China’s Increased Trade and Investment in South Asia (Spoiler … · 2013-05-14 · China’s Increased Trade and Investment in South Asia (Spoiler Alert: It’s The Economy) Prepared

China’s Increased Trade and Investment in South Asia

(Spoiler Alert: It’s The Economy)

Prepared for the U.S. Government Office of South Asia Policy

By Emily Brunjes

Nicholas Levine Miriam Palmer Addison Smith

Workshop in International Public Affairs Spring 2013

©2013 Board of Regents of the University of Wisconsin System All rights reserved.

For additional copies: Publications Office

La Follette School of Public Affairs 1225 Observatory Drive, Madison, WI 53706

www.lafollette.wisc.edu/publications/workshops.html [email protected]

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of the University of Wisconsin–Madison. The school takes no stand on policy issues; opinions expressed in these pages reflect the views of the authors.

The University of Wisconsin-Madison is an equal opportunity and affirmative-action educator and employer.

We promote excellence through diversity in all programs.

Table of Contents

List of Figures ........................................................................................................ ivList of Tables .......................................................................................................... vForeword ............................................................................................................... viiAcknowledgments................................................................................................ viiiExecutive Summary ............................................................................................... ixI Introduction ..................................................................................................... 1II Background ..................................................................................................... 2

A Chinese Outward Direct Investment ....................................................... 2B India-China Rivalry ................................................................................. 3

III China’s Current Involvement in South Asia ................................................... 3A Trade ....................................................................................................... 3

A.1 Rivaling Indian Trade ..................................................................... 5B Outward Direct Investment ..................................................................... 8C Big Projects ........................................................................................... 13D Diplomatic Overtures and Aid .............................................................. 14

IV Modeling Trade and Outward Direct Investment ......................................... 15A Trade ..................................................................................................... 15

A.1 Trade Model .................................................................................. 15A.2 Trade Model Data ......................................................................... 16A.3 Trade Results ................................................................................ 17

B Outward Direct Investment ................................................................... 19B.1 Applying an ODI theory to China ................................................. 20B.2 ODI Model .................................................................................... 20B.3 ODI Model Data ........................................................................... 22B.4 ODI Results ................................................................................... 23B.5 Comparison to Cheung and Qian Model ...................................... 25B.6 ODI Model Sensitivity Analysis ................................................... 26

C Interpretation ......................................................................................... 26V Qualitative Analysis of Big Projects ............................................................. 27

A Possible Geopolitical Explanations of Big Projects .............................. 31VI Putting the Pieces Together ........................................................................... 31VII Going Forward .............................................................................................. 32VIIIConclusion .................................................................................................... 33Appendix A Variable Descriptions and Sources ............................................... 34Appendix B Interpreting Underlying Variables Based on Log Results ............ 36Appendix C ODI Model Country Set ................................................................ 38Appendix D ODI Sensitivity Analysis Results.................................................. 39Appendix E Big Projects Details ....................................................................... 44Appendix F Projecting Trends of Chinese ODI and Trade ............................... 47References ............................................................................................................. 56

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List of Figures

Figure 1: Focus Countries’ Trade with China, 2003-2011 ..................................... 4Figure 2: China’s Share of Focus Countries’ Trade, 2003-2011 ............................ 5Figure 3: China’s Share of Exports from Bangladesh, Nepal, and Sri Lanka,

2003-2011 ....................................................................................................... 5Figure 4: India vs. China Share of Focus Countries’ Exports, 2003-2011 ............. 6Figure 5: India vs. China Share of Focus Countries’ Imports, 2003-2011 ............. 6Figure 6: Imports from India vs. China, 2011: Bangladesh, Nepal, and Sri

Lanka ............................................................................................................... 7Figure 7: Imports from India vs. China, 2011: Bhutan and Maldives .................... 7Figure 8: Exports to India vs. China, 2011: Bangladesh, Nepal, and Sri

Lanka ............................................................................................................... 8Figure 9: Chinese ODI Flow, World vs. Asia, 2003-2011 ..................................... 9Figure 10: Chinese ODI Flow to Focus Countries, 2003-2011 ............................ 10Figure 11: Focus Countries’ Percentage of China’s ODI, 2003-2011 .................. 11Figure 12: Focus Countries’ FDI Inflow, 2003-2011 ........................................... 11Figure 13: China’s Percentage of Focus Countries’ Inward FDI Stock, 2003-

2011 ............................................................................................................... 12Figure 14: Bangladesh’s Inward FDI Stock: India vs. China, 2003-2011 ............ 12Figure 15: Nepal’s Inward FDI Stock: India vs. China, 2003-2011 ..................... 13Figure 16: Sri Lanka’s Inward FDI Stock: India vs. China, 2003-2011 ............... 13Figure F1: Chinese ODI Stock Trend in Bangladesh, 2003-2020 ........................ 47 Figure F2: Chinese and Indian ODI Stock Trend in Bangladesh, 2003-2020 ...... 48Figure F3: Chinese ODI Stock Trend in Nepal, 2003-2020 ................................. 48Figure F4: Chinese and Indian ODI Stock Trend in Nepal, 2003-2020 ............... 49Figure F5: Chinese ODI Stock Trend in Sri Lanka, 2003-2020 ........................... 49Figure F6: Chinese and Indian ODI Stock Trend in Sri Lanka, 2003-2020 ......... 50Figure F7: Trend of China’s Exports to Bangladesh, 2003-2020 ......................... 51Figure F8: China and India Export Trends to Bangladesh, 2003-2020 ................ 51Figure F9: Trend of China’s Exports to Bhutan, 2003-2020 ................................ 52Figure F10: China and India Export Trend to Bhutan, 2003-2020 ....................... 52Figure F11: Trend of China’s Exports to Maldives, 2003-2020 ........................... 53Figure F12: China and India Export Trend to Maldives, 2003-2020 ................... 53Figure F13: Trend of China’s Exports to Nepal, 2003-2020 ................................ 54Figure F14: China and India Export Trend to Nepal, 2003-2020 ......................... 54Figure F15: Trend of China’s Exports to Sri Lanka, 2003-2020 .......................... 55Figure F16: China and India Export Trend to Sri Lanka, 2003-2020 ................... 55

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List of Tables

Table 1: Trade Model Results ............................................................................... 17Table 2: Error Terms for Trade Flows between Focus Countries and China ....... 18Table 3: Expected Versus Actual Trade Flows ..................................................... 18Table 4: ODI Model Results ................................................................................. 23Table 5: ODI Country Fixed Effects ..................................................................... 24Table 6: Estimated Deviation from Expected ODI, by Country ........................... 25Table 7: China’s Big Projects in Bangladesh ....................................................... 28Table 8: China’s Big Projects in Nepal ................................................................. 29Table 9: China’s Big Projects in Sri Lanka .......................................................... 30Table 10: Red Flags .............................................................................................. 33Table A1: ODI Model Variables and Sources ...................................................... 34 Table A2: Trade Model Variables and Sources .................................................... 35Table C1: ODI Model Country Set ....................................................................... 38 Table D1: ODI Sensitivity Analysis with Logged Independent Variables ........... 39 Table D2: ODI Sensitivity Analysis with Logged Independent Variables,

Country Fixed Effects ................................................................................... 40Table D3: ODI Sensitivity Analysis with ODI Flows as Dependent Variable ..... 40Table D4: ODI Sensitivity Analysis with ODI Flows as Dependent Variable,

Country Fixed Effects ................................................................................... 41Table D5: ODI Sensitivity Analysis: Full Sample with Recession Indicator

Beginning in 2008 ......................................................................................... 42Table D6: ODI Sensitivity Analysis: Developing Sample with Indicator

Beginning in 2008 ......................................................................................... 42Table D7: ODI Sensitivity Analysis: Full Sample with Recession Indicator

Beginning in 2009 ......................................................................................... 43Table D8: ODI Sensitivity Analysis: Developing Sample with Recession

Indicator Beginning in 2009 ......................................................................... 43Table E1: Big Project Details: Bangladesh ........................................................... 44 Table E2: Big Project Details: Nepal .................................................................... 45Table E3: Big Project Details: Sri Lanka .............................................................. 46

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Foreword

The La Follette School of Public Affairs at the University of Wisconsin–Madison offers a two-year graduate program leading to a Master of Public Affairs or a Master of International Public Affairs degree. In both programs, students develop analytic tools with which to assess policy responses to issues, evaluate implications of policies for efficiency and equity, and interpret and present data relevant to policy considerations.

Students in the Master of International Public Affairs program produced this report for the U.S. Government Office of South Asia Policy. The students are enrolled in the Workshop in International Public Affairs, the capstone course in their graduate program. The workshop challenges the students to improve their analytical skills by applying them to an issue with a substantial international component and to contribute useful knowledge and recommendations to their client. It provides them with practical experience applying the tools of analysis acquired during three semesters of prior coursework to actual problems clients face in the public, non-governmental, and private sectors. Students work in teams to produce carefully crafted policy reports that meet high professional standards. The reports are research-based, analytical, evaluative, and (where relevant) prescriptive responses for real-world clients. This culminating experience is the ideal equivalent of the thesis for the La Follette School degrees in public affairs. While the acquisition of a set of analytical skills is important, it is no substitute for learning by doing.

The opinions and judgments presented in the report do not represent the views, official or unofficial, of the La Follette School or of the client for which the report was prepared.

Melanie Frances Manion Professor of Public Affairs and Political Science

May 2013

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Acknowledgments

This project could not have been accomplished without the help of a number of University of Wisconsin–Madison professors. First and foremost, we are grateful for Professor Melanie Manion’s guidance and invaluable critiques, which kept us on track and provided structure to our project and final product. Professors Mark Copelovitch and Jon Pevehouse offered very helpful advice on issues of international trade and investment. Professor Menzie Chinn’s knowledge and expertise helped us a great deal in finding the appropriate economic models and data for our report. We would also like to thank Qian Xingwang of State University of New York at Buffalo for providing guidance on the use of the econometric model that he and Yin-Wong Cheung developed. Finally, we would like to thank Keith and the U.S. Government Office of South Asia Policy for the opportunity to work on this project.

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Executive Summary

Chinese economic engagement in South Asia has grown quickly in recent years: trade expanded from approximately $40 billion to $85 billion from 2006 to 2011; bilateral investment grew even more quickly, rising from $500 million in 2005 to $2.8 billion in 2010. These increases create opportunities for growth and development as a wider variety of exports flow from China into the region, and as Chinese outward direct investment improves infrastructure and creates jobs. At the same time, this involvement may challenge India’s regional dominance. In this report we examine Chinese trade and investment flows to South Asia using econometric modeling. We also qualitatively analyze large infrastructure projects China has undertaken in the region. Based on the combined results of these analyses, we conclude that China’s primary motive for increasing involvement in South Asia is economic profit.

In examining China’s economic relationship with South Asia, we focus exclusively on Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka. We use econometric models to determine what China’s expected trade and outward direct investment would be according to normal economic interests. We then compare these predictions to China’s actual economic activity with our focus countries. We conclude that increases in Chinese trade with South Asia grow out of China’s search for wider export markets. Investment reflects Chinese diversification of outward direct investment across developing markets and is consistent with normal economic interests.

Large construction projects, while sometimes pursued for diplomatic reasons, are usually profitable for Chinese firms. To put these diplomacy projects in perspective, we consider China’s increasing diplomatic and economic involvement in Asia and the world. We conclude that China’s behavior is not a specific attempt to counter-balance India’s influence in South Asia, but rather is part of the country’s wider goal to rise in prominence in international affairs. We caution the U.S. government against reading specific geopolitical motives into China’s economic involvement with South Asia.

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I Introduction

China invests heavily in Asia. A large portion of that investment is aimed toward Southeast Asian countries (Shambaugh 2005); however, since 2003 Chinese economic involvement in South Asia has risen noticeably. Between 2003 and 2012, China more than doubled its exports to Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka. Just over a quarter of Bangladesh’s imports now come from China. Additionally, China has offered hundreds of millions of dollars in investment for large infrastructure projects, including port facilities in Bangladesh, Burma, Pakistan, and Sri Lanka (Curtis 2011).

Many possible explanations exist for China’s increased economic activity in South Asia. Normal profit-seeking behavior by Chinese firms and the Chinese state may explain the increase. Geopolitical explanations are equally plausible, especially given the close proximity of Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka to India. Border disputes between China and India dating to the 1960s continue, and the two countries are natural competitors for influence and resources in the region. A desire to project power in the region could motivate China’s strengthening of economic ties with these countries surrounding India.

Such a projection of power through trade, aid, and investment has historical precedent in the rise of other global powers, particularly the United States. In the early 20th century, the United States used a strategy of “dollar diplomacy” to purchase the friendship of countries around the world. Much of its rise to superpower status can be attributed to the network of naval bases and military alliances that dollar diplomacy allowed the United States to build and the preferential access to natural resources abroad that dollar diplomacy secured. Following World War II, the United States invested massively in rebuilding the infrastructure of the former Axis powers and in rebuilding the rest of Europe through the Marshall Plan. This generosity was meant to ensure that as many countries as possible aligned themselves with the United States instead of with the Soviet bloc. Is China acting in a similar way to expand its sphere of influence?

In this report, we explore China’s increasing economic activity in Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka. Specifically, we examine trends of increasing trade flows and increasing outward direct investment (ODI) in these countries. In doing so, we seek to discover whether this phenomenon reflects China’s normal economic interests or if it is an attempt to increase influence and alter the balance of power in the region. In our analysis, we exclude India because we are concerned with China’s challenge to India, not the economic interdependence of the two nations. We also exclude Pakistan and Myanmar because of difficulties in obtaining data.

Our analysis proceeds in four steps. First, we provide background on China’s economic and diplomatic engagement in our focus countries. Second, we use statistical models to examine Chinese trade levels and ODI in these countries, discussing how involvement with each compares to a broader group of China’s

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trade and ODI partners. Third, we qualitatively assess whether economic interests can explain large investment projects and aid. Finally, we present our conclusion about the most plausible explanations for China’s increasing economic engagement in our focus countries.

II Background

China has been diplomatically engaged with South Asia for many decades, but increased economic engagement is relatively recent. Below we provide background for these relations and China’s investment and trade behavior generally. We also outline recent developments on economic and diplomatic fronts with our focus countries.

A Chinese Outward Direct Investment

China formally began setting up international firms after implementing its Open Door Policy in 1978. Until the late 1980s, however, the Chinese government restricted the low level of ODI to state-owned enterprises and incentivized domestic over international investment (Sauvant 2005). Chinese ODI increased slightly in the 1990s. Later, in response to the 1997-1998 Asian financial crisis, the Chinese government tightened government approval procedures to rein in ODI. A fear of capital flight and loss of control of state assets motivated these more stringent policies (Buckley et al. 2008).

Since the implementation of China’s “Go Global” initiative in 2001, the Chinese government has relaxed its foreign exchange controls, approval procedures, and investment restrictions. From 2003 onward, privately owned enterprises have been allowed to apply for permission to invest internationally (Buckley et al. 2008). Since this time, Chinese ODI has rapidly expanded, from less than $3 billion in 2003 to more than $70 billion in 2011 (China Commerce Yearbook 2010 and 2012). While state-owned enterprises continue to be the largest investors—mainly in petroleum, construction, telecommunications, and shipping—private companies such as Lenovo have started to invest abroad (Morck et al. 2008).

Although the majority of China’s ODI is directed toward Southeast Asia, since 2003 China has increased its diplomatic and economic focus on South Asia. It is difficult to discern whether China has geopolitical reasons for strengthening economic ties with South Asia. On the one hand, Chinese objectives could be strictly a function of export-led growth strategies and a desire to expand trade routes. On the other hand, ties to the region could be equally important for China to exercise diplomatic pressure and extend the reach of its military. This potential development is the particular fear of India, China’s prime rival in South Asia.

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B India-China Rivalry

India and China have a complex and sometimes openly contentious relationship. Both countries have rapidly expanding economies and face increasing pressure to secure reliable supplies of natural resources. As rising powers, both countries have ambitious space programs, naval aspirations, and motivations to stake out a sphere of influence in their surrounding region.

More specifically, border disputes arising out of the 1962 Sino-Indian War remain unresolved and continue to cause friction between the two countries. To date, China continues to question Indian sovereignty over the Indian states of Arunachal Pradesh, Jammu, and Kashmir (Curtis 2011). India also continues to host the Dalai Lama and over 100,000 Tibetan refugees, a source of ongoing tension between Beijing and New Delhi.

Moreover, China’s relationship with India’s rival, Pakistan, continues to be a point of tension between the two countries. China has helped Pakistan build nuclear facilities (Times of India 2009) and deep seaports and has provided Pakistan with military hardware such as JF-17 fighter jets (Perlez 2011).

III China’s Current Involvement in South Asia

China is building bridges to South Asia. In 2005, the China Council for the Promotion of International Trade inaugurated the China South Asia Business Forum and the South Asian Countries Trade Fair in Kunming, Yunnan (Sunday Observer 2012). Five hundred South Asian businesspeople and officials attended the fair in 2011 (Plus News Pakistan 2011). Chengdu, Sichuan, began hosting the new China-South Asia Economic Trade Cooperation Fair in 2011 (Ti 2012). China is also developing transportation links from Southwest China to South Asia. Thirty-one international flights fly from Yunnan to 19 countries (Plus News Pakistan 2011), a new direct flight from Chengdu to Sri Lanka was slated to begin in early 2013 (Ti 2012), and plans for a trans-Asian railway project are in progress. The railway is to extend 114,000 kilometers through 28 countries (Plus News Pakistan 2011).

A Trade

China’s trade volume with our South Asian focus countries (Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka) grew roughly six-fold between 2003 and 2011, as shown in Figure 1. This expansion surpasses the increase in China’s world trade volume, which increased four-fold over the same time period. The vast majority of China’s trade growth with the group, however, was in imports by the focus countries; their exports to China increased only slightly. Although focus countries' import growth has been swift, the starting point was very low: China’s exports to these five countries combined made up 0.45 percent of China’s total exports in 2003. In short, imports by these five countries are of small importance to China’s overall trade profile.

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Figure 1: Focus Countries’ Trade with China, 2003-2011

Data Source: United Nations Statistics Division (2013)

As for China’s geopolitical motives, the importance of trade flows for these South Asian economies is even more pertinent to consider. If Chinese trade flows figure large in their economic interests, China stands to have more leverage with these countries if regional disputes arise. Looking again at the 2003 to 2011 time period, China’s share in trade flows for our focus countries has grown. As shown in Figure 2, these countries are growing in their dependence on Chinese imports. The overall share of imports from China has grown for this group from under 15 percent of imports to nearly 25 percent over eight years. The export share bound for China has also increased, but the absolute value of this share is still less than 2 percent of the total. Export share to China for the group quickened after 2009. As shown in Figure 3, Bangladesh, Nepal, and Sri Lanka all saw considerable increases in the share of exports bound for China at that time. This pattern is noteworthy because only eight Asian countries experienced export growth to China between 2008 and 2011 (Sunday Observer 2012).

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Figure 2: China’s Share of Focus Countries’ Trade, 2003-2011


Figure 3: China’s Share of Exports from Bangladesh, Nepal, and Sri Lanka, 2003-2011


A.1 Rivaling Indian Trade

Finally, because we are asking a question about regional dominance, China’s share of trade should be compared to that of India. In contrast to China’s rising share of trade with our focus countries, India’s share fluctuates, with no clear trend. As shown in Figure 4, India clearly dominates in imports from the group: although the trend for export share to China is certainly on the upward slant, it is still far from catching up with India. Imports are an entirely different matter. As

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shown in Figure 5, China surpassed India for import share in 2009, and this edge continues into 2011.

Figure 4: India vs. China Share of Focus Countries’ Exports, 2003-2011


Figure 5: India vs. China Share of Focus Countries’ Imports, 2003-2011


In addition to the overall trend for the group, in Figure 6 through Figure 8 we present snapshots of Indian versus Chinese export and import volumes for each of our focus countries in 2011. This country-by-country comparison shows that the China-Bangladesh trade relationship is particularly strong. Bangladeshi imports from China far surpass those from India, while export volumes to China are nearly as large as those to India. China has much less trade with the other four

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countries. India continues to dominate trade flows there. Bhutan and Maldives had no exports to China in 2011.

Figure 6: Imports from India vs. China, 2011: Bangladesh, Nepal, and Sri Lanka


Figure 7: Imports from India vs. China, 2011: Bhutan and Maldives


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Figure 8: Exports to India vs. China, 2011: Bangladesh, Nepal, and Sri Lanka


B Outward Direct Investment

Chinese ODI has been expanding rapidly over the last 10 years. Initially, China directed this investment almost exclusively toward other Asian countries. As shown in Figure 9, however, beginning in 2008, the rise in investment outside the region has been more rapid. Over this same period, annual ODI flows to Asia as a whole have held steady, while ODI flows to our focus countries have been on the rise, as shown in Figure 10.1

1 ODI can be expressed in terms of the total stock of investment outside the home country or in terms of the flow of new investment in a given year. The value of ODI flow to a particular country can be positive or negative depending on the net of new investments being made and old investments being withdrawn in a given year.

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Figure 9: Chinese ODI Flow, World vs. Asia, 2003-2011

Data Source: China Commerce Yearbook (2010, 2012)

China’s annual ODI flow to Bangladesh shows an overall upward trend; there the Chinese have been active in mining, electricity, and chemical industries (Xinhua News 2012). Increases in ODI flow to Sri Lanka and Nepal have taken off since 2009. In Nepal, “China House” supermarket already does business in five locations and plans to open 10 more stores soon. The Nepalese also hope the Chinese will invest in natural resource extraction (Ti 2012). Chinese investment in Sri Lanka has garnered the most attention, both in the region and in the United States, because of Sri Lanka’s strategic location along trade routes in the Indian Ocean and its location just off the coast of India. The increase in ODI flow to Sri Lanka is very dramatic. At the same time it is not unusual, given the end of the Sri Lankan civil war in 2009 and a similar increase in Sri Lankan total foreign direct investment (FDI). China reports no ODI flow directed toward Bhutan or Maldives.

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Figure 10: Chinese ODI Flow to Focus Countries, 2003-2011

Note: China reports no ODI to Bhutan or Maldives. Data Source: China Commerce Yearbook (2010, 2012)

Compared to ODI flow, ODI stock better illustrates the amount of influence China may have in a given country. The Bangladeshi share of Chinese ODI stock spiked in 2005 and has declined since, as shown in Figure 11. This reduction does not reflect a retreat from Bangladesh (total Chinese ODI stock steadily increased over the time period observed), but rather faster increases in Chinese ODI to the rest of the world. Sri Lanka, on the other hand, has seen its percentage of Chinese ODI stock rise roughly four-fold between 2009 and 2011. Yet this percentage remains quite low (0.04 percent in 2011).

As with trade, it is instructive to examine overall FDI trends within our focus countries and compare these to the Chinese ODI trends in each. Figure 12 shows that in each of the three countries in our focus group that receive ODI from China, total FDI flows are rising. Sri Lanka stands out, because of the large jump in total inward FDI in 2011. This overall trend for Sri Lankan FDI is mirrored in the Chinese ODI trend to Sri Lanka, suggesting that China is not alone in increasing investment in the country since 2009. At the same time, as shown in Figure 13, China’s share in the total Sri Lanka inward FDI has risen since 2009, albeit from a very low point. China has the strongest position in Nepal: Chinese FDI share is greater in Nepal than in Bangladesh or Sri Lanka and is on the rise, although the 0.07 percent share in 2011 is hardly impressive. For all of these countries, most FDI comes from elsewhere.

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Figure 11: Focus Countries’ Percentage of China’s ODI, 2003-2011

Data Source: China Commerce Yearbook (2010, 2012)

Figure 12: Focus Countries’ FDI Inflow, 2003-2011

Data Source: World Bank (2013), World Development Indicators (2013)

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Figure 13: China’s Percentage of Focus Countries’ Inward FDI Stock, 2003-2011

Data Sources: China Commerce Yearbook (2010, 2012); UNCTAD (2013)

Finally, we compare Chinese FDI stock with Indian FDI stock to investigate whether Chinese political leverage in the form of FDI may be a challenge to India. Although Chinese FDI stock has been increasing, Indian FDI stock in each of these countries is also increasing. Bangladesh has seen increasing FDI from both countries. China dominated in the early 2000s, but India has outpaced China in investment in 2010 and 2011, as shown in Figure 14. From 2003 to 2011, India has consistently been a more important investor than China in Nepal and Sri Lanka, as shown in Figure 15 and Figure 16.

Figure 14: Bangladesh’s Inward FDI Stock: India vs. China, 2003-2011

Data Sources: India Department of Economic Affairs (2013); IMF Coordinated Direct Investment Survey (2013a)

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Figure 15: Nepal’s Inward FDI Stock: India vs. China, 2003-2011


Figure 16: Sri Lanka’s Inward FDI Stock: India vs. China, 2003-2011


C Big Projects

A sizable portion of Chinese ODI has come in the form of large construction projects, mainly in the sectors of agriculture, energy, and transportation. Of the countries examined in this report, China’s 2010 and 2011 annual investments were highest in Sri Lanka, where Chinese firms have completed or are in the process of completing a number of large-scale projects. These projects span multiple sectors, including investments in transportation (MENA Report 2013). Many large projects have been conducted in Hambantota, the home province of

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President Rajapaksa. These projects include a 35,000-seat cricket stadium, a large convention center, a $209 million international airport, and a $1.5 billion deep seaport located on strategic sea-lanes (Aneez 2013). Most workers and materials for these projects come from China (Devichand 2010).

China has several ongoing large-scale projects in Bangladesh. These projects are similar to those in Sri Lanka and include deep seaports in Chittagong (Pant 2007) and Sonadia Island (Frankel 2011). China also has plans to build a road and rail link through Myanmar to connect the Chinese city of Kunming with Chittagong (Economist 2013, January 9). Moreover, China has invested in Bangladesh’s agricultural sector, providing $226 million for the Pagla Water Treatment Plant project (Daily Star 2012) and $559 million for a fertilizer factory in Shahjalal (Genota and Luk 2010). Similar to its portfolio in Sri Lanka and Bangladesh, China has invested heavily in Nepalese energy and transportation, including a $1.6 billion hydropower plant and a $1.9 billion railroad project to connect Lhasa to Kathmandu (Unnithan 2011). Other transportation projects include the Pokhara Regional International Airport (MENA Report 2013), a number of roads, bridges, and container depots (Ashok et al. 2010).

D Diplomatic Overtures and Aid

China has been cultivating diplomatic ties with our South Asian focus countries. Most of these overtures build on longstanding diplomatic relations. Nepal-China and Sri Lanka-China diplomatic relations date back to the 1950s; Maldives and Bangladesh have diplomatic ties with China going back to the 1970s. Bhutan has been the one holdout, having left its diplomatic relations management to India for decades. That situation may soon change; Bhutan is now in talks with China to establish formal diplomatic relations as well (Sharma 2013).

During the 2003 to 2011 time period, Chinese high officials interfaced regularly with their counterparts in these countries. Headlines surrounding these diplomatic exchanges involve commitments repeated year after year to “strengthen ties,” “deepen relations,” and “promote friendship.” Our review of news reports indicates an increase in the frequency and intensity of exchanges between China and these small South Asian countries since 2005, when the South Asian Association for Regional Cooperation granted China observer status. China is intent on becoming a permanent member of the association to further enhance its influence in the region. Nepal promoted this bid and Pakistan and Bangladesh seconded, but it was rejected due to opposition from India, backed up by objections from Bhutan, Maldives, and Afghanistan (Abdi 2010).

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IV Modeling Trade and Outward Direct Investment

In this section we present economic theories of trade and ODI and discuss their relevance for China. We then apply econometric models based on these theories to datasets including our focus countries and discuss the results.

Economists have developed a variety of theories to explain patterns of trade and investment among countries. Most of these theories, however, are based on economically developed countries, calling into question their usefulness for developing countries such as China and the countries of South Asia. Furthermore, China’s institutional environment and history of government involvement in business affairs complicate the analysis. In the following section we explain the theories and models we apply, paying special attention to reasons China’s behavior may deviate from the predictions of conventional economic models.

A Trade

Economic trade theory assumes that comparative advantage between countries and barriers to trade determine trade levels. The gravity model of trade assumes that exports and imports between any two countries are inversely proportional to the geographic distance between the countries, because transport costs are one of the most significant trade barriers. This theoretical model has inspired a number of econometric models. Analysis using these models has established the empirical consistency of the gravity model for a wide range of cases. For this reason, we use the gravity model as the basis for our study of China’s exports.

The gravity model assumes that export levels will have the form:

(1)

In this equation, T represents a trade flow (imports or exports); i and j represent a pair of countries engaged in trade; and K, , , and are constants. Although many variations have been used to evaluate the effects of specific policies or other determinants, they all essentially derive from this basic model (Kepaptsoglou et al. 2010). Because our interest is not in determining the effect of new variables, but in determining whether our countries of interest conform to normal economic patterns, we use a simple form of this widely accepted econometric specification.

A.1 Trade Model

Assuming that equation (1) holds, the gravity model can be estimated empirically. Taking the logarithm of both sides of this equation transforms it into an equation suitable for linear regression:

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log log log log

(2)

When a time dimension is added, this equation becomes:

log log log log

(3)

In this econometric equation, represents a constant; represents a time fixed effect; , , and are regression coefficients; and is the error term. The subscript i represents the exporting country, while the subscript j represents the importing country; t represents the time period, which is expressed in years in our analysis. To augment this basic specification, we add three indicator variables for factors commonly assumed to influence bilateral trade. The additional variables are dummy variables indicating whether a pair of countries i and j are adjacent to one another, whether they share a common language, and whether they have a history of colonization by the same country (Melitz 2007; Kepaptsoglou et al. 2010). The colonization dummy does not apply to China but helps make sure that pairs of countries that do share a colonial heritage leading to more trade do not cause omitted-variable bias in the coefficients for the other variables.

log LogLog log

(4)

We believe this specification includes the most important fundamental determinants of variation in trade among countries. If trade volumes between our countries of interest and China deviate substantially from this equation, we assume this deviation indicates trade that does not result from standard economic interests.

A.2 Trade Model Data

We use a global sample to estimate the model before focusing on the predicted values for our countries of interest. We obtain our trade data from the International Monetary Fund (IMF) Direction of Trade Statistics database. We use the logarithm of each country’s reported annual exports to each of its trading partners, expressed in nominal U.S. dollars, for the dependent variable. Our time period for the panel regression is 2009-2011. We add one to all gross export totals to avoid errors caused by instances of zero exports. If exports between an

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export/import pair equal zero for all three years in our panel, we drop that pair from our sample.2

Our gross domestic product data come from the World Bank World Development Indicators. Data for the variables Distance, Adjacency, Common Language, and Common Colonizer all come from the Centre d’Etudes Prospectives et d’Informations Internationales GeoDist dataset (CEPII 2013). For a complete list of variable specifications and data sources, see Appendix A.

A.3 Trade Results

We perform a panel regression according to model (4) above, using time fixed effects. The results are presented in Table 1: every variable included has a statistically significant effect on export levels, and the model explains about half of the variation in export levels, which means the model is a good predictor of trade flows.

Table 1: Trade Model Results Estimate Standard Error t-value

log(GDPit) 1.92 0.008 230.00 ***

log(GDPjt) 1.51 0.008 185.93 ***

log(Distance) -2.21 0.025 -87.07 ***

Adjacency -0.39 0.150 -2.60 **

Common Language 1.96 0.053 37.18 ***

Common Colonizer 2.73 0.196 13.94 ***

R2 0.56

N 7,1157 Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

Next, we use the estimated coefficients to generate point estimates for exports to and from China with our focus countries. In Table 2 we present the difference (or error term) between observed log exports and expected log exports. These results show how well trade flows in these cases conform to global patterns. In Table 3 we present the dollar equivalent of these error terms. These amounts are based on our own calculations, as presented in Appendix B.

2 The IMF Direction of Trade Statistics dataset includes trade flows for 217 countries and entities. For 2009-2011, the dataset includes 23,719 ordered export combinations (exports from Country A to B and from Country B to A are counted separately) with positive export flows for at least one year.

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Table 2: Error Terms for Trade Flows between Focus Countries and China 2009 Error Term 2010 Error Term 2011 Error Term

Chinese Exports to Focus Countries Bangladesh -0.70 -0.67 -0.97 Bhutan -1.03 -2.54 -0.56 Sri Lanka 0.57 0.35 0.21 Maldives 1.85 1.98 2.14 Nepal 0.32 0.36 0.31

Focus Countries’ Exports to China Bangladesh -2.87 -2.59 -2.54 Bhutan N/A N/A N/A Sri Lanka -0.77 -0.82 -1.17 Maldives -0.83 -1.91 -1.22 Nepal -1.72 -1.51 -1.82

Error Term Standard Deviation (Whole Sample): 4.93

Table 3: Expected Versus Actual Trade Flows

2009 2010 2011

Predicted Actual Predicted Actual Predicted Actual Chinese Exports to Focus Countries (Millions of U.S. Dollars)

Bangladesh $9,000 $4,400 $13,300 $6,800 $20,700 $7,800

Bhutan $12 $4 $20 $2 $30 $17

Sri Lanka $900 $1,600 $1,400 $2,000 $2,400 $3,000

Maldives $6 $41 $9 $63 $11 $97

Nepal $300 $400 $500 $700 $870 $1,200

Focus Countries’ Exports to China (Millions of U.S. Dollars)

Bangladesh $1,800 $100 $2,500 $200 $3,800 $300

Bhutan n/a n/a n/a n/a n/a n/a

Sri Lanka $100 $60 $200 $90 $350 $100

Maldives $0.30 $0.12 $0.35 $0.05 $0.42 $0.12

Nepal $30 $5 $50 $10 $80 $13 Source: Author’s Calculations

The expected export levels from China to each of our countries of interest are fairly close to the observed levels. Error terms for China’s exports to our countries of interest range from -2.54 to 2.14. The only country that stands out in this regard is Maldives, which had error terms of 1.85, 1.98 and 2.14 in the years surveyed. These error terms mean that China exported approximately two to three times more than we would expect to Maldives in these years. Not only is Maldives receiving greater than expected exports from China but the discrepancy increases from year to year. China exports less than expected to Bhutan and Bangladesh, and more than expected to Maldives and Nepal, but exports to all four of these countries deviate from the expected levels by a narrower margin than do exports to Maldives. All of the countries of interest exported less to China than would be expected according to the model. The error terms for these countries’ exports to

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China range from -2.87 to -0.77 (with Bhutan omitted because Bhutan does not report exports).

The error terms from the wider sample range from -24.60 to 17.30 with a mean of 0 and a standard deviation of 4.93. This standard deviation means that the differences between the predicted and actual export levels for our countries of interest, even when they seem large, are actually within a range consistent with the model. In sum, conventional economic principles leave none of the trade flows between China and our countries of interest unexplained.

B Outward Direct Investment

Investment abroad and multinational enterprises are commonly understood through Dunning’s ownership-location-internalization (OLI) paradigm (Dunning and Sarianna 2008). The OLI paradigm theorizes that countries move business abroad because of cultural and geographical closeness, raw materials, special taxes or tariffs, and competitive advantages. Scholars debate whether the OLI paradigm can be used to explain investment coming from developing economies, such as China, as it is based on industrialized countries. According to Buckley and colleagues (2008), although China’s case is different from that of industrialized countries, it is close enough to use the typical OLI model with slight modifications based on special consideration of China’s distinctive situation. In addition to being a developing economy, China has an institutional framework, regulations, and political system make its investors different from typical actors in a number of ways.

When seeking to understand China’s increasing investment in South Asia, looking at the climate from which investors come is important. Capital market failures in China inform and alter where and how companies invest abroad. Inefficiency of Chinese capital markets makes it difficult for firms, especially private firms, to access financing and funding, due to restrictions on foreign funding (Buckley et al. 2008). This difficulty funding domestic projects may be part of what motivates firms to invest abroad.

The Chinese government has historically played a role in investment and economic activity, both at home and abroad. Favoritism toward certain types of investment activity distorts economic sectors, as well as the destinations of outward investment. This government effect is evident in disproportionate investment in specific natural resource sectors and propensity to invest in high-risk countries or regions (Buckley et al. 2007). The government may also promote ODI to facilitate export activity or offset high levels of international reserves (Cheung and Qian 2009). In their 2007 analysis of the role of the Chinese government in investment abroad, Buckley et al. compared changes in outward flows with important policy changes and find liberalization of Chinese ODI policies in 1992 had an effect on economic investments abroad.

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Government involvement in economic activity abroad can be helpful and harmful for firms. It can be harmful if the government restricts where and how firms can invest and firms often face unclear regulations. It can be helpful if firms authorized to head abroad receive elevated levels of home country support, such as subsidies, and preferential access to raw materials and cheap capital (Buckley et al. 2008). This home country support distorts economic incentives, giving firms advantages that may help them succeed in places where they otherwise would not.

The distortions in the foreign investment market due to government involvement are important to consider when analyzing economic flows. Government involvement means that investment abroad is not necessarily profit maximizing and thus cannot always be understood through normal firm theories that assume profit maximization. For example, government encouragement of foreign investment creates an agency problem when employees place themselves as overseas managers to increase their income. This agency problem results in unnecessary investment abroad. As private Chinese firms are now permitted to invest abroad, some improvements in efficiency may result, but research in this area is poor (Buckley et al. 2008). The percentage of ODI from private firms is still quite low, at 9.5 percent in 2012, but this amount is a marked increase from the 4 percent private share in ODI in 2010 (Economist 2013, January 19).

B.1 Applying an ODI theory to China

The best ODI theory takes into account as many of the considerations noted above as possible. These considerations can also be used to explain deviations in expected ODI flows. To analyze Chinese ODI to our countries of interest, we replicate the model Cheung and Qian (2009) used that analyzes Chinese ODI flows from 1991 to 2005 by testing for some typical determinants of movement abroad: market access, lower labor costs, natural resources, and country risk. It also includes variables that measure the closeness of the relationship between the two countries, such as export share and percentage of ODI received relative to China’s total, and a variable for each country to capture unspecified country specific features. This statistical analysis does not account for political motivations and other market distortions discussed above. We conduct this analysis using aggregate Chinese ODI data. We would have liked to do a similar analysis for particular sectors, such as natural resource extractive industries and transportation, but China does not report sector-specific ODI numbers.

B.2 ODI Model

Our ODI model employs a panel regression to incorporate data on each country from several years. In this model, variables are lagged to avoid endogeneity problems and account for the time it takes ODI flows to respond to market and institutional changes.

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, 1 , 1 2 , 1 3 , 1

4 , 1 5 , 1 6 , 1 7

1 , 1 2 1 3 , 1 ,

(3)

The dependent variable, , ,represents the logarithm of the total stock of all investments in fixed capital made by Chinese entities, scaled by the host country’s population. The country fixed effect, ,is a dummy variable that captures relevant country characteristics of geographical and cultural closeness. The next three variables, GDP, RGDPpc, and GDPG, account for market-seeking motivations (Cheung and Qian 2009). GDP is the ratio of the host country’s nominal GDP to China’s nominal GDP, RGDPpc is the ratio of the host country’s real per capita GDP to China’s real per capita GDP, and GDPG is the host country’s growth rate. Wage is the average manufacturing wage in the host country relative to China’s. It is included to discover whether a search for lower labor costs motivates Chinese ODI. Raw is the proportion of a country’s raw material exports to merchandise exports and is used to capture any resource-seeking behavior on the part of China. Trend captures changes in China’s general investment behavior over time. Risk is a control for country factors that typically deter ODI, such as institutional instability and natural disasters. XShare accounts for the amount of Chinese exports a host country accepts relative to other countries’ exports. This variable is included because Chinese ODI may be used to assist and facilitate China’s exports to a country (Cheung and Qian 2009). China has been criticized for holding high levels of international reserves and has initiated policies to encourage ODI, reducing the risk of holding such high levels of reserves; Reserve captures this effect and represents China’s total holdings of foreign currency, measured in trillions of U.S. dollars. Finally, Aggl stands for agglomeration and is the ratio of China’s ODI stock in a host country to China’s total ODI stock. Aggl accounts for accumulated Chinese investment in host countries, because ODI typically follows previous investment in a country, to avoid risk or use existing knowledge of the people and economy (Cheung and Qian 2009).

Because data for the Wage variable are the least available, we also run the regression without wage included, as Cheung and Qian (2009) did.

, , , ,

, ,

, ,

, (4)

We examine the statistically significant values to ascertain China’s general ODI behavior. Then we look at the country fixed effects for each of our countries of interest to determine if investment from China is out of the ordinary. If our countries of interest are outliers, it suggests that conventional economic interests cannot explain ODI.

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B.3 ODI Model Data

We draw the data for our ODI model regressions from a number of international bodies, as well as from Chinese government reports. The dependent variable is the logarithm of Chinese ODI stock in a country divided by that country’s population. We obtain all the Chinese ODI data from the annual China Commerce Yearbooks. Prior to 2003, China only reported ODI that flowed from private enterprises that received official permission to invest overseas. From 2003 onward, China has reported total ODI in a manner consistent with Organisation for Economic Co-operation and Development (OECD) and IMF standards. We analyze 2003-2011 ODI flows to make use of this better quality ODI data. We obtain population figures from the World Bank World Development Indicators.

For the independent variables GDP, RDGPpc, and GDPG, we obtained nominal GDP, real GDP per capita, population, and real GDP growth data from the World Bank World Development Indicators. We collected annual data on fuel exports and metal and ore exports from the World Bank as well; both are expressed as a percentage of total merchandise exports. For each country-year, we sum these two export percentages to construct the Raw variable. Finally, we also draw data on China’s total international reserves from the World Development Indicators. The Wage variable is a ratio of the host-country manufacturing wage to China’s. We collected national wage data for this variable from the United Nations International Labour Organization’s database. Reported average wages in manufacturing are available through 2008. We converted these to U.S. dollars using annual average exchange rates available from the IMF International Financial Statistics.

For the Risk variable we opt to use the OECD Country Risk Classification, rather than the International Country Risk Guide Political Risk rating, which Cheung and Qian use. Although the guide rating is better known, the OECD data are more readily available. Like the ICRG rating, the Country Risk Classification takes into account issues such as corruption and threat of conflict. In contrast, this OECD measure is more concerned with the ability of investors to convert currency and recoup their investment and less concerned with specific measures of bureaucratic quality and government stability, compared to the International Country Risk Guide rating. In the Country Risk Classification, some countries in our sample are missing data for some of the years we examine; therefore we impute missing values based on the nearest year for which data are available. As for export data in the XShare variable, we collected trade data from the IMF Directions of Trade Statistics. We use International Labour Organization reported wage data and convert it to U.S. dollars using exchange rates drawn from IMF International Financial Statistics. We include a description of each variable and the corresponding data sources in Appendix A.

The countries in our regression mirror those Cheung and Qian (2009) use, which we did to more readily compare our results to theirs. We use the 50 countries included in their analysis plus Bangladesh, Nepal, and Sri Lanka. Bhutan and

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Maldives are excluded because they receive no ODI from China, which brings our country total to 53. The sample of countries includes developed and developing countries. The sample also draws from a variety of regions. For a complete list of countries included, see Appendix C.

B.4 ODI Results

We use specifications (3) and (4) above to run regressions both for the entire sample of 53 developed and developing countries in our dataset, and for the subset of developing countries. We present our results in Table 4. When all countries are included, RGDPpc, Aggl, and Trend are significant predictors of ODI stock. These results suggest that overall previous investment and market opportunities drive China’s ODI; however, the significance of Aggl may also be due to the continued existence of ODI stock and does not necessarily indicate that countries are receiving new ODI. Reserve is also significant, but negatively so. This result indicates that as China’s stock of foreign reserves increases, ODI stock declines, which is contrary to what we would expect and to what Cheung and Qian (2009) observed. This change in results suggests that reserves are not an important driver of ODI in this time period. Results are not significantly altered when Wage is excluded. Reserve and Aggl become more significant, while RGDPpc drops slightly.

Table 4: ODI Model Results

Full Sample Developing Countries

Model (3) Model (4) Model (3) Model (4)

GDP -0.04 (0.25)

-0.08 (0.14)

-3.99 (4.22)

4.74 *** (1.33)

RGDPpc 0.11** (0.06)

0.03 * (0.02)

-0.10 (0.11)

0.02 (0.02)

GDPG -0.01 (0.02)

0.0006 (0.01)

0.004 (0.03)

-0.008 (0.01)

Raw 0.007 (0.01)

-2.60e-04 (0.002)

-3.94e-04 (0.009)

-4.78e-04 (0.002)

Wage -1.42e-04 (1.25e-04)

------ -1.58e-04 (1.23e-04)

------

Risk 0.22 (0.17)

0.10 (0.07)

0.12 (0.18)

0.11 * (0.07)

XShare -0.24 (0.41)

0.12 (0.31)

-0.19 (0.40)

0.11 (0.30)

Reserve -1.04* (0.55)

-0.87*** (0.20)

-1.80 ** (0.69)

-0.90 *** (0.21)

Aggl 7.56* (4.16)

0.10*** (3.52)

1.11 ** (4.46)

7.37 ** (3.47)

Trend 0.91*** (0.18)

0.72 *** (0.07)

1.09 *** (0.21)

0.75 *** (0.08)

R2 0.73 0.77 0.77 0.78

N 23 53 17 43 Note: Standard errors are in parentheses. ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

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In the developing country subset, Reserve, Aggl, and Trend are all significant. When Wage is excluded, GDP and Risk become significant. The significance of the GDP variable backs up the theory that relative market size is influencing ODI from China to developing countries. Again, Aggl and Trend are significant, while Reserve is significantly negative, suggesting previous investment, but not foreign currency reserves, drives ODI from China to developing countries. Aggl is subject to the same uncertainty of interpretation discussed in the full sample results. One anomaly is that Risk is positive and significant, suggesting that as risk increases, China is more likely to invest in a country. This finding supports the theory that businesses with government assistance are more likely to move into areas that would otherwise be too risky and suggests that the Chinese government is playing a role in supporting ODI to developing countries. The fact that businesses in developing countries have an advantage in working in other developing countries, as they are accustomed to the risks that come along with these locations, may also explain a positive Risk coefficient.

In Table 5 we report the country fixed effect values for Sri Lanka, Bangladesh, and Nepal. Two of our focus countries, Maldives and Bhutan, are excluded from the ODI results, as China did not report ODI stock for those countries in our sample years. For all three countries, the country fixed effect is significant and negative. These countries fall close to the bottom of the range, suggesting that although the amount of ODI to each of the countries has increased recently, each country is still receiving a lower than expected amount of ODI.

Table 5: ODI Country Fixed Effects Full Sample Developing Countries Fixed Effects Standard

Error Fixed Effects Standard

Error Bangladesh -4.58 0.50 -4.80 0.49 Nepal -4.69 0.56 -4.84 0.55 Sri Lanka -3.27 0.46 -3.41 0.45 Fixed Effects Distribution Mean Minimum Maximum Full Sample -1.28 -5.60 4.17 Developing Sample

-1.66 -7.24 4.36

In Table 6 we present the amounts by which the observed ODI levels in each of our focus countries deviate from the level expected in a generic country with the same economic profile. These estimates are based on our own calculations, as presented in Appendix B, and expressed in dollars per person.

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Table 6: Estimated Deviation from Expected ODI, by Country

Full Sample Developing Countries

Measured in U.S. Dollars per Person

Bangladesh -$45 -$56

Nepal -$49 -$58

Sri Lanka -$42 -$49 Source: Author’s Calculations

B.5 Comparison to Cheung and Qian Model

Our results share some similarities and many differences with Cheung and Qian’s findings. Using the whole sample of developed and developing countries, Cheung and Qian find that Wage, Raw, Risk, Trend, XShare, Reserve, and Aggl are all statistically significant. Fewer coefficients are statistically significant in our results. Trend, Aggl, and RGDPpc are all significant in our results, suggesting that previous investment in a country has remained a factor in determining China’s ODI flows, while the per capita GDP of a host country became significant during more recent years. In Cheung and Qian’s analysis, the Reserve variable is significant and has a positive sign. In our results the Reserve variable is also significant, but has a negative sign. This result implies that the amount of foreign currency China holds is no longer a factor in ODI flows, and that more foreign currency decreased ODI in more recent years.

In the developing sample subset, Cheung and Qian find that RGDPpc, Wage, Raw, Trend, XShare, Reserve, and Aggl are all significant. In our developing sample subset, we also find that Aggl and Trend are significant, while GDP is significant as well, suggesting that previous investment in a country has remained a factor in determining China’s ODI flows to developing countries, while GDP of a host country became significant during more recent years. Similar to the whole sample, Reserve is negatively significant, which is the opposite direction of Cheung and Qian’s findings. Further, the Risk variable is significant and is positive. In Cheung and Qian’s time period, Risk was only significant in the total sample and was negative, meaning a riskier country dissuaded ODI flows. This change in results suggests that Chinese companies became more willing to invest in risky developing countries during our time period.

The differences in our findings may be attributed to changes in ODI patterns during our more recent time period or to changes in the Chinese government’s ODI reporting method. The influence of agglomeration and trending appears to have remained constant over the two datasets, while the amount of total reserves China holds has gone from having a positive influence on ODI to a negative influence during our sample years. Raw and XShare are not significant in any of our specifications, which suggests the importance of natural resources and exports from China has declined in more recent years. The insignificance of our Wage variable may be due to changes in ODI behavior, where labor costs are not as important, or data quality problems and lack of data beyond 2008. We are unable

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to compare country fixed effects with Cheung and Qian’s results, as they did not include our countries of interest in their sample.

B.6 ODI Model Sensitivity Analysis

We run sensitivity analysis to test for log specification, the use of ODI flow instead of stock as the dependent variable, and time-specific effects on this model. We present results from each in Appendix D. Estimating variables in logs is a common approach to express coefficients as a percentage increase. Results do not contradict the results of the primary model. Our second sensitivity analysis looks at the use of ODI flows as the dependent variable instead of ODI stock. Looking at ODI flows instead of stock analyzes year specific changes in amounts of ODI to a given country. This option allows us to look more closely at recent trends in investment, whereas ODI stock captures past trends as it measures the buildup of ODI over previous years. These results also do not contradict the results of the primary model.

When looking at time-specific effects, we are specifically interested in the 2008 world economic recession and how it may have affected Chinese ODI. To test for a recession effect, we ran the model adding in a dummy variable for 2008 through 2012. In this specification, Reserve and Aggl become slightly less significant and R2 increases by 0.01. The dummy variable itself is not significant, which suggests the recession did not have a large impact on Chinese ODI. The same results are found in the developing country subsample and when 2009 through 2012 is used as the dummy variable.

C Interpretation

Analytical results for trade and investment suggest a general strategy of diversification away from already-large economies and into smaller economies. These results suggest that China is looking for underexploited opportunities. In the previous periods for which Chinese ODI have been studied, China was still finding opportunities to make investments in high-GDP countries, but it likely exhausted enough of those opportunities that the opportunities in low-GDP countries are now more profitable than those in high-GDP countries. Likewise, in the previous periods for which Chinese exports have been studied, China likely was using its existing connections to export markets to support ODI, but is now coming close to saturating those markets and must look for demand more in places where its connections are not already as strong. This interpretation is generally applicable to all the countries receiving Chinese ODI and exports. Our results are similar to the very recent empirical analyses of Chinese ODI by Li (2013). He finds that China’s ODI since the 2008 global recession is consistent with profit-seeking motives.

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V Qualitative Analysis of Big Projects

Chinese ODI in Sri Lanka, Bangladesh, and Nepal is on the rise. A large portion of this investment has come in the form of big construction projects, mainly in the sectors of agriculture, energy, and transportation. A close examination of these projects does not offer a clear answer as to whether rational economic behavior or geopolitical considerations motivate them; however, a few consistent trends present themselves.

First, the state-owned Export-Import Bank of China funded most big projects. Second, Chinese state-owned enterprises receive contracts for the vast majority of these big projects, with a number of repeat players—namely Sinohydro Corporation, China Harbour Engineering Company, China National Machinery Corporation—receiving multiple contracts. These companies typically use Chinese workers and materials prefabricated in China. Thus, in addition to the long-term economic feasibility of these projects, these investments may be an extension of China’s investment in construction in general. In other words, although some of the money will “leak” into the country in which China invests, if the majority of the money goes back to Chinese firms and workers, then investment in these big projects could be the functional equivalent of a Chinese domestic stimulus. Strengthening this notion is the fact that many of China’s big projects in our countries of interest are similar in nature to projects Chinese state-owned enterprises pursue at home, namely, airports, roads, and power plants.

In Table 7 through Table 9 below, we list the highest-profile projects in Bangladesh, Nepal, and Sri Lanka. (For a more complete list of China’s big project ODI in our focus countries, please refer to Appendix E.) In an attempt to determine whether normal economic interests or geopolitical considerations motivate big projects, we ask four questions. First, does the project facilitate trade? Here, we measure whether the project lowers trade barriers by improving logistics or by lessening transaction costs. Second, could the project facilitate military movement on the part of China? This question revolves around Chinese military logistics. Third, does the project provide potential income to China? This question focuses on the level of rents China might accrue from its investment in big projects. Finally, did the project use Chinese companies, workers, or materials?

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Table 7: China’s Big Projects in Bangladesh Project Facilitate

Trade? Facilitate Military Movement?

Income Potential for China?

Chinese Workers or Materials?

Chittagong Deep Seaport

Yes Yes High Yes

Sonadia Deep Seaport (Proposed)

Yes Yes High Unknown

Dhaka-Chittagong National Highway Upgrade Project

Potentially Potentially Medium Yes

Multiple “Bangladesh-China Friendship” Bridge Projects

No No Low Yes

Fertilizer Factory in Shahjalal

No No Medium Yes

Bangabandhu International Conference Center

No No Low Yes

Source: Authors

The Chittagong port directly borders Northeast India and, like the Hambantota Deep Seaport in Sri Lanka, has raised suspicions in India that China is trying to encircle India and increase its ability to project military power into the region. (Bajaj 2010). Like the Hambantota Deep Seaport, these suspicions can neither be confirmed nor rejected. China has markedly increased its trade with Bangladesh and has a vested economic interest in gaining further commercial access to Bangladeshi markets. Additionally, Bangladesh’s president has publically stated that the port is intended solely for commercial purposes (Kostecka 2010).

The eight so-called “friendship bridges” China has built throughout Bangladesh do not seem to offer much economic utility to China. China funded these bridges to celebrate improved diplomatic relations with Bangladesh; Chinese state media say their construction is “to express the Chinese people’s friendship to the Bangladeshi people.” (People’s Daily 2012). Likewise, the Bangabandhu International Conference Center (also known as the “Bangladesh-China Friendship Conference Center”) does not seem to provide many economic returns to China. Therefore, “dollar diplomacy” likely explains this project better than sound economic investment.

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Table 8: China’s Big Projects in Nepal

Project Facilitates Trade?

Facilitates Military Movement?



Pokhara Regional International Airport

Yes Potentially Medium Yes

750-Megawatt West Seti Hydropower Project

No No Medium Yes

Upper Trishuli 3A and 3B Hydroelectricity Project

No No Medium Yes

Upgrading Ring Road in Kathmandu

Potentially No Low Yes

Tatopani Dry Port Yes No High Yes

The Lumbini Project (Buddha’s Birthplace)

No No Low Yes

Source: Authors

Several of China’s big projects in Nepal have clear economic explanations. China’s investment in multiple hydropower plants, for example, is understandable, given China’s ever-growing demand for energy. As a neighbor of Nepal, China can purchase any excess energy supply from Nepal.

Any discussion of China’s activity in Nepal must take into account Nepal’s large population of Tibetan refugees. Politically, Beijing has a vested interest in getting the Nepalese government to tailor its domestic policy toward Tibetan refugees in a way that benefits China. From a logistical standpoint, multiple road projects are increasing China’s ability to access Nepal. Projects like the $3 billion renovation of Buddha’s birthplace in Lumbini has been interpreted as a direct attempt to undermine the Dalai Lama’s influence by denying him access to the site (Chan 2011).

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Table 9: China’s Big Projects in Sri Lanka Project Facilitate

Trade? Facilitate Military Movement?



Hambantota Deep Seaport

Yes Yes High Yes

Mahinda Rajapaksa International Airport

Yes Potentially High Yes

Norocholai Coal Power Plant

No No Medium Yes

Matara-Kataragama Railway Project

No No High Yes

Mahinda Rajapaksa International (Cricket) Stadium

No No Low Yes

Mahinda Rajapaksa Lotus Pond Theatre

No No Low Yes

Source: Authors

The Hambantota Deep seaport has received a great deal of attention in the media, and a number of Indian security analysts have voiced concern over the port’s close proximity to India (Bajaj 2010). However, economic and geopolitical concerns are not always mutually exclusive. The Hambantota Deep Seaport may be an example of China acting with its economic and geopolitical goals in unison. Located on strategic Asian and European sea lanes, it can serve as a refueling station for commercial as well as military vessels. Moreover, the ability to dock, repair, and refuel strengthens China’s logistical capabilities to ship oil from Iran, Saudi Arabia, and Sudan, thereby increasing China’s energy security. The port also further facilitates trade with a country that is increasing its imports from China, and it is in China’s economic interest to have further commercial access not just to Sri Lanka, but to other surrounding countries as well. Finally, the Sri Lankan president has stated publically that the port is intended for commercial purposes only and will remain so (Kostecka 2010).

Other projects, such as the Mahinda Rajapaksa Cricket Stadium and the Lotus Pond Theatre seem more designed to curry favor with the Sri Lankan government. These projects offer little economic utility to China and mirror projects that China has funded in other countries as part of its diplomacy-through-aid strategy. For example, China also built cricket stadiums for Dominica, Grenada, and St. Lucia following their switch in diplomatic recognition from Taiwan to the mainland (Lum 2008). China gifted the Lotus Pond Theatre to Sri Lanka. The theater offers no economic returns, although Chinese workers constructed the theater using Chinese materials (Sunday Times 2011). Instead of purchasing an economic

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opportunity, these projects are better explained as being used to purchase positive relations with Sri Lanka.

Our qualitative analysis of China’s ODI reveals three kinds of big projects: (1) those that very clearly serve an economic purpose; (2) those that do not serve an economic purpose and are more likely to buy influence with the host country’s government; and (3) those that serve economic and geopolitical interests. The Chinese banks that finance these projects and the Chinese firms that work on them are almost uniformly state-owned enterprises, and the workers and materials used in these projects are often exclusively Chinese. Thus, economic and geopolitical considerations seem to motivate China’s investment in projects.

A Possible Geopolitical Explanations of Big Projects

As discussed above, some of China’s big projects appear geopolitically motivated on their surface, while others seem geopolitically and economically driven. According to the influential “string of pearls” theory, China’s growing access to naval bases and airfields and its improving diplomatic relations with countries encircling India reflects an attempt to establish regional dominance. This theory is based on circumstantial evidence, such as China’s investment in deep seaports and China’s improving military and diplomatic relations with our focus countries. For example, China is Bangladesh’s biggest weapons supplier. On a visit to Bangladesh, then Chinese Foreign Minister Yang Jiechi met with the Bangladesh Army chief general to discuss Chinese training of the Bangladeshi military (Sakhui 2009). Additionally, high-level officials have visited these countries to improve diplomatic relations. In 2012, Wu Bangguo, then chairman of the Chinese National People’s Congress Standing Committee, visited Sri Lanka to discuss trade and investment (Xinhua News 2012). Li Changchun, then Politburo Standing Committee member, has also visited Bangladesh to promote improved economic relations (Daily Star 2012). However, beyond this evidence, our analysis does not support the string of pearls theory.

VI Putting the Pieces Together

Although the trade data show increasing activity in our focus countries, this activity falls well within the expected range when compared to a world sample of China’s trade partners. Our analysis of ODI reveals that China is under-active in our focus countries compared to economic model predictions. If these countries are important to China diplomatically, the economic data do not show it.

Some of China’s big projects in our focus countries, however, suggest something more than economic motives at work. High-profile government and public buildings, like cricket stadiums and convention centers, do not appear to have anything to do with Chinese economic concerns. They look more like diplomatic overtures meant to court the host government. But what kind of strategy do they represent? The Chinese government has repeated this pattern of diplomacy all

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over the developing world, financing imposing structures featuring prominent Chinese seals to remind citizens and governments alike who is responsible for the construction. Furthermore, the Chinese have an explicitly stated interest in greater prominence as a nation in world affairs. Diplomacy with our focus countries is consistent with this narrative. China may have geopolitical motives for some projects in the region, but its ultimate objective is more likely greater prominence in world affairs generally, rather than a particular plan to displace India.

Sri Lankan seaports understandably make India nervous. Looking at the bigger picture, a story about sea-lanes for energy resources and other trade is more plausible than are designs to challenge India militarily. As China has acted provocatively toward Japan and its Southeast Asian neighbors in shared waters, fears of aggression and expansionist tendencies are not unwarranted. Today, however, China is likely more preoccupied with maintaining high economic growth rates at home than with challenging India for dominance in South Asia.

Taken together, the evidence in this report tilts in favor of an economic explanation, rather than a geopolitical explanation, for China’s economic activity in our focus countries. We believe that on both economic and diplomatic fronts China is acting much as it has in other developing countries around the globe. Thus, we conclude that increasing trade and ODI are a reflection of China’s current primary domestic concern: maintaining a high economic growth rate fueled by exports.

VII Going Forward

To anticipate whether Chinese ODI and trade with our focus countries may surpass that of India in coming years, we use the 2003 to 2011 data to estimate trend lines and project through 2020. Charts displaying these trend lines are presented in Appendix F. In ODI, China will not surpass India’s ODI stock levels for any of our focus countries by 2020. If China follows its trend line, it will come closest to India’s 2011 ODI stock in Bangladesh, but reaches less than half for Nepal and Sri Lanka.

In trade trends, China has already surpassed India in exports to Bangladesh. For Bhutan and Nepal, China will not surpass India’s 2011 exports by 2020. If China continues on the projected trend line, it will surpass India’s 2011 exports to Maldives and Sri Lanka by 2016 and 2019, respectively. The projected trend line using data from 2003 to 2011 does not account for the increase in exports from China to Sri Lanka, Nepal, and Maldives since 2009. If the trend line for exports to these countries is projected using only data from 2009 to 2011, it is likely that China would surpass India’s 2011 export levels to these countries by the end of 2014.

The purpose of the analyses in this report is to ascertain if China’s activities in these countries are geopolitically motivated, which would have been our conclusion if economic interests had failed to explain any of the activity.

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In going forward, we believe that developments like the “red flags” listed in Table 10 would indicate more geopolitical motivations.

Table 10: Red Flags

Item Description

Chinese Navy Usage of Deep Seaports

Chinese naval use of deep seaports in Sri Lanka and Bangladesh can come in the form of naval vessels refueling, servicing, or docking for extended periods of time.

Upward Deviation from Projected ODI

We project that China’s ODI in our focus countries will not rival India’s ODI by 2020 (see Appendix F). An upward deviation from this trend line warrants re-examination of China’s motivations.

Upward Deviation from Projected Trade Levels

Although Chinese trade in our host countries has risen dramatically, it still falls within our models’ predictions. An upward deviation from that trajectory would warrant re-examination of China’s motivations.

Increased Proportion of Gifts

Today, the number of gifted government and public buildings remains small compared to other big projects. We suggest being attentive to a growing proportion of gifts compared to more economically sound investments.

The Use of Imports to Gain Leverage over Target Countries’ Policy Decisions

China has tried to use its monopoly control of rare earth elements to gain leverage over Japan. We suggest being attentive to the use of dependency on imports to gain leverage. Bangladesh in particular has become highly dependent on Chinese imports.

Source: Authors

VIII Conclusion

China’s economic engagement with South Asia is clearly increasing. Our econometric analysis of trade and ODI to Bangladesh, Bhutan, Maldives, Nepal, and Sri Lanka, however, does not reveal activity inconsistent with normal economic interests. Our qualitative assessment of big projects suggests that the “string of pearls” theory is plausible, but building ports and roads facilitates trade as much as it increases the ability to project power. Because economic and geopolitical explanations may both hold, we advise that the Office of South Asia Policy continue to follow trade and investment trends. At the same time, we caution against interpreting China’s activity as threatening to regional stability or Indian security.

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Appendix A Variable Descriptions and Sources

In this appendix we provide a description of each variable in our ODI and trade models with their associated sources.

Table A1: ODI Model Variables and Sources

Variable Description Source

ODI The stock of Chinese ODI in a given country in nominal U.S. dollars divided by that country’s population; expressed in logarithmic form.

China Commerce Yearbook (2010, 2012)

GDP The ratio of host country nominal GDP to Chinese nominal GDP; each expressed in U.S.dollars.

World Bank (2013) World Development Indicators

RGDPpc The ratio of host country real GDP per capita to Chinese real GDP per capita; each expressed in constant 2000 U.S. dollars.


GDPG Host country real GDP growth rate. World Bank (2013) World Development Indicators

Risk Country Risk Classification, ranging in value from 1 (lowest risk) to 7 (highest risk); composed of transfer and convertibility risk, and adverse events like war, natural disaster or cases of expropriation. Countries use this classification system to set minimum premium rates in the OECD Arrangement on Officially Supported Export Credits.

OECD (2013)

Raw The share of raw materials (fuel, metals, and ores) in the host country merchandise exports.


Wage Average wage in manufacturing converted to nominal U.S. dollars.

ILO (2013), LABORSTA database; IMF (2013c) International Financial Statistics

XShare The ratio of China’s exports to the host country to total world exports to the country.

IMF (2013b) Direction of Trade Statistics

Aggl The ratio of China’s ODI stock in the host country to China’s total ODI stock worldwide.

China Commerce Yearbook (2010, 2012)

Trend A time trend variable taking on ascending values for the range of years included in the regression (i.e., 2003, Trend=1).

Reserve China’s total international currency reserves, including gold, in trillions of U.S. dollars.


Source: Authors

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Table A2: Trade Model Variables and Sources Variable Description Source log(T) Logarithm of annual gross exports from

country i to country j, expressed in nominal U.S. dollars.

IMF (2013b) Direction of Trade Statistics

log(GDP) Logarithm of Gross Domestic Product, expressed in nominal U.S. dollars.


log(Distance) Logarithm of the distance between the capital cities of a country pair, expressed in kilometers.

CEPII (2013) GeoDist dataset

Common Language

Dummy variable: =1 if country pair share the same language; = 0 if no shared language.


Adjacency Dummy variable: =1 if country pair share a border; = 0 if no shared border.


Common Colonizer

Dummy variable: =1 if country pair have shared history of colonization by the same colonial power, =0 if no shared colonial history.


Source: Authors

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Appendix B Interpreting Underlying Variables Based on Log Results

To make the results of our trade and ODI regression analyses easier to interpret we estimate their dollar equivalents. We present these dollar estimates in Section IA.3 and B.4 in the main body of the report. In this appendix we describe how we calculate these estimates.

Trade Error Term Conversion

For the trade regression we convert the error terms, which correspond to trade flows between China and our focus countries, to expected trade flows in millions of U.S. dollars. To estimate these dollar amounts, we apply the following formula:

In this equation, is the export level in dollars, is a vector of the independent variables used in our trade regression, and is a vector of the regression coefficients estimated by the model. This equation is simply the inverse of the transformation used to produce the dependent variable in the regression model, which is ln . Below we introduce a smearing formula we apply in the case of the ODI country fixed affects. We do not apply this smearing formula, however, in transforming the trade model results because the variance of the trade model error term is too great to produce meaningful smeared estimates.

ODI Country Fixed Effects Conversion

For the ODI regression we convert the country fixed effects for our focus countries, which estimate the deviation in ODI stock for a given country from the mean level estimated by the model, to a deviation expressed in dollars per person. We estimate these values using a smearing formula. Smearing is a method used to produce estimates of an underlying variable based on regressing a transformed version of that variable. In our regression, which involves a logarithmic transformation, the formula for calculating the smeared estimate of the underlying variable is as follows:

In this equation, represents the underlying dependent variable (in this case ODI scaled by host population), represents a vector of the independent variables we use in the regression, represents a vector of the estimated regression coefficients, and represents the variance of the regression error terms (Duan 1983). Because we want to estimate not the value of itself but the effect on of the host being a particular country, we use the following formula to interpret the fixed effects:

Δ

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In this equation, F represents the country’s fixed effect and Δ represents the change that can be attributed to the host being a particular country that is not explained by the other variables in the regression. The estimate of Δ is different for each year, because varies from year to year, so we average all the Δ values for a given country to produce that country’s estimate.

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Appendix C ODI Model Country Set

In this appendix we list the countries in our ODI model regression broken down into developed country and developing country subgroups. With the exception of our focus countries (Bangladesh, Nepal, and Sri Lanka), this list comprises the countries Cheung and Qian (2009) used.

Table C1: ODI Model Country Set

Developed Developing

Australia Algeria Mongolia

Canada Argentina Nepal

Denmark Bangladesh Nigeria

France Brazil Pakistan

Germany Cambodia Papua New Guinea

Italy Chile Peru

Japan Egypt, Arab Rep. Philippines

New Zealand Gabon Poland

United Kingdom Ghana Romania

United States Guinea Russian Federation

Hong Kong SAR, China Singapore

India South Africa

Indonesia Sri Lanka

Kazakhstan Sudan

Korea, Rep. Tanzania

Kyrgyz Republic Thailand

Lao PDR United Arab Emirates

Macao SAR, China Venezuela, RB

Malaysia Vietnam

Mali Yemen, Rep.

Mauritius Zambia

Mexico Source: Authors

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Appendix D ODI Sensitivity Analysis Results

In this appendix we present results from various alternate specifications to check the reliability of our primary ODI model results.

ODI Sensitivity Analysis Results: Logged Variables

In Table D1 we display results for a regression where we log the independent variables in addition to the dependent variable. We do not log Risk, because it only takes on discreet values between zero and seven. We also do not log Aggl, because the size of the existing stock dwarfs changes in ODI levels year to year.

Table D1: ODI Sensitivity Analysis with Logged Independent Variables

Full Sample Developing

log(GDP) 0.76 *** (0.22)

1.01*** (0.24)

log(RGDPpc) 0.35 (0.49)

0.61 (0.50)

log(GDPG) 0.004 (0.12)

-0.11 (0.13)

log(Raw) 0.01 (0.03)

0.0001 (0.03)

Risk 0.15 ** (0.07)

0.14** (0.07)

log(XShare) 0.08 (0.14)

0.05 (0.14)

log(Reserve) 0.78 * (0.40)

1.06 ** (0.44)

Aggl 8.44 ** (3.52)

6.62* (3.47)

Trend 0.26 ** (0.12)

0.20 (0.13)

R2 0.77 0.79

N 53 43 Note: Standard errors are in parentheses. ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

These results are largely consistent with our primary model, which logs only the dependent variable. Differences between the two sets of results are as follows. For full and developing country samples, the r-squared is higher. Here GDP is significant and positive in both samples, supporting the conclusion that Chinese ODI generally follows rational profit motives. In the full sample, GDGPpc is no longer significant. In this version of the model Risk is statistically significant in the full sample, unlike in the original model. The sign on Risk, however, is still positive supporting the conclusion that China is more apt to invest in countries where the political environment is less stable. Finally, Trend is no longer significant in the developing sample, although the sign on Trend is still positive, which is consistent with investment in developing countries increasing over time.

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As in our primary model results, all of our focus countries have below-average country fixed effects, as shown in Table D2. This outcome means they receive less Chinese ODI than what the model would predict for an average country.

Table D2: ODI Sensitivity Analysis with Logged Independent Variables, Country Fixed Effects

Full Sample Developing Sample

Fixed Effects Standard Error


Bangladesh -21.67 10.80 -27.74 11.76

Nepal -20.08 10.92 -25.51 11.91

Sri Lanka -19.97 10.89 -26.05 11.83

Fixed Effects Distribution

Mean Minimum Maximum

Full Sample -19.36 -24.70 -12.53

Developing Sample -25.50 -31.91 -18.94

ODI Flow as the Dependent Variable

In Table D3 we display regression results for a sensitivity analysis using ODI flow from China to a given country as the dependent variable instead of ODI stock.

Table D3: ODI Sensitivity Analysis with ODI Flows as Dependent Variable

Full Sample (with Wage)

Full Sample (no Wage)

Developing (with Wage)

Developing (no Wage)

GDP 0.29 (0.26)

0.23** (0.09)

-1.64 (5.08)

1.46 (1.00)

RGDPpc -0.06 (0.05)

-0.04 *** (0.01)

-0.43 *** (0.13)

-0.07 *** (0.02)

GDPG 0.04* (0.02)

0.01 (0.01)

0.05 * (0.02)

0.01 (0.01)

Raw 0.01 (0.01)

1.29e-04 (0.001)

0.01 (0.01)

-1.27e-05 (1.39e-03)

Wage -2.31e-05 (9.79e-05)

------ -2.03e-05 (1.10e-04)

------

Risk -0.04 (0.17)

0.01 (0.05)

-0.12 (0.22)

0.02 (0.05)

XShare 0.27 (0.55)

0.22 (0.21)

0.36 (0.61)

0.20 (0.23)

Reserve 1.40** (0.54)

0.14 (0.13)

1.36* (0.72)

0.16 (0.16)

Aggl -8.27 ** (3.94)

-8.99 *** (2.37)

-3.24 (4.79)

-9.25 *** (2.63)

Trend -0.34 ** (0.14)

-0.02 (0.05)

-0.39 ** (0.18)

-0.02 (0.06)

R2 0.17 0.13 0.28 0.15

N 23 53 17 43 Note: Standard errors are in parentheses. ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

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One of the main differences between these results and those using ODI stock as the dependent variable is that the r-squared is much lower. Looking at the model without Wage included, many of the variable coefficients in the full sample switch signs (GDP, RGDPpc, Raw, Reserve, and Aggl). Aggl is a negative factor for ODI flows. This result is consistent with the theory that China is diversifying its investment. This effect is not picked up when using ODI stock because ODI has accrued over time. The large existing stock of ODI obscures small changes at the margins. In these results using ODI flow, which is much more variable year to year, this diversification effect can be seen. It is likely we see Aggl as a positive significant variable in our ODI stock results because countries that China invested in previously are still holding a large stock of ODI, even if they no longer receive a large amount each year. Other differences in these results are that GDP becomes significant, while Reserve and Trend are no longer significant.

In the developing subsample without wage included, many variables also switch signs (RGDPpc, GDPG, Reserve, and Aggl). These coefficients tell a story similar to that for the full country sample results explained above. The negative Aggl coefficient demonstrates that China is seeking markets that are not already saturated with Chinese investment. In this sub-sample, many variables are no longer significant (GDP, Risk, Reserve, and Trend). As in our primary model results and our first sensitivity test, all of our focus countries have below-average country fixed effects, as shown in Table D4. This outcome means they receive less Chinese ODI than what the model would predict for an average country.

Table D4: ODI Sensitivity Analysis with ODI Flows as Dependent Variable, Country Fixed Effects

Full Sample Developing Sample



Bangladesh 4.25 0.34 4.12 0.38

Nepal 4.27 0.39 4.15 0.43

Sri Lanka 4.29 0.31 4.20 0.35

Fixed Effects Distribution

Mean Minimum Maximum

Full Sample 4.73 4.18 13.97

Developing Sample

4.62 3.75 14.59

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Recession Indicators

In Table D5 through Table D8 we display results from a sensitivity analysis using recession indicator variables.

Table D5: ODI Sensitivity Analysis: Full Sample with Recession Indicator Beginning in 2008

Estimate Standard Error T-Value

GDP -0.08 0.14 -0.58

RGDPpc 0.03 0.02 1.73 *

GDPG -0.002 0.01 -0.24

Raw -1.23e-04 1.88e-03 -0.07

Risk 0.10 0.07 1.46

XShare 0.14 0.31 0.46

Reserve -0.71 0.24 -2.90 ***

Aggl 10.14 3.52 2.88 ***

Trend 0.69 0.08 8.75 ***

Recession ’08-’11 -0.15 0.13 -1.11

R Squared 0.77

N=53 Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

Table D6: ODI Sensitivity Analysis: Developing Sample with Indicator Beginning in 2008


GDP 4.69 1.33 3.53 ***

RGDPpc 0.021 0.025 0.86

GDPG -9.40e-03 9.91e-03 -0.95

Raw -3.87e-04 1.84e-03 -0.21

Risk 0.11 0.07 1.69 *

XShare 0.12 0.30 0.41

Reserve -0.78 0.26 -2.95 ***

Aggl 7.46 3.48 2.15 **

Trend 0.72 0.08 8.51 ***

Recession ’08-’11 -0.11 0.14 -0.77

R Squared 0.78

N=43 Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively

43

Table D7: ODI Sensitivity Analysis: Full Sample with Recession Indicator Beginning in 2009


GDP -0.08 0.14 -0.55

RGDPpc 0.03 0.02 1.67 *

GDPG 4.52e-04 9.61e-03 0.05

Raw -2.67 e-04 1.88e-03 -0.14

Risk 0.10 0.07 1.45

XShare 0.12 0.31 0.39

Reserve -0.85 0.28 -3.01 ***

Aggl 10.08 3.53 2.86 ***

Trend 0.72 0.09 7.90 ***

Recession ’09-’11 -9.62e-03 0.14 -0.07

R Squared 0.77

N=53 Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

Table D8: ODI Sensitivity Analysis: Developing Sample with Recession Indicator Beginning in 2009


GDP 4.74 1.33 3.56 ***

RGDPpc 0.02 0.02 0.84

GDPG -8.03e-03 9.88e-03 -0.81

Raw -4.97e-04 1.84e-03 -0.27

Risk 0.11 0.07 1.69 *

XShare 0.10 0.30 0.34

Reserve -0.86 0.31 -2.79 ***

Aggl 7.36 3.48 2.12 **

Trend 0.74 0.10 7.52 ***

Recession ’09-’11 -0.03 0.15 -0.18

R Squared 0.78

N = 43 Note: ***, **, and * denote significance at the 1, 5, and 10% levels, respectively.

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Appendix E Big Projects Details

Table E1, Table E2, and Table E3 list some of the larger scale Chinese projects in Bangladesh, Nepal, and Sri Lanka, respectively. We express all monetary values in U.S. dollars, unless otherwise noted.

Table E1: Big Project Details: Bangladesh

Project Year Sector Amount and Type

Source

Chittagong Deep Seaport

2009 Transportation and Logistics

$8.7 billion in three phases

Devichand (2010)

Chittagong is next to Northeast India. China has expressed interest in building a road through Myanmar to connect China to the Bangladeshi city.

Sonadia Deep Seaport

n/a Transportation and Logistics

$5 billion proposed

BBC Monitoring South Asia, (2012, July 20)

This port is located on the Bay of Bengal and has been proposed as an alternative for large vessels too big to navigate the Karnaphuli River on which Chittagong is located.

Introduction of 3G and Expansion of 2G Network

2011 Information Technology

$211 million loan from Export-Import Bank of China

Ministry of Finance Bangladesh (2013)

Loans from the Export-Import Bank of China funds this project.

Fertilizer Factory in Shahjalal

2011 Agriculture $559 million loan People’s Daily (2011)

The Export-Import Bank of China funded this project using concessional loans an interest rate of 2 percent; it was contracted to a Chinese company

Sixth Friendship Bridge over Dhaleswari River

2008 Infrastructure $20 million XinhuaNews (2008)

This project is part of a series of “friendship bridges” built by the China Road and Bridge Corporation.

Seventh Bangladesh-China Friendship Bridge on Arial Khan River

2012 Infrastructure $26 million (2 billion taka)

BBC Monitoring South Asia (2012, January 3)

Bangabandhu International Conference Center

2008 Public Facility / Entertainment

$24 million in grants and interest-free loans

Huan (2010)

This project also goes by the name “Bangladesh-China Friendship Conference Center;” pre-fabricated materials from China used in construction.

Water Treatment Project

2012 Agriculture / Infrastructure

$300 million Financial Express (2012)

The Export-Import Bank of China financed this project with preferential buyer’s credit and concessional loans.

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Table E2: Big Project Details: Nepal

Project Year Sector Amount and Type

Source

750-Megawatt West Seti Hydropower Project

2011 Energy $1.6 billion loan

Unnithan (2011)

The state-owned China Three Gorges Corporation has expressed interest in building this dam.

Pokhara Regional International Airport


$305 million loan

MENAReport(2012)

The Chinese company CAMC constructed this project. The Export-Import Bank of China funded it using soft loans.

The Lumbini Project 2012 Public Facility / Entertainment

$3 billion Unnithan (2011)

This project involves a massive renovation of the Buddha’s birthplace.

Tatopani Dry Port 2012 Infrastructure $14 million BBC Monitoring South Asia (2012, December 20)

This project was contracted to China’s Beijing Real Estate Group Company.

Upper Trishuli 3A and 3B Hydroelectricity Project

n/a Energy $200 million BBC Monitoring South Asia (2012, September 10)

This project was contracted to China Gezhouba Group.

Upgrading Ring Road in Kathmandu

2012 Infrastructure $10 million (Nepalese rupee 547 million grant)

BBC Monitoring South Asia (2012, September 10)

This project was contracted to the Shanghai Construction Group Company.

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Table E3: Big Project Details: Sri Lanka

Project Year Sector Amount and Type Source

Matara-Beliatta Section of the Matara-Kataragama Railway Extension Project.

n/a Transportation and Logistics

$200 million in grants MENA Report (2013)

The Export-Import Bank of China financed this project with preferential buyer’s credit and concessional loans.

Norocholai Coal Power Plant

2007 Energy $450 million (first phase) and $891 million (second phase) in loans

Reuters (2012)

The Chinese company Northwest Electric Power Design Institute designed the power plant. Since opening it has faced repeated shutdowns due to technical failures. .

Hambantota Deep Seaport


$400 million (1st phase) & $810 (2nd phase) in loans

Aneez (2013)

This port is on strategic Asian and European sea lanes. The Export-Import Bank of China will provided $77 million for the seaport’s terminal. China Communications Construction Company built it. It is in President Rajapaksa’s hometown.

Mahinda Rajapaksa International Airport


$209 million in loans Aneez (2013)

Like the Hambantota Deep Seaport, this project is in President Rajapaksa’s hometown. It was contracted to China Harbour Engineering Company.

Mahinda Rajapaksa International (Cricket) Stadium

2010 Construction / Entertainment

$7.8 million in loans Xinhua News (2012)

China built cricket stadiums in Grenada and Dominica because they switched their allegiance from Taiwan to China. The Export-Import Bank of China financed the project, which was contracted to China Harbour Engineering Company and Sinohydro Corporation.

Nelum Pokuna (Lotus Pond) Mahinda Rajapaksa Theatre

2011 Public Facility / Entertainment

$27 million gifted Xinhua News(2011)

A Chinese company constructed this project with materials from China. The opening night’s performance was a Chinese opera.

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Appendix F Projecting Trends of Chinese ODI and Trade

This appendix projects Chinese ODI and exports to our focus countries. We use a linear trend based on recent data to predict values for ODI and exports through 2020. This procedure tells us whether China’s investment in and trade with these countries is likely to surpass India’s over this time period, if China continues on a similar trajectory. As a conservative comparison, we assume that India’s 2011 ODI stock and exports to a given country will remain constant. We then project India’s ODI stock and trade in a same manner as China’s to compare trajectories.

Trends in ODI

In the figures below we create lines of best fit using China’s ODI stock from 2003 to 2011 in Bangladesh, Nepal, and Sri Lanka. We then project for nine years to examine the likely trend of ODI stock through 2020. First, we show China’s trend compared to India’s ODI stock in 2011. Second, we project India’s ODI stock alongside the projected Chinese ODI trend.

In 2011, India had $167 million worth of ODI stock in Bangladesh. As shown in Figure F1, if China continues on its current trajectory it will not approach that amount until after 2020. Figure F2 shows both India and China’s projected ODI stocks in Bangladesh. It does not appear that China will come close to reaching India’s trajectory.

Figure F1: Chinese ODI Stock Trend in Bangladesh, 2003-2020

Data Sources: India Department of Economic Affairs (2013); China Commerce Yearbook (2010, 2012)

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Figure F2: Chinese and Indian ODI Stock Trend in Bangladesh, 2003-2020


In 2011, India had $104 million of ODI stock in Nepal. Figure F3 shows that if China continues its trajectory, Chinese ODI in Nepal will be less than half of India’s 2011 amount by 2020. Figure F4 shows India and China’s projected ODI stocks in Nepal. It does not appear that China will come close to reaching India’s trajectory.

Figure F3: Chinese ODI Stock Trend in Nepal, 2003-2020


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Figure F4: Chinese and Indian ODI Stock Trend in Nepal, 2003-2020


In 2011, India had $574 million of ODI stock in Sri Lanka. As shown in Figure F5, if China continues its trajectory, China’s ODI in Sri Lanka will not even reach half of India’s 2011 amount by 2020. Figure F6 shows India and China’s projected ODI stocks in Sri Lanka. It does not appear that China will come close to reaching India’s trajectory.

Figure F5: Chinese ODI Stock Trend in Sri Lanka, 2003-2020


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Figure F6: Chinese and Indian ODI Stock Trend in Sri Lanka, 2003-2020


Trends in Trade

In the figures below we create lines of best fit using Chinese exports from 2003 to 2011 to our focus countries. We then project for nine years to examine the likely export trend through 2020. First, we show China’s trend compared to India’s exports in 2011. Second, we project India’s exports alongside the projected Chinese export trend.

In 2011, India exported goods worth $3.4 billion to Bangladesh. Figure F7 shows that China was already exporting double India’s amount in 2011. Figure F8 shows India and China’s projected exports to Bangladesh.

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Figure F7: Trend of China’s Exports to Bangladesh, 2003-2020


Figure F8: China and India Export Trends to Bangladesh, 2003-2020


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As shown in Figure F9, in 2011, India exported goods worth $219 million to Bhutan. China has been well below this amount and the trend line shows that it will continue to be far below India’s exports through 2020. Figure F10 shows India and China’s projected exports to Bhutan. It does not appear that China will ever cross India’s trajectory.

Figure F9: Trend of China’s Exports to Bhutan, 2003-2020


Figure F10: China and India Export Trend to Bhutan, 2003-2020


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India exported goods worth $118 million to Maldives in 2011. Figure F11 shows that if China continues its export trajectory, it will surpass India’s 2011 amount in early 2016. Figure F12 shows India and China’s projected exports to Maldives. If both countries maintain the current trend, it does not appear that China will ever cross India’s trajectory. If we projected China’s trend line from only 2009 to 2011, China would surpass India in exports to Maldives much quicker, likely by the end of 2013.

Figure F11: Trend of China’s Exports to Maldives, 2003-2020


Figure F12: China and India Export Trend to Maldives, 2003-2020


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Figure F13 shows that in 2011, India exported goods worth $2.6 billion to Nepal. If Chinese exports to Nepal continue their trajectory, they will still be more than $500,000 below this amount in 2020. Figure F14 shows India and China’s projected exports to the Nepal. If both countries maintain the current trend, it does not appear that China will ever cross India’s trajectory. If we project the trend line from only 2009 to 2011, China would likely surpass India’s 2011 exports to Nepal by the end of 2014.

Figure F13: Trend of China’s Exports to Nepal, 2003-2020


Figure F14: China and India Export Trend to Nepal, 2003-2020


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In 2011, India exported goods worth $4.5 billion to Sri Lanka. Figure F15 demonstrates that if China follows its trend line, it will surpass this amount by 2019. If we projected the trend line from only 2009 to 2011, China would surpass India’s 2011 exports to Sri Lanka much quicker, likely by the end of 2013. Figure F16 shows India and China’s projected exports to the Sri Lanka. If both countries maintain the trend, it does not appear that China will ever cross India’s trajectory.

Figure F15: Trend of China’s Exports to Sri Lanka, 2003-2020


Figure F16: China and India Export Trend to Sri Lanka, 2003-2020


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