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This paper analyses the consequences of heaping in duration models. Heaping is a specific form of response error typical to retrospectively collected labor force status data. Respondents round-off the spell length, when duration data is collected by episode-based questionnaires. Calendar-based questionnaires instead may lead to abnormal concentrations of the start and/or end of spells at specific calendar months. The investigation concentrates on this latter type of heaping, which Kraus and Steiner [1995] identified for the unemployment spell data from the German Socio-Economic Panel (GSOEP). In the special case of an exponential model heaping with a symmetric zero-mean measurement error does not bias the parameter estimate. In the Weibull model with duration dependence, however, it is proven that even such a symmetric heaping would lead to inconsistent estimation. We discuss the bias for general heaping patterns and derive from this a proposal for bias correction. In a number of simulation studies we check the theoretical results. The Monte Carlo simulations also show that an amount of heaping, that characterizes the GSOEP-West does not lead to considerably biased parameter estimates of a Weibull model. However, it clearly leads to spurious seasonal effects. Finally, some directions of future work are indicated.

Heaping and its Consequences for Duration Analysis