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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Quantile

(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: