Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 17, No. 3, 2024 45 The Impact of RCEP on the Restructuring of Supply Chains in the Electronic Machinery and Equipment Industry -- A Comparative Analysis of China and Vietnam Fulong Luo Shanghai Maritime University, Shanghaia, 201306, Chin Abstract: The Regional Comprehensive Economic Partnership (RCEP) focuses on strengthening regional economic integration by lowering tariffs and dismantling trade barriers. This study leverages quantitative methods to assess the influence of RCEP on supply chains in the electronic machinery and equipment sectors of China and Vietnam. The findings indicate significant changes in the import and export volumes of both countries following the implementation of RCEP. Notably, China's import and export volumes have decreased significantly, while Vietnam's exports have shown relative stability. Finally, this paper provides an analysis based on the empirical research results for reference. Keywords: RCEP, Electronic Equipment, China, Vietnam. 1. Introduction Launched by the ten ASEAN member states, the Regional Comprehensive Economic Partnership (RCEP) is a trade accord that includes China, Japan, South Korea, Australia, and New Zealand as key participants. Since its inception in 2012, RCEP has introduced a series of tariff reduction policies that have profoundly impacted ASEAN countries and their neighboring regions. Current international research on the effects of RCEP offers mixed conclusions. The World Bank reports suggest that RCEP could alter the configuration of global supply chains, positioning East Asia and the Asia- Pacific region as more critical players. Conversely, other studies indicate that RCEP may not significantly affect large economies like China. It is crucial to explore which of these perspectives holds more validity. This research seeks to thoroughly assess the effects of RCEP's implementation on the supply chains within China's and Vietnam's electronic machinery and equipment industries using quantitative analysis. The research will uncover RCEP's role in the restructuring of regional supply chains. By conducting an in- depth analysis of the supply chain adjustments and performance of China and Vietnam within the RCEP framework, the study will reveal the actual effects of RCEP on regional economic integration. The empirical evidence offered will aid businesses and policymakers in China and Vietnam, helping them craft well-informed policies and strategies to more effectively address the challenges and opportunities arising from RCEP. 2. Literature Review In recent years, extensive academic research has been conducted on the effects of the Regional Comprehensive Economic Partnership (RCEP) on supply chains. These studies mainly concentrate on how RCEP fosters regional supply chain integration and trade facilitation through tariff reductions, streamlined customs procedures, and the alignment of technical standards. KPMG (2021) highlighted that the implementation of RCEP would significantly enhance regional economic synergies, making supply chains more efficient and stable. Lee (2016) and Kim (2016) examined the effects of RCEP on the textile and apparel supply chains in Asia, finding that the agreement has fostered further integration and development of regional supply chains. However, existing research is mostly at a macro level, with a lack of micro-level analysis on the supply chain adjustment strategies of specific industries and enterprises. Eurasia Review (2021) highlighted the role of geopolitical factors in shaping the stability of RCEP supply chains, with particular emphasis on how security issues in the Taiwan Strait and the South China Sea significantly affect regional trade and supply chains. The Asian Development Bank (ADB, 2021) examined the effects of RCEP on trade flows among member nations, observing that RCEP is likely to drive regional trade growth, especially in high-tariff sectors like machinery, plastics, and electronics. Several studies have further explored the specific impact of RCEP on China's supply chains. Zhou Jialin (2021) used the GTAP model to analyze the effects of RCEP on China's social welfare, GDP, and imports and exports, finding that RCEP could significantly enhance China's position in the global supply chain. Zhou also suggested strategies for Chinese enterprises to increase their competitiveness in response to supply chain risks. Yuhan Zhang (2021), through an empirical survey at the enterprise level, pointed out that companies embedded in the global supply chain are more likely to benefit from RCEP, highlighting the differing impacts of RCEP on various businesses within the supply chain. KPMG's report also noted that RCEP would greatly promote the production and export of electronic machinery and equipment, particularly in Malaysia, where this industry is considered one of the primary beneficiaries of RCEP. The reduction of tariffs and optimization of supply chains have significantly enhanced the international competitiveness of this sector. Although existing literature provides a wealth of background and preliminary evidence, there is still a lack of empirical analysis at the specific industry and enterprise 46 levels. This study will adopt an empirical quantitative analysis approach, focusing on the impact of RCEP on specific industries, while conducting cross-national comparisons to reveal the specific effects of RCEP in different countries. 3. Data Sources and Processing 3.1. Data Sources This study utilizes import and export data from China and Vietnam spanning January 2020 to April 2024, specifically targeting goods under HS code 8517, which pertains to electronic equipment and machinery (e.g., telephones for cellular or other wireless networks, and devices for transmitting or receiving voice, images, or data, including communication equipment for wired or wireless networks like LANs or WANs). The data was sourced from the General Administration of Customs of China, the General Department of Vietnam Customs, the United Nations Comtrade database, and the Global Trade Atlas. 3.2. Data Preprocessing Outlier Treatment: A moving average method was employed to smooth outliers, reducing fluctuations and noise in the data. Seasonal Adjustment: The STL (Seasonal and Trend decomposition using Loess) method was used to perform seasonal adjustment on the data, extracting the trend component and eliminating the effects of seasonal fluctuations. Data Standardization: The adjusted data was standardized so that the mean is 0 and the standard deviation is 1, thereby eliminating differences in scale among the variables. 4. Empirical Analysis 4.1. Model Construction The model is constructed as follows: 𝑌𝑡 = 𝛼 + 𝛽1 × 𝐷𝑡 + 𝛽2 × 𝑇𝑡 + 𝛽3 × (𝐷𝑡 × 𝑇𝑡) + 𝜖𝑡 Where: 𝑌𝑡 represents the standardized import and export values and the standardized revenue values of enterprises at time t. 𝛼 is the constant term, representing the baseline level without intervention and time trend effects. 𝐷𝑡 is the intervention indicator variable, which takes the value of 1 from January 2022 onward and 0 before that. 𝑇𝑡 is the time trend variable, representing the sequence of time. 𝛽1 represents the immediate effect of the intervention, indicating the level change after the intervention is implemented. 𝛽2 represents the effect of the time trend, indicating the trend in the change of import and export values over time. 𝛽3 represents the change in the time trend after the intervention. 𝜖𝑡 is the error term, representing random errors. Variable Configuration: The model's independent variables consist of the constant term, the intervention variable, the time trend variable, and an interaction term (representing the product of the time trend and the intervention variable). The dependent variable is the standardized import and export data. 4.2. Analysis of China's Imports and Exports First, data preprocessing was conducted, which included handling missing values and smoothing the time series data. The data was then divided into two phases: pre-intervention (from January 2020 to December 2021) and post-intervention (from January 2022 to May 2024). A regression discontinuity model was employed to estimate the impact. Table 1. Sharp RD Estimates Using Local Polynomial Regression (China's Imports) Cutoff c = 25 Left of c Right of c Number of obs 24 29 Eff. Number of obs 7 8 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 7.247 7.247 BW bias (b) 10.868 10.868 rho (h/b) 0.667 0.667 Number of obs=53 BW type=mserd Kernel=Triangular VCE method=NN Table 2. Regression Results (China's Imports) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -4.3e+09 5.2e+08 -8.1721 0.000 -5.3e+09 -3.3e+09 Bias-corrected -4.2e+09 5.2e+08 -8.0042 0.000 -5.2e+09 -3.2e+09 Robust -4.2e+09 6.0e+08 -6.9553 0.000 -5.4e+09 -3.0e+09 47 Figure 1. Figure Scatter Plot with Regression Discontinuity (China's Imports) Conventional Method: With a coefficient of -4.3e+09, the data indicates that RCEP's implementation led to a significant drop in import values, approximately $4.3 billion, and this outcome is statistically robust (p-value = 0.000). Bias-Corrected Method: The coefficient of -4.2e+09 reveals a decrease in import values by around $4.2 billion due to RCEP, with the statistical significance confirmed (p-value = 0.000). Robust Method: The coefficient, also at -4.2e+09, supports the statistically significant reduction in import values, validated by a p-value of 0.000. Table 3. Sharp RD Estimates Using Local Polynomial Regression (China’s Exports) Cutoff c = 25 Left of c Right of c Number of obs 24 29 Eff. Number of obs 7 8 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 6.920 6.920 BW bias (b) 11.456 11.456 rho (h/b) 0.604 0.604 Number of obs=53 BW type=mserd Kernel=Triangular VCE method=NN Figure 2. Scatter Plot with Regression Discontinuity (China’s Exports) Table 4. Regression Results (China Exports) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -1.4e+10 5.0e+09 -2.8982 0.004 -2.4e+10 -4.7e+09 Bias-corrected -1.6e+10 5.0e+09 -3.2122 0.001 -2.6e+10 -6.2e+09 Robust -1.6e+10 6.0e+09 -2.6519 0.008 -2.8e+10 -4.2e+09 Given the limited sample size, the study expanded the bandwidth and applied first-order polynomial and second- order bias correction estimates, ultimately settling on a bandwidth of 6.920. Conventional Method: The coefficient of -1.4e+10 reveals that RCEP's implementation slashed import values by roughly $14 billion, a change backed by a statistically significant p- value of 0.004. Bias-Corrected Method: With a coefficient of -1.6e+10, the findings show an even steeper decline in import values by about $16 billion post-RCEP, with strong statistical support (p-value = 0.001). Robust Method: The coefficient of -1.6e+10 points to a $16 billion drop in export values following RCEP, with the statistical significance confirmed by a p-value of 0.008. For imports, the implementation of RCEP significantly reduced China's import value, with a decrease ranging between $4.2 billion and $4.3 billion. The p-values for all methods are less than 0.05, indicating that this effect is statistically significant. For exports, the implementation of RCEP significantly reduced China's export value, with a decrease ranging between $14 billion and $16 billion. The p- values for all methods are less than 0.05, indicating that this effect is statistically significant. 4.3. Analysis of Vietnam's Imports and Exports 4.3.1. Imports Table 5. Sharp RD Estimates Using Local Polynomial Regression (Vietnam’s Imports) Cutoff c = 25 Left of c Right of c Number of obs 24 28 Eff. Number of obs 9 10 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 10.000 10.000 BW bias (b) 10.000 10.000 rho (h/b) 1.000 1.000 48 Table 6. Regression Results (Vietnam’s Imports) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -8.2e+08 2.4e+08 -3.3715 0.001 -1.3e+09 -3.4e+08 Bias-corrected -5.2e+08 2.4e+08 -2.1323 0.033 -1.0e+09 -4.2e+07 Robust -5.2e+08 3.4e+08 -1.5197 0.129 -1.2e+09 1.5e+08 Figure 3. Scatter Plot with Regression Discontinuity (Vietnam’s Imports) Due to significant fluctuations in the original data and insufficient sample size, the study increased the bandwidth and employed first-order polynomial and second-order bias correction estimates. The final bandwidth used was 10. Conventional Method: The coefficient of -8.2e+08 shows that following RCEP's implementation, Vietnam's import value dropped by around $820 million, with this outcome being statistically significant (p-value = 0.001). Bias-Corrected Method: The coefficient of -5.2e+08 indicates a reduction in import value by approximately $520 million post-RCEP, with statistical significance confirmed (p- value = 0.033). Robust Method: The coefficient remains at -5.2e+08, but the result lacks statistical significance (p-value = 0.129). Table 7. Sharp RD Estimates Using Local Polynomial Regression (Vietnam’s Exports) Cutoff c = 25 Left of c Right of c Number of obs 24 28 Eff. Number of obs 5 6 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 5.356 5.356 BW bias (b) 8.994 8.994 rho (h/b) 0.596 0.596 4.3.2. Export Table 8. Regression Results (Vietnam’s Exports) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -1.4e+09 8.0e+08 -1.7588 0.079 -3.0e+09 1.6e+08 Bias-corrected -8.5e+08 8.0e+08 -1.0610 0.289 -2.4e+09 7.2e+08 Robust -8.5e+08 8.8e+08 -0.9615 0.336 -2.6e+09 8.8e+08 For the export data, due to the limited sample size, a smaller bandwidth of 5.356 was used. Figure 4. Scatter Plot with Regression Discontinuity (Vietnam’s Exports) Conventional Method: The coefficient of -1.4e+09 suggests that following RCEP's implementation, Vietnam's export value dropped by about $1.4 billion, with the result being marginally significant (p-value = 0.079). Bias-Corrected Method: The coefficient of -8.5e+08 indicates a decrease, but the result lacks statistical significance (p-value = 0.289). Robust Method: The coefficient remains at -8.5e+08, and the result is similarly not statistically significant (p-value = 0.336). The results indicate that after the implementation of RCEP, Vietnam's import value significantly decreased. Both the Conventional and Bias-Corrected methods show a statistically significant negative impact, with reductions of approximately $820 million and $520 million, respectively. Although the Robust method's result is not significant, the trend is consistent. For exports, while the Conventional method suggests a reduction of about $1.4 billion after the implementation of RCEP, its significance is low (p-value = 0.079). The results from the Bias-Corrected and Robust methods are not significant, indicating that the change in export value is not statistically significant. 4.4. Analysis of Mobile Phone Components in China and Vietnam To further deepen this study, we selected mobile phone accessories and related components classified under the HS code 85177. This includes the following items: 49 Table 9. List of Products under HS Code 85177 HS code Product Name 8517 7100 Various antennas and antenna reflectors and their parts 8517 7910 Parts of digital programmable telephone or telegraph exchanges 8517 7920 Parts of optical terminal equipment and pulse code modulation devices 8517 7930 Parts for smartphones and other handheld (including vehicle-mounted) wireless telephones 8517 7940 Parts for walkie-talkies 8517 7950 Laser transceiver modules for optical communication equipment 8517 7990 Other communication equipment parts listed under heading 85.17 The trade direction in this part of the study focuses on Vietnam's imports from and exports to China, differing from the previously mentioned analysis of the imports and exports of China and Vietnam with the rest of the world. The data selected spans from January 2017 to June 2024 on a monthly basis. The analysis results are as follows: 4.4.1. Import Table 10. Sharp RD Estimates Using Local Polynomial Regression (Vietnam's Imports of Components from China) Cutoff c = 25 Left of c Right of c Number of obs 24 66 Eff. Number of obs 5 6 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 5.445 5.445 BW bias (b) 8.301 8.301 rho (h/b) 0.656 0.656 Table 11. Regression Results (Vietnam's Imports of Components from China) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -3.1e+08 7.0e+07 -4.4201 0.000 -4.5e+08 -1.7e+08 Bias-corrected -2.6e+08 7.0e+07 -3.7074 0.000 -4.0e+08 -1.2e+08 Robust -2.6e+08 8.8e+07 -2.9536 0.003 -4.3e+08 -8.8e+07 Conventional Method: The coefficient is -3.1e+08, indicating that after the implementation of RCEP, Vietnam's import value decreased by approximately $310 million, with the result being marginally significant (p-value = 0.000). Bias-Corrected Method: The coefficient is -2.6e+08, indicating that after the implementation of RCEP, the import value decreased by approximately $260 million, with the result being significant (p-value = 0.000). Robust Method: The coefficient is -2.6e+08, indicating that after the implementation of RCEP, the import value decreased by approximately $260 million, with the result being significant (p-value = 0.003). 4.4.2. Export Due to the limited availability of samples in the original data, the bandwidth was increased. Rdrobust export time, p (2) h (10) c (25) all. Table 12. Sharp RD Estimates Using Local Polynomial Regression (Vietnam's Exports of Components to China) Cutoff c = 25 Left of c Right of c Number of obs 24 66 Eff. Number of obs 14 15 Order est. (p) 1 1 Order bias (q) 2 2 BW est. (h) 15.000 15.000 BW bias (b) 15.000 15.000 rho (h/b) 1.000 1.000 Table 13. Regression Results (Vietnam's Exports of Components to China) Method Coef. Std. Err. z P>|z| [95% Conf. Interval] Conventional -1.6e+09 3.8e+08 -4.1451 0.000 -2.4e+09 -8.4e+08 Bias-corrected -2.1e+09 3.8e+08 -5.4713 0.000 -2.9e+09 -1.4e+09 Robust -2.1e+09 6.9e+08 -3.0422 0.002 -3.5e+09 -7.5e+08 Conventional Method: The coefficient is -1.6e+09, indicating that after the implementation of RCEP, Vietnam's export value decreased by approximately $1.6 billion, with the result being marginally significant (p-value = 0.000). Bias-Corrected Method: The coefficient is -2.1e+09, indicating that after the implementation of RCEP, the export value decreased by approximately $2.1 billion, with the result being significant (p-value = 0.000). Robust Method: The coefficient is -2.1e+09, indicating that after the implementation of RCEP, the export value decreased by approximately $2.1 billion, with the result being significant (p-value = 0.002). Regarding imports, the regression results indicate that after the implementation of RCEP, the import value significantly decreased, with the reduction ranging between $260 million and $310 million. All methods show statistically significant results (p-values are all less than 0.05). For exports, the regression discontinuity results show that after the implementation of RCEP, the export value significantly decreased, with the reduction ranging between $1.6 billion and $2.1 billion, which is much higher than the import reduction, with a similar level of significance. 5. Conclusion This study conducted an in-depth analysis of the effects of the Regional Comprehensive Economic Partnership (RCEP) on the reshaping of supply chains in the electronic machinery and equipment sectors in China and Vietnam. The results clearly show that RCEP's implementation has considerably affected trade flows in both countries. 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