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Why is the gender earnings gap greater in Korea than in the United States?
J. Japanese Int. Economies 21 (2007) 455–469 www.elsevier.com/locate/jjie
Why is the gender earnings gap greater in Korea than in the United States? Donghun Cho ∗ Korea Labor Institute, 9F Korea Federation of Small Business Bldg, 16-2 Youido-dong Yongdungpo-gu, Seoul, 150-740, South Korea Received 17 March 2006; revised 4 October 2006 Available online 21 March 2007
Cho, Donghun—Why is the gender earnings gap greater in Korea than in the United States? It has long been recognized that the gender earnings gap varies across countries. This paper examines the relatively higher gender earnings gap found in the Korean labor market compared to the US labor market. Using the data set representative of the population for both countries, I found that the significant part of the differences in the gender earnings gap simply arise from the differences in the observed characteristics of women among two countries. In particular, relatively lower labor market experience, current job tenure, and educational attainment by Korean female workers play dominant roles in explaining the observed higher earnings gap. In addition, wage structure and labor market discrimination seem to be against Korean female workers compared to US female workers. J. Japanese Int. Economies 21 (4) (2007) 455–469. Korea Labor Institute, 9F Korea Federation of Small Business Bldg, 16-2 Youido-dong Yongdungpo-gu, Seoul, 150-740, South Korea. © 2007 Elsevier Inc. All rights reserved. JEL classification: J16; J31; J80 Keywords: Gender earnings gap; Wage structure; Wage inequality; Decomposition
1. Introduction It has long been recognized that the gender earnings gap varies across countries. In their international comparison, Blau and Kahn (1996) analyzed the gender pay gap in ten industrialized nations and found that wage-setting institutions that affect overall wage inequality played * Fax: +82 2 782 0849.
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important roles in explaining the relatively higher US gender gap than in most other developed countries. For example, the more decentralized wage-setting system in the US labor market leads to relatively higher gender earnings differentials since centralized wage bargaining conducted by many European countries arguably reduces the extent of wage variation across industries, persons, and firms. This paper explores South Korea (henceforth Korea) as an international comparison with the United States to analyze the gender earnings gap. There are a couple of reasons to choose the Korean labor market: First, Korea experienced rapid economic growth during the 1970s and 1980s, which produced some interesting labor market phenomena worth studying.1 For instance, Korea produced an example of rapid industrialization combined with declining wage inequality due to an overall rapid growth of educational achievement. Second, being geographically located in Asia, Korea might reflect some characteristics of institutions and culture that help to understand international labor market structure and labor decisions within households. While US women earn approximately 76 percent of men’s earnings, Korean women earn only about 50 percent of Korean men’s earnings in 2004.2 Before examining the possible sources of a higher gender earnings gap in Korea than in the United States, one’s perception may be that there might exist more serious gender discrimination in the Korean labor market because of its economic position as one of the developing countries. If this anticipation holds true, more severe labor market discrimination would impose more penalties to Korean women at work, resulting in a higher gender earnings gap. Most of the empirical studies on the gender earnings gap have focused both on gender differences in observed characteristics and on gender discrimination at the workplace. However, Blau and Kahn (1996) began to focus on the role of the wage structure—the prices of unmeasured labor market skills and wage inequality as an additional factor when they analyzed the gender earnings gap across countries.3 Following their empirical strategy, several factors associated with the wage structure are investigated in order to address the higher gender earnings gap in Korea. While there seems to be more gender discrimination against Korean women, the majority of the differences in the gender earnings gap simply arise from the differences in the observed characteristics of women among the two countries. For example, the average tenure of Korean women in the sample of workers is much lower compared to men, whereas US women had nearly the same job tenure as men. Furthermore, Korean women’s educational attainment, especially at the four year college level, is substantially lower than men’s while the sample in the US data shows the same educational achievement between men and women. Overall wage inequality, which can be viewed as one of the factors generating a higher gender earnings gap, is found to be the same between the two countries. However, the wage structure and labor market discrimination seem to be slightly against Korean female workers. This paper is organized as follows. Section 2 describes data sources and presents a brief overview of the gender earnings gap in the United States and Korea. Section 3 provides the analytical framework employed in the regression examples of Section 6. Section 4 documents gender 1 Kim and Topel (1995) presented several interesting results about the Korean labor market structure associated with economic growth. For instance, despite rapid economic growth, serious income inequality had not been observed. They also argued that aggregate wage growth was neutral among employment sectors. 2 Note that these calculations are based on log earning differentials: weekly earnings used for the US workers and hourly earnings calculated for the Korean workers. 3 Their methodology was originally developed by Juhn et al. (1991) for analyzing slowdown in black–white wage convergence.
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differences in measured characteristics. Section 5 describes differences in the wage structure, highlighting the roles of overall wage inequality and women’s relative wage position. Section 6 presents main empirical results and Section 7 concludes. 2. Data Among many previous studies on the gender earnings gap, Mincer and Polacheck (1974) argued that women earn less than men because female workers’ weaker labor market attachment leads to lower human capital accumulation (or depreciation of acquired skills)—both firmspecific and general. As a consequence, they claimed that about 70 percent of the gender earnings gap can be explained by differences in the labor market experience.4 According to Mincer and Polacheck, it is therefore crucial to have information on actual labor market experiences for an analysis of the gender earnings gap since potential work experience (e.g., age-7-education) can be a poor proxy for measuring women’s actual work experience.5 I choose to use the following two data sets which include information on current job tenure for both countries: The February Current Population Survey (CPS) for the United States and the August Economically Active Population Survey (EAPS) for Korea. The EAPS, conducted every month by the Korean National Statistical Office since 1963, is a survey for all persons aged 15 years old and over who are not in the armed forces, prisoners, or foreigners. This survey covers about 33,000 households every month and collects several types of information about individuals’ work activities, such as employment status, working hours and current job tenure. EAPS was designed to examine the Korean labor market, but questions about wages were not originally included. However, questions about wages have been included in every August supplemental survey since 2001. This paper uses the 2004 August supplemental EAPS. This CPS-type Korean labor market data allows the researcher to conduct a comparable analysis the analysis comparable between the US and Korea. The 2004 February CPS was used for the US workers since it contains information on job tenure as well as wages, employment, and demographic characteristics.6 Table 1 shows an overview of the descriptive statistics from the 2004 CPS and EAPS. The sample is restricted to full-time (more than 35 hours of work per week for both countries) and non-agricultural sector workers aged 20–65. Weekly earnings are measured for the US workers and hourly earnings are calculated for the Korean workers.7 Here, I excluded the bottom 1 percent of salary workers for both countries. As shown in Table 1, the gender earnings gap (measured by log earnings differentials) is higher in Korea than in the United States—a 0.25 log point difference in the United States and a 0.50 log point difference in Korea. A more detailed examination of the descriptive statistics follows. 4 In contrast to this human capital explanation, the “feedback” hypothesis suggested by Gronau (1988) argued that lower wages of women, which are mainly caused by labor market discrimination, discourage women strongly from staying in the labor market. This labor market separation ultimately leads to less investment in human capital and lower wage growth among female workers. 5 Mincer and Polacheck (1974) used 1967 National Longitudinal Survey of Work Experience which has retrospective information on the actual work experience. 6 All races are included in the CPS sample. Restricting the sample to non-Hispanic whites produces identical results. 7 To calculate hourly earnings for Korean workers, monthly earnings are divided by the monthly hours worked, which were obtained by multiplying the weekly hours by 4.
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Table 1 Descriptive statistics (means): CPS and EAPS from 2004 Variable
US Male
Log of current wages Job tenure for all ages Job tenure for aged 40 or more Experience Age Education Union Marital status Sample
Korea Female
Male
Female
6.64 (0.57) 8.15 (8.27)
6.39 (0.52) 7.35 (7.58)
6.77 (0.55) 6.60 (7.64)
6.27 (0.49) 3.05 (4.47)
11.41 (9.48) 20.10 (10.91) 40.79 (10.88) 13.68 (2.79) 0.14 (0.35) 0.68 (0.46) 4979
9.99 (8.66) 20.17 (11.40) 41.13 (11.18) 13.95 (2.57) 0.13 (0.33) 0.57 (0.49) 3943
9.47 (9.32) 19.82 (10.92) 39.89 (9.76) 13.06 (2.73) 0.19 (0.39) 0.74 (0.43) 11,622
3.65 (5.64) 17.93 (12.59) 37.01 (10.62) 12.07 (2.89) 0.07 (0.26) 0.57 (0.49) 7701
Notes. Standard deviations are in parentheses. The sample is restricted to full-time workers and non-agricultural sector workers aged 20–65. Weekly earnings used for US workers and hourly earnings used for Korean workers. Bottom 1% of salary workers for both countries is excluded. All races are included in the US sample.
3. Empirical methodology8 Blau and Kahn (1996) developed a method of exploring gender earnings gaps across countries, originally adapted from Juhn et al. (1991). Their distinction from previous studies has focused on wage structure as an additional important factor affecting the gender earnings gap. Consider the following (male) wage equation for individual i in country j : Eij = Xij βj + σj θij ,
(1)
where Eij is the log wages; Xij is a vector of characteristics; βj is a vector of coefficients; θij is a standardized residual; and σj is the country’s residual standard deviation of wages. Here, I assume that βs are the same for men and women within a country. θij will reflect the difference in how each unmeasured characteristic is treated (i.e. measuring relative wage levels of men and women after controlling for the observed characteristics). Note that the mean male residual is normalized to be a zero. Using (1), the male–female earnings gap for country j can be written as Dj = Emj − Efj = Xj βj + σj θj ,
(2)
where the m and f subscripts present each gender group (male and female), Xj and θj are gender differences in measured characteristics and standardized residuals respectively. Traditionally, the second term σj θj corresponds to the “unexplained part” in decomposing the gender 8 This section is based on the analytical framework used by Blau and Kahn (1996).
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earning gap—sometimes it can be interpreted as a measure of discrimination against women in the labor market.9 In their studies on gender earnings gaps across countries, Blau and Kahn (1996) interpreted the second term as wage structure—unmeasured prices of skills (θij ) and residual wage inequality (σj ). Then, the gender earnings differentials between the United States and Korea can be decomposed based on (2) DUS − DKorea = (XUS − XKorea )βKorea + XUS (βUS − βKorea ) + (θUS − θKorea )σKorea + θUS (σUS − σKorea ).
(3)
The first term in (3) presents the contribution of US–Korea differences in the observed characteristics among gender groups to the gender earnings gap. For example, relatively lower labor market experiences attained by Korean female workers (i.e. XUS − XKorea < 0) lead to a higher gender earning gap in Korea, all else being equal. The second term shows the contribution of the differences in the returns to the observed labor market qualifications between two countries. For instance, given a (positive) male–female job tenure difference in the US, relatively higher returns to current job tenure in Korea (i.e. βUS − βKorea < 0) would tend to raise the gender earnings gap in Korea. The extent of this contribution depends on the gender distributions of the observed labor market qualifications in the United States. These first two terms may be called the “explained part” of the inter-country decomposition of gender earning gap. Likewise, the third and fourth terms comprise the “unexplained part” which could either be interpreted as the extent of labor market discrimination against women, or as differences in the wage structure (prices of unmeasured skills and residual wage inequality) between two countries. Among these components, there are two possible factors which may constitute the third term in (3). First, labor market discrimination may play some role. Higher labor market discrimination against women in one country will contribute to a higher gender earnings gap in that country after accounting for the measured labor skills. Second, unmeasured characteristics would be reflected in this term since θUS − θKorea measures inter-country differences in the relative percentile ranks of the female wage residuals in the male distribution. However, it is hard to distinguish labor market discrimination from the unmeasured labor market qualifications. Finally, given the same percentile rankings of the female wage residuals, the fourth term in (3) measures the contribution of the difference in male wage residual inequality across the two countries. For instance, if Korea has a higher residual wage inequality than the United States, this probably indicates that the Korean labor market imposes a larger penalty on those in lower wage positions who are more likely to be women, leading to a higher gender earnings gap in Korea. Following Blau and Kahn (1996), I will implement the third and fourth terms by using the entire distribution of male and female residuals from wage regressions for each country.10 Given the concern that decomposition results might be sensitive depending on the choice of the observed characteristics and coefficients, we can rewrite the decomposition equation as follows: DUS − DKorea = (XUS − XKorea )βUS + XKorea (βUS − βKorea ) + (θUS − θKorea )σKorea + θUS (σUS − σKorea ).
(4)
9 Given the assumption that each coefficient is the same, the second term in (2) captures the effect coming from
differences in the coefficients between gender groups. 10 Detailed descriptions of their empirical implementation are skipped here in this paper. The interested readers will find the details in Blau and Kahn (1996).
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Table 2 Educational attainment for US and Korea, 2004 Variable High school incomplete High school graduate Some college College graduate or more
US
Korea
Male
Female
Male
Female
9.31 31.73 27.13 31.81
6.33 30.91 30.94 31.79
12.54 44.27 12.28 30.91
22.87 43.42 16.41 17.30
In this alternative, the difference in characteristics of Korean labor market XKorea is incorporated instead of US labor market qualifications in the second term in (3) and for the first term in (3), US coefficients βUS is substituted for βKorea . Using this alternative, the results will remain almost identical. 4. Gender differences in measured characteristics In this section, we will briefly go over the gender differentials in human capital accumulation for each country. First, the data for Korea show relatively lower experience—particularly firmspecific human capital accumulation—for women. This does not appear in the US data. Most notably, Korean working women possess much lower current job tenure than men on average in the EAPS sample. As shown from Table 1, women’s average tenure is lower by about 3.5 years than men’s. Furthermore, this tenure gap is widened to about 6 years if the sample is restricted to people aged 40 years old or more. In addition, there is an approximate three-year age gap between Korean male and female workers. Note that we have to allow for the mandatory military service (about 3 years) for Korean men. As can be seen in Table 1, despite no difference in the educational achievement between the US male and female workers, Korean female workers’ educational level is lower than their male counterparts. Further detailed breakdowns of the educational attainment for the United States and Korea are summarized in Table 2. The most striking difference is in women’s share of the 4-year or higher college graduates. While the proportion of college graduates are the same between male and female workers in the United States, the share of college graduate for Korean female workers is notably lower than that for Korean male workers: 30.9 percent for males and 17.3 percent for females. Given the (empirical) consensus that whether an individual has a college education tremendously affects lifetime earnings, considerably lower attainment of 4-year (or higher) college diplomas by Korean women relative to Korean men could be a possible explanation for the higher gender earnings gap in Korea. In Korea, female workers tend to be in the relatively less unionized jobs compared to male workers.11 As noted in Table 1, Korean female workers possess relatively lower measured human capital compared to US female workers when women’s labor market characteristics are compared to their male counterparts. However, given the fact that our measured variables of human capital are limited to potential labor market experience, educational level, and job tenure, it may be worth looking at each country’s Labor Force Participation Rate (LFPR) to examine the differences in the strength of women’s labor market attachment. For women aged 15 to 60 in 2004, the 11 Fortunately, the EAPS asks the respondents specifically about their union status and whether or not they are really union members.
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Table 3 Wage inequality for US and Korean Men, 2004 US
Korea
Log wages 90–10 90–50 50–10 Standard deviation
1.501 0.711 0.789 0.569
1.567 0.773 0.791 0.584
Log wage residuals 90–10 90–50 50–10 Standard deviation
1.210 0.504 0.705 0.440
1.087 0.450 0.739 0.396
Notes. Log wage residuals are estimated from pooled wage regressions for each country. In each regression, several explanatory variables—education, potential labor experience, experience square, tenure, union, marital status, occupation, and industry dummies—are included.
LFPR is about 51.3 percent in Korea, compared to about 68.9 percent in the United States.12 Nonetheless, it is hard to conclude that women’s relatively higher LFPR in the United States leads to a higher female–male earning ratios compared to Korea simply on the basis of an indication of strong attachment to the labor market. For instance, as Juhn (2003) pointed out, the dropouts of female workers at the lower tail of the wage distribution would raise the female–male earnings ratios. 5. Differences in wage structure As pointed out by several studies (e.g., Blau and Kahn, 1996) overall wage inequality can play an important role in the gender earnings gap. Interestingly, the examination of wage inequality for the United States and Korea presents similar patterns between the two countries. Male wage inequality is shown to be the same for the United States and Korea based on several measurements of inequality. As shown in Table 3, in the 90th–10th percentile log earnings differentials, the difference is 1.501 in the United States and 1.567 in Korea. The remaining log earnings differentials (90th–50th and 50th–10th differences) also show the similar gaps between the two countries. Kim and Topel (1995) propose that Korea is an example of rapid industrialization combined with sharply declining wage inequality. In their study, they argued that the rapid growth of educational accumulation ultimately reduced overall wage inequality.13 Furthermore, according to the data, this feature of wage inequality in Korea does appear to be found even when several human capital factors—potential market experience, job tenure, education, occupation, and industry dummy variables—are controlled for. If we examine the log wage residuals differentials, as shown from the lower part of Table 3, the 90th–10th differentials appears to be even slightly higher in the United States by 0.123 log points than Korea. This implies that the wage inequality that affects wage structure does not appear to be important in explaining a larger gender earnings gap in Korea. 12 A maximum age of 60 years is set to exclude retired people. 13 Furthermore, the reduced share of the unskilled labor force due to a rapid growth of educational attainment also might
have raised their prices as well.
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Table 4 Wage structure for US and Korea, 2004
Gender log wage differential Gender wage ratio (%) Mean female percentilea Male residual standard deviationb Female residual standard deviation Mean female residualc Mean female residual percentiled
US
Korea
0.2447 76.211 43.19 0.4468 0.4138 −0.1645 44.05
0.5000 59.666 29.54 0.3950 0.3683 −0.1937 39.51
a Mean percentile rankings of the female wages in the male wage distributions. b Calculated from male residuals in the pooled male–female wage regressions. Each regression includes several ex-
planatory variables such as education, labor experience, experience square, job tenure, union, marital status, occupation and industry dummies. c Mean male residual is normalized to a zero. d Mean female residual percentile in the male distribution of wage residuals.
Fig. 1. Cumulative female wages in the male wage distribution: US and Korea, 2004.
In order briefly to examine the possible role of the wage structure on the relatively higher gender earnings gap in Korea, the mean percentile ranks of women in the male wage distributions for the United States and Korea are presented in Table 4.14 While the US women’s mean percentile is positioned at the 43th percentile of men’s earnings, Korean women take a relatively lower position at the 29th percentile. Figure 1 also shows the cumulative female wage functions in the male wage distributions. As can be seen from Fig. 1, wages of Korean female workers are positioned relatively lower in the male wage distribution when we compare the female wage distribution 14 Mean percentile ranks are measured by assigning each women ten percentile ranks based on male wage distributions each for the United States and Korea. More detailed examination of Table 6 is shown in Section 6.
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to the male wages in the United States. For example, in the United States about 17 percent of women fall in the first deciles of the male wage distribution and nearly 33 percent fall in or below the second deciles. In Korea, approximately 40 percent of women belong to the first deciles of the male wage distribution and about 60 percent fall in or below the second deciles of the male distribution. Therefore, the relatively higher gender earnings gap in Korea seems likely to be due to the fact of the lower positions of Korean women in the male wage distributions rather than differences in wage inequality. More detailed empirical analysis will be performed in the following sections. 6. Main results Before we examine detailed decomposition results, Table 4 gives us an overview of the relationship between the wage structure and the gender earnings gap in each country. As noted, the gender earnings gap is higher in Korea (the log wage differential is 25.5 points higher) and Korean female workers have a relatively lower position in the male wage distributions when we compared the US female workers to their male counterparts (see Fig. 1).15 Notably, both for US male and female workers, there exists higher variation in the wage residuals calculated after controlling for several human capital factors as well as occupation and industry dummies—the US male and female residual standard deviations are 0.4468 and 0.4138, respectively, whereas for Korean workers they are 0.3950 and 0.3683. One way of analyzing the sources of the gender earnings gap is to examine the distributions of wage residuals. First, for each country mean female residuals are calculated using pooled wage regressions.16 Note that mean male residuals are always zero by construction and mean female residuals are obtained by subtracting from mean male residuals. This measure is slightly lower in the United States than that in Korea: −0.1645 for the United States and −0.1937 for Korea. This result suggests that the degree to which observed characteristics explain the gender earnings gap is substantially different in the two countries. For example, 32 percent of the gender earnings gap can be explained by observed characteristics in the United States, whereas human capital factors can explain 62 percent of the gender earnings gap in the Korean labor market. The observed difference in the role of human capital components for explaining the gender earnings gap for two countries is very interesting. A possible explanation is that there exists more severe labor market discrimination against women in the US labor market because wage residuals measure partly discrimination in the labor market as well as unmeasured characteristics and their prices. Another possibility is that Korean female workers with observably similar skills might have the same real levels of human capital compared to men. For example, although US female workers have similar labor market experience as male workers, female workers might have weaker labor force market attachment, possibly due to pregnancy and/or child care issues, which is not directly reflected in the data.17 If this argument holds true, as Mincer and Polacheck (1974) argued, a substantial part of the gender earnings gap can be explained by differences in the “actual” labor market experience. 15 Raw gender wage ratios show similar results but the gender gap is a bit of lower than those as measured by log wage
differentials. This paper mainly focuses on gender log wage differentials. 16 While not shown here, results from using male wage regressions are very similar. 17 Relatively lower current job tenure by Korean female workers might support this hypothesis unless Korean female workers move jobs frequently without any detachment in the labor market. A more correct measure of labor market experience is beyond the scope of this paper.
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6.1. Wage regression results Since the decomposition results can be different depending on the selected coefficients, I employ two separate wage equations—pooled and male only—to check the robustness of the results.18 Table 5 represents the results of separate wage regressions by two groups (pooled and male only) for each country. Although some variations exist in the estimated coefficients across the studied groups within one country, the regression results show very similar outcomes in the size of the estimated coefficients. The signs of the estimated coefficients are largely as expected. Main decomposition results are proven to be stable regardless of sample groups included in wage regressions. As can be seen in Table 5, returns to job tenure, union membership, and marriage are higher in Korea than those in the United States while returns to education and labor market experience are lower in Korea.19 Those differences found in the estimated coefficients between two countries, however, are not very influential in explaining the gender earnings gap for the decomposition analysis. The reason is that the variations in estimated coefficients will be multiplied by differences in the observed characteristics (see Eq. (3) in Section 3), which are very small in the United States. Next, wage differentials associated with occupation and industry are examined. There seem to be substantial differences in the estimated wage differentials related with occupation and/or industry between the US and Korean labor market. In particular, most of the estimated coefficients in Korean industry and occupation sectors are relatively higher when compared with the coefficients estimated from the United States. For instance, US workers who work in administrative support occupations have no earnings premium relative to those who work as laborers. In contrast, Korean workers who work in administrative occupations have about 30 percent wage premium compared with Korean laborers. Since the administrative occupation is dominated by female workers in both countries, a substantially higher wage premium for this occupation in Korea might lead to a narrowing of the gender earnings gap.20 This will play a dominant (positive) role in the observed prices in the following decomposition analysis. As in the above occupation analysis, the observed prices related with several industry sectors are examined and they will lead to narrow the gender earnings gap in Korea. 6.2. Decomposition results The decomposition results of the gender earnings gap for the United States and Korea are summarized in Table 6. The coefficients estimated from pooled wage regressions are employed for decomposition analysis. As can be seen in the bottom row, the total difference in the gender earnings gap (US–Korea) is −0.2553 log wage points, showing a relatively higher gender earnings gap in Korea. Among this gap, each numbered column shows its contribution to relative gender earnings gap. Row (1) shows the contribution of the observed characteristics to the 18 Previous studies used only estimation results from male wage regressions since their estimated coefficients are as-
sumed to be more stable across countries. 19 While not shown here, it may be interesting that returns to job tenure for female workers are 10 percent higher than male workers in Korea. Using the US data, most previous researches did not find any differences in returns to job tenure between men and women, whereas Kuhn (1987) found greater returns to job tenure for women in Canada. 20 Notice that the contributions of returns to each occupation on gender earning differentials depend on the male– female distributions in each occupation as well as the differences in the magnitudes of the observed prices between two countries.
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Table 5 Wage regression results for US and Korea, 2004 US Job tenure Experience Education Union Marriage Occupation dummies Executive, administrative, and managerial Professional specialty Service Sales Administrative support including clerical Construction and extraction Installation, maintenance, and repairs Industry dummies Mining and construction Transportation, communications, and public utilities Trade (retail and wholesale) Financial, insurance, and real estate Educational and health services Professional, scientific, and technical services Leisure and hospitality Other services including medical, social, and public administration Adjusted R-squared Number of observations
Korea
Pooled
Men
Pooled
Men
0.010** (0.000) 0.020** (0.001) 0.080** (0.002) 0.097** (0.013) 0.054** (0.010)
0.009** (0.001) 0.023** (0.002) 0.072** (0.003) 0.112** (0.018) 0.093** (0.014)
0.035** (0.000) 0.015** (0.001) 0.054** (0.002) 0.145** (0.008) 0.091** (0.007)
0.028** (0.001) 0.023** (0.001) 0.033** (0.002) 0.124** (0.009) 0.143** (0.010)
0.359** (0.024) 0.190** (0.024) −0.071** (0.024) 0.146** (0.027) −0.068** (0.021) 0.166** (0.025) −0.059** (0.025)
0.438** (0.029) 0.305** (0.028) −0.027 (0.029) 0.243** (0.032) −0.013 (0.028) 0.149** (0.025) −0.022 (0.026)
0.573** (0.028) 0.389** (0.013) 0.027* (0.013) 0.136** (0.016) 0.314** (0.012) 0.245** (0.012) 0.188** (0.011)
0.604** (0.035) 0.424** (0.016) 0.209** (0.022) 0.163** (0.023) 0.382** (0.015) 0.223** (0.014) 0.160** (0.014)
−0.005 (0.025)
−0.023 (0.027)
0.151** (0.011)
0.040** (0.012)
0.024 (0.021) −0.157** (0.020) −0.046* (0.023) −0.261** (0.018) −0.031 (0.020) −0.258** (0.025) −0.105** (0.021) 0.399 8922
0.034 (0.026) −0.128** (0.025) 0.045 (0.032) −0.255** (0.027) −0.005 (0.025) −0.268** (0.036) −0.091** (0.026) 0.412 4619
−0.012 (0.012) −0.077** (0.011) 0.132** (0.013) −0.042** (0.011) 0.038** (0.011) −0.019 (0.025) −0.031** (0.010) 0.541 19,323
−0.102** (0.014) −0.091** (0.014) 0.029 (0.017) 0.044** (0.018) −0.015 (0.014) −0.115** (0.035) −0.067** (0.013) 0.517 11,622
Notes. Laborers and manufacturing are the omitted occupation and industry dummy, respectively. The numbers in parentheses are heteroskedasticity consistent standard errors. ** Significant at 99 percent.
* Significant at 95 percent.
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Table 6 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.3084 −0.0688 −0.0967 −0.0312 −0.0978 −0.0087 0.0814 −0.0071 −0.0198 −0.0003 0.0637 0.0487 −0.0527 0.0235 −0.2270 −0.0292
Total difference in gender earnings gap
−0.2553
Note. The estimated coefficients from pooled male–female wage regressions are used for the decomposition analysis.
gender earnings gap between the two countries. The number is −0.3084, indicating that Korean female workers achieved relatively lower human capital such as education, tenure, and potential labor market experience. This caused a relatively higher gender earnings gap in Korea than the United States. Particularly, among the several observed characteristics, current job tenure has the biggest impact on the relative gender earnings gap in Korean labor market. In contrast to the observed characteristics, the effect of cross-country differences in the observed prices (which corresponds to the second term in Eq. (3)) lowers the gender earnings gap in Korea. As can be seen, occupation and industry seem to play important roles (resulting in lowering the gender earnings gap in Korea) among the prices of measured characteristics.21 Based on results from rows (1) and (2), the explained part as indicated by the observed characteristics and their prices is strongly in favor of the US female workers and explains most of gender earnings gap between the two countries. The third and fourth terms in the decomposition equation (3) are respectively labeled as “residual effects” and “unobserved prices” in Table 6. Broadly speaking, there are three possible candidates that may constitute the unexplained part of the decomposition analysis: labor market discrimination, unmeasured characteristics, and residual wage inequality. From the empirical point of view, it is best to decompose the unexplained part into two parts—residual effects and unobserved prices—as performed in this paper. The residual effects in row (3) have a negative impact on relative female earnings in Korean labor market. Residual effects are empirically implemented by using differences in the mean female residual between two countries evaluated as one country’s residual standard deviation of wages. Negative residual effects can be explained by the relatively higher mean female residuals in the Unites States as shown in Table 4. This negative result can reflect two situations. One is that there exists more severe labor market discrimination 21 As mentioned earlier, administrative support among occupation sectors has the biggest effect. Among industry sectors, educational, medical, and public services play important roles.
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Table 7 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.2701 −0.0422 −0.0776 −0.0452 −0.0883 −0.0203 0.1141 −0.0104 −0.0152 0.0000 0.0671 0.0792 −0.1559 0.0556 −0.1560 −0.1003
Total difference in gender earnings gap
−0.2553
Note. The estimated coefficients from male wage regression are used for the decomposition analysis.
against women in Korea. Another is that Korean female workers might have achieved relatively lower unmeasured labor market qualifications than those by US female workers. However, it is hard to sort out these two possible factors. Lastly, row (4) represents the unobserved prices component of the unexplained part. If each country’s residual wage inequality is the same, this measure will be zero. As we observed, lower wage inequality in Korea (in terms of residual wage) produced a slightly positive impact on the relative female earnings in Korea. However, the effects of the unobserved prices in our decomposition are negligible. Taken as whole, the explained part in the decomposition analysis is strongly in favor of the US female workers and the unexplained part also has a favorable effect on the US female workers. Having separately examined each component in the decomposition analysis, the differences in the observed characteristics play key roles in explaining the relatively higher gender earnings gap in Korea. Here, the decomposition results may be very sensitive to the choice of the estimated coefficients from regression models. While coefficients estimated from pooled wage regressions are used in Table 6, the results using coefficients from male wage regressions are also presented in Table 7. Because of a little variation in the estimated coefficients between pooled and male wage regressions as shown in Table 5, using a male wage regression produces similar results while the extent of unexplained part increases a bit. In addition, using the alternative decomposition method shown in Eq. (4) does not change the main results. These results are shown both in Table 8 and Table 9. 7. Concluding remarks In this paper, I examined the difference in the gender earnings gap between the United States and Korean labor market. Using a data set representative of the population of both countries, the gender earnings gap is substantially higher in Korea than in the United States. Decomposition analysis shows that a significant part of the difference in the gender earnings gap between
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Table 8 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.1618 −0.1019 −0.0285 −0.0398 −0.0133 −0.0166 −0.0652 0.0260 −0.0880 0.0082 −0.0207 0.0233 −0.0527 0.0235 −0.2270 −0.0292
Total difference in gender earnings gap
−0.2553
Notes. Alternative decomposition method (see Eq. (4)) is employed. See the text for the details. The estimated coefficients from pooled male–female wage regressions are used for the decomposition analysis.
Table 9 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.1675 −0.0907 −0.0253 −0.0463 −0.0269 0.0172 0.0115 0.0381 −0.0675 0.0010 0.0058 0.0416 −0.1559 0.0556 −0.1560 −0.1003
Total difference in gender earnings gap
−0.2553
Notes. Alternative decomposition method (see Eq. (4)) is employed. See the text for the details. The estimated coefficients from male wage regressions are used for the decomposition analysis.
these two countries arises from differences in the observed characteristics of women. Unmeasured characteristics and labor market discrimination are factors working slightly against Korean female workers compared to US female workers. These results are robust regardless of which estimated coefficients are used. Relatively lower educational attainment and job tenure by Ko-
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rean female workers compared to their male counterparts play dominant roles in explaining the observed higher gender earnings gap in Korea. In order to analyze the gender earnings gap between the United States and Korea, the decomposition methodology by Blau and Kahn (1996) is employed in the paper. This framework has the advantage of focusing on the role of the wage structure as an additional factor influencing the gender earnings gap. Contrary to what we expected, wage inequality between the two countries is very similar. While labor market structure, reflecting wage inequality and/or discrimination, seems to be slightly against Korean female workers compared to US female workers, it does not seem to play an important role in explaining the higher gender earnings gap in Korea. The differences in labor market qualifications are the major sources for the difference in the gender earnings gap between the United States and Korea. Finally, the following question emerges as a future research agenda: “Why do Korean female workers have significantly lower current job tenure?” One possible speculation is that Korean female workers possess relatively weaker labor force attachment, possibly due to pregnancy and/or child care issues, which is not directly reflected in the data. The atrophy in firm-specific human capital as well as labor market experiences might be one reason why Korean female workers command relatively lower earnings, but more systematic analysis on this issue should be conducted. Acknowledgments I would like to thank Zooyob Anne, Kelly Bedard, Olivier Deschenes, and participants in the labor lunch seminar at University of California, Santa Barbara. Special thanks to my advisor Peter Kuhn for many helpful comments and mentoring. References Blau, F., Kahn, L., 1996. Wage structure and gender earnings differentials: An international comparison. Economica 63, S29–S62. Gronau, R., 1988. Sex-related wage differentials and women’s interrupted careers—The chicken or the egg? J. Lab. Econ. 6 (3), 277–301. Juhn, C., 2003. Labor market dropouts, selection bias, and trends in black and white wages. Ind. Lab. Relat. Rev. 56, 643–662. Juhn, C., Murphy, K., Pierce, B., 1991. Accounting for the slowdown in black–white wage convergence. In: Kosters, M. (Ed.), Workers and Their Wages. AEI Press, Washington, DC, pp. 107–143. Kim, D., Topel, R., 1995. Labor markets and economics growth: Lessons from Korea’s industrialization, 1970–1990. In: Reeman, R.B., Katz, L.F. (Eds.), Differences and Changes in Wage Structures. University of Chicago Press, Chicago, pp. 227–264. Kuhn, P., 1987. Sex discrimination in labor markets: The role of statistical evidence. Amer. Econ. Rev. 79 (3), 536–543. Mincer, J., Polacheck, S., 1974. Family investments in human capital: Earnings of women. J. Polit. Econ. 82 (2), S76– S108.
Why is the gender earnings gap greater in Korea than in the United States? Donghun Cho ∗ Korea Labor Institute, 9F Korea Federation of Small Business Bldg, 16-2 Youido-dong Yongdungpo-gu, Seoul, 150-740, South Korea Received 17 March 2006; revised 4 October 2006 Available online 21 March 2007
Cho, Donghun—Why is the gender earnings gap greater in Korea than in the United States? It has long been recognized that the gender earnings gap varies across countries. This paper examines the relatively higher gender earnings gap found in the Korean labor market compared to the US labor market. Using the data set representative of the population for both countries, I found that the significant part of the differences in the gender earnings gap simply arise from the differences in the observed characteristics of women among two countries. In particular, relatively lower labor market experience, current job tenure, and educational attainment by Korean female workers play dominant roles in explaining the observed higher earnings gap. In addition, wage structure and labor market discrimination seem to be against Korean female workers compared to US female workers. J. Japanese Int. Economies 21 (4) (2007) 455–469. Korea Labor Institute, 9F Korea Federation of Small Business Bldg, 16-2 Youido-dong Yongdungpo-gu, Seoul, 150-740, South Korea. © 2007 Elsevier Inc. All rights reserved. JEL classification: J16; J31; J80 Keywords: Gender earnings gap; Wage structure; Wage inequality; Decomposition
1. Introduction It has long been recognized that the gender earnings gap varies across countries. In their international comparison, Blau and Kahn (1996) analyzed the gender pay gap in ten industrialized nations and found that wage-setting institutions that affect overall wage inequality played * Fax: +82 2 782 0849.
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important roles in explaining the relatively higher US gender gap than in most other developed countries. For example, the more decentralized wage-setting system in the US labor market leads to relatively higher gender earnings differentials since centralized wage bargaining conducted by many European countries arguably reduces the extent of wage variation across industries, persons, and firms. This paper explores South Korea (henceforth Korea) as an international comparison with the United States to analyze the gender earnings gap. There are a couple of reasons to choose the Korean labor market: First, Korea experienced rapid economic growth during the 1970s and 1980s, which produced some interesting labor market phenomena worth studying.1 For instance, Korea produced an example of rapid industrialization combined with declining wage inequality due to an overall rapid growth of educational achievement. Second, being geographically located in Asia, Korea might reflect some characteristics of institutions and culture that help to understand international labor market structure and labor decisions within households. While US women earn approximately 76 percent of men’s earnings, Korean women earn only about 50 percent of Korean men’s earnings in 2004.2 Before examining the possible sources of a higher gender earnings gap in Korea than in the United States, one’s perception may be that there might exist more serious gender discrimination in the Korean labor market because of its economic position as one of the developing countries. If this anticipation holds true, more severe labor market discrimination would impose more penalties to Korean women at work, resulting in a higher gender earnings gap. Most of the empirical studies on the gender earnings gap have focused both on gender differences in observed characteristics and on gender discrimination at the workplace. However, Blau and Kahn (1996) began to focus on the role of the wage structure—the prices of unmeasured labor market skills and wage inequality as an additional factor when they analyzed the gender earnings gap across countries.3 Following their empirical strategy, several factors associated with the wage structure are investigated in order to address the higher gender earnings gap in Korea. While there seems to be more gender discrimination against Korean women, the majority of the differences in the gender earnings gap simply arise from the differences in the observed characteristics of women among the two countries. For example, the average tenure of Korean women in the sample of workers is much lower compared to men, whereas US women had nearly the same job tenure as men. Furthermore, Korean women’s educational attainment, especially at the four year college level, is substantially lower than men’s while the sample in the US data shows the same educational achievement between men and women. Overall wage inequality, which can be viewed as one of the factors generating a higher gender earnings gap, is found to be the same between the two countries. However, the wage structure and labor market discrimination seem to be slightly against Korean female workers. This paper is organized as follows. Section 2 describes data sources and presents a brief overview of the gender earnings gap in the United States and Korea. Section 3 provides the analytical framework employed in the regression examples of Section 6. Section 4 documents gender 1 Kim and Topel (1995) presented several interesting results about the Korean labor market structure associated with economic growth. For instance, despite rapid economic growth, serious income inequality had not been observed. They also argued that aggregate wage growth was neutral among employment sectors. 2 Note that these calculations are based on log earning differentials: weekly earnings used for the US workers and hourly earnings calculated for the Korean workers. 3 Their methodology was originally developed by Juhn et al. (1991) for analyzing slowdown in black–white wage convergence.
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differences in measured characteristics. Section 5 describes differences in the wage structure, highlighting the roles of overall wage inequality and women’s relative wage position. Section 6 presents main empirical results and Section 7 concludes. 2. Data Among many previous studies on the gender earnings gap, Mincer and Polacheck (1974) argued that women earn less than men because female workers’ weaker labor market attachment leads to lower human capital accumulation (or depreciation of acquired skills)—both firmspecific and general. As a consequence, they claimed that about 70 percent of the gender earnings gap can be explained by differences in the labor market experience.4 According to Mincer and Polacheck, it is therefore crucial to have information on actual labor market experiences for an analysis of the gender earnings gap since potential work experience (e.g., age-7-education) can be a poor proxy for measuring women’s actual work experience.5 I choose to use the following two data sets which include information on current job tenure for both countries: The February Current Population Survey (CPS) for the United States and the August Economically Active Population Survey (EAPS) for Korea. The EAPS, conducted every month by the Korean National Statistical Office since 1963, is a survey for all persons aged 15 years old and over who are not in the armed forces, prisoners, or foreigners. This survey covers about 33,000 households every month and collects several types of information about individuals’ work activities, such as employment status, working hours and current job tenure. EAPS was designed to examine the Korean labor market, but questions about wages were not originally included. However, questions about wages have been included in every August supplemental survey since 2001. This paper uses the 2004 August supplemental EAPS. This CPS-type Korean labor market data allows the researcher to conduct a comparable analysis the analysis comparable between the US and Korea. The 2004 February CPS was used for the US workers since it contains information on job tenure as well as wages, employment, and demographic characteristics.6 Table 1 shows an overview of the descriptive statistics from the 2004 CPS and EAPS. The sample is restricted to full-time (more than 35 hours of work per week for both countries) and non-agricultural sector workers aged 20–65. Weekly earnings are measured for the US workers and hourly earnings are calculated for the Korean workers.7 Here, I excluded the bottom 1 percent of salary workers for both countries. As shown in Table 1, the gender earnings gap (measured by log earnings differentials) is higher in Korea than in the United States—a 0.25 log point difference in the United States and a 0.50 log point difference in Korea. A more detailed examination of the descriptive statistics follows. 4 In contrast to this human capital explanation, the “feedback” hypothesis suggested by Gronau (1988) argued that lower wages of women, which are mainly caused by labor market discrimination, discourage women strongly from staying in the labor market. This labor market separation ultimately leads to less investment in human capital and lower wage growth among female workers. 5 Mincer and Polacheck (1974) used 1967 National Longitudinal Survey of Work Experience which has retrospective information on the actual work experience. 6 All races are included in the CPS sample. Restricting the sample to non-Hispanic whites produces identical results. 7 To calculate hourly earnings for Korean workers, monthly earnings are divided by the monthly hours worked, which were obtained by multiplying the weekly hours by 4.
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Table 1 Descriptive statistics (means): CPS and EAPS from 2004 Variable
US Male
Log of current wages Job tenure for all ages Job tenure for aged 40 or more Experience Age Education Union Marital status Sample
Korea Female
Male
Female
6.64 (0.57) 8.15 (8.27)
6.39 (0.52) 7.35 (7.58)
6.77 (0.55) 6.60 (7.64)
6.27 (0.49) 3.05 (4.47)
11.41 (9.48) 20.10 (10.91) 40.79 (10.88) 13.68 (2.79) 0.14 (0.35) 0.68 (0.46) 4979
9.99 (8.66) 20.17 (11.40) 41.13 (11.18) 13.95 (2.57) 0.13 (0.33) 0.57 (0.49) 3943
9.47 (9.32) 19.82 (10.92) 39.89 (9.76) 13.06 (2.73) 0.19 (0.39) 0.74 (0.43) 11,622
3.65 (5.64) 17.93 (12.59) 37.01 (10.62) 12.07 (2.89) 0.07 (0.26) 0.57 (0.49) 7701
Notes. Standard deviations are in parentheses. The sample is restricted to full-time workers and non-agricultural sector workers aged 20–65. Weekly earnings used for US workers and hourly earnings used for Korean workers. Bottom 1% of salary workers for both countries is excluded. All races are included in the US sample.
3. Empirical methodology8 Blau and Kahn (1996) developed a method of exploring gender earnings gaps across countries, originally adapted from Juhn et al. (1991). Their distinction from previous studies has focused on wage structure as an additional important factor affecting the gender earnings gap. Consider the following (male) wage equation for individual i in country j : Eij = Xij βj + σj θij ,
(1)
where Eij is the log wages; Xij is a vector of characteristics; βj is a vector of coefficients; θij is a standardized residual; and σj is the country’s residual standard deviation of wages. Here, I assume that βs are the same for men and women within a country. θij will reflect the difference in how each unmeasured characteristic is treated (i.e. measuring relative wage levels of men and women after controlling for the observed characteristics). Note that the mean male residual is normalized to be a zero. Using (1), the male–female earnings gap for country j can be written as Dj = Emj − Efj = Xj βj + σj θj ,
(2)
where the m and f subscripts present each gender group (male and female), Xj and θj are gender differences in measured characteristics and standardized residuals respectively. Traditionally, the second term σj θj corresponds to the “unexplained part” in decomposing the gender 8 This section is based on the analytical framework used by Blau and Kahn (1996).
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earning gap—sometimes it can be interpreted as a measure of discrimination against women in the labor market.9 In their studies on gender earnings gaps across countries, Blau and Kahn (1996) interpreted the second term as wage structure—unmeasured prices of skills (θij ) and residual wage inequality (σj ). Then, the gender earnings differentials between the United States and Korea can be decomposed based on (2) DUS − DKorea = (XUS − XKorea )βKorea + XUS (βUS − βKorea ) + (θUS − θKorea )σKorea + θUS (σUS − σKorea ).
(3)
The first term in (3) presents the contribution of US–Korea differences in the observed characteristics among gender groups to the gender earnings gap. For example, relatively lower labor market experiences attained by Korean female workers (i.e. XUS − XKorea < 0) lead to a higher gender earning gap in Korea, all else being equal. The second term shows the contribution of the differences in the returns to the observed labor market qualifications between two countries. For instance, given a (positive) male–female job tenure difference in the US, relatively higher returns to current job tenure in Korea (i.e. βUS − βKorea < 0) would tend to raise the gender earnings gap in Korea. The extent of this contribution depends on the gender distributions of the observed labor market qualifications in the United States. These first two terms may be called the “explained part” of the inter-country decomposition of gender earning gap. Likewise, the third and fourth terms comprise the “unexplained part” which could either be interpreted as the extent of labor market discrimination against women, or as differences in the wage structure (prices of unmeasured skills and residual wage inequality) between two countries. Among these components, there are two possible factors which may constitute the third term in (3). First, labor market discrimination may play some role. Higher labor market discrimination against women in one country will contribute to a higher gender earnings gap in that country after accounting for the measured labor skills. Second, unmeasured characteristics would be reflected in this term since θUS − θKorea measures inter-country differences in the relative percentile ranks of the female wage residuals in the male distribution. However, it is hard to distinguish labor market discrimination from the unmeasured labor market qualifications. Finally, given the same percentile rankings of the female wage residuals, the fourth term in (3) measures the contribution of the difference in male wage residual inequality across the two countries. For instance, if Korea has a higher residual wage inequality than the United States, this probably indicates that the Korean labor market imposes a larger penalty on those in lower wage positions who are more likely to be women, leading to a higher gender earnings gap in Korea. Following Blau and Kahn (1996), I will implement the third and fourth terms by using the entire distribution of male and female residuals from wage regressions for each country.10 Given the concern that decomposition results might be sensitive depending on the choice of the observed characteristics and coefficients, we can rewrite the decomposition equation as follows: DUS − DKorea = (XUS − XKorea )βUS + XKorea (βUS − βKorea ) + (θUS − θKorea )σKorea + θUS (σUS − σKorea ).
(4)
9 Given the assumption that each coefficient is the same, the second term in (2) captures the effect coming from
differences in the coefficients between gender groups. 10 Detailed descriptions of their empirical implementation are skipped here in this paper. The interested readers will find the details in Blau and Kahn (1996).
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Table 2 Educational attainment for US and Korea, 2004 Variable High school incomplete High school graduate Some college College graduate or more
US
Korea
Male
Female
Male
Female
9.31 31.73 27.13 31.81
6.33 30.91 30.94 31.79
12.54 44.27 12.28 30.91
22.87 43.42 16.41 17.30
In this alternative, the difference in characteristics of Korean labor market XKorea is incorporated instead of US labor market qualifications in the second term in (3) and for the first term in (3), US coefficients βUS is substituted for βKorea . Using this alternative, the results will remain almost identical. 4. Gender differences in measured characteristics In this section, we will briefly go over the gender differentials in human capital accumulation for each country. First, the data for Korea show relatively lower experience—particularly firmspecific human capital accumulation—for women. This does not appear in the US data. Most notably, Korean working women possess much lower current job tenure than men on average in the EAPS sample. As shown from Table 1, women’s average tenure is lower by about 3.5 years than men’s. Furthermore, this tenure gap is widened to about 6 years if the sample is restricted to people aged 40 years old or more. In addition, there is an approximate three-year age gap between Korean male and female workers. Note that we have to allow for the mandatory military service (about 3 years) for Korean men. As can be seen in Table 1, despite no difference in the educational achievement between the US male and female workers, Korean female workers’ educational level is lower than their male counterparts. Further detailed breakdowns of the educational attainment for the United States and Korea are summarized in Table 2. The most striking difference is in women’s share of the 4-year or higher college graduates. While the proportion of college graduates are the same between male and female workers in the United States, the share of college graduate for Korean female workers is notably lower than that for Korean male workers: 30.9 percent for males and 17.3 percent for females. Given the (empirical) consensus that whether an individual has a college education tremendously affects lifetime earnings, considerably lower attainment of 4-year (or higher) college diplomas by Korean women relative to Korean men could be a possible explanation for the higher gender earnings gap in Korea. In Korea, female workers tend to be in the relatively less unionized jobs compared to male workers.11 As noted in Table 1, Korean female workers possess relatively lower measured human capital compared to US female workers when women’s labor market characteristics are compared to their male counterparts. However, given the fact that our measured variables of human capital are limited to potential labor market experience, educational level, and job tenure, it may be worth looking at each country’s Labor Force Participation Rate (LFPR) to examine the differences in the strength of women’s labor market attachment. For women aged 15 to 60 in 2004, the 11 Fortunately, the EAPS asks the respondents specifically about their union status and whether or not they are really union members.
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Table 3 Wage inequality for US and Korean Men, 2004 US
Korea
Log wages 90–10 90–50 50–10 Standard deviation
1.501 0.711 0.789 0.569
1.567 0.773 0.791 0.584
Log wage residuals 90–10 90–50 50–10 Standard deviation
1.210 0.504 0.705 0.440
1.087 0.450 0.739 0.396
Notes. Log wage residuals are estimated from pooled wage regressions for each country. In each regression, several explanatory variables—education, potential labor experience, experience square, tenure, union, marital status, occupation, and industry dummies—are included.
LFPR is about 51.3 percent in Korea, compared to about 68.9 percent in the United States.12 Nonetheless, it is hard to conclude that women’s relatively higher LFPR in the United States leads to a higher female–male earning ratios compared to Korea simply on the basis of an indication of strong attachment to the labor market. For instance, as Juhn (2003) pointed out, the dropouts of female workers at the lower tail of the wage distribution would raise the female–male earnings ratios. 5. Differences in wage structure As pointed out by several studies (e.g., Blau and Kahn, 1996) overall wage inequality can play an important role in the gender earnings gap. Interestingly, the examination of wage inequality for the United States and Korea presents similar patterns between the two countries. Male wage inequality is shown to be the same for the United States and Korea based on several measurements of inequality. As shown in Table 3, in the 90th–10th percentile log earnings differentials, the difference is 1.501 in the United States and 1.567 in Korea. The remaining log earnings differentials (90th–50th and 50th–10th differences) also show the similar gaps between the two countries. Kim and Topel (1995) propose that Korea is an example of rapid industrialization combined with sharply declining wage inequality. In their study, they argued that the rapid growth of educational accumulation ultimately reduced overall wage inequality.13 Furthermore, according to the data, this feature of wage inequality in Korea does appear to be found even when several human capital factors—potential market experience, job tenure, education, occupation, and industry dummy variables—are controlled for. If we examine the log wage residuals differentials, as shown from the lower part of Table 3, the 90th–10th differentials appears to be even slightly higher in the United States by 0.123 log points than Korea. This implies that the wage inequality that affects wage structure does not appear to be important in explaining a larger gender earnings gap in Korea. 12 A maximum age of 60 years is set to exclude retired people. 13 Furthermore, the reduced share of the unskilled labor force due to a rapid growth of educational attainment also might
have raised their prices as well.
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Table 4 Wage structure for US and Korea, 2004
Gender log wage differential Gender wage ratio (%) Mean female percentilea Male residual standard deviationb Female residual standard deviation Mean female residualc Mean female residual percentiled
US
Korea
0.2447 76.211 43.19 0.4468 0.4138 −0.1645 44.05
0.5000 59.666 29.54 0.3950 0.3683 −0.1937 39.51
a Mean percentile rankings of the female wages in the male wage distributions. b Calculated from male residuals in the pooled male–female wage regressions. Each regression includes several ex-
planatory variables such as education, labor experience, experience square, job tenure, union, marital status, occupation and industry dummies. c Mean male residual is normalized to a zero. d Mean female residual percentile in the male distribution of wage residuals.
Fig. 1. Cumulative female wages in the male wage distribution: US and Korea, 2004.
In order briefly to examine the possible role of the wage structure on the relatively higher gender earnings gap in Korea, the mean percentile ranks of women in the male wage distributions for the United States and Korea are presented in Table 4.14 While the US women’s mean percentile is positioned at the 43th percentile of men’s earnings, Korean women take a relatively lower position at the 29th percentile. Figure 1 also shows the cumulative female wage functions in the male wage distributions. As can be seen from Fig. 1, wages of Korean female workers are positioned relatively lower in the male wage distribution when we compare the female wage distribution 14 Mean percentile ranks are measured by assigning each women ten percentile ranks based on male wage distributions each for the United States and Korea. More detailed examination of Table 6 is shown in Section 6.
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to the male wages in the United States. For example, in the United States about 17 percent of women fall in the first deciles of the male wage distribution and nearly 33 percent fall in or below the second deciles. In Korea, approximately 40 percent of women belong to the first deciles of the male wage distribution and about 60 percent fall in or below the second deciles of the male distribution. Therefore, the relatively higher gender earnings gap in Korea seems likely to be due to the fact of the lower positions of Korean women in the male wage distributions rather than differences in wage inequality. More detailed empirical analysis will be performed in the following sections. 6. Main results Before we examine detailed decomposition results, Table 4 gives us an overview of the relationship between the wage structure and the gender earnings gap in each country. As noted, the gender earnings gap is higher in Korea (the log wage differential is 25.5 points higher) and Korean female workers have a relatively lower position in the male wage distributions when we compared the US female workers to their male counterparts (see Fig. 1).15 Notably, both for US male and female workers, there exists higher variation in the wage residuals calculated after controlling for several human capital factors as well as occupation and industry dummies—the US male and female residual standard deviations are 0.4468 and 0.4138, respectively, whereas for Korean workers they are 0.3950 and 0.3683. One way of analyzing the sources of the gender earnings gap is to examine the distributions of wage residuals. First, for each country mean female residuals are calculated using pooled wage regressions.16 Note that mean male residuals are always zero by construction and mean female residuals are obtained by subtracting from mean male residuals. This measure is slightly lower in the United States than that in Korea: −0.1645 for the United States and −0.1937 for Korea. This result suggests that the degree to which observed characteristics explain the gender earnings gap is substantially different in the two countries. For example, 32 percent of the gender earnings gap can be explained by observed characteristics in the United States, whereas human capital factors can explain 62 percent of the gender earnings gap in the Korean labor market. The observed difference in the role of human capital components for explaining the gender earnings gap for two countries is very interesting. A possible explanation is that there exists more severe labor market discrimination against women in the US labor market because wage residuals measure partly discrimination in the labor market as well as unmeasured characteristics and their prices. Another possibility is that Korean female workers with observably similar skills might have the same real levels of human capital compared to men. For example, although US female workers have similar labor market experience as male workers, female workers might have weaker labor force market attachment, possibly due to pregnancy and/or child care issues, which is not directly reflected in the data.17 If this argument holds true, as Mincer and Polacheck (1974) argued, a substantial part of the gender earnings gap can be explained by differences in the “actual” labor market experience. 15 Raw gender wage ratios show similar results but the gender gap is a bit of lower than those as measured by log wage
differentials. This paper mainly focuses on gender log wage differentials. 16 While not shown here, results from using male wage regressions are very similar. 17 Relatively lower current job tenure by Korean female workers might support this hypothesis unless Korean female workers move jobs frequently without any detachment in the labor market. A more correct measure of labor market experience is beyond the scope of this paper.
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6.1. Wage regression results Since the decomposition results can be different depending on the selected coefficients, I employ two separate wage equations—pooled and male only—to check the robustness of the results.18 Table 5 represents the results of separate wage regressions by two groups (pooled and male only) for each country. Although some variations exist in the estimated coefficients across the studied groups within one country, the regression results show very similar outcomes in the size of the estimated coefficients. The signs of the estimated coefficients are largely as expected. Main decomposition results are proven to be stable regardless of sample groups included in wage regressions. As can be seen in Table 5, returns to job tenure, union membership, and marriage are higher in Korea than those in the United States while returns to education and labor market experience are lower in Korea.19 Those differences found in the estimated coefficients between two countries, however, are not very influential in explaining the gender earnings gap for the decomposition analysis. The reason is that the variations in estimated coefficients will be multiplied by differences in the observed characteristics (see Eq. (3) in Section 3), which are very small in the United States. Next, wage differentials associated with occupation and industry are examined. There seem to be substantial differences in the estimated wage differentials related with occupation and/or industry between the US and Korean labor market. In particular, most of the estimated coefficients in Korean industry and occupation sectors are relatively higher when compared with the coefficients estimated from the United States. For instance, US workers who work in administrative support occupations have no earnings premium relative to those who work as laborers. In contrast, Korean workers who work in administrative occupations have about 30 percent wage premium compared with Korean laborers. Since the administrative occupation is dominated by female workers in both countries, a substantially higher wage premium for this occupation in Korea might lead to a narrowing of the gender earnings gap.20 This will play a dominant (positive) role in the observed prices in the following decomposition analysis. As in the above occupation analysis, the observed prices related with several industry sectors are examined and they will lead to narrow the gender earnings gap in Korea. 6.2. Decomposition results The decomposition results of the gender earnings gap for the United States and Korea are summarized in Table 6. The coefficients estimated from pooled wage regressions are employed for decomposition analysis. As can be seen in the bottom row, the total difference in the gender earnings gap (US–Korea) is −0.2553 log wage points, showing a relatively higher gender earnings gap in Korea. Among this gap, each numbered column shows its contribution to relative gender earnings gap. Row (1) shows the contribution of the observed characteristics to the 18 Previous studies used only estimation results from male wage regressions since their estimated coefficients are as-
sumed to be more stable across countries. 19 While not shown here, it may be interesting that returns to job tenure for female workers are 10 percent higher than male workers in Korea. Using the US data, most previous researches did not find any differences in returns to job tenure between men and women, whereas Kuhn (1987) found greater returns to job tenure for women in Canada. 20 Notice that the contributions of returns to each occupation on gender earning differentials depend on the male– female distributions in each occupation as well as the differences in the magnitudes of the observed prices between two countries.
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Table 5 Wage regression results for US and Korea, 2004 US Job tenure Experience Education Union Marriage Occupation dummies Executive, administrative, and managerial Professional specialty Service Sales Administrative support including clerical Construction and extraction Installation, maintenance, and repairs Industry dummies Mining and construction Transportation, communications, and public utilities Trade (retail and wholesale) Financial, insurance, and real estate Educational and health services Professional, scientific, and technical services Leisure and hospitality Other services including medical, social, and public administration Adjusted R-squared Number of observations
Korea
Pooled
Men
Pooled
Men
0.010** (0.000) 0.020** (0.001) 0.080** (0.002) 0.097** (0.013) 0.054** (0.010)
0.009** (0.001) 0.023** (0.002) 0.072** (0.003) 0.112** (0.018) 0.093** (0.014)
0.035** (0.000) 0.015** (0.001) 0.054** (0.002) 0.145** (0.008) 0.091** (0.007)
0.028** (0.001) 0.023** (0.001) 0.033** (0.002) 0.124** (0.009) 0.143** (0.010)
0.359** (0.024) 0.190** (0.024) −0.071** (0.024) 0.146** (0.027) −0.068** (0.021) 0.166** (0.025) −0.059** (0.025)
0.438** (0.029) 0.305** (0.028) −0.027 (0.029) 0.243** (0.032) −0.013 (0.028) 0.149** (0.025) −0.022 (0.026)
0.573** (0.028) 0.389** (0.013) 0.027* (0.013) 0.136** (0.016) 0.314** (0.012) 0.245** (0.012) 0.188** (0.011)
0.604** (0.035) 0.424** (0.016) 0.209** (0.022) 0.163** (0.023) 0.382** (0.015) 0.223** (0.014) 0.160** (0.014)
−0.005 (0.025)
−0.023 (0.027)
0.151** (0.011)
0.040** (0.012)
0.024 (0.021) −0.157** (0.020) −0.046* (0.023) −0.261** (0.018) −0.031 (0.020) −0.258** (0.025) −0.105** (0.021) 0.399 8922
0.034 (0.026) −0.128** (0.025) 0.045 (0.032) −0.255** (0.027) −0.005 (0.025) −0.268** (0.036) −0.091** (0.026) 0.412 4619
−0.012 (0.012) −0.077** (0.011) 0.132** (0.013) −0.042** (0.011) 0.038** (0.011) −0.019 (0.025) −0.031** (0.010) 0.541 19,323
−0.102** (0.014) −0.091** (0.014) 0.029 (0.017) 0.044** (0.018) −0.015 (0.014) −0.115** (0.035) −0.067** (0.013) 0.517 11,622
Notes. Laborers and manufacturing are the omitted occupation and industry dummy, respectively. The numbers in parentheses are heteroskedasticity consistent standard errors. ** Significant at 99 percent.
* Significant at 95 percent.
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Table 6 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.3084 −0.0688 −0.0967 −0.0312 −0.0978 −0.0087 0.0814 −0.0071 −0.0198 −0.0003 0.0637 0.0487 −0.0527 0.0235 −0.2270 −0.0292
Total difference in gender earnings gap
−0.2553
Note. The estimated coefficients from pooled male–female wage regressions are used for the decomposition analysis.
gender earnings gap between the two countries. The number is −0.3084, indicating that Korean female workers achieved relatively lower human capital such as education, tenure, and potential labor market experience. This caused a relatively higher gender earnings gap in Korea than the United States. Particularly, among the several observed characteristics, current job tenure has the biggest impact on the relative gender earnings gap in Korean labor market. In contrast to the observed characteristics, the effect of cross-country differences in the observed prices (which corresponds to the second term in Eq. (3)) lowers the gender earnings gap in Korea. As can be seen, occupation and industry seem to play important roles (resulting in lowering the gender earnings gap in Korea) among the prices of measured characteristics.21 Based on results from rows (1) and (2), the explained part as indicated by the observed characteristics and their prices is strongly in favor of the US female workers and explains most of gender earnings gap between the two countries. The third and fourth terms in the decomposition equation (3) are respectively labeled as “residual effects” and “unobserved prices” in Table 6. Broadly speaking, there are three possible candidates that may constitute the unexplained part of the decomposition analysis: labor market discrimination, unmeasured characteristics, and residual wage inequality. From the empirical point of view, it is best to decompose the unexplained part into two parts—residual effects and unobserved prices—as performed in this paper. The residual effects in row (3) have a negative impact on relative female earnings in Korean labor market. Residual effects are empirically implemented by using differences in the mean female residual between two countries evaluated as one country’s residual standard deviation of wages. Negative residual effects can be explained by the relatively higher mean female residuals in the Unites States as shown in Table 4. This negative result can reflect two situations. One is that there exists more severe labor market discrimination 21 As mentioned earlier, administrative support among occupation sectors has the biggest effect. Among industry sectors, educational, medical, and public services play important roles.
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Table 7 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.2701 −0.0422 −0.0776 −0.0452 −0.0883 −0.0203 0.1141 −0.0104 −0.0152 0.0000 0.0671 0.0792 −0.1559 0.0556 −0.1560 −0.1003
Total difference in gender earnings gap
−0.2553
Note. The estimated coefficients from male wage regression are used for the decomposition analysis.
against women in Korea. Another is that Korean female workers might have achieved relatively lower unmeasured labor market qualifications than those by US female workers. However, it is hard to sort out these two possible factors. Lastly, row (4) represents the unobserved prices component of the unexplained part. If each country’s residual wage inequality is the same, this measure will be zero. As we observed, lower wage inequality in Korea (in terms of residual wage) produced a slightly positive impact on the relative female earnings in Korea. However, the effects of the unobserved prices in our decomposition are negligible. Taken as whole, the explained part in the decomposition analysis is strongly in favor of the US female workers and the unexplained part also has a favorable effect on the US female workers. Having separately examined each component in the decomposition analysis, the differences in the observed characteristics play key roles in explaining the relatively higher gender earnings gap in Korea. Here, the decomposition results may be very sensitive to the choice of the estimated coefficients from regression models. While coefficients estimated from pooled wage regressions are used in Table 6, the results using coefficients from male wage regressions are also presented in Table 7. Because of a little variation in the estimated coefficients between pooled and male wage regressions as shown in Table 5, using a male wage regression produces similar results while the extent of unexplained part increases a bit. In addition, using the alternative decomposition method shown in Eq. (4) does not change the main results. These results are shown both in Table 8 and Table 9. 7. Concluding remarks In this paper, I examined the difference in the gender earnings gap between the United States and Korean labor market. Using a data set representative of the population of both countries, the gender earnings gap is substantially higher in Korea than in the United States. Decomposition analysis shows that a significant part of the difference in the gender earnings gap between
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Table 8 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.1618 −0.1019 −0.0285 −0.0398 −0.0133 −0.0166 −0.0652 0.0260 −0.0880 0.0082 −0.0207 0.0233 −0.0527 0.0235 −0.2270 −0.0292
Total difference in gender earnings gap
−0.2553
Notes. Alternative decomposition method (see Eq. (4)) is employed. See the text for the details. The estimated coefficients from pooled male–female wage regressions are used for the decomposition analysis.
Table 9 Analysis of differences in the gender earning gap: United States and Korea, 2004 Component
Contribution to the US–Korea earnings differential
(1) Observed characteristics Education Tenure Experience Occupation Industry (2) Observed prices Education Tenure Experience Occupation Industry (3) Residual effects (4) Unobserved prices Explained part: (1) + (2) Unexplained part: (3) + (4)
−0.1675 −0.0907 −0.0253 −0.0463 −0.0269 0.0172 0.0115 0.0381 −0.0675 0.0010 0.0058 0.0416 −0.1559 0.0556 −0.1560 −0.1003
Total difference in gender earnings gap
−0.2553
Notes. Alternative decomposition method (see Eq. (4)) is employed. See the text for the details. The estimated coefficients from male wage regressions are used for the decomposition analysis.
these two countries arises from differences in the observed characteristics of women. Unmeasured characteristics and labor market discrimination are factors working slightly against Korean female workers compared to US female workers. These results are robust regardless of which estimated coefficients are used. Relatively lower educational attainment and job tenure by Ko-
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rean female workers compared to their male counterparts play dominant roles in explaining the observed higher gender earnings gap in Korea. In order to analyze the gender earnings gap between the United States and Korea, the decomposition methodology by Blau and Kahn (1996) is employed in the paper. This framework has the advantage of focusing on the role of the wage structure as an additional factor influencing the gender earnings gap. Contrary to what we expected, wage inequality between the two countries is very similar. While labor market structure, reflecting wage inequality and/or discrimination, seems to be slightly against Korean female workers compared to US female workers, it does not seem to play an important role in explaining the higher gender earnings gap in Korea. The differences in labor market qualifications are the major sources for the difference in the gender earnings gap between the United States and Korea. Finally, the following question emerges as a future research agenda: “Why do Korean female workers have significantly lower current job tenure?” One possible speculation is that Korean female workers possess relatively weaker labor force attachment, possibly due to pregnancy and/or child care issues, which is not directly reflected in the data. The atrophy in firm-specific human capital as well as labor market experiences might be one reason why Korean female workers command relatively lower earnings, but more systematic analysis on this issue should be conducted. Acknowledgments I would like to thank Zooyob Anne, Kelly Bedard, Olivier Deschenes, and participants in the labor lunch seminar at University of California, Santa Barbara. Special thanks to my advisor Peter Kuhn for many helpful comments and mentoring. References Blau, F., Kahn, L., 1996. Wage structure and gender earnings differentials: An international comparison. Economica 63, S29–S62. Gronau, R., 1988. Sex-related wage differentials and women’s interrupted careers—The chicken or the egg? J. Lab. Econ. 6 (3), 277–301. Juhn, C., 2003. Labor market dropouts, selection bias, and trends in black and white wages. Ind. Lab. Relat. Rev. 56, 643–662. Juhn, C., Murphy, K., Pierce, B., 1991. Accounting for the slowdown in black–white wage convergence. In: Kosters, M. (Ed.), Workers and Their Wages. AEI Press, Washington, DC, pp. 107–143. Kim, D., Topel, R., 1995. Labor markets and economics growth: Lessons from Korea’s industrialization, 1970–1990. In: Reeman, R.B., Katz, L.F. (Eds.), Differences and Changes in Wage Structures. University of Chicago Press, Chicago, pp. 227–264. Kuhn, P., 1987. Sex discrimination in labor markets: The role of statistical evidence. Amer. Econ. Rev. 79 (3), 536–543. Mincer, J., Polacheck, S., 1974. Family investments in human capital: Earnings of women. J. Polit. Econ. 82 (2), S76– S108.