Niken Kusumawardhani, Daniel Suryadarma, Luca Tiberti, Veto Indrio
The SMERU Research Institute & Universite Laval, Quebec
What type of cognitive skills lead
to entrepreneurial success?
Evidence from non-farm household enterprises in Indonesia
Micro, Small, and Medium Enterprise in Indonesia
MSMEs contribute to 60%
of GDP and absorbed 97%
workforce in Indonesia
The share of self-
employed to total
workers has remained
between 45% and 48%
during 1994 to 2011,
before dipping in 2014
Share of enterprises who
hire workers increased
from 1% to 3% during the
last two decades
(Sakernas), but majority
are still self-employed
Becoming entrepreneurs
is the way for relatively
unskilled workers to
secure jobs in non-
advanced economies
In advanced
economies,
entrepreneurs are
generally positively
self-selected
Source: https://www.vectorstock.com/royalty-free-vector/vegetables-and-fruits-product-seller-at-the-vector-10777554
Government of
Indonesia has
established programs
to support MSMEs
such as training and
providing capital
Entrepreneurs and Economic Growth
Majority of Indonesia’s self-employed workers are subsistence entrepreneurs, defined
as those who do not have at least one paid employee (de Mel et al., 2008)
Necessity-motivated entrepreneurs have low potential in driving economic growth
High-growth entrepreneurs can create positive externality through innovation
in their ways of doing business
43.4% 46.3% 43.7% 45.2% 43.0% 42.1% 42.3%40.6% 41.3% 42.1%
34.7%32.9%
1.0% 1.4% 1.7% 1.7% 3.0% 2.9% 3.1% 3.0% 3.0% 2.9% 3.6% 3.6%
1994 1996 1997 1998 2002 2003 2004 2005 2006 2011 2014 2015
Sh
are
to
To
tal
Em
plo
ym
en
t (%
)
Subsistence Successful
The Role of Cognitive Skills in Labor Market
Impact of cognitive skills on earnings of
Indonesian workers is modest compared to
general findings in developed countries
Bargain and Zeidan (2017)
Intellectual capacity of an entrepreneur is
positively correlated with the number of
employees employed in the enterprise
Van Praag and Cramer (2001)
Individuals with higher than average cognitive
skills are able to start and run business more
effectively and innovate continuously
Meisenberg (2012)
Specific abilities that contribute to earning
premium for entrepreneurs are mathematical,
technical, and social ability
Hartog et al. (2010)
In this paper, we investigate the returns of cognitive skills of Indonesian non-farm household enterprise operators on the performance of their businesses. We specifically compare the returns to two types of intelligence: fluid and crystallized on non-farm household business performance, taking into account the fact that these types of intelligence are correlated with each other and with education attainment.
Research Objective
Research Object ives
Investigate the returns of cognitive skills of Indonesian non-farm
household enterprises on the performance of their businesses
Specifically, we compare returns of two types of intelligence: fluid
intelligence and crystallized intelligence
Derive policy from our findings to help policymakers identifying
enterprise owners with greater potential for future success
Labor market rewards various types of
intelligence differently, also depends
on labor market context
General ability has two components:
crystallized intelligence (CI) and
fluid intelligence (FI)
CI is acquired through interaction with
environment, level of education, experience
FI is highly influenced by
genetics and biological factors
Conceptual Framework
Fluid intelligence positively
affects individual revenues
both directly and indirectly
through crystallized
intelligence
Hypotheses
If sector-related returns to
intelligence is heterogeneous,
we should expect sorting
based on possessed set of
cognitive skills
1
3
Return to crystallized
intelligence is also positive,
depending on component
related to education and
fluid intelligence
2
Crystallized Intelligence Fluid Intelligence
Type of Cognitive Skills
Usually assessed through tests
on vocabulary, analogies, and
general knowledge
3
Can be continually
improved and generally
increases with age
4
1 Influenced by conditions in
utero, genetics
2 Problem solving skills,
adaptability to change, logical
thinking
3
Taught and often measured as
knowledge1
Related to quality of education,
environment, health2
Generally measured using
cognitive functioning tasks
that rely on working memory
and abstract reasoning3
4Tends to decrease with age and
can be slightly improved with
targeted training
DATA
Indonesian Family Life Survey (IFLS)
A publicly available
longitudinal household survey
fielded in 1993, 1997, 2000,
2007, and 2014; covering 83%
of Indonesia’s population
Household attrition is as
low as 5% each wave.
Overall, 87% of households
are interviewed in all five
waves (Strauss et al., 2016)
Contains information on
household businesses and
detailed information on
respondents’ characteristics
and labor market histories
This study uses IFLS 2007 and 2014 which collects a significantly
richer set of information on household enterprises
Household Businesses in IFLS
Household businesses are categorized into
farm and non-farm. Collected information
between the two are not always similar
No linking of household businesses
between IFLS waves
Identification of the entrepreneur is
possible for most of non-farm business
IFLS has extensively rich information on
household businesses: business’ profit,
assets, capital, and ownership
84% households in our dataset only
has one non-farm business
Each household is asked to provide
information on multiple household
businesses
Performance indicators of household
business that we focus on: annual
profit and total business value
Entrepreneur is the most primarily
responsible person for the daily
business activities (not always owner)
Measurement of Cognitive Skills in IFLS
Math test score is our proxy for crystallized intelligence
Raven’s test score is our proxy for fluid intelligence
A
EK2 contains 5 numeracy problems
and 8 shape matching problems for
individuals age 15-24
EK1 contains 5 numeracy problems
and 12 shape matching problems for
individuals age 7-14
We decide to use EK2 as it is more
complicated and arguably more relevant
with numeracy skills needed for business
A specific section in IFLS collects
information that we use as proxy for
cognitive skills: section EK1 and EK2
A
The numeracy problems in EK2 are
significantly more complicated than
those in EK1
In IFLS 2014, age cutoff is changed:
Raven’s test is for everyone age ≥15,
numeracy test is for everyone age 15-60
Consequently, throughout IFLS 2007 and
2014 participants do not take the test at a
similar age range
Numeracy Test in IFLS
56
84= ...
a.4
7
b.2
3
c.3
4
d.5
6
0.76 - 0.4 - 0.23 = ...
a.0.11
b.0.12
c.0.13
d.0.16
01
Math test score is obtained from number of correct answers
Raven’s Progressive Matrices Test in IFLS
Raven’s test score is obtained from number of correct answers
Dataset Construction
Our sample is constructed
from section NT which
covers information on non-
farm household businesses
and section EK2 which
covers cognitive test score
For section EK2, we stack the
responses from IFLS 2007
and 2014. A subsample of the
stacked dataset would be the
same individuals
01
02
If any of these individuals own
a non-farm business at any time
across IFLS 2007 and 2014, then
we match the business with the
cognitive skills of the owner
A mismatch in merging
entrepreneur and cognitive dataset
should be expected as the tests are
only taken by individuals within
specific age range
03
04
Empirical Strategy
Estimated using OLS, quantile regression, and panel fixed effects with selection
Annual profit
Total value of business
Entrepreneur’s characteristics
Crystallized intelligence
Fluid intelligence
Entrepreneur’s education attainment Community characteristics
Survey year dummy
𝑌𝑖𝑗𝑡 = 𝛽𝑧𝑋𝑗 + 𝛽𝑖𝐼𝑗𝑡 + 𝛽𝐸𝐸𝑗𝑡 + 𝛽𝐶𝐶𝑖𝑡 + 𝛽𝐷𝐷𝑡 + 𝜀𝑖𝑗𝑡
Sample Size 2007 2014
(A) Non-farm entrepreneurs 6010 6246
(B) Have non-missing cognitive scores 961 5457
(A-B) Have missing cognitive scores 5049 789
(C) Supposed to take the test but do not take 26 474
(A-B-C) Have missing score due to survey design 5023 315
Reasons for rejection 2007 2014
Refused 14 223
Cannot read 1 12
Unable to answer 0 6
Not enough time 1 5
Proxy respondent 8 156
Other 0 14
Couldn’t be contacted 2 58
Total 26 474
4.2% of all
entrepreneurs in 2014
Entrepreneurs and Their Cognitive Scores
Probit Result of Missing Cognitive Scores
0,092
0,035
0,031
0,017
0,015
0,001
0,002
0,001
0,001
0,001
Age (years)
% of villages with factories / cottage
industry
Male-headed households (Yes = 1)
Male (Yes = 1)
Ln (profit)
Urban (Yes = 1)
Education attainment (years)
Age squared
Dependency ratio
% of households with access to grid
electricity
-0,962
-0,122
-0,039
-0,024
-0,007
-0,005
-0,002
-0,001
-0,001
-0,001
IFLS 2014 Dummy (Yes =1)
Ethnic Javanese (Yes = 1)
Social sector
Household size
Brain sector
Ln (business value)
Number of employees
Age of business (years)
Height (cm)
Number of non-farm household
businesses at district
Not statistically significant
Statistically significant
Y = 1 for obs. with missing values
on at least one test score; 0 for obs.
with non-missing test scores (both)
Average
Marginal Effects0
Summar y Statist ics
Math score (standardized)
Height (cm)
Age (years)
Male (Yes =1)
Ethnic Javanese (Yes =1)
--0.09 0.99
--0.10 0.98
157.35 8.43
40.45 11.98
0.33 0.47
0.54 0.50
0.45 0.50
Mean Std. Dev
Elementary or below
Junior secondary
Senior secondary / tertiary
0.21 0.41
0.46 0.50
Relatively young
46% have at least 12
years of education
33% have maximum
6 years of education
Raven’s score (standardized)
Characteristics
Entrepreneur
characteristics
Total business value (in Rp 000)
Annual profit (in Rp 000) 1193.19 2974.89
2299.13 8912.87
Performance
indicators
Small but
healthy profit
Implying low
investment in capital
or technology
Summar y Statist ics
Business
sector
classification
Household
characteristics
% of HH with access to grid electricity -0.96 0.07
Mean Std. DevCharacteristics
% of villages with factory or cottage industry
% of villages where a formal financial
institution provides business loan
Number of non-farm household enterprises
-0.63 0.39
0.36 0.15
63.81 44.5
Dependency ratio (%)
Household size
Male-headed household (Yes=1)
Urban (Yes=1)
67 67.57
5.26 2.43
0.88 0.33
0.65 0.48
65% of
non-farm
businesses
are in urban
areas
Community
characteristics
Brawn sector (Yes=1)
Brain sector (Yes=1)
0.03 0.18
0.37 0.48
Social sector (Yes=1) 0.59 0.49
59% are in
sectors that
mainly
require
social skills
RESULTS
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
(1) (2) (3) (4) (5) (6)
Math test score
(standardized)0.114*** 0.238*** 0.030 0.029
(0.023) (0.036) (0.024) (0.037)
Raven’s test score
(standardized)0.149*** 0.311*** 0.057*** 0.070*
(0.023) (0.037) (0.026) (0.040)
Control variables
includedX X X X √ √
Survey year and
province FE included√ √ √ √ √ √
Number of
observations5196 4928 5196 4928 5196 4928
R-squared 0.037 0.027 0.040 0.033 0.141 0.147
OLS Estimation Result
Fluid intelligence has a positive and sizeable effect on business performance
Sub-sample
Estimation
We follow Bargain &
Zeidan (2017) who
separate businesses
into sectors based on
the main skills required
Brawn Sector
• Agriculture, forestry,
fishery
• Mining and quarrying
• Construction
Social Sector
• Finance, insurance, real
estate
• Restaurant, food sales
• Sales: non-food
Brain Sector
• Industry: food
processing, clothing,
and other
• Electricity, gas, water
• Transportation and
communication
• Services: government,
teacher, professionals,
transportation, tricycle,
motorcycle taxi, other
(tailor, hairdressing)
• Other
Require lots of
physical efforts
Require skills in
dealing with
people
Require intense
concentration or
work with computers
Cognitive Skills and Non-Farm Sector Choice
Brain Sector Social Sector
(1) (2)
Math test score (standardized) 0.071 0.036
(0.093) (0.093)
Raven’s test score (standardized) -0.056 -0.110
(0.099) (0.098)
Junior secondary 0.164 0.087
(0.236) (0.234)
Senior secondary / tertiary 0.131 0.034
(0.209) (0.207)
Male (Yes=1) -1.168*** -1.989***
(0.261) (0.258)
Control variables included √ √
Survey year and province FE included √ √
Number of observations 5495 5495
R-squared 0.060 0.060
We find no evidence of sorting into sectors based on intelligence or education
Sub-Sample Estimation: Brain Sector
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
(1) (2) (3) (4) (5) (6)
Math test score
(standardized)0.099*** 0.046 0.088** 0.027
(0.037) (0.057) (0.038) (0.059)
Raven’s test score
(standardized)0.072* 0.096 0.051 0.090
(0.040) (0.061) (0.041) (0.063)
Control variables
included√ √ √ √ √ √
Survey year and
province FE included√ √ √ √ √ √
Number of
observations1936 1839 1936 1839 1936 1839
R-squared 0.185 0.197 0.183 0.198 0.185 0.198
Crystallized intelligence matters for profit in brain-heavy sectors
Sub-Sample Estimation: Social Sector
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
(1) (2) (3) (4) (5) (6)
Math test score
(standardized)0.003 0.026 -0.007 0.013
(0.030) (0.048) (0.031) (0.049)
Raven’s test score
(standardized)0.051 0.067 0.053 0.064
(0.033) (0.050) (0.034) (0.052)
Control variables
included √ √ √ √ √ √
Survey year and
province FE included √ √ √ √ √ √
Number of
observations3084 2915 3084 2915 3084 2915
R-squared 0.134 0.129 0.135 0.129 0.135 0.129
The positive and statistically significant effect of fluid intelligence
doesn’t seem to be concentrated in any sector
Sub-Sample Estimation: Brawn Sector
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
ln (profit)
ln
(business
value)
(1) (2) (3) (4) (5) (6)
Math test score
(standardized)0.056 0.057 0.048 0.063
(0.164) (0.223) (0.164) (0.223)
Raven’s test score
(standardized)0.106 -0.084 0.103 -0.089
(0.148) (0.259) (0.147) (0.261)
Control variables
included √ √ √ √ √ √
Survey year and
province FE included √ √ √ √ √ √
Number of
observations176 174 176 174 176 174
R-squared 0.368 0.260 0.370 0.261 0.370 0.261
No statistically significant correlation of cognitive skills. However, N is very small
Extension 1 : Quantile Regression
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(Ln) Profit (Ln) Business Value
Return on fluid intelligence has a positive trend along the distribution of business value
Extension 2: Panel Fixed Effects with selection
ln (profit)ln (business
value)
(1) (2)
Math test score
(standardized)0.030 0.087
(0.038) (0.066)
Raven’s test score
(standardized)0.093** 0.110
(0.039) (0.067)
Control variables
included √ √
Survey year and
province FE included √ √
Inverse Mills Ratio 0.254 0.526
Inverse Mills Ratio *
survey year dummy -1.384 1.749
Number of
observations1719 1618
R-squared 0.368 0.260
Raven’s test score remains statistically significant for profit
We follow Semykina & Wooldridge
(2010) in estimating panel fixed
effects with selective attrition
Selection variable: proportion of
villages in district with financial
institutions providing business loan
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Why Fluid Intelligence Matters More?
Economic environments in development country setting could change rapidly. In
situation where rules and regulations are still incomplete, ability to quickly adapt
to change and to solve problem is more important than taught knowledge
It implies that basic arithmetic would suffice for non-farm businesses in
Indonesia which are relatively small with little asset and few employees
CONCLUSION
In this paper, we investigate the returns of cognitive skills of Indonesian non-farm household enterprise operators on the performance of their businesses. We specifically compare the returns to two types of intelligence: fluid and crystallized on non-farm household business performance, taking into account the fact that these types of intelligence are correlated with each other and with education attainment.
Research Objective
Key F indings
Fluid intelligence has a positive and sizeable effect on business profit,
even after controlling for possible selection into non-farm entrepreneurship
Crystallized intelligence leads to higher profits only when an entrepreneur is
engaged in the sector that is most appropriate given her/his skills (brain sector)
We find no evidence of entrepreneurs sorting into sectors based on
intelligence or education, presumably due to labor market constraints
Policy Recommendations
Policymakers must invest in
improving long-term health
outcomes as fluid intelligence
matters more
1
Information of skills required in a
specific sector should be made
available to individuals entering the
labor market in order to reduce
skill mismatch
2
Better functioning of labor market
to reduce existing constraints to the
full effectiveness of intelligence factor
in non-farm entrepreneurial sector
3
Thank you
This research was carried out by The SMERU Research Institute, Indonesia
With technical and financial support from Partnership for Economic Policy (PEP)
Under the PEP research and capacity building initiative for
“Policy Analysis on Growth and Employment” (PAGE)
Supported by: