What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach

December 1, 2008

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Rupa Duttagupta, and Montfort Mlachila. What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach, (USA: International Monetary Fund, 2008) accessed November 12, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

Although the economic growth literature has come a long way since the Solow-Swan model of the fifties, there is still considerable debate on the "real' or "deep" determinants of growth. This paper revisits the question of what is really important for strong long-term growth by using a Binary Classification Tree approach, a nonparametric statistical technique that is not commonly used in the growth literature. A key strength of the method is that it recognizes that a combination of conditions can be instrumental in leading to a particular outcome, in this case strong growth. The paper finds that strong growth is a result of a complex set of interacting factors, rather than a particular set of variables such as institutions or geography, as is often cited in the literature. In particular, geographical luck and a favorable external environment, combined with trade openness and strong human capital are conducive to growth.

Subject: Education, Health, Human capital, Population and demographics, Terms of trade

Keywords: Country's trade hub, Economic policy, Growth outcome, Life expectancy, Model state, WP

Publication Details

Pages:

27
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2008/263
Stock No:

WPIEA2008263
ISBN:

9781451871210
ISSN:

1018-5941

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