INTRODUCTION
Problem statement
The gender pay gap is a significant global issue, as highlighted by Nguyet and Binh (2007), who assert that the income disparity between men and women contributes to poverty and impedes development A wider gender earnings gap exacerbates poverty levels, leading to increased malnutrition, illness, and various hardships.
In addition, economic growth will bring more effective for the reduction of poverty level in the country which has a higher level of gender equality
Moreover, inequalities in income between women and men prevent the equal development and this makes the use of resources in society inefficient
Gender inequality in earnings is prevalent in many countries, particularly in developing nations, largely due to traditional views and societal norms that favor male supremacy This perspective restricts women's access to education, training, and professional opportunities, leading to significant disparities in labor distribution and income between men and women across various occupations Additionally, women face limited access to essential resources such as water, transportation, and capital, further hindering their economic advancement and overall status.
In Vietnam, as reported by the International Labor Organization (ILO) on
As of March 7, 2013, Vietnam faced a growing income disparity between men and women, despite having a higher female labor force participation rate of approximately 72%, surpassing many countries globally This trend was concerning, as Vietnam was among the few nations where the gender wage gap widened from 2008 to 2011, unlike the global trend observed during 1999-2007 Furthermore, the 2012-2013 Global Wages Report by the ILO indicated a 2% increase in the gender income gap in Vietnam during this recent period.
In 2011, the General Department of Statistics revealed that women's income was approximately 13% lower than men's A 2012 survey by the General Labor Confederation of Vietnam (VGCL) indicated that female workers earned only 70-80% of their male counterparts' wages The Labor Survey Report from the same year showed that women's average monthly income was consistently lower than men's across all economic sectors, including state, non-state, and foreign investment Even in female-dominated fields such as healthcare, social work, and sales, women still faced salary disparities compared to their male colleagues The VGCL survey further highlighted that women were often relegated to routine roles, while men occupied management positions.
Numerous studies have explored the gender pay gap, identifying key determinants of wage disparities between men and women while proposing solutions to mitigate this issue, including significant works by Anderson et al (2001), Hultin and Szulkin (2003), and Blau and Lawrence (2007) In Vietnam, research by Liu (2002) and Pham and Barry (2007) has also addressed this gap Notably, findings suggest a correlation between the proportion of female managers and the gender wage gap, indicating that workplaces with fewer women in leadership roles tend to exhibit larger income disparities Hirsch's (2013) research in Germany revealed that increasing the female representation in first-level management by 10% points resulted in a 0.5 log point decrease in gender income differences Building on Hirsch’s foundational work, this study examines the relationship between female managerial representation and the gender pay gap in small and medium enterprises (SMEs) in Vietnam, focusing on data from 2007.
Research objective
This study aims to analyze the significant role of women in reducing the gender income gap within the management assembly lines of small and medium-sized enterprises in Vietnam.
This research is also conducted to answer the main question that is gender pay gap low in plants with more female managers?
And for the scope of study, it is undertaken in SMEs in Vietnam in the year of 2007.
Structure of research
This research is structured into five chapters: Chapter one outlines the problem statement and research objectives Chapter two reviews relevant literature, defining key concepts and exploring the causes and factors influencing the gender pay gap Chapter three provides an overview of the gender gap in Vietnam Chapter four details the data description, methodology, and presents estimated results Finally, chapter five concludes with policy suggestions and discusses limitations of the study.
LITERATURE REVIEW
The definitions
Under the leadership of Kim Thi Bui, the director of the Division of Workers’ Compensation (DWC), gender equality is emphasized as a crucial sociological concept that encompasses the roles, responsibilities, and social relationships between men and women.
Gender encompasses the allocation of labor, resources, and benefits between men and women within specific social contexts It is shaped by learning, education, and the diverse differences present in various countries and localities Additionally, gender roles evolve over time, reflecting the dynamics of socio-economic development.
The benefits of gender equality positively impact both women and men, leading to a transformation in the traditional division of labor This shift promotes progress and significantly contributes to enhancing gender equality in society.
The Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) 1978 emphasizes gender equality, ensuring that women and men have equal living conditions and opportunities This equality allows both genders to access resources that benefit themselves and fosters the development of their unique potentials, ultimately contributing to the progress of their countries and reaping the rewards of such development.
Gender inequality, often referred to as "discrimination against women," encompasses any distinctions, exclusions, or restrictions based on gender that adversely affect women's rights and freedoms This discrimination hinders women's ability to fully participate in political, economic, social, cultural, and civil spheres, undermining the principle of equality between men and women regardless of their marital status.
Gender inequality in the labor market manifests through discrimination in access to opportunities, employment, and the inheritance of achievements between male and female workers This human-centered perspective highlights that income inequality is closely related to the distribution of earnings based on gender Consequently, the gender pay gap reflects the unjust income disparity between men and women, despite their comparable capabilities and labor productivity.
The final report by Anderson, Forth, Metcalf, and Kirby (2001) for the Department for Education and Employment outlines various theories explaining the male-female income disparity Human capital theory attributes the earnings gap to differences in innate abilities, education, and experience between genders, which affect productivity In contrast, social construction theories suggest that the historically inferior status of women has led to the undervaluation of traditionally female skills, creating a value system that penalizes both genders Additionally, the dual labor market theory posits the existence of two distinct labor markets: the primary market, which offers higher wages and better conditions for greater productivity, and the secondary market, characterized by lower pay and poor conditions Women’s restricted labor market options often lead them to seek employment primarily in the secondary market, thereby contributing to the gender pay gap.
The Organization for Economic Co-operation and Development (OECD) defines the gender pay gap as the percentage difference between male and female earnings, highlighting the disparity in income between genders.
Cause of gender wage gap and the factors impact on gender wage gap 12 1 Cause of gender wage gap
In conclusion, numerous studies have been conducted over the years regarding the gender pay gap, consistently highlighting the disparities in earnings between males and females.
2.2 Cause of gender wage gap and the factors impact on gender wage gap
2.2.1 Cause of gender wage gap
According to Anderson et al (2001), theories such as the neo-classical labor market highlight individual and institutional factors contributing to the gender pay gap Human capital theory suggests that differences in innate abilities and skills between women and men result in lower productivity for women, thereby explaining the pay disparity Additionally, women's preferences for lower-paying jobs compared to men perpetuate the income gap Discrimination also plays a significant role, with Jewson and Mason (1986) identifying four types: rational-legal, determinist, particularist, and patronage Furthermore, the social undervaluation of traditionally female skills stems from their historically inferior status, while crowding in the labor market leads to an excess supply of labor, ultimately driving wages down (Bergmann, 1971).
Moreover, female labor “faced with the larger degree of monopsony when compared with male labor, and the wage rate for women would be lower”
The internal labor market has historically positioned women at a disadvantage, contributing to the gender earnings gap (Manning, 1996; Kerr, 1950) Additionally, factors such as unionization and collective pay bargaining play a crucial role in this disparity, as women often have less representation in these negotiations, further widening the gender pay gap (Anderson et al., 2001) Other contributing elements to the income difference between men and women include compensating differentials, employer characteristics, and secondary effects.
According to the Fawcett Society, the leading UK charity for gender equality and women's rights, several key factors contribute to the gender pay gap, including the motherhood penalty, concentration in low-wage sectors, and discrimination The motherhood penalty often leads women to opt for part-time work due to childcare responsibilities, resulting in lower hourly wages compared to full-time positions The organization reports that women earn, on average, 19.1% less than men in the same part-time roles, with the gap narrowing to 9.4% in full-time employment Consequently, when women reduce their working hours for childcare, they sacrifice both their current and future income potential.
Occupational discrimination based on gender persists, with women predominantly occupying lower-paid roles in cleaning and catering, while men dominate higher-paying fields like construction and engineering According to the Fawcett Society, women hold 78% of positions in health and social care, which are often low-paid, compared to men who represent 88% of the workforce in STEM industries, known for their higher salaries Discrimination against women in the workplace manifests both directly and indirectly; direct discrimination occurs when women receive lower wages than men for the same job, while indirect discrimination is evident when men earn more than women in roles of equal value.
The European Commission, in collaboration with the Fawcett Society, identifies several key factors contributing to the gender pay gap, including direct discrimination, undervaluation of women's work, labor market segregation, and societal stereotypes, as outlined in their 2011 brochure "Tackling the Gender Pay Gap in the European Union." While legislation has mitigated direct discrimination, it still accounts for only a small portion of the income disparity A significant reason for lower pay among women, even in equal-value jobs, is the undervaluation of their skills compared to men's Women are often employed in lower-paying sectors such as health, education, and public administration, which require similar qualifications and experience Furthermore, research indicates that 32% of female workers engage in part-time jobs compared to just 8% of male workers in Europe, largely due to women's responsibilities in family care.
Meanwhile, in Vietnam, following the research of Nguyet and Binh
In 2007, researchers identified two primary factors influencing the gender earnings gap: noneconomic and economic groups Noneconomic factors encompass traditional gender inequalities and social stereotypes that hinder gender balance and equality These outdated feudal beliefs have historically positioned men as societal leaders and providers, while women were relegated to domestic roles, fostering dependency and limiting women's rights On the other hand, economic factors include employee characteristics such as age, marital status, health, and education, which significantly impact income Higher education often leads to better job opportunities and wages, particularly in skilled professions Additionally, labor elements like profession, experience, and work organization affect earnings; for instance, workers in agriculture typically earn less than those in industry or services In the same roles, income varies based on expertise and experience, with more experienced employees often commanding higher wages due to their efficiency and skill.
2.2.2 The factors impact on gender wage gap
The World Bank (WB) highlighted in 2001 that societal stereotypes and gender inequality significantly hinder the progress towards achieving gender balance and equality between males and females.
During feudal times, societal values regarding women's roles were significantly regressive, making change challenging Men were predominantly seen as the key contributors to social work and production, earning respect for their responsibilities in social management In contrast, women were largely confined to roles related to reproduction and community care, focusing on domestic responsibilities such as homemaking and childcare.
Most of the works often do not generate income and they then completely depend on men, nor have any right to dispose of anything even for themselves
Barriers to education and career opportunities for women significantly hinder their economic advancement, perpetuating income inequalities compared to men.
Worker characteristics encompass various factors, including physical attributes and gender, as well as age, health, and marital status Empirical evidence indicates that an employee's income is significantly influenced by their age.
Young female workers often start with lower wages, which increase as they gain experience and skills, but tend to decline in later years In contrast, young male workers typically see faster income growth (Bojas, 2005) Health disparities also contribute to the gender income gap, as differing gender roles lead to fewer women in certain professions, resulting in lower wages for women Additionally, marital status affects income for both genders; while marriage and children increase financial demands, women often bear greater caregiving responsibilities, limiting their work opportunities and leading to lower earnings compared to men.
Education is a very important factor affecting the income of workers
Wages are set to rise for positions that demand advanced qualifications and specialized skills According to Boris (2005), there is a clear correlation between income and educational attainment, illustrated through the wage and education curve This relationship indicates that employers are prepared to offer salaries that align with the educational levels of their employees, highlighting the connection between years of schooling and earning potential.
Employment factors such as industry, expertise, experience, and type of enterprise significantly influence wages Typically, employees in the agricultural sector earn less than those in other industries due to lower skill and qualification requirements, relying primarily on physical capabilities Within the same industry, wages also vary based on individual expertise and experience; more complex tasks command higher pay, while experienced workers can complete jobs more efficiently, leading to increased earnings.
The type of enterprise significantly influences the earnings disparity between men and women Organizations that adhere to legal regulations typically implement gender equality policies, ensuring that women receive more equitable income in the workplace.
Income is paid to employees with ensuring to cover their life and family
Measurement of gender pay gap
The gender wage gap represents the income disparity between female and male workers, as noted by the Pay Equity Commission in 2012 This gap can also be expressed as a percentage, according to Statistics New Zealand in 2014, using a specific formula.
In which, pay for worker can be measured by hourly, weekly or monthly
More details, when the purpose focuses on analyzing labor market and poverty outcomes for numerous of men and women, monthly earnings are preferable
Hourly earnings tend to be more favorable when comparing smaller groups of workers within the same occupations or enterprises Typically, the earnings gap measured by hours worked is narrower than that assessed over longer periods This discrepancy arises because, in many countries, women generally work fewer hours for income compared to men.
The impact on economic - social development of gender wage gap
Gender inequality in income, identified by the World Bank in 2001, is a significant contributor to poverty and a major barrier to economic development This inequality not only inflicts injustices on women but also adversely affects their families Women's income from labor is essential for their own empowerment and for enhancing the quality of life within their households However, the existing gender income disparity limits women's capacity for labor, restricts their access to technology, education, and training, and adds to their family workload Additionally, the lack of decision-making power within families contributes to higher maternal and infant mortality rates, poorer family health, and decreased school attendance for children, particularly for girls.
Gender income inequalities hinder productivity in both military and business environments, limiting poverty reduction and economic growth These disparities obstruct the accumulation of human capital and restrict access to essential resources and production opportunities, resulting in inefficient resource allocation within society Additionally, lower incomes compared to men diminish women's creative potential and motivation to enhance their labor productivity.
Gender equality in income is a crucial goal for all nations, as it fosters societal progress, prosperity, and sustainable development Achieving this equality reflects a country's commitment to effective guidelines and policies Conversely, income inequality undermines a nation's governance capacity and diminishes the effectiveness of developmental policies.
Haas (2007) conducted a study exploring the relationship between economic development and the gender pay gap, as well as the impact of education attendance on wage inequality The research utilized both supply and demand theories and human capital principles, incorporating Simon Kuznets' inverted-U curve model, which posits a relationship between per capita income and income distribution within a country Data for this study was gathered from the United Nations Human Development Report.
In 2005, the study analyzed the male-to-female earnings ratio as a dependent variable, with GDP per capita serving as the independent variable While the research faced certain limitations, it highlighted that GDP per capita reflects economic development, though it does not encompass all dimensions of development Additionally, the paper did not address the impact of human capital.
The OLS regression analysis revealed that the male-female income gap widens as economic development progresses, except in cases of high per capita income Additionally, there is a positive correlation between wage inequality and gender income differences.
The impact on male – female income difference of female manager
According to Tajfel's (1982) social psychology of intergroup relations, individuals tend to favor members of their own group over those from different groups This theory suggests that women in managerial positions are more likely to support female employees compared to their male counterparts, potentially leading to a reduction in gender wage disparities Numerous studies, including those by Hultin and Szulkin (1999, 2003) and Cohen and Huffman, have explored the impact of managers' gender on the earnings gap between genders.
(2007), Cardoso and Winter-Ebmer (2010), Shin (2012), Hirsch (2013), Hedija
Hultin and Szulkin (1999) analyzed data from the Swedish Level of Living Survey and the Swedish Establishment Survey from 1991 to examine how the gender characteristics of managers influence the wages of subordinate workers in Sweden Their study estimated the wage functions for male and female workers, incorporating the proportion of male managers and supervisors as a key explanatory variable, and differentiated between the private and public sectors The findings revealed that a higher proportion of male managers significantly negatively impacted female wages, with this effect being notably stronger in the private sector compared to the public sector.
In their 2003 study, Hultin and Szulkin found that larger male representation in managerial and supervisory roles correlated with a widening gender wage gap Utilizing matched employer-employee data from various private firms in Sweden, the researchers applied a multilevel model to analyze the impact of male dominance in management on wage disparities Hierarchical linear modeling, as discussed by Bryk and Raudenbush (1992), is an effective statistical approach for nested data, serving purposes such as prediction, data reduction, and causal inference (Gelman, 2012).
Cohen and Huffman (2007) conducted a study on gender income differences using data from large U.S private sector firms collected by the EEOC in 2002 By employing a hierarchical linear model, they decomposed wage variances into individual, job, and local industry components Their research revealed that the gender earnings gap diminishes with increased representation of women in managerial roles, particularly when female managers occupy high-status positions.
Cardoso and Winter-Ebmer (2010) investigated the impact of female employers and gender segregation on wages in Portugal, utilizing a linked employer-employee dataset collected annually by the Ministry of Employment from 1987 to 2000 Their research focused on private firms within the manufacturing and service sectors The OLS regression results indicated that women earned higher wages in female-led firms compared to those in male-led firms, with the male-female income gap decreasing by 1.5% in female-led environments However, the study also revealed that both male and female wages were lower in firms with a higher proportion of female employees.
Shin (2012) analyzed compensation data from 7,711 executives across 831 U.S firms between 1998 and 2005 to explore the relationship between the presence of female directors on compensation committees and the gender pay gap The study hypothesized that a higher number of female leaders would lead to a smaller gender pay gap and enhance the career success of their female subordinates However, this hypothesis was not supported due to insufficient data, highlighting the need for more comprehensive calculations Utilizing random-effect regression with robust standard errors, the research found that increasing female representation in top management significantly impacts the compensation of female executives, ultimately contributing to a reduction in the gender pay gap.
In Hirsch's 2013 study, a cross-sectional model of linked employer-employee data from 2008 was utilized, sourced from the Institute for Employment Research, which matched German plants with their employees The research aimed to determine if the unexplained gender pay gap was narrower in plants with a higher proportion of female managers Utilizing Mincer wage regressions, Hirsch controlled for segregation effects and examined the influence of first and second-level managers on the gender earnings gap The findings aligned with previous studies, revealing a significantly smaller unexplained male-female income difference in plants with more female managers Notably, the impact was more pronounced for second-level managers, who were more involved in hiring, grouping, and promotion decisions.
A study by Hedija (2015) examined how gender characteristics of middle managers affect the wages of their direct subordinates Unlike other research, this study utilized individual company data by focusing on two specific hospitals in the same Czech town Two distinct methods were employed to analyze the influence of gender on subordinate wages.
The analysis utilizing the Mincer-type wage function revealed a 6.18 percentage point reduction in the gender pay gap when women held managerial positions within departments Complementing this, Hedija employed the average treatment effect on the treated estimation method, which indicated a 5.1 percentage point decrease in the gender pay gap for employees under female middle managers compared to those under male middle managers These findings were consistent with results derived from linked superior and subordinate data, reinforcing the impact of female leadership on wage equity.
Conceptual framework of the thesis as below
OVERVIEW OF GENDER WAGE GAP IN VIETNAM
Overview the status of gender gap in earnings in Vietnam
Vietnam has made significant strides in addressing gender discrimination and inequality, as highlighted by various international organizations In 2001, the country was ranked 109th out of 173 on the Human Development Index (HDI), a notable achievement considering its GDP per capita was below $400 Additionally, Vietnam's Gender Development Index (GDI) stood at 89th out of 146 countries, according to the UNDP.
According to the UNDP's Human Development Report in 2006, Vietnam achieved an impressive ranking of 11th globally in terms of gender equality, surpassing many leading developing economies Furthermore, the World Economic Forum's 2007 report on the global gender gap highlighted that Vietnam held the second position in gender equality within the ASEAN and East Asia regions.
Table1: Human development index and Human development index in
Country Rank of Human Development
Index in 173 Countries Rank of Human Development
Gender equality is a fundamental goal for national development in Vietnam, as recognized by the Party and the State This commitment is reflected in the Law on Gender Equality, enacted by the National Assembly on November 29, 2006, and effective from July 1, 2007 Furthermore, the Prime Minister's Decision No 2351/QD-TTg, signed on December 24, 2010, approved the National Strategy on Gender Equality for 2011-2020 The strategy aims to achieve substantive equality between men and women by 2020, ensuring equal opportunities and participation across all sectors, thereby contributing to the country's rapid and sustainable development.
Despite notable advancements in gender equality in Vietnam in recent years, significant gender inequalities persist, particularly in the form of a gender wage gap The ILO report from Bangkok in 2008 highlighted that Vietnam, along with China and Thailand, boasts a women's labor force participation rate of 72.2 percent, surpassing that of the broader East Asia region.
Table2: The share of women’s labor force participation
According to the General Statistics Office of Vietnam (GSO), women make up approximately 50% of the workforce in enterprises, highlighting their significant role alongside men This balance indicates that the rights and responsibilities of male and female workers are becoming increasingly equal across various job sectors However, despite global progress in reducing gender wage disparities, challenges remain in achieving full equality in compensation.
According to the experts of the International Labor Organization, Vietnam is one of the few countries where have the increase in disparities
Table 3: The share of female in plants
Despite similar labor participation rates, men and women tend to occupy different occupations, largely due to the influence of urban industries that reinforce gendered labor divisions In rural areas, where 80% of jobs are in agriculture, career choices are limited, resulting in minimal gender discrimination in career opportunities.
In urban areas, women primarily engaged in trade, light industry—particularly textiles—government offices, and social services, while men dominated skilled sectors like mining, engineering, and manufacturing Consequently, there is a notable underrepresentation of women in administrative and scientific fields.
In industries where women dominate, like textiles and elementary education, men still occupy a significant share of leadership roles Only 23% of women engaged in paid economic activities, compared to 42% of men Additionally, women's average hourly wages are only 78% of men's, highlighting ongoing gender wage disparities (FAO & UNDP 2002).
Recent data highlights the persistent income disparity between genders, with women earning less than men across all sectors The VHLSS 2002 report reveals that the average monthly income for women is just 85% of that of men, with even lower rates in specific sectors: only 66% in agriculture and 78% in industry.
Inequality in labor income stems from various factors such as educational disparities, expertise, work experience, and discrimination, all of which contribute to gender inequality Addressing these issues is essential to achieving equitable labor income for all.
A survey conducted by Nguyet and Binh (2007) revealed that women's earnings were only 86% of men's earnings, with female workers consistently earning about 68% less than their male counterparts across various types of enterprises.
Furthermore, there are differences in income for each age level of workers The average hourly earnings of women than men who had the age of
15 to 25 and 36 to 45 are 92.2% and 92.5%, respectively The difference in income between the two age groups was not more However, at the age of 26 to
The analysis revealed a significant income gap between individuals aged 35 and those aged 46 to 55 This disparity can be attributed to various factors, with differences in job structures across age groups identified as the primary cause.
According to the VHLSS 2006 report, women aged 15 to 25 and 36 to 45 are primarily employed in industries where their average income is comparable to that of men Interestingly, men often work in sectors with average wages that are equal to or even lower than those of female workers Consequently, the average income for women in these age groups is not significantly different from that of their male counterparts.
In the processing industry, the age groups of 26 to 35 and 46 to 55 employed the highest number of male and female workers Notably, the average hourly earnings for women in these age brackets were 73.8% and 65% of their male counterparts, respectively This significant disparity highlights the income gap between male and female employees in the industry (Toan Huy Nguyen, 2010).
In the education sector, Toan Huy Nguyen (2010) observed that female workers earn less than their male counterparts across all educational levels When comparing to 2004, there has been a noticeable disparity in the average income of women workers.
The factors impact on male – female income difference
According to 2006 statistics from GSO, the gender ratio was 96.6 males for every 100 females, with the highest ratio found in those under 19 years old The gender balance was most equal in the 40 to 49 age group, but the ratio declined after age 50 While income generally increases with age, it peaks between 46 to 55 years before declining for both men and women Throughout all age groups, women's earnings consistently fell short of men's income.
Education significantly influences workers' income, with highly qualified individuals earning more than those with lower skills Despite progress in expanding educational opportunities for women, a persistent gap in education levels between genders hinders women's access to high-expertise jobs and equivalent income In 2006, high school enrollment rates were 45.2% for females and 45.7% for males, with a steady increase observed in recent years.
Labor industries exhibit significant disparities in income, with agricultural workers earning less than those in manufacturing and services due to lower skill and qualification requirements According to the VHLSS 2006, nearly half of women were employed in agriculture, compared to one-third of men Additionally, female laborers in agriculture earned only 86.1% of their male counterparts' income, while in the industrial and service sectors, the ratios were 73.7% and 92.9%, respectively.
Living standards and incomes of workers vary significantly between urban and rural areas In 2006, women's participation in economic activities was 56.7% in urban regions and 67.5% in rural areas, compared to 74% and 79.8% for men, respectively This data highlights that women faced lower participation rates and fewer income-generating opportunities than men, resulting in lower overall income levels for women.
METHODOLOGY
The data
The dataset on Small and Medium Enterprises (SMEs) in Vietnam from 2007 provides crucial insights following the implementation of the Enterprise Law of 2000 This comprehensive collection of data highlights the significant role of SMEs in the Vietnamese private sector Conducted six times, with the latest surveys in 2009, 2011, and 2013, these surveys were a collaborative effort between the Institute of Management Central Economy (CIEM) and the Ministry of Planning and Investment of Vietnam (MPI), offering valuable information for understanding the dynamics of SMEs in the country.
The Institute of Labour Science and Social Affairs (ILSSA), part of Vietnam's Ministry of Labour, Invalids and Social Affairs (MOLISA), collaborates with the Economic Development Research Group (DERG) at the University of Copenhagen.
Moreover, the survey was sponsored by the Embassy of Denmark in Vietnam in the Program for Enterprise Development Support (BSPS)
A 2007 survey analyzed data from 2,492 enterprises across ten provinces in Vietnam, including Ha Tay, Ha Noi, Hai Phong, Ho Chi Minh, Long An, Quang Nam, Phu Tho, Nghe An, Khanh Hoa, and Lam Dong.
In 2007, a dataset was created to analyze the specific characteristics of both enterprises and employees, focusing on factors influencing gender income differences This involved a unique matching of employee and employer data from 2,492 enterprises across ten provinces, varying in size, category, legal ownership, and sector The study included 1,043 employees, providing insights into personal characteristics, job features, earnings, and non-wage benefits A subsample of 582 firms was examined, leading to the identification of five distinct occupations while excluding managerial roles, ultimately resulting in 345 job cells for analysis.
The variables
The worker's years of education indicate the total time spent in school by an individual Data on this educational metric was sourced from subject matter experts (SMEs) during the 2007 period In this study, the education variable was developed based on responses to question EqE1 in the employee questionnaire.
Education significantly impacts the gender pay gap, with higher levels of education correlating to a narrower gap Despite this, many girls face daily discrimination from parents and society, limiting their access to education and training opportunities Women lacking qualifications often struggle to secure well-paying jobs and may experience direct and indirect discrimination in the workplace According to the Mincer wage function, increased education leads to higher wages Research by Hultin and Ryszard (2013) and Hedija (2015) supports the idea that education is a key factor in personnel characteristics affecting income Ultimately, higher education for women is essential for achieving better positions in firms, thereby reducing the male-female income disparity.
Seniority refers to the number of years employees have worked for a company, calculated by subtracting the year an employee started from 2007 According to an ILO study, while a long tenure is not linked to productivity in certain lower-level jobs and assembly line positions, wages often do not reflect the duration of employment However, in many countries and job categories, wages tend to rise with increased years of service.
Younger women tend to experience smaller gender income disparities compared to their male counterparts in the same age group, and in some cases, they may even earn more However, as women enter their late 20s and early 30s, the income gap widens significantly due to factors related to childbearing and rearing responsibilities.
Following the study of Hultin and Ryszard (2003), size variable is measured by the logarithm of the number of employees in establishment
The industry variable is categorized using the International Standard Industrial Classification (ISIC), which is divided into 21 sections It is a well-known fact that wage levels are generally lower in smaller enterprises and can vary significantly across different industries.
The Econometrics Models and Results
This study aims to determine if the gender pay gap is lower in organizations with a higher proportion of female managers, utilizing Hirsch's (2013) methodology through augmented Mincer wage regressions The analysis will employ a fully interacted model as the baseline specification.
Lnwi = β1femalei + β2femshmani + β3femalei×femshmani + x’i (γ + femalei × δ) + ui
The article analyzes the daily gross wage of employees, denoted as Lnwi, while incorporating a female dummy variable, femalei, to identify gender differences It also examines the female share among managers, represented as femshmani, and the interaction between female employees and managerial representation, indicated by femalei x femshmani Additionally, the analysis includes a vector of control variables, xi, which encompasses individual human capital factors and plant characteristics, such as age, age squared (agesq), education, seniority, the natural logarithm of plant size (lnsize), and industry classification.
This study identifies β1 as the average unexplained gender pay gap within the sample, while β3 is anticipated to be positive, suggesting that a higher female representation in management correlates with a reduced unexplained gender income disparity.
Ordinary least squares (OLS) regression is a technique of statistical estimation
The linear regression model employs a technique aimed at determining a function that approximates the distribution of discrete samples, allowing for the estimation of unmeasured values Key assumptions of this model, including homoscedasticity, linearity, and the influence of outliers, are relatively straightforward to verify (Craven and Sardar, 2011) To account for segregation effects, the study incorporates job-cell fixed effects, which help reveal the unexplained income differences between male and female employees by addressing unobserved factors related to plant and job segregation The model is estimated using Stata software.
Summary statistics for all explanatory variables are given in the table 4 as bellows:
Variables Mean Std Dev Min Max
Table 5: Wages and female shares in managers
Mean Std.dev Mean Std.dev
Gross daily wage (in 1000 vnd) 45.9795 21.4490 57.1753 27.5101
Table 5 displays sample statistics indicating that male workers earn an average daily wage that surpasses that of female workers by approximately 11.20 Additionally, the raw gender pay gap is quantified at 20.33 log points.
This indicates that there is discrimination about income between male and female workers
Female x Female share in managers 0.2076 0.2013
OLS wage regression with job – cell fixed effect
Female x Female share in managers 0.0620 0.1330
***significant at 1 percent, **significant at 5 percent, * significant at 10 percent (Source: Author’s calculation)
Table 6 presents the key findings from the wage regression analysis based on surveyed plants The average unexplained gender earnings gap is 28.82 log points While the interaction effect between the female dummy and the proportion of female managers is statistically insignificant, it aligns with expectations due to its positive sign Additionally, the variables of age, age squared, and education show significant statistical results at the 10% level.
The level of education significantly influences labor wages, with higher educational attainment leading to increased earnings When accounting for job-cell fixed effects, the unexplained gender income disparity and the interaction between female employees and the proportion of female managers diminish Although the interaction effect on the gender pay gap remains statistically insignificant, a negative relationship between these variables is observed These findings align with previous research by Hirsch (2013) and Hultin and Szulkin (2003), indicating that the gender pay gap is reduced in workplaces with a greater presence of female managers.
CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS
Conclusion
Empirical studies have examined how gender representation among managers impacts the gender pay gap, suggesting that discrimination plays a significant role in earnings disparities This research posits that the gender pay gap is narrower in enterprises with higher participation of female managers.
A study analyzing matched employer and employee data for SMEs in 2007 reveals that while the presence of female managers does not significantly impact the male-female income gap, there is a negative correlation between the two Consequently, the research cannot confirm that a higher proportion of female managers leads to a reduction in the gender pay gap However, it highlights that education plays a crucial role, with significant findings indicating that better education levels, particularly for women, correlate with higher wages This negative relationship between the number of female managers and the gender pay gap aligns with previous research in the field.
Policy implications
Discrimination in women's income arises from various factors, including misconceptions about gender roles, traditional beliefs, and societal prejudices Addressing these issues through educational initiatives can help eliminate such disparities.
The government should implement systematic educational measures and socio-economic policies aimed at achieving gender equality, which will help shift societal perceptions of gender roles This includes encouraging businesses to create a fair competitive environment that offers equal opportunities for both men and women Furthermore, increasing the number of women in managerial positions can significantly reduce income discrimination between genders, prompting businesses to adopt policies that support female leadership Additionally, education plays a crucial role in influencing workers' income, particularly for women, who must continuously enhance their knowledge and skills to narrow the income gap with their male counterparts.
Limitations
The limited matched data for enterprises and employees participating in the SME sample does not adequately represent the broader population of small and medium enterprises in Vietnam Additionally, the absence of data beyond 2007 prevents an analysis of the time variable's impact on the gender pay gap and its relevance to current conditions.
Moreover, the literature shows that determinants of wage come from variety of characteristics of employee and plant (Hirsch, 2013) However, in my research, I focus on a few of these variables
To examine the influence of managerial gender on the gender pay gap, multilevel modeling is a suitable approach for analyzing nested data However, this study did not utilize this model due to constraints related to time and available data.
Mentioned are four limitations in my study that will provide strong basics for further research
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Correlation of variables
Statistics variables
Appendix 2: Statistics variables female -0.0741 0.0968 -0.0702 -0.0183 -0.2110 -0.1247 0.0813 0.0528 1.0000 industry 0.0164 0.0250 0.0242 -0.0398 -0.0087 -0.0323 -0.0908 1.0000 femshman -0.0374 0.0583 -0.1019 -0.2046 -0.1364 -0.4531 1.0000 lnsize -0.0378 -0.0997 -0.0685 0.2870 0.2056 1.0000 lnwage -0.0011 -0.1375 -0.0521 0.4259 1.0000 education -0.0975 -0.1475 -0.1186 1.0000 seniority 0.4729 0.2316 1.0000 cen_age1sq 0.5814 1.0000 cen_age1 1.0000 cen_age1 cen_ag~q senior~y educat~n lnwage lnsize femshman industry female
(obs45) corr cen_age1 cen_age1sq seniority education lnwage lnsize femshman industry female
24.58165 4708226 2350819 Total 50.36048 3.815377 5580147 21.449 4300014 2392863 N÷ 45.97952 3.735837 5733195 27.5101 5053168 2272161 Nam 57.17531 3.939105 5342071 gender wage lnwage femshman by categories of: gender Summary statistics: mean, sd tabstat wage lnwage femshman, by(gender) s(mean sd)
_cons 3.359286 3031156 11.08 0.000 2.763036 3.955536 industry 0856529 2442587 0.35 0.726 -.3948212 566127 lnsize 0249139 0226346 1.10 0.272 -.0196098 0694377 education 0356548 004555 7.83 0.000 0266948 0446148 seniority -.0042775 0052853 -0.81 0.419 -.014674 006119 cen_age1sq -.0004123 0002224 -1.85 0.065 -.0008497 0000252 cen_age1 0052352 0031958 1.64 0.102 -.0010513 0115216 fexfesh 2076764 2013309 1.03 0.303 -.1883556 6037084 femshman -.1421975 1662681 -0.86 0.393 -.4692586 1848636 female -.2882251 1198081 -2.41 0.017 -.5238961 -.0525541 lnwage Coef Std Err t P>|t| [95% Conf Interval]
The regression analysis yielded a total sum of squares of 76.2558393 with a root mean square error (MSE) of 0.41632 The adjusted R-squared value was 0.2181, indicating that the model explains approximately 21.81% of the variance in the dependent variable The residual sum of squares was 58.061992, resulting in an R-squared value of 0.2386, suggesting that 23.86% of the variance is accounted for by the model The F-statistic was 11.66 with a probability value (Prob > F) of 0.0000, confirming the model's statistical significance The analysis included 345 observations and examined variables such as lnwage, female, femshman, fexfesh, cen_age1, cen_age1sq, seniority, education, lnsize, and industry.
Appendix 4: OLS wage regression with job – cell fixed effect
_cons 3.357736 1763228 19.04 0.000 3.010889 3.704583 _Ioccupatio_5 -.0544924 0761862 -0.72 0.475 -.2043593 0953745 _Ioccupatio_4 149934 0830412 1.81 0.072 -.0134174 3132854 _Ioccupatio_3 -.0707198 1105858 -0.64 0.523 -.2882546 1468149 _Ioccupatio_2 -.0245227 0921884 -0.27 0.790 -.2058677 1568224 lnsize 0343101 0222388 1.54 0.124 -.0094361 0780563 education 0331172 0069202 4.79 0.000 0195043 04673 seniority -.0058067 0053335 -1.09 0.277 -.0162982 0046849 cen_age1sq -.0003806 0002209 -1.72 0.086 -.000815 0000539 cen_age1 0060819 0032461 1.87 0.062 -.0003036 0124673 fexfesh 0620072 1330774 0.47 0.642 -.199771 3237855 female -.1767059 0892215 -1.98 0.048 -.3522148 -.0011971 lnwage Coef Std Err t P>|t| [95% Conf Interval]
The analysis yielded a total of 76.26 with a root mean square error (RMSE) of 0.41, indicating the model's accuracy The adjusted R-squared value is 0.23, suggesting that approximately 23% of the variance in the dependent variable is explained by the model The residual sum of squares stands at 56.67, with an R-squared value of 0.26, further confirming the model's fit The F-statistic is 10.47 with a probability greater than F of 0.0000, indicating that the overall model is statistically significant A total of 345 observations were analyzed, with the regression including variables such as gender, age, seniority, education, and occupation, while one occupation category was omitted for clarity.
Variables: fitted values of lnwage Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity estat hettest
Appendix 7: The individual categories of ISIC
D 35 Electricity, gas, steam and air conditioning supply
E 36–39 Water supply; sewerage, waste management and remediation activities
G 45–47 Wholesale and retail trade; repair of motor vehicles
Mean VIF 3.03 industry 1.02 0.976830 education 1.13 0.886052 seniority 1.34 0.747229 lnsize 1.37 0.732004 cen_age1sq 1.59 0.628816 cen_age1 1.89 0.530105 femshman 3.03 0.329786 female 6.81 0.146940 fexfesh 9.12 0.109703 Variable VIF 1/VIF vif
OLS wage regression with job – cell fixed effect
_cons 3.357736 1763228 19.04 0.000 3.010889 3.704583 _Ioccupatio_5 -.0544924 0761862 -0.72 0.475 -.2043593 0953745 _Ioccupatio_4 149934 0830412 1.81 0.072 -.0134174 3132854 _Ioccupatio_3 -.0707198 1105858 -0.64 0.523 -.2882546 1468149 _Ioccupatio_2 -.0245227 0921884 -0.27 0.790 -.2058677 1568224 lnsize 0343101 0222388 1.54 0.124 -.0094361 0780563 education 0331172 0069202 4.79 0.000 0195043 04673 seniority -.0058067 0053335 -1.09 0.277 -.0162982 0046849 cen_age1sq -.0003806 0002209 -1.72 0.086 -.000815 0000539 cen_age1 0060819 0032461 1.87 0.062 -.0003036 0124673 fexfesh 0620072 1330774 0.47 0.642 -.199771 3237855 female -.1767059 0892215 -1.98 0.048 -.3522148 -.0011971 lnwage Coef Std Err t P>|t| [95% Conf Interval]
The regression analysis yielded a total sum of squares of 76.2558393 with a root mean square error (RMSE) of 0.41252, indicating a model fit with an adjusted R-squared value of 0.2323 The residual sum of squares was 56.666671, resulting in an R-squared value of 0.2569 The model's F-statistic was 10.47 with a probability value (Prob > F) of 0.0000, suggesting that the model is statistically significant The analysis included 345 observations, utilizing variables such as female, fexfesh, cen_age1, cen_age1 squared, seniority, education, lnsize, and occupation categories.
Variables: fitted values of lnwage Ho: Constant variance
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity estat hettest
Appendix 7: The individual categories of ISIC
D 35 Electricity, gas, steam and air conditioning supply
E 36–39 Water supply; sewerage, waste management and remediation activities
G 45–47 Wholesale and retail trade; repair of motor vehicles
Mean VIF 3.03 industry 1.02 0.976830 education 1.13 0.886052 seniority 1.34 0.747229 lnsize 1.37 0.732004 cen_age1sq 1.59 0.628816 cen_age1 1.89 0.530105 femshman 3.03 0.329786 female 6.81 0.146940 fexfesh 9.12 0.109703 Variable VIF 1/VIF vif