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Ari Aisen: International Monetary Fund (email@example.com). Francisco Jose Veiga: Universidade do Minho and NIPE Escola de Economía e Gestão, 4710-057 Braga, Portugal (firstname.lastname@example.org)
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.
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).
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 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).