1. Institutions and Female Entrepreneurship
Saul Estrin
LSE, CEPR, IZA
Tomasz Mickiewicz
University College, London
Slides for presentation at BCERC
June 2011, Syracuse

2. Outline The Questions Approach Contribution Literature Hypotheses Data
Results
Conclusions

3. The Questions
Seek to use cross-country heterogeneity to explain likelihood of female entrepreneurial entry
Focus on the impact of alternative national business environments on female entrepreneurship
Concerned with differential effect of the determinants of entrepreneurial activity on female entry
Develop hypotheses with respect to:
Rule of law
Size of the state
Investigates the impact of general measures of rule of law and of the size of the government, but also the impact of the sub- dimensions of each institutional variable that may have specific impact on women

4. Approach
Cross-country analysis using individual data on over 475,000 entrepreneurs and non-entrepreneurs in 55 countries ( )
Combining individual data from Global Entrepreneurship Monitor (GEM) with national institutional data from Polity IV, Heritage Foundation, World Bank (WDI), OECD, EIU
Consider in detail gender specific aspects of rule of law for all and for high growth aspiration projects

5. Contribution
New hypotheses about the impact of institutions on female entrepreneurship; namely women less likely than men to undertake entrepreneurship when:
Rule of law is weaker
State sector is larger
Investigate if those results are stronger/weaker when focus on the sub-indices that may be more relevant to women
Investigate both entry and high aspiration entry as dependent variable

6. Contribution Ctd
Establishes empirically impact of state sector on female entrepreneurship
Establishes empirically that some aspect of welfare and rule of law that are specific for women affect both male and female entrepreneurship
Uses data on entire universe of potential entrepreneurs
Combines individual and cross country data – usually treated separately
Builds on earlier work (esp. Aidis, Estrin, Mickiewicz SBE, 2010) to motivate choice of specification regarding institutional dimensions

7. Literature
General Institutional Theory – North (1990, 1997); Williamson (2000)
Role of entrepreneurs
Ordering of institutions
Williamson (1987), Barzel (1997), Rodrik (2000), Acemoglu and Johnson (2005)
Rule of law / property rights as backbone of market economy
Jutting et al. (2006)
Universality of property rights undermined by gender specific restrictions e.g.. with respect to ownership rights, freedom of choice, mobility, protection against gender-specific violence
de Soto, 2001; Sonin, 2003; Estrin, Korosteleva, Mickiewicz, :
The role of higher order institutions important for higher-value-added types of entrepreneurial activity

8. Institutions and Entrepreneurship
Baumol (1990) – form of entrepreneurial activity depends on institutional context; weak institutions may increase net returns to non-productive or destructive entrepreneurship
Literature suggests two institutional dimensions relevant for entrepreneurship:
Constitutional level: rule of law / property rights (Harper, 2003; confirmed by Johnson et al, 2002, Aidis, Estrin, Mickiewicz, 2009, but not Dermigue- Kunt et al., 2006 nor Klapper et al., 2006)
Governance level: extent of intervention captured by the size of state-sector (Baumol, 1990; de Soto, 1990; Verheul et al., 2001; Aidis, Estrin, Mickiewicz, 2010)

9. Institutions and Female Entrepreneurship
Female entrepreneurship exists everywhere but is always less than male entrepreneurship (except: Philippines – highest; generally high in South East Asia and in Latin America); lowest in United Arab Emirates and low in the Middle East (but not necessary in Far East Muslim countries, e.g. Indonesia)
Verheul, Van Stel and Thurik (2006) only paper attempting to explain cross country variation in female entry rates
Female entrepreneurship explained largely by same factors as male – age, education, birth order etc (Brush, 2007)

10. Hypotheses
H1: Women are less likely than men to undertake entrepreneurial activity in countries where the rule of law is weaker
H2: Women are less likely than men to undertake entrepreneurial activity in countries where the state sector is larger
We need to verify the extent to which the female - specific aspects of rule of law and of state activities are significant

11. Control Variables GDP/capita (PPP) GDP growth
Individual characteristics
Age
Gender
Financial resources
Education
Employment states
Experience
Networking

12. Individual Data
“Nascent entrepreneurs” (start-ups) i.e. individuals between 18 – 64 who have taken some real action towards creating a new business in past year and expect to own a share of the business they are starting
“High aspiration entrepreneurs” – nascent entrepreneurs who expect to employ more than 10 workers within five years
Men on average twice as likely to be involved in entrepreneurship than women – we take this as given and focus on interaction between gender and the institutional variables

13. Table 1. Descriptive statistics: personal characteristics
Variable
Definition and source
Mean
S.D.
Age
The exact age of the respondent
43.13
15.35
Age squared
2096.1
1429.0
Female
1=female, 0 otherwise
.5187
.4996
Employment
1=respondent is either in full or part time employment, 0 if not
.6209
.4852
Post-secondary & higher education
1=respondent has a post secondary or higher education attainment, 0 otherwise
.3421
.4744
Higher education
1=respondent has a higher education attainment
.1352
.3419
Business angel
1=business angel in past three years, 0 otherwise
.0330
.1786
Current owner of business
1=current owner/manager of business, 0 otherwise
.0883
.2837
Knows other entrepreneurs
1=personally knows entrepreneurs, in last two years, zero if not
.3619
.4806
Fear of failure
1=respondent believes that the fear of failure would not prevent him/her from starting a business
.6510
.4766

14. Table 2. Descriptive statistics: institutional variables and macro controls
Definition and source
Mean
S.D.
Constraints on executive
Polity IV ‘Executive Constraints’; scores from 1=”unlimited authority” to 7=”executive parity”; higher denotes less arbitrariness
6.71
.8786
Size of government
Heritage Foundation index based on government expenses (including consumption and transfers) / GDP, higher value → lower spending, range: 0 to 100
46.5
24.1
GDP per capita
GDP per capita at purchasing power parity, constant at 2005 $USD (WB WDI )
26240
10114
GDP growth
Annual GDP growth rate (WB WDI )
3.05
2.28
Restricted freedom of movement
Restricted freedom of movement (opportunity to move freely outside the house) for women (OECD Development Centre), time invariant; range 0-1
.03
.12
Violence against women
Violence against women, lack of relevant legislation (OECD Development Centre), time invariant; range 0-1
.26
.17
Maternity leave
Composite indicator that assesses length of maternity leave and benefits coverage (Economic Intelligence Unit), time invariant, range 0-3.1
2.09
.88
Childcare
Availability, affordability and quality of childcare services, as well as the role of the extended family (Economic Intelligence Unit), time invariant, range 1-5.
3.60
.94

15. Estimation Framework
Ententryijt = f(Strength of Rule of Lawjt, Size of Statejt, GDP growth ratejt, Individual Level Controlsjit, Femaleijt, Interactive effects between institutions and Femalejit)
Estimated using probit, reported with robust standard errors and allowing for possibility that observations are not independent for each country–year sample
Institutional and macro variables lagged one year (apart from those that are time-invariant)
Year dummies
Estimated for entrepreneurial entry and high aspiration entrepreneurial entry

16. (e.g.: Singapore, China, Jordan low; India high)

18. Table 3. Estimation Results Dependent Age Age squared Female
(1)
(2)
(3)
(4)
Dependent
startup
startup,
expects >10 jobs
Age
***
***
*** **
( )
(5.73e-05)
( )
(5.65e-05)
Age squared
-3.02e-05***
-3.37e-06***
-2.98e-05***
-3.29e-06***
(3.99e-06)
(7.47e-07)
(3.95e-06)
(7.37e-07)
Female
***
***
*** **
( )
( )
( )
( )
In employment
0.0141***
***
0.0143***
***
( )
( )
( )
Post-secondary & higher educ.
***
***
***
***
( )
( )
( )
( )
Higher education
( )
( )
( )
( )
Business angel in last 3y
0.0397***
***
0.0393***
***
( )
( )
( )
( )
Current owner of business
***
***
( )
( )
( )
( )
Knows other entrepreneurs
0.0365***
***
0.0362***
***
( )
( )
( )
( )
Fear of failure not prevent start-up
0.0208***
***
0.0206***
***
( )
( )
( )
Size of government (reverse)
***
7.36e-05***
***
6.33e-05***
(8.16e-05)
(1.41e-05)
(7.98e-05)
(1.36e-05)
Constraints on executive *
( )
( )
( )
( )
Gov. size (rev.) x Female
***
3.27e-05**
(4.45e-05)
(1.02e-05)
Constraints on exec x Female
5.15e-05
( )
( )
GDP growth rate
( )
( )
( )
GDP per capita (ppp)
-4.23e-07**
1.17e-08
-4.16e-07**
1.18e-08
(1.37e-07)
(2.59e-08)
(1.35e-07)
(2.53e-08)
Table 3.
Estimation Results

19. Table 4. Estimation Results Dependent Age Age squared Female
(5)
(6)
(7)
(8)
Dependent
startup
startup,
expects>10jobs
Age
***
***
***
( )
(5.56e-05)
( )
(5.59e-05)
Age squared
-3.03e-05***
-3.45e-06***
-3.01e-05***
-3.44e-06***
(3.97e-06)
(7.25e-07)
(7.27e-07)
Female
***
***
***
***
( )
( )
( )
( )
In employment
0.0146***
***
0.0148***
***
( )
( )
( )
Post-secondary & higher educ.
***
***
***
( )
( )
( )
( )
Higher education
( )
( )
( )
( )
Business angel in last 3y
0.0405***
***
0.0401***
***
( )
( )
( )
( )
Current owner of business
***
***
( )
( )
( )
( )
Knows other entrepreneurs
0.0365***
***
0.0361***
***
( )
( )
( )
( )
Fear of failure not prevent startup
0.0204***
***
0.0203***
***
( )
( )
( )
( )
Size of government (reverse)
***
5.98e-05***
***
5.64e-05***
(7.32e-05)
(1.15e-05)
(6.85e-05)
(1.21e-05)
Restricted freedom of movement
***
(0.0129)
( )
Violence against women
0.0111
( )
( )
Gov. size (rev.) x Female
***
2.85e-05***
***
3.66e-05***
(4.04e-05)
(8.32e-06)
(4.11e-05)
(9.76e-06)
Freedom of move x Female
( )
( )
Violence x Female
( )
( )
GDP growth rate
( )
( )
( )
GDP per capita (ppp)
-3.69e-07*
-1.00e-08
-3.29e-07*
1.09e-08
(1.77e-07)
(2.79e-08)
(1.53e-07)
(2.64e-08)
Table 4.
Estimation Results

20. Table 5. Estimation Results Dependent Age Age squared Female
(9)
(10)
(11)
(12)
Dependent
startup
startup,
expects>10jobs
Age
*** **
*** **
( )
(6.24e-05)
( )
(6.33e-05)
Age squared
-3.05e-05***
-3.49e-06***
-3.09e-05***
-3.48e-06***
(4.16e-06)
(8.16e-07)
(4.37e-06)
(8.29e-07)
Female
( )
( )
( )
( )
In employment
0.0142***
***
0.0139***
( )
( )
( )
( )
Post-secondary & higher education
***
***
***
***
( )
( )
( )
( )
Higher education
( )
( )
( )
( )
Business angel in last 3y
0.0385***
***
0.0413***
***
( )
( )
( )
( )
Current owner of business
*** **
( )
( )
( )
Knows other entrepreneurs
0.0352***
***
0.0363***
***
( )
( )
( )
( )
Fear of failure not prevent start-up
0.0206***
***
0.0212***
***
( )
( )
( )
( )
Constraints on executive *
-1.62e-05
-5.20e-05
( )
( )
( )
( )
Maternity leave
***
***
( )
Childcare **
( )
( )
Constraints on exec x Female
( )
( )
( )
( )
Maternity leave x Female
( )
Childcare x Female **
( )
( )
GDP growth rate *
( )
( )
( )
( )
GDP per capita (ppp)
-7.59e-07***
-3.64e-08
-6.80e-07**
-2.06e-08
(1.74e-07)
(3.29e-08)
(2.07e-07)
(3.80e-08)
Table 5.
Estimation Results

21. Results
Controls confirm many findings in literature e.g.. probability of being an entrepreneur less in all countries for women, older people, less educated, less well networked, lack of fear of failure
Entrepreneurship not related to business cycle (except weakly high aspiration is pro-cycle
While rule of law important for entrepreneurship, it does not affect female entrepreneurship specifically
Restrictions on freedom of movement (away from home) imposed on women affect both female and male high aspiration entrepreneurship
Female entrepreneurship is reduced by a large state sector. Holds for entry and for high aspiration female entrepreneurs
Maternity leave and childcare reduce entry and high aspiration entry for both man and women. Plus some additional effects on women.

22. Conclusions
Rule of law is important for entrepreneurship but the effects are the same for male and female entrepreneurs
However detailed elements of the rule of law likely to specifically affect women do impact differentially on high aspiration female entrepreneurship
Differential impact of state sector on female entrepreneurship
Female-specific welfare measures impact both women and men. Good news: impact of these measures stronger on entry than on high aspiration entry.