Is random forest a superior methodology for predicting poverty ? an empirical assessment

Thomas Sohnesen and Niels Stender ()

No 7612, Policy Research Working Paper Series from The World Bank

Abstract: Random forest is in many fields of research a common method for data driven predictions. Within economics and prediction of poverty, random forest is rarely used. Comparing out-of-sample predictions in surveys for same year in six countries shows that random forest is often more accurate than current common practice (multiple imputations with variables selected by stepwise and Lasso), suggesting that this method could contribute to better poverty predictions. However, none of the methods consistently provides accurate predictions of poverty over time, highlighting that technical model fitting by any method within a single year is not always, by itself, sufficient for accurate predictions of poverty over time.

Keywords: Poverty Lines; ICT Applications; Small Area Estimation Poverty Mapping; Poverty Diagnostics; Poverty Monitoring&Analysis; Poverty Assessment; Poverty Impact Evaluation (search for similar items in EconPapers)
Date: 2016-03-18
New Economics Papers: this item is included in nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://documents.worldbank.org/curated/en/777401467987858907/pdf/WPS7612.pdf (application/pdf)

Related works:
Journal Article: Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:7612

Access Statistics for this paper

More papers in Policy Research Working Paper Series from The World Bank 1818 H Street, N.W., Washington, DC 20433. Contact information at EDIRC.
Bibliographic data for series maintained by Roula I. Yazigi ().