The gender wage gap is defined as “the difference between the
median earnings of men and women relative to the median earnings of men” 1.
“The median wage of a woman working full-time is 85% that of a man” in rich and
middle-income countries that the OECD consists of 2. This review will concentrate on five studies that all
examine the existence of the gender wage gaps in many countries, involving several
variables that are tested, and different methods used to gather the data. The inspiration
from Chuang, Lin, and Chui (2017, p. 1) to investigate the gender gap in the
financial industry derived from the part that industry plays in affecting
gender wage gaps for export-oriented countries. Further, they state as Taiwan
is a well know export-oriented economy, the analysis could serve as a
representative case study. The sample chosen was taken from individual level
data from the “Manpower Utilization Survey” from the years 1978 to 2013 which
each year consists of approximately 18000 randomly drawn sample households. The
sample used in the analysis is restricted to paid employees in the private
sector. Through the decomposition, examination, and the breakdown of the
overall gender wage gap, they examine the interindustry gender wage gap in Taiwan,
particularly focusing on the financial industry. They found that during the
period sampled, 2-14% of the overall gender wage gap can relate to workers’
industry association.  Chuang, H-L, et
al., (2017, p.4) state that they will use Mincer’s human-capital earnings
function as a theory underlying the wage equation specification and use it to
compute the log-wage for a representative male and female worker in industry j.
Using the Oaxaca and Blinder (OB) strategy Chuang, H-L, et al., (2017, p.5)
decompose the gender wage gap in industry j into the following explained and
unexplained components.   The first four
terms on the right-hand side of the model display the unexplained components
and the last two terms represent to the explained wage gap in the industry.
However, the OB decomposition suffers from the index-problem that can be split
into two identification problems (IP1 and IP2). Fields and Wolff defined the
industry wage gap for industry j as: The first term is present in the equation in
order to capture the effect of choosing the reference group but does not get
rid of IP2. Horrace and Oaxaca suggested four alternatives to overcome IP2 as
follows;·      ·      ·        
·        
Both  are free from identification problem 2 but
still are affected by identification problem 1. However, the two ranking measures
,
need to imitate the critical values to be able to perform a statistical
inference. Consequently, a measure that was developed by Lin (2007b, 2010) that
resolves both IP1 and IP2 is implemented. This further makes available a
standard error for the significance test.  is recognised as it is free from the choice of
the non-discriminatory wage structure that is not observable as well as the
left-out reference group of any of the dummy variables. Chuang, H-L, et al., (2017, p.6) report that industry variables are of the most interest in studying the gender
wage gap, with the mining industry as the reference group. In order to assess
the contribution of the industry dummies towards explaining
the gender wage gap the OB decomposition techniques are used. From the results
it can be seen that a larger fraction of the explained component is due to the
female-based calculation. This explained proportion rises when industry dummies
are present by 4-10% at 2-8% for the female-based calculation and 5-14% when it
comes to the male-based calculation highlighting the importance of the
including industry variables. The financial industry shows the
smallest gender wage gap with -0.0483 based on  and -0.0494 based on  while the biggest gap is portrayed through the
service industry at -0.1659 based on . Previously
stated in the paper, if an industry has a ranking that is high, which means the
ranking number is small, then the wage gap for that particular industry is
small. Ranking 8th place based on both  and  from 1978 to 1991 (except for 1988), females
faced the largest gender wage gap in the mining industry. Year after year
different industries rank highest before 1997, but the mining industry although
it does not rank lowest has the lowest rankings since 1991. In 1978 agriculture
in the most beneficial industry for women based on the -group
measures however from 1979 to 1997 excluding 1992 and 1995 it changes to the
construction industry. Conversely, the -group
measures the trading industry as the highest-ranking industry in 1978 and after
1996 the financial industry. From 1998 excluding the year 2012, both groups
signal that the highest-ranking industry is the financial industry. When the
ratio of female employment across industries is studied further, after the
increase in female employment over time, almost all sectors have women employed
in them. In the mining industry, between the period 1978-1991 and 1992-2013,
average proportion of female employment rises by 1.83% and 4.12% in the
financial industry. On the whole, the difference in wages for females and males
in the financial industry is very little. Overall evidence shows that Female
workers overtime gain the most beneficial wages from the financial industry.L.N. Christofides
et al (2013) focus their research on understanding the gender wage gap across
26 European countries by using data from the European union statistics on
Income and Living conditions (EU-SILC) in 2007. The two samples that are
examined are the “working sample” and the “alternative sample”. The working
sample consists of workers that are between the ages of 25 and 54 who are not
students, handicapped, retired, doing compulsory community or military service,
or have given up a business. The alternative sample that is also known as the
FTFY sample comprises of workers that must have worked full-time for the whole
of the previous year as well as the requirements of the working sample. Ordinary
least squares (OLS) is used by L.N. Christofides et al (2013 p.89) to estimate
an hourly earnings equation by gender which includes the characteristics that
are relevant and available from the EU-SILC data. Results obtained from OLS
show that the actual gender wage gap is equal to the predicted total gap and generally,
the selection-adjusted gap is even larger indicating positive selection is at
work. Secondly, the average difference in male and
female earnings is decomposed following Oaxaca
and Ransom (1994) as follows,M -F =
(M –
F)N +
M
(M – N)
+ F(N –
F)The
first term ((M –
F)N )
measures
the explained component, the second (M
(M – N)
)
the male advantage and the third (F(N –
F)) the female
disadvantage. The addition of both the male advantage and female advantage represents
the unexplained part. For a number of countries, the unexplained part of the
total is found to be larger than the explained component implying that there
may be an existence of female disadvantage and the data that is accessible does
not explain the behaviour of earnings. The
portion that is explained is negative in Belgium, Greece, Hungary, Iceland,
Italy, Luxembourg, Poland, Portugal, Slovenia, and Spain proposing that female
characteristics are greater than that of males. Furthermore, in the majority of
countries, the offered and total wage gaps are smaller in the working sample of
part-year and part-time workers compared to that of the FTFY sample. In ten
countries of the 26, the public sector of the working sample has a larger
female disadvantage and in the case of the private sector, eight countries have
a smaller gender wage gap. In the alternative sample, nine countries have a
reduced gender gap in the public sector and is ten when there is a larger
disadvantage. These results are only slightly coherent with the understanding
that where FTFY jobs are involved, the public sector is more progressive (N.L. Christofides
et al., 2013, p.92).As the analysis of possible “sticky
floors” and “glass ceiling” effects are not allowed for by the decomposition of
the mean variations, Melly (2005) uses a method call the quantile regression
methodology that decomposes along quantiles of the wage distribution that
addressed any selection issues that may occur. This method lets the
characteristics of workers at different points of the wage distribution have
different effects and in turn affect the decompositions at each point. When
comparing the mean values in the Oaxaca and Ransom decompositions with the
quantile regression total and unexplained gaps at the 50th
percentile, more countries have unexplained components that surpass the total
wage gaps. However, evidence
from Austria, Estonia and the UK show that the total exceeds the unexplained
gap for all the quantiles. Hence, the quantile results highlight the conclusion
that a considerable portion of the earnings gap continues to be unexplained.
Evidence of sticky floors in twelve countries is present with the strongest
results from Cyprus, France, Italy, Luxembourg, Slovenia, and Sweden. Prominent
glass ceiling effects are demonstrated in eleven countries that are Denmark,
Germany, Hungary, the Netherlands, Norway, the Slovak Republic, the Czech
Republic, Finland, Iceland, Slovenia, and the UK. In the FTFY sample, stick
floor behaviour is present in twelve countries as well but instead of Belgium,
Spain is added. Countries exhibiting glass ceiling effects conversely have
increased to twenty-one instead of eleven. Cyprus, Estonia, Lithuania,
Portugal, and Spain do not show evidence of glass ceiling behaviour. The
prevalence of this behaviour in the FTFY sample is coherent with the
interpretation that women are more likely to be at a disadvantage in FTFY
positions, specifically when they are high-paying ones. Generally, in the
public sector, female employees have a lower disadvantage than in the economy
in eight countries and are at a higher disadvantage in ten countries and in
nine countries a lower gender disadvantage is present compared with the private
sector. All findings indicate that gender gaps are bigger when individuals must
be in full time and full year employment.D. Antonczyk et al.
(2010, pp. 835-847) concentrate their study on exploring the link between the
recent rise in wage inequality between 2001 and 2006 in West Germany, as well
as the fall in collective wage bargaining and the progression of the gender
wage gap for West Germany. The focus of
this review will be more on wage inequality and the gender wage gap. The sample
involves repeated cross-sections of the earnings of 440,000 employees, between
the ages of 25 and 55, in 17,000 establishments in 2001 and 750,000 employees
in 22,600 establishments in 2006 taken from the employer-employee data set. All
employees are full-time workers. There are significant changes in the wage
distribution over the years, for example, for both males and females’ real
hourly wages fall below the median whereas they rise for the quantiles that are
beyond the median. Overall, this leads to an growth in wage distribution. Women
are able to achieve most comparatively to men in the bottom part of the wage distribution
from 2001 to 2006.D. Antonczyk et al.
(2010, p. 840-842) propose a sequential decomposition from both the
cross-sections of data in 2001 and 2006. Suggested in DiNardo et al. (1996) and
developed further in Chernozhukov et al. (2008) and Antonczyk et al. (2009),
the decomposition aims to capture wage structure that may be influenced by wage
bargaining, firm characteristics, and personal characteristics. The decomposition
is split into coefficient effects (personal, firm and bargain coverage) the
residual change in overall wage level overtime and characteristic effects (bargain
coverage, firm and personal). The most significant component that increases the
wage inequality is shown by the changes in the firm coefficients. In addition,
changes in residual wages and personal coefficients lead to the rise in wage
inequality whilst personal characteristics work against this trend. Even though
these personal characteristics add to the inequality of wages mainly for
females at the bottom, the effect is typically irrelevant. Changes of wage
differences within and between industry mainly push inequality up which could
imply that the changes of firm wage policies may have increased within and
between industries, perhaps due to the more extensive use of irregular payment
schemes. Wage inequality for both females and males could result from the
unexplained time trends with wages at the top of the distribution escalating and
wage at the bottom declining. This trend causes a consistent reduction in wages
for females of approximately 1.3 percentage points, which is however, quite
insignificant. Overall, all workplace related effects add to the strong increase
in wage inequality. S. Machin, P.A.
Puhani (2003) assess the relevance of subject of degree in explaining a sizeable
proportion of the gender wage differential amongst graduates. With data taken
from the labour force surveys of both the UK and Germany in 1996, they estimate
separate log (earnings) functions for men and women graduates that do and do
not control for subject degree. The log wage difference is slightly smaller in
the UK than it is in Germany at 0.208 and 0.280 respectively. This could coincide
with the view that in Germany, women are not as advance in the wage hierarchy compared
to Britain. However, another reason for this could be that the data collected
from the German labour force survey only measures net income whereas in the UK the
data consists of gross wages.Decomposition of the
earnings is used (S. Machin, P.A. Puhani 2003, p. 396) in three specifications.
The first specification controls for age and age squared like the typical Mincerian
wage equation. Specification two adds a number of other components that are expected
to influence wages, specifically, industry, region, and dummies for part-time
and public-sector employment. Finally, the third specification includes
occupation. Although, there is potential endogeneity meaning it could correlate
with the error term.From specification
one, the results produce analogous results for both countries with
approximately 21%-24% of the gender wage gap between graduates explained by age.
The explained gap almost more than doubles when the subject of degree is added for
both countries. Additionally, by controlling for less detailed degree
categories the explained component of the gap increases from 24% to 43% for the
UK and from 21% to 36% in Germany. However, if detailed degree types are
controlled for then the explained gap further increases to 56% and 41% in the
UK and Germany respectively. This explained increase is statistically
significant in explaining the differentials of wage between females and males. When
the same analysis is undertaken with specification two which incorporates more
variables, a moderately large effect of controlling for subject degree is
present. In both countries, combined subject of degree categories account for a
2% wage premium, doubling to 4% in the UK. However, for this model there is not
much extra impact from detailed degree subject in Germany.  The percentage point increase of the gap explained
by degree type is 7 and 16 for Germany and UK respectively in specification 3
showing that even with occupation, subject degree still matters. On the whole, the
results for both countries are very similar and shows the importance of subject
degree in explaining the wage gap differences between male and female graduates.

S. Brown et al.
(2011) study data from the British household panel survey between 1991 and 2008
of over 5000 private households to examine whether there is a gender wage gap
and if so does the presence of children play an influential role in determining
the gap. The sample consists of 12,921 observations with 53% of the sample
gathered being female. S. Brown et al. (2011, p. 89) decompose the reservation
wage gap into an equation with five different samples used to investigate the
effect of children on the gender reservation wage gap. The results show that
the reservation wage gap is statistically significant and positive and 78% of
the difference in wages continues unexplained. From the explained component,
the number of children is the most important factor. Children alone can explain
why women have higher reservation wages than men, as the negative coefficient
on the variable proposes that it reduces the gap between reservation wages
between men and women. Without the presence of children, the unexplained part
of the gap rises to 99% portraying, observed discrimination for those with
children may have a large influence here. A variable that leads to the
reservation wage gap widening is education. The results indicate that there is
an existence of a reservation wage gap between males and females and the
presence of children plays a substantial role in determining this gap. Without children
almost none of the gap is explained.

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