IDEAS home Printed from
   My bibliography  Save this article

Quantitative estimation of resource nationalism by binary choice logit model for panel data


  • Li, Wenhua
  • Adachi, Tsuyoshi


Since the beginning of this century, another new wave of resource nationalism with unprecedented high frequency and wide area had been profoundly affecting natural resources industry's room of development. It attracted increased attention among researchers and business investors. Numerous descriptive case studies on the issue indicated us how specific economic, political and other factors interacted and further drove resource nationalism strategy and policy making. However, as far as we know, none of them were able to quantitatively dig out the mutual factors across countries that work uniformly in producing resource nationalism. The objective of the study is quantifying significant factors dominating the occurrence of resource nationalism for important metal and energy resources producing countries at global level by binary choice logit model for panel data. Besides finding out the significant variables and their marginal effects to resource nationalism from 2000 to 2013, the regression helps predict up to 89 resource producing countries’, 5 types of base metals’, 4 types of precious metals’, and 3 types of energy resources’ probability of resource nationalism during 2003–2012. The study is a primary trial of researching on resource nationalism and provides some insights for theoretical building and genetic simulation on the issue.

Suggested Citation

  • Li, Wenhua & Adachi, Tsuyoshi, 2017. "Quantitative estimation of resource nationalism by binary choice logit model for panel data," Resources Policy, Elsevier, vol. 53(C), pages 247-258.
  • Handle: RePEc:eee:jrpoli:v:53:y:2017:i:c:p:247-258
    DOI: 10.1016/j.resourpol.2017.07.002

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Bartolucci, Francesco & Pigini, Claudia, 2017. "cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i07).
    2. J. M. Keynes, 1938. "Mr. Keynes's Consumption Function," The Quarterly Journal of Economics, Oxford University Press, vol. 53(1), pages 160-160.
    3. Francesco Bartolucci & Valentina Nigro, 2010. "A Dynamic Model for Binary Panel Data With Unobserved Heterogeneity Admitting a √n-Consistent Conditional Estimator," Econometrica, Econometric Society, vol. 78(2), pages 719-733, March.
    4. Reid W Click & Robert J Weiner, 2010. "Resource nationalism meets the market: Political risk and the value of petroleum reserves," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 41(5), pages 783-803, June.
    5. J. M. Keynes, 1938. "Mr. Keynes's Consumption Function: Reply," The Quarterly Journal of Economics, Oxford University Press, vol. 52(4), pages 708-709.
    6. Chang, Roberto & Hevia, Constantino & Loayza, Norman, 2018. "Privatization And Nationalization Cycles," Macroeconomic Dynamics, Cambridge University Press, vol. 22(2), pages 331-361, March.
    7. Humphreys, David, 2013. "New mercantilism: A perspective on how politics is shaping world metal supply," Resources Policy, Elsevier, vol. 38(3), pages 341-349.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Galina S. CHEBOTAREVA & Wadim STRIELKOWSKI & Viktor A. BLAGININ, 2019. "The renewable energy market: Companies’ development and profitability," Upravlenets, Ural State University of Economics, vol. 10(3), pages 58-69, July.


    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:eee:jrpoli:v:53:y:2017:i:c:p:247-258. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.