Nonlinearities and Robustness in Growth Regressions
探讨增长回归中解释变量选择的稳健性问题，指出传统极端边界分析可能过于悲观，非线性方法能识别更多稳健的增长决定因素。
Much economic growth research has been devoted to determining the explanatory variables that explain cross-country variation in growth rates. A frequently cited problem with this literature is that the number of potential growth regressors is vast, potentially exceeding the number of countries available for study. Thus, researchers are faced with the task of arbitrarily specifying which explanatory variables to include in their growth regressions, raising concerns about how confident we can be in their results. These concerns were magnified by the influential paper of Ross Levine and David Renelt (1992), in which they employ a variation of Edward E. Leamer’s (1983) extreme bounds analysis to test the robustness of conventional growth regression coefficients to changes in the set of conditioning variables. They conclude that the results of this literature are extremely fragile, with the only robust determinants of growth being physical capital investment, initial income, and secondary school enrollment. In contrast, they demonstrate the fragility of a host of fiscal, monetary, and trade policy variables, as well as measures of political and economic stability and economic distortions. There have been two main responses to their findings. The pessimistic response has been to conclude, given the lack of a reliable statistical relationship between conventional macroeconomic indicators and growth, that cross-country growth regressions cannot tell us anything about growth. The more optimistic response has been to argue that the extreme bounds analysis Growth, Education, and invEstmEnt in childrEn †