Estimating Long Run Welfare Outcome in Rotating Panel with Grouped Fixed Effects: Application to Poverty Dynamics in Peru

TL;DR

This paper introduces a grouped fixed effects method to estimate long-term poverty dynamics using rotating panel data, effectively capturing short-term transitions and providing interpretable groupings.

Contribution

It applies GFE to rotating panel data, improving estimates of poverty mobility and persistence over existing synthetic panel methods.

Findings

GFE closely tracks observed poverty transitions.

Predicted transitions remain accurate in out-of-sample validation.

GFE provides more accurate and interpretable poverty mobility measures.

Abstract

Household welfare dynamics are often difficult to investigate due to lack of long-term panel data. Existing methods, such as pseudo-panel and synthetic panel, offer widely used solutions based on repeated cross-section designs, but they do not exploit within-household variation in rotating panel designs, which provide very useful information for estimating long-run dynamics. This paper applies grouped fixed effects (GFE) to estimate poverty mobility and persistence in a rotating panel setting, using National Household Survey on Living Conditions and Poverty (ENAHO) in Peru. Using observed transitions, we show that GFE-implied poverty transitions closely track the data. In a one-step-ahead validation that holds out each household's final observed year, predicted transition shares remain close to realized transition shares, indicating that the method captures short-run entry and exit dynamics out of sample. When benchmarked against synthetic panel point estimates, the GFE approach delivers transition measures that are closer to observed transitions on average, while also providing an interpretable grouping structure that supports richer descriptions of poverty persistence and mobility.