Sullivan County

Key demographics — Sullivan County, Pennsylvania

• Population size

  • 5,840 (2020 Decennial Census)
  • Down ~9% from 6,428 in 2010

• Age

  • Median age: ~54 years (ACS 2019–2023)
  • Under 18: ~16%
  • 18 to 64: ~58%
  • 65 and over: ~26%

• Gender

  • Male: ~52%
  • Female: ~48% (ACS 2019–2023)

• Racial/ethnic composition (ACS 2019–2023; race alone unless noted)

  • White (non-Hispanic): ~94%
  • Black or African American: ~1%
  • American Indian/Alaska Native: ~0–1%
  • Asian: ~0–1%
  • Two or more races: ~3%
  • Hispanic or Latino (any race): ~2%

• Household data (ACS 2019–2023)

  • Households: ~2,650
  • Average household size: ~2.1–2.2 persons
  • Family households: ~60–62% of households
  • Married-couple households: ~50% of households
  • Households with children under 18: ~20%
  • Nonfamily households: ~38%
  • Living alone: ~31%; age 65+ living alone: ~15%

Insights

• Small, rural county with an older age profile (median age ~54).
• Predominantly non-Hispanic White with limited racial/ethnic diversity.
• Household sizes are small and a large share are nonfamily or single-person households.

Sources: U.S. Census Bureau, 2020 Decennial Census; American Community Survey 2019–2023 5-year estimates.

Sullivan County, PA email usage snapshot (modeled from 2020 Census/2022 ACS demographics and Pew email adoption rates)

• Population and density: ~5,800 residents; ~13 people per square mile (among Pennsylvania’s least-dense counties).
• Estimated email users: ~4,000 residents (≈85% of those age 15+).
• Age distribution of email users:
  • 15–29: ~20%
  • 30–49: ~30%
  • 50–64: ~25%
  • 65+: ~25% Older-skewing demographics modestly lower overall adoption versus urban PA, but most seniors still maintain at least one email account.
• Gender split among users: ≈50% female, ≈50% male.
• Digital access and connectivity:
  • About four in five households report a broadband subscription (ACS 2022), but service quality is uneven across mountainous, forested townships.
  • DSL and satellite remain common outside borough centers; fiber build‑outs have accelerated since 2022 via state/federal funding, improving reliability where deployed.
  • Public Wi‑Fi and email access are available through libraries and schools, supplementing home connectivity.

Insights: Despite very low population density and patchy fixed-broadband performance in remote areas, email remains near-universal among working-age adults and widely used by seniors, making it a reliable channel for countywide communication.

Mobile phone usage in Sullivan County, Pennsylvania (2025 snapshot)

User estimates (people, devices, service)

• Population and households: ~5,900 residents; ~2,600 households; ~4,900 adults (18+). Median age ~50 (vs Pennsylvania ~41).
• Mobile phone users (any cellphone): ~4,500 adults (≈92% of adults).
• Smartphone users: ~3,900 adults (≈80% of adults). Feature/basic phone users: ~600 adults (≈12%).
• Wireless subscriptions: ~6,200 active lines (≈105 subscriptions per 100 residents), lower than Pennsylvania’s ≈120 per 100 residents.
• Prepaid vs postpaid: ≈30% prepaid in Sullivan (vs ≈20% statewide), reflecting price sensitivity and coverage-driven carrier hopping.
• Mobile-only internet households (no fixed broadband): ≈22% of households (vs ≈13% statewide).
• Platform split among smartphones: ≈45% iOS, ≈55% Android (statewide closer to even or slightly iOS-leaning).

Demographic breakdown of usage

• Seniors (65+): ~65% smartphone adoption in Sullivan (vs ~75% statewide); ~25% rely on basic phones or voice/text-centric plans.
• Adults 35–64: ~85% smartphone adoption; higher propensity than metro peers to cap data and use Wi‑Fi calling because of patchy radio coverage indoors.
• Ages 18–34: ~96% smartphone adoption, consistent with state; higher use of MVNOs and prepaid tiers to navigate spotty 5G and price.
• Income and plan type: Lower median household income and seasonal employment patterns drive above-average prepaid, hotspot, and MVNO usage; device upgrade cycles skew longer (3–4 years vs ~2–3 statewide).

Digital infrastructure and coverage

• Carriers and relative coverage: Verizon has the broadest footprint across populated corridors (US‑220, PA‑87, PA‑487, PA‑154 and towns like Dushore, Laporte, Muncy Valley, Eagles Mere). AT&T is solid on main routes and public safety (FirstNet Band 14) but thinner off-corridor. T‑Mobile coverage is present in town centers and along major roads with notable rural gaps.
• 4G LTE: Population coverage ≈95–98%; land-area coverage ≈70–80% due to valleys, forest canopy, and state game lands.
• 5G: Population coverage ≈60–70% (primarily low-band/DSS on Verizon and AT&T; more limited mid-band along US‑220). Land-area 5G coverage ≈30–40%. T‑Mobile low-band 5G is present but discontinuous away from towns.
• Capacity: Few sectors support mid-band 5G capacity; sites often rely on microwave backhaul outside US‑220 fiber routes, constraining peak-throughput and resilience during storms.
• Indoors and emergency calling: Wi‑Fi calling is relied upon in many homes and lodgings; E‑911/FirstNet coverage is established on main corridors and around Worlds End State Park and borough centers, but search-and-rescue still encounters dead zones in state forest and deep valleys.
• Outages and seasonality: Summer weekends (Eagles Mere/Worlds End tourism) and weather events can strain limited backhaul and generator-backed sites, causing localized slowdowns or temporary outages.

How Sullivan County differs from Pennsylvania overall

• Adoption mix: Lower overall smartphone penetration (≈80% vs ≈85–88% statewide) and higher basic-phone share, driven by older age structure and coverage constraints.
• Network access: Similar or high population LTE coverage but materially lower land-area coverage; 5G is less available and is mostly low-band, whereas metro counties have broad mid-band 5G.
• Plans and carriers: Higher prepaid/MVNO share and greater Android share; residents optimize for coverage and cost rather than premium unlimited plans.
• Internet reliance: Much higher mobile-only household internet use (≈22% vs ≈13%), reflecting sparse fixed broadband options outside small towns.
• Usage patterns: Voice/SMS remains more prominent; sustained mobile video streaming is less common away from towns because of coverage and capacity, with heavier use of offline media and home Wi‑Fi.

Practical implications

• Providers: The biggest gains would come from adding mid-band 5G on existing sites, extending fiber backhaul beyond US‑220, and infilling along PA‑87/PA‑154 and around Worlds End State Park to convert seasonal load into consistent performance.
• Public sector: Targeted support for rural macro infill, fiber spurs for backhaul, and promotion of Wi‑Fi calling readiness in public facilities will cut emergency dead zones faster than greenfield builds.
• Consumers: Coverage-first carrier choice (often Verizon or AT&T with FirstNet adjacency), Wi‑Fi calling enablement, and external antennas/hotspots remain the most reliable setup for homes and small businesses outside borough centers.

Notes on estimation

• Figures synthesize the 2020 Census/ACS population profile for Sullivan County, Pew Research Center smartphone ownership rates by age and rurality, statewide device adoption benchmarks, FCC mobile coverage data through 2024, and rural carrier deployment norms in Pennsylvania. Where county-specific measurements are unavailable, values are scaled to Sullivan’s age mix, rural density, and observed coverage differentials.

Social media usage in Sullivan County, Pennsylvania — concise snapshot (2025)

Scope and method

• Figures are modeled estimates based on U.S. Census Bureau population for Sullivan County, PA and Pew Research Center’s 2024 U.S. social-media adoption benchmarks, adjusted for the county’s older, rural profile. Percentages refer to adults (18+) unless noted.

User stats

• Population: ~5,900 residents
• Adults (18+): ~4,900
• Active social-media users (18+): ~3,300 (≈67% of adults)
• Teens (13–17) on social media: ~300–400 users (most are on Snapchat, Instagram, YouTube; TikTok common)

Most‑used platforms (estimated adult reach in the county)

• YouTube: ~78%
• Facebook: ~70%
• Pinterest: ~33% (skews female)
• Instagram: ~35%
• TikTok: ~25%
• Snapchat: ~22% (concentrated under 30)
• WhatsApp: ~20%
• X (Twitter): ~18%
• Reddit: ~15%
• LinkedIn: ~12% (lower given the local job mix)

Age profile (share of local social-media users)

• 13–17: ~9–11% of users
• 18–29: ~16–18%
• 30–49: ~28–32%
• 50–64: ~25–28%
• 65+: ~18–22%

Gender breakdown (among local social-media users)

• Women: ~54%
• Men: ~46%
• Platform skew: Women over-index on Facebook and Pinterest; men over-index on YouTube, Reddit, and X.

Behavioral trends and usage patterns

• Facebook is the community hub: heavy use of local Groups and Pages for school updates, volunteer fire/EMS, town events, hunting/fishing clubs, yard sales, and weather/emergency info; engagement leans to shares/comments over original posting.
• Video is ubiquitous but practical: short, informational clips on YouTube and Facebook perform best; livestreams used for meetings, games, and church services.
• Seasonality: noticeable spikes in summer and fall around tourism/outdoors (e.g., state park visits, foliage, fairs); businesses time promotions accordingly.
• Messaging reliance: Facebook Messenger and SMS/WhatsApp for coordination; older users prefer Messenger and Facebook Events.
• Younger cohort behavior: Instagram Stories/Reels, Snapchat streaks/Groups, and TikTok for trends and local highlights; creators are few but influential.
• Access patterns: evening and weekend peaks; rural coverage/broadband gaps push more asynchronous scrolling and downloading over Wi‑Fi.
• Advertising implications: boosted Facebook posts with local geo-targeting outperform most alternatives; Instagram works for visuals (food, lodging, outdoor services); TikTok best used via creator partnerships rather than broad paid buys; calls-to-action tied to dates (fairs, school sports, hunting seasons) lift response.

Sources: U.S. Census Bureau (population, age structure); Pew Research Center, Social Media Use in 2024 (platform adoption by age/community type). Figures are localized estimates derived from these datasets.