IDEAS home Printed from https://ideas.repec.org/a/zbw/espost/240933.html
   My bibliography  Save this article

All Quiet on the Protest Scene? Repertoires of Contention and Protest Actors During the Great Recession

Author

Listed:
  • Hunger, Sophia
  • Lorenzini, Jasmine

Abstract

The choice of specific action repertoires allows protesters to increase their visibility and eventually their success. A rise in protest, i.e. a protest wave, often comes with a qualitative expansion of the conflict, which can take two forms: changes in the action repertoire and a growing diversity of involved actors. In this chapter, we examine the types of protest and the types of actors over time. In so doing, we ask whether and how the Great Recession transformed customary action repertoires in southern, north-western, and eastern Europe. Hence, we show variations in the use of commonplace action forms, i.e. demonstrations, strikes, and confrontational and violent actions. We find that demonstrations and strikes remain the dominant form of protest across regions and time periods, while transformations in the action repertoire of contention, in the form of violent events, took place only in some parts of the south and were short lived. Lastly, we turn to actors and we show that protest events increasingly feature social groups without formal organizational structures. We conclude by arguing that contention repertoires remained largely unaffected by the Great Recession; demonstrations were and remained the prevailing form of protest in all three regions during the whole period under study.

Suggested Citation

  • Hunger, Sophia & Lorenzini, Jasmine, 2020. "All Quiet on the Protest Scene? Repertoires of Contention and Protest Actors During the Great Recession," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 104-127.
  • Handle: RePEc:zbw:espost:240933
    DOI: 10.1017/9781108891660.006
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/240933/1/Full-text-chapter-Hunger-et-al-All-quiet-on.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.1017/9781108891660.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    2. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    3. Nils B. Weidmann, 2016. "A Closer Look at Reporting Bias in Conflict Event Data," American Journal of Political Science, John Wiley & Sons, vol. 60(1), pages 206-218, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kurt Vandaele, 2021. "Applauded ‘nightingales’ voicing discontent. Exploring labour unrest in health and social care in Europe before and since the COVID-19 pandemic," Transfer: European Review of Labour and Research, , vol. 27(3), pages 399-411, August.

    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. Borbáth, Endre & Hutter, Swen, 2020. "Are Political Parties Recapturing the Streets of Europe?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 251-272.
    2. Bremer, Björn & Hutter, Swen & Kriesi, Hanspeter, 2020. "Electoral Punishment and Protest Politics in Times of Crisis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 227-250.
    3. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    4. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    5. Hou-Tai Chang & Ping-Huai Wang & Wei-Fang Chen & Chen-Ju Lin, 2022. "Risk Assessment of Early Lung Cancer with LDCT and Health Examinations," IJERPH, MDPI, vol. 19(8), pages 1-12, April.
    6. Wang, Qiao & Zhou, Wei & Cheng, Yonggang & Ma, Gang & Chang, Xiaolin & Miao, Yu & Chen, E, 2018. "Regularized moving least-square method and regularized improved interpolating moving least-square method with nonsingular moment matrices," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 120-145.
    7. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    8. Lucian Belascu & Alexandra Horobet & Georgiana Vrinceanu & Consuela Popescu, 2021. "Performance Dissimilarities in European Union Manufacturing: The Effect of Ownership and Technological Intensity," Sustainability, MDPI, vol. 13(18), pages 1-19, September.
    9. Candelon, B. & Hurlin, C. & Tokpavi, S., 2012. "Sampling error and double shrinkage estimation of minimum variance portfolios," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 511-527.
    10. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.
    11. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    12. Shuichi Kawano, 2014. "Selection of tuning parameters in bridge regression models via Bayesian information criterion," Statistical Papers, Springer, vol. 55(4), pages 1207-1223, November.
    13. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    14. Enrico Bergamini & Georg Zachmann, 2020. "Exploring EU’s Regional Potential in Low-Carbon Technologies," Sustainability, MDPI, vol. 13(1), pages 1-28, December.
    15. Qianyun Li & Runmin Shi & Faming Liang, 2019. "Drug sensitivity prediction with high-dimensional mixture regression," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-18, February.
    16. Jung, Yoon Mo & Whang, Joyce Jiyoung & Yun, Sangwoon, 2020. "Sparse probabilistic K-means," Applied Mathematics and Computation, Elsevier, vol. 382(C).
    17. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    18. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    19. Soave, David & Lawless, Jerald F., 2023. "Regularized regression for two phase failure time studies," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    20. Moharil Janhavi & May Paul & Gaile Daniel P. & Blair Rachael Hageman, 2016. "Belief propagation in genotype-phenotype networks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(1), pages 39-53, March.

    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:zbw:espost:240933. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.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.