Vertical and Horizontal Education-Job Mismatches in the Korean Youth Labor Market: A Quantile Regression Approach. Ave, Tempe, AZ85281, U.S.

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1 Vertical and Horizontal Education-Job Mismatches in the Korean Youth Labor Market: A Quantile Regression Approach Hong-Kyun Kim a*, Seung C. Ahn b, Jihye Kim c a Dep t of economics, Sogang university, Seoul , Korea b Dep t of economics, Arizona State University and Sogang University, 699s Mill Ave, Tempe, AZ85281, U.S.A c Dep t of education policy and social analysis, Columbia University, 116 th st, New York, NY10027, U.S.A Abstract In an analysis based on a cohort of Korean college graduates, there was a positive relationship between over-education and horizontal mismatches, and in a subsequent regression analysis disregarding horizontal education-job mismatches (over-education), the wage penalty for over-education (horizontal mismatches) was overestimated. Low-ability groups showed significant overestimation, ranging from 8.3% to 89.5%. According to the quantile regression results, the level of wage penalties for over-education and horizontal mismatches varied according to the worker s ability. The relative importance of these penalties varied according to the worker s ability and gender. Specifically, the wage penalty for horizontal mismatches exceeded that for over-education for low-ability male workers, whereas the wage penalty for over-education exceeded that for horizontal mismatches for female workers regardless of their ability. JEL classification: I20, J20, J21, J23, J24 Keywords: Horizontal Mismatch, Over-Education, Wage Penalty, Quantile Regression, Ability Bias Acknowledgments This work was supported by a National Research Foundation of Korea Grant funded by the Korean government (NRF ). *Corresponding Author, tel: ; hongkyun@sogang.ac.kr 1

2 1. Introduction There are two types of education-job mismatches: horizontal and vertical mismatches. A horizontal mismatch refers to a mismatch between the field of study and the job, whereas a vertical mismatch refers to a mismatch between the level of education and the job (e.g., over-education or under-education). Previous studies of education-job mismatches have typically focused on estimating the negative effects of over-education on wages because recent decades have witnessed the rapid expansion of higher education worldwide. Previous studies have found that wage penalties for over-education range from 13% to 19% of well-matched workers earnings (e.g., Bauer, 2002; Cohn & Ng, 2000; Duncan & Hoffman, 1981; Freeman, 1976; Groot & Maassen, 1995, 2000; Hartlog, 1980, 2000; Rumberger, 1981, 1987; Sicherman, 1991; Verdugo & Verdugo, 1992). Until recently, few studies focused on horizontal mismatches. Robst (2007) was the first to estimate wage penalties for horizontal mismatches, followed by Kelly, O Connell, and Smyth (2010), Nordin, Persson, and Rooth (2008), and McGuiness and Sloane (2009), among others. Such studies have estimated that wage penalties for horizontal mismatches range from 10% to 32%, indicating that horizontal mismatches matter for wages as much as over-education. This paper has two objectives. First, the paper demonstrates that the wage effects of over-education and horizontal mismatches should be estimated jointly, not separately. Almost Previous studies have estimated wage penalties for overeducation or those for horizontal mismatches. Thus, their estimates may be biased if there is a correlation between over-education and horizontal mismatches. For example, a regression analysis disregarding the possibility of a horizontal mismatch may overestimate wage penalties for over-education if horizontal mismatches and over-education are correlated. The results based on a cohort of Korean college graduates indicate a positive correlation between over-education and horizontal mismatches: 0.13 and 0.16 for male and female workers, respectively. 1 These 1 This study classifies horizontal mismatches into complete mismatches and partial mismatches. We report only those correlations between over-education and complete 2

3 correlations are not high enough to cause a multicollinearity problem in the regression analysis but high enough for omitted variable bias if both types of mismatches are not controlled for. No study has analyzed this possibility. Second, this paper examines whether the effects of over-education and horizontal mismatches vary across different levels of ability by taking the quantile regression (QR) approach. Several studies of the wage effects of education-job mismatches have taken this approach to investigate whether the effects of over-education or horizontal mismatches (but not both) on wages vary according to the worker s unobservable ability 2 (e.g., Hartlog, Pereira & Vieira, 2001; Budria & Moro-Egido, 2006; McGuinness & Bennett, 2007; Kelly, O Connell & Smyth, 2010). 3 In particular, McGuinness and Bennett (2007) provided a good rationale for using the QR method to examine the wage effect of over-education. For data from individuals with similar levels of education and experience, individual workers wages with other characteristics controlled are mainly sorted by their unobservable abilities. Workers making more than others with the same level of education or experience are those with a higher level of ability. Thus, the k th conditional quantile of the wage distribution (given a set of explanatory variables) can be viewed as the wage of a representative worker whose ability level corresponds to the k th quantile of the ability distribution. Analyzing the relationships between (conditional) quantiles of the wage distribution and a set of explanatory variables, researchers can identify how each variable influences wages of workers of different levels of ability. As McGuinness and Bennett (2007) emphasized, the use of the QR method is appropriate only when the data on individual workers human capital are generally homogeneous. Otherwise, wages can be sorted by a number of other unobservable human capitals (e.g., on-the-job training), but necessarily by ability. Thus, this study mismatches. 2 Most studies have focused on wage penalties associated with over-education. 3 The PSM (propensity score matching) method has been used as an alternative method to control for the ability effect (e.g., McGuinness & Sloane, 2009). For more information on the PSM method, see Cameron and Trivedi (2009). However, previous studies have typically taken the quantile regression approach. 3

4 considers a cohort of Korean college students who graduated in the same year. To the authors knowledge, Kelly, O Connell, and Smyth (2010) were the first to estimate the wage effects of over-education and field mismatches simultaneously. However, they employed the ordinary least squares (OLS) method. In their QR analysis, they focused on how economic returns on various fields of study varied across different earnings quantiles. Thus, to the authors knowledge, this study is the first to estimate the wage effects of over-education and field mismatches simultaneously by taking the QR approach. Estimating the wage effects of over-education and horizontal mismatches simultaneously is particularly important for the Korean youth labor market for two reasons. First, the Korean system of higher education offers too many fields of study, increasing the probability of horizontal mismatches. Nordin, Persson, and Rooth (2008) showed that wage penalties for horizontal mismatches are more likely for Sweden than for other European countries because the Swedish system of higher education offers an excessive number of majors. They found that for Sweden, mismatched males faced a 32% wage penalty, whereas mismatched females faced a 28% wage penalty. Korea also offers highly specialized fields of study in higher education. The 2003 National Follow-Up Survey of College Graduates by the National Science Foundation of the U.S. reported 146 distinct fields, whereas the 2005 Korea National Follow-Up Survey of College and Graduate Graduates on Economic Activity (KCGEA) reported more than 1,000 distinct fields. There is little reason to expect substantial differences in types of available jobs between Korea and the U.S. Thus, this suggests that wage penalties for horizontal mismatches would be greater for Korea. Second, because approximately 80% of high school graduates in Korea go on to college, 4 the cost of over-education is expected to be very substantial in Korea. The major findings are summarized as follows: For both male and female workers, the estimated wage penalty for one type of mismatch (either a horizontal or 4 For example, 83.4% of high school graduates in Korea went on to college in

5 vertical mismatch) obtained disregarding the other type of mismatch was greater than the corresponding estimate reported regardless of the level of ability, implying that previous studies have overestimated wage penalties. In particular, low-ability groups were more likely to be overestimated due to strong positive correlations between over-education and complete horizontal mismatches. Wage penalty was overestimated by 8.3% to 89.4% for low-ability groups. In addition, the effects of over-education and horizontal mismatches on wages varied according to the level of ability. More specifically, wage penalties for horizontal mismatches exceeded those for over-education for low-ability male workers, whereas wage penalties for overeducation exceeded those for horizontal mismatches for female workers regardless of the level of ability. The rest of this paper is organized as follows: Sections 2 discusses the data and sample characteristics, and Section 3 reviews the QR approach. Section 4 presents the regression results, and Section 5 concludes. 2. Data and sample characteristics We obtained the graduate cohort data from the 2005 KCGEA, which followed individuals who graduated with a degree from a two-year college or higher in 2003 and entered the labor market in the same year. The 2005 KCGEA data represented follow-up survey data obtained in 2005, and thus, the data reflected homogeneous individuals in terms of their education and experience levels. The original data set included approximately 25,000 individuals (approximately 5% of the total number of Korean graduates in 2003), but it contained missing values for many individuals. Thus, we obtained a total of 12,666 observations for the analysis. Among these, 51.28% were male, and 47.80%, 45.62%, and 6.58% had a degree from a two-year college, a four-year college, and a graduate school, respectively. The size of this sample far exceeds that typically considered in previous studies. 5 Thus, this study has an advantage over previous studies in terms 5 No study taking the quantile regression approach has analyzed data from more than 2,000 5

6 of representing the population. In addition, the data provided rich information on respondents economic status as well as individual and family characteristics, which is required for examining wage penalties for education-job mismatches. Table 1 lists the variables for the regression analysis and their definitions. The most important factor in analyzing the effects of education-job mismatches on wages is the relationship between the attained education and the job. Whether a worker in our sample matches his/her job vertically or horizontally is determined through the worker s self-assessment. The over-educated (under-educated) group was composed of respondents who indicated that their final degree were higher (lower) than what their job normally required. The vertically matched group included those respondents whose final degree matched what their job normally required. We assigned those respondents who indicated that their field of study was completely (partially) different from what their job normally required to the completely (partially) horizontally mismatched group. The horizontally matched group was composed of respondents who indicated that their field of study matched what their job normally required. Table 2 shows the summary statistics for selected variables for male respondents. The average age of male respondents was 27.93, and their average annual wage was $21,726. As expected, the average level of education for horizontally (vertically) matched respondents was higher than that for horizontally (vertically) completely mismatched respondents. The difference in the level of education between horizontally matched and mismatched respondents exceeded that between vertically matched and mismatched respondents, although the difference was not significant. In addition, wage penalties for complete horizontal mismatches exceeded those for vertical mismatches. Specifically, wages for completely horizontally mismatched male respondents were 7.7% ($1,695) lower than those for their horizontally matched counterparts. By contrast, wages for overindividuals. For example, McGuinness and Bennett (2007) considered data from 1,255 individuals. 6

7 educated male respondents were 6.9% ($1,515) lower than those for their vertically matched counterparts. Table 3 shows the summary statistics for selected variables for female respondents. The average age of female respondents was 24.69, and their average annual wage was $16,152. Thus, on average, female respondents made less than male respondents. In Korea, female workers are generally younger than their male counterparts because all males must complete two-year military service before entering the labor market. On average, male workers enter the labor market three years after their female counterparts. As in the case of male respondents, the average level of education for horizontally (vertically) matched female respondents was higher than that for horizontally (vertically) mismatched respondents. However, there was a difference in the main source of mismatches between male and female respondents. Unlike for male respondents, for female respondents, wage penalties for over-education exceeded those for complete horizontal mismatches. Specifically, while wages for completely horizontally mismatched female respondents were approximately 9.0% ($1,506) lower than those for their horizontally matched counterparts, wages for over-educated female respondents were 9.3% ($1,534) lower than those for their vertically matched counterparts. Table 4 shows the distributions of vertical matches and mismatches. As shown in the table, 70.4% of all respondents had a job that matched their level of education. In addition, 17.4% were over-educated. The results indicate that the likelihood of a vertical match or mismatch depend on the level of education. Respondents with a high level of education were more likely to be vertically matched and less likely to be over-educated. The likelihood of a vertical match varied according to the respondent s gender. In terms of respondents with a twoyear college degree or a graduate degree, female respondents were more likely to be vertically matched than male respondents (70.3% vs. 65.0% for those with a two-year college degree and 80.2% vs. 75.3% for those with a graduate degree). Table 5 reports the distributions of horizontal matches and mismatches. As shown in the table, 37.4%, 44.2%, and 18.4% of all respondents had a job that 7

8 matched, partially mismatched, and completely mismatched their field of study, respectively. The likelihood of a respondent being horizontally matched or mismatched depended on their gender and/or education level. For each level of education (a two-year college degree, a four-year college degree, and a graduate degree), female respondents were more likely be horizontally matched than male respondents. For example, among those with a two-year college degree, 37.4% of female respondents had a job that matched their field of study, whereas only 28.0% of male respondents did. However, this gender gap narrowed as the level of education increased. For both female and male respondents, those with a high level of education were more likely to be horizontally matched. More than 60% of respondents with a graduate degree had a job that matched their field of study. For both female and male respondents, the likelihood of their being completely horizontally mismatched decreased with their education level. It appears that Korean workers are much more likely to be horizontally mismatched than the US workers are. Studying the effects of horizontal mismatches on earnings, Robst (2007) found that 54.8% of the US workers in his sample had jobs matching their fields of study. 6 One possible reason for this large difference in horizontal matches between Korean and U.S. workers is that Korean colleges and graduate schools offer a much wider range of majors. As discussed earlier, the 2003 National Follow-Up Survey of College Graduates by the National Science Foundation of the U.S.A. reported that U.S. colleges and graduate schools offered 146 distinct fields of study, whereas the 2005 KCGEA reported that Korean institutions offered more than 1,000 distinct fields. We now consider the relationships between wages and education-job mismatches. Table 6 shows the percentages of education-job mismatches for three wage groups. Among those male respondents whose wages were lower than the 3 rd decile (bottom 30%), 24.57% were over-educated, and 24.93% were completely horizontally mismatched. By contrast, among those male respondents whose wages 6 For more detail, see Table 1 of Robst (2007). 8

9 were higher than the 7 th decile (upper 30%), 15.22% were over-educated, and 15.86% were completely horizontally mismatched, indicating that the higher the wage, the less likely a mismatch. Figures 1 and 2 provide a better understanding of the relationships between mismatches and wages. In general, education-job mismatches and wages were inversely related. As shown in Table 5 and Figures 3 and 4, female respondents showed similar patterns. Female respondents with higher wages were less likely to have mismatched jobs. The results for over-education in Table 5 are consistent with the findings of McGuinness and Bennett (2007), who considered data from Northern Ireland. Noteworthy in Table 6 is that for each of the three wage groups, male respondents were slightly more likely to be horizontally mismatched than overeducated. With our early finding that complete horizontal mismatch is slightly more likely to occur than over-education for 2-year and 4-year college graduates, this result casts possibility that wage penalties for complete horizontal mismatches are comparable to those for over-education and thus that horizontal mismatches are as likely as over-education to reduce wages. 3. Quantile Regression We employ the QR method to estimate the wage effects of vertical and horizontal mismatches. Here the -th ( (01, )) conditional quantile of the logarithmic wage is a linear function of a set of individual characteristics (Z): 7 Quant [ Ln(W i ) Z i ] = Zi β, (1) where i indexes individuals. The coefficient vector can be consistently estimated by the minimizer of the function ρ ( β) Ln( Wi ) - Ziβ + (1- ) Ln( Wi )- Ziβ. Ln( W i ) >Zi β Ln( W i ) <Zi β This QR provides a series of snapshots that enable the researcher to assess how the (2) 7 For more details, see Buchinsky (2004) and McGuinness and Bennett (2007). The quantile corresponding to 0. 5 is equal to the median. 9

10 relationship between dependent and independent variables evolves with the distributional segments of the dependent variable. Several studies have used the QR method to examine wage penalties for overeducation. For example, Hartlog, Pereira, and Vieira (2001) analyzed data from Portugal covering the years 1982, 1986 and 1992 and found that although the return on surplus schooling 8 was higher for workers with higher wages, wage penalties for over-education remained relatively constant across various levels of wages. However, their sample of workers had heterogeneous characteristics and various levels of experience, and many substituted informal and unobservable human capital investments (e.g., on-the-job training) for the level of formal education. As McGuinness and Bennett (2007) pointed out, such substitution may bias the estimated wage effect of over-education. Using a graduate cohort data set from Northern Ireland, they examined whether the incidence and wage effect of overeducation are specific to individuals of particular ability levels. In their data, individual characteristics such as education and experience levels were relatively homogeneous. Using such data, they assumed that for a group of workers with the same levels of education and experience, the unobservable ability of a worker would be proportional to his or her wage rankings in the group. Under this assumption, they estimated the relationship between over-education and wages by using the QR method and found that for male workers, wage penalties for over-education were greater for those with low and intermediate levels of ability (i.e., low and medium wages). However, male workers were generally less likely to be penalized than their female counterparts. The penalty was much more pervasive and its size only moderately depended on ability for female workers. Our empirical analysis mainly follows that of McGuinness and Bennett (2007). However, we estimate wage penalties for over-education and horizontal mismatches simultaneously. The empirical analysis is based on the following quantile version of 8 The surplus schooling was measured by the difference between the attained education level and the required education level for a job. 10

11 the Mincerian earnings equation: Quant Ln(W) Z = Xβ+ α D+ γ HM + δ VM + θ Field [ ] j j j j j j j j j=2 j=2 j=2 j=2 where W is the after-tax hourly wage of a worker and X is the vector of the worker s socioeconomic and job characteristics, including Age, Mar, and Jobtype. 9 Equation (3) 10 measures wage penalties for education-job mismatches by 2, 3, 2, and 3. Specifically, 2 and 3 reflect wage penalties for complete and partial horizontal mismatches, respectively, whereas 2 and 3 measure those for over- and under-education, respectively. (3) 4. Empirical results Table 7 shows the OLS estimation results for Equation 3. Wage penalties for education-job mismatches were significant for both male and female respondents. Over-educated male respondents earned less than their vertically matched counterparts by 4.48% (see the estimated coefficient of VM2), and completely horizontally mismatched male respondents earned less than their horizontally matched counterparts by 2.88% (see the estimated coefficient of HM2). In terms of female respondents, the wage penalty for over-education was 7.24% of wages for vertically matched female respondents, whereas the wage penalty for complete horizontal mismatches was 2.14%. Thus, as shown in Table 7, wage penalties for over-education exceeded those for complete horizontal mismatches for both male 9 In addition to Jobtype, firm size and firm type were used as a variable to reflect job characteristics in previous studies (McGuinness & Bennett, 2007; Kelly, O connell & Smyth, 2010), but we did not employ these variables because using them would have excluded many observations and because Jobtype is a much more important variable than these two variables as determinant of wage in Korea. The 38.6% of workers has a temporary job as of 2011 in Korea. Workers with temporary job work the same hour as workers with regular job, but earn a lower wage. 10 The reference group for each binary variable in Equation 3 is the variable corresponding to j=1. 11

12 and female respondents, 11 and the penalty gap was wider for female respondents. The effects of the socioeconomic variables on wages were generally consistent with the expectations. Age and wages had expected quadratic relationships for both male and female respondents. Married respondents earned more than singles, and Jobtype had significant positive effects on wages for both male and female respondents. Those respondents with regular jobs earned more than those with temporary jobs by approximately 24%. Unexpectedly, however, respondents with a four-year college degree earned more than those with a graduate degree (see the estimated coefficients of D2 and D3) for both male and female respondents. This result is consistent with the findings of McGuinness and Bennett (2007). One possible explanation for this counterintuitive result is that this study s respondents were in the early stages of their careers. As McGuinness and Bennett (2007) explained, such young workers are likely to be undergoing job training, where the level of education is a less important determinant of labor productivity. As shown in Table 7, the field of study had some influence on wages. There were some gender differences in the effects of the field of study on wages. Among male respondents, those who majored in business and economics (Field4), engineering (Field7), or health care (Field9) earned more than those who majored in computer and IT (reference group), whereas those who majored in architecture (Field2), liberal arts (Field12), natural sciences (Field13), or visual and performing arts (Field16) earned less than the reference group. Among female respondents 12, only those who majored in business and economics made more than the reference group, whereas those who majored in education (Field6), home economics (Field10), 11 This result is consistent with the findings of Kelly, O Connell, and Smyth (2010), who employed data from Ireland. According to their OLS findings, wage penalties for overeducation and horizontal mismatches were 14.4% and 5.0%, respectively. 12 In our data, none of female workers majored in fields related to health profession. None of female workers graduated from medical or dental schools. There are two reasons. First, a relatively smaller number of female Korean students go to medical or dental schools. Second, we lose a large number of sample observations to use Jobtype as a regressor. When Jobtype is not used, our sample contains a small number of female workers who graduated from health profession related schools. 12

13 liberal arts (Field12), social sciences (Field14), or visual and performing arts (Field16) earned less. In general, the effect of the field of study on wages was weaker for female respondents. The OLS estimates may be the biased ones caused by failing to control for the effect of unobservable ability. Thus, to address the potential bias in the OLS estimates, we reestimated Equation (3) by using the QR method. We considered nine wage deciles. Tables 8 and 9 show the results. Following McGuinness and Bennett (2007), we assumed that, with respondents observable characteristics controlled for, their ability levels would be proportional to their wage rankings. Table 8 reports the QR results for male respondents. 13 In terms of wage penalties for horizontal education-job mismatches, there were significant wage penalties for complete horizontal mismatches (coefficient of HM2) for those respondents of low and middle levels of ability but not for those of high levels of ability. Specifically, wage penalties were significant up to the 5 th (conditional) wage decile ( 0. 5 ), whereas they were not significant for the top three (conditional) wage deciles. This indicates that the wage effect of complete horizontal mismatches was negatively correlated with the level of ability. Partial horizontal mismatches generally had no significant effect on wages. Wage penalties for partial mismatches were marginally significant at the 10% level for the 2 nd ( 0. 2 ) and 3 rd ( 0. 3 ) wage deciles but were nonsignificant for all other deciles. The results for partial mismatches are generally consistent with the OLS results in Table 6. As shown in Table 8, wage penalties for over-education were significant at the 1% level for all wage deciles except for the 1 st and 9 th (see the estimated coefficients of VM2) deciles. The level of wage penalties increased with wage deciles (but only slightly). This result is consistent with the notion that the wage effect of overeducation is not highly correlated with the level of ability. The estimated coefficients of HM2 and VM2 indicate that wage penalties on over-education were higher than those on complete horizontal mismatches for the higher order deciles of earnings ( from 5 th decile to 8 th decile) and lower than those on complete horizontal mismatches for the lower order deciles of earnings ( from 1 th decile to 4 th decile).

14 Thus, in contrast to the OLS results, the wage effect of over-education was weaker (stronger) than that of complete horizontal mismatches for respondents of low (high) levels of ability, implying that the OLS results were biased. This result suggests the possibility that the wage effect of horizontal mismatches is as important as that of over-education. For the 9 th (conditional) wage decile, both over-education and complete horizontal mismatches had nonsignificant effects on wages, indicating that neither over-education nor horizontal mismatches led to wage penalties for respondents with the highest level of ability. This result is consistent with the prediction of Human Capital Theory, which posits that the highest able-graduates with complete horizontal mismatches or over-education are likely to have sufficiently flexible jobs that allow them to earn their marginal productivity regardless of entry requirements such as field mismatches or over-education. 13 Returns on education varied across wage segments. The rate of return on a four-year college degree or a graduate degree was higher for respondents of intermediate and high levels of ability than for those with low levels of ability. This result is consistent with the findings of Harmon, Oosterbeek, and Walker (2003), who considered U.K. data. There are two more things worth noting. First, both the rate of return on a four-year college degree and that on a graduate degree varied widely across wage deciles. This result is consistent with the notion that the rapid increase in the worldwide demand for higher education has led to a substantial increase in heterogeneity in terms of workers ability levels. Second, the rate of return on a four-year college degree exceeded that on a graduate degree for most wage deciles (except for the 8 th and 9 th deciles), which provides support for the OLS results. The QR results for the field of study are roughly similar to the OLS results. The QR results in Table 8 generally show that male respondents who majored in business and economics (Field5), engineering (Field8), or health care (Field10) earned more than the reference group (computer and IT), whereas those male 13 For further details, see Hartlog (1997, 2000) and McGuiness and Bennett (2007). 14

15 respondents who majored in architecture (Field3), liberal arts (Field13), or visual and performing arts (Field17) earned less than the reference group. Among the top 10% of respondents in terms of their wages (the 9 th decile), those who majored in business and economics and health care earned, respectively, 11.18% and 8.22% more than the reference group. This result is consistent with the findings of Kelly, O Connell, and Smyth (2010), who employed data from Ireland. 14 In general, the field of study (except for business and economics and health care) was not an important determinant of wages for the highest earners. Table 9 shows the QR results for female respondents. The effects of education-job mismatches on female respondents wages were different from those for male respondents. First, for female respondents, wage penalties for overeducation exceeded those for complete horizontal mismatches for all wage deciles. Second, wage penalties for over-education were significant for all wage deciles, and there was no clear relationship between the level of wage penalties and the level of ability. This finding is consistent with what McGuiness and Bennett (2007) found from Northern Ireland female college graduates. Human capital theory posits that low-ability workers have incentives to compensate for their low ability by increasing their years of schooling. As a consequence, such workers are more likely to be overeducated than high-ability ones, implying that low-ability workers are more likely to face wage penalties for over-education. In this regard, these results provide no support for human capital theory. Instead, this study s results and McGuiness and Bennett s (2007) findings provide support for assignment theory, which posits that wages are determined by workers characteristics (e.g., the level of education) as well as job characteristics. According to this theory, workers wages and marginal productivity need not be equal because jobs can impose wage ceilings. Third, consistent with Mcguinness and Bennett (2007), the results indicate that wage penalties for over-education were greater for female respondents than for male respondents. Wages were lower for over-educated female respondents than 14 They found that medical and veterinary school graduates earned 28.9% more than those graduating with a degree in arts and humanities (the reference group). 15

16 for their well-matched counterparts regardless of their ability. For example, overeducated female respondents with the lowest level of ability (the 1 st decile) earned 8.76% less than their well-matched counterparts, whereas those with the highest level of ability earned 4.6% less. Fourth, as in the case of male respondents, wage penalties for complete horizontal mismatches were significant only for those female respondents of low and intermediate levels of ability. This indicates that the wage effect of horizontal mismatches was more likely than that of over-education to be influenced by the level of ability. Fifth, the estimation results for the rate of return on education for female respondents are generally consistent with those for male respondents. As shown in Table 9, the rate of return on a four-year college degree or a graduate degree was higher for those of intermediate and high levels of ability than for those of low levels of ability. In addition, the volatility of the returns on both 4-year college and graduate school education was big across ability (or wage) deciles. The rate of return on a four-year college degree exceeded that on a graduate degree, but the difference was smaller for female respondents. Finally, the wage effects of the field of study and other exogenous variables for female respondents were generally similar to those for male respondents, although the effects of the field of study were weaker for female respondents. As discussed earlier, there was a significant positive correlation between vertical and horizontal mismatches. Table 10 shows the correlations between overeducation and complete horizontal mismatches across wage deciles. Low-ability respondents and female respondents showed high correlation coefficients. Thus, estimating wage penalties for over-education (horizontal mismatches) by disregarding horizontal mismatches (over-education) overstated the penalties, particularly for low-ability respondents and female respondents. To examine the extent of this overestimation, we reestimated the wage penalty for each mismatch and compared the results with those in Tables 8 and 9. Tables 10 and 11 show the results for male and female respondents, respectively. For both male and female respondents, the estimated penalty for one type of mismatch (either a horizontal or 16

17 vertical mismatch) obtained by disregarding the other type of mismatch exceeded the corresponding estimate in Tables 8 and 9 regardless of the level of ability, implying that previous studies have overestimated wage penalties. In particular, as expected, the bias was more likely for low-ability respondents, which showed significant positive correlations between over-education and complete horizontal mismatches. For complete horizontal mismatches, wage penalties were overestimated by 8.3% to 17.9% for male respondents, whereas they are overestimated 16.3% to 89.4% for female respondents of low levels of ability (up to the 3 rd decile). For over-education, wage penalties were overestimated by 11.1% to 30.8% for male respondents, whereas they were overestimated by 25% to 13.1% for female respondents of low levels of ability (up to the 3 rd decile). 5. Summary and conclusions Analyzing data from a cohort of Korean college graduates, we have examined how large the wage penalties on horizontal and vertical education-job mismatches are. The QR was used to examine the extent to which these penalties varied across respondents of different levels of ability. Over-education and horizontal mismatches were positively correlated, and thus, we estimated these penalties simultaneously to avoid omitted variable bias, which can lead to the overestimation of wage penalties for mismatches. The main results of this paper are as follows. First, there was omitted variable bias, which was more substantial for low-ability respondents, who showed significant positive correlations between over-education and complete horizontal mismatches. This implies that previous studies have overestimated wage penalties because they have estimated wage penalties for one type of mismatch by disregarding the other type. Second, although both over-education and complete horizontal mismatch had substantial effects on wages for both male and female respondents, their effects on wages varied according to the respondent s gender. More specifically, wage penalties for horizontal mismatches exceeded those for overeducation for male respondents of low levels of ability, whereas wage penalties for 17

18 over-education exceeded those for horizontal mismatches for female respondents regardless of the level of ability. Given that the importance of horizontal mismatches as a source of education-job mismatches has been recognized only recently by economists, these results have an important implication: Horizontal mismatches are as important as over-education as a source of education-job mismatches. Causes of horizontal mismatches remain unclear. However, the results of this study and the findings of Nordin, Person, and Rooth (2008) provide a possible explanation. Nordin, Person, and Rooth (2008) showed that the wage penalty for horizontal mismatches exceeded 30% for Sweden. This study s results also indicate that the wage penalty for a mismatch between the field of study and the job was significant for the youth labor market in Korea, although the penalty for Sweden far exceeded that for Korea. Korea and Sweden both have a system of higher education that offers an excessive number of majors. Thus, these findings suggest that an excessive number of majors can lead to large wage penalties for horizontal mismatches. In this regard, offering fewer fields of study may reduce wage penalties for horizontal education-job mismatches. Finally, wage penalties for over-education were significant for both male and female respondents regardless of the level of ability, whereas those for horizontal mismatches were significant only for those respondents of low and intermediate levels of ability. This suggests that the relationship between complete horizontal mismatches and the level of ability is stronger than that between over-education and the level of ability and that wage penalties for complete horizontal mismatches are more likely to be biased than those for over-education if they are estimated without controlling for ability. References Bauer, T. (2002), Educational mismatch and wages: A panel analysis, Economics of Education Review, 21(3), Buchinsky, M. (2004), Changes in the US wage structure : An application of quantile regression, Econometrica, 62(2),

19 Budria, S., and A.I. Moro-Egido. (2006), Overeducation and wages in Europe: Evidence from quantile regression, Studies on the Spanish Economy (FEDEA), Working paper, No 229. Cameron, A.C., and P.K. Trivedi. (2009), Microeconomerics: Methods and applications, Cambridge University Press. Cohn, E., E. Johnson, and C.Y. Ng. (2000), The incidence of overschooling and underschooling and its effect on earnings in the United States and Hong Kong, Research in Labour Economics, 19, Cohn, E., and C.Y. Ng. (2000), Incidence and wage effects of over-schooling and unders-chooling in Hong Kong, Economics of Education Review, 19(2), Daly, M., F. Buchel, and G. Duncan. (2000), Premiums and penalties for surplus and deficit education: Evidence from the United States and Germany, Economics of Education Review, 19(2), Duncan, G., and S. Hoffman. (1981), The incidence and wage effects of overeducation, Economics of Education Review, 1(1), Freeman, R. (1976), The Overeducated American, Orlando, FL: Academic Press. Groot, W., and H. Maassen van den Brink. (1995), Allocation and the returns to overeducation in the United Kingdom, Universiteit van Amsterdam, Tinbergen Institute Discussion Paper TI Groot, W. and H. Maassen van den Brink. (2000), Over-education in the labor market: A meta-analysis, Economics of Education Review, 19(2), Harmon, C., H. Oosterbeek, and I. Walker. (2003), The returns to education: Microeconomics, Journal of Economic Surveys, 17(2), Hartog, J. (1980), Earnings and capability requirements, Review of Economics and Statistics, 62(2), Hartog, J. (1997), Wandering along the hills of ORU land. In: Heijke, J.A.M. (Ed), Education, training and employment in the knowledge based economy, Macmillan, London (in press). Hartlog, J. (2000), Over-education and earnings: Where are we, where should go? Economics of Education Review, 19(2),

20 Hartlog, J., P.T. Pereira, and J.A.C. Vieira. (2001), Changing returns to education in Portugal during the 1980s and early 1990s: OLS and quantile regression estimators, Applied Economics, 33(8), Kelly, E., P. O Connell, and D. Smyth. (2010), The economic returns to field of study and competencies among higher education graduates in Ireland, Economic of education review, 29, McGuinness, S., and J. Bennett. (2007), Overeducation in the graduate labour market: A quantile regression approach, Economic of Education Review, 26, McGuinness, S., and P.J., Sloane. (2009), Labour market mismatch among UK graduates: An analysis using REFLEX data, Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of labor, May. Nordin, M., I. Persson, and D., Rooth. (2008), Education-job mismatch: Is there an wage penalty?, Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of labor, October. Robst, J. (2007), Education and job match: The relatedness of college major and work, Economics of Education Review, 22(1), Rumberger, R.W. (1981), Overeducation in the U.S Labor Market, New York: Praeger, Rumberger, R.W. (1987), The impact of surplus schooling on productivity and wages, Journal of Human Resources, 22(1), Sicherman, N. (1991), Overeducation in the labor market, Journal of Labor Economics, 9(2), Verdogo, R., and N. Verdugo. (1992), Surplus schooling and earnings: Reply to Cohn and to Gill and Sollberg, Journal of Human Resources, 22 (4), Table 1: Variables and definitions Variable Definition 20

21 Ln(W) Age Mar Jobtype Dj HMj Ln (after-tax hourly wage of workers) The age of workers A binary variable for marriage status: coded as 1 if married and 0 otherwise A binary variable for the type of job: coded as 1 if a regular job and 0 if a temporary job A binary variable for the level of education: a two-year college (j = 1), a four-year college (j = 2), and a graduate school (j = 3) A binary variable for horizontal mismatches: a match (j = 1), a complete mismatch (j = 2), and a partial mismatch (j = 3) VMj A binary variable for vertical mismatches: a match (j = 1), over-education (j = 2), and under-education (j = 3) Fieldj A binary variable for 17 fields of study Table 2: Summary statistics for male workers Age Total Annual wage 1) $21,726 (900.34) Education Year complete mismatch (4.42) (4.35) (1.22) Jobtype 2) 0.87 (0.34) $20,429 (922.36) (1.11) 0.80 (0.40) Horizontal Match partial mismatch (4.45) $21,954 (885.0) (1.17) 0.90 (0.30) match (4.41) $22,124 (900.42) (1.31) 0.87 (0.33) Vertical Match overeducation undereducation (4.42) (3.94) $20,581 $21,406 (902.19) (853.99) (1.22) (1.22) (0.39) (0.34) match (4.43) $22,096 (905.45) (1.22) 0.88 (0.32) * Figures in parentheses are standard deviations. Note: 1) $1=1,100won 2) Jobtype is a binary variable that is coded as 1 (0) if a worker had a regular (temporary) job. Table 3: Summary statistics for female workers Horizontal Match Vertical Match 21

22 Age Annual wage Education year Jobtype Total (4.18) $16,152 (687.42) (1.19) 0.77 (0.42) complete mismatch (4.43) $15,224 (668.27) (1.07) 0.71 (0.45) partial mismatch (3.97) $15,986 (681.87) (1.14) 0.75 (0.43) match (4.25) $16,730 (694.99) (1.27) 0.82 (0.38) (4.18) $14,960 (678.90) (1.19) 0.70 (0.46) overeducation undereducation (4.24) $16,322 (704.71) (1.11) 0.78 (0.41) match (3.80) $16,494 (683.10) (1.20) 0.79 (0.41) * Figures in parentheses are standard deviations. Note: 1) $1=1,100won 2) Jobtype is a binary variable that is coded as 1 (0) if a worker had a regular (temporary) job. Table 4: Distributions of vertical matches and mismatches (%) Under-Education Matched Education Over-Education Male Female Total Male Female Total Male Female Total Total year College 4-year College Graduate School Table 5: Distributions of horizontal matches and mismatches (%) 22

23 Complete Match Partial Mismatch Complete Mismatch Men Women Total Men Women Total Men Women Total Total year College 4-year College Graduat e School Table 6: Over-education, complete horizontal mismatch, and the distribution of wages Male Female Wage group Low wage (wage 3 rd decile) Medium wage (3 rd decile < wage 7 th decile) High wage (wage > 7 th decile) Complete Horizontal Overeducatioeducation Over- horizontal complete mismatch mismatch 23.57% 24.93% 26.86% 24.17% 16.02% 17.60% 21.29% 19.12% 15.22% 15.86% 15.46% 15.59% Figure 1: Complete horizontal mismatches and earnings of male workers 23

24 0.05 % over-education Decile Figure 2: Over-education and earnings of male workers Decile Figure 3: Complete horizontal mismatches and earnings of female workers 24

25 0 % over-education Decile Figure 4: Over-education and earnings of female workers Decile Table7: OLS regression results 25

26 Male Female Age (0.0071) *** (0.0071) *** Age (0.0001)*** (0.0001) *** Mar (0.0109) *** (0.0174) ** D (0.0090) *** (0.0100) *** D (0.0162) *** (0.0201) *** HM (0.0114) ** (0.0123) * HM (0.0089) (0.0102) VM (0.0102) *** (0.0104) *** VM (0.0114) (0.0150) Jobtype (0.0117) *** (0.0101) *** Field (0.0444) (0.0207) *** Field (0.0190) ** (0.0296) Field (0.0380) (0.0433) Field (0.0158) *** (0.0189) * Field (0.0382) (0.0344) Field (0.0291) (0.0207) *** Field (0.0136) *** (0.0237) Field (0.0220) (0.0203) Field (0.0216) ** - Field (0.0297) (0.0236) ** Field (0.0346) (0.0507) Field (0.0282) *** (0.0262) ** Field (0.0277) ** (0.0343) Field (0.0213) (0.0207) * Field (0.0236) (0.0338) Field (0.0175) *** (0.0183) *** Const (0.1211) *** (0.1137) *** R Number of observations Standard errors are in parentheses. Variables whose name contains the term field are 26

27 binary index variables for 17 fields of study, including Field1 = Computer & IT; Field2 = Agriculture; Field3 = Architecture; Field4 = Biological Sciences; Field5 = Business & Economics; Feld6 = Communication; Field7 =Education; Field8 = Engineering; Field9 = Languages; Field10 = Health Care; Field11 = Home Economics; Field12 = Law; Field13 = Liberal Arts; Field14 = Natural Sciences; Field15 = Social Sciences; Field16 = Park/Environment/Resources; and Field17 = Visual & Performing Arts. Field10 was excluded from the regression for female workers because no female in the sample specialized in a field related to health care. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Table 8: Quantile regression results from male workers Deciles of hourly wage 1 th 3 rd 5 th 7 th 9 th Age 0.085*** 0.065*** 0.067*** 0.062*** 0.054*** (0.0107) (0.008) (0.008) (0.009) (0.012) *** *** *** *** ** Age 2 (0.0002) (0.0001) (0.0001) (0.0001) (0.0001) MAR 0.074*** 0.066*** 0.069*** 0.058*** 0.073*** (0.0159) (0.012) (0.012) (0.013) (0.019) 0.085*** 0.111*** 0.119*** 0.137*** 0.100*** D 2 (0.0130) (0.010) (0.010) (0.011) (0.015) *** 0.137*** 0.146*** 0.083*** D 3 (0.0214) (0.017) (0.017) (0.020) (0.029) *** *** *** HM 2 (0.067) (0.012) (0.012) (0.014) (0.020) * HM 3 (0.013) (0.009) (0.009) (0.011) (0.015) * *** *** *** VM 2 (0.014) (0.011) (0.011) (0.012) (0.018) VM 3 (0.016) (0.012) (0.012) (0.014) (0.020) Jobtype 0.449*** 0.258*** 0.211*** 0.175*** 0.137*** (0.016) (0.012) (0.012) (0.015) (0.020) Field * (0.063) (0.047) (0.047) (0.055) (0.076) Field *** * * (0.027) (0.020) (0.020) (0.023) (0.031) Field * (0.054) (0.040) (0.040) (0.047) (0.061) Field *** 0.065*** 0.052*** 0.079*** 0.106*** (0.022) (0.017) (0.017) (0.020) (0.027) Field (0.055) (0.040) (0.040) (0.047) (0.066) Field (0.041) (0.031) (0.031) (0.036) (0.047) Field *** 0.060*** 0.050*** 0.044*** (0.020) (0.015) (0.014) (0.017) (0.023) Field ** ** * 27