How to Build a Local Value Score Without Starting a Partisan Firefight

How to Build a Local Value Score Without Starting a Partisan Firefight

by Braden Kelley and Art Inteligencia

In the first piece in this series, we lit the spark: absolute spending debates rarely move the needle, while peer-relative value, what similar communities achieve per dollar, can. This article opens the instrument. Not the full DIY toolkit yet. The method. The experience architecture. The discipline that turns civic anger into civic learning.

From spark to instrument

Innovation without an instrument is just a mood. Human-centered change without a shared unit of sense-making becomes another round of tribal theater. If you felt the first article land – What If You Could Prove the Government is Ripping Us Off? – the next question is practical:

How do we score a place fairly enough that good-faith people on the left, right, and exhausted middle can argue about the same dashboard?

That is an experience-design problem before it is a political problem. People cannot improve what they cannot sense. So we need an interface for civic value that ordinary neighbors can understand in under two minutes, and that a finance director can challenge without inventing motives.

Call the instrument a Relative Value Score (RVS): a transparent, peer-normalized view of outcomes per cost-adjusted dollar, with a clear path to “why the gap.” The revolutionary move is not inventing a grade that flatters your side. It is building a comparison citizens trust enough to use in budget season.

What a Relative Value Score actually measures

A Relative Value Score is not “how big is the budget?” Bigger places spend more. Harder case mixes cost more. Expensive labor markets pay more. Treating raw spend as proof of compassion, or of waste, is how we keep fighting about identity instead of delivery.

Conceptually:

RVS ≈ how close you are to the best peer in your cohort on value delivered per dollar—across a small set of outcome domains, cost-adjusted, with published weights and sources.

Think of it like comparing restaurants of the same type and price band on food quality per dollar, not comparing a food truck to a banquet hall and declaring a winner by total receipts. Futurology loves flashy national indexes. Experience design prefers local instruments that feel fair in the neighborhood where life is actually lived.

Three design rules keep the score human-centered:

  • Outcomes over optics. Prefer measurable delivery (response times, learning growth, pavement condition, permit latency, unit cost of capital) over press-release volume.
  • Cost-adjusted dollars. Adjust for regional price and wage differences so “expensive place” is not automatically “bad place.”
  • Relative, not absolute holiness. Normalize within the peer cohort. The best peer becomes the teacher. Everyone else gets a gap to close—or explain.

Improper payments, fraud risk, and opaque grantee chains still matter. They belong on a related but separate track (more below). Mixing “inefficient” with “corrupt” in one opaque number is how movements lose the room on the first contested claim.

Peers first: the fairness engine

Most “benchmarking” fails before the math starts—because the peer set is a vibes playlist. Comparing a dense coastal city to a rural county, or a high-poverty district to an affluent one using unadjusted test levels, is not accountability. It is ammunition.

Peer cohorts are the fairness engine. Build them first. Argue about them in public. Version them. That is change leadership: put the contested assumptions where people can see them.

Practical cohort dimensions by layer:

  • Cities: population band, density, metro/rural context, regional price/wage index.
  • Counties: population, urban share, mandate set (courts, jails, public health, roads, elections).
  • States: population scale, economic mix, federal transfer share—used carefully; states are multi-product machines.
  • School districts: enrollment, % students in special education / English learners / free-or-reduced lunch (or equivalent need measures), urbanicity, regional educator wages. Prefer learning growth / value-add over raw proficiency levels whenever possible.
  • Water and special districts: service population, infrastructure age/type, treatment complexity, geography—unit cost of delivered service beats “total budget” every time.

A good peer set is small enough to be intelligible and large enough that one outlier cannot redefine “normal.” Publish who is in the cohort and why. Invite the entity to propose alternate peers with evidence. Right-to-reply is not weakness. It is how trust compounds.

Domains, weights, and the “why the gap” story

Do not boil the ocean. Innovation teams ship thin slices. For a first local scorecard, pick 8–12 metrics max across a few domains—not 200 indicators nobody reads.

Example domain shapes (customize by entity type):

  • Service delivery: outcomes or service quality per dollar or per FTE (clearance, response, throughput, condition).
  • Administrative load: central office / admin share versus program or instructional spend.
  • Labor intensity: compensation density (payroll, overtime, benefits where available) versus outputs.
  • Contracting & capital: competitive bid share, concentration, unit cost and schedule slippage on comparable projects.
  • Transparency: timely CAFRs/budgets, machine-readable checkbooks, FOIA latency—inputs to trust, not vanity.

Weights should be public, few, and revisable. Publish a default weighting, and let citizens toggle sensitivity modes (“weight learning growth higher,” “weight admin share higher”) so critics can fork the story without assassinating the messenger. That is open innovation applied to civic sense-making.

The score is the headline. The product is the “why the gap” decomposition: which 2–3 drivers explain most of the distance from the best peer, each with sources and confidence. Without that story, a score is just a ranking for dunking. With it, a score becomes a change agenda.

Two tracks: performance vs. integrity

Human-centered accountability refuses false equivalence. A district can be expensive relative to peers and still honest. An agency can look “lean” on paper and still run integrity red flags. Smash those into one “corruption score” and you have built a weapon, not an instrument.

Keep two panels on the same dashboard:

  • Performance track (RVS): peer-relative outcomes per cost-adjusted dollar. This is the main civic interface.
  • Integrity overlay: audit findings, delayed reporting, sole-source patterns, related-party flags, outcome-light grantee chains—shown with higher evidentiary bars, source documents, and room to reply. Treat AI here as a highlighter for human review, never as a judge pronouncing guilt.

This separation is how a multipartisan movement survives contact with reality. Precision is the revolutionary ethic. Smear is the cheap substitute.

A weekend walkthrough for one jurisdiction

You do not need the full toolkit to practice the method. You need a first mile. Here is a human-sized walkthrough you can start this weekend—before the downloadable suite and guidebooks ship.

  • 1. Name the entity in one sentence. “Our K–12 district,” “our city general fund,” “our water district”—passion plus a border beats rage at “the system.”
  • 2. Pull Tier-A documents. Latest adopted budget, CAFR or annual financial report, salary schedule or checkbook export, and any published outcomes dashboards. Note what is missing.
  • 3. Draft a peer shortlist. Five to fifteen similars using the cohort dimensions above. Write one paragraph defending the set.
  • 4. Choose eight metrics you can source. Prefer unit costs and outcomes over vibes. If a metric cannot be sourced, park it—do not invent it.
  • 5. Build a crude gap view. For each metric, who is best in your peer set? Where are you? What is the story in two drivers?
  • 6. Separate integrity notes. If you see red flags, log them with links and confidence—not as the score itself.
  • 7. Write three budget-season questions. Short enough for a three-minute public comment. Specific enough that staff must answer on the record.

That walkthrough is reconnaissance, not a finished product. Finished products need versioned methods, entity right-to-reply, and consistent cohort rules. That is exactly what the forthcoming DIY tools and role-based guidebooks are for—so local teams are not reinventing discipline in the dark.

What comes next—and how to stay in formation

Method without movement is a PDF nobody uses. Movement without method is a mob with a megaphone. We are building both: open instruments for peer-relative value, and a network of roles—data gatherers, benchmarking intelligence creators, website hosts, promoters, meeting advocates, media partners—who can stand up local scoreboards when the kits are ready.

In the next pieces in this series, we will walk a single jurisdiction end-to-end as a chapter-lead story, then open the downloadable suite: data gathering, careful AI-assisted processing under human guardrails, and public benchmarking sites neighbors can actually use.

Between method and toolkit, stay on the bus.

If this article sharpened your sense of how fair comparison should work, subscribe to Human-Centered Change & Innovation Weekly. That list is how you get first notice when the guidebooks and DIY tools move from spark to usable firepower—practical next steps for the jurisdiction and role you choose.

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Forward this to the person who already knows the peer set in their head—and to the person who keeps saying the comparison “isn’t fair” without ever defining fair. Fairness is designable. That is the work.

Absolute spend is a slogan. Peer-relative value is an instrument. Instruments are how human-centered futures get built.

Frequently Asked Questions

What is a Relative Value Score (RVS)?

A Relative Value Score is a peer-normalized measure of outcomes per cost-adjusted dollar for a city, county, state, school district, or special district. It shows how close an entity is to the best performer among similar peers across a small set of transparent metrics—not whether its total budget is large or small in absolute terms.

How do you choose fair peers for comparison?

Fair peers share similar constraints: size and density for cities; mandate mix for counties; enrollment and student-need mix plus regional wages for school districts; service population and infrastructure profile for water and special districts. Publish the cohort rules, keep the set large enough to be meaningful, and allow entities to propose alternate peers with evidence. Unadjusted “leaderboards” that ignore poverty or cost of living are not fair benchmarks.

Should fraud and performance be one score?

No. Keep a performance track (peer-relative value) separate from an integrity overlay (audits, delayed reporting, sole-source patterns, opaque grantee chains) that requires stronger evidence and human review. Combining them into one “corruption score” destroys credibility and unfairly smears underperforming but honest agencies.

Image Credits: Pexels

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Cursor to clean up the article.

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