Monthly Archive for December, 2016

More apportionment fun

The Census Bureau just released its 2016 Population Estimates. Let’s take a peek at what the Electoral College might look like in 2020 using the PSC Apportionment calculator. The easiest calculation is to just put the 2016 estimates into the calculator (remember to delete Washington, DC). In that scenario:

The losers: Illinois, Michigan, Minnesota and Pennsylvania
The winners: Florida, North Carolina, Oregon, and Texas

If we take the 2010 to 2016 growth rate and extend it to 2020, this is the scenario:

Losers: Alabama, Illinois, Michigan, Minnesota, New York, Ohio, Pennsylvania, Rhode Island, West Virginia
Winners: Arizona, Colorado, Florida (2), North Carolina, Oregon, Texas (3)

Notable in this scenario is Rhode Island losing an electoral seat. It is just slightly larger than Montana – 1,056,426 vs 1,042,520. But, Rhode Island has had an extra seat since 1990 when Montana lost 1 seat in the House of Representatives. Montana is on the cusp of getting that 2nd representative in the House – it just needs ~5,000 more people than our 2020 projection, which is not an unrealistic scenario.

One thing that is unrealistic about the previous scenario is the fast growth of North Dakota during this period. North Dakota has been the fastest growing state for the past 4 years, but its growth rate dropped to 37th fastest in 2016 due to the collapse of oil prices and thus the fracking industry. Thus, a more realistic estimate might be to use the 2015-2016 rate for the last 4 years of the decade. In that scenario, the results are exactly the same, except that now Montana needs only 550 more people to gain a 2nd seat in the House of Representatives.

Resources
Vintage Population Estimates (2016)
http://www.census.gov/programs-surveys/popest/data/tables.html
State Population Estimates (2016)
http://www.census.gov/data/tables/2016/demo/popest/state-total.html
PSC Apportionment Calculator
Representations apportioned to each state (1790 to 2010)
Congressional Apportionment Resource

Mapping Megaregions via ACS commuting data

Two researchers have created a map of megaregions in the US based on commuting data from the American Community Survey (ACS). The results are covered in both the popular press and in PLOS One. The latter provides more details about how they constructed the maps – it wasn’t just via a mapping program.

How 4 Million Commutes Shape America’s ‘Megaregions’
Laura Bliss | Atlantic: City Lab
December 7, 2016

An Economic Geography of the United States: From Commutes to Megaregions
Garrett Nelson and Alisdair Rae
PLOS One
November 30, 2016

If you are so inclined the authors have made their data available for replication via Figshare.

[Additional Media Coverage]

Great Tweetstorm: Most important year in Economics?

This is from the blogger @undercoverhistorian. We had a previous post on the site she maintains. Below is an interesting set of almost 50 tweets – some illustrated – where she defends 1952 as the most important year.

twitter feed
Click here for tweetstorm

Risk Visualization Theater

How to better communicate election forecasts — in one simple chart
Justin Gross | Monkey Cage blog [Washington Post]
November 29, 2016

Most folks were surprised by the results of the 2016 Presidential election and this was in part due to some of the rosy forecasts by some of the poll aggregators, like Huffington Post. But, even when a site had a forecast with a 30% chance of Trump winning, most people have trouble understanding that a Trump victory was possible. The explanation:

But certain representations of probability are more readily grasped than others. In particular, we have trouble understanding risk in terms of the “percent chance” but we do better when simple raw numbers of different outcomes are depicted visually.

Solution: Show the risk as a “Risk Visualization Theater.” Below are the representations of forecasts of victory for Trump via FiveThirtyEight, NYT Upshot, and Huffington Post Pollster. The filled theater seats (in black) represent the chance of a Trump victory. Clearly, the chance of that event happening don’t look so remote in the far left depiction, but look very unlikely as one moves to the right.

Risk via a theater

Missing girls in China maybe weren’t missing after all

China has had a highly unbalanced sex ratio at birth for years leading to an estimate of 30 to 60 million missing girls. The traditional explanation was male preference, exacerbated by the one-child policy, which led to sex selective abortion and/or infanticide. New research presents evidence that maybe the missing girls were never missing after all.

Researchers may have ‘found’ many of China’s 30 million missing girls
Simon Denyer | Washington Post
November 30, 2016

Delayed Registration and Identifying the “Missing Girls” in China
Yaojiang Shi and John James Kennedy | China Daily
November 15, 2016