ࡱ> `X Z E F G H I J K L M N O P Q R S T U V W _ ` b d f hn$$$$$$$$$$$$$$%%%%%[..x/ Nbjbj y%  Qp   8ED $ ^"D$E"EF GKKKL\N\N\N\N\N\N\acN\aKK|KKKN\aaF G^OOOK$alF( GL\OKL\OOZh,\ G`S LD[8\^0^T[GdMxGd \Gd\ KKOKKKKKN\N\OKKK^KKKKGdKKKKKKKKK : Investing in tertiary education: A Simple but Deepened Comparison between University (WO) and Higher Professional Education (HBO)  ERASMUS UNIVERSITY ROTTERDAM Erasmus School of Economics Department of Economics Supervisor: Dr. B.S.Y. Crutzen Date: June 2013 Name: B.M. Mangr Exam Number: 287638 E-mail address:  HYPERLINK "mailto:287638BM@student.eur.nl" 287638BM@student.eur.nl  Abstract Rising tertiary educational costs and budgetary deficits has made the Dutch government and its society wonder in the early nineties and starting the new millennium if they should be the ones who should be paying the lions share for higher tertiary educational levels. This research looks at the wage premiums of tertiary graduates between 1999/2000 and 2005. The results of the Internal Rate of Return method show that the individuals and the Dutch society are the main beneficiaries and are the ones who need to pay for the higher tertiary educational levels. A possible solution is the social feudalism that fits well to this description. A trade off can then be made by changing the current scholarship to the social feudalism (can be considered as a loss for the students, but a gain for the Dutch society) and increasing the age to receive this new type of scholarship thereby retaining the current (income dependable) additional scholarship (considered as a gain for individuals, but a loss for the Dutch society). The proposal in this trade off is a way to keep these higher tertiary educational levels obtainable even for the relatively poor individuals. Keywords: The Netherlands, tertiary education, Internal Rate of Return, individual viewpoint, governmental viewpoint, societal viewpoint, Tempobeurs, and social feudalism. Acknowledgements: I am thankful of having the support of my parents Amriet and Edith Mangr and sister Ann Ramautar-Mangr who encouraged me to keep working on this paper. I owe a lot to my brother Manvi Mangr and my sister Drs. Warsha Mangal-Mangr for reading everything, making sure the structure is clear and coherent. In addition, my thesis supervisor Dr. B.S.Y. Crutzen has been a great help of turning this paper into a success thanks to his advices and communication skills. And finally I am grateful for three persons who gave me the information I needed to create this paper. These persons are in chronological order: Mr. A. Lefeber of CBS Statistics Netherlands who referred me to a research centre in Maastricht (on 16 February 2012), Mr. T. Huijgen of ROA Research Centre for Education and the Labour Market who helped me obtain the Reflex dataset (on 24 February 2012), and Mr. Jorgo Papadolambakis of DUO Dienst Uitvoering Onderwijs who send me the average tuition fees of a pre-university student between the school years 1990/1991 and 1994/1995 (on 22 June 2012). I would like to add that any views expressed here as well as any mistakes and errors that are still in the text are the responsibility of the author. Introduction The financing of tertiary education has been on the news quite a lot and has always been an important topic for politicians to put in their election-programme. My supervisor has encouraged me to write about this topic and I embraced that idea. This topic has affected me personally, because I am following such a tertiary education and will soon enter the labour market to become one of those many tertiary graduates in the Dutch statistics who have benefited from their tertiary degree paid mainly by public money. These privileges to us as students happen, because The Netherlands is a country that attaches great importance to educate its population. That is why most of the education is publicly financed to a certain extent. Furthermore, the European Council has during the Lisbon Strategy in 2000 aimed to make the European Union a knowledge-based economy in 2010 with a highly skilled (tertiary educated) labour force that would provide for economic growth in the whole region. This includes The Netherlands, as they are one of its member states (European Commission 2010: 2). Investing in (tertiary) education was considered a profitable endeavour for everyone. However, according to Greenaway et al. (2007: 298-328) there is a trend coming up, with a government who needs to cutback on its expenditures on (tertiary) education and at the same time have to deal with a greater demand for the same public funds. My research question is therefore: Who should pay for tertiary education? The individual, the government, or the society? I perform a one-country specific analysis, which is about The Netherlands. Two regression methods are used, which are the Ordinary Least Squares (also known as OLS) and the Internal Rate of Return (also known as IRR). The former regression will be used as a first indication to guide the latter regression in an age-income profile by means of a polynomial regression line. This profile consists mainly out of wage premiums from tertiary education, throughout a workers life, to take only their wage effects into account. These calculated IRRs to tertiary education are about the year 2005 when The Netherlands experienced a period of boom. The main results clearly show that the private IRR (the individual) has the highest coefficients when deciding between a higher tertiary education and a lower tertiary education WO(WO) versus HBO(HBO), respectively and therefore makes the individual the one who needs to make a larger contribution to tertiary education. The social IRR (Dutch society) obtains the highest coefficients when the decision is between following a second (or more) tertiary study and only one tertiary study, of which the acquirement of mixed tertiary degrees WO(HBO) and HBO(WO) show the best results. Meaning that the Dutch society as a whole need to keep investing in tertiary education if tertiary graduates want to follow a second (or more) tertiary study. The public IRR (Dutch government) has mostly low positive coefficients, of which, from a financial point of view, there are better alternative investment opportunities available than investing in tertiary education; excluding externalities and the acquirement of mixed tertiary degrees that show a better positive result than the alternatives. Concluding, these results reveal that the individuals and the Dutch society are the ones who need to pay for tertiary education. A scholarship that fits perfectly to this outcome is the social feudalism. This type of scholarship only gives study loans away, which obliges the students to reimburse their borrowed money whether they graduate, or not. The amount of money that is released through reimbursements of the study loans can be reinvested in tertiary education by increasing the age to be eligible to receive this type of scholarship. In other words, the proposal about changing the current scholarship and the way of how the reimbursements are to be spend, makes a trade off possible with the present situation, in which the viewpoints of the student and the Dutch society can be taken. That is, the implementation of the social feudalism can be considered as an extra cost to students for following a tertiary education due to the reimbursements of the study loans, while increasing the age to receive a scholarship can be considered as an extra benefit to the relatively older student who might not be financially able to follow a (second) tertiary education with the current scholarship. The Dutch society sees it the other way around. That is, the implementation of the social feudalism leads to more reimbursements of the study loans, which can be considered as a benefit, while reinvesting this money in tertiary education for scholarships of the relatively older student can be considered as a cost. Note that both viewpoints are compared between the proposal and the Tempobeurs (and the current scholarship). Furthermore, in order to keep equal access for every individual guaranteed, I am in favour of retaining the (income dependable) additional scholarship for individuals with relatively low income(s) (parents). This paper has been divided as follows. Section 1 starts with a literature review containing relevant information about other tertiary studies. Section 2 describes the dataset, the methods and models used, and presents the results of the OLS regression to give a first impression of the data sample. Sections 3-5 take on different views for calculating the IRR to tertiary education, which are the individual, the government, and the society, respectively. Section 6 gives a summary of the main findings with respect to the three viewpoints and ends with the final conclusion. Section 7 is the discussion of my results, in which two discussion points are treated, after which several suggestions are made for future studies about this topic. 1. Literature Review Section 1.1 Founders of econom(etr)ic models for the rate of return to education Education is a much-discussed topic among economists and econometrists. There have been published papers about this subject for over 60 years now, and its popularity is not declining. Most of these papers are about the rate of return to education. Two very well known economists have developed models to calculate such returns to education. The first economist is Becker (1964), who has introduced the Internal Rate of Return (IRR) to education, together with Schultz. They came up with the concept of human capital, which is an idea of individuals who gain skills and knowledge by investing in education, in order to enhance their chances on the labour market. Other investments that also influenced the human capital were: obtaining more labour experience, and on-the-job training (Saxton 2000). The second economist is Mincer (1974), who developed the Mincer equation and came up with a simplified version of the human capital model, by including the variable potential labour experience and putting it in an Ordinary Least Squares regression (OLS) (Card 1999). Section 1.2 Different studies & their results These models made it easier for other econom(etr)ists to work with the human capital model and interpret the coefficients accordingly. They used the human capital model to show a linear connection between education and income. That is, more years of education was directly linked with obtaining a higher ability of the individual and thus receiving a higher income. But a problem with this line of reasoning is that people who repeat a class, automatically obtain at least one year extra education, which would mean that these people had a higher ability than people who did not repeat a class (Van der Meer 2011). Another problem is that, according to Weiss (1995), the added (monetary) benefit for different levels of education were treated the same. That is, a seven-year-old individual in primary school would gain the same amount of (monetary) benefit for one year of education as a 22-year-old individual in tertiary school, which is clearly not correct. Miller (1955) has already shown proof of this in the fifties by creating age-earnings profiles, which also looked at different levels of education. He found out that the incomes of higher educated men peaked approximately ten years later than lower educated men. He also discovered that the age-earnings profiles were concave, which are consistent with the law of diminishing returns to scale. Luckily, Mincers OLS has a dependent variable (LN Wage) that takes this into account. However, Card (1999: 1807) who also used Mincers OLS, discovered a non-linear relation for people who followed tertiary education, for the second time. In his paper it is clearly visible that people who obtain a second tertiary degree (Doctor or advanced degree) earn a lot more (per hour) than people who only have obtained a first tertiary degree, which means that obtaining a second tertiary degree would result in a convex relation in the age-earnings profiles (Card 1999). All these non-linear relationships have convinced econom(etr)ists to adjust the human capital model, by looking at obtained degrees rather than a fixed amount of years of education. This is called credentialism. With the help of the obtained degree, graduated employees could signal employers that they are a high ability person. This signalling effect helped with matching people with different abilities to different jobs. Employers also specified their job qualifications, which is called screening effect, in order to find the right person for the job (Van der Meer 2011, Brown et al. 2007: 58-100). In other words, the additions of Mincers OLS were not useful only for econom(etr)ists, but also for the labour market (employees and employers). However, there are also some biases in Mincers OLS which caused some concern about the true value of the rate of return to education. The three main biases that have received a lot of attention in the literature are: the ability bias, the endogeneity bias of the schooling decision, and the measurement error bias (for instance by Griliches 1977) (Hartog et al. 1999, Card 1993). All these three biases are related to each other and are somewhat solved by using Instrumental Variables (IV) techniques. The ability bias has an upward effect on the OLS coefficient of about a maximum of 10% according to Card (1999) because it assumes some heterogeneity ability that is unobserved. That is, people with more ability are assumed to obtain more education, which lifts the coefficient of the OLS to a higher value. This bias is closely related to the endogeneity bias, which also has an upward effect on the OLS coefficient, because it looks at individuals who let their choice of enrolling for tertiary education depend on their performances at school. Those with high test scores and thus assumed to have a higher ability get enrolled while others do not. According to Micklewright (1989) there is a bias to be found here, which is the time that these individuals assumed to have right before the decision moment is too short. This means that the individual may have decided to invest more time and effort long before they took their final examinations (Oosterbeek 1990: 1364). The IV that was used to control this innate ability had to contain information about the familys ability. Instrumental Variables such as parental education were used, but only to find out that the coefficient of the IV was even larger than the original OLS coefficient. A reason for this finding was found in the third bias. The measurement error bias has a downward effect on the OLS coefficient of about 10%. But when an IV is used, the measurement error is assumed to be zero. This means that there is no downward effect on the IV, which makes the IV coefficient larger than the OLS coefficient. In some studies, however, the IV coefficient is even larger than the 10% that was caused by the measurement error bias (Card 2001). In order to control for the innate ability bias, the econom(etr)ists Ashenfelter et al. (1994) performed a quasi-experimental experiment on twins with different levels of education. They found out that when the twins were assumed to have the same innate abilities, the one who obtained more education, received a higher income than the other one. In fact, there was a 10% increase in the rate of return (Psacharopoulos 1995). This means that the observed ability bias and the measurement error bias roughly cancel each other out (Krueger et al. 2000). There remains still a small difference in the IV and OLS coefficients of around 10%. An explanation to show why the IV can still be larger than the OLS coefficient is by looking how the IV is calculated. The IV looks at the unobserved differences between only two groups, the treatment group which are the individuals that are affected by the change and the comparison group. And the OLS looks at the average of the unobserved differences between all the groups available (Card 2001). When there is a weak correlation between the instrument and the dependent variable, it may lead to a large bias in the IV coefficient (Harmon et al. 1995). Another explanation is that there are other (omitted) variables that influence the unobserved endogeneity bias, making their effect larger than the ability bias effect (Heinrich 2005, Uusitalo 1999). The studies mentioned so far all belong to the type of micro-economic studies that have data on individual observations about their education levels and their income, which are the independent variable and the dependent variable, respectively. There are also macro-economic studies that use data of the countrys average education levels and economic growth, which is the independent and dependent variable, respectively (Vermeer 2011, Venniker 2001). These macro-economic studies take a more general and broader look than micro-economic studies. That is, it sees human capital as a production factor with which it can increase the labour productivity by accumulating either more education, or more labour experience and on-the-job training. This increase in labour productivity takes place when these people get more skilled, gain more knowledge, and get acquainted with new technologies. This leads to a higher level of the initial stock of the human capital, which will also result in more economic growth whereby the society benefits. In short, the micro-economic study mainly looks from the perspective of the individual, while macro-economic studies mainly looks from the perspective of the society (Saxton 2000, Appleby et al. 2002). This is also the reason why macro-economic studies find a higher rate of return to education than micro-economic studies. Especially when the macro-economic studies took the measurement error bias into account. However, Krueger et al. (2000) do want to add that this proof is largely based on their investigation of countries with low levels of education. They noted that their results probably do not apply to the Organisation for Economic Co-operation and Development (OECD) countries, like The Netherlands. For instance, the study performed by Psacharopoulos (1985) also found this result between per capita income levels. That is, countries with low-income levels per capita had a high rate of return to education, while the opposite is true for high-income countries. This is explained by a shortage of skilled labour in the low-income countries, which increases the wages for skilled labour. While in advanced countries there is hardly a shortage for skilled labour, which means that the rate of return here will be much lower. The same kind of reasoning can be applied to the study by Hines et al. (1970) who found out with his dataset that regions with a low level of investment per student had a high (social) rate of return to education, and vice versa. Furthermore, Psacharopoulos et al. (2004) have shown in their study that this result is also visible over time. That is, when the supply of education increases, it will lead ceteris paribus to a decrease in the rate of return to education. A similar conclusion can be drawn for The Netherlands between 1962-1994. Even though there is no distinction made in the education levels in this study (Psacharopoulos et al. 2004). However, a study by Minne et al. (2007b) has shown that advanced countries with a volatile state of technology have a greater need for skilled labour, which indicates a higher rate of return to (tertiary) education and a higher economic growth. This is primarily the case for countries that are already close to the technology frontier like The Netherlands indicating that these countries should invest more in tertiary education in order to develop skilled individuals who can be used for innovation and create new technologies (Minne et al. 2007b, Psacharopoulos et al. 2004). Other micro-economic studies that have been performed over the last few years were about the economic cycle of a specific country. Economists like Lemelin et al. (1994) performed a time-series analysis and discovered that the rate of return to education was inversely related to the economic cycle. That is, in times of an expansion would the rate of return be low, while when there was a recession the rate of return would be high (Appleby et al. 2002). Their observation corresponds with Canton et al. (2005) who ascribed it to the opportunity cost hypothesis. This basically says that when there is a recession, the unemployment rate will be high, so that the opportunity cost to follow an education is lower than when the economy is in an expansion. There were also several micro-economic studies that adjusted the earnings of individuals with differing schooling levels. They accounted for the real growth rate of earnings, the mortality rate, the unemployment rate, taxes, and innate ability. But according to Psacharopoulos (1995), the only thing that happened was that these adjustments were actually cancelling each other out (pluses and minuses), which resulted in an earnings level that was approximately the same as the original one. A micro-economic study by Topel et al. (1992) who looked at the job-changing activity of employees who just started out working found out that when these employees switched jobs a lot, their income growth would be higher in the first ten years than when the employee stayed working for the same employer (Cohn et al. 1998). The micro-economic study by Becker et al. (1979), who used a theoretical model, showed that education is not the only enhancement of opportunity to remain employed. Having rich (and well-educated) parents and/or a good network of family (and/or friends) can greatly minimise the chances to become unemployed (Liu et al. 2000). To calculate the rate of return to education, Becker (1964) had argued to only put human capital variables in the regression model. That is, to avoid having variables that biases the rate of return to education downwards like the study by Monson (1979) (Psacharopoulos 1994). To make sure that the coefficients of every micro-economic study are in correspondence with each other, Mincer (1974) stressed that the control variables should be the same. Otherwise differences in the rate of return to education can be a result (Card 1999). Furthermore, Denison (1967) has shown that an additional year of education can lead to an increase in earning power between 5-6%. But according to Card et al. (1992) there is a limit (Weale 1993). That is: Overeducation is clearly possible (Weale 1993: 729). Econom(etr)ists are not the only ones who make use of the rate of return to education. As said before, employees and employers also use it, in order to increase the chances of a better job match. The government and individuals can also use these rates of return to education. That is, if the government does not have an objective to create equal chances for every individual to obtain the highest education they can follow by way of subsidizing education, then the government can use this rate of return to explain why they should allocate their public funding towards (tertiary) education. According to Psacharopoulos (1995) many authors have calculated the (social) rate of return to education as of the 1960s, which then could be used by the policymakers to justify their spending on education by comparing it with other types of investment like the rate of return to physical capital. Psacharopoulos (1985) discovered that for advanced countries in the 1960s and 1970s the rate of return to physical capital larger was than the rate of return to human capital. This means that investing in education by policy makers may have more to do with other reasons like paternal motives than looking for monetary reasons. According to McIntosh (1998) individuals also use the rate of return to education for their decision to study further after completing their compulsory education (at a certain age). There is however a small difference in gender. That is, males generally also look at the income inequality between tertiary education and secondary education, while females particularly look at their own educational achievements. The unemployment ratio hardly plays a role in this decision. But according to Mincer (1993), tertiary educated individuals will have at least three advantages over secondary education individuals, which are: the higher income, the lower unemployment rate, and a higher income growth (which was already proven by the age-earnings profiles). This lower unemployment rate occurs when there is a better job match between employee and employer, which is more probable in the case of a tertiary individual (Heinrich et al. 2005). When the difference in gender is being considered, Heinrich et al. (2005) found out that the unemployment rate of European women is lower than that of European men. They argue that when women graduate from a secondary or tertiary education, their opportunity costs of not working will be higher than primary educated women or not to be in the labour force at all. This is also a reason why the rate of return to education for women is higher than for men when an OLS regression is performed. But when the tertiary education is considered, the rates of return are almost similar (Psacharopoulos 1994). When a one-country analysis is being performed, the results do not differ. Like the study by Vaillancourt (1996), who found out the same result in Canada. Note that the rates of return for Community College were higher than the universitys Bachelor phase (Boothby et al. 2002). Or the study by Greenaway et al. (2007: 298-328) who also found the same result for The Netherlands in 1997. Hartog et al. (1999) showed in their review of the literature that this result also applies to the economic sector when a simple OLS is used. That is, for The Netherlands in 1982, females working in the private sector received a higher rate of return than their male counterparts. However, for the public sector the result is the opposite. These results are consistent with international literature about the economic sector. Like Psacharopoulos et al. (1994 and 2004), who also found out that working in the private sector received a higher rate of return than in the public sector, because an income earned in the public sector does not reflect the market wage as accurately as the income individuals receive in the private sector. They argue that the labour productivity of higher educated employees in the public sector will be lower compared to working in the dynamic and competitively private sector (The World Bank 2008). Econom(etr)ic researchers also discovered that the private rate of return is higher than the social rate of return; which is the perspective of the individual and society, respectively. For instance Psacharopoulos (1994) noted that the social rate of return incorporates all the costs that was burdened on the society, while the private rate of return only looks at the costs the individual makes. Furthermore, the benefits side only looks at the (additional) income that the individual earns with this tertiary education, which makes this an unequal comparison. Moreover, he shows that the government subsidies become larger when the level of education increases. He argues that this policy of the government has regressive income distribution implications. That is, a larger portion of the income that relatively poor people earn gets distributed to the government by way of taxation, which then will be used for the tertiary education of students and the corresponding institutions. A country that also has this system implemented is The Netherlands. According to Belot et al. (2007) have The Netherlands launched a reform in 1996, which meant a smaller scholarship for students in tertiary education. Surprisingly enough has the reform led to better results by students (by obtaining higher grades and switching less to other fields of study) and a relatively lower regressive income distribution; compared to the situation before the reform. On the flipside, reforms like the one described above lead to a relatively smaller amount of tertiary graduated individuals, compared to other European countries (Groot et al. 2003). Even though The Netherlands has shown a steady growth in the absolute enrolment of tertiary students (see Table 1). Of course, one should not forget that a higher income is also linked directly with a higher amount of (income) taxes to be collected by the tax authorities. According to Demers (1999) are tax collections a (direct) benefit to the state. In his study about Canada he calculated the fiscal rate of return and found out that for tertiary education the fiscal rates of return are pretty high (8.7-11.0%) compared to the primary and secondary education levels, which makes sense because you do not expect an average person who only finished primary education would earn a higher income and thus pay a higher amount of taxes in absolute value than an average tertiary educated individual. Many of these studies took the gross income of individuals to work with, while there are also studies available that looked at the net income of individuals. For instance, Hartog et al. (1988) have shown that when researchers take the net income as their specification to work with, their rates of returns are lower than that of the gross income. Their OLS also proved that The Netherlands in the same year (1982) obtained a higher rate of return for males than for females (Hartog et al. 1999). The use of specifications is also of importance for deciding whether a researcher should look at the nominal years of education or the actual years of education. According to Hartog et al. (1999) utilizing the same dataset, but a different specification can lead to different results. That is, the study about The Netherlands in 1994 by De Koning et al. (1996) who used the actual years of education obtained a lower rate of return than the study by Odink et al. (1997) who used the nominal years of education (Hartog et al. 1999). In short, all these studies calculating the rate of return to tertiary education show a positive result to individuals who have followed and graduated in a tertiary degree. Their society mainly benefits indirectly, like through the income increase by the newly tertiary graduates. This study corresponds a lot to the work performed by Psacharopoulos (1995), of whom framework I am using for calculating the main (monetary) returns to tertiary education with. His profiles concerning the relation between age and earnings mine is between age and income show the influence of a(n) (tertiary) education on the wages very well. The main difference is that this study looks only at the starting incomes of tertiary graduates who obtained their tertiary degree(s) only recently, while Psacharopoulos (1995) has not made this distinction. Section 2 will shed some light on this particular framework and how I have coped with incomes earned by individuals who are middle-aged or may retire soon from the labour market. 2. Data, Methods and Models Section 2.1 Data The dataset that I will be using for solving the research question comes from the Research Centre for Education and the Labour Market (ROA), which is a research institute of the Maastricht University School in Business and Economics. ROA is the coordinator of the project Research into Employment and professional FLEXibility (REFLEX), which goal was amongst others to find out the transition that students made from their acquired degrees in higher education towards employment. This dataset is developed by way of a survey among European graduates of about five years after their graduation in 1999/2000. The study that I will be performing with their dataset is a micro-economic study, focused solely on Dutch tertiary graduates of 1999/2000 and whether these individuals are employed in 2005, or not. That is, I am checking if it is worthwhile for students in The Netherlands to follow a (second) tertiary study when the economy of The Netherlands was in the middle of experiencing a period of boom. However, this study looks only at one year specifically, which means I cannot perform a time series analysis and so the influence of the economic cycle cannot be proven with certainty in this study. This drawback, however, can be altered to an advantage thanks to a large dataset with many variables. The sample that I will be using consists out of 780 individuals. There are 484 females and 296 males between the ages of 26 to 61 in 2005. These individuals are either working fulltime, part-time or are unemployed. I have excluded the self-employed individuals from this sample, because their function in a job is not depended on their schooling level or someones ability to manage (Heinrich et al. 2005). That is, starting a business for oneself does not have to be related to the individuals background or achievements. Furthermore, the focus of this study is on the relationship of tertiary education on income, which means individuals who have received an on-the-job training are also excluded from this study (Psacharopoulos et al. 1979, Hansen 1963). That is, the rate of return of this study will be related more to the acquired tertiary degree than to the on-the-job training. In addition, I would like to point out that the focus of this study is on graduated individuals only, which means that I do not look at individuals who have ceased their study. However, the contribution of these dropouts in the entry rates of tertiary education is taken into account when the tuition fees per student are calculated. That is, even though the student does not finish its study, the government then still would have paid a substantial part of the bill to allow this student to follow a tertiary education. See Table 2: Table 2: Direct costs per student per year* Scholarship (per student)**Subsidy OC&W (per student)**YearHBOWOHBOWO19942290,174621964,21069-9946,49219952466,731082177,04119-10868,6319962508,455882694,593563910,66212169,119972339,240512857,026973979,74713794,1619982536,42152170,782323964,99314701,1819992545,774652419,214884075,4215429,5520002642,2587304151,23515888,482001004527,08816461,142002004792,23616527,082003004835,99916597,96* = These costs are in euros and are already divided by total number of students in calendar years. ** = Own calculations. See also subsection 2.4.1. Other datasets that have been used throughout this study comes mainly from or are derived of official sources. That is, Section 3 has used the tuition fees of the State Secretary of the OCW (2003), the tuition fee for a single year (1992/1993) of the Customer Service of DUO (also known as Dienst Uitvoering Onderwijs), and the Gross Domestic Product deflator (GDP deflator) of the CBS StatLine. See also subsection 2.4.1a. Section 4 has used data coming from the Minister of the OCW (2001-2007) concerning the scholarship and subsidies given by the OCW to the tertiary institutes, and the GDP deflator of the CBS StatLine. See also subsection 2.4.1b. And Section 5 has used the tertiary educational costs, absolute enrolment of tertiary students, and the GDP deflator, among other things, of the CBS StatLine. See also subsection 2.4.1c. The issue of missing data has been taken care of by assuming there is a similar course based on earlier or future data. See also subsection 2.4.1. The tertiary education in The Netherlands is split up into two distinct sections. The first section is vocational higher education (HBO) and is intended for students who study for a specific occupation (or branch of occupations). The objective of these schools is to give their students the practical tools they may need to use later in the workforce. The second section is academic higher education (WO) and is intended for students who want to enrich their intelligence towards a specific direction, which does not have to be job orientated. The objective of these universities is to give their students the theoretical tools they may need to use later in the workforce, for instance in Research & Development. Tertiary education is mainly financed by the government as state aid and in a lesser extent by the individual as tuition fees. There are of course other revenues possible, like contract education in HBO (Minister of the OCW 2001: 64) and acquiring subsidies in WO in order to perform research for (non-)profit institutions (Minister of the OCW 2001: 74) but these revenues will not be taken into account in this study. Section 2.2 Methods In this study I will be focussing on two methods, which already have received a lot of attention in the literature. However, unlike the studies discussed in the literature review, this study is only looking at the tertiary education, while other studies were mainly discussing the relationship between tertiary education and secondary education (and primary education) in a country or a group of countries. Section 2.2.1 Mincers OLS The first method is the basic earnings function of Mincers OLS, which has the years of schooling and the years of working experience and its square as the independent variables, and the natural logarithm of earnings as the dependent variable. See Formula 1: LN Wi = 0 + 1 * Schoolingi + 2 * Experiencei + 3 * Experiencei + i (Formula 1) A great advantage of this method is that the basic earnings function can be extended to include several other independent variables, like: dummy variables for the different education levels of the individuals and their respective gender, and a separate independent variable for their age (Amin et al. 2005, Psacharopoulos 1995, Belzil 2005). See Formula 2: LN Wi = 0 + 1 * WO degree onlyi + 2 * HBOHBOi + 3 * HBOWOi + 4 * WOHBOi + 5 * WOWOi + 6 * Experiencei + 7 * Experiencei + 8 * Genderi + 9 * Agei + i (Formula 2) Other advantages for making an OLS are the relatively easy understanding of its coefficients by looking at their explanation power to see if they are sufficient enough and a smaller need of having a large dataset of individual observations. That is, an OLS can be made even if there are cells that have no value. For instance, when there is no individual available with a particular age for an age-earnings profile, the OLS can still be made (Psacharopoulos 2009, Appleby et al. 2002). However this method also has a few disadvantages. Firstly, an OLS has several biases and even if there is a valid instrument available, certain biases will still remain in the Two-Stage Least Squares regression (2SLS). Secondly, an actual dataset with individual observations needs to be gathered and created in order to make such an OLS (Psacharopoulos 2009). Thirdly, an OLS does not take all the relevant costs into account for calculating the private rate of return or the social rate of return, which limits this method in accuracy and can therefore bias the results. This has to do with certain assumptions of Mincers OLS. These are according to Van Elk et al. (2011): 1) a perfectly functioning of the labour market and the capital market (so people are never unemployed); 2) neglecting direct costs of obtaining a(n) (higher) education; 3) expressing all the benefits in monetary terms; 4) having an infinite time to earn back the indirect costs of obtaining a(n) (higher) education; and 5) having no externalities. Even though these assumptions apply to more methods than the OLS, in order to present reliable results, the use of this method is for this study less desirable, because there are other (simpler) methods available that can be used effectively for calculating the rate of return to (tertiary) education. The second method is the full-discounting method also known as the elaborate method or the Internal Rate of Return (IRR) which uses a cost-benefit approach to solve the following equation. See formula 3.1:  EMBED Equation.3  =  EMBED Equation.3  (Formula 3.1) The left hand side comprises the total costs of acquiring a tertiary degree in N years and the right hand side comprises the total benefits between working with the acquired tertiary degree and retirement. A big advantage of this method is that all the values on each side first need to be adjusted (or discounted) to the same base year (here: 2005) in order to avoid contamination by the inflation of a particular year. Furthermore, this method of using constant prices puts great emphasis on the present than the future when these values are discounted. That is, costs and/or benefits that take place in the far future are valued less than costs and/or benefits that take place only a few years from now (Demers 1999 and 2005). After which an age-income profile can be constructed and these values can get summed up at each side. In the end, this equation will get solved by adjusting the discount (or internal) rate r, on each side until both sides have the same value (Borland et al. 2000, Psacharopoulos 1995, Appleby et al. 2002). The calculated IRR coefficients are then used in combination with other coefficients, like the OLS to answer the (sub) research question(s) whether investing in tertiary education during a workers life should be retained by means of the current scholarship, or not. This advantage plays, according to Psacharopoulos (2009), also a large role when the early income history of the individual is taken into account. That is, when the individual does not have enough working experience obtained with that newly acquired degree, because only (a maximum of) five years have passed. For this reason I have included older individuals into this study. These middle-aged individuals also have obtained a tertiary degree and with their help I am confident of constructing a better age-income profile, than when a fixed income growth is being assumed. As other authors like Becker (1964) and Demers (2005) have done in their papers. However, the disadvantage of requiring a lot of these older observations to obtain a well-behaved age-income profile, as Psacharopoulos (2009) puts it, is partially neutralized by using a polynomial regression line. That is, I am assuming that the polynomial regression line behaves similar to Mincers OLS concerning its handling of empty cells. I have used a second-order polynomial, even though a study by Murphy et al. (1990) has shown that a higher-order polynomial shows an increase in the goodness-of-fit (Card 1999). I am afraid that by using such a higher-order polynomial, I might be biasing the age-income profiles, which can lead to mistakes in the interpretations, as Card (1999) also has stated. Unlike Mincers OLS only taking part of the costs in order to calculate the rates of return, the IRR takes all the monetary costs and monetary benefits into account for the calculation of the rates of return. However, the method of the IRR is also neglecting the possible influence of an individuals ability, its environment like the socio-economic background the individual may come from and luck, just as the biases of Mincers OLS (Appleby et al. 2002, Boothby et al. 2002). Furthermore, Mincers OLS assumes a linear relationship between peoples acquired educational degree and their log income, while Card (1999) already has shown that this is not necessarily true for the tertiary education. That is, the relationship can also have a non-linear relationship: like convex or concave. The IRR does not have this disadvantage and by combining this method with a polynomial regression line in order to deal with empty cells I believe it is still possible to create well-behaved age-income profiles. For the above-mentioned reasons, I will be using the method of Mincers OLS as a first indication of what the rate of return to tertiary education can be when a linear relationship is assumed. This method can be very useful when it gets extended by way of the inclusion of dummy variables to also investigate the effects of gender or other characteristics (Card 1999). A list of descriptive statistics about the variables used in the OLS models is shown in Table 3: Table 3: Descriptive Statistics of the OLS Models VariablesObservations (N)MeanStandard DeviationMinimum valueMaximum valueDependentLN Annual Labour Gross Income643 (780)10.19190.319889.1710.95LN Annual Labour Net Income643 (780)9.83950.264859.0110.48IndependentGross Study Duration (years)643 (780)5.461.896319HBO degree only* (Dummy)366 (448)0.56920.4955701WO degree only (Dummy)140 (170)0.21770.4130201HBOHBO** (Dummy)49 (59)0.07620.2655301HBOWO** (Dummy)64 (72)0.09950.2996101WOHBO** (Dummy)10 (13)0.01560.1238301WOWO** (Dummy)14 (18)0.02180.1460501Job Experience (years)643 (780)5.09952.02463127Job Experience Squared (years)643 (780)30.098046.684311729Age643 (780)30.304.3512656Gender (Dummy)643 (780)0.39970.4902201Private Sector (Dummy)304 (304)0.47280.4996501Public Sector (Dummy)298 (298)0.46350.4990501Supervisor*** (Dummy)170 (170)0.26440.4413501Fulltime (36+ hours) (Dummy)422 (422)0.65630.4753101Graduating in 5 years (Dummy)494 (595)0.76830.4222601Switched Job at least once (Dummy)177 (259)0.27530.4470001*: Two individuals with a second tertiary education are classified as HBO degree only, because the education level of their follow-up study is missing. This also applies to the IRR models. **: The last obtained tertiary degree of individuals who graduated before 1999/2000 is taken as their first tertiary study and the obtained tertiary degree in 1999/2000 as their second. The tertiary degree obtained by the other individuals for their first time in 1999/2000 is considered as their first tertiary study, and the follow-up studies after 1999/2000 as their second. This also applies to the IRR models. ***: Four individuals have not filled out whether they supervise, or not; I have classified them as No Supervisor. This also applies to the IRR models. The number in brackets applies to when the unemployed are included. The main purpose of these simple OLS models is to see which variables are significant and then use these variables in the main method of this study, which is the IRR. This method takes a better look at the influence of education on income and remains effective even when there is a non-linear relationship present. According to Psacharopoulos (1994) the rate of return to education when measured by Mincers OLS depicts only a (marginal) wage effect, because not all the costs are included in this method. The IRR, however, does take all the costs in consideration and is therefore the best method available for this study. Section 2.2.2 OLS Results The OLS results are only used as an indication, because this method can only take certain costs into account for the calculation of the private rate of return to education and the social rate of return to education. This method and its results will get explained extensively in this subsection, while the method of IRR will get explained in subsection 2.4. The results of the method by IRR will be divided into the Sections 3-5 in order to keep an oversight in the calculation of the different rates of return to tertiary education. The OLS only looks at the indirect costs for its calculation of the rate of return to education, which are the opportunity costs (forgone earnings). That is, the individual could have worked instead of following a higher education. It neglects the direct costs of following a tertiary education for obtaining a tertiary degree for the first or second (or even third) time (Education and Manpower Bureau 1999: 9, 12). As mentioned before, the OLS assumes a linear relationship between education and income, and therefore the use of dummy variables is highly recommended, in order to divide the individuals into segments of people that either followed a HBO study, or a WO study, or a combination of both (when more than one study has been followed). The coefficient of the schooling variable can then be interpreted as the average rate of return to an additional year of tertiary education (Psacharopoulos 1995). Mind you, the dependent variable is calculated by only looking at the individuals income, not its earnings, which can influence the age-income profiles positively. In addition, the simple OLS models do not take the unemployed into account, so that these results are comparable with other OLS studies. The upcoming results will get checked and found significant when the P-value of the variable is smaller than 0.100. Variables that have a higher P-value will mainly be used to indicate if that variable has a positive or a negative influence on the dependent variable, LN wage. To avoid variables being correlated to each other, several other independent variables are included in the extended earnings function model to reduce the unobserved characteristics as much as possible. The first OLS model looks at LN Annual Labour Gross Income and shows that the variables Gross Study Duration, Job Experience, Gender, Fulltime (36+ hours), Private Sector, and Supervisor are significant. See Table 4a regression model 5 (Limited Model): Table 4a (Summarized): Gross income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables5 Limited Model5a Limited Model(Constant)9.553(***)9.472(***)Gross Study Duration (years)0.026(***)0.046(**)Gross Study Duration Squared (years)-0.001Job Experience (years)0.029(**)0.030(**)Job Experience Squared (years)-0.001-0.001Age0.0030.003Gender0.061(***)0.059(***)Private Sector0.057(***)0.056(***)Supervisor0.063(***)0.065(***)Fulltime (36+ hours)0.338(***)0.339(***)N643*643*R38.8%39.0%(*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. This table is used as an example of how such an OLS regression model looks like; the other tables concerning the OLS regressions are found in the Appendix. Note that Table 4a in the Appendix also shows the other four OLS regression models, which are used for comparison with the IRR models. I have chosen the variable Gross Study Duration, instead of using the nominal study duration as many authors before me did, because of the large share of students exceeding the nominal study duration. For instance, the nominal study duration for HBO is four years, while in practice students graduated on average approximately one year later (five years in total). Most variables speak for themselves, except for the variables Fulltime (working 36 hours or more) and Supervisor. I have added people who worked less than 36 hours in the sample, because of a low amount of individuals working fulltime. The part-timers work between 12-35 hours a week. The addition of Supervisor plays a larger role for tertiary educated individuals, because according to a study by Butlin et al. (1997) about Canada in 1994, these individuals can find stable, autonomous jobs with a larger prospect of a promotion more easily, which may be attributable by having a larger network. In other words, supervisors (and managers) have the opportunity to develop transferable skills, making them generally more attractive for other organizations to have, where they can obtain a higher (paid) function and are therefore better equipped to recover when they lose their job. See Figure 2 in the Appendix. Furthermore, in their study they found out that men obtained a supervisory function more often than women. In my simple OLS model a similar finding can be made. In addition, even though the variable Job Experience Squared is not significant, it does have a negative coefficient, which is consistent with other literature about Job Experience showing a concave relationship in income over time. In short, these results reveal that this OLS study is comparable with other OLS studies. Meaning that: 1) males earn a higher income than females; 2) working fulltime has a positive effect on the gross income; and 3) working in the private sector has a positive effect on gross income, compared to working in the public sector. Now, when dummy variables replace the variable Gross Study Duration, I find that the dummy variables WO degree only, HBOWO, and WOWO are significant, while HBOHBO and WOHBO are not. The other independent variables remain significant. See Table 4b regression model 5 (Limited Model). The next OLS model looks at LN Annual Net Income by taking account for taxes at the Annual Gross Income and shows a similar picture as the first model. See Table 4c regression model 5 (Limited Model). That is, the same variables are significant and the variable (Job) Experience is showing a concave development, which is due to the negative coefficient of the variable (Job) Experience Squared. When dummy variables replace the variable Gross Study Duration, I find the same results as the first model, meaning that the dummy variables WO degree only, HBOWO, and WOWO are significant, while HBOHBO and WOHBO are not. The other independent variables also remain significant. Only the coefficients are a bit smaller now. See Table 4d regression model 5 (Limited Model). The same OLS regression is performed in Tables 5a-5d (regression model 5 (Limited Model)) but with a change in the reference group, which now refers to WO degree only. Only the dummy variables that are concerned with the education levels are different compared to Tables 4a-4d (regression model 5 (Limited Model)). By comparing the dummy variable WO degree only with the other dummy variables it is clear to see that the tertiary degrees that start with HBO, earn a lower income (gross and net) than the individuals who only obtained one WO degree. The follow-up studies after the tertiary degree WO show a mixed picture. That is, if a WO graduate decides to do a HBO study, (s)he might earn a lower income than someone who did not do a follow-up study. But when the WO graduate decides to do a WO study, (s)he might earn a higher income than someone with a WO degree only. In Tables 6a-6d (regression model 5 (Limited Model)), the OLS results of the two follow-up studies are compared to each other, with HBOHBO as the reference group. The dummy variable WOWO has a positive sign compared to the reference group HBOHBO, indicating that a university graduate might earn a higher income than a (vocational) higher graduate. This is in correspondence with the Tables 4a-4d (regression model 5 (Limited Model)) where amongst other things a WO graduate is compared with a HBO graduate and showing the same result. Furthermore, the dummy variables Private Sector and Supervisor show a higher coefficient than the previous OLS results, which indicate that for a sample that consists only out of individuals who obtained a second (or third) tertiary study these individuals can earn a relatively high income. That is, their high labour productivity and/or their role of supervising other employees can pay off as long as any of these two dummy variables apply to them. Previous studies that looked into the signalling effect and the screening effect have found proof in favour of and against these effects (Brown et al. 2007: 58-100). That is, according to a Dutch study about 1983 by Groot et al. (1994) there is proof of a signalling effect, because individuals who obtained their degree in the nominal years of time received a higher rate of return than individuals that took a longer time to graduate, which is true for both genders (males 5.1% > 4.4% and females 11.7% > 8.7%) (Calculated by Cohn et al. 1998: 267 from Groot et al. (1994), Table 1 page 319 and Table 2 page 320). While a study by Brown et al. (2007) found no evidence of the screening effect in The Netherlands. They reason that it depends upon: the nature of indigenous educational systems and labour markets. (Page 95) In the simple OLS models that I performed, I have not found proof of the signalling effect in The Netherlands. The reasoning by Van der Meer (2011) might also be applied to The Netherlands when the signalling effect is being considered. That is, normally, students are considered to be lazy and unmotivated if they do not graduate within the nominal years. Their private rate of return will be affected and therefore be lower than high ability students. However, The Netherlands is an exception to this rule. In this country, students may perform voluntarily extra curricular activities for instance when they are a member in a students union and perform managerial activities or do an internship that increases their years in tertiary education. This means that a longer stay in tertiary education does not have to be related to a lower private rate of return, as long as the reason for it is shown in the Curriculum Vitae. This is also the main reason why I will be looking at the averaged actual time spend on acquiring a tertiary (or higher) degree, while other authors have chosen to look at the nominal years. Hartog (2000: 133) has shown that mean measures of nominal years can change without any shifts in the production technology or the labour market (Mehta et al. 2011: 9). That is, it depends solely on the educational system as Brown et al. (2007: 84) also had put it. Despite this concern, I believe that calculating the actual rate of return to education when the data is available (or can get approximated by a polynomial regression line) is in this case better than holding on to the nominal rate of return to education. That is, in real life people may also follow tertiary education part-time, or have a part-time job in order to pay for their (tertiary) studies, or gain working experience before their graduation (by way of an internship or voluntary extra curricular activities). All of these individual situations are time consuming and are reasons why a tertiary graduate hardly ever graduate in the nominal years. Other reasons for a student not to graduate in the nominal years are retaking (previously failed) examinations and switching study / specialization (Demers 2005). Seeing that more and more students decide to follow a part-time tertiary education because of work or their family I have added these individuals into the sample. By adding these individuals I have made the sample more comparable to reality. That is, when the sample is compared to the data of the CBS in The Netherlands (CBS StatLine), I find that my sample (of the ROA dataset) is a good comparison with the real situation. These simple OLS models contain the basic independent variables to see whether the relationship with the dependent variable is comparable with the findings of other authors. Even though the coefficient of the schooling variable is a bit low, it does refer to tertiary education, in which this variable has not been divided yet (see regression model 5 (Limited Model) of Tables 4a and 4c) into the different schooling levels HBO, WO, or a combination of both. A similar reasoning applies to the variable Age, because middle-aged individuals are also included in the sample who already have acquired two (or more) tertiary degrees compared to a relatively younger individual who only has acquired one tertiary degree. When the schooling variable does get divided into the different schooling levels, it shows an increase in the rate of return to tertiary education compared to an individual who only acquired one HBO degree. Seeing that the individuals of my sample can only have a maximum of five years of working experience which is the time between graduation in 1999/2000 and filling out this survey in 2005 their increase in Job Experience would be low, which is consistent with the corresponding OLS data results. Surprisingly, the variable Gender shows a positive coefficient in favour of men; while in most other OLS studies it is the other way around. Finally the last three dummy variables speak for themselves. Overall, these simple OLS results are in correspondence with those of other authors, provided that it is only about tertiary education. With the significance of the (dummy) variables now tested, I will be performing a further investigation by putting each of these variables into the models of the IRR, which will get explained in subsection 2.3. Section 2.3 Models All these independent variables will be put into several models in order to find out which variable(s) has the largest effect on the dependent variable income by way of the acquirement of one (or more) tertiary degrees and if there are different rates of return between (the number of) tertiary degrees. 2.3.1 Different scenarios Every model for the IRR is divided into four scenarios. Scenario 1 displays the reality in which only the individuals that have found work are included into the models. Scenario 2 also displays this reality, but with an addition of including the unemployed. Scenario 3 displays a theoretical situation in which every individual that has a job works fulltime, which is set at 40 hours a week. Scenario 4 displays the same situation as scenario 3, but with the inclusion of the unemployed. The addition of the unemployed in a model for only tertiary educated individuals is of course of less importance, than when their unemployment rate is being compared with other levels of schooling, but can still provide useful information about recent graduated tertiary educated individuals who are searching for a job. By adding these unemployed individuals into the models a more complete overview of the reality can be given than when they are ignored. That is, not everyone who graduates can find a job in which they have much in common with, or they may already be satisfied with the job they had during their tertiary education (and earn a relatively moderate income even though they have much more potential), or cannot find a job at all because of being overeducated. These reasons show that ignoring these unemployed individuals can bias the IRR upwards, which is why both scenarios (1 and 2) are carried out. The sample shows a fairly good description of reality concerning the unemployment rates for males, according to Table 9b. The unemployment rates for females, however, are a bit higher but still reasonable low. To see if Scenarios 2 and 4 are viable in this sample, I have also re-tested the previous OLS models by including an independent variable for unemployed individuals (Switched Job at least once) who are familiar with being unemployed. Tables 12a12d show the results. The variable Switched Job at least once is negative and significant, which means that adding the unemployed into the IRR models should not cause problems of inconsistencies. The last two scenarios are included to show the theoretical rate of return to tertiary education if everyone worked fulltime. That is, the sample includes individuals who work part-time, for various reasons, like having to stay at home to watch the children, follow a second tertiary study, have a second job or even doing volunteer work. These reasons are on a direct competition with their time spend on work, indicating a possible downward bias on the real rate of return to tertiary education. 2.3.2 Different types and different studies There are seven different types of models that will get analysed. These seven models are in correspondence with the independent variables that were significant in the previous OLS models, which are: males only (Model A), females only (Model B), males & females (Model C), private sector (Model D), public sector (Model E), supervisor (Model F), and no supervisor (Model G). The models D-G are naturally only for working individuals only, which mean that only the scenarios 1 and 3 will get analysed for these models. Furthermore, this cost-benefit-analysis (CBA) for the models concerning the IRR method consists out of six studies. The studies look between the different tertiary education levels and the number of tertiary degrees, which are: - Study I (1 degree only) : WO vs. HBO; - Study II (2+ degrees only) : WOWO vs. HBOHBO; - Study III (2+ degrees vs. 1 degree) : WOWO vs. WO; - Study IV (2+ degrees vs. 1 degree) : WOHBO vs. WO; - Study V (2+ degrees vs. 1 degree) : HBOHBO vs. HBO; - Study VI (2+ degrees vs. 1 degree) : HBOWO vs. HBO. This specific order is to show the differences in monetary valuation between a WO study and a HBO study, whether the hypothetical individual has followed one or two (or more) tertiary studies; as in the case of Studies 1 and 2. The other studies are put deliberately in this order to show a similar difference in monetary assessment when follow-up studies are concerned, either in the same tertiary level, or a different tertiary level. In addition, Study I will be fully examined, while Studies II-VI will only get analysed by Model C (males and females), because of a low amount of observations at these studies. To get a better overview of the situation, see Figure 3 in the Appendix. 2.3.3 The hypothetical individual The cost-benefit-analyses that will be performed are about hypothetical individuals whose averaged income has been calculated from real individuals of the ROA dataset. There are two main hypothetical individuals assumed for every model in each study. The focus is put on the different choices these two individuals are faced with, with respect to further schooling investments and their subsequent income. The first hypothetical individual is a person (male / female) who after finishing HAVO (a five-year secondary schooling that gives direct access to HBO) at age 17 starts immediately with the tertiary education HBO at the age of 18. This first tertiary education for a HBO student takes, on average, about five years to finish, meaning that graduation in the (schooling) years 1999/2000 will make the average student 22 years old when deciding to follow a second tertiary study, or to work on the labour market straightaway. The second hypothetical individual is a person (male / female) who after finishing VWO (a six-year secondary schooling that gives direct access to WO) at age 18 starts with the tertiary education WO at the age of 19. The first tertiary education for a WO student takes on average seven years to complete. Therefore, graduation in the (schooling) years 1999/2000 will make the averaged student 25 years old for deciding whether to follow a second tertiary study, or not. If both of these individuals decide to follow a second tertiary study, HBO and WO will take on average an additional two and three years, respectively, to acquire this degree. See also Table 13a. For a clearer description, I have added a common age-income profile (See Figure 4 in the Appendix) and Table 13b, where every Study with their hypothetical individuals will get treated. To show how it works, I will present two examples: Example 1: Considering the two hypothetical individuals of Study I. The HBO student starts the HBO tertiary study at age 18 and finishes it five years later. At age 23 (= Q) this student graduates and starts looking for a job, which will last approximately one year, until the age of 24 (= S) where the individual starts working. The WO student starts the WO tertiary study at age 19 and finishes it seven years later. At age 26 (= R) this student will graduate and will look for a job, which also lasts for approximately one year, until the age of 27 (= T) where the individual starts working. Example 2: Now Study IV is considered. The WO student takes the same route as has been explained in Example 1. The WOHBO student starts its study at the age of 19 and finishes it nine (7+2) years later. At the age of 28 (= R) this student graduates and becomes unemployed for a year, until the age of 29 (= T) where the individual starts working. The two hypothetical individuals have in both examples a different income growth rate per year (age), of which the higher educated individual starts lower compared to the lower educated individual, but has a higher (expected) income growth rate which makes an intersection at a later age possible. These individuals keep on working until (and including) their 65th year, at which they can also retire and receive their pension. 2.4 Lemelin Table explained During their stay in tertiary education the students come across several costs and benefits, which have been mentioned earlier in subsection 2.2.1. After their graduation they are assumed to be unemployed for one year, which also brings certain costs and/or benefits to the individual, the government, and the society as a whole. And when they finally start working, the additional benefits compared to a lower tertiary study are to be compared in order to see if the longer study duration can be justified. I implicitly assume that everyone in my sample reaches the pension age of 65 alive, which means I do not adjust the results for premature death like the author Demers (2000) did in his paper. All these different costs and benefits that are related to find out the (Internal) Rate of Return to education have been nicely put into one single table drafted by Lemelin in 1998. A similar table and its components directed at tertiary education are to be discussed in the following subsections. See Table 15: Lemelin Table: Table 15: Lemelin Table SocialPrivatePublic (fiscal)AgentThe CommunityThe StudentGovernmentsCostsDirect costs: Total value of education expenses by the national, provincial, and local governments. Indirect costs: Total value of goods and services not produced (approximated by the total value of gross incomes not received).Direct costs: Total value of tuition fees and related expenses. Indirect costs: Income not received (net of tax) during schooling (opportunity cost) LESS Financial assistance to the student.Direct costs: Subsidies paid to students, tertiary institutions, communities, and companies/non-profit organizations. Indirect costs: Value of taxes not collected on income forgone during schooling.IncomesAdditional production for all of society, approximated by the additional gross incomes received by the most highly educated (including all private benefits).Additional incomes (net of tax payable) received by a tertiary graduate compared to those of someone with a lower tertiary level of education.Total value of tax collected on additional incomes received by the most highly educated.Source: Paper by Appleby et al. (2002: 6). The described costs and benefits (here: incomes) are used to create the necessary polynomial regression line, after which the new calculated values of the polynomial are put into Formula 3 in order to calculate the corresponding IRR. Do note that every amount whether they are costs or benefits is converted to a constant amount with base year 2005 to take care of a possible influence of inflation. In addition, the costs and benefits that are used in this study are per student values, which mean that for every year the monetary values get divided by the total number of students following that study (Minister of the OCW 2002: 141). Furthermore, examples of all the relevant costs and benefits for the three specialised IRRs that are discussed below are shown in Tables 16a16c. The examples show Scenario 1 of Model C for a WO student versus a HBO student, and some remarks starting from the beginning until the end of the age-income profiles. Table 16a: Costs & Benefits for the Private (Internal) Rate of Return Remarks (HBO)HBO ( )Age*WO ( )Remarks (WO)Individuals are allowed to work between the ages 13-17.0130Individuals are allowed to work between the ages 13-17.0140015001600170HBO student starts with the HBO education.0,0018-731,54WO student is following his/her last year in VWO.0,00190,00WO student starts with the WO education. In which (s)he does not receive a scholarship in the last year (7th).0,00200,000,00210,000,00220,00HBO graduate is looking for a job, lasting one year.8576,34230,00Net income received for working with a tertiary degree. The values of the ages 61-65 are the lower boundary** of this age-income profile, to take the excessive incomes into consideration. 14517,82240,0015485,6325-1480,1416382,72268550,80WO graduate is looking for a job, lasting one year.17209,122722452,18Net income received for working with a tertiary degree. The values of the ages 56-65 are the higher boundary** of this age-income profile, to take the excessive incomes into consideration.17964,822821989,2718649,812921620,2819264,103021345,2019807,693121164,0420280,583221076,7920682,763321083,4621014,243421184,0521275,033521378,5521465,103621666,9721584,483722049,3021633,163822525,5521611,133923095,7221518,404023759,8021354,974124517,8021120,844225369,7120816,004326315,5420440,464427355,2919994,234528488,9519477,284629716,5318889,644731038,0218231,304832453,4317502,254933962,7616702,505035566,0015832,055137263,1614890,905239054,2313879,045340939,2212796,485442918,1311643,235544990,9510419,265646760,809124,605746760,807759,245846760,806323,175946760,804816,406046760,804553,286146760,804553,286246760,804553,286346760,804553,286446760,804553,286546760,80 Table 16b: Costs & Benefits for the Public (Internal) Rate of Return Remarks (HBO)HBO ( )Age*WO ( )Remarks (WO)Individuals are allowed to work between the ages 13-17.0130Individuals are allowed to work between the ages 13-17.0140015001600170National government pays out subsidies to tertiary institutes and a scholarship to the HBO student, among other things.-8185,9318-3531,66WO student is following his/her last year in VWO.-7851,0019-15703,76National government pays out subsidies to tertiary institutes and a scholarship to the WO student, among other things. The scholarship for the WO student is not included in the last year (7th).-7926,1320-16852,22-7931,0721-18954,80-7815,3022-20688,20Government receives taxes over gross social assistance.1585,3823-20569,27Taxable income received for working with a tertiary degree. The values of the ages 59-65 are the lower boundary** of this age-income profile, to take the excessive taxable incomes into consideration. 5089,4124-21379,805785,7525-18278,266431,41261572,63Government receives taxes over gross social assistance.7026,382710772,60Taxable Income received for working with a tertiary degree. The values of the ages 59-65 are the higher boundary** of this age-income profile, to take the excessive incomes into consideration.7570,672810445,998064,282910185,868507,20309992,208899,44319865,029240,99329804,319531,86339810,089772,05349882,339961,553510021,0510100,373610226,2510188,503710497,9210225,953810836,0710212,723911240,7010148,804011711,8010034,204112249,389868,914212853,439652,944313523,969386,294414260,979068,954515064,458700,934615934,418282,224716870,847812,834817873,757292,764918943,146722,005020079,006100,565121281,345428,435222550,154705,625323885,443932,135425287,213107,955526755,452233,095628290,171307,545729891,36331,315831559,030,005933239,200,006033239,200,006133239,200,006233239,200,006333239,200,006433239,200,006533239,20 Table 16c: Costs & Benefits for the Social (Internal) Rate of Return Remarks (HBO)HBO ( )Age*WO ( )Remarks (WO)Individuals are allowed to work between the ages 13-17.0130Individuals are allowed to work between the ages 13-17.0140015001600170National/Provincial/Local government pay out subsidies to tertiary institutes and a scholarship to the HBO student, among other things.-11861,6318-4263,194WO student is following his/her last year in VWO.-11418,3719-20387,45National/Provincial/Local government pay out subsidies to tertiary institutes and a scholarship to the WO student, among other things. The scholarship for the WO student is not included in the last year (7th).-12047,4020-25251,43-11989,4721-26511,64-11896,2422-27349,18Society pays out the gross social assistance.-10161,7223-26138,50Gross income received for working with a tertiary degree. The values of the ages 62-65 are negative, but are still above the lower boundary** of this age-income profile.19607,2324-27063,3121271,3825-24171,9122814,1326-10123,43Society pays out the gross social assistance.24235,502733224,78Gross income received for working with a tertiary degree. The values of the ages 58-65 are the higher boundary** of this age-income profile.25535,492832435,2626714,092931806,1427771,303031337,4028707,133131029,0629521,573230881,1030214,623330893,5430786,293431066,3831236,583531399,6031565,473631893,2231772,983732547,2231859,113833361,6231823,853934336,4231667,204035471,6031389,174136767,1830989,754238223,1430468,944339839,5029826,754441616,2629063,184543553,4028178,214645650,9427171,864747908,8626044,134850327,1824795,014952905,9023424,505055645,0021932,615158544,5020319,335261604,3818584,665364824,6616728,615468205,3414751,185571746,4012652,355675447,8610432,145779309,708090,555880000,005627,575980000,004553,286080000,004553,286180000,004553,286280000,004553,286380000,004553,286480000,004553,286580000,00*: The starting age of 13 for the Internal Rates of Return is the official age a person may start to work (even if its just a few hours per week). Source:  HYPERLINK "http://zakelijk.infonu.nl/banen/58773-vanaf-welke-leeftijd-mogen-jongeren-werken.html" http://zakelijk.infonu.nl/banen/58773-vanaf-welke-leeftijd-mogen-jongeren-werken.html (downloaded 21st July 2012). **: The lower and higher boundaries of the age-income profiles are ordered over the scenarios as follows: Type of IRRPrivate (Internal) Rate of ReturnPublic (Internal) Rate of ReturnSocial (Internal) Rate of ReturnIncomesMinimumMaximumMinimumMaximumMinimumMaximumScenario 1 4553.28 46760.80 0.00 33239.20 4553.28 80000.00Scenario 2 4553.28 46760.80 0.00 33239.20- 18369.77 80000.00Scenario 3 11943.51 46760.80 3234.09 33239.20 15177.60 80000.00Scenario 4 6916.55 46760.80 739.78 33239.20- 18369.77 80000.00 2.4.1a Private Internal Rate of Return To calculate the IRR for the individual, only the costs and benefits that the individual faces should be taken into account. That is why many authors work with the (additional) net income of individuals, instead of the (additional) gross income, because every individual has to pay income taxes over their gross income to their government (Demers 2000 and 2005, Appleby et al. 2002). The cost components of the IRR consist out of direct costs and indirect costs (see Table 15). The direct costs are not only costs that need to be paid by the individual for following a tertiary education, like tuition fees, but also other necessary costs, like costs for schoolbooks, living costs, and room rent if the individual was living away from home (Demers 2000 and 2005, Voon 2001, Appleby et al. 2002). The indirect costs are costs to show what the individuals could have earned if they did not follow a higher education, also known as the forgone income (Psacharopoulos 1995, Hines et al. 1970). But seeing that this study compares tertiary educated individuals with other tertiary educated individuals, their ages for obtaining (a) tertiary degree(s) are close to each other and are therefore playing a small role in the upcoming rates of return (see Table 13b). For instance, the opportunity costs for a WO student compared to a HBO student take place in two moments in time. The first moment is when the HBO student starts a tertiary study at age 18, while the WO student is in the last year of secondary school (VWO). Even though primary school and secondary school in The Netherlands are heavily subsidized, the (parents of the) individuals still have to pay a yearly (small) educational tuition. The second moment happens when the HBO student starts working a year after graduation, while the WO student still has three years left (including the +1 year of job searching after graduation) before (s)he can start working. Table 15 also shows that if the individual has received financial assistance by the government that this has to be subtracted from the indirect costs. This financial assistance comes in the form of a scholarship, of which the HBO students started in 1995/1996 and the WO students in 1993/1994. Both types of students fall under the Tempobeurs scholarship, which gave these students financial assistance for their nominal study length plus one-year, after which they could get an additional loan for two years. The only requirement for not having to pay back the scholarship, was that they should get at least 50% of the total credit points every year that they followed a tertiary study (Minister of the OCW 2001: 86, Informatie Beheer Groep 2001:  HYPERLINK "http://www.ib-groep.nl/Images/8587_tcm7-1805.pdf" http://www.ib-groep.nl/Images/8587_tcm7-1805.pdf). For my study I assume an average nominal study length of five years plus an additional one-year, gives a scholarship of six years. Seeing that the actual average study durations from my sample for a HBO student is five years (rounded upwards from 4.51 years) and for a WO student is seven years (rounded upwards from 6.55 years), the HBO student will be fully financed, while the WO student will need a one-year study loan from the government or a different source of income in order to pay the tuition fee. See Table 13a for individuals graduating in 1999/2000 and obtaining their first tertiary education and for individuals graduating for a second (or third) tertiary study after 2000. The models do not take people into account who borrow money from the government, even though it is possible. By adding this assumption, I will not have to look at the repayments, which the students have to make if they have decided to get a study loan. Besides, the sample does not contain information about individuals who have decided to get a study loan, or not. This also influences individuals who decided to do a second (or third) tertiary study. That is, I assume that these individuals do not receive any scholarship and/or study loan, even if they still are eligible for it. Other sources of income for certain individuals are besides a scholarship receiving an allowance from their parents or having an additional job, like a summer job for fulltime students or a part-time job for the part-time students in this sample. However, the IRR models of this study do not take these sources of income into account, just as the OLS models in this study (see subsection 2.2.2). That is, the Tempobeurs scholarship is the only source of income in this study to pay the (direct and indirect) costs for following a tertiary study. The components for the benefits start to count after graduation, when the individual starts by looking for a job. The time between graduating and finding a suitable job takes an average of one year (rounded upwards) in this sample. During this year, I assume these individuals receive social assistance (welfare) net of taxes, which comprises two different amounts that are dependable on their home situation. That is, the amount of welfare is different when you are either single, or living together (can be married or just cohabitants). In Table 16a there is a small difference noticeable in the amounts of welfare given to the tertiary graduates (HBO and WO) who are looking for a job. This difference can be explained by looking at how they are calculated. Firstly, the fractions are taken of the number of singles and the number of people living together. Secondly, each fraction is then multiplied with the amount of welfare net of taxes and summed up. And thirdly, the summed up value is then divided by the total number of individuals in this scenario (singles and people that lived together) to get a new value that is related to the civil status of each tertiary graduate in this data sample concerning its educational level. Keep in mind that a similar calculation is used in Table 16b and Table 16c, which is the public IRR and the social IRR, respectively. Furthermore, its value changes almost every year to account for the inflation. This benefit then also needs to be reduced by the opportunity costs of the lower educated individual who already is active on the labour market and earns an income. However, there are individuals in this sample who still have not found a job after their job search of a year. These long termed unemployed individuals remain receiving welfare net of taxes. In addition, I assume another case for receiving welfare, which is when the individual is a single parent. The other individuals who do find a job are assumed to remain employed and receive the yearly income growth according to the calculated polynomial regression line, until (and including) their retirement age of 65. The purpose of having unemployed individuals in the sample is to give an indication of the actual IRR with respect to reality. That is, almost everyone will experience a period of (in)voluntarily unemployment in his/her life (Appleby et al. 2002: 34). The additional income (or benefit) between a high educated tertiary individual compared to a lower tertiary educated individual should either be positive or zero; otherwise there is no monetary incentive for investing in higher education. However, there are situations thinkable where a lower tertiary educated individual earns more than a higher educated individual. Situations like: 1) a low demand for higher educated individuals during an economic downturn, or 2) when lower tertiary educated individuals have more years of work experience in a certain job sector than the higher tertiary educated individuals, or 3) when a high amount of unemployed tertiary educated individuals pulls the polynomial regression line downwards, which is due to a large supply of high tertiary educated individuals (at a certain study programme); keeping everything else the same (ceteris paribus). Nevertheless, the age-income profiles remain the best available option to provide rates of return to tertiary education while specified in gender, economic sector, and supervising. These profiles have been made with the help of a second-order polynomial regression line. Firstly, the averaged incomes of every tertiary graduate in each educational level (for each model in the data sample) is taken and ordered for each age. Secondly, a second-order polynomial is used to fill in the gaps due to a lack in dispersion over the entire working life of individuals and to make the regression line smoother and thus less influential to outliers. And thirdly, each new value calculated by this polynomial is checked to see if it is between the two boundaries, set out for these age-income profiles to handle excessive income increases, and adjusted accordingly (as shown in Tables 16a16c). After the completion of the polynomial regression lines in the age-(free disposable) income profiles, IRRs for the individual will get calculated, at which a (high) positive value will indicate the attractiveness for the individual at a monetary standpoint to follow a(nother or higher) tertiary education, or not (Demers 2000). In short, the private Internal Rate of Return measures according to ODonoghue (1999): the marginal benefit to the individual to the private cost of extra schooling. (Page 252) And Shahar (2008): Specifically, the value of private rate of return will reflect how a better-informed individual could make a rational decision making of pursuing additional education or end up being employed earlier. (Page 5-6) 2.4.1b Public Internal Rate of Return Only certain costs and benefits that have to do with the government are included in the calculation of the public (fiscal) rate of return. That is, it focuses on the fiscal side of the government, which will get explained shortly. The main reason for looking at the public rate of return is the fact that The Netherlands is a country where individuals who receive high incomes have to pay a higher income tax over their (marginal) incomes. These (income) tax percentages levied on the gross income are shown in Table 17 for The Netherlands between 2001-2005. The cost components of this IRR consist like the private Internal Rate of Return out of direct costs and indirect costs (see Table 15). The direct costs are costs that are paid by the national government in the form of a subsidy to: 1) tertiary institutes, 2) communities (as state aid), 3) companies/non-profit organizations, and 4) households for the purpose of making tertiary education financially attractive (Demers 1999, Appleby et al. 2002). To remain consistent with an 18-year-old HBO student, I also included the public costs of an 18-year-old VWO pupil in these calculations and converted it to constant prices of 2005. However, the first three subsidies of WO students for the years 1994, 1995, and 1996 are due to missing data extrapolated from the years 1997-2003 by way of a linear regression. And the fourth subsidy to households of WO students for the years 1994 and 1995 is guessed also due to missing data by taking the average of the fraction for the remaining years 1996-2001 as to what was spend on WO education compared to the total scholarship given to all students, which was roughly 21% (Minister of the OCW 2007). Table 18 assumption 17 about not paying out a scholarship and/or study loan after the six available years for a Tempobeurs scholarship or following a second tertiary study is also maintained here. The simple reason for this is that the sample does not give any information about individuals receiving a scholarship or borrowing from the government by way of a study loan. The indirect costs are the losses the government has made in terms of forgone taxes (and social contributions) if the higher tertiary student follows a study that lasts longer than the lower tertiary student, measured in actual study duration years. These costs occur only during the (actual) extra study years of the higher tertiary student, because a lower tertiary student starts working earlier compared to a higher tertiary student. (Odink et al. 1998, Demers 1999 and 2005, Appleby et al. 2002). The benefit components of the public IRR starts like the private IRR with an individual looking for a job that lasts one year, after the graduation from tertiary education. During this time of unemployment, the individual receives social assistance, which also is taxed. This is carried out by the tax authorities, which is an institution that collects taxes for the government. Do note that, for the calculations of the age-income profile, this amount will be reduced by the argument made earlier about the opportunity costs of the forgone taxes of an individual who follows a lower tertiary education. Furthermore, the long termed unemployed who keep on receiving social assistance (see Table 18 assumption 20) are taxed by the government over this income. The government sees these taxes as a benefit and are therefore added to the benefit components of the age-(taxable) income profiles. When the higher tertiary educated individual finally does work, its additional income in relation to the lower tertiary educated individual gets taxed at a certain (marginal) tax rate, which may differ each year until (and including) this individual retires at age 65. See also Table 17 (Appleby et al. 2002). Note that in this study, I will not be looking at the effects of the Value Added Tax (VAT), because the dataset does not give information about how the income gets spend. With these costs and benefits the age-(taxable) income profiles are constructed and the public IRR can get calculated. This rate can then be used by the government to tell them whether investing in (tertiary) education is profitable, or not (Demers 1999). The method public Internal Rate of Return is best captured by Appleby et al. (2002): it indicates the proportion in which tax revenues exceed the costs that must be borne to support services provided in education. (Page 5) 2.4.1c Social Internal Rate of Return All the relevant costs and benefits that concern the society should be used in the social Internal Rate of Return. But certain costs and benefits cannot be expressed in monetary units yet. These so-called externalities will get explained in the next subsection with some examples concerning the tertiary sector. In addition, other forms of income, like income in kind also is not included in this study, because the dataset only gives information about employment income ( HYPERLINK "http://hhsa-pg.sdcounty.ca.gov/CalWORKS/44-100/Income_In-Kind/L_income_inkind.htm" http://hhsa-pg.sdcounty.ca.gov/CalWORKS/44-100/Income_In-Kind/L_income_inkind.htm). In other words, this Cost Benefit Analysis study (CBA) looks only at the financial motives for investing in a (higher) tertiary study. And seeing that the perspective of the society about (additional) income concerns both the individual and the government, the gross income of the individual will be used as the benefits (Amin et al. 2005, Psacharopoulos 1995 and 2009, Borland et al. 2000, Minne et al. 2007a). Just as the two other IRRs, consist the cost components of the social Internal Rate of Return also out of direct costs and indirect costs (see Table 15). The direct costs comprise the costs that are spend by all three governments national, provincial, and local for making tertiary education available for students. The social costs of a VWO student at age 18, who is only one year away from entering tertiary education, is the sum of the educational tuition paid by the individual or its parents and the public costs of a secondary student paid by the national government in order to keep the starting ages of the age-income profiles equal to each other. The fourth subsidy of transfers from the government to households for WO students between 1994-1995 applies here too; see subsection 2.4.1b of the public IRR. The extrapolated years, however, do not play a role, because I have used a different source of information to find the total direct costs, which does show the values for 1994-1996 (CBS StatLine 2012). The total direct costs for the 19-year-old WO student entering its first year in tertiary education are different than the following years. That is, the available costs for the year 1994 is presented in a net display that includes the repayments of study loans and other benefits the government may receive, like the returns for investing in Research and Development in its universities or the tuition fees paid by the HBO students while the years starting from 1995 are all presented in a gross display. To counter this effect, I have included the equipment costs and other not-to-be-divided costs as extra costs in the net display. Table 18 assumption 17 applies here too. The indirect costs of the social IRR for following a higher tertiary study compared to a lower tertiary study takes place during the (extra) study years of the higher tertiary student. The total loss in income, tax and labour productivity for not producing goods and services is best approximated by the gross income of individuals of the same age (Borland et al. 2000, Hines et al. 1970, Appleby et al. 2002). The benefit components of the social IRR begin with the individual looking for a job that lasts for one year. During this time of unemployment, the individual receives social assistance (net of taxes) and the government receives these taxes. But the society is the one who pays for this social assistance (taxes included). And even though it should be classified as a cost component, I have added it on the benefit side as a negative value, which makes no difference for the calculations of the social IRR. Furthermore, this negative benefit will be increased by the opportunity cost. That is, the lower educated individual is working during this time and receives a gross income, which is what the higher educated individual could have earned if (s)he did not follow this higher education. The same kind of reasoning applies to the long termed unemployed who receive their (taxed) social assistance from the society; who has to pay for all of this. These negative benefits are also included in the age-(gross) income profiles of the social IRR to show that graduating from a tertiary study has no extra benefit to society if the individual becomes (long termed) unemployed. After one year, the higher tertiary educated individual starts working and its additional gross income compared to the lower tertiary educated individual is added to the age-(gross) income profiles as a positive benefit (most of the time) until (and including) the retirement age of 65 (Appleby et al. 2002). The cost and benefit components are each entered in the age-(gross) income profiles in order to calculate the corresponding social IRR. As already mentioned in Section 1, the purpose of obtaining such a social IRR is that this investment in tertiary education can then be compared with other government investments that are also of great importance for the society (Psacharopoulos 1972, Shahar 2008, Borland et al. 2000). A clearer description of the social IRR comes from David Greenaway et al. (2007): The social rate of return is the discount rate that equates social costs (measured as the value of output forgone, plus teaching costs) to social benefits (measured as higher earnings and higher tax revenues after graduation). (Page 326) The methods of the IRR and the OLS are both dependable on certain assumptions. Table 18 shows the list of the used assumptions in this study: Table 18: List of Assumptions Assumption ##ExplanationAssumption 01The sample (of the dataset) used for this study only considers graduated individuals with one or more tertiary degrees obtained between 1999/2000 and 2005.Assumption 02Only regular tertiary education is taken into account (fulltime and part-time).Assumption 03The study only considers tertiary education as the schooling variable; other forms of training/schooling are excluded. Self-employed people are excluded too.Assumption 04Adjustments to the values of the sample are not made (e.g. premature death). Exceptions are: - Deleting outliers (mean +/- three times the standard deviation) or earning an income below 7.30 per hour (minimum wage per hour in 2005); - Individuals of which the months for starting/ending a tertiary education were missing, I assumed the starting month in September and their graduation month in August (= 11 months). I deleted the observations for individuals where the years were missing.Assumption 05The sample includes middle-aged individuals to show the income increases around their ages when it is not yet contaminated by their working experience at their current educational level.Assumption 06For the OLS model there is a perfectly functioning of the labour market and the capital market considered, it neglects direct costs of obtaining a(n) (higher) education, it expresses all the benefits in monetary terms, it has an infinite time to earn back the indirect costs of obtaining a(n) (higher) education, and there are no externalities assumed.Assumption 07The OLS and IRR models perform a partial equilibrium analysis.Assumption 08The OLS and IRR models work with the (average of) actual years of working experience and the actual years of the study duration.Assumption 09The OLS and IRR models only take the monetary costs and benefits into account for the calculation of the rates of return; externalities are excluded.Assumption 10The OLS models only consider income as the monetary benefits. And the IRR models consider income and social assistance as the monetary benefits, which are paid out to the working population and the unemployed population, respectively. The right of receiving an unemployment benefit directly after getting sacked is ignored. The unemployed are assumed to be put into the welfare system straightaway.Assumption 11How the additional income is being spent is beyond the scope of this study (e.g. influence on Value Added Tax is therefore excluded).Assumption 12The handling of empty cells in an age-income profile for an IRR model is taken care of by using a combination of a polynomial regression line and implementing a minimum and maximum income.Assumption 13All costs and benefits of the age-income profiles are converted to constant prices (2005).Assumption 14For the IRR models, both hypothetical individuals of HBO and WO start and end at the same ages, which are 18 years and 65 years, respectively.Assumption 15The job search of the IRR model lasts for one year, after graduation.Assumption 16The scholarship received by the individual is considered enough to cover the direct and indirect costs the individual might come across. Other forms of income during the tertiary education are excluded in the calculations. See also footnotes 77 and 78 of Section 2.Assumption 17The scholarship lasts for a maximum of six years and does not include a second tertiary study. Getting a study loan is excluded too.Assumption 18The notion follow-up study used here is related to individuals who have obtained a second (or third) tertiary degree in an educational level, which can be different than the educational level of their first tertiary degree.Assumption 19When the graduated individual is unemployed and is living together, his/her partner is considered unemployed too.Assumption 20All gross income is being taxed (e.g. gross social assistance).Assumption 21Every income in the age-income profiles is assumed to be unaffected by earlier working experience obtained by the new tertiary degree (max five years). Making current incomes and future incomes for every (hypothetical) individual more comparable to each other by way of the polynomial regression line. 2.4.2 Role of (positive) externalities The human capital theory see Section 1 believes that the social IRR equals the private IRR, even though both IRRs include different costs and benefits and therefore should not be considered equal to each other. Other theories like the one of credentialism see Section 1 argue that the social IRR is smaller than the private IRR. And they may be right. According to Psacharopoulos (2009) this is caused by the public subsidization of (tertiary) education in The Netherlands, thereby increasing the costs of the social IRR. However, there are other benefits (costs) the so-called externalities that should be included in the social IRR too, making the social IRR higher (lower) than what it is now (Van der Meer 2011, Psacharopoulos 2009). A description of an externality is in the words of Pritchett (2001): the impact of education on aggregate output is greater than the aggregation of the individual impacts. (Page 368) Or in the words of Rosen et al. (2008): An activity of one entity affects the welfare of another entity in a way that is outside the market. (Page 46) Both descriptions reveal in their own words that these externalities get excluded from the CBA, because they cannot get expressed in monetary units as of yet. And, as long as these externalities are not converted to monetary taxes/subsidies, the social IRR will always be smaller than the private IRR, thereby biasing the social IRR downwards and the decisions made by the national government towards investing in (tertiary) education. Examples of positive externalities that take place in general, thanks to tertiary education in comparison with other lower education levels are: 1) public good, 2) better health, 3) smaller use of public services, 4) more social cohesion, 5) lower fertility, and 6) better match between employer and employee. A general example of a negative externality is a point already made in Section 1, which is the regressive income distribution system that favours individuals that have children. That is, these parents can send their children to follow a tertiary education at the cost of the society by way of the scholarship and the subsidies made to the tertiary institutions all at the expense of individuals who do not have children. Examples of (positive) externalities that are more specific to this study of WO versus HBO are: 1) open minded thinking, and 2) globalisation. To sum up, there are a lot of non-monetary benefits to gain next to the monetary benefits for following a(nother or higher) tertiary education for both the individual and the government and thus the society as a whole to keep the access to tertiary education open and affordable for everyone. The following three sections examine (mainly) the financial aspect of this topic and look at who the main beneficiary is and therefore should (continue to) be charged for these costs. 3. The Individual Viewpoint Section 3.1 First sub research question The results of the IRR method concerning the perspective of the individual will get treated in this section. These coefficients will be used to give an answer to the first sub research question, which is: To what extent is it worthwhile for a tertiary student in The Netherlands around the turn of the century to follow a(nother or higher) tertiary education (compared to the one already obtained) when only financial motives are considered? In order to solve this question, the focus of this research begins with subsection 3.2 looking at the differences in coefficients between a HBO study and a WO study with one degree and afterwards with two degrees; which are follow-up studies in the same tertiary education level (1 degree vs. 1 degree and 2 degrees vs. 2 degrees, respectively). After that, the differences between follow-up studies HBO and WO are compared with the original tertiary study that was obtained first (2 degrees vs. 1 degree). Not only are the IRR coefficients compared to each other, but they are also compared with the OLS coefficients, to see if the latter really are a good first indication when only certain costs and benefits are taken into account. Subsection 3.3 treats the biases upwards and downwards that are included in the research models implicitly when certain assumptions were made. They can influence the calculated coefficients positively and/or negatively, depending on which bias has a stronger effect. And subsection 3.4 gives an answer to the sub research question for all six types of students and a short discussion is made about which calculated coefficient should receive more attention than the other and in what degree the bias(es) of the assumptions play a role in them. Section 3.2 Comparison of coefficients: OLS vs. IRR This subsection uses Table 22a for its comparisons. The OLS coefficients are taken from Tables 4d, 5d, and 6d. When a particular OLS coefficient is not significant at a (minimum) significance level of 90%, only the sign will be interpreted correctly, which is shown here with a plus (+) sign and a minus () sign, to indicate a positive and a negative effect, respectively. As already mentioned in Section 2, the OLS models are restricted to the first scenario, but are more diversified in the research models (AG), because all six studies are taken into account. Conversely, the IRR models take all four scenarios into account, but are restricted to only one research model (C) for studies IIVI, because of a low amount of observations. In short, both models have an added benefit for answering the sub research question. Table 22a: Private (Internal) Rate of Return Scenario & ModelOLS / IRRStudy IStudy IIStudy IIIStudy IVStudy VStudy VILong-term interest rate (foreign)Long-term interest rate (domestic)Private Internal Rate of Return1AOLS35.2% (***)++-0.8% (**)+25.2% (**)--IRR9.32%1BOLS18.1% (***)+-17.9% (**)8.1% (**)IRR10.64%1COLS17.9% (***)+-24.5% (***)8.1% (**)IRR9.80%1.22%VNVN3.09%7.20%1DOLS26.7% (***)++-10.9% (**)+18.2% (**)IRR9.37%1EOLS16.3% (***)+-21.3% (**)7.8% (**)IRRVN1FOLS27.1% (***)++-14.0% (***)+17.8% (**)IRR9.21%1GOLS17.7% (***)+-23.4% (***)8.4% (**)IRR9.33%2AIRR11.33%2BIRR3.96%2CIRR3.01%2.50%1.98%7.30%0.49%8.40%3AIRR-2.03%3BIRR10.58%3CIRR10.40%1.72%VNVN3.08%6.23%3DIRR9.38%3EIRR10.88%3FIRR10.40%3GIRRVN4AIRR9.45%4BIRR1.19%4CIRR-0.59%2.23%2.93%8.08%3.90%8.20%(*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. The pluses and minuses are used when the particular coefficient is positive or negative, respectively, but not significant at the minimum significance level of 90%. The abbreviation VN stands for a Very Negative coefficient, which in percentage terms is larger than -100%. Section 3.2.1 Study I (1 degree vs. 1 degree) Starting with the first scenario, a first glance at the coefficients reveals that all IRR models have a lower coefficient than the OLS models. This was foreseen, because the IRR method takes more costs into account than the OLS method. This corresponds with the result found at research model C (9.80% (IRR) < 17.9% (OLS)). When gender is considered, research models A (men) and B (women) show a discrepancy. That is, the OLS model shows that WO educated males earn relatively almost double the additional income of what WO educated females earn compared to their HBO educated genders (35.2% > 18.1%), while the IRR model reveals that the differences between WO males and WO females compared to HBO males and HBO females are rather small, and are here in favour of females (9.32% < 10.64%). The reason is found in the incorporation of the direct costs of the IRR method. That is, on average, females tend to spend less time on obtaining a tertiary degree than males normally do (Demers 2005: 4-5). This finding also applies to this sample about The Netherlands. Seeing that the OLS method has not taken these direct costs into account, different conclusions can be made with the same sample. In this situation is the IRR method superior to the OLS method, whereby the OLS coefficients are to be handled with care. The results from research models D (private sector) and E (public sector) are consistent with the literature. That is, both methods show the private sector being more profitable than the public sector (OLS: 26.7% > 16.3%, and IRR: 9.37% > VN) for a WO graduate compared to a HBO graduate. The OLS results from research models F (supervisor) and G (no supervisor) are also in line with the literature, while the IRR results differ only marginally from each other. That is, being a supervisor is more profitable than being a labourer who does not supervise when only incomes are taken into consideration (OLS: 27.1% > 17.7%). But when educational costs are included, the difference between the coefficients becomes very small (IRR: 9.21% < 9.33%) favouring model G, the model where the individual is not supervising. Scenario 2 shows the results when the income of the unemployed (welfare benefits) is incorporated in the research models. This addition should have a reducing effect on the IRR coefficients, making it smaller than the coefficients in scenario 1. This argument applies to research model C (3.01% < 9.80%). The same argument also applies to research model B (3.96% < 10.64%), but not for research model A, whereby the coefficient in scenario 2 is higher for males than in scenario 1 (11.33% > 9.32%). The reason is found in the relative distributions of the unemployed in the sample. That is, research model A has according to Table 19a in relative terms a better balance of unemployed HBO and WO males (minimum and maximum values of both scenarios do not change). And even though the WO males have relatively more unemployed, their age-income profile reaches until the age of 38, after which the polynomial increases making the reducing effect of the unemployed have a smaller impact on the coefficient. The relative distributions of the unemployed for WO females are different than their HBO counterparts. That is, the WO females have relatively older unemployed individuals according to Table 19a. The latter effect is clearly visible when both genders are included, which make the differences in coefficients between research model B and C similar to each other. A peculiar result is shown in Table 19b where there is a large reduction in the absolute (mean and median) incomes for WO females compared to the HBO females between scenarios 1 and 2. Note that differences in absolute incomes are not reported in the coefficients, only the relative incomes. Scenario 3 depicts a theoretical situation when every individual is working fulltime compared to scenario 1 where individuals could work both part-time and fulltime. The relative distributions should play an important role in the differences between the coefficients of scenarios 1 and 3. However, according to Table 19a is the range in the ages of WO graduated individuals inferior compared to the HBO graduated individuals who can show a better age-income profile (ages 2741 vs. ages 2655, respectively). That is, the polynomial regression line has to fill in many gaps that the WO educated individuals have left unaccounted for. The following results may, therefore, not be too accurate for these research models. For instance, research model C has relatively more HBO part-timers than WO part-timers (128/366 > 39/140), which should lead to a reducing effect on the relevant coefficient when all part-timers are converted to fulltime workers. The result, however, is in favour of scenario 3 (10.40% > 9.80%). An argument can be made when looking at the incomes. That is, WO individuals earn a higher income than HBO individuals receive (according to the mean and median incomes and its peak years of Table 19b). This argument and having a small range in the age-income profile are plausible enough for explaining this sudden increase of the coefficient. The arguments made about the relative distributions and a small range in the age-income profile apply to research model A, by having less WO part-timers compared to HBO part-timers (5/57 < 19/149). This reducing impact becomes larger when the latter argument is taken into account turning the corresponding coefficient negative compared to scenario 1 (-2.03% < 9.32%). The same two arguments apply to research model B only the impact of the age-income profile is non-existent. That is, there are relatively less WO part-timers (34/83) than there are HBO part-timers (109/217) making scenario 3 roughly equal to scenario 1 (10.58% < 10.64%). Research model D has their individuals with relatively low and high incomes well distributed over the age-income profile even though the WO individual has a small age-income profile  for scenario 3 and scenario 1 making the coefficients similar to each other (9.38% H" 9.37%). Scenario 1 of research model E has relatively older individuals that brought the polynomial down, while when everyone would have worked fulltime (scenario 3) an increasing polynomial would be the result. Resulting in scenario 3 having a higher coefficient than scenario 1 (10.88% > VN). And even though working in the private sector is more profitable in absolute terms than working in the public sector (according to the mean and median incomes of Table 19b), in relative terms is the additional income higher when a WO individual works fulltime for the public sector compared to a HBO individual. This is visible in the height of the IRR coefficients. Research model F has a higher coefficient for scenario 3 than scenario 1 (10.40% > 9.21%), because working fulltime gives you a higher wage than working part-time which the polynomial line picks up by filling the gaps and making it steeper. Research model G corresponds with the relative distributions argument, because there are less WO part-timers compared to HBO part-timers (29/95 < 99/262). However, due to the individuals now having a fixed amount of hours for working (fulltime), the polynomial that showed an increasing trend in scenario 1 for the WO individual becomes a decreasing trend in scenario 3. This leads to obtain an age-income profile that has a summed up negative additional income for the WO individuals compared to the HBO individuals, which makes the IRR coefficient to become a VN value for WO individuals. Subsection 3.3.2 bias effect 10 will discuss what happened. These results show that working as a supervisor makes you earn a higher income in both absolute and relative terms, which is in accordance to the literature. The results of scenario 4 show a similar picture as the results of scenario 2, but the effects on the coefficients are more extreme, because there are no more part-timers in the sample that could smooth the effects. That is, the reducing effect of including the unemployed leads to a lower coefficient for research model C, which (compared to scenario 3) even becomes negative (-0.59% < 10.40%). The same argument about part-timers smoothing the polynomial regression line applies to research model B (1.19% < 10.58%), whereby the difference in coefficients of these two scenarios compared to the difference in coefficients of scenarios 1 and 2 has only increased. Research model A also has a higher coefficient compared to scenario 3 (9.45% > -2.03%), which might be caused by the relative distributions of the unemployed in the sample. This is the same argument made in scenario 2. Do note that according to Table 19a, the range of WO males lies between the ages of 27-38, while the HBO males are between the ages of 26-49, which means that for WO males aged 40+ the incomes are all guessed by the model. The range in ages for models B and C are more in line with the HBO individuals, which are close to the retirement age. Section 3.2.2 Study II (2 degrees vs. 2 degrees) Table 22a shows that none of the OLS coefficients of study II are significant at a (minimum) significance level of 90%. Also, there should not be any odd results coming from the polynomial regression line, because the range in ages is well divided over the age-income profile for all scenarios. Research model C of scenario 1 shows positive results for a WO educated individual with a second tertiary study in WO compared to a HBO educated individual with a second tertiary study in HBO for the OLS and the IRR model (+ and 1.22%, respectively). The other research models (A, B, D-G) calculated by the OLS method, all show positive results, but cannot be compared with each, because the coefficients are not significant. The IRR method for the coefficient of research model C is in scenario 2 higher than the coefficient in scenario 1 (2.50% > 1.22%). This finding is not too surprising when there are more HBOHBO individuals that are unemployed compared to the WOWO individuals (see Table 19a). This effect decreases the summed up incomes of the HBOHBO individuals more than the WOWO individuals, which results in an increase in the summed up additional incomes of the WOWO individuals. In scenario 3 reports the IRR method a larger coefficient for research model C compared to scenario 1 (1.72% > 1.22%, respectively). This is again caused by the loss of part-timers that could smooth the age-income profiles. Both follow-up educations have now relatively more extreme age-income profiles. That is, the age-income profile of HBOHBO has become less concave, while that of WOWO has become more convex. The argument made in scenario 2 also applies to scenario 4. That is, the coefficient in scenario 4 is higher than scenario 3 (2.23% > 1.72%), because there are more HBOHBO unemployed individuals than WOWO unemployed individuals. Furthermore, this study shows that the differences in coefficients are now smaller between scenarios 4 and 3, than between scenarios 2 and 1, despite the conversion of part-timers to full-timers. Looking at these differences help to make a better comparison between the actual situation and the theoretical situation. Section 3.2.3 Study III & Study IV (2 degrees vs. 1 degree) The reference group used in this subsection is the WO graduated individuals. They are compared with the two follow-up studies HBO and WO. Table 22a shows that only the signs of the OLS coefficients of study III can be used correctly. Table 19a shows that the range in ages for the follow-up study in study IV is rather limited to the early years of the individuals (between the ages of 29-43), which might have an undesirable effect on the age-income profiles of all four scenarios. The same might apply to scenario 1 and 3 of the WO study, which only has individuals between the ages of 27-41. The OLS coefficients are in scenario 1 negative for research model C, which means that in this general model both follow-up studies after WO have no additional monetary value to the student. The same goes for the IRR method (shown for both studies here with a VN) compared to follow only one tertiary study in WO. The OLS coefficients of the follow-up studies compared to WO are in general negative with respect to the other research models. The follow-up study WO, however, is positive for the males (model A), the private sector (model D), and when the individual is a supervisor (model F), but there is not anything to say about how large the coefficients are. The follow-up study HBO only has negative coefficients to present. The models A, D, and F are comparable with the follow-up study WO, by way of having lower negative coefficients than their counterparts (models B, E, and G, respectively). This implies that the gender male, working in the private sector, and being a supervisor for both follow-up studies have positive effects on the coefficients, whereby the coefficients in study III even become positive, while those from study IV remain negative. In scenario 2 a remarkable change has taken place. The IRR method now shows (high) positive coefficients for study III and study IV compared to scenario 1 (1.98% > VN, and 7.30% > VN, respectively). This change is attributable to the spread of unemployed over the education levels. That is, the follow-up studies have mainly young unemployed individuals (Table 19a mean values: WOWO S1: 32.29 and S2: 31.78; WOHBO S1: 34.80 and S2: 33.77), which keep the age-income profiles increase in value. While the reference group WO has relatively older unemployed individuals (Table 19a mean values: WO S1: 30.63 and S2: 31.34) making the age-income profile decrease. This leads to a (larger) positive coefficient compared to the situation in scenario 1. Scenario 3 has no positive results for following a second tertiary study. That is, when everyone would have worked fulltime, the IRR method shows that there is not an additional monetary benefit for the individual to obtain for doing a follow-up study in either WO (study III: VN) or HBO (study IV: VN). This means that these individuals do not earn as much as they should make compared to the individuals who have not followed a follow-up study. The results of scenario 4 are a combination of scenarios 2 and 3. The influence of the unemployed makes the reference group of WO individuals show a negative polynomial regression line, and the influence of having only fulltime workers makes the age-income profiles a bit steeper. These two effects lead to a higher positive result for studies III and IV compared to scenario 2 (2.93% > 1.98% and 8.08% > 7.30%, respectively). Section 3.2.4 Study V & Study VI (2 degrees vs. 1 degree) When the reference group becomes HBO, the coefficients of both methods become more consistent with each other, which according to Table 19a and Table 22a might be responsible by the number of observations and an even spread of individuals over the ages. However, as in the previous subsection are the OLS coefficients of the same follow-up study insignificant. The signs of these OLS coefficients are sufficient to see what the separate influences are when only the incomes are taken into account. Research model C of scenario 1 shows mixed OLS results. The coefficient of study V is negative, while the coefficient in study VI is positive ( and 8.1%, respectively). The IRR method, however, shows only positive coefficients for the follow-up studies HBO and WO (3.09% and 7.20%, respectively). The other research models show a similar relationship as what was seen in the previous subsection. In other words, a male, or someone who worked in the private sector, or as a supervisor earned in relative terms a higher income than their counterparts. The only difference is that study VI presents positive coefficients, while study IV only has negative coefficients, which is surprising seeing that both studies have a different follow-up study than the original study. The IRR coefficients in scenario 2 show that the influence of the unemployed to the reference group is large, but to the HBOHBO individuals even larger, which might be caused by having relatively older unemployed individuals (Table 19a mean values HBOHBO S1: 32.24 and S2: 33.17). In study V is the IRR coefficient decreasing compared to scenario 1 (0.49% < 3.09%), while in study VI it increases (8.40% > 7.20%). Scenario 3 does not show much difference in the coefficients compared to scenario 1 (study V: 3.08% < 3.09%, and study VI: 6.23% < 7.20%). Do note that the relative distribution of the part-timers in study V was larger for the HBOHBO individuals (26/49) than the reference group (128/366), but at the same time has the former age-income profile become steeper to counter that effect. In study VI there were more part-timers for the reference group than the HBOWO individuals (in relative terms: 128/366 > 14/64), which made the latter age-income profile less steep and resulted roughly in a one percentage point difference in coefficients. The two effects in scenario 3 are both present in scenario 4 for study V. The influence of the unemployed, however, leads to an additional positive effect on the polynomial regression line of the HBOHBO individuals, shifting it higher than the individuals of the reference group. By making the polynomial regression line of HBOHBO steeper and remembering that early incomes are receiving a higher weight than latter incomes, the IRR coefficient becomes higher than scenario 2 (3.90% > 0.49%). In study VI is the polynomial regression line of the reference group only mildly negatively affected by the unemployed, while the HBOWO individuals keep having a positive increasing polynomial regression line. The (converted) fulltime workers of HBOWO make in combination with the unemployed the polynomial regression line shift up, but also a bit flatter compared to scenario 2, leading to two coefficients that are roughly the same (8.20% < 8.40%). To sum up, the OLS coefficients which are only influenced by the incomes of the individuals show a similar relationship with the IRR coefficients. That is, when all the costs and benefits are included in the calculations, the differences become smaller, but the relationship remains intact. This difference is best seen in research model C for all six studies. When the OLS coefficient is (very) positive, the IRR coefficient corresponds with a (large) positive coefficient (studies I and VI). When it is negative or only shows a positive sign, the IRR coefficient is small or nonexistent (studies II, III, IV, and V). Section 3.3 Biases stemming from the assumptions In this subsection are the biases treated, which all have a (presumable) positive and/or negative influence on the coefficients presented in the previous subsection. I will start with the upward and downward biases, and finish with the undecided biases. Section 3.3.1 Upward bias 1. The OLS method focuses on earned incomes only to show wage effects between schooling levels and other individual characteristics concerning jobs thereby neglecting the unemployed and the direct and indirect costs (assumption 6 and assumption 10 of Table 18), which also have an important effect on the coefficient and the resulting conclusion. This is an upward bias, which has been resolved by using a second method: the Internal Rate of Return. 2. The research models use a partial equilibrium analysis (assumption 7 of Table 18) that assumes there is not an influence on the labour market for newly tertiary graduated individuals when only one extra individual is added to the large pool of tertiary graduated. However, looking at Figure 5, there is a steady increase in the percentage of tertiary graduated in the Dutch labour force, which means the partial equilibrium analysis is not valid for implementation to real life. That is, theoretically, every extra newly graduated individual means one tertiary levelled job less available on the labour market, leaving eventually only below-tertiary levelled jobs with a lower income to be earned to be acquired by these newly graduated. This analysis should therefore lead to an upward bias. 3. The polynomial regression line for both methods includes relatively middle-aged individuals who all have received some labour experience with their first tertiary degree, before they obtained their second tertiary degree, despite the assumptions made in Table 18 (assumption 5 and assumption 21). The time between these two graduations can run-up to thirty years. Even though this working experience will get averaged with other individuals, all of these individuals earn an income that is affected by their labour experience. This has a small upward bias on the individuals who have obtained a second tertiary degree for the study IIVI. 4. Assumption 19 in Table 18 leads to an upward bias to all models that includes the unemployed. That is, suppose a situation where only one person of a household is working, while the other one (here classified as the unemployed) watches the children. This simple example shows that assumption 19 where the welfare benefit is multiplied by two may not be valid to all the cohabitant/married unemployed individuals of this sample. 5. The sample used for these studies is about The Netherlands in 2005. This one-year analysis cannot incorporate the whole business cycle in order to average the effects of the economy. In other words, The Netherlands was experiencing a period of boom in 2005-2008 according to Figure 1, which indicates an upward bias effect on the calculated coefficient. 6. According to Figure 1, The Netherlands was experiencing a period of low economic activity before 1997 and a period of boom in 1997-2000. The latter period lasted longer for this sample for which the opportunity cost hypothesis reasons that there will be a downward bias effect on the calculated coefficient. However, the reference category also has this downward bias, which means the downward bias is largely negated. Nevertheless, there is another bias effect present on the coefficients that apply to study II-VI. The Netherlands was experiencing a period of low economic activity in 2000-2004, which is when the individuals were following a second tertiary study. Following the reasoning of the opportunity cost hypothesis, there will be an upward bias effect present on the corresponding coefficients that is considered to be larger than the downward bias mentioned earlier. Section 3.3.2 Downward bias 7. Individuals with missing information are put in the reference categories. In the case of schooling levels, these individuals are classified to the highest tertiary degree they have obtained, which most of the time is HBO degree only. When it is work related, then they are classified to the public sector, or no supervisor. This small upward bias in the reference category leads then to a small downward bias effect in the affected coefficients of models AC, E, and G of study I, study V, and study VI. 8. There is a small downward bias present in assumption 8 of Table 18. By working with averaged real study durations and actual working experience in contrast to the nominal study durations and potential working experience, respectively the cost to the individual who has a longer stay in tertiary education increases, while its working experience is decreasing when (in)voluntary unemployment is taken into account. Both variables are rounded upwards to whole years, which mean the combination of more costs and less income over the years will lead to a lower coefficient for all six studies. However, do note that every study except HBO degree only concerning the actual study duration is affected more or less by this bias, which means the effects can get crossed off each other, leaving only a small effect visible to the coefficient. 9. The intention of scenario 3 and scenario 4 is to show the theoretical IRR to tertiary education when everyone works a fixed amount of 40 hours a week. In other words, the first two scenarios of the sample also included part-timers who did not work fulltime, despite their high wage per hour. These individuals did not receive the wages they could earn and can be considered as a loss to them. Scenario 3 and scenario 4 are therefore used to tackle this specific downward bias effect. Do note that this downward bias effect can turn into an upward bias effect, when the IRR coefficients of the actual situation (scenarios 1 and 2) are higher than the IRR coefficients of the theoretical situation (scenarios 3 and 4). 10. Scenario 3 and scenario 4 also introduced a new small downward bias into the models. That is, there are individuals who have a workweek of more than 40 hours, which means both scenarios decreased the incomes of these individuals. Fortunately, there were only 14 individuals whose incomes were downgraded of which the highest four individuals had a workweek of 50 hours, while the remaining ten individuals are between these two amounts. Note that this downward bias effect can also turn into an upward bias effect, when the IRR coefficient of the reference category is affected more by this downward bias than the comparison category. 11. Then there is also the issue of the scholarship, with respect to assumption 16 and assumption 17 of Table 18. By assuming the scholarship lasting only for a maximum of six years without having considered any other forms of income earned during tertiary education (and the second tertiary studies) it will lead to a downward bias. That is, the calculations in the IRR take the full direct costs of following a seventh year in WO and second tertiary studies into account, while there are individuals in the dataset who have a part-time job or could borrow money from the government by way of a study loan. These actions would lead to lower (or no) costs for these extra study years, making the coefficient higher than what is found now, considering that a WO student has a longer study duration than a HBO student (also applies to a second tertiary study). Note that this downward bias does not apply to HBO degree only individuals. Section 3.3.3 Undecided bias 12. The first assumption in Table 18 leads to a small upward bias to all the coefficients of the first and second tertiary studies (study Istudy VI). That is, by excluding costs some individuals made for following a higher (or second) tertiary degree but did not obtain it (yet), results in having biased coefficients. However, this bias affects all the compared educational categories and their reference categories in the research studies IVI, which means this bias effect, will be classified as undecided. 13. Assumption 4 of Table 18 mentions two adjustments that have been made to the sample. The first adjustment is concerned with the (upper) outliers, which cannot be explained by the model. By deleting these individuals and implementing a maximum income for individuals to earn, I have introduced a downward bias to the coefficients. The second adjustment concerns individuals who earn an income below the minimum wage per hour in 2005. Deleting these individuals and implementing a minimum income for individuals to earn, an upward bias is introduced to the coefficients. Seeing that both adjustments have opposite effects on the coefficients and neither of them is sure enough to have a stronger effect than the other, it is classified as an undecided bias. Section 3.4 Conclusion Now that the coefficients of the two methods have been compared with each other and the effects of the assumptions have been presented, an answer can be given to the first sub research question for all the corresponding studies that have been looked at by way of the research models. Do note that the IRR coefficients of research model 1C reflecting the real situation with respect to the working individual is highly valued in this section than the other IRR coefficients. Section 3.4.1 Study I The results of Table 22a depict a positive picture for an individual to follow a WO study compared to a HBO study, because almost all of the coefficients are significant and positive. The difference in gender is hardly noticeable in the real situation (scenario 1), but becomes more prominent when the unemployed are included and males earn a relatively higher income compared to their female counterpart. Something similar can be said for the private sector and the role of supervisor, which are both good determinants to better replicate the market wage with. These results are the same when only (converted) full-timers are used, even though the differences in coefficients of research model C have widened, thereby turning the coefficient in scenario 4 negative. Furthermore, the upward bias effects (25) for study I are smaller than the downward bias effects (69, 10, and 11). Especially since certain downward biases only affect the WO students, which should make their coefficients even larger. In short, an individual choosing which first tertiary degree to obtain should choose for a WO study, when only monetary incentives are considered. Section 3.4.2 Study II The follow-up study with the same first tertiary degree shows only positive results for a WOWO graduate compared to a HBOHBO graduate. Unfortunately, I cannot say whether the private sector and the role of supervisor are more beneficial than their counterparts, because the OLS coefficients did not give significant results back. The coefficient even increased when the unemployed are included. A similar picture is shown when the (converted) full-timers are used. Only now is the difference in coefficients of scenario 3 and scenario 4 a bit smaller than the difference in the coefficients of scenario 1 and scenario 2. In addition, the upward bias effects (26) are larger than the downward bias effects (8, 9 and 11), but are too small to be of an influence on the calculated coefficient. In short, a follow-up study in the same tertiary degree is more profitable for a WO graduate compared to a HBO graduate, when only monetary incentives are considered. Section 3.4.3 Study III & IV Individuals who have a WO degree and wonder if a second tertiary degree would be beneficial have two options. In order to get promoted they can continue at their present level and get another WO degree (study III) or they can advance in the other tertiary level available and get a HBO degree (study IV). The first situation compared with the referenced category of WO degree only shows negative results in the real situation. The second situation is not much different. Being a male, working in the private sector, or being a supervisor has a positive influence, but it is too small to change these overall negative results. Even the (converted) full-timers show a result that is in favour of the referenced category. Only when the unemployed are included, do the models give positive coefficients in both directions of second tertiary degrees. However, the bias effects may also affect these coefficients. That is, the spread of the unemployed over the referenced category and the two follow-up studies are uneven distributed, favouring the follow-up studies. Additionally, the upward bias effects (26, and 10) are now overwhelming the downward bias effects (8 and 11). In short, following a second tertiary study after having acquired a WO degree shows no additional benefit for the individual, when only monetary incentives are considered. Section 3.4.4 Study V & VI The options for HBO educated individuals are the same as the ones previously described. That is, they too face the option of following a second tertiary education in either HBO (study V) or WO (study VI). The results are positive in the first situation and show high positive returns in the second situation. The determinants for replicating a market wage is just like study I also uphold here, by having (high) positive results for the male, working in the private sector, and being a supervisor. The results for (converted) full-timers only are similar to the first scenario. The results remain positive when the unemployed are added, but they show a much better result for the individuals who decided to do a second tertiary study in WO than in HBO. Moreover, the upward bias effects (26, 9 (study VI), and 10) are dominating the downward bias effects (7, 8, and 11), thereby reducing the calculated coefficients. These upward biases, however, are considered to be too small to change the sub conclusion. In short, HBO graduated individuals should continue to follow another tertiary degree of which the follow-up study in WO, is highly recommended when monetary incentives are considered. 4. The Governmental Viewpoint Section 4.1 Second sub research question This section treats the results of the IRR method with respect to the view of the government. The corresponding coefficients will be used to answer the following sub research question: Is it wise for the Dutch government to invest in (higher) tertiary education around the turn of the century from a monetary standpoint? That is, do the (mostly) future tax benefits compensate the costs made on the tertiary institutions until student graduation? Or should the Dutch government better invest their money in alternative governmental investment opportunities? To answer these questions, I have used a similar layout as Section 3. That is, in subsection 4.2 I will discuss the IRR results and compare them with an alternative governmental expense, which is an average of the long-term interest rates in government bonds of the EU12 area (without The Netherlands). In other words, the coefficients of human capital are being compared with the coefficient of physical capital (here viewed as the social opportunity cost of capital by way of a government bond) (Weale 1993: 729, Voon 2001: 508). Subsection 4.3 looks at the bias effects that certain assumptions have on the IRR coefficients and takes special interest on the notion basic qualification. This notion is important, because by equalizing the ages of the two main hypothetical individuals set at 18 years old for both the HBO student and the WO student, of which the latter is in its last year of pre-university education (VWO6) an important downward bias effect is included in all the IRR models with respect to the public IRR. In other words, the institutional costs the Dutch government makes concerning the 18-year-old WO student should get discarded from the calculations, because these costs would have been made even if the WO student would not follow a (higher) tertiary education. That is, every Dutch individual is supposed to get a basic qualification in order to function properly on the Dutch labour market. This minimum level for my two hypothetical individuals in HBO and WO is set at finishing secondary schooling (HAVO and VWO, respectively). And subsection 4.4 gives an answer to the second sub research question with respect to the six types of students and mentions which IRR coefficient is valued more than the others when the assumptions are taken into consideration. Section 4.2 Comparison of coefficients: IRR vs. government bond (NLD excl.) This subsection uses the IRR coefficients that are mentioned in Table 22b. Just as Section 3, is study I more diversified in the number of research models, while studies IIVI only mention research model C because of a low amount of observations. The arguments with respect to the different studies and scenarios made in Section 3 also apply to this section and will therefore not be repeated (unless it needs to be clarified). Table 22b: Public (Internal) Rate of Return Scenario & ModelOLS / IRRStudy IStudy IIStudy IIIStudy IVStudy VStudy VILong-term interest rate (foreign)*Public Internal Rate of Return1AIRR4.12%3.32% (EU12: NLD excl.)1BIRR5.09%1CIRR4.79%0.25%VNVN2.78%4.97%1DIRR4.86%1EIRRVN1FIRR4.58%1GIRR4.13%2AIRR6.05%2BIRR-2.00%2CIRR-2.12%1.06%2.20%7.71%0.23%5.71%3AIRR-6.70%3BIRR5.78%3CIRR5.81%0.33%VNVN3.66%4.48%3DIRR5.43%3EIRR5.56%3FIRR5.92%3GIRRVN4AIRR4.78%4BIRR-5.94%4CIRR-5.36%0.91%2.67%8.48%3.63%5.77%*: The foreign long-term interest rate of 2005 covers the EU12 member states, which are European countries that have adopted the use of the euro. These are: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, and Spain. Own calculations, based on data from OECD statistics. In the case of the public IRR, the Dutch government cannot lend out money to themselves, so this long-term (capital) interest rate is without The Netherlands. The abbreviation VN stands for a Very Negative coefficient, which in percentage terms is larger than -100%. In addition, the IRR coefficients of model C are now compared with the long-term interest rate (foreign) also included in Table 22b to see which form of capital is considered the best investment opportunity for the Dutch government before any of the bias effects of the assumptions are taken into account when only monetary incentives are considered. Section 4.2.1 Study I (1 degree vs. 1 degree) The IRR coefficients reveal at first sight mostly positive results compared to the lower tertiary education forms. However, these coefficients are to be compared with the long-term interest rate (foreign), which is set (by way of a calculation) at a relatively high average interest rate from which the Dutch government can receive interest payments. Note that this fixed interest rate is an average of the EU12 area without The Netherlands, which means the real long-term interest rate, can lie above and below this average when the Dutch government decides to invest in a certain European country. Scenario 1 shows positive results for research model C (4.79%) and more specifically when gender is concerned it is clear to see that women have a relatively higher IRR than men do (5.09% > 4.12%), but both IRRs are positive. However, do remember that just as in Section 3 the WO males have a poor range in ages for their age-income profile, which makes the polynomial regression line quite uncertain for the higher ages of the model. The research models D and F are both higher than their counterparts E and G, when labour productivity is concerned (4.86% > VN, and 4.58% > 4.13%, respectively). Scenario 2 shows a different picture. Research model C is now negative (-2.12%), which may be the result of WO females having their peak incomes at the start of the age-income profiles, while those of HBO females reach their peak at a later age; shown in Table 20b. The negative result for WO females (research model B: -2.00%) even offsets the additional incomes the WO males received compared to the HBO males (research model A: 6.05%). These income differences are also noticeable in the age-income profiles that are negatively influenced by the addition of the unemployed WO females. It has made these profiles decreasing convex while in scenario 1 it was increasing convex resulting in a negative coefficient to both models B and C. Scenario 3 shows somewhat similar pictures as scenario 1, only now are the coefficients either higher or lower, because of the (converted) full-timers. For instance, models B and C show both a higher positive result (5.78% and 5.81%, respectively), while model A shows a negative result (-6.70%). The latter model has a negative coefficient, because of the relative distributions and having a small range in its age-income profile (see subsection 3.2.1). In addition, model G shows a VN coefficient instead of model E, which does have a coefficient now. And model D could have been higher than its counterpart model E if I had not corrected the incomes with a minimum and a maximum income to deal with the excessive incomes. This means working in the private sector gives a lower additional income than when working in the public sector, but it is still positive (5.43% < 5.56%). And working as a supervisor keeps showing a higher IRR coefficient than working as a labourer (5.92% > VN). Scenario 4 also shows a similar result as scenario 2. That is, model A is now positive (4.78%), while models B and C are negative (-5.94% and -5.36%, respectively). The differences in coefficients are relatively large, because there are only (converted) full-timers in this scenario. The comparison with the long-term interest rate (foreign) shows that in the normal situation (scenario 1) a WO education compared to a HBO education has a higher IRR than investing in an average European country. The decision to invest in tertiary education becomes even firmer when only (converted) full-timers are considered (scenario 3) by way of a higher IRR coefficient. However, not every tertiary educated individual finds a suitable job or becomes unemployed for other reasons and goes therefore on welfare, which has a downward pulling effect on the average incomes of the other individuals of the age-income profiles. In this sample it shows that the IRR becomes negative when the unemployed are taken into account (scenario 2) and becomes even worse when only (converted) full-timers are considered (scenario 4). In these cases the preliminary decision to invest in tertiary education switches to the alternative, favouring the long-term interest rate (foreign) for the Dutch government to spend their money on. Also note that the poor range in age concerning the age-income profile of the WO individual in scenario 1 and scenario 3 may not reflect the right polynomial regression line, which is an argument also made in Section 3. Section 4.2.2 Study II (2 degrees vs. 2 degrees) Scenario 1 shows a positive result for research model C (0.25%), which means the Dutch government receives a positive IRR for a hypothetical working graduate with two WO degrees compared to a hypothetical working graduate with two HBO degrees. The result for model C becomes a bit more positive for the government when the unemployed are included (1.06% in scenario 2). These results remain positive when only (converted) full-time individuals are used in the models of scenario 3 and scenario 4. These IRR coefficients are similar to the actual situation (0.33% and 0.91%, respectively). The comparison of these IRRs with the long-term interest rate (foreign) shows that the Dutch government should invest in a European country, rather than in a second WO tertiary education for WO individuals compared to HBOHBO graduated individuals when costs and benefits are the only measuring instruments to decide on. This preliminary deliberation of the government remains valid even when the investment opportunity in tertiary education has a positive IRR when the unemployed are included, which has a positive (increasing) influence on the corresponding IRR coefficient. Section 4.2.3 Study III & Study IV (2 degrees vs. 1 degree) The reference category is WO and the two follow-up studies that are discussed are WO and HBO, respectively. Keep in mind that scenarios 1 and 3 of the reference category and the values of WOHBO are not very reliable, because of the poor range in age of the individuals (shown in Table 20a), which therefore also have an unreliable effect on the corresponding age-income profiles and the resulting polynomial regression line, as mentioned in subsection 3.2.3. The follow-up study in WO of scenarios 1 and 3 shows a VN coefficient, which means the sum of tertiary costs for following a second tertiary study and their additional benefits is negative. The IRR coefficients of scenario 2 and 4, however, are positive and do not have unreliable values in their age-income profiles, which makes these coefficients more trustworthy (2.20% and 2.67%, respectively). The follow-up study in HBO also shows VN coefficients for scenarios 1 and 3. The IRR coefficients of scenarios 2 and 4 are positive, but unlike the previous follow-up study are these values unreliable in their age-income profiles, which make their coefficients also unreliable (7.71% and 8.48%, respectively). After comparing the coefficients of scenario 2 with the long-term interest rate (foreign) it shows that the Dutch government should only invest in a second tertiary study after WO, if the individual chooses to do a follow-up study in HBO. But when the follow-up study is WO, then the government can do better by investing in an average European country. Do note that this preliminary decision is more certain in the follow-up study in WO (study III) than in HBO (study IV), which is due to the poor range in age of the individuals. Section 4.2.4 Study V & Study VI (2 degrees vs. 1 degree) The reference category changes to HBO and the two follow-up studies discussed are HBO and WO, respectively. These age-income profiles have unlike the previous profiles in subsection 4.2.3 a higher range in age for all four scenarios, making the corresponding polynomial regression lines and their coefficients more credible. Scenario 1 of the same follow-up study as the reference category shows a positive result, but the coefficient decreases to nearly zero when the unemployed are taken into account (2.78% and 0.23%, respectively). The theoretical IRR of both these scenarios are higher when only (converted) full-timers were used (3.66% for scenario 3 and 3.63% for scenario 4, respectively). The IRR coefficient of scenario 1 shows a positive result, when a different follow-up study is chosen compared to the reference category and increases a bit when the unemployed are put in the model (4.97% and 5.71%, respectively). These two coefficients are both quite close to the coefficients of the theoretical situation when only (converted) full-timers are used (4.48% for scenario 3 and 5.77% for scenario 4, respectively). Despite the presentation of positive IRR coefficients, is the comparison with the long-term interest rate leading to a mixed preliminary result. That is, the Dutch government can get a higher positive IRR if the HBO individual has decided to do a HBO study (study V) by investing in an average European country. But if the HBO individual has chosen a WO study (study VI) then the best option for the government is to invest in a second tertiary education. To sum up, the preliminary decisions for the Dutch government about the first two studies in which university education is compared with higher tertiary education are favouring the long-term interest rate (foreign). The Dutch government has in the next two studies mixed favourites concerning the follow-up studies after WO. That is, the alternative government expense gets favoured when the follow-up study is WO, but switches to investing in a second tertiary education when the follow-up study is HBO. Despite having a questionable age-income profile (study IV). The last two studies in which the follow-up studies of a HBO education are discussed show promising results for the Dutch government if every individual was working fulltime, however, this was not the case. Only the HBO individual who obtained a WO degree would earn such a high IRR that it would convince the Dutch government to invest in a second tertiary education, instead of in an average European country which would have been the case, if the HBO individual would choose to obtain a second HBO degree. Section 4.3 Biases stemming from the assumptions This subsection is used to show what kind of (presumable) effect the assumptions have on the resulting IRR coefficients. I will start with the upward biases then move on to the downward biases and finish with the undecided biases. Do note that the bias effect of individuals not obtaining their tertiary degrees for the first or second time corresponding with the first assumption made in Table 18 (or see bias effect 12 in subsection 3.3.3) does not apply to the view of the government. That is, the calculation of the (costs of the) public IRR uses for its denominator all the registered students of a particular year and is not limiting itself to the graduated individuals. Section 4.3.1 Upward bias 1. The partial equilibrium analysis (assumption 7 of Table 18) has a small effect on the benefits of the government. This analysis follows bias effect 2 in subsection 3.3.1 about newly higher tertiary educated individuals having to accept an inferior (below tertiary) job, because there are no jobs at their level (or field of study) available. These individuals will, therefore, earn a lower income than what is predicted by the IRR model and its data sample. A lower income received means they have fewer (income) taxes to pay to the tax authority. The government could see this as a loss in benefits (over time), which makes this analysis have an upward bias effect. 2. The relatively middle-aged individuals in the sample who may have received some labour experience with their previous educational degrees can influence their current incomes. The averaged (income) tax that needs to be paid to the tax authority by the hypothetical individual is therefore upward biased. (See also bias effect 3 in subsection 3.3.1). 3. Tertiary educated individuals without a job can either be single, a single parent, or living together. All three situations comprise a different welfare benefit to be taxed and collected by the tax authority. Assumption 19 in Table 18, however, may overestimate the welfare benefit to the married or cohabiting individuals, which means the tax authority will collect less than what is assumed by this assumption. This could lead to a small upward bias for the government. (See also bias effect 4 in subsection 3.3.1 for an example). 4. The sample presents The Netherlands in 2005 when the country was experiencing a period of boom. There will be an upward bias effect present on the coefficients of the government when only the upper part of the business cycle is taken into account. (See also bias effect 5 in subsection 3.3.1). 5. According to the opportunity cost hypothesis has the period of boom in 1997-2000 a very small downward bias effect on the coefficients of study I-II. The period 2000-2004 is characterized by low economic activity and, therefore, has an upward bias effect on the coefficients of study II-VI. (See also bias effect 6 in subsection 3.3.1). Section 4.3.2 Downward bias 6. Individuals with missing information are put in the reference categories, which can lead to a small upward bias effect to the affected coefficients of models AC, E, and G of study I, study V, and study VI. That is, if the reference category is upward biased, then the resulting coefficient (of the comparison category) will be downward biased. (See also bias effect 7 in subsection 3.3.2). 7. Even though using the average real study durations and actual working experience to calculate the actual costs of following a tertiary education is practical for the IRR method, by rounding these numbers upwards they also introduce a small downward bias effect to the government who are assumed to also pay for all these extended years (rounded upwards). However, in some cases it can have a larger impact on the coefficients when realizing that some of these costs happen relatively early in the age-income profiles with respect to these individuals, which are when discounting does not have a large effect, yet, on these costs. (See also bias effect 8 in subsection 3.3.2). 8. The introduction of scenarios 3 and 4 to the IRR models with a theoretical fixed 40 hours workweek is intended to show what the individuals could have earned if they did not have other obligations like children or a second part-time job. In other words, the tax authority should have received more taxes if everyone would have worked fulltime, which means there is a downward bias present. However, if the coefficients of the actual situation are higher than that of the theoretical situation, then scenarios 1 and 2 will be upward biased. (See also bias effect 9 in subsection 3.3.2). 9. The use of a fixed workweek also resulted in 14 individuals who had a longer workweek and thus now earning a diminished income introduced a small downward bias effect. This means the tax authority will receive (on average) a smaller amount of taxes, thereby negatively influencing the benefits to the government. Note that this downward bias effect can also turn into an upward bias effect, when the IRR coefficient of the reference category is affected more by this downward bias than the compared category. (See also bias effect 10 in subsection 3.3.2). 10. Assumption 11 of Table 18 excludes how the additional incomes after obtaining a higher / second tertiary education get spend. That is, people with more income are assumed to spend a larger part of this income on normal and luxury goods. The government is benefiting by way of the Value Added Taxes that is on these goods. Ignoring these expenses in the IRR coefficients leads to a small downward bias, because the reference categories like HBO only and WO only are affected in the same manner. Note that this bias effect might become an upward bias when the mean incomes of the educational levels are being compared (Table 20b) and are higher for the reference category. 11. The equalization of the ages of the two hypothetical individuals has led to a downward bias effect to all individuals who have started their tertiary educational career with a WO study. The basic qualification is responsible for this as is mentioned in subsection 4.1 and the costs in the first year should not therefore be included in the calculation of the public IRR. Section 4.3.3 Undecided bias 12. The WO individuals had for a few years several costs missing that were needed for the calculation of the public IRR. To solve this problem I used two solutions. Solution 1 is to obtain the costs of the other known years and extrapolate them to the missing years by way of a linear regression (performed on the direct costs for three years, excluding the scholarship). Solution 2 is to take the fraction of the other years with known costs and multiply it with the total costs of the missing years (performed on the scholarship for two years). Seeing that these early costs affecting the public IRR are guessed, there is no way to say with certainty whether there is an upward bias or a downward bias present. For that reason, it is classified as an undecided bias. 13. Individuals who have spend more than six years following a tertiary education which are the WO individuals and the individuals who obtained a second tertiary degree have a right to borrow money from the government as a part of their scholarship. The public costs in the calculations of the IRR models should have been higher, because two years of lending out money to the students have been excluded. This leads to an upward bias effect to the public IRR. However, these same individuals could also have worked in a summer job or a part-time job. The tax authority would then receive (income) taxes, which would increase the public benefits. This would lead to a downward bias effect to the public IRR. Furthermore, the sample does not give information away which student has used the right of a study loan or in which year(s) the student had worked during its tertiary studies. Both biases work in an opposite direction, which leads to classify this also as an undecided bias. (See also bias effect 11 subsection 3.3.2). 14. The implementation of a minimum and a maximum income into the IRR models has led to an upward and a downward bias, respectively, with respect to the individual. The governmental benefits are influenced by means of the marginal taxes on these incomes that increase after each income level (fourth tax scale is the highest, see Table 17). This implementation has opposite effects on the coefficients, which is why it is classified as an undecided bias. (See also bias effect 13 in subsection 3.3.3). Section 4.4 Conclusion With the help of the bias effects in subsection 4.3 a definite answer can be given to the second sub research question. And whether the preliminary conclusions of subsection 4.2 were on the right track, or should be adjusted to the findings in subsection 4.3. Do note that with a change in perspective, the IRR coefficients of research model 2C reflecting the real situation with respect to the graduated individual are now highly valued in this section than the other IRR coefficients. That is, taxes generated by individuals who work in a private/public sector, supervise others or do not, or on the dole are the benefits to the government that matter. It shows the government what kind of rate of return they can expect with the Tempobeurs scholarship considering their many years of investing in tertiary education. Section 4.4.1 Study I Subsection 4.2.1 favoured the alternative investment opportunity. After taking the bias effects of study I into account, of which the upward bias effects (14, and 8) are a bit smaller than the downward bias effects (57, and 911), the preliminary conclusion is assumed to be the right one. Simply because the coefficient in scenario 2 and scenario 4 were both negative, making the additional influence of the downward bias effects on these coefficients too small to alter the decision of the government to invest in a higher tertiary education. In other words, the Dutch government would be financially better off to invest in a European country, rather than in a WO education (compared to a HBO education), when all these monetary effects of the costs and benefits are taken into account. Section 4.4.2 Study II Subsection 4.2.2 also favoured the alternative investment opportunity. And the bias effects in study II do not have an additional influence to change this outcome. That is, the upward bias effects (15, and 8) are dominating the downward bias effects (7, 10, and 11). In other words, the Dutch government would again be financially better off if they would invest in a European country than in a follow-up study of WO in the same tertiary degree (compared to a HBOHBO study), when all these monetary effects of the costs and benefits are taken into account. Section 4.4.3 Study III & IV Subsection 4.2.3 is in favour of investing in the alternative investment opportunity when the follow-up study is WO, but switches to a second tertiary education when the follow-up study is HBO. The bias effects of the assumptions in study III and study IV have a positive influence on the IRR coefficients. That is, the upward bias effects (15, and 9) are dominating the downward bias effects (7, 8, and 10), which result in a high IRR coefficient for scenario 2. In addition, study IVs age-income profile of the WOHBO individuals has been incomplete when the range in the age is taken into account. That is, Table 20a shows the range in ages of this follow-up study between 29- and 43-year-olds, which is very slim for constructing an accurate age-income profile. Despite these reasons is the calculated coefficient considered to be large enough to keep the preliminary conclusion intact. In other words, the Dutch government can be better off by investing in a European country when the follow-up study is WO (compared to a single tertiary study in WO). But when the follow-up study is HBO, then they should invest in a second tertiary study, when all these monetary effects of the costs and benefits are taken into account. Section 4.4.4 Study V & VI Subsection 4.2.4 favours the alternative investment opportunity when the follow-up study is HBO, but switches to (a second tertiary) education when the follow-up study is WO. The influences of the bias effects are too small to change the preliminary decisions with respect to the government. That is, despite having upward bias effects (15, and 9) that are larger than the downward bias effects (6, 7, 8 (study V), and 10 (study V + study VI)), the difference between the IRR coefficients in scenario 2 and the alternative investment opportunity is too large to be influenced by this effect. In other words, the Dutch government should only invest in a second tertiary study after HBO when the follow-up study is WO. If the follow-up study is HBO, then the Dutch government can be financially better off if they would invest in a European country, when all monetary effects of the costs and benefits are taken into account. 5. The Societal Viewpoint Section 5.1 Third sub research question This section looks at the IRR results of a sample of the Dutch labour force from the view of the Dutch society. The coefficients will be used to answer the final sub research question: Should the Dutch society keep on investing in (higher) tertiary education around the turn of the century from a monetary point of view, when other investment opportunities are available with a more reliable rate of return? The viewpoint of the Dutch society is broader than the Dutch government in the room for expenditures. That is, the income taxes of the Dutch labour force that are normally used by the government to invest in (tertiary) education and/or investing in an European country can now also be used to spend on the government bonds of the home country. Now there will be two alternative investment opportunities for the IRR to be compared with. And both alternative investments guarantee a fixed certain return for a number of years. The investments in tertiary education, however, does not only benefit the individual and the government, but also the society by way of externalities (see Section 2). Keep in mind that if individuals stay unemployed after graduating (a maximum of) five years ago, they receive welfare benefits that are paid by the society. These payments will have a negative influence on the IRR coefficients, but are not considered a bias effect, because in reality there (almost) never is a perfect labour market without unemployment. That is, there is bound to be someone in-between jobs. The structure of this section follows a combination of the two previous sections. That is, the IRR coefficients are in subsection 5.2 compared with the OLS coefficients to determine the wage effects of the different education levels when only the benefit side is considered (just as in subsection 3.2). Then a comparison is made between the IRR coefficients and the two alternative investment opportunities to see which rate of return human capital or physical capital is most profitable when a CBA is made. This subsection ends with a preliminary conclusion for each study (just as in subsection 4.2). Subsection 5.3 takes the bias effects of the assumptions into account, of which the basic qualification assumption among others also has an influence on these IRR coefficients (see Section 4). And subsection 5.4 answers the third sub research question for each study by pointing out which IRR coefficient is of importance to the Dutch society while taking also the bias effects into account. Section 5.2 Comparison of coefficients: OLS vs. IRR vs. government bonds (NLD only & EU12 area) This subsection uses the OLS coefficients in Tables 4b, 5b, and 6b to compare them with the IRR coefficients in Table 22c. The OLS coefficients give a better overview of the different research models used in a single scenario (scenario 1), while the IRR coefficients take a closer look at the different scenarios available for a single research model (C). Table 22c: Social (Internal) Rate of Return Scenario & ModelOLS / IRRStudy IStudy IIStudy IIIStudy IVStudy VStudy VILong-term interest rate (domestic)*Social Internal Rate of Return1AOLS42.2% (***)++-0.9% (**)+30.2% (**)3.37% (NLD only) 3.33% (Averaged EU12)IRR5.74%1BOLS21.6% (***)+-21.5% (**)9.6% (**)IRR6.65%1COLS21.4% (***)+-29.5% (***)9.6% (**)IRR6.26%0.52%VNVN2.72%5.75%1DOLS32.1% (***)++-13.0% (**)+21.8% (**)IRR6.26%1EOLS19.5% (***)+-25.6% (**)9.2% (**)IRRVN1FOLS32.5% (***)++-16.8% (***)+21.3% (**)IRR6.00%1GOLS21.2% (***)+-28.1% (***)10.0% (**)IRR5.69%2AIRR8.30%2BIRRVN2CIRRVN1.91%4.58%9.07%5.55%9.85%3AIRR-5.30%3BIRR7.08%3CIRR7.09%0.77%VNVN2.77%5.03%3DIRR6.55%3EIRR7.03%3FIRR7.22%3GIRRVN4AIRR7.54%4BIRRVN4CIRRVN2.22%5.70%9.88%8.31%9.62%*: The foreign long-term interest rate of 2005 covers the EU12 member states, which are European countries that have adopted the use of the euro. These are: Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, and Spain. Own calculations, based on data from OECD statistics. In the case of the social IRR, the Dutch labour force can invest their money into government bonds of The Netherlands, or another country of the EU12 (here: averaged to take the EU12 member states of 2005 into account). (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. The pluses and minuses are used when the particular coefficient is positive or negative, respectively, but not significant at the minimum significance level of 90%. The abbreviation VN stands for a Very Negative coefficient, which in percentage terms is larger than -100%. The arguments with respect to the different studies and scenarios made in Section 3 (and 4) also apply to this section and will therefore not be repeated (unless it needs to be clarified). Furthermore, the IRR coefficients are also compared with the long-term interest rates (domestic) see Table 22c of which the physical capital rate of return in The Netherlands is to be compared first to see which form of capital in The Netherlands is considered the best investment opportunity for the Dutch society before any of the bias effects of the assumptions are taken into account and when only monetary incentives are considered. Afterwards, the physical capital rate of return of the EU12 area is compared to see if they are also profitable if the Dutch society would disperse their risks more, with respect to investing their money. Section 5.2.1 Study I (1 degree vs. 1 degree) The OLS coefficients are in scenario 1 significant from a (minimum of) 90% significance level and they are all positive just as the IRR coefficients, which indicate that the Dutch society can obtain a higher rate of return for a WO student compared to a HBO student. This is visible in the general research model C for both the OLS method and the IRR method (21.4% and 6.26%, respectively). The coefficient of the IRR method is of course lower due to the incorporation of the direct and indirect costs. The difference in actual study durations with respect to gender is also seen here (see Section 3). That is, men have a relatively higher OLS coefficient than women do when only wages matter (42.2% > 21.6%, respectively). But when the actual costs of following a tertiary education are taken into account, then women are receiving a relatively higher coefficient than men do, according to the IRR method (6.65% > 5.74%). The remaining research models in this scenario show a similar picture as in the previous two sections. That is, working in the private sector and being a supervisor can lead to a higher rate of return for both methods compared to their counterparts (OLS: 32.1% > 19.5% and 32.5% > 21.2%, and IRR: 6.26% > VN and 6.00% > 5.69%, respectively). In addition, the former social IRR coefficients are also good indicators of the marginal product of labour in a country, of which the positive values show that a higher tertiary study can still be beneficial to the Dutch society. Table 21a shows for scenario 2 an improvement in the age-income profiles for the general and female gender models (models C and B, respectively) for both education levels. But for the WO student of the male gender model stays the range in age poorly between 27 and 38 years, which results in an IRR coefficient that may not be accurate. The general research model and the female gender model have no additional monetary value to the society (indicated here by two VN coefficients). The male gender model is the only research model that shows a (high) positive IRR coefficient (8.30%), but there is open doubt if this coefficient is correct. The IRR coefficients of the (converted) full-timers in scenario 3 show a similar picture as in scenario 1. That is, an overall good result if everyone would have worked fulltime according to research model C (7.09%). The female model also has a higher IRR coefficient in this scenario than its male counterpart (7.08% > -5.30%). Model As negative result may be partly influenced by the poor range in the available ages of the age-income profile of the WO student. Another reason could be that according to Table 21b the peak of a male WO student is at the early age of 28 after which it is only decreasing, while the peak of a male HBO student reaches his maximum value at the age of 36 meaning that this age-income profile has still an increasing part to show. Note that the absolute income difference in both the mean and the median income favours the male WO student, but has no real influence on the IRR coefficient, because only the relative incomes are taken into account. In other words, even though the additional gross income of the WO individual remained positive, it could not overcome the relatively high additional education costs for following a WO education. Unlike scenario 1, is the IRR coefficient of the private sector now smaller than that of the public sector (6.55% < 7.03%), even though the differences in the absolute incomes differ marginally, according to Table 21b. Being a supervisor does show a better result than its counterpart (7.22% > VN). These former two IRR coefficients also show the theoretical marginal product of labour to the Dutch society, of what could have been obtained if only (converted) full-timers were included in the calculations of these IRR coefficients. Model Gs result might be linked to the relative distributions argument, just as in model A. Only now has it according to Table 21b led to a summed up negative additional income for the WO individuals compared to the HBO individuals, which means there is no additional benefit for the Dutch society to obtain for investing in a WO education, if these individuals do not become a supervisor. Scenario 4 shows a similar picture as scenario 2, but with higher percentages. The general model C is again VN, just as model B, while model A is positive (7.54%). The result of model A is again criticized due to its poor range in ages. The comparison of the general IRR research models (C) with the long-term interest rate (domestic) is favoured by the human capital rate of return when only working individuals are taken into account (scenario 1 and 3), but switches to the physical capital rate of return when also the unemployed are included. Seeing that the Dutch society comprises everyone, its preliminary decision would be to invest in the long-term interest rate (domestic). There would be no difference for them for deciding to invest in The Netherlands or an averaged European country, because these rates of return are close to each other. Section 5.2.2 Study II (2 degrees vs. 2 degrees) All OLS coefficients in scenario 1 are not significant at the (minimum) significance level of 90%. They do, however, have a positive sign, which indicate that the Dutch society benefits from the additional income of the WOWO graduate compared to the HBOHBO graduate. The only IRR coefficient that is available for this scenario is the general research model C, which presents a positive coefficient when the direct and indirect costs are taken into account (0.52%). The IRR coefficient of scenario 2 increases due to the relative distribution argument (1.91%). That is, there are more unemployed individuals in HBOHBO than in WOWO available in the sample, which has a positive effect on the IRR coefficient. The relative distribution argument also applies to scenario 3, which has more HBOHBO part-timers than WOWO part-timers. The conversion to full-timers has a slight positive effect on the IRR coefficient, which raises the coefficient (0.77%). Both of these positive effects of the relative distribution argument come together in scenario 4 and raise the coefficient even more (2.22%). The comparison of these IRR coefficients with the long-term interest rate (domestic) leads to a preliminary decision in favour of the physical capital rate of return for all four scenarios. Section 5.2.3 Study III & Study IV (2 degrees vs. 1 degree) The follow-up studies of a tertiary education in WO are compared with the reference category WO. The poor range in ages in the age-income profiles shown at subsection 3.2.3 also apply to this subsection. The OLS coefficients in scenario 1 primarily show negative results to most compared research models. The models with respect to men (model A), working in the private sector (model D) and being a supervisor (model F) do have a positive influence on the coefficients of both studies, but only the follow-up study in WOWO shows a positive sign. This already indicates that when only the wage effects are taken into account between the follow-up studies and their original study with mostly negative results, the addition of the educational costs with respect to the IRR method will have an additional negative influence on the resulting IRR coefficients. Seeing that the IRR coefficients in study III and study IV cannot be calculated (VN and VN, respectively, of which the latter also suffers from a poor age-income profile), there is a strong believe that the signs of the OLS results are true. The IRR coefficients in scenario 2 are positive and are the result of having relatively older unemployed individuals in the reference category than their follow-up studies pulling the polynomial regression line downwards (study III: 4.58% and study IV: 9.07%). Just as in scenario 1, none of the IRR coefficients can be calculated in scenario 3, when only the (converted) full-timers are taken into account (study III and study IV: VN). Scenario 4 presents high positive results for study III and study IV, which shows what the IRR coefficients could have been if everyone would have worked fulltime and the unemployed were included (5.70% and 9.88%, respectively). The comparisons of scenario 2s IRR coefficient with the long-term interest rate (domestic) favour the human capital rate of return in both studies, of which the coefficient in study III is more certain. That is, the preliminary decision to the Dutch society would be to invest in both second tertiary studies, when only monetary costs and benefits are considered. Section 5.2.4 Study V & Study VI (2 degrees vs. 1 degree) The following two studies have better age-income profiles for both the reference category HBO and its follow-up studies. The OLS coefficients in scenario 1 are negative for study V, but positive for study VI. Models A, D, and F, however, do have a positive effect on both studies, which are shown with a positive sign. The IRR coefficients are both positive, of which a follow-up study in WO is favoured due to a relatively higher coefficient (2.72% (study V) < 5.75% (study VI)). The inclusion of the unemployed in scenario 2 has a beneficial effect on the IRR coefficient of study V (increasing to 5.55%) and study VI (increasing to 9.85%). The IRR coefficients in scenario 3 are similar as those from scenario 1 indicating that the real situation would not be much different than the theoretical situation when everyone would have worked fulltime (2.77% (study V) < 5.03% (study VI)). Scenario 4 shows that including the unemployed in this theoretical situation of (converted) full-timers only has a positive effect on both studies indicating that the reference category is negatively affected by these unemployed, which is mentioned in detail in subsection 3.2.4 (8.31% (study V) and 9.62% (study VI)). The comparisons between scenario 2s IRR coefficient and the long-term interest rate (domestic) show a clear result. That is, the preliminary decisions for the Dutch society would be to invest always in the human capital rate of return concerning follow-up studies of HBO and/or WO after a first tertiary study in HBO. To sum up, the OLS coefficients show a clear overview of what the wage effects are when only the incomes of different tertiary graduated individuals are compared with each other. An exception is the OLS results between study III and study V, of which the signs in both columns of Table 22c are the same. This difference can get explained by the IRR method, with which the construction of the age-income profile is a quick measure to see where the problem lies. In this situation it is caused by the poor range in age of the reference category for study III and study IV. The reference category for study V and study VI does not have a poor range in age and shows therefore a positive IRR coefficient, despite having negative OLS coefficients as its wage effects. This makes the interaction of both the OLS results and the IRR results and its age-income profile to be a good self-monitoring prediction of reality. The preliminary decisions for the Dutch society to invest in human capital are individuals who have followed the follow-up studies after WO (study III and study IV) and after HBO (study V and study VI). The remaining studies I and II are all dominated by the rate of return obtained for physical capital. Section 5.3 Biases stemming from the assumptions The bias effects with respect to the Dutch society are presented here, which all have a (presumable) positive influence, negative influence or both on the IRR coefficients. The calculations of these IRR coefficients make use of all the registered students of a particular year just as the argument made in subsection 4.3 which means the undecided bias effect 12 in subsection 3.3.3 also does not apply to the Dutch society. Section 5.3.1 Upward bias 1. The OLS method only looks at the current incomes of the employed individuals, thereby neglecting among other things the unemployed, and the (in)direct costs for following a tertiary education. This exclusion leads to an upward bias effect in the OLS coefficients to all education levels. The IRR method, however, does takes these individuals and costs into account and does not have this particular upward bias effect. (See also bias effect 1 in subsection 3.3.1). 2. All six studies make use of a partial equilibrium analysis (assumption 7 of Table 18), while in fact every tertiary levelled job that gets taken out of the job pool, will not be available to next-years new tertiary graduated individuals. This means that at least partly the new tertiary graduated will have to do with a below-tertiary levelled job. This leads to a small upward bias effect to the Dutch society. (See also bias effect 2 in subsection 3.3.1). 3. The sample also included relatively middle-aged individuals who have had some working experience between the acquirement of a (non-)tertiary degree and a(nother) tertiary degree. These incomes may have a small upward bias effect on these IRR coefficients. (See also bias effect 3 in subsection 3.3.1). 4. The total direct costs in the calculations of the social IRR coefficient for the hypothetical individual who follows its first year in WO education in 1994 does not include all the tertiary costs compared to the subsequent years. This leads to an upward bias effect to all hypothetical individuals in the sample who started their tertiary educational career by first obtaining a WO education, which are study IIV. 5. A one-year analysis cannot take the whole business cycle into account. The Netherlands was experiencing a period of boom in 2005, which leads to an upward bias effect to all IRR coefficients. (See also bias effect 5 in subsection 3.3.1). 6. The opportunity cost hypothesis has a very small downward bias effect in the coefficient of study I, but a relatively larger upward bias effect in the coefficients of study II-VI. (See also bias effect 6 in subsection 3.3.1). Section 5.3.2 Downward bias 7. Individuals who are unemployed receive welfare, which are paid indirectly by the Dutch labour force. This is why scenarios 2 and 4 can have negative incomes in their age-income profiles, which is visible in Tables 21ab. Assumption 19 in Table 18, however, also assumes that the partner of the unemployed individual is unemployed too, which means this individual will receive a higher welfare benefit than an unemployed who is single or a single parent. Seeing that this does not have to be the case, and these negative incomes can be considered to be too high, there will be a downward bias present in the social IRR coefficients in every model that has a scenario 2 and a scenario 4. 8. Individuals with missing information are put in the reference categories of models AC, E, and G of study I, study V, and study VI. They will cause an upward bias effect in the reference category, which leads to a downward bias in the resulting coefficient (of the comparison category). (See also bias effect 7 in subsection 3.3.2). 9. A small downward bias effect is included in almost all of the compared studies that took longer than the nominal durations of working experience and following a tertiary study. That is, the (averaged) actual durations were rounded upwards. (See also bias effect 8 in subsection 3.3.2). 10. Scenarios 1 and 2 include part-timers that have a downward bias effect on the real rate of return to tertiary education, because these individuals do not work as much as they could have. Scenarios 3 and 4 are used to correct for this effect by way of using only (converted) full-timers. However, if the coefficients in scenarios 1 and 2 are larger than the coefficients in scenarios 3 and 4, then there will be an upward bias present. (See also bias effect 9 in subsection 3.3.2). 11. Scenarios 3 and 4 included a small downward bias effect to the gross incomes of 14 individuals who had a workweek of more than 40 hours, but got reduced to a fixed amount of 40 hours a week. If this downward bias effect affects the reference category more than the comparison category, then there will be an upward bias effect present in these two scenarios. (See also bias effect 10 in subsection 3.3.2). 12. Assumption 11 of Table 18 does not look at how the additional incomes get spend. This is of importance, because all goods in The Netherlands are included with a Value Added Tax, which benefits its government and indirectly also the Dutch society. This means that by neglecting these expenditures, a downward bias effect can be present in the IRR coefficients, as long as the compared category has a mean income that is larger than the referenced category, otherwise there will be an upward bias effect. (See also bias effect 10 in subsection 4.3.2). 13. The argument about the basic qualification of WO individuals starting at their 18th year a year before the hypothetical individual starts with its tertiary education leads to a small downward bias effect in the coefficients of study IIV with respect to the social IRR, because these costs would have been paid by the government anyways, via the Dutch society. (See also bias effect 11 in subsection 4.3.2). 14. Most externalities of tertiary education are positive but almost impossible to quantify in monetary values as is mentioned in subsection 2.4.2. This means there is a large downward bias present in all the compared studies, of which a higher (or second) tertiary study should be affected more than a lower tertiary study. Section 5.3.3 Undecided bias 15. Individuals who needed more time for obtaining their tertiary degree(s) than the six available years of scholarship could consider getting a two-year study loan to pay for their educational costs. This has not been investigated because it was not in the dataset meaning the costs of the government (and indirectly via the Dutch society) could have been higher, indicating a possible upward bias effect. These same individuals could also have had a part-time job (or summer job) to pay for their educational costs, which would lower the (in)direct costs and give rise to a possible downward bias effect. Seeing that both biases are equally plausible and work in an opposite direction, it is classified as an undecided bias. (See also bias effect 13 subsection 4.3.3). 16. The use of a minimum income and a maximum income in the age-income profiles has led to an upward bias effect and a downward bias effect, respectively, on the social IRR. Both effects are contradictory, which means this bias is classified as an undecided bias. (See also bias effect 13 subsection 3.3.3). Section 5.4 Conclusion This subsection is used to give a definite answer to the third sub research question, because it includes the bias effects presented in subsection 5.3 that are specifically used to the viewpoint of the Dutch society. A change in perspective leads to valuing the IRR coefficients of research model 2C more than the other IRR coefficients, since the Dutch society is not limited to the Dutch labour force only (research model 1C). This IRR shows the Dutch society what its students during the years of the Tempobeurs scholarship have achieved in monetary terms and whether their choice to invest in tertiary education has turned out for the best, or not. Section 5.4.1 Study I Subsection 5.2.1 favoured the physical capital rate of return. Considering the high OLS coefficients presented in scenario 1C there can be an argument made for investing in a WO education assuming employed individuals only despite the large upward bias effect (1) of this OLS coefficient shown in subsection 5.3. But the Dutch society also has unemployed graduates who have a negative effect on these coefficients, which are included in the IRR coefficients. The other bias effects affecting the IRR method show that the upward biases (25, and 12) are smaller than the downward biases (69, and 11, 13, and 14). But the difference between the physical capital rate of return and the IRR coefficient of scenario 2C is of such a large extent that these downward bias effects are considered not to be able to bridge the differences. For that reason, the preliminary conclusion stays in effect. In other words, the Dutch society as a whole would be financially better off if they had invested in an averaged European country compared to investing in a WO education, when only monetary costs and benefits are taken into account. Section 5.4.2 Study II Subsection 5.2.2 favoured the physical capital rate of return. The OLS coefficients prefer a WOWO study to a HBOHBO study, which is in correspondence with the IRR coefficient in scenario 1C by showing a positive value. The inclusion of the unemployed by the IRR method in scenario 2 shows a higher result than in scenario 1, but with an IRR coefficient that is still lower than the long-term interest rate (domestic). Seeing that the upward biases (26) are smaller than the downward biases (7, 9, 10, 12, 13 and 14), there is reason to believe that investing in a WOWO education leads to a higher rate of return. However, these downward biases are considered to be a small influence on the IRR coefficients and its resulting conclusion. In other words, the Dutch society can be financially better off if they had invested in an averaged European country rather than in a follow-up study of WO in the same tertiary degree (human capital), when only monetary costs and benefits are considered. Section 5.4.3 Study III & IV Subsection 5.2.3 favoured the follow-up studies in WO (human capital). The OLS coefficients are mostly negative, which indicate that a follow-up study in WO has no additional benefit for the student to obtain, and its society who made it happen. However, the poor range in age affects the values generated in scenario 1 (and 3) of the WO graduate (and the four scenarios of the WOHBO graduate), which means these coefficients may not be reflecting the real social IRR. After having said that, the focus is put again on the IRR coefficients of scenario 2. In subsection 5.3 are the upward biases (2, 3, 5, 6, and 11) slightly smaller than the downward biases (7, 9, 10, 12 and 14). These downward biases do not change the preliminary conclusion. In other words, the Dutch society is financially better off, if they keep on investing in the follow-up studies after WO (human capital), when only monetary costs and benefits are considered. Section 5.4.4 Study V & VI Subsection 5.2.4 favoured the follow-up studies in HBO (human capital). The preliminary conclusion gets verified according to the OLS results in scenario 1, considering the reference category in these last two studies has a relatively better age-income profile to calculate the coefficients with. The bias effects affecting the IRR method show that the upward biases (2, 3, 5, 6, 10 (study VI), and 11) are slightly dominated by the downward biases (7, 8, 9, 10 (study V), 12 (study V + study VI), and 14), which means the social IRR coefficients in scenario 2 should have been a bit higher. Nevertheless, the preliminary decisions to both studies do not change. In other words, the Dutch society is considered to be financially better off, if they keep on investing in the follow-up studies after HBO (human capital) of which the follow-up study in WO is highly recommended , when only monetary costs and benefits are taken into account. 6. Summary and Conclusion Section 6.1 Summary of the main propositions and their results This research paper has tried to find a suitable answer to the research question: Who should pay for tertiary education? The individual, the government, or the society? by doing a cost-benefit-analysis. The used dataset Reflex comes from ROA, which is an extensive questionnaire, but limited by way of treating only tertiary educated individuals who graduated mainly in 1999/2000 and checks if they are employed in 2005, or not. Meaning that comparisons with a (below) secondary educated individual have not been made in this paper, thereby making it less comparable to most other studies about the overall return to education. The time periods of graduating between 1999/2000 and finding a job in 2005 coincide with The Netherlands experiencing a period of boom, which sadly bias the calculated coefficients, downwards and upwards, respectively. The former bias works through the opportunity cost theory, which states that in a booming period the unemployment rate is low making the opportunity costs to follow a (second) tertiary education expensive. The period of 2000-2005 that is used for following a second tertiary education is characterized by having a low economic activity, which somewhat reduce the previous mentioned downward bias effect. The latter bias takes place by way of a low unemployment rate during that year, which makes it easier for the tertiary graduated to find a job. Another factor that should have had less influence on the results is the influence of the unemployed, because this research is about (high ability) tertiary graduates and the questionnaire has taken place in a period of boom. However, Table 9b has shown that the data sample differs a bit from the actual situation, which means the results concerning specific variables, may not be a one-to-one translation to the real world. The focus of this research is on the wage premiums that employed tertiary graduates can receive on top of a low (or first) tertiary education (HBO or WO) with a maximum of five years of working experience over their lifetime by way of an age-income profile. This makes it easier for politicians to decide whether higher (or second) tertiary education is a worthwhile investment opportunity, or not. Unfortunately, some age-income profiles were incomplete making the resulting coefficients also inaccurate. The problem lies with the polynomial regression line that was used to fill in the gaps, but also had to make projections of 20+ years by extrapolating the values that gives rise to extreme values. The implementation of a minimum and a maximum income curbed these values. In the end, some age-income profiles (like study IV) have been made artificially concave, while the others remained being either convex or concave (or even hyperbolic) which is in correspondence to other tertiary studies. Furthermore, the results of the OLS studies are corresponding with earlier research with respect to males earning a relatively higher wage premium than their female counterparts. A similar result has been found for the graduates being employed in the private sector, and working as a supervisor in relation to the public sector and working as a labourer, respectively. However, the results of the IRR studies also relate to other tertiary studies with respect to females earning a relatively higher wage premium than their male counterparts, when educational costs are included in the models. The differences in wage premiums for tertiary graduates concerning the employment in the private sector and as a supervisor also exist in relation to their counterparts and favours the former when part-time graduates are included in the models and the latter when only (converted) fulltime graduates are taken into account making both job directions situational to the individual. The performed research is based on a cost-benefit-analysis, which excludes externalities of tertiary education, because they could not be quantified into monetary terms. However, I have included them in the deliberation process by way as a bias effect on the IRR coefficients. Do note that the reference categories which are also tertiary educational levels also have some of these biases, but in a lesser extent. The final results of these coefficients are then used in the (sub) conclusions to answer the (sub) research questions with, and determine if higher tertiary education can be still beneficial for everyone living in the Dutch community, or whether other investment opportunities should receive a closer look at. The reader should realise that even though the relationships of the IRR coefficients are as follows: [1] Private > [2] Social > [3] Public, the relative incomes and other focus points like the unemployed are taken into account in this study, which means these relations can still get altered accordingly. Section 6.2 Summary of the (sub) conclusions specified per study Study I If individuals want to follow an university education instead of a higher vocational education their contribution should be larger than what they have paid during the Tempobeurs regulation, because the Dutch government and its society do not profit too if the WO graduates finally do find a job, compared to the HBO graduates. Study II A similar finding has been found for individuals who did a follow-up education in the same educational level as their first tertiary study. That is, a follow-up study in WO entails more (financial) benefits to the individuals than to the Dutch government and its society, compared to a HBO graduate. Note that the Dutch government and the Dutch society both have obtained a positive wage premium if the individual follows a follow-up study in WO compared to an individual who does a follow-up study in HBO but have a better alternative investment opportunity with a higher financial rate of return than investing in tertiary education (human capital). In short, both studies argue that individuals who prefer a university education above a higher vocational education should pay a larger contribution in the educational costs. Study III+IV Each follow-up study after WO shows no (positive) wage premium over the (collective) age-income profile(s) for the relevant individuals, which makes it from a financial point of view inadvisable for individuals to follow a second tertiary study WO or HBO after graduating from the university. However, both the Dutch government, as well as the Dutch society see an improvement in their IRR coefficients, which is because of having relatively more unemployed WO graduates that are on welfare. These expenses to the Dutch government and its society has an increasing influence on these IRR coefficients by way of reducing the average wage premium over the different ages in the age-income profiles of the referenced category. Therefore, it is the job of the Dutch government and the Dutch society to make a second tertiary study financially attractive for the WO graduates by investing in second tertiary education. Study V+VI The follow-up studies after HBO show good results high wage premiums for the individuals and the Dutch society, but the Dutch government has a mixed outcome. In other words, the Dutch government can make a profit when HBO graduates do a follow-up education in WO, but when HBO graduates do a follow-up education in HBO, then the alternative investment opportunity (physical capital) shows a higher rate of return making this a better monetary option to choose. Consequently, the Dutch government should invest more in WO as a second tertiary education after HBO. But when HBO is the second tertiary education after having obtained a HBO degree, then the individual and the Dutch society should make a larger contribution by investing in second tertiary education. In short, the research concerning the follow-up studies (study IIVI) reveals positive rates of return to both educational levels with respect to the Dutch government and the Dutch society. Moreover, obtaining two (or more) tertiary degrees in different educational levels is preferred (WOHBO and/or HBOWO). Nonetheless, the Dutch government has, in the case of the graduates with a WOWO degree or a HBOHBO degree, a financially better alternative investment opportunity available to them, with a higher and stable rate of return. This means the Dutch government should make the deliberation whether their objective of getting a satisfied (post) tertiary educated (labour) force is a worthy goal, compared to obtaining a higher financial rate of return. Section 6.3 Final Conclusion This research has shown that between the tertiary studies HBO and WO it is clear that if the individual wants to follow an university education (or a follow-up study in the same direction), he/she has to make a larger financial contribution than was normal during the Tempobeurs regime, because the Dutch government and the Dutch society hardly (or not at all) benefit from this higher tertiary educational level. The Dutch society has the best results highest wage premiums for the follow-up studies after WO and HBO and therefore its mainly their responsibility to keep investing in human capital so that second tertiary education remains available and receives more financial appeal to its students. Nevertheless, the HBO graduate should contribute more if he/she decided to follow a follow-up study in HBO. Furthermore, the Dutch government should provide more financial help if individuals do a follow-up study that is different than their first tertiary educational level. How large this contribution should be is something I leave in the middle, because none of these individuals have perfect foresight about their (future) financial situation after their graduation. That is, I do not assume that these tertiary graduates after having followed an expensive tertiary education even though not paid entirely by them are: 1) happy to sit at home receiving welfare paid by the Dutch society, because they are unemployed, 2) forced to accept a part-time job, because their employer does not have any fulltime jobs to spare, or 3) accepting a job that is not their field of expertise, because all vacancies in their field(s) have already been filled or demand very high requirements for newly graduates. This research shows that not every (post) tertiary education has to generate a positive result for the individual, the government, or the society. And that politicians are advised to be cautious in choosing which (second) tertiary educational level has presented the best additional wage effect (wage premium) to The Netherlands, because it depends on which variables you focus on and whether financial motives concerning these three viewpoints play a role in the decisions. The intention is to make people aware that sending a bag of money to these tertiary institutions can be just as bad as to get it taken away by means of a budget cut, if this money does not get spend well. Any change by politicians ought to be carefully assessed to prevent doing damage on the already sensitive educational policy that The Dutch have implemented in their country. Section 7. Discussions & Other Remarks Section 7.1 First discussion point What kind of scholarship should be used to make / keep everyone happy with respect to the three viewpoints? Section 7.1.1 The transition in scholarships Before 1993 there have been several scholarships put in place to make students aware that their country needs tertiary educated individuals in the labour force. The problem that occurred was that the costs for letting students follow a tertiary education kept on rising, which meant a solution was needed in order to keep these costs in check, without losing the opportunity for these students to follow such an education (State Secretary of the OCW 2003: 25). In the school year 1993/1994 a temporary solution was found when the Tempobeurs scholarship got implemented. Its purpose was to cut down costs by focussing on the study performances of students. The first two school years 1993/1994 and 1994/1995 a temponorm was put in place in which at least 25% of the available study points also known as the European Credit Transfer and Accumulation System (ECTS) were needed each year to be obtained. In the third school year 1995/1996 the temponorm got raised to obtain at least 50% of the available study points each year. If students did not meet these terms they would see their scholarship which was a preliminary gift at that time to be changed to a loan with interest that they needed to pay back (Minister of the OCW 2001: 92, State Secretary of the OCW 2003: 27). The school year 1996/1997 introduced a new scholarship Prestatiebeurs, which had several changes being made compared to the first one, of which the most important one was that the nature of the scholarship got changed from a preliminary gift to a preliminary loan (State Secretary of the OCW 2003: 27). This change was in line with its main goal to further cut down costs, while keeping a tertiary education affordable for students. Other changes that have been made are: A minimum of 50% of the available study points were required to be obtained in the first study year and there was a maximum study length of six years implemented to obtain a diploma (State Secretary of the OCW 2003: 27, 29, Minister of the OCW 2002: 94); A reduction of one year in the previous scholarship while keeping the number of years for receiving money equal (State Secretary of the OCW 2003: 27). This small change resulted in having a less regressive income distribution compared to the Tempobeurs scholarship; And the new scholarship now applies to individuals up to their 30th birthday, which is increased from the 27th birthday that was common during the Tempobeurs regime (State Secretary of the OCW 2003: 42, Hoekstra et al. 1999: 79). In the paper of Belot et al. (2007) among first year tertiary students has the implementation of this new scholarship (also known as reform) led to higher grades and better chances to succeed for their first year. They also found out that these students questioned in 1997 has switched less to other study programmes than the reference group of students questioned in 1995 which is considered as a good result for the government. However, the reform had no effect on the dropout rates of the investigated tertiary students (Belot et al. 2007: 263, Van Elk et al. 2011). In addition, they noted that their results with respect to the reform in 1996 could also have been caused by unobserved factors, which have not been looked at due to a lack in having Instrumental Variables. Furthermore, Belot et al. (2007) have argued that the reform has led to an increase in debt held by the students, because, on average, they finish their tertiary study several months after the nominal study duration, which means they will need to borrow money from the government. There have also been some concerns for a possible loan aversion for new students. And even though the average debt for (graduated) tertiary students increased, the (absolute) enrolment of new tertiary students increased as well (see Table 1 and Figure 5). Van Elk et al. (2011: 66) have argued that students over the years are behaving very price inelastic with respect to tertiary education even when they see their private contributions increase after every reform. This makes the fear of tertiary education becoming inaccessible to students, invalid. That is, students are not afraid of borrowing money from the government. The National Institute for Family Finance Information (also known as NIBUD) backs up this reasoning but warns for the dangers of uncontrollable borrowing by these students. In their investigation they found out that there are a lot of students who do not even know how much their study debt is. Therefore, they would like to make these students more aware of what borrowing from the government actually means with respect to the current scholarship. To this end, they refer to their program Studieleenwijzer, which calculates the amount of interest the student needs to pay back to the government after graduation, and the amount of money that remains for this student (Nibud 2010). Section 7.1.2 Different types of scholarship systems As mentioned earlier, the reform of 1996 has led to more borrowing by the tertiary student of which the government now can receive partly the extra costs invested after the nominal study duration back from the student and can decide whether to reinvest this money in tertiary education, or to another government expense. The government does not have to keep this particular scholarship if it is facing another budget cut. That is, there are other scholarship systems available some that are implemented with success in other OECD countries which the Dutch government and its society can take a look at. There are three types of scholarship systems that will be discussed here, each with their own advantages and disadvantages depending on which viewpoint is taken. The first type is letting the Dutch government finance tertiary education only by way of a system of gifts to its students. The (financial) advantage lies with the students who do not have to make any monetary contribution towards tertiary education. The (financial) disadvantage lies with the Dutch government that has to make all the costs, which they will not get back. The society, as a whole, also does not benefit from such a system, because it is very regressive towards the relatively poor individuals who now are also helping to pay the study costs of the (often) relatively rich students (State Secretary of the OCW 2003: 102, 103). The second type is letting tertiary education get financed by the bank through a system of pure feudalism. The (financial) advantage now lies with the Dutch government, which does not make any costs for tertiary education. The (financial) disadvantage lies with the students, who now have to pay the full tertiary costs by themselves. Seeing that the bank does not know whether they deal with a talented student, or not, they will ask a relatively high price to these students if the students want to borrow money from them (Borland et al. 2000: 8, State Secretary of the OCW 2003: 12). Furthermore, if students finally graduate, they will need to pay a certain amount of money each period even if they do not find a (high paying) job. This may have a repellent effect on risk averse students for not choosing to follow a tertiary education, even if they are qualified for it (Groot et al. 2003: 66). The society, as a whole, also does not benefit from this system despite having a system that is not regressive now , because the high prices asked by the bank will now only be affordable to mainly the relatively richer students. This means this system allows only rich individuals to follow a tertiary education rather than the mostly talented ones (State Secretary of the OCW 2003: 102, 103). The third type looks a lot like the second type, only with the difference that the financing of tertiary education by the bank gets replaced by the Dutch government. The government can cope with less talented students and can ask for a lower price, which allows all students to follow a tertiary education. This system is coupled with a variable feudalism allowing students to borrow a higher amount of money if their living circumstances (or something else) become more expensive. The graduated students reimburse the government with an amount that depends on the incomes of each particular student. In other words, the students reimburse according to their ability to pay. This system of a social feudalism is already applied in the Prestatiebeurs, provided that the student needs a study loan to complete its tertiary study. The advantages lie with the students by way of accessibility and affordability , the Dutch government by way of the reimbursement of almost all the direct study costs that the students make (because not everyone finds a successful job with a high salary) , and the society. The latter benefits by having an almost non-regressive scholarship system, because most of the money that gets borrowed by the students is reimbursed to the government (Van Elk et al. 2011: 65). Moreover, this third type of scholarship system is overall better than the current scholarship system, because of: 1) an expected higher amount of reimbursements by the students; 2) having a scholarship that is not regressive, unlike the Prestatiebeurs during the nominal study duration; and 3) having a well (tertiary) educated labour force (State Secretary of the OCW 2003: 102, 103). Other countries that have already implemented such a social feudalism are: Australia, New Zealand, South Africa, and the United Kingdom. The implementation of these study loans has not led to lower student applications according to Greenaway et al. (2007: 318, 319, 323), but they do warn that the government needs to have a close collaboration with the tax authorities to keep the administration costs to a minimum. Afterwards, the reimbursements can be reinvested into tertiary education, like Australia has done, at which the cost of the distribution of study loans to new students is reduced significantly (Borland et al. 2000: 7 (Table 1.3)). On the other hand, the society (or the Dutch government) can spend these reimbursements also to other government expenses, as long as the society as a whole may benefit from it (financially or otherwise). Section 7.1.3 Social feudalism As a result of the conclusions made in Section 6 whereby those that benefit the most from a tertiary education, by looking at the height of the IRR results, should make a larger contribution is the social feudalism the optimal solution for all three viewpoints. That is, this scholarship meets the needs of equality and equity. The former is maintained by keeping tertiary education accessible to talented students, so that everyone is given an equal chance to follow a tertiary education. And the latter is guarded by keeping tertiary education affordable to all students not only to the relatively rich ones and having a system of reimbursement by way of the ability to pay (Nibud 2010: 44, Van Elk et al. 2011: 65, Borland et al. 2000: 30, Demers 2000: 7, Belot et al. 2007: 262, State Secretary of the OCW 2003: 12-13, 58). According to Van Elk et al. (2011: 15, 67) should this scholarship only lead to a shift in financing, with respect to the society and the individual, which there is assumed to be no (negative) influence on the quality of tertiary education. I am of the opinion that the purpose of the conversion in scholarship next to cutting in the government expenses is to make the students aware that their government sees investing in tertiary education through taxes paid by the Dutch society as a serious matter, and would like to see their students to make the same commitment. I believe that there are students who see the scholarship either the Tempobeurs or the Prestatiebeurs as a means to get free money, without the notion to see it as a real opportunity to invest in their own careers. On the other hand, I do realise that individuals should choose a job that makes them happy rather than only looking at the salary they could earn. That is: someone who thinks they have the qualities required to do well as a teacher, but not as a lawyer, may be well-advised to become a teacher, not a lawyer (Boothby et al. 2002: 41). Additionally, I would like to note that the (income dependable) additional scholarship that exists both in the Tempobeurs and the Prestatiebeurs should be retained for equity reasons (Psacharopoulos 2009: 19). That is, a relatively rich student will probably have a lower study debt after graduation and can cope with it a lot easier compared to a relatively poor student who will borrow a higher (or maximum) amount of money from the government, which will be a lot harder to reimburse even though there is a settlement of ability to pay. And even though loan aversion probably will not be an influence on the students borrowing money from the government, Nibud (2010) does predict a higher study debt from which this process has already taken place. The social feudalism will increase the study debt to around an average of 50.000,- to 60.000,- per student. Therefore, it might be better if there is at least a minimum threshold established  by way of an additional scholarship that does not have to be repaid if the student graduates in time to make sure that every student even those with less wealthy parents can follow a tertiary education without having to worry about the time after their graduation. As an option can the additional scholarship be valid only during the nominal study duration, or only for the first tertiary study. This option can increase students sympathy to accept this new scholarship better, which for equity considerations may result in a higher accessibility and affordability to all students. Furthermore, people should be aware that having a (high) study debt might have a disadvantageous effect on the request to get a mortgage. However, it is up to the graduates to mention they have a study debt, because these debts do not get registered by the Bureau of Credit Registration (also known as the BKR) (Nibud 2010: 21). A separate regulation for the graduates with a (high) study debt may solve this problem. Since, I consider it a bad thing if obtaining a tertiary degree is coupled with having a financial restraint on labour mobility due to a (high) study debt. Likewise, if people get a high mortgage that is nearly impossible to cover the monthly costs if they have not mentioned they also have a study debt to reimburse. A country that already has implemented this scholarship system at which it is working properly is Australia (State Secretary of the OCW 2003: 89). Their scholarship system also takes the incomes after graduating in a specific study programme into account, and adjusts their tuition fees accordingly. ( HYPERLINK "http://studyassist.gov.au/sites/studyassist/helppayingmyfees/csps/pages/student-contribution-amounts" http://studyassist.gov.au/sites/studyassist/helppayingmyfees/csps/pages/student-contribution-amounts) In other words, not all tertiary studies lead to a high paid job, which means having a fixed tuition fee will lead to an unnecessary high study debt for students in certain study programmes. And assuming the introductory period and the redemption period stay the same in the social feudalism two and 15 years, respectively, just as during the Tempobeurs and Prestatiebeurs (State Secretary of the OCW 2003: 35,  HYPERLINK "http://wetten.overheid.nl/BWBR0011453/Hoofdstuk6/Paragraaf61/Artikel67/geldigheidsdatum_wijkt_af_van_zoekvraag/geldigheidsdatum_01-08-2011" http://wetten.overheid.nl/BWBR0011453/Hoofdstuk6/Paragraaf61/Artikel67/geldigheidsdatum_wijkt_af_van_zoekvraag/geldigheidsdatum_01-08-2011) there will be an increase in the number of pecuniary remissions at the end. If The Netherlands does implement the social feudalism, I would strongly advise them to look into this matter and ask for lower tuition fees for study programmes that do not have jobs with relatively high wages to fully reimburse the study debt. I am aware that implementing flexible tuition fees may result in an increase in administration costs like in a situation if the relevant student decides to follow a different tertiary study programme with a different expected income , but I prefer to look at the advantages that it has to offer, like reducing the unnecessary high study debts in order to lower the number of pecuniary remissions at the end of the redemption period. To sum up, the preferred, optimal scholarship to be implemented is the social feudalism with an additional scholarship for the relatively poor students. The advantages to the students in equality and equity are retained, and the direct beneficiaries are the ones that need to make the largest contributions. Seeing that The Netherlands, as a country, is close to the technological frontier, they are always in need of (newly) tertiary graduates who can help the economy by acquiring new ideas (Minne et al. 2007b: 13). And as long as the Dutch society and its government keep on focussing on making their country a knowledge-based economy, they may eventually accomplish this goal or at least partly as it was written in the Lisbon Strategy in 2000 by the European Union (European Commission 2010: 2). Section 7.2 Second discussion point What other consequences are there for using the wage premiums in the age-income profiles concerning the provision of scholarships to the examined individuals of this data sample? Section 7.2.1 Applying wage premiums in the age-income profiles Most authors who perform a micro-economic study about tertiary educated individuals look at the progress of the influence of that particular degree on the income levels of a hypothetic individuals working life by assuming an arbitrarily income growth of one-percent per year. This assumption cautious as it may be results in artificially increasing the gap between the two hypothetical individuals age-income profiles to give a positive result to the person with the highest educational level (Demers 2000: 2, Psacharopoulos 1995: 8-9). These authors also work with the assumption of individuals who follow a tertiary study immediately after graduating from secondary school. Even though in reality there are a lot of individuals who decide to take a break and do something else for a few years like gaining some working experience on the labour market , after which they decide to follow a tertiary study too. The problem with taking these individuals also into account lies with their high age. For example, consider two hypothetical tertiary graduates, of which individual A is 25 years old, and individual B is 50 years old. Individual B will obtain a smaller IRR compared to the much younger individual A, because the relatively older individual has a smaller amount of years to obtain the monetary benefits with that this tertiary degree has to offer (Bjrklund et al. 2002: 207 (Table 10), 208). In other words, individuals who have the same tertiary degree but differ in their graduation years; also see a difference in their incomes with respect to their age-income profiles. The deletion of these individuals from their data sample results in finding only a wage effect on the incomes, because after a certain amount of years the labour experience through its return to experience will have a larger influence on the income than it has now (Psacharopoulos 2009: 4). This study, however, looks at the influence that a tertiary study has on the working life of several tertiary graduates with a different age. These so-called wage premiums of individuals who graduated in a(nother or higher) tertiary degree only five years ago do not have the two problems mentioned above, because only the wage premiums are used in the age-income profiles. That is, by replacing the incomes of the age-income profiles by the wage premiums of the highest obtained tertiary degree, the focus is kept on the educational part rather than the labour experience part. The assumption of keeping labour experience as small as possible is important for the relatively older individuals and is in line with assumptions 05 and 21 of Table 18. The method of IRR is used to take care of the uncertainty in the wage premiums that the relatively older individuals have with respect to their income levels (Demers 1999: 2, Demers 2005: 5). Nevertheless, the profiles with these wage premiums are in line with reality, because the individuals who have taken a break before following a (new) tertiary study can now also be included in my models. The use of the polynomial regression line is a bit dubious, because there are a lot of empty cells in the profiles some more than others filled in by way of interpolation (not bad) and extrapolation (making the uncertainty of these forecasts larger). The extreme deviations, however, have been kept limited thanks to the implementation of a maximum income and a minimum income. Section 7.2.2 A trade off with respect to the scholarship With the help of the results of this method of comparing the summed up wage premiums over the entire working life to the costs incurred for following one (or more) tertiary studies a conclusion was made of which both the individual and the society were benefiting the most out of this arrangement. A logical deduction would be to implement the social feudalism (see subsection 7.2.1) with an increase in the age to be eligible for the student scholarship. This proposal, however, will be limited to a certain age in which it is still beneficial to both the student and the society to earn enough additional income to justify following a(nother or higher) tertiary education until retirement. That is, someone who graduates at age 60 might not earn enough additional income until retirement to reimburse the study loan (= scholarship); assuming this individual can still find a job at such a high age in the labour market we have now. Other countries like Australia and Sweden already have such a system in place maximum age to be eligible for a scholarship is 45 years and 50 years old, respectively and do not worry much about the costs, because they also implemented the social feudalism to get reimbursed at least partly (State Secretary of the OCW 2003: 111). A similar arrangement with respect to the (income dependable) additional scholarship can be made for the relatively poor individuals who are now too old to qualify for the current scholarship. All these changes may make the proposal costly to implement or at least during the transition period , because the current costs to tertiary education keep on rising every year, which is not only noticeable in the increasing number of tertiary graduates (see Figure 5 in the Appendix), but also in the absolute enrolment and direct costs per student every year (Tables 1 and 2, respectively). And changing the present scholarship to a social feudalism probably will not alter this increase in costs, if the Australian case is taken into account (see subsection 7.2.1). The implementation of the social feudalism, however, will also increase the future benefits to society due to the reimbursements of the study loans, which already have been proven by this research. That is, the majority of the relatively older individuals in this study have shown positive wage premiums compared to following only a lower (or one) tertiary study. A possible disadvantage of the proposal may be an increase in the percentage of non-recovery of the debts due to the system of ability to pay back the study loan, which should not be underestimated (Minister of the OCW 2013a: 3). However, considering the situation that we have with the present scholarship in which only the study loan needs to be reimbursed (or the entire scholarship if the student does not graduate in time) I am of the opinion that this disadvantage is only a small inconvenience to society. There are other suggestions to make the reimbursement of the study loan life lasting, like in Australia (State Secretary of the OCW 2003: 118). This suggestion may lead to an increase in the administration costs and the relevant individuals might regret following a(nother or higher) tertiary education if the additional monetary benefit they once saw did not materialize after their graduation. In other words, their investment in themselves has resulted in reimbursing a study loan their entire life, which of course is not the purpose of any scholarship to its students. A suggestion to extend the current redemption period can receive more goodwill than the previous one. That is, it keeps the advantage of paying off a larger part of the study loan to society, while keeping the gift (ex post) for not reimbursing the total study loan intact for the individual (State Secretary of the OCW 2003: 128). The disadvantages are also kept intact with respect to society by way of the administration costs through all new-implemented regulations over time and its individuals. The latter by way of an increase in the amount of interest that the individual also need to reimburse next to the study loan and having to endure a constant burden of reimbursements over a larger portion of its life, despite having the system of ability to pay. Furthermore, that same individual already is paying back some of this relatively high income through the progressive tax rates we have in The Netherlands. Recent developments have ensured that the financing of tertiary education is seen as an investment credit, while it was considered a consumer credit in the situation before April 2013. This change leads to a reduction in the total (extra) monthly costs of the mortgage from 2.0% to 0.7% of the study loan (Minister of the OCW 2013b: 8, Minister of the OCW 2013a: 4). It also makes the suggestion of extending the redemption period a lot easier for the individual to agree with, even though its reimbursements through the annuity system of the study loan increases too; by compounded interest if the individual does not earn as much to pay back in time (State Secretary of the OCW 2003: 56). The question, however, is what the situation concerning these credits becomes when the social feudalism is implemented. That is, if the temporary study loan during the current scholarship is seen as an investment credit now, will it remain that way during the social feudalism? (Minister of the OCW 2013a: 4) Changing it back to a consumer credit makes the extension of the redemption period harder for the individual concerning the lengthier period of reimbursements that have to be made. In addition, closing on a mortgage probably becomes practically impossible, if the study debt needs to be registered at the BKR. That is why I am of the opinion of giving individuals with a study debt a separate regulation so that this particular debt does not need to be registered at the BKR, while keeping the study loan on as an investment credit. These two alterations on the implementation of the social feudalism contain only benefits to both the individual, as the government (and the society who help pay for it). After mentioning the proposal in which the relatively older individuals also should be eligible for a scholarship by reinvesting the reimbursements of the study loans back into this scholarship there are a lot of these individuals who already are following a tertiary education either on their own costs, or through their employer. This situation can remain the same. Though not every relatively older individual will have a job during an economic downturn like nowadays , and when the opportunity costs theory also is taken into account in which it is actually very favourable to follow a tertiary education now in order to make the individuals more versatile than they already are this economic downturn is the perfect time for carrying out the proposal. That is, the individuals with a(n) (additional) tertiary study (in a different specialization) make them more resilient during tough times; like an economic downturn. Furthermore, this social feudalism may help The Netherlands to achieve one of its goals or at least partly to become a knowledge-based economy, as it was written in the Lisbon Strategy in 2000 by the European Union. That is: If countries want to produce a well-qualified population, it would therefore seem important to consider the earlier years spent by individuals in the education system, as well as their final years (McIntosh 1998: 18). Moreover, the positive externalities of a (higher/second) tertiary study on its society should not be forgotten. That is, a tertiary educated (labour) force can be better involved with their society through voter participation and higher job security in times of an economic downturn and have the best intentions for it; through lower crime rates despite having to show an increase in the tax evasion and performing Research and Development (R&D) in which everyone benefits (findings are accessible and therefore transferable). To sum up, the proposal about implementing the social feudalism and increasing the age to be eligible for this type of student scholarship implies the existence of a trade off with respect to the proposal on the one hand, and the Tempobeurs and the current scholarship on the other hand. This trade off can be elaborated by taking the viewpoints of the individuals and the Dutch society. The individuals may consider the implementation of the social feudalism as an extra cost, because these study loans need to be reimbursed whether they graduate, or not. And the increase in age to be eligible to receive a scholarship can be considered as an extra benefit to the relatively older individuals who, despite having positive wage premiums after following a (higher or second) tertiary education do not receive a scholarship under the requirements of the Tempobeurs and the Prestatiebeurs and may not be financially able themselves to follow such a tertiary education , because a (higher or second) tertiary education can help them getting a better bargaining position on the labour market to avoid becoming unemployed and become better citizens for their society. The Dutch society might consider the implementation of the social feudalism as an extra benefit due to the reimbursements of the study loans; thereby keeping the cost structure intact and letting the individuals who benefit from a (higher or second) tertiary education also pay for it. And the increase in age to be eligible of receiving a scholarship might be viewed as an extra cost when the steady increase in the attainment of tertiary education by Dutch individuals between the ages of 25-64 year olds is taken into account, as is shown in Figure 5. Section 7.3 Final remarks The discussions made earlier in this section reveal that the Dutch government and its society have still a lot of (necessary) adjustments to make on the current scholarship for tertiary students in order to make it less costly and more durable. To this end, follow-up studies and other related researches are to be made, which can complement this research and push the Dutch government and its tax paying citizens into the right direction of making The Netherlands a knowledge-based economy. Examples of suggestions that are directly related to this type of research by performing a (similar) micro-economic research to wage premiums are: doing a survey among the same tertiary graduates only five and/or ten years later, in order to see how they endured a different part of the business cycle like during an economic recession with respect to their incomes (Appleby et al. 2002: 2, 10); or doing a survey among tertiary graduates over several consecutive graduation years, in order to take the entire business cycle into account by way of a time series (Appleby et al. 2002: 2, 10); or doing a survey about the current scholarship (Prestatiebeurs), and highlight the differences in coefficients with respect to the former scholarship (Tempobeurs); or working with different independent variables or assumptions like: letting go of the assumption that if the married (or cohabitant living) individual is unemployed, its partner automatically is considered to be unemployed too. And if only one unemployment benefit is used in the calculations of the IRR, the amount of averaged income as a wage premium will decrease, which will have an effect on the resulting IRR; or looking at the employment ratio by means of the (net) hours worked of the labour productivity instead of the unemployed individuals with their unemployment benefits, as in this research study (Borland et al. 2000: 2); or taking a closer look at the difference in jobs. That is, if the job is of a tertiary level, or not, and whether this job belongs to the tertiary study as in study programme of which the individual has a degree for (Cohn et al. 1998: 280-281). The first focus is on the different wage premiums obtained over the corresponding ages of an individuals working life and to find out if there is a positive IRR to be found. If so, then a normal research can be performed to look at the wage effect(s) for each field of study over the entire working life by way of assuming a fixed income growth each year until retirement; or adjusting the retirement age to 67 years in the research models (Bjrklund et al. 2002: 208). The next step after having found a positive wage premium in a particular tertiary educational level is to perform a micro-economic research with a normal age-income profile, which increases with a fixed percentage to resemble the income growth each year until retirement. An example of suggestions of such research is by choosing a fixed age group of five or ten years for example <25-29yrs, 30-34yrs,, and 60-64yrs; or <25-34yrs, 35-44yrs,, and 55-64yrs, respectively, which resembles the postponing of following a tertiary education and calculating the IRR for each of the three viewpoints (Bjrklund et al. 2002: 207-208). Its purpose is to determine up to what age (group) the individual may request a scholarship for following a (higher/second) tertiary education, of which the reimbursement is still as large as possible and thus acceptable to the Dutch government and its society. Afterwards, a new step can be taken of determining how long the optimal period of repayment should last for each age group. These steps can be extended by making the current tuition fee more flexible to account for each field of studys predicted income after graduation, as mentioned earlier in this section. The problem that The Netherlands is facing is that they as a highly developed country in technology have not much to learn from its surrounding countries. That is why they should focus in this area by promoting the knowledge-based economy and encourage new individuals to follow a tertiary education and to conduct R&D, which may lead to new innovations in our country. This may also lead to the creation of new jobs, which in time leads to obtain higher economic growth levels (Van Elk et al. 2011: 70). However, seeing that the positive externalities of university graduates are not well quantitatively measured like the many spill-over effects of university R&D to industrial R&D, which means the calculated coefficients will never reflect the actual social IRR to tertiary education it complicates the work for researchers and politicians who base their policy upon these results to make a sensible judgement (Minne 2007b: 62, Borland et al. 2000: 3). Nevertheless, it is still considered a great loss for not including the university R&D in this research even though there was not relevant data available in the particular data sample which is why I hope that other follow-up studies not only mention it, but also dedicate a whole chapter about it, in order to show its importance to the tertiary university graduates. 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Appendix (Tables 122c): Table 1: Absolute enrolment of Dutch tertiary students* Tertiary education (fulltime & part-time)Period (school years)HBO (men & women) x 1000WO (men & women) x 10001993/1994266,9186,91994/1995270,11851995/1996270,6177,71996/1997274,8166,21997/1998279,9160,71998/1999288,6160,51999/2000303,21632000/2001312,7166,32001/2002321,5173,12002/2003323180,12003/2004335,7189,5*: These values do not include enrolments in the Open University. Source: Statistics Netherlands (StatLine) Table 2 and Table 3 can be found in the text. Tables 4a4d: OLS Limited Models (with HBO degree only as reference group) Table 4a: Gross income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.993(***)9.932(***)9.941(***)10.000(***)9.553(***)Gross Study Duration (years)0.035(***)0.033(***)0.035(***)0.034(***)0.026(***)Job Experience (years)0.0060.016-0.0010.0010.029(**)Job Experience Squared (years)0.000-4.4E-0050.0010.001-0.001Age-0.001-0.003-0.001-0.0010.003Gender0.203(***)0.061(***)Private Sector0.149(***)0.057(***)Supervisor0.115(***)0.063(***)Fulltime (36+ hours)0.338(***)N643643602643643*R5%14.6%9.9%7.5%38.8%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 4b: Gross income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.994(***)9.941(***)9.942(***)10.001(***)9.585(***)WO degree only0.214(***)0.216(***)0.195(***)0.212(***)0.181(***)HBOHBO-0.038-0.012-0.017-0.0160.059HBOWO0.096(**)0.096(**)0.092(**)0.100(**)0.066(*)WOHBO-0.0810.002-0.061-0.0700.078WOWO0.1300.149(*)0.1270.139(*)0.199(***)Job Experience (years)0.0010.013-0.003-0.0030.028(**)Job Experience Squared (years)0.0017.36E-0050.0010.001-0.001Age0.0040.0010.0040.0030.005(*)Gender0.206(***)0.072(***)Private Sector0.126(***)0.043(**)Supervisor0.113(***)0.067(***)Fulltime (36+ hours)0.337(***)N643643602643643*R9.6%19.3%12.8%12.0%42.3%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 4c: Net income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.665(***)9.614(***)9.620(***)9.670(***)9.303(***)Gross Study Duration (years)0.030(***)0.027(***)0.029(***)0.028(***)0.022(***)Job Experience (years)0.0050.0142.43E-0050.0010.024(**)Job Experience Squared (years)0.000-5.7E-0050.0000.000-0.001Age-0.001-0.002-0.001-0.0010.002Gender0.168(***)0.051(***)Private Sector0.124(***)0.048(***)Supervisor0.096(***)0.053(***)Fulltime (36+ hours)0.277(***)N643643602643643*R5.2%14.8%10.1%7.7%38.7%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 4d: Net income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.665(***)9.622(***)9.622(***)9.672(***)9.329(***)WO degree only0.179(***)0.181(***)0.163(***)0.177(***)0.151(***)HBOHBO-0.031-0.010-0.013-0.0130.049HBOWO0.081(**)0.081(**)0.078(**)0.084(**)0.056(**)WOHBO-0.0670.002-0.050-0.0570.065WOWO0.1110.127(*)0.1080.119(*)0.168(***)Job Experience (years)0.0020.012-0.002-0.0020.024(**)Job Experience Squared (years)0.0003.82E-0050.0010.001-0.001Age0.0040.0010.0040.0030.004(*)Gender0.171(***)0.060(***)Private Sector0.104(***)0.036(**)Supervisor0.094(***)0.056(***)Fulltime (36+ hours)0.277(***)N643643602643643*R9.8%19.5%13.0%12.2%42.2%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Tables 5a5d: OLS Limited Models (with WO degree only as reference group) Table 5a: Gross income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.993(***)9.932(***)9.941(***)10.000(***)9.553(***)Gross Study Duration (years)0.035(***)0.033(***)0.035(***)0.034(***)0.026(***)Job Experience (years)0.0060.016-0.0010.0010.029(**)Job Experience Squared (years)0.000-4.4E-0050.0010.001-0.001Age-0.001-0.003-0.001-0.0010.003Gender0.203(***)0.061(***)Private Sector0.149(***)0.057(***)Supervisor0.115(***)0.063(***)Fulltime (36+ hours)0.338(***)N643643602643643*R5%14.6%9.9%7.5%38.8%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 5b: Gross income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)10.208(***)10.157(***)10.137(***)10.213(***)9.766(***)HBO degree only-0.214(***)-0.216(***)-0.195(***)-0.212(***)-0.181(***)HBOHBO-0.252(***)-0.229(***)-0.212(***)-0.228(***)-0.123(***)HBOWO-0.118(**)-0.121(***)-0.103(**)-0.111(**)-0.115(***)WOHBO-0.295(***)-0.215(**)-0.256(**)-0.281(***)-0.103WOWO-0.084-0.068-0.068-0.0720.018Job Experience (years)0.0010.013-0.003-0.0030.028(**)Job Experience Squared (years)0.0017.36E-0050.0010.001-0.01Age0.0040.0010.0040.0030.005(*)Gender0.206(***)0.072(***)Private Sector0.126(***)0.043(**)Supervisor0.113(***)0.067(***)Fulltime (36+ hours)0.337(***)N643643602643643*R9.6%19.3%12.8%12.0%42.3%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 5c: Net income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.665(***)9.614(***)9.620(***)9.670(***)9.303(***)Gross Study Duration (years)0.030(***)0.027(***)0.029(***)0.028(***)0.022(***)Job Experience (years)0.0050.0142.43E-0050.0010.024(**)Job Experience Squared (years)0.000-5.7E-0050.0000.000-0.001Age-0.001-0.002-0.001-0.0010.002Gender0.168(***)0.051(***)Private Sector0.124(***)0.048(***)Supervisor0.096(***)0.053(***)Fulltime (36+ hours)0.277(***)N643643602643643*R5.2%14.8%10.1%7.7%38.7%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 5d: Net income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.844(***)9.802(***)9.784(***)9.849(***)9.481(***)HBO degree only-0.179(***)-0.181(***)-0.163(***)-0.177(***)-0.151(***)HBOHBO-0.209(***)-0.190(***)-0.176(***)-0.189(***)-0.103(***)HBOWO-0.097(**)-0.100(***)-0.085(**)-0.092(**)-0.095(***)WOHBO-0.245(***)-0.179(**)-0.213(**)-0.234(***)-0.086WOWO-0.067-0.054-0.055-0.0580.016Job Experience (years)0.0020.012-0.002-0.0020.024(**)Job Experience Squared (years)0.0003.82E-0050.0010.001-0.001Age0.0040.0010.0040.0030.004(*)Gender0.171(***)0.060(***)Private Sector0.104(***)0.036(**)Supervisor0.094(***)0.056(***)Fulltime (36+ hours)0.277(***)N643 643602643643*R9.8%19.5%13.0%12.2%42.2%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: 41 Individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These 41 individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Tables 6a6d: OLS Limited Models (with HBOHBO as reference group) Table 6a: Gross income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.771(***)9.626(***)9.603(***)9.821(***)9.409(***)Gross Study Duration (years)0.053(**)0.053(**)0.055(**)0.040(*)0.031Job Experience (years)0.0300.0560.0090.0190.047Job Experience Squared (years)-0.000-0.001-5.5E-0056.74E-005-0.002Age-0.004-0.0050.001-0.0030.003Gender0.241(***)0.089Private Sector0.278(**)0.192(**)Supervisor0.262(*)0.180Fulltime (36+ hours)0.316(***)N6363566363*R12.7%22.5%22.7%18.2%47.5%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: Seven individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These seven individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 6b: Gross income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.847(***)9.704(***)9.688(***)9.881(***)9.450(***)WOWO0.1660.1570.1370.1330.121Job Experience (years)0.0260.0520.0070.0140.044Job Experience Squared (years)-0.000-0.001-0.0009.07E-005-0.002Age0.0040.0030.0100.0040.008Gender0.238(**)0.081Private Sector0.259(**)0.174(*)Supervisor0.314(**)0.216(*)Fulltime (36+ hours)0.336(***)N6363566363*R7.6%17.1%15.8%16.0%46.7%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: Seven individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These seven individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 6c: Net income without variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.490(***)9.370(***)9.348(***)9.531(***)9.188(***)Gross Study Duration (years)0.045(**)0.044(**)0.046(**)0.034(*)0.027(*)Job Experience (years)0.0250.0460.0070.0150.038Job Experience Squared (years)-0.000-0.001-3.7E-0057.78E-005-0.001Age-0.004-0.0040.001-0.0020.002Gender0.199(***)0.073Private Sector0.236(**)0.164(**)Supervisor0.220(*)0.150Fulltime (36+ hours)0.260(***)N6363566363*R13.1%22.8%23.5%18.6%47.9%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: Seven individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These seven individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Table 6d: Net income with variables in education levels Regression Models and its (unstandardized) coefficients:Variables1 Standard Model2 Standard Model + Gender3 Standard Model + Private Sector4 Standard Model + Supervisor5 Limited Model(Constant)9.554(***)9.436(***)9.419(***)9.582(***)9.224(***)WOWO0.1410.1330.1150.1130.103Job Experience (years)0.0210.0420.0060.0110.035Job Experience Squared (years)-0.000-0.001-0.0009.76E-005-0.001Age0.0040.0030.0080.0030.006Gender0.196(**)0.067Private Sector0.220(**)0.149(*)Supervisor0.263(**)0.182(*)Fulltime (36+ hours)0.277(***)N6363566363*R7.7%17.2%16.3%16.4%47.1%The coefficients of the OLS regression models 14 are used for comparison with the coefficients of the IRR models. The results of OLS regression model 5 (Limited Model) is used to see what the influences of each variable is on the dependent variable (income), when all the variables are included in one (OLS) regression model. (*) / (**) / (***): Significant at the 90% / 95% / 99% significance level, respectively. *: Seven individuals have not filled out in what economic sector they are working at; for this OLS model they are assumed to be working in the public sector. These seven individuals have been taken into account in the third OLS regression model, in order to be consistent with the IRR models. Tables 7a7d: OLS Full Models (with HBO degree only as reference group) Table 7a: Gross income without variables in education levels (N = 780; R = 39.4%)  Table 7b: Gross income with variables in education levels (N = 780; R = 42.6%)  Table 7c: Net income without variables in education levels (N = 780; R = 39.3%)  Table 7d: Net income with variables in education levels (N = 780; R = 42.6%)  Tables 8a8d: OLS Signal Models (with HBO degree only as reference group) Table 8a: Gross income without variables in education levels (N = 503; R = 40.2%)  Table 8b: Gross income with variables in education levels (N = 503; R = 45.3%)  Table 8c: Net income without variables in education levels (N = 503; R = 39.9%)  Table 8d: Net income with variables in education levels (N = 503; R = 45.1%)  Tables 9: Representative Table 9a: Student types Year 1999/2000*CBS StatLineDatasetEducation LevelGenderFulltime/Parttime/Both**Fulltime/Parttime/Both**HBO First tertiary study***Males & FemalesFT: 67.29% PT: 67.91% Both: 67.39%FT: 59.02% PT: 51.88% Both: 57.44%HBO Second tertiary studyMales & FemalesFT: 0.90% PT: 17.69% Both: 3.50%FT: 15.12% PT: 23.13% Both: 16.79%WO First tertiary study***Males & FemalesFT: 28.34% PT: 13.96% Both: 26.11%FT: 22.76% PT: 18.13% Both: 21.79%WO Second tertiary studyMales & FemalesFT: 3.47% PT: 0.45% Both: 3.00%FT: 3.09% PT: 6.88% Both: 3.97%*: Calculated by taking the full school year of 1999/2000 + the first 4 months of 2000/2001 to get the months of 2000 (September, October, November, and December). **: Fulltime/Part-time/Both refer to the situation of the students when they were studying, not to their working hours. ***: First tertiary study = Bachelor and Doctorate / Master. Own calculations based on data from Statistics Netherlands (StatLine) and own dataset (from ROA). Table 9b: Unemployment rates of 2005 Year 2005OC&WDatasetEducation LevelGenderUnemployment rate of 2005Unemployment rate of 2005*HBOMales3.00% 4.84% (4.69%)Females4.00%12.44% (13.62%)WOMales4.00% 5.47% (5.29%)Females6.00%12.94% (12.35%)*: The unemployment rate of 2005 when also the second tertiary study is accounted for. The rate in brackets is when only the first tertiary study is accounted for. Own calculations based on data from Labour Force Survey Statistics Netherlands (in the document: Kerncijfers 2002-2006 Onderwijs, Cultuur en Wetenschap page 27 (Table 2.22)) and own dataset (from ROA). Tables 10a10b: Instrumental Variables Table 10a: MOD_1 = Annual Labour Gross Income   Table 10b: MOD_2 = Annual Labour Net Income   Table 11: The Dutch labour force in 2005 in percentages of the population Unemployment percentages*Net participation rate**Educational levelsMales (***)Females (***)Males (***)Females (***)BAO (primary)11 (6)17 (11)45 (52)22 (24)MAVO (secondary)8 (4)11 (8)47 (55)36 (36)VBO (secondary)6 (3)12 (8)73 (78)37 (36)HAVO/VWO (secondary)8 (4)10 (6)58 (63)50 (52)MBO (secondary)5 (2)7 (4)81 (85)64 (64)HBO (tertiary)3 (2)4 (3)84 (87)76 (74)WO (tertiary)4 (2)6 (3)85 (90)78 (83)Average6 (3)8 (5)72 (77)54 (52)*: The labour force between the ages of 15-64 who are unemployed (or working less than 12 hours per week). **: The labour force between the ages of 15-64 who are employed (for working at least 12 hours per week). ***: The values in brackets show the corresponding percentages in 2000, in percentages of the population. Source: Labour Force Survey Statistics Netherlands (in the document: Kerncijfers 2002-2006 Onderwijs, Cultuur en Wetenschap page 27 (Table 2.22)). Tables 12a12d: OLS Unemployed Models (with HBO degree only as reference group) Table 12a: Gross income without variables in education levels (N = 780; R = 39.1%)  Table 12b: Gross income with variables in education levels (N = 780; R = 42.6%)  Table 12c: Net income without variables in education levels (N = 780; R = 39.0%)  Table 12d: Net income with variables in education levels (N = 780; R = 42.6%)  Tables 13a13b: Real study durations (averaged) Table 13a: Study durations in tertiary education levels First tertiary education: HBO WONumber of years**: 5 7Follow-up tertiary education*: HBO WONumber of years**: 2 3*: Only individuals who have followed another tertiary degree (after 2000) are taken into account. Own calculations based on data from own dataset (from ROA). **: The Descriptive Statistics of tertiary education: NMin.Max.MeanStd. DeviationFirst tertiary education (HBO)Gross Study Duration (months)4482811250.7611.566Gross Study Duration (years)4483104.511.003First tertiary education (WO)Gross Study Duration (months)1703620574.6924.001Gross Study Duration (years)1703186.552.064Follow-up tertiary education (HBO)Gross Study Duration (months)36 54619.7511.342Gross Study Duration (years)36141.970.971Follow-up tertiary education (WO)Gross Study Duration (months)53106630.7212.969Gross Study Duration (years)53162.911.213 Table 13b: Starting ages of school and work Study I (A = HBO; B = WO)Study II (A = HBOHBO; B = WOWO)ABABQ23Q25R26R29S24S26T27T30Study III (A = WO; B = WOWO)Study IV (A = WO; B = WOHBO)ABABQ26Q26R29R28S27S27T30T29Study V (A = HBO; B = HBOHBO)Study VI (A = HBO; B = HBOWO)ABA BQ23Q23R25R26S24S24T26T27Table 14: Duration of job searching, after tertiary graduation NMin.Max.MeanStd. DeviationFirst tertiary education (HBO N=153 and WO N=66)Unemployed since graduation (months)2190677.6511.271Follow-up tertiary education (HBOHBO N=16 and WOHBO N=5)Unemployed since graduation (months)2116010.4815.942Follow-up tertiary education (HBOWO N=22 and WOWO N=7)Unemployed since graduation (months)290175.313.646 Table 15 and Tables 16a16c: (Costs & Benefits of the HBO and WO student following only one tertiary study, in 2005 constant prices (used for the calculation of the (Internal) Rates of Return)) can be found in the text. Table 17: Income tax percentages 2001-2005 YearLevel 1Level 2Level 3Level 4Tax discount ( )2001*0-32769 (32.35%) 14869.9232769-59520 (37.60%) 12139.0759520-102052 (42%) 19300.18>102052 (52%)3473 gld (1575.98 )2002*0-33785 (32.35%) 15330.9633785-61367 (37.85%) 12516.1761367-105216 (42%) 19897.81>105216 (52%)3630 gld2002 ( )0-15331 (32.35%) 1533115331-27847 (37.85%) 12516.0027847-47745 (42%) 19898.00>47745 (52%)1647 2003 ( )0-15883 (32.90%) 1588315884-28850 (38.40%) 12967.0028851-49464 (42%) 20614.00>49465 (52%)1725 2004 (until 1 July) ( )0-16265 (33.40%) 1626516265-29543 (40.35%) 13278.0029543-50652 (42%) 21109.00>50652 (52%)18252004 (Starting from 1 July) ( )0-16265 (33.70%) 1626516265-29543 (40.65%) 13278.0029543-50652 (42%) 21109.00>50652 (52%)18252005 ( )0-16893 (34.40%) 1689316893-30357 (41.95%) 13464.0030357-51762 (42%) 21405.00>51762 (52%)1894The red values are the differences in income converted to euros (with conversion rate Fl 2.20371 = 1,-). The blue values are the differences in income. And the values in brackets are the income tax percentages. *: The values for the years 2001 and 2002 are shown in Dutch guilders (which is the national currency of The Netherlands before 2002). Sources:  HYPERLINK "http://home.iae.nl/users/kunstein/hypotheek/belasting.html" http://home.iae.nl/users/kunstein/hypotheek/belasting.html &  HYPERLINK "http://www.salarisinfostartpagina.nl/tarieven_en_heffingen_en_tabellen%202003.htm" http://www.salarisinfostartpagina.nl/tarieven_en_heffingen_en_tabellen%202003.htm &  HYPERLINK "http://taxci.nl/read/belastingtarieven_voor_particu" http://taxci.nl/read/belastingtarieven_voor_particu (downloaded 21st July 2012). Table 18 can be found in the text. Table 19a: Descriptive Statistics of the IRR Models: Private Education Levels (Models)*Obs. (N)**S#***Age (years)Income ( )MeanMedianMin. ValueMax. ValueMeanMedianMin. ValueMax. ValueHBO (I-A)149 (170)S130.2929.00264920316.9320236.128978.8032068.60S230.3430.00264919044.02919539.5226916.5532068.60S330.2929.00264920869.533020584.421514106.0431024.60S430.3430.00264919528.372319887.82156916.5531024.60WO (I-A)57 (66)S130.8130.00273823773.7723368.6011340.4034568.80S230.8630.50273822103.83322324.6006916.5534568.80S330.8130.00273824261.037423368.6016734.7934280.80S430.8630.50273822524.655022498.606916.5534280.80HBO (I-B)217 (278)S129.1628.00265517066.3517101.428191.6029646.52S229.4928.00265516083.21315467.8956916.5529646.52S329.1628.00265520051.737319423.421512015.1440360.80S429.4928.00265518413.534918608.68476916.5540360.80WO (I-B)83 (104)S130.5130.00274120744.3220584.4210002.1634856.80S231.6330.00276119135.96119274.8146916.5534856.80S330.5130.00274122926.021822907.5012873.3736251.33S431.6330.00276120877.129121759.106916.5536251.33HBO (I-C, V-C, VI-C)366 (448)S129.6229.00265518389.6718494.628191.6032068.60S229.8129.00265517206.73717101.4226916.5532068.60S329.6229.00265520384.665119887.821512015.1440360.80S429.8129.00265518836.575919007.39656916.5540360.80WO (I-C, III-C, IV-C)140 (170)S130.6330.00274121977.7421593.8010002.1634856.80S231.3430.00276120288.19320448.5856916.5534856.80S330.6330.00274123469.563923020.6012873.3736251.33S431.3430.00276121516.756821969.646916.5536251.33HBO (I-D)173S129.9529.00265119414.1919191.228978.8032068.60S2S329.9529.00265120754.935920236.121514106.0434280.80S4WO (I-D)82S130.6030.00274122198.7221795.6410002.1634856.80S2S330.6030.00274123304.019722919.863212873.3736251.33S4HBO (I-E)169S129.2828.00265517429.7617414.898191.6029646.52S2S329.2828.00265519972.940119503.490512015.1440360.80S4WO (I-E)49S130.5530.00273721557.1620584.4211340.4034856.80S2S330.5530.00273723941.638923194.6017101.4234856.80S4HBO (I-F)102S129.8629.00265519319.8719365.3711340.4028588.60S2S329.8629.00265520775.033320584.421512390.0040360.80S4WO (I-F)43S131.2331.00274123679.1623368.6011340.4034856.80S2S331.2331.00274124831.572324430.915812873.3736251.33S4HBO (I-G)264S129.5329.00265518030.2718146.328191.6032068.60S2S329.5329.00265520233.841019558.871512015.1438216.80S4WO (I-G)97S130.3630.00273821223.5020647.1210002.1633112.60S2S330.3630.00273822865.786922324.6014315.0233960.80S4HBOHBO (II-C, V-C)49 (59)S132.2430.00265418281.0718146.328585.2032764.60S233.1730.00265517175.48517101.4226916.5532764.60S332.2430.00265422234.638321272.866714779.4235560.80S433.1730.00265520458.956719922.65156916.5535560.80WOWO (II-C, III-C)14 (18)S132.2929.00285121212.8119539.528978.8033112.60S231.7829.00285118035.86018494.6226916.5533112.60S332.2929.00285123330.976721744.6015708.2235880.80S431.7829.00285119683.326820440.67866916.5535880.80WOHBO (IV-C)10 (13)S134.8033.50294317430.9216753.1212718.0026152.60S233.7732.00294316068.61114315.0226916.5526152.60S334.8033.50294321461.213621222.6016230.6728704.60S433.7732.00294319168.833420197.42156916.5528704.60HBOWO (VI-C)64 (72)S130.8329.00265620360.2320096.808191.6035720.80S230.5629.00265619538.92919539.5226916.5535720.80S330.8329.00265622456.456721280.6014315.0237160.80S430.5629.00265621402.240220899.73336916.5537160.80*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (14). Table 19b: Descriptive Statistics of the age-income profiles: Private Education Levels (Models)*Obs. (N)**S#***Mean ( )Median ( )Minimum Value ( )****Maximum Value ( )Peak (years) *****HBO (I-A)149 (170)S114563.8017230.884553.2822666.8936S214233.0716977.384553.2820876.9536S317344.0618200.008034.3123303.7436S415725.2218080.886916.5521160.7836WO (I-A)57 (66)S129091.2127728.908058.9042105.2365S235167.4436796.948014.5046760.8050S318267.8819239.148058.9024338.6728S432235.7329062.678014.5046760.8057HBO (I-B)217 (278)S115541.6116846.006407.9519021.3839S213149.7814406.095994.9916148.6131S320800.1021665.408948.5124139.3043S416098.5817419.419037.9318700.1533WO (I-B)83 (104)S132052.66 29293.238888.6246760.8055S215627.5016323.929101.4819245.3127S337422.9746760.808888.6246760.8045S417625.0719663.888073.3521468.3936HBO (I-C, V-C, VI-C)366 (448)S115434.9617502.254553.2821633.1638S213880.4115981.684553.2819207.0737S319559.8920721.608576.3423023.7040S416468.1318143.317434.3919896.2736WO (I-C, III-C, IV-C)140 (170)S132062.4929102.748550.8046760.8056S216414.6117560.248679.4720001.8930S337049.1045875.658550.8046760.8046S417614.0119377.408379.8521591.8634HBO (I-D)173S114742.9216991.344553.2822694.4837S2S320385.9021378.208689.8923566.6041S4WO (I-D)82S132391.2629810.408402.8746760.8055S2S336716.3946760.808402.8746760.8045S4HBO (I-E)169S114465.1216302.934553.2819302.7438S2S318830.3420207.828524.9222813.4040S4WO (I-E)49S113030.4013512.144553.2822204.0827S2S336192.9038030.528806.4746760.8050S4HBO (I-F)102S117613.3618883.198529.3619904.6034S2S319892.8820745.298529.3621395.8136S4WO (I-F)43S132510.6329281.327931.1146760.8056S2S337310.1646760.807931.1146760.8045S4HBO (I-G)264S114893.6916921.224553.2822352.4438S2S319619.9120935.008594.4924018.6341S4WO (I-G)97S129819.2927088.308825.5146760.8062S2S316139.2111943.518825.5123926.1232S4HBOHBO (II-C, V-C)49 (59)S118255.1118876.549920.1820503.7843S214788.0916567.914553.2818815.3539S321326.0622691.419920.1825925.0342S418327.5519800.639791.0323119.3539WOWO (II-C, III-C)14 (18)S122853.1315369.604553.2846760.8056S222348.8614508.604553.2846760.8056S327294.8720591.209119.2346760.8057S425517.3118407.228108.5246760.8056WOHBO (IV-C)10 (13)S131136.9829224.6812680.5646760.8054S229070.3326706.4911921.9046760.8059S333559.8533112.4412680.5646760.8052S431142.1129463.2911921.9046760.8059HBOWO (VI-C)64 (72)S127633.3023650.929863.3446760.8063S227438.8623829.789555.4946760.8064S329624.9626728.049863.3446760.8063S429376.1826939.469555.4946760.8064*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (14). ****: The minimum values can be lower than my appointed minimum incomes, because of the inclusion of the welfare incomes during the transition period (the one-year after graduation and is spend by looking for a job). *****: The (minimum) age at which the wage premium is the highest. Table 20a: Descriptive Statistics of the IRR Models: Public Education Levels (Models)*Obs. (N)**S#***Age (years)Income ( )MeanMedianMin. ValueMax. ValueMeanMedianMin. ValueMax. ValueHBO (I-A)149 (170)S130.2929.0026499234.96619163.87851821.2017731.40S230.3430.0026498395.10818660.4785739.7817731.40S330.2929.0026499623.67729415.57854733.9616975.40S430.3430.0026498735.80168912.1785739.7816975.40WO (I-A)57 (66)S130.8130.00273811747.072811431.40003059.6020031.20S230.8630.50273810591.252810675.4000739.7820031.20S330.8130.00273812085.123811431.406633.6319719.20S430.8630.50273810883.205610801.40739.7819719.20HBO (I-B)217 (278)S129.1628.0026556935.31176898.57851408.4015977.48S229.4928.0026556258.75985718.1055739.7815977.48S329.1628.0026559055.75448576.57853413.4326305.87S429.4928.0026557913.92497987.8066739.7826305.87WO (I-B)83 (104)S130.5130.0027419556.69489415.57852357.8420343.20S231.6330.0027618424.90618469.1865739.7820343.20S330.5130.00274111146.105311097.503863.4721853.94S431.6330.0027619693.377210265.90739.7821853.94HBO (I-C, V-C, VI-C)366 (448)S129.6229.0026557871.50987905.37851408.4017731.40S229.8129.0026557069.42776898.5785739.7817731.40S329.6229.0026559286.95798912.17853413.4326305.87S429.8129.0026558225.79778275.9368739.7826305.87WO (I-C, III-C, IV-C)140 (170)S130.6330.00274110448.491610146.20002357.8420343.20S231.3430.0027619265.95839317.4155739.7820343.20S330.6330.00274111528.419911179.403863.4721853.94S431.3430.00276110155.310610418.36739.7821853.94HBO (I-D)173S129.9529.0026518595.46998408.77851821.2017731.40S2S329.9529.0026519543.06629163.87854733.9619719.20S4WO (I-D)82S130.6030.00274110613.084110292.36002357.8420343.20S2S330.6030.00274111413.931911106.45263863.4721853.94S4HBO (I-E)169S129.2828.0026557196.51167125.10851408.4015977.48S2S329.2828.0026559003.15058634.44063413.4326305.87S4WO (I-E)49S130.5530.00273710140.47329415.57853059.6020343.20S2S330.5530.00273711865.326311305.406898.5820343.20S4HBO (I-F)102S129.8629.0026558512.12928534.62853059.6015211.40S2S329.8629.0026559581.64169415.57853610.0026305.87S4WO (I-F)43S131.2331.00274111716.371611431.40003059.6020343.20S2S331.2331.00274112559.242412200.66323863.4721853.94S4HBO (I-G)264S129.5329.0026557623.99787653.67851408.4017731.40S2S329.5329.0026559173.10298674.46183413.4323983.20S4WO (I-G)97S130.3630.0027389886.44169460.88452357.8418487.40S2S330.3630.00273811071.457410675.404884.9819372.53S4HBOHBO (II-C, V-C)49 (59)S132.2430.0026547800.56277653.67851614.8018235.40S233.1730.0026557054.30196898.5785739.7818235.40S332.2430.00265410631.81379913.80005220.5821105.87S433.1730.0026559405.67948937.3485739.7821105.87WOWO (II-C, III-C)14 (18)S132.2929.0028519930.62348660.47851821.2018487.40S231.7829.0028517888.21387905.3785739.7818487.40S332.2929.00285111477.173010255.405891.7821452.53S431.7829.0028519091.08519311.7023739.7821452.53WOHBO (IV-C)10 (13)S134.8033.5029437142.67526646.87853782.0013447.40S233.7732.0029436249.22554884.9785739.7813447.40S334.8033.50294310051.61979877.40006269.3315295.40S433.7732.0029438486.87459135.9118739.7815295.40HBOWO (VI-C)64 (72)S130.8329.0026569335.45579063.19851408.4021279.20S230.5629.0026568749.52578660.4785739.7821279.20S330.8329.00265610838.97039919.40004884.9822839.20S430.5629.00265610085.98279643.6000739.7822839.20*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (14). Table 20b: Descriptive Statistics of the age-income profiles: Public Education Levels (Models)*Obs. (N)**S#***Mean ( )Median ( )Minimum Value ( )****Maximum Value ( )Peak (years) *****HBO (I-A)149 (170)S15667.766986.870.0010941.9436S25347.466881.000.009706.5936S37116.977691.251314.7211386.2236S46145.207682.88739.789900.9936WO (I-A)57 (66)S115455.6114502.391327.0024486.3765S221759.8421016.411304.8333239.2053S37761.968408.971327.0012138.5028S418750.5215464.571304.8333239.2060HBO (I-B)217 (278)S15937.156877.210.008449.0040S24387.565389.970.006384.7233S39596.9810245.071771.2312028.7243S46429.407459.841815.888218.3835WO (I-B)83 (104)S118881.9415615.821741.3233239.2057S25993.356424.781847.618543.2927S324251.0031934.231741.3233239.2046S47488.768949.24740.4510167.6036HBO (I-C, V-C, VI-C)366 (448)S16100.267292.760.0010225.9538S25104.306167.510.008567.6637S38702.779511.831585.3811194.8040S46662.077817.38739.789036.0237WO (I-C, III-C, IV-C)140 (170)S118667.3215499.431572.6333239.2059S26561.177303.821636.889084.4529S323893.2229833.961572.6333239.2047S47472.068713.52983.7810231.6834HBO (I-D)173S15765.126834.110.0010974.0337S2S39294.879994.701642.0811577.9641S4WO (I-D)82S119036.5916028.911498.7633239.2058S2S323706.9931483.911498.7633239.2047S4HBO (I-E)169S15427.666435.050.008592.2738S2S38189.499198.581559.7111081.8040S4WO (I-E)49S14473.193944.370.0010571.4427S2S322479.1921770.981700.3033239.2052S4HBO (I-F)102S17293.668239.591561.928965.0534S2S38949.069517.081561.929997.2436S4WO (I-F)43S118959.8715525.551263.1933239.2059S2S324159.7031758.731263.1933239.2046S4HBO (I-G)264S15862.206977.010.0010727.9138S2S38749.399692.901594.4511919.4241S4WO (I-G)97S116273.8214083.171709.8132253.1365S2S36253.823234.091709.8111837.6632S4HBOHBO (II-C, V-C)49 (59)S17750.318230.872282.829383.1143S25645.126621.090.008196.3739S39973.1710943.392282.8213296.6341S47634.189137.75739.7811299.8339WOWO (II-C, III-C)14 (18)S112931.626170.520.0033239.2057S212732.925653.640.0033239.2057S315612.879733.881791.1233239.2058S414617.208397.72946.2633239.2057WOHBO (IV-C)10 (13)S118473.7615660.593704.7533239.2056S216484.0613944.583313.9533239.2061S320472.4318483.603704.7533239.2054S418006.6715948.013313.9533239.2061HBOWO (VI-C)64 (72)S114901.6911893.332166.8132062.7865S214721.6112006.562011.3831305.1065S316638.0814233.362166.8132720.3565S416422.8014373.112011.3831827.2865*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (14). ****: The minimum values can be lower than my appointed minimum incomes, because of the inclusion of the welfare incomes during the transition period (the one-year after graduation and is spend by looking for a job). *****: The (minimum) age at which the wage premium is the highest. Table 21a: Descriptive Statistics of the IRR Models: Social Education Levels (Models)*Obs. (N)**S#***Age (years)Income ( )MeanMedianMin. ValueMax. ValueMeanMedianMin. ValueMax. ValueHBO (I-A)149 (170)S130.2929.00264929551.892629400.0010800.0049800.00S230.3430.00264924363.592728200.00-18369.7749800.00S330.2929.00264930493.210230000.0018840.0048000.00S430.3430.00264925188.629928800.00-18369.7748000.00WO (I-A)57 (66)S130.8130.00273835520.842134800.0014400.0054600.00S230.8630.50273828659.096833000.00-18369.7754600.00S330.8130.00273836346.161234800.0023368.4254000.00S430.8630.50273829371.872433300.00-18369.7754000.00HBO (I-B)217 (278)S129.1628.00265524001.659024000.009600.0045624.00S229.4928.00265515128.244121186.00-18369.7745624.00S329.1628.00265529107.491728000.0015428.5766666.67S429.4928.00265519113.732326596.4912-18369.7766666.67WO (I-B)83 (104)S130.5130.00274130301.012030000.0012360.0055200.00S231.6330.00276120804.210527744.00-18369.7755200.00S330.5130.00274134072.127134005.0016736.8458105.26S431.6330.00276123813.850432025.00-18369.7758105.26HBO (I-C, V-C, VI-C)366 (448)S129.6229.00265526261.180326400.009600.0049800.00S229.8129.00265518632.729124000.00-18369.7749800.00S329.6229.00265529671.623028800.0015428.5766666.67S429.8129.00265521418.938927283.3333-18369.7766666.67WO (I-C, III-C, IV-C)140 (170)S130.6330.00274132426.228631740.0012360.0055200.00S231.3430.00276123853.754629766.00-18369.7755200.00S330.6330.00274134997.983834200.0016736.8458105.26S431.3430.00276125971.670732388.00-18369.7758105.26HBO (I-D)173S129.9529.00265128009.664727600.0010800.0049800.00S2S329.9529.00265130298.002129400.0018840.0054000.00S4WO (I-D)82S130.6030.00274132811.804932088.0012360.0055200.00S2S330.6030.00274134717.951634026.315816736.8458105.26S4HBO (I-E)169S129.2828.00265524626.272224540.009600.0045624.00S2S329.2828.00265528976.090628137.931015428.5766666.67S4WO (I-E)49S130.5530.00273731697.632730000.0014400.0055200.00S2S330.5530.00273735806.965134500.0024000.0055200.00S4HBO (I-F)102S129.8629.00265527832.0027900.0014400.0043800.00S2S329.8629.00265530356.674930000.0016000.0066666.67S4WO (I-F)43S131.2331.00274135395.534934800.0014400.0055200.00S2S331.2331.00274137390.814836631.578916736.8458105.26S4HBO (I-G)264S129.5329.00265525654.272725800.009600.0049800.00S2S329.5329.00265529406.943928233.333315428.5762200.00S4WO (I-G)97S130.3630.00273831109.938130108.0012360.0051600.00S2S330.3630.00273833937.244333000.0019200.0053333.33S4HBOHBO (II-C, V-C)49 (59)S132.2430.00265426081.632725800.0010200.0051000.00S233.1730.00265519092.247824000.00-18369.7751000.00S332.2430.00265432866.452031186.666720000.0056666.67S433.1730.00265524727.097728860.00-18369.7756666.67WOWO (II-C, III-C)14 (18)S132.2929.00285131143.428628200.0010800.0051600.00S231.7829.00285122521.260026400.00-7656.3351600.00S332.2929.00285134808.149732000.0021600.0057333.33S431.7829.00285125371.598629752.3810-7656.3357333.33WOHBO (IV-C)10 (13)S134.8033.50294324573.6023400.0016500.0039600.00S233.7732.00294315487.702319200.00-18369.7739600.00S334.8033.50294331512.833331100.0022500.0044000.00S433.7732.00294320825.574129333.3333-18369.7744000.00HBOWO (VI-C)64 (72)S130.8329.00265629695.687529160.009600.0057000.00S230.5629.00265624503.878928200.00-18369.7757000.00S330.8329.00265633295.427031200.0019200.0060000.00S430.5629.00265627703.647330543.3333-18369.7760000.00*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (1 4). Table 21b: Descriptive Statistics of the age-income profiles: Social Education Levels (Models)*Obs. (N)**S#***Mean ( )Median ( )Minimum Value ( )****Maximum Value ( )Peak (years) *****HBO (I-A)149 (170)S119775.5424217.75-9349.0333608.8236S220353.4022215.12-9334.2025345.0126S324024.6525889.38-9349.0334687.6536S424018.2924389.70-9334.2027496.4224WO (I-A)57 (66)S144077.5542231.38-9385.8966591.2065S259475.2180000.00-9319.3280000.0045S325561.1727649.04-9385.8936477.7428S458187.8276255.75-9319.3280000.0046HBO (I-B)217 (278)S120961.7723744.00-10719.7427469.3939S26285.788540.20-10853.8014463.0025S329898.4931897.27-10719.7436168.0243S411305.7213636.55-10853.8018520.3526WO (I-B)83 (104)S150502.2144907.98-10629.9380000.0056S22118.781988.46-18369.7725210.1227S361178.9279296.13-10629.9380000.0046S45805.277855.04-18369.7726903.4027HBO (I-C, V-C, VI-C)366 (448)S121040.2924795.01-10161.7231859.1138S28339.7014487.69-18369.7720383.0535S327789.1030232.53-10161.7234217.6040S413423.7417916.73-10277.1721716.4434WO (I-C, III-C, IV-C)140 (170)S150342.0044602.17-10123.4380000.0058S24536.775815.46-18369.7726313.5427S360495.3676682.35-10123.4380000.0047S46741.299043.04-18369.7728070.3027HBO (I-D)173S120006.2623824.45-10331.9733667.5137S2S329199.5331372.20-10331.9735143.8641S4WO (I-D)82S151041.9645844.86-9901.6380000.0057S2S359941.0378339.63-9901.6380000.0046S4HBO (I-E)169S119401.8722707.79-10084.6227895.0038S2S326551.4529407.40-10084.6233896.2040S4WO (I-E)49S116893.6717457.91-10506.7732775.8827S2S358243.6759795.29-10506.7780000.0051S4HBO (I-F)102S124437.7627122.88-10091.2828869.6634S2S328372.7630262.55-10091.2831393.3136S4WO (I-F)43S151153.6444806.87-9194.3080000.0057S2S361046.7478995.75-9194.3080000.0046S4HBO (I-G)264S120262.7123900.63-10188.9433081.7938S2S327895.4030627.90-10188.9435938.0541S4WO (I-G)97S145718.9941171.47-10535.3180000.0064S2S321863.7515177.60-10535.3135759.6832S4HBOHBO (II-C, V-C)49 (59)S125408.6827106.14-12203.0029885.4243S213804.3217634.27-12007.3220865.3137S330710.0433633.48-12203.0039227.1842S418137.4723031.56-12007.3228252.1638WOWO (II-C, III-C)14 (18)S135267.4521549.72-10910.3980000.0057S230521.1316144.00-10256.7880000.0056S342370.7830332.83-10910.3980000.0057S437231.7723691.24-10232.6980000.0056WOHBO (IV-C)10 (13)S148828.9644883.10-16385.3180000.0055S227509.2931429.43-15235.8537477.0351S353240.4951588.25-16385.3180000.0053S430990.4734487.83-15235.8541380.0249HBOWO (VI-C)64 (72)S142037.2535539.70-12030.1880000.0065S240760.0636416.42-11566.9178020.7365S345724.2240961.40-12030.1880000.0065S444339.6141889.83-11566.9177217.1365*: The letters in brackets refer to the Models. For instance (I-A) refers to Study I Model A, and (IV-C) refers to Study IV Model C. **: The number in brackets refers to Scenarios 2 and 4, when the unemployed are included. ***: S# refers to a Scenario (14). ****: The minimum values can be lower than my appointed minimum incomes, because of the inclusion of the welfare incomes during the transition period (the one-year after graduation and is spend by looking for a job). *****: The (minimum) age at which the wage premium is the highest. Tables 22a-22c: (All Results: OLS Models & IRR Models) can be found in the text. (Figures 15): Figure 1: GDP growth levels in The Netherlands Source: Statistics Netherlands. StatLine. (downloaded 2nd August 2012). Figure 2: Tertiary education is associated with (key) persons in charge Source: Paper by Butlin et al. (1997: 37) (downloaded 13th November 2011). Figure 3: Research models in an organogram  Figure 4: Simple schematic display of the age-income profile  EMBED PBrush  Sources: Paper by Shahar (2008: 15), and paper by Appleby et al. (2002: 14). Figure 5: Tertiary educational attainment in The Netherlands  EMBED Excel.Chart.8 \s Source: OECD Education at a Glance 2012 page 38 (downloaded 10th November 2012).  These four different educational paths to indicate a follow-up study will be used throughout the text without brackets. Meaning that WO(WO), HBO(HBO), WO(HBO), and HBO(WO) become WOWO, HBOHBO, WOHBO, and HBOWO, respectively.  Another paper that found a convex relation between education and income is the one written by Liu et al. (2000), who did a study about Taiwan.  While also low educated individuals can have a high ability but do not obtain more education for several reasons and therefore do not get paid as much as they should get.  IV is mainly used when a change has occurred, like increasing the mandatory schooling age of individuals from 16 to 18 years old. While the OLS is used to calculate the real rate of return for every average individual in the dataset despite the existence of several biases (Card 2001).  This is also the reason why (tertiary) education is seen as a public good. That is, the individual and the society benefit (Saxton 2000).  That is, all these results come from an OLS regression model. The only IRR that is available for The Netherlands is about 1965, which shows a higher rate of return to tertiary education compared to secondary education for both the social rate, and the private rate (Psacharopoulos et al. 2004). Indicating a higher demand for tertiary educated individuals compared to secondary educated individuals.  One-third of the increase in income would be attributable to the job-changing activity, which is 12% per quarter of a year; and 1.75% per quarter of a year for the employees who remained working for the same employer (Cohn et al. 1998).  For instance, according to Mincer (1974) when age is used as a control variable instead of potential labour experience, then the rate of return to education will be lower (Card 1999).  That is, the difference in the rate of return is only 0.2% with a light advantage for men (Psacharopoulos 1994).  Compared to relatively rich people who only pay a relatively small amount of taxes (Psacharopoulos 1994).  To a maximum of five years after graduation, as is presumed in Table 18 assumption 1.  REFLEX is financed as a Specific Targeted Research Project (STREP) of the European Unions Sixth Framework Programme, in which 15 countries participate ( HYPERLINK "http://www.roa.unimaas.nl/" http://www.roa.unimaas.nl/).  See Figure 1 of the Appendix. This figure clearly shows that the GDP growth rate in 2005 of The Netherlands over a period of 16 years (1995-2011) above the trend line is.  Which will be explained in subsection 2.2 and 2.3.  The reason of this exclusion is that on-the-job training is also seen as an example of an investment in human capital, next to following a higher education. See also Section 1. By excluding these individuals, I will be avoiding an interaction that might exist between a certain schooling level and on-the-job training. An example is the Australian study by Borland et al. (1989) that has found an interaction between schooling levels and on-the-job training (Borland et al. 2000: 54).  Although for calculating the tuition fees per student I will not be using this dataset, but the dataset StatLine from the Dutch Centraal Bureau voor de Statistiek (CBS or Statistics Netherlands) ( HYPERLINK "http://statline.cbs.nl/statweb/" http://statline.cbs.nl/statweb/).  Contract education is intended for people who would like to study in a particular subject due to their interest in this course or just to refresh their existing knowledge into this particular course, without having to follow the whole study year ( HYPERLINK "http://www.eur.nl/postacademisch/studievoorlichting/contractonderwijs/" http://www.eur.nl/postacademisch/studievoorlichting/contractonderwijs/).  Other authors look at the potential years of working experience, while I look at the actual years of working experience. As I will explain in the next subsection 2.3, the individuals of my dataset also experience unemployment in their working life, which means calculating the potential years of working experience is, in my opinion, inferior in comparison to take the average of actual years of working experience; provided that the data is available, of course.  To indicate the steepness of the variable Job Experience. When the coefficient of the work experience is divided by the square of the coefficient of the work experience, then the return to experience is given (Belzil 2005).  See Section 1: Literature Review.  This means that previous studies that used the net / gross (hourly) wage have overestimated the return to education when the OLS method was used, because, by not including all the costs in the calculation it will lead to a rosy prospect of the private / social rate of return, respectively. Examples are to be found in the paper by Hartog et al. (1999).  The original equation of a cost-benefit analysis is:  EMBED Equation.3  =  EMBED Equation.3  (Formula 3) Formula 3 has been converted to Formula 3.1 to account for the specifics of this study concerning the tertiary student. Note that an individual is allowed to work at age 13, which means Formula 3 has been adjusted for HBO (WO) individuals by adding an extra variable A to account for the five (six) years in which opportunity costs could have been made. The retirement age is adjusted the same way, and so the maximum years of labour experience becomes 53 years (=6513+1).  In the distant future, t (in years) is large, which means the costs and/or benefits will get divided (or discounted) by a high value, which results in a small number. In the near future, t (in years) will be small, which means the costs and/or benefits will get divided (or discounted) by a small value, which results in a large number.  In other words, the calculated/estimated IRR will be related more to the rate of education (obtained by the higher degree) than to the return to experience (obtained by the actual years of working experience).  Of course it is very reasonable to assume that an older individual would have a lower IRR than a younger individual, simply because a younger individual can obtain a lengthier benefit from the higher degree than the older individual. However, in practice, other authors like Bjrklund et al. (2002) have found out with Swedish data that this line of thought is not so reasonable at all. But this anomaly is not researched in this study, as subsection 7.3 will explain.  As mentioned in Section 1, Card (1999) already has shown that individuals obtaining a tertiary degree and higher may also reveal a convex age-income profile rather than a linear or a concave age-income profile.  Even though their study was performed on young people with either a college-education or a high-school education, their models did reveal a downward bias on the growth rate of their earnings. To avoid such a bias to exist in my study, I have chosen to not use a higher-order polynomial regression line.  Cautionary Note: Even the use of a second-order polynomial regression line has its advantages and disadvantages compared to a linear regression line. An advantage (or disadvantage) is that it can get less (or more) influenced by outliers. Example 1: When the sample consists out of individuals in their 30s and 40s earning a moderate income, while 10 individuals earn a very low income and are in their 60s. The linear regression line will be pulled towards the older low-income people and becomes negative. But in the case of a polynomial regression line, these 10 observations will only make this regression line more concave, which is a good thing. Example 2: When the sample only has increasing values (or only has decreasing values). A linear regression line will show a positive (negative) regression line, but a polynomial regression will show a convex regression line, with an increasing (decreasing) amount to the positive (negative) values, which is a bad thing. These two simple examples are to show that the use of the polynomial regression line is not without risk, especially when there are future projections to be made with these observations. That is why I will be implementing a minimum and a maximum income in order to avoid excessive income values. See subsection 2.4.1 for the specifications.  The only condition when the private rate of return to education of Mincers OLS equals the IRR, is when there are no education costs and when these students do not have a job next to their studies. Willis (1986) has shown this (Bjrklund et al. 2002).  Unlike other authors, like Demers (2000), I will not reduce the additional income by some random percentage in order to take these possible influences into account.  See Section 1: Literature Review.  See also subsection 2.2.2: when the distinction is made between direct costs and indirect costs.  However, some individuals have an additional job next to their study (or only have a summer job), which are forgone earnings too and should therefore also be included in the calculations. For my OLS models, however, I do not take these indirect costs into account.  The distinction is to clarify that other forms of earnings besides wage work are not included into this study. Examples are: the interest of savings accounts, renting a room when you are a homeowner, and receiving a heritage (Hansen 1963).  By ignoring the unemployed, these coefficients will show a rosy prospect of following a tertiary education, because there are situations imaginable where people are in-between jobs for a short time or are long-term unemployed, making the coefficients of the simple OLS models upward biased. However, the simple OLS models are used here for a first indication only, which is reasonable considering the flaws it contains.  That is, I will be using a minimum significance level of 90% in this OLS study.  In addition, all the OLS models that have been created for this study have a F-test that is very significant (P-value of 0.000) meaning that the sample has a Normal distribution unless otherwise stated.  P-values are smaller than 0.100.  This summarized table also has an extra regression model (Regression Model 5a), which is not in the Appendix, in order to show if the educational variable Gross Study Duration has an upwards increasing (= convex) or upwards decreasing (= concave) relation with the LN Annual Labour Gross Income (= Y-variable). The variable Gross Study Duration Squared might not be significant, but does show a negative coefficient, indicating a concave influence. Furthermore, I have performed another regression model with respect to the educational variable Gross Study Duration, this time on the LN Annual Labour Net Income (= Y-variable), and found a similar result as the one found in Regression Model 5a. In order to avoid misunderstandings with the other OLS regression models, I have decided not to mention it in this study, but it is available on request by the author.  There are however more females in my study than males, which means that in absolute terms the situation can be reversed.  The independent variable Gender is 1 for males and 0 for females.  I have also tried to divide the variable Gross Study Duration into eight different fields of study of individuals following a HBO study, a WO study, or a combination of both, and found out that this is not realizable with this sample. That is, there were fields of study that had a small amount of observations, meaning that including them would cause more harm than good to his study. The paper by Bjrklund et al. (2002) also verifies that having a large amount of observations is important when the relationship between jobs and required education becomes difficult due to a difference in the length of schooling levels. Or in my case, in the difference in schooling levels.  The reference group is HBO degree only. Meaning that an individual who has two WO degrees (dummy variable WOWO) is more likely to obtain a 19.9% higher income than an individual that has a HBO degree only.  By splitting up the schooling variable, the variable Age has also become significant now, which shows that the inclusion of these dummy variables is a good thing for this simple OLS model and making its results more robust.  Here too is the reference group HBO degree only.  Do note that in the Tables 5a-5d (regression model 5 (Limited Model)) the P-values of the follow-up studies of WO are insignificant, which means only the sign of the coefficient can be interpreted correctly, not the coefficient itself.  Just as the previous footnote, the P-value in Tables 6a-6d (regression model 5 (Limited Model)) of the follow-up study of WO is insignificant, meaning that only the sign of the coefficient can be interpreted correctly.  See Tables 7a7d. However, this result should not be too surprising, because I have also added part-time students in my dataset. But when I specify the OLS models to look only at the fulltime students who have graduated from one study, I still find no proof of a signalling effect in The Netherlands. That is, the variable Graduating in 5 years has a high P-value and has a negative coefficient when there is no distinction being made in education levels, but it has a positive coefficient when this distinction is being made. This last result, however, does show that there might be a positive relationship when students graduate in the nominal years, but only when the researcher takes account of the difference in length of certain education levels. In addition, the significance of the (other) independent variables has worsened when testing whether this effect exists in The Netherlands. See Tables 8a8d. The main reason for looking only at the reference group HBO degree only for Tables 7 and further is that the other two reference groups WO degree only and HBOHBO have shown poor results in the P-values of the corresponding F-tests, which means that the normality of the distributed population is at stake and thus the understanding of the (sign of the) coefficients.  This is also an example of the signalling effect by way of achieving better social skills (Van der Meer 2011).  In other words, the signalling effect should not be limited to the nominal years of acquiring a tertiary degree, but should be extended to also include a dummy variable of following voluntarily extra curricular activities. Even though this particular variable does exist in my dataset, I have chosen not to investigate it further, because it will complicate the OLS model, which I deliberately intended to keep simple.  Examples are inefficient routing years and repeated years (Brown et al. 2007: 84).  See also the paper by Belot et al. (2007) that shows that, on average, Dutch tertiary students in 1995 needed 6 (HBO) 18 (WO) months extra to finish their study.  See Table 9a. It is clearly visible that in both samples acquiring only one degree in HBO has the most individuals, acquiring two (or more) degrees in WO has the least individuals, and the rest covers everything in between.  See also Section 1.  Even though the dummy variables HBOHBO and WOHBO are not significant, they are still positive, which indicate that they are still higher than an individual who only acquired one HBO degree.  Do note that in those OLS studies the reference group is the individuals that only finished secondary school which are compared with tertiary educated individuals.  That is, for the dummy variable Fulltime: the more hours you work, the more income you earn. For the dummy variable Private Sector: the higher and more competitive the job sector, the higher the income. And for the dummy variable Supervisor: the more responsibilities (in managing) you have, the more income you earn.  I have also tried to include three Instrumental Variables (IV) which were the highest obtained education levels by the father, the mother and the partner of the individual into these models, but found out that two out of the three instruments were too weak to perform a 2SLS model with. That is, only the partner of the individual had a low P-value of 0.040, which makes using it nearly impossible, because I have more than one independent variable to regress. This IV did reveal, however, that the coefficient for schooling has increased to 10.2% (up from 2.6%) for gross income and 8.4% (up from 2.2%) for net income in the simple OLS models. See Tables 10a10b. In other words, the IV lifts up the coefficient for schooling by taking out the measurement errors, as expected. This result is consistent with previous work. See also Section 1.  See Table 11, which is about the unemployed in 2005 with different levels of schooling.  Even though the addition of another independent variable has led to deteriorating P-values of Job Experience and Age, it was expected. That is, the more times an individual switches jobs, the less job experience this person builds up over the years, which naturally gives less information to create a proper image of these independent variables on the dependent variable, LN Wage. But the objective of this thesis is not to calculate the rate of return to experience, but the rate of return to education.  The first study is leading for studies that have a mix of tertiary degrees. This is why I will not perform an analysis between HBOWO vs. WO and WOHBO vs. HBO. Furthermore, I have ignored the analysis between WOWO vs. HBOWO+WOHBO and HBOWO+WOHBO vs. HBOHBO, because the individuals that have acquired a mixture of tertiary degrees also have a separate graduation year in general which means I cannot calculate their rate of return. That is, when a second tertiary study is followed at HBO level it generally takes two years to graduate, while at WO level its three years, indicating a gap of a whole year. This cannot be rationalized towards the IRR models in order to keep some relation with reality.  Do note that the models make an unrealistic assumption about individuals making different education investments. According to Willis (1986) has every individual only one lifecycle income path. That is: It is not correct to assume that students who did not choose for additional schooling would have been able to earn the same additional income if they had. (Page 99) (Odink et al. 1998).  That is, I do not assume that the individual takes a one-year break from school and begins building up a valuable work experience, which might benefit him/her later on.  The WO student begins at the age of 18, in order to keep the ages of both hypothetical individuals on the same starting age.  Note that the well-behaved profiles as described by Psacharopoulos (1995) are not valid in this study. That is, the starting salary for someone with a higher tertiary degree is not very different compared to someone with a lower tertiary degree, which makes an intersection between the two age-income profiles a real possibility.  See Table 14 at how long the job search took in months. I rounded the durations of job searching upwards to a full year, for simplicity considerations. Despite having tertiary graduates who did a follow-up tertiary education in WO and searched for a job the first one-half year and finding a job in the second one-half year. The implementation of considering this as a special case by suddenly working with half years would complicate the models unnecessary, because it would interfere with the interpretation of the first value in the age-income profiles that are made by the polynomial regression line. Note that this is not a real problem and could have been implemented, but has been discouraged to avoid making the already comprehensive models even more difficult to understand.  There are other authors like Psacharopoulos (2009) who use a different name for the same rates of return or subdivide such a rate of return to also include other variables, like externalities.  Do note that the upcoming models in Sections 3-5 use a partial equilibrium analysis, which basically says that for instance: individuals who decide to follow a second tertiary study have no influence on the relative distribution of the income for the demand and supply of second tertiary educated individuals (Borland et al. 2000).  For the calculation of converting school years to calendar years, I made use of the formula: (8/12 * number of students in college year t-1) + (4/12 * number of students in college year t).  However, in The Netherlands the formula to get the net income is: Gross income preliminary wage Tax = Net income. I have used the definitive income tax, so that the formula becomes: Gross income definitive income Tax = Free disposable income. In other words, the results presented in Sections 3-5 are all about free disposable income calculated by taking the four income tax levels into account instead of net income. In addition, other properties that an individual may own like a house or savings are not included here, which means that their taxes are excluded too.  According to the Customer Service of DUO consist the educational tuition out of a mandatory universal tuition fee and an optional fee for books. This book fee is not universal, because it depends on the books the secondary school uses to determine how high this fee should be ( HYPERLINK "http://www.elsevier.nl/web/1038489/Artikel/Nooit-meer-schoolgeld.htm" http://www.elsevier.nl/web/1038489/Artikel/Nooit-meer-schoolgeld.htm & DUO).  For my study I assume that this scholarship is enough to cover the direct and indirect costs.  Note that the hypothetical individual in this study is a graduate with an averaged gross study duration (in years) that is rounded upwards. This means that there are individuals in the sample who are not eligible for the Tempobeurs scholarship, because they enrolled in tertiary education either too early or too late, with respect to the time this scholarship had between implementation and being replaced by a newer one, respectively.  Most tertiary studies take four years to complete, while beta and technical science studies last five years and medical science studies (which are not included in this dataset at all due to a very small number of observations) last six years. Seeing that I do not make a distinction in study programmes due to a low number of observations in some programmes I took the average and rounded it upwards (State Secretary of the OCW 2003).  The main reason for working with the actual average study duration is that while the nominal years may get exceeded often by the individual therefore giving preference for working with the nominal years the government (and the individual) still has to pay for these exceeded years.  Meaning that I will disregard the right an average HBO student has of one-year scholarship in the models, when deciding to do a follow-up tertiary study.  The private payments by parents for their children are excluded, because of the existence of scholarships for the Dutch individuals. Also note that other scholarships are excluded in this study.  When the individual has no more eligible years of scholarship left or is following a follow-up study, then the additional jobs are used here only to be spend on the (direct and indirect) costs of the remaining years to tertiary education. These incomes, however, will not be included in the calculation of the IRRs. The reason of this omission is that the dataset mainly gives information about individuals and their incomes after their graduation. Do note that there were individuals available who earned an income during their second tertiary study, but including them would complicate the IRR models unnecessarily, with respect to the polynomial regression line and its corresponding age-income profiles. That is, by avoiding this complication, I try to be consequent with the other half of second tertiary students who received a scholarship for their studies, but are ignored due to a lack of information about them and a fear of adding even more assumptions to the models that could make the coefficients less trustworthy. I am aware that I may have introduced a downward bias in the coefficients of the second tertiary studies, but this will be discussed more thoroughly in Sections 3-5.  The unemployed individuals of this dataset who did not find a job immediately after their graduation stated they have on average remained in this state between 6-12 months after their graduation, before finally finding a job. See Table 14. Do note that the variable, used here, looks at the (total) number of months of being unemployed between graduating in 1999/2000 and filling this questionnaire in 2005.  That is, even the social assistance is being taxed, because it is considered as social welfare, which means it is placed in Box 1 (taxable income from labour and home) ( HYPERLINK "http://www.homefinance.nl/hypotheek/informatie/maximale-hypotheek-berekenen/uitkering.asp" http://www.homefinance.nl/hypotheek/informatie/maximale-hypotheek-berekenen/uitkering.asp &  HYPERLINK "http://www.homefinance.nl/algemeen/informatie/inkomstenbelasting-box-1.asp" http://www.homefinance.nl/algemeen/informatie/inkomstenbelasting-box-1.asp).  In the case of the individual living together, I assume that the partner will be unemployed too. And therefore their social assistance is two times as much as what a single individual would receive.  This amount is roughly 70% of what a couple receives for living together ( HYPERLINK "http://www.st-ab.nl/2-96043a.htm" http://www.st-ab.nl/2-96043a.htm). For other years the percentages are roughly the same. The amounts of net social assistance over 2005 for singles, single parents, and living together are: 6916.55; 9683.13; and 13833.07, respectively ( HYPERLINK "http://www.st-ab.nl/abwnorm19.htm" http://www.st-ab.nl/abwnorm19.htm &  HYPERLINK "http://www.st-ab.nl/abwnorm20.htm" http://www.st-ab.nl/abwnorm20.htm).  The (net) minimum benefit is put at: 4.553,28 (indicating the minimum wage at 2005 for 12 hours a week) and the (net) maximum benefit is put at: 46.760,80 (indicating the maximum wage at 2005 an average individual could obtain). See footnote 99 for more information.  Not the case in 2005.  In Figure 5 (in the Appendix) it is clearly shown that the percentage of tertiary educated individuals in The Netherlands between 1998-2010 is slowly increasing (OECD 2012: 38). As a consequence, between 2000 and 2005 have the unemployment percentages for the tertiary sector increased as well (see Table 11).  This includes teachers salaries, maintenance costs, rental costs, and the scholarship (and study loan) received by students (CBS StatLine 2012, Borland et al. 2000, Appleby et al. 2002, Minister of the OCW 2003: 86).  The two values of this fourth subsidy are also used in the calculations of the social Internal Rate of Return.  The amounts of taxed social assistance over 2005 for singles, single parents, and living together are: 739.78; 2190.55; and 4536.70, respectively.  The (taxed) minimum benefit is put at: 0,- (indicating that there is no tax to be received for individuals who work at the minimum wage in 2005 for 12 hours a week) and the (taxed) maximum benefit is put at: 33.239,20 (which are the income taxes over a gross income of 80.000,- in 2005). See footnote 99 for more information.  The VAT is considered as a valuable source of income for the government by other authors, like the paper by Borland et al. (2000).  An example of income in kind is when the adult recipient lives with his/her parents/friends, free of charge.  Examples are the transportation costs for students and the costs mentioned at the previous subsection. For an overview of all the costs, see CBS StatLine 2012. There is also an English version available ( HYPERLINK "http://statline.cbs.nl/statweb/?LA=en" http://statline.cbs.nl/statweb/?LA=en ( Themes: Education ( Education expenditure and indicators).  Excluding the subsidy given to households.  The formula I used here is: Total direct costs WO1994 = Total expenditures WO + (extra costs*(Total expenditures WO/Total expenditures)). Do note that these extra costs are also included in the gross display of the years 1995 and so on (CBS StatLine 2012).  Do note that the expenses to academic hospitals are included up to and including 1994. The teaching part, however, comprises all the years (CBS StatLine 2012).  Assuming that if the higher educated individual did not follow a higher tertiary education, (s)he would get the income the lower tertiary educated individual would also get. That is, in the case of forgone income, I assume that there is full employment and perfect competition (Odink et al. 1998).  That is, a positive cost on the left side is equal to a negative benefit on the right side.  The amounts of gross social assistance over 2005 for singles, single parents, and living together are: - 7656.33; - 11873.68; and - 18369.77, respectively.  The (gross) minimum benefit is put at: - 18.369,77 (indicating what the society pays to an unemployed individual who lives with a partner in 2005) and the (gross) maximum benefit is put at: 80.000,- (indicating the gross maximum wage in 2005 an average individual could obtain). This maximum benefit is calculated by taking the individual with the highest income of the data sample and calculates its income increments with a fixed income growth rate set at one-percent a year until that individual reaches the age of 65. Following the reasoning of Demers (2005), O Donoghue (1999), and Appleby et al. (2002) to account for a small and cautious income increase per year a high-income country like The Netherlands can obtain.  Stiglitz (1975) even argues that the signalling model see Section 1 may even lead to a social IRR that is higher than a private IRR (Van der Meer 2011). Van der Meer (2011) backs up this statement by arguing that the Dutch students have a longer stay at tertiary school to make a better match with future employers by following extra curricula, which are shown in their CV. See also, subsection 2.2. But these extra years of public subsidization lead to extra costs for the social rate of return, but not to the private rate of return (because the private IRR does not take these costs into account). Making the social IRR lower than the private IRR.  Like the Pigouvian tax/subsidy tries to do in order to get a socially efficient production in the case of negative/positive externalities. Although the use of Pigouvian subsidies means that the society will have to contribute more (thereby increasing the costs of the social IRR), which also is not a solution by itself (Rosen et al. 2008: 82-84).  The first five examples are explained more in detail. The sixth example is self-explanatory (Demers 1999, Van der Meer 2011). Concerning example 1: Tertiary education is a public good, which is caused by the subsidization of the government so that everyone can follow it. (It is considered non-excludable and non-rival, but with a minimal schooling requirement and with a certain maximum age if the individual wants to get a scholarship for its duration on following this tertiary education). The educated individual with all the knowledge and skills learned is then able to help (or teach) lower educated individuals to do their work more efficient. These spill-over effects are transferable and so tertiary education can be beneficial to the society in this way too. Tertiary education can also lead to a lowering of the social inequality. That is, more income equalization and more equal chance on the labour market (Heinrich et al. 2005, Borland et al. 2000, Van Elk et al. 2011, Groot et al. 2003). Concerning example 2: Tertiary educated individuals receive a higher wage; of which can be spend more on their health (or a healthier lifestyle). According to Kippersluis et al. there is a positive association present between tertiary educated individuals and longevity (Saxton 2000, Demers 1999 and 2005, Kippersluis et al. 2009). Concerning example 3: Tertiary educated individuals have a higher labour force participation (see Table 11), which is mainly caused by their opportunity costs. That is, they do not like to be absent from their work, because then they miss a lot of their income; their income per hour is higher than a relatively lower educated individual. This may lead to a higher labour productivity and thus more value to the employer. In return they get a stable employment and a better job security, because tertiary educated individuals have in general lower unemployment rates (see Table 11) and are thus less dependent on receiving social assistance from the government (The World Bank 2008, Brown et al. 2007: 58-100 and 298-328, Heinrich et al. 2005, ODonoghue 1999, Demers 1999 and 2005). Concerning example 4: A study by Groot et al. (2007) has found out that tertiary educated individuals are less inclined to perform criminal activities, like: shop lifting, vandalism, threat, assault, and injury. This might be caused by their lower time preference or a better control of their emotions than lower educated individuals. This may lead to a better social cohesion of society and to individuals who are more involved with the democratic institutions (like voting). However, do note that committing tax fraud increases with a higher schooling level and income. That is, tertiary educated individuals have more to gain from tax fraud (or tax evasion), because of their higher wages (Psacharopoulos 2009, Greenaway et al. 2007: 298-328, Appleby et al. 2002). Concerning example 5: Tertiary educated individuals are on the one hand more occupied in general with their career, which means their family life will start at a relatively later age, lowering the fertility rate. On the other hand, they are in the position of saving lives, thanks to improved sanitations conditions (Psacharopoulos 1995 and 2009).  These two examples are explained here in detail: Concerning example 1: Getting in touch with international students and getting to know their views about certain aspects like their norms and values with respect to social behaviour; their so-called comings and goings and learning from it in order to get a better understanding of the world or for debating about the best policies, etc. (Borland et al. 2000). Concerning example 2: As already mentioned in Section 1, The Netherlands is a country that is already close to the technology frontier, which means that there are less spill-over effects to obtain from its neighbouring countries. That is why there is a need for a(n) (high and skilled) educated society in which tertiary education is considered to be the key to obtain economic growth, through innovation. Like Research & Development by tertiary educated individuals (Van Elk et al. 2011, Appleby et al. 2002).  There are six different studies with each of them a different coefficient.  However, the IRR coefficient of the public sector could not be calculated, because the summed up income of HBO was higher than the summed up income of WO which showed all negative additional incomes in the age income profile of WO , and is therefore considered to be a very negative value (abbreviated to VN). This finding may lie in having a small number of observations for the public sector of WO (49 according to Table 19a), who are all between the ages of 27-37, which meant that the polynomial regression line had to fill in the gaps until the age of 65. And when the incomes of these individuals already show a decreasing line, the polynomial regression line will continue this trend. This is a situation where the OLS method is valued more than the IRR method.  Do note that the WO supervisors suffer from the same problem as the WO graduates working in the public sector. That is, they too are relatively young (under 40 years) and are small in observations (43 according to Table 19a), which means the coefficient could have been a lot different than what it is now. However, seeing that the OLS coefficient depicts a similar picture, this result can be taken as valid.  Note that both research models A and G do not show a positive coefficient in Table 22a. The difference between them is found in the summed up additional incomes of the age-income profile that is still positive for model A, but negative for model G. For model A it means that the additional incomes are still higher for WO than HBO, but it is not worth the additional costs the individual had to make for following this higher tertiary education. This is why negative coefficients are displayed in Table 22a. The WO individual of model G, however, has no financial benefit over the HBO individual. That is, its additional incomes are negative and then there are still the additional costs made for following that higher tertiary education. This is shown with a VN coefficient short for Very Negative in Table 22a. Note that the VN coefficient must be a very negative value in percentage terms, more than -100% , which means that every other coefficient positive or negative that gets compared to this VN coefficient is considered always a better choice due to the positive wage effects it has on the additional incomes earned.  An explanation can be that study IV uses WO as its reference category, while study VI has HBO as its reference category.  However, foreign studies like Levine (2000) have projected that for 2008 about three out of five newly created jobs in the USA a high industrialized country just like The Netherlands will require some postsecondary education, which means the employers of tomorrow would rather have higher educated people in their companies than lower educated individuals. She does note that there will always be some demand for low-skilled jobs.  Do note that this line of reasoning also applies to the middle-aged individuals with only one tertiary degree for study I. That is, the time between them receiving some labour experience with a non-tertiary degree and their attainment of a tertiary degree.  This term has been explained in Section 1. Basically it shows the relationship of a good economy going hand-in-hand with a low unemployment percentage, which makes the opportunity costs of following a tertiary study expensive, leading to a downward bias on the coefficient. This relationship can also work the other way around towards an upward bias on the coefficient.  The 14 affected individuals are located in the sample as follows: nine HBO degree only (between 41-50 hours), four WO degree only (44 and 45 hours), and one HBOWO (45 hours).  Borrowing money from the government is not reported in the dataset. If, however, the dataset did report such information, then the individual would have to pay its study debt back, which lowers its additional benefits over time. In this situation there would possibly be an upward bias, but if the individual becomes unemployed, its debt will get absolved as well as the upward bias.  According to Cohn et al. (1998: 260) has Chapman (1977) found a similar result with his Australian data concerning an increase in the coefficient when student earnings are allowed in the IRR model.  A minimum of 12 hours a week for the first two scenarios to take the part-timers into account; and a fixed amount of 40 hours a week for the last two scenarios to take only full-timers (or converted part-timers) into account.  See also Section 1. Do note that according to Psacharopoulos (2009: 26) individuals working in the public sector may not represent marginal productivity as good as the private sector. However, they do reveal what individuals earn with these tertiary degree(s).  The bold marked bias effects are considered to have a larger influence on the coefficients than the other mentioned bias effects. They will be used throughout this subsection and the following two Sections.  The numbers come from the OECD that describes a long-term government bond (mostly ten years) as: the instrument whose yield is used as the representative interest rate for this area. See also  HYPERLINK "http://stats.oecd.org/index.aspx?querytype=view&queryname=86#" http://stats.oecd.org/index.aspx?querytype=view&queryname=86# (Press on the red i on the right of Subject and scroll down to the Long-term interest rates, Per cent per annum).  I would like to stress out that this is a CBA whereby positive externalities of having a well-educated population on behalf of equity considerations are excluded in this study. And even though every government expense has its own value towards its society, the study keeps focussing on monetary values only.  See also  HYPERLINK "http://www.cbs.nl/nl-NL/menu/methoden/begrippen/default.htm?conceptid=3544" http://www.cbs.nl/nl-NL/menu/methoden/begrippen/default.htm?conceptid=3544 (Information is in Dutch).  Footnote 105 of Section 3 also applies to this VN coefficient.  If the partner of the unemployed tertiary individual is working then there would be a downward bias present, because the tax authority would then be in the situation of collecting more taxes. However, the dataset does not give information away whether the partner of these individuals is employed, or not.  Do note that the basic qualification argument does not apply to the private IRR, because every (hypothetical) individual still in secondary schooling was supposed to pay in any case a(n) (yearly) educational tuition during this time. See also subsection 2.4.1a.  There are variables in the sample that give information about which student has worked after their graduation of the tertiary education in 1999/2000. There were, however, two individuals who mentioned in which year they started working, while many others only mentioned that they have been working at the same time as following their tertiary studies. 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ŬˬάNHHHHHHH$Ifkd<@$$IfF4r3 7 & y             i       0f&64 Faf4άѬ۬FfC$If  NHHHHHHH$IfkdE$$IfF4r3 7 & y             i     0f&64 Faf4 *2;<=>ABCFfI$If CDGMPSY_bNHHHHHHH$Ifkd^K$$IfF4r3 7 & y             i       0f&64 Faf4bepzFfN$If DЭѭح٭!")*fgnoOPWX()01İŰ>?ѱұJKײزQR޳߳YZ]ch5CJ\mH sH hB*mH phsH hCJaJmH sH hmH sH hCJmH sH LNHHHHHHH$IfkdP$$IfF4r3 7 & y             i     0f&64 Faf4ǭЭѭҭӭ֭׭حFf;T$If ح٭ݭNHHHHHHH$IfkdV$$IfF4r3 7 & y             i       0f&64 Faf4!"#$'()FfY$If )*+,/5;>ANHHHHHHH$Ifkd\$$IfF4r3 7 & y             i     0f&64 Faf4AKU]fghilmnFf]_$If norx{~NHHHHHHH$Ifkda$$IfF4r3 7 & y             i       0f&64 Faf4Ffd$If ®ŮˮѮԮ׮NHHHHHHH$Ifkd3g$$IfF4r3 7 & y             i     0f&64 Faf4׮Ffj$If  $'NHHHHHHH$Ifkdl$$IfF4r3 7 & y             i       0f&64 Faf4'*4>FOPQRUVWFfp$If WXYZ]ciloNHHHHHHH$IfkdUr$$IfF4r3 7 & y             i     0f&64 Faf4oyFfu$If NHHHHHHH$Ifkdw$$IfF4r3 7 & y             i       0f&64 Faf4ȯүگFf2{$If NHHHHHHH$Ifkdw}$$IfF4r3 7 & y             i     0f&64 Faf4()*+./0FfÀ$If 019AFNQW]NHHHHHHH$Ifkd$$IfF4r3 7 & y             i       0f&64 Faf4]`cmwİŰưǰʰаְٰܰFfNJFfT$If$.5>?LS[^djmpzFfFf:$IfȱѱұӱԱױݱFfFfFf $If #&0:AJKRYadjpsvFfFfy$IfDzβײزٲڲݲ !'*FfҲFf_$If*-7AHQRY`hkqwz}ijFfFfE$Ifijγճ޳߳ "(.14?IPYFfFf+$IfYZ:^_`acõεԵJn$If$If7$8$H$Ff:@flnRT67bcABjkϹй$%jk./WX߻MNTUhmH sH hCJmH sH  h5\h5CJ\mH sH h5\mH sH RkdV$$IfF4ִ+ .W" &q            (    )    (    )        0f&    4 Faf4ĶԶ$If kd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4  "2<LR$IfRTkd,$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4TVX^n~$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4·ҷ$Ifkdr$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4 "*36$If 67kd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4789<ENV_b$Ifbckd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4cdehpx$Ifkd[$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4ø͸иظ$If kd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4 $IfkdD$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$-5>A$IfABkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4BCDGOW_gj$Ifjkkd$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4knt}$If kd-$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4Ĺ̹Ϲ$IfϹйkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4йѹҹչ޹$Ifkds$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 !$$If$%kd$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4%)06<FIQY^gj$If jkkd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4klmpx$Ifkd\$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4Ⱥк׺ߺ$Ifkd$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4"+.$If ./kdE$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4/014<DLTW$IfWXkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4XYZ]fow$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd.$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4ƻλӻܻ߻$If ߻kd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4\kdt$$IfF4\+ & q             k  0f&4 Faf4$If  $Ifkdy$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4!$'0\kd$$IfF4\+ & q             k   0f&4 Faf4$If 09AJM$IfMNkd!$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4NOPSTUVWZcl\kd$$IfF4\+ & q             k  0f&4 Faf4$If lt}$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kdz$$IfF4\+ & q             k   0f&4 Faf4$If $Ifkd$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4¼üƼμּ\kd0$$IfF4\+ & q             k  0f&4 Faf4$If ּ޼$Ifkd5$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 \kd$$IfF4\+ & q             k   0f&4 Faf4$If "#)*UV\]½Ƚɽ1289klrsپھ  UV{|Ͽп9:de&'RSjphB*mH phsH hCJmH sH hCJaJmH sH hCJmH sH hmH sH M "$If"#kd$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4#$%()*+,/8A\kd$$IfF4\+ & q             k  0f&4 Faf4$If AIRU$IfUVkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4VWX[\]agknv\kd6$$IfF4\+ & q             k   0f&4 Faf4$If v~$Ifkd; $$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4\kd $$IfF4\+ & q             k  0f&4 Faf4$If $If½kd $$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4½ýĽǽȽɽ̽ҽսؽ\kd $$IfF4\+ & q             k   0f&4 Faf4$If $Ifkd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4 \kd<$$IfF4\+ & q             k  0f&4 Faf4$If %.1$If12kdA$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4234789=CGJR\kd$$IfF4\+ & q             k   0f&4 Faf4$If RZ_hk$Ifklkd$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4lmnqrstux\kd$$IfF4\+ & q             k  0f&4 Faf4$If $Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kdB$$IfF4\+ & q             k   0f&4 Faf4$If ž;־پ$IfپھkdG$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4ھ۾ܾ߾\kd$$IfF4\+ & q             k  0f&4 Faf4$If  $If  kd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4   "'/2\kd$$IfF4\+ & q             k   0f&4 Faf4$If 2:BJRU$IfUVkd$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4VWX[ckpx{$If{|kdV $$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4|}~$Ifkd!$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4ÿ̿Ͽ$IfϿпkd#$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4пݿ$If kd?%$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4 (-69$If9:kd&$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4:;<?HPXad$Ifdekd($$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4efgjs{$Ifkd(*$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4$If kd+$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4$Ifkdn-$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 #&$If&'kd/$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4'(),5>FOR$IfRSkd0$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4SZailu~$If kdW2$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4$Ifkd3$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd5$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 $IfGkd@7$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4Glnp 6BZp$If7$8$H$ hnprPQ  YZde"#pq,-FG!"`a@AHI!")*qryzhmH sH hCJmH sH h5\mH sH h5CJ\mH sH TprtvxPJJJJJJJ$Ifkd8$$IfF4r0 " &`    `    `        n    0f&64 Faf4 $'*5>GPQRSV\behs|Ff?FfC;$If|  FfLFfnHFf D$If !'-03>GPYZ[\_eknq|FfUFf8Q$If  *-3Ffg^FfZ$If39<?JS[defgjpvy|Ff1gFfb$If"#&,58>DGJU^FfoFfk$If^gpqrsv|Ff*}FfxFf`t$If#,-18>DNQW]`cnwFfFf$If FfFfY$If (3=FGJQY_ilrx{~FfFf#$If!"#$'-36FfRFf$If69DMW`aekorx~FfFf$IfNHHHHHHH$Ifkda$$IfF4r0 " &                 n  0f&64 Faf4Ff$If NHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4%.7@ABCFGHFf"$If HIJKNTZ]`NHHHHHHH$Ifkdg$$IfF4r0 " &                 n  0f&64 Faf4`kvFf$If NHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4Ff($If NHHHHHHH$Ifkdm$$IfF4r0 " &                 n  0f&64 Faf4!"#$'()Ff$If )*-369?EHNHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4HKV_hqrstwxyFf.$If yz{|NHHHHHHH$Ifkds$$IfF4r0 " &                 n  0f&64 Faf4Ff$If MNUV67>?~uv34GHUV#$deb̿h5CJ\mH sH hB*mH phsH haJmH sH hCJaJmH sH hmH sH hCJmH sH GNHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4   Ff4$If !$'NHHHHHHH$Ifkdy$$IfF4r0 " &                 n  0f&64 Faf4'2;DMNOPSTUFf$If UVY_bekqtNHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4twFf:$If NHHHHHHH$Ifkd$$IfF4r0 " &                 n  0f&64 Faf4Ff$If  NHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4%-6789<=>Ff@$If >?@ADJPSVNHHHHHHH$Ifkd$$IfF4r0 " &                 n  0f&64 Faf4Valu~Ff$If NHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4FfF$If NHHHHHHH$Ifkd$$IfF4r0 " &                 n  0f&64 Faf4 Ff$If %-2:=CINHHHHHHH$Ifkd$$IfF4r0 " &                  n   0f&64 Faf4ILOZcluvwx{FfFfL$If   *34AHPSY_bepyFf{Ff $If FfFfEFf$If   *5>GHOV^agmps|Fft$Ff $If  &)Ff>-Ff($If),7BLUV]dlou{~Ff6Ff1$If#$%&)/58;FQ[dFf>Ffm:$Ifdeb2LX$If$If[$\$Ff7C*02LXZ02./[\!"OP}~ABef%&,-Z[ab BCIhmH sH hCJmH sH  h5\h5CJ\mH sH h5\mH sH RXZkd|E$$IfF4ִ+ .W" &q            (    )    (    )        0f&    4 Faf4Zbn$If kdF$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4*0$If02kdRH$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4246<N`r$IfkdI$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$IfkdK$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4"+.$If ./kd;M$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4/014=FOX[$If[\kdN$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\]^ajs|$IfkdP$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd$R$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4$If kdS$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4 !$If!"kdjU$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4"#$'09CLO$IfOPkd W$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4PQRU^gqz}$If}~kdX$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4~$If kdSZ$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4$Ifkd[$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 $Ifkd]$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4#+5>A$IfABkd<_$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4BFMSYcfox$If kd`$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4$Ifkdb$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd%d$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4 $Ifkde$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4"*0:=FOYbe$If efkdkg$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4fghks{$Ifkdi$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkdj$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$IfkdTl$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4"%$If %&kdm$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4&'(+,-./2;D\kdo$$IfF4\+ & q             k  0f&4 Faf4$If DNWZ$IfZ[kdp$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4[\]`abeknqz\kdBr$$IfF4\+ & q             k   0f&4 Faf4$If z$IfkdGs$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4\kdt$$IfF4\+ & q             k  0f&4 Faf4$If $Ifkdu$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kdw$$IfF4\+ & q             k   0f&4 Faf4$If   $If kdx$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4#,\kdVz$$IfF4\+ & q             k  0f&4 Faf4$If ,6?B$IfBCkd[{$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4CDEHIJMSVYb\kd|$$IfF4\+ & q             k   0f&4 Faf4$If IJ,-34jkqrSTZ[45bc45vwBCpq>hB*mH phsH hCJmH sH hCJaJmH sH hCJmH sH hmH sH Mbku~$Ifkd~$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4\kd$$IfF4\+ & q             k  0f&4 Faf4$If $Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kd\$$IfF4\+ & q             k   0f&4 Faf4$If $Ifkda$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4 \kd$$IfF4\+ & q             k  0f&4 Faf4$If  ),$If,-kd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4-./2347=@CL\kd$$IfF4\+ & q             k   0f&4 Faf4$If LU^gj$Ifjkkd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4klmpqrstw\kdb$$IfF4\+ & q             k  0f&4 Faf4$If $Ifkdg$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kd $$IfF4\+ & q             k   0f&4 Faf4$If $Ifkd$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4\kd$$IfF4\+ & q             k  0f&4 Faf4$If $IfkdŐ$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4%(+4\kdh$$IfF4\+ & q             k   0f&4 Faf4$If 4=GPS$IfSTkdm$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4TUVYZ[\]`ir\kd$$IfF4\+ & q             k  0f&4 Faf4$If r|$Ifkd#$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4\kdƗ$$IfF4\+ & q             k   0f&4 Faf4$If $Ifkd˘$$IfF4ִ+ .W" &`q    `        (  )()   0f&    4 Faf4$Ifkd|$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4  (14$If45kd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4567:CLV_b$Ifbckd$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4cpw$If kde$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4  (14$If45kdN$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf45<CKNW`jsv$If vwkd$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4wxy|$Ifkd$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkd7$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkdڪ$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4#,6?B$If BCkd}$$IfF4ִ+ .W" &`q    `        (  ) ( )   0f&    4 Faf4CDEHQZdmp$Ifpqkd $$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4qrsv$Ifkdï$$IfF4ִ+ .W" & q             ( )() 0f&    4 Faf4$Ifkdf$$IfF4ִ+ .W" & q             (  ) ( )   0f&    4 Faf4R?@ABCSTbc7$8$H$[$\$>?BCSTUOQbc˹˯ˉxl]Qlj hUmH sH jhcT hUVmH sH jhUmH sH j hUmH sH  hH*jhUmHnHuhCJH*mH sH h6CJ]mH sH hCJmH sH "jhCJUmHnHsH uhB*mH phsH h5CJ\mH sH hhmH sH h5B*\mH phsH 89:;<={].L;h,7$8$H$8>{|]^_ -./0LM;<hi,-ɽܳj+ hUjhUh\aJmH sH jh0JUhCJH*mH sH hCJmH sH j$ hUmH sH %j U hCJUVaJmH sH jhUmH sH hB*mH phsH hmH sH h0  YnBCD`ab3BCfg8<h\aJmH sH h6mH sH h]mH sH j. hUmH sH j, hUmH sH jhUmH sH haJmH sH jh0JUhmH sH jhUh0JmH sH 1`BfDqE"G E   s ~ tldh/0128DqrEF!"#$4MG β֚΃΃΃΃΃yo΃byWyh6aJmH sH h56aJmH sH haJmH sH haJmH sH jh0JUh56aJmH sH haJmH sH j/2 hEHUmH sH j5DU hCJUVaJh5CJ(aJ(mH sH hmH sH jhUmH sH j/ hEHUmH sH j5DU hCJUVaJ"G H E F     s t u } ~   erstuvklmn<=>?@`ab.1?@+,\]#$оh6mH sH h>*mH sH h6mH sH h>*mH sH hmH sH h>*aJmH sH haJmH sH hmH sH jh0JUDl=`?+ V!"P##$$%T&'*@+;-/001^CD7 8   V!W!X!"""P#Q####$$$$$$$$%%%%S&T&U&''((**@+A+++;-<-//0q111223222337777$9%999+<,<B=C==ٳjhUmH sH hmH sH h6mH sH h>*mH sH hmH sH jh0JUh>*aJmH sH haJmH sH E1222377$99+<=E>?ABsC8DHJLgM Q,S\STUfVWZZ[=======E>F>??GAAAAAABBsCtC8D9DEEEEHHJJ.K/KKKKKKKҹȥ҈|mjh6 hUmH sH hB*mH phsH h6]mH sH h6mH sH h]mH sH hmH sH hB*mH phsH jh0JB*Uphjh0JUhmH sH h0JmH sH jhUmH sH j4 hUmH sH &KKNLOLPLLLLLgMhMMMMMMN NOOPPPTPVP\P^PPPPQQ Q Q,S.S\S^SzSSTTU먜Ҩ먜|ҨtthmH sH j<< hB*Uphj'; hB*UphhB*mH phsH jhB*Uphj: hUjhUjh0JUh0JmH sH j]8 hUmH sH hmH sH jhUmH sH *UUUUfVhVWWZZZZ[[[[\\\9\:\;\<\O\P\x\y\\\\\]]J^K^v_w___``eeWfXfhhFjGj>sFuɽɟڔڔڋڀh\aJmH sH hH*mH sH  jhmH sH h0JmH sH &jQ= hB*UmH phsH hB*mH phsH  jhB*UmH phsH hmH sH jh0JUhmH sH h\aJmH sH 2[x\\]J^v__`WfhFjj4noruv%wxzz}o$'LΊFu|uukvvvvv|zzzzzz}}op$%'(LMΊϊz{RSefgNOYZƭƭj? hUmH sH h0JmH sH jv> hUmH sH jhUmH sH h6mH sH jh0JUhmH sH h\aJmH sH hmH sH >ΊzRN\$$.%&&s'T()*H+y,#//^56789\]$$$$$.%/%&&&&&r's't'u'S(T(U())***G+H+I+y,z,#/$///^566666778899;;H<I<L>M>n>>?!???AABBBBcDdDeDhh\mH sH hmH sH haJmH sH Uh6mH sH hmH sH jh0JUM This is possible, because the Dutch society and its government are different entities with each having their own objectives as in how to spend their money best.  The exception is Study I, in which all the research models are examined.  Footnote 105 of Section 3 also applies to this VN coefficient.  Note that the other categorical variables (like gender and working in the private or public sector) have not been taken into account concerning this remark about whether the Dutch society should invest in a WO tertiary education, or not.  Seeing that both long-term interest rates (domestic) are in close proximity to each other, I will stop treating them as a separate alternative investment opportunity for the Dutch society.  For instance, a WO graduate does not have to be educated for the labour market only, like in the case of a HBO graduate. See also subsection 2.4.2.  [1] > [2] Higher costs to be taken into account, because of public subsidization; and [2] > [3] the latter works only with taxable incomes as the benefits, while its costs are only slightly smaller than that of the former.  This was increased to ten years after 2000; the same year in which the annual public transport tickets (the OV studentenkaart) also got included in this scholarship as a preliminary loan.  The Tempobeurs gave a scholarship of the nominal study duration plus one year and a loan for two years. The Prestatiebeurs kept the scholarship with respect to the nominal study duration intact and gave out a loan for three years. As a result, individuals that needed more time to obtain a tertiary degree than the nominal study duration were either forced to find a job or get the study loan from their government.  Six months of study loan for a HBO student and 18 months of study loan for a WO student (of which the last 12 months is due to the reform).  I will only be treating the scholarship systems that are close to the present Dutch scholarship, because it is more likely that one of those will be chosen, rather than a scholarship that is completely different. For an overview of other scholarship systems I refer to Greenaway et al. (2007: 314-318).  Seeing that the relations were mainly Private/Social > Public, it was clear that the individual and the society were to make the largest contributions. The social feudalism is the most proper scholarship system to this end. Do note that a higher IRR in a specific educational level means a larger demand, which should be coupled with a larger educational investment in that specific educational level, and vice versa. However, some jobs on a tertiary level simply do not offer a high salary, which means they will always have a low IRR. This will be discussed later in this subsection (Greenaway et al. 2007: 313-314, Hines et al. 1970: 337, State Secretary of the OCW 2003: 16).  I expect a similar conclusion to be made by other tertiary studies concerning the adoption of the Prestatiebeurs scholarship starting from the school year 1996/1997.  For instance, in the REFLEX dataset there are tertiary graduates who despite having several years of working experience, still earn a relatively low income per income per year (between 9.600,- and 15.900,-). The main  problems of these individuals are that they: 1) work either a lot of part-time  mainly due to their family life  , 2) perform a lot of voluntary work without receiving a form of financial compensation for it  and thus cannot be used in my models  , or 3) stick to their first (tertiary) job and make no effort to switch to a higher paid job, thereby displacing jobs for newly (low) tertiary graduates (Borland et al. 2000: 13-14). That is, it looks like they do not know how much costs the government and the society have made to make sure a tertiary education is available to them to act accordingly, by constantly looking for a better paid job and keep the flow of jobs in motion.  Average study debt per student in 2006: 11.000,- to 2008: 12.500,- (Nibud 2010: 9).  Under the assumption that an individual can borrow yearly 10.000,-, based on the norm amounts of 2011 (Minister of the OCW 2011: 6).  For other ideas that have been proposed by politicians, which are more flexible and pave the way for higher study debts, I refer the reader to the paper Beleidsnotitie Studiefinanciering Studeren is investeren (Minister of the OCW 2011: 3).  According to Appleby et al. (2002: 12) has every study programme not only their own income levels, but also their own skills and (intensity of the) study durations, which their students need to have and endure, respectively.  To have their objective reached in 2010 has been found too optimistic, in retrospect. That is, several misfortunes like the economic crisis and the lack in dividing responsibilities between the European Union and its member states have made this objective unobtainable for the moment (European Commission 2010: 2).  Keeping in mind that the new incomes of the relatively older individual is partly due to their working experience with a lower (tertiary) degree, next to their recently graduation of a (higher) tertiary degree (maximum of five years). So that, theoretically, there will be a permanent gap in incomes between the two profiles.  Note that the graduates in the data sample who are unemployed now probably will not stay that way their entire life. That is, eventually they will find a job with a high (low) wage, which depends on if their job, is in line with their field of study and whether these jobs give high (low) incomes. Furthermore, there will always be some individuals who work either fulltime, or only part-time (or not at all). The latter are probably women who stay at home to take care for the children (Borland et al. 2000: 40).  Note that only individuals, who want and are able to follow a tertiary education, actually do so, while the others skip this costly opportunity due to the schooling premium puzzle. According to Van Elk et al. (2011: 12) people tend to valuate benefits that happen in the future less than benefits that happen now, which is why these future benefits need to get discounted in order to get around this uncertainty.  In this case, the focus shifts away from the parental incomes towards the income of the individual (plus partner and possibly the other residents living in the same house) that will be included to see if that individual really does qualify for an (income dependable) additional scholarship.  Assuming the wage premiums that are calculated in this research correspond more or less with the wage premiums that are found when The Netherlands is experiencing an economic downturn, like an economic crisis (200820xx; see Figure 1 in the Appendix).  This basically keeps the situation as what it is now, only the full scholarship needs to be reimbursed as much as possible by the individuals who profit from it.  The individuals who follow a tertiary education or a different kind of training through their employer have been removed from the data sample, because they could cloud the results of regular education. That is, it was unclear what costs the individuals made for following such an education/training, what this type of education/training actually was, and who actually paid for these costs: the individuals or their employer.  Under the assumption that the unemployed individual has not stepped out of the labour market voluntarily, but instead has been sacked. Note the subtle difference between the both of them, even though they both have a(n) (negative) influence on the IRR, because in both situations there has been a tertiary degree obtained, but no (suitable) job for the individual has been found (Boothby et al. 2002: 42). 9;H<L>?ABBcDEvLGMuMvMxMyM{M|M~MMMMMMMMMMh]h&`#$eDEEEEELvLwLGMHMuMvMwMyMzM|M}MMMMMMMMMMMMMMMMMMMMMMMNyNzN{NN̻ެޔ} h5\hB* \aJmH phsH h5B* aJmH phsH jA hUjBU hCJUVaJhY0JmHnHu h0Jjh0JUjhUhUjh0JUhmH sH haJmH sH , This suggestion is related to the first discussion point about the use of flexible tuition fees in subsection 7.1.3.  These suggestions are related to the second discussion point concerning the issues of extending the age of being eligible to receive a scholarship and increasing the period of repayment in subsection 7.2.2.  Of course this is very optimistic thinking.     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/ / / /  / /  /   44 Ff4p QkdB5$$IfF4 ]R 2h I"    `  ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd9$$IfF4 ]R 2h I"       ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd=$$IfF4 ]R 2h I"    `  ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdYB$$IfF4 ]R 2h I"       ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdF$$IfF4 ]R 2h I"    `  ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdK$$IfF4 ]R 2h I"       ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdpO$$IfF4 ]R 2h I"    `  ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0+++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdS$$IfF4 ]R 2h I"       ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd*X$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd\$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd`$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd2e$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkdi$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkdm$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd:r$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkdv$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkdz$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p QkdB$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / / / /  / /  /   44 Ff4p Qkd$$IfF4 ]R 2h I"      ,  : ::666      <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 5 <#v#v#v,#v:#v 6#v #v <:V F4  0++ + 555,5:5 65 5 </ /  /  /  / / /  / /  /   /   44 Ff4p QkdJ$$IfF4 ]R 2h I"       ,   :  : : 6 6 6       <     0,,,,4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F40555,5:5 65 /  /  /  / /  /  /   /   /   44 Ff4 kd$$IfF4 ]R 2hI"        ,    :   :  :  6  6  6       0((((4 Faf4$$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0)v++ 555,5:5 65 /  /  /  / / / /  /  /   /   44 Ff4p +kd9$$IfF4 ]R 2hI"l       ,   :  : : 6 6 6  `      0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd:$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdQ$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdh$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdİ$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd۴$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd $$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd $$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd7$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdN$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kde$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd|$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"      ,  : ::666       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4  0++ 555,5:5 65 / /  /  /  / / /  / /  /   /   44 Ff4p +kd$$IfF4 ]R 2hI"       ,   :  : : 6 6 6        0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F40555,5:5 65 /  /  /  / /  /  /   /   /   44 Ff4 kd$$IfF4 ]R 2hI"          ,     :     :     :     6     6     6          0((((4 Faf4$$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0)v++ 555,5:5 65 /  /  /  / / / /  /  /   /   44 Ff4p +kd$$IfF4 ]R 2hI"l          ,     :     :     :     6     6     6     `       0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd( $$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0(((( 4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd?$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdV$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kdm$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / / / /  / /  /   44 Ff4p +kd"$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          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+kdO$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p $$If!v h555,5:5:5:56565 65 #v#v#v,#v:#v 6#v :V F4   0++ 555,5:5 65 / /  /  /  / / /  / /  /   /   44 Ff4p +kdS$$IfF4 ]R 2hI"         ,    :    :    :    6    6    6          0((((4 Faf4p !DyK ehttp://studyassist.gov.au/sites/studyassist/helppayingmyfees/csps/pages/student-contribution-amountsyK http://studyassist.gov.au/sites/studyassist/helppayingmyfees/csps/pages/student-contribution-amountsyX;H,]ą'cDyK http://wetten.overheid.nl/BWBR0011453/Hoofdstuk6/Paragraaf61/Artikel67/geldigheidsdatum_wijkt_af_van_zoekvraag/geldigheidsdatum_01-08-2011yK .http://wetten.overheid.nl/BWBR0011453/Hoofdstuk6/Paragraaf61/Artikel67/geldigheidsdatum_wijkt_af_van_zoekvraag/geldigheidsdatum_01-08-2011yX;H,]ą'cuDyK :http://www.iza.org/teaching/belzil_ss2005/mincernotes.pdfyK http://www.iza.org/teaching/belzil_ss2005/mincernotes.pdfyX;H,]ą'cDyK >http://stats.oecd.org/index.aspx?querytype=view&queryname=86#yK 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#vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckds$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 3 m }7y?" &`y   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/  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd $$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd~$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd $$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckdd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4CkdJ$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd0"$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd&$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd+$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd0$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd86$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd;$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4CkdZA$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdF$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd|L$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd R$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4CkdW$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd/]$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckdb$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdQh$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckdm$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckdss$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckdy$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd~$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4$$If!vh5y555i 5#vy#v#v#vi #v:V F40f&6++5y555i 5/  / /  44 Ff4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd&$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd $$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckde$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd؞$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4CkdK$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd1$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 3 m }7y?" &`y    `          V          0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckdp$$IfF4 3 m }7y?" & y              V   0f&6,,,,4 Faf4,$$If!v h5y5555V5555 5 5 #vy#v#v#v#vV#v#v #v #v :V F40f&6++5y5555V55 5 5 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 3 m }7y?" & y               V          0f&6,,,,4 Faf41$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&5q555(5)5(5)5/  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40                          ! " # $ % & ' ( ) * + , - . / 0 1 2 3 4 5 6 7 8 9 : ; < = > ? @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z [ \ ] ^ _ ` a b c d e g h i j k l m n o p q r s t u v w x y z { | } ~  f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6+++5555 5n/  44 Ff4[$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6+++55555j555 /  44 Ff4?kd9$$IfF4 0 b k"dB" &                   j                            0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd=$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdA$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdNF$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4CkdJ$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4CkdO$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd}S$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdW$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4CkdG\$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd`$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckde$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckdvi$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckdm$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd@r$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckdv$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd {$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckdo$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckdԃ$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd9$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckdh$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd͙$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd2$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd $$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd&$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!vh5v555] 5#vv#v#v#v] #v:V F40f&6++5555 5n/  / /  44 Ff4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd,$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /   /  /  / / / / /   44 Ff4Ckd[ $$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd%$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4CkdT"$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd&$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd+$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4Ckd/$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / /  /  / /  /  / / / /   / 44 Ff4Ckd3$$IfF4 0 b k"dB" &`    `          j          0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4CkdM8$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  / / / / / /  /  /  / / / / /  44 Ff4Ckd<$$IfF4 0 b k"dB" &               j   0f&6,,,,4 Faf4$$If!v h5v5555R5555 5 5 #vv#v#v#v#vR#v#v #v :V F40f&6++55555j555 /  /  /  / / / /  /  /  /  / / / / /   44 Ff4CkdA$$IfF4 0 b k"dB" &                j          0f&6,,,,4 Faf41$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&5q555(5)5(5)5/  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  /  / / / /  /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / / / /  44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V F40f&++5q555(5)5(5)5/  /  / / / /  /  / 44 Ff4$$If!vh5q555k#vq#v#v#vk:V F40f&++5q555k/  / /  44 Ff4$$If!vh5q555(5)5(5)5#vq#v#v#v(#v)#v(#v)#v:V 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