The wage gap in accounting positions

Determinantes de las diferencias salariales entre los puestos contables

Abstract

This research aimed to describe the wage differences among accounting occupations in Brazil. A descriptive, documental, and quantitative research was carried out with auditors, accountants, and accounting assistants (occupations defined by the Código Brasileiro de Ocupações - CBO). The study used regressions with binary variables analyzing data from 2009 to 2015. The results showed that low wages are significant among female black or mulatto workers; workers who completed high school and do not have a degree; with a disability; and in their first job or hired under a fixed-term contract. Also, the study observed that individuals who receive a higher wage are accountants, caucasian male, between 30 and 39 years old, holding a degree, and not disabled. The individuals with higher wages have work experience (they are at least in their second work contract), are employed in a company with up to four employees, and are residents of the Southeast region of Brazil.

Keywords:
Wage gap; Determinant factors; Accounting occupations; The human capital theory; The segmentation theory

Resumen

Para describir los determinantes de las diferencias salariales entre los profesionales de contabilidad en Brasil, se realizó una encuesta descriptiva, documental y cuantitativa de profesionales con los cargos de auditor, contador y asistente de contabilidad (de acuerdo con el Código Brasileiro de Ocupações - CBO). Los resultados del uso de regresiones con variables binarias y, en el contexto de las variables analizadas (para los años de 2009 a 2015) son: a) los bajos salarios son significativos entre las profesionales que se declararon negras o mulatas, con educación secundaria, alguna discapacidad y que están en su primer trabajo o contratadas por un tiempo preestablecido; b) la persona que tiende a recibir un salario más alto es quien tiene el cargo de contador, hombre, blanco, entre 30 y 39 años de edad, con educación superior completa y sin discapacidad. Además, esta persona trabaja, al menos, en un segundo empleo, trabaja en una empresa con hasta cuatro empleados y es residente de la región Sudeste de Brasil.

Palabras clave:
Diferencia salarial; Factores determinantes; Puestos contables; Teoría del capital humano; Teoría de la segmentación

Resumo

Com o objetivo de descrever os fatores determinantes das diferenças salariais entre os profissionais da contabilidade no Brasil, foi realizada uma pesquisa descritiva, documental e quantitativa dos profissionais com os cargos de Auditor, Contador e Auxiliar Contábil (segundo o Código Brasileiro de Ocupações - CBO). Os resultados a partir do uso de regressões com variáveis binárias e, no contexto das variáveis ​​analisadas (para os anos entre 2009/2015) são: a) os baixos salários são significativos entre as profissionais do gênero feminino que se declararam negras ou mulatas, com ensino médio, alguma deficiência e que estão no primeiro emprego ou contratadas por tempo pré-estabelecido; b) o indivíduo que tende a receber um salário mais elevado é o ocupante do cargo de Contador, homem, branco, entre 30 e 39 anos de idade, com ensino superior completo e não é deficiente. Adicionalmente, este indivíduo atua, pelo menos, em um segundo emprego, trabalha em uma empresa com até quatro funcionários e é residente da região Sudeste do Brasil.

Palavras-chave:
Diferença salarial; Fatores determinantes; Ocupações contábeis; Teoria do capital humano; Teoria da segmentação

INTRODUCTION

In the contemporary world, wage variation is a feature of the labor market. The determinants of this variation are explicit factors such as skills, knowledge, schooling, and implicit/discriminatory characteristics, for example, due to gender, ethnicity and religion. The modern society, even in developed countries, has not suppressed the differentiated remuneration (wage, bonus, and others) that usually privileges some individuals. Especially those linked to issues extrinsic to the needs for an occupation.

To eradicate wage discrimination practices, many people should be aware of it (all parts of the governments, the community leaders, the educators, and the researchers, etc.). Furthermore, it should be understood as such practices empirically occur at the different levels of the development and in certain spheres analysis. Examples of these spheres are economic, social and cultural (these should be assessed by the country and the geographical areas), business feature (size and sector of economic activity) and peculiarities of certain occupations.

There is a wage inequality in several areas, and this has attracted the attention of several researchers from different theoretical approaches. Among the contributions to understanding this theme are the Human Capital Theory and the Segmentation Theory. The Human Capital Theory, whose precursor is Mincer (1958), that reveals the existence of a relation between investments in education and training of the population and the income distribution.

Some years later, Becker (1964) verified empirically that training and production improvement are the consequences from the investments in education. In other words, education makes people more productive, increasing their wages, which directly affects the economic growth. Therefore, the income of the workers tends to grow according to accumulated human capital.

On the other hand, the Segmentation Theory postulates that workers may have different wages even if they are equally productive. Therefore, the segmentation occurs when workers have similar productive characteristics, but their wages differ. Thus, the wage inequality can be derived from the difference between geographical regions, prejudices as to ethnicity, gender, job experience, among others (SOUZA, 1978).

Each theory presents factors related to the employability and remuneration (sum of the wage, bonus, etc.) of the workers, according to the behavior of the labor market. Both theories seek to explain wage differentiation in the labor market. However, the Human Capital Theory is related on knowledge and training, while the Segmentation Theory is inspired by other issues or factors linked to location or rigid intrinsic characteristics (ethnicity, religion, gender, etc.).

Related to this problem, several researches in the accounting area have focused attention to wage inequality and long-term career development and barriers to occupation of certain prestigious positions in accounting (SILVA, DAL MAGRO and SILVA, 2016; HAYNES, 2017).

In Brazil, where cultural diversity coexists with the ‘myth of racial democracy’, income misdistribution having a significant impact on socioeconomic and regional development. In addition, the different levels of appropriation of education, health and other public services imply on the access to employment opportunities. The regional context is also identified as one of the factors that can determine the wage differential (FIELDS and YOO, 2000; SILVA, DAL MAGRO and SILVA, 2016). However, few researches relate to the thematic of relationship between the profile of accounting positions and their wages, a limitation to which this research proposes to unveil.

In this regards, according with studies focused on the analysis of discriminatory characteristics in specific scenarios, the following research question presents itself: What factors explain the wage gap (consider sum of the wage, bonus, etc. like remuneration) between accounting positions? Consequently, the aim is to describe the personal and contextual factors that explain the occurrence of wage differences between the positions and people who work in the Brazilian accounting area. The personal factors to be investigated are gender, ethnicity, age, schooling, kinds of disability and working time (experience); and contextual factors that are: size of the company and geographic region of its location.

A research for Brazilian case has the following motivations: a) to be largest country in Latin America; b) poor income distribution, that is, social inequality (ONU, 2018); c) ranked 42^nd in the ranking of wage inequality in a list of 44 countries, ranking only in front of Costa Rica and South Africa (OECD, 2018).

According to Fox and Fox (2008) the issue of wage inequality has been ignored by the labor market. The authors consider this as regrettable, because for decades the women have grown in the labor market. In addition, the authors highlight that in the face of (for) structural changes there has been demand for women in the labor market and the supply of jobs to them (for women) will continue to grow in all sectors. In this sense, the research contributes to observe this problem in a specific context.

The research, analyzing all the geographic regions of Brazil, expands the conclusions of the study by Silva, Dal Magro and Silva (2016) who found factors that explain the wage of accountants in specific regions. For Haynes (2017), accounting is a political construct that is already known to perpetuate wage inequality, mainly due to the global challenges related to gender, which throughout history represents a difficult barrier to overcome.

Considering that accounting positions are disseminators and perpetuators of the company information, this study justifies the focus on professionals who represent companies, which are organizations. Therefore, it should at least be an example to society, showing that, in accounting, wage inequality should be as small as possible. Thus, it is possible to see that the accounting area is a promising field for this study.

In addition, there is a need to understand the causes of wage differences between different positions in accounting. In order to develop actions that minimize the discrimination in the accounting area. Therefore, knowing the determinants of the wage difference of the accounting area, it contributes to the reflection and implementation of actions.

Those actions must be taken to reduce wage gaps. Especially those endogenous gaps to discriminatory factors since the solution of this problem contributes to the development of the profession. Thus, it is important to indicate a greater solidity (strength) in this labor market. Furthermore, it stimulates the incoming workers and consolidates some prominence of this profession in the national scenario.

Therefore, an important contribution of this research accrues in the possibility of dimensioning the factors that generate the wage discrimination (core of the problem). Thus, it is feasible to identify the variables that demand the greater attention from those who work in the accounting area, such as professionals or academics.

Knowing the discriminatory forces also help in the identification of the main inequalities that affect the income distribution of a population. The outcomes can subsidize the policymakers to design redistributive measures, as well as the evaluation of their impact on the well-being of a society (CASTELLANO, MANNA and PUNZO, 2016). Undoubtedly, knowing the determinants is important for choosing the better public policies (CAMPANTE, CRESPO and LEITE, 2004). These policies will reduce the wage gap and to improve the income distribution.

The research also contributes to consider the heterogeneity of Brazilian geographic regions. With this empirical strategy, the outcomes are expected to show different wage differentials between the regions of the country. In addition, there is a gender wage gap, which the outcomes of the research make possible to set an agenda that promotes equal wage. As far as the proficiency of the accounting professionals, the research brings relevant things to talk about the formal skills of the accountants. Considering the proficiency for accounting work as essential, consider this variable for public police can result in an accurate training supply in some Brazilian regions.

THEORETICAL FRAMEWORK

The Human Capital Theory and the Segmentation Theory are presented in the following sections. The past-related studies are approached to variables and to results that contribute to the work proposed in this research.

The Human Capital Theory and Segmentation Theory

According to Helal (2005), the theorists of Human Capital Theory, (see for example, Becker, 1964 and Mincer, 1974), consider that the larger human capital stock of the individual, the greater its marginal productivity. Consequently, the higher the economic value of this individual to the job market as well as the greater your chance of getting a job.

As discussed by Pfeffer and Langton (1988), the Human Capital Theory predicts that changes in demographic and productivity characteristics can lead to gaps in wages, regardless of corporate norms or arrangements. In turn, Hanushek (2013) highlight that researches of Schultz (1961), Becker (1964) and, Mincer (1970, 1974) contributed to disseminate the theoretical and empirical approach to the Theory of Human Capital. It is because they have shown that the wage gap is caused by skill differences, that is, by human capital.

Subsequently, Neves and Fernandes (2002) did a review of the contents and observed that the variables schooling and working time are frequently used. Being schooling is considered the main factor of human capital. For the authors, the occupational structure (position) and class position (base managerial authority) are relevant to explain the wage framework process.

In addition, class position plays an important role as mediator between schooling and wage. Also, in relation to education, Mincer (1974) argues that education is responsible for skills. As a result, this variable has been used as a proxy to measure human capital and to understand wage inequality.

Igbaria and Chidambaram (1997) investigated gender gap, human capital variables (age, working time, education and organizational level) and factors that explain career success. The findings concerning women are such that: a) typically have lower education; b) have less working time; c) have less professional and managerial skills. The authors attributed these differences to the higher turnover of women and career breaks to prioritize family. They also concluded that the explanation of human capital is linked to the voluntary choices of individuals in allocating investments such as time and effort at work and in the family.

However, the Human Capital Theory has been criticized. Helal (2005) considers this theoretical approach is restrictive and by having a neoclassical economic approach, disregards the social features of employment and the settlement of wages. In addition, Darity (1993) notes that since 1970 the Segmentation Theory is an alternative or complementary theoretical basis to the Human Capital Theory. Because the Segmentation Theory is used to explain labor market behavior.

The Segmentation Theory presupposes that labor market changes, restructuring, and decentralization can provide insight into how labor market identities are produced from the experience gained by holding positions (BAUDER, 2001). According to Bauder (2001), in contrast to human capital theory, segmentation theory advocates that there is no harmonization with a universal market mechanism, between workers and jobs. Jobs are allocated in segments within the labor market, i.e., kind of job (occupation) and position.

Thus, The Segmentation Theory constitutes a theoretical lens useful to explain the economic marginalization of workers, racial minorities, as well as women (BAUDER, 2001). Morrison (1990) explained that to improve The Segmentation Theory is necessary to create a connection between theory and practice. In addition, the labor market should be considered in its geographical context (local, regional, spatial). Dibb and Simkin (2009) report that there are a few researches devoted to analyzing the ‘practice’ in which segmentation occurs. It can be observed that both The Human Capital Theory and The Segmentation Theory are not free of criticism and have limitations.

However, these issues do not minimize the relevance of these theories as a guide to apply in business and economic sectors. In the case of this research, Human Capital Theory is adopted to explain wages, according to schooling, qualifications, training and professional improvement. The Segmentation Theory, in addition to explaining schooling, considers that wages are most related to the kind of job that workers can have and the position (particularly in this research: Accounting Assistants, Auditors and Accountants).

Determinants of Wage Inequality

The wage gap attracts the attention from a great part of the public and the private organizations. On the other hand, in the academic context (universities), that are based on epistemology, both phenomenological and positivist, the researcher’s intent to understand antecedents, consequences and contexts. They also seek to identify motivating factors of the wage inequality among workers, broadening the scope of research on social inequalities.

Dunlop (1957) stressed the need for collective bargaining agreements for wage negotiations, whether local or regional, because the absence of these agreements may imply wage differences in the same municipality, state, region or country. This author does not consider the situation as beneficial.

Regarding the ethnicity of the individuals and wage difference by geographic region, Campante, Crespo and Leite (2004) explain that in Brazil there is a phenomenon known as “whitening”, that is, the poor individual refers to himself as black or brown, differently from the economically successful person who, although having the same characteristics, identifies himself as Caucasian. This behavior can bias searched outcomes on the wage gap between Caucasians and Blacks, especially in the Northeast and Southeast of Brazil. For the authors, another significant factor in the increase of discrimination is schooling. Ethnicity and schooling tend to influence the entry in the labor market and wages.

Fields and Yoo (2000) quantified the importance of factors that contribute to the reduction of wage inequality in Korea. These factors are tenure or experience, gender, schooling, position wage, company size and region. The most important to explain the inequality of wages are schooling, position and experience. In addition, they found that wage inequality declined among men, but not among women.

Hossain, Haque and Haque (2015) investigated the factors that determine wage differentials in Bangladesh. They found that schooling, age, and gender determined the differences in wages. The authors suggest that this issue should be considered by policymakers to improve income distribution.

In African countries, Agbodji, Batana and Ouedraogo (2015) investigated gender inequality as well as regional inequalities. Regarding gender, women got lower wages than men. In this context, Dreher, Gehring and Klasen (2015) found that gender inequality is one of the key indicators affecting many developing countries. For Jayachandran (2015), men have higher wages than women in almost all countries. Although in recent years, in developing countries, the wage gap is not as large as in the past, even if there is still a difference. However, there is a quasi-consensus that, regardless of the country, there is or should have policy actions to eradicate this inequality.

As reported by Fox (1981), the disparity between men and women in relation to wages, power and prestige occurs almost everywhere in the world. In Fox’s (1981) survey, which compared wages for men and women, the author concluded that wages for women are lower. Another conclusion was that men are less susceptible to wage variations due to differences in ethnicity and citizenship.

Konrad and Pfeffer (1990) analyzed the factors that affect the relationship between productivity, publications and wage. Based on a sample of 5,645 university researchers from 200 educational institutions, the authors concluded that productivity explains wages and that human capital theory explains this relationship. In turn, Gerhart and Milkovich (1990) evaluated the determinants and consequences of wage differences between a group of university and high school managers. To do this, they used longitudinal data from 14,000 managers from 200 educational institutions. Although they concluded that there were significant differences in wages, the research was unable to identify the determinants of the wage gap.

In the context of accounting, Fields (2003) investigated wage inequality in the United States from 1979 to 1999. Explanatory variables include gender, ethnicity, experience (time of employment), schooling, position, economic sector and region. The author concluded that explanatory variables of wage inequality are schooling, position, experience (time of employment) and gender.

Bruno (2010), which aimed to analyze the wage differences of Tunisians in the labor market in Paris, did a similar research. Using a linear regression model, the author concluded that, for the whole sample, gender and nationality was not significant. However, when analyzed by economic sector, it identified significant wages differences between women and men, foreigners and native citizens.

According to Haynes (2008), in developed countries, more and more women and men have equality in the positions held, legal rights, access to education and voting power. The women have been active in areas previously dominated by men, that is, they have diversified their positions. However, while there are improvements in terms of formal equality, inequalities and problems remain. Examples are: (i) small number of women in high political and business positions; (ii) imbalance in domestic work (overload for women); (iii) unequal wage and (iv) sexual harassment.

In turn, Silva, Dal Magro and Silva (2016) aimed to identify the gender inequalities in the accounting, analyzed from the perspective of The Glass Ceiling. They investigated workers in the southern region of Brazil. The results revealed that men more often occupy the positions of auditor and accountant. In addition, men have higher wages, as well as positions held by men are more frequent in large companies. Thus, the authors concluded that there is The Glass Ceiling in the accounting career, which creates an invisible barrier in women´s access to higher positions and better wages.

The research of Castellano Manna and Punzo (2016) aimed to identify the factors that explain the wage gap in France and Italy. The data used was from 2004 to 2007, before the economic crisis. The variables examined was gender, age, schooling, experience (time of job), position and wage. The results confirmed that in Italy, redistributive policies worsen the inequality, and in France, this effect is mitigated by job tenure and wages, and social policies. In addition, the authors emphasize that France is traditionally more egalitarian than Italy. In other words, Italy presents a larger wage gap, even with a similar proportion of individuals of each gender.

Recently Haynes (2017) considered that, in general, in 25 years, many things have changed. However, few changes have occurred in relation to gender wage inequality in accounting area. Although there has been advance in the accounting profession, inequalities persist, and it is worrying. An important change is that women can now choose jobs more flexible, especially part-time.

This allows women to compose multiple tasks, especially childcare and other household chores. Consequently, women who choose to temporarily reduce their workload because of family things tend to be disadvantaged. In many cases, for these women, it makes it difficult to improve their career, as well as suffer imputation of depreciatory stereotypes and discrimination. However, above all there is the explicit question of receiving lower wages.

METHODOLOGICAL PROCEDURES

This research is classified as descriptive, documentary and quantitative. The data was collected in the micro database of the Relação Anual de Informações Sociais (RAIS), available on the website of the Ministério do Trabalho e Emprego (MTE). The selected period was from 2009 to 2015, that is, seven years. The RAIS is a general government database for labor market control. In this database, all companies in the country report annually on a mandatory basis the data about their employees (wages, bonus, workload, time, etc.). Therefore, it allows the generation of longitudinal census statistical data on the employees of the formal labor market.

The sample selected for study (after processing the raw data) is comprised of accounting professionals, with the positions of auditor, accountant, and accounting assistant. Respectively, the employment contract registration, according to the Código Ocupacional Brasileiro (CBO), refers to the positions of: Auditor - CBO 252205, Accountant - CBO 252210 and Accounting Assistant - CBO 413110.

It should be emphasized that the accounting worker population includes only employees hired to work in these positions and does not include partners or any other hiring regime. Thus, the sample size is composed of 60,447 observations for 2009; 72,676 observations for 2010; 86,585 observations for 2011; 88,013 observations for 2012; 91,817 observations for 2013; 91,104 observations for 2014; and 80,881 for 2015.

All annual data was downloaded from MTE database and introduced to the Access software by Microsoft Corporation^®. The first objective was to filter the data for the three positions to be analyzed and seven years. Subsequently, the filtered data was transferred to Excel software. In this stage, was excluded the data with possible registry mistake made by companies. The information excluded was those inherent to employees who did not complete high school and those classified as inactive.

Finally, the following variables was tabulated: payment (sum of wage, bonus and other remunerations), position, gender, ethnicity, age, schooling, kind of disability, company size (number of workers), kind of movement (hire, dismissal, normal work or leave in the reporting year) and State (grouped by geographic region).

The values of wages and other remunerations, 2009-2014, was monetarily updated for the year 2015. This was done with the IGP-M (Índice Geral de Preços de Mercado), estimated by FGV (Fundação Getulio Vargas/Brasil). Thus, it was possible to estimate the average wage of the survey years and estimate for each year, the multiple regression model with dummy variables. For the statistical analysis, the variables were categorized according to Box 1 .

Box 1
Categorization of variables

Box 1
continuation

In the statistical analysis, the methodology of multiple regression with binary variables (dummies) was used, that is, variables assuming values of 0 and 1 (GUJARATI and PORTER, 2011). The regressions were performed using STATA^® statistical software. From this method, it was possible to identify a standard individual that could be compared with the others.

Ex-ante observations and complementary tests showed that: (i) the data are normal (based on sample size); (ii) there are no problems of (perfect) multicollinearity, easily observable by the variables chosen (only dummy variables in the right side of equations to regression analysis); (iii) there is no autocorrelation of the disturbance term because these are regressions on cross-sectional data; (iv) there is no heteroscedasticity, since have not non-binary explanatory variables and; (v) there is no micronumerosity due the sample size.

DATA ANALYSIS AND DISCUSSION

The data analysis was performed comparatively through the multiple regression model with binary variables, based on the accountant position. The accountant position was omitted in the results of the statistical model because the other positions are compared to this, in a percentage difference (as shown in any result of this kind of estimate, for any software). The result is shown in Table 1.

Table 1
Statistical model for 2009 data

Ln Wage (monthly)	Coef.	Hours. Err.	t	P>I t I	[95% Conf. Interv]
Auditor	-0.0034784	0.0091547	-0.38	0.704	-0.0214217	0.0144649
Accounting Assistant	-0.5041989(*)	0.0049432	-102.00	0.000	-0.5138877	-0.4945102
Size:
5 a 9	0.0116508	0.0066198	1.76	0.078	-0.0013240	0.0246256
10 a 19	0.0848258(*)	0.0065152	13.02	0.000	0.0720560	0.0975956
20 a 49	0.1681385(*)	0.0063057	26.66	0.000	0.1557793	0.1804977
50 a 99	0.2463770(*)	0.0069578	35.41	0.000	0.2327397	0.2600142
100 a 249	0.2850203(*)	0.0066012	43.18	0.000	0.2720820	0.2979586
250 a 499	0.3366667(*)	0.0078948	42.64	0.000	0.3211929	0.3521404
500 a 999	0.3449819(*)	0.0093169	37.03	0.000	0.3267207	0.3632431
Above 999	0.4693768(*)	0.0097095	48.34	0.000	0.4503461	0.4884074
Schooling:
Complete High School	-0.3437659(*)	0.0050210	-68.47	0.000	-0.3536072	-0.3339247
Incomplete College	-0.1962161(*)	0.0052004	-37.73	0.000	-0.2064088	-0.1860234
Age:
15 a 17	-0.3682908(*)	0.0229653	-16.04	0.000	-0.4133029	-0.3232787
18a 24	-0.2471285(*)	0.0051779	-47.73	0.000	-0.2572773	-0.2369798
25 a 29	-0.1141342(*)	0.0051368	-22.22	0.000	-0.1242024	-0.1040661
40 a 49	0.0700979(*)	0.0068116	10.29	0.000	0.0567470	0.0834487
50 a 64	0.0500103(*)	0.0107229	4.66	0.000	0.0289934	0.0710272
Above 65	-0.1651046(*)	0.0517790	-3.19	0.001	-0.2665916	-0.0636177
Prentice	-0.5942114(*)	0.0490335	-12.12	0.000	-0.6903172	-0.4981057
Indigenous	-0.0795907	0.0455734	-1.75	0.081	-0.1689147	0.0097332
Black	-0.0411013(*)	0.0107514	-3.82	0.000	-0.0621740	-0.0200285
Asian	0.0574381(*)	0.0203516	2.82	0.005	0.0175490	0.0973273
Mulatto	-0.0484528(*)	0.0048974	-9.89	0.000	-0.0580518	-0.0388539
No ethnicity defined	0.0023379	0.0084505	0.28	0.782	-0.0142250	0.0189009
Female	-0.0949639(*)	0.0038325	-24.78	0.000	-0.1024755	-0.0874522
Disability:
Physical (body)	-0.1515977(*)	0.0453535	-3.34	0.001	-0.2404906	-0.0627047
Deaf	0.0085802	0.0586156	0.15	0.884	-0.1063067	0.1234670
Blind	0.1705001	0.9695885	0.18	0.860	-1.7298970	2.0708970
Mental	-0.1510264	0.3202406	-0.47	0.637	-0.7786990	0.4766462
Multiple Disability	-0.3006791	0.3201747	-0.94	0.348	-0.9282225	0.3268643
Rehabilitation	0.2456006	0.1848923	1.33	0.184	-0.1167890	0.6079902
Type of job:
First job	-0.0946160(*)	0.0065336	-14.48	0.000	-0.1074220	-0.0818101
Reinstated	0.0254369	0.0470334	0.54	0.589	-0.0667486	0.1176224
Temporary	-0.0201764	0.0268535	-0.75	0.452	-0.0728093	0.0324566
Region:
North	-0.1775697(*)	0.0105908	-16.77	0.000	-0.1983277	-0.1568117
Northeast	-0.3013411(*)	0.0060224	-50.04	0.000	-0.3131450	-0.2895373
South	-0.1274950(*)	0.0050477	-25.26	0.000	-0.1373886	-0.1176015
Midwest	-0.1540139(*)	0.0069476	-22.17	0.000	-0.1676312	-0.1403966
_ constant	8.05644(*)	0.0065668	1226.84	0.000	8.043569	8.069311
R²			0.5229
No observations			60.447

• Obs: (*) Statistically significant at 5% Source: Search data.

The standard individual, chosen to differentiate the other positions, due the high wage, was: a) holds the position of accountant; b) male; c) Caucasian; d) age between 30 and 39 years old; e) complete university education (graduate); f) has no disability; g) at least a second job; h) works in a company with up to four employees; i) from de southeast region. The choice of this individual is justified by the following reasons: 1) is the professional with the highest average wage among the positions (in the studied period); 2) is the key professional in the area of the accounting, being responsible for all bookkeeping of companies. The comparative analysis is shown in Tables 1, 2, 3 and 4. The Table 2 shows some of the variables and their percentage variations in relation to the standard individual.

Table 2
Percentage variation in relation to the standard individual

Description	2009	2010	2011	2012	2013	2014	2015
Accountant wage (monthly) * *	3,154	3,351	3,473	3,490	3,550	3,515	3,217
Percentage variation in relation to the wage of:
Auditor	*	161.4%	108.7%	*	91.4%	90.0%	91.8%
Assistant Accounting	49.6%	49.7%	48.8%	51.0%	51.6%	52.6%	47.6%
First job	90.5%	93.1%	93.5%	92.5%	92.6%	91.8%	86.2%
Temporary	*	92.0%	93.4%	*	97.4%	92.5%	*
Female	90.5%	90.0%	90.3%	90.8%	89.9%	89.9%	91.4%
Asian	105.7%	105.7%	107.0%	105.4%	*	95.1%	*
Mulatto	95.2%	92.1%	94.0%	95.4%	95.5%	94.5%	93.2%
Black	95.9%	93.5%	92.6%	94.0%	93.9%	92.6%	93.4%
No ethnicity defined	*	98.5%	98.4%	98.6%	*	97.8%	98.6%
Complete High School	65.6%	63.5%	63.7%	65.3%	66.3%	66.7%	66.5%
Incomplete College	80.4%	77.7%	77.2%	77.1%	77.1%	77.3%	73.5%
Physical (body) disability	84.8%	*	91.4%	83.1%	80.1%	82.9%	78.6%
Deafness	*	*	*	68.5%	78.5%	86.1%	85.7%

• Source: Search data. * Model did not present statistical significance at the level of 5%, for the variable. * * The standard established by the model: accountant, man, Caucasian ethnicity, age between 30 and 39 years old, complete university education (graduate), with no disability, who is at least acting in his second job, who works in a company with up to four employees and who is from the southeast region.

According to the outcomes, the average wage of the auditor in 2010 was 161.4% higher than the average wage of the accountant, with significance of 5%. However, in the period studied there was a big drop in the value of the auditor’s average wage. In 2013, it can be observed that the average wage of the auditor became (8.6%) less than the wage of accountant. This should be investigated in future researches, since there is a trend that auditors have higher wages than accountants do.

In the case of the Accounting Assistant, 2014 was the year of higher average relative value of wages since the average wage of the accounting assistant came over the accountant wage (52.6%). However, in the period studied, this proportion oscillated between 47.6% and 52.6% of the accountant wage, that is, a low oscillation (of 5 percentage points), even with positive and negative variations in this proportion. This means that there was no clear trend of real wage gains proportionally to the accountant position.

These results stand out and, it can be inferred that such behavior derives from the economic crisis that has affected Brazil since 2014. As can it be seen, this result was repeated for all positions (accountant, auditor, and assistant accounting) and wages. In addition, the fall in wages in 2015, according to the Instituto Brasileiro de Geografia e Estatística (IBGE, 2018), between 2014 and 2015, had a reduction in real income (inflation adjusted). According to a survey carried out by the Fundação Instituto de Pesquisas Econômicas (FIPE), associated with the Catho Company, from 2014 to 2015, the wages of the new hires, Brazilian had an average decrease of 2%. It represents the worst result of the last 11 years (CATHO, 2018). From Table 2, in the accounting area, the wage reduction was more drastic, certainly impacted by the reduction in the number of vacancies occupied, with repercussions on wages.

To the kind of employment shown in Table 1, the standard individual is the professional who is not in his or her first job. In other words, the employee already has some experience in accounting or another area. It can be observed that this worker has higher wage in all the analyzed years. Even compared to the others who are in the first job, or in temporary work. This can be explained by their experience in the labor market (working time), which is in line with the results of Fields and Yoo (2000) and Brighenti, Jacomossi and Silva (2015). Fields and Yoo (2000) consider experience as one of the most important factors in explaining wage differentials.

In addition, proportional wages in general (in relation to the accountant position) decrease from 2010 to 2015, in small reductions during the period. That is, which it has a lower value in 2015 for professionals who were not in their first job, because they obtained a percentage of 86.2 % in relation to the wage of the professional who is not in his/her first job (standard individual); and in relation to temporary work, the lowest variation was registered in 2010 (92.0%). Thus, it is noticeable that the professional who is not in his first job receives a higher wage than other professionals do. This can be explained by professional experience. However, the wage variation in the kinds of employment is small, since it does not present a relevant difference.

Regarding gender, there was a reduction in the proportional wages of women from 2009 to 2014 and increased in 2015 (91.4% of the men average wage). That is, in 2015, women had wages a little closer to the standard individual, determinate who is a man. During the period, the difference between men and women was approximately 10% in favor for men. However, the lower wages scenario for women was repeated over the years, showing that men are still more valued when compared to whom is a man.

The evidence of wage inequality between men and women in Brazil is also found in studies conducted in several Brazilian regions and states, the exemple of Brighenti, Jacomossi and Silva (2015) and Silva, Dal Magro and Silva (2016). This same reality is found in other countries, as reported in Fields and Yoo (2000).

Described by Dreher, Gehring and Klasen (2015), gender inequality is one of the main problems affecting a large part of developing countries. Moreover, for Jayachandran (2015), men have higher wages than women in almost all countries/regions and in different economic sectors. This may seem normal, but it is not possible to allow, after so much socioeconomic development, that there are no significant changes in relation to this issue. In the accounting profession specifically, Haynes (2017) points out that, even if there have been advances, inequalities persist and worry.

The Pesquisa Nacional por Amostra de Domicílios (PNAD) of IBGE (2018) also showed that there was a reduction in income inequality between men and women, from 74.5% to 76.1% between 2014 and 2015. However, this change may be related to the Brazilian economic crisis, and therefore may be temporary. That result is also confirmed by Silva, Dal Magro and Silva (2016), which found that which is still difficult for women to take higher positions and wages.

When analyzing the ethnic group, Table 1, it is noted that there is no wage discrimination between blacks and mulattos. The wages ratios of blacks and mulattos, compared to the standard (Caucasian) individual for the period studied, are between 92% and 96%, approximately. In other words, blacks and mulattos have average wages below Caucasians in the range of about 8% to 4%, approximately. An emphasis should be placed to Asian ethnicity, who have slightly higher wages than Caucasians, at around 5% for most of the period analyzed.

Concerning education, individuals with complete high school have lower wages, compared to those with incomplete college and complete college. It should be emphasized that the standard individual who receives a higher wage, it is the individual who has complete college. As the people with complete college, the people with complete high school have lower average wages of about 35%; to those with incomplete college, average wages are lower by around 23%. This result was already expected and is similar to previous studies like Fields and Yoo (2000), Brighenti, Jacomossi and Silva (2015) and Hossain, Haque and Haque (2015).

This result confirms that the Human Capital Theory is emphatic in education and qualification investments, giving the greater wages (Mincer, 1958; Becker, 1964). Concerning the kinds of disabilities, physical (body) and deafness were highlighted. People with different types of disability (body) had a relative average wage, to the standard individual (without any disability), lower by approximately of 17% (considering the period studied). As for those who were deafness, however, the relative average was still lower, approximately to 20%, considered to the standard individual.

In most cases, professionals with disability get lower wages. This result is confirmed by ASID (2018) which notes that most of the disabled are not included in the labor market. In addition, 46.4% of those who declared themselves disabled and occupying different positions receive a minimum wage. Table 3 presents the variation of the age group per year in relation to the standard individual (age between 30 and 39 years).

Table 3
Age Range

Classification	2009	2010	2011	2012	2013	2014	2015
Wage (monthly) Reference * *	3,154	3,352	3,473	3,490	3,551	3,516	3,218
Age Range
Apprentice	40.6%	42.4%	30.2%	48.5%	28.0%	37.4%	34.7%
15 to 17 years	63.2%	61.8%	62.1%	60.9%	63.7%	64.1%	81.8%
18 to 24 years	75.3%	75.0%	74.9%	74.7%	75.2%	75.2%	*
25 to 29 years	88.6%	88.8%	87.9%	87.6%	89.0%	88.7%	100.9%
40 to 49 years	107.0%	107.0%	106.8%	164.0%	107.7%	106.8%	118.8%
50 to 64 years	105.0%	104.1%	102.2%	105.6%	104.6%	105.0%	116.8%
65 years or more	83.5%	82.9%	86.6%	*	*	79.6%	109.4%

• Source: Search data. * Model did not present statistical significance at the level of 5%, for the variable. * * The standard established by the model: Accountant, man, Caucasian ethnicity, between 30 and 39 years of age, complete university education (graduate), with no disability, who is at least works in his second job, who works in a company with up to four employees and who is from southeast region.

It can be observed that, among the age groups, when the age increases, the relative average wage rises gradually against the average wage of the standard individual (30 to 39 years old). People between the ages of 40 and 64 have a relative average wage higher than the average wage of the standard individual (approximately 18%).

Another result here is that it should be highlighted that for people being over 65 years old, the average wage is lower than the average wage standard individual is, i.e. having a wage of about 12% lower than the standard individual. In addition, it has a wage 18% lower than the age range between 50 and 64 years old. In other words, when the person reaches the age 65 years old and he or she needs to continue working, the average wage decreases to approximately 18%. Table 4 presents the wage gap, according to company size, with the standard individual, in this case, the employee of a company with up to four employees.

Table 4
Company size

Classification	2009	2010	2011	2012	2013	2014	2015
Wage Reference (monthly) * *	3,154	3,351	3,473	3,490	3,550	3,515	3,217
Company size
5 to 9 employees	*	102.9%	103.2%	101.8%	101.6%	104.5%	103.6%
10 to 19 employees	108.5%	110.3%	109.6%	108.3%	107.3%	109.3%	109.0%
20 to 49 employees	116.8%	117.1%	116.2%	115.6%	114.0%	114.8%	114.0%
50 to 99 employees	124.6%	122.4%	123.2%	122.3%	119.0%	120.6%	120.7%
100 to 249 employees	128.5%	129.4%	129.8%	127.5%	125.4%	127.0%	126.9%
250 to 499 employees	133.7%	135.9%	133.1%	133.3%	128.9%	129.9%	131.3%
500 to 999 employees	134.5%	135.5%	132.1%	135.6%	135.6%	132.2%	125.1%
1,000 or + employees	146.9%	149.5%	144.5%	141.6%	137.0%	138.2%	132.0%

• Source: Search data. * Model did not present statistical significance at the level of 5%, for the variable. * * The standard established by the model: accountant, man, Caucasian ethnicity, between 30 and 39 years of age, complete university education (graduate), with no disability, who is at least works in his second job, who works in a company with up to four employees and who is from the southeast region.

The standard individual is the person who works in companies with up to four employees, so he is an employee of small businesses. It is noticed that, in general, the more employees in the company, the higher wage of employees in the accounting area. It is common to all years analyzed, since the variation exceeds 100%, that is, the wage of these professionals is higher than the wages of those who work in companies with up to four employees.

It is noteworthy that in 2015, the variation of all professionals was reduced, however, this reduction was small. The outcomes corroborate with the findings of Fields and Yoo (2000), Brighenti, Jacomossi and Silva (2015) and Silva, Dal Magro and Silva (2016) that identified that the size of the company affects the wages. Finally, with respect to Table 5, on the Brazilian regions, there is the wage difference to professional in the Southeast region (individual standard).

Table 5
Geographic Region

Classification	2009	2010	2011	2012	2013	2014	2015
Wage (monthly) * *	3,154	3,351	3,473	3,490	3,550	3,515	3,217
Region
North	82.2%	89.8%	79.5%	78.0%	78.1%	80.3%	80.3%
Northeast	69.9%	71.8%	69.6%	71.5%	72.2%	72.4%	74.9%
South	87.3%	86.1%	86.4%	88.5%	89.0%	89.6%	91.6%
Midwest	84.6%	81.2%	83.3%	85.2%	84.8%	85.7%	88.0%

• Source: Search data. * Model did not present statistical significance at the level of 5%, for the variable. * * The standard established by the model: accountant, man, Caucasian ethnicity, between 30 and 39 years of age, complete university education (graduate), with no disability, who is at least works in his second job, who works in a company with up to four employees and who is from the southeast region.

Accounting employees located in the South are the ones with the smallest wage difference in relation to those who work in the Southeast region (standard individual); except in 2010, in which the lowest difference was observed for the North region (89.8%). Therefore, there is a wage discrimination between the Brazilian regions, in favor to the South region (biggest commercial and industrial region of the country). The South (second Brazilian region in economic issue) and Midwest regions are those that are closer to wages paid in the Southeast. On the other hand, the worst wages are those in the Northeast region. The Northeast region was also identified as the one that pays less to professionals in the research of Campante, Crespo and Leite (2004).

According to the results of Campante, Crespo and Leite (2004), the wage in the Northeast is linked to the fact that poor individuals classify themselves as black or mulatto, increasing the wage gap in this region. However, the current outcomes research is different to the results obtained by these authors, who analyzed intra-region problem. They claim that the Southeast region exceeds the national average of wage discrimination. Fields and Yoo (2000), Agbodji, Batana and Ouedraogo (2015) and Castellano Manna and Punzo (2016) concluded that in many countries there is a wage gap in states and regions, which corroborates with the results found in this research.

From these results, it can be confirmed what the Theory of Segmentation proposes. That is, employees may receive different wages even if they are equally productive. In this case, the segmentation occurs precisely when individuals with similar characteristics receive different wages (SOUZA, 1978).

CONCLUSIONS

This research aimed to describe the determinants that affect the wage gap between employees in accounting in Brazil from 2009 to 2015. The accounting professionals analyzed in this study are Accounting Assistants, Accountants and Auditors. Multiple regressions with dummy variables were estimated to identify the factors that affect the wage, i.e.: position, gender, ethnicity, age, schooling, disability, company size, employee movement (kind) and regions.

The research was performed analyzing employees, so the age of the companies does not interfere in the outcomes. It is worth mentioning that Brazil is interesting for this research, since it is the biggest country in Latin America and being among the countries with the highest wage inequality in the world. Additionally, accounting professionals are part of an important sector for companies as well as are a prominent professional group.

From the results, it can be concluded that, regarding the problem and purpose of the research, a standard individual was chosen, which represents the most frequent characteristics of an accounting professional. The characteristics are: man, Accountant, employee of a company with up to four employees, complete university education (graduate), aged between 30 and 39 years old, white, have no type of disability, are (at least) second job and are of the Southeast region of Brazil.

For higher average wages it is necessary to change at least one of these characteristics (standard individual). The accountant must work in a large company, especially with more than a thousand employees. Or, another possibility of obtaining higher wages is when the employee (Accountant) is in the age group between 40 and 49 years old. Therefore, professionals who have the characteristics of the standard individual, adjusted to these latest changes, have the highest wages (based on data). This endorses what is exposed in the Human Capital Theory and Segmentation Theory.

Based on the results, it is possible to evaluate some issues and propose actions to reduce the wage gap between the companies and among professionals. This contributes to the creation of a wage base at different levels of performance. This action is important for the accounting area because it provides greater guarantee and incentive to the profession in the national scenario (JEACLE, 2011). As already stated by Campante, Crespo and Leite (2004) and Hossain, Haque and Haque (2015), some public policies must be considered to reduce social and wage inequality across the country. On inequality between men and women, Jayachandran (2015) points out that in any country, developed or not, politicians have many tools to accelerate the process of reducing inequality.

The research contributes to some issues that need attention, because they represent discrimination, such as the disabled, whose wage inequality is big. At the same time, women receive lower wages and find it very difficult to occupy higher positions. The region of the country and ethnicity are also factors that make the scenario of inequality relevant and, therefore, some private or public actions are necessary to improve this situation. So, no doubt, know the determining factors is important to review and plan public policies and raise awareness among companies. This to reduce the wage inequality that affects a very large number of people around the world.

The limitations of the study concern to data. It does not present information about the time experience of employees, for example. Therefore, it is suggested to extend this research to analyze the experience of employees and verify if the wage gap can be explained by this factor. Another recommendation would be to analyze the coming years with available data, to observe if there will be reduction of inequality in relation to the factors. Another proposal for future studies is the analysis of the impact of the economic crisis on employment and gender in accounting. This is because there was a significant wage reduction, especially from 2014/2015, and the crisis can be an explanatory factor of this phenomenon.

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• [Original version]

Publication Dates

• Publication in this collection
  10 July 2020
• Date of issue
  Apr-Jun 2020

History

• Received
  02 Oct 2018
• Accepted
  06 Jan 2020