Foreign determinants of local institutions: Spatial dependence and openness

Gerrit Faber a,⁎, Michiel Gerritse b

a Utrecht University School of Economics, P.O. Box 80125, 3508 TC Utrecht, The Netherlandsb Faculty of Economics and Business Administration, VU University, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands

a r t i c l e i n f o a b s t r a c t

Article history:Received 17 November 2009Received in revised form 13 September 2011Accepted 21 September 2011Available online 1 October 2011

There are both empirical and theoretical arguments for the thesis that foreign factors have animpact on domestic institutional quality. Yet the literature is divided over whether exposure(openness to the world economy) or the kind of institutions in surrounding countries (relativelocation) determines the quality of local institutions. This paper confronts these hypothesesempirically, addressing the issues of strong cross-sectional dependence and the endogeneityof openness. In a 107-country cross-section, both trade openness and relative location havea positive impact on local institutions. The institutional quality of neighbouring countries isnot found to be statistically significant when considering openness to foreign direct invest-ments instead of trade, but the statistical performance of that model is poorer.

© 2011 Elsevier B.V. All rights reserved.

JEL classifications:C21F42O19

Keywords:Economic institutionsSpatial dependenceEconomic opennessSpatial econometrics

1. Introduction

It is important to knowwhy countries have different institutions, as these differences explain a substantial part of the variationin income levels across countries (Economides and Egger, 2009). Recent studies point to physical geographical facts and to thesocial and political constellation in countries and regions at the start of modern history as determinants of current institutions(Gallup et al., 1999; Hall and Jones, 1999; Rodrik et al., 2004; Acemoglu et al., 2005a). This puts countries aspiring to improvetheir institutional setting in a predicament: history and physical geography are impossible to change. On top of this, it is arguedthat the process of institutional development is difficult to manipulate. Acemoglu's closed economy model (2005a) shows thatgroups in power have the best opportunities to create and maintain economic institutions that reinforce their political position,thus making institutions highly persistent. While acknowledging these conclusions, this paper asks the question whether externalfactors such as the presence of neighbouring countries and flows of trade and capital have an impact on the kind and quality ofdomestic institutions. Neighbouring countries may have wide implications for domestic institutions. History provides many ex-amples of countries that tried to transplant their institutions to adjacent nations, be it by peaceful or violent means. The wishto exploit foreign opportunities, such as supplying foreign markets and attracting FDI, may lead countries to adapt their policiesand rules, giving rise to institutional change. Likewise, policy competition and imitation are examples of the mechanisms of insti-tutional spillovers in space. There are both theoretical and empirical arguments that integration in the world economy and theinstitutions of neighbouring countries have an impact on domestic institutional quality. This paper delves deeper into these re-lationships. Although the impact of trade integration on long term economic growth has been extensively researched, only a

European Journal of Political Economy 28 (2012) 54–63

⁎ Corresponding author. Tel.: +31 30 2539800; fax: +31 30 2537373.E-mail address: g.faber@uu.nl (G. Faber).

0176-2680/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.ejpoleco.2011.09.005

Contents lists available at SciVerse ScienceDirect

European Journal of Political Economy

j ourna l homepage: www.e lsev ie r .com/ locate /e jpe

number of papers have been written on the impact of trade and openness on domestic institutions (Wei, 2000; Islam andMontenegro, 2002; Dollar and Kraay, 2003; Rodrik et al., 2004; Acemoglu et al., 2005b; Acemoglu and Robinson, 2006). Ob-serving that institutions and trade exert a mutual positive impact on each other (Rodrik et al., 2004: 143),1 we take a closerlook at the impact of trade and geography on domestic institutions. We will approach this question by distinguishing varia-tions of trade and geography in their relevance to institutional and economic processes. Firstly, integration in the world econ-omy may affect domestic structures, as, e.g., exporters pressure their governments to introduce institutions that support theircompetitiveness and foreign investors like adequate protection of their physical and intellectual property. The importance ofopenness per se implies that the relevance of the foreign country is simply in its presence, i.e. the partner country is viewed asgeneric or exposure to the world economy in general drives institutional development. The second line of reasoning is thatinstitutional characteristics of nearby countries matter. Relative location or the geographical position vis-à-vis institutionallylow- and high-ranking countries could determine the local institutional setting. For instance, there may be a spatial spillovereffect in institutions themselves. Bosker and Garretsen (2009) document that countries whose neighbours have poor institu-tions are more likely to experience adversities such as political instability, coups, and civil and external war. Similarly, if coun-tries compete for subsidiaries of multinational firms or specialised workers, spatial patterns would arise in institutionalindicators due to specific policies being adopted (Brueckner, 2003). Such patterns have been confirmed in a panel modelexplaining local institutions that explicitly takes neighbouring countries' institutional quality into account (Kelejian et al.,2007). The objective of this paper is to confront these two lines of reasoning empirically, and determine whether opennessand the institutional quality of neighbouring countries are two independent external factors that impact upon the qualityof domestic institutions.

The paper is composed as follows. The second section will discuss the literature with respect to the possible effects of opennessand their potential relevance for institutional quality and the impact of the geography of institutions. The third section describesthe empirical strategy of the paper and presents the empirical results. Conclusions are drawn in the fourth section.

2. External impacts on domestic institutions

There are several definitions of institutions. North (1991) defines institutions as “the humanly devised constraints that struc-ture political, economic and social interaction. They consist of both informal constraints (…) and formal rules (…)”. At a moreconcrete level, institutions may be defined as particular organisational entities, procedural devices, and regulatory frameworks(IMF, 2003). These have an effect on economic performance by stimulating more or less productive and efficient behaviour of eco-nomic actors. Examples of institutions that promote economic performance include the protection of property rights and the ruleof law. Both stimulate investments in productive capacity.

Underlying this paper are two distinct approaches to external influences on domestic institutions. The first approach positsthat a country's openness with respect to international trade or international investment flows exerts an impact on that nation'sinstitutional quality. Being open makes economic actors dependent on their competitiveness relative to foreign actors and createsincentives to put pressure on their government to improve economic institutions. Wei (2000) analyses the effect of openness oncorruption. This author argues that the “natural openness” of a country – determined by its size and geographical positionamongst other factors – affects its incentives to invest in a corruption-fighting public governance structure. The trade-off betweenmarginal cost and marginal benefits of better institutions will lead to an “equilibrium in which economies may display less cor-ruption and a higher quality of government than naturally less open economies” (Wei, 2000: 2). Islam and Montenegro (2002)have investigated and confirmed this relationship. They argue that there are at least four reasons why more open economieshave better institutions. First, economic agents are more competitive internationally if their domestic environment is charac-terised by better institutions, and thus, countries will try to improve their institutions, in order to attract economic agents andultimately increase overall economic welfare. Second, openness brings more competition among agents, which will make rentseeking and corruptionmore difficult. Third, better institutions are demanded to manage the risks that are associated with tradingwith unknown partners. Finally, there is a learning process based on the institutional conditions under which foreign agentswork.2 One of the conclusions they draw from their empirical research is that “… openness in trade is significantly and consistentlycorrelated with measures of institutional quality that focus on economic features such as the rule of law, corruption and governmenteffectiveness measures” (Islam and Montenegro, 2002: 14). It is plausible that there is a dual causality between trade openness andinstitutions. Rodrik et al. find an effect of openness on institutional quality although the effect of institutional quality on trade open-ness is three times larger (Rodrik et al., 2004: 143). Busse and Gröning (2007) use a dynamic GMM panel estimator to analyse theeffect of trade openness on governance in developing countries. They find a relatively small effect that is close to zero for countrieswith low governance scores in the initial period and negative for resource intensive countries.

The second kind of reasoning with respect to external impacts upon domestic institutions starts from the assumption that in-stitutions in one country affect the institutions in adjacent or nearby countries. Jörgens (2003) distinguishes three mechanismsthat are exemplary for this type of spatial dependence: harmonisation, unilateral imposition and diffusion. Harmonisation isthe result of cooperative decision-making and may take place on different levels (global, regional and sub-regional). Generally,regional and sub-regional harmonisation will result in agreements that have a more binding nature as the number of parties islower compared to global harmonisation, and the diversity of preferences is likely to be smaller and free riding is easier to

1 The impact of institutions on trade is larger and statistically significant. The effect of trade on institutions is found for trade in manufactures (Rodrik et al., 2004).2 This is a case of diffusion to which we return below when dealing with spatial spillovers.

55G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

monitor. There is imposition if institutions or policies are dictated upon countries by other states or international organisations.This may take the form of political or economic coercion, or may involve military threat or occupation. It is plausible that impo-sition will be executedmore effectively in regional settings where actors have higher stakes in exporting their institutions and thecost of imposition is lower. Diffusionmay happen in different ways. Governments may reproduce other countries' institutions as aresult of policy competition or imitate the institutions in other countries because of their proven attractiveness (Easterly andLevine, 1998; Simmons and Elkins, 2004). Diffusion may also occur in reaction to perceived threats from neighbouring countries.An indication that this may happen is found by Bosker and Garretsen (2009) who compute correlations between neighbouringinstitutions and proxies for regional instability. They find significant results for revolutions/coups, political instability, externalwar, the share of the government budget spent on military defence and the number of refugees. The amount of empirical testsof spillovers in institutions is limited. Most authors try to answer the question whether the policies and institutions of neighbour-ing countries have an impact on the incomes of neighbouring countries, not on their institutions (e.g., Ades and Chua, 1997;Murdochand Sandler, 2002). However, Easterly and Levine (1998) show in a cross-section estimation that sub-Saharan African countries im-itate each other's policies. They find “evidence consistent with the view that national economic policies are contagious” (Easterly andLevine, 1998: 122). Simmons and Elkins (2004: 171 ff) propose two broadmechanisms to explain the spread of liberal economic ideasand policies throughout theworld and the clustering of the adoption of these policies over time and space: first, a choice for particularinstitutions elsewhere will change the payoffs of these institutions for domestic policymakers, and second, the institutional qualityelsewhere “can change the information set onwhich governments base their own policy decisions”. Using themean of the dependentvariable for the tenmost competing countries in trade and investment, a spatial lag is constructed to capture the effect of policy com-petition. The imitation and learning effect is approached by taking the policy variables of the top growth decile and relate this to sev-eral diffusion channels: common memberships of preferential trade agreements and bilateral investment agreements, businesscontacts and common dominant religion. Controlling for economic shocks, external political pressure, domestic political economicforces and geography, Simmons and Elkins (2004: 187) find that competition for capital is a highly significant policy diffusionmech-anism. They also conclude that governments liberalise “along the lines of countries with which they share a religious identity.” Giventhe geographical clustering of competitors for capital as defined by the authors – competitors have “similar educational and infra-structural profiles” – and the cultural reference group as a diffusionmechanism, onemay also conclude that the diffusionmechanismsthat were found to be significant have a strong regional slant.

Some empirical studies relate well to this paper. Kelejian et al. (2007) look into spatial spillovers between countries in the de-velopment of institutions. Using a spatial panel model, they explain the quality of economic institutions from a spatial lag of thedependent variable, constructed from the institutions in bordering countries. These authors control for the long-term determi-nants of institutions such as legal origin, resource base, ethnolinguistic fractionalisation and the initial level of GDP per capita.Their main finding is that spatial spillover effects are significant for the explanation of institutional quality. Becker et al. (2009)investigate contagion of corruption between neighbouring countries, by explaining perceived corruption in a country using cor-ruption in other countries weighted by either an adjacency or a distance matrix. Using a GM estimator, these authors find thatcorruption disseminates from one country to its neighbours. This effect declines with geographical distance.

Finally, the paper of Seldadyo et al. (2010) is most closely related to this paper. These authors estimate the effects of nearbycountries' institutional quality on local institutions in a cross-section while taking trade openness into account. To address the po-tential endogeneity of trade openness, they lag the variable by five years. Using this strategy, Seldadyo et al. document an inde-pendent positive effect of trade openness and of the institutional quality of neighbouring countries on domestic institutionalquality.

This paper contributes to the literature on determinants of institutions by comparing a country's openness and the institution-al quality of its (geographical) surroundings as explanations for local institutions. The paper distinguishes itself from the existingliterature by acknowledging this distinction, and by providing a new econometric analysis to address the endogeneity of economicopenness aswell as the issues involving spatial dependence. In particular, compared to Seldadyo et al. (2010), the paper most relatedto this study, we use a different estimator (generalised moments instead of maximum likelihood), and we instrument both tradeopenness and FDI openness. The latter variable is not used by Seldadyo et al. Interestingly, despite these differences the point esti-mates in our baseline specification involving trade openness are comparable and give rise to the same conclusion, viz., that tradeopenness and spatial dependence both explain institutional quality. However, once we replace trade openness with openness toFDI, openness trumps the spatial dependence hypothesis.

3. Empirical evidence

In order to assess the empirical relevance of openness and spatial spillovers for domestic institutions, we take a closer look atthe statistical determinants of institutions. Specifically, in this section we compare the proposed channels of external influences.We model effects of openness (for trade and FDI) and specific spatial dependence among countries using different mechanisms.

To test the hypothesis that neighbouring countries' institutional quality affects the domestic institutional quality, we constructa spatial lag of the indicator for institutional quality. The spatial lag is the sum of other countries' institutional indicators, weightedby an indication of proximity such as geographical distance or contiguity. As such, the lag captures the institutional quality of a

country's peers, allowing the influence of another country to decrease with distance. The lag has the form ∑j∈i−

wijyj, where w is

the weight assigned to the strength of the link between countries i and j (inverse distance or contiguity in this paper), y is the

56 G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

institutional indicator, and i– is the set of all countries except i itself. We normalise the weights such that the partners' weightssum to one for every country (i.e. the weights matrix is row-standardised). As there are serious spatial patterns in ourdependent variable,3 the specification allows for spatial autocorrelation in the error term. The specification to be estimated isthen a so-called spatial autoregressive model with autoregressive disturbances (SARAR):

yi ¼ γ ∑j∈i−

wijyj þαOpennessi þ βXi þ ui

ui ¼ λ ∑j∈i−

wijuj þ εið1Þ

where ε is an i.i.d. error term and x is a vector of control variables. The coefficients of interest are γ, which indicates the effect ofnearby countries' institutions on local institutions, and α, which captures the effects of openness on local institutions.

The spatial lag generates a reflection problem, because country i presumably affects country j, and j affects i (Manski, 1993).Additionally, openness is likely to be endogenous.4 If the institutional setting can promote trade, the coefficient of an OLS regres-sion cannot be trusted to reflect the causal relation that this paper investigates. To address these issues, we use Fingleton and LeGallo's (2008) spatial estimator that allows for endogenous variables on the right hand side. Essentially, the estimator involvesinstrumenting the spatial lag and openness, and transforming the data to account for possibly autoregressive disturbances. To in-

strument the spatial lag in Eq. (1), the lagged dependent variable ∑j∈i−

wijyj is regressed on the lagged exogenous right hand side

variables, i.e. ∑j∈i−

wijXj. The predicted dependent variable of this equation is then used as instrument for the spatial lag in Eq. (1). In

that equation, openness is instrumented by another set of instruments, which we discuss below. The spatial error autoregressiveparameter (λ) is then obtained from the residuals of the 2SLS estimation of Eq. (1) using the moment conditions listed in Kelejianand Prucha (1998). Subsequently, the data are transformed to obtain the coefficients γ, α and β by two stage least squares.

To instrument openness, we follow the established literature (Frankel and Romer, 1999) that explains economic growth fromthe ratio of international trade to GDP. Frankel and Romer argue that the physical geographical barriers to other countries affect acountry's openness to those other countries, but do not affect the local economy in a direct manner. While trade patterns arestrongly determined by distance, policies cannot change the physical distance between countries. Thus, it is possible to exploitexogenous bilateral geographical variation to predict how much a country trades. The strategy by Frankel and Romer has beenrepeated in a number of influential papers, such as Alcalá and Ciccone (2004) and Noguer and Siscart (2005), who provide agood discussion of the instrument. Following this literature, we estimate a bilateral gravity model for trade flows based on phys-ical geographical characteristics like distance and geographical size, and obtain the predictions of bilateral trade from that model.Summing the geographically predicted trade flows for a country gives a measure of that country's openness, which can then in-strument actual openness. In effect, we first estimate the following gravity model:

exportsi;j=GDPi ¼ δGi;j þ φi;j

where G refers to geographical variables like distance, contiguity and size, specific to the country pair i,jwhile φ is a country pair-specific i.i.d error term. The predicted bilateral trade flows (scaled to GDP) from this statistical model can be summed by countryto provide an estimate of the country's openness. Using the estimated coefficients for δ and summing exports from all partner

countries j to home country i gives the predicted trade as a share in country i, or Opennessi ¼ ∑j∈i−

δGi;j. The predicted openness,

aggregated from the bilateral trade estimations, is used to instrument the actual openness. As an extra instrument, we use thepopulation density, which permits us to identify Sargan tests. High population density indicates absence of rugged or inaccessibleterrain and the presence of cities. Under both, investments in infrastructure have a high return, in the sense that the same lengthof road improves local market access more if population density is high. One possible issue is that given a high persistence of pop-ulation density, high historical population densities can have influenced the quality of institutions, thus violating orthogonality ofthe instrument of population density on the error term. However, this assumption is explicitly tested and not rejected in the Sar-gan test. Moreover, excluding this instrument changes the coefficients of interest with no more than 3.5%, leading to no change inthe qualitative conclusions.

We use the total inflow of FDI as a measure of openness to FDI. Clearly, FDI may follow high-quality institutions, compromisingthe causality of FDI in determining local institutions. We exploit the observation that FDI, like trade, is sensitive to geographicaldistance (Eichengreen and Tong, 2007). The motive of market-seeking (horizontal) FDI is to avoid transport cost, making remotelocations more likely candidates. At the same time, vertical FDI, which is motivated by low factor costs, favours nearby locationsbecause it requires shipping intermediates to, and final products from the destination (Barba-Navaretti and Venables, 2004). Weuse the same geographical variables as in the bilateral trade model to predict bilateral inward FDI stocks as a share of GDP from agravity model. Aggregated by country, this yields a variable that describes exogenous variation in the FDI stock to GDP ratio. As an

3 Each of the six individual institutional indicators has been used as a dependent variable. Moran's I test (reported in Table 1) rejects the null of no spatial au-tocorrelation at a 1% significance level, irrespective of using inverse distance or contiguity as weights.

4 Using the instruments discussed in the next paragraph, a Wu–Hausman test of endogeneity in the model excluding the lag suggests this is a minor issue intrade openness (p=0.18), but a serious issue for FDI openness, where the null of exogeneity is rejected with a pb10−5.

57G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

overidentifying restriction, we add the share of a country's surface that lies within 100 km of a coast or river as such accessibilityimproves the ease of shipping manufactured or assembled goods, thus favouring FDI inflows.

3.1. Data

The above strategy is applied to a cross-section of countries in the year 2003. The motivation for a cross-section is twofold.First, information on institutional quality with a long time dimension is scarce. Second, since institutions are arguably endogenousand inert, the literature typically looks at historical factors, makingmany explanatory variables country-specific, not time-specific.The major drawback of a cross-section in this setting is that it does not inform us about the dynamics of institutional spillovers. Itcan shed light on whether institutional spillovers are present, but not on how quick we should expect changes in neighbouringcountries or in trade to impact locally.

As dependent variable, we use the indicators on institutional quality as developed by Kaufmann et al. (2008) in theWorldwideGovernance Indicators project. These are composed using various methods (objective criteria as well as surveys), and commonfactor analysis confirms that the various methods indeed describe a similar trait. Of the six indicators, we focus on the rule oflaw as it is considered as one of the main institutional sources of variation in long term growth, and features a prominent rolein the literature on institutions and growth, such as Rodrik (2003) and Acemoglu and Johnson (2005). In order to check the ro-bustness of our results we report results of similar exercises with other institutional indicators. The descriptive statistics of theinstitutional variables, including a Moran's I test, are reported in Table 1.

For the weights matrices and the gravity model, we depend on bilateral data. We have taken the simple distance betweencountries (major city) in kilometres, a dummy for contiguity, a country's surface in squared kilometres and a dummy for landlock-edness from the CEPII database.5 Bilateral export flows are from the World Bank (Nicita and Olarreaga, 2007), and bilateral in-ward FDI stocks are from the SourceOECD database. While explaining spatial patterns in institutions, we control for the mostprominent historical and geographical explanations for institutional quality. Some of such factors may also have a spatial compo-nent: climate, soil quality or potential for exploitation by other countries are more likely to be similar if countries are neighbours.Failing to include them in the regression may lead to an omitted variable bias and could generate spatial patterns in the errorterm. We use a set of legal origin dummies to capture the effects of the organisation of the judicial system. Using French legal or-igin as reference group, we allow for separate intercepts for British, German, Scandinavian and socialist judicial heritage (La Portaet al., 1999). To capture the effects of ethnically fragmented societies, we include a measure of ethnolinguistic fractionalisation in1985 (Alesina et al., 2003). Finally, first nature geography is captured by the share of land in which plague and leprosy can prevail6;the quality of soil, which promotes agriculture, hydrocarbon deposits as resource measure, and the share of land in the (sub)tropics(by the Koppen–Geiger classification). The last variable captures equatorial climate conditions, so we do not include a country's lat-itude, as we have no theoretical underpinning of how this variable should affect institutions directly, apart from other includedmea-sures. The data on first nature geography are taken from the Center of International Development databases (Gallup et al., 2001).

The gravity equations used to generate instruments for an economy's openness to trade and FDI are reported inappendix Table A.1. We estimate the model in a level–level specification, to circumvent the problem of zero flows. Alternativessuch as a selection or tobit model may pose an endogeneity issue in their treatment of zero flows (e.g. for countries that are ina favourable geographic position for trade, but whose politics disable major trade flows). To capture possible complex border ef-fects, we have added interactions between the border dummy and the other variables, i.e. inverse distance, surface of the twopartners, and landlockedness. The estimated elasticity of distance decay in trade flows at sample means (around −2.2) suggeststhe results are ballpark right in comparison to other literature (see e.g. Anderson and van Wincoop, 2004), but more importantly,the predicted trade flows aggregated by country correlate well with actual trade openness. The amount of observations makes ascatter plot of trade flows and distance unreadable. To plot the distance decay effects, we have estimated a spline regression of thepredicted trade and FDI flows over distance with 20 degrees of freedom. The plots up to 5000 km are provided in the appendix inFig. A.1. The decay of bilateral shares of FDI in GDP and trade in GDP follow similar patterns, but the shares of FDI show strongdecay over the shortest distances, indicating that proximate countries are favoured in FDI flows.

Table 1Descriptive statistics of institutional indicators.

Mean s.d. Moran's Ia

Rule of Law 2.40 1.07 0.40Voice and accountability 2.43 0.97 0.36Political stability and absence of violence 2.16 1.02 0.33Regulatory quality 2.50 1.08 0.42Government effectiveness 2.49 0.99 0.38Control of corruption 2.48 1.10 0.41

a All Moran's I tests are significant at the 1% level.

5 http://www.cepii.fr/anglaisgraph/bdd/distances.htm.6 We have investigated the other major disease types from the World Bank database like yellow fever, leichmaniasis, and dengue, but they do not improve the

model fit, nor change the results.

58 G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

3.2. Results on institutions

Table 2 presents the effects on the indicator rule of law. The first column presents a specification using standardised inversedistance as weights for the spatial lag. The results show that the spatial lag as well as trade openness is estimated with significantpositive parameters in the specification. These estimates are very close to the baseline model of Seldadyo et al. (2010), eventhough the estimator and the treatment of trade openness differ. The marginal effects cannot be taken straight from the table,due to the presence of a spatially lagged dependent variable. In reduced form and matrix notation, the model can be written asy=(I−γW)−1(Xβ+openness α+ε), where W is the spatial weights matrix and I is an identity matrix. A marginal change inopenness in a country leads to a direct change in that country's institutional indicator with the magnitude of α multiplied withthe country's corresponding diagonal element in the matrix (I−γW)−1. Therefore, the average direct impact of a change intrade openness is given by the average diagonal elements in the matrix (I−γW)−1 multiplied with α, in case of model 1 inTable 2: n−1tr(I−γW)−1α=0.25 (see LeSage and Pace (2009: p. 39) for a discussion of the interpretation of spatially lagged var-iables). Indirectly, however, a change in openness can trigger feedback effects because institutional improvement in the homecountry affects foreign countries, which in turn impacts on the home country. Since the row elements of the weight matrixsum to one, the total effect in reduced form can be written as α/(1−γ), which is equal to 0.55 in this case. When assumingthat direct and indirect effects sum to the total effect, these results imply that the indirect effects of trade openness (total effectless the direct effect) account for around half the total effect. Note that this model's interpretation also means that spillovers arerelevant apart from trade openness. For instance, the estimates of model 1 in Table 2 imply that a country whose neighbours havea Scandinavian legal origin have a higher expected quality of rule of law than countries whose neighbours have socialist legal or-igins, keeping other things (including own legal origin) constant.

Our specification shows a small negative autoregressive parameter in the residuals. The F-statistic of a regression of log tradeopenness on the geographically predicted openness and the population density is over 15, indicating the relevance of the instru-ments. Individually, both instruments are also statistically significant at the 5% level in this regression. The Sargan statistic showsno signs of overidentification. The set of variables concerning legal origin is jointly statistically significant (pb10−4) while

Table 2Effects on Rule of Lawa.

Distance Border Top 10 Distance Border Top 10

Lag 0.55b 0.43b 0.37b 0.07 −0.22 0.02(0.21) (0.18) (0.16) (0.44) (0.56) (0.26)

Trade openness (log) 0.25c 0.16d 0.15d

(0.07) (0.08) (0.08)FDI openness (log) 0.69b 0.86 0.73b

(0.31) (0.56) (0.32)British legal origin 0.13 0.13 0.26d 0.38d 0.32 0.28

(0.14) (0.14) (0.14) (0.22) (0.25) (0.22)Socialist legal origin −0.23 −0.74b −0.19 −0.53b −0.24 −0.55d

(0.23) (0.29) (0.23) (0.26) (0.57) (0.30)German legal origin 0.68b 0.64c 0.87c 0.70b 0.77d 0.68b

(0.27) (0.22) (0.23) (0.35) (0.43) (0.34)Scandinavian legal origin 0.98c 0.52 1.06c −0.23 −0.30 −0.22

(0.28) (0.35) (0.27) (0.35) (0.47) (0.37)Ethnolinguistic fractionalisation −0.27 −0.20 −0.14 −0.45 −0.56 −0.39

(0.27) (0.26) (0.30) (0.44) (0.60) (0.48)Soil quality −0.01 −0.01 −0.01 −0.01 −0.01 −0.01

(0.006) (0.006) (0.007) (0.01) (0.01) (0.01)Plague 0.91b 0.89b 0.98c −0.04 0.18 −0.02

(0.37) (0.36) (0.38) (0.60) (0.70) (0.61)Leprosy −0.73c −0.61c −0.81c −0.28 −0.44 −0.34

(0.20) (0.21) (0.20) (0.38) (0.40) (0.38)Hydrocarbon deposits −0.38d −0.36d −0.42d 0.73 0.90 0.64

(0.20) (0.21) (0.24) (0.80) (1.13) (0.78)Share of (sub)tropic area −0.34b −0.37b −0.40b −0.66d −0.60 −0.64d

(0.17) (0.17) (0.20) (0.36) (0.42) (0.38)Constant 3.86c 2.39c 2.72c 2.19b 2.42b 1.95b

(1.38) (0.71) (0.73) (1.11) (1.08) (0.89)−0.12 −0.13 −0.23 0.19 0.24 0.21

F-test openness on instruments 20.38c 20.38c 20.38c 15.97c 15.97c 15.97c

Correlation actual and predicted 0.82 0.85 0.83 0.75 0.68 0.74Sargan test 0.07 0.03 0.06 0.18 0.11 0.13Observations 104 104 104 78 78 78

a Standard errors in parentheses.b pb0.05.c pb0.01.d pb0.1.

59G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

ethnolinguistic fractionalisation and soil quality are not statistically significant. The share of a country in the (sub)tropics, thepresence of oil and disease prevalence have significant effects in line with other literature, with the exception, perhaps, of plagueprevalence which has an apparent positive effect on institutional quality.

Columns 2 and 3 present the same specification using another weight matrix. The second column, which uses contiguity,shows slightly decreased effects of both the spatial lag and trade openness. Following the suggestion of Seldadyo et al., we setthe weights in the spatial lag to zero for all other countries except the 10 nearest (and rescale the weights so they sum toone). Such a change accommodates a cut-off in the number of direct interaction partners and, in practice, on distance. Seldadyoet al. report an improved fit using this weight matrix. Using the 10 nearest neighbours only for spatial weights results in anotherslight drop of the coefficients on trade openness and the spatial lag, as reported in column 3. As a goodness of fit-measure, wefollow Fingleton and Le Gallo (2008), and report the correlation between actual and fitted values of the institutional indicatoras an artificial r-squared. The correlation is highest for the estimation based on contiguity, but the differences are minor. The co-efficients of the controls hardly change across the first columns, with the possible exception of the set of legal origin dummies.Although the t-values differ across specifications, the order of the predicted impact is equal under columns 1 and 3, and whenusing contiguity as weight, only German and Scandinavian legal origin variables switch places (between highest and one buthighest positive effect).

Columns 4–6 repeat the estimation for openness to FDI instead of trade. The instruments are the predicted stocks of inwardFDI, based on a gravity equation of bilateral FDI stocks (reported in A1) and the share of land nearby sea or rivers. Again, the in-struments pass the relevance and exogeneity tests. The most striking feature of the columns 4–6 is that inclusion of FDI opennesshas rendered the spatial lag insignificant using any of the three weights matrices. To test whether this is not the result of sampleselection (we have fewer observations for FDI openness than for trade openness), we have estimated the model for trade open-ness in the FDI sample. The results on openness remain qualitatively similar in the sample of observations on FDI. In fact, usingcontiguity as weights (which provides the best fit in the trade openness model), the coefficients on the lag (0.44, s.e. 0.19) andtrade openness (0.13, s.e. 0.07) closely correspond to their values in column 2. In the estimations with FDI openness, the estimat-ed coefficients of the controls are very similar across choices of weight matrices, like they were in the model that uses trade open-ness. One potential issue in the instrument of predicted FDI is that the gravity model is based on the reports of bilateral stocks ofFDI of OECD countries, so the bilateral FDI-stocks between non-OECD countries are imputed on the basis of geographical charac-teristics among them. To investigate the sensitivity of this issue, we have re-estimated the spatial model using only local acces-sibility (the share of land near coast or river) as an instrument for the actual ratio of inward FDI stock to GDP. This revises thecoefficient on openness to FDI from 0.69 (s.e. 0.31) to 0.72 (s.e. 0.32), and the coefficient estimate of the lag from 0.07 (s.e.0.43) to 0.01 (0.26), the first stage F-statistic being 8.28. Since the results are rather insensitive to this change of instruments,the results seem robust to the fact that the gravity equation is based on relations where at least one partner is an OECD member.

To further investigate possible misspecification and spatial processes in the independent variables, we have estimated Durbinmodels of the specification. The Durbin model is obtained by adding a spatial lag of the controls and of openness to the right handside variables. The spatial lag of predicted openness, obtained using the procedure described at the start of Section 3, serves as aninstrument for the lag of actual openness. Both the exogeneity and instrument relevance tests are passed, but the joint F-test ofthe spatially lagged right hand side variables cannot reject that their coefficients are zero. The spatial Durbin model therefore doesnot seem to statistically significantly improve the model. This holds for both types of openness, using any of the three weightsmatrices studied. Seldadyo et al. reach a similar conclusion regarding the spatial Durbin models, suggesting no misspecificationdue to the spatial patterns in independent variables. As another robustness check, we have re-estimated the same model usinga spatial lag based on random weights.7 This results in an insignificant coefficient of the spatial lag (0.002, p=0.77), ensuringthat the spatial structure is indeed crucial for the estimates on the lagged dependent variable.

Although openness to trade and openness to FDI are conceptually quite different, it is possible to assess the statistical perfor-mance of the two models. It is possible to fit the lags for trade openness and FDI openness in one model, but the endogeneity ofthree explanatory variables would place a heavy burden on the dataset. Instead, we have artificially nested the twomodels, whichinvolves fitting the residuals of one model into the other. Statistical significance of the residuals of a model in another specifica-tion points to residual explanatory power of the model in that other specification. The residuals of the trade-openness modelbased on inverse distance (model 1 in Table 2) produce a coefficient of 0.90 with a p-value of 0.000 in the FDI-openness model(model 4), suggesting residual explanatory power of the trade model in the FDI model. Vice versa, the residuals of the FDI-openness model in the trade-openness model produce a coefficient of 0.16 with a p-value of 0.102. Thus, the trade model encom-passes the FDI model. This conclusion is in line with the higher artificial r-squared on the trade model, although it is muchstronger.

Finally, to investigate the sensitivity of the results to the choice of institutional indicators, we have re-estimated the tradeopenness model on the five other institutional indicators in the Kaufmann indexes. Table 3 reports the relevant coefficient esti-mates (α and γ), omitting the controls for readability and space.8 As can be seen from the table, the measures of government ef-fectiveness, regulatory quality and control of corruption follow the pattern of the indicator of rule of law, with both the lag and themeasure of openness statistically significant. The direct and indirect effects of openness on the government effectiveness are 0.33

7 Studying R&D spillovers, Keller (1998) shows that estimated spillovers based on a spatial lag of random weights could be as large as the spillovers estimatedusing actual weights. Apparently, a model misspecification can lead to the false rejection of the hypothesis that the coefficient on the spatially lagged independentvariable is zero.

8 The regular instrument relevance and exogeneity tests are passed for all indicators.

60 G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

and 0.40; for regulatory quality they are 0.26 and 0.54; for control of corruption, they are 0.25 and 0.20. The measure for politicalstability and absence of violence seems unaffected by both trade openness and the surrounding institutional environment. Theestimated coefficient on the lag of voice and accountability points to a mathematically unstable process, because the coefficientis larger than 1. Since the exogeneity of trade openness in the standard specification cannot be rejected in a Wu–Hausmantest, we have re-estimated the model for voice and accountability using a maximum likelihood estimator that incorporates a Ja-cobian term, leaving aside any endogeneity issues regarding trade openness.9 In this case, the parameter is well behaved, and sug-gests a significant positive spatial lag in addition to a significant positive effect of trade openness. The direct and indirect effect ofopenness on the measure for political stability and absence of violence are 0.15 and 0.33, respectively. The conclusions regardingthe model where FDI is taken as a measure of openness do not change qualitatively when studying different indicators. The spatiallag is not statistically significant for any of the institutional indicators, and the openness variable enters with a statistically signif-icant positive sign, although the magnitude varies slightly.

4. Conclusions

This paper tests two hypotheses that explain foreign impacts on domestic institutional quality: openness to the world econ-omy and the kind of institutions in the surrounding countries. The first explanation posits that openness for trade or FDI positivelyaffects institutional quality. The second explanation argues that the institutional quality of neighbouring countries influences do-mestic institutions, for instance through spillovers, policy competition or imitation. As such, the relative location vis-à-vis insti-tutionally advanced and backward countries could help explain the local institutional quality.

The analysis in this paper shows that both openness to trade and the proximity of institutionally high-ranking countries ben-efit the local institutional indicators. Trade openness positively affects local institutional quality, but due to a strong dependencebetween neighbouring countries, the indirect effects (i.e. via other countries) may be as large as the direct effects in the long run.The causation in this result is established using the physical geographically – and hence exogenously – predicted trade openness.This result holds for the indicator of rule of law, as well as for indicators describing government effectiveness, regulatory quality,control of corruption and voice and accountability. The measure of political stability and absence of violence shows no such pat-terns. This is surprising, given the recent evidence on conflict spillovers (Gleditsch, 2007).

When considering openness to FDI, it is striking that the explanation of relative location is trumped. In contrast to trade open-ness, FDI openness renders the institutional quality of nearby countries insignificant as explanatory variable for local institutionalmeasures, whether proximity is defined by distance or contiguity. FDI openness shows higher distance decay sensitivity thantrade flows do (see Fig. A.1), and arguably, FDI flows are concentrated among institutionally more developed countries. Giventhe sunk cost nature of a direct investment into a country, FDI requires a higher level of commitment and trust in the local insti-tutions than trading with the country does. As such, the trade openness may not be picking up the complete effects of exposure,leaving a residual role for countries that have institutionally high quality neighbours. This may explain the apparent interactionbetween openness to FDI and the spatial lag in institutions in our model, but the methods employed in this paper cannot provideconclusive evidence on this idea. Comparing the overall performance of the models, openness to trade is the preferred specifica-tion for statistical reasons, because that model has residual explanatory power in the model with FDI openness, while the reverseis not true.

9 We thank an anonymous referee for this suggestion.

Table 3Effects of openness and lag for different institutional indicatorsa.

Trade FDI

Lag Openness Lag Openness

Voice and accountability 1.02b 0.21c −0.91 1.37d

(0.40) (0.08) (1.00) (0.79)Political stability and absence of violence 0.47 0.07 −0.83 0.93d

(0.50) (0.10) (0.83) (0.55)Government effectiveness 0.55b 0.33c −0.21 0.83b

(0.23) (0.08) (0.41) (0.36)Regulatory quality 0.69c 0.25c −0.37 0.85b

(0.24) (0.08) (0.55) (0.39)Control of corruption 0.46d 0.25c −0.13 0.77b

(0.24) (0.07) (0.41) (0.34)Voice and accountability: Maximum likelihood 0.70c 0.14c

(0.17) (0.00)

a Estimations based on Eq. (1), controls are not reported.b pb0.05.c pb0.01.d pb0.1.

61G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

Acknowledgements

The authors would like to thank Harry Garretsen (University of Groningen), Henri de Groot (VU University Amsterdam), Olafde Groot (German Institute for Economic Research), Charles van Marrewijk (Utrecht University) and an anonymous referee of theEJPE for constructive suggestions. The authors are responsible for all remaining flaws.

Appendix A

Table A.1Gravity model and the instruments for opennessa.

Effects on trade/GDP or FDI/GDP (bilateral) Trade FDI

Inverse distance 0.18b 2.40b

(0.03) (0.16)Area 1.28b 7.81b

(0.48) (2.64)Area partner 2.45b 8.02

(0.57) (5.33)Landlockedness (×10,000) −0.49c −3.25c

(0.24) (1.59)Border (×10,000) −1.22 48.23b

(1.59) (8.40)Border×inverse distance 0.00 −2.27b

(0.05) (0.29)Border×area 38.49b −10.49

(2.63) (47.64)Border×area partner 56.05b 21.88

(2.78) (61.62)Border×landlockedness (×10,000) −10.04b −14.72

(1.37) (8.97)Constant −0.00 −0.00b

(0.00) (0.00)R-squared 0.06 0.13Overall F-statistic 118.31b 31.38b

Effects on trade/GDP or FDI/GDP (country level)Predicted openness 0.25b 0.40d

(0.05) (0.22)Population density 0.34c

(0.13)Land in 100 km of river/sea 8.69c

(3.16)

a Standard errors in parentheses.b pb0.01.c pb0.05.d pb0.1.

Fig. A.1.

62 G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63

References

Acemoglu, D., Johnson, S., 2005. Unbundling institutions. Journal of Political Economy 113, 949–995.Acemoglu, D., Robinson, J., 2006. Economic Origins of Dictatorship and Democracy. Cambridge University Press, Cambridge.Acemoglu, D., Johnson, S., Robinson, J., 2005a. Institutions as the fundamental cause of long-run growth. In: Aghion, P., Durlauf, S. (Eds.), Handbook of Economic

Growth. Elsevier, North Holland, pp. 385–472.Acemoglu, D., Johnson, S., Robinson, J., 2005b. The rise of Europe: Atlantic trade, institutional change, and economic growth. American Economic Review 95,

546–579.Ades, A., Chua, H.B., 1997. Thy neighbor's curse: regional instability and economic growth. Journal of Economic Growth 2, 279–304.Alcalá, F., Ciccone, A., 2004. Trade and productivity. Quarterly Journal of Economics 119, 613–646.Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., Wacziarg, R., 2003. Fractionalization. Journal of Economic Growth 8, 155–194.Anderson, J.E., van Wincoop, E., 2004. Trade costs. Journal of Economic Literature 42, 691–751.Barba-Navaretti, G., Venables, A.J., 2004. Multinational Firms in the World Economy. Princeton University Press, Princeton.Becker, S.O., Egger, P.H., Seidel, T.B., 2009. Common political culture: evidence on regional corruption contagion. European Journal of Political Economy 25,

300–310.Bosker, M., Garretsen, H., 2009. Economic development and the geography of institutions. Journal of Economic Geography 9, 295–328.Brueckner, J., 2003. Strategic interaction among governments: an overview of empirical studies. International Regional Science Review 26, 175–188.Busse, M., Gröning, S., 2007. Does trade liberalization lead to better governance? An Analysis of the Proposed ACP/EU Economic Partnership Agreements. Ham-

burgisches WeltWirtschafts Institut (HWWI), Hamburg.Dollar, D., Kraay, A., 2003. Institutions, trade, and growth. Journal of Monetary Economics 50, 133–162.Easterly, W., Levine, R., 1998. Troubles with the neighbours: Africa's problem, Africa's opportunity. Journal of African Economies 7, 120–142.Economides, G., Egger, P.H., 2009. The role of institutions in economic outcomes: editorial introduction. European Journal of Political Economy 25, 277–279.Eichengreen, B., Tong, H., 2007. Is China's FDI coming at the expense of other countries? In: Liebscher, K., Christl, J., Mooslechner, P., Ritzberger-Grünwald, D.

(Eds.), Foreign Direct Investment in Europe—A Changing Landscape. Edward Elgar, Cheltenham, pp. 164–175.Fingleton, B., Le Gallo, J., 2008. Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: finite sample properties.

Papers in Regional Science 87, 319–339.Frankel, J.A., Romer, D., 1999. Does trade cause growth? American Economic Review 89, 379–399.Gallup, J.L., Sachs, J.D., Mellinger, A., 1999. Geography and economic development. CID Working Papers, 1. Center for International Development at Harvard

University.Gallup, J., Mellinger, A., Sachs, J.D., 2001. Geography datasets. Center for International development at Harvard University (CID)http://www.cid.harvard.edu/

ciddata/geographydata.htm. accessed at 1 September 2011.Gleditsch, K.S., 2007. Transnational dimensions of civil war. Journal of Peace Research 44, 293–309.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, 83–116.IMF, 2003. World economic outlook 2003. International Monetary Fund, Washington DC.Islam, R., Montenegro, C., 2002. What determines the quality of institutions? Background Paper for the World Development Report 2002. World Bank.Jörgens, H., 2003. Governance by Diffusion – Implementing Global Norms Through Cross-National Imitation and Learning, FFU-report 07–2003. Environmental

Policy Research Centre, Berlin.Kaufmann, D., Kraay, A., Mastruzzi, M., 2008. Governance matters VII: aggregate and individual governance indicators, 1996–2007. World Bank Policy Research

Working Paper 4654. .Kelejian, H., Prucha, I.R., 1998. A generalized spatial two-stage least squares procedure for estimating a spatial autoregressive model with autoregressive

disturbances. Journal of Real Estate Finance and Economics 17, 99–121.Kelejian, H., Murrell, P., Shepotylo, O., 2007. Spatial spillovers in the development of institutions. Electronic Working Papers 07–001. University of Maryland,

Department of Economics.Keller, W., 1998. Are international R&D spillovers trade-related? Analyzing spillovers among randomly matched trade partners. European Economic Review 42,

1469–1481.La Porta, R., Lopez-de Silanes, F., Shleifer, A., Vishny, R., 1999. The quality of government. Journal of Law, Economics, and Organization 15, 222–279.LeSage, J., Pace, R.K., 2009. Introduction to Spatial Econometrics. CRC Publishers, Boca Raton.Manski, C.F., 1993. Identification of endogenous social effects: the reflection problem. Review of Economic Studies 60, 531–542.Murdoch, J.C., Sandler, T., 2002. Economic growth, civil wars, and spatial spillovers. Journal of Conflict Resolution 60, 91–110.Nicita, A., Olarreaga, M., 2007. Trade, production and protection 1976–2004. World Bank Economic Review 21, 165–171.Noguer, M., Siscart, M., 2005. Trade raises income: a precise and robust result. Journal of International Economics 65, 446–460.North, D.C., 1991. Institutions. Journal of Economic Perspectives 5, 97–112.Rodrik, D., 2003. Introduction: what do we learn from country narratives? In: Rodrik, D. (Ed.), In Search of Prosperity; Analytic Narratives on Economic Growth.

Princeton University Press, Princeton, pp. 1–23.Rodrik, D., Subramanian, A., Trebbi, F., 2004. Institutions rule: the primacy of institutions over geography and integration in economic development. Journal of

Economic Growth 9, 131–165.Seldadyo, H., Elhorst, J.P., de Haan, J., 2010. Geography and governance: does space matter? Papers in Regional Science 89, 625–640.Simmons, B.A., Elkins, Z., 2004. The globalization of liberalization: policy diffusion in the international political economy. American Political Science Review 98,

171–189.Wei, S.-J., 2000. Natural openness and good government. World Bank Policy Research Working Paper 2411.

63G. Faber, M. Gerritse / European Journal of Political Economy 28 (2012) 54–63