The World Bank is aiming to end extreme poverty by 2030. Crucial to this goal are techniques for determining which poverty reduction strategies work and which ones do not. But measuring poverty reduction requires measuring poverty in the first place, and it turns out that measuring poverty is pretty hard. The World Bank helps developing countries measure poverty by conducting in-depth household surveys with a subset of the country's population. To measure poverty, most of these surveys collect detailed data on household consumption – everything from food and transportation habits to healthcare access and sporting events – in order to get a clearer picture of a household's poverty status.
Harness the power of these data to identify the strongest predictors of poverty? Right now measuring poverty is hard, time consuming, and expensive. By building better models, we can run surveys with fewer, more targeted questions that rapidly and cheaply measure the effectiveness of new policies and interventions. The more accurate our models, the more accurately we can target interventions and iterate on policies, maximizing the impact and cost-effectiveness of these strategies.