University of Neuchâtel, Institute of Economic Research (IRENE), Neuchâtel
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.
Panel Data Retrospective Data Measurement Error Micro-data weights Labor Markets Transitions Egypt Jordan