Open AccessTechnological unemployment revisited: automation in a search and matching framework
Author(s) -
Dario Cords,
Klaus Prettner
Publication year - 2021
Publication title -
oxford economic papers
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 69
eISSN - 1464-3812
pISSN - 0030-7653
DOI - 10.1093/oep/gpab022
Subject(s) - unemployment , matching (statistics) , automation , context (archaeology) , falling (accident) , labour economics , robot , economics , dreyfus model of skill acquisition , computer science , engineering , artificial intelligence , macroeconomics , economic growth , biology , mechanical engineering , medicine , paleontology , statistics , mathematics , environmental health
Will automation raise unemployment and what is the role of education in this context? To answer these questions, we propose a search and matching model of the labour market with two skill types and with industrial robots. In line with evidence to date, robots are better substitutes for low-skilled workers than for high-skilled workers. We show that robot adoption leads to rising unemployment and falling wages of low-skilled workers and falling unemployment and rising wages of high-skilled workers. In a calibration to Austrian and German data, we find that robot adoption destroys fewer low-skilled jobs than the number of high-skilled jobs it creates. For Australia and the USA, the reverse holds true. Allowing for endogenous skill acquisition of workers implies positive employment effects of automation in all four countries. Thus, the firm creation mechanism in the search and matching model and skill acquisition are alleviating the adverse effects of automation.