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CES Lectures
The political economy of the voting su¤rage.
Institutions and the deep causes of development.
Dr Toke S. Aidt
Cambridge
March 2012
Dr Toke S. Aidt (Cambridge)
Lecture 1
March 2012
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The World Income Distribution
Dr Toke S. Aidt (Cambridge)
Lecture 1
March 2012
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Institutions and the Deep Causes of Development
Accumulation of physical, human and social capital:
provide incentives for building "good" institutions leading to enhanced
economic development.
Geographical factors that a¤ect the disease environment and factor
endowments:
shape the institutional opportunities open to a society.
Inclusive institutions that limit rent seeking, protect property rights,
uphold the rule of law, etc.:
provide incentives for accumulation of physical, human and social
capital leading to enhanced economic development.
Dr Toke S. Aidt (Cambridge)
Lecture 1
March 2012
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De…nition of Institutions
"A set of rules, compliance procedures, and moral and ethical
behavioral norms designed to constrain the behavior of individuals in
the interest of maximizing the wealth or utility of principals" North
(1981, p. 201-202).
Inclusive version exclusive institutions (Acemoglu and Robinson, 2012
or North et al. 2011).
Inclusive institutions (open access societies): entry to economic and
political activities is open; property rights and rule of law upheld.
Example: Germany today; South Africa today.
Exclusive institutions (limit access societies): entry to economy and
political activities is limited; property rights and rule of law is at best
partially upheld. Example: Prussia in 1840. Zimbabwe today.
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The critical junctures vision
Inclusive institutions
AND economic progress
c
Critical
juncture
Exclusive institutions
AND economic stagnation
The grand transition vision
Economic
development
Long run
Short run
institutions
Two competing visions of the world and the role
institutions play in it
The critical junctures (CJ) vision: At critical historical junctures
societies embark on di¤erent paths of economic and political
development.
The grand transition (GT) vision: Political development is the
consequence of long-run economic development and is part of an
interacting set of gradual transitions.
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March 2012
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Does it really matter?
Hugely... think about the interpretation of Figure 1!
First task – which is the topic for this lecture – is to establish if
inclusive institutions are causing development or it is development
that causes inclusive institutions.
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Lecture 1
March 2012
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This lecture: critical junctures or grand transition?
Critical Junctures:
Acemoglu, Daron, Simon Johnson and James A. Robinson (2001) “The
Colonial Origins of Comparative Development: An Empirical
Investigation,” American Economic Review, 91, 1369-1401.
Grand Transition:
Gundlach, Erich, and Martin Paldam, 2009. “A farewell to critical
junctures: Sorting out long-run causality of income and democracy”.
European Journal of Political Economy 25, 340-354.
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Lecture 1
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Overview of Lectures
Lecture 1: Critical junctures or grand transition?
Lecture 2: Inclusive institutions as liberal democracy. Two theories of
su¤rage reform.
Lecture 3: Democracy: Conquered or granted? Empirical evidence on
the causes of democracy.
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March 2012
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The Fundamental Empirical Problem
Institutions are endogenous and/or a¤ected by many factors that also
a¤ect development.
Initial factor endowments
Institutions
Dr Toke S. Aidt (Cambridge)
Accumulation
Lecture 1
Development
March 2012
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IV estimation: a Reminder.
Consider the structural relationship linking institutions to
development:
Yi = αIi + Xi0 β + ui
where i is country index and
Yi is a measure of economic development
Ii is a measure of institutions
Xi is a vector of covariates
ui is all unobserved determinants of Yi
OLS and selection on observables:
Conditional on Xi , Ii and ui are orthogonal (uncorrelated) and OLS is
a consistent estimator of α.
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March 2012
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Biases of OLS
Omitted factors (simplify by setting β = 0):
ui = λLi + ω i
where Li is an omitted determinant of Yi (e.g., education) and
Cov (Ii , ω i ) = 0.
OLS is not a consistent estimator
plim b
αOLS = α + λ
Cov (Ii , Li )
.
Var (Ii )
If λ > 0 (education has positive e¤ect on development) and
Cov (Ii , Li ) > 0 (education is positively correlated with "inclusive"
institutions), then OLS estimate is biased upwards.
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Reverse causality
Yi = αIi + Xi0 β + ui
Ii = υYi + εi
OLS is inconsistent:
plim b
αOLS = α + υ
Cov (Ii , ui )
Var (Ii )
OLS estimate is upwards biased.
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Measurement error (attenuation bias):
Ii = Ti + τ i
or Ti = Ii
τi
where Ti is a noisy signal of "true institutions" Ii and τ i is a classical
measurement error with Cov (Ti , τ i ) = 0. OLS is not a consistent
estimator:
Var (Ti )
.
plim b
αOLS = α
Var (Ti ) + Var (τ i )
The attenuation bias implies that the OLS estimate is downwards
biased.
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Instrumental Variables
Idea: Find a variable which is correlated with institutions (Ii ), but
uncorrelated with the unobserved component of Yi , i.e., uncorrelated
with ui .
Let Zi be a potential instrument and write
Ii = γZi + vi
with Cov (Zi , vi ) = 0. The probability limit of the IV estimator is
plim b
αIV = α +
Cov (Zi , ui )
.
γVar (Zi )
IV estimator is consistent if
Cov (Zi , ui ) = 0, i.e., the instrument is valid.
γ 6= 0, i.e., the instrument is relevant.
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Instrumental Variables in Words
A valid instrument for institutions should be correlated with
institutions, but uncorrelated with any unobserved (or excluded)
determinant of GDP per capita...
The instrumental variable should only a¤ect GDP per capita through
its e¤ect on institutions...
OR
A valid instrument for development (GDP) should be correlated with
GDP, but uncorrelated with any unobserved (or excluded)
determinant of institutions...
The instrumental variable should only a¤ect institutions through its
e¤ect on GDP...
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Measurements
Development = modern GDP per capita.
Survey-based indicators of institutions:
Index of protection against expropriation (ICRG)
Governance matters database (World Bank).
Objective indicators of institutions:
Constraint of executive (Polity IV).
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Colonization as a quasi-natural experiment.
Idea: Estimate the causal e¤ect of institutions on the income
distribution today (i.e., on long-term growth) using colonization as a
quasi-natural experiment (Hall and Jones, QJE 1999; Glaeser and
Schleifer, QJE 2002).
Acemoglu et al. (AER, 2001 & QJE 2002):
Di¤erent colonization policies created di¤erent sets of institutions
("extractive states with exclusive institutions" versus "Neo-Europes
with inclusive institutions").
The optimal colonization strategy was a¤ected by the feasibility of
settlement, the size of the indigenous population, and by the disease
environment.
Once in place, institutions persisted even after independence.
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Acemoglu et al. (AJR): The Colonial Origins of
Development
64 former European colonies.
Outcome variable is GDP per capita (PPP) in 1995.
Indicator of current institutions: Risk of Expropriation, i.e., how likely
it is that private foreign investments are expropriated by government
(0 = high risk; 10 = low risk).
Control variables include: Latitude, continent dummies, legal origin,
various geographical variables, ethnolinguistic fragmentation and
some measures of the disease environment today.
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Graphical Illustration of the OLS Estimate
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Problems with OLS Estimates
Omitted variables: Disease environment at time of settlement ->
disease environment today -> direct e¤ect on GDP (Up).
Reverse causality: Rich countries can a¤ord and prefer better
institutions (Up).
Measurement error: Lot of measurement error (Down).
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AJR’s "Theory" of Development
Settler mortality
Settlement strategy
Early institutions
Current institutions
Current level of
development
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The Structural Model
log yi = µ + αIicurrent + Xi0 γ + εi
Iicurrent
Iiold
Si
= λ1 + β1 Iiold + Xi0 γ1 + r1i
= λ2 + β2 Si + Xi0 γ2 + r2i
= λ3 + β3 Mi + Xi0 γ3 + r3i
where
yi = GDP per capita;
IiCurrent is measure of current institutions and Iiold is measure of
institutions in 1900 or year of independence (democracy score).
Si is measure of European settlement (fraction of the population with
European descent in 1900).
Mi is mortality rate faced by settlers (annualized deaths per 1000).
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Instrumental Variables
Mortality rate of soldiers, bishops and sailors stationed in the colonies
between the 17th and 19th century.
European settler mortality as an instrument for institutions:
Valid: if conditional on controls included in the regressions, the
mortality rates of European settlers more than 100 years ago have no
e¤ect on GDP per capita today, other than through their impact on
institutional development.
Relevant: if the mortality rates of European settlers more than 100
years ago is correlated with measures of current institutions.
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Relevance of Settler Mortality
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How Big is the E¤ect?
Notice that IV estimate (0.94) > OLS estimate (0.52) (which then is
downwards biased)
Compare Nigeria (high risk 5.6) and Chile (low risk 7.8).
The di¤erence in expropriation risk is 2.2 points
Average di¤erence in log points of GDP is 2.2*0.94 = 2.06
7-fold di¤erence (e 2. 06 1 = 6.8)
Actual di¤erence in GDP per capita is 11-fold.
Acemoglu et al (2001) conclude: "Overall, the results show a large
e¤ect of institutions on economic performance."
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Open Questions and Challenges
Is the measure of institutions (expropriation risk) …t for purpose?
(Glaeser et al. JEG 2004)
Deep, durable features or outcomes?
If it is an outcome measure, then impossible to make distinction
between "well-protected property rights due to constraints on
politicians" and "well-protected property rights due to choices made by
politicians, e.g. dictators".
Measurement error in settler mortality data. The Albouy critique:
sparse data on bishops, battle deaths, borders, etc. => irrelevant IV?
Causal e¤ect?
Disease environment (McArthur and Sachs, 2000).
Climate and other geographical characteristics (Engerman and
Sokolo¤, 1991).
Education and human capital (Glaeser et al. 2004).
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Glaeser et al. (JEG, 2004): Building or Bringing?
Suppose that settlers did tend to settle where the disease environment
was benign.
Suppose that settlers brought with them human capital.
So, areas with more settlement became endowed with more human
capital and thus faced a better development prospect.
Implication: Settler mortality could a¤ect development through
education rather than through institutions.
"The sellers a¤ected long-term development, not so much by the
institutions they build, but by what they brought with them:
themselves and their human capital".
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Causal E¤ects: Human Capital
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Causal e¤ects: Human Capital
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The Human Capital Challenge
Serious challenge to the causal order, but not to the idea that
institutions matter.
However...
Are these results sensitive to the choice of measure for institutions
(POLITY IV rather than expropriation risk)?
Lose 17 observations (sample size is 47 versus 64). Are the lost
countries pivotal?
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The Grand Transition
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The Grand Transition
The grand transition (GT) vision: Political development is the
consequence of long-run economic development and is part of an
interacting set of gradual transitions.
Causality runs from economic development (income) to political
institutions (democracy).
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Gundlach and Paldam (EJPE, 2009): A Farewell to Critical
Junctures.
Large world cross section of rich and poor countries.
Outcome variable is the POLITY IV index of political institutions
(capture political authority patterns on a scale from -10 (autocracy)
to +10 (liberal democracy).
Indicator of development is GDP per capita (PPP) in 1995.
Control variables include: Gini, share of mining, legal origin,
ethnolinguistic fragmentation, latitude and many others.
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The correlation between income and democracy
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GP's "Theory" of Development based on Unified Growth Theory, Diamond and Clark
Pre-historical biological and geographical
conditions
Diamond (1997)
Institutional stability of agrarian societies in
the Malthusian era.
Unified growth theory
Change in the composition and size of population in
Unified growth theory
the Malthusian era.
Clark (2007)
Transition from Malthusian to modern growth via
the industrial revolution.
Unified growth theory
Long-run institution-free income differences
Institutions (democracy)
The Structural Model:
log Pi = µ + αyicurrent + Xi0 γ + εi
yicurrent
niold
ASiold
= λ1 + β1 niold + Xi0 γ1 + r1i
= λ2 + β2 ASiold + Xi0 γ2 + r2i
= λ3 + β3 PHi + Xi0 γ3 + r3i
where
Pi = POLITY IV index (the measure of current institutions).
yicurrent = modern GDP per capita;
niold = measure population dynamics in the Malthusian era.
ASiold = stability of agricultural institutions in the Malthusian era.
PHi = pre-historical biological and geographical factors.
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Problems with OLS Estimates
Omitted variables: Stable property rights.
Reverse causality: Better institutions make a country richer (Up).
Measurement error: GDP is measured with a lot of error (Down).
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Instrumental Variables
We do not observe niold or ASiold , but we can measure pre-historical
biological and geographical factors (PHi ):
Number of domesticable big mammals.
Number of annual perennial wild greases.
Relative east-west orientation of a country.
Suitability of climate for agriculture
Valid: if these pre-historical factors (conditional on controls included
in the regressions) have no e¤ect of institutions today other than
through their impact on economic development (as measured by
modern GDP).
Relevant: if these pre-historical factors are correlated with measures
of current GDP per capita.
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Interpretation
The OLS and IV estimates are very similar, so the correlation between
income and democracy seems to represent a causal long-run
relationship from income (GDP) to institutions (democracy).
GDP can explain about 30% of the variation in observed institutions.
Short-run dynamics may be in both directions.
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What about...
The estimations consider a speci…c institution (democracy), but what
if....
income a¤ects broader institutions (property rights, legal systems,
organization of markets etc.) and
broader institutions a¤ect speci…c institutions?
Then, the pre-historical instruments may a¤ect democracy through
other institutions rather than through an income e¤ect.
This bias cannot be ruled out.
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What did we learn?
Economic historians have long argued that institutions are of
paramount importance for economic development (North, 1981,
Engerman et al. 1991, North et al. 2011).
Critical Junctures vision:
Acemoglu et al.: Mortality of European settlers shaped settlement
patterns and institutional outcomes. This put societies on di¤erent
development paths and some end up with democracy and high level of
income for that reason.
Grand Transition vision:
Gundlach and Paldam: The long-run causality runs from income
(economic development) to institutions and is just one facet of a
complex set of interrelated transitions.
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What is next?
The fundamental question is: how do institutions emerge?
Democracy: the extension of the franchise in the West.
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1386
THEAMERICANECONOMICREVIEW
TABLE4-IV
DECEMBER2001
REGRESSIONS
OF LOG GDP PERCAPITA
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)
(4)
(5)
(7)
(3)
(6)
(8)
(9)
Panel A: Two-Stage Least Squares
Average protectionagainst
expropriationrisk 1985-1995
Latitude
0.94
(0.16)
1.00
(0.22)
-0.65
(1.34)
1.28
(0.36)
1.21
(0.35)
0.94
(1.46)
0.58
(0.10)
0.58
(0.12)
0.04
(0.84)
Asia dummy
0.98
(0.30)
-0.92
(0.40)
-0.46
(0.36)
-0.94
(0.85)
Africa dummy
"Other"continent dummy
1.10
(0.46)
-1.20
(1.8)
-1.10
(0.52)
-0.44
(0.42)
-0.99
(1.0)
0.98
(0.17)
-0.34
(0.18)
2.00
(1.40)
0.47
(0.50)
-0.26
(0.41)
1.1
(0.84)
0.33
-0.63
(0.13)
0.40
(0.06)
64
0.46
(0.06)
61
Panel B: First Stage for Average ProtectionAgainst ExpropriationRisk in 1985-1995
Log Europeansettler mortality
-0.61
(0.13)
Latitude
-0.51
(0.14)
2.00
(1.34)
-0.39
(0.13)
-0.39
(0.14)
-0.11
(1.50)
-1.20
(0.22)
-1.10
(0.24)
0.99
(1.43)
Asia dummy
0.47
0.47
0.33
(0.49)
-0.27
(0.41)
1.24
(0.84)
0.30
0.48
(0.07)
37
0.47
(0.07)
37
0.42
(0.06)
64
Africa dummy
"Other"continent dummy
R2
0.27
0.30
0.13
-0.43
(0.17)
0.13
0.28
Panel C: OrdinaryLeast Squares
Average protectionagainst
expropriationrisk 1985-1995
Number of observations
0.52
(0.06)
64
0.47
(0.06)
64
0.49
(0.08)
60
0.47
(0.07)
60
Notes:The dependentvariablein columns (1)-(8) is log GDP per capita in 1995, PPP basis. The dependentvariablein column (9) is log output
per worker,from Hall and Jones (1999). "Averageprotectionagainstexpropriationrisk 1985-1995" is measuredon a scale from 0 to 10, where
a higher score means more protectionagainst risk of expropriationof investment by the government,from Political Risk Services. Panel A
reportsthe two-stage least-squaresestimates, instrumentingfor protectionagainstexpropriationrisk using log settler mortality;Panel B reports
the correspondingfirststage. Panel C reportsthe coefficient from an OLS regressionof the dependentvariableagainstaverageprotectionagainst
expropriationrisk. Standarderrorsare in parentheses.In regressionswith continentdummies,the dummy for Americais omitted. See Appendix
Table Al for more detailed variable descriptionsand sources.
creating a typical measurementerror problem.
Moreover, what matters for current income is
presumablynot only institutionstoday, but also
institutionsin the past. Our measure of institutions which refers to 1985-1995 will not be
perfectly correlatedwith these.19
19We can ascertain,to some degree, whetherthe difference between OLS and 2SLS estimates could be due to
measurementerror in the institutions variable by making
use of an alternativemeasure of institutions, for example,
the constraintson the executive measure. Using this mea-
Does the 2SLS estimate make quantitative
sense? Does it imply thatinstitutionaldifferences
can explain a significantfractionof income difsure as an instrumentfor the protection against expropriation index would solve the measurementerror,but not the
endogeneity problem. This exercise leads to an estimate of
the effect of protection against expropriationequal to 0.87
(with standarderror0.16). 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
magnitudeto explain the difference between the OLS and
2SLS estimates.