A Microsimulation Platform of Firm Evolution Processes
by
Toka S. Mostafa Muhammad
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Department of Civil Engineering
University of Toronto
© Copyright by Toka S. Mostafa 2017
ii
A Microsimulation Platform of Firm Evolution Processes
Toka S. Mostafa
Doctor of Philosophy
Department of Civil Engineering
University of Toronto
2017
Abstract
Firmography is a fundamental component of urban systems that has not received much
research attention. Firmographic events of entry, exit, growth, and relocation affect economic
growth, labour dynamics, and result in a complex system of goods movements. This dissertation
develops a firm modelling framework, called the firmographic engine. The goal of the
framework is to evaluate the implications of policy on firm evolution over time by simulating
and forecasting firm behaviour. The engine is composed of firm generation, market introduction,
performance evaluation, and firm evolution modules. Firm generation simulates entrepreneurial
decisions of firm entry. Market introduction involves the specification and completion of
operational strategies. Performance evaluation assesses the outcomes of the operational
strategies. Firm evolution simulates growth/shrinkage and exit decisions depending on
performance evaluation results.
Three firmographic events are studied in detail: firm start-up size, growth, and survival.
Ordered logit models are estimated for firm start-up size in terms of the firm’s number of
employees and tangible assets. Autoregressive Distributed-Lag models (ARDL) are estimated to
represent firm growth. Parametric and non-parametric analyses for firm survival are presented.
The non-parametric analysis introduces survival and hazard rates characterized by industry class,
province, firm age, and firm size. The parametric analysis includes a discrete-time hazard
duration model of firm failure. The results show that firm start-up size, growth and survival are
influenced by economic growth, industry dynamics, competition, and firm location and
characteristics.
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Models of freight outsourcing decisions by Canadian manufacturers are also presented.
Binary logit and multinomial logit models are estimated. The models show that freight
outsourcing is driven by firm strategies, supplier locations, government incentives, innovation
and technology, industry dynamics, economic conditions, and competition. Models of
international versus local outsourcing are also explored.
Statistics Canada’s T2-LEAP and Survey of Innovation and Business Strategy (SIBS)
databases are utilized for this research. The longitudinal T2-LEAP dataset is used to estimate
models of firm start-up size, firm growth, and firm failure of Canadian firms for the period of
2001 to 2012. The cross-sectional SIBS dataset, for the years of 2009 and 2012, is used to
estimate models of freight outsourcing by Canadian manufacturers.
iv
Acknowledgments
First and foremost, it is by the grace of Allah (my God), the most merciful and most gracious,
that this thesis has been completed. I am sincerely thankful to His countless blessings in every
stage of my life, and surrounding me with all the supportive and loving people throughout my
journey. My Lord, thank you for enabling me to finish this research and hopefully benefiting
others with it.
After quite a long journey, here comes the time to finish my thesis and finally write the part I
was longing for the most; the part where I acknowledge and thank all the people who have
helped to make this thesis come to light. William Arthur Ward said, “Feeling gratitude and not
expressing it, is like wrapping a present and not giving it”. So, allow me to ‘wrap my present’ to
the transportation discipline with this humble acknowledgment section.
My sincere gratitude is to my advisor, Professor Matthew Roorda. Thank you Matt for
always guiding me to the right directions, especially during the most difficult and challenging
moments throughout my PhD. You always saw the best in this research, never lost hope, and
with your wisdom, vision, intellect, patience, continuous support and encouragement, positive
spirit, and experience, this research is completed with success. You are the best supervisor a
graduate student can ever have. Matt, you are an inspiration and an outstanding role model, and
certainly, I am forever grateful to your influence on reshaping my academic personality. People
like you make the world a better place.
Thanks go to my PhD committee members; Professors Eric Miller, Khandker Nurul Habib,
and Shoshanna Saxe, for their feedback on my first thesis draft. Your feedback has helped
improving the clarity of the final version. Special thanks to Professor Habib for his valued advice
in behavioural modelling. Also, special thanks to Professor Hanna Maoh for serving as the
external thesis reviewer. Hanna, my thesis is largely motivated by your PhD research back in
2005, and I greatly appreciated all your comments and suggestions.
Many thanks for Professors Baher Abdulhai and Amer Shalaby for supervising my
comprehensive examination, and for their continuous support throughout my time at U of T.
Prof. Amer, I deeply appreciate your guidance with career path selection. Prof. Baher, it has been
my honor to work with you as a teaching assistant. Your outstanding teaching has always
impressed me and motivated me to pursue research in transportation engineering from the early
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beginning of my master’s degree time. I always look up to you, and wish one day that I become a
great teacher and professor like you.
I want to acknowledge Statistics Canada for approving this research to use their firm micro
databases. This has certainly made this research more valuable to transportation and land use
research in Canada. More specifically, I want to deeply thank Danny Leung, Lydia Couture, and
Douré Grekou for their support to the project, facilitating my time at Statistics Canada, and
accommodating the project requests promptly and cheerfully.
I would also want to acknowledge the financial support I received from the University of
Toronto, Professor Matthew Roorda, Canadian Transportation Research Forum (CTRF), and
Transportation Association of Canada (TAC).
I want to extend my sincere gratitude to my family who have always been the utmost source
of support in every stage of my life. My precious mother, Naida Alrefaie, you are the greatest
gift that is sent to me in this life. Your unconditional love and support has always been the main
driver to any of my achievements. Your faith in my abilities has always given me strength and
lightened the darkest times. This one goes to you and to my late father Sabry Mostafa. My
dearest sisters Tasneem and Takwa, and my dearest brother Tackey El-Deen, you are my pillars
of support, without your unconditional love and encouragement I would have never made it. My
brother-in-law Mohamed, sister-in-law Safaa, my niece Judy, and my lovely nephews Adam,
Eyad, and Omar, your support has always been the brightest side in my PhD life. May we always
be united and never apart.
It has been a long journey, full of ups and downs, challenges, great moments, and most of all;
awesome people. Prophet Muhammad, peace and blessings be upon him, said, "He who does not
thank people, does not thank Allah*”, so, allow me to spend few lines acknowledging everyone I
met inside and outside of U of T who made this journey unique in many dimensions. I would like
to thank my colleagues Wafic El-Assi, Sami Hasnine, Adam Weiss, and Chris Harding for their
help with discrete choice models. Thanks to my lab mates who have made life more fun at U of
T; Islam Kamel, Bryce Sharman, Ahmed Ramadan, Mehdi Nourinejad, Glareh Amirjamshidi,
and Leila Dianat. Thank you, Mohamed Elshenawy, for your support and encouragement during
the thesis writing stage and providing me with excellent tips for efficient writing.
* Authenticated by Ahmed and Timidhi
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My dearest Samah El-Tantawy and Hossam Abdelgawad, I cannot thank the both of you
enough for all the support you have been providing me inside U of T, and being great friends and
family outside U of T. Samah, your ultimate positivity has always been the spark that motivates
people around you, especially me. I still remember how supportive you were in some difficult
times; your patience, excellent advice, and most of all the “never give up” spirit. Hossam, thanks
for always giving me the best advice in my PhD and in my career, and thanks for being a great
brother. May both of you and your kids, Nour and Yussuf, be always blessed and protected.
Most sincerely, I dearly thank you Aya Aboudina for being my great friend, sister, office-
mate, and roommate. Our PhD paths were parallel; we have shared the most moments together,
we travelled together, matured together, laughed a lot, and shared many life changing events. I
cherish this part of the trip greatly. Thank you for being a true friend and being the greatest
support for me over the past six years. My genuine thanks go to my dear friend Nosayba El-
Sayed for all the support and advice that she has been given me. I deeply treasure all the events,
travels, and conversations we shared. I have learned a lot from you and I forever am thankful for
your friendship and being there for me in the most challenging situations.
I am so humbled by the treasured company of my great friends that created the work-life
balance needed to complete my PhD. Nourhan Safwat, Eman Hammad, Somaia Ali, Hoda
Youssef, Sara Alkokhon, Sarah Salem, Mona El-Mosallamy, Rana Morsi, Nagwa El-Ashmawy,
Yasmine Megahed, Lina Elshamy, Mai Hany, Bailasan Khashan, and Sara Anis: you are my
family and I sincerely thank you for everything. A special thank you to my twin ‘non-biological’
sister Aliaa Bassiuoni for her genuine support in levels I cannot count. My family in Ottawa,
Priya and Indu Bakshi, and Basma Elkhateeb: thank you for your great hospitality and support
during my stay in Ottawa. Also, I want to extend my gratitude to Yamen Elbahy for his support.
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To Mom and Dad…
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Copyright Acknowledgment
In this thesis, portions of two chapters (chapters 2 and 7) are published or under review.
These chapters are:
Chapter 2:
Mostafa, T.S and M.J. Roorda (2016). A Review of Firmography in Freight
Microsimulation Modelling. Submitted to Journal of Transport Reviews (Under review).
Chapter 7:
Mostafa, T. and M.J. Roorda (2017). Discrete Choice Modeling of Freight Outsourcing
Decisions of Canadian Manufacturers. Accepted for publication in Transportation Research
Record.
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Table of Contents List of Tables ......................................................................................................................... xiii
List of Figures ........................................................................................................................ xvi
List of Acronyms ................................................................................................................. xviii
CHAPTER 1 Introduction ...................................................................................................... 1
1.1. Introduction .................................................................................................................... 1
1.2. Motivation ...................................................................................................................... 2
1.3. Existing Work ................................................................................................................ 3
1.4. Research Goals, Objectives, and Scope ......................................................................... 4
1.5. Dissertation Structure ..................................................................................................... 6
1.6. Disclaimer ...................................................................................................................... 9
CHAPTER 2 A Review of Firmography in Freight Microsimulation Modelling ............... 10
2.1. Introduction .................................................................................................................. 10
2.2. Firmography and Firm Life Cycle ............................................................................... 10
2.2.1. What influences firmography? ........................................................................ 11
2.2.2. Firm life cycle ................................................................................................. 13
2.2.3. Relationship between firmographic events ..................................................... 14
2.2.4. Firm birth (entry) ............................................................................................. 16
2.2.5. Firm growth ..................................................................................................... 19
2.2.6. Firm exit/survival ............................................................................................ 21
2.2.7. Firm behaviour: and evolutionary perspective ................................................ 23
2.3. Agent-based Models with Firm Microsimulation ........................................................ 24
2.4. Firm Microsimulation and Freight Models .................................................................. 29
2.5. Concluding Remarks and Research Gaps .................................................................... 30
CHAPTER 3 A Framework of Firm Microsimulation: The Firmographic Engine ............. 33
3.1. Introduction .................................................................................................................. 33
3.2. A Conceptual Framework ............................................................................................ 34
3.3. Underlying Modules .................................................................................................... 38
3.3.1. Firm generation ............................................................................................... 38
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3.3.2. Market introduction ......................................................................................... 41
3.3.3. Performance evaluation ................................................................................... 42
3.3.4. Firm Evolution ................................................................................................ 44
3.4. Data Sources ................................................................................................................ 45
3.4.1. T2-Longitudinal Employment Analysis Program (T2-LEAP) ....................... 46
3.4.2. Survey of Innovation and Business Strategies (SIBS) .................................... 47
3.5. Relationship to Other Microsimulation Models of Transportation Systems ............... 51
3.5.1. Integration with FREMIS ................................................................................ 51
3.5.2. Integration with ILUTE ................................................................................... 52
3.6. Concluding Remarks and Future Directions ................................................................ 55
CHAPTER 4 Models of Firm Start-up Size of Canadian Firms .......................................... 58
4.1. Introduction .................................................................................................................. 58
4.2. Determinants of Firm Start-up Size ............................................................................. 60
4.3. Data Description and Basic Analysis ........................................................................... 63
4.4. Model Structure: Ordered Logit Model ....................................................................... 65
4.5. Firm Start-up Employment Size .................................................................................. 66
4.5.1. Order logit model estimation results ............................................................... 67
4.5.2. Results interpretations ..................................................................................... 70
4.5.3. Model validation and goodness-of-fit ............................................................. 77
4.6. Firm Start-up Tangible Assets ..................................................................................... 80
4.6.1. Ordered logit model estimation results ........................................................... 80
4.6.2. Results interpretations ..................................................................................... 83
4.6.3. Model validation and goodness-of-fit ............................................................. 88
4.7. Concluding Remarks and Future Directions ................................................................ 89
CHAPTER 5 Models of Firm Growth of Canadian Firms ................................................... 90
5.1. Introduction .................................................................................................................. 90
5.2. Determinants of Firm Growth ...................................................................................... 91
5.3. Data Description and Basic Analysis ........................................................................... 93
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5.4. Firm Employment Growth Models .............................................................................. 98
5.4.1. Ordered logit model of employment growth ratio .......................................... 99
5.4.2. Panel logistic regression model with random effects .................................... 102
5.4.3. Multilevel model with random effects .......................................................... 109
5.4.4. Autoregressive Distributed-lag Model (ARDL) ........................................... 115
5.5. Firm Tangible Assets Growth Models ....................................................................... 121
5.5.1. Model estimation and result interpretation.................................................... 122
5.5.2. Model validation and goodness-of-fit ........................................................... 123
5.6. Seemingly Unrelated Regression (SURE) ................................................................. 124
5.6.1. Model estimation results and interpretation .................................................. 125
5.6.2. Model goodness-of-fit and validation ........................................................... 129
5.7. Concluding Remarks and Future Directions .............................................................. 131
CHAPTER 6 Survival Analysis of Canadian Firms .......................................................... 134
6.1. Introduction ................................................................................................................ 134
6.2. Literature Review....................................................................................................... 134
6.2.1. Determinants of firm failure .......................................................................... 136
6.3. Data Description ........................................................................................................ 138
6.3.1. Population of study........................................................................................ 138
6.3.2. Explanatory variables .................................................................................... 139
6.4. Data Analysis and Trends .......................................................................................... 141
6.4.1. Data analysis ................................................................................................. 141
6.4.2. Firm entry and exit rates................................................................................ 145
6.5. Survival Analysis ....................................................................................................... 147
6.5.1. Non-parametric analysis ................................................................................ 147
6.5.2. Parametric analysis ........................................................................................ 153
6.5.2.1. Model structure ............................................................................................. 153
6.6. General Notes............................................................................................................. 165
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6.7. Concluding Remarks .................................................................................................. 167
CHAPTER 7 Discrete Choice Models of Freight Outsourcing Decisions of Canadian
Manufacturers 169
7.1. Introduction and Related Literature ........................................................................... 169
7.2. Data Description ........................................................................................................ 171
7.3. Methodology and Model Structure ............................................................................ 174
7.3.1. Binary logit model structure .......................................................................... 175
7.3.2. Multinomial logit (MNL) model structure .................................................... 176
7.4. Model Results ............................................................................................................ 177
7.4.1. Binary logit model results ............................................................................. 177
7.4.2. MNL model results........................................................................................ 183
7.5. Model Validation ....................................................................................................... 187
7.6. Concluding Remarks and Future Research ................................................................ 189
CHAPTER 8 Conclusions .................................................................................................. 192
8.1. Summary of Chapters ................................................................................................ 193
8.2. Main Conclusions and Findings................................................................................. 194
8.3. Research Contributions to the Literature ................................................................... 199
8.4. Future Research ......................................................................................................... 200
References ............................................................................................................................. 208
Appendix A. Additional Tables......................................................................................... 232
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List of Tables
TABLE 2.1 A Summary of Factors Influencing Firmography ..................................................... 12
TABLE 2.2 Summary of Firmographic Microsimulation Research Efforts ................................. 26
TABLE 4.1 Investigated Determinants of Firm Start-up Size ...................................................... 61
TABLE 4.2 Industry Classification 2-digit NAICS Code ............................................................ 62
TABLE 4.3: Start-up Employment Size Ranges for Ordered Logit Model ................................. 68
TABLE 4.4 Ordered Logit Model of Firm Start-up Employment Size: Simple Model ............... 69
TABLE 4.5 Ordered Logit Model of Firm Start-up Employment Size: Detailed Model ............. 70
TABLE 4.6 Ordered Logit Model Probabilities for Each Covariate Independently (Simple
Model) ................................................................................................................................... 76
TABLE 4.7 Ordered Logit Model Probabilities for Each Covariate Independently (Detailed
Model) ................................................................................................................................... 77
TABLE 4.8 Model Estimation Summary and Model Goodness-of-fit ......................................... 80
TABLE 4.9 Cross-Validation Results of Ordered Logit Model of Firm Start-up Employment
Size ........................................................................................................................................ 80
TABLE 4.10 Start-up Tangible Assets Ranges for Ordered Logit Model ............................................ 81
TABLE 4.11 Ordered Logit Model of Firm Start-up Tangible Assets: Model Estimation ............. 82
TABLE 4.12 Start-up Tangible Assets Ordered Logit Model Probabilities for Each Covariate
Independently ................................................................................................................................................ 85
TABLE 4.13 Cross-Validation Results of Ordered Logit Model of Firm Start-up Tangible Assets
........................................................................................................................................................................... 88
TABLE 4.14 Model Estimation Summary and Goodness-of-fit.............................................................. 88
TABLE 5.1 Summary Statistics of Single-Location Small/Medium Sized Firms (less than 100
employee) ....................................................................................................................................................... 95
TABLE 5.2 Correlation Matrix of Firm Attributes ...................................................................................... 97
TABLE 5.3 Correlation Matrix of Industry Characteristics and Economic Indicators ..................... 98
TABLE 5.4. Employment Ratio Classes......................................................................................................... 99
TABLE 5.5 Ordered Logit Model of Employment Growth Ratio - Simple Model ......................... 100
TABLE 5.6 Ordered Logit Model of Employment Growth Ratio - Detailed Model ....................... 101
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TABLE 5.7 Ordered Logit Model of Employment Growth Estimation Summary and Model
Goodness-of-fit ........................................................................................................................................... 102
TABLE 5.8 Random Effects Panel Logistic Regression Model of Employment Size Estimation
Results: Simple Model .............................................................................................................................. 105
TABLE 5.9 Random Effects Panel Logistic Regression Model of Employment Size Estimation
Results: Detailed Model ........................................................................................................................... 106
TABLE 5.10 Estimation Summary and Model Goodness-of-fit of Employment Size ................... 108
TABLE 5.11 Multilevel Random Intercept Only Model of Employment Size: Simple Model
Estimation Results ...................................................................................................................................... 112
TABLE 5.12 Multilevel Random Intercept Only Model of Employment Size: Detailed Model
Estimation Results ...................................................................................................................................... 113
TABLE 5.13 ARDL Model of Employment Size: Estimation Results ................................................ 118
TABLE 5.14 ARDL Model of Employment Size: Estimation Results Summary, Model
Goodness-of-Fit, and Model Validation .............................................................................................. 121
TABLE 5.15 ARDL Model of Tangible Assets: Estimation Results ................................................... 122
TABLE 5.16 ARDL Model of Tangible Assets: Estimation Results Summary, Model Goodness-
of-Fit, and Model Validation .................................................................................................................. 124
TABLE 5.17 SURE Model Estimation Results: Simple Model ............................................................ 126
TABLE 5.18. SURE Model Estimation Results: Detailed Model ......................................................... 128
TABLE 5.19 SURE Model Estimation Summary and Validation Results: Simple Model ........... 130
TABLE 5.20 SURE Model Estimation Summary and Validation Results: Detailed Model ......... 131
TABLE 6.1 Description of Explanatory Variables used in Firm Failure Hazard Duration Model
......................................................................................................................................................................... 140
TABLE 6.2 Survival and Cumulative Hazard Rates of 2001 Firm Population (Period of 2001 to
2012) .............................................................................................................................................................. 148
TABLE 6.3 Survival Rate by Province ......................................................................................................... 151
TABLE 6.4 Survival Rates by Industry ........................................................................................................ 151
TABLE 6.5 Firm Failure Model: Estimation Results ............................................................................... 159
TABLE 6.6 Model Validation and Goodness-of-Fit ................................................................................ 165
TABLE 7.1 Summary of Investigated Explanatory Variables ............................................................... 172
TABLE 7.2 Binary Logit Models for Logistics Outsourcing ................................................................. 179
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TABLE 7.3 Binary Logit Models for Goods Production Outsourcing ................................................ 180
TABLE 7.4 Binary Logit Models for International Outsourcing .......................................................... 182
TABLE 7.5 A MNL Model of Outsourcing: A Simple Model .............................................................. 184
TABLE 7.6 A MNL Model of Outsourcing: A Detailed Model ............................................................ 185
TABLE 7.7 A MNL Model of International Outsourcing ....................................................................... 187
TABLE 7.8 Binary logit model validation ................................................................................................... 189
TABLE 7.9 MNL model validation ............................................................................................................... 189
xvi
List of Figures
FIGURE 1.1 Dissertation Structure ................................................................................................ 8
FIGURE 2.1 Schematic of A Typical Product Life Cycle............................................................ 13
FIGURE 2.2 Relationship Between Market Stress and Firmographic Decisions ........................ 16
FIGURE 2.3 Average Cost and Production Relationship ............................................................. 18
FIGURE 3.1 The Firmographic Engine: A Conceptual Framework ............................................ 36
FIGURE 3.2 The Firmographic Engine: Firm Decision Hierarchy .............................................. 37
FIGURE 3.3 The Firmographic Engine: Firm Decision Hierarchy .............................................. 40
FIGURE 3.4 Market Introduction Module ................................................................................... 42
FIGURE 3.5 Market Introduction Module ................................................................................... 43
FIGURE 3.6 Firm Evolution Module ........................................................................................... 45
FIGURE 3.7 Long Term Strategy, Strategic Focus, and Business Activity Dynamics in SIBS .. 50
FIGURE 3.8 Use of Innovation in SIBS ....................................................................................... 51
FIGURE 3.9 Freight Operations Decision Hierarchy ................................................................... 52
FIGURE 3.10 Overall Structure of ILUTE and Potential Interactions with The Firmographic
Engine ................................................................................................................................... 54
FIGURE 4.1 Firm Start-up and Growth Simulation Configuration ............................................. 59
FIGURE 4.2 Average Number of Employees at Firm Start-up by Industry ................................ 64
FIGURE 4.3 Average Firm Employment Size by Industry (For the period of 2001 to 2012) ..... 65
FIGURE 4.4 Ratio of The Odds of Firm Start-up Employment Size for Some Provinces .......... 71
FIGURE 4.5 Probabilities of Firm Start-up Employment Size for Selected Provinces ............. 72
FIGURE 4.6 Ratio of The Odds by Industry for The Firm Start-up Size: Simple and Detailed
Models................................................................................................................................... 73
FIGURE 4.7 Probabilities of Firm Start-up Employment Size by Industry: Detailed Model ... 73
FIGURE 4.8 Probabilities of Start-up Employment Size for Manufacturers in Different
Provinces-Simple Model ....................................................................................................... 74
FIGURE 4.9 Probabilities of Start-up Employment Size for Manufacturers in Different
Provinces-Detailed Model .................................................................................................... 75
FIGURE 4.10 Probabilities of Start-up Employment Size for Construction Firms in Different
Provinces- Simple Model ...................................................................................................... 75
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FIGURE 4.11 Probabilities of Start-up Employment Size for Construction Firms in Different
Provinces- Detailed Model ................................................................................................... 76
FIGURE 4.12 Tangible Assets Ordered Logit Model - Ratio of the Odds for Some Provinces .. 83
FIGURE 4.13 Tangible Assets Ordered Logit Model - Ratio of the Odds by Industry Class...... 84
FIGURE 4.14 Ordered Logit Model Probabilities for Some Provinces ....................................... 86
FIGURE 4.15 Start-up Tangible Asset Ordered Logit Model Probabilities by Industry Class .... 87
FIGURE 5.1. Firm Population Classified by Province ................................................................. 96
FIGURE 5.2. Firm Population Classified by Industry .................................................................. 96
FIGURE 5.3. Multilevel Model Schematic ................................................................................ 110
FIGURE 6.1 Average Firm Exit Rates by Industry Class (From 2001 to 2012) ........................ 142
FIGURE 6.2 Average Firm Exit Rates by Province (2001 to 2012) .......................................... 143
FIGURE 6.3 Average Firm Exit Rates of Manufacturers by Province ...................................... 143
FIGURE 6.4 Cumulative Distribution of Firm Age at Exit ........................................................ 144
FIGURE 6.5 Population Distribution of Firm Age at Exit ......................................................... 145
FIGURE 6.6 Firm Entry and Exit Rates, and GDP Growth Rates ............................................. 146
FIGURE 6.7 Canadian Studies of Firm Survival Rates .............................................................. 149
FIGURE 6.8 Survival Function by Firm Size ............................................................................. 150
FIGURE 6.9 Survival Function for Selected Provinces. ............................................................ 150
(c) FIGURE 6.10 Survival Function by Age Group .................................................................. 153
FIGURE 6.11 GDP by Province for The Mining, Quarrying, and Oil and Gas Extraction Industry
Class (NAICS 2-Digit Code: 21) ........................................................................................ 156
FIGURE 6.12 GDP by Province for The Manufacturing Industry Class (NAICS 2-digit code:
31-33) a ............................................................................................................................... 157
FIGURE 6.13 Unemployment and GDP Growth Rates for The Province of Ontario ................ 158
FIGURE 6.14 Ratio of The Odds of Failure for Firms Located in Canadian Provinces with
Ontario being the Reference Province ................................................................................ 161
FIGURE 6.15 Ratio of the Odds for Different Industries with Manufacturers being the Base
Industry ............................................................................................................................... 162
FIGURE 7.1 Model structures .................................................................................................... 174
xviii
List of Acronyms
AAE Average Annual Earnings
ACF Autocorrelation Function
ALU Average Labour Unit
APS Adult Population Survey
AR Autocorrelation
ARDL Autoregressive Distributed-lag Model
CMA/CA Census Metropolitan Area/ Census Agglomeration
GEM Global Entrepreneurship Monitor
GDP Gross Domestics Product
GLS Generalized Least Squares
GTHA Greater Toronto and Hamilton Area
HR Human Recourses
i.i.d. Independent and Identically Distributed
IUMs Integrated Urban Models
ILUTE Integrated Land Use, Transportation, Environment
LME Linear Mixed Effects
LR Likelihood Ratio
MAD Median Absolute Deviation
MES Minimum Efficient Scale
MNL Multinomial Logit Model
NAICS North American Industry Classification System
NL Nested Logit
OLS Ordinary Least Squares
SC Supply Chain
SEPH Survey of Employment, Payrolls and Hours
SIBS Survey of Innovation and Business Strategy
xix
SME Small-Medium Sized Establishments/Firms
SURE Seemingly Unrelated Regression
T2-LEAP T2-Longitudinal Employment Analysis Program
1
CHAPTER 1
Introduction
1.1. Introduction
Researchers from regional and transportation planning have focused a large body of research
towards Integrated Urban Models (IUMs) and agent-based microsimulation of urban systems
(Miller et al., 2004; Waddell, 2002; Hunt et al., 2005; Wegener, 1995). IUMs represent urban
activity by simulating the behaviour of interacting agents. IUMs can be used to support strategic
planning by forecasting the effects of policies on demographics and land use changes within
regions. Recent IUMs are focused on representing the system agents at the micro level to provide
better accuracy of the urban systems (Waddell, 2002; Salvini and Miller, 2005). Within urban
systems, firms are recognized as one of the interacting agents whose behaviour influence
regional and local development. Firm entry, exit, growth, and relocation affect labour,
transportation demand, demographic change and other aspects of urban systems.
Firmography, or firm demography, is the study of firm creation, growth, relocation, and
failure. Firm demography is important for several research fields such as microeconomics,
regional science, spatial economics, and transportation planning. Microeconomists are interested
in firmography to observe the economic impacts of labour dynamics, production, sales, and stock
and commodity exchange (Basu at al., 1998; Eidson and Ehlen, 2005; Ehlen et al., 2007).
Transportation planners focus on firm evolution (e.g. firm birth, growth, death, and relocation)
because of their impacts on transportation patterns for passenger and freight trips (Salvini and
Miller, 2005; Roorda et al., 2010). Regional scientists and spatial economists study firmography
because it affects labour force dynamics, labour qualifications and wages, local demand,
demographics, agglomeration economies, urban size, and transportation patterns (Acs and
Storey, 2004; Arauzo-Cardo and Teruel-Carrizosa, 2005; Piacentinoet al., 2016). In addition,
firmographic events induce population demographic changes by attracting skilled labour to
regions where firms are located (van Wissen, 2000).
Freight transportation is a key component of any transportation system. Freight demand is
largely affected by, and affects, land use development projects and the spatial/demographic
composition of urban forms. Current IUMs (such as ILUTE presented by Miller and Salvini,
2
1998) lack the presence of comprehensive micro-level freight models that simulate the behaviour
and interactions of the individual freight agents. In micro-level freight models, firms (including
shippers, carriers, and logistics service providers) are considered the main interacting agents
whose interrelations induce freight demand. The integration of firm micromodels to IUMs is not
only for representing freight demand and studying resulting effects on land use, but also to
provide accurate representation of the job-supply side in the labour market, which is a
fundamental element of IUMs.
Agent-based firmographic models are suitable to simulate firms in freight systems as they
provide a closer representation of the actual interactions of the agents. Such micro-level
modelling can be used to simulate policy effects on firm dynamics and changes in urban form.
This research focuses on studying firm evolution and associated behaviour at the micro level.
Firmographic events of firm entry, growth, exit, and freight outsourcing decisions are the core
firm behaviour investigated in this study. The presented research is to serve for future freight
microsimulation and integration with ILUTE.
This thesis provides three main contributions to the literature:
• Introduction and partial implementation of the first national level firm microsimulation in
Canada using real data. The microsimulation represents firm evolution and models
associated behaviour with impact on freight systems. It incorporates economic growth,
industry dynamics, and firm characteristics and strategic focus in a unified framework.
• Exploration of new sources of firm micro-level databases that are suitable for, but have
not been used before, firm microsimulation.
• Presentation of firm micromodels of firm start-up size, growth, and outsourcing that have
not been introduced earlier in the Canadian literature.
1.2. Motivation
Behavioural freight transportation modelling has recently emerged as an approach to enhance
the quality of freight and logistics decision assessments. Early freight transportation models were
based on the conventional four-stage or commodity-based approaches (Pendyala et al., 2000;
Cambridge Systematics, 1996; Fischer et al., 2000; Cambridge Systematics, 1998). These
approaches are aggregate and were generally developed using the passenger demand modelling
3
paradigm. They fail to represent interaction among system agents; hence their accuracy for
freight modelling is questioned (Roorda et al., 2010). On the other hand, agent-based
microsimulation allows for tracking individual agent decisions, and explicit modelling of
interrelated behaviour.
Firmography is a new approach in freight microsimulation modelling; it has been introduced
in few recent transportation and land use modelling efforts in North America (Maoh and
Kanaroglou, 2005; Kumar and Kockelman, 2008). These efforts considered firms as primary
agents and focused mainly on modelling firm failure, expansion, contraction, and location choice
for specific business segments in certain regions with some data limitations (as will be discussed
in chapter 2).
The majority of firmographic models in the literature (Maoh, 2005; Maoh and Kanaroglou,
2005; van Wissen, 2000; de Bok and Bliemer, 2006; Moeckel, 2005) address firm growth as the
change in the employment size and considered other assets (e.g. vehicle fleets, and number of
business locations) as exogenous variables. Firm growth can be fiscal (e.g. growth in profits,
sales values, and revenues) or physical (e.g. employment, floor space, and machinery and
equipment) depending on each firm’s performance indicators and long-term strategies. It is
expected that correlation exists between different measures of growth. For instance, a firm with
growing sales values may consider increasing their production capacity, and may hire new
employees and expand their physical form in new locations. Models that address firm growth in
a multi-dimensional fashion are not covered in the literature. Firm start-up size models are also
absent at the micro-level. Furthermore, models of other freight-related decisions such as
outsourcing, and commercial vehicle fleet ownership, are missing in the literature. The purpose
of this thesis is to investigate modelling methods to address these gaps in the literature.
1.3. Existing Work
The research of Maoh and Kanaroglou (2005; 2007a; 2007b;2009;2013) is the first (and so
far, the only) attempt in the Canadian literature that addresses firmography in IUMs using firm
microdata. Their study provides a modelling framework of firmographic events for the purpose
of microsimulation. The study focused on small and medium sized business establishments
located in the city of Hamilton, Ontario, Canada. The microsimulation utilized Statistics
Canada’s longitudinal Business Registry (BR) data set for the time interval of 1996 to 2002. The
4
microsimulation presents models of firm failure, mobility, and location choice decisions. Firm
entry and growth are not covered in their microsimulation.
Khan (2002) introduced an agent-based microsimulation of business establishments in a
spatial economic system using a synthetic population. Birth, death, growth, and relocation, are
the firmographic events of consideration in addition to location choice decisions. Birth event is
modelled using probabilistic distributions and Monte Carlo simulation. The growths/shrinkage of
firms are represented in terms of spatial (floor space) expansion of establishments, and
production and consumption of other commodities. The microsimulation is then used for policy
testing scenarios. No other studies are found in the Canadian context of firm microsimulation for
IUMs. Chapter 2 reviews other studies of firmography worldwide and discusses research gaps in
detail.
1.4. Research Goals, Objectives, and Scope
The goal of this research is to introduce a framework of firm microsimulation with a focus on
firm behaviour with direct influence on transportation systems. More specifically, this research
focuses on studying firm behaviour of start-up size, growth, failure, and freight outsourcing with
a case study of Canadian firms.
This research introduces a comprehensive firm microsimulation framework that is called the
firmographic engine. The engine is designed to microsimulate the stages of firm evolution
starting at the entrepreneurial phase of firm entry, and covering the basic firmographic events of
firm birth, growth, relocation, and exit. The engine simulates interactions between individual
firms to replicate freight system dynamics. This research also introduces a suite of firm
behavioural models to formulate parts of the underlying modules of the firmographic engine. The
full implementation and operation of the engine has not yet been completed, however parts of its
components are being implemented, by a research team at the University of Toronto, using the
behavioural models introduced in this thesis.
The microsimulation represents the freight system at the individual firm level, and across
industries in different regions. Firmography is mainly driven by market demand and supply,
economic conditions, and firm attributes such as size, age, and location. The engine incorporates
individual firm characteristics, industry attributes, economic conditions, and market competition
to model firm evolution.
5
This thesis introduces micromodels of firm start-up size, firm growth, firm exit, and freight
outsourcing decisions of Canadian firms. Firmographic events are studied and modelled using
longitudinal micro datasets of Canadian firms in the interval of 2001-2012, with a focus on
single-location, and small and medium sized firms (i.e. 100 employees and less). Small and
medium sized firms account for a large portion of the Canadian GDP. In 2005, small and
medium sized firms accounted for 54.3% of the Canadian GDP (Leung et al., 2012). They also
account for over half of the GDP in all industries except for mining and oil and gas,
manufacturing, transportation and information where large-size firms dominated (Leung et al.,
2012). In 2014, single location firms constituted 93.7% of the entire Canadian firm population,
while single-location and small and medium sized one (i.e. 100 employees and less) constituted
91.7% of the total firm population (Statistics Canada, 2014; 2016c). In 2017, small sized firms
(50 and less employees) contributed with an average of 30% to the Canadian GDP (Statistics
Canada, 2016d).
In this research, firm size is addressed in two aspects; the number of employees and the value
of the tangible assets. Models of firm start-up size and growth consider both aspects of growth
and their potential dependency. For each of the studied firm behaviour, two types of models are
estimated; simple and detailed models. The simple model set utilizes basic firm attributes (e.g.
size, age, industry class, and provincial location), and economic growth indicators (e.g. GDP
growth and provincial unemployment rates). The detailed models use additional firm attributes
(e.g. tangible assets, and sales values) and industry dynamic variables (e.g. entry and exit rates).
The simple models are the foundation of the firmographic engine, while the detailed ones are to
enrich our understanding of firm evolution. Each of the presented models have been validated at
the aggregate level to assess their performance.
The firmographic engine is designed to be integrated with other microsimulation models of
transportation system. Within freight microsimulation, the engine is to be integrated with
FREMIS (Freight Market Interactions Simulation); an agent-based microsimulation of shipper-
carrier interactions in the freight market (Cavalcante and Roorda, 2013). This integration covers
parts of operational strategy formation in the firmographic engine that are related to carrier
selection (discussed in chapter 3).
The firmographic engine provides information on the job supply side (i.e. job creation and
destruction) provided at the firm level in each industry (NAICS 2-digit code). This micro-level
6
information is needed to simulate labour dynamics within IUMs such as ILUTE (Integrated Land
Use, Transportation, Environment) introduced by (Miller et al., 2004). The engine provides
yearly labour dynamics of job creation through firm start-up size and firm growth models, and
job destruction through firm growth and exit models.
Freight outsourcing is a key behaviour in freight systems, and has hardly been covered in the
transport modelling literature. This thesis presents micromodels of freight outsourcing decisions
of Canadian manufacturers. Two aspects of freight activities are considered; logistics and
distribution, and goods production. Models of outsourcing’s geographic location (local vs.
international) are also explored.
Economic growth, firmography, and industry dynamics (i.e. entry and exit rates) are
intertwined. Economic growth induces new opportunities for business ventures which
encourages entrepreneurs to open new firms, and existing firms to grow and expand. Similarly,
new and growing firms, and exiting firms influence the economic growth of a region. On the
industry level, a growing demand generates momentum for firm birth and growth, which boosts
the economy. These two-way relationships between the three entities are entwined and hard to
model. The presented microsimulation represents economic growth and industry dynamics as
external variables to firm evolution processes.
This research explores the use of new data sources for firm microsimulation. Two micro
datasets are utilized to estimate the introduced firm micromodels; the T2-Longitudinal
Employment Analysis Program (T2-LEAP), and Survey of Innovation and Business Strategy
(SIBS) provided by Statistics Canada. T2-LEAP is a micro-level longitudinal dataset of
Canadian firms that was made available for our research for the interval of 2001-2012. SIBS is a
cross-sectional database that collects information about firm strategic focus and use of
innovation and technology.
The long-term goal of the firmographic engine is to be used as a tool to evaluate the effect of
policy change on firm evolution over time. Examples of such policies include trade agreements,
government incentives, and land use and taxation policies.
1.5. Dissertation Structure
This dissertation is composed of eight chapters as summarized in FIGURE 1.1. This chapter
introduces the background, motivation and the gaps this research is aiming to cover. In chapter 2,
7
a comprehensive literature review of firm microsimulation models with an emphasis on freight
systems is presented. The literature review covers different research fields such as urban
planning, economics, regional science, business management, industrial organization, and
transportation and land use. Research gaps in firm microsimulation are stated in chapter 2, which
are the foundation of the remainder of the thesis.
In chapter 3, a framework of firm microsimulation, called the firmographic engine, is
presented. The chapter discusses the main components of the engine. The used data sources to
implement parts of the engine are also discussed in chapter 3. Potential integration with other
microsimulation models for transportation systems is also explored.
Chapter 4 introduces the first set of micromodels of firm events; firm start-up size, expressed
as the number of employees and tangible assets. The models are for for-profit industries only.
The models utilize the longitudinal T2-LEAP database linked to external data of economic
indicators. The ordered logit approach is adopted for firm start-up size models. A literature
review on determinants of firm start-up size is also presented.
Chapter 5 includes a suite of firm growth models (expressed as the number of employees and
tangible assets) for single-location small-medium sized firms (no. of employees less than 100) of
for-profit industries. Several model structures are explored for firm employment growth
including ordered logit, random effects panel logistic regression, random effects-multilevel
regression, and Autoregressive Distributed Lag-Model (ARDL). Only ARDL model structure is
estimated for tangible assets models. Furthermore, seemingly unrelated regression (SURE) is
explored to study the potential correlation between the two aspects of growth of employment and
tangible assets.
Chapter 6 presents survival analyses of single-location small- and medium-sized firms. It
starts with a brief literature on determinants of firm failure. Non-parametric and parametric
analyses of firm survival are explored. The non-parametric analysis presents survival and hazard
rates estimates using Kaplan and Meier and Nelson–Aalen estimators characterized by firm age,
province, and industry. The parametric survival analysis introduces a discrete-time hazard
duration model of failure. A comparison of our findings to other Canadian studies of firm
survival is also presented.
In chapter 7, the cross-sectional SIBS database is used to estimate discrete choice models of
freight outsourcing decisions of Canadian manufacturers. Two modelling structures are explored:
8
binary logit and multinomial logit structures. Models that distinguish outsourcing type (local vs.
international) are introduced. The last chapter, chapter 8, presents a summary of the research,
major findings, contribution to the literature, and future directions.
FIGURE 1.1 Dissertation Structure
(1) Introduction
[Background + gaps + objectives]
(2) Literature review of firm
microsimulation models
(3) The Firmographic Engine: A
Modelling Framework
(5) Firm growth
models
(6) Firm survival
analysis and models
(4) Firm start-up size
models
Firmographic events: Longitudinal analysis
(7) Outsourcing models of freight
activities
Behavioural micromodels: Cross-sectional analysis
(8) Conclusions
[Research contribution +future directions]
9
1.6. Disclaimer
This research utilizes datasets that are owned by Statistics Canada, who has kindly approved
our research team for data access with some restrictions. All statistical information in this
research, which is related to T2-LEAP and SIBS datasets, has been reviewed and approved for
publishing by Statistics Canada. We are only allowed to discuss and publish the analysis, model
estimates, and results that are presented in this thesis. Statistics Canada has their own publicly
available reports and statistics that may overlap with some of the topics discussed in this
research. Any differences between the two sources are because of the nature of the presented
research, the scope, and the used methods and datasets.
10
CHAPTER 2
A Review of Firmography in Freight Microsimulation
Modelling
2.1. Introduction
Firmography is a multidisciplinary research area that attracts researchers from fields
including economics, transportation/land use modelling, sociology, finance, geography, and
spatial sciences (van Wissen, 2000; Kumar and Kockelman, 2008). Firmography analyzes the
major events of firm formation, growth/decline, death, and location/relocation (Khan, 2002;
Maoh, 2005; de Bok, 2007). Firmography has emerged as an approach to enhance the quality of
freight microsimulation models because it allows for understanding the evolution of firm
structure and behaviour at the micro level (van Wissen, 1996; van Dijk and Pellenbarg, 2000;
Wisetjindawat et al., 2007; Kumar and Kockelman, 2008; Abed et al., 2014). Behavioural
models that have introduced firmography in freight microsimulation are scarce in the literature.
In this chapter, a comprehensive review of the state-of-research in firmography is presented,
as it relates to freight models. The chapter starts by explaining firm life cycle and its association
with the firmographic events of firm birth, growth, and survival, and their determinants. Next, a
review of agent-based models that consider firm microsimulation, in the past fifteen years, is
presented. An in-depth review of firm micro-modelling in the field of freight transportation
follows. Finally, the chapter highlights challenges to firmography with some remarks on gaps
observed in the literature for future research directions.
2.2. Firmography and Firm Life Cycle
Firm formation/entry/birth is defined as the event of new firm entry to the market; firms that
did not previously exist (de Bok, 2007; Khan, 2002). Firm exit/closure/failure/dissolution/death
is the event of firm disappearance from the market in a specific year that was present in the
previous years (Khan, 2002). Market exit can either be in the form of merging with other firms
or a complete market exit (Khan et al., 2002; Maoh and Kanaroglou, 2005). A
Migration/relocation event is when an establishment changes its geographic location by either
moving within a specific region (intra-migration), or moving out of the region (out-migration)
11
(Maoh and Kanaroglou, 2007a). Growth/expansion and decline/contraction events are when a
measure of firm size (e.g. employment, and number of business establishments) changes over
time. A firm may operate one or more business establishment. If a firm operates more than one
business establishment it is called a multi-establishment firm, otherwise it is called a single-
establishment firm (Maoh, 2005).
Firms base survival and exit decisions on their performance. Firms evaluate their
performance differently; some may use a single metric to assess their performance, while others
may use multiple metrics depending on their economic sector, long-term strategic focus, and
firm’s life cycle. For example, manufacturers may assess their performance by market growth,
production cost, and sales value, while service providers may focus on service times, and
customer satisfaction. Sometimes performance measures change according to the firm’s life
cycle. New start-ups may focus more on achieving market niches by targeting market share
growth, whereas mature firms may focus on revenues and production cost (Mueller, 1972).
Long-term strategies also influence performance measure selection. For instance, firms with
long-term strategies of market positioning and product leadership may assess their performance
based on customer satisfaction and minimized delivery times, while firms adopting mass market
and low-price long-term strategies may use market share and sales growth rates as performance
indicators.
2.2.1. What influences firmography?
Firm evolution is affected by factors related to firm characteristics and strategies, industry,
economic conditions, location (e.g. proximities to highways), population demographics, and
local policies and government incentives. A summary of such factors found in the literature is
provided in TABLE 2.1 (van Wissen, 2000; Maoh, 2005; Moeckel, 2005; de Bok and Bliemer,
2006; Fritsch et al., 2006; Elgar and Miller, 2006; Hu et al., 2008; Bodenmann and Axhausan,
2010; Baptista and Leitão, 2015). Details of how such factors affect firmographic events are
provided in sections 2.2.4, 2.2.5, and 2.2.6 for firm birth, firm growth, and firm exit.
12
TABLE 2.1 A Summary of Factors Influencing Firmography
Category Factor
Firm related
• Employment size
• Age
• Number of business locations (e.g. production
facilities and warehouses)
• Freight operation decisions (e.g. outsourcing)
• Sales values
• No. of offered products/services
• Growth rates (measured in number of employees,
sales values, profits, or market shares)
• Product Life Cycle (PLC)
• R&D investments
• Business and operational strategies (e.g. financial,
production, marketing, resources and supply
chain, and innovation and technology strategies)
Industry related
• Age of the industry
• Industry Life Cycle (ILC)
• Market competition
• Industry size
• Technological advancements and innovations
• Technology Life Cycle (TLC)
• Availability of materials
Macroeconomic related
• Economies of scale per industry
• Agglomeration economies
• Gross Domestic Product (GDP) and access to
capital (investment potentials)
• Unemployment rates
Location population
demographics
• Average income levels
• Labour market and labour cost
• Population educational level
Location geographic
characteristics
• Space availability and prices
• Location of business owners
• Target customers
• Proximity to other businesses (e.g. suppliers,
carriers, and distribution centres)
• Freeway access and traffic patterns
Local policies • Government financial incentives
• Tax rates and regulations
13
2.2.2. Firm life cycle
The terms Product Life Cycle (PLC), Technology Life Cycle (TLC), and Industry Life Cycle
(ILC) are used in the literature to reflect changes over time within industries, but with some key
distinctions. PLCs are usually measured as a function of the product sales cycles (Levitt, 1965).
A typical PLC is composed of four stages: introduction, growth, maturity, and decline (Cohen,
2010). As explained in FIGURE 2.1, during market introduction, production costs are higher with
a slow growing rate of revenues, while in the growth stage, costs are starting to drop with the
increase of production (approaching economies of scale) and hence revenues increase. Maturity
is attained when constant rates of revenue are achieved with declining costs. When revenues are
declining with the increase of incurred costs, the product is then said to be in a decline stage; that
is usually followed by a failure or product cancellation (Polli and Cook, 1969; Tibben-Lembke,
2002).
FIGURE 2.1 Schematic of a Typical; Product Life Cycle1
TLCs are influenced by technology improvement rates, age of the technology, process
innovation, and market acceptance (Foster, 1988). TLCs and ILCs follow similar life cycles to
PLCs (Lumpkin and Dess, 2001; Haupt et al., 2007) with some differences. TLCs are not
1 This is a common graph that explains product life cycle adopted from Polli and Cook (1969) and Tibben-Lembke
(2002)
$ p
er p
erio
d
Time
Cost Revenue Profit
Introduction Growth Maturity Decline Product Cancellation(Failure )
14
specific to certain products, rather to components or material technologies (Cohen, 2010). On the
other hand, ILCs are addressed differently depending on the field of study. For instance,
economists of the industrial organization address ILCs through entry and exit patterns of firms
(Gort and Klepper, 1982), while in the field of evolutionary economics the scope is shifted to
understand competition within industries (Winter and Nelson, 1982).
In general, firm evolution is affected by all three life cycles (Dinlersoz and MacDonald,
2009). New and technologically thriving industries create opportunities for new products to exist,
and hence higher rates of firm births are expected. Firms shift their focus to production
innovation by introducing new or significantly enhanced products/services to achieve better
market shares (Rainey, 2008). Over time, market stability is achieved, entry rates are decreased,
and firm exits take over, and efforts are shifted to improve the process innovation by improving
production or delivery methods to maintain market shares (Klepper, 1996; Wang, 2006;
Davenport, 2013). Moreover, within industries, new/growing products may result in higher rates
of firm birth and existing firm expansion, while products at the mature and declining stages
increase firm contraction decisions and produce higher exit rates.
Growth of individual firms is a function of the growth of the constituent units of firm
production, i.e. number of products or services (Growiec et al., 2008). Thus, firms follow similar
evolution patterns to product life cycles; starting at market introduction, followed by firm
growth, maturity/market stability, and potential firm exit (decline) (Lewis and Churchill, 1983).
Expansion and contraction decisions of firms are a function of the life cycle stage. Firms
expand during growth stages either geographically by opening new business establishments,
growing their other assets such as their employment and vehicle fleet, or introducing new
products. They may also receive offers from other firms to merge. During decline stages,
shrinkage and exit decisions are likely to occur; shutting down some product lines/services,
relocating to smaller places with employment contraction, or merging/selling to other companies
(Lewis and Churchill, 1983; Nandkeolyar et al., 1993).
2.2.3. Relationship between firmographic events
Firmographic events are interrelated depending on the economic sector, market conditions,
and local policies and regulations. There is a correlation between firm entry and firm exit on the
aggregate level; with higher entry rates, higher exit rates are observed (Geroski, 1991; Mata and
15
Machado, 1996). Higher firm entry rates induce market competition resulting in some firms with
better market shares, and other failure firms who are unable to compete. Hence, it is hard to
study firm birth or death independently without considering the other. Firm survival/failure is
affected by relocation decisions, and entry and exit rates (Maoh and Kanaroglou, 2007b).
Firmographic events are governed mainly by the market economic conditions (van Wissen,
2000). Across industries, increased demands for production increase the chances of firm birth
and growth. This is referred to as the concept of carrying capacity (Hannan and Carroll, 1992;
Hannan and Freeman, 1989). van Wissen (2000), p.116, defined carrying capacity as “the
maximum size a population can attain under the conditions of the current environment”; it is the
market demand. The difference between market demand (Kt) and supply (Xt) is called ‘market
stress’ or ‘market pressure’. Market stress is used as an economic demographic behaviour
predictor. Within industries, if the demand is higher than the supply (i.e. [Kt – Xt] is a positive
value), chances for market growth are increased (either by introducing new firms or expand
existing ones). Firm expansion can happen by opening new business establishments, expanding
employment and/or floor space, or by merger and acquisitions with other firms (Park and Jang,
2011). Alternatively, when supply is larger than demand, the market is in the state of contraction
where firms consider closure and contraction decisions (see FIGURE 2.2). van Wissen (1996)
presented a detailed representation of market demand (K) using spatial input-output models by
industry.
16
FIGURE 2.2 Relationship between Market Stress and Firmographic Decisions
2.2.4. Firm birth (entry)
The decision to start a new firm is based on the intentions of entrepreneurs to be self-
employed and become independent (Shane et al., 1991; Reynolds, 1997; Kolvereid and Isaksen,
2006; Koster, 2006). New independent firms are usually founded by experienced individuals
who prefer to work independently (Birley and Westhead, 1994; van Wissen, 2000). Also,
unemployment affects firm birth; creating a new business is one way of becoming employed
(Beesley and Hamilton, 1994). Once an entrepreneur forms an intention of new business entry,
exploring business opportunities begins (Shook et al., 2003). A decision of starting a new
business venture or selling the idea to other operating firms is then followed. Once the decision
of starting a new venture is selected, a series of strategic decisions need to be made (e.g.
resources, capital investments, and logistics decisions). The entry decision and the characteristics
of the new business are mainly influenced by the founders’ demographics such as age, gender,
education, experience, nationality, and location (Shane et al., 1991; Barkham, 1994; Beesley and
Hamilton, 1994; Reynolds, 1997; van Wissen, 2000; Colombo and Grilli, 2005; Elgar et al.,
Market stress
Demand (Kt) – Supply (Xt)
Market growth
Demand > Supply Market contraction
Demand < Supply
New firms Expand existing
New business
establishments
Expand floor
space/employment
or relocate
Firm closure Firm contraction
Close business
establishments Contract employment or
relocate
Merger and acquisition
17
2009). The social network of entrepreneurs also plays an important role during firm start-up and
influences firm survival (Birley, 1986; Anderson et al., 2005).
Generally, firms tend to start small (in size) to reduce losses in the case of firm failure, and to
minimize sunk costs (Birley, 1986; Mata and Machado, 1996). Also, small new firms are more
flexible in location selection decisions, while larger ones would locate where economies of scale
are present to minimize risks (Mata and Machado, 1996). New start-ups grow gradually when
they start making profit and gain adequate market positions. During the growth stage, firms
might consider expansion either (geographically to target different customers) or in assets and
investments (to enhance the current market status and maintain stability).
It is hard to model the firm entry decision because the non-entry decision is not observed
(Melillo et al., 2013). Therefore, firm entry is mostly addressed on the aggregate level by
estimating the entry rates by considering industry sector, exit rates, and economic conditions
(van Wissen, 2000; Kumar and Kockelman, 2008).
2.2.4.1. Start-up size
One of many decisions new entrants have to determine is their number of employees or, as
commonly referred to in the literature, firm start-up size. Many researchers claim that firm start-
up size is an important determinant of a firm’s subsequent performance and survival (Dunne et
al., 1989; Audretsch and Mahmood, 1994; Mata and Portugal, 1994; Mata et al., 1995; Almus,
2000; Görg et al., 2000). Modelling firm start-up size in a microsimulation context is essential to
understand job creation, because new firms are sources of new employment opportunities. For
example, in the United States, between 1976 and 1984, new firms are found responsible for 74%
of 50.8 million new jobs (Kirchhoff and Philips, 1988). In the United Kingdom, around the same
time, 48% percent of new jobs are the result of new small firms (Gallagher et al., 1991).
Some empirical studies link firm start-up size to founder characteristics, industry size and
growth, location, financing opportunities, market competition, and business strategies (Barkham,
1994; Mata, 1996; Mata and Machado, 1996; Görg et al., 2000; Baum et al., 2001; Görg and
Strobl, 2002; Colombo et al., 2004; Colombo and Grilli, 2005; de Jorge et al., 2010). From an
economic perspective, economies of scale are a major factor influencing firm start-up size (Mata,
1996). Mata and Machado (1996) suggest that firm start-up size increases with economies of
scale and the rate of firm entry and exit per industry. As a result, small firms tend to enter the
market and locate anywhere geographically, while larger ones appear only where agglomeration
18
economies exist. Moreover, the amount of capital required to operate a minimum efficiently
scaled firm (MES) has a negative relationship with the new firm population size, because higher
start-up costs discourage entrepreneurs (Fonseca et al., 2001; Geroski, 1991; Mata et al., 1995).
New firms tend to enter the market below MES which affects the unit production cost compared
to their larger competitors (due to economies of scale), and hence survival is difficult. To
survive, firms need to reduce the production cost by approaching economies of scale to
maximize profits and continue to grow in the market (See FIGURE 2.3).
FIGURE 2.3 Average Cost and Production Relationship2
Financing means also influence many strategic decisions of new firms (Scherr et al., 1993)
including start-up size. Colombo and Grilli (2005) explored the financing means of firm start-ups
in Italy, and they claim that debt-financed firms are not larger in size compared to firms financed
by personal savings of owners, for technology-based firms in manufacturing and service
industries. Robb and Coleman (2010) concluded, based on a study in the United States, that
larger firms tend to use more business debt while small businesses (especially home-based firms)
rely on personal savings and avoid external debt. It seems that the details of the debt-financing
strategies are different from one country to another subject to regulations and economic factors,
2 This is a common graph in economics that explains Long Run Average Cost (LRAC) adopted from Mukherjee et
al. (2004), Mankiw and Taylor (2006), and Found (2012)
Ave
rag
e p
rod
uctio
n c
ost ($
)
Production Q2Q1
C2
C1
Minimum Efficient Scale(MES)
Long Run Average Cost(LARC)
Diseconomies of ScaleEconomies of Scale
19
hence the effect of the adopted financing strategies for new firms would differ from one country
to another.
While most of the previously cited literature deals with firm start-up size as an independent
decision, Melillo et al. (2012) argue that start-up size depends on the venture entry type. They
explained that the number of employees at firm start-up is a range that depends on the type of
entrepreneurship, hybrid or self-employed. Hybrid entrepreneurs (those who are still employed
by other firms) benefit their new businesses by their strong ties to the market, and hence they
tend to start in relatively smaller sizes. Self-employed entrepreneurs start their business
independently and may require more experienced personnel to grow their businesses.
2.2.5. Firm growth
Most studies have addressed firm growth as a function of the change in number of
employees, because of the economic importance of job creation. Fewer studies have considered
firm growth from a financial perspective (Williams et al., 1995; Huynh and Petrunia, 2010; Coad
and Broekel, 2012). Previous studies suggest that firm growth is a function of firm-related and
industry-based factors such as firm age, investments in research and development (R&D), wages,
business strategies, innovation and technology, industry structure, and market competition, (van
Wissen, 2000; Maoh, 2005; Moekel, 2005; Bodenmann and Axhausan, 2010; Haltiwanger et al.,
2013). Growth also can be attributed to other behavioural aspects such as CEO’s traits (Baum et
al., 2001).
One common debate between researchers is whether firm growth is independent from
employment size (known as Gibrat’s law, or the rule of proportionate growth). Gibrat’s law
states that firm size and its relative rate of growth are independent (Gibrat, 1931). Recent studies
have rejected Gibrat’s law (Wagner, 1992; Audretsch, 1995b; Reid, 1995; Harhoff et al., 1998;
Weiss, 1998; Almus and Nerlinger, 1999; Audretsch et al., 1999; Calvo, 2006; Haltiwanger et
al., 2013) with a consistent conclusion that small new firms have higher growth rates compared
to larger ones (Segal and Spivak, 1989; Almus, 2000; Haltiwanger et al., 2013). Furthermore,
Gibrat’s law cannot be generalized to all firm types since firm growth is a function of other
factors such as market segmentation, country, and economic conditions. Lotti et al. (2003)
conducted a study on Italian manufacturers and concluded that Gibrat’s law fails for small new
firms in years immediately following their birth, while the law cannot be rejected for subsequent
20
years for older or large firms. Their conclusion agrees with Segal and Spivak (1989) that Gibrat’s
law holds only for large firms. This can be explained by new entrants trying to grow faster to
reach the economies of scale to reduce their costs and achieve market stability (Cabral, 1995;
Lotti et al., 2003; Park and Jang, 2010).
Some studies (Mansfield, 1962; Evans, 1987b; Das, 1995; Huynh and Petrunia, 2010; Park
and Jang, 2010) argue that employment size has a negative relationship with firm growth while
others state the opposite (Singh and Whittington, 1975). This contradiction might be due to the
differences of the studied firm populations and economic sectors. Firms with previous faster
employment growth are more likely to experience future growth as well, but at a slower rate
(Wagner, 1992; van Wissen, 2000; de Bok and Bliemer, 2006). Similarly, profit impacts
employment growth; profit of prior year positively affects the growth of the current year. At the
same time current and prior growth has impact over profitability (Jang and Park, 2011). In other
words, profit induces growth while growth reduces profitability.
Firm age, and R&D investments are found to have a positive impact on firm growth and
survival (Mueller, 1972; Winter and Nelson, 1982; Geroski, 1991; Das, 1995). Other adopted
business strategies such as production and market strategies influence firm growth. For instance,
manufacturing strategies that focus on product quality and production capacity were found to
have positive influences on firm performance in terms of sales growth (Williams et al., 1995;
Amoako-Gyampah and Acquaah, 2008). Adopting competitive strategies of product low prices,
and product differentiation have a positive effect over firm growth as well (Baum et al., 2001).
Innovation practices positively affect firm growth (Acs and Audretsch, 1988; Soni et al., 1993).
Small new firms are more innovative and perform better than their larger counterparts (Soni et
al., 1993; Tether, 1998).
Market conditions and demand govern firm growth potential; the greater the gap between
demand and market supply, the larger the market growth potential, be it new firms, growth of
existing ones, or merger and acquisition (van Wissen, 2000). Market competition, ILC, and
industry structure (including customer concentration and access to resources) affect firm growth
(Marino and De Noble, 1997; Dinlersoz and MacDonald, 2009). Geroski and Gugler (2004)
modelled the impact of competitor firm growth on the firm employment growth. Their results
indicate that there is no statistical significance between competitor growth and employment
growth except for some specific industries such as manufacturers, where a negative effect is
21
observed. Also, growth rates vary according to competition type; Park and Jang (2010) conclude
that small international firms have higher decline rates compared to their small domestic
counterparts, while large international ones have slower decline rates than large domestic ones.
This is because small domestic firms are more familiar with their markets and customers, thus
they use their resources more optimally compared to international small firms who have limited
learning capacity and usually take a long time to adjust to foreign markets.
The majority of studies that have investigated firm growth present one dimensional models
(e.g. number of employees, sales, or profits). In a unique study, Coad and Broekel (2012)
consider firm growth as the simultaneous growth of both employment and production (sales).
Their analysis underlines that there is no strong relationship between production growth and
employment growth, but there is an inverse association between employment and production
growth; an increase of 1% in employment growth is associated with a 0.1% decrease in
production growth in the following year.
2.2.6. Firm exit/survival
Firms exit the market either by announcing bankruptcy or by merging with other firms (van
Wissen, 2000). Modelling the exit decision as a discrete choice is challenging. Usually, founders
of firms that completely disappear from the market are hard to track making it difficult to
investigate the reasons for the exit decision. Survival and hazard duration models are commonly
used in studies identified in the literature to model firm exit rather than using discrete choice
models (Mata and Portugal, 1994; Bhattacharjee, 2005; Fritsch et al., 2006; Maoh and
Kanaroglou, 2007b; Strotmann, 2007; Nunes and Sarmento, 2010).
Firm survival is affected mainly by two groups of factors; firm specific and industry specific
attributes (van Wissen, 2000; Strotmann, 2007). Firm specific attributes are age, size, growth
rate, PLC, business strategies, location, R&D investments, and innovation practices (Mata et al.,
1995; Stearn et al., 1995; Reynlods, 1997; van Wissen, 2000; de Bok and Bliemer, 2006; Maoh
and Kanaroglou, 2007b; Nunes and Sarmento, 2010; Manzato et al., 2011; Schröder and
Sørensen, 2012). Industry specific factors are the economic sector, ILC, agglomeration
economies, and economies of scale (van Wissen, 2000; Strotmann, 2007; Klapper and
Richmond, 2011). Also, characteristics of business founder (e.g. gender) affect some strategic
22
decisions such as human and financial resources and affect the decisions of business
discontinuance (Carter et al., 1997).
There appears to be a consensus that large and older firms have higher survival probabilities
than small younger ones (Reynolds, 1987; Stearns et al., 1995; van Wissen, 2000; de Bok and
Bliemer; 2006; Maoh and Kanaroglou, 2007b; Nunes and Sarmento, 2010). Exit potential
decreases with increased growth rates; small new firms that experience faster growth rates are
more likely to survive in the market (Mata and Portugal, 1994; de Bok and Bliemer, 2006; Nunes
and Sarmento, 2010). Similarly, production growth rates positively affect firm survival (Schröder
and Sørensen, 2012). Some studies highlighted that firm initial size has an influence on firm
survival (Strotmann, 2007; Schröder and Sørensen, 2012). Firms with large start-up sizes have
better survival chances (Mata and Portugal, 1994). New firms also tend to be more flexible in
changing their strategies to adapt with changes in the economic conditions, market segmentation,
and cope with technological and innovation advancements. Inert firms (firm with more fixed
strategies) have better survival chances compared to firms with a more dynamic nature (van
Wissen, 2000). Several studies in different countries reported that the median life time of newly
born firms is between year five and six after market introduction (Mata and Portugal, 1994;
Wagner, 1994; Audretsch et al., 1999; Bahttacharjee, 2005; Bartelsman et al., 2005; López-
Garcia and Puente, 2006; Schrör, 2009; Nunes and Sarmento, 2010). Moreover, firm growth and
survival seem to be largely affected by the amount of R&D investment (Fritsch et al., 2006;
Nunes and Sarmento, 2010; Sharapov et al., 2011; Zhang and Mohnen, 2013). While survival
probabilities increase with larger R&D investment (Fritsch et al., 2006; Sharapov et al., 2011),
Zhang and Mohnen (2013) pointed that there is an inverted U-shaped relationship between R&D
and firm survival indicating that over investing on R&D increases the risk of firm failure.
Firm survival is related to firm entry and exit rates within industries (Mata and Portugal,
1994; de Bok and Bliemer, 2006). Firms born in industries with higher entry rates have lower
chances of survival because of increased competition (Mata et al., 1995; Nunes and Sarmento,
2010). Foreign firms have lower hazard rates than domestic ones, while state-owned firms have
higher hazard rates than privately owned ones because they are more susceptible to the local
economic conditions (Zhang and Mohnen, 2013). Gross Domestic Product (GDP) growth was
found to have a positive influence over firm survival (Baldwin et al., 2000; Klapper and
23
Richmond, 2011). Furthermore, failure probability is high in industries where minimum efficient
scale is high, demand is low, and the market is narrow (Fritsch et al., 2006; Strotmann, 2007).
Firm location, on the other hand, reflects the macro-economic conditions of the region,
agglomeration economies, and access to resources (Maoh and Kanaroglou, 2007b). Strotmann
(2007) argue that firms in highly agglomerated geographic regions have higher risk of death. To
accept or reject this conclusion, other economic conditions must be taken into consideration such
as the industry class and the market stress. Market stress, that reflects the difference between
market demand and supply, has a negative effect on firm exit; the higher chances for new firms
and more productions are, the lower the exit potential is. PLCs and ILCs also influence firm
survival (Agarwal and Gort, 2002). Firms with products at declining stages, or belong to
industries with a saturated market might consider exiting the market. Also, firms in highly
innovative environments have lower survival chances (Audretsch, 1995a).
2.2.7. Firm behaviour: and evolutionary perspective
Most of the studies surveyed in the literature that addresses firm entry have either
investigated the entrepreneurial side of the decision (Beesley and Hamilton, 1994; Praag, 1997;
Kolvereid and Isaksen, 2006), modelled the employment size at the start-up (Barkham, 1994;
Mata, 1996; Baum et al., 2001; Colombo et al., 2004; de Jorge, 2010), linked regional
characteristics to new firms (Storey, 1982; Reynolds et al., 1994), used probabilistic distributions
and Monte Carlo simulation to identify characteristics of new entries (Khan et al., 2002; Kumar
and Kockelman, 2008), or estimated firm formation at the aggregate level (van Wissen, 2000).
No studies are found that estimate characteristics and strategies of new firms at the micro level.
Firms interact in an agent-based complex adaptive system, where agents learn optimal
strategies through their experience and the experiences of their counterparts. New firms usually
choose successful strategies of the current firm population with similar economic activities.
New/existing firms may also consider introducing innovative strategies that are completely new
to the market. That kind of complex adaptive system is difficult to model using ordinary
behavioural discrete choice models.
Within market segments, successful strategies survive over the years while
weaker/unsuccessful ones vanish over time. This knowledge of strategy performance is
transferred between firm generations through experience. The research field called
24
Organizational Learning is concerned with knowledge creation and transformation within
organizations (Cyert and March, 1963). Evolutionary economics, on the other hand, is a branch
of economics that studies the transformation of the economy by observing the growth behaviour
of economic agents using evolutionary methodologies (Friedman, 1998). Both disciplines use
similar methodologies to model the evolutionary behaviour of firms. Concepts from evolutionary
biology are used to describe the evolution patterns of firms. The firm population follows
Darwin’s natural selection and survival theories in biology. Firms follow the ‘survival of the
fittest’ law; firms with good strategies survive while others with weak strategies do not. These
principles have been employed in evolutionary economics to understand firm evolution and
model firm birth (Johnson et al., 2013). New firms (offspring) adopt a mix of the strategies
employed by the existing firm ‘population’ (mating). They also might launch novel strategies to
compete in the market (mutation). This behaviour can be modelled using Genetic Algorithms to
describe firm birth, and estimate the strategies of the new population of firms (Bruderer and
Singh, 1996). Using the same concepts, evolutionary patterns of firm failure can be captured by
tracking the changes in the strategies of failure firms in a genetic algorithm framework (Chen
and Hsiao, 2008).
2.3. Agent-based Models with Firm Microsimulation
Agent-based microsimulation of firm behaviour is a method that represents interacting agents
within economic markets (Basu et al., 1998; Maoh, 2005; Balmer et al., 2006; Roorda et al.,
2010; Cavalcante and Roorda, 2013). Firm microsimulation differs from one field to another.
Regional scientists and spatial economists focus on firm behaviour that is related to location and
influences the demographics and economic activities within a region (van Wissen, 2000).
Transportation planners focus on firmographic events (e.g. entry, growth, and exit) that induce
changes to transportation patterns, either passenger or freight trips (Salvini and Miller, 2005;
Roorda et al., 2010). The focus of microeconomists is to replicate the economic activities of
individual agents and their effect on the economic market, economic production, and
infrastructure dynamics (e.g. usage of electricity, telecommunications, and transportation) where
production and consumption of economic agents (e.g. households, firms, and government
agencies) are presented. In such models, firms are represented for their economic activities such
25
as labour dynamics, production and selling activities, and stock and commodity exchanges (Basu
et al., 1998; Eidson and Ehlen, 2005; Ehlen et al., 2007).
Most studies that have addressed firmography have only focused on parts of the behaviour
such as birth (Shane et al., 1991; Birley and Westhead, 1994; shook et al., 2003; Kolvereid and
Isaksen, 2006), death (Strotmann, 2007; Nunes and Sarmento, 2010; Zhang and Mohnen, 2013),
growth (Lotti et al., 2003; Huynh and Petrunia, 2010; Park and Jang, 2010), and relocation
(Fritsch et al., 2006; Elgar et al., 2009; Hu et al., 2008; Bodenmann and Axhausan, 2010). In this
section, a review of firm microsimulation efforts, in the past 16 years, that address aspects of
firmography with a focus on spatial demography, and land use and transportation are presented.
TABLE 2.2 provides a summary of such studies highlighting the scope, firmographic events
investigated, data sources, and methods.
26
TABLE 2.2 Summary of Firmographic Microsimulation Research Efforts
Study/year Location Data Scope Firmographic event Methods
SIMFIRMS:
van Wissen
(2000) Netherlands
Firm
Longitudinal
data set- LISA
(1990-1991)
Spatial modelling of
economic
demographic
Firm birth Logistic regression
Exit Logistic regression
Growth Regression
Sample size
10,000 Relocation
Binary, and multinomial logit
models
Maoh and
Kanaroglou
(2005)
The City of
Hamilton,
Ontario, Canada
Business
establishment
longitudinal data
set (1996-2002),
Statistics Canada
Business
Register (BR)
Integrated urban and
land use models
Failure
Discrete hazard duration model
of survival
Mobility (relocation)
Multinomial logit models by
economic sector Location choice
Moeckel
(2005) Dortmund,
Germany
Synthetic micro-
data based on
aggregate data
Integrated land use
and transportation
models,
Urban spatial
economy
Birth
Markov transition probabilities Growth/decline
Closure
Location choice and
relocation decisions
A series of Multinomial logit
models
27
TABLE 2.2 Summary of Firmographic Microsimulation Research Efforts (continued)
Study/year Location Data Scope Firmographic
event
Methods
SMF:
de Bok and
Bliemer
(2006)
South
Holland, the
Netherlands
Firm
Longitudinal
data set- LISA
(1988-1997)
Integrated land use
and transport models
Formation Drawing from distribution at random –
Monte Carlo Simulation
Growth Autoregressive model based on firm and
location attributes
Closure Binary regression model and Monte
Carlo simulation
Migration Joint binary and Multinomial Logit
model
Kumar
and
Kockelman
(2008)
Austin, Texas,
the U.S. A
Firm
longitudinal
data (1998-
2004),
Statistics of
U.S.
Businesses
(SUSB)
• Transportation and
land use interaction
• Commercial vehicle
movement models
Entry Firm birth and death are represented
using 2001-2002 and 2002-2003 rates for
low and high growth respectively. Exit
Growth (expansion/
contraction)
Markov transition probabilities and
Monte Carlo simulation
Relocation Random selection of firm population
with a constant rate of 15%.
Location choice Poisson regression model
Commercial trip
generation Negative binomial regression
Commercial trip
distribution Logit model
28
The work of van Wissen (2000) is a holistic firm microsimulation that addresses all aspects
of firmography. He introduced SIMFIRMS; a spatial demographic microsimulation of firms in
the Netherlands. SIMFIRMS assumes that firms are the primary agents associated with economic
growth. It microsimulates firm birth, growth, migration, and death. Firm birth is microsimulated
at the aggregate level using the concept of carrying capacity. The death module considers the
closure of individual firms as a function of some firm attributes (e.g. age and size), and factors
derived by the economic sector (e.g. availability of infrastructure, accessibility to other firms,
and local taxation). Firm growth is modelled as a function of firm size, location, economic
sector, and market pressure. Firm relocation is derived by two sets of factors; push and pull
factors. Push factors are negative aspects of the current location that cause the firm to consider
relocation (e.g. market segmentation changes, local policies, and location cost). Pull factors are
positive aspects of the new location (e.g. better market segmentation, local policies, and location
cost). Relocation decisions are influenced by age, firm growth rate, and market conditions. He
concluded that older firms are less likely to consider relocation, growing firms are more likely to
move, and the larger the market capacity, the less likely that a firm would relocate (van Wissen,
2000).
Maoh and Kanaroglou (2005) introduced agent-based microsimulation for small and medium
sized business establishments in the City of Hamilton, Ontario, Canada. Their microsimulation is
based on a longitudinal data set obtained from Statistics Canada for the interval of 1996 to 2002.
The microsimulation addresses firm failure, mobility, and location choice decisions. Failure
behaviour is modelled using a discrete hazard duration model of survival.
Moeckel (2005) produced a firm microsimulation with a focus on firm location decisions.
The microsimulation of businesses is integrated with the Integrated Land-Use Modelling And
Transportation System Simulation (ILUMASS). The firmographic events of firm birth,
growth/decline and exit are presented using Markov transition probabilities to estimate the
overall number of births and deaths, and the growth/decline of a simulated firm population.
de Bok and Bliemer (2006) introduced SMF; a firmographic microsimulation of firms in the
South Holland, in the Netherlands. They simulate firms at the micro level in the interval of 1988-
1997. Firm formation, migration, growth, and dissolution are simulated. The microsimulation is
used for spatial policy assessment of infrastructure investments, and industrial or commercial site
planning. Firm exit and birth are simulated using Monte Carlo simulation based on a binary
29
regression model for firm exit, and drawing random samples from distributions for firm birth.
Firm growth is modelled as a function of firm size and location. It is evaluated in two stages; a
tentative size is first calculated based on firm employment, and then is corrected based on the
average firm size within the economic sector. An interesting finding for the overall
microsimulation results is that the micro approach is not proven to perform better at the
neighbourhood level compared to the aggregate approach.
Kumar and Kockelman (2008) conduct a behavioural firm microsimulation for Austin,
Texas. They address firm entry, exit, growth (as a function of employment), and firm location
and relocation decisions. Commercial trip generation and distribution models are also included in
this microsimulation. Firm entry and exit rates for the years of 2001-2002 and 2002-2003, for
low and high growth rates respectively, are used to randomly select firm candidates for birth and
failure from a simulated population. Characteristics of new firms are selected at random from the
existing population distribution. Markov transition probabilities of aggregate firm population and
Monte Carlo simulation are used to represent expansion and contraction dynamics.
Other firm microsimulation efforts are found in the literature that simulate firms based on
fictitious data (Brenner 2001; Otter et al., 2001). Brenner (2001) introduced a comprehensive
approach of simulating firm entry, exit, and growth and observing their influence over the
evolution of industrial clusters. This approach is comprehensive as it includes the influence of
local circumstances of firms such as firm productivity, innovation, firm size, and human capital
over the evolution of industrial clusters.
2.4. Firm Microsimulation and Freight Models
A few research studies have considered firm microsimulation as part of freight modelling (de
Jong and Ben-Akiva, 2007; Wisetjindawat et al., 2007; Kumar and Kockelman 2008; Roorda et
al., 2010; Pourabdollahi et al., 2012; Cavalcante and Roorda 2013). However, such studies
focused primarily on specific behaviour of firms such as shipper and carrier market interactions
(Cavalcante and Roorda, 2013), supply chain decisions of firms (Pourabdollahi et al., 2012), and
estimation of firm commercial trips (Kumar and Kockelman, 2008).
In logistics and supply chain contexts, de Jong and Ben-Akiva (2007) introduced a firm
microsimulation model using Sweden and Norway commodity flow data. The model takes
commodity flows from production to consumption as inputs and then disaggregates these flows
30
to firm-to-firm flows. Next, logistics chains (including choice of consolidation and distribution
centres for transhipment), transportation modes, and shipment size and type are microsimulated.
Similarly, Pourabdollahi et al. (2012) and Samimi et al. (2014) introduced FAME II; a
behavioural freight transportation modelling system that incorporates logistics decisions of
supplier selection, shipment size, and mode choice for the U.S. The goal of this framework is to
simulate freight logistics decisions to estimate freight demand by mode choice and then assign
commodity flows to the traffic network.
For the purpose of microsimulating freight flows, Wisetjindawat et al. (2007) introduced a
firm microsimulation for freight movement decisions considering firm characteristics of location,
employment, and floor space. The decisions of commodity generation, size, and consumption are
modelled at a micro level, to estimate truck flows and traffic assignment. Similarly, Abed et al.
(2014) introduced FALCON; an agent-based freight activities and logistic chains simulator. In
which firm agents are represented either as production, consumption, shippers, or carriers for the
purpose of simulating annual freight flows. Kumar and Kockelman (2008) address firm entry,
exit, survival, and location choice, for Austin, Texas, to estimate commercial trip generation.
Their research is based on aggregate firm birth and exit data. They apply firm entry and exit
rates, estimated for U.S. firms for the years of 2001-2002 and 2002-2003, randomly select exit
firm candidates, and generate firm births. Cavalcante and Roorda (2013) introduced FREMIS; an
agent-based microsimulation for freight market interactions and commodity flow modelling.
FREMIS model shippers and carriers interactions in the formation of contracts, while accounting
for market competition, product differentiation and economies of scale.
At an aggregate level, Gardrat et al. (2014), introduced SIMETAB; an aggregate freight
activity model that simulates the economic structure of an area by simulating the number of
establishments by size and economic sector for a given zone. This tool simulates the urban goods
movement in France using simple data such as population and number of establishments.
2.5. Concluding Remarks and Research Gaps
Firmographic modelling is a promising approach in freight microsimulation; it provides
information about firm behaviour of market entry, exit, expansion/contraction and relocation
decisions, in addition to understanding growth/decline patterns. Firmographic events are affected
by the economic growth, firm characteristics and strategies, industry classification, regulations
31
and local/regional policies, and the geographic locations of firms. While firmography has been
introduced in transportation modelling (Khan et al., 2002; Maoh and Kanaroglou, 2005; Moeckel
2005; Elgar et al., 2009), only a few models are found in the literature that address firmography
with a freight perspective (Kumar and Kockelman, 2008). Evidence from the literature suggest
that there is a need to construct freight models that integrate the individual behaviour of the
freight system agents (i.e. firms) with the changes in the economy, while addressing
heterogeneity between industries.
Current firmographic models have some limitations. They are either designed to address a
specific behaviour, deal with a limited number of firm interactions, target specific firm
populations, or study small-scale geographic regions. Key limitations and research gaps in
firmographic modelling are identified as follows:
• Unavailability of firm micro-data: Data availability, specifically firm micro-data, is
the main constraint to more elaborate firmographic models. The models reviewed in
this chapter use data that are either aggregate, or incomplete. The availability of
longitudinal firm micro-data would facilitate the construction of comprehensive
firmographic models.
• Underrepresentation of large and multi-location firms: While small-medium sized,
and single-location firms may constitute large segments of firm populations in
different areas, large-sized and multi-location firms may have a larger influence over
the economy. The majority of the reviewed firm microsimulation studies focus on
single-location, and/or small-sized firms (e.g. Maoh and Kanaroglou, 2005; Kumar
and Kockelman, 2008), leaving firmographic models of large-sized and multi-
location firms under-researched.
• Lack of firm entry behavioural models: Models of firm entry mostly use probability
distributions and Monte Carlo simulation approaches. This procedure relies on the
collective probabilistic behaviour of the population, hence, the micro-behaviour of
entry decisions is not well captured. Modelling the decision of firm entry requires
data that covers the non-entry behaviour. Micro-models that identify the individual
characteristics of new firms mostly rely on probability distribution and Monte Carlo
simulation, hence their accuracy is questioned. Concepts from evolutionary biology
are probably suitable to model such behaviour, where new firms adopt a combination
32
of strategies similar to the successful ones employed by firms alike, while potentially
introducing some new strategies.
• Unidimensional construct of firm growth: Models of firm growth represent the
change in employment size only, while it might be the combination of the growth of
other assets as well. A correlation between different growth measurements (e.g.
profit, revenues, and employment) is expected (Bruneel et al., 2009; Jang and Park,
2011). Hence, it is worthwhile to explore simultaneous firm growth models, i.e.
modelling firm growth in a multidimensional form taking into account other aspects
of growth such as labour, vehicle fleet, number of business establishments, floor
space, machinery and equipment, market shares, revenues, and sales values.
• A comprehensive agent-based firmographic microsimulation that simulates individual
firm decisions and evolution stages, and models interrelated behaviour within freight
systems is needed. The microsimulation should incorporate the effect of the economy,
market competition, industrial innovation and technological advances, and
firmography altogether. In this fashion, the microsimulation will enable researchers
and decision makers to observe the two way interrelations between freight systems,
and land use and the economy, which eventually can be used for forecasting and
policy assessment. Since firmography is also affected by local/regional policies and
regulations, future research should be conducted across a wider range of countries to
gain better insights about the determinants of firmography.
33
CHAPTER 3
A Framework of Firm Microsimulation: The Firmographic
Engine
3.1. Introduction
The limitations of the current firmographic models have been highlighted in chapter 2, and
the need for having freight micro models that represent behaviour and interactions of the
individual agents in the freight system is clear. The integrated freight microsimulation should
incorporate changes in the economy, and industry dynamics while considering heterogeneity of
firms across industries and regions.
In this chapter, a concept for an agent-based firm microsimulation is introduced, called the
firmographic engine that models the evolutionary stages and behaviour of firms including their
strategic decisions of resource acquisition, outsourcing, and asset expansion/contraction
decisions. This microsimulation builds upon recent attempt of (Roorda et al., 2010) of presenting
an agent-based microsimulation framework that explains the role and function of each agent (i.e.
firm) in the freight system and represents the interactions (both long and short-term) between
each agent in the market through contract formation. The firmographic engine simulates firm
entry, exit, growth, relocation, commercial vehicle ownership, freight outsourcing, and shippers-
carriers interactions.
The firmographic engine mainly microsimulates the dynamics in the freight system, and is
designed to be integrated with ILUTE (Integrated Land Use, Transportation, Environment) by
(Miller and Salvini, 2001); an agent-based microsimulation of activities of individual
households, families, persons, and jobs. This combination will present a fully integrated urban
land use model of both passenger and freight activities. The engine is designed to feed ILUTE
with the supply side of job dynamics in the market throughout tracking of firm birth, growth,
relocation, and death.
The goal of the engine is to be used as a tool for evaluating the implications of policy on
freight systems by simulating individual agents and forecasting their behaviour. Such policies
34
include, for example: infrastructure investment, trade agreements, land use, and taxation policies,
and government incentives.
The chapter first introduces a conceptual framework of the firmographic engine, followed by
details of its underlying modules. In each module, the parts have been implemented using the
behavioural models covered in this research are highlighted. A section about the used data sets
follows. Potential integrations of the engine with other transport models are also discussed. A
summary and future directions are presented at the end of the chapter.
3.2. A Conceptual Framework
The engine is composed of four basic modules: firm generation, market introduction,
performance evaluation, and firm evolution and strategy updates (FIGURE 3.1). Details of each
module are discussed next. The engine is designed to simulate the yearly firm events (e.g. firm
entry, growth, relocation, and exit) for a period of T years. The simulation is executed at the end
of every year, and iterates until the end of the desired time T. For instance, at year t =1, the
simulation starts by running the firm generation module that produces a list of “potential” firms,
Nt, that have their business strategies identified. In this stage, potential entrepreneurs are
identified (using population census and demographic data) and those who are more likely to
establish a new firm are simulated to represent the new firm population. The list of potential new
firms ‘Nt’ is then used as an input to market introduction module that is responsible for
microsimulating the physical entry of firms to the market. In this stage, operational strategies of
resource acquisitions (e.g. hiring employees, establishment of facilities, and contract formation
of other services) are identified. The result of this stage is a list of new physically operating firms
in the market (Nt). In the performance evaluation stage, the list of new operational firms along
with existing firms of previous year (Ot-1), are interacting over the course of the simulation year
(t), and according to some predefined performance indicators of each firm, each have three
decisions: 1) continue existing in the market without changing any of their strategies, 2) continue
existing in the market with changing some of their business strategies, or 3) decide to exit the
market either by declaring bankruptcy or merging with other continuing firms. The result of this
simulation stage is a group of surviving (Ot), and exiting firms (Dt). Firms that decide to continue
existing in the market may wish to revisit their strategies and make some updates depending on
their performance and long-term strategies. Decisions related to strategy updates are simulated in
35
the final stage of ‘firm evolution and strategy updates’. In this stage, decisions related to
expansion/contraction of physical assets, growth/shrinkage in the number of employees,
relocation, opening/closing facilities, and service contracts are simulated. The microsimulation
then steps into the second simulation time instance (t = t + 1) and repeats the same stages again
until the end of simulation time T. FIGURE 3.2 illustrates the decision process of firms in the
firmographic engine.
36
FIGURE 3.1 The Firmographic Engine: A Conceptual Framework
Firm Evolution / Strategy updates
t = 1
Firm
Generation
Performance Evaluation
t = t+1
Start
End
Nt (list of new firms)
Ñt + Ot-1
Dt (Exiting firms)
Ot (Surviving firms) = Ñt + Ot-1 - Dt
Yes
No
Market
Introduction
(operational firms) tÑ
Operational
Strategies
Business
Strategies
Ot (surviving firms )
t = T?
37
FIGURE 3.2 The Firmographic Engine: Firm Decision Hierarchy
Consider
entrepreneurship?
Establish a
firm?
Yes No
Yes No
Continue
existing?
Yes No
(market exit)
Merge/sell to
other firms?
Yes No
(Complete market exit)
Update
strategies?
Yes No
New + continuing firms t+1
t=1
1
2
3
4
t+1
38
3.3. Underlying Modules
3.3.1. Firm generation
This is the initial stage in which entrepreneurs start looking for investment and market
opportunities and define their business strategies accordingly. The output of this module is a list
of potential firms (Nt) with defined business strategies for the current time interval. The results of
this stage are not operational firms, but rather business initiatives that are ready for physical
implementation and introduction to the market. In other words, this stage mainly simulates the
business entry decision at the entrepreneurial stage, where entrepreneurs formulate a group of
strategies for the anticipated firms. Entrepreneurs rely on economic conditions, market demand,
and competition to make a decision of physical market entry or not. Models of entrepreneurial
decision of firm entry are not within the scope of this research. A very useful data source to
model this decision is the Global Entrepreneurship monitor (GEM) - the Adult Population
Survey data set (APS) that is available for the interval of 1998-2010. The APS is designed to
capture aspects of firm creation and entrepreneurship across countries (Global Entrepreneurship
Monitor, 2016). Demographic variables such as respondent age, gender, employment status, and
education are included. The collected information identifies individuals who become active
nascent entrepreneurs in the firm start-up process, either as owner-managers of new firms, or
owner-managers of already established ones. Those who are identified as the owner-managers of
new firms would formulate the population of study for the firm generation module. The survey
also contains information about whether individuals considered starting a business or not, and
whether they have physically established a firm or not. Such decisions can be formulated as in
FIGURE 3.2, in steps (1) and (2).
In the entrepreneurial decision phase, entrepreneurs formulate the firm’s long-term strategy,
and accordingly choose the business strategies that serve their goals. In light of the SIBS (which
will be discussed later), business strategies can include the following (FIGURE 3.3):
a) Production strategy: in which offered products/services, number of product lines, prices
and production quality are identified.
b) Resource strategy: which includes the specification of tangible assets (e.g. buildings,
machinery, equipment, employment, and vehicle fleet), outsourcing of freight operation
decisions, and facility locations.
39
c) Supply chain (SC) strategy: in which the role in SC, potential local/international
suppliers and their location, inputs/outputs of SC, and distribution and logistics
operations are specified.
d) Financial strategy: that includes the specification of financially related activities such as
feasibility studies, payroll, accounting and bookkeeping, and capital investment
management.
e) Marketing strategy: which is responsible for planning promotions and discount systems,
package designs, and media and advertisement activities.
f) Advanced technology usage and innovation strategies: in which decisions about
software, hardware, advanced technologies (e.g. green technologies, and automation),
and process, organizational, product and marketing innovations are made. The SIBS
offers details about the use of innovation in four areas and will be discussed later in
section 3.4.2, FIGURE 3.8.
As indicated in FIGURE 3.3, the engine is to be integrated with a future national economic
indicator module that is responsible for forecasting the economic conditions using some
indicators such as the GDP growth and unemployment rates. This module is to utilize the
research of (Bachmann et al., 2014a and 2014b) that models the effects of global trade patterns
on domestic freight operations in Canada. Currently, the engine uses economic indicators as
exogenous variables to the system. An illustration of how SIBS defines the long-term strategy
and strategic focus is presented in section 3.4.2, FIGURE 3.7. Additionally, FIGURE 3.7 explains
what are the business activity dynamics that are measured thorough the SIBS.
40
FIGURE 3.3 The Firmographic Engine: Firm Decision Hierarchy
Start t = 0
Total market &
investment
potentials (P)
National economic
condition indicator
module
P= P - 1 P = 0 or i = M
Yes
No
1. FIRM GENERATION
Nt: list of firms with strategies (n)
Yes
n =n+1
i = 1
n = 0
i = i + 1 Start firm
?
No
Business strategy
Entrepreneurial phase
Saturation
?
A
Production strategy
• Products lines/ # of
products /provided
services
• Product prices
• Production quality
Supply chain (SC) &
logistics strategy
• Role in SC
• Local/international
suppliers
• SC inputs/outputs
• Distribution and logistics
Resources strategy
• Outsourcing decisions
• Tangible and intangible
assets (e.g. employment,
investments, buildings,
fleet, and machinery)
• Facility locations (local /
international)
• Research and
development activities
• Support services (e.g.
HR, ICT support, and
legal services)
• Sales services
Marketing strategy
• Media and advertisement
• Promotions and discount
systems
• Design packaging of
products
Innovation and
technology strategies
• Software development
• Advanced technology
usages (e.g. green
technologies,
nanotechnologies, and
automation)
• Process, organizational,
product, and marketing
innovations.
Financial strategy
• Capital investments
• Feasibility studies
(expected cost, and
expected revenues)
• Financial services (e.g.
payroll, accounting
services, and
bookkeeping)
Business Strategy Generation / Long-term strategic focus
D
41
3.3.2. Market introduction
This stage is responsible for simulating the firm’s physical entry to the market (FIGURE 3.4).
It simulates the decisions that are related to the application of the operational strategies. It
involves simulating the decisions of hiring employment and acquiring business facilities
including production, logistics, and business service facilities. Resource acquisition also involves
defining other firm tangible assets such as vehicle fleet, machinery and equipment, warehouses,
and transshipment centres. Furthermore, as suggested by (Roorda et al., 2010), interaction
amongst firms (other agents) are defined through contracts. Firms contract with other firms for
other services that are not carried out by the firm. Examples of such contracts are commodity
contracts (e.g. for raw materials), business service contracts (e.g. technical support), and logistics
service contracts (e.g. 3PLs). Contract formation is simulated after the simulation of resource
acquisition, as firms identifies first what are the core activities to be carried out in-house and the
necessary recourses, and what are the activities/services to be outsourced. The result of this
simulation stage is a group of physically operating firms (Ñt) that are interacting in the market
and periodically monitoring their performance.
This module is designed to be integrated with FREMIS, introduced by (Cavalcante and
Roorda, 2013). FREMIS is an agent-based microsimulation of freight market interactions and
commodity flow. FREMIS models shipper-carrier interactions in the formation of contracts,
while accounting for market competition, product differentiation and economies of scale. This
integration will allow the full representation of firm behaviour in the market upon all other firm-
related decisions are modelled. This module utilizes models of firm start-up size that cover the
resource acquisition of employees and tangible assets, that are presented in chapter 4. While
models of freight outsourcing decisions are introduced, their predictive abilities may not be
credible for new firms as the introduced models (in chapter 7) do not include firm age to explain
outsourcing behaviour.
42
FIGURE 3.4 Market Introduction Module
3.3.3. Performance evaluation
New firms (Ñt) along with already existing firms from previous year (Ot-1) interact in the
market, and after one year, each firm evaluates their performance according to predefined
performance measures. Performance measures are selected mainly based on the long-term
strategy and strategic focus of the firm. For instance, firms that employ market positioning and
product leadership as a long-term strategy may adopt performance measures of market/customer
share growth, customer satisfaction, and improved delivery time as they relate to market
leadership. The SIBS collects information of firm performance measure choices, and is
considered a good data source to model decisions related to performance measures selection,
which is not the scope of this research.
After performance evaluation, each firm decides whether to continue existing in the market
or not. A firm can choose to exit the market, if they are not meeting their anticipated
performance, in two forms; sell/merge with other existing firms, or completely disappear from
i = 1
i = n
?
2. MARKET INTRODUCTION
i = i + 1 No
Ñt : operational firms (n)
Operational strategy
FREMIS
Contract formation
• Commodities
• Logistics services
• Business services
Resource acquisitions
• Employees
• Facilities
Commodity production
Business services
Logistics
Vehicles Transshipment centres Warehouses
Yes
Nt: list of firms with strategies (n)
A
B
43
the market by declaring bankruptcy (Khan et al., 2002; Maoh and Kanaroglou, 2005). Models of
firm survival and exit decisions are covered in this research and are presented in chapter 6. A
discrete-time hazard duration model is introduced, which does not identify the type of exit.
Models that specify firm exit type (i.e. merge/sell to other firms, or declare bankruptcy) are not
covered as the provided database does not include the type of exit. The result of this simulation
step is a group of exiting firms (Dt) and a group of continuing firms (Ot). Exiting firms are
removed from the microsimulated firm population, and only continuing firms (Ot) are carried
forward to the next simulation stage (FIGURE 3.5).
FIGURE 3.5 Market Introduction Module
Ñt : operational firms
i = 1
Dt = 0
Ot = 0
Ot-1: previous year survivals
Ñt +Ot-1 Performance Indicators
Survival
?
Yes
Ot = Ot + 1
i = Ñt + Ot-1
?
Yes
No
3. PERFORMANCE EVALUATION
Sell/merge
?
Yes
Exit market Dt = Dt + 1
i = i + 1
No
No
Ot: survivals
Market/Customer
share growth
Gross/Operating
margin growth Sales/revenues
growth
Shareholder
dividends growth Customer
satisfaction
Increased sales
of new products
Improved
delivery time
D
C
B
44
3.3.4. Firm Evolution
Firms that decide to continue existing in the market (Ot), may need to revisit their employed
strategies and make some updates to maintain/enhance their market status and achieve their long-
term goals. Firms decide to update their strategies depending on the current economic conditions,
market demand, industry dynamics (e.g. entry and exit rates), and market competition. Firms
may decide to increase/decrease their production size by introducing new products, enhance the
quality of currently offered products, and/or cancel product lines. Firms may consider changing
their suppliers (either locally or internationally). Decisions of physical expansion or contraction
are also potential in this stage, such as hire/fire employees, expand/contract facilities, open new
locations, and/or close exiting ones. Firms may also relocate to other locations, outsource some
of their activities, introduce new innovation techniques, and/or change their marketing strategy to
enhance their performance and achieve higher market growth. Firms may form new contracts
and/or update/extend/terminate existing ones with other firms (FIGURE 3.6). As discussed in the
firm generation module, section 3.3.1, an external economic condition module (that is not the
subject of this research) is to be integrated to the firmographic engine, which will provide
estimates of economic indicators such as GDP growth and unemployment rates. In this research,
firm growth models of employment and tangible assets (chapter 5), and models of freight
outsourcing decisions (chapter 7) are presented as parts of business and operational strategy
updates. When this simulation stage is finished, the engine is ready to proceed to the next time
instance (t = t+1), and the microsimulation process is repeated until t = T and the simulation
stops.
The firmographic engine considers two aspects of firm growth; the change in the number of
employees, and the change in the firm physical forms indicated by the dollar value of the
tangible assets. We hypothesize that firm employment size affects their tangible assets. In
chapter 4, details of the adopted simulation configuration of employment and tangible assets are
introduced.
45
FIGURE 3.6 Firm Evolution Module
3.4. Data Sources
The major obstacle to fully develop firmographic models is longitudinal firm microdata
availability. Several micro databases of firms in Canada have been investigated, which are
relevant to firmography, but can be used in limited ways due to their confidentiality. Statistics
Canada offers a wide range of firm microdata that is available through the Canadian Centre for
Data Development and Economic Research (CDER). This program offers microdata to support
Operational strategy
4. FIRM EVOLUTION
Business strategy
Production strategy
Resources strategy
SC/logistics strategy
Financial strategy
Innovation and technology
Marketing strategy
Resources
• Expansion
• Contraction
Contracts
• Formations
• Renewals
• Terminations
Strategy updates
Ot: survivals (o)
i = 1
i = Ot
Yes
i = i + 1 No
No
End
Yes
t = T t = t+1 D
National economic
condition indicator
module
C
46
Canadian researchers to conduct analytical research on businesses and economic development
(Statistics Canada, 2016b). Examples of investigated microdata include Annual Survey of
Manufacturing (ASM), Longitudinal Employment Analysis Program (LEAP), T2-LEAP,
Longitudinal Work File (LWF), Capital and Investment Program (CIP), National Accounts
Longitudinal Microdata (NALMF), Survey of Innovation and Business Strategies (SIBS), and
Workplace Employee Survey (WES). Each of the listed datasets could be utilized to estimate
parts of the underlying modules of the firmographic engine. Some of such data sets study labour
dynamics of turnover and mobility (e.g. LWF and WES), some offer longitudinal data of firms’
financial performance (e.g. CIP) and firms’ overall behaviour (e.g. LEAP, T2-LEAP, and
NALMF), and others focus only on firms in specific industries (e.g. ASM). For details about the
listed datasets refer to (Statistics Canada, 2016b).
These data sets have been studied carefully to select the most suitable ones for the scope of
this research and to implement basic parts of the firmographic engine. The T2-LEAP data set has
been selected to estimate the models of firm start-up size, growth, and failure, and SIBS is
selected to estimate the models of manufacturers’ freight outsourcing decisions.
3.4.1. T2-Longitudinal Employment Analysis Program (T2-LEAP)
T2-LEAP offers longitudinal information of Canadian firms that have records in the tax files
from 1984 until 2012. It is the result of merging the LEAP database to Corporate Tax Statistical
Universal File (T2SUF). The LEAP is compiled from three sources: 1) T4 administrative data
available at Canada Revenue Agency (CRA), 2) data from Statistics Canada’s Business Register
(BR), and 3) data from Statistics Canada’s Survey of Employment, Payroll and Hours (SEPH)
(Statistics Canada, 2012a). Statistics Canada considers the LEAP files as the primary source of
information to study employment dynamics at the firm level. For more information on the LEAP
and T2-LEAP refer to their user guide in (Statistics Canada, 2012a).
The T2-LEAP includes yearly information of firm employment size, province, industry class,
sales values, and tangible assets. The data set is linked to CANSIM data to obtain the Canadian
GDP (National GDP, GDP by industry, GDP by province, and GDP growth rates) (Statistics
Canada, 2015a), and yearly unemployment rates (Statistics Canada, 2015b).
Firm population of the years of 2001 to 2012 are the focus of this research. Only for-profit
industries are included while excluding not-for-profit ones from the analysis. For-profit
47
industries are mostly privately owned, and are more susceptible to economic growth. Not-for-
profit entities (i.e. educational service, health care and social assistance, and public
administration) mostly offer public services that are indispensable (e.g. education and health
services) which typically are governmental, and their growth/shrinkage are affected mainly by
public policies, and demand and supply. A discussion of the data filtering and manipulation
techniques is provided with each model in the corresponding chapters.
3.4.2. Survey of Innovation and Business Strategies (SIBS)
SIBS is a cross-sectional data set that contains statistical firm information of strategic
decisions, innovation activities, operational tactics, business strategies, relationship to suppliers,
sales activities, competition, international activities, use of advanced technology and innovation,
and use of government support programs to support innovation. This survey was initiated in
2007 for the purpose of understanding market and policy factors that influence innovation-
oriented business strategies (Statistics Canada, 2012b). This survey is launched every three
years, and collects firm data for a three-year interval. Two cross-sectional data sets of the 2009
and 2012 surveys have been used to estimate models of outsourcing decisions of manufacturers,
presented in chapter 7. The 2009 survey covers the interval of 2007 till 2009, while the 2012 one
covers the period of 2010 till 2012. The surveys are conducted on a sample of enterprises that
have at least 20 employees and annual revenue of $250,000 and more. The firm population is
limited also to enterprises in the following 14 sectors defined according to NAICS:
1. Agriculture, Forestry, Fishing and Hunting
2. Mining, Quarrying, and Oil and Gas Extraction
3. Utilities
4. Construction
5. Manufacturing
6. Wholesale Trade
7. Retail Trade
8. Transportation and Warehousing
9. Information and Cultural Industries
10. Finance and Insurance
11. Real Estate and Rental and Leasing
48
12. Professional, Scientific and Technical Services
13. Management of Companies and Enterprises
14. Administrative and Support, Waste Management and Remediation Services
The 2012 survey targeted a random sample of 7,818 enterprises, and the 2009 targeted 6,233
enterprises across Canada. The manufacturing firm population constitutes 6,233 enterprises
(80%) of the 2012 survey, and 4,599 (74%) of the 2009 survey. In this research, manufacturing
firms are the population under investigation as they account for a large percentage of the
Canadian Domestic Product (GDP) (Richards, Snoddon, and Brown 2014). In addition, the
manufacturing industry involves large freight operations, including outsourcing, locally and
internationally.
Long-term strategy and strategic focus information is collected in detail through the
questionnaire. FIGURE 3.7 presents a schematic of how long-term strategy and strategic focus
are addressed in the SIBS. SIBS assumes that the long-term strategy selection is the core
decision that influences firm’s strategic focus and business activities (Statistics Canada, 2012b).
SIBS summarizes the following areas:
a) Business activities: such as outsourcing, relocation, and expansion/contraction of assets
and facilities of firms. The SIBS includes information that allows estimating models to
describe the behaviour of outsourcing and relocating different business
activities/facilities to other locations in Canada or worldwide. Also, expansion and
contraction of different activities (e.g. new products/services, distribution and logistics
centres, other service facilities) can be captured and modelled using data collected by the
SIBS.
b) Changes to business practices: some firms may wish to change some of their business
practices to accommodate some customer requirements such as implementing specific
cost reduction, improve the quality of some goods/services, or entering new geographic
regions.
c) Use of advanced technology: is addressed in SIBS using nine key technologies of
computerized design and engineering, computerized processing and assembly,
computerized inspection, communication, automated material handling, information
49
integration and control, biotechnologies/bio-products, and green technologies and
nanotechnologies.
d) Human resources practices: which include promotion strategies, performance
management, employment selection practices, training, and incentive programs (e.g.
employee profit-sharing, stock ownership, and merit bonus)
e) Use of innovation: the SIBS collects information for four aspects of innovation;
production innovation, process innovation, organizational innovation, and marketing
innovation (FIGURE 3.8). Statistics Canada (2009) presents a report that includes a
summary of use of innovation in the four identified aspects for Canadian firms.
In addition, the survey includes a section indicating whether firms are utilizing government
support programs for innovation related activities or not. The questionnaire covers federal,
provincial, and municipal programs. Examples of such programs include government training
programs, grants, tax credits, export incentives, and market information services (Statistics
Canada, 2012b).
50
FIGURE 3.7 Long Term Strategy, Strategic Focus, and Business Activity Dynamics in SIBS
Long term strategy
Market positioning
(product leadership) Mass market
(low-price)
Goods and
Services
0: No 1: Yes
Marketing
practices Operations and
business activities Organizational and
management practices
0: No 1: Yes 0: No 1: Yes 0: No 1: Yes
Maintain
existing Introduce
new Maintain
existing Introduce
new Optimize
existing Introduce
new
0: No 1: Yes
Introduce
new Optimize
existing
0: No 1: Yes
Strategic Focus
Business activities • Production of goods/provision of services
• Service centres (customer service, marketing,
sales, after sales, financial, R&D, ICT…etc.)
• Distribution and logistics
Changes to business practices to
respond to customer requirements
(Specific cost reduction, enter new
market, extend business hours…etc.)
Use of advanced technology
(Automation, biotechnologies,
nanotechnologies, green
technologies…etc.)
HR practices
0: No 1: Yes
Internationally Within Canada
0: No 1: Yes
Internationally Within Canada
0 1 or more
Off-shelf
(purchased/leased) Licensing/ customizing
advanced technologies
In conjunction
with others
Use of innovation
Business Activity Dynamics
Production performance
management practices
Relocation Outsourcing
51
FIGURE 3.8 Use of Innovation in SIBS
3.5. Relationship to Other Microsimulation Models of Transportation
Systems
3.5.1. Integration with FREMIS
FREMIS is a microsimulation modelling framework of freight agents to forecast freight
flows. It is focused on shipper decisions of shipment bundling and carrier selection throughout
simulating carrier proposals for contracts (Cavalcante and Roorda., 2013). The potential
integration between FREMIS and the firmographic engine is expected to cover the contract
formation of operational strategies of production, logistics, and/or businesses outsourcing to
other firms. This integration is happening in the two simulation stages of market introduction,
and firm evolution in the firmographic engine. FIGURE 3.9 illustrates the decision tree of
whether a firm chooses to outsource their freight activities, such as logistics and distribution, to
other firms or perform such activities in-house using their own vehicle fleet. Commercial vehicle
ownership decisions have been covered by the authors in a separate research using commercial
Process Innovation
• New/improved method of manufacturing
• New/improved logistics, delivery, or distribution
methods
• New/improved supporting activities (e.g.
maintenance systems, computing…etc.)
Marketing Innovation
• Changes to the design/ packaging of
goods/services
• New media or techniques for goods/services
promotions
• New methods for good/ service placements
• New methods of pricing goods/service
Organizational Innovation
• New business practices for organizing procedure
(SC management, knowledge management,
quality management…etc.)
• New methods of organizing work responsibilities
and decision making
• New methods of organizing external relations
1or more 0
Developed within
enterprise Joint with
others
Developed by
other enterprises
Product Innovation
• New or significantly improved goods
• New or significantly improved services
1, or more 0
Novel to the
market Has some
unique features
Widely available
on the market
52
vehicle travel survey data (Mostafa and Roorda, 2013), and the outsourcing decisions are
covered in this research in chapter 7. FREMIS is intended to cover the third level of the decision
tree (FIGURE 3.9) of shippers when they outsource part/all of their freight operations to other
carriers through long or short-term contracts.
FIGURE 3.9 Freight Operations Decision Hierarchy
3.5.2. Integration with ILUTE
ILUTE is a comprehensive microsimulation model of urban systems to simulate interacting
agents of individuals, households, and firms (Miller et al., 2004). The purpose of ILUTE is to
substitute the conventional four-stage transportation demand models with activity-based models
that produce origin-destination flows by mode, time of day, and trip purpose. ILUTE is designed
to simulate land development, location choices (e.g. dwellings and firms), and auto ownership
(Miller and Salvini, 1998). Another major emphasis of ILUTE is microsimulation of market
demand-supply interactions such as the ones within residential and commercial real-estate
markets, and the job market.
The current firmographic simulation mechanism within ILUTE is restricted to largely
aggregate inputs (Harmon, 2013). No micro-level presentation of the firm has been yet
Freight operations
(e.g. goods production, and logistics and
distribution)
Private fleet
ownership
Outsourcing
3PLs Couriers Individual carriers/
Freight forwarders
Long Term
Cars
(1, 2, 3...N)
Pickups/vans
(1, 2, 3...N)
Trucks
(1, 2, 3...N)
Others
(1, 2, 3...N)
Fleet composition and size Short
Term FREMIS
53
implemented in ILUTE, instead, firm attributes are synthesized and linked to the ‘Job’ entity.
ILUTE synthesizes four firm attributes of firm size classification, multiple firm location
indicator, collective bargaining agreement indicator, and firm location, which are used in the
‘Wage’ model introduced by (Hain, 2010). The current job creation process in ILUTE is random
and relies on synthesized firm data that are based on deterministic growth factors (Harmon,
2013).
The firmographic engine provides micro-level estimation of the number of yearly created
jobs resulting from firm entry, growth, and exit microsimulation. The firm start-up and growth
models can represent job creation (e.g. hiring), and job destruction could be presented through
firm growth and failure/exit models. However, since the merge/selling to other firms is not
identified in the firm failure model, it is not complete yet to track mergers and acquisitions.
Currently, the firmographic engine simulates employment through firm start-up size, growth, and
exit models on the industry level (based on 2-digit NAICS code), which does not give
information about the job type/occupation level. A potential integration between the
firmographic engine and ILUTE is explained in FIGURE 3.10 and can be summarized in the
following:
• The individual modelling of Canadian firms, introduced in the firmographic engine,
provides job supply side interactions. Employment dynamics are captured through
simulating firm entry, growth, and exit which are inputs to labour market modules in
ILUTE. Firm birth and death models in the firmographic models are to potentially
replace current regression-based Job Supply Model in ILUTE (Harmon, 2013).
• The firmographic engine is designed to include models of firm location choice and
migration. Such models are essential to provide location information of firms, and
simulate associated demographic and land us changes. This would allow ILUTE to
present a dynamic business location choice model, instead of the current
representation of locations which is based on static set of spatial probabilities which
do not change as the urban topography evolves (Harmon, 2013).
54
FIGURE 3.10 Overall Structure of ILUTE and Potential Interactions with the Firmographic
Engine - Adapted from (Miller et al., 2010)
Observed Base Year Aggregate
Distribution of Agents & Attributes
(Census, etc.)
Labour Market
Built-Space
Market
Auto Ownership
Activity-Based Daily Travel
(TASHA)
Network Assignment
(MatSim/EMME)
Urban Energy Consumption
Transportation Emissions &
pollution Concentration Dispersion
Demographic
Update
Firmography and
Employment
(The Firmographic Engine)
Commercial Vehicle
Movement
Agent Synthesis
Synthetic Agents @ T
T= T + ΔT
Road & Transit
Network
Evolution
55
3.6. Concluding Remarks and Future Directions
Firmographic models are useful for freight micro-modelling. They provide essential
information of firm entry, growth, relocation, and exit that influence transportation systems at
large. Such events are the main controllers of the job market. They also induce land use and
demographic changes to the surrounding areas. New/expanding firms may attract labour with
specific skills, education, and experience to the firm geographic zones. Also, businesses of the
same industry class may cluster together geographically to benefit from sharing same resources
and services (e.g. agglomeration economies). Such phenomena eventually impact transportation
demand and traffic patterns.
National micro-level firmographic models are scarce in the Canadian literature. In this
research, we propose our concept for the firmographic engine; a firm microsimulation platform
that accommodates changes in the economy and industry activities, and considers differences
between firms across industries and regions in a unified framework. It simulates firm evolution
stages of the early entrepreneurial stage, market entry, growth, and market exit. Throughout the
evolutionary stages, events of firm birth, growth, relocation, death are simulated, in addition to
other firm freight-related decisions. In this chapter, the modelling framework of the firmographic
engine is presented. A full description of the underlying modules is introduced with highlights of
the implemented parts in this research.
Microdata availability is the main challenge for implementing the firmographic engine.
Several microdata sets have been investigated and two sources have been selected; T2-LEAP and
SIBS datasets that are available through Statistics Canada. T2-LEAP is a longitudinal microdata
of firms that covers the Canadian firms (that have tax records) in the interval of 2001-2012. T2-
LEAP is used to estimate behavioural models of firm start-up employment and tangible assets
(used in market introduction stage), firm employment and tangible assets growth (used in firm
evolution and strategy updates), and firm exit/survival (used in performance evaluation stage).
Two cross-sectional datasets of SIBS are used to estimate freight outsourcing models for
Canadian manufacturers. The estimated models are to cover parts of business strategy updates in
firm evolution module. However, such models have not been tested for their predictive abilities
for simulating outsourcing decisions of new firms as no information about firm age was available
in the SIBS.
56
Currently, the estimated models are presented at the Canadian provincial level. To make the
models transferable, models should be re-estimated using land use and demographic variables
(e.g. population density, education levels, land use, and firm densities) instead of the provincial
dummy variables to describe the firm location. This way, models can be applied in locations with
similar attributes.
Firm entry decisions have not been covered in this research. Instead, simple forecasts of entry
rates of simple linear regression models using historical data available (Statistics Canada, 2016a)
are potential candidates. Models that cover the first stage of the entrepreneurial decision of firm
entry (to cover firm generation module) is a logical next step to present firm entry on the micro
level. The APS (Global Entrepreneurship Monitor, 2016) is a suitable data source to identify the
potential entrepreneurs using individual population demographics such as age, gender, and
education. Entrepreneurs that establish new firms can also be modelled using the same data set
and inferences about discrete firm entry can be made. The result of this modelling step covers
firm entry behaviour in the firmographic engine.
Decisions related to performance indicator choices that are addressed in the performance
evaluation stage, can be modelled using data from SIBS. This model can provide explanation of
how firms measure their performance depending on their strategic focus and scope.
Economic growth indicators of GDP and unemployment rates are currently being treated
exogenously to the engine. A future step is to provide a national economic indicator module that
provides forecasts of economic growth using models of global trade pattern effects on domestic
freight operations introduced by (Bachmann et al., 2014a and 2014b). Currently, simple models
(e.g. linear regression) can be used to estimate future GDP and unemployment rates using
historical data publicly available at (Statistics Canada, 2015a; Statistics Canada, 2015b). GDP
rates are classified by industry at the provincial level, while unemployment rates are presented at
the provincial level.
Firm exit/survival models are presented in this research to cover the firm exit decision in
performance evaluation stage. However, the introduced models do not define the exit type (i.e.
bankruptcy vs. merge/sell to other firms) as exit type is not possible to identify using the T2-
LEAP dataset. Once such data is available, competing risk models are potential model structures
to represent firm exit type.
57
Potential integration of the firmographic engine and other transportation microsimulation
models have been discussed. A two-way interaction between the firmographic engine and the
shipper-carrier interactions microsimulation model of FREMIS has been explained to cover other
aspects of firm-to-firm interactions. Furthermore, the engine is a strong candidate to provide
ILUTE with firm-level employment dynamics. Such integrations will provide a fully integrated
urban model of both passenger and freight activities in transportation systems.
58
CHAPTER 4
Models of Firm Start-up Size of Canadian Firms
4.1. Introduction
Firm start-up size is an important determinant of new firms. Modelling firm start-up size at
the micro level is essential to understand job creation because new firms are an important part of
the supply of employment. Also, some studies suggest that firm start-up size influences the
firm’s subsequent performance and survival (Dunne et al., 1989; Audretsch and Mahmood,
1994; Mata and Portugal, 1994). Determinants of firm start-up size have been addressed largely
for European countries (Arauzo-Carod and Segarra-Blasco, 2005;Audretsch et al., 1999;
Barkham, 1994; Görg et al., 2000; Mata and Machado, 1996). To the best of the author’s
knowledge, research studies in a Canadian context that address determinants of firm start-up size
are absent, both in terms of employment size or tangible assets.
This chapter includes models of firm start-up size in terms of employment and tangible assets
for Canadian firms. The models are the foundation of the firm generation module of the
firmographic engine, presented in chapter 3. The majority of studies surveyed in the literature
address firm start-up in terms of number of employees only (Mata, 1996; Mata and Machado,
1996; Görg et al., 2000; Colombo et al., 2004; Arauzo-Carod and Segarra-Blasco, 2005), while
firm size can be multi-dimensional (e.g. number of business establishments, owned vehicles,
and/or warehouses) (Barkham, 1994; Bruneel et al., 2009; Jang and Park, 2011). In this research,
firm size is addressed in two dimensions; the number of employees and the value of tangible
assets. Tangible assets include physical assets such as buildings, land, and machinery and
equipment (Statistics Canada, 2012a). Tangible assets are selected because they represent the
physical size of the firms, and we hypothesize that employment and tangible assets are correlated
(e.g. larger assets require larger number of employees and vice versa). From a transportation and
land use perspective, spatial attributes of the firm (e.g. floor space, and the number of business
establishments), and the vehicle fleet size are of interest. Since disaggregate data are only
available for the dollar value of the tangible assets, tangible assets are utilized as a proxy for
59
other physical indicators of firm size such as floor space, number of business establishments and
fleet ownership.
Models of firm start-up size and growth are configured as in FIGURE 4.1. The selected
sequence starts first (at time t =1) by simulating the firm start-up decision of employment size
(step #1) using basic firm information (e.g. province, industry class, and economic indicators).
The estimated number of employees is then passed as input to estimate the start-up tangible
assets (step #2). In the following year (t = 2), the number of employees is estimated (in step #3)
using previous year’s number of employees (t-1), and this information is then used to estimate
the tangible assets (in step #4) along with the previous year’s tangible assets.
FIGURE 4.1 Firm Start-up and Growth Simulation Configuration
This configuration is based on the assumption that employment and tangible assets are
dependent on what the previous year’s values were. It also assumes that the tangible asset growth
is a function of the number of employees of the current year (t), as in Equations (4.1) and (4.2).
𝑒𝑚𝑝𝑡 = 𝑓(𝑒𝑚𝑝𝑡−1, 𝑓𝑖𝑟𝑚 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠) (4.1)
𝑻𝑨𝒕 = 𝒇(𝑻𝑨𝒕−𝟏, 𝒆𝒎𝒑𝒕, 𝒇𝒊𝒓𝒎 𝒂𝒕𝒕𝒓𝒊𝒃𝒖𝒕𝒆𝒔) (4.2)
While it is more logical that firms decide first on their tangible assets (i.e. purchase new/sell
equipment, or open/close a new business establishment), and then hire/fire employees
accordingly, the aforementioned configuration is chosen for two reasons. First, information of
the tangible assets ($) for firms is not easily available for microsimulation purposes; no public
sources are available for such information on the micro level. Second, simulating employment
Tangible Assets
(TA)
Employment
t = 1 t = 2 t = N
.....
...........
.....
...........
t = 3
1
2
3
4
5
6
2N-1
2N
60
growth is more important for transportation and land use models than tangible assets. In some
situations, simulating the employment growth individually would be more essential than
simulating the tangible assets, and hence they are simulated in separated models, and without
necessitating tangible assets as inputs for employment models.
This chapter is organized as follows. It starts with a review of literature on determinants of
firm start-up size. Then the data sources are explained, followed by explanations of ordered logit
models for start-up employment size and tangible assets. Concluding remarks and future work
are discussed at the end of the chapter.
4.2. Determinants of Firm Start-up Size
The decision of firm start-up size is mainly governed by industry characteristics, firm
characteristics, economic conditions, and founder characteristics (Barkham, 1994; Mata, 1996;
Mata and Machado, 1996; Görg et al., 2000; Colombo et al., 2004; Colombo and Grilli, 2005).
Industry characteristics include economies of scale, industry size and growth, and industry
turbulence (e.g. entry and exit rates) (Mata, 1996; Görg at al., 2000; Arauzo-Carod and Segarra-
Blasco, 2005). Economic conditions are reflected in GDP growth by (Reynolds and White,
1997). Finally, founder characteristics (age, gender, education, human capital, and work skills)
are found in several studies to be the major driving factors to firm start-up size (Colombo et al.,
2004; Mata, 1996; Barkham, 1994).
In this research, determinants of firm start-up size, including firm characteristics, economic
conditions, competition, and industry characteristics, are investigated. Founder characteristics are
not included in this research for two reasons: 1) unavailability of suitable data, and 2) the firm
microsimulation context for the model will simulate individual firms and the surrounding
environment, not the firm founders. The studied determinants are summarized in TABLE 4.1
along with their expected effect on firm start-up size.
61
TABLE 4.1 Investigated Determinants of Firm Start-up Size
Variable Description Expected sign
Industry class A series of dummy variables to represent the 17
classes of the for-profit industries on the 2-digit
NAICS code.
--
Province
A series of dummy variables representing the
province where the firm is located. The
investigated provinces are Ontario, Quebec,
Alberta, British Columbia, Manitoba,
Saskatchewan, Atlantic Canada (including Nova
scotia, New Brunswick, Newfoundland and
Labrador, and Prince Edward Island), and rest of
Canadian provinces and territories (Nunavut,
Yukon, and Northwest Territories)
--
Economic Indicators
Yearly GDP growth rates (%) The percent change in the GDP between year (t)
and year (t-1). Positive
(Log) GDP by industry (2-
digit NAICS code)
The natural logarithmic value of the GDP
classified by the industry for year (t) Positive
Yearly provincial
unemployment rate (%)
The unemployment rate of year (t) on the
province level of where each firm is located Negative
Industry characteristics and competition
Entry rate by industry Yearly firm entry rate on the 2-digit NAICS code Negative Exit rate by industry Yearly firm exit rate on the 2-digit NAICS code Negative
Average firm size (log) The natural logarithmic value of the average firm
size of each 2-digit NAICS code industry Positive
Number of competitors in the
same CMA/CA of the same
NAICS-3 (log)
The natural logarithmic value of the number of
competitors located in the same CMA/CA of
where a firm is located, on the 3-digit NAICS
code.
Negative
We expect that firm start-up size varies across industries in different provinces as highlighted
in similar studies (Audretsch et al., 1999). Therefore, dummy variables of a firm’s industry and
province are considered. The literature indicates that economic growth has an impact on firm
start-up size. A growing economy encourages firms to start large in size, while firms may
choose to start small in size if the economy is declining. Three economic indicators are included
in our models to reflect economic growth: 1) % growth in the GDP, 2) the dollar value of the
GDP by industry (to represent the economic size of the industry), and 3) provincial
unemployment rates.
Mata and Machado (1996) suggest that firm start-up size increases with economies of scale
and the rate of firm entry and exit per industry. Also, the amount of capital required to operate a
62
minimum efficiently scaled (MES) firm has a negative relationship with the new firm size,
because of the higher start-up costs (Fonseca et al., 2001; Geroski, 1991; Mataet al., 1995). Entry
and exit rates by industry, using the 2-digit NAICS code (TABLE 4.2), are included in firm start-
up size models. Firm start-up size and average firm size across industries vary according to the
technology and innovation usage, and economies of scale in each industry (Mata and Machado,
1996). Since it is hard to quantify the MES by industry, average firm size of each industry is
included as an indicator of the MES in the employment dimension. We hypothesize that the
larger the average size of firm within industries, the larger the start-up size of new firms is.
Most of the studies in the literature used regression approaches to investigate determents of
firm start-up size (Mata and Machado, 1996; Görg et al., 2000; Audretsch et al., 1999; Barkham,
1994). In our study, start-up size is considered to be a decision, and discrete choice model
approaches are used to quantify the effect of each of the investigated determinants on the start-up
size choice.
TABLE 4.2 Industry Classification 2-digit NAICS Code
NAICS 2-digit
code Industry Classification
11 Agriculture, Forestry, Fishing and Hunting
21 Mining, Quarrying, and Oil and Gas Extraction
22 Utilities
23 Construction
31-33 Manufacturing
41 Wholesale trade
44-45 Retail trade
48-49 Transportation and warehousing
51 Information and cultural industries
52 Finance and insurance
53 Real estate and rental and leasing
54 Professional, scientific and technical services
55 Management of companies and enterprises
56 Administrative and support, waste management and remediation services
71 Arts, entertainment and recreation
72 Accommodation and food services
81 Other services (except public administration)
63
4.3. Data Description and Basic Analysis
The T2-Longitudinal Employment Analysis Program (T2-LEAP) database provided by
Statistics Canada is used for model estimation. The data provide longitudinal information of
Canadian firms between the years of 2001 and 2012. Only for-profit industries are included, and
not-for-profit industries are excluded from the analysis (i.e. Educational service, Health care and
social assistance, and Public administration) because for-profit industries are mostly privately
owned, and are more susceptible to economic growth. Not-for-profit entities mostly offer public
services that are indispensable (e.g. education and health services) which are typically
governmental, and their growth/shrinkage are affected mainly by public policies, and demand
and supply.
The T2-LEAP includes yearly information of firm employment size, province, industry
class, sales value, and tangible assets. The data set is linked to CANSIM data to obtain the
Canadian GDP (National GDP, GDP by industry, GDP by province, and GDP growth rates)
(Statistics Canada, 2015a) and yearly unemployment rates (Statistics Canada, 2015b). Data are
filtered to only include new firms that entered the market between 2001 and 2012. A new firm is
marked when the first firm record is observed in year (t) and is not observed in previous years (t-
1, t-2…etc.). The 2001 new firm population is excluded from the analysis as the data is left
censored, and it is not evident whether firms that appear in the year 2001 have been present in
previous years or not.
Average firm start-up employment size (for the period of 2001-2012) is classified by industry
as in FIGURE 4.2. It can be seen that firms that belong to ‘Accommodation and food services’
industry have the highest start-up size (4.2 employees) whereas new firms in ‘Mining,
Quarrying, and Oil and Gas Extraction’ industry have the lowest size (1.6 employees). Firm
employment sizes at entry calculated using the T2-LEAP in this research conform to those
published by (Ciobanu and Wang, 2012) for 2000 to 2008. Check TABLE A.1 in Appendix A for
the yearly values of firm start-up by industry for the 2001 to 2012 interval.
64
FIGURE 4.2 Average Number of Employees at Firm Start-up by Industry
We hypothesize that firm start-up employment size is positively influenced by the average
size of firms within the industry (the higher the average firm size is, the higher the probability of
larger start-up size is). FIGURE 4.3 shows that average firm size varies from one industry to the
other. Firms that belong to information and cultural industries rank first, with an average of
364.3 employees, while firms belonging to agriculture, forestry, fishing and hunting industries
have the lowest average firm size of 4.6 employees. Check TABLE A.2 in Appendix A for the
yearly values of the average firm size for each industry class.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Nu
mb
er o
f em
plo
yees
65
FIGURE 4.3 Average Firm Employment Size by Industry
(For the period of 2001 to 2012)
4.4. Model Structure: Ordered Logit Model
Several estimation procedures are available when the response (dependent) variable is
qualitative, one of which is the multinomial model (MNL) structure. However, when the
response variable (y) is ordinal and has more than two (ordered) responses, the MNL structure is
not suitable because MNL ignores the ordinal aspect of the outcome. Ordered logit models (also
known as proportional odds models) provide better representation of the ordering information
(StataCorp, 2013). In the ordered model, the response variable (y) takes values i=1 to N that are
ordered, and the probability of 𝑦𝑖 = 𝑖 is calculated using Equation (4.3)
𝑝𝑖𝑗 = Pr(𝑦𝑖 = 𝑖) = Pr(𝑘𝑖−1 < 𝑥𝑗𝛽 + 𝑢 ≤ 𝑘𝑖) (4.3)
= 1
1 + exp(𝑥𝑗𝛽 − 𝑘𝑖 )−
1
1 + exp(𝑥𝑗𝛽 − 𝑘𝑖−1)
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
Ave
rage
nu
mb
er o
f em
plo
yees
66
where 𝑥𝑗 is a vector of independent variables (covariates), and 𝛽 is a vector of estimated
coefficients of the covariates. The formula indicates that the probability that a specific outcome i
is selected equals to the probability that the utility function (𝑥𝑗𝛽 + 𝑢) is between two
thresholds/cut-points 𝑘𝑖 and 𝑘𝑖−1, given that 𝑘𝑖−1 is always less than 𝑘𝑖. The vector 𝛽 and the
thresholds (ki; i=1 to N-1) are estimated using maximum likelihood estimation. The estimated
model has no constant terms because their effects are captured in the thresholds. A positive sign
of a coefficient indicates that an increase in the associated covariate increases the likelihood of
having higher start up size, and a negative sign indicates the opposite.
The ordered logit model structure is selected for modelling the firm start-up employment
size and firm start-up tangible assets because they can be classified in ranges that are ordinal (as
explained in TABLE 4.3 and TABLE 4.10).
4.5. Firm Start-up Employment Size
Data show that 50% of the firms start with one employee, while 75% of the new firms have
two employees or less. The average start-up size is 2.5 employees with a standard deviation of
4.3. The total number of firms under investigation is 275,517 in for-profit industries. A hold-out
sample of 20% of the records are kept for validation.
Two models are estimated; a simple model (includes basic variables of provincial location,
industry classes, and provincial unemployment rate), and a detailed model that includes
information such as provincial unemployment yearly rates, firm exit rates, average firm size by
industry, and average number of competitors within the same CMA/CA. The simple model is
intended for microsimulation purposes while the detailed one is intended to enhance our
understanding of the behaviour.
First, a simple linear regression model is estimated. The results of the simple and detailed
models are presented in TABLE A.3 and TABLE A.4 in appendix A, along with the model
goodness-of-fit. The dependent variable is the natural logarithm of the number of employees at
firm start-up. The estimates are used as guidelines for selecting the statistically significant
explanatory variables and understanding the expected effect of the covariates in the ordered logit
model structure.
67
4.5.1. Order logit model estimation results
Four classes are identified for the ordered logit model structure for firm start-up employment
size and are shown in TABLE 4.3. The ordered logit model is estimated using the STATA 13
package, and the estimation results of both the simple and detailed models are presented in
TABLE 4.4 and TABLE 4.5. Only variables that are statistically significant at 95% and higher are
included. TABLE 4.4 and TABLE 4.5 include the coefficient estimates, the ratio of the odds, the
P-Values, and the thresholds for each of the identified four employment size classes. A positive
sign of a coefficient indicates that an increase of one unit in the value of the associated covariate
increases the ordered log-odds of being in the higher range for employment size by the
coefficient value, while all other variables are held constant. A negative sign indicates the
opposite. As shown in TABLE 4.5, the log of the odds for firms in Ontario to be in a higher start-
up size category is -0.283 less than firms from other provinces and territories, when other
variables are held constant.
Marginal effects and odds ratios are other statistics that are used to interpret the effect of the
independent variables on the response variable. Marginal effects measure the expected change in
the response variable as a function of the change in the predictors (i.e. explanatory variables) at
specific values (e.g. mean values) of all independent variables (Mood, 2010). It is calculated by
taking the partial derivative of the probability function with respect to the predictor of interest
(Wicksteed, 1910). On the other hand, the odds ratios are obtained by exponentiating the
coefficient estimates (e coef ) (Bland and Altman, 2000). In ordered logit models, odds ratios
indicate that for a one-unit change in a given covariate, the odds for cases in a category greater
than (k) versus being in other categories less than or equal k are the value of the odds ratio
(Brant, 1990; Greenwood and Farewell, 1988; McCullagh, 1980). For example, TABLE 4.5
indicates that the odds for firms located in Quebec to have a start-up employment size in the
higher range (more than five employees) are 0.21 times lower than being in other employment
classes (i.e. five and less employees) compared to other provinces and territories, given that all
other variables are held constant. Likewise, the odds of the response variable to be in the other
combined categories (class 2 to 4; more than one employee) versus being in the first class (start-
up size of one employee) are 0.21 times lower for firms in Quebec compared to firms located
elsewhere, where other variables are held constant. Odds ratios are commonly used with
dichotomous independent variables as they give better interpretations compared to marginal
68
effects (Armstrong and Sloan, 1989; Haddock et al., 1998). Since the presented ordered logit
models in this chapter are mostly dominated by dummy variables, the odds ratios are used to
interpret the effect of independent variables over the response variable.
The estimation results also report the cut-points (thresholds) that are used to differentiate the
adjacent levels of the response variables. Each cut-point separates response variable categories
when all values of the covariates are evaluated at zero. For instance, k1 value (1.164) in TABLE
4.5 is used to differentiate the response variable’s first class from the rest of the classes. Firms
that have a latent variable value less than 1.164 (when all model variables are evaluated at zero),
are classified with a start-up employment size of one employee (the first class). Similarly, firms
that have a value between k1 and k2 of the latent variable are classified in the second employment
class (two employees), and so on.
TABLE 4.3: Start-up Employment Size Ranges for Ordered Logit Model
Class Range
1 1 employee
2 2 employees
3 3-5 employees
4 Greater than 5 employees
69
TABLE 4.4 Ordered Logit Model of Firm Start-up Employment Size: Simple Model
Covariates Coef. Odds Ratio P>|z|
Province
Ontario -0.437 0.646 0.000
Quebec -0.357 0.700 0.000
Alberta -0.692 0.500 0.000
British Columbia -0.378 0.685 0.000
Industry class
Mining, Quarrying, and Oil and Gas Extraction -0.491 0.612 0.000
Construction -0.193 0.824 0.000
Manufacturing 0.324 1.383 0.000
Wholesale trade -0.215 0.806 0.000
Retail trade 0.266 1.305 0.000
Transportation and warehousing -0.775 0.461 0.000
Information and cultural industries 0.127 1.135 0.000
Finance and insurance -0.159 0.853 0.000
Real estate and rental and leasing -0.374 0.688 0.000
Professional, scientific and technical services -0.620 0.538 0.000
Accommodation and food services 1.066 2.905 0.000
Economic indicators of previous year’s (t-1)
Provincial unemployment rate -0.013 0.987 0.000
/k1 -0.226
/k2 0.713
/k3 2.033
70
TABLE 4.5 Ordered Logit Model of Firm Start-up Employment Size: Detailed Model
Covariates Coef. Odds Ratio P>|z|
Province
Ontario -0.283 0.753 0.000
Quebec -0.231 0.794 0.000
Alberta -0.535 0.586 0.000
British Columbia -0.264 0.768 0.000
Industry class
Mining, Quarrying, and Oil and Gas Extraction -1.279 0.278 0.000
Construction -0.277 0.758 0.000
Manufacturing -0.537 0.585 0.000
Wholesale trade -0.524 0.592 0.000
Retail trade -0.538 0.584 0.000
Transportation and warehousing -1.171 0.310 0.000
Information and cultural industries -0.792 0.453 0.000
Finance and insurance -0.750 0.472 0.000
Real estate and rental and leasing -0.509 0.601 0.000
Professional, scientific and technical services -0.726 0.484 0.000
Accommodation and food services 0.807 2.241 0.000
Economic indicators of previous year’s (t-1)
(log) GDP by industry (dollars x 10-10) 0.097 1.102 0.001
Industry characteristics and competition of previous year’s (t-1)
Firm exit rate by industry (%) -0.005 0.995 0.060
(log) Number of competitors (CMA/CA and NAICS 3-Digit
code) -0.056 0.946 0.000
(log) Average firm size by industry (2-digit NAICS) 0.254 1.289 0.000
/k1 1.164
/k2 2.113
/k3 3.431
4.5.2. Results interpretations
The results indicate that firm start-up employment size varies across industries and across
provinces. The coefficients of the province variables (in the simple and detailed models) indicate
that firms located in Ontario, Quebec, Alberta, and British Columbia, when all other variables
are held constant, are less likely to have their start-up employment size in higher classes
compared to firms located in the rest of Canada. FIGURE 4.4 shows that firms located in Alberta
are less likely to have larger start-up employment size, whereas firms located in Quebec are more
likely to have higher start-up employment size compared to other provinces and territories.
71
FIGURE 4.5 shows the ordered logit model probabilities of firms located in the four studied
provinces, when other variables are held constant (calculated based on Equation (4.3), and
TABLE 4.5). These values quantify what the values of the coefficients and odds ratios mean. For
instance, firms located in Alberta have the highest probability (85%) of starting with one
employee, whereas firms in Quebec have a probability of 80% of starting with one employee. On
the other end, firms located in Quebec have a 2.5% chance of starting with five or more
employees while firms located in Alberta have a probability of 1.8% of starting five or more
employees.
FIGURE 4.4 Ratio of the Odds of Firm Start-up Employment Size for Some Provinces
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
Quebec British Columbia Ontario Alberta
Rat
io o
f th
e o
dd
s
Detailed model Simple model
72
FIGURE 4.5 Probabilities of Firm Start-up Employment Size for Selected Provinces
(when other variables are held constant)
Industry class influences the start-up size decision. Some industries require larger start-up
sizes compared to other industries as shown in FIGURE 4.2, for instance, firms in
‘Accommodation and food services’ industry have the highest average start-up employment size
(approximately 4.2 employee). On the other side, model results indicate that firms belonging to
‘Accommodation and food services’ industry have higher odds of starting with employment size
greater than five employees compared to other firms in other industries (FIGURE 4.6).
Comparing the values for ‘Accommodation and food services’ in both figures, we can conclude
that the ordered logit model gives a reasonable representation of the industry class effect on firm
start-up size.
Furthermore, the results show that firms belonging to ‘Mining, Quarrying and Oil and Gas’
industries have a probability of 92% of starting with one employee, whereas firms in
‘Accommodation and food services’ have a 58.8% of starting with one employee (FIGURE 4.7).
Firms belonging to ‘Accommodation and food services’ have a 6.8% chance of starting with
more than five employees while firms in ‘Mining, Quarrying and Oil and Gas’ have a 0.9%
probability of starting with greater than five employees.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 2 3 4
Ord
ered
lo
git
mo
del
pro
bab
ilit
ies
Start-up Employment Size Classes
Alberta Ontario British Columbia Quebec
73
FIGURE 4.6 Ratio of the Odds by Industry for the Firm Start-up Size: Simple and Detailed
Models
FIGURE 4.7 Probabilities of Firm Start-up Employment Size by Industry: Detailed Model
(when other variables are held constant)
Manufacturing and construction firms are explored in more detail to assess the effect of
location on start-up probabilities within industries. The probabilities of the start-up employment
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
Rat
io o
f th
e O
dd
s
Simple model Detailed model
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
Ord
ered
lo
git
mo
del
pro
bab
ilit
ies 1 employee 2 employees 3-5 employees > 5 employees
74
size for manufacturers in different provinces are presented in FIGURE 4.8, and FIGURE 4.9 for
the simple and detailed model, respectively. FIGURE 4.10 and FIGURE 4.11 represent the
probabilities of start-up employment size for construction firms using the simple and detailed
models, respectively. The simple model indicates that, generally, when everything else is held
constant, construction firms have higher probabilities to start with one employee compared to
manufacturers, while manufacturers have higher probabilities to start with two employees
compared to construction firms (FIGURE 4.8 and FIGURE 4.10). There is variation in the
estimated probabilities by the simple and detailed models. This is because the detailed model
accounts for the effects of the economic indicators and the industry variables that explains part of
the behaviour, and hence some variation is expected.
FIGURE 4.8 Probabilities of Start-up Employment Size for Manufacturers in Different
Provinces-Simple Model
0
0.1
0.2
0.3
0.4
0.5
0.6
1 2 3 4
Pro
bab
ilty
Employment Classes
Alberta Ontario British Columbia Quebec
75
FIGURE 4.9 Probabilities of Start-up Employment Size for Manufacturers in Different
Provinces-Detailed Model
FIGURE 4.10 Probabilities of Start-up Employment Size for Construction Firms in Different
Provinces- Simple Model
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4
Pro
bab
ility
Employment Size Class
Alberta Ontario British Columbia Quebec
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1 2 3 4
Pro
bab
iliti
es
Employment Classes
Alberta Ontario British Columbia Quebec
76
FIGURE 4.11 Probabilities of Start-up Employment Size for Construction Firms in Different
Provinces- Detailed Model
The probabilities of the ordered logit for each of the covariates, independently while the
effect of the rest of the variables is held constant in the simple and detailed models, are
calculated using Equation (4.3), and summarized in TABLE 4.6 and TABLE 4.7. Such
probabilities are useful to explain the effect of each of the studied variables, independently, on
the firm start-up employment size.
TABLE 4.6 Ordered Logit Model Probabilities for Each Covariate Independently (Simple Model)
Start-up Employment Class
Covariates 1 2 3 4
Ontario 60.5% 19.2% 14.0% 6.4%
Quebec 58.6% 19.8% 14.8% 6.9%
Alberta 66.4% 17.1% 11.5% 5.0%
British Columbia 59.1% 19.6% 14.6% 6.7%
Mining, Quarrying, and Oil and Gas Extraction 56.6% 20.3% 15.7% 7.4%
Construction 49.2% 22.1% 19.0% 9.7%
Manufacturing 36.6% 23.0% 25.1% 15.3%
Wholesale trade 49.7% 22.0% 18.8% 9.6%
Retail trade 37.9% 23.1% 24.4% 14.6%
Transportation and warehousing 63.4% 18.2% 12.7% 5.7%
Information and cultural industries 41.3% 23.0% 22.8% 12.9%
Finance and insurance 48.3% 22.2% 19.4% 10.0%
Real estate and rental and leasing 53.7% 21.1% 17.0% 8.3%
Professional, scientific and technical services 59.7% 19.4% 14.3% 6.6%
Accommodation and food services 21.5% 19.7% 31.2% 27.6%
Provincial unemployment rate 44.7% 22.7% 21.2% 11.4%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4
Pro
bab
iliti
es
Employment Classes
Alberta Ontario British Columbia Quebec
77
TABLE 4.7 Ordered Logit Model Probabilities for Each Covariate Independently (Detailed Model)
Start-up Employment Class
Covariates 1 2 3 4
Ontario 81.0% 10.7% 6.0% 2.4%
Quebec 80.1% 11.1% 6.2% 2.5%
Alberta 84.5% 8.8% 4.8% 1.9%
British Columbia 80.7% 10.8% 6.1% 2.4%
Mining, Quarrying, and Oil and Gas Extraction 92.0% 4.7% 2.4% 0.9%
Construction 80.9% 10.7% 6.0% 2.4%
Manufacturing 84.6% 8.8% 4.7% 1.9%
Wholesale trade 84.4% 8.9% 4.8% 1.9%
Retail trade 84.6% 8.8% 4.7% 1.9%
Transportation and warehousing 91.2% 5.2% 2.6% 1.0%
Information and cultural industries 87.6% 7.2% 3.7% 1.4%
Finance and insurance 87.2% 7.4% 3.9% 1.5%
Real estate and rental and leasing 84.2% 9.0% 4.9% 1.9%
Professional, scientific and technical services 86.9% 7.6% 4.0% 1.5%
Accommodation and food services 58.8% 19.8% 14.6% 6.8%
(log) GDP by industry x 1010 74.4% 13.8% 8.3% 3.4%
Firm exit rate by industry (%) (t-1) 76.3% 13.0% 7.6% 3.1%
(log) Number of competitors (CMA/CA and NAICS 3-Digit
code) 77.2% 12.5% 7.3% 3.0%
(log) Average firm size (2-digit NAICS) 71.3% 15.2% 9.5% 4.0%
Provincial unemployment rates are found to have a negative effect on firms to start large in
size (TABLE 4.4). GDP growth by industry increases the likelihoods of firms to start with larger
employment size classes as indicated in TABLE 4.5. On the other side, higher firm exit rate
reduces the likelihoods of firms to start large in size (TABLE 4.5), which is intuitive as higher
exit rates may indicate an economic decline in the industry growth. Also, competition is found to
negatively influence the decision of firms to start with large number of employees (TABLE 4.7).
Average firm size is found to have a positive effect on firm start-up size. Firms that belong to
industries with higher average firm size are more likely to start relatively lager in size to reach
the average size quickly (TABLE 4.7) as also explained in (Baldwin et al., 2000; Pagano and
Schivardi, 2003).
4.5.3. Model validation and goodness-of-fit
Model validation was conducted to assess the performance of the estimated model. The
appropriate statistic for assessing the goodness-of-fit of binary choice models is the McFadden
78
pseudo R2 (also known as rho-square). For ordered logit models, maximum likelihood method is
used to estimate the parameters. The R2 reports the variability between the estimated model (full
model) and the null model as follows:
𝑹𝑴𝒄𝑭𝒂𝒅𝒅𝒆𝒏𝟐 = 𝟏 −
𝐥𝐨𝐠(�̂�𝑭𝒖𝒍𝒍)
𝐥𝐨𝐠(�̂�𝑵𝒖𝒍𝒍) (4.4)
where �̂�𝐹𝑢𝑙𝑙 is the maximum likelihood value of the fitted (full) model, and �̂�𝑁𝑢𝑙𝑙is the
likelihood value of the null model (the model with no covariates that includes only intercepts).
In any estimated model (that includes any number of covariates), the reported log-likelihood will
always be higher than the log-likelihood of the null model (a model with no covariates). For a
model that has a very low predictive performance of the outcomes, the ratio between the log-
likelihood of the fitted model and the null model will be close to 1, meaning that the two models
are not much different, and the 𝑅𝑀𝑐𝐹𝑎𝑑𝑑𝑒𝑛2 will be close to zero, indicating a poor predictive
performance of the model (Windmeijer, 1995).
Pseudo R2 increases with the addition of more explanatory variables regardless of their
influence to the dependent variable. This could misleadingly lead to a false interpretation of the
model goodness-of-fit (Theil, 1961). Another measure that considers the number of added
explanatory variables and their effect on the Pseudo R2 is the adjusted-R2 as in:
𝑅𝑎𝑑𝑗2 = 1 − [
(1−𝑅2)(𝑛−1)
𝑛−𝑘−1] (4.5)
where R2 is the pseudo R2 calculated from equation (4.4), n is the total sample size, and k is the
number of explanatory variables. This adjustment indicates that the more explanatory variables
added to the model, the less the adjusted-R2 is unless there is a significant increase in the original
pseudo R2.
Although the pseudo R2 and adjusted-R2 statistics are considered measures of goodness-of-fit
of model estimates, they are not enough to assess the predictive capabilities of the model. Cross-
validation is a common technique that is being widely used in discrete choice model validation
(Roorda et al., 2008; Robin et al., 2009; Habib, 2013). The purpose of cross-validation is to
compare the estimated model outcomes (the dependent variable) for a subset of the data set (a
validation dataset; usually data that are not used in the model estimation), to the actual observed
values of the outcomes in the data. Also, cross-validation limits the problem of model
overfitting. Overfitting occurs when the model ‘memorizes’ the estimation data rather than
79
providing a generalized relationship of the observed behaviour. This often happens when the
model is excessively complex (e.g. the number of estimated parameters are larger than the
number of observations). As a consequence, the model predictions are unreliable as the model in
this case describes the random error or the noise rather than describing the real behaviour
(Hawkins, 2004).
Cross-validation is used to further validate the predictive performance of firm start-up size
models on the aggregate level. For this purpose, a hold-out sample (a validation set) of 20% of
the firm population of investigation (43,281 records) is extracted, and not used in the estimation
process. According to (McFadden, 1978), the predicted total probabilities (for each observation
in the validation dataset) are calculated first and then divided by the validation sample size to
calculate the predicted shares of the four classes of the ordinal outcome of the start-up
employment size (TABLE 4.3). This number is compared to the observed shares of the start-up
employment classes in the sample. The model goodness-of-fit and validation results are
summarized in TABLE 4.8 and TABLE 4.9.
The pseudo and adjusted R2 statistics indicate a poor fit of the model (McFadden, 1978).
However, pseudo and adjusted R2 should not be the only measures to assess the model goodness-
of-fit. Another measure of assessing the overall model significance is a chi-squared test for
testing the null hypothesis that the model coefficients are not statistically different from zero. Let
Lf and L0 be the log-likelihood values of the full and null models respectively. The Likelihood
Ratio (LR) = - 2 (Lf – L0) is approximately χ2 distributed with df-d0 degrees of freedom if the full
model is true. For instance, in TABLE 4.8, prob> chi2 is the probability of obtaining a chi-square
of (16172.25) if the covariates are with no significant effect on the dependent variable (the null
hypothesis is true). This value is the p-value (with a value of 0.00) that when compared to a
critical value (e.g. 0.05 or 0.01 significance level) indicates that the null hypothesis cannot be
true and the model is statically significant at a 99% and higher confidence (i.e. all the model
coefficients are statistically different from zero) (Greene, 2012).
The cross-validation in TABLE 4.9, on the other hand, shows that the detailed model is
perfectly predicting the aggregate behaviour for the second and fourth employment class (zero
difference in the observed and predicted total probabilities), while it under predicts the
probabilities of the first class with 1.8%, and slightly under predicts with a 0.1% for the third
class. Results of cross-validation of the simple model are not included in this research.
80
TABLE 4.8 Model Estimation Summary and Model Goodness-of-fit
Simple model Detailed model
Log likelihood (Null) -261084.65 -191880
Log likelihood (Full) -251559.51 -183794
Number of observations 232,303 167,972
LR chi2(16) 19050.28 16172.25
Prob > chi2 0.000 0.000
Pseudo R2 0.0365 0.0421
Adjusted- R2 0.0364 0.0420
TABLE 4.9 Cross-Validation Results of Ordered Logit Model of Firm Start-up Employment
Size
Detailed Model
Class Observed Predicted Difference (%)
1 56.9% 55.0% -1.9%
2 18.9% 18.9% 0.0%
3 15.8% 15.7% -0.1%
4 8.4% 8.4% 0.0%
4.6. Firm Start-up Tangible Assets
4.6.1. Ordered logit model estimation results
The same data set that is used for estimating firm start-up employment size is used to
estimate the start-up tangible assets model. No descriptive analyses of the tangible assets can be
provided, as per the data confidentiality agreement with Statistics Canada. Outlier analysis
indicates that firms with more than $400,000 of tangible assets should be removed. The final
sample size that is used for model estimation is 272,746 records. 20% of the records are reserved
as a hold-out sample for model validation. Six class ranges are identified and selected for the
ordered logit model. TABLE 4.10 shows such ranges and their distribution in the data set.
81
TABLE 4.10 Start-up Tangible Assets Ranges for Ordered Logit Model
Class number Ranges ($) Frequency
1 0 19.67%
2 0.01 - 4,999.99 19.14%
3 5,000 - 19,999.99 19.26%
4 20,000 - 49,999.99 15.58%
5 50,000 - 99,999.99 10.13%
6 > 99,999.99 16.21%
An ordered logit model is estimated using STATA 13 statistical software package.
Estimation results are summarized in TABLE 4.11. The model of firm start-up tangible assets is
designed to use the estimated number of start-up employees as one of the explanatory variable
(as explained in FIGURE 4.1). Only variables that are significant at a 95% level and higher are
included. For confidentiality reasons, the estimation results for the ‘Utilities’ industry class are
suppressed and only the sign of the coefficient can be included.
82
TABLE 4.11 Ordered Logit Model of Firm Start-up Tangible Assets: Model Estimation
Covariates Coef. Odds
Ratio P>|z|
Number of Employees (log) 0.601 1.825 0.000
Province
Ontario -0.022 0.979 0.038
Quebec 0.186 1.204 0.000
Alberta 0.067 1.070 0.000
Saskatchewan 0.279 1.322 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting 0.984 2.675 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.117 1.124 0.003
Utilities Negative
Construction -0.447 0.640 0.000
Wholesale trade -0.761 0.467 0.000
Retail trade -0.369 0.691 0.000
Transportation and warehousing -0.173 0.841 0.000
Information and cultural industries -1.427 0.240 0.000
Finance and insurance -1.220 0.295 0.000
Real estate and rental and leasing -0.077 0.926 0.023
Professional, scientific and technical services -1.174 0.309 0.000
Management of companies and enterprises -1.203 0.300 0.000
Administrative and support, waste management and remediation
services -0.772 0.462 0.000
Arts, entertainment and recreation -0.196 0.822 0.000
Accommodation and food services 0.561 1.753 0.000
Other services (except public administration) -0.179 0.836 0.000
Economic indicators of year (t)
Yearly provincial unemployment rate (%) -0.047 0.954 0.000
GDP growth (%) 0.007 1.007 0.007
Industry characteristics and competition of year (t)
Yearly firm entry rate by industry class (NAICS2) (%) -0.062 0.940 0.000
(log) # of competitors in the same CMA/CA of the same NAICS-3 -0.056 0.946 0.000
/k1 -2.704
/k2 -1.651
/k3 -0.735
/k4 0.087
/k5 0.800
83
4.6.2. Results interpretations
FIGURE 4.12 shows that firms located in Saskatchewan have higher odds of starting at
higher ranges of tangible assets compared to rest of Canada, while firms in Ontario have the
lowest odds of starting at higher tangible asset ranges.
FIGURE 4.12 Tangible Assets Ordered Logit Model - Ratio of the Odds for Some Provinces
Odds ratios by industry are presented in FIGURE 4.13. Firms that belong to ‘Agriculture,
Forestry, Fishing and Hunting’ industry class have higher odds of higher start-up tangible asset
values compared to other industries. This is due to the nature of this industry which requires
land, machinery and equipment as fundamental assets. Other industries are less dependent on
machinery and equipment as core assets (e.g. Information and cultural industries) and therefore
have lower start-up asset values.
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
Saskatchewan Quebec Alberta Ontario
Rat
io o
f th
e o
dd
s
84
FIGURE 4.13 Tangible Assets Ordered Logit Model - Ratio of the Odds by Industry Class
The ordered logit model probabilities are summarized in TABLE 4.12, using Equation (4.3),
for each variable separately while the rest of the variables are held constant at zero. Employment
size has a positive effect on the start-up tangible assets. An increase of one-unit in the log value
in the number of employees increases the log of the odds by 0.601 (TABLE 4.11). TABLE 4.12,
however, shows that a unit increase in the log number of employees results in a 3.5% chance of
starting the tangible assets in class #1, a 6% chance to be in the second class, a probability of
11.3% to start in the third class, and a 45% chance of starting at value greater than $99,999.99.
0.00
0.50
1.00
1.50
2.00
2.50
3.00
Rat
io o
f th
e o
dd
s
85
TABLE 4.12 Start-up Tangible Assets Ordered Logit Model Probabilities for Each Covariate
Independently
Start-up Tangible Assets Classes
Covariates 1 2 3 4 5 6
Number of Employees (log) 3.5% 6.0% 11.3% 16.6% 17.5% 45.0%
Ontario 6.4% 10.0% 16.5% 19.8% 16.8% 30.5%
Quebec 5.3% 8.5% 14.7% 19.0% 17.4% 35.1%
Alberta 5.9% 9.3% 15.7% 19.5% 17.1% 32.5%
Saskatchewan 4.8% 7.9% 14.0% 18.6% 17.5% 37.2%
Agriculture, Forestry, Fishing and
Hunting 2.4% 4.3% 8.5% 13.8% 16.5% 54.6%
Mining, Quarrying, and Oil and Gas
Extraction 5.6% 9.0% 15.3% 19.3% 17.2% 33.5%
Construction 9.5% 13.6% 19.8% 20.2% 14.6% 22.3%
Wholesale trade 12.5% 16.6% 21.5% 19.4% 12.6% 17.3%
Retail trade 8.8% 12.9% 19.2% 20.3% 15.1% 23.7%
Transportation and warehousing 7.4% 11.2% 17.7% 20.2% 16.1% 27.4%
Information and cultural industries 21.8% 22.6% 22.2% 15.3% 8.3% 9.7%
Finance and insurance 18.5% 20.9% 22.5% 16.8% 9.6% 11.7%
Real estate and rental and leasing 6.7% 10.4% 17.0% 20.0% 16.5% 29.4%
Professional, scientific and technical
services 17.8% 20.5% 22.5% 17.1% 9.9% 12.2%
Management of companies and
enterprises 18.2% 20.8% 22.5% 16.9% 9.7% 11.9%
Administrative and support, waste
management and remediation services 12.7% 16.7% 21.6% 19.3% 12.6% 17.2%
Arts, entertainment and recreation 7.5% 11.4% 17.9% 20.2% 16.0% 27.0%
Accommodation and food services 3.7% 6.2% 11.6% 16.9% 17.6% 44.0%
Other services (except public
administration) 7.4% 11.3% 17.8% 20.2% 16.1% 27.3%
Yearly provincial unemployment rate
(%) 6.6% 10.2% 16.7% 19.9% 16.7% 30.0%
GDP growth (%) 6.2% 9.8% 16.3% 19.7% 16.9% 31.1%
Yearly firm entry rate by industry class
(NAICS2) (%) 6.6% 10.3% 16.8% 19.9% 16.6% 29.7%
# of competitors in the same CMA/CA
of the same NAICS-3 (log) 6.6% 10.3% 16.8% 19.9% 16.6% 29.8%
FIGURE 4.14 shows that the majority of firms in four provinces are more likely to start at
higher start-up tangible asset ranges. Probabilities for all provinces increase with the increase in
the tangible asset ranges, except for the fifth category, that decreases for all provinces compared
to the fourth category.
86
FIGURE 4.14 Ordered Logit Model Probabilities for Some Provinces
The ordered logit model probabilities for each industry class (while the rest of the covariates
are held constant) are provided in TABLE 4.13 and plotted in FIGURE 4.15. There is great
variation in the start-up tangible assets across industries. Firms in information and cultural
industries have the lowest tangible asset values. Firms in ‘Agriculture, Forestry, Fishing and
Hunting’ industries have the highest tangible asset values, followed by firms in ‘Accommodation
and food services’ and ’Mining, Quarrying, and Oil and Gas Extraction’ industries. Such
industries that require larger start-up tangible assets rely on larger investments in land, and
machinery and equipment.
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
1 2 3 4 5 6
Ord
ered
lo
git
mo
del
pro
bab
ilit
ies
Start-up tangible assets classes
Saskatchewan Quebec Alberta Ontario
87
FIGURE 4.15 Start-up Tangible Asset Ordered Logit Model Probabilities by Industry Class
Canadian GDP growth has a positive effect on firm start-up size. A growth of 1% in the
GDP increases the log of the odds of firms to have higher values of start-up size with a
magnitude of 0.007. Provincial unemployment rates have a negative impact on firm start-up
tangible assets indicated by the negative sign of the coefficient (TABLE 4.11). An increase of 1%
in the unemployment rate results in a 6.6% probability of firms to start with no tangible assets
(range #1), and a 30% chance of starting with values greater than $99,999.99. The effect of
Canadian GDP growth and provincial unemployment rates are similar (in terms of the ordered
logit probabilities in TABLE 4.12). Such findings are consistent with outcomes of the start-up
employment size models. Clearly, a thriving economy encourages businesses to grow in size and
decreases the likelihood of failure (van Wissen, 2000).
Firm entry rates by industry have a negative effect on start-up tangible asset values.
TABLE 4.12 indicates that higher entry rates reduce the odds of starting at higher tangible asset
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
Ord
ered
lo
git
mo
del
pro
bab
ilit
ies
$0 $0.01 - $4,999.99 $5,000 - $19,999.99
$20,000 - $45,999.99 $50,000 - $99,999.99 > $99,999.99
88
ranges. Higher entry rates indicate higher competition of new firms in the market, which may
increase the likelihood of failure. Moreover, the number of competitors aggregated to the
CMA/CA and the sub-industry class (NAICS 3-digit code levels) has a negative similar effect on
start-up tangible assets. The values of the calculated probabilities are very close to those
calculated for the entry rates indicating similar impacts. Given these two findings, it can be
concluded that firms are inclined to start smaller to minimize potential sunk costs as a result of
higher competition of similar firms (Cabral, 1995).
4.6.3. Model validation and goodness-of-fit
A cross-validation of the overall shares of the observed and predicted probabilities is
conducted to evaluate the aggregate model performance for each class of the response variable.
A hold out sample of 41,547 records that were not used in the model estimation is used. TABLE
4.13 summarizes the comparison between observed and predicted shares. The maximum
difference between observed and predicted shares is 1.3%. Such small prediction variation
indicates an accepted overall performance of the model.
TABLE 4.13 Cross-Validation Results of Ordered Logit Model of Firm Start-up Tangible Assets
Range Observed shares Predicted shares Difference (%)
1 18.7% 17.5% -1.2%
2 19.0% 17.7% -1.3%
3 18.7% 19.1% 0.4%
4 16.2% 15.9% -0.3%
5 10.4% 10.6% 0.2%
6 16.9% 16.9% -0.1%
The reported pseudo and adjusted R2 are low. However, the chi-square test indicates that the
model coefficients are statistically different from zero with 99% confidence (TABLE 4.14)
TABLE 4.14 Model Estimation Summary and Goodness-of-fit
Number of observations 226040
Log likelihood (Null) -400079.06
Log likelihood (Full) -378437.96
LR chi2(25) 43282.21
Prob > chi2 0.000
Pseudo R2 0.0541
Adjusted R2 0.0540
89
4.7. Concluding Remarks and Future Directions
In this chapter, models of firm start-up size are explored. Start-up size is addressed in two
dimensions: the number of employees and the dollar values of tangible assets. Models of firm
start-up size are not found in the Canadian literature. The presented models fill this gap and
enrich the literature. The models are components of firm start-up in the proposed firmographic
engine. Determinants of firm start-up size are explored and quantified.
Determinants related to firm characteristics, economic conditions, and industry
characteristics are investigated in this research. However, determinants related to the founder
characteristics are not covered. As a future research step, models that simulate the
entrepreneurial decision of the firm start-up could be estimated. A publicly available data source
for this purpose is the Global Entrepreneurship Monitor database (Global Entreprenurship
Monitor, 2015). This database includes information about individual entrepreneurs in different
countries, including Canada. This program conducts yearly surveys on randomly selected
individuals investigating their potential entrepreneurship. The currently published data set covers
the time period from 1998 to 2012. The data includes demographic information of firm founders
such as age, education, gender, and work experience. This data source is considered suitable to
model the firm start-up decision at the entrepreneurial stage, in addition to studying determinants
of firm start-up size related to founder characteristics.
The reported model goodness-of-fit indicates a poor fit. Other model structures that consider
firm start-up size in a continuous fashion is a potential future step. Examples of such models are
Poisson regression that assume that all firms choose their start-up size independently (Kumar and
Kockelman, 2008). Furthermore, the presented ordered logit models ignore the endogeneity
between employment and tangible assets, which induce endogeneity bias. One possibility to
overcome this bias is to explore other model structures that consider the simultaneous decision of
firm start-up size of employment and tangible assets, such as SURE and simultaneous equation
models.
90
CHAPTER 5
Models of Firm Growth of Canadian Firms
5.1. Introduction
This chapter presents firm growth models measured in two dimensions: 1) the change in the
number of employees, and 2) the change in the tangible asset values. These models are the
foundation of the firm evolution module of the firmographic engine of Canada. Several model
structures that address the growth behaviour of the two growth aspects, both individually and
simultaneously, are explored. Panel regression models, multi-level models, a system of related
regression models, and auto-regressive models are estimated. The details, rationale, and results
of each investigated model structure are presented in this chapter.
Our finally selected model structure for firm microsimulation is the Autoregressive
Distributed-lag model (ARDL) as it fits the designed microsimulation configuration of firm start-
up and firm growth discussed previously in chapter 4, FIGURE 4.1. The configuration is based
on the assumption that employment and tangible assets in year (t) are dependent on what the
previous year’s (t-1) values were. It also assumes that tangible assets are a function of the
number of employees of the current year (t). The configuration quantifies the effects of other
firm attributes such as age, provincial location, and industry classification as in Equation (5.1)
and (5.2).
𝑒𝑚𝑝𝑡 = 𝑓(𝑒𝑚𝑝𝑡−1, 𝑓𝑖𝑟𝑚 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠) (5.1)
𝑻𝑨𝒕 = 𝒇(𝑻𝑨𝒕−𝟏, 𝒆𝒎𝒑𝒕, 𝒇𝒊𝒓𝒎 𝒂𝒕𝒕𝒓𝒊𝒃𝒖𝒕𝒆𝒔) (5.2)
The majority of the studies surveyed in the literature address firm growth in terms of number
of employees only (Shanmugam and Bhaduri, 2002; Evans, 1987b; Yasuda, 2005), while firm
size can be multi-dimensional (e.g. profit, revenues, number of business establishments, owned
vehicles, and/or warehouses) (Bruneel et al., 2009; Jang and Park, 2011; Huynh and Petrunia,
2010). In this research, firm size is addressed in two dimensions; the number of employees and
the tangible assets expressed as a dollar value. As explained in chapter 4, tangible assets include
assets with physical form such as buildings, land, and machinery and equipment (Statistics
Canada, 2012a). Tangible assets are selected as they represent the physical size of the firms and
91
we hypothesize that employment and tangible assets are correlated (e.g. larger assets require
larger number of employees and vice versa).
The presented models of firm growth do not actually model the growth directly, instead, they
estimate the expected size (the number of employees and tangible assets values) at a specific
year (t), and this information can be compared to the previous year’s values (t-1) and determine
whether a firm has grown or contracted in size.
This chapter is organized as follows, it starts by briefly reviewing the literature on
determinants of firm growth that are used in this research, followed by an explanation of the
data. Model structures for firm employment including ordered logit, random effects panel
logistic regression, random effects-multilevel regression, ARDL, and seemingly unrelated
regression (SURE), along with the estimation results are then explained. Next, an ARDL model
of tangible assets is presented. Concluding remarks and future work are discussed at the end of
the chapter.
5.2. Determinants of Firm Growth
There is no general agreement on a single measure of firm growth. Size could be measured in
terms of revenues or profits, or by the human and physical capital a firm employs (Barkham et
al., 2002). The way firm size is addressed depends on the field of study. For instance, in the field
of geography and economics, employment is used as a measure of growth as it is an important
economic indicator that is essential for policy makers and is easy to measure. In the field of
finance, fiscal variables such as profits and sales are used to be consistent with other aspects of
businesses (Barkham et al., 2002). However, there is a distinction that should be noted; firm
growth and financial performance are independent. Firms may be performing financially well but
not physically growing, while a firm may be physically growing but financially is performing
poorly. So, the context is important in determining which aspect of growth should be considered.
Firm growth is affected by factors related to firm characteristics, industry characteristics,
location, market demand and supply, competition, and economic conditions (van Wissen, 2000;
Evans, 1987b; Dinlersoz and MacDonald, 2009; Marino and Noble, 1997; Geroski and Gugler,
2004; Barkham et al., 2002). Founder/owner-manager characteristics can also influence growth
within small firms (Barkham et al., 2002). Examples of such characteristics are gender, age,
92
education, work experience, social network, and location (Shane et al., 1991; Barkham, 1994;
van Wissen, 2000; Barkham et al., 2002; Colombo and Grilli, 2005; Elgar et al., 2009)
Age of the firm has been recognized as an important determinant of growth in many studies.
Several studies agree that age has a negative effect on firm size and growth (Jovanovic, 1982;
Evans, 1987a; Evans, 1987b; Variyam and Kraybill, 1992). This could be because younger firms
grow faster to reach economies of scale and attain better market stability compared to older ones
(Cabral, 1995; Lotti et al., 2003; Park and Jang, 2010). Yasuda (2005) has attributed this to the
economic growth vitality; the slow growth of older firms encourages small new firms to enter the
market and create new employment opportunities. Other studies have claimed that age has a
positive impact on size and growth such as Shanmugam and Bhaduri (2002) and Das (1995) for
manufacturers in India.
Firm performance and growth are largely affected by location characteristics such as
population density, access to resources, labour market and demand, access to customers, distance
to suppliers, and land price (Mansfield, 1962; Hoogstra and Dijk, 2004; Maoh and Kanaroglou,
2007a; Elgar et al., 2009). Firm growth patterns also differ across industries (Delmar et al.,
2003).
Firm geographic concentration (competition) has an influence on firm growth (Baldwin and
Gorecki, 1998). It can be positive; higher competition leads to better strategic decisions, and
hence better growth when growth is measured in fiscal terms (Hermalin, 1992). When growth is
measured as number of employees, the effect of competition can be negative as firms benefit
from agglomeration economies as they share services and infrastructures, and hence may
contract in size to minimize costs. Furthermore, firm events of entry and exit influence firm’s
growth and turnover (Kehoe et al. , 2016; Baldwin and Gu, 2006).
Since this research targets firm microsimulation, the focus is on basic firm attributes, general
economic indicators, and basic industry variables that are readily available. More specifically,
the focus is on: a) firm characteristics of age, provincial location, and industry classification, b)
economic indicators of GDP growth and provincial unemployment rates, and c) industry
variables of average firm size, firm entry and exit rates, and local competition.
Also, there is a gap in the literature in introducing firm growth in more than one aspect,
except for one unique study by (Coad and Broekel, 2012) who presented growth as a
simultaneous relationship between employment and production size (expressed as sales values).
93
Their analysis suggests that there is no strong relationship between the two aspects. In this
chapter a potential simultaneity of growth between firm employment size and tangible assets is
investigated by introducing a system of SURE equations.
5.3. Data Description and Basic Analysis
The T2-Longitudinal Employment Analysis Program (T2-LEAP) database, which is provided
by Statistics Canada, is used for model estimation. The data provide longitudinal information of
Canadian firms between the years of 2001 and 2012. We focus on single-location small to
medium sized firms (100 employees and less) as they constitute the largest segment of the firm
population and are more dynamic compared to larger firms (Maoh and Kanaroglou, 2005; Kumar
and Kockelman, 2008). As discussed earlier, only for-profit industries are included and not-for-
profit industries are excluded from the analysis (i.e. educational service, health care and social
assistance, and public administration), refer to section 4.3.
According to the firm start-up and growth microsimulation configuration that has been
explained earlier in chapter 4 (FIGURE 4.1), the growth models depend on the previous year’s
firm characteristics. Therefore, only continuing firms (i.e. excluding the new firms) are included
in the model estimation. A continuing firm is identified in year (t), when records of the same
firms are found in year (t-1).
An outlier analysis has been conducted on the firm and panel levels to exclude deviating
observations. Outlier analysis is performed on the continuous variables of interest included in
TABLE 5.1 such as number of employees, tangible assets, and sales values. For instance, assume
a firm has records for 5 consecutive years. The recorded numbers of employees for each year are
10, 10, 80, 12, and 12. It is intuitive that third observation of 80 employees is an outlier. There
are different outlier labeling methods such as standard deviation method, Z-score method,
median rule, and median absolute deviation (MAD) method. Such methods generate an interval
or criterion that identifies boundaries that if an observation falls beyond them is considered an
outlier. For details about such methods refer to (Seo, 2006). Median absolute deviation method is
used on the entire sample level and on the firm level. In this method, the MAD is calculated
using the following formula:
𝑴𝑨𝑫 = 𝟏. 𝟒𝟖𝟑 ×𝒎𝒆𝒅𝒊𝒂𝒏(|𝒙𝒊 − 𝒎𝒆𝒅𝒊𝒂𝒏(𝒙)|), 𝒊 = 𝟏, 𝟐, … … 𝒏 (5.3)
94
Observations that fall beyond the intervals of Median ± 3 MAD are labeled as outliers and
are excluded from the data set. This technique is performed on the firm level to exclude
deviating observations within the panel.
A preliminary descriptive analysis of the data set is conducted and summarized in TABLE
5.1. The populations of firms classified by province and industry class are presented in FIGURE
5.1 and FIGURE 5.2. It is shown that Ontario contains the majority of firms across Canada with a
share of 34%, followed by Quebec with a share of 22%. Professional, scientific and technical
services, and construction industries are top ranked for the largest share of firms across Canada
with percentages of 14.8% and 14.1% respectively.
95
TABLE 5.1 Summary Statistics of Single-Location Small/Medium Sized Firms (less than 100
employee)
Variables # of obs. Mean Std. Dev.
Firm attributes (current year t)
No. of Employees 6,700,632 6.49 10.64
Firm age 6,700,596 10.43 7.76
log (emp) 6,700,632 1.19 1.06
log (age) 6,700,596 1.97 0.97
log (tangible assets) × 10-6 6,052,401 -2.33 1.93
log (sales values) × 10-6 6,631,898 -1.03 1.52
log (gross profits) × 10-6 5,654,550 -1.69 1.36
Firm attributes (previous year t-1)
log (emp t-1) 6,516,923 1.21 1.06
log (tangible assets t-1) × 10-6 6,080,227 -2.40 1.94
log (sales values t-1) × 10-6 6,637,759 -1.05 1.48
log (profits t-1) × 10-6 5,680,817 -1.72 1.33
Economic Indicators (current year t)
GDP by industry × 10-6 6,649,023 75460 43970
log (GDP by industry) × 10-6 6,649,023 1.84 0.64
GDP growth rate by industry 6,638,144 2.42 3.19
Provincial unemployment rate 6,682,628 7.01 1.81
Provincial employment rate 6,682,628 62.84 3.88
Economic Indicators previous year (t-1)
GDP by industry (10-6) (t-1) 6,701,093 731925 43930
log (GDP by industry t-1) × 10-6 6,638,605 1.82 0.63
GDP by industry and province (t-1) × 10-6 6,701,093 17880 17910
GDP by province (t-1) 6,701,093 314480 175955
Provincial unemployment rate (t-1) 6,682,628 6.99 1.84
Provincial employment rate (t-1) 6,682,628 62.82 3.91
Industry characteristics (current year t)
Entry rate (%) 6,700,632 3.66 1.25
Exit rate (%) 6,014,157 5.23 1.98
Number of competitors within CMA/CA in the NAICS 3-digit 6,700,632 30552 146725
log (number of competitors) 6,700,632 7.09 2.46
Average firm size (NAICS 2-digit) 6,700,632 57.23 83.14
log (average firm size) - NAICS 2-Digit code 6,700,632 3.31 1.16
Industry characteristics of previous year (t-1)
Entry rate 6,175,584 3.89 1.10
Exit rate 6,175,584 5.22 1.97
Number of competitors within CMA/CA in the NAICS 3-digit 6,518,906 6064 12241
log (number of competitors) 6,518,251 6.90 2.22
Average firm size (NAICS 2-digit) 6,700,632 57.91 84.33
log (average firm size) - NAICS 2-Digit code 6,700,632 3.31 1.17
96
FIGURE 5.1. Firm Population Classified by Province
FIGURE 5.2. Firm Population Classified by Industry
Correlation analysis is conducted next to understand the potential relationship between
variables. This analysis is used as a guideline to assess the logical interpretation of the coefficient
signs of the explanatory variables in the final models. TABLE 5.2 shows that that there is a weak
positive correlation between firm age and number of employees. Another two sets of correlations
are observed. First, moderate to strong correlations between the log value of the number of
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
Ontario Quebec Alberta British
Columbia
Atlantic Rest of
Canada
0.00%
2.00%
4.00%
6.00%
8.00%
10.00%
12.00%
14.00%
16.00%
97
employees and tangible assets, sales values, and gross profits. Second, moderate correlations are
observed between tangible assets, sales values, and gross profits. These two groups of
correlations imply that larger firms (those with more employees and/or tangible assets) are more
profitable and have larger sales values. An expected very strong correlation is observed between
sales values and gross profits; as higher sales values increase gross profits.
TABLE 5.2 Correlation Matrix of Firm Attributes
emp
Ag
e
log
(em
p) t
log
(ag
e)
log
(em
p) t
-1
log
(ta
ng
ible
ass
ets)
t
log
(ta
ng
ible
ass
ets)
t-1
log
(sa
les
val
ues
) t
log
(sa
les
val
ues
) t-
1
log
(g
ross
pro
fits
) t-
1
emp 1.00
Age 0.22* 1.00
log(emp)t 0.81 0.27* 1.00
log(age) 0.20* 0.92†† 0.26* 1.00
log(emp)t-1 0.78† 0.31* 0.93†† 0.33* 1.00
log (tangible assets)t 0.46** 0.35* 0.53** 0.34* 0.53** 1.00
log (tangible assets)t-1 0.45** 0.39* 0.52** 0.39* 0.54** 0.97†† 1.00
log (sales values) t 0.64† 0.24* 0.78† 0.23* 0.75† 0.56** 0.54** 1.00
log (sales values) t-1 0.63† 0.28* 0.76† 0.29* 0.78† 0.56** 0.56** 0.95†† 1.00
log (gross profits) t 0.58** 0.17 0.70† 0.17 0.67† 0.52** 0.49** 0.83†† 0.77† 1.00
log (gross profits) t-1 0.58** 0.22* 0.69† 0.24* 0.70† 0.52** 0.52** 0.78† 0.83†† 0.91††
* Weak correlation ** Moderate correlation † Strong correlation †† Very strong correlation
Correlation of industry dynamic variables and economic indicators are calculated and
summarized in TABLE 5.3. A logical weak positive correlation is observed between entry rate by
industry and GDP growth by industry indicating that higher GDP induces higher entry rates. A
weak positive correlation is observed between entry rate by industry and log values of the
number of competitors, indicating that higher entry rates may exert higher competition. A weak
positive correlation is observed between GDP by industry and average firm size by industry
implying that larger industries have higher average firm sizes. The negative correlation detected
between the GDP values and entry rate by industry indicates that larger industries have lower
entry rates.
98
TABLE 5.3 Correlation Matrix of Industry Characteristics and Economic Indicators
GD
P b
y i
nd
ust
ry
(x 1
0-6
)
log
(G
DP
by i
ndu
stry
)
x 1
0-6
GD
P g
row
th r
ate
by
ind
ust
ry (
%)
En
try
rat
e (t
)
Ex
it r
ate
(t)
No
. co
mpet
ito
rs (
t)
log
(N
o. co
mp
etit
ors
)
(t)
Entry rate (t) -0.33* -0.24* 0.30* 1.00
Exit rate (t) -0.01 0.01 -0.08 0.00 1.00
No. competitors (t) -0.02 0.05 0.05 0.13 -0.05 1.00
log (No. competitors) t -0.09 -0.02 0.08 0.20* -0.07 0.66 1.00
Ave. firm size (t) 0.17 0.19 -0.09 -0.03 0.25* -0.21* -0.31*
log (avg. firm size) t 0.25* 0.26* -0.16 -0.06 0.37* -0.22* -0.31*
* Weak correlation
5.4. Firm Employment Growth Models
Several model structures have been estimated and statically evaluated before settling on the
ARDL model to be the better representation of the firm growth. Two modelling approaches are
tested; a discrete choice, and a continuous modelling approach. Employment growth can be
formulated in several ways such as the percent growth from one year to the next, or the
ratio/difference between the number of employees in two consecutive years (e.g. emp(t) /emp(t-1)).
Number of employees can also be modelled for each year independently and the growth can then
be calculated by subtracting the number of employees in year (t) from the number of employees
in previous year (t-1). Continuous modelling approaches (e.g. regression models) are deemed
suitable to model the number of employees. Some discrete choice models have been tried (e.g.
ordered logit models) in addition to a group of continuous models (e.g. panel logistic regression,
ARDL, and multilevel regression models). In this section, some of the model structure estimates,
that may be good representations of firm growth, are presented with explanation of their
limitations. The ARDL model is chosen to simulate firm growth in the firmographic engine. The
rationale for this selection is discussed in section 5.4.4.
99
5.4.1. Ordered logit model of employment growth ratio
The increase or decrease in the number of employees could be perceived as an ordinal
decision where employment is increased or decreased in year (t) as a ratio of employment in year
(t-1). The ratio between the number of employees in year t and year t-1 is first calculated. Then,
an ordinal variable ‘emp_ratio_classes’ is defined to represent the ordinal classes of employment
increase or decrease as in TABLE 5.4.
TABLE 5.4. Employment Ratio Classes
emp_ratio_classes Range Frequency
1 Employment has decreased in year t by more than half of
its value compared to t-1 (emp_ratio < 0.5) 7.45%
2 Employment has decreased by up to half of its value in
year t-1 (0.5≤emp < 1) 16.62%
3 No change (emp_ratio=1) 49.38%
4 Employment has increased by up to 1.5 times its value in
year t-1 (1.5 ≥ emp_ratio>1) 17.22%
5 Employment has increased by more than of 1.5 times its
value in year t-1 (emp_ratio>1.5) 9.32%
Ordered logit model is estimated using ologit function in STATA software to statistically test
and assess the model fitness and logical interpretation. For details about the definition and
mathematical formulation of the ordered logit model, please refer to chapter 4, section 4.4.
Results of the ordered logit model are presented in TABLE 5.5 and TABLE 5.6 for simple and
detailed models respectively.
100
TABLE 5.5 Ordered Logit Model of Employment Growth Ratio - Simple Model
Covariates Coef. Odds
ratio P>|z|
Firm Attributes
Age (log) -0.285 0.752 0.000
Province
Ontario -0.055 0.947 0.000
Alberta -0.056 0.945 0.000
British Columbia -0.060 0.942 0.000
Atlantic Canada -0.039 0.962 0.000
Industry Class
Agriculture, Forestry, Fishing and Hunting 0.070 1.073 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.073 1.076 0.000
Utilities 0.233 1.263 0.000
Construction 0.148 1.159 0.000
Manufacturing 0.106 1.112 0.000
Wholesale trade 0.094 1.099 0.000
Retail trade 0.126 1.135 0.000
Information and cultural industries 0.046 1.047 0.000
Finance and insurance 0.065 1.067 0.000
Real estate and rental and leasing 0.016 1.017 0.000
Professional, scientific and technical services -0.018 0.982 0.000
Administrative and support, waste management and remediation
services 0.091 1.095 0.000
Arts, entertainment and recreation 0.116 1.123 0.000
Accommodation and food services 0.025 1.025 0.000
Other services (except public administration) 0.114 1.121 0.000
Economic indicators
GDP growth (%) by industry 0.007 1.007 0.000
Yearly provincial unemployment rate (%) of t-1 -0.004 0.996 0.000
k1 -3.088
k2 -1.720
k3 0.504
k4 1.783
101
TABLE 5.6 Ordered Logit Model of Employment Growth Ratio - Detailed Model
Covariates Coef. Odds
ratio P>|z|
Firm Attributes
Age (log) -0.338 0.713 0.000
(log) Tangible assets (t-1) in million dollars 0.029 1.030 0.000
(log) Gross profits (t-1) in million dollars 0.054 1.056 0.000
Province
Ontario -0.062 0.940 0.000
Alberta -0.066 0.936 0.000
British Columbia -0.056 0.946 0.000
Atlantic Canada -0.068 0.934 0.000
Industry Class
Agriculture, Forestry, Fishing and Hunting -0.082 0.921 0.000
Utilities 0.170 1.185 0.000
Construction 0.150 1.161 0.000
Manufacturing 0.078 1.081 0.000
Wholesale trade 0.085 1.089 0.000
Retail trade 0.098 1.103 0.000
Information and cultural industries 0.057 1.058 0.000
Finance and insurance 0.099 1.104 0.000
Real estate and rental and leasing -0.028 0.972 0.000
Professional, scientific and technical services 0.046 1.047 0.000
Administrative and support, waste management and remediation services 0.118 1.125 0.000
Arts, entertainment and recreation 0.044 1.045 0.000
Accommodation and food services -0.029 0.972 0.000
Other services (except public administration) 0.056 1.057 0.000
Economic indicators, industry characteristics, and competition
GDP growth (%) by industry 0.009 1.009 0.000
Exit rate by industry of year (t-1) -0.006 0.994 0.000
# of competitors in the same CMA/CA of the same NAICS-3 (log) (t-1) -0.010 0.990 0.000
k1 -3.494
k2 -2.013
k3 0.073
k4 1.469
The signs of the coefficient estimates are logical. For instance, GDP growth by industry class
is found to have a positive effect on firm growth (TABLE 5.5 and TABLE 5.6). Age is found to
have a negative effect on firm growth indicating that older firms have slower growth (TABLE 5.5
and TABLE 5.6). Also, unemployment rates have a negative effect on growth (TABLE 5.5), and
exit rates and competition are found to have a negative effect on growth rates rate (TABLE 5.6).
Although the models look logical, the reported goodness-of-fit of the models (TABLE 5.7)
show a very poor model fit, interpreted from the pseudo and adjusted R2 values. This highlights
102
that ordered logit model structure may not be a good representation of employment growth, and
other model structures should be investigated. MNL structures were also tested, and the reported
goodness-of-fit suggests a very poor model fit as well. The results of the MNL structure has not
been vetted by Statistics Canada, and hence are not included in this research. Results of ordered
logit models are only included as examples of the discrete choice models approach.
TABLE 5.7 Ordered Logit Model of Employment Growth Estimation Summary and Model
Goodness-of-fit
Simple model Detailed model
Number of observations 5,501,728 3,695,280
log likelihood (Null) -7469329.8 -5101586
log likelihood (Full) -7411041.9 -5054397.2
LR chi2 116575.94 94377.7
Prob > chi2 0.000 0.000
Pseudo R2 0.0078 0.0092
Adjusted R2 0.0078 0.0092
5.4.2. Panel logistic regression model with random effects
Another way of modelling the number of employees is to use continuous modelling
approaches, while accounting for the variance that may occur on the firm level. The variance on
the firm level represents the influence of other uncaptured effects related to the firm such as firm
structure and strategic decisions. A panel data set is used, which offers a great source to capture
the individual heterogeneity of firms if appropriate model structures are used (Baltagi, 2008). We
hypothesize that there are differences between firms that have influence on the employment
growth, and which are not captured in the data set. Examples of such effects are the long-term
strategic focus, technologies used, innovation, international activities, and firm structure. There
are several model structures that account for such variance such as panel regression and
multilevel panel regression models. The first model structure that is tested to account for
variance between panel participants is a panel logistic regression with random effects model.
Simply, the model accounts for the variation that could result from one panel participant to
another by estimating a random effects term. The model assumes that variation across panel
participants are random and uncorrelated with the dependent or the independent variables. An
advantage of a random effects model is that it assumes no correlation between error terms and
103
the explanatory variables. This allows for quantification of the effect of time-invariant variables
(e.g. dummy variables of industry class and provincial location) and their inclusion as
explanatory variables. If no random effects are allowed, the effect of the time-invariant variables
can only be considered as fixed effects in the intercept value, and their individual effects are not
estimated (StataCorp, 2013). The random effects model takes the following formulation:
𝒚𝒊𝒕 = 𝛂 + 𝜷𝒙𝒊𝒕 + 𝝂𝒊 + 𝜺𝒊𝒕 (5.4)
for i = 1,…,n panels, where t = 1,….T, νi represents the unit/panel/firm specific error term (also
known as the between-entity error) that are assumed i.i.d. with N(0,𝜎𝜈2). The variance component
model states that:
𝛽𝑥𝑖𝑡 + 𝜈𝑖 + 휀𝑖𝑡 > 0 (5.5)
where 휀𝑖𝑡 is the typical error term that is assumed to be i.i.d. logistic distributed with mean zero
and variance 𝜎ε2 and is not correlated with itself, independent from xit and 𝜈𝑖, and homoskedastic.
The proportion of the total variance contributed by the panel-level variance (𝜌) is another term
that is calculated and reported in the estimation summary (StataCorp, 2013). This term indicates
whether the panel variance is significant or not. The term is calculated as follows:
𝜌 = 𝜎𝜐
2
𝜎𝜐2+ 𝜎𝜀
2 (5.6)
When the value of 𝜌 is zero, the panel-level variance component is neglected and the random
effects part is not statistically significant (StataCorp, 2013). In our case, the model estimates
suggest that the null hypothesis of 𝜌 not being statistically different from zero is rejected and the
variance between panel participants is significant (TABLE 5.8 and TABLE 5.9). The xtreg
function in STATA 13 software is used to estimate the model, which uses a Generalized Least
Square (GLS) transformation for estimation. In this function, estimates of �̂�ε2 and �̂�𝜐
2 are given to
perform the GSL transformation of a variable z (independent or dependent variable) as follows:
𝑧𝑖𝑡∗ = 𝑧𝑖𝑡 − 𝜃�̂�𝑧�̅� (5.7)
where 𝑧�̅� =1
𝑇𝑖 ∑ 𝑧𝑖𝑡
𝑇𝑖𝑡 , and 𝜃�̂� = 1 − √
𝜎ε2̂
𝑇𝑖𝜎𝜈2̂+ 𝜎ε
2̂. When 𝜎𝜈
2 = 0, 𝜈𝑖 is always zero, and 𝜃 = 0.
Alternatively, when 𝜎ε2 = 0, meaning that 휀𝑖𝑡 = 0, and 𝜃 = 1.
104
Dependent and independent variables are transformed using an estimate of 𝜃�̂�. Coefficient
estimates and the conventional variance-covariance matrix are then estimated using Ordinary
Least Square (OLS) regression of 𝑦𝑖𝑡∗ on 𝑥𝑖𝑡
∗ and the transformed constant 1-𝜃�̂�. The OLS is
performed to estimate the following:
(𝑦𝑖𝑡 − 𝜃𝑦�̅�) = 𝛼(1 − 𝜃) + 𝛽(𝑥𝑖𝑡 − 𝜃𝑥�̅�) + {(1 − 𝜃)𝜐𝑖 + (휀𝑖𝑡 − 𝜃휀�̅�)} (5.8)
The model estimation results are shown in TABLE 5.8 and TABLE 5.9 for simple and
detailed models. The reported 𝜌 values of 0.82 and 0.745, in the simple and detailed models
respectively, imply that the panel level variance component is significant and should be
considered in the model.
105
TABLE 5.8 Random Effects Panel Logistic Regression Model of Employment Size Estimation
Results: Simple Model
Covariates Coef. Std.
Err. z P>|z|
Firm Attributes
Age (log) 0.044 0.000 123.740 0.000
Provinces
Quebec 0.042 0.002 19.170 0.000
Alberta -0.135 0.002 -59.700 0.000
British Columbia -0.073 0.002 -31.490 0.000
Atlantic Canada 0.121 0.004 32.530 0.000
Industry Class
Agriculture, Forestry, Fishing and Hunting 0.054 0.008 6.540 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.041 0.011 3.900 0.000
Utilities 0.787 0.032 24.350 0.000
Construction 0.211 0.008 28.150 0.000
Manufacturing 0.748 0.008 93.600 0.000
Wholesale trade 0.376 0.008 47.520 0.000
Retail trade 0.564 0.008 74.360 0.000
Transportation and warehousing 0.033 0.008 4.190 0.000
Information and cultural industries 0.194 0.010 19.880 0.000
Finance and insurance 0.017 0.008 2.000 0.045
Real estate and rental and leasing 0.034 0.008 4.250 0.000
Professional, scientific and technical services -0.077 0.007 -10.350 0.000
Administrative and support, waste management and
remediation services 0.395 0.008 48.970 0.000
Arts, entertainment and recreation 0.375 0.009 39.960 0.000
Accommodation and food services 0.992 0.008 127.590 0.000
Other services (except public administration) 0.294 0.008 37.980 0.000
Economic Indicators
GDP growth (%) by industry 0.003 0.000 42.450 0.000
Yearly provincial unemployment rate (%) of (t-1) -0.007 0.000 -40.510 0.000
_cons 0.716 0.007 97.760 0.000
sigma_u (𝝈𝝊𝟐) 0.849
sigma_e (𝝈𝜼𝟐) 0.381
rho (ρ) 0.832 (fraction of variance due to
u_i)
106
TABLE 5.9 Random Effects Panel Logistic Regression Model of Employment Size Estimation
Results: Detailed Model
Covariates Coef. Std.
Err. z P>|z|
Firm Attributes
Age (log) -0.027 0.000 -60.170 0.000
(log)Tangible assets (t-1) × 10-6 dollars 0.067 0.000 239.580 0.000
(log)Gross profits (t-1) × 10-6 dollars 0.319 0.000 970.720 0.000
Province
Ontario 0.009 0.003 3.060 0.002
Quebec 0.056 0.003 18.670 0.000
Alberta -0.122 0.003 -40.760 0.000
British Columbia -0.012 0.003 -3.810 0.000
Atlantic Canada 0.108 0.004 28.620 0.000
Industry Class
Agriculture, Forestry, Fishing and Hunting -0.225 0.008 -29.570 0.000
Mining, Quarrying, and Oil and Gas Extraction -0.291 0.009 -30.720 0.000
Utilities 0.095 0.026 3.590 0.000
Construction 0.055 0.007 8.080 0.000
Manufacturing 0.293 0.008 37.580 0.000
Wholesale trade 0.038 0.007 5.340 0.000
Retail trade 0.067 0.008 8.590 0.000
Transportation and warehousing -0.138 0.007 -18.970 0.000
Information and cultural industries -0.078 0.010 -7.960 0.000
Finance and insurance -0.037 0.008 -4.670 0.000
Real estate and rental and leasing -0.147 0.007 -20.130 0.000
Professional, scientific and technical services 0.044 0.007 6.440 0.000
Administrative and support, waste management and
remediation services 0.277 0.007 38.310 0.000
Arts, entertainment and recreation 0.325 0.008 39.400 0.000
Accommodation and food services 0.684 0.007 94.840 0.000
Other services (except public administration) 0.305 0.007 42.350 0.000
GDP growth (%) by industry 0.001 0.000 17.710 0.000
Entry rate by industry (%) (t-1) 0.038 0.000 122.830 0.000
Exit rate by industry (%) (t-1) -0.009 0.000 -78.310 0.000
(log) # of competitors in the same CMA/CA of the same
NAICS-3 (t-1) -0.019 0.000 -50.720 0.000
(log) Average firm size by industry (NAICS-2 digit) (t-1) 0.064 0.002 40.020 0.000
_cons 1.385 0.009 154.080 0.000
sigma_u (𝝈𝝊𝟐) 0.586
sigma_e (𝝈𝜼𝟐) 0.343
rho (ρ) 0.745 (fraction of variance due to
u_i)
107
5.4.2.1. Result interpretations
Age is found to have a positive effect on the number of employees in the simple model
TABLE 5.8. In the detailed model, when profits and tangible assets variables are included, the
age is observed to have a negative effect (TABLE 5.9). This indicates that growth in the number
of employees is largely affected by the growth in their profits and tangible assets and the effect
of the age alone may not be sufficient (by comparing the Z values in both tables for age, tangible
assets, and gross profits in TABLE 5.8 and TABLE 5.9). In other words, the simple model
suggests that older firms have larger numbers of employees, while the detailed model suggests
that older firms that have growing tangible assets and profits are less likely to have larger
number of employees.
The province variables in both models have the same sign and almost the same magnitude
indicating that the provincial location effect does not vary when other firm characteristics are
considered. GDP growth is found to have a positive effect on firm employment size in both
models. The simple model indicates that higher unemployment rates (reflecting a declining
economy) reduce the employment size. GDP growth and unemployment rates effects confirm the
hypothesis that a thriving economy encourages firms to grow in size.
Firm entry and exit rates are indications of market conditions and demand that are main
drivers to firm growth decisions (van Wissen, 2000). In other words, the greater the gap between
demand and supply, the larger the chance for market growth. Higher entry rates indicate possible
market growth that can take the form of new firm creation (as indicated by the entry rate) and/or
the growth of existing firms. In the detailed model (TABLE 5.9), firm entry rates within the same
2-digit NAICS code are positively influencing employment size of existing firms. On the other
side, the negative sign of the exit rate covariate (TABLE 5.9) suggests that when a market is
shrinking in size, existing firms are more likely to reduce their employment size.
Competition is found to have a logical negative effect on firm employment size; the higher
the number of competitors in the same sub-industry class (NAICS 3-digit code), that are located
in the same CMA/CA area, the lower the number of employees within a firm is (TABLE 5.9).
This could be due to agglomeration economy effects. When firms with similar activities cluster
together in a specific geographic region, they minimize their costs by sharing assets/services
such as logistics operations and warehouses. This could mean that fewer number of employees
that are required to perform the shared activities.
108
5.4.2.2. Model goodness-of-fit and validation
TABLE 5.10 shows the estimation summary along with the model goodness-of-fit. The
overall R2 of the detailed model indicates a good model fit. A cross-validation technique is
performed to assess the model predictive capabilities. A 20% hold-out sample that has not been
used in the model estimation is used to predict the number of employees using the simple and
detailed models. The number of correct predictions are observed with a 10% marginal error.
Model validation indicates that the detailed model is 63.8% of the time able to correctly predict
the number of employees within a 10% error.
TABLE 5.10 Estimation Summary and Model Goodness-of-fit of Employment Size
Simple Model Detailed Model
Number of observations 5,659,027 4,451,208
Number of groups 1,124,099 914,138
R2
within 0.000 0.119
between 0.133 0.580
overall 0.123 0.579
Adusted-R2
within 0.000 0.119
between 0.133 0.580
overall 0.123 0.579
Wald chi2(23) /(29) 168315.53 1.67E+06
Prob > chi2 0.000 0.000
Validation sample size 945,361
# of correct predictions (within 10% error) 456,650 603,326
% difference 48.3% 63.8%
The reported R2 values do not have all the OLS R2 properties. The OLS mandates that R2 is
equal to the squared correlation between �̂� and y (r is calculated from Equation (5.9)), and also
equals to the fraction of the variation in y explained by �̂� (or the ratio between Var(�̂�) and
Var(y)).
𝑟 = ∑ 𝑦𝑦 ̂−
(∑ 𝑦) (∑ �̂�)
𝑛
√(∑ 𝑦2− (∑ 𝑦)2
𝑛)(∑ �̂�2−
(∑ �̂�)2
𝑛)
(5.9)
109
The reported R2 values are different from the OLS R2 as it is not necessary that the squared
correlation equals to the ratio of the variances between �̂� and y, and the ratio of the variances do
not have to be less than 1 (StataCorp, Stata: Release 13. Statistical Software, 2013). The reported
R2 values are calculated as correlations squared using equations (5.10), (5.11), and (5.12). The R2-
overall corresponds to calculating the correlation squared using equation (5.10), the R2-between
corresponds to Equation (5.11), and the R2-within corresponds to (5.12).
�̂�𝑖𝑡 = �̂� + �̂� 𝑥𝑖𝑡 (5.10)
�̂̅�𝑖 = �̂� + �̂� �̅�𝑖 (5.11)
�̂̃�𝑖𝑡 = (�̂̃�𝑖𝑡
− �̂̅�𝑖) = �̂� (𝑥𝑖𝑡 − �̅�𝑖) (5.12)
In the estimated random effects model, R2-between is used to interpret the ratio of variations
across panelists, and the R2-overall is used to interpret the overall ratio of variances in the
sample, bearing in mind that the predictions under evaluation are the second-round regressions of
𝛾1�̂�𝑖𝑡 from 𝑦𝑖𝑡 = 𝛾1�̂�𝑖𝑡, 𝛾2𝑦�̂̅� from 𝑦�̅� = 𝛾2�̅��̂�, and 𝛾3�̂̃�𝑖𝑡 from �̂̃�𝑖𝑡 = 𝛾3�̂̃�𝑖𝑡 (StataCorp, 2013).
5.4.3. Multilevel model with random effects
Not only is there variability in firm decisions that are related to the firm itself, but there are
also other variabilities related to the industry class and to the provincial location. The hierarchy
of the firms clustered by different levels are presented in FIGURE 5.3. A multilevel approach is
suitable to model the random effects of panel data due to higher levels of data clusters.
The major distinction between panel data models (such as random effects panel logistic
regression) and multilevel models (or mixed effects model) is how each model handles the
regressors xij. Multilevel models treat regressors as non-random variables, while in panel models
regressors are assumed to be random.
110
FIGURE 5.3. Multilevel Model Schematic
The estimation of multilevel models is computationally intensive for large data sets with
lower-level clusters, as the case of the used T2-LEAP data sets. Computation time increases
significantly when the number of estimated parameters increases (StataCorp, 2013). For
example, it took approximately 72 to 96 hours for estimating a random intercept only model that
has twenty-three independent variables using around six million records. Due to time and
hardware limitations, the estimation of the multilevel models with random slopes was not
feasible, and random intercept only models are estimated to demonstrate their statistical
significance.
5.4.3.1. Model description
Linear multilevel models, also known as Linear Mixed-Effects (LME), are a generalization
of the linear regression models that allow for the inclusion of random effects other than those
associated with the overall error term (StataCorp, 2013). The general matrix notation of the
model is represented in Equation (5.13)
𝐘 = 𝛽 𝐗 + 𝑢 𝐙 + 𝜖 (5.13)
where Y is n × 1 vector of responses, X is an n × p covariate matrix for the fixed effects 𝛽 of
the regressors, and Z is n × q covariate matrix for random effects u. The 𝜖 is a vector of errors
that is assumed to be multivariate normal with mean = 0 and variance matrix of 𝜎𝜖2R. Only
random intercept models are estimated in this research for the complexity and computation time
constraints related to other random slope models. A random intercept model takes the form in
Province
Level # 4
Industry
Level # 3
Firm
Level # 2
Observation
Level # 1
111
Equation (5.14), which contains both level-1 (individual observation) and level-2 (the firm level)
effects, and can be written in separate equations as in (5.15) and (5.16).
𝑦𝑖𝑗 = 𝛽0 + 𝛽1𝑥𝑖𝑗 + 𝑢𝑗 + 𝜖𝑖𝑗 (5.14)
𝑦𝑖𝑗 = 𝛾0𝑗 + 𝛽1𝑥𝑖𝑗 + 𝜖𝑖𝑗 (5.15)
𝛾0𝑗 = 𝛽00 + 𝑢0𝑗 (5.16)
The equation of the intercept (5.16) includes the overall intercept mean of 𝛽00 and a cluster-
specific random intercept 𝑢0𝑗. A level-2 random intercept only model is estimated to quantify the
variation on the firm/panel clusters. The mixed function in STATA 13 is used to estimate LME
for simple and detailed models. Estimates are summarized in TABLE 5.11 and TABLE 5.12.
Details of the maximum likelihood estimation technique of the LME are provided in (StataCorp,
2013).
112
TABLE 5.11 Multilevel Random Intercept Only Model of Employment Size: Simple Model
Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log) Age 0.037 0.000 111.71 0.000
Province: Quebec 0.042 0.002 19.13 0.000
Alberta -0.130 0.002 -57.46 0.000
British Columbia -0.073 0.002 -31.85 0.000
Atlantic Canada 0.117 0.004 31.38 0.000
Industry class: Agriculture, Forestry, Fishing and Hunting 0.052 0.008 6.26 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.032 0.011 2.95 0.003
Utilities 0.770 0.033 23.44 0.000
Construction 0.203 0.008 26.57 0.000
Manufacturing 0.740 0.008 90.8 0.000
Wholesale trade 0.369 0.008 45.73 0.000
Retail trade 0.555 0.008 71.83 0.000
Transportation and warehousing 0.024 0.008 2.95 0.003
Information and cultural industries 0.185 0.010 18.69 0.000
Finance and insurance 0.016 0.009 1.85 0.064
Real estate and rental and leasing 0.031 0.008 3.71 0.000
Professional, scientific and technical services -0.085 0.008 -11.25 0.000
Administrative and support, waste management and
remediation services 0.386 0.008 46.98 0.000
Arts, entertainment and recreation 0.367 0.010 38.45 0.000
Accommodation and food services 0.980 0.008 123.65 0.000
Other services (except public administration) 0.288 0.008 36.45 0.000
Economic indicators: GDP growth (%) by industry 0.002 0.000 45.64 0.000
Yearly provincial unemployment rate (%) of (t-1) -0.007 0.000 -41.31 0.000
_cons 0.723 0.007 97.15 0.000
Random-effects Parameters Estimate Std. Err.
Identity (Firm ID): var(_cons) 0.777 0.001
var(Residual) 0.144 0.000
Number of observations 6,601,802
Number of groups 1,140,557
log likelihood (0) -4815186
log likelihood -4815186
Wald chi2(23) 158908.97
Prob > chi2 0.000
Pseudo R2 0.000
Adjusted R2 0.000
LR test vs. linear regression
chibar2(01) 8.8e+06
Prob >= chibar2 0.0000
113
TABLE 5.12 Multilevel Random Intercept Only Model of Employment Size: Detailed Model
Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log) Age -0.032 0.000 -70.01 0.000
(log) Tangible assets ($×10-6) 0.065 0.000 228.08 0.000
(log) Sales (t-1) ($×10-6) 0.323 0.000 952.87 0.000
Province: Ontario 0.008 0.003 2.780 0.005
Quebec 0.055 0.003 17.56 0.000
Alberta -0.122 0.003 -39.12 0.000
British Columbia -0.014 0.003 -4.5 0.000
Atlantic Canada 0.108 0.004 27.18 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting -0.228 0.008 -28.43 0.000
Mining, Quarrying, and Oil and Gas Extraction -0.291 0.010 -29.33 0.000
Utilities 0.099 0.027 3.62 0.000
Construction 0.050 0.007 6.9 0.000
Manufacturing 0.295 0.008 36.04 0.000
Wholesale trade 0.032 0.008 4.2 0.000
Retail trade 0.069 0.008 8.41 0.000
Transportation and warehousing -0.138 0.008 -18.06 0.000
Information and cultural industries -0.070 0.010 -6.88 0.000
Finance and insurance -0.032 0.008 -3.85 0.000
Real estate and rental and leasing -0.145 0.008 -18.87 0.000
Professional, scientific and technical services 0.041 0.007 5.66 0.000
Administrative and support, waste management and
remediation services 0.275 0.008 36.05 0.000
Arts, entertainment and recreation 0.321 0.009 37.02 0.000
Accommodation and food services 0.684 0.008 90.2 0.000
Other services (except public administration) 0.298 0.008 39.39 0.000
Economic indicators, industry characteristics, and competition
GDP growth (%) by industry 0.001 0.000 16.81 0.000
Entry rate by industry (%) t-1 0.038 0.000 118.43 0.000
Exit rate by industry (%) t-1 -0.009 0.000 -78.26 0.000
(log)# of competitors on CMA/CA and NAICS-3 levels (t-1) -0.020 0.000 -50.14 0.000
(log) Average firm size (2-digit NAICS) (t-1) 0.060 0.002 36.99 0.000
_cons 1.417 0.009 151.53 0.000
Random-effects Parameters Estimate Std. Err.
unique_id: Identity (Firm Level)
var(_cons) 0.396 0.001
var(Residual) 0.120 0.000
114
TABLE 5.12 Multilevel Random Intercept Only Model of Employment Size: Detailed Model
Estimation Results (continued)
Number of observations 4,314,653
Number of groups 910,038
log likelihood (0) -2,739,682
log likelihood -2,739,682
Wald chi2(23) 1.58E+06
Prob > chi2 0.000
Pseudo R2 0.000
Adjusted R2 0.000
LR test vs. linear regression
Chibar2(01) 3.30E+06
Prob >= chibar2 0.000
When no random slope components are included in level-3 and higher, and only random
intercepts are considered, the estimation results will divide the variance of the intercept equally
amongst the indicated random-effects clusters (TABLE A.5 and TABLE A.6 in Appendix A). In
order to have a meaningful LME model with more than two levels, random slopes should be
included and identified.
5.4.3.2. Results interpretation
In the presented random intercept only simple model, age is observed to have a positive
influence on firm employment size. Whereas, in the detailed model TABLE 5.12, when other
firm attributes of tangible assets and sales values are included, the age turns to have a negative
effect. This finding is similar to the panel logistic regression with random effect model results,
and similar conclusions can be made.
GDP growth is found to have a positive effect on employment size, whereas unemployment
rates have a negative effect. Both findings highlight that a growing economy encourages firms to
have larger employment sizes and vice versa. Similar to findings of the panel logistics models
discussed in section 5.4.2.1, entry and exit rates, competition, and average firm size have the
same intuitive effects.
Variability between panels has reduced by almost half when other firm related variables are
included (from 0.777 in the simple model to 0.395), which is intuitive. When other variables
related to the panelists are included in the model, the unobserved variability is reduced. A
likelihood ratio (LR) test is reported to compare the model significance to a linear regression
115
model. In both models, the LR test indicates that multilevel models have better representation of
the dependent variable compared to linear regression models.
5.4.4. Autoregressive Distributed-lag Model (ARDL)
We hypothesize that firm growth (either in terms of employment size or tangible assets)
depends on their historical values and trends. More specifically, in a certain year (t), a firm may
choose to increase or decrease their employment size with a specific value based on its previous
year’s value (t-1). This means that there is an autocorrelation between the firm’s size in year (t)
and its size in year (t-1) at least. To verify this hypothesis, autoregressive models for firm
employment size and tangible assets are estimated as measures of growth.
Autoregressive models are considered to be dynamic regression models, in which the effect
of a regressor x on y occurs over time rather than all at once in a specific time instance (t) (Hugh
and Box, 1997). This dynamic specification (particularly in time series and panel data) allows for
quantifying the lag effects on future (expected) forecasts of y (Hendry et al., 1984).
In estimating ARDL models, first, it should be determined whether there is an
autocorrelation between longitudinal observations of the same panel participant or not, and
determine the order of the autocorrelation (i.e. AR (1), or AR (2) …etc.). One simple and
common technique is to use autocorrelation function (ACF). ACF is simply a plot of the
coefficients of correlation ρ, Equation (5.17), between a time series variable (xt) and its lags (xt-1,
xt-2, ..., xt-n).
𝜌 =𝐶𝑜𝑣 (𝑦𝑡,𝑦𝑡−1)
𝜎𝑡𝜎𝑡−1=
𝐸 [ (𝑦𝑡− �̅�𝑡)(𝑦𝑡−1− �̅�𝑡−1)]
𝜎𝑡𝜎𝑡−1 (5.17)
where �̅�𝑡 and �̅�𝑡−1 are the mean values of the variables 𝑦𝑡 and it is lag 𝑦𝑡−1, and 𝜎𝑡 and 𝜎𝑡−1 are
their standard deviations. However, ACF can only be used for a single panelist observation,
which is not our case as there are many panel participants in the dataset. For this, a number of
individual time series panelists are randomly selected and ACF is checked to determine the sign
and order of autocorrelation (if any) for each. The corrgram function in STATA software is
used. This is done for both the number of employees and the tangible assets variables. The ACF
indicate that there is no significant autocorrelation beyond the first lag, showing that the effect of
the autocorrelation beyond the first lag is diminished, and that AR (1) is a suitable order for both
variables. The ACF is also checked for the log value of both variables, and it is concluded the
116
AR (1) is stronger for the log values than the absolute value of the variables. Generally, the AR
(1) values of the log values of both the tangible assets, and the number of employees’ variables,
for the randomly selected panels, are with negative values, indicating a negative effect of the
variable and its lagged value (StataCorp, 2013). The results of the ACF analysis have not been
approved for publication by Statistics Canada, to ensure privacy of the respondents, and
therefore have not been included in the thesis.
A first-order ARDL model structure is used for estimating the yearly number of employees
and tangible assets using a longitudinal panel data. The model accounts for the effect of
autocorrelation between the response variable (at t) and its first lagged value (at t-1). In addition,
the random effects component in the model accounts for explaining the variation on the panel
level as indicated in Equation (5.18).
𝑦𝑖𝑡 = 𝛼 + 𝛽𝕏𝑖𝑡 + 𝜐𝑖 + 𝜖𝑖𝑡 (5.18)
where i = 1,….,N and t = 1,…..,Ti, and
𝜖𝑖𝑡 = 𝜌 𝜖𝑖,𝑡−1 + 𝜂𝑖𝑡 (5.19)
where | 𝜌| <1, and 𝜂𝑖𝑡is independent and i.i.d. with a mean of 0 and variance of 𝜎𝜂2. 𝜐𝑖 is the
part that represents the variation on the panel level and the 𝜖𝑖𝑡 is the component responsible for
addressing the autocorrelation of the first lag (StataCorp, 2013). 𝜐𝑖 is assumed to be realizations
of an i.i.d. process with a mean of 0 and variance 𝜎𝜐2 and is assumed to be independent from the
covariates 𝕏𝑖𝑡.The xtregar function in STATA 13 software package is used to estimate the
ARDL models with random effects for both the number of employees and tangible assets. A
random effects model structure is selected to accommodate time invariant covariates (e.g.
location and industry class dummy variables). The data set for this study can be unbalanced (i.e.
the number of time periods is not the same for every panel) and unequally spaced (i.e. missing
yearly records within the time interval are allowed). The command applies the Prais-Winsten
estimation method, which is explained in detail in (Prais and Winsten, 1954).
5.4.4.1. Model estimation results
The dependent variable is defined as the natural logarithmic value of the number of
employees at time (t) regressed against the variables identified earlier in the data description
section (TABLE 5.1). The estimation results are summarized in TABLE 5.13. The model uses the
first lag of the number of employees as an explanatory variable, and the rest of the covariates
117
represent the change in the number of employees compared to year (t-1). Only variables that are
statistically significant at 99% and higher are included.
118
TABLE 5.13 ARDL Model of Employment Size: Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log) Age -0.049 0.000 -209.650 0.000
(log) No. of employees of previous year's (t-1) 0.898 0.000 3962.720 0.000
Province
Quebec 0.011 0.001 17.170 0.000
Alberta -0.021 0.001 -26.630 0.000
British Columbia -0.009 0.001 -14.200 0.000
Manitoba 0.010 0.001 6.910 0.000
Atlantic Canada 0.005 0.001 3.820 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting -0.049 0.003 -14.640 0.000
Mining, Quarrying, and Oil and Gas Extraction -0.085 0.002 -35.660 0.000
Construction -0.025 0.002 -10.230 0.000
Wholesale trade -0.015 0.002 -7.880 0.000
Retail trade -0.022 0.001 -18.100 0.000
Transportation and warehousing -0.074 0.002 -45.140 0.000
Information and cultural industries -0.094 0.002 -39.610 0.000
Finance and insurance -0.076 0.002 -48.950 0.000
Real estate and rental and leasing -0.068 0.003 -25.770 0.000
Professional, scientific and technical services -0.081 0.002 -35.310 0.000
Management of companies and enterprises -0.091 0.003 -33.030 0.000
Administrative and support, waste management and
remediation services -0.025 0.002 -11.700 0.000
Arts, entertainment and recreation -0.028 0.002 -11.470 0.000
Accommodation and food services 0.020 0.002 11.650 0.000
Other services (except public administration) -0.019 0.003 -6.520 0.000
Economic indicators
GDP growth (%) by industry 0.002 0.000 12.570 0.000
Yearly provincial unemployment rate (%) of (t-1) -0.002 0.000 -11.350 0.000
Industry characteristics and competition
Entry rate by industry (t-1) 0.006 0.000 21.760 0.000
Exit rate by industry (t-1) -0.002 0.000 -13.040 0.000
(log) # of competitors on CMA/CA on NAICS-3 (t-1) -0.004 0.000 -35.120 0.000
(log) Average firm size (2-digit NAICS) (t-1) 0.009 0.001 10.190 0.000
_cons 0.254 0.005 49.910 0.000
rho_ar (ρ) -0.126 estimated AR coefficient
sigma_u ( 𝝈𝝊𝟐)
0.077
sigma_e ( 𝝈𝜼𝟐) 0.389
rho_fov ( 𝝆𝝊) 0.038 fraction of variance due to u_i
119
From TABLE 5.13, the estimated autocorrelation coefficient (ρ) indicates that the
autocorrelation of the first variable lag has a negative effect on the number of employees in year
t, conforming to results found by (Coad and Broekel, 2012; de Bok and Bliemer, 2006). This
confirms ACF explained earlier. This means that a firm with relatively significant growth in one
year is more likely witness a slower growth in the next year. The estimated value of 𝜌𝜐,
previously explained in (5.6), which calculates the proportion of the total variance contributed by
the panel-level variance, indicates a diminished effect of the panel variance for the current model
configuration. A discussion on the model goodness-of-fit is provided in section 5.4.4.2.
5.4.4.2. Results interpretation
The first lagged value of the number of employees was found to have a strong positive effect
on the employment size of year (t) (both in terms of the magnitude and the statistical
significance) which agrees to (de Bok and Bliemer, 2006). Age is found to have a negative effect
on employment size when the value of first lag is included in the model. This indicates that older
firms have slower growth rates. Our finding is in agreement to (Yasuda, 2005; Evans, 1987b;
Variyam and Kraybill, 1992). The values of the location and industry classification dummy
variables show that employment growth varies across industries in different locations.
The economic growth is explained by two covariates, the GDP growth and the provincial
unemployment rate. The positive value of the GDP growth and the negative value of the
unemployment rate covariates indicate that a growing economy encourages firms to grow their
number of employees, and vice versa. Entry and exit rates by industry are another aspect of
economic conditions and market demand and supply. The results show that firms increase their
number of employees with higher entry rates, while they contract their employment with higher
exit rates.
Competition on the other hand is found to have a negative effect on firm employment
growth. Competition is represented as the number of firms that are located in the same CMA/CA
that belong to the same sub-industry class (NAICS 3-digit). Higher competition results in lower
employment firm size. Competition is also the other side to agglomeration economies, meaning
that firms located in highly agglomerated areas are less likely to have larger employment size as
they probably share some activities/resources with other neighbouring firms. Furthermore, the
model suggests that firms that belong to industries that have higher average sizes are more likely
120
to have faster growth rates. Firms tend to grow in size until they reach the average firm size
within the same industry.
5.4.4.3. Model validation and goodness-of-fit
The reported goodness-of-fit of the estimated ARDL model along with a cross-validation
results are summarized in TABLE 5.14. The reported ‘between’ and ’overall’ R2 values imply a
very good model fit compared to the panel regression model with random effects. While the R2
values are important measures of model goodness-of-fit, there are two other parameters whose
statistical significance need be evaluated. The two parameters are the estimated value of ρ, in
Equation (5.19), that specifies whether there is an autocorrelation (AR) of level (1), and the
proportion of the total variance contributed by the panel-level variance 𝜌𝜐 in Equation (5.6). Both
values are reported in TABLE 5.13. For the ρ estimate, a hypothesis testing is conducted to
statistically evaluate the null hypothesis that H0: ρ=0. The modified Durbin-Watson test,
suggested by (Bhargava et al., 1982), is performed and compared to values recommended by
(Verbeek, 2008) to accept or reject the null hypothesis that ρ is not statistically different from
zero. For very large data sets, if the value of Durbin-Watson statistic (dp), calculated using
Equation (5.20), is less than two, then the null hypothesis is rejected, and the value of ρ is
statistically different from zero. The calculated value for the Modified Durbin-Watson (TABLE
5.14) of 1.512 is less than two indicating that the autocorrelation term is significant and should be
included in the model. In other words, the number of employees in one year is significantly
dependent on its value in the previous year.
𝑑𝑝 =∑ ∑ (�̂�𝑖𝑡 −�̂�𝑖,𝑡−1)
2𝑇𝑡=2
𝑁𝑖=1
∑ ∑ �̂�𝑖𝑡 2𝑇
𝑡=1𝑁𝑖=1
(5.20)
Another test statistic that is calculated to evaluate the null hypothesis H0: ρ=0 against H1:
ρ≠0 is the Baltagi-Wu Locally Best Invariant (LBI) test statistics (Baltagi and Wu, 1999)
reported in TABLE 5.14. According to (Baltagi and Wu, 1999), this test statistic accounts for
unequal (missing observations) panel data, and the reported LBI is compared to the critical value
from the upper tail (to test negative autocorrelation) of a N(0,1) distribution. The Baltagi-Wu
LBI value of 2.1 is compared to Z value of a N(0,1) and it can be concluded that the probability
of obtaining a ρ value that is greater than zero is 98%. In other words, the null hypothesis can be
rejected, and it is concluded that ρ is statistically different from zero.
121
The second term, the panel-variance significance 𝜌𝜐 in Equation (5.6) reported in TABLE
5.13, is of a value 0.038 indicates that the variation due to the panel level is diminished.
However, there are no further statistical tests to verify whether it is statistically different from
zero or not. Therefore, the term is kept in the model.
A cross-validation technique is conducted to assess the predictive performance of the model
for each individual year-record for every panel. A 20% hold-out sample that has not been
included in the model estimation has been evaluated using the model. The model is able to
predict the number of employees correctly 85.44% of the time while allowing for a marginal
error of 10%. This highlights that the model has an acceptable predictive performance on the
individual record.
TABLE 5.14 ARDL Model of Employment Size: Estimation Results Summary, Model Goodness-of-
Fit, and Model Validation
Number of observations 4,884,484
Number of groups 998,690
R2 -within 0.192
R2 -between 0.901
R2 -overall 0.861
Adjusted R2 -within 0.192
Adjusted R2 -between 0.901
Adjusted R2 -overall 0.861
Wald chi2(29) 1.93E+07
Prob > chi2 0.000
Modified Bhargava et al. Durbin-Watson 1.512
Baltagi-Wu LBI 2.101
Validation sample size 945,361
Successful prediction (10% error) 807706
% difference 85.44%
5.5. Firm Tangible Assets Growth Models
Per the microsimulation configuration of firm growth (FIGURE 4.1), models of tangible
assets growth use estimates of employment growth model (for the simulated year), and previous
year’s tangible assets as inputs. The same hypothesis assumed in the employment growth ARDL
model (section 5.4.4) is followed, which states that firm increase/decrease their tangible assets of
the current year (t) based on their previous year’s value (t-1) as explained earlier in FIGURE 4.1,
and Equation (5.2).
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5.5.1. Model estimation and result interpretation
The ARDL model estimates are presented in TABLE 5.15. The tangible assets are presented
in their natural logarithmic dollar value ×10-6.
TABLE 5.15 ARDL Model of Tangible Assets: Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log)Tangible assets (t-1) ×10-6 0.926 0.000 2471.390 0.000
(log) number of employees (t) 0.097 0.001 154.860 0.000
(log) age -0.074 0.001 -116.630 0.000
Province
Ontario -0.006 0.002 -3.810 0.000
Quebec 0.013 0.002 7.770 0.000
Alberta 0.027 0.002 14.720 0.000
Saskatchewan 0.029 0.003 8.560 0.000
Industry Class
Agriculture, Forestry, Fishing and Hunting 0.109 0.003 35.710 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.054 0.006 9.610 0.000
Manufacturing -0.041 0.003 -14.760 0.000
Wholesale trade -0.060 0.003 -22.340 0.000
Retail trade -0.111 0.002 -47.690 0.000
Transportation and warehousing -0.029 0.003 -10.300 0.000
Information and cultural industries -0.061 0.005 -12.000 0.000
Finance and insurance -0.068 0.004 -19.370 0.000
Real estate and rental and leasing 0.028 0.003 9.110 0.000
Professional, scientific and technical services -0.063 0.002 -28.820 0.000
Management of companies and enterprises -0.020 0.007 -2.680 0.007
Administrative and support, waste management and
remediation services -0.060 0.003 -20.430 0.000
Arts, entertainment and recreation -0.046 0.004 -10.350 0.000
Accommodation and food services -0.116 0.003 -42.190 0.000
Other services (except public administration) -0.047 0.002 -19.060 0.000
Economic indicator, industry characteristics, and competition
Overall GDP growth (%) 0.001 0.000 5.090 0.000
Exit rate by industry (t) -0.001 0.000 -2.700 0.007
# of competitors on CMA/CA and NAICS-3 levels (log) (t) -0.009 0.000 -28.240 0.000
_cons 0.027 0.004 7.220 0.000
rho_ar (ρ) -0.496 estimated AR coefficient
sigma_u ( 𝝈𝝊𝟐) 0.085
sigma_e ( 𝝈𝜼𝟐) 0.417
rho_fov ( 𝝆𝝊) 0.040 fraction of variance due to u_i
123
The model show that tangible assets (of year t) are greatly dependent on their value in the
previous year (t-1) indicated by the large value of the coefficient (0.926) and large z-value in
TABLE 5.15. Small-medium sized firms (employment less than 100) do not appear to make large
scale expansion of their tangible assets. The mean and standard deviation of the tangible assets
and their first lag values (TABLE 5.1) show a small variation between the two numbers
indicating a small variation which confirms the model results.
The number of employees is found to have a positive effect on the tangible assets. An
increase in the log value of the number of employees by 1 unit (i.e. an increase of 2.72 units in
the number of employees) increases the value of the tangible assets by $1.102×106. Age is found
to have a negative effect on tangible assets. This could have the same analogy followed in ARDL
model of employment size (5.4.2.2) that older firms have slower growth pace.
5.5.2. Model validation and goodness-of-fit
It can be inferred from TABLE 5.16 that the estimated ARDL model has a very goodness-of-
fit indicated by the high values of the R2 (specifically the between and the overall R2). Also, the
values of the Wald Chi2 and prob>chi2 indicate that all model coefficients are statistically
significant from zero. The autocorrelation coefficient ρ=-0.496 reported in TABLE 5.15, implies
that the there is a high negative autocorrelation between the tangible assets and their first lagged
value. Two statistics are reported to assess whether the term ρ is statistically significant from
zero or not; the Modified Bhargava et al. Dubrin-Watson and the Baltagi-Wu LBI in TABLE
5.16. The value of the modified Bargava et al. Dubrin-Watson is less than two rejecting the null
hypothesis that ρ=0 and confirms that it is statistically different from zero. Comparing the
Baltagi-Wu statistics of 2.204 to a standard normal distribution N (0,1), it can be inferred that the
probability of obtaining ρ that is greater than zero is 98.6%. Please refer to section 5.4.4.3 for
details and discussion about these two test statistics. For the panelist-variance significance, the
value of 𝜌𝜐 reported in TABLE 5.15, and calculated from Equation (5.20), imply that variation
due to firm-level variation is small. However, as mentioned earlier, there are no further statistical
tests to reject or accept the hypothesis that it is not statistically different from zero.
A hold-out sample of 20% of the records (that has not been used in the model estimation) has
been used to validate the model. A cross-validation technique is conducted by estimating the
tangible assets values for each individual record in each panel, and compared to their observed
124
values in the hold-out sample. Model validation indicates that the model can correctly predict the
tangible assets 80.82% of the time while allowing for a 10% margin of error. This implies that
the model has a good predictive capability.
TABLE 5.16 ARDL Model of Tangible Assets: Estimation Results Summary, Model Goodness-of-
Fit, and Model Validation
Number of observations 923,828
Number of groups 540,531
R2
within 0.444
between 0.944
overall 0.938
Adjusted R2
within 0.444
between 0.944
overall 0.938
Wald chi2(26) 1.05E+07
Prob > chi2 0.000
Modified Bhargava et al. Durbin-Watson 1.004
Baltagi-Wu LBI 2.204
Validation sample size 1,046,767
Successful prediction (10% error) 845,974
% difference 80.82%
5.6. Seemingly Unrelated Regression (SURE)
Firm growth can be multidimensional; the growth is happening in more than one aspect at the
same time. We hypothesize that a change in a firm’s tangible assets is associated with a change
in their number of employees and vice versa. To model this simultaneous relationship, a
Seemingly Unrelated Regression (SURE) approach is presented to investigate this hypothesis.
This approach is limited for use in a freight microsimulation model because it doesn’t consider
any variability between the panel participants in the data. A future direction for this step is to
estimate a SURE model for a panel that accounts for the unobserved heterogeneity of the panel
units as explained in (Avery, 1977).
SURE models are deemed suitable when a simultaneous relationship between two dependent
variables is suspected, and the dependent variables are explained by the same set of independent
variables. The regression equations of the employment size and the tangible assets (Equation
125
(5.21) and (5.22) are hypothesized and the associated error terms (𝑢1 and 𝑢2) may be correlated
(Cameron and Trivedi, 2009).
log _𝑒𝑚𝑝𝑖 = 𝛽0 + 𝛽𝑖𝑿𝑖 + 𝑢1 (5.21)
log _𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒_𝑎𝑠𝑠𝑒𝑡𝑠𝑖 = 𝛾0 + 𝛾𝑖𝑿𝑖 + 𝑢2 (5.22)
where 𝑿𝑖 is a vector of independent variables, and 𝑢1 and 𝑢2 are the error terms (the residuals)
that are assumed to be correlated. The sureg function in STATA 13 software package is used to
estimate the explained SURE equations. The sureg uses a generalized least-squares (GLS)
algorithm that is described in details in (Greene, 2003), page 341-343. Two types of models are
estimated, simple and detailed models. The simple model includes basic information of the firm
while the detailed one includes more information such as the sales values, and industry
characteristics. The model estimation results are presented in TABLE 5.17 and TABLE 5.18.
5.6.1. Model estimation results and interpretation
Both the simple and detailed systems of equations imply that firm age has a positive effect on
the studied firm growth aspects. This indicates that older firms have more employees and higher
tangible asset values. Unemployment rates in (TABLE 5.17) have a negative effect on
employment size, while GDP growth has a positive effect (TABLE 5.17 and TABLE 5.18). Both
unemployment and GDP growth rates as economic indicators imply that a growing economy
encourage firms to grow in number of employees as explained earlier. Similarly, the value of the
GDP by industry (TABLE 5.17 and TABLE 5.18) has a positive influence on tangible asset
values, indicating that firms belonging to larger industries have higher tangible asset values.
Furthermore, provincial location and industry class dummy variables show variable effects on
the employment size and tangible assets.
Sales values have a large positive impact on both aspects of growth indicated by the
magnitude and z-value of the corresponding coefficient in TABLE 5.18. This is intuitive, as firms
with higher sales values in a particular year (t-1) may consider to physically grow in the
following year (t) to maintain/increase their achieved sales values. Competition has a negative
effect on both aspects of growth as shown in TABLE 5.18. Entry and exit rates are measures of
industry dynamics and are found to have a positive and a negative influence on employment size,
respectively (TABLE 5.18). This is intuitive; higher entry rates indicate higher demand for new or
growing firms, while exit rates imply the opposite.
126
TABLE 5.17 SURE Model Estimation Results: Simple Model
Covariates Coef. Std.
Err. z P>|z|
Equation (1) (log) # of employees
(log) Age 0.244 0.000 523.010 0.000
Province
Quebec 0.005 0.001 4.390 0.000
Alberta -0.196 0.001 -
133.660 0.000
British Columbia -0.081 0.001 -62.350 0.000
Atlantic Canada 0.108 0.002 46.960 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting -0.041 0.003 -15.300 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.022 0.005 4.840 0.000
Utilities 0.694 0.015 45.040 0.000
Construction 0.234 0.002 104.640 0.000
Manufacturing 0.770 0.003 293.820 0.000
Wholesale trade 0.424 0.003 167.100 0.000
Retail trade 0.598 0.002 259.920 0.000
Transportation and warehousing 0.140 0.003 53.380 0.000
Information and cultural industries 0.227 0.004 55.120 0.000
Finance and insurance 0.044 0.003 14.140 0.000
Professional, scientific and technical services -0.061 0.002 -27.190 0.000
Administrative and support, waste management and
remediation services 0.435 0.003 158.900 0.000
Arts, entertainment and recreation 0.432 0.004 114.180 0.000
Accommodation and food services 1.092 0.003 430.600 0.000
Other services (except public administration) 0.316 0.002 128.820 0.000
Economic indicators
GDP growth (%) by industry 0.003 0.000 22.240 0.000
Yearly provincial unemployment rate (%) of (t-1) -0.013 0.000 -40.440 0.000
_cons 0.545 0.003 175.710 0.000
127
TABLE 5.17 SURE Model Estimation Results: Simple Model (Continued)
Equation (2) (log) Tangible assets ($×10-6) Coef. Std.
Err. z P>|z|
(log) Age 0.594 0.001 737.960 0.000
Province
Ontario -0.304 0.003 -97.110 0.000
Quebec -0.294 0.003 -88.520 0.000
Alberta -0.191 0.003 -55.090 0.000
British Columbia -0.347 0.003 -99.620 0.000
Atlantic Canada -0.208 0.004 -48.330 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting 1.343 0.010 131.320 0.000
Mining, Quarrying, and Oil and Gas Extraction -0.392 0.020 -19.800 0.000
Construction -1.063 0.017 -62.530 0.000
Manufacturing -0.654 0.022 -30.360 0.000
Wholesale trade -1.046 0.016 -66.440 0.000
Retail trade -1.058 0.016 -67.660 0.000
Transportation and warehousing -0.429 0.015 -29.320 0.000
Information and cultural industries -1.026 0.014 -71.260 0.000
Finance and insurance -1.524 0.017 -87.780 0.000
Real estate and rental and leasing -0.250 0.021 -11.960 0.000
Professional, scientific and technical services -1.987 0.016 -
126.950 0.000
Administrative and support, waste management and
remediation services -0.906 0.012 -74.460 0.000
Arts, entertainment and recreation 0.213 0.010 20.690 0.000
Accommodation and food services 0.176 0.011 15.930 0.000
Other services (except public administration) -0.609 0.011 -56.250 0.000
Economic indicators
(log) GDP by industry (dollars x 10-10) 0.489 0.007 66.750 0.000
_cons -3.350 0.009 -
360.690 0.000
128
TABLE 5.18. SURE Model Estimation Results: Detailed Model
Covariates Coef. Std.
Err. z P>|z|
Equation (1) (log) # of employees
(log) Age 0.057 0.000 157.06 0.000
(log) Sales (t-1) ($×10-6) 0.540 0.000 2198.09 0.000
Province
Ontario -0.013 0.001 -13.92 0.000
Quebec 0.040 0.001 40.20 0.000
Alberta -0.129 0.001 -121.47 0.000
British Columbia 0.124 0.002 80.11 0.000
Atlantic Canada -0.247 0.004 -61.57 0.000
Industry class
Mining, Quarrying, and Oil and Gas Extraction -0.216 0.004 -58.00 0.000
Utilities 0.167 0.013 12.91 0.000
Construction -0.032 0.003 -10.82 0.000
Manufacturing 0.293 0.002 124.73 0.000
Wholesale trade -0.160 0.002 -66.74 0.000
Retail trade -0.013 0.003 -5.08 0.000
Transportation and warehousing -0.055 0.002 -24.25 0.000
Information and cultural industries 0.014 0.004 3.36 0.001
Real estate and rental and leasing -0.097 0.003 -29.96 0.000
Professional, scientific and technical services 0.041 0.003 15.02 0.000
Administrative and support, waste management and
remediation services 0.254 0.003 94.10 0.000
Arts, entertainment and recreation 0.411 0.003 126.80 0.000
Accommodation and food services 0.749 0.003 290.73 0.000
Other services (except public administration) 0.268 0.004 74.87 0.000
Economic indicators, industry characteristics, and competition
GDP growth (%) by industry 0.003 0.000 24.17 0.000
Entry rate by industry (%) t-1 0.049 0.000 102.15 0.000
Exit rate by industry (%) t-1 -0.008 0.000 -36.53 0.000
(log)# of competitors on CMA/CA and NAICS-3 levels
(t-1) -0.015 0.000 -83.76 0.000
(log) Average firm size (2-digit NAICS) (t-1) 0.026 0.001 20.02 0.000
_cons 1.428 0.006 233.74 0.000
129
TABLE 5.18 SURE Model Estimation Results: Detailed Model (Continued)
Equation (2) (log) Tangible assets ($×10-6)
(log) Age 0.360 0.001 461.85 0.000
(log) Sales (t-1) ($×10-6) 0.635 0.001 1191.81 0.000
Province
Ontario -0.152 0.002 -77.49 0.000
Quebec -0.088 0.002 -41.43 0.000
British Columbia -0.141 0.002 -61.67 0.000
Atlantic Canada -0.146 0.003 -43.35 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting 1.366 0.011 121.86 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.451 0.023 19.27 0.000
Utilities 0.336 0.030 11.30 0.000
Construction -0.345 0.021 -16.79 0.000
Manufacturing -0.094 0.026 -3.60 0.000
Wholesale trade -0.970 0.019 -51.25 0.000
Retail trade -0.922 0.019 -48.88 0.000
Transportation and warehousing 0.148 0.017 8.52 0.000
Information and cultural industries -0.514 0.016 -31.19 0.000
Finance and insurance -0.600 0.021 -28.83 0.000
Real estate and rental and leasing 0.949 0.025 37.24 0.000
Professional, scientific and technical services -0.816 0.019 -43.41 0.000
Administrative and support, waste management and
remediation services -0.447 0.014 -31.83 0.000
Arts, entertainment and recreation 0.189 0.010 18.58 0.000
Accommodation and food services 0.375 0.013 29.98 0.000
Other services (except public administration) -0.214 0.012 -17.49 0.000
Economic indicator, and competition
(log) GDP by industry (dollars x 10-10) 0.064 0.009 7.25 0.000
(log)# of competitors on CMA/CA and NAICS-3 levels
(t-1) -0.088 0.000 -222.16 0.000
_cons -1.585 0.010 -165.98 0.000
5.6.2. Model goodness-of-fit and validation
Estimation summary and model validation results of the SURE models are presented in
TABLE 5.19 and TABLE 5.20. The R2 values of the detailed system of equations indicate a
moderate fit, whereas the simple system of equations indicates a poor fit for both the number of
employees and the tangible assets. The reported R2 values are the percent of the variance
explained by the predictors. The R2 may be used for descriptive purposes, but they are not well-
defined when GLS is used (StataCorp, 2013) and therefore should not be solely used to assess
the model goodness-of-fit. The values of the Chi2 test in both the simple and detailed models
indicate that all model coefficients are statistically different from zero. A cross-validation is
130
performed to further verify the estimation capabilities of the model. The simple system of
equations indicates that it is able to correctly predict the number of employees 50.7% of the time,
and correctly predict the tangible assets 55.8% of the time. The detailed system of equations
imply that the system is 62.1% and 60% of the time is able to correctly predict the number of
employees, and the tangible assets, respectively.
It is noted that the correlation of the residuals is higher in the simple model compared to the
detailed model. The simple model has a correlation of error terms of a value of 0.437 (TABLE
5.19) indicating a moderate correlation between unobserved terms of employment size and
tangible assets models. When more regressors are added to the system of equations, it is noted
that the value of the correlation is reduced to 0.128 (TABLE 5.20). This is intuitive as the more
added explanatory variables that are relevant to both dependent variables, the less the correlation
between the residual terms that accounts for the unobserved effect.
TABLE 5.19 SURE Model Estimation Summary and Validation Results: Simple Model
Equation # (1) log_emp # (2) log_tangible_mill
# of observations 4.90E+06 4.90E+06
Parameters 22 22
RMSE 0.975 1.684
R2 0.158 0.237
Adjusted R2 0.158 0.237
chi2 925861.17 1.54E+06
P 0.000 0.000
Correlation of residuals of Eq. (1) and Eq. (2) 0.437
Validation sample size 1,039,154
# of correct predictions 526484 579415
% of correct predictions 50.66% 55.76%
131
TABLE 5.20 SURE Model Estimation Summary and Validation Results: Detailed Model
Equation #(1) log_emp # (2) log_tangible_mill
# of observations 4.30E+06 4.30E+06
Parameters 26 24
RMSE 0.672 1.455
R2 0.603 0.430
Adjusted R2 0.603 0.430
chi2 6560000 3.27E+06
P 0.000 0.000
Correlation of residuals of Eq. (1) and Eq. (2) 0.128
Validation sample size 1,039,154
# of correct predictions 645433 624019
% of correct predictions 62.11% 60.05%
5.7. Concluding Remarks and Future Directions
In this chapter a range of firm growth models is presented. Employment growth models of
ordered logit, panel logistic regression with random effects, autoregressive distributed-lag, and
multilevel models with random effects are presented. The rationale and statistical significance of
each are discussed in detail. The ARDL model is selected for the proposed firm microsimulation
because it is a better representation of employment growth that fits the designed microsimulation
configuration. It is intuitive that firms grow relative to their size in previous years. ARDL uses
previous year’s firm attributes, economic conditions, industry characteristics, and market
competition to forecast the next year’s size. On the other hand, the presented panel regression
model with random effects can also be used in microsimulation if more weight to the panelist
level variation is desired while ignoring the autocorrelation of the dependent variable.
Firm tangible assets are information that is hard to obtain, and may not be feasible to include
in a parsimonious model of firm growth in a microsimulation setting. Hence, tangible assets are
not included as explanatory variables in the employment growth model to make the
microsimulation of employment achievable. For this reason, only ARDL models are estimated
for tangible assets in case such information is available and can be included in the
microsimulation.
While firm growth is presented in two dimensions that seem to be correlated, firm growth
could better be presented using a single unit the combines both growth aspects, such as
employment per floor space (Hunt et al., 2005; Badoe and Miller, 2000; Miller and Lerman,
132
1981). This information is not available in the used data set. If such information becomes
available, the physical form of the firm could be better explained as the employment per floor
space.
Systems of SURE equations that consider correlation between unobserved terms of the
number of employees and the tangible assets are explored. The SURE may not be suitable for
prediction nor microsimulation purposes because it ignores unobserved heterogeneity between
panelists. SURE do not capture actual correlation of dependent variables, but rather a correlation
between error terms. A future step for this approach is to estimate SURE for panels by
considering that both equations are correlated over time and across units as explained in (Avery,
1977). The effect of autoregression can also be considered by estimating a multivariate time-
series regression of each dependent variable on their lags and on the dependent variable lags as
well (StataCorp, 2013;Kmenta and Gilbert, 1970). Furthermore, a system of simultaneous
equations is another future approach to capture correlation between dependent variables; when
the effect of one dependent variable is endogenous to the other. The estimation of a system of
simultaneous equation models requires enough additional exogenous variables to explain
identified endogenous variables (referred to ‘instruments’). The currently used data set is
dominated by dummy variables, with no enough instruments to be used to estimate the
endogenous variables. When dummy variables, such as industry classification, provincial
location, and corporate type (i.e. private vs. public), are used, the estimation of a system of
structure equation results in the same values as SURE. To have an identified system, a number of
exogenous variables to be instruments is needed to satisfy the condition 𝑚𝑖 ≤ (𝐾 − 𝑘𝑖), where
𝑚𝑖 is the number of endogenous variables in an equation i, K is the total number of all exogenous
variables (including instruments) in the system of equations, and 𝑘𝑖 is the number of exogenous
variables in equation i.
Further discrete choice model approaches such as nested logit (NL) can be explored. Also,
discrete choice models that accommodate panel variance are good candidates to represent firm
growth decisions. Examples of such models are multilevel/mixed MNL, multilevel/mixed NL,
and multilevel ordered logit.
The effect of age on firm growth and employment size is a controversial topic in the
literature. Throughout the models presented in this chapter, it is observed that when age is solely
included to explain employment size, age has a positive effect. Whereas when other firm related
133
attributes that have greater effects on employment size (e.g. tangible assets) are included, the
effect of age is negative. Since other firm attributes of tangible assets and sales values are found
to have higher statistical significance on firm employment size, there are higher confidence that
age has a negative effect on employment size, and it can be concluded that older firms have
smaller growth rates compared to younger ones, agreeing to (Yasuda, 2005; Evans, 1987b;
Variyam & Kraybill, 1992). Young firms grow faster as they need to reach economies of scale to
attain market stability (Cabral 1995, Lotti, Santarelli and Vivarelli 2003, Park and Jang 2010)
Economic growth presented as the GDP and unemployment rate is found to have an
intuitive positive effect on firm size. Firms consider expansion when there is higher demand in
the market. Agglomeration economies (presented as market competition) negatively affect firm
size. When firms geographically cluster together, they are more likely to share common
services/infrastructure, such as warehouses, to minimize cost. As a result, they may consider to
reduce their employment size. There seem to be association between economic growth, firm
entry and exit rates, firm growth, and market competition. In this research, each aspect is treated
exogenously to firm size models. Further models that accounts for such endogeneity are a logical
future step for this research.
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CHAPTER 6
Survival Analysis of Canadian Firms
6.1. Introduction
In this chapter, firm survival analysis for for-profit, single-location, small-to-medium sized
Canadian firms (with 100 or less employees) is introduced for an eleven-year interval (2001 to
2012). More specifically, the focus is on studying the effect of firm characteristics (e.g. firm age
and number of employees), firm location, industry dynamics (e.g. firm entry and exit rates),
market competition, and economic growth (e.g. GDP growth rates, and provincial unemployment
rates), on firm failure. The analysis includes two components: parametric and non-parametric
models. The parametric model is intended to form part of a large-scale firmographic
microsimulation for firms in Canada (Mostafa and Roorda, 2015). The chapter starts with a brief
literature review on determinants of firm failure. Next, the longitudinal database is described,
followed by preliminary analysis of the data to understand firm entry and failure trends. Then,
the chapter presents non-parametric and parametric analyses of firm survival, in which failure
events of the 2001 firm population are observed from 2002 to 2012. The non-parametric analysis
presents survival and hazard rates estimates using Kaplan and Meier, and Nelson–Aalen
estimators characterized by firm age, province, and industry. In the parametric survival analysis,
a discrete-time hazard model of failure for the targeted firm population is introduced. Our results
are compared to other Canadian studies of firm survival analysis (Maoh and Kanaroglou, 2007b;
Baldwin et al., 2000). The chapter ends with some concluding remarks and highlights future
directions.
6.2. Literature Review
Firm failure takes one of two forms; firms either disappear from the market or merge with
other firms (van Wissen, 2000). Generally, firms fail due to unsuccessful business strategies.
Baldwin and Gellatly (2003) indicate that firms fail because of internal deficiencies in core areas
such as management, finance, and marketing. Modelling firm failure as a discrete choice by
considering firm strategies is difficult for two reasons. First, causes of failure are hard to
discover because firm founders are hard to track (especially for firms that disappear from the
135
market). Second, detailed longitudinal data of firm strategies for long periods of time are
necessary to draw sufficient conclusions about failure patterns. Such datasets are rare.
Firm strategies are influenced by external factors such as market dynamics and changes in
the economy. They are the result of competition, changes within the industry, and economic
growth. Since it is difficult to obtain longitudinal data of firm strategies, most studies that
address firm failure use external drivers that influence firm strategy formulation (e.g. firm
characteristics, industry features, location information, and economic indicators) as determinants
of failure (Maoh and Kanaroglou, 2007b; Baldwin et al., 2000). In these studies, survival and
hazard duration models are used to provide models of firm failure based on firm characteristics,
location demographics, and economic indicators as determinants of failure (Mata and Portugal,
1994; Fritsch et al. 2006; Maoh and Kanaroglou, 2007b; Strotmann, 2007; Nunes and Sarmento,
2010; Bhattacharjee, 2005).
The majority of studies that have addressed firm failure are for European firms (Audretsch et
al., 2000; Berglund and Brännäs, 2001; Carree et al., 2008; Fotopoulos and Spence, 2001; van
Wissen, 2000; Lopez-Garcia and Puente, 2006; Dunne and Hughes, 1994; Eriksson and Kuhn,
2006; Harhoff et al., 1998; Wagner, 1999; de Bok and Bliemer, 2006). Few studies are found for
North American firms (Kumar and Kockelman, 2008; Dunne et al., 1988; Audretsch, 1991;
Baldwin et al., 2000; Maoh and Kanaroglou, 2007b; Shapiro and Khemani, 1987). In the
Canadian context, only two studies (Baldwin et al., 2000; Maoh and Kanaroglou, 2007b) have
addressed firm failure from land use and geographic perspectives. Maoh and Kanaroglou (2007)
have addressed firm failure on a local level (i.e. the City of Hamilton in Ontario) while Baldwin
et al. (2000) have presented a study of survival of new firms for Canada.
Firm failure is related to the firm’s performance, productivity and status in the market and
ability to achieve their long-term goals (Agarwal, 1996; Cefis and Marsili, 2006; Siegfried and
Evans, 1994). However, every firm has their own measures of effectiveness depending on
criteria such as the long-term strategy and strategic focus, production capacity, competition, and
industry dynamics. A large body of research has investigated determinants of firm failure using
firm micro-data for the purpose of building fundamental understanding of failure patterns (Acs et
al., 2007; Audretsch et al., 2000; Berglund and Brännäs, 2001; Carree et al., 2008; Fotopoulos
and Spence, 2001; Nyström, 2007; Siegfried and Evans, 1994).
136
6.2.1. Determinants of firm failure
Determinants of firm failure are related to the firm, industry, location and/or macro-economic
conditions (van Wissen, 2000; Strotmann, 2007; He and Yang, 2015). Firm related factors
include firm age, employment size, location, firm growth rate, offered products and services,
business strategies, research and development (R&D) investment, and innovation (Mata et al.,
1995; Stearns et al., 1995; Reynolds, 1997; van Wissen, 2000; de Bok and Bliemer, 2006; Maoh
and Kanaroglou, 2007b; Nunes and Sarmento, 2010). Firm survival patterns vary across
industries depending on the economic growth of the industry, industry size, industry life cycle,
agglomeration economies and economies of scale (van Wissen, 2000; Strotmann, 2007; Klapper
and Richmond, 2011). Below are details of the most common determinants surveyed in the
literature.
6.2.1.1. Type of entry and ownership
Several studies state that firm entry type affects survival (Eriksson and Kuhn, 2006;
Lindholm, 1994; Dietrich and Gibson, 1990;Walsh and Boyland, 1996). Spin-offs (firms that are
formed when a group of persons, who are working in existing firms, decide to open a new firm)
tend to have higher growth and survival rates. Spin-offs benefit from personal networks (that are
transformed by their founders from their previous workplace), and industry-specific knowledge.
Eriksson and Kuhn (2006) investigated growth and survival of Danish private sector spin-offs
during the period of 1981 to 2000. Their results highlight that spin-offs have lower failure risks
compared to other entrants. On the other hand, Zhang and Mohnen (2013) have shown that the
type of ownership affects failure probabilities; they state that domestic firms have lower survival
probability than foreign firms in China. They also show that privately owned firms have higher
survival rates than state-owned firms reflecting the privatization of the Chinese economy and
that privately owned firms are more sensitive to local economic conditions.
6.2.1.2. Age and size
Several studies state that firm size (number of employees) and age affect firm survival;
larger and older firms have higher survival potential (Reynolds, 1987; Stearns et al., 1995; van
Wissen, 2000; de Bok and Bliemer, 2006; Maoh and Kanaroglou, 2007b; Nunes and Sarmento,
2010). Hannan et al. (1998), Lopez-Garcia et al. (2007) find that age and size effects vary across
firms and industries. Studies that have investigated the effect of firm size only confirm also that
137
large firms are less likely to fail (Audretsch and Mahmood, 1995; Dunne and Hughes, 1994;
Ericson and Pakes, 1995; Geroski, 1995; Mata and Portugal, 1999). Other studies report that the
effect of size on firm failure is not uniform and could be nonlinear (Mata and Portugal, 1994;
Pérez et al., 2004; Maoh and Kanaroglou, 2007b). Moreover, firm initial size (also known as
firm start-up size) influences firm survival as highlighted in (Schröder and Sørensen, 2012;
Strotmann, 2007). Mata and Portugal (1994) indicate that firms that start their business with
larger sizes have better survival probabilities. Firm growth rate also affects firm survival. Some
studies find that small new firms that have faster growth rates experience higher survival
probabilities (Mata and Portugal, 1994; de Bok and Bliemer, 2006; Nunes and Sarmento, 2010).
6.2.1.3. Firm strategies
van Wissen (2000) points out that the flexibility of strategy selection influences firm
survival. His study states that firms with more fixed strategies have better survival rates
compared to firms with more dynamic ones. He also finds that older larger firms have reached
the set of strategies that allows them to maintain adequate performance within the market.
6.2.1.4. Research and Development (R&D) investments
The amount of R&D investment also has an influence over firm survival (Fritsch et al.,
2006; Nunes and Sarmento, 2010; Sharapov et al., 2011; Zhang and Mohnen, 2013). Firms that
dedicate more funds for R&D have higher survival chances (Fritsch et al., 2006; Sharapov et al.,
2011). However, Zhang and Mohnen (2013) found that there is an inverted U-shaped
relationship between R&D investments and firm survival indicating that overinvesting in R&D
increases the risk of failure.
6.2.1.5. Industry environment and dynamics
Failure probabilities vary from one industry to another. For instance, survival probabilities
are low in industries where demand is low, the market is narrow, and minimum efficient scale is
high (Fritsch et al., 2006; Strotmann, 2007). Minimum efficient scale is the amount of capital
required for a firm to operate efficiently (Fonseca et al., 2001). Also, firms that belong to highly
innovative industries have higher failure rates (Audretsch, 1995a). Industry dynamics
represented in firm entry and exit rates affect survival of firms within the same industry cohorts
(Mata and Portugal, 1994; de Bok and Bliemer, 2006). New firms that exist in industries with
138
higher entry rates have lower survival chances because of the increased competition of similar
firms (Mata et al., 1995; Nunes and Sarmento, 2010).
6.2.1.6. Agglomeration economies
Firms tend to locate near similar firms to benefit from agglomeration economies. However,
Strotmann (2007) states that firms that locate in highly agglomerated areas have higher risks of
failure. This could be because, beyond a certain level of agglomeration, further increase in the
density of firm clusters in the same industry could result in higher competition.
6.2.1.7. Economic growth and market demand
Generally, it is found that firm survival is positively affected by Gross Domestic Product
(GDP) growth (Klapper and Richmond, 2011; Baldwin et al., 2000). On a macroeconomic level,
market stress, which reflects the difference between market demand and supply, positively
affects firm survival (van Wissen, 2000). Increased GDP reflects higher potential for more
production, leading to lower failure probability. On the other hand, firms that belong to
industries with saturated market have higher failure rates (Audretsch, 1995a).
6.3. Data Description
This study utilizes the T2-Longitudinal Employment Analysis Program (T2-LEAP), a
longitudinal database of the entire Canadian firm population that have records in the tax files
every year. For more information about the database, refer to chapter 3, section 3.4.1.
6.3.1. Population of study
The T2-LEAP database is used to assess firm failure events from 2001 to 2012. Only single-
location small to medium sized firms (employment 100 or less) are considered for this analysis
as they constitute the largest segment of firm population and are more dynamic compared to
larger firms (Maoh and Kanaroglou, 2005; Kumar and Kockelman, 2008). As discussed in
section 4.3, for-profit industries are included and non-for profit ones are excluded from the
analysis.
The T2-LEAP uses the Survey of Employment, Payrolls and Hours (SEPH) to calculate a
measure of employment called Average Labour Unit (ALU). The T4 records do not include
exact information on when and for how long an employee has worked for a firm. Therefore,
Statistics Canada has used the ALU to calculate the average employment an enterprise would
139
have if it paid its workers the average annual earnings (AAE) of a typical worker in the firm’s
particular 4-digit industry, province, and firm size. AAE are obtained from the SEPH (Statistics
Canada, 2012a). The ALU is calculated as follows:
𝐴𝐿𝑈 = 𝐴𝑛𝑛𝑢𝑎𝑙 𝑡𝑜𝑡𝑎𝑙 𝑝𝑎𝑦𝑟𝑜𝑙𝑙
𝐴𝐴𝐸
This procedure for calculating the ALU means that some firms result in zero employment for
some years. This could be because the annual total payroll is missing or the firm is inactive (i.e.
the firm is registered in the tax records but not physically in business). Zero employment firms
are excluded from our analysis. The 2001 firm population size is 970,498 firms. These firms are
tracked until 2012 while marking firm failure events during this period. The total number of
observations is 6,184,894 for the entire period (after exclusion of 20% of the records as a
validation data subset).
6.3.2. Explanatory variables
The T2-LEAP includes firm characteristics such as employment size, NAICS code, province,
Census Metropolitan Area/Census Agglomeration (CMA/CA), the first year a firm has appeared
in the records, gross profits, sales, tangible assets, and revenues. The purpose of the parametric
model (the discrete-time hazard duration model) of firm failure is to be used for firm
microsimulation. Therefore, basic firm information is only included in the models (TABLE 6.1).
Other explanatory variables (e.g. firm age, and number of competitors within CMA/CA with the
3-digit NAICS code) are also investigated. The database is also linked to data extracted from
Statistics Canada’s socioeconomic database, (Statistics Canada, 2015b) that includes annual
GDP growth by industry and province for the study period (2001-2012). Entry and exit rates by
2-digit NAICS code are also calculated and included in the analysis. TABLE 6.1 represents all
variables that are considered in the parametric model of firm failure along with the expected
effect of each variable. A positive sign indicates that an increase in the value of the variable
increases the likelihood of failure, and a negative sign indicates the opposite.
140
TABLE 6.1 Description of Explanatory Variables used in Firm Failure Hazard Duration Model
Variable Description Expected
coef. sign
Firm specific variables
Number of employees
(log)
The natural logarithm of the number of employees in
year (t) Negative
Age of firm
The age of the firm, deduced by subtracting the
current year (t) from the first year the firm has
appeared in the records.
Positive
(Age of firm)2 The square value of the firm age Negative
Employment decrease
A dummy variable indicating whether the firm has
decreased their number of employees in year (t)
compared to its size in year 2001 (the beginning of the
analysis period)
Positive
Employment increase
A dummy variable indicating whether the firm has
increased their number of employees in year (t)
compared to its size in year 2001 (the beginning of the
analysis period)
Negative
Competitors The natural logarithm of competitors in the same
CMA/CA with the same 2-digit NAICS industry Positive
Economic indicators
GDP growth Percent change in GDP by province and industry (2-
digit NAICS) Negative
Unemployment rate Yearly provincial unemployment rate (%) Positive
Entry rate by industry Yearly firm entry rate for each industry (2-digit
NAICS level) (%) Positive
Exit rate by industry Yearly firm exit rate for each industry (2-digit NAICS
level) (%) positive
Location specific variables
Ontario 1 if the firm is located in Ontario; 0 otherwise
Quebec 1 if the firm is located in Quebec; 0 otherwise
Alberta 1 if the firm is located in Alberta; 0 otherwise
British Columbia 1 if the firm is located in British Columbia; 0
otherwise
Manitoba 1 if the firm is located in Manitoba; 0 otherwise
Saskatchewan 1 if the firm is located in Saskatchewan; 0 otherwise
Atlantic Canada
1 if the firm is located in Newfoundland and Labrador, New
Brunswick, Prince Edward Island, or Nova Scotia; 0 otherwise
Canadian Territories 1 if firm is located in Yukon, Nunavut, or Northwest Territories; 0
otherwise
141
TABLE 6.1 (Continued)
Industry specific variables
Agriculture, Forestry,
Fishing and Hunting
1 if the firm belongs to the Agriculture, Forestry, Fishing and
Hunting industry; 0 otherwise
Mining, Quarrying, and
Oil and Gas Extraction
1 if the firm belongs to the Mining, Quarrying, and Oil and Gas
Extraction industry; 0 otherwise
Utilities 1 if the firm belongs to the Utilities industry; 0 otherwise
Construction 1 if the firm belongs to the Construction industry; 0 otherwise
Manufacturing 1 if the firm belongs to the Manufacturing industry; 0 otherwise
Wholesale trade 1 if the firm belongs to the Wholesale trade industry; 0 otherwise
Retail trade 1 if the firm belongs to the Retail trade industry; 0 otherwise
Transportation and
warehousing
1 if the firm belongs to the Transportation and warehousing
industry; 0 otherwise
Information and cultural
industries
1 if the firm belongs to the Information and cultural industries; 0
otherwise
Finance and insurance 1 if the firm belongs to the Finance and insurance industry; 0
otherwise
Real estate and rental and
leasing
1 if the firm belongs to the Real estate and rental and leasing
industry; 0 otherwise
Professional, scientific
and technical services
1 if the firm belongs to the Professional, scientific and technical
services industry; 0 otherwise
Management of
companies and enterprises
1 if the firm belongs to the Management of companies and
enterprises industry; 0 otherwise
Administrative and
support, waste
management and
remediation services
1 if the firm belongs to the Administrative and support, waste
management and remediation services industry; 0 otherwise
Arts, entertainment and
recreation
1 if the firm belongs to the Arts, entertainment and recreation
industry; 0 otherwise
Accommodation and food
services
1 if the firm belongs to the Accommodation and food services
industry; 0 otherwise
Other services (except
public administration)
1 if the firm belongs to the Other services (except public
administration) industry; 0 otherwise
6.4. Data Analysis and Trends
6.4.1. Data analysis
Preliminary data analysis for exit firms are conducted to gain better understanding of the firm
exit behaviour. First, firm failure rates by industry and by province are investigated. FIGURE 6.1
shows the average exit rates of firms by industry of the period of (2001-2012). It shows that
‘Accommodation and food services’ and ‘Information and cultural ‘industries have the highest
142
firm exit rates of ~ 6.5% to 7%. Manufacturing ranks as the 5th highest with a failure rate of ~
5.7%. It is important to highlight that our rates in this study are different from the rates published
by Statistics Canada in (Statistics Canada, 2016a) because the dataset is only for incorporated
firms while their published statistics are for both incorporated and unincorporated firms.
FIGURE 6.1 Average Firm Exit Rates by Industry Class (From 2001 to 2012)
FIGURE 6.2 shows that, on average, Northern Territories have the highest firm exit rates
with a value of 22.6% while Ontario and Quebec have the lowest rates of values of 12.6% and
10.8%, respectively (Statistics Canada, 2016a). For their economic importance, manufacturers
are selected as the reference industry in our analysis to compare the exit propensities of other
industries to it. FIGURE 6.3 focuses on average firm exit rates of manufactures in provinces
across Canada. It is seen that Ontario and Quebec still rank as the two provinces with the lowest
exit rates with a value of ~8.8% (Statistics Canada, 2016a).
0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0
Accommodation and food services
Information and cultural industries
Retail trade
Wholesale trade
Manufacturing
Mining, quarrying, and oil and gas extraction
Transportation and warehousing
Administrative and support, waste…
Arts, entertainment and recreation
Professional, scientific and technical services
Construction
Finance and insurance
Other services (except public administration)
Real estate and rental and leasing
Agriculture, forestry, fishing and hunting
Firm Exit Rate (%)
143
FIGURE 6.2 Average Firm Exit Rates by Province (2001 to 2012)
FIGURE 6.3 Average Firm Exit Rates of Manufacturers by Province
As highlighted in the literature review, firm survival is affected by firm age. The data
indicate that around 20% of firm population exit at the age of 3 years or less, 30% fail at the age
of 5 years or less, 50% fail at the age of 9 years or less, and 75% of firm population fail at the
age of 16 years or less (FIGURE 6.4). Also, the average firm size at failure is 6 employees. Our
0.0
5.0
10.0
15.0
20.0
25.0
Exit
Rat
es (
%)
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
Exit
Rat
es (
%)
144
finding agrees with other similar studies in different countries which report that the median life
time of firms is between five to six years after their establishment (Mata and Portugal, 1994;
Bhattacharjee, 2005; Bartelsman et al., 2005; Lopez-Garcia and Puente, 2006; Schrör, 2009;
Nunes and Sarmento, 2010). FIGURE 6.5 indicates that there is an inverted U-shape relationship
between firm failure and age conforming to similar studies by (He and Yang, 2015; Agarwal et
al., 2004).
FIGURE 6.4 Cumulative Distribution of Firm Age at Exit
0.0%
20.0%
40.0%
60.0%
80.0%
100.0%
120.0%
0 5 10 15 20 25 30
Per
centa
ge
of
Exit
Fir
ms
Firm Age (years)
145
FIGURE 6.5 Population Distribution of Firm Age at Exit*
Data show that 75% of the firm population exit the market with number of employees of two,
while 50% of firms exit with number of employees of one. The average size at exit is 5.6 with a
standard deviation of 31.9.
6.4.2. Firm entry and exit rates
FIGURE 6.6 shows firm entry and exit rates per year. FIGURE 6.6 shows that, in 2008,
Canada’s firm population witnessed a sudden increase in firm exit rates. This is due to the global
recession (defined as two consecutive quarters of negative economic growth) that was sparked
by the US housing market crisis in 2008. This crisis affected Canada’s economy negatively in
two ways: trade, and credit (OECD, 2008). For trade, approximately three-quarters of Canadian
exports are directed to the United States constituting approximately 25% of the Canadian GDP.
This indicates that any change in the US economy has a large effect on many Canadian exporters
(OECD, 2008). For credit, the recession triggered tighter credit conditions, which created a
challenging financing environment in Canada to finance consumers and businesses (OECD,
2008). As a consequence of this recession, GDP growth and firm entry rates declined in the
following year 2009 (FIGURE 6.6). Also, in 2004, there was a significant increase in the firm
* Percentage of exit firms = No. of exit firms (t)/total no. of firms (t)
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
0 5 10 15 20 25 30
Per
centa
ge
of
Exit
Fir
ms
Firm Age (years)
146
exit rates, and this is related to the decline in the Canadian GDP in 2003. According to (Statistics
Canada, 2004) this economic decline was due to major international events that took place
around the year 2003 such as the SARS outbreak, the mid-August power blackout, the war in
Iraq, and natural disasters such as Hurricane Juan in Nova Scotia. We hypothesize that firm
events of entry and exit along with the growth in the GDP are affecting the exit decision on the
firm micro-level, and hence are included in the firm exit parametric model.
FIGURE 6.6 Firm Entry and Exit Rates, and GDP Growth Rates
Firm entry and exit rates are calculated using the T2-LEAP data set for the years 2001-2012.
The resulting firm entry and exit trends are similar to those produced by (Macdonald, 2014;
Ciobanu and Wang, 2012) with lower values due to some differences in the target population and
the methods used to identify entry and exits. Entry and exit rates in our study are only for-profit
industries (excluding firms belonging to educational, healthcare, and public services), while
those published by Macdonald (2014) and Ciobanu and Wang (2012) are for all industries. Also,
Macdonald (2014) and Ciobanu and Wang (2012) used the three-year observation rule described
in (Baldwin et al., 2013), in which entrants are identified when they are active in year (t) and in
subsequent year (t+1), and are inactive in previous year (t-1). Similarly, firm exit is identified
when the firm is active in year t and year (t-1) and is inactive in (t+1). In our study, the two-year
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
8.00%
9.00%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
GD
P G
row
th R
ate
Entr
y a
nd
Exit
Rat
es
YearsFirm entry rates Firm exit rates GDP growth
147
observation rule is used. Simply, for every panelist in the data (the longitudinal records for an
individual firm), for a given year (t), a new firm is identified when it appears in year (t) and not
in (t-1), while firm exit is marked when a firm appears in year (t) and not in year (t+1). New
firms of the year 2001 are not identified because records for the year 2000 are not available.
Similarly, 2012 exits are not identified because the dataset doesn’t include the 2013 records.
Firm entry and exit rates are then calculated by dividing the total number of entrants/exits by the
total number of firms in each year. Entry and exit rates are also calculated for each industry
based on NAICS 2-digit code and used in firm exit parametric model.
6.5. Survival Analysis
6.5.1. Non-parametric analysis
Survivor and the cumulative hazard rates are calculated to analyze how firm survival is
affected by industry class, province, age, and size independently. Then, our results of firm
survival are compared to other similar Canadian studies. Kaplan and Meier, and Nelson –Aalen
estimators are used to produce survival and cumulative hazard rates because the data is right
censored (i.e. exit event is unknown past year 2012). The non-parametric estimator introduced by
(Kaplan and Meier, 1958) of the survival function (St) described in Equation (6.1) is used.
𝑆(𝑡) = ∏ (1 − 𝑑𝑗
𝑛𝑗)𝑗 | 𝑡𝑗 <𝑡 (6.1)
where nj is the number of firms at risk at time tj, and dj is the number of failures at tj. The
survival function indicates the probability of survival beyond time tj, decreases over time. The
cumulative hazard estimator of Nelson –Aalen is a measure of the risk of failure; the greater the
value, the higher the risk (Cleves, 2008). However, unlike the survival function, cumulative
hazard is not a probability, it is a representation of the risk as follows:
𝑯(𝒕) = ∑𝒅𝒋
𝒏𝒋𝒋 | 𝒕𝒋 <𝒕 (6.2)
Both the survival and hazard functions are related; one can be estimated using the other as
follows:
𝐻(𝑡) = − ln 𝑆(𝑡) (6.3)
148
Our survival rates (TABLE 6.2) are higher compared to (Maoh and Kanaroglou, 2007b)
(FIGURE 6.7). This could be for several reasons: first their analysis is focused on enterprises
with employment less than 50 employees. Second, not-for-profit industries are excluded from
our database. Third, our study area is the entire Canadian population while their study focuses
only on the City of Hamilton, Ontario. Fourth, the time interval in both studies is different;
(Maoh and Kanaroglou, 2007b) study the time interval of 1990 to 2002 while our research
studies the interval of 2001 to 2012, however, the rates follow approximately the same trend. On
the other hand, Baldwin et al. (2000) presented a firm survival analysis of new firms in the time
interval of 1984 to 1994. Their survival rates represent the probability that a new firm will live
beyond a certain age. Compared to our analysis (FIGURE 6.7), we can conclude that new firms
have lower survival probabilities compared to the survival of the entire firm population (new and
continuing).
TABLE 6.2 Survival and Cumulative Hazard Rates of 2001 Firm Population
(Period of 2001 to 2012)
Duration (t)
Years
Survival Rate
S(t)
Nelson-Aalen
Cumulative Hazard
1 0.97 0.03
2 0.94 0.06
3 0.88 0.13
4 0.83 0.18
5 0.80 0.22
6 0.76 0.27
7 0.70 0.35
8 0.67 0.39
9 0.64 0.44
10 0.61 0.48
149
FIGURE 6.7 Canadian Studies of Firm Survival Rates
The survival function is also calculated for firms categorized by firm size (FIGURE 6.8). We
can conclude that larger firms have higher survival rates compared to smaller ones. Survival
functions are also calculated categorized by the provincial location of the firm (TABLE 6.3). For
example, FIGURE 6.9 show that firms located in Ontario have higher survival probability
compared to firms in Quebec and Alberta. However, survival rates for firms in Ontario past the
6th year have lower survival chances compared to firms in Quebec. The figure also shows that
firms in Alberta have higher survival probabilities in the first two years compared to Quebec.
This highlights that firm locations significantly influence firm survival. Survival rates by
industry are also calculated (TABLE 6.4)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
Surv
ival
Rat
e
Time (t) in Years
Survivor Function (2001 to 2012) Maoh and Kanaroglou (2007): 1991- 2002
Baldwin et al. (2000): 1984-1994
150
FIGURE 6.8 Survival Function by Firm Size
FIGURE 6.9 Survival Function for Selected Provinces.
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7 8 9 10
Surv
ival
Rat
e
Time in Years (t)
Large: >49 employee Medium 20-49 employee Small: <19 employee
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7 8 9 10
Surv
ival
Rat
e
Time in Years (t)
Ontario Quebec Alberta
151
TABLE 6.3 Survival Rate by Province
Time (t) years 1 2 3 4 5 6 7 8 9 10
Ontario 0.98 0.95 0.90 0.86 0.83 0.77 0.71 0.67 0.64 0.61
Quebec 0.97 0.94 0.83 0.79 0.75 0.72 0.67 0.65 0.62 0.59
Alberta 0.96 0.92 0.87 0.83 0.80 0.77 0.73 0.70 0.66 0.63
British Colombia 0.97 0.94 0.90 0.84 0.79 0.76 0.71 0.68 0.65 0.62
Manitoba 0.97 0.93 0.88 0.85 0.82 0.80 0.75 0.72 0.69 0.66
Saskatchewan 0.97 0.94 0.89 0.85 0.82 0.80 0.76 0.73 0.71 0.68
Atlantic Canada* 0.97 0.94 0.86 0.82 0.79 0.74 0.68 0.64 0.62 0.59
Canadian
territories** 0.98 0.94 0.83 0.65 0.59 0.53 0.46 0.42 0.37 0.33
* New Brunswick, Prince Edward Island, and Nova Scotia
**Yukon, Nunavut, and Northwest Territories
TABLE 6.4 Survival Rates by Industry*
Time (t) years 1 2 3 4 5 6 7 8 9 10
Agriculture, Forestry,
Fishing and Hunting 0.98 0.97 0.93 0.90 0.88 0.86 0.82 0.80 0.77 0.75
Mining, Quarrying, and
Oil and Gas Extraction 0.96 0.92 0.88 0.83 0.79 0.76 0.72 0.69 0.65 0.62
Construction 0.98 0.95 0.89 0.85 0.81 0.78 0.73 0.70 0.67 0.64
Manufacturing 0.97 0.94 0.87 0.82 0.78 0.73 0.67 0.64 0.61 0.58
Wholesale trade 0.97 0.93 0.86 0.81 0.77 0.73 0.67 0.64 0.61 0.58
Retail trade 0.97 0.94 0.85 0.80 0.76 0.71 0.64 0.60 0.57 0.54
Transportation and
warehousing 0.97 0.94 0.87 0.82 0.79 0.74 0.68 0.65 0.62 0.58
Information and cultural 0.97 0.93 0.86 0.81 0.77 0.72 0.66 0.62 0.59 0.56
Finance and insurance 0.97 0.93 0.89 0.85 0.82 0.79 0.75 0.72 0.69 0.66
Real estate and rental and
leasing 0.98 0.96 0.92 0.89 0.86 0.83 0.78 0.76 0.73 0.70
Professional, scientific
and technical services 0.98 0.95 0.89 0.85 0.81 0.77 0.72 0.68 0.65 0.62
Management of
companies and
enterprises
0.97 0.94 0.90 0.86 0.83 0.79 0.75 0.72 0.69 0.66
Administrative and
support, waste
management and
remediation services
0.97 0.95 0.88 0.84 0.80 0.76 0.70 0.67 0.64 0.61
Arts, entertainment and
recreation 0.98 0.95 0.88 0.84 0.81 0.77 0.72 0.69 0.66 0.63
Accommodation and food
services 0.96 0.92 0.80 0.74 0.69 0.63 0.55 0.52 0.48 0.45
Other services (except
public administration) 0.98 0.95 0.89 0.85 0.82 0.78 0.74 0.71 0.69 0.66
*Survival rates for the utilities industry class are suppressed by Statistics Canada for confidentiality
152
(a)
(b)
Group 1 (1- 2 years)
Group 2 (3 years)
Group 3 (4-6 years)
Group 4(7-10 years)
Group 5 (11-16 years)
Group 6 (>16)
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
Surv
ival
Funct
ion
Time in Years (t)
Survival Function by Age Group
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6 7 8 9 10
Surv
ival
Funct
ion
Time in Years (t)
Survival Function by Age Group
Group 1 (1- 2 years) Group 2 (3 years) Group 3 (4-6 years)
Group 4 (7-10 years) Group 5 (11-16 years) Group 6 (>16 years)
153
(c)
FIGURE 6.10 Survival Function by Age Group
As for firm age, it is noted from FIGURE 6.10-b that firms that are ten years or younger have
the same survival trends; survival rates decrease with the increase in age. For instance, firms that
belong to age group 3 (4-6 years) have lower survival rates compared to firms of age group 2 (2-
3 years). This relationship changes beyond the age of ten years; for instance, firms that belong to
age group 5 (11-16 years) have higher survival rates compared to firms in age group 4 (7-10
years). FIGURE 6.10-c shows that survival rates of firms for a duration of two years or less have
similar patterns while for three years and more have other similar patterns for different age
groups.
6.5.2. Parametric analysis
6.5.2.1. Model structure
In this section, the parametric analysis of the firm failure model is presented. The failure
event occurs in a discrete-time fashion; time is observed in discrete units (years) that take
positive integer values (t = 1, 2, 3….). Therefore, a discrete-time hazard duration model is
estimated, which determines the hazard rate of firm (i) that exits the market at time (t), given it
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
1 2 3 4 5 6
Surv
ival
Funct
ion
Age Groups
Survival Patterns by Time
1 Year
2 years
3 Years
4 Years
5 Years
6 Years
7 Years
8 Years
9 Years
10 Years
154
was in operation between 2001 and year Ti=t. Allison (1982) has represented the discrete-time
hazard rate as follows:
𝑷𝒊𝒕 = 𝑷𝒓[𝑻𝒊 = 𝒕 | 𝑻𝒊 ≥ 𝒕, 𝑿𝒊𝒕] (6.4)
where Ti is the discrete random time of the failure event, given it has not occurred before time t,
and Xit is a vector of the observed explanatory variables (that may vary over time) that affect the
failure event. A common formulation for Equation (6.4) is the logistic regression function
presented by (Cox, 1972; Myers et al., 1973) as follows:
𝑃𝑖𝑡 = 1
[1+ 𝑒− (𝛼𝑡+ 𝜷′𝑿𝒊𝒕)] (6.5)
where 𝛼𝑡 is a set of constants, and 𝜷′ is the vector of parameters that quantifies the effect of
the covariates (the explanatory variables) on the failure event. Maximum likelihood estimation
method provides an efficient estimator for the values of 𝛼𝑡 and 𝜷′ . As indicated by (Allison,
1982; Guo, 1993), the log-likelihood function can be presented as follows:
log 𝐿 = ∑ ∑ 𝛿𝑖𝑗𝑡𝑖𝑗=1 log [
𝑃𝑖𝑗
(1−𝑃𝑖𝑗)] + ∑ ∑ log(1 − 𝑃𝑖𝑗)
𝑡𝑖𝑗=1
𝑛𝑖=1
𝑛𝑖=1 (6.6)
where 𝛿𝑖𝑗 =1 if firm i has failed at time j and 0 otherwise. Equation (6.6) represents the log-
likelihood of a logistic regression indicating that discrete-time hazard model can be estimated
using the same routine as the logistic regression model. For this type of setup, and to be able to
estimate equation (6.6), for each panelist (longitudinal firm records) a dependent variable yi = 1 is
coded when the failure event occurs and 0 otherwise. This means that if a firm was present in the
records starting in the year 2001 and has disappeared in 2006, then the variable yi = 0 in the first
5 observations (for the years of 2001 till 2005) and yi =1 in year 2006. However, the data is left
truncated, meaning that the firm population of 2001 includes firms that existed prior to 2001.
This means that some firms have been exposed to risk of failure for some years (r) before it
became under observation for survival analysis. A firm i may have been at risk for r years prior
to 2001 and has failed at time ti corresponding to q years after 2001. This creates q observations
only for firm i in the record set that formulates the log-likelihood function of the hazard model.
A treatment for left truncated data in discrete-time hazard duration models is suggested by
Guo (1993) and Allison (1995) by conditioning the likelihood function in Equation (6.6) on
155
having survived to tr. By taking the logs, and multiplying over the sample, the conditional log-
likelihood for discrete time models is:
log 𝐿 = ∑ ∑ 𝛿𝑖𝑗𝑡𝑖𝑗=1 log [
𝑃𝑖𝑗
(1−𝑃𝑖𝑗)] + ∑ ∑ log(1 − 𝑃𝑖𝑗)
𝑡𝑖𝑗=𝑟+ 1
𝑛𝑖=1
𝑛𝑖=1 (6.7)
Equation (6.7) can be considered a binominal log-likelihood function as equation (6.6) except
that the second summation in the second term sums from r+1 rather than 1. The hazard rate Pij
for observation i at time j can be linked to the observation covariates (as in Equation (6.4))
through a logistic regression.
6.5.2.2. Model specification and hypotheses
As summarized in TABLE 6.1, firm failure is affected by factors related to the firm, market
competition, economic conditions, location and industry. TABLE 6.1 lists the variables under
investigation in terms of their influence over firm failure, along with their expected effect
(negative or positive). For instance, from the data analysis (FIGURE 6.5) and the non-parametric
analysis (FIGURE 6.10), we hypothesize that firm age has a non-linear relationship with firm
failure (i.e. parabolic function), therefore, both the age and the age2 variables are added in our
model. We also hypothesize that employment size has a negative effect on firm exit. Firm
growth on the other hand is hypothesized to have a negative effect on firm exit decisions;
employment decrease may indicate that a firm is in the failure pattern, whereas employment
increase decisions indicate stability in the market. Hence, three variables that represent
employment size and employment growth are included; a continuous variable for employment
size, and two dummy variables indicating whether employment increased or decreased relative to
2001’s size.
Dummy variables that represent the industry class (based on the 2-digit NAICS code) are
tested, as well as dummy variables for the province where the firm is located. In this way,
location and industry effects on firm exit decisions can be captured. We also hypothesize that
market competition has a negative effect on firm survival; higher competition may lead to higher
probability of firm exit. In our model, competition is defined as the number of competitors (firms
within the same Census Metropolitan Area/Census Agglomeration) that are similar in the
services/products offered by their counterparts (based on the NAICS 3-digit code). Economic
156
conditions are also hypothesized to have influence on firm survival. Three economic indicators
are included:
1) The GDP growth rate by industry (2-digit NAICS code) and province. This considers
the diversity of firms in different provinces and industries as some provinces may be
leaders in specific industries and are economically developing faster compared to other
provinces and territories. For instance, Alberta ranks first for GDP for the Mining,
Quarrying, and Oil and Gas Extraction industry class, followed by Saskatchewan
(FIGURE 6.11) while Ontario ranks first in Manufacturing industry followed by
Quebec, Alberta, British Columbia, and the rest of Canada (FIGURE 6.12). In the
model of firm failure, the manufacturing industry is chosen as the reference industry
because it significantly contributes to the Canadian GDP according to Statistics Canada
(2015b). Also, Ontario is chosen to be the reference province as it is the province with
the highest economic activity for manufacturing industry (FIGURE 6.12).
FIGURE 6.11 GDP by Province for the Mining, Quarrying, and Oil and Gas Extraction
Industry Class (NAICS 2-Digit Code: 21)*
* Source: (Statistics Canada, 2015b)
0
10000
20000
30000
40000
50000
60000
70000
80000
GD
P (
do
llar
s X
1,0
00
,00
0)
2001
2012
157
FIGURE 6.12 GDP by Province for The Manufacturing Industry Class
(NAICS 2-digit code: 31-33)
2) Yearly unemployment rate by province: unemployment rate is considered a key
economic indicator that reflects the labour market within a country. Some studies that
addressed firm exit have used unemployment rate as an explanatory variable of the exit
decision (e.g. Maoh and Kanaroglou, 2007b). Unemployment rate aggregated to the
provincial level are used to account for variability amongst provinces in firm failure
behaviour. Unemployment rates are extracted from (Statistics Canada, 2015a) for all
provinces. For example, in the province of Ontario (FIGURE 6.13), there seems to be a
negative relationship between GDP growth and the increase in the unemployment rates
(e.g. check values of year 2009 as a consequence of the 2008 U.S recession as
explained earlier).
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
GD
P (
do
llar
s X
1,0
00
,00
0)
2001
2012
158
FIGURE 6.13 Unemployment and GDP Growth Rates for the Province of Ontario
3) Yearly entry and exit rates: as explained in section four, firm entries and exits both
affect and are affected by GDP growth. They can be used as economic indicators
reflecting the conditions of the market. Firm entry and exit rates are calculated on the
aggregate level of the 2-digit NAICS code.
6.5.2.3. Model results and interpretations
STATA software is used to estimate the discrete-time hazard duration model for firm failure.
A hold-out sample was extracted as a validation data set before the estimation process. A total of
6,184,894 records for the entire observation period, which are the total number of records for
970,498 firms tracked over 2001-2012, are used to estimate the model. TABLE 6.5 presents the
model coefficient estimates, the odds ratio, and the P-values. All the coefficients are reported at
95% and higher confidence interval. The odds ratio is the ratio of the probability that the event of
interest occurs to the probability that it does not (Bland and Altman, 2000). For example, assume
that the probability of success for a particular event is p = 0.8 and the probability of failure is q =
0.2, then the odds of success = 𝑝/𝑞 = 0.8/0.2 = 4. This means that the odds of success are four
to one. In logistic regression, the logit is defined as the log base e (log) of the odds meaning:
𝐿𝑜𝑔𝑖𝑡 (𝑝) = 𝑎 + 𝑏𝑋 or log (𝑝
𝑞) = 𝑎 + 𝑏𝑋 (6.8)
This means that the coefficients in the logistic regression are represented in terms of the log of
the odds. So, a one-unit change in a specific explanatory variable xi results in a bi change in the
log of the odds (UCLA, 2007). Equation (6.8) can be written as follows:
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
0
1
2
3
4
5
6
7
8
9
10
2000 2002 2004 2006 2008 2010 2012 2014
GD
P G
row
th
Unem
plo
ym
ent
Rat
e
Unemployment rate GDP growth
159
𝑒(log
𝑝
𝑞)
= 𝑒(𝑎+𝑏𝑋) or 𝑝
𝑞= 𝑒(𝑎+𝑏𝑥) (6.9)
The odds ratio is simply calculated by exponentiation of the coefficient; Odds Ratio (OR) = 𝑒𝑏.
TABLE 6.5 Firm Failure Model: Estimation Results
Variables Coef. Odds ratio P>|z|
_cons -4.385 0.012 0.000
Firm
characteristics
# of employees (log) -1.211 0.298 0.000
Firm age 0.103 1.109 0.000
Firm age ^ 2 -0.003 0.997 0.000
Employment decrease 0.378 1.459 0.000
Employment increase -0.261 0.770 0.000
Province
Ontario Reference province
Quebec 0.144 1.155 0.000
Alberta 0.105 1.111 0.000
British Colombia 0.118 1.126 0.000
Manitoba 0.205 1.228 0.000
Saskatchewan 0.131 1.140 0.000
Atlantic Canada* 0.256 1.292 0.000
Canadian territories** 0.487 1.627 0.006
Industry Class:
2-Digit NAICS
Code
11 Agriculture, Forestry, Fishing and Hunting -0.504 0.604 0.000
21 Mining, Quarrying, and Oil and Gas Extraction -0.619 0.538 0.000
22 Utilities -0.131 0.877 0.014
23 Construction -0.470 0.625 0.000
31-33 Manufacturing Reference industry
41 Wholesale trade -0.241 0.786 0.000
44-45 Retail trade -0.194 0.824 0.000
48-49 Transportation and warehousing -0.336 0.714 0.000
51 Information and cultural industries -0.681 0.506 0.000
52 Finance and insurance -0.541 0.582 0.000
53 Real estate and rental and leasing -0.693 0.500 0.000
54 Professional, scientific and technical services -0.736 0.479 0.000
55 Management of companies and enterprises -0.822 0.440 0.000
56 Administrative and support, waste management
and remediation services -0.427 0.652 0.000
71 Arts, entertainment and recreation -0.362 0.696 0.000
72 Accommodation and food services -0.256 0.774 0.000
81 Other services (except public administration) -0.237 0.789 0.000
*Newfoundland and Labrador, New Brunswick, Prince Edward Island, and Nova Scotia
** Yukon, Nunavut, and Northwest Territories
160
TABLE 6.5 Firm Failure Model: Estimation Results (Continued)
Competition # of competitors in the same CMA/CA of the
same NAICS-3 (log) 0.058 1.059 0.000
Economic
indicators
% GDP growth by province and Industry
(NAICS2) -0.002 0.998 0.000
Yearly provincial unemployment rate (%) 0.022 1.022 0.000
Yearly firm entry rate by industry class (NAICS2)
(%) 0.081 1.084 0.000
Yearly firm exit rate by industry class (NAICS2)
(%) 0.162 1.176 0.000
The model estimates (TABLE 6.5) confirm our previously stated hypotheses regarding the
effect of the explanatory variables on the firm exit (the determinants of failure). Looking at the
signs of the coefficients, when all other variables are held constant, the number of employees has
a negative impact on firm exit; i.e. larger firms are less likely to fail. The odds ratio implies that
an increase in one unit in the log value of the number of employee reduces the likelihood of
failure by 30%. This result conforms to other studies for Canadian firms conducted by (Maoh
and Kanaroglou,2007b; Baldwin et al., 2000). This also confirms the non-parametric analysis
(FIGURE 6.8) that larger firms have better survival chances. Furthermore, employment growth
(increase or decrease) is found to have a statistically significant effect on firm failure, agreeing
with what (Maoh and Kanaroglou, 2007b) have found in their study. If a firm has increased its
employment size compared to its original size in the base year (2001), it is ~77% less likely to
fail. Firms that shrink in size are approximately 46% more likely to fail.
As expected, and as indicated by the non-parametric analysis (FIGURE 6.10-b and FIGURE
6.10-c), firm age is found to have a non-linear relationship with firm exit. When all other
variables are held constant, exit propensity increases with firm age (explained by the positive
sign of the age covariate), until a limit β1/2β2, and then the exit propensity decreases with firm
age (the negative sign of the age2 covariate), where β1 and β2 are the estimated parameters of the
age and age2 covariates. This means that, older firms have better survival probabilities. Our
results confirm those found by (Baldwin et al., 2000) and are in line with the hypothesis of
“liability of newness” introduced by (van Wissen, 2000).
Firm failure behaviour varies depending on the firm location. As demonstrated in the non-
parametric analysis, firms located in different provinces have different survival behaviour
(FIGURE 6.9). The parametric model confirms that all provinces compared to Ontario have
161
higher risk of failure. This finding complies with the results of (Baldwin et al., 2000). In our
model, for example, compared to Ontario and when all other variables are held constant, firms
located in Alberta are 10% more likely to fail whereas firms located in Quebec have a 14%
greater chance of failure than firms in Ontario. This finding conforms to the results highlighted
in the non-parametric analysis as well (FIGURE 6.9). Generally, exit chances are 11% to 29%
higher for firms located outside of Ontario (except for firms located in Yukon, Nunavut, and
Northwest Territories) compared to firms in Ontario. FIGURE 6.14 ranks the odds of failure for
each province compared to Ontario.
FIGURE 6.14 Ratio of The Odds of Failure for Firms Located in Canadian Provinces with
Ontario being the Reference Province
Our original hypothesis is that failure varies across industries. A group of dummy variables
that indicates the industry class of the firm is included. The results show variability in failure
propensities of firms in different industries illustrated by the odds ratio in FIGURE 6.15. The
manufacturing industry is the reference industry. Compared to manufacturers in Ontario, when
other variables are held constant, all other industries have better survival chances. Firms that
belong to ‘Management of companies and enterprises’ industry class ranks first in firms with
better survival chances. They have an approximately 44% lower chances of failure compared to
manufacturers. Firms that belong to the ‘utilities’ sector are around 88% less likely to fail
compared to manufacturers. This is expected as the data show that Ontario has the highest failure
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
Other provinces**
Atlantic Canada*
Manitoba
Quebec
Saskatchewan
British Colombia
Alberta
Odds ratio
162
shares of manufacturers compared to other provinces and territories (see FIGURE 6.2 and
FIGURE 6.3).
FIGURE 6.15 Ratio of the Odds for Different Industries with Manufacturers being the Base
Industry
Competition on the other hand is found to have a negative impact on firm survival. Higher
number of competitors increases the likelihood of failure. An increase of one unit in the log
value of the number of competitors, categorized in the same industry class (to the level of the 3-
digit NAICS code) and located in the same CMA/CA, increases the likelihood of failure by
approximately 6%. Two effects must be distinguished; the effect of the agglomeration
economies, and the effect of competition. These effects are ‘two sides of the same coin’.
Agglomeration economies, in simple terms, are the benefits firms gain when locating near other
competing firms with similar activities. Such benefits are attained through sharing the same
resources (e.g. goods, labor, technologies, and transportation infrastructure) (Ellison et al., 2010).
Agglomeration economies are similar in concept to economies of scale; when firms with similar
activities cluster together in the same area, their cost of production may decline (Glaeser, 2010).
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Utilities
Retail trade
Other services (except public administration)
Wholesale trade
Accommodation and food services
Transportation and warehousing
Arts, entertainment and recreation
Administrative and support, waste management and…
Construction
Agriculture, Forestry, Fishing and Hunting
Finance and insurance
Mining, Quarrying, and Oil and Gas Extraction
Information and cultural industries
Real estate and rental and leasing
Professional, scientific and technical services
Management of companies and enterprises
Odds ratio (%)
163
A recent example of such agglomeration is the case of Silicon Valley in California, where
several computer-technology related companies have clustered in one area for the purpose of
sharing the same infrastructure and resources. Such clustering has resulted in the concentration
of highly skilled labour in the field of computer-technology in one area, giving new start-ups in
the same area better access to skilled labour (Fallick et al., 2006). This in return gives start-ups
higher chances of survival and growth in addition to benefiting from using the existing
technologically-advanced infrastructure for communication and data sharing, and hence costs are
significantly reduced. Competition on the other hand, is when firms with similar activities locate
near to competitors, and the result of this geographic nearness leads to greater price competition
and results in lower profits (Pasidis, 2013). In other words, when businesses that offer highly
similar (homogenous) products/services (e.g. gas stations) are located near each other, they
compete mainly on the product price, and the benefits of the agglomeration are diminished.
Whereas, businesses that offer similar products with some distinctions (i.e. less homogeneous
such as clothing shops) locate near each other, benefits of agglomeration are stronger because
price competition is less important (Pasidis, 2013). Our findings suggest that benefits of
agglomeration economies are diminished, and competition dominates, indicated by the positive
sign of the coefficient of the number of competitors within the CMA/CA and NAICS 3-digit
code. Our finding is in agreement with the ones stated by (Strotmann, 2007).
The economic conditions of the region where the firm is located is expected to influence firm
survival. Three main economic indicators are used; one that reflects the economic growth of the
region by industry (GDP growth rate by industry and province), one reflects the economic
conditions with respect to labour within the firm’s region (provincial unemployment rate), and
the third reflects the dynamics within the industries (entry and exit rates by industry). As
elaborated earlier; there is a strong association between the overall GDP growth and firm entry
and exit rates and (FIGURE 6.6), the GDP growth and unemployment (FIGURE 6.13). The model
results confirm that there are statistically significant relationships between the used economic
indicators and the firm failure probabilities. The sign of the coefficient covariate confirms our
original hypothesis that GDP growth has a positive impact on survival. The results show that an
increase of 1% in the GDP reduces the likelihood of failure by 99.77% when the rest of the
variables are held constant. Yearly provincial unemployment rates, on the other hand, are found
to have a positive impact on failure. An increase of 1% in unemployment rate per province
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increases the chances of failure by 2%, when everything else is held constant. Industries with
higher entry and exit rates have higher failure chances. Higher entry rates increase competition,
which may result in higher exit rates. Higher exit rates may reflect two things; they may be the
result of higher entry rates, or a reflection of the status of the economic growth. Our results show
that exit rates have a higher impact on firm exit; and increase in 1% of exit rate by industry
increases the odds of failure by 17.6%, whereas the increase of 1% in entry rate by industry
increases the likelihood of failure by 8.4% only.
GDP growth rates, unemployment rates, and firm entry and exit need to be forecast. In a
microsimulation environment, there would be modules or scenarios that forecast such economic
indicators. Yearly GDP by province and industry are publicly available online on Statistics
Canada’s CANSIM tables (Statistics Canada, 2015b). Also, entry and exit rates are available
(from this study) and in other statistics published by Statistics Canada (Statistics Canada, 2016a).
Trends and simple linear regression models are famous methods for forecasting using historical
data and are candidates for such forecasting models.
6.5.2.4. Model Validation
Model validation is an essential step that comes after model estimation to assess the
performance of the estimated model. A discussion about model goodness-of-fit is provided in
chapter 4, section 4.5.3. Cross-validation is used to validate the predictive performance of firm
failure model on the aggregate level. For this purpose, a hold-out sample (a validation set) of
20% of the firm population of investigation (1,570,339 records) was extracted, which was not
used in the estimation process. According to (McFadden, 1978), the predicted total probabilities
(for each observation in the validation dataset) are calculated first and then divided by the
validation sample size to calculate the predicted shares of the binary outcome (firm failure). This
number is compared to the actual observed shares of the failure event in the sample (the number
of failure firms divided by the total number of firms). The validation and goodness-of-fit
summary are presented in (TABLE 6.6), showing an accepted overall performance of the model.
The reported pseudo R2 statistics indicates a good fit of the model as suggested in (McFadden,
1978). Another measure of assessing the overall model significance is a chi-squared test for
testing the null hypothesis that the model coefficients are not statistically different from zero. A
discussion about chi2 is provided in chapter 4, section 4.5.3. Prob > chi2 in TABLE 6.6 when
compared to a critical value (e.g. 0.05 or 0.01 significance level) indicates that the null
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hypothesis cannot be true and the model is statically significant at a 99% and higher confidence
(i.e. all the model coefficients are statistically different from zero) (Greene, 2012).
TABLE 6.6 Model Validation and Goodness-of-Fit
Total number of observations
(Estimation sample size) 6,184,894
Log likelihood (Null model) -1182090
Log likelihood (Full model) -1018971
Pseudo R2 (rho-square) 0.138
Adjusted R2 0.138
LR of chi2(33)* 326238
Prob > chi2 (p-value) 0.00
Observed total probabilities 4.8%
Predicted total probabilities 4.7%
% Difference (Predicted - observed) -0.1%
The cross-validation results indicate that the model slightly under predicts the total failure
probabilities by 0.1%. This value is very small and indicates a very good predictive performance.
6.6. General Notes
The selection of a pooled regression model for the parametric analysis came after trials of
other model structures. First, a panel logistic regression that considers random effects is tested.
In simple terms, the model accounts for the variation that could results from one panelist to the
other by estimating a random effect term.
Pr(𝑦𝑖𝑡 ≠ 0|𝑥𝑖𝑡) = 𝑃 (𝑥𝑖𝑡𝛽 + 𝜈𝑖) (6.10)
for i = 1…, n panelists, where t = 1…., ni, νi are i.i.d. with N(0,σ2ν), and the variance
component model states that:
𝑥𝑖𝑡𝛽 + 𝜈𝑖 + 휀𝑖𝑡 > 0 (6.11)
where 휀𝑖𝑡 are i.id. logistic distributed with mean zero and variance σ2ε and is independent
from 𝜈𝑖. Another term that is estimated to indicate whether the panel regression is significant or
not is the proportion of the total variance contributed by the panel-level variance (𝜌) as:
* Likelihood ratio (LR) chi-square test= -2 x (log(LNull) – log(Lfull))
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𝜌 = 𝜎𝜐
2
𝜎𝜐2+ 𝜎𝜀
2 (6.12)
When the value of 𝜌 is zero, the panel-level variance component is neglected and the panel
logistic model is not statistically different from the pooled model (StataCorp, 2013). In our case,
the model estimates suggest that the null hypothesis of 𝜌 being statistically different from zero
cannot be rejected, i.e. a panel logistic regression is not statistically different from a pooled
logistic regression.
A multilevel model structure is also tested to check if the variance between firm clusters (the
panelist level) is statistically significant or not. The result suggests that the multilevel model is
not statistically different from a pooled logistic model as the variance calculated on the cluster
level (the firm level) is not statistically different from zero. For a quick hands-on on the
multilevel logistic regression models visit melogit chapter in (StataCorp, 2013).
Due to Statistics Canada’s requirements for privacy protection, the results of the indicated
models are not available for publication, and only results of the pooled regression model is
approved for publication.
In the parametric analysis of firm failure, and in order to understand the effect of firm size on
failure events, the number of employees was first discretized into different intervals and
statistically evaluated in the logit regression. It was found that the larger the number of
employees, the less likely a firm to fail. In other words, it has a monotonically decreasing
relationship that can be described by a log function (which is the current used format in the
parametric model for the number of employees). However, in the non-parametric analysis, the
relationship is not the same. In FIGURE 6.8, the curves indicate that very small firms have lower
survival probabilities compared to larger ones. The probability of survival is increased with the
increase in the number of employees to a certain threshold (the medium size class), and then
starts to decrease when the size in the large range. This is because the way the survival function
is calculated does not account for the variability in size for the same firm throughout the years.
Firm change in size by time (shrink or grow); small ones become medium, medium become
large, and so on. Falsely, the survival function, Equation (6.1), considers a firm that changes its
size class to have disappeared from the population, and is marked as a failure, which is not the
case, it has changed in size. Therefore, the results of the non-parametric analysis cannot be fully
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considered when it comes to employment size to understand the effect of firm size on failure
events, and the results of the parametric model is more trusted in this sense.
6.7. Concluding Remarks
The study of firm demography is essential in regional planning. Firm exit results in changes
in labour dynamics, which may result in labour migration across regions, resulting in major
demographic changes within regions. Such behaviour is important to understand and models that
identify drivers to firm failure are needed. Models of firm failure in Canada are limited; the
models found in the literature are either limited to small geographic regions (Maoh and
Kanaroglou, 2007b) or only addresses survival of new firms (Baldwin et al., 2000), leaving room
for further research to cover firm survival in Canada.
Our study explores the patterns of failure for firms in Canada during the period of 2001-
2012. Both parametric and non-parametric analyses indicate that firm employment size, age,
industry classification, and location are influencing firm failure patterns. Our analysis finds that
larger and older firms have a better survival chances compared to smaller younger firms. Market
competition is found to have a negative effect on firm survival; the higher the number of
competitors is, the higher the risk of failure. Economic indicators are also statistically tested in
the discrete-time hazard model. We conclude that a growing economy increases the likelihood of
firm survival. This is indicated by the negative effect of the GDP growth on firm exit decisions,
and the positive effects of provincial unemployment rates, and firm entry and exit rates on firm
exit decisions. These findings at large show that a thriving economy with stable industry
dynamics reduces the likelihood of firm exits.
Several opportunities exist for future research. In our study, economic growth is treated as
an external driver to firm exit, while in fact the relationship between firm demography and the
economy is two-way. A growing economy increases investment potential which is expected to
reduce firm failure and encourage new business. On the other hand, an increase in the number of
firm exits would result in a decline in the economic growth of a region and an increase in the
unemployment rate, and eventually affects the GDP. Modelling such two-way relationships is
challenging, and is left for future research.
Other determinants of failure, for example those related to firm market strategies, have not
yet been explored due to data unavailability. Since it is challenging to contact representatives of
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firms that have already exited the market, a possible technique is stated preference surveys of
firm failure, in which decision makers of firms are interviewed about the main reasons and
conditions that would lead to firm exit.
Our study contributes to the firm demography literature in the Canadian context and is
potentially beneficial to research fields such as regional development, transportation and land
planning. Our conclusions fall in-line with other studies indicating that firm failure patterns are
similar in regions with similar externalities. This research has potential to be incorporated as one
component within a microsimulation model of the urban spatial economy that can be used for
regional development analysis and forecasting.
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CHAPTER 7
Discrete Choice Models of Freight Outsourcing Decisions of
Canadian Manufacturers
7.1. Introduction and Related Literature
One of many decisions firms make, which directly impacts freight systems and the economy,
is outsourcing of freight related activities (such as logistics, and intermediate goods production).
Firms can either choose to perform all of their activities in-house, own subsidiaries to perform
activities, or outsource part/all of their activities to other firms (Abdur Razzaque and Chen
Shang, 1998; Deepen, 2007). Outsourcing is defined as the procurement of products/services
from other sources that are external to the firm (Foster and Muller, 1990). Generally, firms
choose to outsource non-core activities to minimize cost and maximize profits, as outsourcing
reduces capital investments in facilities, equipment, information technology, and workforce
(Fantasia, 1993; Lankford and Parsa, 1999; Montemayor, 2014; Quinn and Hilmer, 1994; Rao
and Young, 1994; Richardson, 1992; Sheffi, 1990). Freight outsourcing allows firms to focus on
the core activities that help them gain competitive advantage, to improve customer deliveries,
and expand in new markets (Lankford and Parsa, 1999; Quinn and Hilmer, 1994; Gonzalez et al.,
2009). However, there are some trade-offs associated with outsourcing related to quality
assurance, protection of shared proprietary data (e.g. product designs), and loss of control (e.g.
supply chain control) (Rao and Young, 1994).
Outsourcing of freight logistics (including transportation, distribution, warehousing,
inventory management, and material handling), and intermediate goods production have strategic
importance. They affect the labour market (Senses, 2004), transportation network efficiency
(Abdur Razzaque and Chen Shang, 1998), and growth of economy (Richardson, 1993).
Globalization has allowed firms to choose to outsource intermediate goods production
internationally to low-cost subcontractors (Lommerud et al., 2003; Lommerud et al., 2009).
International outsourcing may have economic disadvantages for the local labour market. As a
consequence of globalization, there has been concern that labour demand is shifting from
economies with high labour costs to economies with lower costs, which affects job stability and
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increases unemployment of low-skilled workers in economies with high labour costs (Bachmann
and Braun, 2011). Other researchers suggest that international outsourcing has a positive impact
on organizational performance by allowing a focus on core activities, increasing productivity,
which in return increases the demand for local employment (Alexandrova, 2009; Amiti and Wei,
2006; Egger and Egger, 2006; Görg and Strobl). Given the economic importance of outsourcing
type (local vs. international), it is important to explore the underlying factors related to
outsourcing of goods production and logistics, and whether activities are outsourced
internationally or locally.
Freight outsourcing decisions are mainly driven by whether freight operations are within the
firm’s core competency, liability and control, operating costs, information and communication
systems, and market relationships (Rao and Young, 1994). Freight outsourcing is also affected
by other factors related to the firm (e.g. strategic focus, size, production size, and shipment
frequency), the industry (e.g. the nature of the supply chain, the average size of the industry, and
market competition), the economic conditions, location characteristics (e.g. access to resources,
and proximities to highways), and advances in innovation and technology (Abdur Razzaque and
Chen Shang, 1998; Bienstock and Mentzer, 1999; Rao and Young, 1994). Also, outsourcing of
freight operations, and supplier selection decisions might be intertwined. Supplier selection
decisions depend on the transportation cost and the proper mode of transport (Pourabdollahi et
al., 2016). One form of outsourcing can be seen in the shipper’s choice of whether to outsource
logistics activities to other transportation providers or to explicitly choose the desired mode of
transport (Fridstrom and Madslien, 1994; Fries and Patterson, 2008). Fries and Patterson (2008)
provide a distinction between two types of shippers and their preference to outsourcing. They
highlight that “private” shippers prefer to transport their shipments using their own fleet.
Whereas “end-shippers” prefer to completely outsource their freight activities (Fries and
Patterson, 2008).
Our study focuses on Canadian manufacturers, which account for a large percentage of the
Canadian Gross Domestic Product (GDP) (Snoddon et al., 2014). The manufacturing sector
involves large outsourcing operations both locally and internationally (Baldwin and Gu, 2009).
For its economic importance, it is valuable to understand how firm characteristics and economic
conditions influence outsourcing decisions. In this chapter, discrete choice models of freight
outsourcing for Canadian manufacturers are introduced. In addition to building understanding of
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influencing factors contributing to outsourcing, the models are also intended to be integrated
within an agent-based firm microsimulation platform that simulates individual firm decisions and
evolution, and explicitly model interactions between firms (Mostafa and Roorda, 2015). No
previous attempts were found in the literature that introduce discrete choice models to quantify
effects of firm characteristics and the economic environment on freight outsourcing decisions of
manufacturers in a Canadian context. The models consider factors with potential influence over
freight outsourcing such as firm characteristics and strategic focus, market competition, GDP
growth, and international relations. Also, models that examine the outsourcing type (international
vs. local) are introduced. The chapter starts by describing the data and modelling methodology,
and follows with model results and interpretations and model validation.
7.2. Data Description
Two cross-section data sets of the Survey of Innovation and Business Strategy (SIBS),
collected by Statistics Canada, are used. The survey is launched every three years and collects
data for a three-year interval; the 2009 survey covers the period of 2007-2009 and the 2012
survey covers the period of 2010-2012. The surveys were conducted on a sample of enterprises
that have at least 20 employees and annual revenue of at least $250,000. The sample was
stratified according to 14 industry sectors using the North American Industry Classification
System (NAICS). The two data sets include information about 6,233 and 4,559 enterprises in
different industries across Canada for the 2009 and 2012 surveys, respectively (Statistics Canada,
2012b). Approximately 8,000 of these enterprises are manufacturers. The survey collected
information about long-term strategies, outsourcing of business activities, international activities
and relationship with main suppliers, competition, use of advanced technology and innovation,
and use of government support programs for innovation related activities. TABLE 7.1
summarizes all the variables that are considered in this study along with a brief description of
each. All the listed variables have been statistically evaluated in the model estimation, and only
the statistically significant variables are included in the final models discussed in this chapter.
The data are combined with data from Statistics Canada’s socioeconomic database, CANSIM
(Statistics Canada, 2015a), that includes GDP growth for the years investigated in the survey.
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TABLE 7.1 Summary of Investigated Explanatory Variables
Variable Description
Number of employees
Three dummy variables indicating the firm size: 1) small, number of employees <100
2) medium, number of employees between 100 and 250
3) large, number of employees >250
Other firm characteristics
Employees with university degrees (%) Percentage of employees with a university degree
The head office location in Canada A dummy indicating that the head office location is in Canada
More than one profit center A dummy indicating that a firm has more than one profit center
Other subsidiaries in Canada A dummy indicating that a firm has other subsidiary locations in Canada
Provincial location variables A set of 5 dummy variables indicating the firm's provincial location (Ontario, Quebec,
Alberta, British Columbia, and the rest of Canada)
Industry classification (NAICS 3-Digit code) A group of 19 dummy variables representing the sub-industry classification of the
manufacturing industry based on the 3-digit NAICS code (e.g. Food manufacturing (311))
Number of products and services The natural logarithm of number of offered products and services
Long-term strategy: Mass market A dummy variable indicating that a firm's long-term strategic focus is on offering
products/services with low-price and cost leadership
Long-term strategy: Goods or services positioning A dummy variable indicating that a firm's long-term strategic focus is on product
leadership, market segmentation, or product diversification
Age of long-term strategy A variable indicating how long the long-term strategy has been implemented within a firm
Long-term strategy’s performance indicators
Gross/margin operating cost
A set of dummy variables representing 7 different performance indicators a firm may select
to evaluate the performance of the selected long-term strategy
Sales/income growth
Shareholder dividends growth
Market/customer share growth
Customer satisfaction
Sales of new products
Delivery times
Strategic focus
New goods/ services A set of dummy variables for four main strategic foci of a firm. A value of 1 indicates that
a firm is seeking to introduce new or significantly improved
practices/service/goods/activities. A value of 0 indicates that a firm is more focused on
maintaining or expanding the chosen strategic focus
Marketing practices/methods
operations and business activities
Organizational and management practices
Economic indicator: GDP growth on naics-3 level Percent growth of the GDP by sub-industry class
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TABLE 7.1 (Continued)
Operational strategies
Outsource of goods production Indicates a firm outsources their goods production (locally or internationally)
Outsource of logistics Indicates a firm outsources their logistics activities (locally or internationally)
Outsource of goods production (internationally) Indicates a firm outsources their intermediate goods production internationally
Outsource logistics (internationally) Indicates a firm outsources their logistics activities internationally
Opened a new/expand production facility Indicates a firm opened a new production facility or expanded in capacity
Expand production facility by M&A Indicates a firm obtained production capacity by merger and acquisition
Close or contract capacity of production facilities Indicates a firm has closed/contracted the capacity of existing production facilities
Opened new/expanded logistics facilities Indicates a firm has opened a new or expanded the capacity of logistics facilities
Expand logistics by M&A Indicates a firm has obtained logistics capacity by merger or acquisition
Closed/contract capacity of logistics facilities Indicates a firm has closed/contracted the capacity of existing logistics facilities
Use of innovation and advanced technologies
Use of advanced communication technology
A group of dummy variables indicating which type of innovation/advanced technology a
firm may have utilized
Use of advanced automated material handling
Use of process innovation in logistics
Process innovation in new manufacturing methods
Use of Product innovation
Government tax credit programs A group of dummy variables indicating that a firm has utilized different government
support programs for innovation. The government programs can either be federal,
provincial, or municipal.
Government training programs
Government grants
International activities and involvements
Direct exports Indicates that a firm has direct exports outside of Canada
International subsidiaries Indicates that a firm has subsidiaries outside of Canada
International activities Indicates that a firm has activities outside of Canada
International suppliers Indicates that a firm has suppliers outside of Canada
Canada being the main market Indicates that Canada is a firm’s main market
Competition from multinational firms Indicates that a firm has competition from other multinational firms
Competition and market share
Number of competitors <3
A group of three dummy variables indicating the number of competitors for each firm Number of competitors 3-10
Number of competitors >10
Market share of highest selling goods/services in the
main market (%)
A continuous variable representing the percentage of the market share of the highest selling
goods or services a firm is offering in the main market
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7.3. Methodology and Model Structure
We hypothesize that there is correlation between outsourcing of goods production and
outsourcing of logistics and distribution decisions. Therefore, both decisions are considered in a
common modelling framework to capture their interrelation. Outsourcing of goods production,
and outsourcing of logistics and distribution can be formulated as a two independent binary
decisions, while considering the effect of the other decision exogenously (FIGURE 7.1.a), a joint
simultaneous decision (FIGURE 7.1.b), or as a nested structure (FIGURE 7.1.c). It is important to
highlight that nested structures are not sequential models, but rather another representation of a
joint choice model when the choice set is multidimensional and shares observed and unobserved
variables (Ben-Akiva and Lerman , 1985).
FIGURE 7.1 Model structures
Outsource goods
production
Yes No
Outsource logistics
and distribution
Yes No
Outsource
None
Goods production
only
Logistics and
distribution only
Both
Outsource goods
production
Yes No
Yes No
(b) (c)
(a)
Outsource logistics
and distribution
Outsource goods production/logistics
and distribution
Locally Internationally
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In this chapter results of the first two model structures (independent binary choice models,
and a multinomial model of the joint simultaneous decision) are presented. The binary logit
model structure is used to represent the decisions of outsourcing of goods production, and
logistics and distribution decisions. The binary choice structure is also used to model the
outsourcing type (local/international) for firms that choose to outsource part or all goods
production and/or logistics and distribution activities. The results for the nested structure
(FIGURE 7.1.c) suggest that a nested logit model is not statistically different from a multinomial
logit (MNL) model of the joint simultaneous decision.
7.3.1. Binary logit model structure
The outsourcing of the two freight activities of goods production and logistics are binary
choices. In our binary logit models, the dependent variable y=1 if a firm outsources an activity,
and y=0 otherwise. Given a vector of explanatory variables xi, the probability of outsourcing
(yi=1) is represented as follows:
𝐏𝐫(𝒚𝒊 = 𝟏 | 𝒙𝒊) = 𝑭(𝒙𝒊, 𝜷) (7.1)
where 𝜷 is a vector of parameter coefficients of the independent variables xi. Representing this
relationship using a logistic model takes the following form as described in (Ben-Akiva and
Lerman , 1985):
𝐏𝐫(𝒚𝒊 = 𝟏 | 𝒙𝒊) = 𝒆𝑽𝒊
𝟏+ 𝒆𝑽𝒊 (7.2)
where 𝑉𝑖 is the systematic utility, which is a linear function of a set of independent variables xi as
follows:
𝑽𝒊 = 𝜷𝟎𝒊 + 𝜷𝟏𝒊𝒙𝟏𝒊 + 𝜷𝟐𝒊𝒙𝟐𝒊 + ⋯ + 𝜷𝒏𝒊𝒙𝒏𝒊 (7.3)
The vector 𝜷 of can be estimated using Maximum Likelihood Estimation (Greene, 2008),
where the log likelihood function (LL) can be formulated as follows:
𝑳𝑳(𝜷) = ∑ [𝒚𝒊𝒍𝒏[𝑭 (𝒙𝒊, 𝜷)] + (𝟏 − 𝒚𝒊) 𝒍𝒏 [𝟏 − 𝑭 (𝒙𝒊, 𝜷)]]𝒊 (7.4)
where 𝐹 (𝑥𝑖, 𝜷) takes the logit form represented in (7.2)
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Two groups of independent binary logit models are presented in this chapter. One addresses
outsourcing of goods production, and the other addresses outsourcing of logistics and
distribution. For each outsourcing decision model, the outcome of the other decision is included
as an independent binary variable. For example, in the logistics outsourcing model, the goods
production outsourcing is used as an independent variable to capture its effect and significance
on logistics outsourcing decision. The binary logit model structure is also used to represent the
outsourcing type (local/international) decisions for both goods production and logistics and
distribution.
7.3.2. Multinomial logit (MNL) model structure
Correlation analysis of the data and the results of the binary logit models (as shown in the
results section) reveal a high correlation between the decisions of outsourcing of goods
production and outsourcing of logistics. Therefore, a MNL model structure of the joint
outsourcing decisions is tested. MNL is an appropriate approach when the dependent variable is
categorical (more than two categories) and the categories are unordered (Smithson and Merkle,
2013). In the MNL formulation, the probability of alternative i to be chosen amongst a set of
alternatives J is given by:
𝑷𝒓(𝒊) = 𝒆𝑽𝒊
∑ 𝒆𝑽𝒋𝑱
𝒋=𝟏
(7.5)
The log likelihood function can be formulated as follows:
𝑳𝑳(𝜷) = ∑ ∑ 𝜹𝒋𝒕∀ 𝒋 ∈𝑱 ∀ 𝒕 ∈ 𝑻 ×𝒍𝒏 (𝑷𝒓𝒋𝒕(𝜷)) (7.6)
where 𝛿𝑗𝑡= 1 if alternative j is chosen by individual t and 𝛿𝑗𝑡 = 0 otherwise. In the joint model of
freight outsourcing decisions, four decision alternatives are identified as in FIGURE 7.1c:
• Alt. 0: outsource none (the base alternative)
• Alt. 1: outsource goods production only
• Alt. 2: outsource logistics and distribution only
• Alt. 3: outsource both goods production and logistics and distribution
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7.4. Model Results
Three types of models have been estimated for both the binary logit, and MNL model
structures. Results are organized as follows. First, for binary logit model structure, the following
models are presented:
• Logistics outsourcing (simple and detailed models) (TABLE 7.2)
• Goods production outsourcing (simple and detailed models) (TABLE 7.3), and
• International outsourcing (logistics and goods production) (TABLE 7.4)
Then, for the MNL model structure the following models are presented:
• Logistics and goods production outsourcing (simple and detailed models) (TABLE 7.5,
TABLE 7.6)
• International logistics and goods production outsourcing (TABLE 7.7)
The simple models include available generic variables only. These models are intended for use in
a freight microsimulation, in which all inputs must be forecast. The detailed models include more
information about the firm structure, strategic focus, economic growth, market competition, and
use of innovation and advanced technologies. These additional variables would not necessarily
be available within a microsimulation but provide new insights on underlying behaviour.
The models for explaining international outsourcing are based on a subset of the data set that
contains records of all outsourcers. Firms that outsource locally versus internationally are
distinguished. Models for quantifying the effects of firm characteristics and economic
environment on outsourcing type for goods production, and logistics activities are then
estimated.
7.4.1. Binary logit model results
The simple and detailed model estimates for the binary logit model structure are shown in
TABLE 7.2, and TABLE 7.3. TABLE 7.4 provides model estimates for the international
outsourcing models. We hypothesize that outsourcing decisions are different from one industry
to the other. As such, industry effects are represented by dummy variables to identify sub-
industry 3-digit NAICS code. Industries such as wood product, and computer and electronic
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product manufacturers are less likely to outsource their logistics compared to beverages and
tobacco product manufacturers.
Firm size (represented as the number of employees) is found to have an influence over
outsourcing of both decisions; the larger the firm, the more likely they are to outsource their
freight related activities. This finding is in agreement with (Kurz, 2006) for U.S. manufacturers,
which states that outsourcers have significantly greater employment compared to non-
outsourcers. When firms outsource intermediate goods production, they hire more highly-skilled
workers (explained by the positive sign of the percentage of university degree employees in
TABLE 7.2) to perform a firm’s core activities.
Strategic focus of the firm is found to have an impact on goods production outsourcing.
When firms are focused on producing significantly improved or new goods, they are less likely
to outsource production activities. Firms whose strategy is to minimize operating cost, expand
market share growth, or increase the sales of their new products are more likely to outsource
some or all of their production activities.
Growth in the GDP has a positive impact on outsourcing of logistics. When the economy is
growing, manufacturers tend to focus on increasing their market share by focusing on the
production side, and hence are more likely to outsource their logistics. Moreover, firms that
outsource part or all of their goods production are more likely to outsource the associated
logistics and vice versa.
The decisions of expansion and contraction of production facilities are found to have almost
the same impact on the logistics outsourcing. Firms that close production facilities may also want
to close some of their logistics facilities as well. On the other hand, firms that expand their
production facilities may want to focus on the production side, and hence would also want to
outsource logistics. Similarly, closing production or logistics and distribution facilities increase
the likelihood of outsourcing goods production.
The use of advanced communication technologies is found to have a positive influence on
logistics outsourcing. Use of advanced technologies in general indicates an interest in enhancing
production quality to gain higher market share. Hence, outsourcing of logistics is more plausible.
When a firm is using process innovation in introducing new or significantly improved logistics
operations, it is logical that they are focused on administrating their own logistics operations as
179
they are already investing in it, as most likely it is within their goals to enhance their delivery
times (explained by the negative sign of the use of process innovation coefficient in TABLE 7.2).
TABLE 7.2 Binary Logit Models for Logistics Outsourcing*
Simple model Detailed model
Variables Coef. P >|Z| Odds
ratio Coef. P >|Z|
Odds
ratio
_cons -1.090 0.000 0.336 -1.790 0.000 0.167
Industry
Beverage and tobacco products 0.670 0.014 1.960 1.092 0.001 2.980
Textile products -- -- -- -0.340 0.124 0.712
Apparel, leather and allied products -0.348 0.093 0.706 -0.367 0.096 0.690
Wood products -0.440 0.015 0.644 -- -- --
Plastics and rubber products -- -- -- 0.308 0.041 1.360
Non-metallic mineral products -0.548 0.008 0.578 -- -- --
Computer and electronic products -0.467 0.003 0.627 -0.569 0.001 0.567
Furniture and related products -- -- -- 0.330 0.072 1.390
Rest of subindustries Reference
Employment Size
Small-sized firm (less than 100 employees) Reference
Medium-sized firm (100-250 employees) 0.413 0.000 1.511 0.217 0.024 1.242
Large-sized firm (>250 employees) 0.516 0.000 1.675 0.250 0.021 1.280
Location: Ontario 0.242 0.001 1.273 -- -- --
GDP growth on naics-3 level -- -- -- 0.009 0.022 1.009
Other operational strategies
Outsource of goods production -- -- -- 1.415 0.000 4.115
Opened a new/expand production facility -- -- -- 0.467 0.000 1.595
Closed/contracted capacity of production
facility -- -- -- 0.355 0.002 1.426
Use of innovation and advanced technologies
Use of advanced communication tech. -- -- -- 0.245 0.018 1.278
Process innovation in logistics -- -- -- -0.270 0.013 0.764
Use of government tax credit programs -- -- -- 0.140 0.097 1.151
Strategic focus
Gross/margin operating cost -- -- -- 0.181 0.053 1.198
Organizational and management practices -- -- -- -0.130 0.104 0.878
International activities
Direct exports -- -- -- 0.217 0.011 1.242
* All model variables are dummy variables unless otherwise stated
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TABLE 7.3 Binary Logit Models for Goods Production Outsourcing
Simple model Detailed model
Variables Coef. P>|Z| Odds
ratio Coef. P>|Z|
Odds
ratio
_cons -0.763 0.000 0.466 -2.978 0.000 0.051
Industry: Food manufacturing -1.435 0.000 0.238 -1.213 0.000 0.297
Beverage and tobacco products -0.940 0.006 0.391 -- -- --
Apparel, leather and allied products -- -- -- 0.668 0.001 1.951
Wood products -1.409 0.000 0.244 -0.773 0.002 0.462
Paper manufacturing -0.689 0.001 0.502 -- -- --
Petroleum and coal products -1.209 0.026 0.298 -- -- --
Chemical manufacturing -0.805 0.000 0.447 -0.835 0.000 0.434
Plastics and rubber products -0.650 0.000 0.522 -0.424 0.017 0.655
Non-metallic mineral products -1.483 0.000 0.227 -1.011 0.000 0.364
Primary metal manufacturing -0.620 0.001 0.538 -- -- --
Fabricated metal products -0.263 0.040 0.769 -- -- --
Transportation equipment manufacturing -0.569 0.000 0.566 -- -- --
Furniture and related products -0.623 0.001 0.536 -- -- --
Rest of subindustries Reference
Size: Small firm (less than 100 employees) Reference
Medium-sized firm (100-250 employees) 0.401 0.000 1.493 -- -- --
Large-sized firm (>250 employees) 0.501 0.000 1.650 -0.306 0.008 0.736
Employees with university degrees (%) -- -- -- 0.007 0.003 1.007
Production size: # of products and
services(log) -- -- -- 0.068 0.000 1.070
Long-term strategy: Mass market strategy -- -- -- -0.310 0.039 0.734
Improved goods/services -- -- -- -0.218 0.017 0.804
Strategic focus: Gross/margin operating cost -- -- -- 0.297 0.005 1.345
Market/customer share growth -- -- -- 0.191 0.034 1.211
Sales of new products -- -- -- 0.208 0.024 1.231
Other operational strategies
Outsource logistics -- -- -- 1.330 0.000 3.780
Close/ contract capacity of goods production
facilities -- -- -- 0.447 0.001 1.564
Close/contract capacity of logistics facilities -- -- -- 0.355 0.086 1.426
Competition: Number of competitors < 3 Reference
Number of competitors 3-10 -- -- -- 0.328 0.007 1.389
Number of competitors >10 -- -- -- 0.341 0.010 1.406
Process innovation of new manufacturing
methods -- -- -- 0.218 0.002 1.243
International activities -- -- -- 0.660 0.000 1.935
International suppliers -- -- -- 0.392 0.023 1.480
181
Government programs of tax credit are found to have a positive influence over logistics
outsourcing. One example is the Ontario innovation tax credit program. Briefly, this program
allows businesses to claim a 10% of their tax credit for expenditures on scientific research and
experimental development performed in Ontario (Ministry of Finance, 2016). Competition is
found to have a positive impact on goods production outsourcing; the higher the number of the
competitors, the more likely firms would outsource their goods production activities to cope with
the competition and maintain their market share.
TABLE 7.4 presents models for international freight outsourcing. It is found that firms with
international activities/subsidiaries or who have an international market are more likely to
outsource freight activities internationally, as this might be more cost effective.
182
TABLE 7.4 Binary Logit Models for International Outsourcing*
Logistics outsourcing Goods production
outsourcing
Variables Coef. P >|Z| Odds
ratio Coef. P>|Z|
Odds
ratio
_cons -2.617 0.000 0.073 -0.029 0.937 0.972
Industry: Beverage and tobacco products -- -- -- -1.434 0.114 0.238
Apparel, leather and allied products -- -- -- 3.253 0.003 25.856
Printing and related support activities -- -- -- -1.378 0.039 0.252
Rest of subindustries Reference
Size: Small -medium (≤ 250 employees) Reference
Large-sized firm (>250 employees) 0.443 0.065 1.558 -- -- --
Production size: # of product lines (log) -- -- -- 0.190 0.068 1.209
Other characteristics
The head office location in Canada -- -- -- -1.451 0.000 0.234
Other operational strategies
Expand production facility by M&A -- -- -- 1.052 0.009 2.863
Expand logistics by M&A -- -- -- -2.074 0.000 0.126
Use of product innovation -- -- -- 0.477 0.049 1.611
Long-term strategy: Mass market -0.602 0.128 0.548 -- -- --
International involvement
Outsourcing of goods production(intl.) 2.090 0.000 8.087 -- -- --
Outsourcing of logistics (intl.) -- -- -- 2.164 0.000 8.708
International subsidiaries -- -- -- 0.925 0.001 2.522
International activities 0.841 0.001 1.327 -- -- --
Canada being the main market -0.480 0.029 0.619 -0.496 0.039 0.609
Competition from multinational firms 0.599 0.021 1.821 -- -- --
Direct exports 0.433 0.097 1.541 -- -- --
Also, few industries are found to have a statistical significance on goods production
outsourcing. For instance, apparel, leather and allied product manufacturers are more likely to
outsource part of their goods production internationally compared to the rest of manufacturers.
This could be due to resources availability and manufacturing cost, but more information about
outsourcing cost is needed to support this claim.
* All model variables are dummy variables unless otherwise stated
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7.4.2. MNL model results
The main purpose of the MNL models is to explore the effect of the independent variables on
the joint decision of the two freight outsourcing decisions, and whether such effects are
significantly different from the binary logit models or not. TABLE 7.5, TABLE 7.6, and TABLE
7.7 represent the estimates of the three MNL models; the simple model, the detailed, and the
international outsourcing models.
Joint freight outsourcing decisions are different from one industry to the other. For instance,
from the odds ratio in TABLE 7.5 and TABLE 7.6, when all other variables are held constant,
apparel, leather and allied producers are almost 3 times likely to outsource their logistics only,
and are 1.3 times likely to outsource their goods production only than not to outsource any
freight operations, while they are 0.74 ~ 1.1 times likely to outsource both operations than not to
outsource any.
Location of manufacturers also has an effect on outsourcing decisions. Manufacturers in
Ontario are 20% more likely to outsource both freight activities than firms located in Alberta
(calculated from the odds ratio in TABLE 7.5 and TABLE 7.6 This is affected by several groups
of factors related to the location such as the targeted customers, the market, resource availability
and cost of outsourcing. When such information is available, further solid conclusions can be
drawn regarding the effect of firm location on outsourcing decisions.
Professional employment was found to have almost the same effect on outsourcing decisions
(TABLE 7.6). An increase of 1% in the number of employees with university degrees has almost
the same effect across outsourcing decisions compared to the base alternative. Firms that have
other subsidiaries in Canada are generally less likely to outsource freight activities because the
presence of other Canadian subsidiaries may entail expansion in production/logistics operations
and hence are performed within the firm.
Furthermore, firms that target mass markets as their long-term strategy are less likely to
outsource any of their freight operations, which conforms to the results of the binary logit
models. Firms that focus on minimizing operating costs and those whose strategy is to increase
their market share are more likely to outsource goods production only compared to outsourcing
logistics only or outsourcing both goods production and logistics. Firms that aim to increase the
sales of their new products are more likely to outsource logistics compared to the rest of
alternatives. The change in market share of the highest selling goods or services in the main
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market is found to have the same effect on different outsourcing decision alternatives (the
coefficients of all alternatives have the same sign and almost the same magnitude in TABLE 7.6).
Growth in the GDP of each industry is found to have almost the same effect on outsourcing
decisions over the base alternative.
Confirming the conclusions drawn from the binary logit models, firms that employ process
innovation in logistics are less likely to outsource their logistics operations. Also, the use of
government programs of tax credits and trainings are found to have a positive influence on all
outsourcing decisions.
TABLE 7.5 A MNL Model of Outsourcing: A Simple Model*
Coef. P >|Z| Odds ratio
Variables Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3
_cons -1.448 -1.540 -1.602 0.000 0.000 0.000 0.235 0.214 0.201
Industry
Food manufacturing -0.940 -1.235 0.236 0.000 0.000 0.147 0.391 0.291 1.266
Apparel, leather and allied
products 0.240 1.078 -0.299 0.359 0.000 0.377 1.271 2.939 0.741
Wood products -0.943 -1.175 -0.066 0.001 0.001 0.765 0.389 0.309 0.936
Chemical manufacturing -0.262 -0.515 0.108 0.182 0.036 0.561 0.769 0.597 1.114
Plastics and rubber products -0.145 -0.328 0.318 0.476 0.187 0.093 0.865 0.720 1.375
Primary metal
manufacturing -0.507 0.102 0.612 0.106 0.721 0.007 0.602 1.108 1.845
Machinery manufacturing 0.372 0.629 0.261 0.036 0.001 0.177 1.450 1.876 1.299
Computer and electronic
products -0.177 0.611 -0.352 0.418 0.002 0.156 0.838 1.843 0.703
Transportation equipment
manufacturing -0.103 -0.055 0.266 0.553 0.778 0.112 0.902 0.947 1.305
Furniture and related
products 0.054 -0.388 0.369 0.817 0.212 0.100 1.055 0.678 1.446
Miscellaneous
manufacturing 0.297 1.022 0.297 0.232 0.000 0.255 1.346 2.780 1.345
Rest of subindustries Reference
Employment size
Small and medium sized
firms (≤ 250 employees) Reference
Large-sized firm (>250
employees) 0.593 0.088 0.266 0.000 0.545 0.026 1.810 1.092 1.305
Location
Ontario 0.174 0.004 0.257 0.097 0.968 0.011 1.190 1.004 1.293
Alberta -0.075 -0.495 0.059 0.661 0.013 0.714 0.928 0.610 1.061
Rest of provinces in Canada Reference
* All model variables are dummy variables unless otherwise stated
185
TABLE 7.6 A MNL Model of Outsourcing: A Detailed Model
Coef. P >|Z| Odds ratio
Variables Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3
_cons -2.913 -2.698 -2.120 0.000 0.000 0.000 0.054 0.067 0.120
Industry
Food manufacturing -1.218 -1.301 0.413 0.000 0.000 0.012 0.296 0.272 1.511
Apparel, leather and allied
products 0.262 1.136 0.095 0.381 0.000 0.761 1.300 3.115 1.100
Wood products -0.747 -0.652 0.161 0.010 0.043 0.453 0.474 0.521 1.175
Paper manufacturing -0.590 -0.324 -0.171 0.079 0.302 0.541 0.554 0.723 0.843
Chemical manufacturing -0.837 -1.065 -0.002 0.000 0.000 0.993 0.433 0.345 0.998
Plastics and rubber products -0.241 -0.512 0.257 0.261 0.049 0.181 0.786 0.600 1.292
Primary metal products -0.382 -0.013 0.115 0.239 0.968 0.669 0.683 0.988 1.122
Computers and electronics -0.650 -0.293 -0.580 0.006 0.186 0.022 0.522 0.746 0.560
Furniture and related
products 0.290 -0.203 0.412 0.224 0.509 0.070 1.337 0.817 1.510
Miscellaneous
manufacturing 0.248 0.724 0.068 0.322 0.002 0.803 1.281 2.063 1.071
Rest of subindustries Reference
Location: Ontario 0.182 -0.015 0.449 0.114 0.901 0.000 1.200 0.985 1.566
Alberta 0.308 -0.257 0.312 0.095 0.230 0.072 1.361 0.773 1.366
Rest of provinces in Canada Reference
Other firm characteristics
Employees with university
degrees (%) 0.005 0.012 0.001 0.133 0.000 0.733 1.005 1.013 1.001
Other subsidiaries in
Canada -0.021 -0.038 -0.240 0.864 0.770 0.047 0.979 0.962 0.787
More than one profit center 0.130 0.268 0.323 0.293 0.037 0.005 1.138 1.307 1.381
Long-term strategy of
mass market -0.550 -0.107 -0.033 0.006 0.569 0.826 0.577 0.899 0.968
strategic focus
Organizational and
management practices -0.285 -0.366 -0.266 0.009 0.001 0.008 0.752 0.693 0.766
Gross/margin operating cost 0.447 0.153 0.131 0.001 0.242 0.248 1.564 1.165 1.140
Market/customer share
growth 0.206 0.126 0.005 0.063 0.277 0.965 1.229 1.134 1.005
Sales of new products 0.181 0.515 0.023 0.104 0.000 0.827 1.199 1.674 1.023
Other operational strategies
Open new/expand capacity
of production facilities 0.548 0.200 0.489 0.000 0.133 0.000 1.729 1.222 1.631
Close / contract capacity of
production facilities 0.749 0.634 0.505 0.000 0.000 0.001 2.114 1.885 1.657
Open new/expand logistics
facilities -0.137 -0.197 -0.629 0.347 0.223 0.000 0.872 0.821 0.533
Close or contract capacity
of logistics facilities 0.354 -0.058 -0.223 0.104 0.816 0.360 1.425 0.944 0.801
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TABLE 7.6 (Continued)
Market share of highest
selling goods/services in
the main market (%)
-0.005 0.000 -0.002 0.013 0.878 0.150 0.995 1.000 0.998
Use of innovation and advanced technologies
Process innovation of new
manufacturing methods 0.290 0.101 0.237 0.013 0.405 0.025 1.337 1.106 1.267
Process innovation in
logistics 0.068 -0.133 -0.485 0.633 0.402 0.001 1.070 0.875 0.615
Use of advanced
automated material
handling
-0.132 -0.401 0.131 0.345 0.013 0.307 0.876 0.670 1.140
Use of government support programs
Gov. training programs 0.249 0.012 0.196 0.038 0.926 0.084 1.283 1.012 1.216
Gov. tax credit programs 0.282 0.014 0.027 0.021 0.908 0.801 1.325 1.014 1.027
International involvement
International suppliers 0.260 0.464 0.290 0.200 0.040 0.069 1.297 1.590 1.337
International activities 0.399 0.642 0.102 0.001 0.000 0.351 1.490 1.901 1.107
Direct exports 0.396 0.037 0.171 0.002 0.778 0.134 1.486 1.037 1.186
GDP growth on NAICS-
3 level 0.016 0.004 0.008 0.005 0.563 0.134 1.016 1.004 1.008
For international outsourcing decisions (TABLE 7.7), larger firms (with number of
employees >250) are 40% more likely to outsource both freight decisions internationally than not
to outsource any. The production scale is also found to have a positive effect on international
outsourcing; an increase of 1 unit in the log value of the number of goods/services of a firm
increases the likelihood of outsourcing goods production by 20%, outsourcing logistics by
approximately 14% and outsourcing both activities by 10%. Moreover, firms that focus on
maintaining or expanding their sales of the existing products/services are less likely to outsource
goods production, and are more likely to outsource logistics, as firms may outsource logistics to
focus on maintaining a specific level of production quality. Firms that use government training
programs are less likely to outsource any of their freight operations internationally. This is
intuitive because such programs allow firms to enhance their local capabilities through training,
and hence firms use this knowledge to improve their performance locally. On the other hand,
firms that use government grants are more likely to outsource freight internationally. Such
programs, like CanExport (Global Affairs Canada, 2016) and Export Market Access (Ontario
Chamber of Commerce, 2015), aim to help small and medium sized Canadian companies to
increase export opportunities by providing direct finance. This explains the positive sign of the
187
parameter coefficients in the dummy variable of ‘Use of government grants’ in TABLE 7.7. This
finding points to the effectiveness of such programs to help Canadian firms penetrate
international markets. Alternatively, firms that target Canadian markets are approximately
34%~50% less likely to outsource any of their freight activities internationally, unlike firms that
have international subsidiaries, which are approximately 3~3.5 times more likely to outsource
their freight operations internationally than not to outsource any.
TABLE 7.7 A MNL Model of International Outsourcing*
Coef. P >|Z| Odds ratio
Variables Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3 Alt.1 Alt.2 Alt.3
_cons -0.945 -2.004 0.203 0.015 0.000 0.524 0.389 0.135 1.225
Size: Small and medium
firms (≤ 250 employee) Reference
Large-sized firm (>250
employees) 0.192 0.050 0.343 0.609 0.923 0.276 1.212 1.052 1.409
Other firm characteristics
# of products & services (log) 0.178 0.131 0.096 0.001 0.072 0.046 1.194 1.139 1.101
Other subsidiaries in Canada -0.342 -1.113 0.222 0.343 0.038 0.449 0.711 0.329 1.249
Long-term strategy and strategic focus
Age of long-term strategy 0.007 0.027 0.025 0.723 0.201 0.089 1.007 1.027 1.025
Maintaining/expanding sales
of existing goods/services -0.308 0.124 -0.645 0.273 0.747 0.007 0.735 1.132 0.525
Use of advanced automated
material handling -0.531 0.296 0.613 0.193 0.517 0.037 0.588 1.345 1.846
Use of government support programs
Gov. training programs -0.185 -0.349 -0.659 0.544 0.399 0.014 0.831 0.705 0.518
Gov. grants 0.572 0.820 0.220 0.051 0.033 0.391 1.771 2.270 1.246
International involvement
International Subsidiaries 1.122 1.260 1.041 0.002 0.008 0.001 3.071 3.527 2.832
Canada being the main
market -0.699 -0.677 -1.072 0.018 0.083 0.000 0.497 0.508 0.342
7.5. Model Validation
A hold-out sample of 20% of the full sample (for both model structures) was extracted for
model validation. Cross validation techniques are widely used in discrete choice model
validation to evaluate the predictive performance of model estimates by comparing predicted
choice probabilities against observed choice probabilities (Habib, 2013; Robin et al., 2009;
* All model variables are dummy variables unless otherwise stated
188
Roorda et al., 2008). Model performance is evaluated at the aggregate level by comparing the
predicted shares against the observed shares of each choice in the hold-out sample (TABLE 7.8
and TABLE 7.9). The validation results show an accepted overall predictive performance of the
presented models.
In the binary logit model of outsourcing of logistics decisions, the simplified model over
predicts outsourcing by 3.9%, and the detailed and international outsourcing models slightly
under predict outsourcing by 0.9%, and 0.7% respectively. The binary models for goods
production outsourcing over predict by 0.4% and 1% in the simplified and detailed models
respectively, and under predict by 0.2% in the international model.
For the simple MNL model, the cross validation results indicate that the model slightly
under predicts the choice probabilities of the base alternative (no outsourcing) and Alt.3
(outsourcing both goods production and logistics) by 0.3% and 0.7%, over predicts by 1.1% in
Alt.2 (outsource logistics only), and perfectly predicts Alt.1 (outsource goods production only).
For the detailed MNL model, the model under predicts by 0.6% and 0.7% for Alt.1 and Alt.2,
and over predicts by 0.5% and 0.9% for the base alternative and Alt.3.
The McFadden pseudo-R2 (rho-square) and adjusted-R2 values for some of the presented
models may not seem to be high for transportation behavioural models. We believe that this is
because the available data do not include details of many of the main drivers to outsourcing
decisions (e.g. cost), but rather include firm characteristics and aggregate descriptions of the
economic environment.
By looking only at the model goodness-of-fit (psudo-R2 values) for both the binary logit and
MNL models, one can infer that MNL may not offer a better model fit compared to the binary
structures for all models. However, the MNL offers a potential realistic representation of the
simultaneous decisions of outsourcing. Statistically, it may seem that there is no added value of
the MNL relative to the binary logit structures in terms of the goodness-of-fit. If other data sets
that include more details relevant to outsourcing decisions (e.g. cost) are used, further solid
conclusions can be drawn about MNL versus binary logit structures.
189
TABLE 7.8 Binary logit model validation
Logistics and distribution outsourcing
McFadden
pseudo R2
(rho-square)
Adjusted
R2
Observed
total
probabilities
Predicted
total
probabilities
%
Difference
Simple model 0.019 0.017 25.4% 29% 3.90%
Detailed model 0.108 0.104 32.0% 31% -0.90%
International outsourcing
model 0.250 0.240 47.7% 47% -0.70%
Goods production outsourcing
Simple model 0.040 0.037 25.1% 25.5% 0.40%
Detailed model 0.170 0.165 26.7% 27.7% 1.00%
International outsourcing
model 0.318 0.303 58.2% 58.0% -0.20%
TABLE 7.9 MNL model validation
Model type pseudo R2 Adj. R2 Cross validation Alt.0 Alt.1 Alt.2 Alt.3
Simple
model 0.0285 0.0248
Observed total
probabilities 57.9% 14.3% 11.7% 16.1%
Predicted total
probabilities 57.5% 14.3% 12.7% 15.4%
% Difference -0.3% 0.0% 1.1% -0.7%
Detailed
model 0.0814 0.0724
Observed total
probabilities 32.1% 41.4% 19.3% 7.1%
Predicted total
probabilities 32.6% 40.9% 18.6% 8.1%
% Difference 0.5% -0.6% -0.7% 0.9%
International
model 0.1069 0.088
Observed total
probabilities 57.0% 15.3% 12.3% 15.5%
Predicted total
probabilities 57.1% 14.1% 12.4% 16.4%
% Difference 0.2% -1.2% 0.1% 0.9%
7.6. Concluding Remarks and Future Research
Freight outsourcing decisions are a key component of the organizational structure of firms,
which in turn affect how they engage in the freight transportation system. This research studies
the effects of some firm characteristics including strategic focus, firm location, industry class,
economic growth, use of innovation and advanced technologies, market competition, and
international involvement on freight outsourcing decisions. Two groups of models of freight
outsourcing activities of goods production and logistics are presented; binary logit and MNL
190
models. The models generally indicate that when a firm is focused on their core activities they
are more likely to outsource their freight related activities. This can be explained by the positive
sign of the effect of employment size, economic growth, and the use of advanced technologies in
the production process on outsourcing decisions.
Parsimonious models (simple models) are presented that are to be used in a microsimulation
platform for the purpose of agent-based firm micro-modelling. The detailed and international
outsourcing models provide insights on the effect of some policies (such as government
programs), and the use of innovation and advanced technologies on freight outsourcing. They
also provide insights on how the strategic focus and long-term strategies of firms influence their
outsourcing decisions.
The used data set includes only firms with 20 or more employees and annual revenues of at
least $250,000. Small firms (less than 20 employees) are not studied in this research. A potential
direction for this research is to study outsourcing decisions for the small-sized firm and compare
it to outsourcing decisions with medium and large firms and observe variations once suitable
data are available.
Outsourcing decisions may be influenced by other decisions such as relocation of activities,
and/or expansion/contraction of freight facilities (locally/ internationally). Such decisions may
induce some endogeneity to outsourcing decisions. This research offers basic models to
understand outsourcing decisions, some of which are to be used for microsimulation purposes.
Thus, the effect of other decisions is treated exogenously in the presented models. Such
endogeneities can be investigated using other model structures when appropriate data are
available. Other model structures such as mixed logit or probit models could be investigated.
However, the absence of alternative specific variables may result in poor model fitness of mixed
models. Once such data are available, mixed model structures could be explored.
Further improvement to the models of the decision of outsourcing requires detailed data
specific to outsourcing alternatives such as costs and profits, reliability, and delivery times. The
next step of this research is to quantify other key drivers to freight outsourcing. In order to do
that, surveys that are tailored to address outsourcing behaviour would need to be undertaken.
Such surveys would include questions related to the specifics of outsourcing such as operational
costs, delivery times, reliability, liability, resource availability, supplier locations, and target
customer locations. Surveys can either be revealed preferences (RP), stated preferences (SP), or a
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combination of both RP/SP surveys. Further efforts are also needed to incorporate outsourcing
models within an agent-based firm microsimulation platform.
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CHAPTER 8
Conclusions
This research is motivated by the paradigm shift from aggregate four-stage models of
transportation demand towards disaggregate agent-based models that represent the individual
behaviour of the transportation system agents. Freight systems are fundamental components in
transportation systems. Firms are the key interacting agents in freight systems whose dynamics
of entry, exit, relocation, and growth influence the evolution of urban form, employment, and
economic growth.
Firmography is the research field that studies firm evolution processes of entry, growth,
relocation, and exit, at the individual level. Firmographic models can be used to represent these
firm evolution processes in agent-based transportation modelling systems. Research gaps in
firmographic modelling exist in the Canadian context. This research aims to fill the research gaps
by providing a firmographic microsimulation platform for evaluating policy implications on firm
evolution by forecasting their behaviour. Such policies include trade agreements, land use policy,
government incentives, and taxation policy.
This thesis introduces and implements components of a framework of firm microsimulation,
called the firmographic engine that models evolution of firms and their interactions within
freight systems. The firmographic engine extends the framework presented by (Roorda, et al.
2010) which defines firms as interacting agents in freight systems, whose interrelations result in
goods movements. The engine is designed to be integrated with other freight models such as
FREMIS (Cavalcante and Roorda, 2013). The firmographic engine provides information on job
supply-side dynamics that is needed on the micro-level in IUMs, which makes it suitable for
integration with ILUTE (Miller and Salvini, 1998).
Behavioural models of firm start-up size, firm growth, firm exit, and freight outsourcing are
introduced. The presented models capture the effects of firm attributes, firm strategic focus,
economic changes, industry dynamics, and market competition on firm behaviour. Two types of
models have been presented; simple and detailed models. Simple models use basic firm
attributes, such as age, employment size, provincial location, industry class, and general
economic growth indicators (e.g. GDP growth and provincial unemployment rates). Simple
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models are designed to be implemented within the microsimulation platform. Detailed models
are developed for the purpose of gaining a deeper understanding of behaviour using additional
explanatory variables that may not easily be available in a microsimulation model.
8.1. Summary of Chapters
The first chapter highlights the motivation behind this research and states the gaps this
research is trying to cover. Chapter 2 is a comprehensive literature review of firmography and
firm microsimulation in transport modelling with a focus on freight systems. The chapter reviews
studies of firmography, over the past fifteen years, in research areas such as economics,
industrial organization, regional sciences, transportation and land use. This chapter covers basic
principles of firmography, firm life cycle, and relationship between firmographic events, market
dynamics, and economic growth. The summary includes the scope of each study, population of
study, data, the firmographic events under investigation, and the used methods. Firm
microsimulation studies in freight systems are also presented. In addition, the chapter highlights
potential innovative directions for firmographic modelling using concepts of evolutionary
biology and game theoretic approaches. The chapter summarizes major research gaps in
firmographic modelling and potential future research.
Chapter 3 introduces a framework of firm microsimulation that addresses gaps highlighted in
chapter 2. A conceptual framework is presented with underlying modules of firm generation,
market introduction, performance evaluation, and firm evolution and strategy updates. Canadian
sources of firm micro-level data that are used in this research are discussed. Potential integration
of the introduced firm microsimulation and other transportation and land use microsimulations of
FREMIS and ILUTE is illustrated. The chapter explains the firmographic behaviour this research
aims to estimate and implement.
Chapter 4 presents micro-models of firm start-up size. First, determinants of firm start-up
size in the literature are discussed. Data analysis of firm start-up size is introduced. The models
address firm start-up size both in terms of the number of employees and the tangible asset values.
An ordered logit model structure is adopted to represent start-up size decisions. Estimation and
validation results of ordered logit models are discussed.
In chapter 5, several model structures to represent firm growth events are explored. Firm
growth is presented in two dimensions; employment size and tangible assets. Estimation results
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of firm employment growth models of ordered logit, panel logistic regression with random
effects, multilevel models with random effects, and autoregressive distributed-lag (ARDL) are
discussed. ARDL model estimation and results for tangible asset growth are also presented. The
chapter provides a comparison between each of the models, and recommends ARDL models to
represent growth events in the firmographic engine. The chapter also presents seemingly
unrelated regression (SURE) as a simultaneous modelling approach of the firm growth aspects of
employment and tangible assets.
Chapter 6 presents survival analyses of Canadian firms. Parametric and non-parametric
analyses are introduced. The parametric analysis introduces a discrete-time hazard duration
model to represent firm exit events. The non-parametric analysis estimates survival and hazard
rates. The chapter includes a comparison of our results to other Canadian studies of firm survival
analysis introduced by (Maoh and Kanaroglou, 2007b; Baldwin et al., 2000). A discussion of
other explored modelling approached is also provided.
Chapter 7 proposes a suite of freight outsourcing models of Canadian manufactures. Discrete
choice models of binary logit and multinomial logit are introduced. The models quantify the
effect of firm attributes, firm strategic focus, market competition, economic growth, and use of
innovation, advanced technologies, and government incentives on outsourcing decisions. The
chapter presents models of freight outsourcing by geographic selection (local vs. international).
8.2. Main Conclusions and Findings
The review of the current firm microsimulation studies (refer to chapter 2) led to the
following conclusions:
• Firmography is important to freight microsimulation.
• National level firmographic microsimulation is not well-covered in the Canadian
literature.
• Firmography is influenced by firm characteristics, industry dynamics, economic
growth, market demand and competition, firm location, innovation and technological
advancements, and use of and local policies. Such factors should be considered when
developing firm micromodels.
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• Firmographic events of firm entry, growth, relocation, and exit are intertwined and
have a two-way relationship with economic conditions, and hence, should be studied
together.
Analysis and models of firm start-up size (refer to chapter 4) led to the following
conclusions:
• Firms tend to start small (in size) to reduce losses in the case of firm failure. Around
50% of Canadian firms starts with one employee, while 75% of new firms are two
employees or less, and the average firm start-up size of Canadian firms is 2.5
employee.
• Model results indicate that firm start-up employment size varies across industries and
across provinces. Firms located in Quebec have higher odds of starting relatively
large in size compared to the rest of Canada, while firms located in Alberta have 85%
chances of starting with one employee compared to firms located in Ontario, Quebec,
and British Columbia. Firms in the accommodation and food services industry have
higher odds of starting with larger number of employees compared to other industries.
• Provincial unemployment rate, firm exit rate by industry, and competition are found
to have a negative effect on firm start-up employment size. GDP growth, and average
firm size (in each industry) are found to positively influence firm start-up
employment size.
• Tangible assets models indicate that a larger number of employees increases the
likelihood of starting with larger tangible asset values. Approximately, for every three
employees, there is a 45% probability of tangible assets start-up size to be greater
than $99,999.99.
• Firms located in Saskatchewan have higher odds of starting at higher ranges of
tangible assets compared to rest of Canada, while firms in Ontario have the lowest
odds of starting at higher tangible asset ranges.
• Firms that belong to ‘Agriculture, Forestry, Fishing and Hunting’ industry class have
higher odds of having higher start-up tangible asset values compared to other
industries.
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• Provincial unemployment rates, are found to negatively influence firm start-up
tangible asset values, while GDP growth has a positive effect. Clearly, a thriving
economy encourages businesses to grow in size and decreases the likelihood of
failure.
• Firm entry rates by industry have a negative effect on start-up tangible asset values
• The number of competitors aggregated to the CMA/CA and the sub-industry class
(NAICS 3-digit code levels) has a negative effect on start-up tangible assets.
Generally, it can be concluded that firms are inclined to start smaller to minimize
potential sunk costs as a result of higher competition of similar firms.
The following conclusions are drawn from firm growth models (refer to chapter 5):
• The growth in the number of employees is largely affected by the growth in firm
profits and tangible assets, and the effect of the age alone may not be robust.
• GDP growth is found to have a positive effect on firm employment and tangible asset,
whereas unemployment rates have a negative effect. Both findings highlight that a
growing economy encourages firms to grow in size.
• The results show that firms increase their number of employees with higher entry
rates, while they contract their employment with higher exit rates. Higher entry rates
indicate possible market growth that can take the form of new firm creation (as
indicated by the entry rate) and/or the growth of existing firms. Higher exit rates
suggest that when a market is shrinking in size, existing firms are more likely to
reduce their employment size.
• In employment size models, age has a positive effect on employment size when it is
solely included with basic firm characteristics of provincial location and industry
class. Whereas when other firm related attributes that have greater effects on
employment size (e.g. tangible assets) are included, the effect of age is observed to be
negative. Since other firm attributes of tangible assets and sales values are found to
have higher statistical significance on firm employment size, there is higher
confidence that age has a negative effect on employment size, and older firms have
smaller growth rates compared to younger ones, agreeing with (Evans, 1987b;
Variyam & Kraybill, 1992; Yasuda, 2005).
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• Firm employment growth is variable from one firm to the other, across industries in
different regions.
• The first lagged value of the number of employees and tangible assets are found to
have strong positive effects on the employment size and tangible assets of the next
year, respectively.
• The number of employees is found to have a positive effect on the tangible assets. An
increase in the log value of the number of employees by 1 unit (i.e. an increase of
2.72 units in the number of employees) increases the value of the tangible assets by
$1.1 million.
• Competition is found to have a logical negative effect on firm employment and
tangible assets.
Firm survival analysis (refer to chapter 6) led to the following conclusions:
• Larger and older firms have better survival chances compared to smaller younger
firms.
• Market competition is found to have a negative effect on firm survival; the higher the
number of competitors is, the higher the risk of failure.
• A thriving economy with stable industries reduce the likelihood of firm exits.
• Our conclusions fall in-line with other studies indicating that firm failure patterns are
similar in regions with similar externalities.
The following conclusions are drawn from freight outsourcing models (refer to chapter 7):
• Industries such as wood product, and computer and electronic product manufacturers
are less likely to outsource their logistics compared to beverages and tobacco product
manufacturers.
• Firm employment size is found to have an influence over freight outsourcing. The
larger the firm, the more likely they are to outsource their freight related activities.
• Strategic focus of the firm is found to have an impact on goods production
outsourcing. When firms are focused on producing significantly improved or new
goods, they are less likely to outsource production activities. Firms whose strategy is
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to minimize operating cost, expand market share growth, or increase the sales of their
new products are more likely to outsource some or all of their production activities.
• Growth in the GDP has a positive impact on outsourcing of logistics. When the
economy is growing, manufacturers tend to focus on increasing their market share by
focusing on the production side, and hence are more likely to outsource their
logistics.
• There is endogeneity between freight outsourcing activities of goods production and
logistics and distribution. Firms that outsource part or all of their goods production
are more likely to outsource the associated logistics and vice versa
• Firms that close production facilities may also want to close some of their logistics as
well. On the other hand, firms that expand their production facilities may want to
focus on the production side, and hence would also want to outsource logistics.
Similarly, closing production or logistics and distribution facilities increase the
likelihood of outsourcing goods production.
• The use of advanced communication technologies is found to have a positive
influence on logistics outsourcing.
• When a firm is using process innovation in introducing new or significantly improved
logistics operations, it is logical that they are focused on administrating their own
logistics operations as they are already investing in it, as most likely it is within their
goals to enhance their delivery times (i.e. they are less likely to outsource their
logistics operations).
• Government programs of tax credit are found to have a positive influence over
logistics outsourcing.
• Competition is found to have a positive impact on goods production outsourcing; the
higher the number of the competitors, the more likely firms would outsource their
goods production activities to cope with the competition and maintain their market
share.
• Larger firms (with number of employees >250) are 40% more likely to outsource
both goods production and logistics and distribution internationally than not to
outsource any.
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• Firms that focus on maintaining or expanding their sales of the existing
products/services are less likely to outsource goods production, and are more likely to
outsource logistics, as firms may outsource logistics to focus on maintaining a
specific level of production quality.
• Firms that use government grants are more likely to outsource freight internationally.
Such programs, aim to help small and medium sized Canadian companies to increase
export opportunities by providing direct finance. This finding points to the
effectiveness of such programs to help Canadian firms penetrate international
markets.
8.3. Research Contributions to the Literature
This research contributes to the firmography literature in the Canadian region and is
potentially beneficial to research fields such as regional development, transportation and land use
planning. More specifically, the key contributions of the research can be summarized as follows:
• It offers the first national level firm microsimulation in Canada that utilizes microdata
of firms. The microsimulation tracks evolutionary stages of firms across industries in
Canadian provinces. The presented firm behavioural models in this research
incorporate firm attributes, industry characteristics, market competition, and
economic change to quantify their effect on the studied firmographic events of firm
start-up, growth, exit and freight outsourcing.
• It uses two new firm microdata sources (SIBS and T2-LEAP) that have not been
utilized in firm microsimulation in other Canadian research.
• This is the first attempt recorded in the Canadian literature to provide micromodels of
firm start-up size both in terms of employment and tangible assets.
• Exploring new model structures for firm growth such as ARDL, panel logistic
regression, and multilevel random effects models.
• First attempt in the Canadian literature to explore potential relationship between
growth in the number of employees and the firm’s physical form represented as the
tangible assets.
• Presenting firm survival analysis and failure model on a national level.
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• No previous attempts were found in the literature that introduce discrete choice
models of freight outsourcing decisions for Canadian manufacturers. This research
presents models that quantify the effects of the firm attributes, provincial location, use
of advanced technology and innovation, government incentives, and economic
conditions on freight outsourcing decisions. The models distinguish between
domestic and international freight outsourcing.
• The proposed framework is a strong candidate for integration with other
transportation and land use microsimulation of FREMIS (Cavalcante and Roorda.,
2013) and ILUTE (Miller and Salvini, 1998).
8.4. Future Research
This research is still emerging and has many potential significant contributions for further
enhancement and extension. Current research gaps are highlighted with potential future steps in
this section.
1. Endogeneity of firm behaviour, economic growth, and industry dynamics
Economic changes, industry dynamics, and firm evolution are entwined: the change of one
element influences the other. Economic growth encourages firms to grow, and creates
opportunities for new firms to enter the market, which influences competition, and affect entry
and exit rates in each industry. Increased demand and changes in innovation and advanced
technologies, in a specific industry, increase firm entry and growth events, which in turn affects
the economy. New, growing, and exiting firms collectively influence the economic growth
within a region. Currently, the engine considers the effect of economic growth and industry
dynamics exogenously to the firmographic models while the nature of the interactions suggests
endogeneity of the three aspects. The models presented in this research act as first steps to
highlight the determinants of the studied firm behaviour and offer basic models for freight
microsimulation purposes. For this purpose, other decisions are treated exogenously. Also, the
used datasets are not comprehensive enough to model such endogeneity. In a complete system,
these three elements should be modelled simultaneously, which has not been covered yet in any
research area in the literature.
Models that estimate variables of economic growth and industry dynamics are not covered
yet. A simple approach is to estimate simple linear regression models using historical data of
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such variables. Data about GDP, unemployment rates, firm entry and exit rates are publically
available through statistics Canada’s website (Statistics Canada 2015a, Statistics Canada 2015b,
Statistics Canada 2016).
2. Model validation on the firm and provincial levels
Due to restrictions in the data usage, a micro-level validation (using the hold-out sample) was
not conducted, especially with discrete choice models. It was not possible to evaluate the model
on the individual firm level for such a relatively small data set (the hold out sample) given that
the used explanatory variables are mostly dummy variables (especially for the case of the SIBS
database). A validation on the provincial level was also not a possibility due to the same data
usage limitations. For instance, for some regions, the count of firms classified by some industry
classes have too few companies, such that it would be easy to identify unique firms in some
regions which breaches the confidentiality of the data. As a result, it was not possible to perform
the validation on the region level, and we had to settle for evaluating the model at the Canadian
aggregate level. Also, simplified models are to be used for microsimulation purposes for all of
Canada, and hence the model performance on the aggregate is important. Once other data
sources (or the same used ones but offered with fewer restrictions) available, validation on both
the firm and the provincial levels is needed to give more confidence on the individual and
regional performance of the introduced discrete choice models. The continuous models of firm
growth do not have the same constraint, as the number of employees and the tangible assets were
predicted using a 10% marginal error. It was possible to perform model validation on the firm
level and then sum the number of successful predictions on the national level. Also, a provincial
level validation was not possible due to the firm count restriction for some industries in some
provinces.
3. Other techniques to test model forecasting capabilities
In this research, cross-validation techniques are used to assess the predictive capabilities of
the presented firm behavioural models. This has been done by using a hold-out sample of 20% of
the entire data set (that has not used in the model estimation procedure) to estimate forecasts of
the addressed response variables. Another potential technique for further model validation is to
use an entire future year data (i.e. another panel for the year 2013 of the T2-LEAP or another
cross-section of the year 2014 of SIBS) and compare model predictions to actual observed data.
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This procedure would give more confidence in the predictive capabilities of the behavioural
models in the future.
4. Modelling freight outsourcing of small-sized firms
Freight outsourcing behaviour of small-sized firms (employment <20) is not covered. The
SIBS that was used was collected through a survey that targeted firms with 20 or more
employees that have annual revenues of at least $250,000, for the purpose of studying relatively
stable firms. A potential research direction for studying this is to design a survey for small-sized
firms only, to answer the question of whether outsourcing decisions vary significantly between
medium and large firms or not.
5. Firm entry models
Models of firm entry are not well covered in the literature because the non-entry decision is
hard to observe (Melillo et al., 2013). Currently, the firmographic engine’s framework allows for
modelling the entrepreneurial decision of firm entry. The framework is designed to simulate
potential entrepreneurs and then identify those who will establish a new firm. A potential data
source for modelling the entrepreneurial decision is the Adult Population Survey (APS)
publically offered by (Global Entrepreneurship Monitor, 2015). This step is a logical next step to
addressing the complete firm entry decision in the firmographic engine. Another simpler
approach, although not at the micro-level, is to use linear regression models to estimate firm
entry rate by industry. This can be modelled using historical data of firm entry publically
available at Statistics Canada’s website (Statistics Canada 2016a).
6. Models of location choice and business mobility
Firm location choice and migration (mobility) are essential components for transportation
system microsimulation. Firm location influences many other firm decisions such as supplier
selection, outsourcing, and vehicle ownership. It also causes demographic changes to the
surrounding area by attracting employment with suitable skills or attracting other businesses for
agglomeration purposes. Simulating firm location is important to understand goods movements,
and demographic changes, and to estimate these impacts on transportation demand and traffic
patterns. Location choice models are a fundamental next step in the firmographic engine.
Estimation of such models requires firm location data at a finer geographic scope than the
provincial level. The T2-LEAP offers the firm location at the CMA/CA level, which may be
suitable for modelling firm location choice. Firm migration modelling, on the other hand,
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requires longitudinal information of firm locations. The design of the T2-LEAP does not keep
track of historical information of location; it only retains the most recent location information
and updates the entire firm previous records. The Business Registry (BR) database, that is
available through Statistics Canada, is an alternative data source that offers longitudinal firm
location information.
7. Improvements to firm exit/survival models
Firm exit/survival models are presented in this research do not define the exit type (i.e.
bankruptcy vs. merge/sell to other firms) as exit type was not possible to identify using the T2-
LEAP dataset. Once such data is available, competing risk models are potential approaches to
represent firm exit type. Identifying firm exit type is important to simulate employment
dynamics, especially those resulting from merging. When a firm acquires another firm,
potentially their number of employees will increase due to the merger event. In the current
operation of the firmographic engine, this is simulated as employment growth. This may falsely
refer to new employment opportunities (i.e. new jobs in the market) and may lead to partially
misleading representation of job supply-side dynamics.
8. Improvements to freight outsourcing models
Binary and multinomial logit models have been explored and presented in this thesis. Nested
logit models have also been estimated but are not presented in this document because they were
not statistically significant from MNLs. Mixed logit models are potential candidates to represent
outsourcing behaviour. However, mixed and nested logit require data that include information
specific to each alternative (e.g. cost of outsourcing, distance, and delivery times), which is not
part of the SIBS. A potential next step of this part of the research is to quantify other main
drivers to freight outsourcing throughout tailored surveys. Such surveys can be revealed
preference (RP), stated preference (SP), or a combination of both, that include questions related
to the specifics of outsourcing such as operational costs, delivery times, reliability, liability,
resource availability, supplier locations, and target customer locations.
Outsourcing decisions may be influenced by other decisions such as relocation of activities,
and/or expansion/contraction of freight facilities (locally/ internationally). Such decisions may
induce some endogeneity to outsourcing decisions. This research offers basic models to
understand outsourcing decisions, some of which are to be used for microsimulation purposes.
Thus, the effect of other decisions is treated exogenously in the presented models. Such
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endogeneities can be investigated using other model structures when appropriate data are
available. Other model structures such as mixed logit or probit models could be investigated.
However, the absence of alternative specific variables may result in poor model fitness of mixed
models. Once such data are available, mixed model structures could be explored.
9. Improvements to firm start-up size models
Ordered logit model approaches have been explored to address firm start-up size in terms of
the number of employees and tangible assets. The reported model goodness-of-fit indicate poor
fit. Further approaches, such as Poisson regression, can be investigated to enhance the model
goodness of fit. Also, firm start-up employment and tangible assets are endogenous and can be
modelled in a simultaneous fashion using other model structures such as SURE and simultaneous
equation models.
10. Integration with FREMIS
The firmographic engine represents the interactions in freight system of their basic agents
(firms). The firmographic engine is to be integrated with FREMIS; agent-based microsimulation
of shipper-carrier interactions. This integration would offer a complete representation the
interactions between shippers and carriers through contracts. Upon integration with the already
finished components of the firmographic engine that are related to shipper and carrier
interactions, (e.g. outsourcing and vehicle ownership models), and FREMIS, a simultaneous
validation of the two systems is necessary.
11. Development and operation of the firmographic engine
Although the underlying behavioural components of the firmographic engine are not fully
estimated, the physical implementation of the framework (i.e. programming the microsimulation
platform) is currently ongoing by a research team at the University of Toronto. The behavioural
models presented in this research are being implemented and upon completion the
microsimulation of firmographic events is to be tested and validated.
12. Under-representation of behavioural models of multi-establishment firms
The models presented in this research focus on single establishment firms as they constitute
the largest portion of firm population in Canada. However, large-sized multi-establishment firms
may have a larger impact on the economy. Models of firmographic events of large multi-
establishment firms are still under-researched in the literature. The T2-LEAP data set that is used
in this research contains information on multi-establishment firms, which makes it a strong
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candidate for estimating models to simulate the firmographic events of large multi-establishment
firms.
13. Uni-dimensional construct of firm growth
Models of firm growth represent the change in employment size only, while it might be the
combination of the growth of other assets as well. This research explored a potential statistical
model (SURE) that represents the growth in two aspects simultaneously. However, this approach
may not be suitable for microsimulation as it ignores unobserved heterogeneity between firms. A
future step in this direction to estimate other SURE models that considers correlation of the two
aspects of growth over time and across firms. Examples of such models include multivariate
time-series regression, and a system of simultaneous equations. Also, other aspects of growth can
be investigated such as number of business locations, vehicle fleet, floor space, sales, and
revenues.
14. Representing firm size using a single unit
This research presents firm size in two dimensions that seem to be correlated; employment
size and tangible asset values. However, firm size could better be presented using a single unit
that combines both aspects, such as employment per floor space (Badoe and Miller, 2000; Hunt
et al., 2005; Miller and Lerman, 1981). This information is not available in the T2-LEAP dataset.
If such information becomes available, the physical form of the firm could be better explained as
the employment per floor space. Models of firm start-up size and growth can also be reproduced
to reflect this representation.
15. Refining model spatial resolutions
The current behavioural models of the engine are developed at the provincial level. The
models use dummy variables to indicate the provincial location of the firms. Models can be
refined by introducing land use variables of the firm’s CMA/CA location (e.g. population
density, labour, education, income…etc.) to help the models capture the location effect in a
better resolution. When that is done, such models are reasonably transferable and can be used in
similar geographic regions by using location descriptive variables instead of provincial dummy
variables. Such models need to be re-estimated once appropriate data are available.
16. Integration with ILUTE
The model component representing the job supply side in ILUTE is not yet developed at the
micro-level. Once the previous step is achieved, the models are suitable to provide ILUTE with
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suitable labour demand forecasts at the firm micro-level. The engine provides predictions of firm
start-up size by industry class, which can be used to represent the number of jobs created due to
firm entry. It also tracks the change in the number of employees per firm in each industry on
annual basis. It provides firm exit microsimulation to present the job destruction on the micro
level. A further refining extension to the introduced models is to incorporate the job type to
provide accurate information on the job availability once appropriate data are available.
Currently, the next step is to integrate the firmographic engine with ILUTE and provide
employment dynamics using firm start-up size and growth models.
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Concepts of evolutionary biology and game theories in firmographic models
There is a separate field in economics, called evolutionary economics, that is based on the
assumption that economic organizations are dynamic involving transformation and the economic
behaviour driven by the decisions and interactions of diverse agents in the economic system
(including firms, individuals, and economic institutions) (Hodgson, 1993). This research stream
is based on concepts from evolutionary biology that are used to describe the evolution patterns of
firms. In this research stream, firm population is theorized to follow Darwin’s natural selection
and survival theories in biology. Firms follow the ‘survival of the fittest’ law; firms with good
strategies survive while others with weak strategies do not. These principles have been employed
in evolutionary economics to understand firm evolution and model firm birth (Johnson, Price and
Vugt, 2013). Genetic Algorithms have been introduced as a concept to describe firm birth, and
estimate the strategies of the new population of firms (Bruderer and Singh, 1996). Using the
same concepts, evolutionary patterns of firm failure can be captured by tracking the changes in
the strategies of failure firms in a genetic algorithm framework (Chen and Hsiao, 2008).
Furthermore, behaviour of individual firms affects other firms’ (within the same industry
and/or geographic region) strategies and evolution patterns. For instance, when a firm is
considering the introduction of a new product, it studies the current /potential offerings of similar
products by other firms, as well as selecting optimal price and quality strategies to gain new
market shares. Concurrently, other similar-neighbouring firms may need to update some of their
strategies to maintain their status in the market. This is a two-way interaction that can be
represented using a game theoretic approach, in which, individual agents are ‘players’ seeking to
optimize their decisions based on their opponent strategies. Agents adopt mixed cooperative and
competitive strategies based on available information on payoffs of joint actions with their
opponents. The goal of this game is to reach an equilibrium that optimizes the joint agent
decisions based on past payoffs rather than selecting strategies that maximize individual agents’
payoffs (Friesz and Holguín-Veras 2005).
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References
Abed, O., Janssens, D., Wets, G., & BELLEMANS, T. (2014). An agent based freight activities
and logistic chains optimized network simulator (FALCONS).
Abdur Razzaque, M., & Chen Sheng, C. (1998). Outsourcing of logistics functions: a literature
survey. International Journal of Physical Distribution & Logistics Management, 28(2),
89-107.
Acs, Z. J., Armington, C., & Zhang, T. (2007). The determinants of new firm survival across
regional economies: The role of human capital stock and knowledge spillover. Papers in
Regional Science 86, no. 3, 367-391.
Acs, Z. J., & Audretsch, D. B. (1988). Innovation and firm size in
manufacturing. Technovation, 7(3),197-210.
Acs, Z., & Storey, D. (2004). Introduction: Entrepreneurship and economic development.
Regional Studies, 38:8, 871-877.
Agarwal, R. (1996). Technological activity and survival of firms. Economics Letters 52, no. 1,
101-108.
Agarwal, R., & Gort, M. (2002). Firm and product life cycles and firm survival. The American
Economic Review, 92(2), 184-190.
Agarwal, R., Echambadi, R., Franco, A. M., & Sarkar, M. B. (2004). Knowledge transfer
through inheritance: Spin-out generation, development, and survival. Academy of
Management journal 47, no. 4, 501-522.
Alexandrova, M. (2009). International outsourcing: Incentives, benefits and risks for the
companies in SEE countries.
Allison, P. D. (1982). Discrete-time methods for the analysis of event histories. Sociological
methodology 13, no. 1, 61-9.
Allison, P. D. (1995). Survival analysis using SAS: a practical guide. Cary, NC: SAS Institute.
Almus, M., & Nerlinger, E. A. (1999). Growth of new technology-based firms: which factors
matter?. Small business economics, 13(2), 141-154.
Almus, M. (2000). Testing" Gibrat's Law" for Young Firms–Empirical Results for West
Germany. Small Business Economics, 15(1), 1-12.
209
Amiti, M., & Wei, S. J. (2006). Service offshoring and productivity: evidence from the United
States (No. w11926). National Bureau of Economic Research.
Amoako-Gyampah, K., & Acquaah, M. (2008). Manufacturing strategy, competitive strategy and
firm performance: An empirical study in a developing economy
environment. International Journal of Production Economics, 111(2), 575-592.
Anderson, A. R., Jack, S. L., & Dodd, S. D. (2005). The role of family members in
entrepreneurial networks: Beyond the boundaries of the family firm. Family Business
Review, 18(2), 135-154.
Arauzo-Cardo, J.-M., & Teruel-Carrizosa, M. (2005). An urban approach to firm entry: The
effect of urban size. Growth and Change 36, no. 4, 508-528.
Arauzo-Carod, J.-M., & Segarra-Blasco, A. (2005). The determinants of entry are not
independent of start-up size: some evidence from Spanish manufacturing. Review of
Industrial Organization 27, no. 2, 147-165.
Armstrong, B., & Sloan, M. (1989). Ordinal regression models for epidemiologic data. American
Journal of Epidemiology, 129(1), 191-204.
Audretsch, D. B. (1991). New-firm survival and the technological regime. The Review of
Economics and Statistics, 441-450.
Audretsch, D. B. (1995a). Innovation, growth and survival. International journal of industrial
organization 13, no. 4 , 441-457.
Audretsch, D. B. (1995b). Innovation and industry evolution. MIT Press.
Audretsch, D. B., & Mahmood, T. (1994). The rate of hazard confronting new firms and plants
in US manufacturing. Review of Industrial organization 9, no. 1, 41-56.
Audretsch, D. B., & Mahmood, T. (1995). New firm survival: new results using a hazard
function. The Review of Economics and Statistics , 97-103.
Audretsch, D. B., Houweling, P., & Thurik, A. R. (2000). Firm survival in the Netherlands.
Review of industrial organization 16, no. 1, 1-11.
Audretsch, D. B., Santarelli, E., & Vivarelli, M. (1999). Start-up size and industrial dynamics:
some evidence from Italian manufacturing." International Journal of Industrial
Organization 17, no. 7. 965-983.
Avery, R. B. (1977). Error components and seemingly unrelated regressions. Econometrica:
Journal of the Econometric Society, 199-209.
210
Bachmann, C., Kennedy, C., & Roorda, M. (2014b). A Framework for Analyzing The of Global
Trade Patterns on Domestic Freight Operations. Canadian Transportation Research
Forum Conference. Windsor, Canada.
Bachmann, C., Kennedy, C., & Roorda, M. J. (2014a). Applications of random-utility-based
multi-region input–output models of transport and the spatial economy. Transport
Reviews, 34(4), 418-440.
Bachmann, R., & Braun, S. (2011). The impact of international outsourcing on labour market
dynamics in Germany. Scottish Journal of Political Economy, 58(1), 1-28.
Badoe, D. A., & Miller, E. J. (2000). Transportation–land-use interaction: empirical findings in
North America, and their implications for modeling. Transportation Research Part D:
Transport and Environment, 5(4), 235-263.
Baldwin, J. R., Bian, L., Dupuy, R., & Gellatly, G. (2000). Failure rates for new Canadian
firms: New perspectives on entry and exit. Failure Rates for New Canadian Firms: New
Perspectives on Entry and Exit. Statistics Canada.
Baldwin, J. R., & Gellatly, G. (2003). Innovation strategies and performance in small firms.
Edward Elgar Publishing.
Baldwin, J. R., & Gorecki, P. (1998). The dynamics of industrial competition: A North American
perspective. Cambridge University Press.
Baldwin, J. R., & Gu, W. (2006). Competition, firm turnover and productivity growth. Economic
Analysis (EA) Research Paper Series, Available at SSRN 1371593.
Baldwin, J. R., & Gu, W. (2009). Outsourcing and Offshoring in Canada. Available at SSRN
1369254.
Baldwin, J. R., Liu, H., & Wang, W. (2013). Firm Dynamics: Firm Entry and Exit in the
Canadian Provinces, 2000 to 2009. Economic Analysis Division, Statistics Canada.
Balmer, M., Axhausen, K., & Nagel, K. (2006). Agent-based demand-modeling framework for
large-scale microsimulations. Transportation Research Record: Journal of the
Transportation Research Board, (1985), 125-134.
Baltagi, B. (2008). Econometric analysis of panel data. John Wiley & Sons.
Baltagi, B. H., & Wu, P. X. (1999). Unequally spaced panel data regressions with AR (1)
disturbances. Econometric Theory 15, no. 06 , 814-823.
211
Baptista, R., & Leitão, J. (2015). Entrepreneurship, Human Capital, and Regional
Development (pp. 15-28). Heidelberg: Springer.
Barkham, R. J. (1994). Entrepreneurial characteristics and the size of the new firm: a model and
an econometric test. Small Business Economics 6, no. 2, 117-125.
Barkham, R., Gudgin, G., & Hanvey, E. (2002). Determinants of small firm growth: An inter-
regional study in the United Kingdom 1986-90 (Vol. 12). Psychology Press.
Bartelsman, E., Scarpetta, S., & Schivardi, F. (2005). Comparative analysis of firm
demographics and survival: evidence from micro-level sources in OECD countries.
Industrial and Corporate Change 14, no. 3, 365-391.
Basu, N., Pryor, R. J., & Quint, T. (1998). A microsimulation model of the economy.
Computational Economics, 12(3), 223-241.
Baum, J. R., Locke, E. A., & Smith, K. G. (2001). A multidimensional model of venture
growth. Academy of management journal, 44(2), 292-303.
Beesley, M. E., & Hamilton, R. T. (1994). Entry propensity, the supply of entrants and the
spatial distribution of business units. Regional Studies, 28(3), 233-239.
Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to
travel demand (Vol. 9). MIT press.
Ben-Akiva, M. E., Meersman, H., & Voorde, E. Van de (Eds.), 2013. Freight Transport
Modelling. Emerald Group Publishing.
Berglund, E., & Brännäs, K. (2001). Plants' entry and exit in Swedish municipalities. The Annals
of Regional Science 35, no. 3, 431-448.
Bernard, A. B., & Sjoholm, F. (2003). Foreign owners and plant survival (No. w10039).
National Bureau of Economic Research.
Bhargava, A., Franzini, L., & Narendranathan, W. (1982). Serial correlation and the fixed effects
model. The Review of Economic Studies 49, no. 4 , 533-549.
Bhattacharjee, A. (2005). Models of firm dynamics and the hazard rate of exits: Reconciling
theory and evidence using hazard regression models. St. Salvator's College, School of
Economics and Finance.
Bienstock, C. C., & Mentzer, J. T. (1999). An experimental investigation of the outsourcing
decision for motor carrier transportation. Transportation Journal, 39(1), 42-59.
212
Birley, S. (1986). The role of networks in the entrepreneurial process. Journal of business
venturing, 1(1), 107-117.
Birley, S., & Westhead, P. (1994). A taxonomy of business start-up reasons and their impact on
firm growth and size. Journal of Business Venturing, 9(1), 7-31.
Bland, J. M., & Altman, D. G. (2000). The odds ratio. Bmj 320, no. 7247: 1468, 1468.
Bodenmann, B. R., & Axhausen, K. W. (2010). Synthesis report on the state of the art on
firmographics. Institute for Transport Planning and Systems, ETH, Zurich.
Brant, R. (1990). Assessing proportionality in the proportional odds model for ordinal logistic
regression. Biometrics, 1171-1178.
Brenner, T. (2001). Simulating the evolution of localised industrial clusters-an identification of
the basic mechanisms. Journal of Artificial Societies and Social Simulation, 4(3), p.4.
Bruderer, E., & Singh, J. V. (1996). Organizational evolution, learning, and selection: A genetic-
algorithm-based model. Academy of management journal, 39(5), 1322-1349.
Bruneel, J., Clarysse, B., & Wright, M. (2009). Linking entrepreneurial strategy and firm
growth. Ghent University, Faculty of Economics and Business Administration.
Cabral, L. (1995). Sunk costs, firm size and firm growth. The Journal of Industrial Economics,
161-172.
Calvo, J. L. (2006). Testing Gibrat’s law for small, young and innovating firms. Small Business
Economics, 26(2), 117-123.
Cambridge Systematics. (1996). Inc. Quick Response Freight Manual. Final Report. Prepared for
the Federal Highway Administration, September.
Cambridge Systematics. (1998). Collection and Analysis of Commodity Flow Information in the
Portland Metropolitan Area: Compendium of Technical Memoranda and Other Key
Documents. Prepared for Portland Metro and the Port of Portland with ICF Kaiser
Consulting Group and Nelson/Nygaard Consulting Associates.
Cameron, A. C., & Trivedi, P. K. (2009). Microeconometrics using stata (Vol. Vol. 5). College
Station, Texas: Stata press.
Carree, M., Santarelli, E., & Verheul, I. (2008). Firm entry and exit in Italian provinces and the
relationship with unemployment. International Entrepreneurship and Management
Journal 4, no. 2, 171-186.
213
Carter, N. M., Williams, M., & Reynolds, P. D. (1997). Discontinuance among new firms in
retail: The influence of initial resources, strategy, and gender. Journal of business
venturing, 12(2), 125-145.
Cavalcante, R. A., & Roorda., M. J. (2013). Freight Market Interactions Simulation (FREMIS):
An Agent-Based Modeling Framework. Procedia Computer Science, 9, 867-873.
Cefis, E., & Marsili, O. (2006). Survivor: The role of innovation in firms’ survival. Research
Policy 35, no. 5, 626-641.
Chen, L.-H., & Hsiao, H.-D. (2008). Feature selection to diagnose a business crisis by using a
real GA-based support vector machine: An empirical study. Expert Systems with
Applications, 35(3), 1145-1155.
Ciobanu, O., & Wang, W. (2012). Firm dynamics: Firm entry and exit in Canada, 2000 to 2008.
Statistics Canada, (022).
Cleves, M. (2008). An introduction to survival analysis using Stata. Stata Press.
Coad, A., & Broekel, T. (2012). Firm growth and productivity growth: evidence from a panel
VAR. Applied Economics, 44(10), 1251-1269.
Cohen, S. K. (2010). Innovation-driven industry life cycles. Encyclopedia of technology and
innovation management, VK Narayanan, Gina Colarelli O'Connor Eds. Wiltshire: John
Wiley&Sons, 69-76.
Colombo, M. G., & Grilli, L. (2005). Founders’ human capital and the growth of new
technology-based firms: A competence-based view. Research policy 34, no. 6, 795-816.
Colombo, M. G., Delmastro, M., & Grilli, L. (2004). Entrepreneurs' human capital and the start-
up size of new technology-based firms. International journal of industrial organization
22, no. 8, 1183-1211.
Cox, D. R. (1972). "Regression models and life tables. Journal of the Royal Statistical Society
34, 187-220.
Cyert, R. M., & March, J. G. (1963). A behavioral theory of the firm. Englewood Cliffs, NJ, 2.
Das, S. (1995). Size, age and firm growth in an infant industry: The computer hardware industry
in India. International Journal of Industrial Organization, 13(1), 111-126.
Davenport, T. H. (2013). Process innovation: reengineering work through information
technology. Harvard Business Press.
214
de Bok, M., & Bliemer, M. (2006). Infrastructure and firm dynamics: Calibration of
microsimulation model for firms in the Netherlands. Transportation Research Record:
Journal of the Transportation Research Board 1977, 132-144.
de Bok, M. A. (2007). Infrastructure and firm dynamics: a micro simulation approach. TU Delft,
Delft University of Technology.
de Jong, G., & Ben-Akiva, M. (2007). A micro-simulation model of shipment size and transport
chain choice. Transportation Research Part B: Methodological, 41(9), 950-965.
de Jorge Moreno, J., Castillo, L. L., & de Zuani Masere, E. (2010). Firm size and entrepreneurial
characteristics: evidence from the SME sector in Argentina. Journal of Business
Economics and Management, 11(2), 259-282.
Deepen, J. M. (2007). Logistics outsourcing relationships: measurement, antecedents, and
effects of logistics outsourcing performance. Springer Science & Business Media.
Delmar, F., Davidsson, P., & Gartner, W. B. (2003). Arriving at the high-growth firm. Journal of
business venturing, 18(2), 189-216.
Dietrich, G. B., & Gibson, D. V. (1990). New business ventures: the spin-off process. In D. G. F.
Williams, Tehcnology Transfer: a communication prespective (pp. 153-171). London:
SAGE Publications.
Dinlersoz, E. M., & MacDonald, G. (2009). The industry life-cycle of the size distribution of
firms. Review of Economic Dynamics, 12(4), 648-667.
Dunne, P., & Hughes, a. A. (1994). Age, size, growth and survival: UK companies in the 1980s.
The Journal of Industrial Economics, 115-140.
Dunne, T., Roberts, M. J., & Samuelson, L. (1988). Patterns of firm entry and exit in US
manufacturing industries. The RAND journal of Economics, 495-515.
Dunne, T., Roberts, M. J., & Samuelson, L. (1989). The growth and failure of US manufacturing
plants. The Quarterly Journal of Economics, 671-698.
Egger, H., & Egger, P. (2006). International Outsourcing and the Productivity of Low-skilled
Labor in the EU. Economic Inquiry, 44(1), 98-108.
Ehlen, M. A., Scholand, A. J., & Stamber, K. L. (2007). The effects of residential real-time
pricing contracts on transco loads, pricing, and profitability: Simulations using the N-
ABLE™ agent-based model. Energy Economics 29, no. 2, 211-227.
215
Eidson, E. D., & Ehlen, M. A. (2005). NISAC Agent-Based Laboratory for Economics (N-
ABLE™): Overview of Agent and Simulation Architectures. Sandia National Laboratories
Elgar, I., Farooq, B., & Miller, E. (2009). Modeling location decisions of office firms:
Introducing anchor points and constructing choice sets in the model system.
Transportation Research Record: Journal of the Transportation Research Board, 2133,
56-63.
Elgar, I., & Miller, E. (2006). Conceptual model of location of small office firms. Transportation
Research Record: Journal of the Transportation Research Board, (1977), 190-196.
Ellison, G., Glaeser, E. L., & Kerr., W. R. (2010). What causes industry agglomeration?
Evidence from coagglomeration patterns. The American Economic Review 100, no. 3 ,
1195-1213.
Ericson, R., & Pakes, A. (1995). Markov-perfect industry dynamics: A framework for empirical
work. The Review of Economic Studies 62, no. 1 , 53-82.
Eriksson, T., & Kuhn, J. M. (2006). Firm spin-offs in Denmark 1981–2000—patterns of entry
and exit. International Journal of Industrial Organization 24, no. 5, 1021-1040.
Evans, D. S. (1987a). Tests of alternative theories of firm growth. journal of political economy,
95(4), 657-674.
Evans, D. S. (1987b). The relationship between firm growth, size, and age: Estimates for 100
manufacturing industries. The journal of industrial economics, 567-581.
Fallick, B., Fleischman, C. A., & Rebitzer, J. B. (2006). Job-hopping in Silicon Valley: some
evidence concerning the microfoundations of a high-technology cluster. The Review of
Economics and Statistics 88, no. 3 , 472-481.
Fantasia, J. J. (1993). Are you a candidate for third party logistics?. Transportation &
Distribution, 34(1), 30.
Fischer, M., Ang-Olson, J., & La, A. (2000). External urban truck trips based on commodity
flows: a model. Transportation Research Record: Journal of the Transportation
Research Board, (1707), 73-80.
Fonseca, R., Lopez-Garcia, P., & Pissarides, C. A. (2001). Entrepreneurship, start-up costs and
employment. European Economic Review 45, no. 4 , 692-705.
Foster, R. N. (1988). Innovation: The attacker's advantage. Summit Books.
216
Foster, T. A., & Muller, E. J. (1990). Third parties: your passport to profits. Distribution, 89(10),
30-32.
Fotopoulos, G., & Spence, N. (2001). Regional variations of firm births, deaths and growth
patterns in the UK, 1980–1991. Growth and Change 32, no. 2, 151-173.
Found, A. (2012). Economies of Scale in Fire and Police Services in Ontario (No. 12).
University of Toronto, Institute on Municipal Finance and Governance.
Fridstrom, L., & Madslien, A. (1994). Own account or hire freight: a stated preference analysis.
In IABTR Conference, Valle Nevado.
Friedman, D. (1998). Evolutionary economics goes mainstream: a review of the theory of
learning in games. Journal of Evolutionary Economics, 8(4), 423-432.
Fries, N., & Patterson, Z. (2008, October). Carrier or Mode? The Dilemma of Shippers’ Choice
in Freight Modelling. In 8th Swiss Transport Research Conference, at Ascona,
Switzerland.
Fritsch, M., Brixy, U., & Falck, O. (2006). The effect of industry, region, and time on new
business survival–a multi-dimensional analysis. Review of industrial organization 28, no.
3, 285-306.
Gallagher, C. C., Thomason, J. C., & Daly, M. J. (1991). The growth of UK companies and their
contribution to job generation, 1985–1987. Small Business Economics, 3(4), 269-286.
Gardrat, M., Serouge, M., Toilier, F., & Gonzalez-Feliu, J. (2014). Simulating the Structure and
Localization of Activities for Decision Making and Freight Modelling: The SIMETAB
Model. Procedia-Social and Behavioral Sciences,125, 147-158.
Geroski, P. A. (1991). Market dynamics and entry. Blackwell.
Geroski, P. A. (1995). Innovation and competitive advantage. Wokring paper No. 195.
Organisation for Economic Co-operation and Development.
Geroski, P., & Gugler, K. (2004). Corporate growth convergence in Europe. Oxford Economic
Papers, 56(4), 597-620.
Gibrat, R. (1931). Les inégalités économiques. Recueil Sirey.
Glaeser, E. L.(Editor). (2010). Agglomeration economics. University of Chicago Press.
Global Affairs Canada. (2016).online: http://international.gc.ca/canexport/index.aspx?lang=eng.
Global Entrepreneurship Monitor. (2016). Global Entrepreneurship Monitor (GEM). Retrieved
2016, from http://www.gemconsortium.org/data/sets
217
Gonzalez, R., Gasco, J., & Llopis, J. (2009). Information systems outsourcing reasons and risks:
an empirical study. International Journal of Human and Social Sciences, 4(3), 181-192.
Görg, H., Hanley, A., & Strobl, E. (2008). Productivity effects of international outsourcing:
evidence from plant‐level data. Canadian Journal of Economics/Revue canadienne
d'économique, 41(2), 670-688.
Görg, H., Strobl, E., & Ruane, F. (2000). Determinants of firm start-up size: an application of
quantile regression for Ireland. Small Business Economics 14, no. 3 , 211-222.
Görg, H., & Strobl, E. (2002). Multinational companies and indigenous development: An
empirical analysis. European Economic Review, 46(7), 1305-1322.
Gort, M., & Klepper, S. (1982). Time paths in the diffusion of product innovations. The
economic journal, 92(367), 630-653.
Greene,W. H. (2003). Econometric analysis (Vol. 5th ed). Upper Saddle River, NJ: Prentice
Hall.
Greene,W. H. (2008). Econometric analysis.New Jersey,USA:Prentice Hall,2008.
Greene,W. H. (2012). Econometric analysis. 7th ed. Upper Saddle River, NJ: Prentice Hall.
Greenwood, C., & Farewell, V. (1988). A comparison of regression models for ordinal data in an
analysis of transplanted kidney function. Canadian Journal of Statistics, 16(4), 325-335.
Growiec, J., Pammolli, F., Riccaboni, M., & Stanley, H. E. (2008). On the size distribution of
business firms. Economics Letters, 98(2), 207-212.
Guo, G. (1993). Event-history analysis for left-truncated data. Sociological methodology, 217-
243.
Habib, K. M. (2013). A joint discrete-continuous model considering budget constraint for the
continuous part: application in joint mode and departure time choice modelling.
Transportmetrica A: Transport Science 9, no. 2, 149-177.
Haddock, C. K., Rindskopf, D., & Shadish, W. R. (1998). Using odds ratios as effect sizes for
meta-analysis of dichotomous data: a primer on methods and issues. Psychological
Methods, 3(3), 339.
Hain, M. (2010). Labour Market Model of the Greater Toronto and Hamilton Area for
Integration within the Integrated Land Use, Transportation, Environment Modelling
System. MSc. Thesis, University of Toronto.
218
Haltiwanger, J., Jarmin, R. S., & Miranda, J. (2013). Who creates jobs? Small versus large
versus young. Review of Economics and Statistics, 95(2), 347-361.
Hannan, M. T., & Carroll, G. (1992). Dynamics of organizational populations: Density,
legitimation, and competition. Oxford University Press on Demand.
Hannan, M. T., Carroll, G. R., Dobrev, S. D., & Han, J. (1998). Organizational mortality in
European and American automobile industries Part I: Revisiting the effects of age and
size. European Sociological Review 14, no. 3, 279-302.
Hannan, M. T., & Freeman, J. (1989). Organization ecology.
Harhoff, D., Stahl, K., & Woywode, M. (1998). Legal form, growth and exit of West German
firms—empirical results for manufacturing, construction, trade and service industries.
The Journal of industrial economics , 46(4), 453-488.
Harmon, A. (2013). A Microsimulated Industrial and Occupation-Based Labour Market Model
for Use in the Integrated Land Use, Transportation, Environment (ILUTE) Modelling
System. University of Toronto.
Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and
computer sciences 44, no. 1, 1-12.
He, C., & Yang, R. (2015). Determinants of firm failure: empirical evidence from China. Growth
Change, 47, 72–92.
Hendry, D. F., Pagan, A. R., & Sargan, J. D. (1984). Dynamic specification. Handbook of
econometrics, 2, 1023-1100.
Hermalin, B. E. (1992). The effects of competition on executive behavior. The RAND Journal of
Economics, 350-365.
Hodgson, G. M. (1993). Economics and evolution: bringing life back into economics. University
of Michigan Press.
Hoogstra, G. J., & Dijk, J. v. (2004). Explaining firm employment growth: does location matter?
Small business economics, 22(3-4), 179-192.
Haugh, L. D., & Box, G. E. (1977). Identification of dynamic regression (distributed lag) models
connecting two time series. Journal of the American Statistical Association, 72(357),
121-130.
Haupt, R., Kloyer, M., & Lange, M. (2007). Patent indicators for the technology life cycle
development. Research Policy, 36(3), 387-398.
219
Hu, W., Cox, L. J., Wright, J., & Harris, T. R. (2008). Understanding Firms' Relocation and
Expansion Decisions Using Self-Reported Factor Importance Rating. The Review of
Regional Studies, 38(1), 67.
Hunt, J. D., Kriger, D. S., & Miller, E. J. (2005). Current operational urban land‐use–transport
modelling frameworks: A review. Transport Reviews 25, no. 3 , 329-376.
Hunt, J. D., & Stefan, K. J. (2007). Tour-based microsimulation of urban commercial
movements. Transportation Research Part B: Methodological, 41(9), 981-1013.
Huynh, K. P., & Petrunia, R. J. (2010). Age effects, leverage and firm growth. Journal of
Economic Dynamics and Control, 34(5), 1003-1013.
Jang, S. S., & Park, K. (2011). Inter-relationship between firm growth and profitability.
International Journal of Hospitality Management 30, no. 4, 027-1035.
Johnson, D. D., Price, M. E., & Vugt, M. V. (2013). Darwin's invisible hand: Market
competition, evolution and the firm. ournal of Economic Behavior & Organization, 90,
S128-S140.
Jovanovic, B. (1982). Selection and the Evolution of Industry. Econometrica: Journal of the
Econometric Society, 649-670.
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations.
Journal of the American statistical association, 53(282), 457-481.
Kehoe, T., Hur, S., Ruhl, K., & Asturias, J. (2016). Firm Entry and Exit and Aggregate Growth.
Meeting Papers, no. 573. Society for Economic Dynamics.
Khan, A. S., Abraham, J. E., & Hunt, J. D. (2002). Agent-based micro-simulation of business
establishments. Congress of the European Regional Science Association (ERSA).
Dortmund.
Khan, A. S. (2002). A system of microsimulating business establishments: analysis, design and
results. PhD Dessertation, University of Calgary.
Kirchhoff, B. A., & Phillips, B. D. (1988). The effect of firm formation and growth on job
creation in the United States. Journal of Business Venturing, 3(4), 261-272.
Klapper, L., & Richmond, C. (2011). Patterns of business creation, survival and growth:
Evidence from Africa. Labour Economics 18, S32-S44.
Klepper, S. (1996). Entry, exit, growth, and innovation over the product life cycle. The American
economic review, 562-583.
220
Kmenta, J., & Gilbert, R. F. (1970). Estimation of seemingly unrelated regressions with
autoregressive disturbances. Journal of the American Statistical Association 65, no. 329,
186-197.
Kolvereid, L., & Isaksen, E. (2006). New business start-up and subsequent entry into self-
employment. Journal of Business Venturing, 21(6), 866-885.
Koster, S. (2006). Whose child. How existing firms foster new firm formation: individual start-
ups, spin-outs and spin-offs. Ipskamp, Enschede.
Kumar, S., & Kockelman, K. (2008). Tracking size, location, and interactions of businesses:
microsimulation of firm behavior in Austin, Texas. Transportation Research Record:
Journal of the Transportation Research Board 2077, 113-121.
Kurz, C. J. (2006). Outstanding outsourcers: A firm-and plant-level analysis of production
sharing.
Lankford, W. M., & Parsa, F. (1999). Outsourcing: a primer. Management Decision, 37(4), 310-
316.
Leung, D., Rispoli, L., & Chan, R. (2012). Small, medium-sized, and large businesses in the
Canadian economy: Measuring their contribution to gross domestic product from 2001
to 2008. Statistics Canada, Economic Analysis Division.
Levitt, T. (1965). Exploit the product life cycle (Vol. 43). Graduate School of Business
Administration, Harvard University.
Lewis, V. L., & Churchill, N. C. (1983). The five stages of small business growth. Harvard
business review, 61(3), 30-50.
Lindholm, A. (1994). The economics of technology-related ownership changes. Gothenburg,
Sweden: Unpublished doctoral dissertation, Chalmers University of Technology,
Department of Industrial Management and Economics.
Lommerud, K. E., Meland, F., & Sørgard, L. (2003). Unionised oligopoly, trade liberalisation
and location choice. The Economic Journal, 113(490), 782-800.
Lommerud, K. E., Meland, F., & Straume, O. R. (2009). Can deunionization lead to international
outsourcing?. Journal of International Economics, 77(1), 109-119.
Lopez-Garcia, P., & Puente, S. (2006). Business demography in Spain: determinants of firm
survival. Banco de Espana Research Paper No. WP-0608.
221
Lopez-Garcia, P., Puente, S., & Gómez, Á. L. (2007). Firm productivity dynamics in Spain.
Banco de España.
Lotti, F., Santarelli, E., & Vivarelli, M. (2003). Does Gibrat's Law hold among young, small
firms? Journal of Evolutionary Economics, 13(3), 213-235.
Lumpkin, G. T., & Dess, G. G. (2001). Linking two dimensions of entrepreneurial orientation to
firm performance: The moderating role of environment and industry life cycle. Journal of
business venturing, 16(5), 429-451.
Macdonald, R. (2014). Business Entry and Exit Rates in Canada: A 30-year Perspective.
Statistics Canada.
Mankiw, G., & Taylor, M. (2006). Microeconomics. Thomson Learning, chapter 14.
Mansfield, E. (1962). Entry, Gibrat's law, innovation, and the growth of firms. The American
economic review, 52(5), 1023-1051.
Manzato, G., Arentze, T., Timmermans, H., & Ettema, D. (2011). Exploration of Location
Influences on Firm Survival Rates with Parametric Duration Models. Transportation
Research Record: Journal of the Transportation Research Board 2245, 124-1.
Maoh, H. (2005). Modeling Firm Demography in Urban Areas with an Application to Hamilton,
Ontario: Towards an Agent-Based Microsimulation Model. PhD thesis (School of
Geography and Earth Sciences, McMaster University, Hamilton, ON).
Maoh, H. F., & Kanaroglou, P. S. (2005). Agent-based firmographic models: a simulation
framework for the city of Hamilton. Proceedings of PROCESSUS Second International
Colloquium on the Behavioural Foundations of Integrated Land-use and Transportation
Models: Frameworks, Models and Applications. Toronto.
Maoh, H., & Kanaroglou, P. (2007a). Business establishment mobility behavior in urban areas: a
microanalytical model for the City of Hamilton in Ontario, Canada. Journal of
Geographical Systems, 9(3), 229-252.
Maoh, H., & Kanaroglou, P. S. (2007b). Modeling the failure of small and medium size business
establishments in urban areas: an application to Hamilton, Ontario. Hamilton, Ontario:
Centre for Spatial Analysis, McMaster University.
222
Maoh, H. & Kanaroglou, P. (2009) Intrametropolitan Location of Business Establishments:
Microanalytical Model for Hamilton, Ontario, Canada. Transportation Research Record:
Journal of Transportation Research Board. 2133: 33-45.
Maoh, H. & Kanaroglou, P. (2013). Modelling Firm Failure: Towards Building a Firmographic
Microsimulation Model, Employment Location. Pagliara, F., de Bok, M., Simmonds, D.,
Wilson, A.Employment Location in Cities and Regions. 2013: 243-261.
Marino, K. E., & Noble, A. F. (1997). Growth and early returns in technology-based
manufacturing ventures. The Journal of High Technology Management Research, 8(2),
225-242.
Mata, J. (1996). Markets, entrepreneurs and the size of new firms. Economics Letters 52, no. 1,
89-94.
Mata, J., & Machado, J. A. (1996). Firm start-up size: A conditional quantile approach.
European Economic Review 40, no. 6, 1305-1323.
Mata, J., & Portugal, P. (1994). Life duration of new firms. The Journal of Industrial Economics,
227-245.
Mata, J., Portugal, P., & Guimaraes, P. (1995). The survival of new plants: Start-up conditions
and post-entry evolution. International Journal of Industrial Organization 13, no. 4, 459-
481.
McCullagh, P. (1980). Regression models for ordinal data. Journal of the royal statistical
society. Series B (Methodological), 109-142.
McFadden, D. (1978). Quantitative Methods for Analysing Travel Behaviour of Individuals:
Some Recent Developments. In D. A. Hensher, & P. R. Stopher, Behavioural Travel
Modelling (pp. 306-308). London: Croom Helm.
Melillo, F., Folta, T. B., & Delmar, F. (2013). What determines the initial size of new ventures.
DRUID Celebration Conference, 35th, 2.
Miller, E. J., & Lerman, S. R. (1981). Disaggregate modelling and decisions of retail firms: a
case study of clothing retailers. Environment and Planning, 13(6), 729-746.
Miller, E. J., & Salvini, P. A. (1998). The Integrated Land Use, Transportation, Environment
(ILUTE) Modelling System: A Framework. 77th Annual Meeting of the Transportation
Research Board. Washington, DC.
223
Miller, E. J., & Salvini, P. A. (2001). The Integrated Land Use, Transportation, Environment
(ILUTE) microsimulation modelling system: description and current status. In D. (.
Hensher, Travel Behaviour Research: The Leading Edge (pp. 711–724). Pergamon,
Amsterdam.
Miller, E. J., Hunt, J. D., Abraham, J. E., & Salvini, P. A. (2004). Microsimulating urban
systems. Computers, environment and urban systems 28, no. 1, 9-44.
Miller, E., Farooq, B., Chingcuanco, F. & Wang, D. (2010). Microsimulating urban spatial
dynamics: historical validation tests using the ILUTE model system. Workshop on Urban
Dynamics. Marbella, Chile.
Ministry of Finance.(2016).Government of Ontario.
http://www.fin.gov.on.ca/en/credit/oitc/index.html.
Moeckel, R. (2005). Microsimulation of firm location decisions. 9th international conference on
computers in Urban Planning and Urban Management, 7, pp. 2005-29. London.
Montemayor, H. M. V. (2014). Outsourcing Transportation Services: Evidence from Mexican
Maquiladora Industry. International Journal of Operations and Logistics
Management, 3(1), 48-57.
Mood, C. (2010). Logistic regression: Why we cannot do what we think we can do, and what we
can do about it. European sociological review, 26(1), 67-82.
Mostafa, T. S., & Roorda, M. J. (2013). A Framework and Analysis of Firm Evolution Processes.
METRANS 2013 International Urban Freight Conference. Long Beach, CA.
Mostafa, T. S., & Roorda, M. J. (2015). A Conceptual Framework for Modelling Firmography.
Canadian Transportation Research Forum 50th Annual Conference. Montreal, Quebec.
Mostafa, T. S., & Roorda, M. J. (2016). A Review of Firmography in Freight Microsimulation
Modelling. Transport Reviews.
Mostafa, T. S., & Roorda, M. J. (2017). Discrete Choice Modeling of Freight Outsourcing
Decisions of Canadian Manufacturers . Transportation Research Record.
Mostafa, T. S., & Roorda, M. J. (2017). Discrete Choice Modeling of Freight Outsourcing
Decisions of Canadian Manufacturers . 96th Annual Meeting og the Transportation
Research Board. Washington, DC.
224
Mostafa, T., & Roorda, M. (2013). A Structural Equation Model of Commercial Vehicle
Ownership. Paper presented at the hEART Conference - 2nd Symposium of the European
Association for Research in Transportation. Stockholm.
Mueller, D. C. (1972). A life cycle theory of the firm. The Journal of Industrial Economics, 199-
219.
Mukherjee, S., Mukherjee, M., & Ghose, A. (2004). Microeconomics. Pages: 119-148.
Myers, M. H., Hankey, B. F., & Mantel, N. (1973). A logistic-exponential model for use with
response-time data involving regressor variables. Biometrics , 257-269.
Nandkeolyar, U., Rao, S. S., & Rana, K. (1993). Facility life cycles. Omega, 21(2), 245-254.
Nunes, A., & Sarmento, E. (2010). Business survival in portuguese regions. GEMF Working
Papers.
Nyström, K. (2007). An industry disaggregated analysis of the determinants of regional entry and
exit. The Annals of Regional Science 41, no. 4 , 877-896.
OECD. (2008). OECD Economic Surveys: Canada 2008. OECD Publishing.
Ontario Chamber of Commerce.(2015).OCC Funding for Ontario Exporters.Online:
http://www.occ.ca/sme-programs/occ-funding-for-ontario-exporters/.
Otter, H. S., van der Veen, A., & de Vriend, H. J. (2001). ABLOoM: Location behaviour, spatial
patterns, and agent-based modelling. Journal of Artificial Societies and Social
Simulation, 4(4).
Park, K., & Jang, S. S. (2010). Firm growth patterns: examining the associations with firm size
and internationalization. International Journal of Hospitality Management, 29(3), 368-
377.
Pagano, P., & Schivardi, F. (2003). Firm size distribution and growth. The Scandinavian Journal
of Economics, 105(2), 255-274.
Park, K., & Jang, S. S. (2011). Mergers and acquisitions and firm growth: Investigating
restaurant firms. International Journal of Hospitality Management, 30(1), 141-149.
Pasidis, I.-N. (2013, July 16). Why do shops cluster? Spatial competition and collaboration in the
retail sector of f the Netherlands. Amesterdam, Netherlands.
225
Pendyala, R., Shankar, V., & McCullough, R. (2000). Freight travel demand modeling: synthesis
of approaches and development of a framework. Transportation Research Record:
Journal of the Transportation Research Board, (1725), 9-16.
Pérez, S. E., Llopis, A. S., & Llopis, J. A. (2004). The determinants of survival of Spanish
manufacturing firms. Review of Industrial Organization 25, no. 3, 251-273.
Piacentino, D., Espa, G., Filipponi, D., & Giuliani., D. (2016). Firm Demography and Regional
Development: Evidence from Italy. Growth and Change.
Polli, R., & Cook, V. (1969). Validity of the product life cycle. The Journal of Business, 42(4),
385-400.
Pourabdollahi, Z., Karimi, B., Mohammadian, K., & Kawamura, K. (2016). A Hybrid Agent-
based Computational Economics and Optimization Approach for Supplier Selection
Problem 2. In Transportation Research Board 95th Annual Meeting (No. 16-4865).
Pourabdollahi, Z., Mohammadian, A. K., & Kawamura, K. (2012). A behavioral freight
transportation modeling system: an operational and proposed framework. In Proceedings
of the 14th Annual International Conference on Electronic Commerce (pp. 196-203).
ACM.
Praag, C. M. (1997). Determinants of succesful entrepreneurship. The Tinbergen Institute
Research Series no. 112. Tinbergen Institute, Rotterdam
Prais, S. J., & Winsten, C. B. (1954). Trend estimators and serial correlation. Working paper
383, Cowles Commission.
Quinn, J. B., & Hilmer, F. G. (1994). Strategic outsourcing. Sloan management review, 35(4),
43.
Rainey, D. L. (2008). Product innovation: leading change through integrated product
development. Cambridge University Press.
Rao, K., & Young, R. R. (1994). Global supply chains: factors influencing outsourcing of
logistics functions. International Journal of Physical Distribution & Logistics
Management, 24(6), 11-19.
Reid, G. C. (1995). Early life-cycle behaviour of micro-firms in Scotland. Small Business
Economics, 7(2), 89-95.
Reynolds, P. D. (1987). New firms: societal contribution versus survival potential. Journal of
Business Venturing 2, no. 3, 231-246.
226
Reynolds, P. D. (1997). Who starts new firms?–Preliminary explorations of firms-in-gestation.
Small Business Economics 9, no. 5, 449-462.
Reynolds, P., Storey, D. J., & Westhead, P. (1994). Cross-national comparisons of the variation
in new firm formation rates. Regional Studies, 28(4), 443-456.
Reynolds, P. D., & White, S. B. (1997). The entrepreneurial process: Economic growth, men,
women, and minorities. Praeger Pub Text.
Richards, E., Snoddon, C., & Brown, J. (2014). Manufacturing: The Yea 2014 in Review.
Analytical Paper, Analysis in Brief. Statistics Canada. Issue #:2015097,no.97,2014
Richardson, H. L. (1992). Outsourcing: the power worksource. Transportation & Distribution,
July, 22-4.
Richardson, H. L. (1993). Economy spurs growth in outsourcing. Transportation & Distribution,
45-7.
Robb, A. M., & Coleman, S. (2010). Financing strategies of new technology-based firms: a
comparison of women-and men-owned firms. Journal of technology management &
innovation, 5(1), 30-50.
Robin, T., Antonini, G., Bierlaire, M., & Cruz, J. (2009). Specification, estimation and validation
of a pedestrian walking behavior model. Transportation Research Part B:
Methodological 43, no. 1, 36-56.
Roorda, M. J., Cavalcante, R., McCabe, S., & Kwan, H. (2010). A conceptual framework for
agent-based modelling of logistics services. Transportation Research Part E: Logistics
and Transportation Review 46, no. 1, 18-31.
Roorda, M. J., Miller, E. J., & Habib, K. M. (2008). Validation of TASHA: A 24-h activity
scheduling microsimulation model. Transportation Research Part A: Policy and Practice
42, no. 2, 360-375.
Salvini, P., & Miller, E. J. (2005). ILUTE: An operational prototype of a comprehensive
microsimulation model of urban systems. Networks and Spatial Economics 5, no. 2, 217-
234.
Samimi, A., Mohammadian, A., Kawamura, K., & Pourabdollahi, Z. (2014). An activity-based
freight mode choice microsimulation model. Transportation Letters, 6(3), 142-151.
Scherr, F. C., Sugrue, T. F., & Ward, J. B. (1993). Financing the small firm start-up:
Determinants of debt use. The Journal of Entrepreneurial Finance, 3(1), 17.
227
Schröder, P. J., & Sørensen, A. (2012). Firm exit, technological progress and trade. European
Economic Review 56, no. 3, 579-591.
Schrör, H. (2009). Business Demography: employment and survival. eurostat.
Segal, U., & Spivak, A. (1989). Firm size and optimal growth rates. European Economic
Review, 33(1),159-167.
Senses, M. Z. (2004). The Effects of Outsourcing on the Elasticity of Labor Demand. Available
at SSRN 837744.
Seo, S. (2006). A review and comparison of methods for detecting outliers in univariate data
sets. University of Pittsburgh.
Shane, S., Kolvereid, L., & Westhead, P. (1991). An exploratory examination of the reasons
leading to new firm formation across country and gender. Journal of business venturing,
6(6), 431-446.
Shanmugam, K. R., & Bhaduri, S. N. (2002). Size, age and firm growth in the Indian
manufacturing sector. Applied Economics Letters 9, no. 9, 607-613.
Shapiro, D., & Khemani, R. S. (1987). The determinants of entry and exit reconsidered.
International Journal of Industrial Organization 5, no. 1, 15-26.
Sharapov, D., Kattuman, P., & Sena, V. (2011). Technological Environments, R&D Investment,
and Firm Survival. MICRO-DYN Working Paper 34/10.
Sheffi, Y. (1990). Third party logistics: present and future prospects. Journal of Business
Logistics, 11(2), 27.
Shook, C. L., Priem, R. L., & McGee, J. E. (2003). Venture creation and the enterprising
individual: A review and synthesis. Journal of Management, 29(3), 379-399.
Siegfried, J. J., & Evans, L. B. (1994). Empirical studies of entry and exit: a survey of the
evidence. Review of Industrial Organization 9, no. 2, 121-155.
Singh, A., & Whittington, G. (1975). The size and growth of firms. The Review of Economic
Studies, 42(1), 15-26.
Smithson, M., & Merkle, E. C. (2013). Generalized linear models for categorical and continuous
limited dependent variables. CRC Press.
Soni, P. K., Lilien, G. L., & Wilson, D. T. (1993). Industrial innovation and firm performance: A
re-conceptualization and exploratory structural equation analysis. International Journal
of Research in Marketing, 10(4), 365-380.
228
StataCorp. (2013). Stata: Release 13. Statistical Software. College Station, TX: StataCorp LP.
Statistics Canada. (2009). Highlights of the Survey of Innovation and Business Strategy (SIBS).
Retrieved from https://www.ic.gc.ca/eic/site/eas-aes.nsf/vwapj/SIBS_summary-
EISE_resume-eng.pdf/$file/SIBS_summary-EISE_resume-eng.pdf
Statistics Canada. (2004). The economy: Year-end review. Retrieved from anadian Economic
Observer, Vol. 17, no. 4: http://www.statcan.gc.ca/daily-quotidien/040414/dq040414b-
eng.htm
Statistics Canada. (2012a). The T2-LEAP User Guide, 1984-2010. Statiscs Canada.
Statistics Canada. (2012b). Survey of Innovation and Business Strategy. Retrieved from
http://www23.statcan.gc.ca/imdb-bmdi/instrument/5171_Q1_V2-eng.pdf
Statistics Canada. (2014). CANSIM: Table 552-001 – Canadian business patterns, location
counts with employees, by employment size and North American Classification System
(NAICS), Canada and provinces, December 2014. Retrieved 2017 from:
http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=5520001&&pattern
=&stByVal=1&p1=1&p2=31&tabMode=dataTable&csid=
Statistics Canada. (2015a). CANSIM: Table 282-0087 - Labour force survey estimates (LFS), by
sex and age group. Retrieved 2015, from
http://www5.statcan.gc.ca/cansim/a26?lang=eng&retrLang=eng&id=2820087&&pattern
=&stByVal=1&p1=1&p2=37&tabMode=dataTable&csid=
Statistics Canada. (2015b). CANSIM: Table 379-0030 - Gross domestic product (GDP) at basic
prices, by North American Industry Classification System (NAICS), provinces and
territories. Retrieved 2015, from
http://www5.statcan.gc.ca/cansim/a26?lang=eng&id=3790030
Statistics Canada. (2016a). CANSIM: Table 527-0007 - Business dynamics measures, by North
American Industry Classification System (NAICS), provinces and the territories.
Retrieved September 2016, from http://www5.statcan.gc.ca/cansim/a47
Statistics Canada. (2016b). The Canadian Centre for Data Development an Economic Research
(CDER). Retrieved 12 10, 2016, from http://www.statcan.gc.ca/eng/cder/index
Statistics Canada. (2016c). Canadian Economy (NAICS 11-91): Establishments. Retrieved
January 2017 from:
https://www.ic.gc.ca/app/scr/sbms/sbb/cis/establishments.html?code=11-91&lang=eng
229
Statistics Canada. (2016d). Key Small Business Statistics - June 2016. Retrieved January 2017
from: https://www.ic.gc.ca/eic/site/061.nsf/eng/03027.html
Stearns, T. M., Carter, N. M., Reynolds, P. D., & Williams, M. L. (1995). New firm survival:
industry, strategy, and location. Journal of business venturing 10, no. 1, 23-42.
Storey, D. J. (1982). Entrepreneurship and the new firm. Taylor & Francis.
Strotmann, H. (2007). Entrepreneurial survival. Small business economics 28, no. 1, 87-104.
Tether, B. S. (1998). Small and large firms: sources of unequal innovations?. Research
Policy, 27(7),725-745.
Theil, H. (1961). Economic forecasts and policy.
Tibben-Lembke, R. S. (2002). Life after death: reverse logistics and the product life
cycle. International Journal of Physical Distribution & Logistics Management, 32(3),
223-244.
UCLA. (2007). Introduction to SAS. UCLA: Academic Technology Services, Statistical
Consulting Group. Retrieved August 15, 2016, from
http://www.ats.ucla.edu/stat/stata/faq/oratio.htm
van Dijk, J., & Pellenbarg, P. (2000). Demography of firms: progress and problems in empirical
research. In: P.H. Pellenbarg & J. van Dijk (eds) Demography of firms: spatial dynamics
of firm behaviour. Nederlandse Geografische Studies 262. Utrecht/Groningen: Koninklijk
Nederlands Aardrijkskundig Genootschap/faculteit der Ruimtelijke Wetenschappen
Rijksuniversiteit Groningen, p. 325-337.
van Wissen, L. (1996). Demography of the firm: modelling and death of firms using the concept
of carrying capacity. In: Van den Brekel H, Deven F (eds) Population and families in the
low countries 1996/1997. NIDI/CBGS, series. The Hague, Brussels.
van Wissen, L. (2000). A micro-simulation model of firms: applications of concepts of the
demography of the firm. Papers in Regional Science 79, no. 2, 111-134.
Variyam, J. N., & Kraybill, D. S. (1992). Empirical evidence on determinants of firm growth.
Economics Letters, 38(1), 31-36.
Verbeek, M. (2008). A guide to modern econometrics. . John Wiley & Sons.
Waddell, P. (2002). UrbanSim: Modeling urban development for land use, transportation, and
environmental planning. Journal of the American Planning Association 68, no. 3, 297-
314.
230
Wagner, J. (1992). Firm size, firm growth, and persistence of chance: Testing GIBRAT's law
with establishment data from Lower Saxony, 1978–1989. Small Business
Economics, 4(2), 125-131.
Wagner, J. (1994). The post-entry performance of new small firms in German manufacturing
industries. The Journal of Industrial Economics, 141-154.
Wagner, J. (1999). The life history of cohorts of exits from German manufacturing. Small
Business Economics 13, no. 1, 71-79.
Walsh, S. K., & Boyland, R. (1996). Founer background and entrepreneurial success:
Implications of core competence strategy application to new ventures. In P. Reynolads, S.
Birley, J. Butler, W. Bygrave, P. Davidsson, W. Gartner, & P. McDougall, Frontiers of
Entrepreneurship Research (pp. 146-154). Babson Park, US: Babson College.
Wang, Z. (2006). Learning, diffusion and industry life cycle. manuscript, Federal Reserve Bank
of Kansas City.
Wegener, M. (1995). Current and future land use models. Travel Model Improvement Program
Land Use Model Conference. Dallas , Texas.
Weiss, C. R. (1998). Size, growth, and survival in the upper Austrian farm sector. Small Business
Economics, 10(4), 305-312.
Wicksteed, P. H. (1910). The common sense of political economy.
Williams, F. P., D'Souza, D. E., Rosenfeldt, M. E., & Kassaee, M. (1995). Manufacturing
strategy, business strategy and firm performance in a mature industry. Journal of
Operations Management, 13(1), 19-33.
Windmeijer, F. A. (1995). Goodness-of-fit measures in binary choice models. Econometric
Reviews 14, no. 1, 101-116.
Winter, S. G., & Nelson, R. R. (1982). An evolutionary theory of economic change. University
of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical
Research Reference in Entrepreneurship.
Wisetjindawat, W., Sano, K., Matsumoto, S., & Raothanachonkun, P. (2007). Micro-simulation
model for modeling freight agents interactions in urban freight movement. In CD
Proceedings, 86th Annual Meeting of the Transportation Research Board, Washington
DC (pp. 21-25).
231
Yasuda, T. (2005). Firm growth, size, age and behavior in Japanese manufacturing. Small
Business Economics, no. 1(24), 1-15.
Zhang, M., & Mohnen, P. (2013). Innovation and survival of new firms in Chinese
manufacturing, 2000-2006. United Nations University, Maastrecht Economic and Social
Research Training Centre on Innovation and Technology.
232
Appendix A. Additional Tables
TABLE A. 1 Firm Start-up Employment Size by Industry Class
Industry Class
NAICS
2-digit
code
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Avg.
Agriculture, Forestry, Fishing and Hunting 11 2.5 2.4 2.3 2.4 2.1 2.1 2.1 2.1 2.4 2.0 2.0 2.2
Mining, Quarrying, and Oil and Gas Extraction 21 1.7 1.8 1.7 1.7 1.8 1.6 1.7 1.7 1.6 1.4 1.5 1.6
Utilities 22 Suppressed
Construction 23 2.4 2.3 2.1 2.0 2.1 2.1 2.0 2.0 2.0 1.9 1.8 2.1
Manufacturing 31-33 4.0 4.5 3.6 4.1 3.8 3.0 3.5 2.9 2.8 3.2 5.0 3.7
Wholesale trade 41 2.5 2.4 2.2 2.1 2.1 2.1 2.0 2.1 2.4 1.9 1.9 2.1
Retail trade 44-45 3.0 3.0 2.6 2.6 2.5 2.6 2.8 2.5 2.4 2.2 2.8 2.6
Transportation and warehousing 48-49 2.2 2.0 1.9 1.9 2.0 1.9 1.8 1.8 1.8 1.8 1.7 1.9
Information and cultural industries 51 3.9 3.8 3.4 3.9 3.8 3.0 3.2 2.8 2.7 2.6 3.8 3.4
Finance and insurance 52 2.6 2.5 2.4 2.4 2.3 2.5 2.8 2.4 2.4 2.1 2.7 2.5
Real estate and rental and leasing 53 2.3 2.2 2.1 2.0 2.0 2.0 2.0 1.9 1.9 1.8 1.8 2.0
Professional, scientific and technical services 54 1.9 1.8 1.9 1.8 1.8 1.7 1.7 1.7 1.6 1.5 1.6 1.7
Management of companies and enterprises 55 Suppressed
Administrative and support, waste management
and remediation services 56 3.6 3.3 3.2 3.7 3.3 2.7 3.1 2.8 2.6 2.5 3.5 3.1
Arts, entertainment and recreation 71 3.3 3.1 3.1 2.8 2.8 2.7 3.0 2.8 2.6 2.3 3.5 2.9
Accommodation and food services 71 4.8 4.8 4.4 4.2 4.1 3.9 4.0 3.3 3.4 4.0 5.2 4.2
Other services (except public administration) 81 2.2 2.2 2.0 2.0 2.0 2.0 2.0 1.9 1.9 1.8 1.7 2.0
233
TABLE A. 2 Average Firm Size by Industry Class
Industry Class
NAICS
2-digit
code
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Avg.
Agriculture, Forestry, Fishing
and Hunting 11 5.5 4.9 4.6 4.9 4.7 4.5 4.6 5.3 4.6 4.0 3.6 4.0 4.6
Mining, Quarrying, and Oil
and Gas Extraction 21 144.6 131.7 148.0 132.1 130.8 126.5 146.0 162.4 131.9 118.0 126.1 135.4 136.1
Utilities 22 818.0 783.7 682.3 768.5 859.6 913.5 921.1 1116.5 1410.0 1225.8 1359.1 1369.0 1018.9
Construction 23 9.2 9.9 10.4 10.6 11.8 11.6 11.7 12.6 12.5 12.4 13.3 13.6 11.6
Manufacturing 31-33 203.2 186.4 184.3 171.3 177.3 180.3 154.0 143.4 132.4 117.6 109.4 112.1 156.0
Wholesale trade 41 28.0 26.7 25.7 23.2 25.5 26.5 26.2 24.5 24.7 25.2 23.9 23.1 25.3
Retail trade 44-45 142.1 154.1 152.3 152.1 167.5 175.8 168.0 154.0 157.5 156.5 154.4 159.6 157.8
Transportation and
warehousing 48-49 271.7 281.2 240.1 246.3 229.5 242.5 272.4 254.7 242.4 214.3 198.8 211.9 242.1
Information and cultural
industries 51 575.5 535.4 436.1 405.5 369.5 345.3 300.7 345.1 318.5 281.5 240.6 217.9 364.3
Finance and insurance 52 90.0 84.9 80.6 72.5 74.6 75.6 77.8 73.2 80.2 77.0 74.8 79.1 78.4
Real estate and rental and
leasing 53 11.2 11.6 10.6 9.6 9.0 11.2 10.4 9.4 14.8 14.4 13.7 16.6 11.9
Professional, scientific and
technical services 54 17.8 16.1 16.7 15.5 16.0 16.3 15.4 15.7 16.1 14.1 14.4 14.3 15.7
Management of companies and
enterprises 55 9.3 9.0 8.6 9.7 12.2 14.6 17.0 15.0 20.7 20.6 22.9 27.8 15.6
Administrative and support,
waste management and
remediation services
56 27.1 26.3 25.8 24.1 24.3 25.7 24.2 23.5 22.4 21.3 20.9 21.4 23.9
Arts, entertainment and
recreation 71 18.0 18.9 23.6 23.4 25.1 23.8 25.2 26.8 23.5 23.3 22.6 22.1 23.0
Accommodation and food
services 71 48.4 49.2 50.6 46.6 47.2 50.3 48.3 46.2 47.8 49.3 44.6 46.1 47.9
Other services (except public
administration) 81 9.2 7.1 6.2 6.2 7.0 6.6 6.3 6.6 6.6 6.9 6.3 6.7 6.8
234
TABLE A. 3 Linear Regression Model of Firm Start-up Size: Short Model
Number of observation 232236
F( 19,168569) 948.14
Prob > F 0.000
R-squared 0.086
Adj R-squared 0.086
Root MSE 0.704
Source SS df MS
Model 10853.47 20 542.674
Residual 115184.6 15 0.496
Total 126038.1 35 0.543
Covariates Coef. Std. Err. t P>|t|
Province: Rest of Canada Reference
Ontario -0.087 0.007 -11.62 0.000
Quebec -0.050 0.008 -6.05 0.000
Alberta -0.188 0.007 -25.87 0.000
British Columbia -0.081 0.008 -10.43 0.000
Atlantic Canada 0.127 0.011 11.1 0.000
Industry Class
Agriculture, Forestry, Fishing and
Hunting -0.237 0.012 -20.51 0.000
Mining, Quarrying, and Oil and Gas
Extraction -0.284 0.014 -19.84 0.000
Utilities and Manufacturing Reference
Construction -0.246 0.007 -32.83 0.000
Wholesale trade -0.224 0.010 -22.89 0.000
Retail trade -0.057 0.008 -7.42 0.000
Transportation and warehousing -0.384 0.009 -44.4 0.000
Information and cultural industries -0.030 0.013 -2.26 0.024
Finance and insurance -0.217 0.013 -16.5 0.000
Real estate and rental and leasing -0.283 0.011 -25.1 0.000
Professional, scientific and technical
services -0.364 0.007 -48.79 0.000
Management of companies and
enterprises -0.081 0.009 -8.94 0.000
Administrative and support, waste
management and remediation services Reference
Arts, entertainment and recreation -0.107 0.013 -8.06 0.000
Accommodation and food services 0.290 0.008 37.5 0.000
Other services (except public
administration) -0.228 0.008 -27.19 0.000
Economic Indicator of previous year’s (t-1)
Provincial unemployment rate -0.012 0.001 -9.91 0.000
_cons 0.836 0.011 75.89 0.000
235
TABLE A. 4 Linear Regression Model for Firm Start-up Size: Detailed Model
Number of observation 232236
F( 19,168569) 948.14
Prob > F 0.000
R-squared 0.086
Adj R-squared 0.086
Root MSE 0.704
Source SS df MS
Model 10853.47 20 542.674
Residual 115184.6 15 0.496
Total 126038.1 35 0.543
Covariates Coef. Std. Err. t P>|t|
Province: Ontario -0.055 0.009 -6.060 0.000
Quebec -0.021 0.010 -2.080 0.037
Alberta -0.168 0.009 -19.300 0.000
British Columbia -0.064 0.009 -6.930 0.000
Atlantic Canada 0.110 0.014 8.120 0.000
Industry Class
Mining, Quarrying, and Oil and Gas
Extraction -0.322 0.017 -19.530 0.000
Construction -0.070 0.006 -12.070 0.000
Wholesale trade -0.122 0.010 -12.640 0.000
Retail trade -0.107 0.009 -11.590 0.000
Transportation and warehousing -0.328 0.009 -37.660 0.000
Information and cultural industries -0.129 0.017 -7.660 0.000
Finance and insurance -0.203 0.015 -13.150 0.000
Real estate and rental and leasing -0.111 0.011 -9.800 0.000
Professional, scientific and technical
services -0.208 0.006 -33.740 0.000
Accommodation and food services 0.388 0.007 53.570 0.000
Provincial unemployment rate of
previous year -0.013 0.002 -8.540 0.000
Industry dynamics and
competition of previous year’s (t-1)
Firm exit rate by industry (%) -0.004 0.001 -3.840 0.000
(log) Number of competitors
(CMA/CA and NAICS 3-Digit code) -0.014 0.001 -13.880 0.000
(log) Average firm size by industry
(2-digit NAICS) 0.083 0.003 27.480 0.000
_cons 0.571 0.016 36.510 0.000
236
TABLE A. 5 A Linear 3-level Random Intercept Only Model: Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log) Age 0.037 0.000 111.71 0.000
Province
Quebec 0.042 0.002 19.13 0.000
Alberta -
0.130 0.002 -57.46 0.000
British Columbia -
0.073 0.002 -31.85 0.000
Atlantic Canada 0.117 0.004 31.38 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting 0.052 0.008 6.26 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.032 0.011 2.95 0.003
Utilities 0.770 0.033 23.44 0.000
Construction 0.203 0.008 26.57 0.000
Manufacturing 0.740 0.008 90.8 0.000
Wholesale trade 0.369 0.008 45.73 0.000
Retail trade 0.555 0.008 71.83 0.000
trans_48_49 0.024 0.008 2.95 0.003
Information and cultural industries 0.185 0.010 18.69 0.000
Finance and insurance 0.016 0.009 1.85 0.064
Real estate and rental and leasing 0.031 0.008 3.71 0.000
Professional, scientific and technical services -
0.085 0.008 -11.25 0.000
Administrative and support, waste management and
remediation services 0.386 0.008 46.98 0.000
Arts, entertainment and recreation 0.367 0.010 38.45 0.000
Accommodation and food services 0.980 0.008 123.65 0.000
Other services (except public administration) 0.288 0.008 36.45 0.000
Economic indicators
GDP growth (%) by industry 0.002 0.000 45.64 0.000
Yearly provincial unemployment rate (%) of (t-1) -
0.007 0.000 -41.31 0.000
_cons 0.723 0.007 97.15 0.000
Random-effects Parameters Estimate Std. Err.
unique_id: Identity (Firm Level)
var(_cons) 0.388 0.021
ind_id: Identity (Industry Level)
var(_cons) 0.388 0.021
var(Residual) 0.144 0.000
237
TABLE A. 6 A Linear 4-level Random Intercept Only Model: Estimation Results
Covariates Coef. Std.
Err. z P>|z|
(log) Age 0.037 0.000 111.71 0.000
Province
Quebec 0.042 0.002 19.13 0.000
Alberta -0.130 0.002 -57.46 0.000
British Columbia -0.073 0.002 -31.85 0.000
Atlantic Canada 0.117 0.004 31.38 0.000
Industry class
Agriculture, Forestry, Fishing and Hunting 0.052 0.008 6.26 0.000
Mining, Quarrying, and Oil and Gas Extraction 0.032 0.011 2.95 0.003
Utilities 0.770 0.033 23.44 0.000
Construction 0.203 0.008 26.57 0.000
Manufacturing 0.740 0.008 90.8 0.000
Wholesale trade 0.369 0.008 45.73 0.000
Retail trade 0.555 0.008 71.83 0.000
trans_48_49 0.024 0.008 2.95 0.003
Information and cultural industries 0.185 0.010 18.69 0.000
Finance and insurance 0.016 0.009 1.85 0.064
Real estate and rental and leasing 0.031 0.008 3.71 0.000
Professional, scientific and technical services -0.085 0.008 -11.25 0.000
Administrative and support, waste management and
remediation services 0.386 0.008 46.98 0.000
Arts, entertainment and recreation 0.367 0.010 38.45 0.000
Accommodation and food services 0.980 0.008 123.65 0.000
Other services (except public administration) 0.288 0.008 36.45 0.000
Economic indicators
GDP growth (%) by industry 0.002 0.000 45.64 0.000
Yearly provincial unemployment rate (%) of (t-1) -0.007 0.000 -41.31 0.000
_cons 0.723 0.007 97.15 0.000
Random-effects Parameters Estimate Std. Err.
unique_id: Identity (Firm Level)
var(_cons) 0.259 0.041
ind_id: Identity (Industry Level)
var(_cons) 0.259 0.033
ind_id: Identity Province Level)
var(_cons) 0.259 0.040
var(Residual) 0.144 0.000
