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How Does Political Instability Affect Economic Growth?

Author(s):
Ari Aisen, and Francisco José Veiga
Published Date:
January 2011
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I. Introduction

Political instability is regarded by economists as a serious malaise harmful to economic performance. Political instability is likely to shorten policymakers’ horizons leading to suboptimal 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. Political instability as measured by Cabinet Changes, that is, the number of times in a year in which a new premier is named and/or 50 percent or more of the cabinet posts are occupied by new ministers, is indeed globally widespread displaying remarkable regional differences (see Figure 1).

Figure 1.Political Instability Across the World

Source: CNTS (Databanks International, 2007).

Notes: - Five-year averages of the variable Cabinet Changes computed using a sample of yearly data for 209 countries.

   - Cabinet Changes is defined as the number of times in a year in which a new premier is named and/or 50 percent of the cabinet posts are occupied by new ministers.

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 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-GMM3 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 percent 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 II describes the dataset and presents the empirical methodology, section III discusses the empirical results, and section IV concludes the paper.

II. 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, nonoverlapping, five-year periods, from 1960 to 2004.4 Our baseline model includes the following explanatory variables (all except Initial GDP per capita are averaged over each five-year period):

  • Initial GDP per capita (log) (PWT): log of real GDP per capita lagged by one five-year period. A negative coefficient is expected, indicating the existence of conditional convergence among countries.

  • Investment (percent of 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. 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 percent 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:5

  • Inflation rate (IFS).6 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 (percent of 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:7

  • 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.8

  • 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.9

Descriptive statistics of the variables included in the tables of results are shown in Table 1.

Table 1.Descriptive Statistics
VariableObs.MeanSt. Dev.Min.Max.Source
Growth of GDP per capita10980.0160.037-0.3440.347PWT
GDP per capita (log)11978.3151.1585.14411.346PWT
Growth of Physical Capital10820.0280.042-0.1220.463PWT
Physical Capital per capita (log)11748.5631.6274.24411.718PWT
Growth of TFP7030.0000.048-0.5090.292PWT, BL
TFP (log)8088.6320.7635.01012.074PWT, BL
Growth of Human Capital7070.0120.012-0.0270.080BL
Human Capital per capita (log)812-0.3080.393-1.2530.597BL
Investment (percent of GDP)128714.4748.9481.02491.964PWT
Primary School Enrollment128688.50927.7943.000149.240WDI-WB
Population Growth15210.0970.071-0.2810.732PWT
Trade (percent of GDP)128772.52745.2692.015387.423PWT
Government (percent of GDP)128722.16410.5222.55279.566PWT
Inflation [=ln(1+Inf/100)]10800.1560.363-0.0564.178IFS-IMF
Cabinet Changes13220.0440.3580.0002.750CNTS
Regime Instability Index 11302-0.0330.879-0.8948.018CNTS-PCA
Regime Instability Index 21287-0.0140.892-1.0587.806CNTS-PCA
Regime Instability Index 31322-0.0380.684-0.8136.040CNTS-PCA
Violence Index1306-0.0040.786-0.4354.712CNTS-PCA
Political Instability Index1302-0.0040.887-0.7776.557CNTS-PCA
Index of Economic Freedom6795.6821.2082.0048.714EFW
Area 2: Legal Structure and

Security of Property Rights
6465.4241.8461.2719.363EFW
Polity Scale11940.2397.391-10.00010.000Polity IV
Ethnic Homogeneity Index11290.5830.2770.1501.000SFTF
Sources:BL: Updated version of Barro and Lee (2001).CNTS: Cross-National Time Series database (Databanks International, 2007).CNTS-PCA: Data generated by Principal Components Analysis using variables from CNTS.EFW: Economic Freedom of the World (Gwartney and Lawson, 2007).IFS-IMF: International Financial Statistics - International Monetary Fund.Polity IV: Polity IV database (Marshall and Jaggers, 2005).PWT: Penn World Table Version 6.2 (Heston et al., 2006).SFTF: State Failure Task Force database.WDI-WB: World Development Indicators–World Bank.Notes: Sample of consecutive, non-overlapping, five-year periods from 1960 to 2004, comprising the 169 countries considered in the baseline regression, whose results are shown in column 1 of Table 2.
Sources:BL: Updated version of Barro and Lee (2001).CNTS: Cross-National Time Series database (Databanks International, 2007).CNTS-PCA: Data generated by Principal Components Analysis using variables from CNTS.EFW: Economic Freedom of the World (Gwartney and Lawson, 2007).IFS-IMF: International Financial Statistics - International Monetary Fund.Polity IV: Polity IV database (Marshall and Jaggers, 2005).PWT: Penn World Table Version 6.2 (Heston et al., 2006).SFTF: State Failure Task Force database.WDI-WB: World Development Indicators–World Bank.Notes: Sample of consecutive, non-overlapping, five-year periods from 1960 to 2004, comprising the 169 countries considered in the baseline regression, whose results are shown in column 1 of Table 2.

The empirical model for economic growth can be summarized as follows:

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, vi are country-specific effects, μt are period specific effects, and, εit is the error term. With α = 1 + γ, 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”. 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.10 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 nine non-overlapping five-year periods, the bias may still be important.11 First-differencing Equation (2) removes the individual effects (vi) 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 the predetermined 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 estimator12, 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 nonoverlapping five-year periods.

III. 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 nine consecutive and non-overlapping five-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.13

Table 2.Political Instability and Economic Growth
(1)(2)(3)(4)(5)
Initial GDP per capita (log)-0.0087**

(-2.513)
-0.0125***

(-3.738)
-0.0177***

(-4.043)
-0.0181***

(-4.110)
-0.0157***

(-4.307)
Investment (percent of GDP)0.0009**

(2.185)
0.0008***

(2.649)
0.0007**

(2.141)
0.0012***

(2.908)
0.0014***

(3.898)
Primary School Enrollment0.0003***

(3.097)
0.0002*

(1.743)
0.0003

(1.616)
0.0001

(1.134)
0.0001

(0.756)
Population Growth-0.184***

(-3.412)
-0.273***

(-5.048)
-0.232***

(-4.123)
-0.271***

(-5.266)
-0.245***

(-5.056)
Trade (percent of GDP)6.70e-05

(0.957)
0.0001**

(2.344)
2.63e-05

(0.414)
-0.00003

(-0.683)
Inflation-0.0091***

(-2.837)
-0.0027

(-0.620)
-0.0081**

(-2.282)
Government (percent of GDP)-8.22e-05

(-0.229)
9.72e-06

(0.0302)
-0.0004

(-1.366)
Cabinet Changes-0.0239***

(-3.698)
-0.0164**

(-2.338)
-0.0200**

(-2.523)
-0.0244***

(-2.645)
-0.0158**

(-2.185)
Index of Economic Freedom0.0109***

(2.824)
0.0083**

(2.313)
Area2: Legal structure and

security of property rights
0.00360*

(1.681)
Number of Observations990851560588527
Number of Countries169152116120117
Hansen test (p-value)0.2290.3960.3660.1280.629
AR1 test (p-value)1.15e-069.73e-051.64e-052.71e-060.00002
AR2 test (p-value)0.5000.3650.6650.7450.491
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

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 ratios14 have positive and statistically significant coefficients, indicating that greater investment and education promote growth. Finally, population growth has the expected negative coefficient, and Trade (percent of GDP) has the expected sign, but is not statistically significant.

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 (percent of 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 Freedom15 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 (percent of 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 (percent of GDP), Inflation, and Government (percent of 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.16

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)17. 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 variables.

Table 3.Political Instability, Institutions, and Economic Growth
(1)(2)(3)(4)
Initial GDP per capita (log)-0.0216***

(-4.984)
-0.0237***

(-5.408)
-0.0188***

(-4.820)
-0.0182***

(-3.937)
Investment (percent of GDP)0.0011***

(3.082)
0.0006*

(1.773)
0.0018***

(5.092)
0.0014***

(5.369)
Primary School Enrollment0.0003**

(2.106)
0.0003**

(2.361)
0.0002*

(1.784)
0.0001

(0.853)
Population Growth-0.255***

(-5.046)
-0.195***

(-3.527)
-0.228***

(-4.286)
-0.215***

(-3.494)
Trade (percent of GDP)-5.94e-05

(-1.020)
1.63e-05

(0.241)
-8.00e-05

(-1.219)
-4.16e-05

(-0.771)
Inflation-0.0018

(-0.373)
-0.0087***

(-2.653)
Government (percent of GDP)-0.0002

(-0.984)
-0.0004*

(-1.655)
Cabinet Changes-0.0321***

(-3.942)
-0.0279***

(-3.457)
-0.0302***

(-4.148)
-0.0217***

(-3.428)
Index of Economic Freedom0.0085**

(2.490)
0.0080**

(2.255)
Area2: Legal structure and security of

property rights
0.0040**

(2.297)
0.0033*

(1.895)
Polity Scale-0.0006*

(-1.906)
-4.22e-05

(-0.105)
-0.0009*

(-1.864)
7.60e-06

(0.0202)
Ethnic Homogeneity Index0.0449**

(2.316)
0.0560***

(3.728)
0.0301*

(1.671)
0.0201

(1.323)
Number of Observations547520517494
Number of Countries112108113109
Hansen test (p-value)0.6840.9980.6510.992
AR1 test (p-value)3.81e-062.56e-051.10e-054.38e-05
AR2 test (p-value)0.7460.6180.4920.456
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

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.18

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:

  • Regime Instability Index 1: Cabinet Changes and Executive Changes.

  • Regime Instability Index 2: Cabinet Changes, Constitutional Changes, Coups, Executive Changes, and Government Crises.

  • Regime Instability Index 3: Cabinet Changes, Constitutional Changes, Coups, Executive Changes, Government Crises, Number of Legislative Elections, and Fragmentation Index.

  • Violence Index: Assassinations, Coups, and Revolutions.

  • 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.19 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.

Table 4.Indexes of Political Instability and Economic Growth
(1)(2)(3)(4)(5)
Initial GDP per capita (log)-0.0211***

(-4.685)
-0.0216***

(-4.832)
-0.0221***

(-4.789)
-0.0216***

(-4.085)
-0.0216***

(-5.370)
Investment (percent of GDP)0.0012***

(3.006)
0.0011***

(3.091)
0.0011***

(2.778)
0.0010***

(3.190)
0.0011***

(3.126)
Primary School Enrollment0.0003**

(2.156)
0.0002**

(1.964)
0.0002**

(1.972)
0.0004***

(2.597)
0.0003**

(2.496)
Population Growth-0.245***

(-4.567)
-0.214***

(-4.002)
-0.221***

(-4.500)
-0.226***

(-3.869)
-0.220***

(-4.197)
Trade (percent of GDP)-7.06e-05

(-1.058)
-8.92e-05

(-1.391)
-8.19e-05

(-1.268)
-9.30e-05

(-1.109)
-8.95e-05

(-1.392)
Regime Instability Index 1-0.0198***

(-4.851)
Regime Instability Index 2-0.0133***

(-3.381)
Regime Instability Index 3-0.0142***

(-4.246)
Violence Index-0.0046

(-1.197)
Political Instability Index-0.0087**

(-2.255)
Index of Economic Freedom0.0084**

(2.251)
0.0090**

(2.429)
0.0087**

(2.251)
0.0120***

(2.935)
0.0112***

(3.324)
Polity Scale-0.0005

(-1.356)
-0.0005

(-1.311)
-0.0003

(-0.833)
-0.0010**

(-2.296)
-0.0008**

(-2.060)
Ethnic Homogeneity Index0.0497***

(3.150)
0.0497***

(3.094)
0.0530***

(3.177)
0.0429*

(1.832)
0.0376**

(2.349)
Number of Observations547547545547547
Number of Countries112112111112112
Hansen test (p-value)0.5600.4320.4840.5760.516
AR1 test (p-value)3.82e-063.22e-063.60e-066.63e-063.80e-06
AR2 test (p-value)0.6670.2910.4370.2800.233
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004;

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation;

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004;

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation;

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

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.20 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.21 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 five-year period (2000–04) 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.

Table 5.Robustness Tests for Restricted Samples
(1)(2)(3)(4)(5)(6)(7)
Proxy of

Political

Instability
Excluding

Industrial

Countries
Excluding

Africa
Excluding

Developing

Asia
Excluding

Developing

Europe
Excluding

Latin America
Excluding

the 1960s

and 1970s
Excluding

the 2000s
Cabinet

Changes
-0.0282***-0.0285***-0.0342***-0.0280***-0.0282***-0.0309***-0.0326***
(-3.814)(-4.588)(-3.583)(-3.315)(-3.563)(-3.108)(-3.693)
Regime

Instability

Index 1
-0.0191***-0.0154***-0.0198***-0.0185***-0.0167***-0.0159***-0.0136***
(-3.795)(-4.157)(-3.128)(-3.686)(-3.534)(-3.326)(-3.325)
Regime

Instability

Index 2
-0.0161***-0.0107***-0.0141***-0.0131***-0.0117**-0.0160***-0.0141***
(-3.299)(-3.905)(-3.717)(-3.112)(-2.553)(-3.292)(-3.540)
Regime

Instability

Index 3
-0.0161***-0.0118***-0.0148***-0.0145***-0.0096***-0.0165***-0.0146***
(-3.686)(-3.459)(-3.563)(-3.369)(-2.760)(-3.633)(-3.587)
Number of

Observations
415401471506436441488
Number of

Countries
9280979791111112
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - The dependent variable is the growth rate of real GDP per capita.

  • - Each coefficient shown comes from a separate regression. That is, this table summarizes the results of 28 estimations. The complete results are available from the authors upon request.

  • - The explanatory variables used, besides the proxy for political instability indicated in each row, are those of the model of column 1 of Table 3 (for Cabinet Changes) and columns 1 to 3 of Table 4 (for the regime instability indexes).

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - The dependent variable is the growth rate of real GDP per capita.

  • - Each coefficient shown comes from a separate regression. That is, this table summarizes the results of 28 estimations. The complete results are available from the authors upon request.

  • - The explanatory variables used, besides the proxy for political instability indicated in each row, are those of the model of column 1 of Table 3 (for Cabinet Changes) and columns 1 to 3 of Table 4 (for the regime instability indexes).

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

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.

Table 6.Robustness Tests for Alternative Period Lengths
(1)(2)(3)(4)
Proxy of Political Instability4-Year

Periods
6-Year

Periods
8-Year

Periods
10-Year

Periods
Cabinet Changes-0.0298*

(-1.683)
-0.0229**

(-2.470)
-0.0121*

(-1.752)
-0.0231**

(-2.004)
Regime Instability Index 1-0.0081*

(-1.744)
-0.0121***

(-2.842)
-0.0065*

(-1.840)
-0.0213**

(-2.553)
Regime Instability Index 2-0.0077**

(-2.451)
-0.0081**

(-2.291)
-0.0092**

(-2.170)
-0.0078***

(-2.590)
Regime Instability Index 3-0.0065**

(-2.150)
-0.0076**

(-2.217)
-0.0101**

(-2.462)
-0.0069**

(-2.133)
Number of Observations737488390506
Number of Countries11211010997
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - The dependent variable is the growth rate of real GDP per capita.

  • - Each coefficient shown comes from a separate regression. That is, this table summarizes the results of 16 estimations. The complete results are available from the authors upon request.

  • - The explanatory variables used, besides the proxy for political instability indicated in each row, are those of the model of column 1 of Table 3 (for Cabinet Changes) and columns 1 to 3 of Table 4 (for the regime instability indexes).

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - The dependent variable is the growth rate of real GDP per capita.

  • - Each coefficient shown comes from a separate regression. That is, this table summarizes the results of 16 estimations. The complete results are available from the authors upon request.

  • - The explanatory variables used, besides the proxy for political instability indicated in each row, are those of the model of column 1 of Table 3 (for Cabinet Changes) and columns 1 to 3 of Table 4 (for the regime instability indexes).

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

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 installed productive capacity. Thus, we hypothesize that higher political instability is associated with lower productivity growth. Finally, human capital accumulation may also be adversely affected by political instability because uncertainty about the future may induce people to invest less in education.

Construction of the series

The series were constructed following the Hall and Jones (1999) approach to the decomposition of output. They assume that output, Y, is produced according to the following production function:

where K denotes the stock of physical capital, A is a labor-augmenting measure of productivity, and H is the amount of human capital-augmented labor used in production. Finally, the factor share α is assumed to be constant across countries and equal to 1/3.

The series on the stock of physical capital, K, were constructed using the perpetual inventory equation:

where It is real aggregate investment in PPP at time t, and δ is the depreciation rate (assumed to be 6%). Following standard practice, the initial capital stock, K0, is given by:

where I0 is the value of investment in 1950 (or in the first year available, if after 1950), and g is the average geometric growth rate for the investment series between 1950 and 1960 (or during the first 10 years of available data).

The amount of human capital-augmented labor used in production, Hi, is given by:

where si is average years of schooling in the population over 25 years old (taken from the most recent update of Barro and Lee, 2001), and the function φ(si) is piecewise linear with slope 0.134 for si≤4, 0.101 for 4≤si≤8, and 0.068 for si>8. Li is the number of workers (labor force in use).

With data on output, the physical capital stock, human capital-augmented labor used, and the factor share, the series of total factor productivity (TFP), Ai, can be easily constructed using the production function (4).22 As in Hsieh and Klenow (2010), after dividing equation (4) by population N, and rearranging, we get a conventional expression for growth accounting.

This can also be expressed as:

where y is real GDP per capita, k denotes the stock of physical capital per capita, A is TFP, and h is the amount of human capital per capita.

The individual contributions to GDP per capita growth from physical and human capital accumulation and TFP growth can be computed by expressing equation (9) in rates of growth:

Empirical results

Table 7 reports the results of estimations in which the growth rate of physical capital per capita is the dependent variable,23 using a similar set of explanatory variables as for GDP per capita growth.24 Again, Cabinet Changes and the three regime instability indexes are always statistically significant, with a negative sign. Thus, we find strong support for the hypothesis that political instability adversely affects physical capital accumulation. Since the accumulation of capital is done through investment, our results are consistent with those of previous studies which find that political instability adversely affects investment (Alesina and Perotti, 1996; Özler and Rodrik, 1992). There is some evidence that economic freedom is favorable to capital accumulation (column 2), but democracy and ethnic homogeneity do not seem to significantly affect it.25

Table 7.Political Instability and Physical Capital Growth
(1)(2)(3)(4)(5)
Log Physical Capital

per capita (-1)
0.1000***

(8.963)
0.0716***

(6.065)
0.105***

(6.316)
0.105***

(7.139)
0.102***

(7.833)
Log Physical Capital

per capita (-2)
-0.109***

(-9.438)
-0.0846***

(-7.860)
-0.106***

(-6.159)
-0.106***

(-6.973)
-0.103***

(-7.642)
Primary School Enrollment0.0001

(0.764)
0.00003

(0.292)
-0.0001

(-0.855)
-0.0001

(-0.997)
-0.0001

(-1.189)
Population Growth-0.299***

(-5.591)
-0.272***

(-5.730)
-0.212**

(-2.442)
-0.216***

(-2.700)
-0.192**

(-2.474)
Trade (percent of GDP)0.0001**

(2.427)
0.00005

(1.169)
0.00001

(0.234)
0.00001

(0.230)
0.00002

(0.386)
Cabinet Changes-0.0235***

(-2.968)
-0.0195***

(-2.969)
Regime Instability Index 1-0.0108**

(-2.180)
Regime Instability Index 2-0.00932**

(-2.487)
Regime Instability Index 3-0.00906**

(-2.325)
Index of Economic Freedom0.0070**

(2.473)
0.0015

(0.395)
0.0010

(0.282)
0.0004

(0.130)
Polity Scale-0.0001

(-0.414)

-0.0005

(-1.117)
-0.0005

(-1.151)
-0.0004

(-0.940)
Ethnic Homogeneity Index0.0343*

(1.825)
0.0010

(0.0558)
0.0009

(0.0414)
0.0019

(0.0917
Number of Observations899531531531529
Number of Countries155108108108107
Hansen test (p-value)0.05350.5530.1950.4260.213
AR1 test (p-value)0.00000090.000020.00010.00020.00006
AR2 test (p-value)0.1820.9050.9870.9870.928
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

The next step of the empirical analysis was to analyze another possible channel of transmission, productivity growth. The results reported in Table 8 provide clear empirical support for the hypothesis that political instability adversely affects productivity growth, as Cabinet Changes is always statistically significant, with a negative sign.26 Economic freedom, which had positive effects on GDP growth, is also favorable to TFP growth. As can be seen in columns 3 to 5, we find clear evidence that regime instability adversely affects TFP growth. Thus, we can conclude that an additional channel through which political instability negatively affects GDP growth is productivity growth.

Table 8.Political Instability and TFP Growth
(1)(2)(3)(4)(5)
Initial TFP (log)-0.0338***

(-2.871)
-0.0344***

(-3.576)
-0.0299***

(-2.796)
-0.0308**

(-2.525)
-0.0301**

(-2.540)
Population Growth-0.298***

(-3.192)
-0.149

(-1.639)
-0.202*

(-1.837)
-0.189

(-1.367)
-0.156

(-1.150)
Trade (percent of GDP)0.00007

(0.640)
-0.0001

(-1.375)
-0.0002

(-1.632)
-0.0002

(-1.626)
-0.0002

(-1.312)
Cabinet Changes-0.0860***

(-2.986)
-0.0243*

(-1.685)
Regime Instability Index 1-0.0129**

(-1.995)
Regime Instability Index 2-0.0084*

(-1.700)
Regime Instability Index 3-0.0096**

(-1.976)
Index of Economic Freedom0.0190***

(2.794)
0.0225**

(2.380)
0.0225**

(2.399)
0.0197**

(2.340)
Polity Scale-0.0005

(-1.062)
-0.0008

(-1.354)
-0.0008

(-1.099)
-0.0004

(-0.592)
Ethnic Homogeneity Index0.0385*

(1.647)
0.0126

(0.513)
0.0216

(0.914)
0.0237

(1.101)
Number of Observations700502502502498
Number of Countries10591919191
Hansen test (p-value)0.5010.6140.4720.2530.242
AR1 test (p-value)0.00640.000040.000040.000050.00005
AR2 test (p-value)0.6770.8980.9070.8230.811
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Finally, Table 9 reports the results obtained for human capital growth.27 Again, Cabinet Changes and the regime instability indexes are always statistically significant, with the expected negative signs. Regarding the institutional variables, democracy seems to positively affect human capital growth, as the Polity Scale is statistically significant, with a positive sign, in columns 3 to 5. There is also weak evidence in column 4 that ethnic homogeneity is favorable to human capital accumulation. Finally, openness to trade has positive effects on human capital accumulation.

Table 9.Political Instability and Human Capital Growth
(1)(2)(3)(4)(5)
Initial Human Capital

per capita (log)
-0.00608

(-1.313)
-0.0129**

(-2.146)
-0.0122**

(-2.214)
-0.0106

(-1.592)
-0.0121

(-1.604)
Investment (percent of GDP)-0.0001

(-0.723)
0.0002

(1.093)
0.000146

(0.744)
0.000190

(0.876)
0.0002

(1.074)
Population Growth-0.0608***

(-2.772)
-0.0369

(-1.640)
-0.0280

(-1.161)
-0.0160

(-0.676)
-0.0271

(-1.210)
Trade (percent of GDP)0.00009**

(2.488)
0.00006*

(1.868)
0.0000721**

(2.081)
0.0000697**

(1.976)
0.00006*

(1.836)
Cabinet Changes-0.0113**

(-1.976)
-0.00911**

(-2.035)
Regime Instability Index 1-0.00379**

(-2.093)
Regime Instability Index 2-0.00311**

(-2.152)
Regime Instability Index 3-0.00292*

(-1.847)
Index of Economic Freedom-0.0017

(-1.263)
-0.0013

(-0.951)
-0.0016

(-1.171)
-0.0020

(-1.400)
Polity Scale0.0002

(1.490)
0.0004***

(3.217)
0.0004***

(3.198)
0.0005***

(3.170)
Ethnic Homogeneity Index0.0103

(1.638)
0.0098

(1.220)
0.00998*

(1.675)
0.0101

(1.515)
Number of Observations704504504504500
Number of Countries10591919191
Hansen test (p-value)0.4060.6990.6720.7030.678
AR1 test (p-value)0.00000010.000010.000010.000020.00003
AR2 test (p-value)0.7180.5810.5250.6230.675
Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Sources: See Table 1.Notes: - System-GMM estimations for dynamic panel-data models. Sample period: 1960–2004.

  • - All explanatory variables were treated as endogenous. Their lagged values two periods were used as instruments in the first-difference equations and their once lagged first-differences were used in the levels equation.

  • - Two-step results using robust standard errors corrected for finite samples (using Windmeijer’s, 2005, correction).

  • - T-statistics are in parenthesis. Significance level at which the null hypothesis is rejected: ***, 1 percent; **, 5 percent, and *, 10 percent.

Effects of the three transmission channels

The last step of the empirical analysis was to compute the effects of political instability on GDP per capita growth through each of the three transmission channels, using equation (10). The results of this growth decomposition exercise are reported in Table 10, which shows, for each proxy of political instability, the estimated coefficients,28 the effects on GDP per capita growth, and the percentage contributions to the total effects.

Table 10.Transmission Channels of Political Instability into GDP Growth
Proxy of

Political

Instability
Channels of Transmission
Growth of

Physical

Capital pc
Growth of

TFP
Growth of

Human

Capital pc
Total Effect of the 3

Channels on the

Growth of GDP pc
Cabinet

Changes
Coefficient-0.0195***-0.0243*-0.00911**
Effect on GDP-0.0065-0.0162-0.0061-0.0288
Percent of Total

Effect
22.59%56.30%21.11%100%
Regime

Instability

Index 1
Coefficient-0.0108**-0.0129**-0.00379**
Effect on GDP-0.0036-0.0086-0.0025-0.0147
Percent of Total

Effect
24.44%58.40%17.16%100%
Regime

Instability

Index 2
Coefficient-0.00932**-0.00846*-0.00311**
Effect on GDP-0.0031-0.0056-0.0021-0.0108
Percent of Total

Effect
28.71%52.13%19.16%100%
Regime

Instability

Index 3
Coefficient-0.00906**-0.00964**-0.00292*
Effect on GDP-0.0030-0.0064-0.0019-0.0114
Percent of Total

Effect
26.51%56.41%17.08%100%
Sources: See Table 1Notes: - The estimated coefficients were taken from: columns 2 to 5 of Table 7, for the Growth of Physical Capital per capita; columns 2 to 5 of Table 8, for the Growth of TFP; and, columns 2 to 5 of Table 9, for the Growth of Human Capital per capita.

  • - The effects of each channel on the growth of real GDP per capita are obtained by multiplying: the coefficient obtained for the growth of Physical Capital per capita by α=1/3; the coefficient obtained for the growth of TFP by (1-α)=2/3; and, the coefficient obtained for the growth of Human Capital per capita by (1-α)=2/3. That is, we apply equation (10): Δy = αΔ k + (1 – αA + (1 – α) Δh.

Sources: See Table 1Notes: - The estimated coefficients were taken from: columns 2 to 5 of Table 7, for the Growth of Physical Capital per capita; columns 2 to 5 of Table 8, for the Growth of TFP; and, columns 2 to 5 of Table 9, for the Growth of Human Capital per capita.

  • - The effects of each channel on the growth of real GDP per capita are obtained by multiplying: the coefficient obtained for the growth of Physical Capital per capita by α=1/3; the coefficient obtained for the growth of TFP by (1-α)=2/3; and, the coefficient obtained for the growth of Human Capital per capita by (1-α)=2/3. That is, we apply equation (10): Δy = αΔ k + (1 – αA + (1 – α) Δh.

More than half of the total negative effects of political instability on real GDP per capita growth seem to operate through its adverse effects on total factor productivity (TFP) growth, as this channel is responsible for 52.13 percent to 58.40 percent of the total effects. Thus, according to our results, TFP growth is the main transmission channel through which political instability affects real GDP per capita growth. Regarding the other channels, physical capital accumulation accounts for 22.59 percent to 28.71 percent of the total effect, while the growth of human capital accounts for 17.08 percent to 21.11 percent. This distribution of the effects of political instability on GDP growth through the three channels is not surprising. According to the literature on growth accounting, human capital accounts for 10–30 percent of country income differences, physical capital accounts for about 20 percent, and the residual TFP accounts for 60–70 percent (see Hsieh and Klenow, 2010).

Although the total effects of political instability reported in the last column of Table 10 are somewhat smaller than those obtained for the proxies of political instability in the estimations of column 1 of Table 3 (for Cabinet Changes) and of columns 1 to 3 of Table 4 (for the three regime instability indexes), Wald tests never reject the hypothesis that the coefficient estimated for GDP per capita growth is equal to the total effect reported in Table 10.29

IV. Conclusions

This paper analyzes the effects of political instability on growth. In line with the literature, we find that political instability significantly reduces economic growth, both statistically and economically. But, we go beyond the current state of the literature by quantitatively determining the importance of the transmission channels of political instability to economic growth. Using a dataset covering up to 169 countries in the period between 1960 and 2004, estimates from system-GMM regressions show that political instability is particularly harmful through its adverse effects on total factor productivity growth and, in a lesser scale, by discouraging physical and human capital accumulation. By identifying and quantitatively determining the main channels of transmission from political instability to economic growth, this paper contributes to a better understanding on how politics affects economic performance.

Our results suggest that governments in politically fragmented countries with high degrees of political instability need to address its root causes and try to mitigate its effects on the design and implementation of economic policies. Only then, countries could have durable economic policies that may engender higher economic growth.

References
*

Ari Aisen: International Monetary Fund (aaisen@imf.org). Francisco Jose Veiga: Universidade do Minho and NIPE Escola de Economía e Gestão, 4710-057 Braga, Portugal (fjveiga@eeg.uminho.pt)

**

The authors wish to thank John H. McDermott, conference participants at the 2010 Meeting of the European Public Choice Society and at the Fourth Conference of the Portuguese Economic Journal and seminar participants at the University of Minho for useful comments. Finally, we thank Luísa Benta for excellent research assistance.

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.

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).

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 natural 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.

The periods are: 1960–64, 1965–69, 1970–74, 1975–79, 1980–84, 1985–89, 1990–94, 1995–99, and 2000–04.

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.

In order to avoid heteroskedasticity problems resulting from the high variability of inflation rates, Inflation was defined as log(1+Inf/100).

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).

See Benhabib and Rusticini (1996) for a theoretical model relating social conflict and growth.

On the relationship between democracy and growth, see also Acemoglu, et al. (2008).

According to the simulations performed by Judson and Owen (1999), there is still a bias of 20 percent in the coefficient of interest for T=30.

For a detailed discussion on the conditions under which GMM is suitable for estimating growth regressions, see Bond et al. (2001).

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.

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.

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.

Since Investment (percent of 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.

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).

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.

The results for these five 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.

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.

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.

See Caselli (2005) for a more detailed explanation of how the series are constructed. We also follow this study in assuming that the depreciation rate of physical capital is 6 percent and that the factor share α is equal to 1/3. The series of output, investment and labor are computed as follows (using data from the PWT 6.2): Y = rgdpch*(pop*1000), I = (ki/100)*rgdpl*(pop*1000), L = rgdpch*(pop*1000)/rgdpwok. Population is multiplied by 1000 because the variable pop of PWT 6.2 is scaled in thousands.

A second lag of physical capital had to be included in the right hand-side in order to avoid second order autocorrelation of the residuals. Although the coefficient for the first lag is positive, the second lag has a negative coefficient, higher in absolute value. Thus, when we add up the two coefficients for the lags of physical capital, we get negative values whose magnitude is in line with those obtained for lagged GDP per capita in the previous tables.

Since the variable Investment (percent of GDP) – variable ki from the PWT 6.2 - was used to construct the series of the stock of physical capital, it was not included as an explanatory variable. Nevertheless, the results for political instability do not change when the investment ratio is included.

In order to account for interactions among the three transmission channels, we included the growth rates of TFP and of human capital as explanatory variables. None was statistically significant, regardless of the use of current or lagged growth rates. In fact, the same happened in the estimations for the other channels. That is, the growth rate of one transmission channel does not seem to be affected by the growth rates of the other two channels. These results are not shown here in order to economize space, but they are available from the authors upon request.

Data on investment and human capital were used to construct the TFP series. Thus, the variables Investment (percent of GDP) and Primary School Enrollment were not included as explanatory variables in the estimations for TFP growth reported in Table 8. But, when they are included, the results for the other explanatory variables do not change significantly.

Since data on education was used to construct the series of the stock of human capital, Primary School Enrollment was not included as an explanatory variable in the estimations of Table 9. If included, it is statistically significant, with a positive sign, and results regarding the effects of political instability remain practically unchanged.

The coefficients for the proxies of political instability are those reported in columns 2 to 5 of Table 7 (Growth of Physical Capital per capita), Table 8 (Growth of TFP), and Table 9 (Growth of Human Capital per capita).

For example, the estimated coefficient for Cabinet Changes in column 1 of Table 3 is -0.0321, while the total effect of the three channels reported in the last column of Table 10 is -0.0288. The results of the Wald tests were:

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