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Profiling the unemployed : a review of OECD experiences and implications for emerging economics

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  • Loxha, Artan
  • Morgandi, Matteo

Abstract

This paper takes stock of methods to profile the unemployed in public employment services (PESs) in OECD countries, in order to single out suitable approaches for PES in emerging economies. Profiling should enable PESs to segment jobseekers into groups with similar risk of work-resumption, and in turn to determine their level of access to different levels of treatment. In our framework PESs rely to a varying extent on (i) case worker discretion and on (ii) data-intensive approaches. On one hand of the spectrum, PESs may allocate interventions on a first-come-first-serve basis according to broad eligibility criteria (age, unemployment duration). This is likely to either induce deadweight loss or to delay treatment. Most often case managers’ judgment, steered by qualitative guidelines, also plays a role. In this case outcomes depend strongly on the available time and capacity of case managers. An alternative approach is to exploit data about jobseekers to determine the probability of work-resumption according to a statistical model, which then allows the identification of customers most likely to need active labor market interventions. We argue that for PES in emerging economies that show limited case management experience and high customer load, statistical profiling could be a suitable tool to maximize the impact of their scarce resources.

Suggested Citation

  • Loxha, Artan & Morgandi, Matteo, 2014. "Profiling the unemployed : a review of OECD experiences and implications for emerging economics," Social Protection Discussion Papers and Notes 91051, The World Bank.
  • Handle: RePEc:wbk:hdnspu:91051
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    References listed on IDEAS

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    1. O'Connell, Philip J. & McGuinness, Seamus & Kelly, Elish, 2010. "A Statistical Profiling Model of Long-Term Unemployment Risk in Ireland," Papers WP345, Economic and Social Research Institute (ESRI).
    2. Stefanie Behncke & Markus Frölich & Michael Lechner, 2006. "Statistical Assistance for Programme Selection - For a Better Targeting of Active Labour Market Policies in Switzerland," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 4(02), pages 61-68, July.
    3. Stefanie Behncke & Markus Frölich & Michael Lechner, 2006. "Statistical Assistance for Programme Selection - For a Better Targeting of Active Labour Market Policies in Switzerland," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 4(2), pages 61-68, 07.
    4. Randall W. Eberts & Christopher J. O'Leary & Stephen A. Wandner (ed.), 2002. "Targeting Employment Services," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number tes, December.
    5. O'Connell, Philip J. & McGuinness, Seamus & Kelly, Elish & Walsh, John R., 2009. "National Profiling of the Unemployed in Ireland," Research Series, Economic and Social Research Institute (ESRI), number RS10.
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    Cited by:

    1. Apella,Ignacio Raul & Blanco, Gastón, 2015. "Effectiveness of targeting mechanisms utilized in social protection programs in Bolivia," Social Protection and Labor Policy and Technical Notes 100363, The World Bank.
    2. Kerstin Bachberger-Strolz, 2020. "Profiling, Targeting, Algorithmen, künstliche Intelligenz – über die Irrwege einer Debatte in der Arbeitsmarktpolitik," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 46(3), pages 329-363.
    3. World Bank Group, 2016. "Activation and Public Employment Services in Poland," World Bank Publications - Reports 29831, The World Bank Group.
    4. World Bank Group, 2016. "Kazakhstan Jobs Strategy," World Bank Publications - Reports 27266, The World Bank Group.
    5. Anne E Green, 2017. "Implications of technological change and austerity for employability in urban labour markets," Urban Studies, Urban Studies Journal Limited, vol. 54(7), pages 1638-1654, May.
    6. Körtner, John & Bonoli, Giuliano, 2021. "Predictive Algorithms in the Delivery of Public Employment Services," SocArXiv j7r8y, Center for Open Science.

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