Education plays an important role to transfers skill and knowledge toward the increasing in productivity and earning. Using so-called mincer earning function, we investigated the effect of years of schooling, commonly known as return to education, on earning over quantile. By specifying the effect of covariate at different quantile levels we allow the covariate to affect response variable not only at the center of its distribution, but also at its spread. We employed two methods to estimate parameters in mincer equation: (i) Bayesian quantile regression (BQR) and (ii) Bayesian quantile regression with adaptive least absolute shrinkage and selection operator (Lasso) penalty (BALQR). The latter method extends the bayesian Lasso penalty term by employing different penalty function with an adaptive tuning parameter accomodated in the inverse gamma prior distribution. Data used in this paper is samples from workers in agricultural sector in South Sulawesi. Empirical results showed that BALQR outperformed over BQR because it resulted in smaller mean squared error (MSE). In addition, the estimators of the coefficient corresponding to the return to education variable do not monotonically increase over quantile.