This paper examines the properties of instrumental variables (IV) applied to models with essential heterogeneity, that is, models where responses to interventions are heterogeneous and agents adopt treatments (participate in programs) with at least partial knowledge of their idiosyncratic response. We analyze two-outcome and multiple-outcome models, including ordered and unordered choice models. We allow for transition-specific and general instruments. We generalize previous analyses by developing weights for treatment effects for general instruments. We develop a simple test for the presence of essential heterogeneity. We note the asymmetry of the model of essential heterogeneity: outcomes of choices are heterogeneous in a general way; choices are not. When both choices and outcomes are permitted to be symmetrically heterogeneous, the method of IV breaks down for estimating treatment parameters.

This note derives simply computed closed-form expressions for the average treatment effect, the effect of treatment on the treated, the local average treatment effect, and the marginal treatment effect in a latent-variable framework for both normal and nonnormal models. Asymptotic standard errors for versions of these parameters that average over observed characteristics are also obtained. The performances of the derived estimators are also evaluated in Monte Carlo experiments under correct specification and misspecification.

Treatment Effect Bounds under Monotonicity Assumptions: An Application to Swan-Ganz Catheterization by Jay Bhattacharya, Azeem M. Shaikh and Edward Vytlacil. Published in volume 98, issue 2, pages 351-56 of American Economic Review, May 2008

The literature on treatment effects focuses on gross benefits from program participation. We extend this literature by developing conditions under which it is possible to identify parameters measuring the cost and net surplus from program participation. Using the generalized Roy model, we nonparametrically identify the cost, benefit, and net surplus of selection into treatment without requiring the analyst to have direct information on the cost. We apply our methodology to estimate the gross benefit and net surplus of attending college.

One current educational reform seeks to reward the “value added” by teachers and schools based on the average change in pupil test scores over time. In this paper, we outline the conditions under which the average change in scores is sufficient to rank schools in terms of value added. A key condition is that socioeconomic outcomes be a linear function of test scores. Absent this condition, one can still derive the optimal value-added policy if one knows the relationship between test scores and socioeconomic outcomes, and the distribution of test scores both before and after the intervention.Using the National Longitudinal Survey of Youth, we find a nonlinear relationship between test scores and one important outcome: log wages. We find no consistent pattern in the curvature of log wage returns to test scores (whether percentiles, scaled, or raw scores). This implies that, used alone, the average gain in test scores is an inadequate measure of school performance and current value-added methodology may misdirect school resources.

This paper develops methods for evaluating marginal policy changes. We characterize how the effects of marginal policy changes depend on the direction of the policy change, and show that marginal policy effects are fundamentally easier to identify and to estimate than conventional treatment parameters. We develop the connection between marginal policy effects and the average effect of treatment for persons on the margin of indifference between participation in treatment and nonparticipation, and use this connection to analyze both parameters. We apply our analysis to estimate the effect of marginal changes in tuition on the return to going to college.

This paper estimates marginal returns to college for individuals induced to enroll in college by different marginal policy changes. The recent instrumental variables literature seeks to estimate this parameter, but in general it does so only under strong assumptions that are tested and found wanting. We show how to utilize economic theory and local instrumental variables estimators to estimate the effect of marginal policy changes. Our empirical analysis shows that returns are higher for individuals with values of unobservables that make them more likely to attend college. We contrast our estimates with IV estimates of the return to schooling.