Do Institutions have a Greater Effect on Female Entrepreneurs? Saul Estrin LSE, CEPR, IZA And Tomasz Mickiewicz University College, London 1 Slides for presentation at Female Entrepreneurship: Constraints and Oppportunities Conference at the World Bank, June 2 nd 3 rd 2009 Outline Literature Hypotheses Data Results Conclusions 2 1
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: Property rights Role of the state Functioning of financial markets 3 Approach Cross-country analysis using individual data on 360,000 entrepreneurs and non-entrepreneurs in 44 countries Combining individual data from Global Entrepreneurship Monitor (GEM) with national institutional data from Heritage Foundation, World Bank (WDI) and IMF plus GEM-based peer effects Use of factor analysis to reduce multicollinearity and other problems in use of institutional indices 4 2
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 Formal and informal financial sector is weaker Establishes empirically impact of state sector and informal finance on female entrepreneurship Uses data on entire universe of potential entrepreneurs Combines individual and cross country data usually treated separately Factor analysis addresses ad hoc specifications to resolve problems of multicollinearity in institutional indices and generates factors with clear economic interpretation 5 Literature General Institutional Theory North (1990, 1997) Role of entrepreneurs Distinction between formal and informal institutions Williamson (1987), Barzel (1997), Rodrik (2000), Acemoglu and Johnson (2005) 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 Access to formal rights and role of informal sector (de Soto, 2001; Sonin, 2003; Estrin, Korosteleva, Mickiewicz, 2009) The role of formal institutions important for higher-value-added types of entrepreneurial activity 6 3
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 three main institutions relevant for entrepreneurship: 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) State-sector (Baumol, 1990; de Soto, 1990; Verheul et al., 2001) Financial sector (Gros and Steinherr, 2004), both formal and informal (Aidis, Estin, Mickiewicz, 2007; Korosteleva and Mickiewicz 2008) 7 Institutions and Female Entrepreneurship Female entrepreneurship exists everywhere but is always less than male entrepreneurship (except: Thailand) Cross-country variation in female entry rates is large from 0.2% in Japan to 28% in Peru (GEM) 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) Possible difference in determinants of female entrepreneurship with respect to financing (Carter and Rosa, 1998; Bruce, 1999) 8 4
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 H3: Women are less likely than men to undertake entrepreneurial activity in countries where the formal financial sector is weaker H3: Women are less likely than men to undertake entrepreneurial activity in countries where the informal financial sector is weaker 9 Data: Institutions and Factor Analysis Use data reduction methods on Heritage Foundation/Wall Street Journal indicators of institution Unable to use World Bank Doing Business does not match GEM time frame, nor cover all the countries. Also factor analysis collapses due to missing values Heritage Foundation offers 50 independent indicators in 10 broad institutional categories trade policy, fiscal burden, government spending, monetary policy, constraints on FDI, state involvement in banking and finance, labor regulation, security of property rights, business regulation, corruption We apply factor analysis to entire dataset (1995 2008, 164 countries, 9 indicators) 10 5
-1 0 1 2 3.2.4.6.8 0 7/9/2009 Factor Analysis Figure 1 Factor loadings fiscal_free govt_size monet_free financ_free trade_free investm_free busin_free oblimin oblique rotation prop_rights -.2free_corrupt -.2 0.2.4.6.8 Rule of Law (Factor 1) 11 Factor Analysis Figure 2 Countries with rule of law have either large or small state sector BUT... we find no countries without rule of law & large state sector 12 New Zealand Hong Kong Denmark Singapore Netherlands, The United Kingdom Ireland Sweden Finland Australia United States Iceland Canada Switzerland Belgium Germany Chile Norway Spain Italy Israel Portugal Korea, Republic of (South Korea) France Taiwan Hungary Japan Jordan Slovenia Thailand Poland South Africa Greece Brazil Mexico Uganda Peru Croatia Argentina Venezuela India Ecuador China, People's Republic of Russia -3-2 -1 0 1 2 Limited State Sector (Factor 2) 6
Other Institutional Data Formal financial sector ratio of credit provided to the private sector to GDP (World Bank) Informal financial sector prevalence rate of business angels over previous three years (from GEM calculated as country level effects) Some correlation between rule of law and formal financial sector 13 Individual Data nascent entrepreneurs 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 established entrepreneurs individuals who own or manage a company and have paid salaries for more than 42 months 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 four institutional variables 14 7
Control Variables GDP/capita at PPP GDP growth Individual characteristics Age Gender Financial resources Education Employment states Experience Networking 15 Table 1. Descriptive statistics Variable: Obs Mean Std.Dev Min Max Entrepreneurial entry (start-up) 503466 0.03 0.18 0.00 1.00 Male 503466 0.47 0.50 0.00 1.00 Owner/man. of exist. Business 503466 0.05 0.22 0.00 1.00 Business angel in past 3 years 501983 0.03 0.16 0.00 1.00 Knows entrepreneurs in past 2 years 408828 0.35 0.48 0.00 1.00 In employment at time of survey 484814 0.49 0.50 0.00 1.00 Education: secondary or more 460982 0.67 0.47 0.00 1.00 Education: postsec. or more 460982 0.32 0.47 0.00 1.00 Education: higher 460982 0.08 0.26 0.00 1.00 Age 471037 42.97 16.99 1.00 104.00 Annual GDP growth rate 503466 2.95 2.45-10.89 10.06 Log GDP pc (ppp) 503466 10.04 0.65 6.69 10.75 Rule of law (factor 1 score) 503466 1.26 0.75-0.87 2.28 Limited size of the government (factor 2 score) 503466-0.88 1.17-3.28 1.71 Credit to priv. sector / GDP 500583 108.80 44.85 6.88 231.08 16 8
.2.4.6.8 1 7/9/2009 Estimation Framework Ententry ijt = f(strength of Property Rights jt, Level of Welfare Provision and Taxation jt, Availability of Formal Finance jt, Availability of Informal Finance jt, GDP/capita jt, GDP growth rate jt, Individual Level Controls jit, Female ijt, Interactive effects between institutions and Female jit ) Estimated using probit, reported with robust standard errors and allowing for possibility that observations are not independent for each country year sample Estimated for entrepreneurial entry, high aspiration entrepreneurial entry, opportunities entrepreneurial entry and the structure of individual financing High aspiration and opportunity start up jointly estimated with likelihood of start up (bivariate probit) More detailed individual data on financing for subset of start ups only, to explain use of informal and formal finance respectively 17 Figure 3 atios of Female to Male Startup Rates and Ru Thailand Peru TE weak rule of law and weak female entry rates Chile the opposite Russia Venezuela Mexico Ecuador Portugal India Chile China Brazil Australia South Africa Argentina Uganda Hungary Korea US Israel New Zeland Denmark Jordan Canada Ireland Hong Kong UK Singapore Italy Germany Iceland Belgium Netherlands Finland Czech Slovenia Rep. France Greece Taiwan Sweden JapanSpain Croatia Poland Norway -1 0 1 2 3 Rule of Law 18 9
.2.4.6.8 1.2.4.6.8 1 7/9/2009 Figure 4 s of female to male startup rates and size of th Thailand Peru Small state & high female entry rates; Thailand, Peru; some rich English Legal Origin Countries The opposite: France, Sweden Venezuela Mexico Portugal Chile Ecuador Brazil Australia South Africa India China Hungary ArgentinaUganda US Korea Israel New Zeland Denmark Canada Jordan Ireland Hong K Iceland UK Singapore Germany Netherlands Italy Finland Taiwan Sweden Belgium Slovenia Czech Rep. France Japan GreeceRussia Croatia Spain Norway Poland -3-2 -1 0 1 2 Limited state sector 19 Figure 5 s of female to male startup rates and supply of f Developed Formal Finance & Relatively High female Entry Rate: United States The opposite: TE Peru Thailand Venezuela Mexico Ecuador Chile Portugal India Brazil Australia South Africa Uganda China ArgentinaHungary Korea US Israel New Zeland Denmark Canada Jordan Ireland Hong Kong Singapore UKIceland Germany Finland Italy Taiwan Netherlands Slovenia Belgium Czech Rep. France Sweden Russia Greece Japan Croatia Spain Poland 0 50 100 150 200 Credit to Private Sector / GDP 20 10
.2.4.6.8 1 7/9/2009 Figure 6 of female to male startup rates and supply of in Informal Finance And Female Entry: Peru, Ecuador, Uganda The Opposite: Russia, Croatia... Brazil Thailand Venezuela Mexico Portugal India Ecuador Australia Chile South Africa China Hungary Argentina US Korea Israel New Zeland Denmark Ireland Canada Hong Kong UK Singapore Italy Germany Iceland Netherlands Belgium Finland France Russia Sweden Taiwan Czech Rep. Japan Slovenia Croatia Greece Spain Poland Norway Peru Uganda Jordan 0.05.1.15 business angels prevalence rate 21 Table 2. Estimation Results 22 11
Table 3. Financing. Bivariate probit regression 23 Table 4. Estimation Results (with women*employment interaction) 24 12
Table 5. Financing: Bivariate probit regression (with women*employment) 25 Summary of 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 Rule of law does not affect female entrepreneurship Female entrepreneurship is reduced by a large state sector. Holds for high aspiration and opportunity entrepreneurs No effect of formal finance on female entrepreneurship Informal finance is significant for female startup but not for high aspiration or opportunity female entrepreneurship Men more likely than women to rely on formal than informal finance 26 13
Conclusions Rule of law is important for entrepreneurship but the effects are the same for male and female entrepreneurs (up to a certain level of development) Caveat: data on rights of women incomplete so could not use Differential impact of state sector and informal finance on female entrepreneurship important policy implications for institution building process 27 14