INTRODUCTION In the happiness literature, studies that examine the nature of income-happiness relationship often observe that the “average Joe’s happiness” is reducing as income increases over time. Such studies frequently use ordinary least squares (OLS) or ordered probit regressions to estimate the effect of either individual or aggregate income or income growth on the individual or average subjective well-being (SWB) indicators such as life satisfaction or happiness. The conventional regression methods estimate only the mean effects of income on well-being and do not reveal heterogeneity or differential income effects at different points in the well-being distribution. Being average estimates, such estimation methods assume a linear relationship between the dependent variable and the covariates, i.e. the same average effect holds throughout the well-being distribution. Though an understanding of such average effect of economic or income growth on a country’s well-being level can provide useful insights, the averaged coeffcient estimate does not reveal the complete distributional picture of the relationship between income and well-being over the entire distribution. There is no a priori reason that covariate effect to be the same across the entire well-being distribution, as the effects of socioeconomic factors that determine life satisfaction may vary in the lower and upper range of the well-being distribution. Or, the determinants of happiness are not the same as the determinants of unhappiness. Therefore, average effects may well be misleading and the estimation should look beyond the mean effects and capture the differential income effects at every specifc location of the well-being distribution. Such a more comprehensive picture of the differential effect of income on well-being can be estimated by the quantile regression method. The quantile regression model, similar to OLS model in terms of statistical structure, provides a IS THE EFFECT OF INCOME ON HAPPINESS SAME FOR ALL IN INDIA? A PANEL QUANTILE REGRESSION ANALYSIS OF HETEROGENEITY IN THE RELATIONSHIP BETWEEN INCOME AND LIFE SATISFACTION T. Lakshmanasamy* * Professor, Department of Econometrics, University of Madras, Chennai, Tamil Nadu, India. Email: tlsamy@yahoo.co.in Abstract The OLS and ordered probit estimation of a causal relationship between income and happiness assume linearity in individual and average income effects and the same average effect holds over the entire range of subjective well-being distribution. The response of well-being to income changes is not the same for poor and rich, dissatisfed or unhappy, and satisfed or happy people. This paper estimates heterogeneity in the income-life satisfaction relationship at specifc locations of subjective well-being distribution by panel quantile regression method. The data is derived from six waves of the World Value Survey for 12 major states of India for 24 years over the period 1990-2014. The descriptive analysis of well-being distribution across states shows heterogeneity in the income-well-being relationship between states and individuals within states. The across-states over-time panel quantile regression results reveal that estimation of average effect only provides an incomplete picture of the effect of income at both ends of the conditional distribution of well-being indicators, life satisfaction and happiness levels. The quantile regression estimates vastly differ not only from the mean estimates, but also across quantiles. Life satisfaction falls and happiness rises with an increase in average state income and the income effect is strong at the lower end than at the upper end of subjective well-being distribution. Keywords: Life Satisfaction, Happiness, Subjective Well-Being Distribution, NSDP Per Capita, Differential Effects, Quantile Regression JEL classifcation: D31, I31, C23, C35, J 28, J71 Journal of Organisation & Human Behaviour 9 (1 & 2) 2020, 21-35 http://publishingindia.com/johb/