We analyse the income-health relationship for Australian data using a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. We adopt a distributional approach because we want to allow the effect of income on health to vary according to people’s health status. By contrast, conventional regression techniques such as OLS (Ordinary Least Squares) only help identify the effect of the covariates on the mean of the response variable. In our GAMLSS regression analysis, we use a gamma distribution to model the health variable and specify the parameters of this distribution as linear functions of a set of explanatory variables. We find that the risk of ending up in poor, fair or average health is lower for those who have relatively high incomes ($80,000) than for those who have relatively low incomes ($20,000), for both smokers and non-smokers. In relative terms, the risk-lowering effect of income appears to be the largest for those who are in poor health, again for both smokers and non-smokers. We also explore whether quantile regression yields similar results as GAMLSS regression. Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution for high-income and low-income earners as well as for smokers and non-smokers. We find that the results obtained on the basis of quantile regression are to a large extent comparable to those obtained by means of GAMLSS regression. Both techniques point in the direction of a non-uniform effect of income on health, and are therefore promising complements to conventional regression techniques as far as the analysis of the income-health relationship is concerned.