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.

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.

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.

This paper explores issues that arise in the evaluation of social programs using experimental data in the frequently encountered case where some of the experimental treatment group members drop out of the program prior to receiving treatment. We begin with the standard estimator for this case and the identifying assumption upon which it rests. We then examine the behavior of the estimator when the dropouts receive a partial “dose” of the program treatment prior to dropping out of the program. In the case of partial treatment, the identifying assumption is typically violated, thereby making the estimator inconsistent for the conventional parameter of interest: the impact of full treatment on the fully treated. We develop a test of the identifying assumption underlying the standard estimator and consider whether exclusion restrictions produce identification of the mean impact of the program when this assumption fails to hold. Finally, we discuss alternative parameters of interest in the presence of partial treatment among the dropouts and argue that the conventional parameter is not always the economically interesting one. We apply our methods to data from a recent experimental evaluation of the Job Training Partnership Act (JTPA) program.

Using Selection on Observed Variables to Assess Bias from Unobservables When Evaluating Swan-Ganz Catheterization by Joseph G. Altonji, Todd E. Elder and Christopher R. Taber. Published in volume 98, issue 2, pages 345-50 of American Economic Review, May 2008

In this paper we measure the effect of Catholic high school attendance on educational attainment and test scores. Because we do not have a good instrumental variable for Catholic school attendance, we develop new estimation methods based on the idea that the amount of selection on the observed explanatory variables in a model provides a guide to the amount of selection on the unobservables. We also propose an informal way to assess selectivity bias based on measuring the ratio of selection on unobservables to selection on observables that would be required if one is to attribute the entire effect of Catholic school attendance to selection bias. We use our methods to estimate the effect of attending a Catholic high school on a variety of outcomes. Our main conclusion is that Catholic high schools substantially increase the probability of graduating from high school and, more tentatively, attending college. We find little evidence of an effect on test scores.

Joseph Altonji is one of the pioneers of modern labor economics. To mark Joe’s sixty-fifth birthday and honor his illustrious career, a conference was held at his alma mater and employer, Yale University, on September 7–8, 2018. This special issue contains seven of the papers presented by former students, coauthors, and colleagues. Joe has been central to the empirical revolution that has brought vast amounts of individual-level data to some of the most important questions ofbothmicroandmacroeconomics.His seminal breakthroughshave changed thewaywe think aboutnumerous topics, including the studyof consumption behavior and labor supply over time, intrafamily insurance, discrimination, wage determination and learning, school choice and school quality, returns to higher education, and the stochastic process of income. The hallmark of all thiswork is a thoughtful combination of theory anddata, the development ofnewempiricalmethods, thediscoveryofnovel identificationstrategies, and extreme care with extensive robustness checks and attention to data details. Early on in his career Joe was one of the first to develop our understanding of the relationship between intratemporal and intertemporal allocations and the implication this distinction has on estimating elasticities. These

This paper defines and estimates general equilibrium treatment effects. The conventional approach in the literature on treatment effects ignores interactions among individuals induced by the policy interventions being studied. Focusing on the impact of tuition policy, and using estimates from our dynamic overlapping generations general equilibrium model of capital and human capital formation, we find that general equilibrium impacts of tuition on college enrollment are an order of magnitude smaller than those reported in the literature on microeconomic treatment effects. The assumptions used to justify the LATE parameter in a partial equilibrium setting do not hold in a general equilibrium setting. Policy changes induce two way flows. We extend the LATE concept to a general equilibrium setting. We present a more comprehensive evaluation to program evaluation by considering both the tax and benefit consequences of the program being evaluated and placing the analysis in a market setting.