The Census Bureau recently released new migration data based on the 2009-2013 5-year American Community Survey estimates. This data estimates how many people move between each of the country’s metropolitan areas over the course of a year.There are plenty of interesting things that can be teased out of this data, but flow data is always a little bulky to play with. The Washington Post’s Wonkblog put up a great chart showing migration between the country’s largest metro areas: Read Full Article →
As I mentioned in my last post, there are a whole host of considerations to take into account when looking at men’s and women’s wages to investigate any gender-based differences. Sheer earnings numbers are meaningful—after all, a difference in earnings, no matter why it exists, means a difference in what men and women are able to afford to buy. However, when stated without context, these numbers invite debate. In order to gain a more thorough understanding of the difference between men’s and women’s earnings, one reasonable starting point is to control for those things that are widely understood to determine salaries, such as education level, industry of employment, or years of experience.Studies that carefully examine job-related skill-based differences in earnings are not uncommon and are usually called human capital-based studies. Over the last several months, I’ve come across two of particular interest to me: a study conducted for the benefit of university administration at the University of Virginia, and a research letter regarding registered nurse salaries published in the Journal of the American Medical Association. These studies share the goal of describing men’s and women’s earnings while controlling for very specific wage determinants. Each shows a significant difference between men’s and women’s earnings, even after important characteristics have been held constant. Neither definitively states the precise cause of the difference, yet each advances our understanding about the wage gap.
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The financial crisis and its aftereffects had a significant impact on American’s incomes. But the slow income growth that continued after the recession ended has also increased public awareness that income stagnation is a national problem that pre-dates the financial crisis and extends back into the late 1970s. Between 1950 and 1975, the U.S. median household income rose by over 50 percent. Since then income growth has been nearly flat. In Virginia, which had a comparatively healthy economy over the past few decades, the median household income rose less than 2 percent over the entire period between 1990 and 2013. But on the local level in Virginia, changes in income have been considerably more varied since 1990.
Change in Household IncomeLarge swaths of Southside, the Valley, and the Richmond metro area have experienced declines in their median household incomes during the period between 1990 and 2013. Martinsville’s median household income was halved during the past two decades and by 2013 the city had the lowest median household income in the state. Many of the localities whose residents’ incomes rose during this period were in the outer suburbs of Virginia’s Urban Crescent, which stretches from Northern Virginia to Hampton Roads, and grew wealthier as more workers moved out to them during the housing boom. Read Full Article →
Most people probably know that the non-English speaking population in the U.S. is increasing. Only last month the Instituto Cervantes announced that the U.S. now has more Spanish speakers than any other country in the world aside from Mexico. This trend is perhaps best illustrated by public school students who are enrolled in English as a second language courses (ESL). In Fairfax County, Virginia’s largest school division, the portion of ESL students rose from 10 percent in 2000 to 29 percent by 2011.Yet few realize that growth in the number of U.S. residents whose native language is not English has slowed in recent years and, in some places, the number is now declining. In Fairfax County, the percentage of students who needed ESL courses has declined after peaking in 2011 as it has in many other localities with large ESL enrollments, such as Prince William County, Virginia Beach and Harrisonburg. Nationally, the percentage of students enrolled in ESL courses peaked in 2008 and has declined since then as well, most notably in states with a large number of students enrolled in ESL programs, such as California and Texas. Though Virginia has been an exception to national trends, with ESL enrollment still rising, growth statewide has slowed considerably in recent years. Read Full Article →
Religion, race, ethnicity, citizenship, and ancestry are just some of the characteristics used by people to identify themselves, to be part of a bigger community, and to enhance social cohesion. Language plays an important role in this mix. It provides a medium to communicate, carries its own cultural identity, and, like most other social identifiers, it has the ability to unite as well as divide. Demographic data is a constant reminder that diversity in Virginia is rising, so I thought it would be interesting to use language as an indicator of this diverse social landscape and see what languages are most commonly spoken across the Commonwealth.
A while ago, I came across an article exploring language use at the national level, which demonstrated how languages spoken in American homes vary across the 50 states. This map lists Korean as the most commonly spoken language-other than English and Spanish-in Virginia as a whole. However in this analysis, the differences across localities gets eclipsed and we miss out on the variety of languages spoken within the state itself. So let’s take a look at the most common languages spoken in each of Virginia’s cities and counties. Read Full Article →
Most of Dickenson County‘s residents live along the many river valleys that flow down into the Russell Fork which cuts through Virginia’s Cumberland Mountains to create the largest gorge east of the Mississippi. Yet despite its many scenic attractions and the bonus of a low cost of living, Dickenson has been steadily shedding its population, declining by over a third since 1950.Rural counties in Virginia, like Dickenson, have been slowly losing their young adult population for decades as many have moved elsewhere to seek more education and work opportunities. But often rural counties have been able to continue growing by attracting older migrants who are nearing retirement or have already established their careers elsewhere. However, these two migration trends are now creating a new problem for most of Virginia’s counties; the gradual hollowing out of their young adult populations from decades of out-migration combined with a growing retiree age population means that in an increasing number of counties, there are no longer enough families with children to replace the rising number of deaths. Read Full Article →
In my last post, I began a series focused on the gender wage gap, and discussed why demonstrating its existence is not always a simple proposition. Most importantly, I argued, it is not enough to simply look at the difference between men’s and women’s median wages. Not only do these estimates tell us less than we might like (recall those overlapping margins of error), but their analysis also often ends up oversimplifying the conversation by providing only a comparison between all working men to all working women—not, ultimately, the comparison we really want to make.This is because human capital—the abilities people bring with them into the workplace—and wage structure—the amount we compensate various jobs relative to others—affect how much we can reasonably expect people to be paid, as do some outside factors, such as when and where a worker is hired. In other words, there are plenty of reasons other than gender that could explain why two individuals working full time earn different amounts of money. In order to investigate the extent to which a worker’s gender influences his or her wages, we need to make sure we’re comparing workers who are similar in as many other ways as possible. Read Full Article →
Farming has a semi-mythological status unlike any other industry or occupation. In few places is this more true than Virginia, which was founded on farming and particularly tobacco farming. For much of Virginia’s history, its population was shaped by agriculture. Most early Virginians came to the state as either indentured servants or slaves to work in its tobacco and wheat fields. As recently as 1940, farming was still the largest employer in Virginia, with 180,000 farms taking up two-thirds of the state’s land area. Read Full Article →
A couple of weeks back, I attended the 2015 ACS Data Users Conference and heard a lot of concerned researchers discussing the impact of losing the 3-year estimates from the American Community Survey. In the past, the Census Bureau provided separate 1-year, 3-year, and 5-year estimates – but funding constraints have resulted in the discontinuation of the 3-year statistical product which covered communities with a population size of 20,000 or more. Each set of estimates have their relative merits, as explained in detail on their website. The 3 and 5-year estimates are period estimates constructed as a rolling average, these are different from the 1-year values which capture the data collected during the annual cycle. Choice of the data-set and the timeline depends on the goal of the analysis, and it usually involves some tradeoff between currency and precision. Read Full Article →
April 14 of this year marked what is known as Equal Pay Day, the representing “how far into the year women must work to earn what men earned in the previous year.” The choice of date is based on research demonstrating that women earn approximately 77 cents for every dollar earned by their male counterparts. Whether it is 77, 84, or 93 cents on the dollar, there is reason to believe that women earn less than men in this country—but it takes a lot of careful work to meaningfully demonstrate it.Recently, I have been asked for data on men’s versus women’s wages in Virginia, overall, as well as within smaller regions, such as particular localities or zip codes. Each time, the asker—a journalist—has explained that his goal is to find quantitative data addressing the topic of gender wage equity in the workplace. I’m thrilled that journalists are not only interested in asking these questions, but also looking for high-quality data to use as they write about this complex topic. But while comparing wages might appear to be the most obvious way to get at the answer, careful consideration of the data reveals why this task is not as straightforward as it may appear. Read Full Article →
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