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
Kaggle, a platform for predictive modelling and analytics competitions, introduced a section for users to download and analyze public data.
At Kaggle, we want to help the world learn from data. This sounds bold and grandiose, but the biggest barriers to this are incredibly simple. It’s tough to access data. It’s tough to understand what’s in the data once you access it. We want to change this. That’s why we’ve created a home for high quality public datasets, Kaggle Datasets.
Kaggle Datasets has four core components:
- Access: simple, consistent access to the data with clear licensing
- Analysis: a way to explore the data without downloading it
- Results: visibility to the previous work that’s been created on the data
Conversation: forums and comments for discussing the nuances of the data
Current datasets include U.S. Baby Names, 2013 American Community Survey, May 2015 Reddit Comments, U.S. Department of Education: College Scorecard, and Ocean Ship Logbooks (1750-1850).
The U.S. Census Bureau is committing to an open source policy. Their mission, “is to serve as the leading source of quality data about the nation’s people and economy. We honor privacy, protect confidentiality, share our expertise globally, and conduct our work openly. Where possible, the US Census Bureau will actively participate in open source projects aimed at increasing value to the public through our data dissemination efforts.”
Read a current list of the open source projects here.
H/T Flowing Data
Nathan Yau of Flowing Data has been doing some interesting (and beautiful) visualizations of when and how people die. First was Years You Have Left to Live, Probably. Next was Causes of Death. And today he posted How You Will Die.
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.
Read a summary of the findings.
Download the PDF.
Scott Stanley, writing for Family Studies, contrasts his own work with a study by Sarah Mernitz and Claire Kamp Dush which finds that people experience emotional gains when they move in together regardless of marital status. Stanley’s analysis finds that, for a variety of reasons, this isn’t necessarily true.
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.
Philip Cohen’s response to the piece is on his blog, Family Inequality.