Econ-1_Gwq-project

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Groundwater Quality Analysis in Indian Districts (2000-2019)

Overview

This project involves a comprehensive analysis of groundwater quality (GWQ) indicators across Indian districts from 2000-2019. The objective is to understand the relationship between groundwater quality and economic growth, represented by the net state domestic product (SDP) at constant prices. Additionally, the project explores socio-economic factors, including the Gini index, and examines non-linear relationships and regional differences.

Data Sources

1. Groundwater Quality Data: District-year level GWQ indicators measured in milligrams per liter.
2. Economic Output Data (SDP): State-year wise net state domestic product at constant prices, provided by the Reserve Bank of India (RBI) and accessed via the Database for the Indian Economy (DBIE) portal.
3. Gini Index Data: District-level Gini index from the paper by Mohanty et al. (2016).

Project Steps

1. Data Preparation

1. Group Assignment and Indicator Selection:

   • Check the group assignment from the ‘Group Allocation’ sheet.
   • Identify the GWQ indicator assigned to the group from the ‘Dependent Variable Assignment’ sheet.

2. Data Merging:

   • Merge the district-year level GWQ data with the corresponding state-year wise SDP data.
   • Merge the combined dataset with the district-level Gini index.

2. Regression Analysis

Estimate the following regression model:

[ \text{GWQ}{i,t} = \beta_0 + \beta_1 \text{SDP}{i,t} + u_{i,t} ]

where ( i ) indexes districts, ( t ) indexes years, and ( u_{i,t} ) is the random error term.

Regression Steps:

1. Load Data: Import the merged dataset using pandas.
2. Model Estimation: Use statsmodels API to estimate the regression.
3. Result Summarization: Summarize the regression results in a table.

3. Residual Analysis

1. Plot Residuals:

   • Plot the model residuals (( \hat{u}_{i,t} )) against the groundwater quality indicator (Y-axis) and SDP (X-axis).
   • Construct a second plot with ( \hat{u}_{i,t} ) on the Y-axis and SDP on the X-axis.

2. Interpretation: Analyze the plots to understand the residual behavior and whether they meet expectations.

3. Histogram of Residuals:

   • Plot a histogram of ( \hat{u}_{i,t} ).
   • Verify that the sum of residuals (( \sum_{i,t} \hat{u}_{i,t} )) equals zero.

4. Enhanced Model with Environmental Kuznets Curve

Investigate the non-linear relationship between environmental quality and economic growth by enhancing the regression model:

[ \text{GWQ}{i,t} = \beta_0 + \beta_1 \text{SDP}{i,t} + \beta_2 \text{SDP}{i,t}^2 + u{i,t} ]

Enhanced Model Steps:

1. Model Estimation: Estimate the enhanced regression model.
2. Summary Statistics: Prepare a detailed summary statistics table for all variables.
3. Outlier Detection: Identify any outliers and/or influential observations and describe methods to address them.

5. Interpretation of Results

Articulate the relationship between economic growth (as measured by SDP) and groundwater quality based on the regression results. Discuss whether the results align with expectations and existing empirical evidence.

6. Yearly Analysis

Examine whether the relationship between GWQ and economic growth differs by year. Perform regression analysis for each year and compare the results.

7. Regional Analysis

Enhance the model to explore regional differences in the estimates of the Kuznets curve. Use regional definitions provided by the RBI and estimate separate models for each region.

Tools and Libraries

• Python: Programming language used for data analysis and modeling.
• Pandas: Library for data manipulation and analysis.
• Numpy: Library for numerical computations.
• Statsmodels API: Library for statistical modeling and hypothesis testing.

Statistical and Machine Learning Methods

• Regression analysis to assess relationships between variables.
• Visualization of residuals and model diagnostics.
• Identification and handling of outliers and influential observations.
• Exploration of non-linear relationships and regional differences using enhanced regression models.

Conclusion

This project provides a detailed analysis of groundwater quality in relation to economic and socio-economic factors in Indian districts over two decades. By employing advanced statistical and machine learning methods, the study offers insights into the complex interactions between economic growth and environmental quality.

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