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ECONOMETRICS – Master in Economics (University of Valencia)
Taught by Carles Bretó and Pilar Beneito
Final Work.
Questions to be addressed in your report on the article:
1. Point out, in a precise manner, which is the research question of the article.
2. How do the authors justify the value added or ‘contribution’ of their article? How
does it differentiate from other published works in the topic? If possible, indicate the
page number and paragraph where this is explicitly mentioned, and select a sentence
(and copy it) that summarizes the contribution of the article.
3. Copy the (main) estimation model and explain it.
4. Indicate the data source used in the article.
5. Which is the main econometric issue in the article?
6. Explain the strategy used to identify the ‘causal’ effects of interest (the instrumental
variables strategy): which explanatory variables are suspicious of being endogenous,
which intruments are used, how the choice of these instruments is justified.
7. If the article presents tests for endogeneity, validity of instruments and/or weakness
of instruments, point out which tests are used and the conclusions to be drawn from
them.
8. Does the article mention at any point some type of ‘robust’ correction or any other
particular method for estimating standard errors of the estimates? Copy the
sentence/s where this is explicitly pointed out, and try to explain which type of
correction is made (up to your knowledge).
9. As something different from the ‘statistical significance’ of an estimated effect we can
investigate the ‘economic relevance’ of such an effect. This refers to the quantitative
importance of the estimated effec. Select an example on the article where the authors
disentangle quantitatively an estimated impact, and explain it.
10. Observe the titles above and the notes at the bottom of the tables where the authors
present the main estimation results. Indicate which type of information appears in the
titles and in the notes. Does this information suffice to understand the basics of the
empirical exercise being done? If the answer is yes, we say a table is ‘self-contained’.
11. Select some sentence/s or expressions (three, at most) used by the authors to
introduce/explain/describe econometric questions.
12. Sum up the main results and conclusions of the article.
13. Give your opinion about the article (briefly).
Journal of Economic Growth, 9, 131±165, 2004
# 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
Institutions Rule: The Primacy of Institutions Over
Geography and Integration in Economic
Development*
DANI RODRIK
Kennedy School of Government, Harvard University, Cambridge, MA, USA
ARVIND SUBRAMANIAN
International Monetary Fund (IMF), Washington DC, USA
FRANCESCO TREBBI
Department of Economics, Harvard University, Littauer center, Cambridge, MA, USA
We estimate the respective contributions of institutions, geography, and trade in determining income levels
around the world, using recently developed instrumental variables for institutions and trade. Our results indicate
that the quality of institutions “trumps” everything else. Once institutions are controlled for, conventional
measures of geography have at best weak direct effects on incomes, although they have a strong indirect effect by
in¯uencing the quality of institutions. Similarly, once institutions are controlled for, trade is almost always
insigni®cant, and often enters the income equation with the “wrong” (i.e., negative) sign. We relate our results to
recent literature, and where differences exist, trace their origins to choices on samples, speci®cation, and
instrumentation.
Keywords: growth, institutions, openness, geography
JEL classi®cation: F1, N7, O1
Commerce and manufactures can seldom ¯ourish long in any state which does not
enjoy a regular administration of justice, in which the people do not feel themselves
secure in the possession of their property, in which the faith of contracts is not
supported by law, and in which the authority of the state is not supposed to be regularly
employed in enforcing the payment of debts from all those who are able to pay.
Commerce and manufactures, in short, can seldom ¯ourish in any state in which there is
not a certain degree of con®dence in the justice of government.
Adam Smith, Wealth of Nations
*
The views expressed in this paper are the authors’ own and not of the institutions with which they are
af®liated.
132
1.
DANI RODRIK ET AL.
Introduction
Average income levels in the world’s richest and poorest nations differ by a factor of more
than 100. Sierra Leone, the poorest economy for which we have national income statistics,
has a per capita GDP of $490, compared to Luxembourg’s $50,061.1 What accounts for
these differences, and what (if anything) can we do to reduce them? It is hard to think of
any question in economics that is of greater intellectual signi®cance, or of greater
relevance to the vast majority of the world’s population.
In the voluminous literature on this subject, three strands of thoughts stand out. First,
there is a long and distinguished line of theorizing that places geography at the center of the
story. Geography is a key determinant of climate, endowment of natural resources, disease
burden, transport costs, and diffusion of knowledge and technology from more advanced
areas. It exerts therefore a strong in¯uence on agricultural productivity and the quality of
human resources. Recent writings by Jared Diamond and Jeffrey Sachs are among the more
notable works in this tradition (see Diamond, 1997; Gallup et al., 1998; Sachs, 2001).
A second camp emphasizes the role of international trade as a driver of productivity
change. We call this the integration view, as it gives market integration, and impediments
thereof, a starring role in fostering economic convergence between rich and poor regions
of the world. Notable recent research in this camp includes Frankel and Romer (1999) and
the pre-geography work of Sachs (Sachs and Warner, 1995).2 It may be useful to
distinguish between “moderate” and “maximal” versions of this view. Much of the
economics profession would accept the hypothesis that trade can be an underlying source
of growth once certain institutional pre-requisites have been ful®lled. But a more extreme
perspective, and one that has received wide currency in public debates, is that trade/
integration is the major determinant of whether poor countries grow or not. It is the latter
perspective that characterizes such widely cited papers as Sachs and Warner (1995) and
Dollar and Kraay (2004).
Finally, a third group of explanations centers on institutions, and in particular the role of
property rights and the rule of law. In this view, what matters are the rules of the game in a
society and their conduciveness to desirable economic behavior. This view is associated
most strongly with Douglass North (1990). It has received careful econometric treatment
recently in Hall and Jones (1999), who focus on what they call “social infrastructure,” and
in Acemoglu et al. (2001), who focus on the expropriation risk that current and potential
investors face.
Growth theory has traditionally focussed on physical and human capital accumulation,
and, in its endogenous growth variant, on technological change. But accumulation and
technological change are at best proximate causes of economic growth. No sooner have we
ascertained the impact of these two on growthÐand with some luck their respective roles
1 These are ®gures for 2000, and they are expressed in current “international” dollars, adjusted for PPP
differences. The source is the World Development Indicators CD-ROM of the World Bank.
2 One can question whether it is appropriate to treat trade as one of the ultimate determinants of economic
prosperity, but here we are simply following a long literature that has attached central causal importance
to it.
INSTITUTIONS RULE
133
alsoÐthat we want to ask: But why did some societies manage to accumulate and innovate
more rapidly than others? The three-fold classi®cation offered aboveÐgeography,
integration, and institutionsÐallows us to organize our thoughts on the “deeper”
determinants of economic growth. These three are the factors that determine which
societies will innovate and accumulate, and therefore develop, and which will not.
Since long-term economic development is a complex phenomenon, the idea that any
one (or even all) of the above deep determinants can provide an adequate accounting of
centuries of economic history is, on the face of it, preposterous. Historians and many social
scientists prefer nuanced, layered explanations where these factors interact with human
choices and many other not-so-simple twists and turns of fate. But economists like
parsimony. We want to know how well these simple stories do, not only on their own or
collectively, but more importantly, vis-aÁ-vis each other. How much of the astounding
variation in cross-national incomes around the world can geography, integration, and
institutions explain? Do these factors operate additively, or do they interact? Are they all
equally important? Does one of the explanations “trump” the other two?
The questions may be simple, but devising a reasonable empirical strategy for
answering them is far from straightforward. This is not because we do not have good
empirical proxies for each of these deep determinants. There are many reasonable
measures of “geography,” such as distance from the equator (our preferred measure),
percentage land mass located in the tropics, or average temperature. The intensity of an
economy’s integration with the rest of the world can be measured by ¯ows of trade or the
height of trade barriers. The quality of institutions can be measured with a range of
perceptions-based indicators of property rights and the rule of law. The dif®culty lies
instead in sorting out the complex web of causality that entangles these factors.
The extent to which an economy is integrated with the rest of the world and the quality
of its institutions are both endogenous, shaped potentially not just by each other and by
geography, but also by income levels. Problems of endogeneity and reverse causality
plague any empirical researcher trying to make sense of the relationships among these
causal factors. We illustrate this with the help of Figure 1, adapted from Rodrik (2003).
The plethora of arrows in the ®gure, going in both directions at once in many cases,
exempli®es the dif®culty.
The task of demonstrating causality is perhaps easiest for the geographical determinists.
Geography is as exogenous a determinant as an economist can ever hope to get, and the
main burden here is to identify the main channel(s) through which geography in¯uences
economic performance. Geography may have a direct effect on incomes, through its effect
on agricultural productivity and morbidity. This is shown with arrow (1) in Figure 1. It can
also have an indirect effect through its impact on distance from markets and the extent of
integration (arrow (2)) or its impact on the quality of domestic institutions (arrow (3)).
With regard to the latter, economic historians have emphasized the disadvantageous
consequences for institutional development of certain patterns of factor endowments,
which engender extreme inequalities and enable the entrenchment of a small group of
elites (e.g., Engerman and Sokoloff, 1994). A similar explanation, linking ample
endowment of natural resources with stunted institutional development, also goes under
the name of “resource curse” (Sala-i-Martin and Subramanian, 2003).
Trade fundamentalists and institutionalists have a considerably more dif®cult job to do,
134
DANI RODRIK ET AL.
Figure 1. The “deep” determinants of income.
since they have to demonstrate causality for their preferred determinant, as well as identify
the effective channel(s) through which it works. For the former, the task consists of
showing that arrows (4) and (5)Ðcapturing the direct impact of integration on income and
the indirect impact through institutions, respectivelyÐare the relevant ones, while arrows
(6) and (7)Ðreverse feedbacks from incomes and institutions, respectivelyÐare relatively
insigni®cant. Reverse causality cannot be ruled out easily, since expanded trade and
integration can be mainly the result of increased productivity in the economy and/or
improved domestic institutions, rather than a cause thereof.
Institutionalists, meanwhile, have to worry about different kinds of reverse causality.
They need to show that improvements in property rights, the rule of law and other aspects
of the institutional environment are an independent determinant of incomes (arrow (8)),
and are not simply the consequence of higher incomes (arrow (9)) or of greater integration
(arrow (5)).
In econometric terms, what we need to sort all this out are good instruments for
integration and institutionsÐsources of exogenous variation for the extent of integration
and institutional quality, respectively, that are uncorrelated with other plausible (and
excluded) determinants of income levels. Two recent papers help us make progress by
providing plausible instruments. Frankel and Romer (1999) suggests that we can
instrument for actual trade/GDP ratios by using trade/GDP shares constructed on the basis
of a gravity equation for bilateral trade ¯ows. The Frankel and Romer approach consists of
®rst regressing bilateral trade ¯ows (as a share of a country’s GDP) on measures of country
mass, distance between the trade partners, and a few other geographical variables, and then
constructing a predicted aggregate trade share for each country on the basis of the
coef®cients estimated. This constructed trade share is then used as an instrument for actual
trade shares in estimating the impact of trade on levels of income.
Acemoglu et al. (2001) use mortality rates of colonial settlers as an instrument for
INSTITUTIONS RULE
135
institutional quality. They argue that settler mortality had an important effect on the type of
institutions that were built in lands that were colonized by the main European powers.
Where the colonizers encountered relatively few health hazards to European settlement,
they erected solid institutions that protected property rights and established the rule of law.
In other areas, their interests were limited to extracting as much resources as quickly as
possible, and they showed little interest in building high-quality institutions. Under the
added assumption that institutions change only gradually over time, Acemoglu et al. argue
that settler mortality rates are therefore a good instrument for institutional quality. Frankel
and Romer (1999) and Acemoglu et al. (2001) use their respective instruments to
demonstrate strong causal effects from trade (in the case of Frankel and Romer) and
institutions (in the case of Acemoglu et al.) to incomes. But neither paper embeds their
estimation in the broader framework laid out above. More speci®cally, Acemoglu et al.
control for geographical determinants, but do not check for the effects of integration.
Frankel and Romer do not control for institutions.
Our approach in this paper consists of using the Frankel and Romer and Acemoglu et al.
instruments simultaneously to estimate the structure shown in Figure 1. The idea is that
these two instruments, having passed what might be called the American Economic
Review (AER)-test, are our best hope at the moment of unraveling the tangle of cause-andeffect relationships involved. So we systematically estimate a series of regressions in
which incomes are related to measures of geography, integration, and institutions, with the
latter two instrumented using the Frankel and Romer and Acemoglu et al. instruments,
respectively. These regressions allow us to answer the question: what is the independent
contribution of these three sets of deep determinants to the cross-national variation in
income levels? The ®rst stage of these regressions provides us in turn with information
about the causal links among the determinants.
This exercise yields some sharp and striking results. Most importantly, we ®nd that the
quality of institutions trumps everything else. Once institutions are controlled for,
integration has no direct effect on incomes, while geography has at best weak direct
effects. Trade often enters the income regression with the “wrong” (i.e., negative) sign, as
do many of the geographical indicators. By contrast, our measure of property rights and
the rule of law always enters with the correct sign, and is statistically signi®cant, often
with t-statistics that are very large.
On the links among determinants, we ®nd that institutional quality has a positive and
signi®cant effect on integration. Our results also tend to con®rm the ®ndings of Easterly
and Levine (2003), namely that geography exerts a signi®cant effect on the quality of
institutions, and via this channel on incomes.3
3 The Easterly and Levine approach is in some ways very similar to that in this paper. Easterly and Levine
estimate regressions of the levels of income on various measures of endowments, institutions, and
“policies.” They ®nd that institutions exert an important effect on development, while endowments do
not, other than through their effect on institutions. Policies also do not exert any independent effect on
development. The main differences between our paper and Easterly and Levine are the following. First,
we use a larger sample of countries (79 and 137) to run the “horse” race between the three possible
determinants. The Easterly and Levine sample is restricted to 72 countries. Second, Easterly and Levine
do not test in any detail whether integration has an effect on development. For them, integration or open
136
DANI RODRIK ET AL.
Our preferred speci®cation “accounts” for about half of the variance in incomes across
the sample, with institutional quality (instrumented by settler mortality) doing most of the
work. Our estimates indicate that an increase in institutional quality of one standard
deviation, corresponding roughly to the difference between measured institutional quality
in Bolivia and South Korea, produces a two log-points rise in per capita incomes, or a 6.4fold differenceÐwhich, not coincidentally, is also roughly the income difference between
the two countries. In our preferred speci®cation, trade and distance from the equator both
exert a negative, but insigni®cant effect on incomes (see Table 3, panel A, column (6)).
Much of our paper is devoted to checking the robustness of our central results. In
particular, we estimate our model for three different samples: (a) the original 64-country
sample used by Acemoglu et al.; (b) a 79-country sample which is the largest sample we
can use while still retaining the Acemoglu et al. instrument; and (c) a 137-country sample
that maximizes the number of countries at the cost of replacing the Acemoglu et al.
instrument with two more widely available instruments (fractions of the population
speaking English and Western European languages as the ®rst language, from Hall and
Jones, 1999.) We also use a large number of alternative indicators of geography and
integration. In all cases, institutional quality emerges as the clear winner of the “horse
race” among the three. Finally, we compare and contrast our results to those in some
recent papers that have undertaken exercises of a similar sort. Where there are differences
in results, we identify and discuss the source of the differences and explain why we believe
our approach is superior on conceptual or empirical grounds.4
One ®nal word about policy. As we shall emphasize at the end of the paper, identifying
the deeper determinants of prosperity does not guarantee that we are left with clearcut
policy implications. For example, ®nding that the “rule of law” is causally implicated in
development does not mean that we actually know how to increase it under the speci®c
conditions of individual countries. Nor would ®nding that “geography matters”
necessarily imply geographic determinismÐit may simply help reveal the roadblocks
around which policy makers need to navigate. The research agenda to which this paper
contributes is one that clari®es the priority of pursuing different objectivesÐimproving
the quality of domestic institutions, achieving integration into the world economy, or
overcoming geographical adversityÐbut says very little about how each one of these is
best achieved.
trade policy is part of a wider set of government policies that can affect development. Testing for the
effect of policies in level regressions is, however, problematic as discussed in greater detail below.
Policies pursued over a short time span, say 30±40 years, are like a ¯ow variable, whereas development,
the result of a much longer cumulative historical process, is more akin to a stock variable. Thus, level
regressions that use policies as regressors con¯ate stocks and ¯ows.
4 We note that many of the papers already cited as well as others have carried out similar robustness tests.
For example, Acemoglu et al. (2001) document that geographic variables such as temperature, humidity,
malaria risk exert no independent direct effects on income once institutions are controlled for. A followup paper by the same authors (Acemoglu et al., 2003) shows that macroeconomic policies have limited
effects after institutions are controlled. Easterly and Levine (2003) produce similar robustness results on
the geography front. Our contribution is to put these and other tests in a broader framework, including
trade, and to provide an interpretation of the results which we think is more appropriate.
137
INSTITUTIONS RULE
The plan of the paper is as follows. Section 2 presents the benchmark results and
robustness tests. Section 3 provides a more in-depth interpretation of our results and lays
out a research agenda.
2.
2.1.
Core Results and Robustness
Data and Descriptive Statistics
Table 1 provides descriptive statistics for the key variables of interest. The ®rst column
covers the sample of 79 countries for which data on settler mortality have been compiled by
Acemoglu et al.5 Given the demonstrated attractiveness of this variable as an instrument
that can help illuminate causality, this will constitute our preferred sample. The second
column contains summary statistics for a larger sample of 137 countries for which we have
data on alternative instruments for institutions (fractions of the population speaking
Table 1. Descriptive statistics.
Extended Acemoglu
et al. Sample
(79 countries)
Large Sample
(137 countries)
Log GDP per capita (PPP) in 1995 (LCGDP95)
8.03
(1.05)
8.41
(1.14)
Rule of law (RULE)
0.25
(0.86)
0.08
(0.95)
Log openness (LCOPEN)
3.94
(0.61)
4.01
(0.57)
Distance from equator in degrees (DISTEQ)
15.37
(11.16)
23.98
(16.26)
Log European settler mortality (LOGEM4)
(deaths per annum per 1,000 population)
Log constructed openness (LOGFRANKROM)
4.65
(1.22)
2.76
(0.76)
Ð
Ð
2.91
(0.79)
Fraction of population speaking other
European language (EURFRAC)
0.30
(0.41)
0.24
(0.39)
Fraction of population speaking English (ENGFRAC)
0.11
(0.29)
0.08
(0.24)
Notes: Standard deviations are reported below the means. Rule of law ranges between
2.5 and ‡ 2.5.
Openness is measured as the ratio of trade to GDP. Constructed opennessÐthe instrument for opennessÐis
the predicted trade share and is from Frankel and Romer (1999). The Appendix describes in detail all the
data and their sources.
5 Acemoglu et al. actually compiled data on settler mortality for 81 countries, but data on our other
variables are unavailable for Afghanistan (for per capita PPP GDP for 1995) and the Central African
Republic (for rule of law).
138
DANI RODRIK ET AL.
English and other European languages). Data for the Frankel and Romer instrument on
trade, on which we will rely heavily, are also available for this larger sample.
GDP per capita on a PPP basis for 1995 will be our measure of economic performance.
For both samples, there is substantial variation in GDP per capita: for the 79-country
sample, mean GDP in 1995 is $3,072, the standard deviation of log GDP is 1.05, with the
poorest country’s (Congo, DRC) GDP being $321 and that of the richest (Singapore)
$28,039. For the larger sample, mean income is $4,492, the standard deviation is 1.14, with
the richest country (Luxembourg) enjoying an income level of $34,698.
The institutional quality measure that we use is due to Kaufmann et al. (2002). This is a
composite indicator of a number of elements that capture the protection afforded to
property rights as well as the strength of the rule of law.6 This is a standardized measure
that varies between 2.5 (weakest institutions) and 2.5 (strongest institutions). In our
sample of 79 countries, the mean score is 0.25, with Zaire (score of 2.09) having the
weakest institutions and Singapore (score of 1.85) the strongest.
Integration, measured using the ratio of trade to GDP, also varies substantially in our
sample. The average ratio is 51.4 percent, with the least “open” country (India) posting a
ratio of 13 percent and the most “open” (Singapore) a ratio of 324 percent. Our preferred
measure of geography is a country’s distance from the equator (measured in degrees). The
typical country is about 15.4 degrees away from the equator.
2.2.
OLS and IV Results in the Core Speci®cations
Our paper represents an attempt to estimate the following equation:
log yi ˆ m ‡ aINSi ‡ bINTi ‡ gGEOi ‡ ei ;
1†
where yi is income per capita in country i, INSi , INTi , and GEOi are respectively measures
for institutions, integration, and geography, and ei is the random error term. Throughout
the paper, we will be interested in the size, sign, and signi®cance of the three coef®cients a,
b, and g. We will use standardized measures of INSi , INTi , and GEOi in our core
regressions, so that the estimated coef®cients can be directly compared.
Before we discuss the benchmark results, it is useful to look at the simple, bivariate
relationships between income and each of the “deep determinants.” Figure 2 shows these
scatter plots, with the three panels on the left hand side corresponding to the sample of 79
countries and the three panels on the right to the larger sample of 137 countries. All the
plots show a clear and unambiguously positive relationship between income and its
possible determinants. Thus, any or all of them have the potential to explain levels of
income. This positive relationship is con®rmed by the simple OLS regression of equation
6 Acemoglu et al. (2001) use an index of protection against expropriation compiled by Political Risk
Services. The advantage of the rule of law measure used in this paper is that it is available for a larger
sample of countries, and in principle captures more elements that go toward determining institutional
quality. In any case, measures of institutional quality are highly correlated: in our 79-country sample, the
two measures have a simple correlation of 0.78.
INSTITUTIONS RULE
139
Figure 2. Simple correlations between income and its determinants (sample of 79 countries for (a)±(c); sample of
137 countries for (d)±(f )).
(1) reported in column (6) of Table 2. The signs of institution, openness, and geography are
as expected and statistically signi®cant or close to being so. Countries with stronger
institutions, more open economies, and more distant from the equator are likely to have
higher levels of income.
To get a sense of the magnitude of the potential impacts, we can compare two countries,
say Nigeria and Mauritius, both in Africa. If the OLS relationship is indeed causal, the
coef®cients in column (6) of Table 2 would suggest that Mauritius’s per capita GDP should
be 10.3 times that of Nigeria, of which 77 percent would be due to better institutions, 9
percent due to greater openness, and 14 percent due to better location. In practice,
Mauritius’s income ($11,400) is 14.8 times that of Nigeria ($770).
Of course, for a number of reasons described extensively in the literatureÐreverse
140
DANI RODRIK ET AL.
Table 2. Determinants of development: Core speci®cations, ordinary least squares estimates.
Log GDP per capita
Extended Acemoglu
et al. Sample
Acemoglu et al. Sample
Dependent Variable (1)
Geography
(DISTEQ)
(2)
0.74
0.20
(4.48)* (1.34)
Institutions
(RULE)
64
0.25
(4)
(5)
0.32
0.80
0.22
(1.85)** (5.22)* (1.63)
(6)
0.81
0.72
(9.35)* (6.98)*
0.16
(1.48)
0.15
(1.53)
64
0.57
64
0.59
79
0.26
79
0.61
(7)
0.33
0.76
(2.11)** (10.62)*
0.78
0.69
(7.56)* (6.07)*
Integration
(LCOPEN)
Observations
R-square
(3)
Large Sample
79
0.62
(8)
(9)
0.20
(2.48)**
0.23
(2.63)*
0.81
(12.12)*
0.78
(10.49)*
0.08
(1.24)
137
0.42
137
0.71
137
0.71
Notes: The dependent variable is per capita GDP in 1995, PPP basis. There are three samples for which the
core regressions are run: (i) the ®rst three columns correspond to the sample of 64 countries in Acemoglu et
al. (2001); (ii) columns (4)±(6) use a sample of 79 countries for which data on settler mortality (LOGEM4)
have been compiled by Acemoglu et al.; and (iii) columns (7)±(9) use a larger sample of 137 countries. The
regressors are: (i) DISTEQ, the variable for geography, which is measured as the absolute value of latitude
of a country; (ii) Rule of law (RULE), which is the measure for institutions; and (iii) LCOPEN, the variable
for integration, which is measured as the ratio of nominal trade to nominal GDP. All regressors are scaled in
the sense that they represent deviations from the mean divided by the standard deviation. All regressors,
except DISTEQ and RULE, in the three panels are in logs. See the Appendix for more detailed variable
de®nitions and sources. t-statistics are reported under coef®cient estimates. Signi®cance at the 1, 5, and 10
percent levels are denoted respectively by *, **, and ***.
causality, omitted variables bias, and measurement errorÐthe above relationship cannot
be interpreted as causal or accurate. To address these problems, we employ a two-stage
least squares estimation procedure. The identi®cation strategy is to use the Acemoglu et al.
settler mortality measure as an instrument for institutions and the Frankel and Romer
measure of constructed trade shares as an instrument for integration. In the ®rst-stage
regressions, INSi and INTi are regressed on all the exogenous variables. Thus
INSi ˆ l ‡ dSMi ‡ fCONSTi ‡ cGEOi ‡ eINSi ;
2†
INTi ˆ y ‡ sCONSTi ‡ tSMi ‡ oGEOi ‡ eINTi ;
3†
where SMi refers to settler mortality and CONSTi to the Frankel and Romer instrument for
trade/GDP. The exclusion restrictions are that SMi and CONSTi do not appear in equation
(1).
Equations (1)±(3) are our core speci®cation. This speci®cation represents, we believe,
the most natural framework for estimating the respective impacts of our three deep
determinants. It is general, yet simple, and treats each of the three deep determinants
symmetrically, giving them all an equal chance. Our proxies for institutions, integration,
and geography are the ones that the advocates of each approach have used. Our
instruments for institutions and integration are sensible, and have already been
INSTITUTIONS RULE
141
demonstrated to “work” in the sense of producing strong second-stage results (albeit in
estimations not embedded in our broader framework).
Panel A of Table 3 reports the two-stage least squares estimates of the three coef®cients
of interest. The estimation is done for three samples of countries: (i) for the sample of 64
countries analyzed by Acemoglu et al.; (ii) for an extended sample of 79 countries for
which Acemoglu et al. had compiled data on settler mortality; and (iii) for a larger sample
of 137 countries that includes those that were not colonized. In Acemoglu et al., the quality
of institutions was measured by an index of protection against expropriation. We use a rule
of law index because it is available for a larger sample. The IV estimates of the coef®cient
on institutions in the ®rst three columns of panel A are very similar to those in Acemoglu
et al., con®rming that these two indexes are capturing broadly similar aspects of
institutions, and allowing us to use the larger sample for which data on settler mortality are
available.
Columns (4)±(6) report our estimates for the extended Acemoglu et al. sample (which as
we shall explain below will be our preferred sample in this paper). Columns (5) and (6)
con®rm the importance of institutions in explaining the cross-country variation in
development. Once the institutional variable is added, geography and openness do not
have any additional power in explaining development. In