Christopher Taber

Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools.

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.

Estimation of Educational Borrowing Constraints Using Returns to Schooling

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.

An Evaluation of Instrumental Variable Strategies for Estimating the Effects of Catholic Schooling

Several previous studies have relied on religious affiliation and the proximity to Catholic schools as exogenous sources of variation for identifying the effect of Catholic schooling on a wide variety of outcomes. Using three separate approaches, we examine the validity of these instrumental variables. We find that none of the candidate instruments is a useful source of identification in currently available data sets. We also investigate the role of exclusion restrictions versus nonlinearity as the source of identification in bivariate probit models. The analysis may be useful as a template for the assessment of instrumental variables strategies in other applications.

The Rising College Premium in the Eighties: Return to College or Return to Unobserved Ability?

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.

Accounting For Dropouts In Evaluations Of Social Programs

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.

Semiparametric identification and heterogeneity in discrete choice dynamic programming models

Abstract Empirical discrete choice dynamic programming models have become important empirical tools. A question that arises in estimation and interpretation of the results from these specifications is which combination of data and assumptions are needed to overcome problems of heterogeneity, selection, and omited variables bias. This paper addresses this question by considering nonparametric identification of a version of the model that allows for quite general forms of unobservable and information structures. I show that the model can be identified under conditions similar to a static polychotomous choice model. Using a stochastic version of an ‘identification of infinity’ argument, utility can be identified up to a monotonic transformation of the observables under strong support conditions and two types of exclusion restriction. The first type is similar to a standard static exclusion restriction: a variable that influences the first period decision, but does not enter the second period decision directly. The second type requires a variable that does not affect the utility of the first option directly, but is known during the first period, and has predictive power on the choice during the second. I also provide two specifications under which the full error structure can be identified. This requires the additional assumption of stochastic innovations in the observables. I then use the model to estimate schooling decisions in which students deciding whether to drop out of high school account for the option value of attending college.

Econometric Mixture Models and More General Models for Unobservables in Duration Analysis

This paper considers models for unobservables in duration models. It demonstrates how cross-section and time-series variation in regressors facilitates identification of single-spell, competing risks and multiple spell duration models. We also demonstrate the limited value of traditional identification studies by considering a case in which a model is identified in the conventional sense but cannot be consistently estimated.

The changing pattern of wage growth for low skilled workers

We examine the key components that determine an individuals early career wage growth and how these factors have changed for less skilled workers over the last twenty years. In particular, we examine the relative importance of accumulating work experience as compared to the quality of job matches in influencing wage growth. Our main finding is that over this period, the vast majority of the variation in wage growth is due to variability in the return to experience.

Difference-in-Differences Method in Comparative Effectiveness Research: Utility with Unbalanced Groups

BackgroundComparative effectiveness research (CER) often includes observational studies utilizing administrative data. Multiple conditioning methods can be used for CER to adjust for group differences, including difference-in-differences (DiD) estimation.ObjectiveThis study presents DiD and demonstrates how to apply this conditioning method to estimate treatment outcomes in the CER setting by utilizing the MarketScan® Databases for multiple sclerosis (MS) patients receiving different therapies.MethodsThe sample included 6762 patients, with 363 in the Test Cohort [glatiramer acetate (GA) switched to fingolimod (FTY)] and 6399 in the Control Cohort (GA only, no switch) from a US administrative claims database. A trend analysis was conducted to rule out concerns regarding regression to the mean and to compare relapse rates among treatment cohorts. DiD analysis was used to enable comparisons among the Test and Control Cohorts. Logistic regression was used to estimate the probability of relapse after switching from GA to FTY, and to compare group differences in the pre- and post-index periods.ResultsCrude DiD analysis showed that in the pre-index period more patients in the Test Cohort experienced an MS relapse and had a higher mean number of relapses than in the Control Cohort. During the pre-index period, numeric and relative data for MS relapses in patients in the Test Cohort were significantly higher than in the Control Cohort, while no significant between-group differences emerged during the post-index period. Generalized linear modeling with DiD regression estimation showed that the mean number of MS relapses decreased significantly in the post-index period among patients in the Test Cohort compared with patients in the Control Cohort.ConclusionIn this study, an MS population was utilized to demonstrate how DiD can be applied to estimate treatment effects in a heterogeneous population, where the Test and Control Cohorts varied greatly. The results show that DiD offers a robust method for comparing diverse cohorts when other risk-adjustment methods may not be adequate.

Chapter 6 – Identification of Models of the Labor Market ☆

This chapter discusses identification of common selection models of the labor market. We start with the classic Roy model and show how it can be identified with exclusion restrictions. We then extend the argument to the generalized Roy model, treatment effect models, duration models, search models, and dynamic discrete choice models. In all cases, key ingredients for identification are exclusion restrictions and support conditions.