1.

0 FULL REFERENCE OF THE ARTICLE

Hoang Oanh, N. and Hong Ngoc, N. (2020). Gender pay gap in Vietnam: a propensity

score matching analysis. Journal of Economics and Development, Vol. ahead-of-print No.

ahead-of-print. https://doi.org/10.1108/JED-07-2020-0089

2.0 THE OBJECTIVES

This paper investigates the extent, the determinants and the change in the gender pay

gap in Vietnam in the period 2010–2016 in order to provide suggestions for policy

adjustment to narrow gender pay inequality more effectively.

3.0 THE BACKGROUND OF THE ISSUES

Although the currents of modernization surround life now, however the problem of

income gap between the sexes still exists in developing countries, as well as developed

countries. Men tend to earn higher wage rates than women. It was found that wage

differences were influenced by two important categories of factors, namely differences in

individual characteristics referred to as explanatory wage differences and parameter

differences in wage models referred to as unexplained differences.

The segregation in the distribution of jobs and the different wages between male and

female workers is seen to be widening. Female workers often do not earn a decent income

and are somewhat less than male workers due to differences in skills, and the practice of

discrimination by employers against female workers by allocating lower paid jobs. Through

the process of economic development, women began to enter the labor market and had to deal

with the problem of job segregation, wage differences with male workers and so on.

The job market is now more dynamic due to the rapid changes in the use of the latest

technology and the diversity of skills needs by employers. This situation has created great

1
competition between workers, especially between men and women. The implication is that

they have to accept whatever offer is given by the employer which is sometimes not suitable

for the field of study they are getting. In the difficulty of getting a job tend to end up with a

job that is not their choice, although at the same time they are still looking for other jobs that

are more suited to their interests and tastes. A common situation is the exclusion of women

from formal workers which causes them to work in the informal sector. As such, they are

faced with a less conducive work environment, difficult to be promoted, have no opportunity

to improve experience and knowledge, are not given training, and end up receiving low

wages.

In Vietnam, there have been some empirical studies on this topic. These studies,

regardless of being conducted for the whole economy, by economic sector or by income

group, all confirm that gender inequality in pay persists. However, the results of these studies

may not be very convincing given the limitations of the methods applied. . In addition to the

observable determinants including educational qualification level, occupation, economic

sector and industry, unobservable factors are proved to also play an important role in creating

the gender pay gap.

4.0 THE THEORY

The wage segregation model introduced by Oaxaca-Blinder (1973) is to look at wage

differences between men and women. In this model, it is broken down into male wage

structure and female wage structure. To measure the extent of income gap between genders

and the extent of the influence of discrimination, Oaxaca (1973) and Blinder (1973)

decompose the causes of income differences into two parts. The first part is the difference in

the observed variable or commonly referred to as the difference in endowment such as age,

education, experience, and type of job. The second part is the treatment and assessment of

2
differences between the two groups in the job market or differences in unexplained variables

or commonly referred to as discrimination.

This study also aims to determine the factors that influence the participation of the

workforce, the factors that influence the size of male and female income, and the extent of the

contribution of endowment and discrimination factors to income gap between genders.

This study uses the B-O model in describing the analysis of the study. A common

method used to investigate the gender pay gap is the Blinder – Oaxaca (B – O) decomposition

method. This method has been improved by Juhn et al. (1993) and Machado and Mata (2001)

to investigate the change in pay gap over time and to account for differences in labor market

features. However, these methods have certain limitations, including the commonly seen

limitations of the parametric methods and also the problem of comparability in the supports,

thus leading to overestimates of the component of the gap attributable to differences in

individuals ’characteristics (Nopo, 2004). Thus, Nopo (2004) proposed a new non-parametric

matching technique to explain gender pay gap as an alternative to the traditional B – O

method.

The most important advantage of this method is that it produces more robust and

reliable results since it overcomes the heterogeneity of the samples under investigation and

avoids diseases commonly faced by the parametric methods. Until now, the number of

international researches on income inequality employing the matching approach is still small.

5.0 CONCEPTS RELATED TO GENDER IN HRD

5.1 Gender Pay Gap

The gender pay gap refers to the difference in earnings between women and

men. Experts have calculated this gap in a multitude of ways, but the varying

3
calculations point to a consensus: Women consistently earn less than men, and the gap

is wider for most women of color.

The gender pay gap measures the difference between the average earnings of

women and men in the workforce. It is not the difference between two people being

paid differently for work of the same or comparable value, which is unlawful. This is

called equal pay.

The gender pay gap is an internationally established measure of women’s

position in economy in comparison to men. It is the result of the social and economic

factors that combine to reduce women’s earning capacity over their lifetime. The

gender pay gap is influenced by a number of factors, including:

 conscious and unconscious discrimination and bias in hiring and pay

decisions

 women and men working in different industries and different jobs,

with female-dominated industries and jobs attracting lower wages

 lack of workplace flexibility to accommodate caring and other

responsibilities, especially in senior roles

 high rates of part-time work for women

 women’s greater time out of the workforce for caring responsibilities

impacting career progression and opportunities.

 women’s disproportionate share of unpaid caring and domestic work

The gender pay gap can start when women first enter the workforce. A

combination of factors affect women's lifetime economic security and makes it likely

that over a lifetime women will earn less than men, be less likely to advance their

careers as far as men, and accumulate less superannuation and savings than men, and

will therefore be more likely to live in poverty in old age.

4
 The gap between women’s and men’s earnings is a symptom of a broader

cultural problem in workplaces. It reflects the historic and systemic undervaluing of

women’s workplace contributions and the significant barriers that lead to the under-

representation of women in senior executive and management roles.

Closing the gender pay gap goes beyond just ensuring equal pay. It requires

cultural change to remove the barriers to the full and equal participation of women in

the workforce, including the genuine equal choice to access the same career or work

opportunities as men in all occupations, industries and levels of seniority.

Analyzing the most recent Census Bureau data from 2018, women of all races

earned, on average, just 82 cents for every $1 earned by men of all races. This

calculation is the ratio of median annual earnings for women working full time, year

round to those of their male counterparts, and it translates to a gender wage gap of 18

cents. When talking about the wage gap for women, it is important to highlight that

there are significant differences by race and ethnicity. The pay gap is larger for most

women of color.

5.2 Propensity Score Matching

The propensity score method was first introduced by Rosenbaum and Rubin in

1983. The propensity score was defined as the conditional probability of receiving an

intervention based on the characteristics prior to the intervention. This method is a

statistical adjustment method that can be used to analyze data from the design of

observational studies where the design of treatment delivery through randomization

for treatment or non-treatment groups is not possible. Researchers can use propensity

scores on statistics to balance or equalize groups of research subjects so that they can

5
 reduce bias due to non-random treatment. The bias will be reduced when the results of

the comparison between non-treatment and treatment groups are as close as possible.

Propensity Score (PS) is a conditional probability of obtaining certain

treatment based on observed covariates. One of the methods developed from the

propensity score is the Propensity Score Matching (PSM) method. PSM is a method

performed by balancing or equating a group of research subjects with a matching

method. With this method the treatment group is paired with a non-treatment group

based on the observed covariates. In the analysis of observational studies, this method

is used to reduce the bias in the estimation of treatment effects on observational data

due to confounding factors.

Before matching, the propensity score value can be estimated using logistical

regression to facilitate its interpretation (Littnerova, et al., 2013). Then the PSM

method will balance the data of the treatment and control groups by matching based

on the observed covariates (Guo & Fraser, 2010). Several previous studies on PSM

applied in the field of health have begun to develop given the high urgency to reduce

bias between variables in a study.

6.0 CONCLUSION

From this article, the creator also contributes to gender specialization in the labor

market. It is because health and some gender characteristics make women suitable for only

some jobs, and the same is true for men. This fact shows that “not all males are comparable to

all females” even in the support. In other words, the distributions of characteristics can be

different in the support. Matching techniques are a good tool to control such differences and

ensure the comparability of research groups and, therefore, can provide a reliable assessment

6
of gender pay inequality. This study differs from the previous studies on gender pay gap in

Vietnam in that it employs the PSM technique with a focus on the common supports only.

The matching results based on the data sets taken from the Vietnam Household Living

Standards Survey (VHLSS) 2010 and 2016 affirm that gender income inequality in Vietnam,

though persisted, decreased significantly in 2016 compared to 2010, and was insignificant in

many subsamples in 2016. In addition to the observable determinants including educational

level, occupation, economic sector and industry, unobservable factors are proved to also play

an important role in creating the gender pay gap in Vietnam.

Generally do not appear to be different from those of previous studies. However, the

results are more robust and reliable than those obtained by the B–O method found in Amy

(2004) and Nguyen and Hoang (2018) because the matching technique eliminates the

heterogeneity of the samples which the B–O method fails to do. Also, their results are more

robust than those obtained by the improved B–O models found in Pham and Reilly (2007),

Gian (2014) and Vu and Yamada (2018) in the sense that the results from matching

techniques are immune from the diseases commonly faced by parametric estimation

techniques in terms of specification error and uncertainty of functional form. In addition, the

results obtained from a matching allow for a deeper insight into the extent and causes of pay

inequality. Although matching techniques have many advantages over the traditional

parametric methods, they suffer from the dimensionality problem that arises when there are

many explanatory variables in non-parametric setups (Nopo, 2004).

7.0 IMPLICATION

Ideally there are still many other variables that can explain the income gap between

genders, such as, company scale, work breaks, natural inherited talents from parents, and the

7
environment. These variables can be included in future research, so that the contribution of

endowment factors can be greater.

This is the first study using a matching technique to investigate gender wage gap in

Vietnam. With up-to-date data, longer research period and the superiority of the method used

in dealing with sample selection bias, the results obtained are more robust, more detailed and

reliable.

The research findings suggest that policies aimed at mitigating gender pay inequality

should take into account both observable characteristics and unobservable factors such as

unobservable gender differences that affect wages and gender discrimination in pay.

These wage gap calculations reflect the ratio of earnings for women and men across

all industries; they do not reflect a direct comparison of women and men doing identical

work. This is purposeful. Calculating it this way allows experts to capture the multitude of

factors driving the gender wage gap, which include but are not limited to:

Differences in industries or jobs worked. By calculating a wholistic wage gap,

researchers can see effects of occupational segregation, or the funnelling of women and men

into different types of industries and jobs based on gender norms and expectations. So-called

women’s jobs, which are jobs that have historically had majority-female workforces, such as

home health aides and child care workers, tend to offer lower pay and fewer benefits than so-

called men’s jobs, which are jobs that have had predominantly male workforces, including

jobs in trades such as building and construction. These gendered differences are true across

all industries and the vast majority of occupations, at all levels, from frontline workers to

midlevel managers to senior leaders.

Differences in years of experience. Women are disproportionately driven out of the

workforce to accommodate caregiving and other unpaid obligations and thus tend to have less

work experience than men. Access to paid family and medical leave makes women more

8
likely to return to work and more likely to return sooner. However, as of March 2019, only 19

percent of civilian workers had access to paid family leave through their employers and only

40 percent had access to short-term disability insurance benefits to deal with their own

medical needs.

Differences in hours worked. Because women tend to work fewer hours to

accommodate caregiving and other unpaid obligations, they are also more likely to work part

time, which means lower hourly wages and fewer benefits compared with full-time workers.

Discrimination. Gender-based pay discrimination has been illegal since 1963 but is

still a frequent, widespread practice—particularly for women of color. It can thrive especially

in workplaces that discourage open discussion of wages and where employees fear

retaliation. Beyond explicit decisions to pay women less than men, employers may

discriminate in pay when they rely on prior salary history in hiring and compensation

decisions; this can enable pay decisions that could have been influenced by discrimination to

follow women from job to job.

REFERENCES

Blinder, A. (1973). Wage Discrimination: Reduced Form and Structural Estimates. Journal of

Human Resources, Vol. 8 (4), pp. 43.

Guo, S. & Fraser, M. W. (2010). Propensity score analysis: Statistical methods and

applications. Thousand Oaks, CA: Sage Publications.

9
Hoang Oanh, N. and Hong Ngoc, N. (2020). Gender pay gap in Vietnam: a propensity score

matching analysis. Journal of Economics and Development, Vol. ahead-of-print No. ahead-

of-print. https://doi.org/10.1108/JED-07-2020-0089

Juhn, C., Murphy, K. and Pierce, B. (1993), “Wage inequality and the rise in returns to skill”,

The Journal of Political Economy, Vol. 101 No. 3, pp. 410-442.

Littnerova, S., Jarkovsky, J., Parenica, J., Pavlik, T., Spinar, J., & Dusek, L. (2013). Why to

use Propensity Score in Observational Studies? Case Study Based on Data from the Czech

Clinical Database AHEAD 2006-09, cor et Vasa, 55(4), pp. 383-390.

Machado, J. and Mata, J. (2001), “Earning functions in Portugal 1982-1994: evidence from

quantile regressions”, Empirical Economics, Vol. 26 No. 1, pp. 115-134.

Nopo, H.R. (2004), “Matching as a tool to decompose wage gaps”, Working Paper, IZA

Discussion Paper No. 981, Institute of Labor Economics (IZA), Bonn.

Oaxaca, R.L. (1973). Male-female wage differentials in urban labour markets. International

Economic Review 14(3): 693-709.

Pham, H. and Reilly, B. (2007), “The gender pay gap in Vietnam, 1993–2002: a quintile

regression approach”, Journal of Asian Economics, Vol. 185 No. 5, pp. 775-806.

Tijdens, K.G. dan M.Van Klaveren. (2012). Frozen in Time: Gender Pay Gap Unchanged for

10 years. Brussels, ITUC report.

10
Vu, T.M. and Yamada, H. (2018), “Decomposing Vietnamese gender equality in terms of

wage distribution”, Pacific Economic Review, Vol. 23 No. 5, pp. 705-731.

11