nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2016‒08‒14
two papers chosen by
Giovanni Ramello
Università degli Studi del Piemonte Orientale “Amedeo Avogadro”

  1. Global spread of pharmaceutical patent protections: micro evidence from the international equivalents of the drug patents in Japan By OKADA, Yoshimi; NAGAOKA, Sadao
  2. Score-driven dynamic patent count panel data models By Escribano, Álvaro; Blazsek, Szabolcs

  1. By: OKADA, Yoshimi; NAGAOKA, Sadao
    Abstract: We investigate the global spread of pharmaceutical patent protections as acquired by firms, based on a novel global patent database for all significant medical drugs introduced in Japan. It gives us the propensity of filing and grant rate for each country for the granted patents in Japan. Major findings are the following. Both the filing propensity to and the grant rate of major Asian countries approached those of the OECD economies by the early 2000s for chemical substance inventions. However, there still exists substantial heterogeneity with respect to the other drug inventions: crystal, use, formulation or combination, suggesting a significant future room for international harmonization of patent granting standard. We found clear evidence for policy impact on the spread of protections for the two largest non-OECD economies. The Patent Law reform in China in 1993 had an immediate and significant impact on patent filing propensity to China (25 percentage points increase) well before it becoming a WTO member in late 2001. Furthermore, the mailbox application system in India had a substantial effect: the filing propensity reached 80 percent of the number of corresponding EP patent applications around year 2000, well before the year of TRIPS implementation for drug patents.
    Keywords: pharmaceutical patent, chemical substance patent, TRIPS Agreement, India, China, propensity of patent filing, grant rate
    JEL: O34 O38 K29
    Date: 2016–07
  2. By: Escribano, Álvaro; Blazsek, Szabolcs
    Abstract: This paper suggests new Dynamic Conditional Score (DCS) count panel data models. We compare the statistical performance of static model, finite distributed lag model, exponential feedback model and different DCS count panel data models. For DCS we consider random walk and quasi-autoregressive formulations of dynamics. We use panel data for a large cross section of United States firms for period 1979 to 2000. We estimate models by using the Poisson quasi-maximum likelihood estimator with fixed effects. The estimation results and diagnostics tests suggest that the statistical performance of DCS-QAR is superior to that of alternative models.
    Keywords: quasi-maximum likelihood; dynamic conditional score; count panel data; research and development
    JEL: O3 C52 C51 C35 C33
    Date: 2016–07

This nep-ipr issue is ©2016 by Giovanni Ramello. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.