We use a panel dataset of UK workers to look for evidence of compensating wage differentials for workplace risk. Risk data are available at the four-digit industry level or at the three-digit occupation level. We discuss various econometric problems associated with the hedonic wage approach, namely measurement error, instability of the estimates to specification changes, and endogeneity. We find that if we assume a classical measurement error, the true risk signal would be completely drowned out in our data, which would imply a severe downward bias of the OLS coefficient on risk. But this prediction is at odds with our OLS estimates of the VSL, which are large, especially for blue collar workers. Further, the coefficient on risk changes varies dramatically with the inclusion or exclusion of industry and/or occupation dummies, as well as with the addition of nonfatal risk. When we instrument for risk, which we treat as endogenous with wage, and apply 2SLS or a procedure suggested by Garen (1988), we find negative associations between risk and wages for all workers, which is against the notion of compensating wage differentials, or, for blue-collar workers, extremely large VSL figures. Finally, we exploit the panel nature of our data to apply various estimation procedures (the within estimator, GLS and the Hausman-Taylor procedure) that correct for unobserved heterogeneity and endogeneity. The coefficient on risk is usually negative and insignificant for the sample of all workers, which once again questions the notion of compensating wage differentials. For blue-collar workers we obtain reasonable VSLs, but the association between risk and wages is not statistically significant. We conclude that if compensating differentials for risk exist, measurement error, other econometric problems, and the changing nature of labor markets prevent us from observing them. We also conclude that models and techniques for panel data that account for unobserved heterogeneity and endogeneity seem more reliable than the techniques typically employed with cross-sectional data.