The link between intellectual property rights, innovation, and growth: A meta-analysis

Barriers to radical innovation in research institutions in China

“…Governments, by means of policies and regulations, are expected to foster innovative environments and nurture entrepreneurship, as well as helping in the development of a mature national ecosystem and encouraging interactions between all the players in the national innovation system (government, universities and manufacturing industries) (Borges et al , 2020; Neves et al , 2021).…”

Nonlinear System Simulation and Forecasting of Regional Technology Innovation Using System Dynamics Method

“…Using SPSS to analysis the data of Guangdong Free Trade Zone from year 2010 to year 2019, the equations of the model are built and shown as follows: (01) FDI = ACTIVE INITIAL (IF THEN ELSE) Regional open-up policy >0, RANDOM UNIFORM (0.9, 1.1, 1) *(42100.5* regional IPR Protection Index -6.33* RTI capability -388100), 0), +06) (02) OFDI = ACTIVE INITIAL (IF THEN ELSE) Regional open-up policy >0, RANDOM UNIFORM (0.8,1.4, 2) *(8* RTI capability +130000), 0), 227800) (03) RTI capability increment =6200* RTI efficiency -31500 (04) RTI capability = INTEG (RTI capability increment, 14711) (05) Regional R&D investment =113.8* Regional intellectual property protection index -6657 (06) Regional population = INTEG (birth population + immigrate populationdeath populationemigrate population, 10440) (07) Regional live patents quantity =regional population * live patents per 10000 people (08) FCD = FDI /regional GDP (09) ETD = IPR royalties trade deficit /(regional R&D investment + IPR royalties trade deficit) (10) Regional GDP = Active Initial (0.242* live patents quantity +38717.1, 46013.1) (11) Regional IPR protection index = 63+0.000147* RTI capability ( 12) KAC = number of R&D personnel + number of research institutions (13) RTI efficiency = 2E-05 * regional GDP +0.5* incentive effect degree -0.0018* competitive effect degree +6.8 (14) Live invention patents per 10,000 people = 0.0792* knowledge quatity -5.72 (15) Incentive effect degree = ETD * regional IPR protection index (16) IPR royalties trade deficit = 0.5* regional population +4.1* FCD -0.002* RTI capability -5,280 (17) Knowledge quantity = SMOOTH (0.0068* KAC +2.8e-05*(FDI +OFDI) +50, 0.1) (18) Ratio of R&D personnel = (0.00265* regional R&D investment +1.686)/1000 (19) Number of R&D personnel = regional population * ratio of R&D personnel (20) Number of scientific research institution = 13.544* Regional R&D investment -11767.4 (21) Competitive effect degree = FCD * regional IPR protection index V. SIMULATION…”

“…Is IPR protection really improving the RTI, especially in regions that lag in technology? The answers given by current literatures are quite different [17][18][19][20][21][22][23][24].…”

Determinants of innovation outcomes: The role of institutional quality

“…A key insight from this literature is that the institutional configurations favourable to innovation are contingent on a range of factors including the country's level of economic development and its level of innovative capacity (Hudson and Minea, 2013;Anand et al, 2021). In this paper, we apply Unconditional Quantile Regression (UQR) analysis to understand how strong predictors of institutional quality identified by previous research (for summaries, see He and Tian (2020); Neves et al (2021)), vary across the distribution of innovation outcomes rather than only the mean, as is done conventionally (see Becheikh et al (2006)).…”

“…Although there is a growing emphasis on 'soft' or informal institutions including social mores (Donges et al, 2021;Adhikari and Agrawal, 2016) and corporate culture (Sunder et al, 2017), the majority of studies continue to focus on formal economic and political institutions (Acemoglu and Robinson, 2012) including the development of finance and product markets (Moshirian et al, 2021), human resource endowments (Cinnirella and Streb, 2017;Anelli et al, 2020), government policies, regulations and laws, including industrial policy (Cheah and Ho, 2020), competition policy (Anderson et al, 2021), fiscal incentives (Mukherjee et al, 2017), trade policy (Akcigit et al, 2018), and, most notably, the protection of property rights in general and intellectual property rights (IPR) in particular. Indeed, the relationship between the strength of IPR systems and innovation outcomes remains controversial (Sweet and Eterovic, 2019;Woo et al, 2015;Neves et al, 2021). This issue is exacerbated by the reliance of most previous studies on indices (see, for example, Ginarte and Park (1997); Park (2008)) or empirical models employing count data variables such as laws and reforms relating to IPR systems (Allred and Park, 2007;Chen and Puttitanun, 2005;Kanwar and Evenson, 2003).…”