Zipf’s law is a very tight constraint on the class of admissible models of local growth. It says that for most countries the size distribution of cities strikingly fits a power law: the number of cities with populations greater than S is proportional to 1/S. Suppose that, at least in the upper tail, all cities follow some proportional growth process (this appears to be verified empirically). This automatically leads their distribution to converge to Zipf’s law. I.

Several empirical regularities motivate most theories of the distribution of labor earnings. Earnings distributions tend to be skewed to the right and display long right tails. Mean earnings always exceed median earnings and the top percentiles of earners account for quite a disproportionate share of total earnings. Mean earnings also differ greatly across groups defined by occupation, education, experience, and other observed traits. With respect to the evolution of the distribution of earnings for a given cohort, initial earnings dispersion is smaller than the dispersion observed in prime working years. We explore several models that address these stylized facts. Stochastic theories examine links between assumptions about the distribution of endowments and implied features of earnings distributions given assumptions about the processes that translate endowments into earnings. Selection models describe how workers choose a career. Because workers select their best option from a menu of possible careers, their allocation decisions tend to generate skewed earnings distributions. Sorting models illustrate this process in an environment where workers learn about their endowments and therefore adjust their allocation decisions over time. Human capital theory demonstrates that earnings dispersion is a prerequisite for