Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps

By Carlos A Flores, Alfonso Flores-Lagunes, Arturo González, Todd C. Neumann. Review of Economics and Statistics, 2012

177 citations recorded by the source corpus.

Subfields: Demography, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fe37c1cc515cad760d2b9029412264f497cf8d3f

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Citations in the corpus

Partial Identification of Local Average Treatment Effects With an Invalid Instrument (2013, methodology): “Note that to partially identify MATE, and hence LATE, further assumptions are needed because the data have no information on Y (1, D(0)) for compliers, so the terms E[Y (1, D (0)) |c], LMATEc, and LNATEc appearing in equations (11) to (14) are not identified.”
Entropy Balancing for Continuous Treatments (2020, background): “Examples include the evaluation of job training programs with varying duration (Choe, Flores-Lagunes, and Lee 2015; Flores et al. 2012; Kluve et al. 2012; Li and Fraser 2015) or quality (Galdo and Chong 2012) and subsidies of different magnitude to firms or entire regions (Becker, Egger, and von…”
Evaluating continuous training programmes by using the generalized propensity score (2012, background): “At a later stage we found out that Flores et al. (2010) independently propose a similar approach in their paper.”
Difference-in-Differences with a Continuous Treatment (2021, unknown): “[16] Carlos A Flores, Alfonso Flores-Lagunes, Arturo Gonzalez, and Todd C Neumann.”
Assessing the Impact of Crop Diversification on Farm Poverty in India (2015, methodology): “Bia et al. (2011) employ cubic spline estimator and penalized cubic spline estimator while Flores et al. (2011) introduce an inverse weighting kernel estimator in the second stage that involves estimating the DRF and marginal treatment effect functions.”
A Stata Package for the Application of Semiparametric Estimators of Dose–Response Functions (2014, methodology): “Specifically, we develop a set of Stata programs which allow users to (i) estimate the GPS under various alternative parametric assumptions; (ii) impose the common support condition as defined in Flores et al. (2012) and assess the balance of covariates after adjusting for the estimated GPS; (iii) e”