Archive for the 'Areas (Subject)' Category
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Philip Cohen of Family Inequality charts the correlation between marriage and gender inequality:
I used data from this U.N. report on marriage rates from 2008, restricted to those countries that had data from 2000 or later. To show marriage rates I used the percentage of women ages 30-34 that are currently married. This is thus a combination of marriage prevalence and marriage timing, which is something like the amount of marriage in the country. I got gender inequality from the U.N. Development Programme’s Human Development Report for 2015. The gender inequality index combines the maternal mortality ratio, the adolescent birth rate, the representation of women in the national parliament, the gender gap in secondary education, and the gender gap in labor market participation.
The World Bank has launched a gender data portal:
Gender data are one of the most visited parts of our data site, and these new resources make it easier than ever to see our data’s gender dimensions. The country and topic dashboards give an overview of the distribution and trends in data across important themes, and the online tables and book are a useful reference for the most commonly accessed data.
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.
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
Max Ehrenfreund of Wonkblog writes about a new study by former PSC trainee Geoffrey Wodke showing that more intelligent people are no more interested in supporting policies designed to improve racial equality (such as Affirmative Action or school busing):
When you get down to the brass tacks of dealing with racial prejudice, though, more intelligent people seem to tunnel back into the woodwork. The new study revealed that smarter respondents are no more likely to support specific policies designed to improve racial equality — even though they are more liberal on other issues and are more likely to see discrimination as a problem.
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.
In addition to their weekly podcast on data, What’s the Point?, as well as their sports podcast, Hot Takedown, FiveThirtyEight has launched an election podcast called, appropriately enough, FiveThirtyEight Elections.
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