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

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  • Li, Wenhua
  • Adachi, Tsuyoshi

Abstract

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
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    References listed on IDEAS

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    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).
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    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.
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    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.

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