metadata: authors: 0: author: Xiaoyang Chen,Maggie
1: author: Lin,Chuanhao
count: Africa,East Asia and Pacific,World
docna: 0: docna: Foreign Investment across the Belt and Road
            : Patterns, Determinants, and Effects
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the patterns and
            economic effects of foreign direct investment across the
            Belt and Road Initiative countries and assesses the
            potential role of the initiative in shaping the patterns and
            effects. Exploring cross-country bilateral transportation
            cost and foreign investment data, the analysis shows that,
            by reducing overall travel times and transportation costs,
            the proposed Belt and Road Initiative transportation network
            can pave the way for additional investments and increased
            growth in gross domestic product. But the magnitude of the
            effect varies significantly across source and destination
            countries. Aggregate foreign direct investment in Belt and
            Road Initiative countries is predicted to increase by around
            5 percent, with regions such as Sub-Saharan Africa and East
            Asia and Pacific seeing greater potential gains. The
            increase in foreign direct investment can exert a positive
            effect on GDP, trade, and employment growth, especially for
            lower-income countries.
repnme: repnme: Foreign Investment across the Belt and Road
            : Patterns, Determinants, and Effects
display_title: Foreign Investment across the Belt
            and Road : Patterns, Determinants, and Effects
listing_relative_url: /research/2018/10/30486609/foreign-investment-across-belt-road-patterns-determinants-effects
publishtoextweb_dt: 2018-10-10T00:00:00Z
no_of_pages: 77
author_keys: 0: author_key: 1491283
model_extractions: WPS8607 Policy Research Working Paper 8607 Foreign Investment across the Belt and Road Patterns, Determinants and Effects _Maggie Xiaoyang Chen Chuanhao Lin_ Macroeconomics, Trade and Investment Global Practice October 2018
Policy Research Working Paper 8607

# **Abstract**

This paper examines the patterns and economic effects of foreign direct investment across the Belt and Road Initiative countries and assesses the potential role of the initiative in shaping the patterns and effects. Exploring cross-country bilateral transportation cost and foreign investment data, the analysis shows that, by reducing overall travel times and transportation costs, the proposed Belt and Road Initiative transportation network can pave the way for additional investments and increased growth in gross domestic product. But the magnitude of the effect varies significantly across source and destination countries. Aggregate foreign direct investment in Belt and Road Initiative countries is predicted to increase by around 5 percent, with regions such as Sub-Saharan Africa and East Asia and Pacific seeing greater potential gains. The increase in foreign direct investment can exert a positive effect on GDP, trade, and employment growth, especially for lower-income countries.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at xchen@gwu.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Foreign Investment across the Belt and Road: Patterns, Determinants and Effects<sup>1</sup> Maggie Xiaoyang Chen<sup>2</sup> and Chuanhao Lin<sup>3</sup> JEL: F2, F1 Key words: foreign investment, the Belt and Road Initiative, transportation cost, time, growth

> 1 We are grateful to Chunlin Zhang, Michele Ruta, Caroline Freund, Daniel Reyes, Jose Ramon Perea, Ana Fernandez, and participants of the World Bank Belt and Road Initiative Study Author Workshop for helpful comments and suggestions and Haixiao Wu for excellent research assistance. <mark>Funding from the China World Bank Partnership Facility is also acknowledged.</mark>

> 2 George Washington University. Email: <u>xchen@gwu.edu.</u>

> 3 George Washington University. Email: <u><mark>linch@email.gwu.edu.</mark></u>
1. Introduction

In fall 2013, President Xi Jinping introduced the _Belt and Road Initiative_ (BRI) during his visits to Kazakhstan and Indonesia. The initiative aims to revive ancient Silk Roads by encouraging new trade and improving connectivity. The _Belt_ is the land-based “Silk Road Economics Belt” that connects China and Europe through Central Asia and the Russian Federation. The _Road_ is the oceangoing “Maritime Silk Road” that connects China with Southeast Asia, East Africa, the Middle East and ultimately the heart of the Mediterranean. The initiative includes 72 countries around the Belt and the Road and at the moment covers over 60 percent of global GDP and 70 percent of the world population.

The key element of the Belt & Road Initiative is infrastructure investment that improves connectivity and encourages trade and investment across BRI countries. During the Belt and Road Summit in May 2017, the joint communique stated a list of cooperation objectives of BRI. Among many, the two most important objectives are 1) to strengthen physical, institution and people-topeople connectivity among BRI countries; and 2) to expand economic growth, trade and investment. The communique envisions the future of BRI countries to be a win-win situation by creating “prosperous and peaceful community with shared future.” In this paper, we examine the patterns and economic effects of foreign direct investment across BRI countries in comparison to other nations and assess the potential role of BRI, by improving countries’ physical connectivity and infrastructure, in shaping these patterns and effects.

Specifically, we address three broad questions:

- **Patterns.** What are the patterns of foreign investment in BRI nations compared to the rest of the world? How do these patterns vary across countries and over time?

- **Determinants.** Looking at historical data, what have been the roles of various types of transportation cost, infrastructure, and institutional factors in determining the volume and patterns of FDI in BRI countries? Are there synergies between infrastructure investment and general FDI? Which mediating institutional factors are underlying the effects? What is the potential impact of BRI on BRI countries’ receipt of FDI?

- **Effects.** How has inward foreign investment affected the economic growth of host economies? How does the effect vary across income groups and geographic regions? What is the potential impact of BRI on BRI countries’ growth through the channel of FDI?

In recent decades, foreign direct investment (FDI) flows as a share of GDP have more than doubled in both developed and developing nations. While developed countries still account for over 70 percent of the world’s outward FDI flows, developing countries including China have become an increasingly important source of FDI as shown in Figure 1.

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services could foster growth of regional value chains. The above effects could, however, vary significantly across countries depending on countries’ business environment and absorptive capacity.

In this paper, we first document and compare the historical patterns of FDI around the world including BRI and non-BRI countries and investigate how different types of transportation cost and infrastructure have affected FDI. Based on the findings, we then assess the potential roles of BRI in fostering FDI growth by improving different types of connectivity and infrastructure. Finally, we evaluate the effects of FDI on economic growth and how BRI might potentially affect economic growth through the foreign investment channel.

The structure of this paper is as follows. Section 2 reviews the existing literature evaluating the patterns, determinants, and effects of FDI as well as existing analysis on BRI. Section 3 discusses the data sources and the patterns emerging from the data. Section 4 describes the econometric methodology and the empirical findings. Section 5 concludes.

# 2. Literature Review

# 2.1 The Patterns of FDI and the Role of Transportation Cost

An extensive volume of empirical literature in international trade has examined the patterns of FDI as a function of country characteristics including market size, factor endowment, transportation cost, tariff, and other factors such as corporate tax, institutional quality and exchange rate.

The literature shows that the relationship between transportation cost and FDI varies sharply with the nature and type of investment, in particular, between horizontal and vertical/complex FDI. To measure transportation cost, distance or a ratio of cost, insurance and freight (cif) relative to free-onboard import value has usually been used while it is widely acknowledged that distance could capture not only various forms of geographic friction including the costs of communication and monitoring but also other factors such as cultural distance and historical ties (see, for example, Head and Mayer, 2013).

The first stream of studies presents evidence that is in alignment with horizontal FDI by showing a positive relationship between FDI on the one hand and market size and trade cost on the other. Brainard (1997), one of the first empirical studies examining the proximity-concentration trade-off, finds that the patterns in which country characteristics relate to U.S. FDI are broadly in alignment with the market access motive. Specifically, she uses U.S. trade and affiliate sales data from the 1989 BEA Benchmark Survey of U.S. Direct Investment Abroad and finds that FDI increases with hostcountry income and trade cost including the transportation cost to ship goods between the headquarters country and the host country, consistent with the market access motive in horizontal FDI.

Similar findings are shown in Carr, Markusen, and Maskus (2001) who incorporate both horizontal and vertical FDI into a knowledge-capital model of multinational firms and offer support to both market access and knowledge capital hypotheses. In particular, the elasticity of affiliate sales with respect to distance is estimated to range from -0.8 to -1.8, suggesting strong gravity in FDI patterns. Affiliate sales tend to diminish by around 8 to 18 percent when the distance between parent and 4
host countries rises by 10 percent. In fact, the extent of gravity in FDI is comparable to the extent of gravity in trade that has been found in the literature (see, for example, Head and Mayer, 2013).

Yeaple (2003a) extends earlier work by exploring an interaction between country and industry determinants of FDI and offers empirical support to both market access and comparative advantage motives. Specifically, he uses U.S. affiliate sales data in 39 countries and 50 manufacturing industries from the BEA Benchmark Survey of 1994 and finds that U.S. multinational firms from unskilledlabor intensive industries tend to invest in unskilled-labor abundant countries, a result consistent with the hypothesis that countries' factor endowment differences lead to vertical FDI. Unlike in Brainard (1997), the role of transportation cost is found to be negative and statistically insignificant, departing from the expected sign in the context of horizontal FDI.

Irarrazabal, Moxnes and Opromolla (2013) exploit the source of gravity in FDI by introducing intrafirm trade into the framework of Helpman, Melitz and Yeaple (2004) and generating gravity relationships for both exports and multinational production. The model rationalizes gravity in FDI by assuming that the headquarters produce a firm-specific tangible or intangible input that is required for production in any location and trade in such input is subject to trade costs including transportation costs or tariffs in the case of physical inputs and cultural and geographic remoteness from the headquarters in the case of headquarters services. The results suggest that intra-firm trade appears to play a crucial role in shaping the geography of multinational production; to justify the gravity observed, the affiliate's cost share related to input purchases from the headquarters must be about 90 percent. As suggested in the paper, this result may have captured other mechanisms that are dampening firms' multinational production as trade costs increase, such as imperfect transmission of technology between parents and affiliates either because of imperfect codifiability as discussed in Keller and Yeaple (2013) or because of higher frictions in the match between firms and workers.

Addressing the long-standing issue that FDI data are not systematically available across countries and over time, Ramondo, Rodriguez-Clare and Tintelnot (2015) present a comprehensive data set on the bilateral activity of multinational firms using UNCTAD data and an extrapolation procedure, with focus on two variables: affiliate revenues and the number of affiliates across country pairs. Among the various stylized facts, the analysis shows that the effects of distance on multinational production shares are similar to the ones found for trade shares (close to -1) and the extensive margin of multinational production is much more elastic to distance than the intensive margin.

While most empirical studies have examined FDI as bilateral relationships, an emerging literature accounts for the multi-country spatial interdependence of FDI flows predicted in Yeaple (2003b) and Ekholm, Forslid and Markusen (2007) as most multinational firms today employ complex integration strategies and operate multilateral production networks. Head and Mayer (2004) examine hypotheses of export-platform FDI and show that a country's market potential, measured by the distance-weighted sum of domestic and export market size, plays a significant role in countries' ability to attract multinational firms. The results show that Japanese multinationals are more likely to locate in regions proximate to large markets, suggesting that geographic proximity between host and third countries could also influence the investment decisions of multinational firms, especially those seeking to engage in export-platform FDI.

Spatial interdependence across FDI flows is also shown in Baltagi, Egger and Pfaffermayr (2007), Blonigen et al. (2007, 2008), and Chen (2011). Baltagi, Egger and Pfaffermayr (2007) estimate a 5
knowledge-capital model that incorporates spatial correlations in the independent variables and find that third-country characteristics exert significant effects on FDI flows. The linkage between host countries declines with bilateral distance among the host countries. Using sectoral FDI data, Blonigen et al. (2007) examine how investments in third countries affect a country's receipt of U.S. FDI. They find evidence of negative interdependence across proximate host countries, a result consistent with export-platform FDI theory, among European OECD members. The importance of third-country effects in inbound FDI is shown in Blonigen et al. (2008). The authors find a strong parent market proximity effect whereby parent markets' proximity to large third nations increases the volume of FDI. Similarly, Chen (2011) examines the cross-country interdependence in French multinationals' production networks using subsidiary-level data and finds strong spatial interdependence in multinationals' foreign production networks. The role of distance and transportation cost depends on the input-output linkages between subsidiary locations. MNCs are more likely to locate final-good production in countries with large market potential, vertically linked subsidiaries in proximate countries, and horizontally linked subsidiaries in remote locations.<sup>4</sup>

# 2.2 The Impacts of FDI in BRI Countries

The economic impacts of FDI have been studied in a large body of literature, through macro, crosscountry analysis as well as micro, firm-level research. At the macro level, numerous studies, including, for example, the work by Borensztein et al. (1998) and Alfaro et al. (2004), have examined the relationship between FDI and economic growth. Evidence suggests that FDI could exert a positive effect on economic growth when host countries meet certain economic conditions, including sufficient human capital stock and relatively developed financial markets. At the firm level, an extensive volume of research has examined the effects of FDI on host countries in a variety of dimensions including productivity, employment, wage rate, and export performance. The productivity effect of FDI, in particular, has attracted considerable empirical attention.

In this sub-section, we discuss the literature evaluating the impacts of inward FDI in the context of BRI countries. The literature presented here is based primarily on analysis of historical FDI data in OBOR countries prior to the OBOR initiative. Table A.1 in the appendix provides a summary of studies discussed in this Section.

An extensive empirical literature assesses the existence of productivity spillover from multinational to domestic firms. Such spillover could occur to domestic firms acquired by foreign multinational firms, domestic firms competing with the foreign multinationals, as well as those sharing vertical production linkages.

- **Foreign acquisition.** The impacts of foreign acquisition on targeted firms have been shown in terms of wage premium, employment growth, and productivity increase. In terms of wage premium, Lipsey and Sjholm (2004) use Indonesian manufacturing firm-level data to show that foreign firms tend to hire more educated workers and pay a wage premium even

> 4 A prominent literature led by Helpman, Melitz and Yeaple (2004) introduces firm heterogeneity into the decision between exports and horizontal FDI and shows that not only are the most productive firms most likely to engage in FDI, FDI sales relative to exports are also larger in sectors with more firm heterogeneity, higher transportation cost, higher tariff, and greater capital intensity. The work by Helpman, Melitz and Yeaple (2004) is extended in numerous studies including, for example, Yeaple (2009) and Chen and Moore (2010).

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conditional on workers’ education level. Earle, Telegdy and Antal (2013) use linked employer-employee data in Hungary and find the wage premium of foreign acquisition to be around 4.5 percent. The wage premium is observed for all education, experience, gender, and occupation groups as well as across wage quantiles. Divestment to domestic owners, on the other hand, is shown to reverse the effects. On employment, Lipsey et al (2013) find that the shift from domestic to foreign ownership tends to raise employment growth in Indonesia. As for productivity, Arnold and Javorcik (2009) use Indonesian plant level data and find that plant productivity increases about 13.5 percent by the third year under foreign ownership. These productivity improvements occur simultaneously with increases in investment in machinery and equipment, employment, wages and output. Similarly, taking into account endogenous acquisition decisions in China, Wang and Wang (2015) find positive impacts of foreign acquisition on firm productivity, export, output, employment and wages.

- **Spillover to other domestic firms.** In addition to the direct benefits from foreign acquisition on targeted firms, many studies examine the spillover effect of FDI on other domestic firms in the host country. The literature distinguishes three types of spillover: intraindustry or horizontal spillover, inter-industry spillover through backward linkage, and interindustry spillover through forward linkage. The literature provides little evidence in support of intra-industry productivity spillovers from FDI.  Javorcik (2004) and many subsequent studies show that FDI exerts little or negative effects on the productivity of domestic firms in the same industry. Lu et al. (2017), for example, explore the relaxation of FDI regulations upon China's WTO accession to evaluate the spillover effect of horizontal FDI and find FDI to exert either a negative or an insignificant effect on the productivity of Chinese domestic firms.

One of the potential explanations for the limited horizontal spillover is that domestic producers also face crowding out effects in product and factor markets when foreign competitors enter the market. Such crowding out effects tend to be nonexistent or relatively small for firms that are vertically linked to foreign firms (i.e. suppliers or customers of the foreign firms). Other explanations suggested in the literature include, in particular, mediating factors that are discussed below.

While the evidence is ambiguous about the existence of positive and significant horizontal spillover, most studies find positive and significant productivity spillover through backward linkages. A leading study in this literature, Javorcik (2004), finds that a one-standarddeviation increase of foreign presence in the sourcing sectors is associated with 15 percent rise in domestic firms’ output in supplying industries in Lithuania. Blalock and Gertler (2008) use firm level panel data from 1988-1996 and similarly find evidence of positive spillover through backward linkage in Indonesia. In Hungary, Halpern and Murakzy (2007) find evidence of positive horizontal spillover but only for firms that are geographically close to foreign-owned firms. Javorcik and Spatareanu (2009) find evidence of backward linkage spillover in the Czech Republic. Kee (2015) explores a new channel of productivity spillover and finds firms sharing the same suppliers with foreign firms in Bangladesh to experience productivity improvement.

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- **Spillover from FDI in service sectors.** While most studies in the literature focus on spillovers from FDI inflows in manufacturing on manufacturing firms, research has also looked at the effects of FDI inflow in service sectors such as banking and telecommunication on manufacturing firms. Fernandes and Paunov (2012) <mark>examine the impact of FDI inflows in producer service sectors on the TFP of Chilean manufacturing firms and show that forward linkages from FDI in services explain 7% of the observed increase in Chile's manufacturing users' TFP. J</mark> avorcik and Li (2013) use data from Romania in 1997 – 2005 and find that an expansion of global retail chains leads to significant TFP increase in the supplying manufacturing sector. Arnold, Javorcik and Mattoo (2011) find a similar result in the Czech Republic and show that the presence of foreign firms in the service sector is associated with greater productivity in the manufacturing sector. A related study by Arnold et al (2016) finds that service reforms in India benefit both foreign and locally owned manufacturing firms, with the effect on the former being larger.

- **Mediating factors.** The existence and the magnitude of productivity spillover could be conditional on a variety of factors such as the absorptive capacity of countries and firms, forms of FDI, and domestic policies. For example, countries and firms with a stronger absorptive capacity such as R&D capacity and relatively skilled labor would be more likely to experience productivity spillovers. For example, Blalock and Gertler (2009) find that Indonesian firms with more R&D, more educated workers, and smaller technology gaps from foreign firms tend to benefit more from FDI. Another study by Javorcik and Spatareanu (2008) finds that ownership structure can also be a mediating factor. They find that vertical spillovers tend to arise from projects with shared domestic and foreign ownership but not from fully foreign owned plants. Du et al. (2012) examine how industrial policy – specifically tariff liberalization and tax subsidies – affects the magnitude and direction of FDI spillovers in China’s manufacturing sector from 1998 through 2007. The study finds that liberalization measures during the critical 1998–2007 period on balance served to enhance productivity growth in Chinese industry. A study by Farole and Winkler (2014), using World Bank Enterprise Surveys data, shows three types of mediating factors: firms’ absorptive capacity, investors’ spillover potential measured by the share of FDI output sold domestically, and host-country institutional factors. Finally, Espitia et al. (2017) show that distance, the level of development of the host country, and institutions are important mediating factors using panel data for 62 countries from 2005 to 2012.

In addition to firm productivity, employment, wage and export performance, the effects of FDI have also been found on domestic investment and innovation, although these aspects have received far less attention. On domestic investment, Wang (2010) uses data from 50 countries over 1970 to 2004 and finds that FDI had a negative contemporaneous effect on domestic investment but a positive cumulative effect on domestic investment. Miun et al (2002) ask a similar question and find mixed results in the Czech Republic, Hungary, and Poland. In Sub-Saharan Africa, Ndikumana et al (2008) find FDI to crowd out domestic investment. On innovation, Cheung et al (2004) find FDI has increased the number of domestic patent applications in China.

Recently, using a large cross-country firm-level data set, Alfaro and Chen (2017) find factor market reallocation in labor and capital markets from less productive to more productive firms accounts for the majority of aggregate productivity gains from foreign multinational competition. Bao and Chen (forthcoming) show another important source of gains from FDI arises from domestic firms’ 8
responses to the threat of foreign multinational competition by upgrading productivity, raising innovation, investment and wage rate, and altering product composition.

# 2.3 The Determinants and Impacts of Chinese Outward FDI

Within the broad FDI literature, a new emerging strand of research focuses on China’s outward FDI.<sup>5</sup> In its earliest stage, research in this area is primarily descriptive in nature. For example, Cai (1999) proposes that Chinese firms invest overseas to seek markets, natural resources, technology, managerial skills, and financial capital. Wu and Chen (2001) suggest two additional motives, namely, to transfer excessive production capacity overseas and to diversify production lines. A study by Morck, Yeung and Zhao (2008) shows that Chinese multinational firms tend to perform better than other foreign firms in environments with weak domestic institutions, possibly because Chinese firms are better at dealing with governments and operating in a country with inefficient domestic institutions.<sup>6</sup> A new wave of empirical work examines the determinants of Chinese ODI at the aggregate level. For example, Cheung and Qian (2009) find Chinese ODI tends to be attracted to countries with a larger market size and richer resources. Buckley et al (2007) find that Chinese ODI is positively correlated with the levels of host-country political risk and cultural proximity to China. Ramasamy et al (2012) use annual report data from the 200 largest Chinese firms listed in Shenzhen and Shanghai stock markets to compile Chinese ODI measures. They find that while state-controlled firms are attracted to countries with greater natural resources and more risky political environments, private There are also studies analyzing Chinese ODI from the home country perspective. At the regional level, You (2015) finds that Chinese local government policy measured by willingness to approve local ODI and investment in R&D has a positive impact on regional ODI. Another home country characteristic found to have a significant effect is lagged inward FDI. Yao et al (2016) find that a country’s lagged inward FDI stock in China is positively correlated with China’s contemporaneous ODI to that country. A case study by Hertenstein et al (2017) looks into Chinese ODI in the auto component industry and argues that business networks explain the connections between lagged inward FDI and ODI.

More recent studies of Chinese ODI explore firm-level data. Using MOFCOM project data from 1998-2009, Chen and Tang (2014) find that the ex-ante larger, more productive, and more exportintensive firms are more likely to invest abroad. They also find that ODI helps firms to achieve higher TFP, employment, and export intensity and greater product innovation. Wang et al (2016) use firm-level data from Zhejiang Province to examine the impact of financial constraints on firms’ ODI decisions and suggest that lowering a firm’s financial constraints can increase both the probability and volume of ODI. Chen, Dollar, and Tang (2016) focus on the determinants of Chinese ODI in Africa and show that Chinese ODI in Africa is shaped by firms’ profit 5 Another related literature has examined the patterns of FDI from other emerging economies. Gómez-Mera et al. (2015), for example, study the patterns of FDI from Brazil, India, the Republic of Korea, and South Africa and find that FDI from these emerging economies deals with a fundamental trade-off between market attractiveness and the transactions costs of entering distant and remote markets.

6 This “institutional advantage argument” is supported by Cuervo-Cazurra and Genc’s (2008) cross-country analysis, which argues that disadvantages at home due to weak institutions could become advantages abroad.

9
maximization objective and the strategy of choosing locations based on local comparative advantages.

In sum, the literature finds that China’s ODI tends to be attracted to larger markets and rich resource areas just like other countries’ ODI. What differentiates Chinese investors is that they are more willing to invest in risky environments. At the firm level, evidence shows that more productive firms and less financially constrained firms are more likely to invest abroad. Investing abroad also makes investing firms more productive and grow faster. While studies on the determinants of China’s ODI are extensive, relatively little work has been done to evaluate the impacts of Chinese ODI.

# 2.4 Reports on BRI

Existing economic analysis of the BRI is very limited. Most of the available discussions are descriptive reports. For example, Johnson’s (2016) CSIS report provides a general assessment of the BRI and discusses the benefits of BRI to China. It also documents the responses from participating and non-participating countries. The Economist (2016) published a report on BRI countries using data from InfraPPP and Competitiveness Group/Long-term Asset Infrastructure to provide a general outlook of the ongoing infrastructure projects in each BRI country. American Enterprise Institute’s Scissors et al (2017) also provides a descriptive summary of China’s outward investment since the introduction of the BRI and argues that China’s increasing construction projects in BRI could become costly for recipient countries to maintain. Similar concerns have been expressed by Dollar (2017), who observes that most of China’s ODI still flows to rich developed countries. He also points out that Chinese investors’ willingness to take risk will benefit BRI countries. A similar point is made in another report by the Economist (2017) which characterized BRI countries as high opportunities but also high risk countries. UNDP China et al (2017) suggests that the initiative can help BRI countries achieve industrial diversification and economic growth and discussed the conditions for achieving the Sustainable Development Goals.

# 3. Data and Stylized Facts

In this section, we describe the main data sources of FDI and related economic characteristics and an array of stylized facts emerging from the data.

# 3.1 Foreign Investment in BRI: Data and Patterns

The main sources of FDI data used are UNCTAD’s bilateral FDI flow database and China Global Investment Tracker. In addition, we have also collected FDI statistics from individual countries’ official agencies as supplementary data.<sup>7</sup>

> 7 Generally, four types of FDI data have been used for economic analysis in the literature, including (i) aggregate FDI data from international agencies such as UNCTAD and OECD which tend to provide a relatively comprehensive country coverage; (ii) MNC activity data from national agencies, which offer national coverages but usually impose strict nationality and other restrictions on access and are not available systematically across countries; (iii) proprietary firm ownership or mergers and acquisitions (M&A) data sets provided by publishers such as Dun & Bradstreet, Bureau van Dijk,

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<!-- Start of picture text --> aT. aaa<br>A Ns }<br>io GY<br>Investment (millions USD) Cas Xidee.<br>[a(Gl 2860-21870Noo-2860 Data(Gl 2860-21870Noo-2860 Data 2860-21870Noo-2860 DataNoo-2860 Datao-2860 Data Data i}} Piha\iagaay\iagaayaay UAA a ~~ 1<br>fm(ill 11870-2897028970-8991039910-168520(ill 11870-2897028970-8991039910-168520ll 11870-2897028970-8991039910-168520 11870-2897028970-8991039910-16852028970-8991039910-16852039910-168520 &i! . . a . . ”<br><!-- End of picture text --> <!-- Start of picture text --> =.<br>La a . ;<br><5 Umea © OA<br>Construction (millions usb)" usb)" vT : dein<br>[70-2910(Gill 2910-9210No Data(Gill 2910-9210No Data 2910-9210No DataNo Data Data &FF Vimwy4U-wy4U-4U-U- an2424<br>(il 9210-17310 9210-17310 ii >|<br>fmMBMB 17310-2555025550-3830025550-38300 ~ 7 . . é<br><!-- End of picture text -->

<!-- Start of picture text --> aT. aaa =.<br>A Ns } La a . ;<br>io GY <5 Umea © OA<br>Investment (millions USD) Cas Xidee. Construction (millions usb)" usb)" vT : dein<br>[a(Gl 2860-21870Noo-2860 Data(Gl 2860-21870Noo-2860 Data 2860-21870Noo-2860 DataNoo-2860 Datao-2860 Data Data i}} Piha\iagaay\iagaayaay UAA a ~~ 1 [70-2910(Gill 2910-9210No Data(Gill 2910-9210No Data 2910-9210No DataNo Data Data &FF Vimwy4U-wy4U-4U-U- an2424<br>i! (il 9210-17310 9210-17310 ii >|<br>fm(ill 11870-2897028970-8991039910-168520(ill 11870-2897028970-8991039910-168520ll 11870-2897028970-8991039910-168520 11870-2897028970-8991039910-16852028970-8991039910-16852039910-168520 &i! . a . . ” fmMBMB 17310-2555025550-3830025550-38300 ~ 7 . . é<br>i] Time trends of China's investment and construction by sector<br>38To 3<br>a38 5<br>e Ke}<br>25 g a)4<br>Fa 28<br>£9 ee 2<br>a eeaoe aFe<br>3 £<br>8 8 g5 8 g Sos®@ &§ 8 @& =& »& ©RB #28&”<br>Year 6<br>——— Agriculture ——— Chemicals<br>a——— EnergyFinance ———— MetalsEntertalnmenti ° 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>——— Real estate Technology EEE Agriculture MEE Chemicals MEM Energy<br>——— Tourism ———— Transport MEE Entertainment MEME Finance EEE Metals<br>— Utilities (NN Real estate I) Technology MERE Tourism<br>Eaten Chine CdlRe (MA Transport EEE Utilities<br><!-- End of picture text -->
<!-- Start of picture text --> China's outward investment in BRI countries by sector China's outward construction in BRI countries by sector<br>2 3<br>vt<br>re} [-}<br>3 Qa 3<br>2 2<br>ag = ae<br>E a . |<br>=oS || t=sk<br>ejaia = tl o> —<br>° °<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>HE Agriculture ME Chemicals MMMM Energy HEE Agriculture MEE Chemicals MMMM Energy<br>ME Entertainment MEE Finance EEE Metals ME Entertainment MEM Metals MEE Real estate<br>(NN Real estate Technology MEME Tourism (NN Technology Tourism MEE Transport<br>MO Transport MEE Utilities MA Utilities<br><!-- End of picture text -->
|2014-12|Jiangsu Changjiang|STATS<br>ChipPAC|Singapore|Technology|1660|
|---|---|---|---|---|---|

Source: China Global Investment Tracker.

Table 2: China’s top 10 construction projects in BRI countries since 2014

|Year-<br>Month|Constructor|Transaction<br>Partner|Country|Sector|Subsector|Value<br>(million<br>USD)|
|---|---|---|---|---|---|---|
|2017-04|China Railway<br>Engineering||Indonesia|Transport|Rail|3190|
|2016-08|China Railway<br>Engineering||Bangladesh|Transport|Rail|3140|
|2015-12|State Construction<br>Engineering||Pakistan|Transport|Autos|2890|
|2015-08|China Railway<br>Construction and<br>China Railway<br>Engineering||Thailand|Transport|Rail|2840|
|2016-01|Shanghai Electric||Egypt, Arab<br>Rep.|Energy|Coal|2640|
|2017-04|CNPC||Russian<br>Federation|Energy|Gas|2520|
|2016-01|Three Gorges||Pakistan|Energy|Hydro|2400|
|2016-06|Harbin Electric|DEWA|United Arab<br>Emirates|Energy|Coal|2150|
|2016-11|China Railway<br>Contruction, China<br>Railway Engineering,<br>and China<br>Communications<br>Construction||Malaysia|Transport|Rail|2120|
|2016-03|Sinomach|Electricite<br>du Laos|Lao PDR|Energy||2100|

Source: China Global Investment Tracker.

# 3.2 Transportation Cost

To measure transportation costs, we construct detailed transportation cost data by the mode of transportation. First, we measure air distance using the population-weighted great-circle distance from the CEPII GeoDist data set. Second, we measure sea distance using data from Feyrer (2009).<sup>10</sup> Lastly, we construct road distance and driving time data using Google Maps Distance Matrix API.<sup>11</sup>

> 10 We thank Jim Feyrer for kindly sharing the data.

> 11 In a related project, Baniya et al. (2018) examine the role of BRI in international trade by estimating the effects of driving time reductions. We thank Michele Ruta for the valuable suggestion of looking at driving time as a measure of transportation cost.

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For the 4,434 source-host pairs in our sample, all pairs have air distance, 2,715 pairs have sea routes, and 2,366 pairs are connected in Google Maps Distance Matrix.

We find that unit transportation costs vary significantly across regions. One measure for unit transportation cost is average driving distance per hour within each region, which depends on the quality of road as well as congestion.

Using the driving distance and driving time data from Google, we calculated the average driving distance per hour for each WBG region using source-host pairs located within the same region. To see how BRI countries compare with these regions, we report the average driving distance for BRI and non-BRI in Table 3 and Figure 11. We find that North America has the highest driving speed, around 103 km per hour, followed by the Middle East & North Africa (89 km per hour) and Europe & Central Asia (88 km per hour). South Asia has the lowest driving speed among all the regions with an average of 41 km per hour. BRI countries fall behind North America, MENA and ECA regions with an average of 85 km per hour.<sup>12</sup> Table 3: Driving speed across regions

|Region|DrivingSpeed(kmper hour)|
|---|---|
|East Asia & Pacific|57.62335|
|Europe & Central Asia|88.189293|
|Latin America & Caribbean|70.214012|
|Middle East & North Africa|89.411758|
|North America|103.63007|
|South Asia|41.549953|
|Sub-Saharan Africa|69.450729|
|Non BRI|77.015732|
|BRI|85.389648|

> 12 For some country pairs in the East Asia & Pacific region, the drive route involves a ferry. For example, driving from Brunei to Singapore requires a ferry from Sampit to Surabaya.

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<!-- Start of picture text --> Driving Speed Across Region<br>North America<br>Middle East North Africa<br>Europe Central Asia<br>BRI<br>Non BRI<br>Latin America Caribbean<br>Sub-Saharan Africa<br>East Asia Pacific<br>South Asia<br>0 20 40 60 80 100<br>Driving Speed (km/hr)<br><!-- End of picture text -->
upper middle income countries, its airway index and quality of port index are above upper middle income countries. We also report the averages of the three indexes across WBG regions in Table 5 and find that BRI countries are behind North America, East Asia & Pacific and Europe & Central Asia regions but fare better than Latin America & Caribbean, South Asia, and Sub-Saharan Africa.

Table 4: Transport Infrastructure Proxies Average across Income Groups (2012)

||BRI|Non-BRI|High<br>income|Upper<br>middle<br>income|Lower<br>middle<br>income|Low<br>income|
|---|---|---|---|---|---|---|
|Railway index|11.880|13.469|18.479|12.260|6.544|1.173|
|Airway index|3.460|3.794|8.304|2.060|1.319|1.071|
|Quality of<br>Port|4.095|4.405|5.304|3.805|3.703|3.541|

Table 5: Transport Infrastructure Proxies Average across Region Groups (2012)

||BRI|Non-BRI|EAP|ECA|LAC|MENA|NA|SA|SSA|
|---|---|---|---|---|---|---|---|---|---|
|Railway index|11.880|13.469|13.151|17.204|3.158|4.228|28.904|7.940|2.370|
|Airway index|3.460|3.794|4.840|4.899|2.342|5.381|6.627|1.188|1.253|
|Quality of Port|4.095|4.405|4.740|4.462|3.877|4.531|5.650|3.860|3.875|

Zooming into BRI, we also observe a consistent pattern within BRI countries that higher income countries also tend to score higher on the transportation index. The exception is for the railway index, where we found upper middle incomes BRI has higher score than high income groups. This is because Russia has one of the longest railway systems, and one of the highest freight volumes hauled in the world. When we exclude Russia from the calculation, the average of railway index for the upper middle income group drops to 11.46, a level that is close to BRI’s overall average. In addition, we found that BRI’s high income countries score lower than the world’s high income countries in all three types of infrastructure. We report each BRI country’s infrastructure proxies score in the appendix.

Table 6: Infrastructure Proxies within BRI countries based on countries income

|**Country Income**|**Railway**|**Airway**|**PortQuality**|
|---|---|---|---|
|High Income|12.702|7.402|5.011|
|Upper Middle Income|14.963|1.939|3.782|
|Lower Middle Income|8.0430|1.335|3.500|
|Low Income|NA<sup>14</sup>|1.110|2.700|

In the next table, we calculate the averages for the 6 corridors in BRI, while these 6 corridors do not cover every BRI country, they cover countries whose geographic locations have strategic

> 14 Afghanistan and Nepal are the only low-income countries within BRI. For both countries, data for railway goods transported and passengers transported are missing.

19
importance. These 6 corridors are: Table 7: BRI corridor definitions

|**Corridor:**|**Countries:**|
|---|---|
|China, Pakistan Economic Corridor|China, Pakistan|
|China, Mongolia, Russian Federation<br>Economic Corridor|China, Russian Federation, Mongolia|
|New Eurasian Land Bridge Economic<br>Corridor|China, Berlarus, Kazakhstan, Kygyzstan, Poland,<br>Czech Republic, Romania, Ukraine, Slovak<br>Republic, Moldova,|
|China, Central Asia, West Asia Economic<br>Corridor|China, Russian Federation, Afghanistan,<br>Azerbaijan, Georgia, Islamic Republic of Iran,<br>Kygyzstan, Tajikistan, Turkey, Turkmenistan,<br>Uzbekistan|
|China, Indochina Peninsula Economic<br>Corridor|China, Cambodia, Lao PDR, Malaysia, Myanmar,<br>Singapore, Thailand, Vietnam|
|Bangladesh – China-India-Myanmar Economic<br>Corridor|China, Bangladesh, India, Myanmar|

Overall, we see intra-BRI variation across corridors. The China, Mongolia, and Russia Corridor scores the highest in terms of railway infrastructure index, while the China, Indochina Peninsula Economics Corridor scores the highest in terms of port quality and airway. For railway infrastructure, the China, Central Asia, West Asia Economic Corridor appears to be the weak link.

Table 8: Infrastructure Proxies within BRI countries based on corridor

|**Corridor**|**Railway**|**Airway**|**Port**<br>**Quality**|
|---|---|---|---|
|China, Pakistan Economic Corridor|11.022|1.363|4.400|
|China, Mongolia, Russian Federation Economic Corridor|36.440|1.752|3.700|
|New Eurasian Land Bridge Economic Corridor|22.175|1.399|3.444|
|China, Central Asia, West Asia Economic Corridor|5.867|1.518|3.471|
|China, Indochina Peninsula Economic Corridor|6.959|3.973|4.817|
|Bangladesh – China-India-Myanmar Economic Corridor|18.527|1.279|4.200|

The above observations are summarized below:

**Stylized fact 4:** _The infrastructure quality of BRI countries is, on average, lower than that of developed countries and regions including NA, ECA and EAP and exhibits great heterogeneity within BRI._

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# 3.3 FDI Regulations and Trade Agreements

In addition to transportation and infrastructure data, we also obtain information on FDI regulations and trade and investment agreements. To measure FDI regulations across countries, we use the Investing Across Borders data set, which compares FDI laws and regulations across 87 economies. It presents quantitative indicators on economies' laws, regulations, and practices affecting how foreign companies invest across sectors, start businesses, access industrial land, and arbitrate commercial disputes: Specifically, it includes the following indices:

- Investing Across Borders: its offers <mark>measures of statutory restrictions on foreign ownership of equity in new investment projects (greenfield FDI) and on the acquisition of shares in existing companies (mergers and acquisitions).</mark>

- Starting a Foreign Business: it <mark>quantifies the procedural burden that foreign companies face when entering a new market. It comprises 3 components measuring the time needed, procedural steps required, and regulatory regime for establishing a foreign-owned subsidiary.</mark>

- Accessing Industrial Land: it quantifies several aspects of land administration regimes important to foreign companies seeking to acquire land for their industrial investment projects, including the strength of land rights, the scope of available land information, and the process of leasing land in a country’s largest business city.

- Arbitrating Commercial Disputes: <mark>it reflects different aspects of domestic and international arbitration regimes in each country applicable to local and foreign companies, including the strength of the legal framework for alternative dispute resolution, rules for the arbitration process, and the extent to which the judiciary supports and facilitates arbitration.</mark>

Figure 12 compares BRI countries with non-BRI countries as well as high-income OECD countries in the above FDI policy indicators.<sup>15</sup> For each indicator, a higher score implies better performance and more attractiveness to foreign investors.

Figure 13 shows a comparison of FDI openness at the sector level.<sup>16</sup> A few patterns emerge from the data. First, in terms of foreign ownership restrictions, BRI countries, on average, are more restrictive than non-BRI countries and high-income OECD countries. Moreover, as shown in Figure 13, the degree of openness varies across sectors: service sectors such as construction, tourism, retail, media, banking, insurance and telecom tend to see more restrictions in BRI countries than in non-BRI or high-income OECD countries.

Second, BRI countries, on average, impose more restrictions and burdens than OECD high-income countries on starting a foreign business, accessing industrial land, and arbitrating commercial disputes. The gap from high-income OECD countries is particularly pronounced. For example, while the ease index of starting a foreign business is around 80 in high-income OECD, it is around 70 in BRI countries.

> 15 The value of each index is based on the average value of sub-indices.

> 16 Table A.5 reports the summary statistics of the Investing across Borders data.

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<!-- Start of picture text --> Accessing Industrial Land<br>Arbitrating Commercial Disputes<br>Sectoral ownership openness<br>Starting a foreign Business<br>0 20 40 60 80 100<br>MB sx 4M NonsR! )«6=6-séO ECD<br>Source: IAB<br><!-- End of picture text -->
<!-- Start of picture text --> Foreign Equity Owernship Index<br>Agriculture and forestry<br>Banking<br>Construction, tourism, retail<br>Electricity<br>Healthcare, waste management<br>Insurance<br>Light Manufacturing<br>Media<br>Mining, Oil and Gas<br>Telecom<br>Transport<br>0 20 40 60 80 100<br>MH sre = nonsRi §=6s«o&cD<br>Source: IAB<br><!-- End of picture text --> <!-- Start of picture text --> 8 Number of PTAs in BRI vs non-BRI 2 Average PTA depth in BRI vs Non-BRI<br>a<br>z g =~E<br>a re)<br>S Ss &<br>2 ge<br>o<br>S 4<br>1978 1982 1986 1990 1994 1998 2002 2006 2010 2014 1994 1998 2002 2006 2010 2014<br>year year<br>Source: World Bank PTA content Source: World Bank PTA content<br><!-- End of picture text -->
<!-- Start of picture text --> Number of PTA in BR by region wo Average PTA depth within BRI by region<br>8=<br>z<br>g E<br>& 3Bo<br>S g<br>i‘ :ow<br><<br>o o<br>1994 1998 2002 2006 2010 2014 1994 1998 2002 2006 2010 2014<br>year year<br>——— East Asia Pacific ——— Europe Central Asia —— East Asia Pacific ————_ Europe Central Asia<br>——— Middle East North Africa ——— South Asia ——— Middle East North Africa ——— South Asia<br>~~ Sub-Saharan Africa ~~ Sub-Saharan Africa<br>‘Source: World Bank PTA content ‘Source: World Bank PTA content<br><!-- End of picture text -->
<!-- Start of picture text --> BRI countries ranking by average PTA depth<br>‘ee<br>=;<br>onal ee<br>nee<br>wd<br>oonypeeat<br>0 5 10 15 20<br>Average depth of PTAs<br>Source: World Bank PTA content, 2015<br><!-- End of picture text -->
We examine the determinants of FDI flows using the gravity-type empirical specification that has been widely used in previous studies such as the seminal work by Markusen et al. (2001) and Yeaple (2003). The specification is as follows: where subscripts i and j index host and parent countries, respectively, and the subscript t indexes year, BilateralFDI is the bilateral FDI flow from country i to country j, MktSize a vector of GDP of the host and source countries, HCdiff measures the host and source countries’ difference in human capital, Transport is a vector of transportation cost measures between country i and country j including air distance, sea distance, driving distance and driving time, Infrastructure is a vector of indices that measure the quality of trade-related logistic infrastructure including railway, airway, and port quality, PTAdepth measures the depth of the trade agreement between the host and source countries using the WTO RTA data, X is a vector of other country characteristics including a dummy variable for sharing border, a dummy for using same language, a dummy for sharing colonial ties, and a measure of business environment using the number of days required to start a business, D� and D� are vectors of country dummies used to control for all time-invariant country characteristics, and D� ia a vector of year dummies.

Figure A.3 shows positive and statistically significant correlations between FDI inflows and host countries’ GDP per capita, school enrollment, logistics performance, and port infrastructure quality. Countries with a higher income, higher levels of human capital, and better logistic and port infrastructure quality are shown to receive greater FDI inflows.

To examine empirically the effects of host-country economic characteristics, we estimate equation (1) first for a pooled sample of countries and then separating BRI and non-BRI countries to investigate how the determinants of investment flows might vary between BRI and the rest of the world.

Table 9 shows that most of the empirical regularities established in previous studies hold in our broad cross-country data. Most of the factors that have been considered in the literature exert significant and expected effects. As in earlier studies, transportation costs between source and host countries are found in general to reduce FDI, but the effect varies significantly by the mode of transportation. A 1-percent increase in air distance is associated with 0.7-1 percent decrease in the volume of FDI. Land transportation cost, measured by either driving distance or driving time, is also found to exert a significant and negative effect: a 1-percent increase in driving time, for instance, leads to a close to 1-percent decrease in FDI. In contrast, the effect of sea distance is weaker (insignificant in some specifications): a 1-percent increase in sea distance is related to around 0.1percent decrease in FDI flows.

Infrastructure quality is found to matter: the results show a significant and positive relationship between each of the three infrastructure quality measures, namely, railway, airway and port qualities, and FDI inflows. Countries with higher railway and airway indices and better ports tend to attract a greater volume of FDI. For example, improving the level of railway index from BRI countries (11.8) to the level of high-income countries (18.5) is associated with 4.3 percent increase in FDI flows.

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Logistics performance of the source-host pair is also found to be matter: a one-percent increase in the logistics performance index leads to 0.1 to 0.2 percent increase in FDI flows.

Most other country characteristics including market size, contiguity, common language, and colonial ties are also found to exert expected effects. Countries with a larger market size not only attract more FDI but also invest more abroad. Country pairs with contiguity, common languages, or colonial ties are also shown to have greater investment flows. However, the PTA depth is found in Table 9 to have only a weakly positive effect on FDI.

In Table 10, we repeat the analysis using a Poisson Pseudo Maximum Likelihood (PPML) Estimator following Santos Siliva and Tenreyro (2006) to address the zero FDI values in the data sample. The results are qualitatively similar. Both air transportation and land transportation costs, proxied by either distance or driving time, have a significant and negative effect on FDI flows. The effect of sea distance is largely insignificant. The PTA depth in this table is found to be positively and significantly associated with FDI flows, suggesting that deeper trade agreements that cover more provisions have a stronger effect on FDI flows.

We also separately examine FDI flows from OECD source countries using a longer time series of OECD FDI statistics. The results on transportation costs shown in Table 11 are qualitatively similar to Table 9. The elasticities of FDI with respect to air distance, driving distance, and driving time are found to be all around -1 for OECD source countries. The elasticity of FDI with respect to sea distance is also found negative and significant, albeit much smaller in magnitude and around -0.1. OECD countries’ FDI outflows are also responsive to host countries’ airway and port qualities, but insignificantly related to railway. The PTA depth exerts a significant and positive effect on OECD countries’ FDI flows.

<mark>Next, we explore how the effects of distances have evolved over time given the technological improvements in transportation. To do so, we interact the three transportation measures with a vector of dummies that divide the time series into six 5-year windows and allow the distance elasticity of FDI to vary over time. The results are reported in Table 12 and Figure 17. We find that while the sea distance elasticity of FDI has remained stable in the last three decades, there has been a significant rise in the elasticities of FDI with respect to air distance and driving time, with the estimates nearly doubled. This result, in alignment with Feyrer (2009) which shows similar findings for trade, can be explained by the endogenous selection of goods into the different transportation mode and the increasing usage of air and railway transportation with the technological improvement in air and rail transportation and the proliferation of GVCs. Exporters tend to choose air and rail for goods that are more time sensitive and sea for goods that are less time sensitive. Hence, when there is a change in travel time, goods will respond more to travel time in air and railway than in sea. Over time, with falling air and rail transportation costs and an increasing prevalence of GVCs and intermediate trade, more exports are carried out by air and rail than sea, leading to a growing importance of air and land transportation cost in FDI growth.</mark> 27
<!-- Start of picture text --> lo}<br>wo<br>9<br>1985-1989 1990-1994 1995-1999 2000-2004 2005-2009 2010-2015<br>Year<br>—*—_ Air —®— Sea —®— Land<br><!-- End of picture text -->
driving time is found to have the strongest positive effect on FDI in Middle East and North Africa followed by Europe and Central Asia and no significant effect in East Asia Pacific, South Asia and Sub-Saharan Africa. Longer railway is found to have a significant positive effect in South Asia and Middle East and North Africa, but not in the other regions. A better airway index significantly attracts more FDI in East Asia Pacific and Europe and Central Asia, but not in the other regions. The effect of port quality is found to be the strongest for Sub-Saharan Africa, albeit statistically insignificant, followed by Middle East and North Africa and South Asia.

When interacting transportation cost, for instance, driving time with country characteristics, we find in Table 17 that the elasticity of FDI flow with respect to transportation cost rises with host country GDP and the number of days to start a business. FDI flows to countries with a larger market size and less efficient business environment appear to be more affected by transportation cost. The results also suggest that the positive FDI effect of reduced transportation cost can be magnified when accompanied by an improvement in business regulatory environment.

Next, we consider alternative measures of transport cost obtained from de Soyres et al. (2018) which use a network analysis to measure the shortest time needed to travel from one country to another and its implied ad valorem trade cost taking into account the quality and quantity of physical infrastructure, physical obstacles (e.g., rough terrain) and borders, and relative performance measures (e.g., rail service frequency). de Soyres et al. (2018) also compute the measures for both before and after the implementation of BRI taking into account the proposed transportation network as part of BRI.<sup>17</sup> Tables 18 and 19 report the estimation results using the travel time and ad valorem measures, respectively. The results suggest that reducing travel time by one day can increase total FDI inflows to BRI countries by 7 percent, FDI flows within BRI countries by 6 percent, FDI flows from OECD countries to BRI countries by 8 percent, and FDI flows from non-BRI countries to BRI countries by 8 percent.

When using the ad valorem time barrier measure, the results suggest that a 10-percent decrease in the ad valorem time barrier is associated with a 11-percent increase in total FDI inflows to BRI countries, a 10-percent increase in intra-BRI FDI flows, a 13.2-percent increase in FDI flows from OECD to BRI countries, and a 13.3-percent increase in FDI flows from non-BRI to BRI countries.

# 4.2 Determinants of Chinese ODI

In this sub-section, we focus on China’s outward investment; given the available information in the CGIT data set, we can differentiate China’s investment across types and sectors. This enables us to explore potential interactions between China’s construction contracts and its ODI and how the former might serve as a catalyst for the latter.

For China’s investment, the following specification is considered:

# ChinaFDI�� �α� � α� MktSize�� � α� SkillDiff��

# � α� Transport� �α�Infrastructure� �α�PTAdepth� �α� Construction����

> 17 We thank the authors for kindly providing the shipment time and trade cost estimates.

29
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on aggregate values from 2005-2016) Airway and port infrastructure also play a significant role in attracting Chinese foreign investment. Interestingly, a positive relationship is observed between the host-country number of days required to start a business and the volume of FDI flows from China. PTA depth, on the other hand, does not have a significant relation with China’s investment flow.

# 4.3 Missing FDI

In this sub-section, we examine countries’ potential as FDI destination countries and how their potential compares to the realized value of FDI inflow. We refer to the difference between FDI potential and realized FDI as missing FDI. Specifically, we obtain predicted values of FDI based on the baseline estimation results reported in Table 13 and compute the difference between the predicted value of FDI based on each country’s characteristics and the estimated effects of these characteristics on FDI and the country’s actual value of FDI.

Figure 19 plots the ratio of missing FDI relative to actual FDI by country groups. We find the ratio to be the highest (around 4.6) for low-income countries and substantially lower for the rest of the country groups and lowest for the upper-middle income group. Within BRI, we notice that the ratio of missing FDI relative to actual FDI is around 0.7 on average, suggesting a significant potential for attracting additional foreign investment.

<!-- Start of picture text --> Missing FDI/Actual FDI (by income)<br>5.00 4.61<br>4.00<br>3.00<br>2.00<br>0.88 0.95<br>1.00 0.33<br>0.00<br>High income Low income Lower middle Upper middle<br>income income<br><!-- End of picture text --> Figure 19: Missing FDI across Countries

# 4.4 Quantifying the FDI Impact of BRI

Based on the above empirical results, we now estimate the impact of BRI on countries’ ability to attract FDI by reducing transport costs and improving host-country infrastructure. The results in Table 15, in particular, the elasticity of FDI with respect to driving time estimated for BRI host countries, suggest that a 10-percent decrease in driving time between host and source countries could increase FDI flows by 12 percent. Specifically, a 10-percent decrease in driving time could 31
increase intra-BRI FDI flows by about 11.7 percent, FDI flows from OECD by 13.4 percent, and FDI flows from all non-BRI countries by 9.1 percent.

If an improvement in road quality reduces unit driving time in BRI countries to the average level in Europe and Central Asia, total FDI inflow in BRI countries could increase by about 3.6 percent, with 3.5 increase in intra-BRI FDI flow, 4 percent increase in FDI from OECD countries, and 2.7 percent increase in FDI from all non-BRI countries.

The results also suggest that improving the port quality from the current BRI country level (4.1) to the level of high-income countries (5.3) would lead to 10-percent increase in total FDI inflow, 10percent increase in FDI from OECD countries and 12-percent increase in FDI from non-BRI countries. The effects on intra-BRI FDI are statistically insignificant.

Based on the upper-bound estimates in de Soyres et al. (2018), the BRI proposed transportation network can lead to an average reduction of travel time by 0.69 day, or equivalently, around 3.2 percent for BRI destination countries. The estimated travel time reduction varies by origin and is slightly higher for trade within BRI country pairs (3.9 percent) and lower for trade from OECD origins (2.9 percent). Based on the estimates reported in Table 18, the estimated travel time reductions are associated with a 4.97-percent increase in total FDI flows to BRI countries, a 4.36percent increase in FDI flows within BRI, a 4.63-percent increase in FDI flows from OECD countries, and a 5.75-percent increase in FDI flows from non-BRI countries. Across BRI regions, the proposed BRI transportation network is estimated to increase FDI flows to BRI’s East Asia Pacific region by 6.25 percent, Europe and Central Asia by 4.7 percent, Middle East and North Africa region by 3.37 percent, South Asia by 5.19 percent, and 7.47 percent in Sub-Saharan Africa, as shown in Figure 20.

<!-- Start of picture text --> Estimated Effect of the BRI transportation network on FDI<br>by BRI region (%)<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>East Asia Pacific Europe Central Middle East South Asia Sub‐Saharan<br>Asia North Africa Africa<br><!-- End of picture text --> Figure 20: The estimated effects of the BRI transportation network on FDI by BRI region 4.5 The Economic Growth Effects of FDI 32
Next we examine how investment inflows influence host-economy economic growth including the growth of GDP, TFP, technology and trade, taking into account the endogenous patterns of investments.

Specifically, we consider the following specification: where ΔGrowth�� represents ΔGDP��, ΔTFP��, ΔTech�� ΔEmployment��, ΔExport�� and ΔImport��, respectively, which are measured by growth in GDP, total factor productivity, number of patent applications, employment, exports, and imports of host country j at year t, FDI����� is either the actual value or the instrumented value of total investment inflow in country j and year t-1, the coefficient β is the coefficient of interest, and X���� is a vector of other variables that might affect the economic outcomes of interest.

A central challenge in evaluating the economic growth effects of FDI is the endogeneity of FDI flows arising from either its correlations with unobserved factors that could similarly affect economic growth or potential reverse causality between FDI and economic growth. This challenge is particularly difficult to overcome in cross-country analysis for two reasons. First, most country economic characteristics are directly associated with both FDI and economic growth and thus cannot serve as instruments for FDI. Second, some of the instruments that have been considered in recent literature such as Alfaro and Chen (2018) explore supply-side variations across multinational firms and require detailed firm-level operation and investment data that are usually unavailable across a wide range of countries.

In this analysis, we follow the identification strategy employed in Autor et al. (2014) when evaluating the employment effects of imports and instrument FDI inflow to a host country with average FDI inflow to countries that have similar income levels and are located in the same geographic region as the host country. The motivation is to explore variations on the supply of investment which would influence FDI flows to similar countries but are less likely to be associated with economic growth in a particular country via other, non-FDI related channels. We then evaluate how the instrumented FDI inflows affect host-country economic growth and how the effects might vary across countries.

Tables 21 and 22 present the OLS and the IV results, respectively.<sup>20</sup> We find that in both the OLS and IV specifications, FDI inflow tends to exert a positive and significant effect on GDP growth but the effects on TFP and patent growth are statistically insignificant. A 10-percent increase in FDI inflow is associated with a 0.17 percentage-point increase in GDP growth rate, 1.2 percent increase in exports and 1.1 percent increase in imports. The effects of FDI on TFP, patent, and employment growth are found insignificant. The latter results are consistent with the existing literature which shows the effects of FDI on productivity and employment to be largely ambiguous due to the opposing ways through which FDI could influence these outcomes.

> 20 The first-stage IV results are reported in Table A.15. As shown, FDI inflows to the control group countries (countries with similar income and located in the same geographic region) are a good predictor of a host country’s FDI inflows.

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As discussed in Section 2, the extent to which a country could benefit from inward FDI can vary across countries depending on their institutional characteristics and absorptive capacity. Next, we explore the heterogeneity and the possible mediating factors in the economic growth effects of FDI.

Figure 21 plots the estimated effects of FDI on GDP, trade and employment growth by country income groups. In general, the effects of FDI are found to diminish with country income level and the strongest for low-income countries. For example, a 10-percent increase in FDI inflow is found to be associated with 0.45 percentage-point increase in GDP in low-income countries, 0.2 percentage-point increase of GDP in lower-middle-income countries, and 0.08 percentage-point increase of GDP in upper-middle-income countries. The GDP effect in high-income countries is found close to 0 and statistically insignificant. This could be due to the ambiguous effect of M&As, the main type of FDI in high-income countries, on GDP and employment growth. The effect of FDI on import is more pronounced and as the GDP effect diminishes with income level. A 10percent increase in FDI inflow is shown to raise imports by 1.78 percent, 1.3 percent, 0.7 percent, and 0.3 percent in low-, lower-middle-, upper-middle-, and high-income countries, respectively. Similarly, FDI exerts a strong effect on exports, especially for lower-income countries.

The positive impacts of FDI on trade are consistent with the growing prevalence of vertical FDI and global value chains which leads trade and FDI to co-move in the same direction. The stronger effects on lower-income countries are also consistent with the expectation that vertical FDI and subsequently intra-firm trade are more likely to occur between high- and low-income countries because of their complementarity in task comparative advantage. While the employment effect of FDI is generally insignificant, we find the effect to follow a similar pattern with a greater positive effect on lower-income countries.

<!-- Start of picture text --> Estimated effect of a 10% increase in FDI on GDP<br>(in percentage point)<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>Low Income Lower Middle Upper Middle High Income<br>‐0.1<br><!-- End of picture text --> 34
<!-- Start of picture text --> Estimated effect of a 10% increase in FDI on Exports<br>(in percentage point)<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Low Income Lower Middle Upper Middle High Income<br><!-- End of picture text --> <!-- Start of picture text --> Estimated effect of a 10% increase in FDI on Imports<br>(in percentage point)<br>2<br>1.8<br>1.6<br>1.4<br>1.2<br>1<br>0.8<br>0.6<br>0.4<br>0.2<br>0<br>Low Income Lower Middle Upper Middle High Income<br><!-- End of picture text --> Estimated effect of a 10% increase in FDI on Employment (in percentage point) <!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0<br>Low Income Lower Middle Upper Middle High Income<br>‐0.01<br>‐0.02<br>‐0.03<br><!-- End of picture text --> 35
Figure 21: The Heterogeneous effects of FDI on economic growth by country income groups When examining the heterogeneous effect of FDI across geographic regions, we find in Figure 22 the effect to be the strongest in Sub-Saharan Africa, followed by South Asia and East Asia and Pacific. The estimated GDP growth effect for the BRI countries is similar to the estimated effect for Latin America and Caribbean countries.

<!-- Start of picture text --> Estimated effect of a 10% increase in FDI on GDP Growth<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>‐0.05<br><!-- End of picture text --> Figure 22: The Heterogeneous effects of FDI on economic growth by geographic region 4.6 Quantifying the Growth Impact of BRI through the FDI Channel To assess the effect of BRI on economic growth through the channel of foreign investment, we combine the estimation results from the two stages. If BRI reduces driving time between host and source countries by 10 percent, FDI flows into BRI countries are estimated to increase by 12 percent and the 12-percent increase in FDI inflow is estimated to raise GDP growth rate by 0.2 percentage point for BRI countries as a whole. But the effect is estimated to be around 0.5 percentage point increase in GDP growth rate for South Asian BRI countries and 0.35 percentage point increase in GDP growth rate for East Asia & Pacific and Middle East & North Africa BRI countries.

If an improvement in road quality reduces unit driving time in BRI countries to the average level in Europe and Central Asia, total FDI inflow in BRI countries could increase by about 3.6 percent and GDP growth rate in BRI countries is estimated to increase by 0.06 percentage point. The effect again is estimated to be strongest (around 0.16) for South Asia BRI countries.

Improving the port quality from the current BRI country level (4.1) to the level of high-income countries (5.3) would lead to 10-percent increase in total FDI inflow and 0.17 percentage point increase in GDP growth rate in BRI countries. Across regions, the effect on GDP is estimated to be around 0.45 percentage point in South Asia, 0.3 percentage point in East Asia & Pacific and Middle East & North Africa, and negligible in Europe and Central Asia and Sub-Saharan Africa.

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If the proposed BRI transportation network leads to an average 0.69 shipping day reduction, based on the upper bound estimates from de Soyres et al (2018), our analysis suggests that the improved transportation network can raise BRI countries’ GDP growth rate by 0.09 percentage point through the FDI channel. Across regions, the transportation network is estimated, based on Table 18 and Figure 21, to increase GDP growth rate by about 0.08 percentage point in East Asia & Pacific, 0.04 percentage point in Europe and Central Asia, 0.01 percentage point in Middle East & North Africa, 0.13 percentage point in South Asia, and 0.23 percentage point in Sub-Saharan Africa, as shown in Figure 23.

It is noteworthy that the BRI transportation network can also stimulate growth in non-BRI countries through a spillover effect of the transportation network. For example, non-BRI countries in Sub-Saharan Africa may also benefit from access to ports in Kenya and consequently attract more FDI. Our analysis suggests that such spillover is significant in Africa; non-BRI Sub-Saharan African countries are estimated to see a 3.98 percent increase in FDI inflow with the BRI transportation network and, through the increase in FDI, a 0.13 percentage-point increase in GDP growth.

<!-- Start of picture text --> Estimated effect of the BRI transportation network on<br>GDP growth by BRI region<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>East Asia Pacific Europe Central Middle East South Asia Sub‐Saharan<br>Asia North Africa Africa<br><!-- End of picture text --> Figure 23: The estimated effects of the BRI transportation network on GDP growth by BRI region

# 5. Conclusion

In this paper, we evaluate the patterns, determinants and effects of FDI in BRI countries and the potential impacts of BRI on member countries’ FDI and economic growth through reductions of transportation costs. We first document and compare the historical patterns of FDI around the world including BRI and non-BRI countries and investigate how different types of transportation cost and infrastructure have affected FDI. Based on the findings, we then assess the potential roles of BRI in fostering FDI growth by improving different types of connectivity and infrastructure. Finally, we evaluate the effects of FDI on economic growth and how BRI might affect economic growth through the foreign investment channel. The following main findings emerge from the analysis: 37
_Finding 1:_ Reductions in transportation costs raise FDI, but the magnitude of the effect varies by the mode of transportation, with the effects of air and land transportation costs exceeding the effect of sea transportation. Over time, while the sea distance elasticity of FDI has remained stable in the last three decades, the elasticities of FDI with respect to air distance and driving time nearly doubled.

_Finding 2:_ The BRI proposed transportation network can lead to a 4.97-percent increase in total FDI flows to BRI countries, a 4.36-percent increase in FDI flows within BRI, a 4.63-percent increase in FDI flows from OECD countries, and a 5.75-percent increase in FDI flows from non-BRI countries. Across BRI regions, the proposed BRI transportation network could increase FDI flows to BRI’s Middle East and North Africa region by 11.39 percent, the East Asia and Pacific region by 6.56 percent, and Europe and Central Asia by 3.8 percent.

_Finding 3:_ Across transportation modes, a reduction of driving time is found to have a strong positive effect on FDI in Middle East and North Africa followed by Europe and Central Asia. A longer railway is found to have a significant positive effect in South Asia and Middle East and North Africa. A better airway index significantly attracts more FDI in East Asia and Pacific and Europe and Central Asia. The positive FDI effect of reduced transportation cost can also be magnified when accompanied by an improvement in business regulatory environment.

_Finding 4:_ China’s infrastructure investments in BRI countries are found to serve as an effective catalyst for attracting Chinese investments in manufacturing and service sectors. A 10-percent increase in construction contracts is associated with an increase in FDI by 7 percent in the same year, 11 percent the next year, and 16 percent in two years.

_Finding 5:_ The proposed BRI transportation network can increase BRI countries’ GDP growth by 0.09 percentage point through the FDI channel, including 0.08 percentage point in East Asia & Pacific, 0.04 percentage point in Europe and Central Asia, 0.01 percentage point in Middle East & North Africa, 0.13 percentage point in South Asia, and 0.23 percentage point in Sub-Saharan Africa.

_Finding 6:_ The BRI transportation network can also stimulate growth in non-BRI countries, including a 0.13 percentage-point increase in non-BRI Sub-Saharan African countries’ GDP growth, through a spillover effect of the transportation network.

These findings convey several policy implications. First, initiatives improving physical infrastructure such as BRI can stimulate FDI growth and subsequently GDP and trade growth, but the effects of FDI on aggregate productivity and innovation are insignificant. Second, the impacts vary significantly with host- and source-country development level, across types of infrastructure, and over time. Investment allocation should take into account the great heterogeneity in the expected returns across different projects and geographic areas. Third, the positive effects of infrastructure investment can be magnified when accompanied with an improvement in business environment, specifically an improvement in the ease of business entry. Fourth, there exists a significant amount of missing FDI, i.e., unrealized FDI potential, across countries. Market size and unobserved institutional factors play a central role in explaining missing FDI, especially for developing countries.

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