The Atlas of Rural and Small-Town America


USDA’s Economic Research Service releases a lot of state- and county-level information on various socioeconomic and demographic attributes of the population of these geographies.

Ani Ruhil true

This release, called “The Atlas of Rural and Small-Town America”, is periodically updated as new data become available, and offered up as a source of potentially useful indicators of social determinants of health (SDOH). Social determinants of health are all the rage now, and will likely fall by the wayside in a decade. Yet, if SDOH are the currency of the realm today, it makes sense to understand the many ways in which SDOH are measured and available for our sub-state communities. This post summarizes, at a very high level, how a specific Atlas captures and makes available a large set of indicators. Mind you, some of these are very dated, or available for but a very few of the nation’s counties and/or other geographies.

The U.S. Department of Agriculture’s Economic Research Service (USDA ERS) provides a ton of socioeconomic data in what they call the Atlas of Rural and Small-Town America. These data were last revised in June of 2021, and are drawn from the American Community Survey and other sources (including small-area estimates). The central motivation is not only to highlight the value of the ACS as a source of county-level data but also to harness multiple Federal data sources, and to provide an avenue for policymakers, analysts, and stakeholders to understand the challenges and opportunities facing rural and small-town communities.1 Broadly classified the indicators span the following groups:

County Classification

Continuing on from the brief look at Community Resilience Estimates in the last post, here I want to familiarize myself with the Atlas’ contents, starting with the county classification file. Turns out that the indicators in this file do not necessarily span the same time period. For example, some indicators are drawn from the 2015-2019 ACS but others stop with the 2010 Decennial Census, and yet others in 2013, 2011, 2003, and even 2000! As a result, some indicators may be outdated but a few should be stable and usable. For example, the rural urban continuum codes that generate the metro/non-metro flags, the persistent high adult/child poverty indicators (that span four decades of data), the county typology (that splits counties into six distinct classes based on the economy), and then finally the natural amenity flag (counties with high-/low-level of natural amenities) should be stable.

If you want to download these particular indicators for Ohio counties, click the Download data button in the sidebar.


This data-set focuses specifically on our veterans, with measures drawn from the 2015-2019 ACS (which makes it very current and leads me to expect an update once the 2016-2020 ACS is released). The indicators span various attributes of our veterans, including the share of veterans in the population 18+, the percent of veterans serving in the Gulf War, during the Vietnam Era, and during the Korean War, demographic breakdown of the veteran population by gender, race, ethnicity, median income, educational attainment, labor force participation, unemployment rate, income relative to the poverty level, and disability status. Let us dig into the data and see what we can learn.

The tabbed panels that show up below contain small glimpses into some of these indicators, all at the state-level.

Veterans as % of 18+

What state leads in terms of the largest percent of its 18-years or older population being veterans? Alaska does, followed by Virginia, Montana, Wyoming, and Maine, with California, New Jersey, District of Columbia, New York, and Puerto Rico bringing up the rear. Ohio is in the bottom-half.

Female Veterans

While Virginia and Alaska still feature in the top-5, Maryland, District of Columbia, and Georgia are the other top states, with Ohio in the bottom-half.

Median Income

We have information on the median incomes of veterans (the salmon-colored points) and non-veterans (the steelblue-colored points), and this should make for a fascinating comparison. I would hope the veterans fare better given their sacrifices, and sure enough, this indeed rings true. Veterans in the District of Columbia, Maryland, and Virginia have the highest median income, followed by Alaska. My suspicion is that the DC, MD, and VA rise to the top because they are home to a large number of employees who work in the DC area and happen to be veterans. What is surprising is the sharp difference between median incomes of Puerto Rican veterans and their peers in other states. But then again non-veterans’ median income is also very low in PR.


Although a disability is more common among veterans than non-veterans was not exactly a surprise, what alarmed me was the fact that the highest disability rates are for Puerto Rico, and for both groups! Specifically, note that almost 30% of Puerto Rican veterans report a disability veterans. In the Continental United States, West Virginia (25.6%), Arkansas (25.5%), and Oklahoma (24.6%) have the highest percentages of their veterans reporting a disability. The lowest rates are seen in in Maryland and Virginia (13.3% in each), and Delaware (14.0%).


We have a few more domains of The Atlas to explore, and start with income because it is the briefest. The indicators span median household income, income per-capita, the poverty rate (overall and for the 0-17 years-old population), the percent in deep poverty – defined as the percent of the population living in families with income below half of one’s poverty threshold (overall and for the 0-17 years-old population), and then the corresponding number in each category.


The jobs-related indicators are many, and in some cases span multiple years. For example, we have the unemployment rate per annum for 2007 through 2020. We also have the percent employed in 2015-2019 in specific sectors (agriculture, forestry, fishing, and hunting; mining, quarrying, oil and gas extraction; construction; manufacturing; wholesale and retail trade; transportation, warehousing and utilities; information industries; finance and insurance, real estate and rental and leasing; services; and public administration). And then we have the number unemployed per-annum, and the number in the civilian labor force per-annum.

I am curious about sectoral employment in Ohio’s counties so let us run a simple table that shows the percentage of total employment, by sector, in each county.

No surprises here, the Service sector leads in most counties, with manufacturing the leading sector by a nose in only three counties – Shelby (35.6%), Mercer (34.7%), and Holmes (29.5%).


This section of The Atlas is lengthy, with estimates of population size, change in size over time, natural versus migration-based change over time, age, race/ethnicity, nativity, educational attainment, and a handful of household-characteristics rounding out the list. Most data are from 2015-2019, with some spanning the 2010-2019, 2000-2010, and 2018-2019 periods.

Overall, my reaction to The Atlas was, well, let us say I was a little underwhelmed. Why? Meh, I expected to see indicators not available elsewhere. But that is perhaps not the purpose of The Atlas. Time to pin my hopes on some of the other data sources waiting to be tapped.

  1. Details of the Atlas can e found here↩︎


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For attribution, please cite this work as

Ruhil (2022, Feb. 13). From an Attican Hollow: The Atlas of Rural and Small-Town America. Retrieved from

BibTeX citation

  author = {Ruhil, Ani},
  title = {From an Attican Hollow: The Atlas of Rural and Small-Town America},
  url = {},
  year = {2022}