# Data Centers, Submarine Cables, and the Concentrated-Costs / Dispersed-Benefits Frame

**Author:** David Adams · **Date:** 2026-05-17
**Data:** PostGIS `data_centers` DB — `us_dc_sample_geocoded` (1,489 DCs),
`data_center_census_tracts_2024` (611 tracts, ACS 2024 5-yr enriched),
`internet_cables` (693 cables), `internet_city_dominance` (4,552 cities),
`census_tract_acs_2024_selected_states.csv` (83,811 tracts, 46 states).

---

## 1. Are US data centers spatially tied to submarine cables?

Distance from each point to the nearest submarine cable line (km):

| Group | n | Mean | p10 | p25 | **p50** | p75 | p90 | ≤10 km | ≤50 km | ≤100 km | ≤250 km |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| US data centers | 1,489 | 358.7 | 21.6 | 163.1 | **276.1** | 477.4 | 867.4 | 5.2% | 16.8% | 21.4% | 32.2% |
| US population cities | 1,291 | 339.7 | 18.7 | 61.2 | **256.1** | 528.0 | 811.0 | 6.8% | 22.5% | 31.8% | 49.5% |

Mann-Whitney U two-sided: **z = 2.66, p ≈ 0.008** — significant, but in the
*opposite* direction. DCs are **not** systematically closer to cables than
ordinary US cities.

**Interpretation.** At the national level the "cables drive DC siting" story
fails. The largest clusters — Loudoun County VA (Ashburn), central
Washington, Hillsboro OR, Columbus OH, Iowa — are inland, anchored to
terrestrial fiber, cheap power, and tax incentives rather than submarine
landings. Only 21.4% of DCs sit within 100 km of any cable.

---

## 2. Cost concentration at the state level

| Measure | Value |
|---|---:|
| States covered | 46 |
| Gini of DC counts across states | 0.648 |
| HHI of state shares | 0.080 |

Top states by share of US data centers:

| State | DCs | Share | Cumulative |
|---|---:|---:|---:|
| VA | 319 | 21.4% | 21.4% |
| CA | 129 | 8.7% | 30.1% |
| TX | 120 | 8.1% | 38.1% |
| OR | 102 | 6.9% | 45.0% |
| WA |  90 | 6.0% | 51.0% |
| OH |  69 | 4.6% | 55.7% |
| AZ |  60 | 4.0% | 59.7% |
| IA |  58 | 3.9% | 63.6% |

Five states hold **half** of all US data centers.

---

## 3. Cost concentration at the tract level

Much sharper than state-level:

| Measure | Value |
|---|---:|
| DC-hosting tracts | 611 |
| DCs in those tracts | 1,489 |
| Gini of DC counts across DC-hosting tracts | 0.499 |
| HHI of DC shares across DC-hosting tracts | 0.0069 |
| **Top 1% of host tracts (6 tracts) hold** | **14.6% of all DCs** |
| Top 5% of host tracts (30 tracts) hold | 33.3% of all DCs |
| Top 20% of host tracts (122 tracts) hold | 60.6% of all DCs |

Population scaling:

| Metric | Value |
|---|---:|
| Population living in a DC-hosting tract | 2,868,863 |
| Total population (DC-state ACS universe) | 332,343,349 |
| **% of DC-host-state residents in a DC-hosting tract** | **0.86%** |
| DCs per resident, DC-hosting tracts | 1 per 1,927 |
| DCs per resident, DC-state average | 1 per 223,199 |
| **Per-capita DC burden, host vs. average** | **~115×** |

---

## 4. Who bears the costs? (ACS profile of DC tracts vs. peer tracts in same states)

| Field | DC tracts (median) | Non-DC peers (median) | Δ (DC − peer) |
|---|---:|---:|---:|
| Median household income ($) |  91,082 |  76,637 | **+14,446** |
| Per-capita income ($) |  48,111 |  38,546 | +9,565 |
| Broadband subscription (%) |    94.2 |    92.0 | +2.2 |
| Poverty rate (%) |     8.8 |    10.8 | −2.0 |
| Non-Hispanic White (%) |    52.4 |    64.7 | −12.3 |
| Non-Hispanic Black (%) |     6.7 |     3.9 | +2.8 |
| Hispanic/Latino (%) |    11.9 |     9.8 | +2.1 |
| Non-Hispanic Asian (%) |     5.2 |     1.5 | +3.7 |

Population-weighted means in DC tracts: MHI **$109,145**, broadband **93.2%**,
poverty 11.1%. The actual residents of host communities are concentrated in
affluent tech corridors (Loudoun, Silicon Valley, Seattle eastside,
Hillsboro OR).

Primary-industry mix of host tracts (count of tracts):

| Tracts | Primary industry |
|---:|---|
| 351 | Educational services, and health care and social assistance |
| 133 | Professional, scientific, management, administrative, and waste management services |
|  35 | Manufacturing |
|  26 | Arts, entertainment, recreation, accommodation, and food services |
|  22 | Retail trade |
|  14 | Agriculture, forestry, fishing and hunting, and mining |
|  10 | Finance and insurance, and real estate and rental and leasing |
|   9 | Construction |
|   4 | Transportation and warehousing, and utilities |
|   3 | Public administration |

---

## 5. Cable-adjacent vs. inland DC tracts

|  | ≤100 km from a cable | >100 km from a cable |
|---|---:|---:|
| Tracts | 159 | 452 |
| Data centers | 319 | 1,170 |
| Median household income ($) | 106,406 | 86,289 |
| Median broadband (%) | 95.2 | 93.9 |
| Median DC count | 1 | 1 |

Inland DCs are roughly **3.7×** the cable-adjacent count. Coastal/cable
tracts skew even wealthier than inland DC tracts.

---

## 6. Benefit dispersion (broadband subscribers as a benefit proxy)

| Measure | Value |
|---|---:|
| Estimated broadband subscribers (DC states) | 119,719,313 |
| Tracts with subscriber data | 81,839 |
| Gini of subscribers across tracts | 0.253 |
| HHI of subscribers across tracts | 0.00001 |

Side-by-side concentration:

| Series | HHI |
|---|---:|
| DCs across DC-hosting tracts | 0.0069 |
| Broadband subscribers across DC-state tracts | 0.00001 |
| **Concentration ratio** | **~464× more concentrated for DCs** |

---

## 7. Verdict

| Element of the frame | Holds? |
|---|---|
| Costs concentrated geographically | **Yes** — top 6 tracts carry 15% of DCs; <1% of host-state population lives in a DC tract; per-capita burden ~115× the average. |
| Driven by submarine cable infrastructure | **No, broadly** — proximity test fails nationally; submarine cables matter for a coastal subset only. Terrestrial fiber, power, water, land, and tax incentives dominate. |
| Benefits dispersed among users | **Yes** — broadband subscribers ~464× more dispersed (by HHI) than DCs. |
| Classic political failure mode (weak losers vs. diffuse winners) | **No.** Host tracts skew wealthier, higher-income, higher-broadband than peers. The cost-bearing communities are affluent tech corridors with strong bargaining capacity — they tend to convert concentrated costs into concentrated *rents* (tax base, jobs, infrastructure concessions). |

**Bottom line.** The structural asymmetry that defines "concentrated costs /
dispersed benefits" is unambiguous in the data — DC siting is hyper-local
while benefits are continental. But the predicted political dynamic doesn't
fit cleanly, because the loser side here is not weak. A more targeted test
would split host tracts into power-stressed exurban tracts (parts of
Loudoun's edges, central Oregon, Iowa) and urban-suburban tech-corridor
tracts, and look at whether the *exurban* subset shows the weak-loser
pattern (lower income, slower broadband, higher poverty than its
neighbors).

---

## Caveats

- The ACS universe is the 46 DC-host states (already DC-heavy); excludes
  states with no DCs in the sample.
- `data_center_census_tracts_2024` only contains tracts that host at least
  one DC, by construction.
- Broadband-subscription rate is a coarse benefit proxy; cloud services
  benefit any internet user globally, not just local subscribers.
- 45 of 1,489 DCs use city-precision fallback coordinates, so a small share
  of tract assignments are approximate.
- The `logical_dominance_ips` field in `internet_city_dominance` measures
  IP blocks routed/hosted at each city — a supply-side measure that
  duplicates the DC signal, not a demand-side user-location measure. It
  was excluded from the benefit-dispersion calculation for that reason.

## Reproducible scripts

- `load_postgis_internet_cables.py` — ingest cables/landings/cities
- `make_internet_cables_map.py` — render the combined Leaflet map
- `analyze_cables_concentration.py` — state-level + cable-proximity analysis
- `analyze_dc_tract_concentration.py` — tract-level analysis used here