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Routine-biased technological change and wage inequality: do workers’ perceptions matter?

Routine-biased technological change and wage inequality: do workers’ perceptions matter? The Routine-Biased Technological Change (RBTC) has been regarded as a relatively novel technology-based explanation of social changes affecting job and wage polarization. In this paper, we investigate wage inequality between routine and non-routine workers along the wage distribution in Italy. Thanks to unique survey data, we can estimate the wage differential using both the actual and the perceived level of routine intensity of jobs to classify workers. We adopt semi-parametric decomposition techniques to quantify the importance of worker characteristics in explaining the gaps. We also employ non-parametric techniques to account for self-selection bias. We find evidence of a significant U-shaped pattern in the wage gap, according to both definitions, with non-routine workers always earning significantly more than routine workers. Results show that worker characteristics fully explain the gap in the case of perceived routine, while they account for no more than 50% of the gap across the distribution in the case of actual routine. Thus, the results highlight the importance of taking into account workers’ perceptions to reduce the set of omitted vaiables when analyzing determinants of wage inequality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Eurasian Business Review Springer Journals

Routine-biased technological change and wage inequality: do workers’ perceptions matter?

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Publisher
Springer Journals
Copyright
Copyright © The Author(s) under exclusive licence to Eurasia Business and Economics Society 2022
ISSN
1309-4297
eISSN
2147-4281
DOI
10.1007/s40821-022-00222-3
Publisher site
See Article on Publisher Site

Abstract

The Routine-Biased Technological Change (RBTC) has been regarded as a relatively novel technology-based explanation of social changes affecting job and wage polarization. In this paper, we investigate wage inequality between routine and non-routine workers along the wage distribution in Italy. Thanks to unique survey data, we can estimate the wage differential using both the actual and the perceived level of routine intensity of jobs to classify workers. We adopt semi-parametric decomposition techniques to quantify the importance of worker characteristics in explaining the gaps. We also employ non-parametric techniques to account for self-selection bias. We find evidence of a significant U-shaped pattern in the wage gap, according to both definitions, with non-routine workers always earning significantly more than routine workers. Results show that worker characteristics fully explain the gap in the case of perceived routine, while they account for no more than 50% of the gap across the distribution in the case of actual routine. Thus, the results highlight the importance of taking into account workers’ perceptions to reduce the set of omitted vaiables when analyzing determinants of wage inequality.

Journal

Eurasian Business ReviewSpringer Journals

Published: Sep 1, 2022

Keywords: Blinder/Oaxaca; Counterfactual distribution; Italy; Non-parametric methodology; Quantile regression; Routine; Semi-parametric methodology; Wage inequality; J31; J82; C14

References