  It is well-known that value added per worker is extremely heterogeneous among
firms, but relatively little has been done to characterize this heterogeneity
more precisely. Here we show that the distribution of value-added per worker
exhibits heavy tails, a very large support, and consistently features a
proportion of negative values, which prevents log transformation. We propose to
model the distribution of value added per worker using the four parameter
L\'evy stable distribution, a natural candidate deriving from the Generalised
Central Limit Theorem, and we show that it is a better fit than key
alternatives. Fitting a distribution allows us to capture dispersion through
the tail exponent and scale parameters separately. We show that these
parametric measures of dispersion are at least as useful as interquantile
ratios, through case studies on the evolution of dispersion in recent years and
the correlation between dispersion and intangible capital intensity.
