Research on the paths through which digital technology cures the cost disease of the service industry: A study from the perspective of efficiency improvement and structural optimization

doi:10.19571/j.cnki.1000-2995.2025.06.008

Abstract

Abstract: The existence of cost disease in the service industry will affect the productivity of the overall economy and further restrict the momentum of China′s high-quality economic development. Based on this, this paper first sorted out the formation mechanism of cost disease, and then constructed a partial equilibrium model and a multi-sector endogenous growth model from the perspectives of "efficiency improvement" and "structural optimization" to analyze the path of digital technology to cure cost disease. The empirical results obtained from this study based on the panel data of 30 provincial-level regions in China from 2010 to 2022 are as follows: (1) From the perspective of "efficiency improvement", digital technology can significantly promote service innovation, thus facilitating the improvement of production efficiency in the service industry. Digital technology can empower service innovation and cure cost disease. (2) High-skilled labor and per-capita capital can form with digital technology a synergistic effect of "digitalization -high-skills" and "digitalization-capital deepening", and strengthen the efficiency empowerment of digital technology. (3) From the perspective of "structural optimization", digital technology can, on the one hand, moderate the causal chain of "efficiency-price-output" of cost disease, and on the other hand, shorten the gap of technological progress rate between industries, optimize the flow of innovation, and cure cost disease from the structure. This study will provide a theoretical basis and empirical evidence for overcoming the problem of cost disease in the process of "intelligent" digital transformation of the service industry.

Key words: digital technology empowerment, cost disease, service industry innovation, efficiency improvement, structural optimization

Dong Feng, Zhao Xu, Li Chunling, Wang Lun. Research on the paths through which digital technology cures the cost disease of the service industry: A study from the perspective of efficiency improvement and structural optimization[J]. Science Research Management, 2025, 46(6): 73-83.

References

[1] Baumol W. Macroeconomics of unbalanced growth: the anatomy of urban crisis [J]. American Economic Review, 1967, 57(3): 415-426.
[2] 江小涓，靳景．数字技术提升经济效率：服务分工、产业协同和数实孪生[J]．管理世界，2022，38(12)：9-26．
[3] Baumol W, Blackman S, Wolff E. Unbalanced growth revisited: asymptotic stagnancy and new evidence [J]. American Economic Review, 1985, 75(4): 806-817.
[4] Young A. Structural transformation, the mismeasurement of productivity growth, and the cost disease of services [J]. American Economic Review, 2014, 104(11): 3635-3667.
[5] 李晓华．数字技术与服务业“成本病”的克服[J]．财经问题研究，2022，(11)：16-26．
[6] 王恕立，胡宗彪．中国服务业分行业生产率变迁及异质性考察[J]．经济研究，2012，47(04)：15-27．
[7] 蔡跃洲，牛新星．中国数字经济增加值规模测算及结构分析[J]．中国社会科学，2021， (11)：4-30+204．
[8] 刘平峰，张旺．数字技术如何赋能制造业全要素生产率?[J]．科学学研究，2021，39(08)：1396-1406．
[9] 田秀娟，李睿．数字技术赋能实体经济转型发展——基于熊彼特内生增长理论的分析框架[J]．管理世界，2022，38(05)：56-74．
[10] Baumol W, Blackman S, Wolff E. Unbalanced growth revisited: asymptotic stagnancy and new evidence [J]. American Economic Review, 1985, 75(4): 806-817.
[11] 雷小清．信息通信技术对服务业“成本病”的影响研究——基于OECD国家生产率的增长核算分析[J]．财经论丛，2011，(04)：16-21．
[12] 江小涓．高度联通社会中的资源重组与服务业增长[J]．经济研究，2017，52(03)：4-17．
[13] 李帅娜．数字技术赋能服务业生产率：理论机制与经验证据[J]．经济与管理研究，2021，42(10)：51-67．
[14] 谭洪波．全球化分工“治愈”了鲍莫尔成本病吗? ——以美、中、印三国为例[J]．经济体制改革，2018(02)：145-151．
[15] 杜传忠，侯佳妮．制造业与服务业融合能否有效缓解服务业“成本病”——基于WIOD中国数据的经验事实[J]．山西财经大学学报，2021，43(03)：28-42．
[16] Melitz M J. The impact of trade on intra‐industry reallocations and aggregate industry productivity[J]. econometrica, 2003, 71(6): 1695-1725.
[17] Antoniades A. Heterogeneous firms, quality, and trade[J]. Journal of International Economics, 2015, 95(2): 263-273.
[18] Prettner K, Strulik H. Innovation, automation, and inequality: Policy challenges in the race against the machine[J]. Journal of Monetary Economics, 2020, 116: 249-265.
[19] Acemoglu D, Restrepo P. The race between man and machine: Implications of technology for growth, factor shares, and employment[J]. American economic review, 2018, 108(6): 1488-1542.
[20] Jones C I. R & D-based models of economic growth[J]. Journal of political Economy, 1995, 103(4): 759-784.
[21] Aghion P, Howitt P. A model of growth through creative destruction[J]. 1990.
[22] Acemoglu D, Aghion P, Bursztyn L, et al. The environment and directed technical change[J]. American economic review, 2012, 102(1): 131-166.
[23] 易信，刘凤良．金融发展、技术创新与产业结构转型——多部门内生增长理论分析框架[J]．管理世界，2015，(10)：24-39+90．
[24] Acemoglu D. Introduction to modern economic growth[M]. Princeton university press, 2008.
[25] 宋健，郑江淮．产业结构、经济增长与服务业成本病——来自中国的经验证据[J]．产业经济研究，2017，(2)：1-13．
[26] 陈梦根，周元任．数字经济、分享发展与共同富裕[J]．数量经济技术经济研究，2023，40(10)：5-26．
[27] 周小亮，吴武林．中国包容性绿色增长的测度及分析[J]．数量经济技术经济研究，2018，35(08)：3-20．
[28] F?re R, Grosskopf S, Norris M, et al. Productivity growth, technical progress, and efficiency change in industrialized countries[J]. American economic review, 1994: 66-83.
[29] 庞瑞芝，郭慧芳．数字经济能克服服务业“成本病”吗?——来自城市层面的经验证据[J]．经济与管理研究，2023，44(10)：54-74．
[30] 陈景华，王素素，陈敏敏．服务业双向FDI、空间溢出与服务业全要素生产率——来自中国的经验证据[J]．山东财经大学学报，2020，32(01)：87-96．
[31] Shao S, Yang Z, Yang L, et al. Can China's energy intensity constraint policy promote total factor energy efficiency? Evidence from the industrial sector[J]. The Energy Journal, 2019, 40(4).
[32] Fuchs-Schündeln N, Hassan T A. Natural experiments in macroeconomics[M]//Handbook of macroe-conomics. Elsevier, 2016, 2: 923-1012.
[33] Acemoglu D. Technical change, inequality, and the labor market[J]. Journal of economic literature, 2002, 40(1): 7-72.
[34] 随洪光，高钰琳，周瑾，等．劳动力保护抑制了出口产品质量吗？——基于资本深化视角的经验研究[J]．南开经济研究，2023，(11)：149-167．
[35] 韩兆安，吴海珍，赵景峰．数字经济驱动创新发展——知识流动的中介作用[J]．科学学研究，2022，40(11)：2055-2064+2101．

Research on the paths through which digital technology cures the cost disease of the service industry: A study from the perspective of efficiency improvement and structural optimization

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