# Household poverty classification in data-scarce environments: a machine learning approach

arXiv: https://arxiv.org/abs/1711.06813 · HTML: https://mlsanity.com/paper/1711.06813/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Design-based conformal prediction](https://mlsanity.com/paper/2303.01422/) (2023)
- [Machine Intelligence in Africa: a survey](https://mlsanity.com/paper/2402.02218/) (2024)
- [Poverty rate prediction using multi-modal survey and earth observation data](https://mlsanity.com/paper/2307.11921/) (2023)

## Abstract (arXiv)

We describe a method to identify poor households in data-scarce countries by leveraging information contained in nationally representative household surveys. It employs standard statistical learning techniques---cross-validation and parameter regularization---which together reduce the extent to which the model is over-fitted to match the idiosyncracies of observed survey data. The automated framework satisfies three important constraints of this development setting: i) The prediction model uses at most ten questions, which limits the costs of data collection; ii) No computation beyond simple arithmetic is needed to calculate the probability that a given household is poor, immediately after data on the ten indicators is collected; and iii) One specification of the model (i.e. one scorecard) is used to predict poverty throughout a country that may be characterized by significant sub-national differences. Using survey data from Zambia, the model's out-of-sample predictions distinguish poor households from non-poor households using information contained in ten questions.