The tricky thing with this data, and I don’t blame Chetty et al. for this, although I would like them to say more about it, is that they don’t know the race of the children. The data are from tax records, which allow you to know the income and marital status of the parents, but not the race. But they know where they grew up. So if they have a strong effect of the racial composition of the county kids grow up in, but they don’t know the race of the kids, you have to figure a big part of that is race of the kids — and by “you” I mean someone who knows anything about America.
Archive for the 'Socioeconomic & Spatial Mobility/Inequality' Category
The Sunlight Foundation has created a project called Hall of Justice which gathers publicly available criminal justice datasets and research.
While not comprehensive, Hall of Justice contains nearly 10,000 datasets and research documents from all 50 states, the District of Columbia, U.S. territories and the federal government. The data was collected between September 2014 and October 2015. We have tagged datasets so that users can search across the inventory for broad topics, ranging from death in custody to domestic violence to prison population. The inventory incorporates government as well as academic data.
H/T Flowing Data
Jeff Guo of the Wonkblog writes about new research into the reasons behind the educational achievement gap between boys and girls:
A team of economists from MIT, Northwestern, and the University of Florida has been investigating the question of the female advantage using a vast trove of data collected by the state of Florida. In their preliminary research, they have found that upbringing counts for a lot. The gender gap gets wider in poorer families. Girls from disadvantaged backgrounds are much more likely to succeed than boys raised under the same circumstances.
Now, in a new paper released Monday, the economists have found additional evidence that bad schools exacerbate the differences in academic achievement between boys and girls.
Max Ehrenfreund, writing for Wonkblog, examines research presented at the 2016 American Economic Association’s annual meeting by Anuj Shah and collaborators showing that the the poor do better on tests of financial common sense:
If you spend all your time thinking about money, chances are, you’re going to get pretty good at thinking about money. Indeed, new research suggests that the poor — for whom concerns about cash are inescapable — are not as prone to certain financial mistakes often made by the affluent.
“The poor spend a lot more time on mundane, everyday expenses. They’re focused on money,” said Anuj Shah, a psychologist at the University of Chicago and one of the authors of the research, which was published last year and presented earlier this month at the American Economic Association’s annual meeting.
The Bureau of Labor Statistics has released a chart comparing the reasons given for not being in the labor force in 2004 and 2014.
The proportion of the working-age population reporting school attendance as the main reason for being out of the labor force rose from 5.0 percent in 2004 to 6.4 percent in 2014. The percentage who cited illness or disability as the main reason increased from 5.5 percent to 6.5 percent over that same period. The proportion citing home responsibilities declined from 6.0 percent in 2004 to 5.4 percent in 2014.
For more information, see the Beyond the Numbers article “People who are not in the labor force: why aren’t they working?,” by Steven F. Hipple.
H/T Data Detectives
The World Bank has released a new working paper by Neil Fantom and Umar Serajuddin reviewing the World Bank’s classification of countries by income.
The World Bank has used an income classification to group countries for analytical purposes for many years. Since the present income classification was first introduced 25 years ago there has been significant change in the global economic landscape. As real incomes have risen, the number of countries in the low income group has fallen to 31, while the number of high income countries has risen to 80. As countries have transitioned to middle income status, more people are living below the World Bank’s international extreme poverty line in middle income countries than in low income countries. These changes in the world economy, along with a rapid increase in the user base of World Bank data, suggest that a review of the income classification is needed. A key consideration is the views of users, and this paper finds opinions to be mixed: some critics argue the thresholds are dated and set too low; others find merit in continuing to have a fixed benchmark to assess progress over time. On balance, there is still value in the current approach, based on gross national income per capita, to classifying countries into different groups. However, the paper proposes adjustments to the methodology that is used to keep the value of the thresholds for each income group constant over time. Several proposals for changing the current thresholds are also presented, which it is hoped will inform further discussion and any decision to adopt a new approach.
Jay Ulfelder, writing for FiveThirtyEight, argues that the widely held belief that economic inequality causes political upheaval is a difficult thing to prove:
Just because a belief is widely held, however, does not make it true. In fact, it’s still hard to establish with confidence whether and how economic inequality shapes political turmoil around the world. That’s largely because of the difficulty in measuring inequality; on this subject, the historical record is full of holes. Social scientists are busy building better data sets, but the ones we have now aren’t sufficient to make strong causal claims at the global level.
Andrew Flowers of FiveThirtyEight examines a recent study by Lipsey, Farran and Hofer of Tennessee’s voluntary pre-K program, which finds that kids from low income families who went to pre-K tend to fare worse academically than those who did not. Flowers also looks at a new NBER working paper which disputes this result.
The Census Bureau released the 2014 estimates from it Small Area Income and Poverty Estimates program last week.
From Data Detectives:
Tables provide statistics on the number of people in poverty, the number of children younger than age 5 in poverty (for states only), the number of children ages 5 to 17 in families in poverty, the number younger than age 18 in poverty, and median household income. At the school district level, estimates are available for the total population, the number of children ages 5 to 17 and the number of children ages 5 to 17 in families in poverty.
Susan Dynarski examines the research on charter schools for The Upshot:
Social scientists, like medical researchers, can confirm only whether, on average, a given treatment is beneficial for a given population. Not all charter schools are outstanding: In the suburbs, for example, the evidence is that they do no better than traditional public schools. But they have been shown to improve the education of disadvantaged children at scale, in multiple cities, over many years.