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An analysis of sample selection bias in cross-country growth regressions

  • Jayant Ray


    (Columbia University - Department of International & Public Affairs)



    (Columbia University - Department of International & Public Affairs)

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.

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Paper provided by Columbia University, Department of Economics in its series Discussion Papers with number 0102-10.

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Length: 45 pages
Date of creation: 2002
Date of revision:
Handle: RePEc:clu:wpaper:0102-10
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