Wonkblog highlights four maps created by Seth Kadish of Vizual Statistix.
The maps show … the percentage of a county’s population that receives OASDI benefits; the percentage of OASDI beneficiaries who are retired, rather than disabled; the areas where payments to men most greatly outweigh those given to women; and the average monthly OASDI payment, in hundreds of dollars.
Japan population shrank by 268,000 in 2014, the largest reduction on record, and the government has done a terrible job at predicting it’s fertility rate.
The article is based on a WINPEC Working Paper, “Aging and Deation from a Fiscal Perspective” (PDF).
The above cartoon is from the Florida Sun Sentinal back in early 2014 as New York just held on to its ranking as the third largest state. With the release of the most recent population estimates, Florida has now edged out New York.
Florida Passes New York to Become the Nation’s Third Most Populous State, Census Bureau Reports
December 23, 2014
We’ve updated our Apportionment Calculator. See which states are projected to lose/gain seats in 2020 based on the 2014 results.
And, no. North Dakota is not gaining a seat, even as it is the fastest growing state.
Most of the familiar statistical packages social scientists work with are not well-equipped for analysis of text. Python is one tool often used with text data.
Here is a series of Python tutorials posted on Neal Caren’s Github site. Notice the wide-prevalence of code sharing. That is a feature of much of the folks who work in this field.
You can follow his tutorials on Python or take a Coursera course by a UM professor in February. Another option is the Coursera Data Science specialization offered via Johns Hopkins. This set of courses skips Python but includes a snapshot of the variety of concentrations in this field.
Learning Python for Social Scientists [list curated by Neal Caren]
Programming for Everybody (Python) [University of Michigan via Coursera]
Data Science Specialization [Johns Hopkins via Coursera]
Here’s a rendering of that specialization from a student in the Data Toolbox course:
Source: Uri Grodzinski
Who knew that the name Violet was such a good example of a bi-modal distribution?
This was drawn from a very fun post:
How to Tell Someone’s Age When All You Know is Her Name
Nate Silver and Allison McCann | FiveThirtyEight blog
May 29, 2014
We had a previous post on fun with the Social Security names database.
This age of names example is a great applied demography exercise – calculating the median age of names. For that you’ll need a link to the full names database and cohort life tables:
Beyond the Top 1000 Names
Cohort Life Tables for the Social Security Areas by Calendar Year
Here’s also a nice link to some Big Data exercises via Python. There is a lot of code sharing in this GitHub repository.
The Pew Research Center analyzed data from the 2013 American Community Survey and discovered the cliche is true: a man is more likely to marry much a younger woman the second time around.
Read the full article
By: Emily Badger
A couple of years ago, the city of Chicago started a summer jobs program for teenagers attending high schools in some of the city’s high-crime, low-income neighborhoods. The program was meant, of course, to connect students to work. But officials also hoped that it might curb the kinds of problems — like higher crime — that arise when there’s no work to be found.
Read the rest of the article
Research on the program was conducted by the University of Chicago Crime Lab and published in the journal Science.
By: Emily Badger
Via: Washington Post Wonkblog
Despite their ubiquity in the media, gentrifying neighborhoods that evolve over time from low-income to well-off are quite rare. It is far, far more common that once-poor neighborhoods stay that way over time — or, worse, that they grow poorer.
The article is based on the report “Lost in Place” (PDF) by Joe Cortright and Dillon Mahmoudi for City Observatory
See also Marketplace’s series, York & Fig, on neighborhood gentrification.
Alcohol is an entrenched reality of campus life. Read and share this collection of articles on college drinking to inform colleagues and campus discussions, beginning with “A River of Booze: Inside one college town’s uneasy embrace of drinking” by Karin Fischer and Eric Hoover.
By Dan Keating
Over the past 20 years, whites and blacks have experienced opposite trends in segregation. Asians, Hispanics and blacks are moving into historically white neighborhoods. Vastly fewer whites live surrounded by just other white people. Whites look around and see multi-ethnic neighbors. They perceive expanded opportunity and integration because that is what they see. And they think everyone else is experiencing the same things.
But a Washington Post analysis of Census data shows that the experience in historically African American neighborhoods of major cities has been far different, as they have remained heavily isolated. Whites, Asians and Hispanics are not moving into those neighborhoods, and blacks who remain there experience persistent segregation.
Read the full story