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GEOGRAPHY AND INSTITUTIONAL QUALITY

Matthew Browna and Lisa Verdonb

First Draft: May 2012

a Corresponding author. West Virginia Wesleyan College, 59 College Avenue, School of Business; Buckhannon, WV, USA 26201. Email: mattfsubrown@gmail.com. Phone: 1-240-472-7784. b College of Business, Wooster College; Wooster, OH, USA 44691

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Abstract

The relationship between several broad geographic and population characteristics

and the quality of economic institutions in a country is explored. This paper contributes

to our understanding of these issues by introducing new variables to measure ease of exit

and models their relationship with economic institutions as a form of Tiebout sorting.

Countries that are more easily exited are likely to have more market-oriented economic

institution. A significant relationship is demonstrated between economic institutions and

geographic characteristics in numerous specifications with various control variables;

these findings advance our understanding of the determinants of economic institutions

which is currently a weak point in the empirical growth literature.

Keywords: Institutions; Geography; Policy; Economic Freedom. JEL Codes: H11; F59; O17; P16

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1. INTRODUCTION

Following several seminal empirical papers on the role of institutions,

Acemoglu and Johnson (2005) point out, there is a growing consensus among economists

that institutions play an important role in long-run economic performance. What is less

clear is why some countries adopt institutions that are consistent with long-run economic

growth while many others remain constrained by institutions that are harmful to

economic growth and prosperity. These questions are emerging in the literature on

empirical macroeconomics and comparative political economy.

Geography has often entered into discussions of economic performance. A

significant division has emerged in the literature between those that view geographic

characteristics as primary determinants of economics performance and those that view

institutions as the significant driving force in long-term growth. The use of latitude as an

instrument for economic institutions is a common example of how geography,

institutional quality and economic growth of are often linked, although with somewhat

less than satisfying theoretical underpinnings, given the ambiguous understanding of how

latitude impacts institutional quality or growth. Thus economists have often included

geography in discussions of institutions and growth, but the primary focus has been on

institutions and growth and not how geography and other variables influence the

formation of the institutions that drive growth.

This paper introduces new variables based on geographic shape and size and

develops a model of how such geographic characteristics may influence the formation of

institutions by self-interested rulers and thus impact long-term economic growth.

Responding to emigration pressures that result from Teibout competition, rulers will

adjust institutional quality to maximize long-term utility by liberalizing institutions and

policies in countries that are more easily exited in an attempt to maintain a revenue base

and maximize rent extraction. These findings support the hypothesis that geography is an

important determinant of institutional quality (thus influencing long-run growth) and is

based on a more economically sound rationale than the commonly used latitude variable

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and provides a step toward developing a more robust understanding of the determinants

of economic institutions more generally.

A more accurate understanding of the factors that influence institutional quality

may help improve efforts to address poor economic performance around the world. Over

a half-century after the creation of the World Bank and widespread international efforts

to alleviate poverty in poor countries, most of the world’s population continues to live in

conditions that are well below the poverty lines of Western nations. As Easterly (2002)

points out, development programs have undergone periodic fads, each claiming to be the

solution to global poverty and each failing to provide meaningful improvement. But

recognition that institutions are important may not lead to adoption of better institutions.

Many obstacles prevent institutional reforms from taking root. Thus, understanding the

process of institutional reform will enhance our understanding of the critical process of

economic growth and development.

The rest of this paper is organized as follows: first, a literature review covering

the major works on the relationship between economic performance and institutional

quality and the relationship between natural factors, geography and both institutional

quality and economic performance; second, a theoretical explanation of the relationship

between geography and institutional quality; third, a description of major data sources;

fourth, a presentation of the empirical analysis including several robustness checks; and

finally a concluding discussion.

2. LITERATURE REVIEW 2.1 Institutions and Economic Performance Contemporary research on the importance of economic institutions as determinants of

long-run economic performance is largely associated with the work of Douglass North

and a small number of other economists in the post-War period. Although the importance

of institutions has been recognized by many authors since at least Adam Smith (1776)

much of post-war economics research ignored the importance of institutions for

economic growth and development. The seminal textbook of the era, Samuelson’s

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Economics: An Introductory Analysis (1946), does not even include the word

“institutions” in its index. Much of economics followed Samuelson’s example in the

post-war decades leading to many important conceptual gains, but also thus ignoring

institutions. Alternatively, North's less mainstream research over several decades

provides the primary arguments for why institutions matter. "The evolution of institutions

that create an hospitable environment for cooperative solutions to complex exchange

provides for economic growth" (North 1990, vii). According to North, institutions are the

"rules of the game" in an economy or "the humanly devised constraints that shape human

interaction" (North 1990, 3). The broad, yet vague, nature of this definition

simultaneously reveals the importance of institutions and the difficulty of developing

reliable and meaningful measures of them.

North and Thomas (1973) argued that institutions, by shaping the incentives faced by

individuals, were the driving force in long-run economic growth, and that changes in

relative prices, such as their much cited example of feudalism and population change,

were the reason that institutions evolved. Institutions responded efficiently in this

explanation. North (1981) moved away from this efficiency explanation for institutional

change and instead argued that institutions were adopted by self-interested rulers to

satisfy their own interests and that there was no reason to believe that such institutions

would be efficient or lead to maximum economic growth, but only to the increased well-

being of the ruler and his supporters. North (1990) builds on this theory and argues that

inefficient institutions that were adopted in the interest of the ruler or ruling elite may

result in a path dependence in economic and institutional development that long outlasts

the rulers who put the institutions in place.

Other research in the 1980s and 1990s complemented North's emphasis on the

importance of studying institutions. De Soto (1989) argued that economic development in

poor countries was hindered by excessive regulation and bureaucracy that raised

transactions costs so high that it prevented simple entrepreneurial activities from

occurring. De Soto (2000) argued that poor countries lack the foundations of market

economies, like a clear system of property rights, that rich countries developed in the

nineteenth century and that the absence of such clear institutional underpinnings prevents

individuals in poor countries from leveraging their informal, or extra-legal, property

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holdings into more productive economic activity. Rosenberg and Birdzell (1986) support

this view with the argument that economic development in the West was a slow and

gradual process that succeeded through experimentation. "The key elements of the

system were the wide diffusion of the authority and resources necessary to experiment;

an absence of more than rudimentary political and religious restrictions on experiment;

and incentives which combined ample rewards for success" (33). Research on the

importance of firms and contracting (Coase 1937, 1960 and Williamson 1985) and the

price system (Hayek 1945) among many others have led to a large body of work

explaining why institutions are an important, perhaps the most important, explanation for

economic growth.

More recently economists have begun efforts to examine empirically the importance

of institutions in macroeconomic performance. Two important articles in this line of

research were produced by Acemoglu, Johnson, and Robinson in 2001 and 2002. Their

articles are among the most highly cited in the field of empirical macroeconomics and

have made large contributions to the emergence, in its own right, of the field of

comparative political economy as a mainstream area of research. In these companion

pieces Acemoglu et al. investigate how pre-existing conditions impacted the

establishment of institutions in European colonies and how institutions have impacted

long-run growth in those former colonies. Both papers take exception with the argument

of Jeffrey Sachs and others who claim that geographic factors like disease environment

and climate directly impact economic performance today (see for example Sachs 2003).

Instead Acemoglu et al. argue that economic institutions, such as property rights, which

may have originally been influenced by natural conditions, are in fact the primary

determinants of economic performance today.

In their 2001 Colonial Origins paper, Acemolgu et al. use the mortality of colonial

settlers as an instrument for economic institutions. They argue that colonial powers were

more likely to set up “extractive institutions” in colonies where it was difficult to survive,

thus allowing them to extract as much wealth as possible from the colony with little

regard for long-run performance. In colonies where it was easier to survive they

developed “productive institutions,” institutions that respected property rights and rule of

law and encouraged long-run growth. In their 2002 paper, Reversal of Fortunes, they

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used population density as an instrument for institutions. Here they argued that colonies

with dense existing populations where more likely to receive extractive institutions, and

colonies with sparse population densities were more likely to receive productive

institutions.

In both papers they found that their colonial instruments, mortality and population

density, were good predictors of current day institutions and they used this to show that

there is a strong causal relationship between the strength of property rights and current

economic performance. These papers suggest that once institutional factors are taken

properly into account current geographic factors, like disease environment, have no direct

impact on economic performance. Further their papers suggest that economic institutions

often have deep historical roots that may be difficult to easily alter.

Other recent research using various instruments to get around the causality problem

has bolstered the argument that institutions are a primary determinant of economic

growth. Easterly and Levine (2003) reject natural conditions as a cause of long-run

growth beyond the impact those conditions have on institutions and argue that institutions

are the fundamental cause of long-run performance. Rodrick et al. (2004) argue in the

title of their paper that "institutions rule" and conclude "the quality of institutions trumps

everything else" (2004, 135).

But while institutions may rule, exactly which institutions rule and why is still an

open question. Acemoglu and Johnson (2005) identify two types of institutions that

reasonably could be thought to be important for economic growth: property rights

institutions and contracting institutions. They identify property rights institutions as those

that determine how secure property is from expropriation by the state or governmental

entities. Contracting institutions are identified as those that govern the security of

contracts signed by individual economic agents and how well those contracts are

enforced. So the first group of institutions govern how the individual and his property

interact with the state, and the second group of institutions govern how individuals

interact with each other given the existing state.

The goal of Acemoglu and Johnson is to determine the importance of these two

different groups for long-run economic growth. There is much economic theory to

support the importance of both groups. To address problems with endogeneity they use

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instrumental variables to test for a causal relationship between these variables and

economic growth. The instrument for property rights institutions is the colonial mortality

data from the Acemoglu et al. Colonial Origins paper (2001) and the instrument for

contracting institutions is the data on origins of the legal system developed by La Porta et

al. (1997, 1998) and Djankov et al. (2002, 2003), which identifies the legal tradition

(English, French, German etc) that underlies the legal system of each country.

Their analysis suggests that a strong relationship exists between property rights

institutions and economic performance, but that once these are controlled for, contracting

institutions have no statistically significant impact on growth. They speculate that in an

environment of secure property rights economic agents will be able to develop

mechanism, such as reputation monitoring, to overcome shortcomings from a country's

contracting rules. The authors concede that the mechanism by which institutions impact

growth is still something of a black box. But the overwhelming evidence from this

growing body of work on empirical tests of institutions has been that institutions are

important foundations for long-run economic performance. Acemoglu and Johnson's

paper leaves many other types of institutions unaddressed, such as monetary policy,

regulations, and corruption, but it is an important step in helping to determine just what

types of institutions poor countries will need to focus on if they are to encourage long-run

economic growth.

2.2 Geography, Endowments and Institutions

Having collected a large amount of evidence on the importance of institutions for

long-run economic performance, economists have started to ask why some countries

continue to have inefficient institutions. Several papers have begun to address this issue

empirically. The past decade has seen an increasing emphasis on geography in the field

of economic development. What has not been discussed as frequently is the degree to

which geography influences the quality of institutions. Few would debate the notion that

geography plays some role in influencing growth. Many discussions devolve into a

quarrel over relative importance. Dani Rodrik et al. (2004) delineated three main schools

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of thought in the literature – geography, trade and institutions. While all three are relevant

determinants of growth, debate remains regarding interaction and relative impact.

Previously, many had been hesitant to revive such a deterministic conception of

growth as geography. It risks bringing to mind racist development theories that go back at

least as far as Montesquieu (1750) and Machiavelli (1519) (Easterly and Levine 2003, 5).

Yet, empirics suggest equatorial nations are substantially poorer than their counterparts at

higher latitudes (Sachs et al. 1995; 1997).

The most prominent representatives of the Geography School, Jeffrey Sachs et al.

(1998, 2001, 2003) and Jared Diamond (1998), argue that geography has both direct, as

well as indirect effects, the direct being the most significant contributing factor for long-

term growth. Sachs and Diamond approach the topic from slightly different perspectives.

While Sachs illustrates how geography currently impedes economic development,

Diamond tries to explain how geography influenced development over the course of

history.

Gallup and Sachs (1998) note that there are two “unmistakable” correlations with

economic development. The first is that the majority of tropical countries are poor. The

only tropical countries in the top thirty countries in terms of income are Hong Kong and

Singapore. Secondly, coastal economies are richer than landlocked countries. There are

no rich, landlocked countries in the world outside of Europe. Gallup and Sachs argue

that these geographic factors continue to exert a direct impact on growth today. They

estimate the following costs to per capita income for various factors: $4700 for being in

the sub-tropics; $3,500 for being in the southern hemisphere; $10,000 for being socialist;

$5,000 for being landlocked.

In their 2003 work “Institutions Don’t Rule: Direct Effects of Geography on Per

Capita Income,” Sachs et al. flesh out the tropical thesis by focusing on the direct impact

of the malarial environment on growth in equatorial nations. They cite three main ways

that disease climate has a direct effect on income: (1) unhealthy people are less

productive; (2) poor health conditions reduce life expectancy and shorter lives mean less

human capital is accumulated over the lifetime; and (3) poor health may reduce the

ability for human capital investment.

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In addition to the tropical climate, Sachs et al. (1995; 1998; 2003) also discuss the

relevance of coastal proximity for the growth of developing nations. They are not the

first. Given lower transportation costs by sea than via land, one would expect that coastal

nations would benefit more from exchange than their landlocked counterparts. Bauer

devotes a few pages of his 1991 collection of essays, The Development Frontier, to

addressing the relative importance of geography. He notes that while geographic factors

play a significant role in shaping development in the short term, “this elementary analysis

reveals nothing about developments over a longer period” (Baurer 1991, 28).

Jared Diamond’s 1997 book Guns, Germs, and Steel provides a different take on the

role of geography in the history of development. Diamond suggests four main causal

paths through which biological and geographical factors affected development. To

illustrate his hypotheses, he describes the differences in the development of Europe,

Africa and Asia.

First, Diamond attributes divergence in development to biological differences across

continents. Societies with wild plants and livestock capable of being domesticated

(Europe) developed resistance to certain infectious diseases like measles and small pox,

while those who lacked farm animals failed to develop the same immunity. This proved

disastrous for the original populations of the Americas after contact with European

explorers. The other obvious benefit of livestock resides in the productivity gains from

agricultural use. Diamond notes that Africa missed out on these gains due to a disease

climate that limited the number of cattle.

Diamond’s second contention is that diffusion within continents via migration

allowed for greater rates of development in Europe and Asia than in Africa and the

Americas. Climate is relatively uniform along latitudinal lines, and therefore many of the

crops that developed in Europe or Asia were easily transplanted from one continent to

another along similar latitudes. On the contrary, the number of climatic regions in

continents that are oriented primarily along north to south axes did not allow for the same

spread of innovation. “If a productive crop is already available, incipient farmers will

surely proceed to grow it rather than start all over again by gathering its not yet so useful

wild relative and redomesticating it” (179). These continental corridors also allowed for

contact that would have inspired trade, and the “diffusion of technological innovations”

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(179). Diamond notes that diffusion occurred more slowly in Africa and the Americas,

given the north-south axes and geographic barriers.

In addition to diffusion within continents, some factors allowed for diffusion between

different continents. Some continents (Australia, Americas) have traditionally been more

isolated than others. This led to less “interhemispheric diffusion” than that which has

been observed within the Eurasian region, given its “east-west major axis and its

relatively modest ecological and geographical barriers” (407).

Diamond’s fourth hypothesis is that continents benefit from large geographic or

population size. “A larger area or population means more potential inventors, more

competing societies, more innovations available to adopt – and more pressure to adopt

and retain innovations, because societies failing to do so will tend to be eliminated by

competing societies” (407). Essentially, the competition and diffusion created by large

areas for people to interact fostered development over the course of history.

While all of Diamond’s arguments seem plausible he includes one final important

hypothesis as almost an afterthought in his book. He presents a map of the borders of

Europe and China and speculates that differences in their shape could have significantly

influenced their institutional developments. China, he speculates based on the map, was

easier to centralize and bring under the control of one ruling group while Europe was

geographically suited for more local, decentralized control and was harder to reign in by

any one power. As Diamond explains it, Europe is “much more indented and includes

more large peninsulas and two large islands” providing natural barriers to political

centralization. (414).

Hall and Jones (1999) were among the first to empirically explore the effects of

geography on institutions. They found latitude to be a very significant determinant of

institutional quality. Countries that are farther north have significantly more productive

institutions than those closer to the equator. They explored institutions as part of what

they deemed “social infrastructure,” and found them to be productivity enhancing. About

the same time Kaufmann et al. (1999) used percentages of English and European

speaking populations to instrument for institutions. The reasoning, similar to that of Hall

and Jones, is that Europeans have traditionally had better institutions, and generally

settled in climates that were similar to those they were used to in Europe.

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Engerman and Sokoloff (1997, 2000) focus on the type of natural endowments

present in different geographical locations, and how this translates to institutions. Their

2000 study, “Institutions, Factor Endowments, and Paths of Development in the New

World,” centers on the disparity between development in North and South America. The

argument is that given South America’s abundance of resources that lend themselves to

commoditization (rice, silver, sugar cane), economies of scale led to slave labor under a

few ruling elites. This stands in stark contrast to the history of North America, where the

climate allowed for grains like maize and wheat which “permitted relatively small farms

given the technology of the times and may help explain why such a policy of

smallholding was implemented and was effective” (224). Ultimately, extreme inequality

in the majority of the countries in the Americas allowed a few ruling elites to consolidate

political power that still endures. In contrast, due to large amounts of available land and

open immigration, the United States and Canada developed large middle classes that

checked the power of those at the top of the wealth distribution. Because industrialization

required the consent of the majority, these middle classes proved indispensable. This

distinction was also apparent in the United States prior to the Civil War as southern states

developed more extractive, less entrepreneurially focused institutions built around the

slave-driven cotton industry, while the northern states developed more productive

institutions.

Following Engerman and Sokoloff, Acemoglu et al’s 2001 and 2002 pieces

(discussed above) on how pre-existing geographically-determined conditions impacted

the establishment of institutions in European colonies, and how those institutions have

impacted long-run growth in those former colonies, are two of the most influential pieces

in the literature. The Acemoglu et al. papers have been criticized, but primarily for the

validity of their instruments as proxies for institutions not so much for their claims that

the geographic conditions would have influenced institutions (see for example Sachs and

McArther 2001). That being said, Rodrik (2004) argued that a better instrument for

institutions had yet to be discovered.

Easterly and Levine’s 2003 work “Tropics, Germs, and Crops: How Endowments

Influence Development” examines Engerman and Sokoloff’s (1997) hypothesis that

endowments influence institutions. They develop dummy variables to test whether or not

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a country produces any wheat or mining commodities to test the theory. Contrary to the

endowment and policy theorists, Easterly and Levine “find no evidence that endowments

affect country incomes directly other than through institutions, nor … any effect of

policies on development once we control for institutions.” (39). But interestingly, as with

much of this literature, the emphasis of the findings is placed on explaining the role of

institutions in determining growth, and scant discussion is given to the reappearing

assertion that geography is a major factor determining those institutions. Economists, for

the most part, seem content to skip that step in the logic.

Rodrik et al.’s 2004 piece “Institutions Rule: The Primacy of Institutions Over

Geography and Integration in Economic Development” serves as a summary of prior

works in that debate. Rodrik et al. compile the various instruments to argue that

Acemoglu et al’s proxy for institutions is a more significant determinant than any of

Sachs’ geographic instruments, Frankel’s trade variables, or a number of other potential

institutional measures.

2.3 Informal Institutions, Fractionalization and Trust

Williamson (2009) distinguishes between formal institutions, which represent

government enforced constraints, and informal institutions, which represent privately

enforced practices. The contention based on empirical analysis is that informal

institutions have an important role in determining economic outcomes and that formal

institutions matter primarily if they are imbedded within or related to informal

institutions. Her work points to an important role for informal institutions, culture, etc. in

determining formal institutions and thus growth.

These conclusions are supportive of the work of Easterly (2006), which focuses

on the likelihood of failure if outside influences (as development lenders) impose formal

institutions rather than building on organic, bottom-up informal means. Easterly’s

contention is that institutions are important, maybe the most important thing in

determining long-run economic growth, but that so little attention has been paid to

understanding how developing countries work that almost all attempts to improve

institutions have done very little good. As he expresses it “the free market is a universally

useful system. Economic freedom is one of mankind’s most underrated inventions” (72).

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However, while free-markets work well, he points out that free-market reforms are quite

often failures because the designers of reform fail to understand the local underpinnings

of how institutions evolve. The emphasis, he argues, must be placed on understanding

how institutions evolve locally and how assistance can be contributed within that process

rather than aid being used to try to force imported practices to replace local practices. He

clearly contends that productive institutions are not likely to be successfully imposed

though outside pressure such as advocated by the Washington Consensus.

The argument that informal institutions matter a lot in determining formal

institutions and performance is supported by the small line of research on “trust.”

Fukuyama (1996) was one of the first major efforts to discuss the role of trust in

determining economic performance. Trust, he argues, can lower transactions costs by

making it easier for parties to a transaction to agree to terms and feel comfortable about

business dealings. Also trust may contribute to the adoption of more consistently fair

formal institutions by creating a lower sense of unease about how the rest of society

might take advantage of those institutions. Knack and Keefer (1997) have investigated

Fukuyama’s hypothesis empirically using survey data and found that economic

performance is positively correlated with reported levels of trust in a society.

Fractionalization within the population whether on religious, ethnic or linguistic

grounds is thought to be a potential hindrance to greater social cohesion and trust. Recent

empirical work has attempted to develop measures of this type of fractionalization and

test its impact on institutions and growth. Alesina et al (2003) developed the current

standard measures of these three forms of fractionalization for 190 countries. Their study

suggests some importance for fractionalization in determining institutional quality and

economic performance, but is not conclusive. It’s greatest contribution has been spurring

greater interest in the measurement and application of the concept of fractionalization to

empirical growth literature.

A theoretical foundation for the importance of fractionalization in determining

institutional quality was provided by Olson (1982). In The Rise and Decline of Nations

Olson applies the lessons from his 1965 work, The Logic of Collective Action, to the

question of economic growth and performance and economic institutions. He argues that

most development research in economics misses the primary drivers of institutions and

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growth. “They trace the water in the river to the streams and lakes from which it comes

but they do not explain the rain” (1982, 4). Olson (1965) argues that group coordination

is hindered by the free-rider problems; that is, if benefits of group action are dispersed

each individual does not have an incentive to work very hard to achieve that action and

instead has an incentive to free ride on the efforts of others. The larger a group is, and the

more diverse are its members, the less likely it is to pursue actions in its members’

interests. Olson 1982 applies this logic to social evolution and institutions. Societies with

more heterogeneity—fractionalization in the current literature—are likely to have less

social interactions and organization and are less likely to make decisions in the interest of

the whole society—for example, adopting sound economic institutions. Political

entrepreneurs, he argues, are more successful if they deal with homogeneous groups

whose interests can be clearly identified and rewarded. Given the free-rider problem

smaller groups have an advantage in organizing their members to capture the interests of

these political entrepreneurs. As heterogeneity increases, so too does the likelihood that

policies will be adopted to support the interest of only one or a small number of groups

and not society as a whole. The number of distinct interest groups increases the

likelihood of bad outcomes in this reasoning. Thus, in Olson’s model and much of the

political economy literature, fractionalization is expected to play a critical role in

determining if a country will experience positive or negative economic institutions and

performance.

Taken as a whole the institutions and growth literature has advanced rapidly in the

last decade. Significant progress has been made in empirically understanding the role of

institutions in long-term economic performance. Many other issues have been identified

as important factors in the evolution of institutions (such as informal institutions and

fractionalization) which are only beginning to be well understood in relation to the

growth process. A better understanding of the role of factors such as geography,

fractionalization and other variables in the process of institutional development is critical

to continuing to develop a more complete understanding of the process of economic

growth.

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3. THEORETICAL MODEL

When looking for factors to help explain economic institutions geography is

a natural option (as revealed by the preceding discussion). The relationship between

latitude and institutions has long been discussed in the history literature as an

important relationship that may explain economic performance over time. Possible

explanations for latitude’s importance have ranged from the impact of heat on

working conditions and human energy, climate’s impact on agricultural

productivity, and the idea that variation in temperatures at higher latitudes requires

greater ingenuity and adaptability. Since Hall and Jones (1999) it has become

common to use latitude as an instrument for institutional quality in the empirical

growth literature. The strong correlation between economic performance and

latitude makes it impossible to ignore this factor as a potential determinant of

economic performance (Table 7 in the Appendix provides a correlation of .38

between a measure of economic institutions and latitude). Nevertheless, a clear

explanation of the impact of latitude on economic performance is lacking. The two

most common explanations for the importance of latitude discussed in the

economics literature are: 1) that latitude is a good measure of whether a country is

tropical—this is the view closely associated with Jeffrey Sachs that the tropical

disease environment directly impedes economic development; and 2) that latitude is

closely linked with how desirable a location was for colonial powers to develop

long-term settlements—this is the idea closely associated with the work of

Acemoglu and his colleagues. Regardless of the ultimate explanation of why

latitude is so closely related to economic performance and economic institutions, its

strong correlation makes it imperative that latitude be included in the empirical

analysis of the determinants of institutional quality. So for the analyses conducted

here latitude will consistently be included as a control variable to ensure that the

importance of other measures is not unintentionally exaggerated.

Casual empiricism suggests some interesting relationships between

geography and economic institutions. Figure 1 shows the relationship between a

common measure of economic institutions and geographic size. The countries were

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broken down into four quartiles going from smallest geographic area to the largest.

As Figure 1 indicates the simple relationship between area and institutional quality

is negative (higher scores in this measure of institutional quality represent greater

reliance on market allocation). The average economic freedom score (see the next

section for data discussion) for the smallest-by-area quartile is roughly 6.8 while the

average economic freedom score for the largest-by-area quartile is just roughly 5.9.

The middle quartiles fall in between, but do not suggest a clear monotonic pattern.

Figure 1: Economic Institutions and Area by Quartile

The suggestive relationship between geographic size and economic

institutions raises the question: are other measures of country size are correlated

with institutional quality? Figure 2 explores the relationship between population

size and economic institutions with a quartile graph. Here the relationship is much

less clear. The largest-by-population quartile has a higher average economic

freedom score (about 6.7) than the other quartiles (6.05 to 6.26), yet the simple

correlation is not statistically significant at any meaningful level. Population proves

to be not significant in regression analysis in multiple specifications as well and is

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left out of further analysis as it does not appear to be a meaningful measure of

size—at least in terms of trying to explain institutional quality.

Figure 2: Economic Institutions and Population by Quartile

3.1 Why Might Small Countries Have Better Institutions?

Competitive processes may influence the quality of a nation’s institutions and

policies. A smaller country may seem like the ideal place for a rent seeking political elite

to exact maximum control over a populace—with limited resources and technology it is

likely easier to exert control over a small area than a large one. However, as often

happens in economic reasoning, this static view is not likely accurate. Decision-makers in

small countries may have a stronger incentive than those in larger ones to adopt

institutions and policies more consistent with economic liberalism and long-term

economic progress. There are several reasons why this might be the case.

First, exchange with foreigners will be more important for the residents of small

nations. If the residents of small countries are restricted from trading with foreigners, it

will be more difficult for domestic businesses to realize fully the potential gains from

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scale economies. Similarly, domestic entrepreneurs in small countries will often find it

cheaper to obtain financing from investors in other countries. Opportunities for business

expansion and other entrepreneurial activities will be limited by the domestic capital

market if funds cannot also be attracted from abroad. However, foreign investors must

have confidence that they will be treated fairly. This means that the country’s legal,

regulatory, and tax systems must protect property rights, enforce contracts, and apply to

both citizens and foreigners in an even-handed manner. Because the potential gains from

trade with foreigners will be more important for the residents of a small country than for

one with a larger domestic market, decision makers in small countries may have a

stronger incentive to adopt and sustain sound institutions and policies both because the

population has a strong incentive to demand it and because the economic base upon

which the political class can predate will be larger.

Second, compared to large countries, the residents of small nations can generally

exercise the exit option at a lower cost. For the typical person, the distance to the border

will be shorter, thus increasing the number of potential institutional substitutes. Thus,

harmful policies will generally cause a larger share of the population to exit in small

countries than in large ones. This puts still more pressure on the political decision-makers

of small countries to adopt and maintain sound policies. If they do not residents are more

likely to leave and the tax-base will erode. It will also potentially be easier for citizens in

small countries to monitor political agents and to find private solutions to common

problems lessoning the need for intervention (Coase 1960; Olson 1982).

However, the cost and incentive structure relative to economic institutions does

not always favor small countries relative to their larger counterparts. If there are

substantial economies of scale in the provision of institutions or policies generally

provided by governments, small countries may be at a disadvantage. For example, the

cost of operating a central bank may be approximately the same in a small country as for

a large one. Thus, the per-person cost of this service is likely to be greater in small

countries. Furthermore, like language, currency is a network good. The service provided

to each user will be of greater value when the currency is readily accepted by a large

number of people. Therefore, both the per-person cost and the value of the service

provided are likely to work to the disadvantage of small countries attempting to provide

20

and manage their own currency regime or other network goods. Thus absolute size seems

theoretically to be an ambiguous explanation for institutional quality.

3.2 Other Geographic Characteristics Absolute size is not the only way to measure geographic characteristics. Indeed it

may not even be the most accurate method to capture the impact ‘size-like’

characteristics have on institutional quality. Diamond (1997) hypothesized that one of the

reasons Europe may have had an early advantage over China in economic development

was because China was easily controlled by one authority due to its relatively smooth

shape and uninterrupted geography as compared to Europe with its abundance of

peninsulas, islands etc. Diamond’s observation was almost an afterthought placed at the

end of his lengthy book, however, the empirical analysis that follows suggests it is of

major importance in explaining institutional quality. Figure 3 represents the outline of

Europe and China and begs the question Diamond posed about shape and institutions.

Length and shape of borders could help integrate Diamond’s insight into an

empirical analysis of institutional quality. Countries with the shortest borders (smallest

length of total border) have an average economic freedom score over a half a point higher

than countries with the longest borders. However, total border will be very closely related

to total geographic size and so distinguishing the two characteristics is difficult when

analyzing impact on economic institutions.

An alternative approach that would capture the impact of both size and shape

would be to create a new calculated variable called here “exitability” described in

equation 1.

(1) ( )B CEX

A+

=

21

Exitability, EX, is defined as the sum of land borders, B, and coastline, C,

divided by total geographic area, A. This variable more closely captures the idea

Diamond was discussing. A country with an irregularly shaped border would have a

higher ratio of exit options per land area than a country with smooth borders—like

Europe relative to China in Figure 3. From a theoretical perspective, institutional

competition requires available substitutes. Ideally, for competition to be maximized,

the location of any individual within a given country ought to be as close as possible

to a different system of governance. “Exitability”, gives us an approximation of how

easy a country is to leave (an estimate the cost of mobility).

Figure 3: Map of Europe and China: Does Shape Matter?

The exitability score is higher if the length of borders and coast line per total

area is higher and lower when there are shorter borders and coastline relative to total

area. So countries with rather irregular borders have higher exitability while

countries with smoother more regular border have lower scores. China, which

Diamond used as an example, has a relatively low exitability ratio of 0.003. Another

example of a low ratio is Chad with an exitability ratio of 0.004. Both countries

have relatively large landmasses away from any borders and have relatively smooth

borders—the sweeping half-moon shape of China and the more rectangular shape of

Chad. In contrast Denmark has an exitability ratio of 0.17 and Panama is 0.039, both

relatively high exitability numbers. These countries are shaped in such a way that

more of their area is close to a border or coast—the peninsula and many islands of

Denmark and the long narrow isthmus of Panama.

22

One way to consider the importance of ease of exit lies in the ability it

provides citizens to give feedback to the governing party. Individuals will, if

possible, vote with their feet if that is their only option of impacting the institutional

status quo. In this sense we can think of exitability as providing a measure of

Tiebout sorting among countries. Tiebout (1956) developed a novel model for

describing the provision of local public goods. In his model residents would choose

among competing localities for the bundle of publicly provided goods that best fit their

preferences. According to the Tiebout model large governments are inefficient because

they cannot design a bundle of publicly provided goods that satisfies the variety of

preferences among a large populace. Smaller localities in this model are more efficient as

they can tailor their provisions to a more homogeneous population. Individuals can

express their preferences by moving amongst the segmented localities to find the best

match for themselves. This Tiebout sorting process can result in competition (Tiebout

competition) among the various jurisdictions to provide the bundle of services that will

attract population and thus a revenue base for the government. Given that consumers

have heterogeneous preferences and that localities vary in the goods provided

(government programs) and costs (taxes, fees etc.), optimal allocation of citizens,

government programs and taxes are only likely to arise over an extended period of

Tiebout sorting. Optimal allocation arises in Tiebout’s model if information is widely

available (perfect information) and it is easy to move between localities (perfect

mobility). As either of these assumptions is relaxed the optimality of the Tiebout

allocation will diminish.

Applying the logic of Tiebout sorting to the international level we can assume

individual agents have a preference to sort themselves into national jurisdictions that

most closely satisfy their preferences for publicly provided goods and taxes. Here we can

think of the institutional environment as one of the publicly provided goods, or more

accurately a bundle of publicly provided goods. The ability to engage in Tiebout sorting

is clearly impacted by national policies regarding migration; a prohibition against

outward migration would be a clear obstacle to Tiebout sorting—as was often noted

during the era of the Iron Curtain.

23

Thus we can identify at least three major obstacles to Tiebout sorting at the

national level: information, travel costs (the original two constraints) and government

prohibition. Exitability, as described above, will impact how binding each of these

constraints are at the national level. A country with greater exitability will have a

population with more information about other jurisdictions, cheaper access to them by

being located more closely to borders, and an increased difficulty of enforcing border

controls due to the relative abundance of locations from which to exit. Exitability thus

increases the possibility that citizens of a country can engage in Tiebout sorting and seek

out national jurisdictions that more closely align with their preferences.

As North (1981 and 1990) among others explained, rulers will adopt institutions

that allow them to maximize their own welfare given their particular national set of

constraints. If the ruler (or ruling group) wishes to extract rents from the population this

will become more difficult if exit is easier. Thus, with exit options abundant, a more

viable solution for rent extraction may be to adopt institutions that encourage growth

allowing for smaller-percentage wealth transfer from a larger economy rather than large-

percentage wealth transfer from a smaller economy. Greater exit options should increase

competition among national governments to improve institutional quality in order to

retain citizens (revenue). As was widely recognized during the Cold War, population loss

is one of the best indicators of poor governance that exists. Thus, one might expect that

countries with border to area ratios that reduce the cost of exit will generally have more

liberal growth encouraging institutions over the long term as the sorting process occurs.

The ruler’s problem can be captured in a simple utility maximization framework.

Rulers will wish to maximize utility which will be a function of GDP and rent extraction,

among other things:

(2) max ( , ,rU f GDP rent Z= )

The rulers ability to extract utility, Ur, will be a function of both the size of the economic

base and the level at which rents are extracted from the economic base. Acemoglu et al

(2001; 2002; 2004) among others (as discussed above) have provided significant

24

empirical evidence that GDP is a function of economic institutions, EI (and certainly

many other factors):

(3) ( , )GDP f EI Z=

Rent extraction as a percentage of economic output and other forms of utility enhancing

regulation (such as providing benefits and advantages to favored political groups) will

tend to be related to the quality of liberal economic institutions. Thus EI will also

influence the variable rents in equation (2) as well—the ruler will face a tradeoff between

greater percentage rent extraction from a smaller economic base resulting from poor

institutions or a lower percentage level of rent extraction from a larger economic base

resulting from higher quality institutions.

Both GDP and possible rents will be influenced by the exit options of the

population. Thus a main constraint on the ability of rulers to maximize utility is the

ability of the population to exit, which will be a function of the variable exitability, EX,

among other factors:

(4) ( , )g EX X

Taking this into account and combining equations (2) and (3) provides a

simplified version of the ruler’s problem as:

(5) max ( , )

. . : ( , )

rEIU f EI Z

s t g EX X

=

Combining and simplifying the first order conditions from this constrained optimization

problem yields the ruler’s optimal decision rule:

(6) '( , ) ( , )f EI Z g EX X=

25

Thus changes in economic institutions will be impacted by the rulers attempt to maximize

rents based on a given level of exitability. This relationship will be tested empirically to

determine the impact of exitability on institutional quality.

4. DATA

In addition to the variable EX described in equation (1), two other variables were

constructed to test the impact of ease-of-exit on institutional quality. An analogy to the

idea of exitability is the idea that will be called “coastalness”. Several authors have

pointed to the seeming importance of coasts in a country’s economic development

(Bauer; Gallop and Sachs). Coastalness, here, is a calculated variable defined as the

length of coastline divided by total area. As shown in the Appendix there is a positive

correlation between coastalness and economic freedom. With island countries the

measure of exitability is equal to the measure of coastalness. Whereas countries that are

landlocked will have a zero coastalness score yet they still have the possibility of a

relatively high exitability score, like, for example, Austria.

Particularly before the advent of air travel, sea travel often represented the most

economical means of accessing foreign cultures and goods. Thus coastal communities

would not only be more likely to have more contact with people from other societies,

they would also be more likely to become trading hubs among numerous societies that

did not have direct access to sea routes. The potential wealth generation of these

processes should encourage coastal societies to develop institutions conducive to trade.

Smith (1776) summarized the importance of coastalness as follows:

“As by means of water-carriage a more extensive market is opened to every sort of industry than what land-carriage alone can afford it, so it is upon the sea-coast, and along the banks of navigable rivers, that industry of every kind naturally begins to subdivide and improve itself, and it is frequently not till a long time after that those improvements extend themselves to the inland parts of the country.”

Finally another variable has been created to capture the possible impact of shape

on economic institutions. Mathematically, a circle is the shape where most of the area is

26

farthest from a border. If Diamond’s theory of competition holds and Tiebout sorting

leads to leaders adopting more liberal economic institutions, places that are more circular

would likely have less liberal (more authoritarian) economic institutions. Conversely,

those nations whose borders were more irregular and less compact, would be more likely

to have liberal institutions (high levels of market allocation).

This is very similar to the discussion just presented regarding exitability, yet

exitability is dependent on size so it does not capture shape independently. A size-

independent measure of shape has been developed in spatial mathematics (Selkirk 1982).

Selkirk’s measure indicates shape or compactness independent of size; the Selkirk

circularity ratio is used commonly in studies of geography (see van Eck and Koomen

(2008) for a recent example). The ratio is calculated as given in Equation (7):

(7) ( )2

4 ACircularity

perimeterπ

=

Shapes that are perfectly round would have a value of one, while the most elongated

shape would have a value of zero. Equation 7 can be modified to produce an equation

that yields a relationship where more circular shapes are represented by lower rather than

higher numbers; this is referred to here as shape factor. Shape factor, equation (8), has

the desirable characteristic of capturing much of what is included in ‘exitability’, but is

independent of absolute size, which ‘exitability’ is not. Shape factor yields higher values

for greater elongation or “area close to a border” and lower values for greater

circularity—it is essentially a measure of non-compactness.

(8) ( )241 A

ShapeFactorB Cπ

= −+

4.1 Institutional Variables

Prior to the 1990s most of the major work on the relationship between institutions

and economic performance was historical and descriptive. This was largely out of

necessity due to a paucity of quality data on various institutional variables. There are now

several alternative measures available of both political and economic institutions. The

27

major source of data on economic institutions used here is the Economic Freedom of the

World index published by the Fraser Institute (Gwartney and Lawson, 2010).

Produced annually by the Fraser Institute, the Economic Freedom of the World

(EFW) collects both survey and objective data for 130 countries and territories. As the

authors indicate, economic freedom, political freedom and civil freedom are similar but

different. “Economic, political and civil liberties reflect the same fundamental value,” but

they cover different arenas of human action. It is possible for countries to have high

levels of one type of freedom but not another. For example, India is by most measures a

stable democracy with relatively high levels of political freedom, but it has a relatively

“unfree” economy. Alternatively, Singapore has the second highest economic freedom

scores in the world, but has relatively restricted political and civil freedoms.

The EFW score is calculated as a composite of scores in five areas: 1) size of

government, 2) legal structure and security of property rights, 3) access to sound money,

4) freedom to trade internationally, and 5) regulation of credit, labor and business. A

chain-linked version of the dataset is annually updated to maintain continuity between

data over time by applying similar evaluation criteria to previous years (for an

explanation of the methodology behind the index and the chain-linked data see Gwartney

and Lawson (2010)).

Area 1 is Size of Government, which can be broken down into two broad aspects,

government spending and the extent to which government decision making replaces

voluntary decision making. The first two components of Area 1 measure government

consumption as a share of total consumption, the third component measures the degree to

which private enterprise or government controlled enterprises produce the economy’s

output, and the fourth component measures marginal tax rates. Area 1 is based entirely on

objective data.

Area 2 is Legal Structure and Property Rights. This area captures one of the most

fundamental characteristics about institutions as discussed in most of the literature,

namely, how well-defined and secure property rights are in a country. The first

component in Area 2 measures opinions about judicial independence; the second

component measures opinions about the impartiality of courts; the third measures

protection of intellectual property; the fourth, military involvement in the legal or

28

political process; and the fifth component measures the ‘integrity’ of the legal system.

The data in this area are subjective and compiled from the International Country Risk

Guide and the Global Competitiveness Report.

Area 3 is Access to Sound Money. This Area is based on the work of Friedman

and others who have pointed out the importance of low and stable rates of inflation as

sources of economic growth and the dangers of government’s ability to erode property

rights and diminish the foundations of private business arrangements through inflationary

policies. The first three components of Area 3 look at measures of inflation and money

growth. The fourth component looks at the ability of individuals to hold alternative

currencies. The data for this area are objective.

Area 4 is Freedom to Trade Internationally; it contains a mixture of both objective

and subjective (survey) data. Policies that limit international exchange limit the extent of

the market and are clearly contrary to the notion of economic freedom. The first two

components measure tariffs and non-tariff trade barriers. The third component is an

estimated variable determined by regression analysis used to estimate the actual size of

the trade sector compared to the expected size of the trade sector based on national

characteristics such as size, access to coasts, and location. The fourth component

compares the black market exchange rate to the official exchange rate, and the fifth

component measures controls on capital markets.

Area 5 is Regulation of Credit, Labor and Business; this is a mostly subjective

category based on the lack of objective data concerning regulatory burdens, which are

often hidden. This category is broken down into three components which measure

regulation in the credit market, the labor market and regulation of business practices

generally. Although this area is difficult to measure it is a key component of economic

freedom. Given the hidden nature of many regulations, they are a popular object of rent

seeking activity among politicians and special interest groups.

The ten “most free” and ten “least free” economies as measured by the EFW

variable are provided in Table 1. The lack of reliable official data for several countries,

such as North Korea, result in their noticeable exclusion from the list. Summary statistics

for the major data sets is provided in Table 2.

29

Table 1: EFW Ratings for 2008 10 Most Economically-Free

Countries in 2008 Ten Least Economically-Free

Countries in 2008 Hong Kong Algeria Singapore Democratic Republic of Congo New Zealand Burundi Switzerland Guinea-Bissau Chile Central African Republic United States Republic of Congo Canada Venezuela Australia Angola Mauritius Myanmar United Kingdom Zimbabwe

Table 2: Summary Statistics

Variable Observations Mean Std Dev. Minimum Maximum EFW 1690 6.2997 1.1378 2.11 9.21 EFW: Area 1 1755 5.9497 1.5910 0.94 10 EFW: Area 2 1643 5.6631 2.0093 1.02 9.89 EFW: Area 3 1799 7.3181 2.0129 0.06 10 EFW: Area 4 1662 6.4286 1.5181 0.73 9.99 EFW: Area 5 1674 6.0197 1.2107 2.40 8.79 Population 1810 3.87e+07 1.32e+08 0 1.33e+09 Land Area 1669 811312 2030525 2 1.64e+07 Latitude 1737 27.122 17.762 0 65 Exitability 1733 0.0457 0.0931 0.0026 0.7322 Coastalness 1733 0.0350 0.0949 0 0.7034 Shape Factor 1733 0.7517 0.1628 0.1696 0.9972

30

5. EMPIRICAL ANALYSIS The various geographic characteristics discussed above all have potential

explanatory power in the relationship to economic institutions and simple univariate

analysis indicates strong correlations. Multiple regression analysis was conducted to

determine which factors were important in the presence of other variables and controls.

The basic relationship tested can be specified as follows in Equation (9):

(9) i i i iEF Xα β ε= + Ζ +

where EF represents economic freedom in country i , X is the matrix of geographic

variables tested and Z is a vector of control variables, latitude and other natural factors,

and ε is the error term. Results are reported below with robust standard errors to account

for heteroskedasticity as needed.

Table 3 reports the results of regressions using the various geographic measures

discussed above as possible factors influencing economic institutions. In Table 3 the

dependent variable in each regression is the economic freedom of the world score for

each country in 2008. The first model includes the two major size variables, land area and

population and includes latitude as a control variable. Taking latitude into account neither

geographic size or population size is a meaningful predictor of economic institutions,

contrary to some previous speculation. So while we would think of smaller area countries

being closer to alternative regimes, size alone cannot explain the type of mobility that is

expected to lead to greater institutional quality. Land area and population are excluded

from further analysis.

Exitability is included in the second model along with latitude. Exitability is

highly significant and positively correlated with economic freedom. Latitude retains its

positive significance. This supports the idea that ease of exit is important in explaining

institutional variation among countries, suggesting that something similar to Tiebout

competition may be occurring at the national level.

31

Table 3: Geography and Economic Freedom

Dependent Variable: Economic Freedom

Independent Variables

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Land Area -9.97e-09 (3.50e-08)

Population -1.72e-10 (2.31e-10)

Latitude 0.0224*** (0.004)

0.021*** (0.003)

0.021*** (0.004)

0.019*** (0.004)

0.020*** (0.004)

0.019*** (0.004)

Exitability 2.923*** (0.920)

1.329 (3.163)

2.681*** (0.940)

Coastalness 2.870*** (0.947)

1.563 (3.296)

Shape Factor 1.029* (0.565

0.636 (0.529)

Constant 6.064*** (0.164)

5.945*** (0.147)

6.009*** (0.142)

5.35*** (0.429)

5.997*** (0.144)

5.518*** (0.405)

Adj R2 0.17 0.265 0.267 0.197 0.27 0.27

*** significant at 1 percent ** significant at 5 percent * significant at 10 percent

32

The third and fourth models included in Table 3 include the variables coastalness

and shape factor along with latitude. Coastalness along with latitude is highly significant

and positively correlated with economic institutions. Shape factor is positively related to

institutions and it is significant at the 10 percent level, whereas both coastalness and

exitability are significant at the 1 percent level.

The fifth model in Table 3 includes both exitability and coastalness

simultaneously. Exitability and coastalness both report the expected positive signs but

both are insignificant. Exitability and coastalness are correlated and capture much of the

same theoretical explanatory power in terms of ease of exit and availability of substitutes.

So it is probably not surprising that they lose significance when included simultaneously.

A test of joint significance for exitability and coastalness confirms that they are jointly

significant at the 1 percent level. (A correlation matrix is in the appendix.)

Finally, exitability and shape factor are tested together. Exitability remains

positive and highly significant however, shape factor loses significance. Joint

significance was confirmed at the 1 percent level.

The findings in Table 3 support the often observed, if not adequately explained,

correlation between latitude and economic freedom, but they include an important new

finding that adds to the robustness of the geographic explanations of institutional quality:

that ease of exit is an important explanatory factor in the evolution of institutions.

Exitability is significant even though latitude is included. This is an important finding.

Latitude has been used as a sort of catch all for institutional quality as well as the impact

of geography on institutional quality in much of the empirical literature. Included as a

control variable latitude serves as a robustness check on the other variables studied. That

exitability is highly significant (above the 99 percent level) suggests that there are

important geographic factors at work influencing institutional quality that are not

accounted for in the standard ways in the literature. Latitude is something of a dubious

variable for explaining economic performance or institutional quality; although it is

highly significant the explanation for why it is so is elusive. These results have not taken

us any closer to understanding why latitude is important, but they do clarify that latitude

is not an all encompassing geographic instrument for institutional quality as it has

sometimes been used in the literature. The rationale behind the importance of exitability

33

for institutional quality is much more clear and consistent with basic economic processes

described by the Tiebout model.

Consistent with a Tiebout sorting model, a population’s ability to vote with their

feet likely leads to increased competition among governments to improve their

attractiveness to citizens, thus leading to more liberal economic institutions. Geographic

size and population size do not explain variations in institutional quality in a significant

way.

5.1 Robustness

Many economists have argued that natural endowments are important for the

determination of long-term economic well-being. If the so-called ‘resource curse’ leads to

the adoption of inefficient extractive institutions, natural endowments may be more

important for institutional quality than the geographic variables we have been discussing

(see Frankel (2010) for a survey of the resource curse). It is important to try to include

other commonly discussed variables as a robustness check of the explanatory variables

presented in Table 3.

Table 4 presents two tests of robustness for the geographic variables. In the first

model various natural hazards are included as geographically-related independent

variables. These include earthquakes, flooding, droughts, tsunamis, landslides,

hurricanes, avalanches, forest fires, cyclones, windstorms, monsoons, volcanoes,

permafrost, locusts and tornados. Natural conditions and hazards may impact how

institutions evolve to cope with social and economic challenges shaping economic

institutions in the long-run. Three of the hazard variables are significant at meaningful

level. Flooding is negatively related to economic institutions and significant at the 5

percent level while Hurricanes and Tsunamis are positive and significant. Taken as a

whole a test of joint significance reveals the group of hazard variables is significant at the

1 percent level. Importantly, after taking this into account and including latitude, the

regression results affirm the robustness of the variable exitability. Exitability remains

highly significant and positively related to institutional quality.

34

Table 4: Economic Freedom and Geography with Natural Hazards and Endowments

Dependent Variable: Economic Freedom

Independent Variables Model 1 Model 2 Earthquakes 0.0728 Flooding -0.318** Droughts 0.026 Tsunamis 0.448* Landslides -0.321 Hurricanes 0.407** Avalanches 0.560 Forest Fires 0.259 Cyclones 0.355 Wind Storms -0.105 Monsoon -0.460 Volcanoes 0.210 Permafrost -0.016 Locusts -0.016 Tornados -0.146 Fossil Fuels -0.114 Minerals -0.541*** Precious Metals 0.320 Precious Stones -0.054 Arable Land 0.026 Timber 0.146 Latitude 0.023*** 0.025*** Exitability 2.498*** 2.565*** Constant 5.950*** 6.175*** Adjusted R2 0.45 0.36

*** significant at 1 percent ** significant at 5 percent * significant at 10 percent

35

The second model in Table 4 includes natural endowments as the control

variables rather than natural hazards—these include fossil fuels, minerals, stones, arable

land and timber. These factors encompass the factors often included in discussions of the

resource curse theory. Here the results again support the importance of exitability, which

remains positive and highly significant. All but one of the endowment variables is

insignificant. Minerals is negative and highly significant suggesting some negative

impact of an abundance of mineral resources on institutional quality. A test of joint

significance confirms that the natural endowments are significant as a group. After taking

these two additional control variables into account as well as latitude, exitability remains

significantly related to the quality of economic institutions. This further supports the

hypothesis that ease of exit will positively influence institutional quality in the long-term

consistent with the model of Tiebout competition discussed above.

5.2 Geography and Subcomponents of Economic Freedom

The results discussed above suggest an important relationship between economic

freedom and several geographic characteristics, particularly those related to what might

be broadly described as exitability. The construction of the economic freedom dataset

allows us to investigate these relationships further by looking at the potential impact of

geography on various subcomponents of the economic freedom index that represent

different institutional or policy categories. These results will now be briefly discussed.

The five area subcomponents of the economic freedom index are size of

government, legal system and property rights, monetary policy, freedom to trade and

regulatory policy. Table 5 presents multiple regression results for the subcomponents.

Here latitude is included as the standard control variable and exitability is included as the

explanatory variable. Exitability and latitude have by far the most explanatory power in

the area of Property Rights (Area 2 of the EFW). Exitability and latitude explain almost

half the variation in the score of this area of the EFW (adjusted R-sqaure is 0.48) and

both are positively signed and highly significant The next highest level of explanation

comes in the area of Sound Money (Area 3 of the EFW). Here exitability and latitude

explain 27 percent of the variation in the score and both, again, are positive and highly

36

significant. Exitability is insignificant as an explanatory factor for Area 4, Freedom to

Trade Internationally, and significant at the 5 percent level in Area 5, Regulation.

One of the most noticeable results is that latitude is negative and highly

significant in Area 1, Size of Government (latitude is positive and highly significant in

the other areas). Exitability is positive and mildly significant for this area. One possible

explanation for the sign change on latitude in this area is that many northern European

countries have relatively large government expenditures relative to GDP particularly with

respect to social welfare programs but in many other areas retain economic institutions

and policies that result in high economic freedom scores such as strong property rights,

impartial courts, sound money and rule of law.

Relative to the component areas of the EFW, exitability seems to have the most

explanatory power in the area of property rights (Area 2), sound money (Area 3), and

regulation (Area 5). These are certainly areas where individuals would notice direct

impacts on their well-being if the scores were lowered. High levels of inflation and

property seizure along with burdensome regulatory requirements would be meaningful

and noticeable to most economic agents. Whereas restrictions on international trade, Area

4, (although often burdensome) might be less immediately realizable in day-to-day

contexts and thus may result is less pressure for exit given the often “hidden” nature of its

impact on economic well-being. Size of government also may not by itself be viewed as

something that would necessarily result in greater exit. If the government is supplying

services that a relatively homogeneous population values and it does so in a relatively

less burdensome fashion (transparent social welfare programs), government size might

not be automatically curtailed by exit.

The data presented in Table 5 support these hypotheses and help further refine our

understanding of how economic institutions might result from exit and Teibout sorting at

the national level. The more direct the impact a move toward greater intervention is on

individuals, and the more noticeable that impact is, the greater the efforts to exit or find

other means to avoid the increased cost associated with the change. Whereas, institutional

and policy changes that do not directly or as noticeably impact well-being to individuals

or which have hidden costs may be less likely to result in a search for alternative

institutional regimes.

37

Table 5: Geography and Subcomponents of Economic Freedom

Dependent Variables (Five Component Areas of EFW)

Independent Variables

Area 1: Size of Govt

Area2: Property

Area 3: Money

Area 4: Trade

Area 5: Regulation

Latitude -0.022*** 0.058*** 0.041*** 0.020*** 0.010** (2.533) (0.006) (0.005) (0.006) (0.005) Exitability 2.532* 4.317*** 2.697*** 1.979 3.093** (1.328) (1.020) (0.805) (1.588) (1.292) Constant 6.836*** 4.010*** 6.678*** 6.014*** 6.164*** (0.222) (0.202) (0.211) (0.220) (0.190) Adjusted R2 0.12 0.48 0.27 0.107 0.12

*** significant at 1 percent; ** significant at 5 percent; * significant at 10 percent

5.3 Fractionalization Next characteristics of a nation’s populace that may be important determinants of

institutional quality are considered to test the robustness of the findings on exitability and

institutional quality. Three measures of fractionalization from the literature will be

utilized us address this question (Alesina et al 2003). They are ethnic fractionalization,

religious fractionalization, and linguistic fractionalization. In these measures greater

homogeneity is consistent with lower fractionalization scores.

Table 6 presents the results for regressions where economic freedom is the

dependent variable and the three different measures of fractionalization are individually

included as exlanatory variables along with latitude and exitability. By itself (Model 1)

ethnic fractionalization is negative and highly significant (at the 1 percent level), which is

consistent with arguments that greater diversity will lead to lower levels of trust and

attempts to use policies and institutions to extract rents from opposing groups. When

latitude and exitability are included (Model 2) ethnic fractionalziation remains negative

and moderately significant at the 10 percent level.

38

Next religious fractionalization is included as an explanatory variable. The

regression results presented in Table 6 demonstrate no clear impact of religious

fractionalization on economic institutions by itself; religious fractionalization is positive

but insignificant. However, when exitability and latitude are included religious

fractionalization gains moderate significance, and interestly, is postiviely signed,

suggesting that some level of religious fractionalization is positively correlated with

institutional quality.

Finally linguistic fractionalization is included in the regression. Here the finding

is similar to ethnic fractionalization. Linguistic fractionalization is negative and highly

significant on its own and when latitude and exitability are included its significance

decreases, but remains somewhat significant.

In the case of both ethnic fractionalization and linguistic fractionalization as the

variables become less statistically significant the coefficients also decrease by around

half, suggesting their economic significance is significantly less than when tested without

exitability. The picture we see emerging from this dataset is that fractionalization may be

much less important than has previously been presented in the economics literature once

the ability to exit is accounted for. But most significantly for this paper it demonstrates

again that after taking another commonly discussed determinant of economic/institutional

performance into account (fractionalization) exitability remains a robust explanatory

variable for the quality of economic institutions.

39

Table 6: Fractionalization and Economic Freedom

Dependent Variable: Economic Freedom Independent Variables

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Ethnic Fractionalization

-1.545*** (0.303)

-0.712* (0.382)

Religious Fractionalization

0.173 (0.393)

0.582* (0.350)

Linguistic Fractionalization

-1.058*** (0.253)

-0.415* (0.252)

Exitability 2.431*** (0.922)

2.93*** (0.865)

2.739*** (0.967)

Latitude 0.016*** (0.005)

0.023*** (0.004)

0.019*** (0.004)

Constant 7.352*** (0.140)

6.441*** (0.300)

6.581*** (0.181)

5.647*** (0.234)

7.054*** (0.123)

6.151*** (0.214)

Adjusted R2 0.18 0.28 0.00 0.27 0.10 0.28

*** Significant at 1 percent ** Significant at 5 percent * Significant at 10 percent

6. DISCUSSION North (1981) argued that institutions are designed for the utility of the ruler or

ruling elite. The ability of rulers to extract rents from the institutional arrangement is

limited by their ability to maintain control over the population in their country. A key

finding of the empirical work presented here is that the ease of exit from a country is

crucial in determining the quality of economic institutions. New variables were

developed to measure and test this concept with robust results.

Three primary variables were constructed to measure the ease of exit from a

country. The Selkirk Circularity ratio from spatial mathematics was adjusted to yield a

measure called ‘shape factor’; shape factor gives some idea of how elongated or un-

compact a country is, and thus provides an idea of how close any point within the country

40

is to a border. ‘Exitability’ was created to measure the ratio of total borders to area, and

‘coastalness’ was created to measure the ratio of coastlines to total area. Utilizing these

variables the data is supportive of the notion that ease of exit is a key ingredient in

determining institutional quality. Countries which are shaped in such a way as to increase

the proximity of the populace to exit options demonstrate a clear tendency toward greater

institutional quality.

Exitability is a measure of how easy it is for citizens to search out new

institutional regimes and, in effect, vote with their feet for the regime that best suits their

interests. Given the overwhelming evidence for the positive relationship between liberal

economic institutions and higher income that has been demonstrated numerous times in

the literature (and reviewed above) it is no surprise that a type of Tiebout sorting leads to

people choosing less burdensome economic institutions. Thus, rulers of countries where

exit is easier have a stronger incentive to adopt such institutions to retain a taxable

population base. It has also been commonly observed that smaller countries tend to have

freer institutions. This idea is logically appealing for reasons similar to those just listed,

but the data suggest that size alone does not fully capture the true relationship, rather

shape or exitability is what is important.

A geographic variable that is already frequently cited in the literature as being

closely related to institutional quality is latitude. While this relationship is unquestionably

robust in the data the economic intuition for its significance is lacking. However, latitude

provides a useful control variable for other explanations of institutional quality, and when

used it confirms the robustness of exitability as an explanatory factor in multiple

specifications.

Somewhat surprisingly, ethnic fractionalization, or fractionalization measured by

other characteristics like religion and language, proved less important in explaining

institutional quality than is often thought. Once exitability is accounted for,

fractionalization seems to matter less; this suggests ease of exit may allow citizens to self

sort in a way to minimize the negative institutional effects of fractionalization. Again the

force of competitive pressures seems to be more important in determining institutional

quality.

41

That exitability remains robust after accounting for latitude, fractionalization and

other control variables is important. This suggests that exitability may be a major

explanatory factor in the quality of economic institutions. It may also prove useful as an

instrument for institutional quality in the empirical growth literature along with latitude.

And it should contribute to improved empirical investigations into economic performance

and institutional quality.

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APPENDIX

Table 7: Correlation Table EFW Area Latitude Exitability Coastalness Population Population

Density Shape Factor

EFW 1.000 Area 0.011 1.000 Latitude 0.376 0.111 1.000 Exitability 0.298 -0.164 0.055 1.000 Coastalness 0.283 -0.126 0.026 0.986 1.000 Population -0.050 0.453 0.008 -0.100 -0.079 1.000 Population Density

0.294 -0.093 -0.101 0.697 0.688 -0.013 1.000

Shape Factor

0.308 0.189 0.314 0.239 0.236 0.191 0.094 1.000