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Article(id=1316695260667953328, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1730649600000, receivedDateStr=2024-11-04, revisedDate=1741276800000, revisedDateStr=2025-03-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1791854213157, onlineDateStr=2026-10-13, pubDate=1768406400000, pubDateStr=2026-01-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791854213157, onlineIssueDateStr=2026-10-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791854213157, creator=13701087609, updateTime=1791854213157, updator=13701087609, issue=Issue{id=1316695224617914963, tenantId=1146029695717560320, journalId=1308397381743640680, year='2026', volume='44', issue='1', pageStart='1', pageEnd='224', issueExtLink='null', onlineDate='null', pubDate='1768406400000', pubDateStr='2026-01-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791854204563, creator='13701087609', updateTime=1791854713654, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1316697359963239159, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1316697359963239160, tenantId=1146029695717560320, journalId=1308397381743640680, issueId=1316695224617914963, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=75, endPage=85, ext={EN=ArticleExt(id=1316695260944777393, articleId=1316695260667953328, tenantId=1146029695717560320, journalId=1308397381743640680, language=EN, title=“The employment-stabilizing” effect of supply chain digitization, columnId=1309226307205034609, journalTitle=Studies in Science of Science, columnName=Hot Issues, runingTitle=null, highlight=null, articleAbstract=

For a long time, problems such as information lag and cumbersome processes have been widespread under the traditional supply chain management mode. The phenomenon of information silos among enterprises has become the primary obstacle to information connection and efficiency improvement in the supply chain. With the emergence of the digital era, the application of big data, artificial intelligence, the Internet of Things, cloud computing and blockchain technology has successively empowered supply chain management, injecting new vitality into the innovation and efficiency improvement of modern supply chain management models. There are very few studies in the existing literature that examine the employment spillover effects of digital technology application in supply chains. From the perspective of supply chain network construction, This paper intends to explore the impact of supply chain digital technology application on the scale of labor employment of enterprises, and further analysis the impact of the collaborative application of supply chain digital technology of other enterprises in the industrial chain on the scale of labor employment of enterprises in the chain. Compared with the existing literature, the potential contributions of this paper are as follows: Firstly, different from traditional literature that focuses on the analysis of the substitution effect and creation effect of digital transformation on labor employment of enterprises, this paper takes the application of digital technology in supply chain network as an entry point to discuss the spillover effect of supply chain network construction on labor employment of enterprises in the chain. Specifically, this paper clarifies two paths of the employment spillover effect of supply chain digitization from the aspects of commercial credit spillover along the supply chain and smoothing enterprise production, which enriching the existing research on the factors affecting employment in the labor market. Secondly, this paper focuses on the analysis of the synergy between the upstream and downstream of the supply chain network, emphasizing the win-win cooperation of multiple subjects from the perspective of the supply chain, providing theoretical support and empirical evidence for the subsequent promotion and construction of industrial chain digitization. Based on the supply chain research data of listed companies and the perspective of supply and demand network construction, this paper discusses the employment effect of supply chain digitization. The difference-in-differences results show that the enterprise's digitization has a substitution effect on labor employment, while the supply chain digitization within industrial chain can effectively improves the enterprises’ employment scale by smoothing production and reducing the downstream enterprises’ occupation of commercial credit funds, which helps to ease the liquidity constraints of enterprises. In addition, industrial supply chain digitization can effectively cushion the impact of supply chain risks on the labor market, which helps to achieve "the employment-stabilizing". Heterogeneity analysis shows that the employment-stabilizing effect of industrial supply chain digitization is limited by the financing constraints, state-owned enterprises and the enterprises with concentrated purchasing and marketing business show greater spillover effect on employment.The research conclusions of this article are of great significance for the subsequent development of digitized supply chain construction, the maintenance of supply chain security and stability.

, authors=Xiu-mei KAO1, Shen-xiang XIE2, Kuan ZHAN3, authorsList=Xiu-mei KAO, Shen-xiang XIE, Kuan ZHAN, authorCompany=null, correspAuthors=Shen-xiang XIE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1316695261603283124, articleId=1316695260667953328, tenantId=1146029695717560320, journalId=1308397381743640680, language=CN, title=供应链数字化的“稳就业”效应, columnId=1309226307364418163, journalTitle=科学学研究, columnName=热点议题, runingTitle=null, highlight=null, articleAbstract=

结合上市公司供应链研究数据,以供需网络建设为切入点,本文探讨了供应链数字化的就业效应。基于供应链创新与应用试点工作开展的双重差分估计结果显示:(1)企业自身数字化对劳动力就业具有替代效应,而产业链供应链数字化可以通过平滑企业生产、减少下游企业商业信贷资金占用行为缓解企业流动性约束,进而实现链上企业劳动力规模扩张;(2)产业链供应链数字化创新能有效缓冲供应链风险对就业市场产生的冲击,实现“稳就业”;(3)异质性分析表明,融资约束一定程度上限制了供应链数字化就业溢出效应的发挥,国有企业以及购销业务更为集中的企业受产业链供应链数字化影响,其劳动力雇佣规模提升效果更为明显。

, authors=考秀梅1, 谢申祥2, 占宽3, authorsList=考秀梅, 谢申祥, 占宽, authorCompany=null, correspAuthors=谢申祥, authorNote=

考秀梅(1996-),女,博士研究生。占宽(1993-),男,讲师。

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谢申祥(1978-),男,教授、博士生导师, E-mail:。
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考秀梅(1996-),女,博士研究生。占宽(1993-),男,讲师。

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考秀梅(1996-),女,博士研究生。占宽(1993-),男,讲师。

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Variable description

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称 变量定义
lnstaff 劳动力雇佣规模 Ln(员工人数)
Digital 供应链数字化变量1 企业参与试点当年及以后年份定义为1,否则为0
Chaindigital 供应链数字化变量2 详见前文
roa 盈利能力 净利润/总资产余额
lev 资产负债率 负债总额/资产总额
cash 现金资产比率 经营活动产生的现金流量/资产总计
ta 固定资产比例 固定资产净额/资产总计
lnasset 企业规模 Ln(总资产)
age 企业年龄 Ln(企业年龄)
tobinq 托宾Q值 市值/资产总计
), ArticleFig(id=1316695265478820054, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表1, caption=

变量说明

, figureFileSmall=null, figureFileBig=null, tableContent=
变量名称 变量定义
lnstaff 劳动力雇佣规模 Ln(员工人数)
Digital 供应链数字化变量1 企业参与试点当年及以后年份定义为1,否则为0
Chaindigital 供应链数字化变量2 详见前文
roa 盈利能力 净利润/总资产余额
lev 资产负债率 负债总额/资产总额
cash 现金资产比率 经营活动产生的现金流量/资产总计
ta 固定资产比例 固定资产净额/资产总计
lnasset 企业规模 Ln(总资产)
age 企业年龄 Ln(企业年龄)
tobinq 托宾Q值 市值/资产总计
), ArticleFig(id=1316695265550123223, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 2, caption=

Baseline regression result

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(1) (2) (3) (4)
企业自身供应链数字化 产业链供应链数字化 上游供应链数字化 下游供应链数字化
Digitalit -0.115***
(-3.69)
Chaindigitalit 0.168***
(3.04)
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{s}\mathit{u}\mathit{p}\mathit{p}\mathit{l}{\mathit{y}}_{\mathit{i}\mathit{t}}$ 0.133***
(3.07)
$\mathit{C}?\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{c}\mathit{u}\mathit{s}\mathit{t}\mathit{o}\mathit{m}\mathit{e}{\mathit{r}}_{\mathit{i}\mathit{t}}$ 0.065*
(1.71)
控制变量 Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes
城市-年份固定效应 No Yes Yes Yes
N 2437 2437 2437 2437
r2_a 0.948 0.945 0.945 0.944
F 32.454 38.849 39.235 38.700
), ArticleFig(id=1316695265617232088, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表2, caption=

基准回归结果

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4)
企业自身供应链数字化 产业链供应链数字化 上游供应链数字化 下游供应链数字化
Digitalit -0.115***
(-3.69)
Chaindigitalit 0.168***
(3.04)
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{s}\mathit{u}\mathit{p}\mathit{p}\mathit{l}{\mathit{y}}_{\mathit{i}\mathit{t}}$ 0.133***
(3.07)
$\mathit{C}?\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{c}\mathit{u}\mathit{s}\mathit{t}\mathit{o}\mathit{m}\mathit{e}{\mathit{r}}_{\mathit{i}\mathit{t}}$ 0.065*
(1.71)
控制变量 Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes
城市-年份固定效应 No Yes Yes Yes
N 2437 2437 2437 2437
r2_a 0.948 0.945 0.945 0.944
F 32.454 38.849 39.235 38.700
), ArticleFig(id=1316695265701118169, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 3, caption=

Industrial chain synergy

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
供应链数字化非试点企业 供应链数字化试点企业
产业链供应链数字化程度较高组 产业链供应链数字化程度较低组
Chaindigitalit 0.169** 0.341** 0.140
(2.28) (2.39) (1.15)
控制变量 Yes Yes Yes
企业固定效应 Yes Yes Yes
年份固定效应 Yes Yes Yes
城市-年份固定效应 Yes Yes Yes
N 913 920 334
r2_a 0.950 0.942 0.960
F 14.803 14.361 9.411
), ArticleFig(id=1316695265785004250, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表3, caption=

产业链协同作用

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
供应链数字化非试点企业 供应链数字化试点企业
产业链供应链数字化程度较高组 产业链供应链数字化程度较低组
Chaindigitalit 0.169** 0.341** 0.140
(2.28) (2.39) (1.15)
控制变量 Yes Yes Yes
企业固定效应 Yes Yes Yes
年份固定效应 Yes Yes Yes
城市-年份固定效应 Yes Yes Yes
N 913 920 334
r2_a 0.950 0.942 0.960
F 14.803 14.361 9.411
), ArticleFig(id=1316695265881473243, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 4, caption=

Smoothing enterprise production

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2)
产出波动性 产出相对需求波动性
Chaindigitalit -0.563*** -0.332**
(-2.81) (-2.26)
控制变量 Yes Yes
企业固定效应 Yes Yes
年份固定效应 Yes Yes
城市-年份固定效应 Yes Yes
N 2285 2284
r2_a 0.149 0.110
F 2.256 1.241
), ArticleFig(id=1316695265969553628, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表4, caption=

平滑企业生产

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2)
产出波动性 产出相对需求波动性
Chaindigitalit -0.563*** -0.332**
(-2.81) (-2.26)
控制变量 Yes Yes
企业固定效应 Yes Yes
年份固定效应 Yes Yes
城市-年份固定效应 Yes Yes
N 2285 2284
r2_a 0.149 0.110
F 2.256 1.241
), ArticleFig(id=1316695266091188445, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 5, caption=

Commercial credit and corporate liquidity constraints

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5) (6)
下游供应链数字化 上游供应链数字化
销售额 应收账款比例 应收账款周转天数 采购额 应付账款比例 应付账款周转天数
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{s}\mathit{u}\mathit{p}\mathit{p}\mathit{l}{\mathit{y}}_{\mathit{i}\mathit{t}}$ 0.155** -0.021 -6.859
(2.03) (-0.62) (-0.61)
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{c}\mathit{u}\mathit{s}\mathit{t}\mathit{o}\mathit{m}\mathit{e}{\mathit{r}}_{\mathit{i}\mathit{t}}$ 0.187*** -0.040** -14.619**
(3.01) (-2.01) (-1.99)
控制变量 Yes Yes Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes Yes Yes
城市-年份固定效应 Yes Yes Yes Yes Yes Yes
N 2437 2437 2427 2435 2416 2425
r2_a 0.936 0.709 0.709 0.929 0.471 0.534
F 22.803 4.434 4.407 25.068 8.210 6.761
), ArticleFig(id=1316695266204434654, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表5, caption=

商业信贷与企业流动性约束

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5) (6)
下游供应链数字化 上游供应链数字化
销售额 应收账款比例 应收账款周转天数 采购额 应付账款比例 应付账款周转天数
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{s}\mathit{u}\mathit{p}\mathit{p}\mathit{l}{\mathit{y}}_{\mathit{i}\mathit{t}}$ 0.155** -0.021 -6.859
(2.03) (-0.62) (-0.61)
$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{c}\mathit{u}\mathit{s}\mathit{t}\mathit{o}\mathit{m}\mathit{e}{\mathit{r}}_{\mathit{i}\mathit{t}}$ 0.187*** -0.040** -14.619**
(3.01) (-2.01) (-1.99)
控制变量 Yes Yes Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes Yes Yes
城市-年份固定效应 Yes Yes Yes Yes Yes Yes
N 2437 2437 2427 2435 2416 2425
r2_a 0.936 0.709 0.709 0.929 0.471 0.534
F 22.803 4.434 4.407 25.068 8.210 6.761
), ArticleFig(id=1316695266288320735, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 6, caption=

Supply chain digitization and risk buffer

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
新冠疫情对劳动力市场的冲击 依据三分位划分 依据五分位划分
Covidict -0.564*** -1.865*** -1.259***
(-2.66) (-3.79) (-3.66)
Covidict*Treatgrait 0.738*** 0.255**
(3.47) (2.48)
控制变量 Yes Yes Yes
企业固定效应 Yes Yes Yes
年份固定效应 Yes Yes Yes
N 2405 2405 2405
r2_a 0.949 0.949 0.949
F 30.395 26.298 26.377
), ArticleFig(id=1316695266359623904, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表6, caption=

供应链数字化与风险缓冲

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3)
新冠疫情对劳动力市场的冲击 依据三分位划分 依据五分位划分
Covidict -0.564*** -1.865*** -1.259***
(-2.66) (-3.79) (-3.66)
Covidict*Treatgrait 0.738*** 0.255**
(3.47) (2.48)
控制变量 Yes Yes Yes
企业固定效应 Yes Yes Yes
年份固定效应 Yes Yes Yes
N 2405 2405 2405
r2_a 0.949 0.949 0.949
F 30.395 26.298 26.377
), ArticleFig(id=1316695266435121377, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=EN, label=Table 7, caption=

Heterogeneity analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5) (6)
融资约束较高组 融资约束较低组 国有企业 非国有企业 购销业务集中组 购销业务分散组
Chaindigitalit 0.063 0.204*** 0.406*** 0.042 0.214** -0.006
(0.71) (2.77) (3.84) (0.63) (2.38) (-0.07)
控制变量 Yes Yes Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes Yes Yes
城市-年份固定效应 Yes Yes Yes Yes Yes Yes
N 967 1005 921 1149 1119 768
r2_a 0.965 0.940 0.950 0.942 0.942 0.956
F 11.639 16.827 23.999 12.428 15.486 18.098
), ArticleFig(id=1316695266544173282, tenantId=1146029695717560320, journalId=1308397381743640680, articleId=1316695260667953328, language=CN, label=表7, caption=

异质性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
(1) (2) (3) (4) (5) (6)
融资约束较高组 融资约束较低组 国有企业 非国有企业 购销业务集中组 购销业务分散组
Chaindigitalit 0.063 0.204*** 0.406*** 0.042 0.214** -0.006
(0.71) (2.77) (3.84) (0.63) (2.38) (-0.07)
控制变量 Yes Yes Yes Yes Yes Yes
企业固定效应 Yes Yes Yes Yes Yes Yes
年份固定效应 Yes Yes Yes Yes Yes Yes
城市-年份固定效应 Yes Yes Yes Yes Yes Yes
N 967 1005 921 1149 1119 768
r2_a 0.965 0.940 0.950 0.942 0.942 0.956
F 11.639 16.827 23.999 12.428 15.486 18.098
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供应链数字化的“稳就业”效应
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考秀梅 1 , 谢申祥 2 , 占宽 3
科学学研究 | 热点议题 2026,44(1): 75-85
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科学学研究 |热点议题 2026 , 44 (1) : 75 -85
供应链数字化的“稳就业”效应
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考秀梅1, 谢申祥2 , 占宽3
作者信息
  • 1 南开大学经济学院, 天津 300071
  • 2 山东财经大学经济学院, 山东济南 250014
  • 3 武汉轻工大学经济学院, 湖北武汉 430048
通讯作者:
谢申祥(1978-),男,教授、博士生导师, E-mail:。
作者简介:

考秀梅(1996-),女,博士研究生。占宽(1993-),男,讲师。

“The employment-stabilizing” effect of supply chain digitization
Xiu-mei KAO1, Shen-xiang XIE2 , Kuan ZHAN3
Affiliations
  • 1 School of Economics, Nankai University, Tianjin 300071, China
  • 2 School of Economics, Shandong University of Finance and Economics, Jinan 250014, China
  • 3 School of Economics, Wuhan Polytechnic University, Wuhan 430048, China
出版时间: 2026-01-15
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摘要
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结合上市公司供应链研究数据,以供需网络建设为切入点,本文探讨了供应链数字化的就业效应。基于供应链创新与应用试点工作开展的双重差分估计结果显示:(1)企业自身数字化对劳动力就业具有替代效应,而产业链供应链数字化可以通过平滑企业生产、减少下游企业商业信贷资金占用行为缓解企业流动性约束,进而实现链上企业劳动力规模扩张;(2)产业链供应链数字化创新能有效缓冲供应链风险对就业市场产生的冲击,实现“稳就业”;(3)异质性分析表明,融资约束一定程度上限制了供应链数字化就业溢出效应的发挥,国有企业以及购销业务更为集中的企业受产业链供应链数字化影响,其劳动力雇佣规模提升效果更为明显。

供应链  /  数字化  /  稳就业

For a long time, problems such as information lag and cumbersome processes have been widespread under the traditional supply chain management mode. The phenomenon of information silos among enterprises has become the primary obstacle to information connection and efficiency improvement in the supply chain. With the emergence of the digital era, the application of big data, artificial intelligence, the Internet of Things, cloud computing and blockchain technology has successively empowered supply chain management, injecting new vitality into the innovation and efficiency improvement of modern supply chain management models. There are very few studies in the existing literature that examine the employment spillover effects of digital technology application in supply chains. From the perspective of supply chain network construction, This paper intends to explore the impact of supply chain digital technology application on the scale of labor employment of enterprises, and further analysis the impact of the collaborative application of supply chain digital technology of other enterprises in the industrial chain on the scale of labor employment of enterprises in the chain. Compared with the existing literature, the potential contributions of this paper are as follows: Firstly, different from traditional literature that focuses on the analysis of the substitution effect and creation effect of digital transformation on labor employment of enterprises, this paper takes the application of digital technology in supply chain network as an entry point to discuss the spillover effect of supply chain network construction on labor employment of enterprises in the chain. Specifically, this paper clarifies two paths of the employment spillover effect of supply chain digitization from the aspects of commercial credit spillover along the supply chain and smoothing enterprise production, which enriching the existing research on the factors affecting employment in the labor market. Secondly, this paper focuses on the analysis of the synergy between the upstream and downstream of the supply chain network, emphasizing the win-win cooperation of multiple subjects from the perspective of the supply chain, providing theoretical support and empirical evidence for the subsequent promotion and construction of industrial chain digitization. Based on the supply chain research data of listed companies and the perspective of supply and demand network construction, this paper discusses the employment effect of supply chain digitization. The difference-in-differences results show that the enterprise's digitization has a substitution effect on labor employment, while the supply chain digitization within industrial chain can effectively improves the enterprises’ employment scale by smoothing production and reducing the downstream enterprises’ occupation of commercial credit funds, which helps to ease the liquidity constraints of enterprises. In addition, industrial supply chain digitization can effectively cushion the impact of supply chain risks on the labor market, which helps to achieve "the employment-stabilizing". Heterogeneity analysis shows that the employment-stabilizing effect of industrial supply chain digitization is limited by the financing constraints, state-owned enterprises and the enterprises with concentrated purchasing and marketing business show greater spillover effect on employment.The research conclusions of this article are of great significance for the subsequent development of digitized supply chain construction, the maintenance of supply chain security and stability.

supply chain  /  digitization  /  employment-stabilizing
考秀梅, 谢申祥, 占宽. 供应链数字化的“稳就业”效应. 科学学研究, 2026 , 44 (1) : 75 -85 .
Xiu-mei KAO, Shen-xiang XIE, Kuan ZHAN. “The employment-stabilizing” effect of supply chain digitization[J]. Studies in Science of Science, 2026 , 44 (1) : 75 -85 .
正文
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建设现代化供应链体系是畅通国内循环、衔接参与国际循环的重要保障。长期以来,传统供应链管理模式下信息滞后、流程琐碎繁杂问题普遍存在,企业间信息孤岛现象已成为困扰企业间信息对接与供应链效率提升的首要障碍。随着数字时代的到来,大数据、人工智能、物联网、云计算以及区块链技术的应用先后赋能供应链管理,为现代化供应链管理模式创新与效率提升注入了新的活力。
数字技术应用赋能经济发展的优势已被广泛认同并得以验证[1,2],但数字技术对就业的影响效应研究仍未形成一致的结论。一方面,数字技术正迅速侵占过去仅属于人类的技能,并直接替代了部分工作岗位,冲击了劳动力就业水平[3];另一方面,数字化在降低成本、提高生产效率方面的优异表现不仅助推了产业结构升级,更是促进了企业规模的扩张,为劳动力就业提供了更多岗位支撑。
纵观现有关于数字化就业效应探讨的相关文献,一方面,多数文献关注于数字技术应用对其所在企业本身劳动力雇佣数量与雇佣结构的影响[4,5],却忽略了数字技术沿产业链的溢出效应。事实上,数字技术不仅作用于企业自身发展,更是对上下游企业产生了明显溢出,并最终作用于企业生产规模以及企业就业吸纳规模;另一方面,多数文献侧重于考察企业整体数字化运营效率的提升对企业就业吸纳规模的影响,而未着重关注数字化技术应用于供应链构建与供应链管理进而对企业产生的溢出效应。供应链数字技术应用对于突破供给侧卡点、脆弱点,疏通产业链堵点、痛点,增强产业链供应链韧性与安全性具有重要意义,其对企业供应链网络稳定性影响巨大,更易于对链上企业产生溢出作用。
基于此,本文拟基于供应链网络建设视角,探究供应链数字技术应用对企业自身劳动力雇佣规模的影响,并在此基础上进一步考察产业链上其它企业供应链数字技术应用对链上企业劳动力雇佣规模的影响。本文的潜在贡献在于:首先,本文以数字技术在供应链网络内的应用为切入点,探讨其在供应链网络建设的表现对链上企业劳动力就业的影响,特别地,本文从商业信贷沿供应链溢出及平滑企业生产两个方面明确了供应链数字化就业溢出效应的两条路径,填补了现有关于劳动力市场就业影响因素的相关研究中基于供应链网络建设视角的空白,是对现有研究的补充;其次,本文聚焦于供应链网络上下游协同性分析,强调供应链视角上多主体的协同共赢,为后续产业链数字化推广与建设提供了理论支撑与经验证据。
1 制度背景与理论分析
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1.1 制度背景
在党中央的高度重视下,大力发展数字经济已上升为国家战略,数字技术应用于供应链的设想相继推进并得以实践。为深入贯彻并推动供应链体系改革,2018年4月,商务部等8部门发布了《关于开展供应链创新与应用试点的通知》,文件指出将以城市及企业为单位开展供应链创新与应用试点工作,并于同年10月最终确定55座城市及266家企业为试点单位开展为期两年的探索。智慧供应链与数字供应链高度一致的内涵意味着供应链创新与应用针对于供应链数字化创新的政策指向[6-8]。从试点企业的主要任务来看,《通知》指出试点的主要任务是应用现代信息技术,创新供应链技术和模式,实现数据对接,带动上下游企业形成高效的产业供应链。从试点城市的重要任务来看,文件也多次指出要推动企业打造供需对接、资源整合的供应链协同平台,实现系统互联和数据共享。
上述供应链创新与应用试点工作的开展为本文识别供应链数字化创新的“稳就业”效应提供了良好的准自然实验,在此政策背景下,参照刘海建等[8]做法,本文以供应链数字化创新与应用试点工作推行作为企业参与供应链数字化的标志,进而开展后续相关分析。
1.2 理论分析
数字化技术应用显然引致了企业生产方式的转变,并对劳动力市场带来了巨大变数。当智能化数字技术相比于劳动力而言更具比较优势时,企业将投入更多资本开展数字化转型,这在一定程度上必然增加企业对与数字化技术互补的高技能劳动力的需求,却也不可避免地减少了与数字化技术存在替代性的从事常规工作的低技能劳动力需求[9]。因而,供应链数字化创新对劳动力市场整体需求的增减取决于各企业主体现有人力资本结构与供应链数字化技术应用的契合度,在理论层面上,企业自身数字化的就业效应难以判定。
然而,数字化沿产业链溢出的积极效应极大可能促进链上企业劳动力雇佣规模的扩张。已有研究已证实企业数字化沿供应链溢出的积极作用,例如企业数字化转型沿产业链条对上游供应商产生信息溢出,进而缓解“长鞭效应”[10],数字化转型提高企业商业信用供给,进而舒缓下游企业资金流约束[11],以及数字技术应用沿产业链传递实现生产率溢出与创新激励溢出等[1,2]。
基于此,本文提出:
H1:企业自身数字化既有可能创造劳动力需求,亦有可能替代现有劳动力,挤出劳动力就业岗位,而产业链内上下游企业供应链数字化创新将有效发挥数字化的供应链溢出优势,促进链上企业劳动力雇佣规模的扩张。
合理的企业生产计划与稳定的运营环境对企业劳动力雇佣规模扩张具有重要影响,而资金的充裕程度亦很大程度影响企业劳动力雇佣规模。
首先,企业生产与稳定运营角度。企业供应链数字技术应用有效避免了传统供应链中普遍存在的企业信息孤岛现象,有助于纠正企业间信息扭曲偏差,提高供应链匹配度[12]。
信息传递效率的提升对企业稳定运营与平滑企业生产具有重要影响。一方面,企业间信息传递成本得以降低。以物联网和大数据为底层框架而搭建的供应链数字化智能互动平台使得企业信息更易于分享并传递至其外部主体[13],上下游企业原本无法被观测的私人信息(如产品价格信息、产品种类信息以及产品质量等信息)将转变为公共信息,大大降低链上企业的信息搜寻成本[10];另一方面,企业间信息传递精度得以提升。供应链数字化实现了对采购、生产、销售全过程数据的搜集,对数据的采集与整合可实现对供应链全过程的及时监控与分析,智能化分析系统可对包含供应商选择、生产计划、运输规划及库存管理在内的各环节做出科学的数字化决策,提升企业决策效率与准确性。新一代信息技术的应用使得企业可以及时记录供应链网络内企业需求变化及供给变动,并对未来生产及销售状况进行合理预测[10]。就上游厂商而言,下游企业供应链数字化建设能够有效提升其产需感知预测精度与反应速度,提升其向上游供应商企业传递的信息精度,保证信息传递时效,进而削弱需求冲击沿供应链向上游传递的冲击强度,平滑企业生产,稳定企业运营;就下游厂商而言,上游企业供应链数字化管理平台的建设能够使其更好地分析下游客户需求状况,及时调整产品生产,备调存货,提升其对下游企业的供货效率,保障下游企业的供货需要。
由此,本文提出:
H2:产业链内上下游企业供应链数字化建设将有效提升企业间信息传递效率,降低链上企业产出波动性,平滑企业生产,稳定企业运营,进而提升链上企业劳动力雇佣规模。
其次,资金获取角度。作为非正式金融的代表,商业信贷已成为企业融资的重要来源。商业信贷能够有效缓解供应链企业融资约束,然而,企业试图掩盖自身经营困境、夸大企业经营绩效以获取商业信贷的行为明显加剧了商业信用风险,上游企业因此减少商业信用供给以及下游企业延迟支付货款、占用供应链资金的行为严重阻碍了供应链资金的高效流通,降低了商业信贷沿供应链溢出的可实现性。
随着供应链数字技术的推广,供应链可视性的实现以及生产流程数据化的转变使得企业间信息不对称问题得到大幅缓解,商业信用坏账风险大大降低[14],例如,区块链技术的应用有效避免了企业篡改经营数据、编造产品供货信息的可能性。作为商业信用的提供方,上游企业为下游企业提供商业信用供给以稳固供应链关系的意愿将会提升,这表现为上游企业将为下游企业提供更高的货款赊账规模及更长的货款收款周期[15]。
此外,供应链数字化创新深刻改变了企业生产经营模式,有助于提升企业生产效率[16]。供应链数字化发展使得下游客户能够更多参与到产品研发、物流运输等重要运营过程中,有助于挖掘市场新需求,塑造供应链核心竞争力,提升企业营业收入[17]。下游企业自身经营状况的改善以及企业自身资金流通速率的提升将沿供应链传递至其上游企业,作为商业信贷的偿还方,下游客户能够更快向其供应商支付货款[14],减少其对上游企业商业信贷资金的占用行为,提升供应链资金周转效率,进而为上游企业劳动力雇佣规模扩张提供资金保障。
基于此,本文提出:
H3:供应链数字技术应用有助于缓解企业间信息不对称问题,提升上游企业商业信用供给意愿;同时,下游企业供应链数字化能够显著减少其对上游企业商业信用资金占用行为,有效缓解链上企业流动性约束,进而助力企业劳动力规模扩张。
2 研究设计与数据说明
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2.1 研究设计
供应链创新与应用试点工作的开展为本文研究供应链数字化的就业溢出效应提供了较好的切入点。区别于传统基于数字化企业自身视角的探讨,本文在此基础上纳入了基于产业链供应链视角的考察,探究了产业链供应链数字化发展对其链上企业劳动力雇佣水平的影响。
首先,参照(1)式模型就企业自身供应链数字化创新的就业效应进行检验:
$\mathit{L}\mathit{n}\mathit{s}\mathit{t}\mathit{a}\mathit{f}{\mathit{f}}_{\mathit{i}\mathit{t}}={\mathit{\alpha }}_{0}+{\mathit{\alpha }}_{1}\mathit{D}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}{\mathit{l}}_{\mathit{i}\mathit{t}}+\mathit{\alpha }\mathit{\text{'}}{\mathit{X}}_{\mathit{i}\mathit{t}}+{\mathit{\lambda }}_{\mathit{i}}+{\mathit{\lambda }}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$
其中,i、t分别代表企业以及年份。
本文被解释变量为焦点企业员工规模,以Lnstaffit表示。Digitalit为企业是否参与供应链数字化的二元变量,若企业i为试点企业或试点地区内的企业,且$\mathit{t}\ge 2018$,则定义Digitalit为1;否则,为0。
此外,模型中亦纳入一系列控制变量Xit,主要包括企业财务状况指标以及企业经营结构指标,如企业盈利能力、企业资产负债率、现金资产比例、固定资产比例、企业规模、企业年龄变量、托宾Q值;λi、λt分别为企业、年份固定效应;εit为误差项。
其次,参照(2)式模型就产业链供应链数字化创新的就业效应进行检验:
$\mathit{L}\mathit{n}\mathit{s}\mathit{t}\mathit{a}\mathit{f}{\mathit{f}}_{\mathit{i}\mathit{t}}={\mathit{\beta }}_{0}+{\mathit{\beta }}_{1}\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}{\mathit{l}}_{\mathit{i}\mathit{t}}+\mathit{\beta }\mathit{\text{'}}{\mathit{X}}_{\mathit{i}\mathit{t}}+{\mathit{\lambda }}_{\mathit{i}}+{\mathit{\lambda }}_{\mathit{t}}+{\mathit{\lambda }}_{\mathit{c}\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$
其中,i、c、t分别代表企业、所在城市以及年份。
Chaindigitalit为刻画企业所在产业链供应链数字化参与程度的连续变量,该变量定义步骤如下:第一步,根据供应链创新与应用试点地区与企业名单将焦点企业供应商/客户中当年属于供应链数字化试点的企业定义为1,否则设置为0;第二步,以企业供应商采购/客户销售占比为权重对企业进行加权,得到企业采购/销售产品中来自供应链数字化试点企业的比例,以此代表企业上游和下游参与供应链数字化创新程度;第三,取企业上游供应商与下游客户供应链数字化参与程度的均值。此外,本文亦将Chaindigitalit变量拆分为企业上游供应链数字化参与程度变量$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{s}\mathit{u}\mathit{p}\mathit{p}\mathit{l}{\mathit{y}}_{\mathit{i}\mathit{t}}$与企业下游供应链数字化参与程度变量$\mathit{C}\mathit{h}\mathit{a}\mathit{i}\mathit{n}\mathit{d}\mathit{i}\mathit{g}\mathit{i}\mathit{t}\mathit{a}\mathit{l}\_\mathit{c}\mathit{u}\mathit{s}\mathit{t}\mathit{o}\mathit{m}\mathit{e}{\mathit{r}}_{\mathit{i}\mathit{t}}$,分别探讨上游供应链数字化创新与下游供应链数字化创新对企业劳动力雇佣水平的不同影响程度及路径。特别地,模型(2)进一步纳入了城市-时间固定效应λct以控制随时间变化的城市层面因素对企业劳动力雇佣规模的干扰。
本文主要变量以及相关控制变量的定义如表1所示①（① 上市公司并未被强制要求披露企业供应商/客户具体信息,本文仅保留了披露具体企业名称信息的样本。针对该问题可能导致的样本选择偏误,后文也进行了相应稳健性检验。）:
2.2 数据来源与描述性统计
本文回归依托于政策冲击前后企业劳动力规模变动开展后续分析,为尽可能考察到企业受供应链数字化影响的长期效应,本文选用样本期间为2013-2022年,该时段涵盖了供应链创新与应用试点推行过程,可以较好识别供应链数字化的影响。进一步,本文选取A股上市公司为主要研究对象,数据来源于国泰安(CSMAR)数据库,参照已有文献做法,本文对异常值进行了剔除,并进一步剔除了ST和*ST类企业以及金融保险业上市公司。
3 实证结果分析
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3.1 基准回归结果
对假说1进行验证并将基准回归结果汇报于表2。列(1)为参照模型(1)进行分析的回归结果,结果显示企业自身供应链数字化对劳动力产生了替代效应,显著降低了企业劳动力雇佣规模,这表明当前阶段企业自身供应链数字化的就业创造效应小于就业替代效应。列(2)为参照模型(2)进行分析的回归结果,结果表明企业所在产业链条内上下游企业供应链数字化创新存在就业创造效应,有助于焦点企业劳动力雇佣规模的扩张。细分上、下游数字化程度的回归结果表明无论是上游企业供应链数字化还是下游企业供应链数字化,其都将对其产业链上合作企业员工规模扩张产生促进效应。
3.2 供应链数字化的产业链协同效应
供应链数字化创新效应的发挥依赖于上下游供应链网络数据的有效对接,上下游企业供应链数字化的协同创新对于供应链效应优势的发挥具有重要意义。为考察产业链上供应链数字化创新的协同是否影响其就业效应的发挥,进一步进行如下检验。首先,检验企业自身参与数字化创新是否会影响产业链供应链数字化就业创造效应的发挥,以企业是否为供应链数字化试点企业为依据将样本划分为供应链数字化试点企业与非试点企业,针对非试点企业样本进行回归。结果如表3列(1)所示,产业链供应链数字化水平的提升对于非试点的焦点企业具有明显的就业溢出效应。其次,针对试点企业进行考察,并以试点企业所在产业链数字化创新的处理强度变量的中位数为依据划分样本,进行分组回归。回归结果如列(2)-列(3)所示,当产业链供应链数字化水平较低时,其虽未能有效补偿企业自身数字化导致的就业挤出,但随着产业链供应链数字化水平的提升,产业链供应链数字化能够有效发挥协同作用,进而抵消企业自身数字化的就业岗位挤出,并产生较非试点焦点企业下更大的就业创造效应。
以上分析表明,尽管供应链数字化对链上非试点合作企业存在明显的就业溢出效应,但对试点企业而言,其就业效应明显依赖于产业链协同水平。因此,产业链整体供应链数字化水平的提升与协同发展对“稳就业”目标的实现具有重要战略意义。未来供应链数字化建设需着重关注供应链数字技术在产业链上的协同部署与同步应用,进而确保产业链协同就业效应的充分实现。
3.3 平行趋势检验
本文基于双重差分法识别供应链数字化的就业效应,因而处理组与控制组在政策冲击前需满足平行趋势。本文将事前第4期之前年份压缩至事前第4期进行相应回归,平行趋势检验结果如图1所示,政策冲击前处理组与控制组并未表现出显著差异,表明两组别间具有可比性,平行趋势满足。
3.4 稳健性检验
本文基于以下五方面进行了稳健性检验②（② 篇幅限制,稳健性检验的相关说明省略,备索。）。第一,排除企业信息披露的样本自选择偏误问题;第二,考察样本数据量对本文回归结论的干扰;第三,供应链数字化变量基于推广经验的再定义;第四,剔除企业自身数字化水平对识别产业链数字化就业效应的影响;第五,安慰剂检验。以上分析结论与本文基准分析结果一致,验证了本文回归结果的稳健性。
4 拓展分析
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4.1 作用路径
第一,平滑企业生产。为检验产业链供应链数字化能否平滑企业生产,以企业季度生产量的波动性VAR(Productionit)衡量企业生产状况,其中,VAR为年度范围内的季度方差,代表数值波动性,Productionit代表企业季度生产量,企业产量以企业营业成本与新增存货之和代替。回归结果如表4列(1)所示,产业链供应链数字化创新显著降低了焦点企业产出波动性,有助于平滑企业生产。
更进一步,参照Cachon等[18]、Shan等[19]对“长鞭效应”的度量方法,以产出相对需求波动性的比值对产业链供应链数字化建设的信息溢出效应进行检验,其中,企业需求量以企业营业收入表示。回归结果如表4列(2)所示,产业链内供应链数字化显著降低了链上企业产出相对于需求的波动幅度,这意味着产业链供应链数字化能够有效提升供应链信息传递效率,实现信息溢出,平滑企业生产。
第二,商业信贷与企业流动性约束。对假说3进行检验,表5列(1)-列(2)结果显示下游企业供应链数字化显著提升了焦点企业对其前五大客户的总销售额,且焦点企业应收账款比例显著下降。通常而言,应收账款比例下降既可能是由于下游企业资金偿付比例的提升,也有可能是计提坏账损失所导致,为进一步明晰焦点企业应收账款比例下降的缘由,进一步结合焦点企业应收账款周转天数进行分析,结果如列(3)所示,焦点企业应收账款周转天数明显下降表明企业资金回笼效率的提升,这意味着企业应收账款比例下降更可能源于下游企业资金偿付比例的提升。以上分析验证了下游企业数字化减少供应链商业信用资金占用行为,缓解上游企业流动性约束进而提升链上企业劳动力雇佣规模的路径。
相应地,列(4)-列(6)结果表明上游企业供应链数字化明显增加了焦点企业对其前五大供应商的总采购额,但并未对焦点企业应付账款比例与应付账款周转天数产生影响,这表明商业信用供给并未因上游企业数字化转型而增加。产业链供应链数字化对链上企业的商业信用溢出效应可能因议价能力与垄断能力而异[20],垄断能力较强的企业通常倾向于减少甚至不提供其商业信用供给[21]。因而,尽管上游企业参与供应链数字化改革,但其较强的垄断能力亦可能使其无需以提高商业信用供给的方式加固其供应链关系,商业信用供给由上游向下游的传递渠道无法实现。
表5分析综合说明并验证了产业链供应链数字化尤其是下游企业供应链数字化加快资金偿付速率、减缓对上游企业的供应链资金占用问题,以实现舒缓上游企业流动性约束限制,进而提高上游企业劳动力雇佣水平的路径。
4.2 供应链数字化的风险缓冲能力
供应链数字化创新能明显提升供应链运行效率及供应链韧性,从而更好应对供应链风险冲击,减缓其对就业市场的影响。本文以新冠疫情作为供应链风险冲击的代表事件,构建如下(3)、(4)式计量模型进行检验:
$\mathit{L}\mathit{n}\mathit{s}\mathit{t}\mathit{a}\mathit{f}{\mathit{f}}_{\mathit{i}\mathit{t}}={\mathit{\gamma }}_{0}+{\mathit{\gamma }}_{1}\mathit{C}\mathit{o}\mathit{v}\mathit{i}{\mathit{d}}_{\mathit{i}\mathit{c}\mathit{t}}+\mathit{\gamma }\mathit{\text{'}}{\mathit{X}}_{\mathit{i}\mathit{t}}+{\mathit{\lambda }}_{\mathit{i}}+{\mathit{\lambda }}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$
$\mathit{L}\mathit{n}\mathit{s}\mathit{t}\mathit{a}\mathit{f}{\mathit{f}}_{\mathit{i}\mathit{t}}={\mathit{\delta }}_{0}+{\mathit{\delta }}_{1}\mathit{C}\mathit{o}\mathit{v}\mathit{i}{\mathit{d}}_{\mathit{i}\mathit{c}\mathit{t}}+{\mathit{\delta }}_{2}\mathit{C}\mathit{o}\mathit{v}\mathit{i}{\mathit{d}}_{\mathit{i}\mathit{c}\mathit{t}}\mathrm{*}\mathit{T}\mathit{r}\mathit{e}\mathit{a}\mathrm{t}\mathrm{g}\mathit{r}{\mathit{a}}_{\mathit{i}\mathit{t}}+{\mathit{\delta }}_{3}\mathit{T}\mathit{r}\mathit{e}\mathit{a}\mathrm{t}\mathrm{g}\mathit{r}{\mathit{a}}_{\mathit{i}\mathit{t}}+\mathit{\delta }\mathit{\text{'}}{\mathit{X}}_{\mathit{i}\mathit{t}}+{\mathit{\lambda }}_{\mathit{i}}+{\mathit{\lambda }}_{\mathit{t}}+{\mathit{\epsilon }}_{\mathit{i}\mathit{t}}$
其中,Covidict为企业i所在城市c受新冠疫情冲击的影响程度,具体以年末累计确诊人数占全国总确诊人数的比重进行衡量,Treatgrait为企业所在产业链供应链数字化创新的处理强度变量,分别基于城市-年份维度内三分位与五分位水平对其处理强度进行赋值,赋值越高,表明企业所在产业链供应链数字化水平越高。值得说明的是,Covidict变量为城市-年度层面数据,因而,此处未纳入城市-年度固定效应。
回归结果如表6所示,列(1)表明新冠疫情严重冲击了劳动力市场,降低了企业劳动力雇佣规模。基于三分位与五分位对Treatgrait进行赋值、参照式(4)进行回归的检验结果列于列(2)-列(3),随着企业所在产业链供应链数字化水平的提升,新冠疫情对劳动力就业市场的冲击幅度明显降低,表明产业链供应链数字化可有效缓冲供应链风险对就业市场的冲击,助力“稳就业”的实现。
4.3 异质性分析
第一,企业融资约束差异。企业运营资金短缺将遏制企业员工规模的扩张,不利于“稳就业”,相反,较为充裕的资金将为企业员工规模扩张提供更多支撑与空间。依据SA指数是否高于其中值将样本划分为融资约束较高组以及融资约束较低组,分别进行回归,结果如表7列(1)-列(2)所示,融资约束较低组劳动力就业溢出效应更明显,表明融资约束是影响产业链供应链数字化就业效应溢出的重要因素,当企业融资约束较低时,供应链数字化沿产业链溢出的就业创造效应更为显著。
第二,企业股权性质差异。股权性质的差异决定了国有企业在发展国家经济构建新发展格局中承担了更多政策性任务,这包括但不限于在经济下行期雇佣更多员工[22]。以企业是否为国有企业为标准进行分组检验,结果如表7列(3)-列(4)所示,相比于非国有企业,国有企业由于上下游供应链数字化创新产生了更多的劳动需求,创造了更多就业岗位。
第三,购销业务集中度差异。企业购销业务集中度差异可能影响产业链供应链数字化就业效应的发挥。一方面,购销相对集中,与大客户的合作更能有效促进上下游企业间资源与信息整合,提升企业运营效率与企业销售稳定性[23],从而扩大企业劳动力雇佣规模;另一方面,相对分散的购销业务使得企业在与其上下游合作时具有更强的议价能力,从而更有可能获得更多的商业信用供给,舒缓其流动性约束,促进企业劳动力雇佣规模提升。依据企业前五大合作伙伴贸易占比高低将样本划分为购销业务集中组与购销业务分散组,分别进行回归,结果如表7列(5)-列(6)所示,结果表明构建集中的合作关系更有助于供应链数字化协同优势的发挥,助力“稳就业”的实现。
5 结论与政策建议
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本文研究表明企业自身数字化建设对企业劳动力产生了就业替代效应,而上下游供应链数字化水平的提升则可有效抵消该种就业替代效应,沿供应链溢出有效促进链上企业劳动力规模的扩张。进一步分析表明,平滑企业生产、稳定企业运营以及降低下游企业在商业信贷中的资金占用行为、缓解企业流动性约束是产业链供应链数字化促进企业劳动力规模扩张的两大路径。此外,产业链供应链数字化可有效缓冲供应链断裂风险对劳动力市场的冲击,助力“稳就业”目标的实现。融资约束限制了产业链供应链数字化“稳就业”效应的发挥,国有企业相比非国有企业受溢出效应影响吸纳了更多劳动力,且购销业务更为集中的企业劳动力规模扩张更明显。
本文研究结论对于后续供应链数字化建设的开展、供应链安全与稳定的维护和“保市场主体”稳就业压力具有重要意义。基于以上结论,我们提出如下建议:首先,切实把握供应链数字化发展机遇,充分发挥数字化应用在供应链领域的积极作用,为“保市场主体”“稳就业”提供技术支撑。具体而言,要加快推进大数据、人工智能、云计算以及区块链等数字技术在供应链领域的嵌入,提高上下游企业间管理协作与信息对接能力,积极推动企业生产、运输、销售全领域管理效率的提升;其次,着重关注产业链关联溢出效应,积极推进供应链数字化转型在产业链领域的全覆盖,尽可能确保供应链“协同效应”最大效能的发挥。具体而言,政府应承担起相应引导及推广工作,为链上企业联系并提供能够给予大数据、人工智能、云计算及区块链技术支撑并帮助企业实现相应供应链数字化转型业务的公司与服务,必要时给予补贴优惠,助力产业数字化的实现;最后,针对供应链数字化就业溢出效应在不同企业表现异质性的存在,要因地制宜、因时制宜、精准施策。从影响企业供应链数字化就业溢出效应发挥的因素来看,融资约束是直接限制因素,因而推进供应链数字化过程需要充分考虑企业融资约束状况,为企业数字化建设融资服务提供一定的宽松度,激发市场主体活力,助力供应链效率提升沿产业链溢出发挥作用。
基金
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  • 国家社会科学基金资助项目(22VRC107)
  • 国家社会科学基金资助项目(23AZD034)
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  • 接收时间：2024-11-04
  • 首发时间：2026-10-13
  • 出版时间：2026-01-15
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  • 收稿日期：2024-11-04
  • 修回日期：2025-03-07
基金
国家社会科学基金资助项目(22VRC107)
国家社会科学基金资助项目(23AZD034)
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    1 南开大学经济学院, 天津 300071
    2 山东财经大学经济学院, 山东济南 250014
    3 武汉轻工大学经济学院, 湖北武汉 430048

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谢申祥(1978-),男,教授、博士生导师, E-mail:。
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