Redistributive innovation policy, inequality, and efficiency

Basu, Parantap; Getachew, Yoseph

doi:10.1111/jpet.12386

Cited by 5 publications

(7 citation statements)

References 51 publications

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“…The input in the whole process involves many indicators such as talent, capital [36], knowledge, technology and information. Drawing lessons from the practices of [7,8,37], this paper considers the investment indicators selection based on three aspects, talent investment, capital investment and energy input, and uses the full-time equivalent of R&D personnel in industrial enterprises above the designated size, the stock of internal expenditure and the total energy consumption to characterise three indicators of each province, respectively. Meanwhile, the R&D expenditure is accounted for by the perpetual inventory method [38]:…”

Section: Index Selection and Data Sourcesmentioning

confidence: 99%

“…Innovation activities often require a great amount of intellectual resources and material capital. Economically developed areas can gather many outstanding talents and rich funds, so enterprises usually have a more vital drive for technological innovation under this condition [7]. To a certain extent, the intensification of market competition can force enterprises positively to carry out innovation activities to gain more market advantage.…”

Section: Index Selection and Data Sourcesmentioning

confidence: 99%

“…The purpose of this study is to fill the gap between quality and efficiency of TIE by considering the proportion changes between input and output of the production process in the framework of TIE. It is relevant to consider the Schumpeter theory of technological innovation "quantity" under the background of innovation-driven strategy [7,8]. However, they paid little attention to innovation quality and efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

et al. 2022

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Innovation is the engine and accelerator that drives high-quality economic and enterprise development. In recent years, the output of scientific and technological innovation in China has been high, but the phenomenon of low efficiency and low quality of innovation occurs frequently. In this study, first, technological innovation efficiency (TIE) was measured. Then, a dynamic evaluation and analysis of spatial-temporal characteristics of efficiency were performed. Lastly, the driving factors of innovation efficiency were explored. TIE was calculated dynamically in 30 provinces of China from 2011 to 2019 based on the improved super-efficiency SBM-DEA model. Then, the kernel density estimation method was adopted to analyse the spatial-temporal differentiation characteristics and dynamic evolution process of provincial efficiency. The findings confirm that from 2011 to 2019, the top five provinces for TIE in China were Beijing (1.0), Shanghai (0.96), Hainan (0.96), Jilin (0.94) and Tianjin (0.91). The provinces with lowest average efficiency were Qinghai (0.77), Ningxia (0.73) and Inner Mongolia (0.73). The significant differences in the level of technological innovation in different regions were caused by the long-term and in-depth implementation of the government’s strategy of revitalising science and driving innovation in parts of areas. The findings of kernel function confirm that the TIE in most parts of China was gradually polarised. Furthermore, the results show that for every 1 unit of government R&D funding support, the average marginal utility of the expected TIE will reach 0.192, which is more significant in the central and western regions. On this basis, combined with environmental factors of innovation market, infrastructure, financing and enterprise innovation potential, the article also extracts the driving factors that affect the differences in provincial efficiency. The findings provide a reference for guiding provinces to carry out innovation activities independently and improve innovation quality and efficiency.

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Section: Index Selection and Data Sourcesmentioning

confidence: 99%

Section: Index Selection and Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

et al. 2022

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“…According to [6], public investment in innovation and development (R&D) could boost growth and well-being, in the productivity associated with a country or region, according to [7], for [8,9]. Additionally, according to [10], science and technology innovation factors have a strong multiplier effect on regional economic development; hence, the input of new type of a practical patent significantly improves the regional economic level in the current period, and the lag phase effect is further improved.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Growth in the Number of Patents Granted and Its Effect over the Level of Growth of the Countries: An Econometric Estimation of the Mixed Model Approach

2022

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The purpose of this paper is to identify and measure the impact of the variables affecting the increase in the number of patents as a way to advance the development of policies in countries in terms of sustainable development based on innovation. An econometric estimation of a mixed model was used to measure the impact of patent development on the countries analyzed in this research. The findings suggest that economies that have some relevance in research and development have increasing numbers of patents. Thus, the empirical findings relate to the theoretical models that state that comparative advantages may be dynamic due to technological innovation. Finally, this paper shows that innovation is a central parameter to engage in research and develop a knowledge-based economy.

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“…Basu and Getachew () also consider a heterogeneous‐agent growth model where agents are heterogeneous in their ability to generate knowledge.…”

mentioning

confidence: 99%

Competition, patent protection, and innovation with heterogeneous firms in an endogenous market structure

Suzuki

2019

J Public Economic Theory

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This paper revisits the relationship between competition and innovation by incorporating the heterogeneity of R&D efficiency across firms and an endogenous market structure in a dynamic general equilibrium model. Using an analytically tractable model, we show that competition and innovation can have either an inverted-U or a negative relationship, as reported by several empirical studies. Furthermore, we show that the effect of strengthening patent protection on innovation depends on the competition level. In particular, we find a complementary relationship between competition policy and the strengthening of patent protection. 4

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Redistributive innovation policy, inequality, and efficiency

Cited by 5 publications

References 51 publications

Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

Analysis of the Growth in the Number of Patents Granted and Its Effect over the Level of Growth of the Countries: An Econometric Estimation of the Mixed Model Approach

Competition, patent protection, and innovation with heterogeneous firms in an endogenous market structure

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