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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.v10i4.3052</article-id>
      <article-id pub-id-type="publisher-id">10:3:40</article-id>
      <title-group>
        <article-title>Socio-Emotional Characteristics in Early Childhood and Offending Behaviour in
          Adolescence</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Gehrsitz</surname>
            <given-names initials="M">Markus</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
          <xref ref-type="aff" rid="affil-2">2</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Grant</surname>
            <given-names initials="S">Sam</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>McIntyre</surname>
            <given-names initials="S">Stuart</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 Strathclyde, Glasgow, United
        Kingdom</institution></aff>
      <aff id="affil-2"><label>2</label><institution>Institute of Labor Economics (IZA), Bonn,
        German</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>3052</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/3052">This article is available from the
        IJPDS website at: https://ijpds.org/article/view/3052</self-uri>
    </article-meta>
  </front>
  <body>
    <sec>
      <title>Objectives</title>
      <p>We ask whether supervision and license conditions for offenders reduce re-offending. Our goal is to leverage novel administrative data to estimate the causal effect of the 2014 Offender Rehabilitation Act (ORA), a government flagship policy that introduced supervision and license conditions for offenders released from short prison sentences.</p>
    </sec>
    <sec>
      <title>Methods</title>
      <p>We deploy a regression discontinuity design exploiting that ORA quasi-randomly assigned offenders to supervision by probation services upon release, depending on whether they committed the offense that led to incarceration before/after a cut-off date. We obtain a causal effect by combining our quasi-experimental method with administrative data from the Ministry of Justice. By linking these data, we construct offender histories and journeys through the criminal justice system, from initial offense, to court appearance, to prison spell, to probationary period (w/wo supervision and license conditions), to – in many cases – re-offending.</p>
    </sec>
    <sec>
      <title>Results</title>
      <p>We demonstrate that linkage and analysis for policy evaluation of complex administrative dataset is feasible. We identify the universe of prisoners who were affected by the ORA reform. By linking their prison records with probation records, we verify that they were indeed subject to supervision and license conditions. Via linkage to the court records we identify the type of offense and sentence they received. We are also able to link them to further court appearances. This allows us to not only construct detailed offender histories but also re-offending trajectories at the individual level.</p>
      <p>A preliminary analysis of this novel data suggests that supervision and license conditions may reduce re-offending but only in the first few weeks after release from prison.</p>
    </sec>
    <sec>
      <title>Conclusion</title>
      <p>Combining novel administrative data with a credible quasi-experimental approach, we show whether offender supervision and license conditions are an effective and efficient means of decreasing re-offending. We demonstrate that the use of detailed administrative data can be effectively used to evaluate policy interventions in the criminal justice space.</p>
    </sec>
  </body>
</article>