Charles F. Manski – författare

This study is an important first step in the development of a national policy on illegal drugs. It assesses two recent cost-effectiveness studies on cocaine control policy: one by RAND, Controlling Cocaine: Supply Versus Demand Programs, and the other by the Institute of Defense Analyses, An Empirical Examination of Counterdrug Interdiction Program Effectiveness.

This report was prepared in response to a request from the Economic Research Service (ERS) of the U.S. Department of Agriculture (USDA). It summarizes the discussions at a February 1998 workshop convened by the Committee on National Statistics; the Board on Children, Youth, and Families; and the Food and Nutrition Board. The fiscal year 1998 (FY1998) appropriations bill for USDA gave ERS responsibility for all research and evaluation studies on USDA food assistance programs. The bill provided $18 million to fund these studies, an increase from $7 million in FY1997. ERS asked the Committee on National Statistics for assistance in identifying new areas of research and data collection and in further improving the evaluation studies of food assistance programs. By bringing together many who work on evaluation of food assistance programs, policy analysis, survey methods, nutrition, child nutrition and child development, outcome measurement, and state welfare programs, the issues presented and discussed at the workshop provided ERS with information that could be used to develop a framework for their research program.

How should the war on drugs be fought? Everyone seems to agree that the United States ought to use a combination of several different approaches to combat the destructive effects of illegal drug use. Yet there is a remarkable paucity of data and research information that policy makers require if they are to create a useful, realistic policy package-details about drug use, drug market economics, and perhaps most importantly the impact of drug enforcement activities. Informing America's Policy on Illegal Drugs recommends ways to close these gaps in our understanding-by obtaining the necessary data on drug prices and consumption (quantity in addition to frequency); upgrading federal management of drug statistics; and improving our evaluation of prevention, interdiction, enforcement, and treatment efforts. The committee reviews what we do and do not know about illegal drugs and how data are assembled and used by federal agencies. The book explores the data and research information needed to support strong drug policy analysis, describes the best methods to use, explains how to avoid misleading conclusions, and outlines strategies for increasing access to data.Informing America's Policy on Illegal Drugs also discusses how researchers can incorporate randomization into studies of drug treatment and how state and local agencies can compare alternative approaches to drug enforcement. Charting a course toward a better-informed illegal drugs policy, this book will be important to federal and state policy makers, regulators, researchers, program administrators, enforcement officials, journalists, and advocates concerned about illegal drug use.

Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event - a parameter is either identified or not - and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response.Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric.

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.

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.

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.

Economists have long sought to learn the effect of a "treatment" on some outcome of interest, just as doctors do with their patients. A central practical objective of research on treatment response is to provide decision makers with information useful in choosing treatments. Often the decision maker is a social planner who must choose treatments for a heterogeneous population--for example, a physician choosing medical treatments for diverse patients or a judge choosing sentences for convicted offenders. But research on treatment response rarely provides all the information that planners would like to have. How then should planners use the available evidence to choose treatments? This book addresses key aspects of this broad question, exploring and partially resolving pervasive problems of identification and statistical inference that arise when studying treatment response and making treatment choices. Charles Manski addresses the treatment-choice problem directly using Abraham Wald's statistical decision theory, taking into account the ambiguity that arises from identification problems under weak but justifiable assumptions.The book unifies and further develops the influential line of research the author began in the late 1990s. It will be a valuable resource to researchers and upper-level graduate students in economics as well as other social sciences, statistics, epidemiology and related areas of public health, and operations research.

How cutting-edge economics can improve decision-making methods for doctorsAlthough uncertainty is a common element of patient care, it has largely been overlooked in research on evidence-based medicine. Patient Care under Uncertainty strives to correct this glaring omission. Applying the tools of economics to medical decision making, Charles Manski shows how uncertainty influences every stage, from risk analysis to treatment, and how this can be reasonably confronted.In the language of econometrics, uncertainty refers to the inadequacy of available evidence and knowledge to yield accurate information on outcomes. In the context of health care, a common example is a choice between periodic surveillance or aggressive treatment of patients at risk for a potential disease, such as women prone to breast cancer. While these choices make use of data analysis, Manski demonstrates how statistical imprecision and identification problems often undermine clinical research and practice. Reviewing prevailing practices in contemporary medicine, he discusses the controversy regarding whether clinicians should adhere to evidence-based guidelines or exercise their own judgment. He also critiques the wishful extrapolation of research findings from randomized trials to clinical practice. Exploring ways to make more sensible judgments with available data, to credibly use evidence, and to better train clinicians, Manski helps practitioners and patients face uncertainties honestly. He concludes by examining patient care from a public health perspective and the management of uncertainty in drug approvals.Rigorously interrogating current practices in medicine, Patient Care under Uncertainty explains why predictability in the field has been limited and furnishes criteria for more cogent steps forward.

Economists and psychologists have, on the whole, exhibited sharply different perspectives on the elicitation of preferences. Economists, who have made preference the central primitive in their thinking about human behaviour, have for the most part rejected elicitation and have instead sought to infer preferences from observations of choice behaviour. Psychologists, who have tended to think of preference as a context-determined subjective construct, have embraced elicitation as their dominant approach to measurement. This volume, based on a symposium organized by Daniel McFadden at the University of California at Berkeley, provides a constructive engagement between economists and psychologists on the elicitation of preferences.

Economists have long studied policy choice by social planners aiming to maximize population welfare. Whether performing theoretical studies or applied analyses, researchers have generally assumed that the planner knows enough about the choice environment to be able to determine an optimal action. However, the consequences of decisions are often highly uncertain. Discourse on Social Planning under Uncertainty addresses the failure of research to come to grips with this uncertainty. Combining research across three fields – welfare economics, decision theory, and econometrics – this impressive study offers a comprehensive treatment that fleshes out a 'worldview' and juxtaposes it with other viewpoints. Building on multiple case studies, ranging from medical treatment to climate policy, the book explains analytical methods and how to apply them, providing a foundation on which future interdisciplinary work can build.

Economists have long studied policy choice by social planners aiming to maximize population welfare. Whether performing theoretical studies or applied analyses, researchers have generally assumed that the planner knows enough about the choice environment to be able to determine an optimal action. However, the consequences of decisions are often highly uncertain. Discourse on Social Planning under Uncertainty addresses the failure of research to come to grips with this uncertainty. Combining research across three fields – welfare economics, decision theory, and econometrics – this impressive study offers a comprehensive treatment that fleshes out a 'worldview' and juxtaposes it with other viewpoints. Building on multiple case studies, ranging from medical treatment to climate policy, the book explains analytical methods and how to apply them, providing a foundation on which future interdisciplinary work can build.

The original research papers collected in this volume continue the development of discrete choice analysis, of related structural models for analysis of choice behavior, and of the statistical theory used in inference on these models. Most papers in the volume are revised versions of ones presented at a 2005 conference in honor of Daniel L. McFadden, whose fundamental research made discrete choice analysis part of the fabric of modern economics.

Sample data alone never suffice to draw conclusions about populations. Inference always requires assumptions about the population and sampling process. Statistical theory has revealed much about how strength of assumptions affects the precision of point estimates, but has had much less to say about how it affects the identification of population parameters. Indeed, it has been commonplace to think of identification as a binary event – a parameter is either identified or not – and to view point identification as a precondition for inference. Yet there is enormous scope for fruitful inference using data and assumptions that partially identify population parameters. This book explains why and shows how. The book presents in a rigorous and thorough manner the main elements of Charles Manski's research on partial identification of probability distributions. One focus is prediction with missing outcome or covariate data. Another is decomposition of finite mixtures, with application to the analysis of contaminated sampling and ecological inference. A third major focus is the analysis of treatment response. Whatever the particular subject under study, the presentation follows a common path. The author first specifies the sampling process generating the available data and asks what may be learned about population parameters using the empirical evidence alone. He then ask how the (typically) setvalued identification regions for these parameters shrink if various assumptions are imposed. The approach to inference that runs throughout the book is deliberately conservative and thoroughly nonparametric.