Evaluation of the impact of the educational revolution in Peru and the gender wage gap, 2017-2021

1. Introduction

The health crisis highlighted the need for a new social contract that addresses the poor distribution of surplus involving wages, salaries, or dividends from the perspective of Social Economy. Following the health crisis, inequalities were exacerbated due to existing poor redistributive mechanisms (Costas, 2020; Gonzáles, 2021; Singh et al., 2022; Nguyen, 2022; Abdel et al., 2023). It can be observed that the pandemic context fosters situations where income disparities between men and women in developing and developed countries persist despite women’s growing educational attainment (Kireyeva & Satybaldin, 2019; Brzezinski, 2021; Da Costa & Shinkoda, 2021; Afrin & Shammi, 2023).

The educational achievement of women and their value generation through education has led to the prospect of achieving economic equality between men and women. However, women continue to receive lower wages compared to men, reflecting growing inequality in countries such as the United States and Latin American countries. Education is the main factor for women in achieving significant economic success in wage discrimination (Kurek & Górowski, 2020; Litman et al., 2020; England et al., 2020; Reissová & Šimsová, 2019).

There is extensive literature on the persistent gender wage gap in various countries and economic sectors, expressed in working hours, experience, years of education, salary, and labor market. However, there are few studies on the expected return of education on the gender wage gap, which has a significant impact on wage disparity (Fox et al., 2019; Ñiquen, 2019; Smith et al., 2021).

According to Suharyono and Digdowiseiso (2021), in theoretical evidence, salary is the main motivation for labor effort. Empirical evidence has shown a positive effect of education on wage equality in a complex globalization context, where the indecisiveness of public policy managers highlights a concern for the obstacle in hindering women’s continuous development and other associated factors such as stereotypes and ideology restricting women’s access to education (Quadlin et al., 2023; Leibing et al., 2023).

For the International Labour Organization (ILO) in Latin America and the Caribbean, women have had greater participation in the labor aspect in recent decades. However, there is still a 25% economic dependency gap of women on men (Dancausa Millán et al., 2021; Organización Internacional de Trabajo, 2019).

In Peru, simply being a woman evidences the conditioning of receiving different wages, further accentuating the differentiation after the restrictions imposed by the COVID-19 health crisis. Despite the monthly wage gap narrowing from over 20% to 14% since 2016, and the gender gap in hourly wages decreasing from 13.9% to 9.2% in 2010 and 2016, this wage gap is associated with gender disparities related to the labor market’s own dynamics, where women have their own characterization related to variables such as sex, age, marital status, access to health services, among others. Education, along with women’s socioeconomic conditions, influences the probability of having better human capital (Carlosviza et al., 2021; Defensoría del Pueblo, 2019; Rios, 2019; Valdez & Sobrevilla, 2021).

The role of women in economic development is often doubtful, as they are considered inferior, unworthy, and incapable of working. Gender discrimination reduces the economy’s growth capacity and the ability to improve the standard of living (Schober & Winter-Ebmer, 2011; Sugiharti & Kurnia, 2018; Didier, 2021; Alwago, 2023; Bataka, 2024).

Salary is considered a broad topic in economic literature, as it involves both capabilities and skills that result in productivity indicators. It is necessary to point out that the return on education concerning wages is the compensation for education and experience, which starts from different points of the wage distribution. Therefore, the gender wage gap varies across different economic sectors (Castagnetti & Giorgetti, 2019; Tansel et al., 2020; Mandel & Rotman, 2021).

It is essential to indicate that education has generated higher returns on income, validating the theory of investment in education as an investment in human capital. In the case of women, this has led to wage gaps reducing as they gain greater access to education (Barra, 2018; Carlosviza et al., 2021).

Women and men have almost identical human capital; however, the gender wage gap and the trajectory it has created over time in various traditional labor markets have reflected the cumulative effects and gender roles that have emerged throughout life. The drivers of the mentioned gap are productivity factors such as education, skills, and work experience. These individual-level factors most representatively explain the gender wage gap (Chowdhury et al., 2018; Designed & Performed, 2010; Díaz, 2021; Litman et al., 2020; Sterling et al., 2020).

In this context, the present study aims to estimate the impact of education on the gender wage gap in Peru during the period 2017-2021, for which the Tobit estimation, known as the Censored Regression Model, is performed.

In the review of scientific literature, the research by Picatoste et al. (2023) evidenced that the main concerns related to the gender gap are access, use, and outcomes, as well as the wage disparity existing in the European Union. Additionally, using mean comparison, it was shown that the variables significantly influencing the wage gap are related to educational level and social aspects. Meanwhile et al. (2023) in their findings, show that the gender wage gap significantly impacts women’s empowerment due to limitations in women’s economic independence and autonomy.

On the other hand, Penner et al. (2022) in their various studies, indicate that the gender wage gap highlights that various countries can be identified where the wage gap is a crucial aspect of gender differences due to the different jobs that substantially represent wage differences. Using the ordinary least squares methodology, four models related to wage disparity processes between men and women are compared, leading to a gender wage inequality that presents various challenges for the relevance of the 15 countries where the models’ results are compared. However, education is relevant not only as a factor explaining wage inequality between men and women but also between whites and blacks, implying the racist effect on racial inequality disparity, which increases the gap in the labor market with educational interventions that close racial disparities (Zhou & Pan, 2023).

Thus, growing economic inequality has highlighted a variety of literature where wage dispersion involves significant inequality using the VECM model. Considering the structural model reveals that it affects inequality with various impacts on the labor market and becomes relevant in wages with a biased technological shock and structural inequality that negatively impacts working hours and reduces productivity by facing negative inequality in the labor market characterized by high levels of inequality (Hutter & Weber, 2023).

Additionally, in American companies, data shows that female executives represent 6% of the sample, and 26% of women earn less than men. This is given the relevant characteristics where the existence of gender wage gaps within companies is evident, considering the marked income inequality estimated with the entry and exit rate equation methodology, which examines wage gaps (Keller et al., 2023).

Finally, in Europe, it is still evident that women perceive higher levels of income inequality, revealing the persistence of gender wage disparities. In 15 of the 28 countries analyzed, women report the wage gap between incomes and expenditures, highlighting the driving force needed to reduce the gender wage gap and showing a persistence of these disparities (Adriaans & Targa, 2023).

2. Methods

2.3 Sample

The sample includes the number of observations comprising dependent and independent men and women who earn income and are part of the economically active employed population. The inclusion and exclusion criteria are detailed below in Table 1.

Figure 1. Dynamics of the Gender Wage Gap in the Period 2017-2021.

Note: Values from the Encuesta Nacional de Hogares database, 2017-2021, obtained from INEI (2024).

Table 1. Sample for the period 2017-2021.

Year	Gender
	Men	Women
2017	15313	4429
2018	15953	4791
2019	14350	4613
2020	13127	4352
2021	12678	5251

Table 2. Data sources for the variables used in the methodology of this study.

Variables	Type	Description	Unit	Data sources
W_total	Dependent	Monthly income of men and women	Nuevos soles	Household Survey conducted by the National Institute of Statistics and Informatics (INEI)
Age	Explanatory of interest	Age	Years
Minor_6	Explanatory of interest	Number of children under 6 years old	Unit
Between_6_18	Explanatory of interest	Number of children aged between 6 and 18	Unit
Educ	Explanatory of interest	Years of education	Years

Inclusion criteria: Men and women aged 14 and older who are part of the economically active employed population, both dependent and independent, and who earn income during the period 2017-2021.

Exclusion criteria: Men and women aged 14 and older who are part of the economically inactive population and the unemployed during the period 2017-2021.

3. Results

This article is based on the Tobit econometric model, an econometric model used to identify the impact of education on the gender wage gap in Peru during the period 2017-2021. This model used the database of the National Household Survey (ENAHO) 2017-2021, using the total wage (w_total) of men and women and explanatory variables such as age, number of children under 6 years old (under 6), years of education (educ) and children between 6 and 18 years old (between-6-18).

The gender gap in Peru showed a decreasing trend between men and women during the period 2017-2020, with an average reduction of 10% until the year 2020 due to the COVID-19 health crisis. The highest average salary achieved by men was in 2019, reaching S/. 2289.97 soles, while women reached an average salary of S/. 1368.85 soles. In the post-pandemic scenario for the year 2021, the gender gap increased by 3%, with men earning an average salary of S/. 1999.63 soles and women earning an average salary of S/. 1281.16 soles.

While the gender gap has clearly reduced, during the health crisis, men’s salaries showed a greater decline in 2020, with a negative variation of 16.5% compared to 2019. Meanwhile, women experienced a negative variation of only 11.5% during the health crisis compared to 2019. With the post-pandemic economic recovery, women’s salaries showed a more accelerated growth, reaching a variation of 5.7%, while men’s salaries grew by only 4.6%.

In Table 3, the Tobit model estimation shows that education has positively influenced the salaries of both men and women, with a clear wage gap of 137.56 soles. An increase of one year of education in men generates an increase in their salaries by 293.21 soles, while for women, an increase of one year of education generates an increase in their salaries by 155.65 soles.

The marginal effect of the model shows that the probability of women’s incomes improving with an increase in years of education is 2.14%, while for men, the probability of their incomes improving with an increase in years of education is 2.11%, reflecting the greater impact of education on women. In the case of men, the probability of their incomes improving decreases by 0.96% with an increase in the number of children under 6 years old.

The Tobit model shows that the highly significant variables are “educ” (years of education) at 99% and “menor_6” (number of children under 6 years old) at 95%, demonstrating the positive impact of education on the gender wage gap in Peru in 2017.

In Table 4, the Tobit model estimation shows that education has positively influenced the salaries of both men and women, with a clear wage gap of 126.51 soles. An increase of one year of education in men generates an increase in their salaries by 286.30 soles, while for women, an increase of one year of education generates an increase in their salaries by 159.79 soles.

The marginal effect of the model shows that the probability of women’s incomes improving with an increase in years of education is 2.31%, while for men, the probability of their incomes improving with an increase in years of education is 2.16%, reflecting the greater impact of education on women. In the case of men, the probability of their incomes improving decreases by 1.16% with an increase in the number of children under 6 years old, and for women, the probability of their incomes improving with greater age is 0.14%.

Observing the Tobit model, the variables that are highly significant are “educ” (years of education) at 99%, “menor_6” (number of children under 6 years old) at 95%, and “edad” (age in years) at 99%, demonstrating the positive impact of education on the gender wage gap in Peru in 2018.

In Table 5, the Tobit model estimation shows that education has positively influenced the salaries of both men and women, with a clear wage gap of 128.83 soles. An increase of one year of education in men generates an increase in their salaries by 292.53 soles, while for women, an increase of one year of education generates an increase in their salaries by 163.70 soles.

The marginal effect of the model shows that the probability of women’s incomes improving with an increase in years of education is 2.12%, while for men, the probability of their incomes improving with an increase in years of education is 2.01%, reflecting the greater impact of education on women. In the case of men, the probability of their incomes improving decreases by 1.08% with an increase in the number of children under 6 years old.

Observing the Tobit model, the variables that are highly significant are “educ” (years of education) at 99% and “menor_6” (number of children under 6 years old) at 95%, demonstrating the positive impact of education on the gender wage gap in Peru in 2019.

In Table 6, the Tobit model estimation shows that education has positively influenced the salaries of both men and women, with a clear wage gap of 106.18 soles. An increase of one year of education in men generates an increase in their salaries by 261.90 soles, while for women, an increase of one year of education generates an increase in their salaries by 155.72 soles.

The marginal effect of the model shows that the probability of women’s incomes improving with an increase in years of education is 2.35%, while for men, the probability of their incomes improving with an increase in years of education is 2.09%, reflecting the greater impact of education on women. In the case of men, the probability of their incomes improving with an increase in age is 0.07%, and for women, the probability of their incomes improving with greater age is 0.31%.

Observing the Tobit model, the variables that are highly significant are “educ” (years of education) at 99% and “edad” (age in years) at 99%, demonstrating the positive impact of education on the gender wage gap in Peru in 2020.

In the Table 7, the Tobit model estimation shows that education has positively influenced the salaries of both men and women, with a clear wage gap of 101.17 soles. An increase of one year of education in men generates an increase in their salaries by 256.93 soles, while for women, an increase of one year of education generates an increase in their salaries by 155.76 soles.

The marginal effect of the model shows that the probability of women’s incomes improving with an increase in years of education is 2.12%, which is the same as the probability for men. For women, the probability of their incomes improving with an increase in age is 2.12%, while for men, the probability of their incomes improving decreases by 1.16% with an increase in the number of children under 6 years old, and for women, the probability decreases by 1.92%.

Observing the Tobit model, the variables that are highly significant are “educ” (years of education) at 99%, “menor_6” (number of children under 6 years old) at 95%, “entre_6_18” (number of children aged between 6 and 18) at 95%, and “edad” (age in years) at 95%, demonstrating the positive impact of education on the gender wage gap in Peru in 2021.

4. Discussion

The previously found results show that during the analysis period of 2017-2021, years of education have had a positive impact on the gender wage gap in Peru based on the Tobit estimation. These findings are consistent with those of Picatoste et al. (2023) which revealed that the gender gap is a relevant aspect of the European Union, considering that education has significantly influenced the wage gap and is a relevant value in the social aspect.

Similarly, they are related to Reshi and Sudha (2023) whose findings show that the gender wage gap has a significant impact on women’s empowerment due to the limitations of women’s economic independence and autonomy.

On the other hand, Penner et al. (2022) in their various studies, indicate that the gender wage gap highlights that various countries can be identified where the wage gap has been a crucial aspect of gender differences due to different jobs that substantially represent wage differences between men and women, developing wage inequality that faces various challenges in comparing results.

Adriaans and Targa (2023) reveal that women with higher education levels tend to earn higher wages compared to those with lower education levels. However, the gender wage gap persists even among people with similar education levels.

Differences in labor market participation between men and women, such as the proportion of women working full-time versus part-time, can contribute to the wage gap.

The increase in women’s participation in higher education and in academic and professional fields traditionally dominated by men can have a positive impact on reducing the wage gap.

In this way, Hutter and Weber (2023) consider that growing economic inequality has highlighted a variety of literature where wage dispersion involves significant inequality using the VECM model. Considering the structural model reveals that it affects inequality with various impacts on the labor market and becomes relevant in wages with a biased technological shock and structural inequality that negatively impacts working hours and reduces productivity by facing negative inequality in the labor market characterized by high levels of inequality.

Accessible and equitable education provides men and women with the same opportunities to acquire skills and knowledge. This establishes the foundation for fair labor competition and reduces initial disparities that could contribute to wage gaps.

Education provides people with the necessary skills and competencies to perform different labor roles. Acquiring specific skills and advanced knowledge can increase an employee’s perceived value, regardless of gender.

Solid education allows people to make informed career decisions. Encouraging women to enter fields traditionally dominated by men and men to consider options in predominantly female fields can contribute to reducing wage gaps.

Additionally, quality education can offer opportunities for social and economic mobility, allowing people to overcome socioeconomic barriers. This is especially important for groups that have historically faced discrimination, such as women, as education can be a vehicle for empowerment and financial autonomy.

5. Conclusions

Gender wage gap in Peru has shown a decreasing trend between men and women from 2017 to 2020 during the health crisis scenario, only to be reversed in the post-pandemic period of 2021, where the gender gap grew by 3%, with men reaching an average salary of S/.1999.63 soles and women reaching an average salary of S/.1281.16 soles. Despite this, women exhibited a higher accelerated salary growth, with a variation of 5.7%, while men’s salaries only grew by 4.6% in the economic reactivation of 2021 compared to 2020.

The Tobit model estimation in 2017 considers the variables of education (years of education) significant at 99%, minor_6 (number of children under 6 years old) at 95%, and age (years) at 99%. In 2018, the highly significant variables are education (years of education) at 99% and minor_6 (number of children under 6 years old) at 95%. In contrast, in 2019, the highly significant variables are education (years of education) at 99% and age (years) at 99%, and for 2020, the highly significant variables are education (years of education) at 99%, minor_6 (number of children under 6 years old) at 95%, between_6_18 (number of children aged between 6 and 18 years) at 95%, and age (years) at 95%; with education years being highly significant for both men and women at 99% in influencing their salaries during the period 2017-2021.

The Tobit model estimation shows that in 2019, women generated an increase in their salaries by S/.163.70 soles for every additional year of education, while in 2017, men generated an increase in their salaries by S/.293.21 soles for every additional year of education.

During the analysis period of 2017-2021, years of education have had a positive impact on the gender wage gap in Peru, with the greatest impact during the health crisis, as the probability of women’s incomes improving with an increase in years of education was 2.35%, and for men, their greatest impact was in 2018, with a probability of their incomes improving with an increase in years of education at 2.16% in terms of marginal effect.

In conclusion, it is emphasized that the findings recommend promoting gender equality in both access to education and professional training for women. Additionally, states should implement appropriate policies to support salary equities and job opportunities for both men and women.