The Colonial Origins of Comparative Development An Empirical Investigation

By DARON A CEM OG LU,SIMON JOHNSON,ANDJAMESA.ROBINSON*

We exploit differences in European mortality rates to estimate the effect of in st it ution son economic performance.Europeans adopted very different colonization policies in different colonies,with different associated institutions.In places where Europeans faced high mortality rates,they could not settle and were more likely to setup ex tractive institutions.These institutions persisted to the present.Exploiting differences in European mortality rates as an instrument for current institutions,we estimate large effects of institutions on income per capita.Once the effect of institutions is controlled for,countries in Africa or those closer to the equator do not have lower in comes.(JEL O11,P16,P51)

What are the fundamental causes of the large differences in income per capita across countries?Although there is still little consens us on the answer to this question,differen ces in institutions and property rights have received considerable attention in recent years.Countries with better“institutions," more secure property rights,and less dist or

* Acemoglu: Department of Economics,E52-380b,Massachusetts Institute of Technology，Cambridge,MA 02319,and Canadian Institute for Advanced Research (e-mail:daron @ mit.edu);Johnson:Sloan School ofMan- agement, Massachusetts Institute of Technology,Cambridge,MA02319(e-mail:sjohnson @ mit.edu);Robinson Department of Political Science and Department of Economics,210 Barrows Hall,University of California,Berkeley,CA94720(e-mail:[email protected]). We thank Joshua An grist,Abhij it Banerjee,Esther Du flo, StanE n german,JohnGallup,Claudia Gold in,Robert Hall,Chad Jones,Larry Katz,Richard Locke,Andrei Shleifer,Ken Sokoloff,Judith Tendler,three anonymous referees,and seminar participants at the University of California-Berkeley,Brown University,Canadian In stitut e for Advanced Research,Columbia University,Harvar d University,Massachusetts Institute of Technology, National Bureau of Economic Research,Northwestern University,New York University,Princeton University, University of Rochester, Stanford University,Toulouse University, University of California-Los Angeles, and the World Bank for useful comments.We also thank Robert M cCa a for guiding us to the data on bishops'mortality tio nary policies will invest more in physical and human capital,and will use these factors more efficiently to achieve a greater level of income (e.g., Douglass C. North and Robert P.Thomas,1973;Eric L.Jones,1981;North, 1981). This view receives some support from cross-country correlations between measures of property rights and economic development (e.g.,Stephen Knack and Philip Keefe r,1995; PauloMauro，1995;RobertE.HallandCharles 1. Jones, 1999; Dani Rodrik, 1999), and from a few micro studies that investigate the relationship between property rights and investment or output (e.g., Timothy Besley, 1995; Christopher Mazingo, 1999; Johnson et al.,1999).

At some level it is obvious that institutions matter.Witness,for example,the divergent paths of North and South Korea,or East and West Germany,where one part of the country stagnated under central planning and colle ctive ownership, while the other prospered with private property and a market economy. Nevertheless,welack reliable estimates of the effect of institutions on economic per formance.It is quite likely that rich economies choose or can afford better institutions.Perhaps more important, economies that are differentfor a variety of reasons will differ both in their institutions and in their income per capita.

To estimate the impact of institutions one conomic performance, we need a source of exogenous variation in institutions. In this paper, we propose a theory of institutional differences among countries colonized by Europeans,and exploit this theory to derive a possible source of exogenous variation.Our theory rests on three premises:

1.There were different types of colonization policies which created different sets of in stitutions.At one extreme,European powers set up"ex tractive states,'exemplified by the Belgian colonization of theCongo.These in st it ut ions did not introduce much protection for private property,nor did they provide checks and balances against government expr op riation.Infact,the main purpose of the ex tractive state was to transfer as much of the resources of the colony to the colonizer.

At the other extreme,many Europeans migrated and settled in a number of colonies, creating what the historian Alfred Crosby (1986)calls"Neo-Europes."The settlers tried to replicate European institutions,withstrong emphasis on private property and checks against government power.Primary examples of this include Australia, New Zealand, Canada,and the United States.

2.The colonization strategy was influenced by the feasibility of settlements.In places where the disease environment was not favorable to European settlement,the cards were stacked against the creation of Neo-Europes,andthe formation of the extractive state was more likely.

3. The colonial state and institutions persisted even after independence.

Based on these three premises,weusethe mortality rates expected by the first European settlers in the colonies as an instrument for current institutions in these countries.²More specifically，our theory can be schematically summarized as

We use data on the mortality rates ofsoldiers bishops, and sailors stationed in the colonies between the seventeenth and nineteenth centuries, largely based on the work of the historian Philip D. Curtin. These give a good indication of the mortality rates faced by settlers.Europeans were well informed about these mortality rates at the time, even though they did not know how to control the diseases that caused these highmortal it y rates.

Figure 1 plots the logarithm of GDP per capita today against the logarithm of thesettler mortality rates per thousand for a sample of 75 countries(see below for data details).It shows a strong negative relationship.Colonies where Europeans faced higher mortality rates are today substantially poorer than colonies that were healthy for Europeans.Our theory is that this relationship reflects the effect of settler mortalit y working through the institutions brought by Europeans.To substantiate this,weregress current performance on current institutions,and instrument the latter by settler mortality rates Since our focus is on property rights and checks against government power,we use the pro tection against“risk of expropriation”indexfrom Political Risk Services as a proxy for institutions.This variable measures differences in in- stitutions originating from different types of states and state policies.3 There is a strong

FIGURE1.REDUCED-FORM RELATIONSHIP BETWEEN INCOME AND SETTLER MORTALITY

(first-stage)relationship between settler mortality rates and current institutions,which is intere sting in its own right.The regression shows that mortality rates faced by the settlers more than 100 years ago explains over 25 percent of the variation in current institutions.4We also document that this relationship works through the channels we hypothesize: (potential) settler mortality rates were a major determinant of settlements;settlements were a major determinant of early institutions (in practice, institutions in 1900); and there is a strong correlation between early institutions andinstitutions today.Ourtwo-stageleast-squares estimate of the effect of institutions on performance is rel atively precisely estimated and large.For example, it implies that improving Nigeria's institutions to the level of Chile could,inthe long run, lead to as much as a 7-fold increase in Nigeria's income (in practice Chile is over 11 times as rich asNigeria).

The exclusion restriction implied by our in s tru mental variable regression is that,conditional on the controls included in the regression, the mortality rates of European settlers more than 1 o 0 years ago have no effect on GDP per capita today,other than their effect through institutional development. The major concern with this exclusion restriction is that themortal it y rates of settlers could be correlated with the current disease environment, whichmay have a direct effect on economic performance. Inthiscase,our instrumental-variables est imates maybe assigning the effect of diseases on income to institutions.We believe that this is unlikely to be the case and that our exclusion restriction is plausible.The great majority of European deathsin the colonies were caused by malaria and yellow fever.Although the sediseases were fatal to Europeans who had no immunity,theyhad limited effect on indigenous adults whohad developed various types of immunities.These diseases are therefore unlikely tobe the reason why many countries in Africa andAsia are very poor today(see the discussion in Section Ifl,subsection A).This notion is supported by the mortality rates of local people in these areas.For example, Curtin (1968 Table 2)reports that the annual mortality rates of local troops serving with theBritish army inBengal and Madras were respectively 11 and 13 in 1,00o. These numbers are quite comparable to, in fact lower than,the annual mortality rates of British troops serving in Britain,which were approximately 15 in 1,000.Incontrast,themortal it y rates of British troops serving in these colonies were much higher because of their lack of immunity. For example, mortality rates in Bengal and Madras for British troops were between 70 and 170 in 1,000.The view that the disease burden for indigenous adults was not unusual in places like Africa or India is also supported by the relatively high population densitiesin these places before Europeans arrived (Colin McEvedy and Richard Jones, 1975).

We document that our estimates of the effect of institutions on performance are not driven by outliers.For example,excluding Australia,New Zealand,Canada,and the United States does not change the results,nor does excluding Africa. Interestingly,we show that once the effect of institutions on economic performance is controlled for,neither distance from the equator nor the dummy for Africa is significant. These results suggest that Africa is poorer than the rest of the world not because of pure geographic or cultural factors,but because of worse institutions.

The validity of our approach-i.e.,ourexclu- sion restriction is threatened if other factors correlated with the estimates of settler mortality affect income per capita.We adopt twostrategies to substantiate that our results are not drivenby omitted factors.First,we investigate whether institutions have a comparable effect on income once we control for a number of variables potentially correlated with settlermortal it y and economic outcomes.Wefindthat none of these overturn our results;the estimates change remarkably little when we include controlsfor the identity of themain colonizer,legal origin,climate,religion,geography,naturalresources,soil quality,and measures of ethno lingui stic fragmentation.Furthermore,theresults are also robust to the inclusion of controls for the current disease environment(e.g.,the prevalenceof malaria,life expectancy,and infant mortality)and the current fraction of thepopu la tion of European descent.

Naturally,it is impossible to control for all possible variables that might be correlated with settler mortality and economic outcomes.Furthermore, our empirical approach might capture the effect of settler mortality on economic performance,butworking through other channels. We deal with these problems by using a simple over identification test using measures of Europe an migration to the colonies and early in sti- tut ions as additional instruments.Wethenuse over identification tests to detect whether settler mortality has a direct effect on current per for mance.The results are encouraging for our approach;they generate no evidence for a direct effect of settler mortality on economic outcomes.

We are not aware of others who have pointed out the link between settler mortality and in stitutions,though scholars such as William H. McNeill(1976),Crosby (1986),and Jared M Diamond(1997)have discussed the influence of diseases on human history. Diamond (1997), in particular,emphasizes comparative develop ment, but his theory is based on the geographical determinants of the incidence of the neolithic revolution.He ignores both the import ance of institutions and the potential causes of divergence in more recent development, which are the main focus of our paper.WorkbyRonaldE.Robinson and John Gallagher(1961), Lewis H. Gann and Peter Duignan (1962), Donald Denoon (1983), and Philip J. Cain and Anthony G.Hopkins (1993)emphasizes that settler colonies such as the United States and NewZealand are different from other colonies, and point out that these differences were imp ortantfor their economic success.Nevertheless. this literature does not develop the link between mortality,settlements,andinstitutions.

Our argument is most closely related to work on the influence of colonial experience on in stitutions. Frederich A. von Hayek (1960) argued that the British common law tradition was superi or to the French civil law,which was de velop ed during the Napoleonic era to restrain judges'interference with state policies (see also Seymour M. Lipset, 1994). More recently, Rafael La Porta et al.(1998,1999) emphasize the importance of colonial origin(the identity of the colonizer) and legal origin on current institutions,and show that the common-lawcountries and former British colonies have better property rights and more developed financial markets. Similarly, David Landes (1998 Chapters 19 and 20) and North et al. (1998) argue that former British colonies prospered relative to former French, Spanish, and Portuguese colon ies because of the good economic andpolitical institutions and culture they inherited from Britain.In contrast to this approach which focuses onthe identityof the colonizer,we emphasize the conditions in the colonies.Specifically,in our theory-andin thedata-it is not the identity of the colonizer or legal origin that matters,but whether European colonialists could safely settle in a particular location: where they could not settle,they created worse institutions. In this respect, our argument is closely related to that of Stanley L.Engerman and Kenneth L.Sokoloff(1997)whoalsoempha size institutions,but link them to factor en- dow ment s and inequality.

Empirically,our work is related to a number of other attempts to uncover the link between institutions and development,as wellas to Grazie ll aBer to c chi and Fabio Canova(1996) and Robin M. Grier (1999), who investigate the effect of being a colony on postwar growth. Two papers deal with the endo gene it y of inst it ut ions by using an instrumental variables approach as we do here. Mauro (1995) instruments for corruption using ethno linguistic fragmentation. Hall and Jones (1999), in turn, use distance from theequator as an instrument for social infrastructure because, they argue,latitude is correlated with“Western influence," which leads to good institutions.The theoretical reasoning for these instruments is not entirely convincing.It is not easy to argue that the Belgian influence in the Congo, or Western influence in the Gold Coast during the era of slavery promoted good institutions. Ethnolingui stic fragmentation,on the other hand,seems endogenous,especially since such fragmentation almost completely disappeared in Europe during the era of growth when a centralized state and market emerged (see, e.g., Eugen J.Weber, 1976; Benedict Anderson, 1983). Econometric ally, theproblem with both studies is that their instruments can plausibly have a direct effect on performance.Forexample,Willi iam Easterly and Ross Levine(1997)argue that ethno linguistic fragmentation can affect performance by creating political instability. while Charles de Montesquieu [1748] (1989) and more recently David E. Bloom andJeffrey D. Sachs (1998) and John Gallup et al.(1998) argue for a direct effect of climate on per formance. If, indeed, these variables have a direct effect,they are invalid instruments and do not establish that it is institutions that matter.The advantage of our approach is that conditional on the variables we already control for,settler mortal it y more than 1 o 0 years ago should have no effect on output today,other than through its effect on institutions. Interestingly, our results show that distance from the equator does not have an independent effect on economic per formance,validating the useof this variable as an instrument in the work by Hall and Jones (1999).

The next section outlines our hypothesis and provides supporting historical evidence.Section II presents OLS regressions of GDP per capita on our indexof institutions.Section Ill describes our key instrument for institutions,the mortality rates faced by potential settlers atthe time of colonization.Section IV presents our main results.Section V investigates the robustness of our results,and Section VI concludes.

I. The Hypothesis and Historical Background

We hypothesize that settler mortality affected settlements;settlements affected early in st it utions; and early institutions persisted and formed the basis of current institutions.Inthis section,we discuss and substantiate this hypo thesis.The next subsection discusses the link between mortality rates of settlers and settlement decisions,then we discuss differences in colonization policies,and finally,we turn to the causes of institutional persistence.

A.Mortality and Settlements

There is little doubt that mortality rates were a key determinant of European settlements. Curtin (1964,1998) documents how both the British and French press informed the public of mortality rates in the colonies.Curtin(1964)

also document show early British expectations for settlement in West Africa were dashed by very high mortality among early settlers,about half of whom could be expected to die in the first year.In the“Province of Freedom"(Sierra Leone), European mortality in the first year was 46 percent,in Bulama (April 1792-April 1793) there was 61-percent mortality among Europeans. In the first year of the Sierra Leone Company (1792-1793), 72 percent of the European settlers died.On Mungo Park's Second Exp edition(May-November 1805),87 percent of Eu- rope an s died during the overland trip from Gambia to the Niger, and all the Europeans died before completing the expedition.

An interesting example of the awareness of the disease environment comes from the Pilgrim fathers.They decided to migrate to the United States rather than Guyana because of the high mortality rates inGuyana(see Crosby. 1986 pp. 143-44). Another example comes from the Beauchamp Committee in1795, set up to decide where to send British convicts who had previously been sent to the United States. One of the leading proposals was the island of Lemane, up the Gambia River. The committee rejected this possibility because they decided mortality rates would be too high even for the convicts.Southwest Africa was also rejected for health reasons. The final decision was to send convicts to Australia.

The eventual expansion of manyof thecolon ies was also related to the living conditions there.In places where the early settlers faced high mortality rates,there would beless incentive for new settlers to come.5

B.Types of Colonization and Settlements

The historical evidence supports both the notion that there was a wide range of different typesof colonization andthat the presence or absence of European settlers was a key determin ant of the form colonialism took.Historians.

including Robinson and Gallagher (1961),Gann and Duignan (1962), Denoon (1983), and Cain andHopkins(1993),have documented the de- velo p ment of“settler colonies,”where Europeans settled in large numbers, and life was modeled after the home country.Denoon (1983) emphasizes that settler colonies had represent ati ve institutions which promoted what the settl ers wanted and that what they wanted was freedom and the ability to get rich by engaging intrade.Heargues that“there was undeniably something capitalist in the structure of these colonies.Private ownership of land and livestock was well established very early ..." (p. 35).

When the establishment of European-likein stitutions did not arise naturally,the settlers were ready to fight for them against thewishes of the home country. Australia is an interesting example here.Most of the early settlers in A us tr alia were ex-convicts,but thelandwas owned largely by ex-jailors,and there was no legal protection against the arbitrary power of landowners. The settlers wanted institutions and political rights like those prevailing inEngland at the time. They demanded jury trials, freedom from arbitrary arrest, and electoral representation.Although the British government resisted atfirst,the settlers argued that they were British and deserved the same rights as in the home country (see Robert Hughes, 1987). Cain and Hopkins write(1993 p.237）“fromthe late 1840s theBritish bowed tolocal pressures and, in line with observed constitutional changes taking place in Britain herself,accepted theidea that, in mature colonies, governors should in futureform ministries from the majority elements in elected legislatures." They also suggest that “the enormous boom in public investment after 1870[in New Zealand]...was an attempt to buildup an infrastructure...to maintain high living standards in a country where voters expected politicians actively to promote their economic welfare."(p.225).

This is insharp contrast to the colonial expe rien ce in LatinAmerica during the seventeenth and eighteenth centuries,and in Asia and Africa during the nineteenth and early twentieth c enturies.The main objective of the Spanish and the Portuguese colonization was to obtain gold and other valuables from America.Soonafter the conquest, the Spanish crown granted rights toland andlabor(the encomienda)andsetupa complex mercanti list system of monopolies and trade regulations to extract resources from the colonies.

Europeans developed the slave trade in Africa for similar reasons.Before the mid-nineteenth century,colonial powers were mostly restricted to the African coast and concentrated on monopolizing trade in slaves,gold,andother valuable commodities-witness the names used to describe West African countries: the Gold Coast, the Ivory Coast. Thereafter, colonial policy was driven in part by an element of super power rivalry, but mostly by economic motives. Michael Crow der (1968\,\mathrm{~p.~}\,50) ，forexample notes"it is significant that Britain's largest colon yon the West Coast[Nigeria]shouldhave been the one where her traders were most active and bears out the contention that,forBritain ... flag followed trade." Lance E. Davis and Robert A.Huttenback (1987~\mathsf{p}.~307) conclude that “the colonial Empire provides strong eviden ce for the belief that government was attuned to the interests ofbusiness andwilling to divert resources toends that the business comm unity would have found profitable."Theyfind that before 1885 investment in the British empire had a return 25 percent higher than that on domestic investment,though afterwards the two converged. Andrew Roberts (1976 p. 193) writes:“[from] ..1930 to 1940 Britain had kept for itself 2,400,000 pounds in taxes from the Copperbelt, while Northern Rhodesia received from Britain only 136,000 pounds in grants for development." Similarly, Patrick Manning (1982) estimates that between 1905 and 1914, 50 percent of GDP in Dahomey was extracted by the French, and Crawford Young (1994{\mathrm{~\textperthousand~}} 125)notes that tax rates in Tunisia were four times as high as in France.

Probably the most extreme case of extraction was that of King Leopold of Belgium in the Congo. Gann and Duignan (1979 p. 30) argue that following the example of the Dutch in Indonesia,Leopold's philosophy was that “the colonies should be exploited, not by the operationof a market economy,but by state inter ven- tion and compulsory cultivation of cash crops to besoldto and distributed by the state atcontrolled prices." Jean-Philippe Peemans (1975) calculates that tax rates on Africans in the Congo approached 60percent of their income duringthe 1920^{\circ}\mathrm{s} and 1930^{\circ}\mathrm{s} .BogumilJewsiewicki(1983)writes that during theperiod when Leopold was directlyin charge,policy was"based on the violent exploitation of natural and human resources,”with a consequent“destruct ion of economic and social life...[and] ... dismemberment of political structures."

Overall,there were few constraints on state power in the non settler colonies.The colonial powers setup authoritarian and absolutist states with the purpose of solidifying their control and facilitating the extraction of resources. Young

(1994 p. 101) quotes a French official in Africa:

"the European commandant is not posted to observe nature, ... He has a mission ... to impose regulations, to limit individual liberties ...,to collect taxes."Manning \left(1988{\mathrm{~p.~}}84\right) summarizes this as: “In Europe the theories of represent at ive democracy won outover the theorists of absolutism .... But in Africa, the European conquerors set up absolutist governments,based on reasoning similar to that of Louis XlV."

C.Institutional Persistence

There is a variety of historical evidence,aswell as our regressions in Table 3 below,suggesting that the control structures setup in the non settler colonies during the colonial era persisted,while there is little doubt that the institutions of law and order and private property established during the early phases of colonialism in Australia, Canada New Zealand, the United States, Hong Kong, and Singapore have formed thebasisof thecurrentday institutions of these countries."

Young emphasizes that the ex tractive in st it ut ions setup by the colonialists persisted long after the colonial regime ended. He writes “although we commonly described theindependent polities as‘new states,'in reality they were successors to the colonial regime,inheriting its structures,its quotidian routines and practices, and its more hidden normative theories of governance"(1994p.283).An example of the persistence of ex tractive state institutions into the independence era is provided by the persistence of the most prominent ex tractive policies. In Latin America, the full panoply of monopo lies and regulations, which had been created by Spain, remained intact after independence, for most of the nineteenth century.Forced labor policies persisted and were even intensified or reintroduced with the expansion of export agriculture in the latter part of the nineteenth c en- tury. Slavery persisted in Brazil until 1886, and during the sisal boom in Mexico,forced labor was reintroduced and persisted up to the start of the revolution in 1910.Forced labor was also reintroduced in Guatemala andEl Salvador to provide labor for coffee growing.IntheGuate malancase,forced labor lasted until the creation of democracy in 1945. Similarly, forced labor was reinstated in many independent African countries, for example, by Mobutu in Zaire.

There are a number of economic mechanisms that will lead to institutional persistence of this type. Here, we discuss three possibilities.

(1)Setting up institutions that place restrictions on government power and enforce property rights is costly(see,e.g.，Acemoglu and Thierry Verdi er,1998).If the costs of c re- a ting these institutions have been sunk by the colonial powers,then it may not pay the elites at independence to switch to extra ctive institutions.In contrast,when the new elites inherit extractive institutions, they may not want to incur the costs of intro ducing better institutions,and may instead prefer to exploit the existing ex tractive in- st it ut ions for their own benefits.

(2) The gains to an extractive strategy may depend on thesizeof the ruling elite.When this elite is small,each member would have a larger share of the revenues,sotheelite may have a greater incentive tobeextractive.In many cases where European powers setup authoritarian institutions,theydelegated the day-to-day running of the state to a small domestic elite. This narrow group often was the one to control the state after independence and favored ex tractive institutions.ro

(3)If agents make irreversible investments that are complementary to a particular set of institutions,they willbe more willing to support them, making these institutions persist (see, e.g., Acemoglu, 1995). For example,agents who have invested in human and physical capital will be in favor of spending

TABLE1-DESCRIPTIVE STATISTICS

Notes:Standard deviations are in parentheses.Mortality is potential settler mortality,measured in terms of deathsper annum per 1,0oo“mean strength"(raw mortality numbers are adjusted to what they would be if a force of 1,0oo living people were kept in place for a whole year, e.g., it is possible for this number to exceed 1,0oo in episodes of extreme mortality as those who die are replaced with new arrivals).Sources and methods for mortality are described in Section Ill,subsection B,and in the unpublished Appendix(available from the authors;or see A cem og lu et al.,20oo).Quartiles of mortality arefor our base sample of 64 observations.These are:(1) less than 65.4;(2)greater than or equal to 65.4 and less than 78.1;(3)greater than orequal to78.1andless than280;(4)greater than or equal to 280.The number of observations differs by variable;see Appendix Table Al for details.

money to enforce property rights, while those who have less to lose may not be.

II.Institutions and Performance: OL S Estimates

A.Data and Descriptive Statistics

Table 1 provides descriptive statistics for the key variables of interest.The first column is for the whole world,and column(2)is for ourbase sample,limited to the 64 countries that were ex-colonies and for which we have settler mortality,protection against expropriation risk,and GDPdata(this issmaller than the sample in Figure1).The GDP per capita in 1995 is PPP adjusted(a more detailed discussion of alldata sources is provided in Appendix Table A1). Income (GDP) per capita will be our measure of economic outcome.There are large differences in income per capita in both the world sample and our basic sample,and the standard devi ation of log income per capita in both cases is 1.1. In row 3, we also give output per worker in 1988 from Hall and Jones (1999) as an alternative measure of income today. Hall and Jones (1999)prefer this measure since it explicitly refers to worker productivity.Ontheother hand, given the difficulty of measuring the formal labor force, it may be a more noisy measure of economic performance than income per capita.

We use a variety of variables to capture inst it ut ional differences. Our main variable, reported in the second row,is an index of protection against expropriation.These data are from Political Risk Services (see, e.g., William D.Coplinetal.,1991),and were first used in the economics and political science literatures by Knack and Keefe r(1995).Political RiskS ervices reports a value between O and 1 of or each country and year,with O corresponding to the lowest protection against expropriation.We use the average value for each country between 1985 and 1995 (values are missing for many countries before 1985). This measure is appropri ate for our purposes since the focus here is on differences in institutions originating from diffe rent types of states and state policies.We expect our notion of extractive state to corres pond to a low value of this index,whilethe tradition of rule of law and well-enforced proper ty rights should correspond to high values. The next row gives an alternative measure,constraints on the executive in 1990, coded from the Polity Ill data set of Ted Robert Gur rand associates(an update of Gur r,1997).Results using the constraints on the executive and other measures are reported in Acemoglu et al.(2000) and are not repeated here.

The next three rows give measures of early institutions from the same Gur r data set.The first is a measure of constraints on the executive in 1900 and the second is an index of democracy in1900.This information is not available for countries that were still colonies in 1900,so we assign these countries the lowest possible score. In the following row, we report the mean and standard deviation of constraints on the executive in the first year of independence(i.e. thefirst year a country enters the Gurr data set) asan alternative measure of institutions.The second-to-last row gives the fraction of the popul ation of European descent in 1900,whichis our measure of European settlement in the colonies,constructed from Mc Eve dy and Jones (1975)and Curtin et al.(1995).The final row gives the logarithm of the baseline settler mortal it y estimates;the raw data are in Appendix TableA2.

The remaining columns give descriptive statistics for groups of countries at different quartiles of the settler mortality distribution. This is useful since settler mortality is our instrument for institutions (this variable is described in more detail in the next section).

B.Ordinary Least-Squares Regressions

Table 2 reports ordinary least-squares(OLS) regressions of log per capita income on the protection against expropriation variable ina variety of samples.The linear regressions are for the equation

$$
\log y_{i}=\mu+\alpha R_{i}+{\bf X}_{i}^{\prime}\gamma+\varepsilon_{i},
$$

where $y{i}is income per capita in country i,R{i}is the protection against expropriation measure,\mathbf{X}{i}isa vector of other covariates,and\pmb{\varepsilon}{i}isa random error term.The coefficient of interest throughout the paper is_{\alpha}$ ,the effect of institutions on income per capita

Column(1)shows that in the whole world sample thereis a strong correlation between our measure of institutions and income per capita Column(2)shows that the impact of theinstitutions variable on income per capita in our base sample is quite similar to that in the whole world,and Figure 2 shows this relationship dia grammatically for our base sample consisting of 64 countries. The R^{2} of the regression in column (1) indicates that over 50 percent of the variation in incomeper capita is associated with variation in this index of institutions. To get a sense of the magnitude of the effect of institutions on performance, let us compare two countries,Nigeria,which has approximately the 25 th percentile of the institutional measure in this sample,5.6,and Chile,which has approximate ly the 75 th percentile of the institutions index, 7.8. The estimate in column (1), 0.52. indicates that there should be on average a1.14- log-point difference between the log GDP s of the corresponding countries (or approximately a 2-fold difference \cdot e^{1.14}\,-\,\dot{1}\approx\dot{2}.1) . In practice, this GDP gap is 253\;\log points (approximately 11-fold).Therefore， if the effect estimated in Table 2 were causal, it would imply a fairly large effect of institutions on performance, but still much less than the actual income gap between Nigeria and Chile

Many social scientists, including Montesquieu [1784] (1989), Diamond (1997), and

TABLE 2—OL S REGRESSIONS

Notes:Dependent variable:columns (1)-(6),log GDP per capita(PPPbasis)in1995,current prices(from the World Bank's World Development Indicators 1999); columns (7)-(8),log output per worker in 1988 from Hall and Jones (1999).Average protection against expropriation risk is measured on ascalefrom 0 to10,where a higher score means more protection against expropriation,averaged over 1985 to 1995,from Political Risk Services.Standard errors are in parentheses.In regressions with continent dummies,the dummy for America is omitted.See Appendix Table A 1 for more detailed variable definitions andsources.Of the countries in our base sample,Hall andJones do not report output per worker in the Bahamas,Ethiopia andVietnam.

Sachs and coauthors,have arguedfor adirect effect of climate on performance,and Gallup et al.(1998) and Hall and Jones (1999) document the correlation between distance from the e quator and economic performance.To control for this, in columns (3)-(6), we add latitude as a regressor (we follow the literature in using the absolute value measure of latitude,i.e.,distance from the equator, scaled between 0 and 1). This changes the coefficient of the index of institutions little.Latitude itself is also significant and has the sign found by the previous studies. In columns (4) and (6),we also add dummies for Africa,Asia,and other continents,with America as the omitted group.Although protection against expropriation risk remains significant, the continent dummies are also statistically and quantitatively significant.The Africa dummy in column(6)indicates that in our sample African countries are 90 log points (approximately 145 percent)poorer even after taking the effect of institutions into account.Finally,in columns (7)

and (8), we repeat our basic regressions using the log of output per worker from Hall and Jones (1999),with very similar results

Overall, the results in Table 2 show a strong correlation between institutions and economic performance.Nevertheless,there area number of important reasons for not interpreting this relationship as causal.First,rich economies may be able to afford, or perhaps prefer, better institutions.Arguably more important than this reverse causality problem,there are many omit- ted determinants of income differences that will naturally be correlated with institutions. Finally. the measures of institutions are constructed ex post,and the analysts may have had a natural bias in seeing better institutions in richer places. As well as these problems introducing positive bias in the OLS estimates,the fact that the institutions variable is measured with considerable error and corresponds poorly to the “cluster of institutions'that matter in practice creates attenuation and may bias theOL S estimates

FIGURE2.OL S RELATIONSHIP BETWEEN EXPROPRIATION RISK AND INCOME

downwards.Allof these problems could be solved if we had an instrument for institutions. Such an instrument must be an important factor in accounting for the institutional variation that we observe, but have no direct effect on performance. Our discussion in Section I suggests that settler mortality during the time of colonization is a plausible instrument.

III.Mortality of Early Settlers

A.Sources of European Mortality in the Colonies

In this subsection,we give a brief overview of the sources of mortality facing potential settlers. Malaria (particularly Plasmodium falciporum)and yellow fever were the major sources of European mortality in the colonies. In the tropics,these two diseases accounted for 80 percent of European deaths,while g astro in testin al diseases accounted for another 15 percent (Curtin,1989p.30).Throughout the nineteenth century,areas without malaria and yellow fever, such as New Zealand,were more healthy than Europe because themajor causes of death in Europe tuberculosis, pneumonia,and smallpox-were rare in these places (Curtin, 1989 p.13).

Both malaria and yellow fever are transmitted by mosquito vectors. In the case of malaria, the main transmitter is the Anopheles gambia e complex and the mosquito Anopheles funestus, while the main carrier of yellow fever is Aedes aegypti.Both malaria and yellow fever vectors tend to live close to human habitation.

In places where the malaria vector is present. such as the West African savanna or forest,an individual can get as many as several hundred infectious mosquito bites a year. For a person without immunity,malaria(particularly Pl asmodium falciporum) is often fatal, so Europeans in Africa, India, or the Caribbean faced very high death rates.Incontrast,death rates for the adult local population were much lower(see Curtin [1964] and the discussion in our introduction above). Curtin (1998 pp. 7-8) describes this as follows:

Children in West Africa...wouldbeinfec ted with malaria parasites shortly after birth and were frequently reinfected afterwards;if they lived beyond the age of about five, they acquired an apparent immunity.The parasite remained with them, normally in the liver, but clinical symptoms were rare so long as they continued to be infected with the same species of P. falciporum.

The more recent books on mala rio logy confirm this conclusion. For example,“In stable endemic areas a heavy toll of morbidity and mortal it y falls on young children but malaria is a relatively mild condition in adults" (Herbert M. Gilles and David A.Warrell,1993 p.64;see also the classic reference on this topic,Leonard J. Bruce-Chwatt,1980 Chapter 4; Roy Porter, 1996).12 Similarly, the World Health Organization (WHO) points out that in endemic malaria areas of Africa and the Western Pacific today "... the risk of malaria severity and death is almost exclusively limited to non-immunes, being most seriousfor young children over six months of age...surviving children develop their own immunity between the age of 3-5years' (Jose A. Najera and Joahim Hempel, 1996).

Peoplein areas where malaria is endemic are also more likely to have genetic immunity against malaria.Forexample,they tend to have the sickle-cell trait, which discourages the multip li cation of parasites in the blood, or deficiencie s in glucose-6-phosphate dehydrogenase and thalassa emi a traits,which also protect against malaria. Porter (1996 p. 34) writes: “In such a process,...，close to1o0 percent of Africans acquired a genetic trait that protects them against vivax malaria and probably against falc ipo rum malaria as well."Overall,theWHOes- timates that malaria kills about 1 million people peryear,mostof them children.It doesnot,however,generally kill adults who grew up in malariaendemic areas (seeNajera and Hempel,1996).

Although yellow fever's epidemiology is quite different from malaria,it was also much more fatal to Europeans than to non-Europeans whogrew up in areas where yellow fever commonly occurred.13 Yellow fever leaves its surviving victims with a lifelong immunity,which also explains its epidemic pattern, relying on a concentrated non immune population.Curtin (1998\,\mathrm{\bf~p.~}\,10) writes:“Because most Africans had passed through a light case early in life. yellow fever in West Africa was a strangers disease, attacking those who grew up elsewhere."Similarly,Michael B.A.Oldstone (1998p.49) writes:

Most Black Africans and their desc endants respond to yellow fever infection with mild to moderate symptoms such as headache, fever, nausea, and vomiting, and then recover in a few days.Thisoutcome reflects the long relationship between thevirus and its indigenous hosts, who through generations of exposure to the virus have evolved resistance.

In contrast, fatality rates among nonimmune adults,such as Europeans, could be as high as 90percent.

Advances in medical science have reduced the danger posed by malaria and yellow fever. Yellow fever is mostly eradicated(Oldstone, 1998 Chapter 5), and malaria has been eradicate d in many areas.Europeans developed methods of dealing with these diseases that gradually became more effective in the second half of the nineteenth century.For example they came to understand that high doses of quinine,derived from the cinchona bark,acted as a prophylactic and prevented infection or reduced the severity of malaria.Theyalso started to undertake serious mosquito era dic ation efforts and protect themselves against mosquitobites.Further,Europeans also learned that an often effective method of reducing mortality from yellow fever is flight from the area,since the transmitter mosquito, Aedes aegypti, has only a short range.Nevertheless,during much of the nineteenth century,there was almost a complete misunderstanding of the nature of both diseases. For example, the leading theory for malaria was that it was caused by“miasma"from swamps, and quinine was not used widely.The role of small collections of water tobreed mosquitoes and transmit these diseases was not understood.Itwas only in the late nineteenth century that Europeans 14 started to control these diseases.

These considerations,together with the data we have on the mortality of local people and population densities before the arrival of Europeans,make us believe that settler mortality is a plausible instrument for institutional development:these diseases affected European settlement patterns and the type of institutions they setup,but had little effect on the health and economy of indigenous people.15

A final noteworthy feature,helpful in in ter pre ting our results below,is that malaria prevalence depends as much on the microclimate of an area as on its temperature and humidity,or on whether it is in the tropics; high altitudes reduce the risk of infection,so in areas of high altitude, where“hill stations" could be setup,such as Bogota in Colombia mortality rates were typically lower than in wet coastal areas.However,malaria could sometimes be more serious in high-altitude areas.Forexample,Curtin(1989p.47)points out that in Ceylon mortality was lower in the coast than the highlands because rains in the coast washed away the larvae of the transmitter mosquitoes.Similarly,in Madras many coastal regions were free of malaria,while northern India had high rates of infection. Curtin (1998 Chapter 7) also illustrates how there were marked differences in the prev alence of malaria within small regions of Madagascar. This suggests that mortality rates faced by Europeans are unlikely to be a proxy for some simple geographic or cl imactic feature of the country.

B.Data on Potential Settler Mortality

Our dataon the mortality of European settlers come largely from the work of Philip Curtin.Systematic military medical record keeping began only after 1815, as an attempt to understand whysomany soldiers were dying in some places. The first detailed studieswere retrospective and dealt with British forces between 1817 and 1836.TheUnited States and French governments quickly adopted similar methods (Curtin,1989 pp. 3, 5).Some early data are also available for the Dutch East Indies.By the 1870^{\circ}s ,mostEuropean countries published regular reports on the health of their soldiers.

The standard measure is annual i zed deaths per thousand mean strength.This measure reports the death rate among 1,oo 0 soldiers where each death is replaced with a new soldier. Curtin(1989,1998)reviews in detail the c 0 nstruct ion of these estimates for particular places and campaigns,and assesses which data should be considered reliable.

Curtin(1989),Death by Migration,deals primarily with the mortality of European troops from 1817 to 1848.At this time modern medicine was still in its infancy,andthe European militaries did not yet understand how to control malaria and yellow fever. These mortality rates can therefore be interpre ted as reasonable estimates of settler mortality.They are consistent with substantial evidence from other sources(see,forexam- ple,Curtin[1964,1968]).Curtin(1998),Disease and Empire,adds similar data on the mortality of soldiers in the second half of the nineteenth century.'°Inallcases,weusethe earliest available number for each country, reasoning that this is the best estimate of the mortality rates that settlers would have faced. atleast until the twentieth century.

The main gap in the Curtin data is for South America since the Spanish and Portuguese militaries did not keep good records of mortality.Hector Gutierrez(1986)used Vatican records to construct estimates for the mortalit y rates of bishops in LatinAmerica from 1604to1876.Because these data overlap with the Curtin estimates for several co untries,we are able to construct a data series for South America.17 Curtin (1964) also provides estimates of mortality in naval squadrons for different regions which we can use to generate alternative estimates of mortality in South America.Appendix Bin Ace mo glue t al. (20oo),which is available from the authors, gives a detailed discussion of how these data are constructed,and Appendix Table A 5 (available from the authors),shows that these alternative methods produce remarkably similar results. Appendix Table A2 lists our main estimates,and Table A 1 gives information about sources.

IV.Institutions and Performance:IVResults

A.Determinants of Current Institutions

Equation (l) describes the relationship between current institutions and log GDP. In add it ion we have

$$
\begin{array}{r l r}&{}&{R_{i}=\lambda_{R}+\,\beta_{R}C_{i}+\,{\bf X}_{i}^{\prime}\gamma_{R}+\,\nu_{R i},\,\,\,\,\,\,\,\,\,\,\,\,}\ &{}&{C_{i}=\lambda_{C}+\,\beta_{C}S_{i}+\,{\bf X}_{i}^{\prime}\gamma_{C}+\,\nu_{C i},\,\,\,\,\,\,\,\,\,\,\,}\ &{}&{S_{i}=\lambda_{S}+\,\beta_{S}\mathrm{log}\,M_{i}+\,{\bf X}_{i}^{\prime}\gamma_{S}+\,\nu_{S i},}\end{array}
$$

where R is the measure of current institutions (protection against expropriation between 1985 and1995), C is our measure of early(circa 1900) institutions, s is the measure of European settlements in the colony (fraction of thepopulation with European descent in 1900), and M is mortality rates faced by settlers. \mathbf{X} is a vector of co variate s that affect all variables.

The simplest identification strategy might be touse $S{i}(orC{i})）as an instrument forR{i}in equation (1),and we report some of these regres s ions in Table 8.However,to the extent that settlers are more likely to migrate to richer areas and early institutions reflect other character is- tics that are important for income today,this identification strategy would be invalid (i.e.,C{i}andS{i}could be correlated with\varepsilon{i}).Instead,we use the mortality rates faced by the settlers,logM{i}, as an instrument forR{i}This identification strategy will be valid as long as logM{i}is uncorrelated with\pmb{\varepsilon}{i}$ -thatis,if mortality rates of settlers between the seventeenth and nineteenth centuries have no effect onincome today other than through their influence on in st it ut ional development.We argued above that this exclusion restriction is plausible.

Figure 3 illustrates the relationship between the (potential)settler mortality rates and the indexof institutions.We use the logarithm of the settler mortality rates,since there are no theoretical re asons to prefer the level as a determinant of in stitut ions rather than the log,and using the log ensures that the extreme African mortality rates do not play a disproportionate role. As it happens, there is an almost linear relationship between the log settler mortality and our measure of in st it utions.This relationship shows that ex-colonies where Europeans faced higher mortality rates have substantially worse institutions today.

In Table 3, we document that this relationship works through the channels hypothesized in Section I. In particular, we present OLS regressions of equations (2),(3),and (4).In the top panel,we regress the protection against expropriation var iable on the other variables.Column(1)uses constrain ts faced by the executive in 1900 as the regressor,and shows aclose association between early institutions andinstitutions today.Forexample,past institutions alone explain 20 percent of the variation in the indexof current institutions. The second columnadds the latitude variable

FIGURE3.FIRST-STAGE RELATIONSHIP BETWEEN SETTLER MORTALITY AND EXPROPRIATION RISK

with little effect on the estimate.Columns(3) and (4）use the democracy index,and confirm the results in columns (1) and (2)

Both constraints on the executive and democracy indices assign low scores to countries that were colonies in 1900,and do not use the earliest post independence information for Latin American countries and the Neo-Europes.In columns (5) and (6), we adopt an alternative approach and use the constraints on the exec u- ti vein the first year of independence and also control separately for time since independence. The results are similar, and indicate that early institutions tend to persist.

Columns(7)and(8)show the association between protection against expropriation and European settlements. The fraction of Europeans in 1900 alone explains approximately 30 percent of the variation in our institutions variable today. Columns (9) and (10) show the relationship between the protection against expropriation variable and the mortality rates faced bysettlers.This specification will be the first stage for our main two-stageleast-squares estimates(2SLS).It showsthat settler mortality alone explains 27 percent of the differences in institutions we observe today.

Panel B of Table 3 provides evidence in support of the hypothesis that early institutions wereshaped,atleast in part,by settlements,and that settlements were affected by mortality.Columns (1)-(2) and (5)-(6) relate our measure of constraint on the executive and democracy in 1900to the measure of European settlements in 1900 (fraction of the population of European decent). Columns (3)-(4) and (7)-(8) relate the same variables to settler mortality. These regressions show that settlement patterns explain around 50 percent of the variation in early institutions. Finally,columns (9) and (10) show the relationship between settlements and mortality rates.

B.Institutions and Economic Performance

Two-stageleast-squares estimates of e quation (1) are presented in Table 4.Protection against expropriation variable, R_{i} ,istreated as endogenous,and modeled as

$$
R_{i}=\zeta+\beta\,\log\,M_{i}+{\bf X}_{i}^{\prime}\delta+\upsilon_{i},
$$

where M_{i} is the settler mortality rate in 1,000 mean strength.The exclusion restriction is that this variable doesnot appear in (1).

TABLE3-—DETERMINANTS OF INSTITUTIONS

Notes:All regressions are OL S.Standard errors are in parentheses.Regressions with constraint on executive in first year of independence also include years since independence as a regressor.Average protection against expropriation risk is on a scale from O to10,wherea higher score means more protection against expropriation of private investment by government, averaged over 1985 to 1995.Constraint on executive in 1900 is on a scale from 1 to 7,with a higher score indicating more constraints.Democracy in 1900 is on a scalefrom 0 to 10,with a higher score indicating more democracy.European settlements is percent of population that was European or of European descent in 19 o 0.See Appendix Table A 1 for more detailed variable definitions and sources.

Panel A of Table 4 reports 2 SLS estimates of the coefficient of interest, \alpha from equation (1) and Panel B gives the corresponding first stages.18 Column (1) displays the strong firststage relationship between(log)settler mortalit y and current institutions in our base sample also shown in Table 3. The corresponding 2SLS estimate of the impact of institutions on income per capita is 0.94.This estimate ishighly sign if i cant with a standard error of 0.16,andinfact larger than the OLS estimates reported in Table 2. This suggests that measurement error in the institutions variables that creates at tenuation bias is likely to be more important than reverse causality and omitted variables biases. Here we are referring to“measurement error” broadly construed.In reality the set of in st it ut ions that matter for economic performance is very complex,and any single measure is bound to capture only part of the"true institutions,"

TABLE 4 IV REGRESSIONS OF LOG GDP PER CAPITA

Notes: The dependent variable in columns (1)-(8) is log GDP per capita in 1995, PPP basis. The dependent variable in column (9) is log output per worker,from Hall and Jones (1999).“Average protection against expropriation risk 1985-1995"is measured on a scale from 0 to10,where a higher score means more protection against risk of expropriation of investment by the government,from Political Risk Services.Panel A reports thetwo-stageleast-squares estimates,instrumenting for protection against expropriation risk using log settler mortality;Panel B reports the corresponding first stage.Panel Creports the coefficient from an OL S regression of the dependent variable against average protection against expropriation risk.Standard errors are in parentheses.In regressions with continent dummies,the dummy for America is omitted.See Appendix TableA1for more detailed variable descriptions and sources.

creating atypical measurement error problem. Moreover,what matters for current income is presumably not only institutions today,butalso institutions in the past.Our measure of in st it ut ions which refers to 1985-1995 will not be perfectly correlated with these.19

19 We can ascertain, to some degree, whether the differen ce between OL S and 2 SLS estimates could be due to measurement error in the institutions variable by making use of an alternative measure of institutions,forexample the constraints on the executive measure.Using this me a

Does the 2 SLS estimate make quantitative sense?Does it imply that institutional differences can explain a significant fraction of income difference s across countries?Let us once again compare two “typical" countries with high and low expropriation risk, Nigeria and Chile (these countries are typical for the IV regression in the sense that they are practically on the regression line). Our 2 SLS estimate,0.94,implies that the 2.24 differences in expropriation risk between these two countries should translate into 206 log point (approximately 7-fold) difference. In practice, the presence of measurement error complicates this interpretation,because some of the difference between Nigeria andChile's expropriation index may reflect measurement error.Therefore,the 7-fold difference is an upper bound. In any case, the estimates in Table 4 imply a substantial,but not implausibly large,effect of institutional differen ces on income per capita.

Column (2) shows that adding latitude does not change the relationship;the institutions coefficient is now 1.o 0 with a standard error of 0.22.20 Remarkably, the latitude variable now has the “wrong” sign and is insignificant. This result suggests that many previous studies may have found latitude to be a significant determinant of economic performance because it is correlated with institutions (or with the exogenous component of institutions caused by early colonial experience).

Columns (3) and (4) document that our results are not driven by the Neo-Europes. When we exclude the United States, Canada, Australia, and NewZealand,the estimates remain highly sign ificant,and in fact increase a little.Forexample,the coefficient for institutions is now 1.28(s.e. = 0.36) without the latitude control, and 1.21 (s.e. = 0.35) when we control for latitude. Columns (5) and (6)show that our results are also robust to dropping all the African countries from our sample.The estimates without Africa are somewhat smaller,but alsomore precise.For example,the coefficient for institutions is 0.58 (s.e. =~0.1 without the latitude control, and still 0.58 (s.e. = 0.12) when we control for latitude.21

In columns (7)and(8),we add continent dummies to the regressions (for Africa, Asia, and other,with America as the omitted group).The addition of these dummies does not change the estimated effect of institutions,and the dummies are jointly insignificant at the 5-percent level, though the dummy for Asia is significantly different from that of America.The fact that the African dummy is insignificant suggests that the reason why African countries are poorer is not due to cultural or geographic factors, but mostly accounted for by the existence of worse institutions in Africa. Finally, in column (9) we repeat our basic regression using log of output per worker as calculated by Hall and Jones (1999). The result is very close to our baseline result.The 2 SLS coe f- ficient is 0.98 instead of 0.94 as in column (1).22 This shows that whether we use income per capita or output per worker has little effect on our results Overall,the results in Table 4 show a large effect of institutions on economic performance. In the restof thepaper,we investigate the robustness of 23 these results.

22 The results with other covariates are also very similar. We repeated the same regressions using a variety of alternative measures of institutions,including constraints on the executive from the Polity Iff data set,an indexof law and order tradition from Political Risk Services,ameasure of property rights from the Heritage Foundation,ameasureof rule of law from the Fraser Institute,and the efficiency of the judiciary from Business International.Theresults and the magnitudes are very similar to those reported in Table4.We also obtained very similar results with the 1970 valuesfor the constraints on the executive and incomeper capita in 1970,which show that the relationship between institutional measures and income per capita holds across time periods.These results are reported in the Appendix of the working paper version,and are also available from the authors.

23 In the working paper version, we also investigated the robustness of our results in different sub samples with varying degrees of data quality and different methods of construct ing the mortality estimates.The results change very little,for example,when we use data only from Curtin (1989),Death by Migration,when we do not assign mortal it y rates from neighboring disease environments,when

V.Robustness

A.Additional Controls

The validity of our 2 SLS results in Table 4 depends on the assumption that settler mortality in the past has no direct effect on current economic performance.Although this presumption appears reasonable (at least to us), here we substantiate it further by directly controlling for many of the variables that could plausibly be correlated with both settler mortality and economic outcomes,and checking whether the addition of these variables affects our estimates.24 Overall, we find that our results change remarkably little with the inclusion of these variables,and many variables emphasized in previous work become insignificant once the effect of institutions is controlled for

La Porta et al. (1999) argue for the import ance of colonial origin(identityof themain colonizing country)as a determinant of current institutions. The identity of the colonial power could also matter because it might have an effect through culture,as argued by David S.Landes (1998). In columns (1) and (2) of Table5,we add dummies for British and French colonies(colonies of other nations are the omit- ted group).This haslittle affect on our results. Moreover,the French dummyin the first stage is estimated to be zero,while the British dummy is positive, and marginally significant. Therefore, as suggested by La Porta et al. (1998),British colonies appear to have better institutions,but this effect is much smaller and weaker than in a specification that does not control for the effect of settler mortality on institutional development.?5 Therefore, it appears that British colonies are found to perform substantially better in other studies inlarge part because Britain colonized places where settlement s were possible,and this made British colon ies inherit better institutions.Tofurther investigate this issue,columns(3)and(4)estimate our basic regression for British colonies only.They show that both the relationship between settler mortality andinstitutions and that between institutions and income in this sample of 25 British colonies are very similar to those in our base sample. For example, the 2SLS estimate of the effect of institutions on income is now1.07(s.e. =0.24) without controlling for latitude and 1.00 (s.e. =~0.22) with latitude. These results suggest that the identity of the colonizer is not an important determinant of colonization patterns and subsequent in st it ut ional development.

vonHayek(1960)andLaPorta et al.(1999)also emphasize the importance of legal origin. In columns (5)and (6),we control for legal origin.In our sample,all countries have either French or British legal origins, so we simply add a dummy for French legal origin (many countries that are not French colonies nonetheless have French legal origin). Our estimate of the effect of institutions on income per capita is unaffected.26

An argument dating back to Max Weber views religion as a key determinant ofeconomic performance. To control for this,in columns (7) and(8),we add the fraction of the populations that are Catholic,Muslim,andofother religions, with Protestants as the omitted group. In the table we report the joint significance level p -value)of the corresponding F -statistic for these dummies as well as the 2 SLS estimate of

TABLE5—IV REGRESSIONS OF LOG GDP PER CAPITA WITH ADDITIONAL CONTROLS

Notes:Panel A reports thetwo-stageleast-squares estimates with log GDP per capita(PPPbasis)in1995as dependent variable andPanel Breports the corresponding first stage.Thebase case in columns(1)and(2)is all colonies that were neither French nor British. The religion variables are included in the first stage of columns (7) and (8) but not reported here (to save space). Panel C reports the OLS coefficient from regressing log GDP per capita on average protection against expropriation risk, with the other control variables indicated in that column (full results not reported to save space). Standard errors are in parentheses and p values for joint significance tests are in brackets. The religion variables are percentage of population that are Catholics, Muslims, and “"other” religions; Protestant is the base case. Our sample is all either French or British legal origin (as defined by La Porta et al., 1999).

the effect of institutions.27 Finally, column (9) adds all the variables in this tables i mult a- neously.Again,these controls have very little effect on our main estimate.

Another concern is that settler mortality is correlated with climate and other geographic characteristics.Our instrument may therefore be pickingup the direct effect of thesevariables.We investigate this issue in Table 6.In columns (1)and (2),we add a set of tempera ture and humidity variables(all data from Philip M. Parker, 1997). In the table we report joint significance Ie vel s for these var iables. Again, they have little effect on our estimates.

TABLE6-ROBUSTNESS CHECKS FOR IV REGRESSIONS OFLOG GDPPER CAPITA

PanelB:First Stage for Average Protection Against Expropriation Risk in 1985-1995

Notes:Panel A reports the two-stage least-squares estimates with log GDP per capita (PPP basis) in 1995,and Panel B reports the corresponding first stages.Panel C reports theOL S coefficient from regressing log GDP per capita on average protection against expropriation risk,with the other control variables indicated in that column(full results not reported to save space). Standard errors are in parentheses and p -valuesfor joint significance tests are in brackets.All regressions have 64 observations,except those including natural resources,which have 63 observations.The temperature and humidity variables are:average,minimum,and maximum monthly high temperatures,and minimum andmaximum monthlylow temperatures, and morning minimum and maximum humidity,and afternoon minimum and maximum humidity(from Parker,1997). Measures of natural resources are: percent of world gold reserves today,percent of world iron reserves today,percent of world zinc reserves today,number of minerals present in country,and oil resources(thousands of barrelsper capita).Measures of soil quality/climate are steppe (low latitude),desert(low latitude),steppe(middle latitude),desert(middle latitude),drysteppe wasteland,desert dry winter,and highland.See Appendix Table A 1 for more detailed variable definitions and sources.

A related concern is that in colonies where Europeans settled, the current population consists of a higher fraction of Europeans. One might be worried that we are capturing the direct effect of having more Europeans (who perhaps brought a“European culture"orspecia l relations with Europe).To control for this,we add the fraction of the population of European descent in columns (3) and (4) of Table 6. This variable is insignificant, while the effect of institutions remains highly significant，with a coefficient of 0.96 $(\mathbf{s.e.}=$ 0.28).In columns (5)and (6),we control fon measures of natural resources, soil quality (in practice soil types),and for whether the co un- try is landlocked.All these controls are insignificant,and have little effect on our 2 SLS estimate of the effect of institutions on in- come per capita

In columns (7) and (8),we include ethnolinguistic fragmentation as another control and treat it as exogenous.Now the coefficient of protection against expropriation is 0.74 (s.e \mathit{\bar{.}}=\,0.13) ,which is only slightly smaller than our baseline estimate.In Appendix A,we show that the inclusion of an endogenous variable positively correlated with income or institutions will bias the coefficient on in stitutions downwards.Since ethno linguistic fragmentation is likely to be endogenous with respect to development(i.e.,ethno linguistic fragmentation tends to disappear after the formati on of centralized markets;seeWeber [1976]orAndersen[1983]and is correlated with settler mortality,the estimate of 0.74 likely understates the effect of institutions on income.In column(9)of Table 6,weinclude all these variables together. Despite the large number of controls,protection against expropriation on income per capita is still highly significant,with a somewhat smaller co eff icient of 0.71 (s.e. =~0.20) ，which is again likely to understate the effect of institutions on income because ethno linguistic frag mentat ionis treated as exogenous.

Finally,in Table 7,we investigate whether our instrument could be capturing the general effect of disease on development.Sachsanda series of coauthors have argued for the imp ort ance of malaria and other diseases in explaining African poverty (see, for example, Bloom andSachs，1998;Gallup and Sachs,1998;Gallupet al.,1998).Since malariawas one of the main causes of settler mortality,our estimate maybe capturing the direct effect of malaria on economic performance. We are skeptical of this argument since malaria prev alen ce is highly endogenous;it is the poorer countries with worse institutions that have been unable to eradicate malaria.28 While Sachs and coauthors argue that malaria reduces output through poor health, high mortality, and absenteeism, most people who live in high malaria areas have developed some immunity to the disease(see the discussion in Section Iff,subsectionA).Malaria should therefore have little direct effect on economic performance (though, obviously, it will have very high social costs). In contrast, for Europeans, or anyone else who has not been exposed to malaria as a young child, malaria is usually fatal, making malaria prevalence a key determinant of European settlements and institutional development.

In any case, controlling for malaria does not change our results. We do this in columns (1) and (2)by controlling for the fraction of the population who live inan area where falc iporum malaria is endemic in 1994(as constructed and used byGallupet al.,1998).Since malaria prevalence in 1994 is highly endogenous,the argument in Appendix A implies that controlling for it directly will underestimate the effect of institutions on performance. In fact, the coefficient on protection against expropriation is now estimated to be somewhat smaller,0.69 instead of 0.94 as in Table 4. Nevertheless, the effect remains highly significant with a standarderror of 0.25，while malaria itself is insignificant.

In a comment on the working paper version of our study, John W. McArthur and Sachs (2001) discuss the role of geography and institut ions in determining economic performance. They accept our case for the importance of institutions, but argue that more general specifications show that the disease environment and health characteristics of countries (their “geography")matter for economic performance.In particular, they extend our work by controlling for life expectancy and infant mortality, and they also instrument for these health variables using geographic variables such as latitude and mean temperature.Table 7 also expands upon the specifications that McArthur and Sachs suggest. Columns (3)-(6) include life expectancy and infant mortality as exogenous controls. The estimates show a significant effect of in st it u- tions on income,similar to,but smaller than, our baseline estimates.Infant mortality is also marginally significant.Since health is highly endogenous,the coefficient on these variables will be biased up,while the coefficient of institutions will be biased down (see Appendix A). These estimates are therefore consistent with

TABLE7—GEOGRAPHY AND HEALTH VARIABLES

Notes:Panel A reports the two-stage least-squares estimates withlog GDP per capita(PPPbasis)in 1995,and Panel B reports the corresponding first stages.Panel Creports the coefficient from an OL S regression with log GDP per capita as the dependent variable and average protection against expropriation risk and the other control variables indicated in each column as independent variables (full resultsnot reported to save space).Standard errors are in parentheses.Columns (1)-(6)instrument for average protection against expropriation risk using log mortality and assume that the other regress or s are exogenous.Columns(7)-(9)include as instruments average temperature,amount of territory within 100~\mathrm{km} ofthecoast,and latitude (from McArthur andSachs,2001).Columns (10) and(11) use a dummy variable for whether or not a country was subject toyellow fever epidemics before 1900 as an instrument for average protection against expropriation.See Appendix Table A 1 for more detailed variable definitions and sources.

institutions being the major determinant of income per capita differences,with little effect from geography/health variables.

Columns (7)-(9) report estimates from models that treat both health and institutions as endogenous, and following McArthur and Sachs, instrument for them using latitude, mean temperature,and distance from the coast as instrum ents in addition to our instrument,settler mortality.McArthur and Sachs (2001) report that in these regressions the institution variable is still significant, but geography/health are also significant.In contrast to McArthur and Sachs results,we find that only institutions are signif

icant.This difference is due to the fact that McArthur and Sachs include Britain and France in their sample.Britain and France are not in oursample,which consists of only ex-colonies (there is no reason for variation in the mortality rates of British and French troops at home tobe related to their institutional development).It turns out that once Britain and France are left out, the McArthur and Sachs’ specification gene rates no evidence that geography/healthvari- ableshave an important effect on economic 29 performance.

As a final strategy to see whether settler mortality could be proxying for the current disease environment,we estimated models using a yellow fever instrument.This is a dummy var i- able indicating whether the area was ever affected by yellow fever (from Oldstone, 1998; see Appendix TableA1).This is an attractivealternative strategy because yellow fever is mostly eradicated today,so this dummy should not be correlated with the current disease en vironment.The disadvantage of this approach is that there is less variation in this instrument than our settler mortality variable.Despite this,the yellow fever results,reported in columns(10) and (11) of Table 7, are encouraging. The estimate in our base sample is 0.91 (s.e. =\,0.24) comparable to our baseline estimate of O.95 reported in Table 4.Adding continent dummies in column (11) reduces this estimate slightly to 0.90 (s.e. =0.32_{,} .30

29 McArthur and Sachs (2001) also report specifications with more instruments.However,usingsix or seven in s truments with only 64 observations leads to the “too-many

B.Over identification Tests

We can also investigate the validity of our approach by using over identification tests.According to our theory,settler mortality (M) affec ted settlements(S);settlements affected early institutions (C) ; and early institutions affec ted current institutions (R) -cf.，equations (2),(3),and (4).We can test whether any of these variables, C,S. and M ,has a direct effect on income per capita,log y ,by using measures of C and S as additional instruments. The overident if i cation test presumes that one of these instruments,say S ,is truly exogenous,and tests for the exo gene it y of the others,such as settler mortality.This approach is useful since it is a direct test of our exclusion restriction. However, suchtests maynot lead toa rejection if all instruments are invalid,but still highly correlated with each other.Therefore,the results have tobe interpreted with caution.

Overall,the over identification test will reject the validity of our approach if either(i)the equation of interest,(1),does not have a constant coefficient, i.e., $\mathrm{~log~}y{i}=\,\mu\,+\,\alpha{i}R{i}\,+\,\varepsilon{i}whereidenotes country,or (ii)CorShas a direct effect on income per capita,\log{y{i}}(i.e., eitherSorCis correlated with\varepsilon{i})，or (ii) settler mortality,M,has an effect on logy_{i}$ that works through another variable,such as culture

The data support the over identifying restrictions implied by our approach.31 This implies that,subject to the usual problems of power associated with over identification tests,wecan rule out all three of the above possibilities.This gives us additional confidence that settler mortal it y is a valid instrument and that we are estimating the effect of institutions on current performance with our instrumental-variable strategy(i.e.,not capturing the effect of omitted variables).

Notes:Panel A reports the two-stage least-squares estimates with log GDP per capita (PPP basis)in 1995 as the dependent variable,and Panel B reports the corresponding first stage (latitude is included in even-numbered columns but is never significant and not reported here to save space).Panel C reports the p -valuefor the null hypothesis that the coefficient on average protection against expropriation risk in the second-stage regression (i.e.,Panel A) is the same as when instrumented usinglog mortality of settlers in addition to the indicated instruments. Panel D reports results from the regression in which log mortality is included as an exogenous variable and current institutions are instrumented using the alternative instrument indicated.Standard errors are in parentheses.All regressions with constraint on executive and democracy in firstyear of independence also include years since independence as a regressor.All regressions have 60 observations,except those with democracy in 1900 which have 59 observations and those with European settlements in 1900 which have 63 observations.

The results of the over identification tests and related results,are reported in Table 8.In the top panel,Panel A,we report the 2SLS estimates of the effect of protection against expropriation on GDP per capita using ava- riety of instruments other than mortality rates. while Panel B gives the corresponding first stages.These estimates are always quite close to those reported in Table 4.Forexample,in column (1),we use European settlements in 1900 as the only instrument for institutions. This resultsin an estimated effect of O.87 (with standard error 0.14),as compared to our baseline estimate of O.94.The other columns add latitude,and use other instruments such as constraint on the executive in 1900 and in the first year of independence,and democracy in1900.

Panel D reports an easy-to-interpret version of the over identification test.It adds the log of mortality as an exogenous regressor.If mortal it y rates faced by settlers had a direct effect on income per capita,we would expect this variable to come in negative and significant. In all cases, it is small and statistically insignificant. For example, in column (1), log mortal it y has a coefficient of -0.07 (with standard error O.17).This confirms that the impact of mortality rates faced by settlers likely works through their effect on institutions.

Finally,for completeness,in Panel C we reportthe p -valuefrom the appropriate \chi^{2} overident if i cation test. This tests whether the 2SLS coefficients estimated with the instruments indicated in PanelsA and \mathbf{B} versus the coeffici ents estimated using \mathbf{\Psi}(\mathbf{log}) settler mortality in addition to the“true’"instruments are signifi cant ly different(e.g.,in the first column,the coefficient using European settlements alone is compared to the estimate using European settlements and log mortality as instruments).We never reject the hypothesis that they are equal at the5-percent significance level.So these results also show no evidence that mortality rates faced by settlers have a direct effect-or an effect working through a variable other than in st it utions-on income per capita.

VI.Concluding Remarks

Many economists and social scientists beli eve that differences in institutions and state policies are at the root of large differences in income per capita across countries.Thereis little agreement, however, about what determines institutions and government attitudes towards economic progress, making it difficult to isolate exogenous sources of variation in institut ions to estimate their effect on performance. In this paper we argued that differences in coloni al experience could be a source of exo ge nous differences in institutions.

Our argument rests on the following premises:(1)Europeans adopted very different colon iz ation strategies,with different associated institutions. In one extreme, as in the case of the United States,Australia,and New Zealand,they went and settled in the colonies and set up institutions that enforced the rule of law and encouraged investment.In the other extreme,as in the Congo or the Gold Coast, they set up ex tractive states with the intention of transfer ring resources rapidly to the metro pole.These institutions were detrimental to investment and economic progress.(2)The colonization strate gy was in part determined by the feasibility of European settlement.In places where European s faced very high mortality rates,theycould not go and settle,and they were more likely to set up extractive states.(3) Finally,we argue that these early institutions persisted to the present.Determinants of whether Europeans could go and settle in the colonies,therefore. have an important effect on institutions today. We exploit these differences as a source of exogenous variation to estimate the impact of institutions on economic performance.

There is a high correlation between mortality rates faced by soldiers,bishops,and sailors in the colonies and European settlements;between European settlements and early measures of institutions;and between early institutions and institutions today. We estimate large effects of institutions on income per capita using this source of variation.We also document that this relationship is not driven by outliers,andis robust to controlling for latitude,climate,current disease environment, religion, natural resources,soil quality,ethno linguistic frag mentation, and current racial composition.

It is useful to point out that our findings do not imply that institutions today are pre determined by colonial policies and cannot be changed.We emphasize colonial experience as one of the many factors affecting institutions. Since mortality rates faced by settlers are arguably exogenous, they are useful as an instrument to isolate the effect of institutions on performance.Infact,our reading is that these results suggest substantial economic gains from improving institutions,for example as in the case of Japan during the Meiji Restoration or South Korea during the 1960^{,}s

There are many questions that our analysis does not address.Institutions are treated largely as a “black box": The results indicate that reducing expropriation risk(or improving other aspects of the“cluster of institutions"）would result in significant gains in income per capita but do not point out what concrete steps would lead to an improvement in these institutions. Institutional features, such as expropriation risk, property rights enforcement,or rule of law， should probably be interpreted a sane qui librium outcome,related to some more fundamental “institutions," e.g., presidential versus parliamentary system,which can be changed directly. A more detailed analysis of the effect of more fundamental institutions on property rights and expropriation risk is an important area for future study.

APPENDIXA:BIASIN THEEFFECT OF INSTITUTIONS WHEN OTHER ENDOGENOUS VARIABLES ARE INCLUDED

To simplify notation, suppose that $R{i}isexogenous, and another variable that is endogenous,z{i}$ ，suchas prevalence of malariaor ethno linguistic fragmentation,is added to the regression.Then,the simultaneous equations model becomes

$$
\begin{array}{l}{{Y_{i}=\mu_{0}+\,\alpha R_{i}+\,\pi z_{i}+\,\varepsilon_{i}}}\ {{\mathrm{}}}\ {{z_{i}=\mu_{1}+\,\phi Y_{i}+\,\eta_{i},}}\end{array}
$$

where $Y{i}=\log y{i}.We presume that\alpha\ge0\phi<0, and\pi<0, which implies that we interpretz{i}as a negative influence on income. Moreover, this naturally implies that\mathrm{cov}(\eta{i},\,\varepsilon{i})<0and\mathrm{cov}(z{i},R{i})<0, that is, the factorz{i}$ is likely to be negatively correlated with positive in flu ences onincome.

Standard arguments imply that

$$
\begin{array}{l}{\displaystyle\mathrm{plim}\ \hat{\alpha}=\alpha+\frac{\mathrm{cov}(\tilde{R}_{i},\,\varepsilon_{i})}{\mathrm{var}(\tilde{R}_{i})}}\ {\displaystyle\ \ \ \ =\alpha-\kappa\cdot\frac{\mathrm{cov}(z_{i},\,\varepsilon_{i})}{\mathrm{var}(\tilde{R}_{i})},}\end{array}
$$

where \kappa and ${\tilde{R}}{i}are the coefficient and the residual from the auxiliary equation,R{i}=\kappa{0}+\kappa z{i}\;+\;\tilde{R}{i}，andso\kappa\,=\,\mathsf{c o v}(z{i},\,R{i})/\mathbf{v a r}(z{i})\,<O, which is negative due to the fact that cov{\mathbf{\xi}}{R{i}}z{i})<0. The reduced form forz{i}$ is:

$$
z_{i}=\frac{1}{1\,-\,\phi\pi}\,((\mu\,+\,\phi\pi)
$$

Weimpose the regularity condition \phi\cdot\pi<1 so that an increase in the disturbance to the z-equation, $\eta{i},actually increasesz{i}$ Nowusing this reduced form,wecanwrite

$$
\begin{array}{l}{\displaystyle\mathsf{p l i m}\ \hat{\alpha}=\alpha-\kappa\cdot\frac{\mathsf{c o v}(z_{i},\,\varepsilon_{i})}{\mathsf{v a r}(\tilde{R}_{i})}\ }\ {\displaystyle\ \ \ \ \ \ =\alpha-\kappa\cdot\frac{(\sigma_{\varepsilon\eta}+\,\phi\sigma_{\varepsilon}^{2})}{(1\,-\,\phi\pi)\cdot\mathsf{v a r}(\tilde{R}_{i})}}\end{array}
$$

where $\sigma{\varepsilon}^{2}is the variance of\varepsilon, and\sigma{\varepsilon\eta}is the covariance of\varepsilonand\eta$

Substituting for \kappa in (A2),we obtain

plim α

$$
=\alpha-\frac{(\sigma_{\varepsilon\eta}+\,\phi\sigma_{\varepsilon}^{2})}{(1-\,\phi\pi)\cdot\mathrm{var}(\tilde{R}_{i})}\cdot\frac{\mathrm{cov}(z_{i},\,R_{i})}{\mathrm{var}(z_{i})}\,.
$$

Recall that \phi<0 $\sigma{\varepsilon\eta}<0, and{\tt c o v}(z{i},R{i})<0. Therefore, plim\hat{\alpha}<\alpha,and when we control for the endogenous variablez{i}$ ,the coefficient on our institution variable will be biased downwards

Constraint on executive in 1900, 1970, 1990 and in first year of independence: Seven-category scale, from 1 to 7, with a higher score indicating more constraints.Score of 1 indicates unlimited authority; score of 3 indicates slight to moderate limitations; score of 5indicates substantial limitations; score of 7indicates executive parity or subordination.Equal to1if country was not independent atthat date.Date of independence is the first year that the country appears in the Polity II data set.From the Polity Mf dataset,downloaded from Inter-University Consortium for Political andSocial Research.SeeGurr (1997).

Democracy in 1900 and first year of independence:An 11-category scale,from 0 to10,with a higher score indicating more democracy.Points from three dimensions:Competitiveness of Political Participation(from 1 to3);Competitiveness of Executive Recruitment (from 1 to 2, with a bonus of 1 point if there is an election); and Constraints on Chief

Executive (from 1 to 4).Equalto1 if country not independent at that date.From the Polity ll f data set.SeeGurr(1997) European settlements in1900 and percent of European descent 1975:Percent of population European or ofEuropean descent in 1900 and 1975.From Mc Eve dy and Jones(1975)and other sources listed in Appendix Table A 6(available from the authors).

Religion variables:Percent of population that belonged to the three most widely spread religions of the world in 1980 (or for1990-1995for countries formed more recently).The four classifications are:Roman Catholic,Protestant,Muslim, and“other."From La Porta et al.(1999).

French legal origin dummy:Legal origin of the company law or commercial code of each country.Our base sample is all French Commercial Code or English CommonLaw Origin.From LaPorta et al.(1999).

Colonial dummies: Dummy indicating whether country was a British, French, German, Spanish, Italian, Belgian, Dutch, or Portuguese colony.From LaPorta et al.(1999).

Temperature variables:Average temperature,minimum monthly high,maximum monthly high,minimum monthlylow aind maximum monthlylow,all in centigrade.FromParker (1997).

Mean temperature:1987 mean annual temperature in degrees Celsius.From McArthur and Sachs(2001)

Humidity variables: Morning minimum, morning maximum, afternoon minimum, and afternoon maximum, all ir percent.FromParker(1997).

Soil quality: Dummies for steppe (low latitude), desert (low latitude), steppe (middle latitude), desert (middle latitude), dry steppe wasteland, desert dry winter,and highland.FromParker (1997).

Natural resources:Percent of world gold reserves today,percent of world iron reserves today,percent of world zinc reserves today,number of minerals present in country,and oil resources (thousands of barrels per capita.)From Parker (1997).

Dummyfor landlocked:Equal to 1 if country does not adjoin the sea.FromParker(1997)

Malaria in 1994: Population living where falciporum malaria is endemic (percent). Gallup and Sachs (1998).

Latitude:Absolute value of the latitude of the country(i.e.,a measure of distance from the equator),scaled totake values between 0 and 1,where 0 is the equator.From La Porta et al.(1999)

Log European settler mortality: See Appendix Table A2,reproduced below,and Appendix B (available from the authors).

Yellow fever: Dummy equal to 1 if yellow fever epidemics before 1900 and 0 otherwise. Oldstone (1998 p. 69) shows current habitat of the mosquito vector; these countries are coded equal to 1. In addition, countries in which there were epidemics in the nineteenth century,according toCurtin(1989,1998)are also coded equal to 1.

Infant mortality: Infant mortality rate (deaths per 1,000 live births). From McArthur and Sachs (2001)

Life expectancy:Life expectancy at birth in 1995.From McArthur andSachs(2001))

Distance from the coast: Proportion of land area within 100~\mathrm{km} of the seacoast. From McArthur and Sachs (2001).

APPENDIX TABLE A 2---DATA ON MORTALITY

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