AbstractThis paper analyzes the problem of matching heterogenous agents in a Bayesian learning model. One agent gives a noisy signal to another agent, who is responsible for learning. If production has a strong informational component, a phase of cross-matching occurs, so that agents of low knowledge catch up with those of higher one. It is shown that (i) a greater informational component in production makes cross-matching more likely; (ii) as the new technology is mastered, production becomes relatively more physical and less informational; (iii) a greater dispersion of the ability to learn and transfer information makes self-matching more likely; and (iv) self-matching leads to more self-matching, whereas croos-matching can make less productive agents overtake more productive ones.
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Other versions of this item:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- J12 - Labor and Demographic Economics - - Demographic Economics - - - Marriage; Marital Dissolution; Family Structure
- J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
- J41 - Labor and Demographic Economics - - Particular Labor Markets - - - Labor Contracts
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-07-13 (All new papers)
- NEP-CTA-2011-07-13 (Contract Theory & Applications)
- NEP-MIC-2011-07-13 (Microeconomics)
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