Advanced Econometrics is both a comprehensive text for graduate students and a reference work for econometricians. It will also be valuable to those doing statistical analysis in the other social sciences. Its main features are a thorough treatment of cross-section models, including qualitative response models, censored and truncated regression models, and Markov and duration models, as well as a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.

Although the treatment is mathematically rigorous, the author has employed the theorem-proof method with simple, intuitively accessible assumptions. This enables readers to understand the basic structure of each theorem and to generalize it for themselves depending on their needs and abilities. Many simple applications of theorems are given either in the form of examples in the text or as exercises at the end of each chapter in order to demonstrate their essential points.

When more than twenty million immigrants arrived in the United States between 1880 and 1920, the government attempted to classify them according to prevailing ideas about race and nationality. But this proved hard to do. Ideas about racial or national difference were slippery, contested, and yet consequential—were “Hebrews” a “race,” a “religion,” or a “people”? As Joel Perlmann shows, a self-appointed pair of officials created the government’s 1897 List of Races and Peoples, which shaped exclusionary immigration laws, the wording of the U.S. Census, and federal studies that informed social policy. Its categories served to maintain old divisions and establish new ones.

Across the five decades ending in the 1920s, American immigration policy built increasingly upon the belief that some groups of immigrants were desirable, others not. Perlmann traces how the debates over this policy institutionalized race distinctions—between whites and nonwhites, but also among whites—in immigration laws that lasted four decades.

Despite a gradual shift among social scientists from “race” to “ethnic group” after the 1920s, the diffusion of this key concept among government officials and the public remained limited until the end of the 1960s. Taking up dramatic changes to racial and ethnic classification since then, America Classifies the Immigrants concentrates on three crucial reforms to the American Census: the introduction of Hispanic origin and ancestry (1980), the recognition of mixed racial origins (2000), and a rethinking of the connections between race and ethnic group (proposed for 2020).

American agriculture in the twentieth century has given the world one of its great success stories, a paradigm of productivity and plenty. Yet the story has its dark side, from the plight of the Okies in the 1930s to the farm crisis of the 1980s to today's concerns about low crop prices and the impact of biotechnology. Looking at U.S. farming over the past century, Bruce Gardner searches out explanations for both the remarkable progress and the persistent social problems that have marked the history of American agriculture.

Gardner documents both the economic difficulties that have confronted farmers and the technological and economic transformations that have lifted them from relative poverty to economic parity with the nonfarm population. He provides a detailed analysis of the causes of these trends, with emphasis on the role of government action. He reviews how commodity support programs, driven by interest-group politics, have spent hundreds of billions of dollars to little purpose. Nonetheless, Gardner concludes that by reconciling competing economic interests while fostering productivity growth and economic integration of the farm and nonfarm economies, the overall twentieth-century role of government in American agriculture is fairly viewed as a triumph of democracy.

When it comes to sizing up America’s public schools, test scores are the go-to metric of state policy makers and anxious parents looking to place their children in the “best” schools. Yet ample research indicates that standardized tests are a poor way to measure a school’s performance. It is time—indeed past time—to rethink this system, Jack Schneider says.

Beyond Test Scores reframes current debates over school quality by offering new approaches to educational data that can push us past our unproductive fixation on test scores. Using the highly diverse urban school district of Somerville, Massachusetts, as a case study, Schneider and his research team developed a new framework to more fairly and comprehensively assess educational effectiveness. And by adopting a wide range of measures aligned with that framework, they were able to more accurately capture a broader array of school strengths and weaknesses. Their new data not only provided parents, educators, and administrators with a clearer picture of school performance, but also challenged misconceptions about what makes a good school.

With better data, Schneider shows, stakeholders at the federal, state, and local levels can undo the damage of present accountability systems and build greater capacity in our schools. Policy makers, administrators, and school leaders can better identify where assistance is needed. Educators can engage in more evidence-based decision making. And parents can make better-informed choices for their children. Perhaps most importantly, better data can facilitate communication among all these groups, allowing them to take collective action toward shared, concrete goals.

The papers in this volume analyze the deployment of Big Data to solve both existing and novel challenges in economic measurement. 

The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data—such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers—has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics.

Cardiovascular diseases kill and disable more than a million Americans each year. The major types of this complex of diseases are coronary heart disease, hypertensive disease, cerebrovascular diseases, rheumatic heart disease, and congenital malformations of the circulatory system. Authors Moriyama, Krueger, and Stamler relate each of these types to etiology, age of patient at onset, clinical course, and socioeconomic impact on the population. For each type of cardiovascular disease they analyze the quantitative data on the incidence, prevalence, and levels and time trends of mortality and on the demographic characteristics of person affected. They also examine international differences in levels and trends in mortality and point out areas for further research. More than thirty-five figures as well as extensive tables document their text.

Contemporaries of Carroll D. Wright (1840-1909) lived through the transformation of American society by the industrial revolution. For the most part they thought the transformation represented growth and progress, but many also found occasion for doubt and fear in its consequences. Their anxieties collected around the notions of a "labor problem" and "labor reform." Whether from hope or fear, people felt a need for statistical information. On this popular demand Wright built his career as statistical expert and renowned master of "labor statistics." His investigations during thirty-two years of government service (1873-1905) gave form to contemporary ideas and set precedents for modern procedures, as in his seminal studies of wages, prices, and strikes.

In telling how Wright took up this unprecedented career, Mr. Leiby shows the importance of Wright's early years and relates his work to the politics and religion of his time as well as to its social science. In this perspective, the history of the labor bureaus and their voluminous reports take on their original human purposes and meaning.

This is the first book-length treatment of the frequency and distribution of neurologic and sense disorders. It is based primarily on special tabulations of mortality data for 1959–1961 applied to the U.S. census of 1960. Information on incidence, prevalence, and survivorship is provided for each major disorder and a summary chapter considers the frequency and geographic distribution of neurologic disorders in terms of age, sex, color, and demographic characteristics.

This book provides a broad general survey of the principles of scientific method, and a substantial basic knowledge of statistics particularly in its application to scientific research and with special relevance to the fields of the social and biological sciences. The introductory chapters aim to place statistical procedures in the reader’s general philosophy and experience, and to show him their logical importance and practical utility. Mr. Kelley then develops a detailed presentation of essential statistics. Finally, he makes a serious but admittedly initial attempt to release the niceties of middle and advanced mathematics to the peculiar problems that may be found in a given issue and with given data. His book will be of particular value in the teaching of elementary statistics in that it provides a broad scientific and logical approach. Yet, while its earlier chapters constitute a complete first semester course and the later chapters a reference handbook for one going no further, these later chapters also provide the basic topics, procedures and formulas for incorporation into second and third semester courses in applied statistics.

An award-winning math popularizer, who has advised the US Olympic Committee, NFL, and NBA, offers sports fans a new way to understand truly improbable feats in their favorite games.
 
In 2013, NBA point guard Steph Curry wowed crowds when he sunk 11 out of 13 three-pointers for a game total of 54 points—only seven other players, including Michael Jordan and Kobe Bryant, had scored more in a game at Madison Square Garden. Four years later, the University of Connecticut women’s basketball team won its hundredth straight game, defeating South Carolina 66–55. And in 2010, one forecaster—an octopus named Paul—correctly predicted the outcome of all of Germany’s matches in the FIFA World Cup. These are surprising events—but are they truly improbable?
 
In Get in the Game, mathematician and sports analytics expert Tim Chartier helps us answer that question—condensing complex mathematics down to coin tosses and dice throws to give readers both an introduction to statistics and a new way to enjoy sporting events. With these accessible tools, Chartier leads us through modeling experiments that develop our intuitive sense of the improbable. For example, to see how likely you are to beat Curry’s three-pointer feat, consider his 45.3 percent three-point shooting average in 2012–13. Take a coin and assume heads is making the shot (slightly better than Curry at a fifty percent chance). Can you imagine getting heads eleven out of thirteen times? With engaging exercises and fun, comic book–style illustrations by Ansley Earle, Chartier’s book encourages all readers—including those who have never encountered formal statistics or data simulations, or even heard of sports analytics, but who enjoy watching sports—to get in the game.

This book provides a systematic exposition of a group-based statistical method for analyzing longitudinal data in the social and behavioral sciences and in medicine. The methods can be applied to a wide range of data, such as that describing the progression of delinquency and criminality over the life course, changes in income over time, the course of a disease or physiological condition, or the evolution of the socioeconomic status of communities. Using real-world research data from longitudinal studies, the book explains and applies this method for identifying distinctive time-based progressions called developmental trajectories. Rather than assuming the existence of developmental trajectories of a specific form before statistical data analysis begins, the method allows the trajectories to emerge from the data itself. Thus, in an analysis of data on Montreal school children, it teases apart four distinct trajectories of physical aggression over the ages 6 to 15, examines predictors of these trajectories, and identifies events that may alter the trajectories.

Aimed at consumers of statistical methodology, including social scientists, criminologists, psychologists, and medical researchers, the book presents the statistical theory underlying the method with a mixture of intuition and technical development.

This study provides a comprehensive overview of Korea’s macroeconomic growth and structural change since World War II, and traces some of the roots of development to the colonial period. The authors explore in detail colonial development, changing national income patterns, relative price shifts, sources of aggregate growth, and sources of sectoral structural change, comparing them with other countries.

From the health risks of sexual activity to those of pregnancy, abortion, and childbirth, reproduction constitutes enormous risks to a woman's health. Ill-health conditions related to sex and reproduction account for 25 percent of the global disease burden in adult women. In sub-Saharan Africa, they account for over 40 percent. The catastrophic effects of reproductive ill-health, however, are not limited to women; for infants and adult men, they inflict 25 percent and 1 percent respectively of the global burden.

This volume offers comprehensive data and detailed discussions of the epidemiologies of three sexually transmitted diseases, HIV, and five specific maternal conditions, as well as those of congenital anomalies and perinatal conditions. Projections of the HIV epidemic are provided: by 2020 HIV is projected to double to 2.5 percent of the global disease burden.

Health Dimensions of Sex and Reproduction will serve as a comprehensive reference for epidemiologists, public health specialists, practitioners and advocates of STD and HIV prevention, and reproductive and neonatal health.

The 1988 cult classic behind football’s data analytics revolution, now back in print with a new foreword and preface.

Data analytics have revolutionized football. With play sheets informed by advanced statistical analysis, today’s coaches pass more, kick less, and go for more two-point or fourth-down conversions than ever before. In 1988, sportswriters Bob Carroll, Pete Palmer, and John Thorn proposed just this style of play in The Hidden Game of Football, but at the time baffled readers scoffed at such a heartless approach to the game. Football was the ultimate team sport and unlike baseball could not be reduced to pure probabilities. Nevertheless, the book developed a cult following among analysts who, inspired by its unorthodox methods, went on to develop the core metrics of football analytics used today: win probability, expected points, QBR, and more. With a new preface by Thorn and Palmer and a new foreword by Football Outsiders’s Aaron Schatz, The Hidden Game of Football remains an essential resource for armchair coaches, fantasy managers, and fans of all stripes.

Hispanics are less represented in the U.S. Department of Defense (DoD) civilian workforce than in the federal civilian workforce and the civilian labor force. This report assesses what factors might account for Hispanic underrepresentation in DoD. It includes assessments of trends in Hispanic employment and analyses of job applicant data. It also presents findings from interviews with representatives of DoD and of Hispanic-serving institutions.

This magnificent book is the first comprehensive history of statistics from its beginnings around 1700 to its emergence as a distinct and mature discipline around 1900. Stephen M. Stigler shows how statistics arose from the interplay of mathematical concepts and the needs of several applied sciences including astronomy, geodesy, experimental psychology, genetics, and sociology. He addresses many intriguing questions: How did scientists learn to combine measurements made under different conditions? And how were they led to use probability theory to measure the accuracy of the result? Why were statistical methods used successfully in astronomy long before they began to play a significant role in the social sciences? How could the introduction of least squares predate the discovery of regression by more than eighty years? On what grounds can the major works of men such as Bernoulli, De Moivre, Bayes, Quetelet, and Lexis be considered partial failures, while those of Laplace, Galton, Edgeworth, Pearson, and Yule are counted as successes? How did Galton’s probability machine (the quincunx) provide him with the key to the major advance of the last half of the nineteenth century?

Stigler’s emphasis is upon how, when, and where the methods of probability theory were developed for measuring uncertainty in experimental and observational science, for reducing uncertainty, and as a conceptual framework for quantitative studies in the social sciences. He describes with care the scientific context in which the different methods evolved and identifies the problems (conceptual or mathematical) that retarded the growth of mathematical statistics and the conceptual developments that permitted major breakthroughs.

Statisticians, historians of science, and social and behavioral scientists will gain from this book a deeper understanding of the use of statistical methods and a better grasp of the promise and limitations of such techniques. The product of ten years of research, The History of Statistics will appeal to all who are interested in the humanistic study of science.

In Honest Numbers and Democracy, Walter Williams offers a revealing history of policy analysis in the federal government and a scorching critique of what’s wrong with social policy analysis today. Williams, a policy insider who witnessed the birth of domestic policy analysis during the Johnson administration, contends that the increasingly partisan U.S. political environment is vitiating both "honest numbers" — the data used to direct public policy — and, more importantly, honest analysts, particularly in the White House.

Drawing heavily on candid off-the-record interviews with political executives, career civil servants, elected officials and Washington-based journalists, Williams documents the steady deformation of social policy analysis under the pressure of ideological politics waged by both the executive and legislative branches. Beginning with the Reagan era and continuing into Clinton’s tenure, Williams focuses on the presidents’ growing penchant to misuse and hide numbers provided by their own analysts to assist in major policy decisions.

Honest Numbers and Democracy is the first book to examine in-depth the impact of the electronic revolution, its information overload, and rampant public distrust of the federal government's data on the practice of policy analysis.

A hard-hitting account of the factors threatening the credibility of the policymaking process, this book will be required reading for policy professionals, presidential watchers, and anyone interested in the future of U.S. democracy.

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.

This outstanding text by a foremost econometrician combines instruction in probability and statistics with econometrics in a rigorous but relatively nontechnical manner. Unlike many statistics texts, it discusses regression analysis in depth. And unlike many econometrics texts, it offers a thorough treatment of statistics. Although its only mathematical requirement is multivariate calculus, it challenges the student to think deeply about basic concepts.

The coverage of probability and statistics includes best prediction and best linear prediction, the joint distribution of a continuous and discrete random variable, large sample theory, and the properties of the maximum likelihood estimator. Exercises at the end of each chapter reinforce the many illustrative examples and diagrams. Believing that students should acquire the habit of questioning conventional statistical techniques, Takeshi Amemiya discusses the problem of choosing estimators and compares various criteria for ranking them. He also evaluates classical hypothesis testing critically, giving the realistic case of testing a composite null against a composite alternative. He frequently adopts a Bayesian approach because it provides a useful pedagogical framework for discussing many fundamental issues in statistical inference.

Turning to regression, Amemiya presents the classical bivariate model in the conventional summation notation. He follows with a brief introduction to matrix analysis and multiple regression in matrix notation. Finally, he describes various generalizations of the classical regression model and certain other statistical models extensively used in econometrics and other applications in social science.

The United Nations Sustainable Development Goals initiative has the potential to set the direction for a future world that works for everyone. Approved by 193 United Nations member countries in September 2016 to help guide global and national development policies through the year 2030, the seventeen goals build on the successes of the Millennium Development Goals, but also include new priority areas, such as climate change, economic inequality, innovation, sustainable consumption, peace, and justice. Assessed against commonly agreed targets and indicators, the goals should facilitate inter-governmental cooperation and the development of regional and even global development strategies. This book explores, through a series of case studies, the substantial challenges for assembling reliable data and statistics to address pressing development challenges, particularly in Africa. By highlighting the enormous potential value of creating and using high quality data, statistics, and records as an interconnected resource and describing how this can be achieved, the book will contribute to defining meaningful and realistic global and national development policies in the critical period to 2030.

How do you judge the quality of a school, a district, a teacher, a student? By the test scores, of course. Yet for all the talk, what educational tests can and can’t tell you, and how scores can be misunderstood and misused, remains a mystery to most. The complexities of testing are routinely ignored, either because they are unrecognized, or because they may be—well, complicated.

Inspired by a popular Harvard course for students without an extensive mathematics background, Measuring Up demystifies educational testing—from MCAS to SAT to WAIS, with all the alphabet soup in between. Bringing statistical terms down to earth, Daniel Koretz takes readers through the most fundamental issues that arise in educational testing and shows how they apply to some of the most controversial issues in education today, from high-stakes testing to special education. He walks readers through everyday examples to show what tests do well, what their limits are, how easily tests and scores can be oversold or misunderstood, and how they can be used sensibly to help discover how much kids have learned.

Drawing on national, state, and local data, the Urban Child Institute partnered with RAND to explore the social and emotional well-being of children in Memphis and Shelby County, Tenn. The book highlights the importance of factors in the home, child care setting, and community that contribute to social and emotional development.

Statistics-driven thinking is ubiquitous in modern society. In this ambitious and sophisticated study of the history of statistics, which begins with probability theory in the seventeenth century, Alain Desrosières shows how the evolution of modern statistics has been inextricably bound up with the knowledge and power of governments. He traces the complex reciprocity between modern governments and the mathematical artifacts that both dictate the duties of the state and measure its successes.

No other work, in any language, covers such a broad spectrum--probability, mathematical statistics, psychology, economics, sociology, surveys, public health, medical statistics--in accurately synthesizing the history of statistics, with an emphasis on the conceptual development of social statistics, culminating in twentieth-century applied econometrics.

How did Americans come to quantify their society’s progress and well-being in units of money? In today’s GDP-run world, prices are the standard measure of not only our goods and commodities but our environment, our communities, our nation, even our self-worth. The Pricing of Progress traces the long history of how and why we moderns adopted the monetizing values and valuations of capitalism as an indicator of human prosperity while losing sight of earlier social and moral metrics that did not put a price on everyday life.

Eli Cook roots the rise of economic indicators in the emergence of modern capitalism and the contested history of English enclosure, Caribbean slavery, American industrialization, economic thought, and corporate power. He explores how the maximization of market production became the chief objective of American economic and social policy. We see how distinctly capitalist quantification techniques used to manage or invest in railroad corporations, textile factories, real estate holdings, or cotton plantations escaped the confines of the business world and seeped into every nook and cranny of society. As economic elites quantified the nation as a for-profit, capitalized investment, the progress of its inhabitants, free or enslaved, came to be valued according to their moneymaking abilities.

Today as in the nineteenth century, political struggles rage over who gets to determine the statistical yardsticks used to gauge the “health” of our economy and nation. The Pricing of Progress helps us grasp the limits and dangers of entrusting economic indicators to measure social welfare and moral goals.

What gives statistics its unity as a science? Stephen Stigler sets forth the seven foundational ideas of statistics—a scientific discipline related to but distinct from mathematics and computer science.

Even the most basic idea—aggregation, exemplified by averaging—is counterintuitive. It allows one to gain information by discarding information, namely, the individuality of the observations. Stigler’s second pillar, information measurement, challenges the importance of “big data” by noting that observations are not all equally important: the amount of information in a data set is often proportional to only the square root of the number of observations, not the absolute number. The third idea is likelihood, the calibration of inferences with the use of probability. Intercomparison is the principle that statistical comparisons do not need to be made with respect to an external standard. The fifth pillar is regression, both a paradox (tall parents on average produce shorter children; tall children on average have shorter parents) and the basis of inference, including Bayesian inference and causal reasoning. The sixth concept captures the importance of experimental design—for example, by recognizing the gains to be had from a combinatorial approach with rigorous randomization. The seventh idea is the residual: the notion that a complicated phenomenon can be simplified by subtracting the effect of known causes, leaving a residual phenomenon that can be explained more easily.

The Seven Pillars of Statistical Wisdom presents an original, unified account of statistical science that will fascinate the interested layperson and engage the professional statistician.

The Department of Defense Sexual Assault Prevention and Response Office asked the RAND Corporation to conduct an assessment of the rates of sexual assault, sexual harassment, and gender discrimination in the U.S. military. This volume presents survey estimates of how risk of sexual assault and sexual harassment varies across military installations and major commands using data from the 2014 RAND Military Workplace Study.

This solutions manual is a valuable companion volume to the classic textbook Recursive Methods in Economic Dynamics by Nancy L. Stokey, Robert E. Lucas, Jr., and Edward C. Prescott. The exercises in the Stokey et al. book are integral to the text, and thus, a reader cannot fully appreciate the text without understanding the results developed in the exercises. This manual provides detailed answers to the central exercises in Recursive Methods.

The authors’ selection of exercises is designed to maximize the reader’s understanding of Recursive Methods. Solutions are presented to every question in the core chapters on recursive methods, as well as most questions from the chapters on mathematical background. Some questions from the chapters on applications of these techniques to economic models have been reserved so as to provide instructors with a crucial “test bank” of questions.

Efficient and lucid in approach, this manual will greatly enhance the value of Recursive Methods as a text for self-study.

This lively collection of essays examines in witty detail the history of some of the concepts involved in bringing statistical argument "to the table," and some of the pitfalls that have been encountered. The topics range from seventeenth-century medicine and the circulation of blood, to the cause of the Great Depression and the effect of the California gold discoveries of 1848 upon price levels, to the determinations of the shape of the Earth and the speed of light, to the meter of Virgil's poetry and the prediction of the Second Coming of Christ. The title essay tells how the statistician Karl Pearson came to issue the challenge to put "statistics on the table" to the economists Marshall, Keynes, and Pigou in 1911. The 1911 dispute involved the effect of parental alcoholism upon children, but the challenge is general and timeless: important arguments require evidence, and quantitative evidence requires statistical evaluation. Some essays examine deep and subtle statistical ideas such as the aggregation and regression paradoxes; others tell of the origin of the Average Man and the evaluation of fingerprints as a forerunner of the use of DNA in forensic science. Several of the essays are entirely nontechnical; all examine statistical ideas with an ironic eye for their essence and what their history can tell us about current disputes.

A clear explanation and provocative look at the impact of new technologies on world society. In our complex and multicultural society, racial identity is often as much a matter of family background, economic opportunity, and geographic location as it is determined by skin color or hair texture. And yet study after study is released and reported in the media regarding African American test scores, Asian American social mobility, and the white domination of our political institutions. In short, there is a fundamental disconnect between the nuanced understanding many people have of race and the ways it is studied and quantified by researchers. In this timely and hard-hitting volume, Tukufu Zuberi offers a concise account of the historical connections between the development of the idea of race and the birth of social statistics. Zuberi describes the ways race-differentiated data is misinterpreted in the social sciences and asks essential questions about the ways racial statistics are used: What is the value of knowing the income disparities or differences in crime and incarceration rates, differences in test scores, infant mortality rates, abortion frequencies, or choices of sexual partner between different racial groups? When these data are available, what should the principles be guiding their dissemination, interpretation, and analysis? How does the availability of this information shape public discourse, alter scientific research agendas, inform political decision making, and ultimately influence the very social meaning of racial difference? When statistics are interpreted in a racist manner, no matter how inadvertent the racism may be, the public is exposed to seemingly neutral information that in its effect is anything but neutral. Zuberi argues that statistical analysis can and must be deracialized, and that this deracialization is essential to the goal of achieving social justice for all. He concludes by putting forward a principle of racially conscious social justice, offering an incendiary and necessary correction to the inaccuracies that have plagued this topic at the center of American life. "Zuberi, who was named one of Philadelphia's 76 smartest people by Philadelphia Magazine, has written a brilliant new book, Thicker Than Blood. One of the most powerful claims of the book is that instead of being a fixed biological reality, race is instead a socially produced phenomenon. His point is to show just how vicious-especially through the use of statistics-the notion of race has been when it has been employed to protect the interest of those in power (whites), especially those who say that because race does not exist, racism is not real." Michael Eric Dyson in The Chicago Sun-Times "A call to action and, Zuberi hopes, a precursor to a conversation about the real meaning of race, ethnicity, and political power in America." Time Magazine "Tukufu Zuberi's critical assessment of the analysis of racial data in Thicker Than Blood is a tour de force. His discussion and evaluation of the use of racial statistics in historical and cross-cultural contexts is original and important. I strongly feel that all students and scholars in the social sciences should read this thoughtful book." William Julius Wilson Tukufu Zuberi is professor of sociology and director of the African Census Analysis Project at the University of Pennsylvania. He is the author of Swing Low, Sweet Chariot (1995).

A volume of Social Science Frontiers, a series of publications reviewing new fields for social development, aimed at foundation executives, administrators of research grant programs, directors of research organizations, and others concerned with making contemporary social science more useful for the function of social reporting.

For too long the study of impoverished Puerto Ricans living in the fifty states has been undermined by the use of broad generalizations. Puerto Ricans have been statistically grouped with all Latinos, studied with models developed for understanding African-American life, and written about as if New York's Puerto Rican community was the only such community worthy of detailed study. This book changes all that. In this important new work, Susan Baker looks beyond the traditional models and rewrites the origins, current state, and reasons behind Puerto Rican poverty.The book tells the story of how Puerto Ricans have left the Rustbelt cities to return to the island or to seek job opportunities elsewhere. Those left behind are predominantly poor women with dependents who live in segregated neighborhoods with little chance of finding low-skilled jobs because of competition from non-citizen, non-politicized workers.In her alternative explanation, the author presents data from across the country and puts forth an explanation that is grounded in Puerto Rican history and sensitive not only to the interconnectedness of the island and mainland population, but also the increasing distress faced by Puerto Rican women and the sad truth that Puerto Rican citizenship in this country is a second class one.

The future of smart cities has arrived, courtesy of citizens and their phones. To prove it, Daniel T. O’Brien explains the transformative insights gleaned from years researching Boston’s 311 reporting system, a sophisticated city management tool that has revolutionized how ordinary Bostonians use and maintain public spaces. Through its phone service, mobile app, website, and Twitter account, 311 catalogues complaints about potholes, broken street lights, graffiti, litter, vandalism, and other issues that are no one citizen’s responsibility but affect everyone’s quality of life. The Urban Commons offers a pioneering model of what modern digital data and technology can do for cities like Boston that seek both prosperous growth and sustainability.

Analyzing a rich trove of data, O’Brien discovers why certain neighborhoods embrace the idea of custodianship and willingly invest their time to monitor the city’s common environments and infrastructure. On the government’s side of the equation, he identifies best practices for implementing civic technologies that engage citizens, for deploying public services in collaborative ways, and for utilizing the data generated by these efforts.

Boston’s 311 system has narrowed the gap between residents and their communities, and between constituents and local leaders. The result, O’Brien shows, has been the creation of more effective policy and practices that reinvigorate the way citizens and city governments approach their mutual interests. By unpacking when, why, and how the 311 system has worked for Boston, The Urban Commons reveals the power and potential of this innovative system, and the lessons learned that other cities can adapt.

Will America find enough good teachers to staff its public schools? How can we ensure that all our children will be taught by skilled professionals? The policies that determine who teaches today are a confusing and often conflicting array that includes tougher licensing requirements, higher salaries, mandatory master's degrees, merit pay, and alternative routes to certification. Who Will Teach? examines these policies and separates those that work from those that backfire.

The authors present an intriguing portrait of America's teachers and reveal who they are, who they have been, and who they will be. Using innovative statistical methods to track the professional lives of more than 50,000 college graduates, the book describes, in many cases for the first time, just how prospective, current, and former teachers respond to the incentives and disincentives they face. The authors, a group of noted educators, economists, and statisticians, find cause for serious concern. Few academically talented college graduates even try teaching, and many of those who do leave quickly, never to return. Current licensing requirements stifle innovation in training and dissuade many potentially talented teachers at the outset.

But Who Will Teach? shows that we can reverse these trends if we get the incentives right. Although better salaries are essential, especially for new teachers, money is not enough. Potential teachers should be offered alternative paths into the classroom. School districts should improve their recruiting strategies. Licensing criteria should assess teaching skills, not just academic achievement and number of courses completed. The authors offer a promising strategy based on high standards and substantial rewards.

World Inequality Report 2018 is the most authoritative and up-to-date account of global trends in inequality. Researched, compiled, and written by a team of the world’s leading economists of inequality, it presents—with unrivaled clarity and depth—information and analysis that will be vital to policy makers and scholars everywhere.

Inequality has taken center stage in public debate as the wealthiest people in most parts of the world have seen their share of the economy soar relative to that of others, many of whom, especially in the West, have experienced stagnation. The resulting political and social pressures have posed harsh new challenges for governments and created a pressing demand for reliable data. The World Inequality Lab at the Paris School of Economics and the University of California, Berkeley, has answered this call by coordinating research into the latest trends in the accumulation and distribution of income and wealth on every continent. This inaugural report analyzes the Lab’s findings, which include data from major countries where information has traditionally been difficult to acquire, such as China, India, and Brazil. Among nations, inequality has been decreasing as traditionally poor countries’ economies have caught up with the West. The report shows, however, that inequality has been steadily deepening within almost every nation, though national trajectories vary, suggesting the importance of institutional and policy frameworks in shaping inequality.

World Inequality Report 2018 will be a key document for anyone concerned about one of the most imperative and contentious subjects in contemporary politics and economics.

Produced by a team of world-leading economists, this is the benchmark account of recent and historical trends in inequality.

World Inequality Report 2022 is the most authoritative and comprehensive account available of global trends in inequality. Researched, compiled, and written by a team of world-leading economists, the report builds on the pioneering edition of 2018 to provide policy makers and scholars everywhere up-to-date information about an ever broader range of countries and about forms of inequality that researchers have previously ignored or found hard to trace.

Over the past decade, inequality has taken center stage in public debate as the wealthiest people in most parts of the world have seen their share of the economy soar relative to that of others. The resulting political and social pressures have posed harsh new challenges for governments and created a pressing demand for reliable data. The World Inequality Lab, housed at the Paris School of Economics and the University of California, Berkeley, has answered this call by coordinating research into the latest trends in the accumulation and distribution of income and wealth on every continent. This new report not only extends the lab’s international reach but provides crucial new information about the history of inequality, gender inequality, environmental inequalities, and trends in international tax reform and redistribution.

World Inequality Report 2022 will be a key document for anyone concerned about one of the most imperative and contentious subjects in contemporary politics and economics.