Electronic copy available at: http://ssrn.com/abstract=1560524
“ “H Ho ow w d do oe es s p po ol li it ti ic ca al l i in ns st ta ab bi il li it ty y a af ff fe ec ct t e ec co on no om mi ic c
g gr ro ow wt th h? ?” ”
A Ar ri i A Ai is se en n
F Fr ra an nc ci is sc co o J Jo os sé é V Ve ei ig ga a
NIPE WP 5/ 2010
Electronic copy available at: http://ssrn.com/abstract=1560524
“ “H Ho ow w d do oe es s p po ol li it ti ic ca al l i in ns st ta ab bi il li it ty y a af ff fe ec ct t e ec co on no om mi ic c g gr ro ow wt th h? ?” ”
A Ar ri i A Ai is se en n
F Fr ra an nc ci is sc co o J Jo os sé é V Ve ei ig ga a
N NI IP PE E* * W
WP P 5 5/ / 2 20 01 10 0
* NIPE – Núcleo de Investigação em Políticas Económicas – is supported by the Portuguese Foundation for
Science and Technology through the Programa Operacional Ciência, Teconologia e Inovação (POCI 2010) of the
Quadro Comunitário de Apoio III, which is financed by FEDER and Portuguese funds.
* The authors wish to thank Luísa Benta for excellent research assistance.
** The views expressed in this paper are those of the authors and do not necessarily represent those of the Central
Bank of Chile and the International Monetary Fund.
How does political instability affect economic growth?*
Central Bank of Chile and International Monetary Fund
Francisco José Veiga
Universidade do Minho and NIPE
Escola de Economia e Gestão
4710-057 Braga, Portugal
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
1 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.
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 short term 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 Figure 1).
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 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, and inflation. Alesina et al. (1996) use data on
113 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. In a more recent paper, Jong-a-Pin
(2009) also finds that higher degrees of political instability lead to lower economic growth.1 As
regards to private investment, Alesina and Perotti (1996) show that socio-political instability
2 Perotti (1996) also finds that socio-political instability adversely affects growth and investment. For a theoretical
model linking political instability and investment, see Rodrik (1991).
generates an uncertain politico-economic environment, raising risks and reducing investment.2
Political instability also leads to higher inflation as shown in Aisen and Veiga (2006). Quite
interestingly, the mechanisms at work to explain inflation in their paper 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 was 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 addresses these important
questions providing estimates from panel data regressions using system-GMM 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 (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.
3 The periods are: 1960-64, 1965-69, 1970-74, 1975-79, 1980-84, 1985-89, 1990-94, 1995-99, and 2000-04.
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. 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 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,
2007), the Polity IV Database (Marshall and Jaggers, 2005), the State Failure Task Force
database (SFTF), and Gwartney and Lawson (2007).
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
2004.3 Our baseline model includes the following explanatory variables (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 positive coefficient, smaller than 1, is expected, indicating the existence of conditional
convergence among countries;
• 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);
4 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.
5 In order to avoid heteroskedasticity problems resulting from the high variability of inflation rates, Inflation was
defined as log(1+Inf/100)
6 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).
• Primary school enrollment (WDI). Greater enrollment ratios lead to greater human capital,
which should be positively related to economic growth. A positive coefficient is expected;
• 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
political instability. It is essentially an indicator of regime instability, which has been found
to be associated with lower economic growth (Jong-a-Pin, 2009). A negative coefficient is
expected, as greater political (regime) instability leads 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:4
• Inflation rate (IFS).5 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:6
• Index of Economic Freedom (Gwartney and Lawson, 2007). 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). Since all of these are
favorable to economic growth, 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 has important impacts on 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.7
• 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.8
Descriptive statistics of the variables included in the tables of results are shown in Table 1.
-- Insert Table 1 about here --
The empirical model for economic growth can be summarized as follows:
itti it t iit 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
7 See Benhabib and Rusticini (1996) for a theoretical model relating social conflict and growth.
8 On the relationship between democracy and growth, see also Acemoglu, et al. (2008).
parameters and vectors of parameters to be estimated, νi are country-specific effects, μt are period
specific effects, and, εit is the error term. With
, equation (1) becomes:
it t i
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”. 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, εit, even if the latter
is not serially correlated.9 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.10 First-differencing Equation (2)
removes the individual effects (νi) and thus eliminates a potential source of bias:
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. Following Holtz-
Eakin, Newey and Rosen (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
9 See Arellano and Bond (1991) and Baltagi (2008).
10 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.
11 For a detailed discussion on the conditions under which GMM is suitable for estimating growth regressions, see
Bond et al. (2001).
the pre-determined variables, lagged one or more periods. The exogenous variables can be used
as their own instruments.
A problem of this difference-GMM estimator is that lagged levels are weak instruments
for first-differences if the series are very persistent (see Blundell and Bond, 1998). 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
estimator11, which is now common practice in the literature (see Durlauf, et al., 2005, and Beck,
2008). Although several period lengths have been used, most studies work with non-overlapping
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.
12 Their twice lagged values were used as instruments in the first-differenced equations and their once-lagged first-
differences were used in the levels equation.
13 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.
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, most of the other explanatory variables can also be affected by economic
growth. Thus, it is more appropriate to treat all right-hand side variables as endogenous.12
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 ratios13 have positive and statistically significant coefficients,
indicating that greater investment and education promote growth. Finally, population growth has
the expected negative coefficient, and Trade (% GDP) has the expected sign, but is not
-- Insert Table 2 about here --
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
14 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.
15 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
the indirect effect through investment. This direct effect could operate through channels such as total factor
productivity and human capital accumulation.
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 Freedom14 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.15
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
16 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.
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). 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 withdraw 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 multi-dimensional phenomenon, eventually not
well captured by just one variable (Cabinet Changes), we constructed five alternative indexes of
political instability by applying principal components analysis.16 The first three indexes include
variables that are associated with regime instability, the fourth has violence indicators, and the
fifth combines regime instability and violence indicators. The variables (all from the CNTS) used
to define each index were:
o Regime Instability Index 1: Cabinet Changes and Executive Changes.
o Regime Instability Index 2: Cabinet Changes, Constitutional Changes, Coups,
Executive Changes, and Government Crises.
o Regime Instability Index 3: Cabinet Changes, Constitutional Changes, Coups,
Executive Changes, Government Crises, Number of Legislative Elections, and
17 The results for these 5 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.
18 The complete results of the 28 estimations of Table 5 and of the 16 estimations of Table 6 are available from the
authors upon request.
19 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.
o Violence Index: Assassinations, Coups, and Revolutions.
o Political Instability Index: Assassinations, Cabinet Changes, Constitutional Changes,
Coups, and Revolutions.
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 Index is not statistically significant.17 Thus, we conclude that it is regime instability that
more adversely affects economic growth. Jong-a-Pin (2009) and Klomp and de Haan (2009)
reach a similar conclusion.
-- Insert Table 4 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 5 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 to 3 of Table 4 (for the three regime instability indexes) are
estimated using seven alternative restricted samples.18 The first restricted sample (column 1 of
Table 5) includes only developing countries, and the next four (columns 2 to 5) exclude one
continent at a time.19 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 regime 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.
13 Download full-text
-- Insert Table 5 about here --
The results of robustness tests for alternative period lengths are reported in Table 6. The
models of column 1 of Table 3 (for Cabinet Changes) and of columns 1 to 3 of Table 4 (for the
three regime instability indexes) were estimated using consecutive, non-overlapping periods of 4,
6, 8 and 10 years. Again, all estimated coefficients are statistically significant, with a negative
sign, providing further empirical support for the hypothesis that political instability adversely
affects economic growth.
-- Insert Table 6 about here –
3.2. Channels of transmission
In this section, we study the channels through which political instability affects economic
growth. Since political instability is associated with greater uncertainty regarding future
economic policy, it is likely to adversely affect investment and, consequently, physical capital
accumulation. In fact, several studies have identified a negative relation between political
instability and investment (Alesina and Perotti, 1996; Mauro, 1985; Özler and Rodrik, 1992;
Perotti, 1996). Instead of estimating an investment equation, we will construct the series on the
stock of physical capital, using the perpetual inventory method, and estimate equations for the
growth of the capital stock. That is, we will analyze the effects of political instability and
institutions on physical capital accumulation.
It is also possible that political instability adversely affects productivity. By increasing
uncertainty about the future, it may lead to less efficient resource allocation. Additionally, it may
reduce research and development efforts by firms and governments, leading to slower
technological progress. Violence, civil unrest, and strikes, can also interfere with the normal
operation of firms and markets, reduce hours worked, and even lead to the destruction of some