Age-Wage Profiles and Human Capital Depreciation in Italy

This repository contains the code and data used for the analysis in the project titled "Age-Wage Profiles and Human Capital Depreciation in Italy," which is a part of the Labour Economics course.

Overview

The primary objective of this study is to estimate age-wage profiles and human capital depreciation rates for different education levels in Italy. Using a dynamic panel dataset that covers the period from 1980 to 2019, we delve into the intricate relationship between education, wage profiles, and human capital depreciation. Specifically, our analysis focuses on estimating depreciation rates for primary, secondary, and tertiary education levels, thus providing a comprehensive understanding of the differences in human capital depreciation.

Research Questions

1. What are the human capital depreciation rates for primary, secondary, and tertiary education levels in Italy?
2. How does achieving a secondary education level, as opposed to lower secondary, impact the return of human capital and time spent working?
3. Are there significant gender differences in human capital depreciation rates?
4. How does the gender gap in wage-age and hours-age profiles vary across education levels?

Findings

Our analysis yields several important findings:

• Achieving a secondary education level compared to lower secondary leads to a 1% increase in the return of human capital and a 0.5% decrease in time spent working.
• Gender differences in wage-age and hours-age profiles are more pronounced for higher-educated women. The return on the human capital for these women is 2.2% per year lower compared to men.

Repository Structure

/
├── STATA/
│   ├── comp.dta
│   ├── cons.dta
│   ├── ... dta
├── Latex/
│   ├── paper.pdf
├── README.md

Usage

1. Clone this repository to your local machine.
2. Execute main.do to preprocess the dataset.
3. The results, including visualizations and output, will be saved in the directory.

Dataset

The dataset used in this study is provided in the data/ directory. It includes relevant variables required for the analysis.

Contributors

Please feel free to contribute to this project by submitting issues or pull requests.

For questions or further information, contact riccardodalcero99@gmail.com(mailto: riccardodalcero99@gmail.com).