How does political instability affect economic growth?
ABSTRACT The purpose of this paper is to empirically determine the effects of political instability on economic growth. By using the system-GMM estimator for linear dynamic panel data models on a sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher degrees of political instability are associated with lower growth rates of GDP per capita. Regarding the channels of transmission, we find that political instability adversely affects growth by lowering the rates of productivity growth and, to a smaller degree, physical and human capital accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while democracy may have a small negative effect.
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ABSTRACT: This article develops a framework for efficient IV estimators of random effects models with information in levels which can accommodate predetermined variables. Our formulation clarifies the relationship between the existing estimators and the role of transformations in panel data models. We characterize the valid transformations for relevant models and show that optimal estimators are invariant to the transformation used to remove individual effects. We present an alternative transformation for models with predetermined instruments which preserves the orthogonality among the errors. Finally, we consider models with predetermined variables that have constant correlation with the effects and illustrate their importance with simulations.Journal of Econometrics. 09/1990;
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ABSTRACT: This paper focuses on the relationship between political instability, policy--making and macroeconomic outcomes. The theoretical section explores various models that explain the effect of instability (and political uncertainty) on growth, budget formation, inflation and monetary policy. The empirical section discusses the evidence on the predictions generated by theoretical models. Preliminary to this discussion, however, is the analysis of a few general issues concerning the specification and estimation of econometric models with political variables. Some new results are then produced on the empirical relevance of theories of strategic use of fiscal deficit. Copyright Blackwell Publishing Ltd. 2003Journal of Economic Surveys 02/2003; 17(1):1-54. · 1.33 Impact Factor
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ABSTRACT: Estimation of the dynamic error components model is considered using two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator. Both estimators require restrictions on the initial conditions process. Asymptotic efficiency comparisons and Monte Carlo simulations for the simple AR(1) model demonstrate the dramatic improvement in performance of the proposed estimators compared to the usual first-differenced GMM estimator, and compared to non-linear GMM. The importance of these results is illustrated in an application to the estimation of a labour demand model using company panel data.Journal of Econometrics. 11/1998;
European Journal of Political Economy
How does political instability affect economic growth?
Ari Aisen a,* , Francisco José Veiga b,**
a International Monetary Fund, Middle Eastern and Central Asian Department, 700 19th Street
NW, Washington, DC 20431, United States. E-mail address: AAisen@imf.org
b Universidade do Minho and NIPE, Escola de Economia e Gestão, 4710-057 Braga, Portugal.
E-mail address: firstname.lastname@example.org
The purpose of this paper is to empirically determine the effects of political instability on
economic growth. Using the system-GMM estimator for linear dynamic panel data models on a
sample covering up to 169 countries, and 5-year periods from 1960 to 2004, we find that higher
degrees of political instability are associated with lower growth rates of GDP per capita.
Regarding the channels of transmission, we find that political instability adversely affects growth
by lowering the rates of productivity growth and, to a smaller degree, physical and human capital
accumulation. Finally, economic freedom and ethnic homogeneity are beneficial to growth, while
democracy may have a small negative effect.
Keywords: Economic growth; political instability; growth accounting; productivity.
JEL codes: O43, O47
* The views expressed in this paper are those of the authors and do not necessarily represent those of the
International Monetary Fund.
** Corresponding author. Tel.: +351 253604534; fax: +351 253601380.
Political instability is regarded by economists as a serious malaise harmful to economic
performance. Political instability is likely to shorten policymakers’ horizons leading to sub-
optimal macroeconomic policies. It may also lead to a more frequent switch of policies, creating
volatility and thus, negatively affecting macroeconomic performance. Considering its damaging
repercussions on economic performance the extent at which political instability is pervasive
across countries and time is quite surprising. Measuring political instability by Cabinet Changes,
that is, the number of times in a year in which a new premier is named and/or 50% or more of the
cabinet posts are occupied by new ministers, figures speak for themselves. In Africa, for instance,
there was on average a cabinet change once every two years in the period 2000-2003. Though
extremely high, this number is a major improvement relative to previous years when there were,
on average, two cabinet changes every three years. While Africa is the most politically unstable
region of the world, it is by no means alone; as similar trends are observed in other regions (see
The widespread phenomenon of political (and policy) instability in several countries
across time and its negative effects on their economic performance has arisen the interest of
several economists. As such, the profession has produced an ample literature documenting the
negative effects of political instability on a wide range of macroeconomic variables including,
among others, GDP growth, private investment, taxation, public expenditures and investment,
debt and inflation. Brunetti (1997) comprehensively surveys and summarizes the main political
variables affecting economic growth, concluding that, among several variables, measures of
policy volatility and subjective perception of politics are most successful in cross-country growth
regressions, while democracy is the least successful.1 Alesina et al. (1996) use data on 113
1 Carmignani (2003) is another survey of theoretical and empirical models studying the relationship between political
instability, policy-making and macroeconomic outcomes.
countries from 1950 to 1982 to show that GDP growth is significantly lower in countries and
time periods with a high propensity of government collapse. Chen and Feng (1996) show that
regime instability, political polarization and government repression all have a negative impact on
economic growth. In a more recent paper, Jong-a-Pin (2009) uses factor analysis to examine to
effect of 25 political instability indicators and their effect on economic growth. The main finding
is that higher degrees of instability of the political regime lead to lower economic growth.2 As
regards to private investment, Alesina and Perotti (1996) show that socio-political instability
generates an uncertain politico-economic environment, raising risks and reducing investment.3
Political instability leads to higher shares of government spending in GDP (Devereux and Wen,
1998) and political uncertainty in OECD countries tends to reduce public investment (Darby et
al., 2004). Political instability also leads to greater reliance on seigniorage revenues and to higher
inflation as shown in Aisen and Veiga (2006, 2008). Quite interestingly, the mechanisms at work
to explain inflation in their papers resemble those affecting economic growth; namely that
political instability shortens the horizons of governments, disrupting long term economic policies
conducive to a better economic performance.
This paper revisits the relationship between political instability and GDP growth. This is
because we believe that, so far, the profession has been unable to tackle some fundamental
questions behind the negative relationship between political instability and GDP growth. What
are the main transmission channels from political instability to economic growth? How
quantitatively important are the effects of political instability on the main drivers of growth,
namely, total factor productivity and physical and human capital accumulation? This paper
2 A dissenting view is presented by Campos and Nugent (2002), who find no evidence of a causal and negative long-
run relation between political instability and economic growth. They only find evidence of a short-run effect.
3 For a theoretical model linking political instability and investment, see Rodrik (1991). Perotti (1996) also finds that
socio-political instability adversely affects growth and investment. Svenson (1998) and, to a lesser extent, Aron
(2000) show that political instability affects the quality of property rights and institutions, which in turn have an
impact on private investment. Feng (2001) finds that policy uncertainty, measured by the variability of government
capacity, adversely affects private investment,
addresses these important questions providing estimates from panel data regressions using
system-GMM4 on a dataset of up to 169 countries for the period 1960 to 2004. Our results are
strikingly conclusive: in line with results previously documented, political instability reduces
GDP growth rates significantly. An additional cabinet change per year (a new premier is named
and/or 50% of cabinet posts are occupied by new ministers) reduces the annual real GDP per
capita growth rate by 2.39 percentage points. This reduction is mainly due to the negative effects
of political instability on total factor productivity growth, which account for more than half of the
effects on GDP growth. Political instability also affects growth through physical and human
capital accumulation, with the former having a slightly larger effect than the latter. These results
go a long way to clearly understand why political instability is harmful to economic growth. It
suggests that countries need to address political instability, dealing with its root causes and
attempting to mitigate its effects on the quality and sustainability of economic policies
engendering economic growth.
The paper continues as follows: section 2 describes the dataset and presents the empirical
methodology, section 3 discusses the empirical results, and section 4 concludes the paper.
2. Data and the empirical model
Annual data on economic, political and institutional variables, from 1960 to 2004 were
gathered for 209 countries, but missing values for several variables reduce the number of
countries in the estimations to at most 169, which cover all regions of the world. The sources of
economic data were the Penn World Table Version 6.2 – PWT (Heston et al., 2006), the World
Bank’s World Development Indicators (WDI) and Global Development Network Growth
4 System-GMM is a useful methodology to estimate the effects of political instability on growth since it proposes a
clear-cut solution to the endogeneity problem involving these two variables. Using own lagged values as instruments
for contemporaneous political instability, this econometric method allows for the calculation of the causal effect of
political instability on growth independent of the feedback effect of growth on political instability.
Database (GDN), and the International Monetary Fund’s International Financial Statistics (IFS).
Political and institutional data were obtained from the Cross National Time Series Data Archive –
CNTS (Databanks International, 2009), the Polity IV Database (Marshall and Jaggers, 2009), the
State Failure Task Force database (SFTF), and Gwartney and Lawson (2009).
The hypothesis that political instability and other political and institutional variables
affect economic growth is tested by estimating dynamic panel data models for GDP per capita
growth (taken from the PWT) for consecutive, non-overlapping, 5-year periods, from 1960 to
2004.5 Our baseline model includes the explanatory variables common to most growth
regressions found in the literature and a proxy for political instability (all except Initial GDP per
capita are averaged over each 5-year period):
Initial GDP per capita (log) (PWT): log of real GDP per capita lagged by one 5-year period.
A negative coefficient is expected, indicating the existence of conditional convergence among
Investment (% GDP) (PWT). A positive coefficient is expected, as greater investment shares
have been shown to be positively related with economic growth (Mankiw et al., 1992);
Primary school enrollment (WDI). Greater enrollment ratios lead to greater human capital,
which should be positively related to economic growth (Gemmel, 1996).
Population growth (PWT). All else remaining the same, greater population growth leads to
lower GDP per capita growth. Thus, a negative coefficient is expected;
Trade openness (PWT). Assuming that openness to international trade is beneficial to
economic growth, a positive coefficient is expected.
Cabinet changes (CNTS). Number of times in a year in which a new premier is named and/or
50% of the cabinet posts are occupied by new ministers. This variable is our main proxy of
5 The periods are: 1960-64, 1965-69, 1970-74, 1975-79, 1980-84, 1985-89, 1990-94, 1995-99, and 2000-04.
political instability.6 It is related with regime instability, as regime transitions/changes will
surely lead to changes of the premier and of most, if not all, cabinet posts. According to
Jong-a-Pin (2009), regime instability leads to lower rates of economic growth.7 Cabinet
changes can also be seen as an indicator of instability within the regime, or government
instability.8 Since the variable Cabinet Changes is associated with two dimensions of
political instability closely related to policy uncertainty, we believe that it is the best proxy
available for political instability. A negative coefficient is expected, as greater instability of
the regime, or within the regime, would lead to greater uncertainty concerning future
economic policies and, consequently, to lower economic growth.
In order to account for the effects of macroeconomic stability on economic growth, two
additional variables will be added to the model:9
Inflation rate (IFS).10 A negative coefficient is expected, as high inflation has been found to
negatively affect growth. See, among others, Edison et al. (2002) and Elder (2004);
Government (%GDP) (PWT). An excessively large government is expected to crowd out
resources from the private sector and be harmful to economic growth. Thus, a negative
coefficient is expected.
The extended model will also include the following institutional variables: 11
6 Aisen and Veiga (2008) also used Cabinet Changes as the main proxy for political instability. In Aisen and Veiga
(2006), the main proxies for political instability are Cabinet Changes and Government Crises. The latter will be used
below in the construction of indexes for instability within the regime.
7 Cabinet changes has the second largest factor loading in the Regime dimension of the factor analysis performed by
8 In the factor analysis performed by Klomp and de Haan (2009), Cabinet Changes has relatively high factor
loadings in the dimensions of Regime Instability and Government Instability. Both dimensions are found to lead to
increases of growth volatility.
9 Here, we follow Levine et al. (2000), who accounted for macroeconomic stability in a growth regression by
including the inflation rate and the size of government.
10 In order to avoid heteroskedasticity problems resulting from the high variability of inflation rates, Inflation was
defined as log(1+Inf/100).
11 There is an extensive literature on the effects of institutions on economic growth. See, among others, Acemoglu et
al. (2001), Acemoglu et al. (2003), de Hann (2007), Glaeser et al. (2004), and Mauro (1995). The Ethnic
Homogeneity Index and the Polity Scale are those used in Aisen and Veiga (2008). Both were found to be negatively
related to governments’ reliance on seigniorage revenues.
Index of Economic Freedom (Gwartney and Lawson, 2009). Higher indexes are associated
with smaller governments (Area 1), stronger legal structure and security of property rights
(Area 2), access to sound money (Area 3), greater freedom to exchange with foreigners (Area
4), and more flexible regulations of credit, labor, and business (Area 5). According to the
survey of de Haan et al. (2006), which focuses on the empirical studies using this economic
freedom indicator of the Fraser Institute, greater economic freedom stimulates economic
growth. Thus, a positive coefficient is expected;
Ethnic Homogeneity Index (SFTF): ranges from 0 to 1, with higher values indicating ethnic
homogeneity, and equals the sum of the squared population fractions of the seven largest
ethnic groups in a country. For each period, it takes the value of the index in the beginning of
the respective decade. According to Easterly, et al. (2006), “social cohesion” determines the
quality of institutions, which affects whether pro-growth policies are implemented or not.
Since higher ethnic homogeneity implies greater social cohesion, which should result in good
institutions and pro-growth policies, a positive coefficient is expected.12
Polity Scale (Polity IV): from strongly autocratic (-10) to strongly democratic (10). This
variable is our proxy for democracy. According to Barro (1996) and Tavares and Wacziarg
(2001), a negative coefficient is expected.13
Descriptive statistics of the variables included in the tables of results are shown in Table 1.
-- Insert Table 1 about here --
The empirical methodology follows that used by Levine et al. (2000), which is now
common practice in the growth literature. Concretely, we use the system-GMM dynamic panel
estimator, which addresses the econometric problems induced by unobserved country-specific
12 See Benhabib and Rusticini (1996) for a theoretical model relating social conflict and growth.
13 On the relationship between democracy and growth, see also Acemoglu, et al. (2008).
effects and joint endogeneity of the explanatory variables. The model for economic growth can
be summarized as follows:
ittiitt i it t i t iit
where Yit stands for the GDP per capita of country i at the end of period t, Xit for a vector of
economic determinants of economic growth, PIit for a proxy of political instability, and Wit for a
vector of political and institutional determinants of economic growth; α, β, δ, and λ are the
parameters and vectors of parameters to be estimated, i are country-specific effects, t are period
specific effects, and, it is the error term. With
, equation (1) becomes:
One problem of estimating this dynamic model using OLS is that Yi,t-1 (the lagged
dependent variable) is endogenous to the fixed effects (νi), which gives rise to “dynamic panel
bias” (the so-called “Nickel bias”). Thus, OLS estimates of this baseline model will be
inconsistent, even in the fixed or random effects settings, because Yi,t-1 would be correlated with
the error term, it, even if the latter is not serially correlated.14 If the number of time periods
available (T) were large, the bias would become very small and the problem would disappear.
But, since our sample has only 9 non-overlapping 5-year periods, the bias may still be
important.15 First-differencing Equation (2) removes the individual effects (i) and thus
eliminates this potential source of bias:
14 See Arellano and Bond (1991) and Baltagi (2008).
15 According to the simulations performed by Judson and Owen (1999), there is still a bias of 20% in the coefficient
of interest for T=30.
But, when variables that are not strictly exogenous are first-differenced, they become
endogenous, since the first difference will be correlated with the error term. Furthermore, the use
of instruments will also be necessary to deal with the potential endogeneity of the explanatory
variables. Following Holtz-Eakin et al. (1988), Arellano and Bond (1991) developed a
Generalized Method of Moments (GMM) estimator for linear dynamic panel data models that
solves this problem by instrumenting the differenced predetermined and endogenous variables
with their available lags in levels: levels of the dependent and endogenous variables, lagged two
or more periods; levels of the pre-determined variables, lagged one or more periods. The
exogenous variables can be used as their own instruments.
As argued by Levine at al. (2000), there are conceptual and statistical shortcomings with
this difference-GMM estimator. First, conceptually, we would also like to analyze the cross-
country relationship between GDP growth and the explanatory variables, which is eliminated in
the difference estimator. Second, a statistical problem of this difference-GMM estimator is that
lagged levels of the explanatory variables are weak instruments for first-differences if the series
are persistent over time (Blundell and Bond, 1998). The results of Monte Carlo experiments in
small samples indicate that weak instruments may produce biased coefficients.
According to Arellano and Bover (1995), efficiency can be increased by adding the
original equation in levels to the system, that is, by using the system-GMM estimator. If the first-
differences of an explanatory variable are not correlated with the individual effects, lagged values
of the first-differences can be used as instruments in the equation in levels. Lagged differences of
the dependent variable may also be valid instruments for the levels equations.
The estimation of growth models using the difference-GMM estimator for linear panel
data was introduced by Caselli et al. (1996). Then, Levine et al. (2000) used the system-GMM
estimator16, which is now common practice in the literature (Durlauf et al., 2005; Beck, 2008).
Although several period lengths have been used, most studies work with non-overlapping 5-year
3. Empirical results
The empirical analysis is divided into two parts. First, we test the hypothesis that political
instability has negative effects on economic growth, by estimating regressions for GDP per capita
growth. As described above, the effects of institutional variables will also be analyzed. Then, the
second part of the empirical analysis studies the channels of transmission. Concretely, we test the
hypothesis that political instability adversely affects output growth by reducing the rates of
productivity growth and of physical and human capital accumulation.
3.1. Political instability and economic growth
The results of system-GMM estimations on real GDP per capita growth using a sample
comprising 169 countries, and 9 consecutive and non-overlapping 5-year periods from 1960 to
2004, are shown in Table 2. Since low economic growth may increase government instability
(Alesina et al., 1996), our proxy for political instability, Cabinet changes, will be treated as
endogenous. In fact, all the other explanatory variables can also be affected by economic growth.
Thus, we follow the common procedure of treating all right-hand side variables (except the time
dummies) as endogenous in every regression.
16 For a detailed discussion on the conditions under which system-GMM is suitable for estimating growth
regressions, see Bond et al. (2001). Since GMM models can be quite sensitive to the set of instruments and lag length
chosen, it may be useful to compare them with the simple fixed effects results, as the pattern of statistical
significance of the coefficients tends to be similar if there are no specification problems concerning the system-
GMM. Since the results of our fixed effects estimations (not shown here, but available upon request) are quite
similar to those reported in this paper, we believe that our system-GMM models are correctly specified.
When there are several endogenous variables in a model, the number of instruments can
easily become larger than the number of cross-sectional observations (countries). This over-
fitting of the data can bias t-statistics upwards. In order to avoid this problem, and having in mind
the fact that more distant lags are usually weak instruments, we use the smallest possible lag
length: twice lagged values of the dependent and of all explanatory variables are used in the first-
differenced equations and their once-lagged first-differences are used in the levels equation. This
procedure may sometimes go against finding statistically significant results, but avoids eventual
robustness problems resulting from “playing around” with the instrument set and the lag length.
Since Hansen tests never reject the validity of the instrument matrix and second order
autocorrelation is always rejected, it may be safe to assume that our results are valid.
Furthermore, Difference-in-Hansen tests never reject the validity of the subsets of instruments.17
-- Insert Table 2 about here --
The results of the estimation of the baseline model are presented in column 1. The
hypothesis that political instability negatively affects economic growth receives clear empirical
support. Cabinet Changes is highly statistically significant and has the expected negative sign.
The estimated coefficient implies that when there is an additional cabinet change per year, the
annual growth rate decreases by 2.39 percentage points. Most of the results regarding the other
explanatory variables also conform to our expectations. Initial GDP per capita has a negative
coefficient, which is consistent with conditional income convergence across countries.
Investment and enrollment ratios18 have positive and statistically significant coefficients,
indicating that greater investment and education promote growth. Finally, population growth has
17 In order to economize space, the p-values for the Difference-in-Hansen tests are shown only in Tables 2 and 3. The
values for the other tables are available from the authors upon request.
18 The results are virtually the same when secondary enrollment is used instead of primary enrollment. Since we have
more observations for the latter, we opted to include it in the estimations reported in this paper.
the expected negative coefficient, and Trade (% GDP) has the expected sign, but is not
The results of an extended model which includes proxies for macroeconomic stability are
reported in column 2 of Table 2. Most of the results are similar to those of column 1. The main
difference is that Trade (% GDP) is now statistically significant, which is consistent with a
positive effect of trade openness on growth. Regarding macroeconomic stability, inflation and
government size have the expected signs, but only the first is statistically significant.
The Index of Economic Freedom19 is included in the model of column 3 in order to
account for favorable economic institutions. It is statistically significant and has a positive sign,
as expected. A one-point increase in that index increases annual economic growth by one
percentage point. Trade (% GDP) and Inflation are no longer statistically significant. This is not
surprising because the Index of Economic Freedom is composed of five areas, some of which are
related to explanatory variables included in the model: size of government (Area 1), access to
sound money (Area 3), and greater freedom to exchange with foreigners (Area 4). In order to
avoid potential collinearity problems, the variables Trade (% GDP), Inflation, and Government
(% GDP) are not included in the estimation of column 4. The results regarding the Index of
Economic Freedom and Cabinet Changes remain essentially the same.
An efficient legal structure and secure property rights have been emphasized in the
literature as crucial factors for encouraging investment and growth (Glaeser et al., 2004; Hall and
Jones, 1999; La-Porta et al., 1997). The results shown in column 5, where the Index of Economic
Freedom is replaced by its Area 2, Legal structure and security of property rights, are consistent
with the findings of previous studies.20
19 Since data for the Index of Economic Freedom is available only from 1970 onwards, the sample is restricted to
1970 to 2004 when this variable is included in the model.
20 Since Investment (%GDP) is included as an explanatory variable, the Area 2 will also affect GDP growth through
it. Thus, the coefficient reported for Area 2 should be interpreted as the direct effect on growth, when controlling for
In the estimations whose results are reported in Table 3, we also account for the effects of
democracy and social cohesion, by including the Polity Scale and the Ethnic Homogeneity Index
in the model. There is weak evidence that democracy has small adverse effects on growth, as the
Polity Scale has a negative coefficient, small in absolute value, which is statistically significant
only in the estimations of columns 1 and 3. These results are consistent with those of Barro
(1996) and Tavares and Wacziarg (2001)21. As expected, higher ethnic homogeneity (social
cohesion) is favorable to economic growth, although the index is not statistically significant in
column 4. The results regarding the effects of political instability, economic freedom, and
security of property rights are similar to those found in the estimations of Table 2. The most
important conclusion that we can draw from these results is that the evidence regarding the
negative effects of political instability on growth are robust to the inclusion of institutional
-- Insert Table 3 about here --
Considering that political instability is a complex phenomenon that may not be well
captured by just the variable Cabinet Changes, we constructed six alternative indexes of political
instability by applying principal components analysis.22 The first two indexes include variables
that are associated with regime instability, the next two are related with instability within the
the indirect effect through investment. This direct effect could operate through channels such as total factor
productivity and human capital accumulation.
21 Tavares and Wacziarg (2001) justify the negative effect of democracy on growth as the net contribution of
democracy to lowering income inequality and expanding access of education to the poor (positive) at the expense of
physical capital accumulation (negative).
22 This technique for data reduction describes linear combinations of the variables that contain most of the
information. It analyses the correlation matrix, and the variables are standardized to have mean zero and standard
deviation of 1 at the outset. Then, for each of the five groups of variables, the first component identified, the linear
combination with greater explanatory power, was used as the political instability index.
regime, and the last two are composed of violence indicators. The variables (all from the CNTS,
except when indicated in parenthesis) used to define each index were:23
o Regime Instability Index 1: Constitutional Changes, Coups, and Cabinet Changes.
o Regime Instability Index 2: Constitutional Changes, Coups, Cabinet Changes,
Executive Changes, and Regime Crisis Indicator (SFTF).
o Within Regime Instability Index 1: Number of Legislative Elections, Fragmentation
Index, and Government Crises.
o Within Regime Instability Index 2: Number of Legislative Elections, Fragmentation
Index, Government Crises, Executive Changes, and Cabinet Changes.
o Violence Index 1: Assassinations and Revolutions.
o Violence Index 2: Assassinations, Revolutions, Revolutionary War Indicator (SFTF),
Ethnic Wars Indicator (SFTF), and Genocides/Politicides Indicator (SFTF).
The results of the estimation of the model of column 1 of Table 3 using the above-
described indexes are reported in Table 4. While all indexes have the expected negative signs, the
Violence Indexes are not statistically significant.24 Thus, we conclude that regime instability and
within regime instability adversely affect economic growth. Klomp and de Haan (2009) reach a
similar conclusion regarding growth volatility, while the results of Jong-a-Pin (2009) indicate that
it is essentially regime instability that adversely affects economic growth.
-- Insert Table 4 about here --
It is possible that political instability takes different forms in different types of regimes.
This would imply that the proxies used in this paper for political instability may have different
23 The factor analyses of Jong-a-Pin (2009) and Klomp and de Haan (2009) were used as guides to the construction
of the political instability indexes. Concretely, we looked for the variables in our database that had the higher factor
loadings in each of the dimensions of political instability identified in the above-referred studies.
24 The results for these 6 indexes are essentially the same when we include them in other models of Table 3 or in the
models of Table 2. The same is true for indexes constructed using alternative combinations of the CNTS variables.
These results are not shown here, but are available from the authors upon request.
effects on economic growth in democracies and in non-democracies. In order to take that
possibility into account, each proxy was interacted with a dummy variable for democracies and a
dummy for autocracies.25 Thus, in the estimations of Table 5, there is an estimated coefficient for
the effect of each proxy under each regime. Although there are small differences in the size of the
coefficients, we are never able to reject the hypothesis that the coefficients on the interactions
with the proxies of political instability are equal. Thus, we do not find evidence of different
effects under different types of regimes.
-- Insert Table 5 about here --
Several robustness tests were performed in order to check if the empirical support found
for the adverse effects of political instability on economic growth remains when using restricted
samples or alternative period lengths. Table 6 reports the estimated coefficients and t-statistics
obtained for the proxies of political instability when the models of column 1 of Table 3 (for
Cabinet Changes) and of columns 1, 2 and 4 of Table 4 (the two three regime instability indexes
and the second within regime index) are estimated using seven alternative restricted samples.26
The first restricted sample (column 1 of Table 6) includes only developing countries, and the next
four (columns 2 to 5) exclude one continent at a time.27 Finally, in the estimation of column 6,
data for the 1960s and the 1970s is excluded from the sample, while in column 7 the last 5-year
period (2000-2004) is excluded. Since Cabinet Changes and the three political instability indexes
are always statistically significant, we conclude that the negative effects of political instability on
real GDP per capita growth are robust to sample restrictions.
-- Insert Table 6 about here --
25 The dummy variable Democracy takes the value of one when the Polity Scale is greater or equal to zero, and takes
the value of zero otherwise. Autocracy = 1 – Democracy.
26 The complete results of the 28 estimations of Table 6 and of the 16 estimations of Table 7 are available from the
authors upon request. Since reporting the results for all political instability indexes would be too cumbersome, from
now on, we only shown the results for the three indexes that were statistically significant in Tables 4 and 5. The
results for the remaining three indexes are available from the authors upon request.
27 The proxies of political instability were interacted with regional dummy variables in order to test for regional
differences in the effects of political instability on growth. No evidence of such differences was found.