Project title: Effects of agricultural investments on productivity, consumption, and living standards
A. Data
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investments_data.dta: household level dataset. It includes all dependent variables and covariates needed for the analysis
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adults_morbidity_03.dta: individual level dataset. It includes all the information required for running the regressions for health
Since, the files 1 & 2 were too big, we are sharing it through google drive here. Please download the files before running our codes.
https://drive.google.com/drive/folders/1teVZQ0iUVqQQKqReA71so9buM-YTdYS6
- muni_manu_data.csv: contains municipal level data on industrial output in the year 1998
B. Jupyter Notebook
- Copyof_IPPP_Project.ipynb
Contains our codes for analysis along with results description. For reviewing our project please use this file. Please do download the files through google drive before running this file.
C. Summary Statistics
- Panel1.py
Contains codes for creating table of summary statistics for agricultural assets across treatment and control group. It also has codes for TTest for testing if the difference between control and treatment groups are statistically significant.
- Panel2.py
Contains codes for creating table of summary statistics for Head of the household charateristics across treatment and control group. It also has codes for TTest for testing if the difference between control and treatment groups are statistically significant.
- Summary Plots.py
Codes for boxplot for agricultural assets distribution
D. Regressions
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regressions_shortterm.py Contains codes for running short term regressions to evaluate the impact of cash transfers (CT) on the ownership of agricultural assets
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Scraping_regression.py Contains codes for testing if the impact observed on ownership of agricultural assets could be because of difference in industrial output
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regressions_longterm.py Contains codes for testing the impact of CT on living standards in the long run
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regressions_health.py Contains codes to test if the impact observed in the long run is due to health differences
E. Scraping
- Scraping_code.py Contains codes for scraping the industrial data
F. Website
- index.html Contains codes for our website