Is poverty predictable with machine learning? : a study of DHS data from Kyrgyzstan
Year of publication: |
2022
|
---|---|
Authors: | Li, Qing ; Yu, Shuai ; Echevin, Damien ; Fan, Min |
Published in: |
Socio-economic planning sciences : the international journal of public sector decision-making. - Amsterdam [u.a.] : Elsevier, ISSN 0038-0121, ZDB-ID 208905-1. - Vol. 81.2022, p. 1-9
|
Subject: | Generalized linear model | Machine learning | Poverty prediction | XGBoost | Künstliche Intelligenz | Artificial intelligence | Prognoseverfahren | Forecasting model | Armut | Poverty | Kirgisistan | Kyrgyzstan |
-
Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning
McBride, Linden, (2022)
-
Predicting poverty with vegetation index
Tang, Binh, (2022)
-
Comparison of machine learning predictions of subjective poverty in rural China
Maruejols, Lucie Louise, (2023)
- More ...
-
Livelihoods and the Allocation of Emergency Assistance after the Haiti Earthquake
Echevin, Damien, (2011)
-
Heterogeneous beliefs, the term structure and time-varying risk premia
Fan, Min, (2006)
-
Economic overview of 2006 and forecast for 2007
Min, Fan, (2009)
- More ...