Research & Proposals
At StateFinance.org, we believe that open data alone is not enough — it must inspire action. The following policy frameworks, authored by Aegis Analytics, outline practical models for building a more transparent, data-driven, and equitable system of public governance. These proposals complement the data available on StateFinance.org by translating insights into frameworks for reform and accountability.
All materials are freely available for research, discussion, and adaptation.

Model Open Fiscal Data Act (MOFDA)
Overview
The Model Open Fiscal Data Act (MOFDA) is a legislative blueprint for states to require standardized, machine-readable publication of budgets, expenditures, debt, grants, and procurement data. Its goal is to make fiscal reporting transparent, comparable, and accessible—while minimizing the administrative burden for local governments through open-source toolkits, phased compliance, and small-jurisdiction grants.
Prepared by Aegis Analytics (Aug 2025), the Act envisions a national model built around open fiscal data infrastructure, comparable to the federal DATA Act, but scaled to the state and local levels where over $4 trillion in spending occurs annually.
Policy Objectives
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Transparency & Trust: Enable public, media, and researchers to track government spending in real time.
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Efficiency & Cost Savings: Reduce redundant reporting, FOIA costs, and fragmented datasets across thousands of jurisdictions.
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Equity & Accessibility: Provide grants and shared portals to ensure small municipalities can comply.
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Governance & Security: Create a bipartisan Fiscal Data Standards Committee to review schema annually and oversee data quality.
Core Design & Safeguards
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Data Types: Budgets, actual revenues/expenditures, debt schedules, grant flows, and procurement lifecycle (OCDS-aligned).
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Formats: CSV, JSON, and API endpoints with DCAT-US metadata for interoperability.
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Security: Redaction of sensitive vendor and infrastructure details; quarterly cadence for debt data.
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Equity Provisions: Toolkits and micro-grants to small jurisdictions; opt-out clauses for high-risk entities with sunset review.
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Enforcement: “Carrots and sticks”—technical grants, recognition scorecards, and potential penalties (e.g., eligibility restrictions for discretionary funds).
Budget & Cost Model
Five-Year Total Cost: ≈ $52 million (avg. $10.4M/year).
Breakdown:
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$3.5M/year – administrative staff, help desk, and committee operations
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$5M/year – compliance and micro-grants
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$3M initial infrastructure + $1M/year recurring (portals, APIs, toolkit updates)
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$0.5M/year – civic education and data-literacy initiatives
Return on Investment (ROI)
MOFDA’s return potential is massive relative to cost:
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Efficiency Dividend: A mere 0.1% improvement in fiscal efficiency across state/local budgets saves ~$4 B annually — a 400× ROI on program cost.
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Debt Market Benefits: Standardized, high-quality data can improve municipal bond ratings; even a 2–3 bps drop in borrowing costs yields hundreds of millions in interest savings nationally.
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Administrative Productivity: Reduces redundant data cleanup, freeing tens of thousands of staff hours across governments.
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Civic Innovation Multiplier: Open, comparable fiscal data fuels research, oversight, and app development—analogous to how weather or transportation data spawned multi-billion-dollar analytics ecosystems.
Total Potential Annual Value:
If conservatively combining fiscal savings (~$4B), borrowing cost reduction (~$0.3–0.5B), and administrative efficiency (~$0.2–0.4B), the total annual impact could reach $4.5–$5 B, dwarfing the $10 M/year implementation cost.
Implementation Phases
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Year 1: Pilot (5 cities + 1 state agency), toolkit development, schema refinement.
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Year 2: Tier 1 compliance (budgets + actuals).
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Year 3: Tier 2 compliance (debt + grants).
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Year 4: Tier 3 compliance (contracts + metadata).
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Year 5: First national schema review and open-data performance audit.
Strategic Impact
MOFDA transforms fiscal transparency from a compliance burden into a public asset. By harmonizing state/local reporting into open standards, it creates the infrastructure for:
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Better AI-driven fiscal risk detection (fraud, waste, inefficiency).
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Stronger bond market confidence through uniform disclosures.
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An innovation ecosystem around public finance data (watchdogs, startups, civic tech).
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Greater citizen trust through verifiable, accessible spending data.
Summary Table
MetricValue / Estimate
5-Year Cost - $52 M
Annualized Cost - $10.4 M
Potential Annual Savings - $4–5 B+
ROI Ratio ≈ 400×
Payback Period - < 1 month of operation
Coverage - All state + local fiscal entities
Pilot Scale - 5 cities + 1 state agency
Download the full .pdf or .docx here:
American Resilience and Opportunity Accord (AROA)
The American Resilience and Opportunity Accord proposes a new social compact for the age of automation. As AI reshapes corporate and administrative work, AROA ensures that efficiency gains are shared with workers and consumers — not concentrated at the top. It introduces a modest corporate levy that funds Universal Transition Accounts for every worker and rewards companies that raise wages, cut prices, and share profits. By aligning innovation with inclusion, AROA transforms automation from a source of disruption into a driver of lower prices, higher wages, and broader opportunity.
High-Road Business Coalition & Humanitarian Dividend Framework
You can't be a leader if you leave someone behind. The High-Road Business Coalition extends AROA’s mission from national policy to corporate responsibility. Participating companies voluntarily commit to sharing at least 1% of AI efficiency gains through a Humanitarian Dividend — half invested in domestic housing and stability programs, and half in global relief for the world’s most vulnerable. The framework promotes transparency, dignity, and measurable outcomes, with tiered certification for impact reporting and independent assurance. To support this system, the Measurement & Assurance Framework defines three standardized, auditable methods for calculating contributions — from simple fixed proxies to CFO-verified savings-based models. Together, these tools create a scalable, accountable way to ensure that automation’s benefits are shared fairly between workers, consumers, and communities.
Purpose
AROA is a national, incentive-driven framework designed to ensure that AI-driven efficiency gains flow not only to corporate profits but to workers, consumers, and communities. It acts as a “market-compatible social contract” for the automation era — rewarding firms that share their savings while cushioning dislocated workers.
Core Architecture
1. Universal Transition Accounts (UTAs)
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Funding: 0.5 % levy on corporate receipts (above $50 M) or equivalent AI-compute tax.
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Benefits: portable worker accounts covering:
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Wage insurance (up to $20 k per year × 2 years)
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Accredited retraining and certification
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Health-care bridge (up to 6 months)
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Startup grants / loans (up to $50 k)
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Administration: managed through public-private financial institutions; worker-directed within approved categories.
2. Qualified Efficiency Actions (QEAs) — Corporate Incentive Suite
Tax credits to channel AI savings toward social outcomes:
Action Credit Purpose
Frontline wage increases above inflation....................................20%......................Strengthen low/mid-income pay growth
Consumer rebates ≥ 2 % of sales..................................................20%......................Encourage visible price relief
Profit / equity sharing with employees........................................25%......................Align ownership and loyalty
Employer UTA contributions (beyond required minimum).........30%......................Expand safety nets and retraining funds
Credits may offset up to 50 % of corporate tax liability.
3. Worker Power & Fair Process
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Union neutrality required for firms claiming >$10 M credits.
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Fast-track arbitration for automation disputes.
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Encourages “automation dividend” clauses in bargaining agreements.
4. Transparency & Oversight
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Treasury publishes an Annual Efficiency Dividend Report tracking UTA flows, wage/price outcomes, and credit utilization.
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Enforcement targets fraudulent claims rather than micromanaging firm-level savings audits.
Economic Impacts
Indicator Projected Outcome (5 yrs)
Prices............................................3–8 % decline vs. baseline through competition and rebates
Frontline Wages............................+5–10 % sustained growth
Unemployment..............................Temporary +0.3–0.6 pp increase; median reemployment < 60 days
GDP Growth.................................+0.3–0.8 pp annual uplift
Inequality (Gini)............................Modest decline; real household income up
ROI...............................................$1 implementation → $60–100 societal benefit
Example: A $10 B retailer automating admin tasks saves ≈ $750 M. After AROA credits: $375 M rebated to consumers, $187 M to frontline raises, $50 M to UTAs — while retaining R&D capacity and improving margins.
Equity & Regional Design
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Allocate UTA funds by displacement intensity; ≥ 25 % earmarked for rural and non-metro areas.
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Prioritize training pipelines in healthcare, skilled trades, and public-sector digital service.
High-Road Business Coalition for the AROA Humanitarian Dividend
Purpose
To extend AROA’s domestic “share-the-gains” ethos to global and humanitarian outcomes.
Participating firms voluntarily contribute 1 % of AI efficiency gains (0.5 % domestic / 0.5 % global) as a Humanitarian Dividend, in addition to meeting AROA obligations.
Guiding Principles
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Additionality: Beyond CSR and AROA compliance.
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Transparency: Publicly verifiable outcomes.
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Neutrality & Dignity: Non-political, needs-based allocations via vetted providers.
Governance & Integrity
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Independent Advisory Board (labor, consumer, humanitarian, investor, academic).
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Anti-greenwashing guardrails: no credit “cherry-picking,” pass-through minimums (≥ 90 % for global aid), non-substitution of existing CSR funds.
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Public quarterly dashboards and third-party assurance.
Certification Tiers
Tier Contribution Example Impact
Bronze..................1 % (0.5 + 0.5)...................≥ 10 k U.S. households rehoused / 10 M global person-weeks of aid per year
Silver....................1 % + 0.25 % uplift............≥ 20 k HH / 20 M person-weeks
Gold.....................≥ 1.5 % total.....................≥ 35 k HH / 35 M person-weeks + microdata transparency
Measurement & Assurance Framework
To calculate the 1 % Humanitarian Dividend, companies choose from three auditable methods:
Method Basis Verification
Fixed Proxy (default).......................Greater of 5 % of AROA tax credits or 0.05 % of revenue..........................Safe harbor (no AI-savings audit)
Method A – Output Metrics............Hours automated × standard labor rate × realization factor (≈ 0.5)............Third-party attestation
Method B – Finance-Derived...........1 % of verified G&A reduction vs. pre-AI baseline.....................................CFO sign-off + assurance
All require quarterly self-reporting, annual assurance, and recipient transparency for public dashboards.
Strategic Impact
Together, AROA and the High-Road Coalition create a two-tier architecture:
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Domestic Prosperity Engine: Tax-credit and levy system that returns AI efficiency to workers and consumers.
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Global Solidarity Engine: Corporate commitment to share 1 % of automation dividends with society’s most vulnerable, under transparent, audited rules.
In short:
The AROA turns AI’s inevitable efficiency wave into a new era of shared prosperity, fair competition, and humanitarian leadership — backed by data-driven standards, real economic ROI, and voluntary corporate alliances that reward ethical innovation.
Download the full .pdf or .docx here:
