The author considers a class of models that generalizes the popular mixed proportional hazard model for duration data: the generalized accelerated failure-time model. He shows that the generalized accelerated failure-time model is nonparametrically identified (up to a normalization). He then reconsiders the nonparametric identification of the mixed proportional hazard model. He shows that the class of mixed proportional hazard models is not closed under normalization. This implies that a finite mean of the mixing distribution is a necessary condition for (nonparametric) identification of the mixed proportional hazard model. It is impossible to test this hypothesis without imposing arbitrary restrictions on the base-line hazard and/or the regression function. Copyright 1990 by The Review of Economic Studies Limited.

Little is known about the search strategy that employers use in their efforts to fill job vacancies. In this article, we analyze unique micro data to study this search strategy. We conclude that almost all vacancies are filled from a pool of applicants that is formed shortly after the posting of the vacancy. Hence, vacancy durations should be interpreted as selection periods and not as search periods for applicants.

E| Jhhuw

In structural empirical models of labor market search, the distribution of wage offers is usually assumed to be exogenous. However, because in setting their wages profit-maximizing firms should consider the reservation wages of job seekers, the wage offer distribution is essentially endogenous. We investigate whether a proposed equilibrium search model, in which the wage offer distribution is endogenous, is able to describe observed labor market histories. We find that the distributions of job and unemployment spells are consistent with the data, and that the qualitative predictions of the model for the wages set by employers are confirmed by wage regressions. The model is estimated using panel data on unemployed and employed individuals. We distinguish between separate segments of the labor market, and we show that productivity heterogeneity is important to obtain an acceptable fit to the data. The results are used to estimate the degree of monopsony power of firms. Further, the effects of changes in the mandatory minimum wage are examined.

Lancaster and Nickell (1980) have argued that in the proportional hazard model the effects of time dependence (true duration dependence) and unobserved sample heterogeneity (spurious duration dependence) cannot be distinguished. We show that both effects can be distinguished if the model allows for observed explanatory variables in the hazard. We also discuss the application of our result to practical situations.

We study the asymptotic distribution of three-step estimators of a finite-dimensional parameter vector where the second step consists of one or more nonparametric regressions on a regressor that is estimated in the first step. The first-step estimator is either parametric or nonparametric. Using Newey's (1994) path-derivative method, we derive the contribution of the first-step estimator to the influence function. In this derivation, it is important to account for the dual role that the first-step estimator plays in the second-step nonparametric regression, that is, that of conditioning variable and that of argument.

It is shown that in a nonparametric nonseparable triangular system, the conditional moment restriction (CMR) does not identify the average structural function (ASF). The CMR identifies the ASF only if the model is structurally separable in observable covariates and unobservable random errors. This excludes, for instance, random coefficient models in which the CMR in general does not identify the average response. An implication of our results is that empirical researchers should use methods other than CMR if they want to estimate the average response in models that are not additively separable.

We are interested in estimating the average effect of a binary treatment on a scalar outcome. If assignment to the treatment is exogenous or unconfounded, that is, independent of the potential outcomes given covariates, biases associated with simple treatment-control average comparisons can be removed by adjusting for differences in the covariates. Rosenbaum and Rubin (1983) show that adjusting solely for differences between treated and control units in the propensity score removes all biases associated with differences in covariates. Although adjusting for differences in the propensity score removes all the bias, this can come at the expense of efficiency, as shown by Hahn (1998), Heckman, Ichimura, and Todd (1998), and Robins, Mark, and Newey (1992). We show that weighting by the inverse of a nonparametric estimate of the propensity score, rather than the true propensity score, leads to an efficient estimate of the average treatment effect. We provide intuition for this result by showing that this estimator can be interpreted as an empirical likelihood estimator that efficiently incorporates the information about the propensity score.

This paper estimates an education production function using data on the College Scholastic Ability Test score and high school characteristics from Seoul, Korea, where, on entering high school, students are randomly assigned to schools within each school district. We derive a school production function by aggregating the individuals' potential outcomes under the random assignment and no cohort effect assumption. We find that the school production function coefficients differ between districts and that the single-sex school effect estimate is much larger than that found in previous studies.