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Educational mismatch and productivity: evidence from LEED data on Italian firms

  • Laura Bisio
  • Matteo Lucchese

This study aims at evaluating the impact of educational mismatch onto firm-level productivity for a large set of Italian firms. In particular, over (under)-education refers to situations where individual’s educational attainment is higher (lower) than the education required by the job, thereby producing a surplus (deficit) of education. Based on the integration of the LEED (Linked Employer Employee Database) Istat Statistical Register Asia Occupazione – which provides information on workers’ age, professional qualification and educational attainment – and the Istat Frame-SBS Register, we perform an analysis in the spirit of the ORU (Over, Required and Under Education) model proposed by Kampelmann e Rycx (2012). The dataset is based on a large panel of over 55,000 manufacturing and services firms with more than 20 employees, covering the 2014-2019 period. The empirical strategy is based on a two-step procedure: first, ORU indicators are computed at the worker-level; second, we estimate a firm-level productivity (value added per employee) function where the key variables of interest are the ORU indicators collapsed at the firm-level, taking into account both firm and workers characteristics. The productivity function is estimated by GMM-system by Arellano and Bond (1995) e Blundell and Bond (1988). Main results point out that over/under-education affects productivity growth in both manufacturing and services firms: firm’s productivity rises following a one unit increase in mean years of over-education – with spiking results for medium and high-tech manufacturing firms –, whereas a growth in under-education hampers productivity dynamics in high and medium-high tech manufacturing and knowledge-intensive services firms.

  • Keywords:
  • Educational mismatch,
  • Productivity,
  • Linked Employer-Employee Dataset,
  • GMM-System,
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Laura Bisio

ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0003-0922-6359

Matteo Lucchese

ISTAT, Italian National Institute of Statistics, Italy - ORCID: 0000-0001-8331-7393

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  2. Arellano, M., and Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), pp. 29-51.
  3. Blundell, R., and Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), pp. 115-143.
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  • Publication Year: 2023
  • Pages: 299-304
  • Content License: CC BY 4.0
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  • Publication Year: 2023
  • Content License: CC BY 4.0
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Chapter Information

Chapter Title

Educational mismatch and productivity: evidence from LEED data on Italian firms

Authors

Laura Bisio, Matteo Lucchese

Language

English

DOI

10.36253/979-12-215-0106-3.52

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

ASA 2022 Data-Driven Decision Making

Book Subtitle

Book of short papers

Editors

Enrico di Bella, Luigi Fabbris, Corrado Lagazio

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press, Genova University Press

DOI

10.36253/979-12-215-0106-3

eISBN (pdf)

979-12-215-0106-3

eISBN (xml)

979-12-215-0107-0

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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