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A vast literature uses cross-country regressions to find empirical links between policy indicators and long-run average growth rates. But conclusions from those studies are fragile if there are small changes in the independent variables.
We extend the sensitivity analysis of cross-country growth regressions of Levine and Renelt (1992) by introducing a semi-parametric formulation of their regression function. Our results differ from theirs in how certain policy variables affect growth rates. We find that distortion variables, such as the standard deviation of gross domestic credit and inflation and real exchange rate distortions, have a robust negative effect on growth.
This paper addresses the issue of outliers in finance-growth literature and provides a robust sensitivity analysis of some past studies and an updated data set. We employ the robust regression methods of median quantile regression and least trimmed squares. It shows that the findings of past studies are sensitive to outlier observations. Further, we find that the positive effect of financial development on growth disappears and even becomes negative once we use our extended data set of 86 countries over the period 1997-2006. This last finding is consistent with Rousseau and Wachtel (2011). Moreover, we investigate whether our understanding of the finance-growth relationship can further be improved by introducing a measure of R&D into the standard analysis. We note that our measure of R&D has a strong positive effect on growth and may proxy the role of an omitted variable which is highly correlated with economic growth.
The design, implementation, and interpretation of cross- country investigations should be improved. This review of conceptual, methodological, and statistical weaknesses in cross- country studies suggests that existing findings warrant only limited confidence.
Sample sizes in cross-country growth regressions vary greatly, depending on data availability. But if the selected samples are not representative of the underlying population of nations in the world, ordinary least squares coefficients (OLS) may be biased. This paper re-examines the determinants of economic growth in cross-sectional samples of countries utilizing econometric techniques that take into account the selective nature of the samples. The regression results of three major contributions to the empirical growth literature by Mankiw-Romer-Weil (1992), Barro (1991) and Mauro (1995), are considered and re-estimated using a bivariate selectivity model. Our analysis suggests that sample selection bias could significantly change the results of empirical growth analysis, depending on the specific sample utilized. In the case of the Mankiw-Romer-Weil paper, the value and statistical significance of some of the estimated coefficients change drastically when adjusted for sample selectivity. But the results obtained by Barro and Mauro are robust to sample selection bias.
This paper is a substantially revised version of our earlier working paper, "Truth and Robustness in Cross-country Growth Regressions." The most important revisions concern the handling of missing observations in the cross-country data set. In the earlier paper, these had been handled through case-wise deletion-the method common to all previous studies. In this version, they are handled through multiple imputation-a method that retains substantially more of the information content of the data set. Two variants of Leamer's (1983) extreme-bounds analysis are evaluated for their ability to recover the true specification and compared to a cross-sectional version of the general-to-specific search methodology associated with the LSE approach to econometrics. Evaluations are based on a realistic Monte Carlo experiment in which the universe of potential determinants is drawn from those in Levine and Renelt's (1992) study. Levine and Renelt's method is shown to have low size and extremely low power: nothing is robust. Sala-i-Martin's (1997a, b) method is shown to have high size and high power: it is undiscriminating. The general-to-specific methodology 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 Sala-i-Martin's original study. The results are consistent with the Monte Carlo results and suggest that only a few of the 61 potential determinants of growth matter.