Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like 'what to do if there is choice-based sampling?' or 'when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes.

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Some practical guidance for the implementation of propensity score matching

Although published works rarely include causal estimates from more than a few model specifications, authors usually choose the presented estimates from numerous trial runs readers never see. Given the often large variation in estimates across choices of control variables, functional forms, and other modeling assumptions, how can researchers ensure that the few estimates presented are accurate or representative? How do readers know that publications are not merely demonstrations that it is possible to find a specification that fits the author's favorite hypothesis? And how do we evaluate or even define statistical properties like unbiasedness or mean squared error when no unique model or estimator even exists? Matching methods, which offer the promise of causal inference with fewer assumptions, constitute one possible way forward, but crucial results in this fast-growing methodological literature are often grossly misinterpreted. We explain how to avoid these misinterpretations and propose a unified approach that makes it possible for researchers to preprocess data with matching (such as with the easy-to-use software we offer) and then to apply the best parametric techniques they would have used anyway. This procedure makes parametric models produce more accurate and considerably less model-dependent causal inferences.

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Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference

This paper provides an introduction and "user guide" to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a "quasi-experimental" design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. Concepts are discussed using examples drawn from the growing body of empirical research using RD.

Regression Discontinuity Designs in Economics

Many empirical questions in economics and other social sciences depend on causal effects of programs or policies. In the last two decades, much research has been done on the econometric and statistical analysis of such causal effects. This recent theoreti- cal literature has built on, and combined features of, earlier work in both the statistics and econometrics literatures. It has by now reached a level of maturity that makes it an important tool in many areas of empirical research in economics, including labor economics, public finance, development economics, industrial organization, and other areas of empirical microeconomics. In this review, we discuss some of the recent developments. We focus primarily on practical issues for empirical research- ers, as well as provide a historical overview of the area and give references to more technical research.

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Recent developments in the econometrics of program evaluation

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.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

psmatch2 implements full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. This routine supersedes the previous 'psmatch' routine of B. Sianesi. The April 2012 revision of pstest changes the syntax of that command.

PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing

This paper reviews the existing evidence on workplace training in Europe in different data sources - the CVTS, OECD data and the European Community Household Panel. We outline the differences in training incidence and relate these differences to the private costs and benefits of training, and to institutional factors such as unions, employment protection and product market competition. We ask whether there is a case for under-provision of training in Europe and examine alternative policies aiming both at raising training incidence and at reducing inequalities in the provision of skills.

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Workplace Training in Europe

This paper surveys the economics literature on overeducation. The original motivation to study this topic were reports that the strong increase in the number of college graduates in the early 1970s in the US led to a decrease in the returns to college education. We argue that Duncan and Hoffman's augmented wage equation the workhorse model in the overeducation literature in which wages are regressed on years of overschooling, years of required schooling and years of underschooling is at best loosely related to this original motivation. We discuss measurement and estimation issues and give an overview of the main empirical findings in this literature. Finally we given an appraisal of the economic lessons learned.

https://papers.ssrn.com/sol3/Delivery.cfm/dp5523.pdf?abstractid=1771245&mirid=2

Overeducation and Mismatch in the Labor Market

Why do individuals choose different types of post-secondary education, and what are the labor market consequences of those choices? We show that answering these questions is difficult because individuals choose between several unordered alternatives. Even with a valid instrument for every type of education, instrumental variables estimation of the payoffs require information about individuals' ranking of education types or strong additional assumptions, like constant effects or restrictive preferences. These identification results motivate and guide our empirical analysis of the choice of and payoff to field of study. Our context is Norway's post-secondary education system where a centralized admission process covers almost all universities and colleges. This process creates credible instruments from discontinuities which effectively randomize applicants near unpredictable admission cutoffs into different fields of study. At the same time, it provides us with strategy-proof measures of individuals' ranking of fields. Taken together, this allows us to estimate the payoffs to different fields while correcting for selection bias and keeping the next-best alternatives as measured at the time of application fixed. We find that different fields have widely different payoffs, even after accounting for institutional differences and quality of peer groups. For many fields the payoffs rival the college wage premiums, suggesting the choice of field is potentially as important as the decision to enroll in college. The estimated payoffs are consistent with individuals choosing fields in which they have comparative advantage. We also test and reject assumptions of constant effects or restrictive preferences, suggesting that information on next-best alternatives is essential to identify payoffs to field of study.

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Edwin Leuven

Papers

PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing

Workplace Training in Europe

Overeducation and Mismatch in the Labor Market

Field of Study, Earnings, and Self-Selection*

Workplace Training in Europe