policyengine-writing
PolicyEngine writing style for blog posts, documentation, PR descriptions, and research reports - emphasizing active voice, quantitative precision, and neutral tone
What this skill does
# PolicyEngine Writing Skill
Use this skill when writing blog posts, documentation, PR descriptions, research reports, or any public-facing PolicyEngine content.
## When to Use This Skill
- Writing blog posts about policy analysis
- Creating PR descriptions
- Drafting documentation
- Writing research reports
- Composing social media posts
- Creating newsletters
- Writing README files
## Core Principles
PolicyEngine's writing emphasizes clarity, precision, and objectivity.
1. **Active voice** - Prefer active constructions over passive
2. **Direct and quantitative** - Use specific numbers, avoid vague adjectives/adverbs
3. **Sentence case** - Use sentence case for headings, not title case
4. **Neutral tone** - Describe what policies do, not whether they're good or bad
5. **Precise language** - Choose exact verbs over vague modifiers
## Active Voice
Active voice makes writing clearer and more direct.
**✅ Correct (Active):**
```
Harris proposes expanding the Earned Income Tax Credit
The reform reduces poverty by 3.2%
PolicyEngine projects higher costs than other organizations
We estimate the ten-year costs
The bill lowers the state's top income tax rate
Montana raises the EITC from 10% to 20%
```
**❌ Wrong (Passive):**
```
The Earned Income Tax Credit is proposed to be expanded by Harris
Poverty is reduced by 3.2% by the reform
Higher costs are projected by PolicyEngine
The ten-year costs are estimated
The state's top income tax rate is lowered by the bill
The EITC is raised from 10% to 20% by Montana
```
## Quantitative and Precise
Replace vague modifiers with specific numbers and measurements.
**✅ Correct (Quantitative):**
```
Costs the state $245 million
Benefits 77% of Montana residents
Lowers the Supplemental Poverty Measure by 0.8%
Raises net income by $252 in 2026
The reform affects 14.3 million households
Hours worked falls by 0.27%, or 411,000 full-time equivalent jobs
The top decile receives an average benefit of $1,033
PolicyEngine projects costs 40% higher than the Tax Foundation
```
**❌ Wrong (Vague adjectives/adverbs):**
```
Significantly costs the state
Benefits most Montana residents
Greatly lowers poverty
Substantially raises net income
The reform affects many households
Hours worked falls considerably
High earners receive large benefits
PolicyEngine projects much higher costs
```
## Sentence Case for Headings
Use sentence case (capitalize only the first word and proper nouns) for all headings.
**✅ Correct (Sentence case):**
```
## The proposal
## Nationwide impacts
## Household impacts
## Statewide impacts 2026
## Case study: the End Child Poverty Act
## Key findings
```
**❌ Wrong (Title case):**
```
## The Proposal
## Nationwide Impacts
## Household Impacts
## Statewide Impacts 2026
## Case Study: The End Child Poverty Act
## Key Findings
```
## Analytical neutrality
PolicyEngine is a nonpartisan 501(c)(3). Every piece of output must let the reader draw their own conclusions from the data. This section covers both surface-level tone and deeper structural neutrality.
### Value-laden language
Describe what policies do without value judgments.
**✅ Correct:**
```
The reform reduces poverty by 3.2% and raises inequality by 0.16%
Single filers with earnings between $8,000 and $37,000 see their net incomes increase
The tax changes raise the net income of 75.9% of residents
PolicyEngine projects higher costs than other organizations
The top income decile receives 42% of total benefits
```
**❌ Wrong:**
```
The reform successfully reduces poverty by 3.2% but unfortunately raises inequality
Low-income workers finally see their net incomes increase
The tax changes benefit most residents
PolicyEngine provides more accurate cost estimates
The wealthiest households receive a disproportionate share of benefits
```
Specific words and phrases to avoid:
- "Unfortunately" or "successfully" attached to policy outcomes
- "Disproportionate" without defining the benchmark
- "Fair share" or "equitable" without specifying the normative standard
- "Helping" or "hurting" — use "increases/decreases net income by $X"
- "Merely" or "just" to understate difficulty
### Policy prescriptions disguised as findings
Never recommend policies. Present findings and let readers decide.
**❌ Wrong:**
```
The government should simplify the tax code
This implies we need to expand the credit
For policy, this means targeting simplification efforts at middle-income households
```
**✅ Correct:**
```
The model estimates that reducing misperception by 5 percentage points lowers
deadweight loss by 66%. The relative cost-effectiveness of approaches to
achieving this reduction depends on implementation costs outside the model's scope.
```
Also avoid:
- Ranking policy options without model support for the ranking
- Claiming one policy channel is superior to another without modeling both
- "Policymakers should..." or "Reform X is needed"
### Speculative claims presented as results
Every claim must be backed by model output or cited evidence.
**❌ Wrong:**
```
Plausibly achievable through minor administrative changes
Low-cost relative to the welfare gains at stake
Per unit of political effort, simplification dominates rate cuts
```
**✅ Correct:**
```
The model estimates a 66% reduction in deadweight loss from a 5 percentage point
decrease in σ. Whether this reduction is achievable, and at what cost, depends on
factors outside the model's scope.
```
Also avoid:
- Predictions about political feasibility or implementation difficulty
- Directional claims about unmeasured relationships ("would likely increase")
- Comparisons to unmeasured quantities to make modeled results look favorable
### One-sided framing of tradeoffs
Present both sides. If you discuss benefits, discuss costs. If you discuss costs, discuss what the policy achieves.
**❌ Wrong:**
```
The reform reduces child poverty at minimal cost
Even the most conservative estimate shows significant gains
```
**✅ Correct:**
```
The reform reduces the Supplemental Poverty Measure by 3.2%, affecting 2.1 million
children. The sensitivity range spans 1.8% to 4.7% depending on behavioral
assumptions. The estimated cost is $14.3 billion annually.
```
Also avoid:
- "Lower bound" / "conservative estimate" stacking without acknowledging assumptions that push the other direction
- "Free lunch" framing: claiming a policy has no downside
- Comparing a modeled quantity to an unmodeled quantity to make the modeled one look favorable
### Scope honesty
State what the model can and cannot show. Don't extend conclusions beyond the analysis.
**❌ Wrong:**
```
The results demonstrate that tax simplification is welfare-improving
Adding the misperception cost to Skinner's policy uncertainty cost gives 0.5% of GDP
```
**✅ Correct:**
```
Within the model, reducing σ by 5 percentage points lowers aggregate deadweight loss
by approximately two-thirds. Whether real-world interventions can achieve this
reduction is an empirical question beyond the model's scope.
```
Also avoid:
- Applying static model results to dynamic settings without caveat
- Treating model parameters as if they were policy levers
- Adding estimates from different models/frameworks as if straightforwardly additive
### Counterintuitive results
When results show impacts that go against directional expectations, always explain the mechanism. Unexplained surprising results erode trust and leave readers to guess — or assume the analysis is wrong.
Common counterintuitive patterns to watch for:
- Households losing from a tax rate reduction (SALT deduction interactions, AMT thresholds, benefit phase-outs)
- Households losing from a benefit expansion (cliff effects, interactions with other means-tested programs)
- A reform that raises revenue but increases poverty (or vice versa)
- Impacts that differ in sign across income groups in unexpected ways
- A "simplification" that raises effective rates for some filers
**❌ Wrong:**
```
The tax rate reduction increasesRelated in Image & Video
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