be my fault). One methodological remark or question: You write "In the current recovery, the coefficient is 0.5305, which tells you that the economy is growing at a lower rate of accompany productivity growth at present. That means, among other things, that the economy eats into its unemployment pool more quickly for a given labour force growth rate." Which I think is a remarkable finding. One can follow that line of argument easily given the graph and your analysis. However, I wonder whether it is necessary -- or more of a proof for that conclusion -- if you would estimate a single regression model (instead of four separate ones) and include an interaction term allowing the model itself for estimating coefficients for the different slopes. Wouldn't it then be possible -- within that single model approach -- to directly test whether the observed different slopes are -- in statistical terms -- indeed significantly different from each other which in turn would be a statistical test and eventually proof for your (descriptive) claim that the observed differences have more likely than not indeed a meaning? Well, I know that e.g. in sociology the approach of estimating different regression models for different groups, i.e. applying separate models, then comparing and interpreting the resulting coefficients, is more often than not the standard approach. But -- provided there is a large enough sample deriving sufficient statistical power -- I always find the single-model-method including suitable interaction terms more convincing in that it provides a