Growth Econometrics

The Econometrics of Convergence

Durlauf, S. N., Johnson, P. A., and Temple, J. R. W. (2009). The Econometrics of Convergence. In Terence C. Mills and Kerry Patterson (eds.) Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, June.

For more details of the book, see the page on Palgrave’s website

Growth Econometrics

Durlauf, S. N., Johnson, P. A., and Temple, J. R. W. (2005). Growth econometrics. In P. Aghion and S. N. Durlauf (eds.) Handbook of Economic Growth, Volume 1A, North-Holland: Amsterdam, 2005, pp. 555-677.

This chapter provides a survey and synthesis of econometric tools that have been employed to study economic growth. While these tools range across a variety of statistical methods, they are united in the common goals of first, identifying interesting contemporaneous patterns in growth data and second, drawing inferences on long-run economic outcomes from cross-section and temporal variation in growth. We describe the main stylized facts that have motivated the development of growth econometrics, the major statistical tools that have been employed to provide structural explanations for these facts, and the primary statistical issues that arise in the study of growth data. An important aspect of the survey is attention to the limits that exist in drawing conclusions from growth data, limits that reflect model uncertainty and the general weakness of available data relative to the sorts of questions for which they are employed.

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Growth Econometrics

Temple, J. R. W. (2021). Growth Econometrics. Oxford Research Encyclopedia of Economics and Finance, Oxford University Press, online, April 2021.

Growth econometrics is the application of statistical methods to the study of economic growth and levels of national output or income per head. Researchers often seek to understand why growth rates differ across countries. The field developed rapidly in the 1980s and 1990s, but the early work often proved fragile. Cross-section analyses are limited by the relatively small number of countries in the world and problems of endogeneity, parameter heterogeneity, model uncertainty, and cross-section error dependence. The long-term prospects look better for approaches using panel data. Overall, the quality of the evidence has improved over time, due to better measurement, more data, and new methods. As longer spans of data become available, the methods of growth econometrics will shed light on fundamental questions that are hard to answer any other way.

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Growth Econometrics for Agnostics and True Believers

Rockey, James and Temple, Jonathan (2016). Growth Econometrics for Agnostics and True Believers. European Economic Review, 81(1), 86-102.

The issue of model uncertainty is central to the empirical study of economic growth. Many recent papers use Bayesian Model Averaging to address model uncertainty, but Ciccone and Jarocinski (2010) have questioned the approach on theoretical and empirical grounds. They argue that a standard ‘agnostic’ approach is too sensitive to small changes in the dependent variable, such as those associated with different vintages of the Penn World Table (PWT). This paper revisits their theoretical arguments and empirical illustration, drawing on more recent vintages of the PWT, and introducing an approach that limits the degree of agnosticism.

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Growth Regressions and What the Textbooks Don’t Tell You

Temple, Jonathan (2000). Growth regressions and what the textbooks don’t tell you. Bulletin of Economic Research, 52(3), July, 181-205.

In this paper I discuss three econometric problems that are rarely given adequate discussion in textbooks: model uncertainty, parameter heterogeneity, and outliers. I show how Leamer’s extreme bounds analysis can be adapted to address all three problems simultaneously, and present two examples based on an influential cross-country growth paper by Levine and Renelt.

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The Methods of Growth Econometrics

Durlauf, S. N., Johnson, P. A., and Temple, J. R. W. (2009). The methods of growth econometrics. In Terence C. Mills and Kerry Patterson (eds.) Palgrave Handbook of Econometrics, Volume 2: Applied Econometrics. Palgrave Macmillan, June.

This chapter provides an overview of current practices in the econometric analysis of economic growth. We describe some of the main methodologies that have been developed to study growth as well as some of the major empirical findings with which they are associated. Further, we explore the relationship between econometric analyses and growth theories. While we argue that there are a number of respects in which growth econometrics is not adequately integrated with growth theory, we believe that substantial methodological progress has been made.

For more details of the book, see the page on Palgrave’s website

The New Growth Evidence

Temple, J. R. W. (1999). The New Growth Evidence. Journal of Economic Literature, March 1999, 37(1), 112-156. Reprinted in Dutt, A. K. (ed.) The political economy of development, Volume 1. Development, growth and income distribution. Elgar Reference Collection. International Library of Critical Writings in Economics, vol. 140. Cheltenham, U.K. and Northampton, Mass.: Elgar; distributed by American International Distribution Corporation, Williston, Vt., 2002, pp. 31-75.

Why do growth rates differ? This paper surveys the recent empirical literature on economic growth, starting with a discussion of stylized facts, data problems and statistical methods. Six research questions are emphasised, drawing on growth and convergence research. In answering these questions, the paper argues that efficiency has grown at different rates across countries, casting doubt on neoclassical models in which technology is a public good. The latter half of the paper rounds up a variety of findings before providing answers to all six questions, including a short summary of how differences in growth rates arise.

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