The topic of convergence is at the heart of a wide-ranging debate in the growth literature. Empirical studies of convergence differ widely in their theoretical backgrounds, empirical specifications and in their treatment of cross-sectional heterogeneity. Despite these differences, a rate of convergence of about 2% has been found under a variety of different conditions, resulting in the widespread belief that the rate of convergence is a natural constant. We use meta-analysis to investigate whether there is substance to the ‘myth’ of the legendary 2% convergence rate, and to assess several unresolved issues of interpretation and estimation. Our dataset contains approximately 600 estimates taken from a random sample of empirical growth studies published in peer-reviewed journals. We show that publication bias does not interfere with the analysis, and that it is misleading to speak of a natural convergence rate, since estimates of different growth regression! s come from different populations. We find that correcting for the bias resulting from unobserved heterogeneity in technology levels leads to higher estimates of the rate of convergence. We also find that correcting for endogeneity in the explanatory variables has a substantial effect on the estimates, and that measures of financial and fiscal development are important determinants of long-run differences in per-capita income levels.