Geometrically stopped Markovian random growth processes and Pareto tails
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Many empirical studies document power law behavior in size distributions of economic interest such as cities, firms, income, and wealth. One mechanism for generating such behavior combines independent and identically distributed Gaussian additive shocks to log-size with a geometric age distribution. We generalize this mechanism by allowing the shocks to be non-Gaussian (but light-tailed) and dependent upon a Markov state variable. Our main results provide sharp bounds on tail probabilities and simple formulas for Pareto exponents. We present two applications: (i) we show that the tails of the wealth distribution in a heterogeneous-agent dynamic general equilibrium model with idiosyncratic endowment risk decay exponentially, unlike models with investment risk where the tails may be Paretian, and (ii) we show that a random growth model for the population dynamics of Japanese prefectures is consistent with the observed Pareto exponent but only after allowing for Markovian dynamics.
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