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The perpetual inventory method used for the construction of education data per country leads to systematic measurement error. This paper analyses the effect of this measurement error on GDP regressions. There is a systematic difference in the education level between census data and observations constructed from enrolment data. We discuss a methodology for correcting the measurement error. The standard attenuation bias suggests that using these corrected data would lead to a higher coefficient. Our regressions reveal the opposite. We discuss why the measurement error yields an overestimation. Our analysis contributes to an explanation of the difference between regressions based on 5 and on 10 year first-differences.

This paper offers two innovations for empirical growth research. First, the paper discusses principal components augmented regressions to take into account all available information in well-behaved regressions. Second, the paper proposes a frequentist model averaging framework as an alternative to Bayesian model averaging approaches. The proposed methodology is applied to three data sets, including the Sala-i-Martin et al. (2004) and Fernandez et al. (2001) data as well as a data set of the European Union member states' regions. Key economic variables are found to be significantly related to economic growth. The findings highlight the relevance of the proposed methodology for empirical economic growth research.

This paper discusses the history of the use of cross-country regressions in modern growth economics. These regressions continue to be the workhorse of empirical growth analysis even though their meaning continues to be controversial. I argue that the early interpretations of these regressions have proven to be inappropriate and led to substantial exaggeration of the evidentiary support for various new growth theories. On the other hand, I argue that these regressions have a valuable role to play in identifying the modern analog of stylized facts for growth behavior.

The paper discusses three econometric problems that are rarely given adequate discussion in textbooks: model uncertainty, parameter heterogeneity, and outliers. Leamer's extreme bounds analysis can be adapted to address all three problems simultaneously. Two examples are presented based on an influential cross‐country growth paper by Levine and Renelt (American Economic Review, vol. 82, 1992, pp. 942‐63).

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. JEL Classification: O47, C14 Une analyse de sensibilité non‐linéaire des régressions de croissance pour divers pays. Les auteurs utilisent une formulation semi‐paramétrique des équations de croissance de Levine et Renelt (1992) pour divers pays afin de rendre leur analyse de sensibilité plus compréhensive. Les résultats different de ceux de Levine et Renelt en ce que certaines variables de politique affectent les taux de croissance. On découvre que certains facteurs comme l'écart type du crédit intérieur brut et de l'inflation, et des distorsions des taux de change réels, ont un effet négatif important sur la croissance.