注：本文为笔者与王喆博士、蒋殿春教授合作完成的论文，在International Business Review上发表，转载请注明出处。这里仅给出摘要与引言。需要指出的是，本文使用的主要数据来自笔者团队开发的全球数字经济发展指数（TIMG）数据库，欢迎同仁们使用。具体请参加中国社科院金融研究所官网。文中配图摄于南京牛首山，“牛首烟岚”为古金陵十八景之一。
Wang Z, Jiang D, Zhang M. Seeking new location advantages: Analysis of emerging digital cross-border M&As—Based on TIMG index[J]. International Business Review, 2024, 33(2): 102243.
Abstract
This paper examines the location change of emerging digital cross-border mergers and acquisitions (DCBMAs). Based on a country-pair-year sample in 2013–2019 and newly constructed TIMG index, we find evidence that a country’s development level of digital economy has become new location advantage to attract DCBMAs. The possibility and scale of DCBMAs will increase in countries with developed digital infrastructure, digital market, and digital technology. We extend a new distance dimension and find that digital distance may impede DCBMAs between two countries. Further evidence suggests that the relationship of digital location advantages with DCBMAs is also affected by the differences in the income level of acquiring country, the attribute of investors and the characteristics of the digital industry.
Introduction
In recent years, digital economy is becoming a new industrial and investment trend. We have seen a wave of cross-border mergers and acquisitions (M&As) in digital sectors despite the overall downturn in international investment (Eden, 2016, UNCTAD, 2017). According to PitchBook, the aggregate digital cross-border M&As (DCBMAs) rise from 9.1% of total cross-border M&A activities worldwide to 17.3%, and the aggregate dollar volume also rise from 3.3% to 20% in the time period 2009–2019. Not only are developed countries including United States the main participants, some latecomers (like China) also become more and more active in global digital investment (McKinsey Global Institute, 2017). As we can see, many countries are committed to accelerating digital infrastructure construction and making adaptive policies to attract foreign capital inflow. It seems digital economy has become a new source of competitive advantage of one country and especially new path for developing countries to reach the industrial frontier in the digital age. Therefore, increasingly researchers focus on this new phenomenon and one of the most important questions is whether the motivations and geographical location determinants of DCBMAs become different compared with general cross-border M&As.
Based on existing literature, the rise of digital economy may profoundly influence and reshape the geographical landscape of global production and investment. As digital transmission across borders takes place in virtual space at little or zero cost, the constraints of geographical distance on cross-border M&As may be largely weakened (Brynjolfsson & Kahin, 2002; UNCTAD, 2017; Zaheer & Manrakhan, 2001). However, some research like Deardorff (2017) and Jiang and Tang (2021) find that distance still matters in digital trade or FDI. Accordingly, there are increasing discussion on whether multinational enterprise (MNE) activities are more dispersed or concentrated in the digital age (Alfaro and Chen, 2015, Chen and Kamal, 2016, Laplume et al., 2016). At present, no unified conclusion has been reached. Besides, one country's resource endowments and location advantages could also be deeply affected in digital area, which becomes the motivation of the geographic change of cross-border M&As. Some research shows that motivations of FDI in the digital age may be different from traditional FDI. For example, the incentive to expand overseas market quickly may be strengthened, and FDI in digital economy shows obvious intangible assets seeking and knowledge seeking motivation (Nachum & Zaheer, 2002, UNCTAD, 2017). Jiang and Tang (2021) find that digital technology and R&D resources of host countries are the core determinants of digital cross-border M&As.
However, most of existing research remains at the partial or qualitative level, and lack of systematic theoretical discussion and empirical data support. On the one hand, the geography and location change of digital FDI calls for more comprehensive analysis combining international business (IB) and economic geography theory. Prior studies mainly focus on the location advantages of digital economy related to technology or other economic factors, and relatively neglect other soft factors like institution (Ahern & Fracassi, 2015, Bris and Cabolis, 2008, Rossi and Volpin, 2004). Actually, location is one of pillars in eclectic paradigm (Dunning, 1973, Dunning, 1977), and various motivations and country-level determinants have been discussed (Dunning, 2001, Xie et al., 2017). Economic geography theory adds spatial perspective, thus extends location into place and space (Barnes, 1989, Sheppard, 2002). Moreover, emerging relational economic geography literature explores economic changes at various geographical scales in the context of economic, social and institutional environment (Bathelt and Gluckler, 2003, Yeung, 2005). The IB theory embed with geographic relational lens provides adaptability for new trends in internationalization (Buckley and Ghauri, 2004, Deng et al., 2020, McCann, 2011), as institution theory is also gradually integrated into IB theory (Eden & Miller, 2004). This could shed some lights on the explanation to the geography change and determinants in digital international investment.
On the other hand, the digitalization’s effect on location advantages needs more accurate measurement and in-depth empirical examination. Most previous studies use ICT technology or ICT infrastructure to measure the development of digital economy (Adedoyin et al., 2020, Alfaro and Chen, 2015, Ko, 2007, Leitão and Baptista, 2011, Saidi et al., 2018), which largely reflects the early form of digital economy. With the development of frontier technology such as cloud computing, artificial intelligence, blockchain and so on, single indicator of ICT cannot fully representative the latest progress of digital economy. Some international organizations have attempted to compile global digital economy index, such as Digital Economy and Society Index (DESI), Networked Readiness Index (NRI) and World Digital Competitiveness Ranking (European Commission, 2022, IMD, 2022, Portulans Institute, 2022). In spite of this, there is still space for further improvement in the trade-off between comprehensiveness of indicators and breadth of sample coverage.
Therefore, we focus on cross-border M&As aimed at digital sectors because the importance of M&As has been increasingly enhanced compared with greenfield investment in the digital age (Eden, 2016). By constructing a theoretical framework, we believe that location advantages could be reshaped in emerging digital revolution, which may be specific determinants to attract DCBMAs. Specifically, factors influencing a country's digital location can be divided into four dimensions: technology, infrastructure, market and institutional factors. We explore how these factors change in the digital area and the possible effects on cross-border M&As. Furthermore, we put different digital location advantages together and construct TIMG index to measure one country’s digital economy level. TIMG index measures one country’s development of digital economy from the perspective of resource endowment and institutional environment and tries to balance time span and coverage area with finally covering 108 economies over the period of 2013–2019.
In empirical analysis, we identify digital cross-border M&As according to vertical industryification in PitchBook database and examine the effect of digital location factors on DCBMAs based on gravity model. Our sample includes 57 acquiring countries and 72 target countries over the time period 2013–2019, resulting in 27,629 country-pair-year observations. We find empirical evidence that development level of digital economy, as a new location advantage of one country, plays a key role in increasing the possibility and scale of DCBMAs. To be specific, digital infrastructure and digital market are important determinants and motivations to attract DCBMA flows. Furthermore, we test the effect of digital distance on DCBMAs, and find that greater digital distance, which refers to the difference in the development level of digital economy between two countries, may hinder the possibility and scale of DCBMAs. More importantly, the relationship between digital economy level and DCBMAs is also affected by other factors and show different motivations. We mainly consider the impact of income level of acquiring country, the digital attribute of investors and the virtual characteristics of the digital industry. We conduct a series of robustness tests to support these results such as comparing with traditional cross-border M&As, endogeneity test, changing measurement of dependent and independent variables and so on.
This paper contributes to the literature in the following ways. First, our study revolves around recently emerging digital cross-border M&A activities, which enriches international business, economic geography and digital economy theory. Although there has been some discussion on location determinants change of internationalization and international investment in digital age (Alcácer et al., 2016, Alfaro and Chen, 2015, Eden, 2016, UNCTAD, 2017), few studies make in-depth empirical analysis combined with the latest data. This article analyzes the location determinants of DCBMAs from global perspective to find general characteristics and trends, which provides new evidence to understand the impact of digital economy on international investment and MNE’s activities. The heterogeneity analysis also reflects different motivation among different type of targets, investors and countries with different income level, which can make useful suggestion for a country's strategic priorities and path selection in developing digital economy.
Second, this paper identifies digital cross-border M&A deals from micro perspective and construct the sample of DCBMAs at country-pair-year level by summing up micro transaction data. How to define and measure the scope of digital economy has not reached a consensus in the current research field of digital economy. One common method is to identify industry with digital feature or with high digital penetration based on industryification such as NAICS (Jolliff & Nicholson, 2019), which may ignore some emerging digital industries that cannot be included in traditional industryification (ie. Fintech). We adopt a new method to identify the scope of digital economy using vertical industryification in PitchBook, which accurately categorizes the emerging digital industries. Finally, we identify the scope of digital cross-border M&A deals by including different target digital industries based on definition of digital economy, which lays a good foundation for subsequent empirical tests on the motivation and location determinants of DCBMAs.
Third, we analyze the change of location advantages in the digital age based on theoretical framework and further construct a new synthetic index to measure global digital economy development. Existing papers generally use single indicator such as broadband subscriptions, internet penetration, ICT capital as proxies for digital economy level (Abeliansky and Hilbert, 2017, Adarov & Stehrer, 2020; Asongu & Odhiambo, 2019), or focus on indicators in specific digital sectors like e-commerce, artificial intelligence or 3D printing (Abeliansky et al., 2020, Brynjolfsson et al., 2019, Goldfarb and Trefler, 2018). On the one hand, digital economy has developed in multiple dimensions, thus single indicator is difficult to measure comprehensively. On the other hand, indicators adopted by prior papers more reflect the development of information age rather than advanced digital age, or only represent one aspect of the digital economy development. Considering problems above, we set a framework of potential digital advantages combined location selection theory with digital characteristics. Furthermore, we construct new overall index covering 2013–2019 in order to keep track of the latest comprehensive development in the digital economy.
The structure of this paper is as follows. In Section 2, we develop a framework to analyze potential location determinants and advantages in the context of digital economy based on the relevant prior literature. Section 3 reports the sample construction and methodology. Section 4 contains our regression analyses and Section 5 makes robustness checks. Finally, we conclude this paper with Section 6.
