text: 211,43,1119588717059104769,1090429279513522178,Vitor Possebom,"💡Working Paper Update💡I just uploaded a new version of ""Sharp Bounds for the Marginal Treatment Effect (MTE) with Sample Selection"" (<LINK>). Using my partial identification strategy, I analyze the Job Corps Training Program (JCTP) and bound its MTE (figure). <LINK> The lower bound is positive, but it is statistically significant only when the latent heterogeneity is between 0.35 and 0.73. Since the ATT is between $.33 and $.99 and the ATU between $.71 and $3.00, there is some unobserved constraint blocking agents who would benefit. Since my paper is mostly theoretical, analyzing why agents who would benefit from attending the JCTP are not doing so is beyond its scope. But it is an important policy question! Chen, Flores and @A_FloresLagunes (2017, <LINK>) argue that it may be due to lack of childcare services, incomplete information, overconfidence or personal preferences for non-enrollment If you like Monte Carlos, the proposed confidence intervals are (mostly) conservative for the MTE function when the estimated parametric model is correctly specified (Designs 1-3). When the estimated model is misspecified (Designs 4-6), there is undercoverage, <LINK>",https://arxiv.org/abs/1904.08522,"I analyze treatment effects in situations when agents endogenously select into the treatment group and into the observed sample. As a theoretical contribution, I propose pointwise sharp bounds for the marginal treatment effect (MTE) of interest within the always-observed subpopulation under monotonicity assumptions. Moreover, I impose an extra mean dominance assumption to tighten the previous bounds. I further discuss how to identify those bounds when the support of the propensity score is either continuous or discrete. Using these results, I estimate bounds for the MTE of the Job Corps Training Program on hourly wages for the always-employed subpopulation and find that it is decreasing in the likelihood of attending the program within the Non-Hispanic group. For example, the Average Treatment Effect on the Treated is between \$.33 and \$.99 while the Average Treatment Effect on the Untreated is between \$.71 and \$3.00. ",Sharp Bounds for the Marginal Treatment Effect with Sample Selection,5,"['💡Working Paper Update💡I just uploaded a new version of ""Sharp Bounds for the Marginal Treatment Effect (MTE) with Sample Selection"" (<LINK>). Using my partial identification strategy, I analyze the Job Corps Training Program (JCTP) and bound its MTE (figure). <LINK>', 'The lower bound is positive, but it is statistically significant only when the latent heterogeneity is between 0.35 and 0.73. Since the ATT is between $.33 and $.99 and the ATU between $.71 and $3.00, there is some unobserved constraint blocking agents who would benefit.', 'Since my paper is mostly theoretical, analyzing why agents who would benefit from attending the JCTP are not doing so is beyond its scope. But it is an important policy question!', 'Chen, Flores and @A_FloresLagunes (2017, https://t.co/dQe0Ae2FJy) argue that it may be due to lack of childcare services, incomplete information, overconfidence or personal preferences for non-enrollment', 'If you like Monte Carlos, the proposed confidence intervals are (mostly) conservative for the MTE function when the estimated parametric model is correctly specified (Designs 1-3). When the estimated model is misspecified (Designs 4-6), there is undercoverage, https://t.co/7zZ4P9Q2Ww']",19,04,1172