Pareto or log-normal? A recursive-truncation approach to the distribution of (all) cities

Traditionally, it is assumed that the population size of cities in a country follows a Pareto distribution. This assumption is typically supported by nding evidence of Zipf's Law. Recent studies question this nding, highlighting that, while the Pareto distribution may t reasonably well when the data is truncated at the upper tail, i.e. for the largest cities of a country, the log-normal distribution may apply when all cities are considered. Moreover, conclusions may be sensitive to the choice of a particular truncation threshold, a yet overlooked issue in the literature. In this paper, then, we reassess the city size distribution in relation to its sensitivity to the choice of truncation point. In particular, we look at US Census data and apply a recursive-truncation approach to estimate Zipf's Law and a non-parametric alternative test where we consider each possible truncation point of the distribution of all cities. Results con rm the sensitivity of results to the truncation point. Moreover, repeating the analysis over simulated data con rms the di culty of distinguishing a Pareto tail from the tail of a log-normal and, in turn, identifying the city size distribution as a false or a weak Pareto law.

On the real effects of financial pressure: Evidence from euro area firm-level employment during the recent financial crisis.

Using a large panel of unquoted euro-area firms over the period 2003-11, this paper examines the impact of financial pressure on firms’ employment. The analysis finds evidence that financial pressure negatively affects firms’ employment decisions. This effect is stronger during the 2007-2009 financial crisis, especially for firms in the periphery area compared to their counterparts in the core European economies. We also find that impact of financial pressure on employment is more potent for firms classified as financially constrained and operating in periphery economies during the financial crisis.