The Impact of Research and Development Investment on Total Factor Productivity of Animal Husbandry Enterprises: Evidence from Listed Companies in China

Abstract

Improving the total factor productivity (TFP) of animal husbandry enterprises is the key to promoting the sustainable development of animal husbandry. Technological progress is an important driving force for improving the TFP of animal husbandry enterprises, and research and development (R&D) investment determines the speed of technological progress. Based on the data of Chinese animal husbandry enterprises listed on Shanghai and Shenzhen A-shares in China between 2009 and 2022, this article empirically analyzes the impact of R&D investment on the TFP of animal husbandry enterprises and the moderating role of executive incentives in it and discusses the heterogeneity of this impact, using the fixed-effects model. It is found that R&D investment has a significant positive impact on the TFP of animal husbandry enterprises, and the TFP of animal husbandry enterprises increased by 0.00105 for every 1% increase in R&D investment. Equity incentives for executives positively moderated the relationship between R&D investment and the TFP of animal husbandry enterprises, while executive compensation incentives negatively moderated the relationship. Heterogeneity analysis shows that R&D investment has a significant effect on the TFP of non-state-owned animal husbandry enterprises, but has no significant impact on the TFP of state-owned animal husbandry enterprises. The impact of R&D investment on TFP showed a significant promotion in the livestock and poultry breeding and meat product processing industries, but not in the feed production, dairy product processing and animal health industries. R&D investment has a significant effect on the TFP of animal husbandry enterprises in Eastern China and Central China, but has no significant impact on the TFP of animal husbandry enterprises in Western China.

The influence of agricultural insurance on agricultural carbon emissions: evidence from China’s crop and livestock sectors

“…Second, livestock technological progress and technical efficiency are obtained by measuring and decomposing the total green factor productivity of livestock. According to Acosta and Luis (2019) and Yan et al (2023), capital inputs, intermediate consumption, livestock labor inputs, and livestock machinery inputs are selected as input indicators, and livestock gross output value and livestock carbon emissions are selected as output indicators.…”

Assessing Manufacturing Efficiency in Central Plains Cities: A Three-Stage DEA and Malmquist Index Approach

“…He et al [1] calculated the innovation efficiency of China's green low-carbon listed companies from 2016 to 2020 and found that under homogeneous conditions, the innovation efficiency of green low-carbon companies is at a lower level, mainly restricted by scale efficiency, with PTE showing scale heterogeneity. Yan et al [2] found that R&D investment in Jiangsu's listed manufacturing companies has been increasing year by year, but the overall innovation performance has not reached an optimal state, mainly due to low PTE, necessitating an improvement in resource allocation efficiency. Xu and Lu [3] used the Banker, Charnes, and Cooper (BCC) Model of the DEA to measure the input-output efficiency of 60 listed manufacturing companies in Shanghai, Jiangsu, Zhejiang, and Anhui provinces, concluding that the comprehensive efficiency in the Yangtze River Delta region is low and there are significant differences in comprehensive efficiency among regions.…”

“…Many scholars mainly use the first-stage DEA model in analyzing manufacturing efficiency, but the first-stage DEA model does not consider the impact of environmental variables on the efficiency evaluation of decision-making units [6], which may lead to the efficiency values being underestimated or overestimated, and the model's assumptions are inappropriate [7]. Although some scholars have considered the impact of environmental factors, they have only conducted static analyses of manufacturing efficiency using the three-stage DEA model, making it difficult to discern its dynamic trends [2]. Entering the 21st century, the DEA model remains the main model for efficiency research [8].…”