Random forest (RF) is in many fields of research a common method for data‐driven
predictions. Within economics and prediction of poverty, RF is rarely used. Comparing out‐of …

Survey‐to‐survey imputation has been increasingly employed to address data gaps for
poverty measurement in poorer countries. We refine existing imputation models, using 14 …

The increasing growth of forced displacement worldwide has brought more attention to
measuring poverty among refugee populations. However, refugee data remain scarce …

Survey data on household consumption are often unavailable or incomparable over time in
many low-and middle-income countries. Based on a unique randomized survey experiment …

It is costly to collect the household-and individual-level data that underlie official estimates of
poverty and health. For this reason, developing countries often do not have the budget to …

The assignment of social programmes to their target population, known as targeting, is key
to effective policy implementation. Proxy means testing is a widely used targeting approach …

Accurate poverty measurement relies on household consumption data, but such data are
often inadequate, outdated or display inconsistencies over time in poorer countries. To …

Accurate poverty measurement is the prerequisite for policies aiming at reducing poverty.
Yet, development practitioners face the typical challenges that the available household …

In most low-and middle-income countries, household surveys continue to be the main data
source for measuring and monitoring poverty and inequality. At the center of poverty and …

Consumption data from household surveys continue to be the main source of data for
monitoring poverty and inequality in low-and middle-income countries. Differences in food …