Methodology

Whole-worker organizing, developed by Jane McAlevey from her experience in healthcare and public sector campaigns, holds that durable workplace power requires workers to be embedded in community relationships — churches, schools, neighborhood institutions — that extend beyond the job site. A strike only wins if the community supports it. A contract only holds if the workers who signed it have the density and trust to enforce it.

Sectors where workers are already embedded in community life — hospitals, schools, public transit — have structural advantages that sectors without community embeddedness cannot replicate by strategy alone. The community knows who these workers are. When they strike, the community feels it immediately, and often sides with them.

SVS accommodates this theory through two variables: Community Crisis-Creating Reach (can a strike disrupt community life?) and Community-Facing Reach (are workers embedded in community relationships through their work?). The Whole-Worker Bonus rewards sectors where both are present simultaneously, capturing the interaction effect that the additive formula cannot.

Capital disruption theory holds that workers' power derives primarily from their ability to interrupt capital accumulation — to shut down the flows of goods, services, and infrastructure that keep the economy running at scale. Ports can strand billions in cargo. Rail can halt supply chains across multiple industries. Energy upstream can threaten fuel supplies for entire regions.

These sectors can create crises not just for a single employer but for the broader capitalist system. That leverage — what Beverly Silver calls "marketplace power" — is qualitatively different from the leverage of a retail worker whose strike inconveniences one store's customers. The employer's ability to hold out is constrained by pressure from other capital interests who depend on the disrupted sector.

SVS accommodates this theory through Capital Crisis-Creating Reach (how far does a strike disrupt capital's ability to function?) and the Dual-Crisis Bonus, which rewards sectors where capital disruption and community crisis leverage are both present — the most powerful organizing terrain of all.

Formula

County SVS = Σ (sector_SVS × county_employment_in_sector)
→ scaled to 0–100 using absolute benchmark

A county scores high only if it has both a strategically valuable sector and a significant workforce in it. The two factors multiply. A county full of retail workers scores low even if it sits in a swing state. A county with a single large hospital serving a rural region scores high.

Scoring uses an absolute benchmark — the theoretical maximum weighted employment score across all sectors — so a 75 in Pennsylvania means the same as a 75 in Texas. Scores are stable across filtered views.

Known Limitation

Public sector employment is understated. The County Business Patterns dataset (CBP) excludes government employees by design. Counties with large government workforces — DC, state capitals, county seats — show lower Sectoral Value Scores than their actual structural importance warrants. BLS QCEW data partially compensates. See the sector SVS section for the v6 formula that feeds this calculation.

Calculation

Unorganized workforce = Total employment − Estimated union members
→ scaled by county employment share of national total
→ normalized to 0–100

The OPS answers: how much untapped workforce is here to organize? It is a measure of quantity of opportunity, not quality. Quality is captured by the SVS. A county with a massive unorganized workforce scores high regardless of sector composition — organizing potential is a prerequisite for any strategy, even if the sectoral terrain is only moderate.

Union membership data derives from DOL LM-2 filings. Approximately 2,192 union locals are unresolvable — records with PO boxes, international addresses, or addresses that cannot be geocoded to a county FIPS. This represents roughly 20% of all LM-2 filers by count. Union member counts in every county are therefore floors, not ceilings. The model treats this as a confirmed permanent data gap, documented explicitly in the Limitations section.

A sector that can threaten both capital and community simultaneously is qualitatively different from a sector that threatens only one. The Dual-Crisis Bonus captures this.

When organizers in a dual-crisis sector strike, they are not making one argument — they are making two simultaneously: to capital ("your supply chain is disrupted"), and to the community ("your essential service is gone"). These arguments reinforce each other and create pressure from multiple directions at once. Employers face economic loss and political pressure from elected officials responding to constituents. That combination is more than the sum of the two pressures separately.

Why +5 specifically, and not incorporated into the base scale? Because the interaction is conditional. A sector with cap=3 and comm=0 is powerful but one-dimensional. A sector with cap=3 and comm=2 can fight on two fronts. The bonus makes this distinction explicit without distorting the point values of either variable when the bonus does not apply.

Sectors receiving Dual-Crisis Bonus

06 Ports · 07a Trucking General · 07d Couriers Other · 09a Rail Freight · 09b Rail Passenger · 10 Air · 15 Public Utilities · 28 Energy Upstream

A sector where workers are embedded in the communities they can disrupt has whole-worker organizing potential that is more powerful than the sum of its components. The bonus captures McAlevey's core insight: community crisis leverage is most powerful when the workers creating the crisis are also members of the community feeling it.

The teacher who goes on strike is also the neighbor, the parent, the school board meeting attendee. The hospital nurse knows the families of the patients affected. These relationships are not incidental to organizing — they are the mechanism through which community support is built and sustained. A sector that can create a community crisis but has no community-facing relationships must build those relationships from scratch. A sector where the work itself creates them starts with a structural advantage.

Rail Freight has higher capital crisis reach (national vs. state) — a freight rail strike halts supply chains across multiple industries. But Rail Passenger workers interact with commuters and travelers daily; they are embedded in community life in a way freight workers are not. Passenger rail scores state-level capital crisis (not national — one city's commuter system does not threaten national capital), but it scores both community crisis and community-facing reach, triggering the Whole-Worker Bonus. The result: Rail Passenger SVS 55 ranks above Rail Freight SVS 50 despite lower capital crisis reach, because it combines both theories of change simultaneously.

Sectors receiving Whole-Worker Bonus

01 Hospitals · 02 Nursing Homes · 03a Home Health · 03b Outpatient Care · 04 K-12 Education · 05a Public Universities · 05b Private Universities · 09b Rail Passenger · 11 Social Services · 11b Childcare · 12 Federal Hospitals & VA · 13 State Public Health · 14 Public Transit · 16 Public Sanitation · 21 Human Services Admin

Formula

Presidential Sub-score = Swing State Weight + f(county 2024 margin)

Swing State Weights

County-level presidential results from the MIT Election Data + Science Lab, 2020 and 2024 cycles. Both cycles are incorporated to capture trend as well as snapshot. 28 counties have no election data — primarily Alaska borough-level units that do not correspond to standard county-level reporting. Affected counties are flagged in the dataset.

The Statewide Sub-score measures how much organizing in a county can move the needle on statewide electoral contests. Counties in competitive states score higher; counties in safely blue or safely red states score lower, because the electoral marginal value of organizing there is lower for statewide races.

Data sources: MIT Election Lab historical results; Cook Political Report competitiveness ratings for Senate and gubernatorial races.

Critical caveat: The statewide sub-score must be interpreted differently depending on the target race. Presidential, gubernatorial, and Senate races each trigger different intervention logic — they are not interchangeable even within the same state. Cycle-specific race targeting is a planned v7 refinement.

The Congressional Sub-score measures the electoral value of organizing in a county for House of Representatives races. Counties in genuinely competitive districts score highest. The score derives from Cook Political Report district ratings and MIT Election Lab district-level historical results.

Design decision — primary challenges: This model treats primary challenges as a legitimate strategic option. Safe Democratic districts with out-of-step incumbents are scored higher than equivalent safe Republican districts, because a primary challenge in a safe Democratic seat has a plausible path to changing representation. Users who prefer to exclude primary targeting can filter by Intervention Type to focus only on general-election-competitive districts.

Organized Scale — Total union members in the county (raw count, scaled 0–100). Measures the volume of existing organized power available for political deployment. High scale enables Type B and Type C interventions.

Union Culture — Union members as a percentage of total county workforce (density). Measures how normalized collective action is — whether the social infrastructure for organizing exists culturally. Relevant for Type A interventions: a county where workers have neighbors and family in unions is culturally primed for new organizing.

Why kept separate: Union density is a lagging indicator — it is an outcome of past organizing strategy, not a predictor of future organizability. Including it in the OOS would create circular feedback: high-density counties would appear to be good organizing targets simply because they were organized in the past. Infrastructure feeds Intervention Type classification; it does not inflate the organizing opportunity signal.