The setup

If you do applied work on rural America and you need to condition on rurality, you reach often for whatever classification is handy: USDA’s Rural–Urban Continuum Codes (RUCC), the Rural–Urban Commuting Areas (RUCA), the NCHS urban–rural scheme, OMB’s metro/nonmetro line, or a continuous index like the Index of Relative Rurality (IRR). These were built for the USDA, for rural health research, and for federal reporting. None of them were designed for studying how counties run elections, and they often disagree.

How often? Across U.S. counties, the four categorical schemes disagree on 12.6% of cases. That sounds small until you look at where the disagreement lives. The contested counties are not fringe cases out at the edge of the distribution. They sit in the populous middle, where wealthy exurbs share a category boundary with struggling small cities.

So I ask a simple question in the poster: when does the choice of rurality measure actually change what we conclude about how rural counties run elections, and when does it not?

What I did

I pulled four election-administration outcomes from the 2024 EAVS (spanning registration, ballot processing, and poll-worker capacity), plus county-weighted voter confidence from the pooled SPAE 2016–2024 (about 46,000 respondents across 2,562 counties). I excluded the sub-county-jurisdiction states where county-level analysis doesn’t make sense (CT, MA, ME, NH, RI, VT, VA).

For each outcome I estimated the rural gradient, controlling for log population, and then put it through a ladder of progressively stricter inference:

• cluster the standard errors by state, since counties within a state aren’t independent;
• add spatial dependence with Conley distance-based standard errors;
• re-estimate within a single state (Texas), holding policy constant;
• and re-estimate across rurality measures (IRR, RUCC, RUCA, NCHS, and the composite from my rurality package).

An effect only counts as robust if it survives the whole ladder, including the measure swap.

The two findings that matter

Most rural-urban gaps are cross-state. They look real in a naive specification, then vanish the moment the standard errors cluster by state. Adding state fixed effects drives the rural R² gain to near zero for every outcome. The rural signal, where there is one, lives between states, not within them. That makes some sense: states set most of the rules governing election administration, so the state matters more than the county. It also means the cross-state variation is tangled up with everything else that differs across states, which county-level data can’t fully separate.

For the one outcome that survives, the answer flips depending on the measure. Only UOCAVA (overseas and military ballot handling) makes it through every rung of the ladder. It’s also the one outcome that is federally standardized, via the MOVE Act, so state policy explains just 31% of its variance versus 82–99% for state-discretion outcomes like poll-worker ratios. If any county-level rural signal is clean, it should be here. And yet: the effect is negative and significant under IRR, null under NCHS and an IRR alternative, and positive under RUCC and RUCA. The measures disagree on the direction of the effect.

That is the headline. There is no single “rurality.” The choice of measure is part of the research design, not a convenience, and picking the wrong one can hide real variation or manufacture a result that isn’t there.

Why the measures diverge where they do

The schemes mostly agree out in the tails. Searcy County, Arkansas is rural under everything: RUCC 9, NCHS 6, IRR 0.84. The disagreement is concentrated in the contested middle. Two more Arkansas counties make the point:

• Hot Spring County (pop. 33k): RUCC 6 (rural), NCHS 5 (rural), OMB micro, but RUCA 3 (metro).
• Perry County (pop. 10k): RUCC 2 (metro), NCHS 3 (metro), OMB metro, but RUCA 7 (rural).

The disagreement runs in opposite directions for the two counties. Of 394 contested counties nationally, 373 (95%) are RUCA against the rest: commuting flows versus population logic, fighting over exactly the heterogeneous middle of the distribution that these schemes were never built to tell apart.

What I take from this

A few practical implications for anyone conditioning on rurality:

• Report your estimate across several measures. If it’s stable, you have at least some signal. If it flips sign, the effect is dimension-specific and you need to say which dimension.
• Match the measure to your mechanism. Commuting-based, density-based, and population-based schemes are answering different questions.
• Cluster by state. A lot of apparent rural effects are really cross-state variation wearing a rural costume.

And the standard caveats apply: this is observational, not causal; the cross-state variation is entangled with all the state-level differences county data can’t separate; and it’s the 2024 EAVS with sub-county states excluded.

What’s next

• The paper: the full nine-outcome analysis with clustered, spatial, and within-state robustness (forthcoming).
• An identity battery: a validated survey instrument for rural identity and place consciousness, aimed at outcomes like voter confidence that the structural measures don’t predict.
• Sub-county work: an analysis harmonized to minor civil divisions (MCDs, the towns and townships that run elections in place of counties in some states), paired with rurality v0.2. This brings the framework to the sub-county jurisdictions in New England and parts of Wisconsin and Michigan that the county-level analysis has to drop.

Related

The measurement machinery behind this poster is my rurality R package, which provides RUCC, RUCA, and a composite score under one interface (see Software for the Stata port). For the broader research record, see Research.

Rural Notes is a working-notes series. The posts are research-in-progress, not finished work. Subscribe via RSS for occasional updates.

The question

Counties run the bulk American elections at the local level, and for a lot of voters the county election website is the first place they actually meet that work: checking a registration, finding a polling place, requesting a mail ballot, looking up results. So a simple question: what do those sites tell voters, and how evenly?

The honest answer is that we didn’t know. The one well-known study, King and Youngblood’s 2016 audit of Alabama county sites, found that counties provide limited information and fall short on accessibility and usability. But it was one state, almost a decade old, and from before 2020 reorganized how Americans vote. Nobody had checked whether Alabama was typical.

So Will McLean and I built a national audit. We scored the election websites of 2,804 county jurisdictions across 40 states on an 18-item Election Information Content Scale (EICS), extending the King and Youngblood framework with three items that got salient after 2020: mail-ballot tracking, signature cure, and felon-rights restoration. We dropped the states where the county isn’t the unit of election administration (the New England town states, Michigan and Wisconsin, Alaska, and DC), so everything here is conditional on the county-administered model.

Coverage is low, and it clusters

The average county provides 7.2 of 18 items, and the distribution isn’t a smooth normal. It’s more bimodal with a tall spike at zero and a second mode around eight or nine. About 11% of counties score zero. Some have no identifiable election site at all; others have a site that carries none of the 18 items. The top is thin: only 22 counties (0.8%) score 17 or 18. The modal county provides a core set of old, foundational items (registration, polling place, results, absentee) and nothing newer.

Three forces organize the rest of the variation, and they pull in different directions.

A national floor on the “new stuff”

The post-2020 items are scarce almost everywhere. Only 22% of counties give voters a way to track a mail ballot, 10% mention felon-rights restoration, and 4% provide a signature-cure form. Local information simply hasn’t kept up with how people vote now, and that shortfall barely varies across the country. It’s a universal gap, not a rural one.

A state ceiling

Much of the variation occurs at the state level. Mean coverage runs from 14.6 in Florida down to 1.1 in Oklahoma, a 13.5-point spread that’s wider than a full standard deviation of the county distribution. A regression with nothing but state fixed effects gets R² = 0.47: about half of all county-level variation is just between states. That points at state-level institutions (statewide templates, mandates, the capacity a secretary of state’s office pushes down to counties), but sorting out which of those drives it is still open.

The rural gradient, and why it’s mostly about size

Here’s the part that belongs in Rural Notes. Coverage does decline with rurality. Metro counties average 9.1 on the scale, nonmetro-urban 7.3, and rural counties 5.0. The gradient is continuous across the Index of Relative Rurality, and it holds within states: 36 of 38 states with enough counties show the expected negative slope. By every descriptive cut, more rural counties provide less.

But the raw gap is mostly compositional. In a bare bivariate specification rural counties sit 4.1 points below metro. Add state fixed effects and it’s −3.45. Add the demographic and partisan controls and it collapses to −0.81, with the nonmetro-urban gap vanishing entirely. About three-quarters of the rural deficit was never really about rurality. It was about scale.

What absorbs it is log(population), the single strongest predictor in the model: a ten-fold larger county provides about 2.2 more EICS items, holding everything else constant. That’s a fixed-cost story. A bigger jurisdiction can amortize dedicated IT staff, a maintained content-management system, and the ongoing work of keeping a site current over a larger pool of voters. Rural places are, by definition, the small and sparse ones, so scale is the channel rurality mostly runs through here.

This is exactly the kind of thing I hope to consider in this rural notes series. The IRR is a clean, useful continuous measure, but it correlates −0.88 with log(population), and population alone explains about three-quarters of its variance. If you drop the IRR and population into the same model you’ve double-counted size and called part of it “rurality.” So we use categorical RUCC together with log(population) whenever we want to ask how much of the gap is scale, and we never put IRR and population in together. Same finding, different measure, a different story about why.

Infrastructure or content?

Because no-website counties enter at zero, the rural coefficient could mean two very different things: rural counties not putting up a site at all (an infrastructure problem), or rural counties running thinner sites (a content problem). Those call for different fixes, so we split them with a two-stage hurdle.

They separate cleanly. On the question of whether a county has an election website at all, the rural coefficient is a precise zero (+0.4 points, p = .76). Rural counties are no less likely to have a site. The entire rural deficit is on the second margin: content depth among counties that already have a site (−0.83, essentially identical to the pooled estimate). The gap isn’t that rural counties can’t get online. It’s that the ongoing work of filling a site out tracks capacity, and capacity tracks size.

And the residual rural gap that survives the controls isn’t evenly spread either. Fit one model per state and the median state’s rural gap is just −0.24; only three states (Tennessee, Kentucky, North Dakota) show a significant rural deficit after controls. What’s left concentrates in foundational items (results, poll-worker applications, schedules, the polling-place locator) in a handful of states, not a nationwide rural divide. The post-2020 items show no rural gap at all, but only because metro counties don’t provide them either. The national floor leaves nothing to lag behind.

What I take from this

Three patterns, three different problems:

• The national floor on post-2020 information is a universal failure that doesn’t care about urbanicity. Closing it needs a national push.
• The state ceiling is the biggest single source of variation, and it’s the most promising thing left to explain.
• The rural gradient is real but mostly a gradient in county size, with a small rural-specific remainder in foundational items in a few states.

Caveats

This is preliminary, and a few measurement calls are genuinely unresolved (these are where we most want feedback):

• The county site, or the voter’s whole information environment? In high-capacity states the secretary of state’s portal may serve voters a tracker or lookup the county itself doesn’t. Scoring the county site alone answers “what does the county provide?”; crediting the state tool answers “can the voter find it?” We may report both.
• A denominator for the post-2020 items. A county with no signature-cure page in a state that has no signature-cure process is currently scored the same as one that should have it and doesn’t. The floor may mix real gaps with legal non-applicability. The fix is a state-law crosswalk.
• A scale, or a checklist? The EICS is an unweighted sum of 18 binary items. We haven’t validated that they tap one construct, and a polling-place locator and a felon-rights page probably aren’t worth the same point.

We hope to update and expand this project as time and resources allow.

Related

This pairs with the measurement argument behind my rurality R package and a companion ESRA poster on rurality and election administration (also in Rural Notes). For the broader research record, see Research.

Rural Notes is a working-notes series. The posts are research-in-progress, not finished work. Subscribe via RSS for occasional updates.

A field guide to rural America

rurality.app is a new web companion that I created as a complement to my rurality R package. It takes the same county- and ZIP-level rural–urban classifications that the package exposes to R users and puts them behind a browser interface that anyone (e.g., journalists, county officials, students, curious readers) can use without needing to work with R.

The site’s tagline is its premise: a field guide to rural America. I do not really envision this as a database. My hope is that it is an invitation to look more carefully at things you’d otherwise pass over. Rural America is not a single category, and the app is built to make that visible.

What you can do with it

• Look up any U.S. county or ZIP and see its RUCC, RUCA, and composite rurality score
• Browse a national map and select any location to learn more
• Compare up to five places side-by-side
• Download the full dataset
• Read short field-notes on what each measure captures and where it diverges from the others
• Download the field notes

Why a web tool, not just a package

The rurality R package already does some of the heavy lifting for researchers. It includes USDA’s Rural–Urban Continuum Codes, Rural–Urban Commuting Area codes, and a composite rurality score under a single consistent interface. I am working to make sure these merge as cleanly as possible with county FIPS or local zip.

Most of the people who might benefit from looking up rural–urban classifications are not R users. They are reporters writing about a specific county, county officials trying to make sense of a federal funding formula that hinges on metro/non-metro status, students writing papers, and perhaps anyone who has ever read a story about “rural America” and wondered which rural America it was talking about. My hope is that rurality.app makes this an easier process.

I also have been working on expanding my research agenda on rural election administration, measurement, and public opinion. This selfish reason, as much as anything, is why I have created a new way to publicly document that effort. In Rural Notes (the series this post opens) I argue that a finding about rural America is only as good as the rurality measure that produced it. That argument is easier to make when the reader can spend two minutes on rurality.app and see for themselves how a single county can be “rural” under one classification and “metro-adjacent” under another.

The data underneath

Three layers, all keyed by FIPS or ZIP, all available in the rurality R package and rurality-stata:

• Rural–Urban Continuum Codes (RUCC 2023): USDA’s nine-point metro/non-metro scale, one row per county. This is one of the most accessible and (at least in my experience) one of the most commonly used measures since it neatly maps to counties.
• Rural–Urban Commuting Area Codes (RUCA 2020): USDA’s commuting-flow-based classification, finer-grained than RUCC, one row per ZIP code tabulation area.
• A composite rurality score: a continuous measure I built for situations where collapsing nine RUCC buckets into “rural vs. urban” throws away too much signal but keeping all nine as categorical dummies eats degrees of freedom. This one is a work in progress and should be used under that assumption.

The web app and the R/Stata packages are deliberately three faces of the same thing. If you look up a county on rurality.app, the values you see are the values you’d get from rurality_counties() in R. There is no hidden version, no proprietary score behind the web app, and the data will move forward in step when USDA releases new vintages.

Where it fits in the Rural Notes program

rurality.app is the public front door to a research program on three dimensions of rural in which I am currently interested: measurement, public policy (mostly in the form of election administration), and political attitudes and behavior. The posts that follow this one will often use the data the app provides. Sometimes these data will be joined to public-opinion surveys like the Pew American Trends Panel, sometimes joined to election returns, and sometimes just considered on its own. I want to ask substantive questions both about how rural Americans think and experience public policy, and how the answer depends on which “rural” you mean.

The next post in the series will work through that measurement question directly, using the app’s own data layers as the running example. After that, I hope to settle into a rotation of measurement essays, re-analyses of public data, reviews of other people’s work, and short puzzles about what an ideal survey would have to measure.

Try it

rurality.app is live and free. There is nothing to install and no account to create. If you find something broken, confusing, or worth a feature request, you can reach me through the contact form or open an issue on the GitHub repository.

Rural Notes is a working-notes series. The posts are research-in-progress, not finished work. Subscribe via RSS for occasional updates.