Inequality and its different aspects have been one of the most popular areas of interests for the economists. Extensive research has been done to find out the relationship between economic growth and income inequality. In the recent times, with the availability of more advanced data sets, economists have tried to analyse how other factors like level of education, growth rate of population, fertility rates etc. influence income inequality across countries and over time.Kuznets (1955) states that the level of inequality first increases with development in the economy and after a certain point, starts to decrease with further level of development in the economy giving an inverse U-shaped curve. Ahluwalia (1976) analyzes the cross country relationship for different income percentiles. The results established by the paper show that the turning points of the income shares shifts systematically further out as we go down the percentile groups. For the expanded multiple regression equations, Kuznets’ curve flattens with decrease in inequality and hence the turning point shifts out.The literature on development and inequality has advanced a lot over the time period. Kuznets’ relation has been shown to exist over a short term period or not even holding at all. A different curve has been observed over the past decade known as ‘Elephant curve’ which was coined by Milanovi? (2016). He observed the data for 1988 to 2008 and concluded that the inequality in incomes varies with the growth rates as a linear curve in Elephant shaped relation. He observed that Kuznets cycles were visible only for a particular time horizon and were mostly driven by economic changes.Alvaredo (2017) shows that average global growth is relatively low compared to emerging countries’ growth rates. Interestingly, at the world level, growth rates do not rise monotonically with income groups’ position in the distribution. Instead we observe high growth at the bottom, low growth in the middle and high growth at the top of the pyramid. The income growth captured by top earners since 1980 was very large, even if demographically they were a very small group. The paper argues that the global top 1% captured 27% of total income growth between 1980 and 2016, against  12% for the bottom 50%. It also shows that global inequality is likely to further rise in the future, even under optimistic growth assumptions in emerging economies, if countries follow their own inequality trend. These results suggest a necessary discussion over the types of policies implemented by governments to trigger and redistribute income growth. Inequality has increased almost everywhere, but at different speeds revealing the importance of national institutions and policy in the shaping of inequality.Grigoli and Robles (2017) explore the relationship between growth and inequality. They have tried to mark an inequality overhang level in which the slope of the relationship switches from positive to negative thus taking a hump shape. According to them, initial inequality levels matter while determining the relationship. They also try to empirically determine if the relationship is actually linear. The paper finds that at low levels of inequality, increase in inequality affects growth positively. However for higher inequality levels, the negative effects are very strong as the paper shows especially starting the year 2000 as the paper says that by this year inequality levels had risen in almost all countries.Luan and Zhou (2017) focuses on understanding what GDP growth rates zone the economy should aim at, to have economic stability hence less inequalities in income. They show that if the growth is slow or not existing, people become less optimistic towards their future and government, which leads to gaps in income due to reduction in investments. However, extreme growth in the economy also leads to income inequality. The analysis has been done taking gini coefficient as the proxy for income inequality and the explanatory variables are Percentage of contribution of agriculture to GDP, GDP per capita, GDP growth rates, urban population, Political stability and Adult literacy rate. Simple regression model of Gini and GDP per capita explains a very low proportion of variation in inequality. Multiple regression analysis explains a better portion of variation. For comparisons between underdeveloped and developed economies, they took a dummy which showed developed economies had lower Gini coefficients hence lower inequality.Boulier(1975) took into consideration the effect of demographic variables on the income inequality. The paper took up various demographic variables like Rate of growth of population, Fertility rates and Life Expectancy and regressed them on gini coefficient as a measure for income inequality. The rate of growth of population alters the distribution of labour earnings and increases the dependency ratio of adult members and therefore has a positive impact on income inequality. On the other hand, fertility rates have direct effect on rate of growth of population and as fertility rates rise there is less equally sized distribution of income. Gregorio and Lee (1999) presented an empirical analysis about the relationship between education and income inequality using cross-country panel data ranging from 1960 to 1990. The paper concludes that income inequality can be reduced by achieving a higher level of education. Simultaneously a higher government expenditure positively stimulates the average education level across countries. However the analysis fails to explain a considerable amount of dispersion of income inequality both across countries and over the years.Checchi (2000) showed that average years of schooling and per capita GDP negatively offset income inequality. Plotting average years of schooling against income inequality shows a U-shaped curve, the minimum point being 6.5 years. Regression analysis shows that education can explain 3-16% variation in income inequality.4. OBJECTIVES OF STUDY