Pro-active regional policy and the relocation of manufacturing firms: a case study of state-led industrial relocation in Guangdong, China

ABSTRACT

To contribute to the debate on the importance of state vis-à-vis inter-firm competition in regional development, this paper examines a representative dataset of the relocation of manufacturing firms in Guangdong province of China with multinomial logistic regression models. To improve the competitiveness of manufacturing in the Pearl River Delta, the Guangdong government implemented pro-active policies to encourage the relocation of existing manufacturing firms to their designated industrial parks during the 2000s. Although the initial results appear to support the usefulness of relocation policy, further examination reveals its effectiveness depends on the industrial sector and profiles of the relocated firms. In fact, the relocation of large-scale labor-intensive firms is not driven by local government initiatives. The physical proximity of high-technology parks to airports/ports has a bigger impact on the relocation of small-scale locally-funded high-technology firms into designated parks. In the case of locally-funded firms in polluting sectors, they are expanding rather than relocating to designated industrial parks. The empirical evidence indicates the non-binary nature of the industrial relocation policy. The nuances of relocation policy and its multi-scalar effects on relocated firms rejects any simplistic generalization.

KEYWORDS:

• Industrial relocation
• state policy
• manufacturing
• competition
• Pearl river delta
• China

Acknowledgments

This work was financially supported by the National Natural Science Foundation of China (No.41871114, No.41401119); National Natural Science Foundation of Guangdong, China (No.2018A030313293); Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (KF2020-04). The authors would like to express their gratitude for the inspiring comments and suggestions made by the editor and two anonymous referees.

Disclosure statement

No potential conflict of interest was reported by the authors.

Appendix

Table A1: Basic profile of the relocated firms

Table A2: Pairwise correlation matrix of variables

Table A3: Multinomial logistic regression models of industrial parks, Guangdong

Table A4: Robust test - multinomial logistic regression models of industrial parks, Guangdong

Figure A1: Predicted probability of firm relocation in different industrial sectors by type of industrial park.

Source: Calculated by the authors.

Figure A2: Predicted probability of firms with different types of ownership relocating by type of industrial park.

Note: WFVs = foreign JVs

Source: Calculated by the authors.

Notes

1. As a generic analytical framework for the dynamic power relationship between global lead and local firms, the cost-capability ratio is defined as the ratio between costs and a firm’s capability in the GPN 2.0 (Coe and Yeung 2015). As argued by G. Yeung (2016), it is up to individual researcher on how to implement the framework during the operationalization, i.e., researchers have to identify specific indicators that may fit into the framework.

2. See Table A1 in the Appendix.

3. High technology is classified under eight categories in China: information technology, biomedicine and advanced medical apparatus, aerospace, new materials, high-technology services, new energy and energy saving, waste and resources treatments, and advanced manufacturing and robotics (MST 2016).

4. Chinese local governments normally deploy the local media to publicize their important policies, so frequency counts of such news reports is a partial indicator of the importance of policies.

5. See Table A2 in the Appendix.

6. The negative coefficient of labor-intensive firms in scenario 3 is a random effect, with a probability of 0.945.

7. See Figure A1 in the Appendix.

8. The small sample size of 115 firms (6% of the total sample) in other sector firms may have an impact on any random effects.

9. See Figure A2 in the Appendix. An increase in type of ownership from 1 to 2–3 means a change from locally-funded firms to foreign-financed firms from Hong Kong, Macau, and Taiwan-funded to foreign JVs.

10. We separated the scale of investment in the sample into three groups (small, medium and large-scale investment) before estimating the predicted probability of the relocation propensity of firms in different sectors. The results confirm the above conclusion: large-scale labor-intensive firms have the highest tendency (a probability of 0.7181) to relocate to their designated parks while small-scale firms in the high-technology and polluting sectors have higher tendency (0.2679 and 0.3833) to relocate to their designated parks.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41401119,41871114]; Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences [KF2020-04]; National Natural Science Foundation of Guangdong, China [2018A030313293].