The effect of CEO characteristics on executive compensation

Abstract

The aim of this research was to investigate the relation between the characteristics of Chief Executive Officers (CEOs) and the executive compensation of Brazilian companies listed on the stock exchange. The Upper Echelons Theory (Hambrick, 2007) already pointed to a possible combined effect between characteristics and executive compensation. In Brazil, this relation became feasible only with the disclosure requirement established by the Securities and Exchange Commission of Brazil (CVM) Instruction No. 480/2009. Thus, this research contributes to theory and Brazilian literature by exploring this relation. The findings are relevant because they highlight potential criteria used by companies in determining CEO salaries and contribute to explaining variations in executive compensation. Additionally, they provide empirical evidence on which executive characteristics are valued and better compensated in an emerging market. The findings have practical and theoretical implications. For companies, they guide salary policies by showing which characteristics of CEOs influence compensation, aligning incentives with organizational strategy. For academia, they shed light on the determinants of compensation. In the Brazilian context, they reveal how the attributes of CEOs affect their compensation, contributing to an understanding of the impact of executives on organizations. To analyze the models, we used the Generalized Method of Moments (GMM-Sys). Specific attributes of CEOs influence compensation. Education aligned with the business leads to an increase in total compensation, age influences fixed compensation, and duality influences variable compensation. On the other hand, family ties reduce variable compensation, and gender seems to be irrelevant. The results contribute by highlighting these characteristics as determinants of compensation and by showing the combined effect between characteristics and executives' total, fixed, and variable compensation.

Keywords:
executive compensation; characteristics of CEOs; Upper Echelons Theory

Resumo

Esta pesquisa teve como objetivo investigar a relação entre as características dos diretores executivos (Chief Executive Officers - CEOs) e as remunerações executivas de empresas brasileiras listadas em bolsa. A Teoria dos Escalões Superiores (Hambrick, 2007) já apontava para um possível efeito combinado entre características e remunerações executivas. No Brasil, essa relação só se tornou viável com a obrigatoriedade de divulgação estabelecida pela Instrução da Comissão de Valores Mobiliários (CVM) nº 480/2009. Assim, esta pesquisa contribui para a teoria e para a literatura brasileira ao explorar essa relação. Os achados são relevantes por destacarem potenciais critérios utilizados pelas empresas nas definições salariais dos CEOs e por contribuírem para a explicação das variações nas remunerações executivas. Ademais, fornecem evidências empíricas sobre quais características executivas são valorizadas e melhor remuneradas em um mercado emergente. As descobertas têm implicações práticas e teóricas. Para as empresas, orientam políticas salariais ao evidenciar quais características dos CEOs influenciam as remunerações, alinhando os incentivos à estratégia organizacional. Para a academia, esclarecem os determinantes das remunerações. No contexto brasileiro, revelam como os atributos dos CEOs afetam suas remunerações, contribuindo para a compreensão do impacto dos executivos nas organizações. Para a análise dos modelos, utilizamos o Generalized Method of Moments (GMM-Sys). Atributos específicos dos CEOs influenciam as remunerações. A formação alinhada aos negócios proporciona aumento na remuneração total, a idade influencia a remuneração fixa, e a dualidade, a remuneração variável. Por outro lado, laços familiares reduzem as remunerações variáveis, e o gênero parece ser irrelevante. Os resultados contribuem ao destacar essas características como determinantes das remunerações e ao evidenciar o efeito combinado entre características e remunerações totais, fixas e variáveis dos executivos.

Palavras-chave:
remuneração executiva; características dos CEOs; Teoria dos Escalões Superiores

1. INTRODUCTION

Traditional finance defended the rationality of markets and managers, without considering that they have attributes and biases that influence financial decisions. As a result, researchers began to highlight the greater complexity of decision-making, as managers had characteristics that affected their choices. Thus, it wasn't enough just to consider organizational factors; the cognitive bases of executives also had to be taken into account (Shen, 2021). In this sense, the Chief Executive Officer (CEO), being the primary decision-maker, can significantly influence corporate results (Altarawneh et al., 2020).

In the company, CEOs act according to their characteristics. This aroused the interest of scholars in identifying which attributes would be most relevant. However, this task is challenging, as executives have heterogeneous and multidimensional characteristics (Kaplan et al., 2012; Adams et al., 2018). In this context, research has sought to relate the characteristics of executives to various corporate issues and has found that they influence important decisions, such as cost control, resource management, financial policies, investment decisions, and attracting investors (Liu et al., 2018; Altarawneh et al., 2020; Malik et al., 2020; Ghardallou et al., 2020; Silva & Soares, 2024).

One strand of studies on the characteristics of executives has utilized the Upper Echelons Theory (UET) (Hambrick & Mason, 1984) as a framework, emphasizing that CEOs are key players because they possess skills that impact business performance. According to UET, the characteristics of executives have the ability to influence business results and performance, as decisions in the corporate environment are often based on the chief executive's characteristics. In an update of UET, Hambrick (2007) highlighted a relevant research gap, necessary for the advancement of UET and not yet explored at that time. The author invited researchers to investigate the combined effect of characteristics and executive compensation, arguing that managers' behavior would tend to change according to the compensation received.

Studies on executive compensation have advanced since the 1990s, attracting researchers from the fields of economics, finance, accounting, and strategic management, who have focused on the determinants of compensation, the composition of compensation packages, and high salaries (Jensen & Murphy, 1990; Murphy, 1999; Devers et al., 2007; Al-Shammari, 2018). However, this is a complex issue, as salary definitions involve subjective elements, in addition to significant variations between companies and sectors (Frydman & Jenter, 2010; Davis et al., 2013). Furthermore, despite academic attention, there is still no consensus on the determinants and composition of packages, which makes the topic a current field, rich in debate, including in relation to sustainability disclosures (Page, 2018; Grodt et al., 2024).

These discussions are important in developing contexts, as they help to understand the importance attributed to executives and the aspects inherent in corporate governance. In Brazil, studies on executive compensation only gained prominence in the last decade, with CVM Normative Instruction No. 480 of 2009, which made it mandatory to disclose the compensation of executives of publicly traded companies from 2010 onwards (Krauter, 2013; Souza et al., 2017; Aguiar & Pimentel, 2017; Oliveira et al., 2021; Dutra & Ceretta, 2023).

Compensation has shown high growth, leading the media and academia to question the reasons behind high salaries (Murphy, 1999; Hendriks et al., 2023). In the search for explanations, researchers have highlighted that the characteristics of executives play a decisive role in determining compensation (Bussin & Ncube, 2017; Malik et al., 2020; Oliveira et al., 2021). In this way, compensation would reflect the characteristics of the executives, resulting in increases. In this sense, the following question arises: What is the effect of the characteristics of CEOs on executive compensation in the Brazilian context? The aim of this study is to investigate the relation between the characteristics of CEOs and the executive compensation of Brazilian companies listed on the stock exchange.

The study contributes to the literature by revealing which characteristics of CEOs have been valued by companies, offering insights into the impact of executive attributes on decisions (Kaplan et al., 2012; Ghardallou et al., 2020; Shen, 2021). Furthermore, the discussions on CEO compensation are inconclusive, which opens up room for contributions on the determinants. Thus, the research provides evidence on possible determinants of compensation in the Brazilian environment (Finkelstein & Hambrick, 1988; Hendriks et al., 2023). In contrast to other countries, where information on executive characteristics and compensation was already accessible, in Brazil, this transparency was only achieved with CVM Instruction No. 480 of 2009. Thus, the study seeks to fill this gap and contribute to the recent national literature (Pereira et al., 2016; Oliveira et al., 2021; Dutra & Ceretta, 2023).

The results have practical and theoretical implications for companies and for the literature. By identifying the characteristics of CEOs that influence increases in compensation, companies will be able to assess the relevance of these attributes and decide which ones can be incorporated into compensation plans (Jaggia & Thosar, 2022). For literature, it highlights which characteristics are potential determinants, adding empirical findings and opening space for new discussions on the national scene. Furthermore, the study reinforces UET's observation regarding the effect between characteristics and executive compensation, bringing Brazil into these discussions.

2. LITERATURE REVIEW

2.1 Executive Characteristics and Compensation

The Upper Echelons Theory (UET) posits that top executives possess observable characteristics that significantly influence corporate decisions and outcomes (Hambrick & Mason, 1984). According to UET, characteristics such as experience, educational background, and age of the executive enhance the quality of decision-making, leading to more effective, refined, and risky strategies, which can consequently benefit business performance (Hambrick & Mason, 1984; Malik et al., 2020).

In this context, the attributes of executives become relevant. According to Hambrick and Mason (1984), educational background, for example, provides rich and complete information, helping to avoid mistakes and improve profitability and growth. In addition, for the authors, the age of senior executives can also be an indicator of better decisions: younger managers tend to take more risks, while older ones prefer to maintain continuity. Furthermore, for UET, the experience of executives adds improved and useful methods, techniques, and strategies to the decision-making process (Hambrick & Mason, 1984).

Thus, given the growing complexity of the corporate environment, it has become crucial to consider, in addition to organizational factors, the characteristics of managers (Adams et al., 2018; Malik et al., 2020; Shen, 2021). Companies may have resources, but how they are allocated depends largely on the characteristics and skills of the executives (Barker & Mueller, 2002). From this perspective, studies have emerged seeking to understand how these characteristics impact decisions (Liu et al., 2018; Kaur & Singh, 2019; Conyon et al., 2019; Silva & Banda, 2022; Setiawan & Gestanti, 2022; Silva & Soares, 2024). Although the results found are mixed and there is no consensus on which characteristics are most important, research has helped to understand the impact of executives on organizations (Custódio et al., 2014; Altarawneh et al., 2020; Shen, 2021).

In this scenario, the CEO, being at the top of the organizational hierarchy, is considered the main decision-maker. It can strongly influence corporate decisions and, for this reason, studies have focused on investigating its characteristics, considering different markets, countries, and companies (Liu et al., 2018; Kaur & Singh, 2019; Ghardallou et al., 2020; Gupta & Mahakud, 2020; Shen, 2021; Setiawan & Gestanti, 2022; Silva & Soares, 2024). Studies have analyzed characteristics such as educational background, age, gender, and duality, seeking to understand how these factors affect corporate results (Custódio & Metzger, 2014; Conyon et al, 2019; Leszczynska & Chandon, 2019; Gupta & Mahakud, 2020; Ghardallou et al., 2020; Schmid & Baldermann, 2021; Mueller et al., 2021).

In an update of UET, Hambrick (2007) adds a new element to the discussions on the characteristics of executives. For the author, compensation influences strategic decisions and behavior. UET proposes that executive characteristics have an impact on decisions and that compensation acts as an incentive for strategic behavior (Hambrick & Mason, 1984; Hambrick, 2007). Thus, the author suggests a combined effect between characteristics and executive compensation, urging research to explore this relation. However, UET does not explore this issue in depth; Hambrick (2007) merely suggests that further research is needed. The fact is that attracting executives with diverse characteristics is crucial for the company, and compensation encourages the effective utilization of these attributes in the corporate environment (Hambrick, 2007).

The debate on executive compensation intensified from 1990 onwards, focusing on the determinants of high salaries (Murphy, 1999; Devers et al., 2007; Hendriks et al., 2023). In Brazil, it was only with CVM Instruction No. 480 of 2009 that information on executive compensation and characteristics became available, and as a result, studies are still recent on the national scene. In discussions on compensation, it has been pointed out that CEOs have relevant characteristics, which are determinants and help explain high salaries, as well as acting as an incentive for managers to dedicate themselves to the business and use their skills (Bussin & Ncube, 2017; Page, 2018; Malik et al., 2020).

2.2 Research Hypothesis

The characteristics of CEOs have the potential to influence their compensation (Machado et al., 2023). This influence can occur in various ways, either by boosting the company's performance and value, reflected in higher compensation as recognition, or in contexts of weak corporate governance, in which executives can exert direct influence over their own compensation (Jaggia & Thosar, 2022; Chen et al., 2021; Choi et al., 2022). Given the diversity of existing characteristics and their variation between executives, studies have been dedicated to investigating this relation and its impact on the compensation structure (Pereira et al., 2016; Schmid & Baldermann, 2021).

One characteristic that is considered important is the executive's educational background, as it enhances their ability to process complex information. For this reason, research has investigated its impact on companies (Hambrick & Mason, 1984; Barker & Mueller, 2002; Shen, 2021). Studies indicate that CEOs with a background in business, whether in administration, finance, economics, or accounting, at the undergraduate or graduate level, use their theoretical foundation to make better decisions, boosting business performance (Custódio & Metzger, 2014; Gupta & Mahakud, 2020; Ghardallou et al., 2020). As a result, by adding value to the business, executives with this educational background receive higher salaries and, for this reason, academic background is considered a determining factor in executive compensation, shaping salary definitions (Rissatti et al., 2022; Malik et al., 2020; Jaggia & Thosar, 2022; Choi et al., 2022; Machado et al., 2023).

The age of the executive has the potential to impact decisions and is a relevant characteristic in determining compensation (Hambrick & Mason, 1984; Custódio & Metzger, 2014). Studies indicate that companies tend to pay higher salaries to older CEOs, recognizing their experience, decision-making capacity, and ability to identify opportunities, capture resources, and understand the competitive dynamics of the sector (Pereira et al., 2016; Rahman & Mustafa, 2018; Conyon et al., 2019; Schmid & Baldermann, 2021; Ding et al., 2022; Machado et al., 2023). In this way, older CEOs can offer advantages to companies and, therefore, the age of the executive can influence the definition of executive compensation.

The CEO's gender is another relevant determinant of compensation, as it interacts with important variables such as company size, sector, and professional experience (Leszczynska & Chandon, 2019; Schmid & Baldermann, 2021). Studies highlight the importance of studying the gender of the CEO to verify possible pay disparities, since both male and female CEOs are competent in negotiations, strategic planning, quality reporting, risk-taking, and contribute to performance (Lam et al., 2013; Kaur & Singh, 2019; Oliveira et al., 2021; Jaggia & Thosar, 2022). However, studies highlight that there is still a predominance of higher salaries for male CEOs, which reveals gender issues in the definition of compensation, an open gap (Lam et al., 2013; Oliveira et al., 2021). For this reason, the gender of the CEO is a characteristic that has influenced executive compensation.

The duality of positions has also been discussed in studies on compensation (Conyon et al., 2019; Mueller et al., 2021; Dutra & Ceretta, 2023). Studies indicate that CEOs who hold both the positions of chief executive officer and chairman of the board are committed to running the business, as the accumulation of roles requires dedication. However, there are open questions about the impact of duality on results (Krause et al., 2014; Kaur & Singh, 2019). Additionally, some literature highlights that CEOs on the board can influence their own compensation, raising concerns about corporate governance (Shen, 2021; Ding et al., 2022; Machado et al., 2023). Thus, the duality of the CEO can affect executive compensation.

In debates about executive characteristics and compensation, CEOs' family ties have garnered attention due to their potential impact on salary definitions (Graziano and Rondi, 2021; Chen et al., 2021). However, the results are mixed and inconclusive. Some studies suggest that family CEOs receive lower compensation than their non-family peers (McConaughy, 2000; Graziano & Rondi, 2021), as they prioritize the continuity of the company. On the other hand, other research indicates higher salaries for family CEOs (Combs et al., 2010; Chen et al., 2021), possibly due to their influence in setting salaries. The presence of a family CEO raises controversial questions about monitoring, opportunism, and salary preference (Sánchez-Marín et al., 2020). For this reason, it is worth noting that the family ties of CEOs influence their executive compensation.

Given the above, considering that the educational background, age, gender, duality, and family ties of CEOs have the potential to influence the definition of executive compensation, the following central hypothesis is formulated:

HP: The characteristics of CEOs are positively related to the executive compensation of Brazilian companies listed on the stock exchange.

3. METHODOLOGICAL ASPECTS

3.1 Characterization of the Sample, Temporal Space, and Data Collection

The sample consists of publicly traded companies listed on B3, with data available from 2010 to 2022. The choice of 2010 as the starting year is justified by CVM Instruction No. 480 of 2009, which now requires the disclosure of information on executive compensation. Table 1 shows the sample distribution by economic sector.

Table 1
Research sample

Sector	Number of companies	Number of observations
Industrial goods	47	447
Communications	7	38
Cyclical consumption	81	737
Non-cyclical consumption	26	197
Basic materials	31	257
Oil, gas and biofuels	10	94
Health	20	162
Information technology	12	56
Public utility	38	409
Total	272	2,397

• Source: Prepared by the authors.

The personal and curricular information of the CEOs were collected manually from various sources: Formulários de Referência das empresas [Company Reference Forms] (FRE), subitem “Composition and professional experience of management and the fiscal council,” available on the CVM website; company websites, in the investor relations section; CEO profiles on LinkedIn; and, when it was not possible to obtain the information from the sources mentioned, news and interview websites were consulted (G1, O Globo, Veja and Estadão). Executive compensation was collected manually in the FRE, under the item, “Compensation of Administrators” available on the CVM website. The reason for the manual collection was the unavailability of this information in databases. The financial data was obtained from the Refinitiv Eikon ^® database.

3.2 Dependent Variables

In accordance with CVM Normative Instruction No. 480 of 2009, companies must disclose in the FRE information on the compensation of each body, including the Statutory Executive Board, of which the CEO is a member. The FRE displays the components of the compensation packages, as well as the minimum, average, and maximum compensation per body. However, the CEO's compensation is not disclosed individually. Thus, based on information from the statutory executive board, studies have established proxies. Considering that total compensation predominates in studies because it covers the entire salary package, we adopted two proxies for CEOs' total compensation, worked out in natural logarithms:

• Highest compensation of the statutory executive board (RM): The highest compensation of the statutory executive board is disclosed in the FRE. As the position of CEO occupies the top of the organizational hierarchy, it can be inferred that he is the highest-paid member of this board. Studies have utilized this proxy (Costa et al., 2016; Sprenger et al., 2017; Oliveira et al., 2021).

• Average compensation of the statutory executive board (RMED): The FRE discloses the average compensation paid to the statutory executive board. As it is not known how much each member receives, research has considered the average compensation of the board of directors as a proxy for the CEO's compensation (Dutra & Ceretta, 2023; Machado et al., 2023).

The FRE also discloses fixed and variable compensation. Thus, to enrich the discussion on the effect of characteristics on compensation, we included two proxies to represent these types of CEO compensation, calculated using natural logarithms:

• Average fixed compensation of the statutory executive board (RF): Fixed compensation includes salary or pro-labore, direct and indirect benefits, participation in committees, and other fixed compensation. For the study, the total value of fixed compensation divided by the number of members is considered (Rissatti et al., 2019; Ramos et al., 2022).

• Average variable compensation of the statutory executive board (RV): FRE includes variable compensation paid to the statutory executive board, consisting of bonuses, profit sharing, participation in meetings, commissions, other variable compensation, post-employment benefits, benefits motivated by termination of office, and share-based compensation. The study uses the total value of variable compensation divided by the number of members (Rissatti et al., 2019; Ramos et al., 2022; Machado et al., 2023).

3.3 Independent Variables

3.3.1 Characteristics of CEOs

The research hypothesis presented in the literature review was broken down into five statistical hypotheses. As discussed in section 2, the educational background in business, age, gender, duality, and family ties of CEOs have a positive and significant influence on executive compensation. Table 2 shows these characteristics, measurements, and expected relations.

Table 2
Characteristics of CEOs and statistical hypotheses

Variable	Acronym	Measurement	Hypothesis	Expected relation	Authors
Business education	FA	Dummy: 1, if the CEO has an undergraduate or postgraduate degree in administration, accounting, finance, or economics; 0, otherwise.	H1	+	Hambrick and Mason (1984); Custódio and Metzger (2014); Gupta and Mahakud (2020).
Age of the CEO	AGE	CEO's age in years.	H2	+	Custódio and Metzger (2014); Schmid and Baldermann (2021); Oliveira et al. (2021).
CEO's gender	GEN	Dummy: 1, if the CEO is female; 0, if the CEO is male.	H3	+	Lam et al. (2013); Kaur and Singh (2019); Schmid and Baldermann (2021); Mueller et al. (2021); Oliveira et al. (2021).
Duality	DUA	Dummy: 1, if the CEO is chairman of the board of directors; 0, otherwise.	H4	+	Conyon et al. (2019); Jbir et al. (2021); Ding et al. (2022); Machado et al. (2023); Dutra and Ceretta (2023).
Family CEO	CEOFAM	Dummy: 1, if the CEO is a founder, heir, or belongs to the founding family; 0, otherwise.	H5	+	Sánchez-Marín et al. (2020); Graziano and Rondi (2021); Chen et al. (2021).

• Source: Prepared by the authors.

3.3.2 Control variables

To isolate the effect of CEO characteristics on compensation, we adopted control variables highlighted as relevant in the literature. Considering that the Covid-19 pandemic has brought instability to business and may have had an impact on compensation, we have included a dichotomous variable to represent this period of crisis. Recent studies on executive compensation in Brazil (Oliveira et al., 2021; Ramos et al., 2022; Dutra & Ceretta, 2023; Machado et al., 2023) continue to investigate compensation in the period preceding the pandemic. Table 3 shows the control variables, their descriptions, measurements, and expected relations.

Table 3
Control Variables

Variable	Acronym	Description	Measurement	Sign	Authors
Company size	SIZE	Represented by the size of the company's total assets.	Natural logarithm (Ln) of total assets.	+	Lam et al. (2013); Kaur and Singh (2019); Ghardallou et al. (2020); Zhou et al. (2021).
Business performance	ROA	It shows how much profit you can generate in relation to your assets.	LAJIR/Total asset value.	+	Custódio & Metzger (2014); Conyon et al. (2019); Rissatti et al. (2019); Jbir et al. (2021); Zhou et al. (2021); Ding et al. (2022); Choi et al. (2022); Machado et al. (2023).
	Tobin's Q (TQ)	Used to measure the company's market performance.	Market value of shares/Total asset value	+
Indebtedness	IND	This refers to the amount of company debt in relation to total assets.	Total liabilities/Total assets	+/-	Conyon et al. (2019); Ding et al. (2022); Machado et al. (2023); Dutra & Ceretta (2023).
Age of the company	AGECOMP	Time in years since the company was founded.	Measured in years from its foundation.	+/-	Abed et al. (2014); Serra et al. (2016); Setiawan and Gestanti (2022).
Control defined	CONT	Concentration of control of shares of 50% or more in the hands of a single shareholder.	Dummy: 1, if the company has a controlling shareholder; 0, otherwise.	-	Rissatti et al. (2019); Freitas et al. (2020); Peng and Tan (2023).
Pandemic	PAN	Covid-19 pandemic period, considering the years 2020 and 2021.	Dummy: 1, pandemic period; 0, otherwise.	-	Proposed in the study.
The company's sector of activity	SEC	The economic sector in which the company operates.	Dummy: 1, for the company's sector; 0, otherwise.	+	Lam et al. (2013); Falato et al. (2015); Aguiar & Pimentel (2017).

• Source: Prepared by the authors.

3.4 Model and Econometric Treatment

Equation 1 presents the study's general empirical model, which focuses on testing the influence of CEO characteristics on executive compensation, as per the test hypotheses.

$${\text{RM}}_{\mathrm{i},\mathrm{t}}\text{= β}{}_{\text{0}}\text{+ β}{}_{\text{1}}{\mathrm{R}\mathrm{M}}_{\mathrm{t}-1}\text{+ β}{}_2{\text{Σ}\mathrm{C}\mathrm{E}\mathrm{O}s\mathrm{C}\mathrm{h}\mathrm{a}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{c}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\text{+}\text{β}{}_3\text{Σ}{\mathrm{C}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{s}}_{\mathrm{i},\mathrm{t}}\text{+}\mathrm{}\text{β}{}_4\text{Σ}{\mathrm{S}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+\mathrm{}\text{β}{}_5\text{Σ}{\mathrm{Y}\mathrm{e}\mathrm{a}\mathrm{r}}_{\mathrm{i},\mathrm{t}}+{\mathrm{\epsilon }}_{\mathrm{i},\mathrm{t}}$$ (1)

where: "i" and "t" = company and time; _RMi,t = total, fixed and variable CEO compensation; β₁RM_t-1 = lagged total, fixed and variable compensation; β₂ΣCEOs characteristics_st-1 = business education, age, gender, duality and family CEO; β₃ΣControls_i,t = size, ROA, Tobin's Q, indebtedness, age of the company, defined control and pandemic; β₄ΣSector_i,t = dichotomous variables for the sectors according to the B3 sector classification; β₅ΣAno_i,t = dichotomous variables for the period from 2010 to 2022; and ε_i,t = error term.

Studies investigating the relation between the characteristics and compensation of CEOs point to the possibility of endogeneity, which occurs when variables correlate with the residual, i.e., when they can be directly influenced by uncontrolled or unconsidered factors. This problem may arise, among other causes, due to reverse causality (Wooldridge, 2020), as in the case where compensation may influence and be influenced by the variables analyzed. Endogeneity can bias the models, which is why GMM-Sys (Generalized Method of Moments) was adopted, a method considered suitable for mitigating this problem (Arellano & Bond, 1991; Blundell & Bond, 1998).

GMM-Sys is a dynamic panel model that addresses endogeneity by instrumenting the variables, thereby increasing the efficiency of the estimators (Blundell & Bond, 1998; Roodman, 2009). To ensure their robustness, first-order autocorrelation (AR(1)) and second-order autocorrelation (AR(2)) tests were conducted (Arellano & Bond, 1991; Blundell & Bond, 1998), as well as the Hansen test to verify the validity of the instruments (Roodman, 2009). To handle outliers, the data were winsorized at the 1st and 99th percentiles.

4. RESULTS AND DISCUSSIONS

4.1 Descriptive Statistics

Table 4 shows the descriptive statistics for the study variables.

Table 4
Descriptive statistics of the study variables

Panel A - CEO compensation
Variable	Mean	Standard deviation	Minimum	Maximum
RM (R$)	3,996,506.83	6,593,073.06	10,067.20	83,846,204.40
Ln_RM	14.3113	1.5205	9.8782	17.2660
RMED (R$)	2,221,737.45	3,019,722.31	7,500.00	40,061,820.52
Ln_RMED	13.9186	1.3580	9.9522	16.4558
RF (R$)	1,092,835.61	942,073.56	10,800.00	14,973,432.67
Ln_RF	13.6017	0.8660	9.2873	16.5218
RV (R$)	1,725,213.67	2,620,958.05	1,735.00	36,581,437.75
Ln_RV	13.5108	1.5094	8.5106	16.3764
Panel B - Characteristics of CEOs
Variable	Mean	Standard deviation	Minimum	Maximum
Age of the CEO	53.4864	9.5988	25.0000	91.0000
Dummy variables		Observations	Frequency
Business education		1,573	65,62%
Gender		57	2,38%
Duality		292	12,18%
Family CEO		603	25,16%
Panel C - Company characteristics
Variable	Mean	Standard deviation	Minimum	Maximum
Size	21.7816	1.8256	17.4057	25.9613
ROA	0.0507	0.1695	-0.5712	0.3971
TQ	0.7595	0.8970	0.0096	8.0244
Indebtedness	0.7273	0.5796	0.1011	3.3234
Age of the company	42.5428	27.4101	0.0000	131.0000
Dummy variables		Observations	Frequency
Control defined		1,649	68,79%
Pandemic		463	19,32%

• Source: Prepared by the authors.

Panel A details the total fixed and variable compensation of the CEOs. Compensation varies, as evidenced by the minimum and maximum values, as highlighted by researchers (Pereira et al., 2016; Oliveira et al., 2021). This variation can, for example, reflect factors such as the size and age of the company, since smaller and younger organizations tend to adjust compensation to their structure. In terms of total compensation (RM), CEOs received an average annual compensation of R$4 million, with the maximum amount recorded being R$83.85 million. These significant figures give rise to debate about the motivations behind these compensations (Hendriks et al., 2023). The reasons for this, although controversial, highlight the importance of executives in defining salary packages. It can also be seen that the CEOs in the sample have received higher variable compensation (R$1.73 million), compared to fixed compensation (R$1.09 million), as also highlighted by Rissatti et al. (2019). This reiterates the trend for companies to align executive compensation with corporate performance and results.

Panel B shows the characteristics of CEOs. Regarding age, the executives are, on average, 53.49 years old, suggesting a preference for more experienced executives. This finding suggests that companies appear to value the expertise accumulated in the professional careers of executives. However, the sample included CEOs aged between 25 and 91, reflecting the diversity of age groups, as also identified by Oliveira et al. (2021). Additionally, 65.62% of CEOs hold a business degree. This reveals that CEOs in Brazil tend to have an academic background to support decision-making, the formulation of financial policies, and the allocation of resources, thereby adding knowledge to the company (Custódio & Metzger, 2014; Gupta & Mahakud, 2020; Ghardallou et al., 2020).

Additionally, the position of CEO is predominantly held by men, with women accounting for 2.38% of the representation. By highlighting the low female presence in the position of CEO in Brazil, the results suggest the prevalence of cultural and gender barriers (Lam et al., 2013; Oliveira et al., 2021). As for duality, 12.18% of CEOs also hold the position of chairman of the board of directors. This percentage is significant and can affect the independence of the board, transparency, and the balance of decisions. There is also a significant percentage of family CEOs: approximately 25.16% of CEOs are founders, heirs, or members of the founding family. This reveals the strong presence of family CEOs in Brazil, sparking debate about their commitment to the business or pursuit of personal advantages (Sánchez-Marín et al., 2020).

Panel C deals with the characteristics of companies. In terms of size, the companies display a certain homogeneity, with an average of 21.78. Performance, represented by ROA and TQ, had averages of 0.051 and 0.760, respectively. Regarding indebtedness, the companies exhibited variations, with an average of 0.727. In terms of age, there are both young and long-standing companies, with an average age of 42.54 years. It can also be seen that 68.79% have controlling shareholders, showing a concentration of shareholdings. Finally, 19.32% of the observations correspond to the pandemic period.

Before the multivariate analyses, Pearson's correlation and Variance Inflation Factor (VIF) tests were carried out. According to Table 5, none of the independent variables has a correlation above 0.70. In addition, the VIF values were below 2, indicating no multicollinearity problems (Fávero & Belfiore, 2017).

Table 5
Variable correlation matrix

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1)Ln_RM	1.000
(2)Ln_RMED	0.957	1.000
(3)FA	0.053	0.044	1.000
(4)AGE	-0.081	-0.085	-0.190	1.000
(5)GEN	-0.082	-0.070	-0.031	-0.070	1.000
(6)DUA	-0.139	-0.133	-0.029	0.200	0.034	1.000
(7)CEOFAM	-0.046	-0.047	-0.109	-0.013	0.029	0.169	1.000
(8)SIZE	0.580	0.578	-0.016	-0.044	0.022	-0.159	-0.181	1.000
(9)ROA	0.141	0.126	-0.019	0.079	0.042	0.048	0.059	0.166	1.000
(10)TQ	0.067	0.049	-0.103	-0.036	-0.015	-0.019	0.046	-0.035	0.250	1.000
(11)IND	-0.176	-0.168	0.134	-0.008	0.042	0.104	-0.080	-0.200	-0.306	-0.248	1.000
(12)AGECOMP	-0.180	-0.177	0.084	0.219	0.045	0.160	-0.057	-0.222	0.037	-0.088	0.137	1.000
(13)CONT	-0.195	-0.187	-0.014	0.032	0.064	0.055	0.002	0.035	0.107	-0.013	0.044	-0.018	1.000
(14)PAN	0.108	0.099	0.067	-0.041	0.000	-0.043	-0.030	0.102	0.040	0.068	0.048	-0.006	0.013	1.000

• Note: Values in bold are significant up to 5%.
• Source: Prepared by the authors.

Table 5 shows that the characteristics of CEOs influence executive compensation. The CEO's educational background in the business area positively influences compensation; therefore, the importance of this background may translate into better salaries. However, age, gender, duality, and family ties are associated with lower pay for CEOs. Company size and performance have a positive and significant relation with salaries, indicating that larger companies with higher performance pay higher salaries. The pandemic has had a positive influence on compensation, suggesting that even in times of crisis, compensation has increased, reiterating the existence of subjectivity in salary definitions.

4.2 Inferential Statistics

The GMM-Sys adjustment tests are shown in tables 6 and 7. The results of the AR(2) test were not significant; therefore, the results indicate that there is no second-order autocorrelation, indicating that the models are well specified. The Hansen test was also not significant, indicating that there is no correlation between the instruments and the regression residuals, which highlights the validity of the instruments created.

According to the results in tables 6 and 7, current compensation is strongly influenced by compensation in the previous period, as evidenced by the positive and significant relation between lagged variables in all models at the 1% level. These findings suggest that CEOs' past salaries serve as a basis for defining current compensation, reflecting the criteria used in compensation definitions, which are considered complex by the literature (Murphy, 1999; Frydman & Jenter, 2010).

Table 6
GMM-Sys result for total compensation

Variables	RM						RMED
	Hypothesis 1	Hypothesis 2	Hypothesis 3	Hypothesis 4	Hypothesis 5	General model	Hypothesis 1	Hypothesis 2	Hypothesis 3	Hypothesis 4	Hypothesis 5	General model
	(1)	(2)	(3)	(4)	(5)	(6)	(1)	(2)	(3)	(4)	(5)	(6)
RM	0.620***	0.623***	0.628***	0.623***	0.624***	0.623***	0.541***	0.537***	0.543***	0.540***	0.541***	0.540***
RMED t-1	(9.988)	(10.081)	(10.268)	(10.106)	(10.108)	(10.056)	(7.158)	(7.059)	(7.241)	(7.214)	(7.172)	(7.110)
FA	0.072					0.067	0,090*					0,083*
	(1,465)					(1,333)	(1,938)					(1,742)
AGE		-0.001				-0.000		-0.003				-0.002
		(-0,560)				(-0,204)		(-1,245)				(-0,763)
GEN			-0.154			-0.149			-0.050			-0.060
			(-1,161)			(-1,096)			(-0,427)			(-0,519)
DUA				-0.073		-0.070				-0.064		-0.056
				(-0,954)		(-0,929)				(-0,905)		(-0,829)
CEOFAM					0.024	0.045					0.024	0.044
					(0,376)	(0,703)					(0,367)	(0,673)
SIZE	0,215***	0,212***	0,210***	0,211***	0,212***	0,213***	0,233***	0,234***	0,230***	0,232***	0,233***	0,234***
	(5,492)	(5,444)	(5,485)	(5,443)	(5,466)	(5,533)	(5.729)	(5,757)	(5,724)	(5,743)	(5,775)	(5,776)
ROA	0,328*	0,353*	0,362*	0,371**	0,346*	0,347*	0.242	0.271	0.276	0.282	0.262	0.259
	(1,718)	(1,865)	(1,957)	(1,965)	(1,828)	(1,853)	(1,329)	(1,502)	(1,560)	(1,578)	(1,454)	(1,446)
TQ	0,060*	0,058*	0,058*	0,059*	0,059*	0,061**	0.056	0.052	0.053	0.053	0.054	0.056
	(1,920)	(1,852)	(1,844)	(1,869)	(1,899)	(2,018)	(1,520)	(1,434)	(1,452)	(1,446)	(1,487)	(1,577)
IND	-0.028	-0.019	-0.016	-0.014	-0.017	-0.017	-0.028	-0.018	-0.014	-0.014	-0.015	-0.020
	(-0.468)	(-0,318)	(-0,282)	(-0.242)	(-0,291)	(-0,303)	(-0,459)	(-0,300)	(-0,228)	(-0,227)	(-0,251)	(-0,325)
AGECOMP	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001
	(-1,396)	(-1,270)	(-1,263)	(-1,223)	(-1,305)	(-1,138)	(-1,303)	(-1,096)	(-1,206)	(-1,115)	(-1,191)	(-1,030)
CONT	-0,193***	-0,196***	-0,193***	-0,191***	-0,198***	-0,188***	-0,187***	-0,194***	-0,192***	-0,188***	-0,194***	-0,188***
	(-3,075)	(-3,107)	(-3,097)	(-3,041)	(-3,096)	(-3,018)	(-2,731)	(-2,812)	(-2,786)	(-2,740)	(-2,800)	(-2,718)
PAN	0,125**	0.112	0,133**	0,126**	0,152*	0.102	0.001	0.013	0.016	0.010	0.017	0.110
	(1,990)	(1,342)	(2,139)	(1,980)	(1,809)	(1,200)	(0,019)	(0,208)	(0,256)	(0,148)	(0,266)	(1,572)
Time	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Sector	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	0.783	0.860	0.807	0.851	0.785	0.778	1,544***	1,723***	1,579***	1,600***	1,542**	1,596***
	(1,371)	(1,495)	(1,427)	(1,481)	(1,371)	(1,363)	(2,614)	(2,841)	(2,663)	(2,704)	(2,559)	(2,601)
Observations	2,124	2,124	2,124	2,124	2,124	2,124	2,124	2,124	2,124	2,124	2,124	2,124
Companies	272	272	272	272	272	272	272	272	272	272	272	272
Instruments	39	39	39	39	39	43	39	39	39	39	39	43
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-6,998***	-6,997***	-7,004***	-7,021***	-7,015***	-7,018***	-7,352***	-7,304***	-7,339***	-7,343***	-7,340***	-7,348***
AR (2)	0.416	0.426	0.415	0.423	0.431	0.402	0.245	0.253	0.258	0.248	0.261	0.234
Hansen	0.391	0.343	0.312	0.332	0.328	0.362	0.215	0.181	0.160	0.170	0.261	0.213

• Note: RM = highest compensation of the statutory executive board; RMED = average compensation of the statutory board.
• *Significant at the 10% level; **Significant at the 5% level; *** Significant at the 1% level.
• Source: Prepared by the authors.

Table 6 illustrates the impact of CEO characteristics on total compensation. The results indicate that the CEOs' educational background in business has a positive influence on their total compensation, supporting the non-rejection of H1. This result suggests that companies seem to recognize the importance of the CEO's educational background as a differentiator, translating this recognition into higher total salaries. Although there are no laws or rules requiring CEOs to have an educational background, it has been observed that executives who reach this level typically hold a degree (Kaur & Singh, 2019). This result does not exclude the possibility that factors such as practical experience and networking may be decisive in promotion to the position.

As highlighted in the descriptive stage, more than half of the CEOs investigated have a degree in administration, accounting, finance, or economics. According to UET, educational background is an indicator of more strategic and refined decisions, which contribute to corporate results (Hambrick & Mason, 1984). This business-aligned education enables managers to apply theoretical knowledge in decision-making, improving management efficiency and enhancing business performance (Liu et al., 2018; Gupta & Mahakud, 2020; Ghardallou et al., 2020). Thus, by adding knowledge to the business, CEOs with higher academic qualifications tend to receive higher compensation (Malik et al., 2020; Jaggia & Thosar, 2020; Choi et al., 2022).

In Brazil, studies have yielded mixed results, with some suggesting that CEO education increases compensation, while others fail to identify a relation (Pereira et al., 2016; Rissatti et al., 2019; Machado et al., 2023). However, the findings indicate that the CEO's educational background is a determinant of executive compensation. This result provides relevant empirical evidence for Hambrick's (2007) claim that there is a combined effect between the educational background and compensation of CEOs in the Brazilian environment.

Additionally, as shown in Table 6, the variables age, gender, duality, and family CEO did not exhibit statistical significance in relation to total earnings. The results corroborate the literature, which highlights the influence of subjectivity on the criteria used to determine compensation (Finkelstein & Hambrick, 1988; Davis et al., 2013). In addition, they suggest that other characteristics and factors may be considered in salary definitions. Furthermore, in emerging scenarios such as Brazil's, financial restrictions can limit companies' ability to offer higher total compensation, even when executives have attributes and skills that are considered relevant.

Table 7 presents the impact of CEO characteristics on fixed and variable compensation. Fixed compensation has a positive and significant relation with the age of the CEO, which supports the non-rejection of H2. This finding suggests that older CEOs receive higher fixed salaries than their younger counterparts. Because they have professional experience, older CEOs accumulate expertise, incorporating methods and techniques acquired throughout their careers into their businesses, which justifies higher compensation (Custódio & Metzger, 2014; Pereira et al., 2016; Rahman & Mustafa, 2018; Conyon et al., 2019; Machado et al., 2023). In the Brazilian context, the results indicate that companies recognize this differential and reflect it in higher fixed compensation. The findings align with UET and underscore the significant role of the CEO's age in decision-making (Hambrick & Mason, 1984).

Regarding variable compensation, as shown in Table 7, the duality exhibits a positive and significant relation, supporting the non-rejection of H4. Thus, CEOs who hold the position of chairman of the board of directors receive higher variable compensation. This may indicate that, because they perform more roles, executives are paid higher compensation. Studies suggest that this accumulation of roles reflects a greater commitment to the business and influence over compensation policies (Conyon et al., 2019; Kaur & Singh, 2019; Machado et al., 2023; Dutra & Ceretta, 2023). The results differ from those of Rissatti et al. (2019) in Brazil, but highlight two points. Firstly, they show the CEO's influence on the company; secondly, by increasing responsibility for decisions and demanding dedication, they justify receiving greater incentives through variable compensation.

Additionally, regarding variable compensation, the results reveal a negative and significant relation with the CEO's family ties, supporting the rejection of H5. The results suggest that family CEOs receive lower variable compensation. These findings align with the existing literature, which highlights that founding CEOs or heirs receive lower total and variable compensation. However, the reasons for this are diverse and subjective (McConaughy, 2000; Graziano & Rondi, 2021). In the Brazilian context, a plausible explanation is that these family CEOs are focused on driving the business forward, seeking to maximize their earnings as owners (through dividends and share appreciation), rather than maximizing their own compensation.

One point to note is that the gender of the CEO was not statistically significant with any of the forms of compensation, suggesting that gender may not be relevant when defining the total, fixed, and variable compensation of CEOs. However, this is a sensitive issue, as the descriptive stage revealed a predominance of male CEOs in Brazil. This finding is supported in the literature (Lam et al., 2013; Leszczynska & Chandon, 2019), but it differs from the observations made by Oliveira et al. (2021). Thus, the low presence of female CEOs may reflect the existence of barriers to female ascension to leadership positions, making it difficult to draw robust statistical inferences on this issue.

Table 7
GMM-Sys result for fixed and variable compensation

Variables	RF						RV
	Hypothesis 1	Hypothesis 2	Hypothesis 3	Hypothesis 4	Hypothesis 5	General model	Hypothesis 1	Hypothesis 2	Hypothesis 3	Hypothesis 4	Hypothesis 5	General model
	(1)	(2)	(3)	(4)	(5)	(6)	(1)	(2)	(3)	(4)	(5)	(6)
RF/RV t-1	0.520***	0.516***	0.519***	0.523***	0.520***	0.515***	0.330***	0.326***	0.326***	0.322***	0.326***	0.327***
	(8.766)	(8.812)	(8.664)	(8.754)	(8.772)	(8.621)	(5.476)	(5.413)	(5.428)	(5.400)	(5.461)	(5.512)
FA	0,019					0,031	0,098					0,086
	(0,707)					(1,087)	(1,411)					(1,202)
AGE		0,003**				0,003**		-0,001				0,000
		(2,219)				(2,287)		(-0,242)				(0,046)
GEN			-0,049			-0,032			0,044			0,048
			(-0,546)			(-0,362)			(0,187)			(0,201)
DUA				0,022		0,012				0,179*		0,179*
				(0,695)		(0,346)				(1,756)		(1,756)
CEOFAM					0,001	-0,001					-0,231**	-0,237**
					(0,042)	(-0,020)					(-2,216)	(-2,200)
SIZE	0,124***	0,126***	0,123***	0,123***	0,123***	0,126***	0,256***	0,257***	0,256***	0,259***	0,252***	0,257***
	(5,979)	(6,156)	(5,852)	(5,961)	(5,979)	(5,998)	(5,729)	(5,742)	(5,731)	(5,849)	(5,799)	(5,967)
ROA	-0,049	-0,052	-0,038	-0,049	-0,044	-0,051	1,109***	1,160***	1,163***	1,156***	1,233***	1,174***
	(-0,367)	(-0,398)	(-0,292)	(-0,376)	(-0,336)	(-0,395)	(2,861)	(3,039)	(3,050)	(3,025)	(3,251)	(3,038)
TQ	0,021	0,021	0,020	0,020	0,020	0,022	0,195***	0,189***	0,189***	0,192***	0,183***	0,192***
	(0,979)	(0,996)	(0,945)	(0,944)	(0,966)	(1,009)	(3,458)	(3,410)	(3,408)	(3,474)	(3,321)	(3,454)
IND	-0,027	-0,023	-0,023	-0,027	-0,024	-0,025	0,296*	0,315*	0,315*	0,323*	0,298*	0,291*
	(-0,622)	(-0,548)	(-0,542)	(-0,625)	(-0,572)	(-0,576)	(1,651)	(1,757)	(1,755)	(1,791)	(1,682)	(1,657)
AGECOMP	-0,001	-0,001*	-0,001	-0,001	-0,001	-0,001*	-0,008***	-0,008***	-0,008***	-0,008***	-0,008***	-0,008***
	(-1,489)	(-1,703)	(-1,427)	(-1,498)	(-1,442)	(-1,790)	(-3,923)	(-3,896)	(-3,935)	(-4,033)	(-4,060)	(-4,287)
CONT	-0,020	-0,023	-0,022	-0,022	-0,021	-0,024	-0,211**	-0,220**	-0,222***	-0,229***	-0,204**	-0,206**
	(-0,468)	(-0,528)	(-0,501)	(-0,505)	(-0,488)	(-0,545)	(-2,425)	(-2,551)	(-2,582)	(-2,651)	(-2,376)	(-2,390)
PAN	0,015	0,017	0,016	0,016	0,016	0,015	0,644***	0,451***	0,453***	0,469***	0,651***	0,450***
	(0,573)	(0,660)	(0,608)	(0,626)	(0,601)	(0,586)	(5,825)	(4,018)	(4,027)	(4,176)	(5,916)	(3,989)
Time	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Sector	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constant	4,025***	3,889***	4,043***	3,995***	4,026***	3,886***	3,305***	3,435***	3,398***	3,335***	3,496***	3,293***
	(6.790)	(6.546)	(6.862)	(6.691)	(6.676)	(6.416)	(3.059)	(3.185)	(3.172)	(3.088)	(3.244)	(3.010)
Observations	2,114	2,114	2,114	2,114	2,114	2,114	1,501	1,501	1,501	1,501	1,501	1,501
Companies	272	272	272	272	272	272	234	234	234	234	234	234
Instruments	38	38	38	38	38	42	39	39	39	39	39	43
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-5,126***	-5,127***	-5,135***	-5,144***	-5,139***	-5,116***	-6,497***	-6,472***	-6,468***	-6,460***	-6,512***	-6,508***
AR (2)	0.272	0.269	0.272	0.271	0.272	0.270	0.209	0.223	0.222	0.234	0.208	0.210
Hansen	0.199	0.188	0.193	0.201	0.198	0.185	0.321	0.291	0.289	0.276	0.306	0.319

• Note: RF = average fixed compensation of the statutory executive board; RV = average variable compensation of the statutory executive board.
• * Significant at the 10% level; **Significant at the 5% level; *** Significant at the 1% level.
• Source: Prepared by the authors.

The results in tables 6 and 7 show that specific characteristics of CEOs influence executive compensation. Educational background in the business area is a determinant of total compensation, the age of the CEO has an impact on fixed compensation, and duality is relevant in determining variable compensation. However, some of the characteristics analyzed had no impact on certain types of compensation. The diversity of existing executive attributes underscores the possibility that companies may prioritize specific attributes over valuing multiple characteristics when determining salary packages.

According to tables 6 and 7, company size is positively related to all compensation variables, suggesting that larger companies offer higher total, fixed, and variable salaries, as highlighted by national studies (Aguiar & Pimentel, 2017; Oliveira et al., 2021; Machado et al., 2023). Compensation tends to be proportional to the size of the company, reflecting the managerial complexity required of CEOs (Lam et al., 2013; Bussin & Ncube, 2017; Zhou et al., 2021). Furthermore, the results indicate that better-performing companies pay higher total and variable compensation to CEOs, corroborating national (Aguiar & Pimentel, 2017; Souza et al., 2017; Machado et al., 2023) and international (Devers et al., 2007; Conyon et al., 2019; Choi et al., 2022) studies, and consistent with the fact that variable compensation is linked to performance (Jensen & Murphy, 1990).

Regarding indebtedness, according to table 7, companies with indebtedness pay higher variable compensation to CEOs, possibly as a means to align CEOs' interests with the need to improve financial results (Conyon et al., 2019; Choi et al., 2022; Machado et al., 2023). Regarding the age of the company, older companies typically offer lower fixed and variable compensation. According to Abed et al. (2014), younger companies invest more in the qualifications and experience of executives, resulting in higher salaries for managers. Thus, more mature companies tend to pay lower salaries due to greater stability and less need for change.

Defined control showed a negative relation with total and variable compensation, as shown in tables 6 and 7. This result is consistent with national studies, which highlight that executive compensation is lower in companies with controlling shareholders (Pinto & Leal, 2013; Freitas et al., 2020). There are various reasons for this, including the fact that the controlling shareholder has a majority stake in decisions and can influence compensation. The results in tables 6 and 7 indicate that in the pandemic period, CEOs' total and variable salaries were higher. In this case, the compensation may have served as an incentive for managers to develop quick strategies and run the business with greater dedication.

4.3 Additional Analyses

In addition to GMM-Sys, we estimated regressions with fixed effects panels. Generally, the results are similar, but there are some notable differences. Educational background in business is no longer significant for total compensation, suggesting that training does not influence compensation. Gender becomes significant at 10%, with a negative sign for total compensation, indicating a prevalence of lower salaries for female CEOs. However, as only 2.38% of the sample is female, it is not possible to draw more robust conclusions. The “family CEO” variable becomes significant for fixed compensation but ceases to be so for variable compensation, confirming the argument that commitment to the company extends beyond financial incentives.

The fact that the lagged dependent variable is highly significant in GMM-Sys, together with the use of instrumental variables, may explain these differences. Thus, the additional analyses reveal that using methods other than GMM-Sys when studying executive compensation can generate biased results due to the endogeneity problem.

5. CONCLUSIONS

The UET posits that the individual characteristics of executives can interfere with how they process and use information about their roles, companies, and environments, impacting decision-making and reflecting on corporate results (Hambrick & Mason, 1984). UET also points out that there is a combined effect between these characteristics and managers' compensation to be investigated, since executives can shape their behavior according to the compensation they receive (Hambrick, 2007). Given this, the study sought to investigate the relation between the characteristics of CEOs and executive compensation in Brazilian companies listed on the stock exchange.

The salaries paid to executives vary significantly. These findings align with studies that have highlighted the growth in executive salaries, fueling discussions in the media and academia about the composition of compensation packages (Murphy, 1999; Oliveira et al., 2021; Hendriks et al., 2023). In addition, the results revealed that, in Brazil, the CEOs of listed companies have a remarkable educational background aligned with business, which adds useful knowledge, methods, and techniques to processes, decision-making, and the formulation of corporate strategies.

The results suggest that the CEO's educational background in business, age, and duality are related to higher executive compensation, highlighting these characteristics as determinants of compensation and providing empirical implications for Hambrick's (2007) findings. In this sense, companies appear to substantially reward CEOs who possess knowledge from their educational background in business, who are more mature, and who also chair the board of directors. On the other hand, family CEOs receive lower variable compensation, prompting discussion. Furthermore, the results do not indicate gender as a relevant factor in determining salaries.

Regarding limitations, the problem of endogeneity stands out, which, although controlled by GMM-Sys, remains a significant issue in finance and financial accounting research. Furthermore, the characteristics investigated, although supported by the literature, do not exhaust the discussions, as there is a wide range of attributes, many of which are difficult to measure. Furthermore, executive compensation was restricted to the data available in the Reference Forms. Future studies could explore other characteristics and deepen discussions among sectors, as this study does, to help clarify the relation between characteristics and executive compensation in Brazil. Furthermore, research could compare the compensation of Brazilian executives with that of executives in other emerging economies, thereby contributing to the literature on these markets.

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• 4
  This is a bilingual text. This article was originally written in Portuguese, published under the DOI https://doi.org/10.1590/1808-057x20252189.pt.
• 5
  This article stems from a Master's Thesis defended by the author, Francisco Gleisson Paiva Azevedo, in 2024.
• 6
  Paper presented at the XXIV Encontro Brasileiro de Finanças (Brazilian Finance Meeting), Curitiba, PR, Brazil, July 2024.
• DECLARATION OF DATA AVAILABILITY
  The datasets related to this article will be available upon request to the corresponding author.
• FUNDING
  The authors would like to thank the Coordination for the Improvement of Higher Education Personnel (Capes) for its financial support in carrying out this research.

Edited by

• Academic Editor-in-Chief:
  Andson Braga de Aguiar

• Associate Editor:
  Andrea Maria Accioly Fonseca Minardi

Data availability

The datasets related to this article will be available upon request to the corresponding author.

Publication Dates

• Publication in this collection
  01 Dec 2025
• Date of issue
  2025

History

• Received
  01 Aug 2024
• Reviewed
  28 Aug 2024
• Accepted
  15 Apr 2025