Comparing retrospective and panel data collection methods to assess labor market dynamics [feedly]

an article by  Ragui Assaad (University of Minnesota, Minneapolis, USA),  Caroline Krafft (University of Minnesota, Minneapolis, USA; St. Catherine University, St. Paul, USA) and Shaimaa Yassin (University of Lausanne, Switzerland; University of Le Mans, France)  published in IZA Journal of Development and Migration Volume 8  Article 17 (2018)

Abstract

There is potential for measurement problems in both retrospective and panel microdata.

In this paper, we compare results on basic indicators related to labor markets and their dynamics from retrospective and panel survey data in Egypt, in order to determine the conditions under which results are similar or different.

Specifically, we

1. assess the consistency of reporting of time-invariant characteristics in different waves of the panel,
2. compare the retrospective and panel data results on past labor market statuses,
3. assess the consistency of estimates of labor market transition rates across two specific dates by comparing panel and retrospective data,
4. assess the consistency of estimates of the level and trends of annual labor market transition rates across retrospective data from different waves of the survey, and
5. assess whether retrospective data can provide accurate trends of labor market aggregates, such as unemployment rates.

We find that it is possible to garner useful information on labor market dynamics from retrospective data, but one must be cautious about which information to trust and at what level of detail. We conclude with a discussion of implications for future research as well as future survey design.

JEL Classification: C83, C81, J01, J62, J64

Full text (PDF 34pp)

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How real is mobility between low pay, high pay and non-employment?

an article by Dimitris Pavlopoulos (VU University, Amsterdam, The Netherlands) and Ruud Muffels and Jeroen K. Vermunt (Tilburg University, The Netherlands) published in Journal of the Royal Statistical Society: Series A (Statistics in Society) Volume 175 Issue 3 (July 2012)

Summary

The aim of the paper is to investigate the effect of measurement error on low pay transition probabilities.

Our approach combines the virtues of panel regression and latent class models, though it does not require the use of validation or reinterview data. Using British, German and Dutch panel data, we show that the true estimated low pay transition probability is much lower that what previous research has found.

This implies that almost half of the observed transitions can be attributed to measurement error. The highest low pay transition probabilities are found in Germany and the lowest in the Netherlands. When applying this correction for measurement error in a multivariate model of low pay transitions, the results indicate that measurement error attenuates considerably the effects of the main covariates, such as training, job change, change in the type of employment contract and shift from part-time to full-time employment.

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