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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.v10i3.3105</article-id>
      <article-id pub-id-type="publisher-id">10:3:88</article-id>
      <title-group>
        <article-title>Unsupervised evaluation of a large-scale historical population linkage</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author"><name><surname>Whittard</surname><given-names initials="D">Damian</given-names></name><xref ref-type="aff" rid="affil-1">1</xref></contrib>
      </contrib-group>
      <aff id="affil-1"><label>1</label><institution>University of the West of England, Bristol, 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>3105</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/3105">This article is available from the
        IJPDS website at: https://ijpds.org/article/view/3105</self-uri>
    </article-meta>
  </front>
  <body>
    <sec>
      <title>Objectives</title>
      <p>Given the urgency of the transition to net-zero, there is a need for a robust evidence base on green jobs and the green economy to support an environmentally sustainable and equitable economy.</p>
    </sec>
    <sec>
      <title>Methods</title>
      <p>Employing a linked administrative dataset and using both cross sectional and panel estimation techniques, this study examines employment opportunities and estimates the economic benefits of working in green occupations.</p>
    </sec>
    <sec>
      <title>Results</title>
      <p>Contributing to the international literature on pay in green jobs, the study reports a pay premium of four percent after controlling for other factors. Employees covered by collective agreements receive additional pay benefits, yet representation is less prevalent in directly green occupations. In line with research into attitude-behaviour gaps, the study demonstrates that while personal travel behaviours and green employment choices are often inconsistent, when they align this yields a pay dividend.</p>
    </sec>
    <sec>
      <title>Conclusion</title>
      <p>The research makes an important and novel contribution by showing that green employment can partially mitigate inter-occupation pay gaps, while identifying that persistent gender and ethnic pay disparities remain within green occupations. Females appear particularly disadvantaged by domestic and childcare responsibilities. This study also reports sector effects, with more traditional industries such as manufacturing and construction exhibiting entrenched gender biases. The results highlight the need to integrate considerations of inequality into theoretical frameworks that aim to understand and conceptualise the uptake of green jobs.</p>
    </sec>
  </body>
</article>