The most crucial choice a high school graduate makes is whether to attend college or to go to work. Here is the most sophisticated study of the complexities behind that decision.

Based on a unique data set of nearly 23,000 seniors from more than 1,300 high schools who were tracked over several years, the book treats the following questions in detail: Who goes to college? Does low family income prevent some young people from enrolling, or does scholarship aid offset financial need? How important are scholastic aptitude scores, high school class rank, race, and socioeconomic background in determining college applications and admissions? Do test scores predict success in higher education?

Using the data from the National Longitudinal Study of the Class of 1972, the authors present a set of interrelated analyses of student and institutional behavior, each focused on a particular aspect of the process of choosing and being chosen by a college. Among their interesting findings: most high school graduates would be admitted to some four-year college of average quality, were they to apply; applicants do not necessarily prefer the highest-quality school; high school class rank and SAT scores are equally important in college admissions; federal scholarship aid has had only a small effect on enrollments at four-year colleges but a much stronger effect on attendance at two-year colleges; the attention paid to SAT scores in admissions is commensurate with the power of the scores in predicting persistence to a degree. This clearly written book is an important source of information on a perpetually interesting topic.

Almost everyone would like to see the enactment of sound, practical measures to help disadvantaged people get off welfare and find jobs at decent wages, and over the past quarter-century federal and state governments have struggled to develop just such programs. How do we know whether they are having the hoped-for effect? How do we know whether these vast outlays of money are helping the people they are designed to reach?

All welfare and training programs have been subject to professional evaluations, including social experiments and demonstrations designed to test new ideas. This book reviews what we have discovered from past assessments and suggests how welfare and training programs should be planned for the 1990s. The authors of this volume, each a recognized expert in the evaluation of social programs, do more than summarize what we have learned so far. They clarify why the issue of the proper conduct and interpretation of evaluations has itself been a subject of continuing controversy. In part, the problem is organizational, requiring the integrated efforts of social scientists, public officials, and the professionals who execute evaluations. In addition, there is a dispute about scientific method: should evaluators try to understand the complex social processes that make programs succeed (or fail), or should they focus on inputs and outputs, treating the programs themselves as “black boxes” whose machinery remains hidden?

Evaluating Welfare and Training Programs will be important for policy researchers and evaluation professionals, social scientists concerned with evaluation methods, public officials working in social policy, and students of public policy, economics, and social work.

This book is a full-scale exposition of Charles Manski's new methodology for analyzing empirical questions in the social sciences. He recommends that researchers first ask what can be learned from data alone, and then ask what can be learned when data are combined with credible weak assumptions. Inferences predicated on weak assumptions, he argues, can achieve wide consensus, while ones that require strong assumptions almost inevitably are subject to sharp disagreements.

Building on the foundation laid in the author's Identification Problems in the Social Sciences (Harvard, 1995), the book's fifteen chapters are organized in three parts. Part I studies prediction with missing or otherwise incomplete data. Part II concerns the analysis of treatment response, which aims to predict outcomes when alternative treatment rules are applied to a population. Part III studies prediction of choice behavior.

Each chapter juxtaposes developments of methodology with empirical or numerical illustrations. The book employs a simple notation and mathematical apparatus, using only basic elements of probability theory.

This book provides a language and a set of tools for finding bounds on the predictions that social and behavioral scientists can logically make from nonexperimental and experimental data. The economist Charles Manski draws on examples from criminology, demography, epidemiology, social psychology, and sociology as well as economics to illustrate this language and to demonstrate the broad usefulness of the tools.

There are many traditional ways to present identification problems in econometrics, sociology, and psychometrics. Some of these are primarily statistical in nature, using concepts such as flat likelihood functions and nondistinct parameter estimates. Manski's strategy is to divorce identification from purely statistical concepts and to present the logic of identification analysis in ways that are accessible to a wide audience in the social and behavioral sciences. In each case, problems are motivated by real examples with real policy importance, the mathematics is kept to a minimum, and the deductions on identifiability are derived giving fresh insights.

Manski begins with the conceptual problem of extrapolating predictions from one population to some new population or to the future. He then analyzes in depth the fundamental selection problem that arises whenever a scientist tries to predict the effects of treatments on outcomes. He carefully specifies assumptions and develops his nonparametric methods of bounding predictions. Manski shows how these tools should be used to investigate common problems such as predicting the effect of family structure on children's outcomes and the effect of policing on crime rates.

Successive chapters deal with topics ranging from the use of experiments to evaluate social programs, to the use of case-control sampling by epidemiologists studying the association of risk factors and disease, to the use of intentions data by demographers seeking to predict future fertility. The book closes by examining two central identification problems in the analysis of social interactions: the classical simultaneity problem of econometrics and the reflection problem faced in analyses of neighborhood and contextual effects.

Public policy advocates routinely assert that “research has shown” a particular policy to be desirable. But how reliable is the analysis in the research they invoke? And how does that analysis affect the way policy is made, on issues ranging from vaccination to minimum wage to FDA drug approval? Charles Manski argues here that current policy is based on untrustworthy analysis. By failing to account for uncertainty in an unpredictable world, policy analysis misleads policy makers with expressions of certitude. Public Policy in an Uncertain World critiques the status quo and offers an innovation to improve how policy research is conducted and how policy makers use research.

Consumers of policy analysis, whether civil servants, journalists, or concerned citizens, need to understand research methodology well enough to properly assess reported findings. In the current model, policy researchers base their predictions on strong assumptions. But as Manski demonstrates, strong assumptions lead to less credible predictions than weaker ones. His alternative approach takes account of uncertainty and thereby moves policy analysis away from incredible certitude and toward honest portrayal of partial knowledge. Manski describes analysis of research on such topics as the effect of the death penalty on homicide, of unemployment insurance on job-seeking, and of preschooling on high school graduation. And he uses other real-world scenarios to illustrate the course he recommends, in which policy makers form reasonable decisions based on partial knowledge of outcomes, and journalists evaluate research claims more closely, with a skeptical eye toward expressions of certitude.