Quantile regression constitutes a natural and flexible framework for the analysis of duration data in general and unemployment duration in particular. Comparison of the quantile regressions for lower and upper tails of the duration distribution shed important insights on the different determinants of short or long-term unemployment. Using quantile regression techniques, we estimate conditional quantile functions of US unemployment duration; then, resampling the estimated conditional quantile process we are able to infer the implied hazard functions. The proposed methodology proves to be resilient to several misspecification that typically afflict proportional hazard models such as, neglected heterogeneity and baseline misspecification. Overall, the results provide clear indications of the interest of quantile regression to the analysis of duration data.