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A Multi-Regional Input–Output Model to Measure the Spatial Spillover of R&D Capital
Reallocating innovative capital elements can improve the growth of total factor productivity and promote high-quality economic development. The multi-regional multiplier model measures the spatial spillover effects of R&D capital to trace the interregional R&D flows and explore the engines of the longer-term economic growth in China. Results show that the direct R&D intensity in different regions is all concentrated in basic research sectors supported by government funds, and decreased from coastal areas to inland areas. Second, R&D gradually flowed from China’s coastal regions to inland regions, from upstream basic research sectors to downstream infrastructure construction sectors. Third, Guangdong, Jiangsu and Beijing are the main contributors, with R&D spillover intensities reaching 1.69%, 1.40%, and 1.37%, respectively. Xinjiang, Tibet, and Hainan are the main beneficiaries, with R&D inflow intensities reaching 0.49%, 0.53%, and 0.50%, respectively. Finally, the channel of R&D spatial spillover manifests a circular distribution and contact-type and jump-type modes.
A Multi-Regional Input–Output Model to Measure the Spatial Spillover of R&D Capital
Reallocating innovative capital elements can improve the growth of total factor productivity and promote high-quality economic development. The multi-regional multiplier model measures the spatial spillover effects of R&D capital to trace the interregional R&D flows and explore the engines of the longer-term economic growth in China. Results show that the direct R&D intensity in different regions is all concentrated in basic research sectors supported by government funds, and decreased from coastal areas to inland areas. Second, R&D gradually flowed from China’s coastal regions to inland regions, from upstream basic research sectors to downstream infrastructure construction sectors. Third, Guangdong, Jiangsu and Beijing are the main contributors, with R&D spillover intensities reaching 1.69%, 1.40%, and 1.37%, respectively. Xinjiang, Tibet, and Hainan are the main beneficiaries, with R&D inflow intensities reaching 0.49%, 0.53%, and 0.50%, respectively. Finally, the channel of R&D spatial spillover manifests a circular distribution and contact-type and jump-type modes.
A Multi-Regional Input–Output Model to Measure the Spatial Spillover of R&D Capital
Chunyun Wang (author) / Senyu Xing (author) / Lixiao Xu (author)
2023
Article (Journal)
Electronic Resource
Unknown
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