The efficiency and influencing factors of digital inclusive finance empowering rural revitalization:Based on panel data from 30 Provinces(Municipalities and Autonomous Regions)
The Peking University Digital Inclusive Finance Index from 2011 to 2020 was used to calculate the rural revitalization index by adopting the entropy method.Based on the DEA model,the efficiency of digital inclusive finance empowering rural revitalization was calculated,and the influencing factors of efficiency were analyzed by using the Tobit model.The results indicated that the level of comprehensive efficiency in empowering rural revitalization through digital inclusive finance in China was relatively low,and there were regional differences.The development space for empowering rural revitalization through digital inclusive finance was still relative-ly large;technological progress and changes were the main reasons affecting efficiency changes;in terms of influencing factors,the ef-ficiency of financial intermediaries,the scale of financial development,industrial structure,economic development level,fiscal sup-port for agriculture,and the degree of openness to the outside world had a significant positive impact on the efficiency of digital inclu-sive finance in empowering rural revitalization,however,the impact of information infrastructure,transportation level,and economic agglomeration on the efficiency of digital inclusive finance in empowering rural revitalization was not significant.It was proposed to ex-pand the scale of financial development and actively improve the efficiency of financial intermediaries;support the transformation and upgrading of industrial structure,expand the level of opening up to the outside world,and actively cultivate new economic growth points;increase fiscal support for agriculture,promote digital inclusive finance services for agriculture,rural areas,and farmers,and improve the efficiency of digital inclusive finance in empowering rural revitalization.
digital inclusive financerural revitalizationefficiencyinfluencing factorspanel data