Aid, China, and Growth: Evidence from a New Global Development Finance Dataset

AXEL DREHER, Heidelberg University, Germany ANDREAS FUCHS, University of Goettingen, Germany BRADLEY PARKS, William & Mary, USA AUSTIN STRANGE, Harvard University, USA MICHAEL J. TIERNEY, William & Mary, USA

March 2020

Abstract: This article introduces a new dataset of official financing from China to 138 developing countries between 2000 and 2014. It investigates whether Chinese development finance affects economic growth in recipient countries. The results demonstrate that Chinese development finance boosts short-term economic growth. An additional project increases growth by between 0.41 and 1.49 percentage points two years after commitment, on average. While this study does not find that significant financial support from China impairs the overall effectiveness of aid from Western donors, aid from the United States tends to be more effective in countries that receive no substantial support from China.

JEL classification: F35, F43, O19, O47, P33

Keywords: China, foreign aid, overseas lending, Official Development Assistance, development finance, aid effectiveness, economic growth

Acknowledgements: The authors are grateful for generous support from the John D. and Catherine T. MacArthur Foundation (14-106444-000-INP), the William and Flora Hewlett Foundation (#2017-5577 and #2013-8499), Humanity United (#9748421), the Academic Research Fund of Singapore’s Ministry of Education (MOE2014-T2-2-157), and the German Research Foundation (DR 640/5-1 and FU 997/1-1). We thank the Editor and two anonymous reviewers of this journal, David Dollar, Vera Eichenauer, Quan Li, Anna Minasyan, Philip Roessler, Xiang Shao, Zhigang Tao, James Williams, Tianyang Xi, Yang Yao, as well as participants at the “Tracking International Aid and Investment from Developing and Emerging Economies” workshop held at Heidelberg University in September 2017, the Development Seminar at Peking University’s National School of Development in November 2017, the Harvard Shanghai Conference on Africa and Asia in November 2017, the Fudan University School of International Relations and Public Affairs Shipai Workshop in December 2017, the Sheffield Workshop in Political Economy at the University of Sheffield in January 2018, the CSAE Conference on “Economic Development in Africa” at Oxford University in March 2018, the Annual Meeting of the European Public Choice Society at Università Cattolica del Sacro Cuore Rome in April 2018, the Annual Economic Research Southern Africa Workshop on ‘Structural Constraints on the Economy, Growth and Political Economy’ at the University of the Witwatersrandand in September 2019, the Biennial Conference of the Economic Society of South Africa (ESSA) in Johannesburg in September 2019, the Research Seminar of the European Stability Mechanism (ESM) in Luxembourg in November 2019, the Research Seminar of the University College Dublin School of Economics in November 2019, the Gutenberg School of Management and Economics Faculty Seminar Series at the University of Mainz in January 2020, and the Departmental Talk of the Political Science Department of the University of British Columbia in Vancouver in February 2020 for comments on earlier versions of this paper. Excellent research assistance was provided by Melanie Aguilar-Rojas, Omar Alkhoja, Katherine Armstrong, Isabelle Baucum, Zach Baxter, Ellie Bentley, Liliana Besosa, Abegail Bilenkin, Allison Bowers, Ariel Cadby-Spicer, Emma Cahoon, Bree Cattelino, Alex Chadwick, Ava Chafin, Anissa Chams-Eddine, Tina Chang, Harrison Chapman, Yining Chen, Yuning Chen, Zihao Chen, Mengfan Cheng, Michelle Cheng, Tiffanie Choi, Miranda Clarke, Kate Connors, Graeme Cranston-Cuebas, Catherine Crowley, Hali Czosnek, Jenna Davis, Alex DeGala, Hannah Dempsey, Harsh Desai, Weiwei Du, Ashton Ebert, Caleb Ebert, Aili Espigh, Claire Etheridge, Ze Fu, Wesley Garner, Melanie Gilbert, Elizabeth Goldemen, Zijie Gong, Grace Grains, Liz Hall, Thompson Hangen, Sarah Harmon, Ethan Harrison, Collin Henson, Jasmine Herndon, Elizabeth Herrity, Gabrielle Hibbert, Carlos Holden-Villars, Keith Holleran, Weijue Huang, Daniel Hughes, Torey Beth Jackson, Jiaorui Jiang, Emmaleah Jones, Amar Kakirde, Naixin Kang, Ciera Killen, Ian Kirkwood, Emily Koerner, Dylan Kolhoff, Lidia Kovacevic, Martyna Kowalczyk, Mirian Kreykes, Dinu Krishnamoorthi, Isabella Kron, Karthik Kumarappan, Daniel Lantz, Caroline Lebegue, Jade Li, Yuwei Li, Yaseen Lofti, Adriane Lopez, Flynn Madden, Sarah Martin, George Moss, Marie Mullins, Qiuyan Ni, Jack Nicol, Brendan O’Connor, Alexandra Pancake, Carol Peng, Grace Perkins, Charles Perla, Sophia Perrotti, Andrea Powers, Han Qiao, Kamran Rahman, Sarah Reso, David Rice, Natalie Santos, Faith Savaiano, Dominic Scerbo, Leigh Seitz, William Shangraw, Kaitlan Shaub, Samuel Siewers, Kyra Solomon, Yifan Su, Elizabeth Sutterlin, Mahathi Tadikonda, Joanna Tan, Rebecca Thorpe, Jessica Usjanauskas, Emily Walker, Yale Waller, Katherine Walsh, Xinyi Wang, Jason (Jiacheng) Xi, Hanyang Xu, Darice Xue, Erya Yang, Gaohang Yao, Antonio Tianze Ye, Lincoln Zaleski, Jack Zhang, Yue Zhang, Echo Zhong, Joana Zhu, and Junrong Zhu.

The scale and scope of China’s overseas grants and loans now rivals or exceeds that of other major donors and lenders (Horn et al. 2019). Its flagship Belt and Road Initiative (BRI)—a “Belt” of road, rail, port, and pipeline projects that create an infrastructure corridor from China to Central Asia and Europe and a “Maritime Silk Road” that links China to South and Southeast Asia, the Middle East, and Africa through a series of deep-water ports along the littoral areas of the Indian Ocean— has “little precedent in modern history, promising more than [US]$1 trillion in infrastructure and spanning more than 60 countries” (Perlez and Huang 2017). Whether this new source of financing for development promotes economic growth is the subject of continued speculation, controversy, and debate. Some argue that Chinese government institutions and state-owned banks have weak internal systems for appraising the economic viability of candidate projects, and as such are more likely than traditional donors and lenders to fund economically inefficient “white elephant” projects (Dornan and Brant 2014; Financial Times 2016; Pilling and Feng 2018). According to The Economist (2017), “China seems to be repeating many of the mistakes made by Western donors and investors in the 1970s, when money flowed into big … infrastructure projects that never produced the expected economic gains.” Others argue that Chinese banks and government institutions do not adequately account for repayment capacity of borrowers and that Chinese development finance may ultimately dampen the economic growth prospects of borrower countries (Onjala 2018; Hausmann 2019). On the other hand, leaders of many developing countries praise the Chinese government for its willingness to bankroll the “hardware” of economic development—such as roads, railways, power plants, electricity grids, and telecommunication systems—and its ability to quickly implement large-scale infrastructure projects (Swedlund 2017a). Meles Zenawi, former Prime Minister of Ethiopia, claimed that “one of the main reasons for [the] turnaround in the economic fate of Africa is the emergence of the emerging nations in general and China in particular.”2 However, this debate rests upon weak evidentiary foundations. Little is known about the economic effects of Chinese development finance because Beijing shrouds its overseas portfolio of grants and loans in secrecy (Bräutigam 2009: 2). The data that do exist are insufficiently comprehensive and granular. We close this evidence gap by introducing a new, project-level dataset of official financing—including development aid and other forms of government

2 See interview excerpts from 2012 at http://et.china-embassy.org/eng/zagx/t899134.htm (accessed 12 October 2019). 3 financing—from China worldwide over the 2000-2014 period. As we describe in greater detail in Section 1 of this paper, we construct this dataset with the Tracking Underreported Financial Flows (TUFF) methodology developed by Strange et al. (2017a, 2017b), extending AidData’s Chinese Official Finance to Africa Dataset (Strange et al. 2017a) to cover 83 additional countries around the globe over a 15-year period. In total, the new dataset includes 4,304 projects financed with Chinese official development assistance (ODA) or other official flows (OOF) in 138 countries and territories around the world. It is the first dataset that enables researchers to study the effects of China’s official financing (OF) activities on a global scale.3 In this article, we use these data to address two important questions. First, does the receipt of Chinese development finance promote economic growth? Second, does China’s development finance undermine the effectiveness of development finance from Western donors? With respect to the first question, development finance from China may be no different than development finance from other sources. It might spur economic growth via increased physical investment (Clemens et al. 2012; Galiani et al. 2017; Dreher and Langlotz 2019), human capital accumulation (Arndt et al. 2016), household consumption (Jackson 2014; Temple and Van de Sijpe 2017), or firm performance (Chauvet and Ehrhart 2018; Marchesi et al. 2020). On the other hand, several factors could reduce economic growth (Kumar and Woo 2010). First, if China mostly finances unproductive, “white elephant” projects, host governments may find it difficult to service their debts and cover their recurrent expenditures (Christiansen 2010; Dabla-Norris et al. 2012). They might also find themselves using more public funding than would otherwise be necessary to rehabilitate infrastructure that has fallen into disrepair. Second, excessive amounts of debt through Chinese loans could deter foreign investment (Claessens et al. 1996; Pattillo et al. 2004; Ahlquist 2006). Third, a host government that has contracted a high level of Chinese debt might experience foreign exchange shortages, which can lead to import shortages and constrain export growth (Iyoha

3 Previous research focuses on the localized economic development effects of Chinese aid within African countries only (Dreher et al. 2019a). Busse et al. (2016) analyze the growth effects of Chinese aid in Africa. However, they have no direct measure of aid and address endogeneity concerns with a GMM method that relies on internal instruments that are unlikely to be excludable. Bluhm et al. (2019) geocode a subset of the projects that we introduce in this paper and analyze the effect of Chinese transport projects on the spatial distribution of economic activity within countries and subnational regions. In related work, we geocode our project-level data for Africa and investigate whether the birth regions of national leaders receive more aid (Dreher et al. 2019b), and whether such favoritism affects subnational development outcomes (Dreher et al. 2019a). 4

1999). Fourth, unsustainable debt levels can lead to expectations of inflation and exchange rate depreciation (Fischer 1993; Hurley et al. 2019). With respect to the second question, several recent studies suggest that Chinese government financing could undermine the effectiveness of Western donor policies and practices. Hernandez (2017) provides evidence that recipients of Chinese aid receive World Bank loans with fewer policy conditions. Annen and Knack (2019) find that traditional donor countries—members of the OECD’s Development Assistance Committee (DAC)—are substantially less willing to reward countries that have higher-quality policies and institutions with increased aid when those countries also receive large-scale financial transfers from the Chinese government.4 Given that policy and institutional quality are important determinants of public investment efficiency (Denizer et al. 2013; Presbitero 2016) and economic growth (Humphreys and Bates 2005; Jones and Olken 2005), the receipt of Chinese development finance could undermine the growth effects of Western aid by short-circuiting its link to the quality of policies and institutions in recipient countries. To identify whether and how Chinese development finance affects economic growth, we employ instrumental variables (IV) that exploit year-to-year changes in the supply of Chinese development finance in tandem with cross-sectional variation capturing the probability that countries receive a smaller or larger share of such funding (see Section 2). We then investigate the popular but untested claim that Chinese development finance might undermine the effectiveness of Western aid (e.g., Naím 2007; Brazys et al. 2017). In separate tests, we focus on OECD-DAC donors as a whole, the United States, and the World Bank. Our main results, reported in Section 3, suggest that Chinese development projects boost short- term economic growth in recipient countries between one and three years after commitment. They do so by increasing investment and—to a lesser extent—consumption. We do not find evidence that Chinese development projects are more or less effective in countries with high-quality policies and institutions. Nor do we find that significant financial support from China impairs the effectiveness of aid from Western donors (see Section 4). However, aid from the United States

4 Some studies also address the question of whether Chinese government financing undermines the effectiveness of Western donor policies and practices at subnational scales. For example, Brazys et al. (2017) find that the presence of World Bank development projects in Tanzanian localities is associated with lower levels of corruption. However, in cases where Chinese and World Bank projects are geographically co-located, the corruption-reducing effects of World Bank projects vanish. 5 tends to be more effective in countries that receive no substantial support from China.

1. The Global Allocation of Chinese Development Finance 1.1 A New Dataset In this paper, we introduce a new dataset that measures foreign aid and other forms of concessional and non-concessional government financing from China to the developing world between 2000 and 2014. We build upon Strange et al. (2017) who provide data on China’s official financing commitments to 50 African countries over the 2000-2012 period. We extend this work in three ways. First, we leverage a substantially broader set of sources to minimize the effect of incomplete or inaccurate information about individual projects. We standardize and synthesize data from four different source types—official data from Chinese ministries, embassies, and economic and commercial counselor offices; information from aid and debt management systems of finance and planning ministries in counterpart countries; English, Chinese and local-language news reports; and case-studies undertaken by hundreds of scholars and NGOs. In total, our dataset draws upon 15,500 unique sources of information, and it covers 4,304 Chinese development projects (worth approximately US$ 354 billion) that were officially committed, implemented, or completed between 2000 and 2014. Second, we extend the geographical coverage of the dataset to the entire developing world, including 138 countries in five world regions: Africa, the Middle East, Asia and the Pacific, Latin America and the Caribbean, and Central and Eastern Europe. Third, we extend the temporal coverage of the data through 2014. We constructed the dataset using the Tracking Underreported Financial Flows (TUFF) methodology, which we explain in detail in Appendix A1. This method was initially developed by several authors of this paper—in collaboration with AidData, a research lab at William & Mary (Strange et al. 2017b). It codifies a set of open-source data collection procedures that make it possible to identify detailed financial, operational, and locational information about officially financed projects that are not voluntarily or systematically recorded by official donors and lenders through international reporting systems, such as the OECD’s Creditor Reporting System (CRS) or the International Aid Transparency Initiative (IATI). We present the resulting dataset in more detail in Appendix A2. The dataset allows analysts to distinguish between three different categories of Chinese official financing (OF): “ODA-like” projects, which are nominally intended to promote economic or social 6 development and provided at levels of concessionality that are consistent with the ODA criteria established by the OECD-DAC; “OOF-like” projects, which are also financed by the Chinese government, but either have a non-developmental purpose (e.g., export promotion) or have insufficiently high levels of concessionality to qualify as ODA (e.g., loans at market rates); and “Vague OF,” which are Chinese government-financed projects that would qualify as ODA or OOF but cannot be categorized because of insufficient open-source information (Dreher et al. 2018a).7 The vast majority of Chinese government financing each year is OOF-like; ODA-like projects represent only 21% of total Chinese official commitments in financial terms between 2000 and 2014 (see Appendix B2). To analyze the sectoral distribution of Chinese official financing, we also coded the OECD- DAC sector classification (3-digit purpose codes) for all projects. Descriptive statistics indicate that the Chinese government invests far more money in the energy, transportation, industry, mining, and construction sectors than it does in the education, health, and governance sectors (see Appendix B3). However, a measure of project counts, rather than dollar amounts, paints a very different picture: the Chinese government funds more projects in the health, education, and governance sectors than in the economic infrastructure and production sectors. These smaller projects are disproportionately ODA-like, while the large projects in the economic infrastructure and production sectors tend to be funded with OOF-like loans.

1.2 Cross-country Allocation of Chinese Development Finance Our dataset enables us to analyze the allocation of the Chinese government’s global portfolio of aid and debt-financed projects. African countries received the largest proportion (54 percent) of the total number of projects financed by China between 2000 and 2014 (Appendix B5). Seven of the top-ten recipient countries, as measured by the number of projects, are African countries (Appendix B6). This finding is consistent with the conventional wisdom in press accounts (Poplak 2016) and academic sources (Alden et al. 2008; Carmody 2016: ch. 3) that emphasize a new, Chinese-led “scramble for Africa” in the 21st century. However, a different picture emerges when one counts total dollars (rather than projects) committed. Of the 25 largest Chinese projects in financial terms, only six are located in Africa (see Appendices B7 and B8 for details). More broadly, if one measures the average size of Chinese government-financed projects in terms of constant dollars,

7 ODA projects are widely considered to be “development aid” in the strict sense of the term. 7 only one African country is on the list of top 20 recipients (South Africa at #8; see Appendix B9). In panel A of Figure 1, we present a map of the global distribution of Chinese official financing (based on dollar amounts). Russia, Pakistan and Angola are the three largest recipients of Chinese official financing (see also Appendix B10).

INSERT FIGURE 1 HERE

We also examine the factors associated with the allocation of Chinese government grants and loans. Following the aid allocation literature (e.g., Alesina and Dollar 2000; Hoeffler and Outram 2011), Table 1 reports results for simple project allocation regressions. The dependent variable is the number of Chinese government-financed projects in a given country and year.8 In the selection of explanatory variables, we follow Dreher and Fuchs (2015), who study China’s “historical” aid allocation since the 1950s, and Dreher et al. (2018), who study the cross-country correlates of Chinese development finance to Africa.9 As column 1 demonstrates, our results from a simple random-effects panel regression are broadly in line with those reported in previous studies: a country receives more Chinese development projects when its voting is aligned with China in the United Nations General Assembly. Conversely, it receives fewer projects when it recognizes the government in Taipei, Taiwan, rather than the one in Beijing. These results are consistent with the idea that Beijing rewards countries for their political allegiances. Also, commercial ties and ease of communication appear to play a significant role as the number of projects increases with the logged value of China’s existing trade with a particular country (in constant US$) and if the country’s official language is English. Richer countries (oil producers and countries with higher levels of GDP per capita in constant US$) receive fewer projects, suggesting that these countries need less aid, or that it is more difficult to buy policy concessions from them.10 Countries that are

8 Appendix B13 provides the list of all countries included in the allocation analysis. 9 Our control variables originate from Rich (2009), Voeten et al. (2009), Abbas et al. (2010), Mayer and Zignago (2011), Marshall et al. (2013), British Geological Survey (2016), UN Comtrade, and the World Development Indicators. Appendix B14 contains the descriptive statistics of all variables employed in the allocation analysis. 10 We code countries as oil producers if they produced oil in 1999. This measure follows the reasoning in Easterly and Levine (2003), who discuss the benefits of using an exogenous measure of oil. Our finding for oil-producing countries is in line with previous empirical research that refines 8 more indebted receive fewer Chinese government-financed projects, suggesting that Beijing may fear that these countries are less likely to repay their loans. Finally, the political regime type of a recipient country does not affect whether or not it receives projects, and recipient country population size is equally insignificant. INSERT TABLE 1 HERE

1.3 Physical Project Inputs We now turn to a key determinant of the overall supply of Chinese official financing in a given year—the level of domestic industrial overproduction. Following Bluhm et al. (2019), we use this variable as a component of our IV for Chinese financing. Using factor analysis, this variable measures the (logged and detrended) first factor of the Chinese production of six physical project inputs—namely aluminum, cement, glass, iron, steel, and timber, drawing on data from the National Bureau of Statistics of China (NBSC) and the United States Geological Survey (USGS).11 The intuition for this variable builds upon Bluhm et al. (2019) and Dreher et al. (2019a), who suggest that China’s annual production of these raw materials is a key determinant of the availability of Chinese government financing for foreign infrastructure projects. As these two studies explain, the Chinese government considers domestic production inputs, such as steel and aluminum, to be strategically important commodities and therefore produces these materials at levels that substantially exceed domestic demand. Consequently, in years when production volumes of project inputs are high, one would expect the supply of Chinese government financing for economic infrastructure projects abroad to increase. This logic extends to Chinese government-financed projects in all sectors, as the vast majority of projects in other sectors (e.g., hospitals, schools, convention centers, government buildings, and stadiums) also involve physical construction and rely heavily on imported Chinese construction

the conventional wisdom about China using aid to access natural resources (Dreher and Fuchs 2015). 11 We use USGS data on China’s annual production of aluminum in 10,000 tons (https://www.usgs.gov/centers/nmic/aluminum-statistics-and-information, last accessed 12 October 2019). The annual production volumes of cement, pig iron, steel (all in 10,000 tons), timber (in 10,000 cubic meters), and glass (in 10,000 weight cases) have been retrieved via Quandl and complemented with information from the NBSC website (http://www.stats.gov.cn/english/statisticaldata/yearlydata/YB1999e/m12e.htm; last accessed 12 October 2019). 9 materials.12 China suffers from industrial overproduction because many of its state-subsidized firms are over-leveraged, inefficient, and unprofitable (Stanway 2016).13 To address this challenge, Chinese authorities have pursued a two-track strategy of reducing domestic supply and increasing international demand. At home, they have prohibited the development of new production facilities, expedited the closure of inefficient operations, increased the prices of key inputs such as water and power, and imposed higher product quality standards (State Council 2013). In parallel, they have attempted to stimulate global demand by subsidizing overseas infrastructure projects and making these grants, loans, and export credits conditional upon the purchase of Chinese industrial inputs, such as steel, iron, cement, and aluminum. China’s lending operations in Kenya provide a useful illustration of how this international demand stimulation strategy works in practice. Between 2010 and 2014, the Export-Import Bank of China sharply increased lending for road, rail, and bridge projects in Kenya. Chief among these infrastructure projects was the Mombasa-Nairobi Standard Gauge Railway (SGR), which received two loans from the Export-Import Bank of China worth approximately US$3.5 billion. This 475- kilometer railroad required extraordinary amounts of steel, cement, stone, sand, timber, and glass. It also required the acquisition of manufactured goods that depend upon industrial inputs, such as locomotives, train wagons, electricity transmission pylons, and cables (Republic of Kenya 2014a, 2014b; Sanghi and Johnson 2016; Wissenbach and Wang 2017). The Chinese government took several steps to ensure that the vast majority of these project inputs would be sourced from China. The Export-Import Bank of China added a clause to its loan agreements with the Kenyan government that required the borrower to source project inputs from China on a preferential basis (Okoth 2019). China Road and Bridge Corporation (CRBC) and its Chinese subcontractors received generous tax exemptions that Kenyan firms did not enjoy, making it substantially more

12 Appendix B15 lists the five largest projects (by commitment amounts) for the six most frequent sectors. The bulk of these projects are physical infrastructure projects. They not only include economic infrastructure projects like roads and railways, but also social infrastructure projects like schools and hospitals. For example, four out of the five largest health-related projects are hospitals or malaria-treatment centers that use large quantities of building materials. 13 The Chinese government characterizes the problem as “industrial overcapacity,” which is a term that usually refers to the difference between domestic production capacity and actual production for the domestic market. However, for the sake of clarity, we prefer the terms “industrial overproduction” and “excess industrial production” because China overproduces industrial inputs relative to demand on the domestic market. It then attempts to sell its overproduced industrial inputs to foreign buyers (often in developing countries) because of its domestic overcapacity problem. 10 difficult for local firms to supply project inputs. 17 China’s support for the SGR is broadly illustrative of how Chinese grant- and loan-financed projects work. They typically involve physical construction; they usually require construction inputs that are oversupplied in China; and they often obligate recipients to import these inputs on a preferential basis (Mattlin and Nojonen 2015; Copper 2016; Ghossein et al. 2018). The authorities in Beijing have now incorporated this two-pronged strategy of international demand stimulation and industrial production offshoring into the BRI, and codified it in a set of official statements and policy papers (State Council 2013; 2015a, 2015b, 2015c; He 2014; Stanway 2015; Shi 2018), which further underscores why we expect a strong and positive relationship between the domestic production of Chinese construction materials and the supply of Chinese government financing for foreign development projects. Panel A of Figure 2 shows logged production numbers of six construction materials over time: aluminum, cement, glass, iron, steel, and timber. Given that they trend over time, we de-trend them for our analysis. Rather than including six individual variables, using factor analysis, we extract the first factor of the detrended series (which is shown in panel B).

INSERT FIGURE 2 HERE

Returning to the simple allocation regressions in Table 1, column 2 shows that this variable increases the number of Chinese development projects that the average country receives one year later. Relying on a 90% confidence interval, a one standard deviation increase in the production materials leads to an average increase of 0.3-0.6 projects per year. We choose a one-year lag since there is no reason to expect a longer delay between industrial input production and the approval of new Chinese development projects. During our entire period of study, the Chinese government had a well-established set of mechanisms in place to provide foreign grants and loans quickly. 23

17 The implementation of the SGR thus coincided with a major increase in steel imports from China (KNBS 2015, 2017; Business Daily 2018). 23 These mechanisms include grants and interest-free loans (via Economic and Technical Cooperation Agreements) that are administered by the Ministry of Commerce, the Concessional Loan Program, the Preferential Buyer’s Credit Program administered by China Eximbank, and a suite of additional programs and instruments administered by China Development Bank and other state-owned banks and government institutions. 11

Therefore, in years when the authorities considered industrial input overproduction to be a sufficiently large problem to justify the use of international development finance as a remedy, they could secure new loan- and grant-financed project approvals without much delay.24

1.4 Foreign Exchange Reserves Column 3 of Table 1 replaces physical project inputs with a second proxy for over-time variation in the availability of Chinese financing—changes in China’s foreign exchange reserves (see panel C of Figure 2 for a graphical depiction). Specifically, we use the net change in China’s holdings of international reserves resulting from transactions on the current, capital, and financial accounts (in trillions of constant 2010 US dollars), as collected by the World Bank’s World Development Indicators. The intuition for this variable is based on a similar logic to that of production inputs: China’s need to address a domestic oversupply problem. When the Chinese government adopted its “Going Out” strategy in 1999, it did so with the expectation that it would soon face strong macroeconomic headwinds. The country’s foreign exchange reserves were growing due to annual trade surpluses, and the authorities knew that if they allowed these reserves to enter the domestic economy, they would increase the risk of inflation and a currency revaluation (Zhang 2011).25 Therefore, to create favorable conditions for continued economic growth at home, they sharply increased foreign exchange-denominated loans to overseas borrowers on commercial and semi- commercial terms rather than highly concessional terms. 27 Given that the country’s foreign exchange reserves reportedly earned a 3% annual return at home during our period of study (2000- 2014), Chinese government lending institutions had an incentive to price foreign currency- denominated loans to overseas borrowers above this reference rate (Kong and Gallagher 2016).

24 The speed of Chinese aid delivery is frequently praised by political decision-makers in recipient countries. For example, in 2008, the then President of Senegal, Abdoulaye Wade, said that “China [helps] African nations build infrastructure projects in record time … a contract that would take five years to discuss, negotiate and sign with the World Bank takes three months when we have dealt with the Chinese authorities” (Wade 2008). On this point, also see Swedlund (2017: 128- 129). 25 China had an added incentive to lend and invest overseas because of a dwindling number of “bankable” projects at home (Ansar et al. 2016). 27 This incentive to invest in overseas assets strengthened during our fifteen-year period of study, as the country’s foreign exchange reserves soared from roughly US$200 billion in 2000 to US$4 trillion in 2014 (Park 2016). 12

Indeed, our dataset demonstrates that the average annual rate of growth in the provision of (mostly dollar-denominated) Chinese OOF was 2.5 times higher than that of Chinese ODA between 2000 and 2014.30

1.5 Historical and Contemporaneous Probabilities of Receiving Aid Finally, we explore whether past Chinese aid recipients tend to receive more Chinese official financing today. We expect that countries that received funding from China in the past, receive more development funds from China today, controlled for contemporaneous factors that influence China’s allocation of funds. To measure such aid inertia, we therefore construct the share of years in which a recipient country received Chinese aid from 1970-1999 using the data collected in

35 Dreher and Fuchs (2015). More precisely, we define this variable as , = , , , 1 30 𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑦𝑦=1 𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑦𝑦 where , , is a binary variable that equals one when recipient receivedℎ at 30least∑ oneℎ project from Chinaℎ𝐶𝐶𝐶𝐶𝐶𝐶 in𝑖𝑖 𝑦𝑦 year . The computed historical aid probabilities show𝑖𝑖 that Nepal had the highest likelihood of receiving𝑦𝑦 aid between 1970 and 1999 (80%), followed by Mauritania and Tanzania (both 71%). When we include this variable in our regression (in column 4 of Table 1), the results show that past recipients of ODA receive more official financing today, even after controlling for the usual variables included in aid allocation regressions. The coefficient implies that if a country without access to Chinese aid, such as Bhutan, had received as much aid from China as Nepal in the past, it would receive 3.1 additional Chinese government-financed projects per year in our sample period. These results suggest that countries receive projects today for reasons beyond those factors that are typically captured in the aid allocation literature.

30 The average annual rate of growth in ODA and OOF was 37 percent and 90 percent, respectively. In Online Appendix A.3 we describe the relationship between China’s State Administration of Foreign Exchange (SAFE) and the country’s largest single source of official financing to other countries (China Development Bank) to help clarify the process by which surplus foreign exchange reserves increase the supply of Chinese official financing. 35 In Dreher and Fuchs (2015) we draw upon a range of sources to construct our dataset on historical Chinese aid. These include US dollar amounts of loans and donations from various intelligence reports of the CIA (various), an OECD study (1987), and a book written by a German sinologist during the Cold War (Bartke 1989); information on the completion of aid projects from Bartke (1989) and the China Commerce Yearbook (and its predecessors) as published by China’s Ministry of Commerce (Hawkins et al. 2010); as well as the amount of food aid in tons of grain equivalent supplied according to the World Food Programme (2011). 13

Column 5 replaces the historical probability of receiving Chinese aid with the share of years that a country received Chinese government financing during the 2000-2014 sample period. More precisely, we define this variable as , = , , , where , , is a binary variable 1 15 that equals one when recipient received𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 at least15 ∑𝑦𝑦 one=1 𝑝𝑝 𝐶𝐶𝐶𝐶𝐶𝐶project𝑖𝑖 𝑦𝑦 from China𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖in𝑦𝑦 year . Panel B of

Figure 1 presents each country’s𝑖𝑖 probability of receiving Chinese government financ𝑦𝑦ing between 2000 and 2014. Thirteen countries receive official financing commitments from China in all years, and 92 countries are covered in more than half of the years from 2000-2014. The contemporaneous and historical probability measures are strongly correlated (65.4 percent). When we replace the historical probability of receiving projects with the contemporaneous measure, the results are similar, which suggests that the countries receiving aid in more years during our sample period have a higher probability of doing so in any year, even after we control for the conventional determinants of project allocations in our regression framework. Additional results in Table 1 show that these basic results (for the time-varying variables) hold when we run fixed-effects models (columns 6 and 7), collapse the data to a time series of 15 years explaining the totality of Chinese government-financed projects in any given year (columns 8 and 9), and restrict the sample to a cross-section of recipient countries in order to explain the total number of projects received over the sample period with measures that only vary across countries (columns 10 and 11). We make use of this over-time and cross-section heterogeneity in the supply of and recipients of China’s official financing in our IV strategy below.

2. Empirical Strategy Building on the descriptive statistics and allocation regressions in the previous section, we now analyze the causal effects of Chinese development finance on economic growth. We estimate the following regression equation for all recipient countries that are not classified by the World Bank as high-income countries in a given year:

, = , , + , + + + , , (1)

𝑖𝑖 𝑡𝑡 1 𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑡𝑡−2 2 𝑖𝑖 𝑡𝑡−1 3 𝑖𝑖 4 𝑡𝑡 𝑖𝑖 𝑡𝑡 where𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 Growthℎ i,tβ is 𝑂𝑂𝑂𝑂recipient countryβ 𝑝𝑝 i𝑜𝑜’s𝑝𝑝 yearly realβ 𝜂𝜂 GDPβ per𝜇𝜇 capita𝜀𝜀 growth in year t; , , is

𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑡𝑡−2 a measure of Chinese development finance commitments two years before; , 𝑂𝑂𝑂𝑂stands for the recipient country’s (logged) population size, and represent country𝑝𝑝𝑝𝑝𝑝𝑝𝑖𝑖 𝑡𝑡-− 1and year-fixed

𝜂𝜂𝑖𝑖 𝜇𝜇𝑡𝑡 14 effects, respectively, and ԑ is the error term. 36 Standard errors are clustered at the recipient-country level.

We use two measures of , , : the number of Chinese development projects and their 37 logged financial value. The 𝑂𝑂𝑂𝑂latter𝐶𝐶𝐶𝐶𝐶𝐶 comes𝑖𝑖 𝑡𝑡−2 with the obvious advantage that it accounts for the size of projects. However, one important caveat is that 39 percent of the projects lack information on their financial value. Therefore, while we present results using financial values of Chinese development projects in most tables for comparison, our preferred measure is project counts. Of course, Chinese development finance may be endogenous to economic growth. One likely source of endogeneity is reverse causation in which the economic characteristics of recipient countries influence the receipt of Chinese development projects. On the one hand, the Chinese government might provide more support to countries with low growth, which would be consistent with its stated goal to make “great efforts to ensure its aid benefits as many needy people as possible” (State Council 2011). On the other hand, the Chinese government might prefer to channel more development finance to countries with faster growth if these recipients provide more attractive commercial opportunities (Dreher et al. 2018a). The fact that a large number of variables which are excluded from our models potentially correlate with economic growth and the receipt of Chinese development finance also presents a risk of omitted-variable bias. To account for the endogeneity of Chinese development finance, we employ an IV strategy based on our allocation analysis above. Specifically, we estimate the following first-stage regression:

, , = , + , + , + + +

𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑡𝑡−2 1 𝑡𝑡−3 𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 2 𝑡𝑡−3 𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 3 𝑖𝑖 𝑡𝑡−1 4 𝑖𝑖 5 𝑡𝑡 𝑂𝑂𝑂𝑂, . γ 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 ∗ 𝑝𝑝 γ 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 ∗ 𝑝𝑝 γ 𝑝𝑝𝑝𝑝𝑝𝑝 γ 𝜂𝜂 γ 𝜇𝜇 (2)

𝑖𝑖 𝑡𝑡−2 Our𝑢𝑢 first instrument for , , is the interaction of (lagged, detrended, and logged) Chinese production materials, 𝑂𝑂𝑂𝑂𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 𝑡𝑡−2 , which varies over time, and the probability of receiving Chinese development finance𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑀𝑀𝑀𝑀𝑡𝑡−3, , which varies across recipient countries. As discussed above, 𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖

36 We provide a list of all countries included in our analysis in Appendix C1. See https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and- lendinggroups for the World Bank Country and Lending Groups (last accessed September 13, 2017). Table C2 of Appendix C provides descriptive statistics. 37 Note that we added a value of one before taking logs, to ensure we do not lose observations with zero OF. 15 we extract the first factor of the logged and detrended production figures using factor analysis to capture their joint variation, and use it as the time-varying part of our instrument. We interact it with the probability of receiving Chinese development finance as the share of years in the 2000-

2014 period a country has received positive amounts of Chinese development finance, , , 39 which varies between recipient countries exclusively. Our second instrument is the (lagged𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶 and𝑖𝑖 detrended) change in China’s net foreign exchange reserves, , again interacted with the probability of receiving Chinese development finance 𝑅𝑅𝑅𝑅𝑅𝑅,𝑅𝑅𝑅𝑅. 𝑅𝑅Both𝑅𝑅𝑅𝑅𝑡𝑡− 3Material and Reserves affect the availability of Chinese projects over time, while 𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶, affects𝑖𝑖 project availability across recipients (see previous section). We expect positive coefficients𝑝𝑝𝐶𝐶𝐶𝐶𝐶𝐶 𝑖𝑖 on both interacted variables. To be precise, we expect that countries which frequently receive development projects from China will benefit disproportionally from increases in both China’s overproduction of physical project inputs and foreign reserves. An obvious concern is that these instruments violate the exclusion restriction because the probability of receiving Chinese projects may directly affect economic growth (for the same reasons described above). However, our growth regressions control for the effect of the probability of receiving Chinese development finance as well as the levels of Material and Reserves through the inclusion of recipient-country- and year-fixed effects. Given that we control for the effects of the probability of receiving Chinese development finance, its interaction with an exogenous variable results in an exogenous instrument (under the assumption of parallel trends; see Nizalova and Murtazashvili 2016; Goldsmith-Pinkham et al. 2018; Bun and Harrison 2019). The intuition of this approach is that of a (continuous) difference-in-differences regression, where we investigate a differential effect of China’s production of raw materials and changes in foreign reserves on the amount of development finance to countries with a high compared to a low probability of receiving Chinese projects. The identifying assumption is that growth in countries with differing probabilities of receiving Chinese projects will not be affected differently by changes in the availability of construction material and foreign reserves, other than via the impact of China’s development

39 This follows the analyses in Nunn and Qian (2014) and Dreher and Langlotz (2019); also see Werker et al. (2009). When we replace the contemporaneous probability of receiving development finance with the “historical” probability of receiving funding prior to the sample period (as introduced in Section 1 above), the results are similar overall, but in some regressions they are statistically weaker (see Appendix C4). This is unsurprising given that pre-sample probabilities are less directly related to the number of projects in any year and thus a less powerful predictor. 16 finance, controlling for recipient-country- and year-fixed effects. In other words, as in any difference-in-differences setting, we rely on a (conditionally) exogenous treatment and parallel pre- trends across groups in the variables of interest. Controlling for year-fixed effects, Chinese production of input materials and the change in its foreign reserves cannot be correlated with the error term and are thus exogenous to official financing. In order for different trends to affect our results, these trends across countries with a high compared to a low probability of receiving projects from China would have to vary in tandem with year-to-year changes in the production of input materials and foreign exchange reserves.40 Following Christian and Barrett (2017), Figure 2 displays the variation in the availability of Chinese production materials and changes in Chinese foreign exchange reserves (and their detrended versions) in concert with the variation in per-capita aid and growth for two different groups that are defined according to the median probability of receiving aid. The results give little reason to believe that the parallel-trends assumption is violated. The probability-specific trends in Chinese development finance and growth, respectively, are mostly parallel across the regular recipients (those with an above-the-median probability of receiving projects from China) and irregular recipients (those with a below-the-median probability of receiving projects from China). There is also no obvious non-linear trend in regular recipients compared to irregular recipients that is similar for Chinese development finance and growth.41 The exogeneity of our interacted instrument would be violated if changes in detrended input materials or foreign reserves affected recipient-country growth differentially in countries with a high probability of receiving Chinese projects compared to countries with a low probability of receiving Chinese projects for reasons unrelated to China’s official financing. Figure 2 illustrates

40 Given that we have two plausibly valid instruments, we use both of them jointly, which allows us to test the overidentifying restrictions. Below, we show that we obtain similar results when we use them separately, which suggests that both instruments are valid or that both are invalid. We judge the latter to be unlikely as this would suggest that the IVs would have to be correlated with each other in such a way that they are both similarly correlated with the error term of the growth regression for reasons other than Chinese projects. 41 A skeptical reader might be concerned that the emergence of the Global Financial Crisis might drive our results. A first look at the upper left panel of Figure 2 shows that the detrended production of construction materials plateaued from the year 2007 to 2008, while the bottom right panel shows that per-capita GDP growth declined less in the group that is more likely to receive Chinese development finance. It is reassuring that our results hardly change when we exclude the years 2007 and 2008 from our regressions (see Appendix C3). 17 that the first factor of the logged and detrended Chinese input production and the detrended changes in net foreign reserves follow no obvious trends over time. However, production of construction materials could be correlated with overall export volumes or foreign direct investment. Frequent recipients of Chinese government loan- and grant-financed projects also tend to attract higher levels of Chinese trade and investment (e.g., Morgan and Zheng 2019), which could imply that any differential effects of China’s development finance on growth which we observe result from trade and investment rather than government-financed projects. To address this concern, we control for the yearly volume of Chinese foreign direct investment (FDI) outflows (from UNCTAD 2017) and Chinese exports (from the World Bank’s World Development Indicators, WDI) interacted with the probability of receiving Chinese development finance in robustness tests below. Finally, we offer three placebo tests related to the material-based part of our instrument. We test whether (1) Chinese exports, (2) Chinese outward foreign direct investments, and (3) Chinese government-financed projects that do not rely on physical inputs from China (such as budget aid or debt relief agreements) can be predicted with our material-based instrument. For the first two tests, we control for Chinese projects, so that our instrument based on the probability to receive projects should not predict exports or FDI well. If our material-based instrument is valid, it should also not predict projects that do not rely on construction materials. Our specification deviates from the extant literature on aid and growth in a number of ways (e.g., Clemens et al. 2012; Galiani et al. 2017; Dreher and Langlotz 2019). First, we analyze ODA and OOF as separate regressors in addition to investigating them in concert. While existing literature focuses exclusively on the potential growth effects of ODA, it is only one component of 21st-century development finance. During our period of study (2000-2014), most of the official financing provided by China (62-77 percent) and the World Bank (64 percent) was not Official Development Assistance (ODA).42 By contrast, most of official financing provided by the United States and other OECD bilateral donors was ODA.43 This source of variation might help to explain

42 Only 21.6 percent of total official financing from China clearly meets the OECD-DAC criteria for ODA, whereas we lack sufficient information to classify 15.6 percent of China’s official finance as either ODA or OOF. Similarly, 64.3 percent of official financing flows from the World Bank were channeled through the IBRD (OOF) and only the remaining 35.6 percent were channeled through the IDA (ODA) between 2000 and 2014. World Bank data were retrieved from https://data.worldbank.org/ on 12 September 2017. 43 Between 2000 and 2014, the United States provided US$394.6 billion of official financing to other countries. 93 percent of these official financing flows (US$366.4 billion) qualified as ODA 18 heterogeneous “aid” impacts across donors. Indeed, Cordella and Ulku (2007) find that the provision of more concessional forms of development finance increases growth in poor and highly indebted countries. Similarly, Khomba and Trew (2017) conclude that grants are more effective than loans at generating (localized) growth effects. 44 To account for this potential source of variation, we vary our definition of “treatment” and separately investigate the growth effects of more concessional finance (ODA) and less concessional (or market-based) forms of official financing (OOF) from China. In order to do so, we use the interaction of China’s production of construction materials and changes in foreign reserves with the probability of recipient country i receiving Chinese ODA or OOF, respectively. Second, we rely on project commitments rather than disbursements. Given that projects should only affect economic growth after disbursement, the latter are preferable over the former. However, comprehensive data on disbursements from Chinese government-financed projects are not available and are virtually impossible to consistently measure with open-source data collection methods. In our main specification, we lag commitments by two years in order to allow for sufficient time for commitments to affect outcomes. We base our lag duration on a subsample of 300 projects in the dataset for which there is information on the actual project start and end dates.45 The observed average project duration amounts to 664 days, and thus we apply a two-year lag in our baseline regressions.46 This lag structure is also consistent with the conventional wisdom among development practitioners and government officials in host countries that Chinese development projects are quickly implemented (e.g., Swedlund 2017: 128-129). While our data suggest two years may be an appropriate lag period, the 300-project subsample is not necessarily

and 7 percent (US$28.1 billion) qualified as OOF. Between 2000 and 2014, the OECD-DAC as a whole provided US$1.753 trillion of official financing to other countries; 80.6 percent of these flows (US$1.413 trillion) qualified as bilateral ODA and 19.4 percent (US$339.2 billion) qualified as OOF. Data have been retrieved from http://stats.oecd.org/ and AidData’s Core Research Release, Version 3.1 on 6 October 2017. 44 On the other hand, Odedokun (2004) provides evidence that the receipt of grants discourages domestic tax collection and undermines fiscal discipline, and Dovern and Nunnenkamp (2007) find that grants do not provide larger growth dividends than loans. 45 In subsetting the data, we exclude projects with a project length of zero days, which is typically the case for monetary grants. However, even in these cases, the recipient government will need time to implement these projects, which makes a time lag necessary. 46 Historical Chinese aid data also reveal a median of two years between project start and completion (data from Bartke 1989). 19 representative (and may suffer from selection effects), so we perform analyses using various lag lengths below. Third, most previous studies analyze aid amounts either in per-capita terms or as a share of GDP. One disadvantage of this approach is that it restricts the effect of population or GDP to be the same as those of aid. As Annen and Kosempel (2018) argue, there are no obvious theoretical reasons for adopting this approach. Their simulations also show that using aid-over-GDP ratios introduces a downward bias relative to using levels of aid. Therefore, following Ahmed (2016) and many others, we use (logged) commitments in levels as the variable of interest and control for population size (but test robustness to scaling commitments with population or GDP). Fourth, we employ annual data rather than data averaged over three-, four-, or five-year periods (e.g., Clemens et al. 2012; Dreher and Lohmann 2015; Galiani et al. 2017; Dreher and Langlotz 2019). In order for our tests to show an effect of development finance that actually exists with an 80 percent probability, we would require several thousand observations rather than the sample of roughly 420 observations that we would have if we averaged our data over five-year-periods.47 This is a broader empirical challenge within the aid effectiveness literature (Ioannidis et al. 2017).48 However, while much of the literature focusing on Western donors makes use of samples starting in the 1970s, the first year for which we have comprehensive data on Chinese development finance is 2000.49 Our main regressions thus use yearly data rather than averages over longer periods of time since this substantially increases the power of our tests. Our results must therefore be interpreted differently than much of the extant aid effectiveness literature. We primarily test whether Chinese development finance affects growth in the short-run, and we can only draw tentative conclusions about whether it has longer-lasting effects by looking at various lag lengths.50

47 This high number of required observations is driven by our fixed-effects setting, as both country- and time-fixed effects capture most of the variation in the dependent variable so that the variation caused by development finance conditional on these fixed effects is small. 48 According to Ioannidis et al. (2017), only about one percent of the 1,779 estimates in the aid- and-growth literature surveyed have adequate power (see also Doucouliagos 2019; Dreher and Langlotz 2019). 49 Chinese aid volumes are also available for years prior to 1987 (Dreher and Fuchs 2015) but these values are not necessarily comparable to post-2000 data as they are gathered using different data collection procedures. 50 Nevertheless, it is reassuring that we obtain similar results when we use three-year averages rather than annual data. 20

Fifth, we differ from much of the extant literature in our choice of control variables. Our main regressions are parsimonious. They control for fixed effects for years τ and countries η and the

(logged) population size of recipient countries popi. Typical regressions in the aid effectiveness literature include additional control variables such as initial-period per-capita GDP, ethnic fractionalization, assassinations, proxies for institutional and economic policies, and proxies for financial development (e.g., Burnside and Dollar 2000). All of these variables are arguably endogenous and introduce bias even if Chinese development finance is instrumented using a perfectly excludable IV. Given that our exclusion restriction holds absent the inclusion of these control variables, their omission reduces the efficiency of the estimator, but does not bias our estimates.51 3. Does Chinese Development Finance Promote Growth? 3.1 Main Results Table 2 presents our main results on the potential growth effects of Chinese development finance for the 2002-2016 period.52 We show results using OLS in panel A. We start with the number of Chinese projects as the variable of interest in columns 1-3 and then turn to the logged financial amounts in columns 4-6. As shown in column 1, the number of Chinese government-financed projects is positively correlated with economic growth in recipient countries. Relying on a 90% confidence interval,53 an additional Chinese project is associated with a growth rate that is 0.07 to 0.23 percentage points larger. The results are similar when we examine OOF projects only (in column 2) or a more narrowly defined measure of Chinese aid—those projects that meet the OECD- DAC criteria for ODA (column 3). The positive correlations persist when we look at amounts rather than numbers, but they are statistically weaker. Though the point estimate is positive it is estimated less precisely: A doubling of the amount of Chinese OF to the average recipient country is

51 When testing for robustness, however, we do include the variables most commonly used in the aid effectiveness literature. One might object that population size should not be included in fixed effects regressions, given that it hardly varies over time. Omitting population barely changes our results. 52 Recall that we measure Chinese development finance annually between 2000 and 2014 and rely on two-year lags. 53 In what follows, we routinely discuss the range of predicted effects based on the 90% confidence interval, unless stated otherwise. 21 associated with a growth rate that is between -0.02 and 0.03 percentage points larger two years after the commitment.54

INSERT TABLE 2 HERE

The results in panel A reflect correlations that are likely biased due to endogeneity. Therefore, we employ our IV strategy to account for reverse causality and other potential sources of endogeneity. Panel B presents reduced-form estimates, replacing the respective official financing variables with our instruments. Panel C shows the second-stage of the regressions estimated with 2SLS, while we provide the corresponding first-stage results in panel D. In all but one of the first- stage regressions, both instruments show the expected positive sign, i.e., increases in the supply of Chinese project inputs, both material and financial, generate disproportionately large increases in projects for countries that are regular recipients of Chinese support. As illustrated by the Kleibergen-Paap F-test statistics reported at the bottom of the table, our instruments are strong for the regressions focusing on the number of aid projects (columns 1-3), but weaker in two of the three regressions focusing on dollar amounts (in columns 4 and 6 where we focus on total financing and ODA, respectively). While the two instruments are not individually significant at conventional levels in all first-stage regressions, they are jointly significant throughout. This is not surprising given the high correlation between them (0.76), arising from the fact that years with high production volumes are those with larger export surpluses, which in turn increase China’s foreign reserves. Our results are robust when we use either of the two instruments in our regressions (rather than both) and/or when we replace the contemporaneous probability of receiving OF with the arguably more (unconditionally) exogenous measure of the “historical” probability of receiving aid (see Appendix C4 for details). In all regressions, the Hansen test fails to reject the overidentifying restrictions. As the results for the first-stage regressions show, a one standard-deviation increase in both IVs leads to 0.20- 1.53 additional Chinese projects (based on column 1 of panel D).55 According to the reduced-form estimates, a one standard-deviation increase in both IVs increases economic growth by

54 -0.023 * ln(2)=-0.016 and 0.041 * ln(2)=0.028. 55 Lower bound: 0.112 * (-0.502) + 0.610 * 0.430. Upper bound: 0.112 * 5.454 + 0.610 * 1.507. 22 between -0.20 and 1.79 percentage points (based on the 90% confidence interval in column 1 of panel B).56 Turning to our key results, panel C shows that Chinese official financing increases economic growth. According to column 1, Chinese OF projects increase growth, and the same holds when we analyze Chinese ODA separately (in column 3). Overall, the 2SLS effects are stronger than the correlations obtained with OLS. Quantitatively, an additional Chinese project increases growth by between 0.41 to 1.49 percentage points. The corresponding range is an increase of 0.69-2.21 percentage points for ODA, which is substantially larger compared to the OLS estimate. The downward bias of the OLS results is in line with expectations, to the extent that China provides more ODA to needier countries (see Table 1, as well as Dreher and Fuchs 2015; Dreher et al. 2018a). The estimated effects are sizable given average economic growth rates of 2.8 percent for the recipient countries in our sample.57 The same holds when we focus on financial amounts instead of project numbers (in columns 4-6). A doubling of Chinese official finance that the average country receives in a year—amounting to an increase in US$146 million—implies a 0.05-1.33 percentage points increase in growth two years after commitment. The effect is again larger for aid in the strict sense of the term, where a doubling of funding (corresponding to an increase of US$33 million in the average country) would increase growth by between 0.13 to 1.37 percentage points.58 Our results are less clear-cut when we focus on Chinese OOF, which is a less concessional and more commercially oriented type of development finance. While the point estimate of the 2SLS regression in column 2 of Table 2 implies that an additional OOF project increases growth by 0.76 percentage points, the 90% confidence interval ranges from -0.39 to 1.91. It is however important to distinguish a precisely estimated zero-effect from a positive coefficient that is estimated imprecisely (see, e.g., Dreher and Langlotz 2019). 2SLS is always less efficient than OLS, which

56 Lower bound: 0.112 * (-4.722) + 0.610 * 0.536. Upper bound: 0.112 * 4.895 + 0.610 * 2.034. 57 In our dataset, the average financial size of Chinese OF (ODA) projects is US$133 million (US$43 million). 58 To get at the growth effects of aid dollars, we also replicated the analysis with Chinese OF in millions of US dollars rather than logs (see Appendix C11). According to these estimates, an increase in Chinese OF by US$146 million lifts economic growth by 1.6 percentage points, on average. Our estimates imply that a US$33 million increase in Chinese ODA increases economic growth by 2.01 percentage points. 23 indicates a positive and significant effect of OOF projects on growth here. We thus consider it important to also compare the coefficient of the IV estimate to the (significant but biased) coefficient from the OLS regression. According to the t-test from a Seemingly Unrelated Regression (SUR), the coefficients are not statistically different, and the same holds true when we compare the IV estimates for OOF in column 2 with the significant and positive estimates for ODA projects in column 3. For these reasons, we refrain from making strong claims about the potentially differential effects of Chinese ODA and OOF.59

INSERT TABLE 3 HERE

Table 3 investigates the timing of the growth effects in detail. We estimate a variant of Table 2 where we change the lag structure of aid. We change the lag structure of the instruments in analogy—e.g., when we lag China’s official finance by four years, the corresponding instruments are lagged by five years. For the reader’s convenience, we also include the results of our baseline specification where we use the second lag. Our results in column 1 suggest that Chinese projects increase growth from one to three years after being committed, as the three corresponding coefficients are all positive and statistically significant at least at the five-percent level. The coefficient is strongest for the second lag. Coefficients are insignificant at conventional levels contemporaneously, and four as well as five years after commitments; in the sixth year, the coefficient turns negative. When we restrict the regression to the same sample but lag aid by two rather than six years, the coefficient is positive and large, indicating that the longer lag rather than the reduced sample changes the result. As a placebo test, we also investigate the effect of future aid on current growth; the coefficient is negative, with a t-statistic close to zero. Columns 2-6

59 This interpretation finds further support in regressions that use the probability of receiving any project (rather than just OOF projects) as part of our instrument. In such regressions, the coefficients for the number and amounts of OOF projects are high and statistically significant at the one-percent and five-percent levels. While the power of the instrument is slightly lower, the first-stage F-statistics easily remain above ten. While this effect could result in part from the correlation of ODA projects with OOF projects, making the instrument less clearly excludable, this result demonstrates the difficulty in distinguishing between the two types of financing, given the correlation between them and the correlation among the IVs we use for them. Though the share of ODA in all official finance varies substantially across countries (see Appendix B18) the insignificant difference in the effect of the two makes it unlikely that such differences make the aid differentially effective in countries receiving different combinations of financing. 24 replicate the regressions with the alternative dependent variables. The results are similar. Finally, our main results are also similar when we use three-year averages rather than yearly data (see Appendix C12).

INSERT TABLE 4 HERE

In Table 4, we seek to understand the mechanisms linking aid to growth by investigating how Chinese official financing affects specific components of GDP, all measured in differences of logged constant values. Specifically, we investigate the effects of Chinese official financing on gross capital formation, net exports, household final consumption expenditure, and government final consumption expenditure, with overall consumption being the sum of the two, and gross domestic savings. This decomposition creates a basis for expectations about the future effects of Chinese projects: If aid is consumed in its entirety, there would be little reason to expect future growth to increase as a consequence, while strong effects on investment might promote future growth (to the extent investments are productive). As shown in Table 4, Chinese projects increase both investment and consumption in recipient countries, with the effect on investment being stronger in terms of magnitude and statistical significance. Specifically, an additional Chinese project leads to an increase in gross capital formation of 2.4 [0.6; 4.2] percent and an increase in consumption of 0.5 [-0.1; 1.0] percent (90% confidence intervals in brackets). Chinese ODA also increases imports by 2.4 [0.3; 4.4] percent, as one would expect if construction materials for such projects are imported from China or elsewhere. In summary, the positive effects on short-run growth that we observe are best explained via increases in investment and—to a lesser extent— consumption.60 To the extent that these investments are productive, longer-run effects on growth would be a likely consequence; however, we lack the data to test this expectation.

3.2 Robustness and Extensions As an important test for robustness, we explore specifications that control for annual amounts of Chinese outward FDI and exports to a country. The results of these tests are shown in panels A and B of Appendix C7 and confirm our previous results. Panels C and D focus only on the foreign

60 The table also reports results for Foreign Direct Investment. The effect of Chinese projects is positive, on average, but not precisely estimated. 25 reserves-based instrument, and include Chinese overall (rather than country-specific) trade and FDI as interactions with the probability of receiving Chinese aid. This accounts for potential confounding wherein physical-input-based movements in Chinese FDI and exports, rather than development finance, account for differential growth effects in countries that regularly receive Chinese development finance compared to irregular recipients.61 The results are similar. The final test that we report in the table is a placebo regression, where we explain Chinese official financing with future (rather than lagged) values of our instruments. The substantially lower first-stage F- statistics show that our instruments in t+1 do not explain aid in t well. Overall, these tests provide additional support for the main results reported in Table 2. While we do not report these results in a table, we also ran specifications where we instrument Chinese exports and FDI with our instrument based on physical construction materials (controlling for Chinese financing). Results for these placebo regressions show very weak first-stage F-statistics, as one would expect if official financing (rather than exports and investments) is the key method to transfer surplus material to countries that regularly receive Chinese development projects. As a further placebo test, we replaced the number of all projects with the number of projects that should be unrelated to the availability of physical inputs. This subset of projects includes items like budget aid, support to non-governmental organizations, and debt relief agreements. If our instrument captures the availability of physical project inputs it should not be a strong predictor of projects that do not rely on these inputs. Again, the first-stage F-statistics are very low in these placebo regressions, as one would expect. Next, our main results are qualitatively unchanged when we replicate the regressions in Table 2 controlling for the most common determinants of growth found in the aid effectiveness literature: the average number of assassinations in a recipient country, its government surplus as a share of GDP, its rate of inflation, money as a share of GDP, and trade openness (see Appendix Table C6

61 Given that aid is included in exports, we do not use the physical-input-based instrument for this test. Using the physical-input-based instrument, we also tested whether China’s exports of construction materials to a country increase two years after the commitment of Official Finance. Specifically, we use trade data from UN Comtrade and take the sum of exports of iron and steel (SITC code 67), lime, cement, and fabricated construction materials (661), cork and wood (24), aluminum (684), and glass (664) divided by recipient-country GDP. As shown in Appendix C10, the effects of OF on exports of construction material are similar compared to those on growth, as one would expect if the support is given in the form of physical inputs into projects. 26 for details).62 We also explore robustness to outliers. A partial leverage plot of our main result in Table 2 hints at the possibility that a small number of outliers may drive our results (see panel A of Appendix C5). However, when we replicate the regressions without these observations, we find that they do not affect our results in a substantive way (panel B of Appendix C5). Our main conclusions in our 2SLS setting also hold when we replace logged levels of Chinese official financing with per-capita amounts or amounts as a share of recipient-country GDP (Appendix C13).63 Finally, we compare our baseline results with regressions that include only projects that reached at least the implementation stage (Appendix C14) or completion stage (Appendix C15). As one would expect, results are stronger for the subset of projects that reached at least the implementation stage or completion stage than for the full set of projects in the baseline model specification (which include officially committed projects, projects in implementation, and completed projects). We next explore potential heterogeneity in our results. Several studies suggest that aid effectiveness is conditional on recipient political institutions and donor interests that shape allocation patterns. For example, Chinese aid effectiveness may vary across well-governed and poorly-governed countries (Burnside and Dollar 2000; Angeles and Neanidis 2009; Baliamoune- Lutz and Mavrotas 2009; Denizer et al. 2013) or across countries that are more or less politically aligned (Dreher et al. 2015; Dreher et al. 2018b).64 We therefore interact the number of Chinese projects (in terms of OF, OOF and ODA, respectively) with (a) the Burnside-Dollar “good-policy” indicator, (b) the absence of corruption, (c) democratic accountability, (d) the absence of ethnic

62 The excludability of our instrument does not depend on these additional control variables but their inclusion arguably introduces endogeneity, which is why we omit them in our baseline specifications in Table 2. Data originate from Banks and Wilson (2017) and WDI. We linearly interpolate the control variables to maximize sample size. The number of observations is lower compared to those in Table 2 because of countries with data missing for all years. 63 Note that the first-stage F-statistics are weaker, though they remain above the rule-of-thumb value of ten for the regressions including all flows when we use the physical aid-input-based instrument only, with results of the second stage again being similar (not reported in tables). Given that these approaches involve scaling the aid with a variable that should not be predicted by our instruments, the weaker first stage is unsurprising. 64 Others have argued that China is better able than Western donors to transact with poorly governed countries because it employs financial modalities, such as commodity-backed loans, that reduce the risks of financial misappropriation, loan repayment delinquency, and default (e.g., Bräutigam 2011). 27 tensions, (e) the absence of press freedom, (f) the recipient’s voting coincidence with China in the United Nations General Assembly, and (g) a binary indicator for left-wing recipient governments.65 We implement these regressions with a Control Function (CF) approach (based on the residuals from the first-stage regressions shown in Table 2 and using bootstrapped standard errors with 500 replications), assuming that the extent of the bias does not depend on the variable we interact with aid.66 Most interactions are insignificant (see Appendix C8). The exception is the indicator for left- wing governments where we find that ODA is less effective when given to countries with such governments. Finally, we investigate sectoral heterogeneity in the growth effects of aid. Clemens et al. (2012) decompose total aid flows into “early-impact” aid flows (e.g., economic infrastructure) that plausibly affect near-term growth outcomes and aid flows that likely only generate growth over longer periods of time. They find relatively strong impacts of aid on growth when they limit their analysis to “early-impact” aid flows. However, they do not test whether donors are differentially effective at promoting economic growth when they support the same types of “growth” sector activities (e.g., highways, bridges, railroads, dams, airports, seaports, electricity grids). Policymakers in developing countries frequently claim that China is more efficient at implementing social and economic infrastructure projects than its Western counterparts (Wade 2008; Soulé- Kohndou 2016). However, a popular counter-argument suggests that, in its zeal to help partner countries install the “hardware” of economic development in an efficient manner, China has prioritized speed over quality. Critics charge that China has financed white elephant projects—e.g., hospitals without the necessary equipment and personnel and roads that wash away—that provide few economic benefits, while Western donors and lenders have learned through decades of experience to design and implement development projects in careful and sustainable ways (Dornan and Brant 2014; Financial Times 2016: The Economist 2017).

65 Our sources for these data are Dreher and Langlotz (2019), the International Country Risk Guide (ICRG 2017), Voeten et al. (2009), Freedom House (2017), and Beck et al. (2001). 66 An alternative to this approach is 2SLS employing the interaction of the instrument with the variable we interact projects with as second instrument, but this approach treats the interaction with the endogenous variable as a separate endogenous variable and thus “can be quite inefficient relative to the more parsimonious CF approach” (Wooldrige 2015: 429). 28

We estimate the growth effects of aid channeled to three broad sectors as defined by the OECD: Economic Infrastructure & Services, Social Infrastructure & Services, and Production Sectors.67 To instrument these different project numbers and flows, we rely on the sector-specific probability of receiving aid to calculate our interacted instruments. That is, rather than focusing on the probability of receiving any financing, we use the probability of receiving funds in these three sectors. Our results show positive effects of Chinese official finance on recipients’ economic growth in all three broad sectors (see Appendix C9). The positive growth effects are most pronounced in the production sector. The estimated effects are larger for ODA than OOF in all three sectors. It is thus unlikely that differential effects across ODA vs. OOF result from the different sectoral composition of ODA and OOF, respectively.68 Taken together, our results are robust to alterations in the definition of our dependent variable, the inclusion of a number of control variables, and the omission of potential outliers. Placebo regressions show the expected insignificant coefficients. The positive effects of Chinese projects are independent from recipient countries’ governance and institutional quality and visible across different sectors.

4. Does Chinese Development Finance Harm the Effectiveness of Western Aid? This section complements our primary analysis by investigating whether and to what extent Chinese official financing reduces the effectiveness of Western aid. Given that Western bilateral donors only give minimal amounts of OOF, we analyze ODA from all OECD-DAC donors combined and then separately focus on three large Western donors: The United States, the

67 The “Social Infrastructure & Services” category includes health, education, governance, and water supply and sanitation projects; the “Economic Infrastructure & Services” category includes transportation projects (e.g., roads, railways, and airports), energy production and distribution projects, and information and communication technology (ICT) projects (e.g., broadband internet and mobile phone infrastructure); and the “Production Sector” category includes agriculture, fishing, forestry, mining, industry, trade, and tourism projects. Appendix B16 confirms that there are differences in the sectoral composition of ODA and OOF projects, on average. Appendix B18 shows the share of projects received in these sectors in all projects for the countries of our sample. 68 We also experimented with a measure of “Early-impact Financing,” attempting to focus on sectors that might affect growth in the short term according to Clemens et al. (2012). Since we have only 3-digit rather than 5-digit codes, we code a 3-digit sector as “Early-impact Financing” if the larger part of the underlying financial value is in 5-digit sectors that are coded as “Early-impact Aid” in Clemens et al. (2012). The results we obtained with this measure are similar to those reported in Table 2. 29

International Development Association (IDA), and the International Bank for Reconstruction and Development (IBRD). To this end, we introduce basic aid effectiveness regressions using the interaction of a given donor’s total aid budget in a year with the recipient-specific probability of receiving aid from that donor, broadly following Temple and Van de Sijpe (2017). Rather than using variables that affect the availability of aid, aid budgets directly measure the amounts available in a given year. The exclusion restriction relies on the assumption that changes in a donor’s aid budget over time do not differentially affect growth in countries with a low probability of receiving aid from that donor compared to growth in countries with a high probability of receiving aid, other than via the aid from that donor.69 Specifically, we use the interaction of the respective donor’s financial budget, computed as the sum of all its ODA commitments in a given year, with the recipient-specific probability of receiving aid from the respective donor as an instrument for the OECD-DAC, the United States, the two World Bank windows, and—to facilitate comparison—for China as well.70 Building on Lang (2016), we calculate the World Bank’s aid “budget” with measures of its aid resources: the IBRD’s equity-to-loans ratio and the IDA’s “funding position.”71 Lang suggests the IMF’s liquidity ratio interacted with the probability that a country is under an IMF program as an instrument for IMF loans. We follow this approach by using similar proxies for the World Bank. In order to measure the availability of IBRD resources, we rely on the IBRD’s equity-to-loans ratio, which has been

69 Using the interaction with aid budgets is arguably less exogenous than the interaction used in our primary analysis. As Dreher and Langlotz (2019) explain, the exclusion restriction of the interacted aid budget instrument could well be violated “given that growth shocks in recipient countries could directly affect donors’ aid budgets […], while growth shocks in non-recipient countries might not.” They point to a paper by Rodella-Boitreaud and Wagner (2011) who find that donors increase their aid budgets in response to increased demands for their aid rather than just responding with re-allocations of aid. Nevertheless, we believe that this approach is reasonable for the purposes of achieving comparability across donors. 70 For the group of DAC donors our instrument is taken from Dreher and Langlotz (2019). Aggregated aid (across donors) is based on a zero-stage-regression, where bilateral aid from all DAC donors to all recipients is predicted based on the excludable instrument. 71 Alternatively, one might think of aggregating country-specific commitments to derive the Bank’s total “aid budget.” For the Bank, however, we expect the liquidity ratios to be more suitable to indicate budgetary leeway since, unlike the DAC donors, the Bank has no fixed budget that it will spend largely independent of the demand for its resources. 30 consistently reported in the IBRD’s annual financial statements since 1994.72 The equity-to-loans ratio is a measure of the IBRD’s “ability to issue loans without calling its callable capital” (Bulow 2002: 245).73 In order to measure the availability of IDA resources, we rely on a measure of IDA’s “funding position,” which is defined by the World Bank as “the extent to which IDA can commit to new financing of loans, grants and guarantees given its financial position at any point in time and whether there are sufficient resources to meet undisbursed commitments of loans and grants” (IDA 2015: 24). The Bank only began reporting its funding position on an annual basis in 2008, so we reconstruct the 1990-2007 time-series by using the World Bank’s method of calculating this indicator. More specifically, with the information reported in the IDA’s annual financial statements, we first sum the Bank’s net investment portfolio and its non-negotiable, non-interest-bearing demand obligations (on account of members’ subscriptions and contributions) and then divide this figure by the sum of the Bank’s undisbursed commitments of development credits and grants.74 Using this alternative instrument, our findings for Chinese ODA projects and amounts are comparable to those obtained using the previous instruments in Table 2. Again, we find significant, positive growth effects for Chinese ODA projects one, two, and three years after the commitment

72 “Equity” is defined as the sum of usable paid-in capital, general reserves, special reserves, and cumulative translation adjustments. It does not include the “callable capital” that the IBRD’s shareholders are legally obligated to provide if and when it is needed. “Loans” are defined as the sum of loans outstanding and the present value of guarantees. 73 One Executive Director to the World Bank memorably characterized the IBRD’s callable capital in this way: “Management and the Board should think about callable capital as a Christian thinks about heaven, that it is a nice idea but no one wants to go there because the price of admission is death” (quoted in Kapur et al. 1997: 991). 74 Since 2008, the Bank has summed its net investment portfolio and its “unrestricted” demand obligations. However, prior to 2008, the Bank did not separately report its “restricted” and “unrestricted” demand obligations. Therefore, we rely instead on the total non-negotiable, noninterest-bearing demand obligation figures reported in the Bank’s pre-2008 financial reports. The Bank’s “restricted” demand obligations from 2008-2014 were almost negligible (less than 1% of total demand obligations), so this difference in the way IDA’s funding position is calculated from 1999-2007 and 2008-2014 is small and unlikely to be consequential. Likewise, the Bank reported its “net investment portfolio” as a stand-alone figure from 2008 to 2014 but not in earlier years. Therefore, as an approximation of the Bank’s net investment portfolio in each year between 2000 and 2007 we sum “Investments—Notes B and F” and “currencies due from banks” less “net payable from investment securities transactions.” As an approximation of the Bank’s “net investment portfolio” in each year between 1990 and 1999 we sum cash and investments immediately available and not immediately available for disbursement. 31 of aid (see Appendix D1). 75 Again, the coefficient is largest two years after commitment. According to the coefficient, one additional Chinese ODA project increases growth by between 0.68 to 2.48 percentage points two years after commitment. This is remarkably close to the corresponding increase of 0.69-2.21 percentage points for our baseline IV discussed above. Though we would ideally like to focus on the same time periods when comparing across donors, when we restrict the sample for the Western donors and lenders to the period for which we have aid data for China (2000-2014), our instruments for Western aid are insufficiently powerful according to the first-stage F-statistics. Our comparison thus relies on comparable Local Average Treatment Effects (LATEs), but draws from different samples in terms of the time period and recipient countries covered. Besides aid from China, we similarly find that bilateral ODA from Western donors boosts economic growth in recipient countries (columns 3 and 4 of Appendix D1). Focusing on the effects two years after commitment, we find that a doubling of OECD-DAC ODA (US ODA) to the average recipient country increases growth by between 0.40 and 2.33 (0.25 and 1.82) percentage points two years after commitment. Comparing these effects to those of Chinese ODA amounts (in t-2) shown in column 2, we cannot reject the hypothesis that bilateral aid from China and Western donors have equal effects on growth (p-values: 0.79 for DAC and 0.77 for the United States; see last row of Appendix D1). However, we do not observe any significant growth effects of IBRD loans or IDA grants (columns 5 and 6). Compared to these negative coefficients, Chinese ODA registers more positive effects.76 Having benchmarked the effectiveness of Chinese aid vis-à-vis the World Bank, the United States, and OECD-DAC donors as a whole, we next consider whether interactions between Chinese

75 Appendix D2 replicates Table 2 using the aid-budget instrument. Overall, the results are very similar to the ones described above. Appendix D3 reproduces these results with the Cold War probabilities of receiving projects. The results are again qualitatively similar in those specifications with large first-stage F statistics for the number of all projects and ODA projects. 76 These findings should be interpreted with caution, given that the results for the OECD-DAC and the United States are based on a longer panel (1978-2016) as the instrument failed to reach relevance on the shorter 2002-2016 sample. Similarly, the results for the IBRD refer to the 1997- 2016 and for the IDA to the 1993-2016 period. When we disaggregate aid into three sectors (economic infrastructure, social infrastructure, and production) for China, the DAC, and the United States, our results suggest that, irrespective of the source of ODA, support for the “Economic Infrastructure & Services” sector and the “Social Infrastructure & Services” sector consistently yields positive economic growth returns. For “Production Sector” activities, OECD-DAC ODA increases economic growth, but ODA in this same sector from China and the United States does not (see Appendix D4). 32 and traditional donors’ aid impinge upon the effectiveness of that aid. More specifically, we test whether and to what extent Western aid is differentially effective at increasing economic growth when given to countries that do not also receive substantial support from China. Scholars, journalists, and policymakers have previously argued that China’s disregard for the quality of host country policies and institutions diminishes the effectiveness of aid from more “enlightened” donors (Crouigneau and Hiault 2006; Collier 2007; Naím 2007; Pehnelt 2007; Woods 2008; The Economist 2009).77 By way of example, in 2009, the Executive Vice President of the Asia Society relayed a specific account where this dynamic seemed to be at work: “Cambodia was considering a $600m loan from the World Bank that had conditions about transparency and anti-corruption and accountability. The Cambodians basically told the World Bank to go to hell and the next day they received a $601 [million] loan from the Chinese with no conditions” (BBC 2009). Several recent studies suggest that anecdotes like this one may reflect a broader empirical pattern. Hernandez (2017) provides evidence that recipients of Chinese aid receive World Bank loans with fewer conditions. Therefore, to the extent that World Bank conditionality facilitates the adoption of growth-promoting policy and institutional reforms, Chinese aid may slow economic growth by indirectly impeding these reforms. Brazys and Vadlamannati (2018) show that the receipt of Chinese aid reduces the likelihood that host countries will implement market-liberalizing reforms.78 Annen and Knack (2019) also find that Western aid has become more sensitive to the quality of policies and institutions in recipient countries, but this relationship is compromised when the Chinese government provides large-scale financing (in excess of US$2 billion). Therefore, to the extent that high-quality policies and institutions support economic growth in recipient countries, Chinese aid may have detrimental effects on economic growth rates.79

77 Swedlund (2017b) provides a counter-argument. 78 Likewise, Li (2017) finds that Chinese aid has blunted the democratizing effects of DAC aid to Sub-Saharan Africa, while Kersting and Kilby (2014) also recover evidence that Chinese aid undermines democratic governance. 79 On the other hand, Strange et al. (2017a) find that Chinese aid can help prevent civil conflict when recipients are faced with sudden withdrawals of Western aid. Therefore, to the extent that political stability promotes economic growth, Chinese aid could also have indirect positive effects. According to Zeitz (2020), the World Bank reacts to competitive pressure by emulating China’s focus on infrastructure. Whether and to what extent such change in policy affects growth is an empirical question. 33

INSERT TABLE 5 HERE

Arguably, whether or not a country becomes a “Chinese aid orphan” is not exogenous, as China’s aid allocation decisions are made according to commercial, geopolitical, and need-based criteria (Dreher and Fuchs 2015; Dreher et al. 2018a). We address this issue by using the predicted number of Chinese projects rather than the actual number to select the sample of countries that China neglects. Specifically, we use the first-stage regression results from column 1 of Table 2 to split the sample. We define “aid orphans” as countries that are predicted to receive an average number of projects per year below the 60th percentile of the distribution over the sample period (4 projects). Running Seemingly Unrelated Regressions, we compute Wald tests to identify statistically significant differences in the effect sizes compared to the coefficients of the full sample. In addition, we run regressions that restrict the sample period to the years after 2001, where “orphans” are defined in each individual year, rather than for the whole sample period. However, aside from the IBRD, first-stage F-statistics are low, so we only report results for this specification for the regression focusing on the IBRD. Table 5 shows the results, with panel A reporting results for the full sample and panel B those restricted to “Chinese aid orphans.” Again, focusing on the effects two years after commitment, we find that a doubling of DAC ODA (US ODA) to the average Chinese “aid orphan” country increases growth by between 0.46 and 2.58 (0.58 and 3.26) percentage points two years after commitment. The coefficients of IDA credits and IBRD loans are negative but far from reaching statistical significance. Comparing the results of the sample restricted to “aid orphans” to those of the full sample, we find no significant differences for total OECD-DAC aid (see column 1, with the p-value given in the last line of the table), IBRD loans (columns 3 and 4), and IDA credits (column 5). When we focus on aid from the United States (in column 2), results are mixed. While the effect of US aid is positive whether or not we exclude Chinese “aid darlings,” its effect on growth is statistically larger in the sample focusing on China’s “aid orphans” (0.58-3.26) compared to the full sample (0.25-

1.82).80 Overall, claims that Chinese aid systematically impairs the effectiveness of Western aid are not supported by our findings.81

5. Conclusions China has become a major source of global development finance, but the nature and consequences of its official financing activities are poorly understood. The absence of systematic evidence and rigorous analysis on the economic growth effects of Chinese development finance represents a major blind spot in the literature. We address this gap by estimating the average economic growth effects of Chinese development finance. We also test the popular claim that significant support from China impairs the effectiveness of aid from Western donors and lenders. Our results show that Chinese development finance boosts economic growth in recipient countries in the short-run. For the average recipient country, we estimate that an additional Chinese project increases growth between 0.41 to 1.49 percentage points two years after the financial commitment (using a 90% confidence interval). Focusing on amounts rather than project numbers, we find that a doubling of Chinese development finance to the average recipient country— amounting to an increase in US$146 million—implies a 0.05-1.33 percentage points increase in growth two years after commitment. These effects persist across different aid sectors and appear to be driven by increases in investment and—to a lesser extent—consumption. While our analysis shows that the direct effects of Chinese development finance fade four years after the aid has been committed, we lack sufficient data to measure the long-run effects that may result from higher levels of gross capital formation. We also test for heterogeneity across a number of dimensions and do not find that Chinese development finance is differentially effective if given to countries with higher-quality policies or institutions. Nor do we find empirical support for the idea that significant financial support from China impairs the overall effectiveness of aid from Western donors. Overall,

80 Appendix D5 reports results without accounting for the endogeneity of Chinese projects. We define “Chinese aid orphans” as countries that completed fewer projects in the 1960-2005 period than the 60th percentile of the sample. Results are overall similar. The exception is a significantly different coefficient for IBRD funding. While not significantly different from zero, the coefficient is positive and larger than those of the overall sample, indicating that in this—endogenously selected sample—IBRD funding is more effective in countries that did not receive a large number of Chinese projects. 81 We test the robustness of these findings using different thresholds for how we define “aid orphans.” Our results are unchanged when we use the 40th or 70th percentiles to split our sample. 35 this evidence should allay some of the fears that policymakers have expressed about China acting as “rogue donor” that undermines the effectiveness of Western assistance (e.g., Naím 2007; Bolton 2018). Our findings also speak to broader policy and academic debates about the economic impacts of the BRI, a massive, Chinese-led initiative to increase regional connectivity across dozens of countries. The BRI includes hundreds of the very same types of projects that we assess in this article (e.g., roads, railways, tunnels, bridges, power plants, electricity transmission lines, telecommunication networks, and industrial production facilities). However, while most of the projects in our sample have already been implemented and their economic effects can be estimated, this is not the case for the vast majority of projects that have been formally designated as “BRI projects,” as the initiative was not officially launched until late 2013. To the extent BRI projects are financed, allocated, and implemented similarly to the development projects in our dataset, one might expect these projects to also register positive, short-term effects on economic growth. On the other hand, if China’s BRI portfolio is substantially more focused on “connective infrastructure” projects that resolve spatial bottlenecks, increase mobility, generate employment opportunities, create and link new markets, or otherwise reduce costs between subnational jurisdictions and countries, there may be larger and longer-term economic effects that we have not captured in our analysis (de Soyres et al. 2018; Bluhm et al. 2019).82 Future studies should therefore re-evaluate the questions addressed in this paper as more time-series data on Chinese development finance becomes available. Of course, any potential short- and long-term gains from the BRI must be weighed against the various costs of these projects. One particular concern is the extent to which BRI infrastructure projects result in high levels of sovereign debt on lending terms that are less concessional than the debt-financed projects in our dataset. Countries with rising public debt-to-GDP ratios in excess of 50-60% tend to experience economic growth slowdowns (Chudik et al. 2017), and there are now roughly fifteen BRI member countries in immediate danger of crossing—or even far surpassing— these thresholds (Hurley et al. 2019). Therefore, any future attempts to estimate the long-run, economic growth impacts of Chinese official financing ought to address the possibility of sovereign debt accumulation threshold effects, as well as unique features of Chinese government lending

82 Indeed, authoritative Chinese policy documents on the BRI heavily emphasize transportation infrastructure and economic connectivity (e.g., NDRC 2015). 36 such as collateralization (Hausmann 2019) and longer time horizons (Kaplan 2016, forthcoming). Any efforts to make projections about the long-run socioeconomic impacts of Chinese development finance should also account for the fact that Chinese development projects can create negative externalities such as local corruption, public acceptance of authoritarian norms, ethnic tensions, government-initiated violence against political opponents, and environmental degradation (BenYishay et al. 2016; Blair and Roessler 2016; Brazys et al. 2017; Kishi and Raleigh 2017; Isaksson and Kotsadam 2018a, 2018b; Isaksson 2019; Gehring et al. 2019). Some of these questions have already been addressed with the data introduced in this paper, but more research is warranted to fully understand the implications of Chinese development projects around the globe.

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Table 1: Allocation of Chinese official financing (OLS, 2000-2014) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Input factors (t-1) 0.451 0.450 0.394 0.400 44.964 (0.076) (0.075) (0.075) (0.083) (15.925) Reserves (t-1) 2.277 1.701 196.042 (0.446) (0.480) (99.126) OF probability, historic 3.286 41.318 (0.782) (13.347) OF probability, contemp. 3.899 66.033 (0.585) (9.805) UNGA voting alignment 3.285 3.927 3.520 2.571 0.399 6.227 5.570 2526.380 1359.077 0.764 -64.463 (1.305) (1.315) (1.259) (1.229) (1.248) (2.039) (1.964) (1019.900) (1735.639) (20.405) (27.936) Diplomatic relations Taiwan -1.960 -1.923 -1.893 -1.563 -0.308 -0.572 -0.524 -28.273 7.291 (0.385) (0.388) (0.382) (0.359) (0.418) (0.738) (0.722) (7.442) (7.147) Trade with China (log) 0.571 0.404 0.409 0.353 0.332 0.104 0.147 5.148 4.201 (0.083) (0.078) (0.080) (0.082) (0.078) (0.124) (0.123) (2.464) (1.877) Petroleum exporter -1.472 -1.134 -1.132 -0.699 -1.046 -6.596 -12.538 (0.444) (0.451) (0.444) (0.428) (0.377) (6.306) (5.081) Government debt (% of GDP) -0.008 -0.006 -0.006 -0.006 -0.006 -0.003 -0.004 -0.067 -0.055 (0.003) (0.002) (0.002) (0.002) (0.002) (0.003) (0.003) (0.066) (0.061) Democracy (Polity Score) -0.016 -0.012 -0.014 -0.005 -0.019 -0.005 -0.006 -0.078 -0.732 (0.027) (0.026) (0.026) (0.025) (0.022) (0.050) (0.050) (0.320) (0.316) GDP per capita (log) -0.738 -0.705 -0.721 -0.546 -0.301 0.095 0.105 -13.609 -4.423 (0.172) (0.173) (0.172) (0.174) (0.171) (0.340) (0.337) (3.582) (2.750) Population (log) -0.130 -0.022 -0.029 -0.026 -0.006 3.748 3.263 626.343 633.712 -1.354 0.570 (0.139) (0.138) (0.139) (0.122) (0.111) (1.650) (1.672) (129.171) (191.219) (2.559) (2.039) English is official language 1.389 1.343 1.352 1.265 0.888 17.746 13.361 (0.404) (0.398) (0.397) (0.364) (0.349) (5.486) (4.878) Number of observations 1666 1664 1664 1664 1664 1664 1664 15 15 126 126 Number of countries 125 125 125 125 125 125 125 R squared (overall) 0.26 0.28 0.28 0.31 0.36 0.04 0.04 R squared (within) 0.07 0.09 0.08 0.09 0.09 0.10 0.09 0.91 0.86 R squared (between) 0.48 0.51 0.51 0.56 0.65 0.06 0.06 0.59 0.68 Notes: Each column represents one regression. The dependent variable is the number of Chinese government-financed projects in a given country and year. Columns 1-5 use random- effects models; columns 6 and 7 fixed-effects models. Columns 8-11 are OLS regressions. Standard errors in parentheses. Abbreviations: OF—official finance (Official Development Assistance and Other Official Flows). UNGA—United Nations General Assembly. 51

Table 2: Growth effects of Chinese official financing, baseline results (2002-2016) (1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.147 0.203 0.158 0.009 0.025 0.023 (0.049) (0.082) (0.064) (0.019) (0.020) (0.019) (log) Population (t-1) 5.824 6.471 5.794 6.439 6.258 6.342 (2.893) (2.905) (2.856) (2.910) (2.968) (2.890) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 0.086 2.009 0.046 0.160 2.437 0.354 (2.905) (3.858) (2.912) (3.176) (4.007) (3.316) Input (t-3)*probability 1.285 0.501 1.482 1.277 0.518 1.475 (0.453) (0.706) (0.441) (0.473) (0.737) (0.488) (log) Population (t-1) 5.123 6.341 4.268 5.234 6.230 4.615 (3.020) (2.924) (3.067) (3.042) (2.932) (3.087) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.948 0.761 1.450 0.995 0.142 1.086 (0.327) (0.697) (0.465) (0.562) (0.123) (0.544) (log) Population (t-1) 2.137 6.392 0.031 -0.493 5.109 -0.961 (3.367) (2.892) (3.726) (7.775) (3.171) (6.894) Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 2.476 -1.167 3.142 2.394 16.571 3.017 (1.810) (2.928) (1.275) (4.308) (9.464) (4.661) Input (t-3)*probability 0.968 1.192 0.484 0.920 3.735 0.914 (0.327) (0.561) (0.228) (0.656) (1.613) (0.741) (log) Population (t-1) 3.045 -0.022 2.774 5.669 7.882 5.005 (1.377) (0.640) (1.051) (5.265) (4.767) (4.202)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 34.69 34.89 34.92 3.01 37.93 4.00 Kleibergen-Paap F 39.40 28.95 31.67 3.73 18.00 4.33 Hansen J statistic (p-value) 0.43 0.51 0.10 0.62 0.98 0.57

Notes: Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Table 3: Growth effects of Chinese official financing, various lags (2SLS, 2002-2016) (1) (2) (3) (4) (5) (6) (7) (log) (log) (log) All projects OOF projects ODA projects Obs. All amounts OOF amounts ODA amounts Chinese OF (t+1) -0.004 0.406 -0.143 0.131 0.107 -0.054 2103 (0.284) (0.474) (0.365) (0.278) (0.085) (0.260) Chinese OF (t) 0.425 0.476 0.687 0.478 0.122 0.450 2114 (0.273) (0.504) (0.380) (0.331) (0.091) (0.323) Chinese OF (t-1) 0.811 0.840 1.227 0.851 0.154 0.889 2089 (0.286) (0.586) (0.409) (0.463) (0.108) (0.457) Chinese OF (t-2) (baseline) 0.948 0.761 1.450 0.995 0.142 1.086 2061 (0.327) (0.697) (0.465) (0.562) (0.123) (0.544) Chinese OF (t-3) 0.888 0.535 1.541 0.955 0.095 1.270 1915 (0.365) (0.851) (0.509) (0.553) (0.142) (0.632) Chinese OF (t-4) 0.489 0.426 0.977 0.480 0.012 0.802 1768 (0.319) (0.792) (0.415) (0.376) (0.125) (0.415) Chinese OF (t-5) 0.113 0.176 0.416 0.111 -0.027 0.349 1619 (0.281) (0.696) (0.345) (0.291) (0.109) (0.266) Chinese OF (t-6) -0.142 -0.188 0.162 -0.037 -0.060 0.210 1471 (0.263) (0.532) (0.325) (0.267) (0.104) (0.239) Chinese OF (t-2) 2.650 1.523 2.976 1.447 0.335 1.738 1468 (reduced sample) (1.009) (1.382) (1.130) (0.911) (0.267) (1.054)

Notes: Each cell represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-h), denotes Chinese development finance commitments lagged by h years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). The “reduced sample” regression using the second lag restricts the sample to the observations included in the regression using the sixth lag (where we lose three additional observations due to the exclusion of countries with high income, which varies over time). All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

Table 4: Chinese official financing and components of GDP (2SLS, 2002-2016)

coef. s.e. Countries Obs. Cr.-Don. F Kl.-Paap F Hansen J Panel A: Gross Fixed Capital Formation OF 0.024 (0.011) 117 1481 30.00 33.55 0.22 OOF -0.014 (0.027) 117 1481 29.19 22.84 0.44 ODA 0.04 (0.015) 117 1481 28.84 29.55 0.07 Panel B: Gross Fixed Private Capital Formation OF 0.083 (0.071) 103 1215 21.46 34.79 0.37 OOF 0.173 (0.210) 103 1215 14.86 22.52 0.87 ODA 0.112 (0.096) 103 1215 26.00 28.71 0.31 Panel C: Foreign Direct Investment Inflows OF 0.030 (0.050) 149 1852 36.79 32.00 0.72 OOF 0.089 (0.073) 149 1852 47.14 14.43 0.95 ODA 0.038 (0.061) 149 1852 34.83 31.97 0.65 Panel D: Imports OF 0.012 (0.009) 121 1532 32.57 32.10 0.86 OOF -0.016 (0.023) 121 1532 32.53 24.72 0.00 ODA 0.024 (0.012) 121 1532 30.11 27.53 0.18 Panel E: Exports OF 0.003 (0.010) 121 1532 32.57 32.10 0.43 OOF -0.038 (0.022) 121 1532 32.53 24.72 0.36 ODA 0.016 (0.013) 121 1532 30.11 27.53 0.72 Panel F: Consumption, overall OF 0.005 (0.003) 116 1470 31.67 36.03 0.01 OOF 0.004 (0.009) 116 1470 31.67 25.13 0.93 ODA 0.006 (0.004) 116 1470 29.38 30.30 0.00 Panel G: Consumption, household OF 0.004 (0.003) 116 1470 31.67 36.03 0.02 OOF 0.007 (0.009) 116 1470 31.67 25.13 0.49 ODA 0.006 (0.005) 116 1470 29.38 30.30 0.01 Panel H: Consumption, government OF 0.005 (0.009) 116 1470 31.67 36.03 0.10 OOF 0.007 (0.029) 116 1470 31.67 25.13 0.90 ODA 0.007 (0.013) 116 1470 29.38 30.30 0.14 Panel I: Savings OF -0.032 (0.020) 127 1404 26.72 30.51 0.86 OOF -0.005 (0.035) 127 1404 20.90 26.02 0.56 ODA -0.041 (0.032) 127 1404 27.49 24.10 0.94

Notes: Each row represents one regression. The dependent variable is—in first differences, logged constant 2010 US$, with a value of one added before taking logs—indicated in the panel header. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count. In separate rows, we show results for all official finance (OF) projects, Other Official Flows (OOF) projects, and Official Development Assistance (ODA) projects. All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

Table 5: Growth effects of Western official financing for the full sample and the sample for Chinese “aid orphans” (based on the predicted number of Chinese projects) (1) (2) (3) (4) (5) DAC ODA US ODA IBRD IBRD IDA Panel A: Full sample 2SLS estimates – Dependent variable: GDP p.c. growth OF (t-2) 1.972 1.495 -0.375 -0.442 -0.243 (0.845) (0.687) (0.482) (0.397) (1.019) First-stage estimates – Dependent variable: OF (t-2) Budget (t-3)*probability 0.036 1.932 0.141 0.192 -0.025 (0.011) (0.475) (0.085) (0.090) (0.008)

Number of countries 157 157 154 152 157 Number of observations 4995 4996 2808 2370 3373 Kleibergen-Paap F 9.81 16.51 2.75 4.62 9.56

Panel B: Aid orphans 2SLS estimates – Dependent variable: GDP p.c. growth OF (t-2) 2.196 2.765 -0.445 -0.463 -1.550 (0.929) (1.175) (0.323) (0.303) (3.205) First-stage estimates – Dependent variable: OF (t-2) Budget (t-3)*probability 0.047 1.373 0.426 0.577 -0.012 (0.016) (0.422) (0.222) (0.234) (0.009) Number of countries 89 89 89 103 89 Number of observations 2771 2772 1623 1213 1927 Kleibergen-Paap F 8.25 10.56 3.68 6.07 1.70 First year 1978 1978 1997 2002 1993 Last year 2016 2016 2016 2016 2016 Prob > chi2 0.67 0.01 0.83 0.94 0.54

Notes: The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, indicated in the column header, denotes development finance commitments by the respective donor lagged by two years and is measured as logged financial amounts. All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Prob > chi2 reported in the last row corresponds to testing the hypothesis that the effect of DAC/US/IBRD/IDA official finance in countries that are predicted to be “Chinese aid orphans” is different from the effect in the full sample. Columns 1, 2, 3, and 5 define “aid orphans” as countries that are predicted to receive an average number of projects per year below the 60th percentile of the distribution over the sample period according to the regression in column 1 of Table 2 (4 projects). Column 4 restricts the sample period to the years after 2001, where “orphans” are defined in each individual year, rather than for the whole sample period. Standard errors in parentheses.

Figure 1: World maps of Chinese development finance Panel A: Financial value of Chinese development finance between 2000 and 2014 by recipient country (in millions of constant 2010 US dollars)

Legend: (2010 US$, million) <=30000 <=10000 <=5000 <=1000 <=500 0 NA

Panel B: Probability of receiving Chinese development finance between 2000 and 2014 by recipient country

Legend: 100% <100% <=75% <=50% <=25% 0 NA

Figure 2: Chinese development finance and its inputs (2000-2016)

Panel A Panel B 13 1.5

12 1 11

.5 10

9 0 8 -.5 7

(log) Production volume of respective input 6 -1

2000 2004 2008 2012 2016 Detrended first factor of six project inputs

alumn. cement glass -1.5 steel iron timber 2000 2004 2008 2012 2016

Panel C Panel D .5 .4

.4 .3

.3 .2

.2 .1

.1 0 Change in foreign exchange reserves

0 Detrended change in foreign exchange reserves -.1 2000 2004 2008 2012 2016 2000 2004 2008 2012 2016

Panel E Panel F 16 6

12 4

8 2

4 Average Growth p.c. 0

0 -2 Average (log) Chinese Official Finance (t-2) 2000 2004 2008 2012 2016 2000 2004 2008 2012 2016 below median prob below median prob above median prob above median prob

Notes: Panel A shows China’s (logged) production of aluminum (in 10,000 tons), cement (in 10,000 tons), glass (in 10,000 weight cases), iron (in 10,000 tons), steel (in 10,000 tons), and timber (in 10,000 cubic meters) over time. Panel B shows the detrended first factor of the six input materials derived with factor analysis. Panel C reports the change in net foreign exchange reserves (in trillions of constant 2010 US dollars). Panel D reports the detrended values of the latter. Panel E shows average (logged) official finance within the group that is below the median of the probability of receiving it and the group that is above the median over time. Panel F shows the average real GDP per capita growth rate within these two groups over time. For the construction of the averages, we use observations from the sample of column 1 in Table 2.

APPENDIX: NOT FOR PUBLICATION

Appendix A1: The Tracking Underreported Financial Flows (TUFF) Methodology TUFF data collection and quality assurance procedures are documented in their entirety in an accompanying methodology document (Strange et al. 2017b). Here we provide a brief summary. The TUFF methodology is divided into three stages: two stages of primary data collection (project identification and source triangulation) and a third stage to review and revise individual project records (quality assurance). The method standardizes and synthesizes large volumes of information primarily from four sources: English, Chinese and local-language news reports; documents from Chinese ministries, embassies, and economic and commercial counselor offices; the aid and debt information management systems of finance and planning ministries in counterpart countries; and case study and field research undertaken by scholars and NGOs. It represents a systematic, transparent, and replicable way of tracking the identifiable universe of projects financed by donors and lenders who do not publish official financing data at the project level. The method and some of the datasets that it has produced have been subjected to peer review, stress-tested, and substantially improved over time (e.g., Muchapondwa et al. 2016; Strange et al. 2017a; Dreher et al. 2018a).

In the first stage of primary data collection, researchers identify potential projects at the donor/lender-recipient/borrower-year unit of analysis through a standardized set of search queries 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. A machine- learning algorithm is then used to identify the subset of articles retrieved through these Factiva queries that are most likely to contain information about officially-financed projects for the donor/lender of interest (i.e., China in our case).83 Researchers then review each of the Factiva records that the machine-learning algorithm has classified as “relevant” and make case-by-case determinations about whether those records contain information about an officially financed

83 The machine learning tool that is used relies upon large amounts of training data (i.e., past articles that were identified via Factiva and later classified by researchers as containing or not containing information about projects financed by the official donor/lender of interest) to “teach” the algorithm to accurately classify hundreds of thousands of articles into “relevant” and “irrelevant” categories. Use of this tool significantly reduces the amount of time that researchers would otherwise spend reviewing articles that contain no information about projects financed by the official donor/lender of interest (“false positives”). 58 project by the donor/lender of interest. In parallel, researchers retrieve all individual projects that are financed by the donor/lender of interest and recorded in (a) the aid and debt information management systems of recipient/borrower countries, (b) IMF country reports, and (c) the websites of Chinese embassies and Chinese Economic and Commercial Counselor Offices (ECCOs).

Once a potential project has been identified during the first stage of data collection, it is entered into our data management platform with a unique identification number and assigned to a different researcher for a second stage of record review and augmentation. During this second stage, the researcher performs a set of targeted online searches to validate, invalidate, and/or enrich the project-level information that was retrieved in the first stage. These searches are conducted in English, Chinese and recipient/borrower country languages by trained language experts and native speakers in order to improve record accuracy and completeness. The researcher also seeks to collect supplementary information from government sources (e.g., annual reports published by the lender or granting agency), field reports published by NGOs and implementing entities (e.g., private contractors), scholarly research (e.g., case studies of particular projects, doctoral dissertations on the development finance activities of a particular donor/lender in a particular country), and experts with information or knowledge about specific projects that is not in the public domain or is not easily identifiable (e.g., photographic evidence of a project’s current status). This process of project-level investigation and triangulation is designed to reduce the risk of over-reliance on individual sources, such as media reports, that might be inaccurate or incomplete.

The third stage of the TUFF methodology involves the systematic implementation of data quality assurance procedures to maximize the reliability and completeness of project records. First, a set of de-duplication procedures is implemented in order to minimize the risk of double counting. Second, to account for the fact that idiosyncratic coding decisions made by individual researchers can result in inconsistencies across project records, a set of automated data checks are undertaken to limit discretion and eliminate illogical and inconsistent codings.84 Third, each project record in the dataset is vetted by a program manager—who oversees the team of research assistants—or a

84 For example, China Development Bank (CDB) offers finance on commercial, rather than concessional, terms, so an automated decision rule disallows CDB finance from ever being categorized as Official Development Assistance (ODA). Likewise, the China Export-Import Bank offers loans and export credits at commercial and concessional rates, but does not offer grants or interest-free loans, so an automated decision rule disallows any project financed by the China Export-Import Bank to ever be categorized as a grant or interest-free loan. 59 senior research assistant appointed by the program manager to identify potential errors, missing data, or incorrect categorizations. Fourth, the dataset then undergoes another layer of review that focuses specifically on projects with low “health of record” scores and large-scale projects (as indicated by the financial value of the transaction).85 Finally, the dataset as a whole is subjected to several rounds of careful scrutiny by AidData staff and external peer reviewers.86 Internal and external reviewers not only seek to identify errors of omission and commission, but also flag inconsistencies that should be addressed and additional sources that should be consulted.87

85 “Health of record” scores are calculated in order to systematically identify projects that might benefit from additional sourcing or investigation. More specifically, for all projects in the dataset, source triangulation and data completeness scores are calculated. Whereas the source triangulation indicator captures the number and diversity of information sources supporting a given project record, the data completeness indicator measures the extent to which fields/variables for a given project record are populated with missing or vague information. 86 More than 30 external and internal reviewers were involved in this process for the version of the dataset used in this paper. Also, feedback provided by users of a dynamic online platform (at china.aiddata.org) is reviewed and, where appropriate, used to update project records. For example, a PhD student helped AidData to vet and augment project records in the Democratic Republic of the Congo with information she directly gathered through extensive fieldwork in that country. In another instance, Chinese Ministry of Health officials and Chinese university faculty identified missing information about in-kind, medical supply donations made during recurring visits from Chinese medical teams. When a credible source of information about these donations was furnished, new project records were added to the dataset. 87 Among other things, these reviewers (a) generate descriptive statistics with the dataset and compare them to official and third-party estimates to identify anomalies or suspicious results; and (b) review individual project records to suggest potential ways to address errors, biases, and gaps. 60

Appendix A2: Background on New TUFF 1.0 Global Dataset In this paper, we introduce a new dataset that measures foreign aid and other forms of concessional and non-concessional state financing from China to the developing world between 2000 and 2014. More specifically, the dataset captures ODA and OOF from China to 140 countries and territories in five regions of the world: Africa, the Middle East, Asia and the Pacific, Latin America and the Caribbean, and Central and Eastern Europe.88 It includes 4,304 Chinese development projects (worth approximately US$351 billion) that were officially committed, in implementation, or completed between 2000 and 2014. The dataset also includes 630 pledges of support worth an estimated US$137 billion. We could not find evidence that these projects reached the official commitment stage, and exclude these records from our analysis.89 Appendix B1 illustrates the distribution of project status over time. Unsurprisingly, projects announced in recent years are less likely to have reached the completion stage than those announced in earlier years.

The dataset was constructed using the TUFF methodology. This methodology was initially developed by several authors of this paper—in collaboration with AidData, a research lab at the College of William & Mary (Strange et al. 2017b). It codifies a set of open-source data collection procedures that make it possible to identify detailed financial, operational, and locational information about officially financed projects that are not voluntarily or systematically recorded by sovereign donors and lenders through international reporting systems, such as the OECD’s Creditor Reporting System (CRS) or the International Aid Transparency Initiative (IATI). This methodology, which is described at greater length in Appendix A1 and Strange et al. (2017b), standardizes and synthesizes large volumes of information primarily from four sources: English, Chinese and local-language news reports; documents from Chinese ministries, embassies, and economic and commercial counselor offices; the aid and debt information management systems of finance and planning ministries in counterpart countries; and case study and field research undertaken by scholars and NGOs. It represents a systematic, transparent, and replicable way of tracking the identifiable universe of projects financed by donors and lenders who do not publish

88 However, Kiribati received only one project and it was cancelled, which is why it does not appear in the above statistic. We also exclude Palestine from our analysis. 89 We also exclude all 44 suspended or cancelled project records from our analysis and from the descriptive statistics presented in this paper. The same applies to 348 so-called “umbrella” projects that reached the commitment stage. Umbrella projects cover a number of specific sub-projects, and we thus remove them to avoid potential double counting. 61 official financing data at the project level. The methodology and the datasets that it has produced have been subjected to peer review, stress-tested, and substantially improved over time (e.g., Muchapondwa et al. 2016; Strange et al. 2017a; Dreher et al. 2018a).

TUFF-derived data have now been used in dozens of publications in economics and political science (e.g., Hendrix and Noland 2014; Dreher and Fuchs 2015; Hsiang and Sekar 2016; Kilama 2016; Hernandez 2017; Li 2017; Dreher et al. 2018a; Eichenauer et al. 2018; Zeitz 2020). The first empirical application of the TUFF methodology was a dataset that measured 21st-century Chinese official financial flows to Africa (Strange et al. 2013, 2017a). This dataset has been used to study China’s motivations for aid giving in Africa and the intended and unintended impacts of these financial flows in one region of the world (BenYishay et al. 2016; Blair and Roessler 2016; Brazys et al. 2017; Hernandez 2017; Strange et al. 2017a; Isaksson and Kotsadam 2018a, 2018b). Apart from the study of contemporary Chinese aid, researchers have adapted and applied our methodology to identify grants and loans from Gulf Cooperation Council (GCC) members (Minor et al. 2014), under-reported humanitarian assistance flows from Western and non-Western sources (Ghose 2017), foreign direct investment from Western and non-Western sources (Bunte et al. 2018), and pre-2000 foreign aid flows from China to Africa (Morgan and Zheng 2019).

The dataset introduced and used in this paper builds upon and expands the geographical and temporal scope of the earlier dataset of Chinese official financial flows to Africa constructed in collaboration with AidData (see Strange et al. 2017a). It allows one to distinguish between three different types of Chinese official financing. “ODA-like” projects are comparable to ODA in that they are nominally intended to promote economic or social development and they are provided at levels of concessionality that are consistent with the ODA criteria established by the OECD-DAC. “OOF-like” projects are also financed by the Chinese government, but either have a non- developmental purpose (e.g., export promotion) or are insufficiently concessional to qualify as ODA (e.g., loans at market rates). “Vague OF” projects represent official financial flows where there is insufficient open-source information to make a clear determination as to whether the flows are more akin to ODA or OOF (Dreher et al. 2018a). Appendix B2 presents the distribution of these three categories of Chinese official financing over time. The graph in the left panel demonstrates that the vast majority of Chinese projects each year are ODA-like. However, as the right panel shows, these projects represent only 21% of total Chinese official commitments in financial terms between 2000 and 2014.

The dataset introduced in this paper can be used to generate an estimate of global, annual Chinese ODA. Our estimate of average annual Chinese ODA (from 2000-2012), which is measured as the sum of all officially committed projects, projects in implementation, and completed projects during this time period, is US$5.4 billion. Therefore, our global estimates of Chinese ODA are quite similar to those produced by Kitano (2016) and the Chinese government itself. China’s 2014 White Paper (State Council 2014) puts total annual foreign aid from China at about US$4.8 billion (US$14.41 billion over 2010-2012). Kitano (2016) arrives at a slightly higher estimate of US$5.2 billion (in 2012). However, it should be noted that neither Kitano (2016) nor the Chinese government separately measures other sources of Chinese official financing (i.e., OOF). A major advantage of our dataset is that it is at the project level, which allows for analysis at various levels of disaggregation.

To analyze the sectoral distribution of Chinese official financing, we also coded the OECD- DAC sector classification (3-digit purpose codes) for all projects. The conventional wisdom that China funds the “hardware” of development is consistent with the descriptive statistics presented in Appendix B3, which ranks sectors by the number of dollars committed. China invests significantly more money in the “hardware” areas of energy generation and supply, transportation, industry, mining, and construction than it does on the “software” side of development in sectors like education, health, and governance. However, a measure of project counts, rather than dollar amounts, paints a very different picture, as can be seen in the same figure. Because the size of “software” projects are substantially smaller than the large “hardware” projects that dominate the news, the measure using project counts actually shows health, education, and governance as the most prominent sectors. These smaller, “software” projects are disproportionately ODA-like, while the large economic infrastructure projects tend to be funded with OOF-like loans.

A nuanced pattern also emerges when one examines the countries that receive the “most” Chinese official financing. The Chinese State Council’s official White Papers from 2011 and 2014 claim that the vast majority of Chinese aid flows to Africa, rather than other regions of the world. This view is reinforced by press accounts (Poplak 2016) and academic sources (Alden et al. 2008; Carmody 2016: ch. 3) that emphasize a new, Chinese-led “scramble for Africa” in the 21st century. This is also what one observes in our global dataset (see Appendix B4). African countries received a large proportion (59 percent) of the total number of projects financed by China between 2000 and 2014. Seven of the top-ten recipient countries are African countries (Appendix B5).

However, a different picture emerges when one counts total dollars, rather than projects, committed. These cross-country rankings reflect the fact that the number of “mega-projects” in Southeast Asia, the former Soviet Union, and Latin America dwarfs the number of “mega-projects” in Africa.90 Of the 25 largest Chinese projects in financial terms, only six are located in Africa and the largest is #13 on that list (see Appendices B6 and B7 for details). More broadly, if one measures the average size of Chinese government-financed projects in terms of constant dollars, only one African country is on the list of top 20 recipients (South Africa at #8; see also Appendix B9).91

In addition to illustrating the broad empirical patterns that emerge from this new dataset, we want to draw attention to several limitations related to missing data that one needs to keep in mind while conducting analysis. First, as described above, because of insufficiently specific information in the underlying data sources, roughly eight percent of the project records and almost 16 percent of the project dollars are coded as “Vague Official Financing.” These projects receive official financing, but it is not possible (based upon the underlying source documentation) to make a clear determination of whether they qualify as ODA or OOF. Therefore, one needs to explicitly account for this uncertainty.92

Second, a substantial and increasing proportion of project records lack information about the financial amounts committed. The percentage of projects that are missing financial amounts ranges from 20 percent in 2001 to 48 percent in 2014 (Appendix B11). Some types of flows are particularly likely to lack financial amounts. For example, over 90 percent of projects that support technical assistance activities and scholarships lack financial amounts. However, loans include financial amounts 92 percent of the time. This missing data problem should be a second-order

90 We define “mega-projects” as those projects whose financial value exceeds US$ 1 billion. 91 Appendix B10 shows the top-ten recipients of total Chinese official financing from 2000-2014. If no country in a particular region is ranked in the top 10, we list the highest ranked country in each region along with its rank and the total amount of Chinese official financing allocated to that country as it appears in the dataset. The most important recipient of Chinese official financing is Russia, followed by Pakistan and Angola. 92 In this paper, we do so by separately analyzing the effects of Chinese ODA and OOF, where we include Vague OF flows in the latter. We think it is reasonable to assume that most Vague OF is actually OOF since many of the observable attributes of projects coded as Vague OF (e.g., projects in the infrastructure and economic production sectors, projects financed with loans, projects financed by China Development Bank and China Exim Bank) resemble the attributes of OOF projects more so than ODA projects. Therefore, comparisons of the effects of Chinese ODA and OOF (including Vague OF) should help reveal differences in the effects of Chinese ODA and OOF. 64 problem for researchers interested in the aggregate effects of aid on outcomes since loans (where financial information is mostly complete) are typically the largest flows, while technical assistance projects and scholarships (where financial information is relatively incomplete) tend to be small- scale flows (Appendix B12).

A third missing data problem is the incomplete coverage of the Chinese government institutions that financed the projects in the dataset. While the dataset identifies dozens of funding agencies within the Chinese government (including various ministries, Chinese embassies, policy banks, and state-owned news agencies), 78 percent of the project records, which reflect 20 percent of the financial value tracked in the dataset, lack information about the main funding agency responsible for the project. To the extent that the effectiveness of Chinese aid is conditional upon variation in the Chinese government institutions that fund aid projects, this is another limitation of the data that must be acknowledged.93

Finally, it should be noted that the TUFF methodology may be subject to some degree of detection bias—in terms of its ability to identify projects and project financial amounts in countries where English is not the official language (Kilby 2017; Dreher et al. 2018a). However, there are also reasons for optimism. For example, Muchapondwa et al. (2016) find a generally high level of correspondence between the Chinese development project data collected using the TUFF methodology and data collected through field-based, on-site data collection protocols by local enumerators in South Africa and Uganda. The same authors also find that the TUFF methodology is able to identify significantly more projects than field-based methods.

We conclude this section with a brief analysis of the determinants of missing information at the project level. Kilby (2017) reports that projects are more likely to include financial amounts if English is among the official languages of a recipient country, in particular in countries with larger internet penetration. For projects that are non-ODA-like, Kilby shows that financial amounts are more likely to be included with larger press freedom of the recipient country. Broadly following his analysis, columns 1-3 of Appendix B17 regress an indicator that takes a value of one for projects with no financial amounts available (and zero for projects including such amounts) on GDP per

93 We are partially able to address this limitation by decomposing Chinese official financing and separately analyzing Chinese ODA and OOF, as MOFCOM is known to provide the bulk of China’s ODA, while OOF projects are often financed by China Exim Bank and China Development Bank. 65 capita growth, (log) per capita GDP, (log) population, an indicator of press freedom (taken from Freedom House, with larger values indicating less freedom), binary indicators for one of five languages being among a recipient country’s official languages, and a (varying) number of fixed effects—for the sector to which the project belongs; flow class (ODA, OOF, or vague OF); and flow type (such as grant, loan, or debt forgiveness). According to the results in column 1, information on financial amounts is more likely to be missing in countries with lower press freedom as well as in countries that include French or Spanish as official language. When we control for fixed effects for world regions in addition in column 2, projects in richer countries are— surprisingly—less likely to include financial information. However, within-region variation in per capita GDP is comparably low, and there is no such finding in regressions with country-fixed effects (see column 3). Most importantly, there is no significant (conditional) correlation between missing financial amounts and the variables we include in our regressions—economic growth and population.

Columns 4-6 replicate the analysis but focus on a binary variable that indicates that the project is “vague” Official Finance, in the sense that we know it is either ODA or OOF, but cannot classify a project in one of the two categories. We find that projects in richer countries are more likely to have precise information with the exception of the specification where we control for region-fixed effects (in columns 4 and 6). There is again no correlation with our variables of interest, however.

In conclusion, the lack of a significant correlation of missing information with our variable of interest in the main analysis—economic growth—in particular, makes any biases resulting from this missing information unlikely to be consequential.

Appendix A3: How Surplus Foreign Exchange Reserves Increase the Supply of Chinese Official Financing

Understanding the principal-agent relationship between China’s State Administration of Foreign Exchange (SAFE) and the public sector institutions that are responsible for China’s overseas lending portfolio helps clarify the process by which surplus foreign exchange reserves increase the supply of Chinese official financing.94 Consider the relationship between SAFE and the country’s single largest source of official financing to other countries—China Development Bank—at a time when SAFE had strong incentives to use the country’s official lending institutions as vehicles for generating higher investment returns. 95 SAFE invested the lion’s share of China’s foreign exchange reserves in US Treasury bonds prior to 2008. However, it changed course in response to the 2008 Global Financial Crisis. Quantitative easing by the US Federal Reserve weakened the US dollar, which in turn weakened the incentive to invest in US Treasury bonds. At the same time, international commodity prices plummeted, creating an opportunity for SAFE to invest the country’s foreign exchange reserves in undervalued overseas assets. SAFE and CDB negotiated a so-called “entrust loan” agreement in 2008, whereby SAFE would act as the principal and CDB would act as its agent. Under this arrangement, SAFE deposited foreign exchange reserves in the CDB and directed it to engage in international on-lending activities on its behalf. CDB, in exchange, collected a commission (equivalent to approximately 10 basis points). CDB’s foreign currency- denominated loans sharply increased in volume from US$64.5 billion in 2008 to US$260 billion in 2014, and by the end of this period roughly two thirds of CDB’s foreign currency-denominated lending was taking place under the auspices of the “entrust loan” agreement with SAFE.96 CDB was also directed by SAFE to focus its international on-lending activities in the natural resource and energy sector, which is clearly evident in our dataset. Nine of the ten largest CDB loans in our dataset are in the natural resource and energy sector; all of these loans were committed between 2008 and 2014; and all of these loans are priced at commercial rates (i.e., 5.5%-7% interest rate or

94 SAFE is responsible for managing China’s foreign exchange in coordination with the People’s Bank of China. 95 Our description of the relationship between SAFE and CDB draws heavily upon Kong and Gallagher (2016). 96 The vast majority of the loans in our dataset are denominated in US dollars. On this point, see Horn et al. (2019: 16-18). 67

London Inter-bank Offered Rate plus a margin), which is consistent with SAFE’s interest in maximizing investment returns.97 This arrangement between CDB and SAFE reflects a broader process in which China’s excess foreign reserves positively correlate with its outward development finance flows.

97 In the wake of the 2008 Global Financial Crisis, Chen Yuan, the President of China Development Bank, 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). 68

Appendix B: Additional tables and figures of Section 1 and Appendix A

Appendix B1: Proportion of Chinese OF projects by status (2000-2014)

100 62 61 54 61 61 64 65 62 64 61 57 56 50 39 49 80

60 22

18 15 7

40 13 16 24 5 4 9 9 9 15 20 20 8 10 10 19 14 21 18 17 17 15 17 19 16 14 16 20 18 19 16 16 14 16 12 13 12 12 13 11 9 8 9 Share of eachstatus in total number of projects 0 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 Pipeline: Pledge Pipeline: Commitment Implementation Completion

Note: This figure illustrates the distribution of project status for all projects in each year of the dataset. The share of completed projects is shaded red; the share of implemented projects is blue; the share of formally committed projects is orange; and the share of informally pledged projects is green. The figure demonstrates that for almost every year in the dataset, the majority of projects have been completed.

Appendix B2: Variation in flow class over time (2000-2014) 80,000 400 60,000 300 200 40,000 Number of of projects Number Amount in millions of 2009 US$ 2009 of millions in Amount 100 20,000 0 0 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14 ODA-like Vague (OF) ODA-like Vague (OF) OOF-like OOF-like

Note: This figure presents the share of projects in terms of their flow class for each year in the dataset. Projects with flow class “ODA-like” are shaded green, whereas “OOF-like” and “Vague (OF)” are shaded blue and orange, respectively. The contrast between the left- and right-hand side of the figure demonstrates how ODA-like projects constitute the bulk of Chinese official finance in terms of the number of unique projects, but that OOF-like projects comprise the majority of financing in terms of dollar value.

Appendix B3: Largest sectors by financial values and project counts

Note: This figure presents the share of projects by sector for each year in the dataset. Sectoral shares defined in terms of financial value are shaded green, while sectoral shares measured by the number of unique projects are shaded orange. The figure makes it apparent that infrastructure- intensive sectors like Energy Generation and Supply and Transport and Storage account for the majority of financial value of Chinese official finance, but other sectors, such as Health, Education, Government and Civil Society, and Emergency Response account for a disproportionate share of unique projects.

Appendix B4: Flow type and flow class (number of projects)

ODA-like 2014 Grant OOF-like 310 Vague (Official Finance)

ODA-like 416 Loan (excluding debt rescheduling) OOF-like 310 Vague (Official Finance) 274

ODA-like 483 Free-standing technical assistance OOF-like 34 Vague (Official Finance)

ODA-like Export credits OOF-like 127 Vague (Official Finance)

ODA-like 75 Debt forgiveness OOF-like Vague (Official Finance)

ODA-like 132 Scholarships/training in the donor country OOF-like 11 Vague (Official Finance)

ODA-like 4 Vague (undetermined) OOF-like 6 Vague (Official Finance) 81

ODA-like 16 Debt rescheduling OOF-like Vague (Official Finance)

ODA-like Strategic/Supplier Credit OOF-like 11 Vague (Official Finance)

0 500 1,000 1,500 2,000 Number of projects

Note: This figure presents the share of projects in terms of flow type and flow class. As reflected in the figure, most projects where the flow type is “Grant” or “Free-standing technical assistance” qualify as ODA-like financing. The opposite is true for “Export credits,” all of which have a flow class of OOF-like. In contrast, loan-based financing contains a mix of ODA-like and OOF-like projects.

Appendix B5: World regions Total official financing ODA-like Rank World region mil. US$ # mil. US$ # 1 Africa 118,074 34% 2,345 54% 46,052 58% 1,855 59% 2 Central/East. Eur. 56,718 16% 171 4% 2,751 3% 62 2% 3 Latin America 53,389 15% 317 7% 9,877 12% 165 5% 4 South Asia 48,763 14% 423 10% 7,987 10% 294 9% 5 Southeast Asia 39,237 11% 507 12% 5,951 7% 362 12% 6 Central/North Asia 28,491 8% 183 4% 4,391 6% 109 3% 7 Middle East 3,083 1% 93 2% 409 1% 66 2% 8 The Pacific 2,813 1% 265 6% 2,157 3% 227 7% Note: “mil. US$” refers to Chinese official finance and ODA-like financial commitments (2009 US$ millions), followed by a region’s share of the total value. “#” refers to a region’s number of official finance and ODA-like projects, also followed by its share of the total.

Appendix B6: Largest recipient countries (number of projects) Rank Recipient country World region # 1 Cambodia Southeast Asia 168 2 Pakistan South Asia 121 3 Zimbabwe Africa 120 4 Angola Africa 110 5 Sudan Africa 108 6 Tanzania Africa 101 7 Ghana Africa 95 8 Kenya Africa 89 9 Ethiopia Africa 88 10 Sri Lanka South Asia 86 … 18 Papua New Guinea The Pacific 68 … 43 Belarus Central and Eastern Europe 33 43 Kyrgyz Republic Central and North Asia 33 43 Uzbekistan Central and North Asia 33 … 49 Bolivia Latin America and the Caribbean 31 … 64 Yemen Middle East 24 … 136 Sao Tome & Principe Africa 1 136 United Arab Emirates Middle East 1 136 Australia The Pacific 1

Appendix B7: Largest 25 officially-financed Chinese projects by financial amount (in millions of constant 2009 US$) Rank Recipient country Year Title (shortened) Flow class Flow type Amount 1 Russia 2009 Rosneft takes out loan from China Development Bank OOF-like Loan 20,356 2 Russia 2009 China Development Bank to offer loans totaling 25 billion USD in to Russian Rosneft and Transneft OOF-like Loan 13,571 3 Laos 2012 EXIM Bank loan for construction of Kunming-Vientiane high-speed railway link OOF-like Loan 7,625 4 Cuba 2011 China forgives US$ 6 billion worth of Cuban Debt ODA-like Debt forgiveness 6,660 5 Turkmenistan 2009 China Provides 4 Billion USD for South Yolotan—Osman Field Development OOF-like Loan 5,428 6 Turkmenistan 2011 China Provides 4.1 Billion USD for Ioujno-Elotenshoie Field Development OOF-like Loan 4,551 7 Venezuela 2011 ICBC loans Venezuela oil firm 4 billion USD for construction of housing projects OOF-like Loan 4,440 8 Brazil 2010 China Development Bank extends $3.5 billion USD loan to Petrobras from $5 billion line of credit OOF-like Loan 4,402 9 Venezuela 2013 CDB funds $4 billion PDVSA and CNPC joint venture Sinovensa in Orinoco belt OOF-like Loan 4,087 10 Pakistan 2014 Part III: China's financial package loan includes preferential buyer credit for Karachi Nuclear Power Plant OOF-like Export credits 4,001 11 Ukraine 2012 China EXIM Bank agrees USD3B for Ukraine Agricultural Projects OOF-like Loan 3,177 12 Belarus 2013 China Exim Bank and CDB loan 3 billion USD in total for China-Belarus Industrial Park OOF-like Loan 3,050 13 Ethiopia 2013 Chinese Banks Loan 3.3 Billion USD for Addis Ababa-Djibouti Railway Project Vague (OF) Loan 2,847 14 Bahamas 2011 China EXIM Bank loans $2.45 billion to Bahamas for the Baha Mar Resort OOF-like Loan 2,719 15 Ethiopia 2011 China loans 2,400 million USD for Rail Line From Sebeta to Adama in Ethiopia Vague (OF) Loan 2,664 16 Pakistan 2014 China pledges loan of 233.4177 billion rupees to Pakistan for Karachi-Lahore highway Vague (OF) Loan 2,309 17 Pakistan 2014 Part II: China's financial package loan includes buyer credit for Karachi Nuclear Power Plant OOF-like Export credits 2,250 18 South Africa 2013 ICBC signs funding support agreement for South African renewable energy projects OOF-like Loan 2,237 19 Angola 2011 CDB loans $2 billion USD to oil company Sonangol in Angola OOF-like Loan 2,220 20 Ecuador 2011 Ecuador Signs $2B loan with CDB for renewable energy purposes OOF-like Loan 2,220 21 Iran 2014 CMC and SUPOWER signed agreement on the railway electrification program Vague (OF) Loan 2,143 22 Cote d‘Ivoire 2012 Chinese company building railway in Ivory Coast from Man to San Pedro ODA-like Loan 2,118 23 Argentina 2014 China commits 2.1 Billion USD loan for rehabilitation of Belgrano Cargas railway OOF-like Loan 2,100 24 Angola 2014 CDB provided $2 billion USD loan to Sonangol OOF-like Loan 2,000 25 Pakistan 2011 Loans from Silk Road Fund, EXIM, and CDB for Korrak hydropower project/ Korat Dam in Pakistan OOF-like Loan 1,831

Appendix B8: Largest 25 development projects by sector (in millions of constant 2009 US$)

Sector (in alphabetic order) Recipient Year Title (shortened) Flow class Flow type Amount

Action Relating to Debt Cuba 2011 China forgives US$ 6 billion worth of Cuban Debt ODA-like Debt forgiveness 6,660

Agriculture, Forestry and Fishing Ukraine 2012 China EXIM Bank agrees USD3B for Ukraine Agricultural Projects OOF-like Loan 3,177

Banking and Financial Services South Africa 2009 Chinese banks sign $1 billion loan facility for South Africa's Standard OOF-like Loan 1,357 Bank

Business and Other Services Belarus 2013 China Exim Bank and CDB loan for China-Belarus Industrial Park OOF-like Loan 3,050

Communications India 2010 Reliance Industries in India Ordered Equipment from Shanghai Electric OOF-like Loan 1,383

Developmental Food Aid etc. Somalia 2011 China Grants 16 million USD for Humanitarian Interventions in Somalia ODA-like Grant 18

Education Angola 2006 China constructs several institutes in Angola for $93.2 million OOF-like Loan 171

Emergency Response Pakistan 2007 Grant for repatriation of Afghan refugees from Pakistan ODA-like Grant 651

Energy Generation and Supply Russia 2009 Rosneft takes out loan from China Development Bank OOF-like Loan 20,356

General Budget Support Sudan 2012 $1.5 billion loan from China Development Bank OOF-like Loan 1,589

General Environmental Protection Jamaica 2010 China ExIm bank loans Jamaica to repair and protect the shoreline of Vague (OF) Loan 73 Palisadoes

Government and Civil Society Ecuador 2012 China commits a loan of $240 million to Ecuador to set up security Vague (OF) Loan 254 service ECU 911

Health Trinidad & Tobago 2013 Trinidad and Tobago Children's Hospital with concessional loan OOF-like Loan 153

Industry, Mining, Construction Turkmenistan 2009 China Provides 4 Billion USD for South Yolotan, Osman Field OOF-like Loan 5,428 Development

Non-food commodity assistance Pakistan 2010 China donates 40000 wheel chairs to Pakistani charity Bait ul Maal ODA-like Grant 37

Other Multisector Ecuador 2011 Ecuador Signs $2B loan with CDB for renewable energy purposes OOF-like Loan 2,220

Other Social infrastructure and services Venezuela 2011 ICBC loans Venezuela oil firm 4 billion USD for construction of housing OOF-like Loan 4,440 projects

Population Policies etc. Zimbabwe 2012 China provides 4.5 million RMB loan for neonatal equipment in Vague (OF) Loan 1 Zimbabwe

Support to NGOs and GOs Zimbabwe 2010 Zimbabwe miners' association received 10 million USD grant from China ODA-like Grant 13

Trade and Tourism Bahamas 2011 China EXIM Bank loans $2.45 billion to Bahamas for the Baha Mar OOF-like Loan 2,719 Resort

Transport and Storage Laos 2012 EXIM Bank loan for construction of Kunming-Vientiane high-speed OOF-like Loan 7,625 railway link

Unallocated / Unspecified Ethiopia 2006 China loans Ethiopia 500 million USD for unspecified development Vague (OF) Loan 920 projects

Water Supply and Sanitation Cameroon 2009 China loans 366 billion CFA to Cameroon for water distribution project ODA-like Loan 1,052

Women in Development Chad 2012 Grant to Construct Women's Center ODA-like Grant 12

Appendix B9: Average project size by country (in millions of constant 2009 US$) Rank Recipient country Amount 1 Russia 3,052 2 Turkmenistan 1,525 3 Cuba 1,356 4 Brazil 1,218 5 Venezuela 1,122 6 India 796 7 Argentina 773 8 South Africa 628 9 Ecuador 622 11 Kazakhstan 591 11 Iran 430 12 Bahamas 360 13 Montenegro 340 14 Ukraine 314 15 Turkey 301 16 Bosnia-Herzegovina 287 17 Belarus 283 18 Chile 279 19 Pakistan 276 20 Laos 267

… … …

Note: Reports average project size (in millions of 2009 US$) per country over the years 2000-2014 (excluding projects with no information on amount).

Appendix B10: Largest recipient countries (financial values, in millions of constant 2009 US$) Rank Recipient country World region Amount 1 Russia Central and Eastern Europe 36,623 2 Pakistan South Asia 24,325 3 Angola Africa 16,556 4 Ethiopia Africa 14,834 5 Sri Lanka South Asia 12,680 6 Laos Southeast Asia 12,016 7 Venezuela Latin America and the Caribbean 11,219 8 Turkmenistan Central and North Asia 10,676 9 Sudan Africa 10,237 10 Ecuador Latin America and the Caribbean 9,953 … 37 Iran Middle East 2,148 … 58 Fiji The Pacific 1,039 … Note: The table shows the top-ten recipients of total Chinese official financing from 2000-2014. If no country in a particular region is ranked in the top 10, we list the highest ranked country in each region along with its rank and the total amount of Chinese official finance allocated to that country as it appears in the dataset.

Appendix B11: Share of projects with unknown financial amounts by year (2000-2014)

.5 0.48 0.45 0.43 0.43 0.42 0.41

.4 0.37 0.38 0.35 0.33 0.33 0.33 0.30

.3 0.27

0.20 .2 Share of projectswith unknown size .1 0 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09 '10 '11 '12 '13 '14

Appendix B12: Average project size by flow type and share of missing financial values Average project size Missing financial amount Rank Flow type (in US$ million) (share of projects in %) 1 Export credits 333 6 2 Loan (excluding debt rescheduling) 304 8 3 Strategic/Supplier Credit 206 0 4 Debt forgiveness 175 12 5 Debt rescheduling 44 25 6 Vague (undetermined) 41 34 7 Grant 9 40 8 Free-standing technical assistance 8 92 9 Scholarships/training in the donor country 1 92

Appendix B13: List of countries included in the allocation analysis Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, Colombia, Comoros, Congo, Dem. Rep., Congo, Rep., Costa Rica, Cote d'Ivoire, Croatia, Cuba, Czech Republic, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Gabon, Gambia, The, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, India, Indonesia, Iran, Islamic Rep., Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kosovo, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Macedonia, FYR, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Fed. Sts., Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Romania, Russian Federation, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Slovak Republic, Solomon Islands, South Africa, South Sudan, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Swaziland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, Uruguay, Uzbekistan, Vanuatu, Venezuela, RB, Vietnam, Yemen, Rep., Zambia, Zimbabwe.

Note: The country list refers to the estimation sample used in column 1 of Table 1.

Appendix B14: Descriptive statistics (allocation) Variable Obs Mean Std. Dev. Min Max Number of OF projects (t-2) 1,666 2.19 3.20 0.00 35.00 Input factors (t-1) 1,664 -0.22 0.91 -1.64 1.26 Reserves (t-1) 1,664 0.11 0.16 -0.12 0.32 Probability of receiving OF, historic 1,666 0.20 0.22 0.00 0.80 Probability of receiving OF, contemp. 1,666 0.61 0.33 0.00 1.00 UNGA voting alignment 1,666 0.96 0.07 0.54 1.00 Diplomatic relations Taiwan 1,666 0.12 0.33 0.00 1.00 Trade with China (log) 1,666 20.01 2.31 12.04 25.32 Petroleum exporter 1,666 0.51 0.50 0.00 1.00 Government debt (% of GDP) 1,666 56.08 45.30 1.98 487.45 Polity Score 1,666 12.87 5.84 0.00 20.00 GDP per capita (log) 1,666 7.40 1.13 4.81 9.62 Population (log) 1,666 16.14 1.52 12.90 20.97 English is official language 1,666 0.26 0.44 0.00 1.00

Note: Reports descriptive statistics for the estimation sample used in column 1 of Table 1.

Appendix B15: Largest projects (in terms of millions of constant 2009 US$) in the six most prominent sectors (in terms of project numbers) Sector Recipient Year Title Amount Education Angola 2006 China constructs several institutes in Angola for $93.2 million 171 Angola 2006 China constructs institutes and schools for $67.3 million in Angola 124 Angola 2005 China constructs technical schools for $58.9 million in Angola 116 North Korea 2000 China provides 400 million yuan grant to North Korea for school uniforms 112 Gabon 2013 China funds construction of three vocational schools in Gabon 104 Emergency Response Pakistan 2007 Grant for repatriation of Afghan refugees from Pakistan 651 Pakistan 2006 China EXIM Commits $300M USD to Pakistan for Earthquake Relief 552 Senegal 2012 China implements security project in Senegal 53 Sierra Leone 2014 [EBOLA] China donates $48M to Bo City Council 48 Sri Lanka 2005 166 million RMB cash grant for Tsunami relief 40 Energy Generation and Russia 2009 Roseneft takes out loan of 10 billion USD out of available 15 from China Development Bank 20356 Supply Russia 2009 China Development Bank to offer loans totaling 25 billion USD in to Russian Roseneft and Transneft 13571 Brazil 2010 China Development Bank extends $3.5 billion USD loan to Petrobras from $5 billion line of credit 4402 Venezuela 2013 CDB funds $4 billion PDVSA and CNPC joint venture Sinovensa in Orinoco belt 4087 Pakistan 2014 Part III: China's financial package loan includes preferential buyer credit of $4.001 billion USD to Pakistan for Karachi Nuclear 4001 Power Plant's K-2/K-3 Government and Civil Ecuador 2012 China commits a loan of $240 million to Ecuador to set up security service ECU 911 254 Society Fiji 2007 China loans $150 million USD to Fijian government after coup 244 Mongolia 2012 China EXIM Bank Provides 200 Million USD Loan to Mongolian Development Bank for construction of apartments for civil 212 servants Senegal 2005 China assists Senegal in building e-government network 196 Zimbabwe 2010 China pledges to fund new Zimbabwe parliament building 182 Health Trinidad & 2013 China completed the construction of Trinidad and Tobago Children's Hospital with concessional loan of $950 million 153 Tobago Venezuela 2014 The China-Venezuela Fund delivers $127 mln for Surgical Equipment 127 Angola 2007 Complementary Action (#34030): Four Regional Hospitals 124 Kenya 2011 China provides a $9.5 Billion KES concessional loan for the construction of Kenyatta University Teaching, Research and 123 Referral Hospital Niger 2009 China Aids to build the Medical Hospital 115 Transport and Storage Laos 2012 EXIM Bank loan for construction of Kunming-Vientiane high-speed railway link 7625 Ethiopia 2013 Chinese Banks Loan 3.3 Billion USD for Addis Ababa-Djibouti Railway Project 2847 Ethiopia 2011 China loans 2,400 million USD for Rail Line From Sebeta to Adama in Ethiopia 2664 Pakistan 2014 China pledges loan of 233.4177 billion rupees to Pakistan for Karachi-Lahore highway 2309 Iran 2014 CMC and SUPOWER signed agreement on the railway electrification program 2143

Note: We first select the six sectors that have the largest number of projects based on Appendix B3. Then we list here the five projects with the largest commitment amounts in terms of millions of constant 2009 US dollars.

Appendix B16: Share of each broad sector in Chinese development finance by flow class (2000- 2014)

Notes: The upper panel displays the share of each broad sector in the total number of Chinese development projects by flow class. The lower panel displays the share of each broad sector in the total financial value of Chinese development projects by flow class. The “Social Infrastructure & Services” category includes health, education, governance, and water supply and sanitation projects; the “Economic Infrastructure & Services” category includes transportation projects (e.g., roads, railways, and airports), energy production and distribution projects, and information and communication technology (ICT) projects (e.g., broadband internet and mobile phone infrastructure); and the “Production Sector” category includes agriculture, fishing, forestry, mining, industry, trade, and tourism projects.

Appendix B17: Project-level analysis of missing values and socio-economic characteristics

(1) (2) (3) (4) (5) (6) 1 if no financial information 1 if flow class is vague GDP p.c. growth 0.000 0.001 0.001 -0.001 -0.001 -0.002 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (log) GDP p.c. 0.017 0.021 -0.007 -0.011 -0.009 -0.068 (0.01) (0.01) (0.07) (0.01) (0.01) (0.04) (log) Population 0.003 -0.005 0.100 -0.000 -0.000 -0.061 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Less press freedom 0.001 0.001 0.003 0.000 0.000 -0.000 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

French language 0.072 0.047 -0.010 -0.018

(0.02) (0.02) (0.02) (0.02)

English language -0.005 -0.025 -0.002 -0.013

(0.02) (0.02) (0.01) (0.02)

Portuguese language 0.039 0.004 -0.070 -0.084 (0.04) (0.04) (0.04) (0.05)

Spanish language 0.085 0.097 0.005 0.007

(0.05) (0.05) (0.04) (0.05)

Arabic language 0.020 -0.008 0.049 0.034

(0.02) (0.02) (0.02) (0.02)

Number of observations 4114 4114 4150 4114 4114 4150

Notes: Each column represents one regression. The unit of observation is the project level. The dependent variable in columns 1-3 is a binary indicator that takes a value of one if the project record lacks financial information. The dependent variable in columns 4-6 is a binary indicator that takes a value of one if the project is coded as “Vague (Official Finance).” The variables of interest are Growth p.c.; (log) Population; the recipient country’s logged per-capita income ((log) GDP p.c.); the Freedom House index Freedom of the Press (Less press freedom), where larger values indicate less press freedom; and binary variables that take a value of one if the recipient country has one of the following languages as official language: French, English, Spanish, Portuguese, and Arabic. Columns 2 and 5 control for region-fixed effects. Columns 3 and 6 account for country-fixed effects. All regressions include sector- , flow-type, and year-fixed effects; columns 1-3 further include flow-class-fixed effects (see Appendix B4 for flow-type and flow-class, and B5 for regions used here; sector refers to three-digit CRS codes). Standard errors are clustered by recipient country and reported in parentheses.

Appendix B18: Share of Official Development Assistance and Sector-specific Assistance in total Official Finance by Country (2000-2014) (1) (2) (3) (4) (5) (6) (7) (8) Country ODA ODA Social Social Economic Economic Production Production share share share share share share share share per total per total per total per total per total per total per total per total number OF number OF number OF number OF of OF amount of OF amount of OF amount of OF amount projects projects projects projects Haiti 100% 100% 0% 0% 0% 0% 0% 0% Armenia 100% 100% 0% 0% 0% 0% 33% 11% Somalia 100% 100% 0% 0% 50% 4% 0% 0% Cook Islands 100% 100% 0% 0% 71% 85% 14% 0% Grenada 100% 100% 0% 0% 67% 99% 17% 1% Nauru 100% 100% 0% 0% 0% 0% 0% 0% St. Lucia 100% 100% 0% 0% 67% 100% 33% 0% Antigua & Barbuda 100% 100% 50% 0% 50% 3% 0% 0% Timor-Leste 100% 100% 3% 0% 64% 100% 15% 0% Nicaragua 100% 100% 50% 0% 0% 0% 50% 16% Iraq 100% 100% 0% 0% 50% 0% 0% 0% Sao Tome & Principe 100% 0% 100% 0% Niue 100% 100% 50% 0% 50% 100% 0% 0% Niger 97% 92% 10% 0% 53% 14% 14% 0% Papua New Guinea 96% 88% 4% 0% 71% 44% 15% 48% Seychelles 95% 100% 5% 0% 41% 18% 0% 0% Samoa 95% 70% 19% 0% 52% 72% 5% 10% Tonga 95% 100% 19% 0% 50% 5% 17% 33% Lesotho 95% 99% 17% 0% 59% 27% 5% 3% Afghanistan 95% 89% 5% 0% 55% 31% 0% 0% Burundi 94% 91% 18% 0% 48% 12% 2% 0% Micronesia 93% 100% 21% 0% 46% 35% 14% 0% Uganda 93% 76% 14% 0% 62% 23% 11% 6% Central African Rep. 93% 48% 15% 0% 63% 25% 7% 0% Botswana 91% 16% 14% 0% 71% 7% 3% 0% Rwanda 91% 99% 10% 0% 59% 9% 14% 4% Jordan 91% 62% 9% 0% 50% 35% 9% 13% Mauritius 90% 45% 22% 0% 70% 18% 3% 0% Sierra Leone 90% 37% 16% 0% 48% 6% 10% 8% Cape Verde 89% 92% 5% 0% 84% 89% 5% 11% Liberia 89% 66% 9% 0% 60% 68% 13% 3% Mauritania 89% 99% 14% 0% 46% 2% 14% 1% Comoros 88% 100% 15% 0% 69% 16% 4% 0% Eritrea 88% 80% 8% 0% 54% 4% 12% 74% Congo, Dem. Rep. 88% 26% 21% 0% 61% 5% 6% 4% Lebanon 88% 100% 13% 0% 69% 9% 13% 67% Togo 88% 29% 13% 0% 55% 8% 10% 5% Tunisia 86% 85% 14% 0% 64% 80% 9% 0% Congo, Rep. 86% 75% 20% 0% 54% 4% 5% 8% Guinea-Bissau 86% 99% 4% 0% 54% 61% 11% 0% Guinea 86% 34% 20% 0% 66% 19% 3% 1% Vanuatu 86% 95% 14% 0% 71% 46% 3% 2% Namibia 85% 88% 11% 0% 61% 25% 5% 0% Maldives 85% 10% 12% 0% 47% 32% 0% 0% 86

Madagascar 85% 48% 7% 0% 59% 0% 7% 0% South Sudan 85% 24% 15% 0% 30% 1% 11% 11% Tanzania 85% 84% 16% 0% 60% 8% 7% 36% Gabon 85% 68% 15% 0% 62% 29% 15% 18% Malawi 84% 48% 16% 0% 69% 63% 6% 0% Zimbabwe 84% 59% 21% 0% 47% 25% 15% 28% Mozambique 84% 85% 14% 0% 38% 9% 20% 6% Iran 83% 0% 17% 0% 0% 0% 0% 0% Dominica 83% 69% 6% 0% 67% 45% 11% 33% Nepal 83% 94% 17% 0% 59% 15% 4% 2% Zambia 81% 34% 27% 0% 54% 9% 5% 4% Guyana 81% 85% 29% 0% 43% 3% 14% 5% Mali 80% 45% 14% 0% 53% 8% 6% 1% Cuba 80% 99% 0% 0% 20% 1% 0% 0% Benin 80% 36% 12% 0% 56% 22% 8% 8% Senegal 79% 48% 14% 0% 59% 47% 10% 12% Djibouti 79% 7% 25% 0% 58% 3% 0% 0% Cameroon 79% 62% 22% 0% 51% 37% 8% 3% Cote D'Ivoire 79% 90% 9% 0% 55% 11% 18% 23% Kyrgyz Republic 79% 56% 48% 0% 21% 1% 3% 5% Cambodia 79% 35% 32% 0% 37% 2% 14% 6% Mongolia 79% 44% 11% 0% 39% 21% 18% 34% Ghana 78% 49% 22% 0% 56% 19% 6% 3% Fiji 78% 52% 24% 0% 40% 29% 11% 2% Syria 78% 100% 11% 0% 44% 0% 0% 0% Thailand 76% 92% 0% 0% 47% 11% 0% 0% Ethiopia 76% 24% 31% 0% 35% 2% 6% 1% Chad 75% 75% 25% 0% 55% 0% 5% 13% Azerbaijan 75% 63% 0% 0% 25% 9% 50% 54% Kenya 74% 28% 29% 0% 45% 10% 7% 0% Myanmar 74% 38% 18% 0% 36% 1% 10% 40% Moldova 73% 16% 9% 0% 36% 5% 9% 5% Philippines 72% 17% 17% 0% 22% 17% 33% 19% Algeria 71% 100% 14% 0% 57% 100% 29% 0% Colombia 71% 1% 10% 0% 52% 0% 10% 94% Georgia 71% 95% 0% 0% 50% 23% 7% 18% Costa Rica 71% 59% 29% 0% 47% 14% 6% 0% Macedonia, FYR 70% 75% 30% 0% 60% 2% 0% 0% Bangladesh 70% 32% 25% 0% 38% 17% 18% 34% Indonesia 68% 9% 38% 0% 19% 0% 1% 0% Tajikistan 67% 40% 59% 0% 26% 1% 4% 7% Malaysia 67% 0% 17% 0% 17% 0% 0% 0% Yemen 67% 39% 13% 0% 38% 11% 29% 59% Pakistan 66% 10% 29% 0% 22% 1% 4% 0% Laos 66% 6% 26% 0% 36% 3% 16% 5% Uruguay 64% 9% 36% 0% 27% 4% 0% 0% Bolivia 61% 35% 35% 0% 26% 0% 10% 16% Bosnia-Herzegovina 60% 1% 40% 0% 40% 1% 20% 0% Equatorial Guinea 60% 11% 47% 0% 20% 0% 13% 66% Nigeria 59% 43% 37% 0% 41% 1% 10% 3% Peru 59% 6% 23% 0% 55% 7% 9% 0% Jamaica 59% 38% 9% 0% 64% 21% 14% 14% Serbia 57% 50% 29% 0% 43% 0% 21% 33% 87

Viet Nam 54% 8% 31% 0% 40% 2% 17% 18% Korea, Dem. Rep. 54% 100% 23% 0% 46% 47% 8% 26% Sri Lanka 51% 22% 45% 0% 28% 14% 5% 3% South Africa 50% 2% 13% 0% 58% 1% 21% 17% Chile 50% 1% 50% 0% 0% 0% 0% 0% Suriname 50% 3% 42% 0% 33% 3% 17% 13% Libya 50% 1% 25% 0% 25% 0% 0% 0% Sudan 49% 15% 28% 0% 40% 6% 13% 2% Morocco 45% 1% 18% 0% 41% 4% 9% 0% Ukraine 45% 2% 10% 0% 50% 0% 5% 84% Egypt 41% 5% 18% 0% 64% 4% 14% 40% Albania 40% 2% 10% 0% 65% 1% 10% 1% Turkmenistan 38% 6% 50% 0% 0% 0% 50% 99% Ecuador 36% 0% 41% 0% 36% 3% 5% 2% Belarus 36% 2% 45% 0% 27% 1% 12% 12% Turkey 36% 0% 27% 0% 55% 0% 0% 0% Uzbekistan 33% 16% 36% 0% 24% 4% 27% 24% India 33% 0% 53% 0% 27% 0% 0% 0% Angola 31% 6% 37% 0% 44% 12% 12% 6% Mexico 29% 2% 14% 0% 57% 0% 0% 0% Montenegro 25% 89% 75% 0% 25% 0% 0% 0% Kazakhstan 18% 0% 29% 0% 24% 0% 41% 69% Russia 15% 0% 38% 0% 8% 0% 31% 2% Brazil 7% 0% 33% 0% 53% 0% 7% 2% Venezuela 4% 0% 39% 0% 13% 40% 48% 14% Romania 0% 0% 29% 0% 57% 0% 7% 58% Argentina 0% 0% 100% 0% 0% 0% 0% 0% Bulgaria 0% 0% 0% 0% 70% 4% 0% 0%

Note: Column 1 shows the share of ODA projects in all projects. Column 2 shows the share of ODA amounts in all amounts. Columns 3-8 show the share of sector-specific ODA-project numbers in all projects. The “Social Infrastructure & Services” category includes health, education, governance, and water supply and sanitation projects; the “Economic Infrastructure & Services” category includes transportation projects (e.g., roads, railways, and airports), energy production and distribution projects, and information and communication technology (ICT) projects (e.g., broadband internet and mobile phone infrastructure); and the “Production Sector” category includes agriculture, fishing, forestry, mining, industry, trade, and tourism projects.

Appendix C: Additional tables and figures in Section 2

Appendix C1: List of countries included in the effectiveness analysis Afghanistan, Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Burkina Faso, Burundi, Cabo Verde, Cambodia, Cameroon, Central African Republic, Chad, Chile, Colombia, Comoros, Congo, Dem. Rep., Congo, Rep., Costa Rica, Cote d'Ivoire, Croatia, Cuba, Czech Republic, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, Arab Rep., El Salvador, Equatorial Guinea, Eritrea, Estonia, Ethiopia, Fiji, Gabon, Gambia, The, Georgia, Ghana, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, India, Indonesia, Iran, Islamic Rep., Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kosovo, Kyrgyz Republic, Lao PDR, Latvia, Lebanon, Lesotho, Liberia, Libya, Lithuania, Macedonia, FYR, Madagascar, Malawi, Malaysia, Maldives, Mali, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Fed. Sts., Moldova, Mongolia, Montenegro, Morocco, Mozambique, Myanmar, Namibia, Nauru, Nepal, Nicaragua, Niger, Nigeria, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Romania, Russian Federation, Rwanda, Samoa, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Slovak Republic, Solomon Islands, South Africa, South Sudan, Sri Lanka, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Sudan, Suriname, Swaziland, Syrian Arab Republic, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Tuvalu, Uganda, Ukraine, Uruguay, Uzbekistan, Vanuatu, Venezuela, RB, Vietnam, Yemen, Rep., Zambia, Zimbabwe.

Note: The country list refers to the estimation sample used in column 1 of Table 2.

Appendix C2: Descriptive statistics (effectiveness) Variable Obs Mean Std. Dev. Min Max Growth p.c. 2,061 2.85 5.15 -62.23 50.12 Number of OF projects (t-2) 2,061 1.97 3.04 0.00 35.00 Number of OOF/vague projects (t-2) 2,061 1.47 2.33 0.00 24.00 Number of Chinese ODA projects (t-2) 2,061 0.51 1.35 0.00 31.00 (log) Chinese OF amounts (t-2) 2,061 7.93 8.71 0.00 24.06 (log) Chinese OOF/vague amounts (t-2) 2,061 3.68 7.41 0.00 24.06 (log) Chinese ODA amounts (t-2) 2,061 6.31 8.01 0.00 22.39 (log) Population (t-1) 2,061 15.55 2.12 9.16 20.99 Assassinations (t-1) 2,046 0.15 0.93 -1.00 26.00 Government surplus (% of GDP, t-1) 1,612 -1.47 17.68 -206.79 216.63 Inflation (t-1) 1,896 0.73 2.05 -62.73 19.52 Money/GDP (t-1) 1,931 66.66 582.21 -27.78 18347.09 Trade openness (t-1) 1,994 84.47 39.54 0.17 393.38 Note: Reports descriptive statistics for the estimation sample used in column 1 of Table 2. We added a value of one before taking logs of Chinese official finance, in order to not lose observations with zero aid. Descriptive statistics for the IVs can be found in Appendix B14.

Appendix C3: Growth effects of Chinese official finance (without 2007/2008)

Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability -1.401 0.752 -2.097 -1.686 1.372 -2.508 (2.92) (3.67) (2.88) (3.03) (3.57) (3.13) Input (t-3)*probability 1.567 0.693 1.924 1.631 0.678 2.063 (0.57) (0.88) (0.55) (0.60) (0.96) (0.63) (log) Population (t-1) 4.548 5.769 3.621 4.645 5.655 3.967 (3.02) (2.91) (3.07) (3.04) (2.92) (3.09)

Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.973 0.830 1.479 1.084 0.140 1.141 (0.36) (0.82) (0.50) (0.69) (0.13) (0.60) (log) Population (t-1) 1.290 5.563 -0.500 -0.600 4.526 -0.494 (3.51) (2.92) (3.82) (8.23) (3.21) (6.82)

Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 3.01 -0.253 3.392 3.641 17.151 3.903 (1.76) (2.19) (1.35) (4.38) (9.54) (4.73) Input (t-3)*probability 0.849 1.012 0.435 0.576 3.554 0.736 (0.33) (0.42) (0.27) (0.71) (1.67) (0.78) (log) Population (t-1) 3.203 0.259 2.626 4.727 8.018 3.715 (1.34) (0.55) (1.09) (5.26) (4.81) (4.20)

Number of countries 150 150 150 150 150 150 Number of observations 1782 1782 1782 1782 1782 1782 Cragg-Donald F 33.51 42.04 33.01 2.36 35.98 3.72 Kleibergen-Paap F 35.34 27.33 30.29 2.71 17.48 3.97 Hansen J statistic (p-value) 0.17 0.81 0.04 0.24 0.79 0.21

Notes: Excludes the years 2007 and 2008. Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Appendix C4: Growth effects of Chinese aid, alternative instruments (2002-2016)

Table A: Foreign-exchange reserves only

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 6.437 4.464 7.404 6.452 4.969 7.651 (2.55) (4.00) (2.58) (2.95) (4.47) (3.12) (log) Population (t-1) 5.111 6.339 4.231 5.229 6.226 4.592 (3.02) (2.92) (3.07) (3.04) (2.93) (3.09)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.886 0.954 1.336 0.932 0.143 1.015 (0.34) (0.84) (0.46) (0.59) (0.12) (0.56) (log) Population (t-1) 2.420 6.364 0.541 -0.050 5.105 -0.472 (3.40) (2.91) (3.70) (7.71) (3.21) (6.86)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 7.261 4.68 5.543 6.925 34.829 7.539 (0.86) (0.68) (0.70) (2.91) (6.07) (2.84) (log) Population (t-1) 3.036 -0.026 2.762 5.665 7.853 4.991 (1.38) (0.64) (1.05) (5.26) (4.76) (4.20)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 59.44 43.97 65.10 5.19 68.67 7.05 Kleibergen-Paap F 72.02 47.33 61.83 5.65 32.87 7.07

Notes: Uses changes in foreign reserves interacted with the probability of receiving official financing as instrument. Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Table B: Physical project inputs only

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Input (t-3)*probability 1.298 0.801 1.489 1.301 0.882 1.527 (0.42) (0.71) (0.42) (0.49) (0.79) (0.51) (log) Population (t-1) 5.128 6.361 4.272 5.242 6.269 4.640 (2.97) (2.92) (2.98) (2.99) (2.92) (3.01)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.973 0.787 1.579 1.022 0.142 1.125 (0.33) (0.71) (0.49) (0.56) (0.12) (0.55) (log) Population (t-1) 2.020 6.388 -0.542 -0.681 5.112 -1.227 (3.37) (2.89) (3.80) (7.83) (3.15) (6.95)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Input (t-3)*probability 1.333 1.018 0.943 1.273 6.212 1.358 (0.16) (0.17) (0.13) (0.47) (1.04) (0.46) (log) Population (t-1) 3.193 -0.034 3.05 5.799 8.147 5.217 (1.39) (0.65) (1.05) (5.22) (4.79) (4.10)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 67.47 69.05 63.94 5.85 71.54 7.69 Kleibergen-Paap F 72.24 36.88 55.67 7.32 35.39 8.65 Notes: Uses the first factor of six construction materials interacted with the probability of receiving official financing as instrument. Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Table C: Both IVs with historical probabilities

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 4.139 4.139 4.139 4.139 4.139 4.139 (4.79) (4.79) (4.79) (4.79) (4.79) (4.79) Input (t-3)*probability 0.642 0.642 0.642 0.642 0.642 0.642 (0.81) (0.81) (0.81) (0.81) (0.81) (0.81) (log) Population (t-1) 4.964 4.964 4.964 4.964 4.964 4.964 (3.14) (3.14) (3.14) (3.14) (3.14) (3.14)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.646 3.021 0.805 0.810 0.894 0.517 (0.30) (1.73) (0.37) (0.67) (0.64) (0.30) (log) Population (t-1) 3.529 6.071 2.908 0.808 -2.239 2.949 (3.27) (3.39) (3.41) (6.91) (7.81) (4.20)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 3.665 -0.307 3.972 -3.205 -3.587 8.794 (3.86) (2.38) (2.55) (8.09) (10.38) (8.20) Input (t-3)*probability 1.437 0.445 0.992 1.834 1.696 1.103 (0.63) (0.37) (0.46) (1.46) (1.55) (1.46) (log) Population (t-1) 2.354 -0.248 2.602 5.835 8.802 3.875 (1.45) (0.73) (1.07) (5.30) (5.14) (4.11)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 27.93 3.93 32.05 1.46 1.24 5.57 Kleibergen-Paap F 21.80 3.18 25.28 1.12 1.11 3.94 Hansen J statistic (p-value) 0.68 0.51 0.82 0.37 0.42 0.95 Notes: Uses the share of years a recipient received Chinese projects during the 1970-1999 period as part of the interacted instruments (“historic probabilities”). Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Table D: Foreign-exchange reserves only with historical probabilities

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 7.275 7.275 7.275 7.275 7.275 7.275 (3.51) (3.51) (3.51) (3.51) (3.51) (3.51) (log) Population (t-1) 4.955 4.955 4.955 4.955 4.955 4.955 (3.13) (3.13) (3.13) (3.13) (3.13) (3.13)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.681 3.893 0.825 1.264 1.546 0.513 (0.31) (2.75) (0.37) (1.28) (1.91) (0.30) (log) Population (t-1) 3.367 5.947 2.821 -2.382 -8.617 2.976 (3.41) (3.78) (3.52) (11.75) (19.99) (4.28)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 10.689 1.869 8.821 5.758 4.704 14.183 (1.85) (1.09) (1.29) (5.52) (5.89) (5.10) (log) Population (t-1) 2.332 -0.255 2.587 5.806 8.776 3.858 (1.45) (0.72) (1.07) (5.29) (5.13) (4.11)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 47.32 5.05 56.89 1.47 1.09 10.52 Kleibergen-Paap F 33.21 2.93 46.46 1.09 0.64 7.74 Notes: Uses changes in foreign reserves interacted with the share of years a recipient received Chinese projects during the 1970- 1999 period as instrument (“historic probabilities”). Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Table E: Physical project inputs with historical probabilities

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Input (t-3)*probability 1.243 1.243 1.243 1.243 1.243 1.243 (0.60) (0.60) (0.60) (0.60) (0.60) (0.60) (log) Population (t-1) 5.228 5.228 5.228 5.228 5.228 5.228 (2.99) (2.99) (2.99) (2.99) (2.99) (2.99) 1.243 1.243 1.243 1.243 1.243 1.243

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.631 3.105 0.792 0.909 1.058 0.522 (0.30) (1.78) (0.38) (0.76) (0.83) (0.32) (log) Population (t-1) 3.595 6.059 2.966 0.110 -3.845 2.912 (3.22) (3.42) (3.36) (7.71) (9.73) (4.16)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Input (t-3)*probability 1.97 0.4 1.569 1.368 1.175 2.381 (0.30) (0.16) (0.23) (0.98) (0.89) (0.90) (log) Population (t-1) 2.588 -0.268 2.855 5.630 8.573 4.435 (1.41) (0.67) (1.06) (5.20) (5.04) (3.99)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 54.22 7.82 60.67 2.80 2.30 9.96 Kleibergen-Paap F 42.49 6.23 45.40 1.96 1.73 6.99

Notes: Uses the first factor of six construction materials interacted with the share of years a recipient received Chinese projects during the 1970-1999 period as instrument (“historic probabilities”). Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Appendix C5: Growth effects of Chinese official finance, partial leverage plots Panel A: Corresponds to regression shown in column 1 of Table 2

IRQ2004 40

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Growth per capita | X LBR2003 CAF2013 IRQ2003 SSD2012 -40

LBY2011 -60

-1 -.5 0 .5 1 1.5 Number of Chinese Projects | X

Residuals Fitted values

Panel B: Corresponds to regression of column 1 in Table 2, excluding outliers

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-1 -.5 0 .5 1 1.5 Number of Chinese Projects | X

Residuals Fitted values

Appendix C6: Growth effects of Chinese official finance, key results with control variables (2002-2016)

Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 0.915 6.584 0.535 2.442 7.129 2.519 (3.53) (4.86) (3.39) (3.72) (5.19) (3.71) Input (t-3)*probability 1.17 0.124 1.428 1.126 0.175 1.358 (0.58) (0.86) (0.54) (0.59) (0.94) (0.58)

Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.89 0.949 1.464 0.887 0.186 1.001 (0.34) (0.76) (0.50) (0.49) (0.13) (0.50)

Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 3.556 -1.097 3.766 8.529 17.682 8.152 (2.23) (3.45) (1.54) (5.02) (10.96) (5.43) Input (t-3)*probability 0.892 1.263 0.367 0.132 4.44 0.280 (0.40) (0.69) (0.28) (0.74) (2.02) (0.82)

Number of countries 112 112 112 112 112 112 Number of observations 1546 1546 1546 1546 1546 1546 Cragg-Donald F 24.06 25.65 21.68 3.30 33.35 4.31 Kleibergen-Paap F 31.36 23.93 20.97 3.37 20.84 4.14 Hansen J statistic (p-value) 0.51 0.19 0.14 0.22 0.42 0.25 Notes: Includes the average number of assassinations in a recipient country, its government surplus as a share of GDP, its rate of inflation, money as a share of GDP, trade openness, and (log) population as additional control variables. Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

Appendix C7: Growth effects of Chinese official finance, additional controls (2SLS, 2002-2016)

Coef. aid Coef. control Countries Obs. Cr.-Don. F Kl.-Paap F Hansen J Panel A: Chinese exports control OF 0.834 -0.027 130 1523 30.87 36.27 0.32 (0.32) (0.09) OOF 1.176 -0.023 130 1523 34.68 30.47 0.46 (0.73) (0.09) ODA 1.182 -0.028 130 1523 29.06 27.75 0.08 (0.43) (0.09) Panel B: Chinese FDI control OF 1.669 -0.116 101 931 6.29 10.34 0.99 (0.87) (0.19) OOF 1.252 0.055 101 931 4.72 6.58 0.68 (1.36) (0.16) ODA 2.317 -0.075 101 931 7.40 8.17 0.57 (1.21) (0.20) Panel C: Exports*probability of receiving aid control (Reserve-IV only) OF 4.134 -7.966 150 2061 2.96 3.90 (2.35) (5.31) OOF 0.914 0.062 150 2061 20.53 24.18 (1.09) (0.82) ODA 3.191 -3.886 150 2061 12.55 16.49 (1.14) (1.81) Panel D: FDI*probability of receiving aid control (Reserve-IV only) OF 2.148 -1.474 150 2061 7.32 9.90 (0.96) (0.98) OOF 0.584 0.278 150 2061 25.46 28.22 (0.94) (0.35) ODA 2.431 -1.09 150 2061 16.39 24.61 (0.86) (0.63) Panel E: Future values of instruments (placebo) OF 0.962 149 1539 7.28 4.72 0.19 (0.61) OOF 0.367 149 1539 13.32 3.18 0.97 (0.64) ODA 2.013 149 1539 6.57 4.09 0.23 (1.07)

Notes: Includes additional control variables as indicated in the header of each panel. Each row represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t- 2), denotes Chinese development finance commitments lagged by two years and is measured as a project count. In separate rows, we show results for all official finance (OF) projects, Other Official Flows (OOF) projects, and Official Development Assistance (ODA) projects. All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

Appendix C8: Growth effects of Chinese official finance, interactions (CF approach, 2002-2016)

Coef. aid Coef. interact. Countries Obs. Cr.-Don. F Kl.-Paap F Hansen J Panel A: Good Policy (Burnside-Dollar) OF 0.642 0.007 75 1095 26.15 16.22 0.23 (0.38) (0.03) OOF 0.599 0.105 75 1095 23.18 15.38 0.89 (1.11) (0.07) ODA 1.053 -0.005 75 1095 17.20 15.53 0.07 (0.54) (0.03) Panel B: Absence of Corruption (ICRG) OF 0.772 0.052 100 1349 35.90 24.10 0.34 (0.43) (0.08) OOF 0.799 0.161 100 1349 19.61 19.51 0.97 (1.01) (0.17) ODA 1.341 0.030 100 1349 26.88 24.36 0.06 (0.59) (0.09) Panel C: Democratic Accountability (ICRG) OF 0.791 0.047 100 1349 29.96 23.48 0.41 (0.43) (0.04) OOF 0.938 0.063 100 1349 17.21 20.43 0.91 (1.01) (0.09) ODA 1.376 0.054 100 1349 20.79 21.91 0.09 (0.62) (0.05) Panel D: Absence of Ethnic Tensions (ICRG) OF 0.767 0.049 100 1349 33.09 23.30 0.38 (0.44) (0.05) OOF 1.210 -0.046 100 1349 20.19 20.58 0.95 (0.98) (0.07) ODA 1.233 0.088 100 1349 22.57 22.36 0.08 (0.56) (0.06) Panel E: Absence of Press Freedom OF 1.357 -0.000 61 741 24.75 11.37 0.46 (0.77) (0.00) OOF 0.596 -0.004 61 741 21.93 19.57 0.16 (1.51) (0.01) ODA 2.245 0.001 61 741 16.97 11.54 0.50 (1.40) (0.00) Panel F: UNGA voting with China OF 1.032 -0.073 148 2010 38.45 32.68 0.44 (0.48) (0.41) OOF 0.649 0.182 148 2010 28.22 33.07 0.37 (1.13) (1.07) ODA 1.588 -0.130 148 2010 30.62 32.56 0.10 (0.62) (0.59) Panel G: Left-wing recipient government OF 0.989 -0.045 133 1821 33.15 27.99 0.44 (0.43) (0.08) OOF 1.100 0.192 133 1821 32.21 30.57 0.58 (0.82) (0.21) ODA 1.507 -0.193 133 1821 25.43 27.16 0.14 (0.57) (0.08) Notes: Includes variables (Coef. aid) in levels and as interactions with official finance (Coef. interact.) as indicated in the header of each panel. Each row represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth 100 in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count. In separate rows, we show results for all official finance (OF) projects, Other Official Flows (OOF) projects, and Official Development Assistance (ODA) projects. All regressions include (log) Population (t-1) and country- and year- fixed effects as control variables. Estimation is with a Control Function (CF) approach, controlling for the residual of the corresponding first-stage regressions from Table 2. Bootstrapped standard errors with 500 replications in parentheses.

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Appendix C9: Growth effects of Chinese official finance, broad sectors (2SLS, 2002-2016)

Coef. aid Countries Obs. Cr.-Don. F Kl.-Paap F Hansen J Panel A: Economic Infrastructure & Services OF 1.677 150 2061 38.83 12.70 0.28 (1.02) OOF 0.389 150 2061 53.40 20.80 0.89 (0.84) ODA 4.598 150 2061 25.33 5.17 0.04 (2.83) Panel B: Social Infrastructure & Services OF 1.969 150 2061 42.51 33.95 0.21 (0.65) OOF 1.428 150 2061 19.71 23.95 0.18 (1.57) ODA 2.502 150 2061 46.67 32.65 0.02 (0.77) Panel C: Production Sectors OF 6.03 150 2061 17.96 11.02 0.34 (2.37) OOF 2.930 150 2061 17.00 2.75 0.73 (3.51) ODA 8.661 150 2061 20.58 9.05 0.30 (3.86) Notes: Shows separate results for three broad sectors as indicated in the header of each panel. Each row represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count. In separate rows, we show results for all official finance (OF) projects, Other Official Flows (OOF) projects, and Official Development Assistance (ODA) projects. The “Social Infrastructure & Services” category includes health, education, governance, and water supply and sanitation projects; the “Economic Infrastructure & Services” category includes transportation projects (e.g., roads, railways, and airports), energy production and distribution projects, and information and communication technology (ICT) projects (e.g., broadband internet and mobile phone infrastructure); the “Production Sector” category includes agriculture, fishing, forestry, mining, industry, trade, and tourism projects. All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

102

Appendix C10: Effects of Chinese official finance on exports of construction material (2SLS, 2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: Exports/GDP Input (t-3)*probability 0.0059 0.0056 0.0057 0.0083 0.0080 0.0082 (0.00) (0.00) (0.00) (0.00) (0.01) (0.00) (log) Population (t-1) 0.0613 0.0664 0.0588 0.0595 0.0655 0.0575 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02)

Panel B: 2SLS estimates – Dependent variable: Exports/GDP Chinese OF (t-2) 0.0044 0.0055 0.0061 0.0065 0.0013 0.0061 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (log) Population (t-1) 0.0472 0.0666 0.0404 0.0177 0.0552 0.0218 (0.03) (0.02) (0.03) (0.06) (0.03) (0.05)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Input (t-3)*probability 1.3352 1.019 0.9398 1.2648 6.2491 1.3296 (0.16) (0.17) (0.13) (0.47) (1.04) (0.46) (log) Population (t-1) 3.1569 -0.0454 3.0102 6.4126 8.0789 5.8290 (1.38) (0.67) (1.03) (5.00) (4.80) (3.95)

Number of countries 149 149 149 149 149 149 Number of observations 2053 2053 2053 2053 2053 2053 Cragg-Donald F 67.11 68.83 62.71 5.71 71.19 7.33 Kleibergen-Paap F 72.51 36.59 55.68 7.22 35.85 8.39 Notes: Each column per panel represents one regression. The dependent variable in panels A and B is China’s exports of construction material, defined as the sum of exports of iron and steel (SITC code 67), lime, cement, and fabricated construction materials (661), cork and wood (24), aluminum (684), and glass (664) divided by recipient-country GDP, in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

103

Appendix C11: Growth effects of Chinese official financing, unlogged amounts (2002-2016)

(1) (2) (3) All amounts OOF amounts ODA amounts Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.000 0.000 0.001 (0.00) (0.00) (0.00)

(log) Population (t-1) 6.517 6.511 6.471 (2.91) (2.91) (2.90)

Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 0.160 2.437 0.354 (3.18) (4.01) (3.32) Input (t-3)*probability 1.277 0.518 1.475 (0.47) (0.74) (0.49) (log) Population (t-1) 5.234 6.230 4.615 (3.04) (2.93) (3.09) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.011 0.002 0.061 (0.01) (0.00) (0.03) (log) Population (t-1) 10.217 7.262 3.388 (7.02) (3.15) (6.03) Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 636.274 3151.160 -16.264 (769.68) (2155.05) (164.01) Input (t-3)*probability -17.758 -206.593 27.307 (123.82) (353.00) (24.28) (log) Population (t-1) -447.086 -509.715 21.819 (490.45) (496.87) (85.38)

Number of countries 150 150 150 Number of observations 2061 2061 2061 Cragg-Donald F 1.33 10.55 2.03 Kleibergen-Paap F 5.21 6.43 4.94 Hansen J statistic (p-value) 0.26 0.35 0.90

Notes: Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as (unlogged) financial amounts. Column 1 covers all official finance (OF) projects, column 2 focuses on Other Official Flows (OOF), and column 3 focuses on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

104

Appendix C12: Growth effects of Chinese official financing, three-year averages (2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (s-1) 0.177 0.022 0.366 -0.045 -0.011 0.027 (0.08) (0.07) (0.14) (0.05) (0.04) (0.03) (log) Population (s-1) 6.026 6.836 5.354 6.865 6.997 6.764 (2.80) (2.85) (2.79) (2.83) (3.00) (2.83) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (s-1)*probability 1.787 5.025 5.614 7.032 8.178 10.512 (12.68) (14.51) (13.17) (14.85) (16.67) (14.57) Input (s-1)*probability 1.199 0.188 0.893 0.463 -0.121 0.179 (1.73) (2.08) (1.74) (1.98) (2.34) (1.91) (log) Population (s-1) 5.035 6.641 3.587 4.947 6.466 3.937 (3.12) (2.90) (3.32) (3.18) (2.91) (3.30) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (s-1) 0.909 0.809 1.484 3.965 0.176 1.494 (0.40) (0.72) (0.60) (6.66) (0.15) (1.06) (log) Population (s-1) 2.638 6.403 0.791 5.291 4.547 2.180 (3.48) (2.92) (3.84) (24.07) (3.68) (9.71) Panel D: First-stage estimates – Dependent variable: Chinese OF (s-1) Reserves (s-1)*probability 8.916 0.764 8.679 -0.949 51.683 8.388 (5.37) (10.50) (3.19) (12.07) (20.89) (10.87)

Input (s-1)*probability 0.122 1.109 -0.266 0.527 -1.602 -0.116 (0.95) (2.04) (0.50) (1.75) (3.05) (1.58)

(log) Population (s-1) 2.436 0.336 1.725 0.047 10.905 1.136 (1.28) (0.53) (1.06) (6.12) (6.74) (5.68)

Number of countries 151 151 151 151 151 151 Number of observations 706 706 706 706 706 706 Cragg-Donald F 25.24 20.61 32.41 0.11 12.07 1.28 Kleibergen-Paap F 41.99 35.75 45.71 0.23 9.04 1.68 Hansen J statistic (p-value) 0.58 0.79 0.46 0.84 0.94 0.90 Notes: Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in period s, where s is one of five three-year periods (2002-2004, 2005-2007, 2008-2010, 2011-2013, and 2014-2016). The variable of interest, Chinese OF (s-1), denotes Chinese development finance commitments lagged by one three-year period and is measured as the average annual project count in columns 1-3 and as logged average annual financial amounts in column 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

105

Appendix C13: Growth effects of Chinese official financing, aid ratios (2002-2016)

(1) (2) (3) (4) (5) (6) All amounts OOF amounts ODA amounts All amounts OOF amounts ODA amounts per GDP per GDP per GDP p.c. p.c. p.c. Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 5.486 5.559 5.453 0.001 0.001 0.002 (3.12) (3.82) (5.26) (0.00) (0.00) (0.00) (log) Population (t-1) 6.579 6.583 6.591 6.703 6.617 6.688 (2.92) (2.93) (2.93) (2.92) (2.93) (2.93) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability -0.471 1.699 -0.189 -0.471 1.699 -0.189 (3.20) (4.04) (3.33) (3.20) (4.04) (3.33) Input (t-3)*probability 1.365 0.615 1.552 1.365 0.615 1.552 (0.48) (0.74) (0.49) (0.48) (0.74) (0.49) (log) Population (t-1) 5.382 6.333 4.770 5.382 6.333 4.770 (3.06) (2.96) (3.11) (3.06) (2.96) (3.11) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 171.688 105.450 310.368 0.073 0.055 0.101 (71.20) (97.64) (155.27) (0.03) (0.05) (0.06) (log) Population (t-1) 6.078 6.355 6.368 12.532 8.444 11.255 (2.99) (2.90) (3.27) (4.37) (3.61) (3.90) Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability -0.026 0.007 -0.026 -72.838 55.413 -76.160 (0.02) (0.03) (0.02) (63.96) (51.17) (69.53) Input (t-3)*probability 0.011 0.007 0.007 25.534 7.482 21.571 (0.00) (0.00) (0.00) (11.68) (7.66) (13.71) (log) Population (t-1) -0.002 -0.000 -0.001 -92.831 -40.458 -54.343 (0.01) (0.01) (0.01) (38.03) (20.34) (28.64)

Number of countries 149 149 149 150 150 150 Number of observations 2051 2051 2051 2061 2061 2061 Cragg-Donald F 6.04 4.98 6.82 4.29 4.26 4.88 Kleibergen-Paap F 8.40 3.44 3.22 5.45 5.05 2.85 Hansen J statistic (p- value) 0.39 0.85 0.13 0.30 0.88 0.16 Notes: Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as financial amounts divided by recipient-country GDP in columns 1-3 and divided by recipient-country population size in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

106

Appendix C14: Growth effects of Chinese official financing, implemented projects (2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.166 0.197 0.14 0.027 0.020 0.026 (0.07) (0.09) (0.07) (0.02) (0.02) (0.02) (log) Population (t-1) 5.984 6.528 6.029 6.339 6.348 6.373 (2.89) (2.90) (2.87) (2.91) (2.96) (2.89) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 0.484 2.867 0.335 1.060 3.093 0.748 (3.21) (4.13) (3.12) (3.47) (3.99) (3.59) Input (t-3)*probability 1.563 0.216 1.706 1.623 0.140 1.853 (0.47) (0.76) (0.47) (0.51) (0.79) (0.54) (log) Population (t-1) 4.447 6.449 3.644 4.554 6.388 4.153 (2.98) (2.92) (2.99) (2.98) (2.91) (2.98) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 1.286 0.630 1.781 1.657 0.093 1.719 (0.42) (0.74) (0.55) (0.90) (0.10) (0.92) (log) Population (t-1) 2.491 6.591 0.508 -3.567 5.78 -1.802 (3.21) (2.89) (3.56) (9.91) (2.98) (8.61) Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 2.881 -0.665 3.008 0.395 15.216 -0.183 (1.51) (2.91) (1.26) (4.55) (9.08) (5.58)

Input (t-3)*probability 0.799 1.096 0.470 1.016 4.554 1.168 (0.28) (0.56) (0.24) (0.74) (2.07) (0.86)

(log) Population (t-1) 1.404 -0.210 1.615 4.914 6.642 3.506 (1.03) (0.60) (0.82) (4.35) (4.30) (3.87)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 38.81 32.48 38.33 1.99 40.08 2.54 Kleibergen-Paap F 34.25 23.92 28.48 2.55 25.85 2.58 Hansen J statistic (p-value) 0.28 0.46 0.10 0.96 0.65 0.91

Notes: Shows results for projects that have at least reached the implementation stage. Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

107

Appendix C15: Growth effects of Chinese official financing, completed projects (2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: OLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.147 0.185 0.101 0.030 0.011 0.023 (0.08) (0.10) (0.07) (0.02) (0.02) (0.02) (log) Population (t-1) 6.231 6.575 6.281 6.414 6.444 6.476 (2.90) (2.89) (2.88) (2.90) (2.94) (2.91) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Reserves (t-3)*probability 0.204 3.225 0.312 0.670 4.283 0.644 (3.28) (4.23) (3.28) (3.72) (4.23) (3.89) Input (t-3)*probability 1.727 0.051 1.895 2.002 -0.090 2.152 (0.48) (0.79) (0.49) (0.55) (0.85) (0.58) (log) Population (t-1) 4.424 6.525 3.502 4.512 6.486 4.134 (2.96) (2.92) (2.99) (2.94) (2.91) (2.97) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 1.679 0.521 2.199 2.021 0.089 1.847 (0.57) (0.78) (0.70) (1.02) (0.11) (0.91) (log) Population (t-1) 3.437 6.712 1.712 0.658 6.045 4.597 (3.17) (2.92) (3.48) (8.72) (2.93) (7.29) Panel D: First-stage estimates – Dependent variable: Chinese OF (t-2) Reserves (t-3)*probability 1.967 -1.139 2.929 -4.089 23.383 -5.280 (1.31) (2.93) (1.23) (5.03) (11.68) (4.94) Input (t-3)*probability 0.728 1.126 0.371 1.473 3.413 1.741 (0.26) (0.59) (0.24) (0.74) (2.49) (0.82) (log) Population (t-1) 0.501 -0.383 0.690 2.294 4.998 0.348 (0.84) (0.57) (0.70) (3.77) (4.05) (3.64)

Number of countries 150 150 150 150 150 150 Number of observations 2061 2061 2061 2061 2061 2061 Cragg-Donald F 33.24 32.13 33.11 1.47 40.98 2.15 Kleibergen-Paap F 24.22 23.19 21.49 2.70 33.14 2.75 Hansen J statistic (p-value) 0.33 0.40 0.07 0.41 0.58 0.28

Notes: Shows results for projects that have reached the completion stage. Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

108

Appendix D: Additional tables and figures in Section 3

Appendix D1: Growth effects of Chinese and Western aid, aid budget instrument (1) (2) (3) (4) (5) (6) China ODA (log) China ODA (log) DAC (log) US ODA (log) IBRD (log) IDA projects amounts ODA com. com. com. com. t-1 1.376 1.522 1.915 1.394 0.114 0.378 (0.54) (0.97) (0.81) (0.64) (0.40) (1.24) t-2 1.582 1.711 1.972 1.495 -0.375 -0.243 (0.55) (1.00) (0.85) (0.69) (0.48) (1.02) t-3 0.886 0.968 1.968 1.773 -0.656 0.287 (0.51) (0.73) (0.90) (0.67) (0.58) (1.12) t-4 0.727 0.677 2.015 2.433 -0.789 0.717 (0.47) (0.51) (0.97) (0.74) (0.62) (1.14) t-5 0.391 0.352 1.979 2.562 -0.767 -0.076 (0.37) (0.35) (0.97) (0.67) (0.64) (1.35) t-6 -0.118 -0.102 1.99 2.118 -0.632 0.054 (0.33) (0.29) (0.96) (0.68) (0.63) (1.78) t-2 First year 2003 2003 1978 1978 1997 1993 Last year 2016 2016 2016 2016 2016 2016 Number of countries 149 149 157 157 154 157 Number of observations 1913 1913 4995 4996 2808 3373 Cragg-Donald F 53.02 3.35 158.90 134.18 7.03 25.46 Kleibergen-Paap F 30.28 3.87 9.81 16.51 2.75 9.56 Prob > chi2 0.78 0.77 0.01 0.10 Notes: Each cell represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, denoted in the column header, denotes the respective donor’s development finance commitments lagged by h years and is measured as a project count in column 1 and as logged financial amounts in column 2-6. Columns 1-4 and 6 cover Official Development Assistance (ODA) projects, and column 5 focuses on Other Official Flows (OOF) projects. All regressions include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

109

Appendix D2: Growth effects of Chinese aid, aid budget instruments with contemporaneous probabilities (2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Aid budget (t-3)*probability 0.008 -0.001 0.02 0.008 -0.001 0.02 (0.00) (0.02) (0.01) (0.00) (0.02) (0.01) (log) Population (t-1) 3.739 5.359 1.822 3.739 5.359 1.822 (3.39) (3.23) (3.48) (3.39) (3.23) (3.48)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.733 -0.066 1.582 6.370 -0.011 1.711 (0.37) (0.95) (0.55) (36.44) (0.15) (1.00) (log) Population (t-1) 2.295 5.331 -1.567 -39.904 5.448 -7.634 (3.48) (3.25) (4.06) (267.70) (3.41) (11.84) Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Aid budget (t-3)*probability 0.011 0.019 0.013 0.000 0.116 0.012 (0.00) (0.01) (0.00) (0.00) (0.03) (0.01) (log) Population (t-1) 1.971 -0.427 2.143 6.991 8.148 5.528 (1.63) (0.82) (1.23) (5.52) (4.98) (4.59)

Number of countries 149 149 149 149 149 149 Number of observations 1913 1913 1913 1913 1913 1913 Cragg-Donald F 52.52 36.61 53.02 0.02 48.21 3.35 Kleibergen-Paap F 37.72 13.17 30.28 0.03 18.70 3.87 Notes: Uses China’s aid budget interacted with the probability of receiving official financing as instrument. Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

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Appendix D3: Growth effects of Chinese aid, aid budget instruments with “historical” probabilities (2002-2016)

(1) (2) (3) (4) (5) (6) (log) (log) (log) All projects OOF projects ODA projects All amounts OOF amounts ODA amounts Panel A: Reduced-form estimates – Dependent variable: GDP p.c. growth Aid budget (t-3)*probability 0.015 0.03 0.025 0.015 0.03 0.025 (0.01) (0.02) (0.01) (0.01) (0.02) (0.01) (log) Population (t-1) 2.564 3.223 2.377 2.564 3.223 2.377 (3.50) (3.52) (3.47) (3.50) (3.52) (3.47)

Panel B: 2SLS estimates – Dependent variable: GDP p.c. growth Chinese OF (t-2) 0.748 2.755 1.074 -12.521 0.755 0.663 (0.29) (1.97) (0.39) (100.42) (0.66) (0.31) (log) Population (t-1) 2.233 5.918 0.650 94.288 -1.817 0.313 (3.29) (3.60) (3.55) (710.84) (7.94) (4.85)

Panel C: First-stage estimates – Dependent variable: Chinese OF (t-2) Aid budget (t-3)*probability 0.019 0.011 0.023 -0.000 0.039 0.037 (0.00) (0.01) (0.00) (0.00) (0.03) (0.01) (log) Population (t-1) 0.443 -0.978 1.608 7.274 6.674 3.112 (1.74) (0.95) (1.23) (5.65) (5.44) (4.55)

Number of countries 149 149 149 149 149 149 Number of observations 1913 1913 1913 1913 1913 1913 Cragg-Donald F 55.25 7.90 58.44 0.02 3.69 11.37 Kleibergen-Paap F 29.44 3.03 34.32 0.02 1.93 7.50 Notes: Uses China’s aid budget interacted with the share of years a recipient received Chinese projects during the 1970-1999 period as instrument (“historic probabilities”). Each column per panel represents one regression. The dependent variable in panels A and B is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, Chinese OF (t-2), denotes Chinese development finance commitments lagged by two years and is measured as a project count in columns 1-3 and as logged financial amounts in columns 4-6. Columns 1 and 4 cover all official finance (OF) projects, columns 2 and 5 focus on Other Official Flows (OOF), and columns 3 and 6 focus on Official Development Assistance (ODA). All regressions include country- and year-fixed effects as control variables. Standard errors in parentheses.

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Appendix D4: Growth effects of Chinese and Western aid by sector, 1978/2003-2016 Coef. aid Countries Obs. Cr.-Don. F Kl.-Paap F Period Panel A: Economic Infrastructure & Services (Aid-budget IV) China ODA projects 2.816 149 1913 50.44 15.97 03-16 (1.58) (log) China ODA amounts 0.359 149 1913 31.81 9.42 03-16 (0.18) (log) DAC ODA com. 0.876 148 4850 185.94 65.02 78-16 (0.40) (log) US ODA com. 1.426 148 4850 166.27 66.65 78-16

(0.62) Panel B: Social Infrastructure & Services (Aid-budget IV) China ODA projects 3.113 149 1913 66.61 38.39 03-16

(0.94) (log) China ODA amounts 2.06 149 1913 4.77 7.32 03-16

(0.87) (log) DAC ODA com. 1.44 148 4850 223.22 21.35 78-16 (0.62) (log) US ODA com. 1.499 148 4850 260.32 62.56 78-16 (0.56) Panel C: Production Sectors (Aid-budget IV) China ODA projects 18.719 149 1913 10.88 4.83 03-16

(11.71) (log) China ODA amounts -56.229 149 1913 0.02 0.00 03-16

(822.72) (log) DAC ODA com. 2.106 148 4850 80.96 15.43 78-16

(0.94) (log) US ODA com. 0.730 148 4850 108.98 46.14 78-16 (0.77) Notes: Each row represents one regression. The dependent variable is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, denoted in the respective row in the first column, denotes the donor’s development finance commitments lagged by two years and is measured as a project count or logged financial amounts as indicated. The “Social Infrastructure & Services” category includes health, education, governance, and water supply and sanitation projects; the “Economic Infrastructure & Services” category includes transportation projects (e.g., roads, railways, and airports), energy production and distribution projects, and information and communication technology (ICT) projects (e.g., broadband internet and mobile phone infrastructure); and the “Production Sector” category includes agriculture, fishing, forestry, mining, industry, trade, and tourism projects. All regressions cover official development assistance (ODA) projects only and include (log) Population (t-1) and country- and year-fixed effects as control variables. Standard errors in parentheses.

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Appendix D5: Growth effects of Western official financing for Chinese aid orphans (based on number of Chinese Cold War projects)

(1) (2) (3) (4) (5) DAC ODA US ODA IBRD IBRD IDA Panel A: OLS estimates – Dependent variable: GDP p.c. growth OF (t-2) 1.047 0.81 -0.036 0.001 0.301 (0.22) (0.17) (0.02) (0.03) (0.25) (log) Population (t-1) 0.352 -1.061 3.604 11.967 -0.026 (1.89) (2.01) (2.88) (3.49) (3.77) Panel B: Reduced-form estimates – Dependent variable: GDP p.c. growth Budget (t-3)*probability 0.091 4.695 0.035 0.024 0.022 (0.03) (1.28) (0.08) (0.08) (0.03) (log) Population (t-1) -2.632 -2.492 4.098 12.06 -0.054 (2.14) (2.17) (2.96) (3.48) (3.83) Panel C: 2SLS estimates – Dependent variable: GDP p.c. growth OF (t-2) 2.277 4.748 0.135 0.072 -0.986 (1.04) (2.31) (0.31) (0.25) (1.70) (log) Population (t-1) 2.145 -0.509 5.465 12.458 -0.222 (2.72) (3.95) (4.71) (3.76) (3.90) Panel D: First-stage estimates – Dependent variable: OF (t-2) Budget (t-3)*probability 0.04 0.989 0.263 0.338 -0.022 (0.02) (0.45) (0.09) (0.15) (0.01) (log) Population (t-1) -2.098 -0.418 -10.141 -5.559 -0.170 (0.95) (1.09) (4.91) (5.38) (0.82)

First year 1978 1978 1997 2000 1993 Last Year 2016 2016 2016 2014 2016 Number of countries 84 84 84 125 84 Number of observations 2714 2715 1542 924 1843 Cragg-Donald F 119.41 17.95 12.59 10.78 11.29 Kleibergen-Paap F 5.84 4.87 8.09 5.05 2.37 Prob > chi2 0.53 0.00 0.05 0.23 0.90 Notes: Each column per panel represents one regression. The dependent variable in panels A-C is the recipient country’s annual real GDP per capita growth in year t. The variable of interest, indicated in the column header, denotes development finance commitments by the respective donor lagged by two years and is measured as logged financial amounts. Columns 1, 2 and 5 correspond to Official Development Assistance (ODA) projects, and columns 3-4 focus on Other Official Flows (OOF) projects. All regressions include country- and year-fixed effects as control variables. Prob > chi2 corresponds to testing the hypothesis that the effect of DAC/US/IBRD/IDA official finance in countries that are “Chinese aid orphans” is different from the effect in the full sample (as shown in panel A of Table 5). We define “aid orphans” as countries that completed fewer projects in the 1960-2005 period according to data from Dreher and Fuchs (2015) than the 60th percentile of the distribution. Standard errors are in parentheses.

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