05431oam 22012014 450 991082644260332120240402050815.01-4623-7798-X1-4527-8640-21-4518-7121-X97866128421461-282-84214-5(CKB)3170000000055160(EBL)1608096(SSID)ssj0001488723(PQKBManifestationID)11842762(PQKBTitleCode)TC0001488723(PQKBWorkID)11445320(PQKB)11175823(OCoLC)465449025(IMF)WPIEE2008263(MiAaPQ)EBC1608096(EXLCZ)99317000000005516020020129d2008 uf 0engur|n|---|||||txtccrWhat is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach /Rupa Duttagupta, Montfort Mlachila1st ed.Washington, D.C. :International Monetary Fund,2008.1 online resource (29 p.)IMF Working PapersIMF working paper ;WP/08/263Description based upon print version of record.1-4519-1574-8 Includes bibliographical references.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 Checks3. 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; ReferencesAlthough 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.IMF Working Papers; Working Paper ;No. 2008/263Economic developmentCross-cultural studiesEconomic developmentRegional disparitiesExports and ImportsimfLaborimfDemographyimfDemographic Economics: GeneralimfEmpirical Studies of TradeimfHealth: GeneralimfHuman CapitalimfSkillsimfOccupational ChoiceimfLabor ProductivityimfEducation: GeneralimfPopulation & demographyimfInternational economicsimfHealth economicsimfLabourimfincome economicsimfEducationimfPopulation and demographicsimfTerms of tradeimfHealthimfHuman capitalimfPopulationimfEconomic policyimfnternational cooperationimfUnited StatesimfEconomic developmentEconomic developmentRegional disparities.Exports and ImportsLaborDemographyDemographic Economics: GeneralEmpirical Studies of TradeHealth: GeneralHuman CapitalSkillsOccupational ChoiceLabor ProductivityEducation: GeneralPopulation & demographyInternational economicsHealth economicsLabourincome economicsEducationPopulation and demographicsTerms of tradeHealthHuman capitalPopulationEconomic policynternational cooperation338.9Duttagupta Rupa1610000Mlachila Montfort1642167DcWaIMFBOOK9910826442603321What is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach4083241UNINA