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FDI on the move: cross-border M&A and migrant networks

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Abstract

Using data on Chinese outward direct investment and migrant stocks in 96 countries from 2003 to 2014, we find that migrant networks have a positive and significant impact on cross-border mergers and acquisitions (M&A), but not on greenfield investment. The migrant network effect is more pronounced for multinationals with less experience in the host country, especially for initial entrants that face greater firm, industry, and country-level information frictions. These results are robust to various estimation methods, including an instrumental variable approach that addresses potential endogeneity concerns. Our findings demonstrate the importance of knowledge spillovers from migrant networks to multinationals for facilitating entry into new markets.

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Notes

  1. Recent works have examined other determinants of Chinese ODI, such as domestic market frictions and trade and financial linkages (e.g., Chen & Tang 2014; Chen et al., 2019; Aizenman et al., 2018). The roles of migrant networks and different entry modes are not addressed in these studies.

  2. Earlier work includes Gould (1994), Head and Ries (1998), Combes et al. (2005), and Dunlevy (2006), who study the interaction of social networks and trade for US, Canada, France, and US, respectively. Some recent studies demonstrate causality by exploiting quasi-natural experiments (e.g., Steingress, 2018; Cohen et al., 2017; Parsons & Vézina, 2018). In addition, Burchardi and Hassan (2013) study the economic performance of West Germans by examining their social ties to East Germany after the fall of the Berlin Wall.

  3. Other work in this area includes Kugler and Rapoport (2007) and Bhattacharya and Groznik (2008). Gao (2003) and Tong (2005) show that overseas Chinese ethnic networks have a positive correlation with Chinese inward FDI and bilateral investment, respectively. Huang et al. (2013) also analyze Chinese inward FDI, but focus on the performance of industrial firms with investment originating from ethnically Chinese economies (Hong Kong, Macau, Taiwan) versus other countries.

  4. Similarly, Kamal and Sundaram (2016) find that foreign importers are more likely to establish trade partnerships with local exporters that are in the same neighborhood of their previous partners. Bisztray et al. (2018) document evidence that firms also learn from their peers to import.

  5. Transaction values are not always reported due to confidentiality, so we mainly focus on the extensive margin and the counts of M&A deals. UNCTAD (2017) also maintains a database of (non-bilateral) cross-border M&A purchases at the country level. For both the number and value of Chinese M&A purchases, SDC Platinum and UNCTAD (2017) are highly correlated at 0.91 and 0.85, respectively. SDC Platinum captures 38 to 80% of the number of M&A deals annually in UNCTAD (2017), and 42 to 276% of the value.

  6. As stated in database documentation: “Most of the data used to estimate the international migrant stock by country or area were obtained from population censuses. Additionally, population registers and nationally representative surveys provided information on the number and composition of international migrants. In estimating the international migrant stock, international migrants have been equated with the foreign-born population whenever this information is available, which is the case in most countries or areas.” More information is available at http://www.un.org/en/development/desa/population/migration/data/estimates2/estimates15.shtml. Gao (2003) and Tong (2005) study the relationship between Chinese ethnic networks abroad and aggregate FDI using data on ethnic Chinese populations in 1990. For the overlapping year of 1990, the correlation between the population of ethnic Chinese and Chinese migrants from the UN database is high (0.68).

  7. The other SAR of China, Macau, and tax havens like British Virgin Islands and Cayman Islands are excluded because missing data on the control variables.

  8. Although the M&A data are available at the deal level, we are interested in the overall pattern of FDI flows and not just the size of the investment project for individual firms. Hence, we aggregate the M&A data by host country as the outcome variable. However, the detailed deal-level information allows us to decompose M&A by different characteristics and explore the channels through which migrant networks facilitate foreign investment.

  9. Standard errors are clustered by host country for the linear and Poisson models to allow for the correlation of error terms across years within each destination. For the negative binomial model, the estimation of clustered standard errors in Stata is not currently feasible with the CRE approach (see the documentation of the xtnbreg command). However, as noted by Cameron and Trivedi (2009, p. 627), using the negative binomial model “may lead to improved efficiency in estimation and a default estimate of the VCE ([variance-covariance matrix of the estimates]) that should be much closer to the cluster-robust estimate of the VCE, unlike the Poisson panel commands.”

  10. We found that directly applying the method of Burchardi et al. (2019) here yields extreme predictions of migrant stocks. This is in part due to the decreasing stocks observed in some countries, so that their migrant stocks, which were already small to begin with, turned negative in the predictions.

  11. While the UN and World Bank datasets are not identical, the correlation between them is extremely high for the overlapping years of 1990 and 2000 at 0.98.

  12. Although Chinese outward investment only rises significantly at the turn of the 21st century, the overseas Chinese community had been growing long before that, in particular, after restrictions on emigration were lifted in 1860 (Skeldon 1996). Notable examples of mass migration from China can be traced to labor shortages, including the gold rush in US and Australia, and the development of British colonies in Southeast Asia. Due to such labor market shocks, communities were established, forming the Chinese diasporas observed today.

  13. We are also unable to reject the null hypothesis of the overidentification test when the predicted migrant network and, for example, the destination pull factor are used as instruments.

  14. UNCTAD (2017) and Davies et al. (2018) use the same dataset for analyzing global greenfield investment.

  15. We confirm our regression results are also robust when we change the equity threshold of the global ultimate owner to 50.01%.

  16. See Annex Table 8 from (UNCTAD 2017).

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Authors and Affiliations

  1. Department of Economics and Business Economics, Aarhus University, Fuglesangs Allé 4, 8210, Aarhus, Denmark

    Jackie M. L. Chan

  2. Lee Kuan Yew School of Public Policy, National University of Singapore, 469C Bukit Timah Road, Singapore, Singapore

    Huanhuan Zheng

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  1. Jackie M. L. Chan
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The paper has also benefited tremendously from the comments and suggestions of two anonymous referees. The authors are also grateful for the suggestions provided by Yi Lu, Han Qi, Liugang Sheng, Heiwai Tang, Beata Javorcik, and participants of the seminars at Baptist University, Lingnan University, Nanyang Technological University, the Conference on Trade and Development at the Chinese University of Hong Kong, and the Midwest Trade Conference.

Appendix

Appendix

1.1 Definitions of regulatory barriers (Gwartney et al., 2015)

This database covers five areas of economic freedom: (1) Size of government, (2) Legal system and property rights, (3) Sound money, (4) Freedom to trade internationally, (5) Regulation. Our analysis utilizes the following categories related to regulatory barriers and restrictions:

  • Regulation index (Area 5): An average index is constructed from the indices of: (A) Credit market regulations, (B) Labor market regulations, and (C) Business regulations.

  • Business regulations (component of Area 5 (Regulation)): An average index is constructed form the indices of 6 subcomponents: (i) Administrative requirements, (ii) Bureaucracy costs, (iii) Starting a business, (iv) Extra payments/bribes/favoritism, (v) Licensing restrictions. (vi) Cost of tax compliance.

  • Bribes, favoritism (subcomponent (C)(iv) of Area 5 (Regulation)): Based on the Global Competitiveness Report questions: (1) “In your industry, how commonly would you estimate that firms make undocumented extra payments or bribes connected with the following: Import and export permits; Connection to public utilities (e.g., telephone or electricity); Annual tax payments; Awarding of public contracts (investment projects); Getting favorable judicial decisions. Common (= 1), Never occur (= 7)”. (2) “Do illegal payments aimed at influencing government policies, laws or regulations have an impact on companies in your country? 1 = Yes, significant negative impact, 7 = No, no impact at all”. (3) “To what extent do government officials in your country show favoritism to well-connected firms and individuals when deciding upon policies and contracts? 1 = Always show favoritism, 7 = Never show favoritism”.

  • Hiring and firing regulations (subcomponent (B)(ii) of Area 5 (Regulation)): Based on the Global Competitiveness Report question: “The hiring and firing of workers is impeded by regulations (= 1) or flexibly determined by employers (= 7)”.

  • Credit market regulations (component of Area 5 (Regulation)): An average index is constructed from the indices of 3 subcomponents: (i) Ownership of banks, (ii) Private sector credit, (iii) Interest rate controls/negative real interest rates.

  • Foreign ownership restrictions (subcomponent (D)(i) of Area 4 (Freedom to trade internationally)): Based on the following questions from the Global Competitiveness Report: (1) “How prevalent is foreign ownership of companies in your country? 1 = Very rare, 7 = Highly prevalent”; (2) “How restrictive are regulations in your country relating to international capital flows? 1 = Highly restrictive, 7 = Not restrictive at all” (Fig. 2 and Tables 9, 10, 11, 12, 13, 14).

Fig. 2
figure 2

Outward greenfield FDI and M&A from a developing countries and transition economies; b developed economies. Source: UNCTAD (2017)

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Table 9 Summary statistics
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Table 10 Full results from CRE model of Table 2 Panel A
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Table 11 CRE model estimates treating Migrant Network as time-varying
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Table 12 Instrumenting with the components of the predicted migrant network
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Table 13 Overseas Chinese migrant networks and greenfield ODI—Poisson model
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Table 14 Heterogeneity analysis—Poisson model
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Chan, J.M.L., Zheng, H. FDI on the move: cross-border M&A and migrant networks. Rev World Econ 158, 947–985 (2022). https://doi.org/10.1007/s10290-021-00450-1

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