The impact of digitalization on foreign direct investment inflows into cities in China

Abstract

This paper explores the impact of digitalization on Foreign Direct Investment (FDI) inflows in 270 Chinese cities from 2012 to 2019, focusing on regional disparities in income levels. Employing the System Generalized Moment Method (GMM), it aims to bridge the gap in understanding how digitalization influences FDI inflows across regions with different income levels. The findings indicate a positive correlation in low-income cities where digitalization significantly attracts FDI, but this effect is limited in medium and high-income cities. These results highlight that incentives and digital infrastructure development could be crucial for enhancing FDI in lower-income regions, making digitalization a potential strategic tool for economic growth.

Impact Statement

This paper explores the impact of digitalization on Foreign Direct Investment (FDI) inflows in 270 cities in China from 2012 to 2019, focusing on regional disparities in income levels by employing the System Generalized Moment Method (GMM). As previous research on digitalization’s impact on FDI inflows especially at the city level in China is scarce, this study makes two contributions: firstly, we apply the generalized method of moments (GMM) to analyze the digitalization-FDI nexus, providing empirical insights at the city level, which has often been overlooked in previous studies. Secondly, by categorizing cities based on income levels, our study reveals variations in how digitalization impacts FDI across different economic levels. The results of this study offer solutions for economic growth in low-income cities, as the research findings show a positive correlation between digitalization and FDI attraction in low-income cities, although this effect is limited in middle - and upper-income cities. It is emphasized that the development of digital infrastructure is crucial for boosting FDI in low-income regions, thereby making digitalization a potential strategic tool for enhancing economic growth in these areas.

Keywords:

• Digitalization
• foreign direct investment
• regional analysis
• GMM
• China

REVIEWING EDITOR:

• Goodness Aye, Academic Editor, University of Agriculture, Makurdi Benue State, Nigeria

SUBJECTS:

• Economics
• Political Economy
• International Economics

JEL:

• F23
• O33
• O53

Authors contributions

This research paper greatly benefited from the contributions of all three authors. Dr. Tajul Ariffin Masron played a key role in conceptualization, research question formulation, and experimental design. Dansha Zhang’s expertise enhanced data collection, analysis, and interpretation, she also critically revises the paper for intellectual content. Xiutuan Lu contributed significantly to the theoretical framework, literature review, and critical insights. All authors accept accountabilities for all aspects of this work.

Disclosure statement

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

Data availability statement

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

1 In other words, less FDI-receiving areas tend to develop less than high FDI-receiving areas, leading to bigger income gap.

2 Other potential benefits of FDI are creating a more competitive domestic market (Barrios et al., 2005; Ishikawa et al., 2010), bringing a greener technology (Amendolagine et al., 2021; Song et al., 2015), spurring local innovation (Wang & Wu, 2016), improvement in host-country institutions (Kwok & Tadesse, 2006).

3 For example, the internet penetration index it follows: $\mathit{\text{INT}}=\left(\frac{{\mathit{\text{INT}}}_{\mathit{\text{city}}}-{\mathit{\text{INT}}}_{\mathit{\text{lowest}}}}{{\mathit{\text{INT}}}_{\mathit{\text{highest}}}-{\mathit{\text{INT}}}_{\mathit{\text{lowest}}}}\right)\times 100$.

4 The following equations represent the models introduced squared and cubic terms of the digitalization variable. The interpretation of the model symbols is consistent with Equation (3)(3) $${\mathit{\text{FDI}}}_{i,t}={\beta }_0+\gamma {\mathit{\text{FDI}}}_{i,t-1}+\alpha {\mathit{\text{DIG}}}_{i,t}+\beta X_{i,t}+{\lambda }_t+{\delta }_i+{\mu }_{i,t}$$(3) :

${\mathit{\text{FDI}}}_{i,t}={\beta }_0+\gamma {\mathit{\text{FDI}}}_{i,t-1}+{\alpha }_1{\mathit{\text{DIG}}}_{i,t}+{\alpha }_2{\mathit{\text{DIG}}}^2{}_{i,t}+\beta X_{i,t}+{\lambda }_t+{\delta }_i+{\mu }_{i,t}$ and ${\mathit{\text{FDI}}}_{i,t}={\beta }_0+\gamma {\mathit{\text{FDI}}}_{i,t-1}+{\alpha }_1{\mathit{\text{DIG}}}_{i,t}+{\alpha }_2{\mathit{\text{DIG}}}^3{}_{i,t}+\beta X_{i,t}+{\lambda }_t+{\delta }_i+{\mu }_{i,t}$

Additional information

Notes on contributors

Dansha Zhang

Dansha Zhang is currently a PhD student at School of Management, Universiti Sains Malaysia, Malaysia.

Tajul Ariffin Masron

Tajul Ariffin Masron is an Associate Professor at School of Management, Universiti Sains Malaysia, Malaysia.

Xiutuan Lu

Xiutuan Lu is a lecturer at Naning College for Vocational Technology, China.