The Role of Confucius Institutes and One Belt, One Road Initiatives on the Values of Cross-Border M&A: Empirical Evidence from China
Abstract
:1. Introduction
2. Conceptual Background
2.1. Cultural Distance and CBM&A
2.2. Institutional Distance and CBM&A
2.3. Confucius Institutes and CBM&A
2.4. BRI and CBM&A
3. Hypothesis Development
3.1. Cultural Distance and CBM&A Performance
3.2. Institutional Distance and CBM&A Performance
3.3. Confucius Institutes and CBM&A Performance
3.4. BRI and CBM&A Performance
4. Data and Research Methodology
4.1. Data Sources and Sample Selection
- Acquirers are listed on the Shanghai or Shenzhen Stock Exchanges;
- Only the acquisition with the highest transaction value is assessed for firms that conducted more than one CBM&A during one year;
- The M&A do not include British Virgin Islands or Cayman Islands acquisitions;
- The M&A involve non-financial acquirers and targets. Financial firms have different financial reporting systems and regulations, which could bias the results;
- The deal value and accounting information of the acquirer must be available on the WIND database.
4.2. Sample Description
4.2.1. Dependent Variable
4.2.2. Independent Variables
4.2.3. Control Variables
4.2.4. Comprehensive Evaluation Model
4.2.5. Multivariate Regression Model
5. Results and Discussion
5.1. Calculation of the Comprehensive Score
5.2. Correlation Analysis and Descriptive Statistics
5.3. Multivariate Regression Results and Discussion
5.4. Robustness Check
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Capability Indicator | Symbol | Variable | Calculation Methods |
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Profitability |
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Variable | Definition | Data Source | Reference |
---|---|---|---|
PROFIT2 | The comprehensive ratio of CBM&A performance after two years | WIND Database | Shen and Jin [54] and Lin et al. [56] |
CD | The composite index calculated with the Kogut and Singh formula of the cultural distance between China and the target country | Official website of Hofstede | Kogut and Singh [41] |
CD4 | The composite index calculated with the Morosini and Singh formula across Hofstede’s four cultural dimensions as robustness checks | Official website of Hofstede | Morosini et al. [19] |
DIS | The composite index calculated with the Kogut and Singh formula of the institutional distance between China and the target country | World Bank | Kogut and Singh [41] |
CI | Number of Confucius Institutes established in target country i at year t−1 | Official website of Hanban | Lien et al. [12] and Xu and Wang [32] |
OBOR | A dummy variable equal to 1 if the M&A took place in a BRI country and 0 otherwise | Official website of BRI | Liu et al. [48] |
LNV | Log of the amount of the transaction in Chinese yuan | World Bank | Du et al. [59] |
LRW | Labor cost calculated as the log of the target country’s real wages | World Bank | Liu et al. [48] |
LOPEN | Openness calculated as the ratio of the sum of imports and exports to nominal GDP | World Bank | Liu et al. [48] |
LTECH | Technology level calculated as the ratio of the target country’s high-technology exports to nominal GDP | World Bank | Liu et al. [48] |
LINFRA | Infrastructure facilities calculated as the log of fixed telephone subscriptions per 100 people | World Bank | Liu et al. [48] and Kamal et al. [60] |
NRR | Natural resource endowment calculated as the % of ore, mineral, and fuel exports in total merchandised export | World Bank | Liu et al. [48] |
RPT | A dummy variable equal to 1 for a related-party transaction | WIND Database | Du et al. [59] |
STA | A dummy variable equal to 1 for state-owned enterprises and 0 otherwise | WIND Database | Boateng et al. [21] |
AGE | Firm age, from firm establishment to CBM&A (years) | WIND Database | Wu and Xie [61] |
PROFIT2 | CD | DIS | CI | LNV | OBOR | LRW | LOPEN | LTECH | LINFRA | NRR | RPT | STA | AGE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PROFIT2 | 1.000 | |||||||||||||
CD | −0.420 | 1.000 | ||||||||||||
DIS | −0.379 | 0.163 | 1.000 | |||||||||||
CI | 0.335 | −0.110 | −0.413 | 1.000 | ||||||||||
LNV | 0.410 | −0.170 | −0.008 | 0.051 | 1.000 | |||||||||
OBOR | 0.405 | −0.713 | −0.283 | 0.281 | 0.0449 | 1.000 | ||||||||
LRW | −0.087 | 0.201 | 0.502 | 0.074 | 0.0027 | −0.202 | 1.000 | |||||||
LOPEN | −0.048 | −0.558 | 0.249 | −0.285 | −0.0003 | 0.351 | 0.072 | 1.000 | ||||||
LTECH | −0.022 | −0.379 | 0.289 | 0.089 | 0.0217 | 0.220 | 0.305 | 0.742 | 1.000 | |||||
LINFRA | 0.007 | −0.009 | 0.243 | 0.160 | −0.0467 | 0.063 | 0.790 | 0.043 | 0.205 | 1.000 | ||||
NRR | −0.153 | 0.267 | 0.545 | −0.404 | 0.0179 | −0.389 | 0.028 | 0.134 | 0.087 | 0.181 | 1.000 | |||
RPT | 0.010 | −0.090 | -0.052 | 0.039 | −0.0915 | 0.138 | −0.017 | 0.106 | 0.150 | 0.084 | 0.109 | 1.000 | ||
STA | 0.205 | −0.254 | 0.038 | 0.038 | 0.3091 | 0.077 | 0.005 | 0.112 | 0.097 | 0.026 | 0.083 | 0.058 | 1.000 | |
AGE | 0.211 | −0.085 | −0.150 | 0.082 | 0.2578 | 0.099 | −0.077 | 0.005 | 0.002 | 0.103 | 0.118 | 0.156 | 0.144 | 1.000 |
Average | 0.021 | 3.714 | 2.874 | 36.969 | 9.466 | 0.440 | 5.978 | 0.596 | 20.201 | 3.615 | 17.491 | 0.068 | 0.223 | 14.912 |
Std.Dev. | 0.245 | 1.803 | 1.654 | 32.954 | 1.675 | 0.498 | 0.663 | 0.521 | 10.637 | 0.540 | 17.325 | 0.252 | 0.417 | 4.910 |
(1) | (2) | (3) | |
---|---|---|---|
CD | −0.034 *** | −0.023 ** | −0.033 *** |
(−3.498) | (−2.351) | (−3.494) | |
DIS | −0.059 *** | −0.042 *** | −0.062 *** |
(−6.263) | (−4.293) | (−6.906) | |
CI | 0.001 ** | 0.002 *** | 0.0008 ** |
(2.486) | (3.897) | (2.160) | |
LNV | 0.040 *** | 0.039 *** | 0.039 *** |
(6.235) | (6.470) | (6.518) | |
OBOR | 0.068 ** | 0.088 *** | 0.116 *** |
(2.175) | (2.957) | (3.446) | |
CI*CD | 0.0007 *** | ||
(2.814) | |||
CI*DIS | 0.001 *** | ||
(3.772) | |||
OBOR*CD | 0.015 | ||
(0.843) | |||
OBOR*DIS | 0.079 *** | ||
(5.794) | |||
LRW | 0.085 | 0.065 * | 0.082 ** |
(2.545) | (1.848) | (2.621) | |
LOPEN | −0.382 | −0.020 | −0.059 |
(−0.971) | (−1.462) | (−1.583) | |
LTECH | −0.001 | −0.002 | −0.003 |
(−0.693) | (−1.462) | (−1.646) | |
LINFRA | 0.004 | 0.045 | 0.012 |
(0.108) | (1.193) | (0.360) | |
NRR | 0.003 *** | 0.004 *** | 0.004 *** |
(3.136) | (4.537) | (4.570) | |
RPT | 0.016 | 0.031 | 0.030 |
(0.421) | (0.883) | (0.886) | |
STA | −0.004 | 0.013 | 0.030 |
(−0.134) | (0.518) | (1.184) | |
AGE | 0.004 * | 0.004 * | 0.004 ** |
(1.949) | (1.822) | (2.010) | |
Constant | −1.022 | −1.281 | −1.216 |
(−6.557) | (−8.043) | (−8.274) | |
Year dummy | Y | Y | Y |
Industry dummy | Y | Y | Y |
Observations | 192 | 192 | 192 |
Adj. R-squared | 0.764 | 0.788 | 0.798 |
F value | 20.89 | 22.51 | 23.88 |
(1) | (2) | (3) | |
---|---|---|---|
CD4 | −0.032 *** | −0.027 ** | −0.023 ** |
−0.023 ** | (−3.542) | (−2.468) | (−2.595) |
DIS | −0.042 *** | −0.033 *** | −0.051 *** |
(−4.422) | (−3.356) | (−5.568) | |
CI | 0.001 ** | 0.001 *** | 0.001 *** |
(2.361) | (3.076) | (2.833) | |
OBOR | 0.074 *** | 0.083 *** | 0.113 *** |
(2.797) | (3.134) | (3.918) | |
CI*CD4 | 3.22 × 10−5 | ||
(0.112) | |||
CI*DIS | 0.0008 *** | ||
(2.751) | |||
OBOR*CD4 | −0.030 | ||
(−1.621) | |||
OBOR*DIS | 0.086 *** | ||
(5.495) | |||
Constant | −0.919 *** | −1.167 *** | −1.134 *** |
(−6.604) | (−8.433) | (−8.707) | |
Control variables | Y | Y | Y |
Year dummy | Y | Y | Y |
Industry dummy | Y | Y | Y |
Observations | 192 | 192 | 192 |
Adj. R-squared | 0.762 | 0.769 | 0.791 |
F | 21.43 | 20.86 | 23.64 |
(1) | (2) | (3) | |
---|---|---|---|
CD | −0.027 *** | −0.016 ** | −0.026 ** |
(−3.134) | (−1.971) | (−3.135) | |
DIS | −0.058 *** | −0.038 *** | −0.059 *** |
(−6.675) | (−4.097) | (−7.143) | |
CI | 0.001 *** | 0.002 *** | 0.0009 ** |
(2.653) | (4.328) | (2.436) | |
OBOR | 0.074 ** | 0.085 *** | 0.112 *** |
(2.797) | (3.134) | (3.618) | |
CI*CD | 0.0008 *** | ||
(3.607) | |||
CI*DIS | 0.001 *** | ||
(3.809) | |||
OBOR*CD | 0.016 | ||
(1.007) | |||
OBOR*DIS | 0.077 *** | ||
(6.129) | |||
Constant | −1.386 *** | −1.292 *** | −1.424 *** |
(−5.664) | (−5.000) | (−6.070) | |
Control variables | Y | Y | Y |
Year dummy | Y | Y | Y |
Industry dummy | Y | Y | Y |
Observations | 192 | 192 | 192 |
Wald chi2 | 766.93 | 882.22 | 930.15 |
Prob > chi2 | 0.0000 | 0.0000 | 0.0000 |
Log likelihood | 152.6613 | 163.5613 | 167.7511 |
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Jung, J.-Y.; Wang, W.; Cho, S.-W. The Role of Confucius Institutes and One Belt, One Road Initiatives on the Values of Cross-Border M&A: Empirical Evidence from China. Sustainability 2020, 12, 10277. https://doi.org/10.3390/su122410277
Jung J-Y, Wang W, Cho S-W. The Role of Confucius Institutes and One Belt, One Road Initiatives on the Values of Cross-Border M&A: Empirical Evidence from China. Sustainability. 2020; 12(24):10277. https://doi.org/10.3390/su122410277
Chicago/Turabian StyleJung, Jin-Young, Wei Wang, and Sung-Woo Cho. 2020. "The Role of Confucius Institutes and One Belt, One Road Initiatives on the Values of Cross-Border M&A: Empirical Evidence from China" Sustainability 12, no. 24: 10277. https://doi.org/10.3390/su122410277
APA StyleJung, J. -Y., Wang, W., & Cho, S. -W. (2020). The Role of Confucius Institutes and One Belt, One Road Initiatives on the Values of Cross-Border M&A: Empirical Evidence from China. Sustainability, 12(24), 10277. https://doi.org/10.3390/su122410277