skills.rest
xjtulyc
xjtulycCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

regional-economics

Compute location quotients, shift-share decompositions, and Leontief multipliers from regional employment matrices.

33|6|Updated Mar 17, 2026
Search Tags:#spatial econometrics#regional economics#shift-share#location quotient#input-output model#convergence analysis
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill regional-economics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: regional-economics
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/18-urban-science/regional-economics
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill regional-economics

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Regional-economics helps you quantify how and why regional economies change over time by decomposing employment/output shifts, measuring industrial concentration, estimating multiplier effects, and testing spatial patterns that affect policy outcomes.

Core Features & Use Cases

  • Location Quotients (LQ): Identify industry specialization and potential export-base candidates from regional versus national employment shares.
  • Shift-Share Decomposition (Dunn 1960): Break regional industry employment/output change into national share, industry mix, and competitive effects for actionable interpretation.
  • Input-Output (Leontief) Modeling: Convert a technical coefficient matrix into Leontief inverses to compute output multipliers and demand-shock impacts.
  • Convergence & Spatial Econometrics: Estimate beta-convergence and assess sigma-convergence, while supporting spatial autocorrelation and spatial dependence concepts for regional datasets.
  • Use Case: You have regional employment and industry breakdowns across two years and want a policy-ready report distinguishing whether growth is driven by national trends, sector composition, or local competitiveness, including indirect effects from sectoral demand shocks.

Quick Start

Use the regional-economics skill to compute location quotients, run a Dunn shift-share decomposition, estimate convergence metrics, and simulate an input-output demand shock for the specified regions and industries.

Frequently Asked Questions about regional-economics

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I decompose regional employment growth into national, industry mix, and local competitive effects?▼

To perform shift-share decomposition, input regional-by-industry employment matrices for two periods to isolate national share, industry mix, and competitive effects, generating a breakdown distinguishing national trends from local competitiveness.

What is the best way to calculate location quotients for regional industry specialization?▼

Calculate location quotients by comparing regional versus national employment shares to identify industry specialization and potential export-base candidates, requiring regional and national employment data as quantitative inputs.

How do I estimate output multipliers and demand-shock impacts using a Leontief inverse?▼

Estimate output multipliers by converting an input-output technical coefficient matrix into Leontief inverses using matrix algebra, enabling you to simulate demand-shock impacts and compute indirect effects for regional sectors.

Can I use Pandas and Statsmodels for beta-convergence and spatial econometrics workflows?▼

Yes, you can estimate beta-convergence and assess sigma-convergence using NumPy, Pandas, and Statsmodels applied to panel and cross-sectional regional datasets to test spatial dependence and autocorrelation concepts.

What quantitative inputs do I need for regional policy analysis and industrial clustering assessment?▼

Regional policy analysis requires regional-by-industry employment matrices, national and industry growth rates, and an input-output technical coefficient matrix to quantify industrial concentration and multiplier effects.

Why does shift-share analysis distinguish between national trends and local competitiveness in regional economies?▼

Shift-share analysis distinguishes national trends from local competitiveness by isolating the industry mix effect from the regional competitive effect, explaining whether regional economic change aligns with national growth or local advantages.

Related Skills

Explore More Education & Research Skills→
haomingzhaomingz
12

cashflow-valuation

Compute enterprise value from projected free cash flows using DCF.

Community
Advanced
pluginagentmarketplacepluginagentmarketplace
3

red-team-reporting

Generate security assessment reports from structured findings in Markdown, HTML, PDF, or DOCX.

Community
Basic
TurnaboutHeroTurnaboutHero
12

scientist

Analyze structured datasets with hypothesis tests, regression models, and confidence intervals.

Community
Intermediate
ActiveInferenceInstituteActiveInferenceInstitute
13

geo-infer-econ

Analyze regional economic patterns using H3 spatial indexing and Python libraries.

Community
Advanced