1. Are Male Entrepreneurs more Productive than Female Entrepreneurs? Evidence from Transition Economies Shwetlena Sabarwal PREM-Gender Katherine Terrell PREM-Gender and U. of Michigan World Bank Workshop on: “Women in the ECA Region” Jan. 24, 2008

2. Question and Motivation Prior to 1990 entrepreneurship supressed in ECA We know women less likely to enter private sector and less likely to start a business (e.g., Nesporova, 2001; Ganguli and Terrell, 2005) What is the relative performance of men v. women 15 years after the start of transition? Do women underperform compared to men in ECA as found in other countries? (e.g., Brusch, 1992; Rosa et al., 1996) If so, what is constraining women from performing better?

3. Data 2005 Business Enterprise and Performance Surveys (BEEPS) Same sample design and survey instrument for 26 post-socialist economies; manuf/services; sm/lrge Original data set about 9,500 firms but we restrict sample to: Firms where major shareholder is an individual or family (about 7,000 firms) and which have data on sales, capital, labor and materials (about 3,100 firms) We define Female Entrepreneur as the female owner (or primary owner) of an individually or family owned business

4. Female entrepreneurs as a percentage of total in sample

5. Breakdown of entrepreneurial enterprises by gender and size

6. Summary of firm performance by gender (regression sample) Labormale * (mean) female * (mean) Sales (‘000)18951302 Labor (perm.)5241 Capital (‘000)979534 Materials (‘000)930671 Change in sales revenues during last 36 months 25%23% * Numbers have been rounded off.

7. Constraints How problematic are these different factors for the operation and growth of your business? Permits Regulations Anti-competitive Practices of others Corruption Access to Land Electricity Transport Access to Finance Answers: 1= no obstacle…4 = major obstacle We convert to binary variable: 0=no obstacle; 1=obstacle

8. Additional Factors Has the respondent received financing from a bank? (1=Yes; 0=No) Does the respondent belong to a business association? (1=Yes; 0=No)

9. Percentage of entrepreneurs who do not perceive selected constraints as obstacles

10. Percentage of entrepreneurs who do not perceive access to finance as obstacle

11. Methodology Estimate a log-linear Cobb Douglas Production function on pooled data for all 26 countries: This is our base-line; the coefficient of interest is  We estimate for all firms; small v. large; new members of the EU v. Non-EU We add to eqn. the 10 constraints and the constraints interacted with the female ownership dummy Constraints are averaged over an industry and firm size for each country so as to avoid reverse causality

12. Coefficient on female ownership in each sample Baseline Cobb-Douglas Estimate AllSmallLargeNon-EUEU -0.058***-0.080***-0.012-0.073***-0.046*** *** significant at 1%

13. Coefficients on Constraints in TFP regression Coefficient for MaleCoefficient for Female (marginal effect) AllSmallLargeNon- EU EUAllSmallLargeNon-EUEU Permits -0.23-0.12-0.43-0.07-0.39 Permits 0.090.15-0.160.23-0.03 regulation -0.18-0.17-0.180.00-0.37 regulation 0.180.28-0.06-0.030.22 Anti-comp 0.20 0.280.430.10 Anti-comp -0.20-0.300.41-0.31-0.08 corruption 0.100.09 -0.160.27 corruption 0.290.22 0.450.29 Access to land 0.160.220.160.080.11 Access to land -0.19-0.35-0.44-0.22-0.31 electricity 0.040.000.10-0.180.15 electricity 0.01-0.06-0.030.030.54 transport -0.09-0.06-0.52-0.07-0.01 transport 0.060.370.220.15-0.20 Access to finance -0.08-0.14-0.07-0.000.19 Access to finance 0.060.08-0.68-0.020.12 Financing from bank -0.00 -0.01-0.00-0.01 Financing from bank 0.020.030.01-0.010.04 Business association 0.040.050.020.070.00 Business association 0.000.01-0.02-0.080.06 Sig at 1 %Sig at 5%Sig at 10%

14. Summary and conclusions Women owned firms are on average smaller than men’s (in terms of sales, no. of employees, and capital) but the growth in their sales is equivalent On average women underperform compared to men (by 6%) driven by differences in small businesses, no difference in larger ones. gender efficiency gap is larger in non-EU than in EU countries

15. Summary and Conclusions Most factors have similar effect for men and women The perception of the following as obstacles has the same effect on revenue efficiency on men and women: Government regulation and permits (negative effect) Corruption and Anti-competitive practices of others (positive effect) – exception: in Non-EU larger pos. effect on women  Interpretation: these are obstacles to business  barriers to entry of new firms  market share and revenues of existing firms and hence positive correlation with TFP.

16. Summary and Conclusions Access to land is the only factor that affects women’s efficiency differently than men. Pos. effect on men Neg. effect on women (marginal effect is large) Interpretation: “insider-outsider” model? Do not have more information on this variable. Having a loan from a bank does not affect efficiency of either men or women Being a member of a business association improves revenue efficiency of both (v. little)

17. Future Directions Better understanding of the mechanisms driving these pos. v. neg. effects of the constraints and why access to land is such a big factor Examine the effect of constraints on size of business (sales revenue) and perhaps growth of business (growth of sales)