9791221501063-52.pdf

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 produ...

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Γλώσσα:English
Έκδοση: Firenze University Press, Genova University Press 2023
Διαθέσιμο Online:https://books.fupress.com/doi/capitoli/979-12-215-0106-3_52
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spelling oapen-20.500.12657-749242023-08-03T17:59:37Z Chapter Educational mismatch and productivity: evidence from LEED data on Italian firms Bisio, Laura Lucchese, Matteo Educational mismatch Productivity Linked Employer-Employee Dataset GMM-System bic Book Industry Communication::J Society & social sciences 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. 2023-08-03T15:07:01Z 2023-08-03T15:07:01Z 2023 chapter ONIX_20230803_9791221501063_120 2704-5846 9791221501063 https://library.oapen.org/handle/20.500.12657/74924 eng Proceedings e report application/pdf Attribution 4.0 International 9791221501063-52.pdf https://books.fupress.com/doi/capitoli/979-12-215-0106-3_52 Firenze University Press, Genova University Press ASA 2022 Data-Driven Decision Making 10.36253/979-12-215-0106-3.52 10.36253/979-12-215-0106-3.52 9223d3ac-6fd2-44c9-bb99-5b98ca9d2fad 863aa499-dbee-4191-9a14-3b5d5ef9e635 9791221501063 134 6 Florence open access
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language English
description 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.
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publisher Firenze University Press, Genova University Press
publishDate 2023
url https://books.fupress.com/doi/capitoli/979-12-215-0106-3_52
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