“When Life Gives You Lemons”: Using Cross‐Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data

At the core of poverty eradication is the need to eliminate that poverty that is persistent over time (chronic poverty). Unfortunately, traditional approaches to identifying chronic poverty require longitudinal data that is rarely available. In its absence, this paper proposes an alternative approach that only requires 1 year of cross‐sectional data on monetary and non‐monetary poverty. It puts forth two conjectures and contends that the combined profile of a household as both income poor and multidimensionally poor can be used as a proxy of that household being chronically income poor. To explore the viability of this approach, we use a probit model and longitudinal data for three Latin American countries to estimate households’ probabilities of remaining in income poverty based on their past income and multidimensional poverty statuses. We find empirical support for the approach that is significant, consistent across countries, and robust to various controls and periods of analysis.