The SAGE Encyclopedia of World Poverty

The SAGE Encyclopedia of World Poverty

Encyclopedias

Edited by: Mehmet Odekon

Abstract

The SAGE Encyclopedia of World Poverty, Second Edition addresses the persistence of poverty across the globe while updating and expanding the landmark work, Encyclopedia of World Poverty, originally published in 2006 prior to the economic calamities of 2008. For instance, while continued high rates of income inequality might be unsurprising in developing countries such as Mexico, the Organization of Economic Co-operation and Development (OECD) reported in May 2013 even countries with historically low levels of income inequality have experienced significant increases over the past decade, including Denmark, Sweden, and Germany. The U.N. and the World Bank also emphasize the persistent nature of the problem. It is not all bad news. In March 2013, the Guardian newspaper reported, “Some of the poorest people in the world ...

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  • Front Matter
  • Back Matter
    • Antipoverty Organizations
    • Causes of Poverty
    • Children and Poverty
    • Consumption and Poverty
    • Countries: Africa
    • Countries: Americas
    • Countries: Asia
    • Countries: Europe
    • Countries: Pacific
    • Economics of Poverty
    • Education and Poverty
    • Effects of Poverty
    • Health and Poverty
    • History of Poverty
    • Measurements and Definitions of Poverty
    • People
    • Politics and Poverty
    • Poverty Relief Initiatives
    • Religious and Secular Charities
    • Social Assistance
    • Sustainability and Poverty
    • Technology and Poverty
    • U.S. States and Territories
    • Women and Poverty
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      List of Articles

        A B C D E F G H I J K L M N O P Q

      Reader’s Guide

      About the Editor

      Mehmet Odekon is Professor of Economics and Tisch Family Distinguished Professor at Skidmore College in Saratoga Springs, New York. He received his undergraduate degree in economics from Bogazici University (formerly Robert College) in Istanbul, Turkey. He won a Turkish government scholarship to pursue graduate work in the United States and earned his Ph.D. in economics at the State University of New York, Albany. After working at Bogazici University and at the European Institute of Business Administration (INSEAD) in Fontainebleau, France, he joined Skidmore in 1982. Dr. Odekon’s research interests include the political economy of development and globalization and domestic and international poverty and income equality. He is the editor of the Encyclopedia of World Poverty (Sage, 2006), and he coedited Economic Liberalization and Labor Markets (Praeger, 1998), Political Economy of Turkish Liberalization (Lehigh University Press, 1991), and Liberalization and the Turkish Economy (Praeger, 1988). He authored several articles and Costs of Economic Liberalization in Turkey (Lehigh University Press, 2005). In these publications he analyzes the effects of the dominant world economic order on economically disadvantaged groups. Dr. Odekon co-curated an interdisciplinary exhibit at the Tang Teaching Museum, Skidmore College, titled “Classless Society” (November 2013–March 2014). The exhibition, along with its Web site and catalogue, explores the myth that the United States is a classless society. He is currently working on a project on worker-owned cooperatives in the United States. He is an avid supporter of the Liverpool Football Club.

      List of Contributors

      Anil Aba University of Utah

      Yusuf Abdulazeez Usmanu Danfodiyo University

      Biju Abraham Government College Kodanchery, Kozhikode, Kerala

      Angel R. Ackerman Independent Scholar

      Hazel Acosta Independent Scholar

      Lady Jane Acquah The University of Texas at Austin

      Samuel Ojima Adejoh University of Lagos

      Paula Lucía Aguilar University of Buenos Aires

      Patricia Agupusi Brown University

      Ujala Ahsan Independent Scholar

      Bree Akesson McGill University

      Kendra P. Alexander Northwestern University

      Alexander Allison Indiana University–Purdue University Fort Wayne

      Nayeli Berenice Perez Alvarado National Autonomous University of Mexico

      Chinedu Anthony Anene University of Bradford

      Isil Anil Middle East Technical University

      Kepa Artaraz University of Brighton

      H. S. Ashok Bangalore University

      Abdullah Aswat University of Cambridge

      Ahmet Atakisi Trakya University

      Jerry P. Ausburn Hamshire-Fannett Independent School District

      Rojhat B. Avsar Columbia College Chicago

      James Olabisi Ayodele Obafemi Awolowo University

      Şerif Onur Bahçecik Middle East Technical University

      Paola Ballón University of Oxford

      Sonali Chakravarti Banerjee University of Calcutta

      Behroz Baraghoshi Eastern Connecticut State University

      Justice Nyigmah Bawole University of Ghana Business School

      Harlan R. Beckley Shepherd Higher Education Consortium on Poverty

      Nisha Beharie McSilver Institute

      Vyjanti Beharry University of the West Indies at St. Augustine, Trinidad and Tobago

      Orly Benjamin Bar-Ilan University

      Montgomery R. Beyer University of Phoenix

      Haimanti Bhattacharya University of Utah

      Arundhati Bhattacharyya Bhairab Ganguly College, West Bengal State University

      Rabindranath Bhattacharyya University of Burdwan

      Magdalena Bielenia-Grajewska University of Gdansk

      Danielle J. Bird Independent Scholar

      Rebecca Bishop Independent Scholar

      Frank Bliss University of Hamburg

      Stefanie Bognitz Max Planck Institute for Social Anthropology

      Catherine E. Bolten University of Notre Dame

      Helen Bond Howard University School of Education

      Sarah E. Boslaugh Kennesaw State University

      Thomas D. Boston Georgia Institute of Technology

      Dina Bowman University of Melbourne

      Kristin Boza Independent Scholar

      Victoria M. Breting-Garcia Independent Scholar

      Susan Bridle-Fitzpatrick Tulane University

      Roxanne Brizan University of the West Indies at St. Augustine, Trinidad and Tobago

      Chad Broughton University of Chicago

      Justin Bunch Georgia Institute of Technology

      John R. Burch Jr. Campbellsville University

      Shanna L. Burke Simmons College

      Claudio Butticè Independent Scholar

      Sema Buz Hacettepe University

      Michael Calabrese Independent Scholar

      Sandra Calkins Max Planck Institute for Social Anthropology

      Pedro M. Cameselle Fordham University

      Al Campbell University of Utah

      Courtney J. Campbell Vanderbilt University

      Steven Campbell University of South Carolina, Lancaster

      James Park Canfield University of Cincinnati

      Mary Caplan University of Georgia

      John Cappucci University of Windsor

      Juliana Carlson University of Kansas

      Alma J. Carten New York University

      Aziz Çelik Kocaeli University

      Anusha Chaitanya Narmada Bachao Andolan

      T. R. Chandrasekhara National Law School of India University

      Roger Yap Chao Jr. City University of Hong Kong

      Mihika Chatterjee University of Oxford

      Saayan Chattopadhyay Baruipur College

      Yanan Chen Skidmore College

      Kingsley U. Chigbu The University of Texas at Arlington

      Eric K. M. Chong Hong Kong Institute of Education

      Masudul Alam Choudhury International Islamic University

      Anna Julia Cieslewska Jagiellonian University

      Kayelle Clarke The University of the West Indies at St. Augustine, Trinidad and Tobago

      Ian C. Clift Mayo Clinic

      Ryne Clos University of Notre Dame

      Mary Elizabeth Collins Boston University

      Monique Constance-Huggins Winthrop University

      Daniel G. Cooper Adler School of Professional Psychology

      Jessica Coretsi Indiana University–Purdue University Fort Wayne

      Justin Corfield Independent Scholar

      Nicole Corley University of Georgia

      Jessica Cortesi Indiana University-Purdue University Fort Wayne

      Hai-Anh H. Dang World Bank

      Adel Daoud University of Gothenberg

      Katrinell M. Davis University of Vermont

      Carla De Ycaza New York University

      Tiziana C. Dearing Boston College

      Tunde Decker Osun State University

      Mohammed Iqbal Degia Ministry of Foreign Affairs and Foreign Trade, Barbados

      Bronwen Densmore New York City College of Technology

      Ralitza Dimova University of Manchester

      Obediah Dodo Bindura University of Science Education

      Pete Dorey Cardiff University

      Amrita Duraiswamy Sri Ramachandra Medical College

      Steven N. Durlauf University of Wisconsin

      Amitava Krishna Dutt University of Notre Dame

      Jaroslav Dvorak Klaipėda University

      Amitabh Vikram Dwivedi Shri Mata Vaishno Devi University

      Carl Egner Chicago Public Schools

      Christine Erickson Indiana University–Purdue University Fort Wayne

      Michael Eshiemokhai University of Montreal

      Jodi Essey-Stapleton Independent Scholar

      Cristina Faludi Babeş-Bolyai University

      Raúl Fernández-Calienes St. Thomas University

      Rukshan Fernando Azusa Pacific University

      Cindy Ferraino Independent Scholar

      Marisa Fois University of Cagliari

      Olakunle Michael Folami University of Ulster

      Nadja Kurtovic Folic University of Novi Sad

      John Wilson Forje University of Yaounde II

      Izolda Fotiyeva Howard University

      Judith Fox University of Notre Dame

      Samuel Gabriel University of the West Indies at St. Augustine, Trinidad and Tobago

      Sarah B. Garlington Boston University

      Ruchi Singh Gaur Independent Scholar

      Asad Ghalib Independent Scholar

      Abhijit Ghosh University of Michigan

      Charles N. Giberti University of Cincinnati

      Kimberly-Ann Gittens-Baynes University of the West Indies at St. Augustine, Trinidad and Tobago

      Daniel P. Gitterman University of North Carolina at Chapel Hill

      Bren Gray Independent Scholar

      Matthew J. Gritter Angelo State University

      Ana Grondona University of Buenos Aires, CONICET-CCC

      Nizamulmulk Gunes University of Kocaeli

      Gaye Gungor Gediz University

      Vinayak Gunjal Independent Scholar

      Dipin Gupta Independent Scholar

      Jessica Anne Hammer Independent Scholar

      Joseph Hammond Independent Scholar

      Catherine Hawes Independent Scholar

      Thomas L. Head Edith Cowan University

      Glenn Edward Heath Independent Scholar

      Oliver Benjamin Hemmerle Stendahl University

      Magdalena Díaz Hernández University of Seville

      Andrea Hetling Rutgers, The State University of New Jersey

      Gretchen Hoge Rutgers, The State University of New Jersey

      Philip Young P. Hong Loyola University

      Leonard J. Huggins Independent Scholar

      Yameen Huq Georgia Institute of Technology

      Nashaat Hussein Misr International University

      Roukaya Ahmed Ibrahim University of Bristol

      Enrico Ille Ahfad University for Women

      Ana-Cristina Ionescu Independent Scholar

      Victor Manuel Isidro Luna National Autonomous University of Mexico

      Emel Istar Duzce University

      Sharmaine Tia Jackson University of California, Irvine

      Anupama Jacob Azusa Pacific University

      Frank Jacob Queensborough Community College, City University of New York

      Jaewoo Jang Stanford University

      Katarzyna Jarecka-Stępień Jagiellonian University

      Rana Jawad University of Bath

      Laura Johnson Rutgers, The State University of New Jersey

      Myungkook Joo Rutgers, The State University of New Jersey

      Ayanna Julien Independent Scholar

      Laurie E. Kaniarz Foods Resource Bank

      Iİlknur Karaaslan Independent Scholar

      Usman Ahmad Karofi Usmanu Danfodiyo University

      Christine Anne Kelly Fordham University

      Kyle Kelly Skidmore College

      Whitney Key Loyola University

      Santana Khanikar University of Delhi

      Chang-Gi Kim Korea National University of Transportation

      HaeJung Kim West Virginia University

      Yoon Mi Kim Kutztown University

      Njoki W. Kinyatti York College, City University of New York

      Courtney Kisat Southeast Missouri State University

      Kpoti Kitissou Skidmore College

      Jeremy Kleidosty University of Sharjah

      Elise Klein Australian National University

      Andrzej Klimczuk Warsaw School of Economics

      Magdalena Klimczuk-Kochańska Stanislaw Staszic College of Public Administration

      Anna Kotlarska-Michalska Adam Mickiewicz University

      Milena Krkljes University of Novi Sad

      Lynn C. Kronzek Independent Scholar

      Bill Kte’pi Independent Scholar

      Sharanya Kumar Independent Scholar

      Althea La Foucade University of the West Indies at St. Augustine, Trinidad and Tobago

      Tracy C. S. Lau Hong Kong Baptist University

      Christine Laptiste University of the West Indies at St. Augustine, Trinidad and Tobago

      Philipp Hieronymus Lepenies Institute for Advanced Sustainability Studies

      Yan Wing Leung Hong Kong Institute of Education

      Lia Levin Tel Aviv University and King’s College London

      Samuel I. Levy Colegio de la Frontera Sur (ECOSUR)

      Dan A. Lewis Northwestern University

      Hector Lindo-Fuentes Fordham University

      Marieme S. Lo University of Toronto

      Michael Loadenthal George Mason University

      Margaret Lombe Boston College

      Li-Ching Lyu National Taiwan Normal University

      Briony Patricia MacPhee Independent Scholar

      Lily Mafela University of Botswana

      James E. Mahon Washington and Lee University

      Kristen Lynn Majocha University of Pittsburgh at Johnstown

      Thomas N. Maloney University of Utah

      Shefali Manhas Jawaharlal Nehru University

      Jillian Marchant James Cook University

      Jay Marlowe University of Auckland

      Michelle Dawn Martin Independent Scholar

      Leemamol Mathew Bangalore University

      Hiroaki Matsuura University of Oxford

      Mandy M. McBroom Independent Scholar

      Brian P. McCall University of Michigan

      Philip McCallion State University of New York, Albany

      Julia McClure Harvard University

      Gordon C. McCord University of California, San Diego

      Annie McEwen Carleton University

      Stephen V. McGarity University of Georgia

      Mary McKay New York University Silver School of Social Work

      Ruth McRoy Boston College

      Patricia Meador Independent Scholar

      Catherine K. Medina University of Connecticut

      Kelly Melekis University of Vermont

      Rodrigo Meneses-Reyes Center for Research and Teaching in Economics

      Trudy Mercadal Florida Atlantic University

      Micaela Mercado New York University Silver School of Social Work

      Charmaine Metivier University of the West Indies at St. Augustine, Trinidad and Tobago

      Elizabeth Bissell Miller University of Missouri

      William J. Miller Flagler College

      Fazil Moradi Max Planck Institute for Social Anthropology

      Christian Morgner University of Leicester

      Marco Morini International University of Sarajevo

      Katie Moss Independent Scholar

      David Moxley University of Oklahoma

      Ng’ang’a Muchiri University of Miami

      Robert L. Muhlnickel Monroe Community College

      Gift Kim Mushariwa Independent Scholar

      E. Wairimu Mwangi Independent Scholar

      Todd Myers San Diego State University

      Larry Nackerud University of Georgia

      Shailen Nandy University of Bristol

      Tara Natarajan Saint Michael’s College

      April Wagnon Nelms University of North Georgia

      Stephen E. Nepa Temple University

      Bala Raju Nikku University of Science, Malaysia

      Jessica Nobile University of Georgia

      Alison N. Novak Temple University

      Chamunogwa Nyoni Bindura University of Science Education

      Julia Obinger University of Zurich

      Robin O’Brian Elmira College

      Michael O’Brien University of Auckland

      Mehmet Odekon Skidmore College

      James Bamidele Odunbaku Olabisi Onabanjo University

      David Okech University of Georgia

      Eyene Okpanachi University of Alberta

      Rasheed Akanji Okunola University of Ibadan

      Taiwo Olaiya Obafemi Awolowo University

      Samuel Ojo Oloruntoba University of South Africa

      Arzu Ozsoy Ozmen Kocaeli University

      Patit Paban Mishra Sambalpur University

      Dimitrios Pachis Eastern Connecticut State University

      Varsha Pandya Kutztown University

      Hong-Jae Park University of Auckland

      Sundramoorthy Pathmanathan University of Science, Malaysia

      William M. Peaster Independent Scholar

      Gianina Pellegrini Saybrook University

      Sony Pellissery National Law School of India University

      Wilson Amadeo Perez Latin American Social Sciences Institute

      Wilson Perez-Oviedo FLACSO, Ecuador

      Svetlana Perovic University of Montenegro

      David Petrasek University of Ottawa

      James E. Phelan Veterans Health Administration

      Marc Pilisuk Saybrook University and University of California

      Vijayan Pillai University of Texas at Arlington

      Yovanna Pineda University of Central Florida

      Judy L. Postmus Rutgers, The State University of New Jersey

      Steven Pressman Monmouth University

      M. C. L. Provost University of Toronto

      William R. Pruitt Elmira College

      Elizabeth Rholetter Purdy Independent Scholar

      Agnieszka Ewa Pyrzyk Association Centre for Intercultural Initiatives

      Francisco Quiroz Chueca San Marcos National University

      Carolyn Raider Independent Scholar

      Sana Rasul Rais Independent Scholar

      Lauren Ann Ricciardelli University of Georgia

      Carter Ringle Indiana University–Purdue University Fort Wayne

      Brooks B. Robinson Independent Scholar

      Cara Robinson Tennesee State University

      Andreé Robinson-Neal Independent Scholar

      Gisela Robles University of Oxford

      Jaroslaw Richard Romaniuk Case Western Reserve University

      David W. Rothwell McGill University

      Candice Rowser Kingsborough Community College, City University of New York

      Assel Rustemova Tutumlu Gediz University-Izmir

      Mihai Stelian Rusu Babeş-Bolyai University

      Karl Heinz Gaudry Sada University of Freiburg

      Adam Salifu University of Western Sydney

      Wylma C. Samaranayake-Robinson University of Phoenix

      Emily Sanders Garcia University of Vermont

      Juan E. Santarcángelo National Scientific and Technical Research Council

      Doğa Başar Sariipek Kocaeli University

      Jon Daniel Schmid Georgia Institute of Technology

      Sanford F. Schram Hunter College, CUNY

      Ulrike Schuerkens School for the Advanced Studies in the Social Sciences

      Michaela Schulze University of Kassel

      Ewan Scott University of the West Indies at St. Augustine, Trinidad and Tobago

      John C. Scott University of North Carolina at Chapel Hill

      Tamara Seiffer Independent Scholar

      Abdülkadir Şenkal Kocaeli University

      Bodagalu Seshadri Independent Scholar

      Erin Sexton Georgia Institute of Technology

      Elena V. Shabliy Tulane University

      Ritesh Shah University of Auckland

      Syed Faisal Hyder Shah University of Sindh

      Irina Shaorshadze University of Cambridge

      Yasoda Sharma Kutztown University

      Debbie Sharnak University of Wisconsin–Madison

      Richard Sheldon University of Bristol

      Milton Shook Independent Scholar

      Milan Shreshta Arizona State University

      Sebastian Silva-Leander Oxford Poverty and Human Development Initative, University of Oxford

      Anusha Singh Delhi University

      R. P. Singh University of Lucknow

      Atul Singhal Independent Scholar

      Aakanksha Sinha Boston College

      Pravin Sinha Indian Industrial Relations Association

      Diana L. Skelton All Together in Dignity/ATD Fourth World

      Curtis Skinner Columbia University

      Paul Sloan Independent Scholar

      Tanya Sloan Independent Scholar

      Samia Solaiman University of Georgia

      Ludovic A. Sourdot Texas Woman’s University

      Frank Sowa Institute for Employment Research

      Robert Spires Valdosta State University

      Sarah Stanford-McIntyre College of William & Mary

      Richard A. Stein New York University School of Medicine

      Jakub Stępień Jagiellonian University

      Steven Stoll Fordham University

      Tara Stone Independent Scholar

      John F. Struth Nonotuck Resource Associates, Inc.

      Amanda Sturgill Elon University

      Vakur Sumer Selçuk University

      Olatunde Olaseni Taiwo Olabisi Onabanjo University

      Ali Akbar Tajmazinani Allameh Tabataba’i University

      David Takeuchi Boston College

      Karl Theodore University of the West Indies at St. Augustine, Trinidad and Tobago

      Rebecca Leela Thomas University of Connecticut

      Amanda Rowe Tillotson University of Michigan

      Alissa Tolstokorova Independent Scholar

      Trasie A. Topple University of Georgia

      Quyen Tran All Together in Dignity/ATD Fourth World

      Silke Trommer Murdoch University

      Amy Trostle Northern Kentucky University

      Paige Mayleen True California State University, Monterey Bay

      Feyza Turgay Kocaeli University

      Rhea U. Vallente Independent Scholar

      Annette L. Varcoe Independent Scholar

      Tanadej Vechsuruck University of Utah

      Eduardo Torres Veytia National Autonomous University of Mexico

      Udaya R. Wagle Western Michigan University

      Robert Walker New York University Silver School of Social Work

      John Walsh Shinawatra University

      Cheng-Tong Lir Wang University of California, Irvine

      Kate Y. T. Wang National Taiwan Normal University

      Judith Ann Warner Texas A&M International University

      Andrew J. Waskey Dalton State College

      Thomas D. Watts The University of Texas at Arlington

      Richard Weiner Indiana University–Purdue University Fort Wayne

      Mary Rita Weller Kutztown University

      Charles K. Wilber University of Notre Dame

      Renee Wilson-Simmons National Center for Children in Poverty

      Christopher Wimer Columbia University

      Hung Wong Chinese University of Hong Kong

      Mathew Y. H. Wong University of Hong Kong

      Martin Woodside Rutgers University-Camden

      Darrin E. Wright Clark Atlanta University

      Paul Wright California State University, Monterey Bay

      Wang Xianhua Sichuan University

      Wei Yang University of Kent

      Komali Yenneti University of Birmingham

      Emma Yorra Independent Scholar

      Waqar H. Zaidi Lahore University of Management Science

      Michael Zakour West Virginia University

      Stephanie Zehnle University of Kassel

      Scott Zimmer Independent Scholar

      Introduction

      Eight years have passed since the publication of the first edition of the SAGE Encyclopedia of World Poverty. They were an unsettling eight years. Events included the global financial crisis triggered by the collapse of the housing market in the United States; continuing wars in Iraq and Afghanistan; several high-impact natural disasters like the Sichuan earthquake in China; the global food crisis of 2007 and 2008; the Arab Spring; economic crises in Greece, Italy, and Spain; and outbreaks of diseases like the recent Ebola and dengue fever outbreaks in west Africa. Nonetheless, in the midst of these adverse developments, the United Nations Development Programme came up with news that gives a glimmer of hope: the first Millennium Development Goal of halving the number of people living on less than $1.25 a day has already been met. The joy spread among policymakers, politicians, media, and international organizations was a bit overshadowed, though, by the fact that there are still about 1 billion people living in poverty in the world, mostly concentrated in sub-Saharan Africa, south Asia, and southeast Asia.

      It is hard to justify poverty given the economic affluence many enjoy. According to the Central Intelligence Agency (CIA) World Factbook, the gross world product in 2013 calculated at purchasing power parity (PPP) is a staggering $87.25 trillion. If this sum were evenly distributed, per capita income would be $13,100 (in PPP) in contrast to actual per capita incomes of about $700 in Burundi and the Democratic Republic of Congo. Yes, there is significant progress in poverty reduction, but poverty is still a dire problem.

      The distribution of economic gains among different sectors of the world economy is strongly tied to the persistence of poverty in developing countries. In 2013, the world output generated in industrial activity was 31 percent, with 23 percent of the total labor force employed in industry. On the other hand, the world output generated in agriculture was 6 percent of the total, with 35 percent of the total labor force employed in agriculture. This means less income for more workers in agriculture. It is not surprising that the global average Gini index is as high as 38.5 out of 100.

      Solutions to global poverty come with a high price tag. According to the Millennium Project, headed by Jeffrey Sachs, the estimated gap between official development assistance commitments and what is needed to meet Millennium Development Goals in 2014 is estimated to be $74 billion. The problem is exacerbated by the fact that although 22 industrial countries committed about 0.7 percent of their national incomes to Official Development Assistance to developing countries, with the exception of the Scandinavian countries, they did not meet their commitments.

      In the Western world, the interpretation of the successful reduction in the number of people living on less than $1.25 a day has emphasized the positive impact of economic liberalization and globalization on developing countries. The benefits of free markets and free trade policies, it is argued, finally trickled down and helped lift low-income groups out of poverty. It should be recognized, however, that while some countries (like India and China, for example) may have benefitted from liberalization and globalization, this paradigm is not the universal elixir to poverty. In other countries, especially in Latin and Central America, antipoverty programs outside this paradigm have had significant success. For example, in Bolivia, Brazil, Venezuela, Ecuador, and Nicaragua, poverty reduction was achieved through initiatives based on economic democracy; on the rights of indigenous people, women, and children; on bottom-up democratic reorganization; on environmental sustainability; on people’s ownership of natural resources; and on income redistribution schemes.

      The So-Called Great Recession

      Industrial countries have been hit hard by the 2007–08 financial crisis. The so-called Great Recession it triggered in the United States spread to the rest of the industrialized Organisation of Economic Co-operation and Development (OECD) countries. The toll of the recession came in the form of decline in real output, increase in unemployment, increase in income inequality and poverty, and pressures on the budget. The drop in real gross domestic product (GDP) from 2008 to 2010 led to jumps in the unemployment rate, which soared to 9.9 percent in December 2009 in the United States. With the exception of Germany, other industrial OECD countries experienced similar increase in their unemployment rates. The United States succeeded in reducing its unemployment rate to 6.7 percent by mid-2014, while most industrial countries, especially in Europe, still have high rates. Some fervently argue that the persistent unemployment in Europe is the result of their social welfare states and the existing relatively high unemployment benefits that take away the incentive to look for jobs. The claim may or may not be true but the unemployed definitely find it easier to sustain a minimum standard of living thanks to the unemployment benefits and other social expenditures.

      In the United States, high unemployment has also been the result of policy choices. The political impasse between the presidential administration and Congress took its toll, especially on people in low-income groups. In a time of urgent need for fiscal stimulation, budgetary considerations were given priority and fiscal conservatism led to policies not directed to poverty. Similar policies of fiscal contraction are seen in other industrial countries as well. Reactionary popular movements—like Occupy Wall Street in the United States and in numerous other countries—that protest public policies favoring financial sectors and higher income groups, however, have increased public awareness of income inequality and poverty. It is interesting that the first time in a long time in the United States, “socioeconomic class” has become part of public discourse. Stories of the unemployed and the economically struggling appear in the media on a regular basis. Indeed, among industrialized countries the United States today has the highest poverty rate (especially when it comes to children), a relatively low real wage structure, and one of the lowest social expenditure–GDP ratio to fight poverty, despite the fact that the per capita income in 2014 dollars in the United States is over $50,000.

      Slowly Changing Tide

      Occupy movements in dozens of countries can indeed be perceived as displays of discontent with the new global class structure rooted in globalization and economic liberalization. Decades of policies favoring the owners of capital at the expense of labor—especially unskilled, low productivity labor—were bound to create discontent. Policies making the rich richer and counting on its trickle down effects to reach everyone else had limits. The 2007–08 global financial crisis is one of the several failures of globalization and liberalization. Similar crises in Poland, Mexico, Argentina, Turkey, and other countries foreshadowed the 2008 crisis but were perceived as country-specific instead of systematic. Moreover, policy choices by industrial countries and emerging market economies to re-establish the financial order that led to the crisis in the first place were at the surface puzzling, but make sense in the context of a power structure that has been put into place over decades. Economic power leads to political power, and if unchecked, tends to perpetuate itself. Occupy movements raised public awareness concerning the power of the economic and political elite in affecting the lives of people in the street across the globe. But how have these issues been handled by policy makers?

      In the United States, the policy response to popular protests targeted the middle class. In 2009, Vice President Joseph Biden formed a group of experts to define “middle class” in the United States. The task force based its definition on the American Dream, which rested on the ownership of a two-bedroom house with a two-car garage, health care coverage for four, an annual two-week vacation for four, college education for two children, and sufficient savings for retirement. This set the income bracket for the middle class. It was the first official recognition that the United States is not really a classless society, as the popular myth told.

      Similarly, the International Monetary Fund (IMF), World Bank, and other international economic institutions have responded to a climate of awareness about income inequality and poverty. The tide is slowly changing. The IMF is now recommending moderate programs to assist the poor, recommendations that would not have been heard of in the 1990s or in the first decade of the 21st century. Similarly, more and more aggressive antipoverty policies are finding their way into World Bank recommendations. A way to consider this shift is that authorities are starting to pay attention to the demand-side of the economy. If people do not have the purchasing power and favorably affect aggregate demand, ultimately all suffer—rich and poor. Recent economic history supports this picture. Perhaps people are moving toward a middle-class or classless society, and the 21st century will consist of a middle-class era, like the working-class era of the 1960s, and the capitalist-class era that took hold in the post-1980s.

      Next Steps

      The progress made toward meeting the Millennium Development Goals also presents hope for the next step—the Sustainable Millennium Development Goals (SDG). They are 17 ambitious goals that build on the MDGs and that further aim to create a world where all people can lead lives they see as worth living. The SMGs are to end poverty in all its forms everywhere; end hunger, achieve food security and improved nutrition, and promote sustainable agriculture; ensure healthy lives and promote well-being for all at all ages; ensure inclusive and equitable quality education and promote life-long learning opportunities for all; achieve gender equality and empower all women and girls; ensure availability and sustainable management of water and sanitation for all; ensure access to affordable, reliable, sustainable and modern energy for all; promote sustained, inclusive, and sustainable economic growth, full and productive employment and decent work for all, build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation; reduce inequality within and among countries; make cities and human settlements inclusive, safe, resilient and sustainable; ensure sustainable consumption and production patterns; take urgent action to combat climate change and its impacts; conserve and sustainably use the oceans, seas, and marine resources for sustainable development; protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss; promote peaceful and inclusive societies for sustainable development; provide access to justice for all and build effective, accountable and inclusive institutions at all levels; and strengthen the means of implementation and revitalize the global partnership for sustainable development. Wouldn’t you love to live in such a world? I would.

      This Edition

      The second edition of the SAGE Encyclopedia of World Poverty, expanded from three volumes to five, with one full volume of data and primary documents as of 2014, contains over 900 articles. About 175 of the articles are new, original additions, and those from the first edition have been updated. New categories explicitly incorporated into this second edition are health and poverty, education and poverty, environmental sustainability and poverty, technology and poverty, and various different measurements of poverty. More than 100 independent and affiliated scholars have contributed to it, including many new contributors since the previous edition. These scholars are passionately concerned about poverty and related issues, and it was a privilege to read and comment on their contributions. The articles are supplemented with an updated Resource Guide and a Chronology of World Poverty, as well as with updated economic data on U.S. states and individual countries. It is an authoritative and thoroughly investigated source on poverty. It is intended to be a reference source for students, scholars, and anyone ready to engage in the complicated but critical issues related to poverty.

      I hope you find it useful in your studies and research projects.

      Mehmet Odekon

      Skidmore College

      Chronology

      ca. 1790–50b.c.e.

      In Babylon, the Code of Hammurabi includes laws specifying the protection of widows and orphans.

      ca. 500–400b.c.e.

      The Talmud specifies how charitable funds should be collected and distributed in Jewish communities.

      622c.e.

      The Qur’an, considered by Muslims to be the divine revelation of God, includes practicing generous charity as one of the five duties of Muslims.

      1349

      In England, the Statute of Labourers sets restrictions on the movement of workers and establishes the principal of treating the poor as criminals.

      1536

      In England, the Act for the Punishment of Sturdy Vagabonds and Beggars establishes more severe penalties for begging and requires local communities to provide poor relief.

      1601

      In England, the Poor Law creates categories of poor citizens, including the able-bodied, those with disabilities or who are elderly, and children.

      1619

      The first African slaves arrive in the United States.

      1624

      In colonial America, Rhode Island enacts a Poor Law requiring the state to appoint an official to provide support for the impoverished.

      1657

      The Scots Charitable Society is founded in Boston, Massachusetts, making it one of the first voluntary societies to aid the poor.

      1662

      In England, the Law of Settlement and Renewal introduces residency requirements in order for a person to receive poor relief and allows authorities to expel those believed likely to become poor and thus require future relief.

      1692

      In colonial America, Massachusetts establishes a system of child indenture for poor children, with the justification that it would benefit the children to live in a formal family structure.

      1697

      In England, the Workhouse Test Act requires unemployed people to work before being granted assistance.

      1729

      The Ursuline Sisters, a Catholic order in New Orleans, Louisiana, establishes an orphanage to care for women and children who survived a smallpox epidemic and Indian hostilities.

      1773

      The first public mental hospital in the United States is established in Virginia.

      1782

      In England, passage of the Gilbert Act forces the closure of man workhouses, and poor individuals are provided with relief while allowed to continue living in their own homes.

      1789

      The U.S. Constitution includes the famous “three fifths of a man clause,” specifying that for the purpose of calculating a state’s population, a slave counted as 3/5 of a white person.

      1790

      In the United States, the first public state orphanage is created in Charleston, South Carolina.

      1793

      Eli Whitney invents the cotton gin, setting the stage for greatly expanded cotton production in the U.S. south, and thus the impetus to create cotton plantations where the primary labor was supplied by African American slaves.

      1798

      The U.S. Public Health Service is established in an attempt to control the introduction of diseases to east coast cities by the shipping industry.

      1813

      The British reformer Elizabeth Gurney Fry visits London’s Newgate Prison, where she is shocked by the conditions in which women and children are housed.

      1817

      The first free school in the United States for the deaf and hard of hearing, Gallaudet University, is founded in Connecticut.

      1824

      In the United States, the House of Refuge, an institution created to deal with juvenile law-breakers, is founded in New York.

      1834

      In England, the Poor Law Amendment Act of 1834 requires poor individuals seeking relief to live and work in workhouses; as well as the poor, residents of workhouses included orphaned and abandoned children, persons with disability or illness, elderly people, and unmarried mothers.

      1836

      In upper Canada, the Charity Aid Act assigns responsibility for caring for the poor to churches and charities rather than the government.

      1841

      The American reformer Dorothea Dix investigates and publicizes poor conditions for people living in institutions for the insane, leading to the establishment of many state hospitals, intended to provide humane care for the mentally ill.

      1843

      The New York Association for Improving the Condition of the Poor, which later becomes the Community Service Society after merging with the Charity Organization Society, is founded by Robert Hartley and colleagues.

      1844

      In England, the first Young Men’s Christian Association (YMCA) is established in London by George Williams. Seven years later, the first YMCA is founded in North America in Montreal, Canada.

      1853

      The Reverend Charles Loring Brace founds the Children’s Aid Society in New York, making it the first child placement agency not affiliated with an institutional program.

      1860

      In the United States, the state of South Carolina becomes the first to secede from the country on December 20; by May 1861, 10 more states had seceded.

      1861

      In April, the U.S. Civil War begins after the firing on Fort Sumter, South Carolina, by Confederate (Southern) troops.

      1862

      In the Northern U.S. states, Freedmen’s Aid Societies are established to send teachers and material goods to former slaves in the South.

      1862

      U.S. president Abraham Lincoln issues the Emancipation Proclamation, freeing slaves in the states that seceded from the United States.

      1865

      Slavery is abolished in the United States by the Thirteenth Amendment to the Constitution.

      1865

      The Freedmen’s Bureau is founded by the U.S. government with the assistance of private organizations, in order to provide food, clothing, and shelter for feed slaves, to provide education, and to protect the rights of freedmen.

      1866

      In the United States, citizenship is redefined in the United States by the Fourteenth Amendment to the Constitution; among the changes are defining as a citizen anyone other than an American Indian (Native American) who was born in the United States, as well as anyone who was naturalized as a citizen.

      1867

      In Canada, the British North America Act assigns provinces the responsibility for caring for the poor.

      1868

      In Massachusetts, the Board of State Charities begins to pay private families and individuals to care for orphans boarded in their homes.

      1872

      Charles Loring Brace publishes The Dangerous Classes of New York and Twenty Years Work Among Them, informing the larger public of the difficult conditions under which many poor people and immigrants are living.

      1877

      In Buffalo, New York, the first Charity Organization Society in the United States is founded. The practices established by the society include investigation of applicants, use of a central registration system, cooperation among relief agencies, and the use of volunteers to visit the poor and offer advice to them.

      1881

      In the United States, Booker T. Washington founds the Tuskegee Normal and Industrial Institute to provide education and training to African Americans and to increase their economic independence and self-respect.

      1883

      The “Jim Crow” era in the United States begins after the U.S. Supreme Court rules that the Fourteenth Amendment does not apply to privately owned facilities such as railroads, restaurants, and hotels.

      1883

      The British artist and teacher Octavia Hill publishes The Homes of the London Poor, helping to publicize the poor conditions under which many people live and supporting the push for housing reform.

      1884

      In Germany, Chancellor Otto von Bismarck inaugurates a series of social welfare programs, including insurance for sickness, accidents, and old age.

      1886

      The first settlement house in the United States, the Neighborhood Guild, is created in New York City; it is now known as the University Settlement.

      1889

      Jane Addams and Ellen Gates Starr open Hull House in Chicago, a settlement house providing education and other services to the poor. In 1921, Jane Addams is awarded the Nobel Peace Prize for her work.

      1890

      The photographer and journalist Jacob Riis publishes How the Other Half Lives: Studies Among the Tenements of New York, informing the general public of the severe conditions under which many of their fellow citizens live.

      1893

      The Nurses Settlement is founded in New York City by Lillian Wald; it later became the Henry Street Settlement (1895) and ultimately expands to the Visiting Nurse Service of New York, which provides in-home nursing care to the poor of New York City.

      1898

      In New York City, the first school to train social workers is established as the New York School of Philanthropy, later the Columbia University School of Social Work.

      1902

      In the United States, Maryland creates the first Workmen’s Compensation Law, but it is overturned in 1904 as unconstitutional.

      1909

      In the United States, the National Association for the Advancement of Colored People (NAACP) is founded by a group of concerned African American and white citizens.

      1909

      In England, the Royal Poor Law Commission recommends modifying the existing Poor Law by making local governments responsible for the provision of services.

      1910

      Fisk University in Nashville, Tennessee, creates the first training program for African American social workers.

      1910

      Former U.S. president Theodore Roosevelt introduces the concept of the “New Nationalism” in a speech delivered in Kansas; this concept includes strong support for social justice and the importance of the government protecting human rights as well as property rights, and includes practical proposals to create a national health service, an inheritance tax, social insurance for the elderly and disabled, and labor protections, including the eight-hour work day.

      1912

      The U.S. Children’s Bureau is created as the first federal agency focusing exclusively on children; the first head of the bureau is Julia Lathrop.

      1913

      The American social theorist and physician Isaac M. Rubinow publishes Social Insurance, arguing for the creation of a social insurance system covering, among other things, unemployment, illness, old age, and disability.

      1916

      In the United States, the Child Labor Act forbids interstate commerce of goods manufactured by child labor; however, the Act is overturned as unconstitutional in 1918 by the U.S. Supreme Court.

      1917

      The American reformer Mary Ellen Richmond publishes Social Diagnosis, helping to establish casework as a method used in social services, as well as the practice of studying the influence of the environment on poverty and social exclusion.

      1919–20

      In the United States, Attorney General A. Mitchell Palmer leads a movement to round up people believed to be radicals; many are immigrants who have not become citizens and are deported. Today, the Palmer Raids are attributed in part to fear of communism in the United States following the Russian Revolution.

      1920

      The largest and oldest membership-based child welfare organization in the United States, the Child Welfare League, is founded in New York City.

      1920

      In Canada, the Mothers Allowance provides cash assistance to women in Toronto who meet a series of qualifications, including being widowed, being British subjects, and having at least two children.

      1920

      The American Civil Liberties Union is founded, in part in response to the Palmer Raids.

      1921

      The first federally funded social welfare legislation in the United States, the Sheppard-Towner Maternity and Infancy Act, provides federal funds for prenatal and child health care, midwife training, and visiting nurses for pregnant women and new mothers.

      1927

      In Canada, the Old Age Pensions Act creates a means-tested social security pension program for citizens age 70 and older.

      1933–36

      In the United States, a number of federal government programs are created to provide ordinary Americans with relief from the consequences of the Great Depression; these programs, collectively called the New Deal, include the Public Works Administration, the Civilian Conservation Corps, the Rural Electrification Administration, the Tennessee Valley Authority, and the National Recovery Administration.

      1935

      In the United States, the Social Security Act creates a system of old-age pensions for workers and their spouses and children; benefits for the pensioners are funded by a tax paid by those currently employed.

      1939

      In the United States, the federal Food Stamp Program is created to distribute agricultural surpluses and provide nutrition to the poor; in the 21st century the program is known as the Supplemental Nutrition Assistance Program (SNAP).

      1942

      In the United States, the Lanham Act provides federal matching grants to help fund day care centers for working mothers as a response to the many women joining the work force to aid the war effort.

      1944

      In the United States, the G.I. Bill (formally the Servicemen’s Readjustment Act) provides many benefits to those who have served in the American military, including home and business loans, adjustment allowances, and making financial aid available to those who wish to attend college.

      1944

      The Bretton Woods system, created by representatives from the Allied Nations, creates the International Monetary Fund (IMF) and the International Bank for Reconstruction and Development, as well as regulations for international financial transactions, most notably the requirement to tie the value of national currencies to the value of the U.S. dollar.

      1946

      The World Bank begins operations, with its first priority being to loan money to European countries to aid in reconstruction following World War II.

      1946

      In Great Britain, the National Health Service is created.

      1954

      In the Boston suburb of Brookline, Massachusetts, the first urban halfway house for mental patients, Rutland Corner House, is created.

      1960

      At the U.S. Democratic National Convention, John F. Kennedy (later elected president) refers to a “New Frontier,” which is realized during his administration by programs such as increases in unemployment benefits, the minimum wage, and social security benefits, public works programs, and federal aid to cities and states for transportation and other projects.

      1961

      The white American journalist and civil rights activist John Howard Griffin publishes Black Like Me, recounting his experiences living as a black man (aided by makeup) in the U.S. south.

      1961

      In the United States, the Juvenile Delinquency and Youth Offenses Control Act acknowledges that poverty and other social conditions may contribute to youth crime and provides funds for antidelinquency programs in cities.

      1962

      In the United States, the Manpower Development and Training Act provides funds to train displaced and unemployed workers for new jobs.

      1964

      U.S. president Lyndon Johnson declares a War on Poverty as part of his concept of a Great Society program, a series of social interventions intended to diminish or eradicate poverty and related social ills such as racial discrimination. The Economic Opportunity Act creates the Office of Economic Opportunity and leads to the development of many antipoverty and social programs, including Volunteers in Service to America (VISTA), the Job Corps, and the Neighborhood Youth Corps.

      1965

      In the United States, the Administration for Children and Families, later the Department of Health and Human Services, is established.

      1965

      In the United States, Medicare and Medicaid are created as part of Title XIX of the Social Security Act. Medicare is a federal program providing funding for medical care for people over 65, the disabled, and people with renal failure, while Medicaid provides funding for medical care for the poor.

      1966

      The Canada Assistance Plan introduces a system of cost sharing for social and welfare services between the national government and the provinces.

      1969

      U.S. president Richard Nixon proposes something resembling a guaranteed annual income as part of his Family Assistance Plan; it is passed by the House of Representatives but not by the Senate and is withdrawn several years later.

      1972

      In the United States, the Supplemental Security Income (SSI) program is created to provide financial support to the elderly, blind, and disabled.

      1986

      In the United States, undocumented migrants who meet certain conditions (most importantly, having been in the United States continuously prior to January 1, 1982) are granted temporary resident status through the Immigration Reform and Control Act, thus gaining the legal right to work in the United States.

      1988

      In the United States, the Family Support Act provides additional benefits to families receiving Aid to Families with Dependent Children (welfare), including education and training programs and improved support for collecting child support payments.

      1995

      The World Trade Organization (WTO), headquartered in Switzerland, is created to supervise international trade, replacing the General Agreement on Tariffs and Trade (GATT) that had been in force since 1948.

      1995

      The Ontario Works program is inaugurated in Canada, requiring the poor in Ontario to work, be in a training program, or work as a volunteer in order to receive public assistance.

      1996

      In the United States, President Bill Clinton signs the Personal Responsibility and Work Opportunity Act of 1996, setting time limits on how long an individual could receive welfare payments and requiring most to participate in work training. As of July 5, 1997, more than a million fewer people are on the welfare rolls, a change that Clinton claims as evidence that the new legislation is effective.

      1997

      In May, the administration of U.S. president Bill Clinton announces a decrease in welfare caseloads of 20 percent nationally. Various explanations are offered for the drop, including a strong economy with many available jobs, innovation by the states thanks to a federal waivers program, and the earned income tax credit (EITC), which provides money to employed heads of families below a certain income level, thus presumably encouraging their work incentive.

      1999

      The third conference of the World Trade Organization, held in Seattle, Washington, is disrupted by widespread street protests and forced to end earlier than planned, drawing world attention to opposition to the organization.

      2001

      The American journalist Barbara Ehrenreich publishes Nickel and Dimed: On (Not) Getting By in America, describing her difficulties in making ends meet while working a series of low-wage jobs.

      2005

      A report by the European Union (EU) finds that, overall, about 16 percent of the EU population are at risk for poverty, with higher risks for children under 18 years and people age 65 and older (19 percent each). There is also disparity in the poverty rate among countries, with rates of more than 20 percent in Lithuania, Ireland, Poland, Greece, Spain, and Portugal.

      2007

      A report by the Pew Research Center shows that U.S. support for the social safety net has increased substantially since 1994. For instance in 2007, 54 percent of those surveyed said the government should help more poor people, even at the cost of increasing the national debt, as compared with 41 percent who agreed with that statement in 1994.

      2008

      Ontario becomes the first Canadian province to set reduction targets for poverty and the second (after Quebec) to require annual reporting on the success of poverty reduction measures.

      2011

      Census data reveals that Hispanics have the highest rate of poverty among the largest racial ethnic groups in the United States. In 2010, 28.2 percent of American Hispanics were classified as poor by the Supplemental Poverty Measure, as compared to an overall rate of 16.0 percent, 25.4 percent for blacks, 16.7 percent for Asians, and 11.1 percent for whites.

      2012

      A report released by the Office for National Statistics in the United Kingdom reveals that 17.1 percent of the population are at risk for poverty (meaning they have a disposable income of less than 60 percent of the national median), a slightly higher percentage than for the European Union as a whole (16.4 percent).

      2012

      Canada releases a Poverty Trends Scorecard revealing that in 2010, 9.0 percent of Canadians were poor, but that some groups experienced much higher poverty levels, including unattached individuals (26.9 percent), single-parent families (18.7 percent), recent immigrants (17.6 percent), people with disabilities (13.6 percent), and Aboriginal peoples living off reserves (15.2 percent).

      2013

      In October, the Children’s Society releases a study showing that over half (53 percent) of more than 3 million poor children in the United Kingdom reported that their home was insufficiently heated in the past winter, and over three quarters (76 percent) said they often worried about their family’s finances.

      2014

      On January 13, the Pew Research Center releases a report showing that although the poverty rate in the United States has fallen since President Lyndon Johnson declared the War on Poverty in 1964, from 19 percent in 1964 to 15 percent in 2012 (based on census data), many troubling disparities remain. For instance, in 2012, over half (57 percent) of poor Americans were of working age (age 18–64), as compared to 41.7 percent in 1959.

      2014

      In May, the U.S. Census Bureau releases a report showing that in 2012, 14.7 million Americans were near poor, meaning their family income was 100 to 125 percent of the official poverty threshold.

      2014

      On May 6, the Organisation for Economic Co-operation and Development (OECD) releases its Factbook 2014, revealing that the average poverty rate for OECD countries was about 11 percent, but with wide disparities among countries; the poverty rate was over 20 percent in Israel and Mexico and below 7 percent in Denmark, Iceland, and the Czech Republic. The Factbook also revealed that poverty rates rose an average of 1.5 percent across the OECD from the 1990s to the 2010s.

      2014

      On June 13, the Pew Research Center releases a report showing that the poverty rate for American Indians and Alaska Natives is 26 percent, as compared to 11 percent for white Americans, and that Native Americans and Alaska Natives are much less likely than white Americans to be a high school dropout (11 percent compared to 5 percent) and less likely to hold at least a bachelor’s degree (17 percent as compared to 33 percent).

      2014

      On July 28, London mayor Boris Johnson announces that he will not act to counter the trend of separate entrances for richer and poorer tenants in buildings that offer both affordable and high-end housing.

      2014

      In August, a report from the Health and Social Care Information Centre in the United Kingdom reveals that diseases associated with poverty and the Victorian era, such as malnutrition, tuberculosis, and measles, are becoming more prevalent in Britain. Sarah E. BoslaughKeenesaw State University

    • Glossary

      Absolute poverty:

      Poverty so extreme that an individual or family cannot meet their daily needs for food, clothing, and shelter.

      Administrative poverty:

      A category of poverty created by the government, indicating groups of people who are eligible for public assistance (e.g., single mothers, disabled people, low-income households).

      Advocacy NGO:

      A nongovernmental organization that seeks to influence policies and practices in support of a particular cause, rather than focusing on executing particular programs.

      Agency explanations for poverty:

      Explanations that attribute poverty to the failure of the government or to particular agencies to alleviate or prevent poverty. Some scholars do not consider agency explanations to be true explanations of poverty because they do not take into account societal and other factors outside the agencies or government involved.

      Amenities:

      Resources necessary for daily living. Expectations for amenities differ by country and income level, but in the context of housing, examples of amenities could include running water, bathing facilities, heating and cooling systems, and wastewater disposal systems.

      Area deprivation:

      A term with several meanings, depending on the context. One definition simply refers to the presence of a large number of poor people in a given area. A second definition includes the suggestion that a concentration of poor people in an area will create additional disadvantage (e.g., due to prejudice against people from areas known to be poor). A third definition is based on the lack of facilities (e.g., roads, schools) and/or the presence of pollution in an area.

      Asset Vulnerability Framework:

      A method of analysis developed by Caroline Moser that links assets and vulnerability to explain how people cope and how they move in and out of poverty.

      Axiom of Monotonicity:

      A test of poverty measures proposed by the Indian economist Amartya Sen requiring that if the income of someone below the poverty line decreases, the poverty measure must increase.

      Axiom of Transfers:

      A test of poverty measures proposed by the Indian economist Amartya Sen requiring that if there is a transfer from a person below the poverty line to someone better off, the poverty measure must increase.

      Basic income:

      A method of reliving poverty by providing payments to all households or individuals to provide a minimum income; under a basic income scheme, there are no conditions or qualifications placed on receipt of these payments other than age and family status.

      Basic needs:

      Minimum requirements and essential services required by an individual in order to live an adequate life; although the specific designation of basic needs will vary according to the economic and social development of a country, items often included as minimum requirements include food, clothing, shelter, and basic household furniture, while essential services often include potable water, sanitary facilities, transportation, and health care.

      Benchmarking:

      A method of judging the performance or effectiveness of an agency, industrial sector, and so on, by comparing it to the performance of a similar entity.

      Beveridge Report:

      A 1942 report produced by a committee chaired by Sir William Beveridge that created the foundation of the contemporary British welfare state. The Beveridge Report specified six principles of social insurance: comprehensive coverage, classes of contributors (workers, the self-employed, pensioners, etc.), flat-rate benefits, flat-rate contributions, adequate benefit levels, and national administration.

      BIA:

      Behavioral Incidence Analysis, a method of analyzing who benefits from services and how much that benefit is worth to them; for example, how much they would have to pay to receive the same services provided in a particular benefits program.

      Bonded labor:

      A type of labor similar to slavery in which an individual takes out a loan and must pay it back through their labor, with the terms often being such that they can never repay the debt.

      Budget standards:

      A method of setting poverty levels by determining the price of a standard “basket” of goods and services appropriate to different standards of living. Budget standards are not based on actual expenditures by individuals or families but on what some expert committee determines that they “should” spend.

      Calorie-Income Elasticity:

      A concept introduced by Michael Lipton to explain the relationship between income and food consumed. As a rule, as income increases, the proportion of income spent on food decreases so that food expenditures represent a large part of the budget for the poor and a much lower proportion for people who are better off.

      Capacity building:

      A type of intervention that invests in individuals and communities in order to upgrade their skills, improve procedures, and so on, in order to meet development objectives.

      Carbon tax:

      A tax placed on nonrenewable fossil fuels such as gasoline, with the goals of reducing consumption of such fuels, reducing pollution, and raising revenues for the government.

      Charity Organization Society:

      An organization founded in Britain in 1869 to coordinate activities among London charities.

      Child malnutrition:

      According to the World Health Organization (WHO), a child under the age of 5 is malnourished if his or her weight is more than two standard deviations below the age standards published by the WHO.

      Child poverty:

      According to the United Nations International Children’s Fund (UNICEF), child poverty should be evaluated separately from household poverty; children are poor if they do not have the resources needed to develop and thrive.

      Chronic poverty:

      Poverty that lasts for an extended period, as opposed to transitory poverty; also known as persistent poverty.

      Commission of the Inquiry into Poverty:

      A 1975 Australian commission that developed the methodology of the Henderson Poverty Line (HPL) to define poverty.

      Consistent poverty:

      A classification based both on an individual or family’s income and on their inability to provide basic necessities, such as food and appropriate clothing, for themselves.

      Consumption:

      Goods and services used by an individual or household. Consumption has been proposed as an alternative standard to income in determining poverty levels, with the reasoning that consumption is a better measure of the actual standard of living; however, difficulties in measuring consumption have limited its use in measuring and defining poverty.

      Conversion efficiency:

      The relative cost per unit of transforming money into something else, such as food purchased. Because the poor often pay more for goods than people who are better off (due to higher prices, fewer choices of where to shop, etc.), conversion efficiency is typically lower for the poor than for the non-poor.

      Correlates of poverty:

      Factors that have a strong association with poverty, such as a low level of education or living in a particular neighborhood within a city.

      Covariate:

      A negative and unexpected event that affects a large number of people, such as a flood that destroys all the crops in a village.

      Culture of poverty:

      A theory developed by Oscar Lewis stating that the poor shared certain cultural characteristics, such as feelings of helpless and inferiority, relatively low rates of marriage, and families centered around mothers rather than fathers. Lewis argued that these characteristics were an adaptive mechanism for the life circumstances faced by the poor, but his theory has been criticized as blaming the poor for their poverty and ignoring structural conditions as a contribution to poverty.

      Cycle of deprivation:

      A cycle identified by the British politician Keith Joseph in which inadequate parenting led to poor child development and the reproduction of poor parenting in the next generation.

      Deciles:

      The division of a ranked data set into 10 equal parts. This is often done with income, making it easy to make a comparison between, for instance the richest 10 percent (top decile) and poorest 10 percent (bottom decile) within a society.

      Demand analysis:

      A method of analyzing economic relationships by measuring how consumption is affected by income and price, while ignoring supply-side factors.

      Dependency ratio:

      In social statistics, the ratio of household members who are economically dependent (e.g., children, elderly family members) to those who are earning an income.

      Deserving poor:

      A concept most prevalent in the 19th century in England, in which those who were poor were divided into the deserving and undeserving poor. The deserving poor could trace their current status to causes such as illness, injury, or the death of a spouse or parent, and were judged worthy of charity. The undeserving poor were judged to be responsible for their state due to their own behavior and thus were considered not worthy of charity.

      Destitute:

      The condition of having virtually no resources at one’s disposal, whether as a result of poverty or from another source, such as a natural disaster. Historically, the receipt of charity has in some cases depended on being destitute (not merely poor); this was the case, for instance, with the Poor Law in England.

      Dimensions of poverty:

      Different facets of poverty that may not be captured by simple measures of income; examples include poor access to health care, inferior neighborhood schools, and lack of control over one’s daily life.

      Direct impact analysis:

      A method of analyzing the consequences of a policy change in terms of who is directly affected by it, and what those effects are. This method of analysis is limited because it assumes there is no elasticity of demand; that is, that individuals and households will not adjust their demand for some good or service based on changes in price.

      Direct measures of poverty:

      Methods of defining and measuring poverty based on the current condition of an individual or family (that do not have some minimum standard of living).

      Disability:

      A condition that results in the inability to perform an activity expected of an individual within their society, given considerations such as age and gender. Sometimes disability is conceptualized as a condition of an individual and sometimes as an interaction between an individual’s condition and resources available in a society.

      Discrimination:

      Factors and policies at the personal, institutional, or societal levels that act to disadvantage or exclude some individuals from benefits, based on irrelevant qualities such as race or gender.

      Diswelfare:

      A term introduced by R. M. Titmuss in the 1960s as part of his theory that poverty can be explained by the structural aspects of a competitive society. For instance, if there is not enough work available for everyone, some people will by definition be unemployed, and thus are also likely to be poor.

      Dollar a day:

      A measure of poverty introduced to the World Bank in 1990 as a measure of poverty, equivalent in purchasing power parity (PPP) to one 1996 U.S. dollar per day.

      EAPN:

      The European Anti-Poverty Network, a coalition of nongovernmental organizations in European Union countries that fight social exclusion and poverty.

      Empowerment:

      The means by which people gain control over their lives; for instance, by increasing their income, learning to negotiate the power structure, or by participating in processes of political reform.

      Endogenous effect:

      An effect caused by factors within a system (internal changes); for example, increased productivity due to the use of improved technology.

      Equivalence ratio:

      A ratio used to estimate the income required by a household to obtain a particular standard of living. One type of household is chosen as the standard and given the value 1.0, then the resources required by other types of households are defined by a number greater than or less than 1.0. For instance, if a household consisting of two adults is chosen as the standard, that type of household is assigned a value of 1.0. If studies determine that a single-person household requires only 60 percent of the resources of a two-adult household to attain the same standard of living, then a one-person household is assigned a value of 0.60.

      EU:

      The European Union, a union of nations formed in 1993 through the Maastricht Treaty; as of 2014, the EU includes 28 member states, with an additional six country candidates for membership.

      Euro:

      A currency established in 1999 and used by 18 countries; collectively these countries are known as the Eurozone.

      Evaluation:

      The process of analyzing how well a program or policy has achieved its objectives, based on information collected during and after implementation and sometimes information collected before as well.

      Ex-ante analysis:

      An analysis carried out through forecasting or other methods before a proposed policy change takes place.

      Ex-post analysis:

      An analysis carried out after a policy change has taken place, by looking at the actual results of the policy change.

      Exclusion:

      The state or condition of being inadequately integrated into society. There are many definitions of exclusion but they generally cover conditions such as being left out of systems of social protection, being unable to participate in everyday activities due to circumstances such as poverty or disability, and being deliberately shut out of activities due to discrimination.

      Exogenous effect:

      An effect caused by factors outside a system (external changes); for example, decreased sales due to a widespread recession or depression.

      Extreme poverty:

      While there is no single definition for extreme poverty, one definition often cited is that of a household that is unable to meet their needs for food even by spending all of their income on food.

      Family wage:

      Wages sufficient to support a family as a reasonable level. The concept of the family wage developed in 19th-century England, and is attractive in terms of defining wages in terms of needs, but is also complicated because of the assumptions it makes about the roles of men and women as well as the different needs of different families (depending on the number of children and other dependents, high health care expenses caused by chronic illness, etc.).

      Famine:

      A condition in which many people die from lack of adequate food. Famine does not necessarily refer to the absolute lack of food in a region but the inability of individuals to access that food (because it is destined for export, because they cannot afford to buy it, etc.).

      Feminization of poverty:

      A term used by social theorists to describe the fact that an increasing proportion of the world’s poor are female, often with the argument that this condition has been partly produced by cutbacks to social programs such as federal support for children and the elderly.

      FGT Index:

      The Foster, Greer and Thorbecke Index, a method for measuring poverty based on a formula including the level of poverty, population size, number of poor, poverty line, per capital household income, and a fact based on the importance ascribed to the lowest living standards.

      Food energy method:

      A technique for setting the poverty line by determining how much money an individual needs in a given context in order to buy enough food to meet their basic caloric needs.

      Food insecurity:

      A state in which an individual or family is not secure in their ability to obtain sufficient nutritious food for good health: food insecurity has many causes, including poverty, food shortages, or poor distribution of available food.

      Food shortage:

      A condition in which levels of available food are inadequate or are expected to be inadequate in the near future.

      Formative evaluation:

      Evaluation carried out while a program is underway, to assess how well it is performing and suggest changes to make it more effective.

      Fourth world:

      A term describing people living in chronic poverty within developed countries.

      GDP:

      Gross domestic product, the value within a single country of all goods and services produced within a year. GDP per capita is often used as a measure of the relative prosperity of different countries, although it ignores questions of distribution within a country (for instance, a country with a relatively high GDP could still have large numbers of poor people).

      Gender:

      Socially constructed expectations and roles assigned to men and women, on the basis of their sex, in a society.

      Genetic explanations for poverty:

      Theories that explain poverty in terms of the inherited behavior or characteristics of individuals; this type of explanation is not generally accepted today but was popular in the 19th and early 20th centuries.

      Gini coefficient:

      A measure of the equality of income distribution within a geographic area (e.g., a country), with 0 signifying perfect equality (everyone has the same income) and 1 perfect inequality (one person has all the money).

      GNP:

      Gross national product, the value within a single country of all goods and services plus income received from foreign exchange produced within a year. GNP per capita is often used as a measure of the relative prosperity of different countries, although like GDP per capita, it can be misleading because it ignores questions of distribution within a country.

      HDI:

      The Human Development Index, a measure introduced in 1990 by the United Nations Development Programme to compare developmental levels of different countries. The HDI ranges from 0 (least developed) to 7 (most developed), based on the life expectancy, educational attainment, and per capita gross domestic product for a country.

      Head count ratio:

      The proportion of people, families, or households that fall beneath the poverty line. Because of its simplicity, the head count ratio is a popular measure of poverty but ignores the intensity of poverty (a person, household, or family is simply classified as poor or not poor).

      Horizontal equity:

      The principle that people with a similar ability to pay taxes (taking into account expenses as well as income) should pay a similar amount of tax.

      Household models:

      Economic models that treat the household simultaneously as a unit of production and consumption; household models are often used to study reforms in fields such as taxation and agriculture.

      HPI:

      The Human Poverty Index, a term introduced by the United Nations Development Programme in 1999. The HPI for a country is calculated using five components: the percentage of people expected to die prematurely, the percentage of illiterate adults, the percentage of people with access to health care, the percentage of people with access to safe water, and the percentage of malnourished children.

      HPL:

      Henderson Poverty Line, a methodology for defining poverty developed in Australia in 1975 by the Commission of the Inquiry into Poverty, chaired by Roland Henderson.

      Human capital:

      Intangible factors that increase the productivity of an individual, such as education and good health.

      Human trafficking:

      The trade in persons for purposes such as slave labor or prostitution; women and children are the most common victims of human trafficking.

      Idiosyncratic shock:

      A negative and unexpected event that affects only one or a few people (e.g., a family), such as the death of the family’s main breadwinner.

      IDP:

      Internally displaced person, someone who must flee their home due to circumstances such as a natural disaster or warfare but does not leave the boundaries of their country.

      Impoverishment:

      A term introduced in the 1990s to refer to the process of becoming poor. There are many causes of impoverishment, which may occur rapidly or over a period of time; these include degradation of natural resources, lack of access to land and water, commodity price erosion, and long-term illness.

      Inclusive policies:

      Policies that attempt to include poor and vulnerable people in work intended to improve the circumstances of the poor and vulnerable.

      Income distribution:

      The way income is allocated among individuals or families within a country. Many economic studies are based on the way income is distributed within a society—for instance, what percentage of national income goes to the richest 10 percent compared to the poorest 10 percent within a particular country.

      Income maintenance:

      The provision of money to an individual or household when personal income is considered insufficient.

      Indigence:

      The state of lacking the basic means for subsistence, often defined as earning half the income required to meet the poverty level in a given region.

      Indirect measures of poverty:

      Methods of defining and measuring poverty based on the ability of individuals to access the rights, resources, and capabilities to achieve a certain quality of life.

      Infant mortality rate:

      The number of infant deaths per 1,000 live births in a country or other geographic region; a high infant mortality rate is considered an indication of a low level of public health in a country.

      Informal economy:

      The sector of the economy that is not included in calculations of gross domestic product and the like.

      Integrated poverty:

      A French term (pauvreté intégrée) referring to poverty that is concealed due to the individual being integrated into part of a social network or to poverty among employed persons.

      Intergenerational continuity:

      The theory that poverty continues within families for generations, due to reasons such as genetics or familial characteristics. This theory has been challenged by research that found no such continuity in poverty across generations.

      LBW:

      Low birth weight, a term applied to infants weighing less than 2.5 kilograms at birth.

      Lisbon Strategy:

      A 2000 agreement by heads of state of the European Union (EU) to improve the productivity of EU countries through improvement economic and social policies.

      Longitudinal study:

      A research study that observes the same individuals over a period of time, with the goal of noting changes in some characteristics among those studied.

      Marginalization:

      A process in which certain groups are pushed to the edge of society and have little influence or ability to improve their condition. Many factors may contribute to marginalization, including poverty, race and ethnicity, gender, and educational level.

      Market failure:

      A situation in which the free market does not efficiently allocate goods and services. One often-cited example of market failure is health care, and one reason among several cited for this market failure is that there are large differences in information among the parties involved (e.g., among patients, physicians, and insurers).

      Millennium Development Goals:

      A series of eight goals set by the United Nations to improve the lives of people around the world, particularly the poor. The first millennium development goal is to “eradicate extreme poverty and hunger” and it has three targets between 1990 and 2015: to cut in half the proportion of the world’s population who live on less than $1.25 per day; for all people to achieve productive and decent employment, and to cut in half the proportion of the world’s population that suffer from hunger. Other millennium development goals address issues such as education, gender equality, and maternal health.

      Monitoring:

      The process of tracking the inputs and outputs as well as the outcomes and impacts of a policy change or program, in order to assess how well it is achieving the desired results and/or to suggest changes during or after implementation.

      NGO:

      A nongovernmental organization, a private organization that provides services often provided by the government, such as social support services and community development.

      Open method of policy coordination:

      The process used by European Union (EU) countries in which each country creates its own employment and social inclusion policies, but the national policies are evaluated by the other EU countries.

      Operational NGOs:

      Refers to nongovernmental organizations that implement programs, often in development, as opposed to focusing primarily on advocacy.

      Participatory monitoring and evaluation:

      An approach to monitoring and evaluation that includes input from the stakeholders.

      Pathological explanations for poverty:

      Explanations that attribute poverty to the characteristics of the poor, including characteristics attributed to individuals, families, or racial and ethnic groups.

      Poverty line:

      A level of income, as established by a government or nongovernmental entity, below which a person is considered poor. In the United States in 2014, for the 48 contiguous states, the poverty line for a family of one is $11,670, and for a family of four is $23,850. Poverty lines are useful for calculating statistics but may also be controversial because they may not take into account factors such as the cost of living in different areas.

      Poverty maps:

      Maps that show how poverty is distributed geographically within a country or region. Poverty maps are often used in planning service delivery, for instance the placement of health clinics based on residents’ current access to health services.

      Poverty spell:

      A term used in longitudinal studies designating a period of time spent in poverty (because an individual or family may go in and out of poverty many times over a period of time).

      PPP:

      Purchasing power parity, a method of comparing income or the cost of living based on the cost of goods in an individual’s home country. The PPP method of comparing incomes is preferred to looking at raw income statistics because the cost of living differs greatly among countries.

      PRGF:

      Poverty Reduction and Growth Facility, a lending facility established by the International Monetary Fund to provide loans to poor countries in order to promote economic growth and reduce poverty.

      PRSC:

      Poverty Reduction Support Credit, a lending instrument from the World Bank to support a country’s attempts to reduce poverty and promote development.

      PRSP:

      Poverty Reduction Strategy Paper, a type of document prepared by countries with the partnership of international agencies such as the World Bank and International Monetary Fund. PRSPs include information about a country’s economic status and plans for development, including policies and programs intended to promote growth and needs for financing.

      Qualitative analysis:

      Methods of analysis based on analyzing individuals’ own reports of their experiences and perceptions (sometimes literally analyzing the words they use), as compared to methods such as standardized surveys where individuals must choose from among a number of predetermined choices to describe their perceptions and experiences.

      Quantitative analysis:

      Methods of analysis based on statistical methods applied to collected data.

      Quintile:

      The division of a ranked data set into five equal parts. This is often done with income, making it easy to make comparison between; for instance, the richest 20 percent (top quintile) and poorest 20 percent (bottom quintile) within a society.

      Redistribution:

      Distributing money or goods from one sector of society to another, through means such as tax collection and the provision of social benefits.

      Refugees:

      Individuals who cross international boundaries while fleeing war or civil unrest; the term is sometimes broadened to include people who flee their home country for other reasons, such as endemic poverty (economic refugees).

      Relative poverty:

      Poverty described in terms of the expected standard of living and social norms in a given society, including considerations of what an individual is expected to be able to afford even if not strictly available for survival (e.g., to pay for an expensive wedding for one’s children).

      Safety net:

      Programs designed to help people who are in need, such as income support, subsidies for necessary utilities, and feeding programs.

      Scenario analysis:

      A method of analysis used to project the results of a proposed reform under different future conditions (e.g., changes in the value of a national currency).

      Scheduled castes:

      Individuals in India formerly known as “untouchables” who are now eligible for certain benefits and preferential treatment from the government, in recognition of the discrimination they suffered in the past.

      Scheduled tribes:

      Individuals in India recognized as members of specific indigenous tribes who are entitled to affirmative action due to past discrimination; currently there are over 600 scheduled tribes recognized by the Indian government.

      Sensitivity analysis:

      A technique used in statistical analysis that looks at how the results of the analysis change when specific assumptions (e.g., the price of a particular good) are changed.

      Sex ratio:

      The ratio of males to females in a population. Usually, this ratio is approximately 1:1, unless sex selection or migration patterns have favored either males or females.

      SIA:

      Social Impact Assessment, a form of analysis that looks at how the costs and benefits of policy changes are distributed over time.

      Single-good demand model:

      An economic model that measures the willingness of households to pay for goods like water or electricity for which there are no close substitutes.

      SOCAT:

      Social Capital Assessment Tool, a method of evaluating the social capital (including institutions, networks, and attitudes) of a society and how that social capital affects production and policy.

      Social capital:

      Intangible assets in a society, such as social relationships, institutions, and values, that affect both how people interact with each other and how well the society functions as a whole.

      Social exclusion:

      The process of preventing some groups of people from enjoying the full benefits of society, including participating in the making of public policy. Many factors can contribute to social exclusion, including poverty, race and ethnicity, gender, and level of education.

      Structural explanations for poverty:

      Explanations that attribute poverty to structural factors such as the differing access of different groups to resources and opportunities.

      Stunting:

      The failure of children to grow as tall as expected for their age, usually due to malnutrition.

      Subcultural explanations for poverty:

      Explanations for poverty based on the assumption that poor people hold different values than the non-poor.

      Subjective poverty:

      An individual’s belief that they are poor, which need not be based on objective measures such as income.

      Summative evaluation:

      A type of evaluation usually performed after the conclusion of an intervention, in order to judge how successful and effective it was. Often, in order to increase objectivity, summative evaluations are carried out by an individual or group not affiliated with the creation or implementation of the program.

      Supply analysis:

      The analysis of how price changes affects supply at the household, firm, and aggregate levels.

      Transitory poverty:

      Poverty that lasts for only a short period, usually due to temporary circumstances such as loss of a job; after transitory poverty, the individual or family returns to an existence above the poverty line.

      Vulnerability:

      The condition of being at greater than average risk for some hardship or disadvantage, such as illness or poverty. Many factors, alone or in combination, can increase an individual’s vulnerability, including gender, age, poverty, and race or ethnicity.

      Wasting:

      The failure of children to gain weight as expected for their age, usually due to malnutrition or disease; the term wasting may also be applied to adults, for instance, AIDS may be called a wasting disease because it can result in severe weight loss among adults.

      Resource Guide

      Books

      Alexina, Alberto, and Edward L. Glaeser. Fighting Poverty in the U.S. and Europe: A World of Difference. New York: Oxford University Press, 2004.

      Alters, Sandra. World Poverty. Detroit, MI: Gale, Cengage Learning, 2013.

      Auyero, Javier. Patients of the State. Durham: Duke University Press, 2012.

      Ayittey, George B. N. Africa Unchained: The Blueprint for Africa’s Future. New York: Palgrave Macmillan, 2005.

      Bahle, Thomas, Vanessa Hubl, and Michaela Pfeifer. The Last Safety Net: A Handbook of Minimum Income Protection in Europe. Bristol: Policy Press, 2011.

      Banerjee, Abhijit V., and Esther Duflo. Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. New York: PublicAffairs, 2011.

      Baptist, Willie. Pedagogy of the Poor: Building the Movement to End Poverty. New York: Teachers College Press, 2011.

      Barrientos, Armando. Social Assistance in Developing Countries. Cambridge, UK: Cambridge University Press, 2013.

      Berthoud, Richard. Patterns of Poverty Across Europe. Bristol, UK: Policy Press, 2004.

      Bhagwati, Jagdish N., and Arvind Panagariya. Why Growth Matters: How Economic Growth in India Reduced Poverty and the Lessons for Other Developing Countries. New York: Public Affairs, 2013.

      Bhalla, A. S., and Dan Luo. Poverty and Exclusion of Minorities in China and India. New York: Palgrave Macmillan, 2013.

      Bhalla, A. S., and Peter McCormick. Poverty Among Immigrant Children in Europe. New York: Palgrave Macmillan, 2009.

      Blackden, C. Mark, and Quentin Wodon, eds. Gender, Time Use, and Poverty in Sub-Saharan Africa. Washington, DC: World Bank, 2006.

      Boyden, Jo, and Michael Bourdillon, eds. Childhood Poverty: Multidisciplinary Approaches. New York: Palgrave Macmillan, 2012.

      Brockington, Dan. Celebrity Advocacy and International Development. Abingdon, Oxon: Routledge, 2014.

      Buckland, Jerry. Hard Choices: Financial Exclusion, Fringe Banks, and Poverty in Urban Canada. Toronto: University of Toronto Press, 2012.

      Carr, Stuart C. Anti-Poverty Psychology. New York: Springer, 2013.

      Chandy, Laurence, Akio Hosono, Homi Kharas, and Johannes Linn, eds. Getting to Scale: How to Bring Development Solutions to Millions of Poor People. Washington, DC: Brookings Institution Press, 2013.

      Chant, Sylvia, ed. The International Handbook of Gender and Poverty: Concepts, Research, Policy. Cheltenham: Edward Elgar, 2010.

      Clark, David Alexander, ed. Adaptation, Poverty and the Development: The Dynamics of Subjective Well-Being. New York: Palgrave Macmillan, 2012.

      Davis, Deborah S., and Wang Feng, eds. Creating Wealth and Poverty in Postsocialist China. Palo Alto, CA: Stanford University Press, 2009.

      de Boyser, Katrien, ed. Between the Social and the Spatial: Exploring the Multiple Dimensions of Poverty and Social Exclusion. Burlington, VT: Ashgate, 2009.

      Dellink, Rob B., and Arjan Rujs, eds. Economics of Poverty, Environment, and Natural-Resource Use. Dordrecht, Netherlands: Springer 2008.

      Devereux, Stephen, and Melese Getu, eds. Informal and Formal Social Protection Systems in Sub-Saharan Africa. Addis Ababa, Ethiopia: Organisation for Social Science Research in Eastern and Southern Africa; Kampala: Fountain Publishers, 2013.

      Dillinger, William R. Poverty and Regional Development in Eastern Europe and Central Asia. Washington, DC: World Bank, 2007.

      Dowling, J. Malcolm, and Yap Chin-Fang. Chronic Poverty in Asia: Causes, Consequences and Policies. Hackensack, NJ: World Scientific, 2009.

      Dowling, J. Malcolm, and Yap Chin-Fang. Happiness and Poverty in Developing Countries: A Global Perspective. New York: Palgrave Macmillan, 2013.

      Dulal, Hari Bansha. Poverty Reduction in a Changing Climate. Lanham, MD: Lexington Books, 2013.

      Easterly, William. The Tyranny of Experts: Economists, Dictators, and the Forgotten Rights of the Poor. New York: Basic Books, 2013.

      Easterly, William. The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good. New York: Penguin Press, 2006.

      Elkhafif, Mahmoud A. T., Sahar Taghdisi Rad, and Mutasim Elagraa. Economic and Trade Policies in the Arab World: Employment, Poverty Reduction and Integration. New York: Routledge, 2012.

      Ezrow, Natasha M., and Erica Frantz. Failed States and Institutional Decay: Understanding Instability and Poverty in the Developing World. New York: Bloomsbury Academic, 2013.

      Fairbanks, Michael, ed. In the River They Swim: Essays from Around the World on Enterprise Solutions to Poverty. West Conshohocken, PA: Templeton Foundation Press, 2009.

      Fischer, Brodwyn M. A Poverty of Rights: citizenship and Inequality in Twentieth-century Rio de Janeiro. Palo Alto, CA: Stanford University Press, 2008.

      Foster, James E., Suman Seth, Michael Lokshin, and Zurab Sajaia. Measuring Poverty and Inequality: Theory and Practice. Washington, DC: World Bank, 2013.

      Gale, Trevor. Rough Justice: Young People in the Shadows. New York: P. Lang, 2005.

      Galizzi, Paolo, ed. The Role of the Environment in Poverty Alleviation. New York: Fordham University Press, 2008.

      Goldin, Ian. Globalization for Development: Meeting New Challenges. New York: Oxford University Press, 2012.

      Grusky, David B., and Tamar Kricheli-Katz, eds. The New Gilded Age: The Critical Inequality Debates of Our Time. Palo Alto, CA: Stanford University Press, 2012.

      Halkias, Daphne, and Paul Thurman. Entrepreneurship and Sustainability: Business Solutions for Poverty Alleviation From Around the World. Burlington, VT: Gower, 2012.

      Healey, Justin, ed. Poverty and Social Exclusion. Thirroul, NSW: Spinney Press, 2011.

      Hino, Hiroyuki, and Gusatv Ranis, eds. Youth and Employment in Sub-Saharan Africa: Working but Poor. New York: Routledge, 2014.

      Holden, Andrew. Tourism, Poverty and Development. New York: Routledge, 2013.

      Holmen, Hans. Snakes in Paradise: NGOs and the Aid Industry in Africa. Sterling, VA: Kumarian Press, 2010.

      Home, Robert, and Hilary Lim, eds. Demystifying the Mystery of Capital: Land Tenur and Poverty in Africa and the Caribbean. London: GlassHouse Press, 2004.

      Inchauste, Gabriela, and Ernesto Stein, eds. Financing the Family: Remittances to Central America in a Time of Crisis. New York: Inter-American Development Bank, 2013.

      Ingram, Jane Carter, Fabrice DeClerck, and Christina Rumbaitis del Rio, eds. Integrating Ecology and Poverty Reduction: The Application of Ecology in Development Solutions. New York: Springer, 2012.

      Joseph, Richard, and Alexandra Gillies, eds. Smart Aid for African Development. Boulder, CO: Lynne Rienner Publishers, 2009.

      Karells, Charles. The Persistence of Poverty: Why the Economics of the Well-Off Can’t Help the Poor. New Haven: Yale University Press, 2007.

      Karlan, Dean S., and Jacob Appel. More than Good Intentions: How a New Economics is Helping to Solve Global Poverty. New York: Dutton, 2011.

      Kerbo, Harold R. The Persistence of Cambodian Poverty: From the Killing Fields to Today. Jefferson, NC: McFarland, 2011.

      Laderchi, Caterina Ruggeri, and Sara Savastano, eds. Poverty and Exclusion in the Western Balkans: New Directions in Measurement and Policy. New York: Springer, 2013.

      Lötter, Hennie P. P. Poverty, Ethics, and Justice. Cardiff: University of Wales Press, 2011.

      Lu, Calzhen. Poverty and Development in China: Alternative Approaches to Poverty Assessment. New York: Routledge, 2012.

      Makuwira, Jonathan. Nongovernmental Developmental Organizations and the Poverty Reduction Agenda: The Moral Crusaders. Abingdon, Oxon: Routledge, 2014.

      Marlo, Estanislao Gacitua, and Michael Woolcock, eds. Social Exclusion and Mobility in Brazil. Washington, DC: World Bank, 2008.

      Minujin, Alberto, and Shailen Nandy, eds. Global Child Poverty and Well-Being: Measurement, Concepts, Policy and Action. Chicago: Policy Press, 2012.

      Mosley, Paul. The Politics of Poverty Reduction. Oxford: Oxford University Press, 2012.

      Murthy, Ranjani K., and Lakshmi Sankaran. Denial and Distress: Gender, Poverty and Human Rights in Asia. New York: Palgrave, 2003.

      Mwangi, Esther, Helen Markelova, and Ruth Meinzen-Dick, eds. Collective Action and Property Rights for Poverty Reduction: Insights from Africa and Asia. Philadelphia: University of Pennsylvania Press, 2012.

      Nissanke, Machiko, and Erik Thorbecke, eds. Globalization and the Poor in Asia: Can Shared Growth Be Sustained? New York: Palgrave Macmillan, 2008.

      Ping, Huang, and Genevieve Domenech-Chich, eds. Urban Migrants and Poverty Reduction in China. Reading: Paths International, 2012.

      Potts, David, Patrick Ryan, and Anna Toner, eds. Development Planning and Poverty Reduction. New York: Palgrave Macmillan, 2003.

      Poverty and the Environment: Understanding the Linkages at the Household Level. Washington, DC: The World Bank, 2008.

      Robbins, Richard H. Global Problems and the Culture of Capitalism. 6th ed. Boston: Pearson, 2014.

      Rutten, Marcel André, and Dick Foeken, eds. Inside Poverty and Development in Africa: Critical Reflections on Pro-Poor Policies. Boston: Brill, 2008.

      Saunders, Peter. Down and Out: Poverty and Exclusion in Australia. Bristol: Policy Press, 2011.

      Saunders, Peter. The Poverty Wars: Reconnecting Research With Reality. Sydney: University of New South Wales, 2005.

      Schefer, Krista Nadakaukaren. Poverty and the International Economic Legal System: Duties to the World’s Poor. New York: Cambridge University Press, 2013.

      Schiere, Richard. China’s Development Challenges: Economic Vulnerability and Public Sector Reform. New York: Routledge, 2010.

      Selolwane, Onalenna, ed. Poverty Reduction and Changing Policy Regimes in Botswana. New York: UNRISD, 2012.

      Shepherd, Andrew, and Julie Brunt, eds. Chronic Poverty: Concepts, Causes, and Policy. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2013.

      Sigona, Nando, and Nidhi Trehan, eds. Romani Politics in Contemporary Europe: Poverty, Ethnic Mobilization, and the Neoliberal Order. New York: Palgrave Macmillan, 2009.

      Sivini, Giordano. Resistance to Modernization in Africa: Journey Among Peasants and Nomads. New Brunswick, NJ: Transaction Publishers, 2007.

      Sumner, Andy, and Richard Mallett. The Future of Foreign Aid Development Cooperation and the New Geography of Global Poverty. Basingstoke: Palgrave Macmillan, 2013.

      Teichman, Judith A. Social Forces and States: Poverty and Distributional Outcomes in South Korea, Chile, and Mexico. Stanford, CA: Stanford University Press, 2012.

      Thurow, Roger, and Scott Kilman. Enough: Why the World’s Poorest Starve in an Age of Plenty. New York: PublicAffairs, 2009.

      Verner, Dorte, ed. Reducing Poverty, Protecting Livelihoods, and Building Assets in a Changing Climate: Social Implications of Climate Change in Latin America and the Caribbean. Washington, DC: World Bank, 2010.

      World Health Organization. Human Rights, Health, and Poverty Reduction Strategies. Geneva, Switzerland: United Nations Office of the High Commissioner for Human Rights/World Health Organization, 2008.

      Yunus, Mohammad, and Karl Weber. Creating a World Without Poverty: Social Business and the Future of Capitalism. New York: Public Affairs, 2007.

      Zhuang, Juzhong, ed. Poverty, Inequality, and Inclusive Growth in Asia: Measurement, Policy Issues, and Country Studies. London: Anthem Press, 2010.

      Internet Sites

      Asian Development Bank: Poverty Reduction

      http://www.adb.org/themes/poverty/main

      Bureau of Labor Statistics: Experimental Poverty Measures

      http://www.bls.gov/pir/spmhome.htm

      Combating Poverty in Europe (European Commission)

      http://cope-research.eu

      European Platform Against Poverty and Social Exclusion

      http://ec.europa.eu/social/main.jsp?catId=961

      Global Policy Forum: Poverty and Development

      https://www.globalpolicy.org/social-and-economic-policy/poverty-and-development.html

      Global Poverty Project

      http://www.globalpovertyproject.com/about-us

      Inequality Watch

      http://www.inequalitywatch.eu

      National Center for Children in Poverty (Columbia University)

      http://www.nccp.org

      National Poverty Center (University of Michigan)

      http://www.npc.umich.edu/poverty

      OECD: Poverty Reduction and Social Development

      http://www.oecd.org/social/poverty

      Oxford Poverty & Human Development Initiative

      http://www.ophi.org.uk

      Pew Research Center: Poverty

      http://www.pewresearch.org/topics/poverty

      Rural Poverty Portal

      http://www.ruralpovertyportal.org/in/home

      United Nations Development Programme: Poverty Reduction

      http://www.undp.org/content/undp/en/home/ourwork/povertyreduction/overview.html

      United Nations Millenium Project: Fast Facts: The Faces of Poverty

      http://www.unmillenniumproject.org/documents/3-MP-PovertyFacts-E.pdf

      U.S. Census Bureau: Poverty

      http://www.census.gov/hhes/www/poverty/poverty.html

      World Bank: Poverty

      http://www.worldbank.org/en/topic/poverty

      World Food Programme: Hunger

      http://www.wfp.org/hunger/stats

      World Health Organization: Poverty

      http://www.who.int/topics/poverty/en

      Journals

      Acta Sociologica

      Advances in Nutritional Research

      Agriculture & Food Security

      American Economic Review

      American Journal of Sociology

      American Sociological Review

      Annual Review of Nutrition

      Annual Review of Sociology

      Australian and New Zealand Journal of Sociology

      Bangladesh Development Studies

      Basic Income Studies

      Berkeley Journal of Sociology

      British Journal of Sociology

      Bulletin of the American Economic Association

      Cambridge Journal of Regions, Economy, and Society

      Canadian Journal of Economics and Political Science

      Canadian Journal of Sociology

      Canadian Review of Sociology and Anthropology

      Capitalism and Society

      City & Society

      Contemporary Sociology

      Critical Sociology

      Crop Prospects and the Food Situation

      Demographic Research

      Development and Change

      Economic and Labor Relations Review

      Economic Development Journal

      Economic Journal

      Economic Systems

      Economics and Philosophy

      European Journal of Population

      Food Policy

      Global Wage Report

      Hunger

      The Hunger Report

      Journal of Economic Growth

      Journal of Economic Inequality

      Journal of Health & Population in Developing Countries

      Journal of Human Capital

      Journal of Nutrition for the Elderly

      Population Studies

      Poverty & Public Policy

      Research in Human Capital and Development

      Research in Inequality and Social Conflict

      Research in Urban Sociology

      Research on Economic Inequality

      Sociology and Social Research

      Urban Affairs Annual Reviews

      World of Work Report

      Appendix

      Central Intelligence Agency World Factbook Country ComparisonsCentral Intelligence Agency World Factbook Country Comparisons

      Country Comparison pages are presorted lists of Central Intelligence Agency World Factbook Country Comparisons from selected World Factbook Central Intelligence Agency World Factbook Country Comparisons fields. Country Comparison pages are generally given in descending order—highest to lowest. The two exceptions are Unemployment rate and Inflation Rate, which are in ascending—lowest to highest—order.

      Table 1 Gross domestic product (purchasing power parity)
      This table compares the gross domestic product (GDP) or value of all final goods and services produced within a nation in a given year. A nation’s GDP at purchasing power parity (PPP) exchange rates is the sum value of all goods and services produced in the country valued at prices prevailing in the United States.

      Table 2 Gross domestic product (real growth rate)
      Real growth rate compares gross domestic product (GDP) growth on an annual basis adjusted for inflation and expressed as a percentage.

      Table 3 Gross domestic product (per capita PPP)
      Gross domestic product (GDP) per capita (PPP) compares GDP on a purchasing power parity basis divided by population as of 1 July for the same year.

      Table 4 Gross national savings
      Gross national savings is derived by deducting final consumption expenditure from Gross national disposable income, and consists of personal savings, plus business savings, plus government savings, but excludes foreign savings. The figures are presented as a percent of gross domestic product (GDP). A negative number indicates that the economy as a whole is spending more income than it produces, thus drawing down national wealth (dissavings).

      Table 5 Unemployment rate
      Unemployment rate compares the percentage of the labor force that is without jobs.

      Table 6 Unemployment, youth ages 15–24
      Unemployment, youth ages 15–24, gives the percentage of the total labor force ages 15–24 unemployed during a specified year.

      Table 7 Distribution of family income, Gini Index
      Distribution of family income, Gini index, measures the degree of inequality in the distribution of family income in a country. The more nearly equal a country’s income distribution, the lower its Gini index, e.g., a Scandinavian country with an index of 25. The more unequal a country’s income distribution, the higher its Gini index, e.g., a Sub-Saharan country with an index of 50. If income were distributed with perfect equality the index would be zero; if income were distributed with perfect inequality, the index would be 100.

      Table 8 Public debt
      Public debt compares the cumulative total of all government borrowings less repayments that are denominated in a country’s home currency. Public debt should not be confused with external debt.

      Table 9 Inflation rate (consumer prices)
      Inflation rate (consumer prices) compares the annual percent change in consumer prices with the previous year’s consumer prices.

      Table 10 Commercial bank prime lending rate
      Commercial bank prime lending rate compares a simple average of annualized interest rates commercial banks charge on new loans, denominated in the national currency, to their most credit-worthy customers.

      Table 11 Current account balance
      Current account balance compares a country’s net trade in goods and services, plus net earnings, and net transfer payments to and from the rest of the world during the period specified. These figures are calculated on an exchange rate basis.

      Table 12 Labor force
      Labor force compares the total labor force figure.

      10.4135/9781483345727.n888
      The World Bank Open DataThe World Bank Open Data

      Table 1 Income share held by second 20 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

      Table 2 Income share held by third 20 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

      Table 3 Income share held by fourth 20 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

      Table 4 Income share held by highest 20 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

      Table 5 Income share held by highest 10 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.

      Table 6 Income share held by lowest 10 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.

      Table 7 Income share held by lowest 20 percent
      Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

      Table 8 Poverty headcount ratio at $2.00 a day (PPP) (percent of population)
      Population below $2 a day is the percentage of the population living on less than $2.00 a day at 2005 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

      Table 9 Poverty headcount ratio at $1.25 a day (PPP) (percentage of population)
      Population below $1.25 a day is the percentage of the population living on less than $1.25 a day at 2005 international prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

      Table 10 Poverty gap at $2.00 a day (PPP) (percentage of population)
      Poverty gap is the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

      Table 11 Poverty gap at $1.25 a day (PPP) (percentage)
      Poverty gap is the mean shortfall from the poverty line (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.

      Table 12 Gini Index (World Bank estimate)
      Gini index measures the extent to which the distribution of income or consumption expenditure among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

      Table 13 Poverty gap at national poverty lines (percentage)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 14 Poverty headcount ratio at national poverty lines (percentage of population)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 15 Rural poverty gap at national poverty lines (percent)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 16 Rural poverty headcount ratio at national poverty lines (percent of rural population)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 17 Urban poverty gap at national poverty lines (percent)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 18 Urban poverty headcount ratio at national poverty lines (percent of urban population)
      World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e., country–specific) poverty lines.

      Table 19 Annualized growth in survey mean consumption or income per capita, bottom 40 percent (percentage based on 2005 PPP)
      World Bank, Global Database of Shared Prosperity (GDSP), circa 2006–11 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).

      Table 20 Annualized growth in survey mean consumption or income per capita, total population (percentage based on 2005 PPP)
      World Bank, Global Database of Shared Prosperity (GDSP), circa 2006–11 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).

      Table 21 Survey mean consumption or income per capita, bottom 40 percent (2005 PPP $ per day)
      World Bank, Global Database of Shared Prosperity (GDSP) circa 2006–11 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).

      Table 22 Survey mean consumption or income per capita, total population (2005 PPP $ per day)
      World Bank, Global Database of Shared Prosperity (GDSP) circa 2006–11 (http://www.worldbank.org/en/topic/poverty/brief/global-database-of-shared-prosperity).

      10.4135/9781483345727.n889
      U.S. Census Bureau Report “Income, Poverty, and Health Insurance Coverage in the United States: 2012”U.S. Census Bureau Report
      Income, Poverty, and Health Insurance Coverage in the United States: 2012

      Current Population Reports

      Acknowledgments

      Carmen DeNavas-Walt, with the assistance of Jessica L. Semega and Melissa A. Stringfellow, prepared the income section of this report under the direction of Edward J. Welniak, Jr., Chief of the Income Statistics Branch. Bernadette D. Proctor prepared the poverty section under the direction of Trudi J. Renwick, Chief of the Poverty Statistics Branch. Jessica C. Smith prepared the health insurance coverage section under the direction of Brett O’Hara, Chief of the Health and Disability Statistics Branch. Charles T. Nelson, Assistant Division Chief for Economic Characteristics, and Jennifer Cheeseman Day, Assistant Division Chief for Employment Characteristics, both of the Social, Economic, and Housing Statistics Division, provided overall direction.

      David E. Adams, Vonda M. Ashton, Susan S. Gajewski, Tim J. Marshall, and Gregory D. Weyland, Demographic Surveys Division, processed the Current Population Survey 2013 Annual Social and Economic Supplement file. Christopher J. Boniface, Kirk E. Davis, Matthew Davis, Van P. Duong, Thy K. Le, Chandararith R. Phe, and Nora P. Szeto programmed and produced the detailed and publication tables under the direction of Hung X. Pham, Chief of the Survey Processing Branch.

      Kelly Baker, Matthew Herbstritt, and Rebecca A. Hoop, under the supervision of David V. Hornick, all of the Demographic Statistical Methods Division, conducted sample review.

      Lisa Clement, Tim J. Marshall, and Lisa Paska, Demographic Surveys Division, and Roberto Picha and Agatha Jung, Technologies Management Office, prepared and programmed the computer-assisted interviewing instrument used to conduct the Annual Social and Economic Supplement.

      Additional people within the U.S. Census Bureau also made significant contributions to the preparation of this report. Joelle Abramowitz, Bernice Boursiquot, Matthew Brault, Kayla R. Fontenot, Marjorie Hanson, Misty L. Heggeness, John Hisnanick, Charles Hokayem, Yasmin Morgan, Laryssa Mykyta, Len Norry, Kirby G. Posey, and Bruce H. Webster, Jr. reviewed the contents.

      Census Bureau field representatives and telephone interviewers collected the data. Without their dedication, the preparation of this report or any report from the Current Population Survey would be impossible.

      Linda Chen, Donna Gillis, and Donald J. Meyd, of the Administrative and Customer Services Division, Francis Grailand Hall, Chief, provided publications and printing management, graphics design and composition, and editorial review for print and electronic media. General direction and production management were provided by Claudette E. Bennett, Assistant Division Chief, and Barbara J. McCoy, Chief, Publications Services Branch.

      Income, Poverty, and Health Insurance Coverage in the United States: 2012
      INTRODUCTION

      This report presents data on income, poverty, and health insurance coverage in the United States based on information collected in the 2013 and earlier Current Population Survey Annual Social and Economic Supplements (CPS ASEC) conducted by the U.S. Census Bureau.

      Summary of findings:

      • Real median household income in 2012 was not statistically different from the 2011 median income.1
      • The poverty rate in 2012 was not statistically different from 2011.
      • The percentage of people without health insurance decreased between 2011 and 2012, while the number of uninsured in 2012 was not statistically different from 2011.

      For most groups, the 2012 income, poverty, and health insurance estimates were not statistically different from the 2011 estimates. There were a few exceptions. Households in the West and those residing inside principal cities of metropolitan statistical areas experienced increases in median household income. The poverty rate in the West went down in 2012. For health insurance, the uninsured rate for Asians and Hispanics decreased. These results are discussed in more detail in the three main sections of this report—income, poverty, and health insurance coverage. Each section presents estimates by characteristics such as race, Hispanic origin, nativity, and region.2 Other topics covered are earnings, family poverty rates, and health insurance coverage of children.

      The CPS is the longest-running survey conducted by the Census Bureau. The CPS ASEC asks detailed questions categorizing income into over 50 sources. The key purpose of the CPS ASEC is to provide timely and detailed estimates of income, poverty, and health insurance coverage and to measure change in these national-level estimates. The CPS ASEC is the official source of the national poverty estimates calculated in accordance with the Office of Management and Budget’s (OMB) Statistical Policy Directive 14 (Appendix B).

      The Census Bureau also reports income, poverty, and health insurance coverage estimates based on data from the American Community Survey (ACS). The ACS is part of the 2010 Census program and eliminates the need for a long-form census questionnaire. The ACS offers broad, comprehensive information on social, economic, and housing topics and provides this information at many levels of geography.

      Since the CPS ASEC produces more complete and thorough estimates of income and poverty, the Census Bureau recommends that people use it as the data source for national estimates. Estimates for income, poverty, health insurance coverage, and other economic characteristics at the state level can be found on the American FactFinder Web site at <factfinder2.census.gov> and in forthcoming briefs based on the 2012 ACS data. For more information on state and local estimates, see the text box “State and Local Estimates of Income, Poverty, and Health Insurance.”

      The CPS ASEC provides reliable estimates of the net change, from one year to the next, in the overall distribution of economic characteristics of the population, such as income and earnings, but it does not show how those characteristics change for the same person, family, or household. Longitudinal measures of income, poverty, and health insurance coverage that are based on following the same people over time are available from the Survey of Income and Program Participation (SIPP). Estimates derived from SIPP data answer questions such as:

      • What percentage of households move up or down the income distribution over time?
      • How many people remain in poverty over time?
      • How long do people without health insurance tend to remain uninsured?

      The text box “Dynamics of Economic Well-Being” provides more information about the SIPP.

      The income and poverty estimates shown in this report are based solely on money income before taxes and do not include the value of noncash benefits, such as those provided by the Supplemental Nutrition Assistance Program (SNAP), Medicare, Medicaid, public housing, or employer-provided fringe benefits.

      Since the publication of the first official U.S. poverty estimates in 1964, there has been a continuing debate about the best approach to measuring income and poverty in the United States. Recognizing that alternative estimates of income and poverty can provide useful information to the public as well as to the federal government, the OMB’s Chief Statistician formed the Interagency Technical Working Group on developing a Supplemental Poverty Measure. This group asked the Census Bureau, in cooperation with the U.S. Bureau of Labor Statistics (BLS), to develop a new measure that allows for an improved understanding of the economic well-being of American families and how federal policies affect those living in poverty. In November 2011 and November 2012, the Census Bureau released the first sets of estimates for the Supplemental Poverty Measure.3 The text box “Supplemental Poverty Measure” provides more information about this initiative.

      INCOME IN THE UNITED STATES
      Highlights
      • Median household income was $51,017 in 2012, not statistically different in real terms from the 2011 median of $51,100 (Figure 1 and Table 1). This followed two consecutive annual declines.
      • In 2012, real median household income was 8.3 percent lower than in 2007, the year before the most recent recession (Figure 1 and Table A-1).
      • Changes in real median incomes between 2011 and 2012 for family and nonfamily households were not statistically significant (Table 1).
      • For the race and Hispanic-origin groups shown in Table 1, the 2011 to 2012 changes in real median household incomes were not statistically significant (Table 1).
      • The real median incomes of households maintained by a native- or foreign-born person in 2012 were not statistically different from their respective 2011 incomes (Table 1).4
      • The West experienced an increase of 3.2 percent in real median household income between 2011 and 2012, while the changes for the remaining regions were not statistically significant (Table 1).
      • The number of men working full time, year round with earnings increased by 1.0 million between 2011 and 2012; the change for women was not statistically significant (Table 1).
      • The changes in the real median earnings of men and women who worked full time, year round between 2011 and 2012 were not statistically significant (Table 1).
      • The 2012 female-to-male earnings ratio was 0.77, not statistically different from the 2011 ratio (Table 1 and Figure 2).

      Figure 1. Real Median Household Income by Race and Hispanic Origin: 1967 to 2012

      Note: Median household income data are not available prior to 1967. Implementation of 2010 Census population controls began in 2010. For information on recessions, see Appendix A.

      Source: U.S. Census Bureau, Current Population Survey, 1968 to 2013 Annual Social and Economic Supplements.

      Table 1. Income and Earnings Summary Measures by Selected Characteristics: 2011 and 2012

      Household Income

      Median household income was $51,017 in 2012, not statistically different from the 2011 median ($51,100) in real terms, 8.3 percent lower than the 2007 (the year before the most recent recession) median ($55,627), and 9.0 percent lower than the median household income peak ($56,080) that occurred in 1999 (Figure 1 and Table A-1).5

      Type of Household

      Real median incomes in 2012 for family households, $64,053, and nonfamily households, $30,880, were not statistically different from their respective 2011 medians (Table 1). Before 2012, family households had experienced four consecutive annual declines in median income. For nonfamily households, the experience was mixed: real median household income declined between 2007 and 2008, increased between 2008 and 2009, declined between 2009 and 2010, and did not experience a statistically significant change between 2010 and 2011. Among the specific types of family and nonfamily households, the changes in real income between 2011 and 2012 were also not statistically significant.

      Married-couple households had the highest median income in 2012 ($75,694) among family households, followed by households maintained by men with no wife present ($48,634). Family households maintained by women with no husband present had the lowest income ($34,002).

      Race and Hispanic Origin

      Among the race groups, Asian households had the highest median income in 2012 ($68,636). The median income for non-Hispanic White households was $57,009, and it was $33,321 for Black households (Table 1 and Figure 1). For Hispanic households the median income was $39,005. The real median incomes in 2012 of non-Hispanic White households, Black households, Asian households, and Hispanic-origin households were not statistically different from their respective 2011 medians.

      The real median household income for each of the race and Hispanic-origin groups have not yet recovered to their pre-2001 recession median household income peaks. Household income in 2012 was 6.3 percent lower for non-Hispanic Whites (from $60,849 in 1999), 15.8 percent lower for Blacks (from $39,556 in 2000), 7.7 percent lower for Asians (from $74,343 in 2000), and 11.8 percent lower for Hispanics (from $44,224 in 2000) (Table A-1).6

      Comparing the 2012 income of non-Hispanic White households to that of other households shows that the ratio of Asian to non-Hispanic White income was 1.20, the ratio of Black to non-Hispanic White income was 0.58, and the ratio of Hispanic to non-Hispanic White income was 0.68. Between 1972 and 2012, the change in the Black to non-Hispanic White income ratio was not statistically significant.7 Over the same period, the Hispanic to non-Hispanic White income ratio declined from 0.74 to 0.68. Income data for the Asian population was first available in 1987. The 2012 Asian to non-Hispanic White income ratio was not statistically different from the 1987 ratio.

      Age of Householder

      Households maintained by householders aged 45 to 54 had the highest median income in 2012 ($66,411), followed by those with householders aged 35 to 44 ($63,629), those with householders aged 55 to 64 ($58,626), those with householders aged 25 to 34 ($51,381), those with householders 65 years and older ($33,848), and lastly by those maintained by householders aged 15 to 24 ($30,604) (Table 1). As holds true for most characteristics of households, the apparent changes in real median income between 2011 and 2012 by age of householder were not statistically significant.

      Nativity

      In 2012, households maintained by a naturalized citizen ($53,015) or a native-born person ($51,803) had higher median incomes than households maintained by a noncitizen ($37,721) (Table 1).8 The real median incomes of households maintained by a native- or foreign-born person in 2012 were not statistically different from their respective 2011 medians. Before 2012, households maintained by a native-born person had experienced four consecutive annual declines in income. For households maintained by a foreign-born person, the annual income changes for the past 3 years have not been statistically significant, while between 2007 and 2008, these households experienced a statistically significant decline. For households maintained by a naturalized citizen and those maintained by a noncitizen, the 2012 incomes were not statistically different from their respective 2011 incomes.

      Disability Status of Householder

      In 2012, 9.4 percent of householders aged 18 to 64 reported having a disability (8.8 million) (Table 1). The median income of these households was $25,974 in 2012, compared with a median of $61,103 for households with a householder that did not report a disability. Between 2011 and 2012, the changes in real median income were not statistically significant for households maintained by a householder either with a disability or without a disability.

      Region9

      In 2012, households with the highest median household incomes were in the West ($55,157) and the Northeast ($54,627), followed by the Midwest ($50,479) and the South ($48,033).10Between 2011 and 2012, the real median income of households in the West increased by 3.2 percent (Table 1). The changes in the incomes of households in the Northeast, the Midwest, and the South were not statistically significant. Before 2012, the West experienced four consecutive annual declines in income. For the Northeast, 2012 was the fifth consecutive year without a statistically significant annual change. For the Midwest, 2012 was the second consecutive year without a statistically significant annual change. Prior to 2011, the Midwest experienced three consecutive years of annual declines. Recent changes in the median household income for the South were mixed: 2012 was the second consecutive year without a statistically significant annual change; between 2009 and 2010 and between 2007 and 2008, median household income declined; and between 2008 and 2009, the change was not statistically significant.

      Residence

      In 2012, households within metropolitan areas but outside principal cities had the highest median income ($58,780), while households outside metropolitan areas had the lowest ($41,198). Between 2011 and 2012, households residing inside principal cities of metropolitan areas experienced a 3.2 percent increase in real median income (Table 1), while the changes in income of households outside of principal cities and outside of metropolitan areas were not statistically significant.

      Income Inequality

      The Census Bureau traditionally reports two measures of income inequality: (1) the shares of aggregate household income received by quintiles and (2) the Gini index. In addition to these measures, the Census Bureau also produces estimates of the ratio of income percentiles; the Theil index, which is similar to the Gini index in that it is a single statistic that summarizes the dispersion of income across the entire income distribution; the mean logarithmic deviation of income (MLD), which measures the gap between median and average income; and the Atkinson measure, which is useful in determining which end of the income distribution contributed most to inequality.11

      Changes in income inequality between 2011 and 2012 were not statically significant as measured by the shares of aggregate household income by quintiles, the Gini index, the MLD, the Theil index, and the Atkinson measures (Table 2 and A-2). Households in the lowest quintile had incomes of $20,599 or less in 2012. Households in the second quintile had incomes between $20,600 and $39,764, those in the third quintile had incomes between $39,765 and $64,582, and those in the fourth quintile had incomes between $64,583 and $104,096. Households in the highest quintile had incomes of $104,097 or more. The top 5 percent had incomes of $191,157 or more.

      The Gini index was 0.477 in 2012, not statistically different from 2011. Since 1993, the earliest year available for comparable measures of income inequality,12 the Gini index was up 5.2 percent (Table A-2).13

      Comparing changes in household income at selected percentiles shows that income inequality has increased between 1999 (the year that household income peaked before the 2001 recession) and 2012 (Table A-2). Income at the 50th and 10th percentiles declined by 9.0 percent and 14.2 percent, respectively, while the decline in income at the 90th percentile was 1.7 percent. In 2012, the 90th to 10th percentile income ratio was 11.93, not statistically different from the 2011 ratio. Since 1999, the 90th to 10th percentile income ratio increased 14.5 percent, from 10.42 to 11.93.

      Equivalence-Adjusted Income Inequality

      Another way to measure income inequality is to use an equivalence-adjusted income estimate that takes into consideration the number of people living in the household and how these people share resources and take advantage of economies of scale. For example, the money-income-based distribution treats an income of $30,000 for a single-person household and a family household similarly, while the equivalence-adjusted income of $30,000 for a single-person household would be more than twice the equivalence-adjusted income of $30,000 for a family household with two adults and two children. The equivalence adjustment used here is based on a three-parameter scale that reflects:14

      Table 2. Income Distribution Measures Using Money Income and Equivalence-Adjusted Income: 2011 and 2012

      • On average, children consume less than adults.
      • As family size increases, expenses do not increase at the same rate.
      • The increase in expenses is larger for a first child of a single-parent family than the first child of a two-adult family.

      Table 2 shows several income inequality measures, including aggregate income shares and the Gini index, using both money income and equivalence-adjusted income for 2011 and 2012. For both 2011 and 2012, the Gini index was lower when based on an equivalence-adjusted income estimate than on the traditional money-income estimate, suggesting a more equal income distribution. Generally, the shares of aggregate household income received by quintiles show higher shares of income in the lower quintiles and lower shares in the higher quintiles for equivalence-adjusted income when compared with money income. This redistribution would be expected because the lower end of the income distribution has a higher concentration of single-person households and smaller family sizes in relation to those at the upper end of the distribution. Thus, equivalence adjusting increases the relative income of people living in lower-income groups.

      Figure 2. Female-to-Male Earnings Ratio and Median Earnings of Full-Time, Year-Round Workers 15 Years and Older by Sex: 1960 to 2012

      Note: Data on earnings of full-time, year-round workers are not readily available before 1960. Implementation of 2010 Census population controls began in 2010. For information on recessions, see Appendix A.

      Source: U.S. Census Bureau, Current Population Survey, 1961 to 2013 Annual Social and Economic Supplements.

      Based on equivalence-adjusted income, changes in inequality between 2011 and 2012 were not statistically significant as measured by the shares of aggregate household income by quintiles, the Gini index, the MLD, the Theil index, and the Atkinson measures (Table 2). The Gini index was 0.463 in 2012. The MLD was 0.629; the Theil index was 0.405; and the Atkinson measure, calculated with e=0.25 was 0.097 and with e=0.75 was 0.298 in 2012. Table A-3 shows equivalence-adjusted measures of income distribution as well as the Gini index, MLD, Theil index, and Atkinson measure for income years 1967 to 2012. Since 1993, by shares, equivalence-adjusted aggregate income declined in the lowest, second, and third quintiles (13.2 percent, 8.0 percent, and 4.9 percent, respectively).15 The share of equivalence-adjusted aggregate income in the highest quintile increased 4.6 percent. Between 1993 and 2012, the Gini index was up 6.1 percent.16

      Earnings and Work Experience

      In 2012, the real median earnings of men ($49,398) and women ($37,791) who worked full time, year round were not statistically different from their respective 2011 medians (Table 1 and Figure 2). Neither gender group has experienced a significant annual increase in median earnings since 2009. The 2012 female-to-male earnings ratio was 0.77, not statistically different from the 2011 ratio. The female-to-male earnings ratio has not experienced a significant annual increase since 2007.

      The number of men working full time, year round with earnings increased between 2011 and 2012 by 1.0 million, however, the apparent change for women was not statistically significant (Figure 3 and Table A-4).17 For working men and women with earnings, regardless of work experience, the number of men increased by 1.6 million and the number of women by 1.1 million.18 An estimated 71.1 percent of working men with earnings and 59.4 percent of working women with earnings worked full time, year round in 2012, not statistically different from the 2011 percentages.

      Figure 3. Total and Full-Time, Year-Round Workers With Earnings by Sex: 1967 to 2012

      Note: Data on number of workers are not readily available before 1967. People age 15 and older beginning in 1980 and people age 14 and older as of the following year for previous years. Before 1989, data are for civilian workers only. Implementation of 2010 Census population controls began in 2010. For information on recessions, see Appendix A.

      Source: U.S. Census Bureau, Current Population Survey, 1968 to 2013 Annual Social and Economic Supplements.

      In 2012, earnings of full-time, year-round workers aged 15 and older with a disability were generally lower than earnings of those without a disability (Table 1). Men with a disability had median earnings of $41,540 in 2012, compared with $49,806 for men without a disability. Women with a disability had median earnings of $33,790, compared with $37,988 for women without a disability. Between 2011 and 2012, the changes in the median earnings of men and women with or without a disability were not statistically significant.

      POVERTY IN THE UNITED STATES19
      Highlights
      • In 2012, the official poverty rate was 15.0 percent. There were 46.5 million people in poverty (Figure 4 and Table 3).
      • For the second consecutive year, neither the official poverty rate nor the number of people in poverty at the national level were statistically different from the previous year’s estimates (Figure 4 and Table 3).
      • The 2012 poverty rate was 2.5 percentage points higher than in 2007, the year before the most recent recession (Figure 4).
      • In 2012, the poverty rate for people living in the West was statistically lower than the 2011 estimate (Table 3).
      • For most groups, the number of people in poverty did not show a statistically significant change. However, between 2011 and 2012, the number of people in poverty did increase for people aged 65 and older, people living in the South, and people living outside metropolitan statistical areas (Tables 3 and 4).
      • The poverty rate in 2012 for children under age 18 was 21.8 percent. The poverty rate for people aged 18 to 64 was 13.7 percent, while the rate for people aged 65 and older was 9.1 percent. None of these poverty rates were statistically different from their 2011 estimates (Table 3 and Figure 5).20
      Race and Hispanic origin

      The poverty rate for non-Hispanic Whites was 9.7 percent in 2012, lower than the poverty rates for other racial groups. Non-Hispanic Whites accounted for 62.8 percent of the total population and 40.7 percent of the people in poverty. For non-Hispanic Whites, neither the poverty rate nor the number of people in poverty experienced a statistically significant change between 2011 and 2012.

      For Blacks, the 2012 poverty rate was 27.2 percent and there were 10.9 million people in poverty. For Asians, the 2012 poverty rate was 11.7 percent, which represented 1.9 million people in poverty. Among Hispanics, the 2012 poverty rate was 25.6 percent, and there were 13.6 million people in poverty. None of these estimates were statistically different from their 2011 values.

      Figure 4. Number in Poverty and Poverty Rate: 1959 to 2012

      Note: The data points are placed at the midpoints of the respective years. For information on recessions, see Appendix A.

      Source: U.S. Census Bureau, Current Population Survey, 1960 to 2013 Annual Social and Economic Supplements.

      Table 3. People in Poverty by Selected Characteristics: 2011 and 2012

      Figure 5. Poverty Rates by Age: 1959 to 2012

      Note: The data points are placed at the midpoints of the respective years. For information on recessions, see Appendix A. Data for people aged 18 to 64 and 65 and older are not available from 1960 to 1965.

      Source: U.S. Census Bureau, Current Population Survey, 1960 to 2013 Annual Social and Economic Supplements.

      Age

      Between 2011 and 2012, the number of people aged 65 and older in poverty increased to 3.9 million in 2012, up from 3.6 million in 2011, while the poverty rate for this age group was not statistically different at 9.1 percent. Neither the poverty rate nor the number in poverty for people aged 18 to 64 were statistically different from 2011, at 13.7 percent and 26.5 million (Table 3 and Figure 5).

      In 2012, for children under age 18, the survey found no statistically significant change in the poverty rate or the number in poverty (21.8 percent and 16.1 million). The poverty rate for children was higher than the rates for people aged 18 to 64 and those aged 65 and older. Children represented 23.7 percent of the total population and 34.6 percent of the people in poverty.

      Related children are people under age 18 related to the householder by birth, marriage, or adoption who are not themselves householders or spouses of householders.21 The poverty rate and the number in poverty for related children under age 18 were 21.3 percent and 15.4 million in 2012, not statistically different from the 2011 estimates. For related children in families with a female householder, 47.2 percent were in poverty, compared with 11.1 percent of related children in married-couple families.22

      The poverty rate and the number in poverty for related children under age 6 were 24.4 percent and 5.8 million in 2012, not statistically different from the 2011 estimates. About 1 in 4 of these children were in poverty in 2012. More than half (56.0 percent) of related children under age 6 in families with a female householder were in poverty. This was four-and-a-half times the rate for children in married-couple families (12.5 percent).

      Sex

      In 2012, 13.6 percent of males and 16.3 percent of females were in poverty. Neither poverty rate showed a statistically significant change from its 2011 estimate (Table 3).

      Gender differences in poverty rates were more pronounced for the age group 65 and older. The poverty rate for women aged 65 and older was 11.0 percent, while the poverty rate for men aged 65 and older was 6.6 percent. The poverty rate for women aged 18 to 64 was 15.4 percent, while the poverty rate for men aged 18 to 64 was 11.9 percent. For children under age 18, the poverty rate for girls was 22.3 percent and for boys 21.3 percent (Figure 6).

      Nativity

      Of all people, 87.1 percent were native born, 5.9 percent were foreign-born naturalized citizens, and 7.0 percent were foreign-born noncitizens. The poverty rate and the number in poverty for the native born and the foreign born were not statistically different from 2011 (14.3 percent and 38.8 million for the native born and 19.2 percent and 7.7 million for the foreign born in 2012) (Table 3).

      Within the foreign-born population, 45.4 percent were naturalized citizens, while the remaining were not citizens of the United States. The poverty rates in 2012 were 12.4 percent for foreign-born naturalized citizens and 24.9 percent for those who were not citizens, neither statistically different from 2011.

      Figure 6. Poverty Rates by Age by Gender: 2012

      Source: U.S. Census Bureau, Current Population Survey, 2013 Annual Social and Economic Supplement.

      Region

      The poverty rate in the West fell from 15.8 percent in 2011 to 15.1 percent in 2012, while the number in poverty remained unchanged at 11.0 million. For the South, the poverty rate remained unchanged at 16.5 percent in 2012, while the number in poverty increased to 19.1 million, up from 18.4 million in 2011. In 2012, the poverty rate and the number in poverty for the Northeast (13.6 percent and 7.5 million) and the Midwest (13.3 percent and 8.9 million) were not statistically different from 2011 estimates (Table 3).

      Residence

      Inside metropolitan statistical areas, the poverty rate and the number of people in poverty were 14.5 percent and 38.0 million in 2012, not statistically different from 2011. The number in poverty increased for those living outside metropolitan statistical areas to 8.5 million in 2012, from 8.0 million in 2011, while their poverty rate was not statistically different at 17.7 percent in 2012.

      The 2012 poverty rate and the number of people in poverty for those living inside metropolitan areas but not in principal cities were 11.2 percent and 18.1 million. Among those who lived in principal cities, their 2012 poverty rate and the number in poverty were 19.7 percent and 19.9 million. Neither estimate was statistically different from 2011.

      Within metropolitan areas, people in poverty were more likely to live in principal cities in 2012. While 38.5 percent of all people living in metropolitan areas lived in principal cities, 52.4 percent of poor people in metropolitan areas lived in principal cities (Table 3).

      Work Experience

      In 2012, 7.3 percent of workers aged 18 to 64 were in poverty. The poverty rate for those who worked full time, year round was 2.9 percent, while the poverty rate for those working less than full time, year round was 16.6 percent. None of these rates were statistically different from the 2011 poverty rates (Table 3).

      Among those who did not work at least 1 week in 2012, the poverty rate and the number in poverty were 33.1 percent and 15.8 million in 2012, not statistically different from the 2011 estimates (Table 3). Those who did not work in 2012 represented 24.7 percent of all people aged 18 to 64, compared with 59.7 percent of people aged 18 to 64 in poverty.

      Disability Status

      In 2012, for people aged 18 to 64 with a disability, the poverty rate and number in poverty were 28.4 percent and 4.3 million. For people aged 18 to 64 without a disability, the poverty rate and number in poverty were 12.5 percent and 22.2 million. None of these estimates were statistically different from the 2011 estimates. Among people aged 18 to 64, those with a disability represented 7.7 percent of all people in this age group compared with 16.1 percent of people in poverty (Table 3).

      Families

      In 2012, the family poverty rate and the number of families in poverty were 11.8 percent and 9.5 million, neither statistically different from the 2011 estimates (Table 4).

      In 2012, 6.3 percent of married-couple families, 30.9 percent of families with a female householder, and 16.4 percent of families with a male householder lived in poverty. Neither the family poverty rates nor the estimates of the number of families in poverty for these three family types showed any statistically significant change between 2011 and 2012.

      Depth of Poverty

      Categorizing a person as “in poverty” or “not in poverty” is one way to describe his or her economic situation. The income-to-poverty ratio and the income deficit or surplus describe additional aspects of economic well-being. While the poverty rate shows the proportion of people with income below the relevant poverty threshold, the income-to-poverty ratio gauges the depth of poverty and shows how close a family’s income is to its poverty threshold. The income-to-poverty ratio is reported as a percentage that compares a family’s or an unrelated person’s income with the applicable poverty threshold. For example, a family with an income-to-poverty ratio of 125 percent has income that is 25 percent above its poverty threshold.

      The income deficit or surplus shows how many dollars a family’s or an individual’s income is below (or above) their poverty threshold. For those with an income deficit, the measure is an estimate of the dollar amount necessary to raise a family’s or a person’s income to their poverty threshold.

      Ratio of Income to Poverty

      Table 5 presents the number and the percentage of people with specified income-to-poverty ratios—those below 50 percent of poverty (“Under 0.50”), those below 125 percent of poverty (“Under 1.25”), those below 150 percent of poverty (“Under 1.50”), and those below 200 percent of poverty (“Under 2.00”).

      Table 4. Families in Poverty by Type of Family: 2011 and 2012

      In 2012, 20.4 million people reported an income below one-half of their poverty threshold. They represented 6.6 percent of all people and 43.9 percent of those in poverty. One in 5 people (19.7 percent) had income below 125 percent of their threshold, 1 in 4 people (24.6 percent) had income below 150 percent of their poverty threshold, while approximately 1 in 3 (34.2 percent) had income below 200 percent of their threshold (Table 5).

      Of the 20.4 million people with income below one-half of their poverty threshold, 7.1 million were children under age 18, 12.1 million were aged 18 to 64, and 1.2 million were aged 65 years and older. The percentage of people aged 65 and older with income below 50 percent of their poverty threshold was 2.7 percent, less than one-half the percentage of the total population at this poverty level (6.6 percent) (Table 5). The demographic makeup of the population differs at varying degrees of poverty (Figure 7). In 2012 children represented:

      • 23.7 percent of the overall population.
      • 35.0 percent of the population below 50 percent of their poverty threshold.
      • 27.0 percent of people with income between 100 percent and 200 percent of their poverty threshold.
      • 20.3 percent of the people with income above 200 percent of their poverty threshold (Figure 7).

      By comparison, people aged 65 and older represented:

      • 13.9 percent of the overall population.
      • 5.8 percent of people below 50 percent of their poverty threshold.
      • 17.8 percent of the people between 100 percent and 200 percent of their poverty threshold.
      • 14.0 percent of the people with income above 200 percent of their poverty threshold (Figure 7).

      Table 5. People With Income Below Specified Ratios of Their Poverty Thresholds by Selected Characteristics: 2012

      Figure 7. Demographic Makeup of the Population at Varying Degrees of Poverty: 2012

      Note: Details may not sum to totals because of rounding.

      Source: U.S. Census Bureau, Current Population Survey, 2013 Annual Social and Economic Supplement.

      Income Deficit

      The income deficit for families in poverty (the difference in dollars between a family’s income and its poverty threshold) averaged $9,785 in 2012, which was not statistically different from the inflation-adjusted 2011 estimate. The average income deficit was larger for families with a female householder ($10,361) than for married-couple families ($9,348) (Table 6).

      For families in poverty, the average income deficit per capita for families with a female householder ($3,112) was higher than for married-couple families ($2,443). The income deficit per capita is computed by dividing the average deficit by the average number of people in that type of family. Since families with a female householder were smaller on average than married-couple families, the larger per capita deficit for female householder families reflects their smaller average family size as well as their lower average family income.

      Table 6. Income Deficit or Surplus of Families and Unrelated Individuals by Poverty Status: 2012

      For unrelated individuals, the average income deficit for those in poverty was $6,542 in 2012. The $6,279 deficit for women was lower than the $6,873 deficit for men.

      Shared Households23

      While poverty estimates are based on income in the previous calendar year, estimates of shared households reflect household composition at the time of the survey, which is conducted during the months of February, March, and April of each year. In 2013, the number and percentage of shared households was higher than in 2007, prior to the recession. In 2007, there were 19.7 million shared households, representing 17.0 percent of all households; by 2013, there were 23.2 million shared households, representing 19.0 percent of all households. The number of adults in shared households grew from 61.7 million (27.7 percent) in 2007 to 71.5 million (30.2 percent) in 2013.

      Between 2012 and 2013, the number and percentage of shared households increased by an estimated 889,000 households (0.5 percentage points).24 However, change in the number and percentage of additional adults residing in shared households between 2012 and 2013 was not statistically significant. Indeed, there has been no change in the number or proportion of additional adults living in shared households since 2010.

      In 2013, an estimated 10.1 million adults aged 25 to 34 (24.1 percent) were additional adults in someone else’s household. Of these young adults, 5.8 million (13.9 percent) lived with their parents. The change between 2012 and 2013 in the number and percentage of additional adults in this age group living in their parents’ household was not statistically significant.25 Further, there has been no change since 2011 in the number and percent of adults aged 25 to 34 living with their parents.

      It is difficult to assess the precise impact of household sharing on overall poverty rates. In 2012, adults aged 25 to 34 living with their parents had an official poverty rate of 9.7 percent (when the entire family’s income is compared with the threshold that includes the young adult as a member of the family). However, if poverty status were determined using only the additional adult’s own income, 43.3 percent of those aged 25 to 34 would have been below the poverty threshold for a single person under age 65.

      Alternative/Experimental Poverty Measures

      The poverty estimates in this report compare the official poverty thresholds to money income before taxes, not including the value of noncash benefits. The money income measure does not completely capture the economic well-being of individuals and families, and there are many questions about the adequacy of the official poverty thresholds. Families and individuals also derive economic well-being from noncash benefits, such as food and housing subsidies, and their disposable income is determined by both taxes paid and tax credits received. The official poverty thresholds developed more than 40 years ago do not take into account rising standards of living or such things as childcare expenses, other work-related expenses, variations in medical costs across population groups, or geographic differences in the cost of living. Poverty estimates using the Supplemental Poverty Measure (SPM) address many of these concerns. SPM estimates for 2011 were published in November 2012 (www.census.gov/hhes/povmeas/methodology/supplemental/research/Short_ResearchSPM2011.pdf). SPM estimates for 2012 will be released in fall 2013. For more details, see the text box “Supplemental Poverty Measure” on page 2.

      National Academy of Sciences (NAS)-Based Measures

      The Census Bureau currently computes alternative poverty measures based on the 1995 recommendations of the National Academy of Sciences Panel on Poverty and Family Assistance. The NAS-based measures, which use both alternative poverty thresholds and an expanded income definition, provide a consistent time series available from 1999 to the present (www.census.gov/prod/200lpubs/p60-2l6.pdf).26 The Census Bureau will release estimates for these alternative measures for 2012 in fall 2013. Estimates for 2011 for the NAS-based measures can be found at <www.census.gov/hhes/www/povmeas/tables.html>.

      Research Files

      The Census Bureau makes available microdata research files that provide the variables used to construct SPM estimates and NAS-based alternative measures at <www.census.gov/hhes/povmeas/data/public-use.html>. An expanded version of the CPS ASEC public use file includes estimates of the value of taxes and noncash benefits at <http://thedataweb.rm.census.gov/ftp/cps_ftp.html>. Microdata files are currently available for 2011. Data for 2012 will be released later this year.

      CPS Table Creator

      CPS Table Creator is a Web-based tool designed to help researchers explore alternative income and poverty measures. The tool is available from a link on the Census Bureau’s poverty Web site at <www.census.gov/cps/data/cpstablecreator.html>. Table Creator allows researchers to produce poverty and income estimates using their own combinations of threshold and resource definitions and to see the incremental impact of the addition or subtraction of a single resource element. For example:

      • In 2012, the number of people aged 65 and older in poverty would be higher by almost 15.3 million if social security payments were excluded from money income, close to quadrupling the number of elderly people in poverty.
      • If unemployment insurance benefits were excluded from money income, 1.7 million more people would be counted as in poverty in 2012.
      • If SNAP benefits were counted as income, 4 million fewer people would be categorized as in poverty in 2012.
      • Taking account of the value of the federal earned income tax credit would reduce the number of children classified as in poverty in 2011 by 3.1 million.27

      Researchers can also estimate poverty rates using alternative poverty thresholds. Many other countries use relative poverty measures with thresholds that are based on a percentage of median or mean income.28 The Table Creator allows researchers to estimate poverty rates using a relative poverty threshold calculated as any percentage of mean or median equivalence-adjusted income. For example, using poverty thresholds based on 50 percent of median income rather than the official poverty thresholds would increase the overall poverty rate from 15.0 percent to 22.5 percent in 2011.

      HEALTH INSURANCE COVERAGE IN THE UNITED STATES
      Highlights
      • In 2012, the percentage of people without health insurance decreased to 15.4 percent from 15.7 percent in 2011. The number of uninsured people in 2012 was not statistically different from 2011, at 48.0 million (Table 7 and Figure 8).29
      • Both the percentage and number of people with health insurance increased in 2012 to 84.6 percent and 263.2 million, up from 84.3 percent and 260.2 million in 2011 (Table C-1).
      • The percentage of people covered by private health insurance in 2012 was not statistically different from 2011, at 63.9 percent. This is the second consecutive year that the percentage of people covered by private health insurance was not statistically different from the previous year’s estimate. The number of people covered by private health insurance increased in 2012 to 198.8 million, up from 197.3 million in 2011 (Tables 8 and C-1).
      • The percentage and number of people covered by government health insurance increased to 32.6 percent and 101.5 million in 2012 from 32.2 percent and 99.5 million in 2011 (Tables 8 and C-1).
      • The percentage and number of people covered by employment-based health insurance in 2012 were not statistically different from 2011, at 54.9 percent and 170.9 million (Tables 8 and C-1).
      • The percentage and number of people covered by Medicaid in 2012 were not statistically different from 2011, at 16.4 percent and 50.9 million (Tables 8 and C-1). The percentage and number of people covered by Medicare increased in 2012 to 15.7 percent and 48.9 million, from 15.2 percent and 46.9 million in 2011 (Tables 8 and C-1).30
      • Since 2009, Medicaid has covered more people than Medicare (Table C-1).
      • In 2012, the percentage and number of uninsured children under age 18 decreased to 8.9 percent and 6.6 million, down from 9.4 percent and 7.0 million in 2011 (Table 7). In 2012, the uninsured rate for children in poverty, 12.9 percent, was higher than the uninsured rate for children not in poverty, 7.7 percent (Figure 11).
      • The rate and number of uninsured non-Hispanic Whites in 2012 were not statistically different from 2011, at 11.1 percent and 21.6 million. The rate and the number of uninsured Blacks in 2012 were also not statistically different from 2011, at 19.0 percent and 7.6 million (Table 7).
      • The percentage of uninsured Hispanics decreased in 2012 to 29.1 percent, down from 30.1 percent in 2011. The number of uninsured Hispanics in 2012 was not statistically different from 2011, at 15.5 million (Table 7).

      Table 7. People Without Health Insurance Coverage by Selected Characteristics: 2011 and 2012

      Figure 8. Number Uninsured and Uninsured Rate: 1987 to 2012

      Source: U.S. Census Bureau, Current Population Survey, 1988 to 2013 Annual Social and Economic Supplements.

      Type of Coverage

      In 2012, the percentage of people with private health insurance coverage was not statistically different from 2011, at 63.9 percent (Tables 8 and C-1). However, the number of those with private health insurance coverage increased in 2012 to 198.8 million, up from 197.3 million in 2011. Neither the rate nor the number of people covered by employment-based coverage in 2012, 54.9 percent and 170.9 million, was statistically different from 2011. The rate (9.8 percent) and the number of people covered by direct-purchase insurance (30.6 million) in 2012 were not statistically different from 2011.

      The percentage of people covered by government health programs increased to 32.6 percent in 2012 from 32.2 percent in 2011 (Tables 8 and C-1). The number of people covered by government health programs also increased, to 101.5 million in 2012 from 99.5 million in 2011 (Table C-1). The percentage and number of people covered by Medicaid in 2012, 16.4 percent and 50.9 million, were higher than the percentage and the number of people covered by Medicare in 2012, 15.7 percent and 48.9 million. This is the fourth consecutive year that the percentage and number of people covered by Medicaid were higher than the percentage and number of people covered by Medicare.

      The percentage and the number of people with Medicaid coverage in 2012 were not statistically different from 2011, at 16.4 percent and 50.9 million. In 2012, the percentage and the number of people with Medicare coverage increased to 15.7 percent and 48.9 million from 15.2 percent and 46.9 million in 2011.

      The percentage of people with only employment-based coverage in 2012 was not statistically different from 2011, at 44.8 percent (Table 8). The percentage of those covered only by direct-purchase insurance in 2012, 3.6 percent, was not statistically different from 2011. The percentage of those covered only by government health programs increased to 20.7 percent in 2012 from 20.4 percent in 2011. The percentage of those covered only by Medicare increased in 2012 to 5.4 percent, up from 4.9 percent in 2011. The percentage of people covered only by Medicaid decreased to 11.3 percent in 2012 from 11.5 percent in 2011.

      Table 8. Coverage Rates by Type of Health Insurance: 2011 and 2012

      (People as of March of the following year. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see www.census.gov/prod/techdoc/cps/cpsmar13.pdf)

      Coverage type

      2011

      2012

      Any private plan1

      63.9

      63.9

       Any private plan alone2

      52.0

      52.0

      Employment-based1

      55.1

      54.9

       Employment-based alone2

      45.1

      44.8

      Direct-purchase1

      9.8

      9.8

       Direct-purchase alone2

      3.6

      3.6

      Any government plan1

      32.2

      *32.6

       Any government plan alone2

      20.4

      *20.7

      Medicare1

      15.2

      *15.7

       Medicare alone2

      4.9

      *5.4

      Medicaid1

      16.5

      16.4

       Medicaid alone2

      11.5

      *11.3

      Military health care1,3

      4.4

      4.4

       Military health care alone2,3

      1.3

      1.3

      Uninsured

      15.7

      *15.4

      * Changes between the 2011 and 2012 estimates are statistically different from zero at the 90 percent confidence level.

      1 The estimates by type of coverage are not mutually exclusive; people can be covered by more than one type of health insurance during the year.

      2 The estimates by type of coverage are mutually exclusive; people did not have any other type of health insurance during the year

      3 Military health care includes Tricare and CHAMPVA (Civilian Health and Medical Program of the Department of Veteran Affairs), as well as care provided by the Department of Veterans Affairs and the military.

      Source: U. S. Census Bureau, Current Population Survey, 2012 and 2013 Annual Social and Economic Supplements.

      Race and Hispanic Origin

      In 2012, the uninsured rate and the number of uninsured non-Hispanic Whites were not statistically different from 2011, at 11.1 percent and 21.6 million (Tables 7 and C-2). Similarly, the uninsured rate (19.0 percent) and the number of uninsured Blacks (7.6 million) were not statistically different from 2011. The uninsured rate for Asians decreased in 2012 to 15.1 percent, down from 16.8 percent in 2011, while the number of uninsured Asians in 2012, 2.5 million, was not statistically different from 2011.31 Among Hispanics, the uninsured rate decreased in 2012 to 29.1 percent, down from 30.1 percent in 2011. The number of uninsured Hispanics in 2012 (15.5 million) was not statistically different from 2011.

      Age

      The percentage of people under age 65 who were uninsured in 2012, 17.7 percent, was not statistically different from 2011 (Tables 7 and C-3). The percentage of children in 2012 without health insurance decreased to 8.9 percent, down from 9.4 percent in 2011. Among those aged 19 to 25, the uninsured rate in 2012 (27.2 percent) was not statistically different from 2011. The uninsured rate for those aged 65 and older in 2012 (1.5 percent) was not statistically different from 2011. Among those aged 26 to 34, the uninsured rate in 2012 (27.2 percent) was not statistically different from the rate in 2011. For those aged 35 to 44, the rate in 2012 (21.1 percent) was not statistically different from 2011. For those aged 45 to 64, the 2012 rate (16.2 percent) was not statistically different from the rate in 2011.

      From 1999 to 2012, the uninsured rate for those aged 19 to 25 was higher than the rate for those aged 26 to 34 (Figure 9). In 2011 and 2012, the uninsured rates for these two groups were no longer statistically different from each other. In 1999, the uninsured rate for those aged 19 to 25 was 27.7 percent, while the uninsured rate for those aged 26 to 34 was 20.4 percent, a difference of 7.3 percentage points. Since then, the percentage point difference between these two age groups has decreased; in 2012, the uninsured rate for both age groups was 27.2 percent. The uninsured rate for those aged 19 to 25 has decreased 4.2 percentage points since 2009.32

      Figure 9. Uninsured Rates by Age: 1999 to 2012

      Source: U.S. Census Bureau, Current Population Survey, 2000 to 2013 Annual Social and Economic Supplements.

      Nativity

      The rate (13.0 percent) and the number of uninsured (35.1 million) in 2012 for the native-born population were not statistically different from 2011 (Table 7). The rate (32.0 percent) of uninsured in 2012 for the foreign-born population decreased, while the number of uninsured (12.8 million) was not statistically different from the 2011 estimate. Among the foreign-born population, the rate and the number of uninsured in 2012 for naturalized citizens, 18.3 percent and 3.3 million, were not statistically different from 2011 estimates. Both the rate (43.4 percent) and the number of uninsured (9.5 million) noncitizens in 2012 were not statistically different from 2011 estimates. The proportion of the foreign-born population without health insurance in 2012 was about two-and-one-half times that of the native-born population in 2012.

      Economic Status

      The uninsured rate was higher among people with lower incomes and was lower among people with higher incomes (Figure 10). In 2012, 24.9 percent of people in households with annual income less than $25,000 had no health insurance coverage. In 2012, the uninsured rates decreased as household income increased—21.4 percent of people in households with income ranging from $25,000 to $49,999 were uninsured; 15.0 percent of people in households with income ranging from $50,000 to $74,999 were uninsured; and 7.9 percent of people in households with income of $75,000 or more were uninsured. In 2012, the uninsured rate was not statistically different from 2011 for any of the four inflation-adjusted household income ranges.

      Work Experience

      For people aged 18 to 64 who worked at some time during the year, 19.5 percent or 28.4 million, were uninsured in 2012. This percent and number were not statistically different from the 2011 estimates (Table 7). In 2012, full-time, year-round workers were more likely to be covered by health insurance (84.5 percent) than those who worked less than full time, year round (72.3 percent) or nonworkers (74.2 percent).33 Among full-time, year-round workers, the percent and the number of uninsured in 2012 (15.5 percent and 15.3 million) were not statistically different from the 2011 estimates. Among less-than-full-time, year-round workers, the percent and the number of uninsured in 2012 (27.7 percent and 13.1 million) were not statistically different from 2011. For nonworkers, the uninsured rate and the number of uninsured decreased in 2012 to 25.8 percent and 12.3 million, from 26.7 percent and 13.1 million in 2011.

      Disability Status

      Among those aged 18 to 64 with a disability, both the rate and the number of uninsured in 2012 were not statistically different from 2011 estimates, at 1 6.6 percent and 2.5 million (Table 7). For those aged 18 to 64 without a disability, the rate and the number of uninsured in 2012 (21.5 percent and 38.2 million) were also not statistically different from 2011.

      Figure 10. Uninsured Rates by Real Household Income: 1999 to 2012

      Source: U.S. Census Bureau, Current Population Survey, 2000 to 2013 Annual Social and Economic Supplements.

      Children’s Health Insurance Coverage

      In 2012, the rate and the number of children without health insurance decreased to 8.9 percent and 6.6 million, down from 9.4 percent and 7.0 million (Table 7). Uninsured rates for children varied by poverty status, age, race, and Hispanic origin. Figure 11 shows that children aged 12 to 17 had a higher uninsured rate (9.7 percent) than those under age 6 (8.4 percent) and those aged 6 to 11 (8.5 percent).34 Children in poverty were more likely to be uninsured (12.9 percent) than all children (8.9 percent) and children not in poverty (7.7 percent).

      In 2012, the uninsured rates were 6.5 percent for non-Hispanic White children, 9.3 percent for Black children, 8.0 percent for Asian children, and 14.1 percent for Hispanic children.35 During the same time, the numbers of uninsured were 2.5 million non-Hispanic White children, 1.0 million Black children, 290,000 Asian children, and 2.5 million Hispanic children.36 There were no statistical differences in the rate and the number of uninsured between 2011 and 2012 for children in any race group or for Hispanic children.

      Region

      The Northeast had the lowest uninsured rate in 2012 at 10.8 percent. The uninsured rate for the Midwest was 11.9 percent; for the West, 17.0 percent; and for the South, 18.6 percent (Table 7). Between 2011 and 2012, the uninsured rates decreased for the Midwest and the West, while there were no statistically significant differences for the remaining two regions. Between 2011 and 2012, the number of uninsured decreased in the Midwest and the West to 7.9 million and 12.5 million, respectively; there were no statistical differences in the numbers of uninsured for the other two regions.

      Figure 11. Uninsured Children by Poverty Status, Household Income, Age, Race and Hispanic Origin, and Nativity: 2012

      Source: U.S. Census Bureau, Current Population Survey, 2013 Annual Social and Economic Supplement.

      Residence

      The uninsured rate in 2012 for people living inside metropolitan statistical areas decreased to 15.5 percent from 15.8 percent in 2011 (Table 7). In 2012, the uninsured rate was higher among people living in principal cities (18.6 percent) than among people living inside metropolitan areas but outside principal cities (13.5 percent).37 In 2012, the rate and number of uninsured people living outside of metropolitan statistical areas were not statistically different from 2011, at 15.2 percent and 7.3 million.38

      COMMENTS

      The Census Bureau welcomes the comments and advice of data and report users. If you have suggestions or comments on the income and poverty data, please write to:

      Charles T. Nelson

      Assistant Division Chief, Economic Characteristics

      Social, Economic, and Housing Statistics Division

      U.S. Census Bureau

      Washington, D.C. 20233-8500

      or send e-mail to

      <charles.t.nelson@census.gov>

      If you have suggestions or comments on the health insurance coverage data, please write to:

      Jennifer Cheeseman Day

      Assistant Division Chief, Employment Characteristics

      Social, Economic, and Housing Statistics Division

      U.S. Census Bureau Washington, D.C. 20233-8500

      or send e-mail to

      <jennifer.cheeseman.day@census.gov>

      APPENDIX A. ESTIMATES OF INCOME
      How Income Is Measured

      For each person 15 years and older in the sample, the Annual Social and Economic Supplement (ASEC) asks questions on the amount of money income received in the preceding calendar year from each of the following sources:

      • Earnings
      • Unemployment compensation
      • Workers’ compensation
      • Social security
      • Supplemental security income
      • Public assistance
      • Veterans’ payments
      • Survivor benefits
      • Disability benefits
      • Pension or retirement income
      • Interest
      • Dividends
      • Rents, royalties, and estates and trusts
      • Educational assistance
      • Alimony
      • Child support
      • Financial assistance from outside of the household
      • Other income

      It should be noted that although the income statistics refer to receipts during the preceding calendar year, the demographic characteristics, such as age, labor force status, and household composition, are as of the survey date. The income of the household does not include amounts received by people who were members during all or part of the previous year if these people no longer resided in the household at the time of the interview. The ASEC collects income data for people who are current residents but did not reside in the household during the previous year.

      Data on income collected in the ASEC by the U.S. Census Bureau cover money income received (exclusive of certain money receipts such as capital gains) before payments for personal income taxes, social security, union dues, Medicare deductions, etc. Therefore, money income does not reflect the fact that some families receive noncash benefits, such as Supplemental Nutrition Assistance/ food stamps, health benefits, subsidized housing, and goods produced and consumed on the farm. In addition, money income does not reflect the fact that noncash benefits are also received by some nonfarm residents, which often take the form of the use of business transportation and facilities, full or partial payments by business for retirement programs, medical and educational expenses, etc. Data users should consider these elements when comparing income levels.

      Moreover, readers should be aware that for many different reasons there is a tendency in household surveys for respondents to underreport their income. Based on an analysis of independently derived income estimates, the Census Bureau determined that respondents report income earned from wages or salaries more accurately than other sources of income, and that the reported wage and salary income is nearly equal to independent estimates of aggregate income.

      Recessions

      Business cycle peaks and troughs used to delineate the beginning and end of recessions, as shown in the text box above, are determined by the National Bureau of Economic Research, a private research organization. The data points in the time series charts in this report use July as a reference.

      Annual Average Consumer Price Index Research Series (CPI-U-RS) Using Current Methods All Items: 1947 to 2012

      Cost-of-Living Adjustment

      In order to accurately assess changes in income and earnings over time, an adjustment for changes in the cost of living is required. The Census Bureau uses the research series of the Consumer Price Index (CPI-U-RS), provided by the U.S. Bureau of Labor Statistics for 1977 through 2012, to adjust for changes in the cost of living. The indexes used to make the constant dollar conversions are shown in the text box “Annual Average Consumer Price Index Research Series (CPI-U-RS) Using Current Methods All Items: 1947 to 2012.”

      Poverty Threshold Adjustment

      The Office of Management and Budget’s (OMB) Statistical Policy Directive 14 directs the Census Bureau to use the CPI-U to update the poverty thresholds each year for changes in the cost of living. These thresholds are compared to current year (unadjusted for inflation) money income. If alternatively, the CPI-U-RS index were used to inflation-adjust money income in previous years and this income were compared to the current year thresholds, poverty rates would be higher in earlier years. This is because the CPI-U-RS results in a smaller cost of living adjustment over time than the CPI-U used to adjust the thresholds. For example, the official poverty rate for 1978 was 11.4 percent. Using the CPI-U-RS to adjust 1978 income to 2012 dollars and the 2012 thresholds, the poverty rate for 1978 would be 12.8 percent.

      Table A-1. Households by Total Money Income, Race, and Hispanic origin of Householder: 1967 to 2012

      Table A-2. Selected Measures of Household Income Dispersion: 1967 to 2012

      Table A-3. Selected Measures of Equivalence-Adjusted Income Dispersion: 1967 to 2012

      Table A-4. Number and Real Median Earnings of Total Workers and Full-Time, Year-Round Workers by Sex and Female-to-Male Earnings Ratio: 1960 to 2012

      APPENDIX B. ESTIMATES OF POVERTY
      How Poverty Is Calculated

      Following the Office of Management and Budget’s (OMB) Statistical Policy Directive 14, the U.S. Census Bureau uses a set of dollar value thresholds that vary by family size and composition to determine who is in poverty (see the matrix below).

      Poverty Thresholds for 2012 by Size of Family and Number of Related Children Under 18 Years

      Poverty Thresholds for 2012 by Size of Family and Number of Related Children Under 18 Years

      If a family’s total money income is less than the applicable threshold, then that family and every individual in it are considered in poverty. The official poverty thresholds are updated annually for inflation using the Consumer Price Index (CPI-U). The official poverty definition uses money income before taxes and tax credits and excludes capital gains and noncash benefits (such as Supplemental Nutrition Assistance Program benefits and housing assistance). The thresholds do not vary geographically.

      Example: Suppose Family A consists of five people: two children, their mother, their father, and their great-aunt. Family A’s poverty threshold in 2012 was $28,087. Each member of Family A had the following income in 2012:

      Mother

      $11,000

      Father

      8,000

      Great-aunt

      10,000

      First child

      0

      Second child

      0

      Total:

      $29,000

      Since their total family income, $29,000, was higher than their threshold ($28,087), Family A would not be considered “in poverty.”

      While the thresholds, in some sense, represent the needs of families, they should be interpreted as a statistical yardstick rather than as a complete description of what people and families need to live. Many government assistance programs use different income eligibility cutoffs. While official poverty rates and the number of people or families in poverty are important, other poverty indicators are considered in the section, “Depth of Poverty Measures,” and other approaches to setting thresholds and defining resources are discussed in the section, “Alternative Poverty Measures.”

      For a history of the official poverty measure, see “The Development of the Orshansky Poverty Thresholds and Their Subsequent History as the Official U.S. Poverty Measure” by Gordon M. Fisher, available at <www.census.gov/hhes/povmeas/publications/orshansky.html>.

      Weighted average thresholds: Since some data users want a summary of the 48 thresholds to get a general sense of the “poverty line,” the following table provides the weighted average thresholds for 2012. The weighted average thresholds are based on the relative number of families of each size and composition and are not used in computing poverty estimates.

      Weighted Average Poverty Thresholds in 2012 by Size of Family

      (Dollars)

      One person

      11,720

      Two people

      14,937

      Three people

      18,284

      Four people

      23,492

      Five people

      27,827

      Six people

      31,471

      Seven people

      35,743

      Eight people

      39,688

      Nine people or more

      47,297

      Source: U S Census Bureau.

      Table B-1. Poverty Status of People by Family Relationship, Race, and Hispanic origin: 1959 to 2012

      Table B-2. Poverty Status of People by Age, Race, and Hispanic origin: 1959 to 2012

      Table B-3. Poverty Status of Families, by Type of Family: 1959 to 2012

      APPENDIX C. ESTIMATES OF HEALTH INSURANCE COVERAGE
      Quality of Health Insurance Coverage Estimates

      National surveys and health insurance coverage. Health insurance coverage is likely to be underreported on the Current Population Survey (CPS). While underreporting affects most, if not all, surveys, underreporting of health insurance coverage appears to be a larger problem in the Annual Social and Economic Supplement (ASEC) than in other national surveys that ask about insurance. Some reasons for the disparity may include the fact that income, not health insurance, is the main focus of the ASEC questionnaire. In addition, the ASEC collects health insurance information in February through April but asks about the previous year’s coverage. Asking annual retrospective questions appears to cause few problems when collecting income data (possibly because the interview period is close to when people pay their taxes), but it may be less than ideal when asking about health insurance coverage. Compared with other national surveys, the CPS estimate of the number of people without health insurance more closely approximates the number of people who are uninsured at a specific point in time during the year than the number of people uninsured for the entire year. For a comparison of health insurance coverage rates from the major federal surveys, see How Many People Lack Insurance and for How Long? (Congressional Budget Office, May 2003) at <www.cbo.gov/doc.cfm?index=4210>.

      Reporting of coverage through major federal health insurance programs. The CPS ASEC data underreport Medicare and Medicaid coverage compared with enrollment and participation data from the Centers for Medicare and Medicaid Services (CMS).1 Because the CPS is largely a labor force survey, interviewers receive less training on health insurance concepts than labor concepts. Additionally, many people may not be aware that a health insurance program covers them or their children if they have not used covered services recently. CMS data, on the other hand, represent the actual number of people who have enrolled or participated in these programs.

      The State Health Access Data Assistance Center (SHADAC) of the University of Minnesota has worked with the U.S. Census Bureau, CMS, and the Office of the Assistant Secretary for Planning and Evaluation (ASPE) on a research project to evaluate why CPS ASEC estimates of the number of people with Medicaid are lower than counts of the number of people enrolled in the program from CMS. Reports from all four phases of the research project are available from the Census Bureau’s Web site at <www.census.gov/did/www/snacc/>.

      During Phase 1, a database of Medicaid and Medicare enrollment was built using the CMS Medicaid Statistical Information System (MSIS) files merged with CMS Medicare Enrollment Database (EDB) files. The quality of the database was evaluated using two Census Bureau files: the Master Address File/Auxiliary Reference File (MAFARF) and the Person Characteristics File (PCF).

      During Phase 2, files from the MSIS were linked with the CPS ASEC files, and the individual records were compared. The report from Phase 2 showed a gap between CPS ASEC estimates and MSIS files of 2.8 million Medicaid enrollees. A key finding indicating survey response error in the CPS ASEC was that 16.9 percent of people with an MSIS record indicating Medicaid coverage reported in the CPS ASEC that they were uninsured.2 The report found that Medicaid subscribers with longer and more recent enrollment were more likely to report coverage. Respondents for children enrolled in Medicaid were more apt to report coverage for those children than for enrolled adults within the household. Families with lower incomes tended to report coverage more frequently. Individuals who received Medicaid services during the reporting cycle tended to report coverage more often than individuals who had not received services. Reporting differences were also apparent among states.

      Phase 3 of the research project was further broken down into three steps that attempted to account for discrepancies found in Phase 2 between the MSIS records and the CPS ASEC files. These steps focused on determining the number of enrollees who were out-of-scope for the 2001 March CPS interview (people living in institutions and other group quarters are not eligible for CPS ASEC interview; MSIS counts all people, regardless of their living situation). Phase 3 narrowed the gap between CPS ASEC estimates and MSIS files by 1.0 million, to 1.8 million Medicaid enrollees.

      Phase 4 consisted of repeating the Phase 2 process using the National Health Interview Survey (NHIS) data instead of CPS ASEC data. The purpose of this was twofold: to provide explanations for the differences found between NHIS data and MSIS files and to examine how differing survey designs and methodologies affect the survey data and estimates. The report found that the NHIS Medicaid undercount was 27.3 percent in 2001 and 21.7 percent in 2002, but noted that the NHIS added questions in 2004 and these results may not apply to more recent data. The report found higher false-negative reporting for enrollees who were older, had higher incomes, and also had private insurance. False-negative reporting was lower for very low-income enrollees, those on other benefits programs, and those who had recently used Medicaid services. The report found that the dynamics of false-negative reporting was similar in the NHIS and CPS ASEC.

      SHADAC released an imputation adjustment for public use CPS ASEC microdata through its website to help researchers interested in partially adjusting for CPS ASEC Medicaid underreporting.3 This is an experimental imputation and was produced for interested parties to use in their research. The Census Bureau has not evaluated the methodology, and users should be aware that this is not an official data product.

      Enhancements in 2010. SHADAC has also done research to improve the CPS ASEC imputation and allocation processes.4 After evaluating the methodology, the Census Bureau decided to implement these changes for data from the 2000 to 2010 CPS ASEC Supplements. From this point forward, this methodology will be used and is now incorporated into the approved historical series from the 2000 to 2010 CPS ASEC Supplements. For more information on this, see <www.census.gov/hhes/www/hlthins/data/usernote/index.html>.

      There are several ongoing projects aimed at improving the quality of health coverage data from the CPS ASEC. This research includes:

      • 1) cognitive research and held testing to improve the wording of the CPS ASEC health coverage questions;
      • 2) editing and imputation research, including additional research on the use of models that attempt to account for Medicaid underreporting; and
      • 3) expanding the number of studies that match administrative Medicaid data to current survey data to include other surveys, such as the National Health Interview Survey (NHIS) and the American Community Survey (ACS). This research will make it possible to compare and contrast CPS

      ASEC underreporting rates with other surveys, allowing Census Bureau analysts to better understand the nature and impact of CPS ASEC health coverage underreporting.

      After consulting with health insurance experts, the Census Bureau modified the definition of the population without health insurance in the supplement to the March 1998 CPS, which collected data about coverage in 1997. Previously, people with no coverage other than access to the Indian Health Service were counted as part of the insured population. Subsequently, the Census Bureau has counted these people as uninsured.

      In 2009, a modification to uninsured foster children was made. Health insurance experts informed the Census Bureau that all foster children were eligible for Medicaid. The effect of these changes on the overall estimates of health insurance coverage was negligible. This modification was later incorporated into the revision of data from 1999 to 2009.

      Table C-1. Health Insurance Coverage: 1987 to 2012

      Table C-2. Health Insurance Coverage by Race and Hispanic origin: 1999 to 2012

      Table C-3. Health Insurance Coverage by Age: 1999 to 2012

      APPENDIX D. REPLICATE WEIGHTS

      Beginning in the 2011 CPS ASEC report, the variance of CPS ASEC estimates used to calculate the standard errors and confidence intervals displayed in the text tables were calculated using the Successive Difference Replication (SDR) method documented by Fay and Train (1995).1 This method involves the computation of a set of replicate weights which account for the complex survey design of the CPS. The SDR method has been used to estimate variances in the American Community Survey since its inception.

      In previous years, the standard errors of CPS ASEC estimates were calculated using a Generalized Variance Function (GVF) approach. Under this approach, generalized variance parameters were used in formulas provided in the source and accuracy (S&A) statement to estimate standard errors.

      A study by Davern et al. (2006) found that the CPS ASEC GVF standard errors performed poorly against more precise Survey Design-Based (SDB) estimates. In most cases, Davern’s results indicated that the published GVF parameters significantly underestimated standard errors in the CPS ASEC. This and other critiques prompted the Census Bureau to transition from using the GVF method of estimating standard errors to using the SDR method of estimating standard errors for the CPS ASEC. In 2009, the Census Bureau released replicate weights for the 2005 through 2009 CPS ASEC collection years and has released replicate weights for 2010, 2011, and 2012 with the release of the CPS ASEC public use data.

      Following the 2009 release of CPS ASEC replicate weights, Boudreaux, Davern, and Graven (2011) compared replicate weight standard error estimates with SDB estimates. Replicate weight estimates performed markedly better against SDB standard errors than those calculated using the published GVF parameters. The Census Bureau will continue to provide the GVF parameters in the source and accuracy statement.

      Since the published GVF parameters generally underestimated standard errors, standard errors produced using SDR may be higher than in previous reports. For most CPS ASEC estimates, the increase in standard errors from GVF to SDR will not alter the findings. However, marginally significant differences using the GVF may not be significant using replicate weights.

      References:

      Boudreaux, Michel, Michael Davern, and Peter Graven. “Alternative Variance Estimates in the Current Population Survey and the American Community Survey,” presented at the 2011 Annual Meeting of the Population Association of America.

      Davern, Michael, Arthur Jones, James Lepkowski, Gestur Davidson, and Lynn A. Blewett. “Unstable Inferences? An Examination of Complex Survey Sample Design Adjustments Using the Current Population Survey for Health Services Research,” Inquiry. Vol. 43, No. 3, 2006, pp. 283-297.

      Fay, Robert E. and George F. Train. “Aspects of Survey and Model-Based Postcensal Estimation of Income and Poverty Characteristics for States and Counties,” Proceedings of the Section on Government Statistics, American Statistical Association, Alexandria, VA, 1995, pp. 154-159.

      APPENDIX E. ADDITIONAL DATA AND CONTACTS

      Detailed tables, historical tables, press releases, and briefings are available electronically on the Census Bureau’s Income, Poverty, and Health Insurance Web sites. The Web sites may be accessed through the Census Bureau’s home page at <www.census.gov> or directly at <www.census.gov> or directly at <www.census.gov/hhes/www/income/> for income data, <www.census.gov/hhes/www/poverty/> for poverty data, and <www.census.gov/hhes/www/hlthins/> for health insurance data.

      For assistance with income, poverty, or health insurance data or questions about them, contact the U.S. Census Bureau Customer Services Center at 1-800-923-8282 (toll free) or search your topic of interest using the Census Bureau’s “Question and Answer Center” found at <ask.census.gov>.

      Customized Tables
      The CPS Table Creator

      www.census.gov/cps/data/cpstablecreator.html Gives data users the ability to create customized tables from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC). Table Creator can access data back to the 2003 CPS ASEC.

      Data Ferrett

      http://dataferrett.census.gov/ Users can also generate customized tables using the Data Ferrett tool. CPS ASEC files from 1992 to the present are available through Data Ferrett.

      Public Use MicroData
      CPS ASEC

      Microdata for the 2013 CPS ASEC and earlier years is available online at <http://thedataweb.rm.census.gov/ftp/cps_ftp.html#cpsmarch> or via DataFerrett at <http://dataferrett.census.gov>. Technical methods have been applied to CPS microdata to avoid disclosing the identities of individuals from whom data were collected.

      Taxes and Noncash Benefits

      In the early 1980s, the Census Bureau embarked on a research program to examine the effects of taxes and noncash benefits on poverty and income distributional measures. Public use data containing these additional variables are typically released later in the year and are available online at <http://thedataweb.rm.census.gov/ftp/cps_ftp.html#cpsmarch>.

      Research Files

      In addition, the Census Bureau produces special research files that enable researchers to replicate alternative poverty estimates. These files are typically released later in the year and are available online at <www.census.gov/hhes/povmeas/data/index.html>.

      Topcoding

      In its long history of releasing public use microdata files based on the CPS ASEC, the Census Bureau has censored the release of “high income” amounts in order to meet the requirements of Title 13. This process is often called topcoding. During the period prior to the March 1996 survey, this censorship was applied by limiting the values for income amounts to be no greater than a specified maximum value (the topcode). Values above the maximum were replaced by the maximum value. Maximum values varied by income source and year. Beginning with the 1996 survey, the censorship method was modified so that mean values were substituted for all amounts above the topcode (for historically consistent extracts from 1975 to 1995, call the Income Surveys Branch, 301-763-3243).

      Differences in the methods used to censor high-income amounts over time made it difficult to generate consistent time series for important measures of income distribution such as the Gini Coefficient of Income Concentration (GINI), and decile shares. Moreover, using the mean value for all amounts above the topcode made it impossible to examine the distribution of income above the topcode. In an effort to alleviate these problems and improve the overall usefulness of the ASEC, the Census Bureau sponsored research on methods that both met Title 13 requirements and preserved the income distribution above the topcode. This research led to the implementation in the 2011 ASEC of rank proximity swapping methods that switch income amounts above the topcode for respondents that are of similar income rank. Swapped amounts are rounded following the swapping process to provide additional disclosure avoidance.

      Extract files containing swapped income values for survey years 1975 to 2010 are now available on the Census Bureau’s FTP site at <www.census.gov/housing/extract_files>.

      1 “Real” refers to income after adjusting for inflation. All income values are adjusted to reflect 2012 dollars. The adjustment is based on percentage changes in prices between 2012 and earlier years and is computed by dividing the annual average Consumer Price Index Research Series (CPI-U-RS) for 2012 by the annual average for earlier years. The CPI-U-RS values for 1947 to 2012 are available in Appendix A and on the Internet at <www.census.gov/hhes/www/income/data/incpovhlth/2012/CPI-U-RS-Index-2012.pdf>. Consumer prices between 2011 and 2012 increased by 2.1 percent.

      2 Federal surveys give respondents the option of reporting more than one race. Therefore, two basic ways of defining a race group are possible. A group such as Asian may be defined as those who reported Asian and no other race (the race-alone or single-race concept) or as those who reported Asian regardless of whether they also reported another race (the race-alone-or-in-combination concept). The body of this report (text, figures, and tables) shows data using the first approach (race alone). The appendix tables show data using both approaches. Use of the single-race population does not imply that it is the preferred method of presenting or analyzing data. The Census Bureau uses a variety of approaches.

      In this report, the terms “White, not Hispanic” and “non-Hispanic White” are used interchangeably and refer to people who are not Hispanic and who reported White and no other race. The Census Bureau uses non-Hispanic Whites as the comparison group for other race groups and Hispanics.

      Since Hispanics may be any race, data in this report for Hispanics overlap with data for race groups. Being Hispanic was reported by 14.2 percent of White householders who reported only one race, 4.6 percent of Black householders who reported only one race, and 2.6 percent of Asian householders who reported only one race.

      The small sample size of the Asian population and the fact that the CPS does not use separate population controls for weighting the Asian sample to national totals contribute to the large variances surrounding estimates for this group. This means that for some estimates for the Asian population, we are unable to detect statistically significant changes from the previous year. The American Community Survey (ACS), based on a much larger sample of the population, is a better source for estimating and identifying changes for small subgroups of the population.

      The householder is the person (or one of the people) in whose name the home is owned or rented and the person to whom the relationship of other household members is recorded. If a married couple owns the home jointly, either the husband or the wife may be listed as the householder. Since only one person in each household is designated as the householder, the number of householders is equal to the number of households. This report uses the characteristics of the householder to describe the household.

      Data users should exercise caution when interpreting aggregate results for the Hispanic population or for race groups because these populations consist of many distinct groups that differ in socioeconomic characteristics, culture, and recent immigration status. Data were first collected for Hispanics in 1972 and for Asians and Pacific Islanders in 1987. For further information, see <www.census.gov/cps>.

      3 See <www.census.gov/hhes/povmeas/methodology/supplemental/research/Short_ResearchSPM2011.pdf>.

      4 Native-born households are those in which the householder was born in the United States, Puerto Rico, or the U.S. Island Areas of Guam, the Commonwealth of the Northern Mariana Islands, American Samoa, or the Virgin Islands of the United States or was born in a foreign country but had at least one parent who was a U.S. citizen. All other households are considered foreign born regardless of the date of entry into the United States or citizenship status. The CPS does not interview households in Puerto Rico. Of all householders, 85.7 percent were native born; 7.5 percent were foreign-born, naturalized citizens; and 6.8 percent were noncitizens.

      5 The difference between the 1999 and 2007 median household incomes was not statistically significant. The difference between the 2007 to 2012 and 1999 to 2012 percentage changes was not statistically significant.

      6 The differences between the declines for Asian households and non-Hispanic White and Hispanic households were not statistically significant. The difference between the declines for Black households and Hispanic households was also not statistically significant. For non-Hispanic White households, the $60,849 income peak in 1999 was not statistically different from their 2000 median of $60,831. For Blacks, the $39,556 income peak in 2000 was not statistically different from their 1999 median of $38,460. For Hispanics, the $44,224 income peak in 2000 was not statistically different from their 2001 median of $43,531.

      7 The first year that income data for the Hispanic and non-Hispanic White populations were collected in the CPS ASEC was 1972.

      8 The difference between the median incomes of households maintained by a naturalized citizen and households maintained by a native-born person was not statistically significant.

      9 The Northeast region includes Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, and Vermont. The Midwest region includes Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. The South region includes Alabama, Arkansas, Delaware, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia, and the District of Columbia, a state equivalent. The West region includes Alaska, Arizona, California, Colorado, Hawaii, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming.

      10 The difference between the median household incomes for the Northeast and the West was not statistically significant.

      11 An article by Paul Allison, “Measures of Inequality,” American Sociological Review, 43, December 1977, pp. 865 –880, provides an explanation of inequality measures.

      12 Exercise caution when making direct comparisons with years earlier than 1993 because of substantial methodological changes in the 1994 CPS ASEC. In that year, the Census Bureau introduced computer-assisted interviewing and increased income-reporting limits.

      13 For further discussion of how high incomes reported in the CPS ASEC affect income distribution measures, see Semega, Jessica and Ed Welniak, “Evaluating the Impact of Unrestricted Income Values on Income Distribution Measures Using the Current Population Survey’s Annual Social and Economic Supplement (ASEC),” April 2007, <www.census.gov/hhes/www/income/publications/unrestrict-tables/index.html>.

      14 The three-parameter scale used here is the same as the one used in the report The Effect of Taxes and Transfers on Income and Poverty in the United States: 2005, Current Population Reports, P60-232, U.S. Census Bureau, March 2007, <www.census.gov/prod/2007pubs/p60-232.pdf>. The three-parameter scale was applied to the incomes of families and unrelated individuals and assigned to each family member or unrelated individual living within the household. For details on the derivation of the three-parameter scale, see Short, Kathleen, Experimental Poverty Measures: 1999, Current Population Reports, P60-216, U.S. Census Bureau, October 2001, <www.census.gov/prod/2001pubs/p60-216.pdf>.

      15 The differences between the percent declines in the second and third shares of aggregate income were not statistically different from each other.

      16 The change in the money income Gini index between 1993 and 2012 (5.2 percent) was not statistically different from the change in the equivalence-adjusted Gini index during the same period (6.1 percent). The percent changes for the equivalence-adjusted Gini index and the highest quintile were not statistically different from each other.

      17 A full-time, year-round worker is a person who worked 35 or more hours per week (full time) and 50 or more weeks during the previous calendar year (year round). For school personnel, summer vacation is counted as weeks worked if they are scheduled to return to their job in the fall. For detailed information on work experience, see Table PINC-05, “Work Experience in 2012—People 15 Years Old and Over by Total Money Earnings in 2012, Age, Race, Hispanic Origin, and Sex” at <www.census.gov/hhes/www/cpstables/032013/perinc/toc.htm>.

      18 The differences among the 2011 to 2012 increases in the number of men working full time, year round, the number of working men regardless of work experience, and the number of working women regardless of work experience were not statistically significant.

      19 The Office of Management and Budget determined the official definition of poverty in Statistical Policy Directive 14. Appendix B provides a more detailed description of how the Census Bureau calculates poverty.

      20 Since unrelated individuals under 15 are excluded from the poverty universe, there are 468,000 fewer children in the poverty universe than in the total civilian noninstitutionalized population.

      21 Official poverty estimates for children are compiled in two ways—estimates for all children and estimates for related children. In 2012, estimates for all children included an additional 1.2 million children. About 855,000 were members of unrelated subfamilies.

      22 In the text of this report, families with a female householder with no husband present will be referred to as families with a female householder. Families with a male householder with no wife present will be referred to as families with a male householder.

      23 Shared households are defined as households that include at least one “additional” adult, a person aged 18 years or older who is not the householder, spouse, or cohabiting partner of the householder. Adults aged 18 to 24 years who are enrolled in school are not counted as additional adults.

      24 There was no change in the number of shared households between 2010 and 2012.

      25 There was no change in the number or proportion of additional adults aged 18 to 24 years, 35 to 64 years, or 65 years and older residing in someone else’s household between 2012 and 2013.

      26 However, many of the elements of these measures are no longer being updated.

      27 At this time, Table Creator can calculate these estimates for 2011. Data for 2012 from the 2013 CPS ASEC will be added to the Table Creator later this year when the enhanced CPS ASEC file with estimates of noncash benefits, tax credits, and tax liabilities is released to the public.

      28 For example, the Organization for Economic Cooperation and Development (OECD) uses a poverty threshold of 50 percent of median income. The European Union defines poverty as an income below 60 percent of the national median equalized disposable income after social transfers.

      29 For a brief description of how the Census Bureau collects and reports on health insurance data, see the text box “What Is Health Insurance Coverage?” For a discussion of the quality of ASEC health insurance coverage estimates, see Appendix C.

      30 The percentage and number of people covered by Medicaid in 2012, 16.4 percent and 50.9 million, were higher than the percentage and number of people covered by Medicare in 2012, 15.7 percent and 48.9 million.

      31 Due to the small sample size, the changes in uninsured rates for Asians are better interpreted when viewed over a longer time period.

      32 The 2012 uninsured rate for those aged 19 to 25 years was not statistically different from the 2011 uninsured rate. The 2012 uninsured rate for those aged 26 to 34 years was not statistically different from the 2011 uninsured rate.

      33 A full-time, year-round worker is a person who worked 35 or more hours per week (full-time) and 50 or more weeks during the previous calendar year (year-round). For school personnel, summer vacation is counted as weeks worked if they are scheduled to return to their job in the fall.

      34 The uninsured rate for children under the age of 6 (8.4 percent) was not statistically different from the uninsured rate for children aged 6 to 11 (8.5 percent).

      35 In 2012, the uninsured rate for Black children was not statistically different from the uninsured rates for Asian children and White children. In 2012, the uninsured rate for Asian children was not statistically different from the uninsured rates for non-Hispanic White children and White children.

      36 In 2012, the number of uninsured non- Hispanic White children was not statistically different from the number of uninsured Hispanic children.

      37 The 2012 uninsured rate for people living in principal cities (18.6 percent) was not statistically different from the 2011 uninsured rate. The 2012 uninsured rate for people living inside metropolitan statistical areas but outside principal cities (13.5 percent) was not statistically different from the 2011 uninsured rate.

      38 The 2011 uninsured rate for people living inside metropolitan statistical areas (15.8 percent) was not statistically different from the 2011 uninsured rate for people living outside metropolitan statistical areas (15.4 percent). The 2012 uninsured rate for people living inside metropolitan statistical areas (15.5 percent) was not statistically different from the 2012 uninsured rate for people living outside metropolitan statistical areas (15.2 percent).

      1 CMS is the federal agency primarily responsible for administering the Medicare and Medicaid programs at the national level.

      2 For consistency purposes across the MSIS and the CPS, SHADAC removed all MSIS enrollees who received only partial coverage, those who had died before the CPS reporting cycle, and all duplicate person records. Also, all Children’s Health Insurance Program (CHIP) enrollees were removed from the MSIS count.

      3 See <www.shadac.org/publications/medicaid-under-reporting-in-cps-and-one-approach-partial-correction> for more information.

      4 See <www.shadac.org/publications/are-current-population-survey-uninsurance-estimates-too-high-examination-imputation-pro>.

      1 In order to facilitate historical comparisons, the appendix tables display standard errors calculated using the Generalized Variance Function since replicate weights are not available for CPS ASEC collection years prior to 2005.

      10.4135/9781483345727.n890
      U.S. Bureau of Labor Statistics ReportsU.S. Bureau of Labor Statistics Reports

      International Unemployment Rates and Employment Indexes, Seasonally Adjusted, 2009-2013
      Highlights
      • Of the countries covered by the BLS unemployment comparisons program, the unemployment rate in June 2013 decreased for Germany, Italy, and Japan, remained the same in the United States, and increased for Australia, Canada, France, the Netherlands, and Sweden. See Chart 1 and Table 1.
      • The highest unemployment rates for June 2013 were in Italy (12.2 percent) and France (10.7 percent), while the lowest rate for that month was in Japan (3.4 percent).
      • Of the EU countries not covered in the BLS comparisons but tracked by BLS, the unemployment rate in June 2013 decreased in all countries except Belgium. See Chart 2 and Table 2.
      • Employment in June 2013 remained level in Canada, Germany, and Japan, rose in the United States, Australia, and Sweden, and declined in Italy and the Netherlands. See Table 3.

      CHART 1. Unemployment rates adjusted to U.S. concepts, 10 countries, seasonally adjusted, February 2012–June 2013

      NOTE: Latest available monthly data are shown for each country. See Table 1.

      CHART 2. Unemployment rates unadjusted by BLS, 10 European Union countries or areas, seasonally adjusted, February 2012–June 2013

      NOTE: Latest available monthly data are shown for each country. See Table 2.

      TABLE 1. Unemployment rates adjusted to U.S. concepts, 10 countries, seasonally adjusted (in percent)

      TABLE 2. Unemployment rates unadjusted by BLS, 10 European Union countries or areas, seasonally adjusted (in percent)

      TABLE 3. Employment indexes adjusted to U.S. concepts, 10 countries, seasonally adjusted

      Technical notes
      Data adjusted to U.S. concepts

      Data in tables 1 and 3 are on a civilian labor force basis and are from household surveys unless otherwise noted. Although the U.S. lower age limit is 16 years, the age limit for other countries varies from 15 to 16 years. No adjustment is made for the treatment of layoffs. For some countries, no adjustment is made for the treatment of unpaid family workers, persons waiting to start a new job, and passive job seekers (for example, persons only reading newspaper ads as their method of job search). In the United States, job search must be “active,“ such as placing or answering advertisements, and simply reading ads is not enough to qualify as active search. These unadjusted differences have a negligible effect on the comparisons. For further information on comparability issues, see Constance Sorrentino, “International unemployment rates: how comparable are they?” Monthly Labor Review, June 2000, pp. 3-20, at www.bls.gov/opub/mlr/2000/06/art1full.pdf.

      Employment indexes are calculated using employment levels underlying the unemployment rates and therefore are also from household surveys. Household surveys provide greater comparability of labor market trends across countries than establishment surveys, although both types of surveys are used to measure employment. In the United States, the establishment survey provides a highly reliable gauge of monthly change in nonfarm payroll employment while the household survey provides a broader picture of employment including agriculture and the self-employed. For details on the differences between the two U.S. surveys, see www.bls.gov/web/ces_cps_trends.pdf. Note that trends shown in table 3 are for the number of persons in employment and not the number of jobs.

      For further qualifications on data adjusted to U.S. concepts and historical annual figures, see “International Comparisons of Annual Labor Force Statistics, Adjusted to U.S. Concepts, 16 countries” at www.bls.gov/ilc/flscomparelf.htm.

      Unemployment rates unadjusted by BLS

      Data in table 2 are not adjusted by BLS to reflect U.S. concepts. They exclude conscripts but include career military living in private households. These data are prepared by the Statistical Office of the European Communities (EUROSTAT) according to the International Labor Office (ILO) definitions and are called harmonized unemployment rates. For details on methods and concepts, see “European Union labor force survey, methods and concepts, 2001,” at http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-BF-03-002/EN/KS-BF-03-002-EN.PDF. Data are reproduced with permission from EUROSTAT.

      The European Union-27 (EU-27) refers to EU member countries as of January 1, 2007. The EU-27 rate is the population-weighted average for the following 27 countries: Austria, Belgium, Bulgaria, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, and the United Kingdom.

      The Euro area refers to EU member countries that adopted the euro as a common currency. The composition of the euro area changes over time. As the euro area expands, data for new member countries are linked into this moving coverage series. Thus, the euro area rate changes its geographical coverage according to the composition of the euro area during the period to which the data refer. For January 2011 onward, the euro area rate is the population-weighted average for the following 17 countries: Austria, Belgium, Cyprus, Estonia, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Malta, the Netherlands, Portugal, Slovakia, Slovenia, and Spain.

      Technical Notes

      This report presents selected labor force statistics adjusted to U.S. concepts for 1970 onward for the United States and fifteen foreign countries: Australia, Canada, France, Germany, Italy, Japan, the Republic of Korea, Mexico, the Netherlands, New Zealand, South Africa, Spain, Sweden, Turkey, and the United Kingdom. For more information see technical notes or country notes.

      UNEMPLOYMENT

      In the United States, unemployment includes all persons who, during the reference week:

      • Had no employment,
      • Were available for work, except for temporary illness, and
      • Had actively sought work during the 4-week period ending with the reference week.

      Active job search methods are those that have the potential to result in a job offer without further action on the part of the jobseeker. For example, sending a resume to an employer would be considered active, whereas simply reading newspaper advertisements would not.

      Persons who were waiting to start a new job must have fulfilled these criteria to be considered unemployed. However, persons who were waiting to be recalled to a job from which they had been laid off need not have been looking for work.

      The unemployment rate represents the percentage of persons in the labor force who are unemployed.

      EMPLOYMENT

      According to U.S. definitions, employment includes all persons who, during the reference week:

      • Worked at least 1 hour as paid employees, worked in their own business, profession, or on their own farm, or worked at least 15 hours as unpaid workers in a family-operated enterprise, and
      • All those who did not work but had jobs or businesses from which they were temporarily absent due to vacation, illness, bad weather, childcare problems, maternity or paternity leave, labor-management dispute, job training, or other family or personal reasons, regardless of whether they were paid for the time off or were seeking other jobs.

      Each employed person is counted only once, even if he or she holds more than one job. For purposes of industry classification, multiple jobholders are counted in the job at which they worked the greatest number of hours during the reference week.

      Persons whose only activity consisted of work around their own house (painting, repairing, or own home housework) or volunteer work for religious, charitable, and other organizations are excluded.

      EMPLOYMENT BY SECTOR

      Employment levels and distributions are shown for four broad economic sectors: agriculture, industry, manufacturing (a sub-sector of industry), and services.

      Sectoral employment data are based on the North American Industry Classification System (NAICS) for the United States for 2000 onward, Canada for 1976 onward, and Mexico for 2005 onward. Data for Japan are based on the Japanese Standard Industrial Classification System (JSIC). For all other countries covered, sectoral employment data are based on the International Standard Industrial Classification (ISIC). Effects of the change in classification system are discussed in the country notes.

      LABOR FORCE

      The labor force is comprised of persons who are in employment and unemployment. All members of the working-age population are eligible for inclusion in the labor force, and those 16 and over (in the United States; age limits vary by country) who have a job or are actively looking for one are so classified. All others—those who have no job and are not looking for one—are counted as “not in the labor force.”

      The labor force participation rate represents the proportion of the working-age population that is in the labor force. Conversely, the inactivity rate represents the proportion of the working-age population that is not in the labor force. All persons in the civilian non-institutional working-age population who are neither employed nor unemployed are considered not in the labor force. Many who do not participate in the labor force are going to school or are retired. Family responsibilities keep others out of the labor force. Still others have a physical or mental disability which prevents them from participating in labor force activities.

      WORKING-AGE POPULATION

      The labor market statistics provided in this report describe the working-age population. In the United States, the working-age population is more specifically known as the civilian non-institutional working-age population:

      • “Civilian” refers to persons who are not on active duty in the military;
      • “Non-institutional” refers to persons who are not in institutions, such as prison inmates or those in a mental institution; and
      • “Working-age” refers to persons 16 years of age and older.
      International Labor Comparisons

      MAY 2013

      With international trade at an all-time high, global markets are the new normal for U.S. companies and residents. In 2012, total volume of trade (imports and exports) in the United States was nearly $5 trillion—the largest in the world. U.S. international competitiveness can be assessed by comparing key economic measures across countries. These measures include gross domestic product, unemployment rates, compensation costs, labor productivity rates, and consumer prices. In this Spotlight on Statistics, we compare these and other measures across countries in the Americas, Europe, and Asia and the South Pacific to get a glimpse of how individual economies have performed in recent years and historically.

      Real gross domestic product per capita and per hour, in U.S. dollars

      Gross domestic product (GDP) per hour is a general indicator of productivity while GDP per capita is an indicator of overall wealth in a country. Increases in productivity signal a potential for increases in a country’s standard of living. Generally since 1970, overall productivity and wealth have tended to grow together for all countries shown. Countries which gravitate toward the lower right of the chart have lower productivity (GDP per hour) relative to their wealth (GDP per capita) than countries which gravitate to the upper left. Singapore and the United States, for example, have had consistently high wealth, relative to productivity. In contrast, productivity has been relatively higher than wealth in countries such as France and Germany.

      Unemployment rates, 1996-2011

      Although unemployment rates in the countries compared generally remained higher in 2011 than they were in 2008, most countries experienced some recovery in unemployment in 2011: Unemployment rates fell or stayed the same from 2010 to 2011 in all countries compared except Spain and the United Kingdom. Historically, compared with unemployment rates in the 1990s, rates in 2011 were typically lower in a majority of countries. The United States is a notable exception, where unemployment rates in 2011 were about double the rates in the late 1990s.

      Employment-population ratios by sector, 1970-2011

      The percentage of the working age population employed has remained between 50 and 65 percent in most countries covered over the past 40 years, but the share of the working age population employed in each sector has shifted over time. The share of the working age population employed in agriculture dropped by more than half in all countries covered except the Netherlands, and the share of the working age population employed in industry (manufacturing, mining, and construction) fell in all countries covered except the Republic of Korea. In contrast, the share of the working age population employed in services increased in all countries covered, and by 2011, the share was nearly at or above 40 percent in all countries except Italy.

      Labor force size and participation rates by sex, 2011

      Labor force participation rates provide information about what percentage of the working age population is employed or actively seeking work. Labor force participation rates are higher for men than women in all countries compared, but relatively fewer women are working or actively seeking work in Turkey (27 percent) and Mexico (41 percent). On the other end of the spectrum, men and women are much more evenly engaged in the labor force in Canada, New Zealand, and the United States, which have among the highest overall rates of labor force participation.

      Labor force participation rates by sex, 1970-2011

      Men continue to have higher rates of labor force participation than women, but the gap between the two has been narrowing over the past 40 years in all countries compared. In most countries, this is due to a combination of a decreasing rate of participation for men and an increasing rate for women. In 2011, the gap between the sexes was narrowest in Sweden, Canada, and France, and was largest in Turkey, Mexico, and the Republic of Korea.

      Manufacturing average hourly compensation costs in U.S. dollars, by components of compensation, 2011

      Hourly compensation costs measure the average hourly cost to employ a worker, including benefits. Compared to the United States, countries with higher average hourly compensation costs were primarily in northern and western Europe. Countries with lower average hourly compensation costs were primarily in southern and eastern Europe, Asia, and Latin America. Expanding the chart to show components of compensation as a percentage of total compensation reveals that the proportion of directly paid benefits (mostly leave time and bonuses) was lower in the United States than in all countries compared except Israel, while the proportion of social insurance costs tended to be higher than the U.S. level in Europe and Latin America.

      Average hourly compensation costs in sub-manufacturing industries, U.S. dollars, 2011

      Average hourly compensation costs in manufacturing can vary widely by industry. In the United States, average costs in the highest cost industry (other transport equipment) are nearly 2.5 times those of the lowest cost industry (wearing apparel). Selecting other countries shows that, in general, the same industries (for example, petroleum and pharmaceuticals) tend to rank among the highest compensated industries across countries, while the same is true for the lowest compensated industries (apparel, textiles, leather, and wood).

      Average annual percent change in manufacturing productivity, output, and hours worked, 2000-2011

      Increases in labor productivity are approximately equal to the difference between the growth of output and the growth of hours worked; the larger the gap between output and hours, the greater the productivity growth. Since 2000, output has outpaced hours in all countries compared, resulting in increasing productivity. The largest productivity gains were in the Czech Republic, Taiwan, and the Republic of Korea, and were primarily the result of strong output growth (while hours dipped slightly); productivity also increased in the United Kingdom, Spain, and Denmark despite declining output because hours worked declined even more.

      Annual percent change in manufacturing productivity, output, and hours worked, 2010-2011

      In 2011, output growth was the main driver of productivity in many countries, although robust productivity growth in both Spain and the United Kingdom resulted from approximately equal portions of output growth and hours decline. The four countries with red bars at the right of the chart experienced declines in productivity when output was outpaced by hours; Australia, where output fell about 2 percent despite a 2 percent increase in hours, saw the biggest drop in productivity.

      Gap between productivity and real hourly compensation in manufacturing, 1970-2011

      Since 1970, labor productivity has outpaced real hourly compensation in the United States, creating a productivity-compensation gap. Increases in productivity signal a potential for increases in labor income, and by extension, for increases in the standard of living of workers. Although the U.S. gap is the largest among the countries compared, selecting other countries shows the existence of a productivity-compensation gap in all countries except Norway.

      Measures of consumer price inflation, average annual percent changes, 2007-2011

      Consumer price indexes (CPI) and harmonized indexes of consumer prices (HICP) are two measures of consumer price changes. The HICP, however, are adjusted for comparability across countries, whereas the CPI are not adjusted. Over the past 4 years, inflation averaged between 1.5 and 2.5 percent in all but four countries compared. Prices increased at a faster rate in the United Kingdom and Belgium, while Japan was the only country where prices declined since 2007.

      More

      For more information visit International Labor Comparisons at the BLS website.

      TECHNICAL NOTES

      The international comparisons of hourly compensation costs in manufacturing are prepared to assess differences in employer labor costs among countries. BLS compensation data permit more meaningful comparisons of employer labor costs than data based solely on average earnings. Definitions of average earnings vary considerably by country and do not include many items of labor cost that frequently make up a large portion of total cost. BLS compensation data include nearly all labor costs incurred by employers.

      Below is a summary of the concepts used in this report. For more detailed information, see www.bls.gov/ilc/ichcctn.pdf.

      Definitions. Compensation costs include (1) direct pay (all payments made directly to the worker, before payroll deductions of any kind) and (2) social insurance expenditures (employer payments to secure entitlement to social benefits for employees) and labor-related taxes (minus subsidies).

      The data relate to all employees in manufacturing, including part-time and temporary workers. The self-employed, unpaid family workers, contract workers, and workers in private households are excluded.

      Compensation Costs

      Direct Pay

      Employer Social Insurance Expenditures and Labor-related Taxes

      Pay for Time Worked

      Directly-Paid Benefits

      ● Basic wages

      ● Piece rate

      ● Overtime premiums

      ● Shift, holiday, or night work premiums

      ● Cost-of-living adjustments

      ● Bonuses and premiums paid each pay period

      ● Pay for time not worked (vacations, holidays, and other leave, except sick leave)

      ● Seasonal and irregular bonuses

      ● Payments in kind

      ● Allowances for family events, commuting, etc.

      ● Payments to employees’ savings funds

      ● Retirement and disability pensions

      ● Health insurance

      ● Income guarantee insurance

      ● Pay for sick leave

      ● Life and accident insurance

      ● Occupational injury and illness compensation

      ● Unemployment insurance

      ● Severance pay

      ● Other social insurance expenditures

      ● Taxes (minus subsidies) on payrolls or employment

      Methodology. In general, total compensation for each economy is calculated by adjusting earnings series to include items of direct pay, social insurance, and labor-related taxes and subsidies not included in earnings. For countries for which earnings data are not available on a per hour worked basis, BLS makes adjustments in order to approximate compensation per hour worked. Compensation costs are converted to U.S. dollars using the average daily exchange rate for the reference year.

      Earnings statistics are typically obtained from annual establishment surveys. Data on the other components of compensation are typically obtained from periodic labor cost surveys, employer confederations, and other sources.

      For the United States, the methods and results differ somewhat from those of other BLS series on U.S. compensation costs.

      The statistics are adjusted, where possible, to account for major differences in worker and survey coverage. More information on exceptions to these methods, as well as data sources used, may be found in “Country Notes and Data Sources” located at www.bls.gov/ilc/ichccsources.pdf.

      The compensation measures in this report are based on statistics available to BLS as of July 2013.

      Table 1. Hourly compensation costs in manufacturing, U.S. dollars, and as a percent of costs in the United States

      Table 2. Average annual percent change in hourly compensation costs in manufacturing and exchange rates

      Table 3. Components of horly compensation costs in manufacturing, U.S. dollars, 2012