Metro Area
Opportunity
Metro Area
Counties in Metro
| # | County | ST | OPP↓ | Type | Pres | State | Cong | Org | Sect | Infra | 2024 | Swing |
|---|
Top 10 Metro Areas
The Model, Explained
Every score in this model is grounded in social science theory, built from public data, and documented here. Click any component to expand the full explanation — from peer-reviewed framework to organizer's field logic.
Organizing Opportunity Score
The composite terrain score — how favorable is this county for building power?
Primary output · 0–100
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A composite index measuring the structural preconditions for collective action formation, operationalizing McAlevey's (2016) whole-worker organizing framework as a weighted combination of sector-level marketplace bargaining power (Silver, 2003) at 55% and unorganized labor reserve at 45%. Scores are normalized against an absolute national benchmark — not relative rankings — preserving cross-county comparability independent of filter state.
Electoral scores and Infrastructure Score are intentionally excluded from the OOS formula. Combining organizing potential with electoral value would conflate two analytically distinct questions, corrupting both measurements.
A 0–100 score answering one question: how good is this county's ground for building worker power? It combines the leverage of the workers here (what sector they're in) with how many of them are still unorganized. Both have to be true at once to score high.
This is your terrain map before you commit resources. High score means you've got leverage and people to organize — the combination that turns a drive into a power-building project. A high score in a county with no organizers is a call to act. A low score in a county you're already in is a question you need to answer.
Sectoral Value Score (County)
Does this county have strategically powerful workers — and enough of them?
55% of OOS
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County-level weighted summation of sector-specific Strategic Value Scores (SVS), normalized to an absolute benchmark. Implements Silver's (2003) marketplace bargaining power thesis: workers' structural position in production — specifically their capacity for disruptive action — is the primary determinant of leverage, independent of organizational density or political will. A county's sectoral composition is thus the foundational structural variable.
Known gap: County Business Patterns excludes government workers by design. Government-heavy counties (DC, state capitals) are systematically understated.
Two things have to be true at once — it's a multiplication, not an addition. A county scores high only if it has both strategically important workers AND enough of them. A county full of retail workers scores low even in a swing state. A county with a single large hospital system in a rural region scores high.
One hospital system beats five strip malls. It's not headcount — it's who can actually stop something when they walk off. Before you open a campaign, look at what sector you're entering. This score tells you if the terrain has leverage baked into it, before you've talked to a single worker.
Strategic Value Score (Sector)
Boolean rubric: how much structural leverage does this sector have?
Feeds County SVS
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Sector-level boolean rubric operationalizing chokepoint theory (Clawson & Clawson, 1999; Silver, 2003) and McAlevey's (2016) community embeddedness thesis. Points are assigned for: Chokepoint Potential (25 pts) — capacity to disrupt essential flows; Crisis-Creating (20 pts) — a stoppage creates visible public harm; McAlevey Priority (20 pts) — explicitly identified in the whole-worker framework; Community-Facing (15 pts) — workers are embedded in the social fabric beyond the workplace; Non-Offshoreable (10 pts) — cannot be geographically displaced. A ×1.15 multiplier applies when Chokepoint=YES AND Community-Facing=YES, reflecting empirical evidence that these two dimensions produce non-additive leverage when combined.
| Sector | Choke | Crisis | McAl. | Comm. | Non-off | SVS |
|---|---|---|---|---|---|---|
| Hospitals | YES | YES | YES | YES | YES | 100 |
| K-12 Education | YES | YES | YES | YES | YES | 100 |
| Public Admin – Local | YES | YES | YES | YES | YES | 100 |
| Ports / Maritime | YES | YES | YES | NO | YES | 75 |
| Trucking | YES | YES | YES | NO | YES | 75 |
| Warehousing | YES | YES | YES | NO | YES | 75 |
| Utilities | YES | YES | TBD | NO | YES | ~55 (pending) |
| Public Admin – State | YES | YES | TBD | BRDLN | YES | flagged |
| Higher Education | NO | NO | NO | YES | YES | 25 |
| Mfg – Durable | YES | NO | NO | NO | YES | 25 |
| Social Assistance | NO | NO | NO | YES | YES | 25 |
| Public Admin – Federal | NO | NO | NO | NO | YES | 10 |
| Mfg – Non-durable | NO | NO | NO | NO | YES | 10 |
| Construction | NO | NO | NO | NO | YES | 10 |
| Retail | NO | NO | NO | NO | YES | 10 |
| Hospitality | NO | NO | NO | NO | YES | 10 |
| Healthcare – Ambulatory | NO | NO | NO | NO | YES | 10 |
| Other Services | NO | NO | NO | NO | YES | 10 |
| Financial Services | NO | NO | NO | NO | NO | 0 |
| Tech / Information | NO | NO | NO | NO | NO | 0 |
A checklist: Can workers in this sector stop something critical? Is their work woven into community life? Can it be shipped overseas? The answers determine the sector's score. Hospitals and schools answer YES across the board. Financial services and tech answer NO — they can move, they can automate, and nobody's neighborhood depends on them showing up.
Nurses' strike shuts the city down and your neighbor's kid is in that school. That's the combination. Retail scores 10 because Amazon opens a new store. Know which sector you're entering before you spend a year of someone's life on a campaign — the terrain either works for you or against you from day one.
Organizing Potential Score
How large is the unorganized workforce here?
45% of OOS
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Measures the size of the available unorganized labor pool — total county employment minus estimated union members, scaled by the county's share of national employment. Operationalizes McCarthy & Zald's (1977) classical resource mobilization concept of "available population": the raw material for movement formation, analytically independent of structural leverage quality. Combined with SVS, it produces the OOS.
Known ceiling: ~2,192 LM-2 union locals (~20% of filers) are unresolvable due to PO boxes, international addresses, or territories. Union member counts in every county are floors, not ceilings.
How many workers in this county don't have a union yet? This is about quantity of opportunity — the raw material. Sector quality tells you the terrain; this tells you how many people you could bring into the fight. Both have to be strong for a county to score high overall.
A county with 50,000 unorganized hospital workers is a jackpot. A county with 500 unorganized retail workers is a long slog. This score tells you how full the room is before you walk in — before you've knocked a single door or made a single ask.
Presidential Sub-score
Electoral College leverage — how much does this county matter for the presidency?
Goal: Presidential Battlegrounds
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Electoral college leverage score combining state-level swing weight (derived from historical presidential margin tightness and Electoral College value) with county-level 2024 presidential margin. Implements spatial targeting logic from electoral studies literature: organizing resources have highest marginal electoral value in counties within competitive states that are themselves competitive (Hersh, 2015; Enos & Fowler, 2018). State weights: PA/WI=100, AZ/NV=90, GA/MI=85, NC/TX=70, FL=60, others scaled. 28 counties — mostly Alaska boroughs — have no election data.
How much does organizing in this county matter for winning the presidency? Counties in close states that are themselves close score highest — those are the places where mobilizing a few thousand more union households could actually flip the outcome.
If you're in Pennsylvania and your county went R by 3 points, you're gold — that's the margin labor can move. Don't burn your members' shoe leather in a county going D by 30. Take that capacity somewhere it counts. Presidential math is about marginal returns; this score does that math for you.
Statewide Sub-score
Senate and governor — how competitive is this state for statewide power?
Goal: Long-Term State Transformation
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State-level competitive index for statewide office (Senate, governor) derived from historical margin tightness and Cook Political Report ratings. Based on the theoretical premise that labor mobilization has asymmetric electoral returns — high in competitive states, near-zero in noncompetitive states (Key, 1949; Ansolabehere et al., 2006). Critical validity limitation: gubernatorial, Senate, and presidential races each require distinct intervention logic. This sub-score does not distinguish between them — race-specific targeting is a Phase 2 refinement.
How competitive is this state for Senate and governor races? Counties in states with close statewide races score higher because organized labor's electoral work there has real marginal value. But be careful — a competitive state for a Senate race may be irrelevant for the governor's race, and vice versa.
The statehouse controls labor law. A competitive Senate seat is worth years of infrastructure because winning it can change the rules for everyone. But know which race you're targeting — the Senate district map and the governor's map are different games, and your precinct strategy has to match the race.
Congressional Sub-score
House races — general election competition and primary challenge potential.
Goal: Flip the House
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District-level competitiveness score incorporating Cook Political Report ratings and MIT Election Lab historical margins. Includes positive weighting for safe Democratic districts with vulnerable incumbents, reflecting strategic literature on primary elections as an accountability mechanism within legislative bodies (Boatright, 2013). This is a normative model choice: primary challenges are treated as a legitimate tool for legislative accountability.
How much does organizing in this county matter for House races? This covers both tight general-election districts AND safe Democratic seats where a primary challenge could replace a bad incumbent with a better one.
The House is the first lever. A competitive district is obvious. But don't sleep on a Democrat who votes against card check in a safe seat — you can primary them. Safe seat, wrong incumbent? That's a target too. This score accounts for both plays.
Infrastructure Score
Scale and culture — how much organized power already exists here?
Reference score
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Two-dimensional organizational capacity measure: Organized Scale — total union members (raw count, scaled), capturing deployable political resources; Union Culture — union density (members/total employment), capturing the normalized social acceptance of collective action, which Bronfenbrenner (2009) identifies as a significant predictor of new organizing success. Deliberately excluded from the OOS formula: density is an outcome of past strategy, not a predictor of future organizability. Including it would introduce circular measurement.
Two questions: how many union members are here (scale), and what fraction of the workforce are they (culture/density)? Both matter but for different reasons. Scale drives what you can deploy politically right now. Culture shapes whether new organizing will stick.
High scale means you've got members ready to knock doors this cycle. High density means workers in this county grew up with uncles in the union — the culture is already there, and a new drive has air under it from day one. Low on both? You're starting from scratch. That's not a reason not to go — but price it right.
Intervention Type (A / B / C)
What strategic mode does this terrain call for?
Categorical classifier
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Categorical classifier derived from the infrastructure–opportunity matrix, drawing on social movement theory's distinction between mobilization (activating existing resources) and organization-building (creating new resources) as distinct strategic modes with different resource requirements and timelines (McCarthy & Zald, 1977; McAlevey, 2016). Prevents the common error of applying uniform resource allocation to structurally dissimilar terrains.
Type A — Build from scratch where there's opportunity but no unions. Type B — Wake up the existing unions that aren't politically active. Type C — Coordinate unions that are already aligned and active. Different types, different resources, different timelines.
These aren't just labels — they determine your budget, your timeline, and your staff model. A Type A campaign needs an experienced lead organizer and 18 months minimum. A Type C just needs a coordinator and a voter file. Don't run a Type A campaign with Type C resources. Don't waste a Type C terrain by treating it like it needs to be built from scratch.
McAlevey Framework
The whole-worker theory driving sector selection in this model.
Theoretical foundation
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McAlevey's (2016, 2020) whole-worker framework argues that durable political power requires organizing workers as complete social beings embedded in their communities — not merely as economic actors. The framework predicts that sectors with both chokepoint leverage and community embeddedness generate superior long-run political capacity compared to sectors with leverage alone. This is in direct theoretical tension with the Strategic Sector Initiative (SSI), which prioritizes manufacturing based on industrial organization theory. This model sides with McAlevey — the SVS rubric encodes her sector hierarchy, and the ×1.15 multiplier operationalizes her core claim about the non-additive effect of combining leverage with community presence.
Key citations: McAlevey (2016) No Shortcuts; McAlevey (2020) A Collective Bargain; Silver (2003) Forces of Labor (chokepoint theory alignment).
The model follows McAlevey's argument: organize nurses and teachers, not Amazon drivers. Hospitals have more leverage AND nurses live in the communities they serve. Both things are true, and together they're more powerful than either one alone. The SSI framework would say go to manufacturing — this model disagrees.
McAlevey's insight is simple: the best organizers don't see workers — they see whole people who go home to neighborhoods, churches, and school board meetings. If your sector is woven into community life AND can shut something down, you've found the sweet spot. That's not sentiment — it's the empirical record of which campaigns build the kind of power that lasts past a contract.
Two-Actor Negotiating Logic
Bosses and politicians respond to different kinds of pressure.
Theoretical context
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Theoretical distinction between two principal counterparties in labor strategy: private-sector employers — profit-maximizing actors amenable to cost-benefit negotiation, where workplace disruption creates measurable material losses; and political actors — responsive to electoral, ideological, and reputational incentives, capable of ideological intransigence even at substantial material cost (principal-agent theory; public choice literature). Empirically validated by cases where conventional labor leverage failed against ideologically committed political actors: Walker (WI, 2011), DeSantis (FL, 2022). The electoral sub-scores exist precisely because political actors require a different pressure mechanism — the ballot box, not the picket line alone.
You negotiate with bosses and politicians very differently. A boss will calculate what the strike costs them and make a deal. A politician running on an anti-union platform will let the strike drag on and call it a win. Different actor, different logic, different tools needed.
Scott Walker took the hit. DeSantis took the hit. They wanted the fight — it was their brand. Conventional pressure doesn't work on a true believer with a base. You can't bargain someone out of an ideology. You vote them out. That's why this model has electoral scores as a separate layer — because some opponents can only be beaten at the ballot box, not at the table.
What This Model Cannot Do
The model's boundaries — what organizer judgment must supply.
Epistemological limits
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Systematic documentation of measurement validity threats (Cook & Campbell, 1979). The model cannot operationalize: associational power (Levi, 1996), organic leader network centrality, employer-specific financial vulnerability (Bronfenbrenner, 2009), local political contingency, or internal union democracy. These represent genuine construct validity gaps that structural administrative data cannot close — they require ethnographic, participatory, and field research methods. The model is a structural prioritization instrument, not a substitute for ground-level political intelligence.
The model sees structure, not people. It cannot see whether your county has a trusted leader who can move a shop, whether the employer is in financial trouble, whether there's a reform caucus ready to activate, or what happened at the last organizing drive. Those gaps are real and they matter.
This map shows you where to look — not what you'll find when you get there. The next step after a high-scoring county is always talking to people on the ground. The model tells you to go to Scranton; your job is to find out who in Scranton is ready to move, and whether the boss is overextended, and whether the last drive failed because of a snitch or because of a bad lead organizer. The model can't answer any of that. You have to.
Built in collaboration with Claude (Anthropic) for pipeline coding, data validation, literature review, and adversarial testing. All methodological decisions were made by the human researcher. Full documentation at github.com/kpsmas123-bit/labor-organizing-model.
Built by Sam Kaplan-Pettus, recent UC Berkeley Political Science graduate. A personal project exploring how new AI tools can serve the labor movement — inspired by Jane McAlevey's work on labor and political prioritization. Now looking for work and projects at the intersection of labor and national political organizing.
📧 sam.kp@berkeley.edu · 📂 github.com/kpsmas123-bit/labor-organizing-model