Previous research has shown that there is a robust relation between income and life satisfaction. However, the underlying mechanisms of this association are not yet clear. There are several mechanisms that determine this association, absolute income, and relative income. Absolute income is the idea that money buys things that lead to happiness but relative income, whether a person has a higher or lower income in reference to others, may be at least as important. Pervious research on absolute income found a small to moderate positive association between income and life satisfaction (Blanchflower & Oswald, 2004), previous research on relative income shows that one’s subjective ranking of income compared with others reduces well-being, and the effect of perceived relative income was comparable in size to the effect of absolute income.(Layard, Mayraz, & Nickell, 2010). However, not all the studies found support for the relative income hypothesis. The goals of this study were to test the absolute and relative income hypothesis in an extremely large U.S. sample and to examine the role of income inequality as a moderator of the absolute and relative income effects. (Cheung & Lucas, 2016) The study of Cheung & Lucas (2016), used data from more than 1.7 million residents of the United States to determine (1) whether higher household income is associated with higher life satisfaction, (2) whether higher neighborhood income is associated with lower life satisfaction, (3) whether individuals who live in neighborhoods with high-income inequality show a stronger relative income effect, and (4) whether the moderating effect of income inequality on relative income is particularly strong for low-income individuals. To answer these question data from the Behavioral Risk Factor Surveillance System (BRFSS) was used. This data was gathered from the responses of 1.7 million residents across all 50 states of The United States. In the BRFSS data on life satisfaction was gathered using an item that asked how satisfied persons were with their life on a 4 point scale, household income as part of the BRFSS survey used 7 income brackets with a minimum of $10.000 and a maximum of $75.000 or more. The BRFSS study also included items that referred to their background, current educational level and household. These variables were included as covariates in this study. To determine the neighborhood income data from the 2010 ACS 5-year estimates were used. There were done three different analysis which all measured different things using a multilevel model with life satisfaction as the outcome variable, household income as an individual level (level 1) predictor, and county income as a county-level (level2) predictor. (Cheung & Lucas, 2016) The goals of the analysis were (1) to effect of relative income, (2) income inequality as a moderator of the effects of relative income, and (3) the association between relative income and life satisfaction for individuals with low versus high income. Firstly, the results of analysis 1 showed higher household income was significantly associated with higher levels of life satisfaction and county income was negatively associated with life satisfaction. Which justified examining income inequality as a moderator of the association between county income and life satisfaction. Secondly, analysis 2 showed that income inequality moderates the association between relative income and life satisfaction, and the difference in the association between relative income and life satisfaction for countries with high-income inequality. Lastly analysis 3 showed a negative association between county income and life satisfaction was more pronounced for respondents with lower household income living in counties with higher income inequality. There can be drawn three conclusions from this research. Firstly, results from the current study support the relative income hypothesis. Secondly, income inequality moderated the association between relative income and life satisfaction. Lastly, the moderating effect of income inequality was more pronounced for low-income individuals compared with high-income individuals.