This paper empirically assesses the wage effects of the Job Corps program, one of the largest federally funded job training programs in the U.S. Even with the aid of a randomized experiment, the impact of a training program on wages is difficult to study because of sample selection, a pervasive problem in applied microeconometric research. Wage rates are only observed for those who are employed, and employment status itself may be affected by the training program. This paper develops an intuitive trimming procedure for bounding average treatment effects in the presence of sample selection. In contrast to existing methods, the procedure requires neither exclusion restrictions nor a bounded support for the outcome of interest. Identification results, estimators, and their asymptotic distribution are presented. The bounds suggest that the program raised wages, consistent with the notion that the Job Corps raises earnings by increasing human capital, rather than solely through encouraging work. The estimator is generally applicable to typical treatment evaluation problems in which there is nonrandom sample selection/attrition. Copyright

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.

We are pleased to introduce this volume of papers, all of which were presented at a conference in honor of JohnDiNardo held in Ann Arbor, Michigan, in September 2018. After battling leukemia for close to a decade, John left us on August 26, 2017, at the age of 56. The conference was an opportunity for colleagues, coauthors, students, and friends to celebrate John’s life and academic achievements. The proud son of Italian immigrants, John was born and raised in Allen Park, Michigan. He spent most of his academic career at his alma mater, the University of Michigan, where he earned an undergraduate degree and a masters of public policy. After completing his PhD at Princeton in 1990, John spent a year at the RAND Corporation before joining the faculty at the University of California, Irvine, in 1991. He moved back to Michigan in 2001, where he spent the remainder of his career. At this point, convention calls for a discussion of the main research contributions of the scholar being honored, followed by a summary of the contributed papers. However, following convention is not what best describes John DiNardo’s life and career. Armed with a trademark irreverence, 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 matched to stock market data. Event-study estimates show an 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.

Bayesian Learning and the Pricing of New Information: Evidence from Prediction Markets by David S. Lee and Enrico Moretti. Published in volume 99, issue 2, pages 330-36 of American Economic Review, May 2009

In the single-IV model, researchers commonly rely on t-ratio-based inference, even though the literature has quantified its potentially severe large-sample distortions. Building on Stock and Yogo (2005), we introduce the tF critical value function, leading to a standard error adjustment that is a smooth function of the first-stage F-statistic. For one-quarter of specifications in 61 AER papers, corrected standard errors are at least 49 and 136 percent larger than conventional 2SLS standard errors at the 5 percent and 1 percent significance levels, respectively. tF confidence intervals have shorter expected length than those of Anderson and Rubin (1949), whenever both are bounded.

Central to the welfare analysis of income transfer programs is the deadweight loss associated with possible reforms. To aid analytical tractability, its measurement typically requires specifying a simplified model of behavior. We employ a complementary “decomposition” approach that compares the behavioral and mechanical components of a policy’s total impact on the government budget to study the deadweight loss of two unemployment insurance policies. Experimental and quasi-experimental estimates using state administrative data show that increasing the weekly benefit is more efficient (with a fiscal externality of 53 cents per dollar of mechanical transferred income) than reducing the program’s implicit earnings tax.

ORDER TO PROVE PROPOSITION 1, we first present and prove the following lemmas.