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Abstract

This study investigates the intricate relationship between external debt, debt service, and economic growth by using the panel data of 32 Asian Developing Economies (ADE) spanning 1995 to 2020. Employing a two-step system generalized method of moments (GMM) and a dynamic common correlated estimate (DCCE) model, the research explores key macroeconomic channels through which debt influences growth and rigorously tests for debt overhang and crowding-out effects. Findings reveal that public and private investment, total factor productivity, and national savings are pivotal channels transmitting the non-linear effect of external debt on economic growth. Notably, only private and public investment convey the non-linear effects of debt service to economic growth, while productivity and savings convey the linear effect. Evidence of debt overhang and crowding-out effects is identified in the sampled economies. The study suggests strategic measures for developing countries, emphasizing the productive use of accumulated debt, enhanced debt management, and timely project completion. Furthermore, it advocates for fostering economic growth through increased productivity, domestic savings, and private sector expansion to reduce dependence on foreign debt, facilitating both debt repayment and economic self-sufficiency.

Plain language summary

This study explores the intricate links among external debt, debt service, and economic growth in 32 Asian Developing Economies from 1995 to 2020. Using advanced statistical methods like the two-step system generalized method of moments (GMM) and a dynamic common correlated estimate (DCCE) model, the research investigates various macroeconomic pathways, specifically testing for non-linear effects such as debt overhang and crowding-out. Key findings emphasize the significance of public and private investment, total factor productivity, and national savings as pivotal channels for the non-linear impact of external debt on economic growth. Notably, the study reveals that private and public investment exhibit non-linear effects in response to debt service, while productivity and savings show linear effects. The research recommends strategic approaches for developing countries, focusing on judicious debt management, timely project completion, and initiatives to boost productivity, domestic savings, and private sector growth, thereby reducing reliance on foreign debt. Acknowledging valuable insights, the study recognizes limitations in the available data from 32 countries and emphasizes the need for further investigation into mediating and moderating variables in the relationship between external debt and economic growth. Particularly in the context of foreign debt financing policies, the study underscores the importance of exploring threshold values for negative impacts on transmission channels, suggesting avenues for future research to provide a more nuanced understanding of the dynamics involved. In essence, the study offers valuable insights into the nuanced relationship between external debt and economic growth, along with strategic recommendations for sustainable development in developing economies.

Keywords

external debt economic growth non-linearity debt overhang crowding out effect

Introduction

The historical debt crises, such as the OPEC oil price shocks of 1973 and 1979, the Mexico debt crisis of 1980, the Asian crisis of 1997, the 2008 financial crisis, the Euro area sovereign debt crisis of 2009, and the COVID-19 crisis, have prompted the discussions regarding the intricate relationship between debt and economic growth. This recurrent theme in the literature is evident in works such as (Beyene & Kotosz, 2022, 2023; Chudik et al., 2018; Dawood et al., 2021; Haque et al., 2023; Hassan & Meyer, 2021; Lau et al., 2022; Mensah et al., 2019; Necib, 2023; Randi et al., 2023; Xu et al., 2020). While developing economies grapple with various economic and social challenges, the debt crisis stands out as a particularly pressing concern for policymakers striving to attain economic sustainability (Munir & Mehmood, 2018; Zhang et al., 2020). The repercussions of external debt on developing economies have emerged as a persistent and multifaceted challenge. These consequences encompass heightened interest rates, increased resistance to foreign inflows, diminished domestic output, reduced earnings from exports, and constraints on importing modern technological instruments (Siddiqui & Malik, 2001).

In the late 1980s and early 1990s, scant attention was devoted to the mounting foreign debt in Asia’s emerging markets. The Asian financial upheaval of 1997, coupled with recent global economic deceleration and other adverse economic indicators, has prompted heightened scrutiny by observers of Asia’s economic and financial landscape (Lau et al., 2022). The aggregate external debt of Asia stood at US$22.78678 billion in 1995, surged to US$75.35893 billion in 2010, and peaked at US$111.6031 billion in 2015. This upward trajectory persisted until 2020, escalating to US$160.7539 billion during the onset of the COVID-19 crisis (Economist Intelligence Unit, 2020). In the most recent World Economic Outlook (International Monetary Fund, 2022), the debt-to-GDP ratio for emerging and developing Asia in 2022 was 75%, marking a notable rise from 56% in 2019. This considerable escalation can be attributed to the impact of the recent COVID-19 pandemic (Gaspar et al., 2021). According to Srinivasan (2022), Asia’s proportion of total debt, as well as the overall debt burden, has risen markedly from 25% pre-pandemic to 38% at present. Notably, nations at elevated risk, including Laos, Mongolia, Maldives, Papua New Guinea, Sri Lanka, and Pakistan, have already defaulted on their debt obligations. Consequently, numerous countries in the region grapple with escalating debt figures, with some teetering on the precipice of debt distress (UNCTAD, 2022).

In macroeconomic literature, the discourse on the relationship between external debt and economic growth has traditionally been framed within two distinct schools of thought. First, Keynesian economists assert that a rise in debt exerts a positive influence on economic growth. A strand of the empirical studies have lent support to this perspective, revealing evidence of favorable impacts on economic growth (Chaudhry et al., 2017; Hakimi et al., 2019; Joshua et al., 2020; Khursheed & Siddiqui, 2016; Njamen Kengdo et al., 2020; N’zue, 2020). Second, neo-classical economists contend that debt represents a future tax and detrimentally affects economic growth. Numerous studies align with this viewpoint, including (Akinlo, 2020; Asafo, 2019; Chudik et al., 2018; Fosu, 1999; Khodaparasti & Mohammadpour, 2016; Mahmoud, 2015; Mohamad & Abu Bakar, 2022; Senadza et al., 2018; Siddique et al., 2016). These aforementioned studies posit the debt-growth relationship within a linear and direct framework.

Recent research has increasingly directed its focus toward examining the non-linear impact of external debt on economic growth within an indirect framework, a methodological approach deemed more logical (Pattillo et al., 2011). Some empirical studies found evidence of non-linearity (see Alemu et al., 2023; H. Bal et al., 2022; Checherita-Westphal & Rother, 2012; Mohsin et al., 2021; Munir & Mehmood, 2018; Reinhart & Rogoff, 2010). In contrast, other studies found no evidence of non-linearity for selected developing countries (see Asafo, 2019; Asteriou et al., 2020; Hadhek et al., 2014; Le & Phan, 2022; Schclarek, 2004).

In this strand, several hypotheses elucidate the detrimental influence of external debt on economic growth, with debt overhang and crowding-out prominently featured. Researchers have focused on different economic growth determinants as the transmission channels through which external debt negatively affects economic growth, such as public and private investment, total factor productivity, and national savings. The well-established notions of debt overhang and crowding-out lay the groundwork, positing that foreign debt’s negative impact is channeled through specific avenues of economic growth. Clements et al. (2003), Pattillo et al. (2004), Schclarek (2004), Checherita-Westphal and Rother (2012), Shabbir (2013), Silva (2020), Turan and Yanikkaya (2021), Hassan and Meyer (2021), Beyene and Kotosz (2022, 2023), and Haque et al. (2023) have conducted extensive investigations into the transmission channels between external debt and economic growth across diverse country panels. In contrast, the examination of Asian developing economies has been notably limited, with a specific focus observed in the works of Woo and Kumar (2015), D. P. Bal and Rath (2018), and Munir and Mehmood (2018).

The research findings of previous studies about transmission channels between external debt and economic growth are also inconclusive and contradicted. Like many developing economies, most Asian countries have heavily borrowed external debts for socioeconomic development initiatives and infrastructure. However, over the past decade, the huge increase in external debt has received less empirical attention from researchers in the context of Asian economies. In light of the above context, the main questions of this study are: what are the transmission channels through which external debt and external service impact economic growth? And whether the indirect effect of external debt and debt service also presents the different sources of growth determinants. Specifically, this study attempts to answer the following questions: do external debt and debt servicing affect economic growth adversely through investment, productivity, and national savings? Is there any evidence of debt overhang and crowding out effect in Asian developing countries?

The primary motivation behind this paper is to uncover the mediating transmission channels through which external debt and debt service impact economic growth in Asian developing economies (ADE). The study addresses a critical gap in the existing literature by comprehensively examining private investment, public investment, total factor productivity, and national savings as the transmission channels for the non-linear effects of external debt and debt service on economic growth. This approach contributes significantly to the understanding of the intricate dynamics involved in the relationship between debt and economic growth. Furthermore, the study’s contributions are manifold. First, it expands the empirical literature on ADE, an area that has been relatively understudied compared to American and African developing economies. Second, by investigating both main indicators of external debt and debt service, the paper delves into contemporary economic theories such as ‘debt overhang’ and the ‘crowding-out effect.’ This adds a nuanced perspective to the ongoing discourse on the impact of external debt on economic growth.

Third, the study stands out as one of the few that employs advanced econometric methods, specifically the two-step system GMM and DCCE, to explore the transmission channels from external debt and debt service to economic growth. This methodological rigor enhances the reliability and validity of the findings. Fourth, the study addresses the methodological limitations present in previous literature by rigorously accounting for endogeneity, heterogeneity, and cross-sectional dependence. The robustness of the methodology strengthens the credibility of the results, providing a clearer understanding of the non-linear effects of external debt on economic growth. Finally, the findings of the study underscore the significance of public and private investment as the primary channels through which external debt non-linearly influences economic growth. Additionally, productivity and savings are identified as key channels for the non-linear impact of external debt on economic growth, while the relationship with debt service is linear. Importantly, the study provides empirical evidence supporting the existence of debt overhang and the crowding-out effect in Asian developing countries, offering valuable insights for policymakers and researchers alike.

The remainder of the paper summarizes related literature on external debt, channels and growth, and the debt overhang and crowding out effect theories, describe the data and estimation methods, and discusses empirical results.

Related Literature

Theoretical Review

Theoretically, there are two contradicting justifications for the impact of external debt on economic growth in the literature. First, drawing from the Keynesian tradition, the conventional viewpoint contends that heightened external debt signifies augmented capital, output, income, and aggregate demand, particularly beneficial in the short run for developing economies with limited capital stock. This positive influence, however, hinges on judicious fund allocation, directing borrowed resources to productive sectors. Empirical studies (Akram, 2017; Avramovich, 1964; Çiftçioğlu & Sokhanvar, 2018; Cohen, 1993; Dawood et al., 2020; Kassouri et al., 2021; Khodaparasti & Mohammadpour, 2016; Mahmoud, 2015; Necib, 2023; Senadza et al., 2018; Siddique et al., 2016; Yahaya, 2022) reinforce this perspective.

Second, the Neo-Classical theoretical stance asserts that external debt exerts a detrimental impact on economic growth, grounded in the debt overhang hypothesis and the crowding-out effect (Afrin Ale et al., 2023; Dinka’a et al., 2023; Hansen, 2004; Krugman, 1988; Mohamad & Abu Bakar, 2022; Mohsin et al., 2021; Özyılmaz, 2022; Sachs, 1989). The debt overhang hypothesis posits that when creditors or investors anticipate a nation’s debt surpassing its repayment capacity, the expected increase in debt-servicing costs signals reduced domestic and foreign investment, leading to a decline in economic growth (Krugman, 1988). The crowding-out effect occurs when a government allocates resources to debt servicing instead of investing in the social sector and other development projects, resulting in the displacement of private investment (Chudik et al., 2017; Clements et al., 2003; Hansen, 2004). Building on established theories of debt overhang and crowding-out effects, the theoretical foundation anticipates a negative impact of external debt on economic growth through specific channels (Adekunle et al., 2021; Turan & Yanikkaya, 2021). This theoretical perspective recognizes the non-linear nature of these relationships, aligning with the proposition that an optimal level of external debt exists; beyond this threshold, adverse effects like debt overhang and crowding-out may manifest (Azretbergenova et al., 2022; Pattillo et al., 2011).

Contemporary research on external debt’s impact on economic growth has shifted to examine non-linear relationships, emphasizing crucial mediating channels like investment, savings, total factor productivity, and interest rates, departing from a simple linear association. This approach offers a comprehensive understanding of the complex interplay between external debt and economic growth (Afonso & Tovar, 2013; Beyene & Kotosz, 2023; Haque et al., 2023; Pattillo et al., 2011; Qureshi & Liaqat, 2020; Schclarek, 2004; Silva, 2020). In the context of the discussion on the transmission channels between external debt and debt servicing with economic growth, it is crucial to elucidate the dual aspects of these channels for constructing a theoretical framework. Figure A1 in the Appendix delineates the theoretical framework of the study. Three paths connect debt to economic growth: the first path links external debt to determinants of economic growth (channels); the second path directly links channels to economic growth, while the third path moves indirectly from external debt to economic growth through these channels. Our focus lies on the indirect path, given the well-established explanations in empirical studies for the first path and second path.

External debt can exert both positive and negative influences on key economic indicators (transmission channels). When used judiciously, external debt can stimulate private investment by providing additional capital for businesses to fuel expansion and technological advancements. Likewise, it can serve as a valuable source of funds for public investment in infrastructure and development projects, contributing to long-term economic growth. However, the impact is not without challenges. High levels of external debt, especially if denominated in foreign currencies, may increase financial vulnerabilities, potentially limiting private investment and posing risks to fiscal sustainability (Hakimi et al., 2019). Additionally, the efficient use of external debt is crucial for promoting total factor productivity (TFP) through investments in innovation and technology. Careful management is essential to ensure that debt servicing does not become a significant burden, impeding TFP growth, otherwise it reduce TFP (Beyene & Kotosz, 2022). Moreover, while external debt can supplement national savings, overreliance may leave nations vulnerable if debt servicing consumes a substantial portion of income, limiting room for domestic savings (Chaudhry et al., 2009). Policymakers must strike a balance to harness the benefits of external debt while mitigating associated risks.

Public investment, such as government spending on infrastructure, education, and healthcare, can have a significant impact on economic growth. Well-targeted public investment can improve the overall productivity of the economy and create a favorable environment for private sector activities (Aschauer, 1989; Ramey & Zubairy, 2018). Private investment plays a crucial role in economic growth, when businesses invest in new machinery, technology, or infrastructure, it enhances productivity, creates jobs, and stimulates economic activity (Bint-e-Ajaz & Ellahi, 2012; Romer, 1990). TFP measures the efficiency with which inputs (capital and labor) are utilized in the production process. An increase in TFP reflects technological progress, innovation, and improvements in overall efficiency, leading to sustained economic growth (Yalçınkaya et al., 2017). National savings represent the portion of income not consumed and available for investment. Higher national savings can lead to increased capital accumulation, providing funds for investment in productive assets and contributing to long-term economic growth (Ribaj & Mexhuani, 2021).

The impact of external debt on economic growth, particularly through public investment, is multifaceted. Public external debt, when strategically managed, can augment government expenditure, facilitating the payment of salaries, procurement of goods and services, and funding for public investment (Agenor & Montiel, 2015). Assuming a constancy in imports, the utilization of external debt to enhance public investment, support civil servants, and procure domestically produced goods may lead to an increase in GDP. However, the sustainability of this positive effect is contingent upon the extent to which the investment relies on imports (Pattillo et al., 2011; Schclarek, 2004; Silva, 2020). Conversely, challenges arise when higher import costs, unproductive government spending, and elevated debt payments prompt the government to curtail primary expenditures earmarked for public investment. This reduction not only diminishes fiscal space within the government budget but also restricts social transfers, impacting the creation and maintenance of crucial public infrastructure, education, and health initiatives.

The private external debt has the potential to positively influence capital stock, productivity, and overall economic growth, contingent upon the private sector yielding returns surpassing the debt’s cost (Pattillo et al., 2011; Schclarek, 2004). Enhanced productivity, particularly through high-yield investments in advanced technology projects, plays a pivotal role in achieving substantial returns. Additionally, external debt can foster opportunities for new business ventures through mechanisms like mergers, acquisitions, economies of scale, and knowledge enhancement. Conversely, a high level of external debt coupled with elevated interest costs diminishes the capacity of non-financial corporations to invest and expand their capital stock. Consequently, financial institutions may curtail lending due to heightened leveraging, exerting a dampening effect on the investment activities of non-financial corporations. This phenomenon of external debt crowding out private capital expenditure ultimately restrains long-term economic growth (Eberhardt & Presbitero, 2015).

The total factor productivity (TFP), serving as a benchmark for efficiency and the degree of labor and capital utilization, is crucially influenced by external debt (Checherita-Westphal & Rother, 2012; Haque et al., 2023; Pattillo et al., 2011; Schclarek, 2004). Under specific conditions, external debt facilitates heightened levels of investment and capital stock, achieving optimal economies of scale when funds are judiciously employed. Elevated productivity, as a consequence, fosters enhanced competitiveness in domestic markets, propelling increased sales of products and services, leading to higher foreign exchange earnings and, subsequently, economic growth (Silva, 2020).

The impact of external debt on economic growth extends to national savings, constituting a pivotal channel. Silva (2020) underscores that the quantity of external debt held by the private sector, alongside prevailing interest rates, determines a nation’s interest payments. High external debt levels consequently lead to elevated interest payments, influencing the primary income account (Checherita-Westphal & Rother, 2012; Schclarek, 2004). The resultant reduction in the primary income account triggers diminished consumption and savings, culminating in a scenario where external debt hampers economic growth by compelling the imposition of higher taxes on residents—thereby reducing savings. Additionally, a substantial stock of public external debt corresponds to increased payments to foreign institutions, further constraining the primary income account and diminishing the fiscal space in the government budget for project financing. Excessive debt servicing incapacitates the government from investing in crucial sectors such as education and health.

Empirical Literature

There is a limited quantity of empirical studies addressing the mediating transmission channels through which external debt and debt service negatively affect economic growth. The idea behind testing the transmission channels (mediating variables) is to check if a non-linear effect of external debt on economic growth in indirect framework exist to prove the debt overhang and crowding out hypotheses. Clements et al. (2003) investigated the nuanced relationship between external debt and economic growth in 55 low-income countries for period of 1970 to 1999. Using fixed effects and system General Method of Moments, the study revealed a non-linear impact, highlighting thresholds around 30% to 37% of GDP or 115% to 120% of exports. The findings emphasized the indirect effects of debt on growth through its influence on investment, which supported the existence of debt overhang hypothesis. Pattillo et al. (2004) employed the System Generalized Method of Moments (System GMM) to scrutinize the relationship between external debt and economic growth across 61 developing countries from 1969 to 1998. Although shedding light on the channels through which high external debt negatively influences economic growth—particularly the roles of physical capital accumulation and total factor productivity growth. Investigating the relationship between external debt and economic growth for 59 developing and 24 advanced economies for the period from 1970 to 2002, Schclarek (2004) found that external debt mainly influences economic growth through capital accumulation. However, there is limited evidence regarding the role of total factor productivity, and mixed results were found for the transmission through private savings rates.

Checherita-Westphal and Rother (2012) analyze the impact of government debt on per-capita GDP growth in 12 euro area countries over approximately 40 years, starting in 1970. The found the non-linear impact operates through channels such as private saving, public investment, and total factor productivity. Shabbir (2013) explores the relationship between economic growth and external debt in 70 developing countries (1976–2011). The findings support the debt overhang theory, indicating a strong negative impact of external debt and debt servicing on per capita GNI growth. The study underscores the critical role of fixed capital formation for economic well-being. In terms of channels, the research highlights that unsustainable external debt adversely affects economic growth, hampers fixed capital formation, and crowds out the private sector. Akram (2017) investigated the impact of public debt on economic growth and investment in four South Asian countries from 1975 to 2011. Using a hybrid model, the study found that both public external debt and debt servicing negatively affected economic growth and investment, indicating the presence of “debt overhang” and “crowding out” effects.

Estimation methods play a significant role in determining the association between variables, Woo and Kumar (2015) used many homogeneous approaches of pooled OLS, fixed effects, and system GMM to identify transmission channels in Asian economies. They found that labor productivity growth is the primary channel through which debt adversely impacts economic growth. Munir and Mehmood (2018) investigated four Asian economies from 1991 to 2013, using a fixed effect approach. They identified private investment, public investment, and total factor productivity as channels for the negative impact of external debt on economic growth. However, they overlooked the roles of savings and interest rates as key transmission channels. Additionally, they asserted a non-linear relationship between debt and growth in South Asian economies. D. P. Bal and Rath (2018) examine the impact of public debt on economic growth in India (1970–2013). Using a two-step analysis, they find a positive short-run but negative long-run effect of public debt on growth. Employing a Nonlinear ARDL approach, the study confirms a nonlinear impact. Key channels include households’ saving, public investment, and total factor productivity growth. The study suggests focusing on public investment and productivity channels for utilizing public debt in India, advocating borrowing if it contributes to capital formation.

Qureshi and Liaqat (2020) contribute to the empirical literature, employing a panel vector autoregression model to analyze the relationship between external debt and economic growth across 123 countries from 1990 to 2015. The study identified savings and investment as the primary channels through which external debt influences economic growth, providing robust results across various model specifications and controls. Hassan and Meyer (2021) utilized the system GMM methodology to explore the impact of external debt on economic growth in 30 Sub-Saharan African countries from 1985 to 2019. Their findings highlighted three significant transmission channels—private investment, public investment, and total factor productivity—through which external debt negatively influences economic growth in a nonlinear manner in SSA countries. Notably, interest rates were identified as a direct channel, while savings did not emerge as a transmission channel in their analysis.

Beyene and Kotosz (2022) investigated TFP as transmission channel through which external debt affect economic growth severely indebted poor countries (HIPCs). They finding is align with the previous research, indicating that debt significantly reduces both TFP and GDP growth. Moreover, the study underscores the non-linear relationship between external debt, TFP, and GDP growth. Beyene and Kotosz (2023) investigate the non-linear relationship between foreign debt, human capital development (HCD), and economic growth in heavily indebted poor countries (HIPCs) over the period 1990 to 2017. Utilizing seemingly unrelated regressions (SUR) and alternative simultaneous equations models (SEMs), the study reveals a negative non-linear association between foreign debt and HCD. The findings also emphasize the HCD channel as a crucial link through which external debt impacts economic growth in HIPCs. This research contributes valuable insights into debt overhang, crowding-out effects, and recommends policy measures for effective debt management. Haque et al.’s (2023) examined the impact of external debt on GDP growth in LMICs from 1999 to 2019. Using a two-step GMM estimation on data from 30 countries, the research found that external debt does influences LMIC economic growth through the TFP channel. Notably, this relationship is absent in Asian nations during 1999 to 2010 and post-financial crisis periods. For African countries, a negative but minor connection between external debt and TFP is observed. This research highlights nuanced effects and varying patterns in the relationship between external debt, TFP, and economic growth in different regions and timeframes.

This non-linear relationship is inherently logical and theoretically substantiated between external debt and economic growth (Pattillo et al., 2011). The existence of non-linearity aligns with the proposition that there is an optimal level of external debt (Mohsin et al., 2021). Beyond this threshold, the impact on economic growth through these channels may exhibit diminishing returns or adverse effects, such as debt overhang and crowding-out (Abdulhamid et al., 2023; Chudik et al., 2017; Lau et al., 2022). The non-linear impact of external debt and debt service on economic growth can be influenced by factors such as the composition of the debt, its terms, and the efficiency of its utilization. An in-depth analysis employing non-linear regression approaches is crucial to capturing the intricate dynamics and potential channels through which external debt or debt servicing affect economic growth.

The theoretical and empirical review of external debt and debt servicing on economic growth and transmission channels generally indicates a mixed outcome. The previous theoretical frameworks and empirical investigations yield inconclusive results, contingent upon diverse criteria, including the developmental status of the sampled nations, employed methodologies, comprehensiveness of data coverage, and the researchers’ selection of control variables, among other factors. After carefully reviewing the literature, we found three shortcomings in previous studies. Firstly, some studies’ time series were not updated and did not include the current macroeconomic dynamics such as the global financial crisis and current health crises. Secondly, we found that studies mostly focused on external debt accumulation and ignored debt servicing, which causes the crowding-out effect. Thirdly, the previous studies were based on “first-generation” econometric models, by assuming homogeneity and cross-sectional independence, which may produce biased estimates (Mitze & Matz, 2015). Currently, beyond the non-linearities, studies need to emphasize the second-generation econometric modelling to consider the cross-country heterogeneity and cross-sectional dependence (spill-over effect) in the relationship between external debt and growth (Chudik et al., 2017; Eberhardt & Presbitero, 2015; Panizza & Presbitero, 2013; Pescatori et al., 2014). The latest study methodologies have tended to adopt methods that consider these socioeconomic factors and country-specific heterogeneity.

The weaknesses in prior studies on the relationship between external debt and economic growth in Asian countries involve outdated time spans, limited consideration of debt servicing, and dependence on older, potentially biased econometric models. These studies, predominantly Woo and Kumar (2015), Munir and Mehmood (2018), and D. P. Bal and Rath (2018), underscore the critical need for more recent and region-specific research to address these shortcomings. Neglecting the impact of debt servicing, overlooking significant macroeconomic events, and employing outdated methodologies constrain the applicability and reliability of findings. Future investigations should rectify these limitations by employing advanced econometric techniques, integrating current data, and comprehensively understanding the nuanced dynamics of Asian economies in today’s economic context. Therefore, this study re-examine the transmission channels through which external debt affect economic growth negatively in case of ADE. Another novel feature of this study is to investigate the debt overhang and crowding out hypotheses by using the latest data, advanced econometric estimation techniques in non-linear framework.

Methodology

Data Collection and Sample Selection

Since the upshot of mounting debt is likely associated with the state of global markets and occurrences of financial crises, it is crucial to analyze a comprehensive dataset for a large sample of countries and over a sufficiently long horizon. Our dataset comprises balanced panel data for 32 Asian developing countries from 1995 to 2020; see the list of countries in Appendix A1 for further information. Data collection and sample selection are vital components of every study; it is necessary to identify the data source, the selection criteria, and the sample durations. Therefore, we build the sample for this research via a series of stages. First, we analyzed the debt profile and debt vulnerabilities of emerging economies as a result of the increased indebtedness of low and middle-income economies, particularly in the aftermath of the repeated crises between 1970 and 2020. Second, the nominal gross national income (GNI) per capita, which provides an easy-to-understand picture of the country’s life standard, is usually used to determine whether a country is developed or developing. Countries whose nominal GNI is above a certain threshold (which fluctuates somewhat from year to year) are labelled as developed, whilst those with a nominal GNI below the threshold are considered to be developing. For instance, the World Bank considers high-income economies and territories with a GNI of at least $12,696 in 2022. Below this threshold, a country would be called a developing economy. Therefore, we omitted the affluent Asian economies from our sample. Moreover, we chose Asia developing economies because, in the literature, the debt-growth nexus is primarily studied in the context of developed and other developing economies (American, African, and European), whereas empirical research on Asian developing economies is insufficient. We included the sample countries from the list of economies released by the United Nations and the World Bank’s definition of emerging economies and the classification of countries by income level.

We removed Afghanistan, Syria, North Korea, Yemen, and The Timer Least owing to the absence of some needed statistics. Although growth is often defined across longer periods, the sample size for this research is restricted due to the lack of data for many key factors. Specifically, statistics for the primary variable (foreign debt-to-GDP ratio) are only accessible beginning in 1995, whereas the last year for which data are available in the database is 2020. Concerning the selection of explanatory variables, we used the World Development Indicators (WDI), the World Economic Outlook (WEO), the database of The Economist Intelligence Unit (EIU), and the Investment and Capital Stock Dataset (ICSD). Table A2 provides a full overview of variables and their sources. Figure A2 depicts the average development of foreign debt and four channels of transmission dynamics for our sample of 32 countries.

Model Specification

Studies used the growth model to investigate the relationship between external debt and economic growth (Checherita-Westphal & Rother, 2012; Munir & Mehmood, 2018; Woo & Kumar, 2015). Consistent with Munir and Mehmood’s (2018) approach, the empirical growth model is structured on a conditional convergence equation and expanded to incorporate both the total external debt level and external debt servicing. This study used a dynamic modified growth model that considers transmission channels as the dependent variables through which external debt may potentially impact economic growth. As previous studies confirmed that investment, total factor productivity, and national savings have an impact on economic growth (Munir & Mehmood, 2018). The quadratic term is employed to examine the non-linear effects of external debt and debt servicing on economic growth, aligning with recent studies (Hassan & Meyer, 2021; Le & Phan, 2022). As the prime objective of this study is to investigate the channels through which external debt transmits effects on economic growth. The dynamic model for this study is employed for each of the channels as follows:

Z_{ti} = β_{0 i} + β_{1 i} Z_{i, t - 1} + β_{2 i} {ED}_{it} + β_{3 i} {ED}^{2}_{it} + β_{4 i} X_{it} + μ_{i} + ε_{it}

(1)

In the above model, i and t are cross-sectional units and time respectively. Z is the interested transmission channel supposed to investigate as dependent variables, $ED$ and $E D^{2}$ are the external debt and external debt service and their square term, respectively to consider the non-linearity. Many studies utilize external debt stock as a regressor, reflecting the country’s repayment capacity and the presence of debt overhang. Another side, debt service is another crucial indicator; few studies incorporate it to examine the crowding-out effect of debt on growth. X is a vector of control variables. $Z_{i, t - 1}$ is lagged dependent variable to solve the endogeneity problem, $μ_{i}$ is the individual country-specific effect to capture the unobserved heterogeneities among the countries and is the error term.

Following the related literature, this study tests the private investment, public investment, total factor productivity, and national savings as transmission channels through which external debt affect economic growth. Based on the above dynamic model and empirical literature, the following equations present the models to assess the effects of total external debt and external debt service on the dependent variables (transmission channels) to confirm the evidence of debt overhang and crowding out effect, respectively. The significance or otherwise of each variable’s coefficients would assist in establishing whether or not the variable acts as a transmission channel through external debt to affect economic growth in the case of ADE.

\begin{matrix} {PRIGDP}_{ti} = β_{0} + β_{1} L_{PRIGDP}_{ti} + β_{2} {TDPY}_{ti} \\ + β_{3} {TDP Y}^{2}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it} \end{matrix}

(2a)

{PRIGDP}_{ti} = β_{0} + β_{1} L_{PRIGDP}_{ti} + β_{2} {TSPY}_{ti} + β_{3} TSP {Y^{2}}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(2b)

{PBIGDP}_{ti} = β_{0} + β_{1} L_{PBIGDP}_{ti} + β_{2} {TDPY}_{ti} + β_{3} {TDP Y}^{2}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(3a)

{PBIGDP}_{ti} = β_{0} + β_{1} L_{PBIGDP}_{ti} + β_{2} {TSPY}_{ti} + β_{3} TSP {Y^{2}}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(3b)

{FAPG}_{ti} = β_{0} + β_{1} L_{FAPG}_{ti} + β_{2} {TDPY}_{ti} + β_{3} {TDP Y}^{2}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(4a)

{FAPG}_{ti} = β_{0} + β_{1} L_{FAPG}_{ti} + β_{2} {TSPY}_{ti} + β_{3} TSPY {Y^{2}}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(4b)

{PSAV}_{ti} = β_{0} + β_{1} L_{PSAV}_{ti} + β_{2} {TDPY}_{ti} + β_{3} {TDP Y}^{2}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(5a)

{PSAV}_{ti} = β_{0} + β_{1} L_{PSAV}_{ti} + β_{2} {TSPY}_{ti} + β_{3} TSP {Y^{2}}_{ti} + β_{4} X_{ti} + μ_{i} + ε_{it}

(5b)

Where $i$ and $t$ are cross-sectional units and time, respectively, we use four determinants of economic growth as the transmission channels through which external debt affects economic growth. In equations (2a) and (2b), PRIGDP is gross fixed capital formation as a proxy for the investment of the private sector, and L_PRIGDP is its lagged form. In equations (3a) and (3b), PBIGDP is gross fixed capital formation as a proxy for the public sector investment, and L_PBIGDP is its lagged form. In equations (4a) and (4b), FAPG is total factor productivity, and L_FAPG is its lagged form. In equations (5a) and (5b), PSAV is gross national savings, and L_PSAV is its lagged form. Total external debt (TDPY) and debt service (TSPY) are the main explanatory variables. $TDP Y^{2}$ is the square term of total external debt, and $TSP Y^{2}$ is the square term of total debt service.

Regarding $X_{ijt}$ We included a set of explanatory variables as control variables that are consistently associated with growth and to count for each channel’s covariates being tested in line with the literature (e.g., Gómez-Puig et al., 2022; Kassouri et al., 2021; Law et al., 2021). All variables in the models are expressed in their logarithmic form. In the economic growth literature, the growth rate of labor used in the production process and capital accumulation are the critical determinants of growth (Solow, 1956). Therefore, population growth; as a proxy for labor in percentage (DPOP), and the ratio of gross fixed capital formation to GDP (PFIN) are used to proxy for the rate of growth of labor and the accumulation of capital stock, respectively (Chikalipah, 2021). Investment or capital stock (PFIN) is one of the main determinants of economic growth, as it increases employment, income, prosperity, and growth in the economy. According to previous studies, trade openness (PTO) boosts economic growth quality by integrating a country with the rest of the world, which enhances resource allocation efficiency and capital formation. Trade openness; is measured by the sum of exports and imports over GDP (PTO). Furthermore, in terms of the real interest rate (REIR), higher interest rates can increase borrowing costs for debtors; as interest rates rise, the cost of borrowing goes up, especially in international financing markets where the interest rate is not in control of the host country. Real interest rate; is the lending interest rate adjusted for inflation as measured by the GDP deflator (REIR). Control variables also include government consumption (PGCE) as government consumption to GDP, following Turan and Yanikkaya (2021).

To identify that a variable is dealing with a channel through which external debt affects economic growth, the non-linear relationship is more logical and suggested by earlier studies (Hassan & Meyer, 2021; Qureshi & Liaqat, 2020; Silva, 2020). Therefore, we use the square term method to investigate the non-linear relationship between external debt/debt service and transmission channels.

Estimation Techniques

Over the past decade, endogeneity, cross-section dependence, and heterogeneity have received much attention in the emerging panel time-series literature. These issues are based on the realities of a dynamic world, where countries have spill-over effects due to integration and globalization as well as heterogeneous impact across countries, such as the twin oil crises in the 1970s, the Asian financial crisis of 1997, the global financial crisis of 2007 and current health crisis (see Moscone & Tosetti, 2010). However, in most of the early panel research studies, researchers ignored endogeneity issues, and cross-sectional dependence among the cross-sectional units, and assumed homogenous slopes. Furthermore, previous studies investigated the debt-growth relationship in a linear framework, while the latest studies stress using a non-linear framework because it explains the data closely.

Different estimation techniques have been used frequently in the literature for the panel data, such as fixed effects, random effects, and the GMM model. These techniques are the first generation of econometric models that allow intercepts to vary among the cross-sectional unit, but slopes are homogeneous across the cross-sectional groups and assume cross-sectional independence. However, assumptions are invalid in modern econometrics; therefore, these estimates can mislead inference (Chudik & Pesaran, 2015; Harding et al., 2019; Turkay, 2017). Furthermore, Eberhardt and Presbitero (2015) mentioned that cross-sectional dependence occurs due to global shocks and local spill-over effects, yielding seriously biased estimates. Chudik and Pesaran (2015) developed the dynamic common correlated effect estimator (DCCE) to consider the above-mentioned issues, which allows homogenous and heterogeneous coefficients/dynamic common correlated effects. This approach allows for heterogeneous slopes and cross-sectional dependency by incorporating cross-sectional means and lags (Ditzen, 2016).

The study under consideration intends to empirically analyze the transmission channels from external debt to economic growth by using the DCCE approach designed by Chudik and Pesaran (2015). We first apply the two steps system GMM as the baseline model, as it controls for possible endogeneity among variables, measures error, and omitted variable bias. To verify the consistency of the GMM estimator, Arellano and Bond (1991, 1998) and Arellano and Bover (1995) suggested conducting two tests, namely the Hansen J-statistic for over-identifying restriction, which tests the overall validity of the instrumental variables by analyzing the sample analogue of the moment conditions used in the estimation process and the Arrelano Bond test (AR) for autocorrelation in the disturbances, which examines the hypothesis that the error term is not serially correlated in both the single difference regression and the system difference-level regression. Following Pattillo et al. (2003) and Roodman (2009), we applied year (time) dummies to include time effects. Secondly, following the advanced econometricians, to consider the cross-sectional dependence and heterogeneity, we apply the DCCE model of Chudik and Pesaran (2015). It considers three major issues: (a) cross-sectional dependencies, (b) heterogeneity in the parameters, and (c) dynamics (endogeneity). However, the approach of Chudik and Pesaran (2015) makes the accountability of these three issues in a logical manner. The accountability of the first issue took the cross-sectional and lagged averages of the dependent variable as an independent variable. The second issue considered the mean group method that Eberhardt and Presbitero (2015) proposed. The third and the last one incorporated the lag of the dependent variable as an independent one. The DCCE approach can solve all these issues that are discussed earlier.

Empirical Results

The summary of descriptive statistics, correlation analysis, and variance inflation factor (VIF) of the variables employed in all the models are reported in Tables A3 and A4 in Appendix. The results of summary statistics and correlation show that variables are very stable and not highly correlated. The next section investigates four potential channels through which external debt and external debt servicing transmit non-linear effects on economic growth. The mediating transmission channels include public and private investment, total factor productivity, and saving. The two steps system GMM and DCCE are estimated to explore whether theses mediating variables are significant transmission channels or not. The channels under examination are put in the regression as the dependent variables, while external debt, debt service, and control variables are the explanatory variables. The objective is to assess whether the non-linear impact of external debt and debt servicing on economic growth is transmitted through the channels under investigation. For a channel to become a significant means of transmission between external debt and economic growth, both external debt and debt service coefficients should exhibit negativity and significance. The results of the estimation will ultimately confirm the extent to which external debt or debt servicing indirectly influences economic growth through these channels. Each channel is addressed individually based on equations (2) to (5). We carried out the external debt stock and debt service as the main explanatory variables to test the existence of debt overhang and crowding out effect.

Baseline Model

This study specifically delves into investigating how external debt and debt service affect economic growth through the identified transmission channels, grounded in a logical and theoretical basis. The mechanism operates in such a way that as debt accumulation or burden increases, the channels subsequently decrease, leading to a contraction in economic growth. Table 1 reports the GMM estimation results of four transmission channels as outcome variables and their corresponding explanatory variables in columns (1), (2), (3), (4), (5), (6), (7), and (8). In each of the eight equations, the coefficients of the lagged dependent variables are statistically significant at 1%, indicating the persistence and path-dependency of all the dependent variables in Asian developing countries. The diagnostic tests such as AR (2) tests in all eight models the probability values are greater than 5%, implying that all the models are free from the problem of second-order serial correlation. In addition, both Sargan and Hansen tests yield insignificant probability values in all models, which implies that the instruments employed in the models are adequate and not over-identified.

Table 1.

GMM Estimation Results.

Explanatory variables	Dependent variable: private investment		Dependent variable: public investment		Dependent variable: productivity		Dependent variable: savings
Explanatory variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Lag. dependent	0.909*** 0.060	0.962*** 0.034	1.016*** (0.110)	0.986*** (0.097)	0.389** (0.167)	0.426*** (0.149)	0.983*** (0.137)	0.967*** (0.149)
Economic growth	0.097** (0.121)	0.077** (0.124)	0.116** (0.139)	0.105** (0.117)	0.266** (0.112)	0.267** (0.115)	0.065** (0.039)	0.060** (0.039)
Investment*	−0.206** (0.294)	−0.198* (0.331)	−0.012** (0.206)	−0.029** (0.198)	0.074** (0.036)	0.072** (0.036)	0.061** (0.027)	0.067** (0.029)
Trade openness	0.005** (0.131)	0.025** (0.155)	0.164** (0.111)	0.237* (0.135)	0.003 (0.035)	0.0134 (0.038)	0.008 (0.029)	0.002 (0.031)
Population growth	0.0471 (0.042)	0.031 (0.041)	0.101* (0.056)	0.105* (0.056)	0.035 (0.052)	0.036 (0.054)	0.018 (0.012)	0.017 (0.011)
Real interest rate	−0.019 (0.028)	−0.017 (0.029)	0.017 (0.038)	0.016 (0.038)	−0.001 (0.006)	−0.001 (0.006)	−0.009*** (0.003)	−0.008*** (0.003)
Govt’s consumption	−0.059** (0.149)	−0.025** (0.185)	0.101 (0.195)	0.150 (0.175)	0.035 (0.055)	0.022 (0.055)	−0.069 (0.114)	−0.105 (0.114)
External debt	0.006** (0.006)		0.009** (0.005)		0.001** (0.001)		0.002** (0.001)
Squ-external debt	−0.046** (0.034)		−0.047** (0.029)		−0.012** (0.009)		−0.010** (0.004)
Debt service		0.012** (0.013)		0.031** (0.012)		0.002 (0.003)		0.001 (0.001)
Squ-debt service		−0.009** (0.043)		−0.092** (0.041)		−0.003 (0.012)		−0.001 (0.005)
Constant	0.232 (1.425)	0.393 (1.605)	−1.051 (1.037)	−1.792* (1.024)	0.521** (0.247)	0.435* (0.236)	0 (0)	0 (0)
Observations	737	737	737	737	737	737	737	737
Number of countries	32	32	32	32	32	32	32	32
Number of Ints	25	25	12	25	25	25	25	25
AR(1): p-value	0.069	0.062	0.051	0.061	0.051	0.071	0.052	0.090
AR(2): p-value	0.990	0.906	0.286	0.306	0.113	0.143	0.165	0.168
Sargon test	0.289	0.065	0.962	0.599	0.300	0.289	0.945	0.984
p-Value
Hansen test	0.385	0.229	0.976	0.750	0.505	0.385	0.939	0.981
p-Value

Source. The Author’s calculations.

Note. Four channels are dependent variables (private investment, public investment, productivity, and savings). External debt is the main explanatory variable in models (1, 3, 5, and 7), and debt service in models (2, 4, 6, and 8).

Investment* (total, public, and private).

***

, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

The first transmission channel that the study explores is the private investment through which external debt and external debt servicing impact economic growth. Models (1) and (2) in the Table (1) show that an increase in external debt and debt service are positively impacting private investment, respectively. The coefficients of square terms demonstrate that both external debt stock and debt service are negatively affecting private investment. These findings indicate that both external debt and debt service significantly have a non-linear relationship with private investment. It implies the non-linear effect of the external debt stock and debt servicing on economic growth is transmitted through private investment. A clear picture of the non-linear relationship between external debt/ servicing and private investment and being a potential transmission channel, we present the results of the DCCE approach in the next section.

The possible intuition of the results is manifold. First, when external debt stock and debt servicing shoot up, investors’ expectations of returns reduce, and people expect the imposition of higher taxes, which suppress private new investment in the country. Second, the high debt accumulation and debt servicing burden generate uncertainty about the economic conditions of the country, which discourage foreign direct investment. Thus, the results show that private investment is a significant transmission channel through which external debt and debt servicing affect economic growth negatively. In addition, the findings demonstrate that Asian developing countries suffered from the debt crowding effect. Such as Laos, Mongolia, Maldives, and Papua New Guinea, Sri Lanka, Pakistan, where a high external debt burden crowded out private investment due to high taxes and discourage foreign direct investment because of high debt servicing uncertainty. These results are in line with Akram (2017), Munir and Mehmood (2018), Silva (2020), and Hassan and Meyer (2021). The coefficient of public investments is negative and statistically significant at the 5% and 10% levels, respectively, indicating that an increase in public investment leads to a decline in private investment. This finding directs some evidence that public investment is crowding out private investment. Other variables such as economic growth, trade openness, and population growth positively affect private investment, but the latter is insignificant. Real interest rates show a negative and insignificant effect, while government expenses have a negative and significant impact on private investment. Researchers acknowledge the non-linear effects of external debt on growth through the investment channel, few have formalized these dynamics within existing theoretical models. A possible explanation is that, owing to the burden of high debt servicing, governments may be less inclined to undertake challenging reforms, such as trade liberalization or fiscal adjustments. In essence, the impact of debt overhang on growth may not solely be contingent on the volume of investment but also on the efficiency of investment within a challenging macroeconomic policy environment (Pattillo et al., 2011).

Second, Models (3) and (4) show that the external debt and debt service both have significance at the 5% level and bear positive signs. In addition, both their square terms are negative and significant at 5%, indicating that external debt and debt service reduce public investment. As a result, the finding implies that the non-linear effect of external debt and debt service on economic growth is transferred via public investment. This finding is consistent with Hassan and Meyer (2021), Qureshi and Liaqat (2020), Munir and Mehmood (2018), and Silva (2020). The negative effect of external debt and debt service can be explained that excessive debt accumulation is accompanied by a high debt servicing burden, leading to a reduction in capital expenditure for the economy. Thus, the consequences of the negative impact of debt decrease infrastructural development, which slows economic growth. In other words, high external debt and debt service decrease the country’s repayment capacity and decrease the public investment expenditure, respectively. Furthermore, the results also show evidence of debt overhang in the case of ADE. As some of these countries borrow additional external debt to repay old debt obligations, which compelled the governments to cut the incentives and development funds. Both coefficients of private investment are negative and significant at the 5% level. It suggests that private investment negatively influences public investment, in which case a rise in private investment is associated with a decrease in public investment and vice versa. The coefficients of economic growth, trade openness, population growth, interest rate, and government expenditure, are positive, but the latter two variables are insignificant.

Third, Models (5) and (6) show the effect of external debt and debt service on productivity. The coefficients of both variables are positive, and their square terms are negative, but only external debt is significant at a 5% level. The results indicate a case of a non-linear relationship between external debt and total factor productivity. Moreover, the negative effect of external debt on total factor productivity, and consequently on economic growth, could stem from government reluctance to initiate and implement costly economic reforms and policies in the face of enormous external debt stock (Hassan & Meyer, 2021). Particularly, in developing countries, it is believed that most gains from such reforms and policies would accrue to external debt creditors rather than citizens. Billions of external debts are borrowed from different international financial institutions but they are not invested in projects according to agreements, which decreases productivity. The reality is that developing countries have been borrowing since the sixties but their productivity level and technology development is still in the initial stage. Consequently, even domestic sources are used to pay the external debt obligation, which reduces the capacity of productivity in the country. From a debt overhang perspective, the massive external debt accumulation frequently brings uncertainty in the country, which cuts the investment incentives required for technology (Pattillo et al., 2004). This might have a detrimental impact on economic growth because of the total factor productivity decline. This result is related to the findings of Pattillo et al. (2004), Checherita-Westphal and Rother (2012), Munir and Mehmood (2018), and Hassan and Meyer (2021). These researchers found that productivity is a channel through which non-linear effects are transferred from external debt to economic growth in both emerging and industrial economies. Our findings, however, contrast those of Schclarek (2004), who did not find any strong evidence that productivity represents a channel of transmission from external debt to economic growth in developing economies. The coefficients of investment in both models are positive and significant at the level of 5%, indicating that more investment increases productivity. The coefficients of economic growth are equally positive and significant at the 5% level, which denotes that real GDP growth increases productivity. The remaining variables are insignificant.

Fourth, the results of models (7) and (8) report that the external debt and debt servicing variables have positive coefficients while their square terms have negative but only the former is significant. This result shows that external debt non-linearly impacts national savings and it is a channel through which external debt transmits its impact on economic growth. This result aligns with Qureshi and Liaqat (2020), who demonstrated that saving is a channel of transmission between external debt and economic growth. This finding contradicts Munir and Mehmood (2018) and Hassan and Meyer (2021), who found no evidence that private saving is a channel of transmission from external debt to economic growth. The coefficients of economic growth and investment are positive and significant at a 5% level, which indicates that these variables enhance national savings. The coefficients of interest rate are negative and significant, indicating that the interest rate decreases the national savings. The remaining variables, such as population growth and trade openness, are positive but insignificant. The government expenses are negative and insignificant.

Robustness Checks

Table 2 reports the robustness check results by using the DCCE estimator, which is more advanced and recommended by the latest studies against the GMM to consider the cross-sectional dependence and heterogeneity. First, the private investment models in columns (1) and (2) estimated in Table 2 show that the coefficients of external debt and debt servicing are positive. At the same time, their squared terms are negative. Furthermore, the external debt is statistically significant at 1% and the square term at 5%. The debt service is statistically significant at 5%, and its square term is at the same level of significance. These results indicated that the non-linear effect of external debt and debt service on economic growth is transmitted through private investment. Compared to the baseline model results, these results are consistent and have better significance and effect size. The negative effect of external debt and debt service can be explained by the fact that as the economy’s external debt stock and debt service increase, it generates uncertainty in the economy. The results of uncertainty discourage domestic private investment and foreign direct investment. Another argument is that excessive debt stock and debt service increase the debt burden and decrease the country’s repayment capacity. When a country is unable to repay its debt services, the risk of default rises, such as currently Sri-Lanka and Pakistan are in this situation. The results support that private investment is a robust transmission channel through which debt affects economic growth. In addition, the negative effect of external debt stock on growth through private investment is evidence of the debt crowding out effect in the case of ADEs. The external debt service results also identify that the country has a debt burden. To repay the debt obligations, the government increases taxes, which as result decreases the private investment sector and consequently declines economic growth. The coefficients of public investment are significantly negative, which also confirm that public investment crowd out private investment. Other explanatory variables are in line with baseline results.

Table 2.

DCCE Regression Results.

Explanatory variables	Dependent variable: private investment		Dependent variable: public investment		Dependent variable: productivity		Dependent variable: savings
Explanatory variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Lag. dependent	0.087*** (0.195)	0.323*** (0.077)	0.335*** (0.122)	0.156*** (0.136)	−0.042** (0.085)	−0.274** (0.111)	0.077** (0.090)	0.029** (0.093)
Economic growth	0.305*** (0.591)	0.636*** (0.516)	0.0036** (0.648)	0.222*** (0.445)	0.529*** (0.144)	0.439*** (0.133)	0.021*** (0.085)	0.112*** (0.093)
Investment*	−0.283** (0.205)	−0.199** (0.173)	−0.463** (0.439)	−0.077* (0.105)	0.135** (0.054)	0.026** (0.048)	0.089*** (0.031)	0.066* (0.038)
Trade openness	0.365** (0.428)	0.181** (0.286)	0.063 (0.409)	0.015 (0.236)	0.160** (0.073)	0.095* (0.049)	−0.053 (0.033)	−0.019 (0.028)
Population growth	4.899 (3.493)	−0.500 (1.096)	−0.148 (0.866)	1.146 (1.409)	0.025 (0.188)	0.175 (0.166)	0.172* (0.103)	0.089 (0.131)
Real interest rate	−0.003 (0.024)	−0.034 (0.026)	−0.037 (0.038)	−0.033 (0.042)	−0.009 (0.011)	−0.017* (0.009)	−0.002 (0.008)	−0.006 (0.008)
Govt’s consumption	−0.264** (0.269)	−0.244** (0.191)	0.150** (0.229)	0.048** (0.221)	−0.096* (0.101)	−0.029* (0.079)	−0.078* (0.045)	−0.003* (0.049)
External debt	0.110* (0.135)		0.115** (0.171)		0.010** (0.022)		0.005* (0.003)
Squ-external debt	−0.050*** (0.206)		−0.119*** (0.332)		−0.045** (0.063)		−0.029** (0.015)
Debt service		0.348** (0.383)		0.328* (0.703)		0.193 (0.384)		0.106 (0.222)
Squ-debt service		−0.430*** (0.537)		−0.553** (0.985)		−0.262 (0.526)		−0.137 (0.306)
Constant	−0.213 (2.062)	0.810 (1.579)	2.392 (2.274)	−0.225 (1.701)	0.086 (0.434)	−0.495 (0.366)	−0.108 (0.346)	−0.094 (0.274)
Observations	657	657	657	657	657	657	657	657
R ²	0.630	0.630	0.666	0.669	0.649	0.649	0.627	0.639
Number of groups	31	31	31	31	31	31	31	31

Source: The Author’s calculations.

Investment* (total, public, and private).

***

, **, and * indicate 1%, 5%, and 10% level of significance, respectively.

Second, Models (3) and (4) show the robustness check of the impact of external debt and debt service on public investment. The coefficients of both main variables with their square terms are almost consistent with the baseline model results. So, these results support the previously estimated findings in a couple of ways. First, it affirms the nonlinear relationship between external debt and debt servicing, and public investment. Second, it approves public investment as a transmission channel through which external debt and debt servicing affect economic growth. Third, it also endorses the evidence of debt overhang in ADE. Furthermore, the coefficient of private investment is negative and significant at a 5% level, indicating that higher private investments are associated with lower public investment, seeing the same result by Hassan and Meyer (2021). The coefficient of lagged variable, economic growth, and government consumption is positive and significant at 1%, 1%, and 5%, respectively. Other explanatory variables are insignificant at the level, denoting more trade liberalization to enhance public investment.

Third, the total factor of productivity is also an important channel tested in many studies through which external debt has an indirect effect on economic growth. Models (5) and (6) in Table 2 report that only the external debt variable is significant at the 5% level, and its debt square is significant at the 5% level. The variables have expected signs, that is, external debt has a positive sign, and its debt square possesses a negative sign. This supports the hypothesis that productivity is a significant channel through which external debt affects economic growth. These results are in line with baseline model results. In addition, the investment coefficient is positive and significant at the 5% level, indicating that investment increases result in a rise in productivity. Also, the growth rate of GDP is a positive and significant effect on productivity. The coefficient of trade openness is positive and significant, demonstrating that when economies are integrated with the rest of the world, their productivity increases. The interest rate and government consumption harm productivity but are insignificant. Lastly, population growth has a positive effect on growth but an insignificant coefficient. The results are consistent with the baseline.

Fourth, Models (7) and (8) test the robustness of the baseline results about the impact of external debt and debt service on national savings, having a transmission channel from debt to growth. The coefficient of external debt is positive and significant, while its square term is negative and significant. The result confirms the relationship is non-linear between external debt and national savings. In addition, the significance of external debt identifies that national savings are a transmission channel between external debt and growth. These results are consistent with the existing literature and baseline model results. When the debt is high, indicating a high marginal tax on future returns leads the investors to prefer current consumption to future consumption, resulting in a decline in national savings (Sachs, 1989). The coefficients of debt service and its square terms are positive and negative, respectively, but insignificant. The investment coefficient is positive and significant, which illustrates that investment increases savings. In addition, economic growth and past savings also positively affect current savings. Other variables are insignificant.

Conclusion

The intricate relationship between external debt and economic growth has been a focal point in economic studies for decades, with various events, crises, and country-specific factors sparking extensive discussions on the consequences of external debt. Existing empirical literature has delved into the mediating transmission channels between debt and economic growth, revealing a nuanced relationship that goes beyond a simple direct link. This study specifically focuses on the second path of this relationship, emphasizing the importance of understanding the mediating channels through which external debt and debt servicing impact economic growth in Asian developing economies.

The research employs sophisticated econometric tools, specifically the two-step system GMM and DCCE estimators, to address issues of endogeneity, cross-sectional dependence, and heterogeneity. By introducing quadratic terms, the study explores the non-linear relationship between external debt, debt servicing, and economic growth. The findings highlight private and public investments as the most significant channels through which external debt influences economic growth in Asian developing countries. Additionally, total factor productivity and national savings emerge as significant transmission channels. Interestingly, these channels do not show similar significance in the case of debt servicing.

The empirical results underscore the indirect negative effects of external debt and debt servicing on economic growth, pointing toward the existence of debt overhang and crowding-out effects in the examined Asian developing countries. In light of these findings, the study proposes two policy recommendations. Firstly, developing countries should strategically allocate accumulated debt into productive avenues, emphasizing effective debt management concerning interest rates, debt servicing, and project completion. Secondly, attention should be directed toward addressing factors that divert externally borrowed funds into unproductive areas, such as corruption, political instability, and high external dependency. Moreover, fostering human development, boosting domestic savings, and expanding the private sector are suggested avenues for improving economic growth, thereby reducing external debt dependency.

Despite the valuable insights gained, the study acknowledges certain limitations. The use of data from 32 Asian developing countries due to data availability constraints is noted. Future research opportunities are identified in the inclusion of mediating and moderating variables between external debt and economic growth like; capital flight, inflation, government spending, human capital, institutional quality, financial development, and political regime. Moreover, exploring threshold values of external debt and debt servicing for their negative impact on transmission channels presents a promising avenue for future studies, expanding the scope and depth of the research.

Footnotes

Appendix

Table A4.

Matrix of Correlations.

Variables	RYPC	RYPH	TDPY	TSPY	PFIN	PTO	FAPG	GSAV	PBIGDP	PRIGDP	PGCE	REIR	DPOP
RYPC	1.000
RYPH	−0.145	1.000
TDPY	−0.097	−0.039	1.000
TSPY	−0.106	0.271	0.632	1.000
PFIN	0.170	0.054	−0.075	0.000	1.000
PTO	0.013	0.115	0.304	0.193	0.015	1.000
FAPG	0.420	−0.108	−0.197	−0.141	0.076	−0.019	1.000
GSAV	0.121	0.188	−0.276	−0.133	0.515	−0.087	0.134	1.000
PBIGDP	0.062	−0.071	−0.113	−0.155	0.396	0.078	0.045	0.166	1.000
PRIGDP	0.065	0.056	0.023	0.069	0.739	0.055	0.077	0.407	−0.074	1.000
PGCE	−0.184	0.289	0.130	0.114	0.205	0.174	−0.108	−0.098	0.334	0.044	1.000
REIR	−0.042	−0.064	0.161	0.085	0.055	−0.011	−0.098	−0.138	0.091	0.002	0.176	1.000
DPOP	−0.283	−0.003	0.147	0.080	−0.104	0.178	−0.0232	−0.196	0.092	−0.124	0.137	−0.017	1.000
VIF		1.49	2.13	2.00	4.53	1.20	1.08	1.73	2.12	3.5	1.49	1.10	1.10

Note. This Table shows a correlation between variables.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Yunnan Provincial Philosophy and Social Science Innovation Team “Learning from tens of millions of countries construction experince to build a Livable, viable and Beautiful Countryside”(2024CX12)‘ and ’National Social Science Fund Project “Research on Measurement and Promotion Strategy of Rural Digital Governance Efficiency in Southwest China” (23BZZ091).

ORCID iD

Muhammad Dawood

Data Availability Statement

The datasets used and/or analyzed during the current study have been taken from the data collected from the World Development Indicators (WDI), World Economic Outlook (WEO), the Economist Intelligence Unit database (EIU), and Investment and Capital Stock Dataset (ICSD), and made available from the corresponding author on reasonable request.

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External Debt,Transmission Channels,and Economic Growth: Evidence of Debt Overhang and Crowding-Out Effect

Abstract

Plain language summary

Keywords

Introduction

Related Literature

Theoretical Review

Empirical Literature

Methodology

Data Collection and Sample Selection

Model Specification

Estimation Techniques

Empirical Results

Baseline Model

Robustness Checks

Conclusion

Footnotes

Appendix

Declaration of Conflicting Interests

Funding

ORCID iD

Data Availability Statement

References