1

Gender Earnings Gap in Hong Kong

2001-2006

By

Yeung Lai Wa

05016886

Applied Economics Option

As Honours Degree Project Submitted to the

School of Business in Partial Fulfillment

Of the Graduation Requirement for the Degree of

Bachelor of Business administration (Honours)

Hong Kong Baptist University

Hong Kong

April 2008

2

Acknowledgement

I would like to thank Professor Lam Kit Chun for providing me a lot of useful advices

and explanations in my Honours Project. Her advices and explanations really stimulated my

thought to solve my problems.

Doing this Honours Project not only provided me an opportunity to apply the skills and

knowledge learned in the courses in University but also enhanced my analytical and

organizational skills. I hope I can apply what I have learned in this Honours Project in my

future career life.

3

Abstract

This paper examines the gender earnings gap of males and females in Hong Kong in

2001 and 2006 respectively. It is noted that males had more advancement in earnings power

than females in both years. Females earned 25.61% less than males in mean earnings in 2001

and this difference shrank to about 20.21% in 2006. By using the standard Oaxaca (1973) and

Blinder (1973) decomposition based on male wage structure, it is found that the total gender

earnings differential at mean earnings was 0.2330 in 2001 and decreased to 0.1781 in 2006.

In both years, the gender earnings gap arises from the differential of endowment/personal

characteristics is small. Most of the gender earnings gap is unexplained. However, the

unexplained potion of the total gender earnings gap decreased a little bit from 2001 towards

2006.

4

Table of Contents

I. Introduction P.5-P.6

II. Literature Review P.7-P.9

III. Data P.10

IV. Methodology P.11-P.13

V. Empirical Results and Discussions

A. Sample Statistics

B. Analysis of the Regression Results

C. Analysis of gender earnings gap by standard

Oaxaca(1973) and Blinder (1973) decomposition

P.14-P.30

P.14-P.18

P.19-P.23

P.24-P.30

VI. Limitations and Recommendations P.31

VII. Conclusion P.32

VIII. References P.33-P.34

IX. Appendix P.35-P.36

5

I. Introduction

It is generally believed that Hong Kong enjoys high gender equality in wage earnings

with its spirit of equal and fair treatment to human beings so that people seem to pay not

much attention to the issue of gender earnings gap in Hong Kong. However, this may not be

true. Previous studies have found that gender earnings gap did exist in Hong Kong with males

generally enjoyed higher earnings power compared to females in the past several decades

though there was a trend of narrowing gender earnings gap (eg. Lui & Suen, 1993; Chung,

1996; Sung, Zhang & Chan, 2001).

Gender inequality not only affects the division of labor, allocation of resources and

economic welfare between males and females in the family but also influences the economic

incentives faced by female workers beyond the family (Sung et al., 2001). Therefore studying

the issue of gender earnings inequality has great economic and social values: It not only

raises the public’s awareness of this issue but also acts as a useful guide for the government

or other related organizations to carry out appropriate policies for better allocation of

resources and economic welfare between males and females, thereby brings a more equal and

fair labor market.

Gender earnings gap in 2001 and 2006 will be estimated and analyzed in this paper.

Hong Kong society was still suffering economic downturn caused by the Asian Financial

Crisis in 2001. However, as time went by, Hong Kong economy was greatly recovered in

6

2006. It is worth examining how the gender earnings gap was affected in this 5- year period.

Specifically, the main objectives of this paper are the followings:

(1). To analyze how large the gender earnings gap is in 2001 and 2006 and how the gap

differs from 2001 towards 2006.

(2). To investigate how large the gender earnings gap is caused by (i). differential of

gender characteristics/endowments and (ii). the unexplained factors which may partly arise

from market discrimination.

7

II. Literature Review

The first step for studying the issue of gender earnings inequality is to find out factors

which cause gender earnings gap. Blinder(1973) pointed out that ‘‘part of each wage

differential is due to differences in ‘‘objective characteristics’’ such as education and work

experience, while part remains even white-black and male – female differences in these traits

are controlled for’’(p.437). This was similar to what Carnoy (1994) had mentioned that the

between group earnings differentials could be decomposed into the ‘‘attribute’’ component

and the ‘‘price component’’. The ‘‘attribute’’ component refers to characteristics of a group

that affect its earnings which can either be endowed or acquired ( endowed attributes include

being female, white and born in poor family, etc. while acquired attributes include

educational and occupational levels, etc.) (Chung, 1996). On the other hand, the ‘‘price’’

component reflects the value of the attribute in the labor market which is based on market

preference or discrimination towards an individual possessing that attribute (Chung, 1996).

One very important point in studying gender earnings gap is to measure gender earnings

differentials. Blinder (1973) and Oaxaca (1973) introduced a decomposition method to

measure gender earnings differentials. Based on this method, the gender earnings differentials

can be decomposed in two parts:

lnYm - lnYf = βm ( Xm – Xf ) + [ Xf (βm – βf ) + (αm –αf )] (1)

where lnY is average natural logarithm of earnings, X is a vector of average individual

8

characteristics, β is a vector of coefficients, ε and µ are disturbance terms. The first term on

the right hand side of the equation [βm (Xm – Xf )] is the earnings differential attributable to

endowments (observable characteristics ) while the second term [ Xf (βm – βf ) + (αm – αf )] is

the differential due to the coefficients (returns to these characteristics). The second term

exists because the market evaluates the identical set of traits possessed by different members

of demographic groups (gender groups in this paper) differently and it is a reflection of

discrimination (Blinder, 1973). Oaxaca (1973) stated that ‘‘discrimination against females can

be said to exist whenever the relative wage of males exceeds the relative wage that would

have prevailed if males and females were paid according to the same criteria.’’ (p.694).

Oaxaca (1973) also stated that one difficulty of the wage equation when estimating the

gender earnings differentials is that it controls for the major sources of discrimination that

many consider to be against women. He explained that by controlling occupations, some

effects of occupational barriers which act as sources of discrimination will be eliminated., so

another set of equations that do not control for occupations should be estimated.

Blinder (1973), Oaxaca (1973), Ng (2007) & Sebaggala (2007) pointed out that another

problem this decomposition method involves is the familiar index number problem: in

equation (1), the adoption of βm in calculating earnings differential attributable to

endowments indicates that the male earnings structure is assumed to prevail without

discrimination. However, they stated that female wage structure can also be taken as the

9

non-discriminatory benchmark with the following modification of equation (1):

lnYm - lnYf = βf ( Xm – Xf ) + [ Xm(βm – βf ) + (αm –αf )] (2)

They pointed out that using both male and female wage structure as the non-discriminatory

benchmark can result in estimates with a range of possible values.

Nuemark (1988), Cotton (1988), Oaxaca & Ransom (1994) have developed a more

general wage decomposition method. This method uses a pooled sample of the two

demographic groups as the non-discriminatory wage structure which can be expressed as:

lnYm - lnYf = ( Xm – Xf ) β* + [(βm – β

*) Xm + (β* – βf )Xf]

The second term on the right hand side of the equation [(βm – β*) Xm] measures the extra

amount of wage received by males if their sample characteristics were to be rewarded at β*,

β* is the non-discriminatory wage structure (Sebaggala, 2007). The third term [(β*– βf ) Xf ]

estimates the female disadvantage that is equal to the difference between the females’ actual

wage received and the wage they should receive if the non discriminatory wage structure was

implemented (Sebaggala, 2007). However, Appleton et al. (1999) doubted whether the pooled

coefficient can be a good estimator of the non-discriminatory wage structure. It is noted that

both the standard Oaxaca (1973) and Blinder (1973) decomposition and the decomposition

approach developed by Nuemark (1988) or Cotton (1988) are not perfect and encounter their

own problem.

10

III. Data

The data used in this paper comes from 2001 and 2006 population Census aged between

18-60 years old.

Since the earnings structure of employees and the self-employed may be different, so

only employees (work for wage, salary commission, tips or payment in kind) are chosen in

this study.

Besides, to eliminate the effect low income from the large amount of foreign domestic

helpers, foreigners who are not born in Hong Kong or the Mainland of China are excluded.

Only those born in Hong Kong and the Mainland of China are selected in this study in order

to focus on ‘‘local’’ employees (Sung et al., 2001).

11

IV. Methodology

(1). Sample Statistics:

(i). Estimate the sample means of the variables (age, monthly earnings, experience,

experience squared, schooling, different education level attainment, martial status, birth of

place and occupations)

(ii). Analyze the sample characteristics with the estimated sample means

(2). Model Specification:

(i). The model that does not control for occupation (Model 1):

Discussion of the empirical results will be mainly based on this model. The standard

Oaxaca (1973) and Blinder (1973) decomposition will be applied in this paper. Gosse (2001)

pointed out that to get rid of the problem of index problem, the most common way is to do

the decomposition based on the male wage structure, so as to standardize the literature. So

this paper will also be based on the male wage structure.

An Ordinary Least Squares regression for males (m) and females (f) is needed:

lnYm = αm + βm Xm + ε (1)

lnYf = αf + βfXf + µ (2)

where lnY is natural logarithm of monthly earnings, X is a vector of individual

characteristics, β is a vector of coefficients, ε and µ are disturbance terms.

12

The human capital earnings equation by Mincer (1974) will be applied to run the above

regressions:

log(MEARN) = α + β1 (EDUCN) + β2 (EXP) + β3 (EXP)² + β4 (MARIT) +

β5 (BORNPL) + ε (3) where

log (MEARN) is the dependent variable which refers to logarithm of monthly earnings.

The independent variables are the followings:

- ‘‘EDUCN’’ refers to educational attainment (highest level attained) for ‘‘lower

secondary’’(LOWSEC), ‘‘upper secondary’’(UPSEC), ‘‘post secondary’’(POSTSEC),

‘‘university’’(UNIV) and ‘‘postgraduate’’(POSTGRA) with ‘‘primary and below’’ (PRIMB)

as the reference group.

- ‘‘EXP’’ and ‘‘EXP²’’ refer to years of work experience acquired and its square respectively

in which EXP = Age - years of schoolings completed – 6.

- ‘‘MARIT’’ refers to marital status which is a dummy variable for married individuals

(MARRIED), widowed individuals (WIDOWED), divorced/separated individuals

(DIVORCED) with never married individuals (NEVERMARRIED) as the reference group.

- ‘‘BORNPL’’ refers to birth of place which is a dummy variable for individuals born in

Hong Kong (HK) with individuals born in China (CHINA) as the reference group.

13

The following decomposition equation will then be applied:

lnYm - lnYf = βm ( Xm – Xf ) + [ Xf (βm – βf ) + (αm – αf )]

X m and X f can be found from sample means of related variables while βm and βf

can be estimated from the regression equations (1) and (2). Then the explained part

[βm ( Xm – Xf )] which is the wage earnings differential attributable to gender characteristics

and the unexplained part [ Xf (βm – βf ) + (αm –αf )] which is the differential attributable to

returns of these characteristics can be calculated.

(ii) The model that controls for occupations (Model 2):

The operation of this model is similar to model 1 but the dummy variable ‘‘OCCUP’’

(occupations) is incorporated into the human capital earnings equation in this model:

log (MEARN) = α + β1 (EDUCN) +β2 (EXP) +β3 (EXP )² +β4 (MARIT) +

β5 (BORNPL) +β6 (OCCUP) + µ where

‘‘OCCUP’’ refers to occupations which are dummy variables for ‘‘Managers, Professionals

and Associate Professionals’’ (MAN&PROF), ‘‘Clerks’’ (CLERK), ‘‘Service Workers and

Shop Sales Workers’’ (SERVICE) and ‘‘Agricultural and others’’ (AGRI) with ‘‘Elementary,

Plant and Machine Operators and Assemblers’’ (ELEM) as the reference group.

14

V. Empirical results and Discussions

A. Sample Statistics

Table 1: Mean values of variables (2001) Note: standard deviations in parentheses

Variables All Male Female

Monthly earnings 15658.04(15791.6) 17212.18(17350.24) 13703.04( 13325.25)

Log of monthly earnings 9.37 (0.72) 9.48 (0.69) 9.24 (0.72)

Age 36.92 (10.23) 37.73 (10.41) 35.87 (9.90)

Schooling 10.67 (3.78) 10.55 (3.72) 10.82 (3.84)

Education Attainment

Primary and below 0.16 (0.37) 0.16 (0.37) 0.16 (0.37)

Lower secondary 0.20 (0.40) 0.24 (0.43) 0.14 (0.35)

Upper secondary 0.38 (0.49) 0.35 (0.47) 0.42 (0.49)

Post secondary 0.10 (0.29) 0.09 (0.28) 0.11 (0.31)

University 0.13 (0.34) 0.12 (0.33) 0.14 (0.34)

Postgraduate 0.03 (0.18) 0.04 (0.19) 0.03 (0.16)

Experience 20.26 (12.20) 21.19 (12.17) 19.08 (12.13)

Experienced Squared 559.08 (577.82) 597.18 (566.46) 511.15 (543.00)

Marital status

Married 0.59 (0.49) 0.63 (0.48) 0.55 (0.50)

Never married 0.37 (0.48) 0.35 (0.48) 0.40 (0.49)

Widowed 0.01 (0.09) 0.003 (0.05) 0.01 (0.12)

Divorced/Separated 0.03 (0.17) 0.02 (0.14) 0.04 (0.20)

Birth of place

Hong Kong 0.72 (0.45) 0.70 (0.46) 0.73 (0.44)

China 0.28 (0.45) 0.30 (0.46) 0.27 (0.44)

Occupations

Managers 0.07 (0.26) 0.09 (0.29) 0.05 (0.22)

Professionals 0.06 (0.24) 0.07 (0.25) 0.06 (0.23)

Associate professionals 0.18 (0.38) 0.16 (0.37) 0.19 (0.40)

Clerks 0.20 (0.40) 0.10 (0.30) 0.33 (0.47)

Service & Shop Sales Workers 0.17 (0.37) 0.16 (0.37) 0.17 (0.38)

Elementary, craft, plant and

machine operators 0.32 (0.47) 0.42 (0.49) 0.20 (0.40)

Agricultural and Others 0.001 (0.03) 0.002 (0.04) 0.001 (0.02)

Sample Size 24573 13690 10883

15

Table 2: Mean values of variables (2006) Note: standard deviations in parentheses

Variables All Male Female

Monthly earnings 14839.04(15281.70) 16075.24(16622.87) 13373.18 (13373.44)

Log of monthly earnings 9.30 (0.74) 9.37 (0.73) 9.21 (0.74)

Age 38.08 (10.61) 38.86 (10.78) 37.15 (10.33)

Schooling 11.33 (3.79) 11.22 (3.76) 11.45 (3.83)

Education Attainment

Primary and below 0.12 (0.33) 0.12 (0.33) 0.13 (0.33)

Lower secondary 0.19 (0.39) 0.22 (0.42) 0.15 (0.36)

Upper secondary 0.38 (0.48) 0.36 (0.48) 0.39 (0.49)

Post secondary 0.10 (0.30) 0.10 (0.30) 0.11 (0.31)

University 0.16 (0.36) 0.14 (0.35) 0.17 (0.37)

Postgraduate 0.05 (0.22) 0.06 (0.23) 0.05 (0.21)

Experience 20.16 (11.79) 21.06 (11.85) 19.10 (11.64)

Experienced Squared 545.55 (505.14) 583.72 (520.12) 500.30 (482.90)

Marital status

Married 0.57 (0.49) 0.61 (0.49) 0.53 (0.50)

Never married 0.38 (0.49) 0.36 (0.48) 0.40 (0.49)

Widowed 0.01 (0.09) 0.003 (0.06) 0.02 (0.12)

Divorced/Separated 0.04 (0.19) 0.02 (0.15) 0.05 (0.22)

Birth of place

Hong Kong 0.74 (0.44) 0.75 (0.43) 0.74 (0.44)

China 0.26 (0.44) 0.25 (0.43) 0.26 (0.44)

Occupations

Managers and Administrators 0.07 (0.25) 0.08 (0.27) 0.05 (0.23)

Professionals 0.07 (0.25) 0.07 (0.26) 0.06 (0.23)

Associate professionals 0.18 (0.39) 0.17 (0.38) 0.20 (0.40)

Clerks 0.20 (0.40) 0.11 (0.31) 0.31 (0.46)

Service & Shop Sales Workers 0.18 (0.38) 0.17 (0.37) 0.19 (0.39)

Elementary, craft, plant &

machine operators 0.30 (0.46) 0.40 (0.49) 0.19 (0.39)

Agricultural and Others 0.002 (0.04) 0.002 (0.05) 0.001 (0.04)

Sample Size 25812 14003 11809

16

Table 3: Monthly Earnings of different occupations Note: standard deviations in

parentheses

2001 2006

Occupations Male Female Male Female

Managers and

administrators

40203.26

(30783.88)

35115.75

(27104.64)

40416.76

(30548.47)

36278.46

(27834.93)

Professionals

38456.39

(29107.99)

31512.18

(22241.73)

33379.94

(26723.86)

28535.05

(21523.66)

Associate professionals

20427.76

(13923.66)

18970.93

(12270.56)

19690.46

(14088.91)

18087.24

(11871.48)

Clerks

11700.06

(6320.90)

11015.74

(5620.00)

11026.51

(6135.01)

10793.07

(5509.12)

Service & Shop Sales

Workers

13242.98

(7821.94)

8696.53

(5474.97)

12239.98

(7677.24)

8563.16

(6044.59)

Elementary, craft, plant

and machine operators

10486.29

(4915.68)

6541.76

(3450.13)

9513.95

(4761.92)

6111.44

(3398.29)

Agricultural and others

12337.33

(10797.02)

7897.33

(4784.69)

9873.53

(8283.57)

11814.18

(9187.78)

In 2001, the sample size consists of 24573 individuals in total with 13690 males

(55.71%) and 10883 females (44.29%). In 2006, it consists of 25812 individuals in total with

14003 males (54.25%) and 11809 females (45.75%).

In general, the monthly earnings of both males and females in 2006 are slightly less

compared with 2001. In 2001, females earned about 25.61% less than males in mean monthly

earnings. This difference shrank to about 20.21% in 2006.

It is a bit surprised to discover that in both 2001 and 2006, females enjoyed a bit more

advancement than males in regard to education attainment. Females had an average of 10.82

and 11.45 years of schooling in 2001 and 2006 respectively, a bit higher than that of males

17

(10.55 years in 2001 and 11.22 years in 2006). The proportion of both males and females

with higher educational levels (university and postgraduate) in 2006 is slightly higher than

that in 2001, with a bit larger increase for females compared with males. Besides, in both

years, the proportion of females in the higher educational levels (upper secondary, post

secondary and university) is slightly higher than their male counterparts. It can be found that

Hong Kong enjoys high gender equality in receiving education with a trend that even favored

females both in 2001 and 2006.

The sample individuals have an average of around 20 years of working experience in

both years. Males had about 2 more years of working experience than females.

In both years, the married individuals account for about 60% of the total sample. The

proportion of married men is a little bit higher than their female counterparts.

In 2001, about 28% of the individuals of the sample data are born in China. However,

the proportion of the China born individuals decreases a little bit to reach about 25% of the

sample data in 2006.

When focusing on occupations, in both years, less than 20% of the sample individuals

are concentrated in the relatively high income occupations (managers, administrators and

professionals). The male proportion is higher than that of females in managers and

administrators while a similar distribution of males and females is shown in professionals. As

shown by table 3, managers and administrators are the highest earnings group, followed by

18

professionals. Although males generally earn more than females in these high income groups,

the amount that females earn less than males is not very considerable in both years.

Associate professionals are the third highest earnings group and account for about 20%

of the sample size in both years. Besides, about one third of females in the sample are clerks,

nearly triples the number of male clerks in both years. In these two occupations, males and

females have similar monthly earnings.

About 20% of the sample size is concentrated on the service sector. The proportion of

males and females in this sector is quite equal in both years. On the other hand, nearly 40% of

males are concentrated in the elementary, craft and machinery sectors and this proportion

doubles the proportion of their female counterparts in both years. Besides, females earned

much less than males in the service, elementary, craft and machinery sectors. The monthly

earnings of females are at least 50% less than their male counterparts in these sectors.

It is found in both years, males are largely concentrated in the manual related

occupations while a considerable amount of females are concentrated in the clerical sector. It

is noted that females earned much less than males in the relatively low income jobs.

19

B. Analysis of Regression Results

Table 4: Regression Results

Note: t statistics in parentheses. The asterisks *, **, *** represents statistically significant

at 10%, 5% and 1% significance level respectively.

2001 2006

Male Female Male Female

CONSTANT

8.1255***

(356.99)

7.7879***

(291.21)

7.8478***

(321.11)

7.7108***

(298.29)

LOWSEC

0.0919***

(5.68)

0.1626***

(7.70)

0.1562***

(8.71)

0.0788***

(3.78)

UPSEC

0.4094***

(24.67)

0.7040***

(34.21)

0.4516***

(25.15)

0.4991***

(24.77)

POSTSEC

0.8228***

(37.31)

1.1013***

(43.14)

0.7877***

(33.80)

0.8493***

(33.29)

UNIV

1.1726***

(56.68)

1.4539***

(58.02)

1.1281***

(51.44)

1.2114***

(49.97)

POSTGRA

1.5527***

(55.10)

1.7901***

(47.95)

1.4628***

(54.22)

1.5617***

(49.46)

EXP

0.0616***

(37.73)

0.0604***

(34.50)

0.0701***

(38.63)

0.0733***

(38.39)

EXP²

-0.0011***

(-32.85)

-0.0011***

(-27.75)

-0.0013***

(-32.61)

-0.0014***

(-32.44)

MARRIED

0.2082***

(15.94)

0.0078

(0.57)

0.2175***

(15.54)

0.0387***

(2.76)

WIDOWED

0.0926

(1.03)

0.0173

(0.37)

0.1883**

(2.15)

0.0797*

(1.78)

DIVORCED

0.0397

(1.15)

0.0511*

(1.77)

0.0482

(1.41)

0.0068

(0.25)

HK

0.1740***

(16.23)

0.2125***

(16.65)

0.1903***

(16.13)

0.2791***

(21.64)

R-square 0.4277 0.4415 0.3891 0.4008

Adjusted R- square 0.4272 0.4409 0.3886 0.4003

N 13690 10883 14003 11809

20

Table 4 shows the Ordinary Least Squares regression results of male and female

earnings equations for model 1 in 2001 and 2006 respectively. The adjusted R square shows

that about 40% of the variations of the dependent variable (log monthly earnings) of the male

and female earnings equations can be explained by their corresponding independent variables

in both years. This indicates that the variations of the dependent variable can moderately be

explained by their corresponding regression equations in both years.

In both years, all the coefficients of both male and female earnings equations are

statistically significant at 1% of significance level with only a few exceptions.

All the coefficients have the signs as we expected in both years. Both males and females

have positive return to education. The higher the educational level, the higher the rate of

return. Females have a higher rate of return compared to males at each educational level with

the exception of lower secondary level in 2006. To illustrate, compared with the female

individuals attained primary level or below, females who attained university earned 145.39%

more in 2001 and 121.14% more in 2006. However, their male counterparts only earned

117.42% more in 2001 and 121.14% more in 2006 compared with male individuals attained

primary level or below. Besides, the rate of return of males in lower and upper secondary

level increases slightly from 2001 to 2006 while decreases a little bit in post secondary,

university and postgraduate level in this period. However, the rate of return of females in

each educational level decreases significantly from 2001 to 2006.

21

The coefficient of ‘‘Experience’’ and ‘’Experience Square’’ is positive and negative

respectively which is within our anticipation. The coefficient of ‘‘Experience Square’’ is

negative because the age profile is parabolic (Kaufman & Hotchkiss, 2006). Mincer &

Polachek (1974) stated that as job related investment on training controls a return at work,

the older we are, the shorter the duration of work experience and this will weaken our

incentive to invest in our job skills. Besides, they pointed out that as we ages, our human

capital depreciates. To illustrate, we first take a look at the earnings function:

lnY = lnY0 + α1School + α2EXP + α3EXP²

The increase in earnings from an additional year of work experience is

∆(lnY)/∆(EXP) = α2 + 2α3 EXP (Kaufman & Hotchkiss, 2006)

By applying this equation, in 2001, the earnings of males increased by about

0.0616-2(0.0011)(1) =5.95% for the first year of experience. However, their earnings only

increased by about 1.84% in the twentieth year of experience. It is found that in both years,

the rate of return of experience at mean for females (1.99% in 2001 and 1.91% in 2006) is

higher than that of males (1.59% in 2001 and 1.74% in 2006) and the differential is less in

2006.

The earnings of married, widowed and divorced/separated individuals are higher than

those of never married individuals. When focusing on married individuals, married males

earned about 20% more than never-married males in both years. This may be due to less

22

housework responsibility of married men which allows them to be more productive at work

(Becker, 1985). It may also be possible for what Nakosteen and Zimmer (1987) had found

that men possessing higher earnings are more likely to get married. Besides, it is found that

in 2006, married women earned about 4% more than never married women. This positive

earnings premium for females may be because nowadays some households hire domestic

helpers to help married women to do the housework (Jeronimo & Eduardo), especially in the

middle and upper class in Hong Kong. Furthermore, it is found that the earnings premium

arise from marriage is much larger for males than females. This may be because married

women are usually responsible for the major part of the housework (Becker, 1985). Although

the division of housework between men and women nowadays seems to be more equal than

before in Hong Kong, many women still take over the main housework. Hong Kong Young

Women’s Christian Association had interviewed a total of 2089 individuals (916 males and

1173 females) about their division of labor in housework in 2005. The result shows that over

80% of the female interviewees are responsible for doing the major tasks of housework and

taking care of their children.

Males born in Hong Kong earned about 17% and 19% more than those born in China in

2001 and 2006 respectively. On the other hand, Hong Kong born females earned about 21%

and 28% more than those born in China in 2001 and 2006 respectively. A larger earnings

differential is shown between Hong Kong born and China born females compared with their

23

male counterparts. This may be partly due to employer discrimination, and it may also be

possible that compared with Hong Kong born females, China born females are more

influenced by traditional attitudes and they usually have heavier housework, so they tend to

work less (Sung, Zhang, Ng, and Hempel, 2002).

Table 5 (see appendix) shows the Ordinary Least Square regression results of male and

female earnings equations for model 2 in 2001 and 2006 respectively.

This model has added occupational dummies into the earnings equations in hopes of

investigating the impact of the distribution of different occupations of the males of females

on the gender earnings differential, but this approach has assumed that occupations are given

exogenously (Sung et al., 2001). However, occupations are endogenous. In case occupational

determination or segregation is associated with labor market discrimination, this approach

would not be appropriate (Gunderson, 1989). Therefore this model won’t be discussed in

detail in this paper. It is just for reference.

24

C. Analysis of male-female earnings gap by standard Oaxaca (1973) and

Blinder (1973) decomposition (Model 1)

Table 6: Decomposition Results

2001 2006

Gap

contributed

by variable

Gap due to

endowment

differential

Gap due to

coefficient

differential

Gap

contributed

by variable

Gap due to

endowment

differential

Gap due to

coefficient

differential

Education

LOWSEC

-0.0008

(-0.33%)

0.0093

(4.00%)

-0.0101

(-4.33%)

0.0234

(13.11%)

0.0117

(6.56%)

0.0117

(6.55%)

UPSEC

-0.1514

(-65.00%)

-0.0280

(-12.01%)

-0.1234

(-52.97%)

-0.0365

(-20.50%)

-0.0177

(-9.95%)

-0.0188

(-10.55%)

POSTSEC

-0.0490

(-21.04%)

-0.0189

(-8.10%)

-0.0301

(-12.94%)

-0.0180

(-10.09%)

-0.0111

(-6.26%)

-0.0068

(-3.83%)

UNIV

-0.0565

(-24.24%)

-0.0178

(-7.64%)

-0.0387

(-16.60%)

-0.0418

(-23.47%)

-0.0278

(-15.62%)

-0.0140

(-7.85%)

POSTGRA

0.0105

(4.49%)

0.0169

(7.27%)

-0.0065

(-2.78%)

0.0086

(4.82%)

0.0133

(7.49%)

-0.0047

(-2.67%)

Subtotal

for Education

-0.2472

(-106.12%)

-0.0384

(-16.48%)

-0.2088

(-89.62%)

-0.0644

(-36.13%)

-0.0317

(-17.79%)

-0.0327

(-18.35%)

EXP

0.1534

(65.84%)

0.1299

(55.77%)

0.0235

(10.08%)

0.0762

(42.76%)

0.1373

(77.07%)

-0.0611

(-34.31%)

EXP²

-0.1031

(-44.28%)

-0.0929

(-39.89%)

-0.0102

(-4.39%)

-0.0192

(-10.80%)

-0.1043

(-58.54%)

0.0851

(47.75%)

Marital Status

MARRIED

0.1264

(52.45%)

0.0159

(6.84%)

0.1104

(47.41%)

0.1118

(62.77%)

0.0169

(9.51%)

0.0949

(53.26%)

WIDOWED

0.0000

(0.00%)

-0.0011

(-0.46%)

0.0011

(0.45%)

-0.0007

(-0.38%)

-0.0024

(-1.34%)

0.0017

(0.97%)

DIVORCED

-0.0013

(-0.55%)

-0.0008

(-0.35%)

-0.0005

(-0.20%)

0.0008

(0.44%)

-0.0014

(-0.77%)

0.0021

(1.20%)

Subtotal for

Marital status

0.1251

(53.69%)

0.0140

(6.03%)

0.1110

(47.67%)

0.1119

(62.83%)

0.0132

(7.40%)

0.0987

(55.43%)

25

Table 6: Decomposition Results (Continue)

Note: (i). Percentage of the gender earnings differential contributed by variables in terms of

total gender earnings gap in parentheses;

(ii) The log mean earnings differential between males and females is 0.2324 in 2001

and 0.1779 in 2006.

Table 6 shows the explained (due to endowment differential) and unexplained amount

(due to coefficient differential and estimated constant differential) that contribute to the

gender earnings gap by different variables and the explained and unexplained potion of the

total gender earnings gap by using the standard Oaxaca (1973) and Blinder (1973)

decomposition for Model 1 in both 2001 and 2006.

Since males are taken as the comparator group, a positive number in the explained /

unexplained potion indicates a higher earnings power for males (increasing the gender

2001 2006

Birth of place

Hong Kong

-0.0328

(-14.10%)

-0.0047

(-2.00%)

-0.0282

(-12.09%)

-0.0634

(-35.61%)

0.0021

(1.18%)

-0.0655

(-36.79%)

Subtotal for all variables

-0.1047

(-44.95%)

0.0080

(3.42%)

-0.1126

(-48.36%)

0.0410

(23.04%)

0.0166

(9.32%)

0.0244

(13.72%)

estimated constant

differential

0.33762

(144.94%)

0.13708

(76.96%)

Explained potion

(endowment differential)

0.0080

(3.42%)

0.0166

(9.32%)

Unexplained potion

(coefficient differential +

estimated constant differential)

0.2250

(96.58%)

0.1615

(90.68%)

Total differential

0.2330

(100%)

0.1781

(100%)

26

earnings differential) while a negative number indicates a higher earnings power for females

which decreases the gender earnings differential (Gosse, 2001).

It is found that the total gender earnings differential at mean earnings is 0.2330 in 2001

and decreases to 0.1781 in 2006. It can further be split into two components, the explained

and unexplained potion.

The Explained Gender Earnings Gap

The explained amount of the gender earnings differential is 0.008 in 2001 and is

increased to 0.0166 in 2006. The explained potion only accounts for a very small proportion

of the total gender earnings gap (3.42%) in 2001. However, it climbs a little to reach 9.32% in

2006. This shows that the gender earnings differential due to the observable characteristics or

endowments is small in both years, but this differential enlarges a little bit in 2006.

The unexplained Gender Earnings Gap

The amount of unexplained gender earnings differential is 0.2250 in 2001 and is

decreased to 0.1615 in 2006. It is found that the unexplained potion, which may partly arise

from discrimination, accounts for most of the total gender earnings gap, say, 96.58% in 2001

and it decreases to 90.68% in 2006. Sung et al. (2002) had found that the unexplained potion

accounts for 102.8% of the total gender earnings gap in 1996 using the standard Oaxaca

27

(1973) and Blinder (1973) decomposition. It can be found that the unexplained potion of the

gender earnings gap has decreased from 1996 towards 2006.

Gender earnings gap contributed by different variables

1. Education

Education accounts for -106.12% and -36.13% of the total gender earnings gap in 2001

and 2006 respectively which helps to decrease the gender earnings gap. It indicates that

females exceed males (by having higher rate of return) in regard to education attainment. This

may be because women may accept the earnings which may undervalue their characteristics

due to market prejudice or discrimination towards them and the higher educated women can

better tackle these market disadvantages so as to compete with men (Dougherty, 2003). The

results also indicate that the education advancement for females has declined from 2001

towards 2006. Among the -106.12% of the gap contributed by education in 2001, only a small

proportion (-16.48%) is due to the endowment differential between males and female, the

major gap comes from the coefficient differential resulting from higher rate of return of

education to females. However, in 2006, the proportion of the gap arises from endowment

differential and the rates of return to education are similar.

When taking account of different levels of education attainment, almost all the education

levels help to decrease the gender earnings gap, with the exception of postgraduates. In both

28

years, we discover that male postgraduates exceed their female counterparts and increase the

gender earnings gap. Their higher earnings power comes from their endowment advancement

over female postgraduates.

2. Experience

Experience is the main contributor of the gender earnings gap that favors males in both

years. It accounts for 65.84% and 42.76% of the gender earnings gap in 2001 and 2006

respectively. We discover that the endowment differential in which males exceed females

accounts for most of the gap arise from experience. The coefficient differential only accounts

for a small proportion.

The fact that experience accounts for such a considerable amount of the gap can be

explained by what Blinder (1973) had found in his estimates: women shows a flatter

age-earnings profile than men, in which women have less advancement in exhibiting a rise of

their earnings over their life cycle compared with men. Mincer & Polachek (1974) pointed

out that there may be a discontinuous labor force participation of married women, especially

for mothers since they may shift their time in doing housework and raising their children.

This discontinuity may affect some young women to have less job training in their premature

employment compared with their male counterparts with comparable education. They also

stated that married women’s non-participation in the labor force during childbearing may

29

depreciate their skills learned at school and obtained at work. So women exhibit a less steep

earnings profile in their life cycles.

3. Marital Status

Marital status is also a major contributor of the gender earnings gap in both years. It

contributes 53.69% of the gender earnings gap in 2001 and increases a little bit to 62.83% in

2006. Most of the gap comes from the coefficient differential which is the differential

between how male earnings equation would value the characteristic of ‘‘marital status’’ of

the female earnings equation and how female earnings equation actually values them (Blinder,

1973). This indicates that marital status may be a significant source of discrimination. The

endowment differential is not significant in this case.

4. Birth of place

The Hong Kong born group that favors females decreases the gender earnings gap. They

decrease 14.10% of the gap in 2001 and even more, say, 35.61% in 2006. A considerable

amount of this gap arises coefficient differential, the differential between how male earnings

equation would value the characteristic of ‘‘birth of place’’ of the female earnings equation

and how female earnings equation actually values them (Blinder, 1973). This implies that

birth of place may also be a source for gender discrimination which can’t be overlooked.

30

5. Estimated constant

The differential between the estimated constants (a part of the unexplained potion) of

male and female earnings equation is the gender earnings gap when no other variables are

controlled for. This differential accounts for most of the gender earnings gap in both years. It

contributes even more than 100%, say, 144.94% of the gender earnings gap in 2001.

31

VI. Limitations and Recommendations

Sung et al.(2002) pointed out that the unexplained part of the gender earnings gap may

be overestimated since part of the unexplained earnings gap might come from variables such

as working hours and intensity of effort rather than discrimination. Since these variables may

determine productivity, so the explained gender earnings gap might be underestimated if

these variables are omitted. If the Census data contains more productivity-related variables,

the potion of the explained and unexplained gender earnings gap will be more accurate.

.Besides, the importance of discrimination may be underestimated when the sample

individuals are segregated into different occupations since occupational segregation may be

associated with labor market discrimination (Oaxaca, 1973). Estimation of discrimination

will be more accurate if the effect of occupational segregation is incorporated into the model.

In addition, Gosse (2001) stated that the decomposition method only measures the

post–hiring earnings differentials. He pointed out that if the hiring process is subjected to

market discrimination, then decompositions will underestimate the impact of discriminations

on earnings. In such case, pre-market discrimination affecting the gender earnings gap that is

related to productivity is omitted. The decomposition result will be more accurate if more

information about pre-hiring situation such as the information about the hiring process is

provided in the Census data.

32

VII. Conclusion

This paper aims to examine the amount of gender earnings gap in 2001 and 2006 and

how the gap differs in this five-year period. Besides, it also examines how large the gender

earnings gap is caused by observable gender characteristics (endowments) and unexplained

factors (which may partly come from market discrimination) respectively.

Data from 2001 and 2006 Hong Kong Population Census is used to examine the gender

earnings gap using the standard Oaxaca (1973) and Blinder (1973) decomposition based on

the male wage structure. It is found that although the earnings power of males are generally

higher than that of females in both years, the gap shrinks in this 5 year period. The total

gender earnings differential was 0.2330 in 2001 and decreased to 0.1781 in 2006. It is also

found that the endowment differential between males and females is not the major cause of

the gap. Most of the gender earnings gap is unexplained which may partly arise from

discrimination. However, the unexplained potion of the total gender earnings gap decreased a

little bit from 2001 towards 2006.

33

VIII. References

Appleton, S., J. Hoddinott & P Krishnan.(1999).The gender wage gap in three African

Countries. Economic Development and Cultural Change, 47(2): 289-312.

Becker, G.. S.(1985). Human Capital, Effort, and the Sexual Division of Labor. Journal of

Economics 3, S33-58

Blinder, A. S. (1973) Wage Discrimination: Reduced Form and Structural Estimations.

Journal of Human Resources, 8, 436-455.

Carnoy, M. (1994). Faded Dreams. Melbourne: Cambridge University Press.

Chung, Y.–P. (1996). Gender Earnings differentials in Hong Kong: The Effect of the State,

Education, and Employment. Economics of Education Review, 15, 231-243.

Cotton, J. (1988). On the decomposition of wage differentials. Review of Economics and

Statistics, 70, 236-43.

Dougherty, C. (2003, August). Why is the rate of Return to Schooling Higher For Women

Than For Men? UK: Centre for Economic Performance, School of Economics and Political

Science.

Gosse, M. (2001). The Gender Pay Gap in the New Zealand Public Service. Working Paper

No.15, New Zealand: State Services Commission.

Gunderson, M. (1989). Male-Female Wage Differentials and Policy Reponses. Journal of

Economic literature , 27, 46-72.

Jeronimo O. M., & Eduardo L.G. Rios-Neto. Marriage Premium among men and women in

Brazil, downloaded at 26/04/2006,

Available: http://www.ssc.wisc.edu/~jmuniz/muniz%20revised.pdf

Kaufman, BE, & Hotchkiss, JL. (2006). The Economics of Labor Markets. 7th Edition,

Dryden Press

Lui, H.-K. & W. Suen (1993). The Narrowing Gender Gap in Hong Kong: 1976-1986. Asian

Economic Journal, 7(2), 167-180.

34

Mincer, J. (1974). Schooling, Experience and Earnings, New York: National Bureau of

Economic Research.

Mincer, J. & S. Polachek. (1974). Family investment in Human Capital: Earnings of Women.

Journal of Political Economy, Vol. 82 (2, part II): S76-S108.

Nakosteen R. & Zimmer M. (1987). Marital Status and Earnings of Young Men. Journal of

Human Resources, 22(2):248-268.

Neumark, D. (1988). Employer’s discrimatory behaviour and the estimation of wage

discrimination. Journal of Human Resources, 23, 279-295.

Ng, Y. C., (2007, March). Gender Earnings Differentials and Regional Economic

Development in Urban China 1988-97. Review of Income and Wealth, 53(1):148-166.

Oaxaca,R.(1973). Male-Female Wage Differentials in Urban Labor Markets.

International Economic Review, 14, 693– 709.

Oaxaca, R.L. & M.R. Ransom.(1994). On discrimination and the decomposition of wage

differentials. Journal of Econometrics, 61:5-21.

Sebaggala, R. (2007). Wage Determination and Gender Discrimination in Uganda. Research

Series No.50, Economic Policy Research Centre (EPRC), Uganda: Makerere University.

Sung, Y.–W., Zhang J.-S. & Chan C.-S. (2001). Gender wage gap differentials and

Occupational Segregation in Hong Kong, 1981-1996. Pacific Economic Review, 6:3, 345-359

Sung, Y.–W., Zhang J.–S., Ng S.-H. & Hempel P. (2002). Feasibility on Equal Pay For Equal

Work of Equal Value. Research Report 5, Hong Kong: Hong Kong: Equal Opportunity

Commission.

和諧家庭研究系列: 香港性別意識與家庭分工狀況調查。香港基督教女青年會。[網上]

下載於 26/04/2006 網址: http://www.ywca.org.hk/research/200512a/report.pdf

35

IX. Appendix

Table 5: Regression Results (Model 2)

2001 2006

Male Female Male Female

CONSTANT

8.1808***

(374.49)

7.8008***

(293.95)

7.8873***

(343.61)

7.6641***

(304.90)

LOWSEC

0.0660***

(4.32)

0.0923***

(4.63)

0.1219 ***

(7.40)

-0.0019

(-0.10)

UPSEC

0.2307***

(13.96)

0.4428***

(20.79)

0.2506 ***

(14.70)

0.2217***

(11.26)

POSTSEC

0.4527***

(19.77)

0.6642***

(24.87)

0.4214***

(18.62)

0.4148***

(16.54)

UNIV

0.7203***

(31.82)

0.9033***

(33.01)

0.6497***

(29.16)

0.6711 ***

(27.14)

POSTGRA

1.0556***

(35.97)

1.1965***

(31.50)

0.9251***

(34.28)

0.9359 ***

(29.74)

EXP

0.0552***

(35.66)

0.0520***

(31.56)

0.0647***

(38.73)

0.0629***

(35.89)

EXP²

-0.0010***

(-30.76)

-0.0009***

(-24.37)

-0.0011***

(-32.24)

-0.0012***

(-28.52)

MARRIED

0.1962***

(15.93)

0.0097

(0.76)

0.1938***

(15.08)

0.0292**

(2.28)

WIDOWED

0.0243

(0.29)

0.0318

(0.73)

0.1834**

(2.28)

0.0785*

(1.93)

DIVORCED

0.0378

(1.16)

0.0332

(1.23)

0.0421

(1.35)

0.0069

(0.29)

HK

0.1344***

(13.24)

0.1596***

(13.25)

0.1265***

(11.60)

0.1699 ***

(14.10)

MAN&PROF

0.5565***

(40.21)

0.7352***

(36.05)

0.6829***

(50.58)

0.9036***

(47.41)

CLERK

0.0856***

(5.31)

0.2923***

(15.93)

0.1825***

(11.35)

0.4601 ***

(26.34)

36

Table 5: Regression Results (Model 2) (continue)

Note: t statistics in parentheses. The asterisks *, **, *** represents statistically significant

at 10%, 5% and 1% significance level respectively

2001 2006

Male Female Male Female

SERVICE

0.1975***

(15.49)

0.1858***

(10.52)

0.2517***

(18.81)

0.3153***

(18.42)

AGRI

-0.1701*

(-1.68)

0.1175

(0.57)

-0.1846**

(-2.04)

0.6640***

(5.22)

R- Square 0.4932 0.5138 0.4867 0.5042

Adjusted R-Square 0.4927 0.5132 0.4861 0.5036

N 13690 10883 14003 11809