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

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

UNIVERSITY-LEVEL RESEARCH PROJECT
MS:

TOPIC:

INVESTIGATING THE GENDER WAGE GAP IN
VIETNAM BY QUANTILE REGRESSION:
STICKY FLOOR OR GLASS CEILING?

Investigator:
TRẦN THỊ TUẤN ANH

TP.HCM, JULY - 2017


INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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?
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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 femaledominated 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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

-

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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.

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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:
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

ln waget      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
ln wage      schooling    experience    experience2   X  u.

(2)

where X includes explanatory variables which one would like to control for in wage
equation.
After Card (1994), many other studies have expanded Mincer's wage equation by
many ways. Among them, Buchinsky (1994) performed quantile regression on the
Mincer's salary function to examine the marginal effect of explanatory variables on log
wage across different quantiles of wage distribution. Buchinsky's approach has volunteer
for a promising prospect of analyzing the determinant of wages. Especially, once gender
wage gap can be examined across quantiles, Mincer's wage functions are also commonly
used in studies of glass ceiling effects and sticky floor effects.
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:
q

p

j 1

j 1

ln( wage)i      male    j Educationi    j Occupation ji  X i  ui .

13

(3)


INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

Where:
ln(wage)

: logarithm of hourly wage

male:

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

Educationi : dummy variables corresponding to worker‟s highest level of
education.
Occupationi : dummy variables corresponding to groups of occupations
Xi

: Other explanatory variables that are controlled in the wage
equation.

The wage equation and list of explanatory variables will be covered more details at

Section 3.2 of Chapter 3 about research methodology.
2.1.2. Quantile regression
Quantile regression is first introduced by Koenker & Bassett in 1978. Instead of
estimating the parameters of the mean regression by the OLS method, Koenker & Bassett
(1978) proposed estimating the parameter at each quantile of the dependent variable‟s
distribution. Instead of determining the marginal impact of each explanatory variable on
the mean of the dependent variable, the quantile regression will help determine the
marginal impact of the explanatory variable on the dependent variable on each quantiles
throughout the dependent variable‟s distribution.
Consider a linear regression model as follow:
Yi  1  2 X 2i  ...  k X ki  Ui ,

(4)

E (Yi | X 2i ,..., X ki )  1   2 X 2i  ...   k X ki .

(5)

or

X 

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
Q (Yi | X2i ,..., X ki )   1   2 X 2i  ...   k X ki .

(6)

X 

For the regression model (5), the ordinary least squares method is used to estimate the
regression parameters. By OLS, we will obtain the estimation of parameters such that the
squared sum of the errors is minimized.
That means, if the estimated regression model is
Yi  ˆ1  ˆ2 X 2i  ...  ˆk X ki  ei

(7)

X i ˆ

where:
ei

: residuals

ˆ j

: point estimation of  j , j=1,2,…,n.

Then the regression parameters are calculated so that the sum of the squares of ei is
minimal.
n

ˆ*  arg min  (Yi  X iˆ )2 .
ˆR k

i 1

(8)

For quantile regression as (6), the regression parameter will be obtained in order to
minimize the weighted distance from observations to quantile regression line:

1
ˆ  arg min    . Yi  X i    (  1). Yi  X i  .
n {i|Y  X  }
 R
{i|Y  X  }

k

i

i

i

i

(9)

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

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
is (1 – τ).
Regression by OLS method only yields a single regression line that can condition the
conditional mean of the dependent variable Y by the values of the independent variable
X. Meanwhile, the quantile regression shows each particular regression for each quantile
  (0,1)

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

 Fifthly, the quantile regression is particularly suitable for the presence of
heteroskedasticity or for the cases in which dependent variable‟s distribution is
asymmetric.
Disadvantages
In addition to some advantages listed above, the quantile regression still has some
disadvantages as follows:
 Firstly, the calculations in regression are more complicated than OLS. The
estimation of the parameters of the quantile regression is obtained by solving the
linear programming problem. This will be difficult without the support of the
computer.
 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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

(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.

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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
ID

Variables

Notes

1

lnwage

Logarithm of hourly wage

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

2

male

=1 for male workers; =0 for female workers

3

age

Age of worker

4

age2

Age squared

6

married

=1 if worker current marital status is married; =0 otherwise

7

race

=1 if worker race is Kinh or Hoa; =0 otherwise

8

Primary

= 1 if worker‟s highest level of education is primary; =0
otherwise

9

Secondary

= 1 if worker‟s highest level of education is secondary; = 0

otherwise

10

Highschool

= 1 if worker‟s highest level of education is high school; =0
otherwise

11

Vocational

= 1 if worker‟s highest level of education is vocational degree;
=0 otherwise

12

Bachelor

= 1 if worker‟s highest level of education is bachelor; =0
otherwise

13

Postgraduate

= 1 if worker‟s highest level of education is postgraduate; =0
otherwise

14

Manager

=1 if worker occupation is leader/manager; = 0 otherwise

15

High level expert

=1 if worker occupation is high level expert; = 0 otherwise

16

Average level expert

=1 if worker occupation is average level expert; = 0 otherwise

17

Office staff

=1 if worker occupation is office staff; = 0 otherwise

18

Service

=1 if worker occupation is service; = 0 otherwise

19

Manual labourer

=1 if worker occupation is manual labourer; = 0 otherwise

20

Operation worker

=1 if worker occupation is operation worker; = 0 otherwise

21

Private

=1 if worker works in private sector; = 0 otherwise

22

State

=1 if worker works in state sector; = 0 otherwise

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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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:
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

-

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 model is

lnwage  1   2 male+ 3age+  4 age 2  5 married   6 state   7 private
6

5

j 1

j 1

 8 race  9urban +   j education j    j occupation j  u.

(10)

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.
Similarly, the glass ceiling effect occurs when the gender wage differentials is wider
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
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INVESTIGATING THE GENDER WAGE GAP IN VIETNAM BY QUANTILE REGRESSION: STICKY FLOOR OR GLASS CEILING?

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

25