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Job Corps is the United State’s largest and most comprehensive training program for disadvantaged youth aged 16–24 years old. A randomized social experiment concluded that, on average, individuals benefited from the program in the form of higher weekly earnings and employment prospects. At the same time, “young adults” (ages 20–24) realized much higher impacts relative to “adolescents” (ages 16–19). Employing recent nonparametric bounds for causal mediation, we investigate whether these two groups’ disparate effects correspond to them benefiting differentially from distinct aspects of Job Corps, with a particular focus on the attainment of a degree (GED, high school, or vocational). We find that, for young adults, the part of the total effect of Job Corps on earnings (employment) that is due to attaining a degree within the program is at most 41% (32%) of the total effect, whereas for adolescents that part can account for up to 87% (100%) of the total effect. We also find evidence that the magnitude of the part of the effect of Job Corps on the outcomes that works through components of Job Corps other than degree attainment (e.g., social skills, job placement, residential services) is likely higher for young adults than for adolescents. That those other components likely play a more important role for young adults has policy implications for more effectively servicing participants. More generally, our results illustrate how researchers can learn about particular mechanisms of an intervention.

Medical and legal records of 64 inmates receiving mental health services at a maximum-security prison located in the Northeast United States were examined to look at whether prison adjustment is impacted by housing in a mental health residential treatment unit. Inmates in the residential treatment unit, the “treatment group” had a significant decrease in hospitalizations and disciplinary reports while housed in the residential treatment unit. Inmates with a mental health history housed in the general population, the “control group,” did not show a decrease in these behaviors during a similar time period. Results find that inmates referred to the residential treatment unit seem to have high numbers of hospitalizations and segregations while housed in the general population, which level off and become similar to the control group upon entry to the residential treatment unit. Implications for future research evaluating the impact of the residential treatment unit on the behavior of the inmate after he has left the unit are discussed.

Recent empirical studies have improved methodologies for identifying the causal effects of policies especially on a minimum wage hike. This study identifies causal effects of minimum wage hikes across 47 prefectures in Japan from 2008 to 2010 on employment, average hourly wage, work hours, full-time equivalent employment (FTE), total wage costs, average tenure, separation and new hiring in establishments using a micro dataset of business establishments in restaurant, accommodation, and food takeout and delivery industry. Various regression specifications including controls for time-varying regional heterogeneity are implemented by using the bite of the minimum wage in each establishment. First, this study finds that the effects of a revision of minimum wage on employment and FTE in the establishment are statistically insignificant, but the effects on hourly wages and total wage costs are statistically significant. Subsequently, it considers how the establishments react to the increase in total wage costs caused by the revised minimum wage, and finds that separation from the establishment may decrease, and average tenure of workers may increase.

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue.

The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect.

Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South.

The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.

The main goal of employee retention is to prevent competent employees from leaving the company. When analysing the main reasons why employees leave and determining their turnover probability, the question arises: Which retention strategies have an actual effect on turnover and for which profile of employees do these strategies work?

To determine the effectiveness of different retention strategies, an overview is given of retention strategies that can be found in the literature. Next, the paper presents a procedure to build an uplift model for testing the effectiveness of the different strategies on HR data. The uplift model is based on random forest estimation and applies personal treatment learning estimation.

Through a data-driven approach, the actual effect of retention strategies on employee turnover is investigated. The retention strategies compensation and recognition are found to have a positive average treatment effect on the entire population, while training and flexibility do not. However, with personalised treatment learning, the treatment effect on the individual level can be estimated. This results in an ability to profile employees with the highest estimated treatment effect.

The results yield useful information for human resources practitioners. The personalised treatment analysis results in detailed retention information for these practitioners, which allows them to target the right employees with the right strategies.

Even though the uplift modelling approach is becoming increasingly popular within marketing, this approach has not been taken within human resources analytics. This research opens the door for further research and for practical implementation.

We discuss the two most popular frameworks for identification, estimation and inference in regression discontinuity (RD) designs: the continuity-based framework, where the conditional expectations of the potential outcomes are assumed to be continuous functions of the score at the cutoff, and the local randomization framework, where the treatment assignment is assumed to be as good as randomized in a neighborhood around the cutoff. Using various examples, we show that (i) assuming random assignment of the RD running variable in a neighborhood of the cutoff implies neither that the potential outcomes and the treatment are statistically independent, nor that the potential outcomes are unrelated to the running variable in this neighborhood; and (ii) assuming local independence between the potential outcomes and the treatment does not imply the exclusion restriction that the score affects the outcomes only through the treatment indicator. Our discussion highlights key distinctions between “locally randomized” RD designs and real experiments, including that statistical independence and random assignment are conceptually different in RD contexts, and that the RD treatment assignment rule places no restrictions on how the score and potential outcomes are related. Our findings imply that the methods for RD estimation, inference, and falsification used in practice will necessarily be different (both in formal properties and in interpretation) according to which of the two frameworks is invoked.

This paper replicates four highly cited, classic lab experimental studies in the provision of public goods. The studies consider the impact of marginal per capita return and group size; framing (as donating to or taking from the public good); the role of confusion in the public goods game; and the effectiveness of peer punishment. Considerable attention has focused recently on the problem of publication bias, selective reporting, and the importance of research transparency in social sciences. Replication is at the core of any scientific process and replication studies offer an opportunity to reevaluate, confirm or falsify previous findings. This paper illustrates the value of replication in experimental economics. The experiments were conducted as class projects for a PhD course in experimental economics, and follow exact instructions from the original studies and current standard protocols for lab experiments in economics. Most results show the same pattern as the original studies, but in all cases with smaller treatment effects and lower statistical significance, sometimes falling below accepted levels of significance. In addition, we document a “Texas effect,” with subjects consistently exhibiting higher levels of contributions and lower free-riding than in the original studies. This research offers new evidence on the attenuation effect in replications, well documented in other disciplines and from which experimental economics is not immune. It also opens the discussion over the influence of unobserved heterogeneity in institutional environments and subject pools that can affect lab results.

This chapter uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. This chapter extends the Monte Carlo results in Athey and Imbens's (2006) to assess the efficacy of the NL-DID estimators in finite samples. Furthermore, the NL-DID methodology recovers the entire outcome distribution in the absence of treatment. Further, we consider the empirical size and power of tests statistics for equality of mean, medians, and complete distributions as suggested by Abadie (2002). The results show that the NL-DID estimator can effectively be used to recover the average treatment effect, as well as the entire distribution of the treatment effects when there is no selection during the treatment period in finite samples.

The purpose of this study is to determine the extent to which R&D subsidy can affect the innovation process of manufacturing venture firms by examining the output additionality measured as both proximal indicators of innovation and distal indicators of growth. Further, the differences in output additionality between the clusters in the subcontracting regime were examined to investigate whether the effect of R&D subsidy can vary depending on subcontracting practices and structure among large enterprises and venture firms.

This study uses survey data of the Korea Venture Business Association conducted in 2012, 2013, 2014, 2015, and 2016 respectively, which selects a random sample from venture firms by stratified random sampling method based on the industry sector, size and location for each survey year. This study analyzed the data using an endogenous treatment effects model to estimate the average treatment effect of R&D subsidy, yielding more accurate estimates than a traditional treatment effects model by controlling the unobserved endogenous components.

This research found that R&D subsidy may not facilitate the process of transformation of innovation into financial growth even though R&D subsidy can facilitate the innovation process and contribute to producing new and improved products. This research also reveals that the relationship between R&D subsidy and innovation performance for firms heavily dependent on subcontracting is generally much weaker than those for independent subcontractors. Further, the present study exhibits that public R&D subsidy for independently subcontracting venture firms is more effective for the growth in both employment and sales than those for subcontracting with large enterprises or other subcontractors.

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. In addition, venture firms subcontracting with large enterprises or other prime subcontractors tend to achieve incremental innovation with the help of the technology and competence of large companies and run stable businesses through a predetermined market.

R&D subsidy for venture firms does not relieve the burden of liability of newness and smallness of venture firms, especially the disadvantage in market penetration and competition. Further policy measures should be implemented so as to identify and eliminate barriers to market acceptance for new products of venture firms.

This research verifies that the effect of R&D subsidy may harmful to the sales growth of venture firms and the output additionality differs with the degree of dependency on subcontracting practices and structure.

Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of this effect, called treatment effect derivative (TED) can be estimated. We argue here that TED should be employed in most RD applications, as a way to assess the stability and hence external validity of RD estimates. Closely related to TED, we define the complier probability derivative (CPD). Just as TED measures stability of the treatment effect, the CPD measures stability of the complier population in fuzzy designs. TED and CPD are numerically trivial to estimate. We provide relevant Stata code, and apply it to some real datasets.