KnE Social Sciences | The First Economics, Law, Education and Humanities International Conference (The First ELEHIC) | pages: 667–688

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1. Introduction

Some research on the rate of return based on gender also includes research in Indonesia revealed that there is a difference of receiving income between men and women. Aslam (2005) prove women have lower incomes than men [1]. Viktor Pirmana also prove income of men is higher than women [2]. According [3]; Watson (2005), the social benefits of girls in school is significant, especially in developing countries. This is supported by research Schultz, where the increase in the school years for girls reduces infant mortality of 5 to 10 percent [4].

Research refund previous education is very different from my research this because in this study wanted to reveal the return on education and the benefits of marriage (marriage of benefits) based on the externalities of the individual. Externalities of the individual in question is how to express returns obtained education of married women worked when controlled by the characteristics of the respondent, Household Characteristics and Job Characteristics husband.

This study is very important, because of the participation of women regarding the role of tradition and transition. The role of tradition or domestic include women's roles as wives, mothers and household managers. While the role of the transition includes women as workers, community members and human development. In the transition the role of women as workers actively participates in economic activities (for a living) in a variety of activities in accordance with the skills and education that are owned and jobs available. The involvement of women already apparent, but clearly has not been recognized in Indonesia had an impact on women's role in family life.

The phenomenon that occurs in the community are more women looking for extra income to help her husband, but it is also driven by the economic needs of the family, women are also increasingly able to express himself in the family and society. Family economic situation affect the tendency of women to participate in the labor market, in order to help boost the economy of the family.

Some research indicates that the educational investment return rate decreasing trend, it is necessary to evaluate the cause of the decline. If the rate of return on investment is high but rate of school participant are low, indicating that people do not invest optimally in education. Thus, research on the investment returns of education has very important policy implications [5].

Various studies from different parts of the world show the return on investment in education ranges from 5 percent in developed countries and 29% in developing countries [6]. For Indonesia, there is some research on educational investment returns. Duflo (2001) estimate the economic benefits of investing in education in Indonesia ranges from 6.8 to 10.6 percent in 1995. Meanwhile, Psacharopoulos reported that the rate of return on investment in education was 17.0 percent in 1981 [6].

Given there is no meeting point of various studies mentioned above, then a more accurate estimate of how much the return on investment in education, especially married women work, needs to be done to inform policy-making in the education sector. Therefore, the estimated return on investment in education by using current data would be very useful. Such evaluation is important because the rate of return on education in Indonesia varies from time to time, therefore, estimated by using data that is not up to date may not be useful to help capture the current policy.

2. Concept

Theory of human capital

Theodore W. Schultz was the inventor of basic theory or concept of human capital (human capital concept). In his journal, entitled Investment in Human Capital, Schultz argues, the concept of human capital in essence, assumes that humans are a form of capital or capital as well as other forms of capital, such as machinery, technology, land, money, and material [4]. Human beings as human capital is reflected in various forms such as knowledge, ideas (ideas), creativity, skill, and labor productivity. The concept of human capital by Becker (1975) apply economic logic to examine individual investment decisions regarding education and job training, career options and other characteristics related to work. This means that investing in education is done in order to meet expectations of the work to be done in the future. Similarly, in the hope of income received will be greater than the cost of issuance of investing education.

The basic assumption of human capital theory is that a person can increase their incomes by improving education. Each additional year of school means, on the one hand, improve the work ability and the level of a person's income, but, on the other hand, delaying receipt of income during the study follow. Improving the quality of human capital cannot be done in a short time, but it takes a long time. Human capital investment will be actually the same as investing in other production factors. In this case also calculated the rate of return (benefit) from such investments. When someone would make an investment, it must make a cost benefit analysis (cost benefit analysis). Costs are direct costs incurred to attend school plus indirect costs or opportunity costs (opportunity cost). Opportunity cost here is the income received by someone if he chooses to work and do not attend school. While the benefits of educational investment will be revenue to be received in the future after the school year is finished. It is expected from this investment income (benefits) gained far outweigh the costs.

Theory of the allocation of time

According to Becker (1965) in his A Theory of the Allocation of Time states that every individual has the time to be allocated for work or for other activities. With a total time (T) which is owned equally to every individual as many as 24 hours of course time is not only used for work or only allocated to the activities of eating, sleeping, and recreation. Time will be allocated to maximize revenue and leisure activities (relax). The decrease in revenues will affect the time reduction in consumption activities because of time will become increasingly expensive.

Another theory is that supports this theory is a theory about the decision to work (A Theory of The Decision to Work). According to Ehrenberg and Smith, (2012), the allocation of time for work or leisure time is influenced by three factors, namely: First. Opportunity cost (opportunity cost). Viewed from the side of people who allocate time for work, and then the individual does not need time to work. Where the price of spare time owned depending on the level of the wages received. When income increases with the opportunity costs of spare time constant then someone will want to spend more time to spare.

The second factor is the level of well-being, where the level of well-being can be seen from the amount of savings in banks, financial investments, and other physical possessions. The expertise of workers can be considered as something that can be expected to be a source of economic security. When individual workers have a lot of savings that can be cashed, the individual in question tend to favor increasing leisure time than working time. While the third factor is the preference, i.e. a set of options or individual preferences. The options are typically determined and not in real time. Individuals would decide to use more time to work or more leisure time depends on the choice of a choice that is available to him.

The time that each individual will be used to work as much as h hours, then free time (Leisure) owned is equal to (24-h) hours per day (Sudarsono in Marcaine and Mandate 2004, (Termini, 2012)). Leisure time will be used to eat, sleep, housekeeping, child care, recreation and so on. Economically it can be said of people using their time for leisure time can be called consumes time and he will derive satisfaction or utility, whereas individuals who use part of the time for work will also gain the satisfaction or utility because it can consume the goods and services of the wages earned for work.

The decision to work is essentially a decision about how to use the time owned. Individuals can use the remaining time for leisure activities such as active in social activities, culture, care of the household, taking care of children or for vacation while running a hobby of the individual. As for the other things that can affect the time for work, among others the number of dependency, ownership of income of non-workers, even the local culture will also be able to determine the involvement of women in the labor market (Marcaine and Mandate, 2004), the fact that each individual is trying to balance between work and activities house (Grant and Stewart, 2001).

Theory of return on education

It has been explained above that the benefits received by a person who invests in education can also be called a return to education. According to Psacharopoulos simply the educational benefits can be classified in terms of economic and non-economic [6].

Revenue function Mincer

Revenue function Mincer is a revenue model that has been used in many studies in different countries to estimate the effect of investment in human capital to increase revenue. The Mincer function of the standard model is:

Lny =a0+a1S+a2X+a3X2+e

y = log of the individual's income in the period of time

S = the number of years of completed education (years of schooling)

X = the number of individuals employed after completing education (work experience)

e = residual

a 1 = coefficient that indicates the return on investment in education or the rate of return to education (empirical value is 5% -12%).

a 2 = depreciation coefficient that indicates the level of experience they have.

a 3 = coefficient that indicates the level of experience of depreciation.

A = Age obtain employment

In the absence of direct information about work experience, then Mincer proposed a "potential experience" with the assumed start of school age, 7 years, so that X A - S - 7. Although theoretically Mincer deriving equations of the model of choice of school and post-school training decision patterns of variation in income based on age and education has been known at least since the early 1950s (Miller, 1955).

Excess revenue function Mincer described Kruger and Lindahl in his argument that the Mincer demonstrated in the model if the extra year of school is the opportunity cost of time for a student and if the proportion of added time are constant throughout its life, then income (log) will be linearly relate to individual school year; and the slope of this relationship can be viewed as the rate of return [7].

Furthermore Heckman et al [8] says that the revenue model of Mincer was the basis for the study of economics in developing countries for several reasons. The first model is the basis for calculating the rate of return to education. Both Mincer income model is a basis for estimating the quality of education return rate. In addition Mincer income model can be used flexibly in which the model can be modified by adding variables that could theoretically affect revenue. Later models used are also still relevant today.

However, Mincer income model also has some drawbacks. As stated by Hartog [9] that this model does not include an error when measuring education as well as the underlying one to school. In addition, Mincer models will not consider the factors of uncertainty in estimating the income of the individual in the future.

3. Data and Data Analysis Techniques

The data set used in the empirical analysis is National Socioeconomic Survey Indonesia 2014. National Socioeconomic Survey (Susenas) is a household survey on the various socio-economic characteristics of the population, especially those closely related to the measurement of the level of social welfare. The sample size Susenas within one year of activities covers 300,000 households distributed in all provinces in Indonesia, with distribution of the sample every quarter by 75,000 households Susenas Quarterly I, II, III and IV, respectively held in March, June, September and December, Data enumeration results can be presented both national and provincial levels, while the cumulative result of the implementation of the enumeration during the four quarters, the data can be presented to the county or city level.

For the purposes of empirical analysis of this paper, extract data created from the data Susenas 2014. To create, extract data from individual files and file households should be merged. The focus of the data in this study is the work of married women aged 15 to 64 years as a respondent. The independent variables are needed in this research is years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage

Further, the dependent variable in this analysis is the natural logarithm of monthly earnings. The unit of measurement is the rupiah. There is one independent variable that needs to be constructed from other information in the data set, namely potential work experience. Measures of actual labor force experience, an important variable in the study of earnings determination, are absent from the Susenas data sets. However a potential labor force experience variable can be calculated from the information available. Most empirical studies usually use the following basic formula to derive a measure of potential work experience - age minus years of schooling minus official age to start primary school (7). However, for the purposes of calculating potential work experience in this study the following formula will be used: age minus years of schooling minus age first attended primary school. The aim of using this formula is to obtain more precise data on potential work experience since the age individuals first attended primary school varies appreciably. It ranges from 5 to 14 years.

This study used OLS to estimate the coefficients of the independent variables. The coefficients can be used to see the value of the returns to education (education returns). In this study, using a model of the income equation Mincer (1974), as follows:

white Ln Y=α0+α1X yos +α2X exp +α3X exp 2+α4X aor +α5X loed +α6X tre white+α7X rwh +α8X pmj +α9X wsr +α10X ed SLTA +α11X,r SLTA and h> SLTA white+α12X eol +α13Xt04 years +α14X dom +α15X pm h+α16X afm white+α17X yom +e

Where:

Y = Income

X yos = Years of schooling

X exp = Experience

X exp2 = Experience2

X aor = The Age of respondents

X loed = The Level of education

X tre = The tipe respondents' education

X rwh = The respondents working of hours

X pmj = The position in the main job respondents - workers or professional

X wsr= The work sector of respondent- formal / informal

X ed SLTA = The Education of respondents and highest husband SLTA

X, r=s> SLTA = The Education of respondents and the husband is higher than high school

X, r SLTA and h> SLTA = Highest education high school respondents, higher than high school Husband

X eol = Respondents living in the era of

X t-0-4 years = The number of toddler son

X dom = Domicile households

X pm -h = The position in the main job husband's respondents - workers or professional

X afm = The Age at first marriage

X yom = Years of marriage

4. Empirical

This study aimed to determine the return on investment in education (rate of return to education) employment of married women in Indonesia of the data Susana's 2014. The results are presented through descriptive analysis and regression analysis between the dependent variable and the independent variables are shown in (Appendix).

The dependent variable in this study is independence years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage.

The population is in this paper is the employment of married women in Indonesia aged 15-64 years amounted to 31,450,273 with the status of work, wage, and provide complete information about the variables needed in the research. Revenue per month received by respondents is the income received from the results of work in the form of wages / salaries or benefits of the respondent's main job. The average net income is usually received during the month of his major work is Rp.1.254.123 with a standard deviation value which is quite large, which is 752600. This shows there is an imbalance or are large differences in the distribution of labor income data of married women in Indonesia, in the model used variable returns to education in the natural logarithm of earnings or variable income per month.

An examination of all independent variables on the dependent variable using a multiple linear regression analysis. The results of the analysis are presented in Table 13 in the annex. Significance test the influence of all independent variables together with dependent variable using the F test F test is used to determine the ability of independent variables years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage In explaining the dependent variable is income, If the probability of error rate test of the F-count is smaller than a certain level of significance (5% significance), then the model tested is significant.

The results if the data shows the value of the F-count amounted to 964,954.2 with a probability of error rate of 0.000 is smaller than the expected level of significance (0.0% < 5%), it can be said that years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage Married women in the labor force Indonesia simultaneous effect on income.

Significance test the influence of the independent variable on the dependent variable individually using the t test. The results if the data matches with a probability of error rate are smaller than the significance level expected (0,0% < 5%), thus years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage married women in the labor force Indonesia is partially affected significantly affect revenue. But a different effect each of these as there are positive and also a negative effect.

Testing the effect of years of schooling on revenue generating a regression coefficient of 0.213, a positive influence and significant impact on the level of labor income of married women in Indonesia. This shows that the regression coefficient of 0.213 the length of education has a positive regression coefficient directions which means any increase in the length of education one year would raise revenues by 21.3%. The results of this study are supported by Purnastuti, Miller and Salim [1] which states that income is also influenced by education with the results of a regression coefficient of 0.055.

Testing the effect of potential work experience to income generates regression coefficient of 0208. The result of the analysis shows that there is a positive and significant influence of work experience potential to income. This means that any potential increase of 1 year working experience will increase revenue by 20.8 percent. The results of this study are supported by Purnastuti, Miller and Salim [1] which states that income is also influenced by the potential work experience with the results of the regression coefficient of 0.006

Testing the effect of the potential of quadratic work experience to income yields a regression coefficient of -0.125, with a probability of error rate of 0.0009 smaller than the expected level of significance (0.0009% < 5%), then the potential work experience of labor squared has a negative coefficient, which identifies the decreasing marginal return. The result of the analysis shows that the marginal increase of work experience potential will be followed by the decreasing marginal increase of income. The income-experience profile will peak at work experience > 20 years. This indicates that workers with working experience > 20 years of marginal revenue will increase with years of work experience, after working experience reaching a certain year marginal revenue increase will decrease.

Then testing the age of respondents to income, showed a positive and significant influence because the average age of women workers aged 15 to 64 years of Indonesia is a productive age of 25-39 years. This means that productive age allows time, energy and opportunities to increase revenue. Another case with the level of education to education shows the effect of -0.060 which means the level of education has a negative effect on return. When being checked to the level of education respondents it turns out the level of education is a junior high school. This education is still classified as basic education so it can be concluded that education only junior level will not affect income.

Then the influence of the type of education is also negative at -0.020 indicating that the type of education cannot increase the return of female labor. This is due to the average of respondents attending public schools. Public education is a primary and secondary education that prioritizes the extension of knowledge required by learners to continue education to a higher level which means not yet have the skills to earn income.

Working hours of respondents and job sectors of respondents have a positive and significant impact on the income of female workers aged 15 - 64 years in Indonesia because basically working hours have exceeded 20 hours a week and is said to have a maximum. Maximum working hours will increase revenue. While the job sector is the formal sector. This means that the formal sector is a business sector that already has a permit from the government. The formal sector can be a comfortable sector because it gets clear income every month. But when testing the position in the main job respondents by Income showed a negative effect of -0.40 caused by the married woman on average work as laborers / employees.

Tests comparison of education of women with spouse (husband) influence significantly with negative direction. This means that the returns obtained by women workers aged 15 to 64 years in Indonesia is low when compared to the level of education with a partner. This is due to the average level of education of respondents and husband only graduated from junior high school (table 1). Thus the education of the respondent and the low pair resulted in a low return.

The respondent's order has a positive and significant impact on the income. This is due to the average workforce of Indonesian married women aged 15-64 years old living in the new order. Others with husband occupation positions and the number of children under five who owned negatively affects income. This condition is caused by the position of the husband of the respondent is the self-employed / professional who is believed to have a non-fixed income that negatively affects the income of the respondent. Once the number of children under five is negative, because on average the respondent has 1 child under five. Having a toddler is very time-consuming, especially working mothers. This variable describes that the return of married female workers is getting smaller when they have children aged 0 - 4 children. Women who are children of toddlers will need more time to take care of the baby at home, so the time to work less and less. This condition makes the income smaller. The mother cannot be free to work because the time is much taken care of children aged 0 - 4 years. This means that respondents who have children aged 0 - 4 years the rate of return is small, otherwise if the respondent does not have children 0 - 4 years, the more available for work.

Testing influence of residence (domicile) to income yield regression coefficient equal to -0.117 which influence negative but significant. This negative influence indicates because on average the domicile of women workers aged 15 - 64 years is in rural areas. Thus it can be concluded that the regression coefficient domicile of -0.117 indicates that the labor who live in the village have lower income compared to workers who live in the city.

Variables The first respondents' age of marriage had a positive and significant effect on the return of Indonesian married women aged 15-64 years old due to the age of marriage of 21 respondents and said to be old enough and mature in their thinking. The first woman marries faster to cover his chances of working for money. Furthermore, the length of marriage also has a positive and significant effect on the income of respondents which is caused by the average length of marriage of Indonesian women between 15- 64 years old is 11-20 years old. The longer a woman is bound in marriage the more comfortable, thereby having the calm to work harder so that income increases.

Assessment of the goodness of fit of the regression model using Adjusted R-Square size. The Adjusted R-Square value of the OLS regression result is 0.411 meaning that the independent variables in the model are able to explain the dependent variable by 41.1 percent. This means independent variables (years of education, potential work experience, potential quadratic work experience, age, working hours, employment sector, occupation type, occupation, age, life on governmental order, level of education, respondents with spouse, place of residence, husband occupation, first marriage and long marriage) able to explain the change of dependent variable (income) equal to 41, 1 percent while the rest 58, 9 percent explained by other variable not submitted in this research.

5. Conclusion

The based on the results of modeling the return on investmen in educatin, it can be drawn that the opinion in influenced by the opinion years of schooling, experience, experience 2 , the age of respondents, the level of education, the type respondents' education, the respondents working of hours, the position in the main job respondents - workers or professional, the work sector of respondent- formal / informal, the education of respondents and highest husband SLTA, the education of respondents and the husband is higher than high school, highest education high school respondents, higher than high school husband, the era of live, the number of toddler son, domicile households, the position in the main job husband's respondents - workers or professional, the age at first marriage, and years of marriage. The variable contribution to income are 41, 1 percent and 58, 9 percent, the rest are explained by variable which is not proposed in this research.

Externalities due to marriage variable it significantly affects the return of Indonesian married women workforce aged 15-64 years, for example: The number of toddler son, the compared education of respondent and husband's respondent, Working Hours respondent husband, The position in the main job husband's respondents, The work sector of husband's respondent, The education level of the respondent husband, The Age at first marriage, and Years of marriage. Unobservable variable is a novelty in this paper significantly affect the return of Indonesian female workers aged 15-64 years ie Externalities due to marriage variable added with The type respondents' education, The work sector of respondent and Respondents living in the era. The Variabel have a significant effect on income but the inflence there are positif and negatif.

Acknowledgements

Thank you to the BPS institutions for providing 2014 Susenas data for this journal and also the continuity of STIE "KBP" which has given permission to carry out this research.

Appendix

fig-1.jpgfig-2.jpg

Source: Calculated based on Susenas 2014

Figure 1

The income of respondents based on the school year.

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fig-4.jpg

Source: Calculated based on Susenas 2014

Figure 2

The income of respondents based on work experience.

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Source: Calculated based on Susenas 2014

Figure 3

The income of respondents by a long marriage.

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Source: Calculated based on Susenas 2014

Figure 4

The respondents' income by age at first marriage.

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Source: Calculated based on Susenas 2014

Figure 5

The respondents' income based on the respondents' education.

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Source: Calculated based on Susenas 2014

Figure 6

The respondents' income by type of education.

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Source: Calculated based on Susenas 2014

Figure 7

The respondents' income by era.

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Figure 8

The respondents' income by age.

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Figure 9

The respondents' income by type of work.

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Figure 10

The income respondents by job sector respondents.

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Figure 11

The income respondents based on working hours.

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Figure 12

The income respondents based on domicile.

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Table 14

The result of regression analysis.

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References

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Purnastuti, Losina., Miller, Paul., and Salim, Ruhul (2012). Economic Returns to Schooling in A Less Developed Country: Evidence for Indonesia. Journal of European Economy. Vol. 11. Sepecial Issue.

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Pirmana, Viktor. 2006. Earning Differential Between Male-Female In Indonesia: Evidence From Sakernas Data. Bandung: Fakultas Ekonomi Universitas Padjajaran. http://core.ac.uk/download/files/153/9317806.pdf. Diakses pada 14 Januari 2016.

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Herz, B. and Sperling, G. (2005); what works in girls education; UK

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Kimenyi, S Mwangi., Mwabu, Germano., and Manda, Kulundu. 2006. “Human Capital Externalities and Private Returns to Education in Kenya”. Eastern Economic Journal, Vol. 32, No. 3, 493-513.

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Psacharopoulos, G. and Patrinos, H. (2004), Returns to Investment in Education: A Global Update., Education Economics, 12 (2), pp. 111-134.

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Kruger, Alan B. and Michael Lindahl (2000) “Education for Growth in Sweden and The World”

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Hartog, Joop. (2006). A Risk Augmented Mincer Earning Equation? Taking Stock. Universiteit van Amsterdam.

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