0Banking on the Belt and Road: Insightsfrom a new global dataset of 13,427
Chinese development projects
Ammar A. Malik, Bradley Parks, Brooke Russell, Joyce Jiahui Lin,
Katherine Walsh, Kyra Solomon, Sheng Zhang, Thai-Binh Elston, andSeth Goodman
AidData, William & Mary

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Abstract
Over the last two decades, China has provided record amounts of international development finance and establisheditself as a financier of first resort for many low- and middle-income countries (LMICs); however, its grant-giving andlending activities remain shrouded in secrecy. Our paper introduces a uniquely comprehensive and granular dataset ofinternational development finance from China. It captures 13,427 projects worth $843 billion across 165 countries inevery major world region over an 18-year period. Five key insights emerge from the dataset. First, we document an
extraordinary expansion in China’s overseas development finance program during the first two decades of the 21st
century. With annual international development finance commitments hovering around $85 billion a year, China now
outspends the U.S. and other major powers on a 2-to-1 basis or more. It is doing so with semi-concessional and nonconcessional debt rather than aid: since the introduction of the Belt and Road Initiative (BRI), China has maintained a
31-to-1 ratio of loans to grants and a 9-to-1 ratio of OOF to ODA. Second, China’s state-owned commercial banks haveassumed an increasingly important role during the BRI era by organizing lending syndicates and other co-financing
arrangements that make it possible to undertake bigger-ticket infrastructure projects. The number of “mega-projects”—financed with loans worth $500 million or more—being approved each year tripled during the first five years of BRI
implementation. Third, increasing levels of credit risk have created pressure for stronger repayment safeguards. Chiefamong these safeguards is collateralization, which has become the linchpin of China’s implementation of a high-risk,
high-reward credit allocation strategy. In the interest of securing energy and natural resources that it lacks in sufficientquantities at home and maximizing investment returns on surplus dollars and euros, China has rapidly scaled up theprovision of foreign currency-denominated loans to resource-rich countries that suffer from high levels of corruption.
These loans are collateralized against future commodity export receipts to minimize repayment and fiduciary risk andpriced at relatively high interest rates (nearly 6%). Fourth, although the implementation of the BRI has not prompted
any major changes to the sectoral or geographical composition of the country’s overseas development financeprogram, it has marked an important transition in how China bankrolls infrastructure projects. The majority of its
overseas lending was directed to sovereign borrowers (i.e., central government institutions) during the pre-BRI era, butnearly 70% is now directed to state-owned companies, state-owned banks, special purpose vehicles, joint ventures,and private sector institutions. These debts, for the most part, do not appear on government balance sheets in LMICs.
However, most of them benefit from explicit or implicit forms of host government liability protection, which has blurred
the distinction between private and public debt and introduced major public financial management challenges for
LMICs. We find that Chinese debt burdens are substantially larger than research institutions, credit rating agencies, orintergovernmental organizations with surveillance responsibilities previously understood: 42 LMICs now have levels ofdebt exposure to China in excess of 10% of GDP. These debts are systematically underreported to the World Bank’s
Debtor Reporting System (DRS) because, in many cases, central government institutions in LMICs are not the primaryborrowers responsible for repayment. We estimate that the average LMIC government is underreporting its actual andpotential repayment obligations to China by an amount that is equivalent to 5.8% of its GDP. Collectively, theseunderreported debts are worth approximately $385 billion. Fifth, we find that 35% of the BRI infrastructure project
portfolio has encountered major implementation problems—such as corruption scandals, labor violations,environmental hazards, and public protests—but the Chinese government’s infrastructure project portfolio outside of
the BRI has encountered fewer implementation problems. We also find that BRI infrastructure projects are less likely toface problems during implementation when they are undertaken by host country organizations (or organizations thatare neither from China nor host countries).

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Acknowledgments
We thank David Dollar, Sebastian Horn, Nadège Rolland, Anna Gelpern, Laura Metzger, Alex Wooley, and John Custer
for comments on an earlier version of this paper. We thank Parker Kim and Sarina Patterson from AidData for copyediting, formatting, and graphic design of the final publication. We also owe a debt of gratitude to the large team ofresearch assistants—including Aiden Daly, Alex McElya, Alyson Reynolds, Amelia Grossman, Andrew Brennan, Andrew
Tanner, Anna East, Annika Suri, Arushi Aggarwal, Bat-Enkh Baatarkhuu, Beneva Davies-Nyandebo, Carina Bilger, Carina
Rosenberg, Carlos Hoden-Villars, Caroline Duckworth, Caroline Morin, Catherine Pompe van Meerdervoort, Cathy
Zhao, Celeste Campos, Chifang Yao, Christian Moore, Claire Schlick, Claire Wyszynski, Connor Sughrue, Dan Vinton,
Eileen Dinn, Elizabeth Fix, Elizabeth Pokol, Emma Codd, Emmi Burke, Erica Stephan, Fathia Dawodu, Fei Wang,
Florence Noorinejad, Gabrielle Ramirez, Georgiana Reece, Grace Klopp, Haley VanOverbeck, Han (Harry) Lin, Hannah
Slevin, Harin Ok, Isabel Ahlers, Jason Bao, Jiaxin Tang, Jiaying Chen, Jim Lambert, Jingyang Wang, Jinyang Liu,
Joanne Lee, Johnny Willing, Jordan Metoyer, Juhwan Cho, Julia Tan, Julian Allison, Lukas Franz, Kacie Leidwinger,
Kathryn Yang, Kathryn Ziccarelli, Kathrynn Weilacher, Kieffer Gilman Strickland, Kiran Rachamallu, Leslie Davis, Linda
Ma, Lydia Vlasto, Marty Kibiswa, Mary Trotto, Mattis Boes, Maya Priestley, Mengting Lei, Mihika Singh, Natalie Larsen,
Natalie White, Olivia Le Menestrel, Olivia Yang, Paige Groome, Patrick Loeffler, Pooja Tanjore, Qier Tan, Raul De La
Guardia, Richard Robles, Rodrigo Arias, Rory Fedorochko, Ryan Harper, Sam LeBlanc, Sania Shahid, Sarah Farney,
Sariah Harmer, Sasan Faraj, Sebastian Ruiz, Shannon Dutchie, Shishuo Liu, Sihan (Brigham) Yang, Solange Umuhoza,
Taige Wang, Tasneem Tamanna Amin, Thai-Binh Elston, Undra Tsend, Victoria Haver, Wassim Mukayed, Wenye Qiu,
Wenzhi Pan, Williams Perez-Merida, Xiangdi William Wu, Xiaofan Han, Xinxin (Cynthia) Geng, Xinyao Wang, Xinyue
Pang, Xuejia (Stella) Tong, Xufeng Liu, Yannira Lopez Perez, Yian Zhou, Yiling (Elaine) Zhang, Yiwen Sun, Youjin Lee,
Yunhong Bao, Yunji Shi, Yunjie Zhang, Yuxin Qin, Yuxin (Susan) Shang, Zihui Tian, Ziqi Zheng, Ziyi (Zoey) Jin, and Ziyi Fu—who helped construct the dataset that we introduce in this paper. We are especially grateful to a group of staff andresearch associates—including Wenyang Pan, Molly Charles, Steven Pressendo, Paige Jacobson, Jack Mackey,
Samantha Rofman, David (Joey) Lindsay, John Jessen, Lincoln Zaleski, Mengfan Cheng, and Siddhartha Ghose—who
provided excellent project, team, and data management support.
This study was made possible through a cooperative agreement (AID-OAA-A-12-00096) between USAID's Global
Development Lab and AidData at William and Mary under the Higher Education Solutions Network (HESN) Program.
We also gratefully acknowledge financial support from the William and Flora Hewlett Foundation, the Ford Foundation,and the Smith Richardson Foundation. The views expressed here do not necessarily reflect the views of the William andFlora Hewlett Foundation, the Ford Foundation, the Smith Richardson Foundation, USAID, or the United States
Government. We also acknowledge that previous versions of the dataset introduced in this study would not have beenpossible without generous financial support from the John D. and Catherine T. MacArthur Foundation, Humanity
United, United Nations University-WIDER, the Academic Research Fund of Singapore’s Ministry of Education, and theGerman Research Foundation. AidData’s research is guided by the principles of independence, integrity, transparency,
and rigor. A diverse group of funders support AidData’s work, but they do not determine its research findings orrecommendations.
This study draws selectively upon evidence, argumentation, and prose from our joint work with Axel Dreher, Andreas
Fuchs, Austin Strange, and Michael Tierney for a new book publication entitled Banking on Beijing: The Aims andImpacts of China’s Overseas Development Program (Dreher et al. forthcoming). Over the last ten years, an even larger
group of coauthors and collaborators—including Ariel BenYishay, Samantha Custer, Sebastian Horn, Anna Gelpern,
Christoph Trebesch, Scott Morris, Alysha Gardner, Richard Bluhm, Paul Raschky, Roland Hodler, Daniel Nielson, Daniel
Runfola, Rachel Trichler, Lukas Wellner, and Edwin Muchapondwa—has helped refine and expand the dataset uponwhich this study is based by piloting coding procedures, recommending new sources and methods, and scrutinizing
preliminary project records.

Citation
Malik, A., Parks, B., Russell, B., Lin, J., Walsh, K., Solomon, K., Zhang, S., Elston, T., and S. Goodman. (2021). Banking
on the Belt and Road: Insights from a new global dataset of 13,427 Chinese development projects. Williamsburg, VA:
AidData at William & Mary.

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1. Introduction
After eight years of implementation, China’s Belt and Road Initiative (BRI) stands at a crossroads. With 140 participating
countries, nearly a thousand projects underway, and hundreds of billions of dollars committed, the ambition of Beijing’s
flagship infrastructure program is extraordinary and without precedent. However, it is also facing major implementation
challenges on the ground in host countries. Xi Jinping has described the BRI as the “project of the century” and “aroad for peace, prosperity, opening-up, and innovation, connecting different civilizations” that will “build a broad
community of shared interests.” He has also framed it as “a new option for other countries and nations who want tospeed up their development while preserving their independence.”1 Beijing’s narrative of “South-South cooperation”
emphasizes solidarity with the Global South and the opportunity for low- and middle-income countries (LMICs) to
pursue an alternative model of development that is free from the dictates of foreign powers. As the world’s largest
developing country, China describes participation in the BRI as an opportunity for other countries in the Global Southto learn from its experience with pulling hundreds of millions of people out of poverty. In juxtaposition to a Westernmodel of development that has traditionally emphasized “software” investments and reforming policies andinstitutions, Beijing has put forward a model that focuses on “hardware” investments to ease infrastructure bottlenecks,
crowd-in private capital, and drive sustainable and equitable economic growth.2
When China initially launched the BRI in 2013, countries from every corner of the globe were eager to participate.
However, with the passage of time, enthusiasm has waned. There is a growing appreciation for the fact that, while
Chinese infrastructure projects often generate short-term economic benefits, their long-term risks need to be carefully
managed.3 Many foreign leaders continue to lavish praise upon Beijing for addressing unmet infrastructure needs (lestthey alienate a uniquely important patron), but China is facing “BRI backlash” in a growing number of countries across
Africa, Asia, Latin America, and Central and Eastern Europe.4 Some LMIC policymakers have cancelled or mothballed
high-profile BRI projects because major changes in public sentiment have made it difficult to maintain close relations
with China. Others, with concerns of their own, have decided to take a second look at whether the benefits of BRI
participation outweigh the risks. The COVID-19 pandemic has also put Beijing on its back foot, as many BRI projects
have encountered implementation challenges and a growing number of borrowers have struggled to repay theirChinese debts.5
China’s political leadership is seemingly aware of the fact that it will need to address the concerns of host countries tosustain elite and public support for its flagship infrastructure initiative. One of the first signs that Beijing knew that acourse correction was necessary came in September 2018 when Xi Jinping announced that BRI funds were “not to be
spent on any vanity projects but in places where they count the most.”6 Around the same time, the Chinese authorities
recalibrated their public messaging, emphasizing that the BRI of the future would be “cleaner” and “greener.”
However, strategists in Beijing are still grappling with the question of how to take the global connectivity initiative
forward. They could: (1) try and win to over general public in BRI participant countries and hope that their efforts will
result in electoral success of pro-Beijing political parties; (2) curry favor with incumbent leaders and bank on theirloyalty to Beijing and political survival; or (3) take an entirely different tack and “multilateralize” the BRI by co-financing,

1 President Xi Jinping delivered this speech at The Belt and Road Forum for International Cooperation in 2017. See http://

www.xinhuanet.com/english/2017-05/14/c_136282982.htm.
2 Another important feature of China’s model is its “portfolio approach.” Chin and Gallagher (2019: 256) explain that “[w]hereas

Western-backed [development finance institutions] and [multilateral development banks] conduct individual project financing, China’s
policy banks, at home and abroad, take a ... portfolio approach and finance what they refer to as ‘strategic credit spaces’ where
bundles of loans or lines of credit are issued for an array of coordinated and corresponding projects.” Coordinated public investment
strategies have a rich intellectual history related to “big push” theory (Rosenstein-Rodan 1943) and “growth pole” theory (Perroux
1950; Hirschman 1958). According to Chin and Gallagher (2019: 251), “[s]ome in the senior ranks of the Chinese state policy banks
have drawn inspiration from [“big push” theory], including the former chief economist at the China Development Bank [CDB], Lixing
Zou, who saw CDB as having played such a coordinating role within the Chinese growth miracle.”
3 For studies that measure the socioeconomic impacts of Chinese development projects, see Bluhm et al. (2018), Martorano et al.

(2020), and Dreher et al (2021a, 2021b, forthcoming). For studies that measure the negative, unintended impacts of Chinese
development projects, see Brazys et al (2017), Dreher et al. (2019, forthcoming), Isaksson and Kotsadam (2018a, 2018b), Isaksson(2020), and Iacoella et al. (2021).
4 See Aamir (2018), Balding (2018), Mundy and Hille (2019), Rolland (2019), and Parks (2019).
5 At least 28 countries that have recently sought or secured debt relief from their official creditors in China. See http://www.sais-cari.org/debt-relief
6 This quote is drawn from Shepherd and Blanchard (2018).

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co-designing, and co-implementing infrastructure projects with OECD-DAC and multilateral development financeinstitutions and subjecting these projects to international standards and safeguards.7
Until recently, Beijing did not have much reason to worry about competition in the global infrastructure finance market.
However, with new leadership in White House and strong bipartisan support in Congress, the U.S. government and its
allies in the industrialized world are seeking to develop a viable alternative to the BRI. Under the auspices of the Build
Back Better World (B3W) initiative that the G7 announced in June 2021, an effort is now underway to create a separate,
global infrastructure initiative that is guided by the principles of sustainable and transparent financing, good
governance, public sector mobilization of private capital, consultation and partnership with local communities, andstrict adherence to social and environmental safeguards.8
For countries in the developing world that face multi-billion-dollar deficits and fiscal crises due to pandemic-induced
revenue shortfalls, strategic competition between China and the G7 could create an extraordinary opportunity toaddress unmet financing needs in several key infrastructure sectors. However, it is unlikely that the benefits ofparticipation in B3W and BRI will accrue evenly across LMICs. Countries with weak environmental standards, labor
protections, and anti-corruption institutions are probably less well-positioned to benefit from a “golden era” of global
infrastructure financing.9
In this pivotal moment, it is more important than ever that G7, Chinese, and host country policymakers base theirdecisions upon hard evidence rather than opinions and conjecture. AidData’s team of more than 135 faculty,
professional staff, and research assistants has sought to establish a stronger evidentiary foundation for analysis anddecision-making by painstakingly assembling a comprehensive dataset of Chinese government-financed development
projects. The purpose of this paper is to introduce AidData’s Global Chinese Development Finance Dataset (Version2.0) and highlight the ways that it can be put to productive use.
Section 2 provides an overview of the dataset’s most important features and how it has evolved over time toaccommodate changes in the nature, scale, composition, and geographical distribution of Chinese development
finance that have taken place since the BRI was introduced. In Section 3 of the paper, we use the 2.0 dataset to answer
the following questions:●

What is the true scale, scope, and composition of China’s overseas development finance program? Whichcountries and sectors has Beijing prioritized? Are Chinese development projects primarily financed with aid(ODA) or debt (OOF)? How does the concessionality of China’s overseas lending compare to theconcessionality of lending from other official creditors?

●

How has the BRI altered China’s overseas development finance program? How much financing and what typesof financing does Beijing provide to BRI participants? What does Chinese development finance look like incountries that opt out of BRI?

●

What are the specific terms and conditions that govern official sector loans from China? How do Chinese stateowned lenders minimize repayment risk? How do they balance risk and reward?

Then, in Section 4, we use the 2.0 dataset to answer a set of questions about Chinese debt:●

How much debt have LMIC governments accumulated from Chinese state-owned lenders? What is the scale ofthe so-called “hidden debt” problem? Which countries have the highest levels of sovereign debt exposure andhidden debt exposure to China?

7 One of the first signs that China was considering a course correction came when Xi Jinping announced in 2017 that “[w]e will [...]

strengthen international cooperation on anticorruption in order to build the Belt and Road Initiative with integrity” (quoted in AbiHabib 2018). Then, in 2018, he gave a speech at the Second Belt and Road Forum for International Cooperation where he announced
that China would “adopt widely accepted rules and standards and encourage participating companies to follow general internationalrules and standards in project development, operation, procurement and tendering and bidding (Xi 2019)
8 B3W will also focus specifically on infrastructure investments that support climate change mitigation and adaptation, global health,

digital technology, and gender equity and equality. See https://www.whitehouse.gov/briefing-room/statements-releases/2021/06/12/
fact-sheet-president-biden-and-g7-leaders-launch-build-back-better-world-b3w-partnership/
9 Such countries will most likely find it difficult to participate in B3W. They may not have difficulty participating in the BRI but

evaluating whether the rewards of BRI participation outweigh the risks will likely be a significant challenge. While some LMIC
government agencies lack the capacity to evaluate these risks, others have incentives to maximize short-term rewards and discountlong-run risks.

5●

Are LMIC governments fully disclosing their actual and potential repayment obligations to China via official
reporting systems—like the World Bank’s Debtor Reporting System (DRS)? If not, why not?

●When and how do Chinese state-owned lenders provide debt relief to borrowers who cannot meet their
repayment obligations?

In Section 5, we use the 2.0 dataset to answer a set of questions about the implementation of BRI infrastructure
projects:●

What types of problems do BRI infrastructure projects most frequently—and least frequently—encounter
during implementation? Corruption scandals, labor strikes, environmental disasters, defaults, bankruptcies,
public protests?

●

How are BRI infrastructure projects different from other Chinese government-financed infrastructure projects?
How do these two types of projects fare during implementation?

●

When Chinese state-owned lenders enter into co-financing arrangements, do BRI infrastructure projects
perform any differently?

●

Which organizations are responsible for implementing BRI infrastructure projects? Do these projects fare any
better or worse during implementation when they are undertaken exclusively by host country organizations?
What if they are jointly implemented by Chinese organizations and host country organizations? Whichorganizational characteristics correlate with better project performance?

Finally, in Section 6, we conclude by highlighting several new and improved features of the 2.0 dataset that may beparticularly useful to analysts and decision-makers.

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2. AidData’s Global Chinese Development Finance
Dataset, Version 2.0
2.1 The evolution of AidData's research program on Chinese
development finance since 2013
Over the last two decades, China has provided record amounts of international development finance and establisheditself as a financier of first resort for many low-income and middle-income countries (LMICs); however, its grant-giving
and lending activities remain shrouded in secrecy. Beijing’s reluctance to disclose detailed information about its
overseas development finance portfolio has made it difficult for LMICs to objectively weigh the costs and benefits of
BRI participation. This informational void has also made it difficult for bilateral aid agencies and multilateraldevelopment banks to determine how they can confront and compete—or coordinate and collaborate—with Chinese
government institutions. As the G7 rolls out the B3W initiative, access to timely, reliable, and comprehensive
information about Chinese development projects will be essential for evidence-based decision making.
Since 2013, AidData has led a far-reaching effort to shed light on official financial transfers from China by developing atransparent, systematic, and replicable methodology—called Tracking Underreported Financial Flows (TUFF)—that
enables the collection of detailed financial, operational, and locational information about Chinese governmentfinanced development projects. The application of the TUFF methodology initially resulted in several region-specificdatasets, which were published between 2013 and 2017 (Muchapondwa et al. 2016; BenYishay et al. 2016; Strange etal. 2017; Bluhm et al. 2018; Dreher et al. 2018, 2019).10 Then, in October 2017, AidData released the first global
version of its dataset which captured 4,300 projects worth $350 billion across 138 countries (Dreher et al. 2021,
forthcoming).11 The earlier versions of this dataset were used in over 250 research publications that were cited over
4,000 times. It was also used more than 450 times in international media outlets like the Financial Times, the New YorkTimes, the Wall Street Journal, the Washington Post, the Economist, and BBC World Service. The China-specificsections of AidData’s website, featuring thousands of project webpages, have now been viewed over 1.3 million times
by visitors from more than 180 countries, which reflects the high level of demand that exists for reliable information
about China’s overseas development program.

2.2 Key features of AidData’s Global Chinese Development
Finance Dataset, Version 2.0
AidData’s objective is to facilitate rigorous analysis and support evidence-based decision-making by providing
comprehensive, reliable, and granular data on China’s overseas development finance program. To this end, we have reengineered the TUFF methodology to make more extensive use of official sources, such as grant and loan agreements
published in government registers and gazettes, official records extracted from the aid and debt information
management systems of host countries, annual reports published by Chinese state-owned banks, Chinese Embassyand Ministry of Commerce websites, reports published by parliamentary oversight institutions in host countries, andour own direct correspondence with finance ministry officials in developing countries.12 The latest (2.0) version of ourdataset captures 13,427 projects in 145 countries supported by financial and in-kind transfers from official sector
institutions in China It covers every low-income, lower-middle income, and upper-middle income country and territory
across every major world region, including Africa, Asia, Oceania, the Middle East, Latin America and the Caribbean,
and Central and Eastern Europe (including 20 additional countries where systematic searches were undertaken but no

10 Version 1.1 of AidData's Chinese Official Finance to Africa Dataset was released in April 2013. Version 1.2 of AidData's Chinese

Official Finance to Africa Dataset was released in September 2015. A subnationally georeferenced iteration of Version 1.2 wasreleased in January 2017. At the same time, we released a subnationally georeferenced dataset of Chinese development projects in
the Tropical Andes of South America, the Great Lakes of Africa, and the Mekong Delta of Southeast Asia.
11 At the time of its release, AidData referred to this dataset as the Global Chinese Official Finance Dataset (Version 1.0). We will refer

to future versions of the dataset as AidData’s Global Chinese Development Finance Dataset.
12 Custer et al. (2021)

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Chinese government-financed projects were identified).13 With respect to temporal coverage, the dataset tracks
projects over eighteen commitment years (2000-2017), with details on the timing of project implementation over a 22year period (2000-2021).14 10,849 of the 13,427 records in the dataset are formally approved, active, and completed
projects. These projects are collectively worth $843.1 billion. The remaining 2,577 projects in the 2.0 dataset are (i)projects that secured official financial or in-kind commitments from China but were subsequently suspended orcancelled, (ii) projects that secured pledges of financial or in-kind support from official sector institutions in China butnever reached the formal approval (official commitment) stage; and (iii) so-called “umbrella” projects that are designed
to support multiple, subsidiary projects.15
One of the most important features of the dataset is its comprehensive scope. It covers all regions, all sectors, and all
sources and types of financial and in-kind transfers from government and state-owned institutions in China. There areother datasets that capture official financial transfers from China to a single sector (e.g., energy) or region (e.g., Latin
America), or that only track certain types of financial flows (e.g. loans) and funding sources (e.g. China’s policy banks).16
However, 2.0 version of AidData's dataset is unique in that it captures the full range of projects that align with theOECD definitions of Official Development Assistance (ODA) and Other Official Flows (OOF). Any project that benefits
from financial or in-kind support from any official sector institution in China is included. The projects in the 2.0 dataset
are supported by 334 unique official sector institutions in China, including central government agencies (like the
Ministry of Commerce, the Ministry of Foreign Affairs, and the Ministry of Agriculture), regional and local government
agencies (like Chongqing Municipal Health Commission and Tianjin Municipal Government), state-owned enterprises(like China National Petroleum Corporation, China National Aero-Technology Import & Export Corporation, and China
Machinery Engineering Corporation), state-owned policy banks (like China Development Bank and China Eximbank),state-owned commercial banks (like Bank of China, China Construction Bank, and the Industrial and Commercial Bankof China), state-owned funds (like the Silk Road Fund), and non-profit government organizations (like Hanban and the
China Foundation for Poverty Alleviation). For a comparison of the 2.0 dataset to other Chinese development financedatasets, see Table A-1 in the Appendix.
The scale, diversity, quality, and transparency of the sourcing that underpins the 2.0 dataset also sets it apart fromothers. We assembled the dataset with 91,356 sources (including 63,464 unique sources in more than a dozen
languages, of which 34,075 are official sources).17 Whereas the average project record in the 1.0 dataset was based
upon 3.6 sources, the average project record in the 2.0 dataset is based upon 6.8 sources. Perhaps most importantly,
89% of the project records in the 2.0 version of our dataset are underpinned by at least 1 official source (compared to
62% of the project records in the 1.0 dataset). Another new feature of the 2.0 dataset is the inclusion of a “staff
comments” field in which we identify the assumptions, logic, and evidence that we used to address challenging coding
and categorization determinations. In the interest of exposing our coding and categorization determinations to public

We used the 2.0 version of the TUFF methodology to systematically search for projects supported by official financial
and in-kind transfers from China across 165 countries and territories. We identified Chinese government-financed
projects in 145 countries and territories and no such projects in 20 countries and territories. For more details, see Table
A-2 in the Appendix. The dataset provides comprehensive coverage across low-income, lower-middle income, andupper-middle income countries and territories. 11 high-income countries and territories are also included in the 2.0
dataset to help ensure comprehensive coverage in every major world region to the extent possible.
13

14 The 2.0 dataset includes three different “year” variables: a project’s official commitment year, its implementation start year, and its

completion year. As such, it captures variation in project implementation over a twenty-one-year period (2000-2021).
15 For analysis that requires the aggregation of projects supported by official financial and in-kind commitments from China, werecommend using the Recommended_for_Aggregates marker variable to isolate formally approved, active, and completed projects(and avoid double-counting projects and monetary amounts). For analysis of suspended projects, cancelled projects, and projects
backed by informal pledges, we do not recommend using the Recommended_for_Aggregates filter.
16 For a comparison of the 2.0 dataset to other Chinese development finance datasets, see Table A-1 in the Appendix.
17 In many cases, official sources provide information about multiple projects, which is one of the main reasons why the total numberof unique sources is 63,464 but the total number of sources is 91,356. The 2.0 version of AidData’s Global Chinese Development
Finance Dataset relies primarily upon Chinese-, English-, Spanish-, French-, Portuguese-, Russian-, and Arabic-language sources.
However, for certain countries, it uses other local language sources (e.g., Farsi sources in Iran, Dutch sources in Suriname, Vietnamesesources in Vietnam).

8
scrutiny and promoting replicable research findings, we disclose all the sources that were used to construct the dataset
at the project level.18
These sourcing improvements have enabled AidData to (1) capture financial and in-kind transfers from an expanded
pool of official donors and lenders, (2) document more of the terms and conditions that govern financial agreementswith LMICs, (3) track the implementation of projects over time and geographic space with higher levels of
measurement precision, and (4) construct detailed narrative descriptions that explain how Chinese development
projects are being designed, implemented, monitored, and evaluated in practice. The 2.0 dataset includes 70 variables
and fields, which not only provide granular details about individual projects but also make it possible to compare
Chinese development finance with other sources of development finance, including members of the OECD-DAC andmultilateral institutions. These variables and fields fall into several categories:●

Basic Project Information: The 2.0 dataset provides foundational information about each project, including its
title in English, Chinese, and host country languages, a unique and stable project identification number, thedate of the official commitment, the monetary value of the official commitment, the currency in which theofficial commitment was denominated, the identity of the funder and recipient, the primary purpose of the
project, and URLs for all of the sources that supported the creation of the project record.19●

Transactional Details: The 2.0 dataset identifies the nature of the financial or in-kind transfer (e.g., grant, loan,
technical assistance, export buyer’s credit, export seller’s credit, supplier’s credit, debt forgiveness, debt
rescheduling, scholarship/training) supporting each project in the dataset. Whenever applicable, it documents
loan and export credit pricing details (interest rate, maturity, grace period, management fee, commitmentfee); levels of financial concessionality, as measured by the OECD-DAC’s grant element calculator; themonetary value and timing of disbursements and repayments; the use of credit enhancements, including
guarantees, insurance, and collateral; the establishment of special purpose vehicles, subsidiary on-lending
arrangements, and escrow/proceeds/revenue/special accounts; and the monetary value and timing ofunderlying commercial contracts. The dataset also provides stable URLs to a large number of unredacted
grant, loan, export credit, debt forgiveness, and debt rescheduling agreements whenever they have beensuccessfully retrieved. AidData published a subset of these financing agreements at the time of the release of
the “How China Lends” study (Gelpern et al. 2021). However, the 2.0 dataset provides the full set ofagreements retrieved by AidData.

●

Development Finance and Sector Categorization: To facilitate comparisons to other international sources ofdevelopment finance, the 2.0 dataset categorizes projects as Official Development Assistance (ODA) or Other
Official Flows (OOF) based on measurement of the project’s primary intent and the concessionality level of thefinancing provided for the project (see Box 2 for more details). AidData adheres to OECD-DAC reporting
directives that define specific eligibility criteria for ODA and OOF. The 2.0 dataset also assigns 3-digit OECDsector codes and names to all projects using OECD-DAC classification criteria, which again enables
comparisons to other bilateral and multilateral sources of international development finance that use the same
criteria. In a new book entitled Banking on Beijing: The Aims and Impacts of China's Overseas Development
Program, our longtime collaborators from Heidelberg University, the University of Göttingen, the University ofHong Kong, and William & Mary argue that the failure to use common definitions and consistent

18 To provide users with an objective way to evaluate the sourcing that underpins individual project records, we also provide two

project-level metrics. The Source Quality metric varies on a scale of 1 to 5, with 1 indicating exclusive reliance upon unofficial sources(e.g., media reports) and 5 indicating reliance upon multiple, official sources. The Data Completeness metric varies on a scale of 1 to
5, with 5 indicating that the foundational fields of the project record are complete. The “threshold” for a score of 5 is similar to key
fields from OECD Official Finance data (firm commitment year, transaction amount, flow type/flow class, funding agency, andimplementing or receiving agency). For more information on the criteria used to construct these metrics, see Custer et al. (2021).
19 AidData’s 2.0 dataset is different from other Chinese development finance datasets in that it measures financial commitments inconstant (i.e., inflation-adjusted) U.S. dollars (USD). It does so by capturing official financial commitments in their original currencies ofdenomination, converting these financial amounts into nominal USD values at the average exchange rates that were in effect during
the commitment years, and subsequently converting the nominal USD values to constant 2017 USD values using the OECD’s deflation
methodology (to adjust for inflation and ensure comparability over time and geographic space). For a broader comparison of the 2.0
dataset to other Chinese development finance datasets, see Table A-1 in the Appendix.

9
measurements of international development finance has led to misleading and inaccurate comparisons, whichhave confused rather than informed decision-makers (Dreher et al. forthcoming).20●

Stakeholder Organizations: The 2.0 dataset provides detailed information about the organizations that are
involved in Chinese government-financed development projects. Specifically, it provides information about
five different types of organizations for each project: (1) the official sector institution in China that is
responsible for providing funding and/or in-kind support for the project; (2) the co-financing institutions frominside and outside of China that are supporting the same project; (3) the recipient institutions that are
responsible for managing incoming funds and in-kind transfers; (4) the contractors and subcontractors that are
responsible for project implementation, and (5) the third-parties that provide repayment guarantees, credit
insurance policies, and collateral which can be seized in the event of default. Each of these organizations iscategorized by type (i.e., Government Agency, State-Owned Bank, State-Owned Company, State-Owned
Fund, Intergovernmental Organization, Special Purpose Vehicle/Joint Venture, Private Sector, NGO/CSO/
Foundation) and country of origin (i.e., China, Recipient Country, or Other).21 The dataset identifies 334 official
sector institutions in China that provide funding and/or in-kind support, 460 co-financing institutions (some ofwhich are traditional bilateral and multilateral agencies that have chosen to participate in syndicated loans orother types of consortia with Chinese counterparts), 2,450 recipient institutions, 3,523 implementing
institutions, and 227 third-parties (“accountable agencies”) that provide repayment guarantees, credit
insurance policies, and collateral which can be seized in the event of default.

●

Spatio-Temporal Rollout of Project Implementation: The 2.0 dataset provides an unprecedented level ofdetail on project implementation start dates, project completion dates, and the precise geographical locations
where projects take place. AidData was able to identify precise implementation start dates for 5,539 projects
and precise project completion dates for 6,061 projects. The 2.0 dataset also provides data on the originallyscheduled project implementation start dates and completion dates, so that users can determine if projects
have been implemented on schedule, behind schedule, or ahead of schedule. Additionally, for 3,285 projects
that have physical footprints or involve specific locations, the 2.0 dataset extracts point, polygon, and line
vector data via OpenStreetMap URLs and provides a corresponding set of GeoJSON files. In Section 6 of thispaper, we explain how these data on the spatio-temporal rollout of project implementation can be used tomeasure the intended and unintended impacts of Chinese development projects.

●

Project Risks, Setbacks, Achievements, and Failures: The 2.0 dataset provides a suite of variables and fields
that allow users to track projects over their full life cycles. Each project is assigned to one of six “Status”
categories: Pipeline: Pledge, Pipeline: Commitment, Implementation, Completed, Suspended, or Cancelled.22
The 2.0 dataset also provides detailed project narratives that “tell the story” of each project (in the“Description” field). The narratives document the various risks and challenges that arose during project design
and implementation (e.g., protests, scandals, and public health restrictions) and how funding, receiving,
implementing, and accountable institutions responded to these risks and challenges. They also capture project
achievements and failures, contractor performance vis-à-vis deadlines and deliverables, and findings fromproject audits and evaluations. The average length of each project narrative in the 2.0 dataset is 144 words,

20 The definition and measurement of “aid” is a case in point. There is an internationally accepted definition and standard formeasuring aid that is called Official Development Assistance (ODA). However, to the best of our knowledge, there is no organization
other than AidData that uses the formal definition of ODA to determine which Chinese government-financed activities qualify as aid in
the strict sense of the term. Consequently, some analysts claim that China’s aid giving rivals that of other major donors, while others
claim that China is a relatively small player in the aid market. These disagreements are rooted in basic definitional and methodologicaldifferences (Strange et al. 2017; Dreher et al. 2018, forthcoming)
21 Over half (58%) of the implementing institutions in the 2.0 dataset are from China, with another 35% from host countries. The

remaining 8% consists of organizations outside China and the host country (e.g., intergovernmental organizations).
22 A project assigned to the “Pipeline: Pledge” category is one that an official sector institution in China indicated it was interested insupporting (or willing to consider supporting) but did not result in an official commitment. Projects assigned to this category include
those that are identified in letters of intent, term sheets, memoranda of understanding, and non-binding announcements. All projects
given a status designation of Pipeline: Commitment, Implementation, Completed, Suspended, or Cancelled reached the official
commitment stage (i.e., a binding, written agreement that governs the provision of financial or in-kind support for a specific project/purpose was signed by an official sector donor or lender in China and an entity in a recipient country). A project assigned to the“Pipeline: Commitment” category is one that is backed by an official commitment but has not yet entered implementation. A project
assigned to the “Implementation” category is one that is backed by an official commitment and has begun implementation withfinancial or in-kind support from the source of the commitment. A project assigned to the “Completion” category is one that isbacked by an official commitment and that reached completion with financial or in-kind support from the sources of the commitment.
Projects assigned to the “Suspended” and “Cancelled” categories are those that were backed by an official commitment but
subsequently suspended or cancelled. The coding of the “Status” field in the 2.0 dataset is based on sources that were available aslate as August 2021.

10
and when all the narratives are stitched together, they are as long as 19 full-length books (1.93 million words).23
Additionally, AidData has created individual webpages (with stable URLs) for each project—and projectnarrative—on the aiddata.org website.
In the next section of this paper, we use the 2.0 dataset to explore the nature, scale, and geographical distribution ofChina’s overseas development finance program. We also explore whether the rollout of the BRI has coincided with any
major changes in China’s overseas development finance program.

23 A typical, full-length book includes 100,000 words.

11

3. The changing nature, scale, composition, anddistribution of Beijing’s overseas development
finance program
3.1 How much is China spending—and where is the money going?
Figure 1 demonstrates that, during the first thirteen years of the 21st century (2000-2012), China and the United States
were overseas spending rivals: average annual development finance commitments from China amounted to $32 billion
and average annual development finance commitments from the U.S. were roughly on par (nearly $34 billion).
However, during the first five years of BRI implementation (2013-2017), China’s overseas development finance program
underwent a dramatic expansion, with Beijing outspending Washington on a more than two-to-one basis (see Figure2).24 China spent $85.4 billion a year (on average) and the U.S. spent $37 billion a year (on average) during thisperiod.25

Figure 1: International development finance from China and the G7, 2000-2012$500

USD 2017 billions

$375

$250

$125

$0

United States

China
ODA

Japan

Germany
OOF

FranceCanada United Kingdom Italy
Vague

Source: OECD-DAC and AidData.

24 By the fourth year (2016) of BRI implementation, China was outspending the U.S. on a 3.6-to-one basis (see Figure 3, below).
25 These estimates are based on Figure 2.

12Figure 2: International development finance from China and the G7, 2013-2017$500

USD 2017 billions

$375

$250

$125

$0

China

United States
ODA

Japan

Germany
OOF

UnitedKingdom

FranceItaly

CanadaVague

Source: OECD-DAC and AidData.
The 2.0 dataset also reveals that Beijing and Washington are delivering very different types of overseas development
finance (see Figure 3). Whereas the U.S. provided 73% of its international development finance via grants and highly
concessional loans (or “ODA” as defined by the OECD-DAC and described in Box 1) between 2000 and 2017, China
only provided only 12% of its international development finance via ODA during the same period. Beijing financed thelion’s share (81%) of its international development finance portfolio with semi-concessional and non-concessional loans
and export credits (or “OOF” as defined by the OECD-DAC and described in Box 1).26 Figure 4 also demonstrates thatthe overseas spending behavior of other members of the G7 (Canada, France, Germany, Italy, Japan, the United
Kingdom) more closely resembles that of the U.S. than China: most of their international development financecommitments were provided as ODA rather than OOF between 2000 and 2017.27

26 While 81% of China’s overseas development finance portfolio was provided via OOF-like commitments and 12% was provided via

ODA-like commitments between 2000 and 2017, the remaining 7% consisted of “Vague (Official Finance)” commitments that werebacked by an official commitment from China but could not be reliably categorized as ODA or OOF because of insufficiently detailed
information. Projects in this residual category primarily consist of (a) those with an unspecified “Flow Type” (i.e., values of “Vague
TBD”); and (b) those financed with loans for which AidData lacks the borrowing terms (interest rates, grace periods, or maturity dates)
needed to make financial concessionality determinations.
27 However, there is significant variation within the G7. According to Figure 4, between 2000 and 2017, more than 73% of official

financing from the U.S., 90% of official financing from the United Kingdom, and 87% of official financing from France was provided via
ODA. The composition of official financing was more evenly distributed between ODA and OOF in Germany, Japan, Canada, andItaly during this period.

13

Figure 3: Official Development Assistance (ODA) and Other Official Flows (OOF) from the
U.S. and China, 2000-2017

$120

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
OOF from US
ODA from US

Vague (OF) from China
OOF from China
ODA from China

Source: OECD-DAC and AidData.

Figure 4: China’s ODA and OOF portfolio compared to G7 countries, 2000-2017$900

USD 2017 billions

$675

$450

$225

$0

China

United States
ODA

Source: OECD-DAC and AidData.

Japan

Germany
OOF

France United Kingdom Canada
Vague

Italy

14

Box 1:
How
AidDatameasuresfinancialconcessionality
and project
intentintent
Box
1: How
AidDatameasuresfinancialconcessionality
and projectAs partAsofpartits data
and classification
system,AidDatadesignates
each projectas Official
Developmentof itscollection
data collection
and classification
system,AidDatadesignates
each projectas Official
Assistance
(ODA)
or
Other
Official
Flows
(OOF)
based
on
measurement
of
(a)
the
concessionality
level
Development Assistance (ODA) or Other Official Flows (OOF) based on measurement of (a) the of the financing
provided
for a project,leveland of(b)thethefinancing
intent of the
project.
is a uniquefeatureof theof2.0
concessionality
providedforThisa project,and (b)the intent
thedataset
project.that
Thissets
is ait apart fromother publicly
availablethat measure
Chinesefinanceavailable(see Table
A-1 Figurein theunique featureof datasetsthe 2.0 datasetthat sets
it apartdevelopmentfrom other publicly
datasetsthat A5-1}}
measureAppendix).
Itisespecially
usefulfor
analysts
andpolicymakers
who
wish
to
compareorbenchmarkChineseChinese development finance (see Table A-1 in the Appendix). It is especially useful for analysts anddevelopment
financeagainstinternationalsources Chineseof development
finance.
policymakers
whowish otherto compare
or benchmark
development
finance against otherinternational sources of development finance.
For loans and export credits, AidData uses the OECD-DAC concessionality calculator to determine the “grantelement”
the andfinancialtransfer.
Thismeasure,varies from concessionality
0 percent to 100
percent, tocapturesthe generosityofFor ofloans
exportcredits,
AidDatauseswhich
the OECD-DAC
calculator
determinethe28 Loans and export credits provided ona loan/export
creditortheextent
to
which
it
is
pricedbelow-market
rates.
“grant element” of the financial transfer. This measure, which varies from 0 percent to 100 percent,marketcaptures
terms have
grant elementof 0 percent.
Pureorgrantshave atograntelementof 100percent. To rates.
calculatethe28
the agenerosityof a loan/export
creditthe extent
which
it is priced
below-market
grant elementof exporta loan/export
thatonis provided
on below-market(concessional)
terms, one
calculate
Loans andcredits credit
providedmarket terms
have a grant elementof 0 percent.
Pureneeds
grantstohavea29
the discounted
cost (orpresent value”)
of thethefutureservicethatcredit
will bethat
madeby the borrower.
grant elementof “net100 percent.
To calculate
grantdebt
elementof paymentsa loan/export
is provided
onThis calculation
requiresinformation
about
the
face
value,
maturitylength,grace
period,andinterest
rate
of
the
loan/
below-market (concessional) terms, one needs to calculate the discounted cost (or “net present value”) of
30
exportthecredit.
future debt service payments that will be made by the borrower. This calculation requires information

about the face value, maturity length, grace period, and interest rate of the loan/export credit.
AidData also codes the intent of each project in its dataset. Projects with “development intent” are those that areprimarily
oriented
the intent
promotionof economic
development
and welfare
in the recipient
country.
AidDataalsotowards
codes theof eachproject in its
dataset. Projects
with “development
intent”
areProjects
those with“commercial
intent”
are
those
that
primarily
seek
topromotethecommercial
interestsofthecountry
fromwhich thethat are primarily oriented towards the promotion of economic development and welfare in the recipientfinancialtransfer
has originated(e.g., encouraging
theexportChinese seek
goods
services).
Projects withcountry.
Projectswith “commercial
intent” arethose
thatofprimarily
to andpromotethe commercial“representational
intent”
are thosethat primarily
seek transfer
to promotea bilateral relationshipwith anothercountryinterests of thecountry
from which
the financial
has originated(e.g., encouraging
the exportof orotherwisepromotethelanguage,
culture,orvaluesofthecountry
from
which
the
financialtransferhasoriginated(e.g.,
Chinese goods and services). Projects with “representational intent” are those that primarily seek tothe establishmentofaConfucius
Instituteor
Chineseculturalcenter).
Projects
with“militaryintent”
are
those
that
seek
promote a bilateral relationship with another country or otherwise promote the language, culture, orto promotesecurity
interestsof the thecountry
fromtransfer
which thetransferor strengthen
valuestheof thecountry
from which
financialhasfinancial
originated(e.g.,originates
the establishmentof a the lethal
force capabilities
of military
in thecenter).
recipientcountrywith.31 “military intent” are those that seek to
Confucius Instituteor institutions
Chinese cultural
Projects
promote the security interests of the country from which the financial transfer originates or strengthen the
The OECD
Official Development
Assistance in(ODA)
activitiescountry
as thoselethaldefines
force capabilities
of military institutions
the recipient. provided on highly concessional terms(with a minimum grant element of 25 percent) and with development intent.32 It defines Other Official Flows (OOF) as
activities
on lessOfficial
concessionalterms (witha grant (ODA)
element
below 25
percent)
and/oronactivities
Theprovided
OECD defines
Development
Assistanceactivities
as those
providedhighly without
development
intent.
ODA
projects
arewidelyconsideredtobe“development
aid”
in
the
strictsense
term. The
concessional terms (with a minimum grant element of 25 percent) and with development intent.of
It thedefinessum ofOtherODA Official
and OOFissometimes
referredtoasOfficial
Financing(OF)orOverseas
Development
Finance(ODF).
Flows (OOF) as activities provided on less concessional terms (with a grant element below
25 percent) and/or activities without development intent. ODA projects are widely considered to be“development aid” in the strict sense of the term. The sum of ODA and OOF is sometimes referred to as
Official Financing (OF) or Overseas Development Finance (ODF).

Beijing’s adoption of the “Going Out” strategy in 1999 was a watershed moment that dramatically altered the scale,scope, and composition of China’s overseas development finance program. It was the point at which Beijing tasked its
state-owned “policy banks”—China Eximbank and China Development Bank—with helping Chinese firms gain afoothold in overseas markets. The strategy came into existence because of several challenges that the country faced at
28 In theory, grant element calculators can generate values above 100 or below 0. However, in the 2.0 dataset, we bound grantelement values so that loans cannot assume negative values (since negative values imply lending terms that are “less favorable thanmarket terms,” which is nonsensical if market terms are risk-adjusted prices agreed by willing buyers and sellers of credit) or values inexcess of 100%.
29 We address this measurement issue at greater length in Morris et al. (2020).
30 It also requires actual or assumed information about discount rates, loan repayment profiles, and the number of repayments madeeach year. AidData relies upon the following assumptions in its grant elements calculations: a fixed, 10% discount rate, equal principal
repayments, and two repayments per year. If grace period is not provided, the grant element calculator assumes no grace period isprovided. Grant element calculators can also incorporate commitment fees and management fees. However, AidData has not yet
adapted its grant element calculations to account for such fees.
31 As we describe at greater length in Section 6 of this paper, AidData now provides data on projects with military intent that arebacked by official financial flows from China.
32 The OECD is phasing in a tiered system to measure financial concessionality with different grant element thresholds (and discountrates) for countries at different income levels. However, to ensure consistency and comparability across the 18-year period in ourdataset, AidData uses a unified 25 percent grant element threshold (and a fixed, 10% discount rate) to make financial concessionality
determinations.

15home. First, the country suffered from a domestic industrial overproduction problem because its state-owned steel,iron, cement, glass, aluminum, and timber companies were over-leveraged, inefficient, and unprofitable. Beijing viewed
domestic industrial overproduction as a threat to the country’s long-term growth prospects and a potential source ofsocial unrest and political instability. It wanted to reduce domestic supply (through the offshoring of industrial input
production facilities) and increase international demand (by encouraging foreign buyers to purchase more industrial
inputs from China). Second, Beijing faced a foreign exchange oversupply problem: annual trade surpluses facilitated a
rapid expansion in foreign exchange reserves, and the country risked macroeconomic instability (inflation or a currency
revaluation) if it allowed these reserves to enter the domestic economy, so the authorities decided to instead look for
productive overseas outlets where they could park their excess dollars and euros.33 Third, Beijing recognized that tosustain high levels of domestic economic growth it would need to scour the globe for those natural resources that it
lacked in sufficient quantities at home.
To address these three challenges, the Chinese government enlisted the support of its policy banks. They were given a
mandate to support overseas projects focused on infrastructure, industrial production, and natural resource acquisition
and facilitate the participation of Chinese firms in these projects. China Eximbank and China Development Bank
responded in three ways: they increased foreign currency-denominated lending at or near market rates; contractually
obligated overseas borrowers to source project inputs (such as steel and cement) from China; and made it easier forcountries to secure and repay loans with the money that they earned from natural resource exports to China (Dreher etal. forthcoming). Consequently, in the eighteen-year period following Beijing’s adoption of the “Going Out” strategy,
China’s overseas development spending skyrocketed—from $3.6 billion per year in 2000 to $77 billion per year in 2017(see Figure 5).

Figure 5: Official development finance commitments from China, 2000-2017$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
ODA-like

OOF-like

Vague (Official Finance)

Source: AidData.
During the second half of the 20th century, the Chinese government managed a relatively small portfolio of overseas
development projects and its Ministry of Commerce (MOFCOM) financed most of these projects with renminbidenominated grants and interest-free loans (i.e., highly concessional development finance). A small percentage of theportfolio consisted of bankable, revenue-generating projects. Beijing instead prioritized projects—like the constructionof presidential palaces and parliamentary complexes—that would help cultivate and cement diplomatic ties andpolitical alliances with other countries. However, during the first two decades of the 21st century, China’s foreign

33 The country’s foreign exchange reserves increased from roughly $200 billion in 2000 to $4 trillion in 2014 (Park 2016).

16
exchange reserves soared to record levels and domestic industrial overproduction became a major problem, whichgave China a stronger incentive to invest in overseas assets via foreign currency denominated lending.34
Beijing’s pivot towards less concessional and more commercially-oriented lending—denominated in dollars and euros
rather than renminbi—is clearly evident in AidData’s 2.0 dataset. In the eight-year period between the adoption of the“Going Out” strategy and the 2008 Global Financial Crisis, Beijing ramped up foreign currency-denominated lending
and dialed down renminbi-denominated lending (see Figure 6). It also began to behave more like a banker and lesslike a benefactor: whereas Beijing issued 1.7 dollars of OOF for every dollar of ODA in 2000, it issued 5.6 dollars of
OOF for every dollar of ODA in 2007.

Figure 6: Composition of loan portfolio by currency of denomination, 2000-2017% of total value in constant USD

100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
USD

Other

EUR

CNY

Source: AidData.
Another key inflexion point came in 2008. The overall size of China’s overseas development program nearlyquadrupled between 2008 and 2009—from $28 billion to $98 billion a year—in the immediate aftermath of the 2008
Global Financial Crisis. The sharp and sudden drop in international asset prices super-charged Beijing’s benefactor-tobanker transition.35 Prior to 2008, Beijing parked most of its excess foreign exchange reserves in U.S. Treasury bonds.
However, when quantitative easing by the U.S. Federal Reserve weakened the U.S. dollar, Beijing decided to invest its
excess foreign exchange reserves in higher-yield (undervalued) overseas assets.36 It entrusted its surplus dollars andeuros to the country’s state-owned banks and tasked them with the pursuit of higher investment returns via foreign
currency-denominated international lending (Dreher et al. 2021a).37 As Figure 5(see above) demonstrates, the rate of

34 In joint work with a group of longtime collaborators from Heidelberg University, the University of Göttingen, the University of Hong

Kong, and William & Mary, we have statistically modeled the relationship between the overall size of China’s overseas development
finance program and the severity of its foreign exchange oversupply problem in any given year. We estimate that a one-standarddeviation-increase in China’s foreign exchange reserves increases the overall size of its overseas development finance program by 170
to 476 percent. Likewise, we have modeled the relationship between the overall size of China’s overseas development financeprogram and its level of domestic industrial production in any given year. We estimate that a one-standard-deviation-increase inindustrial input production leads to an increase in the overall size of China’s overseas development program by 176 to 461 percent(Dreher et al. forthcoming).
35 Dreher et al. (forthcoming) discuss this 21st century transition from benefactor to banker at greater length.
36 With the country’s foreign exchange reserves yielding an estimated 3% annual return at home, Beijing had an incentive to price its

foreign currency-denominated loans to overseas borrowers above this reference rate (Kong and Gallagher 2016).
37 By 2009, the country’s leadership tacitly acknowledged that its motivation for engaging in large-scale overseas lending was related

to its surplus of foreign exchange reserves. Wen Jiabao, the then-Premier of the State Council, stated that “We should hasten theimplementation of our ‘going out’ strategy and combine the utilization of foreign exchange reserves with the ‘going out’ of ourenterprises” (Anderlini 2009).

17
growth in the provision of Chinese OOF was nearly double that of Chinese ODA between 2008 and 2013.38 By the firstyear of BRI implementation, Beijing was issuing more than 9.25 dollars of OOF for every dollar of ODA. Beijing also
reduced the proportion of its international development finance program financed via grants to 1.5% by 2013 (from ahigh of 31% in 2001).39 Then, during the first five years of BRI implementation, China solidified its position as theworld’s largest official creditor to the developing world.40 It maintained a 9-to-1 ratio of OOF to ODA and a 31-to-1
ratio of loans to grants between 2013 and 2017.41

Figure 7: Grant-loan composition of China’s official development finance portfolio,
2000-2017
100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Source: AidData.

Grant

Loan

Vague

Note: All grant-like transfers (grants, technical assistance, scholarships/training activities, and debt forgiveness) arecollapsed into a single “Grant” category. All loans, export buyer’s credits, and supplier’s credits/exports seller’s credits
are collapsed into a single “Loan” category. A residual “Vague” category captures projects with unidentifiable flow
types.
AidData’s 2.0 dataset also provides a unique opportunity to identify the regions and countries that have received themost—and least—Chinese development finance. According to Table 1, development finance commitments fromofficial sector institutions in China were broadly distributed between 2000 and 2017 across Asia (29%), Africa (23%),
Latin America and the Caribbean (24%), Eastern and Central Europe (18%), the Middle East (4%), and Oceania (1%).
However, the overall picture looks different when these official sector financial transfers are disaggregated into ODA
and OOF. According to Table 2, African countries received 42% of all Chinese ODA between 2000 and 2017, which isconsistent with Beijing’s official position that the most of its foreign aid budget is earmarked for Africa.42 The 2.0

38 Over time, Washington has moved in the opposite direction of Beijing. Whereas 46% of U.S. development finance commitments

were provided via ODA in 2000, this figure skyrocketed to 95% in 2017. By contrast, the proportion of Chinese development financecommitments provided via ODA shrank from 15% in 2000 to 9% in 2017 (see Figure 3above).
39 Figure 7 demonstrates that China increased the proportion of its international development finance program financed via loans to

98% (from 69% in 2001).
40 Horn et al. (2019: 14) provide evidence that China became the world’s largest official creditor to the developing world, surpassing

both the World Bank and the IMF, in 2011.
41 Over the entire 18-year period of measurement in AidData’s 2.0 dataset, China’s global OOF portfolio exploded in size, increasing

24-fold. China’s ODA portfolio also expanded, but to a much lesser extent. It increased roughly 4-fold between 2000 and 2017.
42 State Council (2011, 2014, 2021). According to China’s latest white paper, 44.65% of the country's foreign aid portfolio was

earmarked for Africa between 2013 and 2018 (State Council 2021).

18
dataset indicates that the biggest African recipients of Chinese ODA are Ethiopia, the Republic of Congo, Sudan,
Ghana, Zambia, Kenya, Cameroon, Senegal, Mali, and Cote d’Ivoire (see Table 3). It also indicates that, over the same
18-year period, Asia was the second largest regional recipient of Chinese ODA (see Table 2).43 It received 38% of all
Chinese ODA between 2000 and 2017.44 The biggest Asian beneficiary of Chinese ODA was North Korea. It secured
approximately $7.17 billion between 2000 and 2017 primarily via large-scale food and fuel donations (see Table 3 andTable A-3b).

Table 1: Chinese official finance by region, 2000-2017
RegionProject count% of project countTotal USD 2017 billions% of USD 2017 billions

Africa

5,152

47%

207.4

25%

America

1,284

12%

192.3

23%

Asia

2,801

26%

245.7

29%

Europe

521

5%

150.2

18%

Middle East

340

3%

36.2

4%

Multi-region

25

0.2%

0.1

0.01%

Oceania

726

7%

11.1

1%

10,849

100%

843

100%

Total

Table 2: Chinese ODA and OOF by region, 2000-2017
Flow class

ODA

RegionProject count% of project
count

Total USD 2017
billions

Africa

4,056

54%

Asia

1,767

23%

Middle East

243

3%

9.2

9%

America

679

9%

8.1

8%

Oceania

628

8%

2.4

2%

Europe

173

2%

1.0

1%

Multi-region

21

0%

0.1

0%

Total

7,567

100%

101

100%

Asia

913

33%

197.4

29%

America

514

18%

174.0

26%

41.7
38.0

% of USD 2017
billions
42%
38%

43 The 2.0 dataset indicates that North Korea is the largest Chinese ODA recipient in Asia. It secured more than $7 billion of Chinese

ODA between 2000 and 2017 (see Table 3).
44 According to China’s latest white paper, 36.82% of the country's foreign aid portfolio was earmarked for Asia between 2013 and2018 (State Council 2021).

19

OOF

Europe

320

12%

145.8

21%

Africa

853

31%

135.9

20%

Middle East

83

3%

20.4

3%

Oceania

95

3%

8.7

1%

Multi-region

4

0%

-

0%

2,782

100%

682.1

100%

Total

Table 3: Top 25 recipients of Chinese ODA and OOF, 2000-2017
Top 25 recipients of Chinese ODA, 2000-2017

Top 25 recipients of Chinese OOF, 2000-2017

Recipient

Recipient

USD 2017 billions

USD 2017 billions

Iraq

8.15

Russia125.38

DPRK

7.17

Venezuela

85.54

Ethiopia

6.57

Angola40.65

Indonesia

4.42

Brazil

39.08

Congo

4.24

Kazakhstan

39.01

Pakistan

4.18

Indonesia

29.96

Sri Lanka

4.17

Pakistan

27.84

Cuba

3.35

Viet Nam

16.35

Bangladesh

2.95

Ecuador

15.92

Myanmar

2.72

Laos

12.36

Sudan

2.57

Peru

10.51

Uzbekistan

2.24

Iran

10.42

Ghana

2.22

Malaysia

9.67

Cambodia

2.20

Ethiopia

8.90

Zambia

2.10

Argentina

8.55

Kenya

2.03

Turkmenistan

8.52

North Korea

1.71

Sudan

7.85

Cameroon

1.46

Cambodia

7.76

Mozambique

1.40

Sri Lanka

7.69

Viet Nam

1.37

Kenya

7.02

Senegal1.27

Bangladesh

6.88

Nepal

1.08

Nigeria

6.82

Mali

1.06

Belarus6.73

20

Ecuador

1.05

India

6.65

Cote d'Ivoire

0.92

Myanmar

6.63

China’s provision of OOF, which almost exclusively consists of loans and export credits priced at or near market rates,reflects a very different set of priorities (see Table 2 above). Russia is by far Beijing’s largest OOF recipient, securing 107
loans and export credits worth $125 billion from official sector institutions in China between 2000 and 2017. Most ofthese debts were contracted by Russian state-owned enterprises in the oil and gas sector, priced at LIBOR or EURIBOR
plus a margin, and collateralized with the proceeds from oil and gas sales to China.45 Venezuela is Beijing’s second
largest OOF recipient. It secured $86 billion of non-concessional and semi-concessional debt—mostly via loans
collateralized against future oil export receipts—from China’s state-owned policy banks and commercial banks.
However, Asia was the most popular regional destination for Chinese OOF transfers between 2000 and 2017.46
Kazakhstan, Indonesia, Pakistan, Vietnam, Laos, Malaysia, Turkmenistan, Cambodia, Sri Lanka, Bangladesh, India, andMyanmar were among the largest Asian recipients of Chinese OOF during this period (see Table 3above). Significant
shares of China’s global OOF portfolio were also earmarked for Latin America and the Caribbean, Eastern and Central
Europe, and Africa.47 Yet, for every dollar that China lent to Asia between 2000 and 2017 at or near market rates, it
only lent $0.88 to Latin America and the Caribbean, $0.74 to Eastern and Central Europe, $0.69 to Africa, $0.10 to theMiddle East, and $0.04 to Oceania (see Table 2 above).
The 2.0 dataset can also be used to evaluate the risk profile of China’s overseas lending portfolio.48 According to Table
4, 89% of official sector lending from China between 2000 and 2017 supported countries that scored below the global
median on the WGI Control of Corruption Index.49 We also find evidence of concentrated effort in countries that pose
especially high levels of fiduciary risk: 65% of official sector lending from China between 2000 and 2017 supported
countries within the bottom quartile of the WGI Control of Corruption Index. A separate, but related, insight from thedataset is that Beijing disproportionately lends to countries that perform poorly on conventional measures ofcreditworthiness. According to Table 5, approximately 69% of official sector lending from China between 2000 and2017 supported countries that scored below the global median on a popular measure of repayment risk (based on
average sovereign credit ratings from Moody’s, Standard & Poor’s, and Fitch Ratings).50 Beijing’s willingness to pursue
large-scale lending operations in high-risk LMICs sets it apart from members of the OECD-DAC. In a book that willsoon be published by Cambridge University Press, we and our longtime collaborators from Heidelberg University, theUniversity of Göttingen, the University of Hong Kong, and William & Mary show that OECD-DAC members neither
favor nor disfavor LMICs which pose high levels of fiduciary risk and repayment risk (Dreher et al. forthcoming).

Table 4: Chinese official sector lending and corruption, before and after BRI% of official sector lending from China
Control of corruption (year - 1)

All years

2008-2012 (pre-BRI)

2013-2017 (BRI)

45 See, for example, Project ID#85211, 66689, 43069, 43012, and 67166.
46 29% of non-concessional and semi-concessional lending (i.e., OOF) from official sector institutions in China supported Asianborrowers between 2000 and 2017 (see Table 2 above).
47 26% of Chinese OOF was directed to Latin America and the Caribbean between 2000 and 2017, while 21% was directed to Eastern

and Central Europe and 20% was directed to Africa during the same period (see Table 2 above).
48 In this study, we use the terms “official sector lending” and “overseas lending” from China to refer to loans and export credits

issued by Chinese government institutions and Chinese state-owned institutions.
49 To calculate this summary statistic, we first measure the percentage of official sector lending from China in each year of the periodof measurement (2000-2017) that was directed to countries which performed below the global median on the Control of Corruption
index in the preceding year. We then calculate the average annual percentage over the eighteen-year period. We rely on the Control
of Corruption index from the Worldwide Governance Indicators (WGI) Dataset, which is available via www.govindicators.org.
50 To calculate this summary statistic, we first measure the percentage of official sector lending from China in each year of the periodof measurement (2000-2017) that was directed to countries which performed below the global median on the “sovrate” index from
Kose et al. (2017) in the preceding year. We then calculate the average annual percentage over the eighteen-year period. The
resulting figure is based on the set of countries for which average sovereign credit ratings from Moody's, Standard & Poor's, and Fitch
Ratings are available. In any given year, the sovrate index from Kose et al. (2017) is unavailable for approximately 15% of the countries
in the 2.0 dataset. The sovrate index varies from 0 to 21, with high scores indicating lower levels of sovereign credit (i.e., repayment)
risk.

21

Above median

11%

12%

10%

Below median

89%

88%

90%

Top 3 quartiles

35%

29%

43%

Bottom quartile

65%

71%

57%

Note: The percentages reported in this table are based on the set of countries for which WGI Control of Corruption scores areavailable

Table 5: Chinese official sector lending and repayment risk, before and after BRI% of official sector lending from China
Repayment risk (year - 1)

All years

2008-2012 (pre-BRI)

2013-2017 (BRI)

Above median

31%

36%

32%

Below median

69%

64%

68%

Top 3 quartiles

64%

60%

65%

Bottom quartile

36%

40%

35%

Note: The percentages reported in this table are based on the set of countries for which average sovereign credit ratings from
Moody's, Standard & Poor's, and Fitch Ratings are available

3.2 How has China’s overseas development finance programchanged during the BRI era?
For many journalists and policymakers, the Belt and Road Initiative (BRI) is synonymous with China’s overseas
development finance program. However, it is important to remember how Beijing has defined the purpose and scopeof the initiative. When Xi Jinping first launched the BRI in 2013, he referred to it as the “One Belt, One Road” (OBOR)
initiative. It was described at that time as a “Belt” of road, rail, port, and pipeline projects that would create an
infrastructure corridor from China to Central Asia and Europe and a “Maritime Silk Road” that would link China toSouth and Southeast Asia, the Middle East, and Africa through a series of deep-water ports along the littoral areas of
the Indian Ocean (Perlez and Huang 2017; Mauk 2019).51 The Chinese authorities subsequently expanded thedefinition of the BRI to include “five connectivities” (see Box 2), but there is little question that the core focus of theinitiative is infrastructure connectivity.52

51 Initially, the “Belt” was expected to consist of six overland corridors: China–Mongolia–Russia Economic Corridor, the New EurasianLand Bridge, the China–Central Asia–West Asia Economic Corridor, the China–Indochina Peninsula Economic Corridor, the China–
Pakistan Economic Corridor, and the Bangladesh–China–India–Myanmar Economic Corridor (World Bank 2019). However, it was later
expanded to include various corridors in Sub-Saharan Africa and Latin America and the Caribbean.
52 In his keynote address at the 2017 Belt and Road Forum for International Cooperation, President Xi Jinping emphasized that“[i]nfrastructure connectivity is the foundation of development through cooperation. We should promote land, maritime, air andcyberspace connectivity, concentrate our efforts on key passageways, cities and projects and connect networks of highways, railways
and seaports ...” (Xi 2017).

22

Box 2: The “Five Connectivities” of the BRI
In a widely cited September 2013 speech at Kazakhstan’s Nazarbayev University, President Xi Jinping
shared his vision for the establishment of an overland economic corridor between China and Europe called
the “Silk Road Economic Belt.” Then, in October 2013, he gave a speech to Indonesia’s Parliament in
Boxwhich
2: The“Five Connectivities” of the BRI
he outlined a separate but related vision for the establishment of a sea route from China to Europe(via South and Southeast Asia, Oceania, East Africa, and the Middle East) called the “Maritime Silk Road.”In a widely cited September 2013 speech at Kazakhstan’s Nazarbayev University, President Xi Jinping shared his vision
In order to implement the “One Belt, One Road” (OBOR) initiative, Xi indicated that China would workfor the establishment of an overland economic corridor between China and Europe called the “Silk Road Economic
with partner countries to build five “connectivities” or “links”: (1) physical connectivity via infrastructureBelt.” Then, in October 2013, he gave a speech to Indonesia’s Parliament in which he outlined a separate but relatedbuilding; (2) policy coordination; (3) unimpeded trade; (4) financial integration; and (5) people-to-people
vision for the establishment of a sea route from China to Europe (via South and Southeast Asia, Oceania, East Africa,
exchanges.53 The goal of OBOR, which later became known as the BRI, was to improve interconnectivity
and the Middle East) called the “Maritime Silk Road.” In order to implement the “One Belt, One Road” (OBOR)
within Eurasia, a longstanding ambition of Chinese grand strategy (State Council 2016).
initiative, Xi indicated that China would work with partner countries to build five “connectivities” or “links”: (1) physicalconnectivity via infrastructure-building; (2) policy coordination; (3) unimpeded trade; (4) financial integration; and (5)During his speech at Nazarbayev University, Xi also outlined a set of guiding principles for the initiative. He
people-to-people exchanges.53 The goal of OBOR, which later became known as the BRI, was to improve
reaffirmed that China would “never intervene in [the] internal affairs” of partner countries and it wouldinterconnectivity within Eurasia, a longstanding ambition of Chinese grand strategy (State Council 2016).54
strive for “peaceful coexistence” by forging partnerships characterized by “mutual benefits.” He also
signaled an “openness” to include any willing country in the initiative, which paved the way for the BRI’s
During his speech at Nazarbayev University, Xi also outlined a set of guiding principles for the initiative.55 He reaffirmed
expansion beyond Eurasia.
that China would “never intervene in [the] internal affairs” of partner countries and it would strive for “peacefulcoexistence” by forging partnerships characterized by “mutual benefits.” He also signaled an “openness” to include
Participation in the BRI has increased over time (see Figure 8a below and Table A-17 in the Appendix). An
any willing country in the initiative, which paved the way for the BRI’s expansion beyond Eurasia.
initial group of 31 countries—primarily from Europe and Asia—joined the initiative between 2013 and2016. Then, in 2017 and 2018, the BRI’s geographical scope expanded dramatically to 120 countries,
Participation in the BRI has increased over time (see Figure 8 Figure BRI_rollout}} below and Table abc Figure A4-1}} inincluding large swathes of Africa, Latin America and the Caribbean, the Middle East, and Oceania. The list
the Appendix).56 An initial group of 31 countries—primarily from Europe and Asia—joined the initiative between 2013of participants in the initiative grew even further—to 140 countries—between 2019 and 2021.
and 2016. Then, in 2017 and 2018, the BRI’s geographical scope expanded dramatically to 120 countries, including
large swathes of Africa, Latin America and the Caribbean, the Middle East, and Oceania. The list of participants in theinitiative grew even further—to 140 countries—between 2019 and 2021.57

53 According to Rolland (2017), President Xi’s vision for a Eurasian continent that is integrated around China is a “grand strategy” thatall key components of the Chinese state have adopted.
54 In China’s geostrategic worldview, the BRI is a manifestation of the country’s emergence as a global economic powerhouse driven

by sustained domestic economic growth over multiple decades.
55 Xi Jinping motivated these remarks by invoking two millennia of Chinese trade and cultural relations with Central Asia (Xi 2013).
56 For this analysis, we measure the year in which a country “joined the BRI” as the first year in which it signed a BRI cooperationagreement or a formal BRI memorandum of understanding (MOU) with China. We used the “Official BRI participants by year of
joining” dataset made available by the Council of Foreign Relations (CFR) in March 2021 as a baseline (Sacks 2021; Hillman and Sacks
2021). For countries where CFR designated the year as “unknown,” we conducted desk research to confirm the first year in which a
BRI cooperation agreement or MOU was signed between China and that country.
57 The additional countries that joined the initiative between 2019 and 2021 primarily came from Africa, Latin America and the

Caribbean, the Middle East, and Oceania. However, there is still considerable uncertainty about the exact nature, scale, and scope of
the BRI. According to the New York Times, “China has never released any official map of Belt and Road routes nor any list of
approved projects, and it provides no […] guidelines on what it means to be a participant” (Mauk 2019).

23

Figure 8a: BRI signatories by year of signing, 2013-2021

Source: AidData.

The BRI is very much an extension and expansion of the “Going Out” strategy, which was adopted by Jiang Zemin in1999 and reaffirmed by Hu Jintao during his time in office (2002-2012).58 In order to manage the country’s domestic
industrial overproduction problem and foreign exchange oversupply problem, the Chinese government has rapidly
scaled up the provision of loans and export credits to foreign borrowers that are denominated in (domesticallyoversupplied) dollars and euros and priced at or near market rates. The projects financed with these loans and export
credits usually require construction inputs—like steel, cement, timber, and glass—that are oversupplied in China andcontractually obligate borrowers to import these inputs on a preferential basis.59 The BRI has therefore been describedas “Xi Jinping’s branding of his predecessors’ ‘going global’ strategy” (Brautigam 2019).60
Yet, it is still possible that the introduction of the BRI has altered the scale and geographical distribution of Chinese
development finance. The comprehensive nature of AidData’s 2.0 dataset—during the BRI era and the pre-BRI era—
provides a unique opportunity to test whether and to what extent participation in the BRI influences the overall size ofthe financial envelope that Beijing makes available to individual countries. To do so, we take advantage of the BRI’s
staggered rollout and calculate the average, annual monetary value of official financial commitments from China toeach BRI participant country before and after it joined the global infrastructure initiative. Our findings suggest that, on
average, official financial commitments from China increase by 63% after a country officially joins the BRI (see Table
A-21 in the Appendix).61 However, a significant increase in funding from Beijing would have likely taken place in thesecountries even if they chose not to participate in BRI. As a point of comparison, we calculate the average increase in

58 The “Going Out” strategy is also known as the “Going Global” strategy.
59 Dreher et al. (forthcoming) provide a more detailed explanation of how this strategy is implemented in practice.
60 Brautigam (2019) attributes the durability of this strategy—under Jiang Zemin, Hu Jintao, and Xi Jinping—to the fact that “China

has excess foreign exchange, construction capacity, and mid-level manufacturing and needs to send all of these overseas.”
61 This analysis is based on the five-year period before and after the BRI was announced (2008-2017). For each country that joined the

BRI, we calculate the rate of increase between (a) the average annual official commitment amount from China between 2008 and theyear before the country joined the BRI, and (b) the average annual official commitment amount from China between the year in which
the country joined the BRI and 2017. We then calculate a weighted average across all BRI participant countries, which indicates thatthe average annual official commitment increase after a country signs a BRI MOU with China is 63%. We exclude Russia from theanalysis because its pre-BRI funding includes a significant outlier—a single $32 billion oil prepayment facility (the largest project in the
2.0 dataset).

24
official financial commitments from China among countries that chose not to join the BRI over the same time period.62
The average increase within this cohort (47%) suggests that a large portion of the 64% funding increase experienced by
BRI participant countries would have likely materialized even if they formally opted out of Beijing’s flagshipinfrastructure initiative. This finding underscores a broader point: China has made significant efforts to position the BRI
as something new, but it is part and parcel of the “Going Out” strategy that was adopted more than two decades ago.
As further evidence that the BRI represents an effort to double down on the “Going Out” strategy, consider how the“color of the money” from Beijing to countries around the globe has changed since 2013. During the thirteen-year
period preceding the BRI (2000-2012), China spent 5.3 dollars of OOF for every dollar of ODA. However, during thefirst five years of BRI implementation (2013-2017). China spent 9.1 dollars of OOF for every dollar of ODA. AidData’s
2.0 dataset also indicates that, in all but two regions of the world, Beijing moved away from ODA and towards OOF
during the BRI era (see Table 6). In the Middle East, the composition of China’s international development financeportfolio became substantially less concessional and more commercially oriented. From 2000 to 2012, every ODA
dollar that Beijing spent on development projects in the Middle East was matched by a dollar of OOF. However, during
the first five years of BRI implementation (2013-2017), the Chinese ODA-to-OOF ratio in the Middle East fell
precipitously, with each ODA dollar being matched by 31.5 dollars of OOF. Official financial commitments from China
to Eastern and Central Europe, Africa, Asia, and Latin America and the Caribbean have also become less concessionaland more commercially oriented over time. Between 2000 and 2012, every ODA dollar that Beijing spent on
development projects in Eastern and Central Europe was matched by 106.4 dollars of OOF. However, during the first
five years of BRI implementation (2013-2017), Eastern and Central Europe received 216.8 dollars of Chinese OOF forevery dollar of Chinese ODA. Similarly, Africa and Asia witnessed significant declines in their Chinese ODA-to-OOF
ratios since the BRI was introduced.63 Oceania and Latin America and the Caribbean were the only two regions of theworld where Beijing’s ODA-to-OOF ratio increased during the BRI era. For every ODA dollar that Beijing gave to
Oceania between 2000 and 2012, it lent more than 35 dollars at semi-concessional or commercial rates (OOF).
However, during the first five years of BRI implementation (2013-2017), Beijing pivoted towards more grant-giving andhigh concessional lending in Oceania, with each dollar of ODA being matched by 15 dollars of OOF. A similar pivot
took place in Latin America and the Caribbean.64

Table 6: Ratio of Chinese ODA to OOF commitments
ODA to OOF ratio
Region2000-2017

2000-20122013-2017

Africa1 : 3.3

1 : 2.4

1 : 4.6

America

1 : 21.5

1 : 35.4

1 : 15.3

Asia

1 : 5.2

1 : 4.1

1 : 6.7

Europe1 : 144

1 : 106.4

1 : 216.8

Middle East

1 : 2.2

1:1

1 : 31.5

Oceania

1 : 3.6

1 : 3.7

1 : 3.4

62 More specifically, we calculate the average annual increase in official commitments from China for non-BRI countries (i.e., those whohave never signed a BRI MOU as well those who had not signed one by the end of 2017) between 2013 and 2017. The weighted
average increase across all non-BRI countries is 47%.
63 According to Table 6, every ODA dollar that Beijing spent on development projects in Africa was matched by 2.43 dollars of OOF

between 2000 and 2012. However, during the first five years of BRI implementation (2013-2017), Africa received 4.6 dollars of Chinese
OOF for every dollar of Chinese ODA. Between 2000 and 2012, every ODA dollar that Beijing spent on development projects in Asia
was matched by 4.1 dollars of OOF. However, during the first five years of BRI implementation (2013-2017), Asia received 6.7 dollars
of Chinese OOF for every dollar of Chinese ODA.
64 According to Table 6, every ODA dollar that Beijing spent on development projects in Latin America and the Caribbean wasmatched by 35.4 dollars of OOF between 2000 and 2012. However, during the first five years of BRI implementation (2013-2017),
Latin America and the Caribbean received 15.3 dollars of Chinese OOF for every dollar of Chinese ODA.

25
Another unique aspect of the 2.0 dataset is that every project is assigned a single, 3-digit OECD sector code using aninternationally accepted set of classification criteria. This feature enables cross-donor and cross-lender comparisons—at
global, regional, national, and subnational scales—since most official sources of international development finance(including all the members of the OECD-DAC and the most multilateral institutions) use the same criteria. It also
facilitates analysis of sectoral patterns and trends over space and time. One question that naturally arises about the BRI
is whether its introduction has changed the sectoral composition of China’s overseas development finance portfolio.
When the global infrastructure initiative was first launched, the Chinese Government signaled that it would prioritize
investments in “hardware” sectors, like construction, industry, mining, energy, and transportation. Figure 8b
demonstrates that there was a modest shift in this direction after the introduction of the BRI, but Beijing alreadyassigned a high level of priority to these sectors before the initiative was announced. In the thirteen-year period(2000-2012) preceding the BRI, 63% of China’s overseas development finance portfolio was dedicated to three“hardware” sectors (Energy; Industry, Mining, and Construction; and Transport and Storage). Then, during the first five
years of BRI implementation, this figure increased to 76%.65

Figure 8b: Chinese development finance commitments by sector, 2000-2017
BRI announced$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Industry, mining, and construction

EnergyTransport and storage

Other

Source: AidData.

Although the BRI did not bring about a major change in the sectoral composition of China’s international lending andgrant-giving program, it did mark an important transition in how China finances big-ticket infrastructure projects. Prior
to 2013, Beijing primarily lent through its policy banks—China Eximbank and China Development Bank—togovernment agencies and state-owned enterprises in low-income and middle-income countries (see Section 3.1,
Figure 9 and Figure 10). However, shortly after the BRI was announced in 2013, China’s state-owned commercial
banks66—including Bank of China, ICBC, China Construction Bank, and Bank of Communications—assumed more
65 During the first five years of BRI implementation, the energy sector experienced more growth—in absolute terms—than any othersector. Total official development finance commitments from China for energy sector projects soared from $15 billion in 2013 to $28
billion in 2017 (see Figure 8b and Figure A-1 in the Appendix).
66 AidData classifies the following institutions as Chinese state-owned commercial banks: China Construction Bank Corporation (CCB),

Industrial and Commercial Bank of China (ICBC), Bank of China (BOC), China Bank of Communications (BoCom or BoComm),
Agricultural Bank of China, Postal Savings Bank of China (PSBC), China Bohai Bank, Bank of Shanghai, China CITIC Bank, China
Merchants Bank, Huaxia Bank Co., Ltd., and China Everbright Bank Co., Ltd. This group of banks includes so-called shareholding
commercial banks that are subsidiaries of state-owned enterprises (e.g., China CITIC Bank) and city commercial banks (i.e., Bank of
Shanghai).

26
prominent roles in the country’s overseas lending program.67 They did so at the behest of the central government(Zhou et al. 2018). Figure 9 indicates that, during the pre-BRI era, these institutions collectively lent $2.39 billion peryear on average to the countries and territories captured in our dataset, but during the first five years of BRI theycollectively lent $11.06 billion per year on average. That’s close to a five-fold increase. By 2017, the overseas lending
activities of China’s state-owned commercial banks (worth $15 billion) made them the third largest (collective) source ofofficial sector lending from China.68 This shift calls attention to the increasingly commercial orientation of Chinese
development finance because China’s state-owned commercial banks price their loans on substantially lessconcessional terms than the loans offered by other Chinese government lending institutions like China Eximbank andChina’s Ministry of Commerce (see Table 7).69

Figure 9: China’s official lending portfolio by funding agency type, 2000-2017$120

BRI announced

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
CDB
CEXIM
MOFCOM
State-Owned Commercial Banks

Central/Regional Government (non-MOFCOM)/State-Owned Fund
Combination of State-Owned Policy & Commercial Banks
SOEs
Unspecified/Other

Source: AidData.

67 In all subsequent summary statistics and figures that refer to official sector lending from China, we combine all of the projects in the

2.0 dataset that were financed with loans, export buyer’s credits, and supplier’s credits/export seller’s credits.
68 CDB and China Eximbank are the two largest sources of official sector funding from China identified in the 2.0 dataset (see Figure

9).
69 Rather than issuing loans and export credits with fixed and subsidized (i.e., below-market) interest rates, China’s state-owned

commercial banks usually tether the pricing of their overseas loans to floating market interest rates like LIBOR or EURIBOR plus a
margin to account for borrower-specific risk and repayment capacity.

27

Figure 10: China’s official lending portfolio by borrower type, 2000-2017$120

BRI announced

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Government agency

SOE

SPV

Private sector

State owned bank

Other

Source: AidData.

Table 7: Average lending terms by funding agency type
Funding agency

Grant element (%)

Interest rate (%)

Grace period (years)

Maturity (years)

MOFCOM

74.7%

0.0%

8.6

19.7

CEXIM

41.5%

3.2%

2.9

17.0

CDB

18.1%

5.3%

1.2

11.5

State-owned commercial banks 22.0%

4.3%

1.0

10.1

Combination of state-owned
policy & commercial banks

24.6%

4.9%

2.2

12.2

SOEs

20.0%

2.9%

0.3

9.1

Note: All interest rates, maturities, grace periods, and grant elements are reported as weighted averages. They areweighted according to the monetary values of loans (and export credits) in constant 2017 USD.

Another core feature of the BRI is the scale of the projects being financed. The 2.0 dataset demonstrates that the
average monetary value of a loan from an official sector institution in China increased by 27% during the first five years

28of the BRI (2013-2017).70 Whereas the average monetary value of a loan during the pre-BRI era (2000-2012) was $258
million, it was $328 million during the first five years of the BRI (2013-2017).71 Chinese state-owned lenders have also
ramped up the provision of credit for “mega-projects.” In an average year during the 13-year period preceding the BRI(2000-2012), Beijing approved 11 loans worth more than $500 million. By contrast, in an average year during the first
five years of BRI implementation (2013-2017), Beijing green-lit 36 loans worth more than $500 million.72
Larger loans present larger risks. Therefore, Chinese state-owned banks have pursued a variety of measures to mitigate
these risks. One of these measures is co-financing. The 2.0 dataset demonstrates that official sector debt from China isincreasingly being issued via large, syndicated loans that are backed by multiple Chinese state-owned banks.73 Figure
11 shows a significant increase in the percentage of China’s overseas lending portfolio being co-financed between
2000 and 2017. At the beginning of the period of measurement (2000), no official sector lending from China was cofinanced. However, by 2017, 32% was co-financed. A good example of this trend is the 485MW Hussein CombinedCycle Gas Turbine Project in Jordan, which was co-financed in 2016 by two Chinese state-owned banks (China
Construction Bank and the Industrial and Commercial Bank of China) and a group of Arab and multilateraldevelopment finance institutions (the European Bank for Reconstruction and Development, the International FinanceCorporation, Europe Arab Bank, and the OPEC Fund for International Development) through a $350 million syndicatedloan agreement.74

Figure 11: Official sector lending from China that is co-financed, 2000-2017

BRI announced$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Co-financed

Not co-financed

Source: AidData.

70 The median monetary value of a loan from an official sector institution in China increased by 41% during the first five years of the

BRI (2013-2017).
71 Between 2000 and 2017, the average monetary value of a loan from an official sector institution in China increased by 340% (from$60 million to $264 million).
72 In an average year during the 13-year period preceding the BRI (2000-2012), Beijing approved 5 loans worth more than $1 billion.

However, in an average year during the first five years of BRI implementation (2013-2017), Beijing green-lit 16 loans worth more than
$1 billion.
73 Banks typically use syndicated loans—that is, loans issued jointly by a group (“syndicate”) of banks—to share credit risk and financeprojects that they would not otherwise finance on their own.
74 See Project ID#72504 and #64473 in the 2.0 dataset for more details.

29
Table 8 highlights a separate but related insight from the 2.0 version of AidData’s dataset: the fact that the averagemonetary value of a loan issued by a syndicate of Chinese state-owned banks is $1.292 billion, which far exceeds theaverage value of “bilateral” loans issued by China Development Bank ($575 million), China Eximbank ($198 million),and the country’s state-owned commercial banks ($234 million). Illustrative transactions from the 2.0 dataset include the$2 billion syndicated buyer’s credit loan that China Development Bank, China Construction Bank, Bank of China, theIndustrial and Commercial Bank of China, and the Bank of Communications issued in 2016 for the 1980MW Vinh Tan 3
Coal-Fired Power Plant Construction Project in Vietnam and the $1.4 billion loan that China Eximbank and theIndustrial and Commercial Bank of China jointly issued in 2017 for the 870MW Suki Kinari Hydropower Project inPakistan.75

Table 8: Official sector lending from China by funding agency and type of borrower
Funding agency
Borrower type

CEXIM

CDB

Stateowned
commercial
banks

Combinationof stateowned policy&
commercial
banks

MOFCOM SOEs

Averageloan size(millions
USD
2017)

Government agency

63.1%

37.1%

22.6%

5.8%

99.2%

12.0%

210.4

SPV

20.4%

14.3%

37.0%

55.0%

0.0%

5.4%

438.9

SOE

9.5%

31.0%

23.1%

7.9%

0.5%

80.9%

572.0

Private sector

1.4%

8.0%

10.0%

9.8%

0.0%

1.2%

194.7

State-owned bank

2.8%

7.2%

4.6%

17.5%

0.3%

0.0%

282.7

Average loan size (USD
millions 2017)

198.0

575.0

234.3

1,292.2

19.2

576.5

Although Chinese state-owned lenders are approving bigger loans and taking on more credit risk during the BRI era,
there are some indications that steps are being undertaken to mitigate risk. Table 9 suggests that when China’s stateowned policy banks and state-owned commercial banks pursue co-financing and syndicated loan arrangements, they
are less likely to support projects in countries that pose high levels of repayment risk.76 However, we do not find thatthey are less likely to support projects in countries that pose high levels of fiduciary risk (see Table 10 Figure ). In
Section 5, we explore a related question: whether projects backed by multiple financiers encounter fewer risks andproblems during implementation.77

75 See Project ID#39014 and #65742 in the 2.0 dataset for more details.
76 According to Table 9, 47% of the debt issued for projects co-financed by China’s state-owned policy banks and commercial banks

has supported countries performing above the global median on a popular measure of repayment risk (based on average sovereign
credit ratings from Moody’s, Standard & Poor’s, and Fitch Ratings). However, this percentage is considerably lower for projects
bilaterally financed by MOFCOM (11%), China Eximbank (16%), CDB (30%), and individual state-owned commercial banks (27%)
77 When projects are co-financed through a syndicated loan or otherwise, the co-financiers often coalesce around a common set ofdue diligence procedures and standards that will guide the design and implementation of the project.

30

Table 9: Chinese official sector lending and repayment risk, by funding agency% of official sector lending from China by funding agency
Repayment risk
(year - 1)

CEXIM

CDB

State-owned
commercial
banks

Combination of stateowned policy & commercial
banks

SOEs

MOFCOM

Above median

16%

30%

27%

47%

87%

11%

Below median

84%

70%

73%

53%

13%

89%

Top 3 quartiles

54%

62%

68%

80%

89%

20%

Bottom quartile

46%

38%

32%

20%

11%

80%

Note: The percentages reported in this table are based on the set of countries for which average sovereign credit ratings from
Moody's, Standard & Poor's, and Fitch Ratings are available

Table 10: Chinese official sector lending and corruption, by funding agency% of official sector lending from China by funding agency
Control ofcorruption (year 1)

CEXIM

CDB

State-owned
commercial banks

Combination of stateowned policy & commercial
banks

SOEs MOFCOM

Above median

11%

10%

15%

12%

6%

18%

Below median

89%

90%

85%

88%

94%

82%

Top 3 Quartiles

40%

31%

43%

45%

19%

52%

Bottom Quartile

59%

69%

57%

55%

81%

48%

Note: The percentages reported in this table are based on the set of countries for which WGI Control of Corruption scores areavailable

There are also some indications that, over time, China has rebalanced the risk profile of its overseas lending portfolio.
Figure 12 documents a decline, since the introduction of the BRI, in the percentage of official sector lending from
China supporting countries that score within the bottom quartile of the WGI Control of Corruption Index—from 78% in2013 to 39% in 2017.78 Figure 13 also provides evidence of a downward trend during the 21st century in thepercentage of official sector lending from China supporting countries below the global median on a popular measureof repayment risk (based on average sovereign credit ratings from Moody’s, Standard & Poor’s, and Fitch Ratings).79
This rebalancing of risk in Beijing’s overseas lending portfolio is related to China’s state-owned commercial banks,
which are responsible for a growing percentage of official sector lending from China but have lower levels of risk
appetite than the country’s state-owned policy banks (see Figure 9 , Table 10,and Table 9). Nevertheless, an importantcaveat is that there are still high baseline levels of risk in the borrower country composition of China’s overseas lending
portfolio, so while it is true that Beijing is gradually ratcheting down the percentage of its lending portfolio that
supports countries posing the highest levels of fiduciary risk and repayment risk, these countries are still among themost important borrowers in its overseas lending portfolio.

78 To calculate these summary statistics, we follow the same procedures that are described in footnote 50. However, instead of

calculating an annual average over the entire eighteen-year period, we report each annual estimate.
79 To calculate these summary statistics, we follow the same procedures that are described in footnote 51. However, instead of

calculating an annual average over the entire eighteen-year period, we report each annual estimate.

31

Figure 12: Official sector lending from China by level of corruption risk, 2000-2017
100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Bottom quartile

Top 3 quartiles

Source: AidData.

Figure 13: Official sector lending from China by level of repayment risk, 2000-2017
100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Below MedianAbove Median

Source: AidData.
As Chinese state-owned lenders have taken on bigger projects and sought to manage credit risk more effectively, they
have also ratcheted up their use of so-called “credit enhancements.” Across the full 2.0 dataset, we find that at least
61% of China’s official sector lending has benefited from a direct or indirect pledge of collateral, a repayment
guarantee (usually from the host government), and/or credit insurance (usually purchased by the borrower from

32
Sinosure).80 This ratio has fluctuated significantly from year-to-year, but generally trended upward over time (see Figure14). During the first four years of measurement in the 2.0 dataset (2000-2003), 31% of official sector lending from China
benefited from one or more credit enhancements. By 2017, this ratio increased to 52%. The average ratio during thefirst five years of BRI implementation (2013-2017) was 58%.

Figure 14: Chinese official sector lending with credit enhancements$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Lending with credit enhancement(s)Lending with no credit enhancements

Source: AidData.

Among these credit enhancements, we find that collateralization is the most popular tool among Chinese state-owned
lenders.81 The 2.0 dataset indicates that at least 44% of official sector lending from China is collateralized, while at least
17% is backed by a repayment guarantee and at least 13% is insured.82 It also demonstrates that levels of
collateralization vary considerably across Chinese state-owned lenders and over time. According to Table 11, just undera third of China Eximbank’s overseas lending portfolio is collateralized and more than two-thirds of CDB’s overseas

80 Sinosure is the shorthand term for the China Export & Credit Insurance Corporation. It provides short-term, medium-term, and long-term buyer’s credit and seller’s credit insurance products. It also provides overseas investment (debt and equity) insurance.
81 The 2.0 dataset includes a Yes/No marker called “Collateralized/Securitized,” which indicates if it is known that one or more sourcesof collateral/security can be seized in the event the borrower defaults on its repayment obligations. It is set to “Yes” when (i) a loan/
export credit is collateralized through a formal lien or security interest; (ii) a borrower is required to deposit project-related revenues orunrelated revenues in a special account, escrow account, proceeds account, or revenue account that can be accessed (and debited)by the lender; and/or (iii) a security agent is appointed (to enforce rights against the collateral in the event that the borrower defaults
on its repayment obligations). AidData also codes all pre-export finance (PXF) facilities as collateralized since they are almost always
secured by an assignment of rights by the producer under an ‘offtake contract’ (i.e., a sale and purchase contract between the
producer and a buyer of that producer of goods or commodities), and a collection account charge over a bank account into which
proceeds due to the producer from the buyer of the goods or commodities under the offtake contract are credited.
82 We use the “at least” qualifier because AidData only codes loans and export credits as being collateralized, guaranteed, or insured

when it has evidence that one of these credit enhancements was issued. However, there are almost certainly collateralized,
guaranteed, or insured transactions in the 2.0 dataset that are not identified as such because no such evidence was identifiable.

33
lending portfolio is collateralized.83 These ratios have not changed much since the BRI was introduced. However, the2.0 dataset calls attention to a significant increase in collateralization during the BRI era among Chinese state-owned
commercial banks: whereas 27% of the overseas lending from Chinese state-owned commercial banks wascollateralized between 2008 and 2012, 44% was collateralized during the first five years of BRI implementation(2013-2017).84 This trend is notable since much of the growth in China’s overseas lending portfolio between 2013 and2017 took place among its state-owned commercial banks (see Figure 9 above).

Table 11: Percent of lending collateralized by funding agency, 2008-2012 and 2013-2017
Funding agency% lending collateralized(2008-2012)% lending collateralized(2013-2017)

CEXIM

30%

29%

CDB

67%

70%

State-owned commercial banks

27%

44%

Combination of state-owned policy &commercial banks

76%

68%

SOEs

50%

71%

MOFCOM0%0%

Another insight from the 2.0 dataset is that Chinese state-owned lenders favor collateralization when the stakes areespecially high: at least 40 of the 50 largest loans (worth $254.4 billion) in the 2.0 dataset are collateralized (see Table
A-3a in the Appendix). As additional evidence that Chinese state-owned lenders use the tool of collateralization
strategically and selectively, consider Table 12, which shows that 83% of collateralized lending from official sector
institutions in China between 2000 and 2017 supported countries scoring within the bottom quartile of the WGIControl of Corruption Index.85 Table 13 also provides evidence that Chinese state-owned lenders are more likely tocollateralize loans to countries which pose higher levels of repayment risk: whereas 62% of collateralized lending fromofficial sector institutions in China between 2000 and 2017 supported countries that score below the global median on
a popular measure of repayment risk (based on average sovereign credit ratings from Moody’s, Standard & Poor’s, andFitch Ratings), only 38% supported countries falling at or above the global median.86 In Table 14 , we identify the ten
countries—Venezuela, Peru, Turkmenistan, Equatorial Guinea, Russia, Uganda, DRC, Congo-Brazzaville, Ecuador, andAngola—in the 2.0 dataset that have contracted the highest levels of collateralized debt from Chinese state-owned
lenders. Seven of these countries rank below the global median on the previously mentioned measure of repayment

83 In previous work with a group of collaborators from the Center for Global Development, the Peterson Institute for International

Economics, and the Kiel Institute for the World Economy, we analyzed a sample of 100 loan contracts that were issued by official
Chinese creditors between 2000 and 2020. We found that 29% of the loans in that smaller sample were collateralized. 75% of the
CDB loans and 22% of the China Eximbank loans in that sample were collateralized (Gelpern et al. 2021).
84 We use the 5-year period immediately preceding the BRI (2008-2012) as a baseline for comparison because most of China’s high-risk, high-reward overseas lending began after the 2008 Global Financial Crisis. Table 11 identifies a similar BRI era increase incollateralization among Chinese SOE lenders: from 50% between 2008-2012 to 71% between 2013-2017.
85 We find that 94% of collateralized lending from official sector institutions in China between 2000 and 2017 supported countries

scoring below the global median on the WGI Control of Corruption Index (see Table 12 ). To calculate this summary statistic, we firstcategorize all collateralized lending from official sector institutions in China in each year of the period of measurement (2000-2017)that is directed to countries which performed above or below the global median on the Control of Corruption index in the preceding
year. We then calculate the average annual percentage over the eighteen-year period. We rely on the Control of Corruption index
from the Worldwide Governance Indicators (WGI) Dataset, which is available via www.govindicators.org. We follow an analogous setof procedures to calculate the percentage of collateralized lending from official sector institutions in China supporting countries thatscore within the bottom quartile of the WGI Control of Corruption Index.
86 To calculate these summary statistics, we followed the same procedures that are described in footnote 86 but we restricted ouranalysis to collateralized lending from official sector institutions in China.

34risk.87 These patterns in the dataset reinforce a key point: Beijing is more willing to bankroll projects in risky countries
than other official creditors, but it is also more aggressive than its peers at positioning itself at the front of therepayment line (via collateralization).88

Table 12: Chinese official sector lending and corruption, by lending type
% of Chinese lending with and without credit enhancements
Control ofcorruption (year 1)

Collateralized
lending

Non-collateralized
lending

Lending with credit
enhancement (s)Lending without credit
enhancement(s)Above median

6%

14%

9%

14%

Below median

94%

86%

91%

86%

Top 3 quartiles

17%

49%

27%

48%

Bottom quartile

83%

51%

73%

52%

Note: The percentages reported in this table are based on the set of countries for which WGI Control of Corruption scores areavailable.

Table 13: Chinese official sector lending and repayment risk, by lending type
% of Chinese lending with and without credit enhancements
Repaymentrisk (year - 1)

Collateralized
lending

Non-collateralized
lending

Lending with credit
enhancement (s)Lending without credit
enhancement(s)Above median

38%

25%

36%

23%

below Median

62%

75%

64%

77%

Top 3 quartiles 64%

64%

63%

66%

Bottomquartile

36%

37%

34%

36%

Note: The percentages reported in this table are based on the set of countries for which average sovereign credit
ratings from Moody's, Standard & Poor's, and Fitch Ratings are available.

87 Here again, we use the sovrate index from Kose et al (2017). 6 of the 10 countries (Venezuela, Uganda, DRC, Congo-Brazzaville,

Ecuador, and Angola) rank in the bottom quartile of this index. 7 of the 10 countries (Russia, Venezuela, Uganda, DRC, Congo,
Ecuador, and Angola) rank below the median.

88 As Gelpern et al. (2021) explain, Chinese state-owned banks have positioned themselves as senior creditors whose loans should be

repaid on a priority basis by requiring their borrowers to maintain significant cash balances in lender-controlled bank or escrowaccounts. These informal collateral arrangements, which are rarely used by other bilateral and multilateral creditors, effectively put
Chinese lenders at the front of the repayment line because they can simply dip into their borrower’s accounts to collect unpaid debts.

35

Table 14: Top 10 borrowers by percent of lending that is collateralized
Borrowers% collateralized lending

Venezuela

92.5%

Peru

90.0%

Turkmenistan

88.6%

Equatorial Guinea

80.3%

Russia76.6%

Uganda74.3%

Democratic Republic of the Congo

67.4%

Congo-Brazzaville

67.3%

Ecuador

59.0%

Angola56.5%

Note: This table only includes countries that secured more than 10 official
sector loans from China between 2000 and 2017.

As Chinese state-owned lenders have sought to bankroll more costly, complex, and risky projects under the auspices of
the BRI, they have also prioritized different types of borrowers. Figure 10 above demonstrates that Beijing has directeda growing percentage of its overseas lending to project companies—or so-called special purpose vehicles (SPVs).89
Whereas 8% of China’s official sector lending was directed to these SPVs in 2000, this number soared to 33% by 2017.
This trend is part of a larger shift from full-recourse sovereign lending to limited-recourse project finance transactions
that began around the time of the 2008 Global Financial Crisis and accelerated during the first five years of BRI
implementation.
A loan that is directly issued to a government agency is called a full-recourse sovereign loan. The repayment of thistype of debt does not depend upon the financial viability of a project or the cash flow generated by any particularasset. The sovereign government borrower guarantees the repayment of the loan, regardless of whether the project
supported by the loan generates enough revenue to facilitate repayment. The creditor has a legal right to seize any
and all assets of the borrower until the full amount of the debt is recovered (i.e., it has “full recourse” to the assets ofthe borrowing government). By contrast, when a project is financed with a limited-recourse or no-recourse structure,
the loan that is used to finance the acquisition, construction, and/or maintenance of an asset—such as a toll road, aseaport, or an electricity grid—is exclusively repaid with the cash flow generated by the asset (e.g., toll revenue,
container fees, or electricity sales), and the creditor either has no claim (“recourse”) or a limited claim to any otherassets as a basis for recovering the debt. In a standard, limited-recourse or no-recourse project finance transaction, acreditor lends to an independent legal entity that is established for the express purpose of developing, owning, andoperating a specific project. This entity is often called a special purpose vehicle (SPV) because it is only allowed toengage in activities that relate to a specific purpose (project), and it is legally prohibited from incurring debts or
obligations that are not related to that purpose (project).90
AidData’s 2.0 dataset highlights a strong emphasis on lending to SPVs (i.e., limited-recourse and no-recourse project
finance transactions) during the BRI era. Figure 10 above and Figure 15 below demonstrate that, during the thirteenyear period preceding the BRI (2000-2012), SPVs accounted for 17% of official sector lending from China. However,during the first five years of BRI implementation (2013-2017), this figure shot up to 26%. In absolute terms, averageannual lending to SPVs increased from $8.2 billion during the 2000-2012 period to $13.2 billion during the 2013-2017
period. By 2017, one-third of all official sector lending from China was directed to SPVs.

89 Also see Figure 15 below.
90 SPVs are sometimes also referred to as “bankruptcy-remote” entities because they minimize bankruptcy risk.

36

Figure 15: Official sector lending from China to SPVs, 2000-2017$120

USD 2017 billions

$90

$60

$30

$0

2001 2002 2003 2004 2005 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
SPV borrowers

Other borrowers

Source: AidData.
Limited-recourse and no-recourse project finance transactions are often attractive to LMIC governments because they
make it possible to finance public infrastructure “off-balance sheet.” But why would Chinese state-owned lenders wantto move in this direction? Sovereign loans provide full recourse to the assets of government borrowers in the event ofdefault, so one would think that loans with a no recourse or limited recourse structure would be less attractive to them.
At the same time, it is important to remember that an unprecedented expansion in China’s overseas lending program
took place before the BRI was launched, and many LMIC governments accumulated substantial amounts of debt
between 2000 and 2012 (as we discuss at greater length in Section 4). Consequently, when Beijing announced the“Project of the Century” in 2013, many sovereign borrowers in LICs and MICs did not have a lot of room on theirbalance sheets to take on more big-ticket loans for large-scale infrastructure projects.91 This demand-side constraint
forced China’s state-owned banks to look for “work-arounds” that would allow them to deliver on Xi Jinping’s signatureforeign policy initiative. The most important path that they chose was increased lending to SPVs, and to account for thelimited recourse that such lending provides to host government assets, Chinese state-owned lenders doubled down on
the use of so-called credit enhancements. In the 2.0 dataset, we find that official creditors from China are nearly threetimes as likely as to issue loans to SPVs with some type of guarantee, credit insurance, or pledge of collateral as they
are loans to government borrowers.92
By the end of the fifth year of BRI implementation, the borrower type composition of China’s overseas lending portfolio
looked substantially different than it did at the turn of the century. Whereas 55% of the official sector lending from
China was directed to government agencies in 2000, 30% was directed to government agencies in 2017. This decline
coincided with an expansion of lending to SPVs, private sector institutions, state-owned banks, and state-owned
companies: whereas 31% of China’s official sector lending was directed to these institutions in 2000, this number

91 At the Belt and Road Forum for International Cooperation in May 2017, Xi Jinping referred to the BRI as the “Project of theCentury” (Clover et al. 2017).
92 Over an eighteen-year (2000-2017) period, we find that official creditors from China are 2.8 times as likely as to issue loans to SPVs

with some type of guarantee, credit insurance, or pledge of collateral as they are loans to government borrowers.

37
soared to 68% in 2017.93 These changes in the composition of China’s overseas lending portfolio were largely the resultof a shift towards limited-recourse project finance transactions. In Section 4, we take a closer look at this shift awayfrom full-recourse sovereign lending and consider the implications for LMIC governments as they relate to “hiddendebt” and the management of contingent public sector liabilities.

3.3 A primer on Chinese loan pricing practices
A longstanding question about China’s overseas lending program is whether its loans are more or less concessional
than the loans that LMICs can obtain from traditional bilateral and multilateral creditors. According to the 2.0 version of
our dataset, the average interest rate of a loan from an official sector institution in China is 4.2%,94 while the average
maturity length is 9.4 years and the average grace period is 1.8 years.95 The internationally-accepted way of measuring
a loan’s level of concessionality—i.e., the generosity of a loan or the extent to which it is priced below-market rates—isto calculate its “grant element” (which we discuss in Box 2). AidData calculates the grant element of every loan in its
2.0 dataset (for which the necessary pricing details are available) using OECD-DAC measurement standards.96 Analysisof these data indicates that, between 2000 and 2017, the average grant element of a loan issued by an official sector
institution in China was 28.4% (see Figure 16 below).97 As of 2017, it was 35%. Both figures are considerably lower than
the average grant element of loans (64%) issued by OECD-DAC creditors.98 Official sector loans from China are alsosignificantly less concessional than loans from the World Bank.99 Yet, there is some evidence of progressivity in theways that Chinese state-owned lenders price their loans. The average grant element of a loan from official sector
institutions in China to a borrower in a low-income country is 41%, while the average grant element of loan to aborrower in a lower-middle income country is 37% and the average grant element of a loan to a borrower in an uppermiddle income country is 21%.

93 The 2.0 dataset suggests that another area of specialization among Chinese state-owned lenders is borrower type. According toTable 15 , in our dataset, 53% of the loans that are provided to government agencies in borrower countries come from China
Eximbank. By contrast, CDB specializes in lending to state-owned banks and state-owned enterprises. In our dataset, nearly 50% of
the loans issued to state-owned banks in borrower countries come from CDB, and 51% of the loans issued to state-owned enterprises
come from CDB. Responsibility for lending to SPVs is more evenly distributed across China’s state-owned policy banks andcommercial banks.
94 As a point of comparison, across all ODA loans issued by all OECD-DAC members in 2018, the average interest rate was 1.1% andthe average maturity length was 28 years (OECD 2020: 5). However, lenders that participate in the OECD’s Creditor Reporting System(CRS) do not voluntarily disclose the pricing of their OOF loans.
95 The unweighted (simple) averages are as follows: 3.2% interest rate, 14.6-year maturity length, and 5.2-year grace period. Unlessotherwise specified, all of the grant elements, interest rates, maturity lengths, and grace periods that we report for Chinese loans andexport credits in this study are weighted according to the monetary values of loans (and export credits) in constant 2017 USD.
96 If any loan or export credit in the 2.0 dataset does not specify a grace period, AidData calculates its grant element by assuming thatno grace period was extended to the borrower. When Chinese state-owned lenders issue loans and exports without grace periods,
they typically only report an interest rate and maturity length.
97 In Figure 16 and Figure 17, average loan concessionality is calculated as the weighted average grant element for all loans with

sufficiently complete information on lending terms (to use the OECD-DAC grant element calculator).
98 This benchmark rate is based on all ODA loans issued by OECD-DAC members in 2017 (OECD 2020: 4). Lenders that participate in

the OECD’s Creditor Reporting System (CRS) do not voluntarily disclose the pricing of their OOF loans. Therefore, it is currently notpossible to generate an average grant element of OOF loans issued by OECD-DAC creditors, but it is important to keep in mind thatthe vast majority of official financing from OECD-DAC members is provided via ODA.
99 The World Bank is a particularly relevant benchmark institution since it provides loans for large-scale infrastructure projects and has

two different funding windows—the International Development Association (IDA) and the International Bank for Reconstruction andDevelopment (IBRD)—for the provision of ODA and OOF. In Morris et al. (2020), we demonstrate that the average grant element of aWorld Bank loan is approximately 17 percentage points higher than the average grant element of an official sector loan from China.
World Bank lending terms are more favorable to borrowers than Chinese lending terms because they generally include lower interest
rates and longer maturities.

38

Table 15: Breakdown of borrower type by funding agency
Borrower type
Funding agency

Government
agencyState-ownedbank

Privatesector

SOE

SPV

CEXIM

53%

17%

8%

14%

32%

CDB

36%

49%

53%

51%

26%

State-owned commercial bank

6%

9%

18%

11%

18%

Combination of state-owned policy &commercial banks

1%

25%

13%

3%

21%

MOFCOM

1%

0%

0%

0%

0%

SOEs

2%

0%

1%

22%

2%

Figure 16: Annual weighted average of grant element for loans from all funders, 2000-2017

BRI announced

50%

Average grant element

40%

30%

20%

10%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Source: AidData.

39

Figure 17: Annual weighted average of grant element for loans from select funding
agencies, 2000-2017
BRI announced

50%

40%

30%

20%

10%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
EXIM

CDB

State-owned commercial banks

Source: AidData.
The 2.0 dataset also shows that, during the BRI era, China’s official sector lending portfolio has become slightly moreconcessional. Figure 16 above provides evidence that the average grant element of official sector lending from China
increased by 2.5 percentage points—from 32.5% to 35%—between 2013 and 2017. This shift towards moreconcessional lending might, at first blush, seem inconsistent with the BRI’s emphasis on profitable, revenue-generating
projects and the growing involvement of state-owned commercial banks in China’s overseas lending program.100
However, to better understand the reason for this modest increase in the grant element of official sector lending from
China, one needs to examine differences in concessionality levels and trends across different official creditors in
China.101 Table 7 above suggests that, with the passage of time, a division of labor has emerged among the various
government and state-owned entities that support China’s overseas lending portfolio. As the longtime administrator of
the country’s interest-free loan program, MOFCOM’s Department of Foreign Assistance issues loans on extremely
generous terms—with average concessionality rates (i.e. grant elements) that approach 75%—and it usually supports
projects that generate diplomatic and geostrategic benefits rather than commercial benefits.102 Projects financed with
MOFCOM loans often support the construction of presidential palaces, parliamentary complexes, theaters, opera
houses, convention centers, stadiums and other facilities that cater to governing elites in major urban centers. By
contrast, CDB and the country’s state-owned commercial banks prioritize revenue-generating projects—like oil
refineries, gas pipelines, mines, factories, and power plants—and price their loans at or near market rates. In fact, Table
7 above demonstrates that the average interest rates, maturities, grace periods, and grant elements of loans issued by
CDB and state-owned commercial banks are very similar: 10 to 11.5 year maturities, 1 to 1.2 year grace periods, and

100 In 2016, an official with China’s National Development and Reform Commission (NDRC) told the Financial Times that “these days

we need viable projects and a good return. We don’t want to back losers” (Financial Times 2016).
101 Here we use the term “official sector lending from China” to capture loans and export credits issued by Chinese government

institutions and Chinese state-owned institutions.
102 In August 2021, China International Development Agency (CIDCA), the Ministry of Commerce (MOFCOM), and the Ministry ofForeign Affairs (MOFA) reviewed and approved a new set of foreign aid administration measures. These measures specify that, as of
October 1, 2021, CIDCA will be responsible for all planning, policymaking, regulatory, and supervisory functions that support thecountry’s foreign aid program. MOFCOM will continue to implement foreign aid projects, among other line ministries (including
MOFA). See http://www.cidca.gov.cn/2021-08/31/c_1211351312.htm

40
4.4% to 5.3% interest rates.103 The average grant element of a CDB loan is 18.1% and the average grant element of aloan issued by a Chinese state-owned commercial bank is 22.1%. China Eximbank is something of a hybrid institution;
it not only offers highly concessional loans with low, fixed interest rates and long repayment schedules and grace
periods (like MOFCOM), but also loans at or near market rates with shorter repayment schedules and grace periods(like CDB and the state-owned commercial banks).104 The compositional diversity in its portfolio is reflected in an
average grant element of 41.5%, which is considerably lower than the concessionality level of a typical MOFCOM loan
but considerably higher than the concessionality level of a typical loan issued by CDB or a Chinese state-owned
commercial bank.
In Figure 17 above, we take a closer look at why the average level of concessionality in China’s official sector lending
portfolio increased modestly during the first five years of BRI implementation. We do so by measuring the average
grant element of lending from China’s state-owned policy banks (CDB and China Eximbank) and state-owned
commercial banks between 2000 and 2017. According to Figure 17 above, there was a decline in the average
concessionality level of lending from China’s state-owned commercial banks between 2013 and 2017—from 24% to20%.105 This trend is consistent with the increasingly commercial orientation of China’s overseas lending activities
during the BRI era. There was also a decline in the average concessionality of lending from China Eximbank (from 50%
in 2013 to 44% in 2017), which suggests a shift towards more commercial lending within one of the country’s two major
policy banks after the BRI was announced.
Yet curiously, the overseas lending portfolio of the country’s other major policy bank (CDB) did not become lessconcessional. In fact, according to Figure 17 above, CDB’s loan portfolio became substantially more concessional
during the BRI era, which is difficult to reconcile with Beijing’s assertion that CDB is a commercial bank adhering tocommercial lending practices. If we further probe the question of why the average concessionality level of CDB’s loan
portfolio increased sharply between 2016 and 2017, we quickly find evidence of the “hidden hand of the state”
guiding CDB’s lending behavior. Although the institution usually lends at a floating market interest rate (e.g., LIBOR or
EURIBOR plus a margin),106 the sharp increase in CDB loan concessionality took place (primarily) because the bankdeviated from its own loan pricing guidelines for a strategically important mega-infrastructure project in Indonesia.107
The Jakarta-Bandung High Speed Rail (HSR) Project was originally going to be financed by the Japan International
Cooperation Agency (JICA). In January 2014, JICA included the project in its Overseas Development Aid plan forIndonesia and agreed in principle to bankroll 75% of the total cost of the project at a 0.1% interest rate so long as thehost government provided a loan repayment guarantee. However, Beijing was determined to win the contract for the
HSR project and sought to outcompete Tokyo on several dimensions, including cost, speed of implementation, andlevel of public liability (Harner 2015; Rachman and Lamboge 2020; Liao and Katada 2021). In early 2015, Indonesian
President Joko Jokowi invited China to submit an alternative proposal, and it proposed a lower cost version of the
project that could be fully financed with a government-guaranteed (CDB) loan at a 2% interest rate and implemented
on a shorter timeline (3 years rather than 5 years). Then, in September 2015, when President Jokowi was expected toannounce the winning bidder, he surprised everyone by rejecting Beijing’s offer and Tokyo’s offer and “cancelling” theproject. The stated rationale for his decision was that the project would lead to a ballooning of public debt. Tokyo
responded by offering a 50% reduction in the amount of debt that would need to be backed by a sovereign guarantee.
103 The average loan pricing data in our dataset is largely consistent with the average loan pricing data that CDB has publicly

disclosed (at an aggregate level). The CDB’s self-reported average interest rate of “loans and advances” was 5.25% in 2015 and4.40% in 2016 (CDB 2016).
104 Another apparent area of specialization among Chinese state-owned lenders is borrower type. According to Table 15 above, 53%of the lending in the 2.0 dataset that are issued to government agencies in borrower countries come from China Eximbank. By
contrast, CDB seems to specialize in lending to state-owned banks and state-owned enterprises. In our dataset, just over 50% of theloan value issued to state-owned banks in borrower countries come from CDB, and 51% of the lending issued to state-owned
enterprises come from CDB. Responsibility for lending to SPVs is more evenly distributed across China’s state-owned policy banks andcommercial banks.
105 This trend should be considered in light of another trend that we previously highlighted in Figure 9 above: the fact that a large

proportion of the growth in China’s overseas lending portfolio between 2013 and 2017 took place among its state-owned commercial
banks.
106 According to Chen (2020: 442), “[i]nterviews with CDB loan managers and employees of enterprises that have received CDB loans

suggest that ... [the] [i]nterest rates of foreign-currency [CDB] loans are presented in the form of LIBOR + n · 100 [basis points].” In the
2.0 dataset, we find that the vast majority of CDB loans are denominated in dollars or euros and priced at LIBOR or EURIBOR plus a
margin.
107 An important caveat is that AidData uses a fixed discount rate rather than country-specific discount rates to calculate levels of loan

concessionality (see Box 2). We do so to minimize complexity and ensure consistency with prevailing international measurement
standards. However, our approach does not account for borrower country-specific levels of risks (i.e., different levels of uncertainty
about future cash flows across countries). On this point, see Schlegl et al. (2019).

41
Beijing responded by scrapping the sovereign guarantee requirement altogether and proposing an off-government
balance sheet transaction, in which CDB would work around the country’s public debt ceiling by extending a loan to an
SPV (jointly owned by Chinese and Indonesian state-owned companies) rather than the Government of Indonesia.108
Beijing’s revised offer was sufficiently attractive to seal the deal. On May 14, 2017, China Development Bank (CDB)
signed a $3.9675 billion loan agreement with PT Kereta Cepat Indonesia China—a special purpose vehicle that is
jointly owned by a set of Indonesian state-owned enterprises (60% equity stake) and a set of Chinese state-owned
enterprises (40% equity stake)—for the Jakarta-Bandung High Speed Rail Project.109 The loan was issued in two
tranches (a USD-denominated tranche worth $2.3805 billion and an renminbi-denominated tranche worth $1.587
billion) and priced on exceptionally generous terms. The dollar-denominated tranche (captured via Project ID#61320 in
the 2.0 dataset) carried a 40-year maturity, 10-year grace period, and 2% interest rate, while the renminbi-denominatedtranche (captured via Project ID#61321 in the 2.0 dataset) carried a 40-year maturity, 10-year grace period, and 3.46%
interest rate. As such, the grant element of the dollar-denominated tranche was 69% and the grant element of therenminbi-denominated tranche was 56%—levels of loan concessionality that far surpass the OECD’s 25% threshold for
Official Development Assistance (ODA) and more closely resemble the levels of concessionality that are observed when
MOFCOM’s Department of Foreign Assistance issues interest-free loans or China Eximbank issues preferential loans.110
The fact that one of the largest CDB transactions in our global dataset would qualify as foreign aid or ODA under any
reasonable standard (set by the OECD or the Chinese Government) belies the claim that CDB is a nothing more than acommercial bank following commercial lending practices.111 During a recent debate about which Chinese lenders
should be required to participate in the Debt Service Suspension Initiative (DSSI), Beijing took the position that CDB’s
participation in the DSSI should be optional since only official creditors are expected to participate in the initiative.112
However, the Jakarta-Bandung High Speed Rail Project demonstrates that Beijing is trying to have it both ways: it iscontent to let the CDB operate on a commercial basis when this approach is consistent with the government’s policy
objectives, but it is also willing to intervene and direct the bank to lend at below-market rates when it decides thatconcessional lending will more effectively advance the government’s policy objectives.113
The heavily subsidized nature of CDB lending for the Jakarta-Bandung High Speed Rail (HSR) Project is certainly
unusual, but that does not mean that the institution’s broader international loan portfolio is shielded from the policy
directives of the central government. Quite the opposite. Our analysis of the broader set of CDB-financed projects inthe 2.0 dataset suggests that the institution’s overseas lending activities are being directed by the central government—even when its loans are priced on commercial rather than concessional terms—and it has been tasked with a “highrisk, high-reward” credit allocation strategy that sets it apart from other Chinese state-owned lenders. CDB, which isthe single largest official source of international credit from China (see Figure 9 above), is more willing than its peers tolend to countries that are plagued by high levels of corruption. According to Table 10 above, nearly 70% of CDB’s
overseas lending between 2000 and 2017 supported countries within the bottom quartile of the WGI Control ofCorruption index.114 By way of comparison, 59% of overseas lending from China Eximbank and 57% of the country’s

108 After the Asian Financial Crisis, Indonesia passed fiscal reform legislation, which established an outstanding government debt

ceiling of 60% of GDP and a budget deficit ceiling of 3% of GDP.
109 The purpose of the project is to construct a 142.3 km high-speed railway from the city of Jakarta to Bandung, the capital of West

Java. Upon completion, the railway is expected to reduce the travel time between Jakarta and Bandung from more than 3 hours toless than 40 minutes.
110 According to Table 7 , CDB’s average level of loan concessionality is 18.1%.
111 For econometric evidence that political factors significantly influence the allocation of CDB credit, see Ru (2018).
112 In its latest public filing with the Hong Kong Stock Exchange, CDB describes itself in the following manner: “We are a state-owned

development finance institution. We report directly to the State Council on important matters relating to our business and operations,and [...] [o]ur operations are subject to the direct leadership of the State Council, in support of the development of key sectors andweak areas in the PRC economy. To anchor our mission of supporting national development and delivering a better life for the people,
we align our business focus with China’s major medium- and long-term economic development strategies. We are currently wholly
owned, directly or indirectly, by the PRC government […]” (CDB 2020).
113 During recent policy discussions regarding the DSSI, Beijing has tried to characterize CDB as a commercial bank that should be

exempt from the international rules and norms that guide the behavior of official creditors. However, CDB’s own statements areinconsistent with this claim. In a public filing with the United States Securities and Exchange Commission (SEC), CDB acknowledgedthat w[e] generally make hard loans to [borrowers] that, based on our credit evaluation process, are considered to be capable ofrepaying the debt obligations and are responsible for the construction and operation of the projects being financed”, but “[w]e may
select projects for soft loan financing primarily based on the government’s policy considerations […]” (CDB 2005).
114 This figure increases to 89% if one considers the percentage of CDB lending that supported countries below the (global) medianon the WGI Control of Corruption index.

42state-owned commercial banks between 2000 and 2017 supported countries within the bottom quartile of the WGIControl of Corruption index.115
CDB is also more aggressive about collateralizing its debts than other Chinese state-owned lenders. During the first
five years of BRI implementation, 70% of CDB’s overseas lending portfolio was collateralized, but substantially smaller
percentages of the overseas lending portfolios overseen by China Eximbank (29%) and the country’s state-owned
commercial banks (44%) were collateralized (see Table 11 above). Collateralization has evidently become a “go-to” risk
mitigation tool for CDB when it transacts with countries that pose high levels of corruption risk: 82% of CDB’s
collateralized lending goes to countries within the bottom (fourth) quartile of the WGI Control of Corruption index,
while only 18% goes to countries that fall within the first, second, and third quartiles of the of the WGI Control ofCorruption index (see Table 16).116

Table 16: Chinese collateralized lending and corruption, by funding agency% of Chinese collateralized lending by funding agency
Control ofcorruption (year 1)

CEXIM

CDB

State-owned
commercial banks

Combination of stateowned policy & commercial
banks

SOEs

MOFCOMTop 3 quartiles

20%

18%

28%

21%

5%

0%

Bottom quartile

80%

82%

72%

79%

95%

0%

Note: The percentages reported in this table are based on the set of countries for which WGI Control of Corruption scores areavailable.

One explanation for this pattern is that the collateralization mechanism is itself a deterrent to financial misappropriation
and repayment delinquency. Gelpern et al. (2021) provide contractual evidence that CDB often requires borrowers todeposit a portion of the cash proceeds from their commodity exports into lender-controlled bank accounts, which puts
the lender in a position to seize liquid assets (without having to go to court) if borrowers fail to honor their repayment
obligations. This arrangement, which has been described as an “agency of restraint,” may provide a partial explanation
for why the CDB issues such large amounts of debt to resource-rich countries that pose high levels of fiduciary risk(e.g., Venezuela, Angola, Equatorial Guinea, Congo-Brazzaville, Turkmenistan).117
However, another important part of the story is that CDB is a yield-maximizing agent of the state tasked with lending toresource-rich countries that pose especially high levels of fiduciary risk (where there is not much competition from otherofficial creditors) to achieve outsized investment returns. Table 17 provides evidence that when CDB lends to countries
that pose especially high levels of fiduciary risk, it attaches higher interest rates to its loans—specifically, weighted
average interest rates of 5.8% for countries in that fall within the bottom quartile of the WGI Control of Corruption
index and weighted average interest rates of 4% for countries that fall within the top three quartiles of the WGI Control
of Corruption index.118 China Eximbank has followed a similar strategy, but it has not done so as aggressively as CDB.

115 These figures increase to 89% and 85%, respectively if one considers the percentage of lending from China Eximbank and thecountry’s state-owned commercial banks that supported countries below the (global) median on the Control of Corruption index.
116 The same can be said of other Chinese state-owned lenders (see Table 16).
117 Brautigam (2011) puts it this way: “[c]ommodity-backed loans are a pre-commitment technique. They allow a government to havepublic works expenditures today, paying for them with future exports. In weak governments, rather than trying directly to improve thehost government’s accountability mechanisms, or forcing improvements through conditionality, the Chinese accept that institutional
development is a long-term process. They manage their fiduciary responsibility by keeping control over the finances and almost nevergiving cash. As one African official told me: ‘with China you never see that money.’”
118 The grant element metric that we previously discussed can also be used as a gauge of whether and when borrowing terms are

more or less favorable to a lender. According to Table 17, the weighted average grant element of a CDB loan that is offered to a
country in the bottom (fourth) quartile on the WGI Control of Corruption index is only 15%. It is nearly twice as high (28%) for
countries that fall within the top three quartiles on the WGI Control of Corruption index.

43
Table 17 also indicates that China’s state-owned commercial banks are not balancing risk and reward in the sameway.119

Table 17: CDB, CEXIM, and state-owned commercial banks’ lending terms, by level offiduciary risk in borrower countries
CDB

CEXIM

State-owned commercial banks

Control ofcorruption (year –1)

Interest rate

Grant element

Interest rate

Grant element

Interest rate

Grant element

Top 3 Quartiles

4.0%

28%

2.9%

44%

4.7%

25%

Bottom Quartile

5.7%

15%

3.4%

40%

4.0%

19%

Note: The percentages reported in this table are based on the set of countries for which WGI Control of Corruption scores areavailable. All interest rates and grant elements are reported as weighted averages. They are weighted according to the monetary
values of loans (and export credits) in constant 2017 USD.

CDB’s unique “high-risk, high-reward” credit allocation strategy traces its origins to the 2008 Global Financial Crisis.
When international commodity prices plummeted, the Chinese government gave CDB a special assignment: investing
the country’s excess foreign exchange reserves in undervalued—and potentially high-yield—overseas assets via foreign
currency-denominated international lending (Dreher et al. 2021a).120 A particularly high level of priority was assigned tooverseas assets in the natural resource sector. Chen Yuan, the President of China Development Bank at the time, said
that “[e]veryone is saying we should go to the western markets to scoop up [underpriced assets]. … I think we should
not go to America’s Wall Street, but should look more to places with natural and energy resources” (Anderlini 2009).
That is precisely what happened: CDB’s foreign currency-denominated lending to overseas borrowers soared to record
levels after 2008 (see Figure 9 above) and the vast majority of its lending was priced on commercial rather thanconcessional terms, collateralized against commodity exports, and directed to resource-rich countries that pose
especially high levels of fiduciary risk. Therefore, the story of how CDB became Beijing’s biggest lender to thedeveloping world is the story of a profit-maximizing surrogate of the state making high-risk, high-reward investments inundervalued assets located in poorly-governed countries with large natural resource endowments.

119 Table 17 provides a comparable set of summary statistics for China Eximbank and the country’s state-owned commercial banks (thesecond- and third-largest official sources of international credit from China). It reveals a substantially smaller difference (0.5% as
opposed to 1.7% in CDB’s case) in the weighted average interest rate of China Eximbank lending to countries that fall within thebottom quartile of the WGI Control of Corruption index (3.4%) and the weighted average interest rates of China Eximbank lending tocountries that fall within the top three quartiles of the WGI Control of Corruption index (2.9%). The interest rates attached to China
Eximbank loans are also considerably lower than those attached to CDB loans. Table 17 does not suggest that Chinese state-owned
commercial banks are pursuing the same “high-risk, high-reward” credit allocation strategy. They attach lower interest rates tocountries that pose especially high levels of fiduciary risk and higher interest rates to countries that pose relatively lower levels of
fiduciary risk.
120 Under a so-called “entrust loan” agreement that was signed in 2008, China’s State Administration of Foreign Exchange (SAFE)

entrusted a portion of the country’s foreign exchange reserves to CDB and directed it to engage in international lending activities on
its behalf (Kong and Gallagher 2017).

44

4. Managing debt risks during the BRI era
4.1 The rise of hidden debt and the fall of sovereign debt
In Section 3, we provided evidence of an extraordinary expansion in China’s overseas development finance program
during the 21st century, which was initially fueled by non-concessional and semi-concessional loans and exports credits(i.e., OOF rather than ODA) from the country’s state-owned policy banks (China Eximbank and CDB). We also
demonstrated that, during the BRI era, China’s state-owned commercial banks have assumed an increasingly importantrole—by organizing lending syndicates and other co-financing arrangements that make it possible to undertake morecostly and complex infrastructure projects. Over time, larger loans have led to higher levels of credit risk, which has inturn created pressure for stronger repayment safeguards, including collateralization, credit insurance policies, and thirdparty guarantees.
We now turn our attention to another defining feature of the BRI era: the fact that China has rapidly expanded its
lending for infrastructure projects in LMICs while at the same time shrinking the percentage of its overseas loan
portfolio that supports sovereign borrowers (i.e., central government institutions) in LMICs. Beijing has attempted tosquare this circle by ramping up its lending to SPVs, state-owned banks, state-owned companies, and private sector
institutions in the developing world. However, it has done so in ways that blur the distinction between private andpublic debt and create major public financial management challenges for LMICs.
According to the 2.0 dataset, only 31% of China’s official sector lending was directed to project companies (SPVs),
state-owned companies, state-owned banks, and private sector institutions in 2000. However, by 2017 (the fifth year of
BRI implementation), this figure soared to 68% (see Figure 10 in Section 3).121 This extraordinary increase coincidedwith a sharp decline in China’s official sector lending to government agencies—from 55% in 2000 to 30% in 2017 (seeFigure 10 in Section 3). At first blush, one might think that government agencies in LMICs do not have much reason to
be concerned about the rapid accumulation of Chinese debt by project companies (SPVs), state-owned companies,
state-owned banks, and private sector institutions in their countries. These repayment liabilities, for the most part, do
not appear on government balance sheets in LMICs.
However, these off-government balance sheet transactions raise questions and concerns about undisclosed
government repayment liabilities in LMICs because Beijing’s state-owned banks have relied on hybrid financial
arrangements that selectively mix and match elements from full-recourse sovereign lending and elements from limitedrecourse project finance. In some cases, they have lent to a private company or SPV but demanded that the hostgovernment issue a sovereign guarantee in support of the loan, which means that the loan issued could become apublic debt that host country taxpayers are responsible for repaying (if the borrower goes bankrupt or the project inquestion does not generate sufficient revenue).122 In other cases, they have lent to a state-owned enterprise in the hostcountry (or an SPV that is wholly or partially-owned by a state-owned enterprise in the host country) without
demanding a sovereign guarantee, thereby creating uncertainty about who will assume responsibility for repayment in
the event that the borrower goes bankrupt or defaults on its obligations. Another practice that has become increasingly
popular is issuing a loan to an SPV or private sector institution without requiring a formal repayment guarantee fromthe host government but demanding a guaranteed return on equity from the host government.123 In principle, this typeof loan is not a public sector liability since the borrower is an independent legal entity and the host government has no
repayment obligation in the event of default. However, in practice, a host government-guaranteed return on equity

121 This increase was largely driven by the expansion in lending to SPVs, which we documented in Section 3. Also see Figure 15 inSection 3.
122 According to the 2.0 dataset, 36% of official sector lending from China to SPVs, 23%% of official sector lending from China to

private sector institutions, 34% of official sector lending from China to state-owned banks, and 11% of official sector lending from
China to state-owned companies is backed by a formal repayment guarantee.
123 This practice is especially prevalent among independent power projects (IPPs) that are being financed with a mix of Chinese debt

and equity under the auspices of the China–Pakistan Economic Corridor (CPEC) initiative. In June 2013, Pakistan’s National ElectricPower Regulatory Authority (NEPRA) set a 17% return on equity (ROE) “floor” for coal-fired IPPs. Then, to entice Chinese investors
and provide additional reassurance to Beijing’s state-owned banks, NEPRA increased the ROE floor to 24.5% in June 2014. The
Government of Pakistan even went a step further by guaranteeing the payment obligations of the state-owned Central Power
Purchasing Agency (CPPA) to the companies (special purpose vehicles) that own and operate independent power plants (Downs2019). The Government of Pakistan has also issued ROE guarantees and CPPA payment guarantees for Chinese government-financed
renewable energy projects (e.g., Arif Habib Corporation Limited 2017).

45
implies that the borrower will be able to consistently service its Chinese debts, which is tantamount to an explicit loan
repayment guarantee that that host government does not need to disclose.124
None of these lending practices are intrinsically harmful to the interests of developing countries. However, they raise
concerns about “hidden debts” and questions about whether LMIC governments are effectively monitoring andmanaging their contingent liabilities (World Bank and IMF 2020a; G7 2020, 2021; IMF 2021). Figure 18 arrays thedifferent types of borrowers to which Chinese state-owned banks lend on a “public liability” spectrum. On the far lefthand side of the spectrum is a sovereign debt transaction in which the borrower is the central government institution,and the lender has full recourse to the assets of the borrower. On the far right-hand side of the spectrum is a loan
issued to a private entity without a government repayment guarantee.

Figure 18: Official Chinese sector lending on the “public liability” spectrum

We will soon demonstrate that Chinese state-owned banks increasingly lend to borrowers that fall somewhere in the“murky middle” of this spectrum. This type of lending comes in different varieties. One variety—situated left-of-centeron the public liability spectrum in Figure 18 —is when an official Chinese creditor issues a loan to a private or public
sector entity in an LMIC that is backed by an explicit government repayment guarantee (i.e., a “sovereignguarantee”).125 Take for example the $127.5 million loan that China Eximbank issued in 2017 to a private company—
124 The World Bank describes the nature of the problem in the following manner: “[a]nother category of contingent liabilities that is

growing rapidly and poses a potential risk to the debt portfolio is the guarantees given in the context of public-private partnerships orexternal liabilities issued through off-shore or off-balance sheet mechanisms by both public and private domestic entities.
Governments are often obligated to guarantee above average income streams to attract private investors and the scope ofguarantees offered to make PPPs look viable may be substantial, including loan repayments, guaranteed rates of return, minimum
income streams, guaranteed currency exchange rates and compensation, should new legislation affect an investment’s profitability.
Some of the guarantees associated with PPPs are explicit and stated in contractual agreements but more often they are implicit[contingencies] which may translate into financial obligations: the timing and magnitude depends on the occurrence of a future event
outside the control of the government. Measurement problems are compounded by the fact that current account practice permits
governments to keep the costs and liabilities associated with PPPs off-balance sheet, thus circumventing budgetary constraints andobfuscating scrutiny by the national legislature.” See https://datatopics.worldbank.org/debt/QuarterlyBulletin-January2019
125 A sovereign guarantee is a guarantee from a government institution (typically the Ministry of Finance) that a repayment obligation

will be satisfied if the primary borrower defaults. When a government institution issues a sovereign guarantee, there is no immediate
impact on the liquidity of the government institution, but its assets are put at risk.

46
called Ahmed Siyam Holdings Pvt Ltd—to support the construction of a luxury resort with 509 rooms on Noonu Atoll in
the Maldives (captured via Project ID#54281). The owner of Ahmed Siyam Holdings Pvt Ltd was a member ofparliament and a key political ally of the Maldivian president (Abdulla Yameen Abdul Gayyoom) at the time that theloan was contracted.126 For reasons that were never disclosed, the Government of the Maldives issued a sovereignguarantee in support of the loan (Auditor General’s Office of the Republic of Maldives 2020). Therefore, the private
debt contracted by Ahmed Siyam Holdings Pvt Ltd would automatically become a public debt if the resort project wasinsufficiently profitable, and the borrower defaulted on its repayment obligations. This worst-case scenario nearly cameto pass. Due to economic difficulties resulting from the COVID-19 pandemic, Ahmed Siyam Holdings Pvt Ltd failed tomeet a $10 million loan repayment obligation that was due to China Eximbank on July 21, 2020. The very next day,
China Eximbank warned the Government of the Maldives that, as the ultimate guarantor of the private debt, it was
responsible for the loan repayment obligations of Ahmed Siyam Holdings Pvt Ltd. and failing to meet these obligations
would result in a sovereign default, thereby jeopardizing the country’s international credit ratings and its
macroeconomic stability (Aiham 2020; Mohamed 2020; Macan-Markar 2020).127
However, debts and debtors that are located between right-of-center and center on the public liability spectrum in
Figure 18 above can create major public financial management problems for LMICs.128 Take for example public-private
partnership (PPP) projects, which have become increasingly popular among Chinese state-owned lenders during the
BRI era. When LMIC governments engage in PPP projects, they can provide explicit or implicit forms of liability
protection to the project companies (SPVs) that own public infrastructure assets. Explicit forms of liability protection are
codified in laws and contracts, but implicit forms of liability protection arise when there are expectations that the hostgovernment will bail out a project company (SPV) if it cannot repay its debts. Bova et al. (2016) provide evidence that
implicit forms of contingency liability protection are especially problematic. They construct a comprehensive dataset ofcontingent liability realizations in developed and developing countries over a 25-year period and find that “[t]heaverage fiscal cost of a contingent liability realization is 6 percent of GDP but costs can be as high as 40 percent for
major financial sector bailouts.” They also find that more than 80% of all contingent liability realizations result from
implicit forms of liability protection.
The China-Laos Railway Project, which we discuss at greater length in Box 3, illustrates why implicit forms of liability
protection from LMIC governments can be especially problematic. To implement this $5.9 billion project on a PPP
basis, three Chinese state-owned companies and a Lao state-owned enterprise established a joint venture (SPV) called
the Laos-China Railway Company Limited (LCRC). The co-owners of the LCRC agreed to provide equity contributions
that would cover 40% of the total project cost. The remaining 60% was financed by the LCRC with a $3.54 billion debt
financing package from China Eximbank. The Government of Laos did not issue a sovereign guarantee in support ofthe debt financing package that was contracted by the LCRC. Therefore, the project is not a contingent liability for theGovernment of Laos because it provided an explicit form of liability protection to the LCRC. It is a contingent liability
for the Government of Laos because it is potentially “too big to fail.” That is to say, if the railway (a public infrastructure
asset) is insufficiently profitable, the Laotian authorities may face political, reputational, and/or moral pressures to bailout the LCRC.129 However, since the LCRC is jointly owned by three Chinese state-owned companies that hold a 70%
equity stake and a Lao state-owned enterprise that holds a 30% equity stake, there is uncertainty about whether the
Chinese side or the Lao side would feel more compelled to bail out the LCRC if it became financial distressed and/or it
defaulted on its repayment obligations to China Eximbank. In the event that the Chinese side was less willing than the
Lao side to abandon the railway, the host government would potentially have no liability at all for the $3.54 billion debt
that was contracted by the LCRC.130 However, if the Lao side had less appetite than the Chinese side to let the railway
fail, the host government could end up assuming responsibility for the entire $3.54 billion debt—or more.131 Herein lies

126 President Abdulla Yameen Abdul Gayyoom was eventually jailed on charges related to the leasing of an island for resort

development in exchange for bribes worth $1.17 million (Avas 2020).
127 This issue was ultimately resolved when the Maldivian government threatened to exercise its mortgage rights if Ahmed Siyam

Holdings Pvt Ltd did not deposit the overdue payment in an escrow account within five business days. However, this case still
highlights the blurry nature of the distinction between private and public debts that are owed to Chinese state-owned lenders. See
Macan-Markar (2020).
128 Here we are referring to the third and fourth categories from the left on the public liability spectrum.
129 Even in wealthy, industrialized countries, these types of off-government balance sheet transactions have a checkered history. Takefor example the PPP that was established during the late 1990s to refurbish the London Underground (“Tube”). It underperformed
financially and the central government eventually had to step into the breach and bail out an SPV that could not manage its debt
repayment obligations to the tune of £1.6 billion (Butcher 2012; Schaefer 2018).
130 Supalak Ganjanakhundee of the ISEAS–Yusof Ishak Institute argues that “China would not allow Laos to go bankrupt because it

could boomerang on Chinese companies and creditors” (Macan-Markar 2021).
131 The “or more” qualifier is an important one because, as an independent legal entity, the LCRC can assume any number of financial

obligations other than the $3.54 billion debt financing package that it received from China Eximbank.

47
the core challenge of “hidden debts” that benefit from implicit forms of liability protection: the problem is less about
the host government knowing that it will need to service an undisclosed debt (with a known monetary value) than it isabout the host government not knowing the monetary value of debt that it may or may not have to service in the
future.132
Contingent liabilities are especially dangerous in countries with high baseline levels of public indebtedness.133 Laos isagain a case in point. Without any consideration of contingent liabilities, its public debt-to-GDP ratio stands
somewhere between 55% and 60%, and history suggests that countries with rising public debt-to-GDP ratios in excess
of 50–60 percent tend to experience economic growth slowdowns (Chudik et al. 2017). Laos would therefore be in the“danger zone” even if it had no contingent liabilities. But it does. International credit rating agencies estimate that the
China-Laos Railway Project and other projects being implemented on a PPP basis may represent an additional 30% of
GDP (Barney and Souksakoun 2021).134 Our own dataset suggests that the Laotian government’s true level ofcontingent liability due to official sector lending from China may be as high as 35% of GDP (see Table A-4 in the
Appendix). These “known unknowns” have real consequences. In August 2020, Moody’s downgraded the country’s
international credit rating from B3 to Caa2—one notch above a rating of debt default (Moody’s 2020). Fitch Ratings
followed suit in September 2020, citing concerns about contingent liability risk (Fitch Ratings 2020). The limited liability
corporation structure of the LCRC (described in Box 3) and the various project companies responsible for other PPP
projects has no doubt shielded the Government of Laos from some degree of repayment risk, but it has alsoheightened concerns about hidden debt and implicit forms of liability protection (i.e., whether the Laotian authorities
would bail out the LCRC in a situation of financial distress or default).

132 The fact that the LCRC is jointly owned by three Chinese state-owned companies (with a 70% equity stake) and a Lao state-owned

enterprise (with a 30% equity stake) does not imply that, in the event of default, the Chinese state-owned companies would legally be
responsible for 70% of LCRC’s outstanding debts to China Eximbank. Nor does it imply that the Lao state-owned company would be
responsible for 30% of the LCRC’s outstanding debts to China Eximbank. The LCRC is a limited liability corporation, so in the eventthat it defaults on its repayment obligations to China Eximbank, the equity holders in the LCRC are not legally obligated to assume
responsibility for its outstanding debts.
133 According to the World Bank and the IMF, the country faces a high risk of debt distress (World Bank and IMF 2019).
134 Most of the other projects being implemented as PPPs in Laos are hydropower projects financed by Chinese state-owned banks(Barney and Souksakoun 2021).

48

Box 3:
Hiddendebt debt
in a China
Eximbank-financed
PPP projectin Laos
Box
3: Hidden
in a China
Eximbank-financed
PPP projectin Laos
The Lao-China
RailwayRailway
Company(LCRC)(LCRC)
is a special
purposevehiclevehicle(i.e., projectcompany)that wasestablished
as a
The Lao-China
Company
is a special
purpose(i.e., projectcompany)
thatwaslimitedestablishedliability corporation(LLC)
tofinance,design,construct,andmanagea418
kilometer
railway
betweenthe
as a limited liability corporation (LLC) to finance, design, construct, and manage a 418
Chinesecity of Kunming
and the theLaotiancapitala public-private
partnership(PPP) onbasis
(World Bank
kilometer
railway between
ChinesecityofofVientiane
Kunmingon
andthe Laotian capitalof Vientianea public135
2020: 20).
total cost(PPP)of theChina-Laos
Railway
Project
is The$5.9totalbillion—equivalent
to roughly
one-thirdof Laos’
privateThepartnershipbasis(World Bank
2020:20).135
cost of the China-Laos
Railway
Projectis
GDP—anditisbeing
financedaccording
to
a
60:40
debt-to-equity
ratio($3.54billion
of
debt
and
$2.36
billion$5.9 billion—equivalent to roughly one-third of Laos’ GDP—and it is being financed according to a 60:40ofequity).debt-to-equity
LCRC directlyratio
secured$3.54
billionof debt
fromofChina
Eximbank,
and theGovernmentof Laos and($3.54billionof debt
andfinancing$2.36 billion
equity).
LCRC directly
secured$3.54 billion
the Chinese
Government
contributed$730
millionand$1.63
billionofequityfinancing,respectively.
Inorder
to makeof debt financing from China Eximbank, and the Government of Laos and the Chinese Government
its $730
million equity
the project,
Government
Laos secured$480tomillionfrom China
contributed$730contribution
million and to$1.63
billion ofthe
equityfinancing,ofrespectively.
In aorder
make loan
its $730
136 The $3.54 billion debt
Eximbank
andequity
it agreed
to provide
million theof itsown fundingof(inannualinstalments).
millioncontribution
to$250the project,
GovernmentLaossecureda $480 million
loan from China
secured
by
LCRC,
which
is
jointly
owned
by
three
Chinesestate-owned
companies
thatcollectively
hold$3.54
a 70% equity
Eximbank and it agreed to provide $250 million of its own funding (in annual instalments).136 The
137
stake andone
Laostate-owned
enterprise
that
owns
a
30%
equitystake,
isnotbackedbyasovereignguarantee.
billion debt secured by LCRC, which is jointly owned by three Chinese state-owned companies that
However,
in
the
event
that
the
China-Laos
Railwayisnotprofitable,theLaotianauthorities
“will
be
under
considerable
collectively hold a 70% equity stake and one Lao state-owned enterprise that owns a 30% equity stake, ispressure
cover any
(Hurley
et al. 2019:
The Governmentof Laos Railway
is optimistic
137 However,nottobacked
by a[financial]sovereignlosses”
guarantee.
in 153).
the eventthat the China-Laos
is notthat the railway
138 but economic modelingwill turnaprofit
by
2027,
its
sixth
year
of
expected
operation(ObeandKishimoto2019),
profitable, the Laotian authorities “will be under considerable pressure to cover any [financial] losses”
suggeststhat theRailway
is “unlikely of
to Laosbringismajor
economic
benefits
andpotential
to present a(Hurley
et al.China-Laos
2019: 153).
The Government
optimisticthat therailway
will[has]turnthea profitby 2027,very large
contingent
foroperation
the Lao PDR”
2020: 1). The
commercial
viability modelingof the railway
will ultimately
138 but economic
its sixth
year of liability
expected(Obe(Lane
and Kishimoto2019),
suggeststhat
hinge upon
its ability toincreasecargo andbetweenThailandbut there
significant
the China-Laos
Railway
is “unlikely
to passenger
bring majortraffic
economic
benefits
and and[has]China,
the potential
to ispresenta
139 There is also
uncertainty
aboutcontingent
when the railway
the(Lane
Thai sidetheTheborderwill reachcompletion.
very large
liability connection
for the Lao on
PDR”
2020:of1).
commercial
viabilityof the railway
will
uncertainty
abouthinge
how uponmuch itscargo
andtopassenger
trafficand
withinLaos willtraffic
movebetween
from theThailand
road network
to thebut
railwayultimately
abilityincrease cargopassenger
and China,
and if theLaotianGovernment
will adopt
trade
reforms thatborderclearancethere
is significant
uncertainty
aboutthewhen
thefacilitation
railway connection
on arethe needed
Thai sidetoofreducethe border
will
reach
times orthe regulatory
reformsin thelogistics about
sectorhowthat muchare neededmarket traffic
entry (World
Bankwill2020).
139 There
completion.
is also
uncertainty
cargo toandeasepassenger
within Laos
movefrom the road network to the railway and if the Laotian Government will adopt the trade facilitation
There reforms
are two ways
in whichthetoChina-Laos
Railway
Projecttimes
is being
financedwith “hidden
debt.”First, the$480
that areneededreduce borderclearanceor theregulatory
reforms in
the logistics
sectormillionthat
China
that
the Government
Laos2020).
secured to finance its equity stake in the LCRC is a classic
areEximbank
needed toloaneasemarketentry (Worldof
Bankcase of “double-gearing” or “pyramiding,” which raises an important question about the financial health of the projectcompany.
AnareequityshouldanChina-Laos
asset, but ifRailway
it is financed
debt,financed
it is a liability,
which suggeststhat the asset
There
two stakeways inwhichbethe
Projectwith
is beingwith “hidden
debt.” First,
may betheworthless.
In
otherwords,
theproject
company
structurethat
underpinstheChina-Laos
Railway
Project$480 million China Eximbank loan that the Government of Laos secured to finance its equity stake in is
140 Second, if theseemingly
beingisused
to obscuredebt accumulation
rather than for
limitedliability
protection.
the LCRC
a classic
case ofmore“double-gearing”
or “pyramiding,”
which
raisesan importantquestion
about
LCRC isinsufficiently
profitable,
anywhere
between
0%
and
100%
of
the
$3.54
billion
debt
that
China
Eximbank
the financial health of the project company. An equity stake should be an asset, but if it is financedwith issued
141
to the debt,
LCRC itcould
becomewhich
a repaymentof theGovernmentof Laos.
ofthe
theproject
equity holders
in theis a liability,
suggestsobligationthat the asset
may
be worthless.
In otherNone
words,
company
LCRC havelegalliabilityfor
any
unpaiddebts
oftheproject
company
since
it
was
established
as
an
LLC.
However,structure that underpins the China-Laos Railway Project is seemingly being used to obscure more debt
since theChina-Laosrather
Railway
is aforpublicinfrastructure
asset being
financed
PPP
arrangement,
there is a non140 Second,
accumulation
thanlimitedliability protection.
if throughthe LCRCa isinsufficiently
profitable,
anywhere between 0% and 100% of the $3.54 billion debt that China Eximbank issued to the LCRC could
become a repayment obligation of the Government of Laos.14` None of the equity holders in the LCRC
have legal liability for any unpaid debts of the project company since it was established as an LLC.
However, since the China-Laos Railway is a public infrastructure asset being financed through a PPP
arrangement, there is a non-trivial possibility that the Laotian (and/or Chinese) authorities would feelcompelled to bail out the LCRC if it defaulted on its repayment obligations to China Eximbank.142

135 The LCRC is identified as an LLC (有限公司) on its website. See http://www.lcrc.ltd/index.htm
136 The $480 million loan agreement that the Government of Laos and China Eximbank signed on September 8, 2016, carries thefollowing borrowing terms: 2.3% interest rate, 25-year maturity, and 5-year grace period. The borrower reportedly pledged revenues
from a bauxite mine and three potash mines as sources of collateral to secure the loan.
137 The $480 million loan is captured via Project ID#33726 in AidData’s 2.0 dataset. It is also included in the China’s Overseas

Development Finance Database maintained by Boston University’s Global Development Policy Center. The $3.54 billion debt
financing package that China Eximbank issued to the LCRC is captured via captured via Project ID#85304 in AidData’s 2.0 dataset. It is
not included in the database maintained by Boston University’s Global Development Policy Center.
138 President Thongloun Sisoulith has said “I am not concerned much about the burden of debt or the construction of the high-speed

railway” (Obe and Kishimoto 2019).
139 China wants to establish a high-speed railway from Bangkok to the Laotian border in Nong Khai Province (via Nakhon Ratchasima)and eventually create a “Pan-Asia Railway Network” that connects Kunming—the capital city of southwestern China’s Yunnan Province—with Thailand, Laos, Malaysia, Singapore, and Indonesia. However, the railway segment in Thailand may not be complete until 2030.
140 We are grateful to Anna Gelpern for sharing this insight.
141 This fact stands in tension with the way that LMIC governments disclose their actual and potential debt obligations via international

reporting systems. For example, the World Bank’s Debtor Reporting System (DRS) only tracks the obligations of “public sector[entities] in which the government holds a fifty percent or more share.” See https://datatopics.worldbank.org/debt/QuarterlyBulletinJanuary2019

49
trivial possibility
that
LaotianChinese)publicauthorities
would
feelassociatedcompelledwith
to bailthe LCRC
if it defaulted
Ultimately,
thethetrue
size of(and/or
the contingent
sectorliability
the out
China-Laos
Railway142
on its repayment
obligations to
China Eximbank.
Project is unknowable(ex-ante),
but that does not mean that it is inconsequential. Uncertainty about theGovernment of Laos’ true level of liability has already prompted significant concern among credit rating
Ultimately,
the truesize of the contingent
public sector
associatedwith the China-Laos
Projectagencies,
intergovernmentalorganizations
with liability
surveillance
responsibilities,
and foreignRailway
creditors
and isunknowable(ex-ante),
but
that
does
not
mean
that
it
is
inconsequential.
Uncertainty
about
the
Government
investors (Barney and Souksakoun 2021). Moody's downgraded Laos’ credit rating to "junk" status inof Laos’true levelof liability
already
promptedsignificant
concernamongcreditAugust2020,hasandFitch Ratings
took
a similar action
one
month
later,rating
citingagencies,
concerns intergovernmental
about contingent
organizations
withsurveillance
responsibilities,
andforeigncreditors
andinvestors(Barneyand Souksakoun
2021).
liability risk. Since then, the government’s foreign exchange reserves have dipped below$900 million,
Moody'swhich
downgradedLaos’
creditratingto"junk"
statusinAugust
2020,
and
Fitch
Ratings
tookasimilaractionis less than its annual debt repayment obligations (worth more than $1 billion), and the country’s one
month later,
citingconcerns
about
contingent
liability risk.
the government’s
foreignexchangereserves
rate of economic
growthhas
sharply declined.
As ofSincelate then,2020 andearly 2021, theLaotianauthorities
werehave dipped
million, default
which isandlessurgently
than its seeking
annual debt
(worthmore143
than$1 billion),on thebelow
brink $900
of sovereign
debtrepayment
relief from obligations
their Chinese
creditors.
In orderand theto
country’sof economic
has
sharplydeclined. Asof late 2020
the Laotian
authorities
free up rateliquidity,
they soldgrowtha majorpublicinfrastructure
asset—alarge andpart earlyof the2021,
country’s
electricity
143 In order to freewere ontransmission
the brink of grid—tosovereigna default
andurgently
seekingdebt
relieffrom
theirChinesecreditors.
Chinese state-owned enterprise for $600 million (in an apparent debt-for-equity
up liquidity,they
144 sold a major public infrastructure asset—a large part of the country’s electricity transmission grid—to aswap).
Chinese state-owned enterprise for $600 million (in an apparent debt-for-equity swap).144
Table 18 and Figure 19 indicate that, between 2000 and 2017, only 40.6% of China’s $800 billion overseas lending
portfolio was directed to government borrowers (i.e., debts and debtors that fall within the first category that sits on
the far left-hand side of the spectrum in Figure 18 above). An additional 12.8% was directed to private sector
borrowers with no explicit or implicit forms of government liability protection (i.e., debts and debtors that fall within thelast category on the far right hand side of the spectrum).145 44.1% of China’s overseas lending portfolio was directed to
the “murky middle”: debts and debtors that fall within the second, third, and fourth categories in the middle of the
public liability spectrum. These loans to the “murky middle” come in two flavors: those that benefit from explicit formsof government liability protection (5.5%) and those that benefit from implicit forms of government liability protection(38.6%). The former consist of loans from official sector institutions in China to private or public sector entities in hostcountries that have secured explicit host government repayment guarantees (i.e., “sovereign guarantees”), while thelatter consist of loans to host country SOEs and state-owned banks and SPVs with some degree of host government
ownership.146 Across the entire eighteen-year period of measurement (2000-2017) in the 2.0 dataset, we find that
official sector lending from China to the “murky middle” amounted to $353 billion.
142 The China-Laos Railway Project also highlights an important methodological difference between how official sector lending from

China to joint ventures (i.e., project companies that are jointly owned by Chinese and host country entities) is recorded in the 2.0
version of AidData’s Global Chinese Development Finance Dataset and the Chinese Loans to Africa Database maintained by Boston
University's Global Development Policy Center and the China Africa Research Initiative at the Johns Hopkins University School of
Advanced International Studies (SAIS-CARI). The Chinese Loans to Africa Database does not record the face values of loans that areissued to joint ventures. It first identifies the share of the joint venture that is owned by a host country institution and then multiplies
that share by the face value of the loan (to estimate the size of host country institution’s liability). AidData does not follow this practice.
Whenever an official sector loan from China is issued to a joint venture, AidData records the entire face value of the loan. It does so
because, in the event of default, the equity holders in a joint venture typically do not have a legal obligation to assume responsibility
for the outstanding debts of the joint venture. So long as the joint venture was established as a limited liability corporation (LLC), theequity holders are shielded from legal liability for outstanding debts. In the event of default, any repayment obligations assumed byequity holders would be the result of implicit rather than explicit forms of liability protection (e.g., public or political pressure to bailout a project company in financial distress). For a broader comparison of the 2.0 version of AidData’s Global Chinese Development
Finance Dataset and the Chinese Loans to Africa Database maintained by Boston University's Global Development Policy Center andthe China Africa Research Initiative at the Johns Hopkins University School of Advanced International Studies (SAIS-CARI), see Table
A-1 in the Appendix.
143 The last sovereign default to take place in Asia was in Myanmar in 2002.
144 In September 2020, China Southern Power Grid Co. was granted a majority ownership stake in Électricité du Laos Transmission

Company Limited (EDLT) in exchange for a $600 million fee (equity infusion). Under a 25-year concession agreement, EDLT is
responsible for managing the country’s high-voltage transmission network above 230 kilovolts. Électricité du Laos (EDL)—another Laostate-owned power company—has retained responsibility for the country’s low-voltage transmission network under 230 kilovolts. In
August 2021, Fitch Ratings announced that “[w]e expect the [Government of Laos] to pursue further asset sales and concessionagreements to shore up external liquidity over the next few years, particularly as the new administration has opted for a greater focus
on equity over debt financing” (Fitch Ratings 2021).
145 An additional 2.5% of China’s overseas lending portfolio between 2000 and 2017 was “unallocable” (to any one of the fivecategories along the public liability spectrum depicted in Figure 18 above) because the borrowers were not based in host countries(e.g., loans issued to Chinese companies).
146 AidData has successfully identified information on the ownership structure of 90% of the individual SPVs (including joint ventures)in the 2.0 dataset. It was not able to identify the ownership structure of the remaining 10% of loans to SPVs in the 2.0 dataset. Given
that it is difficult to know if an SPV borrower stands to benefit from an implicit form of host government liability protection without
knowing if the host government has an ownership stake in an SPV, AidData erred on the side of caution and classified all these loans
as “private.”

50

Figure 19: China’s overseas lending portfolio by level of public liability, 2000-2017$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Government

Government-guaranteed

HiddenPrivateUnallocable

Source: AidData.
Note: The five categories of debt that are listed in this figure directly correspond to the five categories of debt in Table 18 below.
“Government” debt refers to full-recourse loans to sovereign governments. “Government-guaranteed” debt refers to loans issued to
private or public entities with explicit host government repayment guarantees. “Hidden” debt refers to loans issued to entities thatare wholly or partially owned by the host government with implicit host government repayment guarantees. “Private” debt refers toloans issued to private entities without host government repayment guarantees. “Unallocable” debt refers to loans issued toborrowers that are not based in host countries (e.g., loans issued to Chinese companies).

Table 18: China's overseas lending portfolio by level of public liability, 2000-2017
Debt type

USD 2017 billions% of portfolio

Government debt

325

40.6%

Government-guaranteed debt

44

5.5%

Hidden debt

309

38.6%

Private debt

103

12.8%

Unallocable

20

2.5%

Total

800

100%

Note: The five categories of debt that are listed in this figure directly correspond to the five categories of debt in Figure 19 above.
“Government” debt refers to full-recourse loans to sovereign governments. “Government-guaranteed” debt refers to loans issued to
private or public entities with explicit host government repayment guarantees. “Hidden” debt refers to loans issued to entities thatare wholly or partially owned by the host government with implicit host government repayment guarantees. “Private” debt refers toloans issued to private entities without host government repayment guarantees. “Unallocable” debt refers to loans issued toborrowers that are not based in host countries (e.g., loans issued to Chinese companies).

51

4.2 Which countries have the highest levels of sovereign debt
exposure and hidden debt exposure to China?
AidData’s 2.0 dataset sheds light on a key feature of Beijing’s overseas lending program that is not yet widely or fully
understood by policymakers: the fact that many Chinese debts in the murky middle of the “public liability” spectrum(depicted in Figure 18 above and quantified in Table 18 and Figure 19 above) are not disclosed by LMIC governments
because they are not, strictly speaking, the borrowers responsible for repayment.147 Hidden debts—undisclosed debts
that could become government repayment obligations in the future—are problematic even in normal times, but they
are particularly worrisome during the COVID-19 pandemic because the repayment capacities of LMIC borrowers aresubstantially weakened and China’s debtors are finding it increasingly difficult to engage in collective restructuringnegotiations.148
To be clear, loans from official sector institutions in China that benefit from explicit host government guarantees arecontingent public sector liabilities that need to be carefully managed (as illustrated by the China Eximbank-financed
resort project in the Maldives that we previously discussed). However, they are supposed to be treated as “sovereigndebt” liabilities by the authorities in LMICs and reported as such to intergovernmental organizations withmacroeconomic surveillance responsibilities (e.g., the World Bank and IMF), so AidData does not classify them as“hidden debt.” We classify debts to official sector institutions in China as “hidden” if they are contracted by stateowned enterprises, state-owned banks, or state-owned special purpose vehicles/joint ventures without explicit hostgovernment guarantees.149 We classify these debts as “hidden” because they are rarely disclosed as potential hostgovernment repayment obligations but may benefit from implicit forms of government liability protection (and thus
become host government repayment obligations in the event of default by the primary borrowers).150
Figure 20 below and Table A-4 in the Appendix put the scale and scope of the problem into perspective by identifying
which borrower countries are most at risk of debt distress because of the loans that they have contracted from Chinesestate-owned entities. On the x-axis, we plot the cumulative amount of Chinese debt contracted by government
agencies in host countries and entities that received (explicit) repayment guarantees from government agencies in hostcountries between 2000 and 2017 as a percentage of host country GDP. On the y-axis, we plot the cumulative amountof Chinese debt contracted by host country SOEs and state-owned banks and SPVs with some degree of hostgovernment ownership between 2000 and 2017 as a percentage of host country GDP.151 The first source of variation isa measure of sovereign debt exposure to China and the second source of variation is a measure of hidden debt
exposure to China (i.e., debts to Chinese state-owned entities that could become host government repayment
obligations in the future). Whereas Horn et al. (2019: 14) estimate that “[m]ore than two dozen countries now owe morethan 10% of their GDP to the Chinese government,” we find that 44 countries now owe more than 10% of their GDP to
the Chinese government. More specifically, we find that 38 countries have levels of sovereign debt exposure to China

147 World Bank and IMF (2020b: 16).
148 They are finding it increasingly difficult to engage in collective restructuring negotiations because their non-Chinese creditors are

reluctant to engage unless their borrowers fully disclose the nature and scale of their outstanding debts to Chinese creditors (Bavier
and Strohecker 2021) This is, in many cases, not possible because of binding confidentiality clauses in Chinese loan contracts (Gelpern
et al. 2021).
149 State-owned entities are among the biggest beneficiaries of implicit forms of host government liability protection (Cebotari 2008;

Bova et al. 2016). Therefore, we treat any degree of host government ownership of a borrowing entity as an indicator of whether itstands to benefit from an implicit form of host government liability protection. The key distinction between a borrowing entity that isstate-owned and a borrowing entity that is privately-owned is that the former faces a less credible (i.e., “softer”) budget constraint
than the latter. Loss-making entities that are privately-owned typically go bankrupt, but loss-making entities that are state-owned“typically do not face the threat of bankruptcy, especially if they are responsible for the provision of socially sensitive goods andservices, or if they are large employers” (Ter-Minassian 2017: 4). As the World Bank puts it, “[a]lthough non-guaranteed borrowing bystate-owned enterprises constitutes only an implicit contingent liability there is a general expectation the government will step in if a
default occurs and empirical evidence confirms this to be the case.” See https://datatopics.worldbank.org/debt/QuarterlyBulletinJanuary2019
150 Our definition of “hidden debt” is conservative in that it excludes loans from official sector institutions in China that benefit fromexplicit host government guarantees (i.e., government-guaranteed debt). In principle, all government-guaranteed debts should be
disclosed via official reporting systems like the World Bank’s Debtor Reporting System (DRS). However, in practice, governmentguaranteed debts are underreported in official reporting systems.
151 The debt stock measures on the y-axis and x-axis are based on cumulative loan commitments. They do not account for loan

disbursements or repayments. In Figure A-2 in the Appendix, we present the same summary statistics but only for BRI participant
countries.

52
that exceed 10% of host country GDP,152 and 10 countries have levels of hidden debt exposure to China in excess of
10% of host country GDP.153 4 countries (Laos, Mozambique, Angola, and Tonga) straddle these two categories, withsovereign debt exposure to China in excess of 10% of host country GDP and hidden debt exposure to China in excess
of 10% of host country GDP (see Figure 20).154 All of these countries have recently sought debt relief from their official
creditors in China.155

Figure 20: Sovereign vs. hidden debt to China, all countries

Note: The following countries have hidden or sovereign debt less than or equal to 10% of their GDP Afghanistan, Albania, Algeria, American Samoa, Argentina, Armenia, Aruba, Azerbaijan, Bahamas, Bangladesh, Barbados, Belize,
Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, British Virgin Islands, Bulgaria, Burkina Faso, Burundi, Cayman
Islands, Central African Republic, Chad, Chile, Colombia, Comoros, Cook Islands, Costa Rica, Cote d'Ivoire, Cuba, Curacao,
Democratic People's Republic of Korea, Dominican Republic, Egypt, El Salvador, Eswatini, Fiji, French Polynesia, Gambia, Georgia,
Ghana, Grenada, Guam, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, India, Indonesia, Iran, Iraq, Israel, Jordan,
Kiribati, Kosovo, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Malaysia, Mali, Marshall Islands, Mauritius, Mexico,
Micronesia, Moldova, Morocco, Myanmar, Nauru, Nepal, New Caledonia, Nicaragua, Nigeria, Niue, North Macedonia, Northern
Mariana Islands, Oman, Pakistan, Palau, Panama, Paraguay, Peru, Philippines, Puerto Rico, Romania, Russia, Rwanda, Saint Lucia, Sao
152 These thirty-eight countries are Venezuela, Congo-Brazzaville, Equatorial Guinea, Djibouti, the Maldives, Angola, Suriname,Samoa, Kyrgyz Republic, Laos, Zambia, Tajikistan, Vanuatu, Sudan, Antigua and Barbuda, Mozambique, Montenegro, Cambodia,
Sierra Leone, South Sudan, Eritrea, Tonga, Cameroon, Dominica, Gabon, Belarus, Mongolia, Ecuador, Zimbabwe, Sri Lanka, Jamaica,
Kenya, Mauritania, Togo, Ethiopia, Cape Verde, Senegal, and Niger.
153 These ten countries are the Democratic Republic of the Congo, Papua New Guinea, Angola, Mozambique, Namibia, Brunei,

Kazakhstan, Tonga, Turkmenistan, and Laos.
154 Laos is in a class of its own, with exceptionally high levels of sovereign debt exposure (29.4% of GDP) and hidden debt exposure(35.4% of GDP) to China. According to the 2.0 dataset, Laos’ overall level of debt exposure to China is equivalent to 64.8% of GDP(see Table A-4 in the Appendix). Interestingly, we find that many of Laos’ largest sovereign debts (see Project ID#33809, 33726,
64813, 63452, 64776) and hidden debts (see Project ID#63762, 63450, 85304, 73301, 67486) are related to similar projects—namely,hydropower generation and transmission and the construction of the China-Laos Railway.
155 See http://www.sais-cari.org/debt-relief53Tome and Principe, Serbia, Seychelles, Saint Maarten (Dutch part)*, Solomon Islands, Somalia, South Africa, St. Kitts and Nevis, St.
Martin (French part), St. Vincent and the Grenadines, Syrian Arab Republic, Tanzania, Thailand, Timor-Leste, Trinidad and Tobago,
Tunisia, Turkey, Turks and Caicos Islands, Tuvalu, Uganda, Ukraine, Uruguay, Uzbekistan, Viet Nam, Virgin Islands (U.S.), West Bank andGaza Strip, Yemen

Figure 20 provides several additional insights. The northwestern quadrant highlights a cohort of six countries where
hidden debt exposure to China is high (over 10% of GDP) but sovereign debt exposure is worth less than 10% of GDP.
These countries include Turkmenistan, Kazakhstan, Brunei, Namibia, Democratic Republic of the Congo, and Papua
New Guinea. At the same time, one can see that 34 countries have levels of hidden debt exposure to China worth lessthan 10% of GDP but levels of sovereign debt exposure to China that exceed 10% of GDP. There is also considerable
variation within the southeastern quadrant of Figure 20. Eight of the 34 countries (Congo-Brazzaville, Equatorial
Guinea, Djibouti, the Maldives, Suriname, Kyrgyz Republic, Samoa, and Zambia) in this cohort have levels of sovereign
debt exposure to China that exceed 25% of GDP, and notably, at least six of these countries have recently sought debt
relief from their official creditors in China.156 The remaining 26 countries in the southeastern quadrant (Tajikistan,Vanuatu, Sudan, Antigua and Barbuda, Cambodia, Montenegro, Sierra Leone, Venezuela, Eritrea, South Sudan,
Belarus, Cameroon, Dominica, Gabon, Mongolia, Ecuador, Zimbabwe, Sri Lanka, Ethiopia, Jamaica, Kenya, Mauritania,
Togo, Cape Verde, Niger, and Senegal) have levels of sovereign debt exposure to China that fall somewhere between
10% and 25% of GDP. At least 11 of these countries have recently sought debt relief from their official creditors in
China.157 However, as we describe in Box 4, when LMIC borrowers seek short-term relief from Beijing, they risk
repaying their creditors substantially more over the lifetime of a loan (in net present value terms) than they would in theabsence of a rescheduling.

156 These countries include Congo-Brazzaville, Djibouti, the Maldives, Kyrgyz Republic, Samoa, and Zambia. See Lanuola Tusani T - Ah

Tong (2021) and http://www.sais-cari.org/debt-relief
157 These countries include Tajikistan, Vanuatu, Montenegro, Sierra Leone, Venezuela, Cameroon, Ecuador, Zimbabwe, Sri Lanka,

Ethiopia, Kenya, and Mauritania. See The Sunday Mail (2020) and http://www.sais-cari.org/debt-relief

54

Box 4: Anatomy of a high-stakes debt rescheduling with China Eximbank158
Between 2006 and 2013, the Republic of Congo (ROC) went on a borrowing spree. It signed a $1.6 billion“strategic partnership” with China Eximbank on June 19, 2006, which allowed it to obtain loans for bigticket infrastructure projects through a collateralization mechanism: Société Nationales des Pétroles
Congolais (SNPC)—the country’s state-owned oil company—agreed to deposit a portion of the cash
proceeds from its oil exports into an escrow account that is controlled by China Eximbank. A slew of
infrastructure projects was approved and implemented through this mechanism, and after
the ROC
158
Box 4exhausted: Anatomyof alinehigh-stakes
debt
reschedulingwith China
Eximbank
the firstof credit, China
Eximbank
extended another$1.6 billion
line of credit in 2013
through the same collateralization arrangement. Then, global oil prices declined sharply—from more than
Between2006
and 2013,
thetoRepublic
Congo
(ROC)
went on a borrowing
It signed
a $1.6billion$100
a barrelin 2013just overof$40
a barrelin 2016—and
it became spree.
significantly
moredifficultfor “strategic
thepartnership”
with
China
Eximbank
on
June
19,
2006,
which
allowedittoobtainloans
forbig-ticket
infrastructure
ROC to service its debts to the Chinese lender. The Congolese authorities approached the IMF for a
projects
throughcollateralization
des Pétroles
Congolais (SNPC)—thebailout,
buta its
initial request mechanism:
was rejected.Société
Public Nationales
debt had reached
an unsustainablelevel (nearly country’s
90% of stateownedtheoil country’s
company—agreed
deposita portionthethecash
proceeds
from bilateral
its oil exports
intosoanthe
escrowGDP) and to
China
Eximbank
wasof
nowcountry’s
largestcreditor,
IMF account thatis controlled
by thatChina
slew beof infrastructure
projects
wasapproved andimplemented
through thissignaled
its Eximbank.
assistance A
wouldcontingent upona debtreschedulingdealwith China Eximbank.
mechanism,
and
after
the
ROC
exhausted
the
first
line
of
credit,China
Eximbank
extendedanotherbillionCalixte Nganongo, the country’s Finance Minister, initiated negotiations with China Eximbank $1.6
in late2017line ofcredit in2013
throughthesamecollateralization
arrangement.
Then,
globaloilpricesdeclined
sharply—frommore
but did not reach a final debt restructuring agreement until April 2019.
than $100 a barrel in 2013 to just over $40 a barrel in 2016—and it became significantly more difficult for the ROC toserviceThe
its debts
thetheChineselender.
The Congolese
approachedIMF
a bailout,but
its initial
lendertoandborrower
restructured
8 loansauthorities(worth $1.32
billion) forthea setof forhousing,dam,
road,requestwas
rejected.
Publicdebt
hadreachedanunsustainable
level(nearly90%
ofthecountry’s
GDP)andcommercial real estate, and water treatment plant projects that would require near-term debt service, China
Eximbank
wasnowrecently
the country’s
largestbilateral
creditor,sorequirethe IMFdebt
signaled
that
its assistancewouldperiods
be contingent
ratherthancontracted
loansthat did
not yetservice(because
their grace
upon awere
debtstillreschedulingwith
China Eximbank.
Nganongo,
thecountry’s atFinance
Minister,in effect).159deal
Prior
to rescheduling,all 8Calixteof theseloans were
contracted
an interest
rateinitiatedofnegotiations
Eximbank
in between
late 201713
butanddid20
notreachanda finalrestructuring
April 2019.
0.25%.with
TheirChina
maturities
variedyears,
theirdebt
grace
periods fellagreement
somewhereuntil
between3 and 5 years. When the debt rescheduling deal was struck in April 2019, Congo-Brazzaville’s outstanding
The lenderand thetoborrower
restructured
8 loans(worth$1.32 billion)
for billion,
a set ofandhousing,road,ofcommercial
realobligations
China Eximbank
for these
8 loansamounted
to $1.6
underdam,
the terms
theestate,rescheduling,
and water treatment
plant projects
that towould
requiredebt service,rather thanunderrecently
contracted
Congo-Brazzavilleagreedrepay
33% ofnear-term
its outstanding
debt obligations
eachof
159 Prior to rescheduling, allloans thatnot (approximately
yet require debt$532
service(becausegraceFor
periods
were still67%
in effect).
the 8didloans
million)withintheir
3 years.
the remainingof its outstandingdebt
8 of these
loans
were
contractedataninterest
rateof
0.25%.
Their
maturities
variedbetweenand 20 years,
andobligations under each of the 8 loans, Congo-Brazzaville agreed to meet its obligations 13
accordingtotheir grace
periodsfell
somewhere
between3
and5
years.
When
the
debt
reschedulingdeal
wasstruck
in
April
2019,
extended maturities but higher interest rates. More specifically, China Eximbank agreed to extend theCongo-Brazzaville’s
outstanding
obligations
to China
forattachedthese 8 loans
to $1.6
maturity of eachloan by 15
years, increase
the Eximbank
interest rate
to theamounted
single largest($1 billion,
billion) andloan under
the termsof theCongo-Brazzavilleagreedto repayits outstanding
to 2%,
andrescheduling,increase the interest
rates attachedto theother 33%
sevenofloans
to 1.5%. debt obligations under eachof the 8 loans (approximately $532 million) within 3 years. For the remaining 67% of its outstanding debt obligations
under Consequently,
each of the 8 loans,
Congo-Brazzavilleagreedmeet
its obligations
accordingto extended
after thedebt restructuring
dealtowasstruckin April 2019,the net present
valuematurities
of total but
higherrepaymentsinterest rates.
More
specifically, China
Eximbank
agreedincreased
to extendfrom
the $1.3
maturityof eachloan
by 15 years,tofromCongo-Brazzavilleto China
Eximbank
billion(beforerestructuring)increase
thebillion
interest
raterestructuring).
attached to thesinglelargest did($1 billion)
to 2%,whenand increase
the interest
rates attached$1.6(afterChina
Eximbank
not takeloan
a haircut
it restructured
Congoto the Brazzaville’s
other seven debts.
loans to1.5%.
Quite the opposite: it successfully negotiated $300 million in additional loan

repayments from Congo-Brazzaville (in net present value terms), which would not have occurred in theConsequently,
the debt restructuring
deal was struck in April 2019, the net present value of total repayments from160
absence afterof a restructuring.
Congo-Brazzaville to China Eximbank increased from $1.3 billion (before restructuring) to $1.6 billion (after
restructuring). China Eximbank did not take a haircut when it restructured Congo-Brazzaville’s debts. Quite theopposite: it successfully negotiated $300 million in additional loan repayments from Congo-Brazzaville (in net present
value terms), which would not have occurred in the absence of a restructuring.160

Another way to estimate the scale of the “hidden debt” problem is to compare reported flows and unreported flows. In
June 2020, the World Bank took the unprecedented step of publishing previously confidential data on debt flows (andstocks) that can be disaggregated according to individual pairs of borrower and creditor governments.161 Data on
official sector loans from China are voluntarily reported by 119 LMIC governments to the World Bank through its

158 An earlier version of this case study was published in Gardner et al. (2020).
159 The eight China Eximbank loans that were rescheduled supported the following projects: Djiri Water Treatment Plant Extension

Project (ID#69323), Djiri Water Treatment Distribution Network Project (ID#69355), Liouesso Hydroelectric Dam Project (ID#31028),
Brazzaville Shopping Center Project (ID#58408), Mpila Social Housing Project (ID#31029), Mpila Commercial Area (Twin Towers)
Project (ID#58720), Mpila Memorial Project (ID#58721), and Phase 2 (375 km Dolisie-Brazzaville Section) of the National Route 1
Project (ID#369). The largest of the eight loans was a $1 billion loan to support the construction of a segment of National Route 1(NR1) between Dolisie and Brazzaville. This project was officially completed on March 1, 2016. However, the road was not wellmaintained by the Congolese authorities after its completion.
160 The temporary repayment relief that China Eximbank provided in April 2019 was apparently insufficient. In June 2021, Congolese

President Denis Sassou Nguesso asked Xi Jinping for another major debt rescheduling.
161 See https://datatopics.worldbank.org/debt/ids/

55
Debtor Reporting System (DRS).162 The DRS data cover the same eighteen-year period captured in AidData’s 2.0
dataset as well as two additional years (2018 and 2019). In total, AidData’s 2.0 dataset captures $676 billion of official
sector loan commitments from China to LMIC borrowers with explicit or implicit host government liability protection(i.e., loans that represent actual or potential host government repayment obligations) between 2000 and 2017.163 The
DRS captures $310 billion of official sector loan commitments from China to LMIC borrowers with explicit hostgovernment liability protection between 2000 and 2017.164 This comparison suggests that AidData has captured
approximately $366 billion of official sector debt flows from China that represent actual or potential LMIC government
repayment obligations but are not recorded in the DRS (i.e., the main international reporting system for LMIC
governments to disclose their repayment obligations).165
One of the reasons why this gap is so large is that AidData’s 2.0 dataset includes official sector loans from China which
benefit from implicit host government liability protection (e.g., the $3.54 billion debt financing package that China
Eximbank issued to the LCRC for the China-Laos Railway Project).166 A second reason is that AidData provides
substantially greater coverage of loans contracted by public sector entities other than central government institutions(e.g., state-owned banks and enterprises) in LMICs.167 A third reason is that not all LMIC governments voluntarily
disclose their debts via the DRS. The World Bank is very much aware of these underreporting problems. In November
2020, it acknowledged that “[t]he availability of comprehensive, timely and consistent public sector debt data
produced by borrowing countries needs to improve, especially for (i) non-standard debt instruments; (ii) debt
contracted by public sector entities other than the central government; (iii) limited information on terms and conditionsof some debt instruments; and (iv) information on the terms of official bilateral debt restructuring” (World Bank and
IMF 2020b: 16). They also warned that “[p]ublic debt disclosure remains particularly weak in countries at high risk ofdebt distress and fragile states [and] [...] there is a risk that public sector indebtedness is substantially larger in several[low-income and middle-income countries] than publicly reported” (World Bank and IMF 2020b: 16).
To better understand where underreporting problems are most and least acute, we compare the levels of hostgovernment debt exposure to China that are documented in AidData’s 2.0 dataset and the DRS (in Table A-27 in the
Appendix). To maximize comparability across the AidData and DRS datasets, we first calculate the sum of all Chinese
loan commitments from 2000-2017 (recorded by AidData) and the sum of all Chinese loan commitments from
2000-2017 (recorded in the DRS) and transform all of these values into constant 2017 USD.168 Then, we normalize the
AidData-based measure of debt exposure to China and the DRS-based measure of debt exposure to China by host
162 The DRS has been in existence since 1952 (World Bank 2021). It is designed to capture loans issued to government borrowers andprivate borrowers with government repayment guarantees. In total, 120 countries participate in the DRS. China is one of theparticipating countries. Therefore, we restrict our analysis of the DRS to the other 119 participating countries.
163 This figure includes lending to host government agencies, host country state-owned enterprises, host country state-owned banks,

SPVs with some degree of host government ownership, and privately-owned institutions that secured host government repayment
guarantees. It excludes lending to private sector institutions without any explicit or implicit liability protection from the hostgovernment. It was calculated in constant 2017 USD.
164 Across the 119 countries that voluntarily disclosed their debts to official sector institutions in China via the DRS, AidData’s 2.0

dataset records $645 billion of official sector lending commitments from China to host government agencies, host country stateowned enterprises, host country state-owned banks, SPVs with some degree of host government ownership, and privately-owned
institutions that secured host government repayment guarantees between 2000 and 2017.
165 According to World Bank (2021), the “Debtor Reporting System (DRS) is the most detailed single source of verifiable informationon the external indebtedness of low- and middle-income countries. All countries that borrow from IBRD or IDA agree to report,
annually, loan-by-loan on stocks and flows for long-term external debt owed by a public agency or a private agency with a publicguarantee […].”
166 AidData’s 2.0 dataset may also provide better coverage of official sector loans from China which benefit from explicit hostgovernment liability protection (i.e., sovereign guarantees). The DRS seeks to measure all government and government-guaranteed
debt from official sector loans from China. However, underreporting of government-guaranteed debt is a known problem in the DRS.
This AidData and DRS coverage issue warrants further investigation.
167 According to the World Bank, “[b]y far the most significant omission in DRS reports relates to borrowing by state-owned

enterprises on their own account, without benefit of a government guarantee.” See https://datatopics.worldbank.org/debt/
QuarterlyBulletin-January2019
168 Consistent with our previous analysis of these data, the AidData-based measure of debt exposure to China captures official sector

lending from China to host government agencies, host country state-owned enterprises, host country state-owned banks, SPVs withsome degree of host government ownership, and privately-owned institutions that secured host government repayment guarantees(as a percentage of host country GDP). However, it excludes official sector lending from China to private sector institutions without any
explicit or implicit liability protection from the host government. The DRS-based measure of debt exposure to China captures official
sector lending from China to central government borrowers and private and public sector borrowers with government repayment
guarantees (as a percentage of host country GDP).

56
country GDP.169 Figure 21 below provides a country-by-country comparison of the differences across these two
measures. It demonstrates that only 49 governments provide a relatively complete picture of their actual and potential
repayment obligations to official creditors in China through the DRS.170 The vast majority of governments substantially
underreport their actual and potential repayment obligations to China—or do not disclose any of their repayment
obligations to any external creditors—through the DRS. By way of illustration, consider Venezuela. Its government has
voluntarily disclosed $699 million of official sector loan commitments from China to the DRS between 2000 and 2017.
Yet, according to AidData’s 2.0 dataset, Venezuela contracted $74.7 billion of sovereign debt (i.e., government andgovernment-guaranteed debt) and an additional $16.3 billion of hidden debt (i.e., potential government repayment
obligations resulting from debts contracted by state-owned banks, enterprises, and SPVs) from official sector
institutions in China between 2000 and 2017. This comparison implies that Venezuela’s level of Chinese debt exposureis underreported in the DRS by $90.3 billion, which is equivalent to roughly 20% of Venezuela’s GDP.171
The two “delta” (Δ) variables in Table A-27 in the Appendix are country-level barometers of unreported (public) debt
exposure to China. One is measured in terms of inflation-adjusted U.S. dollars and the other is measured as apercentage of host country GDP. Table A-27 in the Appendix demonstrates that, while Venezuela is an extreme
example, it is hardly an isolated case of underreporting. Across the 154 LMIC governments that are jointly covered by
AidData and the DRS, we find that 57% (88 governments) have underreported their levels of Chinese debt exposure to
the DRS.172 The total amount of official sector debt to China that these governments have opted not to report to the
DRS is $385 billion. The average government in this cohort has underreported its actual and potential repayment
obligations to official sector creditors in China by 5.8% of its GDP.173 This problem has grown worse over time. Figure
22 below shows that, among the LMIC governments jointly covered by AidData and the DRS, average annual
underreporting of repayment liabilities to official sector creditors in China was $13 billion during the pre-BRI period(2000-2012). However, during the first five years of BRI implementation, average annual underreporting was $40 billion.
Figure 21 below also calls attention to a subset of countries where underreporting to the DRS is particularly acute. Itshows that 58 LMIC governments have levels of underreported debt to China that are equivalent to 1% of GDP or
more and 29 LMIC governments have levels of underreported debt to China that are equivalent to 5% of GDP or more.
Underreporting is especially severe in Equatorial Guinea, Suriname, Congo-Brazzaville, Turkmenistan, Laos, Venezuela,
Antigua and Barbuda, Sierra Leone, Montenegro, Sudan, Kazakhstan, Democratic Republic of the Congo, South
Sudan, Namibia, Eritrea, Papua New Guinea, Iran, and Djibouti. These 18 LMIC governments all have levels of
underreported debt to China in excess of 10% of GDP.174

169 We also normalize host country GDP to constant 2017 USD.
170 More specifically, 49 governments (that participate in the DRS) meet one of two conditions: (1) there is no monetary difference

between the Chinese loan commitments that they reported to the DRS between 2000 and 2017 and the Chinese loan commitments
recorded in AidData’s 2.0 dataset, or (2) the Chinese loan commitments that they have reported to the DRS between 2000 and 2017
exceed the Chinese loan commitments recorded in AidData’s 2.0 dataset. 70 governments (that participate in the DRS) do not meeteither of the conditions. An additional 35 LMICs in the AidData’s 2.0 dataset do not participate in the DRS.
171 This $90.3 billion figure is based on the sum of Venezuela’s sovereign debt and hidden debts as recorded by AidData ($91 billion)

less the $699 million that the Venezuelan authorities voluntarily disclosed to the World Bank via the DRS.
172 This calculation is based on the total number of LMICs with positive “AidData-DRS Δ in constant 2017 USD” values in Table A-27 in

the Appendix. Given that the DRS is only designed to capture the repayment obligations of LMICs, this calculation excludes the 11
high-income countries that are included in AidData's 2.0 dataset.
173 In a handful of countries (Angola, Belarus, Mauritania, Ghana, Grenada, and Burundi), the DRS records significantly higher levels of

official sector debt from China (worth 2% of host country GDP or more) than AidData, which suggests that there are still someunderreported debt flows in AidData’s 2.0 dataset. AidData will investigate and address these issues prior to the release of the next
iteration of its Global Chinese Development Finance Dataset.
174 Table A-27 in the Appendix also indicates that at least one high-income government (Brunei) has a level of underreported debt toChina in excess of 10% of GDP.

57

Figure 21: Country-by-country comparison of DRS and AidData measures of hostgovernment debt exposure to China

58

Source: AidData and World Bank Debtor Reporting System.
Note: Unless otherwise specified, the AidData, DRS, and GDP data in this table are reported in constant (2017) U.S. dollars. It was notpossible to reliably deflate GDP into constant (2017) U.S. dollars for three countries with especially high levels of inflation (Venezuela,
Zimbabwe, and South Sudan). Therefore, for these three countries, we calculated the variables that are expressed as a percentage ofhost country GDP by dividing Chinese loan commitment values in nominal dollars by GDP in nominal dollars for each year

59

Figure 22: Underreporting of sovereign debt exposure to China via the World Bank’s Debtor
Reporting System (DRS), 2000-2017$100

USD 2017 billions

$75

$50

$25

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Additional sovereign and hidden loan commitments (AidData)
Sovereign loan commitments (DRS)

Source: AidData and World Bank Debtor Reporting System.

60

5. What does the 2.0 dataset tell us about BRI
implementation?
5.1 BRI project implementation risks, setbacks, achievements, andfailures: The need for a stronger evidentiary foundation
The empirical literature on the BRI has largely focused on the overall volume of lending and how it has changed fromyear-to-year (Kratz et al. 2020; Ma et al. 2021). However, policymakers and researchers still know relatively little about
the implementation of BRI projects because publicly available datasets provide few details about when, where, bywhom, and how they are implemented. With the release of the 2.0 version of AidData’s Global Chinese Development
Finance Dataset, we seek to close this evidence gap. The latest version of our dataset identifies the specific contractors
and sub-contractors responsible for the implementation of Chinese development projects (in countries that do and do
not participate in the BRI), which opens up new opportunities to analyze whether projects fare better or worse when
they are undertaken by specific organizations or types of organizations. It also identifies the precise calendar days when
projects were originally expected to start and end, and the precise calendar days when implementation actuallycommenced and concluded. A separate, but related, feature of the 2.0 dataset is that it captures the geographicalfootprints of Chinese development projects with a high degree of locational precision.175 In Section 6, we demonstratethat the “art of the possible”—with respect to measuring the intended and unintended impacts of Chinese
development projects—is vastly greater with highly precise data on spatio-temporal rollout of project
implementation.176 Analysts and decision-makers who wish to better understand BRI project effectiveness and valuefor-money now have many more ways of doing so.177
Another aspect of AidData’s 2.0 dataset that sets it apart from others is the inclusion of “cradle to grave” narratives
that “tell the story” of each project. These narratives consist of 1.93 million words (roughly the same number of words
one would find in 19 full-length books) across 13,427 projects. They provide detailed information about how Chinese
development projects are being implemented in practice and where/why they are failing or faltering. They capture ex
ante project risks, including economic and financial viability concerns (e.g., results of cost-benefit analysis undertaken,
sufficiency of the revenues generated by the project facilitate loan repayment), land acquisition and involuntary
resettlement concerns, and concerns related to environmental degradation and preservation of cultural heritage. They
then describe efforts to mitigate these risks during implementation—for example, through environmental and social
impact assessments, compensation to involuntarily displaced communities, and renegotiated loan terms andconditions after borrowers go into default or fall behind on their repayment schedules. The narratives also document
how Chinese donors and lenders as well as their contractors have responded to implementation challenges and various
host country concerns—for example, by increasing on-site supervision, temporarily or permanently halting
construction, withholding grant or loan disbursements, easing or hardening loan repayment terms, threatening topursue or pursuing litigation, activating cross-cancellation or cross-default contract clauses, or declaring force majeure.
The rich, qualitative information that is contained in these project narratives, we will soon demonstrate, can beleveraged to better understand the types of problems that Chinese development projects encounter most and least
frequently during implementation.

175 In the 2.0 dataset, we have calculated a Project Implementation Score for every project that has entered implementation orreached completion. The ordinal, five-point score provides an indication of how many implementation details are captured in eachproject record. More specifically, it measures how many of the following fields are populated: implementing agency, implementation
start date (actual or planned), completion date (actual or planned), geographic location, and a specified sector allocation. The average
Project Implementation Score in the 2.0 dataset is 3.4.
176 Also see Chapters 7 and 8 of Dreher et al. (forthcoming).
177 A growing number of projects being financed by OECD-DAC members and multilateral development banks are subjected torigorous impact evaluations—i.e., those that establish a credible counterfactual and address the question of what would have
happened in the absence of the project to isolate the change in an outcome that is attributable to that project as opposed to otherfactors (BenYishay 2018). However, the vast majority of these evaluations are of health, nutrition, education, and other social sector
projects (Cameron et al. 2016). Projects in “hardware” sectors—like transportation and energy infrastructure—remain under-evaluated.
With data from household surveys, satellites, mobile phones, and real-time sensors, it is increasingly feasible to measure the net,
attributable impacts of such projects. However, as Dollar (2008) explains, “Western donors have by and large gotten out of hard
infrastructure sectors … and [t]hey [instead] channel their assistance overwhelmingly to social sectors or to infrastructure sectors suchas water supply and sanitation that have direct effects on household health.”61

Box 5: How AidData measures BRI infrastructure projects underway between 2013
and2021
Box 5:
How
AidData measures BRI infrastructure projects underway between 2013 and 2021
To analyze
the implementation
BRI infrastructure
projects
do in Section
5 ofthis paper),one toTo analyze
the implementationof BRI of
infrastructure
projects(as we (asdo wein Section
5 of thispaper),
one needs
needs between
to differentiate
between BRI
infrastructure
and thegovernment-financed
other Chinese government-financed
differentiate
BRI infrastructure
projects
and theprojects
other Chinese
infrastructure projects inprojects dataset.
in the 2.0
versiondataset.
doinsowhich
by first
identifying
the year joinedinthe 2.0infrastructure
version of AidData’sWe
do so ofbyAidData’s
first identifying
theWe
year
each
country officiallythe BRI
178 Then, for each recipient country
which
each
country
officiallyjoinedthe
BRI(between
2013and2021).
178(between 2013 and 2021). Then, for each recipient country in the dataset, we identify the subset of projects thatin the dataset,
identify thesubsettheof projects
that were
implemented
betweenthewere implemented
orwecompletedbetweenyear in which
the country
joined or
BRIcompleted
and presentday. Wedoyearso by using
which thejoinedBRIandthepresent
We do so
bythe
using
severalvariablesin timing
our dataset
thatseveralinvariables
in country
our dataset
that
track
statusday.
of projects
andprecise(calendar
day)of theirtrack the statusof projects and the precise (calendar day) timing of their implementation and completion:
implementationand completion:●●with completion
dates
fall between
a country
BRI
and present● projects
projectswith completion
dates that
fallthat
betweenwhen awhen
countryjoined joined
the BRIthe
andpresentday; day;with implementation
start thatdates
fall between
a country
BRI
and present● projects
projectswith implementation
start dates
fallthat
betweenwhen awhen
countryjoined joined
BRI andpresent
day (note:day(note:
some
of
these
projects
have
beencompletedwhile
othersarestillin
implementation);some of these projects have been completed while others are still in implementation); andandprojects that were officially committed (via legally-binding agreements) in the same year when a country joined
projects
officially committed
legally-binding
in the
same
year
when
a
the●BRI—or
anythatyearwere
thereafter—andthat we (viaknowhave enteredagreements)
implementation
(even
if we
lack
a specificcountryjoinedthe
BRI—orany
year
thereafter—and
that
we
know
have
enteredimplementation
project implementation start date) or reached completion (even if we lack a specific project completion date)(even if we lack a specific project implementation start date) or reached completion (even if welack
a specificproject
completiondate)
After identifying
thissubset ofChinesegovernment-financed
development projects in BRI participant countries, we●

further prune the sample to only include infrastructure projects (by using a set of keyword search procedures).179
After identifying
subset ofChinesegovernment-financed
development
projects
in BRI
participant“Infrastructure
projects”thisgenerally
includeprojects
that involve physicalconstruction
activities(e.g.
roads, railways,
countries,
wefurtherprune
thesampleto
onlyincludeinfrastructure
projects
(by
using
a
set
of keyword
pipelines, transmission lines, fiber optic networks, etc.) and exclude projects that involve the provisionof cash,179 “Infrastructure projects” generally include projects that involve physicalsearchprocedures).
technical
assistance,scholarships,
equipment, or supplies.
construction activities (e.g. roads, railways, pipelines, transmission lines, fiber optic networks, etc.) andexcludeprojects
that involve
the provisionof cash,government-financed
technical assistance, infrastructurescholarships, equipment,or
By following
theseprocedures,
we identify
949 Chinese
projects underway
in BRI
supplies.
180
participant countries between 2013 and 2021. Table 19 and Table 20 present the ten countries with the most BRI

infrastructure projects and the highest levels of financing for BRI infrastructure projects. There is considerable overlap
By these
followingprocedures,
we identify
949 Chinese
government-financed
infrastructure
projectsPakistan, Sribetweentwo these
lists, including
5 countries
in South,Southeast,or Central Asia (Cambodia,
Indonesia,
180 Table 19 and Table 20 present the ten
underwayin
BRI
participant
countries
between
2013
and
2021.
Lanka, Kazakhstan) and 1 country in Eastern Europe (Belarus). In Figure 23 , we present the broader portfolio of
countries
with themost for
BRIBRI
infrastructure
projects
and(worththe highest
levels offinancinginfor
BRI major
infrastructure
ChineseGovernment
funding
infrastructure
projects$170 billion)
underway
every
world region
projects.
There
is considerable overlap between these two lists, including 5 countries in South, Southeast,
between2013 and2021.
or Central Asia (Cambodia, Indonesia, Pakistan, Sri Lanka, Kazakhstan) and 1 country in Eastern Europe(Belarus). In Figure 23, we present the broader portfolio of Chinese Government funding for BRI
infrastructure projects (worth $170 billion) underway in every major world region between 2013 and 2021.

178 We measure the year in which a country “joined the BRI” as the first year in which it signed a BRI cooperative agreement or BRI

MOU. See footnote 54 for more details.
179 We isolate infrastructure projects by identifying all projects with title fields or description fields that included one or more of thefollowing keywords: construc*, build, rehabilit*, upgrad*, renovat*, exten*, restor*, built, groundbreaking, fiber, power plant,
expansion, electrification, hydro*, instal*, foundation. We also exclude all projects that are assigned to the following flow type
categories: debt forgiveness, debt rescheduling, scholarships, training, or free-standing technical assistance activities.
180 941 of these BRI infrastructure projects reached implementation or completion. Eight of them were officially committed BRI

infrastructure projects that were subsequently suspended or cancelled.

62

Table 19: Top 10 countries with BRI
infrastructure projects underway, by projectvalue

Table 20: Top 10 countries with BRI
infrastructure projects underway, by projectcountCountry

2017 USD billions

Country

Project countPakistan

27.3

Cambodia82

Indonesia

20.3

Pakistan

71

Kazakhstan

12.1

Indonesia

71

Malaysia

8.4

Mongolia

43

Cambodia

6.8

Belarus

35

Kenya

6.3

Myanmar

33

Belarus

6.1

Cameroon

30

Sri Lanka

5.5

Sri Lanka

25

Bangladesh

5.0

Angola25

Ethiopia

4.1

Kazakhstan

21

Figure 23: Map of Chinese government funding for BRI infrastructure projects underway
between 2013 and 2021

Source: AidData.

63
The 2.0 dataset reveals a great deal of implementation variation—from project-to-project and country-to-country—inthe BRI infrastructure portfolio. For example, with precise data on project implementation start dates and completion
dates, it is now possible to measure exactly how long it takes to implement BRI infrastructure projects in different
settings. Table 21 demonstrates that, in Iran, the average BRI infrastructure project that reaches completion takes 7.1
years (2,585 days) to implement.181 By way of comparison, the average BRI infrastructure project in Azerbaijan—Iran’snorthern neighbor—takes less than 1 year (307 days) to implement (see Table 22. Our dataset also reveals that a whole
host of BRI infrastructure projects in Iran never reach completion but are instead suspended, cancelled, or put in a stateof abeyance.

Table 21: Top 10 countries with longest average implementation times for BRI infrastructure
projects
Year

Average days to implement

Nigeria

3333 days (9.1 years)

Iran

2585 days (7.1 years)

Gabon

2471 days (6.8 years)

Russia1874 days (5.1 years)

Viet Nam

1783 days (4.9 years)

Sri Lanka

1684 days (4.6 years)

Ecuador

1594 days (4.4 years)

Algeria

1554 days (4.3 years)

Serbia1534 days (4.2 years)

Congo

1494 days (4.1 years)

Note: This table excludes countries with less than 5 BRI infrastructure projects.

181 In Table 21 and Table 22 , we use the variables in in our dataset (start_actual and end_actual) that measure the precise calendar

days when project implementation actually commenced and concluded to calculate the average number of days it takes to complete
a BRI project (as defined in Box 5) in a given country. We only report average project implementation times for countries with at least
five observations (BRI projects).

64

Table 22: Top 10 countries with shortest average implementation times for BRI infrastructure
projects
Year

Average days to implement

Philippines

156 days (0.4 years)

Azerbaijan

307 days (0.8 years)

Cabo Verde

371 days (1 years)

Myanmar438 days (1.2 years)

Mongolia

498 days (1.4 years)

Moldova

506 days (1.4 years)

Micronesia

524 days (1.4 years)

Madagascar

528 days (1.4 years)

South Africa

608 days (1.7 years)

Tanzania

623 days (1.7 years)

Note: This table excludes countries with less than 5 BRI infrastructure projects.
Iran’s experience with the BRI highlights a broader constraint that influences the implementation of BRI infrastructure
projects around the globe: the ease with which Chinese state-owned lenders can make use of the international financial
system and thereby facilitate foreign currency-denominated payments to Chinese suppliers of goods and services.
Beijing created a single state-owned bank—the Bank of Kunlun—to facilitate transactions with Iran and minimize theexposure of other Chinese state-owned banks to international sanctions.182 However, in 2012, the U.S. Treasury
sanctioned the Bank of Kunlun and cut off its access to U.S. dollars and the U.S. financial system.183 The Bank of Kunlun
responded by shifting gears and facilitating transactions with Iran in renminbi and euros. But this strategy also failed.
After the U.S. Government announced that it would withdraw from the “Iran nuclear deal,” the Bank of Kunlun decided
to stop facilitating euro-denominated transactions in August 2018 in anticipation of another wave of U.S. sanctions.184
Then, on November 5, 2018, the U.S. Treasury sanctioned a wide swathe of Iranian individuals and entities andthreatened to impose secondary sanctions on any non-U.S. individuals or entities. To avoid exposure to secondary
sanctions, the Bank of Kunlun immediately suspended renminbi-denominated transactions (for non-humanitarian
activities). Without any way of paying for Chinese goods and services with dollars, euros, or renminbi, it has become
increasingly difficult for Iranian government entities and companies and their Chinese counterparts to implement BRI
projects.185
The Tehran-Mashhad High-Speed Railway Electrification Upgrading Project (captured via ID#67389 in the 2.0 dataset)is a case in point. Originally billed as a flagship BRI project, it sought to electrify a 926-kilometer railway that runs from
Tehran to the eastern city of Mashhad in Khorasan Razavi Province. Its ultimate goal was to transport 25 million
passengers and 10 million tons of cargo per year. The Iranian Government signed a commercial contract with China
National Machinery Import and Export Corporation (CMC) in June 2015 and later secured a $1.5 billion loan from
China Eximbank to finance 85% of the cost of the commercial contract. The project commenced on February 6, 2016,
and was scheduled to reach completion within 48 months (February 6, 2020). However, as of June 2019, it had still only

182 The Bank of Kunlun is majority-owned by China National Petroleum Corporation (CNPC), a Chinese state-owned oil company.
183 The U.S. Treasury sanctioned the Bank of Kunlun for funneling money to an organization associated with Iran’s Revolutionary

Guards.
184 The formal name of the “Iran nuclear deal” is the Joint Comprehensive Plan of Action (JCPOA).
185 This constraint is crucial because most loans from Chinese state-owned banks are issued in order to help Iranian borrowers

purchase goods and services from Chinese suppliers. Consequently, the U.S. Treasury’s actions have brought many BRI projects in Iranto a grinding halt.

65
achieved a 3% completion rate. In January 2021, CMC withdrew from the project because U.S. sanctions and “currency
problems” made it too difficult to proceed.186
Several countries, such as Cuba and Sudan, have encountered similar obstacles to BRI project implementation.
However, others have found ways to circumvent international sanctions with the support of Chinese state-owned banks(Rapoza 2014; Marson and Ostroukh 2016). Russian state-owned companies in the oil and gas sector are subject to U.S.
and E.U. sanctions that severely restrict their access to long-term, dollar- and euro-denominated loans, but our dataset
reveals that these companies still represent some of Beijing’s biggest borrowers (see, for example, Project ID#43012,
43069, 85211, 67166, 43002, 67039 and Table A-3a in the Appendix).187 They have accessed substantial amounts ofChinese credit via pre-export finance (PXF) facilities, which can be designed to exploit loopholes in the internationalsanctions regime.188

5.2 What types of problems do BRI infrastructure projects andother Chinese government-financed infrastructure projects
encounter during implementation?
The “cradle to grave” project descriptions in the 2.0 dataset highlight a wide range of other impediments to BRI
implementation, including strikes, riots, public protests, wars, corruption scandals, natural disasters, public healthrestrictions, political transitions, bankruptcies, loan defaults, contractual disputes, lawsuits, and ruptures in diplomatic
relations.189 To leverage the vast trove of qualitative information that is contained in these descriptions, we use a set ofsystematic search and categorization procedures to quantify and compare the prevalence of different types ofproblems among (a) BRI infrastructure projects and (b) Chinese government-financed infrastructure projects that are
implemented outside of the BRI. We first define four major problem types related to (1) scandals, controversies, or
alleged violations; (2) financial wrongdoing; (3) community or ecosystem harm; and (4) underperformance vis-à-vis
project objectives (described in more detail below). We then identify a set of corresponding keywords for eachproblem type that, if present in a project’s “description” field within the 2.0 dataset, may indicate it encountered acertain type of problem. For each problem type, we isolate all projects in our two subsamples of interest—all BRI
infrastructure projects and Chinese government-financed infrastructure projects that are implemented outside of the
BRI—with “description” fields that contain one or more of the pre-specified keywords.190 This analysis encompasses
projects backed by official financial (or in-kind) commitments that reached implementation or completion in thesesearches. We also include suspended and cancelled projects in order to avoid sample selection bias. After identifying
the subset of projects with “description” fields that contain one or more of the pre-specified keywords, we manuallyreview each of the corresponding project descriptions to eliminate “false positives”—e.g., a description that references
the keyword “lawsuit” to describe litigation affecting an earlier phase of a project backed by a non-Chinese financier
but not the subsequent, Chinese government-financed phase of the project. More specifically, we confirm or
disconfirm that the projects in question encountered the types of problems that we intended to identify with the prespecified set of keywords. However, at the manual review stage, we do not seek to differentiate between problems thatwere effectively or ineffectively managed. Nor do we seek to independently evaluate the veracity of any reported
instances of harm, wrongdoing, or poor performance. Our goal is to identify which types of problems arose during
implementation and we treat any reported instances of harm, wrongdoing, or poor performance as problems
186 The parties reportedly explored the possibility of implementing the project through an EPC+F arrangement, whereby China

Eximbank would extend the loan to CMC (the EPC contractor) and the Iranian Government would issue a sovereign guarantee insupport of the loan. Our dataset demonstrates that Chinese state-owned banks have attempted to implement many projects through
EPC+F arrangements (to avoid making any foreign exchange transfers to their Iranian counterparts). However, these arrangements
have proven difficult to implement for several different reasons (Imam Jomeh Zadeh et al. 2020; Bahadoran-Baghbaderani andMohamadi 2021).
187 The 2.0 dataset indicates that Russia is China’s biggest borrower country (see Table A-3a in the Appendix), which is largely the

result of Chinese debts incurred by Russian state-owned oil and gas companies over the last 15 years. However, our dataset also
reveals that some big-ticket infrastructure projects in Russia backed by Chinese state-owned banks have encountered major problems
because of U.S. and E.U. sanctions (e.g., Project ID#43054).
188 A PXF facility is an arrangement in which a commodity producer gets up-front cash from a customer in return for a promise to

repay the customer with that commodity in the future (potentially at a discount). Transaction lawyers advertise that they can help
clients craft PXF facility agreements in ways that exploit the so-called “Trade Finance Exemption” in international sanctions.
189 The project descriptions in the 2.0 dataset reveal that, in 2020 and 2021, many BRI infrastructure projects encounteredimplementation difficulties related to the COVID-19 pandemic, including work stoppages, higher rates of illness among construction
workers, difficulty accessing key project inputs, and weakened loan repayment capacities.
190 All searches are conducted in English because the project descriptions in the 2.0 dataset were prepared in English.

66(regardless of their merit and how they were handled). However, we are not able to account for “false negatives” (i.e.,cases in which a specific type of problem arose during project implementation, but the problem was not captured in
the “description” field within AidData’s 2.0 dataset), so the summary statistics that we report should be treated aslower-bound estimates.
Our analysis suggests that at least 7% of the BRI infrastructure projects in the 2.0 dataset encountered some type ofscandal, controversy, or alleged violation during implementation.191 However, these “problem projects” are unevenly
distributed across Beijing’s global portfolio. Table 23 indicates that 59 of the 65 projects that fall within this category
are located in just six countries: Pakistan (10 projects), Indonesia (9 projects), Malaysia and Vietnam (5 projects each) as
well as Kenya, Kyrgyz Republic, and Papua New Guinea (4 projects each).

Table 23: Top 10 countries with BRI infrastructure project descriptions that refer to scandals,
controversies, or alleged violations
Country

Project CountUSD 2017 millions

Pakistan

10

5,675

Indonesia

9

5,224

Malaysia

5

18,863

Viet Nam

5

2,747

Kenya

4

5,047

Kyrgyz Republic

4

1,055

Papua New Guinea

4436

Cambodia

2

860

Mozambique2

768

Belarus2727

To better understand the nature of the problems affecting BRI infrastructure projects during implementation, we apply
three additional sets of keyword search terms to the project descriptions in our dataset. First, we seek to identify BRI
infrastructure projects that were affected by claims of corruption or financial wrongdoing. Our analysis suggests that atleast 3% of the BRI infrastructure projects in the 2.0 dataset encountered this type of problem.192 These projects, as
Table 24 demonstrates, are heavily concentrated in 6 countries: Pakistan, Kyrgyz Republic, Indonesia, Kenya, Zambia,
and Papua New Guinea.

191 These 65 projects are collectively worth $48 billion. We identified these projects by first applying the following keyword searchterms to the BRI infrastructure project descriptions in the 2.0 dataset: allege*, allegation, criticiz*, criticism, complain*, controvers*,
fiasco, turmoil, protest*, violat*, noncomplian*, non-complian*, breach*, probe*, audit*, inquiry, scrutin*, uncover*, scandal, dispute*,
fined, sued, lawsuit, arbitrat*, court, and litigat*. We then reviewed each of the identified project descriptions “by hand” to remove
false positives.
192 We identified these 30 projects (worth $30 billion) by applying the following keyword search terms to the BRI project descriptionsin the 2.0 dataset: misuse*, abuse*, bid-rig*, misappropriat*, mismanage*, steal, theft, stole*, corrupt*, bribe*, graft, fraud, kickback,siphon*, embezzle*, illicit, illegal, inflat*, overprice*, over-price*, wrongdoing, collusion, collude*, loot*, plunder*, prosecut*, arrest,
convict*, criminal, extradit*, imprison, and blacklist*. We then reviewed each of the identified project descriptions “by hand” toremove false positives.

67

Table 24: Top 10 countries with BRI infrastructure project descriptions that reference claimsof corruption or other types of financial wrongdoing
Country

Project countUSD 2017 millions

Pakistan

4

2,675

Kyrgyz Republic

4

1,055

Indonesia

4

980

Kenya

3

3,552

Zambia3585

Papua New Guinea

3411

Malaysia

2

14,541

Cameroon

2

227

Sri Lanka

2

209

Venezuela

1

3,901

The qualitative project descriptions in the 2.0 dataset shed additional light on the specific types of financial
wrongdoing that reportedly took place.193 For example, in Papua New Guinea (PNG), China Eximbank provided a $229
million preferential buyer’s credit (PBC) in 2016 for a project that involved laying 5,600 kilometers of submarine fiber
optic cable to connect coastal and maritime provincial centers across the country (see Project ID#61213).194 The project
became a subject of local controversy because the proceeds of the loan were used to finance a commercial contract—
between PNG DataCo Ltd. (a PNG state-owned enterprise) and Huawei Technologies Co Ltd. (“Huawei”)—that wasallegedly overpriced by 30-50%.195 In the Kyrgyz Republic, China Eximbank issued a $386 million loan for themodernization of the Bishkek thermal power plant (TPP) in 2013 (see Project ID#39620). Then, during the middle ofwinter in 2018, the power plant failed, and local residents were left with no heating. When civil society organizations
followed the paper trail related to the China Eximbank-financed modernization project, evidence of embezzlement
emerged. An investigation resulted in the firing of Prime Minister Sapar Isakov. In total, 30 government officials—
including another (former) Prime Minister (Jantoro Satybaldiev), a former mayor of Bishkek (Kubanychbek Kulmatov),
and a former Energy Minister (Osmonbek Artykbaev)—were charged with corruption and using their positions to lobby
for the selection of a Chinese company (TBEA) as the contractor for the project. Prosecutors estimate that bid-rigging
and the inflated cost of the sole-source contract issued to TBEA cost the Government of the Kyrgyz Republic as muchas $111 million. Nurlan Omurkul, the director of the Bishkek TPP, later acknowledged that “I’ve worked my whole life inpower and heating plants and knew all along that the Chinese price of $386 million was too expensive” (Higgins 2019).
Second, we attempt to identify BRI infrastructure projects that are associated with harm to local communities or
ecosystems. Our analysis suggests that at least 1.5% of BRI infrastructure projects in the 2.0 dataset were affected by

193 By identifying projects that faced claims of corruption or other types of financial wrongdoing, AidData is not rendering judgment

about the veracity of the underlying claims. Nor is AidData implying that the financiers or implementers of these projects werenecessarily responsible for financial wrongdoing. It is important to remember that a large proportion of China’s overseas development
finance program takes place in countries that pose high levels of fiduciary risk (see Table 4 above in Section 3).
194 The Ministry of Treasury of the Government of Papua New Guinea then on-lent the proceeds of the PBC to Kumul Consolidated

Holdings (a PNG state-owned enterprise), which in turn use the proceeds to finance approximately 85% of the cost of a $270 million
commercial contract with PNG DataCo Ltd. and Huawei Technologies Co Ltd.
195 Concerns have also arisen that revenue generated by the Australian Government grant-financed Coral Sea Cable System (CSCS) is

being used to “cross-subsidize” the Kumul Submarine Cable Project (i.e., facilitate loan repayment to China Eximbank).

68
problems related to human and environmental hazards.196 Table 25 indicates that these “problem projects” aredisproportionately located in three countries: Indonesia, Papua New Guinea, and Belarus. The 700MW Sumsel-1 Power
Plant Construction Project in Indonesia, which is being financed by ICBC, Bank of China, and China Construction Bank,is an illustrative case.197 It involves the construction of a coal-fired power plant—with two, 350MW power generation
units (Unit 1 and Unit 2)—and an 80 kilometer, 275kV transmission line. In March 2020, more than a hundred
construction workers from the project site went on strike to protest health and safety violations, workplace
discrimination, illegal layoffs, and the failure to provide overtime pay to employees. The construction of the plant has
also reportedly caused flooding and damage to a nearby oil palm plantation.198

Table 25: Seven countries with BRI infrastructure project descriptions that reference
community or ecosystem harm
Country

Project countUSD 2017 millions

Indonesia

6

4,651

Papua New Guinea

2176

Belarus2

727

Cambodia1759

Zambia1

164

Pakistan1

93

Myanmar1-

Third, we seek to identify BRI infrastructure projects that have underperformed vis-à-vis their original objectives (e.g.,borrowers that default on their repayment obligations, infrastructure assets that are underutilized or less profitable than
expected, companies that end up going into bankruptcy, contractors that fail to meet key milestones that are specified
in their contracts). Our analysis suggests that at least 10% of the BRI infrastructure projects in the 2.0 dataset fall into
this category.199 While some of these projects are located in “hotspots” that we have already discussed, others arenot.200 Take, for example, the Djibouti-Addis Ababa Railway Project, which is the largest BRI project in Ethiopia andDjibouti. The Ethiopian government contracted three China Eximbank loans worth $2.49 billion to finance theconstruction of one segment of the railway (measuring approximately 650 kilometers in length) that falls within its
jurisdictional boundaries. In parallel, the Djiboutian government secured a $491.7 million China Eximbank loan tofinance the construction of a 100 kilometer segment that passes through its territory at the Ethiopian border andterminates at the Port of Doraleh.201 The railway was completed and put into operation in January 2018, but it has

196 We identified these 14 projects (worth $6.5 billion) by applying the following keyword search terms to the BRI project descriptionsin the 2.0 dataset outside the emergency response and disaster preparedness sectors: disaster, destroy*, destruction, ruin*, despoil,
degrad*, pollut*, displace*, hazard*, exploit*, involuntary, unsafe, cracks, substandard, low-quality, and defective. We also excluded all
projects assigned to the emergency aid/disaster preparedness sectors. We then reviewed each of the identified project descriptions“by hand” to remove false positives.
197 For more detailed information about this project, see Project ID#69419, #69418, and #62064.
198 Then, in March 2020, the project's sponsor declared force majeure and stated that construction would be delayed because of the

COVID-19 pandemic. Labor negotiations were also delayed due to a COVID-19-related travel ban preventing employees of
Guangdong Engineering from returning from China.
199 We identified these 91 projects (worth $54 billion) by applying the following keyword search terms to the BRI project descriptionsin the 2.0 dataset: freeze, froze*, halt*, suspend*, cancel*, withheld, postpon*, abeyance, withhold, abandon*, mothball*, threat*,renege*, renegotiat*, default*, arrears, solvency, insolvent, cross-subsidize, unprofitable, losses, loss-making, bankrupt*,
underperform*, reschedul*, rescope*, jeopard*, rescind*, revoke*, remedia*, distress*, shutter*, seiz*, expropriat*, confiscat*, failure,delay*, and overrun*. We then reviewed each of the identified project descriptions “by hand” to remove false positives.
200 These projects, as Table 26 demonstrates, are heavily concentrated in 4 countries: Indoneisa, Sri Lanka, Pakistan, and Malaysia.
201 The three China Eximbank loans that supported the Ethiopian segment of the railway are captured in Project ID#70083, #70085,

and #70086. The China Eximbank loan that supported the Djiboutian segment of the railway is captured in Project ID#46183.

69
generated substantially less railway revenue than originally expected, which has made it difficult for the Ethiopian
government and the Djiboutian government to service their debts.202 In Ethiopia’s case, China Eximbank responded byextending the loan repayment period from 15 years to 35 years. In Djibouti’s case, China Eximbank agreed to extend
the maturity of the loan from 15 years to 25 years, lengthen the grace period from 5 years to 10 years, and modestly
reduce the interest rate (from 6-month LIBOR plus 300 basis points to 6-month LIBOR plus 210 basis points). However,it remains to be seen if the Djibouti-Addis Ababa Railway will find a path to commercial viability; according to the Chief
Economist of Sinosure, China’s state-owned overseas insurance provider has already written off approximately $1
billion in losses due to the project’s poor commercial performance (Pilling and Feng 2018).

Table 26: Top 10 countries with BRI infrastructure project descriptions that reference
underperformance vis-à-vis original objectives
Country

Project countUSD 2017 millions

Indonesia

12

6,239

Sri Lanka

11

3,933

Pakistan

7

1,800

Malaysia

5

18,863

Kenya

4

5,197

Papua New Guinea

4211

Ethiopia3

2,420

Kazakhstan

3

1,759

Belarus3

1,013

Viet Nam

3

664

Given that the 2.0 dataset provides a comprehensive picture of China’s overseas development finance portfolio aroundthe globe, it also provides a unique opportunity to check how BRI infrastructure projects have fared in comparison toother Chinese government-financed infrastructure projects. More specifically, from an ease of project implementation
perspective, is participation in the BRI a privilege or a penalty? Table 27 suggests that it may be the latter. When wecompare the implementation of BRI infrastructure projects to Chinese government-financed infrastructure projects thatare implemented outside of the BRI, we find that BRI infrastructure projects on average encounter longer delays during
implementation. Across all sectors where Chinese infrastructure projects are taking place, the average difference in the
duration of the implementation delay is 272 days: it takes 771 days, on average, to implement an infrastructure projectwith funding from the Chinese government outside of the BRI; however, it takes 1,047 days, on average to implement a
BRI infrastructure project. The same pattern is present in the three infrastructure sectors where China has the biggest
financial footprint: (1) construction, industry, mining; (2) transport and storage; and (3) energy.
The underlying reason(s) why BRI infrastructure projects are facing longer delays in implementation are not yet widely
understood, but there are several competing and complementary hypotheses that merit attention. One possibility isthat BRI infrastructure projects are oversized and overpriced, which leads to higher levels of host country criticism and ahigher probability that the underlying commercial contracts financed with Chinese loans and export credits will be
renegotiated.203 The China Eximbank-financed East Coast Rail Link Project in Malaysia—captured via Project ID#85858,

202 The three China Eximbank loans that supported this project are captured in Project ID#70083, #70085, and #70086.
203 Although this problem has potentially become more acute during the BRI era, it clearly existed before China introduced its

flagship, global infrastructure initiative in 2013. ​​Mohamed Nasheed, who served as the President of the Maldives from 2008-2012,
puts it this way: “[t]hey came in; they did the work and sent us the bill. So it’s not the loan interest rates as such but the costing itself.
They over-invoiced us and charged us for that and now we have to repay the interest rate and the principal amount. … I can’t see how
our development can be rapid enough to have the amount of savings to re-pay China” (The Economic Times 2019).

70#49309, and #73473 and described in Box 6—is a case in point. Another possibility is that BRI infrastructure projects
are larger and more complex and thus more likely to encounter problems related to human health and safety,
environmental protection, compensation for involuntarily displaced communities, and cultural heritage preservation.204

Box 6:
The
consequencesof overpricing
in a China
Eximbank-financed
Box
6:implementation
The implementationconsequencesof overpricing
in a China
Eximbankproject
financed project
On September
26, 2016,
Ministryof Finance
Incorporated(MOF Inc.)
Malaysia
establisheda wholly-owned
On September
26, the2016,
the Ministryof Finance
Incorporated(MOFofInc.)of Malaysia
establisheda whollysubsidiaryandsubsidiaryspecial purpose
vehiclecalled vehicle
Malaysia
Rail Link
Sdn Bhdor MRLSB)
to design,
finance,owned
and special
purposecalledMalaysia
Rail (MRL
Link Sdn
Bhd (MRLor MRLSB)
toimplement,
andmaintainthe East Coast
Rail Linkthe(ECRL).
One month
later,
ChinaOne
Communications
Construction
design,finance,
implement,
and maintain
East Coast
Rail Link(ECRL).
month later, China
Company
Ltd (CCCC) and
MRLSB signed
a commercial
contract
worth RMB
72.8
billion (46 billion
ringgit)forRMB
Phase 1
Communications
Construction
Company
Ltd (CCCC)
and MRLSB
signeda commercial
contract
worthof the 72.8
ECRLbillion
Project.
and
MRLSB
signeda loanagreement
RMB
61.88 billion (39.1(46China
billionEximbank
ringgit) forPhase
1 ofsubsequently
the ECRL Project.
China
Eximbank
andworth
MRLSB
subsequently
billion signed
ringgit)afor
Phase
1 of theworth
ECRL RMB
Project.
Then,
on May2017,
CCCCforandMRLSB
an Engineering,loan
agreement
61.88
billion(39.113,billionringgit)Phase
1 ofsigned
the ECRL
Project.
Procurement,
Construction
andCCCC
Commissioning(EPCC)
worth RMB
14.2 billion Construction(9 billion ringgit)
Then, onMay 13, 2017,
and MRLSB
signedcontract
an Engineering,
Procurement,
andfor Phase 2 of
the ECRL
Project. However,in contract
August 2018,
electedPrime
Minister
MahathirMohamadcancelled
the ECRL
Commissioning(EPCC)
worthnewly
RMB 14.2
billion(9 billion
ringgit)for Phase
2 of the
ECRL Project.
Project.
and statedthat it could
developed
by a Malaysian
forcancelled
less than half
the valueof theHowever,in August
2018,have
newlybeen
electedPrime Minister
Mahathircompany
Mohamadthe ECRL
Project.
contracts
hadthatbeen
won byCCCC.
in January
an investigation
thethanWallhalf
Streetthat
andthatstatedit could
have
beenThen,developedby a2019,
Malaysiancompany forbyless
theJournal
value ofrevealed
thethe Chinese
Government
had previously
recommended
that the2019,previous
governmentby
in Malaysia
price the
ECRL (andcontractsthat had been
won by CCCC.
Then, in January
an investigation
the Wall Street
Journalanother
China Eximbank-financed
project) at an
above-market
values in orderthat
to generate
excesscash that in
it could use
revealed
that the Chinese Government
had
previously recommended
the previous
government
to repay
the maturing
debts
of (and
1MDB(a stateChina
investment
fund).205 Afterproject)
projectatcancellation,
Chinavalues
Eximbank
Malaysia
price the
ECRL
another
Eximbank-financed
an above-market
in informed
the new
Malaysiangovernmentthat itthat
could
eitheruse
renegotiatethematuring
original commercial
contractsor payterminationorder
to generate
excess cash
it could
to repay thedebts of 1MDB
(a state
investment
205 21.78
costs ofabout
billion ringgit.
The Malaysian
government
chosetheto new
renegotiate.
On
April 12, 2019,fund).
After projectcancellation,
China Eximbank
informed
Malaysiangovernmentthat CCCC
it couldand
MRLSBeither
signeda Supplemental
Agreement(SA) regarding
and Phase 2costsof theProcurement,
renegotiate
the originalcommercial
contractsPhaseor pay1terminationof Engineering,
about 21.78 billion
ringgit.
Construction& Commissioning(EPCC)ECRL Project,which
reducedthe overall
cost of signed
the ECRL
The Malaysian
government
chosefortothe
renegotiate.
On April
12,
2019, CCCC
and MRLSBa Project from65.5 billion
ringgit toAgreement
44 billion ringgit.
The contract
renegotiation
led to major
implementation delays. As
Supplemental(SA) regarding
Phase
1 and Phase process
2 of thehas
Engineering,
Procurement,of March
2021, the project
had only achieved
a 20%
rate.
Thereduced
originally
expectedproject
completion
date
Construction& Commissioning(EPCC) forthe completion
ECRL Project,
which
the
overall costof theECRL
was June30, 2024,
but itbillion
has been
pushedtoringgit.
December
2026.renegotiation process has led to major
Projectfrom 65.5
ringgitto 44 backbillionThe 31,
contract
implementation delays. As of March 2021, the project had only achieved a 20% completion rate. The
originally expected project completion date was June 30, 2024, but it has been pushed back to December
31, 2026.

Table 27: Average days to implement and complete a project, BRI vs. non-BRI infrastructure
projects
Sectors

BRI infrastructure projects

Non-BRI infrastructure projects

All sectors

1,047

Energy

1,295

1,260

Industry, mining, and construction

1,156

961

Transport and storage

1,320

1,096

771

According to Table 28 , while at least 6.9% of the BRI infrastructure project descriptions in the dataset refer to some
type of scandal, controversy, or alleged violation that arose during implementation, only 3.7% of non-BRI infrastructure
project descriptions include such references. Similarly, when we look for evidence of problems related to corruption orother forms of financial wrongdoing across the two subsamples, we find that 3.2% of the BRI project descriptions in the
2.0 dataset identify these types of problems, but only 1.4% of the non-BRI infrastructure project descriptions do so.
With respect to projects that have potentially underperformed vis-à-vis their original objectives or brought harm to

204 There are of course other potential explanations, including the possibility that the larger size of BRI infrastructure projects makes

them more prone to cost overruns (and thus implementation delays) or that BRI infrastructure projects are hastily conceived and-implemented due to political pressures from Beijing.
205 The Prime Minister (Najib Razak) was, at the time, accused of stealing $681 million from 1MDB (Wright and Hope 2019).

71
local communities and ecosystems, there are few differences across the subsamples. However, we find large differences
across the subsamples when we compare the proportions of BRI infrastructure spending and non-BRI infrastructure
spending that are associated with these different types of problems (see Table 29 ). More than twice as much BRI
infrastructure spending (25.9%) is associated with some type of scandal, controversy, or alleged violation than non-BRI
infrastructure spending (9.8%). The proportion of BRI infrastructure spending affected by problems related to financial
wrongdoing (16.1%) is approximately five times higher than the proportion of non-BRI infrastructure spending (2.9%).
With respect to problems related to local community or ecosystem harm, we find a 3.6% prevalence rate in the BRI
infrastructure portfolio and a 1.8% rate in the non-BRI infrastructure portfolio. When we consider projects that haveapparently underperformed vis-à-vis their original objectives, we find a 29.4% prevalence rate in the BRI infrastructure
portfolio and a 18.4% rate in the non-BRI infrastructure portfolio. If we define the prevalence rate as exposure to any
one of these problems, the BRI infrastructure portfolio registers a 34.9% problem prevalence rate, and the non-BRI
infrastructure portfolio registers a 21.4% problem prevalence rate.206

Table 28: BRI vs. non-BRI infrastructure project descriptions that reference a problem type,by % of project descriptions
Problem type

BRI infrastructure projects

Non-BRI infrastructure
projects

Scandals, controversies, or alleged violations

6.9%

3.7%

Financial wrongdoing

3.2%

1.4%

Community or ecosystem harm

1.5%

0.8%

Underperformance vis-à-vis original objectives

9.6%

8.2%

Any of these 4 types of problems

12.8%

10.1%

Table 29: BRI vs. non-BRI infrastructure projects that reference a problem type, by % oftransaction values

Problem type

BRI infrastructure projects

Non-BRI infrastructure
projects

Scandals, controversies, or alleged violations

25.9%

9.8%

Financial wrongdoing

16.1%

2.9%

Community or ecosystem harm

3.6%

1.8%

Underperformance vis-à-vis original objectives

29.4%

18.4%

Any of these 4 types of problems

34.9%

21.4%

However, we also find that these types of problems are not equally prevalent across all types of BRI infrastructure
projects (see Table 30). When BRI infrastructure projects are exclusively undertaken by host country organizations (ororganizations that are neither from China nor the host country), they are less likely to encounter these types of

206 This estimate is broadly consistent with a previous estimate generated by a private consultancy. RWR Advisory maintains a

proprietary database of BRI projects, which reportedly shows that 32% of the BRI project portfolio (by monetary value) has
encountered problems during implementation (Kynge 2018). However, since the RWR Advisory database is shielded from public
scrutiny, we have not been able to conduct an in-depth comparison to AidData’s 2.0 dataset.

72
problems during implementation.207 Yet, among BRI infrastructure projects that are co-financed, these types ofproblems are more likely to arise.208 These sources of variation underscore the importance of better understanding how
the upstream financing and implementation arrangements that govern BRI infrastructure projects—and other Chinese
government-financed development projects—influence downstream outcomes.

Table 30: Prevalence rate of problems among BRI Infrastructure projects, by type offinancing and implementation arrangement

Cofinancing

Implementation

Problem prevalence rate

% of all BRI
infrastructure
projects

Cofinanced with any type of agency

21%

14%

Implemented solely by a host-country
agency10%14%Implemented by neither a non-Chinese
agency nor a host-country agency

6%

14%

Implemented solely by a Chinese agency

40%

35%

Co-implemented by a Chinese & hostcountry agency

44%

37%

Note: In this table, the problem prevalence rate is defined as the percentage of projects that encountered at least oneof the following types of problems: (1) scandals, controversies, or alleged violations; (2) financial wrongdoing; (3)community or ecosystem harm; and (4) underperformance vis-à-vis project objectives. We do not disaggregate thesample of co-financed projects into projects co-financed by Chinese and non-Chinese organizations due to small
sample sizes.

5.3 Learning from failure: A closer look at suspended andcancelled Chinese development projects
The 2.0 dataset is also different from other publicly available datasets of Chinese development finance in that it
captures project suspensions and cancellations (see Table A-1 in the Appendix).209 AidData has previously faced
criticism for publishing data on projects backed by official commitments that are subsequently suspended or

207 We find that 14% of all BRI infrastructure projects in the 2.0 dataset are exclusively implemented by organizations that are neither

from China nor the host country, 35% are exclusively implemented by Chinese organizations, 14% are exclusively implemented byhost country organizations, and 37% are jointly implemented by Chinese and host country organizations. Yet we find a proportionallylower problem prevalence rate among BRI infrastructure projects exclusively implemented by organizations that are neither from
China nor the host country (6%) and among BRI infrastructure projects exclusively implemented by host country organizations (10%).
The opposite is true of BRI infrastructure projects exclusively implemented by Chinese organizations and jointly implemented by
Chinese and host country organizations. They have proportionally higher problem prevalence rates—40% and 44%, respectively.
208 Whereas 14% of all BRI infrastructure projects in the 2.0 dataset involve co-financing and 86% do not, we find a proportionallyhigher problem prevalence rate (21.5%) among BRI infrastructure projects that involve co-financing and a proportionally lower
problem prevalence rate (78.5%) among BRI infrastructure projects that do not. This pattern in the data does necessarily imply that cofinancing is the “culprit.” It may simply reflect the fact that co-financed projects are larger in size and larger projects are riskier thansmaller projects. This issue merits further empirical investigation.
209 Users of the 2.0 dataset can identify all suspended and cancelled projects by sorting on the status variable. Users should also keep

in mind that suspended, cancelled, and pledged but not committed projects will be excluded if the Recommended_for_Aggregates
filter is applied.

73
cancelled.210 However, we maintain that it is important to systematically track these projects and subject them toanalysis. Shielding suspended and cancelled projects from public scrutiny leaves analysts and decision-makers with anincomplete picture of China’s overseas development finance portfolio. It also limits opportunities to learn from failure.
Figure 24 shows that, during the BRI era, Beijing has faced more project suspensions and cancellations than it had
during the pre-BRI era. A similar, post-2013 trend is observed in BRI participant countries.211 A “BRI backlash” isevidently underway, but it is concentrated in a particular set of countries.212 In Table 31, we calculate the aggregatemonetary value of all project suspensions and cancellations in BRI participant countries. Many of the countries thatenter the “top ten” list—including Malaysia, Kazakhstan, Ecuador, and Costa Rica—are places where there is clear
evidence of BRI “buyer’s remorse.” In Malaysia, the Prime Minister cancelled Phase 1 of the China Eximbank-financed
East Coast Rail Link Project based on concerns about overpricing and corruption. He told the New York Times that“[i]t’s all about borrowing too much money, which we cannot afford and cannot repay because we don’t need theseprojects in Malaysia” (Beech 2018). In Kazakhstan, CDB issued a $1.5 billion loan (backed by a sovereign guarantee) toan SPV called Astana LRT LLP—that is owned by the City of Astana—for the Astana Light Rail Project in 2015
(ID#53930).213 However, two years later, the President of Kazakhstan ordered an investigation into the officials who
initiated the project. The chief executive of Astana LRT LLP was accused of embezzling project funds and fled thecountry. The local authorities suspended the construction of the railway, and the half-finished project became a sourceof public discontent. In Figure 25, we present the broader set of BRI projects (scaled by their financial values) aroundthe globe that were suspended or cancelled between 2013 and 2021.214

Figure 24: Suspensions and cancellations of Chinese government-financed projects in BRI vs.
non-BRI countries, 2000-2017
BRI announced
20

15

All
countries

10

5

BRI
participant
countries

02000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Source: AidData.

210 The main criticism that AidData has faced is that users of its dataset may generate inflated estimates of official development

finance commitments from China by including suspended and cancelled projects in their analyses (see Chapter 3 of Dreher et al.
forthcoming). However, for users who wish to estimate official development finance commitments from China, AidData has
consistently provided guidance on how to isolate the formally approved, active, and completed Chinese government-financed
projects in its datasets (i.e., by using the “status” and “umbrella” variables to identify all projects with a status designation of Pipeline:
Commitment, Implementation, and Completion that have not also been designated as umbrella agreements).
211 The “BRI participant countries” category in Figure 24 captures all projects that were officially committed during or after 2013 but

subsequently suspended or cancelled among the subset of countries that signed a BRI MOU/cooperation agreement during or after2013.
212 Chinese development projects have raised concerns about corruption, overpricing, political capture, debt sustainability, andenvironmental degradation in at least two dozen countries. These concerns have had knock-on effects. A growing number ofpoliticians from LMICs have cancelled or mothballed high-profile Chinese development projects because major changes in public
sentiment have made it difficult for them to maintain close relations with China (Aamir 2018; Balding 2018; Mundy and Hille 2019;
Rolland 2019; and Parks 2019).
213 The Government of Kazakhstan provided a sovereign guarantee in support of the loan that was issued to the SPV.
214 Figure 25 includes projects that were officially committed during or after 2013 but subsequently suspended or cancelled among

the subset of countries that signed a BRI MOU/cooperation agreement during or after 2013.

74

Table 31: Monetary value of project suspensions and cancellations in BRI countries,
2013-2021
Country

USD 2017 millions

Malaysia

11,576.2

Kazakhstan

1,474.7

Bolivia

1,025.0

Kenya

952.5

Costa Rica

889.3

Sudan

674.6

Ethiopia

534.4

Ecuador

417.3

Zambia411.0

Cameroon

310.5

Figure 25: Map of suspended and cancelled project financing in BRI countries, 2013-2021

Source: AidData.
At the same time, the 2.0 dataset demonstrates that some countries are encountering fewer obstacles than others
during the implementation of BRI infrastructure projects. Table 32 provides a list of the countries in the dataset with thehighest number of BRI infrastructure projects completed ahead of schedule. Kenya is at the top of the list. Beijing’s
flagship BRI project in Kenya—the Mombasa-Nairobi Standard Gauge Railway (SGR)—is an illustrative case. The
purpose of this project, which was financed with two China Eximbank loans worth approximately $3.5 billion (captured
via ID#37103 and ID#31777 in the 2.0 dataset), was to construct a 475-kilometer railway from the port of Mombasa to
Nairobi. Its ultimate objective was to transport passengers and cargo from Nairobi to the southeastern port city of
Mombasa at 120 kilometers per hour and reduce travel times and transport costs by as much as 60 percent. The

75
project was implemented at an astonishingly fast pace.215 It commenced on December 12, 2014, and was completed
on May 31, 2017, eighteen months ahead of schedule. This achievement is remarkable in light of the scale andcomplexity of the project and the significant obstacles that arose during project implementation (Dreher et al.
forthcoming).

Table 32: Number of BRI infrastructure projects completed ahead of schedule, by country
Year

Projects ahead of schedule

Kenya

5

Indonesia

3

Mongolia

3

Cameroon

3

Cambodia

2

Lao People's Democratic Republic2

Zambia2

Maldives

2

Pakistan

1

Belarus1

Kyrgyz Republic

1

Sri Lanka

1

Papua New Guinea

1

Uzbekistan

1

Senegal1

Serbia

1

Ghana

1

Fiji

1

Suriname1

Georgia1

Rwanda1

215 Mega-transport projects like this one are notoriously difficult to implement and often beset by delays and cost overruns (Dimitriou

et al. 2014; Flyvbjerg et al. 2002).

76
The ease of project implementation typically depends—at least in part—on host country choices and characteristics. In
the case of the Mombasa-Nairobi SGR, President Kenyatta acted as the “project owner” and assumed responsibility for
resolving politically contentious matters that could delay or derail the project. He also identified a specific goal toguide the efforts of everyone involved in project implementation: putting the railway into operation before he stood for
re-election in August 2017. At the same time, project implementation outcomes often depend upon the specificcontractors who are responsible for implementation. Here again, the case of the Mombasa-Nairobi SGR is instructive.
Conservationists sounded the alarm when they learned that the railway would run through the Tsavo National Park
wildlife sanctuary. But China Road and Bridge Corporation (CRBC)—the Chinese state-owned enterprise responsible
for project implementation—moved quickly to mitigate the risk of biodiversity loss by undertaking various in-situ
conservation activities. It created access corridors for wildlife migration, building some bridges as high as seven meterstall to ensure the safe passage of giraffes and elephants from one side of the park to the other. Alongside the railway, itinstalled dust-suppressing sprinklers, noise screens, and drinking water facilities for wildlife to mitigate concerns about
health hazards during the construction phase and water scarcity during dry seasons. Another implementation challenge
was the fact that the construction of the railway would require the displacement of nearby residents. To help resolve
contentious disputes, CRBC hired local people who were trusted by local residents, such as pastors from nearby
churches. These “community liaison officers helped CRBC understand and respond to local grievances—a tactic thatindependent observers have described as “an effective strategy for gaining greater [local] acceptance of the SGR”
(Wissenbach and Wang 2017: 14).
In Section 6, we will explain how several features of AidData’s 2.0 dataset have opened up new opportunities toevaluate supply-side and demand-side factors that affect project implementation outcomes.

77

6. New and improved features of the dataset forprospective users to consider
By way of conclusion, we would like to highlight several new and improved features of the 2.0 dataset that may be
useful to analysts and decision-makers:(1) Locational Precision. Previous versions of the dataset used a point-based geocoding methodology to identify thephysical locations of Chinese development projects (AidData Research and Evaluation Unit 2017; Bluhm et al. 2018;
Dreher et al. forthcoming). These locational data opened up new opportunities for social scientists to measure theeffects of Chinese development projects at subnational rather than national scales (e.g., BenYishay et al. 2016; Isaksson
and Kotsadam 2018a, 2018b; Brazys et al. 2017; Bluhm et al. 2018; Brazys and Dukalskis 2019; Martorano et al. 2020;
Dreher et al. forthcoming, 2021b; Iacoella et al. 2021; Blair and Roessler 2021). However, most of these data weregenerated with a non-trivial amount of spatial measurement imprecision.216 The latest (2.0) version of our dataset
provides substantially more precise locational information about Chinese development projects. The physicalboundaries and exact locations of schools, hospitals, stadiums, government buildings, power plants, and factories are
now represented with polygons.217 Likewise, we have used polygons to precisely characterize the geographical scopeof special economic zones, industrial parks, mining concessions, protected areas, and plots of land under cultivation.
Line vectors (i.e., “squiggly” lines) are provided to capture the exact routes of roads, bridges, tunnels, railways, powerlines, canals, and pipelines. Technically-savvy users can now access our data via GeoJSON files, which can be mergedwith georeferenced outcome and covariate data to undertake geospatial impact evaluations and other types ofgeospatial analysis.218 We also provide OpenStreetMap URLs—as well as stable hyperlinks to GeoJSON files (onAidData’s GitHub repository)—so that anyone can instantly visualize the geographical locations and features of Chinese
development projects. An important caveat is that we are only able to provide these geospatial details for the subset
of projects in our dataset that have physical footprints (e.g., roads, railways, transmission lines) or involve activities at
specific locations (e.g., medical teams stationed at a given hospital, equipment given to park rangers to patrol a welldemarcated protected area). Out of the 13,427 projects in the 2.0 version of AidData’s Global Chinese Development
Finance Dataset, 3,285 projects (worth $410 billion) have corresponding GeoJSON files and OpenStreetMap URLs.219
By way of illustration, Figure 26 uses all of the GeoJSON files for a single country (Laos) to visualize the geographicallocations and features of Chinese development projects that were underway in that country between 2000 and 2021.220
The long, teal, “squiggly” line that runs from the country’s border with China in the north to its border with Thailand in
the south represents the precise path of the China-Laos Railway Project (which we discussed at length in Section 4).221
Most of the orange lines and polygons in Figure 26 represent hydroelectric power plants and transmission lines that
evacuate power to residential and commercial customers. In Figure 27, we “zoom in” on the area surrounding thecapital (Vientiane) of Laos to take a closer look at the nature of the locational data that are included in AidData’s 2.0

216 For example, AidData may have identified the district in which a hospital was located but not its exact location, or the starting

point and end point of a road but not the precise route between those two points.
217 The polygons in the 2.0 dataset are closed shapes made up of straight-line segments that seek to capture the precise boundaries

of a geographical area or feature. When physical boundary data are not available, but a precise location is available, AidData provides
points rather than polygons. However, note that all features (captured via GeoJSON files) are represented as polygons in their final
form; for example, points and line segments are buffered slightly to form a polygon.
218 When spatial measurement imprecision is minimized, analysts can conduct geospatial impact evaluations (GIEs) with substantiallygreater accuracy. If “treated” locations are measured with a significant amount of error, GIE estimates of project impact can be biased
toward zero (akin to false negatives) and thereby understate—or mask—the true effects of Chinese development projects (BenYishay
et al. 2017; Marty et al. 2019).
219 However, given that many Chinese development projects support multiple segments of physical infrastructure or otherwise takeplace in multiple locations, there are substantially more OpenStreetMap URLs in our dataset than there are projects with GeoJSON
files and OpenStreetMap URLs. In total, our dataset provides 3,285 GeoJSON files and 7,809 OpenStreetMap URLs for 3,285 Chinese
government-financed projects. As a point of comparison, the geolocated dataset of Chinese overseas development finance produced
by Boston University’s Global Development Policy Center provides spatial data (polygons, lines, and points) for 615 Chinese
government-financed projects (Gallagher and Ray 2020; Ray et al. 2021). For a broader comparison of the 2.0 version of AidData’s
Global Chinese Development Finance Dataset and various Chinese development finance datasets maintained by Boston University's
Global Development Policy Center, see Table A-1 in the Appendix.
220 The colors in the map represent the OECD sector codes to which projects in the 2.0 dataset have been assigned.
221 The GeoJSON file for this project (accessible via https://github.com/aiddata/china-osm-geodata) was constructed with thefollowing OpenStreetMap URL: https://www.openstreetmap.org/relation/10168930#map=15/17.8810/102.7150

78
dataset. In the northwestern section of the map, one can see a teal-colored polygon north of the Mekong River. This
geographical feature captures the precise boundaries of Wattay International Airport, which was upgraded with apreferential buyer’s credit from China Eximbank (captured via Project ID#35756). Approximately 5 kilometers southeastof that airport, one can see three polygons—in light blue and dark blue—that capture the exact locations and physicalboundaries of Mahosot Hospital, Hospital 103, and a National Cultural Hall. All these facilities were rehabilitated with
grant funding from the Chinese government (see Project ID#64210, #64211, and #85264). The orange lines that run
from north to south represent China Eximbank-financed transmission lines bringing electricity from the 130MW Nam
Khan 2 Hydropower Plant to the country’s capital, as well as a China Eximbank-financed project that involves thereconstruction of the 115kV Donekoy substation, the 115kV Sokpaluang substation, and transmission lines between thesubstations (captured via Project ID#33809 and #64786). The teal line that runs from north to south represents one
small segment of the China-Laos Railway (captured via Project ID#33726 and #85304). Sandwiched in between theorange and teal lines are two additional polygons—one in pink and another in yellow—that represent the precise
locations and physical boundaries of the National University of Laos (where a Confucius Institute and a Huawei cloudcomputing laboratory are housed) and the International Conference Center, both of which have benefited from
Chinese government grant funding (see Project ID#35757, #43956, and #73289).222
To illustrate the value of these precise locational data when they are analyzed in conjunction with high-resolution
outcome data, consider a single project from the 2.0 dataset: the 230kV Nabong-Nam Ngum 1-Hin Heup Power
Transmission Line Project in Laos, which was financed by China Eximbank in August 2016 financed with a $162.35
million preferential buyer's credit.223 A core objective of this project was to facilitate the distribution of electricity fromthe Nam Ngum 1 hydroelectric dam by constructing a 90-kilometer transmission line from the Nam Ngum 1 substation
to the Hinheup (Hin Heup) substation and expanding existing substation capacity. Project implementation commenced
in November 2016 and ended in May 2018 (eighteen months ahead of schedule). A formal completion ceremony took
place on July 4, 2018, and the project warranty period officially ended on September 2, 2019. We can explore whether
electrification rates increased in the areas near the project site by merging AidData’s georeferenced project data withhigh-resolution satellite imagery on nighttime light output from NOAA’s Visible Infrared Imaging Radiometer Suite(VIIRS). To do so, we first use the GeoJSON files in the 2.0 dataset to identify the precise locations and boundaries of
the Nam Ngum 1 and Hinheup (Hin Heup) substations (with polygons) and the precise path of the 90 km transmission
line that runs between the substations (with a line vector). Figure 28 visualizes these project features in a map.224 Then,
to identify geographical areas that could potentially benefit from the transmission line and substations, we create fivekilometer buffers around these project features (i.e., the intervention sites). We then calculate zonal statistics for these“catchment areas” to produce aggregate measures of our outcome of interest. Since the data from VIIRS are available
before and after project implementation took place, we can measure the rate of change in nighttime light output
during the “pre-treatment” period (2014-2017) and the “post-treatment” period (2017-2020). This analysis, which isvisually represented in Figure 28, suggests that the geographical areas within a five-kilometer buffer around thesubstations and transmission line experienced a 26 percent increase in the average rate of change in nighttime light
output between the periods before and after project implementation.

222 There are several additional Chinese government-financed projects that are captured via lines and polygons in Figure 27, but for

which we did not have enough space to create inset boxes. These include Triumphal Arch Park, Saysettha Comprehensive
Development Zone, a satellite ground control station, and a Lao National Television building (captured via Project ID#64695, #33755,#64693, #64690, #64770, and #64663).
223 This China Eximbank-financed project is captured in AidData’s 2.0 dataset via ID#63448. China Eximbank also financed theconstruction of the Nam Ngum 1 hydroelectric power plant (as captured via ID#63447 in AidData’s 2.0 dataset). Neither of these
China Eximbank-financed projects are included in the two relevant datasets (China’s Global Energy Finance Dataset and China’s
Overseas Development Finance Dataset) published by Boston University’s Global Development Policy Center.
224 These GeoJSON files can be accessed from AidData’s GitHub repository via https://github.com/aiddata/china-osm-geodata.

79

Figure 26: Precise locations and physical boundaries of 66 Chinese government-financed
projects in Laos

80

Figure 27: Precise locations and physical boundaries of Chinese government-financed
projects in the areas surrounding Vientiane, the capital of Laos

81

Figure 28: Changes in nighttime light output before and after implementation of China
Eximbank-financed 230kV Nabong-Nam Ngum 1-Hin Heup Power Transmission Line Projectin Laos

82(2) Exact timing of project implementation. Existing Chinese development finance datasets do not adequately
address increasing levels of demand for data on the timing of project implementation (i.e., when treatment “turns on”
and “turns off”) among social scientists. Nor do they adequately address demand among policymakers andpractitioners for rich details on project implementation. To ease these informational constraints, AidData has prioritized
the collection of high-precision (i.e., calendar day) data on the implementation start dates and completion dates ofChinese development projects. These variables were included in earlier iterations of our dataset. However, in the 2.0
version of our dataset, these variables have vastly improved coverage rates. In the 1.0 version of our dataset, we were
only able to identify precise implementation start dates for 745 projects and precise project completion dates for 906
projects. In the 2.0 version of our dataset, we have identified precise implementation start dates for 5,539 projects(worth $438 billion) and precise project completion dates for 6,061 projects (worth $333 billion). With calendar daylevel information on the timing of project implementation and exact locational details, users of our dataset can now
measure the spatio-temporal rollout of project implementation with a high level of precision.
To provide an illustration of how these high-precision data on the timing of project implementation data can be put toproductive use, consider the design of a new study that seeks to measure the public opinion impacts of Chinese
government-financed development projects (Wellner et al. 2021). To do so, the authors use a Regression Discontinuity
Design (RDD) that leverages data on Chinese development project implementation and completion dates as well as
time-stamped, respondent-level data from the Gallup World Poll (GWP). The GWP dataset consists of approximately
1.5 million survey responses across 125 countries and nearly 2,000 subnational jurisdictions from 2006 to 2020.225
Wellner et al. (2021) exploit the staggered roll-out of the GWP and the availability of precise interview dates bycomparing respondents who were interviewed in the month before and the month after the occurrence of a particular
type of Chinese project “event.”226 They estimate the public opinion impacts of several different types of Chinese
project “events,” including implementation start dates and completion dates. Similarly, to estimate the local corruption
impacts of Chinese development finance, Isaksson and Kotsadam (2018b) leverage variation in the timing of project
implementation. More specifically, they compare Afrobarometer survey responses from people who lived near Chinese
development projects that were already underway with Afrobarometer survey responses from people who lived near
Chinese development projects that had not yet entered implementation.
(3) Organizational responsibility for project implementation. Another question that has prompted speculation anddebate among scholars and policymakers is the extent to which the outcomes of BRI projects—and Chinese
development projects more generally—depend upon the organizations responsible for implementation (e.g., Jansson
2013; Farrell 2016). Recognizing that this question cannot be answered without a stronger evidentiary foundation,
AidData has made a concerted effort to systematically populate the implementing_agencies variable in the 2.0 dataset—in particular, for large, “implementation-intensive” projects.227 In the 1.0 version of our dataset, we were able toidentify implementing agencies for 29% of projects (1,272 out of 4,373 projects) worth $223 billion (USD 2014).
However, in the 2.0 version of our dataset, we have managed to identify implementing agencies for 63% of projects(6,886 out of 10,849 projects) worth $682 billion. These implementing agencies include 1,045 government agencies(30%), 958 state-owned companies (27%), 554 private sector organizations (16%), and 189 special purpose vehicles/
joint ventures (5%). The remaining 770 implementing agencies (22%) consist of non-governmental organizations(NGOs), inter-governmental organizations (IGOs), and organizations that AidData could not reliably assign to acategory (“unspecified”). 35% of these implementing agencies are from China, while 58% are from host countries. 7%
are neither from China nor the host country where the project is based (e.g., IGOs). We anticipate that these data will
create new opportunities to evaluate the supply-side and demand-side factors that affect project implementation
outcomes.
In order to better understand the relationship between implementing agencies and project outcomes, we have also
constructed detailed narratives—in the “description” field of the 2.0 dataset—that provide an overview of project
accomplishments and failures, contractor performance vis-à-vis deadlines and deliverables, and efforts to manage andmitigate social, environmental, and fiduciary risks (e.g., pollution increases and health hazards near the project site,
public protests from local stakeholders, embezzlement scandals, indictments, prosecutions, and convictions of project
personnel). In Section 5, we provided an empirical demonstration of how the information in the 2.0 project narratives
can be put to productive use (through keyword searching). We stopped short of analyzing the prevalence of different
225 They focus on the following GWP survey question: “Do you approve or disapprove of the job performance of the leadership ofChina?”
226 They analyze these data with an event-study model that includes high-dimensional fixed effects. By using fixed effects at thecountry-level and province-year level and including a battery of variables that control for individual, geographical area, and survey
characteristics, the timing of the GWP interviews can be considered as-if random, thereby providing a plausibly exogenous source ofvariation necessary for causal inference.
227 There are many different types of transactions in our dataset that are not “implementation-intensive,” such as cash grants,

commodity (e.g., oil, coal, rice, corn, wheat) donations, equipment donations, balance of payments support, pre-export financefacilities, debt forgiveness, scholarships, and trainings.

83
types of implementation problems in projects that are undertaken by different types of contractors, but we anticipate
that this may be a fruitful avenue for future analysis.228
A cursory examination of the contractors involved in suspended and cancelled projects from the 2.0 dataset also
underlines the need to better understand the relationship between project performance and the organizations
responsible for implementation. It is notable that 100% of the contractors listed in Table 33 are state-owned
enterprises.229 The fact that there are no Chinese private enterprises on the list suggests that the 2.0 dataset may be
useful to those who wish to understand if Chinese development projects fare any better or worse during
implementation when they are undertaken by specific organizations.230 It is our hope that the dataset will also create astronger basis for determining which organizational characteristics correlate with better project performance.

Table 33: List of Chinese implementing agencies Involved in two or more suspended orcancelled projects
Implementing agency

Number of suspended
or cancelled projects

USD 2017 millions

China Harbour Engineering Co., Ltd.

4

993

China National Aero-Technology Import & Export
Corporation (CATIC)

3

158

China Petroleum Pipeline Bureau

2

1,906

China Railway International Group Co., Ltd. (CRIG)

2

1,478

ZTE Corporation

2

1,028

China State Construction Engineering Corporation(CSCEC)

2

483

AVIC International Holding Corporation

2

410

Sinohydro

2310

China International Water and Electrical Corporation
(CWE)

2

275

China Road & Bridge Corporation (CRBC)

2

269

Shandong Hi-Speed Group Corporation

2

218

Dongfang Electric Corporation (DEC)2

204

China Railway Engineering Corporation 7th Bureau(CREC7)

2

194

China Railway Construction Corporation (CRCC)

2171

China Shandong International Economic and TechnicalCooperation Corporation (SIETC)

2

169

China Civil Engineering Construction Corporation(CCECC)

2123

228 For example, one could explore if reported cases of financial wrongdoing are more prevalent among Chinese government-

financed development projects that are implemented by Chinese state-owned enterprises or Chinese private enterprises.
229 Jansson (2013) argues that Chinese state-owned enterprises are more willing than Chinese private enterprises to engage in risky

projects and activities.
230 The 2.0 dataset demonstrates that Chinese private enterprises are very much involved in the implementation of Chinese

government-financed development projects.

84

Note: Since multiple implementing agencies can be involved in a single project, the project counts and monetary amounts in thistable may be counted multiple times(4) Lending terms and conditions. Since the fall 2017 release of the 1.0 version of the dataset, AidData has prioritized
the collection of more detailed information on the terms and conditions that govern the loan and export credit
contracts issued by Chinese state-owned entities—when they are first issued and when they are subsequently revised.
We have done so, in part, by systematically implementing search procedures that enable the identification of loan andexport credit agreements in the official registers/gazettes and parliamentary websites of LMICs (Gelpern et al. 2021:
11). We have also done so by taking special care to retrieve the terms and conditions that are documented in the debt
information management systems of borrower countries. For 50% of the loans and export credits (and 56% of the debt
in monetary terms) in the 2.0 dataset, we have obtained the three key pricing details that are needed to calculate
financial concessionality levels (i.e., grant elements). By way of comparison, only 29% of the loans and export credits in
the 1.0 dataset included all three of these pricing details (see Table 34). Whereas 35% of the loans and export credits in
the 1.0 dataset identified an interest rate, this figure has increased to 53% in the 2.0 dataset. Similarly, 37% of the loans
and export credits in the 1.0 dataset identified a maturity length and 28% identified a grace period, but these coverage
rates have increased to 63% and 41%, respectively, in the 2.0 dataset. To the best of our knowledge, AidData is the
only organization that produces a global dataset of Chinese development finance and publishes loan and export credit
pricing details at the transaction level. Our 2.0 dataset identifies 1,659 interest rates, 1,940 maturity lengths, and 1,285
grace periods across 3,103 loans and export credits in Africa, Asia, Oceania, Eastern and Central Europe, the Middle
East, and Latin America and the Caribbean.231

Table 34: Loans with face values and borrowing terms, AidData 2.0 vs. 1.0 dataset
Variable

1.0 dataset coverage

2.0 dataset coverage

Transaction amount

92%

89%

Interest rate

35%

53%

Maturity

37%

63%

Grace period

28%

41%

Grant element

29%

50%

Three additional features of the 2.0 dataset may be of interest to users. First, we have added commitment fee andmanagement fee variables to the dataset to capture an important but a poorly understood feature of Chinese loan
pricing practices.232 These fees vary considerably across loans.233 While some include management and commitment
fees as low as 0.08%, others include fees as high as 1.45%. Second, we have added three variables that capture thedifferent types of “credit enhancements” used by Chinese state-owned lenders to increase their repayment prospects:(i) formal and informal pledges of collateral that can be seized in the event that borrower defaults on its repayment
231 There is one region-specific dataset—the Chinese Loans to Africa Database maintained by Boston University's Global

Development Policy Center and the China Africa Research Initiative at the Johns Hopkins University School of Advanced International
Studies (SAIS-CARI)—that provides such information. As a point of comparison, it identifies 589 interest rates, 435 maturities, and 359
grace periods across 1144 loans and export credits to countries in Africa. 51% of the loans and export credits in the Chinese Loans to
Africa Database have a specified interest rate, while 38% have a specified maturity length and 31% have a specified grace period.
232 A commitment fee is a fee that a borrower must pay to compensate the lender for its commitment to lend; it is usually payablesemi-annually and the size of the fee is usually based on a fixed percentage of the undisbursed loan amount. A management (or“front-end”) fee is a one-time, lump sum fee that is charged as a percentage of the face value of the loan.
233 To illustrate how these fees work in practice, consider the $492,400,000 preferential buyer’s credit (PBC) agreement that theGovernment of Bolivia signed with China Eximbank in 2015 for the Rurrenabaque-Riberalta Highway Project. This loan was provided
with a 3% interest rate, a 0.25% commitment fee, and a 0.25% management (front-end) fee. Upon signature of the PBC agreement,
the Government of Bolivia had to remit a lump-sum payment of $1.231 million (.25% x $492,400,000) to China Eximbank. In the 39
days that elapsed between the start date of the loan (November 14, 2015) and the first date when the Government of Bolivia was
expected to pay a commitment fee, the undisbursed balance of the loan was still $492,400,000, so China Eximbank charged theGovernment of Bolivia a pro-rated commitment fee of $133,358 [.25% x $492,400,000 x (39/360)]. Then, in the first six months of
2016, China Eximbank disbursed $98,480,000 to the Government of Bolivia, leaving the undisbursed balance of the loan at$415,918,000 and Government of Bolivia a commitment fee charge of $558,699 (Banco Central de Bolivia 2016). This PBC was later
split two smaller PBCs (worth $216,515,817 and $275,884,183), which are captured via Project ID#38832 and #52515 in the 2.0
dataset.

85
obligations, (ii) the issuance of any repayment guarantees by parties other than the borrower, and (iii) the acquisition of
credit insurance. The 2.0 version of our dataset reveals that 44% of official sector lending from China (in monetary
terms) is collateralized, 17% is backed by third-party repayment guarantees, and 13% is insured. 61% is backed by atleast one type of credit enhancement. Third, we have made special efforts to document the debt cancellation andrescheduling actions of Chinese state-owned lenders. With the 1.0 version of our dataset, Horn et al. (2019: 32) identify
140 Chinese debt cancellation and rescheduling actions between 2000 and 2017. They find that these types of actions
are far more common among official Chinese creditors than private external creditors (banks and bondholders).
However, the 2.0 version of our dataset identifies an even larger number (212) of debt cancellation and reschedulingactions by Chinese state-owned lenders during the same 18-year period: 134 debt cancellation actions in 64 countries
and 78 debt rescheduling actions in 36 countries.
(5) ODA- and OOF-Adjacent Financial Transfers. In previous iterations of our dataset, we only included projects
backed by official financial transfers with development, commercial, or representational intent. We did not include
projects backed by official financial transfers with military intent. Nor did we include projects financed with aid or debt
from Huawei Technologies Co., Ltd. (“Huawei”) or any of its subsidiaries (due to a lack of agreement about whether
Huawei should be treated as an “official” source of financing). We adhered to these practices to ensure that every
financial transfer in the dataset was consistent with the ODA or OOF eligibility criteria (as described in the OECD-DAC
reporting directives). In our view, this approach is important because it enables users of our dataset to make crossdonor and cross-lender comparisons—globally, regionally, nationally, and sub-nationally—since most international
donors and lenders use the same development finance categorization criteria. In the 2.0 version of AidData’s Global
Chinese Development Finance Dataset, we do not deviate from these practices. However, given that some analysts
and decision-makers have expressed interest in learning more about Huawei-financed projects and projects withmilitary intent that are backed by official financial flows, we have included two additional datasets that capture these“ODA-adjacent” and “OOF-adjacent” projects in separate worksheets within the master .csv data file.234 AidData's
Global Huawei Finance Dataset captures 153 Huawei-financed projects worth $1.4 billion in 64 countries. AidData's
Global Chinese Military Finance Dataset captures 392 projects, worth $8.3 billion in 89 countries that have a military
purpose and are financed by official sources in China (e.g., the Ministry of Defense, the People’s Liberation Army, andmilitary SOEs like NORINCO and Poly Technologies).235

234 Consistent with OECD-DAC reporting directives, we identify projects as having military intent if they seek to promote the security

interests of the country from which the financial transfers originate or strengthen the lethal force capabilities of military institutions in
the recipient country. When military institutions are involved in a project with a humanitarian or development purpose, we code theproject as having development intent. Other activities that involve the military but qualify as having development intent include
peacebuilding and peacekeeping operations (when peacekeepers are executing specific development-related activities, such as
medical personnel providing medical training and medical care, engineering personnel deployed to build transportation
infrastructure, etc.); security system management and reform efforts that reinforce civilian control; removal of landmines and explosive
remnants of war; reintegration of demobilized military personnel into the economy; conversion of production facilities from military tocivilian outputs; repatriation and demobilization of armed factions, and disposal of their weapons; technical co-operation to control,
prevent and/or reduce the proliferation of small arms and light weapons; and efforts to demobilize, disarm, release, reintegrate,
repatriate and resettle child soldiers. The direct provision of equipment or funds to military institutions (e.g., defense ministries,
different branches of the armed forces) do not meet the OECD-DAC reporting directives for development-related purposes and are
identified as having military intent in AidData’s 2.0 dataset.
235 We provide GeoJSON files (on AidData’s GitHub repository) for 19 projects in AidData's Global Chinese Military Finance Dataset

and 21 projects in AidData's Global Huawei Finance Dataset.

86

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Appendix: Supplementary Information
Section 1. Figures and tables referenced in the report
Figure A-1: Chinese development finance commitments by sector, 2000-2017
BRI announced$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Industry, mining, construction
Other multisector
Others

Source: AidData.

EnergyCommunications

Transport and storage
General budget support

94

Figure A-2: Sovereign vs. hidden debt, BRI participant countries

Source: AidData.
The following countries have hidden or sovereign debt less than or equal to 10% of their GDP Afghanistan, Albania, Algeria, Armenia, Azerbaijan, Bangladesh, Barbados, Benin, Bolivia, Bosnia and Herzegovina,
Botswana, Bulgaria, Burundi, Chad, Chile, Comoros, Cook Islands, Costa Rica, Cote d'Ivoire, Cuba, Dominican
Republic, Egypt, El Salvador, Fiji, Gambia, Georgia, Ghana, Grenada, Guinea, Guyana, Indonesia, Iran, Iraq, Kiribati,
Lebanon, Lesotho, Liberia, Libya, Madagascar, Malaysia, Mali, Micronesia, Moldova, Morocco, Myanmar, Nepal,
Nigeria, Niue, North Macedonia, Oman, Pakistan, Panama, Peru, Philippines, Romania, Russia, Rwanda, Serbia,
Seychelles, Solomon Islands, Somalia, South Africa, Tanzania, Thailand, Timor-Leste, Trinidad and Tobago, Tunisia,
Turkey, Uganda, Ukraine, Uruguay, Uzbekistan, Viet Nam, Yemen

95

Table A-1: AidData 2.0 dataset comparison to other datasets
Scope & coverage
Institution &GeograSectors
Financiers
Datasetphies

Dataset summary
Types offlows

Project details reported

Sources# of# of
Total $
publicly
records fields
trackedavailable

Time
frame

Financialdetails

SubImplemen- Avg
InvolvedOECDnational
tationdescription
agenciesclassifications location
details
lengthdata

AidDataGlobalChineseDevelopment
Finance
Dataset(Version
2.0)

All

All Official
Entities:
EXIM, CDB,
People's
Loans,
Bank ofGrants,China, State- Scholarshi
Owned
ps,
Global Commercial Technical(165
Banks,
Assistance
13,386
countrie Government , Debts)
Agencies,
Rescheduli
Stateng and
Owned
Debt
Companies Forgivenes(totaling 369 s
Chineseofficial
entities)

60+

90,000$851
BillionTransaction
Amount (inLocal
Currency,
Nominal
USD,
Constant
2000-2
USD),
017,
Interest
with
Rate,
implemMaturity,
entatio
GracenPeriod,details
Manageme
througnt Fee,h 2021
Commitme
nt Fee,Grant
Element,
Insurance,
Guarantee,
Collateral

None

$245
BillionTransaction
2000-2 Amount (in020
NominalUSD)

Funding
Agency, Commitm
Receiving ent year
AgencyNone$462
BillionTransaction
2008-2 Amount (in019
NominalUSD)

Funding
Agency, Commitm
Receiving ent year
AgencyFunding,
Status,coCommitm
financing,ent year,
implemeplanned/nting,~142
actualreceiving,words
implement
andation andaccounta
completioble
n dates.
agencies

Sectors, Flow ~3,000Class (ODA, physical
OOF)
locations.

None

City orprovince
location

None

Physicallocationsfor 615
projects

Boston University
China’s
GlobalEnergyEnergyFinance
Database

Global(73
EXIM, CDB
countries)

China’s
Overseas
DevelopAll
ment
Finance
Database

Global(94
EXIM, CDB
countrie
s)

Loans

Loans

281

858

11

8

~5 words~5 words

96

China’s
GlobalPower
Power
Database

Global(83
EXIM, CDB,
countrie Investorss)

Nonconcession
77
al Loans &FDI

17

Funding/
Investing Year ofentity,Commissio None
Receiving n, status
agencyNone

2000-2 None033
provided

None

$153
BillionTransaction
Amount (inNominalFunding
USD),
Agency,Collateral, Receiving Commitm
2000-2
Interest
Agency, ent year,
019
Rate,
Impleme status
LIBOR
nting
Rate, Grace Agency
Period,Term

Transaction
2005-2 Amount (in Funding
020
NominalAgency
USD)

None

None

None

~7 words

Sectors

None

Commitment Year

None

None

None

None

~5 words

None

None

China Africa Research Initiative and Boston University

ChineseLoans toAll
AfricaDatabase

All Lenders(EXIM, CDB,
Private/Commercial
Banks,
AfricaState(54
Owned
Loans
countrie
Companies,s)
PrivateBusinesses,
People's
Bank of
China)

1,141

15

Boston University and Inter-American Dialogue
ChinaLatin
America All
Finance
Database

Latin
American & theCaribbe Exim, CDB
an (18countrie
s)

Loans

99

6

None

$137
Billion

Latin
America
Miningn & the 5 Chinese&
Caribbe Commercial Loans
Infrastru
an (9
Banks
cture
countrie
s)

40

6

None

None

2012-2
None
020

$6.7
billionTransaction
Impleme2011-2 Amount
nting
020
(Nominal
AgencyUSD)

Inter-American Dialogue
ChinaLatin
AmericaCommercial
Loans
Tracker

None

Lowy InstitutePacific
Aid Map All(v2020)

Pacific
Islands EXIM, and(8
Government
countrie Agenciess)

Loans,
Grants,Technical
Assistance

287

~20

~450
SourcesCommitment Year,Completio ~75 words Sectors
n Year,Status

Geocoordinates
for 250+
projects

97

Table A-2: Countries Included in the 2.0 Dataset
Country

Row Count

Afghanistan

123

Albania

47

Algeria

40

American Samoa

No projects foundAngola350

Antigua and Barbuda*

59

Argentina*

67

Armenia

40

Aruba*

No projects foundAzerbaijan

27

Bahamas**31

Bangladesh

120

Barbados*

74

Belarus112

BelizeNo projects found

Benin

107

BhutanNo projects foundBolivia

103

Bosnia and Herzegovina

29

Botswana

88

Brazil134

British Virgin Islands**

No projects foundBrunei Darussalam**

26

Bulgaria

42

Burkina Faso

No projects found

Burundi113

Cabo Verde85

Cambodia

308

Cameroon

132

Cayman Islands**

No projects foundCentral African Republic

107

98

Chad

96

Chile*

32

Colombia

46

Comoros

68

Congo

168

Cook Islands

34

Costa Rica

49

Cote D'Ivoire

94

Cuba

118

Curacao**

2

DPRK

126

Democratic Republic of the Congo 249
Djibouti

96

Dominica

83

Dominican Republic

5

Ecuador

122

Egypt

71

El Salvador

1

Equatorial Guinea

121

Eritrea61

Eswatini

No projects foundEthiopia

218

Fiji

152

French Polynesia**

2

Gabon

82

Gambia16

Georgia

43

Ghana

156

Grenada93

Guam**

1

GuatemalaNo projects found

Guinea101

Guinea-Bissau

78

99

Guyana

69

Haiti

33

Honduras

3

India

55

Indonesia

257

Iran

77

Iraq

20

Israel**15

Jamaica

79

Jordan72

Kazakhstan

124

Kenya

191

Kiribati

1

KosovoNo projects foundKyrgyz Republic96

Lao People's Democratic Republic

240

Lebanon71

Lesotho114

Liberia

144

Libya

7

Madagascar

111

Malawi155

Malaysia

39

Maldives

77

Mali

149

Marshall Islands

41

Mauritania

99

Mauritius

95

Mexico59

Micronesia

142

Moldova

43

Mongolia

139

Montenegro

20

100

Morocco59

Mozambique

117

Myanmar280

Namibia

140

Nauru

13

Nepal

157

New Caledonia**

No projects foundNicaragua

1

Niger

115

Nigeria

104

Niue

23

North Macedonia

35

Northern Mariana Islands*

No projects found

Oman*

10

Pakistan

280

Palau

1

Panama*8

Papua New Guinea

145

Paraguay

3

Peru

97

Philippines

92

Puerto Rico*

No projects foundRomania

28

Russia146

Rwanda104

Saint Lucia

15

Samoa

123

Sao Tome and Principe

22

Senegal83

Serbia66

Seychelles*

101

Sierra Leone

166

Sint Maarten (Dutch part)*

1

101

Solomon Islands

No projects foundSomalia50

South Africa

105South Sudan

120

Sri Lanka

174

St. Kitts and Nevis

No projects foundSt. Martin (French part)

No projects foundSt. Vincent and the Grenadines

No projects found

Sudan

285

Suriname

63

Syrian Arab Republic

60

Tajikistan

110

Tanzania

204

Thailand55

Timor-Leste

75

Togo

120

Tonga

110

Trinidad and Tobago*

29

Tunisia75

Turkey59

Turkmenistan

38

Turks and Caicos Islands**

No projects foundTuvaluNo projects found

Uganda144

Ukraine70

Uruguay*

49

Uzbekistan

149

Vanuatu

120

Venezuela

84

Viet Nam

111

Virgin Islands (U.S.)**No projects foundWest Bank and Gaza Strip

30

Yemen

74

102

Zambia167

Zimbabwe

204*These countries and territories were classified as middle-income atsome point during the data collection commitment year period(2000-2017), but by 2017 they were classified as high-income.
**These countries and territories were classified as high-income forthe entire data collection commitment year period (2000-2017).

Table A-3a: The 50 largest loans in the 2.0 dataset, by 2017 USD value
Project ID

Recipient

Commitment
YearFunding Agency

85211

Russia

2013

43069

Russia43012

Russia

37808

37804

Venez
uela

Venez
uela

Receiving Agency

USD
2017
Billions

Collatera
lizedChina National Petroleum
Corporation (CNPC)PJSC Rosneft Oil Company

32.06

Yes

2009

China Development Bank(CDB)PJSC Rosneft Oil Company

19.56

Yes

2009

China Development Bank(CDB)OAO AK Transneft

13.04

Yes

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |China Venezuela Joint Fund |Government of Venezuela

12.50

Yes

2010

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |China Venezuela Joint Fund |Government of Venezuela

12.09

Yes

2005

China Development Bank
(CDB) | Export-Import
Bank of ChinaPJSC Rosneft Oil Company |Vnesheconombank (VEB Bank)

11.30

Yes

Asia Gas Pipeline LLP (AGP)

9.93

Yes

2010

67166

Russia

54489

Kazak
hstan

2008

Bank of China (BOC)|
China Development Bank
(CDB)

39118

Venez
uela

2015

China Development Bank
(CDB)

Government of Venezuela

9.83

Yes

38420

Brazil2009

China Development Bank
(CDB)

Petróleo Brasileiro S.A. (Petrobras) 9.13

Yes

7.07

Yes

6.35

No

53063

Angol
a

2016

China Development Bank
(CDB)

Government of Angola |Sociedade Nacional deCombustiveis de Angola(Sonangol)

67002

Russia2006

Bank of China (BOC)

Taihu Limited

103

35985

Venez
uela

2007

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |Government of Venezuela |Pétroleos de Venezuela S.A.
(PDVSA)

6.25

Yes

Minera Las Bambas S.A.C. (Minera
Las Bambas)
5.77

Yes

55426

Peru

2014

Bank of China (BOC) |China Development Bank
(CDB) | Export-Import
Bank of China | Industrial
and Commercial Bank of
China (ICBC)

67064

Russia2016

China Development Bank(CDB)OAO Yamal LNG

5.73

Yes

37528

Venez
uela

2009

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |Government of Venezuela |Pétroleos de Venezuela S.A.
(PDVSA)

5.21

Yes

53068

Brazil2016

China Development Bank
(CDB)

Petróleo Brasileiro S.A. (Petrobras) 5.12

Yes

59384

Brazil2017

China Development Bank
(CDB)

Petróleo Brasileiro S.A. (Petrobras) 5.00

Yes

4.92

Yes

39099

Venez
uela

2015

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |China Venezuela Joint Fund |Government of Venezuela

41089

Venez
uela

2015

China Development Bank
(CDB)

Government of Venezuela

4.92

Yes

85858

Malay
sia

2016

Export-Import Bank of
China

Malaysia Rail Link Sdn Bhd (MRL)

4.87

No

4.86

Yes

38316

Venez
uela

2013

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |China Venezuela Joint Fund |Government of Venezuela

67062

Russia2016

Export-Import Bank ofChina

OAO Yamal LNG

4.85

Yes

39997

Kazak
hstan

2012

Bank of China (BOC) |China Development Bank
(CDB)

Asia Gas Pipeline LLP (AGP)

4.79

Yes

4.27

Yes

37838

Venez
uela

2011

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |Government of Venezuela |Pétroleos de Venezuela S.A.
(PDVSA)

37915

Venez
uela

2012

Industrial and Commercial
Bank of China (ICBC)

Pétroleos de Venezuela S.A.
(PDVSA)

4.07

Yes

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |China Venezuela Joint Fund |Government of Venezuela

4.07

Yes

38163

Venez
uela

2012

104

41905

Turkm
enistan

2013

China Development Bank
(CDB)

Turkmengaz

3.98

Yes

40393

Turkm
enistan

2009

China Development Bank
(CDB)

Turkmengaz

3.91

Yes

39567

Kazak
hstan

2009

Export-Import Bank of
China

Mangistau Investments B.V.
(MIBV)

3.91

Yes

38053

Venez
uela

2013

China Development Bank(CDB)Petrolera Sinovensa, S.A.

3.90

No

41918

Pakistan

2014

Export-Import Bank of
China

Government of Pakistan | Pakistan
Atomic Energy Commission

3.86

No

China Development Bank
(CDB)

Banco de Desarrollo Económico y
Social de Venezuela (BANDES) |Government of Venezuela |Pétroleos de Venezuela S.A.
(PDVSA)

3.86

Yes

38380

Venez
uela

85304

Lao
Peopl
e's
Demo
cratic
Repub
lic
2016

Export-Import Bank of
China

Laos-China Railway Company

3.63

Yes

484

Equat
orial
Guine
a

2006

Export-Import Bank of
China

Government of Equatorial Guinea

3.53

Yes

38170

Brazil2015

China Development Bank
(CDB)

Petróleo Brasileiro S.A. (Petrobras) 3.44

No

2017

Bank of China (BOC) |China Bank ofCommunications (BoComor BoComm) | China
Development Bank (CDB)| Industrial andCommercial Bank of China(ICBC)
Oman Ministry of Finance

3.20

No

Sociedade Nacional deCombustiveis de Angola(Sonangol)

3.02

Yes

2014

66543

Oman

47101

Angol
a

2010

Industrial and Commercial
Bank of China (ICBC)

61056

Kazak
hstan

2009

China Development Bank(CDB)

KAZ Minerals PLC

3.00

No

39720

Baha
mas

2010

Export-Import Bank of
China

Baha Mar Ltd.

2.96

Yes

105

73145

Demo
craticRepub
lic of
the
Cong
o
2008

Export-Import Bank of
China

Sino–Congolais des Mines(Sicomines SARL)

2.69

Yes

73852

Angol
a

2013

China Development Bank
(CDB)

Sonangol Finance Limited

2.43

Yes

36064

Peru

2010

Export-Import Bank of
China

Minera Chinalco Perú S.A.

2.42

Yes

37002

Argen
tina

2014

China Development Bank
(CDB)

Argentina Ministry of Economy

2.41

Yes

61320

Indon
esia

2017

China Development Bank(CDB)PT Kereta Cepat Indonesia China(KCIC)

2.38

Yes

59129

Ethiopia
2007

China Development Bank
(CDB)

Ethiopian Telecommunications
Corporation | ZTE Corporation

2.34

No

67003

Russia2014

Bank of China (BOC)

Unspecified Project Company

2.31

Yes

67039

Russia2016

Bank of China (BOC)

Gazprom

2.27

Yes

58413

Venez
uela

2016

China Development Bank
(CDB)

Pétroleos de Venezuela S.A.
(PDVSA)

2.25

Yes

41917

Pakistan

2014

Export-Import Bank of
China

Government of Pakistan | Pakistan
Atomic Energy Commission

2.17

No

106

Table A-3b: Top 25 Chinese government grant-financed projects from the 2.0 dataset, by
2017 USD value
Proje
ct ID

Recipient

Commitment year
Funding agency

USD 2017
millions

Project title

8634
0

DPRK

2008

China Ministry ofCommerce483.49Chinese Government donates 500,000
tons of crude oil in 2008

8633
8

DPRK

2006

China Ministry ofCommerce

426.88Chinese Government donates 500,000
tons of crude oil in 2006

8633
9

DPRK

2007

China Ministry ofCommerce

413.98Chinese Government donates 500,000
tons of crude oil in 2007

392.81Chinese Government provided RMB 2.22
billion grant for New Yalu River Bridge
Construction Project

391.00Chinese Government donates 500,000
tons of crude oil in 2005

6776
8

DPRK

2010

China Ministry ofCommerce

8633
7

DPRK

2005

China Ministry ofCommerce

371.38Chinese Government donates 500,000
tons of crude oil in 2011 (Linked to
Project ID#41496)

6333
2

DPRK

2011

China Ministry ofCommerce

8634
2

DPRK

2010

China Ministry ofCommerce352.07

Chinese Government donates 500,000
tons of crude oil in 2010

8634
3

DPRK

2012

China Ministry ofCommerce

351.07

Chinese Government donates 500,000
tons of crude oil in 2012

8634
4

DPRK

2013

China Ministry ofCommerce

348.92Chinese Government donates 500,000
tons of crude oil in 2013

8633
6

DPRK

2004

China Ministry ofCommerce

300.76Chinese Government donates 500,000
tons of crude oil in 2004

230.00Chinese Government provides $230
million grant for New Gwadar
International Airport Construction Project

227.45Chinese Government donates 472,167
tons of crude oil to North Korea in 2003

221.98Chinese Government provides RMB 1.5
billion grant for Government Complex
and Parliamentary Complex Construction
Project (Linked to Project ID#71727)

217.02Chinese Government donates 437,500
tons of crude oil in 2000 (Linked with
Project ID#63154, #63155)

5242
4

Pakistan

2017

China Ministry ofCommerce

6330
6

DPRK

2003

China Ministry ofCommerce

5378
6
6315
2
5354
1

5242
7

Tajikistan

DPRK

Pakistan

Costa Rica

2017

China Ministry ofCommerce

2000

China Ministry ofCommerce

2011

China Ministry ofCommerce

210.31Chinese Government provides RMB
1.273 billion grant for Post Flood (N-35,
N-5, N-55) Road Rehabilitation Project

2007

Unspecified
ChineseGovernment
Institution

202.99Chinese Government commits 130 million
USD in aid to Costa Rica

107

8634
8
6315
5

DPRK

DPRK

2017

China Ministry ofCommerce

2002

China Ministry ofCommerce

186.18Chinese Government donates 500,000
tons of crude oil in 2017182.21Chinese Government donates 437,500
tons of crude oil in 2002 (Linked with
Project ID#63152, #63154)

4954
2

Africa, regional

2006

China Ministry ofCommerce

176.95Chinese Government provides RMB 800
million grant for African Union
Conference Center and Office Complex
Construction Project (Linked to Project
ID#52335)

8634
6

DPRK

2015

China Ministry ofCommerce

175.33Chinese Government donates 500,000
tons of crude oil in 2015

8634
7

DPRK

2016

China Ministry ofCommerce

162.62Chinese Government donates 500,000
tons of crude oil in 20162015

China Ministry ofCommerce

156.42Chinese Government provides RMB 1
billion grant for Morodok Techo National
Stadium Construction Project147.98Chinese Government provides RMB 1
billion grant through ETCA for Upgradingof the Kodari Highway and Rasuwa
Bridge Construction Project

136.89Chinese Government provides $105
million grant for National Football
Stadium Construction Project

135.21Chinese Government provides RMB 710
million grant for Bangladesh-China
Friendship Exhibition Center
Construction Project

4888
7

7179
1
3726
6

3407
0

Cambodia

Nepal

Costa Rica

Bangladesh

2017

China Ministry ofCommerce

2009

China Ministry ofCommerce

China Ministry ofCommerce

2008

Table A-4: Sovereign and hidden debt exposure by borrower country, 2000-2017
CountryISO
code

Hidden debt exposure to China
as % of GDP (AidData)

Sovereign debt exposure to
China as % of GDP (AidData)

Afghanistan

AFG

0%

0%

Albania

ALB

0%

1%

Algeria

DZA

0%

0%

American Samoa

ASM

0%

0%

Angola

AGO

12%

38%

Antigua and Barbuda

ATG

0%

20%

Argentina

ARG

0%

1%

Armenia

ARM

0%

0%

Aruba

ABW

0%

0%

Azerbaijan

AZE

1%

0%

108

Bahamas

BHS

0%

1%

Bangladesh

BGD

1%

2%

Barbados

BRB

4%

1%

Belarus

BLR

1%

14%

Belize

BLZ

0%

0%

Benin

BEN

0%

5%

Bhutan

BTN

0%

0%

Bolivia

BOL

0%

5%

Bosnia and Herzegovina

BIH

2%

4%

Botswana

BWA

0%

7%

Brazil

BRA

2%

0%

British Virgin Islands

VGB

0%

0%

Brunei Darussalam

BRN

14%

0%

Bulgaria

BGR

1%

0%

Burkina Faso

BFA

0%

0%

Burundi

BDI

1%

1%

Cabo Verde

CPV

0%

10%

Cambodia

KHM

1%

19%

Cameroon

CMR

0%

14%

Cayman Islands

CYM

0%

0%

Central African Republic

CAF

5%

3%

Chad

TCD

0%

7%

Chile

CHL

0%

0%

Colombia

COL

0%

0%

Comoros

COM

0%

3%

Congo

COG

0%

53%

Cook Islands

Cook

0%

8%

Costa Rica

CRI

0%

1%

Cote d'Ivoire

CIV

0%

5%

Cuba

CUB

0%

4%

Curacao

CUW

0%

0%

DPRK

PRK

0%

0%

109

Democratic Republic of the
Congo

COD

11%

6%

Djibouti

DJI

2%

47%

Dominica

DMA

0%

14%

Dominican Republic

DOM

0%

0%

Ecuador

ECU

2%

13%

Egypt

EGY

1%

1%

El Salvador

SLV

0%

0%

Equatorial Guinea

GNQ

0%

50%

Eritrea

ERI

2%

15%

Eswatini

SWZ

0%

0%

Ethiopia

ETH

5%

11%

Fiji

FJI

0%

8%

French Polynesia

PYF

0%

0%

Gabon

GAB

0%

14%

Gambia

GMB

0%

1%

Georgia

GEO

0%

0%

Ghana

GHA

1%

6%

Grenada

GRD

0%

7%

Guam

GUM

0%

0%

Guatemala

GTM

0%

0%

Guinea

GIN

4%

0%

Guinea-Bissau

GNB

0%

1%

Guyana

GUY

0%

5%

Haiti

HTI

0%

0%

Honduras

HND

0%

1%

India

IND

0%

0%

Indonesia

IDN

2%

0%

Iran

IRN

6%

8%

Iraq

IRQ

0%

0%

Israel

ISR

0%

0%

Jamaica

JAM

0%

11%

Jordan

JOR

0%

0%

110

Kazakhstan

KAZ

16%

2%

Kenya

KEN

0%

11%

Kiribati

KIR

0%

0%

Kosovo

XKX

0%

0%

Kyrgyz Republic

KGZ

1%

30%

Lao People's Democratic
Republic

LAO

35%

29%

Lebanon

LBN

0%

0%

Lesotho

LSO

0%

5%

Liberia

LBR

0%

1%

Libya

LBY

1%

0%

Madagascar

MDG

0%

1%

Malawi

MWI

1%

4%

Malaysia

MYS

0%

2%

Maldives

MDV

0%

40%

Mali

MLI

0%

5%

Marshall Islands

MHL

0%

0%

Mauritania

MRT

0%

11%

Mauritius

MUS

3%

3%

Mexico

MEX

0%

0%

Micronesia

FSM

1%

0%

Moldova

MDA

0%

0%

Mongolia

MNG

4%

14%

Montenegro

MNE

0%

19%

Morocco

MAR

0%

0%

Mozambique

MOZ

12%

20%

Myanmar

MMR

7%

5%

Namibia

NAM

12%

3%

Nauru

NRU

0%

0%

Nepal

NPL

0%

1%

New Caledonia

NCL

0%

0%

Nicaragua

NIC

0%

0%

Niger

NER

1%

10%

111

Nigeria

NGA

0%

2%

Niue

NIU

0%

0%

North Macedonia

MKD

0%

8%

Northern Mariana Islands

MNP

0%

0%

Oman

OMN

1%

7%

Pakistan

PAK

1%

7%

Palau

PLW

0%

0%

Panama

PAN

0%

0%

Papua New Guinea

PNG

11%

6%

Paraguay

PRY

0%

0%

Peru

PER

0%

0%

Philippines

PHL

0%

0%

Puerto Rico

PRI

0%

0%

Romania

ROU

0%

0%

Russia

RUS

8%

0%

Rwanda

RWA

0%

3%

Saint Lucia

LCA

0%

0%

Samoa

WSM

0%

30%

Sao Tome and Principe

STP

0%

0%

Senegal

SEN

0%

10%

Serbia

SRB

0%

4%

Seychelles

SYC

0%

0%

Sierra Leone

SLE

1%

19%

Sint Maarten (Dutch part)

SXM

0%

0%

Solomon Islands

SLB

0%

0%

Somalia

SOM

0%

0%

South Africa

ZAF

1%

1%

South Sudan

SSD

0%

15%

Sri Lanka

LKA

0%

12%

St. Kitts and Nevis

KNA

0%

0%

St. Martin (French part)

MAF

0%

0%

St. Vincent and the Grenadines

VCT

0%

0%

Sudan

SDN

7%

21%

112

Suriname

SUR

0%

34%

Syrian Arab Republic

SYR

0%

0%

Tajikistan

TJK

3%

24%

Tanzania

TZA

0%

5%

Thailand

THA

0%

0%

Timor-Leste

TLS

0%

0%

Togo

TGO

2%

11%

Tonga

TON

21%

15%

Trinidad and Tobago

TTO

0%

2%

Tunisia

TUN

0%

0%

Turkey

TUR

0%

0%

Turkmenistan

TKM

23%

2%

Turks and Caicos Islands

TCA

0%

0%

Tuvalu

TUV

0%

0%

Uganda

UGA

0%

8%

Ukraine

UKR

0%

1%

Uruguay

URY

0%

0%

Uzbekistan

UZB

9%

2%

Vanuatu

VUT

0%

23%

Venezuela

VEN

4%

18%

Viet Nam

VNM

3%

3%

Virgin Islands (U.S.)

VIR

0%

0%

West Bank and Gaza Strip

PSE

0%

0%

Yemen

YEM

0%

2%

Zambia

ZMB

5%

27%

Zimbabwe

ZWE

8%

21%

Note: Unless otherwise specified, the data in this table are reported in constant (2017) U.S. dollars. It was not possible
to reliably deflate GDP into constant (2017) U.S. dollars for three countries with especially high levels of inflation(Venezuela, Zimbabwe, and South Sudan). Therefore, for these three countries, we calculated the variables that are
expressed as a percentage of host country GDP by dividing Chinese loan commitment values in nominal dollars by
GDP in nominal dollars for each year.

113

Section 2. Characteristics of China’s overseas development financeportfolio (summary statistics)
Figure A-3: Official finance portfolios of China and G7 countries, 2000-2017
BRI announced$140

USD 2017 billions

$105

$70

$35

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Total OOF from G7 Countries
Total ODA from G7 Countries

Source: AidData and OECD DAC.

Chinese Vague
Chinese OOF
Chinese ODA

114

Figure A-4: Total official financial flows from G7 vs. China, 2000-2017$2,000

USD 2017 billions$1,500$601$1,000$60$1,217
$500

$682

$101

$0

G7
ODA

China
OOF

Vague

Source: AidData and OECD DAC.

Figure A-5: Total official financial flows from G7 vs. China, 2013-2017$600

USD 2017 billions

$450

$156
$36

$300

$150

$0

$39
G7
ODA

Source: AidData and OECD DAC.

$353

$418

China
OOF

Vague

115

Figure A-6: Chinese official development finance flows by project count, 2000-2017

1200

Project count

900

600

300

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
ODA-like

OOF-like

Vague (Official finance)

Source: AidData.

Table A-5: Chinese official finance by region, before and during BRI

Pre-BRI (2000-2012)

BRI (2013-2017)

RegionProject count% of project
count

Total USD 2017
billions% of USD 2017
billions

Asia

1,637

26%

119.6

29%

Africa

3,045

49%

99.3

24%

America

653

10%

98.5

24%

Europe

278

4%

72.7

17%

Middle East

189

3%

18.6

4%

Oceania

429

7%

7.1

2%

Multi-region

15

0%

0.0

0%

Total

6,246

100%

415.9

100%

Asia

1,164

25%

126.1

30%

Africa

2,107

46%

108.1

25%

America

632

14%

93.8

22%

Europe

243

5%

77.6

18%

Middle East

151

3%

17.6

4%

Oceania

297

6%

4.0

1%

116

Multi-region

10

0%

0.1

0%

Total

4,604

100%

427

100%

Figure A-7: China’s development finance portfolio by income bracket, 2000-2017

120

USD 2017 billions

90

60

30

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Low

Lower-middle

Upper-middle

High

Source: AidData.
Note: Not all high-income countries are covered in our dataset.

Table A-6: China’s development finance portfolio by income bracket, before and during BRI
Income bracket

Pre-BRI(2000-2012)

Project count % of project countUSD 2017 billions % of USD 2017 billions

Low-income

2,882

46%

84.6

20%

Lower-middle
income

2,070

33%

114.1

27%

Upper-middle
income

1,043

17%

206.6

50%

High-income

137

2%

9.1

2%

Unallocated

114

2%

1.5

0%

Total

6,246

100%

415.9

100%

Low-income

1,334

29%

34.9

8%

117

BRI
(2013-2017)

Lower-middle
income

1,746

38%

155.2

36%

Upper-middle
income

1,260

27%

174.6

41%

High income

216

5%

61.7

14%

Unallocated

48

1%

0.7

0%

4,604

100%

427.2

100%

Total

Table A-7: China’s overseas development finance portfolio by financial flow type, before andduring BRI

Flow type

Pre-BRI(2000-201
2)

BRI(2013-201
7)

Project
count% of projectcount

USD 2017
billions% of USD 2017
billions

Grant

3,553

57%

16.3

4%

Free-standing technical assistance

595

10%

0.1

0%

Scholarships/training in the donor
country

254

4%

0.0

0%

Loan

1,090

17%

274.9

66%

Export Buyer's Credit

449

7%

96.6

23%

Supplier's Credit/Export Seller'sCredit

94

2%

15.0

4%

Debt forgiveness

111

2%

12.5

3%

Debt rescheduling

49

1%

-

0%

Vague TBD

51

1%

0.5

0%

Total

6,246

100%

415.9

100%

Grant

2,418

53%

9.9

2%

Free-standing technical assistance

380

8%

0.1

0%

Scholarships/training in the donor
country

318

7%

0.0

0%

Loan

1,075

23%

310.1

73%

Export buyer's credit

290

6%

100.4

24%

Supplier's credit/export seller'scredit

32

1%

3.1

1%

Debt forgiveness

17

0%

3.3

1%

Debt rescheduling

25

1%

-

0%

Vague TBD

49

1%

0.3

0%

118

Total

4,604

100%

427.2

100%

Table A-8: Chinese development finance portfolio by sector, 2000-2017
SectorProject count% of projectcount

USD 2017
billions% of USD
2017 billions

Industry, mining, construction

302

4.8%

123.3

29.7%

Energy

431

6.9%

89.5

21.5%

Other multisector

123

2.0%

55.3

13.3%

Transport and storage

553

8.9%

50.1

12.0%

Communications

326

5.2%

28.3

6.8%

Action relating to debt

175

2.8%

14.5

3.5%

Other social infrastructureand services

428

6.9%

13.9

3.3%

General budget support

35

0.6%

6.3

1.5%

Banking and financial services

44

0.7%

5.2

1.3%

Water supply and sanitation

134

2.1%

5.2

1.3%

Agriculture, forestry, fishing

380

6.1%

4.7

1.1%

Trade policies and regulations 51

0.8%

4.4

1.1%

Government and civil society
Pre-BRI(2000-201 Health2)
Education

792

12.7%

3.9

0.9%

875

14.0%

3.3

0.8%

866

13.9%

2.6

0.6%

Other commodity assistance

18

0.3%

1.5

0.4%

Business and other services

29

0.5%

1.1

0.3%

Developmental food aid/foodsecurity assistance

103

1.6%

1.0

0.2%

Emergency response

484

7.7%

0.7

0.2%

Unallocated/unspecified

41

0.7%

0.7

0.2%

General environmental
protection

33

0.5%

0.11

0.0%

Reconstruction relief andrehabilitation

13

0.2%

0.08

0.0%

Disaster prevention andpreparedness

4

0.1%

0.01

0.0%

Population policies/
programmes andreproductive health

6

0.1%

0.00

0.0%

119

Total

100.0%

416

100.0%

Industry, mining, Construction 245

5.3%

130.9

30.6%

Energy

436

9.5%

120.1

28.1%

Transport and storage

450

9.8%

72.3

16.9%

Other multisector

71

1.5%

25.5

6.0%

General budget support

14

0.3%

20.2

4.7%

Communications

166

3.6%

12.6

3.0%

Banking and financial services

59

1.3%

8.1

1.9%

Other social infrastructureand services

321

7.0%

6.2

1.5%

Water supply and sanitation

129

2.8%

5.6

1.3%

Action relating to debt

51

1.1%

5.3

1.2%

Agriculture, forestry, fishing

221

4.8%

3.5

0.8%

Government and civil society

465

10.1%

3.4

0.8%

Business and other services

46

1.0%

2.9

0.7%

826

17.9%

2.5

0.6%

573

12.4%

2.3

0.5%

Trade policies and regulations 38

0.8%

2.3

0.5%

Unallocated/unspecified

30

0.7%

2.2

0.5%

Emergency response

359

7.8%

0.9

0.2%

Other commodity assistance

16

0.3%

0.15

0.0%

General environmental
protection

28

0.6%

0.06

0.0%

Developmental food aid/foodsecurity assistance

43

0.9%

0.05

0.0%

Reconstruction relief andrehabilitation

12

0.3%

0.05

0.0%

Disaster prevention andpreparedness

4

0.1%

0.004

0.0%

Population policies/
programmes andreproductive health

1

0.0%

-

0.0%

Total

4,604

100.0%

427

100.0%

BRI
Education(2013-201
7)
Health

6,246

120

Figure A-8: Chinese ODA commitments by sector, 2000-2017
$18
$16
$14

USD 2017 billions

$12
$10
$8
$6
$4
$2
$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Transport and storage
EnergyAction relating to debt
Industry, mining, construction
Communications
Other social infrastructure and services
Others

Source: AidData.
Figure A-9: Chinese OOF commitments by sector, 2000-2017$100

USD 2017 billions

$75

$50

$25

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Industry, mining, construction
Other multisector
Others

Source: AidData.

EnergyCommunications

Transport and storage
General budget support

121

Figure A-10: Sectoral allocation of Chinese government grants in USD, 2000-2017$4

USD 2017 billions

$3

$2

$1

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
EnergyTransport and storage
Other social infrastructure and services
Government And civil society
Health
Education
Others

Source: AidData.

Figure A-11: Sectoral allocation of Chinese government grants by project count, 2000-2017$700

USD 2017 billions

$525

$350

$175

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Education
Government And civil society
HealthEmergency response
Other social infrastructure and services
Agriculture, forestry, fishing
Others

Source: AidData.

122

Figure A-12: Sectoral allocation of official sector loans from China, 2000-2017$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Industry, mining, construction
Other multisector
OthersEnergyCommunications

Transport and storage
General budget support

Source: AidData.

Figure A-13: Top five sectors by funding agency, 2000-2017$300

USD 2017 billions

$225

$150

$75

$0
Industry, mining, construction

Source: AidData.

EnergyTransport and storage Other multisector

China Development Bank (CDB)
China National Petroleum Corporation (CNPC)
Industrial and Commercial Bank of China (ICBC)

Communications

Export-Import Bank of China
Bank of China (BOC)
Others123

Table A-9: Top 25 recipients of grants and loans from official sector institutions in China,
2000-2017
Top 25 recipients of grants
Recipient

Top 25 recipients of loans
USD 2017 billions

Recipient

USD 2017
billions

DPRK

7.17

Russia151.80

Pakistan

1.11

Venezuela

81.96

Suriname

1.10

Angola

50.47

Nepal

0.69

Brazil38.97

Sri Lanka

0.60

Indonesia

36.04

Cambodia

0.54

Kazakhstan

35.19

Bangladesh

0.46

Pakistan

33.15

Afghanistan

0.43

Viet Nam

18.37

Kenya

0.42

Ecuador

16.92

Lao People's Democratic Republic

0.40

Ethiopia

16.26

Tajikistan

0.38

Iran

15.27

Africa, regional

0.36

Lao People's Democratic
Republic

14.57

Zimbabwe

0.33

Sudan

11.94

Myanmar

0.33

Sri Lanka

11.63

Liberia

0.32South Africa

10.88

Malawi

0.31

Peru

10.59

Grenada

0.30

Kenya

10.16

Sudan

0.30

Malaysia

10.06

Uganda0.29

Cambodia

9.75

Costa Rica

0.29

Bangladesh

9.26

Mali

0.29

Myanmar9.07

Kyrgyz Republic

0.28

India

8.86

Democratic Republic of the
Congo

0.28

Turkmenistan

8.78

Tanzania

0.27

Argentina

8.65

Belarus0.27

Zambia8.29

124

Section 3. Financiers and recipients/borrowers
Table A-10: Top 25 loan-financed projects and commitments by funding agency

Funding agency

USD 2017
millions% USD 2017
millions

Project
count

China Development Bank (CDB)

312,819.6

39.1%

640

Export-Import Bank of China

272,112.8

34.0%

1499

Combination of Agencies

77,528.3

9.7%

85

Industrial and Commercial Bank of China (ICBC)

42,034.6

5.3%

237

China National Petroleum Corporation (CNPC)

34,140.4

4.3%

5Bank of China (BOC)30,215.6

3.8%

121

China Construction Bank Corporation (CCB)

3,430.7

0.4%

26

China Ministry of Commerce

3,291.3

0.4%

188

Unspecified Chinese Government Institution

2,854.6

0.4%

147

PetroChina

2,371.7

0.3%

2People's Bank of China (PBC)

2,334.8

0.3%

46

ICBC Financial Leasing Co., Ltd. (ICBCFL) (ICBC Leasing)

2,174.2

0.3%

2

Huarong Energy Africa Co., Ltd. (华融能源⾮洲有限公司)

2,000.0

0.2%

1China Machinery Engineering Corporation (CMEC)

1,425.7

0.2%

6

Unspecified Chinese Bank

1,386.9

0.2%

4Agricultural Bank of China

1,246.3

0.2%

7

China Electric Power Equipment and Technology Company (CET)

1,119.9

0.1%

3

China National Machinery Industry Corporation (Sinomach)

932.8

0.1%

4

Silk Road Fund

796.0

0.1%

1

ZTE Corporation

707.3

0.1%

5

MCC-JJJ Mining Development Company Limited

626.0

0.1%

1

China National Building Material Company (CNBM)

400.0

<0.1%

1

Aluminum Corporation of China (CHINALCO)

373.8

<0.1%

1

Postal Savings Bank of China (PSBC)(中国邮政储蓄银行)

350.0

<0.1%

1

Poly Technologies

343.6

<0.1%

6

125

Table A-11: Top 25 grant-financed projects and commitments by funding agency

Funding agency

USD 2017
millions% USD 2017
millions

Project
count

China Ministry of Commerce

21,770.2

83%

1,167

Unspecified Chinese Government Institution

3,498.7

13%

3,211

China National Petroleum Corporation (CNPC)

155.0

1%

37

ZTE Corporation

77.7

0.3%

42

Combination of Agencies

73.1

0.3%

29

China Ministry of Defense

71.1

0.3%

19

China Ministry of Finance

55.7

0.2%

16

CITIC Construction Co., Ltd. (中信建设有限责任公司)

54.2

0.2%

5

China Ministry of Agriculture

53.6

0.2%

8

Jilin Provincial Government

53.6

0.2%

1

Chinese Embassy

46.8

0.2%

641

China Machinery Engineering Corporation (CMEC)

40.1

0.2%

3

People's Liberation Army of China

36.7

0.1%

14

Hanban (Confucius Institute Headquarters)

28.7

0.1%

270

China Ministry of Foreign Affairs

25.7

0.1%

20

Shenzhen Municipal People's Government

22.0

0.1%

2

CNPC AktobeMunaiGas JSC (CNPC AMG)

21.4

0.1%

28

Export-Import Bank of China

15.9

0.1%

1

China International Center for Economic and Technical
Exchanges (CICETE)

11.3

<0.1%

2

China Road & Bridge Corporation (CRBC)

10.7

<0.1%

9

National Development and Reform Commission (NDRC)

10.7

<0.1%

5

People's Government of Guangdong Province (GDPTO)

10.5

<0.1%

7

China Harbour Engineering Co., Ltd.

9.2

<0.1%

16

China Ministry of Environmental Protection

6.1

<0.1%

19

China National Energy Administration (NEA)

5.6

<0.1%

1

126

Table A-12: ODA-financed projects and commitments by funding agency

Funding agency

USD 2017
millions% USD 2017
millions

Project
count

Export-Import Bank of China

44,344.8

44.1%

466

China Ministry of Commerce

28,256.4

28.1%

1,480

Unspecified Chinese Government Institution

15,648.4

15.6%

4,207

China Development Bank (CDB)

7,924.5

7.9%

24

Bank of China (BOC)

1,152.9

1.1%

5

Combination of Agencies

1,084.6

1.1%

38

Industrial and Commercial Bank of China (ICBC)

694.9

0.7%

9

People's Bank of China (PBC)

290.6

0.3%

9

China National Petroleum Corporation (CNPC)

174.1

0.2%

46

Sinochem Corporation

148.0

0.1%

2

China Ministry of Finance

104.8

0.1%

17

State Administration of Foreign Exchange (SAFE)

97.8

0.1%

1

China Ministry of Defense

69.8

0.1%

21

ZTE Corporation

67.0

0.1%

43

CITIC Construction Co., Ltd. (中信建设有限责任公司)

54.2

0.1%

7

China Ministry of Agriculture

53.6

0.1%

25

Jilin Provincial Government

53.6

0.1%

2

Chinese Embassy

46.5

<0.1%

561

China Machinery Engineering Corporation (CMEC)

40.1

<0.1%

3

People's Liberation Army of China

36.7

<0.1%

52

China Ministry of Foreign Affairs

25.9

<0.1%

20

Shenzhen Municipal People's Government

22.0

<0.1%

1

CNPC AktobeMunaiGas JSC (CNPC AMG)

17.8

<0.1%

25

CNMC Industrial Zone Development Zambia Limited

14.5

<0.1%

1

China International Center for Economic and Technical
Exchanges (CICETE)

11.3

<0.1%

2

127

Table A-13: Top 25 OOF-financed projects and commitments by funding agency

Funding agency

USD 2017
millions% USD 2017
millions

Project
count

China Development Bank (CDB)

281,572.4

41.3%

494

Export-Import Bank of China

211,519.5

31.0%

905

Combination of Agencies

76,199.6

11.2%

95

China National Petroleum Corporation (CNPC)

34,126.9

5.0%

5Industrial and Commercial Bank of China (ICBC)

30,459.0

4.5%

187

Bank of China (BOC)

26,412.2

3.9%

103

China Construction Bank Corporation (CCB)

3,381.5

0.5%

24

PetroChina

2,371.7

0.3%

2

ICBC Financial Leasing Co., Ltd. (ICBCFL) (ICBC Leasing)

2,174.2

0.3%

2

China Machinery Engineering Corporation (CMEC)

1,336.7

0.2%

5Agricultural Bank of China

1,246.3

0.2%

7

Unspecified Chinese Bank

1,192.7

0.2%

3

China Electric Power Equipment and Technology Company(CET)

1,119.9

0.2%

3

China National Machinery Industry Corporation (Sinomach)

932.8

0.1%

4

Silk Road Fund

796.0

0.1%

1

ZTE Corporation

718.0

0.1%

8

Unspecified Chinese Government Institution

691.8

0.1%

298

MCC-JJJ Mining Development Company Limited

626.0

0.1%

1

China National Building Material Company (CNBM)

400.0

0.1%

1

Aluminum Corporation of China (CHINALCO)

373.8

0.1%

1

China Ministry of Commerce

357.4

0.1%

42

Postal Savings Bank of China (PSBC)(中国邮政储蓄银行)

350.0

0.1%

1

Poly Technologies

343.6

0.1%

6

China Merchants Bank Co., Ltd.

295.0

<0.1%

1

China National Aero-Technology Import & Export
Corporation (CATIC)

242.4

<0.1%

4

Figure A-14: Official sector loans from China with various types of credit enhancements, %by transaction value

128

80%

60%

40%

20%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Source: AidData.

Collateralized
Combined (at least 1 type of credit enhancements)

InsuredGuaranteed

Figure A-15: Official sector loans from China with various types of credit enhancements$80

USD 2017 billions

$60

$40

$20

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Collateralized
Source: AidData.

InsuredGuaranteed

Combined (at least 1 type of credit enhancements)

129

Figure A-16: Official sector loans from China with various types of credit enhancements, %by project count
50%

40%

30%

20%

10%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Collateralized

Source: AidData.

InsuredGuaranteed

Combined (at least 1 type of credit enhancements)

130

Figure A-17: Percent of official sector lending with credit enhancements
100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Lending with credit enhancement(s)Source: AidData.

Lending with no credit enhancements

131

Table A-14: Top 10 borrowers by levels of collateralization

Top 10 borrowers by % of lending that iscollateralized

Top 10 borrowers by % of loans thatare collateralized

Top 10 borrowers by totalmonetary value of collateralized
lending

Borrowers

Collateralized%

Borrowers

Collateralized%

Borrowers

Venezuela

92.5%

Democratic
Republic of theCongo

69.57%

Russia

96.1

Peru

90.0%

Congo

68.75%

Venezuela

84.2

Turkmenistan

88.6%

Venezuela

67.19%

Angola29.7

Equatorial Guinea

80.3%

Equatorial Guinea

66.67%

Kazakhstan

21.1

Russia76.6%

Angola46.96%

Brazil19.8

Uganda74.3%

Uganda33.33%

Ecuador

9.8

Democratic Republic 67.4%of the CongoPeru

33.33%

Indonesia

9.6

Congo

67.3%

Sierra Leone

27.27%

Peru

9.5

Ecuador

58.0%

Ecuador26.19%

Turkmenistan

7.9

Angola

56.5%

Benin

25.00%

Democratic
Republic of the
Congo

5.0

Note: This table only includes
countries that secured more than 10
official sector loans from China
between 2000-2017.

Note: This table only includes
countries that secured more than
10 official sector loans from China
between 2000-2017.

USD 2017
billions

132

Section 4. Project implementation
Figure A-18: Percentage of projects completed on-time, ahead of schedule, or behindschedule, 2000-2017
BRI announced
100%

75%

50%

25%

0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Ahead of schedule

Behind scheduleOn-time

Source: AidData.
Note: Percentages reflect the % of projects completed ahead of schedule, behind schedule, or on-time for the 431
projects in the 2.0 dataset that have both the planned and actual completion dates filled in.

Figure A-19: Project suspensions and cancellations, 2000-2017
BRI announced

USD 2017 billions

12

9

6

3

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
All countries

Source: AidData.

BRI participant countries

133

Figure A-20: Map of project suspensions and cancellations by transaction amount, allcountries, 2000-2017

Source: AidData.

134

Figure A-21: Development finance project cycle

PledgeImplementation

A Chinese Official Entity signals
its intent to provide some typeof official development flow.
This often takes the form ofMOUs, letters of exchange, or
announcements at diplomatic
events. Pledges indicate an
intent to provide goods,
services, or funding, but these
intentions are not binding, so apledge is not considered afully-fledged project yet.

This signals when the project
funded through Chinese Official
Development Finance Flowsmoves from the planning stage
to actual execution of the
project activities (such as
construction beginning for an
infrastructure project, personnel
deployment for training
activities, etc.)

Commitment
A Chinese Official Entity
signs a legally-binding
commitment to provide
funding for a project.
This often takes the form
signed loan contracts,
signed grant
agreements, etc.

Suspension &Cancellation
Some projects may
encounter problems
after theCommitment stage these projects canbe suspended(funding and/or
construction on the
project is halted) or
fully cancelled.

Completion
This signals when the project
activities funded through
Chinese Official Development
Finance Flows are fully
executed. Indications of projectcompletion include a successfultransfer of goods and services(e.g., donations handed over,trainings completed), handover
ceremonies such as when an
infrastructure project begins
operations or is handed overfrom the contractor
implementing the project tothe project owner.

135

Figure A-22: Chinese development finance by project status category, 2000-2017

Cancelled2%
Suspended
2%

Pipeline: Pledge
19%

Pipeline: Commitment
8%$1,104 billion inCompletion
44%

Chinese development
finance flows,
2000-2017

Implementation25%

Source: AidData.
Note: This figure includes all “recommended for aggregates” projects (officially committed, implemented, andcompleted projects) plus pledged, suspended, and cancelled projects. No umbrella projects are included. For any
analysis that requires the aggregation of projects supported by official financial (or in-kind) commitments from China,
including analysis of monetary amounts and project counts, AidData recommends excluding pledged, suspended, andcancelled projects (since pledged, suspended, and cancelled projects represent financial transfers that likely did notoccur or will not occur). We included these projects in this figure to show the full spectrum of projects captured in the
2.0 dataset.

136

Figure A-23: Distribution of Chinese development project status over time by project count,
2000-2017

1,400

Project count

1,050

700

350

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Pipeline: pledge
Suspended

Pipeline: commitment
Cancelled

Implementation

Completion

Source: AidData.

Figure A-24: Distribution of Chinese development project status, 2000-2017$160

USD 2017 billions

$120

$80

$40

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Pipeline: pledge
Suspended
Source: AidData.

Pipeline: commitment
Cancelled

Implementation

Completion

137

Figure A-25: Distribution of BRI infrastructure projects by status, 2013-2021:
Cancelled
7%
Suspended
1%
Implementation
30%$184 billion in BRI
infrastructure
flows, 2000-2017

Completion
62%

Source: AidData.

Note: This figure includes all “recommended for aggregates” projects (officially committed, implemented, andcompleted projects) plus pledged, suspended, and cancelled projects that meet the criteria for BRI infrastructure
financing (as described in Box 5 in the report). We have included these to show the full spectrum of BRI financial flows
for infrastructure projects captured in the 2.0 dataset.

138

Table A-15: Top 20 Chinese implementing agencies by project countImplementing agency

Project count

Huawei Technologies Co., Ltd.

164

SinoHydro

162

Chinese Embassy

134

China Road & Bridge Corporation (CRBC)

132

China Machinery Engineering Corporation (CMEC)102

ZTE Corporation

84

China Harbour Engineering Co., Ltd.

83

China State Construction Engineering Corporation(CSCEC)83

China Civil Engineering Construction Corporation(CCECC)

69

China Development Bank (CDB)

63

Shanghai Construction Group General Co.

61

China Gezhouba Group Company Ltd. (CGGC)

59

Nuctech Company Limited (Tongfang Vision
Technology Co., Ltd.)

57

Beijing Construction Engineering Group Co., Ltd.
(BCEG)

55

China CAMC Engineering Co., Ltd.

52

China Communications Construction Co., Ltd.
(CCCC)

51

China National Machinery Industry Corporation(Sinomach)

43

China Geo-Engineering Corporation (CGC)

42

China International Water and Electrical
Corporation (CWE)

42

China National Complete Plant Import & Export
Corporation Group (COMPLANT)

42

139

Table A-16: Top 20 Chinese and non-Chinese implementing agencies by number of BRI
infrastructure projects

Implementing agency

Project
count

China Road & Bridge Corporation (CRBC)

14

China Harbour Engineering Co., Ltd.

8

China National Heavy Machinery Corporation (CHMC) | Electricite du Cambodge

7

China Machinery Engineering Corporation (CMEC)

7

PowerChina Huadong Engineering Corporation Limited

7

TBEA Co., Ltd.

6

Government of Pakistan

5

SinoHydro

5

China Harbour Engineering Co., Ltd. | Hengyi Industrial (Brunei) Co., Ltd.

5

Guangdong Foreign Construction Co., Ltd (GDFC)

5China First Metallurgical Construction Group Co. (CFMCC) | MCC Ruba International Construction
Company (Pvt) Ltd.

4

Government of Angola | SinoHydro

4

China Railway 14th Bureau Group Co., Ltd. (CRCC14)

4Nuctech Company Limited (Tongfang Vision Technology Co., Ltd.)

4

China CAMC Engineering Co., Ltd.

4

China Gezhouba Group Company Ltd. (CGGC)

4

Beijing Construction Engineering Group Co., Ltd. (BCEG)

4Power Construction Corporation of China Ltd. (POWERCHINA)

4

China Road & Bridge Corporation (CRBC) | Government of the Kyrgyz Republic

4

China Road & Bridge Corporation (CRBC) | Guangzhou Wanan Construction Supervision, Co. Ltd.

4

140

Section 5. BRI participation and financing
Table A-17: Year that countries first signed a BRI cooperative agreement or MOU
BRI country

Year signed

Afghanistan

2013

Albania2017

Algeria2018

Angola2018

Antigua and Barbuda

2018

Armenia

2015

Austria2018

Azerbaijan

2015

Bahrain2018

Bangladesh

2019

Barbados

2019

Belarus

2013

Benin

2019

Bolivia2018

Bosnia andHerzegovina

2017

Botswana

2021

Brunei Darussalam

2018

Bulgaria

2015

Burundi2018

Cabo Verde2018

Cambodia2013

Cameroon

2015

Chad

2018

Chile

2018

Comoros

2019

Congo

2018

Cook Islands

2018

Costa Rica2018

Cote D'Ivoire

2017

141

Croatia

2017

Cuba

2019

Cyprus2019

Czech Republic

2015

Democratic Republicof the Congo

2021

Djibouti

2018

Dominica

2018

Dominican Republic

2018

Ecuador

2018

Egypt

2016

El Salvador

2018

Equatorial Guinea

2019

Estonia2017

Ethiopia

2018

Fiji

2018

Gabon

2018

Gambia2018

Georgia

2016

Ghana

2018

Greece2018

Grenada2018

Guinea2018

Guyana2018

Hungary

2015

Indonesia

2015

Iran

2018

Iraq

2015

Italy

2019

Jamaica2019

Kazakhstan

2015

Kenya

2017

Kiribati

2020

142

Kuwait2018

Kyrgyz Republic2013

Lao People's
Democratic Republic

2018

Latvia2016

Lebanon2017

Lesotho2019

Liberia

2019

Libya

2018

Lithuania

2017

Luxembourg

2019

Madagascar

2017

Malaysia

2017

Maldives

2017

Mali

2019

Malta

2018

Mauritania

2018

Micronesia

2018

Moldova2013

Mongolia

2013

Montenegro

2017

Morocco2017

Mozambique

2018

Myanmar2016

Namibia

2018

Nepal

2017

New Zealand

2017

Niger

2019

Nigeria

2018

Niue

2018

North Macedonia

2013

Oman

2018

Pakistan

2013

143

Panama2017

Papua New Guinea

2016

Peru

2019

Philippines

2017

Poland2015

Portugal

2018

Qatar

2019

Romania

2015

Russia2017

Rwanda

2018

Samoa

2018

Saudi Arabia

2018

Senegal2018

Serbia2015

Seychelles

2018

Sierra Leone

2018

Singapore

2018

Slovakia

2015

Slovenia

2017

Solomon Islands

2019

Somalia

2015

South Africa

2015South Korea

2018

South Sudan

2018

Sri Lanka

2017

Sudan

2018

Suriname

2018

Tajikistan

2018

Tanzania

2018

Thailand2014

Timor-Leste

2017

Togo

2018

Tonga

2018

144

Trinidad and Tobago

2018

Tunisia2018

Turkey2015

Uganda2018

Ukraine2017

United Arab Emirates

2018

Uruguay

2018

Uzbekistan

2015

Vanuatu

2018

Venezuela

2018

Viet Nam

2017

Yemen

2017

Zambia2018

Zimbabwe

2018

Note: We measure the year in which a country “joined the BRI” as the first year in which it signed a BRI cooperationagreement or a formal BRI memorandum of understanding (MOU) with China. We used the “Official BRI participants by
year of joining” dataset made available by the Council of Foreign Relations (CFR) in March 2021 as a baseline (Sacks
2021; Hillman and Sacks 2021). For countries where CFR designated the year as “unknown,” we conducted desk
research to confirm the first year in which a BRI cooperation agreement or MOU was signed between China and thatcountry.

Figure A-26: BRI infrastructure projects underway by project count, 2013-2021

Source: AidData.

145

Figure A-27: BRI infrastructure projects underway by transaction value, 2013-2021

Source: AidData.
Note: Financial values are in constant 2017 USD.

146

Table A-18: BRI infrastructure projects underway by country
Country

USD 2017 billions

Project countPakistan

27.32

71

Indonesia

20.26

71

Kazakhstan

12.07

21

Malaysia

8.38

6

Cambodia

6.77

82

Kenya

6.31

18

Belarus

6.15

35

Sri Lanka

5.45

25

Bangladesh

5.02

9

Ethiopia

4.13

13

Cameroon

3.94

30

Venezuela

3.90

1

Viet Nam

3.81

12

Iran

3.12

7

Russia3.09

3Uzbekistan

3.08

15

Turkey2.81

15South Africa

2.55

8

Lao People's Democratic Republic

2.48

20

Serbia

2.45

8

Kyrgyz Republic

2.11

21

Cote d'Ivoire

2.08

8

Myanmar2.02

33

Zambia1.82

15

Ecuador

1.78

7

Brunei Darussalam

1.75

5

Angola1.74

25

Papua New Guinea

1.57

16

Maldives

1.51

10

Mozambique

1.43

10

Senegal

1.42

10

147

Zimbabwe

1.36

6

Nigeria

1.36

6

Mongolia

1.25

43

Uganda

1.09

4

Egypt

0.89

9

Bolivia0.82

3North Macedonia

0.77

10

Bosnia and Herzegovina

0.70

2

Gabon

0.67

3

Djibouti

0.67

3

Nepal

0.65

17

Ghana

0.60

6

Azerbaijan

0.54

5

Cuba

0.45

5

Jamaica

0.39

3Suriname

0.37

3

Congo

0.37

4

Thailand

0.37

3

Mali

0.36

3

Tajikistan

0.32

4

Morocco0.29

2Philippines

0.24

8

Guinea

0.22

4

Sudan

0.21

3

Madagascar

0.17

5

Gambia0.16

4Bulgaria

0.15

1

Togo

0.14

5

Afghanistan

0.13

14

Burundi0.12

6

Liberia

0.12

2Antigua and Barbuda

0.10

2

Vanuatu

0.07

2

148

Mauritania

0.07

6

Samoa

0.07

6

Tanzania

0.07

8

Tunisia0.07

2

Sierra Leone

0.06

2

Guyana0.06

4Grenada0.06

1

Cabo Verde

0.05

8

Panama0.05

1

Costa Rica

0.05

2

Romania

0.05

1

Lebanon

0.05

8

Fiji

0.04

4

South Sudan

0.03

3

Micronesia

0.03

6

Benin

0.03

2

Algeria

0.03

2

Comoros

0.03

1

Georgia

0.03

4

Rwanda0.03

2

Seychelles

0.02

3

Moldova

0.02

6

Peru

0.02

2

Uruguay

0.02

1

Namibia

0.02

3

Dominica

0.01

2

Niue

0.01

1

Armenia0.01

2

Cook Islands

0.01

1

Albania

0.01

3

Democratic Republic of the Congo

0.01

1

Iraq

0.00202

Somalia

0.00203

149Montenegro

0.0001

1

Timor-Leste

-

3

Chad

-

1

Ukraine-2

Equatorial Guinea-

1

169.52941

Total

Table A-19: Sectoral breakdown of BRI infrastructure projects underway

Sector

USD 2017 billions

Energy61.77

Transport and storage

49.04

Industry, mining, construction

36.54

Communications

4.94

Other social infrastructure and services

4.08

Other multisector

3.53

Water supply and sanitation

3.40

Health

1.52

Government and civil society

1.18

Education

1.00

Agriculture, forestry, fishing

0.89

Trade policies and regulations

0.36

Action relating to debt

0.35

Business and other services

0.30

Unallocated/unspecified

0.27

Banking and financial services

0.23

Emergency response

0.10

Reconstruction relief and rehabilitation

0.02

150

Table A-20: Income bracket breakdown of BRI infrastructure projects underway
Income bracket of recipient country

USD 2017 billions

Lower-middle income

94.4

Upper-middle income

52.5

Low-income

20.4

High-income

2.3

Table A-21: Chinese official financial commitments per country before and after joining the
BRI (weighted average)
Country

Average commitments per year
before BRI

Average commitments per year after
BRI%
increase

Kazakhstan

4,452.79

2,635.09-41%

Indonesia

1,803.17

6,418.48

256%

Viet Nam

1,548.96

249.95-84%

Iraq

1,158.18

14.86

-99%

Pakistan

1,146.31

5,110.67

346%

Kenya

980.38884.62-10%

Sri Lanka

962.88

1,742.91

81%

Malaysia

931.791,675.19

80%

Cambodia908.74844.63-7%

Belarus895.99647.18-28%

Myanmar770.19208.26-73%

Uzbekistan

707.70658.77-7%

Papua New Guinea

628.26460.78-27%

South Africa

459.392,344.50

410%

Cameroon

437.69804.22

84%

Cote d'Ivoire

302.71119.98-60%

Kyrgyz Republic251.02307.3822%

Serbia240.90420.7975%

Ukraine196.10

0.10

-100%

Mongolia

158.30282.4978%

Turkey130.68

1,591.68

1118%

Montenegro

113.43

3.09

-97%

151

Philippines

103.73463.79347%

Maldives

103.72686.60

562%

Nepal

92.74

191.52

107%

Bosnia and Herzegovina

91.99

698.02659%

Morocco

91.96

-

-100%

Thailand

68.01

0.02

-100%

Egypt

50.46

2,223.22

4305%

Bulgaria

45.60

106.97135%

Georgia

39.35

18.39

-53%

Romania

24.27

15.64

-36%

Afghanistan

16.31

33.13

103%

Panama

9.21

-

-100%

Yemen

8.65

142.701549%

Lebanon

7.04

29.48

319%

Moldova

6.91

4.92

-29%

Madagascar

6.65

173.752514%

Timor-Leste

5.46

1.20

-78%

Armenia

5.23

16.19

209%

Somalia

2.85

5.70

100%

Somalia

1.9

5.7

204%

North Macedonia

1.57

154.329741%

Albania

0.69

5.15

646%

Azerbaijan

0.67

189.6428190%

19,970.5

32,591.7

63%

Total

Note: This table excludes Russia, which is an outlying observation. All commitment values are measured inconstant 2017 USD.

152

Section 6. Metadata
Overview of TUFF 2.0 methodology
AidData’s Global Chinese Development Finance Dataset (Version 2.0) was collected using Tracking Underreported
Financial Flows (TUFF). TUFF is a transparent, systematic, and replicable methodology that enables the collection ofdetailed financial, operational, and locational information about Chinese ODA- and OOF-financed projects (Custer et
al. 2021). It is undertaken in three stages: (1) project identification, (2) project verification and enhancement, (3a)project-level data quality assurance, and (3b) quality assurance of the dataset as a whole.
The 2.0 version of AidData’s Global Chinese Development Finance Dataset was collected using a re-engineeredversion of the TUFF methodology (“TUFF 2.0”) that involved three major improvements (Custer et al. 2021). First,
instead of relying on media sources to identify individual projects, AidData began its search process by systematicallyreviewing tens of thousands of primary, official sources. These sources include unredacted grant and loan agreements
published in government registers and gazettes, official records extracted from the aid and debt information
management systems of host countries, annual reports published by Chinese state-owned banks, Chinese Embassyand MOFCOM websites, reports published by parliamentary oversight institutions in host countries, and AidData’s
direct correspondence with finance ministry officials in developing countries. Official source retrieval was undertaken ona country-by-country basis to comprehensively track the full range of financial and in-kind transfers from official sector
institutions in China. Then, as a supplement, AidData conducted a set of systematic search procedures in Factiva—a
Dow Jones-owned media database that draws on approximately 33,000 media sources worldwide in 28 languages,
including newspapers and radio and television transcripts—to identify non-official sources that also provide useful
information about Chinese government-financed projects. Second, TUFF 2.0 involved the implementation of anenhanced set of data quality assurance protocols to identify important project implementation details, such as calendar
day-level commencement and completion dates, precise geographical locations and features of project activities, andthe contractors and subcontractors responsible for implementation. Third, TUFF 2.0 prioritized the collection of moredetailed information on the terms and conditions that govern the loan and export credit contracts issued by Chinesestate-owned entities, such as maturities, grace periods, interest rates, grant elements, commitment fees, management
fees, and the use of credit enhancements (including collateral, insurance, and repayment guarantees).
The construction of the dataset was only possible because of the collective efforts of hundreds of professional staff,
faculty, research assistants, and expert reviewers at multiple institutions over the last five years. More details on the full
methodology and how it was applied to construct the 2.0 version of AidData’s Global Chinese Development Finance
Dataset can be found in Custer et al. (2021).
AidData’s Global Chinese Development Finance Dataset (Version 2.0) systematically captures official financial transfersfrom China to 165 countries worldwide, including all low- and middle-income countries (as of 2017), as well as 11
countries and territories that were designated as high-income countries over the entire data collection commitment
year period (2000-2017). This dataset seeks to provide comprehensive coverage of all low- and middle- income
countries, as well as full coverage of the five major regions, including Asia, Africa, Latin America and the Caribbean, theMiddle East, and Eastern Europe. The 11 high-income countries were included to help ensure comprehensive coveragein each region to the extent possible. In total, we have captured Chinese Government-financed development projects
in 145 countries, meaning we found no projects in 20 countries and territories despite systematic searches.

153

Figure A-28: Coverage of AidData’s Global Chinese Development Finance Dataset, Version
2.0

Source: AidData.
The 2.0 dataset is built upon a wide array of publicly available sources. For each project in the dataset, all the sources
used to confirm the existence of the project and construct the project record are linked and included in the final
version of the dataset. Table A-22 provides an overview of the nature and the number of sources used to construct the
2.0 dataset. In many cases, official sources provide information about multiple projects, which is one of the main
reasons why the total number of unique sources is 63,464 but the total number of sources is 91,356.

Table A-22: Source counts for AidData's Global Chinese Development Finance Dataset,
Version 2.0

Source type

# ofsourcereferences

Sourcecategory

Source type

# ofuniquesources

Sourcecategory

Donor/recipient official
source35996

Official

Donor/recipient official source

24662

Official

Implementing/intermediaryorganization source

8957

Official

Implementing/intermediaryorganization source

6518

Official

Other official source (nondonor, non-recipient, nonimplementing)

5504

Official

Other official source (non-donor,
non-recipient, non-implementing)

2895

Official

NGO/civil society/advocacy(non-donor, non-recipient,
non-implementing)2019

Nonofficial

NGO/civil society/advocacy (nondonor, non-recipient, nonimplementing)1065

Nonofficial

Academic journal article

2038

Nonofficial

Academic journal article

686

Nonofficial

Other academic (working
paper, dissertation)

2542

Nonofficial

Other academic (working paper,
dissertation)

840

Nonofficial

Media report

30958

Nonofficial

Media report

24470

Nonofficial

154

Social media, including
unofficial blogs

946

Nonofficial

Social media, including unofficial
blogs

763

Nonofficial

Other

2396

Nonofficial

Other

1565

Nonofficial

Total

91356

Total

63464

Figure A-29: Distribution of projects by commitment year, implementation start year, andcompletion year
1,200

Project count

900

600

300

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Commitment year
Source: AidData.

Implementation year

Completion year

155

Figure A-30: Financial flows by commitment year, implementation start year, and completion
year
$120

USD 2017 billions

$90

$60

$30

$0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Commitment year

Implementation year

Completion year

Source: AidData.

Table A-23: Project counts by year of actual start of project implementation
Year

Total # of projects# in implementation

2000

58

2001

75

2002

93

2

2003

139

3

2004

143

2

2005

173

3

2006

245

10

2007

268

19

2008

293

9

2009

350

18

2010

349

17

2011

396

29

2012

360

42

2013

358

46

1
3

156

2014

400

29

2015

460

48

2016

552

83

2017

634

132

2018

123

62

2019

57

50

2020

6

6

2021

2

2

Blank

5,311

640

Table A-24: China’s overseas development finance portfolio, by project value thresholds
Threshold% of total
projects% of total financial
value

Project countUSD 2017 billions

Above $1 billion

1%

53%

154

447

Between $500m and$1 billion

2%

13%

167

113

Between $250m and
$500m

3%

13%

320

114

Between $50m and
$250m

11%

15%

1,175

130

Between $1m and
$50m

28%

5%

3,005

39

Under $1 million

16%

0.1%

1,722

0.5

Blank

40%

0%

4,306

-

Total

100%

100%

10,849843

157

Table A-25: Breakdown of project records with missing transaction amounts
Flow type% of projects

Project count

Grant

57%

2,453

Free-standing technical assistance

21%

921

Scholarships/training in the donor country

12%

504

Loan

5%

235

Vague TBD

2%

74

Debt rescheduling

2%

74

Export buyer's credit

1%34

Debt forgiveness

0%8

Supplier's credit/export seller's credit

0%

3

Total

100%

4,306

Table A-26: Availability of actual project commencement and completion dates, 1.0 vs. 2.0
datasetImplementation
dates

1.0 dataset

2.0 dataset

Actualimplementationdate

745 projects (17% ofofficially committed
projects)

5,539 projects (51.1% of officiallycommitted projects)

Actualcompletion date

906 projects (20.7% ofofficially committed
projects)

6,061 projects (55.9% of officiallycommitted projects)

158

Table A-27: Country-by-country comparison of DRS and AidData measures of host government debt exposure to China

Country

Reports
to DRS

Sovereigndebt
exposure
to China(AidData)

Sovereig
n debtexposure
to China(DRS)

Hiddendebt
exposure
to China(AidData)

Sovereignand hidden
debt
exposure to
China(AidData)

AidDataDRS Δ inconstant
2017 USD

Sovereigndebt exposure
to China as %of GDP(AidData)

Sovereigndebt
exposure to
China as % of
GDP (DRS)

Hidden debt
exposure to
China as % of
GDP
(AidData)

Sovereign andhidden debt
exposure to
China as % of
GDP (AidData)

AidDataDRS Δ in %of GDP

Afghanistan

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Albania

Y

86

0

0

86

86

0.6%

0.0%

0.0%

0.6%

0.6%

Algeria

Y

59

5

42

102

97

0.0%

0.0%

0.0%

0.1%

0.1%

American Samoa

N

0

-

0

0

0

-

-

0.0%

0.0%

0.0%

AngolaY

39,88859,771

12,41252,301(7,470)

37.8%

56.6%

11.8%

49.5%

-7.1%

Antigua andBarbuda

N

270

-

0

270

270

19.7%

-

0.0%

19.7%

19.7%

Argentina

Y

6,437

7,467

365

6,801

(665)

1.3%

1.5%

0.1%

1.3%

-0.1%

Armenia

Y

24

25

0

24

(0)

0.2%

0.2%

0.0%

0.2%

0.0%

Aruba

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Azerbaijan

Y

0

0

448

448

448

0.0%

0.0%

1.1%

1.1%

1.1%

Bahamas

N

114

-

0

114

114

1.0%

-

0.0%

1.0%

1.0%

Bangladesh

Y

7,115

4,328

1,750

8,865

4,537

2.4%

1.5%

0.6%

3.0%

1.6%

Barbados

N

22

-

167

189

189

0.5%

-

4.1%

4.6%

4.6%

Belarus

Y

7,496

9,740

481

7,977

(1,763)

13.7%

17.8%

0.9%

14.6%

-3.2%

Belize

N

0

7

0

0

(7)

0.0%

0.4%

0.0%

0.0%

-0.4%

Benin

Y

839

903

40

879

(24)

5.4%

5.8%

0.3%

5.7%

-0.2%

Bhutan

Y

0

0

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Bolivia

Y

1,904

2,117

103

2,007

(110)

5.2%

5.8%

0.3%

5.5%

-0.3%

Bosnia andHerzegovina

Y

692

0

294

986

986

3.6%

0.0%

1.5%

5.2%

5.2%

159

Botswana

Y

1,258

168

0

1,258

1,090

7.2%

1.0%

0.0%

7.2%

6.3%

BrazilY

2,258

20,68032,11634,374

13,694

0.1%

1.0%

1.6%

1.7%

0.7%

British Virgin
Islands

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Brunei
Darussalam

N

0

-

1,750

1,750

1,750

0.0%

-

13.5%

13.5%

13.5%

Bulgaria

Y

44

44

330

374

330

0.1%

0.1%

0.5%

0.6%

0.5%

Burkina Faso

Y

0

94

0

0

(94)

0.0%

0.6%

0.0%

0.0%

-0.6%

Burundi

Y

41

139

33

74

(65)

1.3%

4.3%

1.0%

2.3%

-2.0%

Cabo Verde

Y

172

54

0

172

117

10.3%

3.3%

0.0%

10.3%

7.0%

Cambodia

Y

4,725

4,602

133

4,857

255

19.2%

18.7%

0.5%

19.7%

1.0%

Cameroon

Y

5,464

6,069

0

5,464

(605)

13.9%

15.4%

0.0%

13.9%

-1.5%

Cayman Islands

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Central African
Republic

Y

63

81

119

182

101

2.8%

3.6%

5.2%

8.0%

4.4%

Chad

Y

769

322

31

801

479

7.1%

3.0%

0.3%

7.4%

4.4%

Chile

N

14

-

582

596

596

0.0%

-

0.2%

0.2%

0.2%

Colombia

Y

50

0

50

100

100

0.0%

0.0%

0.0%

0.0%

0.0%

Comoros

Y

38

38

0

38

0

3.1%

3.1%

0.0%

3.1%

0.0%

Congo

Y

6,201

2,675

51

6,252

3,577

53.0%

22.8%

0.4%

53.4%

30.6%

Cook Islands

N

33

-

0

33

33

8.3%

-

0.0%

8.3%

8.3%

Costa Rica

Y

435

693

168

603

(90)

0.7%

1.2%

0.3%

1.0%

-0.2%

Cote d'Ivoire

Y

2,760

2,853

3

2,763

(90)

4.5%

4.7%

0.0%

4.6%

-0.1%

Cuba

N

3,441

-

202

3,643

3,643

3.6%

-

0.2%

3.8%

3.8%

Curacao

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

160

Democratic
People's
Republic of
Korea

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Democratic
Republic of the
Congo

Y

2,443

767

4,624

7,067

6,300

6.0%

1.9%

11.4%

17.4%

15.5%

Djibouti

Y

1,511

1,240

58

1,568

328

46.8%

38.4%

1.8%

48.5%

10.1%

Dominica

Y

63

71

0

63

(9)

13.7%

15.6%

0.0%

13.7%

-1.9%

Dominican
Republic

Y

0

53

0

0

(53)

0.0%

0.1%

0.0%

0.0%

-0.1%

EcuadorY

1,267

13,3172,372

15,042

1,724

13.0%

13.7%

2.4%

15.4%

1.7%

Egypt

Y

2,852

3,337

1,722

4,574

1,236

1.3%

1.5%

0.8%

2.1%

0.6%

El Salvador

Y

0

236

0

0

(236)

0.0%

1.0%

0.0%

0.0%

-1.0%

Equatorial
Guinea

N

5,251

-

0

5,251

5,251

49.7%

-

0.0%

49.7%

49.7%

Eritrea

Y

796

235

104

900

666

14.9%

4.4%

1.9%

16.9%

12.5%

Eswatini

Y

0

179

0

0

(179)

0.0%

3.9%

0.0%

0.0%

-3.9%

EthiopiaY

10,85015,666

4,557

15,407

(260)

10.9%

15.8%

4.6%

15.5%

-0.3%

Fiji

Y

385

338

0

385

47

8.4%

7.4%

0.0%

8.4%

1.0%

French Polynesia

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Gabon

Y

2,303

1,973

0

2,303

330

14.4%

12.4%

0.0%

14.4%

2.1%

Gambia

Y

25

0

0

25

25

1.4%

0.0%

0.0%

1.4%

1.4%

Georgia

Y

8

8

0

8

(0)

0.0%

0.0%

0.0%

0.0%

0.0%

Ghana

Y

3,800

6,306

385

4,185

(2,121)

5.7%

9.4%

0.6%

6.2%

-3.2%

Grenada

Y

73

136

0

73

(63)

6.8%

12.8%

0.0%

6.8%

-6.0%

Guam

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Guatemala

Y

0

67

0

0

(67)

0.0%

0.1%

0.0%

0.0%

-0.1%

161

Guinea

Y

59

779

532

591

(189)

0.5%

6.3%

4.3%

4.8%

-1.5%

Guinea-Bissau

Y

10

0

0

10

10

0.7%

0.0%

0.0%

0.7%

0.7%

Guyana

Y

342

392

0

342

(51)

4.6%

5.2%

0.0%

4.6%

-0.7%

Haiti

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Honduras

Y

287

561

0

287

(274)

1.3%

2.5%

0.0%

1.3%

-1.2%

India

Y

0

336

0

0

(336)

0.0%

0.0%

0.0%

0.0%

0.0%

Indonesia

Y

4,954

3,904

17,283

22,236

18,332

0.5%

0.4%

1.6%

2.0%

1.7%

Iran

Y

7,617

2,743

5,861

13,478

10,734

7.6%

2.7%

5.9%

13.5%

10.7%

Iraq

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Israel

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Jamaica

Y

1,481

1,481

18

1,500

19

11.1%

11.1%

0.1%

11.2%

0.1%

Jordan

Y

31

63

0

31

(32)

0.1%

0.1%

0.0%

0.1%

-0.1%

Kazakhstan

Y

3,172

3,236

27,27130,443

27,207

1.8%

1.9%

15.7%

17.5%

15.7%

Kenya

Y

9,287

9,796

22

9,309

(487)

10.7%

11.3%

0.0%

10.7%

-0.6%

Kiribati

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Kosovo

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Kyrgyz Republic

Y

2,233

2,206

105

2,338

132

30.2%

29.8%

1.4%

31.6%

1.8%

Lao People's
Democratic
Republic

Y

5,575

8,144

6,692

12,267

4,123

29.4%

43.0%

35.4%

64.8%

21.8%

Lebanon

Y

24

0

0

24

24

0.1%

0.0%

0.0%

0.1%

0.1%

Lesotho

Y

100

88

0

100

12

4.9%

4.3%

0.0%

4.9%

0.6%

Liberia

Y

54

63

0

54

(9)

1.0%

1.1%

0.0%

1.0%

-0.2%

Libya

N

0

-

391

391

391

0.0%

-

1.3%

1.3%

1.3%

Madagascar

Y

186

191

0

186

(5)

1.4%

1.4%

0.0%

1.4%

0.0%

Malawi

Y

340

305

135

476

170

3.5%

3.2%

1.4%

4.9%

1.8%

162

Malaysia

N

6,975

-

1,529

8,504

8,504

2.1%

-

0.5%

2.6%

2.6%

Maldives

Y

1,502

1,415

0

1,502

88

40.3%

37.9%

0.0%

40.3%

2.3%

Mali

Y

923

841

2

925

85

5.4%

4.9%

0.0%

5.4%

0.5%

Marshall Islands

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Mauritania

Y

750

979

0

750

(229)

11.0%

14.3%

0.0%

11.0%

-3.3%

Mauritius

N

391

-

383

774

774

3.3%

-

3.2%

6.5%

6.5%

Mexico

Y

38

0

0

38

38

0.0%

0.0%

0.0%

0.0%

0.0%

Micronesia

N

0

-

3

3

3

0.0%

-

0.8%

0.8%

0.8%

Moldova

Y

0

48

14

14

(34)

0.0%

0.5%

0.1%

0.1%

-0.3%

Mongolia

Y

1,636

1,267

520

2,156

889

13.6%

10.5%

4.3%

17.9%

7.4%

Montenegro

Y

1,012

105

0

1,012

908

18.7%

1.9%

0.0%

18.7%

16.8%

Morocco

Y

256

751

385

641

(110)

0.2%

0.7%

0.3%

0.6%

-0.1%

Mozambique

Y

2,637

3,163

1,550

4,187

1,024

19.9%

23.8%

11.7%

31.5%

7.7%

Myanmar

Y

3,322

7,540

4,874

8,196

656

4.9%

11.2%

7.2%

12.1%

1.0%

Namibia

N

323

-

1,425

1,747

1,747

2.7%

-

11.8%

14.5%

14.5%

Nauru

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Nepal

Y

395

363

0

395

32

1.2%

1.1%

0.0%

1.2%

0.1%

New Caledonia

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Nicaragua

Y

0

10

0

0

(10)

0.0%

0.1%

0.0%

0.0%

-0.1%

Niger

Y

1,386

1,516

107

1,494

(22)

10.4%

11.4%

0.8%

11.2%

-0.2%

Nigeria

Y

6,444

6,375

52

6,496

121

1.7%

1.7%

0.0%

1.7%

0.0%

Niue

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

North
Macedonia

Y

964

780

0

964

184

8.2%

6.6%

0.0%

8.2%

1.6%

Northern
Mariana Islands

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

163

Oman

N

4,998

-

410

5,408

5,408

7.0%

-

0.6%

7.6%

7.6%

PakistanY

24,38329,402

1,802

26,185(3,217)

7.4%

8.9%

0.5%

7.9%

-1.0%

Palau

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Panama

N

59

-

0

59

59

0.1%

-

0.0%

0.1%

0.1%

Papua New
Guinea

Y

1,347

1,350

2,618

3,965

2,615

5.8%

5.9%

11.4%

17.2%

11.3%

Paraguay

Y

49

0

0

49

49

0.1%

0.0%

0.0%

0.1%

0.1%

Peru

Y

0

0

243

243

243

0.0%

0.0%

0.1%

0.1%

0.1%

Philippines

Y

639

2,048

478

1,117

(931)

0.2%

0.6%

0.1%

0.3%

-0.3%

Puerto Rico

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Romania

N

253

-

0

253

253

0.1%

-

0.0%

0.1%

0.1%

RussiaY00

117,681

117,681

117,681

0.0%

0.0%

7.6%

7.6%

7.6%

Rwanda

Y

302

236

0

302

65

2.9%

2.2%

0.0%

2.9%

0.6%

Saint Lucia

Y

0

19

0

0

(19)

0.0%

1.2%

0.0%

0.0%

-1.2%

Samoa

Y

250

236

0

250

14

29.9%

28.2%

0.0%

29.9%

1.7%

Sao Tome andPrincipe

Y

0

0.4

0

0

(0)

0.0%

0.1%

0.0%

0.0%

-0.1%

Senegal

Y

2,463

1,866

0

2,463

597

10.2%

7.7%

0.0%

10.2%

2.5%

Serbia

Y

1,992

2,078

203

2,195

117

4.2%

4.4%

0.4%

4.6%

0.2%

Seychelles

N

2

-

0

2

2

0.2%

-

0.0%

0.2%

0.2%

Sierra Leone

Y

733

69

44

776

708

18.6%

1.7%

1.1%

19.7%

18.0%

Sint Maarten(Dutch part)

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Solomon Islands

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Somalia

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

South Africa

Y

2,498

4,000

4,545

7,043

3,043

0.8%

1.2%

1.4%

2.1%

0.9%

164

South Sudan

N

2,132

-

0

2,132

2,132

14.6%

-

0.0%

14.6%

14.6%

Sri LankaY10,394

10,326374

10,768

442

11.7%

11.6%

0.4%

12.1%

0.5%

St. Kitts and
Nevis

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

St. Martin(French part)

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

St. Vincent and
the Grenadines

Y

0

43

0

0

(43)

0.0%

5.4%

0.0%

0.0%

-5.4%

Sudan

Y

8,786

5,144

3,087

11,872

6,729

20.9%

12.3%

7.4%

28.3%

16.0%

Suriname

N

902

-

0

902

902

34.0%

-

0.0%

34.0%

34.0%

Syrian Arab
Republic

Y

131

67

0

131

64

0.3%

0.1%

0.0%

0.3%

0.1%

Tajikistan

Y

2,148

2,095

225

2,373

278

24.5%

23.9%

2.6%

27.0%

3.2%

Tanzania

Y

2,887

850

0

2,887

2,036

4.8%

1.4%

0.0%

4.8%

3.4%

Thailand

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Timor-Leste

Y

0

0

0

0

0

0.0%

0.0%

0.0%

0.0%

0.0%

Togo

Y

837

932

143

980

48

11.3%

12.5%

1.9%

13.2%

0.7%

Tonga

Y

69

152

98

167

15

14.6%

32.2%

20.8%

35.4%

3.2%

Trinidad andTobago

N

461

-

0

461

461

2.2%

-

0.0%

2.2%

2.2%

Tunisia

Y

180

154

11

191

36

0.5%

0.4%

0.0%

0.5%

0.1%

Turkey

Y

1,124

1,524

1,806

2,930

1,406

0.1%

0.2%

0.2%

0.3%

0.2%

Turkmenistan

Y

796

316

8,111

8,907

8,591

2.2%

0.9%

22.5%

24.7%

23.9%

Turks and Caicos
Islands

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Tuvalu

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Uganda

Y

2,874

2,873

0

2,874

1

8.2%

8.1%

0.0%

8.2%

0.0%

Ukraine

Y

1,670

0

0

1,670

1,670

1.5%

0.0%

0.0%

1.5%

1.5%

165

Uruguay

N

49

-

47

97

97

0.1%

-

0.1%

0.2%

0.2%

Uzbekistan

Y

1,553

2,854

6,010

7,562

4,708

2.4%

4.4%

9.2%

11.6%

7.2%

Vanuatu

Y

196

184

0

196

12

22.5%

21.2%

0.0%

22.5%

1.3%

VenezuelaY74,741

699

16,32991,07090,371

17.5%

0.4%

4.0%

21.5%

21.1%

Viet Nam

Y

7,617

4,574

7,205

14,823

10,249

2.9%

1.8%

2.8%

5.7%

3.9%

Virgin Islands(U.S.)

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

West Bank andGaza Strip

N

0

-

0

0

0

0.0%

-

0.0%

0.0%

0.0%

Yemen

Y

310

389

0

310

(80)

2.5%

3.1%

0.0%

2.5%

-0.6%

Zambia

Y

6,608

6,066

1,337

7,945

1,880

27.1%

24.8%

5.5%

32.5%

7.7%

Zimbabwe

Y

1,652

3,146

1,448

3,099

(46)

13.0%

20.3%

8.0%

21.0%

0.7%