MSCI Inclusion and Digital Transformation Analysis

This repository contains code for analyzing the relationship between MSCI inclusion (capital market liberalization) and digital transformation of firms.

Project Overview

This research examines how inclusion in the MSCI Emerging Markets Index affects companies' digital transformation efforts. The analysis uses difference-in-differences and other econometric approaches to identify causal relationships and underlying mechanisms.

Project Structure

patie_preprocess/
├── config.py                      # Configuration settings
├── main.py                        # Main entry point
├── src/
│   ├── data_preprocessing.p       # Data preparation
│   ├── analysis/                  # Analysis modules
│   │   ├── descriptive.py         # Descriptive statistics
│   │   ├── hypothesis.py          # Hypothesis testing
│   │   ├── models.py              # Econometric models
│   │   └── mechanisms.py          # Mechanism analysis
│   └── visualization/             # Visualization modules
│       ├── descriptive_plots.py   # Descriptive visualizations
│       ├── model_plots.py         # Model result visualizations
│       └── mechanism_plots.py     # Mechanism visualizations
├── dataset/                          # Data directory
└── results/                       # Generated figures

Installation

1. Clone this repository
2. Install dependencies:

   pip install -r requirements.txt
   

Usage

Run the full analysis:

python main.py

The script will:

1. Preprocess the data
2. Run descriptive analysis
3. Perform hypothesis testing
4. Conduct model analysis
5. Analyze potential mechanisms
6. Generate visualizations for all analyses

Analysis Workflow

1. Data Preprocessing: Cleans, transforms, and prepares data for analysis
2. Descriptive Analysis: Generates summary statistics and correlation matrices
3. Hypothesis Testing: Tests key hypotheses about MSCI inclusion and digital transformation
4. Model Analysis: Implements DiD models, event studies, matching methods, and robustness checks
5. Mechanism Analysis: Explores potential causal mechanisms including financial access, corporate governance, and investor scrutiny

Requirements

• Python 3.7+
• pandas
• numpy
• matplotlib
• statsmodels
• scikit-learn
• seaborn
• pathlib

Author

License

[License Information]