A commonly used migration regression relates population changes to local labor demand shocks, often yielding small or statistically insignificant coefficients despite sizable effects on labor market outcomes. We argue that such findings need not imply that mobility is unresponsive to local economic conditions. Rather, conventional migration regressions can be biased, as they do not reflect how workers choose among competing locations. In a parsimonious equilibrium model, local population growth depends on differences between local shocks and those in relevant destinations; when these shocks are correlated, estimated responses are attenuated. Moreover, with industry-level shocks, standard shift-share measures misrepresent locations’ effective exposure, leading to further attenuation. Using simulation experiments based on Brazilian and German administrative data, we show attenuation can exceed 50% and that conventional regression estimates can be highly imprecise. We propose theory-grounded empirical approaches that correct the biases, deliver estimates with a clear structural interpretation, and improve the precision of estimated mobility responses.