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

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            Author(s)
            Bisio, Laura
            Lucchese, Matteo
            Language
            English
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            Abstract
            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.
            Book
            ASA 2022 Data-Driven Decision Making
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/154930
            Keywords
            Educational mismatch; Productivity; Linked Employer-Employee Dataset; GMM-System; thema EDItEUR::J Society and Social Sciences
            DOI
            10.36253/979-12-215-0106-3.52
            ISBN
            9791221501063
            Publication date and place
            Florence, 2023
            Series
            Proceedings e report,
            Pages
            6
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            Credits


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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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