Showing papers by "Joshua D. Angrist published in 1995"

Identification and Estimation of Local Average Treatment Effects

Abstract: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity

Abstract: Two-stage least squares (TSLS) is widely used in econometrics to estimate parameters in systems of linear simultaneous equations and to solve problems of omitted-variables bias in single-equation estimation. We show here that TSLS can also be used to estimate the average causal effect of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. The average causal effect in which we are interested is a conditional expectation of the difference between the outcomes of the treated and what these outcomes would have been in the absence of treatment. Given mild regularity assumptions, the probability limit of TSLS is a weighted average of per-unit average causal effects along the length of an appropriately defined causal response function. The weighting function is illustrated in an empirical example based on the relationship between schooling and earnings.

Split-Sample Instrumental Variables Estimates of the Return to Schooling

Abstract: This article reevaluates recent instrumental variables (IV) estimates of the returns to schooling in light of the fact that two-stage least squares is biased in the same direction as ordinary least squares (OLS) even in very large samples. We propose a split-sample instrumental variables (SSIV) estimator that is not biased toward OLS. SSIV uses one-half of a sample to estimate parameters of the first-stage equation. Estimated first-stage parameters are then used to construct fitted values and second-stage parameter estimates in the other half sample. SSIV is biased toward 0, but this bias can be corrected. The splt-sample estimators confirm and reinforce some previous findings on the returns to schooling but fail to confirm others.

Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants

Abstract: This study uses Social Security data on the earnings of military applicants to the all-volunteer forces to compare the earnings of Armed Forces veterans with the earnings of military applicants who did not enlist. Matching, regression, and Instrumental Variables (IV) estimates are presented. The matching and regression estimates control for most of the characteristics used by the military to select qualified applicants from the military applicant pool. The IV estimates exploit an error in the scoring of exams used by the military to screen applicants between 1976 and 1980. All the estimates suggest that soldiers who served in the early 1980s were paid considerably more than comparable civilians while in the military. Military service also appears to have led to a modest (less than 10 percent) increase in the civilian earnings of nonwhite veterans while actually reducing the civilian earnings of white veterans. Most of the positive effects of military service on civilian earnings appear to be attributable to improved employment prospects for veterans.

The economic returns to schooling in the West Bank and Gaza strip

Abstract: The author uses micro data from the Labor Force Surveys conducted in the West Bank and the Gaza Strip during 1981-91 to show that during 1981-87 wage differences between schooling groups fell by well over one-half. This sharp reduction is associated with large increases in the size of the educated Palestinian labor force. Since the returns to schooling for Israeli Jews were stable, the decline in returns to schooling for Palestinians is consistent with the notion that the returns to schooling in the territories were determined largely by the forces of supply and demand in a segmented market for skilled labor. Copyright 1995 by American Economic Association.

Average Causal Response with Variable Treatment Intensity

Abstract: In evaluation research, an average causal effect is usually defined as the expected difference between the outcomes of the treated, and what these outcomes would have been in the absence of treatment. This definition of causal effects makes sense for binary treatments only. In this paper, we extend the definition of average causal effects to the case of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. We show that given mild regularity assumptions, instrumental variables independence assumptions identify a weighted average of per-unit causal effects along the length of an appropriately defined causal response function. Conventional instrumental variables and Two-Stage Least Squares procedures can be interpreted as estimating the average causal response to a variable treatment.

Split Sample Instrumental Variables

Abstract: Instrumental Variables (IV) estimates tend to be biased in the same direction as Ordinary Least Squares (OLS) in finite samples if the instruments are weak. To address this problem we propose a new IV estimator which we call Split Sample Instrumental Variables (SSIV). SSIV works as follows: we randomly split the sample in half, and use one half of the sample to estimate parameters of the first-stage equation. We then use these estimated first-stage parameters to construct fitted values and second-stage parameter estimates using data from the other half sample. SSIV is biased toward zero, rather than toward the plim of the OLS estimate. However, an unbiased estimate of the attenuation bias of SSIV can be calculated. We us this estimate of the attenutation bias to derive an estimator that is asymptotically unbiased as the number of instruments tends to infinity, holding the number of observations per instrument fixed. We label this new estimator Unbiased Split Sample Instrumental Variables (USSIV). We apply SSIV and USSIV to the data used by Angrist and Krueger (1991) to estimate the payoff to education.

Conditioning on the Probability of Selection to Control Selection Bias

Abstract: Problems of sample selection arise in the analysis of both experimental and non-experimental data. In clinical trials to evaluate the impact of an intervention on health and mortality, treatment assignment is typically nonrandom in a sample of survivors even if the original assignment is random. Similarly, randomized training interventions like National Supported Work (NSW) are not necessarily randomly assigned in the sample of working men. A non- experimental version of this problem involves the use of instrumental variables (IV) to estimate behavioral relationships. A sample selection rule that is related to the instruments can induce correlation between the instruments and unobserved outcomes, possibly invalidating the use of conventional IV techniques in the selected sample. This paper shows that conditioning on the probability of selection given the instruments can provide a solution to the selection problem as long as the relationship between instruments and selection status satisfies a simple monotonicity condition. A latent index structure is not required for this result, which is motivated as an extension of earlier work on the propensity score. The conditioning approach to selection problems is illustrated using instrumental variables techniques to estimate the returns to schooling in a sample with positive earnings.

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.

Estimating the Labor Market Impact of Voluntary Military Service Using Social Security Data on Military Applicants

Abstract: This study uses Social Security data on the earnings of military applicants to the all-volunteer forces to compare the earnings of Armed Forces veterans with the earnings of military applicants who did not enlist. Matching, regression, and Instrumental Variables (IV) estimates are presented. The matching and regression estimates control for most of the characteristics used by the military to select qualified applicants from the military applicant pool. The IV estimates exploit an error in the scoring of exams used by the military to screen applicants between 1976 and 1980. All the estimates suggest that soldiers who served in the early 1980s were paid considerably more than comparable civilians while in the military. Military service also appears to have led to a modest (less than 10 percent) increase in the civilian earnings of nonwhite veterans while actually reducing the civilian earnings of white veterans. Most of the positive effects of military service on civilian earnings appear to be attributable to improved employment prospects for veterans.

Conditioning on the Probability of Selection to Control Selection Bias

Abstract: Problems of sample selection arise in the analysis of both experimental and non-experimental data. In clinical trials to evaluate the impact of an intervention on health and mortality, treatment assignment is typically nonrandom in a sample of survivors even if the original assignment is random. Similarly, randomized training interventions like National Supported Work (NSW) are not necessarily randomly assigned in the sample of working men. A non- experimental version of this problem involves the use of instrumental variables (IV) to estimate behavioral relationships. A sample selection rule that is related to the instruments can induce correlation between the instruments and unobserved outcomes, possibly invalidating the use of conventional IV techniques in the selected sample. This paper shows that conditioning on the probability of selection given the instruments can provide a solution to the selection problem as long as the relationship between instruments and selection status satisfies a simple monotonicity condition. A latent index structure is not required for this result, which is motivated as an extension of earlier work on the propensity score. The conditioning approach to selection problems is illustrated using instrumental variables techniques to estimate the returns to schooling in a sample with positive earnings.

Average Causal Response with Variable Treatment Intensity

Abstract: In evaluation research, an average causal effect is usually defined as the expected difference between the outcomes of the treated, and what these outcomes would have been in the absence of treatment. This definition of causal effects makes sense for binary treatments only. In this paper, we extend the definition of average causal effects to the case of variable treatments such as drug dosage, hours of exam preparation, cigarette smoking, and years of schooling. We show that given mild regularity assumptions, instrumental variables independence assumptions identify a weighted average of per-unit causal effects along the length of an appropriately defined causal response function. Conventional instrumental variables and Two-Stage Least Squares procedures can be interpreted as estimating the average causal response to a variable treatment.

The Economic Returns to Schooling in the West Bank and Gaza Strip

Abstract: The author uses micro data from the Labor Force Surveys conducted in the West Bank and the Gaza Strip during 1981-91 to show that during 1981-87 wage differences between schooling groups fell by well over one-half. This sharp reduction is associated with large increases in the size of the educated Palestinian labor force. Since the returns to schooling for Israeli Jews were stable, the decline in returns to schooling for Palestinians is consistent with the notion that the returns to schooling in the territories were determined largely by the forces of supply and demand in a segmented market for skilled labor. Copyright 1995 by American Economic Association.(This abstract was borrowed from another version of this item.)

Non-Parametric Demand Analysis with an Application to the Demand for Fish

Abstract: Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors. The paper also shows how to compute the weights underlying IV estimates of average derivatives in a simultaneous equations model. These ideas are illustrated using data from the Fulton Fish market in New York City to estimate an average elasticity of wholesale demand for fresh fish. The weighting function underlying IV estimates of the demand equation is graphed and interpreted. The empirical example illustrates the essentially local and context-specific nature of instrumental variables estimates of structural parameters in simultaneous equations models.

Introduction to the JBES Symposium on Program and Policy Evaluation

Abstract: Meyer. Each of the articles in this group relies on changes in state (or provincial) policies over time to identify the effect of policy changes. States instituting reforms constitute the treatment group, whereas nonreforming states are assumed to provide a valid control group. The treatment-control parison is made after differencing state observations across years. For example, in their article on Canadian national health insurance, Jon Gruber and Maria Hanratty use the fact that Canadian provinces introduced national health insurance at different times to estimate the effect of national health insurance on changes in labor-market outcomes. The presumption is that contemporaneous changes in neighboring provinces provide a control for labor-market trends that are unrelated to health-care reform. Similarly, in their article on minimum wages, Taeil Kim and Lowell Taylor use the variation afforded by California's increase in the state minimum wage in the late 1980s. The presumption underlying this work is that, within narrowly defined sectors engaged in retail trade, changes in employ

Non-Parametric Demand Analysis with an Application to the Demand for Fish

Abstract: Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous equations context and generalizes earlier research on average derivative estimation to models with endogenous regressors. The paper also shows how to compute the weights underlying IV estimates of average derivatives in a simultaneous equations model. These ideas are illustrated using data from the Fulton Fish market in New York City to estimate an average elasticity of wholesale demand for fresh fish. The weighting function underlying IV estimates of the demand equation is graphed and interpreted. The empirical example illustrates the essentially local and context-specific nature of instrumental variables estimates of structural parameters in simultaneous equations models.

Identification and Estimation of Local Average Treatment Effects

Abstract: We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.