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

Understand

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.