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What is Really Good for Long-Term Growth? Lessons from a Binary ClassificationTree (BCT) Approach Author info | Abstract | Publisher info | Download info | Related research | Statistics Montfort Mlachila
Rupa Duttagupta
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.
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Paper provided by International Monetary Fund in its series IMF Working Papers with number
08/263.
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Length: 27 pages
Date of creation: 04 Dec 2008Date of revision:
Handle: RePEc:imf:imfwpa:08/263Contact details of provider: Postal: International Monetary Fund, Washington, DC USA Phone: (202) 623-7000 Fax: (202) 623-4661 Email: Web page: http://www.imf.org/external/pubind.htm More information through EDIRC
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Keywords: Economic growth ; Trade liberalization ; Trade policy ; Human capital ; Economic models ; Other versions of this item:
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