Measuring the elasticity of taxable income (ETI) is central for tax policy design. Yet, there are few arguments which support or infirm that current methods yield measurements of the ETI that can be trusted. Our first purpose is to use simulation methods to assess the bias and precision of the prevalent methods used in the literature (IV estimation and bunching methods). Thereby, we aim at (i) explaining the huge differences in empirical results, and (ii) providing arguments in favor of or against using these methods. Our second purpose is to suggest indirect inference estimation to improve the quality of the measurement. We find that the IV regression estimators may suffer from considerable bias and be quite imprecise, whereas the bunching estimators perform better in our controlled environment. We also show that using more of the information available in the data, estimators based on indirect inference principles produce more precise estimates of the ETI than any of the most commonly used methods.