Endogenous Learning in Input-Output Economies

Abstract

Consider a multi-sector general equilibrium model where firms have incomplete information about the returns to scale of their production and where that information is sequentially updated once real production is observed. What is the impact of this learning dynamics on the market-wise equilibrium objects? Under which conditions are firms able to efficiently learn their actual returns to scale? At which rate does this learning happen? In this work, we analyze endogenous learning mechanisms and their implications for the market-wise equilibrium objects in the multi-sector model. Our results shed light on how idiosyncratic shocks translate into the learning dynamics of the input-output elasticity structure. Particularly, we observe that (i) all the relevant information in the learning dynamics is encoded in the input decisions; (ii) firms are able to learn the actual returns to scale independently from the manner in which input decisions are taken; (iii) the mismatch between the true (unknown) returns to scale and those predicted by firms critically affects the aggregate production, which is amplified when the economy is capital-intensive.