Debt trapped: Analysing the impact of IMF on economic growth and human development in highly indebted countries, with a focus on corruption

Abdou, Doaa M. Salman; El-Ahmar, Ahmed Adel; Youssri, Dina; Klose, Jens Article Debt trapped: Analysing the impact of IMF on economic growth and human development in highly indebted countries, with a focus on corruption Ekonomika Provided in Cooperation with: Vilnius University Press Suggested Citation: Abdou, Doaa M. Salman; El-Ahmar, Ahmed Adel; Youssri, Dina; Klose, Jens (2025) : Debt trapped: Analysing the impact of IMF on economic growth and human development in highly indebted countries, with a focus on corruption, Ekonomika, ISSN 2424-6166, Vilnius University Press, Vilnius, Vol. 104, Iss. 1, pp. 30-47, https://doi.org/10.15388/Ekon.2025.104.1.2 This Version is available at: https://hdl.handle.net/10419/323170 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ 30 Ekonomika ISSN 1392-1258 eISSN 2424-6166 2025, vol. 104(1), pp. 30–47 DOI: https://doi.org/10.15388/Ekon.2025.104.1.2 Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development in Highly Indebted Countries, with a Focus on Corruption Doaa M. Salman Abdou* October University for Modern Sciences and Arts, Cairo, Egypt Email: [email protected] ORCID: https://orcid.org/0000-0001-5050-6104 Ahmed Adel El-Ahmar October University for Modern Sciences and Arts, Cairo, Egypt Email: [email protected] ORCID: https://orcid.org/0000-0002-9160-2001 Dina Youssri German University, Cairo, Egypt Email: [email protected] ORCID: https://orcid.org/0000-0003-3516-3865 Jens Klose THM Business School, Giessen, Germany Email: [email protected] ORCID: https://orcid.org/0000-0001-6234-5272 Abstract. Being indebted represents significant risks associated with global financial instability in a world where financial stability hangs precariously between debt and economic growth. The International Monetary Fund (IMF) casts a critical eye over countries navigating the perilous seas of fiscal responsibility, aiming to improve their economic performance. Hence, evaluating the connection between IMF loans and sustainable growth in highly indebted countries is crucial. This study aims to examine the impact of IMF loans on real GDP and human development in a panel of the 13 most indebted countries from 1997 to 2020, by using pooled OLS and fixed-effect estimators. The article contributes to the existing literature in two ways. On the one hand, a broad set of human development indicators is analysed. On the other hand, corruption is incorporated into the analysis, explicitly measuring the simultaneous effects of IMF loans and corruption. It has been found that IMF loan growth tends to lower GDP growth, human development, and mortality. IMF loans often come with conditions that may lead to austerity measures. While these measures can negatively impact economic growth in the short term, they might also redirect resources toward social programs which improve health outcomes, thereby reducing mortality rates. When corruption is considered, a reduction in corruption leads to more effective IMF loans, increased human development, and decreased mortality even further. Therefore, it is recommended that IMF loans should always be accompanied with incentives to reduce corruption. Keywords: IMF loans, HDI, indebted countries, corruption, economic growth, life expectancy, mortality, education. Received: 17/09/2024. Revised: 21/11/2024. Accepted: 05/01/2025 Copyright © 2025 Doaa M. Salman Abdou, Ahmed Adel El-Ahmar, Dina Youssri, Jens Klose. Published by Vilnius University Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Contents lists available at Vilnius University Press * Correspondent author. Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 31 Introduction Human development is widely recognized as one of the key drivers of a country’s economic growth. Improved education and health lead to the acquisition of skills, particularly the ability to innovate, which can boost economic growth. Moreover, human development enhances people’s choices and diversifies them in a way that enables them to lead longer, healthier, and more fulfilling lives. Given its importance, the United Nations has addressed human development extensively in its Sustainable Development Goals (SDGs), with seven of the 17 goals focusing on different aspects of human development, such as reducing poverty, improving health and education, and promoting gender equality. However, most countries fail to develop balanced debt management policies that can help them to achieve growth even if they receive external assistance from institutions such as the International Monetary Fund (IMF) and the World Bank (Elkhalfi et al., 2024). The IMF plays a critical role in developing countries, where corruption can misdirect the use of IMF loans, with funds getting wasted on projects that do not actually benefit the country. This study aims to examine the impact of IMF loans on economic growth and human development in the most indebted countries. Some scholars have expressed concern that the engagement is a debt trap, a ruse towards modern neo-colonization and resource extinction in Africa. However, others have documented the significance of such investments in attaining SDGs (Bo et al., 2024) This article extends previous research in the literature in two ways. First, it starts with measuring the effects of IMF loans on a broad set of dependent variables. These are, on the one hand, the real GDP growth as the common proxy for economic growth, and, on the other hand, the Human Development Index (HDI), secondary school enrollment, life expectancy, and mortality rate, which are used to measure human development. Second, a new dimension is added to the literature by adding corruption to the analysis. With this factor being considered, we can verify whether IMF loans have different effects in highand low-corruption economies. This study focuses on the transmission channels and discusses various factors that might mediate the indebtedness-mortality link. For example, we note that higher levels of debt accumulation result in increased austerity. The following section discusses the literature review, followed by an overview of the data used. Finally, the results, conclusion and policy recommendations are outlined. Literature Review The classical school, in contrast with Keynesian economists, believed in the Government’s role in regulating the market and correcting imbalances through public borrowing as part of Government intervention in the economy to ensure upward economic evolution. They believed that accumulation of debt led to a crowding-out effect, thereby decreasing private investments, which deteriorated economic growth. The Debt Overhang Theory, established by Krugman in 1988, explains the situation in which the accumulated loans and debt make the country unable to repay them, thus decreasing its expenditure ability on public ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 32 projects such as infrastructure, social programs, or financing the recurrent expenditure. In 1964, Gary Becker explored how education and training contribute to economic success, and explained why developed countries accumulate wealth while developing countries remain poor due to labour (under)productivity. The model states that a higher investment in capital per worker leads to a higher output, with investment per worker being the main variable positively impacting changes in capital per worker. Therefore, debt should be directed to finance education, improve infrastructure, and develop healthcare services so that to improve human capital and technology, which are considered the main sources for the country’s future income generation. The effectiveness of IMF programs and loans on the countries receiving help has already been investigated extensively. This also holds, among other points, for the empirical response to economic growth and human development. Barro and Lee (2005) found that IMF loans reduce economic growth rates in their sample of 130 countries between 1975 and 1999. Meanwhile, Bird and Rowlands (2017) discovered that the impact of IMF loan programs on economic growth in low-income countries is generally positive, however, it depends on the country’s performance, debt and aid dependency, IMF resources, and the recent history of IMF engagement. Later, Hackler et al. (2020) estimated empirically for 93 countries between 2000 and 2014 what effects the compliance to IMF loan conditions tends to have on economic growth. The authors established that meeting the IMF conditions can change the economic growth rate either way depending on the nature of the condition. Siddique et al. (2021), by using panel data covering 70 countries during the period of 1980–2018, determined that IMF loans had a positive impact on economic growth for upper-middle-income countries. Kuruc (2022) used a synthetic control analysis of IMF interventions in 399 different crisis periods between 1970 and 2013. With this approach, he could show that IMF programs led to higher economic growth than in a situation without a program in place. Another strand of literature focuses on the role of IMF programs for human development. Easterly (2003), by using data from 1980 to 1998, established that IMF and World Bank programs have no significant direct effect on poverty rates. However, he found that the growth elasticity of poverty is significantly negative, which means that the poor segment of the population benefits less from economic expansion under a program. Muhamed and Gaas (2016) discovered that IMF programs had a negative impact on human development in developing countries. Bird et al. (2021) determined that IMF programs did not significantly increase poverty or income inequality by using a sample of 48 countries in the years ranging from 1990 to 2015. Stubbs et al. (2021) focused on the austerity policies often associated with IMF programs and their effects on poverty and income inequality. In a sample covering 79 countries between 2002 and 2018, they ascertained that stricter austerity policies in IMF programs led to more poverty and higher income inequality. Biglaiser and McGauvran (2022) found that IMF loans reduce human development in developing countries in a sample of 81 countries between 1986 and 2016. This result holds in particularly concerning poor nations. Corruption is a factor that may affect the effectiveness of IMF loans and is associated with lower growth and more poverty. A Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 33 recent strand of the literature emphasizes the importance of e-government in combating corruption and promoting transparency (Seiam and Salman, 2024). Apeagyei et al. (2024) showed that many sub-Saharan African countries suffer from poverty and income inequality, deteriorating public health, educational outcomes, increasing child mortality, and corruption. The IMF provides financial assistance to countries facing balance of payments problems, and numerous sub-Saharan nations have engaged with the IMF through various lending programs. This study contributes to the ongoing discussion on the effectiveness of IMF loans. First, we are one of the few studies that estimate the effects of IMF loans on economic growth and human development simultaneously. Second, to the best of the researchers’ knowledge, they are the first to investigate the role of corruption in IMF programs and their effect on growth and development. Third, we focus on a unique database with 13 highly indebted countries in the years ranging between 1997 and 2020. The study’s main hypotheses are: • IMF loans tend to lower the GDP growth and human development (e.g., life expectancy, education) in the short term. • The effectiveness of IMF loans improves when corruption is reduced, thus leading to enhanced human development and decreased mortality. Methodology and Data Our panel dataset is comprised of 13 countries which received IMF loans during the period from 1997 to 2020. These countries are Angola, Argentina, Ecuador, Egypt, Ghana, Ivory Coast, Kenya, Morocco, Nigeria, Pakistan, South Africa, Tunisia, and Ukraine. For each of these countries, we gathered data on five dependent variables, representing measures of either economic growth or human development. Economic growth is approximated by the real gross domestic product (GDP). Additionally, we incorporated two of the three sub-indices used in the HDI calculation. Thus, the second measure represents the share of secondary school enrollment, while the third measure represents life expectancy. The fourth measure is the mortality rate, which is expected to negatively correlate with life expectancy. As for independent variables, we collected data on the two primary variables of interest: the amount of money received through IMF loans, and the country’s corruption index. For the corruption index, we utilized the Corruption Perception Index issued by Transparency International1. It is of importance to note that an increase in this index indicates a reduction in corruption within the country. Furthermore, we included a set of six additional control variables in all the estimation equations. These include Gross capital formation (GCF), Government expenditures on education (as a percentage of the total Government expenditures), trade in services, consumer price index, foreign direct investments (FDI), and the size of the population. To ensure the stationarity of the underlying time-series 1 https://www.transparency.org/en/cpi/2022 ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 34 data, all the variables were transformed into growth rates. For example, the price index was transformed into the inflation rate. Descriptive statistics along with unit root tests for all variables are presented in Table 1 in the Appendix. Correlation Analysis The analysis reveals a strong positive correlation between IMF loans and GDP in the studied countries, thereby indicating that as IMF loans increase, GDP also rises. There are several other significant correlations: a weak negative correlation exists between the mortality rate and both IMF loans and corruption. Secondary school enrollment shows a weak positive correlation with GDP, IMF loans, and corruption, along with a strong negative correlation with the mortality rate. Human development exhibits a weak positive correlation with GDP, IMF loans, and corruption, but strong negative correlations with the mortality rate and positive correlations with secondary school enrollment. Government expenditure on education has weak negative correlations with GDP and IMF loans, but weak positive correlations with corruption, mortality, secondary school enrollment, and human development. Inflation negatively correlates with GDP, corruption, secondary school enrollment, and human development. Foreign direct investment (FDI) shows weak negative correlations with GDP, IMF loans, secondary school enrollment, and human development, while strongly correlating positively with inflation. Gross capital formation has weak negative correlations with GDP and IMF loans, and trade in services negatively correlates with GDP. Population growth is weakly negatively correlated with GDP, IMF loans, corruption, secondary school enrollment, and Government expenditure. For detailed correlations, refer to Table 2 in the Appendix. Estimation Methodology The study used a cross-dependence test to determine the suitability of pooled versus random effects models. The model includes five dependent variables, and employs two estimation methods: pooled ordinary least squares (OLS), assuming a common intercept for all the countries under analysis, and fixed-effects estimation, allowing for individual country intercepts. Detailed findings are presented in Tables 3 to 7 in the Appendix. Two different types of estimation are employed: first, a pooled ordinary least squares (OLS) estimation, which assumes that all countries have the same intercept (see Equation (1)); and second, a fixed-effects estimation, which allows each country to have an individual intercept (see Equation (2)). 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (1) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (2) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (3) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (4) (1) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (1) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (2) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (3) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (4) (2) In both equations, the index i signals the country and t stands for the time period (years in our case). y denotes one of the five dependent variables (GDP, HDI, School, Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 35 Life, or Mortality). α represents the common or individual intercept, while β1 and γx are the coefficients for IMF loans and the control variables (GCF, Education, Trade, Price, FDI and Population), respectively. It should be noted that x ranges from 1 to 6 in line with the responses to be estimated for each of the six control variables. Finally, εit measures the error term. 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (1) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (2) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (3) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (4) (3) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (1) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (2) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (3) 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖+ 𝛽𝛽1𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 + 𝛽𝛽2𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖𝑖𝑖 ∙𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖𝑖𝑖 + 𝛾𝛾𝑥𝑥𝑍𝑍𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖 (4) (4) In Equations (1) and (2), the effect of corruption is not included. This is deliberate to first demonstrate the overall effects of IMF loans on the dependent variables. In a subsequent step, however, we incorporate corruption. This is done in two ways: firstly, by adding corruption as an additional regressor. Secondly, by adding the product of IMF and corruption, we demonstrate how the dependent variable changes concerning IMF loans and changes in corruption. Equations (3) and (4) depict these adjusted specifications using pooled OLS and fixed-effects, respectively. Results This section presents and discusses the empirical results. It begins with the specifications that do not consider the influence of corruption, as outlined in Equations (1) and (2). The results are presented in Table 8. The response of GDP growth was found to be significantly negative in both specifications (2.1 and 2.2), although the effect tends to be small. This suggests that an increase in IMF loans is associated with lower economic growth in the short term, possibly due to the stringent reform packages typically associated with IMF support. This finding reinforces the results of previous studies by Barro and Lee (2005). However, there are three exceptions worth noting. Firstly, gross capital formation shows a positive and significant effect on economic growth. A one percent increase in gross capital formation leads to an increase of 0.15% in economic growth. This result is logical as gross capital formation is a component of GDP. Secondly, GDP growth reacts negatively to an increase in the price index, thereby indicating that higher inflation rates reduce economic growth, with everything else being equal. This result is expected as increased prices diminish product demand. Thirdly, population growth appears to positively influence economic growth, as expected, thus indicating that countries with larger populations tend to produce more goods and services. Concerning the response to HDI growth (2.3 and 2.4), the results suggest a weakly negative effect of IMF loan growth on HDI growth, which becomes significant in the pooled OLS Equation (2.3). This implies that, at least in the short term, IMF loans and the austerity programs often associated with them tend to reduce human development. This finding is consistent with previous studies by Muhamed and Gaas (2016) and Biglaiser and McGauvran (2022). The control variables tend to be predominantly significant in these specifi- ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 36 Table 8. Baseline results GDP HDI School Life Mortality (2.1) (2.2) (2.3) (2.4) (2.5) (2.6) (2.7) (2.8) (2.9) (2.10) Pooled OLS Fixed effects Pooled OLS Fixed effects Pooled OLS Fixed effects Pooled OLS Fixed effects Pooled OLS Fixed effects IMF -0.004** (0.002) -0.003** (0.002) -0.001* (0.000) -0.001 (0.000) 0.001 (0.007) 0.003 (0.007) 0.000 (0.000) 0.000 (0.000) -0.002** (0.001) -0.001** (0.001) GCF 0.150*** (0.015) 0.147*** (0.015) 0.013*** (0.004) 0.014*** (0.003) 0.099* (0.060) 0.117* (0.061) 0.002 (0.002) 0.002 (0.002) -0.000 (0.007) -0.004 (0.006) Education 0.006 (0.015) 0.002 (0.015) 0.008** (0.004) 0.007** (0.003) -0.112* (0.062) -0.121* (0.063) -0.000 (0.002) 0.000 (0.002) -0.001 (0.007) 0.002 (0.006) Trade 0.011 (0.012) 0.010 (0.012) 0.008*** (0.003) 0.009*** (0.003) 0.091* (0.049) 0.103** (0.050) 0.001 (0.002) 0.002 (0.002) -0.001 (0.006) -0.001 (0.005) Price -0.022* (0.013) -0.029** (0.015) 0.006** (0.003) 0.005 (0.003) -0.078 (0.050) -0.122** (0.059) 0.000 (0.002) -0.000 (0.002) -0.007 (0.006) 0.001 (0.006) FDI 0.001 (0.001) 0.001 (0.001) 0.000 (0.000) -0.000 (0.000) -0.008** (0.003) -0.009*** (0.003) 0.000 (0.000) -0.000 (0.000) -0.000 (0.000) 0.000 (0.000) Population 0.633*** (0.202) -0.937 (0.789) 0.296*** (0.049) 0.597*** (0.177) 2.417** (0.941) 7.486* (4.22) 0.274*** (0.031) 0.443*** (0.106) 0.143 (0.096) -0.713** (0.328) C2.152*** (0.411) 0.340*** (0.100) -2.300 (1.774) 0.117* (0.065) -3.658*** (0.194) Adj. R² 0.382 0.400 0.266 0.396 0.067 0.053 0.241 0.475 0.006 0.256 N249 249 249 249 189 189 249 249 249 249 Notes. C stands for the constant (only for pooled OLS. Fixed effects constants are available from the authors upon request), N denotes the number of observations, whereas Adj. R² is the adjusted R² value. All other abbreviations are explained in the main text. Standard errors are given in parentheses, ***/**/* stands for the significance of the coefficients at 99%, 95% and 90% levels, respectively. Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 37 cations. Gross capital formation (GCF) growth exhibits a robust positive effect on HDI growth, which is reasonable as investments may occur in sectors that enhance human development, such as education or healthcare. Unsurprisingly, the response of HDI growth to the Government’s schooling expenditure increases is significantly positive, as this directly contributes to human development. Additionally, increasing international trade growth was found to increase HDI growth, likely due to the broader range of goods and services available via imports, which can improve access to items such as medication. Interestingly, the inflation rate initially appears to have a significantly positive effect on HDI growth in the pooled OLS Equation (2.3). This result is puzzling, as one might expect a negative response, given that higher inflation rates could deter investments aimed at improving human development. However, the significance diminishes once fixed effects have been incorporated into the estimation (2.4). Finally, according to the results, human development growth increases with population growth. One possible explanation for this is that an increasing population necessitates the development of critical infrastructure, leading to improvements in healthcare and education systems. When examining the first factor contributing to HDI, which is School growth, we find that growth in IMF loans tends to have no effect (3.5 and 3.6). This suggests that, in the short term, an IMF credit does not significantly contribute to the development or improvement of the schooling system. This finding aligns with the expectations, as establishing or enhancing a schooling system is typically a mediumto long-term endeavour. Regarding the control variables, we observe similar significantly positive reactions to school growth as we found concerning HDI growth for variables such as gross capital formation (GCF), trade, and population growth. Thus, it can be inferred that HDI growth, particularly in terms of school growth, is partly driven by these factors. Moreover, we now find that the response of school growth to the inflation rate has the expected significantly negative impact, as higher prices tend to discourage investments in the schooling system. However, the result regarding Government expenditures on education is puzzling, as it is found to have a significantly negative effect on school growth. One possible explanation is that the effects of these expenditure increases do not materialize immediately, and may even hinder schooling improvement due to factors such as renovations of school buildings. Lastly, we find a significantly negative effect of foreign direct investment (FDI) growth on school growth. One explanation for this could be that, in many of the countries under investigation, which are predominantly low-income countries, FDI investments are mainly directed towards sectors requiring unskilled labour. Consequently, an increase in FDI may lead to a greater demand for unskilled workers, thus prompting individuals to forgo or reduce their education. When examining the effects on growth in life expectancy, the results are presented in columns 2.7 and 2.8 of Table 8. Finally, when examining the response of mortality growth (2.9 and 2.10), we find the expected significantly negative response, which corresponds to the significantly positive effect we found regarding growth in life expectancy. This suggests that, as the population grows, mortality rates tend to decrease, which is consistent with the notion that larger populations may lead to an improved access to healthcare and other life-saving resources. ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 44 Roberts, R. O. (1942). Ricardo’s theory of public debts. Economica, 9(35), 257–266. https://doi. org/10.2307/2549539 Siddique, I., Hayat, M., Naeem, M., Ejaz, A., Spulbar, C., Birau, R., and Calugaru, T. (2021). Why Do Countries Request Assistance from International Monetary Fund? An Empirical Analysis. Journal of Risk and Financial Management. https://doi.org/10.3390/jrfm14030098 Seiam, D. A., & Salman, D. (2024). Examining the global influence of e-governance on corruption: a panel data analysis. Future Business Journal, 10(1), 29. https://doi.org/10.1186/s43093-024-00319-3 Stubbs, T., Kentikelenis, A., Ray, R. and, Gallagher, K. P. (2022). Poverty, Inequality, and the International Monetary Fund: How Austerity Hurts the Poor and Widens Inequality. Journal of Globalization and Development, 13(1), 61–89. https://doi.org/10.1515/jgd-2021-0018 Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 45 Appendix Table 1. Descriptive statistics and unit root tests GDP HDI School Life Mortality IMF Corruption GCF Education Trade Price FDI Population Descriptive Statistics Mean 3.51 0.90 1.47 0.53 -3.38 34.26 -0.62 3.50 0.32 4.24 13.29 33.04 1.80 Maximum 15.33 4.40 128.27 2.49 2.07 985.04 0.64 57.12 97.03 186.45 325.00 2873.16 3.76 Minimum -15.14 -3.82 -33.33 -2.01 -10.70 -95.20 -1.50 -47.12 -67.71 -55.80 -1.20 -2182.55 -1.05 Standard Deviation 4.10 0.95 11.04 0.65 1.61 132.20 0.46 14.65 13.81 21.43 7.01 304.87 1.04 Panel Unit Root Tests Levin et al. (2002) -3.90 (0.00) -3.95 (0.00) -8.09 (0.00) -7.03 (0.00) -2.32 (0.01) -9.81 (0.00) -2.40 (0.01) -9.44 (0.00) -11.64 (0.00) -10.48 (0.00) -2.81 (0.01) -10.72 (0.00) -1.68 (0.05) Notes. Panel unit root test testing for a common unit root as introduced by Levin et al. (2002). For the unit root tests, we show the corresponding p-values in parentheses. Table 2. Pairwise correlation Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) GDP 1 (2) IMF 0.501* 1 (3) Corr 0.037 -0.104 1 (4) Mortality -0.175* -0.205* -0.493* 1 (5) School 0.287* 0.129* 0.207* -0.587* 1 (6) HDI 0.382* 0.310* 0.443* -0.880* 0.604* 1 (7) GEI -0.188* -0.058 0.398* -0.475* 0.273* 0.383* 1 (8) price -0.187* -0.068 -0.209* 0.361* -0.119* -0.235* -0.043 1 (10) FDI -0.172* -0.115* 0.130* 0.136* -0.015 -0.080 0.189* 0.560* 1 (11) GCF -0.281* -0.361* -0.059 0.086 0.045 -0.102 0.068 0.120* 0.147* 1 (12) Trade -0.511* -0.205* -0.029 0.032 -0.062 -0.127* 0.221* 0.403* 0.440* 0.289* 1 (13) POP -0.207* -0.298* -0.377* 0.713* -0.336* -0.712* -0.381* 0.220* -0.011 0.109 0.023 1 Source: based on the author’s calculations ISSN 1392-1258 eISSN 2424-6166 Ekonomika. 2024, vol. 104(1) 46 Table 3. Cross dependence test RGDP [CountryID,t] = Xb + u[CountryID] + e[CountryID,t] Estimated results: | Var sd = sqrt(Var) ---------+----------------------------- logReal~P | .7390468 .8596783 VIF 1/VIF FDI 1.699 .589 INF 1.695 .59 TISI 1.459 .686 POP 1.369 .731 GEI 1.321 .757 LIMFL 1.229 .814 GCFI 1.168 .856 Mean VIF 1.42 . e | .0247097 .1571933 u | 1.502486 1.225759 Test: Var(u) = 0 chibar2(01) = 1708.87 Prob > chibar2 = 0.0000 Table 4. Hausman Test Hausman (1978) specification test Coef. Chi-square test value 2.33 P-value .969 Table 5. Normality Test Jarque-Bera normality test: 28.59 Chi(2) 6.2e-07 Jarque-Bera test for Ho: normality: Table 6. VIF Test VIF 1/VIF FDI 1.699 .589 INF 1.695 .59 TISI 1.459 .686 POP 1.369 .731 GEI 1.321 .757 LIMFL 1.229 .814 GCFI 1.168 .856 Mean VIF 1.42 . Doaa M. Salman Abdou et al. Debt Trapped: Analysing the Impact of IMF on Economic Growth and Human Development... 47 Table 7. Breusch-Pagan for Heteroscedasticity Breusch-Pagan / Cook-Weisberg test for heteroscedasticity Ho: Constant variance Variables: fitted values of LRGDP chi2(1) = 0.09 Prob > chi2 = 0.76