Productivity, Profitability and GrowthGiulio Bottazzi, Marco Grazzi, Angelo Secchi and Federico...

Productivity, Profitability and Growth

Giulio Bottazzi1 Marco Grazzi1 Angelo Secchi2

Federico Tamagni1

1 LEM, Scuola Superiore Sant’Anna

2 DSE, University of Pisa

MFJ-RIETI-WASEDA International ConferenceMaison Franco Japonaise, Tokyo, 14-15 November, 2008

Giulio Bottazzi, Marco Grazzi, Angelo Secchi and Federico Tamagni Productivity, Profitability and Growth

Theoretical Background

Market efficiency: underlying selection process which operateson heterogeneous producers, via reallocation of market sharesfrom the least to the more “efficient” business units

Definition of fitness: productivity, profitability, growth ?

Transmission mechanism: Which dimension is the first driver?What’s the genotype? What’s the phenotype?












Empirical Background

Meso-economic analysis Sectoral decomposition of productivitydynamics: entry, exit and reallocation among incumbents.

Micro-economic analysis Characteristics of entrants or exitingfirms.

Shortcomings Only one dimension considered. No bridgebetween technical efficiency dynamics and the financialconditions often affecting firms exit.












3 issues and 3 lines of investigation

A first issue regards the possibility that productivity is not theonly and the proper fitness measure (cfr. Foster et al., 2008)



We explore existence and asses the strength of the link betweenPRODUCTIVITY and PROFITABILITY . . .




This naturally brings us to the productivity-growth andprofitability-growth links




. . . focusing at the same time on thePRODUCTIVITY-GROWTH and PROFITABILITY-GROWTHlinks





Another relatively unexplored issue concerns the role of “time”(if any)





we look at the relations between the three measures also usingdifferent time horizons


Previous analysis

Bottazzi, Secchi and Tamagni “Productivity, profitability and Financialperformance”, Industrial and Corporate Change, forthcoming.

-1

-0.5

0

0.5

1

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

gT

S

MFP

-1

-0.5

0

0.5

1

1.5

-3 -2 -1 0 1 2 3

gT

S

MFP

Figure 12: Scatter plot of the empirical relationship between Multi-Factor Productivity (MFP) andTotal Sales Growth in 2002 for the Manufacturing left and Service right industry. Both the twovariables are normalized removing the sectoral mean.

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4

MF

P

ROS

-2

-1

0

1

2

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5

MF

P

ROS

Figure 13: Scatter plot of the empirical relationship between Profitability (ROS) and Multi-FactorProductivity (MFP) in 2002 for the Manufacturing left and Service right industry. Both the twovariables are normalized removing the sectoral mean.

-1

-0.5

0

0.5

1

1.5

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4

gT

S

ROS

-1

-0.5

0

0.5

1

-0.4 -0.2 0 0.2 0.4 0.6

gT

S

ROS

Figure 14: Scatter plot of the empirical relationship between Profitability (ROS) and Total SalesGrowth in 2002 for the Manufacturing left and Service right industry. Both the two variables arenormalized removing the sectoral mean.

32


Outline

1 Data Description

2 Static unconditional analyses

3 Panel data analyses

4 Deepening the investigations


Data description

Micro.3: integrated data bank including firms with 20 or moreemployees over the time period 1989− 2004. It is based on threedifferent sources

census of all the Italian firms with more than 20 employeesbefore the 1997 and with more than 100 employees in the period1998− 2004

representative sample of firms with a number of employeesincluded in the range 20− 99 rotating every five years

balance-sheet provided by CEBI, the Italian official member ofthe European Committee of Central Balance-Sheet Data Offices

The multiple sources allow us to accomplish two objectives: enhance thecompleteness and check for reliability of the balance sheet data


Data description







Data description







Data description







Data description







Database time span

220

230

240

250

260

270

280

1990 1992 1994 1996 1998 2000 2002 2004

REAL GDP (billions of Euro)

Out of SME

Devaluation

Recession

Fixed Exch. Rates


Database time span

15000

15500

16000

16500

17000

17500

18000

18500

1990 1992 1994 1996 1998 2000 2002 2004

EMPLOYMENT, (Thousands of Persons)

Out of SME

Devaluation

Recession

Fixed Exch. Rates


This paper focuses on the manufacturing sector

we consider 60084 firms over the period 1989− 2004

they represent more than 50% of the GDP generated by all theItalian manufacturing firms and more than 40% of the totalemployment in manufacturing

Proxies selection:

TS =Total Sales (Annual Revenues)

L = Number of Employees

VA = Value Added (TS − cI)

GOM = Gross Operative Margin (VA− wL)


This paper focuses on the manufacturing sector

we consider 60084 firms over the period 1989− 2004

they represent more than 50% of the GDP generated by all theItalian manufacturing firms and more than 40% of the totalemployment in manufacturing

Proxies selection:

TS =Total Sales (Annual Revenues)

L = Number of Employees

VA = Value Added (TS − cI)

GOM = Gross Operative Margin (VA− wL)


Measures of performances

We define growth, profitability and productivity

Productivity

Πt =VAt

Lt(1)

Profitability

Pt =GOMt

TSt(2)

GrowthgTS

t = ∆ log(TSt) (3)


1 Data Description





Relation Π ∼ Growth

-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π

Manufacture of machinery

2004mean

median



-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004mean

median

-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π

Manufacture of furniture

2004mean

median


Binned Statistics

Simple statistics of dependent variable by quantile of independentvariable. For pairs (Xi, Yi)

S[ Yi | Xi ∈ bin ] S = E, V, . . .

Account for noise in both variables. Let

Yi = f (Xi) + εi ,

if Xi = X + ηi and ηi � X then

Yi = f (X) + f ′(X)ηi + εi

and hence

E[ Yi | Xi ∈ bin ] = f (X)V[ Yi | Xi ∈ bin ] = f ′2σ2

η + σ2ε .



-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation



-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation

-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation


Kernel Regression

Generalization of Binned Statistics: smooth weighting ofobservation pairs

From the definition of Conditional Expectation:

E[Y | X] =∫

dY YP(Y | X) =∫

dY YP(Y, X)

P(X)

Using the “plug-in” principle: P → P

E[Y | X] =∫

dY Y

∑i K

(Y−Yi

hY, X−Xi

hX

)∑

i K(

X−XihX

) =

∑i YiK

(X−Xi

hX

)∑

i K(

X−XihX

)with symmetric kernel function K(·) .


-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation

Non parametric regression


-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation


-1

-0.5

0

0.5

1

0 20 40 60 80 100

gTS

Π


2004Binned relation



Relation ROS ∼ Growth

-1

-0.5

0

0.5

1

-0.1 0 0.1 0.2 0.3 0.4

gTS

Π


2004mean

median

-1

-0.5

0

0.5

1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3

gTS

Π


2004mean

median



-1

-0.5

0

0.5

1

-0.1 0 0.1 0.2 0.3 0.4

gTS

Π


2004Binned relation

-1

-0.5

0

0.5

1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3

gTS

Π


2004Binned relation



-1

-0.5

0

0.5

1

-0.1 0 0.1 0.2 0.3 0.4

gTS

Π


2004Binned relation


-1

-0.5

0

0.5

1

-0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3

gTS

Π


2004Binned relation



Relation Π ∼ ROS

-0.2

-0.1

0

0.1

0.2

0.3

0.4

20 30 40 50 60 70 80 90 100 110

gTS

Π


2004mean

median

-0.2

-0.1

0

0.1

0.2

0.3

0 10 20 30 40 50 60 70 80

gTS

Π


2004mean

median


Relation Π ∼ ROS

-0.2

-0.1

0

0.1

0.2

0.3

0.4

20 30 40 50 60 70 80 90 100 110

gTS

Π


2004Binned relation

-0.2

-0.1

0

0.1

0.2

0.3

0 10 20 30 40 50 60 70 80

gTS

Π


2004Binned relation


Relation Π ∼ ROS

-0.2

-0.1

0

0.1

0.2

0.3

0.4

20 30 40 50 60 70 80 90 100 110

gTS

Π


2004Binned relation


-0.2

-0.1

0

0.1

0.2

0.3

0 10 20 30 40 50 60 70 80

gTS

Π


2004Binned relation



Kendall’s τ

Global, non-parametric measure of probabilistic dependence

Couples of ordered variables (Xi, Yi) i ∈ 1, . . . , N

Consider the G = N(N − 1)/2 matching

(Xi − Xj)(Yi − Yj) ={

1 concordant−1 discordant

i, j ∈ N (4)

Kendall’s coefficient reads

τ =Nc − Nd

G= Pr{concordant} − Pr{discordant} (5)

with Nc = # of concordant and Nd = # of discordant.


Kendall’s τ




(Xi − Xj)(Yi − Yj) ={


i, j ∈ N (4)


τ =Nc − Nd




Kendall’s τ




(Xi − Xj)(Yi − Yj) ={


i, j ∈ N (4)


τ =Nc − Nd




Kendall’s τ




(Xi − Xj)(Yi − Yj) ={


i, j ∈ N (4)


τ =Nc − Nd




Kendall’s τ - Manufacture of machinery

Rank Correlations

Year Πt vs gTSt Πt vs Pt Pt vs gTS

t

1992 0.168 0.000 0.486 0.000 0.119 0.001

1994 0.182 0.000 0.503 0.000 0.150 0.000

1996 0.113 0.002 0.474 0.000 0.056 0.135

1998 0.111 0.002 0.495 0.000 0.073 0.055

2000 0.115 0.000 0.473 0.000 0.057 0.035

2002 0.170 0.000 0.490 0.000 0.141 0.000

2004 0.115 0.000 0.510 0.000 0.123 0.000


Kendall’s τ - Manufacture of furniture

Rank Correlations

Year Πt vs gTSt Πt vs Pt Pt vs gTS

t

1992 0.133 0.000 0.359 0.000 0.099 0.001

1994 0.157 0.000 0.331 0.000 0.119 0.000

1996 0.129 0.000 0.359 0.000 0.121 0.000

1998 0.154 0.000 0.381 0.000 0.093 0.001

2000 0.101 0.000 0.399 0.000 0.070 0.000

2002 0.057 0.003 0.401 0.000 0.059 0.002

2004 0.177 0.000 0.397 0.000 0.132 0.000


Relationships: summing up

Productivity

Profitability Growth

��

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weak

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?


A couple of remarks

Even at this “exploratory” stage of our investigation two caveatdeserve to be underlined

heterogeneity: one has to take into account the role of firmheterogeneity

robustness: we check if this preliminary result survives tovarious extensions


A couple of remarks

Even at this “exploratory” stage of our investigation two caveatdeserve to be underlined

heterogeneity: one has to take into account the role of firmheterogeneity

robustness: we check if this preliminary result survives tovarious extensions


1 Data Description





Panel data analysis

We exploit the panel dimension of our data to include the effect ofunobserved, firm specific variables. We estimate

Yi,t = c + ui + αXi,t + εi,t , (6)

where Y and X cab be gTSt , Πt and Pt respectively. We also include

year dummies to control for common time effects.


Panel data analysis

We exploit the panel dimension of our data to include the effect ofunobserved, firm specific variables. We estimate

Yi,t = c + ui + αXi,t + εi,t , (6)

where Y and X cab be gTSt , Πt and Pt respectively. We also include

year dummies to control for common time effects.


A measure for the strength of relations

In order to say something about the relative strength of therelationships we consider

SYX = |α|σX

σY, (7)

where α is the Fixed Effects estimate of the α coefficient, and σstands for the standard deviation of the two variables included in eachregression.

Note: SYX is the square root of a standard R2, computed ignoring theterms due to annual dummies and fixed effects



Gi,t = c + αΠi,t + ui + εi,t Πi,t = c + αGi,t + ui + εi,t

Sector α S2YX R2 α S2

YX R2

291 0.0035* 0.0957 0.2819 11.4861* 0.0167 0.6999292 0.0052* 0.1616 0.2631 11.5384* 0.0225 0.7252294 0.0062* 0.1561 0.3338 11.4820* 0.0325 0.6964295 0.0061* 0.1566 0.2385 10.4497* 0.0260 0.6892297 0.0027* 0.0612 0.2554 9.5740 * 0.0112 0.6806311 0.0040* 0.1194 0.3941 10.6308* 0.0152 0.7840312 0.0046* 0.1076 0.3098 8.7446 * 0.0150 0.7420316 0.0050* 0.1771 0.2847 9.5601 * 0.0130 0.8063343 0.0043* 0.0851 0.2261 6.3745 * 0.0090 0.7486361 0.0057* 0.1335 0.2824 8.7656 * 0.0186 0.7317362 0.0047* 0.0896 0.2231 8.2525 * 0.0171 0.6605366 0.0043* 0.1602 0.3194 11.9030* 0.0166 0.7810

Table: Contemporaneous relationship between Growth (G) and Productivity(Π) - Fixed Effects regressions.


Relation P ∼ Growth

Gi,t = c + αPi,t + ui + εi,t Pi,t = c + αGi,t + ui + εi,t


YX R2

291 0.8955* 0.1489 0.3150 0.0922* 0.0458 0.6200292 0.8275* 0.0701 0.2559 0.0483* 0.0228 0.5755294 0.6559* 0.0384 0.2996 0.0359* 0.0145 0.5698295 0.9674* 0.0826 0.2302 0.0508* 0.0292 0.5425297 0.3341* 0.0104 0.2405 0.0145* 0.0023 0.6443311 0.7336* 0.0631 0.3927 0.0466* 0.0186 0.6707312 0.4656* 0.0393 0.2871 0.0298* 0.0049 0.7485316 0.7092* 0.0553 0.2766 0.0329* 0.0098 0.6950343 0.0306* 0.1768 0.3519 6.0417* 0.1929 0.3229361 0.6907* 0.0545 0.2843 0.0541* 0.0257 0.5087362 1.5110* 0.1609 0.2871 0.0777* 0.0857 0.4797366 0.7879* 0.0967 0.3270 0.0743* 0.0355 0.5923

Table: Contemporaneous relationship between Growth (G) and Profitability(P) - Fixed Effects regressions.


Relation Π ∼ P

Πi,t = c + αPi,t + ui + εi,t Pi,t = c + αΠi,t + ui + εi,t


YX R2

291 100.6240* 0.2355 0.7655 0.0031* 0.4000 0.6946292 119.1753* 0.2809 0.7999 0.0029* 0.4400 0.7499294 116.2409* 0.2279 0.7389 0.0024* 0.3501 0.6765295 102.8542* 0.2459 0.7683 0.0035* 0.5209 0.6627297 147.5963* 0.2660 0.7887 0.0023* 0.4220 0.7339311 111.2658* 0.2039 0.8492 0.0029* 0.5177 0.7598312 77.2837 * 0.1803 0.8192 0.0033* 0.3565 0.7460316 96.1510 * 0.1649 0.8295 0.0024* 0.3256 0.7599343 0.1489 * 0.0007 0.7035 0.0139* 0.0061 0.1296361 77.1432 * 0.1646 0.7759 0.0035* 0.4329 0.6370362 77.3997 * 0.0952 0.6808 0.0019* 0.2201 0.5142366 93.8103 * 0.1660 0.8264 0.0032* 0.5462 0.6852

Table: Contemporaneous relationship between Productivity (Π) andProfitability (P) - Fixed Effects regressions.


A comment on heterogeneity

In all the relationships the single regressor considered displays only aweak effect, largely residual with respect to the one provided by theunobserved component. Two possible explanations

we know that all the variables involved (Total sales, ValueAdded, Employees) are very sticky over time then firm-specificfactors ui are very likely to capture a significant fraction of eachdependent variable

strength of the relationships largely depends on the idiosyncraticcharacteristics of the firm: links are weak on average while it isheterogeneity that matters most












Deepening the analysis

We begin considering

1 the role (if any) of different time horizons

2 a first attempt to perform international comparisons

3 the role (if any) of investments

4 a finer look at the relation between productivity and profitability























Extending the time horizon


Towards long-run relations

It can be the case that the relations under investigation are intrinsicallydynamic. Hence we define 5-years time average of our proxies

Xi,p =15

5∑t=1

Xi,t X = Π, P, G , (8)

and we run again the same regressions using time-averages

Yi,t = c + ui + αXi,t + εi,t . (9)


Towards long-run relations

It can be the case that the relations under investigation are intrinsicallydynamic. Hence we define 5-years time average of our proxies

Xi,p =15

5∑t=1

Xi,t X = Π, P, G , (8)

and we run again the same regressions using time-averages

Yi,t = c + ui + αXi,t + εi,t . (9)

Essentially the conclusions reached so far are confirmed!


Growth vs. productivity

Gi,s = c + αΠi,s + ui + εi,s Πi,s = c + αGi,s + ui + εi,s


YX R2

291 0.0012 * 0.0168 0.6681 6.1466 * 0.0033 0.8515292 0.0024 * 0.0503 0.6533 8.0713 * 0.0075 0.8662294 0.0042 * 0.1337 0.7335 11.7205 * 0.0180 0.9022295 0.0019 * 0.0205 0.6058 5.0902 * 0.0045 0.8151297 -0.0008 0.0075 0.7015 -4.2439 0.0015 0.8690311 0.0015 * 0.0187 0.7937 7.7622 * 0.0074 0.8706312 0.0004 0.0012 0.7515 1.1392 0.0002 0.8972316 0.0044 * 0.1555 0.6809 10.6302 * 0.0138 0.9048361 0.0014 * 0.0113 0.6035 2.8530 * 0.0015 0.8579362 0.0012 0.0125 0.6239 3.9450 0.0017 0.8597366 0.0011 0.0126 0.6348 3.8956 0.0014 0.8796

Table: Contemporaneous relationship between 5-years averaged Productivity(Π) and 5-years averaged Growth (G) - Fixed Effects regressions.


Growth vs. profitability

Gi,s = c + αPi,s + ui + εi,s Pi,s = c + αGi,s + ui + εi,s


YX R2

291 0.4657 * 0.0542 0.6777 0.0823 0.0271 * 0.7719292 0.4349 * 0.0263 0.6521 0.0343 0.0084 * 0.8032294 0.6590 * 0.0509 0.7273 0.0563 0.0270 * 0.8013295 0.3314 * 0.0127 0.6071 0.0236 0.0048 * 0.7583297 -0.7356 * 0.0721 0.7156 -0.0697 0.0365 * 0.7976311 0.5488 * 0.0385 0.8035 0.0888 0.0617 * 0.7512312 -0.0010 0.0000 0.7401 -0.0001 0.0000 0.9058316 0.4265 * 0.0255 0.6712 0.0221 0.0035 * 0.8786361 0.3330 * 0.0143 0.6055 0.0257 0.0052 * 0.7628362 0.2261 0.0079 0.6269 0.0317 0.0065 0.6602366 0.5070 * 0.0587 0.6544 0.0783 0.0268 * 0.7663

Table: Contemporaneous relationship between 5-years averaged Profitability(P) and 5-years averaged Growth (G) - Fixed Effects regressions.


Productivity vs. profitability

Πi,s = c + αPi,s + ui + εi,s Pi,s = c + αΠi,s + ui + εi,s


YX R2

291 83.7196 * 0.1847 0.8975 0.0040* 0.6131 0.8133292 117.7079 * 0.3698 0.8969 0.0026* 0.2475 0.9158294 112.8553 * 0.2042 0.9228 0.0031* 0.5918 0.8686295 115.3268 * 0.3023 0.8825 0.0032* 0.4449 0.8575297 126.7378 * 0.1894 0.8971 0.0022* 0.4031 0.8496311 99.7595 * 0.1560 0.9304 0.0033* 0.6821 0.8545312 98.9326 * 0.2221 0.9544 0.0037* 0.5999 0.9249316 116.9078 * 0.2157 0.9341 0.0026* 0.4153 0.9084361 81.6753 * 0.1915 0.8591 0.0027* 0.2634 0.8345362 72.4282 * 0.1002 0.8892 0.0032* 0.5194 0.7482366 81.3128 * 0.1424 0.9141 0.0036* 0.5886 0.8252

Table: Contemporaneous relationship between 5-years averaged Productivity(Π) and 5-years averaged Profitability (P) - Fixed Effects regressions.


Productivity vs. profitability

The picture is even clearer if with introduce a lag (i.e. we use theaverage of the previous 5 years) in the independent variable


International comparisons(a first attempt)


French data

We have access (thanks to a research project with Nadia Jacoby,Université Paris I Panthéon-Sorbonne) to the EAE individualfirm databank maintained by the French Statistical Office(INSEE).

This database contains longitudinal data on a virtually exhaustivepanel of French firms with 20 employees or more over the period1989-2004.

Firms are classified according to their sector of principal activity.We exclude firms that have undergone any kind of modificationof structure, such as merger or acquisition or any other kind ofstructural modification.

The database encompasses about 22000 firms per year for eachyear of the period.


Productivity

Profitability Growth

��

��

��

��

��

��+

��

��

��

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?

?


Introducing investments


The role of investments

What is (if any) the role of investments?

We introduce a new dimension: investments(I) are defined as (log) ofthe variation of (real) tangible assets,

next we correlate I with Π,P and G using the same functional formwith fixed effects.











Essentially Investments do not change the picture: they are not relatedwith any of our proxy!


Investments and growth

Ii,s = c + αGi,s + ui + εi,s Gi,s = c + αIi,s + ui + εi,s


YX R2

291 0.8793 * 0.0051 0.7799 0.0077 * 0.0090 0.7076292 0.3908 0.0017 0.7804 0.0053 0.0025 0.7304294 0.5609 0.0044 0.7865 0.0069 0.0034 0.8143295 0.2291 0.0009 0.7962 0.0070 0.0028 0.6489297 0.3914 0.0013 0.8554 0.0077 0.0068 0.6746311 -0.0459 0.0000 0.8030 -0.0007 0.0000 0.8311312 0.7926 0.0071 0.8390 0.0108 0.0103 0.8057316 0.5333 0.0030 0.8205 0.0068 0.0044 0.7805361 0.8832 * 0.0078 0.7631 0.0129 * 0.0168 0.6517362 0.7252 0.0070 0.8002 0.0162 0.0197 0.6658366 0.9857 0.0079 0.8053 0.0107 0.0141 0.7400

Table: Contemporaneous relationship between 5-years averaged Investment(I) and 5-years averaged Growth (G) - Fixed Effects regressions.


Investments and profitability

Ii,s = c + αPi,s + ui + εi,s Pi,s = c + αIi,s + ui + εi,s


YX R2

291 0.1969 0.0001 0.7798 0.0003 0.0000 0.8527292 2.3873 * 0.0114 0.7838 0.0031 * 0.0049 0.8584294 3.1311 * 0.0154 0.7850 0.0035 * 0.0078 0.8474295 0.1181 0.0000 0.7964 0.0003 0.0000 0.7496297 1.7316 0.0036 0.8562 0.0029 0.0071 0.7979311 2.1351 0.0093 0.8032 0.0043 0.0092 0.8046312 3.2138 0.0350 0.8402 0.0050 0.0074 0.9263316 1.1636 0.0024 0.8197 0.0014 0.0012 0.8753361 1.2978 0.0023 0.7606 0.0013 0.0012 0.8284362 1.3928 0.0032 0.7994 0.0020 0.0025 0.8230366 2.1108 0.0082 0.8080 0.0026 0.0036 0.8729

Table: Contemporaneous relationship between 5-years averaged Investment(I) and 5-years averaged Profitability (P) - Fixed Effects regressions.


Investments and productivity

Ii,s = c + αΠi,s + ui + εi,s Πi,s = c + αIi,s + ui + εi,s


YX R2

291 0.0053 0.0030 0.7788 0.3495 0.0012 0.8626292 0.0104 * 0.0147 0.7822 0.6420 * 0.0030 0.9016294 0.0168 * 0.0326 0.7883 0.7260 * 0.0046 0.9209295 0.0019 0.0004 0.7954 0.1680 0.0002 0.8477297 0.0122 0.0180 0.8567 1.0067 0.0084 0.9020311 0.0075 0.0082 0.8037 0.6886 0.0032 0.8764312 0.0116 0.0112 0.8387 0.5919 0.0042 0.9011316 0.0081 0.0083 0.8201 0.3402 0.0009 0.9402361 0.0228 * 0.0353 0.7646 0.7594 * 0.0085 0.8846362 0.0098 0.0142 0.7992 0.6761 0.0031 0.9063366 0.0070 0.0051 0.8032 0.3740 0.0013 0.8992

Table: Contemporaneous relationship between 5-years averaged Investment(I) and 5-years averaged Productivity (Π) - Fixed Effects regressions.


A closer look to diversity offirms


Quantile investigations

Traditionally (at least in economics) to bring order out of the diversityof empirical observations we explore how AVERAGES of certainvariables depend on the values of other “conditioning” variables. Veryoften this way is very effective

Still one may question whether the exclusive focus on conditionalmean relations ignores some “charm of variety” in matters statistical.

Quantile regressions offers the opportunity for a more complete viewof the statistical landscape and the relationships among stochasticvariables


Quantile investigations

Traditionally (at least in economics) to bring order out of the diversityof empirical observations we explore how AVERAGES of certainvariables depend on the values of other “conditioning” variables. Veryoften this way is very effective

Still one may question whether the exclusive focus on conditionalmean relations ignores some “charm of variety” in matters statistical.

Quantile regressions offers the opportunity for a more complete viewof the statistical landscape and the relationships among stochasticvariables


Two examples

0.001

0.0015

0.002

0.0025

0.003

0.0035

0 1 2 3 4 5 6 7 8 9 10

Effe

ct o

n Pr

ofita

bilit

y

Productivity quantiles


QuantileOLS


Two examples

0.0013

0.0014

0.0015

0.0016

0.0017

0.0018

0.0019

0.002

0.0021

0.0022

0.0023

0.0024

0 1 2 3 4 5 6 7 8 9 10

Effe

ct o

n Pr

ofita

bilit

y

Productivity quantiles


QuantileOLS


Concluding remarks

To sum up our investigations in a conclusion

we have a lot of work to do!

can we bring some order out of all these sources ofheterogeneity? Do sectoral patterns emerge?

exploring the “fixed effect” black-box

deeper investigation of the directions of causation; what are wemissing?


Concluding remarks







Concluding remarks







Concluding remarks







Concluding remarks







Date post:	27-Mar-2021
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Productivity, Profitability and GrowthGiulio Bottazzi, Marco Grazzi, Angelo Secchi and Federico...

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