On synthetic income panels

In many developing countries, the increasing public interest for economic inequality and mobility runs into the scarce availability of longitudinal data. Synthetic panels based on matching individuals with the same time-invariant characteristics in consecutive cross-sections have been proposed as a substitute to such data - see Dang and Lanjouw (2014). The present paper improves on the calibration methodology of such synthetic panels in several directions: a) it abstracts from (log) normality assumptions; b) it improves on the estimation of auto-correlation of unobserved determinants of (log) earnings; c) it considers the whole mobility matrix rather than mobility in and out of poverty. We exploit the cross-sectional dimension of a national-representative Mexican panel survey to evaluate the validity of this approach. The income mobility matrix in the synthetic panel calibrated on the former turns out to be very close to the observed matrix in the latter.