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Article

Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries

by
Tarek Eldomiaty
1,*,
Islam Azzam
1,
Jasmine Fouad
1,
Hussein Mowafak Sadek
2 and
Marwa Anwar Sedik
3
1
Onsi Sawiris School of Business, The American University in Cairo, AUC Avenue, P.O. Box 74, New Cairo 11835, Egypt
2
College of Management and Technology, Arab Academy for Science Technology and Maritime Transport, P.O. Box 2033, Cairo 11736, Egypt
3
Faculty of Business and International Trade, Department of Finance, Misr International University, P.O. Box 1, Heliopolis, Cairo 11757, Egypt
*
Author to whom correspondence should be addressed.
Risks 2025, 13(6), 111; https://doi.org/10.3390/risks13060111
Submission received: 23 March 2025 / Revised: 15 May 2025 / Accepted: 3 June 2025 / Published: 12 June 2025
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Abstract

This paper examines the impacts of observed versus uncertain (stochastic) institutional quality of corporate debt financing. This paper compares the impacts of two distinct levels of institutional quality in developed and developing economies. World governance indicators (WGIs) are used as proxies for institutional quality. Stochastic Geometric Brownian Motion (GBM) is used to quantify the institutional uncertainty of WGIs. The results of GLS estimates using a sample of 309 nonfinancial listed firms in G8 countries and 373 nonfinancial listed firms in MENA countries covering the years 2016–2022 show (a) positive (negative) stochastic impacts of voice and accountability (government effectiveness and political stability) on debt financing in the G8 and MENA regions; (b) although potential improvements in institutional quality are shared concerns among G8 and MENA countries, the former outperforms the latter in terms of creditors’ contract protection and enforcement, paving the way for public policy makers in the MENA region to enhance regulations that protect debt contractual obligations; (c) macroeconomic variables have sporadic impacts; GDP growth is significant in G8 but not in MENA countries; (d) the negative impacts of inflation rates are consistent in both regions; and (e) unemployment plays a negative signaling role in the G8 region only. This paper contributes to the related literature by examining the uncertain impact of institutional quality on corporate debt financing. This paper offers implications for policy makers, directing them to focus on institutional endeavors in a way that helps companies secure the debt financing required to support investment growth.

1. Introduction

Institutional arrangements remain intrinsically uncertain. Corporate managers cannot expect either the institutional plans set out by government officials or the impact of those plans. Therefore, effective financial plans must consider the impact of current (or observed) and expected institutional changes. Intrinsically, the impacts of institutional changes at large, particularly institutional quality, offer empirical boundaries between macroeconomic and microeconomic analyses of business corporations. As the classification of business systems recognizes a number of economic regions, a question arises to what extent the potential for convergence must be considered by both corporate managers and public policy makers. Accordingly, the objectives of this paper are twofold. First, this paper aims to investigate country-level determinants of capital structure via analyses of companies from the Group of Eight and Middle East and North Africa blocs. Second, this paper aims to quantify institutional uncertainty by estimating stochastic WGIs via GBM. The essence of institutional quality is addressed through an examination of two different regions, namely G8, representing developed countries, and MENA, representing countries. This paper contributes to related studies in terms of comparing the observed (historical) and stochastic effects of WGIs on corporate capital structure in two institutionally different economies, namely G8 and MENA. The stochastic estimates provide a quantification of institutional uncertainty.
The rest of the paper is organized as follows: Section 2 presents a literature review of the impact of institutional quality on financing decisions in developed and developing economies. Accordingly, several hypotheses are developed. Section 3 describes the methodology, including the data, variables, trends in institutional quality in the G8 and MENA regions, statistical specification tests, and methods of statistical estimation. Section 4 discusses the results. Section 5 concludes the paper.

2. Literature Review and Hypothesis Development

This section discusses the impacts of institutional quality and macroeconomic factors on debt financing in developed and developing countries. Accordingly, a number of related hypotheses are developed.

2.1. Impact of Country-Level Institutional Quality on Corporate Business

The evolving contributions of institutional quality to macroeconomic performance (Acemoglu et al. 2005; Greif 2006) have been extended to the micro level (firm level) as a natural distinction between developed and developing economies that have been examined in the course of new institutional economics (Joskow 2008). North (1990, 1993, 2005, 2008) has provided extensive examples of how strong institutions help countries and firms grow faster than weak institutions do. Williamson (2002) offers a distinguished treatment of how strong institutional governance helps develop quality contracts between corporate stakeholders, including financiers. These contracts and institutional uncertainty affect all corporate finance decisions. Hence, it is befitting to investigate the impact of institutional factors on the corporate capital structure. Furthermore, Hartwell (2018) argues that the stability of institutional arrangements matters, which can be considered a convenient opening to further examine the role of institutional uncertainty. Therefore, prevailing institutional uncertainty offers a plausible motivation in this paper to quantify the effects of institutional quality on the corporate management of capital structure.
Country-specific determinants usually include institutional and macroeconomic environments (Jaworski and Czerwonka 2019; Jõeveer 2013). Institutional determinants capture the traditions and institutions through which authority is exercised in a country (Kaufmann et al. 2011). These include the processes by which governments are selected, monitored, and replaced; the capacity of governments to effectively formulate and implement sound policies; and the degree of respect that citizens and the state have for the institutions governing their economic and social interactions (World Bank 2022). Therefore, as economic regulations at the macro level govern corporate business, a growing body of research has examined the impact of different elements of institutional quality on corporate business. Faccio (2006) investigated country-level institutional features conducive to the high prevalence of political connections. Boubakri et al. (2012a) conclude that firms with strong political ties are associated with lower capital costs in comparison to firms with weak political ties. This conclusion offers shareholders positive confidence to invest in firms with strong political ties. Strong institutions reduce firms’ reliance on political connections, lowering debt levels (Boubakri et al. 2012b; Öztekin 2015). Cahan et al. (2009) suggest that high country-level institutional quality is necessary (but insufficient) for earnings quality to be reflected in share prices. That is, countries with strong institutions are associated with high value-relevance earnings quality. The opposite is true for countries with weak institutions. The impact of weak institutions is also extended to the firm’s operational level. In West African countries, which are associated with weak institutions and a low level of private sector participation, firm executive directors exercise more power to expropriate remuneration. Furthermore, in North Africa, government effectiveness, corruption control, and political stability help curb expropriation by firm directors (Hearn 2013, 2014). Therefore, as corporate financial activities are influenced by the quality of institutional arrangements, corporate financing decisions per se are worth further examination.

2.2. Institutional Quality and Corporate Financing Decisions in Developed Countries

Cross-country studies on capital structure have provided evidence that capital structure decisions are influenced not only by firm-specific characteristics but also by the institutional and macroeconomic environment of a country (Beck et al. 2002; Antoniou et al. 2006; de Jong et al. 2008; Lopez-Iturriaga and Rodriguez-Sanz 2008). However, most studies have focused on firm-specific factors, paying less attention to country-specific factors (Bilgin 2019). Additionally, most empirical studies examining capital structure focus primarily on developed economies, which has led to a growing research interest in the context of developing countries (Kumar et al. 2017). In particular, capital structure in the MENA region is receiving increased attention (Cherni 2022). Nevertheless, significant institutional and macroeconomic differences between developed and developing economies are expected to affect the capital structure choices made by firms (Booth et al. 2001). Previous studies have examined limited aspects of how institutional environments affect corporate financing decisions (Awartani et al. 2016; Etudaiye-Muhtar et al. 2017).
Empirical studies confirm that institutional quality is negatively associated with debt levels in both developed and developing countries (Seifert and Gonenc 2016; Saona et al. 2020; Wei and Zhou 2018). In addition, countries with strong institutions facilitate creditor protection, reduce default risk, and increase long-term debt reliance (La Porta et al. 1998; Mendoza et al. 2021). Additionally, countries with strong legal systems and transparent markets access financing more easily (Rajan and Zingales 1995). Political connections shape corporate finance, with firms with politically connected boards exhibiting greater leverage and lower financial transparency (Belghitar et al. 2019).
Government effectiveness significantly influences firms’ capital structure by shaping regulatory environments, financial market access, investor confidence, and economic stability. Toader et al. (2022) examined firms in Romania, Bulgaria, and Hungary and reported that institutional factors such as government effectiveness directly impact capital structure, whereas macroeconomic effects are inconsistent. Matemilola et al. (2019) reported that institutional quality positively affects capital structure in Asia, Latin America, and Eastern Europe but is insignificant in Africa.
As regulatory quality reflects a country’s ability to formulate and enforce policies that support growth in the private sector, ease of business, and public interest protection, Iona et al. (2023) reported that increased credit market freedom enhances capital structure, financial flexibility, and the corporate investment climate. Hemmelgarn and Teichmann (2014) linked corporate income tax changes to shifts in bank leverage, dividends, and earnings management. Claessens and Laeven (2004) argued that greater economic freedom increases banking profitability by expanding lending opportunities. Chortareas et al. (2013) and Gropper et al. (2015) demonstrated that economic freedom improves banking efficiency and profitability. The quality of regulations also positively influences domestic and international investment (Xu 2019; Tag and Degirmen 2022), banking stability (Asteriou et al. 2021), and corporate innovation (Zhu and Zhu 2017). The rule of law plays a crucial role in shaping a firm’s capital structure by influencing access to debt and equity financing. Strong legal protections enhance creditor confidence, facilitating external financing (Qian and Strahan 2007). In developed economies, the relative extent of controlling corruption strengthens legal systems, enhances creditor protection, and increases firms’ access to long-term debt (de Jong et al. 2008). Studies confirm that reduced corruption improves financial stability, facilitates bank credit (Liu et al. 2020), and supports foreign investment (Jain et al. 2017). The usefulness of the abovementioned findings in developed economies requires further comparative insights from developing economies, as detailed in the following subsection.

2.3. Institutional Quality and Corporate Financing Decisions in Developing Countries

Better governance encourages investor confidence, reduces risk premiums, and improves financial stability (Belkhir et al. 2016). Conversely, weak governance forces companies to depend on short-term debt, as creditors adjust terms to mitigate risk (Demirgüç-Kunt and Maksimovic 1999). Additionally, firms in weaker institutional environments tend to rely more on higher leverage (Fan et al. 2012). Furthermore, investors avoid countries with poor governance due to expropriation risk (Çam and Özer 2021).
Studies conducted in Brazil, Malaysia, Indonesia, and Pakistan have confirmed that such firms have easier access to credit (Khwaja and Mian 2005; Claessens et al. 2008). Evidence from Egypt and Pakistan shows that politically linked firms receive larger loans on the basis of favoritism rather than financial strength (Diwan and Schiffbauer 2018). While political ties offer financial advantages, they raise concerns about inefficiencies, weaker reporting, and reduced investor protection (Chaney et al. 2011).
Political stability significantly influences firms’ capital structure by shaping access to financing, investor confidence, and regulatory predictability (Ahmed and McMillan 2023). Alim et al. (2024) conclude that, in Pakistan, political instability restricts financing and raises uncertainty. Studies have shown that firms in politically unstable regions reduce leverage to mitigate risk (Dessai et al. 2008; Goldman et al. 2009). Cao et al. (2013) reported that firms maintain financial flexibility by limiting debt during uncertain periods. Political risk increases capital costs (Cashman et al. 2015) and shortens debt maturity (Pan et al. 2019). At the macro level, political uncertainty lowers investment and economic output (Baker et al. 2016). Nguyen and Tran (2017) reported that government effectiveness in Vietnam positively correlates with debt financing. Alves and Ferreira (2011) reported that institutional factors significantly impact capital structure, with weak shareholder rights reducing market leverage. Cho et al. (2014) observed a negative association between creditor protection and long-term debt, suggesting that firms avoid excessive obligations to maintain control. Studies in Africa highlight the influence of legal frameworks on debt selection (Chipeta and Deressa 2016; Gwatidzo and Ojah 2014). Wong et al. (2024) linked strict competition laws to reduced reliance on bank loans. As corruption affects corporate financing decisions, evidence largely supports a negative impact. That is, corruption reduces long-term debt reliance and increases short-term borrowing (Lemma 2015; Colonnelli and Prem 2017). High levels of corruption increase credit risk (La Porta et al. 1997) and hinder bank lending (Weill 2011). In emerging markets, control of corruption positively influences leverage (Singh and Kannadhasan 2020). Notably, as institutional factors govern macroeconomic activities, it is worth further discussing the impacts of the fundamental macroeconomic factors of corporate financing decisions, as detailed in the following subsection.

2.4. Macroeconomic Determinants of Capital Structure

Macroeconomic factors include GDP growth, inflation, interest rates, stock market development, and unemployment, among other variables, and these factors influence firms’ access to financing, debt–equity decisions, and overall capital structure stability (Mokhova and Zinecker 2014). The macroeconomic environment is a key determinant of firms’ capital structure and significantly influences firms’ capital structure decisions (Gajurel 2006). These determinants include GDP growth, stock and bond market development, inflation, unemployment, and interest rates (Memon et al. 2015; Temimi et al. 2016; Rehman 2016; Huong 2017; Ben Hamouda et al. 2023). These variables shape the economic environment, affecting financing decisions. Pecking order theory suggests that firms reduce debt during economic booms. Additionally, interest rates and stock market conditions affect firms’ financing choices, influencing debt levels on the basis of market performance and borrowing costs (Tai 2017).

2.4.1. GDP Growth

Bokpin (2009) reported a strong negative association between GDP growth and capital structure. Gajurel (2006) reported that GDP growth negatively affects total and short-term debt but positively influences long-term debt, as firms prioritize internal financing during economic booms.

2.4.2. Inflation Rate

The impact of inflation on capital structure remains debated in the financial literature. Basto et al. (2009) reported no significant relationship, whereas Gajurel (2006) argued that firms prefer long-term debt in inflationary economies since short-term interest rates rise more rapidly. However, Tehrani and Khoi (2017) and Muthama et al. (2013) reported a negative association, suggesting that firms avoid additional debt because of increased revenue volatility. Mokhova and Zinecker (2014) reported that the effect of inflation varied across European countries, being positive in France and Greece but negative in countries such as Germany and Poland.

2.4.3. Unemployment

High unemployment rates increase the risk of financial distress, encouraging firms to adopt conservative financing strategies to mitigate potential costs associated with layoffs and decreased demand. Korajczyk and Levy (2003) highlight that external factors, including unemployment, play a crucial role in shaping corporate financing decisions. Camara (2012) analyzed the relationship between unemployment and capital structure adjustments and reported that higher unemployment rates can lead firms to reduce leverage to maintain financial flexibility. Mokhova and Zinecker (2014) reported that while unemployment positively affects total leverage, it negatively impacts long-term debt.

2.5. Hypothesis Development

The abovementioned findings of previous related studies help develop a number of testable hypotheses:
H1: 
There is a positive association between political stability and debt financing.
H2: 
There is a positive association between government effectiveness and debt financing.
H3: 
There is a positive association between regulatory quality and debt financing.
H4: 
There is a positive association between firms’ political connections and debt financing.
H5: 
There is a positive association between the protection of investors’ rights and debt financing.
H6: 
There is a positive association between control of corruption and debt financing.
H7: 
There is a negative association between GDP growth and capital structure.
H8: 
There is a negative association between the inflation rate and capital structure.
H9: 
There is a negative association between unemployment and capital structure.

3. Methodology

This paper employs a quantitative regression analysis to estimate the observed and stochastic impacts of institutional quality on corporate debt financing. What follows is a further detailed description of the data, the measurement of the dependent and independent variables, the statistical specification tests, and the regression estimation method.

3.1. Data

In this paper, the data are divided into three groups. The first group is obtained from the financial reports of nonfinancial firms listed in the major indices in the G8 and the MENA region countries and is obtained from Investing.com. The G8 countries are Canada, France, Germany, Italy, Japan, Russia, the United Kingdom, and the United States of America, which are considered the developed countries in the study. The MENA region countries are Bahrain, Egypt, Iraq, Jordan, Lebanon, Morocco, Oman, Saudi Arabia, Tunisia, and the United Arab Emirates, which are considered underdeveloped countries in the study. This paper examines a sample of 309 nonfinancial listed firms from G8 countries and 373 nonfinancial listed firms from MENA countries. The dataset spans the seven-year period from 2016 to 2022. The second group is the institutional data related to the WGIs and is obtained from the World Bank’s Government Governance (http://info.worldbank.org/governance/wgi/ (accessed on 18 March 2024)); these data represent the quality of the institutional environment of the country. The World Bank’s Worldwide Governance Indicator (WGI) database provides time-varying measures of country governance effectiveness for more than 200 countries and territories, updated annually to reflect reforms. Since 1996, WGIs have assessed governance via six key indicators: voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. These indicators aggregate data from 31 sources, including surveys, business reports, NGOs, and public institutions, providing a comprehensive global governance evaluation. The third group consists of macroeconomic data obtained from the IMF database.

3.2. Dependent Variable

The dependent variable employed to measure corporate capital structure is the ratio of long-term debt to total assets, which is a proxy for corporate capital structure. This ratio represents the percentage of a firm’s assets financed with long-term debt, which encompasses loans or other debt obligations lasting more than one year.

3.3. Independent Variables

Notably, the well-known theories of capital structure not only imply but also require certain institutional qualities. Trade-off theory (Modigliani and Miller 1958, 1963; Miller 1977) assumes an optimal debt ratio; hence, capital structure can be reached when bankruptcy costs associated with debt financing offset tax savings (or shields). The latter is influenced to a large extent by institutional quality, which determines the extent to which policy makers design a tax policy that helps firms reduce the cost of financing. Pecking order theory (Frank and Goyal 2003, 2009) assumes that firms depend on internal equity financing and then resort to external financing using either debt or equity. The latter assumes that financial market regulations (reflecting institutional quality) allow for raising the required external financing at a low cost. The agency theory of free cash flow (Jensen 1986; Richardson 2006) assumes that firm managers should make financing decisions after taking into consideration the required future investments. In settings with lower country-level corporate governance quality, it may not be possible to rely on institutional mechanisms to curb overinvestment. Hence, management may resort to company-level mechanisms, such as the use of debt financing. This would suggest a preference for debt versus equity financing (Farinha 2003). Therefore, it is plausible to assume that institutional quality is a cornerstone and backbone of capital structure theories. This also justifies the direct focus on the impact of pillars of institutional quality on firms’ financing decisions.
The independent variables are classified into three groups as reported in Table 1. The first group comprises the WGIs used as proxies for country institutional quality, namely voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and control of corruption. The second group includes the macroeconomic variables employed in the research, which are GDP growth (as a proxy for economic growth), the inflation rate, and the unemployment rate. The third group includes control variables and proxies for the firm’s size and country effect. Firm size was examined via the natural log of total assets.

4. Results and Discussion

Table 2 and Table 3 present the descriptive statistics for the dependent variable (debt ratio) and the independent variables, which include the WGI indicators and macroeconomic determinants for the G8 and MENA countries.
The descriptive statistics show that, on average, there are relative differences between G8 countries’ and MENA countries’ firms in terms of their ratio of long-term debt to total assets. Table 2 shows that the debt ratio has a mean of 0.238993 for firms in developed countries; however, Table 3 shows that the debt ratio has a mean of 0.1401321 for firms in developing countries. It is clear that, in developed countries, firms rely more on long-term borrowing.
With respect to the institutional quality indicators, Table 2 shows that the developed countries have stronger institutional quality than that shown for the developing countries in Table 3, and this appears in the higher scores of each indicator from the six indicators compared with the scores of the developing countries.
Concerning the macroeconomic indicators, the results show that, on average, there are differences between firms in G8 countries and those in MENA countries in terms of each independent variable. Table 3 shows that developed countries have a stronger economic foundation than developing countries, and this appears in the lower inflation rate and unemployment rate scores; conversely, the GDP growth in developing countries indicates a better situation than that in developed countries.

4.1. Observed Versus Uncertain Institutional Quality

As economic progress is associated with the pace of institutional quality, it is plausible to examine the differences in institutional quality between the G8 countries and MENA countries. Notably, the advances in stochastic modeling in institutional finance address the intrinsic aspects of corporate financing and economics. That is, finance managers are usually concerned with imminent outcomes (either quarterly or annually), whereas institutional finance is concerned with expected outcomes associated with expected institutional arrangements (Dupačová et al. 2003; Consigli et al. 2017). The latter is constrained by two characteristics. The first takes into consideration that institutional arrangements take a long time to affect corporate finance decisions. The second characteristic is the uncertainty surrounding the institutional outcome. As institutional changes take a longer time than corporate finance outcomes do, the latter have long been characterized by “short-termism”. In these cases, stochastic simulation offers an outlay of expected outcomes of institutional changes that help managers plan for uncertain financial decisions (Stein 1989). This paper extends the awareness of corporate finance managers and policy makers regarding the exogenous factors that help them make efficient and effective financing decisions. The uncertainty of institutional quality offers empirical guidelines for the improvement of institutional arrangements to help promote corporate business, which benefits society at large.
The observed (historical) institutional quality is measured by the six pillars of WGIs. The expected progress in institutional quality can be conveniently estimated stochastically via Geometric Brownian Motion as follows: The stochastic WGIs are estimated via Geometric Brownian Motion (Feynman 2013; Ibe 2013) as follows: Δ W G I t + Δ t = W G I t   μ   Δ t + W G I t   σ   ε   Δ t , where Δ W G I t + Δ t is the change in the score of WGI; μ and σ are the mean and volatility in the percentage change in WGIs, respectively; and ε is the Weiner process, which is measured as the normal distribution with a mean = 0 and standard deviation = 1. The number of iterations for each country/company = 6, which equals the number of years under examination. Figure 1 and Figure 2 compare the average scores of WGIs in the G8 and MENA countries.
Figure 1 and Figure 2 illustrate the empiricism of institutional quality. That is, what distinguishes developed countries from developing countries is an increasing improvement in institutional quality. In the case of G8, Figure 1 shows that the expected (stochastic) average WGI is greater than that observed in every country. In contrast, Figure 2 shows that the expected (stochastic) averages of WGIs are very close to the observed averages, illustrating a slow pace of improvement in institutional quality in MENA countries.
This section compares the impacts of observed and uncertain (stochastic) institutional quality on corporate debt financing. The dependent variable is the debt ratio. The independent variables include the six pillars of WGIs, macroeconomic variables, and dummy variables as proxies for the effects of size and country. A panel regression model is employed. Linearity versus nonlinearity is determined via the regression equation specification error (RESET) (Ramsey 1969; Thursby and Schmidt 1977; Thursby 1979; Sapra 2005; Wooldridge 2006). The results are reported in Appendix A. In addition, fixed and random tests (Hausman 1978; Hausman and Taylor 1981) and cross-sectional estimation techniques are employed. The results are reported in Appendix B. Multicollinearity is examined, and the variables associated with a VIF > 10 are excluded. The homogeneity of the residuals is examined via the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity. The results are reported in Appendix C. The GLS is used for the final estimates. The results are reported in Table 4.
Table 4 reports the results for the observed (historical) and stochastic estimates of the institutional determinants of capital structure. The results offer further insights into the composition and impacts of institutional quality. In the G8 countries, three institutional pillars are highly multicollinear, namely regulatory quality, rule of law, and control of corruption, which are associated with VIF scores of 97.12, 879.04, and 250.73, respectively. In MENA countries, five institutional pillars are highly multicollinear as well, namely political stability, government effectiveness, regulatory quality, rule of law, and control of corruption, which are associated with VIF scores of 32.40, 35.11, 24.64, 124.24, and 67.80, respectively. These results offer further insights that must be considered when using WGIs as proxies for institutional quality. The authors argue that, as the scores of the pillars of WGIs reflect a quantification of attitudes, it remains highly likely that the responses of the respondents in each country to the collective pillars converge, resulting in high multicollinearity. High VIF scores are still observed in the case of stochastic simulations of WGIs.

4.2. Effects of Voice and Accountability on Corporate Capital Structure

In the case of G8 firms, the positive effects of voice and accountability suggest that stronger creditor protection and contractual commitments increase firms’ access to debt financing (Matemilola et al. 2019). Furthermore, politically connected firms tend to sustain higher debt levels because of privileges associated with government affiliations (Smith 2016). This relationship is robust as long as a positive stochastic effect is sustained. In the case of firms in the MENA region, the opposite (negative) effect is observed. Nevertheless, the stochastic estimate shows that when voice and accountability improve, a positive effect is expected.

4.3. Effect of Political Stability on Corporate Capital Structure

In the case of G8 firms, the positive effect of political stability suggests that during periods of high political instability, firms tend to reduce investments and leverage (Julio and Yook 2012). Nevertheless, the stochastic estimate is statistically insignificant.
The estimates for the firms in the MENA region provide peculiar insights. That is, although the observed (historical) variable is omitted, the negative stochastic estimate shows that an expected increase in political stability would encourage firms to raise equity rather than debt financing.

4.4. Effect of Government Effectiveness on the Corporate Capital Structure

The listed firms in both the G8 and MENA economies show that higher government effectiveness is associated with lower debt financing. These findings are consistent with the argument of Saona et al. (2020) that as governance improves, firms increasingly avoid debt in favor of equity financing. Furthermore, Cho et al. (2014) reported that greater creditor protection is associated with lower long-term debt utilization, contradicting the assumption that stronger creditor safeguards lead to increased long-term borrowing.
In terms of the effect of firm size on capital structure, total assets in G8 and MENA listed firms are negatively associated with capital structure, which contradicts the argument that large firms typically secure more debt financing due to lower bankruptcy risk (Titman and Wessels 1988).
The results provide interesting insights into the effects of comparative fundamental macroeconomic factors on corporate capital structure. The negative effect of GDP growth in both regions suggests that, during economic booms, firms experience greater profitability and increased retained earnings, reducing their reliance on external debt, which is in line with the pecking order theory. This negative effect is expected, as the stochastic estimate is still negative in the case of G8. Nevertheless, in the case of MENA countries, the expected effect of GDP growth on capital structure is insignificant, suggesting that the expected patterns of corporate debt financing may not be related to GDP growth. The results show that the negative effects of inflation on debt financing prevailed and are expected to prevail in the G8 and MENA countries. Higher inflation increases borrowing costs, discouraging debt financing (Nejad and Wasiuzzaman 2015).
In the G8 listed firms, the negative effect of unemployment suggests that high unemployment signals economic distress, which is usually associated with reduced consumer spending and therefore lower borrowing capacity (Mokhova and Zinecker 2014). Nevertheless, the stochastic expected effect is statistically insignificant, suggesting that the negative effect may disappear.
The results of cross-sectional dependence in Table 4 show extended confirmation of the link between institutional quality and financing decisions. The observed (historical) and stochastic estimates show that in both regions, cross-sectional dependence is significant. This is extended evidence of the convergence for G8 in terms of institutional quality and financing patterns. The same is also true for the MENA region. Nevertheless, as the results in Table 4 show a potential stochastic convergence of the impacts of voice and accountability and inflation rates between the G8 and MENA regions, a convenient econometric robustness test is to further examine this impact by combining both regions in one panel. The results are reported in Table 5.
The results in Table 5 have significant implications for the impacts of voice and accountability on firms’ debt financing. Although the treatment of each region separately shows an indication of convergence, the aggregate treatment of voice and accountability in both regions as one panel has an insignificant effect on firms’ debt financing. That is, the impact of voice and accountability (being a proxy for creditor protection and contractual commitments) in the G8 economies differs from that in the MENA economies. This implication extends what is shown in Figure 1 and Figure 2, namely that the improvement in institutional quality in the G8 economies is progressing more than that in the MENA economies. Furthermore, the robust and negative impact of inflation rates on debt financing remains common in both economies. Furthermore, the results of the cross-sectional dependence test indicate the dependence between the two regions, which reflects an extended interest in both G8 and MENA in improving the quality of creditor protection and contractual commitments.

5. Conclusions

This paper quantifies the contribution of institutional uncertainty to corporate financing decisions. The essence of institutional quality is presented through a comparison between two different economies, namely G8 and MENA. As WGIs are common indicators of institutional quality, this paper contributes to related studies in terms of examining the extent to which different institutional arrangements have impacts on financing decisions. WGIs are used as proxies for institutional factors. Annual data from 2016 to 2022 offer the following insightful implications:
  • As the pillars of WGIs reflect a quantification of attitudes, high multicollinearity exists, which must be taken into consideration in future studies.
  • Compared with MENA countries, G8 countries are characterized by stronger voice and accountability, resulting in more reliance on debt financing by the former than the latter. The expected (stochastic) effect remains positive in G8, indicating perseverance in protecting creditors’ contractual rights. The same is true in MENA countries, where an expected improvement in voice and accountability is observed.
  • Political stability in G8 sustains debt financing, although a stochastic negative effect is estimated, indicating that potential political instability may result in companies relying on less debt financing. The same negative stochastic effect is estimated in MENA countries, which have the same implications.
  • Government effectiveness has a crucial negative effect on debt financing in both G8 and MENA listed firms. This finding implies that stronger government effectiveness may lead to an expected reduction in debt financing and consequently more reliance on equity financing.
  • The effect of firm size is still persistent over time and across different institutional arrangements. That is, large firms are able to secure and afford debt financing.
  • The effects of macroeconomic factors reflect the essence of institutional quality. That is, in both regions, the observed and stochastic progress in GDP growth results in less debt financing. Nevertheless, the listed companies in the MENA region exhibit insignificant stochastic effects, indicating that the expected patterns of corporate debt financing may not be related to GDP growth. In addition, the observed and stochastic effects of inflation remain negative for corporate debt financing in the G8 and MENA economies. The unemployment rate plays a signaling role in G8 only, although the stochastic effect is statistically insignificant.
Notably, the results offer significant insights for both corporate managers and policy makers. That is, corporate managers can use the results as a guide to where firms (being multinationals) move capital to invest in countries that are institutionally favorable. These results also guide policy makers to focus on the institutional pillars (specifically, protecting creditors’ contractual rights, political stability, and government effectiveness) that help companies raise financing for investment opportunities that contribute to the welfare of society. The abovementioned implications can be extended to overcome the limitations of extreme institutional quality. That is, as the G8 and MENA countries offer examples of high and low institutional quality, other economic regions, such as Asia at large, offer examples of transitional and progressing institutional quality that are approaching the G8. Companies in these economies must have followed certain financing patterns that helped achieve noticeable growth within Asia and the global economy as well. Furthermore, as the results of the stochastic estimation of institutional quality show potential convergence in the impact of voice and accountability, a road map can be developed for future studies to examine the potential impacts of changes in the quality of debt regulations on corporate debt financing.
Nevertheless, the abovementioned results and conclusions are limited to two regions, namely G8 and MENA. Extended research examining other economic regions is warranted to offer insights into the global view of the impacts of institutional quality on corporate businesses at large and on corporate finance in particular.

Author Contributions

Conceptualization, T.E. and I.A.; methodology, T.E.; software, H.M.S.; validation, J.F., M.A.S. and H.M.S.; formal analysis, T.E.; investigation, J.F.; resources, H.M.S.; data curation, M.A.S.; writing—original draft preparation, T.E.; writing—review and editing, T.E.; visualization, I.A.; supervision, J.F.; project administration, I.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data that support the findings of this study are available from the Reuters Finance Centre (https://www.reuters.com/markets/) (accessed on 18 March 2024).

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Appendix A. Linearity Versus Nonlinearity Test: G8 and MENA Countries1

CountriesResults
G8F(3, 2145) = 1.745 (Prob > F = 0.1560)
MENAF(3, 2588) = 2.32 (Prob > F = 0.191)

Appendix B. Hausman Test: G8 and MENA Countries2

CountriesResults
G8chi2 (10) = 12.97 (Prob > chi2 = 0.2252)
MENAchi2 (5) = 18.65 (Prob > chi2 = 0.0022)

Appendix C. Heteroscedasticity Test: G8 and MENA Countries (Breusch–Pagan/Cook–Weisberg Test)3

CountriesResults
G8chi2 (1) = 171.22 (Prob > chi2 = 0.0000)
MENAchi2 (1) = 303.38 (Prob > chi2 = 0.0000)

Notes

1
The results indicate that, at the 95% confidence level, the null hypothesis of the Ramsey RESET test is not rejected for both the G8 and MENA countries, suggesting that the linear model is appropriate.
2
The results indicate that the random-effects model is appropriate for the G8 countries, as the p value associated with the test exceeds 5%, whereas the fixed-effects model is appropriate for the data of the MENA countries, given that the corresponding p value is below 5%.
3
The results indicate that the null hypothesis of the Breusch–Pagan/Cook–Weisberg test for heteroskedasticity is rejected at the 95% confidence level. This suggests that the variance of the residuals is not constant, which requires the use the robust estimators.

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Figure 1. Comparative institutional quality in the G8 countries.
Figure 1. Comparative institutional quality in the G8 countries.
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Figure 2. Comparative institutional quality in MENA countries.
Figure 2. Comparative institutional quality in MENA countries.
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Table 1. List of the dependent and independent variables: WGIs, macroeconomic variables, and control variables.
Table 1. List of the dependent and independent variables: WGIs, macroeconomic variables, and control variables.
VariableConceptual DefinitionOperational DefinitionAuthors
Corporate Capital StructureLong-term debt ratioLong-term debt to total assets(Abor 2005; Eldomiaty 2008; Ebaid 2009; Omran and Pointon 2009; Youssef and El-Ghonamie 2015; Bandyopadhyay and Barua 2016; Shambor 2017; Obay 2018)
WGIsVoice and AccountabilityRefers to perceptions of the extent to which citizens have freedom of expression and media and are able to select governmentVoice and Accountability Index constructed by the World Bank(Kaufmann et al. 2011)
Political StabilityRefers to political stability and absence of violence in a countryPolitical Stability and Absence of Violence Index constructed by the World Bank
Government EffectivenessRefers to perceptions of the quality of public service, the quality of civil service, and the degree of the government’s independence from political pressureGovernment Effectiveness Index constructed by the World Bank
Regulatory QualityRefers to the quality of a country’s regulatory environmentRegulatory Quality Governance Index constructed by the World Bank
Rule of LawRefers to the perceptions of the extent to which agents have confidence in and abide by the rules of society, the police, and the courtsRule of Law Index constructed by the World Bank
Control of CorruptionRefers to the control of corruption in a countryControl of Corruption Index constructed by the World Bank
ControlEconomic GrowthGDP growthGDP growth rate (calculated as the percentage change in GDP per capita across years)Cohen and Zarowin (2010); Chen et al. (2015)
Inflation RateInflationAnnual percentages of average consumer prices are year-on-year changes
Unemployment RareUnemployed workers are those who are currently not working but are willing and able to work for pay, currently available to work, and have actively searched for workThe number of unemployed persons as a percentage of the labor force (the total number of people employed plus unemployed)
Firm SizeThe size of the firmNatural logarithm of total assets
Country MembershipThe country included in the analysisDummy variables (binary 0, 1) taking the value of 1 for a given country and 0 otherwiseObay (2018)
Table 2. Descriptive statistics for G8 countries.
Table 2. Descriptive statistics for G8 countries.
Descriptive Statistics
VariableNMin.Max.MeanStd. Deviation
Debt Ratio21630.00001710.96410.2389930.172486
Voice and Accountability21630.14492750.96601940.77446350.226162
Political Stability21630.1523810.92857140.5528160.1574594
Government Effectiveness21630.2594340.96666660.82356760.1579148
Regulatory Quality21630.13207550.97619050.82745350.2021162
Rule of Law21630.12264150.96666660.79519690.2336246
Control of Corruption21630.16666670.96190480.79738530.2330293
GDP Growth2163−0.106450.060260.0103360.0363128
Inflation Rate2163−0.006480.053750.015660.0116451
Unemployment Rate21630.023580.1180.06099610.0244892
Table 3. Descriptive statistics for MENA countries.
Table 3. Descriptive statistics for MENA countries.
Descriptive Statistics
VariableNMin.Max.MeanStd. Deviation
Debt Ratio26110.0001370.79840.14013210.374187
Voice and Accountability26110.04926110.56521740.17278270.1198541
Political Stability26110.01415090.71904760.27542810.1454232
Government Effectiveness26110.07547170.90476190.53310310.1753695
Regulatory Quality26110.09047620.82857140.50591630.1681739
Rule of Law26110.02380950.78773580.51652340.1671129
Control of Corruption26110.0523810.83490560.52362970.1760254
GDP Growth2611−0.250.151990.01233620.0393075
Inflation Rate2603−0.01931.44510.067920.0502162
Unemployment Rate26110.03580.190750.09907540.0481625
Table 4. Observed and stochastic institutional determinants of the corporate capital structure in G8 countries and MENA countries.
Table 4. Observed and stochastic institutional determinants of the corporate capital structure in G8 countries and MENA countries.
Dependent:
Debt Ratio		Observed Institutional Determinants of Capital StructureStochastic Institutional Determinants of Capital Structure
G8MENAG8MENA
(Constant)0.417 ***
(0.051)		0.184 *
(0.101)		−0.092
(0.165)		0.417 ***
(0.071)
Voice and Accountability0.106 ***
(0.006)		−0.178 ***
(0.023)		0.387 ***
(0.095)		0.1494 **
(0.088)
Political Stability0.067 ***
(0.005)		 0.026
(0.029)		−0.142 ***
(0.034)
Government Effectiveness−0.311 ***
(0.074)		 −0.015
(0.107)		−0.358 ***
(0.031)
GDP Growth−0.199 ***
(0.068)		−0.402 **
(0.181)		−0.000046 ***
(0.000008)		0.000008
(0.000008)
Inflation Rate−0.345 ***
(0.037)		−0.1987 ***
(0.018)		−0.851 ***
(0.183)		−0.074 ***
(0.030)
Unemployment Rate−0.355 ***
(0.020)		−0.889
(0.920)		0.111
(0.072)		−0.049
(0.087)
Size Effect (Natural Log of Total Assets)YesYesYesYes
G8 Country Effect (Dummy Variables Taking Binary Values)Yes Yes
MENA Country Effect (Dummy Variables Taking Binary Values) Yes Yes
Time0.0014
(0.0013)		0.0024
(0.0033)		0.0021
(0.0209)		0.0093
(0.0063)
N2163260316352238
R ¯ 2 0.55020.50600.96630.9722
Pesaran Cross-Sectional Dependence (CD) Test648.24 ***20.99 ***611.93 ***14.87 ***
* Significant at 10%; ** significant at 5%; *** significant at 1%; robust standard errors in parentheses.
Table 5. Robustness of the potential stochastic convergence of institutional quality between the G8 region and the MENA region.
Table 5. Robustness of the potential stochastic convergence of institutional quality between the G8 region and the MENA region.
Dependent:
Debt Ratio		Stochastic Institutional Determinants of Capital Structure (G8 and MENA)
(Constant)0.1499
(0.0329) ***
Voice and Accountability0.1030
(0.072)
Inflation Rate−0.0882 ***
(0.0264)
Size Effect (Natural Log of Total Assets)Yes
G8 and MENA Country Effect (Dummy Variables Taking Binary Values; 1 = G8, 0 = MENA)Yes
Time0.00043 ***
(0.00015)
N3876
R ¯ 2 0.9864
Pesaran Cross-Sectional Dependence (CD) Test43.844 ***
*** Significant at 1%.
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Eldomiaty, T.; Azzam, I.; Fouad, J.; Sadek, H.M.; Sedik, M.A. Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries. Risks 2025, 13, 111. https://doi.org/10.3390/risks13060111

AMA Style

Eldomiaty T, Azzam I, Fouad J, Sadek HM, Sedik MA. Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries. Risks. 2025; 13(6):111. https://doi.org/10.3390/risks13060111

Chicago/Turabian Style

Eldomiaty, Tarek, Islam Azzam, Jasmine Fouad, Hussein Mowafak Sadek, and Marwa Anwar Sedik. 2025. "Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries" Risks 13, no. 6: 111. https://doi.org/10.3390/risks13060111

APA Style

Eldomiaty, T., Azzam, I., Fouad, J., Sadek, H. M., & Sedik, M. A. (2025). Stochastic Uncertainty of Institutional Quality and the Corporate Capital Structure in the G8 and MENA Countries. Risks, 13(6), 111. https://doi.org/10.3390/risks13060111

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