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        <title type="main" level="a">Educational mismatch and productivity: evidence from LEED data on Italian firms</title>
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          <persName n="1" ref="https://orcid.org/0000-0003-0922-6359" type="ORCID">
            <forename>Laura</forename>
            <surname>Bisio</surname>
            <placeName type="affiliation">ISTAT, Italian National Institute of Statistics, Italy</placeName>
          </persName>
          <persName n="2" ref="https://orcid.org/0000-0001-8331-7393" type="ORCID">
            <forename>Matteo</forename>
            <surname>Lucchese</surname>
            <placeName type="affiliation">ISTAT, Italian National Institute of Statistics, Italy</placeName>
          </persName>
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          <resp>This is a section of <title>ASA 2022 Data-Driven Decision Making</title>(DOI: <idno type="DOI">10.36253/979-12-215-0106-3</idno>) by </resp>
          <name>Enrico di Bella, Luigi Fabbris, Corrado Lagazio</name>
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      <publicationStmt>
        <publisher>Firenze University Press</publisher>
        <pubPlace>Firenze</pubPlace>
        <date when="2023">2023</date>
        <idno type="DOI">https://doi.org/10.36253/979-12-215-0106-3.52</idno>
        <availability>
          <p>Available for academic research purposes</p>
          <p>Open Access</p>
          <p>Copyright Author(s)</p>
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            <p>Content licence CC BY 4.0</p>
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      <abstract xml:lang="en">
        <p>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.</p>
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        <keywords>
          <list>
            <item>Educational mismatch</item>
            <item>Productivity</item>
            <item>Linked Employer-Employee Dataset</item>
            <item>GMM-System</item>
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      <p>It is available online at https://doi.org/10.36253/979-12-215-0106-3.52<ref target="https://doi.org/10.36253/979-12-215-0106-3.52" /></p>
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          <head>References</head>
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