Supply Chain Relationships, Resilience, and Export Product Quality: Analysis Based on Supply Chain Concentration
Abstract
:1. Introduction
2. Theoretical Analysis and Hypothesis
2.1. Size
2.2. Efficiency
3. Research Design
3.1. Model Setup
3.2. Variable Measurement
3.2.1. Dependent Variable
3.2.2. Independent Variable
3.2.3. Control Variables
3.3. Data Source and Processing
4. Empirical Results and Discussion
4.1. Baseline Model Results and Discussion
4.2. Robustness Tests
- Endogeneity test: Because firms with a higher export product quality are likely to be stronger and have more trading partners to choose from, they may try to reduce the transaction volume with any single partner to avoid constraints. Therefore, the baseline model may have endogeneity issues. To test for endogeneity, this study used the average supply chain concentration at the provincial and industry levels as an instrumental variable. The results in Table 3 show that the instrumental variable passed the under-identification test and the weak instrumental variable test, suggesting that the choice of instrumental variable was reasonable. Moreover, even when using the instrumental variable, the result for the supply chain concentration remained significantly negative. This indicated that the previous estimates were likely not significantly affected by endogeneity.
- Changes in variable measurement: As previously mentioned, the supply chain concentration () was divided into supplier concentration () and customer concentration () for testing. Referring to the methods of Krolikowski and Yuan [7] and Tana and Chai [48], the supplier concentration () was measured by the proportion of purchases from the top-five suppliers to the total procurement. Similarly, the customer concentration () was measured by the proportion of sales to the top-five customers to the total sales. The results in columns (1) and (2) of Table 4 showed that both the supplier concentration and customer concentration had significantly negative impacts on the quality of export products. This indicated that an increase in either the supplier concentration or customer concentration would negatively affect export product quality. Additionally, since there are differences in the elasticity used to calculate export product quality in various studies, we also tested the robustness of the results by changing the elasticity. Following the suggestion of Anderson and Wincoop [49] that the elasticity can be set to 10, which has been adopted in some literature, the export product quality was recalculated with the adjusted elasticity, and the regression analysis was conducted again. The result in column (3) of Table 4 showed that even after changing the elasticity, the impact of the supply chain concentration remained significantly negative. This suggested that differences in elasticity may not fundamentally affect the results.
- Addition of control variables: To enhance the robustness of the previous estimates, additional macrolevel control variables were included in the analysis. Since per capita GDP () and industrial agglomeration () may affect the results of the baseline model at the macrolevel, these two variables were added to the baseline model for estimation. Per capita GDP () was calculated as the ratio of provincial GDP to the total population, while industrial agglomeration () was measured using the method of Kosfeld and Titze [50]: measured by the ratio of each province’s share of national industrial added value to its share of national total output. The results in column (4) of Table 4 showed that, even after adding these control variables, the impact of the supply chain concentration on export product quality remained significantly negative, suggesting that the previous estimates were likely robust.
- Adjustment of estimation method: As some literature, such as Brandt et al. [51], analyzed import and export issues at the four-digit code level, the product fixed effects were also tested at the four-digit code level. The results in column (5) of Table 4 showed that, even after changing the fixed level of product fixed effects, the impact of the supply chain concentration remained significantly negative. This indicated that different levels of fixed effects may not fundamentally affect the results.
- Replacement of samples: Since different samples may yield different results, several methods were used to replace the samples for testing. Firstly, considering that the global financial crisis of 2008 may have impacted import and export trade [52], only samples from post-2008 were retained for regression, with the results presented in column (6) of Table 4. Secondly, as the export situation of non-manufacturing products may differ [51], only manufacturing data were retained for re-estimation, with the results shown in column (7) of Table 4. Finally, as to whether to applying winsorization and truncation may lead to different results, these methods were tested, and the results are presented in columns (8) and (9) of Table 4, respectively. The results in columns (6) to (9) of Table 4 indicated that the impact of the supply chain concentration remained significantly negative, suggesting that differences in samples may not significantly affect the results.
5. Further Discussion
5.1. Heterogeneity Analysis
5.1.1. High-Tech Industry
5.1.2. Connection with Banks
5.1.3. CEO’s Overseas Background
5.1.4. Top Executives’ Digital-Related Expertise
5.2. Mechanism Analysis
5.3. Supply Chain Concentration, Resilience, and Export Product Quality
5.3.1. Infrastructure Resilience: Regional Fixed Asset Investment
5.3.2. Firm Structure Resilience: Overseas Subsidiaries
5.3.3. Industrial Structure Resilience: Industrial Upgrading
6. Conclusions and Policy Implications
6.1. Conclusions
6.2. Policy Implications
6.3. Limitations and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Raman, K.; Shahrur, H. Relationship-Specific Investments and Earnings Management: Evidence on Corporate Suppliers and Customers. Account. Rev. 2008, 83, 1041–1081. [Google Scholar] [CrossRef]
- Acemoglu, D.; Carvalho, V.M.; Ozdaglar, A.; Tahbaz-Salehi, A. The Network Origins of Aggregate Fluctuations. Econometrica 2012, 80, 1977–2016. [Google Scholar] [CrossRef]
- Kolay, M.; Lemmon, M.; Tashjian, E. Spreading the Misery? Sources of Bankruptcy Spillover in the Supply Chain. J. Financ. Quant. Anal. 2016, 51, 1955–1990. [Google Scholar] [CrossRef]
- Upson, J.E.; Wei, C. Supply Chain Concentration and Cost of Capital. Account. Financ. 2024, 64, 607–634. [Google Scholar] [CrossRef]
- Murshed, M.; Dao, N.T.T. Revisiting the CO2 Emission-Induced EKC Hypothesis in South Asia: The Role of Export Quality Improvement. GeoJournal 2022, 87, 535–563. [Google Scholar] [CrossRef]
- Peters, J. Buyer Market Power and Innovative Activities. Rev. Ind. Organ. 2000, 16, 13–38. [Google Scholar] [CrossRef]
- Krolikowski, M.; Yuan, X. Friend or Foe: Customer-Supplier Relationships and Innovation. J. Bus. Res. 2017, 78, 53–68. [Google Scholar] [CrossRef]
- Zhu, X.; Zhao, Q.; Yao, X. How Inventory Flexibility Affects Productivity: The Moderating Roles of Digital Transformation and Supply Chain Concentration. J. Manuf. Technol. Manag. 2024, in press. [Google Scholar] [CrossRef]
- Jiang, M.; Fang, J.; Yang, Y.; Yu, C.; Li, J. Supply Chain Concentration, Industry Concentration and Enterprise Innovation Performance. Financ. Res. Lett. 2024, 63, 105394. [Google Scholar] [CrossRef]
- Liu, W.; Zhuo, Q.R.; Qin, L.B.; Liu, Y. Supply Chain Risks and Corporate Innovation Strategy: From the Perspective of Breakthrough Innovation and Incremental Innovation. Appl. Econ. Lett. 2024, in press. [Google Scholar] [CrossRef]
- Fang, M.; Liu, F.; Park, K. Is Inventory Performance Helping to Improve SME Credit Ratings? The Moderating Role of Supply Chain Concentration. Appl. Econ. Lett. 2024, 31, 880–884. [Google Scholar] [CrossRef]
- Campello, M.; Gao, J. Customer Concentration and Loan Contract Terms. J. Financ. Econ. 2017, 123, 108–136. [Google Scholar] [CrossRef]
- Shi, R.; Yin, Q.; Yuan, Y.; Lai, F.; Luo, X. The Impact of Supply Chain Transparency on Financing Offerings to Firms: The Moderating Role of Supply Chain Concentration. Int. J. Oper. Prod. Manag. 2024, 44, 1568–1594. [Google Scholar] [CrossRef]
- Ma, J.; Gao, D. The Impact of Sustainable Supply-Chain Partnership on Bank Loans: Evidence from Chinese-Listed Firms. Sustainability 2023, 15, 4843. [Google Scholar] [CrossRef]
- Khandelwal, A.K.; Schott, P.K.; Wei, S.-J. Trade Liberalization and Embedded Institutional Reform: Evidence from Chinese Exporters. Am. Econ. Rev. 2013, 103, 2169–2195. [Google Scholar] [CrossRef]
- Shi, B.; Shao, W. Measurement and Determinants of Export Product Quality for Chinese Enterprises: A Micro Perspective on Cultivating New Competitive Advantages in Export. J. Manag. World 2014, 9, 90–106. (In Chinese) [Google Scholar]
- Anwar, S.; Sun, S. Foreign Direct Investment and Export Quality Upgrading in China’s Manufacturing Sector. Int. Rev. Econ. Financ. 2018, 54, 289–298. [Google Scholar] [CrossRef]
- Zhang, T.; Xing, Y.; Shang, H. Foreign Bank Entry and Export Quality Upgrading: Evidence from a Quasi-Natural Experiment Set in China. Empir. Econ. 2024, 66, 1975–2005. [Google Scholar] [CrossRef]
- Xiong, R.; Zhang, H.; Zhang, C.; Mu, G.; Wei, P. The Impact of Foreign Divestment on Chinese Firms’ Export Quality. Singap. Econ. Rev. 2024, in press. [Google Scholar] [CrossRef]
- Hayakawa, K.; Mukunoki, H.; Yang, C. Liberalization for Services FDI and Export Quality: Evidence from China. J. Jpn. Int. Econ. 2020, 55, 101060. [Google Scholar] [CrossRef]
- Oladi, R.; Beladi, H.; Chau, N. Multinational Corporations and Export Quality. J. Econ. Behav. Organ. 2008, 65, 147–155. [Google Scholar] [CrossRef]
- Yan, Z.; Sui, S.; Wu, F.; Cao, L. The Impact of Outward Foreign Direct Investment on Product Quality and Export: Evidence from China. Sustainability 2023, 15, 4227. [Google Scholar] [CrossRef]
- Liu, Q.; Qiu, L.D.; Zhan, C. FDI Inflows and Export Quality: Domestic Competition and within-Firm Adjustment. J. Dev. Econ. 2024, 170, 103293. [Google Scholar] [CrossRef]
- Wu, J.; Ding, X.; Liu, X. Governmental Pressures and Firms’ Export Product Quality: Evidence from China. Financ. Res. Lett. 2023, 55, 103850. [Google Scholar] [CrossRef]
- Leng, X.; Li, P.; Zheng, Y. Does the Expansion of Local Government Debt Affect the Export Quality? Manag. Dec. Econ. 2023, 44, 2495–2515. [Google Scholar] [CrossRef]
- Xiong, M.; Zhu, L. Mandatory Pollution Abatement and Firm Export Product Quality. Econ. Anal. Policy 2023, 80, 1–12. [Google Scholar] [CrossRef]
- He, Z.; Tang, Y. Local Environmental Constraints and Firms’ Export Product Quality: Evidence from China. Econ. Model. 2023, 124, 106326. [Google Scholar] [CrossRef]
- Chiappini, R.; Gaglio, C. Digital Intensity, Trade Costs and Exports’ Quality Upgrading. World Econ. 2024, 47, 709–747. [Google Scholar] [CrossRef]
- Wang, F.; Ye, L. Digital Transformation and Export Quality of Chinese Products: An Analysis Based on Innovation Efficiency and Total Factor Productivity. Sustainability 2023, 15, 5395. [Google Scholar] [CrossRef]
- Zhang, Q.; Duan, Y. Digital Empowerment and Export Quality: The Moderate Effect of Market Segmentation. Financ. Res. Lett. 2024, 63, 105334. [Google Scholar] [CrossRef]
- Zhang, Q.; Duan, Y. How Digitalization Shapes Export Product Quality: Evidence from China. Sustainability 2023, 15, 6376. [Google Scholar] [CrossRef]
- Wang, Q.; Chen, S.; Wang, Y. An Empirical Study on the Impact of Digital Economy Innovation Development on the Export Quality of Chinese Electromechanical Products. Sustainability 2023, 15, 16908. [Google Scholar] [CrossRef]
- DeStefano, T.; Timmis, J. Robots and Export Quality. J. Dev. Econ. 2024, 168, 103248. [Google Scholar] [CrossRef]
- Lin, C.; Xiao, S.; Yin, Z. How Do Industrial Robots Applications Affect the Quality Upgrade of Chinese Export Trade? Telecommunications 2022, 46, 102425. [Google Scholar] [CrossRef]
- Lu, J.; Wang, T.; Yuan, Y.; Chen, H. Do Industrial Robots Improve Export Product Quality of Multi-Product Enterprises? Evidence in China. Emerg. Mark. Financ. Trade 2024, 60, 1691–1715. [Google Scholar] [CrossRef]
- Hu, D.; Huang, Y.; Ge, H. Corporate Financing Constraints and Export Product Quality: Based on the Perspective of Dual Institutional Differences. Appl. Econ. Lett. 2023, in press. [Google Scholar] [CrossRef]
- Choi, B. The Impact of Financial Development on Innovation-Based Exports: Do All Firms Benefit Equally? Q. Rev. Econ. Financ. 2023, 88, 81–100. [Google Scholar] [CrossRef]
- Ma, D.; Zhu, Y.; Yang, Y. How Green Finance Affects Export Production Quality: Fresh Evidence from China. Energy Econ. 2024, 131, 107381. [Google Scholar] [CrossRef]
- Sui, H.; Geng, S.; Zhou, J.; Raza, A.; Aziz, N. Fiscal Institutional Reform and Export Product Quality: A Quasi-Experimental Research on Counties Managed Directly by Provinces. Econ. Model. 2023, 126, 106383. [Google Scholar] [CrossRef]
- Qiu, G.; Si, D.-K.; Hu, D.; Li, X. Banking Deregulation and Export Product Quality. Pac. -Basin Financ. J. 2023, 82, 102166. [Google Scholar] [CrossRef]
- Ridder, M.D. Market Power and Innovation in the Intangible Economy. Am. Econ. Rev. 2024, 114, 199–251. [Google Scholar] [CrossRef]
- Gosman, M.; Kelly, T.; Olsson, P.; Warfield, T. The Profitability and Pricing of Major Customers. Rev. Account. Stud. 2004, 9, 117–139. [Google Scholar] [CrossRef]
- Piercy, N.; Lane, N. The Underlying Vulnerabilities in Key Account Management Strategies. Eur. Manag. J. 2006, 24, 151–162. [Google Scholar] [CrossRef]
- Cohen, D.A.; Li, B. Customer-Base Concentration, Investment, and Profitability: The U.S. Government as a Major Customer. Account. Rev. 2020, 95, 101–131. [Google Scholar] [CrossRef]
- Bernini, M.; Guillou, S.; Bellone, F. Financial Leverage and Export Quality: Evidence from France. J. Bank. Financ. 2015, 59, 280–296. [Google Scholar] [CrossRef]
- Ciani, A.; Bartoli, F. Export Quality Differentiation under Credit Constraints. World Econ. 2020, 43, 1398–1433. [Google Scholar] [CrossRef]
- Fan, H.; Li, Y.A.; Yeaple, S.R. Trade Liberalization, Quality, and Export Prices. Rev. Econ. Stat. 2015, 97, 1033–1051. [Google Scholar] [CrossRef]
- Tana, G.; Chai, J. Digital Transformation: Moderating Supply Chain Concentration and Competitive Advantage in the Service-Oriented Manufacturing Industry. Systems 2023, 11, 486. [Google Scholar] [CrossRef]
- Anderson, J.E.; Wincoop, E.V. Trade Costs. J. Econ. Lit. 2004, 42, 691–751. [Google Scholar] [CrossRef]
- Kosfeld, R.; Titze, M. Benchmark Value-Added Chains and Regional Clusters in R&D-Intensive Industries. Int. Reg. Sci. Rev. 2017, 40, 530–558. [Google Scholar]
- Brandt, L.; Biesebroeck, J.V.; Wang, L.; Zhang, Y. WTO Accession and Performance of Chinese Manufacturing Firms. Am. Econ. Rev. 2017, 107, 2784–2820. [Google Scholar] [CrossRef]
- Bricongne, J.-C.; Fontagné, L.; Gaulier, G.; Taglioni, D.; Vicard, V. Firms and the Global Crisis: French Exports in the Turmoil. J. Int. Econ. 2012, 87, 134–146. [Google Scholar] [CrossRef]
- Kupiec, P.H.; Sylla, R.; Pollock, A.J.; Calomiris, C.W.; Ely, B. American Enterprise Institute Roundtable: Government Policies Reshape the Banking System. J. Appl. Corp. Financ. 2021, 33, 52–69. [Google Scholar] [CrossRef]
- Danese, P.; Bortolotti, T. Supply Chain Integration Patterns and Operational Performance: A Plant-Level Survey-Based Analysis. Int. J. Prod. Res. 2014, 52, 7062–7083. [Google Scholar] [CrossRef]
- Lee, H.; Chung, C.C.; Beamish, P.W. Configurational Characteristics of Mandate Portfolios and Their Impact on Foreign Subsidiary Survival. J. World Bus. 2019, 54, 100999. [Google Scholar] [CrossRef]
- Ceccobelli, M.; Gitto, S.; Mancuso, P. ICT Capital and Labour Productivity Growth: A Non-Parametric Analysis of 14 OECD Countries. Telecommun. Policy 2012, 36, 282–292. [Google Scholar] [CrossRef]
- Ullah, N.; Zada, S.; Siddique, M.A.; Hu, Y.; Han, H.; Vega-Muñoz, A.; Salazar-Sepúlveda, G. Driving Factors of the Health and Wellness Tourism Industry: A Sharing Economy Perspective Evidence from KPK Pakistan. Sustainability 2021, 13, 13344. [Google Scholar] [CrossRef]
Variables | Obs. | Mean | S.D. | Min. | Max. |
---|---|---|---|---|---|
Quality | 397,403 | 0.5291 | 0.2626 | 0 | 1 |
SCC | 397,403 | 0.2313 | 0.1304 | 0.0250 | 0.9560 |
Age | 397,403 | 2.5732 | 0.4073 | 0.6931 | 3.8918 |
SO | 397,403 | 0.4945 | 0.5000 | 0 | 1 |
Capital | 397,403 | 0.2066 | 0.1283 | 0.0002 | 0.8000 |
OC | 397,403 | 0.3881 | 0.1540 | 0.0431 | 0.8855 |
FC | 397,403 | −1.0125 | 0.0721 | −1.5050 | 1.4604 |
Variables | Dependent Variable: Quality | ||
---|---|---|---|
(1) | (2) | (3) | |
SCC | −0.0407 *** | −0.0409 *** | −0.0456 *** |
(−4.9422) | (−4.9316) | (−5.4571) | |
Age | −0.0311 *** | ||
(−4.6878) | |||
SO | 0.0029 | ||
(0.7618) | |||
Capital | 0.0145 ** | ||
(1.9661) | |||
OC | −0.0457 *** | ||
(−3.7439) | |||
FC | 0.0455 *** | ||
(2.9073) | |||
Constant | 0.5386 *** | 0.5387 *** | 0.6792 *** |
(275.3483) | (273.1744) | (26.8392) | |
Firm FE | Yes | Yes | Yes |
Product FE | Yes | Yes | Yes |
Destination FE | Yes | Yes | Yes |
Year FE | No | Yes | Yes |
N | 397,021 | 397,021 | 397,021 |
Variables | First Stage | Second Stage |
---|---|---|
Dependent Variable: SCC | Dependent Variable: Quality | |
(1) | (2) | |
IV | 0.4977 *** | |
(143.1366) | ||
SCC | −0.0377 ** | |
(−1.9646) | ||
Age | −0.0507 *** | −0.0307 *** |
(−30.6147) | (−4.5847) | |
SO | −0.0063 *** | 0.0029 |
(−7.9847) | (0.7767) | |
Capital | −0.0096 *** | 0.0143 * |
(−4.9267) | (1.9424) | |
OC | 0.0305 *** | −0.0461 *** |
(7.8340) | (−3.7706) | |
FC | 0.1808 *** | 0.0435 *** |
(22.7838) | (2.6805) | |
Firm FE | Yes | Yes |
Product FE | Yes | Yes |
Destination FE | Yes | Yes |
Year FE | Yes | Yes |
Under-identification Test | 7408.82 *** | |
Cragg–Donald Wald F Statistic | 88,846.17 | |
Kleibergen–Paap Wald RK F Statistic | 20,488.08 | |
N | 397,021 | 397,021 |
Variables | Dependent Variable: Quality | ||||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
SCC | −0.0456 *** | −0.0451 *** | −0.0489 *** | −0.0332 *** | −0.0366 *** | −0.0455 *** | −0.0642 *** | ||
(−5.4571) | (−5.3958) | (−5.8771) | (−3.0206) | (−3.7698) | (−5.2829) | (−6.6342) | |||
SC | −0.0147 ** | ||||||||
(−2.4380) | |||||||||
CC | −0.0425 *** | ||||||||
(−6.2796) | |||||||||
Age | −0.0294 *** | −0.0311 *** | −0.0285 *** | −0.0285 *** | −0.0288 *** | −0.0175 * | −0.0401 *** | −0.0253 *** | −0.0290 *** |
(−4.4443) | (−4.6995) | (−4.2773) | (−4.2773) | (−4.4049) | (−1.6523) | (−5.5305) | (−3.1670) | (−3.3466) | |
SO | 0.0034 | 0.0020 | 0.0037 | 0.0037 | 0.0036 | 0.0180 *** | 0.0053 | 0.0026 | −0.0002 |
(0.9019) | (0.5323) | (0.9806) | (0.9806) | (0.9512) | (3.2227) | (1.1920) | (0.6744) | (−0.0397) | |
Capital | 0.0133 * | 0.0160 ** | 0.0134 * | 0.0134 * | 0.0085 | 0.0079 | 0.0127 | 0.0126 ** | 0.0010 |
(1.8076) | (2.1769) | (1.8220) | (1.8220) | (1.1491) | (0.8363) | (1.5201) | (1.6906) | (0.1195) | |
OC | −0.0468 *** | −0.0476 *** | −0.0438 *** | −0.0438 *** | −0.0277 ** | −0.0364 ** | −0.0549 *** | −0.0449 *** | −0.0421 *** |
(−3.8319) | (−3.8975) | (−3.5768) | (−3.5768) | (−2.2768) | (−2.2057) | (−4.1393) | (−3.6612) | (−3.2645) | |
FC | 0.0375 ** | 0.0451 *** | 0.0399 ** | 0.0399 ** | 0.0524 *** | 0.0182 | 0.0342 * | 0.0476 *** | 0.0825 *** |
(2.3993) | (2.8879) | (2.5365) | (2.5365) | (3.3349) | (0.9391) | (1.9280) | (2.7767) | (4.4127) | |
PCGDP | −0.0049 | ||||||||
(−0.3468) | |||||||||
IA | 0.0331 *** | ||||||||
(3.4442) | |||||||||
Constant | 0.6600 *** | 0.6788 *** | 0.6792 *** | 0.3213 *** | 0.6748 *** | 0.6011 *** | 0.6834 *** | 0.6666 *** | 0.7162 *** |
(26.3422) | (27.0130) | (26.8392) | (3.1265) | (26.7806) | (16.4612) | (24.5059) | (22.9410) | (22.5098) | |
Firm FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Product FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Destination FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
N | 397,021 | 397,021 | 397,021 | 396,988 | 397,318 | 240,581 | 289,388 | 397,021 | 363,725 |
Variables | High-Tech Industry | Connection with Banks | Overseas Background | Digital-Related Expertise | ||||
---|---|---|---|---|---|---|---|---|
Yes | No | Yes | No | Yes | No | Yes | No | |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | |
SCC | −0.0154 | −0.0639 *** | −0.0254 | −0.0497 *** | 0.0491 | −0.0387 *** | −0.0296 | −0.0478 *** |
(−1.3375) | (−4.7753) | (−0.9407) | (−5.4699) | (1.1900) | (−3.5572) | (−0.6332) | (−5.5043) | |
Age | −0.0430 *** | 0.0022 | −0.1288 *** | −0.0222 *** | 0.0117 | −0.0473 *** | −0.0697 ** | −0.0248 *** |
(−5.5512) | (0.1595) | (−3.5844) | (−3.1901) | (0.1868) | (−5.6299) | (−2.4677) | (−3.4963) | |
SO | 0.0094 * | −0.0027 | 0.0366 | −0.0005 | 0.0295 ** | 0.0135 ** | 0.0673 *** | −0.0051 |
(1.8486) | (−0.4574) | (1.3580) | (−0.1229) | (2.1175) | (2.1464) | (8.0986) | (−1.2321) | |
Capital | −0.0032 | 0.0455 *** | 0.0074 | 0.0166 ** | −0.1624 *** | 0.0299 *** | −0.0252 | 0.0211 *** |
(−0.3169) | (3.9029) | (0.2680) | (2.0455) | (−5.8984) | (3.0373) | (−0.6984) | (2.6899) | |
OC | −0.0188 | −0.0378 * | 0.2012 *** | −0.0441 *** | −0.3781 *** | −0.0313 * | 0.0409 | −0.0569 *** |
(−1.1955) | (−1.7850) | (2.7043) | (−3.4139) | (−3.4916) | (−1.9053) | (0.6485) | (−4.5300) | |
FC | 0.0389 * | 0.0163 | 0.0258 | 0.0477 *** | 0.3774 *** | 0.0010 | −0.0176 | 0.0529 *** |
(1.9273) | (0.6179) | (0.5061) | (2.8549) | (6.1175) | (0.0496) | (−0.2564) | (3.2451) | |
Constant | 0.6707 *** | 0.5869 *** | 0.8314 *** | 0.6554 *** | 1.0310 *** | 0.6627 *** | 0.6419 *** | 0.6799 *** |
(21.6714) | (12.5898) | (7.0582) | (24.3601) | (5.1087) | (20.9261) | (6.1995) | (25.3979) | |
Firm FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Product FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Destination FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
N | 223,851 | 172,477 | 59,745 | 336,771 | 24,542 | 233,066 | 30,772 | 363,676 |
Variables | Size 1 | Size 2 | TFP | SCE |
---|---|---|---|---|
(1) | (2) | (3) | (4) | |
SCC | −0.7836 *** | −1.1370 *** | −0.4269 *** | −0.5728 *** |
(−6.0544) | (−7.1508) | (−2.7758) | (−5.0661) | |
Age | 0.3501 *** | 0.1809 | 0.4987 *** | −0.0619 |
(2.7791) | (1.2717) | (3.1295) | (−0.6085) | |
SO | 0.1177 ** | 0.0715 | 0.0919 | 0.0755 * |
(2.0863) | (1.0490) | (1.5956) | (1.9113) | |
Capital | −0.4160 *** | 0.2640 * | −0.2498 ** | −0.0800 |
(−3.4181) | (1.8266) | (−2.1490) | (−0.7435) | |
OC | 0.1937 | 0.5322 ** | 0.3942 * | −0.1382 |
(0.8152) | (2.3255) | (1.7169) | (−0.8043) | |
FC | −2.6878 ** | −1.6715 ** | −2.6692 ** | 0.9034 ** |
(−2.3734) | (−2.1627) | (−2.2094) | (2.4036) | |
Constant | 18.2220 *** | 5.5202 *** | 7.2573 *** | 5.9185 *** |
(15.9836) | (6.5929) | (5.7766) | (13.2614) | |
Firm FE | Yes | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes | Yes |
N | 4571 | 4559 | 4027 | 4571 |
Variables | Dependent Variable: Quality | ||
---|---|---|---|
(1) | (2) | (3) | |
SCC | −0.3214 *** | −0.0749 *** | −0.0776 *** |
(−5.0443) | (−4.5671) | (−5.3149) | |
RFAI | −0.0062 | ||
(−1.4619) | |||
SCC × RFAI | 0.0300 *** | ||
(4.4195) | |||
OS | −0.0261 | ||
(−1.6330) | |||
SCC × OS | 0.1768 *** | ||
(3.7095) | |||
IU | −0.0057 | ||
(−1.1085) | |||
SCC × IU | 0.0284 ** | ||
(2.4968) | |||
Age | −0.0311 *** | −0.0125 | −0.0317 *** |
(−4.6826) | (−1.2419) | (−4.7746) | |
SO | 0.0025 | −0.0099 | 0.0031 |
(0.6695) | (−1.0798) | (0.8102) | |
Capital | 0.0135 * | 0.0223 * | 0.0149 ** |
(1.8326) | (1.9273) | (2.0179) | |
OC | −0.0434 *** | −0.0661 *** | −0.0446 *** |
(−3.5429) | (−4.1463) | (−3.6352) | |
FC | 0.0410 *** | 0.0453 ** | 0.0442 *** |
(2.6221) | (2.1903) | (2.8151) | |
Constant | 0.7307 *** | 0.6396 *** | 0.6846 *** |
(15.3118) | (17.5874) | (26.5645) | |
Firm FE | Yes | Yes | Yes |
Product FE | Yes | Yes | Yes |
Destination FE | Yes | Yes | Yes |
Year FE | Yes | Yes | Yes |
N | 397,002 | 248,974 | 396,663 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Chen, R.; Xu, H. Supply Chain Relationships, Resilience, and Export Product Quality: Analysis Based on Supply Chain Concentration. Sustainability 2024, 16, 8743. https://doi.org/10.3390/su16208743
Chen R, Xu H. Supply Chain Relationships, Resilience, and Export Product Quality: Analysis Based on Supply Chain Concentration. Sustainability. 2024; 16(20):8743. https://doi.org/10.3390/su16208743
Chicago/Turabian StyleChen, Renhao, and Helian Xu. 2024. "Supply Chain Relationships, Resilience, and Export Product Quality: Analysis Based on Supply Chain Concentration" Sustainability 16, no. 20: 8743. https://doi.org/10.3390/su16208743
APA StyleChen, R., & Xu, H. (2024). Supply Chain Relationships, Resilience, and Export Product Quality: Analysis Based on Supply Chain Concentration. Sustainability, 16(20), 8743. https://doi.org/10.3390/su16208743