TY - JOUR
T1 - Cross-country concentration and specialization of mining inventions
AU - Fernandez, Viviana
N1 - Publisher Copyright:
© 2021, Akadémiai Kiadó, Budapest, Hungary.
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - C4index
KW - Gini coefficient
KW - Herfindahl–hirschman index
KW - Lorenz curve
KW - Panel-regression models
KW - Patent families
UR - http://www.scopus.com/inward/record.url?scp=85110536170&partnerID=8YFLogxK
U2 - 10.1007/s11192-021-04044-4
DO - 10.1007/s11192-021-04044-4
M3 - Article
AN - SCOPUS:85110536170
SN - 0138-9130
VL - 126
SP - 6715
EP - 6759
JO - Scientometrics
JF - Scientometrics
IS - 8
ER -