Analyzing the Income Inequality of India vs.

China Using Gini Coefficient and

Lorenz Curve
Aadarsh Ashok Atishay Jain Arun Pratap Singh Navneet Singh Rishi Prasad Agrahari
2024PGP001 2024PGP100 2024DPM09 2024PGP258 2024PGP504

ABSTRACT METHODOLOGY RESULTS CONCLUSION & INFERENCES

The Methodology Used for Calculating the Gini Coefficient and Lorenz Have plotted the Lorenz Curve of India and China for the years 1980 and
This work is done to compare the income inequalities of one Curves 2014. Have also plotted the trend of the Gini Coefficient over the period. This analysis explores the changes in income inequality in India and China
from 1980 to 2014 using the Gini coefficient and Lorenz curve. The
major economy and one growing economy, China and India. The • Using income data from the Global Consumption and Income
following are the key observations:
Gini index and Lorenz curve are popular measures. The Gini Project to draw Lorenz curves and compare country income Lorenz and Gini Coefficient Plots
Index is a number used to understand the unequalness of distribution changes over time. Income here does not account for • Rising Inequality: Over the years, both countries have seen their
Income distribution in a country. It has a limitation in that it does taxes levied within the country, as this can vary with the country,
From the plotted data, income inequality grow. The Gini coefficient reflects this.
not provide significant information about inequality at the end of and hence the data will not be consistent/normalized. • China’s path has been quite unpredictable, with sudden jumps and
• The Data Source Comprised 14 columns containing the country, the
we see that income
the strata, i.e., richest and poorest people. At the same time, the inequality has risen drops in inequality. It shows that income distribution has been all
year, and the income, grouped by percentiles of 10. The income over the place at times.
Lorenz curve visually illustrates the distribution of income across
was converted to the purchasing power of the US Dollar in the year more clearly for China • India has followed a steadier course. There have been fewer
different segments of the population. The project offers valuable
2005. A snippet of the data set is included below. (from the solid blue line surprises here, with inequality rising gradually.
insights to compare the socio-economic landscape of both
to the red dotted line) • The Lorenz Curve: This gives us a visual idea of how income is spread
countries.
than for India (from the across the population. In China, the gap between the richest and
orange dashed line to poorest has widened much more dramatically than in India over time.
• China’s curve reveals a sharp increase in inequality, highlighting the
INTRODUCTION the green dashed line).
growing distance between rich and poor.
• India’s curve tells a different story—while inequality is rising, it’s
• The trend of the Lorenz curve was plotted for the years 1980 and
happening more slowly.
The Gini index devised by statistician Corrado Gini, is largely 2014 for the chosen countries of India and China.
• Economic and Political Influence: The trends in inequality are deeply
accepted measure of socioeconomic inequality, majorly in • Further, the Gini Coefficient was calculated from the curve, and we
plotted the results as well. connected to the economic and political situations in each country.
income and wealth distribution. • China’s rapid reforms and market shifts have played a role in its
Income inequality phenomena can be significantly seen in the • Using this data, we can correlate the changes in income inequality
with the prevailing economic and political situations in both volatile inequality patterns.
developing nations like India and China. The uneven distribution • India’s rise in inequality has been more consistent, but the 2008
countries and draw parallels of similarity and difference.
of wealth between rich and poor in these countries is large. global financial crisis did throw things off course for a while.
• We can also chart out the changes in the ratio between interdecile
Graham (1995) regarded income inequality as line drawn income levels, to get a more appropriate understanding of the • Comparing the Extremes: When looking at the income of the richest
between the rich and the poor This study aims to analyze the income inequality situation in both countries. 10% compared to the poorest 10%, there’s a clear difference between
quantitatively inequality extent in India and China the two countries:
• China has had bigger ups and downs, with some periods showing a
The Gini index is a derivative of the Lorenz curve (Lorenz,1905). It
is an integral that talks about the deviation of Lorenz's curve from
CODE SNIPPET massive jump in the gap between rich and poor.
• India, meanwhile, has seen a steady increase in this gap, with only
perfect equitability. The index calculation is performed by the 2008 crisis slowing things down briefly.
dividing the area between the perfect equality line and the Code Snippet for Lorenz Curve Extraction
Lorenz curve (A) by the total area under the perfect equality line
(A + B).
def create_c_income_shares(data, year, country): Final Thoughts:
query = (data["Country"] == country)& (data["Year"] == year)
If the index is near zero where area A is small, the distribution of decs = data.loc[query, [y for y in data.columns if “percentile" in y]]
total_inc = data.loc[query, “Income"] In short, both China and India have seen more income inequality over
income can be said to be more equal, while if the index is near total_incr_values=10*total_inc
time, but the way it’s played out has been different. China’s changes
one where area A is large, the distribution of income can be said c_incr_share = decs.csum(axis=1) / total_incr.values[0]
c_incr_share.index = [country + ", " + str(year)] have been faster and more unpredictable, likely due to its rapid
to be more unequal c_incr_share.columns = range(1, len(c_incr_share.columns) + 1)
return c_incr_share economic shifts. India’s inequality has grown more steadily. These
insights could help guide future policies to tackle inequality in both
countries.
From the plot, we see that the trend for the Gini Coefficient is
Code Snippet for Gini Coefficient Extraction upward, showing income inequality increased. We also see that
def gini_coeff (y):
China has a Gini Coefficient that is swinging quite erratically, in REFERENCES
y = np.double(y.values)
z=y.sum() comparison to India, which is a smooth curve.
var = y / z
y = var Graham, (1995): Poverty and Health Risks: Issues and Concept.
temp1= np.subtract.outer(y, y)
temp2 = np.abs(temp1)
m = temp.mean(temp2)
Interdecile Ratio Plot Sitthiyot, T., & Holasut, K. (2020). A simple method for
rm = m / np.mean(y)
measuring inequality. Palgrave Communications, 6(1), 1-9.

g_c = 0.5 * rm Lorenz, M. O. (1905). Methods of measuring the concentration

return g_c of wealth. Publications of the American statistical
association, 9(70), 209-219.

Code Snippet for Interdecile Calculation Gini, C. (2005). On the measurement of concentration and
variability of characters, METRON-International Journal of
df["NinetyTen"]=df[“tenthdecileincome"]/df[“firstdecileincome"]
df["NinetyFifty"]=df[“tenthdecileincome"]/df[“fifthdecileincome"]
Statistics, Vol.
df["FiftyTen"]=df["Decile 5 Income"]/df[“firstdecileincome"]
plot_df = df.loc[df["Country"].isin(countries), ["Country", "Year",
"NinetyFifty"]]
fig, ax = plt.subplots()
for country, style in zip(countries, ["-", "-.", "dashed", ":"]):
plot_df_country = plot_df.loc[plot_df["Country"] == country]
ax.plot(plot_df_country["Year"], plot_df_country["NinetyFifty"], Plots of the ratio of the Income of the 90th Percentile to the 10th and the
50th Percentile. We can see that through the years, China has seen
label=country, linestyle=style)

erratic behavior, but India has been steadily increasing, save for the
financial crisis of 2008.