On the measurement of political instability and its impact on economic growth

European Journal of Political Economy 25 (2009) 15–29

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European Journal of Political Economy j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / e j p e

On the measurement of political instability and its impact on economic growth Richard Jong-A-Pin ⁎ Department of Economics and Econometrics, University of Groningen, Nettelbosje 2, PO Box 800, 9700 AV Groningen, The Netherlands

a r t i c l e

i n f o

Article history: Received 4 January 2007 Received in revised form 10 September 2008 Accepted 10 September 2008 Available online 4 October 2008 JEL classification: O40 D74

a b s t r a c t We examine the multidimensionality of political instability using 25 political instability indicators in an Exploratory Factor Analysis. We find that political instability has four dimensions: politically motivated violence, mass civil protest, instability within the political regime, and instability of the political regime. We examine the causal impact of political instability on economic growth using a dynamic panel system Generalized Method of Moments model and find that the four dimensions of political instability have different effects on economic growth. Only the instability of the political regime has a robust and significant negative effect on economic growth. © 2008 Elsevier B.V. All rights reserved.

Keywords: Political instability Factor analysis Economic growth

1. Introduction It is widely believed that political instability is detrimental for the economic growth performance of countries. Indeed, there are various studies that report a negative and significant correlation between political instability and economic growth (e.g. Gupta, 1990; Barro, 1991; Alesina et al., 1996; Perotti, 1996; Ades and Chua, 1997). These studies are complemented with several contributions providing a theoretical linkage between political instability and economic growth (e.g. Benhabib and Rustichini, 1996; Brock Blomberg, 1996; Svensson, 1998; Devereux and Wen, 1998; Darby et al., 2004; Ghate et al., 2003). However, the framework in which the relation between political instability and economic growth is analyzed has recently come under attack. De Haan (2007), among others, argues that many of the variables used in the empirical analyses on economic growth, including political instability, are measured with error. If so, this severely affects the reliability of obtained estimates. Besides measurement error, other studies point to the fact (and also provide statistical evidence) that a negative correlation need not imply a causal relationship (Campos and Nugent, 2002). In this paper, we focus on the measurement of political instability and reexamine the causal linkage between political instability and economic growth. There have been several attempts to tackle the problem of measurement error. Most studies that have focused on the impact of political instability on economic growth have constructed one dimensional indexes using principal components analysis (PCA) (Perotti, 1996), discriminant analysis (Gupta, 1990), or logit analysis (Alesina et al., 1996). However, there is ample evidence from political science that political instability is multidimensional (Rummel, 1963, 1966; Tanter, 1966; Feierabend and Feierabend, 1966; Morrison and Stevenson, 1971; Hibbs, 1973), although this literature has not reached consensus as to the appropriate number of dimensions. As these studies all find that political instability has more dimensions, the solutions as proposed above may still suffer from measurement error, or, at least ignore some aspects of political instability when analyzing the impact on economic growth.

⁎ Tel.: +31 503634757; fax: +31 503637018. E-mail address: [email protected]. 0176-2680/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ejpoleco.2008.09.010

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Table 1 Correlation matrix of the political instability indicators (I)

(II)

(III)

(IV)

(V)

(VI)

(VII)

(VIII)

(IX)

(X)

(XI)

(XII)

(XIII)

(XIV)

(XV)

(XVI)

(XVII)

(XVIII)

(XIX)

(XX)

(XXI)

(XXII)

(XXIII)

(XXIV)

(XXV)

1 0.22 0.46 0.25 0.10 0.20 0.32 0.23 0.12 0.10 0.16 0.19 0.05 0.15 −0.13 −0.03 −0.07 −0.28 −0.08 0.12 −0.01 0.19 0.12 0.13 0.33

1 0.17 0.39 0.06 0.47 0.05 0.46 0.05 0.08 0.21 0.26 0.15 0.10 −0.26 −0.06 −0.07 −0.18 −0.10 0.21 0.12 0.22 0.09 0.06 0.07

1 0.23 0.17 0.23 0.70 0.18 0.17 0.16 0.17 0.13 −0.08 0.32 −0.27 −0.39 −0.21 −0.54 −0.14 0.00 −0.12 0.16 0.15 0.34 0.61

1 0.11 0.25 0.26 0.31 0.19 0.21 0.47 0.54 0.15 0.27 −0.24 −0.15 −0.07 −0.17 −0.16 0.23 0.15 0.37 0.07 0.13 0.13

1 0.12 0.21 0.17 0.11 0.17 0.08 0.05 −0.04 0.13 −0.18 −0.08 0.03 −0.07 0.11 −0.18 −0.13 0.05 0.03 0.07 0.08

1 0.06 0.74 0.01 0.11 0.17 0.14 0.06 0.10 −0.16 −0.21 −0.14 −0.14 0.05 0.03 −0.04 0.11 0.23 0.12 0.08

1 0.12 0.32 0.27 0.27 0.19 −0.12 0.40 −0.27 −0.40 −0.19 −0.53 −0.13 0.00 −0.11 0.20 0.13 0.32 0.51

1 −0.03 0.14 0.21 0.17 0.12 0.17 −0.13 −0.12 −0.03 −0.10 0.12 0.06 0.03 0.15 0.11 0.12 0.09

1 0.50 0.30 0.38 −0.19 0.33 −0.21 −0.19 −0.15 −0.24 −0.09 −0.05 −0.11 0.27 0.15 0.01 0.18

1 0.40 0.31 − 0.04 0.52 − 0.31 − 0.24 − 0.16 − 0.29 0.01 − 0.19 − 0.18 0.22 0.14 − 0.01 0.16

1 0.54 0.15 0.34 −0.26 −0.19 −0.10 −0.22 −0.23 0.17 0.02 0.38 0.14 0.04 0.11

1 0.24 0.26 −0.18 0.01 0.02 −0.02 −0.24 0.29 0.21 0.64 0.04 0.04 0.04

1 −0.07 0.03 0.18 0.22 0.21 −0.07 0.20 0.29 0.17 0.00 0.02 −0.09

1 −0.26 −0.27 −0.14 −0.29 0.00 −0.05 −0.18 0.22 0.11 0.02 0.17

1 0.40 0.16 0.57 −0.04 0.30 0.21 −0.15 −0.11 −0.11 −0.21

1 0.41 0.66 −0.04 0.21 0.26 0.00 −0.16 −0.30 −0.32

1 0.47 0.05 0.15 0.27 0.07 − 0.16 − 0.22 − 0.25

1 0.12 0.23 0.31 −0.03 −0.16 −0.30 −0.48

1 − 0.47 − 0.30 − 0.23 − 0.05 − 0.08 − 0.12

1 0.58 0.29 −0.01 0.01 0.02

1 0.17 −0.09 −0.01 −0.07

1 0.06 0.02 0.08

1 −0.01 −0.01

1 0.09

1

Note: The table shows pairwise correlation coefficients. The Roman numbers refer to: (I) Assassinations, (II) Strikes, (III) Guerilla warfare, (IV) Major government crises, (V) Purges, (VI) Riots, (VII) Revolutions, (VIII) Demonstrations, (IX) Coups d'etat, (X) Major constitutional changes, (XI) Cabinet changes, (XII) Changes in the chief executive, (XIII) Number of elections, (XIV) Political regime changes, (XV) Government stability, (XVI) Ethnic tensions, (XVII) Religious tensions, (XVIII) Internal conflicts, (XIX) Years of largest party in government, (XX) Fractionalization, (XXI) Polarization, (XXII) Number of veto players that drop from office, (XXIII) Minor civil conflicts, (XXIV) Medium civil conflicts, (XXV) civil war.

R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

(I) (II) (III) (IV) (V) (VI) (VII) (VIII) (IX) (X) (XI) (XII) (XIII) (XIV) (XV) (XVI) (XVII) (XVIII) (XIX) (XX) (XXI) (XXII) (XXIII) (XXIV) (XXV)

R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

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Fig. 1. Catell's scree test for both samples.

Quite surprisingly, the multidimensionality of political instability is only tentatively addressed when it comes to analyzing the impact on economic growth. Ghate et al. (2003) examine the growth effects of the dimensions as identified by Hibbs (1973) and differentiate between a political violence dimension and a collective protest dimension. Furthermore, Chen and Feng (1996), and Campos and Nugent (2002) analyze the growth effects of mild and severe political instability. In this paper, our first aim is to examine the multidimensionality of political instability and to derive new measures for political instability. To that end, we employ an Exploratory Factor Analysis (EFA) on a set of 25 political instability indicators. EFA differs from PCA, since the latter is a data reduction method to extract as much of the variance contained in a set of indicators, while factor analysis is based on a model and extracts only the information common to all indicators (Wansbeek and Meijer, 2000).1 Previewing the factor analysis results: we find that the following dimensions of political instability can be distinguished: (1) politically motivated violence, (2) mass political violence, (3) instability within the political regime, and (4) instability of the political regime. Our second aim is to re-examine the causal relation between political instability and economic growth. To that end, we use the system Generalized Method of Moments (GMM) dynamic panel estimator as developed by Blundell and Bond (1998) and combine it with the Granger-causality framework (Granger, 1987) as proposed by Campos and Nugent (2002). We focus on 5-year averages in a panel of about 90 countries over the period 1974–2003 and find both a contemporaneous relation as well as a Granger causal relation running from the instability of the political regime to economic growth. Furthermore, we find some evidence for two-way causality between economic growth and the instability within countries and find that economic growth has a causal impact on political violence. The remainder of this paper is organized as follows. In Section 2, we discuss our data and the factor analysis results. In Section 3, we provide some descriptive statistics of the different dimensions of political instability. In Section 4, we examine the effect of political instability and economic growth. The sensitivity of our baseline estimation results is examined in Section 5. Section 6 concludes. 2. Data and factor analysis results We examine the dimensionality of political instability using an Exploratory Factor Analysis. The aim of the factor analysis is to separate the information that is common to all indicators from the information that is unique to a single indicator. By assuming that the observed indicators are “generated” by a linear combination of unobserved factors and some individual error term, a simple model structure is imposed on the covariance matrix of the indicators. Various studies have used principal components analysis, which is akin to factor analysis. However, we prefer factor analysis, since it assumes a model and extracts only the information common to all indicators, while principal components analysis is a data reduction method to extract as much of the variance contained in a set of indicators. When the model is correctly estimated and interpreted, it is possible to obtain values for the underlying factors, i.e., the separate dimensions of political instability. These values of the dimensions of political instability can be used to evaluate the correlation

1 There are a number of other differences between this study and earlier work. Firstly, we utilize the information contained in 25 political instability indicators – a set larger than in any previous study. Secondly, in contrast to previous work, this study decides upon the appropriate number of dimensions on the basis of various statistical tests. Thirdly, also in contrast to previous work, this study does not restrict the different dimensions to be uncorrelated with each other. Fourthly, the measurement error of the individual indicators is assessed and the different dimensions of political instability are subjected to a cross country comparison. Finally, the factor analysis results are subjected to an extensive robustness analysis.

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R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

Table 2 Rotated factor loadings matrix and unique variance estimates

Notes: Factor loadings are estimated using Maximum Likelihood. The method of rotation is Oblimin. The left panel shows the estimates for the sample 1974–2003, for which 21 indicators are used. The right panel shows the estimates for the sample 1984–2003, for which 25 indicators are used. For illustrative purposes, the factor loadings larger than 0.3 (in absolute terms) are in grey.

with individual indicators, but can also be used in empirical applications to obtain more reliable estimates of the role of political instability in economics. To that end, we obtain factor scores based on the regression predictor, which gives the conditional expectation of the factors given all the indicators (see Wansbeek and Meijer, 2000). To select indicators for the factor analysis that proxy for political instability, we rely on the surveys by Brunetti (1997), Aron (2000), and Carmignani (2003). We use data from the following commonly used data sets: Databanks International (2005), International Country Risk Guide (2005), Polity IV (Marshall and Jaggers, 2002), International Peace Research Institute Oslo (Gleditsch et al., 2002), and Database of Political Institutions (Beck et al., 2001). On the basis of the surveys mentioned above, the available indicators, and the assumptions underlying the factor model, which require that the political instability indicators are “generated” by a few latent variables, and that the measurement errors are uncorrelated, we have selected 25 political instability indicators for our analysis. These indicators, their definitions and sources are listed in Appendix A. As to the appropriate time period for our analysis, we face a trade-off between the number of available indicators and the length of the period. That is, the indicators of the ICRG data set comprise the period 1984–2003, whereas the other data sets provide at least ten more years of data. To ensure that our analysis does not hinge upon either of these choices, we provide estimation results for two samples. The first analysis is based on the period 1984–2003 and consists of 25 political instability indicators. The other is based on the period 1974–2003 and consists of 21 political instability indicators. Both analyses are based on 5-year averages of the available indicators.2 The pair-wise correlation matrix of the political instability indicators can be found in Table 1. To extract the appropriate number of factors in the EFA, we use various statistical tests. First, we consider Catell's scree test (Cattell, 1966). This graphical method shows the eigenvalues on the vertical axis and the number of factors on the horizontal axis. Fig. 1 shows the scree plots for the 1974–2003 sample (left panel) and the 1984–2003 sample (right panel), respectively. It can be seen in Fig. 1 that in both cases four factors have a large eigenvalue relative to the other factors and explain a relatively large part of the variance contained in all indicators. Hence, according to the scree test four factors are appropriate. Next, we perform a Likelihood Ratio (LR) test, which compares both four factor models against the alternative of a saturated model. In both cases the test rejects the null-hypothesis that the estimates are equal in favor of the (restricted) four factor model.3 As the scree test is often criticized that it relies on subjective criteria and the LR test is sensitive to over-fitting of the model, we also use various other statistical tests. First, we examine for which factor model we find the lowest value of Akaike's information criterion and the Schwarz criterion. Next, we apply the three information criteria as proposed by Bai and Ng (2002). Finally, we also 2

For our analysis, the 5-year average of the period is only included if at least 3 out of 5 observations are non-missing. For the first sample (1974–2003, 21 indicators), the LR statistic is 627.33 (p-value 0.00), while for the second model (1984-2003, 25 indicators) case LR = 905.59 (p-value: 0.00). 3

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Table 3 Correlation matrices of the identified dimensions of political instability Violence

Protest

Within

Regime

Sample: 1974–2003 Violence Protest Within Regime

1.00 0.23⁎ −0.05 0.35⁎

1.00 0.08 0.28⁎

1.00 0.10⁎

1.00

Sample: 1984–2003 Violence Protest Within Regime

1.00 0.21⁎ −0.09 0.36⁎

1.00 0.04 0.30⁎

1.00 −0.06

1.00

Note: The upper panel shows the correlation coefficients between the factors of the four factor model for the sample 1974–2003 and excludes the ICRG indicators. The lower panel shows the correlation coefficients of the sample 1984–2003 using all available indicators. ⁎ indicates significant at the 5% level.

consider the test of Onatski (2007). In all cases, we find the minimal test statistic (for Onatski: the maximal) in case of a factor model with four factors, which leads us to the conclusion that extracting four factors is most appropriate.4 The estimation results of the rotated factor solution are shown in Table 2. Since the Oblimin rotation minimizes the correlation between columns of the factor loadings matrix (also known as the pattern matrix), the general pattern that arises is that every indicator has a high loading on one factor, while it has low loadings on the other factors. The indicators with high factor loadings can be used to interpret the factors. It is clear that the same indicators have high loadings on the specific factors in both samples. In both cases, the first factor has high loadings for indicators that relate to incidences of political violence and warfare. Therefore, we label this factor as: “politically motivated violence”. Indicators that are associated with “mass civil protest” are clearly the only indicators with high factor loadings on the second factor and, therefore, we label this factor accordingly. A number of indicators that relate to the instability of governments/cabinets such as fractionalization and polarization have high factor loadings on the third factor. We label this factor as the “instability within the political regime”. Finally, several indicators that reflect changes in the polity, political leaders, and constitution have high loadings on the fourth factor. This factor, in turn, is labelled: “instability of the political regime”.5 Apart from the factor loadings, Table 2 also reports the estimates of the measurement errors of the individual indicators. These unique variances refer to the variance contained in the individual indicators which cannot be attributed to any of the factors. It can be seen that some indicators contain more information about the “true” level of political instability than others. Whereas, guerrilla warfare seems to be a good proxy for politically motivated violence, and riots capture mass civil protest, the majority of indicators have over 50% unique variance. In other words: these indicators contain relatively much variance that cannot be attributed to one of the political instability dimensions. 3. Political instability: some descriptive statistics The factor scores we obtained reflect different dimensions of political instability. In contrast to previous work, we did not restrict these dimensions to be orthogonal. In Table 3, correlation matrices are shown of the factor score predictions. Again, it is clear that the results of both samples are very similar. Furthermore, it can be seen that the factors moderately correlate, which implies that they indeed reflect different dimensions of political instability, although some correlation coefficients do significantly differ from 0. To gain further insight in the different dimensions of political instability, we show in Fig. 2 our factor scores for four selected countries: Botswana (BWA), Israel (ISR), the Netherlands (NLD), and Sudan (SDN) for each 5-year period under consideration.6 Furthermore, we provide rankings of the most stable countries and the most unstable countries for each political instability dimension in Tables 4a–d.7 These rankings are based on the median observation for each country. The reported rank correlation coefficient compares the median ranking with a ranking based on the minimum level of political instability and the maximum level of political instability. As

4 We also considered the so-called Kaiser criterion, which states that all factors with eigenvalues greater than one should be included in the model. As the scree plots indicate, the first model has 5 factors with eigenvalues greater than one, while the second model has 7 factors with eigenvalue greater than 1. However, these solutions are so-called Heywood cases. That is, some of the unique variances of the indicators in these models are estimated smaller than zero. In general a Heywood case is an indication of a poorly specified model (Heywood, 1931). 5 We performed several sensitivity checks to examine the robustness of our results. First, we examined all solutions of the factor model when one of the twenty-six indicators was excluded from the model. Second, we examined all solutions of the factor model when one country was omitted from the sample. Finally, we checked whether the factor solutions would be different if different time spans (5 or 20 year averages) are used instead of 10-year averages. Our results are robust for all these sample changes. 6 The 5 year periods considered here are 1 = 1974-1978, 2 = 1979-1983, 3 = 1984-1988, 4 = 1989-1993, 5 = 1994-1998, 6 = 1999-2003. 7 All factor scores are available on request.

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R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

Fig. 2. Comparison of factor scores between and within selected countries.

the rank correlation coefficients indicate, there are differences between the alternative rankings, but these differences are generally small. On the basis of Fig. 2 and Tables 4a–d, the following observations can be made. Firstly, the figure shows that the dimensions of political instability vary both within and between countries. Furthermore, it also reflects that the dimensions are quite persistent over time. Secondly, the rankings reflect that countries ending up in the top 10 of most (un)stable countries on one dimension do not necessarily end up in the top 10 of any other dimension. This reinforces the view of the correlation matrices reported in Table 3. Thirdly, countries that have high factor scores on the “Within” dimension are typically countries that also have high scores on democracy indices, while countries that have low scores are typically autocracies. Indeed, the correlation between the “within” variable and, for instance, the democracy variable (polity index) from Marshall and Jaggers (2002) is 0.79. Finally, we compare our factor scores representing different dimensions of political instability with some of (the most prominent) indices that are used in the literature. The indices we compare are: the sum of political revolutions and coups d'etat of Barro (1991), the indicator of political assassinations as used by Barro (1991), the index of Perotti (1996), which is based on PCA, the

Table 4a Ranking most stable and unstable countries: politically motivated violence Ranking

Country

1 Mongolia 2 Russia 3 Korea, Dem. Rep. 4 Vietnam 5 Albania 6 Bulgaria 7 Singapore 8 Japan 9 Taiwan 10 Ivory Coast … … 110 Angola 111 Guatemala 112 Cambodia 113 Myanmar 114 Peru 115 Lebanon 116 Colombia 117 Sri Lanka 118 Philippines 119 Afghanistan Spearman rank correlation with median ranking:

obs

median

minimum

r min

maximum

r max

5 3 6 6 6 5 6 5 2 6 … 5 5 3 3 5 3 6 6 5 4

−0.60 −0.59 −0.59 −0.57 −0.56 −0.56 −0.55 −0.55 −0.55 −0.54 … 1.51 1.70 1.87 1.94 2.03 2.09 2.11 2.14 2.32 2.41

−0.64 −0.63 −0.60 −0.61 −0.60 −0.60 −0.57 −0.59 −0.57 −0.56 … 0.92 −0.45 1.52 0.54 1.09 1.75 0.36 −0.08 1.14 1.78

2 3 7 5 9 8 16 12 18 21 … 114 87 117 113 115 118 110 104 116 119 0.83

−0.48 −0.29 −0.55 1.44 −0.26 −0.36 −0.51 −0.50 −0.52 0.09 … 1.82 3.44 1.96 2.38 3.16 2.49 6.19 2.63 3.57 3.13

11 55 1 95 57 46 5 7 3 77 … 98 114 100 106 112 108 119 109 115 111 0.79

Note: The table shows the ranking of countries on the basis of the median factor score. A low ranking refers to a stable country, a high ranking refers to an unstable country. obs is the number of factor scores available for the particular country, median is the median factor score, minimum is the minimum factor score, r min is the ranking on the basis of the minimum factor score. Maximum = the maximum factor score, r max is the ranking on the basis of the maximum factor score. The Spearman rank correlation is calculated for the median rank and the minimum rank, and the median rank and the maximum rank, respectively.

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R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29 Table 4b Ranking most stable and unstable countries: mass civil protest Ranking

Country

obs

1 Uganda 5 2 Cuba 6 3 Mozambique 5 4 Laos 5 5 Singapore 6 6 Angola 5 7 Namibia 2 8 Gambia 4 9 Yemen, Rep. 2 10 Lesotho 4 … … … 110 China 6 111 Serbia 6 112 Poland 4 113 Argentina 4 114 Pakistan 2 115 Israel 6 116 Korea, Rep. 6 117 Russia 3 118 South Africa 6 119 India 6 Spearman rank correlation with median ranking:

median

minimum

r min

maximum

r max

− 0.56 − 0.55 − 0.54 − 0.54 − 0.54 − 0.52 − 0.52 − 0.51 − 0.50 − 0.50 … 0.81 0.83 0.88 1.15 1.47 1.66 1.94 2.59 2.65 3.26

− 0.74 − 0.56 − 0.68 − 0.60 − 0.58 − 0.62 − 0.56 − 0.53 − 0.51 − 0.54 … 0.26 − 0.37 − 0.28 0.32 1.25 0.46 − 0.18 − 0.12 − 0.35 0.00

1 19 2 6 10 4 18 40 48 33 … 113 82 94 116 119 117 100 105 83 110 0.80

−0.42 −0.35 −0.30 −0.40 −0.48 −0.48 −0.48 −0.50 −0.49 0.01 … 1.12 2.88 3.88 1.95 1.69 3.38 6.63 6.54 6.96 8.38

10 15 19 11 6 5 4 1 2 47 … 96 111 114 108 105 113 117 116 118 119 0.77

Note: The table shows the ranking of countries on the basis of the median factor score. A low ranking refers to a stable country, a high ranking refers to an unstable country. obs is the number of factor scores available for the particular country, median is the median factor score, minimum is the minimum factor score, r min is the ranking on the basis of the minimum factor score. Maximum is the maximum factor score, r max is the ranking on the basis of the maximum factor score. The Spearman rank correlation is calculated for the median rank and the minimum rank, and the median rank and the maximum rank, respectively.

two indices as proposed by Hibbs (1973) (also based on PCA), and the predicted probability of a government change of Alesina et al. (1996), which is obtained from a logit model. Table 5 shows the correlation between these indices and variables that reflect the different dimensions of political instability. The table shows that the indices by Barro (1991), Perotti (1996) reflect politically motivated violence, while the indices of Hibbs (1973) capture both violence as well mass civil protest. However, the proxies used in the literature are not strongly related to our variables for the instability of the political regime as well as the instability within the political regime. The highest correlation coefficient for these dimensions is 0.49 (the revolutions and coups d'etat variable of Barro (1991) with the instability of the political regime). The index of Alesina et al. (1996), which is based on a logit model, is the only index that is moderately related with three out of the four dimensions that we distinguish in the data. 4. The impact of political instability on economic growth Using the newly obtained variables that reflect different dimensions of political instability, we re-examine the impact of political instability on economic growth. We have an unbalanced data set of (about) 90 countries for the period 1974–2003, which is split up in 6 equal periods of 5 years in order to examine long term growth effects. In our analysis, we focus on two different aspects of the relation between political instability and economic growth. Firstly, do the different dimensions have different effects on economic growth? Secondly, is the relation between political instability and economic growth a causal one? To address the latter question adequately, we follow the approach of Campos and Nugent (2002), who use a dynamic panel framework in which they use the concept of Granger causality (Granger, 1987). This approach amounts to evaluating the lagged impact of political instability on current values of economic growth, whilst controlling for the lagged effect of economic growth (and other explanatory variables). More specifically, we use the dynamic panel system GMM estimator of Blundell and Bond (1998). This choice is motivated by the fact that this estimator allows us to model both the lagged dependent variable as well as country fixed effects. In our view, including country fixed effects in the model is particularly important, since many of the significant variables identified by the empirical growth literature (such as ethno-linguistic fractionalization or geographical variables) are time invariant (an overview of economic growth determinants can be found in Durlauf et al., 2005). Furthermore, the GMM approach can be utilized to take account of the potential endogeneity of political instability by using lagged political instability variables as instrumental variables. The baseline model we estimate is an augmented version of the model of Islam (1995), who derives an estimable panel regression specification from the Solow (1956) growth model.8 In the next section, we also probe the robustness of our results

8

See also Mankiw et al. (1992).

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Table 4c Ranking most stable and unstable countries: instability within the political regime Ranking

Country

1 Cambodia 2 Iraq 3 Somalia 4 China 5 Myanmar 6 Russia 7 Mongolia 8 Korea, Dem. Rep. 9 Cuba 10 Romania … … 110 Sweden 111 Denmark 112 Netherlands 113 Belgium 114 Norway 115 Bolivia 116 Ecuador 117 Israel 118 Italy 119 Pakistan Spearman rank correlation with median ranking:

obs

median

minimum

r min

maximum

r max

3 5 3 6 3 3 5 6 6 5 … 6 6 6 6 6 4 5 6 5 2

−1.34 −1.13 −1.13 −1.11 −1.05 −1.05 −1.02 −0.99 −0.98 −0.98 … 1.35 1.38 1.44 1.52 1.54 1.55 1.64 1.68 1.93 1.97

−1.73 −1.25 −1.50 −1.43 −1.19 −2.01 −1.33 −1.20 −1.21 −1.06 … 0.94 1.22 0.90 0.89 1.20 1.17 1.53 1.32 0.97 1.72

2 13 6 7 18 1 8 16 15 29 … 109 116 107 106 115 114 118 117 110 119 0.90

−1.25 −0.45 −0.73 −0.55 −0.86 −0.95 −0.27 −0.91 −0.85 0.98 … 1.66 1.76 1.69 1.94 1.71 1.76 2.10 2.02 2.42 2.22

1 19 6 10 4 2 31 3 5 80 … 104 110 106 113 107 109 115 114 118 117 0.85

Note: The table shows the ranking of countries on the basis of the median factor score. A low ranking refers to a stable country, a high ranking refers to an unstable country. obs is the number of factor scores available for the particular country, median is the median factor score, minimum is the minimum factor score, r min is the ranking on the basis of the minimum factor score. Maximum is the maximum factor score, r max is the ranking on the basis of the maximum factor score. The Spearman rank correlation is calculated for the median rank and the minimum rank, and the median rank and the maximum rank, respectively.

Table 4d Ranking most stable and unstable countries: instability of the political regime Ranking

Country

obs

median

minimum

r min

maximum

r max

1 Indonesia 2 Namibia 3 Cuba 4 Jamaica 5 Germany 6 Singapore 7 Botswana 8 Cyprus 9 Zimbabwe 10 Ivory Coast … … 110 Ecuador 111 Central African Republic 112 Cambodia 113 Italy 114 Lebanon 115 Pakistan 116 Afghanistan 117 Niger 118 Bangladesh 119 Burundi Spearman rank correlation with median ranking:

5 2 6 5 5 6 6 1 5 6 … 5 3 3 5 3 2 4 2 1 2

−0.86 −0.86 −0.82 −0.79 −0.77 −0.74 −0.73 −0.68 −0.67 −0.66 … 0.72 0.81 0.91 1.02 1.24 1.37 1.85 1.85 1.95 2.27

−0.92 −0.90 −0.83 −0.82 −1.15 −0.84 −0.89 −0.68 −0.86 −0.83 … −0.49 −0.17 0.14 0.13 0.76 0.55 0.53 1.60 1.95 1.32

10 11 25 35 1 22 13 59 17 32 … 84 106 112 111 116 115 114 118 119 117 0.75

−0.15 −0.81 −0.43 0.04 −0.41 −0.06 −0.42 −0.68 2.23 1.77 … 1.11 0.81 1.44 2.36 1.79 2.18 2.60 2.11 1.95 3.21

17 1 5 30 7 24 6 2 111 99 … 78 68 90 113 100 110 117 107 106 118 0.53

Note: The table shows the ranking of countries on the basis of the median factor score. A low ranking refers to a stable country, a high ranking refers to an unstable country. obs is the number of factor scores available for the particular country, median is the median factor score, minimum is the minimum factor score, r min is the ranking on the basis of the minimum factor score. Maximum is the maximum factor score, r max is the ranking on the basis of the maximum factor score. The Spearman rank correlation is calculated for the median rank and the minimum rank, and the median rank and the maximum rank, respectively.

including various alternative “growth determinants” in our model specification that may affect the relation between political instability and economic growth. The baseline model specification is:
lnðgit Þ ¼ α i þ ut þ βln gi;t−1 þ γlnðZit Þ þ λXit þ eit

R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

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Table 5 Correlations between the dimensions of political instability and other indices used in the literature Index:

Violence

Protest

Regime

Within

Revolutions and Coups Barro (1991) Assassinations Barro (1991) PC index Perotti (1996) PC Protest Hibbs (1973) PC Violence Hibbs (1973) Logit Alesina et al. (1996)

0.81 0.53 0.79 0.22 0.87 0.55

0.09 0.22 0.23 0.98 0.29 0.64

0.49 0.23 0.42 0.23 0.33 0.41

−0.10 0.12 −0.04 0.06 −0.02 0.14

Note: The indices are calculated using the methodology and variables as described in the relevant reference using our sample of countries for the period 1974– 2003. PC denotes Principal Component.

Table 6 Baseline estimation results Dependent variable: real GDP growth per capita

(1)

(2)

(3)

(4)

Period of factor analysis

74–03

74–03

84–03

84–03

Contemporaneous vs. Granger Causality

Cont.

Granger

Cont.

Granger

Lagged real GDP growth per capita

−0.230 (1.80)⁎ −0.048 (2.74)⁎⁎⁎ 0.010 (1.15) −0.035 (1.50) 0.012 (0.85) 0.104 (3.90)⁎⁎⁎ 0.003 (0.15) −0.398 (1.41) −0.117 (1.59) 322 90 15.80 0.00 −1.31 0.19 73.42 0.31 78.96 0.00

−0.154 (1.16) −0.046 (2.01)⁎⁎ 0.011 (0.89) 0.037 (1.62) 0.016 (0.94) 0.096 (2.76)⁎⁎⁎ −0.021 (1.05) −0.175 (0.37) −0.082 (0.83) 317 94 6.28 0.10 −0.46 0.64 60.76 0.31 74.19 0.00

−0.132 (0.91) −0.023 (1.31) 0.010 (1.09) −0.025 (0.91) 0.022 (1.92)⁎ 0.083 (3.63)⁎⁎⁎ 0.007 (0.47) −0.212 (0.63) −0.110 (1.64) 310 86 25.57 0.00 −0.09 0.93 34.14 0.73 92.52 0.00

−0.063 (0.63) −0.074 (2.55)⁎⁎ 0.003 (0.21) 0.049 (2.00)⁎⁎ 0.018 (0.82) 0.061 (1.79)⁎ −0.020 (1.02) 0.397 (0.68) −0.065 (0.53) 236 87 1.58 0.45 n.a. n.a. 29.06 0.18 23.14 0.00

Regime instability Mass civil protest Within instability Politically motivated violence Investment Secondary school enrollment Population growth Constant Observations Countries F-test time fixed effects p-value Arellano Bond test for autocorrelation p-value Hansen test for over-identifying restrictions p-value Wald test p-value

Notes: Estimation method: System GMM (Blundell and Bond, 1998), Corrected z-statistics in parentheses, ⁎ significant at 10%; ⁎⁎ significant at 5%; ⁎⁎⁎ significant at 1%.

where git is the real GDP growth rate per capita in country i for the 5-year period starting in year t, αi refers to a country fixed effect and φt is a time fixed effect.9 The vector Zit contains the baseline explanatory variables from the Solow framework. The vector consists of: the ratio of investments to GDP at the beginning of period t, the level of secondary school enrolment in country i at the beginning of period t, and population growth in country i in period t. Apart from the schooling variable, which is taken from Barro and Lee (2000), all variables are from the Penn World Table 6.2 (Heston et al., 2002). The vector Xit contains the four political instability indicators for country i in period t.10 Finally, εit is a random error term. Appendix B contains descriptive statistics of all variables used in the analysis. Table 6 shows the baseline estimation results. In column 1, we include the contemporaneous values of political instability using the factor scores obtained from the 1974–2003 sample, while we use the lagged values in column 2. In columns 3 and 4 we follow the same procedure, but there we use the factor scores obtained from the 1984–2003 sample. The specification tests for residual autocorrelation and over-identifying restrictions that are reported at the bottom of Table 6 indicate no misspecification.

9 We test for the presence of time fixed effects using an F-test that tests the linear restriction that all time effects equal 0. If the tests rejects the null-hypothesis (on the 10% significance level), we include time fixed effects in the model. If the null-hypothesis is not rejected, we omit time fixed effects from the specification. 10 When Granger causality is addressed we include the lagged values of the political instability indicators.

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R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

Table 7 Sensitivity tests Dependent variable: real GDP growth per capita

Lagged real GDP growth per capita Investment Secondary school enrollment Population growth Regime instability Mass civil protest Within instability Politically motivated violence Democracy (Polity IV)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

−0.131 (0.82) 0.099 (2.75)⁎⁎⁎ −0.011 (0.66) −0.217 (0.46) −0.045 (2.13)⁎⁎ 0.012 (1.12) 0.031 (0.87) 0.012 (0.67) −0.003 (0.58)

−0.156 (1.08) 0.107 (3.02)⁎⁎⁎ −0.020 (1.12) −0.290 (0.66) −0.043 (1.98)⁎⁎ 0.011 (0.90) 0.046 (1.44) 0.009 (0.51)

−0.127 (0.80) 0.096 (2.84)⁎⁎⁎ −0.014 (0.79) −0.236 (0.53) −0.047 (1.95)⁎ 0.012 (1.05) 0.025 (0.68) 0.011 (0.60)

−0.040 (0.39) 0.056 (1.76)⁎ −0.022 (1.53) 0.229 (0.56) −0.048 (3.79)⁎⁎⁎ 0.014 (1.05) 0.017 (0.76) 0.020 (1.87)⁎

− 0.021 (0.20) 0.077 (2.55)⁎⁎ − 0.016 (1.00) − 0.079 (0.18) − 0.047 (3.49)⁎⁎⁎ 0.011 (0.91) 0.019 (0.87) 0.017 (1.38)

−0.054 (0.72) 0.100 (3.53)⁎⁎⁎ −0.012 (0.32) 0.048 (0.15) −0.071 (3.61)⁎⁎⁎ 0.018 (1.50) 0.012 (0.53) 0.014 (1.06)

−0.092 (0.77) 0.097 (2.24)⁎⁎ −0.034 (1.92)⁎ −0.428 (1.46) −0.054 (3.29)⁎⁎⁎ 0.008 (0.74) 0.055 (2.72)⁎⁎⁎ 0.023 (1.78)⁎

− 0.169 (1.32) 0.093 (2.66)⁎⁎⁎ − 0.024 (1.11) 0.299 (0.66) − 0.039 (2.09)⁎⁎ 0.011 (1.03) 0.052 (2.79)⁎⁎⁎ 0.016 (1.43)

−0.150 (1.33) 0.098 (3.06)⁎⁎⁎ −0.023 (1.16) −0.128 (0.24) −0.047 (2.68)⁎⁎⁎ 0.008 (0.72) 0.045 (1.95)⁎ 0.016 (1.21)

−0.016 (0.16) 0.049 (1.60) −0.019 (1.12) 0.383 (0.76) −0.041 (2.74)⁎⁎⁎ 0.015 (1.19) 0.032 (1.34) 0.018 (1.43)

−0.042 (0.92)

Democracy (Vanhanen) Autocracy (Przeworski et al.)

0.034 (0.54)

Rule of law

0.023 (2.45)⁎⁎

0.017 (1.95)⁎ − 0.002 (0.23)

Corruption Wage inequality

0.127 (0.45) −0.020 (1.14)

Globalization

− 0.070 (6.62)⁎⁎⁎

Inflation Government share in GDP Constant Observations Countries F-test time fixed effects P-value Arellano bond test for autocorrelation P-value Hansen test for over-identifying restrictions P-value Wald test P-value

−0.102 (1.04) 316 93 9.18 0.03 −0.36 0.72 59.22 0.36 94.79 0.00

−0.107 (1.04) 314 93 5.00 0.17 −0.33 0.74 59.01 0.37 57.94 0.00

−0.109 (0.99) 315 92 9.75 0.02 −0.36 0.72 61.38 0.29 89.94 0.00

−0.099 (0.96) 298 88 8.61 0.04 0.51 0.61 53.76 0.56 82.40 0.00

− 0.057 (0.47) 298 88 9.62 0.02 0.54 0.59 52.14 0.62 72.13 0.00

−0.133 (1.06) 195 78 7.85 0.05 −0.65 0.52 54.63 0.53 70.31 0.00

−0.022 (0.25) 290 85 5.50 0.14 0.76 0.45 57.30 0.43 51.64 0.00

− 0.118 (1.34) 303 92 3.45 0.33 − 0.86 0.39 52.75 0.60 144.57 0.00

−0.064 (7.56)⁎⁎⁎ −0.019 (0.46) −0.058 (0.32) 317 94 5.66 0.13 −0.33 0.74 58.08 0.40 43.27 0.00

−0.099 (0.90) 289 87 4.81 0.19 −0.82 0.41 55.35 0.50 351.66 0.00

Notes: Estimation method: System GMM (Blundell and Bond, 1998), Corrected z-statistics in parentheses, ⁎ significant at 10%; ⁎⁎ significant at 5%; ⁎⁎⁎ significant at 1%.

Table 8 Estimation results when specific regions are excluded from the sample Dependent variable: real GDP growth per capita

Region excluded: Sub Saharan Africa

Southeast Asia

Middle East & North Africa

Latin America

Regime instability

−0.047 (2.14)⁎⁎ 0.016 (1.77)⁎ 0.001 (0.03) 0.000 (0.01) 233 67

−0.051 (2.52)⁎⁎ 0.018 (1.07) 0.022 (0.89) 0.009 (0.70) 284 83

−0.042 (1.89)⁎ 0.012 (1.01) 0.044 (1.70)⁎ 0.010 (0.49) 295 87

−0.047 (2.26)⁎⁎ 0.009 (0.85) 0.021 (0.82) 0.020 (1.13) 245 72

Mass civil protest Within instability Politically motivated violence Observations Countries

Notes: Estimation method is System GMM (Blundell and Bond, 1998). The used model specification is the same as column 2 table 2.5. For brevity sake, the estimation results for the other control variables and the specification tests are suppressed. Corrected z-statistics in parentheses, ⁎ significant at 10%; ⁎⁎ significant at 5%; ⁎⁎⁎ significant at 1%.

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Table 9 Examination of reverse causality (1)

(2)

(3)

(4)

Dependent variable:

Violence

Protest

Within

Regime

Lagged real GDP growth per capita

−0.588 (1.77)⁎ 0.669 (7.80)⁎⁎⁎

0.178 (0.83)

0.617 (2.72)⁎⁎⁎

−0.554 (1.02)

Politically motivated violence Mass civil protest

0.608 (7.98)⁎⁎⁎

Within instability

0.516 (5.28)⁎⁎⁎

Regime instability Constant Observations Countries F-test time fixed effects P-value Arellano bond test for autocorrelation P-value Hansen test for overidentifying restrictions P-value Wald test P-value

0.001 (0.03) 321 102 5.35 0.15 −0.44 0.66 24.65 0.22 89.10 0.00

−0.141 (3.97)⁎⁎⁎ 321 102 10.42 0.02 −0.73 0.47 22.51 0.31 74.02 0.00

0.173 (3.04)⁎⁎⁎ 321 102 26.34 0.00 −0.70 0.48 16.51 0.68 103.58 0.00

0.192 (2.49)⁎⁎ −0.311 (4.96)⁎⁎⁎ 321 102 20.54 0.00 0.96 0.34 22.13 0.33 33.48 0.00

Notes: Estimation method is System GMM (Blundell and Bond, 1998), Corrected z-statistics in parentheses, ⁎ significant at 10%; ⁎⁎ significant at 5%; ⁎⁎⁎ significant at 1%.

For the 1974–2003 sample, we find that the instability of the political regime is the only dimension of political instability that is significantly related to economic growth — both contemporaneous and lagged. For the 1984–2003 sample, the results are more diverse. Here, we find that both the instability of the political regime and the instability within the political regime exert a lagged effect on economic growth. The impact of the “within” dimension, however, is positive. Quite remarkably, we find in one specification that political violence has a positive impact on economic growth. As to the control variables, we find that only the investment-GDP ratio is strongly positively related to economic growth. The baseline estimates clearly indicate that the different dimensions have different effects on economic growth. Not only the significance of the estimated coefficients differ, also the sign, and size of the estimated impact is different. In the conclusion of the paper, we provide our views on the relation with the existing literature on the topic. However, we first examine the robustness of the baseline estimation results. 5. Robustness analysis An often recurring critique in the empirical growth literature relates to the proper model specification. As Durlauf and Quah (1998) put it: economic growth theories are open-ended, i.e., one theory does not necessarily invalidate a different theory. Similarly, it may be that omitted variables drive the results reported above. Therefore, we test the sensitivity of our estimates including a range of alternative variables. These variables are: democracy (3 measures taken from Marshall and Jaggers (2002), Przeworski et al. (2000), and Vanhanen (2000), respectively), the rule of law (ICRG, 2005), corruption (ICRG, 2005), wage inequality (UTIP, 2006), a measure of openness (proxied by the KOF globalization index of Dreher, 2006), inflation (World Bank Development Indicators, 2007), and the size of government as a share of GDP (Heston et al., 2002). As our benchmark model, we use the specification of column 2 in Table 6 (hence focusing on the lagged effect of political instability for the sample 1974–2003). The results are reported in Table 7. In columns 1–9, we have included each of these control variables one by one. In column 10, we have added all significant control variables to the benchmark specification. As these variables are often significant in cross-country growth analysis, it is remarkable to observe that almost all of these variables are insignificant in the dynamic panel framework. One possible explanation is the inclusion of country fixed effects in our regressions.11 Yet, we still find that (the lagged value of) the instability of the political regime is significant in all specifications. Furthermore, we find that in some of the specifications the instability within the political regime is, again, positively related to economic growth. To further check the robustness of the results, we examine whether the results are driven by specific regions. This choice is motivated by the findings of Campos and Nugent (2002), who argue that evidence for a causal effect of political instability on

11 In fact, when we estimate a simple pooled time series cross-section model (without country and time specific effects), many of the control variables turn out to be significant.

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economic growth is driven by the set of Sub-Saharan African countries in the sample. In Table 8, we report estimation results when we exclude different regions from the sample. It turns out the exclusion of different regions does not influence our main finding that the instability of the political regime causally affects economic growth. Finally, we examine the possibility of reverse causality running from economic growth to the level of political instability. We use a similar setup as above, using each of the political instability dimensions as the dependent variable. The results are reported in Table 9. Apart from the finding that the dimensions of political instability are persistent over time, we find that higher growth rates lead to lower levels of politically motivated violence. Furthermore, we find that economic growth spurs the instability within the political regime, which indicates a two-way causal relationship between these variables. Finally, we find no effect running from economic growth to the other dimensions of political instability. This leads us to the conclusion that there is a one-way causal impact of instability of the political regime on economic growth, while there seems to be no relation (whatsoever) between mass civil protest and economic growth. 6. Conclusion In this paper, we have examined the multidimensionality of political instability. Moreover, we have examined whether the different dimensions of political instability have a different impact on economic growth and whether this effect is causal. We have used an Exploratory Factor Analysis and conclude that four dimensions of political instability can be distinguished. These dimensions are: (1) politically motivated violence, (2) mass civil protest, (3) instability within the political regime, and (4) instability of the political regime. The dimensions identified by our analysis are similar to the proposed framework of Sanders (1981), who argues that the first two dimensions are challenges to the political regime, while the latter two comprise actual changes of government or the political regime. We do not fully share this view, however, since the instability reflected by the third dimension clearly not only refers to actual changes, but also the potential for change as reflected by, e.g., the number of elections and the degree of fractionalization. When we include the dimensions of political instability in an empirical growth model, we find that two dimensions have a causal impact on economic growth. Firstly, we find that the instability of the political regime has a negative impact on economic growth. In our view, the instability of the political regime (and not instability within the political regime) comes closest to the concept that scholars have in mind when they refer to the uncertainty that investors face concerning the security of property rights as in e.g. Svensson (1998). However, in our regressions, we find that even when we control for formal institutions that reflect the security of property rights (rule of law) in countries, the instability of the political regime remains significant. One plausible explanation is that the security of property rights reflects de jure uncertainty, while the instability of the political regime measures de facto uncertainty. Second, we find some evidence that more instability within the political regime is good for economic growth. This finding is in contrast with the predictions of the model of Darby et al. (2004), who argue that political instability within governments reduces the probability of re-election, which leads to lower public investments and, hence, lower economic growth rates. However, this finding does lend support for the model of Besley et al. (2005). In their model, more political competition is good for economic growth as incompetent politicians can be held accountable for the implementation of inappropriate government policies and will be replaced by more competent politicians. Thirdly, we find that there is also some evidence for reverse causality. That is, economic growth fosters instability within the political regime, whereas the lack of growth triggers politically motivated violence. As we find that this dimension has overlap with democracy, it can be interpreted that democracy (or political competition) not only triggers economic growth, but that there are clearly feedback effects (and vice versa). The second effect is in line with results of, e.g., Miguel et al. (2004), who also use an instrumental variables set-up and find that, in Sub-Saharan Africa, adverse economic shocks increase the probability of civil conflict. There are, of course, several limitations to our study. One issue that is hard to address is the effect of missing observations and data quality. This may potentially bias the estimates, as it is likely that data is missing for highly unstable polities. Furthermore, a different source of measurement error may still be present in the data as it is likely that highly repressive regimes under (or over) report incidences of revolt, or peaceful protest. In these regimes, the press is typically under control of the political leader and is used as an instrument for propaganda. In our view, the net effect of this type of measurement error is a priori unclear. A second, more pressing issue, relates to the chosen framework of 5-year economic growth averages as the dependent variable in the empirical analysis. It may well be that the growth rate at the beginning of the period is high, while in the second half of the period the growth rate may be low (or even negative). In such a situation, the 5-year average may be meaningless, especially when the break in the economic growth rate is caused by political instability. Alternative approaches such as Hausmann et al. (2005), who focus on variables that trigger periods of rapid economic growth, and, Hausmann et al. (2006), who focus on triggers of growth collapses, seem very promising to deal with this problem. Acknowledgments I would like to thank participants of the 2006 SOM PhD. Conference, at the University of Groningen, participants of the EPCS annual meeting 2006 in Turku, Finland and participants of the 15th Silvaplana workshop in Political Economy in Silvaplana, Switzerland for helpful comments. I would also like to thank Jakob de Haan, Henri de Groot, Bart Los, Erik Meijer, Mark Mink and three anonymous referees for their valuable comments and suggestions that improved this paper markedly.

R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

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Appendix A. List of variables, definitions and sources

Indicator

Definition

Source

Assassinations

Any politically motivated murder or attempted murder of a high government official or politician.

Cabinet changes

Databanks International (2005) Databanks International (2005) Gleditsch et al. (2002) Databanks International (2005) Databanks International (2005) Databanks International (2005) ICRG (2005)

The number of times in a year that a new premier is named and/or 50% of the cabinet posts are occupied by new ministers. Dummy variable, 1 if at least 1000 battle related deaths per year in a conflict between the government of a state and internal opposition groups without foreign intervention and 0 otherwise. The number of extraconstitutional or forced changes in the top government elite and/or its effective control of the nation's power structure in a given year. Any rapidly developing situation that threatens to bring the downfall of the present regime, excluding situations of revolt aimed at such overthrow. Any peaceful public gathering of at least 100 people for the primary purpose of displaying or voicing their opposition to government policies or authority, excluding demonstrations of a distinctly anti-foreign nature. An assessment of the degree of tensions within a country which is attributable to racial, nationality or language divisions. The number of times in a year that effective control of the executive changes hands. Databanks International (2005) The probability that two deputies picked at random from the legislature will be of different parties. Beck et al. (2001) An assessment of the governments ability to carry out its declared programs and its ability to stay in office. ICRG (2005) Any armed activity, sabotage, or bombings carried on by independent bands of citizens or irregular forces and Databanks aimed at the overthrow of the present regime. International (2005) An assessment of political violence in the country and its actual or potential impact on governance. ICRG (2005) The number of basic alterations in a state's constitutional structure, the extreme case being the adoption of a Databanks new constitution that significantly alters the prerogatives of the various branches of government. International (2005) Dummy variable, 1 if there are more than 25 battle related deaths per year and a total conflict history of more Gleditsch et al. than 1000 battle related deaths, but fewer than 1000 per year (between the government of a state and internal (2002) opposition groups without foreign intervention) and 0 otherwise. Dummy variable, 1 if there are at least 25 battle related deaths per year for every year in the period in a conflict Gleditsch et al. (2002) between the government of a state and internal opposition groups, without foreign intervention and 0 otherwise. The number of elections held for the lower house of a national legislature in a given year. Databanks International (2005) Maximum polarization between the executive party and the four principle parties of the legislature. Beck et al. (2001) Number of years that the party of the chief executive has been in office. Beck et al. (2001)

Civil war Coups d'etat Major government crises Demonstrations Ethnic tensions Executive changes Fractionalization Government stability Guerilla warfare Internal conflicts Major constitutional changes Medium civil conflicts

Minor civil conflicts

Number of elections Polarization Years of ruling party in office Purges Regime changes Religious tensions Revolutions riots

Number of systematic repressions (or eliminations) by jailing or execution of political opposition within the rank of the regime or the opposition. Dummy variable, 1 if the variable "durable" is 0 in the polity IV dataset, which means that a new regime has started or that the state is in anarchy, 0 otherwise. An assessment of the degree of tensions within a country which is attributable to religious divisions. Any illegal or forced change in the top governmental elite, any attempt at such a change, or any successful or unsuccessful armed rebellion whose aim is independence from the central government. Any violent demonstration or clash of more than 100 citizens involving the use of physical force.

Number of veto players The percent of veto players that drop from the government given the senate does not change. who drop from office Strikes Any strike of 1,000 or more industrial or service workers that involves more than one employer and that is aimed at national government policies or authority.

Databanks International (2005) Marshall and Jaggers (2002) ICRG (2005) Databanks International (2005) Databanks International (2005) Beck et al. (2001) Databanks International (2005)

Appendix B. Descriptive statistics

Variable

N

Mean

St. dev.

Min

Max

Economic growth

631

0.05

0.20

−1.7

0.9

Factor scores: 1974–2003 Politically motivated violence Mass civil protest Instability of the political regime Instability within the political regime

546 546 546 546

−0.01 0.06 −0.08 0.04

0.97 1.01 0.77 0.92

−0.7 −0.7 −1.2 −2.0

6.2 8.4 3.6 2.6

Factor scores 1984–2003 Politically motivated violence Mass civil protest

376 376

−0.01 0.11

0.99 1.06

−0.9 −0.7

5.2 7.4 (continued on next page)

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R. Jong-A-Pin / European Journal of Political Economy 25 (2009) 15–29

Appendix B (continued) Variable

N

Mean

St. dev.

Min

Max

Factor scores 1984–2003 Instability of the political regime Instability within the political regime

376 376

−0.05 0.07

0.82 0.94

− 1.4 − 1.6

3.9 3.1

Baseline control variables GDP per capita Investment GDP ratio Population growth Secondary school enrollment

763 769 810 627

8190.64 16.08 0.09 26.98

9226.96 9.43 0.07 16.99

171.4 0.2 − 0.3 0.4

84408.2 60.6 0.7 72.3

Other control variables Democracy (Polity IV) Democracy (Vanhanen) Autocracy (Przeworski et al.) Globalization Rule of law Corruption Wage inequality Inflation Government share in GDP

738 773 782 642 483 483 439 708 763

0.28 0.46 0.58 1.88 3.61 3.29 0.06 85.15 21.23

7.63 0.50 0.49 0.86 1.58 1.41 0.07 1041.54 9.81

− 10 0 0 0.61 0 0 0.00 − 23.48 3.23

10 1 1 5.21 6 6 0.88 26762.02 73.17

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