On Synthetic Income Panels - Héctor Moreno; François Bourguignon; Hai-Anh Dang (Global Labor Organization (GLO), 2021)
In many developing countries, the increasing public interest in monitoring economic inequality and mobility is hindered by the scarce availability of longitudinal data. Synthetic panels based on matching individuals with the same time-invariant characteristics in consecutive cross-sections have been recently proposed as a substitute to such data. We extend the methodology to construct such synthetic panels in several directions by: a) explicitly assuming the unobserved or time variant determinants of (log) income are AR(1) and relying on pseudo-panel procedures to estimate the corresponding auto-regressive coefficient; b) abstracting from (log) normality assumptions; c) generating a close to perfect match of the terminal year income distribution and d) considering the whole income 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. With the median estimate of the AR coefficient, the income mobility matrix in the synthetic panel closely approximates that of the genuine matrix observed in the actual panel, except for out-lying values of the AR coefficient.
Keywords: D31; I32; 330; Synthetic panel; income mobility; Mexico