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On the macroeconomic effects of heterogeneous productivity shocks

  • Christian Jensen EMAIL logo
Published/Copyright: September 15, 2015

Abstract

The conventional wisdom that producer heterogeneity washes out, and is therefore irrelevant for the aggregate economy, does not apply when producers compete monopolistically. Despite this, the effects of such heterogeneity can be reproduced with an appropriately redefined representative-agent framework where the equilibrium values of aggregates are expressed in terms of the moment generating function of the distribution of heterogeneity, or its asymptotic distribution. Increased heterogeneity raises aggregate productivity and production, more so the fiercer competition is. We propose a framework where the entire distribution of heterogeneity matters, yet computationally requires no more than a representative-agent model.

JEL: E20; E23

Corresponding author: Christian Jensen, Department of Economics, University of South Carolina, 1014 Greene Street, SC 29208, Columbia, Tel.: +1-803-777-2786, e-mail:

Appendix: Proof proposition 4

It is first necessary to establish that if a random variable converges in distribution toward 𝒟t with moment generating function Γ𝒟t, then in the limit, its moment generating function will equal Γ𝒟t.

Proposition 7Let Γait (θt–1) be the moment generating function of the random variable aiti. If assumption 2 holds and Γ𝒟texists, then

(64)limMtΓait(θt1)=ΓDt. (64)

Proof. Since the characteristic function φait of any random variable ait always exists and is given by

(65)φait(τ)=E(eτait1) (65)

where

(66)eτait1=cos(τait)+1sin(τait) (66)

is a bounded continuous function, and aitdDt as Mt→∞, it follows from the Helly-Bray theorem, see Loève (1963, 182), that φait converges toward the characteristic function of the distribution 𝒟t as Mt→∞. Hence, in the limit, the two characteristic functions are identical. When the moment generating function of ait exists, it is related to the characteristic function of ait through the relation

(67)Γait(τ)=φait(τ1) (67)

so when Mt→∞, and the characteristic functions of ait and 𝒟t become identical, their moment generating functions become the same too, so Γait exists and equals Γ𝒟t.

This result can now be combined with that on convergence in probability (proposition 2) to compute aggregate total factor productivity.

Proposition 4

Proof. Proposition 2 implies that conditional on θt, for any ε>0,

(68)Pr(|At(Γait(θt1))1θt1|ε)0 (68)

when It→∞. Proposition 7 implies that

(69)limMt(Γait(θt1))1θt1=ΓDt1θt1 (69)

since the function f(ω)=ω1θt1 is continuous. Combining the two conditions, (68) and (69), we have

(70)Pr(|AtΓDt1θt1|ε)0 (70)

when It→∞ and Mt→∞, which is equivalent to the statement (47) in the proposition.      □

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Published Online: 2015-9-15
Published in Print: 2016-1-1

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