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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IJPDS</journal-id>
      <journal-title-group>
        <journal-title>International Journal of Population Data Science</journal-title>
        <abbrev-journal-title>IJPDS</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="epub">2399-4908</issn>
      <publisher>
        <publisher-name>Swansea University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.23889/ijpds.v10i3.3294</article-id>
      <article-id pub-id-type="publisher-id">10:3:262</article-id>
      <title-group>
        <article-title>Employment Data Lab: Reflections on the first two years</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Aujla</surname>
            <given-names initials="K">Kavandeep</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="affil-1"><label>1</label><institution>Department for Work and Pensions, London,
        United Kingdom</institution></aff>
      <pub-date date-type="pub" publication-format="electronic">
        <day>01</day>
        <month>06</month>
        <year>2025</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2025</year>
      </pub-date>
      <volume>8</volume>
      <issue>4</issue>
      <elocation-id>3294</elocation-id>
      <permissions>
        <license license-type="open-access"
          xlink:href="https://creativecommons.org/licences/by/4.0/">
          <license-p>This work is licenced under a Creative Commons Attribution 4.0 International
            License.</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://ijpds.org/article/view/3294">This article is available from the
        IJPDS website at: https://ijpds.org/article/view/3294</self-uri>
    </article-meta>
  </front>
  <body>
    <sec>
      <title>Objectives</title>
      <p>Shortly after rollout, the Employment Data Lab was showcased at the 2023 ADR conference. We
        have now gathered two years of practical experience of linking charity and third-party data
        with Government datasets to conduct evaluations. We will share our reflections from the
        first ten projects: both good and bad.</p>
    </sec>
    <sec>
      <title>Method</title>
      <p>The Employment Data Lab was developed to evaluate local employment programmes run by
        charities and local authorities. The Data Lab’s unique position, with access to a plethora
        of administrative datasets, allows impact evaluations of local programmes efficiently and
        securely. Data lab analysts use the statistical technique of propensity score matching to
        create a realistic, counterfactual comparison group. They then compare the treatment group
        of participants with the constructed comparison group to accurately evaluate employment
        programmes.</p>
    </sec>
    <sec>
      <title>Results</title>
      <p>We will present an overview of ten completed evaluations, reflecting on using linked
        administrative datasets in the Employment Data Lab. The Data Lab began with just Department
        for Work and Pensions (DWP) and Her Majesty’s Revenue and Customs (HMRC) administrative
        datasets. Overtime the service has been extended to include other datasets involving
        education and housing and homelessness data. This expansion has allowed the Data Lab to
        evaluate a wider set of programmes. Moving beyond employment, to support initiatives
        providing skills/sports training and helping people at risk of homelessness. In time, the
        service will expand further by adding Ministry of Justice data to help a wider set of
        charities.</p>
    </sec>
    <sec>
      <title>Conclusion</title>
      <p>The Data Lab has helped a wide variety of external organisations to harness the power of
        linked administrative data. Since inception, this free service has been a tremendous
        success. Furthermore, increased Government emphasis on more devolved programmes and localism
        mean the Data Lab will go from strength to strength.</p>
    </sec>
  </body>
</article>