IDEAS home Printed from https://ideas.repec.org/p/aeb/wpaper/201804y2018.html
   My bibliography  Save this paper

Efficient bargaining versus Right to manage in the era of liberalization

Author

Listed:
  • Natasha Miaouli

    (Athens University of Economics and Business)

  • Panagiota Koliousi

    (Athens University of Economics and Business)

Abstract

We compare product and labour market liberalization under the two trade union bargaining models: the Right- to- Manage (RTM) model and the Efficient Bargaining (EB) model. The vehicle is a dynamic general equilibrium (DGE) model that incorporates two types of agents (capitalists and workers), imperfectly competitive product and labour markets. The model is solved numerically employing common parameter values and data from the euro area. A key message is that product market deregulation is favourable under any labour market structure while opting for labour market deregulation one should provide special attention to the structu- re of the labour market such as the bargaining system of unions. If the prevailing way of bargaining is the RTM model then restructuring both markets is beneficial for all agents.

Suggested Citation

  • Natasha Miaouli & Panagiota Koliousi, 2018. "Efficient bargaining versus Right to manage in the era of liberalization," Working Papers 201804, Athens University Of Economics and Business, Department of Economics.
  • Handle: RePEc:aeb:wpaper:201804:y:2018
    as

    Download full text from publisher

    File URL: http://www2.econ.aueb.gr/uploadfiles/AllDP052017
    File Function: Released version, 2018
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hall, Peter & Yao, Qiwei, 2003. "Inference in ARCH and GARCH models with heavy-tailed errors," LSE Research Online Documents on Economics 5875, London School of Economics and Political Science, LSE Library.
    2. Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2016. "Nonstationary GARCH with t-distributed innovations," Economics Letters, Elsevier, vol. 138(C), pages 19-21.
    3. Jianqing Fan & Lei Qi & Dacheng Xiu, 2014. "Quasi-Maximum Likelihood Estimation of GARCH Models With Heavy-Tailed Likelihoods," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 178-191, April.
    4. Peter Hall & Qiwei Yao, 2003. "Inference in Arch and Garch Models with Heavy--Tailed Errors," Econometrica, Econometric Society, vol. 71(1), pages 285-317, January.
    5. Christian Francq & Jean‐Michel Zakoïan, 2012. "Strict Stationarity Testing and Estimation of Explosive and Stationary Generalized Autoregressive Conditional Heteroscedasticity Models," Econometrica, Econometric Society, vol. 80(2), pages 821-861, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stelios Arvanitis, 2017. "Non-Emptyness of Stochastic Dominance Effiicient Sets via Stochastic Spanning," Working Papers 201710, Athens University Of Economics and Business, Department of Economics.
    2. Helen Caraveli & Ioannis Chatzigiatroudakis & Evangelos Paravalos, 2018. "Determinants of growth differences between Eastern and Southern EU countries: A panel-data approach," Working Papers 201803, Athens University Of Economics and Business, Department of Economics.
    3. Bitros, George C. & Nadiri, M. Ishaq, 2017. "Behavior of business investment in the USA under variable and proportional rates of replacement," MPRA Paper 80594, University Library of Munich, Germany.
    4. Stylianos G. Gogos & Dimitris Papageorgiou & Vanghelis Vassilatos, 2017. "Rent Seeking Activities and Aggregate Economic Performance - The Case of Greece," Working Papers 201712, Athens University Of Economics and Business, Department of Economics.
    5. Bertsatos, Georgios & Sakellaris, Plutarchos & Tsionas, Mike G., 2017. "Did the financial crisis affect the market valuation of large systemic U.S. banks?," Journal of Financial Stability, Elsevier, vol. 32(C), pages 115-123.
    6. George C. Bitros, 2017. "Still in the Woods," Working Papers 201711, Athens University Of Economics and Business, Department of Economics.
    7. Arvanitis, Stelios & Louka, Alexandros, 2017. "Stable limits for the Gaussian QMLE in the non-stationary GARCH(1,1) model," Economics Letters, Elsevier, vol. 161(C), pages 135-137.
    8. George C. Bitros, 2017. "Germany and Greece: A mapping of their great divide and its EU implications," Working Papers 201706, Athens University Of Economics and Business, Department of Economics.
    9. Ryoko Ito, 2016. "Asymptotic Theory for Beta-t-GARCH," Cambridge Working Papers in Economics 1607, Faculty of Economics, University of Cambridge.
    10. Li, Dong & Ling, Shiqing & Zhu, Ke, 2016. "ZD-GARCH model: a new way to study heteroscedasticity," MPRA Paper 68621, University Library of Munich, Germany.
    11. Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2016. "Nonstationary GARCH with t-distributed innovations," Economics Letters, Elsevier, vol. 138(C), pages 19-21.
    12. Rasmus Søndergaard Pedersen & Anders Rahbek, 2015. "Nonstationary ARCH and GARCH with t-distributed Innovations," CREATES Research Papers 2015-27, Department of Economics and Business Economics, Aarhus University.
    13. Li, Dong & Zhang, Xingfa & Zhu, Ke & Ling, Shiqing, 2018. "The ZD-GARCH model: A new way to study heteroscedasticity," Journal of Econometrics, Elsevier, vol. 202(1), pages 1-17.
    14. Conrad, Christian & Mammen, Enno, 2016. "Asymptotics for parametric GARCH-in-Mean models," Journal of Econometrics, Elsevier, vol. 194(2), pages 319-329.
    15. Javed Farrukh & Podgórski Krzysztof, 2017. "Tail Behavior and Dependence Structure in the APARCH Model," Journal of Time Series Econometrics, De Gruyter, vol. 9(2), pages 1-48, July.
    16. Wang, Hui & Pan, Jiazhu, 2014. "Normal mixture quasi maximum likelihood estimation for non-stationary TGARCH(1,1) models," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 117-123.
    17. Mo Zhou & Liang Peng & Rongmao Zhang, 2021. "Empirical likelihood test for the application of swqmele in fitting an arma‐garch model," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(2), pages 222-239, March.
    18. Wang, Xuqin & Li, Muyi, 2023. "Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 184(C).
    19. Aguilar, Mike & Hill, Jonathan B., 2015. "Robust score and portmanteau tests of volatility spillover," Journal of Econometrics, Elsevier, vol. 184(1), pages 37-61.
    20. Jungsik Noh & Sangyeol Lee, 2016. "Quantile Regression for Location-Scale Time Series Models with Conditional Heteroscedasticity," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 700-720, September.

    More about this item

    Keywords

    J5; I1;

    JEL classification:

    • J5 - Labor and Demographic Economics - - Labor-Management Relations, Trade Unions, and Collective Bargaining
    • I1 - Health, Education, and Welfare - - Health

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aeb:wpaper:201804:y:2018. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Katerina Michailidou (email available below). General contact details of provider: https://edirc.repec.org/data/auebugr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.