Essays on Labor Market and Human Capital - Korea and Germany

Contents Tables...................................................................................................................12 Figures .................................................................................................................15 General Introduction .........................................................................................17 1. What can Job Training do? Is University the Sole Way of Life? – The Effects of School Education and Job Training on Wages in Korea ........................................................................................................25 1.1. Introduction .......................................................................................25 1.2. Education System in Korea ..............................................................28 1.3. Theory, Model and Problems of Estimation...................................31 1.4. Data and Descriptive Statistics ........................................................38 1.5. Estimation Methods and Results .....................................................40 1.5.1. Random Effects Estimation....................................................41 1.5.2. Heckman 2 Step Estimation ...................................................43 1.5.3. Other Findings........................................................................46 1.6. Conclusion..........................................................................................48 References ...........................................................................................................52 Appendix .............................................................................................................56 2. Effects of School Education and Job Training on Earnings in Korea and Germany. A Comparative Study .......................................75 2.1. Introduction .......................................................................................75 2.2. Difference of School Education and Job Training between Korea and Germany ..........................................................78 2.2.1. School Systems in Korea and Germany.................................78 2.2.2. Vocational Training at the Upper Secondary Education in Korea and Germany ...........................................................82 2.2.3. Job Training after Schooling in Korea and Advanced Job Training in Germany ..............................................................84 2.3. Model and Methods...........................................................................86 2.4. Data and Descriptive Statistics ........................................................88 2.4.1. Data.........................................................................................88 2.4.2. Descriptive Statistics ..............................................................89 2.5. Estimation Results.............................................................................93 2.5.1. Estimation Results using the Variables: Education Levels and Job Training.....................................................................93 2.5.2. Estimation Results using the Variables: Educational Duration and Job Training .....................................................96 2.5.3. Job Training Effects on Earnings including Interaction Terms and Variables for Earlier Years in Job Training.........98 2.5.4. Other Findings........................................................................99 2.6. Conclusion .............................................................................................101 References .........................................................................................................105 Appendix ...........................................................................................................109 3. Women’s Career and Children – Human Capital and Earnings of Women in Korea and Germany. A Comparative Study .......................142 3.1. Introduction .....................................................................................142 3.2. Education, Employment and Fertility Rate of Women in Korea and Germany .......................................................................146 3.3. Model and Methods.........................................................................150 3.4. Data and Descriptive Statistics ......................................................154 3.5. Estimation Results...........................................................................157 3.5.1. Effects of Human Capital on Earnings for Women using the Quantile Regression Method..........................................157 3.5.2. Discontinuity of Earnings due to Children using the RD Design ............................................................163 3.6. Conclusion...........................................................................................166 References .........................................................................................................171 Appendix ...........................................................................................................175 Closing Remarks...............................................................................................200 References .........................................................................................................206 Tables Table 1.1: School System in Korea ...............................................................30 Table 1.2: Biases of Mincer Earning Estimation in Educational Level and Job Training..................................................................................37 Table 1.3: Descriptive Statistics....................................................................57 Table 1.4: Descriptive Statistics for Wage Function Heckman 2 Step.........61 Table 1.5: Effects of School Education and Job Training on Wages ...........64 Table 1.6: Probit Estimation Results of Selection Function by Heckman 2 Step ......................................................................65 Table 1.7: Effects of Experience on Wages ..................................................66 Table 1.8: Effects of Tenure on Wages.........................................................67 Table 1.9: Effects of Family Background on Wages ....................................70 Table 1.10: Effects of Working Contracts, Working Conditions and Trade Unions on Wages ...............................................................71 Table 1.11: Effects of Firm Size (Number of Employees) on Wages ............72 Table 1.12: Effects of Family Residences on Wages......................................73 Table 1.13: Effects of Firm Types on Wages..................................................74 Table 2.1: School Systems in Korea and Germany.......................................79 Table 2.2: Upper Secondary and Post-Secondary Non-Tertiary Education in Korea and Germany .................................................................81 Table 2.3: Classification of Education Levels ............................................109 Table 2.4: Comparison of School Education in Germany and Korea according to ISCED-97 ..............................................................110 Table 2.5: Educational Duration in Germany .............................................117 Table 2.6: Educational Duration in Korea...................................................117 Table 2.7: Descriptive Statistics for the Korean Data KLIPS.....................118 Table 2.8: Descriptive Statistics for the German Data GSOEP ..................120 Table 2.9: Estimation Results using Variables: Educational Level and Job Training in Korea.................................................................122 Table 2.10: Estimation Results using Variables: Educational Level and Job Training in Germany ...........................................................123 Table 2.11: Estimation Results using Variables: Educational Years and Job Training in Korea.................................................................124 Table 2.12: Estimation Results using Variables: Educational Years and Job Training in Germany ...........................................................126 Table 2.13: Job Training Effects on Earnings including Interaction Terms (Educational Levels and Job Training) in Korea............127 Table 2.14: Job Training Effects on Earnings including Interaction Terms (Educational Levels and Job Training) in Germany.......128 Table 2.15: Estimation Results using Variables: Job Training in Earlier Years...........................................................................129 Table 2.16: Estimation Results – Age and Tenure in Korea.........................130 Table 2.17: Estimation Results – Age and Tenure in Germany....................130 Table 2.18: Estimation Results – Family Characteristics in Korea ..............133 Table 2.19: Estimation Results – Family Characteristics in Germany .........133 Table 2.20: Estimation Results – Firm Characteristics in Korea ..................134 Table 2.21: Estimation Results – Firm Characteristics in Germany.............135 Table 2.22: Estimation Results – Family Residence in Korea......................136 Table 2.23: Estimation Results – Family Residence in Germany.................137 Table 3.1: Descriptive Statistics for the Korean Data KLIPS for Women .......................................................................................175 Table 3.2: Descriptive Statistics for the German Data GSOEP for Women .......................................................................................177 Table 3.3: Descriptive Statistics of Main Variables after Treatment Assignment for Each Model in Korea........................................179 Table 3.4: Descriptive Statistics of Main Variables after Treatment Assignment for Each Model in Germany ..................................180 Table 3.5: Estimation Results for Women – Age and Tenure in Korea .....181 Table 3.6: Estimation Results for Women – Age and Tenure in Germany.................................................................................181 Table 3.7: Estimation Results for Women – Number of Children in Korea ......................................................................................184 Table 3.8: Estimation Results for Women – Number of Children in Germany.................................................................................184 Table 3.9: Estimation Results for Women – Family Characteristics in Korea ......................................................................................186 Table 3.10: Estimation Results for Women – Family Characteristics in Germany.................................................................................188 Table 3.11: Estimation Results for Women – Educational Levels in Korea ......................................................................................190 Table 3.12: Estimation Results for Women – Educational Levels in Germany.................................................................................190 Table 3.13: Estimation Results for Women – Educational Years in Korea ......................................................................................191 Table 3.14: Estimation Results for Women – Educational Years in Germany.................................................................................193 Table 3.15: Estimation Results for Women – Having Children in Korea (Women between the Ages of 30 and 55, Cutoff Point: 4 Years) ................................................................194 Table 3.16: Estimation Results – Having Children in Germany (Women between the Ages of 30 and 55, Cutoff Point: 10 Years) ..............................................................195 Table 3.17: Estimation Results – Having Children in Korea (Women between the Ages of 30 and 40, Cutoff Point: 4 Years) ................................................................196 Table 3.18: Estimation Results – Having Children in Germany (Women between the Ages of 30 and 40, Cutoff Point: 7 Years) ................................................................197 Table 3.19: Estimation Results – Having Children in Korea (Women between the Ages of 40 and 55, Cutoff Point: 4 Years) ................................................................198 Table 3.20: Estimation Results – Having Children in Germany (Women between the Ages of 40 and 55, Cutoff Point: 12 Years) ..............................................................199 Figures Figure 1.1: Histogram: Log Monthly Net Wages...........................................56 Figure 1.2: Effects of Experience on Wages ..................................................68 Figure 1.3: Experience-Wage Profiles............................................................68 Figure 1.4: Effects of Tenure on Wages.........................................................69 Figure 1.5: Tenure-Wage Profiles ..................................................................69 Figure 2.1: Effects of Educational Levels and Job Training on Earnings in Korea ........................................................................................94 Figure 2.2: Effects of Educational Levels and Job Training on Earnings in Germany...................................................................................96 Figure 2.3: Effects of Educational Years and Job Training in Korea ..........125 Figure 2.4: Effects of Educational Years and Job Training in Germany .....125 Figure 2.5: Age-Earnings Profiles in Korea .................................................131 Figure 2.6: Age-Earnings Profiles in Germany ............................................131 Figure 2.7: Tenure-Earnings Profiles in Korea ............................................132 Figure 2.8: Tenure-Earnings Profiles in Germany .......................................132 Figure 2.9: Earnings Effects of Marriage .....................................................138 Figure 2.10: Earnings Effects of Household Head .........................................138 Figure 2.11: Earnings Effects of Having Children .........................................139 Figure 2.12: Earnings Effects of Working in Public Sector...........................139 Figure 2.13: Earnings Effects of Working Overtime .....................................140 Figure 2.14: Earnings Effects of Working Part time ......................................140 Figure 2.15: Earnings Effects of Firm Size by Employee in Korea...............141 Figure 2.16: Earnings Effects of Firm Size by Employee in Germany..........141 Figure 3.1: Public Spending for Family in Cash, Services and Tax Measures.......................................................................147 Figure 3.2: Total Fertility Rates from 1970 to 2006.....................................149 Figure 3.3: Effects of Educational Levels on Women’s Earnings in Korea ......................................................................................161 Figure 3.4: Effects of Educational Levels on Women’s Earnings in Germany.................................................................................162 Figure 3.5: Age-Earnings Profiles for Women in Korea..............................182 Figure 3.6: Age-Earnings Profiles for Women in Germany.........................182 Figure 3.7: Tenure-Earnings Profiles for Women in Korea .........................183 Figure 3.8: Tenure-Earnings Profiles for Women in Germany....................183 Figure 3.9: Effects of Having Children for Women in Korea......................185 Figure 3.10: Effects of Having Children for Women in Germany.................185 Figure 3.11: Effects of Marriage for Women .................................................187 Figure 3.12: Effects of Female Household Head ...........................................189 Figure 3.13: Effects of Single Mother ............................................................189 Figure 3.14: Effects of Educational Years for Women in Korea ...................192 Figure 3.15: Effects of Educational Years for Women in Germany ..............192 General Introduction Excessive competition for university entrance is a very serious problem in Korea1. Since 2004, more than 80% of all high school graduates have gone on to university or junior college. In 2008, this reached 83.8%, although only 76.7% of graduates from all tertiary educational institutions found a job. Only 56.1% of all graduates have a regular job (Korean Educational Development Institute (2009)). The percentage of the unemployed who graduated from a university was 12.6% among all the registered unemployed in 2000, 17.1% in 2004, 19.5% in 2007 and 22.04% in 2009 (Korean Statistical Information Service (2010)). In spite of this fact that more highly educated people suffer more seriously from unemployment, the desire to go to university still keeps growing in Korea. On the other hand, the number of vocational high school graduates continues to fall. Job training is generally neglected in Korea. In human capital theory, as developed by Becker (1962, 1964), a positive correlation between earnings and the level of skills − school education and on-the-job training − and the reduction of the unemployment rate with the increasing level of skills was determined to be the stylized fact of human capital investment. However, the current situation in the Korean labor market does not serve to justify this theory of human capital. In studying the contradiction between theory and reality in Korea, this study seeks to uncover some reasons behind the biased behavior of Koreans concerning human capital investment, which focuses too much on university education. Starting from this critical mind-set, this study further compares the situation in Korea with that in Germany in order to find a possible solution, which could lead to a balance between labor supply and -demand concerning university graduates and graduates from vocational high schools. With this in mind, this study seeks to determine the effects of German vocational education and training. Finally, this study focuses on human capital investment and women’s earnings. Korea and Germany share the common problem of a low female fertility rate. In the theory of human capital for women as developed by Mincer und Ploachek (1974), childbirth and childcare are recognized as a career hindrance for working women. With the growing educational level of women, women’s ambition in working life becomes higher. However, the social progress concerning family life lags far behind women’s enhanced self-consciousness. This disparity could be reflected in the low fertility rate. This study tries to find the effect of having 1 The national description is written as Korea instead of South Korea in this study. children on women’s careers, as measured by their earnings, and finally compares the difficulties of having children for working women in Korea and Germany. This dissertation is comprised of the following three empirical studies: The first essay is entitled “What can Job Training do? Is University the Sole Way of Life? - The Effects of School Education and Job Training on Wages in Korea.” This study, in the framework of human capital investment theory (Becker (1962, 1964), Mincer (1974)), attempts to find the reasons behind the overheated fever surrounding university entrance in Korea, while simultaneously neglecting job training. Although the theory of human capital has already pointed out that job training is directly oriented to increasing productivity, unlike school education, the significance of job training in human capital investment is almost entirely neglected in Korea. Why does Korea invest so much in school/university education while neglecting job related education and training? Concerning this question, a possible hypothesis would be as follows: Korea gives a great deal of weight to school and university education, because the effect of graduating university on wages is much greater than wage effects associated with other lower educational levels. This wage difference, however, can not be compensated for with any other human capital investment in Korea. Job training, which generally enhances one’s productivity and earnings parallel with formal education, has only a marginal influence on wages. So, Koreans do not recognize job training as an alternative factor in human capital investment. This is the primary reason why people invest exclusively in school and university education and consider job training to be of no account. To test the hypothesis, this study estimates different wage effects between educational levels and job training and to this end, compares the effect of university education with that of job training, which comprises a new project in Korea. Concerning the educational system, the present school system in Korea was influenced by the USA and was established in 1949. The school system mainly consists of 6 years of elementary school (comparable to Grundschule in Germany: ISCED-972 level 1), 3 years of junior high school (Real-/Hauptschule or Gymnasium: ISCED-97 level 2), and 3 years of senior high school (Berufsschule or Gymnasiumoberstufe: ISCED-97 level 3 and 4). At the tertiary level (ISCED2 ISCED-97 stands for the International Standard Classification of Education 1997 by UNESCO. ISCED is a framework to collect and report data on educational programs varying widely between countries with a similar level of education. It helps with the compilation of internationally comparable education statistics and indicators. See OECD (1999). 97 level 5 and 6), there are 2-3 years of junior college (Fachschule or Meisterschule in Germany) or 4 years of bachelor’s studies. It proceeds then with a 2 years master’s course and a further 3 years or more for the doctoral course. Senior high school is divided into vocational high school (such as Berufsschule) and academic high school (such as Gymnasiumoberstufe). Vocational senior high school in Korea even teaches pupils theories concerning vocational specialties between the 11th and 12th classes, but this school does not carry out vocational training, which would enable pupils to acquire vocational qualifications. After finishing school education, job training is generally carried out by the company, the government or on one’s own initiative. In order to estimate the effects of school education and job training on wages, this study employs the Mincer earnings model. The model, however, reveals some problems in estimating the effects of school education and job training on wages by means of ordinary least squares (OLS). i.e. measurement errors associated with the educational level, unobserved omitted ability bias and selfselection bias concerning schooling and job training, and sample selection bias on wage data in this study. Ashenfelter and Zimmerman’s studies (1993, 1997) demonstrated that omitted ability and the oppositely directed measurement error offset their respective biases in a sample of the same size. Concerning the remaining selection biases, this study makes a couple of assumptions about the Korean data deduced from the particular educational and social circumstances in Korea. These assumptions enable us to compare educational effect on wages with the job training effect, although the job training variable still suffers from omitted ability and selection biases. The estimation methods utilized in this essay are the random effects method and the Heckman 2 step method (Heckman (1979)). An advantage of the random effects method is its competence, which removes serial correlations between unobserved individual effects and idiosyncratic errors in the panel data. The Heckman 2 step method corrects bias due to sample selection. Data, used in this study, is taken from the ‘Korean Labor and Income Panel Study (KLIPS)’ which is comparable to the ‘German Socio-Economic Panel SOEP (GSOEP).’ This survey was launched in 1998 with 5,000 households and their 13,321 family members, and composed of employees, self-employed workers, non-employed people and members of the economically inactive population. From this data between 2002 and 2006, this study uses 6,649 observations for males aged between 26 and 55. The dependent variable of this study is the natural log of individual monthly net wages from their main job. The main explanatory variables are levels of school education and job training experience in employees’ whole working life. Other control variables are 10 labor market experience dummies, 10 tenure dummies, 5 year dummies, 16 dummies of residence of house, 202 industry dummies, 19 firm type dummies, 8 firm size dummies, 5 casual worker dummies, and dummy variables for regular worker, part time, overtime, union, shift work, household head and having children. Additionally, this study employed 4 house ownership dummies, natural log household sustenance allowance for parents, natural log non household labor income, natural log household financial wealth and natural log household debt in order to correct the sample selection bias in the estimation obtained via the Heckman 2 step. At the end of the essay, this study attempts to bring to light whether job training could compensate or noticeably reduce the wage difference attributable to different educational levels. This study found a small wage effect due to job training. Koreans may not be aware of job training as an alternative factor in human capital investment. In the second essay, this study conducts a comparative analysis. The subject of the study is “Effects of School Education and Job Training on Earnings in Korea and Germany. A Comparative Study.” The central question of this essay is: why is a university education in Korea considered to be so much more important than is the case in Germany? In attempting to answer this question, Korean researchers often advance the hypothesis that the earnings difference between university graduates and workers with a lower level of education is larger in Korea than in Germany, where vocational training at school and on-the-job training for adults are well developed. This job training reduces earnings differences between educational levels and ameliorates inefficient competition for university entrance (Kim (2006)). This study estimates effects attributable to school education and job training on earnings and compares these effects between Korea and Germany. A comparative study, in which the earnings effects of human capital are estimated and compared in the same empirical framework, is a new endeavor in the study of human capital investment, but also in other thematically related areas of empirical study in Korea. This comprises the main contribution of this study. Job training in Germany is primarily carried out at vocational school (Berufsschule) in the dual system. In this system, pupils take part in vocational training for 3-4 days a week in a firm. For an additional 1-2 days, pupils study vocational oriented theories at school. Firms which conduct vocational training are responsible for the vocational qualification of pupils, corresponding to regulated qualification standards. At the end of their training, these pupils have to pass an examination to demonstrate their practical and theoretical competence in order to finish the training course. After successful completion of training, they obtain a certificate, which serves as their vocational qualification. In 2004, 52.5% of labor market entrants in the relevant age group took an accredited job, which is acquired at vocational school in the dual system. 20% of matriculating students had vocational training in 2005. Graduates of vocational school have a further chance of acquiring additional vocational qualifications at a Fachschule or a Meisterschule – i.e. the advanced vocational school - when they gain practical experience in an appropriate job according to the rules (Hippach-Schneider et al. (2007)). In Korea, 3 years of vocational senior high school does not provide a job oriented qualification program for pupils. Even if they could participate in vocational practice in a firm, this practice does not furnish pupils with any accredited vocational qualification. This, as a rule, takes only 34 hours to 6 months. There is no standard curriculum for the practice and qualification exam or certifications, which demonstrate pupils’ vocational qualification. For all senior high school graduates, the percentage of vocational high school graduates is 31%. In 2004, only 11% of new comers in the labor market were graduates from vocational senior high school (Korean Educational Development Institute (2005), pp. 31). From the view-point of labor supply, the vocational senior high school does not play a significant role. In general, job training is mainly carried out by firms and the government, or individuals voluntarily take part in any job training for one’s own qualification. The proportion of participants averages about 5% of the entire population aged 16 or older. This second essay, based on the Mincer earnings model (1974), employs the quantile regression method (Koenker and Bassett (1978), Koenker (2005)) for estimating school and job trainings effects on earnings in Korea and Germany. Quantile regression enables us to estimate the effects of education and job training at different distributional points of log earnings, which are taken to be the 10th, 25th, 50th, 75th and 90th quantile points in this study. Thus, we are able to analyze not only earnings differences between educational levels, but also the differences between various distributional points for a particular level. In a comparative study, this advantage especially expands the comparability manifold. The quantile regression method allows us to measure earnings differences attributable to the variables of school education and job training throughout the whole distribution in Korea compared to those in Germany as well as to look at the effect of workers’ heterogeneity on the earnings between both countries. This essay uses the 2002 to 2007 data for male employees aged between 18 and 55 taken from the ‘Korea Labor and Income Panel Study (KLIPS)’ and the ‘German Socio-Economic Panel (GSOEP).’ GSOEP was launched in 1984 and surveyed 5.921 households and their 12,290 family members aged 16 and older. The dependent variables of this study are log yearly earnings of employees in both countries. The independent variables are mainly educational levels/duration and indicator of completed job training in the last a year. Using these variables, this study utilizes 7,359 observations for 2,709 individuals from the KLIPS und 13,011 observations for 4,129 individuals from the GSOEP. According to estimation results, this study falsifies the initial hypothesis. Earning differentials between education levels are higher in Germany than in Korea, although German job training compensates for these differentials more strongly than Korean job training. This thesis then turns its attention to working women with/without children and their careers, as measured by their earnings, in the third essay. Since 2005, Korea has recorded the lowest fertility rate across all OECD countries. This fertility rate equaled only 1.08 babies per woman aged between 15 and 49 in 2005. In Germany, although the fertility rate has not declined dramatically, it has remained below the natural replacement rate of 2.1 babies since the 1970’s. Entitled “Women’s Career and Children -Human Capital and Earnings of Women in Korea and Germany. A Comparative Study,” this essay seeks to study the impact of childbirth and childcare on the earnings of women. Following the study concerning human capital and women by Mincer and Polachek (1974, 1978), this thesis advances the hypothesis of earnings discontinuities suffered by working mothers due to problems associated with childbirth and childcare. This study expected an earnings decrease for working women due to childbirth and childcare. The purpose of this study is to identify any such earnings effects linked to having children for women in Korea and Germany. As the first comparative study concerning human capital and women’s earnings, this study also plans to demonstrate the effects of education, experience and family characteristics on earnings in both countries. In Korea, research on the human capital of women and the subject of children and their effect on women’s earnings remains an undeveloped area of study. A comparison of human capital effects on women’s earnings in Korea with similar effects in Germany is, in general, a completely new undertaking, which will contribute to the study of human capital research and the labor markets in Korea. Observing the participation rates in the higher level of education, the female entry rate for a junior college averages 53% based on each year of relevant age (i.e. 17% OECD on average), and the entry rate for university amounts to 59% (63% OECD). The employment rate of Korean women between the ages of 25 and 64 was 58.3% (64.9% OECD) in 2007. Compared to the OECD average (79.5%), women with a tertiary education had much lower employment rates in Korea (61.2%), while women with an education level below upper secondary had much higher employment rates (58%) compared to the OECD average (47.8%) for this group of women in 2007. Korea is therefore only an exceptional case among all OECD countries, where women who attain a tertiary level of education generally have a higher participation rate in the labor market than women with lower educational levels. Average annual earnings for females as a percentage of those of males aged between 25 and 64 equal 65% for women with a tertiary education. This amounts to 47% for women with an upper secondary educational level. One third of working women have a precarious working contract. Public support in monetary payments stands at 0.175% of GDP, which averages 2.4% for 24 OECD countries (OECD (2007)). Women aged between 15 and 49 had just 1.08 babies on average in 2005. This fertility rate is the lowest of 30 OECD countries in 2005 (OECD (2009b)). In Germany, the female entry rate to institutes for higher vocational qualification such as Fachschule or Meisterschule averaged 16% based on each year of relevant age for women in 2007. The entry rate to Fachhochschule and university was 35% for females. The employment rates for females between the ages of 25 and 64 were high at 67.3% for all educational levels in 2007. German women who have attained a tertiary level of education had an average participation rate of 80.6% in the labor market. This participation rate is higher than the rate for women with an education level below upper secondary and an education level of upper secondary. Average annual earnings for German women as a percentage of men’s earnings were 59% for holders of a tertiary education degree in 2007. This percentage was also 59% for women with an education level of upper secondary (OECD (2009a)). Public support exclusively for families was equal to a total of 3.0% of GDP in 2003 (OECD (2007)). This level is higher than the 2.4 % OECD average. However, formal childcare for children younger than three years old is especially inadequate. The fertility rate in Germany (1.34 babies in 2005) is higher than Korea. But since the 1970’s, the fertility rate has fallen below the mortality rate (OECD (2009b)). The model by Mincer and Polachek (1974), on which this study is based, divides the labor market experience of women into three phases – employment phase before marriage, home time for childcare and employment phase after home time, which differentiates the human capital earnings model for women with children from the model for men or women without children. By means of quantile regression, this study demonstrates, in a general fashion, human capital ef- fects on women’s earnings. Regression discontinuity design method (Thistlethwaite and Campbell (1960)) contributes to the estimation of effects of having a child or two children on women’s earnings and further identifies any earnings discontinuity for women due to childbirth and childcare along the tenure earnings function, which is a main interest of this study. For this estimation, the data of females aged between 30 and 55 is taken from the ‘Korean Labor and Income Panel Study (KLIPS)’ and the ‘German Socio-Economic Panel (GSOEP)’ between 2002 and 2007. This study makes use of 3,066 observations in Korea and 8,260 observations in Germany. The main variables utilized in this study are the log gross yearly labor earnings of individuals as dependent variable, and age, tenure, dummies for marital status, single mother, household head, children number and educational levels/years as the independent variables. This study found earnings discontinuities for women aged between 30 and 40 in Germany and women aged between 40 and 55 in Korea. The German women suffer from a more than 60% earnings drop due to having children at an average tenure of 7 years. Unexpectedly, the Korean women show a 37% earnings gain attributable to children at an average tenure of 4 years. The main part of this doctoral thesis begins with an analysis of the human capital effect on wages in Korea, focusing on school education and job training. Thereafter, this thesis compares the influence of school education and job training on earnings in Korea with the situation in Germany. Then, I focus on the subject of the human capital of women, primarily women’s careers and the influence of children on these career-paths. In the following sections of this thesis, the three essays present the backgrounds of the papers, hypotheses, theories and models for each paper, estimation strategies, estimation results, interpretation of findings and conclusions. The Thesis ends with various closing remarks.