Does China’s Aid to Ethiopia Promote Exports from Ethiopia to China?

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Simachew Zelalem Mengistu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7529955/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract China has emerged as Ethiopia’s largest bilateral development partner, financing large-scale infrastructure projects under the framework of South–South cooperation and the Belt and Road Initiative (BRI). Conventional wisdom suggests that such aid should facilitate trade by reducing costs, enhancing industrial capacity, and integrating Ethiopia into global markets. This article investigates whether Chinese aid has indeed promoted Ethiopian exports to China. The evidence demonstrates that, despite massive inflows of Chinese aid, Ethiopia’s exports to China remain stagnant and concentrated in a handful of primary commodities. Instead, Chinese aid has primarily fueled Ethiopia’s external debt burden, widened trade imbalances, and reinforced structural asymmetries. Chinese aid has been more effective in creating debt obligations than in promoting Ethiopian export growth. International Economics International Business Aid for Trade Export Ethiopia China 1. Introduction In the last two decades, China has positioned itself as a key development partner for Ethiopia, channeling substantial infrastructure financing into the country through South–South cooperation initiatives and the Belt and Road Initiative (BRI). Projects such as the Addis Ababa–Djibouti Railway, hydropower dams, and industrial parks are widely promoted as instruments to reduce trade costs, support industrial growth, and thereby boost Ethiopia’s exports. Ethiopia’s government, in particular, has framed these initiatives as catalysts not only for domestic transformation but also for expanding its trade footprint, especially with China. However, while such infrastructure investments hold theoretical promise for trade facilitation, empirical analysis often highlights structural imbalances. Indeed, Deborah Bräutigam, a leading scholar on Chinese aid in Africa, emphasizes that although China’s infrastructure-focused approach appears to combat typical aid conditionalities, the actual developmental outcomes—particularly for export diversification and trade growth—are highly contested and context dependent (Bräutigam, 2011 ; TARRÓSY, 2020 ). In Ethiopia’s case, the expected alignment between China aid and export performance remains weak: exports remain concentrated in a few primary commodities, and export trade flows between Ethiopia and China have become increasingly asymmetrical. This article examines whether Chinese aid to Ethiopia has effectively enhanced Ethiopian exports to China or instead contributed to heightened debt burdens and trade dependency. It argues that despite the infrastructure investments funded by China, Ethiopia’s export gains have been marginal, while debt pressures have intensified—highlighting the need for critically evaluating the developmental logic of such aid relationships. 2. Literature Review 2.1. Aid, trade, and the “Aid for Trade” (AfT) proposition A large body of work contends that aid targeted at trade-related constraints—ports, roads, customs, standards, power—should reduce trade costs and raise exports. The OECD–WTO “Aid for Trade” agenda formalized this logic: help low-income economies build “supply-side capacity and trade-related infrastructure” so they can access markets and export more, with export diversification a key success metric. “Aid for Trade at a Glance” reports synthesize evaluations and econometric evidence showing mixed but often positive effects at the aggregate level, especially when infrastructure and trade facilitation are the focus (OECD, 2024b , 2024a ). Micro-to-macro channels are widely discussed. Infrastructure lowers logistics and inventory costs; trade facilitation reforms cut dwell time and uncertainty; quality infrastructure (standards, SPS, TBT) enables compliance with destination-market requirements; and firm-capability support (management, finance, clusters) helps exporters upgrade. Empirical studies frequently find that “hard” and “soft” trade facilitation correlate with higher export performance, with some suggesting stronger effects from physical infrastructure combined with regulatory reform (Portugal-Perez & Wilson, 2012 ). Yet meta-evidence also cautions against blanket optimism. Early and subsequent studies report that “aid to productive capacity” often has weak or insignificant links to export growth compared with infrastructure or border reforms; others document that AfT can increase recipient exports to donors but also increase recipient imports from donors (with ambiguous net effects). Such findings foreshadow a potential divergence between aid’s intent and its realized trade outcomes—especially when aid is commercially tied or when productive capabilities lag (Hühne et al., 2014 ). 2. 2. China’s development finance model and the aid–trade nexus China’s contemporary development finance differs from OECD-DAC conventions in instruments, institutions, and disclosure. A growing empirical literature—enabled by Aid data project-level datasets and the CARI/BU “Chinese Loans to Africa” database—shows that Chinese flows range from grants and zero-interest loans to concessional loans, export credits, suppliers’ credits, and other official finance, much of it for large infrastructure. Several studies associate Chinese finance with short-run output gains, while underscoring risks from opacity, collateralization, and sovereign debt vulnerabilities. Aid Data and collaborators emphasize that Beijing often “banks” on big-ticket infrastructure via state policy banks (Eximbank), with projects frequently implemented by Chinese SOEs and private contractors under tied procurement. This architecture can spur construction booms and donor-country exports of equipment and services, but its trade-creation effects for recipients hinge on downstream integration: connecting infrastructure to export-capable firms, standards, finance, and buyer networks. Where these links are weak, large projects may raise debt without catalyzing export diversification (Malik et al., 2021 ). In Africa, loan volumes surged during 2000–2016, then slowed after 2017 amid debt stress and China’s own recalibration. New data indicate lending picked up again modestly in 2023, but with greater selectivity and more financial-sector or renewable-energy orientation. Ethiopia has been among the top five African sovereign borrowers from Chinese creditors since 2000—an important contextual fact for parsing the aid–trade relationship in the Ethiopian case (Collins, 2019 ). 2.3. Ethiopia’s industrial policy, SEZs/industrial parks, and export ambitions Ethiopia’s industrialization strategy since the mid-2000s has relied on active industrial policy—targeting light manufacturing (textiles/apparel, leather), import-substitution in selected intermediates, and export-oriented industrial parks—supported by infrastructure megaprojects (power, rail, roads). Still, the country’s structural transformation and manufacturing export share have remained modest relative to ambition. A strand of research focuses on the Ethiopia–China interface in industrial zones. Bräutigam and Tang’s comparative work on China-backed Special Economic Zones (SEZs) in Africa—covering Ethiopia’s Eastern Industrial Zone (EIZ)—posits potential for technology and managerial spillovers but highlights execution risks: enclave dynamics, limited local linkages, and capability bottlenecks. Subsequent assessments of Ethiopia’s parks (Hawassa, Bole Lemi, and others) show sizable job creation but persistent challenges—power reliability, input sourcing, quality control, compliance—with uneven export performance and thin supplier ecosystems. Recent multilateral diagnostics echo these mixed results. UNCTAD’s 2024 review notes long construction lead times and under-capacity in several parks, compounded by conflict disruptions in the north; World Bank analyses show industrial parks contributing a non-trivial but still limited share of manufacturing exports and often registering negative net-export positions once imported inputs are accounted for. These patterns complicate any simple mapping from aid-financed infrastructure and zones to sustained export growth (UNCTAD; World Bank, 2023 ). 2.4. Ethiopia–China trade structure: concentration, asymmetry, and “who exports what?” Despite infrastructure build-outs and industrial-park expansion, Ethiopia’s exports to China remain concentrated in primary commodities—especially sesame—plus coffee and a few minerals. UNCTAD’s green export review (leather and sesame) shows that in the late 2000s and mid-2010s, China consistently absorbed the majority of Ethiopia’s sesame shipments. More recent bilateral snapshots still list coffee, oily seeds (including sesame), and some ores among Ethiopia’s top exports to China. Meanwhile, Ethiopia imports a wide range of manufactured goods from China (machinery, textiles, electronics, chemicals), producing a persistent bilateral trade deficit (UNCTAD, 2018 ). This composition suggests that the country has not achieved significant upgrading or diversification of exports to China, despite aid-financed infrastructure ostensibly aimed at reducing trade costs and enabling manufacturing export takeoff. Aggregate figures reported in open data portals show bilateral exports fluctuating at relatively low levels by comparison with imports—another sign that aid and infrastructure, on their own, have not sufficed to transform export structure toward higher-value goods destined for the Chinese market. 2.5. The Addis Ababa–Djibouti Railway and the logistics corridor hypothesis A flagship of China–Ethiopia cooperation is the standard-gauge, electrified Addis Ababa–Djibouti Railway (AA–DJ), designed to cut transit times to port and lower logistics costs. The “logistics corridor” hypothesis holds that such a line should enable scale economies and reliability gains for exporters. However, multiple sources indicate operational and financial headwinds: lower-than-projected traffic, coordination issues with trucking and port handling, and debt-service concerns that led Ethiopia (and Djibouti for its segment) to seek loan restructuring with China Exim bank. In 2018, Ethiopia announced a rescheduling that extended the railway loan’s maturity, and project-level documentation indicates maturity/grace-period extensions tied to weak commercial performance. The railway’s mixed record underscores a broader point in the literature: mega-infrastructure can be a necessary but not sufficient condition for export growth. Without consistent power, industrial inputs, standards compliance, order aggregation, and downstream firm capabilities, logistics upgrades may fail to translate into sustained increases in export volumes and values. In Ethiopia’s case, the logistics bottleneck was only one of several constraints; park-level operational issues and firm-level capability gaps have often proved binding. 2.6. Debt dynamics and the “aid-as-debt multiplier” concern Parallel to trade outcomes, the literature tracks rising sovereign liabilities. IMF debt sustainability assessments for Ethiopia have repeatedly flagged external debt vulnerabilities tied to export-linked indicators (e.g., PV of external debt to exports), with debt judged unsustainable in recent updates. Because a large share of Ethiopia’s external borrowing financed infrastructure with long gestation and uncertain FX returns, the debt service burden can crowd out the very investments in capabilities and quality infrastructure that would make exports competitive. In this sense, certain aid/loan packages can become debt multipliers when export earnings do not materialize as projected (McNabb, 2016 ). Broader analyses of Chinese lending confirm this macro picture: loan volumes peaked and later moderated amid rising recipient distress and China’s shift toward “sustainable lending” principles. Ethiopia’s prominent position among top African borrowers heightens the salience of debt-export linkages for the aid–trade debate in the Ethiopian context. 2.7. Preferential market access: zero-tariff initiatives and their limits A related (and often optimistic) argument is that China’s unilateral tariff preferences for least-developed countries (LDCs) should lower price wedges and pull in African exports. In 2023, China granted Ethiopia zero-tariff treatment on 98% of tariff lines; by late 2024, Beijing announced 100% product coverage for all LDCs with diplomatic ties. In principle, these reforms should boost margins for eligible products. In practice, however, preference utilization depends on rules-of-origin compliance, product standards, buyer relationships, and consistent supply—all areas where firm-level and institutional capabilities in Ethiopia remain uneven. This limits the translation of preferential tariffs into realized export growth, especially beyond a narrow set of primary commodities already demanded by Chinese buyers. 3. Econometric Model 3.1. Model Specification To quantify the effect of Chinese aid on Ethiopia’s exports to China, the study applies a time-series regression model within the framework of trade–aid nexus analysis: $$\:{{\Delta\:}\text{ln}\left(EXP\right)}_{t}=\:\alpha\:+{\beta\:}_{1}{\Delta\:}{AID}_{t}+{\beta\:}_{2}{{\Delta\:}GDP}_{CHN,t}+{\beta\:}_{3}{{\Delta\:}GDP}_{ETH,\:t}+{\beta\:}_{4}{{\Delta\:}EXR}_{t}+{\beta\:}_{5}{{\Delta\:}IMP}_{t}+{\mathcal{E}}_{t}$$ Where: \(\:{EXP}_{t}\) = Ethiopia’s exports to China (in USD), \(\:{AID}_{t}\) = Value of Chinese aid/loans to Ethiopia in year (USD millions), \(\:{GDP}_{CHN,t}\) = China’s GDP (proxy for import demand from Ethiopia), \(\:{GDP}_{ETH,\:t}\) = Ethiopia’s GDP (proxy for supply capacity and production base), \(\:{EXR}_{t}\) = Ethiopia’s exchange rate against the USD (competitiveness factor), \(\:{IMP}_{t}\) = Ethiopia’s imports from China (to capture asymmetric aid–trade effect), and \(\:{\mathcal{E}}_{t}\) = Error term Expected Signs \(\:{\beta\:}_{1}\) ​: Expected positive, if Chinese aid stimulates Ethiopia’s exports through infrastructure and capacity-building. But may be insignificant/negative, if aid mainly fuels debt and import dependence. \(\:{\beta\:}_{2}\) ​: Positive — higher Chinese GDP should increase demand for imports. \(\:{\beta\:}_{3}\) ​: Positive — stronger Ethiopian economy should enhance production/export supply. \(\:{\beta\:}_{4}\) ​: Ambiguous — depreciation may make Ethiopian goods cheaper abroad but also raises cost of imported inputs. \(\:{\beta\:}_{5}\) ​: Negative — rising imports from China may crowd out Ethiopia’s capacity to export, worsening the trade deficit. 3.2. Estimation Techniques To empirically examine the relationship between Chinese aid and Ethiopia’s exports to China, the study employed a multi-step econometric strategy designed to ensure both statistical rigor and robustness of inference. 1. Stationarity Testing As a first step, the time-series properties of the variables were assessed. The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were applied to detect the presence of unit roots in each series. Establishing the order of integration was necessary to avoid spurious regressions and to determine the appropriate estimation approach. If variables were found to be non-stationary in levels but stationary in first differences (I (1)), subsequent cointegration analysis was undertaken. 2. Cointegration and Long-Run Relationship 2. Cointegration and Long-Run Relationship To explore the possibility of a long-run equilibrium relationship between Chinese aid and Ethiopia’s exports to China, the Johansen Cointegration Test was applied. This test identifies whether multiple non-stationary variables share a common stochastic trend. If evidence of cointegration was found, a Vector Error Correction Model (VECM) was estimated, capturing both the short-run dynamics and the long-run equilibrium adjustment process. 3. Short-Run Dynamics In the absence of cointegration, the study employed alternative dynamic specifications such as the Autoregressive Distributed Lag (ARDL) model or Vector Autoregression (VAR). The ARDL framework is particularly useful given its flexibility with mixed orders of integration (I(0) and I(1)) and its ability to estimate both short- and long-run coefficients. The VAR model was considered for purely short-run analysis when no long-run equilibrium was established. 4. Robustness Checks Several robustness measures were incorporated to enhance the reliability of the results. Newey–West heteroskedasticity and autocorrelation consistent (HAC) standard errors were used to correct for potential autocorrelation and heteroskedasticity in the residuals. Multicollinearity among regressors was assessed through the Variance Inflation Factor (VIF), ensuring that estimated coefficients were not biased by redundant explanatory variables. Hypothesis Null Hypothesis (H₀): Chinese aid does not significantly promote Ethiopian exports to China ( \(\:{\beta\:}_{1}\) = 0). Alternative Hypothesis (H₁): Chinese aid significantly promotes Ethiopian exports to China ( \(\:{\beta\:}_{1}\) ≠0). Given Ethiopia’s structural challenges and trade imbalance, it is anticipated that β1 will be statistically insignificant or negative, supporting the conclusion that Chinese aid has not fostered exports but instead exacerbated debt burden. 4. Data & Period The study relies on annual data covering the period from 2000 to 2023. The dataset has been compiled from multiple credible sources, including the United Nations Comtrade Database, AidData, the China–Africa Research Initiative (CARI) Database, the World Bank’s World Development Indicators (WDI), the International Monetary Fund’s Debt Sustainability Analysis (IMF DSA), and publications from the National Bank of Ethiopia. These diverse sources provide a comprehensive foundation for examining the economic and financial dynamics relevant to the analysis. 5. Results and Discussion of the study 5.1. Results Table 1 summarizes the econometric findings. Chinese aid has a negative and insignificant effect on exports. China’s GDP and Ethiopia’s GDP both have positive and significant impacts. Imports from China are negative and significant, indicating structural asymmetry. Table 1 Regression Results: Effect of Chinese Aid on Ethiopian Exports to China (2000–2023) Predictor B SE (robust) t- value p- value Δln(AID) –0.045 0.037 –1.21 .236 Δln(GDP_CHN) 0.328 0.092 3.56 .002 Δln(GDP_ETH) 0.204 0.081 2.52 .019 Δln(EXR) 0.067 0.049 1.37 .185 Δln(IMP) –0.192 0.075 –2.56 .017 5.2. Diagnostics Breusch–Godfrey tests revealed no autocorrelation; Breusch–Pagan indicated no heteroskedasticity; Jarque–Bera confirmed approximate normality. Granger causality tests showed that aid does not Granger-cause exports at 1–2 lags. 5.3. Discussions 5.3.1. Aid-for-Trade and the Missing Export Link Theoretically, aid that reduces trade costs or builds productive capacity should enable export growth. The WTO–OECD “Aid for Trade” framework emphasizes that infrastructure and trade facilitation investments improve competitiveness by lowering transport costs, streamlining customs, and strengthening supply chains. In Ethiopia’s case, China’s aid was heavily concentrated in large-scale infrastructure projects such as railways, dams, and industrial parks — all of which are consistent with the Aid-for-Trade rationale. However, the econometric results indicate that Chinese aid (ΔlnAID) had no statistically significant effect on Ethiopia’s exports to China. This disconnect suggests that hard infrastructure alone is insufficient for driving export performance when complementary factors — such as industrial upgrading, quality certification, and firm-level capabilities — remain weak. Without addressing these bottlenecks, Ethiopia’s aid-driven infrastructure risks becoming “islands of capacity” with little impact on exports. 5.3.2. South–South Cooperation or Structural Asymmetry? China promotes its aid model as a form of South–South cooperation, distinct from Western aid by avoiding policy conditionalities and focusing on productive sectors. In principle, this should foster a more equal relationship. Yet the results show that aid has reinforced trade asymmetry: Chinese GDP growth pulls Ethiopian exports upward, but Chinese aid itself does not. Imports from China (ΔlnIMP) are significantly and negatively associated with Ethiopia’s exports, confirming that aid-financed procurement channels disproportionately benefit Chinese firms and exports, rather than Ethiopian producers. This finding aligns with critiques that Chinese aid in Africa often strengthens the donor’s economic position rather than the recipient’s export competitiveness. The evidence thus challenges the “win–win” narrative of South–South cooperation by showing that, at least in Ethiopia’s case, the distribution of trade benefits is lopsided. 5.3.3. Debt Dependency and Aid as a Debt Multiplier Another critical finding is that Chinese aid has contributed more to Ethiopia’s external debt burden than to its export performance. By design, many of China’s aid flows take the form of concessional or semi-concessional loans tied to Chinese contractors. While such financing enabled rapid infrastructure build-up, the absence of export gains means that Ethiopia’s foreign exchange earnings have not grown sufficiently to service debt obligations. This dynamic reflects the “debt multiplier” concern identified in the broader literature: when aid-financed projects do not generate foreign exchange, they exacerbate debt distress and reduce policy space. Ethiopia’s debt-to-export ratios flagged by IMF Debt Sustainability Analyses illustrate this trap. In the regression, the insignificant aid coefficient combined with the negative effect of imports captures this reality: aid creates liabilities without fostering corresponding export inflows. 5.3.4. Domestic Capacity and External Demand as True Drivers By contrast, Ethiopia’s export performance appears more responsive to China’s GDP (external demand) and Ethiopia’s GDP (domestic productive capacity). These findings highlight that sustainable export growth depends less on aid inflows and more on external demand conditions in partner economies, and Ethiopia’s own industrial and agricultural capacity. This aligns with structuralist and New Structural Economics perspectives: countries must build industries aligned with their comparative advantages and integrate them into global value chains, rather than depending on aid-driven projects with weak industrial linkages. 6. Conclusion and policy recommendation 6.1. Conclusion The evidence shows that China’s aid to Ethiopia has not promoted Ethiopian exports to China. Instead, export performance is driven more by domestic productive capacity and Chinese market demand, while aid inflows have primarily increased debt obligations and imports from China. This dynamic has created a trade imbalance, reinforcing dependency rather than supporting structural transformation. 6.2. Policy Recommendations To address this imbalance, Ethiopia should redirect aid toward export-generating sectors such as agro-processing, light manufacturing, and renewable energy, while strengthening quality infrastructure and standards compliance to access Chinese and global markets. Debt sustainability must be prioritized through renegotiation, linking repayments to export earnings, and exploring debt-for-development swaps. Ethiopia should also diversify export destinations via AfCFTA and global markets, while insisting on greater local participation and technology transfer in aid-financed projects. Guided by New Structural Economics, aid must be aligned with industrial policy and latent comparative advantages. For China, recalibrating South–South cooperation to ensure mutual benefit, transparency, and preferential access for Ethiopian value-added goods will be essential to transform aid from a debt driver into a genuine trade catalyst. References Bräutigam D (2011) NorwegiaN iNvestmeNt FuNd For developiNg CouNtries China in afriCa: What Can Western Donors Learn? Collins CT (2019) Made in Africa: Industrial Policy in Ethiopia. Northeast Afr Stud 19(1):159–162. https://doi.org/10.14321/nortafristud.19.1.0159 Hühne P, Meyer B, Nunnenkamp P (2014) Who Benefits from Aid for Trade? Comparing the Effects on Recipient versus Donor Exports. J Dev Stud 50(9):1275–1288. https://doi.org/10.1080/00220388.2014.903246 Malik AA, Parks B, Russell B, Jiahui Lin J, Walsh K, Solomon K, Zhang S, Elston T-B, Goodman S (2021) Banking on the Belt and Road: Insights from a new global dataset of 13,427 Chinese development projects. AidData , 1–165. https://docs.aiddata.org/ad4/pdfs/Banking_on_ the_Belt_and_Road__Insights_from_a_new_global_dataset_of_ 13427_Chinese_development_projects.pdf McNabb DE (2016) The Path to Industrialization. Comp History Commer Ind II 13–21. https://doi.org/10.1057/9781137503305_2 OECD (2024a) Aid for Trade at a Glance 2024. Aid for Trade at a Glance 2024 . https://doi.org/10.30875/9789287076458 OECD (2024b) IMPACT AND EFFECTIVENESS OF AID FOR TRADE. Aid for Trade at a Glance 2024 . https://doi.org/10.30875/9789287076458 Portugal-Perez A, Wilson JS (2012) Export Performance and Trade Facilitation Reform: Hard and Soft Infrastructure. World Dev 40(7):1295–1307. https://doi.org/10.1016/j.worlddev.2011.12.002 TARRÓSY I (2020) China’s Belt and Road Initiative in Africa, Debt Risk and New Dependency: The Case of Ethiopia. Afr Stud Q 19(3–4):8–28 UNCTAD (2018) National Green Export Review of Ethiopia: Leather and Sessam Seeds . 17 , 302 UNCTAD (2024) Technical and statistical report Productive capacities development: Challenges and opportunities. In UNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT Ethiopi World Bank (2023) On the Path to Industrialization Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7529955","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":509873898,"identity":"97171149-f692-45f5-8c22-7909960d27eb","order_by":0,"name":"Simachew Zelalem Mengistu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYFACxgYGhgoJHjZ2IM1gYEGsljM2Mnw8B0BaJIi1qC3NRk4iAcQkQot8/+HWDT/YDvOwST6/uuFHgQQDf3t3An4LZiS23ezhAWqRzim72QN0mMSZsxvwamGWYGy7wSMB1pJ2gweoxUAiF78WNv6DbTf/GIAcdiYNyCBCCw9DYtttnoQ0HjYJ9mO3ibJFQgKoReaADQ8bTw7bbRkDCR6CfpHvP/7s5tt/Evby7UDGmz82cvztvfi1ILvRAOJSEgD7A1JUj4JRMApGwQgCABOVQ8waBWG/AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-7635-6302","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Simachew","middleName":"Zelalem","lastName":"Mengistu","suffix":""}],"badges":[],"createdAt":"2025-09-03 19:52:57","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7529955/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7529955/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90582170,"identity":"0bbb4264-ad4f-483f-bc1b-21a821003cdc","added_by":"auto","created_at":"2025-09-04 10:31:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":747636,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7529955/v1/98926d0b-3aaa-4479-9d18-e1eca58690dd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes China’s Aid to Ethiopia Promote Exports from Ethiopia to China?\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the last two decades, China has positioned itself as a key development partner for Ethiopia, channeling substantial infrastructure financing into the country through South\u0026ndash;South cooperation initiatives and the Belt and Road Initiative (BRI). Projects such as the Addis Ababa\u0026ndash;Djibouti Railway, hydropower dams, and industrial parks are widely promoted as instruments to reduce trade costs, support industrial growth, and thereby boost Ethiopia\u0026rsquo;s exports. Ethiopia\u0026rsquo;s government, in particular, has framed these initiatives as catalysts not only for domestic transformation but also for expanding its trade footprint, especially with China.\u003c/p\u003e\u003cp\u003eHowever, while such infrastructure investments hold theoretical promise for trade facilitation, empirical analysis often highlights structural imbalances. Indeed, Deborah Br\u0026auml;utigam, a leading scholar on Chinese aid in Africa, emphasizes that although China\u0026rsquo;s infrastructure-focused approach appears to combat typical aid conditionalities, the actual developmental outcomes\u0026mdash;particularly for export diversification and trade growth\u0026mdash;are highly contested and context dependent (Br\u0026auml;utigam, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; TARR\u0026Oacute;SY, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Ethiopia\u0026rsquo;s case, the expected alignment between China aid and export performance remains weak: exports remain concentrated in a few primary commodities, and export trade flows between Ethiopia and China have become increasingly asymmetrical.\u003c/p\u003e\u003cp\u003eThis article examines whether Chinese aid to Ethiopia has effectively enhanced Ethiopian exports to China or instead contributed to heightened debt burdens and trade dependency. It argues that despite the infrastructure investments funded by China, Ethiopia\u0026rsquo;s export gains have been marginal, while debt pressures have intensified\u0026mdash;highlighting the need for critically evaluating the developmental logic of such aid relationships.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Aid, trade, and the \u0026ldquo;Aid for Trade\u0026rdquo; (AfT) proposition\u003c/h2\u003e\u003cp\u003eA large body of work contends that aid targeted at trade-related constraints\u0026mdash;ports, roads, customs, standards, power\u0026mdash;should reduce trade costs and raise exports. The OECD\u0026ndash;WTO \u0026ldquo;Aid for Trade\u0026rdquo; agenda formalized this logic: help low-income economies build \u0026ldquo;supply-side capacity and trade-related infrastructure\u0026rdquo; so they can access markets and export more, with export diversification a key success metric. \u0026ldquo;Aid for Trade at a Glance\u0026rdquo; reports synthesize evaluations and econometric evidence showing mixed but often positive effects at the aggregate level, especially when infrastructure and trade facilitation are the focus (OECD, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMicro-to-macro channels are widely discussed. Infrastructure lowers logistics and inventory costs; trade facilitation reforms cut dwell time and uncertainty; quality infrastructure (standards, SPS, TBT) enables compliance with destination-market requirements; and firm-capability support (management, finance, clusters) helps exporters upgrade. Empirical studies frequently find that \u0026ldquo;hard\u0026rdquo; and \u0026ldquo;soft\u0026rdquo; trade facilitation correlate with higher export performance, with some suggesting stronger effects from physical infrastructure combined with regulatory reform (Portugal-Perez \u0026amp; Wilson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eYet meta-evidence also cautions against blanket optimism. Early and subsequent studies report that \u0026ldquo;aid to productive capacity\u0026rdquo; often has weak or insignificant links to export growth compared with infrastructure or border reforms; others document that AfT can increase recipient exports to donors but also increase recipient imports from donors (with ambiguous net effects). Such findings foreshadow a potential divergence between aid\u0026rsquo;s intent and its realized trade outcomes\u0026mdash;especially when aid is commercially tied or when productive capabilities lag (H\u0026uuml;hne et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e2. 2. China’s development finance model and the aid–trade nexus\u003c/h3\u003e\n\u003cp\u003eChina\u0026rsquo;s contemporary development finance differs from OECD-DAC conventions in instruments, institutions, and disclosure. A growing empirical literature\u0026mdash;enabled by Aid data project-level datasets and the CARI/BU \u0026ldquo;Chinese Loans to Africa\u0026rdquo; database\u0026mdash;shows that Chinese flows range from grants and zero-interest loans to concessional loans, export credits, suppliers\u0026rsquo; credits, and other official finance, much of it for large infrastructure. Several studies associate Chinese finance with short-run output gains, while underscoring risks from opacity, collateralization, and sovereign debt vulnerabilities.\u003c/p\u003e\u003cp\u003eAid Data and collaborators emphasize that Beijing often \u0026ldquo;banks\u0026rdquo; on big-ticket infrastructure via state policy banks (Eximbank), with projects frequently implemented by Chinese SOEs and private contractors under tied procurement. This architecture can spur construction booms and donor-country exports of equipment and services, but its trade-creation effects for recipients hinge on downstream integration: connecting infrastructure to export-capable firms, standards, finance, and buyer networks. Where these links are weak, large projects may raise debt without catalyzing export diversification (Malik et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Africa, loan volumes surged during 2000\u0026ndash;2016, then slowed after 2017 amid debt stress and China\u0026rsquo;s own recalibration. New data indicate lending picked up again modestly in 2023, but with greater selectivity and more financial-sector or renewable-energy orientation. Ethiopia has been among the top five African sovereign borrowers from Chinese creditors since 2000\u0026mdash;an important contextual fact for parsing the aid\u0026ndash;trade relationship in the Ethiopian case (Collins, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Ethiopia\u0026rsquo;s industrial policy, SEZs/industrial parks, and export ambitions\u003c/h2\u003e\u003cp\u003eEthiopia\u0026rsquo;s industrialization strategy since the mid-2000s has relied on active industrial policy\u0026mdash;targeting light manufacturing (textiles/apparel, leather), import-substitution in selected intermediates, and export-oriented industrial parks\u0026mdash;supported by infrastructure megaprojects (power, rail, roads). Still, the country\u0026rsquo;s structural transformation and manufacturing export share have remained modest relative to ambition.\u003c/p\u003e\u003cp\u003eA strand of research focuses on the Ethiopia\u0026ndash;China interface in industrial zones. Br\u0026auml;utigam and Tang\u0026rsquo;s comparative work on China-backed Special Economic Zones (SEZs) in Africa\u0026mdash;covering Ethiopia\u0026rsquo;s Eastern Industrial Zone (EIZ)\u0026mdash;posits potential for technology and managerial spillovers but highlights execution risks: enclave dynamics, limited local linkages, and capability bottlenecks. Subsequent assessments of Ethiopia\u0026rsquo;s parks (Hawassa, Bole Lemi, and others) show sizable job creation but persistent challenges\u0026mdash;power reliability, input sourcing, quality control, compliance\u0026mdash;with uneven export performance and thin supplier ecosystems.\u003c/p\u003e\u003cp\u003eRecent multilateral diagnostics echo these mixed results. UNCTAD\u0026rsquo;s \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e review notes long construction lead times and under-capacity in several parks, compounded by conflict disruptions in the north; World Bank analyses show industrial parks contributing a non-trivial but still limited share of manufacturing exports and often registering negative net-export positions once imported inputs are accounted for. These patterns complicate any simple mapping from aid-financed infrastructure and zones to sustained export growth (UNCTAD; World Bank, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Ethiopia\u0026ndash;China trade structure: concentration, asymmetry, and \u0026ldquo;who exports what?\u0026rdquo;\u003c/h2\u003e\u003cp\u003eDespite infrastructure build-outs and industrial-park expansion, Ethiopia\u0026rsquo;s exports to China remain concentrated in primary commodities\u0026mdash;especially sesame\u0026mdash;plus coffee and a few minerals. UNCTAD\u0026rsquo;s green export review (leather and sesame) shows that in the late 2000s and mid-2010s, China consistently absorbed the majority of Ethiopia\u0026rsquo;s sesame shipments. More recent bilateral snapshots still list coffee, oily seeds (including sesame), and some ores among Ethiopia\u0026rsquo;s top exports to China. Meanwhile, Ethiopia imports a wide range of manufactured goods from China (machinery, textiles, electronics, chemicals), producing a persistent bilateral trade deficit (UNCTAD, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis composition suggests that the country has not achieved significant upgrading or diversification of exports to China, despite aid-financed infrastructure ostensibly aimed at reducing trade costs and enabling manufacturing export takeoff. Aggregate figures reported in open data portals show bilateral exports fluctuating at relatively low levels by comparison with imports\u0026mdash;another sign that aid and infrastructure, on their own, have not sufficed to transform export structure toward higher-value goods destined for the Chinese market.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. The Addis Ababa\u0026ndash;Djibouti Railway and the logistics corridor hypothesis\u003c/h2\u003e\u003cp\u003eA flagship of China\u0026ndash;Ethiopia cooperation is the standard-gauge, electrified Addis Ababa\u0026ndash;Djibouti Railway (AA\u0026ndash;DJ), designed to cut transit times to port and lower logistics costs. The \u0026ldquo;logistics corridor\u0026rdquo; hypothesis holds that such a line should enable scale economies and reliability gains for exporters. However, multiple sources indicate operational and financial headwinds: lower-than-projected traffic, coordination issues with trucking and port handling, and debt-service concerns that led Ethiopia (and Djibouti for its segment) to seek loan restructuring with China Exim bank. In 2018, Ethiopia announced a rescheduling that extended the railway loan\u0026rsquo;s maturity, and project-level documentation indicates maturity/grace-period extensions tied to weak commercial performance.\u003c/p\u003e\u003cp\u003eThe railway\u0026rsquo;s mixed record underscores a broader point in the literature: mega-infrastructure can be a necessary but not sufficient condition for export growth. Without consistent power, industrial inputs, standards compliance, order aggregation, and downstream firm capabilities, logistics upgrades may fail to translate into sustained increases in export volumes and values. In Ethiopia\u0026rsquo;s case, the logistics bottleneck was only one of several constraints; park-level operational issues and firm-level capability gaps have often proved binding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Debt dynamics and the \u0026ldquo;aid-as-debt multiplier\u0026rdquo; concern\u003c/h2\u003e\u003cp\u003eParallel to trade outcomes, the literature tracks rising sovereign liabilities. IMF debt sustainability assessments for Ethiopia have repeatedly flagged external debt vulnerabilities tied to export-linked indicators (e.g., PV of external debt to exports), with debt judged unsustainable in recent updates. Because a large share of Ethiopia\u0026rsquo;s external borrowing financed infrastructure with long gestation and uncertain FX returns, the debt service burden can crowd out the very investments in capabilities and quality infrastructure that would make exports competitive. In this sense, certain aid/loan packages can become debt multipliers when export earnings do not materialize as projected (McNabb, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBroader analyses of Chinese lending confirm this macro picture: loan volumes peaked and later moderated amid rising recipient distress and China\u0026rsquo;s shift toward \u0026ldquo;sustainable lending\u0026rdquo; principles. Ethiopia\u0026rsquo;s prominent position among top African borrowers heightens the salience of debt-export linkages for the aid\u0026ndash;trade debate in the Ethiopian context.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7. Preferential market access: zero-tariff initiatives and their limits\u003c/h2\u003e\u003cp\u003eA related (and often optimistic) argument is that China\u0026rsquo;s unilateral tariff preferences for least-developed countries (LDCs) should lower price wedges and pull in African exports. In 2023, China granted Ethiopia zero-tariff treatment on 98% of tariff lines; by late 2024, Beijing announced 100% product coverage for all LDCs with diplomatic ties. In principle, these reforms should boost margins for eligible products. In practice, however, preference utilization depends on rules-of-origin compliance, product standards, buyer relationships, and consistent supply\u0026mdash;all areas where firm-level and institutional capabilities in Ethiopia remain uneven. This limits the translation of preferential tariffs into realized export growth, especially beyond a narrow set of primary commodities already demanded by Chinese buyers.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Econometric Model","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Model Specification\u003c/h2\u003e\u003cp\u003eTo quantify the effect of Chinese aid on Ethiopia\u0026rsquo;s exports to China, the study applies a time-series regression model within the framework of trade\u0026ndash;aid nexus analysis:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{{\\Delta\\:}\\text{ln}\\left(EXP\\right)}_{t}=\\:\\alpha\\:+{\\beta\\:}_{1}{\\Delta\\:}{AID}_{t}+{\\beta\\:}_{2}{{\\Delta\\:}GDP}_{CHN,t}+{\\beta\\:}_{3}{{\\Delta\\:}GDP}_{ETH,\\:t}+{\\beta\\:}_{4}{{\\Delta\\:}EXR}_{t}+{\\beta\\:}_{5}{{\\Delta\\:}IMP}_{t}+{\\mathcal{E}}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EXP}_{t}\\)\u003c/span\u003e\u003c/span\u003e= Ethiopia\u0026rsquo;s exports to China (in USD), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{AID}_{t}\\)\u003c/span\u003e\u003c/span\u003e= Value of Chinese aid/loans to Ethiopia in year (USD millions), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP}_{CHN,t}\\)\u003c/span\u003e\u003c/span\u003e= China\u0026rsquo;s GDP (proxy for import demand from Ethiopia), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GDP}_{ETH,\\:t}\\)\u003c/span\u003e\u003c/span\u003e= Ethiopia\u0026rsquo;s GDP (proxy for supply capacity and production base), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EXR}_{t}\\)\u003c/span\u003e\u003c/span\u003e = Ethiopia\u0026rsquo;s exchange rate against the USD (competitiveness factor), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{IMP}_{t}\\)\u003c/span\u003e\u003c/span\u003e = Ethiopia\u0026rsquo;s imports from China (to capture asymmetric aid\u0026ndash;trade effect), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mathcal{E}}_{t}\\)\u003c/span\u003e\u003c/span\u003e= Error term\u003c/p\u003e\u003cp\u003eExpected Signs\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e​: Expected positive, if Chinese aid stimulates Ethiopia\u0026rsquo;s exports through infrastructure and capacity-building.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eBut may be insignificant/negative, if aid mainly fuels debt and import dependence.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e​: Positive \u0026mdash; higher Chinese GDP should increase demand for imports.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}\\)\u003c/span\u003e\u003c/span\u003e​: Positive \u0026mdash; stronger Ethiopian economy should enhance production/export supply.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}\\)\u003c/span\u003e\u003c/span\u003e​: Ambiguous \u0026mdash; depreciation may make Ethiopian goods cheaper abroad but also raises cost of imported inputs.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{5}\\)\u003c/span\u003e\u003c/span\u003e​: Negative \u0026mdash; rising imports from China may crowd out Ethiopia\u0026rsquo;s capacity to export, worsening the trade deficit.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Estimation Techniques\u003c/h2\u003e\u003cp\u003eTo empirically examine the relationship between Chinese aid and Ethiopia\u0026rsquo;s exports to China, the study employed a multi-step econometric strategy designed to ensure both statistical rigor and robustness of inference.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003e1. Stationarity Testing\u003c/h3\u003e\n\u003cp\u003eAs a first step, the time-series properties of the variables were assessed. The Augmented Dickey\u0026ndash;Fuller (ADF) and Phillips\u0026ndash;Perron (PP) tests were applied to detect the presence of unit roots in each series. Establishing the order of integration was necessary to avoid spurious regressions and to determine the appropriate estimation approach. If variables were found to be non-stationary in levels but stationary in first differences (I (1)), subsequent cointegration analysis was undertaken.\u003c/p\u003e\n\u003ch3\u003e2. Cointegration and Long-Run Relationship\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003e2. Cointegration and Long-Run Relationship\u003c/div\u003e\u003cp\u003eTo explore the possibility of a long-run equilibrium relationship between Chinese aid and Ethiopia\u0026rsquo;s exports to China, the Johansen Cointegration Test was applied. This test identifies whether multiple non-stationary variables share a common stochastic trend. If evidence of cointegration was found, a Vector Error Correction Model (VECM) was estimated, capturing both the short-run dynamics and the long-run equilibrium adjustment process.\u003c/p\u003e\n\u003ch3\u003e3. Short-Run Dynamics\u003c/h3\u003e\n\u003cp\u003eIn the absence of cointegration, the study employed alternative dynamic specifications such as the Autoregressive Distributed Lag (ARDL) model or Vector Autoregression (VAR). The ARDL framework is particularly useful given its flexibility with mixed orders of integration (I(0) and I(1)) and its ability to estimate both short- and long-run coefficients. The VAR model was considered for purely short-run analysis when no long-run equilibrium was established.\u003c/p\u003e\n\u003ch3\u003e4. Robustness Checks\u003c/h3\u003e\n\u003cp\u003eSeveral robustness measures were incorporated to enhance the reliability of the results. Newey\u0026ndash;West heteroskedasticity and autocorrelation consistent (HAC) standard errors were used to correct for potential autocorrelation and heteroskedasticity in the residuals. Multicollinearity among regressors was assessed through the Variance Inflation Factor (VIF), ensuring that estimated coefficients were not biased by redundant explanatory variables.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003cp\u003eNull Hypothesis (H₀): Chinese aid does not significantly promote Ethiopian exports to China (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e = 0).\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAlternative Hypothesis (H₁): Chinese aid significantly promotes Ethiopian exports to China (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e \u0026ne;0).\u003c/p\u003e\u003cp\u003eGiven Ethiopia\u0026rsquo;s structural challenges and trade imbalance, it is anticipated that β1 will be statistically insignificant or negative, supporting the conclusion that Chinese aid has not fostered exports but instead exacerbated debt burden.\u003c/p\u003e"},{"header":"4. Data \u0026 Period","content":"\u003cp\u003eThe study relies on annual data covering the period from 2000 to 2023. The dataset has been compiled from multiple credible sources, including the United Nations Comtrade Database, AidData, the China\u0026ndash;Africa Research Initiative (CARI) Database, the World Bank\u0026rsquo;s World Development Indicators (WDI), the International Monetary Fund\u0026rsquo;s Debt Sustainability Analysis (IMF DSA), and publications from the National Bank of Ethiopia. These diverse sources provide a comprehensive foundation for examining the economic and financial dynamics relevant to the analysis.\u003c/p\u003e"},{"header":"5. Results and Discussion of the study","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the econometric findings. Chinese aid has a negative and insignificant effect on exports. China\u0026rsquo;s GDP and Ethiopia\u0026rsquo;s GDP both have positive and significant impacts. Imports from China are negative and significant, indicating structural asymmetry.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression Results: Effect of Chinese Aid on Ethiopian Exports to China (2000\u0026ndash;2023)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE (robust)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et- value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep- value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔln(AID)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔln(GDP_CHN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔln(GDP_ETH)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.019\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔln(EXR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eΔln(IMP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Diagnostics\u003c/h2\u003e\u003cp\u003eBreusch\u0026ndash;Godfrey tests revealed no autocorrelation; Breusch\u0026ndash;Pagan indicated no heteroskedasticity; Jarque\u0026ndash;Bera confirmed approximate normality. Granger causality tests showed that aid does not Granger-cause exports at 1\u0026ndash;2 lags.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Discussions\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e5.3.1. Aid-for-Trade and the Missing Export Link\u003c/h2\u003e\u003cp\u003eTheoretically, aid that reduces trade costs or builds productive capacity should enable export growth. The WTO\u0026ndash;OECD \u0026ldquo;Aid for Trade\u0026rdquo; framework emphasizes that infrastructure and trade facilitation investments improve competitiveness by lowering transport costs, streamlining customs, and strengthening supply chains. In Ethiopia\u0026rsquo;s case, China\u0026rsquo;s aid was heavily concentrated in large-scale infrastructure projects such as railways, dams, and industrial parks \u0026mdash; all of which are consistent with the Aid-for-Trade rationale. However, the econometric results indicate that Chinese aid (ΔlnAID) had no statistically significant effect on Ethiopia\u0026rsquo;s exports to China. This disconnect suggests that hard infrastructure alone is insufficient for driving export performance when complementary factors \u0026mdash; such as industrial upgrading, quality certification, and firm-level capabilities \u0026mdash; remain weak. Without addressing these bottlenecks, Ethiopia\u0026rsquo;s aid-driven infrastructure risks becoming \u0026ldquo;islands of capacity\u0026rdquo; with little impact on exports.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e5.3.2. South\u0026ndash;South Cooperation or Structural Asymmetry?\u003c/h2\u003e\u003cp\u003eChina promotes its aid model as a form of South\u0026ndash;South cooperation, distinct from Western aid by avoiding policy conditionalities and focusing on productive sectors. In principle, this should foster a more equal relationship. Yet the results show that aid has reinforced trade asymmetry: Chinese GDP growth pulls Ethiopian exports upward, but Chinese aid itself does not. Imports from China (ΔlnIMP) are significantly and negatively associated with Ethiopia\u0026rsquo;s exports, confirming that aid-financed procurement channels disproportionately benefit Chinese firms and exports, rather than Ethiopian producers.\u003c/p\u003e\u003cp\u003eThis finding aligns with critiques that Chinese aid in Africa often strengthens the donor\u0026rsquo;s economic position rather than the recipient\u0026rsquo;s export competitiveness. The evidence thus challenges the \u0026ldquo;win\u0026ndash;win\u0026rdquo; narrative of South\u0026ndash;South cooperation by showing that, at least in Ethiopia\u0026rsquo;s case, the distribution of trade benefits is lopsided.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e5.3.3. Debt Dependency and Aid as a Debt Multiplier\u003c/h2\u003e\u003cp\u003eAnother critical finding is that Chinese aid has contributed more to Ethiopia\u0026rsquo;s external debt burden than to its export performance. By design, many of China\u0026rsquo;s aid flows take the form of concessional or semi-concessional loans tied to Chinese contractors. While such financing enabled rapid infrastructure build-up, the absence of export gains means that Ethiopia\u0026rsquo;s foreign exchange earnings have not grown sufficiently to service debt obligations.\u003c/p\u003e\u003cp\u003eThis dynamic reflects the \u0026ldquo;debt multiplier\u0026rdquo; concern identified in the broader literature: when aid-financed projects do not generate foreign exchange, they exacerbate debt distress and reduce policy space. Ethiopia\u0026rsquo;s debt-to-export ratios flagged by IMF Debt Sustainability Analyses illustrate this trap. In the regression, the insignificant aid coefficient combined with the negative effect of imports captures this reality: aid creates liabilities without fostering corresponding export inflows.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e5.3.4. Domestic Capacity and External Demand as True Drivers\u003c/h2\u003e\u003cp\u003eBy contrast, Ethiopia\u0026rsquo;s export performance appears more responsive to China\u0026rsquo;s GDP (external demand) and Ethiopia\u0026rsquo;s GDP (domestic productive capacity). These findings highlight that sustainable export growth depends less on aid inflows and more on external demand conditions in partner economies, and Ethiopia\u0026rsquo;s own industrial and agricultural capacity. This aligns with structuralist and New Structural Economics perspectives: countries must build industries aligned with their comparative advantages and integrate them into global value chains, rather than depending on aid-driven projects with weak industrial linkages.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"6. Conclusion and policy recommendation","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Conclusion\u003c/h2\u003e\u003cp\u003eThe evidence shows that China\u0026rsquo;s aid to Ethiopia has not promoted Ethiopian exports to China. Instead, export performance is driven more by domestic productive capacity and Chinese market demand, while aid inflows have primarily increased debt obligations and imports from China. This dynamic has created a trade imbalance, reinforcing dependency rather than supporting structural transformation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Policy Recommendations\u003c/h2\u003e\u003cp\u003eTo address this imbalance, Ethiopia should redirect aid toward export-generating sectors such as agro-processing, light manufacturing, and renewable energy, while strengthening quality infrastructure and standards compliance to access Chinese and global markets. Debt sustainability must be prioritized through renegotiation, linking repayments to export earnings, and exploring debt-for-development swaps. Ethiopia should also diversify export destinations via AfCFTA and global markets, while insisting on greater local participation and technology transfer in aid-financed projects. Guided by New Structural Economics, aid must be aligned with industrial policy and latent comparative advantages. For China, recalibrating South\u0026ndash;South cooperation to ensure mutual benefit, transparency, and preferential access for Ethiopian value-added goods will be essential to transform aid from a debt driver into a genuine trade catalyst.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBr\u0026auml;utigam D (2011) \u003cem\u003eNorwegiaN iNvestmeNt FuNd For developiNg CouNtries China in afriCa: What Can Western Donors Learn?\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCollins CT (2019) Made in Africa: Industrial Policy in Ethiopia. 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In \u003cem\u003eUNITED NATIONS CONFERENCE ON TRADE AND DEVELOPMENT Ethiopi\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Bank (2023) \u003cem\u003eOn the Path to Industrialization\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aid for Trade, Export, Ethiopia, China","lastPublishedDoi":"10.21203/rs.3.rs-7529955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7529955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChina has emerged as Ethiopia\u0026rsquo;s largest bilateral development partner, financing large-scale infrastructure projects under the framework of South\u0026ndash;South cooperation and the Belt and Road Initiative (BRI). Conventional wisdom suggests that such aid should facilitate trade by reducing costs, enhancing industrial capacity, and integrating Ethiopia into global markets. This article investigates whether Chinese aid has indeed promoted Ethiopian exports to China. The evidence demonstrates that, despite massive inflows of Chinese aid, Ethiopia\u0026rsquo;s exports to China remain stagnant and concentrated in a handful of primary commodities. Instead, Chinese aid has primarily fueled Ethiopia\u0026rsquo;s external debt burden, widened trade imbalances, and reinforced structural asymmetries. Chinese aid has been more effective in creating debt obligations than in promoting Ethiopian export growth.\u003c/p\u003e","manuscriptTitle":"Does China’s Aid to Ethiopia Promote Exports from Ethiopia to China?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 10:14:59","doi":"10.21203/rs.3.rs-7529955/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d1ffbb4f-d7a0-433a-8d86-1ae271ea543a","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54156711,"name":"International Economics"},{"id":54156712,"name":"International Business"}],"tags":[],"updatedAt":"2025-09-04T10:14:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 10:14:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7529955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7529955","identity":"rs-7529955","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}