The demand-side politics of China’s global buying spree: managers’ attitudes toward Chinese inward FDI flows in comparative perspective

Abstract

I investigate public opinion toward Chinese FDI inflows in advanced economies, comparing attitudes toward such investment with attitudes toward American and European FDI inflows. I am interested in whether concerns with technology transfer (and related job losses) commonly associated with Chinese FDI resonate among the key target audience, namely managers. Accordingly, I expect managers to less strongly support Chinese FDI inflows relative to FDI inflows from advanced economies when they are employed in high R&D industries. I expect both self-interested and socio-tropic motives to drive the split in how managers view Chinese FDI vs. European and American FDI. Because technology transfer occurs in acquisitions, I also expect industry-level exposure to Chinese acquisitions to reinforce the negative joint impact of being a manager and employment in high R&D industries on support for Chinese FDI. Using original survey data from Switzerland, I find robust evidence for my expectations. The findings point to occupational characteristics and the fear of technology transfer as key drivers of opposition to Chinese FDI in advanced economies, and suggest that the demand-side politics of Chinese inward FDI is unique.

Keywords:

• FDI preferences
• advanced economies
• occupations
• public opinion
• China
• globalization backlash
• political economy

Acknowledgements

Earlier versions of this paper were presented at the University of Reading (June 2017); the Congress of the Swiss Sociological Association in Zurich (June 2017); FORS – the Swiss Centre of Expertise in the Social Sciences in Lausanne (December 2017); the Annual Convention of the International Studies Association in San Francisco (April 2018); the University of Bern (March 2019), and the World Trade Forum in Bern (October 2019). For comments and suggestions, I thank seminar/conference participants, especially Matt Amengual, Jonas Bunte, Nana De Graaff, Valentino Desilvestro, Manfred Elsig, Jonathan Golub, Lukas Linsi, Marcelo Olarreaga, Alexandre Pollien, Wanlin Ren, Tim Vlandas, Patrick Wagner and Patrick Ziltener.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The DOI for the MOSAiCH 2015 survey is https://doi.org/10.23662/FORS-DS-683-2

Notes

1 Top and high-ranked managers are used interchangeably in this study. When the term manager is used, it refers to top managers.

2 In 2015, foreign companies invested CHF 68 billion in companies in Switzerland, of which CHF 51 billion originated in Europe. The single largest European investor was the UK (CHF 14 billion), followed by Luxembourg (CHF 14 billion), the Netherlands (CHF 9 billion), Austria (CHF 4 billion), Sweden and Belgium (CHF 2 billion each), and Germany and France (CHF 1 billion each).

3 The ratio of Chinese FDI stock to GDP was 0.74 and 2.26 in Japan and the US, respectively.

4 The overall OECD FDI Regulatory Restrictiveness scores in 2015 were as follows (1 = full restriction; 0 = no restriction): 0.033 for the sample of 23 EU countries for which data is available; 0.066 for the OECD average; 0.083 for Switzerland; 0.089 for the US; and 0.386 for China. Data source is OECD.Stat, OECD FDI Regulatory Restrictiveness Index (database accessed on December 12, 2018).

5 77.1% of the respondents to the main survey answered the drop-off questionnaire.

6 Exact wording of the survey questions and answer responses in the three national languages are shown in Online Appendix Table A1.

7 For instance, if a respondent said Chinese companies investing in Switzerland is quite good for Switzerland (score of 4) and neither good/nor bad for her personally (score of 3), she gets a score of 3.5 (mean of 3 + 4) on the variable Pro-Chinese FDI. This variable ranges between 1 (if a respondent answered two times very bad) and 5 (if she answered twice very good).

8 The other groups are: 2) Professionals (e.g., science/health/teaching/business & administration/ICT/legal professionals); 3) Technicians and Associate Professionals (e.g., science/health/etc associate professionals); 4) Clerical Support Workers (e.g., customer services clerks); 5) Services and Sales Workers (e.g., personal care workers); 6) Skilled Agricultural, Forestry and Fishery Workers; 7) Craft and Related Trades Workers; 8) Plant and Machine Operators and Assemblers; 9) Elementary Occupations (e.g., cleaners); 0) Armed Forces Occupations.

9 The results are robust to coding the food industry as medium-low R&D industry as suggested in the OECD taxonomy (results available upon request).

10 The cut-off point is 13^th February 2015, the date when the first interview took place.

11 Database accessed April 16, 2019.

12 All reported predicted probabilities, calculated with the remaining variables held at their means, are statistically significant.

13 The correlation between Education and Build & make (-.35) indicates that they are conceptually different.

14 The correlation coefficient between Nationalism and Cosmopolitanism is -.53.

15 The result is not driven either by how managers perceive European FDI as being distinct from Chinese and American FDI. The coefficients for the triple interaction with a dummy for EU observations are all positive and statistically insignificant (results available upon request).

16 The coefficient for the triple interaction term in Model 2 of Table A8 borders significance level (p-value=.104). In the fully-specified model, this coefficient is significant at the 95% level (results available upon request).

17 The EU launched internal discussions over the creation of a new mechanism for screening FDI at the EU level in 2017 (see Meunier, 2019b).

Additional information

Funding

This work was supported by the Swiss National Science Foundation under Grant number PP00P1_163745).

Notes on contributors

Damian Raess

Damian Raess is an SNSF Assistant Professor of Political Science at the World Trade Institute, University of Bern. He is the principal investigator of the project BRICS Globalization and Labor Protections in Advanced and Emerging Economies (https://bricsglobalization.weebly.com/). He is the co-author (with Dora Sari) of the most comprehensive dataset on labor provisions in preferential trade agreements (LABPTA dataset).