Personas Explained: How Civitas Models Real Household Impacts

Introduction: Beyond Aggregate Statistics

When policymakers debate a new tax reform or trade tariff, the headlines focus on aggregate numbers: "GDP grows 2.1%," "deficit falls by $400B," "inflation rises 0.3 percentage points." These statistics matter—but they obscure the most important question for real people: What happens to households like mine?

Aggregate statistics can hide profound distributional effects: a policy that grows GDP by 2% might simultaneously devastate working families while enriching the top 1%. A tariff that "protects jobs" in manufacturing might raise prices so much that the protected workers end up worse off. A universal basic income might lift the bottom quintile while triggering inflation that erodes middle-class savings.

Civitas solves this problem with personas—detailed household archetypes representing different wealth levels, life stages, and risk exposures. When you run a simulation, Civitas doesn't just project economy-wide outcomes; it translates those macro shifts into tangible impacts for each persona: changes in after-tax income, wealth accumulation, housing affordability, and exposure to hazards like climate shocks or pandemics.

This article is your field guide to the persona system. You'll learn how personas are constructed, how projections work under the hood, how to read impact charts, and how to create custom personas that model your own household—so you can answer the question that matters most: "What would this policy do to me?"

The Persona Library: Quintiles, Archetypes, and International

Civitas ships with three categories of personas, each serving a different analytical purpose:

1. Quintile Representatives (Q1–Q5 + Top 0.1%)

These six personas represent the US income/wealth distribution:

• Maya (Q1): Single mother, $28K income, $8K net worth, renting. High exposure to housing cost shocks, childcare policy, EITC changes.
• Jordan (Q2): Retail worker, $48K income, $35K net worth. Sensitive to payroll taxes, minimum wage, trade-driven price shifts.
• Taylor (Q3): Mid-career professional, $78K income, $120K net worth, owns home. Mortgage interest deduction, 401(k) policy, healthcare premiums.
• Alex (Q4): Dual-income household, $135K income, $450K net worth. Capital gains rates, SALT deduction caps, college savings incentives.
• Morgan (Q5): High earner, $320K income, $2.1M net worth. Top marginal rates, estate taxes, corporate tax incidence.
• Charles (Top 0.1%): Ultra-wealthy, $1.8M income, $18M net worth. Wealth taxes, carried interest, unrealized gains taxation.

These personas anchor quintile comparison sets, the default view in the Dashboard. When you score a bill, Civitas shows a pie chart breaking down winners and losers across the distribution.

2. Hazard-Exposure Archetypes

These personas model households with elevated risk to specific hazards:

• Climate archetypes: Coastal homeowners (storm surge), Phoenix retirees (heat), California families (wildfire).
• Pandemic archetypes: Frontline workers, multigenerational households, immunocompromised individuals.
• War/conflict archetypes: Military families, defense contractors, import-dependent businesses.
• Opioid crisis archetypes: Rural communities, households with addiction history.

Each hazard archetype carries exposure weights (0.0–1.0) that amplify the impact of hazard channels. For example, when HeatIntensity rises due to climate policy inaction, the Phoenix retiree persona experiences outsized healthcare costs and property value declines.

3. International Personas

Civitas includes personas representing households in trading-partner nations (e.g., Chinese manufacturer, Mexican assembly worker, German exporter). These personas help model cross-border spillovers from US trade policy—tariffs, export controls, sanctions. International personas are experimental and use simplified assumptions, but they surface second-order effects often ignored in domestic-only analysis.

Users can also create custom personas via JSON import or the Personas page clone workflow (more on this in Section 5).

How Persona Projections Work

When you run a simulation, Civitas projects 120 months of outcomes for each persona in your comparison set. Here's the step-by-step flow:

Step 1: Compose Policy Events into Lever Deltas

A bill is a collection of policy events (tax reforms, spending programs, tariffs, etc.). The Policy Event Composer merges these events into a single Economy Levers representing the cumulative policy shift from baseline.

This additive composition means provisions don't overwrite each other—they stack. A bill with three tariff provisions applies all three, compounding trade impacts.

Step 2: Run Monthly Simulation Steps

The EconomySimulator steps forward month-by-month, updating:

• Macro state: GDP growth, inflation, unemployment, interest rates, trade balance.
• Fiscal state: tax revenue, spending, deficit, debt-to-GDP.
• Hazard channels: climate intensity, pandemic waves, war escalation (driven by policy choices or exogenous shocks).

At each step, the simulator applies feedback loops: deficits push up interest rates, tariffs raise prices and reduce imports, AI labor displacement shifts wage distribution.

Step 3: Project Per-Persona Outcomes

For each persona, the simulator calculates:

1. After-tax income: Applies marginal rates, EITC, child tax credits, payroll taxes. Adjusts for inflation and wage growth.
2. Wealth accumulation: Savings rate × disposable income, plus investment returns (equity/bond mix), minus housing costs.
3. Hazard impacts: Multiplies hazard intensity by persona exposure weights, subtracts from wealth/income (e.g., storm damage, medical bills).
4. Ending wealth tier: Classifies persona into Q1/Q2/Q3/Q4/Q5/Top0.1% based on final net worth.

Civitas frames persona outcomes by ending wealth tier, not just percentage change—because a 90% loss on $18M still leaves you wealthy, while a 10% loss on $40K can mean eviction.

Step 4: Classify Winners and Losers

The Lived Impact Classifier compares baseline vs counterfactual for each persona.

This classification powers the pie chart on the Dashboard: green slices for winners, red for losers, gray for neutral.

Step 5: Compute Distributional Verdict

Civitas compares Q1 (Maya) vs Top 0.1% (Charles) to classify the bill:

• Progressive: Maya gains more (or loses less) than Charles.
• Regressive: Charles gains more (or loses less) than Maya.
• Neutral: Both experience similar outcomes.
• Mixed: Some quintiles gain, others lose, with no clear pattern.

This verdict appears as a badge on the Bills page and in AI chat summaries.

Reading Persona Impact Charts

The Dashboard presents persona outcomes in three visual formats:

1. Pie Chart: Winners and Losers

The persona impact pie breaks down your comparison set by outcome:

• Green slices: Personas who end wealthier (or in a higher tier) under the policy.
• Red slices: Personas who end poorer (or in a lower tier).
• Gray slices: Personas with neutral outcomes (< $50K wealth delta, no tier shift).

Hover over a slice to see the persona name, wealth delta, and tier shift. Click to drill into that persona's 120-month trajectory.

Pro tip: Switch comparison sets to see different breakdowns. The default "Quintile" set shows distributional effects; the "Climate" set shows who bears climate risk.

2. Trajectory Lines: 120-Month Projections

The cartesian chart plots wealth over time for each persona in your comparison set. Two lines per persona:

• Dashed line: Baseline (current law).
• Solid line: Counterfactual (with your bill).

The gap between lines is the policy impact. Look for:

• Divergence: Lines that start together but split—indicating the policy's effect compounds over time.
• Crossovers: A persona who gains initially but loses later (or vice versa)—common with deficit-financed stimulus or delayed tax hikes.
• Tier boundaries: Horizontal gridlines mark quintile thresholds. Watch for personas crossing into new tiers.

Pro tip: Use the timeline scrubber to step through month-by-month. The pie chart updates to show winners/losers at that point in time, revealing how distributional effects evolve.

3. KPI Tiles: Macro Summary

The top of the Dashboard shows four KPI tiles:

• GDP Growth: Annualized real GDP growth rate at the selected month.
• Inflation: CPI year-over-year change.
• Unemployment: U3 rate.
• Deficit: Federal deficit as % of GDP.

Each tile shows baseline → counterfactual with a delta. Use these to contextualize persona impacts: if inflation spikes, real income losses are expected; if unemployment falls, wage gains should follow.

4. Validation Panel (Historical Bills Only)

For historical bills (TCJA, CARES, ACA, etc.), Civitas shows a validation panel comparing modeled outcomes to CBO scores:

• Expected deficit: $1.9T (CBO estimate)
• Modeled deficit: $2.1T (Civitas projection)
• Verdict: ≈ (within 15% tolerance)

This transparency lets you assess model accuracy and adjust your confidence in projections.

Creating Custom Personas for Your Analysis

The built-in personas are useful starting points, but the real power of Civitas emerges when you model yourself. Custom personas let you answer: "What would this bill do to my household?"

Method 1: Clone and Edit

The fastest way to create a custom persona:

1. Navigate to Personas page.
2. Find the built-in persona closest to your situation (e.g., Taylor for mid-career homeowner).
3. Click Clone.
4. Edit the cloned persona:
   • Adjust Income to match your household.
   • Set Wealth to your current net worth.
   • Toggle OwnsHome if you rent.
   • Add StudentDebt, MortgageBalance, RetirementSavings fields.
   • Adjust HazardExposure weights (e.g., boost HeatIntensity if you live in Arizona).
5. Save with a recognizable name (e.g., "Me - 2025").

Method 2: JSON Import

For advanced users, Civitas supports JSON persona definitions:

{
  "id": "me-2025",
  "name": "Me - 2025",
  "description": "My household: $95K income, $180K net worth, homeowner",
  "income": 95000,
  "wealth": 180000,
  "ownsHome": true,
  "studentDebt": 32000,
  "mortgageBalance": 280000,
  "retirementSavings": 85000,
  "hazardExposure": {
    "HeatIntensity": 0.4,
    "PandemicIntensity": 0.5
  }
}

Save this as me.json and import via Personas → Import. The persona appears in your library and can be added to custom comparison sets.

Method 3: AI-Assisted Persona Generation

In the Personas page, click Ask AI and describe your household:

"Create a persona for a 34-year-old software engineer in Seattle, $140K salary, $220K net worth, renting, $45K in student loans, high exposure to tech layoffs."

The AI generates a JSON definition, explains the hazard exposure weights, and offers to save it to your library.

Using Custom Personas in Simulations

Once created, custom personas work exactly like built-ins:

1. Navigate to Groups page.
2. Create a new comparison set (e.g., "My Family").
3. Add your custom persona(s) plus relevant built-ins for context (e.g., add Taylor to compare yourself to the Q3 average).
4. Return to Dashboard, select your custom group, run a simulation.

The pie chart and trajectory lines now show your household impact alongside others.

Pro Tips

• Model multiple scenarios: Create personas for "Me - 2025," "Me - 2030 (promoted)," "Me - 2035 (retired)" to see how policy impacts evolve with your life stage.
• Share personas: Export to JSON and share with family/friends so they can model themselves.
• Update annually: Clone last year's persona, adjust income/wealth, re-run simulations to track how policy changes affect your trajectory.

Using Comparison Sets for Group Analysis

Comparison sets (also called groups) let you analyze policy impacts on specific populations. Civitas ships with six built-in sets:

Built-In Comparison Sets

1. Quintile (default): Maya, Jordan, Taylor, Alex, Morgan, Charles—full income/wealth distribution.
2. Life Stage: Young worker, mid-career, pre-retiree, retiree—focuses on age-related policy impacts (Social Security, Medicare, student loans).
3. Climate: Coastal homeowner, Phoenix retiree, California family—highlights climate hazard exposure.
4. Pandemic: Frontline worker, multigenerational household, immunocompromised—models pandemic policy (lockdowns, stimulus, healthcare).
5. War/Conflict: Military family, defense contractor, import-dependent business—trade/defense policy impacts.
6. International: Chinese manufacturer, Mexican worker, German exporter—cross-border spillovers from US policy.

Creating Custom Comparison Sets

Navigate to Groups page:

1. Click New Group.
2. Name your set (e.g., "Rust Belt Workers").
3. Add personas:
   • Search built-ins (e.g., Jordan for Q2 worker).
   • Add custom personas (e.g., your "Detroit Autoworker" persona).
   • Mix quintiles, archetypes, and custom personas freely.
4. Save.

Your custom group appears in the Dashboard's comparison set dropdown.

Example: Analyzing a UBI Proposal

Suppose you want to model a $1,000/month UBI funded by a VAT. Create a comparison set:

• Maya (Q1): Likely big winner—UBI exceeds VAT burden.
• Jordan (Q2): Modest winner—UBI slightly exceeds VAT.
• Taylor (Q3): Neutral—UBI ≈ VAT.
• Alex (Q4): Modest loser—VAT exceeds UBI.
• Morgan (Q5): Big loser—VAT far exceeds UBI.
• Retiree (custom): Test if fixed income + UBI keeps pace with VAT inflation.

Run the simulation, scrub the timeline, and watch the pie chart shift as inflation dynamics kick in.

Example: Modeling a Green New Deal

Create a comparison set:

• Coastal homeowner: Wins from storm mitigation infrastructure.
• Phoenix retiree: Wins from heat adaptation subsidies.
• Coal miner (custom): Loses from fossil fuel phase-out.
• Solar installer (custom): Wins from renewable energy jobs.
• Charles (Top 0.1%): Check if wealth taxes fund the program equitably.

This set surfaces the just transition question: who bears the cost of decarbonization, and are displaced workers compensated?

Exporting and Sharing Groups

Groups are stored locally but can be exported to JSON

Share the JSON with collaborators, who can import it into their Civitas instance. This enables reproducible analysis: "Here's the comparison set I used for my climate policy report—import it and verify my results."

Real-World Applications

Personas transform Civitas from an academic toy into a practical tool for policy advocacy, journalism, and personal financial planning. Here are real-world use cases:

1. Advocacy: Making the Case for Policy Change

Scenario: A nonprofit advocates for expanding the Child Tax Credit (CTC).

Workflow:

1. Create a comparison set: Maya (single mother, Q1), Jordan (Q2 worker with kids), Taylor (Q3, two kids).
2. Build a bill: Increase CTC from $2,000 to $3,600 per child, make it fully refundable.
3. Run simulation, generate persona impact charts.
4. Export charts to a report: "Maya's household gains $4,800/year, lifting her from Q1 to Q2 by year 5. Jordan's kids get stable childcare, boosting his labor force participation."
5. Present to legislators with lived impact framing, not just aggregate budget scores.

Why it works: Personas make abstract policy concrete. Legislators see constituents, not statistics.

2. Journalism: Explaining Who Wins and Loses

Scenario: A reporter covers a proposed tariff on Chinese imports.

Workflow:

1. Use the International comparison set (Chinese manufacturer, US consumer, US steelworker).
2. Build a bill: 25% tariff on Chinese goods.
3. Run simulation, note:
   • US consumer (Taylor): Loses $1,200/year to higher prices.
   • US steelworker (custom persona): Gains $3,500/year from protected wages.
   • Chinese manufacturer: Loses $18K/year from reduced exports.
4. Write article: "Tariff protects 50K steel jobs but costs every US household $1,200—net loss for Q1-Q3."
5. Embed Civitas charts, link to shareable bill URL (via UrlEmbedCodec).

Why it works: Readers see the trade-off in human terms, not just "tariffs raise revenue."

3. Personal Finance: Planning for Policy Risk

Scenario: You're a high earner (Q5) worried about wealth tax proposals.

Workflow:

1. Clone Morgan, adjust to your income/wealth.
2. Build three bills:
   • Warren wealth tax: 2% on net worth > $50M.
   • Sanders wealth tax: 1% on net worth > $32M.
   • Wyden unrealized gains tax: Annual mark-to-market on tradable assets.
3. Run simulations, compare your 10-year trajectory under each.
4. Adjust financial plan: Shift assets to tax-advantaged accounts, consider Roth conversions, model emigration scenarios.

Why it works: Civitas lets you stress-test your financial plan against plausible policy futures.

4. Academic Research: Validating Distributional Models

Scenario: An economist studies the TCJA's long-run distributional effects.

Workflow:

1. Load the TCJA (2017) bill from the historical library.
2. Run simulation with quintile comparison set.
3. Compare Civitas projections to CBO, TPC, and JCT estimates.
4. Note discrepancies (e.g., Civitas models behavioral responses to corporate rate cuts; CBO assumes static incidence).
5. Publish paper: "Civitas projections align with CBO on aggregate deficit but show larger Q5 gains due to investment feedback loops."

Why it works: Open-source simulation engine enables transparent, reproducible distributional analysis.

5. Hazard Modeling: Climate Policy and Equity

Scenario: A city planner models a carbon tax with rebate.

Workflow:

1. Create comparison set: Coastal homeowner, Phoenix retiree, low-income renter (Maya), suburban commuter (Taylor).
2. Build a bill: $50/ton carbon tax, rebate 100% via per-capita dividend.
3. Run simulation, note:
   • Maya: Wins—low carbon footprint, full rebate.
   • Taylor: Neutral—commute costs offset by rebate.
   • Phoenix retiree: Loses—high AC costs, fixed income.
   • Coastal homeowner: Wins—home value protected by climate mitigation.
4. Recommend: Add heat adaptation subsidies for Phoenix retiree, funded by higher tax on top quintile.

Why it works: Personas surface environmental justice issues invisible in aggregate climate models.

Conclusion: From Aggregates to Lived Experience

Personas are the bridge between macroeconomic theory and lived experience. They transform Civitas from a budget calculator into a distributive justice simulator—a tool for asking not just "Does this policy grow the economy?" but "Who benefits, and who pays?"

By modeling yourself, your community, or vulnerable populations, you gain the power to:

• Advocate with data-driven stories.
• Report with empathy and precision.
• Plan for policy risk.
• Research with transparency.

The next time you hear a politician tout a "pro-growth" tax cut or a "deficit-neutral" spending bill, fire up Civitas, load the personas, and ask: "Pro-growth for whom? Neutral for whom?" The answer might surprise you—and it will certainly inform your vote, your voice, and your future.