Political Instability, Institutions, and Economic Growth.
Ryan A. Compton*
University of Manitoba
compton@cc.umanitoba.ca
Daniel C. Giedeman
Grand Valley State University
giedemad@gvsu.edu
Noel D. Johnson
California State University – Long Beach
njohnso3@csulb.edu
Draft: Summer 2006
Please do not cite.
Comments and suggestions are welcome and greatly appreciated!!
Abstract
Recent research has shown that institutions contribute significantly to long-run economic growth.
Paradoxically, a separate line of research has produced mixed results concerning the effect of
political instability on economic growth. How can it be that political institutions matter, but that
during periods of political instability there is no systematic effect on growth? We attempt to
resolve this paradox by modeling an institutional equilibrium as a coordination game whose
payoffs are related to the value of the nexus of contracts governed by the prevailing institutions.
The stability of this equilibrium in the face of political instability depends on the extent to which
formal political institutions govern the underlying nexus of contracts. If formal political
institutions matter, then political instability should simultaneously cause a large decline in the
value of contracts and a change in the institutional equilibrium is more probable. If informal
institutions play a greater role, then political instability should have less impact on the value of
trades and the opportunity cost of institutional change is correspondingly higher. We test this
theory using a System-GMM panel estimator approach and panel data on political instability,
institutions, and economic growth.
JEL Classifications: N0, 011, 040,
Key Words: Economic History, Growth, Institutions
. Previous versions of this paper have circulated under the title “Does it Take a Revolution? An Empirical Study of
Political Instability and Economic Growth in the Long Run.”
* Contact author: Department of Economics, University of Manitoba, 501 Fletcher Argue Bldg. Winnipeg, MB,
Canada, R3T 3V5. We would like to thank Christa Brunnschweiler, Janice Compton, Arthur Denzau, Ross Hanig,
Talan Iscan, Ivan Jeliazkov, Douglass North, John Olson, John Serieux, Wayne Simpson, Steven Yamarik, as well
as participants at the 2005 Western Economics Association Annual Meetings, 2006 Midwest Economics Association
Annual Meetings, Northwestern’s Economic History Seminar, and 2006 Canadian Economics Association Annual
Meeting for helpful suggestions and comments. The usual disclaimer applies.
I. Introduction
Among politicians and the economists who “whisper in their ears” there is a belief that
democratic institutions are good for growth. Unfortunately, while this idea may have very good
theoretical and moral support, the empirical evidence for democratic institutions as a catalyst for
growth is ambiguous at best. In a recent study Dani Rodrik and Romain Wacziarg find that
among 24 countries that experienced a democratic transition, half found their average growth
rate fell in the following ten years (and about a third experienced negative growth).1 The
heterogeneity in countries’ growth experiences implies that something is missing from our
analyses. We propose that this missing element is a more nuanced understanding of what
institutions are and how institutional change occurs. The focus of most studies on formal
political institutions ignores the full range of both formal and informal institutions that operate in
most countries and their importance for determining whether political instability positively or
negatively impacts growth. For both theoretical and empirical reasons this focus is entirely
understandable. We believe, however, it is at least possible to identify those circumstances in
which a focus on formal institutions is more appropriate and those in which it is not.
Several components are necessary for a study of this type. First, there needs to be a
theoretical framework describing an “institutional equilibrium” that includes both formal and
informal institutions. This theory must also yield hypotheses that can be tested using available
data. We propose that an institutional equilibrium should be thought of as governing the writing
and enforcement of a “nexus of contracts.” Some of these contracts depend on formal and others
on informal institutions. The stability of the institutional equilibrium stems from the value of the
contracts it governs. The reason people do not often change the way property rights are defined
and enforced is because of the potential losses to those who benefit from them. Thinking of
1 Cite AER Proceedings paper.
1
institutions in this way allows us to formulate hypotheses concerning how the changing value of
the nexus of contracts impacts institutional change and ultimately the effect of political
instability on growth.
We hypothesize that periods of extreme political instability tend to reduce the value of
contracts enforced by formal institutions more than they reduce the value of contracts enforced
by informal institutions. As such, countries in which trade is structured predominantly through
informal institutions should experience less of a reduction in output during these political shocks.
In those countries in which contracts lose value during periods of political uncertainty, we
hypothesize that institutional change is more likely to occur, thus creating the potential for a
change in growth regime. In effect, we interpret periods of political instability as a natural
experiment to study the stability of institutional equilibria across countries at different times. By
looking at many of these events we hope to say something more general about those countries
which respond to political instability and those which do not.
In Section II below we describe in more detail our theoretical hypothesis. In Section III
we describe our data, including our measures of political instability and our proxies for the
degree of formal institutions in a country. In Section IV we use data on 134 instances of political
instability across 69 countries between 1870 and 2000 to perform a simple “Before and After”
examination of the effect of political instability on growth. In Section V we use a System GMM
approach on a subset of the data from 1960 to 2000 to determine whether or not countries that
rely more on formal institutions are more responsive to political instability than countries that
rely more on informal institutions. Section VI concludes.
2
II. Institutional Equilibrium as a Nexus of Contracts
We take as our starting point that a country’s “institutional equilibrium” plays a
significant role in determining its rate of economic growth.2 We also believe that the stability of
the institutional equilibrium in the face of political instability depends on the type of institutions
involved. In what follows we will: (1) define what we mean by formal and informal institutions;
(2) define an institutional equilibrium; and (3) construct some hypotheses about the relationship
between an institutional equilibrium and political instability.
Any trade that has value also has associated with it positive transaction costs.
Transaction costs are all of the costs associated with engaging in trade that are not explicitly
captured by price including search costs, negotiation costs, and enforcement costs.3 Institutions
exist to lower the transaction costs associated with trade and may be either formal or informal.4
Formal institutions are defined as “explicit,” or, “written down”. Informal institutions are the
implicit rules we obey when engaging in trade. As an illustration, consider institutions which
governed land transactions in the nineteenth century U.S. versus those in present-day Peru.
After the American Revolution two important documents were adopted which established
the formal rules governing property rights over land. The Land Ordinance of 1785 laid out the
precise rules by which public domain lands were settled. It allowed for the scientific delineation
of lands, prohibited the Federal Government from taxing land after it was ceded, and allowed for
the creation of states after sufficient settlement. The Northwest Ordinance of 1787 established
that property rights in land would be fee simple. Landowners were explicitly guaranteed
2 See for example, Hall and Jones (1998), Edison (2003), Engerman and Sokoloff (2003), Rodrik et al (2002), or
Acemoglu et al (2004).
3 See North and Thomas (1973), p. 93, or Furubotn and Richter (2000), ch. 2.
4 North (1990), p. 61.
3
perpetual possession, freedom of alienation, right of will, direct inheritance (in case of no will),
and right of waste.
The Land Ordinance and the Northwest Ordinance were formal institutions that also
happened to be highly conducive to economic growth. Perhaps the clearest example of this
comes from the history of how the public domain was allocated during the nineteenth century.
As more territories were acquired, huge amounts of land had to be transferred into the private
sector. In 1836 more than 3 million acres were sold to Illinois alone. During the 1850’s the
government sold almost 50 million acres. The distribution of the returns from the sales of these
lands was certainly not equitable.5 However, the land did eventually end up in the hands of those
who valued it most, farmers.6 In effect, formal government institutions led to secure property
rights and lowered transaction costs enough so that the Coase Theorem held. The initial
distribution of land didn’t matter, it was eventually traded to those with the highest valuations.
The U.S. is a good example of a state in which formal institutions led to low transaction
costs which, in turn, led to economic growth. Where formal institutions fail to lower transaction
costs (or actually raise them) individuals conspire to create informal institutions to facilitate
trade. An example of this process is outlined by Hernando de Soto in present day Peru.7 De
Soto and his colleagues quantified the transaction costs of using the formal legal system in Peru
by opening a garment workshop on the outskirts of Lima. This 1 employee business took 289
days and $1,231 (or 31 times the average monthly wage) to acquire the proper paperwork to
5 The government often set limits on minimum parcel size rather than minimum price which, when combined with
preemption, led to speculators buying-up huge tracts of land and then reselling them at higher prices to smaller
farmers. For an overview of this process see Hughes and Cain (2003), pp. 94-100, Atack and Passell (1994), ch.9.
For the specific argument that government policy led to speculation and inefficient allocation see Gates (1936).
6 Fogel and Rutner (1972) address this question as does Swierenga (1966). Fogel and Rutner find, for example, that
after the Civil War, the average time a speculator held a plot of land before selling was 32 months.
7 De Soto (2000).
4
open.8 Similarly, de Soto and his team observed that it took six years and eleven months to
obtain legal authorization to build a house on state-owned land.9 Acquiring the legal title alone
required 728 steps.
As a result of the high transaction costs of using formal institutions in Peru, individuals
have created informal ways to trade land. For example by forming legal agricultural
cooperatives that buy large tracts of land, but then illegally convert that land into smaller parcels
to sell to independent farmers. The property rights over these smaller holdings derives not from
the central government, but from the informal arrangement with the cooperative. As a result of
arrangements like these, de Soto claims that only 30% of homes in Peru have a legitimate legal
title.10 Aside from the poor incentives this creates with regards to capital improvement, de Soto
claims this creates large amounts of “dead capital.” It is impossible to borrow on the capital
represented by your house if your deed is not recognized by the bank.
These examples from the U.S. and Peru illustrate two points. First, the set of institutions
which are “binding” depends on the transaction costs associated wih using them. If formal
institutions come with very high transaction costs, then informal institutions are likely to take
their place. Second, not all institutions are created equal. The formal laws governing land
transactions in the U.S. allow individuals to borrow on their homes and to transfer ownership
relatively easily. In Peru, since property rights are not explicit, this is often impossible. In the
Peruvian case, informal institutions are superior to existing formal institutions. By no means,
however, do they represent the best possible institutional equilibrium. How do we explain how
rational individuals arrive at an institutional equilibrium that is suboptimal, like that in Peru?
What are the conditions that would cause a society to move from one equilibrium to another?
8 Ibid., p. 19.
9 Ibid., p. 20. Fogel’s and Rutner’s 32 months for the 19th century U.S. doesn’t look so bad after all…
10 Ibid., p. 252.
5
A convenient way to think of this problem is in terms of game theory. Transaction costs
arise largely because of prisoners’ dilemma problems. The reason it is costly for someone in
Peru to purchase a legitimate lease on a small farm is because it is in the interests of a bureaucrat
to raise the cost of doing so in order to benefit himself. In this case, the informal solution is for
individuals to form collectives and negotiate land contracts outside the legal system. This is, of
course, not the only possible solution to this particular prisoner’s dilemma problem. Another
option would be for individuals to act to reform (overthrow) the existing formal laws, or, to use
some other informal allocation mechanism (perhaps based on trust within religious groups).11 `
The main point is that once a cooperative solution to the Prisoner’s Dilemma problem has
emerged, it is then in the interests of all players to continue to cooperate according to that
solution, even if it is not the best solution to the Prisoner’s Dilemma problem from their point of
view. The reason for this is because engaging in institutional change would reduce the value of
existing contracts that are defined and enforced through the current institutional equilibrium.
Figure 1: A Coordination Game
Player A
Formal Informal
Formal 3,3 1,1 Player B
Informal 1,1 3,2
Consider the game portrayed in Figure 1. There are two organizations, or players: A and B.
Each player chooses whether to support formal or informal institutions.12 Player A’s decision to
support formal or informal institutions depends on B’s decision because if they do not agree on
11 For example, see Grief (1989) or Richman (2006).
12 The choice need not be “formal or informal,” it could be between any number of different types of institutions, but
this set-up will facilitate our point and also be consistent with a choice set that has more than two dimensions.
6
the institutional framework, then many valuable trades will not take place. The two Nash
Equilibria for this game are {Formal, Formal} and {Informal, Informal}.13 These two equilibria
do not possess the same welfare properties. {Formal, Formal} is Pareto Optimal to {Informal,
Informal}. This need not be the specific case, but in general, it will be the case that some
institutional equilibria are more growth enhancing than others.
What role does political instability play in causing players A and B to change their
strategies? By most definitions, political instability corresponds to a period during which the
enforcement of formal institutions is highly uncertain. The most obvious examples of this come
from capital markets. Consider Figure 2, which shows the yield spread between short-term
bonds in France and Britain between 1870 and 1885.
13 We adopt the convention of writing the equilibrium choice as {Player A’s choice, Player B’s choice}.
7
Figure 2: French-UK Bond Spread and French Growth 1867-1878
This period is interesting because it includes the Franco-Prussian War, the Paris
Commune, and the political transformation of France from a Monarchy under Napoleon III to a
Republic. The fact that many of the institutions which were enforced by the Monarchy were
thrown into question as a result of the political instability is reflected by the fact that the risk
premium on government debt rose so high. The interest rate on French government bonds rose
56 basis points between August 1870 and September 1870, the month Napoleon III was captured
by the Prussians at Sedan. In the subsequent months, during which the socialist Paris Commune
seized control of the capital and there was debate about the new form of government, the interest
rate rose another 80 basis points. This political uncertainty also reduced the value of the “nexus
of contracts” which were enforced through formal institutions. At precisely the time political
uncertainty was at its worst, 1870 and 1871, per capita growth of GNP went from positive to
negative.
Political instability does not impact all types of institutions in the same way. In general,
formal institutions are more adversely affected by political instability than informal institutions.
8
In the game above, political instability would lower the payoffs associated with coordinating on
{Formal, Formal}, but not on {Informal, Informal}. If society started out in the equilibrium
{Informal, Informal}, political instability would reduce the expected net benefit to B of
switching strategies and increase the stability of the institutional equilibrium. Political instability
reinforces the use of informal institutions in this society and should have no noticeable effect on
the value of trades, since trades take place outside of the context of formal institutions anyway.
What if the society began in the equilibrium {Formal, Formal}? Then the result of the
instability would make B more likely to switch his strategy to {Informal, Informal}, because the
opportunity cost of doing so has decreased.
We do not argue, as this simple model may imply, that societies will ineluctably evolve
towards informal institutions. In reality, there are many potential sets of institutions, both formal
and informal on which society may coordinate. Our point is simply that countries that rely more
on informal institutions to structure their transactions will be less likely to respond to political
shocks. This is true because the opportunity cost of engaging in institutional change depends, in
large part, on the value of “nexus of contracts” enforced by existing institutions. If political
instability does not affect contracts defined and enforced by informal institutions, then
organizations will be less likely to expend resources to change the institutional equilibrium.
This theoretical framework implies the following hypotheses:
(1) Not all periods of political instability will result in a significant decrease in the value
of trades.
(2) Countries that experience a decrease in the value of trade during the period of
political instability will be more likely to exhibit a change in their long-run growth
regime.
(3) Political instability should impact countries that rely on formal institutions more than
countries which rely on informal institutions.
9
There are two caveats which accompany these hypotheses. First, our claims concern
institutional stability. We make no claims of knowing why political instability occurs in the first
place. Second, there is no claim here for a “Political Coase Theorem.” In other words, just
because institutional change may be more likely in certain contexts does not necessarily mean
that society will coordinate on a new equilibrium which is Pareto Superior to the old one.
In what follows we examine propositions (1) and (2) using a simple “Before and After”
framework, and then in section IV adopt a panel approach to empirically test proposition (3).
III. Data
Clearly, given the nature of this paper’s focus, identifying episodes of political instability
is fundamental. Therefore a key dataset is the Polity IV dataset from the University of
Maryland’s Center for International Development and Conflict Management. Covering 1800-
2003, this dataset has annual coded information on regime and authority characteristics for a
wide range of countries (all independent states).14 While measuring political regimes and
authority characteristics is a difficult task across countries, and even more so over time, the
Polity database series is highly regarded and well suited for questions such as ours.
The Polity IV data set contains a large number of political variables. The variable of
interest for our purpose is the “Polity” variable which indicates the degree of autocracy or
democracy in a country for a given year.15 Within the Polity variable there are three
standardized codes for special political circumstances where a polity no longer operates properly.
The first code, “-88”, indicates a transition period, the idea being that new polities may be
preceded by a transition period determined by the executive or legislature. This is a period where
14 Further detail on the Polity IV dataset can be found at http://www.cidcm.umd.edu/inscr/polity.
15 The components of the Polity variable measure the type of formal political institutions enjoyed by the country.
-10 indicates high autocracy while +10 indicates high democracy. For a detailed list of these components see the
Polity IV code manual.
10
new institutions are planned, legally constituted, and established. The second code, “-77”,
indicates an interregnum period where the central political authority essentially collapses.
Finally, “-66” indicates a period of interruption, where a country is occupied by a foreign power
(but where the polity reestablishes itself once war has come to an end).
As an illustration of these standardized codes consider Uganda’s recent history. In 1966
Uganda’s leader, Milton Obote, was implicated (along with Idi Amin) in a gold smuggling plot.
In order to avoid prosecution, Obote used his executive powers to suspend the constitution and
have parliament arrested. After being cleared by the judiciary of wrong-doing, Obote launched a
coup against the ceremonial president Edward Mutesa II, thus becoming the sole leader of the
country. The years 1966-1967 are coded as –88 in the polity dataset. In contrast to Obote’s
“legal” removal of Mutesa, Idi Amin’s forced exile in 1979 by invading Tanzanian forces is
coded as –66. Finally, the years 1985-1986 when, first Obote, and then his successor Tito
Okello, were “illegally” deposed are coded as –77.
It is the codes for these three political circumstances that we use as our primary indicator
of episodes of extreme political instability, and which allow us to consider the relationship
between political instability, institutional change, and economic growth. We would expect that it
is during uncertain times such as these that we might see the value of contracts decline, and the
opportunity cost of engaging in institutional change decline, as laid out by our earlier theoretical
discussion. This instability measure is denoted “PI1” in this paper. In order to examine the
sensitivity of our results to our choice of political instability, we also employ two other political
instability proxies. Our second proxy is based on when there is a change in our polity variable
score by 3 or more (and is denoted PI2), while our third proxy is based on a change in the polity
score of 5 or more (and is denoted PI3).
11
Due to the well known difficulty of measuring institutions generally, and formality of
institutions specifically, we consider three different proxies for formal institutions. One is based
on tax revenue relative to GDP, where higher measured tax revenue relative to GDP indicates a
higher degree of formal institutions. We also consider Clague et al. (1999)’s contract-intensive
money, which is a widely used institutional measure (again we consider higher use of contract
intensive money as an indicator of a more formalized institutional equilibrium), and
infrastructure quality measures from Calderon and Serven (2004), where countries with higher
infrastructure quality are taken as having a more formal institutional equilibrium.
Beyond our political instability and institutional proxies, the dataset for this study
consists of our dependent variable, economic growth, which is measured as the growth of real
GDP per capita, and standard control variables which include lagged initial real per capita GDP
and average years of schooling as well as the black market premium, investment, trade openness,
and inflation.16
IV. A “Before and After” Examination of the Effect of Political Instability on Long-Run
Economic Growth
Recall there are a number of testable implications we consider. These are that: (1) not all
periods of political instability result in a reduction in the value of trade (i.e. growth of real GDP
per capita); and (2) countries that experience a decrease in the value of trade during the period of
instability should be more likely to exhibit a change in their long-run growth regime. As well, a
caveat raised is that the change in growth regime for countries which experienced a decrease in
trade is not necessarily for the better. Our approach to get at these points is a basic “Before and
After Test” which examines the growth characteristics of countries which experienced periods of
16 See the appendix for data sources.
12
extreme political instability (as measured by our Polity variable scores of –66,-77,-88).17 More
specifically for a given episode of political instability we measure the average annual growth rate
of real per capita GDP during the period of political instability, as well as the average annual
growth rate of real per capita GDP five and ten years following the instability relative to the
same length of time prior to the instability.18
Using this approach we are able to determine which countries experienced a reduction in
the value of trade during the political instability (as proxied by negative real GDP growth) as
well as examine whether countries experienced increased or reduced growth in the years after the
political instability relative to the years prior to the political instability19.
First, let’s consider what we find in terms of growth using the entire sample of countries.
Table 1: Full Sample Statistics
Average Growth During
Political Instability
10 Year Average Growth
Difference
5 Year Average Growth
Difference
-1.1
(8.0)
0.46
(3.8)
1.4
(4.8)
Parenthesis indicate standard deviation
Figure 3: Average Growth During Period of Instability (Entire Sample)
0
5
10
15
20
25
30
-40 -30 -20 -10 0 10 20 30
Growth
17 Rodrik and Wacziarg (2005) employ a similar approach.
18 Our before and after approach is based on percentage change in real per capita GDP.
19 Table 1-4 in the appendix provide an extensive summary of these before and after results.
13
Figure 4: Change in 10 Year Average Growth (Entire Sample)
0
4
8
12
16
20
-10 -8 -6 -4 -2 0 2 4 6 8 10 12
Change in Growth
Figure 5: Change in 5 Year Average Growth (Entire Sample)
0
2
4
6
8
10
12
14
-10 -5 0 5 10 15
Change in Growth
From the sample statistics and plots detailed in figures 3-5, a number of points emerge.
First is that, on average, during periods of political instability countries tend to experience low
growth.20 This is evident in Table 1 which indicates an average annual growth rate of –1.1
percent during episodes of political instability. However, in Figure 3 it is also evident that the
experience of countries differs widely.
Another point concerns the relative change in growth experienced by these countries. In
Table 1, when comparing the average growth rate for the five years following the instability
relative to the five years prior to the instability, countries grow 1.4% faster annually in the five
20 This is in line with results seen in the literature (see Alesina and Perotti, 1996).
14
years after the instability relative to their experience for the five years leading up to the political
instability, while the 10 year difference indicates a 0.46% average annual increase.21 Again,
examining the plots in Figures 4 and 5, we see the experiences vary widely.
Having considered what the entire sample tells us, we see these results are in line with a
number of papers in the literature. However, we also see the experiences differ dramatically
across countries with respect to growth during the instability as well as the change in growth
after the instability. Importantly, this has shed some light on our first testable implication, which
is that periods of political instability will not necessarily always result in a decrease in the value
of trades. Figure 3 certainly makes that point.22
With that, let’s consider dividing the sample based on the growth experiences of
countries during periods of political instability. This will allow us to consider testable
implication two as well as our caveat.
Table 2: Comparing Across Samples
Average Growth
During Political
Instability
Difference in 10
Year Average
Growth
Difference in 5
Year Average
Growth
Negative Growth Sample -6.2
(7.0)
1.2
(4.5)
2.3
(5.4)
Positive Growth Sample 4.3
(4.9)
-0.4
(2.7)
0.5
(2.8)
Table 2 indicates that on average, countries which experienced negative average growth
during their period of political instability went on to experience increased long run growth
relative to their pre-instability rates of growth as well as relative to the countries which had
experienced positive growth during their episode of political instability. Figures 6 and 7 also
21 The finding that political instability does not have a large effect on growth in the long run is seen in papers such as
Levine and Renelt (1992), Campos and Nugent (2000), Haber et al (2003). Our result appears in line with this earlier
research.
22 The appendix contains statistical tests of the results found in this section.
15
confirm this as the sample which experienced negative growth during their period of instability
has many more cases than the positive growth instability group of large increases in their growth
relative to their pre-instability periods.23 This of course is what we would expect given our
theory that during periods where the value of trade is reduced, institutional change may be
brought about with a resulting relative increase in long-run economic growth occurring.
Figure 6: Change in 10 Year Average Growth (Negative Growth Instability Sample)
0
1
2
3
4
5
6
7
8
-6 -4 -2 0 2 4 6 8 10 12
Change in Growth
Figure 7: Change in 10 Year Average Growth (Positive Growth Instability Sample)
0
2
4
6
8
10
12
-10 -8 -6 -4 -2 0 2 4 6 8 10
Change in Growth
As well concerning our caveat that in the case of countries which experienced a reduction
in the value of trade, it was not necessarily the case that they would expect a subsequent increase
in their long-run rates of growth, in Figure 6, a number of countries which experienced negative
23 For sake of space, 5 year results can be found in Figures 8 and 9 in the appendix.
16
growth during their period of instability went on to experience reduced rather than increased long
run economic growth rates.
As a whole these findings lend support to our theory and its testable implications. For
further evidence, section V below provides the results of our System GMM model which
examines the political instability-institutions-economic growth linkage.
V. Empirical Approach and Results
Empirical Approach
The use of panel data has become widespread in the growth literature due to limitations
of cross-section growth regressions highlighted in work such as Levine and Renelt (1992). As
has been raised in numerous studies, averaging data over long periods of time wastes valuable
information in terms of dynamics, while cross-section models are also known to suffer from
omitted variable bias due to heterogeneity as well as problems with endogeneity. In keeping
with the recent literature, we use a dynamic panel approach based on Blundell and Bond (1998)
to assess the relationship between political instability, institutions, and economic growth.24
Following Beck and Levine (2004), consider first the standard model we wish to
estimate:
yi,t - yi,t-1 = â yi,t-1+ ã´Xi,t +çi + åi,t (1)
where for country i (i=1…N) at time t (t=1…T), y is the logarithm of real GDP per capita, X is
the set of explanatory variables including our institutions and political instability measures, ç is
an unobserved country specific fixed-effect, and å is the error term. A potential problem exists in
(1) in that the lagged dependent variable yi,t-1 may be correlated with the country fixed effect çi.
Following Arellano and Bond (1991) it is common to difference equation (1) such that it
becomes
24 Bond, Hoeffler and Temple (2001) and Hoeffler (2002) each provide an excellent explanation of this approach.
17
(yi,t - yi,t-1) – (yi,t - yi,t-2) = â(yi,t-1 - yi,t-2)+ ã´(Xi,t - Xi,t-1) + åi,t - åi,t-1 (2)
which eliminates the country fixed-effect. This however introduces a new bias as
(yi,t-1 - yi,t-2) and (åi,t - åi,t-1) are now correlated. As well there exists the potential problem of
endogeneity of variables in the X vector, and so as a result this requires the use of instruments.
Assuming the error term is not serially correlated and the explanatory variables are weakly
exogenous (i.e. not correlated with future error terms), Arellano and Bond (1991) propose GMM
estimation in the form of the Difference GMM estimator which uses the following moment
conditions:
E[yi,t-s(åi,t - åi,t-1)] = 0 for s ¡Ý 2; t=3,...,T (3)
E[Xi,t-s(åi,t - åi,t-1)] = 0 for s ¡Ý 2; t=3,...,T (4)
A well noted problem with the Arellano and Bond estimator however is that lagged levels
often prove to be poor instruments for first differences for series which approach a random
walk.25 As a result, the System GMM estimator methodology developed by Arellano and Bover
(1995) and Blundell and Bond (1998) which combines the regression in differences with a
regression in levels in order to estimate a system has become widely used to overcome this
problem.
With System GMM, the difference equation is as detailed in (2), where as discussed,
appropriately lagged levels of yi,t and Xi,t are used as instruments. The other equation in System
GMM is the levels equation as detailed in (1) where
yi,t - yi,t-1 = â yi,t-1+ ã´Xi,t +çi + åi,t.
Here lagged differences serve as instruments for the corresponding variables in the levels
regression. This requires the assumption that the differences of the right-hand side variables are
25 See Blundell and Bond (1998).
18
uncorrelated with the country specific effects in the levels equation. The additional moment
restrictions resulting from the levels part of the system is then
E[(yi,t-s - yi,t-s-1)(çi + åi,t)] = 0 for s = 1 (5)
E[(Xi,t-s - Xi,t-s-1)(çi + åi,t)] = 0 for s = 1. (6)
In summary, System GMM uses the moment conditions (3)-(6) and the system set up in
order to estimate the parameters of our model. Lastly, in order to test the validity of our
instruments, we employ the Arellano-Bond test for autocorrelation as well as the Hansen test of
over-identifying restrictions.
Results
Our results are based on 5 year-average panel data from 1960-2000. Our empirical model
follows Beck and Levine (2004). The dependent variable is growth of real GDP per capita,
while our control variables include education (average years of schooling), initial real GDP per
capita, openness (trade/GDP), inflation, investment, and a black-market premium. Our measures
of political instability are again based on the polity variables in the Polity IV database. Our
measures of formal institutions include government tax revenue divided by GDP, contractintensive
money, and infrastructure quality.26 Finally, in order to capture the institutional
equilibrium-political instability link, we interact our political instability measure with our formal
institutions proxy. The System GMM results based on our primary political instability measure,
PI1, are seen in Table 3 to Table 6, while the results based on the PI3 measure are found in
Tables 7 -10 in the appendix.
It is important to note the construction of the interaction term. Recall our institutional
measure is based on a 5-year average while the instability measure is a 1-0 dummy based on
26 At this point only results for contract intensive money and tax ratio institutional proxies are available. Also for the
sake of space only the results for our primary instability indicator (PI1) based on codes -66,-77,-88 indicating the
polity is no longer functioning properly, and an alternative (PI3) based on a polity score change of 5 are reported.
19
whether there was an episode of political instability during the 5 year period. Therefore an
interaction is based on the average institutional measure for the 5-year period and the 1-0
dummy. However our story concerns the institutional equilibrium up to the instability event and
how that determines the impact of political instability on institutional change. One could imagine
however that should the institutional equilibrium change as a result of the instability, the 5-year
average used in the interaction will give a false sense of the institutional equilibrium at the time
of political instability.27 As a result of this concern, for each institutional measure and political
instability measure we consider: (1) a standard interaction using the average institutional proxy
during the 5 year period interacted with the political instability dummy of that period (as seen in
tables 3 and 5); and (2) an interaction based on the previous period’s average institutional proxy
and the current period political instability dummy (as seen in tables 4 and 6).
Let’s first consider the institutional proxy based on the tax ratio and the political
instability measure PI1. Table 3 again is based on a standard interaction while Table 4 has the
interaction based on the lagged institutional measure (and current political instability dummy).
27 For example, a country with a high degree of formal institutions which experiences political instability at the
beginning of the 5 year period and as a result moves say to a more informal institutional equilibrium for much of the
rest of the period would therefore give an average institutional formality measure that is much less than the actual
institutional formality at the time of the instability. This of course is problematic.
20
Table 3: Political Instability (PI1), Institutions (tax ratio) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.041
(0.173)
0.017
(0.579)
0.081**
(0.016)
0.173***
(0.000)
0.049
(0.120)
0.169***
(0.000)
Initial GDP per Capitaa -0.002
(0.398)
-0.000
(0.903)
-0.005
(0.159)
-0.013***
(0.000)
-0.005
(0.181)
-0.012***
(0.000)
Average Years of Schoolinga 0.008**
(0.039)
0.008*
(0.074)
0.011***
(0.007)
0.006**
(0.038)
0.011**
(0.011)
0.007**
(0.020)
Trade Opennessa 0.020***
(0.003)
-0.000
(0.878)
Inflation -0.027***
(0.000)
-0.018***
(0.001)
Investmenta 0.025***
(0.000)
0.028***
(0.000)
Black Market Premiumb -0.011***
(0.002)
-0.003
(0.157)
Formal Institutionsa 0.011**
(0.037)
0.001
(0.815)
0.007
(0.140)
0.003
(0.534)
0.007
(0.179)
-0.002
(0.645)
Political Instability -0.037***
(0.009)
-0.041***
(0.010)
-0.046***
(0.008)
-0.042***
(0.008)
-0.039***
(0.002)
-0.037***
(0.002)
Interaction -0.015**
(0.045)
-0.017**
(0.038)
-0.021**
(0.036)
-0.017**
(0.042)
-0.017**
(0.013)
-0.014**
(0.025)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.959 0.910 0.702 0.758 0.294 0.502
Hansen Test (p-values) 0.769 0.969 0.993 0.970 0.999 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported
21
Table 4: Political Instability (PI1), Institutions (tax ratio), and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.112***
(0.002)
0.082**
(0.047)
0.111***
(0.002)
0.205***
(0.000)
0.073**
(0.039)
0.174***
(0.000)
Initial GDP per Capitaa -0.008*
(0.052)
-0.003
(0.410)
-0.009**
(0.043)
-0.014***
(0.000)
-0.006
(0.113)
-0.012***
(0.000)
Average Years of Schoolinga 0.011**
(0.027)
0.010*
(0.070)
0.014***
(0.006)
0.007*
(0.054)
0.012**
(0.021)
0.008**
(0.023)
Trade Opennessa 0.023***
(0.002)
0.000
(0.870)
Inflation -0.028***
(0.000)
-0.018***
(0.001)
Investmenta 0.024***
(0.000)
0.028***
(0.004)
Black Market Premiumb -0.012***
(0.003)
-0.003
(0.335)
Formal Institutionsa 0.010*
(0.094)
0.002
(0.761)
0.004
(0.514)
0.002
(0.727)
0.010
(0.115)
0.000
(0.934)
Lag Formal Institutionsa 0.008
(0.202)
0.004
(0.585)
0.006
(0.370)
0.005
(0.432)
0.004
(0.570)
-0.002
(0.743)
Political Instability -0.050*
(0.063)
-0.051**
(0.029)
-0.071***
(0.007)
-0.045*
(0.067)
-0.061***
(0.004)
-0.042***
(0.010)
Interaction -0.021
(0.139)
-0.022*
(0.088)
-0.033**
(0.018)
-0.018
(0.158)
-0.029**
(0.015)
-0.016*
(0.086)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.803 0.862 0.320 0.987 0.886 0.865
Hansen Test (p-values) 0.885 0.978 0.984 0.950 0.999 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported
In both Tables 3 and 4, the coefficients for our control variables generally appear as
would be expected, while political instability is negative and statistically insignificant across the
regression. One possible concern is that our measure of formal institutions (PI1) while positive is
not statistically significant (with 1 exception in each table). However this is not necessarily a
finding contradicting the importance of institutions. Recall we are trying to capture the degree of
formalness of institutions not necessarily good versus bad institutions. While the extent of tax
revenue relative to GDP is likely a good proxy for the formality of the institutional equilibrium it
does not necessarily also capture “good versus bad” institutions (in fact two countries may rely
22
largely on formal institutions but one relies on good formal institutions while the other largely
bad formal institutions). Now importantly for our purposes, in Table 3 the interaction of political
instability with formal institutions is negative and statistically significant in all our model
specifications, and is negative and largely significant (though significance depends on the control
variables) in Table 4. This negative and generally significant finding for the interaction term is
consistent with our idea that political instability will have a greater negative impact on countries
in which formal institutions play a greater role than for countries which are structured more by
informal institutions.
Table5: Political Instability (PI1), Institutions (CIM) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.103***
(0.001)
0.112***
(0.000)
0.115***
(0.000)
0.176***
(0.000)
0.070***
(0.005)
0.167***
(0.000)
Initial GDP per Capitaa -0.008**
(0.020)
-0.007**
(0.060)
-0.010***
(0.070)
-0.011***
(0.001)
-0.004
(0.129)
-0.010***
(0.001)
Average Years of Schoolinga 0.010**
(0.011)
0.010**
(0.042)
0.129***
(0.004)
0.004
(0.334)
0.010***
(0.006)
0.005*
(0.089)
Trade Opennessa 0.018***
(0.009)
-0.002
(0.460)
Inflation -0.024***
(0.000)
-0.022***
(0.000)
Investmenta 0.020***
(0.006)
0.028***
(0.000)
Black Market Premiumb -0.007*
(0.072)
-0.002
(0.002)
Formal Institutionsa 0.039***
(0.001)
0.035***
(0.004)
0.038***
(0.000)
0.032***
(0.000)
0.027**
(0.018)
0.020***
(0.005)
Political Instability 0.007
(0.598)
0.006
(0.636)
-0.010
(0.347)
0.000
(0.992)
-0.000
(0.986)
-0.023**
(0.033)
Interaction 0.067
(0.120)
0.059
(0.191)
-0.003
(0.921)
0.034
(0.450)
0.040
(0.330)
-0.039
(0.174)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.585 0.731 0.225 0.666 0.464 0.980
Hansen Test (p-values) 0.976 1.000 1.000 0.999 1.000 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported
Tables 5 and 6 again consider our primary political instability measure (PI1), however
this time uses contract intensive money (CIM) as the measure of formal institutions in the
23
economy. Again the signs of our control variables are generally in line with the literature. The
results in however are much less favourable for our hypothesis. Overall our measure of formal
institutions (contract intensive money) proves to be positive and statistically in all regressions in
both tables. This is not surprising given that CIM is also an excellent and widely used proxy for
“good” institutions. However the use of CIM rather than the tax ratio as our measure of formal
institutions appears to reduce the significance of political instability relative to tables 3 and 4 and
more importantly the interaction term.
Table 6: Political Instability (PI1), Institutions (CIM) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.060*
(0.052)
0.033
(0.256)
0.053**
(0.023)
0.120***
(0.000)
0.030
(0.322)
0.141***
(0.000)
Initial GDP per Capitaa -0.003
(0.324)
-0.001
(0.679)
-0.004
(0.129)
-0.007**
(0.025)
-0.000
(0.989)
-0.009***
(0.004)
Average Years of Schoolinga 0.009**
(0.048)
0.008
(0.138)
0.012**
(0.015)
0.004
(0.314)
0.007*
(0.100)
0.007**
(0.045)
Trade Opennessa 0.020***
(0.006)
-0.004
(0.148)
Inflation -0.024***
(0.000)
-0.023***
(0.000)
Investmenta 0.018**
(0.011)
0.027***
(0.000)
Black Market Premiumb -0.005
(0.174)
-0.000
(0.866)
Formal Institutionsa 0.099***
(0.000)
0.084***
(0.000)
0.080***
(0.000)
0.084***
(0.000)
0.082***
(0.000)
0.058***
(0.004)
Lag Formal Institutionsa -0.061**
(0.012)
-0.054**
(0.031)
-0.053**
(0.032)
-0.056**
(0.013)
-0.050*
(0.060)
0.046**
(0.041)
Political Instability -0.015
(0.298)
-0.020
(0.201)
-0.027**
(0.021)
-0.014
(0.279)
-0.025
(0.162)
-0.028**
(0.025)
Interaction -0.021
(0.633)
-0.038
(0.400)
-0.066**
(0.033)
-0.022
(0.574)
-0.042
(0.428)
-0.052*
(0.097)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.094 0.133 0.118 0.125 0.575 0.711
Hansen Test (p-values) 0.986 1.000 1.000 0.999 1.000 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported
24
VI. Conclusion
This study is premised on the idea that if institutions are important, then a deeper
understanding of how institutional change comes about is essential. Where our paper attempts to
contribute to this area is by considering one avenue which may bring about regime change –
during episodes of extreme political instability (broadly defined). More specifically, we lay out a
theory which links political instability, institutional change, and economic growth.
Using a before and after approach to compare the growth experiences of countries during
episodes of political instability as well as changes in their growth regimes following the political
instability, the data is suggestive of many of the ideas our theory lays out. More specifically, the
results indicate that political instability does not necessarily entail negative growth during the
period of instability, or even reduced growth over the long run following the period of instability.
Splitting countries based on their growth experiences during the periods of political instability
we see that the growth experiences in the years following the instability do seem to differ. We
tie these results into how political instability may alter the institutional equilibrium, and more to
the point, formal institutions.
Using System GMM and panel data, our results for our hypothesis linking political
instability and institutions with institutional change provides mixed evidence at this point that
political instability may affect countries organized by formal institutions more significantly than
it affects countries organized by informal institutions. Additional data and techniques will
investigate this further in future drafts.
25
Bibliography
Acemoglu, Daron, Simon Johnson, and James A. Robinson. "Institutions as the Fundamental
Cause of Long-Run Growth." In National Bureau of Economic Research Working
Papers, 2004.
Alesina, Alberto, and Roberto Perotti. "Income Distribution, Political Instability, and
Investment." European Economic Review 40, no. 6 (1996): 1203-28.
Arellano, Manuel and Stephen Bond. "Some Tests of Specification for Panel Data: Monte Carlo
Evidence and An Application to Employment Equations." The Review of Economic
Studies 58, no. 2 (1991): 277-297.
Arellano, Manuel and Olympia Bover. "Another Look at the Instrumental-variable Estimation of
Error-components Models." Journal of Econometrics 68, no. 1 (1995): 29-52.
Atack, Jeremy and Peter Passell. A New Economic View of American History (2nd Edition).
W.W. Norton, 1994.
Barro, Robert J. and Jong-Wha Lee. "International Data on Educational Attainment: Updates and
Implications." CID Working Papers, no. 42, April 2000.
Beck, Thorsten, Ross Levine and Norman Loayza. "Finance and the Sources of Growth."
Journal of Financial Economics 58, no.1-2 (2000): 261-300.
Beck, Thorsten and Ross Levine. "Stock Markets, Banks, and Growth: Panel Evidence."
Journal of Banking & Finance 28, no. 3 (2004): 423-442.
Blundell, Richard and Stephen Bond. "Initial Conditions and Moment Restrictions in Dynamic
Panel Data Models." Journal of Econometrics 87, no. 1 (1998): 115-43.
Bond, Stephen, Anke Hoeffler and Jonathon Temple. "GMM Estimation of Empirical Growth
Models." CEPR Discussion Papers, no. 3048, November 2001.
Calderon, Cesar and Luis Serven. "The Effects of Infrastructure Development on Growth and
Income Distribution." World Bank Policy Research Working Papers, Paper 3400, 2004.
Campos, Nauro F. and Jeffrey B. Nugent. "Who is Afraid of Political Instability?" Journal of
Development Economics 67, no. 1 (2002): 157-72.
Clague, Christopher, Philip Keefer, Stephen Knack and Mancur Olson. ”Contract-Intensive
Money: Contract Enforcement, Property Rights, and Economic Performance." Journal of
Economic Growth 4, no.2 (1999): 185-211.
26
Edison, Hali. "Testing the Links: How Strong are the Links Between Institutional Quality and
Economic Performance?" Finance and Development 40, no. 2 (2003): 35-37.
Engerman, Stanley L. and Kenneth L. Sokoloff. "Institutional and Non-Institutional
Explanations of Economic Differences." In National Bureau of Economic Research
Working Papers, 2003.
Fogel, Robert W. and Jack L. Rutner. "The Efficiency Effects of Federal Land Policy, 1850-
1900: Some Provisional Findings." in W.O. Aydelotte, A.G. Bogue and R.W. Fogel The
Dimensions of Qualitative Research in History. Princeton: Princeton University Press,
1972.
Furubotn, Eirik G. and Rudolf Richter. Institutions and Economic Theory: The Contribution of
the New Institutional Economics. Ann Arbor: University of Michigan Press, 2000.
Gates, Paul Wallace. "The Homestead Law in an Incongruous Land System." American
Historical Review, 41, no. 4 (1936): 652-681.
Grief, Avner. "Reputation and Coalitions in Medieval Trade: Evidence on the Maghribi Traders."
Journal of Economic History 49, no. 4 (1989): 857-882.
Haber, Stephen, Armando Razo, and Noel Maurer. The Politics of Property Rights. Cambridge:
Cambridge University Press, 2003.
Hall, Robert E. and Charles I. Jones. "Why do Some Countries Produce so Much More Output
per Worker than Others?" In National Bureau of Economic Research Working Papers,
1998.
Heston, Alan, Robert Summer and Bettina Aten. Penn World Table (Version 6.1), Center for
International Comparisons at the University of Pennsylvania (CICUP), October 2002.
Hoeffler, Anke. "The Augmented Solow Model and the African Growth Debate." Oxford
Bulletin of Economics and Statistics 64, 2 (2002): 135-158.
Hughes, Jonathon and Louis P Cain, American Economic History (6th Edition). Reading:
Addison-Wesley, 2003.
Levine, Ross and David Renelt. "A Sensitivity Analysis of Cross-Country Growth Regressions."
The American Economic Review 82, no. 4 (September, 1992): 942-63.
Nkurunziza, Janvier D. and Robert H. Bates. "Political Institutions and Economic Growth in
Africa." Center for International Development Working Papers, No. 98, March 2003.
North, Douglass Cecil. Structure and Change in Economic History. New York: W.W. Norton
and Company, Inc., 1981.
27
_____. Institutions, Institutional Change, and Economic Performance, The Political Economy of
Institutions and Decisions. Cambridge ; New York: Cambridge University Press, 1990.
_____. Understanding the Process of Economic Change. Princeton, New Jersey: Princeton
University Press, 2005.
North, Douglass Cecil and Robert P. Thomas. The Rise of the Western World: A New Economic
History. New York: Cambridge University Press, 1973.
Richman, Barak. "How Community Institutions Create Economic Advantage: Jewish Diamond
Merchants in New York" Law and Social Inquiry 32, 2 (2006): forthcoming.
Rodrik, Dani and Roman Wacziarg. "Do Democratic Transitions Produce Bad Economic
Outcomes?" American Economic Review, 95, no. 2 (May 2005): 50 - 55.
Rodrik, Dani, Arvind Subramanian, and Francesco Trebbi. "Institutions Rule: The Primacy of
Institutions over Geography and Integration in Economic Development." In National
Bureau of Economic Research Working Papers, 2002.
Roodman, David. Xtabond2: Stata Module to Extend Xtabond Dynamic Panel Data Estimator
Center for Global Development, Washington D.C. 2005.
http://econpapers.repec.org/software/bocbocode/s435901.html
Swierenga, Robert P. “Land Speculator ‘Profits’ Reconsidered: Central Iowa as a Test Case.”
Journal of Economic History, 26, no. 1 (1966): 1-28.
28
Appendix I
Table 1: Before and After Summary Table: Latin America*
Country Episode Duration Polity
Score
Before
Instability
Polity
Score After
Instability
Regime
Length
Pre-
Instability
Regime
Length
Post-
Instability
Average
Annual
Growth
During
Instability
Change in
10 Year
Average
Growth
Rate
Change in
5 Year
Average
Growth
Rate
Argentina -88 1946-1948 -8 -9 3 7 6.5 -0.2 -2.4
-88 1955-1957 -9 -1 7 9 2.1 0.7 1.1
Bolivia -88 1952-1956 -5 -3 16 8 -2.8 -4.7 -4.7
Brazil -88 1930-1934 -3 -7 36 11 -1.2 -0.3 -0.2
-88 1945-1946 -7 7 11 1 7.9 0.3 0.6
-88 1964-1965 3 -9 1 8 -0.3 2.2 1.4
Chile -88 1924-1925 3 -2 33 10 -0.1 0.2 1.5
Cuba -88 1952-1955 3 18 -3.8 -3.55 -3.18
-77 1959-1960
-88 1960-1961 -7 15
Dom Rep -88 1961-1963 -9 28 1.5 2.4 2
-77 1963-1965
-88 1965-1966 -3 12
El Salvador -88 1948-1950 -8 -6 4 6 -10.6 -1.2 -2.3
-88 1979-1984 -6 6 3 7 -5.5 -0.4 -3
Guatemala -88 1985-1986 -6 3 1 10 -3.4 1.5 3.7
Haiti -88 1986-1988 -9 -7 9 1 -2.1 -3.7 -2.2
Honduras -88 1980-1982 -1 6 8 4 -1.6 -1.7 -4.5
Mexico -77 1911-1917 -9 -1 30 3 0.7 -1.8 -0.1
Nicaragua -66 1926-1928 -3 -3 17 8 13.5 -3.7 -3.7
-77 1979-1981 -8 -5 43 3 -8.9 -5.3 -4
Peru -88 1919-1920 2 -9 33 11 14.2 -0.8 -2.4
1930-1933 -9 2 11 17 -10.7 -2.3 -1.5
1978-1980 -7 7 1 10 0.4 -4 -3.8
Uruguay -88 1971-1973 8 -8 20 5 -3.6 0.9 2.1
Venezuela -88 1935-1936 -9 -6 15 0 8.4 -0.9 4.8
* Note: Where sample coverage makes comparing 10 year growth differences impossible, the results for the 5 year comparisons
are included in the 10 year growth differences column. Also, where a number of events of political instability run together (or are
within a few years of each other), the averages over those periods are used and included as a single event.
29
Table 2: Before and After Summary Table: Europe*
Country Episode Duration Polity
Score
Before
Instability
Polity
Score After
Instability
Regime
Length
Pre-
Instability
Regime
Length
Post-
Instability
Average
Annual
Growth
During
Instability
Change in
10 Year
Average
Growth
Rate
Change in
5 Year
Average
Growth
Rate
Austria -88 1918-1920 -4 8 51 12 -2.4 6.4 12.7
-88 1933-1934 8 12 -3.2 10.5 18.6
-77 1938-1945
-88 1945-1946 10 current
Belgium -66 1914-1915 7 7 1 4 -7 1.3 0.3
-66 1939-1944 10 10 9 current -4.2 4.5 4.1
Bulgaria -88 1934-1935 -3 -10 15 8 -9.7 -6.5 1.2
-77 1943-1944 -10 8
-88 1944-1946 -6 1
Czech Rep -77 1939-1945 7 20
-88 1947-1948 -7 20
-77 1968-1969 -7 -7 20 20 4.3 -1.2 0.3
Denmark -88 1901-1915 -3 10 31 25 2.2 -0.1 -0.4
-66 1940-1945 10 10 25 current -1 2.2 4.5
Finland -88 1930-1931 10 4 11 14 -1.9 -2.4 0.9
France -88 1851-1852 6 -8 4 8 6.2 0.4 -0.3
-88 1860-1863 -8 -6 8 6 0.8 -1.4 -1.4
-88 1870-1877 -3 7 1 21 2.1 -0.8 1.3
-88 1944-1946 -9 10 4 12 28 9 15.7
Germany -88 1867-1871 -7 -4 5 7 1.4 -0.3 0.4
-88 1918-1919 2 6 9 14 -13.3 5.7 8.3
-66 1945-1949 -9 10 12 41 -12.6 3.5 7.5
-88 1989-1990 10 10 41 current -3.8 -0.3 -0.9
Greece -77 1941-1944 -8 8 5 5 -15.8 8.8 14.7
-88 1974-1975 -7 8 7 11 -3.9 -5.3 -4.7
Hungary -66 1944-1945 -1 25
-88 1945-1948 -7 8
-66 1956-1957 -7 -7 8 31 -5.3 -2.4 0.2
-88 1989-1990 -2 10 1 current -1.8 -0.1 -1.7
Italy -88 1922-1928 -1 -9 22 15 1.6 0.4 3.3
-77 1943-1945 -9 15 1.5 4.4 6.2
-88 1945-1947 10 current
Netherlands -66 1940-1945 10 10 22 present -13 12 16.5
Norway -66 1940-1945 10 10 42 current -4.2 1.9 2.6
Portugal -88 1910-1911 2 7 3 15 1.1 0.7 1
-88 1926-1930 7 -9 15 44 2.1 -1.3 -2.1
-88 1974-1976 -9 9 44 7 -3.9 -4.9 -4.1
Romania -88 1989-1990 -8 5 13 6 -10.9 0.4 -0.6
Spain -88 1868-1871 -6 9 2.5 -0.6 1.7
-88 1874-1876 -1 3
-88 1930-1931 -6 7 4 7 -3.9 -4.8 -6.8
-88 1975-1978 -7 9 37 4 2.6 -4 -5.2
Sweden -88 1907-1917 -4 10 37 current 0.3 1.2 0
Yugoslavia -88 1937-1939 -9 2
-77 1941-1944
30
Country Episode Duration Polity
Score
Before
Instability
Polity
Score After
Instability
Regime
Length
Pre-
Instability
Regime
Length
Post-
Instability
Average
Annual
Growth
During
Instability
Change in
10 Year
Average
Growth
Rate
Change in
5 Year
Average
Growth
Rate
-88 1951-1953 -7 -7 6 27
Yugo
(Bosnia)
-77 1992-1995 -5 -7 2 2 -5.4 8.8 8.8
* Note: Where sample coverage makes comparing 10 year growth differences impossible, the results for the 5 year comparisons
are included in the 10 year growth differences column. Also, where a number of events of political instability run together (or are
within a few years of each other), the averages over those periods are used and included as a single event.
31
Table 3: Before and After Summary Table: Africa*
Country Episode Duration Polity
Score
Before
Instability
Polity
Score After
Instability
Regime
Length
Pre-
Instability
Regime
Length
Post-
Instability
Average
Annual
Growth
During
Instability
Change in
10 Year
Average
Growth
Rate
Change in
5 Year
Average
Growth
Rate
Benin -88 1963-1965 2 -7 3 4 3.5 0.7 0.4
-88 1990-1991 -7 6 10 current 0.6 0.7 2.4
Burkina
Faso
-88 1977-1978 -4 5 8 3 9.2 2.8 2.9
Burundi -88 1965-1966 -3 -7 2 17 2 2.5 6.9
-88 1992-1993 -7 5 -4.8 -4.8 -2.2
-77 1993-1996 -5 2
Chad -88 1978-1979 -7 3 0.6 -0.5 -2.3
-77 1979-1984
-88 1984-1985 -7 6
-88 1991-1992 -7 -4 6 4 6.6 -2.6 0.2
Ethiopia -77 1974-1975 -9 -7 28 9 -1.1 -2.6 -0.9
-77 1991-1993 -8 7 -0.8 -0.8 2.2
-88 1993-1995 1 current
Gabon -88 1990-1991 -9 -4 22 current 3.4 1.2 4.9
Ghana -88 1969-1970 -7 3 4 1 7.5 -3 -0.9
-88 1978-1979 -7 6 7 3 6.7 -1.6 -4
-88 1991-1992 -7 -1 9 4 1.1 2.7 0
Liberia -77 1990-1996 -6 5 1.5 -4.5 -4.5
-88 1996-1997 0 current
Madagascar -88 1991-1992 -6 9 17 4 -1.7 3 0.3
Mali -88 1991-1992 -7 7 12 5 -3.1 1.1 -0.6
Niger -88 1991-1992 -7 8 4 3 -9.1 4.6 1.8
Nigeria -88 1978-1979 -7 7 13 4 3.3 -9.4 -9.5
Rwanda -88 1993-1994 -7 -6 2 6 -42.1 7.1 7.1
Senegal -88 1962-1963 -1 -7 3 11 1.3 -2.6 -1.3
Sierra
Leone
-88 1968 -7 1 1 2 8.1 -1.7 -0.5
South
Africa
-88 1992-1994 5 9 3 current -0.4 2.2 2.2
Sudan -88 1964-1965 -7 6 0.2 -0.9 2.1
-88 1969-1971 -7 13
-88 1985-1986 -7 7 13 3 -9.9 -0.7 2
Togo -88 1991-1993 -7 -2 13 9 -11.7 3 3
Uganda -88 1966-1967 7 -6 3 2 2.7 -2.2 -0.9
-66 1979-1980 -7 3 8 5 -9.3 1.8 3.1
-77 1985-1986 3 -7 5 7 -1.3 6.1 2.4
Zimbabwe -88 1979-1980 4 5 10 3 7 -1.8 3.6
* Note: Where sample coverage makes comparing 10 year growth differences impossible, the results for the 5 year comparisons
are included in the 10 year growth differences column. Also, where a number of events of political instability run together (or are
within a few years of each other), the averages over those periods are used and included as a single event.
32
Table 4: Before and After Summary Table: Asia and Middle East*
Country Episode Duration Polity
Score
Before
Instability
Polity
Score After
Instability
Regime
Length
Pre-
Instability
Regime
Length
Post-
Instability
Average
Annual
Growth
During
Instability
Change in
10 Year
Average
Growth
Rate
Change in
5 Year
Average
Growth
Rate
Afghanistan -77 1978-1979 -7 -7 1 8 1.5 0 2.2
-77 1992-1996 -8 -7 3 5 -2.6 5.2 5.2
Cambodia -88 1970-1972 -9 15 1.4 0 0
-77 1976-1976
-66 1979-1988
-88 1988-1993 1 4
Iran -88 1979-1981 -10 -6 24 16 -12.1 -4.3 0.2
Japan -66 1945-1952 1 10 78 current 7.9 4.9 7.2
Kuwait -66 1990-1991 -10 -9 4 1 -23.2 9.9 10.4
Laos -77 1961-1973 -1 1 1 0.6 1.3
-88 1973-1975 -7 current
Nepal -88 1957-1959 -7 2 7 2 4.4 -1.7 -1.7
Pakistan -88 1969-1971 1 8 1.9 0.3 -0.9
-77 1971-1971
-88 1972-1973 -8 4
Philippines -88 1986-1987 -6 8 2 5 1 1.6 4.2
Singapore -66 1963-1965 7 -2 4 current -4.7 8.1 8.9
S. Korea -88 1960 -4 8 8 1 -1.3 1.3 1.1
-88 1987-1988 -5 6 6 10 10.4 -1.3 -1.7
Thailand -88 1968-1969 -7 9 4.9 -0.4 -2
-88 1973-1974
-88 1977-1978 2 10
Turkey -88 1960-1961 4 9 6 4 -0.7 -0.1 5.5
Yemen -88 1990-1993 -5 -2 2 10 -1.9 3.6 3.6
* Note: Where sample coverage makes comparing 10 year growth differences impossible, the results for the 5 year comparisons are
included in the 10 year growth differences column. Also, where a number of events of political instability run together (or are within
a few years of each other), the averages over those periods are used and included as a single event.
33
Change in 5 Year Annual Growth Rates
Figure 8: Change in 5 Year Average Growth (Negative Growth Instability Sample)
0
4
8
12
16
-5 0 5 10 15 20
Change in Growth
Figure 9: Change in 5 Year Average Growth (Positive Growth Instability Sample)
0
4
8
12
16
20
-10 -5 0 5 10 15
Change in Growth
34
Appendix II: Statistical Tests of the Before and After Results
Testing the hypothesis that the Positive and Negative Group growth rates have identical
variances and means during instability
Test 1
Ho = Variances are equal during instability
H1= Variances differ during instability
Mean1 eq: -6.223 (variance=49.13) (se=0.9815)
Mean2 eq: 4.318 (variance=23.929) (se=0.7061)
Single sided probability that the two variances are equal (f- test): 0.00659   Reject the null of equality
Test 2
Ho = Means are equal during instability
H1 = Means differ during instability
Difference between means:
-10.5414 (sd=11.4451) (se=1.1971)
95% CI: -12.8878<diff<-8.1951 (Wald)
t-value of difference: -8.806; df-t: 91
probability: 0 (left tail pr: 1)
doublesided p-value: 0   Reject Ho that the means are identical
Testing the hypothesis that 10 year difference growth rates for Positive and Negative
have identical variances and means
Test 3
Ho = variance of positive and variance of negative equal
H1 = variance of positive and variance of negative not equal
mean1 eq: 1.243 (variance=20.767) (se=0.6381)
mean2 eq: -0.363 (sd=2.74) (se=0.3955)
Single sided probability that the two variances are equal (f-test): 0.00026   Reject Ho that the variances
are identical
Test 4
Ho = means of positive and negative samples are identical
H1 = means of positive and negative samples differ
difference between means:
1.6065 (sd=6.8306) (se=0.7434)
95% CI: 0.1496<diff<3.0635 (Wald)
t-value of difference: 2.161; df-t: 84
probability: 0.983012 (left tail pr: 0.01699)
doublesided p-value: 0.034   Reject Ho that means of samples are identical
35
Appendix III: Country Coverage for System GMM Estimation
Algeria Gabon New Zealand
Argentina Gambia Nicaragua
Australia Ghana Niger
Austria Great Britain Nigeria
Barbados Greece Norway
Belgium Guatemala Pakistan
Belize Haiti Panama
Bolivia Honduras Papua New Guinea
Botswana Hungary Paraguay
Brazil Iceland Peru
Burkina Faso India Philippines
Burundi Indonesia Portugal
Cameroon Iran Rwanda
Canada Ireland Senegal
C.A.R. Israel Sierra Leone
Chad Italy South Africa
Chile Jamaica Spain
Colombia Japan Sri Lanka
Congo Jordan Sweden
Costa Rica Kenya Switzerland
Cote d’ Ivoire Korea Syria
Cyprus Lesotho Tanzania
Denmark Luxembourg Thailand
Dom.Republic Madagascar Togo
Ecuador Malawi Trinidad and Tobago
Egypt Malaysia Turkey
El Salvador Mauritius Uganda
Ethiopoia Mexico Uruguay
Fiji Morocco USA
Finland Nepal Venezuela
France Netherlands Zaire
Zimbabwe
Note: Actual country coverage varies slightly depending on institutional proxy.
36
Appendix IV: Data
Variable Measure Source
growth constructed as log difference of real
GDP per capita (rgdpch)
PWT 6.1
Initial GDP per capita real GDP per capita at beginning of
each 5 year period (rgdpch)
PWT 6.1
schooling average years of schooling in the
total population 25+ (tyr)
Barro and Lee (2000)
trade openness sum of exports and imports to GDP
(openc)
PWT 6.1
inflation constructed as log difference of CPI
(fp.cpi.totl)
World Development Indicators
investment Investment share of real GDP (ki) PWT 6.1
black market premium ratio of black market exchange rate
and official exchange rate minus 1
Data underlying Beck, Levine and
Loayza (2000) and Beck and Levine
(2004). Original source is Pick’s
Currency Yearbook.
political instability 1 dummy variable constructed for
polity code of -66, -77, or -88
Polity IV Database
political instability 2 dummy variable constructed for
change of polity2 score of 3 or more.
Polity IV Database
political instability 3 dummy variable constructed for
change of polity2 score of 5 or more.
Polity IV Database
tax ratio ratio of tax revenue (s05f3/100) to
GDP (s16f3)
Cross National Time Series
Database
CIM contract intensive money: ratio of
non-currency component of M2 to
total M2.
IFS
infrastructure TBA Calderon and Serven (2004)
Summary Statistics
Variable Observations Mean Standard Dev. Min Max
Growth 751 0.0178 0.0303 -0.1151 0.1576
Initial GDP 749 6146 6135 321 34372
Education 660 4.5897 2.8293 0.042 12.179
Trade Openness 755 0.5949 0.3303 0.0874 2.424
Inflation 697 0.1403 0.2864 -0.0305 3.446
Investment 755 0.1575 0.0861 0.0102 0.4393
Black Market
Premium
569 0.6144 5.1878 -0.0525 109.9
PI1 729 0.0836 0.2770 0 1
PI2 729 0.2318 0.4222 0 1
PI3 729 0.1934 0.3952 0 1
Tax Ratio 644 0.2153 0.1132 0.0211 0.800
CIM 703 0.7601 0.1581 0.2113 0.9841
37
Appendix V: Additional Results
Table 7: Political Instability (PI3), Institutions (tax ratio) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.082**
(0.017)
0.005
(0.891)
0.093**
(0.012)
0.190***
(0.000)
0.046
(0.172)
0.162***
(0.000)
Initial GDP per Capitaa -0.004
(0.285)
0.001
(0.747)
-0.006
(0.135)
-0.014***
(0.001)
-0.001
(0.686)
-0.012***
(0.001)
Average Years of Schoolinga 0.008*
(0.060)
0.005
(0.318)
0.010**
(0.024)
0.006*
(0.054)
0.008*
(0.068)
0.007**
(0.032)
Trade Opennessa 0.024***
(0.001)
0.000
(0.934)
Inflation -0.025***
(0.000)
-0.015***
(0.002)
Investmenta 0.026***
(0.000)
0.027***
(0.000)
Black Market Premiumb -0.011***
(0.001)
-0.004
(0.115)
Formal Institutionsa 0.011**
(0.021)
-0.000
(0.899)
0.009
(0.107)
0.004
(0.325)
0.004
(0.404)
-0.001
(0.780)
Political Instability -0.045**
(0.009)
-0.043**
(0.020)
-0.032*
(0.070)
-0.037**
(0.025)
-0.011
(0.585)
-0.011
(0.823)
Interaction -0.019**
(0.021)
-0.019**
(0.031)
-0.013
(0.125)
-0.014*
(0.071)
-0.001
(0.859)
-0.002
(0.823)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.870 0.747 0.670 0.600 0.348 0.698
Hansen Test (p-values) 0.739 0.972 0.971 0.938 1.000 1.000
p-values in parentheses;*, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported
38
Table 8: Political Instability (PI3), Institutions (tax ratio), and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.125***
(0.001)
0.073**
(0.067)
0.107***
(0.004)
0.228***
(0.000)
0.063*
(0.073)
0.186***
(0.000)
Initial GDP per Capitaa -0.008*
(0.051)
-0.003
(0.502)
-0.008*
(0.058)
-0.16***
(0.000)
-0.006
(0.160)
-0.013***
(0.001)
Average Years of Schoolinga 0.011**
(0.036)
0.009
(0.109)
0.012**
(0.014)
0.008**
(0.047)
0.012**
(0.033)
0.009**
(0.027)
Trade Opennessa 0.018***
(0.009)
0.002
(0.708)
Inflation -0.024***
(0.000)
-0.015***
(0.006)
Investmenta 0.027***
(0.000)
0.028***
(0.000)
Black Market Premiumb -0.012***
(0.007)
-0.003
(0.298)
Formal Institutionsa 0.008
(0.135)
0.000
(0.886)
0.003
(0.563)
0.000
(0.881)
0.009
(0.176)
0.001
(0.809)
Lag Formal Institutionsa 0.009
(0.207)
0.003
(0.644)
0.005
(0.498)
0.006
(0.426)
0.001
(0.863)
-0.002
(0.755)
Political Instability -0.040**
(0.044)
-0.040*
(0.056)
-0.025
(0.248)
-0.037**
(0.041)
-0.018
(0.486)
-0.020
(0.361)
Interaction -0.017*
(0.087)
-0.017*
(0.088)
-0.009
(0.390)
-0.013
(0.131)
-0.005
(0.662)
-0.005
(0.611)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.772 0.807 0.350 0.970 0.798 0.796
Hansen Test (p-values) 0.791 0.982 0.988 0.976 0.998 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported here
39
Table 9: Political Instability (PI3), Institutions (CIM) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.105***
(0.001)
0.093***
(0.002)
0.100***
(0.000)
0.174***
(0.000)
0.054**
(0.029)
0.157***
(0.000)
Initial GDP per Capitaa -0.007**
(0.044)
-0.004
(0.210)
-0.008**
(0.017)
-0.011***
(0.005)
-0.002
(0.375)
-0.009***
(0.002)
Average Years of Schoolinga 0.008**
(0.011)
0.006*
(0.092)
0.103***
(0.005)
0.001
(0.597)
0.009**
(0.011)
0.006*
(0.070)
Trade Opennessa 0.015***
(0.004)
-0.002
(0.518)
Inflation -0.026***
(0.000)
-0.019***
(0.000)
Investmenta 0.021***
(0.004)
0.025***
(0.000)
Black Market Premiumb -0.008**
(0.035)
-0.003
(0.206)
Formal Institutionsa 0.038***
(0.000)
0.037***
(0.000)
0.036***
(0.000)
0.032***
(0.000)
0.019*
(0.072)
0.014**
(0.035)
Political Instability -0.006
(0.449)
-0.004
(0.607)
-0.005
(0.459)
-0.007
(0.340)
-0.005
(0.507)
-0.006
(0.317)
Interaction 0.004
(0.847)
0.001
(0.931)
-0.004
(0.802)
-0.004
(0.826)
0.009
(0.640)
0.004
(0.692)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.493 0.641 0.174 0.613 0.580 0.880
Hansen Test (p-values) 0.991 1.000 1.000 1.000 1.000 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported here
40
Table 10: Political Instability (PI3), Institutions (CIM) and Growth
Regressors (1) (2) (3) (4) (5) (6)
Constant 0.054*
(0.090)
0.044
(0.156)
0.065**
(0.014)
0.126***
(0.001)
0.030
(0.311)
0.154***
(0.000)
Initial GDP per Capitaa -0.003
(0.463)
0.000
(0.934)
-0.004
(0.161)
-0.006*
(0.075)
-0.000
(0.960)
-0.009***
(0.005)
Average Years of Schoolinga 0.008*
(0.071)
0.005
(0.255)
0.010**
(0.025)
0.002
(0.520)
0.008*
(0.093)
0.006
(0.101)
Trade Opennessa 0.016***
(0.003)
-0.003
(0.273)
Inflation -0.023***
(0.000)
-0.019***
(0.000)
Investmenta 0.019**
(0.013)
0.027***
(0.000)
Black Market Premiumb -0.006
(0.109)
-0.001
(0.453)
Formal Institutionsa 0.099***
(0.000)
0.094***
(0.000)
0.087***
(0.000)
0.086***
(0.000)
0.078***
(0.001)
0.062***
(0.003)
Lag Formal Institutionsa -0.062***
(0.009)
-0.058**
(0.012)
-0.055**
(0.016)
-0.057**
(0.008)
-0.051**
(0.043)
-0.048**
(0.032)
Political Instability -0.003
(0.890)
-0.004
(0.593)
-0.008
(0.321)
-0.010
(0.275)
-0.002
(0.839)
-0.009
(0.229)
Interaction -0.003
(0.890)
-0.009
(0.673)
-0.020
(0.288)
-0.016
(0.497)
0.015
(0.538)
-0.010
(0.588)
AB test for AR(1) in first diff
(p-values)
0.000 0.000 0.000 0.000 0.000 0.000
AB test for AR(2) in first diff
(p-values)
0.080 0.099 0.066 0.104 0.516 0.532
Hansen Test (p-values) 0.983 1.000 1.000 0.999 1.000 1.000
p-values in parentheses; *, **, *** indicate significance at the 10%, 5%, and 1% level.
a variable is included as log(variable); b variable is included as log(1+variable)
Time dummies are included in the regressions but are not reported here
41