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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.
We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is distributed among many models, suggesting the superiority of BMA over any single model. Out-of-sample predictive results support that claim. In contrast with Levine and Renelt (1992), our results broadly support the more “optimistic” conclusion of Sala-i-Martin (1997b), namely, that some variables are important regressors for explaining cross-country growth patterns. However, the variables we identify as most useful for growth regression differ substantially from Sala-i-Martin’s results.