LEADER 05630oam 22012854 450 001 9910961116303321 005 20250426110149.0 010 $a9786612842146 010 $a9781462377985 010 $a146237798X 010 $a9781452786407 010 $a1452786402 010 $a9781451871210 010 $a145187121X 010 $a9781282842144 010 $a1282842145 035 $a(CKB)3170000000055160 035 $a(EBL)1608096 035 $a(SSID)ssj0001488723 035 $a(PQKBManifestationID)11842762 035 $a(PQKBTitleCode)TC0001488723 035 $a(PQKBWorkID)11445320 035 $a(PQKB)11175823 035 $a(OCoLC)465449025 035 $a(IMF)WPIEE2008263 035 $a(MiAaPQ)EBC1608096 035 $a(IMF)WPIEA2008263 035 $aWPIEA2008263 035 $a(EXLCZ)993170000000055160 100 $a20020129d2008 uf 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aWhat is Really Good for Long-Term Growth? Lessons from a Binary Classification Tree (BCT) Approach /$fRupa Duttagupta, Montfort Mlachila 205 $a1st ed. 210 1$aWashington, D.C. :$cInternational Monetary Fund,$d2008. 215 $a1 online resource (29 p.) 225 1 $aIMF Working Papers 225 0$aIMF working paper ;$vWP/08/263 300 $aDescription based upon print version of record. 311 08$a9781451915747 311 08$a1451915748 320 $aIncludes bibliographical references. 327 $aContents; 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 327 $a3. 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 330 3 $aAlthough 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. 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