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This study exploits district-level variation in the timing and intensity of civil war violence to investigate whether early-life exposure to civil wars affects labor-market outcomes later in life. In particular, we examine the impacts of armed conflict in Peru, a country that experienced the actions of a tenacious, brutally effective war machine, the Shining Path, between 1980 and 1995. This study finds that the most sensitive period to early-life exposure to civil war violence is the first 36 months of life. A one standard deviation increase in civil war exposure leads to a four percent fall in adult monthly earnings. Neither fetal, nor pre-school, periods significantly affect long-run earnings. Substantial heterogeneity in the earnings impacts emerge when considering variation in the types of civil war violence. Sexual violations disproportionally affected the wages of women, while torture and forced disappearances disproportionally affected the wages of men. Evidence on intervening pathways suggests that health rather than schooling is the most important channel in connecting early-life exposure to civil war and adult earnings.

This paper addresses the selection of smoothing parameters for estimating the average treatment effect on the treated using matching methods. Because precise estimation of the expected counterfactual is particularly important in regions containing the mass of the treated units, we define and implement weighted cross-validation approaches that improve over conventional methods by considering the location of the treated units in the selection of the smoothing parameters. We also implement a locally varying bandwidth method that uses larger bandwidths in areas where the mass of the treated units is located. A Monte Carlo study compares our proposed methods to the conventional unweighted method and to a related method inspired by Bergemann et al. (2005). The Monte Carlo analysis indicates efficiency gains from all methods that take account of the location of the treated units. We also apply all five methods to bandwidth selection in the context of the data from LaLonde's (1986) study of the performance of non-experimental estimators using the experimental data from the National Supported Work (NSW) Demonstration program as a benchmark. Overall, both the Monte Carlo analysis and the empirical application show feasible precision gains for the weighted cross-validation and the locally varying bandwidth approaches. -- Matching ; cross-validation ; kernel regression ; Monte Carlo simulation

This paper addresses the selection of smoothing parameters for estimating the average treatment effect on the treated using matching methods. Because precise estimation of the expected counterfactual is particularly important in regions containing the mass of the treated units, we define and implement weighted cross-validation approaches that improve over conventional methods by considering the location of the treated units in the selection of the smoothing parameters. We also implement a locally varying bandwidth method that uses larger bandwidths in areas where the mass of the treated units is located. A Monte Carlo study compares our proposed methods to the conventional unweighted method and to a related method inspired by Bergemann et al. (2005). The Monte Carlo analysis indicates efficiency gains from all methods that take account of the location of the treated units. We also apply all five methods to bandwidth selection in the context of the data from LaLonde's (1986) study of the performance of non-experimental estimators using the experimental data from the National Supported Work (NSW) Demonstration program as a benchmark. Overall, both the Monte Carlo analysis and the empirical application show feasible precision gains for the weighted cross-validation and the locally varying bandwidth approaches.

AbstractThis study investigates the causal impacts of integrating mobile phone technologies into traditional public labor-market intermediation services on employment outcomes. By providing faster, cheaper and up-to-date information on job vacancies via SMS, mobile phone technologies might affect the rate at which offers arrive as well as the probability of receiving a job offer. We implement a social experiment with multiple treatments that allows us to investigate both the role of information channels (digital versus non-digital) and information sets (restricted [public] versus unrestricted [public/private]). The results show positive and significant short-term effects on employment for public labor-market intermediation. While the impacts from traditional labor-market intermediation are not large enough to be statistically significant, the unrestricted digital treatment group shows statistically significant short-term employment effects. As for potential matching efficiency gains, the results suggest no statistically significant effects associated with either information channels or information sets.JEL classification codes:I3, J2

AbstractUsing longitudinal data for Canada, we analyze the incidence and wage returns to employer supported course enrollment for men and women. Availability of confidential data, along with a relatively rich set of observable covariates, lead us to the estimation of difference-in-differences matching models of the effect of employer supported course enrolment on wages. The estimated average treatment effects on the treated range from 5.5 to 7.2 percent for men and 7.1 to 9.0 for women. While high-skilled workers show disproportionately higher rates of participation in employer-supported training, we observe no wage premiums for these types of workers. Statistically significant positive wage returns are found, on the other hand, for low-skilled workers.JEL codes:C14, I20, J24, J31, M53