Truth and Robustness in Cross-country Growth Regressions

Kevin D. Hoover, Stephen J. Perez
2001 Social Science Research Network  
We thank Oscar Jorda, Judith Giles, Wayne Joerding, and participants in seminars at the University of California, Irvine, and the University of Victoria for helpful comments on an earlier draft. We also thank Orley Ashenfelter for his help in getting this project off the ground. Abstract The work of Levine and Renelt (1992) and Sala-i-Martin (1997a, b) which attempted to test the robustness of various determinants of growth rates of per capita GDP among countries using two variants of Edward
more » ... mer's extreme-bounds analysis is reexamined. In a realistic Monte Carlo experiment in which the universe of potential determinants is drawn from those in Levine and Renelt's study, both versions of the extreme-bounds analysis are evaluated for their ability to recover the true specification. Levine and Renelt's method is shown to have low size and extremely low power: nothing is robust; while Sala-i-Martin's method is shown to have high size and high power: it is undiscriminating. Both methods are compared to a cross-sectional version of the generalto-specific search methodology associated with the LSE approach to econometrics. It is shown to have size near nominal size and high power. Sala-i-Martin's method and the general-to-specific method are then applied to the actual data from the original two studies. The results are consistent with the Monte Carlo results and are suggestive that the factors that most affect differences of growth rates are ones that are beyond the control of policymakers. JEL Classification: C4, C8, O4
doi:10.2139/ssrn.258608 fatcat:fsx2n7sgmbajtgikptwkd4v6tu