This paper seeks to identify whether there is a representative empirical Okun's law coefficient (OLC) and to measure its size. We carry out a meta-regression analysis on a sample of 269 estimates of the OLC to uncover reasons for differences in empirical results and to estimate the ‘true’ OLC. On statistical (and other) grounds, we find it appropriate to investigate two separate subsamples, using respectively (some measure of) unemployment or output as dependent variable. Our results can be summarized as follows. First, there is evidence of type II publication bias in both subsamples, but a type I bias is present only among the papers using some measure of unemployment as the dependent variable. Second, after correction for publication bias, authentic and statistically significant OLC effects are present in both subsamples. Third, bias-corrected estimated true OLCs are significantly lower (in absolute value) with models using some measure of unemployment as the dependent variable. Using a bivariate MRA approach, the estimated true effects are −0.25 for the unemployment subsample and −0.61 for the output subsample; with a multivariate MRA methodology, the estimated true effects are −0.40 and −1.02 for the unemployment and the output subsamples respectively.