Do financially sophisticated CEOs receive higher compensation? Evidence from Brazil

ABSTRACT

This study examines the relationship between the financial sophistication characteristics of Chief Executive Officers (CEOs) and executive compensation through an analysis of 272 publicly traded Brazilian companies from 2010 to 2022. The models were estimated using the Generalized Method of Moments (GMM-Sys). The CEOs investigated possessed substantial academic qualifications and professional experience to support decision-making. Academic backgrounds in business and international education have been increasingly sought after and have led to higher compensation, establishing themselves as determinants of remuneration. We identified that companies have been offering higher compensation to CEOs wwho have experience in their sector, experience in the financial sector, and have held previous financial roles. The findings highlight important questions regarding financial constraints that limit the ability to hire highly qualified CEOs due to challenges in providing adequate compensation. This study contributes to both companies and the literature on national and emerging markets by offering insights into how CEOs are compensated and identifying the characteristics that influence compensation variations, addressing areas that remain underexplored in Brazil.

KEYWORDS:
Executive compensation; CEO characteristics; Financial sophistication; Academic qualifications; Professional experience

RESUMO

O estudo analisa a relação entre as características de sofisticação financeira dos Chief executive officers (CEOs) e remunerações executivas por meio de análise com 272 empresas brasileiras de capital aberto de 2010 a 2022. Os modelos foram estimados pelo Generalized Method of Moments (GMM-Sys). Os CEOs investigados possuem considerável formação acadêmica e experiência profissional para subsidiar a tomada de decisão. As características de formação acadêmica nos negócios e formação internacional têm sido requeridas e proporcionado melhores remunerações, constituindo-se como determinantes das remunerações. Identificamos que as empresas têm remunerado mais expressivamente CEOs com experiências no setor de atuação, setor financeiro e em cargos financeiros. Os achados suscitam questões pertinentes sobre restrições financeiras que dificultam a contratação de CEOs com muitas características dada a dificuldade de remunerá-los adequadamente. O estudo contribui com empresas e literatura nacional e de países emergentes, evidenciando aspectos sobre como os CEOs são remunerados e que características têm contribuído para a variação dos salários, conduzindo a constatações importantes e que ainda são incipientes no Brasil.

PALAVRAS-CHAVE:
Remuneração executiva; Características dos CEOs; Sofisticação financeira; Formação acadêmica; Experiência profissional

1. INTRODUCTION

The Chief Executive Officer (CEO) holds the highest position in an organization's hierarchy and is the primary decision-maker. Studies have focused on these executives, hypothesizing that their characteristics influence how they process and use information (Kaplan et al., 2012; Shen, 2021). In this context, the Upper Echelons Theory (UET) by Hambrick and Mason (1984) posits that the observable characteristics of executives can influence corporate performance and outcomes. According to UET, factors such as age, professional experience, and education can shape managers' decision-making behavior. This concept has supported studies seeking to understand the impact of executives on organizations (Conyon et al., 2019; Main & Silva, 2021; Setiawan & Gestanti, 2022).

Hambrick (2007), in an update to UET, highlighted the existence of a relationship between executive characteristics and compensation, as behaviors tend to shift based on the remuneration received. Subsequently, studies emerged exploring this relationship, indicating that much remains to be investigated. The relationship investigated varies across individuals, companies, sectors, countries, and compensation plans, with research predominantly focused on developed economies (Conyon et al., 2019; Malik et al., 2020; Schmid & Baldermann, 2021; Jaggia & Thosar, 2022).

Executive compensation is a controversial topic, as both media and academia have concentrated on the debate surrounding high compensations (Devers et al., 2007; Hendriks et al., 2023). Moreover, the relationship between optimal hiring and remuneration is not straightforward, and the effects of CEO compensation on performance are unclear and heterogeneous among corporations, contributing to the lack of consensus regarding its determinants (Frydman & Jenter, 2010). According to Murphy (1999), factors such as professional experience and education could be considered in defining executive compensations. Thus, compensation would reflect managers' attributes.

Companies tend to seek talented leaders; therefore, a CEO with an academic background and professional expertise would be deemed competent for strategic decision-making, becoming financially sophisticated (Custódio & Metzger, 2014; Bortoli & Soares, 2021). According to Bortoli and Soares (2021), financial sophistication, defined as a cluster of knowledge stemming from academic training and professional experience related to business, can contribute to a CEO's decision-making.

The CEO's academic background has been recognized as a key factor in determining compensation, as it enhances their expertise in business management (Custódio et al., 2013; Gupta & Mahakud, 2020). Professional experience has also been discussed in the executive compensation literature. Custódio et al. (2013) and Conyon et al. (2019) highlight that CEOs who develop skills throughout their professional careers tend to receive higher compensation. Consequently, the financial sophistication characteristics of CEOs represent potentially valuable assets for companies and may impact their remuneration. In light of the above, this study seeks to examine the relationship between CEOs' financial sophistication characteristics and executive compensation.

This study addresses Hambrick's (2007) call, contributing to the theoretical framework and existing studies that have explored this subject. It contributes to the growing body of literature exploring executive characteristics by incorporating financial sophistication into these discussions and linking it to CEO compensation. The research sheds light on potential determinants of CEO compensation and helps to understand whether the investigated characteristics are valued and how they are being rewarded.

This relationship has been more prominently examined in studies on developed economies, where greater transparency facilitates such analyses (Conyon et al., 2019; Schmid & Baldermann, 2021; Jaggia & Thosar, 2022). To address this gap, this study seeks to enrich the literature on emerging markets, particularly in the Brazilian context. In Brazil, studies on this relationship are relatively recent, likely because information on executive compensation only became available following the enactment of CVM Normative Instruction No. 480 in 2009. This regulation also disclosed curriculum details of CEOs in publicly listed companies (Pereira et al., 2016; Oliveira et al., 2021). As such, this paper contributes to the literature on Brazil, which remains underdeveloped (Oliveira et al., 2021; Dutra & Ceretta, 2023).

2. LITERATURE REVIEW

2.1 Financial sophistication charecteristics

CEOs possess valuable characteristics for businesses that can render them financially sophisticated (Custódio & Metzger, 2014). Bortoli and Soares (2021) define the financial sophistication characteristics of CEOs through the construct of "Financial Sophistication," which the authors describe as a combination of knowledge stemming from academic qualifications and professional experience in finance.

Academic qualifications are a key characteristic, as highly educated executives are better equipped to process complex information and act as valuable resources for their companies (Saidu, 2019; Shen, 2021). Such business-focused education provides concrete advantages, offering CEOs keen insight into financial policy decisions (Gupta & Mahakud, 2020). According to Ghardallou et al. (2020), corporate performance improves when a CEO holds a background in management, finance, economics, or accounting. A CEO’s education aligned with business needs influences investments, R&D expenditures, and the company’s financial health (Naseem et al., 2020; Setiawan & Gestanti, 2022). Studies also emphasize the value of international education, highlighting its role in helping CEOs manage economic uncertainties and identify opportunities for foreign investment (Jiang & Liu, 2020; Sun et al., 2021).

Professional experience is another crucial factor examined in the literature, which suggests that more extensive experience enhances a CEO's ability to manage businesses effectively (Ghardallou et al., 2020; Sun et al., 2021). This experience can be gained in various ways: experience as a CEO allows executives to understand the company better and contribute to strategic changes (Naseem et al., 2020; Darouichi et al., 2021), sector-specific experience equips CEOs with acumen to handle competition and identify investment opportunities (Ma et al., 2021; Agnihotri & Bhattacharya, 2021), andexperience in the financial sector enhances the ability to analyze banking information and improves the quality of information disclosure (Gounopoulos & Pham, 2018; Gupta & Mahakud, 2020; Shahab et al., 2020). Financial experience in roles such as CFO, accountant, or controller prepares CEOs for strategic decisions, including debt management and resource acquisition (Serra et al., 2016; Gounopoulos & Pham, 2018; Oradi et al., 2020; Li et al., 2023). International experience broadens strategic global perspectives and fosters sustainable innovation (Jiang & Liu, 2020; Schmid & Baldermann, 2021).

2.2 The influence of CEO characteristic on executive compensation

A CEO’s educational background is a significant determinant of compensation (Banghøj et al., 2010; Cole & Mehran, 2016). CEOs in possession of a business-aligned education demonstrate enhanced skills, and their compensation often serves as an incentive for taking calculated risks (King et al., 2016; Jaggia & Thosar, 2022). According to Malik et al. (2020), well-compensated CEOs play a pivotal role in advancing Corporate Social Responsibility initiatives. Choi et al. (2022) argue that compensation acts as a moderating factor between a CEO's education level and corporate value. In Brazil, studies examining the relationship between CEOs' academic backgrounds and their compensation are relatively recent and have yielded mixed results. Some studies have identified positive relationships (Pereira et al., 2016), while others have reported negative associations (Machado et al., 2023) or no significant relationship (Rissatti et al., 2019; Dutra & Ceretta, 2023).

Research has also delved into the relationship between CEOs' professional experience and their compensation. Findings suggest that executives with professional experience, including international exposure, tend to receive higher compensation packages (Custódio et al., 2013; Conyon et al., 2019; Schmid & Baldermann, 2021). Additionally, Jbir et al. (2021) noted that compensation levels help control opportunistic behavior. In the Brazilian context, studies examining the relationship between compensation and CEO experience have yielded divergent results. Pereira et al. (2016) and Machado et al. (2023) found that tenure and international experience are associated with higher compensation. However, Dutra and Ceretta (2023) argue that CEOs' professional experience does not appear to significantly influence salary determinations.

Considering that CEOs with academic qualifications and professional experience are financially sophisticated, contributing valuable knowledge and skills to their companies, they are likely to receive higher compensation. Based on this premise, the following hypotheses are proposed

H1: The financial sophistication characteristics of CEOs are positively and significantly associated with their compensation in Brazil.

H1a: A CEO's academic background is positively and significantly associated with their compensation in Brazil.

H1b: A CEO's professional experience is positively and significantly associated with their compensation in Brazil.

3. METHODOLOGY

3.1 Sample and data

The sample comprised 272 publicly traded Brazilian companies over the period from 2010 to 2022. Table 1 displays the sample distribution for the study. The selected timeframe is justified by the implementation of CVM Normative Instruction No. 480 in 2009, which standardized the disclosure of information in companies’ reference forms (CRF). This regulation facilitated the identification of CEOs' personal and professional data and mandated the disclosure of executive compensation details.

Table 1
Sample Distribution

Sector	Companies	Percentage ( %)	Observations
Industrial Goods	47	17.22 %	447
Communications	7	2.56 %	38
Cyclical Consumer Goods	81	29.78 %	737
Non-Cyclical Consumer Goods	26	9.52 %	197
Basic Materials	31	11.36 %	257
Oil, Gas, and Biofuels	10	3.66 %	94
Healthcare	20	7.33 %	162
Information Technology	12	4.40 %	56
Utilities	38	13.92 %	409
Total	272	100 %	2397

• Source: Prepared by the authors.

Data on the personal and financial sophistication characteristics of CEOs were manually collected from several sources, in the following order: (I) Companies' Reference Forms (CRF); (II) Company websites; (III) Bloomberg; (IV) LinkedIn; (V) Refinitiv Eikon^®; and (VI) News websites (G1, Veja, Valor, and Estadão). Executive compensation values were manually retrieved from the CRF in the section titled “13 - Compensation of Administrators.” Financial data for the control variables were sourced from the Refinitiv Eikon^® database.

3.2 Dependent variables

In Brazil, data on CEO compensation is not disclosed separately. Section 13 of the CRF provides information about the compensation of the Statutory Board, of which the CEO is a member. The CRF includes details on the number of board members, the components of the compensation packages, and the average, minimum, and maximum amounts paid to executives by governing body. Using this information, four proxies were adopted to represent CEO compensation, as shown in Table 2.

Table 2
Dependent Variables

Variable	Acronym	Measurement	Authors
Highest compensation of the Statutory Board	Ln_RM	Ln (Highest compensation of the Statutory Board)	Sprenger et al. (2017); Oliveira et al. (2021);
Average compensation of the Statutory Board	Ln_RMED	Ln (Total compensation of the Statutory Board / Number of members)	Aguiar & Pimentel (2017); Ramos et al. (2022); Dutra & Ceretta (2023); Machado et al. (2023).
Average fixed compensation of the Statutory Board	Ln_RF	Ln (Fixed compensation of the Statutory Board / Number of members)	Rissatti et al. (2019); Machado and Sonza (2023); Ramos et al. (2022).
Average variable compensation of the Statutory Board	Ln_RV	Ln (Variable compensation of the Statutory Board / Number of members)	Rissatti et al. (2019); Ramos et al. (2022); Machado et al. (2023).

• Source: Prepared by the authors.

Compensation data were adjusted for inflation using 2010 as the base year, referencing the IPCA index based on information from the IBGE website. This adjustment was made to ensure the comparability of values over time, enabling an analysis of the real evolution of compensation and its relationship with the explanatory variables.

3.3 Financial sophistication charecteristics

The variables that comprise the construct of Financial Sophistication proposed by Bortoli and Soares (2021) were utilized. While the authors introduced a metric called the Financial Sophistication Index, this study examines the characteristics in their raw form to analyze the relationships individually. As a result, the characteristics that define the financial sophistication of the CEO are evaluated separately, rather than being combined into a composite index. The definitions and measurements of these sophistication characteristics are grounded in the Upper Echelons Theory and prior studies, as detailed in Table 3 (Custódio et al., 2013; Custódio & Metzger, 2014; Gounopoulos & Pham, 2018; Gupta & Mahakud, 2020; Jiang & Liu, 2020; Oradi et al., 2020; Darouichi et al., 2021; Schmid & Baldermann, 2021; Sun et al., 2021; Ma et al., 2021; Bortoli & Soares, 2021; Ding et al., 2022; Li et al., 2023).

Table 3
Characteristics of Financial Sophistication

Variable	Acronym	Description/Measurement
Finance-related Education	FF	Dummy: 1 - The CEO holds an undergraduate or postgraduate degree in business administration, accounting, finance, or economics; 0 - Otherwise.
International Education	FI	Dummy: 1 - The CEO holds an undergraduate or postgraduate degree obtained abroad; 0 - Otherwise.
CEO Experience	EXP	Years of tenure as CEO in the current company.
Sector Experience	EXPS	Years of experience as CEO in the same sector.
Experience in the Financial Sector	EXPSF	Dummy: 1 - The CEO has experience working in companies within the financial sector; 0 - Otherwise.
Financial Experience	EXPF	Dummy: 1 - The CEO has experience in financial roles, such as CFO, auditor, accountant, or controller; 0 - Otherwise.
International Experience	EXPI	Dummy: 1 - The CEO has experience working in companies outside Brazil; 0 - Otherwise.

• Source: Prepared by the authors.

3.4 Control variables

Studies have identified that CEO age (Custódio & Metzger, 2014; Conyon et al., 2019), gender (Oliveira et al., 2021), duality (Dutra & Ceretta, 2023), family CEO status (Graziano & Rondi, 2021), company size (Aguiar & Pimentel, 2017; Zhou et al., 2021), corporate performance (Conyon et al., 2019), leverage (Choi et al., 2022; Machado et al., 2023), company age (Serra et al., 2016), and defined control (Freitas et al., 2020) are important factors in explaining compensation. Additionally, considering that the Covid-19 pandemic had significant impacts on businesses, a dichotomous variable for the period was included. Table 4 presents the control variables and their measurements.

Table 4
Control Variables

Variable	Acronym	Measurement	Sign	Authors
CEO Age	AGE	Age in years.	+	Custódio & Metzger (2014); Conyon et al. (2019)
CEO Gender	GEN	Dummy: 1 - Female, 0 - Male.	+/-	Lam et al. (2013); Oliveira et al., (2021).
Duality	DUA	Dummy: 1 - CEO is also the chairperson of the board, 0 - Otherwise.	+	Ding et al. (2022); Machado et al., (2023).
CEO familiar	CEOFAM	Dummy: CEO is the founder or part of the founding family, 0 - Otherwise.	+/-	Graziano and Rondi (2021).
Company Size	TAM	Natural logarithm of total assets.	+	Ghardallou et al. (2020); Zhou et al. (2021).
Corporate Performance	ROA	EBIT / Total assets.	+	Conyon et al. (2019); Zhou et al (2021); Choi et al (2022).
	Tobin's Q (QT)	Market value / Total assets.	+
Leverage	END	Total liabilities / Total assets.	+/-	Ding et al. (2022); Machado et al., (2023).
Company Age	IDAEMP	Company age in years.	+/-	Serra et al (2016); Setiawan & Gestanti (2022).
Defined Control	CDEF	Dummy: 1 - The company has a controlling shareholder, 0 - Otherwise.	-	Rissatti et al. (2019); Freitas et al. (2020).
Pandemic	PAN	Dummy: - Pandemic period (2020 and 2021), 0 - Otherwise.	-	Proposed in this study

• Source: Prepared by the authors.

3.5 Econometric procedures

This study recognizes the potential for endogeneity. Executive compensation can both influence and be influenced by the variables under analysis, as well as by the omission of certain variables, which could lead to challenges and compromise the robustness of the findings. To mitigate this problem, the Systemic GMM (Generalized Method of Moments) approach was employed, a method recommended for analyzing compensation and executive characteristics (Aguiar & Pimentel, 2017; Shahab et al., 2020; Oliveira et al., 2021; Zhou et al., 2021; Machado et al., 2023; Dutra & Ceretta, 2023).

GMM was developed by Arellano and Bover (1991) and Blundell and Bond (1998). It is a dynamic panel regression method that effectively addresses endogeneity by using the variables themselves as instruments, ensuring efficiency in the estimation of asymptotic parameters. To adjust and validate the models, first- and second-order autocorrelation tests, along with the Hansen test (Roodman, 2009), were performed. Additionally, to manage outliers, the data were winsorized at the 1st and 99th percentiles. Equations 1 and 2 present the models tested.

$${\text{RM}}_{i,t}\text{ou}{\text{RMED}}_{i,t}\text{ou}{\text{RF}}_{i,t}\text{ou}{\text{RV}}_{i,t}\text{= β}{}_{\text{0}}\text{+}\sum _{}^2{FA}_{i,t}\text{+}\mathrm{}\sum _{}^{11}{Controles}_{i,t}\text{+}\sum _{}^9{Setor}_{i,t}+\sum _{}^{13}{Ano}_{i,t}+{\epsilon }_{i,t}$$ (1)

$${\text{RM}}_{i,t}\text{ou}{\text{RMED}}_{i,t}\text{ou}{\text{RF}}_{i,t}\text{ou}{\text{RV}}_{i,t}\text{= β}{}_{\text{0}}\text{+}\sum _{}^5{EP}_{i,t}\text{+}\mathrm{}\sum _{}^{11}{Controles}_{i,t}\text{+}\sum _{}^9{Setor}_{i,t}+\sum _{}^{13}{Ano}_{i,t}+{\epsilon }_{i,t}$$ (2)

When:

RM_i,t = Highest compensation of the Statutory Board for company i in period t;

RMED_i,t = Average compensation of the Statutory Board for company i in period t;

RF_i,t = Fixed compensation of the Statutory Board for company i in period t;

RV_i,t = Variable compensation of the Statutory Board for company i in period t;

FA_i,t = Academic background of the CEO of company i in period t;

EP_i,t = Professional experience of the CEO of company i in period t.

4. RESULTS AND DISCUSSION

4.1 Descriptive analyses

Table 5 presents executive compensation in a general perspective and by economic sector, adjusted for inflation. As observed in other studies, compensation exhibited significant fluctuations in the Brazilian context during the period from 2010 to 2022 (Pereira et al., 2016; Oliveira et al., 2021).

Panel A: CEO Compensation Variables Obs. Means Standard Deviations Minimums Maximums RM 2397 2,505,137.57 3,876,748.66 7,160.77 56,110,254.73 RMED 2397 1,407,190.35 1,790,492.55 3,916.07 20746987.15 RF 2387 708,628.95 569,512.84 5,639.15 7,818,274.16 RV 1737 1,071,183.84 1,555,655.95 1,291.37 18,055,340.16 Panel B: CEO Compensation by Economic Sector RM Variables Obs. Means Standard Deviations Minimums Maximums Industrial Goods 447 1,964,220.10 2,164,526.44 9,246.87 14,821,771.35 Communications 38 5,778,158.93 9,608,945.19 177,946.36 56,110,254.73 Cyclical Consumer Goods 737 2,247,760.97 3,267,324.54 8,649.30 23,750,897.70 Non-Cyclical Consumer Goods 197 4,191,465.82 5,363,788.08 7,160.77 32,324,524.01 Basic Materials 257 3,088,499.96 4,555,402.09 7,625.59 38,052,084.93 Oil, Gas, and Biofuels 94 2,973,649.58 4,121,026.67 49,279.98 21,386,858.00 Healthcare 162 3,621,151.06 4,465,114.03 35,326.11 40,555,864.65 Information Technology 56 2,037,819.89 2,136,926.61 173,757.53 10,932,487.21 Utilities 409 1,591,461.58 2,603,371.31 12,438.52 27,527,328.60 RMED Sectors Obs. Means Standard Deviations Minimums Maximums Industrial Goods 447 1,156,145.95 1,016,365.11 13,145.54 5,851,755.99 Communications 38 3,351,616.36 4,513,598.22 199,874.76 20,746,987.15 Cyclical Consumer Goods 737 1,200,488.19 1,272,633.61 7,020.31 9,067,706.49 Non-Cyclical Consumer Goods 197 2,313,156.42 2,554,632.10 7,832.84 12,645,836.36 Basic Materials 257 1,731,082.22 2,289,147.02 3,916.07 17,344,644.62 Oil, Gas, and Biofuels 94 1,879,487.14 2,343,914.87 35,810.52 11,048,617.93 Healthcare 162 1,780,209.88 2,332,444.12 16,546.49 19,773,137.45 Information Technology 56 1,145,794.88 1,023,530.42 138,273.83 5,234,312.16 Utilities 409 1,012,974.45 1,316,176.50 13,158.69 12,806,345.44 RF Sectors Obs. Means Standard Deviations Minimums Maximums Industrial Goods 441 678,715.89 368,755.83 40,224.88 2,523,256.40 Communications 38 1,242,053.23 1,168,879.45 152,420.95 5,233,828.04 Cyclical Consumer Goods 733 657,545.55 446,945.02 5,639.15 4,752,439.74 Non-Cyclical Consumer Goods 197 990,069.91 989,427.15 7,832.84 7,818,274.16 Basic Materials 257 827,262.69 680,221.13 11,777.94 3,705,649.44 Oil, Gas, and Biofuels 94 812,157.04 661,718.61 62,188.24 5,210,578.37 Healthcare 162 819,907.63 660,706.77 23,363.60 4,533,526.49 Information Technology 56 585,502.35 208,396.80 159,196.82 1,358,493.91 Utilities 409 521,756.64 324,909.55 17,372.64 2,463,785.33 RV Sectors Obs. Means Standard Deviations Minimums Maximums Industrial Goods 310 882,420.90 801,173.35 1,526.09 5,153,699.22 Communications 25 2,907,723.36 3,204,175.11 33,151.50 12,459,910.11 Cyclical Consumer Goods 488 1,021,215.95 1,360,878.79 10,452.20 14,164,807.25 Non-Cyclical Consumer Goods 144 1,602,853.45 1,660,413.69 1,974.26 10,234,004.25 Basic Materials 190 1,430,320.10 2,127,251.62 2,669.90 14,012,404.46 Oil, Gas, and Biofuels 64 1,998,913.67 2,466,414.27 29,205.76 12,156,514.60 Healthcare 136 1,415,314.71 2,075,570.07 2,730.88 18,055,340.16 Information Technology 54 724,483.81 994,902.02 11,233.97 4,781,804.23 Utilities 326 472,215.54 843,702.92 1,291.37 10,783,212.21 Source: Prepared by the authors.

Panel A: CEO Compensation
Variables	Obs.	Means	Standard Deviations	Minimums	Maximums
RM	2397	2,505,137.57	3,876,748.66	7,160.77	56,110,254.73
RMED	2397	1,407,190.35	1,790,492.55	3,916.07	20746987.15
RF	2387	708,628.95	569,512.84	5,639.15	7,818,274.16
RV	1737	1,071,183.84	1,555,655.95	1,291.37	18,055,340.16
Panel B: CEO Compensation by Economic Sector
RM
Variables	Obs.	Means	Standard Deviations	Minimums	Maximums
Industrial Goods	447	1,964,220.10	2,164,526.44	9,246.87	14,821,771.35
Communications	38	5,778,158.93	9,608,945.19	177,946.36	56,110,254.73
Cyclical Consumer Goods	737	2,247,760.97	3,267,324.54	8,649.30	23,750,897.70
Non-Cyclical Consumer Goods	197	4,191,465.82	5,363,788.08	7,160.77	32,324,524.01
Basic Materials	257	3,088,499.96	4,555,402.09	7,625.59	38,052,084.93
Oil, Gas, and Biofuels	94	2,973,649.58	4,121,026.67	49,279.98	21,386,858.00
Healthcare	162	3,621,151.06	4,465,114.03	35,326.11	40,555,864.65
Information Technology	56	2,037,819.89	2,136,926.61	173,757.53	10,932,487.21
Utilities	409	1,591,461.58	2,603,371.31	12,438.52	27,527,328.60
RMED
Sectors	Obs.	Means	Standard Deviations	Minimums	Maximums
Industrial Goods	447	1,156,145.95	1,016,365.11	13,145.54	5,851,755.99
Communications	38	3,351,616.36	4,513,598.22	199,874.76	20,746,987.15
Cyclical Consumer Goods	737	1,200,488.19	1,272,633.61	7,020.31	9,067,706.49
Non-Cyclical Consumer Goods	197	2,313,156.42	2,554,632.10	7,832.84	12,645,836.36
Basic Materials	257	1,731,082.22	2,289,147.02	3,916.07	17,344,644.62
Oil, Gas, and Biofuels	94	1,879,487.14	2,343,914.87	35,810.52	11,048,617.93
Healthcare	162	1,780,209.88	2,332,444.12	16,546.49	19,773,137.45
Information Technology	56	1,145,794.88	1,023,530.42	138,273.83	5,234,312.16
Utilities	409	1,012,974.45	1,316,176.50	13,158.69	12,806,345.44
RF
Sectors	Obs.	Means	Standard Deviations	Minimums	Maximums
Industrial Goods	441	678,715.89	368,755.83	40,224.88	2,523,256.40
Communications	38	1,242,053.23	1,168,879.45	152,420.95	5,233,828.04
Cyclical Consumer Goods	733	657,545.55	446,945.02	5,639.15	4,752,439.74
Non-Cyclical Consumer Goods	197	990,069.91	989,427.15	7,832.84	7,818,274.16
Basic Materials	257	827,262.69	680,221.13	11,777.94	3,705,649.44
Oil, Gas, and Biofuels	94	812,157.04	661,718.61	62,188.24	5,210,578.37
Healthcare	162	819,907.63	660,706.77	23,363.60	4,533,526.49
Information Technology	56	585,502.35	208,396.80	159,196.82	1,358,493.91
Utilities	409	521,756.64	324,909.55	17,372.64	2,463,785.33
RV
Sectors	Obs.	Means	Standard Deviations	Minimums	Maximums
Industrial Goods	310	882,420.90	801,173.35	1,526.09	5,153,699.22
Communications	25	2,907,723.36	3,204,175.11	33,151.50	12,459,910.11
Cyclical Consumer Goods	488	1,021,215.95	1,360,878.79	10,452.20	14,164,807.25
Non-Cyclical Consumer Goods	144	1,602,853.45	1,660,413.69	1,974.26	10,234,004.25
Basic Materials	190	1,430,320.10	2,127,251.62	2,669.90	14,012,404.46
Oil, Gas, and Biofuels	64	1,998,913.67	2,466,414.27	29,205.76	12,156,514.60
Healthcare	136	1,415,314.71	2,075,570.07	2,730.88	18,055,340.16
Information Technology	54	724,483.81	994,902.02	11,233.97	4,781,804.23
Utilities	326	472,215.54	843,702.92	1,291.37	10,783,212.21

• Source: Prepared by the authors.

Regarding total compensation, according to Table 5, CEOs received average salaries of BRL 2,505,137.57 and BRL 1,407,190.35 (Panel A), with a maximum of BRL 56,110,254.73, approximately 22 times the average, indicating exceptionally high compensation levels. Among the sectors, the communications sector has the smallest number of companies but pays the highest compensation, as also noted by Oliveira et al. (2021). This finding aligns with the growth of the technology sectors in the contemporary market. Additionally, it is observed that variable compensation exceeds fixed compensation, a result aligned with prior studies (Rissatti et al., 2019). Variable compensation serves as an incentive for managers to apply their skills effectively in business management.

According to Figure 1, compensation levels rise and fall in synchrony, with the variable portion showing an upward trend over time. In recent years, significant salary growth has been observed, as highlighted in recent literature (Hendriks et al., 2023). In 2020 and the subsequent years, despite the widespread impact of the Covid-19 pandemic across all sectors of society, total and variable compensation levels increased. This suggests that CEO compensation in Brazil was minimally affected by the crisis. Such findings reinforce the notion that subjective factors may play a significant role in determining salary structures.

Figure 1
Executive Compensation

The companies in the sample were led by 613 CEOs during the period from 2010 to 2022. Table 6 presents the financial sophistication characteristics of these CEOs.

Panel A: Dummy Variables Frequency Relative Frequency ( %) Finance-related Education 402 65.58 % International Education 215 35.07 % Experience in the financial sector 136 22.19 % Financial Experience 198 32.30 % International Experience 124 20.23 % Panel B: Variables in Years Mean Standard Deviation CEO Experience 7.04 9.66 Experience in the Sector 22.39 12.29 Source: Prepared by the authors.

Panel A: Dummy Variables
	Frequency	Relative Frequency ( %)
Finance-related Education	402	65.58 %
International Education	215	35.07 %
Experience in the financial sector	136	22.19 %
Financial Experience	198	32.30 %
International Experience	124	20.23 %
Panel B: Variables in Years
	Mean	Standard Deviation
CEO Experience	7.04	9.66
Experience in the Sector	22.39	12.29

• Source: Prepared by the authors.

According to Table 6, 65.58 % of CEOs have finance-related education, reflecting a high level of theoretical knowledge to support decision-making. Additionally, 35.07 % of CEOs have international education. Regarding experience, 22.19 % of CEOs have experience in the financial sector, while 32.30 % have experience in financial roles, having held positions such as auditor or accountant. Moreover, 20.23 % of CEOs have worked in companies outside Brazil, contributing international experience to their organizations. On average, CEOs have 7 years of experience in their current role and 22.39 years of experience within the same sector, underscoring their strategic insights into competitiveness.

Table 7 provides the descriptive statistics for all variables used in the research.

Table 7
Statistics of Research Variables

Variables	Mean	Standard Deviation			Minimum	Maximum	Obs. Total
		Overall	Between	within
Ln_RM	13.9042	1.4689	1.3913	0.6410	9.5400	16.7556	2397
Ln_RMED	13.5098	1.3066	1.2543	0.1234	9.5699	16.0110	2397
Ln_RF	13.1949	0.8066	0.6753	0.3894	10.0767	15.2824	2387
Ln_RV	13.0901	1.4418	1.3306	0.7506	8.1543	15.9429	1737
EXP	7.0392	9.6656	7.8094	5.0471	0.0000	64.0000	2397
EXPS	22.3871	12.290	9.9147	7.0410	0.0000	72.0000	2397
AGE	53.4864	9.6008	7.5823	6.3119	25.000	91.0000	2397
TAM	21.7816	1.7832	1.6903	0.4202	17.4057	25.9613	2397
ROA	0.0507	0.1203	0.0791	0.0925	-0.5712	0.3971	2397
QT	0.7595	0.8969	0.7222	0.5231	0.0096	8.0244	2397
END	0.7273	0.5196	0.4472	0.2509	0.1011	3.3234	2397
IDAEMP	42.5428	27.4102	27.0485	3.2831	0.0000	131.0000	2397
Variables	Dummy “0”			Dummy “1”			Obs. Total
	Fr.			Fr.
FF	34.38 %			65.62 %			2397
FI	65.33 %			34.67 %			2397
EXPSF	80.31 %			19.69 %			2397
EXPF	68.25 %			31.75 %			2397
EXPI	80.68 %			19.32 %			2397
GEN	97.62 %			2.38 %			2397
DUA	87.82 %			12.18 %			2397
CEOFAM	74.94 %			25.16 %			2397
CDEF	31.21 %			68.79 %			2397
PAN	80.68 %			19.32 %			2397

• Source: Prepared by the authors.

4.2 Main results

Tables 8, 9, 10, and 11 present the results of the Systemic GMM analyses and the tests for model adjustments. The AR(1) test does not reject the hypothesis of first-order autocorrelation. However, the AR(2) test did not show significance, indicating the absence of second-order autocorrelation in the models, confirming that they are well specified. The Hansen test also did not show significance, supporting the hypothesis that the instruments are exogenous. This affirms the validity of the instrument sets.

The results of the Systemic GMM reveal that the rate of variation in compensation at time t is influenced by the variation in compensation at time t-1. As observed in the descriptive phase and highlighted in recent studies (Hendriks et al., 2023), compensation shows significant growth. In this context, variations in prior salaries serve as a basis for salary increases. Additionally, when defining contracts, promotions, and career progressions, the compensation paid to CEOs in previous years is taken into account, among other parameters. However, it is important to highlight that determining compensation is a complex process influenced by a variety of subjective factors that differ between companies (Frydman & Jenter, 2010).

Table 8
Systemic GMM Analysis for the Highest Compensation of the Statutory Board

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Ln_RM t-1	0.641***	0.638***	0.644***	0.644***	0.646***	0.644***	0.644***	0.636***
	(9.957)	(10.035)	(10.063)	(10.041)	(10.107)	(10.122)	(10.077)	(9.947)
FF	0.069							0.043
	(1.434)							(0.869)
FI		0.108***						0.103**
		(2.656)						(2.560)
EXP			0.001					0.001
			(0.216)					(0.169)
EXPS				0.001				0.002
				(0.638)				(1.064)
EXPSF					0.057			0.050
					(1.132)			(0.932)
EXPF						0.056		0.027
						(1.369)		(0.646)
EXPI							0.021	-0.010
							(0.464)	(-0.228)
AGE	-0.000	0.000	-0.001	-0.002	-0.001	-0.001	-0.001	-0.001
	(-0.193)	(0.086)	(-0.614)	(-0.842)	(-0.476)	(-0.488)	(-0.528)	(-0.347)
GEN	-0.073	-0.076	-0.078	-0.075	-0.077	-0.076	-0.076	-0.060
	(-0.600)	(-0.617)	(-0.617)	(-0.587)	(-0.606)	(-0.601)	(-0.598)	(-0.498)
DUA	-0.073	-0.073	-0.076	-0.072	-0.076	-0.069	-0.074	-0.081
	(-0.983)	(-0.977)	(-0.999)	(-0.956)	(-1.018)	(-0.917)	(-0.985)	(-1.071)
CEOFAM	0.042	0.048	0.029	0.027	0.045	0.038	0.038	0.042
	(0.675)	(0.782)	(0.493)	(0.426)	(0.714)	(0.620)	(0.612)	(0.676)
TAM	0.203***	0.200***	0.201***	0.201***	0.199***	0.201***	0.200***	0.204***
	(5.090)	(5.106)	(5.052)	(5.056)	(5.053)	(5.098)	(5.082)	(5.158)
ROA	0.334*	0.345*	0.358*	0.354*	0.369**	0.365**	0.357*	0.333*
	(1.779)	(1.862)	(1.928)	(1.897)	(1.995)	(1.968)	(1.918)	(1.776)
QT	0.059**	0.056*	0.058*	0.057*	0.058*	0.057*	0.057*	0.060**
	(1.993)	(1.851)	(1.952)	(1.901)	(1.946)	(1.935)	(1.913)	(2.022)
END	-0.017	-0.010	-0.009	-0.008	-0.006	-0.010	-0.009	-0.009
	(-0.303)	(-0.180)	(-0.159)	(-0.143)	(-0.102)	(-0.169)	(-0.158)	(-0.157)
IDAEMP	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001
	(-1.154)	(-1.229)	(-1.069)	(-1.059)	(-1.027)	(-1.095)	(-1.054)	(-1.298)
CDEF	-0.180***	-0.184***	-0.181***	-0.180***	-0.180***	-0.182***	-0.182***	-0.178***
	(-2.889)	(-2.941)	(-2.895)	(-2.881)	(-2.887)	(-2.919)	(-2.906)	(-2.886)
PAN	-0.100*	-0.101*	-0.060	-0.062	-0.090	-0.089	-0.092*	-0.080
	(-1.756)	(-1.769)	(-0.917)	(-0.949)	(-1.605)	(-1.599)	(-1.646)	(-1.181)
Time/Sector	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes
Constant	0.680	0.774	0.759	0.761	0.726	0.702	0.754	0.685
	(1.210)	(1.394)	(1.356)	(1.349)	(1.305)	(1.261)	(1.348)	(1.231)
Observations	2124	2124	2124	2124	2124	2124	2124	2124
Companies	272	272	272	272	272	272	272	272
Instruments	43	43	43	43	43	43	43	49
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-7.555***	-7.557***	-7.559***	-7.561***	-7.555***	-7.584***	-7.556***	-7.561***
AR (2)	0.521	0.527	0.532	0.534	0.524	0.521	0.526	0.516
Hansen	0.390	0.406	0.334	0.336	0.334	0.352	0.345	0.417

• Note: Significance= ^*** p < 0.01, ^** p < 0.05, ^* p < 0.1.
• Source: Prepared by the authors.

According to Table 9 (models 1 and 2), academic background characteristics demonstrate a positive and significant relationship, thereby supporting H1a. The results indicate that a background in finance and international education contributes to an 8.1 % and 10.3 % increase in compensation, respectively. Supported by the UET and corroborated by previous studies, CEOs with higher educational levels provide valuable benefits and strengthen strategic management (Hambrick & Mason, 1984; Gupta & Mahakud, 2020). When this education aligns with finance, it enhances corporate performance and positively influences investment decisions, equipping companies to better navigate market challenges (Ghardallou et al., 2020; Naseem et al., 2020; Main & Silva, 2021; Setiawan & Gestanti, 2022). These findings suggest that Brazilian companies recognize the value of CEOs with a business-oriented education, promoting the use of these skills in corporate decision-making.

Furthermore, the results indicate that international academic backgrounds are highly regarded in Brazil. Such qualifications provide CEOs with insights into international business dynamics and facilitate the identification of foreign investment opportunities (Jiang & Liu, 2020; Sun et al., 2021). These findings align with previous studies (Conyon et al., 2019; Schmid & Baldermann, 2021), demonstrating that international education for CEOs is highly valued in Brazil, encouraging companies to offer higher compensation as recognition.

In Brazil, studies examining the relationship between a CEO's academic background and their compensation have yielded mixed results (Pereira et al., 2016; Machado et al., 2023; Dutra & Ceretta, 2023). This study makes a significant contribution by analyzing an extended period, providing valuable insights for the national literature, and highlighting that CEOs' academic backgrounds are in demand and have a measurable influence on their compensation.

Table 9
Systemic GMM Analysis for the Average Compensation of the Statutory Board

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Ln_RMED t-1	0.542***	0.537***	0.535***	0.540***	0.546***	0.539***	0.540***	0.539***
	(7.102)	(7.091)	(7.100)	(7.082)	(7.190)	(7.131)	(7.094)	(7.238)
FF	0.081*							0.048
	(1.650)							(0.944)
FI		0.103***						0.093**
		(2.676)						(2.408)
EXP			0.001					0.001
			(0.407)					(0.417)
EXPS				0.002				0.003
				(0.887)				(1.248)
EXPSF					0.080*			0.070
					(1.932)			(1.628)
EXPF						0.070*		0.036
						(1.750)		(0.857)
EXPI							0.058	0.026
							(1.300)	(0.587)
AGE	-0.001	-0.001	-0.003	-0.003	-0.002	-0.002	-0.002	-0.003
	(-0.708)	(-0.524)	(-1.217)	(-1.545)	(-1.098)	(-1.051)	(-1.042)	(-1.085)
GEN	-0.066	-0.069	-0.067	-0.063	-0.064	-0.062	-0.059	-0.043
	(-0.569)	(-0.580)	(-0.551)	(-0.515)	(-0.538)	(-0.516)	(-0.485)	(-0.364)
DUA	-0.057	-0.059	-0.067	-0.058	-0.062	-0.054	-0.063	-0.076
	(-0.847)	(-0.860)	(-0.991)	(-0.838)	(-0.894)	(-0.787)	(-0.908)	(-1.117)
CEOFAM	0.042	0.048	0.021	0.022	0.046	0.037	0.041	0.039
	(0.652)	(0.741)	(0.346)	(0.337)	(0.706)	(0.574)	(0.632)	(0.624)
TAM	0.233***	0.231***	0.235***	0.233***	0.229***	0.234***	0.231***	0.232***
	(5.702)	(5.712)	(5.895)	(5.741)	(5.692)	(5.784)	(5.698)	(5.834)
ROA	0.244	0.255	0.272	0.264	0.278	0.279	0.268	0.244
	(1.370)	(1.452)	(1.548)	(1.500)	(1.605)	(1.595)	(1.528)	(1.386)
QT	0.056	0.054	0.056	0.054	0.056	0.055	0.056	0.060*
	(1.591)	(1.498)	(1.591)	(1.519)	(1.579)	(1.552)	(1.558)	(1.697)
END	-0.023	-0.013	-0.011	-0.011	-0.012	-0.013	-0.010	-0.012
	(-0.378)	(-0.211)	(-0.187)	(-0.179)	(-0.204)	(-0.224)	(-0.172)	(-0.194)
IDAEMP	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001	-0.001
	(-1.067)	(-1.109)	(-1.005)	(-0.946)	(-0.955)	(-0.998)	(-0.957)	(-1.231)
CDEF	-0.185***	-0.189***	-0.190***	-0.186***	-0.178***	-0.187***	-0.188***	-0.177***
	(-2.657)	(-2.700)	(-2.726)	(-2.660)	(-2.605)	(-2.702)	(-2.699)	(-2.594)
PAN	-0.149**	-0.037	-0.185***	-0.082	-0.028	-0.075	-0.033	-0.095
	(-2.429)	(-0.411)	(-3.010)	(-0.903)	(-0.315)	(-0.815)	(-0.368)	(-1.043)
Time/Sector	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes
Constant	1.501**	1.472**	1.596***	1.496**	1.363**	1.430**	1.461**	1.315**
	(2.474)	(2.471)	(2.644)	(2.491)	(2.285)	(2.416)	(2.442)	(2.209)
Observations	2124	2124	2124	2124	2124	2124	2124	2124
Companies	272	272	272	272	272	272	272	272
Instruments	43	43	43	43	43	43	43	49
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-7.366***	-7.318***	-7.356***	-7.345***	-7.367***	-7.357***	-7.350***	-7.446***
AR (2)	0.231	0.236	0.237	0.244	0.212	0.242	0.220	0.207
Hansen	0.242	0.252	0.192	0.199	0.176	0.204	0.217	0.248

• Note: Significance= ^*** p < 0.01, ^** p < 0.05, ^* p < 0.1.
• Source: Prepared by the authors.

Based on Tables 8, 9, 10, and 11, certain characteristics of CEOs' professional experience were not significant, resulting in the rejection of H1b. These findings diverge from international studies (Custódio et al., 2013; Schmid & Baldermann, 2021) but align with the results of Dutra and Ceretta (2023) in the Brazilian context. The results indicate that specific CEO experiences are associated with increases in their compensation.

As shown in Tables 8, 9, 10, and 11 (Model 5), CEO experience in the financial sector is linked to an 8 % increase in total compensation and a 12 % increase in variable compensation. This experience enhances their ability to process banking information, and due to their familiarity with such environments, CEOs tend to better understand debt acquisition processes and demonstrate greater acumen in managing investor relations (King et al., 2016; Shahab et al., 2020). Thus, companies in Brazil appear to value this experience, which is reflected in higher compensation.

According to the data in Tables 9 and 10 (Model 6), the results also reveal that CEOs’ financial experience contributes to a 7 % increase in total compensation and a 5 % increase in fixed compensation. Studies suggest that this type of experience equips managers to effectively navigate dynamic environments, make informed accounting decisions, and strengthen internal controls, thereby minimizing inefficiencies in investments (Gounopoulos & Pham, 2018; Oradi et al., 2020; Li et al., 2023). In Table 10 (Models 4 and 8), the results show that sector-specific experience contributes to a 0.3 % increase in fixed compensation. Sector experience helps CEOs understand the sector’s nuances, optimize resource allocation, and navigate competitive environments (Custódio et al., 2013; Ma et al., 2021; Agnihotri & Bhattacharya, 2021).

The findings from Tables 10 (Models 3 and 8) and 11 (Models 3 and 8) indicate that CEO tenure is associated with a 0.3 % reduction in fixed compensation and a 1.3 % reduction in variable compensation. This trend aligns with the Upper Echelons Theory, which posits that more experienced managers may become less inclined to take risks and could develop opportunistic behavior over time (Naseem et al., 2020; Oware & Awunyo-Vitor, 2021; Jbir et al., 2021). The results suggest that CEOs with extended tenure may be less valued, reflected in reductions to their compensation. An alternative explanation is that newly hired CEOs often receive an initial premium as an incentive to accept the position, which exceeds any subsequent salary increases during their tenure in the same role.

Table 10
Systemic GMM Analysis for the Average Fixed Compensation of the Statutory Board

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Ln_RF t-1	0.544***	0.545***	0.545***	0.549***	0.545***	0.544***	0.543***	0.549***
	(8.036)	(8.097)	(8.119)	(8.104)	(8.097)	(7.937)	(7.943)	(7.948)
FF	0.022							0.013
	(0.802)							(0.463)
FI		-0.030						-0.039
		(-1.180)						(-1.497)
EXP			-0.002*					-0.003**
			(-1.657)					(-2.144)
EXPS				0.003*				0.003**
				(1.799)				(2.215)
EXPSF					0.038			0.025
					(1.366)			(0.842)
EXPF						0.050*		0.044
						(1.898)		(1.557)
EXPI							0.028	0.018
							(0.855)	(0.570)
AGE	0.003**	0.002*	0.004***	0.001	0.003**	0.003**	0.003**	0.002
	(2.270)	(1.762)	(2.587)	(1.051)	(2.261)	(2.348)	(2.201)	(1.539)
GEN	-0.012	-0.017	-0.018	-0.008	-0.013	-0.009	-0.011	-0.003
	(-0.146)	(-0.208)	(-0.216)	(-0.102)	(-0.162)	(-0.108)	(-0.126)	(-0.038)
DUA	0.018	0.018	0.032	0.017	0.013	0.020	0.016	0.034
	(0.546)	(0.563)	(0.993)	(0.510)	(0.400)	(0.600)	(0.490)	(0.986)
CEOFAM	-0.001	-0.008	0.012	-0.013	0.003	-0.003	-0.000	0.007
	(-0.036)	(-0.251)	(0.359)	(-0.385)	(0.091)	(-0.080)	(-0.002)	(0.197)
TAM	0.118***	0.118***	0.116***	0.116***	0.117***	0.119***	0.117***	0.115***
	(5.499)	(5.566)	(5.442)	(5.437)	(5.508)	(5.474)	(5.495)	(5.337)
ROA	-0.055	-0.046	-0.054	-0.058	-0.041	-0.045	-0.050	-0.048
	(-0.457)	(-0.380)	(-0.449)	(-0.491)	(-0.345)	(-0.375)	(-0.418)	(-0.395)
QT	0.019	0.018	0.017	0.017	0.019	0.020	0.018	0.019
	(0.917)	(0.894)	(0.851)	(0.846)	(0.945)	(0.962)	(0.871)	(0.903)
END	-0.014	-0.012	-0.017	-0.010	-0.010	-0.013	-0.011	-0.014
	(-0.355)	(-0.308)	(-0.430)	(-0.267)	(-0.250)	(-0.331)	(-0.287)	(-0.355)
IDAEMP	-0.001**	-0.001*	-0.001**	-0.001*	-0.001*	-0.001**	-0.001**	-0.001*
	(-2.056)	(-1.921)	(-1.963)	(-1.936)	(-1.938)	(-1.999)	(-1.985)	(-1.912)
CDEF	-0.028	-0.028	-0.028	-0.028	-0.028	-0.028	-0.029	-0.027
	(-0.695)	(-0.694)	(-0.705)	(-0.693)	(-0.702)	(-0.695)	(-0.728)	(-0.676)
PAN	-0.190***	-0.188***	-0.189***	-0.191***	-0.187***	-0.188***	-0.189***	-0.189***
	(-6.785)	(-6.784)	(-6.878)	(-6.873)	(-6.816)	(-6.837)	(-6.858)	(-6.628)
Time/Sector	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes
Constant	3.539***	3.551***	3.571***	3.516***	3.522***	3.508***	3.570***	3.475***
	(5.552)	(5.590)	(5.627)	(5.488)	(5.562)	5.500)	5.584)	(5.461)
Observations	2114	2114	2114	2114	2114	2114	2114	2114
Companies	272	272	272	272	272	272	272	272
Instruments	42	42	42	42	42	42	42	48
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-5.259***	-5.267***	-5.275***	-5.294***	-5.287***	-5.309***	-5.265***	-5.313***
AR (2)	0.335	0.348	0.324	0.337	0.336	0.325	0.332	0.331
Hansen	0.135	0.137	0.144	0.122	0.134	0.130	0.135	0.116

• Note: Significance= ^*** p < 0.01, ^** p < 0.05, ^* p < 0.1.
• Source: Prepared by the authors.

Analyzing the results of the Systemic GMM, it was observed that while some financial sophistication characteristics proved relevant, others were not significant. CEOs with these characteristics possess greater know-how for managing businesses and, as a result of their value, could receive higher compensation (Custódio & Metzger, 2014; Ghardallou et al., 2020; Bortoli & Soares, 2021; Main & Silva, 2021). However, the findings suggest that, in Brazil, the financial sophistication characteristics of CEOs do not appear to significantly influence increases in compensation. This raises important discussions: it underscores that CEO salary definitions are influenced by subjective factors that go beyond technical qualifications; companies may prioritize specific attributes over a broad range of characteristics; and it invites speculation that resource constraints might also limit the characteristics companies seek in their CEOs in emerging markets like Brazil.

Table 11
Systemic GMM Analysis for the Average Variable Compensation of the Statutory Board

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Ln RV t-1	0.328***	0.324***	0.327***	0.326***	0.328***	0.325***	0.325***	0.332***
	(5.558)	(5.570)	(5.540)	(5.508)	(5.587)	(5.519)	(5.518)	(5.698)
FF	0.081							0.061
	(1.129)							(0.826)
FI		0.034						0.030
		(0.429)						(0.370)
EXP			-0.013**					-0.012**
			(-2.415)					(-2.314)
EXPS				-0.001				0.002
				(-0.207)				(0.521)
EXPSF					0.120*			0.128
					(1.652)			(1.627)
EXPF						-0.024		-0.079
						(-0.319)		(-1.038)
EXPI							0.029	0.005
							(0.350)	(0.060)
AGE	0.000	-0.000	0.003	-0.000	-0.000	-0.001	-0.000	0.003
	(0.063)	(-0.018)	(0.811)	(-0.002)	(-0.048)	(-0.187)	(-0.114)	(0.790)
GEN	0.045	0.045	0.011	0.043	0.039	0.041	0.052	0.004
	(0.188)	(0.185)	(0.045)	(0.181)	(0.165)	(0.167)	(0.213)	(0.018)
DUA	0.180*	0.174*	0.253**	0.177*	0.164	0.177*	0.174*	0.241**
	(1.766)	(1.722)	(2.413)	(1.756)	(1.626)	(1.736)	(1.705)	(2.349)
CEOFAM	-0.235**	-0.237**	-0.182*	-0.239**	-0.222**	-0.246**	-0.241**	-0.154
	(-2.179)	(-2.188)	(-1.665)	(-2.192)	(-2.118)	(-2.306)	(-2.240)	(-1.390)
TAM	0.258***	0.257***	0.247***	0.258***	0.256***	0.257***	0.257***	0.243***
	(5.975)	(5.937)	(5.710)	(5.948)	(5.967)	(5.962)	(5.995)	(5.659)
ROA	1.175***	1.217***	1.227***	1.214***	1.246***	1.210***	1.212***	1.186***
	(3.041)	(3.168)	(3.239)	(3.182)	(3.263)	(3.162)	(3.168)	(3.013)
QT	0.191***	0.185***	0.182***	0.187***	0.188***	0.187***	0.188***	0.190***
	(3.434)	(3.400)	(3.390)	(3.485)	(3.433)	(3.421)	(3.426)	(3.553)
END	0.296*	0.309*	0.299*	0.303*	0.309*	0.308*	0.306*	0.297*
	(1.688)	(1.763)	(1.721)	(1.738)	(1.754)	(1.756)	(1.744)	(1.721)
IDAEMP	-0.008***	-0.008***	-0.008***	-0.008***	-0.008***	-0.008***	-0.008***	-0.008***
	(-4.266)	(-4.235)	(-4.245)	(-4.241)	(-4.184)	(-4.232)	(-4.237)	(-4.225)
CDEF	-0.204**	-0.210**	-0.204**	-0.212**	-0.205**	-0.211**	-0.212**	-0.198**
	(-2.377)	(-2.447)	(-2.455)	(-2.489)	(-2.394)	(-2.489)	(-2.480)	(-2.376)
PAN	0.299***	0.306***	0.303***	0.312***	0.314***	0.309***	0.310***	0.289***
	(2.794)	(2.864)	(2.823)	(2.901)	(2.931)	(2.900)	(2.903)	(2.641)
Time/Sector	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes	Yes/Yes
Constant	3.132***	3.256***	3.337***	3.234***	3.190***	3.281***	3.243***	3.263***
	(2.864)	(3.019)	(3.156)	(3.021)	(2.974)	(3.029)	(3.005)	(3.083)
Observations	1.501	1.501	1.501	1.501	1.501	1.501	1.501	1.501
Companies	234	234	234	234	234	234	234	234
Instruments	43	43	43	43	43	43	43	49
Prob>F	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
AR (1)	-6.610***	-6.581***	-6.601***	-6.596***	-6.593***	-6.595***	-6.602***	-6.588***
AR (2)	0.204	0.217	0.234	0.212	0.185	0.216	0.214	0.205
Hansen	0.323	0.301	0.294	0.298	0.245	0.304	0.305	0.261

• Note: Significance= ^*** p < 0.01, ^** p < 0.05, ^* p < 0.1.
• Source: Prepared by the authors.

4.3 Additional analyses

To assess the effects of the explanatory variables across the distribution of CEO compensation, the sample was divided into the 25th and 75th percentiles for each of the study’s dependent variables. The GMM-Sys method was applied to estimate the models for each segmented sample. The objective was to analyze the relationships between CEO characteristics and various levels of compensation, recognizing that lower and higher salaries may be associated with distinct attributes. This approach has the potential to yield novel findings and provide valuable insights.

Table 12 summarizes the results, offering a comprehensive perspective on the impact of the explanatory variables at the extremes of the compensation distribution. For the model adjustment tests, the AR(2) test did not show significance in any of the models, indicating the absence of second-order autocorrelation in the residuals. Similarly, the Hansen test also showed no significance in any of the tests, further confirming the validity of the instrument sets. The regression analysis applied to the 25th and 75th percentiles reveals significant insights into how CEOs’ academic backgrounds and professional experience relate to their compensation levels.

Table 12
GMM Analysis with Samples Segregated into the 25th and 75th Percentiles for Each Dependent Variable

	RM			RMED		RF		RV
	P25	P75	P25	P75	P25	P75	P25	P75
FF	0.154*	-0.068	0.088	0.002	-0.047	-0.011	0.262*	0.181***
	(1.732)	(-1.137)	(0.720)	(0.035)	(-0.728)	(-0.350)	(1.656)	(0.812)
FI	0.293***	0.041	0.108	0.082*	-0.072	-0.018	0.362**	0.003
	(3.883)	(0.681)	(0.811)	(1.824)	(-1.300)	(-0.407)	(2.394)	(0.041)
EXP	-0.001	0.007*	-0.002	-0.001	0.004	-0.003	0.015*	-0.008**
	(-0.215)	(1.661)	(-0.383)	(-0.286)	(1.420)	(-1.486)	(1.645)	(-1.975)
EXPS	-0.001	0.006**	0.001	0.002	0.004	0.003	-0.007	0.006*
	(-0.240)	(2.173)	(0.244)	(0.950)	(1.348)	(1.017)	(-0.999)	(1.759)
EXPSF	0.162*	-0.073	0.152	0.014	0.151**	0.057*	-0.067	-0.169***
	(1.676)	(-1.227)	(1.467)	(0.310)	(2.566)	(1.666)	(-0.551)	(-2.896)
EXPF	0.230***	0.094*	0.125	0.108**	0.103*	0.091**	0.032	-0.019
	(3.762)	(1.667)	(1.521)	(2.404)	(1.811)	(2.527)	(0.226)	(-0.347)
EXPI	0.321***	0.101	0.259*	0.046	0.080	-0.017	0.167	-0.090
	(2.983)	(1.617)	(1.817)	(1.161)	(1.489)	(-0.554)	(1.133)	(-1.347)
Observations	472	412	462	422	450	438	265	280
Companies	76	98	80	94	88	93	71	75

• Note: FF - Finance-related education; FI - International education; EXP - CEO experience; EXPS - Sector experience; EXPSF - Financial sector experience; EXPF - Financial experience; EXPI - International experience. Significance= ^*** p < 0.01, ^** p < 0.05, ^* p < 0.1.
• Source: Prepared by the authors.

The 25th percentile represents the companies offering the lowest compensation among the analyzed firms. As shown in Table 12, variations in compensation at these companies are positively associated with academic background, financial sector experience, financial experience, and international experience. Variations in fixed compensation are specifically linked to financial sector experience and financial expertise. In contrast, variations in variable compensation are associated with academic background and CEO tenure.

Given that smaller and younger companies generally offer more modest salaries in line with their size and maturity stage, the results suggest that these companies value the CEO’s academic background as a crucial factor for effective business management. This recognition, reflected in compensation, underscores the understanding that a CEO’s qualifications significantly influence their leadership abilities, decision-making processes, and the company’s growth trajectory. Moreover, the professional expertise of CEOs emerges as a key factor enabling these companies to excel in competitive markets, fostering business expansion and consolidation. All types of experience, except sector-specific experience, were positively associated with compensation variations at the 25th percentile. This suggests that companies with lower salary levels may rely more heavily on the professional experience of their CEOs.

At the 75th percentile, which encompasses companies offering the highest compensation, there is a clear emphasis on valuing CEOs' academic backgrounds. These companies provide superior total and variable compensation packages to those with business-aligned and international education-attributes that equip CEOs to craft strategies ensuring organizational continuity and success. Specific experience, such as CEO tenure, sector-specific expertise, and roles in finance, are linked to increases in total compensation. In terms of fixed compensation, both financial sector experience and broader financial expertise stand out as significant factors driving increases in CEO remuneration.

For companies offering higher salary levels, sector-specific experience is associated with increases in variable compensation. However, CEO tenure and financial sector experience appear to be less influential in this segment. These findings suggest that for companies offering superior compensation packages, the differentiating factor lies less in general sector experience or CEO tenure and more in a blend of academic credentials and targeted professional experiences. As such, larger and more established companies tend to prioritize leadership profiles that are globally oriented and academically robust, capable of fostering innovation and maintaining competitiveness in an ever-evolving business landscape.

Several key points deserve attention. In terms of total compensation, CEOs' financial and international education is significant at the lower percentile but not at the upper percentile. This finding suggests that academic qualifications are more highly valued at lower salary levels, likely because they equip CEOs with essential skills for career advancement. In larger companies, however, this attribute may be supplanted by the executive’s professional expertise. Regarding total compensation, the CEO’s international experience is significant at the lower percentile but not at the upper percentile. This indicates that, at lower salary levels, international experience is perceived as a differentiating factor, providing companies with expertise in global negotiations and facilitating entry into new economic contexts.

For variable compensation, CEO tenure demonstrates negative significance at the upper percentile and positive significance at the lower percentile. This suggests that executives hired at higher salary levels may encounter less generous adjustments over time, or adjustments contingent on meeting specific performance goals. When it comes to financial sector experience, it is negatively significant at the upper percentile, while showing no relevance at the lower percentile. This finding implies that executives with extensive financial sector experience may have their variable compensation tied to stricter performance goals, potentially leading to reduced earnings if these targets are not achieved.

Overall, the results underscore the complexity of the relationship between CEOs' education, experience, and their compensation levels. The factors driving compensation at lower salary ranges may differ from those influencing executives at the upper end of the pay scale. While academic background is valued across both lower and higher-paying companies, the specific experiences that shape compensation vary depending on the organizational and market context. These findings are consistent with prior tests, suggesting that companies employ diverse compensation strategies tailored to their development stage or the unique challenges of their markets, seeking CEOs with profiles that align with their specific needs.

5. CONCLUSIONS

This study examined the relationship between the Financial Sophistication characteristics of CEOs and their compensation in publicly traded Brazilian companies. The findings suggest that CEOs with finance-related academic backgrounds and international education receive higher compensation. In the Brazilian context, as observed in Denmark (Banghøj et al., 2010), the United States (Cole & Mehran, 2016), the United Kingdom (Conyon et al., 2019), China (Choi et al., 2022), and Europe (Schmid & Baldermann, 2021), CEOs’ academic qualifications are increasingly valued, driving higher compensation. Academic education emerges as a key determinant of CEO compensation in Brazil, potentially incentivizing executives to apply their expertise to organizational processes.

However, the results indicate that companies prioritize rewarding specific professional experiences, placing greater value on expertise acquired in the financial sector, roles involving financial responsibilities, and experience within the same industry. In contrast, CEO tenure is linked to reductions in both fixed and variable compensation. This observation aligns with studies from Pakistan (Naseem et al., 2020) and India (Oware & Awunyo-Vitor, 2021), which suggest that long-tenured CEOs may exhibit opportunistic behavior and are often viewed as less valuable investments. Furthermore, the findings indicate that salary adjustment policies may be creating a compensation disparity, favoring newly hired executives over those with longer tenures.

Although academic background appears to be relevant in salary determinations, certain characteristics related to professional experience were not significant, making it impossible to conclude that financially sophisticated CEOs receive higher compensation in Brazil. This finding raises important considerations: in an emerging economy, financial constraints may prevent companies from hiring CEOs with numerous qualifications, owing to challenges in providing adequate compensation. Furthermore, many of the observed characteristics remain underdeveloped among executive directors in Brazil.

Among the study’s limitations, compensation data are restricted to information disclosed in the CRF, which has been available only since 2010 and is sometimes incomplete. Future research could examine compensation in specific sectors-particularly the financial sector-or investigate in greater depth why the communications sector offers the highest compensation. Comparisons of executive compensation in Brazil with that of other emerging economies would also prove insightful. Additionally, new studies could explore other executive attributes, focusing on specific cognitive elements that may inform discussions between national research and studies in emerging economies.

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• DATA AVAILABILITY
  The datasets related to this article will be available upon request to the authors.

Edited by

• EDITOR-IN-CHIEF
  Márcia D’Angelo

• ASSOCIATE EDITOR
  Lucas Godeiro

Data availability

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

Publication Dates

• Publication in this collection
  27 July 2026
• Date of issue
  2026

History

• Received
  10 June 2024
• Reviewed
  07 Oct 2024
• Accepted
  27 Dec 2024