Abstract
This article focuses on the cross-country distribution of mining patent families during the period of 1970 − 2018. Alternatives measures of concentration indicate that only a few countries account for most mining inventions (e.g., China, Germany, Japan, the United States), and that the composition of such countries has remained relatively stable over time. This is also true for patent families of all technology fields. On the other hand, the evidence shows that those countries relatively specialized in mining technologies do not necessarily have a high share of mining inventions (e.g., Peru and Indonesia). These stylized facts are complemented with panel-regression models of the number of mining patent families—distinguishing between patented inventions and utility models—-, of mining patent-family ranks, and of relative specialization in mining. The empirical findings show that important drivers of mining patent families are mineral prices and production, family features, and the number of patent families of all technology fields (i.e., overall inventive performance). Moreover, the evidence shows that increments in mineral rents/GDP have a positive impact on the likelihood of relative specialization in some mining technologies, such as Blasting, Mining, and Processing.
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Notes
The World Bank defines mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.
In related literature, Bickenbach, Bode, and Krieger‐Boden (2013) show that the difference between absolute and relative Theil localization indices of economic activity depends only on how strongly the patterns of industrial specialization and of regional concentration of the aggregate economy differ from the corresponding references.
The original WIPO mining database included about 45,000 records that did not have any information on country of origin and assignee type (business, research center, educational institution, or individual). These were deleted. In addition, many records had missing information on country of origin, which was entered manually when possible. This task was relatively simple for educational institutions and businesses (typically, headquarter location was considered). Missing information on individuals was kept this way to avoid possible errors.
Computations and graphs are obtained using the Stata lorenz routine developed by Jann op cit.
Patent rights are essentially territorial, that is, they apply only in the jurisdiction of the patent office that grants the right.
A single filing of a PCT application is made with a Receiving Office (RO) in one language. It then results in a search performed by an International Searching Authority (ISA), accompanied by a written opinion about the patentability of the invention. Ultimately, the grating of patents remains under the control of national and regional offices due to the territorial nature of patent protection.
We rule out any collinearity issues among the explanatory variables due to the approximately linear association between the mining and all-technology ranks (Fig. 3). Indeed, the mean variance inflation factor (VIF) for the mining and all-technology ranks and lagged mineral rents/GDP is 3.85, whereas none of the individual VIFs exceed 6. In practice, VIFs greater than 10 are considered problematic (e.g., Greene 2018, pages 94–95). Similarly, the mean VIF for the mining and all-technology ranks and lagged log mineral production is 3.95. Once again, the individual VIFs do not exceed 6.
The RSI for each mining technology is computed as \({\text{log}}_{10} \left( {\frac{{{\raise0.7ex\hbox{${{\text{P}}_{{\text{C}}}^{{{\text{MT}}_{{\text{i}}} }} }$} \!\mathord{\left/ {\vphantom {{{\text{P}}_{{\text{C}}}^{{{\text{MT}}_{{\text{i}}} }} } {{\text{P}}_{{\text{C}}}^{{\text{M}}} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\text{P}}_{{\text{C}}}^{{\text{M}}} }$}}}}{{{\raise0.7ex\hbox{${{\text{P}}_{{\text{W}}}^{{{\text{MT}}_{{\text{i}}} }} }$} \!\mathord{\left/ {\vphantom {{{\text{P}}_{{\text{W}}}^{{{\text{MT}}_{{\text{i}}} }} } {{\text{P}}_{{\text{W}}}^{{\text{M}}} }}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{${{\text{P}}_{{\text{W}}}^{{\text{M}}} }$}}}}} \right)\) where \({\text{P}}_{{\text{C}}}^{{{\text{MT}}_{{\text{i}}} }}\) and \({\text{P}}_{{\text{W}}}^{{{\text{MT}}_{{\text{i}}} }}\) represent the number of patent families in mining technology (MT) ‘i’ filed by country C and across the world, respectively, whereas \({\text{P}}_{\text{C}}^{\text{M}}\) and \({\text{P}}_{\text{W}}^{\text{M}}\) are the mining patent families filed by country C and across the world, respectively.
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Funds provided by Programa de Apoyo a la Investigación (PAI)-UAI, Grant DII_IND_2020_06, are greatly acknowledged.
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Appendix
Robustness check: time sub-periods. See Tables
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Fernandez, V. Cross-country concentration and specialization of mining inventions. Scientometrics 126, 6715–6759 (2021). https://doi.org/10.1007/s11192-021-04044-4
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DOI: https://doi.org/10.1007/s11192-021-04044-4