The Pew Research Center examined the racial/ethnic make up of 29 groups, including Protestant denominations, other religious groups and three religiously unaffiliated groups. The analysis included 5 racial and ethnic groups: Hispanics, non-Hispanic whites, blacks, Asians, and other/mixed-race.
More than one-quarter of the 100 largest metropolitan areas experienced white losses in both cities and suburbs. Less than half (45) of the these areas followed the traditional patterns of white city loss and suburban gain—including Midwest areas such as Columbus, Kansas City, and Minneapolis-St. Paul.
Jia Zhang of FiveThirtyEight built a Twitter bot which pulls data from the U.S. Census and creates mini-narrative. For example, “I haven’t moved recently. I work for a private company. I was widowed.”
Census data is often seen at a large scale — atlases, research studies and interactive visualizations all offer the view from 10,000 feet. But there are people inside those top-line numbers. And when you start to look at the people in the data sets, you get a glimpse of their lives. Just a few descriptors — how much they work, whom they take care of, where they were born — can give us a sense of the people around us.
Emily Badger and Christopher Ingraham of Wonkblog use data from the Annie E. Casey Foundation’s Kids Count to map the best and worst states for children on a variety of indicators, including poverty, food security, housing, family structure, education, exercise, and incarceration rates.
Thinking, Fast and Slow? Some Field Experiments to Reduce Crime and Dropout in Chicago
by Sara B. Heller, Anuj K. Shah, Jonathan Guryan, Jens Ludwig, Sendhil Mullainathan, Harold A. Pollack #21178
Researchers at the University of North Carolina at Chapel Hill are working on a project called DataBridge to create an archive for data sets and metadata that would otherwise be lost once the papers they were produced for are published.
Emily Badger of Wonkblog examines the policy effects of economic segregation, particularly the skewed view the wealthy have of poverty:
The wealthy, surrounded by other wealthy people, generally believed the U.S. population was wealthier than it actually is. It’s easy to imagine why they might make this mistake: If you look around you and see few poor people — on the street, in your child’s classroom, at the grocery store — you may think poverty is pretty rare.
See also: Dawtry, Sutton & Sibley, Why Wealthier People Think People Are Wealthier, and Why It Matters
The Half-Life of Happiness: Hedonic Adaptation in the Subjective Well-Being of Poor Slum Dwellers to a Large Improvement in Housing
by Sebastian Galiani, Paul J. Gertler, Raimundo Undurraga #21098
The Weaker Sex? Vulnerable Men, Resilient Women, and Variations in Sex Differences in Mortality since 1900
by Mark R. Cullen, Michael Baiocchi, Karen Eggleston, Pooja Loftus, Victor Fuchs #21114
Multidimensional poverty measurement and analysis is evolving rapidly. Quite recently, a particular counting approach to multidimensional poverty measurement, developed by Sabina Alkire and James Foster, has created considerable interest. Notably, it has informed the publication of the Global Multidimensional Poverty Index (MPI) estimates in the Human Development Reports of the United Nations Development Programme since 2010, and the release of national poverty measures in Chile, Mexico, Colombia, Bhutan and the Philippines. The academic response has been similarly swift, with related articles published in both theoretical and applied journals.
The high and insistent demand for in-depth and precise accounts of multidimensional poverty measurement motivates this book, which is aimed at graduate students in quantitative social sciences, researchers of poverty measurement, and technical staff in governments and international agencies who create multidimensional poverty measures.
How Does Health Promotion Work? Evidence From The Dirty Business of Eliminating Open Defecation
by Paul Gertler, Manisha Shah, Maria Laura Alzua, Lisa Cameron, Sebastian Martinez, Sumeet Patil #20997