Gender Wage Gaps in Hong Kong

1. Gender Wage Gaps in Hong Kong Junsen Zhang Department of Economics 2009.11.6

2. Review on Gender Wage Gap Literature • Two problems: • Gender wage gap on a year or between years? • Considering gender differences in labor participation or not? • Research of four categories: • A: Gender wage gap on a specific year, not considering gender differences in labor participation (many) • B: Gender wage gap between years, not considering gender differences in labor participation (many) • C: Gender wage gap on a year, considering gender differences in labor participation (several) • D: Gender wage gap between years, considering gender differences in labor participation (so far, none)

3. Examples • Category A: Oaxaca (1973), Liu et al. (2000). • Category B: Blau and Kahn (1997), Zhang et al. (2008). • Category C: Hunt(2002). • The lower gender wage gap is not a good news for women. Many low-quality female workers are forced to quit labor market.

4. What We Do? • We do the job of category A but on the distributional level. • We do the job of category B but on the distributional level. • We do the job of category D on the mean level.

5. Three Papers • First paper is “Gender Wage Gaps in Hong Kong in 2006: Evidence from the Whole Distribution.” • Second paper is “The Effect of Economic Restructuring from 1981 to 2006 on Gender Wage Gaps in Hong Kong.” • Third paper is “FemaleLabor Participation and Gender Wage Gaps from 1991 to 2006 in Hong Kong.”

6. Paper I: Facts

7. Paper I: Facts Figure 1: Gender Wage Gaps at Different Percentiles, 2006 • Gender wage gaps are wider at both the lower positions and the higher positions. “glass ceiling” and “sticky floor” both exist. • This will be hidden at the mean level analysis.

8. Paper I: Method • Machado and Mata (2005, Journal of Applied Econometrics). • Step 1: run quantile regression for male sample and female sample respectively. • Step 2: counterfactual analysis--- “what will the distribution of female wages be like if they were paid according to male quantile regression coefficients”. • Step 3: counterfactual analysis--- “what will the distribution of female wages be like if their characteristics(edu, exp., etc.) were the same with male’s”. • Step 4: separate “coefficient effect” and “characteristics effect”.

9. Paper I: Results • At lower positions, the gap is mostly explained by coefficients.

10. Paper I: Conclusion & Policy Implication • According to Oaxaca (1973), the part explained by coefficients (prices) are regarded as “discrimination”. • Gender wage gaps are wider at both the lower positions and the higher positions. However, female workers at the lower positions are more discriminated. • Anti-discrimination effort should be more emphasized on females at the lower positions.

11. Paper II: Facts Figure 2: Change of Gender Wage Gap between 1981 and 2006 • At the lower positions, gender wage gaps decrease greatly; at the higher positions, gender wage gaps decrease slightly or increase. • Question: females at lower/higher positions benefit more from economic restructuring?

12. Paper II: Method • Machado and Mata (2005) • It is useful to manage distributional analysis. • Lam and Liu (2002) • It is useful to manage between-year analysis. • We combine them.

13. Paper II: Method • Machado and Mata (2005) • Already discussed in the first paper. • Lam and Liu (2002, Journal of Labor Economics) • M and f denote males and females; t and t’ denote two years. and denote characteristics and coefficients. • The first term on the right-hand is relative price effect and the second term is general price effect. The third term is the relative quantity effect and the fourth term is general quantity effect.

14. Paper II: Decomposition Results for the Lower Positions

15. Paper II: Decomposition Results for the Higher Positions

16. First, female workers at lower positions and higher positions of the wage distribution both benefit from economic restructuring. • Second, female workers at higher positions benefit more from economic restructuring. At lower positions of the wage distribution, economic restructuring reduces gender wage gap by 0.262 log wage points; at higher positions, economic restructuring reduce gender wage gap by 0.880 log wage points. • Third, female workers at higher positions are greatly hindered by effects of the constant term, which is often regarded as effects of unobserved factors often attributed to discrimination.

17. Paper II: Policy Implication • Women at the lower positions do not benefit more from economic restructuring. • We need to give them extra help.

18. Paper III: Facts • In Hong Kong, higher female labor participation is companied with lower gender wage gap. • LPR: 43.37%(1991) , 46.46% (2006) • Wage ratio (F/M): 0.73(1991), 0.81(2006). • In other countries, lower female labor participation is companied with lower gender wage gap. • East German. • China Mainland. • Explanation: Low-quality women quit or participate in the labor market. • Why is Hong Kong’s story different?

19. Paper III: Method • Simpler version of BFL(2002). • Bourguignon, F., F. Ferreira and P. Leite, “Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions across Countries”. • Two kinds of counterfactual analyses: • Labor participation counterfactual analysis; • Wage counterfactual analysis.

20. Paper III: Labor Participation Counterfactual Analysis • Step 1: logit model for females of 2006 (=t) to get labor participation equation: • Step 2: logit model for females of 1991 (=t’) to get labor participation equation: • Step 3: Counterfactual analysis • If a female of 2006 has the same education as a female of 1991, will she participate in labor market? • If a female of 2006 decides her action according to the education coefficient of 1991, will she participate in labor market? • Then we can separate different effects on labor participation.

21. Paper III: Wage Counterfactual Analysis • Four situations: • A:present working status is employed, predicted is employed; • B:present working status is employed, predicted is unemployed; • C:present working status is unemployed, predicted is employed; • D: present working status is unemployed, predicted is unemployed. • Her wage will be: • A:her actual wage; • B:0; • C: predict her wage according to 2006 wage equation; • D:0. • Then we can separate different effects on female’s wages and further on the gender wage gap too.

22. Paper III: Results

23. Education will help a woman to find a job more in 2006 than 1991. • Women with higher ages tend to find a job more easily in 2006 than in 1991. • Marriage’s negative effect is less in 2006 than in 1991.

25. From Column 1, we can see four factors can explain higher female LPR in 2006. • Higher education. • Higher positive effect of education. • Higher positive effect of age. • Less negative effect of marriage. • However, from Column 2, we can see only higher education can explain why higher female labor participation is companied with lower gender wage gap. This factor explains Hong Kong’s different story. The other three factors show the same story with Germany and China mainland.

26. Paper III: Policy Implication • Female’s higher education in Hong Kong is a major advantage. Because of this, Hong Kong need not worry about the “contradiction” of policies to encourage female labor market participation and policies to narrow gender wage gaps.

27. Thank You!