The Impact of Digital Talent Inflow on the Co-Agglomeration of the Digital Economy Industry and Manufacturing
Abstract
:1. Introduction
2. Literature Review
3. Theoretical Mechanism
4. Indicator Selection and Model Setup
4.1. Explanatory Variable
4.2. Explained Variable
4.3. Control Variables
4.4. Data Description
4.5. Benchmark Regression Model Specification
5. Empirical Results and Analysis
5.1. Analysis of the Spatiotemporal Evolution Characteristics of Digital Talent Inflow and the Co-Agglomeration of the Digital Economy Industry and Manufacturing
5.1.1. Analysis of the Spatiotemporal Evolution Characteristics of Digital Talent Inflow
5.1.2. Analysis of the Spatiotemporal Evolution Characteristics of the Co-Agglomeration of the Digital Economy Industry and Manufacturing
5.2. Benchmark Regression Results
5.3. Endogeneity Test
5.3.1. Granger Causality Tests
5.3.2. Instrumental Variable Method
5.4. Robustness Test
5.4.1. Alternative Variable Measurement Methods
5.4.2. Changing the Statistical Model
5.4.3. Winsorization
5.4.4. Adjusting the Sample Period
5.4.5. Multi-Dimensional Fixed Effects
5.4.6. Inclusion of Lagged Terms
5.4.7. Nonlinearity Test
5.4.8. Omitted Variable Test
5.5. Analysis of the Mechanism of Action
5.5.1. Model Specification
5.5.2. Mechanism of Digital Technology Innovation
5.5.3. Mechanism of Digital Knowledge Spillover
5.5.4. Mechanism of Digital Knowledge Flow
5.5.5. Mechanism of Entrepreneurial Activity in Digital Enterprises
5.5.6. Mechanism of Urban Industrial Structure Upgrading
5.5.7. Mechanism of Digital Economy Policy
5.6. Heterogeneity Test
5.6.1. Heterogeneity in Manufacturing Levels
5.6.2. Heterogeneity of Digital Infrastructure
5.6.3. Heterogeneity of City Levels
5.6.4. Heterogeneity of Economic Structure
5.7. Spatial Spillover Effect Analysis
5.7.1. Spatial Econometric Model and Spatial Weight Matrix Settings
5.7.2. Spatial Autocorrelation Test
5.7.3. Results of Spatial Spillover Effect Tests
6. Discussion and Practical Implications
7. Conclusions and Recommendations
7.1. Conclusions
7.2. Recommendations
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
- ①
- The patent has been cited 0 times globally, involving one IPC subgroup. It has one R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 1. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTjc%2Bia3DlyNy2r4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ②
- The patent has been cited 0 times globally, covering three IPC subgroups. It has one R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 3. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTgIVAapXs1ivGr4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ③
- The patent has been cited 0 times globally, encompassing 21 IPC subgroups. It has seven R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 5. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTioXUtEwpcGEmr4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ④
- The patent has been cited eight times globally, involving two IPC subgroups. It has one R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 8. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTg7hW7UgtYpU2r4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ⑤
- The patent has been cited five times globally, pertaining to one IPC subgroup. It has two R&D personnel invested, no licensing has occurred, but it has been transferred twice, and the technological advancement score is 9. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTiLRkGif1%2F0Q2r4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ⑥
- The patent has been cited eight times globally, encompassing five IPC subgroups. It has two R&D personnel invested, no licensing has occurred, but it has been transferred once, and the technological advancement score is 10. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTh%2F%2FdXTEDAbPWr4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ⑦
- The patent has been cited 23 times globally, covering nine IPC subgroups. It has 10 R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 10. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDThswefhd%2Bl%2Fg2r4kAd0KKkg&local=zh (accessed on 9 August 2024)
- ⑧
- The patent has been cited 41 times globally, involving two IPC subgroups. It has four R&D personnel invested, no licensing or transfer has occurred, and the technological advancement score is 10. URL: https://www.incopat.com/detail/init2?formerQuery=3eQEo0gaDTielNTJVgZfGGr4kAd0KKkg&local=zh (accessed on 9 August 2024)
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Core Classification | Industry Segmentation of the National Economy | Core Classification | Industry Segmentation of the National Economy |
---|---|---|---|
Digital product manufacturing | Manufacturing of computer | Digital technology applications | |
Manufacturing of communication equipment | Software development | ||
Manufacturing of broadcasting and television equipment | Integrated circuit design | ||
Manufacturing of radar and supporting equipment | Information system integration services | ||
Manufacturing of nonprofessional audiovisual equipment | IoT technology services | ||
Manufacturing of smart consumer devices | Operation and maintenance services | ||
Manufacturing of electronic devices | Information processing and storage support services | ||
Manufacturing of electronic components and specialized electronic materials | Information technology consulting services | ||
Manufacturing of other electronic devices | Digital content services | ||
Digital product services | Retail of computers, software, and auxiliary equipment | Other information technology service industries | |
Retail of communication equipment | Telecommunication | ||
Digital factor-driven industries | Internet platform | Broadcasting and television transmission services | |
Broadcast | Satellite transmission services | ||
Television | Internet access and related services | ||
Film and television program production | Internet search services | ||
Integrated broadcasting and television control | Internet security services | ||
Distribution of movies and radio and television programs | Internet data service | ||
Movie screening | Other Internet-related services | ||
Sound recording |
Variable | Unit | N | Mean | SD | Min | Max |
---|---|---|---|---|---|---|
The co-agglomeration of the digital economy industry and manufacturing | / | 4573 | 0.600 | 0.403 | 0.061 | 6.936 |
Digital talent inflow | / | 4573 | 1.200 | 1.519 | 0.100 | 22.278 |
Level of financial development | Ten thousand yuan | 4573 | 17.234 | 1.219 | 14.240 | 21.848 |
Level of informatization | % | 4573 | 2.618 | 1.969 | 0.232 | 29.434 |
Economic development level | Billion | 4573 | 7.259 | 0.994 | 4.251 | 10.707 |
Level of openness | USD/10,000 people | 4573 | 13.135 | 1.686 | 5.492 | 16.877 |
Level of urbanization | % | 4573 | 53.967 | 16.206 | 3.191 | 100.000 |
Degree of government intervention | Ten thousand yuan | 4573 | 14.642 | 0.946 | 11.721 | 18.358 |
Industrial structure | % | 4573 | 87.372 | 8.193 | 50.110 | 99.970 |
Investment in innovative personnel | People | 4573 | 8.478 | 1.170 | 3.689 | 13.483 |
Total market size | Ten thousand yuan | 4573 | 15.444 | 1.090 | 12.119 | 19.013 |
Transportation infrastructure | Kilometer | 4573 | 6.427 | 0.973 | 1.099 | 9.614 |
Variable | Cor | |||||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
DT | 0.172 *** | 0.161 *** | 0.208 *** | 0.125 ** | 0.128 ** | 0.199 *** |
(0.041) | (0.040) | (0.045) | (0.050) | (0.050) | (0.045) | |
Constant | 0.394 *** | 0.407 *** | 0.350 *** | −0.639 | −0.123 | −0.175 |
(0.042) | (0.042) | (0.054) | (0.472) | (0.509) | (1.249) | |
N | 4573 | 4573 | 4573 | 4573 | 4573 | 4573 |
Adj R2 | 0.420 | 0.524 | 0.822 | 0.509 | 0.563 | 0.828 |
Control variables | NO | NO | NO | YES | YES | YES |
Urban fixed effects | NO | YES | YES | NO | YES | YES |
Fixed year effects | NO | NO | YES | NO | NO | YES |
Explanatory Variable | Explained Variable | Lag Order Test | Granger Causality Test | ||
---|---|---|---|---|---|
Order | BIC | p-Value | Conclusion | ||
The inflow of digital talent | The co-agglomeration of the digital economy industry and manufacturing | Lag 1 | −25,035.839 * | 0.000 | The inflow of digital talent is a Granger cause of the co-agglomeration of the digital economy industry and manufacturing. |
Lag 2 | −22,261.273 | ||||
Lag 3 | −19,969.573 | ||||
Lag 4 | −17,206 | ||||
Lag5 | −14,822.049 | ||||
The co-agglomeration of the digital economy industry and manufacturing | The inflow of digital talent | Lag 1 | −9562.043 * | 0.108 | The co-agglomeration of the digital economy industry and manufacturing is not a Granger cause of the inflow of digital talent. |
Lag 2 | −8261.085 | ||||
Lag 3 | −6824.807 | ||||
Lag 4 | −5567.507 | ||||
Lag 5 | 5307.609 |
Variable | DT | Cor |
---|---|---|
Model 1 | Model 2 | |
DT | 0.194 *** | |
(0.008) | ||
IV | 0.100 *** | |
(0.003) | ||
Kleiberen–Paap rk LM statistic | 22.942 *** | |
Kleiberen–Paap rk Wald F statistic | 113.281 | |
{16.380} | ||
Constant | −0.794 | −2.324 *** |
(1.713) | (0.487) | |
N | 4573 | 4573 |
Adj R2 | 0.861 | 0.839 |
Control variables | YES | YES |
Urban fixed effects | YES | YES |
Fixed year effects | YES | YES |
Variable | Cor1 | Cor2 | Cor3 | Cor | ||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
DT | 0.026 *** | 0.257 *** | 0.215 *** | |||
(0.007) | (0.045) | (0.050) | ||||
DT1 | 2.767 *** | |||||
(0.595) | ||||||
DT2 | 0.220 *** | |||||
(0.044) | ||||||
DT3 | 28.299 *** | |||||
(6.053) | ||||||
Constant | 2.486 *** | 1.546 | −0.464 | 0.254 | −0.192 | 0.302 |
(0.540) | (1.856) | (1.423) | (1.301) | (1.230) | (1.310) | |
N | 4573 | 4573 | 4573 | 4573 | 4573 | 4573 |
Adj R2 | 0.834 | 0.746 | 0.800 | 0.814 | 0.833 | 0.812 |
Control variables | YES | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES | YES |
Variable | Cor | ||||
---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
DT | 0.199 *** | 0.202 *** | 0.253 *** | 0.129 *** | 0.147 *** |
(0.004) | (0.044) | (0.050) | (0.034) | (0.030) | |
Constant | −2.402 *** | −0.222 | 0.316 | 0.509 | 2.699 *** |
(0.477) | (1.484) | (0.940) | (0.763) | (0.898) | |
N | 4573 | 4573 | 4505 | 3672 | 3766 |
Adj R2 | 0.829 | 0.841 | 0.798 | 0.852 | |
Control variables | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES |
Variable | Cor | |||||
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
DT | 0.199 *** | 0.243 *** | 0.279 *** | |||
(0.045) | (0.053) | (0.054) | ||||
LDT1 | 0.208 *** | |||||
(0.046) | ||||||
LDT2 | 0.218 *** | |||||
(0.046) | ||||||
LDT3 | 0.226 *** | |||||
(0.045) | ||||||
DT2 | −0.005 | |||||
(0.003) | ||||||
Constant | −0.175 | −0.733 | −0.623 | −1.087 | −1.690 | 0.088 |
(1.253) | (1.628) | (1.252) | (1.237) | (1.272) | (1.183) | |
N | 4573 | 4437 | 4304 | 4035 | 3766 | 4573 |
Adj R2 | 0.827 | 0.871 | 0.840 | 0.850 | 0.862 | 0.834 |
Control variables | YES | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES | YES |
Fixed effects of provinces | YES | NO | NO | NO | NO | NO |
Interaction terms between provinces and cities | NO | YES | NO | NO | NO | NO |
Category | Keywords |
---|---|
Artificial Intelligence Technology | Artificial intelligence, Business intelligence, Image understanding, Investment decision support system, Intelligent data analysis, Intelligent robots, Machine learning, Deep learning, Semantic search, Biometric technology, Face recognition, Speech recognition, Authentication, Autonomous driving, Natural language processing |
Big Data Technology | Big data, Data mining, Text mining, Data visualization, Heterogeneous data, Credit investigation, Augmented reality, Mixed reality, Virtual reality |
Cloud Computing Technology | Cloud computing, Stream computing, Graph computing, In-memory computing, Multi-party secure computing, Brain-like computing, Green computing, Cognitive computing, Converged architecture, Hundred-million-level concurrency, EB-level storage, Internet of Things, Cyber-physical systems |
Blockchain Technology | Blockchain, digital currency, distributed computing, differential privacy technology, intelligent financial contracts |
Application of Digital Technology | Mobile Internet, Industrial Internet, Mobile interconnection, Internet healthcare, E-commerce, Mobile payment, Third-party payment, NFC payment, Smart energy, B2B, B2C, C2B, C2C, O2O, Online payment network, Smart wearables, Smart agriculture, Smart transportation, Smart healthcare, Smart customer service, Smart home, Smart investment advice, Smart cultural tourism, Smart environmental protection, Smart grid, Smart marketing, Digital marketing, Unmanned retail, Internet finance, Digital finance, Fintech, Financial technology, Quantitative finance, Open banking |
Variable | Quantity | Cor | Quality | Cor | Knowledge | Cor |
---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
DT | 0.385 *** | 0.173 *** | 0.212 *** | 0.195 *** | 0.348 *** | 0.188 *** |
(0.072) | (0.048) | (0.066) | (0.046) | (0.079) | (0.046) | |
Quantity | 0.069 *** | |||||
(0.014) | ||||||
Quality | 0.019 *** | |||||
(0.006) | ||||||
Knowledge | 0.032 *** | |||||
(0.008) | ||||||
Constant | −1.004 | −0.106 | −2.631 | −0.125 | −4.393 | −0.035 |
(3.469) | (1.257) | (3.910) | (1.259) | (4.122) | (1.264) | |
Sobel test | 0.027 *** | 0.004 *** | 0.011 *** | |||
N | 4573 | 4573 | 4573 | 4573 | 4573 | 4573 |
Adj R2 | 0.890 | 0.840 | 0.866 | 0.830 | 0.833 | 0.834 |
Control variables | YES | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES | YES |
Variable | Flow | Cor | Active | Cor | Upgrading | Cor | Cor |
---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model7 | |
DT | 0.502 *** | 0.135 *** | 0.009 *** | 0.112 ** | 6.205 *** | 0.197 *** | 0.115 *** |
(0.050) | (0.045) | (0.001) | (0.053) | (1.814) | (0.045) | (0.037) | |
Flow | 0.128 *** | ||||||
(0.014) | |||||||
Active | 9.771 *** | ||||||
(1.820) | |||||||
Upgrading | 0.000 | ||||||
(0.000) | |||||||
Policy | 0.001 | ||||||
(0.001) | |||||||
DT × Policy | 0.004 *** | ||||||
(0.001) | |||||||
Constant | −3.966 | 0.334 | −0.078 ** | 0.588 | −361.713 ** | −0.033 | −0.123 |
(2.452) | (1.193) | (0.037) | (1.360) | (151.022) | (1.219) | (1.249) | |
Sobel test | 0.064 *** | 0.087 *** | 0.002 *** | ||||
N | 4573 | 4573 | 4573 | 4573 | 4573 | 4573 | 4335 |
Adj R2 | 0.745 | 0.857 | 0.776 | 0.865 | 0.925 | 0.829 | 0.850 |
Control variables | YES | YES | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES | YES | YES |
Indicator | Keywords of Digital Economy Policy |
---|---|
Digital Economy Policy | Digital economy, Intelligent economy, Information economy, Knowledge economy, Smart Economy, Digitalized information, Modern information network, ICT, Communication infrastructure, internet, Cloud computing, Blockchain, IoT, Digitization, Digital village, Digital industry, E-commerce, 5G, Digital infrastructure, Artificial intelligence, Electronic commerce, Big data, Datafication, Industrial digitization, Digital industrialization, Data capitalization, Smart city, Cloud service, Cloud technology, Cloud platform, E-government, Mobile payment, Online, Information industry, Software, Information Infrastructure, Information technology, Digital life. |
Variable | Cor | |||||||
---|---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
DT | 0.089 *** | 0.185 *** | 0.247 *** | 0.014 | 0.216 *** | 0.035 * | 0.047 | 0.240 *** |
(0.034) | (0.044) | (0.065) | (0.017) | (0.057) | (0.019) | (0.036) | (0.058) | |
p-value of inter group coefficient | Not involving grouping | 0.067 | 0.003 | 0.005 | ||||
Constant | −0.647 | 0.456 | −3.542 | 0.436 | −3.375 | 2.071 *** | 1.094 | −1.426 |
(1.285) | (1.242) | (3.573) | (0.631) | (4.210) | (0.680) | (1.021) | (2.808) | |
N | 4573 | 4573 | 2099 | 2224 | 1751 | 2652 | 1751 | 2652 |
Adj R2 | 0.704 | 0.821 | 0.878 | 0.904 | 0.864 | 0.871 | 0.864 | 0.871 |
Control variables | YES | YES | YES | YES | YES | YES | YES | YES |
City fixed effects | YES | YES | YES | YES | YES | YES | YES | YES |
Fixed year effects | YES | YES | YES | YES | YES | YES | YES | YES |
Year | Global Moran’s I Index | |||||
---|---|---|---|---|---|---|
Digital Talent Inflow | Co-Agglomeration of the Digital Economy Industry and Manufacturing | |||||
Economic Matrix | Adjacency Matrix | Distance Matrix | Economic Matrix | Adjacency Matrix | Distance Matrix | |
2006 | 0.158 *** | 0.167 *** | −0.018 *** | 0.094 *** | 0.453 *** | −0.074 *** |
2007 | 0.163 *** | 0.169 *** | −0.015 *** | 0.093 *** | 0.432 *** | −0.079 *** |
2008 | 0.161 *** | 0.176 *** | −0.014 *** | 0.089 *** | 0.387 *** | −0.069 *** |
2009 | 0.158 *** | 0.158 *** | −0.014 *** | 0.073 ** | 0.343 *** | −0.059 *** |
2010 | 0.182 *** | 0.148 *** | −0.015 *** | 0.044 | 0.304 *** | −0.051 *** |
2011 | 0.188 *** | 0.135 *** | −0.015 *** | 0.031 | 0.252 *** | −0.042 *** |
2012 | 0.171 *** | 0.125 *** | −0.013 *** | 0.038 | 0.210 *** | −0.033 *** |
2013 | 0.219 *** | 0.150 *** | −0.015 *** | 0.054 * | 0.184 *** | −0.027 *** |
2014 | 0.237 *** | 0.164 *** | −0.016 *** | 0.070 ** | 0.162 *** | −0.022 *** |
2015 | 0.230 *** | 0.148 *** | −0.015 *** | 0.085 *** | 0.151 *** | −0.018 *** |
2016 | 0.223 *** | 0.151 *** | −0.015 *** | 0.097 *** | 0.152 *** | −0.015 *** |
2017 | 0.215 *** | 0.181 *** | −0.014 *** | 0.111 *** | 0.165 *** | −0.013 *** |
2018 | 0.224 *** | 0.120 *** | −0.013 *** | 0.122 *** | 0.175 *** | −0.011 *** |
2019 | 0.227 *** | 0.144 *** | −0.014 *** | 0.130 *** | 0.185 *** | −0.010 *** |
2020 | 0.229 *** | 0.151 *** | −0.013 *** | 0.132 *** | 0.208 *** | −0.010 *** |
2021 | 0.222 *** | 0.166 *** | −0.014 *** | 0.135 *** | 0.227 *** | −0.012 *** |
2022 | 0.222 *** | 0.181 *** | −0.015 *** | 0.132 *** | 0.236 *** | −0.012 *** |
Variable | Cor | |||||
---|---|---|---|---|---|---|
Direct Effect | Overflow Effect | Direct Effect | Overflow Effect | Direct Effect | Overflow Effect | |
Model 1 | Model 2 | Model 3 | ||||
DT | 0.181 *** | 0.050 *** | 0.186 *** | 0.082 *** | 0.195 *** | −0.277 ** |
(0.004) | (0.014) | (0.004) | (0.011) | (0.004) | (0.124) | |
W×Cor | 0.212 *** | 0.447 *** | −0.906 *** | |||
(0.028) | (0.017) | (0.290) | ||||
sigma2_e | 0.024 *** | 0.021 *** | 0.025 *** | |||
(0.001) | (0.000) | (0.001) | ||||
N | 4573 | 4573 | 4573 | |||
R2 | 0.117 | 0.411 | 0.434 | |||
Control variables | YES | YES | YES | |||
City fixed effects | YES | YES | YES | |||
Fixed year effects | YES | YES | YES |
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Li, X.; Chen, Z.; Chen, Y. The Impact of Digital Talent Inflow on the Co-Agglomeration of the Digital Economy Industry and Manufacturing. Systems 2024, 12, 317. https://doi.org/10.3390/systems12080317
Li X, Chen Z, Chen Y. The Impact of Digital Talent Inflow on the Co-Agglomeration of the Digital Economy Industry and Manufacturing. Systems. 2024; 12(8):317. https://doi.org/10.3390/systems12080317
Chicago/Turabian StyleLi, Xiumin, Zishuo Chen, and Yaqi Chen. 2024. "The Impact of Digital Talent Inflow on the Co-Agglomeration of the Digital Economy Industry and Manufacturing" Systems 12, no. 8: 317. https://doi.org/10.3390/systems12080317