MEMORY BIAS IN RETROSPECTIVELY COLLECTED EMPLOYMENT CAREERS: A MODEL-BASED APPROACH TO CORRECT FOR MEASUREMENT ERROR.

Event history data constitute a valuable source to analyze life courses, although the reliance of such data on autobiographical memory raises many concerns over their reliability. In this paper, we use Swedish survey data to investigate bias in retrospective reports of employment biographies, applying a novel model-based latent Markov method. A descriptive comparison of the biographies as reconstructed by the same respondents at two interviews carried out about 10 years apart reveals that careers appear simpler and less heterogeneous and have fewer elements and episodes when reported at a point long after their occurrence, with a particularly high underreport of unemployment. Using matching techniques, the dissimilarity between the two reconstructions turns out to be unaffected by respondents' sociodemographic characteristics but particularly affected by the occurrence of unemployment spells and career complexity. Using latent Markov models, we assume correlated errors across occasions to determine the measurement error and to obtain a more reliable estimate of the (true) latent state occupied at a particular time point. The results confirm that (correlated) measurement errors lead to simplification and conventionalism. Career complexity makes recall particularly problematic at longer recall distances, whereas unemployment underreporting also happens very close to the interview. However, only a small fraction of respondents make consistent errors over time, while the great majority makes no errors at all. [ABSTRACT FROM AUTHOR]