The Optimal Distribution of the Tax Burden over the Business Cycle

This paper analyses optimal income taxes over the business cycle under a balanced-budget restriction, for low, middle and high income households. A model incorporating capital-skill complementarity in production and differential access to capital and labour markets is developed to capture the cyclical characteristics of the US economy, as well as the empirical observations on wage (skill premium) and wealth inequality. We .nd that the tax rate for high income agents is optimally the least volatile and the tax rate for low income agents the least countercyclical. In contrast, the path of optimal taxes for the middle income group is found to be very volatile and counter-cyclical. We further find that the optimal response to output-enhancing capital equipment technology and spending cuts is to increase the progressivity of income taxes. Finally, in response to positive TFP shocks, taxation becomes more progressive after about two years.

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.