Breaking the Vicious Circle: Macroeconomic Perspectives on Policy Interventions for Female Labor Supply and the Gender Wage Gap

ABSTRACT

This paper examines the interplay between the female labour supply and wages through human capital accumulation in South Korea, highlighting the significant gender wage gap and low female labor participation. Our findings suggest that wage subsidies alone are insufficient for narrowing the gender wage gap. Targeted wage subsidies for households with infants and toddlers can mitigate career interruptions and enhance human capital for women. However, it also attracts lower-productivity women into the labor market, further exacerbating the gender wage gap. Therefore, direct labour market structural reforms, such as the Anti-Gender Discrimination Act, are essential.

KEYWORDS:

• Female labour supply
• gender wage gap
• wage subsidy

JEL CODE:

• H31
• J24
• J31

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Notes

1 The average gender wage gap of OECD countries is 13.0%, as of 2018.

2 As of 2016, the ratio of women’s LFP to men’s LFP was 74.0%, which is 5.5%p below the OECD average of 79.5%, ranking it 31st.

3 Population Trend Survey, Statistics Korea.

4 As of 2015, the gender wage gaps (%) of major countries were as follows:

Table

5 Even though it was not presented in this paper, we conducted a fixed effect panel regression analysis, and estimated the effect in a country (within effect), which indicated a significant negative correlation between the two variables.

6 According to SF 2.4 of the OECD Family Database, in 2016, the ratio of out-of-wedlock childbirths to total childbirths in Korea was 1.9%, ranking Korea the lowest among the 45 countries surveyed.

7 This study examines the effects of the government’s policies on women’s LFP on the condition that each household has a certain number of children. However, the government’s policies may affect households’ fertility rates, which would in turn affect women’s LFP. This study follows a previous study by Song and Wu (2015), which shows that the government’s policies have a limited impact on fertility rates and focuses on the analysis of the female labour supply by presuming that childbirth is exogenously determined.

8 A productivity gap between men and women with the same level of education is presumed to be maintained without any change during their lifecycle once it is set as an initial condition.

9 Men's willingness to work might also be affected by the changes in the economic environment, such as subsidies. Nonetheless, the model assumes that the male’s labour supply is constant as there is a long history of gender-based division of labour, where men are expected to be the primary breadwinners, while women are expected to take care of the household and children.

10 According to Yoo et al., (2018), ‘79.5% of daycare centres have children on the waiting list for enrolment, with an average number of 23.34 children waiting. Among public and national daycare centres, the proportion with children on the waiting list is 94.7%, and the average number of children waiting is 106.75, which is the highest compared to other types of facilities. Moreover, this number tends to increase in larger cities and as the size of the daycare centres increases.

11 In general, households’ labour is divided into economic and non-economic activities, with economic activities being further divided into employment and unemployment. In this model, as companies accommodate all labour supply through flexible wage adjustments, unemployment does not occur.

12 The representative examples of companies’ inflexible labour demand are wage rigidity, which is introduced in a New Keynesian literature, and wage determination, which is achieved through bargaining between worker and firm in a searching-matching model. These concepts may assist in the analysis of a short-term situation where companies’ labour demand fails to increase flexibly to match an increase in labour supply, thus leading to unemployment. This short-term analysis would be useful for examining cases where middle-aged and elderly women who had withdrawn from the labour market later rejoin the market (both as employed and unemployed), rather than cases of young women’s career interruptions, which are discussed thoroughly in this study.

13 This refers to the situation where $\theta \left(I,l_f\right)=1\mathrm{\forall I},l_f$ in Equation (3).

14 This refers to the situation where $\theta \left(I,l_f\right)>1\mathrm{\forall I},l_f$, $\theta \left(I,l_f=0\right)=0\mathrm{\forall I}$ in Equation (3). The reason why $\theta (I,l_f=\overline{l}$ is that the same amount of fiscal expenditure as that in Scenario 1-1-a is paid to a smaller number of beneficiaries as subsidies.

15 This refers to ${\tau }_{\mathrm{s}}>0$ in Equation (3), and ${\tau }_{\mathrm{s}}$ was adjusted to achieve the same amount of fiscal expenditure as in Scenario 1-1-a.

16 Unlike the subsidy policy simulation, this simulation assumes that the change occurs in the labour market itself, making it impossible to compare the results of the former policy with those of the latter. Instead, we look at the direction in which major variables move when the effects of the former policy are achieved by the acceleration of the pace at which women’s human capital is accumulated.

17 This study does not take into account the cases where different wages are paid due to gender discrimination, even though females have the same productivity as males. Instead, it focuses on the cases where there is gender ‘discrimination’ in terms of promotions and appointments, leading to ‘differences’ in productivity and personal traits, which would in turn cause a gender wage gap.