Determinants of Economic Growth: Will Data Tell?

Abstract

Many factors inhibiting and facilitating economic growth have been suggested. Will international income data tell which matter when all are treated symmetrically a priori? We find that growth determinants emerging from agnostic Bayesian model averaging and classical model selection procedures are sensitive to income differences across datasets. For example, many of the 1975-1996 growth determinants according to World Bank income data turn out to be irrelevant when using Penn World Table data instead (the WB adjusts for purchasing power using a slightly different methodology). And each revision of the 1960-1996 PWT income data brings substantial changes regarding growth determinants. We show that research based on stronger priors about potential growth determinants is more robust to imperfect international income data.

Growth econometrics for agnostics and true believers

“…One reason for using Bayesian approaches is that conventional significance tests are rarely a sound basis on which to form policy or take decisions, as Brock and Durlauf (2001) and Brock et al (2003) emphasized. 15 In this paper, we have followed much of the growth literature in emphasizing posterior inclusion probabilities. One justification is that a theorist might be interested in knowing which variables appear to influence growth, while taking a conservative approach that is wary of 'false positives'.…”

Growth, Inequality, and Social Welfare: Cross-Country Evidence

“…The growth correlates include a measure of financial development (M2 as percentage of GDP), the Sachs-Warner indicator of trade openness, the Chinn-Ito Index of financial openness, the inflation rate, the general government budget balance, life expectancy, population growth, the Freedom House measure of civil liberties and political rights, the frequency of revolutions, and a dummy variable indicating whether the country was party to a civil or international war in a given year. Most of these variables have been identified as important correlates of growth in one or more of three prominent meta-analyses of growth determinants (Fernandez, Ley and Steel (2001a), Sala-i-Martin (2004) and Ciccone et al (2010)). We also consider some additional variables that have been found to be significant correlates of inequality in the much smaller existing cross-country literature on determinants of inequality.…”

Endogeneity and panel data in growth regressions: A Bayesian model averaging approach

“…It was proposed as a method to overcome the sensitivity of results with respect to the set of controlling variables in a regression. Since then, BMA has been applied widely in the empirical growth literature (e.g., Durlauf et al, 2008;Prüfer and Tondl, 2008;Winford and Papageorgiou, 2008;Ciccone and Jarocinski, 2010;CrespoCuaresma et al, 2011) and in other areas of economics (e.g., Koop and Tole, 2003;Tobias and Li, 2004). Recent papers have contributed towards the development of summary measures of the output (Ley and Steel, 2007;Doppelhofer and Weeks, 2009); led to greater understanding of prior assumptions (e.g., Ley and Steel, 2009;; and extended the technique in ways that are relevant to growth regressions such as threshold models (Crespo-Cuaresma and Doppelhofer, 2007), heteroscedasticity (Doppelhofer and Weeks, 2008), endogeneity (Cohen-Cole et al, 2009;Lenkoski et al, 2011;Koop et al, 2011;Karl and Lenkoski, 2012), and panel data models (León-González and Montolio, 2004;Moral-Benito, 2010;2012, Chen et al 2011.…”