The Growth Effects of Property Rights: The Role of Finance

World Development Vol. 40, No. 9, pp. 1784–1797, 2012 Ó 2012 Elsevier Ltd. All rights reserved. 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

http://dx.doi.org/10.1016/j.worlddev.2012.04.020

The Growth Effects of Property Rights: The Role of Finance NILOY BOSE * University of Wisconsin-Milwaukee, USA National Science Foundation, USA ANTU PANINI MURSHID University of Wisconsin-Milwaukee, USA

and MARTIN A. WURM * Pacific Lutheran University, Washington, USA Summary. — Using a variety of statistical approaches, we show that the relationship between property rights and growth is nonlinear; stronger enforcement of property rights raises growth up to a point before growth begins to decline. We provide a simple theoretical rationale for this conclusion using a model with informational asymmetries in the financial sector. Stronger property rights have two opposing effects. On the one hand it increases capital formation and growth. On the other hand it encourages bad borrowing practices. Thus there exists an optimal level of property rights which maximizes growth. However, as financial markets mature, the negative effects associated with stronger property rights become weaker. Ó 2012 Elsevier Ltd. All rights reserved. Key words — financial development, growth, institutions, property rights, thresholds

1. INTRODUCTION

therefore important for the proper functioning of financial systems (Acemoglu et al., 2001; Levine, 1998). But could this interaction also go the other way? Could financial development also be a necessary precursor for property rights to be effective? Miletkov and Wintoki (2008) provide a simple yet compelling example. They argue that protecting the rights of parties to financial transactions is costly, and legislation to strengthen these rights is difficult to justify in the absence of deep financial markets. This is one rationale for why finance can catalyze institutional change, but it is not the only rationale. In theory drawing a line from finance to property rights should not require assumptions that depart from conventional wisdom. As an example consider an argument based on the following three widely accepted stylized facts. First, financial development exerts a positive influence on growth (Goldsmith, 1969; McKinnon, 1973). Second, informational asymmetries between borrowers and lenders can impair allocative efficiency in financial markets (Stiglitz & Weiss, 1981; Williamson, 1987). Third, developed financial markets are better able to resolve informational frictions by virtue of better accounting or auditing standards and other forms of monitoring (Demirgu¨ßcKunt & Huizinga, 2000; Demirgu¨ßc-Kunt, Laeven, & Levine, 2004).

There is a wide consensus that property rights are good for growth and investment (Acemoglu, Johnson, & Robinson, 2001; Hall & Jones, 1999; Johnson, McMillan, & Woodruff, 2002). So why are these rights not universal? One possible explanation is that some states lack the ability to carry out institutional reforms. However, another possibility is that strengthening property rights is not always optimal. While the benefits of doing so seem obvious, it is conceivable that furthering these rights imposes costs on society. “Policing property” for instance could be prohibitively expensive in the presence of a weak judiciary. These costs could also arise through more subtle channels. For instance, stronger property rights can impose higher “tariffs” on imitation, reducing technological transfer rates, while increasing market concentration (Furukawa, 2007; Helpman, 1993). Alternatively, stronger property rights can lead to a misallocation of talent from productive to unproductive sectors (Acemoglu & Verdier, 1998). In the presence of these tradeoffs, we might expect the relationship between property rights and growth to be nonlinear. This paper provides evidence that this is the case, but the form of this nonlinearity hinges on the degree of financial development. This focus on the interaction between property rights, financial markets, and economic growth is not new. However, the bulk of the existing literature attempts to establish these links by drawing a line from property rights to financial development (and then from financial development to growth). For instance, Levine, Loayza, and Beck (2000) suggest that legal and regulatory reforms that strengthen creditor rights, contract enforcement, and accounting practices, foster financial development, and this induces faster growth. Stronger property rights can also facilitate credit by allowing borrowers to post collateral (Besley & Ghatak, 2009). According to this view property rights—legal traditions more generally—are

* The authors are grateful to seminar participants at Georgia Tech, the Midwest Economics Association, and the University of Wisconsin-Whitewater. In addition, we would like to extend a special thanks to three anonymous referees for insightful comments and criticisms. The authors are alone responsible for any remaining errors and omissions. This paper was completed during Niloy Bose’s appointment at the National Science Foundation. Any opinions, conclusions, and recommendations expressed in the paper are those of the authors and do not reflect the views of the National Science Foundation. Final revision accepted: January 9, 2012. 1784

THE GROWTH EFFECTS OF PROPERTY RIGHTS: THE ROLE OF FINANCE

Against this backdrop, we claim that property rights can have important effects on the quality of financial contracts. 1 We establish this by presenting an analytical framework that is based on a simple two period overlapping-generations growth model, where a group of noncreditworthy borrowers, that are underprivileged in the sense that their abilities to operate productive technologies are limited, is a priori indistinguishable from creditworthy loan applicants. When screening is costly, the participation of this group in credit markets dilutes the contract offered to the credit worthy borrowers, compromising the allocative efficiency of financial markets. Under this scenario, alternative income sources for noncreditworthy borrowers, can improve efficiency by reducing their incentives to participate in credit markets. According to our theory, slack property rights are the source of these outside opportunities, which can be substantial. A lack of enforcement of copyright law in China and India for instance has allowed software piracy to flourish. The Business Software Alliance Report (2009) estimated the loss in sales due to software piracy in China and India alone at $9.4 billion. If slack property rights provide a source of outside income, then the introduction of better enforcement mechanisms can have two opposing effects on economic growth. On the one hand, stronger property rights can foster productive investment. On the other hand, they can erode outside opportunities for noncredit worthy borrowers, which can, in turn, crowd out good borrowers from credit markets, thus generating a potentially adverse growth effect. This tradeoff points to an optimal level of property rights that is consistent with growth maximizing objectives. Significantly, our theory suggests that lower agency costs and better mechanisms for separating good and bad borrowers will affect the nature of this tradeoff. By implication the optimal level of property rights will vary across countries according to the efficiency of financial intermediaries. In countries where monitoring costs are low, a high level of property rights is consistent with maximizing growth objectives. In countries where the financial infrastructure is weak, optimal growth is consistent with weaker property rights enforcement. To be sure the implication is not that financial development is a pre-requisite for property reform, but that financial development may be a prerequisite for property reform to have the growth implications that we seek. These results are similar to an earlier contribution by Helpman (1993), where stronger intellectual property rights protections can have mixed effects. One the one hand, stronger IPR protections can provide greater incentives to invest in research and development. On the other hand strengthening IPRs can reduce technological diffusion through imitation. For developing countries, which are further away from the technological frontier, this latter channel is likely to be more important. The growth effects of stronger IPRs should therefore be a function of economic development. The evidence that we present here is broadly consistent with these conjectures. Within a group of countries, the relation between property rights and growth is described by an invertedU shaped function. However, these nonlinearities are more strongly evident in countries where financial systems are weak. In identifying this structure within the data we consider a variety of empirical approaches, from varying coefficients models to threshold regression and dynamic GMM estimation. The remainder of this paper is organized as follows. In Section 2, we present our theoretical framework and its implications for the property rights-growth relationship. Section 3 describes our data and methodology. In Section 4 we present

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our main empirical findings. Section 5 concludes with some remarks. 2. THEORETICAL FRAMEWORK The structure of our economy follows the framework proposed in Bencivenga and Smith (1993). There is an infinite sequence of two-period lived overlapping generations with three groups of participants—households, firms and predatory agents, each of unit mass. All agents are risk neutral with a preference for second-period consumption. Below we provide a detailed description of agents’ behavior as well as other characteristics of the economy. (a) Households and lenders Households are providers of funds in this economy. When young they are endowed with one unit of labor and e -units of a tangible asset (e.g., land) which can be used to produce output. In the absence of strict enforcement of property rights, households lose part of their claim to this endowment. Suppose after expropriation a household owns ep-units of assets [where p e (0, 1) measures the degree to which the property rights are enforced—higher values of p correspond to stronger property rights], which yield f(ep) units of output in the first period of their lives. We assume that the technology, f(.), is subject to diminishing returns, i.e. f 0 > 0 and f 00 < 0. In addition a young household supplies their labor inelastically in the market at the ruling wage wt. Total first-period earnings are therefore wt + f(ep). Since households only have a preference for second-period consumption, these earnings are saved. This generates time-t + 1 capital which is allocated into productive activities. We suppose that every household has the opportunity to convert their time-t earnings into time-t + 1 capital on a one-to-one basis. Alternatively households can loan their earnings to firms in return for time-t + 1 capital. In either case, the old are the owners of capital, and they finance their consumption by renting it to time-t + 1 output producers. (b) Firms Each young firm is endowed with a linear technology that converts one unit of time-t output into Q > 1 units of timet + 1 capital. Since firms are not endowed with their own output, they need access to external funds in order to “operationalize” their capital-production technologies. For this, young firms need to borrow from the concurrent generation of young households. In the final period of their lives, firms become output producers. Output is produced by combining time-t + 1 capital and labor supplied by young households (born at time-t + 1) with a common, nonstochastic technology. A mature firm employing lt+1 units of labor and kt+1 units of capital is able to produce yt+1 units of output according to the following technology: k c k htþ1 l1h y tþ1 ¼  tþ1 ;

c; h e ð0; 1Þ

ð1Þ

where k denotes the average capital stock per firm. This specification allows for externalities in the production process. For simplicity, it is assumed that c = 1  h. This reduces the model to a simple Ak endogenous growth model in which externalities in production exactly offset diminishing marginal returns to capital. Finally, without any loss of generality, it is assumed that capital depreciates fully in the production process. A part of output is paid to young households in the form of wages, which later become a source of capital for future

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production, and a part is paid to adult households—the owners of capital. In a competitive equilibrium labor and capital are paid their marginal products (w and q). Since lt+1 = 1: wtþ1 ¼ ð1  hÞk tþ1

ð2aÞ

qtþ1 ¼ hl1h tþ1 ¼ h

ð2bÞ (c) Predatory agents

each of the loan contracts—(Rs, qs) and (Rn, qn)—yields zero economic profit for a lender. Specifically, for a given level of property rights, p, a given amount of loanable funds, x, and for a given rental rate on time-t + 1 capital, qt+1, the optimal contract solves the following maximization problem: max qtþ1 ½ð1  uÞðQ  Rn Þqn þ uðQ  Rs Þqs 

u;Rn ;Rs ;qn ;qs

Subject to: qn Rn  qn ¼ 0;

Our third group of agents are neither households nor firms. Unlike households this group is not endowed with marketable labor and unlike firms they lack the skills to operate capital or output-production technologies effectively. Predatory agents are however born with an endowment of labor resources, which though nontradable, can be used in one of two activities. First, a young predatory agent can “work” to appropriate a faction, (1  p), of assets from their contemporaries, which yields an amount f[e(1  p)] of old-age consumption. Recall the parameter p measures the degree to which the property rights are enforced. Alternatively, like a regular firm, a predatory agent can access credit markets to run capital-production projects. However, unlike regular firms, success is uncertain. In particular, a capital project operated by a predator converts one unit of output into Q units of capital with probability p < 1. Further, suppose that pQ < 1. Accordingly, predatory agents must misrepresent themselves as firms in order to gain access to credit markets. (d) Credit markets Recall that a lender can convert output into capital on a one-to-one basis. By contrast the expected return on a capital project managed by a predatory agent is given by pQ < 1. Accordingly, it is in the interest of lenders to only lend to legitimate firms. However, we assume that a borrower’s type is her own private information and lenders cannot distinguish between types without undertaking a costly screening process that requires incurring a cost which is assumed to be proportional in the amount d to the size of the loan contract. In practice, banks and other financial institutions incur various agency costs in conducting their operations. These include costs associated with processing information, enforcing contracts, screening, and monitoring borrowers (see for instance Diamond, 1984; Gurley & Shaw, 1960). For the purposes of the present analysis, we consolidate these into a single composite cost of screening, d, which serves as our indicator of financial development. The general consensus is that this cost is higher in less advanced economies, where financial markets are less mature and institutional structures are less established. The loan contract offered by a lender takes the following form: A lender screens an applicant with probability u e (0, 1). In the event that the applicant is a legitimate firm, the lender offers the contract (Rs, qs), where Rs is the gross lending rate and qs the loan quantity. If the applicant is a predatory agent the loan is denied. In the event the applicant is not screened [with probability (1  u)], the lender offers the contract (Rn, qn), where Rn and qn denote the gross lending rate and the loan quantity, respectively. We assume that lenders operate in a competitive environment. Under competition, any contract that makes extra-economic profit for the lender cannot survive in equilibrium as lenders would compete among each other and try to win over borrowers by offering them part or all of the extra-economic profit. This amounts to saying that, in equilibrium, lenders would be maximizing the utility of the borrowers subject to the constraint that

ð3Þ

qs Rs  dqs ¼ 0

ð4Þ

qtþ1 pð1  uÞðQ  Rn Þqn 6 f ½ð1  pÞe

ð5Þ

qn 6 x

ð6Þ

x 1þd

ð7Þ

0
ð8Þ

qs 6

As noted earlier the goal of lenders is to do the best they can for firms. Therefore, Eqn. (3) simply represents the expected utility of (legitimate) firms. Eqn. (4) specifies the zero profit constraints faced by lenders in nonscreening and screening states, respectively. The incentive compatibility constraint is given by (5). The left hand side of Eqn. (5) represents a predatory agent’s expected profit from entering the loan market undetected. As an alternative, a predatory agent is able to engage in predation and obtain f[(1  p)e] amounts of output for final consumption. Thus, by satisfying Eqn. (5), a lender is able to keep the nonlegitimate firms out of the credit market. Finally, Eqns. (6) and (7) are the upper bounds on loans in nonscreening and screening states, respectively. The following proposition summarizes the solution to the above contracting problem. Proposition 1: If Q > 1 + d and if qt+1p(Q  1)x > f[(1  p)e] then the terms of the loan contract in equilibrium f ½ð1pÞe are: u ¼ 1  qtþ1 ; Rn = 1; Rs = (1 + d); qn = x and pðQ1Þx s x q ¼ 1þd . The intuition is straightforward. The condition Q > 1 + d makes screening viable. Moreover, if qt+1p(Q  1)x < f[(1  p)e] there is no incentive for predatory agents to enter the loan market, and the problem is trivial. The expressions for Rs and Rn follow from the zero profit constraints [Eqn. (4)]. Also note that the utility of legitimate firms is increasing in loan size. Thus, after screening, if a lender learns with certainty that an applicant is credit-worthy, it is optimal to offer x : the applicant the maximum available funds—qs ¼ 1þd Finally, when qt+1p(Q  1)x < f[(1  p)e], lenders must make provisions to deter noncredit-worthy borrowers by making sure their incentive compatibility constraints are satisfied. The expression for the optimal screening probability, u, is then obtained by taking (5) as an equality at obtained values of Rn and Rs. Finally, note that lower values of p (weaker property rights) increase predatory income and therefore the opportunity costs of nonworthy borrowers from participating in the formal loan market. This reduces the incidence of screening in equilibrium. In the next section we exploit this relationship in understanding the connection between the level of financial development and property rights. (e) Growth rates and property rights In this economy, time-t + 1 capital is generated by legitimate firms, both those that are screened and those that are

THE GROWTH EFFECTS OF PROPERTY RIGHTS: THE ROLE OF FINANCE x not screened. A screened firm receives qs ¼ 1þd amount of Qx loans and produces 1þd amount of capital. Whereas a nonscreened firm produces Qqn = Qx units of capital. Since the size of firms is normalized to 1, time-t + 1 capital stock is given by:   x þ ð1  uÞx k tþ1 ¼ Q½uqst þ ð1  uÞqnt   Q u 1þd

By substituting expressions for u (from proposition 1), wt and qt+1 [from Eqs. 2(a) and 2(b)], and noting that the size of loanable funds, x, is given by wt + f[(1 - p)e], we obtain an expression for the growth rate of capital stock per firm: 2 k tþ1 Q g¼ ¼ hpðQ  1Þð1 þ dÞ kt   1  hpðQ  1Þð1  hÞ þ fhpðQ  1Þf ðepÞ þ df ½ð1  pÞeg kt The above equation has very straightforward and testable < 0. Accordingly, implications. It is easily verifiable that @g @d financial development has a positive impact on growth. The relationship between the level of property rights, p, and the growth rate, however, is less straightforward. In particular, f 0 ðepÞ @g d > 0ð< 0Þ () f 0 ½ð1pÞe > ð<Þ hpðQ1Þ . Note that since f 00 < 0, @p 0

f ðepÞ is decreasing in the value of p. Therethe expression f 0 ½ð1pÞe fore, for a given set of parameter values, the economy’s growth rate may actually fall rather than increase when the value of p is pushed too high implying a nonlinear inverted “U” shaped relationship between growth and property rights. Hence there exists p = p* at which growth is maximized. In addition our analysis suggests that the positive growth effects of property rights are limited by the development of financial markets. This result should be intuitive. An increase in p has two opposing effects on growth. On the one hand, stronger property rights mean fewer resources are diverted through predation, raising the quantity of investable funds. On the other hand, a higher p generates stronger incentives for predators to enter credit markets. This has a negative impact by raising costs of financial intermediation. This second effect is stronger at low levels of financial development where the marginal cost of screening, d, is high. Therefore countries with less developed financial markets are more likely to experience adverse growth effects when property rights are enforced beyond a certain level. In contrast, these negative effects should be weaker or absent altogether in countries where financial markets have already reached a threshold level of maturity (and d is low). Additionally an implication of this is that the level of property rights consistent with growth maximizing objectives, p*, may be a function of the development of financial markets, i.e. p* = p*(d) .

3. DATA AND METHODOLOGY (a) Methodology With few exceptions the empirical literature assumes that growth is linear and additively separable in its arguments. But as the discussion in Section 2 suggests, this may not be the case; growth can be a complex nonlinear process—difficult to isolate in simple linear regressions. 3 In this section, we outline a variety of empirical approaches that relax the assumption of linearity. In particular, we estimate a simple semiparametric model, where growth is linear in each of its determinants except in

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its relationship with property rights, where we allow for some nonlinearities. Specifically, we regress growth against property rights and property rights squared, but we allow the coefficients on these terms to be functions of the level of financial development. This specification allows for very general interactions between property rights and finance. The two growth equations that we estimate are: gi ¼ X0i b þ c/i þ h1 ðui Þpi þ h2 p2i þ ui

ð9aÞ

gi ¼ X0i b þ c/i þ h1 pi þ h2 ðui Þp2i þ ui

ð9bÞ

In both of these specifications we regress the average growth rate in country i, gi, against a measure of the size of the financial system which we denote by ui (see data discussion below), property rights, pi, property rights squared, p2i , and a set of other controls, Xi. These are the usual suspects that we find in growth equations, including initial real per capita GDP, average years of secondary male schooling, and a measure of investment-inefficiencies proxied using the relative price of nonresidential investment. This baseline specification is then extended in a number of ways which we discuss below. In Eqn. (9a) the coefficient on property rights h1(.) is allowed to vary smoothly with ui, holding constant the effect of property rights squared, h2, and in Eqn. (9b), we hold constant the effect of property rights, h1, while allowing the coefficient on property rights squared, h2(.), to vary smoothly with ui. The “smoothers” in (9a) and (9b) can be estimated using penalized regression splines (see Wood, 2000 for details). In particular, we estimate our model using a set of procedures developed by Wood (2009). Our approach to modeling nonlinearities in the propertyrights growth equation is similar to some recent analyses on the finance-growth relationship (Stengos & Liang, 2005 is a recent example). There the authors considered a variety of semiparametric and nonparametric approaches, which as is the case here, provide an important exploratory foundation. In particular, of interest is the pattern in the variation of the regression functions h1(.) and h2(.). We might want to know for instance if this variation suggests an approximate classification of countries into distinct growth regimes, across which property rights have markedly differing impacts. It is difficult however to test this assertion without introducing some additional structure on the form of this heterogeneity. As an example consider the following simplification of the nonlinear interaction in Eqs. (9a) and (9b): gi ¼ X 0i b1 ½Ið/i 6 sÞ þ X 0i b2 ½1  Ið/i 6 sÞ þ h1;1 ½Ið/i 6 sÞpi þ h1;2 ½1  Ið/i 6 sÞpi þ h2;1 ½Ið/i 6 sÞp2i þ h2;2 ½1  Ið/i 6 sÞp2i þ ui

ð10Þ

Here I(.) is an indicator variable, which assumes the value 1 if the variable ui exceeds the threshold s and is 0 otherwise. In general the effects of all explanatory variables differ depending on the regime, however it is feasible to restrict some of the model parameters (we expand on this point in section four below). Although in general the threshold, s, is unknown, it can be estimated by minimizing a loss function across values of u (Hansen, 2000). However, since s is not identified under the null hypothesis (“no threshold”), classical test statistics are not asymptotically chi-squared. Nevertheless a likelihood ratio test of the null can still be formed using bootstrapping methods suggested in Hansen (2000). The difficulty with this analysis is that both property rights and financial development are endogenous. As a result we

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present a second set of evidence where we attempt to resolve the identification issue using the Arellano-Bover system GMM panel estimator (Arellano & Bover, 1995). Panel methods are not a panacea for resolving identification issues. However since finding exogenous sources of variation in the cross-section is difficult, dealing with identification in panels may be the only viable approach. We link our panel analysis to our analysis of thresholds, by splitting our data into two sub-samples based on the threshold estimates generated in Section 4(c). Thus we estimate two sets of panel regressions; one for the low finance group for which /i;j 6 ^s and one for the high finance group where /i;j > ^s.

identify complex, potentially nonlinear, relationships. However drawing structural inferences from cross-country analyses is difficult. Fortunately the FI measure has a relatively long time dimension. The Fraser Institute rates countries between 1970 and 2000 at regular five-year intervals. Since 2000 these ratings were published annually. The time-series component of these data provides a basis for resolving the identification issue within a panel framework, while also allowing us to control for country-specific effects. Unfortunately the time dimension of the HF measure is relatively short; ratings on property rights are available (on an annual basis) from 1995, making these data less than ideal for panel analysis.

(b) Data

(ii) Measures of finance The World Bank’s Financial Structure Database provides data on a wide array of country-level financial indicators. Of these, measures of the size of the financial system, which we denote here by u, continue to be the most widely used proxy for the efficiency of financial markets. Researchers have often used u as a proxy for agency costs d, such as those associated with processing information, enforcing contracts, and screening. The assumption is that these costs decline as the size financial system increases, and therefore the efficiency of the financial system improves (see for instance Levine, 1997). While there are several indicators of financial depth, recent research has focused on the volume of credit supplied by the financial system to the private sector (normalized by GDP). The intuition underlying this measure is straightforward: financial systems that allocate more credit to the private sector are likely to monitor firms more closely and exercise greater corporate control (Beck, Demirgu¨ßc-Kunt, & Levine, 2000). Our analysis is based on this indicator.

The dependent variable in our sample is the growth rate of real GDP per capita adjusted for international differences in purchasing power parity. Our period of investigation begins in 1970, the first year for which data on property rights are available. We include a number of control variables in our regressions, which are described in Table 4 in Appendix A, along with the list of countries covered by our analysis. In addition Table 5 provides summary statistics and cross-country correlations. Below, we describe in more detail, our two key control variables—property rights and financial development. (i) Measures of property rights Measures of the international variation in the enforcement of property rights fall into two classes. One class aims to capture the security of intangible assets—specifically intellectual property. Another class aims to assess the scope of laws and regulations governing the security of property as they apply more generally. Since our focus is not exclusively on intellectual property, it makes sense to draw from the latter group. Below we consider two measures of property rights—the Fraser Institute (abbreviated FI) measure of property rights published in the Economic Freedom of the World: 2009 Annual Report and the Heritage Foundation’s (abbreviated HF) Property Freedom index. An important feature of both indexes is that they do not simply reflect laws on the books, but the overall legal environment as it relates to the protection of property rights and the overall quality of legal institutions. In particular, the Fraser Institute rates countries on a scale from 0 to 10—zero being the lowest—on the independence of the judiciary, the impartiality of the courts, the basis of the protection of property, the degree of military interference, the integrity of the legal system, the degree of enforcement of legal contracts, and the extent of regulatory restrictions on the sale of real property. Similarly the Heritage Foundation’s assessment of property rights also provides a broad assessment of individuals’ abilities to accumulate private property as secured by transparent legislation and government enforcement, together with the likelihood of expropriation, the efficiency of the judiciary, the presence of corruption within the judiciary, and the enforceability of contracts. The Heritage Foundation measure varies on a scale from 0 to 100, with higher values indicating stronger property rights. For our purposes, it is useful to rescale these data (limiting the variation in the index from 0 to 10). Both indexes have been extensively used in the literature (see for instance Acemoglu et al., 2001; La Porta, Lopez-deSilanes, Pop-Eleches, & Shleifer, 2002; and Levine, 2005). In part, this may be a reflection of their broad cross-country coverage. This large cross-country dimension [141 countries (FI) and 183 countries (HF)] is useful since our intent here is to

4. RESULTS (a) Initial look at the data In this section, we provide evidence on the relation between property rights and economic growth. In Table 1 we report the results from a simple regression of the growth rate of real GDP per capita against our two measures of property rights. In each case growth is an average from 1970 to 2005. In Table 1 columns (1)–(5), property rights are an average of the FI index from 1970 to 2005 and from columns (6) to (10) they are an average of the HF index from 1995 to 2005. In both cases, we find that the effect of property rights on growth is economically and statistically significant: a one point increase in FI scale translates into a 0.24 percentage point rise in the trend annual growth rate. The corresponding figure for a one point increase in the HF index is 0.31%. When we include the square of property rights as an additional regressor we find some evidence of nonlinearities in the FI data [Table 1, columns (2) and (3)]; the coefficient on the square term is negative and statistically significant. The results are somewhat different when using the HF index; the square term is not statistically significant in both basic and extended specifications. At first blush these mixed findings provide only loose support for our theory where the relation between property rights and growth is nonlinear. However, the analysis in Section 2 suggests the relationship between property rights and growth that is more complex. According to our theoretical model, nonlinearities in the property rights-growth relationship are to be expected, however the degree to which this relationship is nonlinear varies with the level of financial depth. Thus when

THE GROWTH EFFECTS OF PROPERTY RIGHTS: THE ROLE OF FINANCE

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Table 1. Property rights and growth OLS regressions Dependent variable: Average growth rate of real GDP per capita, 1970–2005 Fraser Institute Measure of Property Rights (1–1)

(1–2)

(1–3)

(1–4)

(1–5)

Heritage Foundation Measure of Property Rights (1–6)

(1–7)

(1–8)

(1–9)

(1–10)

0.8106 0.7318 0.6412 0.6392 0.6389 0.8508 0.8393 0.8366 0.7092 0.7095 (5.88***) (5.86***) (4.28***) (4.24***) (4.20***) (6.30***) (6.22***) (6.04***) (4.73***) (4.71***) Education 0.6615 0.7832 0.7227 0.7266 0.7247 0.4883 0.5085 0.5698 0.5473 0.5474 (2.63***) (2.80***) (2.49**) (2.50**) (2.48**) (1.96*) (1.94*) (2.03**) (1.95*) (1.94*) Price of capital 0.0077 0.0085 0.0068 0.0067 0.0068 0.0079 0.0081 0.0039 0.0042 0.0042 (1.78*) (1.94*) (1.15) (1.10) (1.12) (2.09**) (2.10**) (0.71) (0.72) (0.72) Finance 1.0620 0.8958 0.9033 0.6654 0.8677 0.9778 0.9499 1.0450 0.9160 1.0058 (6.11***) (4.32***) (4.17***) (2.35**) (3.90***) (5.35***) (4.71***) (2.74***) (2.50**) (3.97***) Financial integration 0.4113 0.4418 0.4214 0.4696 0.4789 0.4655 (1.55) (1.60) (1.52) (1.26) (1.76*) (1.71*) Trade openness 0.7294 0.7344 0.7328 0.0158 0.7962 0.7918 (2.50**) (2.48**) (2.48**) (0.53) (2.72***) (2.71***) Black market premium 0.2714 0.3117 0.2789 0.0044 0.2731 0.2648 (0.35) (0.40) (0.35) (0.05) (0.38) (0.37) Property rights 0.2352 1.5268 1.5549 1.5450 1.6105 0.3073 0.4959 2.4825 0.5218 0.5212 (2.21**) (3.21*) (3.29***) (3.24***) (3.14***) (3.20*) (1.54) (1.61) (1.44) (1.36) Property rights2 0.1156 0.1188 0.1367 0.1299 0.0163 0.000 0.0263 0.0193 (0.65) (0.00) (0.88) (0.39) (2.90*) (2.98***) (3.18***) (1.93*) Finance  property 0.0574 0.0174 (0.69) (0.22) Finance  property2 0.0016 0.0004 (0.18) (0.06) Number of observations 87 87 82 82 82 91 91 82 82 82 R2 0.55 0.59 0.63 0.63 0.63 0.57 0.57 0.61 0.61 0.61 Initial income

Notes: t-Statistics are reported below the coefficients in parentheses. At the bottom of the table, we report the R-square from each regression and number of observations that constitute the sample. Estimation was based on OLS. The dependent variable is the average annual growth rate from 1970 to 2005. Initial income is measured in 1970, and all other variables are period averages. All variables, except the relative price of capital are the in logs. However for the black market premium, we take the alternative transformation log(1 + bmp) due to a large number of negative values. * Statistical significance at 10% level. ** Statistical significance at 5% level. *** Statistical significance at 1% level.

we examine patterns within data across a sample of countries where measures of financial depth vary substantially, evidence of nonlinearities across the entire sample may be weak, as the coefficient on property rights and/or property rights squared may vary across countries with differing degrees of financial development. One way we could attempt to isolate nonlinearities in the data is by interacting the property rights and property rights squared variables with our measure of financial development. However, we find these interactions are statistically insignificant [Table 1, columns (4), (5), (9), (10)]. Even so, this does not imply that the growth-effect of property rights is unchanging with financial development. It could simply be that the partial derivative of growth with respect to property rights (or property rights squared) changes, but nonlinearly, with financial development. To examine this issue more carefully, below we consider a model specification that allows interactions between finance and property rights, and finance and property rights squared, without imposing a linear structure on the model. (b) Varying coefficients model In particular here we consider a generalization where the marginal effects of (a) property rights and; (b) property rights squared, are allowed to vary smoothly and nonparametrically with financial development. We consider these interactions in turn. That is first we estimate an additive model, where the coefficient on property rights, h1(.), is allowed to vary with financial development, u, while holding the effect of property rights-squared, h2, constant. Next, we hold h1 constant while

allowing h2(.) to vary smoothly with u [see regression specifications (9a) and (9b) in Section 3]. The estimates of the regression functions, h1(.) and h2(.), are presented in Figure 1. 4 Allowing for parameter heterogeneity reveals a great deal of structure within the data. If we fix h2 while allowing h1(.) to vary with u then evidence of two forms of nonlinearity emerge. First the coefficient on property rights squared is negative and highly statistically significant [(h2 =0.17; tstat = 2.96) using the FI index and (h2 =0.13; tstat =2.78) on the basis of the HF measure], while the coefficient on property rights h1(.) is positive for all, or most, values of u (Figure 1). When we estimate our model using the HF data, over a range of values [when u  log (finance) lies approximately between (1, 1.5) ] h1(.) < 0. However, importantly there are only two observations within our data for which u lies within that range—the Democratic Republic of Congo (DRC) and Sierra Leone. The positive values of h1(.) coupled with negative coefficients on h2 suggest that, for any given value of u (u > 1.5 when property rights are measured using the HF index), the relationship between property rights and growth is described by an inverted-U shaped pattern. Second for both measures in Figure 1, h1(.) is increasing in u. Hence the turning point in the relationship between property rights and growth shifts to the right as u increases, i.e., the optimal level of property rights, p*, at which growth is maximized, increases with financial development. By examining the profile of h1(.) carefully, we can examine how p* varies with u (Figure 2A). The country with lowest financial development is the Democratic Republic of Congo (DRC), where the private

WORLD DEVELOPMENT

0

4

1

θ2 = -0.17 (tstat = -2.96)

2

θ1(φ)

0

2

θ1(φ)

θ2 = -0.13 (tstat = -2.78) -4

coefficient on property rights

4 3

Foundation’s Data

-1

coefficient on property rights

Coefficient on Property Rights: Heritage

Coefficient on Property Rights: Fraser Institute’s Data

-2

1790

-1

0

1 2 3 4 log of financial development

5

-1

1 2 3 4 log of financial development

5

Coefficient on Property Rights Squared: Heritage Foundation’s Data

Coefficient on Property Rights Squared:

-0.6 -1

0

1

2

3

4

5

log of financial development

-0.2 -0.4 -0.6 -0.8

θ1 = 1.66 (tstat = 3.20)

-1.0

-0.5

θ1 = 1.89 (tstat = 3.00)

coefficient on property rights square

-0.4

-0.3

-0.2

θ2(φ)

θ2 (φ)

-1.2

-0.1

0.0

0.0

Fraser Institute’s Data

coefficient on property rights square

0

-1

0

1

2

3

4

5

log of financial development

Figure 1. Varying effect of property and property rights squared on growth: varying coefficients analysis. Notes: figures based on the estimation of a varying coefficients model, where the coefficients on property rights and property rights squared are smooth functions of financial development; all other coefficients are constant. The dashed lines denote 95% standard error bands.

credit to GDP ratio averaged 0.45% between 1970 and 2005. For the DRC, the negative growth-effect of enforcing property rights almost immediately outweighs any beneficial effects that it may have [p* < 1, on the FI scale, and p* is negative (outside the range of possible values) based on HF index]. As private credit ratios increase to between 15% and 20% of GDP, p* increases abruptly to between 6.3 and 6.6 (FI scale), and to between 5.4 and 6.3 (on the HF index). As the volume of private credit continues to increase (beyond 20 percent of GDP) however the optimal value of p increases only slightly from 6.6 to 7.7 on the FI scale. In the Heritage Foundation data, the benefits of strengthening property rights do increase steadily—as u increases to 100% of GDP p* rises to 9.2 —however the function p*(u) flattens significantly for u > 20%. These profiles suggest a “regimed” pattern within the data. For u < 20% of GDP the marginal effect of stronger financial markets on  optimal property rights, p*, is large. Above this threshold dp falls sharply; the optimal value of property rights, d/ p*, does not increase much when measured on the FI scale and increases, at a much slower rate, when p* is measured using the HF scale.

If we fix h1 but allow h2(.) to vary with u a similar pattern emerges. The coefficient on property rights is always positive and statistically significant while the coefficient on property rights squared is negative for all values of u (Figure 1). Again these results provide evidence of a negative quadratic relationship between property rights and growth. Moreover, since h2(.) is increasing (decreasing in an absolute sense) with u, this effect is stronger in countries with lesser developed financial structures. The variation in h2(.) with u again implies that optimal property rights are a function of financial development (Figure 2B). In the case of the HF data p* increases steadily with u. However, with the FI data the pattern is again more “regimed” in the sense that the function p*(u) flattens significantly beyond a threshold value of u between (15% and 20%). Below we formally examine whether these patterns within the data are consistent with the presence of multiple regimes. (c) Threshold regressions Our analysis in this section applies methods developed in Hansen (2000) to formally test for threshold effects in the prop-

THE GROWTH EFFECTS OF PROPERTY RIGHTS: THE ROLE OF FINANCE

gi ¼ X 0i b þ h1;1 ½Ið/i 6 sÞpi þ h1;2 ½1  Ið/i

10 π* Heritage Foundation

6 sÞpi þ h2;1 ½Ið/i 6 sÞp2i þ h2;2 ½1  Ið/i

π* Fraser

6 sÞp2i þ ui

Optimal Value of Property Rights (π*)

5

0

-5

-10

-15 0

20

40

60

80

100

120

Financial Development (φ)

Figure 2A. Optimal property rights: calculations based on regression (9A).

12

π* Heritage Foundation Optimal Value of Property Rights (π*)

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8

π* Fraser

4

0 0

20

40

60

80

100

120

Financial Development (φ)

Figure 2B. Optimal property rights: calculations based on regression (9B).

erty rights growth relationship. Hansen’s approach offers two advantages. First, the sample splits are determined endogenously. Hence countries are not grouped into regimes in an ad hoc manner. This contrasts with our informal analysis based on the plots of h1(.) and h2(.). Second, it offers a way to formally test whether the regression coefficients across the two regimes are different on the basis of objective statistical criteria. Below we estimate two versions of Eqn. (10). In version one, we allow all the regression coefficients to change as the threshold variable, u, crosses an endogenously determined threshold s. Next, we constrain the slope coefficients on all our variables, except property rights, property rights squared, and the intercept, to be the same across the two regimes. That is we estimate the following regression:

ð10aÞ

In the case, where the regressors, Xi, are uncorrelated with the level of financial development, ui, constraining the coefficients on Xi will not bias the test. Hence evidence of a threshold effect will be based solely on the additional explanatory power of allowing the effect of property rights and property rights squared to change across regimes. 5 In addition to constrained and unconstrained alternatives, below we estimate two model specifications: a baseline—where we control for initial income, the relative price of capital, schooling, and financial development—and an extended specification—where we also control for openness and an indicator of policy inefficiency (the black market premium). In each case, we find strong evidence of threshold effects within the data: the null hypothesis of “no threshold” can be rejected at better than 1% in most cases (Table 2). Some of the variation across the two sub-samples can be attributed to differences in the coefficients on our other control variables. For instance, evidence of convergence effects is stronger in the high finance group. However, much of the variation across the two samples can be captured by the differing effects of property rights on growth. As a result, the R-square from the unconstrained and constrained regressions are similar. In the unconstrained model specifications the split into low and high financial categories happens when the private credit to GDP ratio is approximately between 16.9% and 18% in the FI data and 16.4% in the HF data. This corresponds to the 23rd to 25th percentile of banking development. Constraining the slope coefficients implies a slightly lower estimated threshold when property rights are measured using the FI index. Our threshold regressions point to strong nonlinearities, which is consistent with our varying coefficients analysis. Moreover the relationship between property rights and economic growth exhibits greater concavity for countries in the low finance group. In the general constrained specification for instance, the regression coefficient on property rights squared in the FI data is 1.0 in the low group and 0.2 in the high group [Table 2, columns (5) and (6)]. In the HF data, the respective relevant coefficients are 0.7 and 0.1 [Table 2, columns (11) and (12)]. The implication then is that the beneficial effects of stronger property rights may not be realized unless stronger enforcement of property law is accompanied by developments in the financial sector. This conclusion is truer for countries with low quality financial structures. For these countries, there may exist a low level of property rights consistent with growth-maximizing objectives. For instance in the low financial group, the optimal level of property rights is between 3.8 and 4.6 on FI scale, and between 3.3 and 3.6 on the Heritage scale. Thus what constitutes an optimal level of property rights is not a strict metric. Benin, Mozambique, Nepal, and Syria are examples of countries that lie close to this range. Property is only weakly protected in these countries, the courts are inefficient, and expropriation is possible if not common. Even so the growth rate in these four countries averaged 1.5% over the period of investigation. To either side of this optimal range, growth in countries was significantly lower. For instance, in the DRC and Haiti, where property rights were weakest, average growth rates were negative (3.5% and 0.3%, respectively). Examples of countries with property rights enforcement that exceeded the optimal threshold

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Table 2. Property rights and growth: threshold regressions Dependent variable: Average growth rate of real GDP per capita, 1970–2005 Fraser Institute Measure of Property Rights Unconstrained

Initial income Education Price of capital

Constrained Regime 1 finance 6 14.47 (2–3)

Regime 2 finance > 14.47 (2–4)

Regime 1 finance 6 14.47 (2–5)

Regime 2 finance > 14.47 (2–6)

Regime 1 finance 6 16.34 (2–7)

Regime 2 finance > 16.34 (2–8)

Regime 1 finance 6 16.34 (2–9)

Regime 2 finance > 16.34 (2–10)

Regime 1 finance 6 16.34 (2–11)

Regime 2 finance > 16.34 (2–12)

0.0210 (0.09) 0.7184 (2.40**) 0.0099 (1.36) 0.7655 (3.26***)

1.0526 (6.84***) 1.0809 (3.26***) 0.0145 (3.62***) 0.7610 (2.67***)

0.8165 (6.50***) 0.9002 (3.88***) 0.0098 (2.45**) 0.8695 (4.59***)

0.8165 (6.50***) 0.9002 (3.88***) 0.0098 (2.45**) 0.8695 (4.59***)

1.0288 (7.98***) 0.5520 (2.07**) 0.0120 (3.11***) 0.7064 (2.65***)

0.9100 (7.23***) 0.5783 (3.09***) 0.0059 (1.71*) 1.1079 (6.47***)

0.9100 (7.23***) 0.5783 (3.09***) 0.0059 (1.71*) 1.1079 (6.47***)

2.5293 (4.85***) 0.1879 (4.66***) 65 14.47 (0.01) 13.47, 30.10 0.66

6.4569 (3.12***) 0.8557 (3.29***) 16 14.47 (0.00) 13.90, 15.81 0.71

2.3970 (5.16***) 0.1830 (4.75***) 71 16.34 (0.00) 16.34, 18.32 0.70

0.8168 (5.62***) 0.8332 (3.55***) 0.0101 (1.84*) 0.8893 (4.74***) 0.2393 (1.10) 0.5737 (2.29**) 0.9571 (1.68*) 2.5802 (5.91***) 0.1941 (5.26***) 68 16.34 (0.00) 16.34, 16.34 0.71

0.3331 (1.24) 0.4726 (2.77***) 0.0074 (1.74*) 1.3806 (8.95***)

8.3548 (4.15***) 1.0353 (4.28***) 22 18.01 (0.02) 13.90, 58.02 0.70

0.8168 (5.62***) 0.8332 (3.55***) 0.0101 (1.84*) 0.8893 (4.74***) 0.2393 (1.10) 0.5737 (2.29**) 0.9571 (1.68*) 7.6049 (4.20***) 1.0195 (4.62***) 14 16.34 (0.00) 10.76, 19.49 0.66

3.7349 (6.91***) 0.5243 (5.96***) 22

1.6093 (4.03***) 0.0917 (3.00***) 69

3.0467 (3.99***) 0.4377 (3.71***) 22

1.4905 (3.81***) 0.0877 (2.87***) 69

0.8277 (6.10***) 0.5467 (2.88***) 0.0057 (1.07) 1.0819 (6.65***) 0.3181 (1.43) 0.5487 (2.12**) 0.1873 (0.47) 4.5274 (5.31***) 0.6913 (5.78***) 17

0.8277 (6.10***) 0.5467 (2.88***) 0.0057 (1.07) 1.0819 (6.65***) 0.3181 (1.43) 0.5487 (2.12**) 0.1873 (0.47) 1.3667 (3.12***) 0.0808 (2.42**) 65

Trade openness Black market premium

Property rights2 Number of observations Threshold p-value Confidence Interval R2

Constrained

Regime 2 finance > 18.01 (2–2)

Financial integration

Property rights

Unconstrained

Regime 1 finance 6 18.01 (2–1)

Notes: t-statistics are reported below the coefficients in parentheses. At the bottom of the table the figures in parentheses are p-values associated with the linearity test. The dependent variable is the average annual growth rate from 1970 to 2005. Initial income is measured in 1970, and all other variables are period averages. All variables, except the relative price of capital are the in logs. However for the black market premium, we take the alternative transformation log(1 + bmp) due to a large number of negative values. * Statistical significance at 10% level. ** Statistical significance at 5% level. *** Statistical significance at 1% level.

WORLD DEVELOPMENT

Finance

Heritage Foundation Measure of Property Rights

Table 3. Panel GMM regressions Dependent variable: Average growth rate of real GDP per capita five year intervals between 1970 and 2005 Fraser Institute Measure of Property Rights Regime 1 finance 6 14.47

Education Price of capital Finance

Regime 1 finance 6 14.47

Regime 2 finance > 14.47

(3–1)

(3–2)

(3–3)

(3–4)

(3–5)

(3–6)

(3–7)

(3–8)

(3–9)

0.8156 (1.63) 0.9763 (1.15) 0.0193 (1.85*) 0.8113 (1.93*)

0.6790 (1.93*) 0.5084 (0.85) 0.0158 (1.82*) 0.2941 (0.75)

0.6502 (1.83*) 1.3254 (2.37**) 0.0088 (1.02) 0.0139 (0.04)

0.3480 (0.99) 1.0577 (1.95*) 0.0128 (1.29) 0.4375 (0.56) 1.2602 (1.49) 1.0023 (1.17)

0.7283 (1.77*) 1.0419 (1.39) 0.0137 (1.31) 0.4119 (1.01) 0.6791 (1.72*) 0.6777 (1.41)

4.6413 (2.38**) 0.5251 (2.21**)

2.3495 (3.30***) 0.1863 (3.75***)

1.7283 (3.10***) 0.1300 (3.15***) 0.1741 (2.05**)

1.6421 (2.13**) 0.1196 (2.16**)

3.7367 (1.85*) 0.4247 (1.82*)

1.9804 (2.65***) 0.1479 (2.65***)

0.5302 (1.07) 1.0100 (1.50) 0.0209 (2.10**) 0.2752 (0.33) 0.4827 (0.41) 0.1860 (0.12) 0.4279 (0.41) 4.4930 (2.84***) 0.5010 (2.54**)

1.1147 (3.54***) 1.1806 (1.88*) 0.0231 (1.79*) 1.0537 (2.01**) 1.2133 (2.03**) 1.2745 (2.23**) 2.5630 (1.94*) 0.7801 (1.24) 0.0576 (1.14)

0.9804 (3.39***) 0.5090 (0.90) 0.0265 (2.29**) 0.6143 (1.55) 0.9676 (1.95*) 0.9799 (2.27**) 1.7402 (2.46**) 0.6131 (1.14) 0.0293 (0.70) 0.0760 (0.92)

0.2194 (3.25***) 216 65 (0.00) (0.33) (0.00) (1.00) 137

70 29 (0.07) (0.80) (0.54) (1.00) 46

361 75 (0.00) (0.17) (0.00) (1.00) 174

58 27 (0.23) (0.28) (0.31) (1.00) 33

284 72 (0.00) (0.08) (0.02) (1.00) 138

204 63 (0.00) (0.85) (0.00) (1.00) 150

Trade openness Black market premium

Property rights2 Growth, lagged once Growth, lagged twice Number of observations Number of countries First order serial correlation Second order serial correlation Sargan test Hansen test Number of instruments

Regime 2 finance > 14.47

0.5059 (1.21) 1.2839 (2.05**) 0.0166 (1.71*) 1.0145 (1.69*)

Financial integration

Property rights

Regime 1 finance 6 14.47

75 31 (0.04) (0.84) (0.58) (1.00) 49

374 77 (0.00) (0.05) (0.01) (1.00) 134

288 74 (0.00) (0.02) (0.00) (1.00) 152

THE GROWTH EFFECTS OF PROPERTY RIGHTS: THE ROLE OF FINANCE

Initial income

Regime 2 finance > 14.47

Notes: t-statistics are reported below the coefficients in parentheses. At the bottom of the table the figures in parentheses are p-values. The dependent variable is the average growth rate in five year intervals between 1970 and 2005. For each period, property rights (measured using the Fraser Institute’s scale), income and education are initial values, except for the final period where property rights are an average from 2000 to 2005. Financial development, financial integration, trade openness and the black market premium are period averages. In Eqns. (3)–(7) and (3–9) the final period is dropped, as the black market premium data do not extend beyond 2000. All variables, except the relative price of capital are the in logs. However for the black market premium, we take the alternative transformation log(1 + bmp) due to a large number of negative values. * Statistical significance at 10% level. ** Statistical significance at 5% level. *** Statistical significance at 1% level.

1793

1794

WORLD DEVELOPMENT

include, Papua New Guinea (FI = 4.7, HF = 5), Malawi (FI = 5.0, HF = 5) and Zambia (FI = 5.4, HF = 5). In those countries the average growth rate was also significantly lower (0.1%) compared to our optimal group. In the high finance group the nonlinearity in the relationship between property rights and growth is weaker, consequently optimal value of property rights is significantly higher—between 6.6 and 6.8 on the FI scale and between 8.3 and 8.8 on the HF scale. On the FI scale the highest level of average property rights between 1970 and 2005 is 8.6 and on the Heritage scale the maximum value is 9. The optimal level of property rights is therefore close to the upper end of the support of the measure, at least on the basis of the HF data. (d) Panel regressions While our threshold analysis reveals important nonlinearities within the data, we cannot make structural inferences on the basis of this evidence since the explanatory variables, in particular our measures of financial development and property rights, are correlated with innovations to the response variable. In addressing this endogeneity issue we face two hurdles. First, instrumental variables estimation within endogenous threshold models is available only with certain restrictions. 6 Second, identifying instruments which capture the exogenous cross-country variation in property rights institutions and financial development is difficult (see for instance Barro, 2000). Since it is difficult to isolate the structural component in the relationship between property rights and growth and finance and growth in the cross-section, we attempt to generate some traction on this issue by exploiting the time-series variation in the FI data which dates back to 1970. Although the median country rating has changed little over time, increasing from 6.25 in 1970 to 6.7 in 2005, in some countries the extent of enforcement of property rights has changed substantially. In Chile, for instance, under Pinochet property rights were strengthened. As a result between 1970 and 2005, Chile’s property rights rating increased by 4.11 points. By contrast Venezuela, which has introduced various land reforms, has seen its property rights rating drop by 2.16 points since 1970. This variation, over time, in the FI index provides some traction for isolating relationships within countries. Below we estimate the property rights-growth relationship in a panel, using as instruments, lags of the endogenous regressors. In particular, we estimate our model using the system-GMM estimator (Arellano & Bover, 1995) which is particularly well suited for confronting the issue of endogeneity when variables exhibit a large degree of persistence—a characteristic often found in macro panels. Below we report results based on the one-step variant of this estimator, since the standard errors in the two-step estimator tend to be downward biased in finite samples. In our baseline specification we regress the average 5-year growth rate in each of the following time periods j = 197074, 7579, . . ., 20002005, against average values of the ratio of private credit to GDP and the relative price of capital over the same periods, as well as income and secondary school enrollment at the start of each period. In addition, we extend this specification by first including controls trade and financial openness. In our final specification, we also included the average size of the black market premium in each period up to 2000. The period from 2000 to 2005 was excluded from our analysis, as no data on the black market premium were available over this time. Since our goal is to examine how the effect of property rights on growth changes across re-

gimes, we split our data into two subsamples using our earlier threshold estimate from the constrained threshold regression (private credit 14.5% of GDP) and estimate the relationship between property rights and finance within each subsample. Even though lags of the finance and property rights variables are used as instruments in the GMM procedure, 7 shocks to growth could be serially correlated, invalidating our instruments. By construction, even when the errors are i.i.d., the first-differenced errors will be first-order serially correlated. Not unexpectedly then evidence against the null of zero autocorrelation in the first-differenced errors is always rejected. The crucial test however is that second-order serial correlation in the residuals is absent. This was not always the case, particularly in the high-finance regime, that is in the sample where private credit to GDP exceeded the threshold 14.5% of GDP. Consequently, for this sub-sample, we considered alternative specifications, where we included lags of the dependent variable in our model of increasingly higher orders until there was no significant evidence of second-order serial correlation in the first-differenced errors. As with our threshold and varying coefficient analysis, our GMM results point to nonlinearities in the relationship between property rights and growth: stronger property rights raise growth initially, however above a threshold between 4.4 and 4.5 on the FI scale, further improvements in property rights lower growth. As before the concavity in the property rights-growth relationship is more pronounced in the low finance group, although nonlinearities were also evident in the high group. In that regime the coefficient on property rights squared varied between 0.19 and 0.12 [Table 3, columns (2) and (4)]—significantly lower in absolute sense, when compared to the low regime [Table 3, column (1)]. In the most general specification, which included the black market premium, this coefficient was even lower. Optimal property rights in the high group suggested a value between 6.3 and 6.9, though in one specification we estimated p* to be 10.5. Although identification is a difficult nut to crack, these results are promising. Moreover, they are both qualitatively and quantitatively consistent with our earlier cross-country analysis. There is then a tentative basis for concluding that improvements in the property rights can imply tradeoffs that negatively influence growth. This nonlinear relationship is particularly strong when financial institutions are initially weak. In those countries property rights reforms have not always been associated with stronger economic growth and have in some cases implied lower growth. Similar patterns are evident to some extent in countries with stronger financial markets. But within this high group, the departures from linearity and montonicity are less pronounced. 5. CONCLUSION Conventional wisdom dictates that institutional reforms that better protect property will raise growth. The evidence that we present here is not inconsistent with this view—property rights can elevate the growth prospects of countries—however we also find evidence of nonlinearities in this relationship. For countries with weak banking systems, where informational frictions are most pronounced, the beneficial effects that flow from securing property can quickly give way to their negative effects which could be operating through the financial system. By contrast, in countries where the volume of credit supplied by the banking system is relatively high, the relationship between property rights and growth exhibits lesser concavity and as a result the optimal level of property rights is higher.

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NOTES 1. The introduction of civil courts in colonial India for instance had two opposing effects on financial contracts in agricultural markets. On the one hand, the average cost of loans fell. On the other hand, increased competition undermined long-term credit relationships, leaving farmers more exposed to negative price and output shocks (Kranton & Swamy, 1999). 2. Due to the Ak technology this also represents the output growth rate of the formal economy. 3. Increasingly growth empirics are shifting toward more nonlinear approaches such as the one that we take here (see for instance Durlauf & Johnson, 1995; Liu & Stengos, 1999). 4. For space considerations we have not reported the linear coefficients and t-stats in our model. However, these will be made available upon request. 5. In general Xi and /i will not be uncorrelated, and the coefficients on our other explanatory variables, Xi, could also switch across regimes. Restricting the model coefficients on Xi could therefore impart a bias in

our estimates of h1 and h2 which may (erroneously) lead to the rejection of the null hypothesis: h1,1 = h1,2 and h2,1 = h2,2. This is why we also consider specification (10) where the model parameters on each of our controls are allowed to vary freely across regimes. 6. For instance, Caner and Hansen (2004) relax the exogeneity assumption for the explanatory variables but continue to assume the threshold variable is exogenous. Some progress in this area however is being made. In a recent working paper, Kourtellos, Stengos, and Tan (2007) consider an estimator where both slope and threshold variables are endogenous. The so called “THRET” estimator in Kourtellos et al. (2007) may provide some traction for resolving identification issues, as long as appropriate exogenous sources of variation can be found. Unfortunately this is a tall order in many macroeconomic datasets. 7. Second (and higher) order lags of the levels of private credit and the index of property rights are used as instruments in the first differenced equation, while first lags of the first difference of private credit and property rights are used as instruments in the levels equation.

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Gurley, J. G., & Shaw, E. S. (1960). Money in a theory of finance. Washington, DC: Brookings Institution. Gwartney, J., & Lawson, R. (2009). Economic freedom of the world: 2009 Annual Report. Vancouver, BC: The Fraser Institute. Hall, R. E., & Jones, C. I. (1999). Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics, 114(1), 83–116. Hansen, B. E. (2000). Sample splitting and threshold estimation. Econometrica, 68(3), 575–604. Helpman, E. (1993). Innovation, imitation and intellectual property rights. Econometrica, 61(6), 1247–1280. Heston, A., Summers, R., & Aten, B. (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006. Holmes, K. R., Feulner, E. J., & O’Grady, M. A. (2008). 2008 Index of economic freedom. Washington, DC and New York, NY: Heritage Foundation and Dow Jones and Company, Inc. Johnson, S., McMillan, J., & Woodruff, C. M. (2002). Property rights and finance. American Economic Review, 92(5), 1335–1356. Kourtellos, A., Stengos, T., & Tan, C. M. (2007). Threshold regression with endogenous threshold variables. Mimeo University of Guelph. Kranton, R., & Swamy, A. (1999). The hazards of piecemeal reform: British civil courts and the credit market in colonial India. Journal of Development Economics, 58(1), 1–24. Lane, P. R., & Milesi-Ferretti, G.-M. (2007). The external wealth of nations mark II: Revised and extended estimates of foreign assets and liabilities, 1970–2004. Journal of International Economics, 73(2), 223–250. La Porta, R., Lopez-de-Silanes, F., Pop-Eleches, C., & Shleifer, A. (2002). The Guarantees of Freedom. Harvard Institute of Economic Research Working Paper 1943. Levine, R. (1997). Financial development and economic growth: Views and agenda. Journal of Economic Literature, 35(2), 688–726. Levine, R. (1998). The legal environment, banks and long-run economic growth. Journal of Money, Credit and Banking, 30(3), 596–613. Levine, R. (2005). Finance and growth: Theory and evidence. In P. Aghion, & S. N. Durlauf (Eds.), Handbook of economic growth (1st. ed., pp. 865–934). Amsterdam: Elsevier. Levine, R., Loayza, N. V., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31–77.

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Liu, Z., & Stengos, T. (1999). Non-linearities in cross-country growth regressions: A semiparametric approach. Journal of Applied Econometrics, 14(5), 527–538. McKinnon, R. (1973). Money and capital in economic development. Washington, DC: Brookings Institution. Miletkov, M., & Wintoki, M. (2008). The role of financial intermediation in the evolution of property rights. FMA Working Paper. Stengos, T., & Liang, Z. (2005). Financial intermediation and economic growth: A semiparametric approach. In C. Diebolt, & C. Kystson (Eds.), New trends in macroeconomics (1st. ed., pp. 39–52). New York, NY: Springer. Stiglitz, J. E., & Weiss, A. (1981). Credit rationing in markets with imperfect information. American Economic Review, 71(3), 393–410. Williamson, S. D. (1987). Costly monitoring, loan contracts, and equilibrium credit rationing. Quarterly Journal of Economics, 102(1), 135–145.

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APPENDIX A See Appendix Tables 4 and 5.

Table 4. Variable descriptions, data sources and list of countries Variable

Description

Growth

Growth of real GDP per capita, calculated as the log difference of chained real GDP (series rgdpch, Heston, Summers, & Aten, 2006) Explanatory variables benchmark specification Real GDP per capita adjusted for differences in purchasing power (series rgdpl, Heston et al., 2006) Average years of secondary schooling attainment in the male population over age 25. Barro and Lee (2001) Relative price of investment calculated as the log difference in the price of investment and the price of GDP (series pi & p respectively, Heston et al., 2006) Private credit provided by depository banks as percent of GDP. Beck et al. (2000), Financial Structure Database (update through 2006). Rating of private property ranging from 0 to 10, higher values indicating stronger property rights. Source: economic Gwartney and Lawson (2009). Freedom of the World: 2009 Annual Report. Vancouver, BC: Fraser Institute Protection of private property, ranging from 0 to 10, higher values indicating stronger property rights. Source: Holmes, Feulner, and O’Grady (2008). 2008 Index of Economic Freedom: Freedom#8, property rights. Washington, DC and New York, NY: Heritage Foundation and Dow Jones and Company, Inc. Explanatory variables extended specification Sum of stocks of (the absolute value of) external assets plus external liabilities as a percentage of GDP (Lane & MilesiFerretti, 2007) Total volume of trade flows expressed as a percent of GDP (series NE.TRD.GNFS.ZS, World Bank, 2008) Black market exchange rate / official exchange rate 1. Easterly (2001) Country list Australia, Fiji, Hong Kong, Indonesia, Japan, Korea, Malaysia, New Zealand, Papua New Guinea, Philippines, Singapore, and Thailand Hungary Argentina, Barbados, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad &Tobago, Uruguay, and Venezuela Algeria, Bahrain, Egypt, Israel, Jordan, Malta, Portugal, Syria, and Tunisia

Initial income Education Relative price of capital Financial development Property Rights Fraser Institute Property Rights Heritage Foundation

Financial Integration Trade openness Black market premium East Asia and the Pacific Eastern Europe Latin America and the Caribbean Middle East and North Africa North America South Asia Sub Saharan Africa

Western Europe a

Canada, United States Bangladesh, India, Nepal, Pakistan, and Sri Lanka Benin, Burundi, Cameroon, Democratic Republic of Congo, Republic of Congo, The Gambiaa, Ghana, and Kenya, Lesothoa, Malawi, Mali, Mauritania, Mauritius, Mozambique, Senegal, Sierra Leone, South Africa, and Sudana, Swazilanda, Togo, Zambia, and Zimbabwe Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Netherlands, Norway, Spain, Sweden, Switzerland, and United Kingdom

Data limitations imply that the country was included in the Heritage Foundation sample only.

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Table 5. Cross-country summary statistics and correlations Panel A Variable

No. of observations

Mean

Standard deviation

Minimum

Maximum

91 91 91 91 91 82 82 82 91 87

1.78 7.45 0.10 37.72 3.30 0.06 4.09 0.17 5.80 5.50

1.55 1.49 0.86 40.34 0.87 0.70 0.56 0.25 2.16 1.81

3.50 4.80 2.18 24.02 0.81 1.29 2.82 0.01 1.00 2.28

5.75 10.13 1.65 137.65 5.01 3.24 5.99 1.17 9.00 8.53

Growth Initial income Education Price of capital Finance Financial integration Trade openness Black market premium Heritage Foundation Fraser Institute Panel B Growth Growth Initial income Education Price of capital Finance Financial Integration Trade openness Black market premium Heritage Foundation Fraser Institute

1.00 0.24 0.45 0.44 0.63 0.15 0.34 0.36 0.53 0.48

Initial income 1.00 0.79 0.73 0.67 0.38 0.14 0.42 0.77 0.78

Education

1.00 0.75 0.67 0.25 0.14 0.44 0.74 0.71

Price of capital

1.00 0.64 0.17 0.10 0.52 0.76 0.72

Finance

1.00 0.35 0.29 0.53 0.73 0.72

Financial integration

Trade openness

Black market premium

Heritage Foundation

Fraser Institute

1.00 0.71 0.17 0.35 0.33

1.00 0.22 0.25 0.25

1.00 0.49 0.49

1.00 0.91

1.00

Notes: Growth is the average growth rate from 1970 to 2005; Initial income is the log of real GDP per capita in 1970 (or the earliest date available); Education is the average years of male secondary school attainment between 1970 and 1999; Price of Capital is an average from 1970 to 2005; Finance is the average private credit to GDP ratio from 1970 to 2005; Financial integration is the average of the sum of external assets plus liabilities from 1970 to 2005; Trade openness is the average trade volume between 1970 and 2005; Black market premium is the average of the log of (1+) black market premium between 1970 and 2000; Heritage Foundation is the average of the Heritage Foundation’s index from 1995 to 2005. Fraser Institute is the average of the Fraser Institute’s index from 1970 to 2005.