1.

Record Nr.

UNINA9910464251303321

Autore

Duttagupta Rupa

Titolo

What is really good for long-term growth? : lessons from a binary classification tree (BCT) approach. / / Rupa Duttagupta and Montfort Mlachila ; authorized for distribution by Martín Cerisola

Pubbl/distr/stampa

[Washington, District of Columbia] : , : International Monetary Fund, , 2008

©2008

ISBN

1-4623-7798-X

1-4527-8640-2

1-4518-7121-X

9786612842146

1-282-84214-5

Descrizione fisica

1 online resource (29 p.)

Collana

IMF Working Papers

IMF working paper ; ; WP/08/263

Altri autori (Persone)

MlachilaMontfort

CerisolaMartin

Disciplina

338.9

Soggetti

Economic development

Economic development - Regional disparities

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

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