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Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

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.

In this paper, we study partial identification of the distribution of treatment effects of a binary treatment for ideal randomized experiments, ideal randomized experiments with a known value of a dependence measure, and for data satisfying the selection-on-observables assumption, respectively. For ideal randomized experiments, (i) we propose nonparametric estimators of the sharp bounds on the distribution of treatment effects and construct asymptotically valid confidence sets for the distribution of treatment effects; (ii) we propose bias-corrected estimators of the sharp bounds on the distribution of treatment effects; and (iii) we investigate finite sample performances of the proposed confidence sets and the bias-corrected estimators via simulation.

The purpose of this paper is to estimate the impact of one productive development program (PROPYME) in a developing nation like Costa Rica. This program seeks to increase the capacity of small and medium-sized firms (SMEs) to innovate.

Impacts have been estimated assuming that beneficiary firms are trying to maximize their profits and that PROPYME aims to increase these firms productivity. The impacts were measured in terms of three result variables real average wages employment demand and the probability of exporting. A combination of fixed effects and propensity score matching techniques was used in estimations to correct for any selection bias. The authors worked with panel data companies treated and untreated for the period 2001-2011.

PROPYME’s beneficiaries performed better than other firms in terms of labor demand and their probability of exporting. In addition, the dose and the duration of the effects of the treatment (timing effects) are important.

The authors study the impact in ways that go beyond the average treatment effects on the treated (ATT) usually estimated in the existing literature. Specifically, the research focusses on the identification of the timing or dynamic effects (i.e. how long should we wait to see results?) and treatment intensity (dosage effects).

Se estima el impacto de un programa de desarrollo productivo (Propyme) en un país en vías de desarrollo como Costa Rica. El Propyme busca incrementar la capacidad innovadora de las pequeñas y medidas empresas (pymes) costarricenses.

el impacto se ha estimado y evaluado asumiendo que las pymes beneficiaras buscan maximizar sus beneficios y que Propyme se enfoca en incrementar la productividad de esas empresas. El impacto se valoró en función de tres variables: salarios reales medios, empleo demandado y la probabilidad de exportar. Se utilizó una combinación de técnicas de efectos fijos y emparejamiento en las estimaciones con el fin de prevenir sesgos de selección. Se trabajó con un panel de datos, incluyendo empresas tratadas (beneficiarias de Propyme) así como no tratadas para el periodo 2001-2011.

los beneficiarios de Propyme tuvieron mejor desempeño que las restantes empresas en términos de empleo demandado y su posibilidad de exportar. Adicionalmente los efectos dinámicos (dosis y duración) de los tratamientos son importantes.

este artículo evalúa el impacto de una forma que va más allá de lo usual en la literatura por medio de los efectos promedios de los tratamientos sobre los beneficiarios. Esto por cuanto se enfoca en efectos dinámicos como la duración así como la intensidad.

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

Lechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection-on-observables-type assumptions (sequential conditional independence assumptions). Lechner (2004) proposed matching estimators for this framework. However, many practical issues that might have substantial consequences for the interpretation of the results have not been thoroughly investigated so far. This chapter discusses some of these practical issues. The discussion is related to estimates based on an artificial data set for which the true values of the parameters are known and that shares many features of data that could be used for an empirical dynamic matching analysis.

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.

Purpose – A placebo effect is a (positive) change in health outcomes that is due to a (positive) change in beliefs about the value of a treatment. Placebo effects might be “behavioral,” in the sense that revised beliefs lead to behavioral changes or new actions that in turn yield changes in health outcomes. Placebo effects might also include a “physiological” component, which refers broadly to non-behavioral, brain-modulated mechanisms by which new beliefs cause changes in health outcomes. Nearly all formal economic models of human behavior are consistent with behavioral placebo effects, but strongly inconsistent with their physiological counterparts. The reason is that the latter effects can imply that expectations enter, rather than multiply, state-contingent preferences. It is therefore unfortunate that little evidence exists on physiological placebo effects. We report data from novel clinical experiments with caffeine that seek to provide such evidence.

Methods – Subjects visit the clinic on multiple occasions. On each visit they ingest either a placebo or caffeine pill. Subjects only know the probability with which the pill includes caffeine. We obtain physiological measurements prior to ingestion and at 30, 60, and 90min after ingestion. Importantly, we constrain subjects to remain seated and read pre-selected magazines during the interval between treatment and outcome measurement.

Findings – Our design provides particularly clean inference because it (i) eliminates the possibility of behavioral confounds; (ii) provides for measurements at the individual level; (iii) manipulates beliefs without deception; and (iv) uses salient rewards. We find evidence for the existence of physiological placebo effects mediated by expectations.

Implications – Our results are consistent with the possibility that the prefrontal cortex provides external, top-down control that modulates physiological outcomes, and make a case for the importance of research geared toward developing appropriate and tractable frameworks that accommodate non-linear relationships between expectations and preferences.