AN ECONOMIC EXPLANATION OF THE NATIONALIZATION OF ELECTORAL POLITICS

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systems, if we ran an interaction between the output gap and the dummy variable
separating electoral systems, the high correlation between individual fixed effects and
the dummy variable would generate a severe problem of multicollinearity. The
descriptive statistics are displayed in Table 1. x
Table 1: Descriptive statistics*
Nationalization Output gap Democracy Majoritarian PR and mixed
Mean 0.797 -0.004 54.48 0.266 0.734
Median 0.823 -0.003 42.00 0 1
Maximum 0.949 0.151 210.00 1 1
Minimum 0.387 -0.160 0 0 0
Std. deviation 0.104 0.046 46.38 0.442 0.442
Observations 384 384 384 384 384
*For model 3 using Bochsler's measure in Table 2.
The pooled time-series cross-sectional models are estimated using a fixed-effect
specification to account for idiosyncratic effects for different countries. To correct for
heteroscedasticity, standard errors are replaced with clustered residuals. Furthermore,
the inclusion in the model of both fixed effects and the lagged value of the dependent
variable makes the Least Square Dummy Variable (LSDV) biased (Nickell, 1981). To
measure the bias in the estimates and to take into account the strongly unbalanced panel
(the number of observations goes from 1 in Ghana to more than 20 in the United
Kingdom and the United States), the bias-corrected LSDV estimators for standard
autoregressive panel data models suggested by Bruno (2005) are also computed. xi With
respect to serially correlated errors, the LM test shows a first-order serial correlation in
the residuals when using Bochsler's measure, but not when using Kasuya and Moenius’
measure. As the lag of the dependent variable is included in the model, the model using
Bochsler's measure is also estimated using a Marquardt NLLS algorithm, which follows
an iterative procedure. In sum, the specification is run using the standard LSDV
estimation, the Bruno correction of the Nickell bias in the LSDV estimation, and the
NLLS estimator to deal with serial correlation.