I posted recently about how the state-by-state unemployment rate has changed during my lifetime. The result was a small multiples grid that put the states in context with one another.
Today I’ve created a new version aimed at identifying more precisely how each state has differed from the national unemployment rate during the last four decades. The lines show the percentage point difference — above (worst) or below (better) — from the national rate.
This view allows us easily to identify the most anomalous states in both directions (West Virginia, for example, had quite an unemployment spike during the 1980s; South Dakota, on the other hand, has never been worse than the national rate).
There’s plenty more to explore in this quick remix:
There’s good news this week in the monthly jobs report, the latest sign that the economy, however grudgingly, has healed from the financial crisis nine years ago:
The unemployment rate fell to 4.6 percent, the Labor Department said, from 4.9 percent. The last time it was this low was August 2007. That was the month, you may recall, when global money markets first froze up because of losses on United States mortgage-related bonds: early tremors of what would become a recession four months later and a global financial crisis nine months after that.
These things, of course, are cyclical. Here’s how the unemployment rate has changed, by state, during my lifetime:
As FiveThirtyEight notes, turnout in the 2016 presidential election isn’t dramatically lower than it was four years ago, according to the latest estimates. And with many mail-in and provision ballots still being counted, the 2016 turnout rate could still change:
Approximately 58.1 percent of eligible voters cast ballots in last week’s presidential election, according to the latest estimates from Michael McDonald, associate professor at the University of Florida, who gathers data at the U.S. Elections Project. That’s down only slightly from 2012, when turnout was 58.6 percent, and well above 2000’s rate of 54.2 percent. Turnout may end up being higher than in any presidential election year between 1972 and 2000….
We won’t have final turnout numbers for weeks or months because some states are still counting ballots; millions remain uncounted. That means estimates based solely on votes counted so far will understate turnout — though already more presidential votes have been counted this year than in 2012 (contrary to reports that fewer voters turned out this year). In the meantime, most news organizations rely on estimates from McDonald.
Here’s a quick look at historic turnout in both midterm and general elections, according to estimates compiled by McDonald:
Earlier I used small multiples to show how each Major League Baseball team’s 2016 season progressed relative to the .500 line. Here are those same line charts, but this time I’ve grouped them by division:
Last week I published a new heatmap exploring the popularity of American birthdays. The chart, which uses darker shades to represent higher average birth counts on specific days, can give the impression that some birthdays are much more common than others.
In reality, outside of some special occasions, namely major holidays, there isn’t a huge amount of diversity in the data set, which has two decades of births aggregated by day. Most birthdays, including my own, are fairly average — especially in the first six months of the year. For example:
It’s baby season in America, with September the busiest month for births on average in the last two decades. So it seemed like the right time to remix this blog’s most-popular post: How Common is Your Birthday?
That old heatmap, which highlighted specific dates for popularity, has been viewed more than 500,000 times here and published across the web. But it was flawed, namely that it used ordinal data (birthday ranks by date) rather than continuous data (actual births counts by date). This graphic finally addresses that problem:
The previous posts relied on two data sets from the World Health Organization, which calculates consumption (in liters and grams) based on surveys and actual import, exports and sales data. The organization, a reader noted recently, also breaks down the consumption totals proportionally by beverage.
This chart shows each country and its relative tastes for beer, wine, spirits and “other,” which, in South Korea at least, is mostly soju, a fermented rice beverage that’s not easily categorized.
A few weeks ago I posted about gender gaps in alcohol consumption around the world.
In some countries — South Korea, for example — men and women consume quite different amounts of booze, according to the World Health Organization. Fueled by a love for soju, South Korea’s men are among the heaviest drinkers in the world, consuming about 78 grams per day — nearly twice as much as other men on average. Its women drink only slightly more than their counterparts abroad, on average.
But that data only averaged daily consumption, by country, among people who list themselves as “drinkers”. The organization also has estimates about per-capita consumption amounts based on countries’ import, export and sales data, normalized with their adult populations. Depending on your question, that might be more useful information.
Those data, which also offer a breakdown of alcohol types (beer, wine, spirits and “other”), tell a different story. Instead of leading the world, by that measure South Koreans rank farther down a list of 196 countries: 35th.
Note: I followed my wife, a foreign correspondent for NPR News, to Seoul last year. This is one of a series of posts exploring our adopted country’s demographics, politics and other nerdy data stuff. Let me know if you have ideas for future posts.
I never lived in a high-rise building before moving to South Korea, but now home is 35 stories above central Seoul. The view is pretty great — when, of course, it isn’t obscured by pollution.
I’m just one of about 10 million Seoul residents in a geographic footprint the size of Chicago, so high-rise residential seems normal. How common is it, though, and how has that changed over time? These charts attempt to answer.