This article considers a model in which the productivity of a worker depends on his experience, ability, and position in the firm. It is shown that workers are sorted in positions based on comparative advantage. Furthermore, workers are induced to retire when productivity is equal to the value of time after adjusting for the reallocation of other workers by positions that would occur if the worker retired.

The primary goal of this article is to specify and estimate a structural simultaneous search model and then determine the empirical importance of simultaneous search. The results indicate that new labor force entrants search simultaneously. A secondary goal is to identify and estimate job offer arrival rates and wage offer rejection probabilities separately. The results indicate that a significant portion of unemployment spells are caused by slow arrival rates, but policies intended to speed arrival rates would increase the average length of unemployment spells.

In this paper, a firm maximizes profits over choices of wage schedules and hiring schedules in a model with (1) turnover costs, (2) a productivity function that depends on position and experience, and (3) employee utility functions that depend on monetary compensation and position. It is shown that firms may have reason to encourage employees to retire before their reservation wage is greater than their marginal product. However, if an alternative definition of marginal product is used, the usual relation holds.

This paper considers a matching model of which the search behaviour of Jovanovic (1979) and Albrecht and Jovanovic (1984) are special cases. The nature of the generality is that each unemployed worker has more information than firms about his average productivity. This results in wage offers falling over the length of an unemployment spell because a long spell is a signal of lower than average productivity. The results of the paper are similar to the scarring effect of Heckman and Borjas (1980) and Greenwald (1986) and the discouraged worker effect of Schweitzer and Smith (1974).

THERE HAS BEEN MUCH INTEREST over the years in estimating discrete choice models. However, with the exception of multinomial logit models, agents have been restricted to few choices so that multivariate integrals could be feasibly integrated numerically.2 Pakes and Pollard (1989) and McFadden (1989) have independently developed the method of simulated moments (MSM) to deal with estimating a wide class of models of which high order discrete choice models are a subset. One of the problems a researcher must handle when using MSM for a discrete choice model is how to smooth the discrete simulated random variables. This is necessary to keep small the number of draws required to simulate derivatives and to simulate variation in the data. Geweke (1989) and McFadden (1989) have suggested importance sampling methods (and other methods) to smooth the simulated variables for general error structures. In this paper, I present a factor analytic smoothing method that can be applied to probit problems. Although it is not as general as the methods Geweke and McFadden suggest, it is easy to use and has clear intuition. Furthermore, simulated probabilities will be unbiased, will be bounded between zero and unity, and will have smaller variances than unsmoothed probabilities always and smaller variances than importance sampling estimates for a large class of probabilities. The second section of this paper develops notation, defines the problem, and presents the smoothing method and an algorithm to employ it. The last section presents Monte Carlo comparisons of the smoothing method to the importance sampling method.

This paper considers two problems that frequently arise in dynamic discrete choice problems but have not received much attention with regard to simulation methods. The first problem is how to simulate unbiased simulators of probabilities conditional on past history. The second is simulating a discrete transition probability model when the underlying dependent variable is really continuous. Both methods work well relative to reasonable alternatives in the application discussed. However, in both cases, for this application, simpler methods also provide reasonably good results. Copyright 1994 by MIT Press.

A dynamic programming model of job exit behavior and retirement is constructed and estimated using the method of simulated moments. The model and estimation method allow for both unobserved individual effects and unobserved job-specific "match" effects. The model is estimated using two different assumptions about individual discount factors. First, a static model, with the discount factor equal to zero, is estimated. Then a dynamic model, with the discount factor equal to .95 is estimated. In both models, it is found that bad health, age, and lack of education increase the probability of retirement. The dynamic model performs better than the static model and has different implications for retirement behavior. The job-specific effects are an important source of unobserved heterogeneity. Copyright 1991 by The Econometric Society.