Alternative Methods for Evaluating the Impact of Training Programs", (with discussion (1989)
by J Heckman, J Hotz
Venue: Journal of the American Statistical Association
Nonparametric estimation of average treatment effects under exogeneity: a review
by Guido W. Imbens - REVIEW OF ECONOMICS AND STATISTICS , 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional-form assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this
...erlap in the covariate distributions. The rst of these assumptions is in principle untestable. Nevertheless a number of approaches have been proposed that are useful for addressing its credibility (=-=Heckman and Hotz, 1989-=-; Rosenbaum, 1984b). One may also wish to assess the responsiveness of the results to this assumption using a sensitivity analysis (Rosenbaum & Rubin, 1983b; Imbens, 2003), or, in its extreme form, a ...
Regression Discontinuity Designs: A Guide to Practice
by Guido Imbens, Thomas Lemieux , 2007
Large Sample Properties of Matching Estimators for Average Treatment Effects
by Alberto Abadie, Guido W. Imbens - ECONOMETRICA 74,235-267 , 2006
"... Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not ap ..."
Matching estimators for average treatment effects are widely used in evaluation research despite the fact that their large sample properties have not been established in many cases. The absence of formal results in this area may be partly due to the fact that standard asymptotic expansions do not apply to matching estimators with a fixed number of matches because such estimators are highly nonsmooth functionals of the data. In this article we develop new methods for analyzing the large sample properties of matching estimators and establish a number of new results. We focus on matching with replacement with a fixed number of matches. First, we show that matching estimators are not N1/2-consistent in general and describe conditions under which matching estimators do attain N1/2-consistency. Second, we show that even in settings where matching estimators are N1/2-consistent, simple matching estimators with a fixed number of matches do not attain the semiparametric efficiency bound. Third, we provide a consistent estimator for the large sample variance that does not require consistent nonparametric estimation of unknown functions. Software for implementing these methods is available in Matlab, Stata, and R.
Recent Developments in the Econometrics of Program Evaluation
by Guido W. Imbens, Jeffrey M. Wooldridge , 2008
Simple and Bias-Corrected Matching Estimators for Average Treatment Effects
by Alberto Abadie, Guido Imbens , 2002
Efficient semiparametric estimation of quantile treatment effects
by Sergio Firpo, Dan Mcfadden, Deb Nolan, David Reinstein, Paul Ruud , 2003
"... This paper presents calculations of semiparametric efficiency bounds for quantile treat-ment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric e ..."
This paper presents calculations of semiparametric efficiency bounds for quantile treat-ment effects parameters when selection to treatment is based on observable characteristics. The paper also presents three estimation procedures for these parameters, all of which have two steps: a nonparametric estimation and a computation of the difference between the so-lutions of two distinct minimization problems. Root-N consistency, asymptotic normality, and the achievement of the semiparametric efficiency bound is shown for one of the three estimators. In the final part of the paper, an empirical application to a job training program reveals the importance of heterogeneous treatment effects, showing that for this program the effects are concentrated in the upper quantiles of the earnings distribution.
Moving the Goalposts: Addressing Limited Overlap in Estimation of Average Treatment Effects by Changing the Estimand
by Richard K. Crump, V. Joseph Hotz , Guido W. Imbens, Oscar Mitnik , 2004
"... Estimation of average treatment effects under unconfoundedness or selection on observables is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In this ..."
Estimation of average treatment effects under unconfoundedness or selection on observables is often hampered by lack of overlap in the covariate distributions. This lack of overlap can lead to imprecise estimates and can make commonly used estimators sensitive to the choice of specification. In this paper we develop formal methods for addressing such lack of overlap in which we sacrifice some external validity in exchange for improved internal validity. We characterize optimal subsamples where the average treatment effect can be estimated most precisely, as well optimally weighted average treatment effects. We show the problem of lack of overlap has important links to the presence of treatment effect heterogeneity: under the assumption of constant conditional average treatment effects (conditional on covariates) the treatment effect can be estimated much more precisely. The efficient estimator for the treatment effect under the assumption of a constant conditional average treatment effect is shown to be identical to the efficient estimator for the optimally weighted average treatment effect. We also develop tests for the null hypotheses of a constant and a zero conditional average treatment effect. The latter is shown to be much more powerful than
...(x) = e(x) · (1− e(x)). 8 Testing In this section we discuss some statistical tests. Most of the statistical tests discussed in this literature focus on the appropriateness of the various estimators (=-=Heckman and Hotz, 1989-=-). Some attempt to provide assessments of the unconfoundedness assumptions (Imbens, 2004). We focus on three different hypotheses concerning the conditional average treatment effect τ(x). The first hy...
Nonparametric Tests for Treatment Effect Heterogeneity
by Richard K. Crump, V. Joseph Hotz, Guido W. Imbens, Oscar A. Mitnik - FORTHCOMING IN THE REVIEW OF ECONOMICS AND STATISTICS , 2007
"... In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on t ..."
In this paper we develop two nonparametric tests of treatment effect heterogeneity. The first test is for the null hypothesis that the treatment has a zero average effect for all subpopulations defined by covariates. The second test is for the null hypothesis that the average effect conditional on the covariates is identical for all subpopulations, i.e., that there is no heterogeneity in average treatment effects by covariates. We derive tests that are straightforward to implement and illustrate the use of these tests on data from two sets of experimental evaluations of the effects of welfare-to-work programs.
2012. "Long-Run Impacts of Unions on Firms: New Evidence from Financial Markets, 1961–1999." Quarterly
by David S. Lee, Alexandre Mas - Journal of Economics
"... We estimate the effect of new private-sector unionization on publicly traded firms ' equity value in the United States over the 1961–1999 period using a newly assembled sample of National Labor Relations Board (NLRB) representation elections matchedtostock market data. Event-study estimates showan a ..."
We estimate the effect of new private-sector unionization on publicly traded firms ' equity value in the United States over the 1961–1999 period using a newly assembled sample of National Labor Relations Board (NLRB) representation elections matchedtostock market data. Event-study estimates showan average union effect on the equity value of the firm equivalent to $40,500 per unionized worker, an effect that takes 15 to 18 months after unionization to fully materialize, and one that could not be detected by a short-run event study. At the same time, point estimates from a regression discontinuity design—comparing the stock market impact of close union election wins to close losses—are considerably smaller and close to zero. We find a negative relationship between the cumulative abnormal returns and the vote share in support of the union, allowing us to reconcile these seemingly contradictory findings. JEL Codes: J01, J08, J5, J51. "Laymen and economists alike tend, in my view, to exaggerate greatly the extent to which labor unions affect the structure and level of wage rates. " 1 "Everyone 'knows ' that unions raise wages. The questions are how much, under what conditions, and with what effects on the overall performance of the economy. " 2 I.
Does digital divide or provide? The impact of cell phones on grain markets in Niger
by Jenny C. Aker - BREAD Working Papers , 2008
"... Abstract. Due partly to costly information, price dispersion across markets is common in developed and developing countries. Between 2001 and 2006, cell phone service was phased in throughout Niger, providing an alternative and cheaper search technology to grain traders and other market actors. We c ..."
Abstract. Due partly to costly information, price dispersion across markets is common in developed and developing countries. Between 2001 and 2006, cell phone service was phased in throughout Niger, providing an alternative and cheaper search technology to grain traders and other market actors. We construct a novel theoretical model of sequential search, in which traders engage in optimal search for the maximum sales price, net transport costs. The model predicts that cell phones will increase traders' reservation sales prices and the number of markets over which they search, leading to a reduction in price dispersion across markets. To test the predictions of the theoretical model, we use a unique market and trader dataset from Niger that combines data on prices, transport costs, rainfall and grain production with cell phone access and trader behavior. We first exploit the quasi-experimental nature of cell phone coverage to estimate the impact of the staggered introduction of information technology on market performance. The results provide evidence that cell phones reduce grain price dispersion across markets by a minimum of 6.4 percent and reduce intra-annual price variation by 10 percent. Cell phones have a greater impact on price dispersion for market pairs that are farther away, and for those with lower road quality. This effect becomes larger as a higher percentage of markets have cell phone coverage. We provide empirical evidence in support of specific mechanisms that partially explain the impact of cell phones on market performance.