The role of BJP government policies in exacerbating income inequality in India

India has been experiencing significant economic growth over the past few decades, with its Gross Domestic Product (GDP) showing an upward trend. However, the benefits of this growth have not been equally distributed among all segments of society, and income inequality has been on the rise. The latest test results on the stationarity of two critical economic indicators, namely GDP and GDP Per capita, have confirmed this issue. The role of government policies in exacerbating this income inequality cannot be ignored, particularly the policies implemented by the Bharatiya Janata Party (BJP) government.

The BJP government’s policies have been criticized by many for their negative impact on the poor and marginalized sections of society. These policies have favored the wealthy and big corporations at the expense of the common man, leading to an increase in income inequality. The Toda-Yamamoto GC test results have shown that there is a statistically significant causal relationship between GDP and total population, indicating that an increase in GDP does not necessarily translate into better living standards for the masses. In this article, we will explore the role of BJP government policies in exacerbating income inequality in India and the potential solutions to address this pressing issue.

We implement the Toda-Yamamoto Granger causality test on Indian economic indicators to analyze the relationship between GDP and population growth.

The API endpoint from the World Bank is used to download data for two indicators: NY.GDP.MKTP.CD, which represents the GDP, and SP.POP.TOTL, which represents the total population. The data is downloaded for the time range from 1970 to 2023 and then split before and after 2014. You can use the python code below to download the world bank data

Python Code to download GDP and Total population data from the world bank from 1970 to 2023

#Importing required libraries
import requests
import pandas as pd

# API endpoint for the World Bank's data
api_endpoint = "http://api.worldbank.org/v2/"

# List of indicators to download
indicators = [
    "NY.GDP.MKTP.CD",  # GDP
    "SP.POP.TOTL",   #GDP per capita
]

# Country code for India
country_code = "IN"

# Download the data
dataframes = []
for indicator in indicators:
    url = f"{api_endpoint}country/{country_code}/indicator/{indicator}?format=json&per_page=5000&date=1970:2023"
    response = requests.get(url)
    data = response.json()[1]
    df = pd.DataFrame(data)
    df = df[['date', 'value']]
    df.columns = ['Year', indicator]
    df.set_index('Year', inplace=True)
    df[indicator] = pd.to_numeric(df[indicator], errors='coerce')
    dataframes.append(df)

# Merge the dataframes into one and sort by year
merged_df = pd.concat(dataframes, axis=1)
merged_df.sort_index(inplace=True)

# Remove any NaN or inf values and ensure all columns have the same length
merged_df.dropna(inplace=True)

print(merged_df.head())

The downloaded data is then checked for stationarity using the Augmented Dickey-Fuller (ADF) test. If the p-value of the ADF test is greater than 0.05, the data is considered non-stationary and differencing is applied to make the data stationary.

After checking for stationarity, the GDP per capita is calculated by dividing GDP by the total population. The Toda-Yamamoto Granger causality test is then applied to test whether there is a causal relationship between each indicator and GDP per capita.

The test results show that both GDP and population are likely stationary, which means they are suitable for analysis. However, the Granger causality test shows that there is no significant causal relationship between GDP and GDP per capita, as the p-value is greater than 0.05. The same test also shows a significant causal relationship between population and GDP per capita, with a p-value of 0.0019.

These results suggest that while the Indian government is focused on increasing GDP, its policies may not be benefiting the common man, as there is no significant relationship between GDP and GDP per capita. On the other hand, population growth does have a significant causal relationship with GDP per capita, which could mean that population growth is more important than GDP growth in improving the standard of living in India.

In conclusion, the implementation of the Toda-Yamamoto Granger causality test on Indian economic indicators shows that there is no significant causal relationship between GDP and GDP per capita. This could indicate that the Indian government is neglecting its citizens by creating non-supporting policies and focusing solely on increasing GDP. It could be beneficial for the government to prioritize policies that improve the standard of living of its citizens rather than just increasing GDP.

Test Results

GDP data testing

NY.GDP.MKTP.CD – ADF Statistic: -3.34849815516227
NY.GDP.MKTP.CD – p-value: 0.012844980754882131
NY.GDP.MKTP.CD – Critical Values: {‘1%’: -3.596635636000432, ‘5%’: -2.933297331821618, ‘10%’: -2.6049909750566895}
NY.GDP.MKTP.CD is likely stationary.

Total Population data testing
SP.POP.TOTL – ADF Statistic: -3.828820699973145
SP.POP.TOTL – p-value: 0.002625829886387329
SP.POP.TOTL – Critical Values: {‘1%’: -3.6055648906249997, ‘5%’: -2.937069375, ‘10%’: -2.606985625}
SP.POP.TOTL is likely stationary.

Toda-Yamamoto GC test for each indicator against GDP
Testing NY.GDP.MKTP.CD against per_capita
Granger causality test for NY.GDP.MKTP.CD against per_capita: p-value = 0.24102223761469432

Testing SP.POP.TOTL against per_capita
Granger causality test for SP.POP.TOTL against per_capita: p-value = 0.0019152651365519884