IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-23-00210.html
   My bibliography  Save this article

Construction, extractive and mining global investment intentions: a network analysis

Author

Listed:
  • Gilson Silva jr

    (Federal University of Santa Catarina - Brazil)

  • Jose-Maria Silveira

    (STATE UNIVERSITY OF CAMPINAS - BRAZIL)

  • Henrique Richter

    (CARAVELA CONSULTING)

Abstract

We characterized the networks of greenfield investment intentions before and after the 2008 global financial crises in two key sectors, construction and extractive and mining, using network indicators (cohesion and centre periphery) and blockmodels. The main results are i) the number of lines informs extract and mining network is substantially thinner than construction, ii) the 2008 crises impact on networks was substantially higher in extractive and mining than in construction, which suggests that construction web of believes was more resilient to exogenous shock, iii) network graphs show us deep change in web configuration after crises. We didn't find any study using that methodology to analyze greenfield investment intentions, particularly in those sectors.

Suggested Citation

  • Gilson Silva jr & Jose-Maria Silveira & Henrique Richter, 2024. "Construction, extractive and mining global investment intentions: a network analysis," Economics Bulletin, AccessEcon, vol. 44(2), pages 586-600.
  • Handle: RePEc:ebl:ecbull:eb-23-00210
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2024/Volume44/EB-24-V44-I2-P44.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daron Acemoglu & Ufuk Akcigit & William Kerr, 2016. "Networks and the Macroeconomy: An Empirical Exploration," NBER Macroeconomics Annual, University of Chicago Press, vol. 30(1), pages 273-335.
    2. Daron Acemoglu & Munther A. Dahleh & Ilan Lobel & Asuman Ozdaglar, 2011. "Bayesian Learning in Social Networks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 78(4), pages 1201-1236.
    3. Davide Castellani & Katiuscia Lavoratori, 2020. "The lab and the plant: Offshore R&D and co-location with production activities," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 51(1), pages 121-137, February.
    4. Gale, Douglas & Kariv, Shachar, 2003. "Bayesian learning in social networks," Games and Economic Behavior, Elsevier, vol. 45(2), pages 329-346, November.
    5. Jing Du & Dong Zhao & Ou Zhang, 2019. "Impacts of human communication network topology on group optimism bias in Capital Project Planning: a human-subject experiment," Construction Management and Economics, Taylor & Francis Journals, vol. 37(1), pages 44-60, January.
    6. Peter M. DeMarzo & Dimitri Vayanos & Jeffrey Zwiebel, 2003. "Persuasion Bias, Social Influence, and Unidimensional Opinions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(3), pages 909-968.
    7. Acemoglu, Daron & Ozdaglar, Asuman & ParandehGheibi, Ali, 2010. "Spread of (mis)information in social networks," Games and Economic Behavior, Elsevier, vol. 70(2), pages 194-227, November.
    8. Cristina Poleacovschi & Amy Javernick-Will & Tony Tong, 2017. "The link between knowledge sharing connections and employee time savings: A social network analysis," Construction Management and Economics, Taylor & Francis Journals, vol. 35(8-9), pages 455-467, September.
    9. Matthew O. Jackson, 2014. "Networks in the Understanding of Economic Behaviors," Journal of Economic Perspectives, American Economic Association, vol. 28(4), pages 3-22, Fall.
    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. Buechel, Berno & Hellmann, Tim & Klößner, Stefan, 2015. "Opinion dynamics and wisdom under conformity," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 240-257.
    2. Jadbabaie, Ali & Molavi, Pooya & Sandroni, Alvaro & Tahbaz-Salehi, Alireza, 2012. "Non-Bayesian social learning," Games and Economic Behavior, Elsevier, vol. 76(1), pages 210-225.
    3. Rusinowska, Agnieszka & Taalaibekova, Akylai, 2019. "Opinion formation and targeting when persuaders have extreme and centrist opinions," Journal of Mathematical Economics, Elsevier, vol. 84(C), pages 9-27.
    4. Daron Acemoğlu & Giacomo Como & Fabio Fagnani & Asuman Ozdaglar, 2013. "Opinion Fluctuations and Disagreement in Social Networks," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 1-27, February.
    5. Fernandes, Marcos R., 2023. "Confirmation bias in social networks," Mathematical Social Sciences, Elsevier, vol. 123(C), pages 59-76.
    6. Foerster, Manuel, 2018. "Finite languages, persuasion bias, and opinion fluctuations," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 46-57.
    7. Syngjoo Choi & Edoardo Gallo & Shachar Kariv, 2015. "Networks in the laboratory," Cambridge Working Papers in Economics 1551, Faculty of Economics, University of Cambridge.
    8. Foerster, Manuel, 2019. "Dynamics of strategic information transmission in social networks," Theoretical Economics, Econometric Society, vol. 14(1), January.
    9. Ding, Huihui & Pivato, Marcus, 2021. "Deliberation and epistemic democracy," Journal of Economic Behavior & Organization, Elsevier, vol. 185(C), pages 138-167.
    10. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    11. Syngjoo Choi & Sanjeev Goyal & Frederic Moisan & Yu Yang Tony To, 2023. "Learning in Networks: An Experiment on Large Networks with Real-World Features," Management Science, INFORMS, vol. 69(5), pages 2778-2787, May.
    12. John Barrdear, 2014. "Peering into the mist: social learning over an opaque observation network," Discussion Papers 1409, Centre for Macroeconomics (CFM).
    13. Michel Grabisch & Antoine Mandel & Agnieszka Rusinowska & Emily Tanimura, 2015. "Strategic influence in social networks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01158168, HAL.
    14. Battiston, Pietro & Stanca, Luca, 2015. "Boundedly rational opinion dynamics in social networks: Does indegree matter?," Journal of Economic Behavior & Organization, Elsevier, vol. 119(C), pages 400-421.
    15. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2014. "Information percolation in segmented markets," Journal of Economic Theory, Elsevier, vol. 153(C), pages 1-32.
    16. Berno Buechel & Stefan Klößner & Martin Lochmüller & Heiko Rauhut, 2020. "The strength of weak leaders: an experiment on social influence and social learning in teams," Experimental Economics, Springer;Economic Science Association, vol. 23(2), pages 259-293, June.
    17. Förster, Manuel & Mauleon, Ana & Vannetelbosch, Vincent J., 2016. "Trust and manipulation in social networks," Network Science, Cambridge University Press, vol. 4(2), pages 216-243, June.
    18. Brandts, Jordi & Giritligil, Ayça Ebru & Weber, Roberto A., 2015. "An experimental study of persuasion bias and social influence in networks," European Economic Review, Elsevier, vol. 80(C), pages 214-229.
    19. Duffie, Darrell & Malamud, Semyon & Manso, Gustavo, 2015. "Reprint of: Information percolation in segmented markets," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 838-869.
    20. Jakob Grazzini & Domenico Massaro, 2021. "Dispersed information, social networks, and aggregate behavior," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 1129-1148, July.

    More about this item

    Keywords

    investment; intentions; construction; extractive and mining; network analysis;
    All these keywords.

    JEL classification:

    • Q3 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation
    • G1 - Financial Economics - - General Financial Markets

    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:ebl:ecbull:eb-23-00210. 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: John P. Conley (email available below). General contact details of provider: .

    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.