The Wall Street Journal recently released a fantastic interactive graphic that shows trends in monthly food stamp participation, by state, from 1990 through 2013. Most states follow the overall national trend: participation rises in the mid-1990s, gradually declines through the boom years of the late 1990s and early 2000s, flattens slightly through the 2000s, and then sharply increases following 2008.

Some states, like Alaska, have strong seasonal fluctuations in participation rates, although the magnitude of this variation has diminished in recent years.

In other states, program participation spikes dramatically following natural disasters. In Louisiana, SNAP enrollment nearly doubled in late 2005 following Hurricane Katrina. Damaging hurricanes in 2008 (Gustav and Ike) and 2012 (Isaac) led to similar spikes in enrollment.

Interested in exploring more? We have an interactive map of SNAP recipients by county for Virginia in 2010. For even greater detail, the USDA Economic Research Service provides a detailed interactive map of SNAP participation, benefit amounts, and socioeconomic indicators by county for 2006-2010.

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Rebecca Tippett is a Research Associate at the University of Virginia’s Weldon Cooper Center for Public Service where she studies household economic well-being and produces population estimates and projections.

This map, built on data from the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS), shows the prevalence rate of adult obesity by state for 1985 to 2010. 

Prior to 1991, no state had an adult obesity rate greater than 15%. Seven years later, in 1998, the highest obesity rates were above 20%. Three years later, in 2001, obesity rates in Mississippi exceeded 25%, and rates in other states soon rose. By 2010, no state had an adult obesity rate less than 20%; adult obesity rates were equal to or greater than 25% in nearly three-quarters (36) of the states.

Data for 2011 shows that adult obesity rates remain high across the nation; though prevalence rates continue to rise, there is some evidence that the rate of increase has slowed in recent years.

Obesity is a major risk factor for many of the leading causes of preventable death, such as heart disease, stroke, and type 2 diabetes, and is associated with significantly higher medical costs. Given the size of the epidemic, even small changes in obesity prevalence would result in substantial medical savings, as well as improvements in longevity.

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Rebecca Tippett is a Research Associate at the University of Virginia’s Weldon Cooper Center for Public Service where she studies household economic well-being and produces population estimates and projections.

• Virginia 2010

• Fredericksburg City 2010
• Richmond-Pertersburg Metro Area 2010
• Martinsville 2010
• Lynchburg City 2010
• Harrisonburg City 2010
• Staunton-Waynesboro 2010
• Roanoke-Salem 2010

Plus the ones from the previous post:

• Northern Virginia 2010
• Charlottesville City 2010
• Winchester City 2010
• Hampton Roads 2010

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Dustin Cable is a Policy Associate at the University of Virginia’s Weldon Cooper Center for Public Service where he conducts research on topics that lie at the intersection of demographics, politics, and public policy.

Take the City of Charlottesville as an example: The great thing about dot density maps is that they elegantly convey a lot of data in a small space.  Total population, population density, geographic distribution, and race/ethnicity are displayed in a single visual.  Also, by incorporating the racial and ethnic data, the extent and degree of residential segregation manifests itself.  These maps still work for even the most densely populated areas… Fairfax, the Beltway, and Manassas: Norfolk, Portsmouth, Newport News, and Hampton: However, like the Charlottesville example, some of most interesting maps are for Virginia’s smaller cities and towns. Winchester City:

Despite their utility and beauty, these maps have their limitations.  They are bounded by the highest resolution possible with census data, namely population data by Census Block, the smallest unit of census geography (roughly equivalent to a city block in a urban area).  The dots are randomly placed within Census Blocks so sometimes may not represent actual residences for some larger area and less-populated Census Blocks.

Note:  The racial categories for White, Black, Asian, and Other are all non-Hispanic.  Hispanic dots represent a person of any race, but are usually categorized as Hispanic White or Hispanic Other.

High-resolution images available upon request.

– Dustin Cable is a Policy Associate at the University of Virginia’s Weldon Cooper Center for Public Service where he conducts research on topics that lie at the intersection of demographics, politics, and public policy.

In the spirit of March Madness, the Census Bureau has developed their own bracketology-themed population game. You should take a few minutes and play a round. It’s pretty fun.

You’ll find match-ups of states or metro areas, and you simply pick the one with the larger population. You’ll go through all the pairings until you’ve selected what you think is the state or metro area with the largest population in the country.

The Census Bureau has developed quite a few tools and games like this to showcase their data.  You can find the entire gallery on their webpage: http://www.census.gov/dataviz/

Among those of us who love old maps, the good people at the Atlas of Historical County Boundaries project have digitized and uploaded historical information on the shape of American counties.  With this data one can animate how America’s political boundaries have changed since the founding of the Massachusetts Bay and Virginia Colonies.  The above video shows historic county boundaries from 1630 to 1910 (shortly after Oklahoma and Indian Territory joined to form the State of Oklahoma in 1907).  Please note these boundaries show the creation of government-defined geographic units, not necessarily where population is located.

Another great thing about this data is the level of detail available.  For instance, focusing on the monumental changes that Virginia has gone through is quite interesting:

Note the emergence of many of Virginia’s Independent Cities at the turn of the 20th Century.

Things get more interesting when these county files are merged with historical census data.  Inspired by our previous post on “Every person gets a dot,” I decided to look at county population dot densities from the first United States Census of 1790 to the recent 2010 Census.  Here, every dot represents 5,000 people:

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Dustin Cable is a Policy Associate at the University of Virginia’s Weldon Cooper Center for Public Service where he conducts research on topics that lie at the intersection of demographics, politics, and public policy.

This past Tuesday, the Census Bureau released a report, Disability Characteristics of Income-Based Government Assistance Recipients in the United States: 2011, which uses 2011 American Community Survey data to document the disability prevalence and type among U.S. adults 18 and older receiving need-based public assistance. Nationally, 30.4% of adults receiving need-based government assistance report some type of disability. Virginia, like many of the states along the Appalachian mountains, has a slightly higher rate of disability among adults receiving need-based aid: 33.4%.

This high disability prevalence among individuals receiving need-based aid reflects both high unemployment rates among the disabled population and, among the population that is working, lower average wages. These broad patterns are replicated in Virginia.

In 2011, among working-age Virginians (ages 16 to 64):

• 60% with a disability were out of the labor force, meaning they are neither working nor looking for work. This is triple the out-of-the-labor-force proportion of non-disabled working-age Virginians (20%).
• Among individuals in the labor force, Virginians with disabilities reported higher unemployment rates, greater part-time work, and lower employment in professional occupations.

Consequently, compared to non-disabled Virginians, individuals with any disability report lower median incomes and are more likely to fall below the poverty line, receive food stamps, and rely on public health insurance, such as Medicaid, for medical care. While participation in these need-based government transfer programs is significant for alleviating poverty, the poverty rate among disabled Virginians remains high: 20%, even after accounting for taxes and transfers.

For more information, please visit our resources on the Social Safety Net and Working-Age Virginians with Disabilities.

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Rebecca Tippett is a Research Associate at the University of Virginia’s Weldon Cooper Center for Public Service where she studies household economic well-being and produces population estimates and projections.

This map provides another way of visualizing population density, but this time with a layer of political consequence. Each of the states is centered around one of the 50 largest cities. Freeman’s algorithm then used proximity, urban area, and commuting patterns to further group counties around these cities. After this initial modeling was completed, he manually adjusted the map to account for a variety of other factors, such as compact shapes and equal populations.

Freeman notes that this map has the advantage of ending disproportionate representation in the House and Senate and suggests that states, much like House and Senate districts, could be redistricted after each census to reflect changing population patterns.

While Freeman is careful to note that this is “not a serious proposal,” I enjoyed this visualization because it challenges the idea of states as fixed geographical/political entities, while still providing meaningful groupings.

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Rebecca Tippett is a Research Associate at the University of Virginia’s Weldon Cooper Center for Public Service where she studies household economic well-being and produces population estimates and projections.

The map shows a dot representing every person counted by the 2010 U.S. Census, the 2011 Canadian Census, and the 2010 Mexican Census. There are 454,064,098 dots – one for each person.

I think the coolest thing about this map is that you can zoom in to a very detailed level and try to find your own dot (you can toggle labels on and off at the top left of the screen to help orient yourself on the map).

Although I should clarify that when I say, “your own dot,” I don’t mean it literally. The Census takes many precautions to protect personal information, so Martin-Anderson didn’t actually place a dot exactly where each person lives. He randomly placed points in each Census block to represent the total population count – the most detailed piece of information he can get – in each block.

This is evident from one of his FAQs:

Q: [Why does] this [map show that] someone lives in the middle of a lake.

A: The census reported that someone lives in a block which includes a lake, and that’s where their dot was randomly placed. Also, some people live in the middle of lakes.

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Susan Clapp is a statistician at the University of Virginia’s Weldon Cooper Center for Public Service. She is an expert in data sources, statistical methods, and teaching us all how to understand and use data well. 

The central thesis of this line of argument is perhaps best summarized by George Mason University economist Donald Boudreaux, whom Edsall quotes:

“[O]ur larger, more central, and most important point is that middle-class Americans are today far better off economically than they were 30 or 40 years ago, regardless of how their well-being today compares to that of rich Americans.”

This line of argumentation defines one of the primary characteristics of improved economic well-being as having access to better and more affordable goods and services than previous generations. As Kevin Hassett and Aparna Mathur write in the Wall Street Journal:

“[T]he access of low-income Americans—those earning less than $20,000 in real 2009 dollars—to devices that are part of the “good life” has increased [between 2001 and 2009]. The percentage of low-income households with a computer rose to 47.7% from 19.8% in 2001….

The percentage of low-income homes with air-conditioning equipment rose to 83.5% from 65.8%, with dishwashers to 30.8% from 17.6%, with a washing machine to 62.4% from 57.2%, and with a clothes dryer to 56.5% from 44.9%.”

The argument that the poor are somehow “doing okay” because they have access to air conditioners, time saving devices, and computers is a distraction from a larger discussion that is worth having, and ignores key issues underlying the consumption theory.

How do we conceptualize economic well-being?

The argument about consumption inequality versus income inequality points to a broader issue of how we conceptualize economic well-being. Should poverty be an absolute metric, defined as the ability to afford a set market basket of goods that is deemed minimally sufficient (i.e., the current U.S. federal poverty line)? (And, what is minimally sufficient? Should it be limited to what is necessary for basic human functioning, or should it be based on what it takes to be self-sufficient in a given society?) Or, should we use poverty metrics that are relative, such as the proportion of individuals earning less than 50% of median income?

Like any metric, both have strengths and weaknesses. Absolute metrics give us a measure of deprivation, while relative metrics provide a better picture of the potential for inequality to exert negative effects on overall societal well-being. As such, the choice of metric leads to different conversations about the consequences of poverty and how best to target public policies.

Are improvements in consumption equal to increased economic well-being?

While it may be true that low- and moderate-income households have access to certain goods and services previously out of their reach, the consumption thesis focuses on an absolute standard and ignores that consumption patterns are typically relative to changes in living standards. The definition of the “good life” is not static. Many of these items owned by low-income households, such as refrigerators for food storage and microwaves for cooking, are no longer considered luxuries, and other items, such as access to computers and corresponding computer skills, are often key to successful labor market entry.

Ultimately, while it is important that low- and moderate-income households have access to goods and services that improve their lives, that does not by definition translate into economic well-being. The proponents of the benefits of increased buying power for the poor fail to mention the source of this consumption financing. If consumption is maintained (or increased) primarily by reliance on borrowing (debt), this is a serious problem that has the potential to dramatically undermine both the short-term and long-term economic well-being of these households.

Compared to non-poor households, poor individuals and households spend a larger proportion of income on basic necessities and are less likely to own assets, such as a car or a house, and are less likely to have savings. In addition, low-income workers are significantly less likely than their higher earning counterparts to have access to workplace fringe benefits, such as health insurance and retirement accounts. Consequently, lower-income households often have no or limited capacity to weather financial emergencies.

If we consider economic well-being to entail being able to both afford day-to-day necessities and build savings for the future, then consumption alone fails to provide a full picture of economic well-being.

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Rebecca Tippett is a Research Associate at the University of Virginia’s Weldon Cooper Center for Public Service where she studies household economic well-being and produces population estimates and projections.

For information on economic well-being in the Commonwealth, please visit our our Economic Security for Virginia’s Families and The Social Safety Net pages.