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Impact of the Digital Economy on the Total Factor Productivity of Agricultural Product Processing Industry
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Xiaoyan HAN1, Xingwei WANG1, Zehao HUANG1, Jing CHEN2, Di CHEN1
Smart Agriculture | 2026, 8(3) : 215 - 225
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Smart Agriculture | 2026, 8(3): 215-225
• Special Issue--Digital Technologies Reshaping Agriculture and Agricultural Economics •
Impact of the Digital Economy on the Total Factor Productivity of Agricultural Product Processing Industry
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Xiaoyan HAN1, Xingwei WANG1, Zehao HUANG1, Jing CHEN2, Di CHEN1
Affiliations
  • 1.College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China
  • 2.Liaoning Institute of Agricultural Mechanization, Shenyang 110866, China
  • HAN Xiaoyan, E-mail:

About Author:

HAN Xiaoyan, E-mail:

Published: 2026-05-30 doi: 10.12133/j.smartag.SA202601038
Outline
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[Objective] The agricultural product processing industry constitutes an important component in the economic system that plays a pivotal role in the construction of the entire agricultural industry chain and the realization of rural industrial revitalization. Despite China's agricultural product processing industry is experiencing the paradigm shift from a recovery-oriented development phase toward a high-quality development trajectory, there remains a gap between its current status and both the strategic development targets and the industrial benchmarks established by advanced economies. The purpose of the research is to: (1) Calculate the total factor productivity (TFP) within China's agricultural product processing industry to serve as a proxy variable for the high-quality development of the industry; (2) Identify the impact of the digital economy as an emerging economic paradigm on the total factor productivity of China's agricultural product processing industry and elucidate the mediating role of research and development investment in this relationship; (3)Analyze the heterogeneous impacts of the digital economy on the TFP of the agricultural product processing industry across varying governance environments, business operational conditions and enterprise management proficiency levels. [Methods] First, based on the panel data of agricultural product processing related enterprises listed on China's A-share market from 2012 to 2024, the TFP was measured by the Solow residual approach and the Levinsohn-Petrin (LP) method. Through the comprehensive construction of an indicator system and the utilization of provincial-level panel data, the developmental level of the digital economy was systematically measured. Second, a two-way fixed effects model was employed to identify the causal effect of the digital economy on TFP. In order to ensure the accuracy of data, robustness checks were conducted by replacing the baseline model with a Tobit model and by using alternative measures of the dependent variable. To address potential endogeneity, the one-period lagged value of digital economy development was used as an instrumental variable and the two-stage least squares (2SLS) method was adopted. In addition, a mediation model was introduced to test the channel effect of RD expenditure. Finally, the heterogeneity of the digital economy's impact on total factor productivity was analyzed from the perspectives of digital government, the business environment and enterprise management expense ratios. [Results and Discussions] The digital economy can effectively enhance the total factor productivity of the agricultural product processing industry. Mechanism analysis indicated that the enterprise RD investment played a partial mediating role in this relationship. These results remained robust after endogeneity treatment and robustness tests. Moreover, the impacts of the digital economy demonstrated significant heterogeneity when examined across different regional and enterprise dimensions. Specifically, with a relatively low level of digital government, the digital economy significantly inhibited the improvement of TFP; by contrast, with a more favorable business environment, the digital economy significantly promoted TFP growth. The conclusion demonstrated that only when digital government or business environment reaches advanced levels can it synergistically enhance the TFP of China's agricultural product processing industry with the digital economy. There was a significant negative relationship between management expense ratio and the impact of digital economy on TFP, which indicated enhancing management efficiency constituted a crucial pathway for improving firm-level total factor productivity. [Conclusions] Although the digital economy exerted substantial promotional impacts on the high-quality development of China's agricultural product processing industry, that still necessitated the attainment of specific thresholds across governmental governance environments, market operational conditions and enterprise management capabilities. Consequently, the efforts should be made to promote the deep integration between the digital economy and the agricultural product processing sector. On the one hand, the construction of digital government should be enhanced by extensively applying digital technologies to government service domains, implementing precision policy formulation to improve service accessibility and establishing stable, equitable, transparent and predictable policy and business environments for agricultural processing enterprises. On the other hand, agricultural processing enterprises should strategically leverage digital technologies to foster comprehensive innovation across technological, organizational and managerial dimensions, thereby realizing the optimization of their management tools and operational processes, the improvement of efficient resource allocation and utilization and the rational cost control. Ultimately, in the modern digital economy landscape, the overall operational efficiency and competitive advantage of the agricultural product processing sector can be enhanced.

digital economy  /  total factor productivity  /  RD investment  /  agricultural product processing industry  /  two-way fixed effects model
韩晓燕, 王星蔚, 黄泽澔, 陈菁, 陈迪. 数字经济对农产品加工业全要素生产率的影响. 智慧农业(中英文), 2026 , 8 (3) : 215 -225 . DOI: 10.12133/j.smartag.SA202601038
Xiaoyan HAN, Xingwei WANG, Zehao HUANG, Jing CHEN, Di CHEN. Impact of the Digital Economy on the Total Factor Productivity of Agricultural Product Processing Industry[J]. Smart Agriculture, 2026 , 8 (3) : 215 -225 . DOI: 10.12133/j.smartag.SA202601038
  • General Project of Department of Education of Liaoning Province(JYTYB2024033)
Year 2026 volume 8 Issue 3
117
54
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doi: 10.12133/j.smartag.SA202601038
  • Receive Date:2026-01-30
  • Online Date:2026-10-16
  • Published:2026-05-30
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  • Received:2026-01-30
Funding
General Project of Department of Education of Liaoning Province(JYTYB2024033)
Affiliations
    1.College of Economics and Management, Shenyang Agricultural University, Shenyang 110866, China
    2.Liaoning Institute of Agricultural Mechanization, Shenyang 110866, China

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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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