Impact of Macro-Economic Factors on CEO Compensation: Evidence from JSE-Listed Banks

Abstract

The debate over CEO compensation persists despite extensive efforts by academics and technocrats to understand its determinants. Most research has focused on how firm-specific characteristics and CEO-specific traits influence CEO compensation. However, the results have been contradictory, indicating that other factors may also play a role. This study examines the impact of macroeconomic factors on the compensation of CEOs. It examines how price variables such as interest rates, inflation, and exchange rates affect the fixed salaries and total compensation of CEOs at six South African banks listed on the Johannesburg Stock Exchange. Conducted over a 15-year period, this quantitative longitudinal study utilized secondary data from annual reports and the IRESS database. Panel data regression analysis was employed to interpret the data. The findings reveal a positive relationship between interest rates and fixed salaries, as well as between exchange rates and fixed salaries. Additionally, interest rates and total compensation are positively related, and exchange rates also have a positive relationship with fixed salaries. Understanding how macroeconomic conditions influence CEO pay helps Compensation Committees contextualize performance. It allows them to differentiate between achievement driven by a CEO’s abilities and that resulting from external factors, ensuring fair compensation and minimizing excessive rewards for “luck”. This knowledge supports the adjustment of incentive plans based on relative performance and economic-adjusted metrics, reducing the cyclical influence of macroeconomic variables on firm performance and, ultimately, CEO compensation.

1. Introduction

Commercial banks are the core of any country’s financial system (Nguyen, 2022). They enhance the economic growth of any country through the mobilization of savings, allocation of capital, financial inclusion, and support for small to medium-sized enterprises (Mohamed, 2024). Internal factors, which are bank-specific and macro-economic factors, affect the efficiency and profitability of commercial banks (Khan, 2022). Macro-economic conditions not only impact bank profitability but also affect the compensation of Chief Executive Officers (CEOs) (Luijsterburg, 2024). CEO compensation continues to draw attention from scholars, and the subject is considered a controversial one. According to Luijsterburg (2024), CEO compensation is influenced by a multitude of factors, including performance, company industry, company size, and CEO-specific characteristics such as age, tenure, and gender. Contradictory results have emerged on the impact of these factors on CEO compensation. A study by M. Bussin et al. (2023) found a positive relationship between company performance and CEO compensation among the top 100 companies by market capitalization listed on the JSE. Conversely, Sikawa et al. (2020) study on determinants of CEO compensation among FSTE 100 companies found no relationship between return on assets and CEO compensation. On the impact of company size on CEO compensation, Anne et al. (2021) found a positive relationship between company size and CEO compensation among 40 companies listed on the Nairobi stock exchange while Rullahi et al. (2024) found no relationship among banks listed in Nigeria. Such contradictory findings can be attributed to other factors, such as macro factors. A qualitative study by Dhliwayo and Bussin (2019) that explored uncontrollable factors that impact CEO compensation is South Africa emphasize macro-economic factors as influencing CEO compensation. Luijsterburg (2024) argues that examining macroeconomic factors as determinants of CEO compensation can help explain the exaggeration of CEO salaries in instances where they are not related to organizational performance. CEOs sometimes earn large sums of money despite unfavourable macroeconomic conditions (Oxelheim et al., 2008). Changes in the macroeconomic environment impact organizational performance and compensation that is dependent on performance (Oxelheim & Wihlborg, 2008). Macro fluctuations in interest rates, exchange rates, and inflation are considered sources of either good or bad luck for organizational performance, particularly when management is unable to adjust for such fluctuations (Chiu et al., 2016).

Fluctuations in macroeconomic factors account for seven to twelve percent changes in CEO compensation in the US (Chiu et al., 2016). Studies that have sought understanding the relationship between macroeconomic factors have been limited. Chiu et al. (2016) investigated this relationship in the US, and no studies have explored the impact of these factors in emerging economies, such as South Africa. Similarly, Luijsterburg (2024) investigated the impact of macroeconomic conditions in Europe. Executive compensation is mainly tied to performance measures, which are also impacted by the macroeconomic environment in which an organization operates. It is salient for remuneration committees or management to consider macroeconomic fluctuations when designing CEO incentives (Chiu et al., 2016).

Notably, despite the significant impact of macroeconomic factors on organizational performance, there are no studies that have examined the macro determinants of CEO compensation in South Africa. Kieviet and Scholtz (2025) argue that, despite South Africa being a pioneer in emerging market governance, it is characterized by economic instability. The authors note that this instability makes executive remuneration an area of interest for JSE-listed firms. Macro-economic factors have a direct impact on organizational performance, which is a critical determinant of CEO compensation. According to Carrothers (2019), CEO compensation attracts much public scrutiny during periods of macroeconomic distress. Therefore, understanding the relationship between these variables is important. Luijsterburg (2024) notes that understanding the effects of macroeconomic factors on CEO compensation is important for organizations and various stakeholders, as it gives a deeper understanding of the CEO compensation setting process. It also helps to determine whether the CEO compensation increase is economically justified or a result of managerial power (Reda & Kestenbaum, 2024).

Compensation in the financial services industry attracts much attention from the media, as most CEO compensation in financial institutions is not aligned with company performance (Le et al., 2025; Malik & Shim, 2019). Numerous studies (Kieviet et al., 2024; Azazi, 2020; Ahamed, 2022) have focused on the relationship between performance and CEO compensation. Kieviet and Scholz (2025) argue that other external and internal factors also impact this performance. This study aims to empirically investigate whether the relatively high salaries in the banking industry are aligned with fluctuations in the macroeconomic environment. South Africa is considered a country with the highest wage inequality, with bank executives among the top-paid executives in the country (Teuterberg, 2025). The Reserve Bank of South Africa aims to keep the inflation rate between 3% and 6%. The average inflation rate for the period under study was 5.2 percent, with a peak of 7.8 percent in 2022, mainly due to the COVID-19 pandemic (Focus Economics, 2025). The interest rate averaged 6.3 percent over the last decade, with the SARB repo rate averaging 8.39% and the broader interest rate averaging 9.86 percent.

The study focuses exclusively on the six banks listed on the Johannesburg Stock Exchange (JSE) that meet the criteria of being continuously listed during the study period and having complete data on annual compensation of their CEOs. These six banks represent the entire population of JSE-listed commercial banks that satisfy this requirements. Moreover, JSE-listed banks operate within a highly regulated and relatively homogeneous institutional environment, which enhances internal validity by reducing regulatory and governance-related heterogeneity that could confound the relationship between macro-economic factors and CEO compensation.

Understanding this relationship provides insights into whether CEO compensation is a result of firm performance, managerial skill, or broader economic forces that are beyond individual control. Macroeconomic factors, such as inflation, GDP growth, interest rates, and unemployment, can impact corporate profitability, risk, and market expectations, all of which influence compensation decisions. The study employs price variables to investigate the impact of macro economic variables on CEO compensation. Chiu et al. (2016) argues that price variables respond rapidly to underlying unobservable shocks compared to quantity variables which are typically observed with a substantial lag. Price variables adjust quickly to both domestic and foreign conditions which can impact organizational performance and also signal underlying macro-economic shocks. (Chiu et al., 2016). By examining these dynamics, researchers can assess the fairness and efficiency of executive pay systems, contribute to policy discussions on income inequality, and help boards design compensation packages that effectively balance motivation, accountability, and long-term value creation across different economic climates. The following section discusses the literature review.

2. Literature Review

2.1. CEO Compensation

Mohammed et al. (2023) note that, despite heterogeneity in pay practices, CEO compensation comprises four components: salary, bonuses, payouts from long-term incentives paid in cash or stock. Frydman and Jenter (2010) argue that the level and composition of CEO compensation have not been static over the years. The authors indicate that prior to 1970, CEO compensation was mainly at low to moderate levels, consisting of stock options. Between 1970 and 1990, all components experienced drastic increases. Between 2000 and 2008, pay levels declined, while restricted options replaced stock options. This study adopts t M. H. Bussin and Carlson (2020) conceptualization of fixed pay as the basic salary plus benefits, and total compensation as the sum of fixed pay and short-term incentives.

Zvinavashe (2022), identifies firm performance, corporate governance, market performance, and firm size as determinants of CEO compensation. Maloa and Bussin (2016) highlight that CEO specific characteristics such as education, gender, tenure and experience account for differences in CEO compensation. On the other hand, Malik and Shim (2019) identify CEO-specific characteristics, the organization’s economic characteristics, corporate governance, and social and political forces as key determinants of CEO compensation. Inclusion of social and political forces indicates the influence of exogenous factors in determining CEO compensation. Chiu et al. (2016) note that CEO compensation depends on luck when it is determined by factors beyond management’s control, such as macroeconomic factors. The authors argue that temporary changes in the macroeconomic environment impact compensation based on performance, especially cash compensation.

2.2. Macro-Economic Factors

Macro-economic factors and industry-wide factors are beyond managerial influence and control; however, management can mitigate their impact on performance measures and compensation through risk management techniques (Oxelheim et al., 2008). Macro-economic factors are critical indicators of a country’s economic health (Keswani et al., 2024). Macroeconomic factors are categorized into two main types: quantity and price variables. Quantitative variables include GDP, GDP growth, employment rates, and price variables, including interest rates, inflation, exchange rates, and stock market indices (Chiu et al., 2016). Price variables reflect underlying macroeconomic shocks, which are more visible compared to other macroeconomic variables (Chiu et al., 2016). The authors further highlight that using price variables is advantageous because they adjust quickly to domestic and foreign conditions, which impact company performance. This study employed price variables to examine the impact of macroeconomic factors on the compensation of CEOs.

One significant macroeconomic variable that influences the performance of organizations is the exchange rate. Moyo and Tursoy (2020) argue that the exchange rate fluctuations control the bank profitability levels of banks. Elhussein and Osman (2019) highlight that exchange rate volatility affects bank performance by impacting expected cash flows and profitability. Morina et al. (2020) argue that the exchange rate influences a country’s economy by affecting domestic price levels, the profitability of goods and services, and investment decisions. This highlights the notion that exchange rates impact organizational performance and growth, which are key factors in determining the compensation levels of CEOs. Taiwo and Adesola (2013) found a positive relationship between the exchange rate and bank loan losses, a measure of bank performance, among banks in Nigeria. In contrast, Elhussein and Osman (2019) reported a weak negative correlation between the exchange rate and bank performance among 37 banks in Sudan.

Interest rates are among the macroeconomic price variables. Ahmed et al. (2018) note that the interest rate is a salient macroeconomic variable that impacts economic growth. Musah et al. (2018) assert that the interest rate is one of the key determinants of bank profitability. Similarly, Mabati and Onserio (2020) concur that the interest rate is a key determinant of an organization’s profitability; they further note that the interest rate is expressed as a percentage over one year. High-interest rate fluctuations impact returns and decision-making among financial organizations, particularly in investment decisions (Ahmed et al., 2018). Investment decisions have a significant impact on the growth and profitability of organizations. Musah et al. (2018) investigated the impact of interest rates on bank profitability in Ghana and found a positive and statistically significant relationship between interest rates and bank profitability. Regression analysis was employed in a study by Mabati and Onserio (2020), which found a strong correlation between interest rates and bank performance, measured by return on assets, among banks in Kenya.

The inflation rate is a key macroeconomic variable. According to Khursheed and Sheik (2022), the inflation rate is a crucial macroeconomic variable affecting CEO compensation. Cevik et al. (2024) argue that inflation hurts an organization’s performance. Almansour et al. (2021) also contend that macroeconomic factors, such as inflation, influence performance through their relationship with stock prices. Inflation affects interest rates and liquidity preferences, which in turn impact bank profitability (Nawir et al., 2025). Almansour et al. (2021) established a strong negative relationship between inflation and bank performance, while. Moyo and Tursoy (2020) investigated the impact of inflation and exchange rate on South African banks and found a significant inverse relationship between inflation and bank performance.

Macro-economic factors have a lagging effect and may not immediately impact CEO compensation. As indicated in the literature review, their impact is primarily due to their effect on organizational performance, which in turn affects compensation. According to Oxelheim et al. (2010), performance-based compensation schemes for executives are either weakened or strengthened by macroeconomic factors. Chiu et al. (2016) note that when macroeconomic factors favour the CEO, they are considered a form of luck. Very few studies have explored the impact of macroeconomic variables on CEO compensation, with all the studies done in developed countries.

A study by Luijsterburg (2024) among US companies, using data from 2006 to 2023, found a positive relationship between macroeconomic factors and CEO compensation. The study found that the effect was more pronounced on the equity component of CEO compensation. Chiu et al. (2016) investigated the influence of macroeconomic variables among 2019 firms in the United States of America. The study found that macroeconomic variables indirectly affect CEO compensation through organizational performance, which is directly influenced by fluctuations in the macroeconomy. The authors note that their findings indicate that a large share of changes in CEO compensation is attributed to macroeconomic factors. A study by Oxelheim et al. (2010) found that the influence of macroeconomic factors on CEO compensation was substantial among 127 Swedish companies.

There have been limited studies that sought to comprehend how macro-economic factors impact CEO compensation.

Table 1. Summary of Macro-economic and Executive Compensation Studies.

Continent	Country/ Region	Study (Authors, Year)	Macroeconomic/ Business-Cycle Factors	Key Findings on Macro Factors & Executive Pay
North America	United States	Chiu et al. (2016)—Macroeconomic Fluctuations as Sources of Luck in CEO Compensation	GDP growth, inflation, interest rates, exchange-rate changes	Using 2091 US firms, decomposes CEO pay into firm-specific and macro “luck”; shows that macroeconomic fluctuations significantly affect CEO compensation and that firms only partially filter out this “luck” when setting pay.
	United States	Luijsterburg (2024) The Effects of Macroeconomic Factors on CEO Compensation	Inflation, Exchange rate, GDP, Unemployment, Market performance, Market risk, Investor sentiment, Policy uncertainty, geopolitical risk, terrorism index.	CEO compensation is significantly influenced by macroeconomic conditions, with indicators such as inflation, GDP growth, unemployment, and market performance showing strong associations with changes in executive pay. The study concludes that compensation outcomes are shaped not only by firm-level performance, but also by wider economic environments and systemic risk factors, underscoring the importance of incorporating macroeconomic dynamics into executive pay analysis.
Europe	Europe (multi-country)	Oxelheim et al. (2008)—Executive Compensation and Macroeconomic Fluctuations.	Interest rates, inflation, exchange rates, real activity	Theoretically and empirically shows that European executives’ pay is materially affected by macroeconomic fluctuations and argues that compensation should adjust for macro “noise” to avoid overpaying for luck.
Asia	India (Asia, emerging market)	Tomar and Korla (2011)—Global recession and determinants of CEO compensation: listed Indian firms	Global financial crisis (recession vs. pre-crisis period)	Using 132 Indian firms, examines CEO pay determinants around the global recession, showing that crisis-period macro distress alters the strength of the pay performance link and the role of stock-price changes.
	Asia (regional)	Sun et al. (2010)—Executive compensation in Asia: A critical review and outlook.	Regional growth, liberalization, crisis episodes	Reviews Asian evidence and argues that liberalization, rapid growth, and crisis episodes have shaped the level and structure of executive compensation, highlighting the role of macro and institutional context in pay design.
Africa	South Africa (SOEs)	Bezuidenhout (2021)—The effect of the economic crisis on the pay–performance link in South African state-owned enterprises.	Domestic economic crisis period	Finds that during economic crisis the relationship between performance indicators and CEO pay in SOEs changes, suggesting that fiscal stress and macro downturns weaken traditional pay performance links.

summaries these studies.

Based on the literature discussed on macroeconomic factors, this study proposes the following hypothesis for fixed salary.

H₁. 

Interest rate does not significantly influence the CEO’s fixed salary.

H₂. 

Exchange rate does not significantly influence the CEO’s fixed salary.

H₃. 

Inflation rate does not significantly influence the CEO’s fixed salary.

Regarding Total CEO compensation, this study proposes the following hypothesis

H₄. 

Interest rate does not significantly influence the CEO’s total salary.

H₅. 

Exchange rate does not significantly influence the CEO’s total salary.

H₆. 

Inflation rate does not significantly influence the CEO’s total salary.

Chiu et al. (2016) argue that organizations respond differently to different sets of macroeconomic variables. Nawir et al. (2025) also note that bank-specific matters affect the impact of inflation on bank performance. The impact of other bank-specific factors on the relationship between macroeconomic factors and performance led to the inclusion of bank size as a control variable in the study.

3. Methodology

The study’s objective is to investigate the relationship between macro-economic factors and the compensation of CEOs of Johannesburg Stock Exchange (JSE)-listed commercial banks. The banks used for the study were ABSA, Capitec, First Rand, Investec, Nedbank and Standard. Annual compensation data were used, and annual macro-economic variables were used for the study. A quantitative research approach was employed, specifically utilizing panel data analysis. The same method was adopted by Kieviet et al. (2024) when they investigated the relationship between company performance and CEO compensation of JSE-listed banks. The study sample comprised six commercial banks listed on the JSE, and data were collected from 2010 to 2024. Choi and Lam (2017) note that a minimum of 10 observations per variable is sufficient to produce statistically sound results. Although this study benefits from a 15-year longitudinal dataset, the sample size is inherently constrained to 90 observations (six banks over fifteen years) due to the limited availability of publicly disclosed CEO remuneration information. Only JSE-listed banks are required to publish detailed executive pay data, making it methodologically impractical to expand the sample beyond these institutions without compromising the consistency and reliability of the dataset. Consequently, while the dataset captures the full population of eligible banks with transparent remuneration reporting, its relatively small cross-section may limit the statistical power of the analysis and reduce the generalisability of the findings to the wider banking sector. This limitation is acknowledged, though mitigated to some extent by the depth of the time dimension, which enhances the study’s capacity to examine long-term patterns and macroeconomic dynamics The data was sourced from the Bank’s financial statements and the IRESS database. The following section discusses the dependent and independent variables which are summarised in

Table 2. Definition of variables.

Variable	Definition of Variables	Data Source	Expected Sign
Dependent Variables
Salary (SAL)	Sum of basic salary and benefits not linked to performance	Coetzee and Bezuidenhout (2019)	+
Total Compensation (TC)	Consists of salary, long-term bonus, short-term bonus, loyalty bonus, pension compensation, and incentive measures (stock options, stock-based awards)	Bouteska et al. (2024)	+
Explanatory variables
Interest Rate (INT)	The money market rate serves as a proxy for the interest rate and is represented by the monthly average of daily interbank lending rates (%)	World Development Indicators	+/−ve
Inflation (INF)	The Consumer Price Index (CPI) is the most widely recognized proxy to measure of inflation.	World Bank Development Indicators	-
Exchange Rate (EXCH)	The exchange rate used in this context refers to the value of the South African Rand in relation to the US dollar.	SARB website	+
Control Variable
Return on Investment (ROA)	${\displaystyle \frac{Net income}{Total Assests}}$	Marozva and Makina (2020)	-
Total assets (TA)	Represents the overall size and resource base of an organization	Sultana et al. (2025)	+
COVID_19	Takes the value of 1 during the COVID period, otherwise zero (0)	Iyke (2020)	+/−

.

3.1. Theoretical Framework

The agency theory underpins this study. It is the main framework used to explain and understand executive compensation (Le et al., 2025). Jain et al. (2024) points out that CEO compensation is usually viewed through the agency framework, where pay based on performance serves as a mechanism to align stakeholder interests. The author also notes that agency theory justifies high pay tied to performance. Ramgath and Bussin (2022) mention that agency theory highlights the problems that often come up, mainly in the form of conflicts of interest, between the agent, who is the CEO, and the principal, who are the shareholders. These problems happen due to information asymmetry, where the principal lacks information and delegates tasks to an informed agent, whose actions influence the benefits for both parties (Cao et al., 2021). The authors also note that the emphasis on performance in the theory supports the inclusion of both long-term and short-term incentives for CEOs, rather than just offering a fixed salary. Based on the principles of agency theory, CEO compensation structures are expected to respond to macroeconomic changes, keeping pay aligned with performance.

Managerial power theory suggests that executive compensation may reflect the ability of CEOs to influence pay-setting processes rather than purely market-driven performance incentives. In line with this theory, compensation contracts are shaped by managerial dominance, weak governance, and board dependence, which allow executives to extract rents beyond what would be predicted by firm fundamentals or economic conditions (Bebchuk & Fried, 2004). Incorporating this theoretical lens provides a useful counterbalance to agency-based interpretations, highlighting that macroeconomic factors may interact with organizational power dynamics to shape pay outcomes. This theory is particularly relevant in highly concentrated industries such as banking, where CEO discretion and information asymmetry may be elevated, allowing external shocks to be strategically leveraged in support of favourable pay adjustments (Frydman & Jenter, 2010).

Optimal contracting theory proposes that executive compensation packages are designed to align shareholder and managerial interests by linking pay to performance outcomes that reflect value creation (Jensen & Meckling, 1976). The theory emphasize that compensation reduces agency conflicts and motivates managers to act in the best interests of principals, particularly in environments characterized by information asymmetry. From this perspective, external macroeconomic factors such as interest rates, inflation rates and exchange rates play a critical role in shaping executive remuneration structures, as boards attempt to structure contracts that reward managerial effort while filtering out shocks unrelated to executive decision-making (Murphy, 1999). Integrating optimal contracting theory into the present study reinforces the argument that CEO compensation responds systematically to external economic forces rather than being driven by purely internal power dynamics or institutional pressures. This lens therefore, supports the analytical focus on macroeconomic factors as determinants of compensation outcomes in the banking sector

3.2. Model Specification

The study evaluates the hypotheses using a fixed effects model. The baseline panel regression model was specified as follows:

$${Sal}_{it}=\alpha +{\beta }_1{INT}_{it}+{\beta }_2{INF}_{it}+{\beta }_3{EXCH}_{it}+{\beta }_4{ROA}_{it}+{\beta }_5{TA}_t+{\beta }_{6}COVID\_19+{\mu }_i+{\epsilon }_{it}$$

(1)

$${TC}_{it}=\alpha +{\beta }_1{INT}_{it}+{\beta }_2{INF}_{it}+{\beta }_3{EXCH}_{it}+{\beta }_4{ROA}_{it}+{\beta }_5{TA}_t+{\beta }_{6}COVID\_19+{\mu }_i+{\epsilon }_{it}$$

(2)

where the subscript i denotes the bank (i = 1, 2, …, 6), and t represents the fiscal year (2009, 2010, …, 2024). Accordingly, $\alpha$, β, $\mu$ and ε denote the intercept, the coefficient estimates, the individual fixed effects, and the error term, respectively. CEO compensation is measured using two proxy variables: total CEO compensation for bank i at time t (${TC}_{it}$) and CEO salary for bank i at time t (${Sal}_{it}$), hence the two equations.

4. Preliminary Analysis

4.1. Descriptive Statistics

This section presents the descriptive statistics for the key variables included in the analysis and provides an initial overview of their characteristics and distributions. The descriptive analysis helps to identify observable trends and preliminary relationships. These insights are salient for interpreting subsequent empirical findings and assessing model suitability.

Table 3. Descriptive Results.

Descriptive Statistics. Variables	Mean	Median	Maximum	Minimum	Std Dev	Skewness	Kurtosis	Obs
SAL	9168.46	8428.00	23,190.48	3558.84	3890.90	1.58	5.75	90
TC	24,520.45	22,414.00	77,381.25	6300.00	14,455.26	1.35	5.05	90
TA (Billions)	790.40	807.92	1662.27	4.72	472.01	−0.08	1.93	90
INT	4.25	3.75	7.50	2.31	1.38	0.72	2.87	90
EXCH	13.33	14.04	18.71	6.62	3.60	−0.30	2.11	90
INF	4.97	5.13	6.90	3.10	0.97	−0.15	2.76	90
ROA	1.88	0.51	6.234	−0.42	6.95	7.71	65.95	90

summarizes the descriptive statistics of the study. The findings indicate that the highest total compensation earned by a CEO in the banking sector during the research period was R77.3 million, with an average compensation of R24.5 million and a minimum of R6.3 million. The maximum salary, which encompasses both basic salary and benefits, reached R23.1 million. The average salary was R9.1 million, with a minimum of R3.5 million. The analysis reveals that the interest rate peaked at 7.5, with an average of 4.25 and a minimum of 2.3. The exchange rate had a maximum of 18.7, an average of 13.3, and a minimum of 6.6. The inflation rate explanatory variable demonstrated a maximum of 6.9, an average of 4.9, and a minimum of 3.1 during the period under study. The control variables included total assets, which ranged from a maximum of R1.6 trillion to an average of R790 billion and a minimum of R4.7 billion. Lastly, ROA, another important control variable, showed a maximum of 62.34, an average of 1.88, and a minimum of 0.42.

Although some variables exhibit non-normal distributions, the fixed effects (FE) estimator remains valid and appropriate for this analysis. The fixed effects model is fundamentally based on the assumptions of the Gauss-Markov theorem, which states that linear estimators remain unbiased and consistent even when regressors are not normally distributed, provided the error term satisfies classical assumptions (Wooldridge, 2010). This is reinforced by the central limit theorem, which supports the robustness of inference in panel settings with sufficient data (Hsiao, 2014). Also, as long as the model handles heteroskedasticity and serial correlation through robust standard errors, the non-normality of regressors does not compromise the validity of the results (Gujarati & Porter, 2009).

4.2. Correlation Analysis

This section presents and discusses the correlation analysis used to explore the strength and direction of associations among the key variables prior to multivariate estimation. Correlation analysis provides an important preliminary diagnostic tool to identify potential linear relationships and patterns within the dataset Examining these correlations helps to determine whether observable associations exist between CEO compensation and price macroeconomic variables.

Table 4. Correlation Analysis Summary.

Variable.	SAL	TC	TA	INT	EXCH	INF	ROA
SAL	1.0000
TC	0.3405 ***	1.0000
TA	−0.0632	0.0439	1.0000
INT	0.4065 ***	0.3313 ***	0.2140 *	1.0000
EXCH	0.6159 ***	0.4189 ***	0.3631 ***	0.5958 ***	1.0000
INF	0.0532	0.1117	−0.0604	0.3235 ***	0.1544	1.0000
ROA	−0.1557	0.0826	−0.3435 ***	−0.1091	−0.1653	0.0691	1.0000

Level of significance * p < 0.05, *** p < 0.001.

summarizes the correlations.

There is a positive and weak correlation between fixed salary and interest rate. This means that as the interest rate increases, so does the fixed salary of CEOs. The correlation between interest rates and total compensation is positive and weak. Implying that as the interest rate increases, the fixed salary of the CEO also increases. Exchange rate and fixed salary have a strong and significant correlation. This suggests that an increase in the exchange rate is associated with a rise in the CEO’s fixed salary. The exchange rate and total compensation exhibit a weak positive correlation, indicating that as the exchange rate appreciates, the total compensation of the CEO also increases. There is a weak correlation among the independent variables, signifying the absence of multicollinearity. The absence of multicollinearity is confirmed by the variance inflation factors (VIF), which are less than 5 as exhibited in

Table A1. Test for multicollinearity on independent variables.

Variable	VIF	1/VIF
EXCH	2.29	0.437164
INT	2.05	0.487623
LTA	1.74	0.573593
COVID_19	1.65	0.606767
ROA	1.64	0.608781
INF	1.27	0.784521
Mean VIF	1.77

Source: Author’s compilation.

under Appendix A.

The analysis also recognizes potential endogeneity concerns, including the possibility of reverse causality between macroeconomic conditions and CEO remuneration, as well as the risk of omitted variable bias. To mitigate these risks, the modelling strategy incorporates bank-specific fixed effects and relevant control variables, which help to account for unobserved heterogeneity and isolate within-entity variation over time.

4.3. Unit Root Tests

Stationarity tests were conducted using the Levin–Lin–Chu (LLC), Im–Pesaran–Shin (IPS), and Bai and Ng unit root procedures, and the results indicated that all variables were stationary at level.

5. Empirical Results and Discussion

A series of diagnostic tests were performed on the pooled OLS, fixed effects, and random effects models to identify the most appropriate model specification, as reported in Table 4 and

Table 5. Stationarity tests of variables using LLC, IPS and Bai & Ng unit roots.

Variable	Intercept	Intercept and Trend	None	Diagnosis
Stationary tests of variables using the Levin, Lin & Chu (LLC)test
TC	−2.6669 ***	−1.7756 **	−7.0881 ***	I(0)
Sal	−2.7788 ***	2.7970 ***	−3.2649 ***	I(0)
EXCH	−1.7875 **	−3.5871 ***	−4.1005 ***	I(0)
INT	−5.0873 ***	−8.2203 ***	−3.0800 ***	I(0)
LTA	−6.9317 ***	−7.9270 ***	−4.5842 ***	I(0)
ROA	−5.9815 ***	−4.0722 ***	−9.3834 ***	I(0)
INF	−4.6580 ***	−3.6682 ***	−8.5749 ***	I(0)
Stationary tests of variables using the Im, Pesaran and Shin W-stat (IPS) test
TC	−3.4835 ***	−1.9291 **	N/A	I(0)
Sal	−2.9516 ***	−2.4183 ***	N/A	I(0)
EXCH	−2.2138 ***	−2.4928 ***	N/A	I(0)
INT	−2.1837 ***	−3.9421 ***	N/A	I(0)
LTA	−5.1668 ***	−4.5805 ***	N/A	I(0)
ROA	−5.7601 ***	−3.6653 ***	N/A	I(0)
INF	−2.0365 ***	−3.1055 ***	N/A	I(0)
Stationary tests with CSD using the Bai and Ng test
TC	−2.0378 **	−2.3966 ***	−2.0623 **	I(0)
Sal	−2.4276 **	−2.9201 ***	−2.9964 ***	I(0)
EXCH	−2.4492 ***	−12.4959 ***	−2.4492 ***	I(0)
INT	−2.2283 **	−2.6975 ***	−2.3213 **	I(0)
LTA	−2.2287 **	−2.5217 **	−2.1362 **	I(0)
ROA	−2.4179 ***	−4.5280 ***	−2.4189 ***	I(0)
INF	−2.8648 ***	−3.1159 ***	−2.9438 ***	I(0)

Note ***; **; indicates that we reject the null hypothesis of unit root tests at 1% and5% respectively. Source: Author’s compilation.

. These included tests for the joint validity of cross-sectional individual effects, the Breusch–Pagan Lagrange Multiplier (LM) test for random effects (Breusch & Pagan, 1980), the Hausman specification test for heterogeneity (Hausman, 1978) and cross-sectional independence.

The Hausman test produced a statistically significant result, indicating that the regressors were not exogenous; therefore, the fixed effects model was deemed the most appropriate specification. Furthermore, the Chow test (F-test) assessing poolability and the presence of individual effects was also significant, further supporting the robustness of the fixed effects approach. Thus, the fixed effects estimator was employed to investigate the impact of macro-economic factors on CEO compensation among JSE-listed banks in order to control for unobserved, time-invariant heterogeneity across banks. JSE-listed banks differ systematically in organizational culture, ownership structures, governance quality, risk appetite, business models, and historical performance trajectory. These factors are assumed to unlikely vary meaningfully over time but are highly correlated with CEO compensation levels. Ignoring this heterogeneity would result in omitted variable bias and inconsistent estimates, rendering pooled OLS inappropriate. Similarly, the random effects estimator assumes no correlation between bank-specific characteristics and the explanatory variables, an assumption that is implausible in this context, given that macro-economic conditions (such as inflation, GDP growth, or interest rates) influence strategic decision-making differently depending on bank size, risk exposure, and compensation philosophy. Fixed effects, therefore, allow the model to isolate within-bank variation over time, producing more reliable inference regarding the true relationship between macroeconomic dynamics and CEO pay.

Breusch–Pagan/Cook–Weisberg test was additionally employed to examine the presence of homoscedasticity, and the Wooldridge test was employed to test for serial correlation. Although the results suggested no evidence of serial correlation, the residuals were found to be heteroscedastic. To address this issue, the fixed effects model was estimated using Driscoll and Kraay’s robust standard errors, which are robust to heteroscedasticity and cross-sectional dependence. Although the Driscoll–Kraay standard errors estimator performs most efficiently in panels with relatively large time dimensions, it is also appropriate for datasets characterized by a small cross-sectional dimension and moderate time series length, provided that T is sufficiently large (Driscoll & Kraay, 1998; Hoechle, 2007). With 15 annual observations per bank, the structure of our panel meets this requirement. Recent applications further demonstrate the suitability of Driscoll–Kraay corrections in macro-panel settings with comparable or shorter time dimensions (Ditzen, 2019).

Table 6. Effects of Macroeconomic variables on CEO’s Fixed salary.

	Pooled Effects	Fixed Effects	Random Effects	FGLS
Variables	Sal	Sal	Sal	Sal
INT	0.00805	0.0487 ***	0.00805	0.00846
	(0.0118)	(0.0124)	(0.0118)	(0.0119)
INF	−0.0188	−0.0121	−0.0188	−0.0194
	(0.0132)	(0.0141)	(0.0132)	(0.0133)
EXCH	0.0320 ***	0.0266 ***	0.0320 ***	0.0324 ***
	(0.00484)	(0.00631)	(0.00484)	(0.00478)
LTA	−0.1978 ***	0.147 ***	−0.1978 ***	−0.113 ***
	(0.0559)	(0.093)	(0.0559)	(0.0304)
ROA	−0.00641 *	0.0685 ***	−0.00641 *	−0.00656 **
	(0.00254)	(0.00598)	(0.00254)	(0.00210)
COVID_19	0.00464	0.0970 ***	0.00464	0.00610
	(0.0362)	(0.0397)	(0.0362)	(0.0363)
_cons	4.428 ***	2.338	4.428 ***	4.555 ***
	(0.483)	(1.643)	(0.483)	(0.270)
N	90	90	90	90
R2	0.564	0.271	0.564	-
Fixed Effects-Test	-	2.81 **	-	-
F-Stats/Wald Chi2	110.70 ***	18.21 ***	110.70 ***	116.92 ***
Pesaran’s test CSD	-	−1.93	−1.22	-
Hausman Test	-	43.66 ***	43.66 ***	-
Wooldridge test (F-Stat)		1.442

Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001.

summarises the effects of macro economic variables on CEO fixed salary.

The study’s findings revealed a positive and significant correlation between the CEO’s fixed salary and the interest rate. This suggests that executive fixed compensation is responsive to macroeconomic conditions. The positive and significant relationship between the interest rate and fixed compensation suggests the impact of macroeconomic factors on compensation that is not typically linked to performance, as fixed compensation is usually not tied to any performance metrics. The relationship can be attributed to the link between interest rate and inflation. As higher interest rates are experienced, companies tend to adjust executive remuneration in line with inflation as well, which will have been impacted by interest rates. The study’s findings align with those of Chiu et al. (2016), who found a strong and significant relationship between the interest rate and CEO cash compensation. Conversely, Luijsterburg (2024) found that interest rates had no significant effect on CEO compensation. The contrasting findings can be attributed to the differing economies in which the studies were conducted.

There is a positive and significant correlation between the exchange rate and CEO fixed compensation. The positive and significant relationship reiterates the impact of macroeconomic conditions on the compensation of CEOs. Le et al. (2025) note that cross-listed banks in developing countries show a preference for high fixed CEO compensation. Most of the banks investigated in the study have some international exposure. Investec Bank is dual listed in South Africa and the UK. Standard Bank and ABSA have operations across Africa. When exchange rates fluctuate and banks are cross-listed, the CEO will benefit from such fluctuations when currencies in their host countries depreciate. The positive relationship between the Exchange rate and fixed salary can be attributed to the continued depreciation of the South African Rand against the United States dollar, which in turn has increased the total assets of banks. Total assets are linked to firm size, and the bigger the organization, the higher the fixed salary is in most cases. The study by Luijsterburg (2024) revealed a positive and significant relationship between the fixed salary and the Exchange rate.

Total assets and fixed salary have a positive and significant relationship. Bortoluzzo et al. (2024) argue that small or large banks can enhance their profitability. The authors note that for small banks, profitability is achieved through niche strategies, while large banks have the advantage of strengthened firm capabilities, leading to greater profitability. Regehr and Sengupta (2016) highlight that increased bank size results in reduced costs, coupled with lower risks, which in turn increase profitability through reduced average costs and fewer losses. Hence, the enhanced performance resulting from the bank’s size can be used to explain the positive and significant correlation between total assets and total compensation. Oxelheim et al. (2008) argue that CEO compensation changes by approximately 2.4% for each 10% increase in firm size, while a 10% increase in firm performance results in a 0.8% increase in CEO compensation. The results align with those of Sikawa et al. (2020), who found a positive relationship between total assets and CEO compensation.

The fixed pay and return on assets have a positive and significant relationship. The relationship can be explained by the importance of company profitability in enabling the company to pay high salaries, thus linking fixed pay to the company’s past performance. Clementi and Cooley (2023), emphasize the importance of past performance in determining the CEO’s current and deferred compensation, as it can serve as a measure of the CEO’s ability to perform effectively. The study’s findings align with M. H. Bussin and Ncube (2017), who found a positive relationship between CEO fixed pay and firm performance among state-owned organizations in South Africa.

The study’s findings revealed a positive and significant relationship between COVID-19 and CEOs’ fixed pay. This positive connection can be linked to the business performance during the pandemic. Ilić and Lepojević (2022) argue that the business performance of banks influenced the compensation of CEOs during this period. Clementi and Cooley (2023) suggest that a company’s past performance influences its ability to offer above-average current salaries. Therefore, the business performance of banks during the pandemic justified an increase in the fixed salaries of CEOs. The study’s findings are consistent with those of Ilić and Lepojević (2022), who found a positive correlation between COVID-19 and fixed pay for CEOs among banking sector executives.

Table 7. Effects of Macroeconomic variables on CEO’s Total Compensation.

	Pooled Effects	Fixed Effects	Random Effects	FGLS
Variables	TC	TC	TC	TC
INT	0.0193	0.0130 ***	0.0193	0.0215
	(0.0176)	(0.0017)	(0.0176)	(0.0228)
INF	−0.00298	0.00540	−0.00298	−0.00706
	(0.0197)	(0.0198)	(0.0197)	(0.0255)
EXCH	0.0283 ***	0.0218 *	0.0283 ***	0.0300 **
	(0.00738)	(0.00889)	(0.00738)	(0.00920)
LTA	0.135	0.422 ***	0.135	0.113
	(0.109)	(0.0271)	(0.109)	(0.0585)
ROA	0.0709 ***	0.0147 ***	0.0709 ***	0.0113 **
	(0.00432)	(0.0084)	(0.00432)	(0.00405)
COVID_19	0.208 ***	0.465 ***	0.208 ***	0.0164
	(0.0543)	(0.0559)	(0.0543)	(0.0700)
_cons	2.690 **	0.248	2.690 **	2.861 ***
	(0.938)	(2.314)	(0.938)	(0.519)
N	90	90	90	90
R2	0.456	0.465	0.456	-
Fixed Effects-Test	-	18.78 ***	-	-
F-Stats/ Wald Chi2	60.18 ***	11.28 ***	60.18 ***	41.59 ***
Pesaran’s test CSD	-	−0.893	−0.116	-
Hausman Test	-	51.01 ***	51.01 ***	-
Wooldridge test(F-Stat)		3.656

Robust Standard errors in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001.

summarizes empirical results on the effect of macroeconomic variables on CEO total compensation.

There is a positive and significant relationship between total compensation and interest rate. The finding suggests that increases in the interest rate environment are linked to higher pay outcomes for bank executives. One possible interpretation is that rising interest rates may strengthen bank profitability through improved net interest margins (Ozdemir & Altinoz, 2024) thereby enhancing performance metrics that underpin compensation policies and structures. This aligns with optimal contracting theory, which proposes that executive pay is structured to reward value creation driven by external conditions. The result may also indicate that boards adjust remuneration packages to retain or incentivise CEOs during periods of economic tightening, when strategic decision-making becomes more critical. The study’s findings align with those of Chiu et al. (2016), who discovered a positive and significant relationship between interest rates and total compensation.

The study’s findings indicate a positive and significant relationship between the exchange rate and the CEO’s total compensation. The positive and significant relationship indicates that changes in the exchange rate have an impact on the CEO’s total compensation. This suggests that firm-specific characteristics do not solely determine the total compensation of the CEO. The impact of the exchange rate on CEO compensation can be attributed to the international exposure of the banks under study. Exchange rate fluctuations are closely linked with the bank’s revenue and profitability. Exchange rate appreciation has a positive effect on stock returns (Salisu et al., 2022). Correspondingly, Bruno et al. (2022) note that in emerging markets, stock returns increase as the local currency appreciates against the dollar. This impacts profitability. The total compensation of CEOs is linked to performance and profitability (M. Bussin et al., 2023). Improved firm performance, such as that resulting from exchange rate fluctuations, translates to higher total compensation. Therefore, remuneration committees are encouraged to be cognizant of macroeconomic factors when structuring CEO compensation. The study’s findings align with those of Luijsterburg (2024), who found a positive and significant relationship between the exchange rate and total compensation.

The study findings also exhibited a positive and significant correlation between total assets and total compensation. This suggests that as the bank expands in terms of total assets, the CEO’s total compensation also increases. This shows the impact of firm size on the total compensation of CEOs. Anne et al. (2021) argue that larger organizations attract highly qualified CEOs, who only accept high compensation, resulting in high incentive compensation. This increase explains the positive relationship between total compensation and total assets, as total compensation includes incentive pay. The findings of the study align with those of Hallock’s (2011) study, which examined 2300 CEOs in publicly traded companies in the United States of America. A 1% increase in company size, measured by assets, resulted in a 0.33% increase in the CEO’s total compensation.

The study findings showed a positive and significant correlation between COVID-19 and total compensation. This shows that the COVID-19 pandemic did not negatively affect CEO’s total compensation. The positive impact can be attributed to the firm’s performance, which remained unaffected by the pandemic. Ye et al. (2023) note that the positive relationship between COVID-19 and CEO total compensation was a result of firm performance, which was not significantly affected by the pandemic. Total compensation is closely tied to a firm’s performance. The positive impact can also be attributed to the symbolic nature of pay cuts among CEOs during the pandemic. Bedford et al. (2023) argue that a reduction in total compensation did not accompany most pay cuts among Australian executives. The authors argue that pay cuts were substituted with increases in bonuses. Bonuses form part of the total compensation for CEOs. The study’s findings align with those of Ye et al. (2023), who found a positive and significant relationship between COVID-19 and the CEO pay ratio among S&P 500 firms. Similarly, Ilić and Lepojević (2022) found a positive relationship between COVID-19 and total CEO compensation among executives in the banking sector.

A positive relationship also existed between ROA and total compensation. The relationship cements the notion that total compensation is a product of company performance. The findings of the study are in line with Zandi et al. (2019) study, which found a positive correlation between CEO compensation and ROA. A similar study by Ndofirepi (2015) in South Africa found a positive and significant relationship between ROA and CEO compensation, both the fixed salary and total compensation.

6. Limitations and Recommendations

A key limitation of this study arises from the restricted sample size of six banks, which constrains both the statistical power and generalisability of the findings. Although the focus on listed commercial banks enhances comparability and sectoral relevance, it limits variation in organizational characteristics, governance practices, and compensation structures. In addition, the study’s reliance on price-based macroeconomic variables such as inflation, interest rates, and exchange rates may not fully capture the broader economic environment influencing executive compensation decisions. While these indicators act as important proxies for external shocks, they do not reflect quantity macroeconomic conditions such as employment trends, GDP, financial regulation severity, technological shifts, or geopolitical risk, all of which can affect bank performance and compensation structures. Price variables may also exhibit endogeneity concerns, particularly through feedback loops between bank performance, CEO decision-making, and macroeconomic outcomes.

Future studies could expand the sample beyond six banks to enhance external validity and improve the representativeness of findings across the financial sector. A larger sample would allow for more detailed analysis of bank-specific characteristics, including ownership structures, governance arrangements, and strategic positioning, while reducing sensitivity to outliers. Comparative analyses across countries or regions would also be valuable in assessing whether the impact of macroeconomic conditions on CEO compensation is consistent across different regulatory and institutional environments. Such extensions would not only strengthen the generalisability of results but also enable researchers to identify sectoral or regional patterns that may not be evident within a restricted banking sample. Further research could incorporate broader macroeconomic indicators and non-price variables to capture the multidimensional nature of the economic environment. Variables such as labour market conditions, GDP, technological change, and regulatory developments may offer deeper insights into the mechanisms that shape executive remuneration structures. Incorporating organization specific factors such as governance measures like board independence, ownership structures, or remuneration committee characteristics can also clarify how internal organizational dynamics interact with external shocks to influence compensation structures. Methodologically, future studies may benefit from exploring nonlinear relationships, applying alternative modelling techniques such as dynamic panel approaches, or conducting causal inference analyses to address potential endogeneity. Such extensions would offer a richer and more holistic view of how macroeconomic forces shape executive compensation in the banking industry.

7. Conclusions

This study establishes a positive and statistically significant relationship between exchange rate movements and CEO compensation, as well as a positive link between interest rates and CEO pay within the banking industry. However, the Inflation rate did not have any relationship with either fixed salary or total compensation. These findings suggest that macroeconomic conditions have a significant influence on the dynamics of executive remuneration in financial institutions. In the banking sector, an appreciation of the exchange rate can enhance profitability through increased foreign investments, a higher valuation of foreign-denominated assets, and improved cross-border economic activities, all of which can lead to higher CEO compensation. Similarly, rising interest rates can expand net interest margins and boost bank earnings, which may justify increased rewards for top executives.

For remuneration committees in the banking industry, these results have important implications. The impact of fluctuations in exchange and interest rates on CEO pay highlights the necessity for committees to distinguish between compensation outcomes driven by macroeconomic conditions and those stemming from managerial performance. To ensure fairness, accountability, and alignment with stakeholder interests, remuneration committees should incorporate macro-adjusted metrics or relative performance measures that account for external economic effects. Moreover, considering that banking performance is vulnerable to monetary policy and exchange rate volatility, pay structures should emphasize long-term value creation and risk management over short-term gains. Ultimately, understanding the macroeconomic factors influencing CEO compensation enables remuneration committees to design more resilient, transparent, and equitable compensation frameworks in the banking sector.