企业数字化转型对就业规模及结构影响的实证研究

doi:10.11959/j.issn.2096-6652.202336

Abstract

Abstract:

With the advent of the digital age, people are paying more and more attention to the employment scale and employment structure of enterprises.Used the enterprise panel data of listed companies from 2011 to 2019, the impact of enterprise digital transformation on employment scale and employment structure was studied deeply by the fixed-effect model and mediation effect model.The results show that, firstly, the digital transformation of enterprises significantly expand the employment scale of enterprises.At the same time, the impact of digitalization on the job structure has a strong＆quot;knowledge bias＆quot;, showing a bias towards high skills and knowledge, and an increase in the demand for talents who master knowledge related to cutting-edge industries.The results of the intermediary effect test show that the digital transformation of enterprises indirectly promotes the employment scale of enterprises through market scale effect and product innovation.The results of heterogeneity analysis show that the high degree of digitalization of the environment and the degree of digitalization of non-state-owned enterprises have a more significant impact on the employment structure.

Key words: digital transformation, employment scale, employment structure

CLC Number:

TP39

Jiting HUANG,Kexin GUO,Jiayin QI. An empirical study on the impact of enterprise digital transformation on employment scale and structure[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 352-365.

Figures/Tables 16

References 31

[1]	秦蕊, 梁小龙, 李娟娟 ,等. 平行科研院所:从数字化转型到智能化变革[J]. 智能科学与技术学报, 2023,5(2): 212-221.
	QIN R , LIANG X L , LI J J ,et al. Parallel scientific research institutes:from digital transformation to intelligent revolution[J]. Chinese Journal of Intelligent Science and Technology, 2023,5(2): 212-221.
[2]	王飞跃, 缪青海, 张军平 ,等. 探讨AI for Science的影响与意义:现状与展望[J]. 智能科学与技术学报, 2023,5(1): 1-6.
	WANG F Y , MIAO Q H , ZHANG J P ,et al. The DAOs to AI for Science by DeSci:the state of the art and perspective[J]. Chinese Journal of Intelligent Science and Technology, 2023,5(1): 1-6.
[3]	李雯轩, 李晓华 . 全球数字化转型的历程、趋势及中国的推进路径[J]. 经济学家, 2022(5): 36-47.
	LI W X , LI X H . The history and trend of global digital transformation and China's promotion path[J]. Economist, 2022(5): 36-47.
[4]	ACEMOGLU D , RESTREPO P . Robots and jobs:evidence from US labor markets[J]. Journal of Political Economy, 2020,128(6): 2188-2244.
[5]	FREY C B , OSBORNE M A . The future of employment:how susceptible are jobs to computerisation?[J]. Technological Forecasting and Social Change, 2017,114: 254-280.
[6]	ACEMOGLU D , RESTREPO P . Unpacking skill bias:automation and new tasks[J]. AEA Papers and Proceedings, 2020,110: 356-361.
[7]	STEVENSON B . AI,income,employment,and meaning[M]// The Economics of Artificial Intelligence:An Agenda,Chicago: University of Chicago Press, 2019: 189-195.
[8]	DEKLE R . Robots and industrial labor:Evidence from Japan[J]. Journal of the Japanese and International Economies, 2020,58:101108.
[9]	LORDAN G , NEUMARK D . People versus machines:The impact of minimum wages on automatable jobs[J]. Labour Economics, 2018,52: 40-53.
[10]	韩民春, 庞思明 . 工业机器人应用对制造业劳动生产率的影响研究——基于欧美13个国家数据的经验分析[J]. 工业技术经济, 2021,40(1): 13-21.
	HAN M C , PANG S M . Research on the influence of industrial robot application on manufacturing labor productivity:experience analysis based on data from 13 countries in Europe and America[J]. Industrial Technology ＆ Economy, 2021,40(1): 13-21.
[11]	李磊, 王小霞, 包群 . 机器人的就业效应:机制与中国经验[J]. 管理世界, 2021,37(9): 104-119.
	LI L , WANG X X , BAO Q . The employment effect of robots:mechanism and evidence from China[J]. Journal of Management World, 2021,37(9): 104-119.
[12]	孙早, 侯玉琳 . 工业智能化如何重塑劳动力就业结构[J]. 中国工业经济, 2019(5): 61-79.
	SUN Z , HOU Y L . How does industrial intelligence reshape the employment structure of labor force[J]. China Industrial Economics, 2019(5): 61-79.
[13]	易露霞, 吴非, 常曦 . 企业数字化转型进程与主业绩效——来自中国上市企业年报文本识别的经验证据[J]. 现代财经(天津财经大学学报), 2021,41(10): 24-38.
	YI L X , WU F , CHANG X . Enterprise digital transformation process and main business performance:empirical evidence from the text recognition of the annual reports of listed companies in China[J]. Modern Finance and Economics(Journal of Tianjin University of Finance and Economics), 2021,41(10): 24-38.
[14]	徐金球 . 数字化与企业弹性——基于新冠病毒感染疫情冲击的经验证据[J]. 上海对外经贸大学学报, 2023,30(1): 41-56.
	XU J Q . Digitalization and enterprise resilience:empirical evidence based on the impact of COVID-19[J]. Journal of Shanghai University of International Business and Economics, 2023,30(1): 41-56.
[15]	赵宸宇 . 数字化转型对企业劳动力就业的影响研究[J]. 科学学研究, 2023,41(2): 241-252.
	ZHAO C Y . Research on the impact of digital transformation on enterprises' labor employment[J]. studies in science of science, 2023,41(2): 241-252.
[16]	张车伟, 王博雅, 高文书 . 创新经济对就业的冲击与应对研究[J]. 中国人口科学, 2017(5): 2-11.
	ZHANG C W , WANG B Y , GAO W S . Innovation economy and it's impact on employment and the countermeasures[J]. Chinese Journal of Population Science, 2017(5): 2-11.
[17]	刘志成, 刘斌 . 贸易自由化、全要素生产率与就业——基于2003—2007年中国工业企业数据的研究[J]. 南开经济研究, 2014(1): 101-117.
	LIU Z C , LIU B . Trade liberalization,total factor productivity and employment:based on 2003—2007 China's industrial enterprises database[J]. Nankai Economic Studies, 2014(1): 101-117.
[18]	刘一顺, 阳春华, 黄科科 . 知识驱动的冶金企业原料供应链订单分配方法[J]. 智能科学与技术学报, 2022,4(3): 355-370.
	LIU Y S , YANG C H , HUANG K K . Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises[J]. Chinese Journal of Intelligent Science and Technology, 2022,4(3): 355-370.
[19]	康孟珍, 王秀娟, 李冬 ,等. 基于联邦学习的分布式农业组织[J]. 智能科学与技术学报, 2022,4(2): 288-297.
	KANG M Z , WANG X J , LI D ,et al. Distributed agricultural organization based on federated learning[J]. Chinese Journal of Intelligent Science and Technology, 2022,4(2): 288-297.
[20]	赵烁, 施新政, 陆瑶 ,等. 兼并收购可以促进劳动力结构优化升级吗?[J]. 金融研究, 2020(10): 150-169.
	ZHAO S , SHI X Z , LU Y ,et al. Can mergers and acquisitions promote the optimization and upgrading of labor structure?[J]. Journal of Financial Research, 2020(10): 150-169.
[21]	郭怡笛 . 人工智能对我国技能劳动力需求变动的影响研究[D]. 济南:山东大学, 2022.
	GUO Y D . Research on the influence of artificial intelligence on the demand change of skilled labor force in China[D]. Jinan:Shandong University, 2022.
[22]	戚聿东, 肖旭 . 数字经济时代的企业管理变革[J]. 管理世界, 2020,36(6): 135-152.
	QI Y D , XIAO X . Enterprise management reform in the era of digital economy[J]. Journal of Management World, 2020,36(6): 135-152.
[23]	吴非, 胡慧芷, 林慧妍 ,等. 企业数字化转型与资本市场表现——来自股票流动性的经验证据[J]. 管理世界, 2021,37(7): 130-144.
	WU F , HU H Z , LIN H Y ,et al. Digital transformation of enterprises and performance of capital market:empirical evidence from stock liquidity[J]. Management World, 2021,37(7): 130-144.
[24]	张刚, 袁帅, 张玉巧 . 技术进步、产业升级与结构性失业[J]. 现代管理科学, 2018(5): 73-75.
	ZHANG G , YUAN S , ZHANG Y Q . Technological progress,industrial upgrading and structural unemployment[J]. Modern Management Science, 2018(5): 73-75.
[25]	林知微, 张嵩川, 王成吉 ,等. 区块链技术综述:在下一代智能制造中的应用[J]. 智能科学与技术学报, 2023,5(2): 200-211.
	ZHANG Z W , ZHANG S C , WANG C J ,et al. A survey on application of block chain in next generation intelligent manufacturing[J]. Chinese Journal of Intelligent Science and Technology, 2023,5(2): 200-211.
[26]	郑建明, 李金甜, 刘琳 . 新三板做市交易提高流动性了吗?——基于"流动性悖论"的视角[J]. 金融研究, 2018(4): 190-206.
	ZHENG J M , LI J T , LIU L . Can NEEQ market making improve liquidity? from the perspective of liquidity paradox[J]. Journal of Financial Research, 2018(4): 190-206.
[27]	赵涛, 张智, 梁上坤 . 数字经济、创业活跃度与高质量发展——来自中国城市的经验证据[J]. 管理世界, 2020,36(10): 65-76.
	ZHAO T , ZHANG Z , LIANG S K . Digital economy,entrepreneurial activity and high-quality development:empirical evidence from China city[J]. Journal of Management World, 2020,36(10): 65-76.
[28]	潘雅茹, 龙理敏 . 数字经济对中小企业就业需求及结构的影响——基于深圳主板上市公司的经验证据[J]. 华东经济管理, 2022,36(11): 85-94.
	PAN Y R , LONG L M . Impact of digital economy on employment demand and structure of SMEs:empirical evidence based on companies listed on the Shenzhen main board[J]. East China Economic Management, 2022,36(11): 85-94.
[29]	荆文君, 孙宝文 . 数字经济促进经济高质量发展:一个理论分析框架[J]. 经济学家, 2019(2): 66-73.
	JING W J , SUN B W . Digital economy promotes high-quality economic development:a theoretical analysis framework[J]. Economist, 2019(2): 66-73.
[30]	张峰, 刘曦苑, 武立东 ,等. 产品创新还是服务转型:经济政策不确定性与制造业创新选择[J]. 中国工业经济, 2019(7): 101-118.
	ZHANG F , LIU X Y , WU L D . Product innovation or service transition:economic policy uncertainty and manufacturing innovation choice[J]. China Industrial Economics, 2019(7): 101-118.
[31]	陈思, 何文龙, 张然 . 风险投资与企业创新:影响和潜在机制[J]. 管理世界, 2017(1): 114-134.
	CHEN S , HE W L , ZHANG R . Venture capital and enterprise innovation:influence and potential mechanism[J]. Management World, 2017(1): 114-134.

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类别	结构化特征词
人工智能技术	人工智能、商业智能、图像理解、投资决策辅助系统、智能数据分析、智能机器人、机器学习、深度学习、语义搜索、生物识别技术、人脸识别、语音识别、身份验证、自动驾驶、自然语言处理
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变量	变量名称	符号	备注
	企业就业规模	Emp	企业员工总人数对数值
		Prop_graduate＆up	研究生及以上学历占比
	学历结构	Prop_undergraduate	本科学历占比
		Prop_ specialty	专科学历占比
被解释变量		Prop_high＆under	高中及以下学历占比
		Prop_production	生产岗位占比
	岗位结构	Prop_finance	财务岗位占比
		Prop_sale	销售岗位占比
		Prop_Tech	技术岗位占比
核心解释变量	企业数字化水平	DIGI	企业年报MD＆A数字化词频对数值
	人工智能技术	AI	结构化特征词词频
	区块链技术	Block_Chain	结构化特征词词频
口径拆分解释变量	云计算技术	Cloud_comp	结构化特征词词频
	大数据技术	Big_Data	结构化特征词词频
	数字技术运用	Digi_tech_ap	结构化特征词词频
	企业年龄	ComP_age	企业年龄对数值
	营业收入增长率	Income_GRA	营业收入增长额 / 上年营业收入总额
控制变量CVs	基本每股收益	EPS	来自年报财务摘要
	资产负债率	Asset_DRA	企业当年总负债 / 企业当年总资产
	流动比率	CR	企业当年流动资产与流动负债之比
	人均GDP	GDP_per	企业当年所在省份人均GDP
	年份固定效应	Year	—
固定效应	省份固定效应	Prov	—
	行业固定效应	Ind	—
	企业固定效应	Com	—
中介变量	营业收入	Revenue	企业当年营业收入对数值
	公司专利累计	Patent	年度发明专利申请量
虚拟变量	环境数字化程度	—	企业所在地级市数字化水平
	企业所有制性质	—	国有企业为1，反之为0
滞后变量	—	L.+变量符号	表示滞后一期的变量

对比项	M1	M2	M3	M4	M5
L.DIGI	0.144^***	0.058^***	0.054^***	0.052^***	0.050^***
	(15.706)	(8.685)	(8.044)	(8.841)	(8.321)
ComP_age	—	0.615^***	0.314^**	0.328^***	0.321^**
		(7.230)	(2.514)	(2.884)	(2.445)
Income_GRA	—	0.175^***	0.172^***	0.163^***	0.168^***
		(7.599)	(7.627)	(7.105)	(7.366)
EPS	—	0.096^***	0.100^***	0.102^***	0.098^***
		(8.149)	(8.690)	(10.177)	(9.908)
Asset_DRA	—	0.967^***	0.998^***	1.025^***	1.020^***
		(13.719)	(14.038)	(16.241)	(16.546)
CR	—	-0.000^***	-0.000^***	-0.000^***	-0.000^***
		(-9.255)	(-10.873)	(-10.321)	(-10.385)
GDP_per	—	0.000^***	0.000^**	0.000^**	0.000^***
		(3.212)	(2.509)	(2.639)	(4.482)
CVs	否	是	是	是	是
Com	是	是	是	是	是
Year	否	否	是	是	是
Ind	否	否	否	是	是
Prov	否	否	否	否	是
N	19 088	19 088	19 088	19 088	19 088
R²	0.053	0.211	0.216	0.245	0.259
注：（1）*、、*分别代表1%、5%、10%的显著性水平；（2）括号中的数字是经过聚类稳健标准误调整的t值；（3）“是”表示模型中加入了控制变量或该固定效应，“否”为没有加入。下文同。

对比项	M1	M2	M3	M4
L.DIGI	-0.661^***	0.040	0.218	0.415^**
	(-5.053)	(1.469)	(1.610)	(2.550)
常数项	49.871^***	0.956	23.741^***	6.723
	(3.167)	(0.443)	(3.786)	(0.483)
CVs	是	是	是	是
Year/Ind/Prov/Com	是	是	是	是
N	16 555	16 555	16 555	16 555
R²	0.081	0.071	0.054	0.065

对比项	M1	M2	M3	M4
L.DIGI	0.070^***	0.331^**	-0.073	-0.832^***
	(3.044)	(2.693)	(-0.596)	(-4.000)
常数项	3.798	34.389^***	3.073	-41.606^**
	(1.045)	(5.335)	(0.335)	(-2.610)
Cvs	是	是	是	是
Year/Ind/Prov/Com	是	是	是	是
N	16 555	16 555	16 555	16 555
R²	0.068	0.161	0.038	0.215

An empirical study on the impact of enterprise digital transformation on employment scale and structure

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Figures/Tables 16

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对比项	M1	M2	M3
dt	0.005(0.148)	—	—
du×dt	0.069^*(1.842)	0.050^**(2.119)	—
du	0.000(.)	—	—
du×dt×DIGI		—	0.051^***(9.648)
Cvs	是	是	是
Com	是	是	是
Year/Ind/Prov	否	是	是
N	19 088	19 088	19 088
R²	0.206	0.242	0.247

对比项	M1	M2
L.DIGI	0.004(0.354)	0.054^***(4.769)
环境数字化程度	-0.002(-0.042)	—
数字化_环境	0.039^***(3.305)	—
是否国有企业	—	0.200^**(2.453)
数字化_国有	—	-0.052^**(-2.640)
CVs	是	是
Year/Ind/Prov/Com	是	是
N	16 442	16 240
R²	0.236	0.242

对比项	环境数字化程度低				环境数字化程度高
对比项	Prop_production	Prop_finance	Prop_sale	Prop_Tech	Prop_production	Prop_finance	Prop_sale	Prop_Tech
L.DIGI	0.683(1.011)	0.016(0.349)	-0.576^*(-1.961)	0.172 (0.387)	-0.780^***(-4.680)	0.049^*(1.761)	0.261^*(1.808)	0.485^***(2.750)
CVs	是	是	是	是	是	是	是	是
Year/Ind/Prov/Com	是	是	是	是	是	是	是	是
N	1 804	1 804	1 804	1 804	14 638	14 638	14 638	14 638
R²	0.115	0.064	0.175	0.129	0.091	0.070	0.054	0.065

对比项	非国有企业				国有企业
对比项	Prop_production	Prop_finance	Prop_sale	Prop_Tech	Prop_production	Prop_finance	Prop_sale	Prop_Tech
L.DIGI	-0.535^***(-4.168)	0.034(1.240)	0.286^*(1.853)	0.459^**(2.601)	-0.902^**(-2.714)	0.035(0.878)	0.033(0.151)	0.357(1.233)
CVs	是	是	是	是	是	是	是	是
Year/Ind/Prov/Com	是	是	是	是	是	是	是	是
N	10 218	10 218	10 218	10 218	6 022	6 022	6 022	6 022
R²	0.105	0.089	0.059	0.070	0.055	0.023	0.073	0.116

对比项	M1	M2	M3
L.DIGI	0.140^***	102.750^***	0.126^***
	（18.11）	（10.96）	（16.95）
公司专利累计			0.00013^***
			（20.55）
CVs	是	是	是
Year/Ind/Prov/Com	是	是	是
N	16555	16555	16555
R²	0.26	0.03	0.28
Sobel 检验	0.013^***
	机制有效——正向传导
间接效应检验（p值）	0.000
	间接效应成立

对比项	M1	M2	M3
L.DIGI	0.140^***（18.11）	0.077^***（9.85）	0.084^***（15.97）
营业收入			0.726^***（138.66）
CVs	是	是	是
Year/Ind Prov/Com	是	是	是
N	16 555	16 555	16 555
R²Sobel检验	0.26	0.43	0.66
R²Sobel检验	0.013^***机制有效——正向传导
间接效应检验（p值）	0.000间接效应成立

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[8]	Weiyi YU, Tao CHEN, Junping ZHANG, Hongming SHAN. A survey of deep learning-based MRI stroke lesion segmentation methods [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 293-312.
[9]	Peiding PI, Qinghua NI, Jing YANG, Mengzhen KANG, Xuanhao LI, Yingkun DU, Fei-Yue WANG. Parallel Sharhili: a new approach to sustainable development and intelligent management of ecological resources [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 283-292.
[10]	Xiaojia XU, Yongcai WANG, Deying LI. A survey on network tomography technology [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 163-179.
[11]	Zhiwei LIN, Songchuan ZHANG, Chengji WANG, Yiwei ZHOU. A survey on application of block chain in next generation intelligent manufacturing [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 200-211.
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[13]	Zhouyu GU, Yuecheng YU, Tiantian Zhe. Rapider-YOLOX: lightweight object detection network with high precision [J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(1): 92-103.
[14]	Fengtao XIANG, Jiongming SU, Xueqiang Gu, Wanpeng ZHANG. Research on the spread and countermeasures of COVID-19 using agent-based modeling [J]. Chinese Journal of Intelligent Science and Technology, 0, (): 51-57.
[15]	Xiaofeng CONG, Jie GUI, Jun ZHANG. Underwater image enhancement network based on visual Transformer with multiple loss functions fusion [J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(4): 522-532.

对比项	Emp	Prop_production	Prop_Tech	Prop_graduate＆up	Prop_undergraduate	Prop_high＆under
L.AI	0.101^***(5.596)	-1.333^**(-2.065)	1.965^***(4.070)	0.283^*(1.833)	0.083(0.136)	-4.089^***(-3.823)
L.Block_Chain	-0.010(-0.183)	-2.960(-1.672)	2.812**(2.658)	0.612(1.481)	0.267(0.206)	-5.702***(-10.495)
L.Cloud_comp	0.048^***(3.723)	-1.199^***(-2.867)	0.900(1.318)	0.161(1.195)	1.442^***(3.270)	-6.648^***(-7.455)
L.Big_Data	0.103^***(5.196)	-1.960^**(-2.581)	0.682(1.203)	0.011(0.086)	0.689^*(1.914)	-3.785^***(-4.351)
L.Digi_tech_ap	0.054^***(4.893)	-1.330^***(-2.935)	0.566(1.638)	-0.029(-0.418)	0.801^***(3.086)	-0.773(-1.529)
Cvs	是	是	是	是	是	是
Year/Ind/Prov/Com	是	是	是	是	是	是
N	16 555	16 555	16 555	16 555	16 555	16 555