Poverty at Higher Frequency

Abstract

Poverty is typically measured as insufficient yearly income or consumption. In practice, however, poverty is marked by seasonality, economic instability, and illiquidity across months. To capture within-year variability, we extend traditional poverty measures to include a temporal dimension. Using panel data from rural India, we show how conventional poverty measures can distort understandings of poverty: exposure to poverty is wider and more common than typically measured, and poverty entry and exit are not sharp transitions. Accounting for within-year variability improves predictions of anthropometrics, and targeting transfers to challenging periods can reduce poverty most effectively by compensating for imperfect consumption smoothing.