English, Article, Journal or magazine article edition: Seam Bias, Multiple-State, Multiple-Spell Duration Models and the Employment Dynamics of Disadvantaged Women John C. Ham; Xianghong Li; Lara Shore-Sheppard

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Bookmark: http://trove.nla.gov.au/version/141840
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Seam Bias, Multiple-State, Multiple-Spell Duration Models and the Employment Dynamics of Disadvantaged Women
Author
  • John C. Ham
  • Xianghong Li
  • Lara Shore-Sheppard
Physical Description
  • preprint
Notes
  • Panel surveys generally suffer from “seam biasâ€--too few transitions observed within reference periods and too many reported between interviews. Seam bias is likely to affect duration models severely since both the start date and the end date of a spell may be misreported. In this paper we examine the employment dynamics of disadvantaged single mothers in the Survey of Income and Program Participation (SIPP) while correcting for seam bias in reported employment status. We develop parametric misreporting models for use in multi-state, multi-spell duration analysis; the models are identified if misreporting parameters are the same for fresh and left-censored spells of the same type. We extend these models to allow misreporting to depend on individual characteristics and for a certain fraction of the sample never to misreport. These extensions are informative about misreporting, but do not affect estimates of the hazard functions. We compare our results to two approaches used previously: i) using only data on the last month of reference periods and ii) adding a dummy variable for the last month of the reference periods. We find that there are important differences between our estimates and those obtained from ii), and very important differences between our estimates and those obtained from i). Finally, we also consider three alternative models of misreporting and are able to reject them based on aggregates of our micro data.
  • RePEc:nbr:nberwo:15151
Language
  • English
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