income

Data Drives Community Change in Southwest Missouri

In southwest Missouri, there is a bit of a chicken and egg problem.

What came first, the high teen birth rates or the low access to primary care physicians?  Children eligible for free and reduced lunch or the unemployment rate? What is the root cause of the disparities in southwest Missouri?

Birth rates for teens in McDonald County are four times that of the national average. Over 30 percent of the population is living below the Federal Poverty Level (FPL). Sixty-nine percent of schoolchildren are enrolled in free and reduced lunch programming. Twenty-five percent of children experienced food insecurity, while six percent of those children were deemed ineligible for assistance. More than 5,000 families are eligible to utilize the Women, Infants, and Children (WIC) program – but only about 11 percent of those families are are using the WIC services.

“It is difficult to move out of McDonald County,” Debbie Markman, Resource Development Director for the Economic Security Corporation (ESC) said. “You either have zero income or less than $300 per month or you make over the 50 percent of the area median gross income because you work at the chicken plants that they pay well and provide good benefits. We have people doing ‘sustenance living’ – growing their own food and sometimes are living in places that don’t have electricity or running water. It’s like the county is split in two.”

For four counties in southwest Missouri – Barton, Jasper, Newton, and McDonald – navigating the challenges of their community needs falls under the mission of the Economic Security Corporation (ESC). ESC works with communities to alleviate the conditions of poverty and provide individuals and families with opportunities that will enable them to achieve economic security.

When the time came to update the community needs assessments for the four counties, ESC turned to Community Commons to explore the areas of greatest need in their communities and to present that data in a compelling way to board members. The utilization of the community data provided unparalleled insight into a path toward community-wide economic security.

“The data we discovered has started so many conversations and really has given us a place to start from,” Markman said. “We’ve been able to talk about why things are happening and what can be done to fix it. Our goal is developing programs that have evidence-based processing behind it.”

ESC is now leading an effort to address three to five major priorities and hope to start implementation on these priorities over the next five years.

Data Viz: Broadband Access and Usage

A recent report from the United States Census Bureau highlights usage rates of broadband across the nation. The report shows that high income areas, like the Pacific Coast, New Hampshire, and Massachusettes, are better connected and have higher rates of utilization.

Alternatively, states with low median household incomes, like Mississippi, Arkansas, and New Mexico, reported the lowest rates of broadband usage. These states were also more likely to access the internet from mobile devices than from a computer.

The Digital Divide: Percentage of Households With Broadband Internet Subscription by State

The data also show that non-Hispanic whites have more access to digital devices –  65 percent of non-Hispanic whites reported owning a computer (desktop or laptop), a mobile device, and a broadband connection, compared with 55 percent of Hispanics and 49 percent of non-Hispanic blacks.

The Digital Divide: Percentage of Households With Broadband Internet Subscription by State

Overlay broadband data, demographics, and income in our MapRoom:

Click the map to zoom to your area.

Read more: https://census.gov/newsroom/press-releases/2017/internet-use.html

Mapping Poverty in the Appalachian Region

The Appalachian region is home to hardworking individuals who value their families, community, and living in the natural beauty of one of America’s most beautiful regions. For generations, families have earned a living on jobs provided by the region’s primary industry- coal. In its heyday of the 1910s and 20s, more than 700,000 jobs were provided by the coal industry. In Appalachia, that number now hovers around 44,000 – with not much coming in to fill the void.

When the demise of the coal industry began in the 1940s, unemployment and poverty hit the region hard. Those with higher education went to other states for better jobs and higher wages- a trend we still see today, especially among young adults. More recently, the outsourcing of jobs overseas has caused soaring unemployment in a number of counties.

The outlook for coal is only expected to worsen as federal regulations, the decreasing cost of natural gas and the increasing costs of mining in the region continue. However, people in some of Appalachia’s most impoverished counties are coming up with their own ways of building a future without coal.

Still, poverty, unemployment, and low-paying wages persist. While communities across the US struggle with poverty, some of the most impoverished- and unnoticed- are in Appalachia.

Poverty in Appalachian Counties 

Appalachia has some of the highest poverty rates in the US. The US poverty rate is 15.6 percent, while the Appalachian region is 19.7 percent. However, what is most revealing is when you compare an Appalachian state’s poverty level to the same state’s Appalachian region’s poverty level. For example, in Virginia the poverty rate is 11.5 percent, much lower than the US rate. However, when you look at Virginia’s Appalachian region’s poverty rate, it increases considerably to 18.8 percent (Fahe.org). As depicted in the map below, the same is true for states like Kentucky and West Virginia as well. That’s the difference that frequently goes unnoticed.

CC Map Poverty3

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Unemployment in Appalachian Counties Relative to US Rate

Unemployment in the Appalachian region is 6.5 percent, and in the US it’s 6.2 percent. While this difference is not startling at first glance, when you look to specific counties, it becomes a much bigger issue. When coal companies and manufacturing plants close their doors, a jobs void affects workers in the surrounding areas. In Lewis County, KY, when Nine West shoe company closed their plant to move overseas, the county’s unemployment rate climbed to 12 percent.

CC Map Unemployment

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Appalachian Counties’ Per Capita Income

Unemployment is only one facet of the poverty in the region. As you can see from the map below, the Appalachian region has some of the lowest wages in the US. The average income in the Appalachian region is $37,260 and $46,049 in the US. What’s perhaps most telling is the labor market engagement map. It’s an index that describes the level of employment, labor force participation, and level of education in a census tract. Much of the region is plagued by poor labor market engagement, a direct reflection of poor job prospects and low wages.

CC Map Per Capita Income

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CC Map labr mrkt engagement

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Building Opportunity in Appalachia

The data and statistics look disheartening, but fortunately one of the greatest assets the region has are its people.

In an interview with The Atlantic, Peter Hillie, President of the Mountain Association for Community Economic Development (MACED) says unlike the past, Appalachia’s future prosperity will require more than one industry to come in and save the region, “There’s not a silver bullet,” he said, “There’s just a lot of little silver BBs.” One of those BBs is getting young people to stay.

In the same interview, Ada Smith from Lechter County, KY said “For people who grow up here or have roots in this place, parents and grandparents who know there’s not a lot of opportunity here encourage their loved ones to go and find jobs elsewhere.” Some counties are beginning to see the return of young people who left. One man returned from Louisville to open a tattoo parlor, others have opened restaurants, t-shirt companies and record shops. With a loan from MACED, one former coal miner even bought sheep from Vermont to start his own business- Good Shepherd Cheese.

Perhaps most inspiring is the work done by Smith and other young people from Appalachian states. Dismayed by the perception that staying in poor Appalachia counties is equated with failure, the group created Stay Together Appalachian Youth (STAY). Made up of young people from Appalachian states, the group discusses how they can better their local communities and work to encourage other young people to stay and work towards the same goal. “I feel like people are having conversations and willing to try different things that they never would have before,” Smith said. The enthusiasm for the movement is catching on, as more young people are moving to Whitesburg (where Smith lives) for the sense of community among other young adults that live there.

Building prosperity in Appalachia requires a homegrown effort, with commitments and investments from everyone. It needs people thinking about how they can work across county lines and state lines to move forward as a region –with its young people as the catalyst.

Data Viz of the Week: Index Scores Summarize Community Conditions

Along with single-indicator map layers, Community Commons offers several indices that provide a community score for a particular topic. An index is based on several indicators put together, and thus allows you to summarize many factors while looking at just a single score.

Though they can often be intimidating, indices are a great way to understand how your community is doing in comparison to others.

This feature will present just three of the several indices offered in the Community Commons map room. Try adding one to your map to see how your community ranks.

Low Transportation Cost Index

This index takes into account several transportation-related indicators and scores the transportation cost for each census tract around the country. In this case, the higher the score, the lower the transportation cost. Using this index layer may come in handy when thinking about proposals to increase public transportation in your community.

Low Transportation Cost Index sized

Labor Market Engagement Index

The labor market engagement index is based on educational attainment, employment level, and labor force participation in a particular area. Using these three criteria, this index provides a score from 1-100 to summarize the intensity of the labor market and human capital in a census block group. These scores might be useful in determining where to focus on increasing employment opportunities in your neighborhood.

Labor Market Engagement Index sized

Per Capita Income Disparity Index

Indices can also be helpful in displaying disparities within and between communities. The per capita income disparity index is based on data from the American Community Survey to illustrate where income disparities exist across race/ethnicity in communities. This index provides a summary score for communities across the country, where a high score indicates high disparity.

Per Capita Income Disparity Index sized

Using an index can be helpful when you want to provide a summary of many indicators in an easy-to-understand score. Try adding one of these indices to your map today!

At Community Commons, we love to explore data and create new indices. We partner wth CARES to do these analyses, such as what we created with Environments Supporting Healthy Eating (ESHE). Look for an in depth feature on the ESHE Index later in July. If you have data you’d like to explore as an index, please contact CARES at cares.missouri.edu

Mapping Factors that Influence Social Mobility

Upward social and economic mobility from one generation to the next is harder in the U.S. compared to other developed countries. While many Americans have access to resources and opportunities that bolster their chances of achieving the  “American Dream” for their families, many more lack the opportunity to simply break from the cycle of poverty. The difference between the two is often separated by just one street.

In a study looking at social and economic mobility in the US, Harvard and UC-Berkeley economics professors aptly stated, “The U.S. is better described as a collection of societies, some of which are `lands of opportunity’ with high rates of mobility across generations, and others in which few children escape poverty.”

The researchers describe high mobility areas as generally having:

  • Less residential segregation
  • Less income inequality
  • Better primary schools
  • Greater social capital
  • Greater family stability

The income gap between wealthy and non-wealthy Americans shows no signs of slowing down or reversing. Of course, this is not a new or unique trend, especially in some of America’s major cities. However, the starkest disparity is seen in the nation’s capitol. Poorer Americans not only have lower incomes, but also have more debt and less savings than higher income earners. In Washington, D.C., for example, the top 20 percent earn $151,132 per year, while the bottom 20 percent earn Less than $20,151 per year. In an area with one of America’s highest costs of living, the disparities echoe across neighborhoods, schools, and family households.

Let’s look at how the researcher’s indicators of social mobility play out in Washington, D.C.

Segregation in Residential Areas

RacialSegnMajMoinSchlsDC

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Income Inequality and Poverty

Average Family IncomeDC

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PopBelowPovDC

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Quality of Schools

SchoolProfIndexDC

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Family Stability

SingFemHousDC

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Outlook for social mobility in the U.S.

So, where are the most upwardly mobile cities?  Salt Lake City, Pittsburgh, and San Jose top the list. That means, children born into low income families in these cities generally fare better at moving up the income ladder as they age compared to other U.S. cities.

Why do these areas see higher intra- and intergenerational mobility? It depends.

San Jose has less concentrated poverty, proximity to innovative areas like Silicon Valley, and fewer single-family households, yet it’s schools under-perform and it has a higher level of racial and economic segregation. In Salt Lake City, they have a strong public school system, solid middle-class, and less income inequality.

However, despite these high rankings, the all too familiar trends of increasing rent and fewer middle class jobs, which many parts of the country are already experiencing, is beginning to impact even these cities. A similar problem is beginning to grow in San Jose as housing becomes unaffordable and middle class jobs dwindle. And as Salt Lake City becomes more diverse, increasing segregation and intergenerational poverty is making community workers take notice.

Issues like the non-ceasing, coast to coast trend of unaffordable housing and a weakening middle class have gained national attention. But it’s at the local level  where we’ll see real change- school funding, neighborhoods, access to jobs, affordable transportation, more housing options, and inclusive communities – are all factors researchers have underscored time and time again as having the greatest impact on upward mobility. When segregation of affluence, race, and resources exists, all members of the community see less upward social mobility. Most factors that impact social mobility are conditions children are born into- and where they’re born into it. For the U.S., creating one “land of opportunity” means building from the neighborhood on up.

To locate areas of opportunity in your community, check out Community Commons’ Location Opportunity Footprint Tool (LOFT).

Does Growing Up In Assisted Housing Affect Earning Potential?

Originally published on the U.S. Census Bureau Research Matters Blog on April 1, 2016 by Mark J. Kutzbach, Center for Economic Studies

In 2000, nearly 3 million children under age 18 lived in voucher-supported or public housing sponsored by the U.S. Department of Housing and Urban Development (HUD). Although assisted housing programs have been in place for some time, research on the long-term effects on resident children is scarce and hampered by methodological limitations. To shed light on this topic, my colleagues and I combined Census Bureau data with administrative data to track children through assisted housing and into the labor force as adults.

The Census Bureau often combines survey and administrative datasets to produce new statistics, but these data can also help answer complex research questions. For this project, we identify families with multiple teenage children counted in the 2000 Census and link them to HUD administrative records to observe how they move into and out of assisted housing between 1997 and 2005. We then match the children to their adult earnings from 2011 to 2013 using data from the Longitudinal Employer-Household Dynamics program.

To identify the impact of assisted housing on earnings, we compare adult earnings between siblings who experienced different amounts of assisted housing as teenagers. As siblings share many of the background characteristics that affect adult earnings — for example, household poverty and parental motivation — we are able to distinguish the effect of assisted housing participation from the effect of any other shared childhood experiences.

Assisted Housing and Poverty

We also look at whether public and voucher housing might have different effects on boys and girls. In public housing, a household lives in a project run by the local housing authorities, whereas in voucher-based housing, the housing authority pays a large portion of a household’s rent and utilities in private housing chosen by the household.

We first observe that children growing up in assisted housing tend to have lower adult earnings compared with other children, even those from similarly low-earning households. However, observing a difference in adult earnings between children who participated in assisted housing and those who did not is not enough to conclude that the assisted housing participation caused the difference. For example, participating households are required to earn below specified thresholds in order to be eligible. Beneficiary children are therefore likely to come from impoverished backgrounds and — even in the absence of assisted housing participation — earn less as adults.Voucher Housing and African AmericansHousing Voucher Legend

When we use only between-sibling differences, we find that assisted housing participation is associated with increases in adult earnings for girls and only modest, often statistically insignificant, decreases in earnings for boys. In other words, holding constant family characteristics, the negative effect of assisted housing disappears for most children.

The overall result that assisted housing raises earnings for girls more than boys might depend on the community under study. To shed additional light on this difference, we look at results separately for white, black and Hispanic households. The between-siblings effects are consistently positive only for black non-Hispanics, who represent roughly half of all HUD residents (and are mostly not distinguishable from zero effect for whites and Hispanics). The figure below shows both the between-siblings estimates and the naïve estimates, those that do not compare siblings, for black non-Hispanic households.

In the between-siblings model, girls in black non-Hispanic households earn 6.5 percent more for each year spent in voucher housing and 4.3 percent more for each year spent in public housing while a teenager. Boys in black non-Hispanic households earn 2.6 percent more for each year they spent in voucher housing and 3.8 percent more for each year spent in public housing. The difference in the effects of voucher housing for girls in black non-Hispanic households relative to boys is statistically significant.

How might housing assistance affect children? While housing assistance relieves families of a major expenditure, other studies have shown that it may also concentrate low-earning households together in large public housing buildings or low-income neighborhoods, exposing children to high-poverty settings. Thus, the net effects are not certain without an empirical analysis. When distinguishing between the effects of assisted housing programs and family characteristics, we found that more time spent in assisted housing participation for siblings led to increases in adult earnings, especially for black non-Hispanic girls.

We are doing more research to try to unravel what aspects of housing assistance might have the greatest effects and to explore other adult outcomes that may be influenced by housing. We are also trying to understand why we observe girls benefiting more than boys for any type of housing assistance.

For more information, see Childhood Housing and Adult Earnings: A Between-Siblings Analysis of Housing Vouchers and Public Housing, a joint paper written by Fredrik Andersson, Office of the Comptroller of the Currency; John C. Haltiwanger, University of Maryland and U.S. Census Bureau; Mark J. Kutzbach, U.S. Census Bureau; Giordano Palloni, International Food Policy Research Institute; Henry O. Pollakowski, Harvard University; and Daniel H. Weinberg, Virginia Tech.

Mapping the Impact of Rising Rents

There is a growing shortage of affordable housing in the U.S. With more middle class families turning towards renting instead of home ownership, low-income families are being squeezed out of rentals that were once affordable. But for both middle class and low-income families, renting continues to take up a growing percentage of household income.

The Shift Towards Renting

Demand for rental housing is at its highest since the 1960’s- and home ownership at a 48-year low. While home ownership is still more affordable than renting in many U.S. markets, it is not an option for individuals or families who do not qualify for loans, have incomes that are too low, or are saddled by debt like student loans. In 2008, a quarter of rental applicants were paying off student debt, by fall 2015 that number had risen to half.

With high-income earners forgoing home ownership, increasing urban populations, baby boomers downsizing, and the housing crash fresh in the minds of would-be homeowners, vacancy rates for rentals are at an all-time low. And with that comes ever increasing rent costs.

Rent Taking Up More of Americans’ Paychecks

Affordable rent is considered less than 30 percent of a household’s income, though today, most rents surpass that. According to the National Low Income Housing Coalition’s 2016 Out of Reach Report, U.S. renters need to earn an average of $20.30 per hour to afford a modest two- bedroom apartment- the average wage of a U.S. renter is $15.42.

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What about those making the federal minimum wage of $7.25? First, there is no state where a full-time worker making minimum wage could afford a fair market value, one-bedroom apartment – anywhere. Second, in order to afford a one-bedroom apartment, that worker would need to put in 90 hours per week at work or 112 hours per week for a two-bedroom apartment. For single moms or dads not only holding down full-time employment, but also part-time evening work, that is often a reality- and the only option.

 Housing Wages (By State) Needed to Afford a Modest Two-Bedroom Apartment

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Click the map to view report and other graphics

But let’s take a closer look at it this way, too:

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Unaffordable rent is a critical issue millions of Americans face. These severely cost-burdened households are a critical issue for the U.S. as well. With more and more of workers income going towards rent, less and less is being spent in the market. The Out of Reach report also found that families who spend more than half their income on housing spend 50 percent less on clothing, one-third less on food, and 80 percent less on medical care compared to those with affordable rent.

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It’s interesting to note that when we look at labor force participation, it seems to be worse in many areas that have more cost-burdened households and higher rents.

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More “Low-Income” Housing is Not Always the Answer

As long as higher-income earners are willing to pay high prices, there will continue to be a shortage of affordable apartments. With federal housing funds cut in half over the last 10 years, and fewer families qualifying for rental subsidies, the shortage will remain. Though building more “low-income” housing (via tax credits to developers) often seems to be the go-to solution, that in fact has shown to be not only ineffective overall, but actually more expensive than simply increasing the purchasing power of renters.

Four Maps of American Health You Need to See

This post was originally published by Samantha MacDonald on December 4, 2015 on the ESRI Insider Blog.

Eat your fruits and veggies. Drink plenty of water. Exercise 30 minutes a day. That’s the perfect recipe for good health, right? Well, not quite.

The truth is, your health depends on much more. Thanks to advances in mapping technology, today’s health professionals know that where you live, work, learn, and play has an incredible impact on your well-being.

Interactive maps paint the picture of community health, showing the spatial relationship between disparities, illness, and location—like visualizing how a neighborhood with no playgrounds influences childhood obesity. Maps offer rich insights that can help drive positive change in Americans’ lives.

Check out these four maps to explore the health challenges that permeate the United States and how place plays a pivotal role.

Education and Health

According to the Centers for Disease Control and Prevention (CDC), “education is one of the strongest predictors of health; the more schooling people have, the better their health is likely to be.”

Understanding and communicating the health benefits of an education may help increase graduation rates. This interactive map explores the spatial relationship between high school completion and health quality.

ESRI Map 1

Access to Health Care

Access to basic health care varies radically from place to place across the country and is inadequate in many parts of the nation. These county-level maps present two ways of examining a fundamental community health challenge: to the left of the slider bar is a look at doctors’ offices relative to population: to the right, primary health providers.ESRI Map 2

Linking Obesity and Diabetes

Fact 1: Close to one-third of US adults are obese.

Fact 2: Almost 90 percent of people with newly diagnosed type 2 diabetes are overweight.

These county-level maps reflect the close links between these key community health challenges.

ESRI Map 3

Poverty and Health

Poverty can have a significant impact on health. Poverty among school-age children (5 to 17 years old and living with families) increased in more than a quarter of US counties during 2007–2011. This map shows how poverty in school-age children in the United States has changed during 2005–2011 by school district.ESRI Map 4

Explore Similar Maps on Community Commons

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Population with Only a High School Diploma

Medically Underserved Area:Population

Medically Underserved Areas

Diabetes Incidence, New Cases by County

Diabetes Incidence, New Cases

Population Below the Poverty Level, Children (age 0-17), Total by County

Population of Children Below the Poverty Level

Obese Adults (Age 20+) Percent by County

Obese Adults (Age 20+)

Data Viz of the Week: Gini Coefficient

Developed in 1912 by Corrado Gini, the Gini coefficient (or Gini index) measures income distribution among the residents of a specified geography, such as a country, a state, or a census tract. A Gini coefficient of 1 means all income belongs to a single individual, while a coefficient of 0 reflects a perfectly even distribution. The measure is most often used to look at income inequality.

See the Gini index below in various geographies for the United States. Simply click on any of the large maps to enter the interactive map environment and zoom to your own location. Geography is changed by clicking on the drop down on the right side of the map room.

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Gini county

A countyH county

Gini tract

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See all the great tools we offer on Community Commons, search our data list, and visit our support area to learn more.