Benchmarking parameter estimates in logit models of binary choice and semiparametric survival models - ARCHIVED

An approach to exploiting the data from multiple surveys and epochs by benchmarking the parameter estimates of logit models of binary choice and semiparametric survival models has been developed. The goal is to exploit the relatively rich source of socio-economic covariates offered by Statistics Canada's Survey of Labour and Income Dynamics (SLID), and also the historical time-span of the Labour Force Survey (LFS), enhanced by following individuals through each interview in their six-month rotation. A demonstration of how the method can be applied is given, using the maternity leave module of the LifePaths dynamic microsimulation project at Statistics Canada. The choice of maternity leave over job separation is specified as a binary logit model, while the duration of leave is specified as a semiparametric proportional hazards survival model with covariates together with a baseline hazard permitted to change each month. Both models are initially estimated by maximum likelihood from pooled SLID data on maternity leaves beginning in the period from 1993 to 1996, then benchmarked to annual estimates from the LFS from 1976 to 1992. In the case of the logit model, the linear predictor is adjusted by a log-odds estimate from the LFS. For the survival model, a Kaplan-Meier estimator of the hazard function from the LFS is used to adjust the predicted hazard in the semiparametric model.