Gender Earnings Differentials in Hong Kong 2006 & Tam Wai Kit. Applied Economics Concentration. Mak Tsz Hong. Applied Economics Concentration

Transcription

1 Gender Earnings Differentials in Hong Kong 2006 & 2011 BY Tam Wai Kit Applied Economics Concentration Mak Tsz Hong Applied Economics Concentration An 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 2012

2 I. Introduction The traditional societal belief emphasized sexual division of labour by the segregation of productive and reproductive roles. In past few decades, women were commonly considered to be responsible for the bearing of children, caring and nurturance (Westwood et al., 1997). Although OECD (2002) revealed some of the occupations are still thought to be discriminating against women by gender stereotyping, it is undoubtedly to admit that women are now more welcomed by labour market because of changing social norms and more chances to receive higher education (Lee et al., 2009). In order to response to public concerns for gender discrimination in workplace, the HKSAR Government (the Government) established Equal Opportunities Commission (the Commission) in After the establishment of the Commission, both men and women tend to be received the same treatment in workplace. Currently, gender discrimination, in terms of equal treatment 1, seems to be equalized. However, in terms of income, this may not be the case. Sung et al. (2001) revealed that women generally earned less than men between 1981 and 1996, holding other things being constant. Moreover, Fan and Lui (2003) had the same opinion after analysing 1981 and 1991 Population Census Data. In addition, both Sung et al. (2001) and Fan and Lui (2003) agreed that the gender earnings gap in Hong Kong was narrowing from 1981 to That is, the gender discrimination, in term of income, was improving over time. 1 It is defined as job assignmentsand promotion/employment opportunities in workplace

3 In fact, the existence of gender earnings differential may affect family decisions and resource allocations by discouraging the incentives of women to participate into labour market (Lee et al., 2009). As a result, investigating and decomposing the gender gap in Hong Kong can provides insights to the Government and relevant parties to implement policies that can promote gender earnings equality 2 and then utilize resources in a better way for the sake of maximizing economic welfare to the society. In this paper, there are two main objectives to be achieved. To start with, it investigates the impact of various socio-economic factors on income earned by men and women in 2006 and Studying the 2006 By-census Population and 2011 Census Population data can extend the study of the trend of gender earnings differential in Hong Kong since previous studies 3 investigated the gap between 1976 and After that, it will find out and decompose the gender earnings gap in Hong Kong in these years by Neumark Decomposition Method in order to investigate the seriousness of gender discrimination 4 and provide policy implications to the Government as well as relevant parties. 2 Both sexes should be paid the same income in labour market if they have the same objective characteristic (e.g. education level, holding other things being constant). 3 It includes Lui and Suen (1993), Suen (1995), Chung (1996), Sung et al. (2001) and Fan and Lui (2003). 4 It is defined as gender income inequality

4 II. Literature Review A. Mincer Earnings Function Mincer (1958) investigated the determinants of income inequality among individuals and linked the relationship between income, schooling and experience. The theoretical model showed that there was a premium for additional schooling and experience. Becker (1964) then introduced Human Capital Theory to indicate the positive effects of schooling and on-job training on individual s income. These two researches provided a foundation for subsequent researchers to further investigate the income inequality. Mincer (1958) suggested a basic econometrics model to identify the major determinants affecting individual s income and this model was then named as Mincer Earnings Function. The function is shown as below: InY = α + β 0 S + β 1 EXP + β 2 EXP 2 + ε Where InY is the dependent variable that represents natural logarithm of individual s income; α is a constant term; β i s are corresponding coefficients of independent variables; S is the years of schooling; EXP is the years of working experience; EXP 2 is the square of years of working experience and ε is an error term. In past few decades, labour economists strived hard on investigating determinants (other than schooling and working experience) affecting individual s income. It is because omitted variable bias in original Mincer Earnings Function may misestimate the coefficients of those independent variables (Griffin and Ganderton, 1996)

5 To begin with, Beyer and Knight (1989) suggested that individual s occupation should have significant effect on individual s income because of different job responsibilities, training costs incurred by workers, values of personal characteristics (e.g. natural ability) and natures of labour markets (e.g. monopsony market) among different occupations. Moreover, Korenman and Neumark (1991), and Breusch and Gray (2004) indicated married person should earn more income than single person. The underlying reason of such scenario is related to intra-household specialization after marriage (Bardasi and Taylor, 2008). That is, both men and women can specialize in what they have comparative advantage after marriage. Therefore, this enables them to earn more income in workplace. Furthermore, Lam and Liu (2002), and Lang (2005) pointed out that immigrants should earn less than natives. It is because the divergence comes from the lower skill price for immigrants education in labour market. That is, immigrants are paid less even they have the same educational level as locals. As a result, it is a part of workplace discrimination. In addition, Murphy and Topel (1990), and Neal (1995) mentioned that workers who are working in particular industries (e.g. Utility sector) should be paid more. Later, they further addressed this scenario reflects the relationship between wage premium and industry-specific skills. That is, it is concluded that some industry-specific skills will compensate the workers more. As a result, it is reasonable to include this factor in Mincer Earnings Function. To conclude, it is worthwhile to include the above factors as controlling variables in following sections in order to obtain a better estimation of the coefficients in Mincer Earnings Equation

6 B. Gender Earnings Gap Gender, unfortunately, was also found to be an important contributor of wage differential among individuals. In Hong Kong, Lui and Suen (1993) found that the gender earnings gap existed from 1976 to 1986 and it was narrowing over time. Moreover, they addressed the major contributors of the narrowing gender gap should be the increase in education level and labour force participation of women. On the other hand, Sung et al. (2001) also pointed out that the narrowing gender gap from 1986 to 1996 was mainly attributed to the economic restructuring 5 in Hong Kong and rise of women s education attainment. 6 In order to know more about why gender earnings gap existed in labour market, labour economists introduced some decomposition methods to observe the gender gap in detail (Lee et al., 2009). Among different decomposition methods, Blinder-Oaxaca Decomposition Method is the most popular one. The decomposition method is shown as below: In Y m InY f = β m i (X im X i f ) + ( X i f (β m i β f i ) + (α m α f )) Where m and f are male and female (respectively), In Y is average natural logarithm of income from major employment, X i is vector of average individual objective characteristics (e.g. education and working experience), β i s are the returns to those characteristics and α is the intercept in Mincer Earnings Function. In Y m InY f is the total earnings differential between male and female, β m i (X im X if ) represents the earnings differential contributed by the gender differences in objective characteristics and X if (β m i β f i ) is the earnings 5 Women changed their job from low-wage occupations (e.g. Operators and Clerks) to high-wage occupation (e.g. Services workers) under the economic restructuring of Hong Kong. 6 Both Lui and Suen (1993) and Sung et al. (2001) used Oaxaca-Blinder Decomposition to find out the major contributors of the narrowing gender earnings gap in Hong Kong

7 differential contributed by the gender differences in returns to those characteristics. By investigating the above equation, it is observed that Blinder (1973) suggested certain portion of the differential can be explained by endowments component. On the other hand, the remaining portion of the differential should be explained by price component. That is, this component investigates whether labour market prices the same objective characteristic differently between men and women. Therefore, this component is attributed to discrimination. Even though Blinder-Oaxaca Decomposition Method is widely used by labour economists, there is a structural problem involved in this method. Oaxaca (1973) and Blinder (1973) revealed that this method will involve familiar index number problem. That is, this approach assumed either male or female wage structure can be the non-discriminatory benchmark. However, both male and female wage structures can be discriminated in some occasions. Therefore, Neumark (1988) concluded the problem of this method is on the choice of nondiscriminatory benchmark and introduced a new method called Neumark Decomposition Method. The decomposition method is: In Y m InY f = β i (X im X if ) + [ X im (β i m β i ) + (α m α )] +[ X if (β i β i f ) + (α α f )] - 7 -

8 In this equation, β i s 7 are the estimated coefficients of returns to those objective characteristics of pooled sample 8 while α is the intercept of the pooled sample s regression. 9 On the other hand, β i (X im X if ) is similar to what Oaxaca-Blinder Decomposition Method suggests, which represents the endowment component (explained portion). Dissimilar to previous method, Neumark (1988) divided the price component (unexplained portion) into two parts: [ X im (β m i β i ) + (α m α )] measures pure male advantage while [ X if (β i β f i ) + (α α f )] estimates pure female disadvantage. These two sub-components are to observe whether the market over/undervalue the male and female objective characteristics. Taking the familiar index number problem into account, this paper will use Neumark Decomposition Method to find out and decompose the gender earnings differential in Hong Kong from 2006 to β i means the non-discriminatory benchmark. 8 This sample includes both men and women. 9 Neumark Decomposition Method assumes there is no gender difference in the world physically. That is, β i s can evaluate the returns to objective characteristics in the absence of gender discrimination

9 III. Methodology This paper will use 5% sample data sets of 2006 Population By-census and 2011 Population Census to investigate the situation of gender earnings gap in Hong Kong. These two sample sets include a broad range of demographic and socio-economic characteristics (e.g. monthly income and education level) of one-tenth of households. Since International Standard Classification of Occupations 2008 (ISCO-08) and Hong Kong Standard Industrial Classification (HSIC) Version 2.0 were introduced in 2008 and 2009 respectively, ISCO-88 (for occupational data) and HSIC Version 1.0 (for industrial data) will be used in order to ensure the data comparability among different years. Furthermore, this paper further focuses on the individuals who were aged between 16 and 65 and claimed to be employed with positive monthly income (from main employment only) during the enumeration periods. There are mainly two research objectives in this paper. To begin with, it will investigate the impacts of various social-economic determinants (e.g. marriage status) on income in 2006 and Then, it will find out and decompose the gender earnings gap in these years by Neumark Decomposition Method in order to investigate the situation of gender discrimination (in terms of income) and provide policy implications to the Government as well as relevant parties. In order to meet the research objectives, Mincer Earning Function and Neumark Decomposition Method will be used

10 To start with, this paper will generate the descriptive statistics of all variables related to the model specifications (e.g. education attainment). Then, this paper will use the Mincer Earnings Function (with Ordinary Least Square Method) to investigate the impacts of various social-economic determinants on male and female income. For this function, additional variables (i.e. place of birth, marital status, industry and occupation) will be added to the standard form of the function. The model is shown as below: Male: InY m = α m + β i m0 Education i m + β m1 Experience m +β m2 Experience m 2 + β m3 Immigrant m + β i m4 Marriage i m + β i m5 Industry i m + β i m6 Occupation i m + ε m Female: InY f =α f + β i f0 Education i f + β f1 Experience f +β f2 Experience f 2 + β f3 Immigrant f + β i f4 Marriage i f + β i f5 Industry i f + β i f6 Occupation i f + ε f

11 Where: Variables m and f InY m and InY f α m and α f β m0 i, β m4 i, β m5 m6 i, β i Explanations Male and female (respectively) Natural logarithm of monthly income from major employment Intercept of the function Corresponding vectors of estimated coefficients and β i f0, β i f4, β i f5, β i f6 β m1,.., β m3 Corresponding estimated coefficients and β f1,, β f3 Education i m and Education i f Vector of educational dummy variables: UPSEC (Upper secondary level), POSTSEC (Post-secondary level), UNVI (University level) and POSTGRAD (Post-graduate or above) Reference group: LOWSEC (Lower secondary or below) Experience m and Years of working experience (i.e. Experience = Age Schooling 6) Experience f Experience 2 m and 2 Experience f Immigrant m and Immigrant f Square of years of working experience Dummy variable of place of birth IMMIGRANT (Not born in Hong Kong) Reference group: LOCAL (Born in Hong Kong) Marriage i m and Marriage i f Vector of marital dummy variables MARRIED (Married), WIDOWED (Widowed) and DIV_SEP (Divorced or Separated) Reference group: NEVER_MARRIED (Never married) Industry i m and Industry i f Vector of occupational dummy variables MANU (Manufacturing), UTILITY (Electricity, Gas and Water), CONS (Construction), TRANS (Transport, Storage and Communication), FIN_EST (Financing, Insurance, Real Estate and Business Services) and SERVICES (Community, Social and Personal Services) Reference group: WH_RE (Wholesale, Retail and Import/Export Trades, Restaurants and Hotels) Occupation i m and Occupation i f Vector of industrial dummy variables MANAGER (Managers and Administrators), PRO (Professionals), APRO (Associate Professionals), CLERK (Clerks), SER_SALES (Services Workers and Shop Sales Workers), CRAFT (Craft and Related Workers) and PLANT (Plant and Machine Operators and Assemblers) Reference group: ELE (Elementary Occupations) ε m and ε f Error term of the function

12 Finally, this paper will use Neumark Decomposition Method with the results of male and female regressions to find out the amount of gender earnings differential and how much the earnings gaps are explained by those determinants and price component. In Y m InY f = β i (X im X if ) + [ X im (β i m β i ) + (α m α )] +[ X if (β i β i f ) + (α α f )] Where: Variables Explanations m and f Male and female (respectively) In Y m and InY f Average natural logarithm of monthly income from major employment X im and X if Vector of average individual objective characteristics β i m and β i f (β i ) Vector of returns to objective characteristics (Non-discriminatory benchmark) α m and α f (α ) Intercept of the function (Non-discriminatory benchmark) β i (X im X if ) Portion of earnings differential contributed by the gender differences in objective characteristics Endowment component / Explained portion (Evaluate the gender differences at β i ) [ X im (β i m β i ) +(α m α )] Portion of earnings differential contributed by pure male advantage 10 Male advantage component (Evaluate the differences at X im ) [ X if (β i β i f ) Portion of earnings differential contributed by pure female disadvantage Female disadvantage +(α α f )] f component (Evaluate the differences at X i ) 10 Since β i estimates the returns to objective characteristics in absence of gender discrimination, measuring the difference between this non-discriminatory benchmark and male/female returns to characteristics can reflect the male advantage/female disadvantage in labour market

13 IV. Results of the Study A. Descriptive Statistics Table 1: Sample statistics for the samples in 2006 & 2011 (Note: Standard deviations are in parenthesis) Male (N=75118) Female (N=72819) Male (N=77498) Female (N=82313) Mean Income ( ) ( ) ( ) ( ) Age (11.33) (10.51) 40.82(11.90) (10.93) Lower Secondary or Below 0.34 (0.48) 0.29 (0.45) 0.29 (0.46) 0.26 (0.44) Upper Secondary 0.40 (0.49) 0.45 (0.50) 0.39 (0.49) 0.44 (0.50) Post-secondary 0.05 (0.22) 0.05 (0.22) 0.06 (0.23) 0.06 (0.23) University 0.15 (0.36) 0.17 (0.37) 0.17 (0.38) 0.18 (0.38) Postgraduate 0.06 (0.24) 0.04 (0.20) 0.08 (0.27) 0.06 (0.24) Experience (13.06) (12.39) (13.73) (12.82) Single 0.36 (0.48) 0.41 (0.49) 0.34 (0.48) 0.39 (0.49) Married 0.62 (0.49) 0.52 (0.50) 0.62 (0.48) 0.53 (0.50) Widowed 0.003(0.06) 0.02 (0.13) (0.06) 0.02 (0.15) Divorced/Separated 0.03 (0.16) 0.05 (0.22) 0.03 (0.17) 0.06 (0.24) Manufacturing 0.10 (0.30) 0.08 (0.28) 0.06 (0.23) 0.04 (0.19) Wholesale and Retail 0.23 (0.42) 0.29 (0.45) 0.26 (0.44) 0.32 (0.46) Utility 0.01 (0.09) (0.04) 0.01 (0.09) (0.04) Construction 0.12 (0.33) 0.01 (0.11) 0.13 (0.34) 0.02 (0.13) Transportation 0.16 (0.37) 0.06 (0.24) 0.15 (0.36) 0.06 (0.23) Finance and Real Estate 0.19 (0.40) 0.16 (0.36) 0.21 (0.41) 0.16 (0.36) Services 0.18 (0.39) 0.39 (0.49) 0.18 (0.39) 0.42 (0.49) Managers 0.09 (0.28) 0.05 (0.22) 0.11 (0.31) 0.06 (0.24) Professionals 0.08 (0.26) 0.05 (0.22) 0.08 (0.27) 0.06 (0.23) Associate Professionals 0.17 (0.37) 0.17 (0.38) 0.19 (0.40) 0.18 (0.38) Clerks 0.11 (0.31) 0.27 (0.44) 0.12 (0.32) 0.25 (0.43) Services and Sales Workers 0.17 (0.37) 0.17 (0.37) 0.16 (0.36) 0.16 (0.37) Craft 0.15 (0.36) 0.02 (0.13) 0.13 (0.34) 0.01 (0.11) Operators and Assemblers 0.10 (0.30) 0.01 (0.11) 0.09 (0.28) 0.01 (0.08) Elementary Occupations 0.14 (0.35) 0.26 (0.44) 0.13 (0.33) 0.27 (0.45) Immigrant 0.30 (0.46) 0.39 (0.49) 0.29 (0.45) 0.42 (0.49)

14 Table 1 shows the mean values of different demographic and socioeconomic characteristics of the selected samples in 2006 and In these samples, it includes 75,118 men and 72,819 women in 2006 while it consists of 77,498 men and 82,313 women in Taking a quick glance at the table, it is found that men earn more income than women in both years. In 2006, men earn $4, more than women on average. After five years, the nominal income differential becomes larger and men earn $5,860.6 more than women in Moreover, the table reveals that around 70 percent of male and female samples attain secondary education level or below in Unsurprisingly, the situation is almost the same in 2011 and the underlying reason should be the mandatory education policy in Hong Kong. At the same time, only around 25% of them achieve university level or above in both years. This scenario may be related to the limited capacities of tertiary institutions. On the other hand, men generally possess more years of working experience than women in both 2006 and But, the gender difference in years of experience slightly diminishes from 2.71 years to 2.41 years among these five years. Furthermore, the descriptive statistics suggest that over 60% of men and 50% of women are claimed to be married in 2006 and On the other hand, there are 35% of men and 40% of women claimed to be single in these two enumeration periods. In addition, the statistics imply that men (23%) are more likely to work in Wholesale and Retail sector while women (39%) are more likely to participate into Services sector in By observing the figures of industrial segregation in 2006, there is an interesting finding that around 70% of women works in Wholesale and Retail, and Services sectors. Nevertheless, the distribution of male workers across industries is much even than that of female workers. Fan

15 and Lui (2003) explained this scenario that women have comparative advantage in participating into Service sector. Even in 2011, the situation is more or less the same as that in As well, the table indicates that more than half of the women work as Clerks and Elementary Workers in 2006 and Unfortunately, it is expected that the workers in these two occupations are paid much less compared with those in other occupations. On the contrary, the distribution of male workers across occupations is much even that that of female workers (similar to the situation of industrial segregation). At the same time, there is higher proportion of men (compared to female figures) working in high earnings occupations (e.g. Managers and Professionals) in both enumeration years. Therefore, occupation segregation seems to be one of the major reasons contributing to the gender earnings differential. Finally, the sample statistics point out that there are around 30% of men and 40% of women claiming to be immigrant (i.e. the place of birth is not in Hong Kong) in both years

16 B. Analysis of Regression Results Table 2: Regression results in 2006 & 2011 (Note: Standard errors are in parenthesis) Male (N=75118) Female (N=72819) Male (N=77498) Female (N=82313) Intercept *** (0.0097) *** (0.0095) *** (0.0096) *** (0.0093) Upper Secondary *** (0.0053) *** (0.0055) *** (0.0053) *** (0.0054) Post-secondary *** (0.0100) *** (0.0100) *** (0.0095) *** (0.0095) University *** (0.0081) *** (0.0077) *** (0.0081) *** (0.0079) Postgraduate *** (0.0105) *** (0.0116) *** (0.0100) *** (0.0105) Experience *** (0.0006) *** (0.0006) *** (0.0006) *** (0.0006) Experience Squared *** ( ) *** ( ) *** ( ) *** ( ) Married *** (0.0055) *** (0.0048) *** (0.0052) *** (0.0046) Widowed *** (0.0332) *** (0.0149) *** (0.0319) (0.0131) Divorced/Separated *** (0.0129) ** (0.0094) ** (0.0114) * (0.0083) Manufacturing *** (0.0075) *** (0.0078) *** (0.0087) *** (0.0102) Utility *** (0.0220) *** (0.0467) *** (0.0200) *** (0.0418) Construction *** (0.0079) *** (0.0168) *** (0.0072) *** (0.0142) Transportation *** (0.0069) *** (0.0084) *** (0.0064) *** (0.0082) Finance and Real Estate *** (0.0065) *** (0.0063) *** (0.0059) *** (0.0059) Services *** (0.0062) *** (0.0053) *** (0.0058) *** (0.0050)

17 Managers *** (0.0095) *** (0.0108) *** (0.0092) *** (0.0099) Professionals *** (0.0102) *** (0.0108) *** (0.0104) *** (0.0104) Associate Professionals *** (0.0076) *** (0.0074) *** (0.0074) *** (0.0071) Clerks *** (0.0082) *** (0.0068) *** (0.0079) *** (0.0065) Services and Sales Workers *** (0.0076) *** (0.0068) *** (0.0076) *** (0.0065) Craft *** (0.0075) *** (0.0158) *** (0.0077) *** (0.0175) Operators and Assemblers *** (0.0084) *** (0.0183) *** (0.0084) *** (0.0216) Immigrants *** (0.0045) *** (0.0046) *** (0.0042) *** (0.0044) R-Square Adjusted R Square The p-value significant at 1 percent, 5 percent and 10 percent are indicated by *, ** and *** respectively. Table 2 shows the regression results of male and female samples in 2006 and According to above regression result, most of the variables are significant at 1 % significance level except a few of them (e.g. Windowed and Divorced/Separated). This implies that the factors are vitally important in determining male and female income. To start with, table 2 lists out the regression results of a series of education dummies. The coefficients of education dummies indicate the additional rate of return to education after the attainment of an education level as compared to lower secondary education level or below. Therefore, the rate of return to an education level can be calculated by subtracting the coefficient of previous education level from the coefficient of the observing education level. In 2006, it is found that the rate of return to post-secondary, university and postgraduate education for men are 9.6%, 20% and 22% respectively. By the same token, the rates of return to these

18 education levels for women are 11.7%, 18.3% and 26.3% respectively. Undoubtedly, postgraduate degree yields the highest rate of return for both sexes in Nevertheless, this is not the case in In this year, university degree yields the highest rate of return for both sexes (Male: 23.2%; Female: 26.7%). On the other hand, the rate of return to post-secondary education for women decreases sharply from 28% to 20% among these five years. Secondly, the regression results show that years of experience (EXP) have a positive impact on both male and female income. However, the negative coefficients of squared EXP indicate that their earnings will rise over time, reach the peak at a moment and decline thereafter. That is, both men and women have their earnings profiles with concave shape in 2006 and It is believed that this should be related to human capital depreciation and slower process of human capital accumulation after the midlife. By further investigating the figures, it is found that men will reach their maximum earnings 11 when they have 31 years of experience in 2006 (2011: 32 years). On the other hand, women with 32 years of experience in 2006 (2011: 33 years) can also reach their maximum earnings. As a result, it is expected that women have a flatter earnings profile than men. Thirdly, the results addressed that married men earn 16% (in both years) more than single men whereas married women earn 4% (2011: 6%) more than single women in Therefore, this finding indicates that marriage exerts stronger positive income effect on men. 11 EXP = and women s earnings Coefficient of EXP 2 Cofficient of squared EXP can calculate how many years of working experience can maximize men s

19 Fourthly, the table shows that the industrial dummies represent the additional rate of return to working in a sector as compared to working in Wholesale and Retail sector. From the table, it is observed that Utility sector elevates 30% and 20% of male and female earnings respectively in This sector yields the highest relative rate of return across industries. Nevertheless, the relative rate of return to this sector for men greatly decreases by around 10% in On the other hand, the relative rate of return to Construction sector for men increases sharply by 6%. For the other industries, the situation is more or less the same as that in Fifthly, the coefficients of occupational dummies identified how much (in percentage) an individual earns more by an occupation as compared to working as an Elementary Worker. The table revealed that the earnings of Managers and Professionals are higher than that of the workers in reference group by more than 110% for both sexes in both years. Surprisingly, women have higher rate of return than men in every occupation during these two enumeration periods. Last but not least, male immigrants earn 3% (2011: 5%) less than local male in 2006 while female immigrants earn 16% (in both years) less than local female. This shows that the earnings differential between immigrants and locals are larger in female sample

20 C. Neumark Decomposition Method Table 3: Results of Neumark Decomposition Method Total Gap Endowment Differential Male Advantage Female Disadvantage Total Gap Endowment Differential Male Advantage Female Disadvantage Education Attainment Working Experience % % % % % % % % % % % % % % % % Marital Status % % % % % % % % Employment Sector % % % % % % % % Occupations % % % % % % % % Place of Birth % % % % % % % % Subtotal % % % % % % % % Summary of the decomposition: Table 4:Summary of the results Explained Portion % % Unexplained Portion for Male Advantage % % Unexplained Portion for Female Disadvantage % % Total differential % 100% Table 3 shows the results of Neumark Decomposition Method. By investigating the figures in this table, important conclusions can be drawn to provide policy implications to the Government and relevant parties. In the following, this paper will investigate different objective characteristics one-by-one in details

21 i. Education Attainment According to Human Capital Theory, it suggests that knowledge and skills acquired from education can raise individual s earnings. Therefore, it is believed that education can reduce the gender gap through increasing the women participation rate in higher education. However, this is not the case in Hong Kong. The endowment differential due to education attainment only reduce the total gap by 1.6% in Worst still, the endowment differential component even widens the gap by 1.3% in It is because the distribution of male and female sample in different education levels are almost the same (except postgraduate level) in 2006 and As a result, the human capital endowment cannot alter the gap much. The impact of education attainment on the gap is mostly from discrimination portion. In particular, the female discrimination portion in 2011 attributes much to the insignificance effect of education attainment on narrowing the gap. (Given the fact that education attainment can slightly reduce the gap by 6.8% in 2006). ii. Working Experience Table 3 indicates that working experience is the most influential factor among different characteristics. It enlarges the gender gap by 37.7% and 25% in 2006 and 2011 respectively. Nevertheless, the influence of explained portion is decreasing over time. It is because the estimated coefficients of working experience for pooled sample is smaller in 2011 (See Appendix - Table A). As a result, the impact of endowment differential component greatly decreases from +10.4% to +6% in 2011 even the gender difference in working experience just slightly decreases in this year. By the same token, the results show that discriminations in favor of men (2006: +10.9%; 2011: +7.1%) and against women (2006: +16.4%; 2011: +11.9%) are still the major contributors to the expanding effect of this objective characteristics even the influence of discrimination portion is also reducing over time

22 iii. Marital Status It is observed that marital status raises the gender gap by 20.1% and 22.3% in 2006 and 2011 respectively. Moreover, unexplained portion is the major contributor of the expanding effect of this characteristic. Within five years, the discriminations in favor of men (From +9.9% to +12.2%) as well as against women (From +6.8% to 7.4%) in respect of marital status contribute more to the gap in The unexplained portion in this category can be explained by the potential risk of quitting the job. In the viewpoint of employers, they have a stronger incentive to provide on-the-job training and promotion opportunities for men because men are more likely to stay at the firm after marriage. That is, the firms consider whether they can recoup the cost of training from the employees after providing training. As women are typically expected to focus on family issues after marriage, this can explain why marital status widens the gender gap in Hong Kong through the enlargement of unexplained portion. iv. Employment Sector The results show that employment sector widens the gap by 10.9% and 9.2% respectively. However, the market discrimination is found to be not prevalent across employment sectors since the unexplained portion has a small effect on the gap in both years. On the other hand, the explained portion remains around +9% in both 2006 and The scenario can be explained by the distribution of male and female samples in different industries. As women expect their working life is shorter than that of men, they are more likely to participate in some industries which do not require much on the skills. 12 However, those industries (e.g. Services sectors) normally have lower wage premiums for the workers (See Appendix Table A). By the same token, men participate into the sectors that have higher wage premiums (e.g. Utility, 12 It is because they have much less time than men for capturing the benefits after gaining the skills

23 Transportation and Financial sectors). As a result, this explains why the explained portion of employment sector contributes much to the gender gap in Hong Kong. v. Occupations Among different objective characteristics, occupation is the second most influential factor among different characteristics. It narrows the gap by 27% and 39% in 2006 and 2011 respectively. Table 3 identified that explained portion has expanding effect on the gap while unexplained portion has narrowing effect. Furthermore, both portions become much more influential in 2011 (compared with the figures in 2006). Similar to employment sector, the shorter expected working life influences women s human capital investment 13. Given that high earnings occupations (e.g. Manager) require more specialized and advanced skills, women, who invest less on human capital, should earn less income than men (Tam, 1997). It is because it is more difficult for them to work in a higher position without suitable skills. Hence, this explains why the explained portion has positive impact on the gap. On the other hand, the discriminations against men and in favor of women in labour market are also the reasons why the unexplained portion has narrowing effect on the gap in both years. vi. Place of Birth The gender earnings gap increases by 14% and 16% in 2006 and 2011 respectively because of this characteristic. By observing the figures in Table 3, it is found that the expanding effect of this factor is mainly from explained portion (around +5%) and male advantage portion (around +9%). This situation can be explained by Theory of Market Discrimination. It is because perception from the employers will lower relative wage of minority workers (i.e. immigrants). As higher proportion of women sample is regarded as Immigrants, it can answer 13 Women, who are normally responsible for child care and housework, may be discouraged from investing in market human capital (Becker, 1985)

24 why the explained portion attributes to the expanding effect of this objective characteristic. On the other hand, the discrimination in favor of men even further increases the impact of the characteristic on the gap in Hong Kong. Table 4 summarizes the results of Neumark Decomposition Method. It shows that the gender earnings gap in Hong Kong widens from to within 5 years. Moreover, the explained portion explains relatively large proportion of the gender gap in both years. In particular, 7% more of the gender gap are explained by this portion in On the contrary, the explanatory power of unexplained portion decreases over time. Therefore, the relevant parties should pay more attention on the explained portion of the gender gap. That is, the parties have to focus on women s working experience, employment sector and occupation as the explained portion of these three characteristics have great impacts on altering the gap. Referring to the findings mentioned as above, the relevant parties should provide more incentive programmes for encouraging women to participate more in trainings, which equip them with advanced skills and it is useful for them to change their jobs to high-wage occupations/industries. For example, the Government can provide women with subsidized vocational trainings and child-care programmes for women so as to reduce their direct and indirect cost of receiving advanced training

25 V. Conclusions To begin with, the means of relevant variables provide an important implication about the pattern of gender mix in different industries and occupations. The results indicate that higher proportion of male sample concentrates on high earnings industries. Moreover, men are more likely to work in high earnings occupations (e.g. Manager and Professionals). On the contrary, women are more likely to work in low earnings industries and occupations. Secondly, both education level, working experience, marital status (except Widowed, Divorced and Separated ), industry segregation, occupation segregation and birth of place have significant effects on individual s income at 1% significance level in the regression model. Moreover, most of the variables (except Immigrants ) are positively related to individual s income. The results are consistent with what previous studies suggests. Lastly, the Neumark Decomposition Method indicates that marital status, occupation and working experience are the most influential objective characteristics contributing to the gender gap in Hong Kong. In addition, the gap is found to be widening from 2006 to This results contradicts to previous researches like Lui and Suen (1993), Suen (1995), Chung (1996), Sung et al. (2001) and Fan and Lui (2003), which suggest the gender earnings gap is narrowing in Hong Kong over time. By investigating the summarized results of Neumark Decomposition Method, it is found that the explanatory power of those objective characteristics increases greatly (from 56.1% to 63.5%) in Contrarily, the discrimination portion (i.e. Male Advantage and Female Disadvantage ) becomes smaller after 5 years. This shows that the expansion of gender earnings gap is mainly due to endowments differential but not market discrimination. Besides,

26 this decomposition method suggests that working experience, employment sector and occupation explain much to the gender earnings gap in terms of explained portion. Therefore, the relevant parties should focus on these three objective characteristics to reduce the gap in Hong Kong. That is, it is suggested that the parties can provide incentive programmes for persuading women to invest more on human capital (e.g. vocational trainings). For example, the Government can provides subsidies for child-care programmes and vocational trainings. Therefore, it can reduce their direct and indirect costs of receiving training. It is useful for them to work in high earnings industries/occupations (Given the fact that most of the high-wage jobs requires employees to have special skills or experiences)

27 VI. Appendix Table A: Regression Results of Pooled Sample 2006 (N= ) 2011 (N=159811) Coefficient Standard Error Coefficient Standard Error Intercept *** *** Education Attainment & Experience Upper Secondary *** *** Post-secondary *** *** University *** *** Postgraduate *** *** Experience *** *** Experience Squared *** *** Marital Status Married *** *** Widowed *** Divorced/Separated *** Employment Sector Manufacturing *** *** Utility *** *** Construction *** *** Transportation *** *** Finance and Real Estate *** *** Services *** *** Occupation Managers *** *** Professionals *** *** Associate Professionals *** *** Clerks *** *** Services and Sales Workers *** *** Craft *** *** Operators and Assemblers *** *** Place of Birth Immigrants *** *** R-Square Adjusted R-Square The p-value significant at 1 percent, 5 percent and 10 percent are indicated by *, ** and *** respectively

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