DOI: https://doi.org/10.62204/2336-498X-2025-1-5

ESCAPING THE SOVEREIGN DEBT DOOM LOOP:

EVIDENCE FROM UKRAINE’S ECONOMY

Hennadii Hryhoriev,

Doctor of Economics, Associate Professor,

National University of Kyiv – Mohyla Academy, Ukraine,

gennadyi.grygoriev@ukma.edu.ua; ORCID: 0000-0003-2349-3875

Annotation. In this scientific article, with the help of the method of simulation modeling, an attempt is made to identify the endogenous factors of the economy’s exit from the debt trap. The problem of breaking the “doom loop” during the war and in the post-war period remains an insufficiently studied phenomenon due to the novelty of this phenomenon in the 21st century: the Ukrainian-Russian war is the first war of such a scale.

The result of writing a research paper is as follows: identification of patterns and mechanisms for overcoming exogenous negative economic shocks (debt burden) through endogenous factors of internal economic potential; construction of a macroeconomic model which allows to identify the scenario of debt burden on the country’s economy under the conditions of its potential growth in export potential in wartime and post-war economy.

Keywords: sovereign debt doom loop; system dynamics; economic growth and debt.

Introduction. Sovereign debt doom loop is a relatively new phenomenon in the theory of sovereign debt, but it is gaining more and more scientific and practical importance. The world economy is experiencing a difficult period of significant growth of debt in relation to gross output  “and expected to reach about 93 percent of global GDP by the end of 2024” [2]. Since Russian war invasion into Ukraine in February 2022 the level of sovereign debt has increased critically due to external borrowing from developed countries and international financial organizations, which allows to support the country’s economy during war and macroeconomic instability. The economic growth of a country like Ukraine largely depends on its own export potential and relates to the possibility of entering the world financial markets for lending and export support. At the same time, external dependence on loans can lead to an increase in debts and difficulties in repayment of loans, which can create problems for the economic development of the country.

Thus, it is important to scientifically identify the factors that affect the balance of domestic and international trade for the country, but also the priority of endogenous economic development aimed at reducing dependence on external loans, because in the case of dependence, the country depends on external loans to maintain its export potential which may lead to an increase in the national debt of the country. External loans usually come in the form of loans or bonds that will need to be repaid in the future, which can lead to an increase in the country’s debt burden.

The country’s fiscal policy under the influence of the debt burden is under significant pressure, as the tax burden on economic entities increases (to strengthen the country’s defense capability), which in turn leads to a fall in the level of consumer spending, investment and production, which can negatively affect economic growth and employment in the country. In addition, rising taxes may also cause negative reactions from markets and investors. With the simultaneous growth of the external debt burden, this situation leads to an even greater increase in tax payments for debt service and to a decrease in the country’s credit rating, which can complicate access to financing on international markets in the post-war period and worsen the country’s financial stability and economic stability.

Study analysis and problem statement. The theory of sovereign debt makes it possible to provide a scientific explanation for the processes of accumulation and management of public external debt and its impact on macroeconomic stability and social well-being of the country.

  The problem to be solved is the evolution of national welfare under the burden of external debt overhang. Excessive foreign debt indicates the resilience of the national economy in the event of external shocks, the ability to adapt in difficult economic conditions. The consequence of the existence of such debt resilience in the conditions of war can be a rapid economic rise of the economy of Ukraine after the end of the war with the corresponding breaking of the doom loop between the troubled national banking sector and sovereign debt.

Western European scientists are considered the founders of modern studies of the “sovereign debt doom loop” problem [6].  In [4,9] the relationship between banks’ sovereign exposure and default incentive  demonstrates a sovereign debt doom loop cycle, in which each element reinforces the influence of the others, ultimately threatening both national and international economic stability. From the point of view of system dynamics, a “self-reinforcing loop” appears, which means the following: a fall in economic indicators can lead to an increase in sovereign debt spreads and a fall in the country’s sovereign ratings. As a result, the cost of borrowing on foreign markets increases, which will again lead to a drop in the economic indicators of the country’s development and to possible economic crisis.

Certain aspects of the impact of the war on the growth of sovereign debt has been  revealed in the article [10].

However, the issue of sovereign debt doom loop in wartime conditions is not sufficiently disclosed and needs to be studied carefully.

The purpose of the study is to investigate the long-term strategy of Ukraine’s exit from the sovereign debt doom loop and to provide recommendations on possible improvement of the economic situation in the country while reducing the debt burden

To achieve the research objective, the method of system dynamics simulation modeling was used, using the principle “other things being equal”, which allows to find the most important regularities in the development of the system. Fit statistics methods were used to assess how well a statistical model fits the observed data. Fit statistics provide a measurement of the goodness of fit of a model by comparing the model’s predicted values to the actual observed values (reference mode). There are many measures we can use, such as correlation, r-squared, mean average error, and mean squared error. These are part of behavioral tests in system dynamics and other statistical tools. We should focus on fit statistics, especially amplitude, mean, and phasing, using Theil inequality statistics to break down error into three parts.

Study results. According to the IMF, the national debt of Ukraine may exceed 105% in 2025, and in 2026 the dynamics of its growth will slow down somewhat [3]. Such growth has a critical negative impact on the stability of the national fiscal space, which may shrink to a critical limit soon. As of November 2024, the EU’s strategic macro-financial support for Ukraine will play a crucial role in preventing such a critical contraction. At the same time, the negative dynamics of the growth of Ukraine’s national debt may override such efforts (Fig. 1).

According to the data in Figure 1, there is a stable trend towards the growth of the national debt. So, in 2019, the national debt amounted to 47.8 billion. US dollars, and in 2029 will reach 293.09 billion. US dollars (according to Statista forecasts). The critical increase in the national debt can be justified by complex factors, among which the military actions on the territory of Ukraine, caused by Russian military aggression and the economic crisis and instability because of such aggression, play a primary role. Of particular concern is the extremely rapid growth of the national debt from 2022, which is again caused by Russian military aggression against Ukraine. The rapid exponential growth of loans and interest payments, in accordance with the law of the development of systemic phenomena, will be inhibited by the development of countercyclical processes due to the significant growth of GDP in the post-war and postwar periods, which is the subject of this study. A decisive role is played by the ability to maximize the mobilization of financial resources through the effective formation of an institutional environment with the appropriate selection of tools and critical points of influence on the system to adjust fiscal rules. The process of modeling. There are 5 steps of modelling, accepted in system dynamics science:
Step 1. Definition of the issue. The dynamic issue is formulated as: «Why the sovereign debt stock is increasing in Ukraine and is it possible to stabilize it in the long – run period of time? ».
Step 2. Dynamic hypothesis formulation
For this part of modelling, we construct a reference mode. The reference behavior reflects the pattern of the most characteristic and problematic variable (or group of variables) selected for analysis. The basic for analysis is the national debt in relation to the gross domestic product for 2013 – 2029 period.

As the Figure 2 shows, the growth of the national debt to GDP will occur in the period until 2025, which roughly corresponds to various forecasts regarding the end of the war in Ukraine. After 2025, there could be a fall in debt related to the country’s economic recovery with significant regaining of access to export markets lost during the war.

Figure 3 depicts a Reference Behavior Pattern for the issue (constructed in Stella Architect modelling tool), built on the base of Figure 2.  As you can see, we extend the time period to 2040 for a better understanding of the processes of development of the economy of Ukraine in the post-war period. We suppose that debt/GDP ratio of Ukraine will increase again as OECD assume, that “in 2040 the global economy will be lower than in pre – COVID period” [7], which could have a negative impact on different economies due to the highere level of external debt of individual countries around the world.

On Figure 3 the number 1 on vertical scale indicates 100% and number 2 – 200%

Step 3. Constructing a model and formulating the hypothesis.

On this step we initialize the model in steady – state, in which the inflows to the stock will equal to the outflows the outflows from the stock (Figure 4).

A hypothesis is visualized to explain reference behavior. In our case, it’s a flow-generating process, so we build the co-flow model on the basis of [8, Chapter 9].  We will provide an explanation. Flows in system dynamics occur within a closed system in time. The creation of resource flows occurs with the help of the processes of generation of such flows. When we talk about creating a co-flow model, we mean developing a model that reflects the relationships between different flows within a system. The co-flow model allows us to understand how different flows are connected and how changes in one of them can affect other flows in the system. By studying the co-flow model, we can gain insight into system dynamics, identify feedback loops, and understand cause-and-effect relationships between different flows. This allows you to model the system’s behavior over time and explore different scenarios to better understand how changes in one part of the system can affect the entire system.

The “starting point” model contains two stocks and one converter with relative measures: debt/GDP, economic growth rate and net export/GDP (the last one is also a stock, though presented in the form of converter). The interplay between the debt-to-GDP ratio and the net export-to-GDP ratio may be represented as a feedback loop. The debt-to-GDP ratio indicates the total debt of a nation in relation to its gross domestic product, which serves as an indicator of economic performance. Meanwhile, the net export-to-GDP ratio reflects the balance of a country’s exports and imports relative to its GDP.

When a nation experiences high debt levels compared to its GDP, it may face obligations that could deteriorate its economic stability. This situation may result in a decline in net exports, as the country may be unable to sustain high import levels, thereby diminishing its overall economic output and GDP. Such a reduction in net exports can lead to higher  economic difficulties, potentially leading to an increase in the debt-to-GDP ratio.

On the other hand, the high level of  net exports can stimulate GDP and economic expansion, enabling a country to manage its debt more effectively. This positive  feedback loop can assist in achieving a sustainable equilibrium between debt levels and economic output.

The term “Growth rate” reveals how swiftly a country’s debt is increasing or decreasing in comparison to its GDP.

The structure of the model also presents the stock of national external debt, which indicates the need to create the foundations of economic competition to stimulate the growth of export potential over time.

The growth of export potential to cover the negative burden from the debt burden should be 30% over a period of 20 years, since the critical debt burden on the economy exceeds the permissible 50% and equals around 100% of GDP [3]. The remaining 20% of the debt coverage should be due to the stimulation of endogenous potential and development and reparations from the Russian Federation to Ukraine, caused by military aggression.

The economic growth could not last forever. The average speed of export growth may cease to increase in 2030: according to ITR Economics “the next Great Depression will begin in 2030 and last well into 2036” [5]. It explains the cessation of exponential growth, based on export expansion and transition to the model of overshoot and collapse.

It is also worth noting that a significant simplification of the model is the establishment of the debt level as a constant that does not change over time.

We pointed out that foreign debt significantly inhibits economic growth in the long term, despite some stimulation of the national economy in the short term. Foreign debt puts pressure on the entire financial and economic system of the country and screens out weak economic players from the market. Inefficient enterprises or sectors of the economy are not always able to adapt to the new competitive environment. Under conditions of serious pressure from the external debt factor, such enterprises or sectors of the economy cannot withstand competitive changes and may be forced out of the market, which will lead to structural changes in the economy and possible economic growth.

Displacement of individual enterprises from the market occurs through the following mechanisms:

1. Increasing interest rates. The increase in external borrowing leads to higher interest rates and more expensive loans for private companies and the crowding out of uncompetitive companies.

“The National Bank of Ukraine has implemented a tight monetary policy” [1]. What implications will this have for Ukraine, which is currently at war?

A long-term high real interest rate can clearly have a negative impact on the Ukrainian economy, which will lead to a reduction in both investment (both domestic and foreign) and the level of consumption, which will lead to a decrease in economic potential and growth. In addition, credit risks for enterprises and individuals may increase, which may subsequently lead to an increase in the number of defaults and bankruptcies, as well as increase the risk of sovereign default because of the difficulty of accessing financing for enterprises of the national economy, a drop in the level of production and an increase in unemployment. In this case, two negative effects arise.

1. The effect of crowding out. When the external state debt increases, the purchase of shares of enterprises by investors may be replaced by government bonds, which leads to a decrease in available capital for private capital and complicates the creation of the economic potential of enterprises.
2. Endogenous macroeconomic shocks. Financial crises caused by the growth of external debt can lead to a reduction in investment and government orders for private enterprises and their displacement from the market.

We observe the steady state of economic growth at this point of analysis, though the fragile balanced state may be easily distorted by introducing some additional parameters.

 The outflow of interest payments is partially contributing to the overall financial burden stemming from economic growth stocks. Companies are struggling to adjust to evolving market conditions, which hampers their ability to repay loans and subsequently invest in updating their capital assets, thereby hindering their competitiveness. Consequently, these companies risk falling behind more successful and efficient competitors in the marketplace.

The level of debt in the model is significantly lower than the level of economic growth. The latter should be sufficient to pay the former. At this stage, the debt level is set at -1, which is twice as low as the level 1 of economic growth. -1 indicates a decrease in the overall level of debt.

Interest payment outflow should be set at a level lower than economic growth – at the level of 0.05. Under these parameters, the inflow of economic growth will be equal to the outflow of economic growth and the economy will be in a steady state.

In the model, the outflow of interest payments is anticipated to place increased financial pressure on the economy, as these obligations must be met regardless of economic performance. This could lead to a situation where the income produced from economic expansion fails to counterbalance the interest payment outflow, jeopardizing economic stability and increasing the likelihood of falling into a debt trap. Additionally, the focus on servicing interest payments may exacerbate the challenges faced by weaker businesses, which might struggle to fulfill their financial obligations, ultimately leading to greater market consolidation and the removal of less efficient companies.

The key issue is to increase the export potential of the economic system to prevent it from falling into a new debt trap and crowding out inefficient enterprises from the market: weak, inefficient companies or industries are prone to being absorbed or displaced by stronger and more successful competitors. This can occur as a result of the processes of market consolidation, mergers and acquisitions, when large players absorb small players unable to compete on equal terms. We can integrate this logic by introducing a model variable that captures average growth for weak enterprise, separated from the average interest payments outflow.

In the proposed model, the parameter investment decisions bias is introduced. Regardless of the actual financial stability or attractiveness, investors prefer to invest in companies that, from their point of view, are the most attractive. This approach leads to the possibility that successful and innovative companies become “unnoticed” by potential investors. Thus, investment decisions bias leads to distortion of investment decisions and missing the opportunity to invest in little-known but promising companies.

Also, a new variable average growth/weak enterprise is introduced into the model, because of the activities of the inefficient enterprises described above. After the introduction of this variable, economic growth maintains its steady state with export growth. At the same time, after the possible expansion of the time horizon, exports will begin to significantly lag economic growth. This indicates the possibility of a gradual reorientation of the national economy to an internal (endogenous) type of economic development. If the planning horizon is increased to 18 years, the number of inefficient enterprises may increase significantly, by approximately 1.5. times This can be explained by the action of other factors that are not included in the model. We can assume that the country pays insufficient attention to innovative renewal, which leads to a long-term deterioration of its economic condition.

As net export growth occurs, with the debt parameter unchanged, the number of inefficient enterprises should decrease. The graph with average growth per weak enterprises indicates that the number of inefficient enterprises initially remains unchanged (for about three years), after that it rapidly decreases by about 10%, after which it increases slightly and remains within the “steady state” during the entire studied time interval (Figure 5).

This model is too optimistic, as it indicates exponential economic growth against the background of a fall in the debt/GDP outflow parameter. There is also a drop in the debt repayment outflow indicator that is unsatisfactory for this stage of the analysis.

The number of inefficient enterprises per unit of debt is decreasing and stabilizing thanks to the growth of export potential. The debt/GDP ratio is declining as export potential increases and the number of inefficient enterprises is relatively stable. A concern is the level of  debt interest payments, which cannot be reduced despite economic growth.

Final modelling part:

To transform the above mentioned optimism into more realistic scenario we need to admit, that in the case of Ukraine its economy  is completely depended on external loans, at least in the short run. While economic growth can be financed through external borrowing in the short term, over-reliance on external borrowing could create vulnerabilities that affect long-term growth and sustainability. These economies must strike a balance between borrowing and strengthening domestic economic strength to ensure that they do not become overly dependent on external financing. To present a realistic development scenario we are forced to replace the economic growth stock with debt/GDP to emphasize the fact that the main problem lies in the level of foreign debt, which does not allow the economy to achieve a high level of growth.

 The following variables were added to the model: rate of economic growth was added to economic growth inflow (determines how quickly the economic growth inflow may occur) along with economic growth capacity (determines the highest sustainable rate of economic growth that a system can attain is determined by a range of factors, including available resources (including external financing), infrastructure, technological advancements, and policy frameworks).

The variable lack of investment resources reflects the challenges posed by economies’ ability to invest in growth-enhancing activities. Against the backdrop of existing debt levels, it becomes clear that economies must maintain a precarious balance between managing debt and creating an environment conducive to domestic and foreign investment. The variable Investment climate represents the overall attractiveness of an economy for investment and can affect the availability of financial resources to pay interest.

Compared to Figure 5, which shows the exponential growth of exports, Figure 6 shows a more moderate growth in exports.

On this step we use fit statistics module in Stella Architect to test the model. Fit statistics (goodness of fit) is a behavioral reproduction test. It provides important quantitative metrics for evaluating model performance and ensures that the model’s structure and parameters accurately represent the dynamics of the real system.    

The way we want to approach it is to include fit statistics, paying attention to features of amplitude, mean and the phasing. Theil inequality statistics is a way to do that.

The question is how to obtain a reasonable fit between reference behaviour pattern (Figure 3) and simulated data (Figure 6).  To do this we add the module called Debt/GDP fit where we compare Reference mode debt/GDP with Debt/GDP (simulated value). The fit statistics module structure is presented   on Figure 7 upper panel. On the Figure 7 lower panel we present the graphical results of goodness of goodness of fit results.  The simulated behaviour and reference mode have the same general shape, though they don’t match perfectly.

The actual fit statistics calculation is presented in Table 1. with the norm of correlation, R – squared; mean squared error (MSE) along with three inequality statistics: Um is looking for mean structure; Us as a measure of standard deviation amplitude; Uc a measure of phasing. 

The results of testing are quite good with a correlation of 0,952 and R squared 0,907. The errors are decomposed, and we see that most of the errors are in mean structure (Um). Step 5. Policy design and evaluation.

We have chosen the following policies (leverage points to intervene in the dynamic structure with appropriate structure redesign) available for further research.

– Debt restructuring mechanism. Effective debt restructuring involves not only its postponement, but also managing the strategy of economic growth, considering the temporary reduction of the debt burden

– Economic growth strategies. We believe that in the long term, an import substitution strategy is important, in contrast to an effective short-term export promotion policy, which can create an endogenous boost to GDP growth and decrease sovereign debt level

– Macroprudential policy. Policies may be formulated to promote a more sustainable balance between public and private debt, potentially incorporating restrictions on the volume of short-term debt that can be issued.

– Partial (at least) debt writing off, which will lead to the release of economic resources for the post-war renewal of the economy

– Reduction of the tax burden in the post-war period, which allows the national economy of Ukraine to reduce the expenses of enterprises and increase their economic efficiency.

Conclusions:

– The sovereign debt doom loop underscores the relationship among escalating debt levels, economic metrics, and heightened borrowing expenses, especially in the context of wartime scenarios. For Ukraine, a continued dependence on external financing threatens to intensify its economic difficulties.

– Using the method of system dynamics, a model of partial equilibrium of the economic system has been built (equilibrium of economic growth against the background of export growth). It was theoretically approved that the national economy may simultaneously achieve economic growth steady state and export which contributes to sovereign debt relief.

– Goodness of fit results (Debt/GDP reference mode with Debt/GDP simulated model) have approved the reliability of modelling.

– Policy design and evaluation for assessing Ukraine’s post-war potential growth driven by export potential and external borrowing effects has been provided.

– The practical implication of sovereign debt doom loop issue is to provide proactive actions to facilitate sovereign debt resilience and foster economic stability because of making recommendations for Ministry of Finance of Ukraine, National Bank of Ukraine and other governmental institutions in their coordination with international financial institutions.

– Further research will be devoted to the implementation of the scenario approach to the above developed model of sovereign debt.

References:

1. Bogdan T. (2024). Monetary and debt exhaustion: What threatens the high cost of servicing the domestic national debt during the war, available at: https://biz.censor.net/columns/3522564/skilky-vytrachaye-derjava-na-obslugovuvannya-vnutrishnogo-borgu
2. Dabla – Norris E., Furceri D., Lam R., Menkulasi J. (2024). Global public debt is probably worse than it looks. URL: https://www.imf.org/en/Blogs/Articles/2024/10/15/global-public-debt-is-probably-worse-than-it-looks
3. Danishevska K. IMF forecast: Ukraine’s national debt to exceed 100% GDP in 2025, available at: https://newsukraine.rbc.ua/news/imf-forecast-ukraine-s-national-debt-to-exceed-1729662602.html
4. Gómez-Puig, M., & Sosvilla Rivero, S. (2024). The diabolic loop between sovereign and banking risk in the euro area. IREA–Working Papers, 2024, IR24/06.
5. ITR Economics. What Will the World Look Like After the 2030s Great Depression?, available at: https://blog.itreconomics.com/blog/what-will-world-look-like-after-2030s-great-depression#:~:text=How%20Long%20Will%20the%202030s,before%20the%20Great%20Depression%20began.
6. Leiss, W. (2011). The doom loop in the financial sector: And other black holes of risk. University of Ottawa Press.
7. Organisation for Economic Co-operation and Development. (2022) Global plastics outlook: Policy scenarios to 2060. OECD Publishing, 2022.
8. Richmond, B., & Peterson, S. (2001). An introduction to systems thinking. Lebanon, NH: High Performance Systems., Incorporated.
9. Rojas, L. E., & Thaler, D. (2024). The bright side of the doom loop: banks’ sovereign exposure and default incentives. – Banco de España Working papers № 2409
10. Trofimchuk, M., & Trofimchuk, O. (2023). Specific features of Ukraine’s public debt management in conditions of war. Herald of Economics, (3), 198-211.
11. Ukraine: National debt from 2019 to 2029, available at: https://www.statista.com/statistics/531998/national-debt-of-ukraine/#:~:text=After%20the%20tenth%20consecutive%20increasing,increasing%20over%20the%20past%20years.
12. Ukraine: national debt in relation to gross domestic product (GDP) from 2013 to 2029, available at:

https://www.statista.com/statistics/427246/national-debt-of-ukraine-in-relation-to-gross-domestic-product-gdp/