Local governments across the country have come under increasing fiscal strain in recent years, with several being forced to declare bankruptcy. The problems range from pension programs and decaying infrastructure to falling revenues from industrial and sales taxes as manufacturing gets offshored and shopping happens online. In Virginia, cities are further constrained by annexation laws that prevent them from expanding with their metropolitan area and gaining revenue from greenfield development or wealthier suburbs.
Recently, the Bureau of Labor Statistics published a map of industry sectors with the highest employment by state over the past couple of decades. The map shows clearly America’s shift from manufacturing to retail to healthcare. Retail trade has led in Virginia since 1996, even as the rest of the states have been taken over by healthcare.
In his 2014 State of the Union address, President Obama called on Congress to “give American a raise” by increasing the federal minimum wage. For the second year in a row he argued “that in the wealthiest nation on earth, no one who works full time should have to live in poverty”. Even with the presidential priority of raising the Federal minimum wage, the 2014 House bill was voted down. In spite of this, many states and cities have opted to raise the basic hourly wage independent of the federal government.
Raising the minimum wage will impact employers and employees alike, and through them the larger society. While fewer than three percent of US workers* earn the minimum wage (or less), 18 percent earn less than $10.10/hour (the amount proposed by the President). Understanding how an increased minimum wage will affect individuals first requires examining common arguments about low-wage workers.
Like many people, I’ve been inclined to explain Virginia’s decades of explosive population growth in terms of migration and the Federal government’s expansion in Northern Virginia. While that’s certainly part of the equation, “natural increase” has actually driven most of the growth, just as it has across the country. Natural increase simply means more people are born than die in a year. Even in Northern Virginia and Hampton Roads, natural increase is the largest generator of population growth. But “natural increase” does not mean that we are having lots and lots of babies. In fact, it has much more to do with the fact that we had a lot of babies a while back and since then people started living a lot longer.
You hear, on this blog and elsewhere, about the “aging population,” but I wanted to show exactly what that means. Here’s the one gif you need to see to understand population growth in Virginia:
When it comes to interactive data visualizations, I am a junky. I don’t mean the dime-a-dozen country maps showing the favorite baby name/band/movie/current fad for each state. I mean the kind that present information in a way that surprises me, even when I am relatively familiar with the data.
Today I spent quite a bit of time exploring this interactive chart of Jobs by State and Salary, created by Dr. Nathan Yau over at FlowingData. In this chart, Yau shows the number of people employed and the median income for all jobs in a state. The top image shows the occupations in Virginia with a median annual salary of roughly $33,000 or more highlighted in green.
Recently, I’ve been comparing a number of traits of metropolitan areas based on distance from the core. Here I’m looking at the average densities of each metro area as you travel outwards from the center, calculated using census blocks and 2010 short-form census data. I’ve graphed them in groups of three. Cities with a strong core will have high densities on the left (near the center) that fall off as you travel outwards. Cities whose densities fall off quickly on the right have clearer edges, while those that taper off slowly are more spread out. Click on the graphs to view them full screen.
First are the three major metro areas. Note that the Northern VA graph includes only Virginia census blocks, not the rest of the DC area. Northern VA has the largest population by far, with fairly high densities even several miles into the suburbs. Richmond has the smoothest curve. I used downtown Norfolk as the core for Hampton Roads, but the area’s polycentricity is obvious.
This week, the Demographics Research Group updated its profile of Virginia’s regions. The eight regions of the Commonwealth were identified by the Demographics Research Group based on proximity, geography, demographic characteristics and shared socioeconomic conditions. While there are many shared characteristics across Virginia’s regions, our profile shows that a number of differences exist as well.
Northern Virginia stands out the most among Virginia’s regions, but this is not a new trend as Charles Grymes notes on Virginia Places:
“Northern Virginia has been “different” ever since Lord Fairfax established a land office issuing Northern Neck deeds independently from the colonial government in Williamsburg”Continue reading →
Last week, the Bureau of Labor Statistics released results from the 2013 American Time Use Survey. This survey, administered every year for the last decade, asks respondents–selected from people who have recently completed the Current Population Survey–to keep a diary of how they spent their time for a full 24 hour period. These data allow us to understand something about the “average” day not only for the population overall, but also for various subgroups, such as the unemployed or elderly. As you read, consider: How helpful is information about the “average” respondent?
While there is a fair amount of data to mine, let’s start at the highest level, by looking at time use on an “average” weekday across all respondents:Perhaps most notable is the sheer amount of sleep people seem to be getting: apparently, a healthy 8.5 hours a night. Not bad!
…however. As detailed over at Wonkblog, it turns out that “sleeping” is what happens between getting into bed and getting out of bed, and also takes into account naps. This way of calculating sleep doesn’t differentiate between deep slumber and “dozing off to Netflix” (I know I’m not the only one). Similarly, things like “reading in bed” could feasibly occur during these otherwise allocated sleep hours.
And though 99.9 percent of all respondents reported that they slept at some point in the previous 24 hours, the time reported for other activities is averaged across everyone in the survey, whether they did or did not report them in their time-use “diary”. This is where we need to pay attention to what we mean by “average”. Continue reading →