An econometric analysis of the India Human Development Survey-II (IHDS-II, 2011-12) investigating the heterogeneous mechanisms of employment marginalization across social groups (Muslims, Adivasis, and Dalits) in India.
This project analyzes data from 204,568 individuals across 371 districts to move beyond uniform "binary" views of inequality. Our research Identifies three distinct "traps" that fragment the Indian labor market:
- The Muslim Geographic Trap: A quantity problem. Muslims suffer from geographic segregation in economically stagnant districts.
- The Adivasi (ST) Subsistence Mirage: A quality problem. High work intensity (distress employment) in subsistence economies masked by low per-job earnings.
- The Dalit (SC) Distributed Barrier: An access problem. Hiring discrimination and wage penalties that persist consistently across all geographies.
The analysis is organized into a sequential pipeline of R scripts located in the scripts/ folder:
| Step | Script | Description |
|---|---|---|
| 00 | 00_setup.r |
Dependency management and environment setup. |
| 01 | 01_data_loading.r |
Raw data ingestion (IHDS-II rda files). |
| 02 | 02_data_preparation.r |
Variable cleaning, recoding, and subsetting. |
| 03 | 03_descriptive_stats.r |
Initial group-level summary and wealth-work paradox analysis. |
| 04 | 04_regression_models.r |
Core logistic and OLS models for employment and earnings. |
| 05 | 05_Interactions_Subgroups.r |
Complex interaction effects and heterogeneity checks. |
| 07 | 07_final_report.r |
Generation of tabular results and regression summaries. |
| 08 | 08_visualizations.r |
Code for generating the core diagnostic plots. |
| 09 | 09_robustness_checks.r |
Validation of results across different model specifications. |
| 10 | 10_geographic_analysis.r |
District-level correlation and Muslim geographic trap analysis. |
| 11 | 11_sc_st_geographic_analysis.r |
Comparative geographic analysis for SC and ST groups. |
| 12 | 12_employment_quality.r |
Analysis of contract types, informality, and formal benefits. |
- District Fixed Effects (FE): Used to isolate geographic effects from individual-level identity penalties.
- Geographic Correlation Analysis: Mapping group concentration against district-level economic outcomes.
- Wage Regressions: Controlling for education, age, gender, and location to estimate adjusted wage penalties.
- Comparison/Falsification: Using a three-way group comparison (Muslim-SC-ST) to validate historical and geographic narratives.
- Clone the repository:
git clone https://github.com/ashwinnsr/IIHDS_Project.git
- Run the full analysis pipeline:
Note: Ensure you have placed the IHDS-II
source("scripts/run_analysis.r").rdafiles in the root directory.
Key outputs are saved in output/plots/, including:
- Wealth-Employment Paradox: Bubble charts of economic distress vs. work.
- Correlation Plots: District concentration vs. employment rates.
- Contract Distribution: Informalization metrics across social groups.
For the formal write-ups, refer to the LaTeX_Submission/ folder, which contains the APA 7 formatted manuscript.
License: MIT
Author: Ashwin Sreekumar (CHRIST Deemed to be University, Bangalore)