Table 4: Benchmark regressions for proximate correlates of growth polarity, 1968–2008, with real GDP growth rates<sup>*</sup>

||**P1**|**P2**|**P3**|**P4**|**P5**|**P6**|
|---|---|---|---|---|---|---|
|Population|-2.078|-0.053|-1.827|2.767|2.304|-2.794|
|growth|(1.39)|(0.18)|(1.43)|(1.64)<sup>_∗_</sup>|(1.41)|(2.33)|
|Investment|0.302|0.083|0.282|0.254|0.257|0.560|
||(0.22)|(0.13)|(0.36)|(0.24)|(0.14)<sup>_∗_</sup>|(0.37)|
|Enrollment|0.116|-0.037|0.335|-0.055|0.009|-0.063|
||(0.04)<sup>_∗∗∗_</sup>|(0.06)|(0.13)<sup>_∗∗_</sup>|(0.04)|(0.05)|(0.12)|
|Income per||||0.095|0.103|0.168|
|capita||||(0.02)<sup>_∗∗∗_</sup>|(0.02)<sup>_∗∗∗_</sup>|(0.05)<sup>_∗∗∗_</sup>|
|Life||||-0.237|-0.361|-0.352|
|expectancy||||(0.11)<sup>_∗∗_</sup>|(0.19)<sup>_∗_</sup>|(0.48)|
|Dependency||||-0.494|-0.442|0.032|
|ratio||||(0.12)<sup>_∗∗∗_</sup>|(0.16)<sup>_∗∗∗_</sup>|(0.30)|
|Government||||-0.887|-1.085|-1.545|
|size||||(0.23)<sup>_∗∗∗_</sup>|(0.35)<sup>_∗∗∗_</sup>|(0.68)<sup>_∗∗_</sup>|
|Adjusted _R_<sup>2</sup>|0.070|||0.274|||
|_F_|||2.709<sup>_∗∗∗_</sup>|||3.067<sup>_∗∗∗_</sup>|
|Hansen _J_|||31.644|||60.344|
|AR(2)|||-0.169|||0.429|
|Estimator|OLS|RE|S-GMM|OLS|RE|S-GMM|
|Instruments|||42|||74|
|_N_|682|682|682|633|633|633|

> * Growth rates calculated from GDP data in real 2000 U.S. dollars. Sampling frame selected to maximize sample size, and is hence unbalanced. Data periodicity are fiveyear averages. All variables are in log form. Heteroskedasticity and autocorrelationrobust standard errors are reported in parentheses. A lagged dependent variable (GMM only), period fixed effects, and a constant term (all specifications) were included in the regressions, but not reported.<sup>_∗_</sup> indicates significance at 10 percent level,<sup>_∗∗_</sup> indicates significance at 5 percent level, and<sup>_∗∗∗_</sup> indicates significance at 1 percent level.

Indeed, per capita income appears to be strongly correlated with growth polarity. In some ways, this is not entirely surprising: economies that have historically been significant drivers of global growth are also generally the ones that have become developed, and this is reflected in the strong positive coefficient on the GDP per capita variable. The magnitude of this effect is relatively small, however, despite its strong statistical significance: a one percent increase in per capita income raises the polarity measure by about 0.1 percent.

Across the three specifications, a one percentage point increase in the size of government leads to a decrease in the polarity measure that ranges [0 _._ 9 _,_ 1 _._ 5] percent. This negative coefficient for government size should be interpreted in light of the fact that the specification already controls for per capita income levels. Since more developed economies tend, for political economy reasons, to have greater government expenditures as a result of more advanced social insurance programs (Iversen & Cusack 2000), they also possess, in general, larger governments. The alleviated by the logarithmic transform. For the panel regressions, normal probability plots (shown in Figure A.3 the annex) suggest that non-normality in the residuals is not an issue (we refrain from formal statistical tests for the normality of errors in this case, since such tests are typically rejected for large sample sizes). The Kolmogorov-Smirnov test statistic was applied to the cross-section regressions that follow, however, and the results (available on request) are supportive of the normality of the error distribution.

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