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Asymmetric Effects of National-based Active Labour Market Policies

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  • Carlo Altavilla

    (University of Naples Parthenope, CESifo and CSEF)

  • Floro Ernesto Caroleo

    (University of Naples Parthenope)

Abstract

Labour market policies settled at national level imply a “one-size-fits-all” labour market strategy. This strategy might not sufficiently take into account region-specific economic structures. We employ a panel factor-augmented vector autoregression (FAVAR) to evaluate whether active labour market programmes (ALMPs) might asymmetrically affect labour markets at regional level in a data-rich environment. The paper focuses on Italian regions. Our results suggest that while in the South employment is mainly driven by social and economic context variables, in the North the employment dynamics is significantly explained by policy interventions. Finally, we suggest two main policy implications. First, the success of active policies depends on the regional labour market conditions. Second, policymakers should adjust labour policy strategy to the regional economic structure

Suggested Citation

  • Carlo Altavilla & Floro Ernesto Caroleo, 2011. "Asymmetric Effects of National-based Active Labour Market Policies," CSEF Working Papers 293, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
  • Handle: RePEc:sef:csefwp:293
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    Cited by:

    1. Goulas, Eleftherios & Zervoyianni, Athina, 2018. "Active labour-market policies and output growth: Is there a causal relationship?," Economic Modelling, Elsevier, vol. 73(C), pages 1-14.
    2. Elzbieta Antczak & Ewa Galecka-Burdziak & Robert Pater, 2016. "Efficiency in spatially disaggregated labour market matching," Working Papers 2016-010, Warsaw School of Economics, Collegium of Economic Analysis.
    3. Wapler, Rüdiger & Werner, Daniel & Wolf, Katja, 2014. "Active labour-market policies in Germany : do regional labour markets benefit?," IAB-Discussion Paper 201428, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    4. Wolfgang Dauth & Reinhard Hujer & Katja Wolf, 2016. "Do Regions Benefit from Active Labour Market Policies? A Macroeconometric Evaluation Using Spatial Panel Methods," Regional Studies, Taylor & Francis Journals, vol. 50(4), pages 692-708, April.
    5. M. Agovino & A. Rapposelli, 2017. "Speculation on a Flexicurity Index for Disabled People: The Italian Case," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 130(1), pages 389-414, January.
    6. repec:ces:ifodic:v:14:y:2016:i:3:p:19255698 is not listed on IDEAS
    7. Ana Millán & José María Millán & Concepción Román, 2016. "The Role of Start-up Incentives on Entrepreneurship Dynamics in a Post-Crisis Era: Evidence from European Countries," ifo DICE Report, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 14(03), pages 29-35, October.
    8. Elżbieta Antczak & Ewa Gałecka‐Burdziak & Robert Pater, 2019. "What Affects Efficiency In Labour Market Matching At Different Territorial Aggregation Levels In Poland?," Bulletin of Economic Research, Wiley Blackwell, vol. 71(2), pages 160-179, April.

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    More about this item

    Keywords

    Active Labour Market Policies; FAVAR.;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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