Showing papers by "Joshua D. Angrist published in 1999"

Empirical Strategies in Labor Economics

Abstract: This chapter provides an overview of the methodological and practical issues that arise when estimating causal relationships that are of interest to labor economists. The subject matter includes identification, data collection, and measurement problems. Four identification strategies are discussed, and five empirical examples – the effects of schooling, unions, immigration, military service, and class size – illustrate the methodological points. In discussing each example, we adopt an experimentalist perspective that emphasizes the distinction between variables that have causal effects, control variables, and outcome variables. The chapter also discusses secondary datasets, primary data collection strategies, and administrative data. The section on measurement issues focuses on recent empirical examples, presents a summary of empirical findings on the reliability of key labor market data, and briefly reviews the role of survey sampling weights and the allocation of missing values in empirical research. © 1999 Elsevier Science B.V. All rights reserved.

Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement

Abstract: The twelfth century rabbinic scholar Maimonides proposed a maximum class size of 40. This same maximum induces a nonlinear and nonmonotonic relationship between grade enrollment and class size in Israeli public schools today. Maimonides’ rule of 40 is used here to construct instrumental variables estimates of effects of class size on test scores. The resulting identiecation strategy can be viewed as an application of Donald Campbell’s regression-discontinuity design to the class-size question. The estimates show that reducing class size induces a signiecant and substantial increase in test scores for fourth and efth graders, although not for third graders. When asked about their views on class size in surveys, parents and teachers generally report that they prefer smaller classes. This may be because those involved with teaching believe that smaller classes promote student learning, or simply because smaller classes offer a more pleasant environment for the pupils and teachers who are in them [Mueller, Chase, and Walden 1988]. Social scientists and school administrators also have a longstanding interest in the class-size question. Class size is often thought to be easier to manipulate than other school inputs, and it is a variable at the heart of policy debates on school quality and the allocation of school resources in many countries (see, e.g., Robinson [1990] for the United States; OFSTED [1995] for the United Kingdom; and Moshel-Ravid [1995] for Israel). This broad interest in the consequences of changing class size

Instrumental Variables Estimates Of The Effect Of Subsidized Training On The Quantiles Of Trainee Earnings

Abstract: The effect of government programs on the distribution of participants? earnings is important for program evaluation and welfare comparisons. This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on the quantiles of outcome variables. This quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression when selection for treatment is exogenously determined. The QTE estimator can be computed as the solution to a convex linear programming problem, although this requires first-step estimation of a nuisance function. We develop distribution theory for the case where the first step is estimated nonparametrically. For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles. Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution.

Jackknife instrumental variables estimation

Abstract: Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for models with more instruments than endogenous regressors. These estimators can be interpreted as instrumental variables procedures using an instrument that is independent of disturbances even in finite samples. Independence is achieved by using a `leave-one-out' jackknife-type fitted value in place of the usual first-stage equation. The new estimators are first-order equivalent to 2SLS but with finite-sample properties superior to those of 2SLS and similar to LIML when there are many instruments. Moreover, the jackknife estimators appear to be less sensitive than LIML to deviations from the linear reduced form used in classical simultaneous equations models.

How Large are the Social Returns to Education? Evidence from Compulsory Schooling Laws

Abstract: Average schooling in US states is highly correlated with state wage levels, even after controlling for the direct effect of schooling on individual wages. We use an instrumental variables strategy to determine whether this relationship is driven by social returns to education. The instrumentals for average schooling are derived from information on the child labor laws and compulsory attendance laws that affected men in our Census samples, while quarter of birth is used as an instrument for individual schooling. This results in precisely estimated private returns to education of about seven percent, and small social returns, typically less than one percent, that are not significantly different from zero.

How Large are the Social Returns to Education? Evidence from Compulsory Schooling Laws

Abstract: Average schooling in US states is highly correlated with state wage levels, even after controlling for the direct effect of schooling on individual wages. We use an instrumental variables strategy to determine whether this relationship is driven by social returns to education. The instrumentals for average schooling are derived from information on the child labor laws and compulsory attendance laws that affected men in our Census samples, while quarter of birth is used as an instrument for individual schooling. This results in precisely estimated private returns to education of about seven percent, and small social returns, typically less than one percent, that are not significantly different from zero.

New Evidence on Classroom Computers and Pupil Learning

Abstract: The question of how technology affects learning has been at the center of recent debates over educational inputs. In 1994, the Israeli State Lottery sponsored the installation of computers in many elementary and middle schools. This program provides an opportunity to estimate the impact of computerization on both the instructional use of computers and on pupils' test scores. Results from a survey of Israeli school-teachers show that the influx of new computers increased teachers' use of computer-aided instruction (CAI) in the 4th grade, with a smaller effect on CAI in 8th grade. CAI does not appear to have had educational benefits that translated into higher test scores. Results for 4th graders show sharply lower Math scores in the group that was awarded computers, with smaller (insignificant) negative effects on verbal scores. Results for 8th graders' test scores are very imprecise, probably reflecting the much weaker first-stage relationship between program funding and the use of CAI in 8th grade. The estimates for 8th grade Math scores are also negative, however.

Comment on James Heckman, "Instrumental Variables: A Study of Implicit Behavioral Assumption Used in Making Program Evaluations

Abstract: In a recent paper in this journal, Heckman discussed the use of instrumental variables methods in evaluation research and our local average treatment effects (LATE) interpretation of instrumental variables estimates. This comment provides additional background for Heckman's paper, and a review of our rationale for focusing on LATE. We also show that a set of assumptions proposed by Heckman as an alternative to the LATE assumptions are not compatible with either latent-index assignment models or the definition we proposed for an instrument.

When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects

Abstract: The problem of how to control for covariates is endemic in evaluation research. Covariate-matching provides an appealing control strategy, but with continuous or high-dimensional covariate vectors, exact matching may be impossible or involve small cells. Matching observations that have the same propensity score produces unbiased estimates of causal effects whenever covariate-matching does, and also has an attractive dimension-reducing property. On the other hand, conventional asymptotic arguments show that covariate-matching is (asymptotically) more efficient that propensity score-matching. This is because the usual asymptotic sequence has cell sizes growing to infinity, with no benefit from reducing the number of cells. Here, we approximate the large sample behavior of difference matching estimators using a panel-style asymptotic sequence with fixed cell sizes and the number of cells increasing to infinity. Exact calculations in simple examples and Monte Carlo evidence suggests this generates a substantially improved approximation to actual finite-sample distributions. Under this sequence, propensity-score-matching is most likely to dominate exact matching when cell sizes are small, the explanatory power of the covariates conditional on the propensity score is low, and/or the probability of treatment is close to zero or one. Finally, we introduce a random-effects type combination estimator that provides finite-sample efficiency gains over both covariate-matching and propensity-score-matching.

When to Control for Covariates? Panel-Asymptotic Results for Estimates of Treatment Effects

Abstract: The problem of how to control for covariates is endemic in evaluation research. Covariate-matching provides an appealing control strategy, but with continuous or high-dimensional covariate vectors, exact matching may be impossible or involve small cells. Matching observations that have the same propensity score produces unbiased estimates of causal effects whenever covariate-matching does, and also has an attractive dimension-reducing property. On the other hand, conventional asymptotic arguments show that covariate-matching is (asymptotically) more efficient that propensity score-matching. This is because the usual asymptotic sequence has cell sizes growing to infinity, with no benefit from reducing the number of cells. Here, we approximate the large sample behavior of difference matching estimators using a panel-style asymptotic sequence with fixed cell sizes and the number of cells increasing to infinity. Exact calculations in simple examples and Monte Carlo evidence suggests this generates a substantially improved approximation to actual finite-sample distributions. Under this sequence, propensity-score-matching is most likely to dominate exact matching when cell sizes are small, the explanatory power of the covariates conditional on the propensity score is low, and/or the probability of treatment is close to zero or one. Finally, we introduce a random-effects type combination estimator that provides finite-sample efficiency gains over both covariate-matching and propensity-score-matching.

Estimation of Limited-Dependent Variable Models with Dummy Endogenous Regressors: Simple Strategies for Empirical Practice

Abstract: Applied economists have long struggled with the question of how to accommodate binary endogenous regressors in models with binary and non-negative outcomes. I argue here that much of the difficulty with limited-dependent variables comes from a focus on structural parameters, such as index coefficients, instead of causal effects. Once the object of estimation is taken to be the causal effect of treatment, a number of simple strategies is available. These include conventional two-stage least squares, multiplicative models for conditional means, linear approximation of nonlinear causal models, models for distribution effects, and quantile regression with an endogenous binary regressor. The estimation strategies discussed in the paper are illustrated by using multiple births to estimate the effect of childbearing on employment status and hours of work.

Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings

Abstract: This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on quantiles. The quantile treatment effects (QTE) estimator reduces to quantile regression when selection for treatment is exogenously determined. QTE can be computed as the solution to a convex linear programming problem, although this requires first-step estimation of a nuisance function. We develop distribution theory for the case where the first step is estimated nonparametrically. For women, the empirical results show that the JTPA program had the largest proportional impact at low quantiles. Perhaps surprisingly, however, JTPA training raised the quantiles of earnings for men only in the upper half of the trainee earnings distribution. Copyright The Econometric Society 2002. (This abstract was borrowed from another version of this item.)