To meet the Sustainable Development Goals (SDGs) by 2030, we need more data. Collecting data can be time-consuming and expensive, but doesn’t have to break the bank.

Governments can select the data collection methods and analytical tools that will best help them reach their SDG targets. Fortunately, there are several approaches at hand.

Our research shows that longitudinal data on household expenditure may be a better way of measuring poverty and income inequality in Asia and the Pacific than the cross-sectional data analysis currently used across the region.

Longitudinal data tracks the same kinds of data on the same subjects over long periods of time, whereas cross-sectional data is collected from many subjects at a single point in time.

For example, using three rounds of household expenditure survey data collected from the Philippines in 2003, 2006, and 2009, we identified the proportion of the country’s population.