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The

Colonial Origins

of

Comparative

Development:

An Empirical Investigation

By

DARON ACEMOGLU, SIMON JOHNSON,

AND

JAMES A. ROBINSON*

We

exploit differences

in

European mortality

rates to

estimate the

effect

of institu-

tions

on 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

set

up

extractive 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 controlledfor, countries in Africa or those closer to

the equator do not

have lower incomes. (JEL 011, P16, P51)

What are the fundamental causes of

the

large differences in income per capita across

countries? Although there

is

still little

con-

sensus

on

the

answer

to this question,

differ-

ences

in

institutions and property rights have

received considerable attention in recent

years.

Countries with better

"institutions,"

more secure property rights, and less distor-

tionary 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 Keefer, 1995;

Paulo Mauro, 1995; Robert E. Hall and

Charles

I. 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 collec-

tive ownership,

while

the other prospered

with private property

and

a

market

economy.

Nevertheless,

we lack reliable

estimates

of

the effect of institutions

on

economic

perfor-

mance.

It

is

quite likely that rich economies

choose

or

can afford better institutions. Per-

haps

more

important,

economies that are

dif-

ferent

for a

variety

of reasons will differ both

*

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 of Man-

agement,

Massachusetts Institute

of

Technology,

Cam-

bridge,

MA

02319 (e-mail: sjohnson@mit.edu); Robinson:

Department of Political Science and Department of Eco-

nomics, 210

Barrows

Hall, University

of

California,

Berke-

ley, CA 94720 (e-mail: jamesar@socrates.berkeley.edu).

We thank Joshua Angrist, Abhijit Banerjee, Esther Duflo,

Stan

Engerman,

John

Gallup,

Claudia

Goldin,

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

Insti-

tute

for Advanced

Research, Columbia University,

Har-

vard

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

McCaa for guiding us to the data on bishops' mortality.

1369

1370 THE

AMERICAN ECONOMIC REVIEW

DECEMBER 2001

in their institutions and in their income per

capita.

To estimate the impact of institutions on eco-

nomic performance, we need a source of exog-

enous 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

insti-

tutions. At one

extreme, European powers

set

up

"extractive

states," exemplified by

the Bel-

gian colonization of the Congo. These institu-

tions did not introduce

much

protection

for

private property,

nor did

they provide

checks

and balances

against government expropria-

tion. In

fact, the main purpose

of

the extractive

state was to transfer

as

much

of

the resources

of the

colony

to the colonizer.

At the other

extreme, many Europeans

mi-

grated

and settled

in a

number

of

colonies,

creating

what the historian Alfred

Crosby

(1986) calls "Neo-Europes."

The settlers tried

to replicate European institutions, with strong

emphasis

on

private property

and

checks

against government power. Primary examples

of this include

Australia,

New

Zealand,

Can-

ada,

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,

and the

formation of

the

extractive state was more

likely.

3. The colonial state and institutions

persisted

even after

independence.

Based on these three

premises,

we use

the

mortality

rates

expected by

the first

European

settlers

in the

colonies

as

an instrument

for

current institutions in these countries.2 More

specifically, our theory can be schematically

summarized as

(potential) settler

>

settlements

mortality

early current

institutions institutions

current

performance.

We use

data

on

the mortality

rates of

soldiers,

bishops,

and sailors stationed in

the colonies

be-

tween 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

high mor-

tality

rates.

Figure 1 plots the logarithm of GDP per

capita today against

the

logarithm

of the

settler

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 to-

day substantially poorer than colonies that were

healthy

for

Europeans.

Our

theory

is that this

relationship reflects the effect of settler mortal-

ity working through

the

institutions

brought by

Europeans.

To

substantiate

this,

we

regress

cur-

rent

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

protec-

tion

against

"risk of

expropriation"

index from

Political Risk Services as

a

proxy

for institu-

tions.

This variable measures differences in in-

stitutions

originating

from different

types

of

states and state

policies.3

There is a

strong

1

By "colonial experience" we do not only mean the

direct control of the colonies by European powers, but

more

generally, European influence

on the rest of the world. So

according

to this

definition,

Sub-Saharan Africa was

strongly affected by "colonialism" between the

sixteenth

and

nineteenth

centuries

because

of

the Atlantic

slave trade.

2

Note that although only

some countries were colonized,

there

is

no selection bias

here. This

is

because the question

we are interested in is the

effect of colonization policy

conditional on being colonized.

3Government expropriation

is not the only institutional

feature that matters. Our

view

is that there

is

a "cluster

of

VOL.

91

NO. 5

ACEMOGLU ET

AL.:

THE

COLONIAL ORIGINS OF DEVELOPMENT 1371

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PANGA

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FJ

GUY

AGO

Xi

PAKIND

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GMB

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co

BGD

NERMD

NGA

tl

6

ETH

TA

SI

n-

6

0

2

4

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8

Log

of

Settler

Mortality

FIGURE

1. REDUCED-FORM RELATIONSHIP BETWEEN INCOME AND

SETTLER

MORTALITY

(first-stage) relationship between settler mortal-

ity rates and current institutions, which is inter-

esting

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.4 We 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

determi-

nant of early institutions (in practice, institu-

tions in

1900);

and there

is

a

strong

correlation

between

early

institutions and institutions to-

day. Our two-stage least-squares estimate

of

the

effect

of

institutions

on

performance

is

rela-

tively precisely

estimated and

large.

For

ex-

ample,

it

implies

that

improving Nigeria's

institutions to the level

of Chile could, in the

long run, lead to as much

as a 7-fold increase in

Nigeria's income (in practice

Chile is over 11

times as rich as Nigeria).

The exclusion restriction

implied by our in-

strumental variable regression

is that, condi-

tional on the controls included

in

the regression,

the mortality

rates of European settlers more

than 100 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 the mor-

tality

rates

of

settlers

could be correlated with

the current

disease environment,

which

may

have a direct effect on economic performance.

In this

case,

our

instrumental-variables

esti-

mates may

be

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

deaths in the colonies were

caused

by

malaria

and

yellow

fever.

Although

these

dis-

eases

were fatal to

Europeans

who

had

no

im-

munity, they had

limited effect on indigenous

adults

who had

developed

various

types

of

im-

munities. These diseases

are therefore

unlikely

to be the reason

why

many

countries

in Africa

and Asia are

very poor

today (see

the discussion

in Section III, subsection

A).

This notion is

institutions," including constraints on

government expropri-

ation, independent judiciary, property rights

enforcement,

and institutions

providing equal

access to

education

and

ensuring civil liberties, that

are

important to

encourage

investment and growth. Expropriation risk is related to all

these institutional

features.

In

Acemoglu

et al.

(2000),

we

reported similar results with other

institutions variables.

4

Differences in

mortality

rates are not the

only,

or even

the main, cause of variation in

institutions. For our empir-

ical

approach to work, all we need is that

they are a source

of exogenous variation.

1372 THE

AMERICAN ECONOMIC REVIEW

DECEMBER 2001

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 the British

army

in

Bengal

and Madras were

respectively

11

and

13

in

1,000. 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. In contrast, the mor-

tality 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

be-

tween 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

den-

sities

in

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

con-

trolled

for,

neither distance

from

the

equator

nor

the

dummy

for

Africa

is

significant.

These re-

sults

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.,

our exclu-

sion restriction-is threatened

if

other factors

correlated

with

the estimates

of

settler

mortality

affect income

per capita.

We

adopt

two strate-

gies

to

substantiate that our results are not

driven

by

omitted factors.

First,

we

investigate

whether institutions have

a

comparable

effect

on

income once

we

control

for a

number of

variables

potentially

correlated with settler

mor-

tality

and economic

outcomes.

We find that

none

of

these overturn

our

results;

the estimates

change remarkably

little when

we

include con-

trols

for the

identity

of the

main

colonizer, legal

origin, climate, religion,

geography,

natural re-

sources,

soil

quality,

and measures

of

ethnolin-

guistic fragmentation.

Furthermore,

the

results

are also

robust to the inclusion

of

controls for

the

current disease environment

(e.g.,

the

prev-

alence of

malaria,

life

expectancy,

and infant

mortality) and the current fraction

of the popu-

lation of European descent.

Naturally, it is impossible to control

for all

possible variables that might be correlated

with

settler mortality and economic outcomes.

Fur-

thermore, our empirical approach

might capture

the effect

of

settler mortality on economic

per-

formance, but working through other

channels.

We

deal

with

these

problems by

using

a

simple

overidentification test

using

measures

of

Euro-

pean migration to the colonies and

early insti-

tutions

as

additional instruments. We then use

overidentification tests to detect

whether settler

mortality has

a direct

effect

on current perfor-

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 insti-

tutions, 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

geograph-

ical determinants

of

the incidence

of

the

neolithic revolution.

He

ignores both

the impor-

tance of institutions and the

potential

causes

of

divergence

in more recent

development,

which

are the main focus of our

paper.

Work

by

Ro-

nald

E.

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

New

Zealand

are

different

from

other colonies,

and

point

out that these differences were

impor-

tant

for their economic

success.

Nevertheless,

this literature

does

not

develop

the link between

mortality, settlements,

and institutions.

Our

argument

is most

closely

work

on the influence of colonial

experience

on

insti-

tutions. Frederich A.

von

Hayek (1960)

argued

that the

British

common law tradition was

su-

perior

to the French

civil

law,

which was devel-

oped 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

VOL 91 NO.

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ACEMOGLU

ET

AL:

THE

COLONIAL ORIGINS OF DEVELOPMENT 1373

the colonizer) and legal origin on current insti-

tutions, and show that the common-law coun-

tries and former British colonies have better

property rights and more developed financial

markets. Similarly, David Landes (1998 Chap-

ters 19 and 20) and North et al. (1998)

argue

that former British colonies prospered

relative

to former French, Spanish, and Portuguese col-

onies because of the

good

economic

and

polit-

ical institutions and culture they inherited

from

Britain. In contrast to this approach

which

focuses on the

identity

of the

colonizer,

we

emphasize

the

conditions

in the colonies. Spe-

cifically, in our theory-and in the

data-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)

who also em-

phasize institutions, but

link them to factor en-

dowments

and

inequality.

Empirically,

our work is related to a number

of other attempts to uncover the link

between

institutions and

development,

as

well

as to

Graziella Bertocchi 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 endogeneity of

in-

stitutions

by using

an instrumental variables

approach as we do here. Mauro (1995) instru-

ments

for

corruption using ethnolinguistic

frag-

mentation.

Hall

and Jones

(1999),

in

turn,

use

distance

from the

equator

as an instrument for

social infrastructure

because, they argue,

lati-

tude 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. Ethnolin-

guistic fragmentation,

on the other

hand,

seems

endogenous, especially

since such

fragmenta-

tion 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).

Econometrically,

the

problem

with both studies

is that their instruments can

plausibly

have

a

direct effect on performance. For example, Wil-

liiam Easterly and Ross Levine (1997) argue

that ethnolinguistic fragmentation can affect

performance by creating political instability,

while Charles de Montesquieu [1748] (1989)

and more recently David

E.

Bloom and Jeffrey

D.

Sachs

(1998)

and John

Gallup

et al.

(1998)

argue for a direct effect of climate on perfor-

mance. 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 mor-

tality

more than

100

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 perfor-

mance, validating

the use of

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 index of institutions.

Section

III de-

scribes

our

key instrument for institutions, the

mortality rates faced by potential settlers

at

the

time of colonization. Section

IV

presents

our

main

results.

Section V

investigates

the robust-

ness

of our

results,

and Section VI

concludes.

I.

The

Hypothesis

and Historical

Background

We

hypothesize

that

settler

mortality

affected

settlements;

settlements

affected

early

institu-

tions;

and

early

institutions

persisted

and

formed the basis of current institutions.

In this

section,

we discuss

and substantiate this

hypoth-

esis.

The

next subsection discusses

the link be-

tween

mortality

rates of settlers and

settlement

decisions,

then we

discuss differences in colo-

nization 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)

1374

THE

AMERICAN ECONOMIC REVIEW

DECEMBER 2001

also documents how

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 Europe-

ans. In the

first

year of the Sierra Leone Com-

pany (1792-1793),

72

percent of

the

European

settlers died. On Mungo Park's Second Expedi-

tion

(May-November 1805), 87 percent

of Eu-

ropeans 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 Pil-

grim

fathers.

They

decided to

migrate

to

the

United States

rather than

Guyana

because

of the

high mortality rates in Guyana (see Crosby,

1986

pp. 143-44).

Another

example

comes

from the

Beauchamp

Committee in

1795,

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

many

of the col-

onies was

also related

to the

living

conditions

there. In

places

where

the

early

settlers

faced

high mortality rates,

there would be less incen-

tive for new settlers to come.5

B.

Types of Colonization

and

Settlements

The historical evidence

supports

both the no-

tion

that there

was a wide

range

of different

types

of colonization and that the

presence

or

absence of

European

settlers was a

key

deter-

minant of the

form

colonialism took.

Historians,

including

Robinson and

Gallagher (1961), Gann

and

Duignan (1962),

Denoon

(1983),

and

Cain

and Hopkins (1993), have documented the de-

velopment

of

"settler colonies," where Europe-

ans

settled

in

large numbers,

and

life was

modeled after the home

country.

Denoon

(1983)

emphasizes

that settler

colonies

had

representa-

tive institutions which

promoted

what the set-

tlers wanted and

that what they wanted was

freedom and

the

ability to get rich by engaging

in trade.

He

argues

that "there was

undeniably

something capitalist

in the

structure

of

these

colonies. Private

ownership of

land and live-

stock was well established

very early ..." (p.

35).

When

the

establishment of

European-like

in-

stitutions

did not

arise

naturally,

the settlers

were

ready

to

fight

for them

against

the wishes

of the home

country.

Australia is

an

interesting

example

here. Most of the

early

settlers

in

Aus-

tralia

were ex-convicts, but the land was owned

largely by ex-jailors,

and there was no

legal

protection against

the

arbitrary power

of land-

owners. The settlers wanted

institutions

and

po-

litical

rights

like

those

prevailing

in

England

at

the time.

They

demanded

jury trials,

freedom

from

arbitrary arrest,

and

electoral representa-

tion.

Although

the

British

government

resisted

at

first,

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)

"from

the late

1840s

the British bowed to local

pressures and,

in line

with

observed constitutional

changes

taking place

in Britain

herself, accepted

the idea

that,

in

mature

colonies, governors

should in

future

form ministries from the

majority

ele-

ments in elected

legislatures." They

also

sug-

gest

that

"the enormous

boom

in

public

investment

after 1870

[in

New

Zealand]

... was

an

attempt

to build

up

an infrastructure

... to

maintain

high living

standards

in

a

country

where voters

expected politicians actively

to

promote

their

economic welfare."

(p. 225).6

5

Naturally,

other

factors also influenced

settlements.

For

example, despite

the

relatively high mortality rates, many

Europeans migrated

to

the Caribbean because of the very

high incomes there

at

the time (see, e.g., Richard S. Dunn,

1972; David

W.

Galenson, 1996; Engerman and Sokoloff,

1997; David Eltis, 2000).

6

Robert H. Bates (1983 Chapter 3) gives

a nice

example

of the influence

of

settlers on policy in Africa. The British

colonial government pursued many policies that depressed

the price of cocoa, the main produce of the farmers in

Ghana. In contrast,

the British

government supported

the

prices faced by

the

commercial cereal farmers in Kenya.

VOL. 91 NO.

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ACEMOGLU

ET

AL.:

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COLONIAL ORIGINS OF DEVELOPMENT 1375

This is in sharp contrast to the colonial expe-

rience in Latin America during the seventeenth

and eighteenth centuries, and in Asia

and

Africa

during the nineteenth and early twentieth cen-

turies. The

main

objective of the Spanish

and

the Portuguese colonization was to

obtain

gold

and other valuables from America. Soon after

the conquest,

the

Spanish crown granted rights

to land and

labor

(the encomienda)

and

set

up

a

complex mercantilist system of monopolies and

trade

regulations

to extract

resources

from the

colonies.7

Europeans developed

the

slave trade

in Af-

rica

for

similar reasons. Before the

mid-nine-

teenth

century,

colonial

powers

were

mostly

restricted to

the African coast

and concentrated

on

monopolizing

trade

in

slaves, gold,

and other

valuable commodities-witness

the names used

to describe West Aflican countries: the

Gold

Coast,

the

Ivory

Coast.

Thereafter,

colonial

pol-

icy was driven

in

part by an element

of

super-

power rivalry,

but

mostly by economic

motives.

Michael Crowder

(1968 p. 50),

for

example,

notes

"it is significant

that

Britain's largest

col-

ony

on

the

West

Coast

[Nigeria]

should have

been the one

where her

traders were most active

and bears out the

contention that,

for Britain

...

flag

followed trade."8 Lance

E.

Davis

and

Robert A. Huttenback (1987 p. 307) conclude

that

"the colonial Empire provides strong evi-

dence for

the belief

that

government

was at-

tuned to

the

interests of business and willing to

divert resources to ends that the business com-

munity

would

have found profitable." They find

that before 1885 investment in the British em-

pire 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 p.

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

opera-

tion of a market

economy,

but

by

state interven-

tion and

compulsory cultivation of

cash

crops to

be

sold

to and

distributed by the state

at

con-

trolled prices." Jean-Philippe Peemans (1975)

calculates that tax rates

on

Africans in the

Congo approached 60 percent of

their income

during

the

1920's and 1930's. Bogumil

Jew-

siewicki

(1983)

writes

that during

the

period

when

Leopold

was

directly

in

charge, policy

was

"based

on the

violent exploitation

of

natural

and

human

resources,"

with a

consequent

"de-

struction

of economic and

social

life

...

[and]

...

dismemberment of

political

structures."

Overall,

there were few

constraints

on state

power

in the nonsettler

colonies.

The

colonial

powers

set

up

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 (1988 p. 84)

summa-

rizes

this

as:

"In

Europe

the theories of

repre-

sentative

democracy

won out over

the

theorists

of absolutism.... But

in

Africa,

the

European

conquerors

set

up

absolutist

governments,

based

on

reasoning

similar to that

of

Louis XIV."

Bates shows that this was

mainly because in Kenya, but

not

in Ghana, there were a

significant number

of

European

settler

farmers,

who

exerted considerable pressure

on

policy.

7

See James Lang

(1975) and

James Lockhart and

Stuart B. Schwartz

(1983). Migration to Spanish America

was

limited by

the

Spanish Crown,

in

part

because of a

desire to keep

control

of the

colonists

and limit their

independence (see, for

example, John

H.

Coatsworth,

1982). This

also

gives

further

support to our

notion

that

settlers were able

to

influence the

type

of institutions set

up

in

the

colonies,

even

against

the wishes

of the home

country government.

8

Although

in

almost

all

cases the main

objective

of

colonial

policies

was to

protect economic interests and

obtain profits,

the

recipients

of

these profits varied. In

the

Portuguese case,

it

was

the

state; in the Belgian case, it was

King Leopold; and

in the

British case, it was often private

enterprises

who

obtained concessions or

monopoly trading

rights

in Africa

(Crowder, 1968 Part III).

1376 THE

AMERICAN ECONOMIC

REVIEW

DECEMBER 2001

C. Institutional Persistence

There is a variety of historical evidence, as well

as our regressions in Table 3 below, suggesting

that the control structures set up in the nonsettler

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 the basis of the current-

day institutions of these countries.9

Young emphasizes that

the

extractive institu-

tions

set

up by

the

colonialists

persisted long

after the colonial

regime

ended. He

writes

"al-

though

we

commonly

described the

indepen-

dent 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 gov-

ernance" (1994 p. 283). An example of the

persistence

of extractive state institutions into

the

independence

era is

provided by

the

persis-

tence

of

the most

prominent

extractive

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 agri-

culture

in

the latter part

of

the nineteenth

cen-

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 and El Salvador to

provide

labor for coffee

growing.

In

the Guate-

malan

case,

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

Verdier, 1998).

If

the costs of cre-

ating

these institutions have been sunk

by

the colonial

powers, then

it

may

not

pay the

elites

at

independence

to

switch

to

extrac-

tive institutions.

In

contrast,

when the new

elites

inherit extractive institutions, they

may not

want to incur the costs of introduc-

ing better

institutions, and may instead

prefer to

exploit

the

existing extractive

in-

stitutions

for

their

own

benefits.

(2) The gains

to

an

extractive strategy may

depend

on

the

size

of the

ruling

elite. When

this elite

is

small,

each

member would have

a

larger

share

of

the

revenues,

so the elite

may have a

greater

incentive to

be

extrac-

tive.

In

many

cases

where

European powers

set

up

authoritarian

institutions, they

dele-

gated

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

extractive

institutions.

10

(3)

If

agents

make irreversible

investments

that

are

complementary

to

a

particular

set

of

institutions, they

will be more

willing

to

support

them, making

these

institutions

per-

sist

(see,

e.g., Acemoglu, 1995).

For

exam-

ple, agents

who

have invested in human and

physical

capital

will be in favor of

spending

9

The thesis that institutions persist for a long time goes

back at least to Karl A. Wittfogel (1957), who argued that

the

control structures set up by the large "hydraulic" empires

such

as China, Russia, and the Ottoman Empire persisted for more

than 500 years to

the

twentieth century. Engerman and

Sokoloff (1997), La Porta et al. (1998, 1999), North et al.

(1998), and Coatsworth (1999) also argue that colonial insti-

tutions persisted. Engerman

et

al. (1998) provide further

evi-

dence supporting this view.

10

William Reno (1995),

for

example, argues

that the

governments of

postindependence Sierra

Leone adopted the

tactics and institutions

of

the British

colonizers to cement

their political power

and extract resources

from the rest of

society. Catherine

Boone (1992) provides

a similar analysis

of the evolution

of the modern

state in Senegal. Most

scholars also

view the roots of authoritarianism

under

Mobutu in the colonial

state practices

in the Belgian Congo

(e.g., Thomas

M. Callaghy, 1984,

or Thomas Turner and

Young, 1985, especially

p. 43).

The situation in Latin

America is similar.

Independence

of most Latin American

countlies came

in the early nineteenth

century

as

domestic

elites took advantage of the invasion

of Spain by Napoleon

to

capture

the control of

the

state.

But,

the

only thing

that

changed was the

identity of the recipients

of the rents (see,

for example, Coatsworth,

1978, or

John

Lynch, 1986).

VOL 91 NO.

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ET

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THE

COLONIAL ORIGINS OF DEVELOPMENT

1377

TABLE

1-DESCRIPTIVE

STATISTICS

By quartiles of

mortality

Whole world Base sample (1) (2)

(3) (4)

Log GDP per capita (PPP)

in

1995 8.3

8.05

8.9

8.4

7.73

7.2

(1.1) (1.1)

Log output per worker

in

1988 -1.70 -1.93 -1.03 -1.46 -2.20 -3.03

(with

level of

United

States

(1.1) (1.0)

normalized to

1)

Average protection against

7 6.5

7.9

6.5 6

5.9

expropriation risk, 1985-1995 (1.8) (1.5)

Constraint on

executive

in

1990 3.6

4

5.3

5.1

3.3 2.3

(2.3) (2.3)

Constraint

on

executive

in

1900 1.9 2.3 3.7 3.4 1.1 1

(1.8) (2.1)

Constraint

on

executive

in

first year 3.6 3.3 4.8 2.4 3.1

3.4

of

independence (2.4) (2.4)

Democracy

in

1900 1.1 1.6 3.9 2.8

0.19

0

(2.6) (3.0)

European

settlements

in

1900 0.31

0.16

0.32 0.26 0.08 0.005

(0.4) (0.3)

Log European settler mortality n.a. 4.7 3.0 4.3

4.9

6.3

(1.1)

Number of

observations

163

64

14

18

17

15

Notes: Standard deviations

are in

parentheses. Mortality

is

potential

settler

mortality,

measured

in

terms

of

deaths

per

annum

per 1,000

"mean

strength" (raw mortality

numbers are

adjusted

to what

they would

be if a force of

1,000 living people

were

kept

in

place

for a whole

year, e.g.,

it is

possible

for this

number

to exceed

1,000

in

episodes

of extreme

mortality as

those

who

die are

replaced

with new

arrivals).

Sources

and methods for

mortality

are described

in

Section

III, subsection B,

and

in the

unpublished Appendix (available

from

the

authors;

or see

Acemoglu

et

al., 2000). Quartiles

of

mortality

are for 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

or

equal

to

78.1 and less than

280; (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:

OLS 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 our base

sample, limited to

the 64 countries

that

were

ex-colonies

and for which we

have

settler mor-

tality, protection against expropriation risk,

and

GDP data

(this is smaller

than the

sample

in

Figure 1).

The GDP

per capita

in

1995 is

PPP

adjusted (a more detailed discussion of all data

sources is

provided

in

Appendix Table Al).

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 devia-

tion 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 alterna-

tive measure of income today. Hall and Jones

(1999) prefer this measure since it explicitly

refers to worker productivity. On the other

hand, given the difficulty

of

measuring

the for-

mal labor

force, it may

be a more

noisy

measure

of

economic performance

than income

per

capita.

We use a

variety

of variables to

capture

in-

stitutional differences. Our

main

variable,

re-

ported

in

the second

row,

is

an index of

protection against expropriation.

These data are

from Political Risk Services (see, e.g., William

D.

Coplin

et

al., 1991),

and

were first

used in the

economics and

political

science literatures

by

Knack and Keefer

(1995).

Political Risk Ser-

vices

reports a value between 0 and 10

for each

country and year, with 0 corresponding to

the

1378

THE

AMERICAN ECONOMIC

REVIEW

DECEMBER

2001

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 appro-

priate for our purposes

since the focus here is on

differences

in

institutions

originating from dif-

ferent types of states

and state policies. We

expect our notion of

extractive state to corre-

spond to a low value of

this index, while the

tradition of rule of

law

and

well-enforced prop-

erty rights should

correspond to high values.11

The next

row gives an

alternative measure, con-

straints on the

executive in

1990, coded from

the

Polity

III

data

set of Ted

Robert Gurr and

associates

(an update

of

Gurr, 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 Gurr data set. The

first

is

a measure of

constraints

on the

executive

in

1900

and the

second

is an index

of democ-

racy in 1900. 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.,

the first

year

a

country

enters

the Gurr data

set)

as an alternative measure of institutions. The

second-to-last row

gives

the fraction of the

pop-

ulation of

European

descent

in

1900,

which is

our measure of

European

settlement

in the

col-

onies,

constructed from

McEvedy

and

Jones

(1975)

and Curtin et al.

(1995).

The final row

gives the logarithm

of

the baseline settler mor-

tality estimates;

the raw data

are

in

Appendix

Table

A2.

The

remaining

columns

give descriptive

sta-

tistics

for

groups

of

countries

at different

quar-

tiles 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

B.

Ordinary Least-Squares Regressions

Table 2 reports ordinary least-squares (OLS)

regressions of log per capita income on the

protection against expropriation variable in a

variety of samples. The linear regressions are

for

the equation

(1)

logyi=i+

aRi+Xy+

ei,

where

yi

is income per capita in country i,

Ri

is

the

protection against expropriation measure,

Xi

is

a vector

of other

covariates,

and

ei

is a

random

error

term.

The coefficient of interest

throughout

the

paper

is

a,

the effect of

institu-

tions on

income per capita.

Column

(1)

shows

that in the whole world

sample

there is a

strong

correlation between our

measure

of institutions and

income

per capita.

Column

(2)

shows that the

impact of

the

insti-

tutions variable on

income

per capita

in

our base

sample

is

quite

similar to that

in

the whole

world,

and

Figure

2

shows this

relationship

di-

agrammatically

for our base

sample consisting

of 64 countries.

The

R2

of

the

regression

in

column

(1)

indicates that

over 50

percent

of

the

variation

in

income

per capita

is

associated

with

variation

in

this

index

of

institutions. To

get

a

sense of the

magnitude

of

the effect of institu-

tions on

performance,

let

us

compare

two coun-

tries, Nigeria,

which has

approximately

the

25th

percentile

of the institutional measure

in

this

sample, 5.6,

and

Chile,

which

has

approxi-

mately

the 75th

percentile

of the institutions

index,

7.8. The estimate

in

column

(1), 0.52,

indicates

that

there

should

be on

average

a

1.14-

log-point

difference between the

log

GDPs

of

the

corresponding

countries

(or approximately

a

2-fold

difference-e1

. 14- 1

2.1).

In

prac-

tice,

this

GDP

gap

is 253

log points (approxi-

mately 1-fold). Therefore,

if the effect

estimated

in

Table 2 were

causal,

it

would

im-

ply

a

fairly large

effect of institutions

on

per-

formance,

but

still much

less

than

the actual

income

gap

between

Nigeria

and

Chile.

Many

social

scientists, including

Monte-

squieu [1784] (1989),

Diamond

(1997),

and

"

The

protection against expropriation variable is spe-

cifically for

foreign investment,

since Political and Risk

Services construct these data

for

foreign

investors. How-

ever, as noted

by

Knack and Keefer

(1995),

risk of

expro-

priation of foreign

and domestic investments are very highly

correlated,

and risk of

expropriation of foreign investment

may

be more

comparable

across

countries.

In

any case, all

our results hold also with a

variety of

other measures of

institutions (see

Tables 4a, b, c, d, and e in Acemoglu et al.,

2000, available

from the authors).

VOL. 91 NO.

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THE

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1379

TABLE 2-OLS REGRESSIONS

Whole Base Whole Whole Base Base Whole

Base

world sample world world sample sample world

sample

(1)

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

(7)

(8)

Dependent variable

is log output

per

Dependent

variable is

log

GDP

per capita

in

1995 worker

in

1988

Average protection

0.54 0.52 0.47 0.43 0.47

0.41

0.45 0.46

against expropriation (0.04) (0.06) (0.06) (0.05) (0.06) (0.06) (0.04)

(0.06)

risk, 1985-1995

Latitude 0.89 0.37 1.60 0.92

(0.49) (0.51) (0.70) (0.63)

Asia

dummy

-0.62 -0.60

(0.19)

(0.23)

Africa dummy -1.00 -0.90

(0.15) (0.17)

"Other"

continent dummy

-0.25

-0.04

(0.20) (0.32)

R2

0.62 0.54 0.63 0.73 0.56 0.69 0.55 0.49

Number of observations 110 64

110 110 64 64

108

61

Notes:

Dependent

variable:

columns

(1)-(6), log

GDP

per capita (PPP basis)

in

1995,

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

a scale from 0

to

10,

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

Al

for more detailed variable

definitions

and

sources. Of the

countries in our

base

sample,

Hall and Jones

do not

report output per

worker

in

the

Bahamas,

Ethiopia,

and Vietnam.

Sachs and coauthors, have argued

for

a direct

effect

of

climate on performance, and Gallup et

al. (1998) and Hall and Jones (1999) document

the correlation between distance from the equa-

tor 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 institu-

tions 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 Amer-

ica 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 are a 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 consider-

able error and

corresponds poorly

to

the "cluster

of

institutions" that matter

in

practice creates

attenuation and may

bias the

OLS

estimates

1380

THE

AMERICAN ECONOMIC

REVIEW

DECEMBER

2001

10

HKG

S

CAN

r) |

~~~~~~~~~~~~MLTBHS

H

GM

PER

DOMTW)

N- 8

SLV

BO[GU

IDN

. ~~~HTI

SDN

'MM

TGO

RD 7 A l

NEBRGD

NGA

0~~~~~~~~

a)

EhE

TZA

m~ 6m

0

4'

4

6

8

10

Average

Expropriation

Risk 1985-95

FIGURE

2.

OLS

RELATIONSHIP

BETWEEN EXPROPRIATION

RISK AND INCOME

downwards. All of 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

perfor-

mance. 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

set-

tlers. Malaria (particularly

Plasmodium

falcipo-

rum) 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

gastrointes-

tinal diseases accounted

for

another

15

percent

(Curtin, 1989 p. 30). Throughout

the

nineteenth

century,

areas without malaria and

yellow fever,

such as

New Zealand,

were

more

healthy

than

Europe because

the

major causes

of

death

in

Europe-tuberculosis, pneumonia, and small-

pox-were rare

in

these places (Curtin, 1989

p. 13).

Both malaria

and yellow fever are

transmit-

ted by mosquito

vectors.

In

the case

of malaria,

the main transmitter

is the Anopheles gambiae

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

Plas-

modium

falciporum)

is

often fatal, so

Europe-

ans

in

Africa,

India,

or the Caribbean

faced very

high death rates.

In

contrast, death rates

for

the

adult

local

population

were much lower

(see

Curtin

[1964]

and the discussion

in

our

intro-

duction

above).

Curtin

(1998 pp.

7-8)

describes

this as follows:

Children

in

West

Africa ... would be

in-

fected with

malaria parasites

shortly after

birth and were frequently

reinfected

after-

wards;

if

they lived beyond

the

age

of

about

five,

they acquired

an

apparent

im-

munity. The parasite

remained

with them,

normally

in the

liver,

but clinical symp-

toms were rare so

long

as

they

continued

to be infected with the

same species

of P.

falciporum.

VOL.

91

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1381

The more recent books on malariology confirm

this conclusion. For

example,

"In

stable en-

demic

areas a heavy toll

of

morbidity and mor-

tality

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 Organiza-

tion

(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,

be-

ing most serious

for

young children over six

months of age... surviving children develop their

own

immunity

between the

age

of

3-5 years"

(Jose

A.

Najera and Joahim Hempel, 1996).

People

in

areas where malaria is endemic are

also more

likely

to have

genetic immunity

against

malaria. For

example, they

tend to have

the sickle-cell

trait,

which

discourages

the mul-

tiplication

of

parasites

in the

blood,

or deficien-

cies

in

glucose-6-phosphate dehydrogenase and

thalassaemia

traits,

which

also protect against

malaria.

Porter

(1996 p. 34)

writes:

"In

such a

process,...,

close to 100

percent

of

Africans

acquired

a

genetic

trait that

protects

them

against

vivax malaria and

probably against

fal-

ciporum

malaria

as well."

Overall,

the

WHO es-

timates that malaria kills about

1

million

people

per year,

most

of

them children. It does

not,

how-

ever, generally

kill

adults who

grew up

in

malaria-

endemic areas

(see Najera

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

who

grew up

in areas

where

yellow

fever

com-

monly

occurred.13

Yellow fever leaves its

sur-

viving

victims with

a

lifelong immunity,

which

also

explains

its

epidemic pattern, relying

on

a

concentrated nonimmune

population.

Curtin

(1998 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 else-

where." Similarly, Michael B. A. Oldstone

(1998 p. 49) writes:

Most Black Africans and their

descen-

dants respond

to

yellow fever infection

with mild to moderate

symptoms such as

headache, fever, nausea, and vomiting,

and then recover

in

a few

days.

This

out-

come reflects the

long relationship

be-

tween the virus 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

90 percent.

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 eradi-

cated 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.

They

also

started

to undertake serious mosquito

eradica-

tion efforts and

protect

themselves

against

mos-

quito

bites.

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 to

breed

mosquitoes

and

transmit these diseases was

not

understood.

It

was

only

in

the late nineteenth

century

that

Europeans

started to control these diseases.

14

12

Because malaria species are

quite local, a person may

have immunity to the local version

of malaria,

but be

highly

vulnerable to malaria a short distance

away.

This is

proba-

bly the explanation for why

Africans had

such

high mortal-

ity

when

they were forced to

move by colonial powers.

(Curtin

et

al., 1995 p. 463).

13

Because yellow fever struck

Europeans

as

an epi-

demic, many of the very high

death rates we report below

for European troops are from

yellow

fever.

14

Even during the early twentieth century, there

was

much confusion about

the

causes

of

malaria

and

yellow

1382

THE

AMERICAN ECONOMIC REVIEW

DECEMBER 2001

These considerations, together with the data

we have on the mortality of local people and

population densities before the arrival of Euro-

peans, make us believe that settler mortality is a

plausible instrument for institutional develop-

ment: these diseases affected European settle-

ment patterns and the type of institutions they

set

up,

but had

little

effect on

the

health and

economy

of

indigenous people.15

A final noteworthy feature, helpful in in-

terpreting

our

results

below,

is

that malaria

prevalence depends as much on the microcli-

mate

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 set

up,

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.

For

example,

Curtin

(1989 p. 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

transmit-

ter

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

preva-

lence 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 climac-

tic feature of the country.

B. Data on Potential Settler Mortality

Our

data on the

mortality of European set-

tlers come largely from the work

of

Philip

Curtin.

Systematic military

medical record

keeping began only after 1815, as an attempt

to understand why so many soldiers were

dying in some places. The first detailed stud-

ies were

retrospective

and

dealt with British

forces between

1817

and

1836.

The

United

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's, most Euro-

pean

countries

published regular reports

on

the

health

of

their soldiers.

The standard measure is annualized deaths

per thousand mean strength. This measure

reports

the

death rate among 1,000 soldiers

where

each death is replaced with a

new soldier.

Curtin

(1989, 1998)

reviews in

detail

the con-

struction

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 mod-

ern

medicine was still

in its

infancy,

and the

European

militaries did not

yet

understand

how

to

control

malaria

and

yellow

fever.

These

mortality

rates

can therefore

be

inter-

preted

as

reasonable estimates of settler

mor-

tality. They

are consistent

with

substantial

evidence

from

other sources

(see,

for exam-

ple,

Curtin

[1964, 1968]).

Curtin

(1998),

Dis-

ease and

Empire,

adds

similar data

on the

mortality

of soldiers

in the

second

half of the

nineteenth

century.

16

In

all

cases,

we

use the

fever. The Washington

Post on Nov. 2,

1900 wrote: "Of all

the silly and nonsensical

rigmarole of yellow

fever that has

yet found its way

into print ... the silliest

beyond compare is

to

be found in the

arguments

and theories generated by

a

mosquito hypothesis"

(quoted in

Oldstone, 1998 pp.

64-65).

Many campaigns

during the nineteenth

century had very

high mortality rates.

For example, the

French campaign

in

Madagascar

during

the

1890's and French

attempts to build

the Panama Canal

during

the

1880's

were mortality disas-

ters, the first due

to malaria, the second

due to yellow fever

(see Curtin, 1998,

and David McCullogh,

1977). In Panama,

to

stop

ants

the French

used water

pots

under the

legs

of

beds

in

barracks and

hospitals.

These

pots

provided

an ideal

milieu for the breeding

of Aedes aegypti,

causing very high

rates

of

mortality

(Oldstone, 1998 p. 66).

15

In Acemoglu

et al. (2001), we document

that many of

these areas

in

the tropical zone were

richer

and more

densely

settled in 1500 than the temperate

areas later settled

by

the

Europeans.

This also

supports

the

notion that the

disease environment

did

not create an absolute disadvantage

for these countries.

16

These numbers

have to be used with

more care be-

cause there was a

growing

awareness of how to avoid

epidemics of the

worst tropical diseases, at

least during

short military campaigns.

For example,

the campaign

in

Ethiopia at the end

of the nineteenth century

had very low

mortality rates because

it was short and well

managed (see

Figure 1). Although

the mortality rates from this

successful

campaign certainly

underestimate

the

mortality

rates faced

VOL. 91 NO. 5 ACEMOGLU

ET

AL: THE

COLONIAL ORIGINS OF DEVELOPMENT 1383

earliest available number

for

each country,

reasoning that this is the best estimate

of

the

mortality rates that settlers would have faced,

at least 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 mor-

tality. Hector Gutierrez (1986) used Vatican

records to construct estimates for the mortal-

ity rates of bishops

in

Latin America from

1604 to 1876. Because these data overlap

with the Curtin estimates

for

several coun-

tries,

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

gener-

ate alternative estimates

of

mortality

in

South

America. Appendix

B

in Acemoglu et al.

(2000),

which is

available

from

the

authors,

gives a detailed discussion of how these data

are

constructed,

and

Appendix

Table A5

(available

from the

authors), shows that

these

alternative methods

produce remarkably

sim-

ilar results.

Appendix Table

A2 lists our

main

estimates,

and Table

Al

gives

information

about sources.

IV. Institutions and Performance: IV Results

A.

Determinants

of Current Institutions

Equation (1) describes

the

relationship

be-

tween current institutions and

log

GDP.

In

ad-

dition

we have

(2)

Ri

=

AR

+

OR

Ci

+

X>iYR

+

VRi,

(3)

Ci

=

AC

+

,Bcsi

+

Xj')/c

+

1Ci,

(4)

Si

=

As

+

(351og

Mi

+

X1,yS

+

vSi,

where

R

is

the

measure

of

current

institutions

(protection against

expropriation between 1985

and 1995), C is

our measure

of

early (circa

1900)

institutions, S is the measure of European

settlements

in the

colony (fraction of the popu-

lation with

European descent in 1900), and M is

mortality rates

faced by settlers.

X is

a vector

of

covariates that

affect all variables.

The

simplest

identification strategy might be

to use

Si

(or

Ci)

as an instrument for

Ri

in

equation (1),

and we report some of these re-

gressions

in Table

8. However,

to

the extent that

settlers are

more

likely to migrate to richer areas

and

early

institutions reflect other

characteris-

tics that are

important for

income

today,

this

identification

strategy

would be invalid

(i.e.,

Ci

and

Si

could be correlated with

sk). Instead,

we

use the

mortality

rates faced

by

the

settlers, log

Mi,

as an instrument for

Ri.

This

identification

strategy

will

be

valid as long as log

Mi

is

uncorrelated

with

si-that

is,

if

mortality

rates

of settlers between the seventeenth

and

nine-

teenth centuries have

no

effect on income

today

other than

through

their

influence on institu-

tional development.

We

argued

above that this

exclusion restriction is

plausible.

Figure 3 illustrates

the

relationship

between

the

(potential)

settler

mortality

rates and the index of

institutions. We use the

logarithm

of the

settler

mortality rates,

since

there

are

no

theoretical rea-

sons

to prefer

the level

as a determinant

of

insti-

tutions 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 institu-

tions.

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

Sec-

tion I. In

particular,

we

present

OLS

regressions

of

equations (2),

(3),

and

(4).

In

the

top panel,

we

regress

the

protection

against expropriation

vari-

able

on the other variables. Column

(1)

uses con-

straints

faced

by

the executive in

1900

as the

regressor, and

shows a close

association between

early

institutions and institutions

today.

For

exam-

ple, past

institutions

alone

explain

20

percent

of

the

variation

in the

index

of

current institutions.

The

second column adds

the latitude

variable,

by potential settlers

in

Ethiopia,

we did

not exclude

this

country because

excluding

it

would have helped

our

hy-

pothesis.

17

Combining

data from a variety of sources

will

in-

troduce

measurement error in our estimates of

settler

mortality.

Nevertheless, since we are using settler

mor-

tality as an

instrument, this measurement error

does

not

lead to inconsistent

estimates of the effect of

institutions

on

performance.

1384

THE AMERICAN ECONOMIC

REVIEW

DECEMBER

2001

10

USA

NZL

CAN

AUS

SGP

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IND

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0)

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v

8

'

MYS

,fflLANGAB

i?r)

Z

A

^~~~~~L

JAM

-F,6

._

T

~~~~~~~~CMR

GINGH

NABGA

MDG

4

SDN

MLI

4

~~~~~~~~~~~HTI

ZAR

2

4

6

8

Log

of

Settler

Mortality

FIGURE

3.

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 democ-

racy indices assign low

scores to countries that

were colonies

in

1900,

and do not use the ear-

liest postindependence

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 execu-

tive

in 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

be-

tween

protection against

expropriation

and

Euro-

pean 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

be-

tween

the

protection against

expropriation

vari-

able

and the

mortality

rates faced

by

settlers. This

specification

will

be the

first

stage

for our main

two-stage least-squares

estimates

(2SLS).

It shows

that

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

were

shaped, at least

in

part, by settlements,

and

that settlements were affected by mortality. Col-

umns (1)-(2) and (5)-(6) relate our measure of

constraint

on

the executive and democracy in

1900 to 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 regres-

sions

show that settlement patterns explain around

50

percent

of

the variation

in

early institutions.

Finally, columns (9) and (10) show the relation-

ship between settlements and mortality rates.

B. Institutions and Economic

Performance

Two-stage least-squares estimates

of

equa-

tion

(1) are presented

in

Table

4. Protection

against expropriation variable,

Ri,

is treated as

endogenous, and modeled as

(5)

Ri

=

+

log

Mi

+

X'8

+

vi,

where

Mi

is

the

settler

mortality

rate in

1,000

mean strength.

The exclusion restriction is that

this variable does not

appear

in

(1).

VOL. 91 NO. 5 ACEMOGLU

ET

AL.: THE COLONIAL

ORIGINS

OF DEVELOPMENT

1385

TABLE 3-DETERMINANTS OF INSTITUTIONS

(1) (2)

(3) (4) (5) (6) (7) (8) (9) (10)

Panel A

Dependent

Variable Is

Average

Protection

Against Expropriation

Risk in

1985-1995

Constraint on executive

in 0.32 0.26

1900 (0.08) (0.09)

Democracy

in

1900

0.24 0.21

(0.06) (0.07)

Constraint on executive

in

first

0.25

0.22

year

of

independence

(0.08) (0.08)

European settlements

in

1900

3.20 3.00

(0.61) (0.78)

Log European settler mortality

-0.61 -0.51

(0.13) (0.14)

Latitude 2.20

1.60 2.70 0.58 2.00

(1.40)

(1.50) (1.40) (1.51) (1.34)

R2

0.2

0.23

0.24 0.25

0.19

0.24

0.3 0.3 0.27 0.3

Number

of

observations

63 63

62 62

63

66 66 64 64

Dependent

Variable Is

European

Dependent

Variable Is

Constraint

Dependent

Variable

Is Settlements in

Panel

B on

Executive

in

1900

Democracy

in

1900 1900

European settlements

in

1900 5.50 5.40 8.60

8.10

(0.73) (0.93)

(0.90) (1.20)

Log European settler mortality

-0.82

-0.65

-1.22 -0.88 -0.11

-0.07

(0.17) (0.18) (0.24) (0.25) (0.02) (0.02)

Latitude 0.33 3.60

1.60 7.60

0.87

(1.80)

(1.70) (2.30) (2.40) (0.19)

R2

0.46 0.46 0.25

0.29

0.57

0.28 0.37 0.31 0.47

Number of observations 70 70

75 75 67 67 68 68 73 73

Notes: All

regressions

are OLS.

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

0 to

10,

where a

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 scale

from 0

to

10,

with a

higher

score

indicating

more

democracy. European

settlements is

percent

of

population

that was

European

or of

European

descent

in

1900. See

Appendix

Table Al for more

detailed variable

definitions and

sources.

Panel A of Table 4 reports 2SLS estimates

of the coefficient of interest,

a

from equation

(1) and Panel B gives the corresponding first

stages.18 Column (1) displays the strong first-

stage relationship between (log) settler mortal-

ity 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

is highly sig-

nificant with a standard

error of

0.16, and

in

fact

larger than the OLS estimates

reported in

Table 2. This suggests that

measurement error

in the institutions variables

that creates attenu-

ation 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

institu-

tions that matter

for economic

performance is

very complex,

and

any single

measure is bound

to

capture only part

of the "true

institutions,"

18

We have also

run these regressions

with

standard

errors

corrected for

possible clustering

of the

mortality

rates

assigned

to

countries

in

the same

disease environment. This

clustering has little

effect on the standard errors, and does

not change our

results.

1386

THE AMERICAN ECONOMIC REVIEW

DECEMBER

2001

TABLE 4-IV

REGRESSIONS OF LOG GDP PER CAPITA

Base

Base Base sample,

Base Base sample sample dependent

Base sample Base sample sample sample with with variable is

Base

Base without without without without continent continent log output

sample

sample Neo-Europes Neo-Europes Africa Africa dummies dummies per worker

(1)

(2)

(3)

(4) (5)

(6)

(7)

(8) (9)

Panel A: Two-Stage Least Squares

Average protection against 0.94

1.00

1.28 1.21 0.58 0.58 0.98 1.10 0.98

expropriation

risk

1985-1995 (0.16)

(0.22) (0.36) (0.35) (0.10) (0.12) (0.30) (0.46) (0.17)

Latitude -0.65 0.94 0.04

-1.20

(1.34) (1.46) (0.84) (1.8)

Asia

dummy

-0.92 -1.10

(0.40) (0.52)

Africa

dummy

-0.46 -0.44

(0.36) (0.42)

"Other" continent

dummy -0.94 -0.99

(0.85) (1.0)

Panel B: First

Stage

for

Average

Protection

Against Expropriation

Risk

in

1985-1995

Log European settler mortality -0.61 -0.51 -0.39 -0.39

-1.20

-1.10 -0.43 -0.34 -0.63

(0.13)

(0.14) (0.13) (0.14) (0.22) (0.24) (0.17) (0.18) (0.13)

Latitude 2.00 -0.11

0.99 2.00

(1.34) (1.50) (1.43) (1.40)

Asia

dummy 0.33 0.47

(0.49) (0.50)

Africa

dummy -0.27

-0.26

(0.41) (0.41)

"Other" continent

dummy

1.24 1.1

(0.84) (0.84)

R2

0.27

0.30 0.13 0.13 0.47

0.47

0.30 0.33

0.28

Panel

C: Ordinary Least Squares

Average protection against

0.52

0.47 0.49

0.47

0.48 0.47

0.42

0.40 0.46

expropriation

risk

1985-1995

(0.06)

(0.06) (0.08) (0.07) (0.07) (0.07) (0.06) (0.06) (0.06)

Number of

observations

64 64 60 60

37 37

64 64 61

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 to

10,

where

a

higher

score means more

protection against

risk

of

expropriation

of investment

by

the

government,

from Political

Risk

Services. Panel

A

reports

the

two-stage least-squares estimates,

instrumenting

for

protection against expropriation

risk

using log

settler

mortality;

Panel

B

reports

the

corresponding

first

stage.

Panel

C

reports

the

coefficient from

an

OLS

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

Table

Al

for more detailed variable

descriptions

and sources.

creating

a

typical

measurement error problem.

Moreover, what

matters

for

current income

is

presumably not only institutions

today, but also

institutions in the past.

Our measure of institu-

tions which refers to

1985-1995 will not be

perfectly correlated with

these.19

Does the 2SLS

estimate make quantitative

sense? Does it imply

that institutional

differences

can explain a significant

fraction

of income dif-

19

We can ascertain, to some degree,

whether the differ-

ence

between

OLS

and

2SLS estimates

could

be

due

to

measurement error in the institutions variable

by making

use of an alternative measure of institutions,

for example,

the constraints on the executive measure.

Using this mea-

sure as an

instrument

for

the protection

against expropria-

tion index

would solve the measurement error,

but not the

endogeneity problem.

This exercise

leads to an estimate

of

the effect

of

protection against

expropriation equal

to 0.87

(with standard

error 0.16). This

suggests that "measurement

error"

in

the

institutions

variables

(or

the

"signal-to-noise

ratio" in the institutions

variable)

is of the right

order of

magnitude

to explain the difference

between the

OLS and

2SLS estimates.

VOL. 91 NO.

5

ACEMOGLU ET AL.:

THE

COLONIAL ORIGINS OF DEVELOPMENT 1387

ferences across countries? Let us once again com-

pare

two

"typical"

countries with

high

and

low

expropriation risk, Nigeria and Chile (these coun-

tries are typical for the IV regression in the sense

that they are practically on the regression line).

Our 2SLS 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 be-

tween

Nigeria

and

Chile'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 differ-

ences on

income per capita.

Colunm

(2)

shows that

adding

latitude does

not

change

the

relationship;

the

institutions

coefficient is now 1.00 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 determi-

nant of economic performance because

it

is

correlated with institutions

(or

with the

exoge-

nous

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

New

Zealand,

the estimates remain

highly signif-

icant,

and in

fact increase

a little. For

example,

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 sam-

ple.

The

estimates without

Africa are somewhat

smaller,

but

also more

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 dum-

mies 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

differ-

ent 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

ac-

counted 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

veiy

close to our baseline result. The 2SLS

coef-

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

rest of the

paper,

we

investigate

the

robustness of

these results.

3

20

In 2SLS

estimation,

all covariates that are included in

the second

stage,

such

as latitude, are also included

in

the

first stage. When these first-stage effects are of no

major

significance

for

our argument,

we

do not report them

in

the

tables to save

space.

21

We should note at this point that

if

we limit the

sample

to

African

countries

only, the first-stage relationship

using

the protection against expropriation variable

becomes

con-

siderably weaker, and the 2SLS effect of institutions is no

longer significant. The 2SLS effect of institutions continue

to be significant when we use some (but not all) measures of

institutions. Therefore, we conclude that the relationship

between settler mortality and institutions is weaker within

Africa.

22

The results with other covariates are also very similar.

We repeated the same regressions using a variety of alter-

native measures of institutions, including constraints

on

the

executive from the Polity III data set, an index of law and

order tradition from Political Risk Services, a measure of

property rights from the Heritage Foundation, a measure

of

rule of law from the Fraser Institute, and the efficiency

of the

judiciary

from Business International. The results

and the

magnitudes

are

very

similar to those

reported

in

Table 4. We also obtained very similar

results

with the 1970

values for the constraints

on the

executive

and income

per

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 subsamples

with

vary-

ing degrees of data quality and different methods

of

con-

structing 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

mor-

tality rates from neighboring disease environments, when

1388 THE

AMERICAN ECONOMIC REVIEW

DECEMBER 2001

V.

Robustness

A. Additional

Controls

The validity of our

2SLS 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 impor-

tance

of colonial origin

(identity

of

the

main

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

Table

5,

we add dummies for British and French

colonies

(colonies

of

other nations are the omit-

ted

group).

This has little affect on our

results.

Moreover,

the French

dummy

in 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.25

Therefore,

it

ap-

pears

that

British colonies are found

to perform

substantially better

in

other studies

in large part

because Britain colonized places

where settle-

ments were

possible,

and this made British col-

onies inherit

better institutions.

To further

investigate this issue, columns (3)

and (4) esti-

mate our basic

regression

for British colonies

only. They show

that both the

relationship be-

tween settler mortality and

institutions 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 now 1.07 (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

institu-

tional

development.

von

Hayek (1960)

and La Porta

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

of

economic

performance.

To control for

this,

in

columns

(7)

and

(8),

we

add

the fraction of the

populations

that are

Catholic, Muslim,

and

of other reli-

gions,

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 2SLS estimate of

the use data for Latin America from

naval

stations

instead

of

bishops, and

when

we do not use data from small African

samples. These results are available

in

Appendix Table

A5

available

from the

authors,

or in

Acemoglu

et al.

(2000).

24

Joseph N. Altonji et al.

(2000) develop an

econometric

methodology to assess the

importance of omitted variable

bias. The basic idea is that

if

the estimate

of

the coefficient

of interest does

not

change

as additional covariates are

included in the regression,

it is less likely to change if we

were able to add some

of

the

missing

omitted

variables.

Our

methodology

here is an informal version of

this approach.

25

Moreover, the British

colonial dummy is negative and

significant

in the

second

stage.

The net effect of

being

a British

colony

on

income per capita

is in

fact negative. More specif-

ically, British colonies

have, on average, an index of institution

that

is

0.63 points lower. Given the 2SLS estimate of 1.10,

this translates

into 69

log points higher

income

per capita

for

British colonies

(1.10

X

63

69).

The

second-stage

effect

of

being

a

British colony

is -78

log points,

im-

plying -9 log point (approximately 10 percent) negative

net effect of being a British colony. A possible explana-

tion

for

this

pattern

is

that

(Anglo-Saxon?)

researchers

are overestimating how "bad" French institutions are, and

the second-stage

regression

is

correcting

for

this.

26

The first stage shows that French legal origin is asso-

ciated with worse institutions, but similarly, the net effect of

having

French

legal origin

is

actually positive: -67

X

1.1

+

89

=

15 log points (approximately

15

percent).

VOL. 91 NO. 5 ACEMOGLU ETAL.: THE COLONIAL ORIGINS OF DEVELOPMENT 1389

TABLE 5-IV REGRESSIONS OF LOG GDP PER CAPITA WITH ADDITIONAL CONTROLS

British British

Base Base

colonies colonies Base

Base

Base Base

sample sample only only sample sample sample sample sample

(1) (2) (3) (4)

(5)

(6)

(7)

(8) (9)

Panel A: Two-Stage Least Squares

Average protection against 1.10 1.16 1.07 1.00 1.10 1.20 0.92 1.00 1.10

expropriation risk, 1985-1995 (0.22) (0.34) (0.24) (0.22) (0.19) (0.29) (0.15) (0.25) (0.29)

Latitude -0.75 -1.10 -0.94 -1.70

(1.70) (1.56) (1.50) (1.6)

British colonial

dummy

-0.78 -0.80

(0.35) (0.39)

French colonial dummy -0.12 -0.06 0.02

(0.35) (0.42) (0.69)

French

legal origin dummy 0.89

0.96 0.51

(0.32) (0.39) (0.69)

p-value

for

religion variables [0.001] [0.004]

[0.42]

Panel B: First Stage for Average Protection Against Expropriation Risk

in

1985-1995

Log European settler mortality -0.53 -0.43 -0.59 -0.51 -0.54 -0.44 -0.58 -0.44 -0.48

(0.14) (0.16) (0.19) (0.14) (0.13) (0.14) (0.13) (0.15) (0.18)

Latitude 1.97 2.10 2.50 2.30

(1.40) (1.30) (1.50) (1.60)

British colonial

dummy 0.63 0.55

(0.37) (0.37)

French colonial

dummy 0.05

-0.12

-0.25

(0.43) (0.44) (0.89)

French

legal origin -0.67

-0.7 -0.05

(0.33) (0.32) (0.91)

R

2

0.31 0.33 0.30 0.30 0.32

0.35 0.32 0.35 0.45

Panel

C:

Ordinary

Least

Squares

Average protection against 0.53

0.47

0.61 0.47 0.56 0.56 0.53 0.47 0.47

expropriation risk,

1985-1995

(0.19) (0.07) (0.09) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06)

Number of observations 64

64

25 25

64

64 64 64

Notes: Panel

A

reports

the

two-stage least-squares

estimates with

log

GDP

per capita (PPP basis)

in

1995

as

dependent variable,

and Panel B

reports

the

corresponding

first

stage.

The base 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

table simulta-

neously. Again, these

controls have very little

effect on our main estimate.

Another concern

is that settler mortality is

27

The religion dummies are significant

in

the first stage,

but once

again they

are estimated to have

offsetting

effects

in

the second stage, implying

little net

effect

of

religion

on

income.

correlated with

climate and

other geographic

characteristics.

Our instrument

may therefore

be picking up

the direct effect

of these vari-

ables. We investigate

this issue

in Table 6. In

columns

(1)

and (2),

we add a set of

temper-

ature and humidity variables

(all data

from

Philip M. Parker,

1997). In

the table we

report joint

significance levels

for these vari-

ables.

Again,

they have little

effect

on our

estimates.

1390 THE AMERICAN

ECONOMIC REVIEW DECEMBER 2001

TABLE 6-ROBUSTNESS

CHECKS

FOR IV

REGRESSIONS OF LOG GDP PER CAPITA

Base Base

Base Base Base Base Base Base Base

sample sample

sample sample sample sample sample sample sample

(1) (2)

(3) (4) (5) (6) (7) (8) (9)

Panel A:

Two-Stage Least Squares

Average protection against

0.84

0.83 0.96

0.99

1.10 1.30 0.74

0.79

0.71

expropriation risk, 1985-1995 (0.19) (0.21)

(0.28) (0.30) (0.33) (0.51) (0.13) (0.17) (0.20)

Latitude 0.07

-0.67

-1.30

-0.89 -2.5

(1.60)

(1.30) (2.30) (1.00) (1.60)

p-value for temperature variables [0.96] [0.97]

[0.77]

p-value for humidity variables [0.54] [0.54]

[0.62]

Percent of European descent in 1975

-0.08 0.03 0.3

(0.82) (0.84) (0.7)

p-value for soil quality

[0.79] [0.85] [0.46]

p-value for natural resources [0.82] [0.87]

[0.82]

Dummy for being landlocked 0.64

0.79 0.75

(0.63) (0.83) (0.47)

Ethnolinguistic fragmentation

-1.00 -1.10

-1.60

(0.32) (0.34)

(0.47)

Panel B:

First Stage for Average Protection

Against Expropriation

Risk

in

1985-1995

Log European settler mortality -0.64 -0.59 -0.41 -0.4 -0.44

-0.34 -0.64 -0.56 -0.59

(0.17) (0.17)

(0.14) (0.15) (0.16) (0.17) (0.15) (0.15) (0.21)

Latitude 2.70

0.48 2.20 2.30 4.20

(2.00)

(1.50) (1.50) (1.40) (2.60)

R

2

0.39

0.41 0.34

0.34

0.41

0.43 0.27 0.30 0.59

Panel C: Ordinary Least

Squares

Average protection against

0.41 0.38 0.39 0.38 0.46 0.42 0.46 0.45 0.38

expropriation risk,

1985-1995

(0.06) (0.06)

(0.06) (0.06) (0.07) (0.07) (0.05) (0.06) (0.06)

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 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. 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 and maximum

monthly

low

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

barrels

per capita).

Measures of

soil

quality/climate

are

steppe (low latitude),

desert

(low

latitude), steppe (middle latitude),

desert

(middle latitude), dry steppe

wasteland,

desert

dry winter,

and

highland.

See

Appendix

Table

Al

for more detailed

variable

definitions and sources.

A

is that in colonies

where

Europeans settled, the current

population con-

sists

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" or

spe-

cial

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 sig-

nificant, with a coefficient of 0.96 (s.e.

=

0.28).

In columns

(5)

and

(6),

we control for

measures of natural resources, soil quality (in

practice

soil

types), and

for whether the

coun-

try is landlocked. All these controls are insig-

nificant, and have little effect on our 2SLS

estimate of the effect of institutions on

in-

come per capita.

In

columns (7) and (8), we include

ethno-

linguistic fragmentation as another control

and

treat

it

as exogenous. Now the

coefficient

VOL. 91

NO. S

ACEMOGLU

ET AL:

THE COLONIAL

ORIGINS OF

DEVELOPMENT

1391

of

protection

against

expropriation

is

0.74

(s.e.

=

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

insti-

tutions

downwards.

Since

ethnolinguistic

fragmentation

is

likely

to

be

endogenous

with

respect

to

development

(i.e.,

ethnolinguistic

fragmentation tends to

disappear after the

for-

mation of centralized

markets; see Weber

[1976]

or

Andersen

[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,

we include

all

these variables

together.

Despite

the

large

number of

controls, protection

against

expro-

priation on

income

per

capita

is

still

highly

significant, with a

somewhat

smaller

coeffi-

cient

of

0.71

(s.e. =

0.20),

which is

again

likely

to understate the effect of

institutions

on income

because

ethnolinguistic

fragmen-

tation is treated as

exogenous.

Finally,

in Table

7,

we

investigate

whether

our

instrument

could

be

capturing

the

general

effect of disease on

development. Sachs

and a

series of coauthors

have

argued for the

impor-

tance of malaria

and other diseases in

explain-

ing

African

poverty

(see,

for

example,

Bloom

and

Sachs,

1998;

Gallup

and

Sachs, 1998;

Gallup

et

al., 1998). Since

malaria

was one of

the main causes

of

settler

mortality,

our esti-

mate

may

be

capturing

the direct effect of

ma-

laria

on

economic

performance.

We are

skeptical

of this

argument

since

malaria

preva-

lence

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 absen-

teeism,

most

people

who live

in

high

malaria

areas

have

developed some

immunity to the

disease (see

the

discussion in

Section III, sub-

section A).

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

set-

tlements 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

in

an area

where

falcipo-

rum malaria is

endemic

in

1994

(as

constructed

and used

by

Gallup

et

al., 1998).

Since malaria

prevalence in

1994 is

highly

endogenous, the

argument

in

Appendix A

implies

that

control-

ling for

it

directly will

underestimate

the effect

of institutions on

performance.

In

fact,

the co-

efficient 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 stan-

dard

error 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 insti-

tutions in

determining

economic

performance.

They accept

our

case for the

importance

of

institutions,

but

argue

that

more general

speci-

fications show

that the disease environment and

health characteristics

of countries

(their

"geog-

raphy")

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 sug-

gest.

Columns

(3)-(6)

include

life

expectancy

and infant

mortality

as

exogenous

controls. The

estimates show a

significant

effect

of

institu-

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 insti-

tutions will be biased down

(see Appendix

A).

These estimates are therefore

consistent with

28

For

example,

the

United States eliminated malaria

from

the Panama

Canal

Zone, and Australia

eliminated

it

from

Queensland (see

Crosby, 1986 pp.

141-42). Even

in

Africa,

there have

been very

successful

campaigns

against

malaria,

including those in

Algeria and that conducted

by

the Rio-Tinto

Zinc

mining company

in Zambia (then

Northern

Rhodesia).

The

WHO's

Roll Back

Malaria

program contains a number of

effective

recommendations for

controlling malaria that are

relatively

straightforward

to

implement if

families have

enough

money (e.g.,

insecticide-treated bed

nets).

1392 THE AMERICAN ECONOMIC

REVIEW DECEMBER 2001

TABLE 7-GEOGRAPHY AND HEALTH

VARIABLES

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

(6) (7) (8) (9) (10)

(1

1)

Yellow fever

instrument for

average

Instrunienting only

for

average

Instrumenting

for all

protection against

protection against expropriation

risk

right-hand-side

variables

expropriation risk

Panel A:

Two-Stage

Least

Squares

Average protection against

0.69 0.72 0.63 0.68 0.55 0.56 0.69 0.74 0.68 0.91 0.90

expropriation risk, 1985-1995 (0.25) (0.30) (0.28) (0.34) (0.24)

(0.31) (0.26) (0.24) (0.23) (0.24) (0.32)

Latitude

-0.57

-0.53

-0.1

(1.04) (0.97)

(0.95)

Malaria in 1994 -0.57 -0.60

-0.62

(0.47) (0.47)

(0.68)

Life

expectancy

0.03

0.02

(0.02) (0.02)

(0.02)

Infant

mortality

-0.01

-0.01 -0.01

(0.005)

(0.006) (0.01)

Panel B: First

Stage

for

Average

Protection

Against Expropriation

Risk in 1985-1995

Log

European

settler

mortality

-0.42 -0.38 -0.34 -0.30 -0.36 -0.29 -0.41 -0.40 -0.40

(0.19) (0.19) (0.17) (0.18) (0.18)

(0.19) (0.17) (0.17) (0.17)

Latitude 1.70 1.10 1.60 -0.81 -0.84 -0.84

(1.40) (1.40)

(1.40) (1.80) (1.80) (1.80)

Malaria in 1994 -0.79 -0.65

(0.54) (0.55)

Life

expectancy

0.05 0.04

(0.02) (0.02)

Infant

mortality

-0.01

(0.01) (0.01)

Mean temperature

-0.12 -0.12

-0.12

(0.05) (0.05) (0.05)

Distance

from coast

0.57

0.55

(0.51) (0.52) (0.52)

Yellow fever dummy

-1.10 -0.81

(0.41) (0.38)

R2

0.3

0.31 0.34

0.35 0.32

0.34

0.37 0.36 0.36

0.10

0.32

Panel C:

Ordinary

Least

Squares

Average protection against

0.35

0.35 0.28

0.28

0.29 0.28 0.35 0.29 0.29 0.48 0.39

expropriation risk,

1985-1995

(0.06) (0.06) (0.05) (0.05) (0.05)

(0.05) (0.06) (0.05) (0.05) (0.06) (0.06)

Numnber

of observations

62 62

60 60 60

60

59 59 64 64

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

the coefficient

from an

OLS

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

results not

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

regressors

are

exogenous.

Columns

(7)-(9)

include

as

instruments

average temperature,

amount of

territory

within 100

km

of the

coast,

and latitude

(from

McArthur

and

Sachs, 2001).

Columns

(10)

and

(11)

use

a

dummy

variable

for

whether or not a

country

was

subject

to

yellow

fever

epidemics

before 1900

as an instrument for

average protection

against

expropriation.

See

Appendix

Table Al for more detailed

variable

definitions

and

sources.

institutions being the major determinant of in-

come per capita differences,

with little

effect

from

geography/health

variables.

Columns

(7)-(9) report estimates from mod-

els 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 in-

struments

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-

VOL. 91

NO.

5

ACEMOGLU

ET

AL.:

THE COLONIAL

ORIGINS

OF

DEVELOPMENT

1393

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

our sample, which consists of

only

ex-colonies

(there is no

reason for variation

in

the mortality

rates of British and

French troops at home to be

related to

their

institutional

development).

It

turns out that once Britain and France are left

out,

the

McArthur and Sachs' specification gen-

erates no

evidence

that

geography/health vari-

ables have an

important

effect on

economic

performance.29

As a final

strategy

to see whether settler mor-

tality could be

proxying for the current disease

environment, we

estimated

models

using

a

yellow

fever

instrument.

This is a

dummy

van-

able

indicating

whether the area was

ever

af-

fected

by yellow

fever

(from Oldstone, 1998;

see

Appendix

Table

Al).

This is an attractive

alternative

strategy because yellow fever

is

mostly

eradicated

today,

so

this

dummy

should

not be

correlated with the current disease envi-

ronment. 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 esti-

mate in our

base

sample

is

0.91 (s.e.

=

0.24)

comparable

to our

baseline

estimate of

0.95

reported

in

Table

4.

Adding

continent dummies

in

column

(11)

reduces this estimate

slightly

to

0.90

(s.e.

=

0.32).3

B.

Overidentification

Tests

We can also

investigate the validity of our

approach by using overidentification tests. Ac-

cording

to our

theory, settler mortality (M) af-

fected

settlements (S);

settlements

affected

early

institutions

(C);

and

early

institutions af-

fected

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 overi-

dentification

test

presumes

that

one of these

instruments, say

S,

is truly exogenous, and tests

for the

exogeneity

of the

others,

such

as settler

mortality.

This

approach

is useful since it is a

direct

test of

our exclusion restriction. However,

such tests

may

not lead to a

rejection

if

all

instruments are

invalid,

but

still

highly

corre-

lated with each other.

Therefore,

the

results

have to be

interpreted

with caution.

Overall,

the

overidentification test

will

reject

the

validity

of

our

approach

if

either

(i)

the

equation

of

interest, (1),

does not have

a con-

stant

coefficient, i.e., log

yi

=

jt

+

aiRi

+

si,

where i denotes

country,

or

(ii) C

or S has a

direct effect

on

income

per capita, log

yi

(i.e.,

either

S or C is correlated with

si),

or

(iii)

settler

mortality, M,

has an effect on

log

yi

that

works

through

another

variable,

such as culture.

The data

support

the

overidentifying

restric-

tions

implied by

our

approach.31

This

implies

that, subject

to the usual

problems

of

power

associated with overidentification

tests,

we can

rule

out all three of the

above

possibilities.

This

gives

us additional confidence that settler mor-

tality

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).

29

McArthur and Sachs (2001) also report specifications

with more

instruments. However, using six

or seven instru-

ments

with only 64 observations leads to the "too-many-

instruments" problem, typically biasing the IV estimate

towards the OLS estimate (see John Bound

et

al., 1995). We

therefore did not pursue these estimates further.

Finally, McArthur

and Sachs also

argue

that our ex-

colonies

sample may

not have

enough geographic

variation.

In their

view,

this

may

be

why

we

do not

find

a role for

geographic variables. Nonetheless, there is substantial vari-

ation

in the

geography

variables

in our

sample

which

in-

cludes countries such as

Canada,

the United

States,

New

Zealand, and Australia.

The standard

deviation

of distance

from

the equator

in

the world

is

1.89, greater than 1.33 in

our

sample.

This

is mainly because there

are

a large number

of

European countries with high latitudes

in

the world

sample, but not in our sample.

30

If

we

drop

the

Neo-Europes (not reported here),

the

estimate is

still

similar and

highly significant, 1.05 (s.e.

=

0.35).

31

In

some specifications,

the

overidentification

tests us-

ing measures of early institutions reject

at

that 10-percent

level

(but

not at the 5-percent level). There

are in fact good

reasons to expect institutions circa 1900

to

have

a

direct

effect on income today (and hence the

overidentifying

tests

to reject our restrictions): these

institutions should affect

physical

and human

capital

investments at

the

beginning

of

the century, and have some effect on

current income levels

through this channel.

1394 THE AMERICAN ECONOMIC REVIEW DECEMBER 2001

TABLE 8-OVERIDENTIFICATION TESTS

Base Base Base Base Base Base Base Base Base Base

sample sample sample sample sample sample sample sample sample sample

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Panel A: Two-Stage Least Squares

Average protection against expropriation 0.87 0.92

0.71 0.68 0.72

0.69

0.60 0.61

0.55 0.56

risk, 1985-1995 (0.14) (0.20) (0.15) (0.20) (0.14) (0.19) (0.14) (0.17) (0.12) (0.14)

Latitude -0.47

-0.34 0.31 -0.41 -0.16

(1.20) (1.10) (1.05) (0.92) (0.81)

Panel B: First Stage for Average Protection Against Expropriation Risk

European settlements

in

1900 3.20 2.90

(0.62) (0.83)

Constraint

on

executive

in

1900 0.32 0.26

(0.08) (0.09)

Democracy

in

1900

0.24

0.20

(0.06) (0.07)

Constraint

on

executive

in first

year

of 0.25 0.22

independence (0.08) (0.08)

Democracy in first year of independence 0.19 0.17

(0.05) (0.05)

R

2

0.30 0.30

0.20 0.24 0.24 0.26

0.19 0.25

0.26

0.30

Panel C: Results from Overidentification Test

p-value (from chi-squared test) [0.67] [0.96] [0.09] [0.20] [0.11] [0.28] [0.67] [0.79] [0.22] [0.26]

Panel D: Second

Stage

with

Log Mortality

as

Exogenous

Variable

Average protection against expropriation

0.81 0.88 0.45

0.42

0.52 0.48 0.49

0.49

0.4 0.41

risk, 1985-1995 (0.23) (0.30) (0.25) (0.30) (0.23) (0.28) (0.23) (0.25) (0.18) (0.19)

Log European settler mortality -0.07 -0.05 -0.25 -0.26 -0.21

-0.22 -0.14 -0.14

-0.19 -0.19

(0.17) (0.18) (0.16) (0.17) (0.15) (0.16) (0.16) (0.15) (0.13) (0.12)

Latitude -0.52

0.38 0.28 -0.38 -0.17

(1.15) (0.89) (0.86) (0.84) (0.73)

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-value

for 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

using log 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

first year

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

overidentification 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

a

va-

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. For

example,

in

column (1), we use

European settlements

in

1900 as

the

only

instrument for institutions.

This results in an

estimated effect of 0.87

(with standard error

0.14), as compared to our

baseline estimate of

0.94.

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

in 1900.

Panel D reports

an easy-to-interpret

version

of the overidentification

test.

It

adds

the log of

mortality as an exogenous

regressor.

If

mor-

tality 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

insig-

nificant.

For example,

in

column (1),

log

mor-

tality

has

a

coefficient

of

-0.07

(with

standard

error

0.17). This confirms

that the

VOL. 91 NO. 5 ACEMOGLU ET AL.: THE

COLONIAL ORIGINS OF DEVELOPMENT

1395

impact of

mortality rates faced by settlers

likely

works through their effect on

institutions.

Finally,

for completeness, in Panel C we re-

port the p-value

from

the appropriate

x2

overi-

dentification test. This tests whether the 2SLS

coefficients estimated with the instruments

in-

dicated

in

Panels

A

and

B

versus

the coeffi-

cients estimated

using (log)

settler

mortality

in

addition to

the "true" instruments are signifi-

cantly

different

(e.g.,

in

the first

column, the

coefficient

using European settlements

alone is

compared

to the

estimate using European

set-

tlements

and

log mortality

as

instruments).

We

never reject

the hypothesis that they are equal at

the

5-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 institu-

tions-

on

income

per capita.

VI.

Concluding

Remarks

Many

economists and social

scientists

be-

lieve that

differences

in

institutions and

state

policies

are at

the

root

of

large

differences

in

income per

capita

across countries. There is

little

agreement, however,

about

what deter-

mines institutions and

government

attitudes to-

wards

economic

progress, making

it

difficult

to

isolate

exogenous

sources of

variation

in insti-

tutions to estimate their effect on

performance.

In

this

paper

we

argued

that differences

in

co-

lonial

experience

could be a source

of

exoge-

nous

differences

in institutions.

Our

argument

rests on the

following pre-

mises:

(1)

Europeans adopted very

different col-

onization

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

extractive

states with

the

intention of transfer-

ring

resources

rapidly

to the

metropole.

These

institutions were detrimental to investment and

economic

progress. (2)

The colonization strat-

egy

was in

part

determined

by

the

feasibility

of

European

settlement.

In

places

where

Europe-

ans faced

very high mortality rates, they

could

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 in-

stitutions; 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, and

is

robust to controlling for latitude, climate, cur-

rent disease

environment, religion, natural

resources, soil quality, ethnolinguistic fragmen-

tation,

and

current racial

composition.

It is

useful

to

point out that our findings

do

not

imply

that

institutions

today

are

predeter-

mined

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

argu-

ably exogenous, they

are useful

as an

instru-

ment

to

isolate the effect of institutions

on

performance.

In

fact,

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 re-

ducing 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

as an

equilib-

rium

outcome,

related to some more funda-

mental

"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

1396

THE

AMERICAN

ECONOMIC

REVIEW

DECEMBER

2001

rights and

expropriation

risk is

an

important

area

for

future

study.

APPENDIX

A:

BIAS

IN

THE

EFFECT OF

INSTITUTIONS

WHEN

OTHER

ENDOGENOUS

VARIABLES

ARE

INCLUDED

To

simplify

notation,

suppose that

Ri

is ex-

ogenous,

and

another variable

that

is

endoge-

nous,

zi,

such as

prevalence

of

malaria

or

ethnolinguistic

fragmentation,

is added to the

regression.

Then,

the

simultaneous

equations

model

becomes

Y

i-ko

+

aRi

+

'zi

+

?i

Zi=

1

+

Yi

+

qi,

where

Yi

=

log

yi.

We

presume

that

a

>

0,

4

<

0,

and

7n

<

0,

which

implies

that

we

interpret

z1

as a

negative

influence on

income.

Moreover,

this

naturally

implies

that

cov(rji, 8j)

<

0

and

cov(zi,

Ri)

<

0,

that

is,

the factor

za

is

likely

to

be

negatively

correlated

with

positive

influ-

ences

on

income.

Standard

arguments

imply

that

cov(Rf,

Si)

plim =a+

=

+

var(i)

cov(zi,

E,)

a

{-

Kva

var(R;)

e

where K

and

Ri

are the

coefficient

and

the

residual

from the

auxiliary

equation,

Ri

=

Ko

+

KZi

+

Ri,

and

so

K

=

cov(zi,

Ri)/var(zi)

<

0,

which

is

negative

due

to

the

fact that

cov(Ri,

Zi)

< 0.

The reduced form

for

zi

is:

1

(Al)

zi

-

+

4iaRi

+

(psi

+

ij).

We

impose

the

regularity condition

4

-

i

<

1,

so that

an

increase in

the

disturbance to

the

z-equation,

m1i,

actually

increases

zi.

Now

using

this

reduced

form, we

can

write

(A2)

plim

a^

=

a

-

K

v

C

(Zi

)

va(ff?

if)

+

4o)02

=~~ ~(,7 0E - K@-

(1

-

07T)

*

var(Ri)

where

oi

is the

variance of

s, and

u,,

is

the

covariance of s and

-q.

Substituting

for K

in

(A2),

we

obtain:

plim

&

(of?

+

efo?) cov(zi, Ri)

(1

-

4) 7)

*

var(Ri)

var(z,)

Recall

that

4

<

0,

o

<

0,

and

cov(zi,

Ri)

<

0.

Therefore, plim

a^

<

a,

and when we

control

for the

endogenous variable

zi,

the coefficient

on our

institution variable

will

be

biased

downwards.

VOL. 91 NO.

5

ACEMOGLU ET

AL..

THE COLONIAL

ORIGINS

OF

DEVELOPMENT 1397

APPENDIX

TABLE Al: DATA

DESCRIPTIONS AND SOURCES

Log GDP per capita,

1975

and

1995: Purchasing Power Parity Basis, from

World

Bank, World Development Indicators,

CD-Rom, 1999.

Log output per worker, 1988: As

used

in

Hall

and

Jones (1999),

from

www.stanford.edu/-chadj.

Average protection against

expropriation risk, 1985-1995: Risk of expropriation of private foreign investment by

government, from 0 to 10, where

a

higher score means less risk. Mean

value for all

years

from

1985 to 1995. This data

was previously used by Knack and

Keefer (1995)

and

was organized

in

electronic form by the IRIS Center (University of

Maryland); originally

Political Risk Services.

Constraint on executive

in

1900,

1970,

1990 and in first

year

of

independence: Seven-category scale,

fiom

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 5

indicates substantial

limitations; score

of

7 indicates executive parity or subordination. Equal to 1 if

country

was

not independent at that

date. Date of independence

is

the first year that the country appears in the Polity HI data

set. From the Polity

HI

data set, downloaded from

Inter-University

Consortium for Political and Social

Research. See Gun

(1997).

Democracy in 1900 and first year of

independence: An 1 1-category scale, from 0 to 10, with a higher score indicating

more democracy. Points from three dimensions:

Competitiveness

of Political

Participation (from

1 to

3); 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). Equal

to 1 if

country

not

independent

at

that date.

From the

Polity

III

data set. See Gurr (1997).

European

settlements

in

1900 and

percent

of

European

descent

1975:

Percent of

population European or

of

European

descent in

1900

and

1975.

From

McEvedy

and

Jones

(1975)

and other

sources listed in

Appendix

Table

A6

(available

from the authors).

Ethnolinguistic fragmentation:

Average of five different indices of ethnolinguistic fragmentation. Easterly and Levine

(1997),

as used

in La

Porta et al.

(1999).

Religion variables:

Percent of

population

that

belonged

to the three most

widely spread religions

of

the world

in

1980

(or

for

1990-1995

for

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

Common Law

Origin.

From La

Porta

et al.

(1999).

Colonial dummies:

Dummy

indicating

whether

country

was a

British, French, German, Spanish, Italian, Belgian, Dutch,

or

Portuguese colony.

From

La

Porta et al.

(1999).

Temperature

variables:

Average

temperature,

minimum

monthly high,

maximum

monthly high,

minimum

monthly low,

and maximum

monthly low,

all

in

centigrade.

From

Parker

(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

in

percent.

From Parker

(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.

From

Parker

(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).

Dummy

for landlocked:

Equal

to 1 if

country

does not

adjoin

the sea. From Parker

(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 to take

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

to Curtin

(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

and

Sachs

(2001).

Distance

from

the

coast: Proportion

of land area

within 100 km of the seacoast. From McArthur and Sachs

(2001).

1398 THE AMERICAN ECONOMIC REVIEW DECEMBER 2001

APPENDIX TABLE A2-DATA ON MORTALITY

Average Average

protection protection

Log GDP against Log GDP against

Abbreviated

per capita expropriation Main Abbreviated per capita expropriation

Main

name

used (PPP)

in

risk mortality

name

used (PPP)

in risk

mortality

Former colonies

in

graphs 1995 1985-1995 estimate Former colonies

in

graphs 1995 1985-1995 estimate

Algeria

DZA

8.39 6.50 78.2 Jamaica

JAM

8.19 7.09 130

Angola AGO 7.77 5.36 280 Kenya KEN 7.06 6.05 145

Argentina ARG 9.13 6.39 68.9 Madagascar

MDG 6.84

4.45 536.04

Australia

AUS

9.90

9.32

8.55 Malaysia

MYS

8.89 7.95

17.7

Bahamas BHS 9.29 7.50 85 Mali

MLI

6.57 4.00 2940

Bangladesh BGD 6.88 5.14 71.41 Malta

MLT 9.43

7.23

16.3

Bolivia BOL

7.93

5.64 71 Mexico

MEX 8.94 7.50

71

Brazil BRA 8.73 7.91 71 Morocco

MAR

8.04 7.09 78.2

Burkina Faso BFA 6.85 4.45 280 New Zealand

NZL

9.76 9.73 8.55

Cameroon

CMR

7.50 6.45 280 Nicaragua

NIC

7.54

5.23 163.3

Canada CAN

9.99 9.73

16.1

Niger

NER

6.73

5.00 400

Chile CHL 9.34 7.82

68.9

Nigeria

NGA 6.81

5.55 2004

Colombia COL 8.81 7.32

71

Pakistan PAK 7.35 6.05 36.99

Congo (Brazzaville)

COG

7.42 4.68 240 Panama PAN 8.84 5.91 163.3

Costa Rica

CRI

8.79 7.05 78.1 Paraguay

PRY 8.21

6.95

78.1

C6te d'Ivoire

CIV

7.44

7.00

668 Peru

PER

8.40 5.77 71

Dominican Republic DOM 8.36 6.18 130 Senegal

SEN

7.40 6.00 164.66

Ecuador

ECU

8.47 6.55

71 Sierra Leone SLE

6.25

5.82 483

Egypt EGY 7.95 6.77

67.8

Singapore SGP 10.15

9.32 17.7

El

Salvador

SLV

7.95 5.00 78.1 South

Africa

ZAF 8.89 6.86 15.5

Ethiopia

ETH 6.11 5.73 26 Sri Lanka

LKA

7.73

6.05 69.8

Gabon GAB

8.90

7.82

280 Sudan

SDN 7.31

4.00

88.2

Gambia GMB 7.27

8.27 1470 Tanzania

TZA

6.25

6.64 145

Ghana

GHA

7.37 6.27 668

Togo

TGO

7.22

6.91

668

Guatemala

GTM

8.29

5.14 71 Trinidad and

Tobago

TTO 8.77

7.45

85

Guinea GIN

7.49 6.55 483 Tunisia TUN

8.48

6.45

63

Guyana

GUY 7.90

5.89

32.18

Uganda

UGA

6.97

4.45

280

Haiti HTI

7.15

3.73 130

Uruguay

URY

9.03 7.00 71

Honduras

HND 7.69 5.32

78.1

USA USA 10.22 10.00

15

Hong Kong HKG 10.05

8.14

14.9

Venezuela

VEN

9.07

7.14 78.1

India IND 7.33

8.27 48.63

Vietnam

VNM

7.28 6.41 140

Indonesia

IDN 8.07 7.59 170 Zaire

ZAR 6.87

3.50 240

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