[gov3009-l] Rubin on "Are Job-Training Programs Effective?"

Matt Blackwell mblackwell at iq.harvard.edu
Mon Mar 7 10:24:59 EST 2011

Hi all,

We hope you can join us at the Applied Statistics Workshop this
Wednesday, March 9th, when we are excited to have Don Rubin, the John
L. Loeb Professor of Statistics here at Harvard University, who will
be presenting recent work on job-training programs. You will find an
abstract below. As usual, we will begin with a light lunch at 12 noon,
with the presentation starting at 12:15p and wrapping up by 1:30p.

"Are Job-Training Programs Effective?"
Don Rubin
John L. Loeb Professor of Statistics, Harvard University
Wednesday, March 9th 12:00pm - 1:30pm
CGIS Knafel K354 (1737 Cambridge St)

In recent years, job-training programs have become more important in
many developed countries with rising unemployment. It is widely
accepted that the best way to evaluate such programs is to conduct
randomized experiments. With these, among a group of people who
indicate that they want job-training, some are randomly assigned to be
offered the training and the others are denied such offers, at least
initially. Then, according to a well-defined protocol, outcomes, such
as employment statuses or wages for those who are employed, are
measured for those who were offered the training and compared to the
same outcomes for those who were not offered the training. Despite the
high cost of these experiments, their results can be difficult to
interpret because of inevitable complications when doing experiments
with humans. In particular, some people do not comply with their
assigned treatment, others drop out of the experiment before outcomes
can be measured, and others who stay in the experiment are not
employed, and thus their wages are not cleanly defined. Statistical
analyses of such data can lead to important policy decisions, and yet
the analyses typically deal with only one or two of these
complications, which may obfuscate subtle effects. An analysis that
simultaneously deals with all three complications generally provides
more accurate conclusions, which may affect policy decisions. A
specific example will be used to illustrate essential ideas that need
to be considered when examining such data. Mathematical details will
not be pursued.

Gov3009 website: http://www.iq.harvard.edu/events/node/1208


Matthew Blackwell
PhD Candidate
Institute for Quantitative Social Science
Department of Government
Harvard University
url: http://people.fas.harvard.edu/~blackwel/

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