unlicensed investigating the gender wage gap in vietnam by quantile regression sticky floor or glass ceiling

If the gender wage gap at lower tail quantiles is wider than gap at the middle quantiles, it will result in a sticky floor effect.. The glass ceiling effect in wage existed if the gender

TABLE OF CONTENTS

TABLE OF CONTENTS 2

LIST OF TABLES 4

LIST OF FIGURES 5

CHAPTER 1: INTRODUCTION 6

1.1 INTRODUCTION 6

What is glass ceiling? 6

What is sticky floor? 6

1.2 THE NECESSARY OF INVESTIGATING THE STICKY FLOOR AND GLASS CEILING IN VIETNAM 8

1.3 OBJECTIVES 10

1.4 CONTRIBUTIONS 10

1.5 STRUCTURES 11

CHAPTER 2: LITERATURE REVIEW 12

2.1 BACKGROUND 12

2.1.1 Mincer-type wage equation 12

2.1.2 Quantile regression 14

2.2 LITERATURE REVIEW 17

2.3 THE RESEARCH GAPS 20

CHAPTER 3: METHODOLOGY 21

3.1 DATA 21

3.2 VARIABLES AND MINCER-TYPE WAGE EQUATION 21

3.3 QUANTILE REGRESSION OF WAGE EQUATION 24

CHAPTER 4: RESULTS AND DISCUSSION 26

4.1 DESCRIPTIVE STATISTICS 26

4.2 RESULTS 28

4.2.1 The distribution of wage: Kernel density wage estimation 28

4.3 THE GENDER WAGE DIFFERENTIALS ACROSS THE DISTRIBUTION 34

a.Entire sample 35

b By urban – rural areas 38

c By sectors 43

d By education 48

e By occupations 51

4.4 RESULTS OF THE STICKY FLOOR AND GLASS CEILING EFFECTS IN THE VIETNAM LABOUR MARKET 52

CHAPTER 5: CONCLUSION 56

5.1 CONCLUSION 56

5.2 POLICY IMPLICATIONS 56

REFERENCES 60

APPENDIX A: QUANTILE REGRESSION OF WAGE EQUATION BY EDUCATION 63

APPENDIX B: QUANTILE REGRESSION OF WAGE EQUATION BY OCCUPATION 69

LIST OF TABLES

Table 1: List of variables 21

Table 2: The percentage of male and female labourers in entire sample and in each subsample 26

Table 3: Comparison of lnwage between male and female groups 27

Table 4: Quantile wage regression in entire sample 36

Table 5: Quantile wage regressions in urban areas 39

Table 6: Quantile wage regressions in rural areas 41

Table 7: Quantile wage regressions in state sector 44

Table 8: Quantile wage regressions in private sector 46

Table 9: Summary about stick floor and glass ceiling in Vietnam 53

LIST OF FIGURES

Figure 1: Density functions of male and female (log) hourly wages 30

Figure 2: Density functions of male and female (log) hourly wages in urban and rural 30

Figure 3: Density functions of male and female (log) hourly wages in state sector and private sector 31

Figure 4: Density functions of male and female (log) hourly wages by qualifications 32

Figure 5: : Density functions of male and female (log) hourly wages by occupations 34

Figure 6: Gender wage gap in entire sample by OLS and quantile regression 38

Figure 7: Gender wage gap in urban area by OLS and quantile regression 40

Figure 8: Gender wage gap in rural by OLS and quantile regression 43

Figure 9: Gender wage gap in state sector by OLS and quantile regression 46

Figure 10: Gender wage gap in private sector by OLS and quantile regression 48

Figure 11: Gender wage gap by education 50

Figure 12: Gender wage gap by occupation 52

CHAPTER 1: INTRODUCTION

1.1 INTRODUCTION

Inequality between men and women in the labour market is one of the issues that are

of great interest in labour economics Many empirical studies have shown that wages of males are higher than for females This happens in most countries around the world Most

of these studies focus on the average gender wage gap However, in modern labour economics, an interesting phenomenon also attracts the attention of researchers, that is the gender wage gap at the upper and lower tails of wage distribution are usually higher than that at middle If the gender wage gap at lower tail quantiles is wider than gap at the middle quantiles, it will result in a sticky floor effect If the gender wage gap at upper quantiles is higher than the middle units, the glass ceiling is called to be existed

What is glass ceiling?

Glass ceiling can be interpreted as the phenomenon whereby women do quite well in the labour market up to a point after which there is an effective limit on their prospects Glass ceiling implies that there seems to be an invisible barrier to female workers in occupation, in promotion or in wage that prevents females to reach the top compared to male workers who have the same productivity characteristics The glass ceiling effect in wage existed if the gender wage gap at the top of the wage distribution is wider than other position, suggesting that females in wage ceiling have lower pay than their male counterparts

What is sticky floor?

The sticky floor effect occurs when the gender wage gap widen at the lower tail of the wage distribution This mentions to the case where women at the bottom of the wage distribution are more discriminated against than men and they may face greater disadvantages than at other quantiles

Why gender wage gap, sticky floor and glass ceiling effects exist?

There are many reasons for existence of gender wage gap in which women often receive

a lower wage:

- Due to differences of labour characteristics such as education level, health, etc

- Due to the occupational segregation

- Due to the discrimination against women, especially in some Asian countries where the male – dominated thought still exists

- And other reasons

The sticky floor effect may be occurred due to:

- Low - paid careers are often associated with women, such as maids, secretaries, housekeepers, clerks, tailors, etc Even if doing jobs of equal value, women earn less than men One of the main reasons is the way females' competences are valued compared to males'

- Men and women with their own characteristics are often suitable for different industries In fact, the male-dominated industries often pay more than female- dominated industries Men are still able to participate in the dominant women's sector, but they may demand higher compensation than women receive to do the job

- Getting married and having children can affect female workers' productivity and hence lead to income diversification between males and females After marriage, men may feel more responsibility for the family and work hard to support their families Meanwhile, women will be responsible for housework and caring for children, so women may reduce their participation in labour force and their productivity will be reduced In addition, women have tendency to choose less demanding jobs and lose the opportunity to find or maintain good occupations The glass ceiling effect that exists may be due to:

- Men still perceive more promotions than women

- It is popular to disregard the women‟s potential to fulfil senior or managerial positions amongst women themselves as well as their male colleagues

- Women often find that it is hard to obtain the education and training required to be promoted into leader or managerial positions

- The prejudices either conscious or unconscious in society regarding gender still exist that may limit the women‟s opportunity to get promotions Sometimes, women may get promoted but with lower wage than men counterparts

- An employer may care about a woman‟s marital status as signal of family responsibilities, less flexibility and less productivity And this may reduces the employment prospects of married women and lower the level of wages that women can command

One should pay attention to gender wage gap, sticky floor and glass ceiling effects Firstly, low wages increase the dependence of women on men in the home, so the role of women in the family may be overlooked, which lead to the case that women shoulder almost the entire burden of family planning or lead to domestic violence The fact that women are responsible for housework also contributes to lower productivity in women's main work Or long term violence can affect physical health and mental health This in turn decreases the productivity of women Secondly, wages in the workplace of women are lower than men, so the pension will also be lower Women retire earlier than men, while the average life expectancy of women is higher than that of men Thus, women will experience a longer retirement period than men with lower wages, and women will face economic difficulties in their old age

The presence of sticky floor and glass ceiling is also one of the important sign of gender inequality in particular and social inequality in general This can be seen as a consequence of the development progress of a country Therefore, it is necessary to investigate the existence of sticky floor effect and glass ceiling effect

1.2 THE NECESSARY OF INVESTIGATING THE STICKY FLOOR AND

GLASS CEILING IN VIETNAM

Nowadays sustainable development is a global concern In development process, most regions and countries encounter many common challenges One of the most popular challenges is the problem of increasing inequality in society along with economic growth Therefore, gender equality is one of the important criteria for assessing the sustainable development of a country As other countries, Vietnam is also oriented towards sustainable development Therefore, the improvement of gender wage gap is also one of the urgent requirements in global integration context Investigating the existence of the glass ceiling sticky floor effect will determine the segments where the gender wage inequality actually occurred, and thereby help the government to build strategies for improving the gender inequality efficiently and effectively

In addition, many studies reveal that inequality hurts economic growth Overcoming the effect of sticky floor and glass ceiling will create conditions for both men and women

to contribute significantly to country‟s development The fact that female workers are stuck in low-income or bound with invisible barriers in high-income workers may limit their ability to contribute The 17th sustainable development goals of United Nation mention that “Achieve gender equality and empower all women and girls”

In Vietnam, there are some empirical studies that demonstrate statistical evidence of gender wage gap Liu (2004) uses data from VHLSS 1992-1998 to investigate gender wage inequality in Vietnam by multiple linear regression and the Oxaca – Blinder (1973) decomposition Hung PT (2007) employs quantile regression to analyze the gender wage differential with the data for the period from 1992 to 2002 Anh T.T.T (2015) also uses quantile regression and Machado- Mata (2015) analyzed the gender wage gap All above studies show the existence of gender wage inequality in Vietnam with strong statistical evidence However, none of these papers has really focused on analyzing glass ceiling and sticky floor effects

In addition, it is important to know at which quantiles of wage distribution the wage inequality is stronger If the existence of the glass ceiling and sticky floor effects are

cofirms, this will provide important guidance for policy makers to focus specifically on specific income groups where the gender wage inequality is most serious

1.3 OBJECTIVES

The study aims to achieve the following objectives:

- Investigate the existence of glass ceiling and sticky floor on Vietnam‟s labour market

- Investigate the floor stickiness and glass ceiling effects by groups which formed

by living areas (urban – rural), by sectors (state - private), by education and by

occupations

1.4 CONTRIBUTIONS

By employing quantile regression on the VHLSS 2014, the results of this research project have helped the article to contribute as follow

- Firstly, with the latest data available, this study reinforces the empirical evidence

of the existence of gen wage inequality in Vietnam This is consistent with previous research in Vietnam

- Secondly, this paper sheds light on the overview of gender wage inequality in Viet Nam By investigating the existence of glass ceiling and sticky floor of wages we confirm that the gender wage inequality mainly occurs in the low wage group (sticky floor effect) and be less severe in high wage group (no glass ceiling effect)

- This study also clarifies the glass ceiling and sticky floor effect in each group of labour (urban - rural, state - private, educational, occupational groups Specifically, in terms of urban and rural areas, the sticky floor exists in both regions, but the glass ceiling exists only in rural areas In terms of state and private sectors, the glass ceiling exists in both sectors, while the stick floor is only present in the private sector The cause may be that males are often assigned senior or important position than females Females are still able to participate in high-level leadership but in fact Such cases are quite rare If this happens, females

often receive lower wages than men for the same position One other reasonable explanation for this result is the difference in wage policy for two sectors The private sector is often more competitive and there are no strict wage scales as in the state sector

1.5 STRUCTURES

The remaining of this study is organized as follow:

- Chapter 2 deals with a theoretical background and literature review

- Chapter 3 presents the research methodology used by this study to investigate the sticky floor and glass ceiling effect

- Chapter 4 shows the results of the research and the discussion of the results

- Chapter 5 summarizes some key results, policy implications, limitation of the study

CHAPTER 2: LITERATURE REVIEW

2.1 BACKGROUND

In the representative study of Albrecht et al (2003) and Arulampalam et al (2007), the statistical evidence of the glass ceiling and sticky floor is found by indicating the wider gender wage differentials at the lower and upper tails of the wage distribution On average, the gender wage gap is possible to estimate by using ordinary least squares (OLS) and other mean regression However, OLS can not investigate the gap beyond of the mean of the dependent variable So it does not help in examining the glass ceiling and the sticky floor Many statistical tools have been introduced to perform regression in other quantiles of wage distribution However, with the introduction of the quantile regression by Koenker & Bassett (1978), the investigation of gender wage differentials throughout the wage distribution becomes more easily Since then, quantile regression has become an effective empirical tool for examining the existence of sticky floor and glass ceiling

Thus, in addition to the descriptive statistics, this study estimates the extended Mincer-type wage equation by quantile regression to reveal statistical evidence of sticky floor and glass ceiling, then to determine the magnitude of the effect

2.1.1 Mincer-type wage equation

The most popular specification of empirical wage equations which are used for the analysis is the Mincer wage equation Mincer (1974) introduces a wage equation that demonstrates the relationship between the logarithm of wage (or compensation/income) with some variables such as years of schooling, work experience, and the square of work experience According to Mincer (1974), the wage equation bases in a assumption: all individuals are identical so they require a wage differential to work in occupations that require longer schooling period In literature, the simplest model of Mincer's wage formula will be of the form:

ln wage t schooling experience 

experience2 

(1)

This is the form of static Mincer wage equation, which is used extensively in wage differential analysis One of the popular studies, by inheriting Mincer's (1974) wage equation, was developed by Card (1994) in which the wage function was expanded as follow

In addition, many different studies include different explanatory variables in the expanded Mincer (1974) or they may change the form of traditional variables such as education or experience by dummy variables In the study of the glass ceiling effect in Sweden, Albrecht et al (2003) used age and age squared instead of years of experience and experience squared Furthermore, the dummy variables corresponding to worker‟s highest level of education is also added instead of year schooling Other dummies for living areas (urban - rural), economic sectors (public – private), marital status and some other variables on labour characteristics and trades are also included into the wage model Based on the expanded Mincer wage equation in Albrecht et al (2003), this study employed the following wage equation to investigate the glass ceiling and sticky floor:

ge) i male j Education i 

j Occupation ji X iu i (3)

Where:

ln(wage) : logarithm of hourly wage

male: : dummy variable that take the value 1 if worker is male and 0 if

otherwise

Education i : dummy variables corresponding to worker‟s highest level of

education

Occupation i : dummy variables corresponding to groups of occupations

X i : Other explanatory variables that are controlled in the wage

Consider a linear regression model as follow:

This mean regression is often used to analyze the marginal effects of explation variables on the expected value of the dependent variable However, in order to analyze the marginal impact of independent variables on the dependent variable's quantiles, Koenker & Bassett (1978) proposed the quantile regression model as follows

That means, if the estimated regression model is

Formula (9) shows that the parameter estimation in the regression function at each quantile based on all observations of sample Each observation is assigned a

corresponding weight In particular, the weight of observations above the quantile regression line is τ and the weight of the observations below the quantile regression line

Advantages and disavantages of quantile regression

After Koenker and Bassett (1978) introduced the first quartile regression model, a number of studies were conducted to overcome the shortcomings and to expand the quantile regression Quantile regression is becoming more well-developed and popular as

a quantitative tool in economic research According to Koenker (2005) and Hao & Naiman (2007), quantile regression has the following advantages

Advantages

 Firstly, the quantile regression allows for a detailed exploration of the relationship between the dependent and explanatory variables throughout the dependent variable‟s distribution, not only at the mean of dependent variable as OLS does

 Secondly, although quantile regression requires many lots of complicated calculations, the development of mathematics and statistics along with the support

of information technology help performing quantile regression easily and quickly

 Thirdly, in OLS regression, outliers greatly affected the estimation results Meanwhile, the quantile regression is robustness to presence of outliers

 Fourthly, testing the hypothesis of the parameters of quantile regression is not based on the normality of the error Furthermore, these tests does not require any assumptions about the distribution of the regression error

 Fifthly, the quantile regression is particularly suitable for the presence of heteroskedasticity or for the cases in which dependent variable‟s distribution is asymmetric

 Secondly, one have to conduct each regression function for each dependence‟s quantile to show the full landscape of the marginal effect of independent variables

on the dependent variable, while OLS performs only one conditional mean regression

 Thirdly, the application of quantile regression to nonlinear functions is rather limited The treatment for autocorrelation or endogeneity in quantile regression has not been fully developed

2.2 LITERATURE REVIEW

Adamchik et al (2003) measures the relative economic welfare of women in Poland during the transition The authors analyse the male-female wage differential over the period from 1993 to 1997 after providing an account of gender differences in several labour market outcomes Their results show most of the explained portion of the wage differentials may be contributed to industrial and occupational segregation They also confirm that a substantial part of the wage gap remains unexplained

Albrecht et al (2003) use 1998 data to show that the wage gap between males and females in Sweden rises throughout the wage distribution and move faster in the top quantiles They explain this as a strong glass ceiling effect Albrecht et al (2003) also

performed decomposition by quantile regression to investigate the cause of gender gap After controlling age, education, sector, industry, and occupation, they conclude that the glass ceiling still persists to a considerable extent

Booth et al (2003) employ data from the British Household Panel Survey to reveal that full-time women are more likely than men to be promoted Accounting for individual characteristics, they indicate that females may receive smaller wage increases consequent upon promotion, although females are promoted at almost same rate as men They construct a new “sticky floors” model of pay and promotion to explain for their results In sticky floor model, women are just as likely as men to be promoted but they stuck at the bottom of the wage scale for the new grade

Kee et al (2005) analyses Australian gender wage gaps in both public and private sectors across the wage distribution by using the HILDA survey and quantile regression techniques Additionally, the authors perform quantile regression counterfactual decomposition analysis to examine whether differences in gender characteristics, or differing returns between genders is attributed to the gap Kee et al (2005) detect a strong glass ceiling effect in the private sector Moreover, after controlling for many relevant factors, the acceleration in the gender gap across the distribution does not vanish This proposes that the wage gap mainly causes by returns to genders

Using data from the European Community Household Panel, De la Rica (2008) analyzes the gender pay gap across the wage distribution in Spain by quantile regression and panel data techniques There exists the glass ceiling for highly educated workers, because the gap increases as moving up along the distribution However, the gap decreases for less-educated workers The author argues that this can be explained by statistical discrimination exerted by employers in countries where less-educated women have low participation rates

Using 1987, 1996, and 2004 data, Chi & Li (2008) show that the gender earnings gap in urban of China has increased throughout the earning‟s distribution, and the gap was greater at the lower quantiles This can be interpreted as strong evidence of sticky

floor effect They also decompose wage distributions and find that the gender endowment differences contribute less to the overall gender earnings gap than do return to labour market characteristics They also find that phenomenon “sticky floor” can be concerned with female production workers in low-paid occupation group working in non-state owned firms

Agrawal (2013) examines the gender pay gap in the rural and urban areas in India Their findings show evidence of the sticky floor effect in the urban sector and evidence

of the glass ceiling effect in the rural sector The gender wage gap is decomposed to clarify the contributions of coefficients and characteristics The results show the presence

of discrimination against women Aditionally, women at the bottom of the wage distribution encounter more discrimination than those at the top

Christofides et al (2013) consider the gender wage differentials in 26 European countries with data in 2007 from Income and Living Conditions of the European Union Statistics The magnitude of the gender wage differentials differ considerably among countries The gap cannot be explained fully by the labourer‟s characteristics Using quantile regressions, the authors reveal that the glass ceilings and sticky floors effects exists in several countries They also find larger glass ceilings for full-time full-year employees They suggest that country institutions and policies are relevant to unexplained gender wage gaps in systematic ways

Finseraas et al (2016) study discrimination among recruits in the Norwegian Armed Forces during bootcamp They find that female candidates are perceived as less suited to

be squad leaders than their identical male counterparts They also find that intense collabourative exposure to female colleagues reduces discriminatory attitudes: Male soldiers who were randomly assigned to share room and work in a squad with female soldiers during the recruit period do not discriminate in the vignette experiment

In Vietnam, Pham and Reilly (2006) demonstrated the gender gap in Vietnam by using VHLSS 1998 and 2002 Anh T.T.T (2015), compared to the VHLSS data for 2002 and 2012 using the quantitative regression and the decomposition method Machado-Mata

(2005), shows evidence that the gender wage differential occurs on all quantiles and the wage gap is entirely due to the difference in returns to labour characteristics received by men and women However, Anh.T.T.T (2015) does not examine the existence of glass ceiling and sticky floor on the labour market in Vietnam

2.3 THE RESEARCH GAPS

Most of previous studies in Vietnam demonstrate a strong statistical evidence of the existence of gender wage gap in Viet Nam but almost no studies have investigated the sticky floor and glass ceiling effects, that mean they do not determine whether the wage gap is stronger at low quantiles (sticky floor) or at high quantiles (ceiling effect) of the wage distribution The existence of sticky floor and glass ceiling effects will help to figure out a detailed picture of gender wage inequality in Vietnam Identifying the existence of these two effects will help governments as well as policy makers to improve the gender wage inequality in Vietnam labour market In addition, sticky floor and glass ceiling effects should also be considered in each group of workers which divided by urban-rural areas, by state - private sectors, by educational level and by occupation groups Detailed research in each group will also help to improve Vietnam's wage inequality policies, to be more efficient and effective

CHAPTER 3: METHODOLOGY

3.1 DATA

This study uses the dataset of VHLSS 2014 to accomplish the research objectives The VHLSS dataset collects information on a sample of households and communes that serves to assess the living standards across the country and regions This includes the objective of assessing poverty and the economic inequality The VHLSS survey consists

of households, household members and communes in all provinces/cities The VHLSS sampling method is implemented through the consultancy and supervision of the National Institute of Statistical Sciences, UNDP and the World Bank, to ensure representative representation of the sample selected for the overall study Because of the representative sample of the VHLSS, the VHLSS data is suitable for constructing the wage equation to investigate the existence of glass ceiling and sticky floor in Vietnam

The total number of households surveyed in VHLSS 2014 is 46,995 households in

3133 communes across 63 provinces Information on employment and wages is provided

in Section 4A of the questionnaire The sample comprises all the respondents in Section 4A but excludes members out of working age The sample also excludes members who are self-employed workers

3.2 VARIABLES AND MINCER-TYPE WAGE EQUATION

Using the VHLSS 2014 and referring to study of Albrecht et al (2003), this study employs an extension of Mincer wage equation with the independent variables listed in Table 1 The dependent variable is logarithm of hourly wage Taking hourly pay will rule out the difference in wage due to being full-time or part-time workers, as well as rule out all factors that affect the working time of workers such as housework, childcare, etc

Table 1: List of variables

2 male =1 for male workers; =0 for female workers

Because this research‟s objectives are to investigate the existence of glass ceiling

and stocky floor and determine how wide the gaps are, the variable male is the key

explanatory variable This is a dummy variable, taking value 1 if the worker is male and zero if the worker is female The regression coefficient of this dummy variable will help

to measure the gender wage gap

In addition to gender dummy variable, the wage regression also includes other independent variables as control variables Quantitative variables in the model are age and squared age Qualitative variables such as educational level, gender, marital status, occupation, field of activity, economic type, ethnicity, urban-rural area are added as dummy variables The control variables are divided into three groups: a group of variables related to individual characteristics, a group of variables related to work characteristics, and a group of other factors

Group of variables related to individual characteristics contains:

- Age and Age squared

- The highest level of education of worker is demonstrated by a set of dummy

variables: Primary, secondary, highs chool, vocational degree, bachelor,

postgraduate

- Marital status is represented by dummy variable married

- Race is expressed by a dummy variable named race, which takes value 1 if

worker‟s race is Kinh or Hoa and takes value 0 for otherwise

Group of variables related to work characteristics contains:

- Occupations are represented by a set of dummy variables: Manager, High level

expert, Average level expert, Office staff, Service, Manual labourer, Operation worker

- Sector includes state sector and private sector

Group of other control factors includes:

- Urban – rural which is represented by dummy variable urban

3.3 QUANTILE REGRESSION OF WAGE EQUATION

The wage equation of this study is constructed as an extension of Mincer wage equation which are referred to Albrecht et al (2003) Estimation method is the quantile regression Although quartile regression can be estimated for every quantile τ ϵ (0,1), we only report the results for some regular quantiles such as 0,1 – 0.25 – 0.5 – 0.75 – 0.9 These quantile are chosen because this is a combination of quartiles and deciles which are commonly used in statistics

The explanation of variables is listed on Table 1

The quantile regression will be performed at some typical quantiles: 0.1 – 0.25 – 0.5 – 0.75 – 0.9 The coefficient of the gender dummy variable will show the gender wage differentials at each quantile The sticky floor effect occurs when females at the lower tail

of the wage distribution are at a greater disadvantages and the gap is wider at this lower tail Thus, according to Booth et al (2003), in order to verify the existence of the sticky floor in Vietnam, the coefficient of the gender dummy variable at quantile 0.1 is compared with that of quantiles 0.25 and 0.5 If the gender wage gap at quantile 0.1 is significantly greater than the gap at 0.25 and 0.5, there is statistical evidence for the existence of sticky floor in Vietnam

at he upper tail of the wage distribution Therefore, according to Arulampalam et al (2007), in order to verify the existence of the glass ceiling, the coefficient of the gender dummy variable at quantile 0.9 is compared with that of quantile 0.5 and 0.75 If the

gender wage gap at 0.9 is significant greater than the gap at 0.5 and 0.75, there is statistical evidence for the existence of glass ceiling in Vietnam

In order to figure out the overall picture of the sticky floor and glass ceiling in Vietnam‟s labour market, this study will conduct the analysis over the entire population and some subpopulations

- Sticky floor and glass ceiling effect in urban and rural areas

- Sticky effect and glass ceiling effect in state sector and private sector

- Sticky floor and glass ceiling effect in groups divided by education

- Sticky floor and glass ceiling effect in groups divided by occupations

CHAPTER 4: RESULTS AND DISCUSSION

4.1 DESCRIPTIVE STATISTICS

Table 2 shows the percentages of male and female workers in the sample as well as

in each subgroup The total number of observation in entire sample is 5512 observations,

of which the number of female workers is 2407 (about 43.67%) and the number of male workers is 3105 (about 56.33%) In the sample, there are 1454 (26.38%) workers employed in the private sector, of which 618 (42.5%) were male, and 836 male (57.5%) The number of people working in the public sector is 785 (14.24%), with the proportion

of men in this group being 51.3% and 48.7% for women

For each group formed by education, the number of workers with bachelor degree is

1774 (about 24.93%) which is the highest proportion; of which 53.9% of those are female, 46.1% of those are male The proportion of workers with postgraduate qualifications was relatively small at about 1.40% (= 77/5512), of which the proportion

of men with postgraduate qualifications was much higher than that of women (65.5% versus 34.5%) At the remaining levels of education such as primary, lower secondary, highschool and vocational levels, the proportion of male workers is always higher than that of female workers

Table 2: The percentage of male and female labourers in entire sample and in each subsample

Sample

Count

e Percent

Table 3: Comparison of lnwage between male and female groups

Sample

chi2

(1) (2) (3) (4) (5)= (3) - (1) (6) (7)= (4) - (2) Entire sample 2.88 2.95 3.03 3.06 0.15 7.77*** 0.11 31.71***

Urban – rural areas

Urban 2.70 2.81 2.87 2.93 0.17 5.97*** 0.11 37.94*** Rural 3.06 3.08 3.22 3.24 0.16 6.06*** 0.16 19.81***

Public – private sectors

Private 2.86 2.91 3.06 3.07 0.20 5.43*** 0.16 30.20*** State 3.26 3.31 3.43 3.48 0.18 5.61*** 0.16 23.36***

Educations

Primary 2.54 2.67 2.76 2.85 0.22 5.147*** 0.18 15.12*** Secondary 2.74 2.81 2.83 2.89 0.09 2.14** 0.08 4.31** Highschool 2.75 2.86 2.93 3.00 0.19 3.32*** 0.15 10.64*** Vocational 3.00 3.05 3.18 3.22 0.18 4.31*** 0.16 20.48***

Bachelor 3.27 3.35 3.53 3.57 0.26 6.97*** 0.22 38.97*** Postgraduate 3.76 3.86 4.00 3.94 0.24 1.94* 0.08 2.52

Occupations

Manager 3.52 3.65 3.80 3.85 0.28 2.12** 0.20 5.42** HighLevelExpert 3.44 3.46 3.69 3.69 0.24 5.96*** 0.23 28.69*** AverageLevelExpert 3.16 3.26 3.33 3.35 0.17 2.87*** 0.09 3.65* OfficeStaff 2.94 3.02 3.12 3.22 0.17 2.07** 0.20 7.34*** Service 2.59 2.68 2.73 2.81 0.14 2.20** 0.12 6.82*** ManualLabourer 2.59 2.73 2.96 3.01 0.37 9.76*** 0.28 70.00*** OperationWorker 2.89 3.00 3.13 3.14 0.24 5.71*** 0.14 19.56*** LowSkilledLabourer 2.48 2.53 2.63 2.72 0.14 3.71*** 0.19 21.73***

*,**,***: significant at 10%, 5%, 1% respectively

Table 3 demonstrates the mean and median wages of the two groups of male and

female over the entire sample as well as subsamples The log wage‟s mean value of males

is higher than that of females on the whole sample This not only occurs in the entire

sample but also in every subsample which are split by urban – rural areas, by state –

private sectors, by education and by occupations All gender wage differentials are

statistically significant, suggesting that the gender wage gap actually exists

Table 3 also shows the median wage differentials between men and women Similar

to the mean wage differentials, the median of male wages is always higher than that of

females over the whole samples as well as in all subsamples considered All the median

wage gap between men and women is always statistically significant

These early comparisons show that male wage tend to be higher than female wages in

both cases of mean wage and the median wage However, this comparison does not help

to see whether there is a sticky floor and glass ceiling In the next step, it is necessary to

describe the whole distribution of the wage variable One of the appropriate descriptions

is to use the kernel density function

4.2 RESULTS

4.2.1 The distribution of wage: Kernel density wage estimation

The density function is used to describe the probability density of a random variable

To describe the density function of the log wage variable, this study uses the kernel density function The kernel density function is a non-parametric method for estimating the density function of a random variable Kernel density estimation is a basic data smoothing problem where inferences about the population are based on a sample data The meaning of a kernel function is quite similar to a histogram, but is smooth and graphed on a solid line The highest peak density of the kernel density function indicates the mode of that variable

a Over entire sample

Figure 1 shows estimated kernel densities of male and female wages over the sample data The male kernel density function is represented by the solid line and the female kernel wage density function is the dashed line The results in Figure 1 show that men's wage kernel density functions are almost on the right side of women‟s (except for some middle positions) In addition to Table 3, this result indicates that male wages are not only higher than women's at median, but also at almost other quantiles

Figure 1: Density functions of male and female (log) hourly wages

b By regions: urban – rural

Figure 2: Density functions of male and female (log) hourly wages in urban and rural

Figure 2 shows the wage density function of male and female workers in urban and rural areas It can be seen that in both areas the wage density of males is always at the

right of the female worker, indicating that the wages of the male group are higher than that of the female in all quantiles

c By sector

Figure 3: Density functions of male and female (log) hourly wages in state sector and private sector

Figure 3 shows the kernel density function of male and female workers in both the private and public sectors Both figures show that wages of male workers are always higher than that of female, reflecting by the fact that the men's wage density function is almost always on the right side of the women‟s

d By education

Males Females

Figure 5: : Density functions of male and female (log) hourly wages by occupations

When classifying the whole sample into subsamples by degrees as well as by occupation, the wage density kernel functions of the male workers are always on the right side of the female workers‟s This is also a signal that male wages are higher than females throughout the wage distribution However, this is not enough to conclude about the existence of the sticky floor and glass ceiling The existence of these effects will be verified after considering the quantile regression results of wage equation as in Section 4.3

4.3 THE GENDER WAGE DIFFERENTIALS ACROSS THE DISTRIBUTION

In order to verify the existence of sticky floor and glass ceiling effect, the wage equation (10) are estimated by quantile regression at quantiles 0.1 – 0.25 – 0.5 – 0.75 – 0.9 The regression coefficient of gender dummy variable will help to measure the gender wage gap The sticky floor is considered to exist when the gen wage gap at the lower quantile of wage distribution is wider than gap at other quantiles The glass ceiling effect

is determined to exist when gender wage gap is wider at top quantiles of wage distribution In this study, based on the studies of Booth et al (2004) and Arulampalam et

al (2007), the stick floor was determined to exist when the gender wage gap at quantile 1.1 is significantly greater than that of quantiles 0.25 and 0.5 The glass ceiling is determined to exist when the gender wage gap at quantile 0.9 is significantly greater than 0.75 and 0.5

In order to figure out the overall picture of the sticky floor and glass ceiling in Vietnam‟s labour market, this study will conduct the analysis over the entire population and some subpopulations

- Sticky floor and glass ceiling effect in urban and rural areas

- Sticky effect and glass ceiling effect in state sector and private sector

- Sticky floor and glass ceiling effect in groups divided by education

- Sticky floor and glass ceiling effect in groups divided by occupations

a Entire sample

Table 4 presents the results of the wage regression by using the entire sample The

key variable to consider in this regression is gender the dummy variable, named male,

because the coefficient of this dummy variable reveals the gender wage gap after controlling for other factors Column (1) of Table 4 shows the regression result obtained

by OLS Columns (2) - (6) show the coefficients and standard error obtained by quantile regression The results are also shown in Figure 6 The gender gap of mean wage is expressed by the horizontal line The folded line represents the regression coefficient of the gender dummy variable in every quantile (0,1)

Table 4: Quantile wage regression in entire sample

[-11.97] [-11.52] [-12.76] [-11.54] [-7.44] [-3.24] married 0.108*** 0.201*** 0.114*** 0.0720*** 0.0355* 0.0627**

[5.11] [4.98] [4.75] [4.29] [1.87] [2.30] staterun 0.215*** 0.294*** 0.212*** 0.164*** 0.193*** 0.165***

[7.84] [5.14] [6.50] [7.30] [7.46] [4.39] private 0.172*** 0.179*** 0.151*** 0.141*** 0.149*** 0.137***

[7.37] [3.67] [5.49] [7.62] [7.19] [4.75] foreign 0.305*** 0.320*** 0.334*** 0.278*** 0.297*** 0.233***

[9.75] [4.80] [9.12] [11.09] [10.57] [5.83] race -0.0146 0.00385 0.0619 -0.0173 -0.0355 -0.0491

[-0.43] [0.05] [1.53] [-0.63] [-1.17] [-1.14] urban 0.116*** 0.172*** 0.129*** 0.104*** 0.0858*** 0.0944***

[6.89] [4.73] [6.38] [7.64] [5.65] [4.40] Primary 0.168*** 0.337*** 0.167*** 0.132*** 0.117*** 0.148***

[5.04] [4.91] [4.31] [4.97] [3.90] [3.56] Secondary 0.262*** 0.437*** 0.275*** 0.190*** 0.173*** 0.212***

[7.51] [6.17] [6.89] [6.98] [5.63] [4.96] Highschool 0.283*** 0.332*** 0.274*** 0.245*** 0.245*** 0.323***

[7.16] [4.22] [6.16] [8.06] [7.10] [6.78] Vocational 0.351*** 0.472*** 0.331*** 0.308*** 0.323*** 0.357***

[9.41] [5.96] [7.48] [10.15] [9.37] [7.46] Bachelor 0.423*** 0.534*** 0.440*** 0.380*** 0.423*** 0.510***

[9.29] [5.62] [8.52] [11.03] [10.81] [9.08] Postgraduate 0.713*** 0.865*** 0.769*** 0.696*** 0.704*** 0.727***

[9.94] [4.97] [7.98] [10.81] [9.65] [7.12] Manager 0.513*** 0.356*** 0.427*** 0.548*** 0.508*** 0.491***

[7.86] [2.64] [5.88] [11.21] [9.01] [5.78] HighLevelExpert 0.444*** 0.493*** 0.431*** 0.425*** 0.389*** 0.403***

[10.25] [5.61] [9.08] [13.13] [10.07] [6.89] AverageLevelExpert 0.250*** 0.325*** 0.285*** 0.265*** 0.214*** 0.164***

[6.18] [3.92] [6.21] [8.27] [5.61] [2.86] OfficeStaff 0.0888* -0.00136 0.121** 0.112*** 0.131*** 0.153***

[1.90] [-0.01] [2.40] [3.21] [3.28] [2.65]

Service -0.0968***

[-2.69]

-0.167** 2.43]

[ 0.108***

[-2.83]

-0.0948*** 3.64]

[ 0.0343 [-1.17]

-0.0527 [-1.29] SkillLabourer 0.119* 0.0855 0.125 0.101* 0.124** 0.120

[1.68] [0.61] [1.64] [1.93] [2.16] [1.48] ManualLabourer 0.1000*** 0.147*** 0.132*** 0.103*** 0.0911*** 0.0379

[3.80] [2.69] [4.41] [5.05] [3.90] [1.14] OperationWorker 0.189*** 0.267*** 0.250*** 0.196*** 0.139*** 0.0884**

[6.17] [4.00] [6.88] [7.81] [4.79] [2.12] Intercept 0.520*** -1.522*** 0.0343 1.105*** 1.673*** 2.108***

[4.55] [-7.20] [0.29] [13.83] [18.71] [15.76]

*,**,*** : significant at 10%, 5%, 1% respectively

Figure 6 demonstrates the gender wage gap in mean and in each quantile across the

wage distribution The horizontal dashed line represents the gender gap in mean wage

and it is constant across all quantiles The folded line represents the variation of gender

wage gap across quantiles As we can see from Table 4 and Figure 6, the gender wage

gap tends to be higher at bottom of the wage distribution The folded line in Fig 6 have

the tendency to be higher at lower quantiles than that of middle quantiles, indicating that

this statistical evidence supports the existence of a sticky floor in Vietnam labour market

However, the regression result on the whole sample did not provide statistical evidence

for the existence of the glass ceiling because the gender variable‟s coefficient at quantile

0.9 is not greater than at 0.5 and 0.75