The overall goal of this project is to improve measurements of poverty dynamics, and generally measurements of economic mobility, in data-scarce environments, as well as to evaluate the impact of programs and policies to alleviate poverty.
The study of mobility in and out of poverty has increasingly captured the attention of policy makers and researchers. The types of policies needed to counteract persistent poverty may be quite different from those required to address transient poverty or movements in higher parts of the income distribution. When measuring mobility and poverty dynamics, longitudinal (panel) datasets, which follow individuals or households over time, represent the most appropriate source of information. Unfortunately, such data are often not available, or of low quality, not only in low-income countries but also in most high-income ones. These data-scarce environments are therefore common in contexts where dynamics of income and poverty would be more important to be assessed.
Approaches to the measurement of poverty transitions and economic mobility in absence of panel data have been recently proposed in the literature. They use repeated cross-sectional surveys and are commonly referred to as pseudo-panels.
The project first seeks to validate these approaches, providing robust evidence about accuracy of pseudo-panel estimates relative to benchmarks based on estimates directly derived from genuine panel data. As an alternative to the existing literature on pseudo-panels, a class of dynamic multilevel models will be proposed to track the temporal (and the spatial) dynamics of the data. Alternative ways to build pseudo-panels will be also explored following a statistical matching approach. Within this framework, it will be possible to evaluate the performance of poverty-alleviating programs. New methods for evaluating the distributional (as opposed to just average) impact of those policies will be finally investigated.
