Undisclosed Debt Sustainability

Alfaro, Laura; Kanczuk, Fábio

doi:10.1257/pandp.20221000

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…The models' out-of-sample prediction shows a general decline in performance over time. This decline emphasizes the changing nature of IMF-supported program use that is influenced by either supply changes like IMF policy adjustments or country demand shifts such as the emergence of alternative funding sources e.g., the rise of China as a lender of last resort (Alfaro & Kanczuk, 2019). Additionally, we note a performance dip during global crises (Asian Financial Crisis,Global Financial Crisis,, which underscores the inherent difficulty in predicting such events (Aikman et al, 2021).…”

Section: Introductionmentioning

confidence: 78%

Predicting IMF-Supported Programs: A Machine Learning Approach

Batsuuri

2024

IMF Working Papers

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and the participants at the FIN Economists' Group Seminar for their valuable comments. We are also grateful to the Strategy, Policy & Review -Risk Unit team for their consultations regarding data and technical aspects. We would like to especially thank Maksym Ivanyna, Clément Marsilli, Kevin Wiseman, Ritong Qu, and Xin Weining for sharing their valuable advice and insights on the technical aspects of machine learning modeling. The study also greatly benefitted from comments received during the interdepartmental review process.

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Section: Introductionmentioning

confidence: 78%

Predicting IMF-Supported Programs: A Machine Learning Approach

Batsuuri

2024

IMF Working Papers

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“…Despite the increased Chinese activities in Africa as shown by trade, foreign direct investment, official aid and debt, there seems to be doubt on the impact of such on growth of African countries. The impact of Chinese finance on growth of developing economies is ambiguous as Chinese debt and aid bear hidden costs, which are consequential for macroeconomic stability (Alfaro & Kanczuk, 2019). Similarly, Chinese investment are concentrated in the primary sector, where Chinese companies are in the process of extracting raw materials for the homeland (Carmignani & Chowdhury, 2012).…”

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confidence: 99%

Untitled

2023

RJOAS

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Industrialization of the Chinese economy has made China a formidable force in the global economy. Similarly, Africa has witnessed increased Chinese activity as shown by rising figures in trade with China, Chinese foreign direct investments, Chinese aid and debt to Africa. Despite the rise in Chinese activities in Africa, there is doubt about the impact of such on growth of African economies. Chinese aid and debt are observed to bear hidden costs that outweigh the benefits. Whereas Chinese trade and investments are claimed to be resource seeking and not genuine. The World Bank also observed that Chinese finance is secretive and does not comply with the best practices of good governance. In this regard, this paper aims to assess the impact of Chinese activities in Southern Africa. The paper uses an explanatory research design. On determining the effect of Chinese activities, we adapt a Solow-growth model. A panel data set is assembled using individual countries' time series data on the variables of interest. The study uses real GDP growth rate as the dependent variable. The explanatory variables in this paper are GDP per capita, trade with the rest of the world, rest of the world foreign direct investment, Chinese trade, Chinese debt, Chinese FDI, Chinese aid and population growth. Document analysis is the major data collection tool in this paper. Data on Chinese debt, investments, trade and aid is on documents of the Ministry of Commerce of China and John Hopkins University China Africa Research Initiative. Whereas, data on world trade, FDI inflows, population growth, real GDP growth and GDP per capita is on World Bank and IMF public data sets. We use Stata 15 for data analysis. The diagnostic tests showed that we have a problem of heteroscedasticity and auto-correlation. Whereas, the Hausman model choice test showed that the random effects model is the most efficient and consistent than the fixed effects model. We therefore adopt the Feasible Generalised Least Squares regression (FGLS); as this model is robust under autocorrelation and heteroscedasticity. The regression results show that GDP per capita and rest of the word FDI are the only statistically significant explanatory variables at 5% level of significance. All the other variables that is Chinese trade, Chinese debt, Chinese FDI, Chinese aid, population growth and trade with the rest of the world became statistically significant at 10% level. The study recommends that Southern African countries need to establish a thriving democracy and improve the quality of institutions as a way to attract long term investments from the rest of the world. On dealing with China, the paper observes that there is a possibility that all citizens can benefit from Chinese activities and as a region there is need to change the current systems that benefit the political elite and support corruption at the expense of sustainable and inclusive economic development.

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Borrowing from China and Sovereign Credit Risk

Kondo,

Mkhitaryan,

Sosa-Padilla

2024

AEA Papers and Proceedings

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China's lending to developing nations has surged since the mid-2000s, making it these nations' largest official creditor. Contracts frequently feature unique terms, indicating China's substantial privileges as a lender. However, other lenders are also involved. We examine how these lending relationships with China affect access to credit from international bondholders. Combining data on external marketable debt and prices with Chinese lending data, we find that Chinese funding events decrease marketable debt issuance and sovereign yields. Conversely, debt restructuring with China leads to higher spreads. These findings underscore the impact of China's lending on international credit access.

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Undisclosed Debt Sustainability

Cited by 5 publications

References 17 publications

Predicting IMF-Supported Programs: A Machine Learning Approach

Predicting IMF-Supported Programs: A Machine Learning Approach

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Borrowing from China and Sovereign Credit Risk

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