  This study proposes a reversible jump Markov chain Monte Carlo method for
estimating parameters of lognormal distribution mixtures for income. Using
simulated data examples, we examined the proposed algorithm's performance and
the accuracy of posterior distributions of the Gini coefficients. Results
suggest that the parameters were estimated accurately. Therefore, the posterior
distributions are close to the true distributions even when the different data
generating process is accounted for. Moreover, promising results for Gini
coefficients encouraged us to apply our method to real data from Japan. The
empirical examples indicate two subgroups in Japan (2020) and the Gini
coefficients' integrity.
