“When Life Gives You Lemons”: Using Cross‐Sectional Surveys to Identify Chronic Poverty in the Absence of Panel Data
提出一种仅需一年横截面数据（收入与多维贫困）来识别慢性贫困的替代方法，并用三个拉美国家的面板数据验证其有效性。
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.