To be able to redress retrospective panels into random samples and correct for any recall
and/or design bias the data might suffer from, this paper builds on the methodology proposed by
Langot and Yassin (2015) and extends it to correct the data on the individual transaction level
(i.e. micro level). It creates user-friendly weights that can be readily used by researchers relying
on retrospective panels extracted from the Egypt and Jordan Labor Market Panel Surveys
(ELMPS and JLMPS respectively). The technique suggested shows that it is sufficient to have
population moments - stocks and/or transitions (for at least one point in time) to correct overor
under-reporting biases in the retrospective data. The paper proposes two types of microdata
weights: (1) naive proportional weights and (2) differentiated predicted weights. Both
transaction-level weights i.e. for each transition at a certain point in time, as well as panel
weights i.e. for an entire job or non-employment spell, are built. In order to highlight the
importance of these weights, the paper also offers an application using these weights. The
determinants of labor market transitions in Egypt and Jordan are analyzed via a multinomial
regression analysis with and without the weights. The impact of these weights on the regressions
estimations and coefficients is therefore examined and shown significant among the different
types of labor market transitions, especially separations.