TY  - JOUR
AU  - Ham,John C.
AU  - Li,Xianghong
AU  - Shore-Sheppard,Lara
TI  - Seam Bias, Multiple-State, Multiple-Spell Duration Models and the Employment Dynamics of Disadvantaged Women
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 15151
PY  - 2009
Y2  - July 2009
UR  - http://www.nber.org/papers/w15151
L1  - http://www.nber.org/papers/w15151.pdf
N1  - Author contact info:
John Ham
University of Maryland
Department of Economics
3105 Tydings Hall
College Park, MD 20742
Tel: 818-439-3531
E-Mail: ham@econ.umd.edu

Xianghong Li
Department of Economics
Vari Hall 1068, 4700 Keele St
Toronto, ON, M3J 1P3, Canada
E-Mail: xli@econ.yorku.ca

Lara Shore-Sheppard
Department of Economics
Williams College
24 Hopkins Hall Drive
Williamstown, MA 01267
Tel: 413/597-2226
Fax: 413/597-4045
E-Mail: lshore@williams.edu
AB  - Panel surveys generally suffer from “seam bias”--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.
ER  -