INTRODUCTION
Problem statement
The gender pay gap is a significant global issue that contributes to poverty and hinders development According to Nguyet and Binh (2007), the disparity in income between men and women not only exacerbates poverty but also leads to increased malnutrition, illness, and various hardships As the earnings gap widens, the negative consequences on society become more pronounced.
Economic growth is more effective in reducing poverty levels in countries with higher gender equality Additionally, income disparities between women and men hinder equitable development, leading to inefficient resource utilization within society.
Gender inequality in earnings is prevalent in many countries, particularly in developing nations, largely due to traditional views and societal beliefs in male supremacy This mindset restricts women's access to education, training, and professional opportunities, leading to significant disparities in labor distribution and income between genders 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 gender wage gap despite having a higher female labor force participation rate of approximately 72%, surpassing many countries globally Contrary to the global trend of decreasing wage disparities, Vietnam's gender income gap increased by 2% between 2008 and 2011, as reported by the 2012-2013 Global Wages Report by the ILO Data from the General Department of Statistics in 2011 revealed that women's earnings were approximately 13% lower than men's A survey by the General Labor Confederation of Vietnam in 2012 indicated that female workers earned only 70-80% of their male counterparts' wages This disparity was evident across all economic sectors, including state, non-state, and foreign investment, with women consistently earning less than men, even in female-dominated fields such as healthcare and sales The VGCL survey highlighted that women often occupied lower-level positions, while men held management roles, further contributing to the wage gap.
Numerous studies have explored the gender pay gap, identifying key factors influencing wage disparities between men and women and proposing solutions to address this issue Notable research includes works by Anderson et al (2001), Hultin and Szulkin (2003), and Blau and Lawrence (2007), among others In Vietnam, studies such as Liu (2002) and Pham and Barry (2007) have also contributed to this discourse A significant finding indicates a correlation between the proportion of female managers and the gender wage gap, highlighting that workplaces with more male managers tend to exhibit wider income disparities Research utilizing multilevel models on Swedish data revealed that the gender pay gap was more pronounced in environments with fewer women in managerial roles Additionally, Hirsch's (2013) study on Germany demonstrated that a 10% increase in female representation in first-level management resulted in a 0.5 log point decrease in the gender income gap Building on Hirsch's findings, this study aims to investigate the relationship between female managerial representation and the gender pay gap specifically within 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 assembly lines of managers in 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
The research is structured into five chapters: Chapter one outlines the problem statement and research objectives Chapter two discusses relevant concepts from the literature review, including definitions 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 estimated results Finally, chapter five concludes with policy recommendations and acknowledges the study's limitations.
LITERATURE REVIEW
The definitions
Under the leadership of Kim Thi Bui, the director of the Division of Workers’ Compensation (DWC), gender equality is framed as a sociological concept that encompasses the roles, responsibilities, and social relationships between men and women It addresses the division of labor, resources, and benefits within specific social contexts Gender is shaped by learning and education, reflecting the diversity and differences across countries and regions Additionally, it evolves over time in response to socio-economic development.
The benefits of gender equity positively impact both women and men, driving a transformation in the traditional division of labor By fostering progress and collaboration, these benefits contribute significantly to advancing gender equality in society.
The Convention on the Elimination of All Forms of Discrimination Against Women (CEDAW) 1978 emphasizes the importance of gender equality, defining it as the state in which women and men have equal living conditions and opportunities This equality enables both genders to access and utilize resources effectively, fostering the discovery and development of their potential Ultimately, this balance contributes significantly to the development of nations and allows everyone to benefit from such progress.
Gender inequality and discrimination against women encompass any distinctions, exclusions, or restrictions based on gender that harm or impede women's recognition and the exercise of their human rights and fundamental freedoms This issue affects various domains, including political, economic, social, cultural, and civil spheres, emphasizing the need for 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 job achievements between male and female workers This human-centered perspective highlights that income inequality is tied to the distribution of earnings based on gender Consequently, the gender pay gap reflects the unjust income disparities faced by men and women, despite their equivalent capabilities and productivity levels.
The final report by Anderson, Forth, Metcalf, and Kirby (2001) for the Department for Education and Employment outlines several theories that explain the gender pay gap Human capital theory suggests that differences in innate abilities, education, and experience between men and women lead to variations in productivity and earnings Additionally, theories on the social construction of skills indicate that the historically inferior status of women has resulted in the undervaluation of traditionally female skills, creating a value system that penalizes both genders The dual labor market theory further explains the gender pay gap by identifying two distinct labor markets: the primary market, which offers higher wages and better working conditions, and the secondary market, characterized by lower pay and poorer conditions Due to limitations in labor market options, women are often concentrated in the secondary market, exacerbating the gender pay gap.
The Organization for Economic Co-operation and Development (OECD) defines the gender pay gap as the difference in earnings between males and females, represented as a percentage of male earnings.
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, consistently highlighting the definition of the gender pay gap, which refers to the disparity 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
The gender pay gap can be explained through various theories and factors According to Anderson et al (2001), neo-classical labor market theories highlight individual and institutional influences on this disparity Human capital theory suggests that differences in innate abilities and skills between men and women contribute to lower productivity for women, thus affecting their earnings Additionally, women's choices often lead them to lower-paying jobs, perpetuating income differences Discrimination plays a significant role, with Jewson and Mason (1986) identifying four types: rational-legal, determinist, particularist, and patronage The undervaluation of women's traditional skills, stemming from their historically inferior status, further exacerbates the issue Bergmann (1971) notes that labor market crowding results in excess supply, driving down wages Manning (1996) highlights that women face greater monopsony power, leading to lower wage rates, while Kerr (1950) points out the weak position of women in internal labor markets, contributing to the gender earnings gap Moreover, Anderson et al (2001) emphasize that unionization and collective bargaining significantly impact pay disparities, as women are often less represented in these negotiations Other factors, such as compensating differentials, employer characteristics, and secondary effects, also contribute to the income gap between men and women.
The Fawcett Society, the UK's leading charity for gender equality and women's rights, identifies several key factors contributing to the gender pay gap, including the motherhood penalty, concentration in low-wage sectors, and discrimination Women often opt for part-time work due to childcare responsibilities, resulting in lower hourly wages compared to full-time positions This leads to an average income disparity where women earn 19.1% less than men in similar part-time roles, with the gap narrowing to 9.4% in full-time work Additionally, women are disproportionately represented in low-paid sectors such as health and social care, while men dominate higher-paying STEM industries Discrimination persists in the workplace, manifesting as direct discrimination when women receive lower wages for the same job as men, and indirect discrimination where men earn more in roles of equivalent value.
The European Commission, in collaboration with the Fawcett Society, identifies several key factors contributing to the gender pay gap, including direct discrimination, the undervaluation of women's work, labor market segregation, and societal stereotypes According to their 2011 brochure, "Tackling the Gender Pay Gap in the European Union," direct discrimination accounts for only a small portion of the income disparity due to effective EU and national legislation A significant reason for women earning less than men for equivalent roles is the lower valuation of women’s competencies, particularly in sectors like health, education, and public administration, which tend to be underpaid Research indicates that 32% of female workers are employed part-time compared to just 8% of male workers, largely due to women's responsibilities in family care.
Meanwhile, in Vietnam, following the research of Nguyet and Binh
The gender earnings gap is influenced by two primary groups: economic and noneconomic factors Traditional gender inequality, rooted in feudal conceptions, represents the noneconomic aspect, where societal stereotypes hinder gender balance and equality Historically, men were viewed as the primary breadwinners, responsible for societal roles, while women were confined to domestic duties, rendering them dependent and without rights On the economic side, factors such as employee characteristics, education, labor elements, and geography play significant roles Key characteristics include age, marital status, health, and financial resources Education is crucial, as higher qualifications lead to better-paying jobs, while labor elements like profession and work experience also impact earnings Generally, employees in agriculture earn less than those in industry or services due to lower requirements, and within the same occupation, experience and expertise significantly influence wages, with more experienced workers typically earning higher salaries.
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 men and women.
The feudal perspective on women's roles in family and society is outdated, yet difficult to change, as men are traditionally seen as the primary contributors to social and economic activities, earning respect for their roles in production and management In contrast, women are often relegated to responsibilities such as homemaking and childcare, which typically do not provide any income, leading to their complete dependence on men and lack of autonomy This dependence restricts women's access to education and career opportunities, perpetuating economic disparities and income inequality between genders.
Worker characteristics, including age, health, and marital status, significantly influence income disparities between genders Empirical evidence indicates that a young female worker typically earns a lower wage, which tends to increase as she gains experience and develops her human capital, but may decline in older age In contrast, young male workers often experience faster wage growth than their female counterparts (Bojas, 2005) Health also plays a crucial role in the income gap, as gender differences result in women being concentrated in fewer career fields, leading to lower wages Additionally, marital status impacts both male and female workers, as marriage and parenting increase living expenses and the need for additional income However, women often bear greater family responsibilities, which limits their work opportunities and contributes to a persistent income gap compared to men.
Education significantly influences worker income, as higher qualifications and complex skills lead to increased wages According to Boris (2005), the wage and education curve illustrates the correlation between income and years of schooling, indicating that businesses are prepared to offer salaries that reflect each educational level.
Employment factors such as industry, expertise, experience, and type of enterprise significantly influence worker compensation Generally, employees in the agricultural sector earn less than those in other industries due to the lower skill and qualification requirements, where physical capability is often prioritized Additionally, within the same industry, wages are affected by individual expertise and experience; more complex tasks typically offer higher pay, and seasoned workers can execute their jobs more efficiently, leading to increased earnings compared to their less experienced counterparts.
The type of enterprise significantly influences the income disparity between men and women Organizations that are strictly regulated by law tend to adhere to gender equality policies, resulting in more equitable earnings for female employees.
Employee income is designed to support their livelihoods and families However, due to varying living standards and expenses across different regions, income levels also differ significantly Typically, workers in urban areas earn higher wages compared to those in rural regions, even for jobs that share similar nature and complexity.
Measurement of gender pay gap
The gender wage gap highlights the disparity in earnings between male and female workers, as defined by the Pay Equity Commission (2012) This gap can also be expressed as a percentage, according to Statistics New Zealand (2014), using a specific formula to quantify the difference in income.
Worker compensation can be assessed on an hourly, weekly, or monthly basis, with monthly earnings being more suitable for analyzing labor market dynamics and poverty outcomes among diverse groups of men and women In contrast, hourly wages are more effective for comparing earnings among smaller groups of workers within the same occupations or enterprises, as the discrepancies measured over shorter time frames tend to be less pronounced This is largely due to the tendency of women to work fewer hours for income compared to men in many countries worldwide.
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 hinders economic development This inequality not only affects women directly but also has detrimental effects on their families Women's labor income is crucial for sustaining their households and improving family quality of life However, gender income disparity restricts women's ability to regenerate labor, access technology, education, and training, and increases their workload challenges Additionally, the lack of decision-making power within families contributes to higher maternal and infant mortality rates, adversely affecting family health and reducing school attendance for children, particularly 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 productive resources, impeding the ability to engage in production activities and leading to inefficient resource allocation Additionally, lower incomes compared to men diminish women's creative potential and motivation, ultimately affecting their labor productivity.
Gender equality, particularly in income, is a crucial goal for all nations as it fosters societal progress, prosperity, and sustainable development Achieving income equality reflects a country's commitment to effective policies and guidelines aimed at these objectives Conversely, gender income inequality undermines a nation's governance capacity, ultimately diminishing the effectiveness of development policies.
Haas (2007) conducted a study exploring the relationship between economic development and the gender pay gap, focusing on the impact of education attendance on wage inequality The research utilized both supply and demand theories and human capital concepts, 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 sourced from the United Nations Human Development Report.
In 2005, a study examined the relationship between male-female earnings ratios and GDP per capita as independent variables While the research faced limitations, it highlighted that GDP per capita indicated economic development, albeit not encompassing all developmental aspects, and did not account for human capital The OLS regression analysis revealed that the income disparity between genders widened with economic growth, except at higher levels of per capita income, where wage inequality and gender income differences were positively correlated.
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 suggests that women in managerial positions may be more inclined to support female employees compared to their male counterparts, potentially influencing wage disparities and narrowing the gender income gap Numerous studies, including those by Hultin and Szulkin (1999, 2003) and Cohen and Huffman, have examined how the gender characteristics of managers affect earnings differences 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 employees, 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 and supervisors negatively impacted female wages, with this effect being significantly stronger in the private sector compared to the public sector.
In 2003, Hultin and Szulkin found that larger male participation in managerial and supervisory roles contributed to a widening gender income gap Their research utilized matched employer-employee data from private firms in Sweden, applying a multilevel model to assess the impact of male representation in management on the gender wage disparity Hierarchical linear modeling, as discussed by Bryk and Raudenbush (1992), is an effective statistical approach for analyzing nested data, serving purposes such as prediction, data reduction, and causal inference (Gelman, 2012).
Cohen and Huffman (2007) conducted research on gender income differences using data from large U.S private sector firms collected by the U.S Equal Employment Opportunity Commission (EEOC) in 2002 Utilizing a hierarchical linear model, they decomposed wage variances into three components: individuals, jobs, and local industries Their study revealed that the representation of women in managerial roles, particularly those in high-status positions, significantly reduces the gender earnings gap for both men and women.
Cardoso and Winter-Ebmer (2010) investigated the impact of female employers and gender segregation on wages in Portugal, utilizing a comprehensive dataset collected annually by the Ministry of Employment from 1987 to 2000 This dataset encompassed private firms in the manufacturing and service sectors Their OLS regression analysis revealed that women earned higher wages in female-led firms compared to male-led firms, with the male-female income gap decreasing by 1.5% in female-led environments However, the study also indicated that overall wages for both genders tended to be 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 investigate the correlation between the presence of female directors on compensation committees and the gender pay gap The study hypothesized that a higher representation of women in leadership roles would lead to reduced earnings disparities for female employees However, this hypothesis was not supported due to insufficient data, highlighting the need for more extensive calculations Utilizing random-effect regression with robust standard errors, the research found that increased female leadership positively impacted compensation for female executives, ultimately contributing to a narrower gender pay gap.
In Hirsch's 2013 study, a cross-sectional model utilizing linked employer-employee data from 2008 was employed, with data collected by the Institute for Employment Research, linking German plants to their employees The research aimed to determine whether the unexplained gender pay gap was smaller in plants with a higher proportion of female managers By applying Mincer wage regressions and controlling for segregation effects through job-cell effects, Hirsch examined the influence of first and second-level managers on the gender earnings gap The findings aligned with previous research, indicating that the unexplained income difference between males and females was significantly lower in plants with more female managers Notably, the effect was more pronounced among second-level managers, who are more involved in hiring, grouping, and promotion decisions.
A study by Hedija (2015) examined the impact of gender characteristics in middle managerial positions on the wages of subordinates, utilizing data from two Czech hospitals in the same town Unlike previous research, this study focused on individual company data linking superiors and subordinates Two methods were employed: the Mincer-type wage function and the average treatment effect on the treated estimation The analysis revealed that the gender pay gap decreased by 6.18 percentage points when a female manager was present in the department Additionally, the average treatment effect method indicated a 5.1 percentage point reduction in the gender pay gap for employees under female middle managers compared to those under male managers, with consistent findings across both methodologies.
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 reducing gender discrimination and inequality, as highlighted by various international organizations In 2001, the country was positioned 109th out of 173 nations on the Human Development Index (HDI), surpassing expectations for a nation with a GDP per capita below $400 Additionally, Vietnam's Gender Development Index (GDI) ranked 89th out of 146 countries, according to the UNDP.
According to the 2006 Human Development Report by UNDP, Vietnam ranked 11th globally in gender equality, surpassing several leading developing economies Additionally, the 2007 Global Gender Gap Report published by the World Economic Forum highlighted Vietnam's strong position, ranking it second 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 of national development in Vietnam, emphasized by the Party and State This commitment is reflected in the Gender Equality Law enacted by the National Assembly on November 29, 2006, which took effect on 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 ensure substantive equality between men and women by 2020, promoting equal opportunities and participation across all sectors, including politics, economics, culture, and society, thereby contributing to the country's rapid and sustainable development.
Despite recent advancements in gender equality in Vietnam, significant gender inequalities persist, particularly in the wage gap A 2008 ILO report highlighted that Vietnam, alongside China and Thailand, boasts a women's labor force participation rate of 72.2%, 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), female participation in enterprises is approximately 50%, highlighting the significant role women play alongside men in the workforce This statistic indicates a growing equality in rights and responsibilities between genders in various job industries However, despite global trends towards reducing gender wage disparities, experts from the International Labor Organization note that Vietnam is one of the few countries experiencing an increase in these disparities.
Table 3: The share of female in plants
Although labor participation rates for men and women were comparable, they were predominantly found in different occupations Urban areas showcased a diverse range of industries that reinforced gender-based job segregation In contrast, rural regions saw 80% of employment concentrated in agriculture, leading to limited career choices and minimal gender discrimination in professional opportunities.
In urban areas, women predominantly engage in trade, light industry—particularly textiles—government roles, and social services, while men are more represented in skilled sectors such as mining, engineering, and manufacturing Despite women often making up the majority in fields like the textile industry and elementary education, men still hold a significant share of higher leadership positions Notably, only 23% of women participate in paid economic activities compared to 42% of men, and women earn, on average, only 78% of men's hourly wages (FAO & UNDP 2002).
Recent data highlights persistent income disparities between men and women across all sectors, with women earning an average of 85% of men's income, as reported by the VHLSS 2002 In the agricultural sector, this figure drops to 66%, while in the industrial sector, women earn 78% of their male counterparts' income The inequality in labor income is influenced by various factors, including differences in education, expertise, work experience, and discrimination, all of which must be addressed to tackle gender inequality effectively.
According to a survey conducted by Nguyet and Binh (2007), women's earnings were found to be only 86% of men's earnings, and across various types of enterprises, female workers consistently earned about 68% less than their male counterparts.
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 between 46 to 55 This disparity can be attributed to various factors, with differences in job structure 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 incomes are comparable to those of men Interestingly, male workers are often found in sectors with average wages that are equal to or even lower than those of female workers As a result, the average income for women in these two 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, women in these age brackets earned an average of 73.8% and 65% of what their male counterparts made, respectively This significant wage disparity highlights the ongoing income inequality between male and female employees in the sector (Toan Huy Nguyen, 2010).
In the education sector, Toan Huy Nguyen (2010) noted that female workers earn less than their male counterparts across all educational levels When compared to 2004, the average income for women workers remains lower, highlighting ongoing gender income disparities in the workforce.
In 2006, efforts to narrow the income gap between genders showed some progress, yet gender inequality in earnings remained evident Female employees with the same level of university education earned only 85.82% of what their male counterparts made.
Female workers face significant inequalities in the workplace, particularly in the area of labor contracts Many businesses fail to offer long-term contracts to female employees, and when contracts expire for those who are pregnant or caring for children under 12 months, employers often do not renew them This practice contradicts the Labor Code, which prohibits the unilateral termination of contracts for pregnant employees or those with young children As a result, numerous women are deprived of maternity benefits due to the expiration of their labor contracts.
The factors impact on male – female income difference
According to GSO statistics from 2006, the gender ratio was 96.6 males for every 100 females, with the highest ratio observed in individuals under 19 years of age The gender balance was most even among those aged 40 to 49, but it declined for those aged 50 and older Generally, income levels increased with age, peaking between 46 to 55 years, after which earnings for both men and women began to decline Throughout all age groups, women's earnings consistently fell short of men's income.
Education significantly influences workers' income, with those possessing higher qualifications earning more than their less skilled counterparts Despite advancements in educational opportunities for women, a persistent gap in education levels between genders hinders women's access to high-expertise jobs and equivalent income to men According to the General Statistics Office (GSO, 2006), high school enrollment rates were 45.2% for females and 45.7% for males, showing a steady increase in recent years.
Labor income disparities exist across various industries, 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 At that time, the average hourly income for female agricultural workers was just 86.1% of their male counterparts, while the ratios in the industrial and service sectors were 73.7% and 92.9%, respectively.
Living standards and worker incomes 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 indicates that women's economic participation was lower than that of men, resulting in fewer income-generating opportunities and consequently lower earnings for women.
METHODOLOGY
The data
In 2007, comprehensive surveys of Small and Medium Enterprises (SMEs) in Vietnam were conducted, providing crucial data following the implementation of the Enterprise Law of 2000 These surveys, executed in collaboration with the Institute of Management Central Economy (CIEM), the Ministry of Planning and Investment (MPI), and the Institute of Labour Science and Social Affairs (ILSSA), were also supported by the Economic Development Research Group (DERG) of the University of Copenhagen and sponsored by the Embassy of Denmark in Vietnam under the Program for Enterprise Development Support (BSPS) Over the years, these surveys have been conducted six times, with the latest iterations occurring in 2009, 2011, and 2013, offering invaluable insights into the private sector landscape of Vietnamese SMEs.
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 comprehensive dataset was created to analyze the specific characteristics of enterprises and employees, focusing on factors such as gender income disparities This analysis utilized a unique matched dataset of 2,492 enterprises across ten provinces, varying in size, category, legal ownership, and sector, alongside 1,043 employees whose personal characteristics, job features, earnings, and non-wage benefits were documented The study narrowed its focus to a subsample of 582 firms, ultimately categorizing the data into 345 job cells by matching employee and enterprise information and excluding managerial occupations while distinguishing five specific job types.
The variables
The worker's year of education indicates the total years spent in school by an individual Data for this metric was sourced from SMEs during the 2007 period In this study, the education variable was developed based on responses to the EqE1 question in the employee questionnaire.
Education significantly impacts the gender pay gap, with higher education levels correlating to a narrower gap Despite this, many girls face discrimination that limits their access to education, resulting in fewer opportunities for social education and training 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, higher education leads to increased wages Research by Hultin and Ryszard (2013) and Hedija (2015) supports the notion that education is a key factor in understanding gender income disparities Ultimately, women's higher education contributes to their advancement in the workplace, effectively reducing the male-female income gap.
Seniority refers to the length of time employees have worked for a company, calculated by subtracting the year an employee started from 2007 According to an ILO study, while extensive tenure may not correlate with productivity in lower-level jobs and assembly lines, wages typically do not reflect the number of years worked However, in many countries and across various job categories, there is a trend of increasing wages with additional years of service.
Younger women tend to experience smaller gender income disparities compared to their male counterparts within the same age groups, with some even earning more than men However, as women reach their late 20s and early 30s, the income gap widens significantly due to the impact of childbearing and rearing responsibilities.
According to Hultin and Ryszard (2003), the size of an establishment is quantified by the logarithm of its employee count Industries are classified into 21 sections based on the ISIC categories It is widely recognized that wage levels are generally lower in smaller enterprises and can differ significantly across various industries.
The Econometrics Models and Results
This study aims to investigate the relationship between the gender pay gap and the presence of female managers in plants, utilizing Hirsch's (2013) methodology through augmented Mincer wage regressions The analysis will be grounded in a fully interacted model as the baseline specification.
Lnwi = β1femalei + β2femshmani + β3femalei×femshmani + x’i (γ + femalei × δ) + ui
The variable Lnwi represents the daily gross wage of employee i, while femalei serves as a dummy variable indicating gender The term femshmani denotes the proportion of female managers, and the interaction between femalei and femshmani is captured by the product femalei x femshmani Additionally, xi is a vector of control variables that encompasses factors related to the individual's human capital and plant characteristics, including age, age squared (agesq), education level, seniority, the natural logarithm of plant size (lnsize), and industry classification.
In this study, β1 represents the average unexplained gender pay gap within the sample, while β3 is anticipated to be positive, suggesting that a higher female share in management correlates with a reduced unexplained gender income difference The research employs Ordinary Least Squares (OLS) regression, a widely used statistical estimation technique in linear regression models, aiming to approximate the function that represents sample distributions and estimate unmeasurable values The model's assumptions regarding homoscedasticity, linearity, and the influence of outliers are relatively straightforward to verify (Craven and Sardar, 2011) To account for segregation effects, job-cell fixed effects are incorporated, allowing the study to analyze the unexplained male-female income difference within jobs, addressing unobserved factors related to plant and job segregation The model is estimated using Stata, with summary statistics for all explanatory variables presented in Table 4.
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 revealing that the average daily wage for male workers exceeds that of female workers by approximately 11.20 Additionally, the raw gender pay gap is quantified at 20.33 log points, highlighting the existence of income discrimination between male and female employees.
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
Table 6 presents the key findings from the wage regression analysis of surveyed plants, revealing an average unexplained gender earnings gap of 28.82 log points While the interaction between the female dummy variable and the proportion of female managers shows an insignificant statistical result, it aligns positively with expectations Additionally, the variables for age, age squared, and education demonstrate significant statistical relevance at the 10% level.
Higher education levels significantly increase labor wages, as evidenced by the strong correlation between education and earnings When controlling 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 is statistically insignificant, a negative relationship exists between the number of female managers and the gender pay gap These findings align with previous research by Hirsch (2013) and Hultin and Szulkin (2003), which indicates that workplaces with a higher proportion of female managers tend to have a reduced gender pay gap.
CONCLUSION, POLICY IMPLICATIONS AND LIMITATIONS
Conclusion
Empirical studies have examined how gender representation among managers influences the gender pay gap, suggesting that earnings disparities can often be attributed to discrimination This research posits that the gender pay gap is reduced in organizations with a higher presence of female managers.
A study utilizing matched employer and employee data from SMEs in 2007, while controlling for employment segregation through job-cell fixed effects, reveals that the presence of female managers does not significantly impact the male-female income gap, showing a negative correlation instead Consequently, the research cannot confirm that a higher proportion of female managers leads to a reduction in the gender pay gap However, it consistently finds that education plays a crucial role, indicating that wages increase with higher education levels, particularly for female workers Furthermore, the observed negative relationship between the number of female managers and the gender pay gap aligns with findings from previous research in this area.
Policy implications
Discrimination in women's income arises from various factors, including societal misconceptions about gender roles and traditional prejudices, which can be addressed through educational initiatives To promote gender equality, the government should implement comprehensive educational measures and socio-economic policies that foster a fair competitive environment, ensuring equal opportunities for both men and women Furthermore, increasing the number of women in management positions can significantly reduce income disparities, prompting businesses to adopt policies that encourage female leadership Additionally, education plays a crucial role in influencing worker 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 involved in SMEs in the sample does not adequately represent the broader population of small and medium enterprises in Vietnam Additionally, the absence of comprehensive data, particularly for the year 2007, prevents an analysis of the time variable's effect on the gender pay gap, thereby obscuring its current impact.
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 explore the influence of managerial gender on the gender pay gap, multilevel modeling is a suitable approach for analyzing nested data However, due to time constraints and data limitations, this study did not implement this model.
Mentioned are four limitations in my study that will provide strong basics for further research
Anderson, Tracy, et al "The gender pay gap." London: Women and Equality Unit
Becker, Gary S The economics of discrimination University of Chicago press,
Blau, Francine D., and Lawrence M Kahn "The gender pay gap." The
Bojas , George J (2005),Labor Economics, McGraw-Hill, Third Edition
Bryk, Anthony S., and Stephen W Raudenbush Hierarchical linear models: applications and data analysis methods Sage Publications, Inc, 1992
Cardoso, Ana Rute, and Rudolf Winter-Ebmer "Female-led firms and gender wage policies." Industrial & Labor Relations Review 64.1 (2010): 143-163
Cardoso, Ana Rute, and Rudolf Winter-Ebmer "Female-led firms and gender wage policies." Industrial & Labor Relations Review 64.1 (2010): 143-163
Cohen, Philip N., and Matt L Huffman "Working for the woman? Female managers and the gender wage gap." American Sociological Review 72.5 (2007): 681-704
Cohen, Philip N., and Matt L Huffman "Working for the woman? Female managers and the gender wage gap." American Sociological Review 72.5 (2007): 681-704
Craven, B D., and Sardar MN Islam Ordinary least-squares regression SAGE Publications, 2011
Gelman, Andrew "Multilevel (hierarchical) modeling: what it can and cannot do." Technometrics (2012)
Haas, Sherri "Economic development and the gender wage gap." The Fifth
Hamermesh, Daniel "Fun with matched firm-employee data: Progress and road maps." Labour Economics 15.4 (2008): 662-672
Hedija, Veronika "The Effect of Female Managers on Gender Wage Differences." Prague Economic Papers 2015.1 (2015): 38-59
Hirsch, Boris "The impact of female managers on the gender pay gap: Evidence from linked employer–employee data for Germany." Economics
Hultin, Mia, and Ryszard Szulkin "Mechanisms of inequality Unequal access to organizational power and the gender wage gap." European Sociological
Hultin, Mia, and Ryszard Szulkin "Mechanisms of inequality Unequal access to organizational power and the gender wage gap." European Sociological
Hultin, Mia, and Ryszard Szulkin "Wages and unequal access to organizational power: An empirical test of gender discrimination."Administrative Science
Jensen, Paul H "Exploring the uses of matched employer–employee datasets." Australian Economic Review 43.2 (2010): 209-216
Larsen, Anna Folke, John Rand, and Nina Torm "Do recruitment ties affect wages? An analysis using matched employer–employee data from Vietnam."Review of development economics 15.3 (2011): 541-555
Liu, Amy YC "Gender wage gap in transition in Vietnam." (2002)
Manning, Alan The gender pay gap No 200 Centre for Economic Performance, LSE, 2006
Nghiên cứu của Nguyễn Thị và Lê Thị An Bình đã chỉ ra rằng thu nhập của người lao động ở Việt Nam đang gặp nhiều khó khăn Bài viết cũng đề xuất một số giải pháp nhằm cải thiện tình hình này, góp phần nâng cao đời sống cho người lao động Tạp chí đã đăng tải những thông tin quan trọng liên quan đến vấn đề này.
Pham, Thai-Hung, and Barry Reilly "The gender pay gap in Vietnam, 1993– 2002: A quantile regression approach." Journal of Asian Economics 18.5 (2007): 775-808
Shin, Taekjin "The Gender Gap in Executive Compensation The Role of Female Directors and Chief Executive Officers." The Annals of the American Academy of Political and Social Science 639.1 (2012): 258-278
Spencer, Neville "Gender pay gap widens." (2015)
Tajfel, Henri "Social psychology of intergroup relations." Annual review of psychology 33.1 (1982): 1-39
Torm, Nina The union wage gap among Vietnamese SMEs Working Paper, Department of Economics, University of Copenhagen, 2011.
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
corr cen_age1 cen_age1sq seniority education lnwage lnsize femshman industry female
Nam 57.17531 3.939105 5342071 gender wage lnwage femshman by categories of: gender
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]
Source SS df MS Number of obs = 345
reg lnwage female femshman fexfesh cen_age1 cen_age1sq seniority education lnsize industry
Appendix 4: OLS wage regression with job – cell fixed effect
_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]
Source SS df MS Number of obs = 345 i.occupation _Ioccupatio_1-5 (_Ioccupatio_1 for occ~n==LĐ nghiệp vụ omitted)
xi: reg lnwage female fexfesh cen_age1 cen_age1sq seniority education lnsize i.occupation
Variables: fitted values of lnwage
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
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
OLS wage regression with job – cell fixed effect
_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]
Source SS df MS Number of obs = 345 i.occupation _Ioccupatio_1-5 (_Ioccupatio_1 for occ~n==LĐ nghiệp vụ omitted)
xi: reg lnwage female fexfesh cen_age1 cen_age1sq seniority education lnsize i.occupation
Variables: fitted values of lnwage
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity
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