[Ml-stat-talks] Donald Rubin in TODAY's colloquium at ORFE
rigollet at Princeton.EDU
Fri Oct 5 11:08:47 EDT 2012
=== ORFE Colloquium Announcement ===
DATE: Today, October 5, 2012
LOCATION: Room 101, Sherrerd Hall
SPEAKER: Donald Rubin, Department of Statistics, Harvard University
TITLE: Evaluating the Effect of Training on Wages in the Presence of Noncompliance, Nonemployment,
and Missing Outcome Data
ABSTRACT: The effects of a job training program, Job Corps, on both employment and wages are evaluated using
data from a randomized study. Principal stratification is used to address, simultaneously, the complications of
noncompliance, wages that are only partially defined because of nonemployment, and unintended missing outcomes.
The first two complications are of substantive interest, whereas the third is a nuisance. The objective is to find a
parsimonious model that can be used to inform public policy. We conduct a likelihood-based analysis using finite
mixture models estimated by the expectation-maximization (EM) algorithm. We maintain an exclusion restriction
assumption for the effect of assignment on employment and wages for noncompliers, but not on missingness. We
provide estimates under the “missing at random” assumption, and assess the robustness of our results to deviations
from it. The plausibility of meaningful restrictions is investigated by means of scaled log-likelihood ratio statistics.
Substantive conclusions include the following. For compliers, the effect on employment is negative in the short term;
it becomes positive in the long term, but these effects are small at best. For always employed compliers, that is,
compliers who are employed whether trained or not trained, positive effects on wages are found at all time periods.
Our analysis reveals that background characteristics of individuals differ markedly across the principal strata. We found
evidence that the program should have been better targeted, in the sense of being designed differently for different
groups of people, and specific suggestions are offered. Previous analyses of this dataset, which did not address all
complications in a principled manner, led to less nuanced conclusions about Job Corps.
This is a joint work with Paolo Frumento, Fabrizia Mealli and Barbara Pacini
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