A sovereign credit rating serves as a vital measure of a country’s financial health and creditworthiness, determined by specialized credit rating agencies. These ratings provide a comprehensive evaluation of a nation’s ability to meet its debt obligations, considering economic and political factors from both short-term and long-term perspectives. Expressed through alphanumeric scores, sovereign credit ratings specifically reflect a country’s long-term foreign currency outlook. The publication of these ratings is a pivotal event, influencing investor confidence and economic stability, both at the time of release and in its aftermath [1]. Over the past few years, there has been a growing demand for credit ratings as a means for countries to assess their creditworthiness [2]-[4]. This enables them to access global capital markets and affects their borrowing costs. Countries without credit ratings are unable to secure loans from global capital markets and must rely on official loans from multilateral institutions or individual governments, as well as aid or grants [5]. The Credit Rating Agencies (CRAs) play an important role in this structure. Among them S&P, Moody’s, and Fitch are particularly prominent, providing investors with assessments of credit risk in the form of ratings. According to the ratings agencies, lower-rated governments pose greater credit risks to investors. As a result, these governments face higher borrowing costs when they issue bonds [6]. The lack of a credit rating can hinder a sovereign’s ability to invest in development projects, stimulate economic growth, and meet financial obligations [5]. The sovereign ratings provided by the major credit rating agencies typically cover more than 100 countries worldwide. However, it is noteworthy that many African countries receive lower ratings on the global scale. The importance of credit rating agencies in Africa has been highlighted during the recent discussions on the global Debt Service Suspension Initiative (DSSI), led by the World Bank and the International Monetary Fund (IMF). The DSSI was initiated in response to concerns about the sustainability of public sector debt burdens in many African countries, and aimed to alleviate some of these financial burdens [6]. While numerous studies have examined the factors that determine sovereign credit ratings in different economies, little attention has been paid to East African countries. Our research examines this gap by analyzing a sample of 7 countries in East Africa from 2010 to 2022 to assess the criteria and factors considered in assessing creditworthiness. The credit ratings of East African countries are particularly low for a number of reasons such as high debt levels and difficulties in managing debt obligations which can affect a country’s creditworthiness and lead to lower credit ratings. To improve their credit ratings, governments should take a number of measures. First, governments should prioritize improving the long-term determinants of financial operations, such as improving the fiscal balance and external surplus, to enhance overall solvency. Second, East African countries should take steps to establish and support their own sovereign credit rating agencies and research organizations to enhance their presence in global markets. Third, there is a need for increased financial and policy support for research initiatives focused on sovereign credit ratings. Fourth, it is imperative to implement continuous monitoring and early warning strategies to mitigate risks. In addition, countries need to consider changing the currency structure of loans and diversifying reserves [4]. Rating levels aren’t solely determined by economic indicators; several other factors also play a role. For example, in evaluating sovereign ratings, take into account aspects such as the state of democracy, the quality of healthcare, the competitiveness of the education system, and security [7]. In addition, models that predict the future of corporate and sovereign ratings include other independent factors not just economic and financial variables [8]. Factors that determine sovereign ratings go beyond current economic and financial indicators and include factors that vary across rating categories and countries undergoing structural changes [9]. Therefore, a comprehensive consideration of factors beyond economic indicators is important when assessing and predicting rating levels.

In recent years, a list of publications has treated the subject of credit rating determinants and creditworthiness and categorized them into two different aspects, where the first aspect focuses on economic fundamentals as explanatory variables and the other aspect focuses on political fundamentals. In an early paper [10], a study was conducted on a cross-sectional analysis of Moody’s and Standard & Poor’s (S&P) ratings, and found that GDP growth, per capita income, inflation, external debt, and other explanatory dummy variables of economic development for the default history and foreign currency debt are significant explanatory variables for sovereign credit rating field. However, no significant correlation was found between the external balance or fiscal balance and sovereign credit rating. [11][12] also found that sovereign credit ratings are primarily impacted by factors such as per capita income, government revenue, fluctuations in the real exchange rate, inflation rates, and historical instances of default. In the same way, [13] investigated the factors behind sovereign credit ratings issued by Moody’s and S&P using a sample of 81 economies in 2001 and discovered the same characteristics that significantly influence ratings [14] conducted a study that examined the drivers of sovereign credit ratings in 95 countries from the end of 1995 to the end of 1996, They found that qualitative, political, and social indicators as well as economic factors such as GNP per capita and inflation, were significant. From the arguments that political variables should be incorporated, [15], in their study with the aim to distinguish the long-term and short-term effects of macroeconomic and fiscal variables on sovereign credit ratings, they emphasized the significance of qualitative variables. They argued that government effectiveness has a lasting impact on SCRs. In the same way, [16] conducted a study involving 36 developing countries from 1996 to 2006. They extended their research by introducing new political and institutional variables, such as adherence to the rule of law, protection of property rights, and the presence of a robust and independent judicial system. Their findings indicated that political variables played a vital role in influencing and determining SCRs. Meanwhile, [17] in their study on Moody’s, Standard Poor’s, and Fitch sovereign rating models, sought to discover major factors of sovereign ratings. Moreover, the sovereign credit ratings are not affected by the estimation methodologies used. Economic strength, inflation, and governance are discovered to be the major predictors of sovereign credit ratings, before fiscal strength, domestic political risk, and banking system size. [18] in their study examining 50 developing countries from 1987 to 2003, their findings indicated that sovereign ratings are influenced significantly by economic factors, including inflation rate, default history, GDP growth rate, and commitment to trade [19]. They analyze the factors influencing sovereign credit ratings using panel data encompassing 86 countries from 1993 to 2013. Exploring whether the average credit ratings of nations vary based on regions and during periods of crisis and stability. They found that macroeconomic, external, governmental, and qualitative factors significantly impact sovereign credit ratings [20], in the case of developed and developing nations over the period between 2002 and 2016. They conclude that credit rating exhibits a positive correlation with per capita Gross Domestic Product (GDP), the degree of economic advancement, and export levels. Moreover, the research revealed a negative association between credit ratings and inflation rates, debt levels, and the history of defaults. According to [21], the determinants of sovereign credit ratings vary across African regions and income levels. Throughout all regions and income groups, the most important determinants were developmental indicators [21] found that the determinants commonly identified in the existing literature may not be as relevant to African sovereigns. Instead, developmental variables, income levels, and regional distinctions appear as crucial considerations when assessing sovereign credit ratings in Africa. Similarly, in the study [1], the relationship between sovereign credit ratings assigned by S&P (a well-known credit rating agency) and macroeconomic variables was investigated for different income groups in 105 countries. The study found that certain macroeconomic variables hold economic significance in determining sovereign credit ratings for different income groups of countries. For low-income and middle-income countries, the study revealed that per capita income, growth rate, inflation rate, government debt, budget balance, and current account balance were identified as significant macroeconomic variables. These factors played a crucial role in determining the sovereign credit ratings of these countries. On the other hand, for high-income countries, the study identified different significant macroeconomic variables, which include Per capita income, inflation rate, unemployment rate, and government-related factors. These variables were found to have a considerable impact on determining the sovereign credit ratings of high-income countries. According to [14], the assessment of ratings is not solely reliant on existing economic and financial indicators. Furthermore, the significance of economic variables differs among distinct rating categories, and financial indicators reveal variations based on countries’ development levels. In highly-rated countries, economic variables hold less importance, whereas in lower-rated countries, factors such as inflation, GNP per capita, foreign reserves, and current account balance play a significant role in determining sovereign ratings. From the proponents who advocate for limiting Sovereign Credit Rating (SCR) analysis to economic factors, [5] conducted a study on 53 countries to identify the factors that determine sovereign credit ratings. In their study, they utilized 9 macroeconomic variables and expanded their analysis by including 3 qualitative variables: history of default, economic development, and economic freedom. The outcomes of their study aligned with previous research, indicating that certain macroeconomic variables significantly influence credit ratings. Additionally, the variable of economic freedom played a crucial role in determining sovereign credit ratings. The existing literature on the determinants of sovereign credit ratings in East African countries appears to be limited, with most research focusing on other regions. One key research gap is the need for a more comprehensive understanding of how the importance of various factors, such as GDP growth, external debt, current account balance, inflation, and rule of law may differ across East African countries with varying income levels. While previous studies have examined macroeconomic variables and institutional quality as determinants of sovereign credit ratings, there is a lack of in-depth analysis on how these factors may be shaped by the income levels of the East African countries. A deeper exploration of the diverse effects of these variables, stratified by high-income, middle-income, and low-income countries, could provide valuable insights allowing for a better understanding of the unique drivers and constraints faced by East African countries at different stages of economic development when it comes to achieving favorable sovereign credit ratings. Addressing this research gap by explicitly incorporating the role of income levels as a moderating factor could yield important insights to guide policymakers and stakeholders in the region as they work towards enhancing their countries’ creditworthiness and access to global financial markets.

We use nine rating factors as independent variables in our model. We include GDP growth, GDP per capita, external debt, current account balance, inflation rate, unemployment, corruption, political stability, and rule of law. Based on a comprehensive analysis of various sources of information, it has been determined that the most reliable and credible source of a chronological sequence of macroeconomic indicators used as explanatory variables in our study is the World Development Indicators database and the IMF’s Economic Outlook Indicators, which have emerged as the primary repository of this data. Nevertheless, there is a need to obtain data on credit ratings from the three international rating agencies, i.e., Standard Poor’s, Moody’s, and Fitch, for an appropriate period of time. Data on sovereign credit ratings are obtained from https://www.globaleconomy.com/ (see Table 1). To support data transparency, we added a source note highlighting how data quality may influence our findings and encouraging thoughtful interpretation.

Table 1. Explanations of the explanatory variables.

Our country selection is based on a geographical standpoint, with a particular focus on countries in the East Africa region. For example, Tanzania and Burundi were not included in our sample due to limited data availability. This implies that not all the countries encompassed by the East African sub-region were selected for our study. In total, our research covers 7 of the 21 countries listed under East Africa in the World Population Review. These 7 countries were classified into 3 groups: low, middle, and high-income. We use 13 years of information from 2010 to 2022. Our dataset comprises a well-balanced panel for East African developing countries, focusing on the 3 prominent rating agencies, Fitch, Standard & Poor’s, and Moody’s (see Table 2).

Table 2. The summary statistics report for each variable was generated across all of the observations that have S&P, Fitch, and Moody’s ratings for the years 2010 to 2022.

This study employs a quantitative research methodology, using econometric models and statistical analysis techniques. The sample includes a selection of East African countries, and the analysis covers 13 years. The independent variables identified in the literature that influence sovereign credit ratings are tested for East African countries and their income groups by using various panel data techniques. Panel data methods are preferred because they will increase the number of observations and the type of data used in this study a combination of cross-section and time series. Using a multi-year ordered probit regression model, which allows us to incorporate time dynamics into our analysis and assess how the importance of different factors may evolve over years or economic cycles, pooling data over several years can potentially increase the sample size, provide more statistical power and reduce the impact of outliers, which may be particularly relevant in the context of East African countries where data may be limited or subject to volatility. Some determinants of sovereign credit ratings may have cumulative or lagged effects. An ordered probit model allows us to capture these dynamics and understand how past economic, financial, or political events affect current credit ratings. Our approach is similar to the cross-sectional multivariate ordered response model for analysis used by [22] to investigate the determinants of the level of non-work activities across different types of activities.

The ordered probit regression model.

Will take into account the latent variable equation:

where:

y i t * is the unobserved latent variable representing the creditworthiness of country i in year t. X is a vector of explanatory variables for example GDP growth, unemployment, external debt stock, etc. β is a vector of coefficients to be estimated, ε is the error term, assumed to follow a standard normal distribution. μ₁, μ₂, …., μ_J−1 are the unknown threshold parameters representing the cutoff points between credit rating categories.

The observed ordinal credit rating, y_it, is then determined by the threshold parameters μ:

y_it = 1 if y_it ≤ μ₁

y_it = 2 if μ_1it ≤ μ₂ (3)

y_it = 3 if μ2 < y_it ≤ μ₃

y_it = J if y_it > μ_J-1

Given that Φ ( − ∞ ) = 0 and Φ ( + ∞ ) = 1

The log-likelihood function for the sample is estimated using conditional probabilities, and the parameters that maximize this function are determined.

The factors influencing sovereign credit ratings can be significant if all countries undergo evaluation using a consistent approach that takes into account macroeconomic variables. Including country-specific characteristics as fixed or random effects in the model would undermine the study’s objectives. Nonetheless, random effects ordered probit models are estimated to see how the potential estimation strategy affects the outcomes.

The random effect model using the maximum likelihood estimation is given as

P ( y i t > k | k , x i t , v i ) = Φ ( β ' x i t + v i − k k ) *

For i = 1 , … , n panels, where t = 1 , … , n i , v i are independent and identically distributed (iid), v i ∼ N ( 0 , σ v 2 ) and k is a set of cutpoints k 1 , k 2 , … , k q − 1 where q is the number of possible outcomes; and Φ ( · ) is the normal probability density function. From * we the probability of observing the outcome k for response y i t :

Whose k 0 is taken as − ∞ and k q as + ∞ , we express the model in terms of the latent linear response, where y i t are generated from the latent continuous response such that;

Since v i is iid, and the correlation between successive disturbances is given as ρ = σ v 2 / ( σ v 2 + 1 ) . The conditional probabilities for the rating outcomes of the countries can be written as:

From the conditional probabilities, the conditional likelihood for a country is the product of all those probabilities for the available years T :

The probability functions in the conditional likelihood include the random country-specific component v i . The unconditional likelihood is obtained by integrating the marginal likelihood over all possible values of v i . Let v ˜ = v i / σ v , the likelihood function takes the form:

where

The growing interconnectedness of global markets and cross-border investments has elevated the importance of credit ratings. This study seeks to deepen our understanding of the macroeconomic factors that influence sovereign credit ratings, with a particular focus on East African countries. The findings show that all the analyzed indicators significantly contribute to the credit ratings assigned by rating agencies. Notably, variables such as GDP, unemployment, corruption, and the passage of time were found to be statistically significant in the estimation of the multi-year ordered probit model. The results indicate that as time progresses, sovereign credit ratings tend to decline, suggesting a trend of worsening creditworthiness, likely driven by increasing debt levels and political instability. Surprisingly, corruption was found to have a positive impact on credit ratings in East Africa. This unexpected result implies that in this regional context, higher levels of corruption may be linked to other factors that indirectly improve credit ratings, or the effects of corruption may be outweighed by stronger positive influences, such as economic growth.

A comparison of the rating models used by various credit rating agencies reveals that several key determinants are consistently applied across the board. Governance variables and inflation, in particular, are recognized as significant by all agencies. Countries that score higher on governance indicators tend to receive more favorable ratings from every agency. The seven East African countries analyzed in this study are classified as developing nations. To stimulate economic growth, these governments must build strong institutional frameworks and adopt strategies to enhance their competitiveness. Moreover, addressing a range of critical issues is essential. Given the high-value rating agencies place on governance indicators, East African nations should prioritize improving their economic institutions. Key areas of focus include strengthening corruption control, enhancing government effectiveness, upholding the rule of law, coordinating monetary policies, and improving infrastructure to align with the standards of Africa’s best-performing regions.

While this study offers valuable contributions and important insights, it is not without limitations, as is common in any research effort. These limitations refer to potential shortcomings that lie beyond the researchers’ control. In our case, the lack of prior research focused on the selected East African countries presents a challenge. Apart from the illustrative variables sourced from the World Bank Indicator (WDI), there are significant delays in obtaining credit rating data from these countries. Additionally, the quality of data in these emerging nations raises concerns, potentially impacting the study’s overall reliability.

In conclusion, this paper examines the factors influencing sovereign credit ratings in selected East African countries based on their income levels. However, it does not encompass all countries in the region. Future research should expand on this by exploring additional macroeconomic factors that impact sovereign credit ratings.

Dataset is readily available online.

The authors acknowledge the Taiyuan University of Technology Institutional Review Board for approving this study.

The conceptualisation, curating data and analysis of the work on the factors influencing Sovereign Credit Ratings in East Africa were done by M. N., I. M., M. S. & C. M. All the authors were actively involved in performing the Ordered Probit Model analysis and discussions on findings as well as the conclusion of the paper. All authors were major contributors to the manuscript’s writing. All authors read and approved the final manuscript. Besides, M.S. supervises the entire work.

The work was ethically approved and accepted by the Taiyuan University of Technology Institutional Review Board (TYUTIRB/SEM/2024/305). Authorities and participants were informed about the study’s goal and other details. None of the authors was forced to participate in the study.