Arbeitspapier
Which Model for Poverty Predictions?
OLS models are the predominant choice for poverty predictions in a variety of contexts such as proxy-means tests, poverty mapping or cross-survey impu- tations. This paper compares the performance of econometric and machine learning models in predicting poverty using alternative objective functions and stochastic dominance analysis based on coverage curves. It finds that the choice of an optimal model largely depends on the distribution of incomes and the poverty line. Comparing the performance of different econometric and machine learning models is therefore an important step in the process of opti- mizing poverty predictions and targeting ratios.
- Sprache
-
Englisch
- Erschienen in
-
Series: GLO Discussion Paper ; No. 468
- Klassifikation
-
Wirtschaft
Personal Income, Wealth, and Their Distributions
Equity, Justice, Inequality, and Other Normative Criteria and Measurement
Incomes Policy; Price Policy
Economic Development: Human Resources; Human Development; Income Distribution; Migration
- Thema
-
Welfare Modelling
Income Distributions
Poverty Predictions
Imputations
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Verme, Paolo
- Ereignis
-
Veröffentlichung
- (wer)
-
Global Labor Organization (GLO)
- (wo)
-
Essen
- (wann)
-
2020
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Verme, Paolo
- Global Labor Organization (GLO)
Entstanden
- 2020
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