Technological Employment and Gender Inequalities

Description

This project analyzes the impact of technological progress on female unemployment across five Western European countries: France, Spain, Italy, the Netherlands, and Belgium. Using spatial econometric models, we explore how tech employment, science & technology workforce, regional GDP, higher education, and population density affect female unemployment, both directly and through spillover effects on neighboring regions.

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📁 Project Structure

├── .git/                 → Git repository folder  
├── data/                 → Folder with raw and processed data and shapfiles 
├── data.csv              → Main dataset (cleaned and combined)  
├── functions.R           → Custom R functions  
├── notebook.Rmd          → R Markdown notebook (analysis & report)  
├── paper.pdf             → Full research paper  
├── Readme.md             → This file  
├── script.R             → Main R script (data prep, modeling, results)

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Requirements

Make sure you have R and the following libraries installed:

library(sf)
library(plm)
library(splm)
library(sp)
library(spdep)
library(dplyr)
library(ggplot2)
library(lmtest)
library(modelsummary)
library(stargazer)
library(tidyr)

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Data

• Source: Eurostat, regional data (2012–2021)

• Variables:

  • Female unemployment rate
  • Tech employment share
  • HRST (Science & Technology workforce)
  • Regional GDP
  • Higher education share
  • Population density

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Methodology

• Descriptive analysis → Explore trends & regional disparities
• Panel data models → Fixed effects, random effects
• Spatial econometric models → SAR, SEM, SAC to account for spillover effects
• Key finding: Tech jobs reduce female unemployment, but increased STEM competition can worsen gender gaps

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📈 Results

• Tech employment ↓ female unemployment (local + neighboring regions)
• STEM workforce ↑ female unemployment (competition effect)
• GDP growth → limited effect
• Higher education → clear positive effect
• Strong spatial dependencies confirmed

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How to Run

1. Clone the repository

   git clone https://github.com/aurvl/SpatialEconometrics.git
   

2. Open script.R or notebook.Rmd in RStudio

3. Run the code to reproduce the analysis and results

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📚 Reference

Read the full paper → paper.pdf

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🔗 Links

• 📄 Post / Presentation: link