ERS Charts of Note
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Monday, August 1, 2022
Self-employed workers were more than twice as likely to lack health insurance compared with those employed by private firms or government in 2018, regardless of whether they lived in metropolitan or nonmetropolitan counties. Self-employed working-age adults (ages 26–64) with health insurance were covered more widely across sources. Like those employed by private firms and government, they were insured through employer-based group health insurance plans more than any other source. (Many of these individuals could be receiving coverage through another household member’s employer-based plan.) Even so, a much smaller share of self-employed workers was covered by employer-based plans than those employed by private firms or government (50.5 percent versus 82.4 percent, respectively, in nonmetro counties). Instead, self-employed working-age adults were insured through direct-purchase health insurance plans at more than three times the rate of those employed by private firms or government. Similarly, public health insurance (e.g., Medicaid, Medicare) rates for self-employed working-age adults were nearly twice that of those employed by private firms or government. A version of this chart appears in the ERS publication Health Care Access Among Self-Employed Workers in Nonmetropolitan Counties published May 2022.
Wednesday, July 27, 2022
As the effects of the Coronavirus (COVID-19) pandemic deepened in 2020, a greater share of employed people reported lacking health insurance coverage, regardless of residential location or whether they were self-employed. Self-employed workers were already more often uninsured than those employed by private industry or government, and the gap persisted through the end of 2020. Self-employed workers started the pandemic with uninsured rates of between 24 percent and 28 percent, and these rates remained relatively stable through July 21, 2020. Thereafter, the percentage of uninsured individuals increased, and between August 19 and December 21, 2020, around 33 to 34 percent of self-employed workers were uninsured. The rates of uninsured individuals among other workers followed the same trend, with rates of 15 to 16 percent at the beginning of the pandemic increasing to around 27 percent by the end of 2020. The increases correspond both to a decrease in health insurance coverage through employer-based plans as job losses grew and to slight declines in coverage through direct-purchase plans among the self-employed. This chart appears in the ERS publication report Health Care Access Among Self-Employed Workers in Nonmetropolitan Counties, published May 2022.
Tuesday, July 19, 2022
Self-employed workers are individuals who work for themselves and have not incorporated their businesses. A higher proportion of nonmetropolitan workers are self-employed than metropolitan workers, according to a recent study by the USDA, Economic Research Service. ERS researchers used workforce data from the U.S. Bureau of the Census’ 2014–18 American Community Survey (ACS) to classify counties by the percentage of self-employed workers. Counties with a share of self-employed workers in the top 25 percent were considered to have a high level of self-employment. In these counties, 9.1 percent to 36.7 percent of workers were self-employed. High self-employment counties were primarily nonmetropolitan (702 counties versus 84 metropolitan counties). They were largely clustered throughout the Great Plains and upper Mountain West. This figure appears in the ERS publication Health Care Access Among Self-Employed Workers in Nonmetropolitan Counties, published May 2022.
Thursday, July 7, 2022
To understand how the availability of health care for self-employed workers in metropolitan and nonmetropolitan areas has changed over time, researchers at USDA, Economic Research Service (ERS) examined the average rate of doctors with medical degrees (not with osteopathic degrees) per 10,000 people. In this chart, they focused on differences between metropolitan and nonmetropolitan counties with high and low rates of self-employment. The researchers found that between 1960 and 2017, doctors have been far more available in metropolitan counties with low self-employment rates than in other counties. They also found that doctors have been the least available in nonmetropolitan counties with high self-employment rates. The largest gap in doctor availability existed between these two groups: in 2017, there was an average of 23 doctors per 10,000 residents in low self-employment metropolitan counties versus 7 doctors in high self-employment nonmetropolitan counties. However, the average rates of doctors and trends in those rates have changed over time. Between 1970 and 2000, the average rate of doctors increased in both metropolitan and nonmetropolitan counties. But since 2000, the average rate of doctors started to decrease in nonmetropolitan counties. By 2017, the average rate of doctors in low self-employment nonmetropolitan counties fell below the rate in high self-employment metropolitan counties. Thus, people living in nonmetropolitan counties, particularly those with a higher proportion of self-employed workers, generally had less access to doctors. A version of this chart appears in the ERS publication Health Care Access Among Self-Employed Workers in Nonmetropolitan Counties, published May 2022.
Thursday, November 15, 2018
Compared to traditional medical delivery systems, telehealth—health services or activities conducted through the internet—allows people to more actively participate in their health care. It also facilitates timely and convenient monitoring of ongoing conditions. To better understand the factors affecting telehealth use, ERS researchers examined rural residents’ participation in three telehealth activities: online health research, online health maintenance (such as contacting providers, maintaining records, and paying bills), and online health monitoring (the transmission of data gathered by remote medical devices to medical personnel). The ERS analysis looked at a number of socioeconomic factors—including family income, educational attainment, age, and employment type and status—that may affect a person’s choice to engage in telehealth activities. Findings show that participation rates for telehealth activities in 2015 increased with the level of educational attainment. For example, rural residents with college degrees were over 5 times more likely to conduct online health research than residents without a high school diploma, and more than 10 times as likely to engage in the other telehealth activities. This chart appears in the November 2018 ERS report, Rural Individuals’ Telehealth Practices: An Overview.
Wednesday, July 25, 2018
Veterans constitute a rapidly aging and increasingly diverse group disproportionally living in rural America. Nearly 18 percent of veterans lived in rural (nonmetro) counties in 2015, compared to 15 percent of the U.S. adult civilian population. Veterans were also overrepresented in some rural counties: about 10 percent of all rural civilian adults were veterans, but in some rural counties, that share reached as high as 25 percent. The U.S. counties with the highest shares of veterans tended to have significant concentrations of elder veterans (65 years or older), relative to the Nation as a whole. About 24 percent of all U.S. counties—often completely rural counties not adjacent to metro areas—had concentrations of elder veterans. By comparison, 28 percent of all U.S. counties—predominantly large urban counties (not shown)—contained concentrations of working-age veterans (18 to 65 years old). Areas with concentrations of both groups were mostly in rural counties adjacent to metro areas (19 percent). Many of these counties contained or were near military installations, reserve bases, or training areas. This chart appears in the September 2017 Amber Waves data feature, “Veterans Are Positioned To Contribute Economically to Rural Communities.”
Tuesday, May 29, 2018
On May 29, 2018, the Chart of Note article “Rural economies depend on different industries” was reposted to correct the industry classification of a few counties and, in the legend, show the number of rural counties only, instead of all counties.
Rural counties depend on different industries to support their economies. Counties’ employment levels are more sensitive to economic trends that strongly affect their leading industries. For example, trends in agricultural prices have a disproportionate effect on farming-dependent counties, which accounted for nearly 20 percent of all rural counties and 6 percent of the rural population in 2017. Likewise, the boom in U.S. oil and natural gas production that peaked in 2012 increased employment in many mining-dependent rural counties. Meanwhile, the decline in manufacturing employment has particularly affected manufacturing-dependent counties, which accounted for about 18 percent of rural counties and 22 percent of the rural population in 2017. This chart is based on the ERS data product for County Typology Codes, updated May 2017.
Tuesday, February 13, 2018
Nearly 19 million veterans lived in the United States in 2015. Almost 18 percent of them lived in rural (nonmetro) counties, compared to 15 percent of the U.S. adult civilian population. About 45 percent of rural veterans were working age (18 to 64 years old); the rest were elder veterans (65 years or older). Overall, about 21 percent of elder rural veterans reported currently working (full- or part-time) or having last worked (if retired or unemployed) in the agriculture industry. By comparison, less than 3 percent of working-age veterans reported the same. Instead, working-age veterans relied more on the manufacturing industry for employment. About 19 percent of working age veterans reported currently working or having last worked in manufacturing, compared to 7 percent of elder veterans. Both working age and elder veterans relied about equally for employment in some industries—including education and health, wholesale and retail trade, and construction. This chart appears in the September 2017 Amber Waves data feature, "Veterans Are Positioned To Contribute Economically to Rural Communities."
Thursday, September 28, 2017
Veterans tend to have higher earnings compared to nonveterans. In 2015, rural veterans who were full-time wage and salary workers had median earnings of about $50,000. That’s $11,000 more than the median earnings of their nonveteran counterparts. Earnings for veterans and nonveterans varied by industry, however. For example, compared to nonveterans, the median earnings for veterans was $29,000 higher in financial services, $20,000 higher in education and health, and $11,500 higher in transportation and utilities. Differences in median earnings by industry between veterans and nonveterans generally track closely with educational attainment. However, in 2015, even in industries where fewer veteran than nonveteran earners had a college degree, the median income for veterans was near or greater than that of nonveterans. This may be explained by a variety of factors, including differences in demographic composition and job skills. For example, veterans tend to be older and are predominantly male, and thus on average more likely to have higher earnings than the general population. This chart appears in the September 2017 Amber Waves data feature, "Veterans Are Positioned To Contribute Economically to Rural Communities."
Monday, September 19, 2016
Between 2010 and 2015, the population of rural and small-town America declined by 0.3 percent, according to Census population estimates. This loss of 137,000 people was a relatively small change that masked larger racial-ethnic trends. The non-Hispanic White population declined by 738,000 in rural (nonmetro) counties, while all other racial-ethnic groups increased by 601,000. The rural Hispanic population alone grew by 376,000 (10 percent) during this time period. The increasing Hispanic population helped nearly 10 percent of rural counties (188 counties) in Texas, New Mexico, and 32 other states maintain population growth, continuing a 30-year trend. Immigration and domestic migration drove this trend early on as Hispanic workers filled jobs in textiles, food processing, and other agricultural-related industries. Today, immigration has slowed and most of the growth in the rural Hispanic population comes from natural increase (more births than deaths). The resulting change in the composition of Hispanic families may lead to new community needs for housing, schools, and family services. Find county-level maps and data on the U.S. Hispanic population in ERS’s Atlas of Rural and Small-Town America.
Tuesday, June 7, 2016
Racial and ethnic minorities made up 21 percent of rural residents in 2014. Hispanics (who may be of any race) and Asians are the fastest growing minority groups in the United States as a whole and in rural areas. Over 2010-14, the rural Hispanic population increased 9.2 percent, and their share of the total rural population rose from 7.5 to 8.2 percent. Asians and Pacific Islanders represent a small share of the rural population—about 1 percent—but their population grew by 18 percent between 2010 and 2014, while rural Native American and Black populations grew at more modest rates. This is in contrast to the rural non-Hispanic White population, which declined by 1.7 percent between 2010 and 2014. Overall rural population loss (which was -0.2 percent for the period) would have been much higher if not for the growth in the rural racial and ethnic minority groups. Rural minorities tend to be younger on average and have larger families than non-Hispanic Whites, and this, along with net migration, is reflected in the varying growth rates. This chart updates one found in the ERS publication, An Illustrated Guide to Research Findings from USDA's Economic Research Service.
Thursday, March 3, 2016
The proportion of adults lacking a high school diploma or equivalent declined in rural America (defined here as nonmetro counties), from 32 percent in 1990 to 15 percent in 2014. The proportion of rural adults with college degrees also increased from 12 to 19 percent during that time. Despite these overall gains, educational attainment varies widely across rural areas. ERS’s latest county typology classifies low-education counties as those where at least one of every five working-age adults (age 25-64) has not completed high school. In an average of data over 2008-12, ERS identified 467 low-education counties in the United States, 367 of which were rural. Eight out of 10 of all low-education counties are located in the South. Three-fourths of rural low-education counties also qualified as low-employment in the latest ERS county typology. Over 40 percent of rural low-education counties were both low-employment and persistently poor, reflecting the difficulty that adults without high school diplomas have in finding and retaining jobs that pay enough to place them above the poverty line. This map is part of the ERS data product on County Typology Codes, released December 2015.
Tuesday, August 4, 2015
The number of rural (nonmetropolitan) counties that lost population in 2010-14 reached a historic high of 1,310. The recent economic recession, increased global competition, and technological changes led to widespread job losses in rural manufacturing. Population loss occurred throughout the eastern United States, especially in manufacturing-dependent regions such as along the North Carolina-Virginia border and southern Ohio. Population growth did occur in 666 nonmetro counties. Large sections of the northern Great Plains started to gain population after decades of persistent decline, due largely to the inmigration of workers capitalizing on the shale oil and gas production boom. Nonmetro counties in southeastern New Mexico and parts of eastern Texas also gained population from energy-related job growth. This chart appears in the August 2015 Amber Waves finding, “Population Loss in Nonmetro Counties Continues.”
Monday, June 29, 2015
During 2010-14, the number of nonmetro counties that lost population reached a historic high of 1,310. County population loss stems from two possible sources: more people leaving a county than moving into it (net outmigration) and/or more people dying than are being born (natural decrease). Historically, the vast majority of counties that lost population still continued to experience natural increase, just not enough to offset losses from net outmigration (this scenario describes less than half of the 2010-14 population loss counties). The number of nonmetro counties with population loss from both net out-migration and natural decrease grew from 387 before the recession (2003-07) to 622 during 2010-14. Clusters of counties experiencing this demographic ‘double-jeopardy’ have expanded, especially in Alabama, southern Appalachia, along the Virginia-North Carolina border, and in New England. The rising number of double-jeopardy counties signals new challenges in maintaining future population growth and sustained economic development. This map is based on information found in the Population & Migration topic page, updated June 2015.
Thursday, May 14, 2015
Small population size and geographic remoteness provide benefits for residents and visitors alike, but those same characteristics often create economic and social challenges. Job creation, population retention, and provision of goods/services (such as groceries, health care, clothing, household appliances, and other consumer items) require increased efforts in very rural, remote communities. The newly updated ERS Frontier and Remote area (FAR) codes identify remote areas of the United States using travel times to nearby cities. Results for level one FAR codes (which include ZIP code areas with majority of their population living 60 minutes or more from urban areas of 50,000 or more people) show that 12.2 million Americans reside more than a one-hour drive from any city of 50,000 or more people. They constitute just 3.9 percent of the U.S. population living in territory covering 52 percent of U.S. land area. Wyoming has the highest share of its population living in FAR level one ZIP code areas (57 percent), followed by Montana, North Dakota, South Dakota, and Alaska. This map, along with the full detail of FAR codes levels 1-4 may be found in the ERS data product, Frontier and Remote Area Codes, updated April 2015.
Wednesday, February 25, 2015
The estimated 42 million African Americans living in the United States in 2013 made up close to 13 percent of the population. During a post-recession population slowdown in the United States, African Americans have continued to experience relatively high rates of population growth, the result of higher fertility rates and a younger average population. Population estimates from the U.S. Census Bureau show gains among African Americans in all urban/rural county types except for the most sparsely-settled and remote areas (nonadjacent rural) during 2010-13. For the total population, suburbanization trends in the U.S. slowed markedly with the onset of the housing crisis and recession. Suburban fringe counties (metro outlying) now show slower rates of growth than the central cities of metro areas, although the African American population growth rate has not yet experienced this historic shift. Similarly, the African American population continues to show gains in those nonmetro counties most likely to be suburbanizing (nonmetro adjacent) at a time when those counties show overall population declines. This chart expands on one found in Shifting Geography of Population Change, a chapter in the ERS website topic page on Rural Population and Migration.
Wednesday, May 14, 2014
Population change is varied across rural and small-town America. Since 2010, over 1,200 rural (nonmetropolitan) counties have lost population, with declines totaling nearly 400,000 people. At the same time, the population of just over 700 rural counties grew, together adding just over 300,000 residents. New regional patterns of growth and decline emerged in recent years. Areas of population decline appeared for the first time in the eastern United States, including New England, the North Carolina-Virginia border, and southern Ohio. Falling birth rates, an aging rural population, and a declining manufacturing base contributed to population downturns in these regions. In the Mountain West, population growth also slowed considerably, and in some cases turned negative, for the first time in decades, affecting numerous counties in western Colorado and Wyoming, central Oregon, and northern Idaho. In contrast, an energy boom has spurred population growth in sections of the northern Great Plains that had previously experienced long-term population declines. This map is found in the ERS topic page on Rural Population and Migration, updated April 2014.
Wednesday, October 23, 2013
Metropolitan (metro) counties have fared better than both micropolitan and noncore counties (shown in the map) following the 2007-09 recession. ERS researchers generally define “rural” as micropolitan and noncore counties (together referred to as nonmetropolitan or nonmetro counties), and “urban” as metropolitan or metro counties. During the National economic recovery period between 2010 and 2012, employment increased by 2.5 percent in metro counties, compared with 1.1 percent in micropolitan, and 0.5 percent in noncore counties. Metro counties are densely settled counties with an urban core population of 50,000 or more, and outlying counties tied to the central core by labor force commuting. Micropolitan counties are similar to metro counties, but include an urban core with a population between 10,000 to 49,999, and outlying counties tied to the core by commuting. Noncore areas are the remaining counties that are neither metro nor micropolitan. As of February 2013, the Office of Management and Budget identified 1,167 metro counties, 641 micropolitan counties, and 1,335 noncore counties. This map is found in the ERS topic page on Rural Classifications, updated in May 2013.
Thursday, July 11, 2013
All sectors of the economy were not equally affected by the 2007-09 economic recession and the subsequent recovery. Specialization within local economies has shaped county-to-county differences in recent rural (nonmetro) growth in jobs. Boosted by high farm income and, in some areas, booming gas-extraction activities, farming-dependent counties have seen job growth for the first time in many years, growing during and after the recession. Manufacturing counties, affected by global competition, showed weak job growth in the early 2000s, followed by substantial losses during the recession. Recreation counties, which experienced above-average job growth in 2001-07, lost jobs in 2007-09 as their housing markets collapsed and the recession reduced tourism. Weak postrecession job growth did not bring jobs back to prerecession levels in most nonmetro counties by 2011. County economic types were defined by ERS in 2004 and are scheduled to be updated next year. This chart combines the ERS county typology codes with employment data from U.S. Department of Commerce, Bureau of Economic Analysis.
Friday, June 14, 2013
The Urban Influence (UI) codes classify all U.S. counties, as well as “municipios” in Puerto Rico, by size of metropolitan area, adjacency to a metropolitan or micropolitan area, and size of the largest town. These codes are updated every 10 years after the release of new decennial census data and updated metropolitan and micropolitan areas by the Office of Management and Budget (OMB). The latest UI codes were released in May 2013. Compared with 2003, in 2013 there are an additional 78 metropolitan counties, 114 counties moved from nonmetropolitan to metropolitan, and 36 counties changed from metropolitan to nonmetropolitan for a total of 1,167. The number of nonmetro counties fell from 2,053 to 1,976 between 2003 and 2013. The UI codes enable users to analyze the diversity of rural counties by their size, and access to larger economies that serve as centers of trade, finance, information, and communications. The codes provide a more finely articulated measure of rural and take advantage of OMB’s metropolitan, micropolitan, and noncore classification system. ERS uses the codes extensively in its research on rural labor, poverty, population change, employment, and unemployment. This map is found on the ERS website as part of the Urban Influence Codes data product, updated May 2013.