REVERSAL OF FORTUNE: GEOGRAPHY AND DARON …pages.ucsd.edu/~mnaoi/page4/POLI227/files/page1_8.pdftion and Marginalization” in Bergen, The Canadian Institute of Advanced Re-search,

REVERSAL OF FORTUNE: GEOGRAPHY ANDINSTITUTIONS IN THE MAKING OF THE MODERN

WORLD INCOME DISTRIBUTION*

DARON ACEMOGLU

SIMON JOHNSON

JAMES A. ROBINSON

Among countries colonized by European powers during the past 500 years,those that were relatively rich in 1500 are now relatively poor. We document thisreversal using data on urbanization patterns and population density, which, weargue, proxy for economic prosperity. This reversal weighs against a view thatlinks economic development to geographic factors. Instead, we argue that thereversal reflects changes in the institutions resulting from European colonialism.The European intervention appears to have created an “institutional reversal”among these societies, meaning that Europeans were more likely to introduceinstitutions encouraging investment in regions that were previously poor. Thisinstitutional reversal accounts for the reversal in relative incomes. We providefurther support for this view by documenting that the reversal in relative incomestook place during the late eighteenth and early nineteenth centuries, and resultedfrom societies with good institutions taking advantage of the opportunity toindustrialize.

I. INTRODUCTION

This paper documents a reversal in relative incomes amongthe former European colonies. For example, the Mughals in Indiaand the Aztecs and Incas in the Americas were among the richestcivilizations in 1500, while the civilizations in North America,New Zealand, and Australia were less developed. Today theUnited States, Canada, New Zealand, and Australia are an orderof magnitude richer than the countries now occupying the terri-tories of the Mughal, Aztec, and Inca Empires.

* We thank Joshua Angrist, Abhijit Banerjee, Olivier Blanchard, AlessandraCassella, Jan de Vries, Ronald Findlay, Jeffry Frieden, Edward Glaeser, HerschelGrossman, Lawrence Katz, Peter Lange, Jeffrey Sachs, Andrei Shleifer, FabrizioZilibotti, three anonymous referees, and seminar participants at the All-Univer-sities of California History Conference at Berkeley, the conference on “Globaliza-tion and Marginalization” in Bergen, The Canadian Institute of Advanced Re-search, Brown University, the University of Chicago, Columbia University, theUniversity of Houston, Indiana University, Massachusetts Institute of Technol-ogy, National Bureau of Economic Research summer institute, Stanford Univer-sity, the Wharton School of the University of Pennsylvania, and Yale Universityfor useful comments. Acemoglu gratefully acknowledges financial help from TheCanadian Institute for Advanced Research and the National Science FoundationGrant SES-0095253. Johnson thanks the Massachusetts Institute of TechnologyEntrepreneurship Center for support.

© 2002 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnology.The Quarterly Journal of Economics, November 2002

1231

Our main measure of economic prosperity in 1500 is urban-ization. Bairoch [1988, Ch. 1] and de Vries [1976, p. 164] arguethat only areas with high agricultural productivity and a devel-oped transportation network can support large urban popula-tions. In addition, we present evidence that both in the timeseries and the cross section there is a close association betweenurbanization and income per capita.1 As an additional proxy forprosperity we use population density, for which there are rela-tively more extensive data. Although the theoretical relationshipbetween population density and prosperity is more complex, itseems clear that during preindustrial periods only relativelyprosperous areas could support dense populations.

With either measure, there is a negative association betweeneconomic prosperity in 1500 and today. Figure I shows a negativerelationship between the percent of the population living in townswith more than 5000 inhabitants in 1500 and income per capitatoday. Figure II shows the same negative relationship betweenlog population density (number of inhabitants per square kilome-ter) in 1500 and income per capita today. The relationships shownin Figures I and II are robust—they are unchanged when wecontrol for continent dummies, the identity of the colonial power,religion, distance from the equator, temperature, humidity, re-sources, and whether the country is landlocked, and when weexclude the “neo-Europes” (the United States, Canada, New Zea-land, and Australia) from the sample.

This pattern is interesting, in part, because it provides anopportunity to distinguish between a number of competing theo-ries of the determinants of long-run development. One of the mostpopular theories, which we refer to as the “geography hypothe-sis,” explains most of the differences in economic prosperity bygeographic, climatic, or ecological differences across countries.The list of scholars who have emphasized the importance ofgeographic factors includes, inter alia, Machiavelli [1519], Mon-

1. By economic prosperity or income per capita in 1500, we do not refer to theeconomic or social conditions or the welfare of the masses, but to a measure oftotal production in the economy relative to the number of inhabitants. Althoughurbanization is likely to have been associated with relatively high output percapita, the majority of urban dwellers lived in poverty and died young because ofpoor sanitary conditions (see, for example, Bairoch [1988, Ch. 12]).

It is also important to note that the Reversal of Fortune refers to changes inrelative incomes across different areas, and does not imply that the initial in-habitants of, for example, New Zealand or North America themselves becamerelatively rich. In fact, much of the native population of these areas did notsurvive European colonialism.

1232 QUARTERLY JOURNAL OF ECONOMICS

tesquieu [1748], Toynbee [1934–1961], Marshall [1890], andMyrdal [1968], and more recently, Diamond [1997] and Sachs[2000, 2001]. The simplest version of the geography hypothesisemphasizes the time-invariant effects of geographic variables,such as climate and disease, on work effort and productivity, andtherefore predicts that nations and areas that were relatively richin 1500 should also be relatively prosperous today. The reversalin relative incomes weighs against this simple version of thegeography hypothesis.

More sophisticated versions of this hypothesis focus on thetime-varying effects of geography. Certain geographic character-istics that were not useful, or even harmful, for successful eco-nomic performance in 1500 may turn out to be beneficial later on.A possible example, which we call “the temperate drift hypothe-sis,” argues that areas in the tropics had an early advantage, butlater agricultural technologies, such as the heavy plow, croprotation systems, domesticated animals, and high-yield crops,have favored countries in the temperate areas (see Bloch [1966],Lewis [1978], and White [1962]; also see Sachs [2001]). Althoughplausible, the temperate drift hypothesis cannot account for the

FIGURE ILog GDP per Capita (PPP) in 1995 against Urbanization Rate in 1500

Note. GDP per capita is from the World Bank [1999]; urbanization in 1500 ispeople living in towns with more than 5000 inhabitants divided by total popu-lation, from Bairoch [1988] and Eggimann [1999]. Details are in Appendices 1and 2.

1233REVERSAL OF FORTUNE

reversal. First, the reversal in relative incomes seems to be re-lated to population density and prosperity before Europeans ar-rived, not to any inherent geographic characteristics of the area.Furthermore, according to the temperate drift hypothesis, thereversal should have occurred when European agricultural tech-nology spread to the colonies. Yet, while the introduction of Eu-ropean agricultural techniques, at least in North America, tookplace earlier, the reversal occurred during the late eighteenth andearly nineteenth centuries, and is closely related to industrializa-tion. Another version of the sophisticated geography hypothesiscould be that certain geographic characteristics, such as the pres-ence of coal reserves or easy access to the sea, facilitated indus-trialization (e.g., Pomeranz [2000] and Wrigley [1988]). But we donot find any evidence that these geographic factors caused indus-trialization. Our reading of the evidence therefore provides littlesupport to various sophisticated geography hypotheses either.

An alternative view, which we believe provides the best ex-planation for the patterns we document, is the “institutions hy-pothesis,” relating differences in economic performance to theorganization of society. Societies that provide incentives and op-portunities for investment will be richer than those that fail to doso (e.g., North and Thomas [1973], North and Weingast [1989],

FIGURE IILog GDP per Capita (PPP) against Log Population Density in 1500

Note. GDP per capita from the World Bank [1999]; log population density in1500 from McEvedy and Jones [1978]. Details are in Appendix 2.


and Olson [2000]). As we discuss in more detail below, we hy-pothesize that a cluster of institutions ensuring secure propertyrights for a broad cross section of society, which we refer to asinstitutions of private property, are essential for investment in-centives and successful economic performance. In contrast, ex-tractive institutions, which concentrate power in the hands of asmall elite and create a high risk of expropriation for the majorityof the population, are likely to discourage investment and eco-nomic development. Extractive institutions, despite their adverseeffects on aggregate performance, may emerge as equilibriuminstitutions because they increase the rents captured by thegroups that hold political power.

How does the institutions hypothesis explain the reversal inrelative incomes among the former colonies? The basic idea isthat the expansion of European overseas empires starting at theend of the fifteenth century caused major changes in the organi-zation of many of these societies. In fact, historical and econo-metric evidence suggests that European colonialism caused an“institutional reversal”: European colonialism led to the develop-ment of institutions of private property in previously poor areas,while introducing extractive institutions or maintaining existingextractive institutions in previously prosperous places.2 Themain reason for the institutional reversal is that relatively poorregions were sparsely populated, and this enabled or inducedEuropeans to settle in large numbers and develop institutionsencouraging investment. In contrast, a large population and rela-tive prosperity made extractive institutions more profitable forthe colonizers; for example, the native population could be forcedto work in mines and plantations, or taxed by taking over existingtax and tribute systems. The expansion of European overseasempires, combined with the institutional reversal, is consistentwith the reversal in relative incomes since 1500.

Is the reversal related to institutions? We document that thereversal in relative incomes from 1500 to today can be explained,

2. By the term “institutional reversal,” we do not imply that it was societieswith good institutions that ended up with extractive institutions after Europeancolonialism. First, there is no presumption that relatively prosperous societies in1500 had anything resembling institutions of private property. In fact, theirrelative prosperity most likely reflected other factors, and even perhaps geo-graphic factors. Second, the institutional reversal may have resulted more fromthe emergence of institutions of private property in previously poor areas thanfrom a deterioration in the institutions of previously rich areas.


at least statistically, by differences in institutions across coun-tries. The institutions hypothesis also suggests that institutionaldifferences should matter more when new technologies that re-quire investments from a broad cross section of the society be-come available. We therefore expect societies with good institu-tions to take advantage of the opportunity to industrialize, whilesocieties with extractive institutions fail to do so. The data sup-port this prediction.

We are unaware of any other work that has noticed or docu-mented this change in the distribution of economic prosperity.Nevertheless, many historians emphasize that in 1500 the Mu-ghal, Ottoman, and Chinese Empires were highly prosperous, butgrew slowly during the next 500 years (see the discussion andreferences in Section III).

Our overall interpretation of comparative development in theformer colonies is closely related to Coatsworth [1993] and En-german and Sokoloff [1997, 2000], who emphasize the adverseeffects of the plantation complex in the Caribbean and CentralAmerica working through political and economic inequality,3 andto our previous paper, Acemoglu, Johnson, and Robinson [2001a].In that paper we proposed the disease environment at the timeEuropeans arrived as an instrument for European settlementsand the subsequent institutional development of the former col-onies, and used this to estimate the causal effect of institutionaldifferences on economic performance. Our thesis in the currentpaper is related, but emphasizes the influence of population den-sity and prosperity on the policies pursued by the Europeans (seealso Engerman and Sokoloff [1997]). In addition, here we docu-ment the reversal in relative incomes among the former colonies,show that it was related to industrialization, and provide evi-dence that the interaction between institutions and the opportu-nity to industrialize during the nineteenth century played a cen-tral role in the long-run development of the former colonies.4

3. In this context, see also Frank [1978], Rodney [1972], Wallerstein [1974–1980], and Williams [1944].

4. Our results are also relevant to the literature on the relationship betweenpopulation and growth. The recent consensus is that population density encour-ages the discovery and exchange of ideas, and contributes to growth (e.g., Boserup[1965], Jones [1997], Kremer [1993], Kuznets [1968], Romer [1986], and Simon[1977]). Our evidence points to a major historical episode of 500 years where highpopulation density was detrimental to economic development, and therefore shedsdoubt on the general applicability of this recent consensus.


The rest of the paper is organized as follows. The next sectiondiscusses the construction of urbanization and population densitydata, and provides evidence that these are good proxies for eco-nomic prosperity. Section III documents the “Reversal of For-tune”—the negative relationship between economic prosperity in1500 and income per capita today among the former colonies.Section IV discusses why the simple and sophisticated geographyhypotheses cannot explain this pattern, and how the institutionshypothesis explains the reversal. Section V documents that thereversal in relative incomes reflects the institutional reversalcaused by European colonialism, and that institutions startedplaying a more important role during the age of industry. SectionVI concludes.

II. URBANIZATION AND POPULATION DENSITY

II.A. Data on Urbanization

Bairoch [1988] provides the best single collection and assess-ment of urbanization estimates. Our base data for 1500 consist ofBairoch’s [1988] urbanization estimates augmented by the workof Eggimann [1999]. Merging the Eggimann and Bairoch seriesrequires us to convert Eggimann’s estimates, which are based ona minimum population threshold of 20,000, into Bairoch-equiva-lent urbanization estimates, which use a minimum populationthreshold of 5000. We use a number of different methods toconvert between the two sets of estimates, all with similar re-sults. Appendix 1 provides details about data sources and con-struction. Briefly, for our base estimates, we run a regression ofBairoch estimates on Eggimann estimates for all countries wherethey overlap in 1900 (the year for which we have most Bairochestimates for non-European countries). This regression yields aconstant of 6.6 and a coefficient of 0.67, which we use to generateBairoch-equivalent urbanization estimates from Eggimann’sestimates.

Alternatively, we converted the Eggimann’s numbers using auniform conversion rate of 2 as suggested by Davis’ and Zipf ’sLaws (see Appendix 1 and Bairoch [1988, Ch. 9]), and also testedthe robustness of the estimates using conversion ratios at theregional level based on Bairoch’s analysis. Finally, we con-structed three alternative series without combining estimatesfrom different sources. One of these is based on Bairoch, the


second on Eggimann, and the third on Chandler [1987]. All fouralternative series are reported in Appendix 3, and results usingthese measures are reported in Table IV.

While the data on sub-Saharan Africa are worse than for anyother region, it is clear that urbanization in sub-Saharan Africabefore 1500 was at a higher level than in North America orAustralia. Bairoch, for example, argues that by 1500 urbaniza-tion was “well-established” in sub-Saharan Africa.5 Becausethere are no detailed urbanization data for sub-Saharan Africa,we leave this region out of the regression analysis when we useurbanization data, although African countries are included in ourregressions using population density.

Table I gives descriptive statistics for the key variables ofinterest, separately for the whole world, for the sample of ex-colonies for which we have urbanization data in 1500, and for thesample of ex-colonies for which we have population density datain 1500. Appendix 2 gives detailed definitions and sources for thevariables used in this study.

II.B. Urbanization and Income

There are good reasons to presume that urbanization andincome are positively related. Kuznets [1968, p. 1] opens his bookon economic growth by stating: “we identify the economic growthof nations as a sustained increase in per-capita or per-workerproduct, most often accompanied by an increase in populationand usually by sweeping structural changes. . . . in the distribu-tion of population between the countryside and the cities, theprocess of urbanization.”

Bairoch [1988] points out that during preindustrial periods alarge fraction of the agricultural surplus was likely to be spent ontransportation, so both a relatively high agricultural surplus anda developed transport system were necessary for large urbanpopulations (see Bairoch [1988, Ch. 1]). He argues “the existenceof true urban centers presupposes not only a surplus of agricul-

5. Sahelian trading cities such as Timbuktu, Gao, and Djenne (all in modernMali) were very large in the middle ages with populations as high as 80,000. Kano(in modern Nigeria) had a population of 30,000 in the early nineteenth century,and Yorubaland (also in Nigeria) was highly urbanized with a dozen towns withpopulations of over 20,000 while its capital Ibadan possibly had 70,000 inhabit-ants. For these numbers and more detail, see Hopkins [1973, Ch. 2].


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tural produce, but also the possibility of using this surplus intrade” [p. 11].6 See de Vries [1976, p. 164] for a similar argument.

We supplement this argument by empirically investigatingthe link between urbanization and income in Table II. Columns(1)–(6) present cross-sectional regressions. Column (1) is for 1900,the earliest date for which we have data on urbanization andincome per capita for a large number of countries. The regressioncoefficient, 0.038, is highly significant, with a standard error of0.006. It implies that a country with 10 percentage points higherurbanization has, on average, 46 percent (38 log points) greaterincome per capita (throughout the paper, all urbanization ratesare expressed in percentage points, e.g., 10 rather than 0.1—seeTable I). Column (2) reports a similar result using data for 1950.Column (3) uses current data and shows that even today there isa strong relationship between income per capita and urbanizationfor a large sample of countries. The coefficient is similar, 0.036,and precisely estimated, with a standard error of 0.002. Thisrelationship is shown diagrammatically in Figure III.

Below, we draw a distinction between countries colonized byEuropeans and those never colonized (i.e., Europe and non-Euro-pean countries not colonized by Western Europe). Columns (4) and(5) report the same regression separately for these two samples. Theestimates are very similar: 0.037 for the former colonies sample, and0.033 for the rest of the countries. Finally, in column (6) we addcontinent dummies to the same regression. This leads to only aslightly smaller coefficient of 0.030, with a standard error of 0.002.

Finally, we use estimates from Bairoch [1978, 1988] to con-struct a small unbalanced panel data set of urbanization andincome per capita from 1750 to 1913. Column (7) reports a re-

6. The view that urbanization and income (productivity) are closely related isshared by many other scholars. See Ades and Glaeser [1999], De Long andShleifer [1993], Tilly and Blockmans [1994], and Tilly [1990]. De Long andShleifer, for example, write “The larger preindustrial cities were nodes of infor-mation, industry, and exchange in areas where the growth of agricultural pro-ductivity and economic specialization had advanced far enough to support them.They could not exist without a productive countryside and a flourishing tradenetwork. The population of Europe’s preindustrial cities is a rough indicator ofeconomic prosperity” [p. 675].

A large history literature also documents how urbanization accelerated inEurope during periods of economic expansion (e.g., Duby [1974], Pirenne [1956],and Postan and Rich [1966]). For example, the period between the beginning ofthe eleventh and mid-fourteenth centuries is an era of rapid increase in agricul-tural productivity and industrial output. The same period also witnessed a pro-liferation of cities. Bairoch [1988], for example, estimates that the number of citieswith more than 20,000 inhabitants increased from around 43 in 1000 to 107 in1500 [Table 10.2, p. 159].


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gression of income per capita on urbanization using this paneldata set and controlling for country and period dummies. Theestimate is again similar: 0.026 (s.e. � 0.004). Overall, we con-clude that urbanization is a good proxy for income.

II.C. Population Density and Income

The most comprehensive data on population since 1 A.D.come from McEvedy and Jones [1978]. They provide estimatesbased on censuses and published secondary sources. Whilesome individual country numbers have since been revised andothers remain contentious (particularly for pre-Columbian Meso-America), their estimates are consistent with more recent re-search (see, for example, the recent assessment by the Bureauof the Census, www.census.gov/ipc/www/worldhis.html). We useMcEvedy and Jones [1978] for our baseline estimates, and testthe effect of using alternative assumptions (e.g., lower or higherpopulation estimates for Mexico and its neighbors before thearrival of Cortes).

FIGURE IIILog GDP per Capita (PPP) in 1995 against the Urbanization Rate in 1995

Note. GDP per capita and urbanization are from the World Bank [1999]. Ur-banization is percent of population living in urban areas. The definition of urbanareas differs between countries, but the usual minimum size is 2000–5000 inhabi-tants. For details of definitions and sources for urban population in 1995, see theUnited Nations [1998].


We calculate population density by dividing total populationby arable land (also estimated by McEvedy and Jones). Thisexcludes primarily desert, inland water, and tundra. As much aspossible, we use the land area of a country at the date we areconsidering.

The theoretical relationship between population density andincome is more nuanced than that between urbanization andincome. With a similar reasoning, it seems natural to think thatonly relatively rich areas could afford dense populations (seeBairoch [1988, Ch. 1]). This is also in line with Malthus’ classicwork. Malthus [1798] argued that high productivity increasespopulation by raising birthrates and lowering death rates. How-ever, the main thrust of Malthus’ work was how a higher thanequilibrium level of population increases death rates and reducesbirthrates to correct itself.7 A high population could therefore bereflecting an “excess” of population, causing low income per cap-ita. So caution is required in interpreting population density as aproxy for income per capita.

The empirical evidence regarding the relationship betweenpopulation density and income is also less clear-cut than therelationship between urbanization and income. In Acemoglu,Johnson, and Robinson [2001b] we documented that populationdensity and income per capita increased concurrently in manyinstances. Nevertheless, there is no similar cross-sectional rela-tionship in recent data, most likely because of the demographictransition—it is no longer true that high population density isassociated with high income per capita because the relationshipbetween income and the number of children has changed (e.g.,Notestein [1945] or Livi-Bacci [2001]).

Despite these reservations, we present results using popula-tion density, as well as urbanization, as a proxy for income percapita. This is motivated by three considerations. First, popula-tion density data are more extensive, so the use of populationdensity data is a useful check on our results using urbanizationdata. Second, as argued by Bairoch, population density is closely

7. A common interpretation of Malthus’ argument is that these populationdynamics will force all countries down to the subsistence level of income. In thatcase, population density would be a measure of total income, but not necessarilyof income per capita, and in fact, there would be no systematic (long-run) differ-ences in income per capita across countries. We view this interpretation asextreme, and existing historical evidence suggests that there were systematicdifferences in income per capita between different regions even before the modernperiod (see the references below).


related to urbanization, and in fact, our measures are highlycorrelated. Third, variation in population density will play animportant role not only in documenting the reversal, but also inexplaining it.

III. THE REVERSAL OF FORTUNE

III.A. Results with Urbanization

This section presents our main results. Figure I in the intro-duction depicts the relationship between urbanization 1500 andincome per capita today. Table III reports regressions document-ing the same relationship. Column (1) is our most parsimoniousspecification, regressing log income per capita in 1995 (PPP basis)on urbanization rates in 1500 for our sample of former colonies.The coefficient is �0.078 with a standard error of 0.026.8 Thiscoefficient implies that a 10 percentage point lower urbanizationin 1500 is associated with approximately twice as high GDP percapita today (78 log points � 108 percent). It is important to notethat this is not simply mean reversion—i.e., richer than averagecountries reverting back to the mean. It is a reversal. To illustratethis, let us compare Uruguay and Guatemala. The native popu-lation in Uruguay had no urbanization, while, according to ourbaseline estimates Guatemala had an urbanization rate of 9.2percent. The estimate in column (1) of Table II, 0.038, for therelationship between income and urbanization implies that Gua-temala at the time was approximately 42 percent richer thanUruguay (exp (0.038 � 9.2) � 1 � 0.42). According to our estimatein column (1) of Table III, we expect Uruguay today to be 105percent richer than Guatemala (exp (0.078 � 9.2) � 1 � 1.05),which is approximately the current difference in income per cap-ita between these two countries.9

The second column of Table III excludes North African coun-tries for which data quality may be lower. The result is un-

8. Because China was never a formal colony, we do not include it in oursample of ex-colonies. Adding China does not affect our results. For example, withChina, the baseline estimate changes from �0.078 (s.e. � 0.026) to �0.079 (s.e. �0.025). Furthermore, our sample excludes countries that were colonized by Euro-pean powers briefly during the twentieth century, such as Iran, Saudi Arabia, andSyria. If we include these observations, the results are essentially unchanged. Forexample, the baseline estimate changes to �0.072 (s.e. � 0.024).

9. Interestingly, these calculations suggest that not only have relative rank-ings reversed since 1500, but income differences are now much larger than in1500.


changed, with a coefficient of �0.101 and standard error of 0.032.Column (3) drops the Americas, which increases both the coeffi-cient and the standard error, but the estimate remains highlysignificant. Column (4) reports the results just for the Americas,where the relationship is somewhat weaker but still significant atthe 8 percent level. Column (5) adds continent dummies to checkwhether the relationship is being driven by differences acrosscontinents. Although continent dummies are jointly significant,the coefficient on urbanization in 1500 is unaffected—it is �0.083with a standard error of 0.030.

One might also be concerned that the relationship is beingdriven mainly by the neo-Europes: United States, Canada, NewZealand, and Australia. These countries are settler colonies builton lands that were inhabited by relatively undeveloped civiliza-tions. Although the contrast between the development experi-ences of these areas and the relatively advanced civilizations ofIndia or Central America is of central importance to the reversal andto our story, one would like to know whether there is anything morethan this contrast in the results of Table III. In column (6) we dropthese observations. The relationship is now weaker, but still nega-tive and statistically significant at the 7 percent level.

In column (7) we control for distance from the equator (theabsolute value of latitude), which does not affect the pattern ofthe reversal—the coefficient on urbanization in 1500 is now�0.072 instead of �0.078 in our baseline specification. Distancefrom the equator is itself insignificant. Column (8), in turn, con-trols for a variety of geography variables that represent the effectof climate, such as measures of temperature, humidity, and soiltype, with little effect on the relationship between urbanization in1500 and income per capita today. The R2 of the regressionincreases substantially, but this simply reflects the addition ofsixteen new variables to this regression (the adjusted R2 in-creases only slightly, to 0.27).

In column (9) we control for a variety of “resources” whichmay have been important for post-1500 development. These in-clude dummies for being an island, for being landlocked, and forhaving coal reserves and a variety of other natural resources (seeAppendix 2 for detailed definitions and sources). Access to the seamay have become more important with the rise of trade, andavailability of coal or other natural resources may have differenteffects at different points in time. Once again, the addition ofthese variables has no effect on the pattern of the reversal.


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Finally, in columns (10) and (11) we add the identity of thecolonial power and religion, which also have little effect on ourestimate, and are themselves insignificant.

The urbanization variable used in Table III relies on work byBairoch and Eggimann. In Table IV we use data from Bairoch andEggimann separately, as well as data from Chandler, who pro-vided the starting point for Bairoch’s data. We report a subset ofthe regressions from Table III using these three different seriesand an alternative series using the Davis-Zipf adjustment toconvert Eggimann’s estimates into Bairoch-equivalent numbers(explained in Appendix 1). The results are very similar to thebaseline estimates reported in Table III: in all cases, there is anegative relationship between urbanization in 1500 and incomeper capita today, and in almost all cases, this relationship isstatistically significant at the 5 percent level (the full set ofresults are reported in Acemoglu, Johnson, and Robinson [2001b]).

III.B. Results with Population Density

In Panel A of Table V we regress income per capita today onlog population density in 1500, and also include data for sub-Saharan Africa. The results are similar to those in Table IV (alsosee Figure II). In all specifications we find that countries withhigher population density in 1500 are substantially poorer today.The coefficient of �0.38 in column (1) implies that a 10 percenthigher population density in 1500 is associated with a 4 percentlower income per capita today. For example, the area now corre-sponding to Bolivia was seven times more densely settled thanthe area corresponding to Argentina; so on the basis of thisregression, we expect Argentina to be three times as rich asBolivia, which is more or less the current gap in income betweenthese countries.10

The remaining columns perform robustness checks, andshow that including a variety of controls for geography and re-sources, the identity of the colonial power, religion variables, ordropping the Americas, the neo-Europes, or North Africa has very

10. The magnitudes implied by the estimates in this table are similar to thoseimplied by the estimates in Table III. For example, the difference in the urban-ization rate between an average high and low urbanization country in 1500 is 8.1(see columns (4) and (5) in Table I), which using the coefficient of �0.078 fromTable III translates into a 0.078 � 8.1 � 0.63 log points difference in current GDP.The difference in log population density between an average high-density andlow-density country in 1500 is 2.2 (see columns (6) and (7) in Table I), whichtranslates into a 0.38 � 2.2 � 0.84 log points difference in current GDP.


little effect on the results. In all cases, log population density in1500 is significant at the 1 percent level (although now some ofthe controls, such as the humidity dummies, are also significant).

TABLE IVALTERNATIVE MEASURES OF URBANIZATION

Dependent variable is log GDP per capita (PPP) in 1995

Basesample

(1)

With continentdummies

(2)

Withoutneo-Europes

(3)

Controllingfor latitude

(4)

Controllingfor resources

(5)

Panel A: Using our base sample measure of urbanization

Urbanization in 1500 �0.078 �0.083 �0.046 �0.072 �0.058(0.026) (0.030) (0.026) (0.025) (0.029)

R2 0.19 0.32 0.09 0.24 0.45Number of observations 41 41 37 41 41

Panel B: Using only Bairoch’s estimates

Urbanization in 1500 �0.126 �0.107 �0.089 �0.116 �0.092(0.032) (0.034) (0.033) (0.036) (0.037)


Panel C: Using only Eggimann’s estimates

Urbanization in 1500 �0.041 �0.043 �0.022 �0.036 �0.022(0.019) (0.019) (0.018) (0.019) (0.023)


Panel D: Using only Chandler’s estimates

Urbanization in 1500 �0.057 �0.072 �0.040 �0.054 �0.049(0.019) (0.021) (0.019) (0.019) (0.025)


Panel E: Using Davis-Zipf Adjustment for Eggimann’s series

Urbanization in 1500 �0.039 �0.048 �0.024 �0.040 �0.031(0.015) (0.020) (0.014) (0.015) (0.017)


Standard errors are in parentheses. Dependent variable is log GDP per capita (PPP) in 1995. Basesample is all former colonies for which we have data. Urbanization in 1500 is percent of the population livingin towns with 5000 or more people. In Panels B, C, D, and E, we use, respectively, Bairoch’s estimates,Eggimann’s estimates, Chandler’s estimates, and a conversion of Eggimann’s estimates into Bairoch-equiva-lent numbers using the Davis-Zipf adjustment. Eggimann’s estimates (Panel C) and Chandler’s estimates(Panel D) are not converted to Bairoch-equivalent units. The continent dummies, neo-Europes, and resourcesmeasures are as described in the note to Table III. For detailed sources and descriptions see Appendix 2. Thealternative urbanization series are shown in Appendix 3.


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ende

nt

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able

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ta(P

PP

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ase

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ple

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mer

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nie

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rw

hic

hw

eh

ave

data

.Pop

ula

tion

den

sity

in15

00is

tota

lpop

ula

tion

divi

ded

byar

able

lan

dar

ea.S

eeT

able

III

for

anex

plan

atio

nof

the

sam

ple

and

cova

riat

esin

each

colu

mn

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orde

tail

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urc

esan

dde

scri

ptio

ns

see

App

endi

x2.


The estimates in the top panel of Table V use variation inpopulation density, which reflects two components: differences inpopulation and differences in arable land area. In Panel B weseparate the effects of these two components and find that theycome in with equal and opposite signs, showing that the specifi-cation with population density is appropriate. In Panel C we usepopulation density in 1000 as an instrument for population den-sity in 1500. This is useful since, as discussed in subsection II.C,differences in long-run population density are likely to be betterproxies for income per capita. Instrumenting for population den-sity in 1500 with population density in 1000 isolates the long-runcomponent of population density differences across countries (i.e.,the component of population density in 1500 that is correlatedwith population density in 1000). The Two-Stage Least Squares(2SLS) results in Panel C using this instrumental variables strat-egy are very similar to the OLS results in Panel A.

III.C. Further Results, Robustness Checks, and Discussion

Caution is required in interpreting the results presented inTables III, IV, and V. Estimates of urbanization and population in1500 are likely to be error-ridden. Nevertheless, the first effect ofmeasurement error would be to create an attenuation bias toward0. Therefore, one might think that the negative coefficients inTables III, IV, and V are, if anything, underestimates. A moreserious problem would be if errors in the urbanization and popu-lation density estimates were not random, but correlated withcurrent income in some systematic way. We investigate this issuefurther in Table VI, using a variety of different estimates forurbanization and population density. Columns (1)–(5), for exam-ple, show that the results are robust to a variety of modificationsto the urbanization data.

Much of the variation in urbanization and population densityin 1500 was not at the level of these countries, but at the level of“civilizations.” For example, in 1500 there were fewer separatecivilizations in the Americas, and even arguably in Asia, thanthere are countries today. For this reason, in column (6) we repeatour key regressions using variation in urbanization and popula-tion density only among fourteen civilizations (based on Toynbee[1934–1961] and McNeill [1999]—see the note to Table VI). Theresults confirm our basic findings, and show a statistically signifi-cant negative relationship between prosperity in 1500 and today.Columns (7) and (8) report robustness checks using variants of


the population density data constructed under different assump-tions, again with very similar results.

Is there a similar reversal among the noncolonies? Column(9) reports a regression of log GDP per capita in 1995 on urban-ization in 1500 for all noncolonies (including Europe), and column(10) reports the same regression for Europe (including EasternEurope). In both cases, there is a positive relationship betweenurbanization in 1500 and income today.11 This suggests that thereversal reflects an unusual event, and is likely to be related tothe effect of European colonialism on these societies.

Panel B of Table VI reports results weighted by population in1500, with very similar results. In Panel C we include urbaniza-tion and population density simultaneously in these regressions.In all cases, population density is negative and highly significant,while urbanization is insignificant. This is consistent with thenotion, discussed below, that differences in population densityplayed a key role in the reversal in relative incomes among thecolonies (although it may also reflect measurement error in theurbanization estimates).

As a final strategy to deal with the measurement error inurbanization, we use log population density as an instrument forurbanization rates in 1500. When both of these are valid proxiesfor economic prosperity in 1500 and the measurement error isclassical, this procedure corrects for the measurement error prob-lem. Not surprisingly, these instrumental-variables estimatesreported in the bottom panel of Table VI are considerably largerthan the OLS estimates in Table III. For example, the baselineestimate is now �0.18 instead of �0.08 in Table III. The generalpattern of reversal in relative incomes is unchanged, however.

Is the reversal shown in Figures I and II and Tables III, IV,and V consistent with other evidence? The literature on thehistory of civilizations documents that 500 years ago many partsof Asia were highly prosperous (perhaps as prosperous as West-ern Europe), and civilizations in Meso-America and North Africawere relatively developed (see, e.g., Abu-Lughod [1989], Braudel[1992], Chaudhuri [1990], Hodgson [1993], McNeill [1999], Po-meranz [2000], Reid [1988, 1993], and Townsend [2000]). In con-

11. In Acemoglu, Johnson, and Robinson [2001b] we also provided evidencethat urbanization and population density in 1000 are positively correlated withurbanization and population density in 1500, suggesting that before 1500 therewas considerable persistence in prosperity both where the Europeans later colo-nized and where they never colonized.


TA

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trast, there was little agriculture in most of North America andAustralia, at most consistent with a population density of 0.1people per square kilometer. McEvedy and Jones [1978, p. 322]describe the state of Australia at this time as “an unchangingpalaeolithic backwater.” In fact, because of the relative backward-ness of these areas, European powers did not view them asvaluable colonies. Voltaire is often quoted as referring to Canadaas a “few acres of snow,” and the European powers at the timepaid little attention to Canada relative to the colonies in the WestIndies. In a few parts of North America, along the East Coast andin the Southwest, there was settled agriculture, supporting apopulation density of approximately 0.4 people per square kilo-meter, but this was certainly much less than that in the Aztec andInca Empires, which had fully developed agriculture with a popu-lation density of between 1 and 3 people (or even higher) persquare kilometer, and also much less than the correspondingnumbers in Asia and Africa [McEvedy and Jones 1978, p. 273].The recent work by Maddison [2001] also confirms our interpre-tation. He estimates that India, Indonesia, Brazil, and Mexicowere richer than the United States in 1500 and 1700 (see, forexample, his Table 2-22a).

III.D. The Timing and Nature of the Reversal

The evidence presented so far documents the reversal inrelative incomes among the former colonies from 1500 to today.When did this reversal take place? This question is relevant inthinking about the causes of the reversal. For example, if thereversal is related to the extraction of resources from, and the“plunder” of, the former colonies, or to the direct effect of thediseases Europeans brought to the New World, it should havetaken place shortly after colonization.

Figure IV shows that the reversal is mostly a late eighteenth-and early nineteenth-century phenomenon, and is closely relatedto industrialization. Figure IVa compares the evolution of urban-ization among two groups of New World ex-colonies, those withlow urbanization in 1500 versus those with high urbanization in1500.12 We focus on New World colonies since the societies came

12. The initially high urbanization countries for which we have data and areincluded in the figure are Bolivia, Mexico, Peru, and all of Central America, whilethe initially low urbanization countries are Argentina, Brazil, Canada, Chile, andthe United States.


under European dominance very early on. The averages plottedin the figure are weighted by population in 1500. In addition, inthe same figure we plot India and the United States separately(as well as including it in the initially low urbanization group).The figure shows that the initially low urbanization group as awhole and the United States by itself overtake India and the ini-tially high urbanization countries sometime between 1750 and 1850.

Figure IVb depicts per capita industrial production for theUnited States, Canada, New Zealand, Australia, Brazil, Mexico,and India using data from Bairoch [1982]. This figure shows thetakeoff in industrial production in the United States, Australia,Canada, and New Zealand relative to Brazil, Mexico, and India.Although the scale makes it difficult to see in the figure, percapita industrial production in 1750 was in fact higher in India, 7,than in the United States, 4 (with U. K. industrial production percapita in 1900 normalized to 100). Bairoch [1982] also reportsthat in 1750 China had industrial production per capita twice the

FIGURE IVaUrbanization Rate in India, the United States, and New World Countries

with Low and High Urbanization, 800–1920Note. Urbanization is population living in urban areas divided by total popula-

tion. Urban areas have a minimum threshold of 20,000 inhabitants, from Chan-dler [1987], and Mitchell [1993, 1995]. Low urbanization in 1500 countries areArgentina, Brazil, Canada, Chile, and the United States. High urbanization in1500 countries are Bolivia, Ecuador, Mexico, Peru, and all of Central America. Fordetails see Appendix 1.


level of the United States. Yet, as Figure IVb shows, over the next200 years there was a much larger increase in industrial produc-tion in the United States than in India (and also than in China).

This general interpretation, that the reversal in relative in-comes took place during the late eighteenth and early nineteenthcenturies and was linked to industrialization, is also consistent withthe fragmentary evidence we have on other measures of income percapita and industrialization. Coatsworth [1993], Eltis [1995], Enger-man [1981], and Engerman and Sokoloff [1997] provide evidencethat much of Spanish America and the Caribbean were more pros-perous (had higher per capita income) than British North Americauntil the eighteenth century. The future United States rose in percapita income during the 1700s relative to the Caribbean and SouthAmerica, but only really pulled ahead during the late eighteenthand early nineteenth centuries. Maddison’s [2001] numbers alsoshow that India, Indonesia, Brazil, and Mexico were richer than theUnited States in 1700, but had fallen behind by 1820.

U. S. growth during this period also appears to be an indus-try-based phenomenon. McCusker and Menard [1985] and Galen-son [1996] both emphasize that productivity and income growthin North America before the eighteenth century was limited.During the critical period of growth in the United States, between

FIGURE IVbIndustrial Production per Capita, 1750–1953

Note. Index of industrial production with U. K. per capita industrialization in1900 is equal to 100, from Bairoch [1982].


1840 and 1900, there was modest growth in agricultural outputper capita, and very rapid growth in industrial output per capita;the numbers reported by Gallman [2000] imply that between1840 and 1900 agricultural product per capita increased by about30 percent, a very small increase relative to the growth in manu-facturing output per capita, which increased more than fourfold.

IV. HYPOTHESES AND EXPLANATIONS

IV.A. The Geography Hypothesis

The geography hypothesis claims that differences in eco-nomic performance reflect differences in geographic, climatic, andecological characteristics across countries. There are many differ-ent versions of this hypothesis. Perhaps the most common is theview that climate has a direct effect on income through its influ-ence on work effort. This idea dates back to Machiavelli [1519]and Montesquieu [1748]. Both Toynbee [1934, Vol. 1] and Mar-shall [1890, p. 195] similarly emphasized the importance of cli-mate, both on work effort and productivity. One of the pioneers ofdevelopment economics, Myrdal [1968], also placed considerableemphasis on the effect of geography on agricultural productivity.He argued: “serious study of the problems of underdevelop-ment . . . should take into account the climate and its impacts onsoil, vegetation, animals, humans and physical assets—in short,on living conditions in economic development” [Vol. 3, p. 2121].

More recently, Diamond [1997] and Sachs [2000, 2001] haveespoused different versions of the geography view. Diamond, forexample, argues that the timing of the Neolithic revolution hashad a long-lasting effect on economic and social development.Sachs, on the other hand, emphasizes the importance of geogra-phy through its effect on the disease environment, transportcosts, and technology. He writes: “Certain parts of the world aregeographically favored. Geographical advantages might includeaccess to key natural resources, access to the coastline and sea—navigable rivers, proximity to other successful economies, advan-tageous conditions for agriculture, advantageous conditions forhuman health” [2000, p. 30]. Also see Myrdal [1968, Vol. 1, pp.691–695].

This simple version of the geography hypothesis predictspersistence in economic outcomes, since the geographic factorsthat are the first-order determinants of prosperity are time-in-


variant. The evidence presented so far therefore weighs againstthe simple geography hypothesis: whatever factors are importantin making former colonies rich today are very different from thosecontributing to prosperity in 1500.

IV.B. The Sophisticated Geography Hypotheses

The reversal in relative incomes does not necessarily reject amore sophisticated geography hypothesis, however. Certain geo-graphic characteristics that were not useful, or that were evenharmful, for successful economic performance in 1500 may turnout to be beneficial later on. In this subsection we briefly discussa number of sophisticated geography hypotheses emphasizing theimportance of such time-varying effects of geography.13

The first is the “temperate drift hypothesis,” emphasizing thetemperate (or away from the equator) shift in the center of eco-nomic gravity over time. According to this view, geography be-comes important when it interacts with the presence of certaintechnologies. For example, one can argue that tropical areasprovided the best environment for early civilizations—after all,humans evolved in the tropics, and the required calorie intake islower in warmer areas. But with the arrival of “appropriate”technologies, temperate areas became more productive. The tech-nologies that were crucial for progress in temperate areas includethe heavy plow, systems of crop rotation, domesticated animalssuch as cattle and sheep, and some of the high productivityEuropean crops, including wheat and barley. Despite the key roleof these technologies for temperate areas, they have had muchless of an effect on tropical zones [Lewis 1978]. Sachs [2001, p. 12]also implies this view in his recent paper when he adapts Dia-mond’s argument about the geography of technological diffusion:“Since technologies in the critical areas of agriculture, health, andrelated areas could diffuse within ecological zones, but not acrossecological zones, economic development spread through the tem-

13. Put differently, in the simple geography hypothesis, geography has a main effecton economic performance, which can be expressed as Yit � �0 � �1 � Gi � �t � �it,where Yit is a measure of economic performance in country i at time t, Gi is a measureof geographic characteristics, �t is a time effect, and �it measures other country-time-specific factors. In contrast, in the sophisticated geography view, the relationshipbetween income and geography would be Yit � �0 � �1 � Git � �2 � Tt � Git � �t � �it,where Tt is a time-varying characteristic of the world as a whole or of the state oftechnology. According to this view, the major role that geography plays in history isnot through �1, but through �2.


perate zones but not through the tropical regions” (italics in theoriginal; also see Myrdal [1968], Ch. 14).

The evidence is not favorable to the view that the reversalreflects the emergence of agricultural technologies favorable totemperate areas, however. First, the regressions in Tables III, IV,and V show little evidence that the reversal was related to geo-graphic characteristics. Second, the temperate drift hypothesissuggests that the reversal should be associated with the spread ofEuropean agricultural technologies. Yet in practice, while Euro-pean agricultural technology spread to the colonies between thesixteenth and eighteenth centuries (e.g., McCusker and Menard[1985], Ch. 3 for North America), the reversal in relative incomesis largely a late eighteenth- and early nineteenth-century, andindustry-based phenomenon.

In light of the result that the reversal is related to industri-alization, another sophisticated geography hypothesis would bethat certain geographic characteristics facilitate or enable indus-trialization. First, one can imagine that there is more room forspecialization in industry, but such specialization requires trade.If countries differ according to their transport costs, it might bethose with low transport costs that take off during the age ofindustry. This argument is not entirely convincing, however,again because there is little evidence that the reversal was re-lated to geographic characteristics (see Tables III, IV, and V).Moreover, many of the previously prosperous colonies that failedto industrialize include islands such as the Caribbean, or coun-tries with natural ports such as those in Central America, India,or Indonesia. Moreover, transport costs appear to have beenrelatively low in some of the areas that failed to industrialize(e.g., Pomeranz [2000], Appendix A).

Second, countries may lack certain resource endowments,most notably coal, which may have been necessary for industri-alization (e.g., Pomeranz [2000] and Wrigley [1988]). But coal isone of the world’s most common resources, with proven reservesin 100 countries and production in over 50 countries [World CoalInstitute 2000], and our results in Table III and V offer littleevidence that either coal or the absence of any other resource wasresponsible for the reversal. So there appears to be little supportfor these types of sophisticated geography hypotheses either.14

14. Two other related hypotheses are worth mentioning. First, it could beargued that people work less hard in warmer climates and that this matters more


IV.C. The Institutions Hypothesis

According to the institutions hypothesis, societies with asocial organization that provides encouragement for investmentwill prosper. Locke [1980], Smith [1778], and Hayek [1960],among many others, emphasized the importance of propertyrights for the success of nations. More recently, economists andhistorians have emphasized the importance of institutions thatguarantee property rights. For example, Douglass North startshis 1990 book by stating [p. 3]: “That institutions affect theperformance of economies is hardly controversial,” and identifieseffective protection of property rights as important for the orga-nization of society (see also North and Thomas [1973] and Olson[2000]).

In this context we take a good organization of society tocorrespond to a cluster of (political, economic, and social) institu-tions ensuring that a broad cross section of society has effectiveproperty rights. We refer to this cluster as institutions of privateproperty, and contrast them with extractive institutions, wherethe majority of the population faces a high risk of expropriationand holdup by the government, the ruling elite, or other agents.Two requirements are implicit in this definition of institutions ofprivate property. First, institutions should provide secure prop-erty rights, so that those with productive opportunities expect toreceive returns from their investments, and are encouraged toundertake such investments. The second requirement is embed-ded in the emphasis on “a broad cross section of the society.” Asociety in which a very small fraction of the population, forexample, a class of landowners, holds all the wealth and politicalpower may not be the ideal environment for investment, even if

for industry than for agriculture, thus explaining the reversal. However, there isno evidence either for the hypothesis that work effort matters more for industryor for the assertion that human energy output depends systematically on tempera-ture (see, e.g., Collins and Roberts [1988]). Moreover, the available evidence onhours worked indicates that people work harder in poorer/warmer countries (e.g.,ILO [1995, pp. 36–37]), though of course these high working hours could reflectother factors.

Second, it can be argued that different paths of development reflect the directinfluence of Europeans. Places where there are more Europeans have becomericher, either because Europeans brought certain values conducive to develop-ment (e.g., Landes [1998], and Hall and Jones [1999]), or because having moreEuropeans confers certain benefits (e.g., through trade with Europe or becauseEuropeans are more productive). In Acemoglu, Johnson, and Robinson [2001b] wepresented evidence showing that the reversal and current income levels are notrelated to the current racial composition of the population or to proxies of whetherthe colonies were culturally or politically dominated by Europeans.


the property rights of this elite are secure. In such a society, manyof the agents with the entrepreneurial human capital and invest-ment opportunities may be those without effective property rightsprotection. In particular, the concentration of political and socialpower in the hands of a small elite implies that the majority of thepopulation risks being held up by the powerful elite after theyundertake investments. This is also consistent with North andWeingast’s [1989, pp. 805–806] emphasis that what matters is:“ . . . whether the state produces rules and regulations that bene-fit a small elite and so provide little prospect for long-run growth,or whether it produces rules that foster long-term growth.”Whether political power is broad-based or concentrated in thehands of a small elite is crucial in evaluating the role of institu-tions in the experiences of the Caribbean or India during colonialtimes, where the property rights of the elite were well enforced,but the majority of the population had no civil rights or propertyrights.

It is important to emphasize that “equilibrium institutions”may be extractive, even though such institutions do not encour-age economic development. This is because institutions areshaped, at least in part, by politically powerful groups that mayobtain fewer rents with institutions of private property (e.g.,North [1990]), or fear losing their political power if there isinstitutional development (e.g., Acemoglu and Robinson [2000,2001]), or simply may be reluctant to initiate institutional changebecause they would not be the direct beneficiaries of the resultingeconomic gains. In the context of the development experience ofthe former colonies, this implies that equilibrium institutions arelikely to have been designed to maximize the rents to Europeancolonists, not to maximize long-run growth.

The organization of society and institutions also persist (see,for example, the evidence presented in Acemoglu, Johnson, andRobinson [2001a]). Therefore, the institutions hypothesis alsosuggests that societies that are prosperous today should tend tobe prosperous in the future. However, if a major shock disruptsthe organization of a society, this will affect its economic perfor-mance. We argue that European colonialism not only disruptedexisting social organizations, but led to the establishment of, orcontinuation of already existing, extractive institutions in previ-ously prosperous areas and to the development of institutions ofprivate property in previously poor areas. Therefore, Europeancolonialism led to an institutional reversal, in the sense that


regions that were relatively prosperous before the arrival of Eu-ropeans were more likely to end up with extractive institutionsunder European rule than previously poor areas. The institutionshypothesis, combined with the institutional reversal, predicts areversal in relative incomes among these countries.

The historical evidence supports the notion that colonizationintroduced relatively better institutions in previously sparselysettled and less prosperous areas: while in a number of coloniessuch as the United States, Canada, Australia, New Zealand,Hong Kong, and Singapore, Europeans established institutions ofprivate property, in many others they set up or took over alreadyexisting extractive institutions in order to directly extract re-sources, to develop plantation and mining networks, or to collecttaxes.15 Notice that what is important for our story is not the“plunder” or the direct extraction of resources by the Europeanpowers, but the long-run consequences of the institutions thatthey set up to support extraction. The distinguishing feature ofthese institutions was a high concentration of political power inthe hands of a few who extracted resources from the rest of thepopulation. For example, the main objective of the Spanish andPortuguese colonization was to obtain silver, gold, and othervaluables from America, and throughout they monopolized mili-tary power to enable the extraction of these resources. The min-ing network set up for this reason was based on forced labor andthe oppression of the native population. Similarly, the BritishWest Indies in the seventeenth and eighteenth centuries werecontrolled by a small group of planters (e.g., Dunn [1972, Chs.2–6]). Political power was important to the planters in the WestIndies, and to other elites in the colonies specializing in planta-tion agriculture, because it enabled them to force large masses ofnatives or African slaves to work for low wages.16

What determines whether Europeans pursued an extractive

15. Examples of extraction by Europeans include the transfer of gold andsilver from Latin America in the seventeenth and eighteenth centuries and ofnatural resources from Africa in the nineteenth and twentieth centuries, theAtlantic slave trade, plantation agriculture in the Caribbean, Brazil, and FrenchIndochina, the rule of the British East India Company in India, and the rule of theDutch East India Company in Indonesia. See Frank [1978], Rodney [1972],Wallerstein [1974–1980], and Williams [1944].

16. In a different vein, Europeans running the Atlantic slave trade, despitetheir small numbers, also appear to have had a fundamental effect on the evolu-tion of institutions in Africa. The consensus view among historians is that theslave trade fundamentally altered the organization of society in Africa, leading tostate centralization and warfare as African polities competed to control the supplyof slaves to the Europeans. See, for example, Manning [1990, p. 147], and also


strategy or introduced institutions of private property? And whywas extraction more likely in relatively prosperous areas? Twofactors appear important.

1. The economic profitability of alternative policies. Whenextractive institutions were more profitable, Europeanswere more likely to opt for them. High population density,by providing a supply of labor that could be forced to workin agriculture or mining, made extractive institutionsmore profitable for the Europeans.17 For example, thepresence of abundant Amerindian labor in Meso-Americawas conducive to the establishment of forced labor sys-tems, while the relatively high population density in Af-rica created a profit opportunity for slave traders in sup-plying labor to American plantations.18 Other types ofextractive institutions were also more profitable indensely settled and prosperous areas where there wasmore to be extracted by European colonists. Furthermore,in these densely settled areas there was often an existingsystem of tax administration or tribute; the large popula-tion made it profitable for the Europeans to take control ofthese systems and to continue to levy high taxes (see, e.g.,

Wilks [1975] for Ghana, Law [1977] for Nigeria, Harms [1981]) for the Congo/Zaire, and Miller [1988] on Angola.

17. The Caribbean islands were relatively densely settled in 1500. Much ofthe population in these islands died soon after the arrival of the Europeansbecause of the diseases that the Europeans brought (e.g., Crosby [1986] andMcNeill [1976]). It is possible that the initial high populations in these islandsinduced the Europeans to take the “extractive institutions” path, and subse-quently, these institutions were developed further with the import of slaves fromAfrica. An alternative possibility is that the relevant period of institutionaldevelopment was after the major population decline, but the Caribbean still endedup with extractive institutions because the soil and the climate were suitable forsugar production, which encouraged Europeans to import slaves from Africa andset up labor-oppressive systems (e.g., Dunn [1972] and Engerman and Sokoloff[1997, 2000]).

18. The Spanish conquest around the La Plata River (current day Argentina)during the early sixteenth century provides a nice example of how populationdensity affected European colonization (see Lockhart and Schwartz [1983, pp.259–260] or Denoon [1983, pp. 23–24]). Early in 1536, a large Spanish expeditionarrived in the area, and founded the city of Buenos Aires at the mouth of the riverPlata. The area was sparsely inhabited by nonsedentary Indians. The Spaniardscould not enslave a sufficient number of Indians for food production. Starvationforced them to abandon Buenos Aires and retreat up the river to a post atAsuncion (current day Paraguay). This area was more densely settled by semi-sedentary Indians, who were enslaved by the Spaniards; the colony of Paraguay,with relatively extractive institutions, was founded. Argentina was finally colo-nized later, with a higher proportion of European settlers and little forced labor.


Wiegersma [1988, p. 69], on French policies in Vietnam, orMarshall [1998, pp. 492–497], on British policies in India).

2. Whether Europeans could settle or not. Europeans weremore likely to develop institutions of private propertywhen they settled in large numbers, for the natural reasonthat they themselves were affected by these institutions(i.e., their objectives coincided with encouraging good eco-nomic performance).19 Moreover, when a large number ofEuropeans settled, the lower strata of the settlers de-manded rights and protection similar to, or even betterthan, those in the home country. This made the develop-ment of effective property rights for a broad cross sectionof the society more likely. European settlements, in turn,were affected by population density both directly and in-directly. Population density had a direct effect on settle-ments, since Europeans could easily settle in large num-bers in sparsely inhabited areas. The indirect effectworked through the disease environment, since malariaand yellow fever, to which Europeans lacked immunity,were endemic in many of the densely settled areas [Ace-moglu, Johnson, and Robinson 2001a].20

Table VII provides econometric evidence on the institutionalreversal. It shows the relationship between urbanization or popu-lation density in 1500 and subsequent institutions using threedifferent measures of institutions. The first two measures refer tocurrent institutions: protection against expropriation risk be-tween 1985 and 1995 from Political Risk Services, which approxi-mates how secure property rights are, and “constraints on theexecutive” in 1990 from Gurr’s Polity III data set, which can bethought of as a proxy for how concentrated political power isin the hands of ruling groups (see Appendix 2 for detailedsources). Columns (1)–(6) of Table VII show a negative relation-

19. Extraction and European settlement patterns were mutually self-rein-forcing. In areas where extractive policies were pursued, the authorities alsoactively discouraged settlements by Europeans, presumably because this wouldinterfere with the extraction of resources from the locals (e.g., Coatsworth [1982]).

20. European settlements shaped both the type of institutions that developedand the structure of production. For example, while in Potosı (Bolivia) miningemployed forced labor [Cole 1985] and in Brazil and the Caribbean sugar wasproduced by African slaves, in the United States and Australia mining companiesemployed free migrant labor and sugar was grown by smallholders in Queensland,Australia [Denoon 1983, Chs. 4 and 5]. Consequently, in Bolivia, Brazil, and theCaribbean, political institutions were designed to ensure the control of the labor-ers and slaves, while in the United States and Australia, the smallholders and themiddle class had greater political rights [Cole 1985; Hughes 1988, Ch. 10].


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ship between our measures of prosperity in 1500 and currentinstitutions.21

It is also important to know whether there was an institu-tional reversal during the colonial times or shortly after indepen-dence. Since the Gurr data set does not contain information fornonindependent countries, we can only look at this after indepen-dence. Columns (7)–(9) show the relationship between prosperityin 1500 and a measure of early institutions, constraint on theexecutive in the first year of independence, from the same dataset, while also controlling for time since independence as anadditional covariate. Finally, the second panel of the table in-cludes (the absolute value of) latitude as an additional control,showing that the institutional reversal does not reflect somesimple geographic pattern of institutional change.

The institutions hypothesis, combined with the institutionalreversal, predicts that countries in areas that were relativelyprosperous and densely settled in 1500 ended up with relativelyworse institutions after the European intervention, and thereforeshould be relatively less prosperous today. The reversal in rela-tive incomes that we have documented so far is consistent withthis prediction.

Notice, however, that the institutions hypothesis and thereversal in relative incomes do not rule out an important role forgeography during some earlier periods, or working through insti-tutions. They simply suggest that institutional differences are themajor source of differences in income per capita today. First,differences in economic prosperity in 1500 may be reflecting geo-graphic factors (e.g., that the tropics were more productive thantemperate areas) as well as differences in social organizationcaused by nongeographic influences. Second and more important,as we emphasized in Acemoglu, Johnson, and Robinson [2001a],a major determinant of European settlements, and therefore ofinstitutional development, was the mortality rates faced by Eu-ropeans, which is a geographical variable. Similarly, as noted byEngerman and Sokoloff [1997, 2000], whether an area was suit-able for sugar production is likely to have been important in

21. When both urbanization and log population density in 1500 are included,it is the population density variable that is significant. This supports the inter-pretation that it was the differences between densely and sparsely settled areasthat was crucial in determining colonial institutions (though, again, this may alsoreflect the fact that the population density variable is measured with less mea-surement error).


shaping the type of institutions that Europeans introduced. How-ever, this type of interaction between geography and institutionsmeans that certain regions, say Central America, are poor todaynot as a result of their geography, but because of their institu-tions, and that there is not a necessary or universal link betweengeography and economic development.

V. INSTITUTIONS AND THE MAKING OF THE MODERN WORLD

INCOME DISTRIBUTION

V.A. Institutions and the Reversal

We next provide evidence suggesting that institutional dif-ferences statistically account for the reversal in relative incomes.If the institutional reversal is the reason why there was a rever-sal in income levels among the former colonies, then once weaccount for the role of institutions appropriately, the reversalshould disappear. That is, according to this view, the reversaldocumented in Figures I and II and Tables III, IV, V, and VIreflects the correlation between economic prosperity in 1500 and in-come today working through the intervening variable, institutions.

How do we establish that an intervening variable X is re-sponsible for the correlation between Z and Y? Suppose that thetrue relationship between Y, and X, and Z is

(1) Y � � � X � � Z � �,

where � and are coefficients and � is a disturbance term. In ourcase, we can think of Y as income per capita today, X as ameasure of institutions, and Z as population density (or urban-ization) in 1500. The variable Z is included in equation (1) eitherbecause it has a direct effect on Y or because it has an effectthrough some other variables not included in the analysis. Thehypothesis we are interested in is that � 0; that is, populationdensity or urbanization in 1500 affects income today only viainstitutions.

This hypothesis obviously requires that there is a statisticalrelationship between X and Z. So we postulate that X � � Z �v. To start with, suppose that � is independent of X and Z andthat v is independent of Z. Now imagine a regression of Y on Zonly (in our context, of income today on prosperity in 1500,similar to those we reported in Tables III, IV, V, and VI):


Y � b � Z � u1. As is well-known, the probability limit of theOLS estimate from this regression, b, is

plimb � � � � .

So the results in the regressions of Tables IV, V, VI, and VII areconsistent with � 0 as long as � � 0 and � 0. In this case, wewould be capturing the effect of Z (population density or urban-ization) on income working solely through institutions. This is thehypothesis that we are interested in testing. Under the assump-tions regarding the independence of Z from � and �, and of X from�, there is a simple way of testing this hypothesis, which is to runan OLS regression of Y on Z and X:

(2) Y � a � X � b � Z � u2

to obtain the estimates a and b. The fact that � in (1) is indepen-dent of both X and Z rules out omitted variable bias, so plima �� and plimb � . Hence, a simple test of whether b � 0 is all thatis required to test our hypothesis that the effect of Z is through Xalone.

In practice, there are likely to be problems due to omittedvariables, endogeneity bias because Y has an effect on X, andattenuation bias because X is measured with error or correspondspoorly to the real concept that is relevant to development (whichis likely to be a broad range of institutions, whereas we only havean index for a particular type of institutions). So the above pro-cedure is not possible. However, the same logic applies as long aswe have a valid instrument M for X, such that X � � � M � , andM is independent of � in (1). We can then simply estimate (2)using 2SLS with the first-stage X � c � M � d � Z � u3. Testingour hypothesis that Z has an effect on Y only through its effect onX then amounts to testing that the 2SLS estimate of b, b, is equalto 0. Intuitively, the 2SLS procedure ensures a consistent esti-mate of �, enabling an appropriate test for whether Z has a directeffect.

The key to the success of this strategy is a good instrumentfor X. In our previous work [Acemoglu, Johnson, and Robinson2001a] we showed that mortality rates faced by settlers are a goodinstrument for settlements of Europeans in the colonies and thesubsequent institutional development of these countries. Thesemortality rates are calculated from the mortality of soldiers,bishops, and sailors stationed in the colonies between the seven-


teenth and nineteenth centuries, and are a plausible instrumentfor the institutional development of the colonies, since in areaswith high mortality Europeans did not settle and were morelikely to develop extractive institutions. The exclusion restrictionimplied by this instrumental-variables strategy is that, condi-tional on the other controls, the mortality rates of Europeansettlers more than 100 years ago have no effect on GDP per capitatoday, other than their effects through institutional development.This is plausible since these mortality rates were much higherthan the mortality rates faced by the native population who haddeveloped a high degree of immunity to the two main killers ofEuropeans, malaria and yellow fever.

Table VIII reports results from this type of 2SLS test usingthe log of settler mortality rates as an instrument for institu-tional development. We look at the same three institutions vari-ables used in Table VII: protection against expropriation riskbetween 1985 and 1995, and constraint on the executive in 1990and in the first year of independence. Panel A reports results fromregressions that enter urbanization and log population density in1500 as exogenous regressors in the first and the second stages,while Panel B reports the corresponding first stages. Differentcolumns correspond to different institutions variables, or to dif-ferent specifications. For comparison, Panel C reports the 2SLScoefficient on institutions with exactly the same sample as thecorresponding column, but without including urbanization orpopulation density.

The results are consistent with our hypothesis. In all col-umns we never reject the hypothesis that urbanization in 1500 orpopulation density in 1500 has no direct effect once we control forthe effect of institutions on income per capita, and the addition ofthese variables has little effect on the 2SLS estimate of the effectof institutions on income per capita. This supports our notion thatthe reversal in economic prosperity reflects the effect of earlyprosperity and population density working through the institu-tions and policies introduced by European colonists.

V.B. Institutions and Industrialization

Why did the reversal in relative incomes take place duringthe nineteenth century? To answer this question, imagine a soci-ety like the Caribbean colonies where a small elite controls all thepolitical power. The property rights of this elite are relatively wellprotected, but the rest of the population has no effective property


rights. According to our definition, this would not be a societywith institutions of private property, since a broad cross section ofsociety does not have effective property rights. Nevertheless,when the major investment opportunities are in agriculture, thismay not matter too much, since the elite can invest in the land

TABLE VIIIGDP PER CAPITA AND INSTITUTIONS

Institutions asmeasured by:

Dependent variable is log GDP per capita (PPP) in 1995

Averageprotection against

expropriationrisk, 1985–1995

Constraint onexecutive in

1990

Constraint onexecutive in first

year ofindependence

(1) (2) (3) (4) (5) (6)

Panel A: Second-stage regressions

Institutions 0.52 0.88 0.84 0.50 0.37 0.46(0.10) (0.21) (0.47) (0.11) (0.12) (0.16)

Urbanization in 1500 �0.024 0.030 �0.023(0.021) (0.078) (0.034)

Log population densityin 1500

�0.08 �0.10 �0.13(0.10) (0.10) (0.10)

Panel B: First-stage regressions

Log settler mortality �1.21 �0.47 �0.75 �0.88 �1.81 �0.78(0.23) (0.14) (0.44) (0.20) (0.40) (0.25)

Urbanization in 1500 �0.042 �0.088 �0.043(0.035) (0.066) (0.061)

Log population densityin 1500

�0.21 �0.35 �0.24(0.11) (0.15) (0.17)

R2 0.53 0.29 0.17 0.37 0.56 0.26Number of observations 38 64 37 67 38 67

Panel C: Coefficient on institutions without urbanization or population density in 1500

Institutions 0.56 0.96 0.77 0.54 0.39 0.52(0.09) (0.17) (0.33) (0.09) (0.11) (0.15)

Standard errors are in parentheses. Dependent variable is log GDP per capita (PPP) in 1995. Themeasure of institutions used in each regression is indicated at the head of each column. Urbanization in 1500is percent of the population living in towns with 5000 or more people. Population density is calculated as totalpopulation divided by arable land area. Constraint on the executive in 1990, 1900, and the first year ofindependence are all from the Polity III data set. Regressions with constraint on executive in first year ofindependence use the earliest available date after independence, and also include the date of independenceas an additional regressor.

Panel A reports the second-stage estimates from an IV regression with first-stage shown in Panel B.Panel C reports second-stage estimates from the IV regressions, which do not include urbanization orpopulation density and which instrument for institutions using log settler mortality. Log settler mortalityestimates are from Acemoglu, Johnson, and Robinson [2001a]. For detailed sources and descriptions seeAppendix 2.


and employ the rest of the population, and so will have relativelygood incentives to increase output.

Imagine now the arrival of a new technology, for example, theopportunity to industrialize. If the elite could undertake indus-trial investments without losing its political power, we may ex-pect them to take advantage of these opportunities. However, inpractice there are at least three major problems. First, those withthe entrepreneurial skills and ideas may not be members of theelite and may not undertake the necessary investments, becausethey do not have secure property rights and anticipate that theywill be held up by political elites once they undertake theseinvestments. Second, the elites may want to block investments innew industrial activities, because it may be these outside groups,not the elites themselves, who will benefit from these new activ-ities. Third, they may want to block these new activities, fearingpolitical turbulence and the threat to their political power thatnew technologies will bring (see Acemoglu and Robinson [2000,2001]).22

This reasoning suggests that whether a society has institu-tions of private property or extractive institutions may mattermuch more when new technologies require broad-based economicparticipation—in other words, extractive institutions may be-come much more inappropriate with the arrival of new technolo-gies. Early industrialization appears to require both investmentsfrom a large number of people who were not previously part of theruling elite and the emergence of new entrepreneurs (see Enger-man and Sokoloff [1997], Kahn and Sokoloff [1998], and Rothen-berg [1992] for evidence that many middle-class citizens, innova-tors, and smallholders contributed to the process of earlyindustrialization in the United States). Therefore, there are rea-sons to expect that institutional differences should matter moreduring the age of industry.

If this hypothesis is correct, we should expect societies withgood institutions to take better advantage of the opportunity toindustrialize starting in the late eighteenth century. We can testthis idea using data on institutions, industrialization, and GDPfrom the nineteenth and early twentieth centuries. Bairoch[1982] presents estimates of industrial output for a number ofcountries at a variety of dates, and Maddison [1995] has esti-

22. In addition, industrialization may have been delayed in some cases be-cause of a comparative advantage in agriculture.


mates of GDP for a larger group of countries. We take Bairoch’sestimates of U. K. industrial output as a proxy for the opportunityto industrialize, since during this period the United Kingdom wasthe world industrial leader. We then run a panel data regressionof the following form:

(3) yit � �t � �i � � � Xit � � � Xit � UKINDt � �it,

where yit is the outcome variable of interest in country i at datet. We consider industrial output per capita and income per capitaas two different measures of economic success during the nine-teenth century. In addition, �t’s are a set of time effects, and �i’sdenote a set of country effects, UKINDt is industrial output in theUnited Kingdom at date t, and Xit denotes the measure of insti-tutions in country i at date t. Our institutions variable is againconstraint on the executive from the Gurr Polity III data set. Asnoted above, this variable is available from the date of indepen-dence for each country. Since colonial rule typically concentratedpolitical power in the hands of a small elite, for the purpose of theregressions in this table, we assign the lowest score to countriesstill under colonial rule. The coefficient of interest is �, whichreflects whether there is an interaction between good institutionsand the opportunity to industrialize. A positive and significant �is interpreted as evidence in favor of the view that countries withinstitutions of private property took better advantage of the op-portunity to industrialize. The parameter � measures the directeffect of institutions on industrialization, and is evaluated at themean value of UKINDt.

The top panel of Table IX reports regressions of equation (3)with industrial output per capita as the left-hand-side variable(see the note to the table for more details). Column (1) reports aregression using only pre-1950 data. The interaction term � isestimated to be 0.132, and is highly significant with a standarderror of 0.26. Note that Bairoch’s estimate of total U. K. indus-trialization, which is normalized to 100 in 1900, rose from 16 to115 between 1800 and 1913. In the meantime, the U. S. per capitaproduction grew from 9 to 126, whereas India’s per capita indus-trial production fell from 6 to 2. Since the average differencebetween the constraint on the executive in the United States andIndia over this period is approximately 6, the estimate impliesthat the U. S. industrial output per capita should have increasedby 78 points more than India’s, which is over half the actualdifference.


In column (2) we extend the data through 1980, again with noeffect on the coefficient, which stays at 0.132. In columns (3) and(4) we investigate whether independence impacts on industrial-ization, and whether our procedure of assigning the lowest scoreto countries still under colonial rule may be driving our results. Incolumn (3) we include a dummy for whether the country is inde-pendent, and also interact this dummy with U. K. industrializa-tion. These variables are insignificant, and the coefficient on theinteraction between U. K. industrialization and institutions, �, isunchanged (0.145 with standard error 0.035). In column (4) wedrop all observations from countries still under colonial rule, andthis again has no effect on the results (� is now estimated to be0.160 with standard error 0.048).

In columns (5) and (6) we use average institutions for eachcountry, X� i, rather than institutions at date t, so the equationbecomes

yit � �t � �i � � � X� i � UKINDt � �it.

This specification may give more sensible results if either varia-tions in institutions from year to year are endogenous with re-spect to changes in industrialization or income, or are subject tomeasurement error. � is now estimated to be larger, suggestingthat measurement error is a more important problem than theendogeneity of the changes in institutions.

An advantage of the specification in columns (5) and (6) isthat it allows us to instrument for the regressor of interest X� i �UKINDt, using the interaction between U. K. industrializationand our instrument for institutions, log settler mortality Mi (sothe instrument here is Mi � UKINDt). Once again, institutionsmight differ across countries because more productive or other-wise different countries have different institutions, and in thiscase, the interaction between industrialization and institutionscould be capturing the direct effects of these characteristics oneconomic performance. To the extent that log settler mortality isa good instrument for institutions, the interaction between logsettler mortality and U. K. industrialization will be a good instru-ment for the interaction between institutions and U. K. industri-alization. The instrumental-variables procedure will then dealwith the endogeneity of institutions, the omitted variables bias,and also the attenuation bias due to measurement error. The


TA

BL

EIX

TH

EIN

TE

RA

CT

ION

OF

U.

K.

IND

US

TR

IAL

IZA

TIO

NA

ND

INS

TIT

UT

ION

S

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)

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sin

gda

tath

rou

gh19

80(a

llda

ta)

(2)

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mer

colo

nie

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sin

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lypr

e-19

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)

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nie

s,u

sin

gon

lyda

tapr

e-19

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dfo

rin

depe

nde

nt

cou

ntr

ies

(4)

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mer

colo

nie

s,w

ith

aver

age

inst

itu

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rea

chco

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try,

usi

ng

only

pre-

1950

data

(5)

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mer

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age

inst

itu

tion

sfo

rea

chco

un

try,

usi

ng

only

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,we

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oast

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enya

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occo

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eria

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s:17

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800,

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3,an

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lth

ough

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for

all

cou

ntr

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for

all

date

s.F

orde

tail

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esan

dde

scri

ptio

ns

see

App

endi

x2.


2SLS estimates reported in columns (7) and (8) are very similar tothe OLS estimates in columns (5) and (6), and are highlysignificant.23

In columns (9) and (10) we add the interaction betweenlatitude and industrialization. This is useful because, if thereason why the United States surged ahead relative to Indiaor South America during the nineteenth century is its geo-graphic advantage, our measures of institutions might be proxy-ing for this, incorrectly assigning the role of geography to in-stitutions. The results give no support to this view: the esti-mates of � are affected little and remain significant, while theinteraction between industrialization and latitude is insignifi-cant. Panel B of Table IX repeats these regressions using log GDPper capita as the left-hand-side variable (the interaction termis now as Mi � ln(UKINDt) since the left-hand-side variable is logof GDP per capita). The results are broadly similar to those inPanel A.

Overall, these results provide support for the view that in-stitutions played an important role in the process of economicgrowth and in the surge of industrialization among the formerlypoor colonies, and via this channel, account for a significantfraction of current income differences.

VI. CONCLUSION

Among the areas colonized by European powers during thepast 500 years, those that were relatively rich in 1500 are nowrelatively poor. Given the crude nature of the proxies for prosper-ity 500 years ago, some degree of caution is required, but thebroad patterns in the data seem uncontroversial. Civilizations inMeso-America, the Andes, India, and Southeast Asia were richerthan those located in North America, Australia, New Zealand, or

23. Despite our instrumental-variables strategy, the interaction betweeninstitutions and the opportunity to industrialize may capture the possible inter-action between industrialization and some country characteristics correlated withour instrument. For example, with an argument along the lines of Nelson andPhelps [1966] or Acemoglu and Zilibotti [2001], one might argue that industrialtechnologies were appropriate only for societies with sufficient human capital, andthat there were systematic cross-country differences in human capital correlatedwith institutional differences. This interpretation is consistent with our approach,since the correlation between institutions and human capital most likely reflectsthe fact that in societies with extractive institutions the masses typically did notor could not obtain education. In other words, low levels of human capital mayhave been a primary mechanism through which extractive institutions delayedindustrialization.


the southern cone of Latin America. The intervention of Europereversed this pattern. This is a first-order fact, both for under-standing economic and political development over the past500 years, and for evaluating various theories of long-rundevelopment.

This reversal in relative incomes is inconsistent with thesimple geography hypothesis which explains the bulk of the in-come differences across countries by the direct effect of geo-graphic differences, thus predicting a high degree of persistencein economic outcomes. We also show that the timing and natureof the reversal do not offer support to sophisticated geographyviews, which emphasize the time-varying effects of geography.Instead, the reversal in relative incomes over the past 500 yearsappears to reflect the effect of institutions (and the institutionalreversal caused by European colonialism) on income today.

Why did European colonialism lead to an institutional rever-sal? And how did this institutional reversal cause the reversal inrelative incomes and the subsequent divergence in income percapita across the various colonies? We argued that the institu-tional reversal resulted from the differential profitability of alter-native colonization strategies in different environments. In pros-perous and densely settled areas, Europeans introduced ormaintained already-existing extractive institutions to force thelocal population to work in mines and plantations, and took overexisting tax and tribute systems. In contrast, in previouslysparsely settled areas, Europeans settled in large numbers andcreated institutions of private property, providing secure prop-erty rights to a broad cross section of the society and encouragingcommerce and industry. This institutional reversal laid the seedsof the reversal in relative incomes. But most likely, the scale ofthe reversal and the subsequent divergence in incomes are due tothe emergence of the opportunity to industrialize during thenineteenth century. While societies with extractive institutionsor those with highly hierarchical structures could exploit avail-able agricultural technologies relatively effectively, the spread ofindustrial technology required the participation of a broad crosssection of the society—the smallholders, the middle class, and theentrepreneurs. The age of industry, therefore, created a consid-erable advantage for societies with institutions of private prop-erty. Consistent with this view, we documented that thesesocieties took much better advantage of the opportunity toindustrialize.


APPENDIX 1: URBANIZATION ESTIMATES

This is a shortened version of the Appendix in Acemoglu,Johnson, and Robinson [2001b].

1. Urbanization in 1500

Our base estimates for 1500 consist of Bairoch’s [1988] as-sessment of urbanization augmented by the work of Eggimann[1999]. Merging these two series requires us to convert Eggi-mann’s estimates, based on a minimum population threshold of20,000, into Bairoch-equivalent urbanization estimates, based ona minimum population threshold of 5000.

To construct our base data, we run a regression of Bairochestimates on Eggimann estimates for all countries where theyoverlap in 1900 (the year for which we have the largest number ofBairoch estimates for non-European countries). There are thir-teen countries for which we have good overlapping data. Thisregression yields a constant of 6.6 and a coefficient of 0.67.

We use these results to convert from Eggimann to Bairoch-equivalent urbanization estimates in Colombia, Ecuador, Guate-mala (and other parts of Central America), Mexico, and Peru inthe Americas. We also use this method for all North Africancountries and for India (and the rest of the Indian subcontinent),Indonesia, Malaysia, Laos, Burma/Myanmar, and Vietnam inAsia. See Appendix 2 for the precise numbers we use.

There are a number of countries for which Bairoch deter-mines that there was no real urbanization or no pre-European“settled agriculture.” In these cases, a reasonable interpretationof Bairoch is that there was no urban population using his defi-nition. In our baseline data we therefore assume zero urbaniza-tion for the following countries: Argentina, Brazil, Canada, Chile,Guyana, Paraguay, Uruguay, the United States, and Australia.

For countries where Bairoch determines there was some lowlevel of urbanization, associated with fairly primitive agriculture,he assesses that the urbanization rate was 3 percent. We use thisestimate for Cuba, the Dominican Republic, Haiti, and Jamaicain the Americas. We also use this estimate for Hong Kong, thePhilippines, and Singapore in Asia and for New Zealand. In theAppendix of Acemoglu, Johnson, and Robinson [2001b], wepresent qualitative evidence documenting the low levels of urban-ization in countries with assigned values of 0 percent or 3 percenturbanization in our baseline data.


While the data on sub-Saharan Africa are worse than for anyother region, it is clear that urbanization before 1500 was at ahigher level than North America or Australia (see the Appendixof Acemoglu, Johnson, and Robinson [2001b] for detailed discus-sion and sources). Given the weakness and incompleteness ofdata for sub-Saharan Africa, we do not include any estimates inour baseline urbanization data set. We do, however, include all ofsub-Saharan Africa in our baseline population density data.

We have checked the robustness of our results using alter-native methods of converting Eggimann estimates into Bairoch-equivalent numbers. We have calculated conversion ratios at theregional level (e.g., for North Africa and the Andean regionseparately). We have also constructed an alternative series usinga conversion rate of 2, as suggested by Davis’ and Zipf ’s Laws (seeBairoch [1988], Chapter 9.)24 We have also used Bairoch’s overallassessment of urbanization for broad regions, e.g., Asia, withoutthe more detailed information from Eggimann (see the Appendixin Acemoglu, Johnson, and Robinson [2001b] for more detail). Wehave also used estimates just from Bairoch, just from Eggimann,and just from Chandler. See Table IV for relevant regressions.

Our baseline estimates and the most plausible alternativeseries are shown in Appendix 2. We have also calculated urbani-zation rates for all European countries and non-European coun-tries that were never colonized. We have also checked Bairoch’sestimates carefully for these countries against the work of Bai-roch, Batou, and Chevre [1988], Chandler and Fox [1974], deVries [1984], and Hohenberg and Lees [1985]. Our discussion ofurbanization in European and never colonized countries is notreported here to conserve space, but it is available from theauthors.

2. Urbanization from 1500 to 2000

Eggimann’s data only cover countries that are now part ofthe “Third World.” He therefore does not provide any informationon the timing of urbanization changes in settler colonies. Bairochdoes have some information on urbanization in the United States,Canada, and Australia, but only from 1800 [Bairoch 1988, Table13.4, p. 221]. For a more complete picture of urbanization from800 to 1850 across a wide range of countries, we therefore rely

24. We are using a conservative version of Davis’ law. See the Appendix inAcemoglu, Johnson, and Robinson [2001b] for a more detailed discussion.


primarily on Chandler’s estimates. We should emphasize, how-ever, that wherever there is overlapping information, these esti-mates are broadly consistent with the findings of Eggimann andBairoch.25 As before, we convert urban population numbers intourbanization using population estimates from McEvedy andJones [1978].

Chandler’s data enable us to see changes in urbanizationover time across countries, but because his series ends in 1850 (or1861 for the Americas), we cannot follow the most importanttrends into the twentieth century. In addition, Chandler’s dataare reported at 50-year intervals from 1700 (100-year intervalsbefore that), which is only enough to show the broad pattern.

We therefore supplement the analysis with data from twoother sources. The UN [1969] provides detailed urbanization datafrom 1920, focusing on localities with 20,000 or more inhabitants(i.e., the same criterion as Chandler uses outside of Asia). How-ever, this still leaves a gap between 1850 and 1920.

We complete this composite series using data from Mitchell[1993, 1995]. His urbanization data start in 1750, provide infor-mation every ten years from 1790 for most countries, and run to1980. The only disadvantage of this series is the relatively latestarting date. The criterion for inclusion in Mitchell’s series isalso a little different—cities that had at least 200,000 inhabitantsaround 1970—but this seems to produce broadly consistent esti-mates for overlapping observations. We use these data both tocomplete the Chandler series for Mexico, India, and the UnitedStates (see Figure IVa) and to provide alternative estimates forthe timing of urbanization changes within the Americas.

The data shown in Figure IVa are from Chandler (through1850), Mitchell (for 1900), and the UN (for 1920 and 1930),converted to Bairoch-equivalent units using the conservativeZipf-Davis adjustment (i.e., multiplying the estimates by 2).

25. The only point of disagreement is whether there was any urbanization inthe area now occupied by the United States in 1500. Chandler lists one town(Nanih Waiya) but does not give its population. He also does not indicate anyurbanization either before or after this date. Bairoch argues there was no pre-European urbanization and the latest archaeological evidence suggests villagesrather than towns [Fagan 2000]. We therefore follow Bairoch in assigning a valueof zero. For supportive evidence see Waldman [1985, p. 30].


AP

PE

ND

IX2:

VA

RIA

BL

ED

EF

INIT

ION

SA

ND

SO

UR

CE

S

Var

iabl

eD

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(PP

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per

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and

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in1

A.D

.,10

00,

and

1500

(als

olo

gpo

pula

tion

in15

00an

dlo

gar

able

lan

din

1500

)

Log

arit

hm

ofpo

pula

tion

den

sity

(tot

alpo

pula

tion

divi

ded

byto

tal

arab

lela

nd)

in1

A.D

.,10

00,

1500

.

McE

vedy

and

Jon

es[1

978]

.

Urb

aniz

atio

nin

1960

and

1995

Per

cen

tof

popu

lati

onli

vin

gin

urb

anar

eas

in19

60an

d19

95,

asde

fin

edby

the

UN

(typ

ical

ly20

,000

min

imu

min

hab

itan

ts).

Wor

ldB

ank,

Wor

ldD

evel

opm

ent

Indi

cato

rs,

CD

-Rom

,19

99.

For

mor

ede

tail

,se

ep.

159

ofth

eW

orld

Ban

k’s

Wor

ldD

evel

opm

ent

Indi

cato

rs19

99(h

ard

copy

).U

rban

izat

ion

in10

00,

1500

,an

d17

00P

erce

nt

ofpo

pula

tion

livi

ng

inu

rban

area

sw

ith

apo

pula

tion

ofat

leas

t50

00in

1000

,15

00,

and

1700

.

Bai

roch

and

supp

lem

enta

lso

urc

es,

asde

scri

bed

inA

ppen

dix

1.

Eu

rope

anse

ttle

men

tsin

1800

and

1900

Per

cen

tof

popu

lati

onth

atw

asE

uro

pean

orof

Eu

rope

ande

scen

tin

1800

and

1900

.R

ange

sfr

om0

to0.

99in

our

base

sam

ple.

McE

vedy

and

Jon

es[1

978]

and

oth

erso

urc

esli

sted

inA

ppen

dix

Tab

le5

ofA

cem

oglu

,Jo

hn

son

,an

dR

obin

son

[200

0].

Ave

rage

prot

ecti

onag

ain

stex

prop

riat

ion

risk

,19

85–1

995

Ris

kof

expr

opri

atio

nof

priv

ate

fore

ign

inve

stm

ent

bygo

vern

men

t,fr

om0

to10

,w

her

ea

hig

her

scor

em

ean

sle

ssri

sk.

We

calc

ula

ted

the

mea

nva

lue

for

the

scor

esin

all

year

sfr

om19

85to

1995

.

Dat

ase

tob

tain

eddi

rect

lyfr

omP

olit

ical

Ris

kS

ervi

ces,

Sep

tem

ber

1999

.T

hes

eda

taw

ere

prev

iou

sly

use

dby

Kn

ack

and

Kee

fer

[199

5]an

dw

ere

orga

niz

edin

elec

tron

icfo

rmby

the

IRIS

Cen

ter

(Un

iver

sity

ofM

aryl

and)

.T

he

orig

inal

com

pile

rsof

thes

eda

taar

eP

olit

ical

Ris

kS

ervi

ces.


AP

PE

ND

IX2

(CO

NT

INU

ED

)

Var

iabl

eD

escr

ipti

onS

ourc

e

Con

stra

int

onex

ecu

tive

in19

70,

1990

,an

dfi

rst

year

ofin

depe

nde

nce

Ase

ven

-cat

egor

ysc

ale,

from

1to

7,w

ith

ah

igh

ersc

ore

indi

cati

ng

mor

eco

nst

rain

ts.

Sco

reof

1in

dica

tes

un

lim

ited

auth

orit

y;sc

ore

of3

indi

cate

ssl

igh

tto

mod

erat

eli

mit

atio

ns;

scor

eof

5in

dica

tes

subs

tan

tial

lim

itat

ion

s;sc

ore

of7

indi

cate

sex

ecu

tive

pari

tyor

subo

rdin

atio

n.

Sco

res

of2,

4,an

d6

indi

cate

inte

rmed

iate

valu

es.

Pol

ity

III

data

set,

dow

nlo

aded

from

Inte

r-U

niv

ersi

tyC

onso

rtiu

mfo

rP

olit

ical

and

Soc

ial

Res

earc

h.

Var

iabl

ede

scri

bed

inG

urr

[199

7].

Per

cen

tof

Eu

rope

ande

scen

tin

1975

reli

gion

vari

able

sP

erce

nt

ofpo

pula

tion

that

was

Eu

rope

anor

ofE

uro

pean

desc

ent

in19

75.

Ran

ges

from

0to

1in

our

base

sam

ple.

McE

vedy

and

Jon

es[1

978]

.

Per

cen

tage

ofth

epo

pula

tion

that

belo

nge

din

1980

(or

for

1990

–199

5fo

rco

un

trie

sfo

rmed

mor

ere

cen

tly)

toth

efo

llow

ing

reli

gion

s:R

oman

Cat

hol

ic,

Pro

test

ant,

Mu

slim

,an

d“o

ther

.”

La

Por

taet

al.

[199

9].

Col

onia

ldu

mm

ies

Du

mm

yva

riab

lein

dica

tin

gw

het

her

cou

ntr

yw

asa

Bri

tish

,F

ren

ch,

Ger

man

,S

pan

ish

,It

alia

n,

Bel

gian

,D

utc

h,

orP

ortu

gues

eco

lon

y.

La

Por

taet

al.

[199

9].

Tem

pera

ture

vari

able

sT

empe

ratu

reva

riab

les

are

aver

age

tem

pera

ture

,m

inim

um

mon

thly

hig

h,

max

imu

mm

onth

lyh

igh

,m

inim

um

mon

thly

low

,an

dm

axim

um

mon

thly

low

,al

lin

cen

tigr

ade.

Par

ker

[199

7].


Hu

mid

ity

vari

able

sH

um

idit

yva

riab

les

are

mor

nin

gm

inim

um

,m

orn

ing

max

imu

m,

afte

rnoo

nm

inim

um

,an

daf

tern

oon

max

imu

m,

all

inpe

rcen

t

Par

ker

[199

7].

Soi

lqu

alit

yM

easu

res

ofso

ilqu

alit

y/cl

imat

ear

est

eppe

(low

lati

tude

),de

sert

(low

lati

tude

),st

eppe

(mid

dle

lati

tude

),de

sert

(mid

dle

lati

tude

),dr

yst

eppe

was

tela

nd,

dese

rtdr

yw

inte

r,an

dh

igh

lan

d.

Par

ker

[199

7].

Nat

ura

lre

sou

rces

Mea

sure

sof

nat

ura

lre

sou

rces

are

perc

ent

ofw

orld

gold

rese

rves

toda

y,pe

rcen

tof

wor

ldir

onre

serv

esto

day,

perc

ent

ofw

orld

zin

cre

serv

esto

day,

perc

ent

ofw

orld

silv

erre

serv

esto

day,

and

oil

reso

urc

es(t

hou

san

dsof

barr

els

per

capi

tato

day)

.

Par

ker

[199

7].

Coa

lD

um

my

vari

able

equ

alto

1if

cou

ntr

yh

aspr

odu

ced

coal

sin

ce18

00.

Wor

ldR

esou

rces

Inst

itu

te[1

998]

and

Ete

mad

and

Tou

tain

[199

1].

Lan

dloc

ked

Du

mm

yva

riab

leeq

ual

to1

ifco

un

try

does

not

adjo

inth

ese

a.P

arke

r[1

997]

.

Isla

nd

Du

mm

yva

riab

leeq

ual

to1

ifco

un

try

isan

isla

nd.

DK

Pu

blis

hin

g[1

997]

.

Lat

itu

deA

bsol

ute

valu

eof

the

lati

tude

ofth

eco

un

try,

scal

edto

take

valu

esbe

twee

n0

and

1,w

her

e0

isth

eeq

uat

or.

La

Por

taet

al.

[199

9].

Log

mor

tali

tyL

ogof

esti

mat

edse

ttle

rm

orta

lity.

Set

tler

mor

talit

yis

calc

ulat

edfr

omth

em

orta

lity

rate

sof

Eur

opea

n-bo

rnso

ldie

rs,s

ailo

rs,a

ndbi

shop

sw

hen

stat

ione

din

colo

nies

.It

mea

sure

sth

eef

fect

sof

loca

ldi

seas

eson

peop

lew

itho

utin

heri

ted

orac

quir

edim

mun

itie

s.

Ace

mog

lu,

Joh

nso

n,

and

Rob

inso

n[2

001a

],ba

sed

onC

urt

in[1

989]

and

oth

erso

urc

es.


AP

PE

ND

IX3

Bas

eu

rban

izat

ion

esti

mat

ein

1500

Sou

rce

ofba

seu

rban

izat

ion

esti

mat

ein

1500

Urb

aniz

atio

nes

tim

ate

in15

00u

sin

gon

lyin

form

atio

nfr

omB

airo

ch

Urb

aniz

atio

nes

tim

ate

in15

00u

sin

gon

lyin

form

atio

nfr

omE

ggim

ann

Urb

aniz

atio

nes

tim

ate

in15

00u

sin

gon

lyin

form

atio

nfr

omC

han

dler

Dav

is-Z

ipf

adju

stm

ent

appl

ied

toE

ggim

ann

seri

es

Pop

ula

tion

den

sity

in15

00

Pop

ula

tion

den

sity

in15

00

Pop

ula

tion

den

sity

in15

00

For

mer

colo

nie

sin

clu

ded

inou

rba

sesa

mpl

efo

ru

rban

izat

ion

For

mer

colo

nie

sin

clu

ded

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sesa

mpl

efo

rpo

pula

tion

den

sity

but

not

for

urb

aniz

atio

n

Arg

enti

na

0.0

Bai

roch

0.0

0.0

0.0

0.0

0.11

An

gola

1.50

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dan

14.0

3A

ust

rali

a0.

0B

airo

ch0.

00.

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00.

00.

03B

aham

as1.

46S

uri

nam

e0.

21B

angl

ades

h8.

5E

ggim

ann

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vert

edto

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roch

9.0

2.9

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823

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Bar

bado

s1.

46T

anza

nia

1.98

Bel

ize

9.2

Egg

iman

n(3

.8%

)co

nve

rted

toB

airo

ch

7.0

18.0

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7.6

1.54

Ben

in4.

23T

ogo

4.23

Bol

ivia

10.6

Egg

iman

n(E

cuad

oran

dB

oliv

ia)

con

vert

edto

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roch

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83B

otsw

ana

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nid

adan

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obag

o1.

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zil

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roch

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12B

urk

ina

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o4.

23U

gan

da7.

51

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ada

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roch

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Bu

run

di25

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Zai

re1.

50C

hil

e0.

0B

airo

ch0.

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00.

80C

amer

oon

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bia

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Col

ombi

a7.

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ann

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vert

edto

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roch

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e0.

79

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ica

9.2

Egg

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n(3

.8%

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nve

rted

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54C

entr

alA

fric

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epu

blic

1.50

Dom

inic

anR

epu

blic

3.0

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roch

3.0

0.0

.0.

01.

46C

had

1.00


Alg

eria

14.0

Egg

iman

nco

nve

rted

toB

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ch.

11.0

11.0

22.0

7.00

Com

oros

4.48

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ador

10.6

Egg

iman

n(E

cuad

oran

dB

oliv

ia)

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roch

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5.0

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2.17

Con

go1.

50

Egy

pt14

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ggim

ann

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atem

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yan

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gK

ong

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iopi

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Egg

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1.54

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on1.

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ti3.

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esia

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aysi

a)co

nve

rted

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ch

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ana

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a8.

5E

ggim

ann

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edto

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roch

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1.8

5.8

23.7

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ren

ada

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uin

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aos

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iman

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aos

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1.73

Ken

ya2.

64

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Lan

ka8.

5E

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ann

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vert

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roch

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o0.

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occo

17.8

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iman

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33.3

9.08

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agas

car

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Mex

ico

14.8

Egg

iman

nco

nve

rted

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012

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524

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62M

alaw

i0.

79


AP

PE

ND

IX3

(CO

NT

INU

ED

)

Bas

eu

rban

izat

ion

esti

mat

ein

1500

Sou

rce

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seu

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esti

mat

ein

1500

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atio

nes

tim

ate

in15

00u

sin

gon

lyin

form

atio

nfr

omB

airo

ch

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aniz

atio

nes

tim

ate

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gon

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han

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ent

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ann

seri

es

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ula

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ula

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in15

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rted

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i1.

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Mau

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New Z

eala

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00.

37M

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biqu

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ista

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5E

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ann

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u10

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1.56

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azil

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Sal

vado

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ggim

ann

(3.8

%)

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vert

edto

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roch

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1.54

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nis

ia12

.3E

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edto

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roch

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111

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Leo

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outh

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S.

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nt

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tnam

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DEPARTMENT OF ECONOMICS, MASSACHUSETTS INSTITUTE OF TECHNOLOGY

SLOAN SCHOOL OF MANAGEMENT, MASSACHUSETTS INSTITUTE OF TECHNOLOGY

DEPARTMENTS OF POLITICAL SCIENCE AND ECONOMICS, UNIVERSITY OF CALIFORNIA,BERKELEY

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Documents

REVERSAL OF FORTUNE: GEOGRAPHY AND DARON …pages.ucsd.edu/~mnaoi/page4/POLI227/files/page1_8.pdftion and Marginalization” in Bergen, The Canadian Institute of Advanced Re-search,