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Jole Contribution Framing

ASecurity

Use when articulating what is new in a Journal of Labor Economics (JOLE) manuscript for a general labor-economics audience — the marginal contribution, the labor lesson, and why a labor economist outside the subfield should care. Frames the pitch; it does not run analysis.

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Added 6/5/2026
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Security Analysis

A100/100

Scanned 6/5/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill jole-contribution-framing --agent claude-code

Installs into .claude/skills of the current project.

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SKILL.md
---
name: jole-contribution-framing
description: Use when articulating what is new in a Journal of Labor Economics (JOLE) manuscript for a general labor-economics audience — the marginal contribution, the labor lesson, and why a labor economist outside the subfield should care. Frames the pitch; it does not run analysis.
---

# Contribution Framing (jole-contribution-framing)

## When to trigger

- A referee or reader cannot quickly say what the paper adds to labor economics
- The result is "a coefficient" with no articulated lesson
- The contribution is over-claimed (global statement from a local estimate) or under-claimed (real lesson buried)
- You need a one-paragraph "what's new for labor" statement for the intro and cover letter

## What "contribution" means at JOLE

JOLE is a **general-interest labor-economics** journal published for the **Society of Labor Economists (SOLE)**, so the contribution must register with labor economists **beyond your own subfield**, not only with specialists on your exact policy or dataset. A JOLE contribution typically takes one of these shapes:

1. **A new credible answer** to a first-order labor question (e.g., a returns-to-X, a labor-supply elasticity, an effect of an institution) where prior estimates were not well identified.
2. **A new fact** about the labor market made possible by novel or newly linked data (e.g., firm–worker registers, administrative earnings), disciplined against measurement error.
3. **A new mechanism** distinguishing between competing labor-economic explanations (e.g., taste-based vs. statistical discrimination; human capital vs. signaling).
4. **A theoretical advance** with a labor-economic payoff (a model that reorganizes how we interpret a body of labor evidence).

The marginal contribution must be **calibrated**: it should match exactly what the design and sample support, because JOLE's word economy (~20,000 words) and single-blind labor referees both punish padding and over-claiming.

## Framing the contribution

- **One-sentence claim.** "We show that [margin] responds to [variation] by [magnitude], implying [labor lesson]." If you cannot fill the blanks, the contribution is not yet sharp.
- **Against the frontier, not a survey.** Name the two or three closest papers and say precisely what you add (better identification, new data, new mechanism, new population) — this hands off to jole-literature-positioning.
- **External relevance.** State what a labor economist studying a different country/policy/firm learns from your result.
- **Scope discipline.** A clean local estimate (one reform, one register) is a strength; do not inflate it into a universal structural parameter without support.
- **Theory vs. empirics.** If the paper is theoretical, the contribution is the labor insight, not the math; if empirical, it is the identified answer or fact, not the regression count.

## Checklist

- [ ] One-sentence contribution with margin, variation, magnitude, and lesson
- [ ] Registers with labor economists **outside** your subfield
- [ ] Positioned against the 2–3 closest labor papers, not a literature survey
- [ ] External relevance ("beyond this setting") stated
- [ ] Claim is calibrated to what the design/sample supports — no global-from-local leap
- [ ] Fits the word economy (the contribution is one paper, not three)

## Anti-patterns

- "We are the first to study X" with no statement of the labor lesson
- A local estimate sold as a universal parameter
- A contribution only specialists on your exact dataset would notice
- Listing what you did (regressions, robustness) instead of what you learned
- A theory paper framing the math as the contribution rather than the labor insight
- Over-claiming that a single referee will puncture in one line

## Output format

```
【Contribution type】new answer / new fact / new mechanism / theory advance
【One-sentence claim】margin + variation + magnitude + lesson:
【Closest papers】[2–3] — what we add vs. each:
【External relevance】beyond this setting:
【Calibration】claim matches design/sample? [Y/N]
【Next step】jole-tables-figures or jole-writing-style
```

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