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What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach / / Rupa Duttagupta, Montfort Mlachila



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Autore: Duttagupta Rupa Visualizza persona
Titolo: What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach / / Rupa Duttagupta, Montfort Mlachila Visualizza cluster
Pubblicazione: Washington, D.C. : , : International Monetary Fund, , 2008
Descrizione fisica: 1 online resource (29 p.)
Disciplina: 338.9
Soggetto topico: Economic development
Economic development - Regional disparities
Exports and Imports
Labor
Demography
Demographic Economics: General
Empirical Studies of Trade
Health: General
Human Capital
Skills
Occupational Choice
Labor Productivity
Education: General
Population & demography
International economics
Health economics
Labour
income economics
Education
Population and demographics
Terms of trade
Health
Human capital
Population
Economic policy
nternational cooperation
Soggetto geografico: United States
Altri autori: MlachilaMontfort  
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: Contents; I. Introduction; II. A Few Notes on the Growth Literature; Tables; 1. Most Significant Variables in Selected Growth Studies; III. The Binary Classification Tree (BCT) Approach; IV. Properties of the Data; 2. Definition of Variables; V. The Results; A. Baseline Model: What is Good for Strong Growth?; Figures; 1. Distribution of Growth; 3. Growth Rate for Top Quartile; 4. What is Really Good for Growth: Ranking of Indicators; 2. Baseline Model; 5. Median Values of Key Indicators in Baseline Model; B. Alternative Specifications and Robustness Checks
3. Out of Sample Forecast (I)-Advanced Economies4. Out of Sample Forecast (II)-Highly Indebted Poor Countries; 6. The Do's and Don'ts of Growth; VI. Concluding Remarks; Appendix; I. Description of the Database; References
Sommario/riassunto: 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.
Titolo autorizzato: What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach  Visualizza cluster
ISBN: 1-4623-7798-X
1-4527-8640-2
1-4518-7121-X
9786612842146
1-282-84214-5
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910788341203321
Lo trovi qui: Univ. Federico II
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Serie: IMF Working Papers; Working Paper ; ; No. 2008/263