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Systematic thinking for social action

TLDR
In this paper, Alice M. Rivlin examines the contributions that systematic analysis has made to decision-making in the government's "social action" programs education, health, manpower training, and income maintenance and concludes by urging the widespread implementation of social experimentation and acceptability by the federal government.
Abstract
How can we identify who benefits from government programs aimed at solving our social problem and who pays for them? With so many problems, how can we allocate scarce funds to promote the maximum well-being of our citizens?In this book, originally presented as the third series of H. Rowan Gaither Lectures in Systems Science at the University of California (Berkeley). Alice M. Rivlin examines the contributions that systematic analysis has made to decisionmaking in the government's "social action" programseducation, health, manpower training, and income maintenance. Drawing on her own experience in government, Mrs. Rivlin indicates where the analysts have been helpful in finding solutions and wherebecause of inadequate data or methodsthey have been no help at all.Mrs. Rivlin concludes by urging the widespread implementation of social experimentation and acceptability by the federal government. The first in such a way as to permit valid conclusions about their effectiveness; the second would encourage the adoption of better ways of delivering services by making those who administer programs responsive to their clients. Underlying both is the requirement from comprehensive, reliable performance measures."

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Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: the Case of Manpower Training

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Choosing among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training

TL;DR: In this article, the authors explore the value of simple specification tests in selecting an appropriate nonex-experiment estimator for a manpower training program and find that a simple testing procedure eliminates the range of nonexperimental estimators at variance with the experimental estimates of program impact.

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