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

Zhang, Dansha; Masron, Tajul Ariffin; Lu, Xiutuan Article The impact of digitalization on foreign direct investment inflows into cities in China Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Zhang, Dansha; Masron, Tajul Ariffin; Lu, Xiutuan (2024) : The impact of digitalization on foreign direct investment inflows into cities in China, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-15, https://doi.org/10.1080/23322039.2024.2330458 This Version is available at: https://hdl.handle.net/10419/321461 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 The impact of digitalization on foreign direct investment inflows into cities in China Dansha Zhang, Tajul Ariffin Masron & Xiutuan Lu To cite this article: Dansha Zhang, Tajul Ariffin Masron & Xiutuan Lu (2024) The impact of digitalization on foreign direct investment inflows into cities in China, Cogent Economics & Finance, 12:1, 2330458, DOI: 10.1080/23322039.2024.2330458 To link to this article: https://doi.org/10.1080/23322039.2024.2330458 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 02 Apr 2024. Submit your article to this journal Article views: 3641 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE The impact of digitalization on foreign direct investment inflows into cities in China Dansha Zhang a , Tajul Ariffin Masron b and Xiutuan Lu a a School of Finance and Economics, Nanning College for Vocational Technology, Nanning, China; b School of Management, Universiti Sains Malaysia, Minden, Malaysia 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 lowincome 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. ARTICLE HISTORY Received 8 September 2023 Revised 20 February 2024 Accepted 10 March 2024 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 Introduction China’s Foreign Direct Investment (FDI) landscape has witnessed a significant transformation following the country’s accession to the World Trade Organisation (WTO) in 2001. Despite global economic uncertainties, including the COVID-19 pandemic, China has seen resilient growth in FDI inflows, which increased annually by over 6% from 2020 to 2022 (Ministry of Commerce of China, 2022). The importance of FDI to host economies has been well documented in the literature (see Bermejo Carbonell & Werner, 2018; Borensztein et al., 1998; Iamsiraroj, 2016), including the case of China (see Su & Liu, 2016; Tang et al., 2008; Zhang, 2001). However, the inflows of FDI have been unevenly distributed, as shown in Figure 1, with a pronounced concentration in coastal regions (Lee & Chang, 2009; Rodrik, 1999), and could be the answer to the findings of unfavorable effect of FDI on income inequality specifically in China as observed by Gries and Redlin (2009), Zheng et al. (2021), Chen and Wu (2005), and Miyamoto and Liu (2005) amid the positive outcome of FDI to host areas. 1 CONTACT Tajul Ariffin Masron [email protected] School of Management, Universiti Sains Malaysia, 11800 Minden, Penang, Malaysia ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2330458 https://doi.org/10.1080/23322039.2024.2330458 With the importance of FDI inflows as a source of capital, technology, and management skills to local industry for the development of the host economy or area, 2 lagged areas in China could get more advantageous by more inflows of FDI. Several strategies have been stated as the key to the success of China in luring a high volume of FDI and becoming among the world largest recipients of FDI could be large domestic market, labor quality (as well as cost), agglomeration, infrastructure and institutional quality (Boermans et al., 2011; Li & Park, 2006; Na & Lightfoot, 2006), leveraging on the rapid development of digitalization across the globe, we are interested on the role of digital transformation on FDI inflows into cities in China. While traditional theories like the Eclectic Paradigm (Dunning, 1977;Dunning,2002)have laid a foundational understanding of location and internalization advantages, they largely overlook the increasingly pivotal role of digital infrastructure in the economic landscapes of cities. Digitalization can be defined by Brennen and Kreiss (2016) as the adoption or increased use of digital or computer technology across various sectors. From Figure 2, we can observe a rapid improvement in digital transformation across China between 2012 and 2019, but mainly occurs in the coastal and Eastern areas. Several studies examine the effects of digitalization, starting from the role of government initiatives to encourage more private innovation (Wang et al., 2023), till the application in various areas such as economic development (S. Luo et al., 2023; Wang, et al., 2022), regional disparities (Liu et al., 2024), manufacturing sector (Miao, 2022), small and medium enterprises (Zhou & Liao, 2024) and environmental issue (J. Wang, Dong, et al., 2022). However, research on digitalization’s impact on FDI inflows especially at the city level in China, to our limited reading, is scarce. A central question our research seeks to answer is whether increasing digitalization across all cities in China will help promote FDI inflows. This study has two contributions. We first apply the generalized method of moments (GMM) to analyze the digitalization-FDI nexus, offering empirical insights at the city level—a scale often overlooked in previous studies (Noussan & Tagliapietra, 2020). Second, by categorizing cities based on income levels, our study reveals variations in how digitalization impacts FDI across different economic levels. This paper analyses the determinants of FDI in the context of city-level digitalization in China from 2012 to 2019. The remainder of the paper is organized as follows. The related literature is briefly reviewed in Section 2. The model construction and methodology are outlined in Section 3. The empirical results and discussion are presented in Sections 4 and 5, and Section 6 concludes the study. Figure 1. Spatial distribution of China’s FDI inflows performance in 2012 and 2019. Source: China Urban Statistics Yearbook (2013–2020). Figure 2. Spatial distribution of China’s digitalization in 2012 and 2019. Source: China Urban Statistics Yearbook (2013–2020). 2 D. ZHANG ET AL. Literature review This literature review discusses two critical streams of research relevant to our study, which include theoretical frameworks and the empirical review of the relationship between digitalization and FDI, to find the gaps in the existing research and propose the hypotheses of this paper. Theoretical framework of digitalization and FDI Theoretically, many classical theories can explain FDI activities, in which Ownership, Location and Internalization (OLI) Framework or Eclectic Paradigm (Dunning, 1981,1988; Dunning, 1977) explains why MNEs choose to invest in particular host countries, or why they choose to invest in particular locations within particular host countries. The location advantages of host countries are ignored by theories like Monopoly Advantage Theories. Meanwhile, Internalization Theories, which is taken into account by the Eclectic Paradigm, suggest the location advantages of host countries as an important influencing factor into the research framework. The theory of location advantage uses policies, economic variables, and production costs to explain why different locations are more or less attractive for FDI. According to location advantage, location determinants like market size, labor cost, infrastructure development, government incentives, location, and openness are considered the main location determinants of FDI. Based on the Eclectic Paradigm and other relevant theories, this paper draws on previous studies for variable selection. Digitalization can be regarded as technological progress, which greatly changes the way and speed of information transmission (Cenamor et al., 2017; Ciampi et al., 2022), and for multinational enterprises, digitalization helps to improve the return on investment (Luo, 2021). Investment income is the focus of multinational enterprises when making investment (Dunning & Lundan, 2010). Digitalization improves the attractiveness of FDI by reducing the information cost required by enterprises for crossborder investment. Specifically, if the local digitalization level is high, market phenomena have been formed and recorded in the institution, which means more data sources and makes it easier for investors to obtain and analyze, the collection of data on newly developed markets will also become more precise, which is also convenient for enterprises to make relevant investment plans (George & Schillebeeckx, 2022; Luo, 2021). Digitalized equiped region also enables foreign enterprises to quickly get familiar with the market and social situation of the host country (World Economic Forum, 2021). In addition, digitalization reduces the cost of talent search. Regions with higher levels of digital development often mean stronger technological and talent strength, which provides a large number of human resources for multinational enterprises (Grimpe et al., 2023). Moreover, digitalization can rely on information and communication technology to shorten the communication distance, effectively reduce the communication cost caused by geographical distance in business activities of enterprises, and enable enterprises to better integrate into the market environment of the host country. Dunning’s Eclectic Paradigm is based on the old reality of the 1970s and 1980s. These realities and the assumptions behind them have changed considerably. Therefore, this paper believes that theoretically, digitalization can be introduced into the location advantage of OLI framework as a new motivation for transnational investment. In addition to digitalization as a factor affecting FDI inflows, theories such as Neoclassical growth theory, Investment Development Path (IDP) theory and New trade theory also believe that other factors also have an important impact on FDI inflows. In Neoclassical Growth Theory, per capita GDP growth rate is considered to represent the overall improvement of the economy, indicating that a country has a good investment environment and potential large market demand, which is one of the factors considered by multinational enterprises to choose investment places. The Investment Development Path Theory highlights the importance of government spending, especially in infrastructure, in attracting FDI (Narula & Dunning, 2010). This aligns with the ‘location advantages’in the Eclectic Paradigm. What is more, urbanization also plays a significant role in attracting international capital, as New trade theory indicated that agglomeration economies in urban areas are key factors influencing FDI (Fujita & Thisse, 1996; Guimar~ aes et al., 2000; Tuan & Ng, 2004). Past studies have also shown that the exchange rate has an uncertain impact on FDI inflows. Cushman (1985) and Froot and Stein (1991) argue that the COGENT ECONOMICS & FINANCE 3 depreciation of the Renminbi promoted the inflow of foreign direct investment (FDI) based on the ‘Relative Production Cost Theory’(Cushman, 1985) and the ‘Relative Wealth Hypothesis Theory’(Froot & Stein, 1991). They believed that the depreciation of a country’s currency would lower the production costs of local goods, increase the return on FDI, and enhance the relative wealth of foreign investors. Empirical reviews of digitalization and FDI With China’s emergence as a digital leader (Woetzel et al., 2017), the role of digitalization in FDI has gained empirical attention. Studies indicate that digital factors like communication facilities and internet infrastructure attract FDI (Boermans et al., 2011; Mensah & Traore, 2022). According to Ha and Huyen (2022), digitalization is crucial in attracting FDI in the shortand long-term. They assert that digitalization can help overcome the challenges posed by the COVID-19 pandemic by leveraging data in 23 European countries. Some studies have shown that individual indicators of digitalization have a positive attractiveness of FDI, such as communication facilities and telecommunication level (Boermans et al., 2011; Mensah & Traore, 2022). Boermans et al. (2011) show that provinces with good communication facilities attract foreign investors. Mensah and Traore (2022) indicate that making high-speed internet an infrastructure quality indicator induces FDI in the banking and technology sector in the Belt and Road Initiative (BRI) countries. In another study, Sinha and Sengupta (2019) discerned that Information and communication technology significantly enhances productivity, efficiency, FDI inflows, and economic growth in developing countries. Digitalization can be measured using various relevant indicators, some of which are discussed as follows. First is internet coverage, an indicator often used to measure digitalization. Choi (2003) selects 14 investing countries and 53 host countries to examine the relationship between the Internet and FDI. The study finds that for every 10% increase in internet-related indicators, FDI increases by at least 2%. Second, the proportion of computer services and software employees reflects the degree to which an economy leverages digital technologies and employs workers with digital skills to drive growth, innovation, and productivity (Kraus et al., 2021). The increasing use of digital technologies in businesses has led to a growing demand for workers with skills in computer services and software development (M€ oller et al., 2020). Third, telecommunications services, including advanced services such as broadband internet, mobile data, and other high-speed connectivity services, represent the extent to which people can access telecommunications services such as telephone, internet, and other communication technologies. Navas-Sabater et al. (2002) discuss how access to information and communications technologies has become crucial to sustainable economic development and poverty reduction agendas. Fourth, the postal service amount can reflect the efficiency and coverage of a city’s postal service. This also reflects digitalization through e-commerce. Efficient postal services can promote the flow of goods and documents, which may attract foreign investors seeking to establish a business in a city. Fifth is mobile phone penetration, an indicator used to measure digitalization (Corrocher & Ordanini, 2002). According to China Internet Network Information Center, in 2020, 99.7% of China’s Internet users (986 million) accessed the internet via their mobile phones, while 32.8% and 28.2% of them accessed the internet via desktop and laptop, respectively (Wang & Liu, 2021). Sixth is government science and technology (S&T) expenditure, an essential characteristic of digitalization. It refers to the allocation of fiscal revenue by the treasury to fulfill the goal of S&T transformation and innovation. Knowles et al. (2021) discuss that foundational research software infrastructure is critical for accelerating science but is often unsustainably funded. Solving this problem requires an appreciation of the importance of digital public goods and a commitment to invest in government. However, some studies believe that digitalization can have stochastic influences on FDI inflows. For example, Sangroya et al. (2010) indicate that adopting a cloud computing paradigm may have adverse effects on the data security of service consumers. Brougham and Haar (2018) show that employees’awareness of technological advancements is negatively related to organizational commitment and job satisfaction. Thus, FDI may become less attractive in the eyes of domestic regions. 4 D. ZHANG ET AL. Despite the extensive literature, there is a gap in understanding the comprehensive impact of digitalization, particularly at the city level, on FDI in China. Existing studies often rely on single indicators of digitalization. Our study addresses this gap by employing a comprehensive digitalization index, considering various digital aspects. We hypothesize a positive relationship between the level of digitalization and FDI inflows that will be tested using a dynamic model across most cities in China. Methodology In developing our model to examine the relationship between digitalization and FDI inflows in Chinese cities, we have drawn upon extensive literature and empirical studies, which has guided our variable selection, ensuring that each variable is relevant and provides meaningful insights into the dynamics of FDI. The model aims to quantify the factors influencing FDI inflows into Chinese cities empirically. The literature has highlighted the importance of government spending, GDP growth, urbanization, and exchange rates in determining FDI inflows. To incorporate these insights, the FDI inflows model is specified as follows: FDIi,t¼b0þb1GOVSi,tþb2GDPCi,tþb3URBPi,tþb4EXRTi,tþli,t(1) Where bs are parameters to be estimated. lis the stochastic term, i and t refer to cities and years, respectively. FDI denotes FDI inflows to Chinese cities, the amount of foreign capital utilized annually. GOVS is government spending, GDPC represents the annual per capita real GDP growth rate, URBP stands for the proportion of permanent urban residents in the city’s population, including the city’s urban and rural areas, EXRT is the exchange rate, the amount of 1 US dollar in Chinese yuan. Aiming to explore the relationship between digitalization and FDI inflows, the model is expanded to include digitalization (DIG) as a key variable: FDIi,t¼b0þb1GOVSi,tþb2GDPCi,tþb3URBPi,tþb4EXRTi,tþb5DIGi,tþli,t(2) Traditional panel estimators such as pooled OLS, random effect, and fixed effect, can be biased and inconsistent due to the correlation between the lagged dependent variable and the error term (Ibrahim & Law, 2014). Considering these econometric concerns, this study applies a panel data model using the Generalized Method of Moments (GMM) technique for estimation, addressing endogeneity and biases associated with non-exogenous explanatory variables. This method aligns with methodologies outlined in studies by Holtz-Eakin et al. (1988), Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). Hence, in GMM format, setting X to represent the vector of explanatory variables, the dynamic panel model can be simplified as follows: FDIi,t¼b0þcFDIi,t−1þaDIGi,tþbXi,tþktþdiþli,t(3) In this study, all variables enter in natural logarithmic as indicated by ln, where cis the coefficient of the lagged dependent variable, ais the coefficient of the core variable DIG, X is a set of other independent variables, and ktis a period-specific effect common to all countries diis the city-specific effect, li,tis the random variable. The GMM estimator uses lagged explanatory variables as instrumental variables, which are used to address the possible correlation between the lagged dependent variable and the error term as well as the endogeneity of the explanatory variables. This method, known as first-order difference GMM, transforms Equation (2) into first-order differences and helps to eliminate city-specific effects. However, Arellano and Bover (1995), Blundell and Bond (1998) and other studies have shown that using lagged levels of variables as differential regression instruments may lead to weak instrumental variables and biased estimates. In addition, Bun and Kiviet (2006) show that this method would lead to significant deviations in the first-order difference GMM of highly persistent variables. To address these issues, this study employs a two-step system GMM estimator, which is more efficient than the first-order difference GMM estimator. Newey and West (1987) propose the two-step GMM estimator using the optimal weighting matrix, which showed better efficiency compared with the one-step estimator. Windmeijer (2005) also agrees on the effectiveness of two-step system GMM estimator. COGENT ECONOMICS & FINANCE 5 It is necessary to conduct specification tests and diagnostic procedures to ensure the consistency of the GMM estimator. The Hansen overidentifying restriction test is included to assess the validity of the instrument and model specification. In addition, first-order serial correlation (AR(1)) and second-order serial correlation (AR(2)) need to be performed. When the model is valid, the null hypothesis of the absence of first-order serial correlation should be rejected, but the null hypothesis of the absence of second-order serial correlation should not be rejected. This digitalization (DIG) index measurement involves three stages. The first stage involves identifying six indicators as the dimensions in digitalization. They are Internet penetration rate (INT), the proportion of computer services and software employees per 100 People (COM), total telecommunications service amount per 100 people (TEL), postal service amount per 100 people (POS), mobile phone user penetration rate (MPH), science and technology expenditure of government (SCE). The sub-indicators for the DIG index were selected with the support of relevant literature, including Boermans et al. (2011) and Mensah and Traore (2022), who emphasized the importance of digital infrastructure elements such as telecommunications facilities and mobile connectivity, and the following: Studies by Ha and Huyen (2022) and Choi (2003) highlight the importance of Internet penetration, e-commerce, and government technology investment as indicators of digitization in attracting investment. In the second stage, each of them is to be normalized into a standard measurement by turning them into an index following the same formula by the United Nations in the construction of the Human Development Index (HDI) as follows: 3 Dimension index ¼actual value-minimum value maximum value-minimum value 100 The third step, following the methodology used in constructing the HDI index, the third step involves computing the composite DIG index by averaging the six dimensions: DIG ¼INT þCOM þTEL þPOS þMPH þSCE 6  The rest of the variables are discussed in Table 1. The study uses panel data collected from 270 Chinese cities between 2012 and 2019. Table 1 displays the variables utilized in the study and their respective data sources. Most variables utilized in this study are sourced from the China Urban Statistics Yearbook for 2013– 2020. Exchange rate data is obtained from China’s National Bureau of Statistics for the same period. Results Descriptive and correlation analyses are omitted to conserve space but are available upon request. This study presents full-sample regression analysis results using the two-step system GMM estimator, as shown in Table 2. To assess the contribution of each sub-indicator, separate regression analyses for subindicators were conducted in models 2a to 2f. Specification tests conducted before interpreting the regression results show that the lagged dependent variable is significant and positive across various estimations, validating the dynamic model. The null hypothesis of first-order autocorrelation (AR1) is not accepted, while the absence of second-order autocorrelation (AR2) cannot be rejected. The Hansen test supports the validity of the instruments used in the study. The analysis reveals that overall, DIG significantly positively impacts FDI, suggesting that an increase in the digitalization level corresponds with a rise in FDI. Within the regression results for sub-indicators, Table 1. List of variables, descriptions, and sources. Variables Proxy Descriptions Sources FDI FDI inflows Amount of foreign direct investment utilized per year China Urban Statistics Yearbook (2013–2020)GOVS Government spending Amount of government fiscal spending GDPC Economy development Annual per capita real GDP growth rate URBP Urbanization level Proportion of permanent urban residents in the city’s population EXRT Exchange rate Period average price, Chinese Yuan (CNY) per 1 US Dollar (USD) DIG Digitalization Index Digitalization Index is calculated by UNDP method 6 D. ZHANG ET AL. government Science and Technology Expenditure (SCE) notably positively influences FDI, indicating SCE as a potent factor within the context of digitalization’s impact on FDI attraction. Certain sub-indicators like INT, COM, and POS did not significantly impact FDI in the whole sample, potentially due to low digitalization levels in cities. Addressing this, squared (DIG 2 ) and cubic (DIG 3 ) terms of digitalization were introduced 4 . Results in Table 3 indicate that even with these adjustments, the impact of squaring the digitalization level on FDI attractiveness remained insignificant in model 3(a, c, e, g, and i). Adding the cubic term of DIG in model 3(b, d, f, h, and j) reveals that sub-indicators such as INT and COM negatively affect FDI attractiveness at higher DIG levels, while other sub-indicators remain statistically insignificant. Discrepancies in the entire sample results may be attributed to regional imbalances (Li & Park, 2016). To further explore this, the model was re-estimated by dividing the total sample based on the average per capita income from 2012 to 2019 into low- (Table 4), middle- (Table 5), and high-income (Table 6) city sub-samples. Table 2. Regression Results of the Full sample [DV: FDI]. Model 1: DIG ¼DIG Model 2a: DIG ¼INT Model 2b: DIG ¼COM Model 2c: DIG ¼POS Model 2d: DIG ¼MPH Model 2e: DIG ¼SCE Model 2f: DIG ¼TEL FDI t-1 0.755 0.743 0.732 0.693 0.745 0.753 0.721 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) DIG 0.5120.081 0.144 0.082 0.059 0.136−0.010 (0.068) (0.360) (0.194) (0.150) (0.633) (0.059) (0.849) GOVS 0.337 0.369 0.408 0.403 0.346 0.257 0.429 (0.049) (0.012) (0.020) (0.005) (0.015) (0.133) (0.005) GDPC −0.042 −0.039 −0.052−0.036 −0.030 −0.051−0.052 (0.187) (0.171) (0.086) (0.226) (0.352) (0.065) (0.084) URBP −0.051 0.356 0.412 0.363 0.327 0.054 0.686 (0.894) (0.375) (0.343) (0.352) (0.453) (0.856) (0.089) EXRT −2.250 −2.737−3.139 −2.723−2.259 −1.451 −3.802 (0.248) (0.081) (0.100) (0.077) (0.125) (0.475) (0.015) Model criteria #Obs 1,512 1,512 1,512 1,512 1,512 1,512 1,512 #Cities 270 270 270 270 270 270 270 AR(1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AR(2) 0.924 0.973 0.985 0.969 0.978 0.995 0.978 Hansen 0.030 0.078 0.112 0.111 0.081 0.308 0.128 Note. The p-values are reported in parentheses. ,, and  denote 10%, 5%, and 1% level of significance, respectively. The model is estimated with 2-step system GMM.AR and Hansen refer to p-value. Table 3. Regression results of the full sample with square and cubic [DV: FDI]. Model 3a Model 3b Model 3c Model 3d Model 3e Model 3f Model 3g Model 3h Model 3i Model 3j DIG ¼INT DIG ¼COM DIG ¼POS DIG ¼MPH DIG ¼TEL FDI t-1 0.753 (0.000) 0.746 (0.000) 0.801 (0.000) 0.750 (0.000) 0.758 (0.000) 0.755 (0.000) 0.743 (0.000) 0.735 (0.000) 0.691 (0.000) 0.692 (0.000) DIG 0.511 (0.091) 0.343 (0.048) 0.175 (0.041) 0.245 (0.024) −0.107 (0.638) 0.030 (0.805) 0.038 (0.760) 0.208 (0.177) −0.329 (0.469) −0.164 (0.519) DIG 2 −0.069 (0.136) −0.068 (0.278) 0.017 (0.431) −0.005 (0.951) 0.023 (0.458) DIG 3 −0.009 (0.049) −0.050 (0.067) 0.000 (0.751) −0.063 (0.134) 0.001 (0.482) GOVS 0.330 (0.010) 0.327 (0.010) 0.314 (0.019) 0.297 (0.023) 0.257 (0.044) 0.277 (0.034) 0.276 (0.010) 0.257 (0.036) 0.358 (0.008) 0.348 (0.009) GDPC −0.022 (0.513) −0.021 (0.527) −0.043 (0.194) −0.057 (0.075) −0.025 (0.388) −0.025 (0.393) −0.037 (0.301) −0.038 (0.356) −0.047 (0.225) −0.047 (0.211) URBP 0.340 (0.327) 0.342 (0.331) 0.190 (0.544) 0.436 (0.192) 0.267 (0.467) 0.290 (0.425) 0.470 (0.188) 0.354 (0.365) 0.888 (0.023) 0.892 (0.022) EXRT −2.639 (0.063) −2.476 (0.073) −2.233 (0.139) −2.066 (0.159) −1.108 (0.465) −1.533 (0.307) −1.678 (0.100) −1.182 (0.362) −2.517 (0.103) −2.611 (0.072) Model criteria #Obs 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512 1,512 #Cities 270 270 270 270 270 270 270 270 270 270 AR(1) 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AR(2) 0.976 0.969 0.952 0.989 0.972 0.976 0.957 0.966 0.963 0.966 Hansen 0.117 0.127 0.309 0.288 0.199 0.183 0.0561 0.0933 0.0267 0.0255 Note. The p-values are reported in parentheses. ,, and  denote 10%, 5%, and 1% level of significance, respectively. The model is estimated with 2-step system GMM. AR and Hansen refer to p-value. COGENT ECONOMICS & FINANCE 7 Francis, J., Zheng, C. C., & Mukherji, A. (2009). An institutional perspective on foreign direct investment. Management International Review,49(5), 565–583. https://doi.org/10.1007/s11575-009-0011-x Froot, K. A., & Stein, J. C. (1991). Exchange-rates and foreign direct investment –An imperfect capital markets approach. The Quarterly Journal of Economics,106(4), 1191–1217. https://doi.org/10.2307/2937961 Fujita, M., & Thisse, J. F. (1996). Economics of agglomeration. Journal of the Japanese and International Economies, 10(4), 339–378. https://doi.org/10.1006/jjie.1996.0021 George, G., & Schillebeeckx, S. J. D. (2022). Digital transformation, sustainability, and purpose in the multinational enterprise. Journal of World Business,57(3), 101326. https://doi.org/10.1016/j.jwb.2022.101326 Grimpe, C., Sofka, W., & Kaiser, U. (2023). Competing for digital human capital: The retention effect of digital expertise in MNC subsidiaries. Journal of International Business Studies,54(4), 657–685. https://doi.org/10.1057/s41267021-00493-4 Gries, T., & Redlin, M. (2009). China’s provincial disparities and the determinants of provincial inequality. Journal of Chinese Economic and Business Studies,7(2), 259–281. https://doi.org/10.1080/14765280902847783 Guimar~ aes, P., Figueiredo, O., & Woodward, D. (2000). Agglomeration and the location of foreign direct investment in Portugal. Journal of Urban Economics,47(1), 115–135. https://doi.org/10.1006/juec.1999.2138 Ha, L. T., & Huyen, N. T. T. (2022). Impacts of digitalization on foreign investments in the European region during the COVID-19 pandemic. Development Studies Research,9(1), 177–191. https://doi.org/10.1080/21665095.2022.2074863 Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395. https://doi.org/10.2307/1913103 Iamsiraroj, S. (2016). The foreign direct investment–economic growth nexus. International Review of Economics & Finance,42, 116–133. https://doi.org/10.1016/j.iref.2015.10.044 Ibrahim, M. H., & Law, S. H. (2014). Social capital and CO 2 emission—output relations: A panel analysis. Renewable and Sustainable Energy Reviews,29, 528–534. https://doi.org/10.1016/j.rser.2013.08.076 Ishikawa, J., Morita, H., & Mukunoki, H. (2010). FDI in post-production services and product market competition. Journal of International Economics,82(1), 73–84. https://doi.org/10.1016/j.jinteco.2010.04.004 Kwok, C. C., & Tadesse, S. (2006). The MNC as an agent of change for host-country institutions: FDI and corruption. Journal of International Business Studies,37(6), 767–785. https://doi.org/10.1057/palgrave.jibs.8400228 Knowles, R., Mateen, B. A., & Yehudi, Y. (2021). We need to talk about the lack of investment in digital research infrastructure. Nature Computational Science,1(3), 169–171. https://doi.org/10.1038/s43588-021-00048-5 Ko, K. W. (2007). Internet externalities and location of foreign direct investment: A comparison between developed and developing countries. Information Economics and Policy,19(1), 1–23. https://doi.org/10.1016/j.infoecopol.2006.08.001 Kraus, S., Jones, P., Kailer, N., Weinmann, A., Chaparro-Banegas, N., & Roig-Tierno, N. (2021). Digital transformation: An overview of the current state of the art of research. SAGE Open,11(3), 215824402110475. https://doi.org/10. 1177/21582440211047576 Kurniawati, M. A. (2022). Analysis of the impact of information communication technology on economic growth: Empirical evidence from Asian countries. Journal of Asian Business and Economic Studies,29(1), 2–18. https://doi. org/10.1108/JABES-07-2020-0082 Lee, C. C., & Chang, C. P. (2009). FDI, financial development, and economic growth: International evidence. Journal of Applied Economics,12(2), 249–271. https://doi.org/10.1016/s1514-0326(09)60015-5 Li, S., & Park, S. H. (2006). Determinants of locations of foreign direct investment in China. Management and Organization Review,2(1), 95–119. https://doi.org/10.1111/j.1740-8784.2006.00030.x Li, X., & Park, S.-R. (2016). Trade characteristics of foreign direct investment inflows in China. China Finance Review International,6(2), 177–207. https://doi.org/10.1108/CFRI-12-2014-0100 Liu, H., Wang, X., Wang, Z., & Cheng, Y. (2024). Does digitalization mitigate regional inequalities? Evidence from China. Geography and Sustainability,5(1), 52–63. https://doi.org/10.1016/j.geosus.2023.09.007 Luo, S., Yimamu, N., Li, Y., Wu, H., Irfan, M., & Hao, Y. (2023). Digitalization and sustainable development: How could digital economy development improve green innovation in China? Business Strategy and the Environment,32(4), 1847–1871. https://doi.org/10.1002/bse.3223 Luo, Y. (2021). New OLI advantages in digital globalization. International Business Review,30(2), 101797. https://doi. org/10.1016/j.ibusrev.2021.101797 Mensah, J. T., & Traore, N. (2022). Infrastructure quality and FDI inflows: Evidence from the arrival of high-speed internet in Africa.https://ideas.repec.org/p/wbk/wbrwps/9946.html. Miao, Z. (2022). Industry 4.0: Technology spillover impact on digital manufacturing industry. Journal of Enterprise Information Management,35(4/5), 1251–1266. https://doi.org/10.1108/JEIM-02-2021-0113 Miyamoto, K., & Liu, H. (2005). An analysis of the determinants of provincial-level performance in China’s economy. Comparative Economic Studies,47(3), 520–542. https://doi.org/10.1057/palgrave.ces.8100056 M€ oller, K., Sch€ affer, U., & Verbeeten, F. (2020). Digitalization in management accounting and control: an editorial. Journal of Management Control,31(1-2), 1–8. https://doi.org/10.1007/s00187-020-00300-5 Na, L., & Lightfoot, W. S. (2006). Determinants of foreign direct investment at the regional level in China. Journal of Technology Management in China,1(3), 262–278. https://doi.org/10.1108/17468770610704930 Narula, R., & Dunning, J. H. (2010). Multinational enterprises, development and globalization: Some clarifications and a research agenda. Oxford Development Studies,38(3), 263–287. https://doi.org/10.1080/13600818.2010.505684 14 D. ZHANG ET AL. Navas-Sabater, J., Dymond, A., & Juntunen, N. (2002). Telecommunications and information services for the poor: Toward a strategy for universal access. The World Bank Group. Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica,55(3), 703–708. https://doi.org/10.2307/1913610 Noussan, M., & Tagliapietra, S. (2020). The effect of digitalization in the energy consumption of passenger transport: An analysis of future scenarios for Europe. Journal of Cleaner Production,258, 120926. https://doi.org/10.1016/j.jclepro.2020.120926 Rodrik, D. (1999). Where did all the growth go? External shocks, social conflict, and growth collapses. Journal of Economic Growth,4(4), 385–412. https://doi.org/10.1023/A:1009863208706 Sangroya, A., Kumar, S., Dhok, J., & Varma, V. (2010 Towards analyzing data security risks in cloud computing environments [Paper presentation]. Paper Presented at the 4th International Conference on Information Systems, Technology and Management, Mar 11–13, Bangkok, Thailand. Sinha, A., & Sengupta, T. (2019). Impact of natural resource rents on human development: What is the role of globalization in Asia Pacific countries? Resources Policy,63, 101413. https://doi.org/10.1016/j.resourpol.2019.101413 Song, M., Tao, J., & Wang, S. (2015). FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis. Annals of Operations Research,228(1), 47–64. https://doi.org/10.1007/s10479-013-1442-0 Su, Y., & Liu, Z. (2016). The impact of foreign direct investment and human capital on economic growth: Evidence from Chinese cities. China Economic Review,37(February), 97–109. https://doi.org/10.1016/j.chieco.2015.12.007 Tang, S., Selvanathan, E. A., & Selvanathan, S. (2008). Foreign direct investment, domestic investment and economic growth in China: A time series analysis. The World Economy,31(10), 1292–1309. https://doi.org/10.1111/j.14679701.2008.01129.x Tuan, C., & Ng, L. F. Y. (2004). Manufacturing agglomeration as incentives to Asian FDI in China after WTO. Journal of Asian Economics,15(4), 673–693. https://doi.org/10.1016/j.asieco.2004.05.014 Wang, J., Dong, K., Dong, X., & Taghizadeh-Hesary, F. (2022). Assessing the digital economy and its carbon-mitigation effects: The case of China. Energy Economics,113(September), 106198. https://doi.org/10.1016/j.eneco.2022.106198 Wang, J., Wang, B., Dong, K., & Dong, X. (2022). How does the digital economy improve high-quality energy development? The case of China. Technological Forecasting and Social Change,184(November), 121960. https://doi.org/ 10.1016/j.techfore.2022.121960 Wang, L., & Liu, C. (2021). Lost in mobile? Exploring the mobile internet digital divide among Chinese college students. International Journal of Educational Technology in Higher Education,18(1), 1–17. https://doi.org/10.1186/ s41239-021-00267-w Wang, X. C., Li, Y., Tian, L. W., & Hou, Y. (2023). Government digital initiatives and firm digital innovation: Evidence from China. Technovation,119, 102545. https://doi.org/10.1016/j.technovation.2022.102545 Wang, C. C., & Wu, A. (2016). Geographical FDI knowledge spillover and innovation of indigenous firms in China. International Business Review,25(4), 895–906. https://doi.org/10.1016/j.ibusrev.2015.12.004 Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics,126(1), 25–51. https://doi.org/10.1016/j.jeconom.2004.02.005 Woetzel, J., Seong, J., Wang, K. W., Manyika, J., Chui, M., & Wong, W. (2017). China’s digital economy: A leading global force.https://www.mckinsey.com/featured-insights/china/chinas-digital-economy-a-leading-global-force. World Economic Forum. (2021). The Davos agenda 2021.https://www.weforum.org/focus/the-davos-agenda-2021/. Zhang, K. H. L. (2001). How does foreign direct investment affect economic growth in China? Economics of Transition,9(3), 679–693. https://doi.org/10.1111/1468-0351.00095 Zheng, L., Shepherd, D., & Batuo, M. E. (2021). Variations in the determinants of regional development disparities in rural China. Journal of Rural Studies,82,29–36. https://doi.org/10.1016/j.jrurstud.2020.08.011 Zhou, C., & Liao, J. (2024). Home country digital finance development and post-entry internationalization speed of emerging market SMEs: Empirical evidence from China. International Review of Financial Analysis,91, 103016. https://doi.org/10.1016/j.irfa.2023.103016 Zhu, Y. X., Liu, Z. M., Feng, S. Y., & Lu, N. (2022). The role of fiscal expenditure on science and technology in carbon reduction: Evidence from provincial data in China. Environmental Science and Pollution Research,29(54), 82030– 82044. https://doi.org/10.1007/s11356-022-21500-7 COGENT ECONOMICS & FINANCE 15