Journal of Economic Geography Advance Access originally published online on December 3, 2008
Journal of Economic Geography 2009 9(3):295-328; doi:10.1093/jeg/lbn047
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Economic development and the geography of institutions
*Department of International Economics & Business, Faculty of Economics and Business, University of Groningen, The Netherlands.
email <e.m.bosker{at}rug.nl>
Date submitted: 14 December 2006 Date accepted: 16 October 2008
JEL classifications: O1, F5, O57
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Abstract |
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TOP
Abstract
1. IntroductionTo explain cross-country income differences, research has recently focused on the so-called deep determinants of economic development, notably institutions and geography. This article shows that it is not only absolute geography, in terms of for instance climate or being landlocked, but also relative geography, the spatial linkages between countries, that matters for a country's GDP per capita. More specifically, we analyze the importance of the geography of institutions. We show that apart from its own institutions, the institutional quality in neighboring countries is also important for a country's economic development. This finding is robust to various alternative specifications of relative geography, sample size and the inclusion of additional controls.
Keywords: relative geography, economic development, institutions
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1. Introduction |
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The question why we observe such large income differences between countries is arguably the most important question in economics. Lately, research on this issue has focused on the so-called deep or fundamental determinants of economic development. Three determinants have been singled out, institutions, geography and economic integration (openness). Papers by inter alia Hall and Jones (1999), Acemoglu et al. (2001), Easterly and Levine (2003) and Rodrik et al. (2004) present strong, though not undisputed, empirical evidence in favor of institutions over geography and openness. Or in the words of Rodrik et al. (2004): institutions rule! Openness is deemed irrelevant and geography has at most an indirect impact, via institutions, on per capita income levels.
The goal of the present article is to extend this line of research by allowing geography to play an additional role. Instead of defining a country's geography only in absolute terms, that is independent from the location and characteristics of other countries, we also look upon geography in relative terms. In effect, we argue that a country's location (clearly a deep determinant) not only determines its absolute geography but it also pins down its position on the globe vis-à-vis all other countries, which may in turn affects a country's own level of economic development by determining the type and importance of a country's international relations. The latter implies that a country's prosperity is not only a function of its own deep determinants but potentially also of these determinants in other countries.
Instead of merely analyzing whether or not a country is better off if surrounded by high-income neighbors (see e.g. Easterly and Levine, 1998; Redding and Venables, 2004), we take the observation that institutions rule as our starting point and show that it is the geography of institutions that matters too. In particular, we estimate an extended version of Rodrik et al.'s (2004) baseline model including a measure of the institutional quality of neighboring countries; hereby using econometric techniques that explicitly address the endogeneity issues characterizing the new, or deep, growth empirics literature and that properly take countries’ interlinkages into account. Our results show that the institutions of other (neighboring) countries exert a significant impact on a country's own GDP per capita. This is the main finding of our article and we show that it is robust to alternative measures of relative geography, alternative samples and a varying list of controls.
The article is organized as follows. Section 2 first briefly discusses the recent literature on the relevance of the so-called deep determinants of economic development, notably institutions and geography. It then motivates our choice to look at the spatial or geographical nature of institutions by pointing out how the institutions of other countries might matter as well. In Section 3, we present our dataset along with some descriptive statistics, introduce our main specification and discuss the (spatial) econometric techniques involved in the estimation process. Section 4 discusses the estimation results. Section 5 concludes.
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2. The deep determinants of income and the role of geography |
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2.1. Own institutions and absolute geography
Income differences between countries are large and persistent. To explain these differences, economists have traditionally called upon growth theory, considering factor inputs and factor productivities as the prime explanatory variables of a country's economic growth. As Rodrik et al. (2004, p. 132–3) emphasize, the basic problem with this standard approach is that it is merely concerned with the proximate causes of economic growth. If it is for instance found that income differences are due to differences in labor productivity, this is begging the question what drives the latter. To explain income differences, we therefore need to understand the deep or fundamental determinants of economic growth.
In the recent empirical literature on the fundamental causes of income or growth differences, three deep determinants have in particular been emphasized: institutions, geography and economic integration. A main stimulus for what might be called the new growth empirics is the use of instruments that allow one to adequately deal with endogeneity. To be able to conclude that cross-country variations in these kind of deep determinants cause the observed cross-country income differences, one wants to exclude the feedback from income itself or a third variable of interest.1 Since geography is meant to refer to physical geography only, the exogeneity of this determinant is commonly taken for granted. This is however not true for institutions or economic integration (openness) and it is here that the introduction of new instruments has been important. Following the work of notably Acemoglu et al. (2001), Hall and Jones (1999) and Frankel and Romer (1999), the estimation problems arising from the endogeneity of, respectively, institutions and economic integration can be adequately dealt with.
Even though there are differences in the specifications used and in the deep determinants actually included in the analysis (any combination of institutions, geography or integration can be found in the literature), the main conclusion is that institutions have a strong and direct impact on income. Geography is found to be at best of only indirect importance in explaining income differences (via its impact on institutions), and economic integration, when set against institutions and geography, does not have a significant impact on income. This consensus view is best exemplified by the seminal paper by Rodrik et al. (2004) that will therefore serve as a benchmark for our own analysis.
The methodology used and the conclusions reached by Rodrik et al. (2004) and other related papers have, however, not remained unchallenged. Sachs (2003) and Carstensen and Gundlach (2006) for instance strongly dispute the alleged irrelevance of physical geography and attempt to show that the use of alternative, more accurate, measures of geography (i.e. tropical disease indicators; most notably malaria incidence) does reveal that geography is as important as institutions. Glaeser et al. (2004) argue that institutions are poorly measured and identified in the new growth empirics and once this is acknowledged, other and more standard determinants (human capital) are far more important in explaining differences in economic prosperity. As to the conclusion that economic integration or openness is not important, Alcalá and Ciccone (2004) for instance use an alternative measure of openness and then show that openness is very significant in explaining cross-country differences in productivity.2
In our view, the new or deep growth empirics can, however, be criticized for a different and arguably more fundamental reason. Our criticism deals not with the exact definitions of the three deep determinants of income in Rodrik et al. (2004), our concern is first and foremost with the limited role that relative geography or space plays in the analysis. Following a distinction made by Krugman (1993), the current literature only looks at the role of absolute, or first nature, geography by e.g. looking at the impact of variables, such as distance to the equator, climate or disease environment in explaining cross-country income differences. Relative, or second nature, geography does not play a part at all. As a result, the relative geography of a country, i.e. the location of a country vis-à-vis other countries (in our view also clearly a deep determinant), is no issue. It is only differences in absolute geography between otherwise spatially independent countries that matter. So, for the income of an individual country only the geography in terms of its own climate, health environment or its access to the sea is thought to be important. A country's geographical location, i.e. its position on the globe, however not only determines its own first nature geography characteristics. It also pins down which other countries are located close by, hereby having a big impact on the type and importance of a country's international relations.3 This neglect not only holds for economic interdependencies but also for political and, most relevant here, institutional interdependencies that may exist between (neighboring) countries.
2.2 Relative geography: the role of neighboring institutions
The idea that relative geography is relevant for economic outcomes is not new. Indeed, this basic notion defines what the literature on economic geography at large is all about. The notion that spatial interdependencies are important lies at the heart of for instance the new economic geography (NEG) approach (Fujita et al., 1999). In a nutshell and when applied to this article's topic, this approach argues that a country's income is higher the higher its market access (MA), i.e. the closer it is located to other high-income countries. Crafts and Venables (2003), Redding and Venables (2004) and Mayer (2008), among others, all provide empirical evidence that a higher level of MA indeed increases a country's income level (see also the survey on NEG empirics by Head and Mayer, 2004).
While not denying the importance of MA or spatial income interdependencies, we take a somewhat different approach. Given the aforementioned consensus on the importance of institutions in the recent ‘deep growth’ literature, we introduce second nature geography in a different way and look at the effect of the quality of institutions in neighboring countries on a country's own economic prosperity. In particular, we want to find out if the geography of institutions matters in understanding cross-country income differences.
Why should the institutions of country i matter for the income level of country j? Instead of developing a fully developed theoretical model, we will outline possible channels of transmission that have been put forward in the literature, some of which have also been empirically verified in related research. Several authors have discussed channels through which the institutional setup in neighboring countries may be of importance for a country's own economic development. We make a distinction as to the path or route along which the institutional setup of country i can have an impact on the income of country j, and argue that basically three possible channels of influence exist.
The first, and rather indirect, route occurs when institutions in a neighboring country lead to economic, social or political outcomes in that country that in turn have an impact on your country's income level. This indirect route boils down to asking how outcomes in neighboring country i that are a result of that country's institutional setup, like its income level or degree of political stability, can influence the income level of country j. A number of these indirect channels have already been analyzed. To begin with, Easterly and Levine (1998) show that the poor economic performance of one country (as a result of for instance bad institutions) indeed negatively affects income levels in its neighboring countries. Ades and Chua (1997) provide evidence that instability in neighboring countries (measured by the number of revolutions and coups) has a negative effect on the economic performance of a country itself. Regional instability disrupts trade and thus depresses income, especially for landlocked countries that are depending on trade routes (access to the sea) through neighboring territory. Also instability in neighboring countries often results in inflows of large numbers of refugees (Moore and Shellman, 2007) that not only have to be looked after but also contribute significantly to the spread of diseases like malaria (Montalvo and Reynal-Querol, 2007). It also results in increased military expenditures to prevent the spreading of conflict and/or to deter potential future military aggression from unstable neighbors (thereby crowding out more productive investment by the government). In a similar vein, Murdoch and Sandler (2002) show that civil war in neighboring countries disrupts economic activity at home.
The second, and more direct, route occurs when institutions in a neighboring country affect economic, social or political outcomes in your own country that in turn have an impact on your country's income level. Here, one can for instance think of a more direct relationship between neighboring institutions and your income when a neighboring country directly interferes into your internal affairs by supporting the opposition or financing rebel groups and in doing so affect your economic development. In the political science literature this role of ‘nosy neighbors’ is well documented (see e.g. Gleditsch and Beardsley, 2004), as is the spread of (internal) conflict from one country to another (Salehyan and Gleditsch, 2006; Salehyan, 2008). Another example is that the quality of neighboring institutions impacts on your income level via its impact on the behavior of foreign economic agents. From the empirical trade and FDI literature, it is well-established that ceteris paribus bad institutions decrease trade and FDI flows between a country and the rest of the world. Given the quality of your own institutions, the quality of neighboring institutions may help to determine whether trade or FDI flows from third countries end up in your country or in the neighboring country. Here, bad neighboring institutions with respect to contract enforcement or property rights can even improve your income when your country is preferred as a trading or investment partner because of the poor quality of institutions in nearby countries.4 Another more direct link between neighboring institutions and your income can arise when due to imperfect information on financial markets, the (poor) neighboring institutions are seen as representative for a region as a whole by outsiders. An example is the well-documented contagion effect with respect to financial flows to emerging markets (Kaminsky and Reinhart, 2000). Once financial flows to a particular country come to a halt because of (alleged) malfunctioning domestic financial institutions, typically the financial flows to and hence the income levels of seemingly similar—and not infrequently neighboring—countries also suffer irrespective of their own institutions.
Finally, neighboring institutions can directly affect the quality of your own country's institutions and thereby impact on your income level (see also Kelejian et al., 2008 for a discussion and examples). An example of this route not only occurs when countries copy each other's successful, but also sometimes unsuccessful (e.g. the import substitution policies followed by many developing countries in the 20th century) policies. Simmons and Elkins (2004) show that countries indeed copy policies from each other, and from neighbors, in particular, that were proven to be successful and avoid those that failed. This copying behavior can be initiated by the country itself but it can also be institutionalized in the sense that countries are forced to do so in order to meet certain standards that have to be met before entering a regional trade bloc or a monetary union (think of the entry criteria for the economic and monetary union in the EU). More generally, the work by institutional economists like North (1990) provides many examples how institutions spread from one country to neighboring countries. This spreading is easier when countries share some basic characteristics (religion, history) that make it more likely that the institutional transmission occurs between countries that are literally neighbors (again see Simmons and Elkins, 2004).
Overall, this brief (and certainly nonexhaustive) discussion of three basic channels by which the quality of neighboring institutions can leave its mark on your country's income level is meant to emphasize the potential importance of relative geography—or more specifically the geography of institutions—for economic development. When analyzing the importance of institutions for economic development, it may be restrictive to focus exclusively on a country's own institutions. In the empirical section of the article, we verify this and analyze to what extent the geography of institutions matters for economic development by extending the new growth empirics framework so that it explicitly allows relative geography—i.e. neighboring institutions—to play a role. Note that disentangling exactly which of the three above-mentioned channels is most important lies beyond the scope of this article; we focus on a broad measure of neighboring institutions instead.5 In Section 4.4, however, we will illustrate the importance of some of these channels by looking at the correlation of several indicators with our measure of neighboring institutional quality.
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3. Model specification, dataset and estimation strategy |
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3.1. Model specification
Following the exposition in Rodrik et al. (2004) the benchmark empirical specification of this article is the following equation:
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| (1) |
i is a random error term. Furthermore, Zi denote the variable(s) used to instrument the measure for institutions to correct for potential endogeneity problems caused by reverse causality (higher income results in better institutions), omitted variables or measurement error. Rodrik et al. (2004) use, following Acemoglu et al. (2001), European settler mortality as the only instrument for their baseline sample. Given the resulting limitation that this imposes on the number of countries (79 former colonies) that can be included in the analysis, they also resort to the use of two other instruments, introduced by Hall and Jones (1999), i.e. percentage of population speaking English and percentage of population speaking a European language, and consider a much larger sample of 137 countries.6
To incorporate the main point of this article in the empirical specification, we add a term capturing the quality of institutions in one's neighboring countries to Equation (1):
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| (2) |
, but now also on that of its neighbors: 
. As in Equation (1), institutions need to be instrumented as well in order to resolve potential endogeneity problems, see Section 3.3 below.
The spatial weight matrix, W, defines the spatial reach of the impact of institutions. In our benchmark model, we will take the concept of neighboring institutions quite literally and base this W on contiguity, or more formally:
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| (3) |
We have decided to use this simple contiguity-based weighting matrix, which gives equal weight to all neighbors, as our baseline specification (also used by Ades and Chua, 1997). Other weighting schemes have been used in the literature on spillovers between neighboring countries however, for example, weighting each neighbor by the size of its total GDP (Easterly and Levine, 1998), weighting each neighbor by the length of the common border (Murdoch and Sandler, 2002) or by considering the n-nearest countries as being neighbors. We use the simple spatial weight matrix in (3) in our baseline specification7 as we think it captures in a clean and simple way the main point that we want to make in this article, namely that relative geography matters.8 Also we think that for cross-country spillovers in either institutions or institution-induced outcomes the spatial reach is often quite limited. The effects of e.g. political instability or even civil unrest are generally mostly felt in neighboring countries in terms of refugees, disrupted infrastructure or reduced trade flows. In line with Kelejian et al. (2008, p. 8), we think that the distance effect in these spillovers is such that the direct or indirect impact is typically the strongest between countries that border each other.
3.2. The dataset
Regarding the choice of variables for institutions and geography, we again follow Rodrik et al. (2004) by taking the Rule of Law variable due to Kaufmann et al. (2005)9 as our baseline institutional measure and the absolute distance from the equator in degrees as our baseline geography measure. As dependent variable we collected GDP per capita (PPP adjusted) in 1995 from the 2003 version of the World Development Indicators.
To give an idea of the spatial distribution of both GDP per capita and institutions, Figures 1a and b show the distributions of these two variables across the countries in our dataset. As can be seen both income per capita and the quality of institutions are not randomly distributed across the globe. Instead, relatively high levels of income per capita are geographically clustered in the Americas and Europe and clusters of relatively good institutions are located in North America, Europe and the southern tip of Africa and South America. Similarly, relatively low levels of income per capita are geographically clustered in Africa and Asia, and clusters of relatively bad institutions are located in Africa, Middle- and South America and parts of the Middle-East. Whereas the current literature implicitly explains this clustering of similar income levels by country-specific differences in e.g. absolute geography and institutions, this article looks whether or not this spatial pattern of institutions also contributes to the observed income differences.
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Table 1 provides some additional descriptive statistics. The countries in our sample have an average GDP per capita of I$ 7518 with a large variation across countries (s.d. of I$ 7230); ranging from I$ 450 per capita in the poorest country (Tanzania) to I$ 33,256 per capita in the richest country (Luxembourg). The typical country is located at 22.67° above or below the equator with Uganda the closest and Iceland the furthest away from the equator.
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Regarding the institutional quality measure, Table 2 and Figure 2 give some more detail about both the institutional and the neighboring institutional qualities.
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Figure 2 shows the relationship between own and neighboring institutions (pairwise correlation between the two is 0.73 [P-value 0.00] – the dotted line). Countries below (above) the solid 45° line are countries with better (worse) institutions than their average neighbor. Table 2 provides some more detail on this, showing that Hong Kong, followed by Kuwait and Chile, has the best institutions relative to its neighbor(s). Similarly, the Philippines has the worst institutions relative to its neighbors followed by Yemen and Iraq. In absolute terms, the Seychelles (followed by the Dominican Republic, Jamaica and Sierra Leone) have the worst neighbors in terms of institutions whereas the Democratic Republic of Congo has the worst own institutions (followed by Somalia, Liberia and Iraq).
Note that our baseline sample consists of 147 countries. Rodrik et al. (2004) use a sample of only 79 countries as their baseline sample because they deem settler mortality (taken from Acemoglu et al., 2001) to be a ‘better’ instrument (based on the result of overidentification tests), than the Hall and Jones (1999) instruments, i.e. percentage of population speaking a European language and percentage of population speaking English (see also Acemoglu, 2005).10 Settler mortality rates are, however, only available for 79 countries (former colonies only). Besides the more standard argument of improving inference when using more observations (see also McArthur and Sachs, 2001), we have a more fundamental reason to use the largest possible sample. Given our aim to assess the importance of relative geography, more particularly the institutional setting in neighboring countries, we do need to have data available on these neighbors! To make our point, Figure 3 shows a map of the countries included in our 147-country sample and also a map of the 79 countries included in Rodrik et al.'s baseline sample.
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From Figure 3 it is immediately clear that the 147 sample adds many countries in Europe, the Middle-East, Asia and Africa [still missing in our sample are mainly former communist countries (most of them were part of the USSR)]. Restricting our analysis to the 79 country sample would lead to a number of countries (mainly in Africa and Asia) having far fewer neighbors than they in actual fact have, e.g. Angola, Tanzania, Cameroon, India, Vietnam, Laos and Egypt, and even leads to ‘artificial islands’, i.e. South Africa and Hong Kong. Using the largest possible sample avoids or at least limits this problem.11
3.3. Estimation strategy
Given the model specification as given by Equation (1), Rodrik et al. (2004) apply a 2SLS estimation procedure. Given the fact that the institutional measure is expected to be correlated to the error term due to reverse causality, measurement error or omitted variable bias, a simple OLS regression on Equation (1-ii) would result in biased and inconsistent estimates of all parameters of interest. 2SLS solves these endogeneity problems if a ‘good’ instrument(s)12 is used for the institutional quality measure. In the first stage (1-i), the institutional measure is regressed on the instrument(s) and the geography measure (and possible additional controls), and in the second stage (1-ii) the effects of geography and institutions are estimated by regressing log GDP per capita on the geography measure and the fitted institutions obtained from the first stage (and possible additional controls), respectively.
Can one use the same estimation strategy when taking relative geography into account by also trying to estimate the effect of neighboring countries’ institutions on one's own GDP per capita? And related to this: do we also have to instrument our measure of neighboring institutions, and if so, how to do this? As mentioned before, the reason to use a 2SLS estimation procedure in the standard case, our model (1), is that the institutional quality measure is likely to be correlated to the second stage error term, making OLS a biased and inconsistent estimator. More formally,
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| (4) |
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| (5) |
- In the first stage, (2-i), regress own institutions on the instrument, geography and possible additional controls, and obtain the fitted institutions,
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- Using the obtained fitted institutions construct also neighboring countries’ fitted institutions, i.e.
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- In the second stage, (2-ii), regress GDP per capita on geography, possible additional controls and
and 
. Given the validity of the chosen instrument, the parameters obtained will be unbiased and consistent estimates of the parameters of interest.
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4. Estimation results |
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4.1. Baseline model
As we explained in the previous section, our baseline specification closely follows the baseline specification in Rodrik et al. (2004) because we basically want to extend their analysis by adding spatial institutions. Table 3 presents the estimation results for our baseline model. The relevant comparison with the Rodrik et al. (2004) study is their Table 3. The log of GDP per capita is thus regressed on the preferred measures for geography, institutions and, in our extension, neighboring institutions. Since a main conclusion from the Rodrik et al. (2004) study is that the integration (openness) variable is invariably insignificant, we decided to omit that variable and we thereby concentrate on the main issue for the present article, the relationship between geography (absolute and relative) and institutions.13 Columns (1–5) replicate the findings by Rodrik et al. (2004).14 Institutions are always very significant. Absolute geography is significant when included on its own, but ceases to be significant or is significant with the wrong (i.e. unexpected) sign once we include (instrumented) institutions. It seems to be at most of only indirect relevance, given the fact that it is significant in the first stage [see columns (5) and (7)]. Note also that the instruments have the expected sign and are significant too.
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Our principal interest in Table 3 is, however, with columns (6 and 7). Two results stand out. First, neighboring institutions matter. Neighboring institutional quality has a significant positive effect on economic prosperity. Second, a country's own institutions continue to be relevant but, when including also neighboring institutional quality, the estimated coefficient is lower compared to the 2SLS results in column (5). However, the estimated effect of own institutions is (not unexpectedly) larger than that of neighboring institutions; even more so when one keeps in mind that the estimated coefficient on neighboring institutions measures, given the way we have specified the spatial weight matrix W, the effect of a similar institutional improvement in all neighboring countries. Combining these two findings, we thus find that institutions still rule but geography also matters in the sense that the geography of institutions (here, neighboring institutions) helps to explain income differences in our sample of 147 countries. Note also that the Lagrange Multiplier (LM) test for the existence of a spatial error structure, see the last line of Table 3, indicates that there is evidence for spatial dependence as long as we have not included neighboring institutions among our set of regressors. In our preferred estimation, column (7), the IV estimation with neighboring institutions, the LM test results indicate that there is no longer evidence of a spatial structure in the error term (and the spatial dependence has been picked up by neighboring institutions).
In the remainder of this section, we want to find out if this main result still holds when considering alternative measures of spatial institutions, after including additional control variables, for alternative country subsamples, and, recall Section 2, for specifications that also include a different measure of relative geography, namely neighboring countries’ income level or MA, among the set of explanatory variables.
4.2. Robustness checks
As a first robustness check, we estimated our 2SLS baseline model with spatial institutions while using alternative spatial measures. Recall that in the baseline model we use contiguity based on sharing a common land border letting island nations, who do not share a land border with any other country, ‘share a common border’ with their nearest neighbor (determined on the basis of the distance between capital cities).
Columns (2) and (3) in Table 4 indicate whether our results are sensitive to the definition of neighbor for the island countries in our sample.15 In column (2) the reference city, chosen to determine the nearest neighbor, is a country's main city instead of its capital city, and in column (3) an island is assigned two (nearest) neighbors instead of one. It is clear that the results do hardly change. When we dismiss contiguity all together and define spatial institutions in terms of nearest neighbors for all 147 countries in our sample, columns (4) and (5) of Table 4 give the estimation results for various nearest neighbor measures, where 10 or 5 refers to the number of neighboring countries that we took into account in the definition of nearest neighbor. Invariably, n-nearest neighbors are determined by the distance between the corresponding capital cities. Finally, column (6) shows the results when using an area-weighted measure of contiguity in order to see whether giving more weight to larger neighbors affects the results. As can be seen, the results for the significance of institutions and neighboring institutions are not affected. Also, just as in our baseline results in Section 4.1, absolute geography is significant but has the wrong sign.
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The second robustness check concerns the sample composition. Table 5 illustrates for four subsamples of our overall sample of 147 countries whether the main results are driven by a particular group of countries (again, only the second stage estimation results are shown). In order to avoid loss of information about the neighbors, the neighboring institutional variable in these regressions is based on the whole 147 sample. That is to say, fitted (neighboring) institutions are obtained from the first stage results using the 147-country sample, and next the second stage is done using a specific subsample.
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Again the conclusions with respect to our main variables of interest, institutions and neighboring institutions, are not affected. When excluding Africa or the Western countries from the sample in columns (2 and 3), the significance of neighboring institutions drops slightly compared to the baseline sample (to the 5.9% or the 4.9% level, respectively), suggesting that it is the variance between Africa and the Western countries that contributes to the more significant results in the baseline sample (see also Easterly and Levine, 1998 for a similar finding). Column (4) shows that our main results also come through when not considering the island countries in our sample indicating that the results are not driven by our (to some extent arbitrary) assignment of ‘neighbors’ to these island countries. Finally, we have excluded the current (2008) EU Member States from our sample (column 5) to check whether our results could be entirely driven by the entry process toward, and membership of, the EU that goes along with a convergence process of national institutions ‘enforced’ by EU rules and legislation. Column 5 shows that the main results remain unchanged when the current EU Member States are excluded. Along similar lines, our results also come through if we exclude the 15 European countries that were a member of the EU in 1995 from our sample or if we exclude all former communist countries from our sample (results not shown here but available upon request).
Arguably the most important robustness check that we carried out deals with the extension of our baseline model by adding additional controls that have been suggested in the literature. Since we are using Rodrik et al. (2004) as our benchmark, we based our selection of additional explanatory variables largely on the control variables used in that paper (Rodrik et al. (2004), Table 6, p. 148). More specifically, see our Table 6 below, we decided to add as controls three regional dummies, other measures of absolute geography, such as e.g. malaria incidence and having direct access to the sea, the identity of the (European) colonizer and religion. Some of these variables are added to see whether or not the chosen measure of absolute geography, i.e. distance to the equator, might be an insufficient proxy to capture the effect of absolute geography. Variables like e.g. access to the sea, being an island, malaria incidence or even regional dummies can be argued (see e.g. Sachs, 2003 and Carstensen and Gundlach, 2006) to capture absolute geography effects much more accurately.16
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The main reason to add these additional controls, like the regional dummies and the other geography variables, is that we set out to estimate the importance of the spatial dependence between countries (via our neighboring institutions variable) but in Tables 3–5
it could be that the found relevance of the geography of institutions is simply due to omitted spatial heterogeneity, i.e. omitted variables that are regional in nature like the regional dummies and the landlocked or malaria variable. Of these additional control variables, three variables in particular have been subject of discussion in the literature because of their alleged importance: the Sub-Saharan Africa dummy, the landlocked variable and malaria (see Appendix A for definitions of all included controls).17 The estimation results (both the first and second stage results are shown in Table 6) lead to the following conclusions. First, and most importantly, our main results carry through. In all specifications, both own and neighboring institutions are significant (and absolute geography, as measured by distance to the equator, only has an indirect impact on GDP per capita). Second, when comparing our results to the corresponding results in Rodrik et al. (2004), it is clear that the coefficient on own-country institutions is much lower in our case. Whereas we find a coefficient of approximately 0.7–0.9, in Rodrik et al. (2004) the corresponding coefficient is in the range of approximately 1.5–2.5. A third conclusion is that of the additional absolute geography control variables, the landlocked variable seems to be most important in the sense that it is significant in all of the specifications shown in Table 6. This is not in line with the findings in Rodrik, et al. (2004), where the landlocked variable is not significant. The fact that we do find a significantly negative, direct effect of being landlocked is probably due to the larger sample that we use. Out of the additional 68 countries that we have in our sample compared to the sample in Rodrik et al. (2004), 13 countries are actually landlocked (examples include Malawi, Nepal, Mongolia and Zimbabwe, see also Figure 3). This allows us to better pinpoint the effect of being landlocked. Interestingly, the Sub-Saharan Africa dummy is not significant once landlocked and/or malaria are also added as a controls, see columns (4) and (8). Also of interest is the finding, in line with Rodrik et al. (2004) that the malaria variable is significant when included on its own (column 6) but no longer so when the regional dummies are added as controls (column 8).
4.3. Spatial GDP: including Market Access
In Section 2 we referred to the NEG literature where income differences between countries are explained by the differences between countries in terms of their so-called market access (MA), i.e. spatial income dependence between countries. It might therefore be argued that the real importance in terms of including relative geography is not so much spatial institutions but spatial income levels. To verify whether our results are sensitive to the inclusion of such a MA measure, Table 7 below adds a measure of MA to our set of explanatory variables where the MA of country j is measured as
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| (6) |
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The first column in Table 7 again shows our baseline results and the second column illustrates that, when looked upon in isolation, MA has the expected significant positive effect on GDP per capita. Following Redding and Venables (2004), MA is instrumented by the log distance to each of the world's three major economic centers (New York, Brussels and Tokyo). When we then add own-country institutions, see column (3), MA has the wrong sign. This also holds when neighboring institutions are added, see column (4). More importantly, the addition of the MA variable does not alter our main finding that institutions matter and also that neighboring institutions play a role. The fifth column of Table 7 confirms this. After controlling for regional effects and other measures of absolute geography, where the landlocked dummy again is significant, own and neighboring institutions remain significant and MA is now insignificant (but positive). Note also that the fact that the spatial GDP variable is no longer significant in the specification with neighboring institutions and the control variables implicitly indicates that there is no spatial dependence left once the latter variables are included in the regression (confirming the results of the LM tests mentioned earlier).18
As to the question why our findings differ from those of Redding and Venables (2004), a number of possibilities arise. They do for instance not instrument institutions and only look at own country institutions; also their sample is somewhat smaller (101 countries) to the effect that their sample does not include a number of (African) countries with poor institutions. Moreover, the focus of their analysis is different. Even though they report estimation results for the specification with our simple MA variable and institutions (their footnote 15, p. 65), the bulk of their paper and estimation results deals with MA measures that are grounded in a NEG model. To align our results somewhat more with those in Redding and Venables (2004), Table 7 (see column 6) also provides estimation results based on their measurement of MA. That is, we first estimate a gravity trade equation and then use the results to construct a (theoretically grounded) measure of MA (see Appendix A for further details on the estimation strategy and the trade data used). Next, we plug this trade-based MA variable in the model. The results are shown in Table 7, column (6). A comparison of columns (5) and (6) reveals that the main results are in a qualitative sense not affected by this alternative measure of MA. MA is still insignificant and institutions still matter, although the significance of neighboring institutions has now dropped somewhat (significant at the 8% level). We do, however, not want to stress this last result too much because the limited availability of bilateral trade data forced us to estimate the Redding and Venables (2004) MA specification for 84 countries only. Compared to our full sample of 147 countries this is not only a substantial reduction of our sample, but it also implies, recall Figure 3, that this measure of MA includes much less (neighboring) countries compared to the simple market potential measure constructed using (6).
4.4. Indirect channels of influence
In Section 2.2, we outlined possible channels through which neighboring institutions could directly or indirectly influence income levels in other countries. Now that we have established that neighboring institutions do indeed matter, we round up our analysis by investigating for our sample of 147 countries whether some of these channels that have been mentioned in the literature seem to be more relevant than others. Table 8 suggests some possible channels of influence. It shows the correlation between several outcome variables in country i on the one hand and the quality of institutions in country i's neighbors on the other hand.
|
The general message that Table 8 conveys, corroborating some of the findings in e.g. Easterly and Levine (1998), Ades and Cha (1997), Murdoch and Sandler (2002) or Moore and Shellman (2007), is that countries surrounded by neighbors with poor institutions experience more armed conflicts (both external and civil wars) and more political turmoil. Also, these countries attract more (prima facie) refugees and their governments tend to spend a larger part of their budget on the military. Besides these more politically oriented channels, the geography of institutions also affects countries’ economic relations—in Table 8 expressed by the four trade-related variables. Poor institutions in neighboring countries are negatively associated with a country's openness (trade as a percentage of GDP), and lower trade levels with both its direct neighbors as well as with other countries. Interestingly, countries surrounded by neighboring countries with good institutions not only trade more with both these countries and the rest of the world, but they also generally trade relatively more with their neighbors.
Note however that Table 8 only provides tentative evidence into the relevance of some of the channels mentioned in Section 2.2. On a more general level, the question why and how neighboring institutions, and for that matter also own-country institutions, have an impact on economic development is not clearly answered by the IV-framework on which our analysis is based. This is generally true for the new growth empirics at large where the way in which institutions influence income essentially remains essentially a black box. This is aptly summarized by Rodrik et al. (2004, pp. 153–4) where they state that even a powerful instrument is a long cry from a ‘full theory of cause and effect’.
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5. Summary and conclusions |
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To explain cross-country income differences, economic research has recently focused on the so-called deep determinants of economic development, notably institutions and geography. This article sheds a different light on these determinants. Based on a sample of 147 countries, we show that is not so much a country's absolute geography, in terms of for instance its climate, but its relative geography in terms of its institutions that matters for economic development. Apart from a country's own institutions, institutions in neighboring countries turn out to be relevant as well. This finding is robust to various alternative specifications of relative geography, sample size, and additional controls including neighboring countries’ income levels.
In line with the seminal paper by Rodrik et al. (2004) that served as our benchmark paper, we also come the conclusion that a country's institutions are invariably significant in explaining cross-country differences in GDP per capita; that is the exogenous variation in institutions as captured by our instruments is always significant. Also absolute geography (when measured by a country's distance to the equator) only has an indirect impact on GDP per capita in the baseline specification; it is only through institutions that this version of geography matters.
Our contribution is to show the importance of the overlooked relative aspect of geography, which in our view can also be considered a deep determinant. A country's location not only determines its absolute geography but it also pins down its position on the globe vis-à-vis all other countries, which in turn affects a country's own level of economic development. More specifically we find that the geography of institutions matters as well. A country's GDP per capita does not only depend on own-country institutions but also on the quality of institutions in its neighboring countries as well. This is our main result and it shows that economic development does not take place in isolation. Instead, a country is directly affected by what happens in its neighboring countries. Low institutional quality in neighboring countries specifically seems to increase the chance of armed conflict, political turmoil and refugee flows. It also deters trade not only with these neighbors, but also with other countries. Moreover, our estimation results indicate that by excluding the spatial feature of institutions, studies like Rodrik et al. (2004) probably overestimate the relevance of own-country institutions.
Given the importance that is nowadays attached to good governance by domestic as well as international policy makers like the IMF or the World Bank, a policy implication of our analysis is that economic development is not only stimulated by improved own-country institutions but also by better institutions across the region. Not only the extent and the type of institutional change matters, but its effectiveness also depends on where it is implemented due to the spillover effects it has on other neighboring countries. In fact, good institutions may only be of limited use if a country continues to be surrounded by neighbors with very poor institutions. This illustrates the relevance of regional development policies; of concerted efforts to raise institutional standards in a group of neighboring countries at the same time. Also, it suggests the need to aid countries that are unable to deal with the adverse effects of being surrounded by ‘bad’ neighbors themselves in order to prevent negative spillover effects.
Finally, we think that our analysis may give rise to two fruitful directions for future research. The first is to get a better understanding of the transmission channels through which neighboring institutions matter (see Table 8 above). The second is to look more closely at alternative measures of spatial interdependency. Instead of focusing only on physical distance, one could think of other reasons (e.g. common language, religion or history, political or economic alliances) why neighboring countries might matter for a country's own economic prosperity (see also Simmons and Elkins, 2004). In addition to employing such alternative measures of space to measure the extent of countries’ interrelatedness, a related avenue for future research would be to improve our understanding of the relative importance of possible different ways in which relative geography or location matters (e.g. spatial institutions versus spatial GDP) for a country's economic development.
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Appendix A: Data, definitions and sources |
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GDP per capita in 1995
Purchasing Power Parity basis, from World Bank, World Development Indicators, 2003. For 11 countries with no data provided by the World Bank, we used the CIA World Factbook, 1995 or 1996
Institutions
Rule of Law index. Refers to 2000 and approximates for 1990's institutions, from Kaufmann et al. (2005): http://www.worldbank.org/wbi/governance/govdata/index.html.
Geography
Distance from the equator measured as abs(latitude)/90, from Hall and Jones (1999): http://emlab.berkeley.edu/users/chad/HallJones400.asc.
Percentage speaking European language
Percentage of the population speaking one of the major languages of Western Europe at birth, i.e. English, French, Spanish, Portuguese or German. From Hall and Jones (1999): http://emlab.berkeley.edu/users/chad/HallJones400.asc.
Percentage speaking English
Percentage of the population speaking English at birth, from Hall and Jones (1999): http://emlab.berkeley.edu/users/chad/HallJones400.asc.
Sub-Saharan Africa
Dummy variable taking value 1 if a country belongs to Sub-Saharan Africa and 0 otherwise.
Latin America and the Caribbean
Dummy variable taking value 1 if a country belongs to Latin America or the Caribbean and 0 otherwise.
East–South-East Asia
Dummy variable taking value 1 if a country belongs to East–South-East Asia and 0 otherwise.
Landlocked
Dummy variable taking value 1 if a country has no direct access to the sea and 0 otherwise.
Island
Dummy variable taking value 1 if a country is an island and 0 otherwise.
Area
Land area (in square kilometers), from CEPII: http://www.cepii.fr/distance/geo_cepii.xls.
Identity main European colonizer
Dummy variables taking the value 1 if a country's main colonizer was one of the following Western European countries: Great Britain, France, Spain, Portugal, The Netherlands, Belgium or Germany and 0 otherwise. From CEPII: http://www.cepii.fr/distance/geo_cepii.xls.
Religion
Variables measuring the percentage of the population that belonged to one of the following religions: Buddhism, Hinduism, Catholicism, Islam and Protestantism. From Barro (1999) (for 12 countries we used the CIA World Factbook).
Distance to economic center
Variables measuring the distance of a country's capital city to one of the following economic centres: New York (USA), Tokyo (Japan) and Brussels (EU). From CEPII: http://www.cepii.fr/distance/geo_cepii.xls.
GDP in 1995
Purchasing Power Parity basis, from World Bank, World Development Indicators (2003). For 11 countries with no data provided by the World Bank, we used the CIA World Factbook (1995) or (1996).
Relative geography
Contiguity: A matrix indicating for all country pairs if the two countries are contiguous, 1, or not, 0. From http://www.cepii.fr/distance/geo_cepii.xls.
Distances: A matrix containing the distance between capital (or main) cities for all country pairs. From http://www.cepii.fr/distance/geo_cepii.xls.
Assassinations
Average number of assassinations per million inhabitants over the period, 1960–1990. From Barro and Lee (1994).
Revolutions/coups
Average number of revolutions and coups over the period 1960–1990. From Barro and Lee (1994).
Political instability
From Knack and Keefer (1995).
External war
Dummy variable taking value 1 if a country was involved in at least one external war during the period 1960–1990 and 0 otherwise. From Barro and Lee (1994).
Percentage of government budget spent on the military
Average 1989–1999, from World Bank, World Development Indicators (2003).
Prima facie refugees
Number of refugees granted refugee status on a prima facie/group basis, average 1994–2000. From UNHCR, Statistical Yearbook 2003: http://www.unhcr.org/cgi-bin/texis/vtx/statistics/.
Refugees per GDP
Total number of refugees and person of concern to UNHCR divided by the Gross Domestic Product, average 1994–2000. From UNHCR, Statistical Yearbook 2003: http://www.unhcr.org/cgi-bin/texis/vtx/statistics/.
Percentage of arms in total imports
Average 1989–1999, from World Bank, World Development Indicators (2003).
Percentage of workforce in the military
Average 1989–1999, from World Bank, World Development Indicators (2003).
Malaria (MALFAL)
The percentage of the population at risk of contracting falciparum malaria. From The Earth Institute at Colombia University: http://www.earthinstitute.columbia.edu/about/director/malaria/index.html#datasets.
Malaria ecology
An ecologically based spatial index of the stability of malaria transmission. From The Earth Institute: http://www.earthinstitute.columbia.edu/about/director/malaria/index.html#datasets.
Civil war
Percentage of years at war during the period 1990–2000. From Hegre and Sambanis (2006).
Trade
Bilateral trade flows. Collected for 97 countries in 1995. From CEPII's Trade and Production database, available at: http://www.cepii.fr/anglaisgraph/bdd/TradeProd.htm.
Market Access a la Redding and Venables 2004 (see Table 7, column 6)
MA constructed in the same manner as in Redding and Venables (2004) (see their Sections 4.1 and 4.2), where we use the estimated coefficients of the following bilateral trade equation to construct MA (using data for 1995 and employing PPML estimation following Santos Silva and Tenreyro, 2006):
where Xij denotes trade between importer i and exporter j, Dij the distance between I and j, Bij is a dummy variable that takes the value 1 if i and j share a common border and ij a well-behaved error term. Given that we also have information on each country's ‘internal trade’ (= total production – total exports), we also explicitly estimate the effect of internal distance [measured as Dii = 0.66(areai/
)1/2]. Results are available upon request.
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Appendix B. The validity of percentage of speaking European language as an instrument |
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As mentioned in the text, Rodrik et al. (2004) decide to use 79 countries as their baseline sample. The reason for this is that:
‘We ... prefer this sample to the 137-country sample because settler mortality appears to be a superior instrument to those used in the 137-country sample (ENGFRAC and EURFRAC). ... the instruments for the IV regressions in the 137-country sample fail to pass the overidentification tests ...’ (Rodrik et al., 2004, p.143).This failure of the language instruments to pass the overidentification test also applies to our 147 country sample. Failure of these two instruments (or any instruments in general) to pass these tests sheds considerable doubt on the resulting second stage estimates when making use of these instruments. However, not being able to use the 147 country sample would leave us with considerable problems (see main text) when trying to estimate the effect of neighboring institutions. The solution to the problem that we have resorted to is to use the variable ‘% of the population speaking a European language’ as our only instrument. We think we have good reasons to believe in the validity of this instrument. Our argument goes as follows:
(1) Failing to pass the overidentification test does not necessarily mean that both instruments are invalid. What is tested is if one instrument is valid given that the other instrument is valid. Rejecting the null hypothesis may as well be interpreted as evidence that only one of the two instruments is correlated with the second stage error term.
(2) How to establish if this is the case? To do this we have come up with the following estimation strategy. Ideally we would have liked to have a third (and valid) instrument in case of our 147 country sample so that we can test the validity of each of the language instruments separately using the overidentification test. We have to admit that we do not have such an instrument in our 147 country sample case, however in case of Rodrik et al.'s (2004) baseline sample of 79 countries we do have such an instrument, namely the Acemoglu et al. (2001) measure of settler mortality. So by restricting ourselves to the 79 country sample, we have performed 2SLS estimation using any subset of the three instruments we have at hand in that case.
(3) Table A1 shows the results. The first three columns show the first and second stage results when using only one of the available instruments in the first stage. In that case overidentification tests, i.e. tests to establish the uncorrelatedness of the instrument with the residuals in the second stage, cannot be preformed and the only way to establish whether or not an instrument is valid is to look at the F-statistic on the instrument in the first stage, i.e. the instrument has to be correlated to the institutional quality measure. All three potential instruments pass this simple test. So (econometrically speaking) there is no reason to write off any of the instruments at this stage (to believe in any of the instruments in this case, relies solely on the ‘story’ behind the instrument).
Next, columns (4–6) show the results when using a combination of two of the three instruments in the first stage. Column (4) confirms the finding by Rodrik et al. (2004) in case of their 137 country sample and our finding in case of our 147 country sample: when including both language instruments the overidentification tests are not passed. This also holds when including settler mortality and percentage speaking English as instruments (column 5). However, when including settler mortality and percentage speaking a European language (column 6) the overidentification test is passed with flying collars (P-value 0.586).19 As we have noted above failing to pass the overidentifaction test can be interpreted as telling you that at least one of the instruments is not appropriate. We take the fact that the overidentification test is passed when including both settler mortality and percentage of population speaking a European language, but not so when any of these two are included in combination with percentage of population speaking English, as evidence that this latter instrument is invalid whereas there is no evidence to believe that percentage of population speaking a European language is not a good instrument.20 Column (6) confirms this finding, as when including all three instruments, the overidentification test is again not passed. Given the result in column (5), we can also explicitly test for the validity of percentage of population speaking English by calculating the difference in the J-statistic between columns (6 and 5) and comparing this to the 
(1) distribution. This results in rejecting the validity of percentage of population speaking English. Note also that the estimated second stage coefficient on the institutional quality measure is always within 1 s.d. when using either only settler mortality, only percentage speaking a European language or both of them as instruments, whereas the coefficient drops substantially when including also percentage speaking English.
Combining the above-presented evidence and the need to have an as large as possible sample for our purpose of estimating the effect of relative geography in the form of neighboring institutions (see main text), we have decided to use percentage of population speaking a European language as instrument21 in all of our 147 country regressions presented in the main text.
Table A1 Results on the instrument validity
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Notes: Our sample of 79 differs from Rodrik et al.'s (2004) baseline sample in one aspect. Our sample does not include Vietnam because of the unavailability of the language variables, whereas Rodrik et al. (2004) exclude the Central African Republic from their baseline sample due to data unavailability reasons. When using the whole 147 country sample when settler mortality is not included as instrument, results are very similar to the 79 country sample. t-values in parentheses.
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Appendix C: 2SLS when estimating the effect of neighboring institutions |
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The question whether or not to instrument neighboring institutions boils down to whether or not this measure can be expected to be correlated to the error term, see (5). As mentioned in the main text there are three main reasons why this could be the case, and here we will briefly comment on each of these.
Reverse causality
Can we expect that neighboring institutions are also directly influenced by one's own level of GDP per capita, i.e. 
? If this would be the case the need to instrument would immediately be established. However a direct effect of one's own level of GDP per capita on the institutions in one's neighboring countries may not be that convincing, although not unthinkable either. Still even if this ‘direct’ form of reverse causality would not be an issue, it is still true that it is likely that one's own level of GDP per capita affects the institutional quality in one's own country (one of the reasons to instrument own institutions),
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| (AC1) |
, establishing the need for instrumentation of neighboring institutions. To show this rewrite neighboring institutions in structural form using (AC1) and (2-ii), to get:
|
| (AC2) |
|
|
if22,
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| (AC3) |
which is clearly not equal to zero (in the case of for example k = 2, the term in (AC3) will represent the correlation of a country's own shock with the shock to that country's neighbors’ neighbors, which clearly includes the country's own shock).
Measurement error
Can we expect our institutional variable to be measured with error, i.e.
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| (AC4) |
|
| (AC5) |
Omitted variables
If we expect the presence of an omitted variable that explains GDP per capita and that is correlated to our measure of institutional quality, the need to instrument would immediately be present. This is clearly not unthinkable in the case of neighboring institutions. Instead of instrumenting, however, one would much rather include the important omitted variable in the regression. The latter is what is done in the numerous robustness checks provided in the main text. But, of course, one cannot include variables in these robustness checks for which no data is available. So instrumenting neighboring institutions on the basis of omitted variable bias is also a valid reason, maybe not that a convincing reason though as omitted variable bias can be argued to be a problem in any empirical study (and clearly IV-estimation is not so common in many studies).
Given that we need to instrument, we will now show that neighboring ‘fitted institutions’ constructed by multiplying ‘fitted institutions’ obtained from the first stage, (2-i), by the spatial weight matrix, W, is a valid way to solve the endogeneity problems involved in the neighboring institutions variable. For ease of exposition we will show this for the following case (in vector notation):23
|
| (AC6) |
, and that 
. The fitted values of X obtained from the first stage, (AC6-i) are
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| (AC7) |
:
|
| (AC8) |
? This can be checked rewriting (AC8) by first substituting (AC6-ii) for Y and adding and subtracting 
:
|
| (AC9) |
|
| (AC10) |
using (AC7) and (AC6-ii) to get:
|
| (AC11) |
, taking the expectation and using the assumptions that 
and 
, that:
|
| (AC12) |
, we are also interested in the accuracy of the estimated parameter, 
? Using (AC11) and denoting 
and 
it follows that:
|
| (AC13) |
,
is the variance of 
,
, respectively, and 
the covariance between
and 
. The (AC13) can be estimated consistently by substituting 
,
,
and 
for 
,
,
and 
, respectively.
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Acknowledgements |
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We would like to thank three anonymous referees and the editor, Diego Puga, for very useful comments and suggestions.
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Notes |
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1 Measurement error is a third source of endogeneity problems.
2 See Section 2.3 in Rodrik et al (2004) for a reaction to Sachs (2003) and Alcalá and Ciccone (2004). Another recent critique, see for instance Rajan and Zingales (2006), is whether institutions can really be considered to be deep determinants to begin with.
3 Leaving scope for thought experiments such as ‘What would happen to the Netherlands (keeping its own characteristics fixed) were it to be located next to Mozambique and South Africa instead of next to Belgium and Germany?’ (see Redding and Venables, 2004, for such a thought experiment involving Zimbabwe).
4 Similarly, bad (first nature) geography is not inevitably only a bad thing. Nunn and Puga (2007) show for African countries that were raided by slave traders that bad geography (in their case, ruggedness) impeded slave trade and thereby indirectly had a positive impact on economic development (the direct impact of bad geography on economic development is always negative).
5 Note that the same holds for own country institutions. Also in that case the exact channels through which own institution matter remain under investigation (see Rodrik et al., 2004, Section 3.3 and Table 3; and Acemoglu and Johnson, 2005).
6 The use of these two instruments for their largest sample is not without problems, see Appendix B.
7 In Section 4.2 we assess the robustness of our results by using alternative spatial weight matrices. Note also that the use of GDP weights is likely to result in an endogenous spatial weight matrix, making inference problematic (Anselin, 1988).
8 We do not see a clear-cut reason to, a priori, give more weight to for example larger neighbors (even less so in terms of their GDP) when considering the possible spillovers from bad or good institutions. Spillovers from bad institutions in a small neighboring country may have just as large an effect on one's own country as those from larger neighboring countries [e.g. civil unrest from Burundi (a small country) spilling over to Rwanda (also a small country) and affecting Uganda, the Dem. Rep. of Congo and Tanzania (all larger countries)].
9 Note that we have taken the Rule of Law variable from the most recent version of the Kaufmann et al. indicators as they are supposed to supersede (see http://www.worldbank.org/wbi/governance/govdata/index.html) the older version(s).
10 See Appendix B for a detailed discussion of the solution to this problem in case of our 147-country sample.
11 Admittedly, the problem still remains in our sample, but too a much lesser extent (basically only for countries bordering a former communist country).
12 ‘Good’ meaning (1) significantly related to the institutional quality measure, i.e. significant in the first stage, and (2) uncorrelated with the residual in the second stage. The second requirement can be explained more intuitively by saying that the effect the instrument has on GDP per capita goes entirely through its effect on institutional quality.
13 Adding integration (openness) instrumented by the Frankel and Romer (1999) instrument does not change any of the results shown in our article. Furthermore integration is, as in Rodrik et al. (2004), invariably insignificant. Results are available upon request.
14 To arrive at the corresponding coefficients in Table 3 of Rodrik et al. (2004) for the geography (distance to the equator) and institutions (rule of law), our estimated coefficients should be multiplied by the standard deviation, 0.172 and 1.030 for geography and institutions, respectively.
15 Note that the island countries in our sample (see top panel of Figure 2), are real islands as well as countries (like South Korea) that because of lack of data (North Korea is not in our sample) would have no neighbors in terms of contiguity.
16 Note that the landlocked variable merely serves here as an indicator of first nature or physical geography and not as a measure of the degree of openness or economic integration (although we do not deny that there is a clear link between being landlocked and openness).
17 Adding other measures of absolute geography, e.g. share of land in the tropics or a measure of soil quality, does not affect the results (not shown here, but available upon request).
18 The estimated model including a market access measure comes very close to a spatial lag model and such a model implicitly allows for a spatial error (this can be easily seen when rewriting a spatial lag model in structural form). The insignificance of the market access variable (i.e. spatial lag) hereby corroborates the results obtained from the LM tests shown in Tables 3 and 6.
19 Also the F-statistic on the instruments in the first stage is always significant.
20 When one takes the validity of settler mortality as instrument for granted [as Rodrik et al. (2004) for example do], the results of the Hansen J-test when including both settler mortality and percentage speaking a European language can be interpreted as a direct test (which is accepted) of the relevance of the latter.
21 Note that the results for the 79 country sample (Table A1, column 2) and the 147 country sample (Table 3, column 5) in the main text are quite similar.
22 Note that the condition in (AC3) also assumes that the effects of own institutions, 
, and neighboring institutions, 
, are not equal to zero.
23 The result is easily extended to including also X, the endogenous variable in nonspatial form, and/or other endogenous/exogenous variables. One can also generalize the result regarding the variance and allow for heteroscedasticity of 
.
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References |
|---|
-
Acemoglu D. Constitutions, politics, and economics: a review essay on persson and Tabellini's ‘The economic effects of constitutions’. Journal of Economic Literature (2005) XLIII:1025–1048.
Acemoglu D, Johnson S. Unbundling institutions. Journal of Political Economy (2005) 113:949–995.[CrossRef][Web of Science]
Acemoglu D, Johnson S, Robinson JA. The colonial origins of comparative development: an empirical investigation. American Economic Review (2001) 91:1369–1401.[Web of Science]
Ades A, Chua HB. Thy Neighbor's curse: regional instability and economic growth. Journal of Economic Growth (1997) 2:279–304.[CrossRef]
Alcalá F, Ciccone A. Trade and productivity. Quarterly Journal of Economics (2004) 119:613–646.[CrossRef][Web of Science]
Anselin L. Spatial Econometrics: Methods and Models (1988) London: Kluwer.
Barro RJ. Determinants of democracy. Journal of Political Economy (1999) 107:S158–S183.[CrossRef][Web of Science]
Barro RJ, Lee J. Data set for a panel of 138 countries. NBER (1994) Available online at: http://www.nber.org/pub/barro.lee/readme.txt [Accessed 25 November 2008].
Carstensen K, Gundlach E. The primacy of institutions reconsidered: direct income effect of malaria prevalence. The World Bank Economic Review (2006) 20(3):309–339.
Central Intelligence Agency. CIA World Factbook (1995) Washington, DC: US Central Intelligence Agency.
Central Intelligence Agency. CIA World Factbook (1996) Washington, DC: US Central Intelligence Agency.
Crafts N, Venables AJ. Globalization in history: a geographical perspective. In: Globalization in Historical Perspective—Bordo M, Taylor AM, Willianson JG, eds. (2003) Chicago: The University of Chicago Press. 323–364.
Easterly W, Levine R. Troubles with the neighbours: Africa's problem, Africa's opportunity. Journal of African Economics (1998) 7(1):120–142.
Easterly W, Levine R. Tropics, germs, and crops: how endowments influence economic development. Journal of Monetary Economics (2003) 50:3–40.[CrossRef][Web of Science]
Frankel JA, Romer D. Does trade cause growth? American Economic Review (1999) 89:379–399.[Web of Science]
Fujita M, Krugman P, Venables AJ. The Spatial Economy (1999) Cambridge, MA: MIT Press.
Glaeser E, LaPorta R, Lopes-de-Silanes F, Shleifer A. Do institutions cause growth? Journal of Economic Growth (2004) 9:271–303.[CrossRef][Web of Science]
Gleditsch KS, Beardsley K. Nosy neighbors. Journal of Conflict Resolution (2004) 48:379–402.
Hall R, Jones CI. Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics (1999) 114:83–116.[CrossRef][Web of Science]
Head K, Mayer T. The empirics of agglomeration and trade. In: Handbook of Regional and Urban Economics—Henderson V, Thisse J-F, eds. (2004) IV. Amsterdam: North Holland. 2609–2665.
Hegre H, Sambanis N. Sensitivity analysis of empirical results on civil war onset. Journal of Conflict Resolution (2006) 50:508–535.
Kaminsky G, Reinhart C. On crises, contagion and confusion. Journal of International Economics (2000) 51:145–168.[CrossRef][Web of Science]
Kaufmann D, Kraay A, Mastruzzi M. Governance matters IV: governance indicators for 1996–2004. In: World Bank Policy Research Working Paper (2005) Washington: World Bank. No. 3630.
Kelejian H, Murrell P, Shepotylo O. Spatial Spillovers in the Development of Institutions. In: Mimeo (2008) University of Maryland.
Krugman P. First nature, second nature, and metropolitan location. Journal of Regional Science (1993) 33:129–144.[CrossRef][Web of Science]
Mayer Th. Market potential and development. In: CEPR Discussion Paper (2008) London. no. 6798.
McArthur JW, Sachs JD. Institutions and geography: comment on Acemoglu, Johnson and Robinson (2000). In: NBER Working Paper (2001) Cambridge, MA. no. 8114.
Montalvo JG, Reynal-Querol M. Fighting against malaria: prevent wars while waiting for the ‘miraculous’ vaccine. Review of Economics and Statistics (2007) 89:165–177.[CrossRef][Web of Science]
Moore WH, Shellman SM. Whither will they go? A global analysis of refugee flows, 1955-1995. International Studies Quarterly (2007) 51:811–834.[CrossRef][Web of Science]
Murdoch JC, Sandler T. Economic growth, civil wars and spatial spillovers. Journal of Conflict Resolution (2002) 46(1):91–110.
North D. Institutions, Institutional Change and Economic Performance (1990) Cambridge: Cambridge University Press.
Nunn N, Puga D. Ruggedness: the blessing of bad geography in Africa. In: CEPR Discussion Paper (2007) London. no. 6253.
Rajan RG, Zingales L. The Persistence of Underdevelopment: Institutions, Human Capital or Constituencies. In: NBER Working Paper (2006) Cambridge MA. no. 12093.
Redding S, Venables AJ. Economic geography and international inequality. Journal of International Economics (2004) 62(1):53–82.[CrossRef][Web of Science]
Rodrik D, Subramanian A, Trebbi F. Institutions rule, the primacy of institutions over geography and integration in economic development. Journal of Economic Growth (2004) 9:131–165.[CrossRef][Web of Science]
Sachs JD. Institutions Don’t Rule: direct Effects of Geography on Per Capita Income. In: NBER Working Paper (2003) Cambridge, MA. no. 9490.
Salehyan I. No shelter here: rebel sanctuaries and international conflict. Journal of Politics (2008) 70(1):54–66.[Web of Science]
Salehyan I, Gleditsch KS. Refugees and the spread of civil war. International Organisation (2006) 60(2):335–366.
Santos Silva JMC, Tenreyro S. The log of gravity. Review of Economics and Statistics (2006) 88:641–658.[CrossRef][Web of Science]
Simmons BA, Elkins Z. The globalization of liberalization: policy diffusion in the international political economy. American Political Science Review (2004) 98(1):171–189.[CrossRef][Web of Science]
World Bank. World Development Indicators 2003 CD Rom. (2003) Washington, DC: International Bank for Reconstruction and Development.
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