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

Author

Listed:
  • Silvia Vannutelli

    (Northwestern University)

  • Sergio Scicchitano

    (INAPP (National Institute for the Analysis of Public Policies)
    GLO (Global Labor Organization))

  • Marco Biagetti

    (INAPP (National Institute for the Analysis of Public Policies)
    GLO (Global Labor Organization))

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.

Suggested Citation

  • Silvia Vannutelli & Sergio Scicchitano & Marco Biagetti, 2022. "Routine-biased technological change and wage inequality: do workers’ perceptions matter?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 409-450, September.
  • Handle: RePEc:spr:eurasi:v:12:y:2022:i:3:d:10.1007_s40821-022-00222-3
    DOI: 10.1007/s40821-022-00222-3
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    Cited by:

    1. Caselli, Mauro & Fracasso, Andrea & Traverso, Silvio, 2021. "Robots and risk of COVID-19 workplace contagion: Evidence from Italy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    2. Caselli, Mauro & Fracasso, Andrea & Marcolin, Arianna & Scicchitano, Sergio, 2021. "The reassuring effect of firms' technological innovations on workers' job insecurity," GLO Discussion Paper Series 938, Global Labor Organization (GLO).

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    More about this item

    Keywords

    Blinder/Oaxaca; Counterfactual distribution; Italy; Non-parametric methodology; Quantile regression; Routine; Semi-parametric methodology; Wage inequality;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • J82 - Labor and Demographic Economics - - Labor Standards - - - Labor Force Composition
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

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