What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach
29 Pages Posted: 18 Dec 2008
Date Written: December 2008
Abstract
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.
Keywords: Economic growth, Trade liberalization, Trade policy, Human capital, Economic models
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Truth and Robustness in Cross-Country Growth Regressions
By Kevin D. Hoover and Stephen J. Perez
-
Truth and Robustness in Cross-Country Growth Regressions
By Kevin D. Hoover and Stephen J. Perez
-
The Properties of Automatic Gets Modelling
By David F. Hendry and Martin Krolzig
-
Selecting a Regression Saturated by Indicators
By Soren Johansen, David F. Hendry, ...
-
Weak Identification of Forward-Looking Models in Monetary Economics
-
Searching for the Causal Structure of a Vector Autoregression
By Kevin D. Hoover and Selva Demiralp