The changes in the distribution of earnings during the 1980s have been studied extensively. The two most striking characteristics of the decade are (a) a large increase in the college/high school wage gap, and (b) a substantial rise in the variance of wage residuals. While this second phenomenon is typically implicitly attributed to an increase in the demand for unobserved skill, most work in this area fails to acknowledge that this same increase in demand for unobserved skill could drive the evolution of the measured college premium. In its simplest form, if higher ability individuals are more likely to attend college, then the increase in the college wage premium may be due to a increase in the relative demand for high ability workers rather than an increase in the demand for skills accumulated in college. This paper develops and estimates a dynamic programming selection model in order to investigate the plausibility of this explanation. The results are highly suggestive that an increase in the demand for unobserved ability could play a major role in the growing college premium.

This paper measures the importance of borrowing constraints on education decisions. Empirical identification of borrowing constraints is secured by the economic prediction that opportunity costs and direct costs of schooling affect borrowing-constrained and unconstrained persons differently. Direct costs need to be financed during school and impose a larger burden on credit-constrained students. By contrast, gross forgone earnings do not have to be financed. We explore the implications of this idea using four methodologies: schooling attainment models, instrumental variable wage regressions, and two structural economic models that integrate both schooling choices and schooling returns into a unified framework. None of the methods produces evidence that borrowing constraints generate inefficiencies in the market for schooling in the current policy environment. We conclude that, on the margin, additional policies aimed at improving credit access will have little impact on schooling attainment.

Gibbons and Katz’s asymmetric information model of the labor market predicts that wage losses following displacement should be larger for layoffs than for plant closings. This was borne out in their empirical work. In this article, we examine how the difference in wage losses across plant closing and layoff varies with race and gender. We find that the basic prediction by Gibbons and Katz holds only for white males. We augment their asymmetric information model with heterogeneous human capital and show that this augmented model can match the data.

We estimate a life-cycle model of consumption, human capital investment, and labor supply. The interaction between human capital and labor supply toward the end of the life cycle is most novel. The estimates replicate the main features of the data, in particular the large increase in wages and small increase in labor supply at the beginning of the life cycle and the small decrease in wages but large decrease in labor supply toward the end. We show that incorporating human capital is critical when analyzing changes to Social Security.

In this paper, we develop a model that captures key components of the Roy model, a search model, compensating differentials, and human capital accumulation on‐the‐job. We establish which components of the model can be non‐parametrically identified and which ones cannot. We estimate the model and use it to assess the relative contribution of the different factors for overall wage inequality. We find that variation in premarket skills (the key feature of the Roy model) is the most important component to account for the majority of wage variation. We also demonstrate that there is substantial interaction between the other components, most notably, that the importance of the job match obtained by search frictions varies from around 4% to around 29%, depending on how we account for other components. Inequality due to preferences for non‐pecuniary aspects of the job (which leads to compensating differentials) and search are both very important for explaining other features of the data. Search is important for turnover, but so are preferences for non‐pecuniary aspects of jobs as one‐third of all choices between two jobs would have resulted in a different outcome if the worker only cared about wages.

The goal of this paper is to use a semiparametric reduced form model to estimate the effects of various tuition subsidies. This approach expands on the tuition subsidy example in Ichimura and Taber (2000) in a number of dimensions. It has become common practice in the empirical literature to refer to any nonstructural empirical analysis as "reduced form." This is not the traditional sense of the phrase. A classic reduced form analysis (see e.g. Marschak, 1953) first specifies a structural model and then derives the reduced form parameters in terms of the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural parameters. While many recent studies have asserted to taking a reduced form approach, the structural model which the reduced form model should correspond is rarely specified. We explicitly specify a structural model and use the implied reduced form structure to estimate the effect of tuition subsidy policies. Specifying the underlying model has the advantage of being explicit about the assumptions that justify the analysis. This avoids Rosenzweig and Wolpin's (2000) criticism of work on natural 'natural experiments' that often leaves these conditions implicit. Our structural model is based on the model studied by Keane and Wolpin (1999). It is highly nonlinear and allows for more unobserved heterogeneity than the typical simultaneous equations framework that most previous work has used in reduced form estimation. Using hte specified structural model, we examine the assumptions discussed in Ichimura and Taber (2000) to justify reduced form estimation of the policy effects

Propensity-score matching is a nonexperimental method for estimating the average effect of social programs (see William Cochran, 1968; Paul Rosenbaum and Donald Rubin, 1983; James Heckman et al., 1998b). The method compares average outcomes of participants and nonparticipants, conditioning on the propensityscore value. The average comparison measures the average impact of a program. This methodology has received much attention recently in econometrics (see Heckman et al., 1996, 1997, 1998a, b; Jinyong Hahn, 1998; Rajeev Dehejia and Sadek Wahba, 1999; Jeffrey Smith and Petra Todd, 2000; Keisuke Hirano et al., 2000). The underlying identification requirement of the matching methodology is that the program choice is independent of outcomes conditional on a certain set of observables. While intuitively attractive in that the method replicates features of randomized experiments within observational data, the identification requirement excludes a possibility that the program-choice decision could be correlated with the outcomes given the set of observables (see Heckman et al., 1997, 1998b). Unobservables that are correlated both with an outcome and the program choice are not allowed. There are some efforts to estimate more general models using nonparametric methods (see Whitney Newey and James Powell, 1989; Heckman, 1997; Alberto Abadie, 2000; Serge Darolles et al., 2000; Matali Das, 2000; JeanPierre Florens, 2000; Ichimura and Taber, 2000). One such effort is the use of the instrumental-variable methods. Heckman (1997) has shown that the set of assumptions to justify instrumental-variable methods are very restrictive from the perspective of behavioral models of program participation. We show that his conditions justifying instrumental-variable methods actually justify the matching method as a special case.1 This observation ties the limitations of the matching method in line with those of instrumental-variable methods and also is useful in constructing specification tests for matching methods when valid instrumental variables are available. This is analogous to testing the validity of the identification conditions for ordinary least-squares (OLS) estimators when there are overidentifying instrumental variables. We then present two different propensityscore methods that are based on instrumental variables. Both methods include standard propensity-score matching as special cases. They help reduce the dimension of the conditioning variables without invoking functional-form assumptions in the same way that the standard propensity-score matching helps reduce the dimension of the conditioning variables. We show how to use these ideas to construct estimators that can be easily implemented.