Too Cool for School? Patterns and Determinants for Dropouts and Re-
enrollers in Upper Secondary Education in Norway
Martine Jonette Hoseth Myklebust
Thesis submitted for the degree of
Master in Economic Theory and Econometrics 30 Credits
Department of Economics Faculty of Social Sciences UNIVERSITY OF OSLO
May 2021
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Too Cool for School? Patterns and Determinants for Dropouts and Re-enrollers in Upper Secondary Education in Norway
Martine Jonette Hoseth Myklebust
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© Martine Jonette Hoseth Myklebust 2021
Too Cool for School? Patterns and Determinants for Dropouts and Re-enrollers in Upper Secondary Education in Norway
Martine Jonette Hoseth Myklebust http://www.duo.uio.no/
Trykk: Reprosentralen, Universitetet i Oslo
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Abstract
This thesis investigates the different patterns and determinants for different groups of students in upper secondary education in Norway that enrolled into high school in 2008. The students are followed for a ten-year period after starting school in order to investigate their movements in and out of education. The main focus is on dropout students that re-enrolled back into education after some period outside, and what differentiates these students from dropouts that did not re-enroll. The findings are consistent with the international literature.
I found that dropout students constitute approximately one third of the school cohort, and that the majority of dropouts re-entered education at some point. Despite the high re- enrollment rate did half of the dropout group remain outside school by the end of the analysis period as permanent dropouts. 20% of the sample is categorized as dropouts five years after enrolling into upper secondary education. This share decreases with four percentage points to 16% in 2018, ten years after starting high school. By the end of the analysis period 82% of the sample is registered with completion of a high school degree, implying that 18% did not obtain a degree during the analysis period. By having an analysis period for ten years, this life course perspective helps understand and observe the movements in and out of education. The analysis shows that individuals keeps moving in and out of education throughout the whole analysis period.
Ordinary students have higher educated parents and higher average grades compared to dropout students. The ones that re-enroll have higher grade averages compared to the dropouts that never re-enroll. The most common for individuals that choose to leave school drops out one time and re-enrolls back one time. The northern region has the highest share of dropout students, and the county of Finnmark is the only council municipality in Norway that has equal shares of dropouts and ordinary students. As a comparison is the average for the whole sample a dropout rate of 35%. Five years after enrolling into upper secondary education Sogn og Fjordane has the highest share of students that completed with a high school degree. This pattern is unchanged until ten years after enrolling into upper secondary education.
Grades from lower secondary school matter for the movements in and out of school and is related to gender and parental education. Pupils with highly educated parents produces higher grade averages in upper secondary education, and girls dominate the upper part of the
V grade distribution. Boys drop out to a larger extent than girls, as they dominate all of the dropout categories. Grade averages is higher for ordinary students compared to dropouts and re-enrollers.
The regression conducted on dropout probability shows that mother’s education and higher grade averages has negative significant association on the probability of becoming a dropout student, while going to school in northern Norway is associated with an increases in the probability of becoming a dropout compared to going to school in South. The regression on re-enrollment probability shows that having a foreign citizenship decreases the probability of becoming a re-enroller while mother’s education and high average grades increases the probability of becoming a re-enroller. Mother’s education seems to matter more for
movements in and out of school more than does father’s education. School region seems to have a bigger impact associated with dropout probability than re-enrollment probability.
Both regressions were conducted using the same model specification and the same regressors. These findings imply that identical model specifications and controlling for the same background characteristics has different implications on the probability to drop out and re-enroll respectively.
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Acknowledgements
This master thesis represents the end of my studies at the Department of Economics at the University of Oslo. It has been five incredible years, mainly thanks to my fellow students that has motivated me, guided me through numerous mandatory assignments and made me laugh almost every single day. Thanks to my family, friends and significant other for unconditional love and support during this period. It has been comforting to know that I have people around me that cheer at me when I do not believe in myself. Especially do I want to thank my fellow student and incredibly good friend Mia that has listened to my frustration, given me valuable advice in life and co-walked with me home from university at late nights. Writing a thesis itself has been the hardest task of my life, and it has truly been an emotional rollercoaster. I do not think the pandemic has worked as a countercyclical effect either.
I want to give a sincere thanks to my supervisors Monique de Haan at Department of
Economics and Marte Rønning at Statistics Norway for good guidance. Monique has been a comforting and supportive supervisor that has taught me a lot about method and econometrics during this process. I also want to provide gratitude to Kaja Reegård at NIFU for valuable discussions of the topic and engaging conversations on the field of educational economics.
You did really motivate me to take on to this project and you are part of the reason this thesis was evolved.
This thesis is written as a part of the project “The comeback kids: A longitudinal study of dropouts’ re-enrollment in upper secondary education” at NIFU. The project is financed by the Research Council of Norway (project number 283408). I also want to thank NIFU for giving be the opportunity to participate at meeting with the focus group, and that I got to write a thesis within such an important and relevant theme.
The data preparation and estimation has been carried out in STATA16. Codes can be provided on request.
Eventual mistakes or inaccuracies are solely my own.
Martine Jonette Hoseth Myklebust Oslo, 20. May 2021
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Contents
1 Introduction ... 1
2 Background ... 5
2.1 Literature review ... 5
2.1.1 Literature on dropouts ... 5
2.1.2 Literature on re-enrollers ... 8
2.2 Theory behind the decision ... 10
2.2.1 Education as an investment ... 11
2.2.2 Education as signaling ... 12
2.2.3 The optimal level of education ... 15
3 Institutional setting ... 17
4 Data ... 19
5 Empirical approach ... 24
6 Results from the empirical analysis ... 27
6.1 Descriptive evidence ... 27
6.1.1 Gender ... 32
6.1.2 Mother’s education ... 33
6.1.3 Father’s education ... 34
6.1.4 School region... 35
6.1.5 Grades... 37
6.1.6 Years spent outside education ... 41
6.2 Main results ... 43
6.2.1 Regression on dropouts ... 44
6.2.2 Regression on re-enrollers ... 49
6.2.3 Comparing the two regressions ... 54
7 Discussion ... 57
8 Conclusion ... 61
9 References ... 63
10 Appendix ... 69
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List of figures
Figure 1. Percentage of pupils that are outside school and have not passed education. Years
2015-2019. Source: Udir ... 3
Figure 2. Number of dropouts and re-enrollers during the analysis period ... 22
Figure 3. Chart over the different student groups ... 27
Figure 4. Dropout and re-enrollment occurrences ... 30
Figure 5. Distribution of grade averages by student groups ... 38
Figure 6. Student status and years spent outside education ... 41
List of tables
Table 1. Overview of the different student groups... 20Table 2. Summary statistics for two subgroups and the whole sample. Mean coefficients, standard deviation in parenthesis ... 23
Table 3. Dropout occurrences ... 28
Table 4. Re-enrollment occurrences... 28
Table 5. Dropout and re-enrollment occurrences for all individuals ... 29
Table 6. Years spent outside education ... 31
Table 7. Gender distribution by student groups ... 32
Table 8. Distribution of mother’s education by student groups ... 33
Table 9. Distribution of father’s education by student groups ... 34
Table 10. Distribution of students groups within regions ... 35
Table 11. Distribution of grade averages by student groups ... 37
Table 12. Distribution of grade averages by gender ... 39
Table 13. Distribution of grade averages by parental education ... 40
Table 14. Regression results for the probabiltiy of becoming a dropout ... 45
Table 15. Regression results for the probability of becoming a re-enroller ... 50
Table 16. High school completion within council municipality, by year. ... 69
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1 Introduction
Graduation from secondary school is viewed as the minimum level of educational competence needed for successful participation in the labor market (Bushnik et al., 2004).
Youths take education in order to meet formal qualifications and hence to be a productive worker in a technological society with more specific skills demanded in the labor force. In Norway students have a statutory right to upper secondary education as youths or adults. The reasons to leave school for a youth in a complex life situation can be many. Reasons like working, being pregnant, going to military or traveling abroad are some of the reasons pupils have to leave school early. Approximately one third of a school cohort drops out of high school across OECD countries (Bennett et al., 2020). Developing effective tools to identify the students that don’t follow linear paths of education is important. The phenomena of students dropping out of education is a problem of social and economic disadvantage. One of the challenges in motivating more youths to stay in school is to find ways to handle pupil diversity, since every student has different needs and ways of learning.
In this thesis I investigate patterns and determinants for movements in and out of upper secondary education in Norway. Every year a new cohort enters the Norwegian education system. In the school year of 2020/2021, 246.838 pupils entered upper secondary education in Norway (SSB, 2021). 78% of Norwegian pupils’ complete upper secondary education within five years (SSB, 2020), and the share of completion has been rather stable for the past years. I have studied pupil movements in and out of education, from dropouts to potential re-enrollers. I investigate how re-enrollers differ in characteristics from dropouts that don’t re-enroll, and I will identify possible determinants of re-enrollment. We know little about what characterizes re-enrollers regarding demographic and academic traits. It is also little knowledge about how differences between gender and ethnic minorities within the same risk groups respond differently to a re-enrolling process.
Early school leaving for an individual is not a phenomenon that occurs overnight. The process that ends with dropping out of high school could have started early in life, as a result of a long-time mis relationship between the school and the individual (Markussen, 2016).
There is a lot of literature on students that drop out of upper secondary education. Yet there is still much unknown about those that decide to return to education after some time outside
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schooling. Re-enrollment into upper secondary education is a topic of high economic
relevance and interest. The government invests a lot of money and thought to the problem of early school leavers, and the societal costs of dropouts in every new school cohort is
estimated to be approximately 5 billion NOK (Lillejord et al., 2015).
The educational policy in Norway has been built on school being free, reducing inequality and hindering all forms of marginalization (Imsen & Volckmar, 2014). Leaving school early can have negative consequences for both the individual itself and the society as a whole. Being outside school is associated with several risk factors, such as greater risk of poverty, unemployment, incarceration, health problems and dependence on public assistance (Rumberger & Lim, 2008).
Upper secondary education in Norway is a right, and not a duty. Even though it is voluntary has the path into upper secondary education turned into almost mandatory in order to obtain a stable attachment to the working life (Ministry of Education, 2021). This imply that in practice, upper secondary education is the only real alternative for young adults in Norway. This may also be the reason that as much as 97 percent of pupils that finish primary school enroll straight into upper secondary education (Aanerud, Holmsetg & Johansson., 2013). The youths that are 16 years old today do not have alternative paths into the labor force as the youths had some decades ago. Even in vocational professions that are mainly based on raw labor is it required with formal qualifications and certificates in order to do the job.
Despite this can it still be the best alternative for an individual to drop out, take a break from school or to do something completely different after finishing lower secondary school. The focus of this thesis is to examine what the factors are that are associated with leaveing school in the first place, and what characteristics the group of students that returns back has. Since the Norwegian government uses so much resources and funding in order to avoid dropouts, a closer look at what determines if a dropout becomes a re-enroller will give valuable
knowledge to the field.
The educational system is prone to reforms being introduced in order to give attention to certain challenges. Reform 94 was introduced to give youths a statutory right to upper secondary education. In 2006 the Knowledge Promotion Reform (KPR) was introduced in order to acknowledge the low completion rate and the big dropout rate in upper secondary education in Norway (Ministry of Foreign Affairs, 2005). It has been reported a yearly positive trend in the completion rate since the introduction of the reform. In total, the
3 excessive share of students that completed upper secondary education due to KPR was 5 percentage points by 2018. The government has recently come up with a new proposal for a reform, called the finishing reform. They have a goal that nine out of ten pupils will finish and pass upper secondary education by 2030 (Ministry of Education, 2021). In order to fulfill that goal, they suggest removing the time restriction of five years on the right to upper secondary education. This will result in giving everyone the right to education up to the point where they obtain study or vocational competence. This extended right will give pupils that of different reasons need more time to finish, and that is in danger of falling out if they have to follow a linear path of education, the flexibility they need in order to be able to complete.
Figure 1. Percentage of pupils that are outside school and have not passed education. Years 2015-2019. Source: Udir
I use high quality Norwegian registry data from Statistics Norway through Oslo Fiscal Studies (OFS). In the data sets I can follow all individuals in Norway from 1970 to 2018 identified by an ID number. This allows me to merge different data sets in order to study different characteristics on each student group. I am interested in comparing ordinary students that follow the regular paths in education to dropouts and re-enrollers with non-linear paths of education. A dropout is identified by not being enrolled into high school for a particular year,
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and at the same time not being registered with a high school degree from before. A dropout is an individual that leaves school at some point during the analysis period. The dropouts are divided into two groups; the ones that re-enroll and the ones that are permanent dropouts. For different birth cohorts in the data set I am defining the different groups by yearly observations of attendance in education and obtaining a degree. I examine three birth cohorts that enrolled into upper secondary education in 2008. The individuals are followed for ten consecutive years after they start high school, usually the year they turn 16 years.
The main findings is that one third of the sample drops out at one point, and 70% of the dropouts are re-enrollers. Despite this high re-enrollment rate is half of the dropouts still registered as a dropout by the end of the period, hence being a permanent dropout. The variables that has the highest associations with dropout probability are not the same variables that has significant associations for re-enrollment probability. Going to school in Northern Norway is associated with a higher dropout probability as that region has the highest share of dropout students, as well as the highest share of permanent dropouts. Girls dominate the higher part of the grade distribution, and boys dominate all of the subgroups of dropouts.
Parental education seems to matter for both the grades the pupils obtain, but also whether a student is categorized as a dropout or not. Five years after enrolling into upper secondary education the share of dropoutsis 20% , while it decreased to 16% ten years after enrolling.
Four years after enrolling is 64% of the sample registered as having a high school degree.
This share increases to 82% in 2018, ten years after the individuals enrolled into upper secondary education. The remaining 18% did not obtain a degree during the analysis period, hence being categorized as permanent dropouts.
The rest of the thesis is organized as follows. The background chapter includes both a literature review and a conceptualizing of the theoretical framework on educational decisions.
Then follows a chapter on institutional setting with a description of the Norwegian education system. Next follows the data chapter and empirical approach where the method of a linear probability model is described. The results from the empirical analysis is in two parts, one descriptive and one on main findings from the regression. To finish up is there a chapter on discussion and on conclusion separately, followed by appendix.
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2 Background
The background chapter is divided into two parts. First I review the relevant literature on dropouts and re-enrollers respectively. Next follows a conceptualizing of schooling decisions within a theoretical framework. Individual decisions to drop out and re-enroll is discussed within the framework of human capital theory and signaling theory.
2.1 Literature review
While the literature that investigates characteristics on dropouts is substantial, is the literature on re-enrollers much more scarce. Section 2.1.1 will give an overview of the literature on dropouts and section 2.1.2 will focus on studies investigating re-enrollers.
2.1.1 Literature on dropouts
The literature on dropouts in the Norwegian education system is relatively broad.
Studies in the recent years have examined the different characteristics and reasons for why some pupils leave school early. Understanding the factors that put some pupils at risk of dropping out constitutes a good fundament for decision making and implementation of measures and interventions.
Norway has higher expenses per pupil than the mean in the OECD countries (Lillejord et al., 2015). Dropping out is not only related to lack of formal qualifications, but also the risk of being excluded in the labor market and to have poorer health later in life. On this basis Falch et al. (2009) estimated that a 10 percent reduction in the dropout rate (from 30 to 20) would save the society for between 5.4 and 8.8 billion NOK per year. There is reason to believe that a reduced dropout rate has potential to contribute to increased welfare, in terms of increased productivity, reduced extent of public social security and benefit schemes and reducing the unequal income distributions in society (Falch et al., 2009). It is not feasible to believe that it is possible to eliminate the rate of dropouts completely, but the Norwegian government has a stated goal to reduce the number of dropouts by as much as possible.
The definition of dropouts varies in the literature, but the most common way to define it are pupils that are not registered with a high school degree within five years after enrolling.
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Young dropouts, defined as youths in the age 16 to 24 that are not in education and lack a high school diploma has been stable in the period 1994-2006 (von Simson, 2014).
Regional differences are expected in such a long and diverse country as Norway. Falch et al. (2010) found that the completion rate in Northern Norway is clearly lower compared to the rest of the country, also when controlling for primary school grades, distance to school and other factors. They also found an indication that a higher use of social services in Northern Norway is due to a low level of obtained education in that region.
Lamb & Markussen (2011) conducted an analysis containing Norway and other countries, and examined the dropout patterns across countries. Across all of the countries in their analysis was it a group of between 20 to 40 percent of the pupils that did not pass education. Problems around dropout rates are not only a problem in Norway, but also internationally. The educational system is a good enough educational provision for the majority, but not good enough for every student. The research points to different factors that can explain the variation in dropout and competence acquisition. Among these are unequal social background, earlier school performance, as well as academic and social engagement.
School performance before upper secondary education seems to have the biggest impact on the probability for whether pupils drop out or finishes and pass upper secondary education (Markussen, 2010). Falch et al. (2010) found some similar results that, controlled for other factors, an increase in the average grade in lower secondary school with one grade point increased the probability for that the pupil finished upper secondary education with almost 30 percentage points. Similar results were found in their data, that among pupils with at least 55 primary school points from primary school did 99 percent pass, compared to 13 percent of the individuals with less than 25 primary school points (SSB, 2014).
Vogt et al. (2020) investigates education, work and welfare trajectories for three different birth cohorts of early school leavers in Norway between the ages of 16 and 26. Their concern is the issue of early school leaving due to claims that low-skilled young people, and especially males, are excluded from the labor market. They did find some indications of increased labor market exclusion among early school leavers, however the majority still followed trajectories characterized by employment or further education. Male early school leavers did consistently predominate the trajectories leading to middle or high incomes. This implies that a gender-segregated labor market consistently appears to be providing low-skilled
7 men with more economically rewarding life course trajectories. Female school leavers, on the other hand, are overrepresented among those who follow low-income trajectories or receive temporary health-related benefits.
The dropouts that did not re-enroll in school or employment two to five years after dropping out described a lack of support and access to material, academic and social resources. Bridgeland, Dilulio and Morison (2006) found that 74% of young disadvantaged high school dropouts stated that they would have stayed in school longer if they could have made the decision again, suggesting that the decision to drop out may be perceived differently later in life.
Bunting et al. (2017) complemented the view of youths at risk with a perspective of socially mediated relationships. Their experiences suggest that, in order to succeed, students need customized support throughout the process from school to the workplace and that many pupils depend on this support. The higher education parents have, the higher is the share of students that gained study or vocational competence after five years, according to Støren et al.
(2007). Living situation does also seem relevant. 70% of youths that lived with both their parents at age 15 got a diploma after five years, compared to 50% for those that did not live with both parents. Bratsberg et al. (2010) found that youths from families with highly educated parents and high income had far lower risk for long lasting exclusion from school, compared with youths from families with low education and low income.
The association between individual grade variance and educational attainment was investigated by Sandsør (2020). She used the grade point average to determine entrance into upper secondary education in Norway, taking use of the second moment of individual grade distribution. Since the grade point average is calculated from grades 1 to 6 for the same 13 subjects, there is a limited number of possible average values and within variation. She found that grade variance is negatively associated with educational attainment across the grade distribution and for both genders. These differences are not driven by background characteristics, implying that being a generalist pupil with similar skills across different subjects, hence having low grade variance, is beneficial with respect to educational attainment.
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2.1.2 Literature on re-enrollers
Litterature on re-enrollment is not as substantial as for the groups of dropouts, but there is still relevant findings in the literature. It is expected that the re-enrollers as a group are heterogeneous with respect to duration of their time outside, reasons for returning to school and what they did outside. Existing research focuses on the relevance of institutional arrangements in relation to re-enrolment, and especially conditions related to re-enrollment age or the gap between the statutory rights to upper secondary education for youths and adults (Sterri et al., 2015).
Lillejord et al. (2015) conducted a systematic overview of studies published between 2010 and 2014. The overview included different measures relevant for Norway that had documented effect of completion and dropouts in lower and upper secondary education. It concluded that it was hard to point at which individual measures that was most effective. The reason for this is that school reforms in Norway often is introduced with many measures at the same time, making it hard to estimate the effects from single measures and separating the effects.
Bennett et al. (2020) exploited reforms that enabled access to high school for adults over the age of 25. They evaluated the causal impact of a second chance of completing high school as adults on labor market outcomes in Norway. By reducing the opportunity cost of re- enrolling in high school, these reforms significantly increased education among women, but not men. The results suggested that financial support may matter, as increases in student support pushes women back to education, and that childbearing is important for both drop out and returning decisions among women.
Steig et al. (2018) conducted a series of focus group interviews with 15 youths that has dropped out, focusing on their experiences with resuming with education or training. They found substantial differences between regions, gender and educational programs. The students pointed at the importance of self-determined decisions, experiencing sufficient competence and the need for support from significant others when resuming back to education or training.
Ramsdal & Wynn (2021) investigated how youths who had dropped out of high school experienced their re-enrollment processes. Early in the process the youths focused on their experiences with lack of inner motivation, lack of endurance and confusion concerning what
9 they wanted to do with their lives. Later on, they described positive changed in inner
motivation and reduced confusion about future goals. The feeling of being stuck developed into a feeling of moving forward during the re-enrollment process. The weakness of this study is that the sample size consists of six youths from Northern Norway and is therefore not representative for the group of re-enrollers.
A Norwegian study conducted by Olsen, Anvik & Breimo (2019) focused especially on re-enrollment of the pupils outside education and training (NEET) that had mental health problems. Their findings indicated that close monitoring and follow-up was particularly important in the process of re-enrolling. The ultimate challenge for the NEETs in the re- enrollment process was to make the pupils stay in work or education after they had re- enrolled. The problem with external validity also arises when this study focused on NEETS, which is a narrow group, but in addition has mental health problems. It might be that these results are not as profound for the re-enrollers that worked in the time outside, or that did not have mental issues. Yet is the finding of the importance of emotional, academic and social support in the re-enrollment process also found in other studies and in countries outside Norway. Rumberger & Lim (2008) published a paper in cooperation with the California Dropout Research Project and the dropouts often felt that they were left to fend for
themselves, and for many was the only support they could rely on from their mothers when they met social or educational challenges. This highlights the importance of an external support system around the pupil that can help push them back into education when they are not able to motivate themselves.
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2.2 Theory behind the decision
When looking at education as any other arbitrary investment, the optimal allocation would be to invest to the point where the marginal return of education equals the marginal cost of taking the education. The true cost is rather loss of potential income if the individual were to work at a paid job instead of taking education. This is an example of the opportunity cost, which is the value of the best alternative that got chosen away when the individual chose between two mutually exclusive alternatives. Individuals that chooses to be in school are willing to give up earnings today in order to gain higher earnings in the future. The trade-off between lower earnings today and higher earnings in the future, as well as the financial and institutional constraints that limit access to education institutions, determines the distribution of educational attainment in the population (Borjas, 2015, p. 201).
Individuals expect returns on their investments, otherwise the investment would not necessarily have been made. In the same way do we expect to be rewarded with higher earnings in the future when we collect the returns on the education investment. It is reasonable to assume that individuals choose the level of human capital investments that maximizes the present value of lifetime earnings. A rational student will make a decision whether or not to attend school based on the present value of different schooling scenarios.
The two options that will be compared is the present value of earnings with low level of education and the value of earnings with high level of education. The student knows for sure that there will be a loss of potential income in the period where the excessive schooling appears. There is a lot of uncertainty related to this kind of decision as the amount of information the individual has at the time when the decision is made is rather scarce. The observed education decisions are a consequence of expectations, preferences and
opportunities (Manski, 1993). There are many factors that can complicate this process. Facing the lack of information about the true returns of education in the future is important, also preferences can change over time and available opportunities and personal situations can differ (Jensen, 2010)
I will further discuss implications of education according to two different economic approaches, one that treats education as an investment and one that focuses on education as a way to signal abilities.
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2.2.1 Education as an investment
Knowledge in terms of human capital is one of society’s most important resources.
Economic theory highlights different incentives for individuals to get education. We acquire human capital through school investments, and the skills that are gained in school are an increasingly important component of our stock of knowledge. The value of the future work force, in terms of human capital, is estimated to be up to 81% of the national wealth in Norway (Ministry of Finance, 2013). Individuals invest time and resources in gaining new knowledge with hopes for higher salaries and broader opportunities in the labor market (Macedo et al., 2018).
When treating education as an investment in human capital the interpretation is that education increases the productivity of the worker and hence raises the future wage. Going to school is costly in two ways. First, every year spent in school is a year spent not working and not getting a wage. For most students the earnings they forgo is from a low-wage part-time job, but it is still counted as an opportunity cost from attaining school. Secondly, the students face some direct costs and expenses for tuition, books and other fees. In Norway these costs are not substantial since students get funds to attain school, free transportation to school and possibility to get scholarships.
The gains from education is that getting more knowledge and skills will make the future productivity higher. A higher wage is paid to workers that have invested more time and resources into education. Being compensated for the private costs is an important incentive in order to make individuals invest in education. If students that stayed longer in school earned less than students that dropped out, no one would finish high school because they would not have had an incentive to do so.
When discussing returns on investments the rate of discount plays a role. The discount rate can differ between individuals. The higher the discount rate, the less value is attached to the future. This implies that students with high discount rate acquire a lower level of
education, such as students dropping out. The interpretation is that returns to education is collected in the far future, and this is close to irrelevant for a dropout with high discount rate that cares more about the present utility of being outside school. The level of discount rate also depends on the rate of time preference, hence how an individual feel about giving up utility today in order to get rewarded more in the future. In general individuals who are more
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oriented around present time have a higher discount rate and hence are less likely to stay in school (Becker & Mulligan, 1997)
According to the definition of the different student groups, dropouts are placed within the group of individuals with high discount rate and oriented around present time. It can also be that dropouts finds the opportunity cost of going to school too high. An example would be if they got a job offer somewhere outside school and found it to be more appropriate than to continue with taking education. Ordinary students choose to never drop out, hence has a lower discount rate and are not as oriented around present time according to this theory. The
ordinary students may not find other options other than going to school to be valid or good enough with regards to future labor market opportunities and wages. The behavior of the group of re-enrollers is not properly taken care of within this framework. A re-enroller has at one point in time chosen to leave school prior to graduation, and at some later point decided to go back to school. Such preferences evolving through time is not taken into account due to the fact that the theoretical framework is static, while real decisions regarding educational attainment is dynamic and hence can change over time.
2.2.2 Education as signaling
When evaluating education as a signaling device the attention is put to how individuals at best can show their abilities. According to this theory, education increases future earnings because education signals the abilities of the individual. On this basis is abilities rewarded with higher wages. This signaling mechanism plays an important role when an eventual employer is not able to observe the abilities of the individual directly.
A signaling mechanism can be helpful in a situation with asymmetric information. In the labor market the different actors hold more information about certain areas than the other.
For the worker knowledge about own abilities, interests and skills can be hidden from the employer, whereas the employer knows more about the contract and the workplace. There is always some uncertainty related to an individual’s personal contribution to a firm, and education can work as a way of sorting workers according to these qualifications.
In the framework of educational abilities, I assume that finishing with a diploma signals as high ability. Getting a diploma requires some knowledge, qualifications and skills that
separates the different students from each other and does therefore function as a signaling tool
13 with good precision. The comparison is hence between high school graduates as high ability and those that did not finish with a degree as low ability. As an example, consider two students that attain upper secondary school, whereas one of them chooses to finish and the other one chooses to drop out. The student that quits before finishing with a diploma earns a low wage from age 17 until retirement since he is counted as low ability. The student that finishes with a diploma forgoes some of the earnings for the years it takes to finish school, and then earns a higher wage until retirement, since the high productivity gained from education is rewarded with future earnings.
The costs of taking education differs between individuals. Within a signaling framework is the assumption that dropouts find it costlier to obtain a certain level of
education crucial. This is the mere reason some individuals manage to obtain a diploma while others don’t. Fees to attend school is identical for all individuals, but for those that is having a hard time with school, finishing a high school degree can be challenging both monetarily and personally. A dropout may have to study longer, hire tutors, buy study guides and special classes in order to finish a degree compared to the students that did not drop out in the first place. For a student with poorer preconditions to take education it might cost more in effort, hours spent studying and a feeling that education is a battle to overcome. Theoretical models suggest that one can understand the dropout process as a development that begins early in the school system. School failure may culminate an attitude of pupils rejecting or being rejected by the school (Finn, 1989).
Without a signaling device individuals would be paid the same wage irrespective of whether they had obtained a high school diploma or not. Such lack of information about abilities could result in a mismatching of workers and jobs, since all individuals are treated identically due to the lack of information about true productivities. Such situation would be profitable for the individuals classified as low ability, since they would get a higher wage than their productivity accounts for. The opposite would be true for the ones that had obtained high abilities from finishing a high school degree, where their wage would be dragged down by the low ability of the dropouts. This kind of mismatching would not be efficient, as high
productivity workers could get easy tasks and low productivity workers could get tasks that they were unable to solve. Mismatching of workers and jobs would have a detrimental effect on national income (Borjas, 2015, p. 232).
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High school diploma functions as credible information provided about the individuals ability so that the individual can be allocated into the correct ability group. This is efficiency of allocation, and something that has positive repercussions.
The signaling device serves as a way to separate individuals based on abilities
obtained from education. High school graduates are able to demonstrate the abilities they have gained from education, compared to the ones that did not obtain a diploma. The theory builds on a static decision based on the trade-off between making the effort to be categorized as a high ability worker, or not make the effort to finish high school and hence be defined as low ability. Individuals will either take education such that they obtain a high school diploma or drop out and leave at some point before. If an individual considers the future gains from being able to signal high ability as bigger than the costs of going to school, the individual would take education according to this theory.
In reality it can be hard for an individual to estimate the long-term future gains from a high school degree, and even how high these returns might be individuals would make
decisions based on other factors than just future earnings. How pupils get along with teachers, if they are being bullied or if they find courses interesting are among factors that can affect the decision to leave school early. Self-perception, aspirations, social engagement and school climate are factors that can change over time and is not taken into account in the theory.
In terms of the definition of student groups would dropouts not get a high school diploma and hence signal their low ability, as they find the costs of education too high. In this way the ordinary students can separate themselves out from the rest, signaling their high abilities. Re-enrollers that ended as dropouts would be classified as low ability, while the re- enrollers that graduated would be defined as high ability. This is not intuitive, because if a re- enroller truly was high ability, it would not make sense that the pupil dropped out in the first place. The signaling theory is suffering the same problem as the human capital theory in not being able to describe the behavior for the re-enrollers. These theories do not do well in describing why some individuals first drop out and next re-enroll. This is expected, as static theoretical frameworks rarely is capable of explaining dynamic processes.
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2.2.3 The optimal level of education
Higher levels of education are associated with higher wages and lower unemployment rates. As any other investment decisions, the revenues need to be weighed against the cost of investment. The marginal rate of return to school can be estimated by how much earnings would increase for an individual that attains schooling for one additional year. The percentage increase in earnings from one additional year of schooling is the marginal rate to school. This cannot be forever increasing because of the law of diminishing returns. It is more costly for a highly educated individual to stay longer in school due to the opportunity cost. The curve of the marginal rate of return to education is hence a declining function of the level of schooling.
A general property of optimal investment decisions is that the stopping rule maximizes an individual’s present value of lifetime earnings.
Making well-informed decisions in the present based on future returns of education can be difficult. A schooling decision is influenced by many external factors and not just the monetary value of the lifetime cash flow. The true reward to schooling is hard to estimate and there is a lot of uncertainty about revenues for the individual. As mentioned above can the costs of taking education differ between individuals. Economic and social situations change unpredictably, making it very difficult to forecast how rewards to schooling skills and careers develop. Uncertainty plays a role in every decision, and human capital investments makes no exception.
If education leads to increased wage and opportunities in the labor market, why does not all individuals obtain a certain level of education? According to economic theory there are two main reasons individuals obtain different levels of schooling; either there are differences in marginal rate of returns to education, or there are different discount rates for individuals.
The investment of education is easier to perform for an individual with lower discount rate or higher marginal rate of return to education.
When evaluating educational attainment, we are subject to a selection bias that makes us unable to truly compare the returns for individuals. We cannot observe the same person both dropping out and not dropping out at the same time. This makes it possible that it is some kind of selection happening that makes some of the individuals dropping out in the first place. We know that individuals differ in interests, abilities, knowledge, skills and schooling
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engagement, and it is possible that some of these factors influences the schooling decision without being able to make count of it in the theory.
The signaling theory says that education functions as a mechanism to best sort the right workers for the right jobs. On the other side does the human capital theory says that education increases individual skills and knowledge, making them more productive workers.
It is not an easy task to determine which of these two theories that may be more correct, but regardless is it observed that higher educated workers earn higher wages in the labor market (Bennett et al., 2020), and that students that don’t finish education is at risk of being excluded from the labor market, as well as having health problems later in life (Lillejord et al., 2015). It is hard to prove which of the two theories that are most substantial since both theories predict a positive correlation between education and higher wages (Bedart, 2001). Both theories are able to explain the differences in acts of dropouts and ordinary students, yet is neither capable of describing the group of re-enrollers.
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3 Institutional setting
The Norwegian educational system is built on a principle that everyone has equal rights to participate and that education is free. Children start primary school at the age of six and follow compulsory schooling for the following ten years. The first seven years constitute primary school, and the following three is lower secondary school. As soon as the youths are 15 years old, they choose whether they want to apply for upper secondary education.
Norwegian students have a guaranteed admission to upper secondary education since the introduction of Reform 94. The pupils can rank three choices of study program, and they are guaranteed to get accepted at some educational program (Sandsør, 2020). The grades received in the last semester of 10th grade is used to determine the acceptance into upper secondary education. This grade point average contains both grades in subjects as well as some finishing exams in the last year of lower secondary education.
97,7% of Norwegians pupils enrolls into upper secondary education the same year as they finish lower secondary education (SSB, 2019). When enrolling from basic education the youths can choose between different types of study programs in upper secondary education.
The main categories are to either choose a direction towards general academic studies, or to take a vocational qualification where they obtain a specific profession. There are many different types of educations within both of these categories. The goal of upper secondary education is to qualify students for work through vocational programs, or higher education thorough general studies (Markussen et al., 2011)
The higher education entrance qualification programs they can choose from is Art, design and architecture, Media and communication, Music, dance and drama, Specialization in general studies and Sports and physical education. These programs are mainly theoretical and provide the youths with a good foundation for later studies at a university or a college (Utdanningsdirektoratet, 2021). All of these programs have a duration of three years, where the students can choose different topics and specializations. After finishing the three years they receive a diploma for completed upper secondary education that qualifies them for higher education later on.
The vocational options are more comprehensive. The most common structure of vocational programs is to have two years in school followed by two years as an apprentice in
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a firm. This model holds for the eight vocational programs and leads to a professional certificate after a passed theoretical exam and a practical test. The youths gain vocational skills and are able to be a skilled worker in their respective field. There are also some other types of vocational paths that have different structures. There is one type that consist of one year in school followed by three years working as an apprentice. This is categorized as a special pathway, but it also results in a professional certificate for the pupil. Common for all of the vocational qualifications is that the pupils can attain a supplementary year after finishing their ordinary paths if they wish to enter higher education later on.
Every county administration has a task to provide pupils with a follow-up service.
They are responsible with providing education, employment or suitable activities for the young people that have dropped out of school. Their main task is to help youths in the age 16- 19 outside work or education to make transition to competence giving education or work (Markussen, 2010). The service has few available measures to offer the dropouts, as they almost only can offer the dropouts to return to the same school that they left in the first place.
Depending on the dropout reason some pupils might not find this option optimal, for example if the dropout reason was bullying or poor relations with teachers and other students. Re- enrolling is considered an important task as a part of the Norwegian welfare policy. Despite this does only one third of the dropouts receive satisfactory assistance and guidance from the service (Riksrevisjonen, 2016). This means that the majority of the dropouts are depending on own drive, support from family or other care persons in order to take the step back into
education again. A consequence can be that many of the dropouts enter into marginalization processes, are being passive and potentially depending on disability benefit schemes. These marginalization processes often involve being outside education, employment or training, hence being categorized as a NEET (Sandsør, 2020). A lack of ability for the system to follow up and guide pupils into school again can result in potential re-enrollers being stuck in a category as a dropout student.
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4 Data
Using high quality Norwegian registry data from 1970 to 2018 I examine the
movements in and out of school for individuals in upper secondary education in Norway. I do not differentiate on what activities the individuals are doing in their time outside education.
All individuals can be identified and followed through the data sets by an ID variable. The sample is restricted to students born in 1991-1993 that enrolled into upper secondary
education in 2008. It is the 1992 cohort that is supposed to start high school that year, but the sample also includes individuals from the cohorts the year before and after in case there are individuals that are starting one year earlier or one year later. The year of ordinary enrollment into upper secondary education is when the individual is 16 years old. I started out with 61,163 observations, but the final sample includes 57,593 pupils after excluding some individuals due to lack of observations in relevant variables1.
The data set is registry data from Statistics Norway, accessed through Oslo Fiscal Studies (OFS). The unit of observation is individuals. I have used different data sets in order to constitute a data set with the variables of interest. The data sets that has been used is on education, individual characteristics, demography, social background and grades.
The individuals are followed for ten years after starting high school in 2008. The movements of the individuals are followed through the upper secondary educational system, defining two main groups of students. The definitions of the different groups are based on school attendance and obtainment of high school degree2. School attendance is a variable that confirms whether an individual was registered within a Norwegian educational institution for a respective year. Obtainment of high school degree is a variable that states for each
individual the highest obtained educational level for a given year3. The individuals that are not in school for a particular year and have not obtained a high school degree from before is defined as a dropout for the respective year4. An ordinary student is an individual that is either
1 Removing individuals start started prior to 2008, that did not enroll in 2008, that was registered with a high school degree before 2011, that had parents with unobserved education, without numeric grades or school region
2 NUS2000 codes are used as the basis to define the groups
3 In order to make sure that the education is finished and passed
4 Educational level >3 implies that an individual has finished and passed at least final upper secondary education, hence having a high school degree
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registered in school for a particular year or have obtained a high school degree from before. I have constructed summary variables that indicates whether an individual has been registered as a dropout or not during the whole period, as well as for each consecutive year in the analysis period.
The group of dropouts is further divided into two subgroups. A re-enroller is a dropout that came back to education after some time outside. That means that these individuals were first registered as a dropout, but then was registered inside an educational institution
sometime after. A non-re-enroller is a dropout that never returned back to education after leaving, hence had status as a dropout for all the following years after dropping out one time.
Another group of students that are a type of dropout is what is called permanent dropouts.
These are the individuals that still had status as a dropout by the end of the analysis period in 2018.
Table 1. Overview of the different student groups
Group N Fraction of whole sample
Whole sample 57593 100%
Ordinary students 37824 66%
Dropouts 19769 34%
Re-enrollers 13833 24%
Re-enrollers that ended as dropouts 3214 6%
Re-enrollers that did not end as dropouts 10619 18%
Non-re-enrollers 5936 10%
Permanent dropout 9150 16%
As seen from the population sample, two thirds of the sample follows linear paths of education, and one third drops out at some point during the analysis period. The majority of the dropouts re-enrolls back into education at some point, hence giving school a second chance. Most of the re-enrollers did not end as dropouts, meaning that they either obtained a high school degree or stayed in school until the end of the period. About half of the
individuals that drop out was still registered as a dropout by the end of the analysis period.
21 The group of permanent dropouts is a result of both the dropouts that never re-enrolled (2/3), and the dropouts that re-enrolled at some point but still ended the period as a dropout (1/3).
The aim for the thesis is to compare the different groups of pupils in terms of
background characteristics. We know a lot about school dropouts, but we do not know much about those that return to school after a time outside. There are two interesting questions to ask. Firstly, who are the students that leave school in the first place. Do they have non educated mothers, low grades or a particular gender? Secondly, what separates the dropout students that chooses to take the step to re-enroll back into high school. Is it students going to school in a specific region, students with foreign citizenship or girls? These kinds of variables are included in the analysis in order to determine what characteristics that are associated with the probability of becoming a dropout and a re-enroller
A variable on gender is generated in order to see the different compositions of gender within the different groups. The variable is binary, and takes on the value 1 for female, and 0 for male. Citizenship is interesting to look at, and therefore is a binary variable on whether an individual is registered with a foreign citizenship created. Since upper secondary school is the responsibility of the council municipalities in Norway, a variable on school region is included instead of birth region. The division of regions follows the old council municipalities and regions prior to the merging of the communes that was decided by the parliament in Norway in 2015. The regions take on values for South, East, West, Mid and North. Parental education is defined for both parents separately in order to see whether there are differences in the coefficients of education of mothers and fathers. The parental education variables are
registered when the pupil is 16 years old and takes on nine different values, spanning from no education to researcher title. A variable on grades from lower secondary school is created, taking on the mean value for the final assessment grades registered in the 10th grade.
It is the council municipalities that has the responsibility for following up the students that leave school early. The follow up service is required by law and their main tasks is to help youths in the age 16-19 outside work and education to make the transition into education or work (Markussen, 2010). Being able to examine patterns and determinants of the different groups makes it possible to customize different measures for the groups of students that are not following the linear paths of education. It is also worth to mention that there are
substantial differences of dropout and re-enrollment rates within the council municipalities that is not reflected in the presentation of the numbers for the whole sample.
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Figure 2. Number of dropouts and re-enrollers during the analysis period
The tendencies in the data set are in line with previous research on the field. The students that drop out and later return comes back short time after dropping out, while the ones that have been outside for a while has a smaller probability of taking the step back into education (Bratsberg et al., 2010). The fraction of dropouts is rather stable beyond the three first years after starting high school.
Following is a table of summary statistics for the variables used in the analysis on the groups of dropouts, ordinary students and the whole sample.
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Table 2. Summary statistics for two subgroups and the whole sample. Mean coefficients, standard deviation in parenthesis
Re- enrollers
Dropouts Ordinary students
Whole sample
Female 0.44
(0.496)
0.42 (0.494)
0.51 (0.500)
0.48 (0.500)
Foreign 0.01
(0.084)
0.01 (0.089)
0.01 (0.081)
0.01 (0.084) Mother’s education 3.87
(1.636)
3.73 (1.620)
4.43 (1.631)
4.19 (1.661) Father’s education 3.91
(1.598)
3.77 (1.572)
4.42 (1.637)
4.20 (1.644) School region 3.33
(1.020)
3.32 (1.018)
3.25 (0.962)
3.27 (0.982)
Grades 3.60
(0.831)
3.48 (0.824)
4.32 (0.739)
4.03 (0.868)
N 13833 19769 37824 57593
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5 Empirical approach
In the thesis I investigate the different patterns and determinants for different groups of students. I examine specifically what makes some dropouts differ from others by returning back to school after early leaving, and, hence becoming a re-enroller. I am also looking into what background characteristics that are associated with the probability of becoming a dropout in the first place. Any findings that suggests that the groups respond differently on different background characteristics is of interest. Determinants such as parental education is well known to play a role on the school performance of individuals. It is also common knowledge that there is male dominance in dropout rates. I do therefore expect to find that parental education and gender matters for the behavior of both groups of dropouts and re- enrollers.
The statistical associations are going to be estimated using a linear probability model, which is a special case of the Ordinary Least Squares (OLS) regression model for a binary dependent variable.
The conditional expectation equals the probability for success in the dependent
variable, 𝑌 = 1, conditional on the regressors 𝑋1𝑖, 𝑋2𝑖, … . , 𝑋𝑘𝑖 (Stock and Watson, 2014). The linear probability model with multiple regressors is the following:
𝐸[𝑌𝑖|𝑋1𝑖, 𝑋2𝑖, … , 𝑋𝑘𝑖] = Pr(𝑌 = 1|𝑋1𝑖, 𝑋2𝑖, … , 𝑋𝑘𝑖) = ß0+ ß1𝑋1𝑖+ ß2𝑋2𝑖+ ⋯ + ß𝑘𝑋𝑘𝑖 (1)
𝑌𝑖 = ß0 + ß1𝑋1𝑖+ ⋯ + ß𝑘𝑋𝑘𝑖+ 𝑢𝑖
The coefficients on ß𝑗 equals the change in the probability that 𝑌𝑖 = 1 associated with a unit change in 𝑋𝑗.
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𝜕 Pr(𝑌𝑖 = 1|𝑋1𝑖, 𝑋2𝑖, … , 𝑋𝑘𝑖)
𝜕𝑋𝑗 = ß𝑗 (2)
𝑌𝑖 is the dependent binary outcome variable and 𝑋1𝑖,…,𝑘𝑖 are regressors. I am going to construct one model for the probability of becoming a dropout, and one for the probability to become a re-enroller, given that an individual has dropped out. It is interesting to examine what characteristics are associated with re-enrolling back into education, but this decision is closely related to the characteristics that influenced an individual become a dropout in the first place. This two-sided modelling is done in order to determine what background
characteristics are associated with the different groups of students in different ways.
In order to estimate causal effects with the linear probability model is there some assumptions that needs to be fulfilled. I will not stress the validity of these assumptions in this thesis as I am not aiming at estimating causal effects, but rather taking use of the linear
probability model to extend the descriptive analysis, and to be able to include multiple characteristics simultaneously.
The model interpretation is which associations there is between the explanatory variables and the probability of success in the dependent variable. The dependent outcome is being measured by a binary variable that takes on the value of 1 if there is success, and 0 otherwise. The aim is to find what characteristics that is associated with the probability of becoming a dropout, and the probability that dropouts becomes re-enrollers. By examining these background characteristics, I estimate how much these characteristics is associated with the probability of being categorized within these student groups. The background
characteristics that are examined in both models are gender, citizenship, parental education, school region and grade averages from lower secondary school. I also included an interaction term between gender and grades. This is included in order to allow for the association
between grades and the probability to drop out or re-enroll to differ between males and females. The interaction term is interpreted as the difference in the association of having higher grade averages for females compared to males. The two linear probability models used in the regressions are the following:
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𝑃𝑟(𝐷𝑟𝑜𝑝𝑜𝑢𝑡 = 1|𝑋1𝑖, … , 𝑋𝑘𝑖)
= ß0+ ß1∗ 𝐹𝑒𝑚𝑎𝑙𝑒 + ß2∗ 𝐹𝑜𝑟𝑒𝑖𝑔𝑛 + ß3∗ 𝑀𝑜𝑡ℎ𝑒𝑟𝑠 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + ß4 ∗ 𝐹𝑎𝑡ℎ𝑒𝑟𝑠 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + ß5∗ 𝑆𝑐ℎ𝑜𝑜𝑙 𝑟𝑒𝑔𝑖𝑜𝑛 + ß6∗ 𝐺𝑟𝑎𝑑𝑒𝑠 + ß7 ∗ (𝐹𝑒𝑚𝑎𝑙𝑒 𝐺𝑟𝑎𝑑𝑒𝑠)
(3)
Pr(𝑅𝑒 − 𝑒𝑛𝑟𝑜𝑙𝑙𝑒𝑟 = 1|𝑋1𝑖, … 𝑋𝑘𝑖)
= ß0+ ß1∗ 𝐹𝑒𝑚𝑎𝑙𝑒 + ß2∗ 𝐹𝑜𝑟𝑒𝑖𝑔𝑛 + ß3∗ 𝑀𝑜𝑡ℎ𝑒𝑟𝑠 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + ß4∗ 𝐹𝑎𝑡ℎ𝑒𝑟𝑠 𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 + ß5∗ 𝑆𝑐ℎ𝑜𝑜𝑙 𝑟𝑒𝑔𝑖𝑜𝑛 + ß6∗ 𝐺𝑟𝑎𝑑𝑒𝑠 + ß7∗ (𝐹𝑒𝑚𝑎𝑙𝑒 𝐺𝑟𝑎𝑑𝑒𝑠)
(4)
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6 Results from the empirical analysis
The empirical analysis consists of two parts. The first part is descriptive results and how the composition of the different student groups are in the data set. The second part includes the results obtained from the empirical analysis through linear probability models as explained in chapter 5.
6.1 Descriptive evidence
In this chapter I will discuss and describe the descriptive findings I have explored in the data set. The background characteristics on gender, citizenship, parental education, school region and grades form lower secondary school.
The general findings in the sample is that approximately one third of students drops out at some point during the period. Re-enrollers accounts for more than two thirds of the dropouts, and about one third does never re-enroll after leaving school.
Figure 3. Chart over the different student groups
Whole sample
All students in the sample are born in 1991-1993 and enrolled into upper secondary education in 2008
Ordinary students (65%)
Students that did not drop out during the
analysis period
Dropouts (35%)
Students that drop out at some point during the period Re-enrollers (70%)
Dropouts that re-enrolled back into education
Not re-enrollers (30%) Dropouts that never re-enrolled
back into education
Re-enrollers that ended as dropouts
(24%)
Re-enrollers that did not
end as dropouts (76%)
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Among the ones that drop out approximately one half of them remain as a dropout by the end of the period, in the group of permanent dropouts. Permanent dropouts consist of the dropouts that never re-enrolled and the re-enrollers that left after returning to school. Among dropouts it most common to drop out one time during the period followed by the second most common scenario to drop out two times during the period. Students that drop out three times constitutes only 3.17% of the dropout group, and the last 0.23% of students dropping out is allocated between four or five times. That means that only 3.4% of the individuals that at some point drops out are allocated between dropping out more than two times during the period.
Table 3. Dropout occurrences
Number of dropout occurrences for the group of dropouts
Frequency Percentage
Dropped out one time 14921 75%
Dropped out two times 4184 21%
Dropped out three times 616 3%
Dropped out four times 47 0%
Dropped out five times 1 0%
N 19769 100%
The pattern for the re-enrollers shows the same movements as for the dropouts. This is expected as in order to be a re-enroller the individual needs to have been defined as a dropout in the first place. Among those that actually re-enroll into education the majority enroll only one time, while some re-enrolls two times back into upper secondary education. The last 1.4%
of the re-enrollers have enrolled back into education three or four times during the period.
Table 4. Re-enrollment occurrences
Number of re-enrollment occurrences for the group of re-enrollers
Frequency Percentage
Re-enrolled one time 11686 84%
Re-enrolled two times 1960 14%
Re-enrolled three times 174 1%
Re-enrolled four times 13 0%
N 13833 100%
