The study, published in the Atlantic Economic Journal, revisits earlier research from Pennsylvania that identified important differences in SNAP participation between rural and urban counties. Ohio offered a useful test because it contains a more balanced mixture of rural and non-rural counties: 47 of its 88 counties were classified as rural during the analysis, compared with 48 rural counties among Pennsylvania’s 67 counties. Ohio also combines Appalachian communities, large urban centers and economies shaped by manufacturing and post-industrial change. Nearly 2.8 million Ohio residents live in rural counties, giving the state one of the largest rural populations in the United States. By examining a neighboring state with similar but not identical characteristics, the researchers sought to determine whether the Pennsylvania pattern reflected a broader regional phenomenon or merely the conditions of one state.

To investigate the difference, Tuyen Pham, Christian Swart and G. Jason Jolley assembled annual county-level data covering 2010 through 2019. The dependent variable was each county’s SNAP participation rate, while the explanatory variables included poverty, unemployment, educational attainment, age structure, racial composition and employment in different industries. The researchers excluded data from 2020 onward because the COVID-19 pandemic disrupted employment, household finances and public-benefit behavior on an unprecedented scale. Including those years could have made it difficult to distinguish long-standing rural–urban patterns from temporary effects associated with the pandemic, emergency assistance and changes in economic activity.

The statistical model was designed not only to compare rural and non-rural counties, but also to test whether the same social and economic pressures had different effects in the two settings. At its core, the model estimated SNAP participation as a function of a rural-county indicator, a set of changing county characteristics, interactions between rural status and those characteristics, year-fixed effects and an error term. The interaction terms are particularly important: for a non-rural county, the effect of a variable is represented by its ordinary coefficient, whereas in a rural county the effect is the sum of that coefficient and the rural interaction coefficient. This allows, for example, unemployment or low education to have a different relationship with SNAP participation in a rural county than in an urban one.

Year-fixed effects helped account for influences that affected all Ohio counties in a particular year, including national policy changes and broad economic shocks. The researchers also clustered errors at the county level, a technique that addresses serial correlation when observations from the same county are followed repeatedly over time. Without that adjustment, statistical tests could exaggerate the apparent precision of the results because conditions in a county during one year are not independent of conditions in the next. The model explained a large share of the observed variation in county participation rates, with an R² value of 0.87. That does not prove that every factor affecting SNAP use was captured, but it indicates that the included variables collectively tracked substantial differences across counties and years.

The rural advantage remained after the analysis controlled for measurable socioeconomic conditions. Rural Ohio counties had a SNAP participation rate 13.4 percentage points higher than non-rural counties, with a probability value below 0.01. In practical terms, the result means that two counties with comparable measured poverty, unemployment, demographic composition, educational attainment and industry mix could still differ substantially in SNAP participation simply because one was rural and the other was not. The estimate should not be interpreted as evidence that rural residents are inherently more dependent on food assistance. Rather, it points to geographic conditions, institutional access, social norms or unmeasured economic characteristics that may influence the movement from eligibility to actual enrollment.

Across Ohio as a whole, several familiar indicators of economic hardship were strongly associated with higher SNAP participation. Counties with higher poverty and unemployment rates tended to have greater use of the program, while counties with lower educational attainment also showed higher participation. These relationships are consistent with SNAP’s role as a means-tested support program: as household resources fall and joblessness rises, more residents are likely to meet eligibility requirements or need help purchasing food. At the same time, the study found that counties with a larger share of younger residents and a larger manufacturing workforce tended to have lower participation rates. Those associations do not necessarily mean that young people or manufacturing employment reduce food insecurity. They may instead reflect differences in household structure, wages, benefit eligibility, labor-market stability or the demographic composition of counties.

The analysis also revealed that rural context altered the relationship between demographic characteristics and participation. Older adults aged 65 and over and people without a high school diploma were less likely to participate in SNAP in rural regions than their urban counterparts, even when other observed characteristics were held constant. The researchers suggest that stigma may help explain this pattern. In smaller communities, applying for or receiving public assistance may be more visible, potentially discouraging eligible residents from enrolling. Limited transportation, fewer social-service offices, reduced broadband access and weaker connections to outreach organizations could also make application and recertification more difficult, although the model was not designed to measure each of those mechanisms directly.

The findings broadly echo the Pennsylvania study that motivated the research, while also sharpening the policy challenge. If rural counties have higher participation overall but some vulnerable rural groups participate at lower rates than comparable urban residents, then a single statewide strategy may miss important barriers. The authors point to targeted workforce development and digitalization efforts, including community outreach, as possible ways to connect people experiencing hardship with assistance. Such measures could include more effective information delivery and easier access to enrollment services, although the study itself does not test particular interventions or establish which would be most successful. Increasing participation among eligible households matters beyond food budgets: previous research cited by the authors has linked SNAP access with reduced hunger and broader economic benefits for states.

Ohio’s political landscape limited what the researchers could say about another possible influence on SNAP participation: party control of state government. Republicans controlled the governorship, State Senate and State House for nearly the entire study period from 2011 through 2019, leaving too little variation to identify a reliable partisan effect. Consequently, political control was omitted from the explanatory model rather than treated as if it could be meaningfully estimated. The study also could not examine trends before 2010 because comparable explanatory variables were unavailable for earlier years. Those limitations mean the results describe a defined decade rather than a complete historical trajectory. Even so, the consistency of the Ohio findings with earlier evidence from Pennsylvania strengthens the case that rural–urban differences in SNAP enrollment are not merely a statistical curiosity. They may reflect persistent differences in need, access and social attitudes—differences that policymakers will need to understand if food-assistance programs are to reach all eligible households.

Understanding SNAP participation requires a multifaceted approach, as eligibility and enrollment are influenced by a complex web of economic, demographic, and geographic factors. The research team meticulously analyzed data from Pennsylvania, a state with heterogeneous landscapes ranging from densely populated urban centers like Philadelphia to expansive rural counties characterized by agricultural economies and limited infrastructure. This dichotomy serves as an ideal laboratory for exploring how place impacts public program participation.

One of the most prominent findings from the study is the persistent gap in SNAP engagement between rural and urban residents. Despite rural communities often facing higher rates of poverty and limited access to diverse food outlets, their SNAP participation rates tend to lag behind those in urban areas. This paradox suggests that economic need does not straightforwardly translate to program uptake. Instead, barriers such as distance to enrollment offices, internet connectivity issues complicating online applications, and social stigma uniquely affect rural applicants.

The researchers applied advanced econometric modeling techniques to isolate and quantify these influences. By controlling for income levels, household size, age distributions, and employment status, the study discerned that geographic disparities were not solely an artifact of economic conditions. The rural environment itself imposes structural challenges that reduce program participation rates, pointing to systemic gaps in outreach and assistance delivery mechanisms tailored for rural populations.

This revelation is particularly significant in the context of the evolving food landscape in the United States. Urban areas often benefit from concentrated social services, including local nonprofits and government agencies that actively promote SNAP enrollment. Conversely, rural counties may lack such dense institutional networks, placing greater onus on individuals to navigate the application process independently. Furthermore, transportation barriers influence the ability of rural residents to access physical service centers, leading to reliance on online platforms—a method hindered by the persistent digital divide.

The digital divide’s impact on SNAP participation emerges as a critical element of the study. While urban internet penetration rates have surged, rural broadband access remains patchy, limiting seamless online application submissions and communications. This technological handicap exacerbates existing socioeconomic disadvantages and can lead to delayed or forgone aid. The authors argue that expanding broadband infrastructure could be a game changer for rural SNAP participation, emphasizing the need for integrated policy frameworks that combine technological advancement with social service outreach.

Another layer of complexity is the perception and stigmatization of government assistance programs in rural communities. Cultural attitudes toward self-reliance and skepticism of public aid can discourage eligible individuals from enrolling in SNAP. The study employed qualitative data gathered through surveys and interviews, uncovering that social stigma often stagnates outreach efforts. Participants expressed worries about judgment from neighbors and extended family, which are amplified in close-knit rural settings where privacy is limited.

Alcantara, Kolesar, and Jozefowicz’s analysis also touches upon transportation infrastructure as a determinant of SNAP enrollment disparities. Rural residents frequently need to travel substantial distances to reach Department of Human Services offices or partner organizations that facilitate benefits enrollment. Inadequate public transit further compounds this difficulty, heightening the opportunity cost of applying for and maintaining SNAP benefits. Contrastingly, urban dwellers generally have multiple enrollment locations within accessible ranges, minimizing logistical hurdles.

Policy implications drawn from the study advocate for a tailored approach to SNAP outreach in rural areas. The conventional “one size fits all” model fails to account for geographic particularities that shape access and participation. Decentralized service delivery, mobile enrollment units, and community-led awareness campaigns are among the strategic innovations recommended to boost rural enrollment rates. Importantly, the study underscores fostering partnerships with trusted local institutions such as churches, schools, and agricultural cooperatives to break down stigma and build credibility.

The research also recognizes the role of Medicaid expansion and other social programs in modulating SNAP participation. Counties with higher rates of Medicaid enrollment often demonstrate elevated SNAP utilization due to streamlined eligibility determination and coordinated outreach efforts. Urban counties in Pennsylvania tend to leverage integrated service models more effectively than rural counterparts, reinforcing the need for cross-sector collaboration to enhance program penetration uniformly.

Economic fluctuations and employment patterns further influence SNAP participation patterns. The research identifies seasonal agricultural work prevalent in rural Pennsylvania as generating income volatility, which complicates consistent eligibility verification and benefits retention. In contrast, urban employment, while also subject to instability, typically involves a broader service-sector economy with different temporal income dynamics. Understanding these labor market differences is critical for designing responsive SNAP policies that accommodate diverse income cycles.

Critically, the study situates these findings within the broader context of food insecurity trends. Rural areas, despite lower SNAP participation, often face higher food insecurity rates due to isolation, poverty, and fewer retail food options. Urban locales, while benefiting from higher program enrollment, contend with different challenges such as food deserts and affordability issues in economically marginalized neighborhoods. The nuanced understanding offered by this research helps policymakers prioritize interventions aligned with local realities rather than aggregated statewide data.

From a methodological perspective, the research combines robust quantitative analysis with qualitative insights to yield a comprehensive portrait of SNAP dynamics. Utilizing county-level administrative data, the authors correlated SNAP caseloads with census economic indicators and conducted semi-structured interviews to probe attitudinal factors. This mixed methods approach provides a richer, more layered understanding than purely statistical trend analyses would permit, offering actionable insights for academics and practitioners alike.

The implications of this research extend beyond Pennsylvania, reflecting broader national patterns where geographic and infrastructural disparities inhibit equitable access to social safety nets. Rural-urban splits in SNAP participation highlight a critical dimension of inequality that calls for targeted solutions embedded in place-sensitive policy design. Furthermore, the study amplifies calls to address the digital divide and cultural factors that mediate public program engagement in America’s hinterlands.

In conclusion, Alcantara, Kolesar, and Jozefowicz’s contribution presents a compelling case for reimagining SNAP outreach and implementation strategies to bridge the rural-urban divide. Their findings advocate for multimodal interventions that integrate transportation support, expand broadband access, combat stigma, and harness local partnerships. Doing so not only promises to improve SNAP participation rates but also advances the broader goal of mitigating food insecurity and enhancing social equity in Pennsylvania and across the United States.

As social assistance programs face ongoing scrutiny and evolving demands, this research stands out for its rigorous examination of place-based disparities and practical recommendations. It urges policymakers, social workers, and technologists to collaborate in crafting inclusive and accessible safety nets that transcend geographic boundaries. Only by recognizing and addressing the unique constraints faced by rural communities can the vision of equitable food security be realized on a national scale.

Subject of Research: Rural and urban disparities in SNAP participation within Pennsylvania counties.

Article Title: Rural–Urban Differences in SNAP Participation in Pennsylvania Counties.

Article References:
Alcantara, A., Kolesar, K. & Jozefowicz, J.J. Rural–Urban Differences in SNAP Participation in Pennsylvania Counties. Atl Econ J (2025). https://doi.org/10.1007/s11293-025-09822-6

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