Predicting Poverty with Missing Incomes

Arbeitspapier

Poverty prediction models are used by economists to address missing data issues in a variety of contexts such as poverty profiling, targeting with proxy-means tests, cross-survey imputations such as poverty mapping, or vulnerability analyses. Based on the models used by this literature, this paper conducts an experiment by artificially corrupting data with different patterns and shares of missing incomes. It then compares the capacity of classic econometric and machine learning models to predict poverty under these different scenarios. It finds that the quality of predictions and the choice of the optimal prediction model are dependent on the distribution of observed and unobserved incomes, the poverty line, the choice of objective function and policy preferences, and various other modeling choices. Logistic and random forest models are found to be more robust than other models to variations in these features, but no model invariably outperforms all others. The paper concludes with some reflections on the use of these models for predicting poverty.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: GLO Discussion Paper ; No. 1260

Classification and Topics

• Classification: 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
• Subject: income modeling ; Income Distributions ; Poverty Predictions ; Imputations

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Verme, Paolo
• Event: Veröffentlichung
• (who): Global Labor Organization (GLO)
• (where): Essen
• (when): 2023

Further information

• Handle: http://hdl.handle.net/10419/270738
• Last update: 10.03.2025, 11:43 AM CET