1. The study examines whether gender gaps in labor markets "push" women into necessity entrepreneurship or self-employment out of lack of alternatives. 2. The results show a positive and robust effect of gender employment gaps on necessity entrepreneurship for women, but no link for aspirational entrepreneurship. 3. Specifically, the multi-level regressions find that larger gender employment gaps are associated with higher rates of necessity self-employment among women, providing evidence that gender inequality in labor markets can push women into starting businesses out of necessity rather than by choice.

1. Pushed into necessity?
Pushed into necessity?
Gender gaps in the labor market and entrepreneurship of women
Work in progress. All comments welcome
Joanna Tyrowicz and Magdalena Smyk
FAME|GRAPE & University of Warsaw
May 2018

2. Pushed into necessity?
Motivation
Pushed or pulled?
Long standing debate: pushed or pulled
mixed evidence, rarely direct
positive (aspirations) vs. negative - lack of alternatives

3. Pushed into necessity?
Motivation
Pushed or pulled?
Long standing debate: pushed or pulled
mixed evidence, rarely direct
positive (aspirations) vs. negative - lack of alternatives
Story behind “discrimination” – can arguments work?
no: clients’ taste + co-workers’ taste
yes: statistical
Methodological constraints
usually cannot observe that someone was “discriminated against”...
... prior to becoming self-employed

4. Pushed into necessity?
Motivation
Preview of results
Our contribution
1 Split by necessity and aspirations self-employment

5. Pushed into necessity?
Motivation
Preview of results
Our contribution
1 Split by necessity and aspirations self-employment
2 Exploit cross-country & time variation of labor market gaps

6. Pushed into necessity?
Motivation
Preview of results
Our contribution
1 Split by necessity and aspirations self-employment
2 Exploit cross-country & time variation of labor market gaps
⇒ estimates available for gender employment & wage gaps (GEG, GWG)

7. Pushed into necessity?
Motivation
Preview of results
Our contribution
1 Split by necessity and aspirations self-employment
2 Exploit cross-country & time variation of labor market gaps
⇒ estimates available for gender employment & wage gaps (GEG, GWG)
Intuitions ⇒ hypotheses
Inequality as a push factor may matter for necessity SE ...
... but should not matter for aspirations
Wage inequality may operate weaker than employment inequality

8. Pushed into necessity?
Motivation
Preview of results
Our contribution
1 Split by necessity and aspirations self-employment
2 Exploit cross-country & time variation of labor market gaps
⇒ estimates available for gender employment & wage gaps (GEG, GWG)
Intuitions ⇒ hypotheses
Inequality as a push factor may matter for necessity SE ...
... but should not matter for aspirations
Wage inequality may operate weaker than employment inequality
We find that:
positive & robust effect of GEG/GWG on necessity female entrepreneurship
no link for aspirational entrepreneurship

9. Pushed into necessity?
Motivation
Structure of the talk
1 Motivation
2 Insights & theory
3 Method
4 Data
5 Results
6 Conclusions

10. Pushed into necessity?
Insights & theory
Insights from earlier studies
Unemployment as push factor to entrepreneurship
positive - lower opportunity costs (Evans and Jovanovic 1989, Blanchflower and
Meyer 1994)
negative - lack of physical capital (Johansson 2000, Hurst and Lusardi 2004)

11. Pushed into necessity?
Insights & theory
Insights from earlier studies
Unemployment as push factor to entrepreneurship
positive - lower opportunity costs (Evans and Jovanovic 1989, Blanchflower and
Meyer 1994)
negative - lack of physical capital (Johansson 2000, Hurst and Lusardi 2004)
Entrepreneurship among women
different types of products and services (Coleman 2000, Verheul et al. 2006,
Orser et al. 2006)
enhancing women’s relative power in the household (returns from education,
children development, work-life balance; Minniti and Naude 2010)

12. Pushed into necessity?
Insights & theory
Insights from earlier studies
Unemployment as push factor to entrepreneurship
positive - lower opportunity costs (Evans and Jovanovic 1989, Blanchflower and
Meyer 1994)
negative - lack of physical capital (Johansson 2000, Hurst and Lusardi 2004)
Entrepreneurship among women
different types of products and services (Coleman 2000, Verheul et al. 2006,
Orser et al. 2006)
enhancing women’s relative power in the household (returns from education,
children development, work-life balance; Minniti and Naude 2010)
gendered institutions - women react stronger to institutional barriers (Estrin and
Mickiewicz 2011)
education, empowerment, unadjusted wage gap (Kobeissi 2010)

13. Pushed into necessity?
Insights & theory
Insights from earlier studies
Unemployment as push factor to entrepreneurship
positive - lower opportunity costs (Evans and Jovanovic 1989, Blanchflower and
Meyer 1994)
negative - lack of physical capital (Johansson 2000, Hurst and Lusardi 2004)
Entrepreneurship among women
different types of products and services (Coleman 2000, Verheul et al. 2006,
Orser et al. 2006)
enhancing women’s relative power in the household (returns from education,
children development, work-life balance; Minniti and Naude 2010)
gendered institutions - women react stronger to institutional barriers (Estrin and
Mickiewicz 2011)
education, empowerment, unadjusted wage gap (Kobeissi 2010) ⇒ our study

14. Pushed into necessity?
Insights & theory
How to conceptualize
Extend the model by Fonseca et al (2001)
V - SE payoff, U - work payoff, K - start-up cost and α - distribution of
enterpreneurial skill
Individuals may have a gender

15. Pushed into necessity?
Insights & theory
How to conceptualize
Extend the model by Fonseca et al (2001)
V - SE payoff, U - work payoff, K - start-up cost and α - distribution of
enterpreneurial skill
Individuals may have a gender ⇒ women are disadvantaged in employment /
wages (but not productivity): U(1 − gap).

16. Pushed into necessity?
Insights & theory
How to conceptualize
Extend the model by Fonseca et al (2001)
V - SE payoff, U - work payoff, K - start-up cost and α - distribution of
enterpreneurial skill
Individuals may have a gender ⇒ women are disadvantaged in employment /
wages (but not productivity): U(1 − gap).
m and f - costs of being self-employed are also gender-specific.

17. Pushed into necessity?
Insights & theory
How to conceptualize
Extend the model by Fonseca et al (2001)
V - SE payoff, U - work payoff, K - start-up cost and α - distribution of
enterpreneurial skill
Individuals may have a gender ⇒ women are disadvantaged in employment /
wages (but not productivity): U(1 − gap).
m and f - costs of being self-employed are also gender-specific.
For becoming self-employed:
M: (α − m)V − K > U ⇒ Sm =
U + K
V
+ m,
W: (α − f )V − K > U(1 − gap) ⇒ Sw =
U + K − gap ∗ U
V
+ w

18. Pushed into necessity?
Insights & theory
How to conceptualize
Extend the model by Fonseca et al (2001)
V - SE payoff, U - work payoff, K - start-up cost and α - distribution of
enterpreneurial skill
Individuals may have a gender ⇒ women are disadvantaged in employment /
wages (but not productivity): U(1 − gap).
m and f - costs of being self-employed are also gender-specific.
For becoming self-employed:
M: (α − m)V − K > U ⇒ Sm =
U + K
V
+ m,
W: (α − f )V − K > U(1 − gap) ⇒ Sw =
U + K − gap ∗ U
V
+ w
This yields a gap in
1
1 − F(Sw )
−
1
1 − F(Sm)
= −
gap ∗ U
V
+ (w − m) (1)
negative so long as m is sufficiently smaller than w.
(w − m) is likely to be a country specific effect.

19. Pushed into necessity?
Method
What do we do
1 GEM: separate self-employment out of necessity and aspirational self-employment

20. Pushed into necessity?
Method
What do we do
1 GEM: separate self-employment out of necessity and aspirational self-employment
2 Indicators of gender wage gap and employment gap adjusted for individual
characteristics

21. Pushed into necessity?
Method
What do we do
1 GEM: separate self-employment out of necessity and aspirational self-employment
2 Indicators of gender wage gap and employment gap adjusted for individual
characteristics
3 Use theoretical model for econometric specification

22. Pushed into necessity?
Method
What do we do
1 GEM: separate self-employment out of necessity and aspirational self-employment
2 Indicators of gender wage gap and employment gap adjusted for individual
characteristics
3 Use theoretical model for econometric specification
4 Multi-level regression (following Estrin and coauthors)

23. Pushed into necessity?
Method
How to measure gender inequality?
Gender wage gaps
Nopo et al (2011) for 64 countires (cross-section)
Tyrowicz & van der Velde (2016) for app. 500 data points (time and
cross-section)

24. Pushed into necessity?
Method
How to measure gender inequality?
Gender wage gaps
Nopo et al (2011) for 64 countires (cross-section)
Tyrowicz & van der Velde (2016) for app. 500 data points (time and
cross-section)
Gender employment gaps
Goraus , Tyrowicz & van der Velde (2016) for app. 1200 data points (time and
cross-section)

25. Pushed into necessity?
Method
How to measure gender inequality?
Gender wage gaps
Nopo et al (2011) for 64 countires (cross-section)
Tyrowicz & van der Velde (2016) for app. 500 data points (time and
cross-section)
Gender employment gaps
Goraus , Tyrowicz & van der Velde (2016) for app. 1200 data points (time and
cross-section)
Gender equality index from ISD
robustness check

26. Pushed into necessity?
Method
Data on entrepreneurship and self-employment
Global Entrepreneurship Monitor
Adult Population Survey - representative for working population
At least two thousand respondents from each participating country (100+)
Questions (among others) on entrepreneurship activities, aspirations and plans
Three parts of the survey:
new firms ⇒ someone who has just established a firm
established firms ⇒ characteristics
business plans for the future ⇒ aspirations

27. Pushed into necessity?
Data
Matching between GEM and our data sources
GEM and GEG/GWG data available for diverse countries / years
Exact matching: that same year & country in both
Yields: 25 countries for GEG and 21 countries for GWG (dominated by
developed)

28. Pushed into necessity?
Data
Matching between GEM and our data sources
GEM and GEG/GWG data available for diverse countries / years
Exact matching: that same year & country in both
Yields: 25 countries for GEG and 21 countries for GWG (dominated by
developed)
If exact unavailable, inexact matching: +/- 5 years of GEM data relative to
GEG/GWG data
Yields 26 countries for GEG & GWG

29. Pushed into necessity?
Data
Matching between GEM and our data sources
GEM and GEG/GWG data available for diverse countries / years
Exact matching: that same year & country in both
Yields: 25 countries for GEG and 21 countries for GWG (dominated by
developed)
If exact unavailable, inexact matching: +/- 5 years of GEM data relative to
GEG/GWG data
Yields 26 countries for GEG & GWG
Check: ISD data yield 80 countries

30. Pushed into necessity?
Data
Descriptive statistics for women in the sample
Coverage by ISD GEG exact GWG exact
SE 5.8% 3.4% 3.7%
Necessity SE 1.6% 0.6% 0.8%
Opportunity SE 3.9% 2.6% 2.7%
EGA 23.5% 13.7% 12.1%

31. Pushed into necessity?
Data
Descriptive statistics for women in the sample
Coverage by ISD GEG exact GWG exact
SE 5.8% 3.4% 3.7%
Necessity SE 1.6% 0.6% 0.8%
Opportunity SE 3.9% 2.6% 2.7%
EGA 23.5% 13.7% 12.1%
Tertiary education 33.3% 34.4% 35.8%
Knows entrepreneur 32.6% 30% 30.5%
Knows business angel 2.5% 1.7% 2%

32. Pushed into necessity?
Data
Descriptive statistics for women in the sample
Coverage by ISD GEG exact GWG exact
SE 5.8% 3.4% 3.7%
Necessity SE 1.6% 0.6% 0.8%
Opportunity SE 3.9% 2.6% 2.7%
EGA 23.5% 13.7% 12.1%
Tertiary education 33.3% 34.4% 35.8%
Knows entrepreneur 32.6% 30% 30.5%
Knows business angel 2.5% 1.7% 2%
GEG exact match 23% 23% 32.6%
GWG exact match 20.2% 20.2% 20.2%
ISD 0.76 0.79 0.79

33. Pushed into necessity?
Data
Descriptive statistics for women in the sample
Coverage by ISD GEG exact GWG exact
SE 5.8% 3.4% 3.7%
Necessity SE 1.6% 0.6% 0.8%
Opportunity SE 3.9% 2.6% 2.7%
EGA 23.5% 13.7% 12.1%
Tertiary education 33.3% 34.4% 35.8%
Knows entrepreneur 32.6% 30% 30.5%
Knows business angel 2.5% 1.7% 2%
GEG exact match 23% 23% 32.6%
GWG exact match 20.2% 20.2% 20.2%
ISD 0.76 0.79 0.79
Countries 80 25 21
Country-year groups 394 185 53

34. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***

35. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***
GWG exact match 0.0021

36. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***
GWG exact match 0.0021
GEG inexact match 0.0064***

37. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***
GWG exact match 0.0021
GEG inexact match 0.0064***
GWG inexact match 0.0046*

38. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***
GWG exact match 0.0021
GEG inexact match 0.0064***
GWG inexact match 0.0046*
ISD gender equality -0.0205***

39. Pushed into necessity?
Results
Necessity self-employment for women (multi-level regression)
Necessity SE (1) (2) (3) (4) (5)
for women
Country-year groups 185 53 191 175 185
Observations 339,702 101,616 344,308 326,663 339,702
GEG exact match 0.0060***
GWG exact match 0.0021
GEG inexact match 0.0064***
GWG inexact match 0.0046*
ISD gender equality -0.0205***
Necessity SE - men 0.6242*** 0.6315*** 0.6237*** 0.9931*** 0.6346***
Age -0.0001*** -0.0001*** -0.0001*** -0.0001*** -0.0001***
Tertiary education 0.0004 0.0000 0.0004 0.0003 0.0003
Knows entrepreneur 0.0071*** 0.0079*** 0.0072*** 0.0072*** 0.0071***
Knows business angel 0.0118*** 0.0103*** 0.0116*** 0.0111*** 0.0118***
Constant 0.0010 0.0049*** 0.0011 -0.0021 0.0185***

40. Pushed into necessity?
Results
Opportunity self-employment and entrepreneurial growth aspirations (MLR)
(1) (2) (3) (4) (5) (6)
Opportunity SE for women =1 EGA
GEG inexact match 0.002 0.030
GWG inexact match 0.005 -0.021
ISD gender equality 0.029* 0.048
y variable - men 0.480*** 0.639*** 0.728*** 0.819*** 0.767*** 0.904***
Age -0.001*** -0.001*** -0.001*** -0.006*** -0.006*** -0.006***
Tertiary education 0.013*** 0.012*** 0.017*** 0.027*** 0.024*** 0.035***
Knows entrepreneur 0.040*** 0.040*** 0.049*** 0.055*** 0.058*** 0.070***
Knows bus angel 0.056*** 0.055*** 0.066*** 0.058*** 0.052*** 0.070***
Constant 0.007** -0.001 -0.027** 0.195*** 0.223*** 0.142***
Country-year groups 191 175 394 189 173 391
Observations 344,308 326,663 535,615 25,373 24,737 46,737

41. Pushed into necessity?
Conclusions
Conclusions
Robust evidence for link between GEG/GWG and necessity self-employment
among women

42. Pushed into necessity?
Conclusions
Conclusions
Robust evidence for link between GEG/GWG and necessity self-employment
among women
Weak or no evidence for aspirations

43. Pushed into necessity?
Conclusions
Conclusions
Robust evidence for link between GEG/GWG and necessity self-employment
among women
Weak or no evidence for aspirations
Previous results were due to country specificity (no macro effects once accounting
for country fixed effects)

44. Pushed into necessity?
Conclusions
Conclusions
Robust evidence for link between GEG/GWG and necessity self-employment
among women
Weak or no evidence for aspirations
Previous results were due to country specificity (no macro effects once accounting
for country fixed effects)

45. Pushed into necessity?
Conclusions
Conclusions
Robust evidence for link between GEG/GWG and necessity self-employment
among women
Weak or no evidence for aspirations
Previous results were due to country specificity (no macro effects once accounting
for country fixed effects)
What to do next?
Obtain counterfactual scenarios
Evaluate the effects of excess necessity on productivity

46. Pushed into necessity?
Conclusions
Thank you!

47. Pushed into necessity?
Conclusions
Macro-level approach: necessity female self-employment
Necessity SE - women (1) (2) (3) (4) (5)
for women
GEG exact match -0.0011
GWG exact match -0.0023
GEG inexact match -0.0017
GWG inexact match -0.0006
ISD gender equality -0.0013
Necessity SE 0.2780*** 0.2953*** 0.2967*** 0.3004*** 0.2779***
(men)
Tertiary education -0.0006 0.0030 -0.0009 -0.0009 -0.0005
(women)
Knows entrepreneur 0.0070* 0.0152 0.0074* 0.0080* 0.0066
(women)
Business angel 0.0856*** 0.1719*** 0.0798*** 0.0796*** 0.0906***
(women)
Constant -0.0004 -0.0045 -0.0003 0.0004 0.0004
Observations 185 53 191 175 185
Countries 25 21 26 26 25
Rˆ2 0.4589 0.6676 0.4688 0.4795 0.4587

48. Pushed into necessity?
Conclusions
Macro-level approach: opportunity self-employment and entrepreneurial
growth aspirations
(1) (2) (3) (4) (5) (6)
Opportunity SE - women (average) EGA - women (average)
GEG inexact match -0.005 0.222*
GWG inexact match 0.002 0.027
ISD gender equality 0.013 -0.165
y variable - men 0.303*** 0.308*** 0.545*** 0.779*** 0.747*** 0.653***
Tertiary education 0.009** 0.011*** 0.007 0.115 0.067 0.033
(women)
Knows entrepreneur 0.014 0.015* 0.033*** -0.241 -0.259 0.037
(women)
Business angel 0.044 0.045 0.003 2.013* 1.621 -0.360**
(women)
Constant 0.001 -0.001 -0.021** -0.087 0.010 0.144
Observations 191 175 394 191 175 394
Countries 26 26 80 26 26 80
Rˆ2 0.453 0.474 0.647 0.585 0.500 0.365