research-ideation

name	research-ideation
description	Generate research questions from economic phenomena
workflow_stage	ideation
compatibility	["claude-code","cursor","codex","gemini-cli"]
author	Awesome Econ AI Community
version	1.0.0
tags	["research","ideation","hypothesis","brainstorming"]

Research Ideation

Purpose

This skill helps economists generate and refine research questions by applying economic thinking frameworks. It guides the process from observing phenomena to formulating testable hypotheses.

When to Use

• Starting a new research project
• Brainstorming dissertation topics
• Finding angles for policy analysis
• Developing extensions of existing work

Instructions

Step 1: Understand the Starting Point

Ask the user:

1. Do you have a phenomenon, puzzle, or data source in mind?
2. What field/subfield of economics?
3. What's your methodological preference? (Theory, empirical, structural)
4. Any constraints? (Data access, timeline, etc.)

Step 2: Apply Ideation Frameworks

Use these approaches:

1. The Puzzle Approach

• What's surprising about current patterns?
• What contradicts conventional economic wisdom?
• Where do standard models fail?

2. The Policy Approach

• What policies lack rigorous evaluation?
• What natural experiments remain unexploited?
• What interventions might solve important problems?

3. The Data Approach

• What new data sources have become available?
• What can existing data tell us that hasn't been explored?
• What linkages between datasets are possible?

4. The Extension Approach

• How can seminal papers be extended?
• What mechanisms remain unexplored?
• Can methods from one field apply to another?

Step 3: Evaluate and Refine

For each idea, assess:

• Feasibility: Can this be done with available data/methods?
• Contribution: What's new here?
• Interest: Who cares about this question?
• Identification: Can effects be credibly estimated?

Example Interaction

User Query: "I'm interested in labor economics and have access to LinkedIn data"

Generated Response:

Research Ideas: LinkedIn Data × Labor Economics

1. Network Effects in Job Search

Question: How do professional networks affect job transitions and wage changes?

Puzzle: Standard search models assume random matching, but most jobs come through networks. How much do networks matter for outcomes?

Approach:

• Track profile updates signaling job changes
• Measure network overlap between current/future employers
• Instrument with exogenous network shocks (school alumni connections)

Contribution: Quantify the causal role of networks vs. unobserved ability

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2. Skill Signaling and Returns

Question: Do workers who list specific skills on profiles earn wage premiums?

Puzzle: Are listed skills signals of ability, or just cheap talk? What's the return to skill acquisition vs. skill signaling?

Approach:

• Compare wages before/after workers add skills
• Use skill endorsements as validation
• Exploit skill assessment features as quasi-experiments

---

3. Gender Gaps in Self-Promotion

Question: Do men and women describe equivalent achievements differently?

Puzzle: Lab evidence shows women understate accomplishments. Does this appear in real profiles and affect outcomes?

Approach:

• NLP analysis of profile language (adjectives, quantification)
• Compare language for workers in same role/company
• Link to recruiter contact rates if available

---

4. Remote Work and Labor Market Geography

Question: How has remote work changed the geographic reach of job matching?

Approach:

• Compare pre/post-2020 job applications by distance
• Measure changes in location signals on profiles
• Study wage convergence across cities

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Evaluation Matrix

Idea	Data Feasibility	Identification	Policy Relevance
Network effects	High	Medium (need IV)	High
Skill signaling	High	Medium	Medium
Gender language	High	High (descriptive)	High
Remote geography	High	High (COVID natural experiment)	High

Recommended Next Steps

1. For empirical work: Start with Idea #4 (clearest identification)
2. For policy impact: Idea #1 has most implications for job matching platforms
3. For methodological novelty: Idea #3 combines NLP with quasi-experimental methods

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Frameworks for Generating Questions

The "5 Whys" for Economics

Start with an observation and drill down:

1. GDP growth is slowing → Why?
2. Productivity is stagnant → Why?
3. Investment is low → Why?
4. Uncertainty is high → Why?
5. Policy is unpredictable → Testable: Does policy uncertainty cause low investment?

The "What If" Generator

• What if [policy X] were implemented?
• What if [technology Y] became widespread?
• What if [assumption Z] were relaxed?

The Cross-Field Pollinator

Take a method from one field and apply to another:

• IO techniques → Labor markets (how do firms choose wages like prices?)
• Finance models → Education (returns to schooling as asset pricing)
• Macro shocks → Micro outcomes (firm-level effects of exchange rate changes)

Common Pitfalls

• ❌ Questions that are too broad ("What causes inequality?")
• ❌ Questions without clean identification ("Does education cause income?")
• ❌ Questions without data ("Were medieval peasants happy?")
• ❌ Questions already well-answered

References

• Shapiro (2022) How to Get Started on Research in Economics
• Angrist & Pischke on Mostly Harmless research design
• AEA Research Pipelines

Changelog

v1.0.0

• Initial release with ideation frameworks