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Endogenous Learning in Multi-Sector Economies

Stefano Nasini () and Rabia Nessah ()
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Stefano Nasini: IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France
Rabia Nessah: IESEG School of Management, Univ. Lille, CNRS, UMR 9221 - LEM - Lille Economie Management, F-59000 Lille, France

Authors registered in the RePEc Author Service: Kristiaan H. J. Kerstens

No 2021-EQM-08, Working Papers from IESEG School of Management

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 these learning dynamics on the marketwise 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 multisector model. Our results shed light on how idiosyncratic shocks translate into the learning dynamics of the returns to scale and the input-output elasticity structure. Particularly, (i) we observe that all the relevant information in the learning dynamics is encoded in the input decisions and the manner in which input decisions are taken; (ii) we deduce conditions under which firms are able to learn the actual returns to scale; (iii) we uncover cases in which the incorrect knowledge of returns to scale does not translate into input misallocation. On the empirical side, the proposed analysis of the endogenous learning dynamics is complemented with an estimation approach that allows testing the presence and level of learning using available input-output data. The empirical figures reveal the presence of sizable learning processes (driven by underestimations and overestimations of the returns to scale parameters) in different sectors.

Keywords: : Multi-sector general equilibrium model; Bayesian learning; Returns to scale; In- complete information (search for similar items in EconPapers)
JEL-codes: C11 C13 D5 D51 D83 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2021-10, Revised 2023-01
New Economics Papers: this item is included in nep-eff, nep-ene and nep-env
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