Journal of Development Economics

Journal of Development Economics 99 (2012) 92 104 Contents lists available at SciVerse ScienceDirect Journal of Development Economics journal omepage: www.elsevier.com/locate/devec Investor protection and income inequality: Risk saring vs risk taking Alessandra Bonfiglioli Institute for Economic Analysis (CSIC) and CEPR, Spain article info abstract Article istory: Received 24 May 2010 Received in revised form 28 September 2011 Accepted 28 September 2011 JEL classification: D31 E44 O16 Keywords: Investor protection Income inequality Optimal financial contracts Risk taking Risk saring Tis paper studies te relationsip between investor protection and income inequality. In te presence of market frictions, better protection makes investors more willing to take on entrepreneurial risk wen lending to firms, tereby improving te degree of risk saring between financiers and entrepreneurs. On te oter and, by increasing risk saring, investor protection also induces more risk taking. By increasing entrepreneurial risk taking, it raises income dispersion. By reducing te risk faced by entrepreneurs, it reduces income volatility. As a result, te relationsip between investor protection and income inequality is non monotonic, since te risk-taking effect dominates at low levels of investor protection, wile risk saring becomes stronger wen more risk is taken. Empirical evidence from up to sixty-seven countries spanning te period 1976 2004 supports te predictions of te model. 2011 Elsevier B.V. All rigts reserved. 1. Introduction Te literature on institutions, law and economics as sown tat investor protection affects significantly te financial structure of an economy, and as investigated te effects of financial development on economic performance in terms of GDP growt, productivity and investment. 1 Wat as received less attention is tat investor protection, troug its effect on financial structure and te allocation of risk, may influence te risk taking beavior of investors and firms, tereby affecting income inequality. To fill tis gap, tis paper investigates te link between investor protection and income inequality, bot teoretically and empirically. It proposes a model were investor protection promotes risk saring between financiers and entrepreneurs, tereby inducing more risk taking in te economy. Better risk saring and wider risk taking, in turn, affect income inequality in opposite ways. Te main results of te model are ten confronted wit te data. To formalize tese ideas, I construct a simple model of investors and entrepreneurs were agents are risk averse and eterogeneous in ability. Investors decide ow to allocate teir endowment between safe loans (debt) and diversified portfolios of risky (equity-like) assets, wile entrepreneurs face a coice between a safe and a risky Institute for Economic Analysis (CSIC), Campus UAB, 08193 Bellaterra (Barcelona), Spain. Tel.: +34 93 5806612; fax: +34 93 580 1452. E-mail address: alessandra.bonfiglioli@iae.csic.es. 1 See, among oters, Acemoglu and Jonson (2005), Beck and Levine (2004), La Porta et al. (1997, 2006), Levine (2005) and references terein. tecnology, wose probability of success depends on ability. Starting up a firm entails a fixed entry cost tat entrepreneurs must cover by borrowing. Financial markets are subject to a moral azard problem arising from te non-observability of output to financiers. Measures of investor protection alleviate tis financial friction. In particular, I assume tat investor protection promotes transparency by imposing a cost to misreport cas flow. 2 Better guarantees generate more confidence among investors, tereby making tem more willing to insure te entrepreneurs troug lending. It follows tat in financial systems wit stronger investor protection tere is more equity-like external finance relative to debt, wic offers entrepreneurs a iger degree of risk saring. Finally, I rule out wealt eterogeneity among agents, so tat all inequality is due to idiosyncratic factors (ability), financial market conditions and income risk. 3 In te model, better investor protection affects income inequality in two ways. (i) It improves risk saring, tereby reducing income volatility for risky entrepreneurs; and (ii) it raises te sare of risky firms, and ence agents exposed to earnings risk. Wile (i) tends to 2 Investor protection takes te form of a iding cost also in Agion et al. (2005), Castro et al. (2004) and Lacker and Weinberg (1989). In tis paper, like in te two latter, te cost is proportional to te idden amount, wile in te first, it equals a fraction of te initial investment. 3 Using microdata, Hurst and Lusardi (2004) sow tat wealt may not be te key factor affecting entrepreneurial coices, wile Ardagna and Lusardi (2008) provide evidence tat skills and te fear of failure are among te most important determinants of entrepreneursip. Tis lends support to my modeling coice of abstracting from wealt eterogeneity to better focus on oter factors, suc as risk and ability. 0304-3878/$ see front matter 2011 Elsevier B.V. All rigts reserved. doi:10.1016/j.jdeveco.2011.09.007

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 93 reduce inequality, (ii) raises it. Te analysis sows tat te risk taking effect (ii) dominates wen investor protection is low since risky entrepreneurs still face a considerable earnings risk, wile te risk saring effect (i) prevails wen investor protection is ig since better insurance applies to a large mass of risky entrepreneurs. Hence, te relationsip between investor protection and income inequality is predicted to be non-monotonic. Moreover, since investor protection affects te financial structure of te economy, te same non-monotonic relationsip olds between te sare of equity-like externalfinance and inequality. To evaluate empirically te main results of te model, I consider a dataset covering up to sixty-seven countries observed between 1976 and 2004. Te coice of a cross-country analysis is dictated by te fact tat investor protection is generally set by law and ence exibits little witin-country variation. I adopt two proxies for inequality: first, te Gini coefficient of te income distribution, wic is available for a relatively large sample of countries and years. Altoug te model refers to entrepreneurs, wic belong to te top income percentiles, tree main arguments may justify te use of a general indicator of inequality. 4 First, recent evidence from several countries suggests tat a large fraction of te variation in income inequality over te last two decades is explained by canges at te top of te distribution (see, among oters, Atkinson et al., 2009 and Heatcote et al., 2010). Second, employees normally earn iger wages and are subject to iger employment risk wen working in more productive and riskier firms. 5 Hence te results obtained for entrepreneurs may be expected to trickle down to all workers. Finally, te model could also be interpreted as one of occupational coice àlakilstrom and Laffont (1979), were eac agent can eiter be a worker receiving a fixed wage or an entrepreneur facing risk. In tis case, te implications on earnings inequality would refer to te entire population. Noneteless, for robustness, I replicate part of te analysis proxying inequality wit te ratio of te top 1st to 10t percentiles of te income distribution. Te main sortcoming wit tis variable, recently compiled by Alvaredo et al. (2011), is its very limited cross-sectional availability (at present, te database covers only 23 countries). Turning to te independent variables, I proxy investor protection wit te de jure index compiled by La Porta et al. (2006), and estimate its non-linear relationsip wit inequality. Next, I evaluate te teoretical mecanism following a two-step approac. I first sow tat better protection tends to coincide wit a iger sare of equity-like external finance, and ten I estimate a non-linear relationsip between te indicator of financial structure and inequality on a wider cross-section and a panel. Te results suggest tat inequality varies non-monotonically bot wit investor protection and te relative weigt of equity-like finance, as predicted by te model. Te paper is related to four main strands of literature. Acemoglu and Jonson (2005), as well as La Porta et al. (1998), sow tat investor protection, and in general institutions aimed at contractual protection, affect te financial structure of an economy by promoting te development of stock markets, but ave unclear effects on economic performance. No attention was devoted, owever, to study te effects on inequality. Teoretical contributions from te growt literature (see Agion and Bolton, 1997; Banerjee and Newman, 1993; Galor and Zeira, 1993, and Greenwood and Jovanovic, 1990, among oters) ave proposed explanations for te relationsip between financial development, inequality and growt. In most of tese models, income inequality originates from eterogeneity in te initial wealt distribution, paired 4 Note tat, if investor protection affected inequality among te poor in a different way and troug anoter cannel, my estimates would suffer from attenuation bias. 5 Evidence tat more productive firms pay iger wages is provided, among oters, by Oi and Idson (1999). wit credit market frictions. 6 As te poorest are subject to credit constraints, tey are prevented from making te efficient investment, wic affects te dynamics of wealt and income. I depart from tis approac in two main respects. First, te financial friction affects te sare of risk borne by agents, rater tan te amount of external finance available to tem. Second, I consider a different source of ex-ante eterogeneity (in productivity) wic, togeter wit te extent of risk saring and risk taking, ultimately determines te income distribution. 7 Tis paper also contributes to te recent literature on te macroeconomic implications of entrepreneursip wic addresses te effects of financial frictions on investment, growt and volatility troug teir impact on entrepreneurial coices (see Quadrini, 2009 for a review). Te papers focusing on distributional issues tend to consider financial frictions as a factor tat perpetuates and exacerbates wealt inequality by affecting te investment and saving coices of entrepreneurs, and abstract from entrepreneurial risk saring and risk taking. Oter papers, suc as Castro et al. (2004) and Micelacci and Fabiano (2011), relate financial institutions and entrepreneursip to growt troug risk saring, risk taking, and managerial ability, but do not study inequality. Tesmar and Toenig (fortcoming) point out tat better risk saring may induce iger risk taking and raise volatility. Caselli and Gennaioli (2001) sow tat weak contract enforcement deteriorates productivity (TFP) by discouraging untalented family-firm owners from iring competent managers (as in Burkart et al., 2003). Te vast empirical literature on financial development and economic performance (see Levine, 2005 and references terein) provides evidence tat deeper financial markets foster growt. Very little attention was paid to te effects of financial development on income inequality. Two recent contributions (see Beck et al., 2007 and Clarke et al., 2006) sow tat iger availability of credit to te private sector tends to reduce income inequality. My results are consistent wit tis evidence, but also provide a novel insigt suggesting tat equity-like finance may increase inequality. Te remainder of te paper is organized as follows. Section 2 presents te model of entrepreneurial coice and sows ow earnings and te degree of risk taking vary in equilibrium wit investor protection. In Section 3, I caracterize analytically and by means of numerical solution ow income inequality responds to canges in investor protection and financial structure. Section 4 briefly discusses some reasons wy investor protection may be imperfect and vary across countries. Section 5 provides empirical evidence from up to sixtyseven countries over te period 1976 2004 supporting te main results of te model, and Section 6 concludes. 2. Te model In tis section, I propose a simple static model were risk-averse agents, eterogeneous in teir entrepreneurial ability, ave to coose between safe and risky projects and need external finance. Asymmetric information in te financial market generates a moral azard problem tat makes it too costly for some entrepreneurs to finance risky projects. Investor protection may alleviate moral azard, tereby easing te conditions of access to finance and promoting bot risk saring and risk taking. 6 Te financial friction may consist in te non-observability of ex-post outcomes as in Banerjee and Newman (1993) and Galor and Zeira (1993), or of effort as in Agion and Bolton (1997). 7 Similarly to tis paper, in Acemoglu and Zilibotti (1999) income inequality is generated by managerial incentives. Antunes et al. (2008) propose a quantitative model wit eterogeneity in wealt and ability were weak financial institutions inder growt and raise income inequality. Yet, bot papers abstract from firm-specific idiosyncratic risk.

94 A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 2.1. Set up Consider a small open economy populated by a continuum of riskaverse agents wose preferences are represented by V ¼ E½uc ðþš; were E is te expectation operator, c is consumption of a omogeneous good, and te utility function satisfies te following properties: u >0, u b0 and lim c 0 u (c)=. Te price of te consumption good is determined on te world market and is assumed to be constant and normalized to 1. Agents are eterogeneous in teir ability, denoted by [0, 1], drawn from a continuously differentiable distribution G(), but ave no wealt endowment. Tey work as self-employed entrepreneurs and can coose to produce te consumption good using eiter a safe or a risky tecnology. Te former generates a constant level of production wic is independent of ability: y S ðþ ¼ y S ¼ B: Te performance of te risky tecnology depends on entrepreneurial ability. 8 For simplicity, I assume tat ability only affects te probability of success and not te quantities produced. 9 In particular, an entrepreneur wit ability generates output yðþ ¼ yh ¼ A wit probability y L ¼ ϕa wit probability 1 were ϕ (0,1) and y H and y L denote production in te good and bad state respectively. Tis implies tat a firm's expected cas flow is [+(1 )φ]a, wic is increasing in ability. Success is i.i.d. witin eac ability group, ence tere is no aggregate risk and total production of entrepreneurs wit ability equals g()[+(1 )φ]a, were g() is te density of te ability distribution. Eac entrepreneur, regardless of er tecnological coice, as to pay a fixed entry cost of 1 tat can be covered by raising funds on te international financial market. Here, I assume tat investors are atomistic and risk-averse, and ave perfect information about te risk-free interest rate (r, normalized to 1), production tecnologies (B, A and ϕ), te individual ability of eac entrepreneur () and er tecnological coice, but cannot observe final output (y). Te financial contract entails te commitment of te firm to repay after production a certain amount, possibly contingent on te reported realization of output. Given tat ex-ante information is perfect, entrepreneurs using te safe tecnology are known to generate wit certainty a cas flow of B and tus face a fix repayment equal to te international gross risk-free rate, 1, wic gives tem a payoff of w S =B 1. Te situation is different if te borrower runs a risky project. Once production as occurred, an unlucky entrepreneur can only report output y L = ϕa, and ence repay to investors te cas flow minus er earnings: y L w L (). If successful, te entrepreneur may misreport te output realization and pretend to be in te bad state, in order to repay y L w L () instead of y H w H (). However, I assume tat measures of investor protection, specific to te borrower's country, make misreporting costly. For every unit of idden cas flow, te firm incurs a cost p [0, 1], so tat te payoff from misreporting is w L ()+ (1 p)(y H y L ). 10 I focus on optimal 8 See Fairlie and Robb (2003) and Sciller and Crewson (1997) for empirical studies on te determinants of entrepreneurial success, mainly among small firms. 9 Ability can be considered as playing a twofold role. It enances te cance of success in risky enterprises, as assumed in te model. But it may also raise productivity regardless of te tecnological coice. In te next section, I argue tat tis second effect can be introduced into te model witout affecting te qualitative results. Te relevant assumption is tat ability is more important in te risky sector, wic seems realistic. 10 See Castro et al. (2004) for a similar way of modeling te optimal financial contact. financial contracts tat maximize te entrepreneur's expected utility subject to (1) an incentive-compatibility constraint, making trutful reporting preferable, and (2) te outsiders' participation constraint, requiring tat investors be indifferent between lending to all entrepreneurs wit ability and buying te risk-free asset. 11 In equilibrium, eac entrepreneur wit ability as rational expectations and cooses tecnology to maximize er expected utility, given te level of investor protection and te optimal financial contract {w H (), w L ()}. 2.2. Solution To find te equilibrium, I proceed backwards and start by solving for te optimal financial contract, first under efficient markets, and ten wit asymmetric information for a given level of investor protection. Next, I caracterize te tecnological coice, and finally I sow ow te equilibrium varies wit te degree of investor protection. For simplicity, I assume tat ϕabbb A, wic implies tat te risky tecnology is on average more productive tan te safe one for some entrepreneurs. 12 2.2.1. Optimal financial contract: efficient markets If investors could perfectly observe te cas flow of a firm, misreporting would be impossible, and ence te optimal financial contract would simply maximize te expected utility of a risk-averse borrower wit success probability subject to te participation constraint of a perfectly diversified lender. Tus, investors would provide entrepreneurs wit full insurance in excange for an expected gross return equal to te safe rate: w H ðþ ¼ w L ðþ ¼ w FB ðþ ¼ A þ ð1 ÞϕA 1; were w FB () denotes te efficient, first-best, payoff of a risky entrepreneur wit ability, wic is equal to er expected cas flow, increasing in ability, minus te risk-free interest rate repayment. 2.2.2. Optimal financial contract: asymmetric information If te cas flow cannot be observed by outsiders, entrepreneurs may ave an incentive to misreport, and ence te first-best contract is incentive compatible only if investor protection drives te gain from misreporting down to zero, wic appens only for p=1. If investor protection is not perfect (0 pb1), first-best contracts are not incentive compatible since entrepreneurs in te good state would gain (1 p)(1 ϕ)a>0 from misreporting. Due to risk aversion, agents want to minimize te difference between payoffs in te two states. However, tis is possible only up to te point were bot te incentive-compatibility constraint and te investors' participation constraint old wit equality, so tat te optimal financial contract satisfies: w H ðþ ¼ w L ðþþð1 pþð1 ϕþa; ð1þ w L ðþ ¼ ϕa 1 þ pð1 ϕþa: ð2þ Note tat bot payoffs are increasing in entrepreneurial ability since tey are correlated to expected cas flow, and tat w H () >w FB ()>w L () is needed to offset te temptation to misreport. 11 Note tat a pooled portfolio of loans to te i.i.d. entrepreneurs wit ability yields a safe return, so tat investors face no risk. It follows tat te participation constraint is te same as in te case of competitive, risk-neutral financiers wit a single borrower wit ability. 12 In te interest of space, te analytical caracterization is reported in te online appendix (ttp://bonfiglioli.iae-csic.org/ineqfin_jde_app.pdf).

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 95 2.2.3. Tecnological coice Entrepreneurs wit ability will coose te risky tecnology if it gives at least te same expected utility as te safe one. Since te state-contingent payoffs from te risky project in Eqs. (1) and (2) increase wit ability (), wile te difference between tem is independent of it, expected utility, is also increasing wit ability. Expected utility of a safe entrepreneur, instead, is constant. Tis implies tat te solution to te tecnological coice problem features a tresold ability level suc tat te agents wit ability iger tan coose te risky tecnology wile tose wit lower ability coose te safe project. Tis property is formalized in Lemma 1. Lemma 1. Tere exists a unique suc tat, u(w H ()) + (1 )u(w L ()) u(w S )), and {w H (), w L ()} is te optimal and incentive compatible financial contract. Note tat, due to risk aversion, te expected payoff of risky entrepreneurs wit ability equal to te tresold, w H ( )+ (1 ) w L ( ), must be iger tan te safe earnings, w S. 2.2.4. Investor protection and te equilibrium To study ow investor protection affects te equilibrium of te model, I first focus on te optimal financial contract and ten on tecnological coice. Te optimal payoffs in (1) and (2) are, respectively, decreasing and increasing in investor protection, and ence te wedge between tem is decreasing in p. Tis appens because, wen te unit cost of iding cas flow is ig, te temptation to misreport is low and ence a smaller deviation from te first best of state-invariant payoffs is enoug to acieve trut-telling. Notice, moreover, tat for p=0, te financial contract is akin to debt, implying a constant repayment equal to te risk-free rate, and te entire risk is borne by te entrepreneur. As investor protection increases, investors bear more and more risk, wic makes te financial contract closer to equity. Since equilibrium earnings and expected utility are functions of investor protection and te tecnological parameters, also te tresold ability varies wit p, A, ϕ and B, asformalizedinlemma2. Lemma 2. Te tresold ability is a decreasing function of investor protection (p) and tecnological level of te risky sector (A); it increases wit te riskiness of te risky tecnology (inverse of ϕ) and te productivity of te safe one (B): b0; A b0; b0; ϕ B > 0 Intuitively, stronger investor protection allows entrepreneurs to better sare risks wit investors, tereby raising te expected utility drawn from te risky project. Since tis is increasing in ability, a rise in p makes te risky tecnology preferable to te most able among safe entrepreneurs, i.e. reduces tresold ability. Higer A implies tat productivity of te risky project increases, and more so in te good state. As a consequence, payoffs rise but also te wedge between tem. Since te overall effect on expected utility is positive, a more productive tecnology reduces te tresold ability. Te parameter ϕ captures te riskiness of te risky tecnology (maximum risk for ϕ=0, no risk for ϕ =1), and also affects its expected productivity. If it grows, it makes te risky option preferable to te most able among safe entrepreneurs because it reduces te volatility of state-contingent earnings and increases teir expected value, tereby raising expected utility. Trivially, iger productivity in te safe industry, B, makes it more attractive, tereby inducing te least able among risky entrepreneurs to adopt te safe tecnology, wic rises te tresold. Te tresold also depends on risk aversion, since te curvature of te utility function affects expected utility for given probability of success (, i.e., ability). For instance, under CRRA utility wit relative risk aversion equal to or iger tan one, te risky tecnology is not run in equilibrium ( =1) as long as te earnings of te most able in te bad state are non positive, i.e., for p (1 ϕa)/[(1 ϕ)a], wic is positive for ϕab1. Alternatively, wen risk aversion is sufficiently low, tere may be entrepreneurs coosing te risky project even in te absence of investor protection ( p =0 = max b1). Note tat safe entrepreneurs are indifferent between raising external finance troug standard debt and equity-like contracts. Risky firms instead can only be started if financed troug equity-like instruments. Tis implies tat, in te presence of an infinitesimal cost of signing te optimal financial contract, safe entrepreneurs coose debt, and ence te financial structure of te economy, described by te weigt of equity in total external finance, is captured in te model by te size of te risky sector, wic is denoted as η 1 G( ) for empirical purpose. Tis measure is decreasing in te tresold ability and increasing in investor protection, and varies wit tecnological parameters. Corollary 1. Te weigt of equities in total external finance, η, is decreasing in te tresold ability ( ), te riskiness of te risky tecnology (inverse of ϕ) and te productivity of te safe one (B): η 0; η ϕ 0; η B 0; it is increasing in investor protection (p) and tecnological level of te risky sector (A): η 0; η A 0: Finally, as proved in Lemma 3, average entrepreneurial earnings, E½wŠ ¼ G S 1 w þ E ½wjŠgðÞd; and ence te average income of te economy, are increasing in investor protection. Lemma 3. Average entrepreneurial earnings, E½wŠ, are increasing in investor protection, p. E½wŠ 0: Intuitively, an increase in te cost of misreporting, p, gives risky entrepreneurs better insurance, tereby encouraging more agents to coose te risky tecnology wit a iger payoff. Tis implies tat also aggregate production and welfare increase wit investor protection. 13 3. Evaluating income distribution In tis section, I study ow investor protection affects income inequality. I consider measures of inequality accounting for te entire distribution of income suc as te variance and te Gini coefficient, and sow analytically and by means of numerical solution ow 13 In te next session, I discuss alternative assumptions under wic perfect investor protection may not be socially optimal or politically viable.

96 A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 tese respond to canges in investor protection and in te financial structure. Intuitively better investor protection affects inequality troug two cannels: by reducing te gap between state-contingent earnings of entrepreneurs, it reduces income differentials among agents wit te same ability,, and ence overall inequality. I refer to tis as te risk saring effect. On te oter and, a drop in te tresold ability implies tat a mass g( ) of agents switces to statecontingent earnings, wic is likely to translate into iger inequality. I refer to tis as te risk taking effect. 3.1. Analytical results I evaluate analytically te strengt of te two cannels and te overall effect of investor protection on inequality by focusing on te variance of te earnings distribution, wose derivative wit respect to p is: ¼ 2 ð 1 p Þð1 ϕþ2 A 2 1 ð1 Þg ð Þd þ Var : ð3þ Te first term captures te risk saring effect of investor protection, wic tends to reduce inequality and is stronger te larger is te mass of risky entrepreneurs, i.e. te lower te tresold ability, as analytically proven in Lemma 4. Lemma 4. For a given tresold ability of risky entrepreneurs, better investor protection reduces te variance of te earnings distribution: Var j 0: Te second term in (3) captures te risk taking effect, wereby investor protection may increase inequality. Tis appens to te extent tat te marginal risky entrepreneurs make te earnings distribution more dispersed, tat is if te earnings of agents wit ability differ enoug from te average, as specified in Assumption 1. Assumption 1. Te tresold ability in te absence of investor protection, p =0, satisfies: p¼0 > Ψ p¼1; wit Gð Þ p¼1 þ 1 gðþd þ p¼1 p¼0 Ψ 2 Gð Þ p¼1 þ 1 gðþd 1 : p¼0 2 Te following analytical results (Lemma 5, and Propositions 1 and 2) are obtained under Assumption 1. 14 Lemma 5. Tere exists at least one p [0,1) suc tat te variance of earnings is increasing in te tresold ability: Var > 0: Te overall impact of investor protection on income inequality depends on te strengt of te risk saring and risk taking effects. In particular, wen te cost of misreporting, and ence te size of te risky sector, is close to its maximum, te risk taking effect is weak, since te marginal entrepreneurs do not add muc to te existing mass of risky firms. Te risk saring effect is instead very strong since it applies to nearly all potential risky entrepreneurs, and ence an increase in p reduces inequality. Wen investor protection is very low, tere is a small mass of risky firms in te economy and ence te risk taking effect is stronger at te margin, wile te risk saring effect is weak since it applies to few entrepreneurs. It follows tat inequality may be a non-monotonic function of investor protection, as proven in Proposition 1. Proposition 1. Te variance of earnings is increasing in investor protection for low values of p and decreasing for ig p: lim p 0 ðwþ ðwþ > 0 and lim b0: p 1 Since, from Corollary 1, te relative weigt of equity-like instruments in overall external finance, η, is continuous and monotonic in investor protection, also te relationsip between η and income inequality follows te non-monotonic pattern of Proposition 1, to te extent tat te variation in financial structure is driven by canges in investor protection. Proposition 2. For given parameters {A, B, ϕ} and ability distribution G(), te variance of earnings is increasing in te relative weigt of equity-like instruments in overall external finance, η 1 G( ), for low values of η and decreasing for ig η: ðwþ ðwþ lim > 0 and lim b0: η η p¼0 dη η η p¼1 dη After caracterizing analytically te main forces driving te relationsip between investor protection and inequality, it is useful to pause and consider weter canging some assumptions would alter te results. First, ability is assumed to affect productivity only wen te entrepreneur cooses te risky tecnology. If tis assumption was relaxed, te qualitative results are likely to old as long as te expected marginal return to ability is lower in te safe tan in te risky sector. Intuitively, tis would raise te degree of inequality under p=0, and may weaken te risk taking effect, toug it does not seem to cange te forces giving rise to te non-monotonic relationsip between investor protection and inequality. 15 Te financial friction in te model is given by ex-post asymmetric information about entrepreneurial outcomes, generating a moral azard problem. It may be argued toug tat credit markets are also subject to ex-ante asymmetric information on te quality of te borrowers, wic may give rise to adverse selection. If in addition ability was ex-ante unobservable, investors would ave to induce entrepreneurs to trutfully reveal it by offering tem a menu of financial contracts suc tat agents wit a certain level of ability would be worse off declaring a iger, since earnings are proportional to it. Terefore, te degree of risk saring would be decreasing in ability. Tis may weaken bot te risk saring and te risk taking effect. Wile adding tis friction to te model would complicate te analytical caracterization, it does not seem to qualitatively affect te forces beind te non-monotonic relationsip between investor protection and inequality, since te risk taking effect would 14 Notice tat tis conditon is always satisfied if te casflow in te bad state is nonpositive, i.e., ϕa 1. 15 Wat may cange is te parameter restriction in Assumption 1, but not te fact tat te risk taking effect is more likely to dominate at low levels of investor protection and te risk saring effect becomes stronger as p increases.

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 97 still be stronger at low levels of investor protection and risk saring would dominate at ig levels. Finally, te assumption of fixed capital requirement implies tat entrepreneurs only decide weter to take risk or not (extensive margin), but not ow muc risk to take (intensive margin). In reality, te degree of entrepreneurial risk taking varies along bot margins, and it is terefore meaningful to wonder ow te results would cange if also te intensive margin was introduced in te model. Castro et al. (2004) sow, witin a similar framework, tat investor protection may ave an ambiguous effect on te intensive margin of risk taking (i.e. continuous capital investment in te risky tecnology). Hence, it is not a priori obvious weter introducing te intensive margin would strengten or weaken te risk taking effect. Moreover, te presence of te intensive margin is unlikely to alter te risk saring effect. 3.2. Numerical solution I now move to numerical solution to compute alternative measures of inequality and study ow tey vary wit investor protection, p. 16 First, I solve te model under different riskiness parameters to generate variation in te tresold ability for given p. 17 Based on te earnings distributions derived numerically, I ten compute two indicators of inequality, te variance and te Gini coefficient, and plot tem against investor protection. Note tat tis numerical solution as no quantitative aim. All details are given in te online appendix. Fig. 1 sows tat bot measures of inequality may be non-monotonic in investor protection. Note first tat in te simulation for low risk, nearly all entrepreneurs coose te risky tecnology independently of investor protection, so tat Assumption 1 is violated and te risk taking effect is not at work. Only in tis special case, inequality is unambiguously decreasing in p. Wen Assumption 1 olds, for iger riskiness, inequality is increasing in investor protection wen p is sufficiently low, and becomes decreasing wen p gets ig enoug. Te risk saring cannel is effectively illustrated by te downwardsloping lines for te low-risk tecnology, wic exibit a sarp decline in inequality associated to an increase in p wen nearly all firms adopt te risky tecnology independently of investor protection. Te risk taking effect is instead captured by te initial upwardsloping part of te lines for te middle and ig risk cases, and by te fact tat, for any value of p, inequality is iger wen te riskiness is lower and ence risk taking is larger. 4. Optimality and political viability of investor protection In te model, perfect investor protection is ex-ante Pareto efficient, since it raises bot aggregate income and te expected utility of all agents. Tis is due to te absence of costs, wic implies tat te level of investor protection tat bot te social planner and individual agents would cose is p = 1. In te real world owever, we do not observe perfect investor protection, and reforms aimed at improving it may be opposed by different interest groups in te society, as argued by te literature on te political determinants of financial institutions (see Caselli and Gennaioli, 2008; Pagano and Volpin, 2005; Perotti and von Tadden, 2006; Rajan and Zingales, 2003). Assuming tat enforcing investor protection entails a cost, c(p) wit c ( ) >0, e.g. given by te monitoring and judicial activities, would immediately imply tat a benevolent social planner would 16 Tis numerical solution as no quantitative aim. All details are given in te Appendix. 17 Recall tat, as predicted by Lemma 2 te ability of te marginal risky entrepreneur,,isincreasingininvestorprotection(p) and decreasing in tecnological riskiness. Alternatively, I could assume a CRRA utility function and let te risk aversion parameter vary. Te results, available upon request, are analogous. coose imperfect investor protection. Te reason is tat te marginal value of investor protection, i ð1 ϕþað1 Þ 1 0 u w L ðþ u w H ðþ gðþd; tends to zero for p=1. Moreover, if te cost as to be financed troug uniform lump-sum taxation, even a socially optimal pb1 could be politically difficult to implement because tere would be a constituency against it. In particular, an increase in investor protection would be opposed by te least and te most able agents since tey would bear te cost witout enjoying enoug benefit from risk saring. In oter words, te constituency against investor protection would be formed by te more productive, and ex-post ricer, incumbents of te risky sector and te least able outsiders, wic recalls te result in Pagano and Volpin (2006) tat managers may form a coalition wit workers to oppose te reform. Interestingly, if investor protection was cosen according to te preferences of a median voter wo would always coose te safe project, te prevailing p would be zero! In te model, te entry of new entrepreneurs in te risky sector does not affect te payoffs for te incumbents. If te safe tecnology was assumed to be employed to produce a omogeneous final good, wile te risky one produced differentiated intermediate goods, entry would erode te profits of risky incumbents, tereby giving tem an additional reason to oppose te reform, as suggested, for instance, by Rajan and Zingales (2003). Safe entrepreneurs, on te oter and, migt benefit from te increased competition in te risky sector, and terefore switc in favor of better investor protection if tis gain outweiged its cost. Te model also abstracts from wealt eterogeneity. If tis was introduced, te implications for income inequality would become more complicated to derive. Yet, more insigts may arise for te political economy of investor protection, because te composition of te constituencies considered above would also be affected by wealt. On te one and ricer agents, as entrepreneurs, do not benefit muc from investor protection since safe returns from investment account for a larger part of teir total income. On te oter and, as investors, tey would benefit from te effect tat investor protection migt ave on te interest rate. 18 5. Empirical analysis In tis section, I evaluate empirically te main teoretical predictions derived in Section 3. Since investor protection is generally determined by law, it is not expected to exibit large variation across geograpical areas or sectors witin a country. Hence, cross-country data, possibly wit time variation, seem more appropriate for empirical purpose. First, I assess te overall relationsip between investor protection and income inequality on a cross-section using a timeinvariant de jure indicator of investor protection and a general measure of income inequality. Next, I evaluate te teoretical mecanism and provide evidence from a wider cross-section and a panel tat te weigt of equity-like finance increases wit investor protection (Corollary 1) and sares a non-monotonic relationsip wit inequality (Proposition 2). Moreover, I sow tat te main result olds even wen considering only inequality at te top of te income distribution, and tat risk taking, captured by firm entry, is positively correlated wit inequality as in te model. Finally, I sow tat te results are specific to investor protection and financial structure, as postulated by te model, and do not apply to more general indicators of financial development. 18 As sown in Bonfiglioli (2005), in a general equilibrium version of te model, investor protection would raise productivity and ence te interest rate.

98 A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 Gini coefficient 0.35 0.3 0.25 0.2 0.15 0.1 0.05 ALESSANDRA BONFIGLIOLI Investor Protection and Inequality Investor Protection and Inequality 1 ig risk mid risk low risk Variance 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ig risk mid risk low risk 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 investor protection 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 investor protection Fig. 1. Investor Protection and Income Inequality. 5.1. Data Te first empirical task is to measure te main variables of interest: inequality and investor protection. In most of te analysis, I proxy te dependent variable, inequality, wit te Gini coefficient of te income distribution from Dollar and Kraay's (2002) database. 19 Tis is a widely used measure and is available for a relatively large sample of countries and years. Altoug te teory proposed in te paper refers to entrepreneurs, wo usually belong to te top percentiles of te income distribution, tree arguments may justify te use of a general indicator of inequality. 20 First, recent evidence from several countries sows tat a large fraction of te variation in income inequality over te last two decades is explained by canges at te top of te distribution (see, among oters, Atkinson et al., 2009 and Heatcote et al., 2010), wic suggests tat te effects of investor protection on inequality at te top of te distribution are likely to sow up even on data for te entire population. Second, employees normally earn iger wages and are subject to iger employment risk wen working in more productive and riskier firms. 21 Hence te results obtained for entrepreneurs may be expected to trickle down to all workers. Finally, te model could be interpreted as one of occupational coice à la Kilstrom and Laffont (1979), were eac agent can eiter be a worker receiving a fixed wage or an 19 Tis database relies on four sources: te UN-WIDER World Income Inequality Database, te ig quality sample from Deininger and Squire (1996), Cen and Ravallion (2001), and Lundberg and Squire (2000). Te original sample consists of 953 observations, wic reduce to 418 separated by at least five years, on 137 countries over te period 1950 1999. Countries differ wit respect to te survey coverage (national vs subnational), te welfare measure (income vs expenditure), te measure of income (net vs gross) and te unit of observation (ouseolds vs individuals). For better comparability, data from Deininger and Squire are usually adjusted by adding 6.6 to te Gini coefficients based on expenditure. Here, te adjustment was made in a sligtly more complicated way to account for te variety of sources; see Dollar and Kraay (2002) for details. 20 Tis would be a serious concern if investor protection affected inequality among te poor in te same way as predicted by te model, but troug a different cannel, tereby generating a bias in favor of te model. If, owever, tere was no effect, or an opposite one, tis would just be a source of attenuation bias. 21 Evidence tat more productive firms pay iger wages is provided, among oters, by Oi and Idson (1999). entrepreneur facing risk. In tis case, te implications on earnings inequality refer to te entire population. Yet, to assess te robustness of my empirical results to te measure of inequality, I replicate part of te analysis using te data on top income percentiles collected by Alvaredo et al. (2011). Unfortunately, tese data are available for a limited number of countries (23 in te data release of Marc 2011), toug over a reasonably long time-spam, wic restricts my sample to 16 countries wit 5- year observations from 1976 to 2004. Proxying inequality wit te ratio of te average income of te top 1 and 0.1 over te one of te top 10 of te income distribution, I obtain te same results, in line wit te model predictions. As regards te main explanatory variable, I first consider a de jure measure of investor protection, i.e., te index of sareolder protection compiled by La Porta et al. (2006), tat takes values between 0 (no protection) and 10 (maximum protection). Tis index is available for 49 developed and developing countries and as no time variation, wic is its main limitation. Next, I adopt an alternative approac and evaluate te predictions of te model in two steps. I first take to te data Corollary 1, arguing tat te relative weigt of equity in external finance sould be a positive function of investor protection, and ten Proposition 2, establising te non-monotonic relationsip between te indicator of financial structure and inequality. 22 Te advantage of tis strategy is tat it allows me to enlarge te cross-sectional dimension and to exploit te time variation, since data on inequality and financial structure are available for a large number of countries and years. In particular, I proxy te size of te stock market relative to overall external finance wit te ratio of stock market capitalization over credit to te private sector, were te data on stock market capitalization and credit to te private sector as a ratio of GDP are taken from te 2009 update of te database on Financial Development and Structure by Beck et al. (2000). A preliminary data inspection lends grapical support to tis two-step procedure. Consistently wit te empirical evidence in La Porta et al. (2006), te plot in Fig. 2 suggests tat better 22 In te model, financial structure varies wit oter parameters, as summarized in Corollary 1. Tese variables may only affect quantitatively but not qualitatively te non-monotonicity of te relation between investor protection and inequality troug financial structure, for instance by determining were its sign is inverted.

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 99 0.5 1 1.5 2 2.5 Investor Protection and Financial Structure Cross-section 1980-2000 HKG ZAF SGP FIN MY S GBR PER DNK AUS CHE CHL ME X JOR LKA PHL BEL SWE BRA ARG IRL IND TUR NLD CAN USA NGA ISR VEN GRC ITA ESP EGY KEN FRA NZL PAK JPN DEUECU COL IDNTHA KOR NOR PRT AUT URY 0 2 4 6 8 10 IP = inv estor protection SM = Stock market cap/private credit Fitted values, slope 0.069 (t=3.84) Fig. 2. Investor Protection and te Financial Structure from a cross-section of 47 countries, 1980 2000. 20 30 40 50 60 Financial Structure and Income Inequality Panel 1976-2000 BRA_1991 COL_1991 ZAF_1991 PAN_1991 KEN_1991 CHL_1986 MEX_1991 COL_1976 MEX_1986 ZMB_1996 CHL_1976 RUS_1996 TUR_1991 RUS_1991 MYS_1976 MYS_1991 COL_1986 MEX_1981 CRI_1996 THA_1991 PHL_1996 HND_1991 PER_1991 CHL_1991 THA_1976 ECU_1991 JAM_1986 PER_1986 MYS_1986 MYS_1981 PHL_1981 TTO_1986 THA_1986 THA_1996 JOR_1976 JOR_1991 PHL_1991 TUN_1986 USA_1991 HKG_1991 PRY_1991 VEN_1976 VEN_1986 VEN_1991 TUR_1986 NPL_1991 VEN_1981 JAM_1991 AUS_1986 IDN_1981 BGD_1991 DNK_1986 GRC_1981 GRC_1986 MUS_1991 JOR_1986 JOR_1996 PRT_1976 SGP_1986 SGP_1991 PRT_1991 KOR_1976 ISR_1991 DNK_1991 IDN_1986IDN_1991 IND_1981 PAK_1981 IND_1976 PAK_1976 NOR_1986 PAK_1986 LKA_1991 ITA_1986 IND_1986 EGY_1991 USA_1986 PRT_1986 FRA_1986 KOR_1991 JPN_1986 IDN_1996 IND_1991 KOR_1981 ESP_1986 NZL_1986 SWE_1986 PAK_1996 LKA_1986 CAN_1991 GBR_1991 AUS_1991 AUT_1986 KOR_1986 NLD_1991 NLD_1986 DEU_1991 DEU_1986 CAN_1986 FIN_1986 JPN_1991 BEL_1991 GBR_1986 LUX_1991 NOR_1991 SWE_1991 ESP_1996 HUN_1991 SVK_1996 FIN_1991 SVK_1991 GHA_1991 GHA_1996 0 1 2 3 SM = Stock Market Capitalization/Private Credit GINI Quadratic Interpolation Fig. 3. Financial Structure and Income Inequality from a panel of 58 countries, 1976 2000. investor protection is associated to a larger relative size of te stock market. 23 Fig. 3 plots instead five-year observations of te Ginis against relative stock market size. Despite sowing unconditional correlations only, it is suggestive of a non-monotonic relationsip, as predicted by te model. 24 Finally, since risk taking is an important determinant of inequality in te model, it would be desirable to also control for it in te empirical analysis. I take data on firm entry as a proxy for te extensive margin of entrepreneurial risk taking and control for it in te specifications for inequality among top income levels. Entry is computed as te percentage annual growt rate in te number of establisments as reported by te UNIDO database. Te main sortcoming wit tis control variable is te time span it covers. 25 Oter control variables are uman capital, proxied by te sare of population aged above 25 years wit completed secondary education from Barro and Lee (2000, updated in 2010), real per capita GDP, government expenditure and trade (Export+Import) as a sare of GDP from te Penn World Tables 6.3 (Heston et al., 2009). Wen combining te data sources for te main dependent and explanatory variables, I am left wit two cross-sections of 47 and 67 countries between 1980 and 2000 and two unbalanced panels of 58 and 16 countries observed over te period 1976 2004. 26 5.2. Cross-sectional estimates First, I focus on te cross-section of 47 countries and estimate wit Ordinary Least Squares te following equation for te overall effect of investor protection on inequality: Gini i ¼ α 0 þ α 1 IP i þ α 2 IP IP H IGH þ α 3 X i þ ε i ; were Gini is te measure of income inequality, i is te country index, IP is te indicator of investor protection, IP _HIGH is a dummy taking value one for IP above te median, X is a vector of control variables, and ε is te error term. Following te empirical literature on income inequality, I include in X te log of te real per capita GDP and its square to account for te Kuznets' ypotesis, uman capital, government 23 Te OLS and IV regressions reported in te next subsection confirm tat te correlation between investor protection and te ratio of stock market capitalization over private credit is positive and significant. 24 In te regression analysis, te non-monotonicity is sown to be statistically significant and robust to te inclusion of controls and te exclusion of outliers. 25 Altoug data are available for up to 116 countries, observations start in te late Nineties for most countries in te sample, wic makes te overlap wit te Ginis too limited for econometric analysis. 26 Te countries in eac sample are reported in te Appendix. ð4þ expenditure to account for te degree of redistribution, and trade. All variables are expressed in period average for te period 1980 2000. Te model predicts a non-monotonic relationsip between investor protection and inequality, wic is consistent wit a positive α 1 and a negative α 2. Table 1 reports te estimated coefficients. In column 1, I only control for investor protection and education and obtain a non significant α 1, suggesting tat tere is no clear correlation. As soon as I allow for non-linearity in investor protection, I obtain significant coefficients wit te expected sign: positive α 1 and negative α 2. Tese results old if I add te oter controls, in columns 3 and 4. In column 5, te dummy capturing te non linearity accounts for observations of IP above te 60t percentile instead of te median. Altoug more imprecise, te coefficients maintain te expected signs. To correct for possible simultaneity, in columns 6 and 7, I regress te last available observation of te Gini on te same variables as in columns 2 and 3. Te estimates are qualitatively and quantitatively very close to te ones for period averages. Tese results suggest tat te relationsip between investor protection and income inequality is non-monotonic in te way predicted by te model. As a first step in te evaluation of te teoretical mecanism, I follow La Porta et al. (2006) and regress te ratio of stock market capitalization over total credit to te private sector, SM, on real per capita GDP and an index of efficiency of te judiciary system (eff_jud). I first estimate te equation wit OLS and ten wit Two-Stages Least Squares instrumenting investor protection wit dummies for legal origins. Te results, reported in Table 2, exibit positive and significant coefficients for investor protection bot wit OLS and 2SLS Instrumental Variables, wic is in line wit Corollary 1. Te second step is to consider te relationsip between financial structure and inequality on a wider cross-section. In particular, I estimate te following equation Gini i ¼ ~α 0 þ ~α 1 SM i þ ~α 2 ðsm i Þ 2 þ ~α 3 X i þ ε i ; and report te results in Table 3. Te estimates suggest financial structure to be an important, yet so far overlooked, covariate of inequality. In particular te significant estimates of a positive ~α 1 and a negative ~α 2, are consistent wit te model prediction. Te fact tat te results are robust to controlling for per capita GDP and its square reassures tat te non-monotonic relationsip between relative stock market size and inequality is not spuriously driven by te level of income of a country. Robustness to te inclusion of oter controls in columns 4 and 8 also suggests tat te results are not driven by redistributive policies or openness to trade, wic may be correlated wit financial structure. As in Table 1, to correct for a possible simultaneity, in columns 5 8, I regress te last available observation of te Gini on te same variables

100 A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 Table 1 Investor protection and income inequality (Gini) cross-section 1980 2000. 1 2 3 4 5 6 7 IP 0.128 2.004** 2.370*** 2.253** 0.585 2.215** 2.427*** [0.457] [0.959] [0.844] [0.855] [0.736] [1.015] [0.893] IP_HIGH 1.562** 1.796*** 1.717** 0.354 1.713** 1.852** [0.724] [0.664] [0.651] [0.612] [0.781] [0.710] SCHOOL 0.260*** 0.251*** 0.07 0.063 0.059 0.260*** 0.03 [0.076] [0.071] [0.097] [0.112] [0.102] [0.091] [0.134] LRGDP 124.462*** 129.804*** 114.407*** 152.672*** [31.944] [33.031] [38.293] [34.153] LRGDP^2 7.201*** 7.522*** 6.674*** 8.862*** [1.827] [1.899] [2.182] [1.958] GOV 0.123 [0.081] TRADE 0.011 [0.011] R^2 0.237 0.31 0.519 0.536 0.433 0.274 0.533 Countries 47 47 47 47 47 47 47 Gini year 1980 2000 1980 2000 1980 2000 1980 2000 1980 2000 Last obs. Last obs. IP index Investor protection Investor protection Investor protection Investor protection Investor protection Investor protection Investor protection Note: te dependent variable is te Gini coefficient of te income distribution. Te regressors are: IP= investor protection, IP_HIGH= IP*dummy for IP>median (60t percentile in column 5), SCHOOL= sare of people above 25 years wit completed secondary education, LRGDP(2)= log of real per capita GDP (squared), GOV= government expenditure, TRADE= (import+export)/gdp. Estimation is performed wit Ordinary Least Squares. Robust standard errors are reported in brackets. *, **, and *** stand for 10, 5, and 1 per cent significance level. as in columns 1 4 and obtain estimates qualitatively and quantitatively very close to te ones for period averages. Back of te envelope calculations suggest tat an increase in te relative size of te stock market sould reduce inequality only after getting to a level (between 1.34 and 1.53) tat only a few countries ave reaced in te sample. 5.3. Panel estimates Next, I exploit te time-series variation in te data for bot financial structure and inequality, and estimate wit least squares te following equation: Gini it ¼ β 0 þ β 1 SM it þ β 2 ðsm it Þ 2 þ β 3 X it þ ν it ; ð5þ Table 2 Investor protection and financial structure cross-section 1980 2000. 1 2 3 OLS 2SLS 2nd stage 2SLS 1st stage IP 0.059*** 0.061** [0.019] [0.026] LRGDP 0.05 0.053 0.574 [0.058] [0.071] [0.510] EFF_JUD 0.053* 0.054* 0.011 [0.028] [0.032] [0.231] UK legal origin 2.728** [1.185] FR legal origin 1.22 [1.250] GE legal origin 1.448 [1.373] R^2 0.327 0.282 0.497 Sargan over-id 0.202 Wu-Hausman F 0.898 Countries 46 46 46 Dep. variable SM SM IP IP index Investor protection Investor protection Investor protection Note: te dependent variable is eiter SM=stock market capitalization as ratio of credit to te private sector or IP= index of investor protection, as specified. Te regressors are: IP, LRGDP= log of real per capita GDP, EFF_JUD= index of efficiency of te judiciary and legal origins. Estimation is performed wit Ordinary Least Squares and Two-Stages Least Squares. Robust standard errors are reported in brackets. *, **, and *** stand for 10, 5, and 1 significance level. P-values are reported for te Sargan test of over-identification and te Wu-Hausman test of endogeneity. were time subscripts refer to non-overlapping 5-year periods between 1976 and 2000, all regressors are te same as described above, and ν it is te error term. I estimate Eq. (5) bot considering ν it as a random effect, tereby exploiting bot time and cross-sectional variation, and under te assumption tat ν it =η i + ε it were η i is te country fixed effect. In te second case, te link between financial structure and inequality is identified out of witin-country variation. In bot cases, te reported standard errors are clustered by country and robust. A positive estimate for β 1 and a negative one for β 2 would be consistent wit te model prediction tat an increase in te weigt of stocks in te financial structure tends to raise inequality until it becomes ig enoug so tat te sign of te relationsip canges. Te results are reported in Table 4. Te first two rows tend to confirm te result of Table 3, tat inequality is non monotonic, as suggested by te model, in te relative size of te stock market, altoug te positive estimate for β 1 is now significant even wen I do not control for te quadratic term. Tis pattern is robust to adding te oter controls. Te regressions in columns 5 and 10 suggest tat te results are not sensitive to te exclusion of Gana, wic appears as an outlier in Fig. 3. Notice tat te results of positive β 1 and, especially, negative β 2 old stronger wen te coefficients are estimated wit random effects. Tis makes te evidence more consistent wit te predictions of te model, because te cross-sectional variation in SM, accounted for in tis specification, is more likely to be generated by differences in investor protection tan time-series canges. Te evidence presented so far is obtained using a broad measure of inequality, based on te entire income distribution, wile it may be argued tat te model applies to entrepreneurs wo are more likely to belong to te top end of te income distribution. I address tis concern by estimating Eq. (5)for two indicators of income inequality among te ric, suc as te ratio of te average income of te top 1 (or 0.1) over te top 10 of te income distribution. Note tat data availability is limited to a maximum of 16 OECD countries, observed between 1976 and 2004. Te results, reported in Table 5, are consistent wit te ones obtained for te Gini coefficients. Using te same restricted sample of OECD countries I am also able to account for te role of risk taking, wic is wat drives te positive effect of investor protection on inequality in te model. In particular, in columns 3 and 6 8, I add to te specification te annual growt rate of establisments, as a proxy of firm entry, capturing te extensive margin of risk taking. Consistently wit te teory, te estimates for entry are positive and tend to reduce te size and significance

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 101 Table 3 Financial structure and income inequality (Gini) cross-section 1980 2000. 1 2 3 4 5 6 7 8 SM 3.292 11.459** 12.415** 13.487*** 3.603 13.636*** 15.402*** 16.809*** [2.350] [4.395] [4.890] [4.758] [2.774] [4.775] [5.211] [5.076] SM^2 4.146** 4.125* 4.398** 5.093*** 5.460** 5.739*** [1.677] [2.260] [2.019] [1.782] [2.381] [2.078] SCHOOL 0.187*** 0.215*** 0.056 0.002 0.191** 0.226*** 0.028 0.029 [0.062] [0.066] [0.072] [0.078] [0.077] [0.080] [0.092] [0.098] LRGDP 33.089 46.268* 32.287 48.162* [24.631] [23.589] [25.307] [24.330] LRGDP^2 2.054 2.872** 2.063 3.034** [1.371] [1.314] [1.408] [1.357] GOV 0.311** 0.369** [0.147] [0.141] TRADE 0.008 0.003 [0.016] [0.018] R^2 0.433 0.459 0.53 0.574 0.401 0.433 0.518 0.565 Countries 67 67 67 67 67 67 67 67 Gini year 1980 2000 1980 2000 1980 2000 1980 2000 Last obs. Last obs. Last obs. Last obs. Note: Te dependent variable is te Gini coefficient of te income distribution. Te regressors are: IP= investor protection, IP_HIGH= IP*dummy for IP>median, SM=stock market capitalization as ratio of credit to te private sector, Scool= sare of people above 25 years wit completed secondary education, LRGDP(2)= log of real per capita GDP (squared), GOV= government expenditure/gdp, TRADE= (import+export)/gdp. Estimation is performed wit OLS. Robust standard errors are reported in brackets. *, **, and *** stand for 10, 5, and 1 significance level. of te linear coefficients for relative stock market size, suggesting tat its positive correlation wit inequality is driven by a iger exposition to risk. Finally, note tat te evidence of a non-monotonic relationsip between investor protection, financial structure, and income inequality, reported in tis section, is specific to investor protection and financial structure, as postulated by te model, and does not generalize to oter, more general, measures of financial development, as sown in te online appendix. 6. Conclusions Tis paper provides teoretical and empirical support for a systematic relationsip between investor protection, financial structure and income inequality. Wile tere are contributions addressing te effects of investor protection on financial structure and economic growt troug risk saring and risk taking, little attention as been paid to te implications for income distribution. To fill tis gap, I develop a simple static model wit risk-averse agents, eterogeneous in teir ability, tat can produce using eiter a safe or a risky tecnology. I assume tat entrepreneurs ave to borrow funds in order to start teir business, and tat tere are financial frictions, arising from te nonobservability of a firm's cas-flow to investors. In tis framework, I study ow investor protection, by alleviating frictions, affects optimal financial contracts, te tecnological coice of agents wit different ability and te distribution of teir earnings. Better investor protection affects income inequality in two opposite ways. By improving risk saring between entrepreneurs and financiers, it reduces income volatility. On te oter and, by inducing more agents to coose te risky tecnology, it may increase te dispersion of te earnings realizations. Te first, risk saring, effect reduces inequality, wile risk taking tends to raise it. Te overall impact of investor protection on inequality is sown to be non-monotonic. In particular, te risk taking effect dominates at low levels of investor protection, Table 4 Financial structure and income inequality (Gini) panel 1976 2000. 1 2 3 4 5 6 7 8 9 10 SM 2.265** 7.743*** 7.164** 7.105** 6.975** 1.871* 6.262* 7.469* 7.663** 8.908** [0.911] [2.708] [2.976] [3.081] [3.253] [1.007] [3.153] [3.735] [3.501] [3.630] SM^2 2.814** 2.649* 2.724** 2.591* 2.267 2.756 3.197* 4.038** [1.293] [1.408] [1.373] [1.590] [1.594] [1.841] [1.658] [1.733] SCHOOL 0.194*** 0.219*** 0.105* 0.111* 0.112* 0.171*** 0.195*** 0.179*** 0.237*** 0.249*** [0.045] [0.046] [0.057] [0.062] [0.062] [0.052] [0.050] [0.062] [0.070] [0.071] LRGDP 31.565** 33.634** 29.789* 19.452 18.08 21.37 [15.675] [15.736] [15.690] [24.946] [26.898] [29.020] LRGDP^2 1.962** 2.095** 1.886** 1.033 0.902 1.06 [0.893] [0.901] [0.899] [1.458] [1.587] [1.709] GOV 0.12 0.102 0.142* 0.129 [0.083] [0.081] [0.082] [0.083] TRADE 0.009 0.012 0.051 0.075** [0.018] [0.018] [0.031] [0.032] R^2 witin 0.118 0.157 0.122 0.13 0.143 0.118 0.157 0.175 0.221 0.251 R^2 between 0.168 0.204 0.367 0.377 0.374 0.166 0.2 0.159 0.133 0.118 Obs. 113 113 113 113 111 113 113 113 113 111 Countries 58 58 58 58 57 58 58 58 58 57 Country-FE No No No No No Yes Yes Yes Yes Yes Note: te dependent variable is te Gini coefficient of te income distribution. Te regressors are: SM=stock market capitalization as ratio of credit to te private sector, Scool= sare of people above 25 years wit completed secondary education, LRGDP(2)= log of real per capita GDP (squared), GOV= government expenditure, TRADE= (import +export)/gdp. Estimation is performed wit Least Squares wit random effects (columns 1-5) and country fixed effects (columns 6-10). Columns 5 and 10 are estimated on te sample witout Gana. Robust standard errors are clustered by country and reported in brackets. *, **, and *** stand for 10, 5, and 1 significance level.

102 A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 Table 5 Financial structure and top income inequality panel 1976-2004. 1 2 3 4 5 6 8 10 Top 1 to 10 Top 1 to 10 Top 1 to 10 Top 1 to 10 Top 1 to 10 Top 1 to 10 Top 0.1 to 10 Top 0.1 to 10 SM 1.760*** 1.667*** 0.626*** 1.139** 0.908 0.718* 5.918*** 11.256** [0.402] [0.484] [0.238] [0.513] [0.534] [0.346] [2.100] [3.905] SM^2 0.932*** 0.967*** 0.318*** 0.686*** 0.665** 0.352** 2.266* 2.748*** [0.203] [0.256] [0.099] [0.220] [0.253] [0.130] [1.305] [0.896] SCHOOL 0.009 0.005 0.006* 0.028** 0.027* 0.01 0.012 0.167 [0.006] [0.007] [0.004] [0.013] [0.013] [0.008] [0.034] [0.102] LRGDP 19.056*** 43.548*** 19.341** 39.615** 146.341* 155.049* [6.698] [7.176] [7.434] [13.663] [74.978] [72.843] LRGDP^2 0.981*** 2.214*** 0.996** 2.023** 7.643** 7.849** [0.332] [0.358] [0.380] [0.686] [3.659] [3.444] ENTRY 1.390*** 1.650*** 0.421 0.088 [0.255] [0.392] [0.503] [0.357] R^2 witin 0.647 0.716 0.874 0.675 0.754 0.883 0.84 0.901 R^2 between 0.193 0.33 0.355 0.127 0.175 0.288 0.344 0.00358 Obs. 42 42 24 42 42 24 24 24 Countries 16 16 14 16 16 14 14 14 Country-FE No No No Yes Yes Yes No Yes Note: te dependent variables are: average income of top 1 over top 10 percentile (columns 1-6) and of top 0.1 over top 10 percentile (columns 7-10) of te income distribution. Te regressors are: SM=stock market capitalization as ratio of credit to te private sector, Scool= sare of people above 25 years wit completed secondary education, LRGDP= log of real per capita GDP, ENTRY= percentage annual cange in te number of establisments. Estimation is performed wit Least Squares wit random effects (columns 1-3 and 7-8) and country fixed effects (columns 4-6 and 9-10). Robust standard errors are clustered by country and reported in brackets. *, **, and *** stand for 10, 5, and 1 significance level. and is outweiged by risk saring wen investor protection is ig. In te empirical section, I provide evidence from a cross-section of up to sixty seven countries and a panel of up to fifty-eigt countries over te period 1976 2004 tat is consistent wit te main teoretical predictions. Te model is deliberately kept simple to empasize te mecanism linking investor protection to income inequality. It follows tat its implications for economic performance and welfare may appear simplistic: aggregate income increases wit investor protection due to risk taking, and welfare increases due to iger output and better risk saring. Yet, an interesting insigt is tat investor protection, troug its positive effect on aggregate output and te nonmonotonic impact on inequality, generates a Kuznets' curve. Contrary to existing models, tis inverse-u saped relationsip between GDP and inequality is generated by te development of financial institutions, rater tan by wealt accumulation. Moreover, as discussed in paper, te model may be easily modified to address a number of issues, suc as te political economy of financial institutions. Acknowledgement I tank Francesco Caselli (te Editor) and tree anonymous Referees for teir insigtful comments. I also benefited from comments by: Pilippe Agion, Amparo Castelló Climent, Antonio Ciccone, Giovanni Favara, Gino Gancia, Nicola Gennaioli, Gita Gopinat, Jon Hassler, Ross Levine, Torsten Persson, Andrei Sleifer, Jaume Ventura, Fabrizio Zilibotti and seminar participants at Banco de España, Bocconi University, CREI and UPF, European Central Bank, IAE-CSIC, IIES, SIFR, University of Amsterdam, Universidad Carlos III de Madrid, Universitat Autonoma de Barcelona, University of Warwick, te 2007 NBER Summer Institute, 2006 Annual Meeting of te European Economic Association, 2006 CEPR European Summer Symposium on International Macroeconomics, 2004 Annual Meeting of te Society for Economic Dynamics, 2004 European Winter Meeting of te Econometric Society. Te support of te Spanis Ministry of Education and Science (grant ECO2008-04785) and of te Barcelona GSE researc network and Generalitat de Catalunya (grant 2009 SGR 1126) is gratefully acknowledged. All remaining errors are mine. Appendix A. Proofs Lemma 1. Te assumptions tat A> B+1>ϕA and u >0, imply tat agents wit =1 always coose te risky tecnology since V R (1)= u(w H (1))= u(a 1)> u(b)= V S ; wile agents wit =0 always make te safe coice since V R (0)=u(w L (0))= u(ϕa 1)b V S. To prove tat tere exist a unique ability (0,1) suc tat V R ()b(>) V S for all >(b), I just need to sow tat V R is increasing in. Te derivative of V R w. r. t. under te optimal financial contract is V R ¼ u wh ðþ u w L ðþ þ pð1 ϕþa½u w H ðþ þð1 Þu w L ðþ Š > 0 since w H ()>w L (). Terefore, tere exist a unique tresold ability suc tat V R ( )= V S and >, u(w H ())+ (1 )u(w L ()) > u(b). Lemma 2. To prove tat te tresold ability is decreasing in investor protection, I caracterize as implicit function of p, V R ; p ¼ V S ; and obtain its derivative wit respect to p as ¼ V R V R! V R 1 : To prove tat tis derivative is negative, I just need to sow tat R V is positive, since by Lemma 1 > 0. I obtain V R ¼ 1 u w L u w H i ð1 ϕþa 0; since utility is concave and w L () w H (), implying tat 0.

A. Bonfiglioli / Journal of Development Economics 99 (2012) 92 104 103 Moreover, p 1lim ¼ 0 since p 1limwH ()=p 1limw L ()= w S. Te tresold ability varies wit te tecnological parameters A, ϕ and B as follows A ¼ V R A since ¼ V S B V R! V R 1 b0; ϕ ¼ V R ϕ! 1 > 0 V R t A ¼ u w H it ½ϕ þ pð1 ϕþþð1 pþð1 ϕþš þð1 Þu w L it ½ϕ þ pð1 ϕþš > 0; V R t ϕ ¼ 1 u and w H it V S t B ¼ u ðb 1Þ > 0: Ap þ 1 u w L it Corollary 1. Te derivative of η w.r.t. is η ¼ g 0: Te derivative of η w.r.t. ϕ is η ϕ ¼ η ϕ 0 since b0 by Lemma 2. ϕ Te derivative of η w.r.t. B is η B ¼ η B 0! V R 1 b0; B A 1 p > 0 since > 0 by Lemma 2.Te derivative of η w.r.t. p is B η ¼ η 0 since b0 by Lemma 2. Te derivative of η w.r.t. A is η A ¼ η A 0 since b0 by Lemma 2. A Lemma 3. Te derivative of average entrepreneurial earnings w.r.t. is E½wŠ ¼ g w S w H 1 w L i 0; since, by risk aversion and te definition of, w S w H ( ) (1 )w L ( ). Lemma 4. To prove tat te partial derivative of Var(w) w.r.t. p is iger te iger is te tresold ability, I obtain its derivative w.r.t. and sow tat it is positive: Var ¼ 21 p ð Þð1 ϕþ 2 A 2 1 gðþ 0: Lemma 5. Derive te partial derivative of Var(w) w.r.t. te tresold, : Var ¼ g w L w H i w L þ w H i 2E½wŠ i i þgð Þ w S w L ð Þ w S þ w L ð Þ 2E½wŠ Tis is positive, Var > 0, iff w H w L i 2E½wŠ w H w L i w S w L i 2E½wŠ w S w L i > 0; tat is, since bot 2E½wŠ w S w L ð Þ and w H ( ) w L ( ) are positive, iff: 2E½wŠ w H ð Þ w L ð Þ 2E½wŠ w S w L ð Þ ws w L ð Þ w H ð Þ w L ð Þ > 0: Substituting for E½wŠ, w H ( ), w L ( ), w S, and (B ϕa)/a(1 ϕ) = p =1, and multiplying and dividing by A(1 ϕ) delivers, after furter simplifications, te following condition: G ð Þ p¼1 þ 1 gðþd p ð 1 p Þ=2 p¼1 p > 0; Gð Þ p¼1 þ 1 gðþd p p¼1 =2 ð1 pþ wic olds for te tresold ig enoug relative to its firstbest value, p =1.Sinceteigest is associated to p =0, condition for te existence of at least one value of p so tat Var > 0 is tat p¼0 p¼1 > G ð Þ p¼1 þ 1 gðþd þ p¼1=2 0 Gð Þ p¼1 þ 1 gðþd 1=2 : 0 Proposition 1. Compute te limit of at te extreme values of p. For p 0, p =0 and satisfying Lemma 5, so tat lim p 0 Var > 0, i u w L lim p 0 ¼ p¼0 1 p¼0 u w H p¼0 p¼0 ð1 ϕþa b0; u w H u w L ence: lim p 0 ¼ Var 0: p¼0 p¼0 For p 1, w H () w L () for any, and w H ( )=w L ( )=w S, ence 0, Var 0 and 0. To verify tat it approaces zero from above, express in second order Taylor expansion in a neigborood of p =1: lim p 1 ¼ ðp 1 Var Þd2 ðþ 2 ¼ ðp 1Þ21 ϕ ð Þ 2 A 2 1 ð1 ÞgðÞdb0: p¼1 Proposition 2. For given parameters and ability distribution, te derivative of te variance of earnings w.r.t. η can be expressed as: dη ¼ dη 1 ¼ g d 1 :

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