This paper develops IV estimators for unconditional quantile treatment effects (QTE) when the treatment selection is endogenous. In contrast to conditional QTE, i.e. the effects conditional on a large number of covariates X, the unconditional QTE summarize the effects of a treatment for the entire population. They are usually of most interest in policy evaluations because the results can easily be conveyed and summarized. Last but not least, unconditional QTE can be estimated at the root n rate without any parametric assumption, which is obviously impossible for conditional QTE (unless all X are discrete). In this paper we extend the identification of unconditional QTE to endogenous treatments. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. Several types of estimators are proposed: regression, propensity score and weighting estimators. Root n consistency, asymptotic normality and attainment of the semiparametric efficiency bound are shown for our weighting estimator, which is extremely simple to implement. We also show that including covariates in the estimation is not only necessary for consistency when the instrumental variable is itself confounded but also for efficiency when the instrument is valid unconditionally. Monte Carlo simulations and two empirical applications illustrate the use of the proposed estimators.
This paper shows nonparametric identification of quantile treatment effects (QTE) in the regression discontinuity design. The distributional impacts of social programs such as welfare, education, training programs and unemployment insurance are of large interest to economists. QTE are an intuitive tool to characterize the effects of these interventions on the outcome distribution. We propose uniformly consistent estimators for both potential outcome distributions (treated and non-treated) for the population of interest as well as other function-valued effects of the policy including in particular the QTE process. The estimators are straightforward to implement and attain the optimal rate of convergence for one-dimensional nonparametric regression. We apply the proposed estimators to estimate the effects of summer school on the distribution of school grades, complementing the results of Jacob and Lefgren (2004).
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In this paper, we discuss the implementation of various estimators proposed to estimate quantile treatment effects (QTE). We distinguish four cases: conditional and unconditional QTE with exogenous or endogenous treatment variable. Therefore, the ivqte command covers four different estimators: the classical quantile regression estimator of Koenker and Bassett (1978) extended to heteroskedasticity consistent standard errors, the IV quantile regression estimator of Abadie, Angrist, and Imbens (2002), the estimator for unconditional QTE proposed by Firpo (2007), and the IV estimator for unconditional QTE proposed by Frölich and Melly (2007). The implemented IV procedures estimate the causal effects for the sub-population of compliers and are well-suited for binary instruments only. This command also provides analytical standard errors and various options for nonparametric estimation. As a by-product, the command locreg implements local linear and local logit estimators for mixed data (continuous, ordered discrete, unordered discrete and binary regressors).
Allocating people into social programmes on the basis of statistical tools has gained increasing acceptance in recent years. The need for better targeting of active labour market programmes recently has arisen in Switzerland, too. Statistically assisted programme allocation could be an effective tool to increase the effectiveness of labour market policies. It is a software tool that assists the caseworkers in public employment centres to find adequate measures for unemployed people to get them back to work. This paper gives a short overview of the methodologies applied in various countries and assesses their capabilities to serve the purpose of improving overall programme outcomes. Based on these experiences, an approach suitable for Switzerland is proposed and its potential benefits are investigated. On the basis of very rich data about programme participants in 1998 and 1999, a reallocation into programmes is simulated retrospectively. The reallocation is done according to individual-specific estimates of the usefulness of programmes for each person. The results of the simulations show that the reemployment share could have been improved substantially. Hence, a statistical allocation system could be very fruitful for Switzerland.
(http://www.sjes.ch/papers/2003-III-4.pdf)
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We estimate the effects of active labour market policies (ALMP) on subsequent employment by nonparametric instrumental variables and matching estimators. Very informative administrative Swiss data with detailed regional information are combined with exogenous regional variation in programme participation probabilities, which generate an instrument within well-defined local labour markets. This allows pursuing instrumental variable as well as matching estimation strategies. A specific combination of those methods identifies a new type of effect heterogeneity. We find that ALMP increases individual employment probabilities by about 15% in the short term for unemployed that may be called 'marginal' participants. The effects seem to be considerably smaller for those unemployed not marginal to the participation decision
