W HERE AND W HAT TO S TUDY ?
An empirical analysis of how personal characteristics affect choice of higher education in Norway
Clara Christine Bergene
Thesis Submitted for the Degree of Master in Economics
30 Credits
The Department of Economics The Faculty of Social Science
The University of Oslo
May, 2021
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Where and What to Study?
An empirical analysis of how personal characteristics affect choice of higher education in Norway
Clara Christine Bergene
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© Clara Christine Bergene 2021
Where and What to Study?
Clara Christine Bergene http://www.duo.uio.no/
Trykk: Reprosentralen, Universitetet i Oslo
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Abstract
It is well known that there are differences between genders and students with different socio- economic background when it comes to higher educational attainment. However, how does personal characteristics affect the applicants preferences when one apply to higher education in Norway? And how does these preferences affect the applicants’ educational attainment after six years? In this paper we try to answer these questions. Based on registry data from the National educational database in Norway, applications of first time applicants through The Norwegian University and College Admission Services (NUCAS) from 1999-2012 are used to quantify the applicants’ preferences in terms of field-of study and location. Ordinary least square regression model is used to examine the within-group differences in the preferences between different personal characteristics. In order to analyze how the preferences affect the educational attainment six years after the applicants first applied through NUCAS a linear probability model is used. As a results of the analysis it is found that male applicants have a stronger preference for both field of study and location than female applicants. This indicates that male applicants are more specific in their applications than women. In addition, the stronger the preference for location is, the less likely it is that an applicant has completed higher education six years after they first applied. These, and other results from this study, are reviewed and compared to existing studies on higher education.
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Acknowledgment
Writing this thesis has been a highly rewarding process. The Norwegian Research Council supported this research under project no. 275906. I want to thank my supervisor, Edwin Leuven for guiding, supporting and helping me in an excellent way. I would also like to thank my co-students at the Department of Economics for making the best possible work
environment, even under a pandemic. I want to thank friends and family for showing me much love and support throughout my whole time as a student. Last but not least, Bjørn – thank you for being you and supporting me as best as possible through this thesis (even on long distance).
Any mistakes, faults or inconsistencies are entirely my fault, I take full responsibility for them.
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Content
1 Introduction ... 1
2 Background ... 4
2.1 Literature review ... 4
2.1.1 Socioeconomic Status ... 4
2.1.2 Gender ... 7
2.2 The Norwegian Educational System ... 8
3 Data ... 10
3.1 Data Source and Variables ... 10
3.2 Descriptive Statistics ... 12
4 Empirical Approach ... 19
4.1 Quantification of Preferences ... 19
4.1.1 Descriptive Statistics of the Preferences ... 21
4.2 Regression ... 26
5 Results ... 30
5.1 Preferences ... 30
5.2 Preferences Impact on Attained Education ... 39
6 Conclusion ... 45
References ... 47
A.1 Number of Applicants from Each Region ... 52
A.2 Number of Priorities to Each Ditailed Field ... 53
A.3 Number of Priorities to Each Location ... 55
A.4 Using Matrix Norms to Quantify The Applicants’ Preferences ... 57
A.5 Correlation Between SES and Gender ... 60
A.6 Robustness Checks ... 61
A.7 Stata Code ... 74
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List of Figures
Figure 1 Number of Priorities per Applicant ... 15
Figure 2 Home Region of the Applicants ... 16
Figure 3 Number of Priorities to the Different Counties and Aggregated Fields ... 17
Figure 4 Comparing the Preferences for Field of Study and Location using the Different Benchmarks ... 22
Figure 5 Comparing the Different Benchmarks for Both Preferences ... 23
Figure 6 Share of Applicants with the Different Preferences ... 24
List of Tables
Table 1 Personal Characteristics for the Sample ... 14Table 2 Personal Characteristics for Groups with Different ... 18
Table 3 Personal Characteristics of Fifferent Preferences by First Choice ... 25
Table 4 Personal Characteristics of Different Preferences by Mode ... 26
Table 5 Regression Results for Preferences ... 31
Table 6 Regression Results for the Preferences Including Control Preferences ... 36
Table 7 Within group differences on the level of education using the mode as the benchmark for preferences ... 41
Table 8 Within group differences on the level of education using the first choice as the benchmark for preferences ... 42
Table 9 Example of correlation between SES and Grades by looking on the within group differences for educational attainment ... 60
Table 10 Robustness Check for Preference for Field Using the Mode of the Applicant as the Benchmark ... 61
Table 11 Robustness Check for Preferences for Field Using the First Choice of the Applicant as the Benchmark ... 62
Table 12 Robustness Check For Preference For Field Using the Mode of the Applicant as the Benchmark and Controlling for the Preference for Location ... 63
Table 13 Robustness Check For Preference for Field Using the First Choice Of the Applicant as the Benchmark Controlling for the Preference for Location ... 64
Table 14 Robustness Check for the Preference on Location Using the Mode of the Applicant as The Benchmark ... 66
Table 15 Robustness Check for The applicants Preference on Location Using the Applicants First Choice as the Benchmark ... 67
Table 16 Robustness Check on Applicants' Preference for Location Using the Applicants' Mode as Benchmark and Controlling for Preferences for Field ... 68
Table 17 Robustness Check for Preferences for Location Using the Applicants' First Choice as the Benchmark and Controlling for Preferences for Field ... 69
Table 18 Robustness Check for Attained Education Using the First Choice as the Benchmark ... 70
IX Table 19 Robustness Check for Attained Education Using the Mode as the Benchmark ... 72
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1 Introduction
As children we learn that we can become what we want. A good public educational system and public funding of students have made the threshold to take higher education in Norway quite low, but the question still remains: how are individual choices about higher education made, and what influences these choices? These general questions are the starting points of this article.
It is well documented that there is a strong socio-economic and gender gradient in educational attainment (Bailey & Dynarski, 2011; Black et.al, 2005; Bakken et.al, 2016). This gender gradient is often even starker when looking at type of education (Black et.al, 2005; Patnaik et.al, 2020; Boring & Brown, 2016). This raises the question to what extent these differences can be explained by preferences on the one hand, or opportunities on the other. Preferences in applications are not documented to the same extent as attendance. Delaney and Devereux have done some research on it in Ireland. They have found that there are some differences in application pattern, both between gender and people with different socio-economic status.
According to their research, women are more likely to make safe choices, by including programs with lower admission standards in their applications (Delaney & Devereux, 2021).
In another study the same researchers find that those from highly privileged schools are more likely to prioritize specific study programs and higher ranked institutions (Delaney &
Devereux, 2020). In the US there has been conducted studies on the differences in application preferences of groups with unequal socio-economic status (SES), race and ethnicity (Dillon & Smith, 2017; Black et.al., 2020; Hoxby & Avery,2012) They find that many of the effects are financially motivated. For this reason - and due to the application processes being very different in the US and Norway - these findings are not easily applicable to a Norwegian context.
Little is thus known about which factors influence how people prioritize when it comes to higher education in a Norwegian context. This is an important gap in our understanding of this subject. This paper tries to make progress on these questions by studying preferences for
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field-of-study and preferences for location among higher education applicants in Norway. To investigate why there might be differences, we control for gender, SES, regional differences and grade points in order to see if there are structural differences between the groups. We have chosen these factors in this study, as previous research has shown that these factors have an impact on the choice of higher education (Patnaik et.al, 2020).
Understanding how people prioritize location and field of study, can shed light on gender and socio-economic gaps in educational aspirations and outcomes. It thus has the potential to inform higher education policy in public systems which have to trade-off regional supply of education versus centralization. Norway is a large, sparsely populated country, with a stated political goal of maintaining decentralized population centers all over the country. An important aspect of this policy is to keep institutions for higher education spread throughout the country. It is important to investigate if the supply for, and distribution of, higher
education corresponds to the applicants’ preferences. This research can therefore answer questions about the access to higher education in Norway and add valuable information for future policy making.
Individual choices on what and where to study have a massive impact on different important variables. It corelates with variables such as future wages (Crawford, et.al. 2016) and health (Silles, 2009). Since the choices of higher education are impactful, it is important to deepen our understanding of what motivates these choices. One goal of this present article is to look into whether or not parental level of education influence their children's applications for higher education, and if so how. We know that there is a spillover effect from parents' educational level, to the education level of their children (Bakken et.al , 2016). This in turn creates a spillover effect on SES. The result of this study can thus shed some light on how education level and SES is reproduced from generation to generation – another important political question.
Our analysis builds on high quality Norwegian registry data on the universe of higher
education applicants. These data allow us to control for the factors of interest and investigate
3 the preferences of many of the individuals that have applied through the Norwegian
Universities and College Admission Service (NUCAS) since 1998. As SES is controlled for, we are only interested in the first time applicants between the age of 19 and 21. The
information on application lists allow us to characterize preferences and more specifically lets us quantify the extent to which people exhibit preferences for specific fields of study, and locations. We use two different benchmarks in order to identify the applicants’ preferences, their first choice and the most common choice in their application. This is done in terms of both location and field of study. As a result, we use an ordinary least square (OLS) model is used to determine how gender, SES, home region and grades affect the preferences. In addition we investigate the affect the applicants’ preferences have on their attained education six years after that the applicant applied for the first time.
The existing literature has mostly focused on the differences within the students in higher education, and not on the differences between applicants. This study lets us see if the differences that we know are present after admission are already observable in the
applications of the future students. In addition we can contribute with relevant information about preferences for higher education that can inform future policy making and political decisions about the supply and distribution of higher education.
We find that men are more specific in their applications, that they have a stronger preference for both field and place of study than their female counterpart. It is also found that applicants with low SES have a stronger preference for field of study and a weaker preference for
location than applicants with high SES. In addition it is found that the applicants with a strong preference for location are less likely to complete higher education six years after they first applied to higher education than applicants with a weaker preference for location. Also, applicants with 1-10 priorities and a strong preference for field are more likely to complete higher education than those with a weaker preference for field.
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2 Background
2.1 Literature review
This article looks at how gender, SES, geography and grades influence how individuals prioritize when they apply to higher education, and how their preferences influences their educational attainment after six years. The following section will summarize the knowledge about the relationship between these factors and educational choices in general, and
preferences when applying to higher education more specifically.
2.1.1 Socioeconomic Status
We know that children's ambitions for higher education correlates with their parents' levels of higher education. Children with parents with a higher socioeconomic status tend to be more ambitious regarding higher education, compared to their peers with a lower socioeconomic status (Bakken et.al , 2016). For instance, children of parents with higher education are more likely to take a graduate degree than children that do not have parents with higher education (Patnaik et.al., 2020). Studies also show that children from a lower socioeconomic
background are more open to take vocational education than children from a higher
socioeconomic background (Bakken, et.al., 2016). Also, Patnaik et.al. find that individuals from different SES have different preferences when it comes to field of study. For example, individuals with lower SES in the US are overrepresented in fields such as nursing, education and business (Patnaik et.al, 2020). These differences are important to study as choices in higher education have a major impact on the applicants´ careers and their future earnings (Altonji et.al, 2016).
These differences partly reflect the fact that children with a higher socioeconomic background tend to receive better grades than children from a lower socioeconomic background, and thus
5 have more opportunities (Bakken, 2009). However, Hoxby and Avery find that students from lower income families in the US, who are very high achieving, are much less likely to apply for selective colleges or universities with high test- and grade scores than high-income students with equal achievements. This is the case even though they have the same opportunities of enrollment as their high-income counterparts when they apply (Hoxby &
Avery, 2012). Thus, the differences in education based on SES, cannot be wholly explained by differences in results. These results are in accordance with results from an Irish study. Irish students from advantaged high schools are more likely to apply for more prestigious
institutions. Applicants with high SES are also more likely to get enrolled into these prestigious institutions, compared with applicants with lower SES (Delaney & Devereux, 2020).
Undermatching1 and overmatching in college and university applications in the US are influenced by financial considerations. Applicants from lower SES are more likely to undermatch, due to the higher tuition fees associated with higher quality colleges in the US.
These effects are also sensitive to proximity to different education institutions. For instance, undermatching is less likely to occur when the applicants live close to a public in-state
educational institution, as the applicants can live home and reduce their travel costs as well as their tuition fee by being in state applicants (Dillon & Smith, 2017). Black, Asian and
Hispanic applicants are more sensitive to distance to college than their white counterparts (Black et.al., 2020). It is important to keep in mind that the application process for US universities is different from the Norwegian application process, as there is no centralized application portal. In addition, the financial aspect is less relevant in Norway, as there are no tuition fees to public universities and colleges, and students receive economic support2 from the government when attending higher education.
1 Meaning that an applicant applies to colleges that the applicant is overqualified for based on its results, overmatching is the opposite. (Dillon & Smith, 2017)
2 All students in programs that are certified by Lånekassen can receive basic support, where 40% gets converted to a grant if you fulfill the criterion such as living away from your parents, completing your education program or have income and assets below the limit. (Lånekassen, u.d.)
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There have been some studies on individual preferences in higher education in Ireland, which looks at the importance of proximity to institutions of higher education. Flannery and
Cullinan (2014) find that there is no significant correlation between living far from a
university and attending higher education in general. They do however find that the applicants who live far from a university are less likely to attend a university, and more likely to attend a university college (Flannery & Cullinan, 2014). The more advantaged high schools in Ireland are also on average closer to universities than less advantaged schools (Delaney & Devereux, 2021). Numbers from Statistics Norway indicate that the counties in Norway in 2016 with the highest share of individuals with higher education were Oslo, Akershus, Sør Trønderlag and Hordaland (Statistisk Sentralbyrå, 2017).3 These are also some of the most populated counties (Statistisk Sentralbyrå, 2021) and houses Norway’s most prestigious institutions of higher education.4 These results suggest a possible connection between geographical proximity to prestigious universities, and the attendance of these universities.
According to research by Delaney and Devereux students from advantaged schools also tend to cluster their applications in terms of program selectivity rather than field of study.
Meaning, that they are more likely to cluster their fields by programs within fields than fields at a general basis. In addition they find that applicants from disadvantaged schools are more likely to be enrolled into their top priorities, and apply to more programs within the same field of study – meaning that they are less selective in the combination of field and location than those from advantaged schools. These differences are smaller when you look at enrollment data, than when you look at application data (Delaney & Devereux, 2020). Irish studies such as the ones mentioned above are very relevant to Norwegian studies, as the application process for tertiary education in Ireland is quite similar to the Norwegian.5
3 Counties before the regional reform (2019-2020) in Norway. (Kommunal- og moderniseringsdepartementet, 2019)
4QS top university ranking ranks the University of Oslo, University of Bergen and the Norwegian University of Science and Technology (NTNU) in Trondheim as the three top ranked universities in Norway. (QS Top Universities , 2021)
5 Ireland have a centralized application portal where they can list priorities from different institutions. (Delaney
& Devereux, 2020)
7 Previous literature has shown that there is a relationship between the level of parental
education and the grades of their children. Anders Bakken found that when the level of parental education increases the grades of the students tend to increase as well (Bakken, 2009). Other studies have suggested that the effect of parental education on their children's grades diminishes as we control for more variables such as parental income and marital status (Hægeland, et.al., 2010). There are thus some doubts about the causal relationship between parental education and the grades of their children, even though there is a clear correlation between grades and SES.
2.1.2 Gender
Men and women differ dramatically in their choices of education (Patnaik et.al., 2020;
McNally, 2020; Gemici & Wiswall, 2014). 60% of those who attend higher education in Norway are women. 78% of those who attend studies within health, social- and sport fields are women, while 65 % of the students in science, technology, engineering and mathematical (STEM) fields are men (Barne-, ungdoms- og familiedirektoratet, 2021). These differences are apparent before individuals apply for higher education. A Canadian study found evidence that female high school students are less likely to choose STEM courses in high school, and are therefore often not STEM prepared, meaning that they lack the knowledge and the qualifications to be admitted into STEM programs in institutions of higher education
(McNally, 2020.) Studies from the United States have shown that men were better at adapting to the market demand when it came to business and science skills during the 1980s and 1990s, which led to a bigger gender gap within those fields in this period. (Gemici & Wiswall, 2014) The gender differences in studies within science, arts and engineering in the US have not decreased even though more and more women are taking higher education. (Gemici &
Wiswall, 2014)
When it comes to the gender differences among applicants for higher education, Boring and Brown finds that there are different trends between men and women. For instance, male students at Sciences Po in France tend to list higher ranked universities in their top three priorities more often than women when they apply for exchange programs (Boring & Brown,
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2016). Another study finds that women are more likely than men to cluster their top priorities by field of study rather than by institutions. As female applicants tend to be more risk averse when applying to higher education, meaning that they are more likely to include programs with lower admission standards, that they are overqualified for. Less high-achieving women do however tend to act more like men when filling out their application (Delaney &
Devereux, 2021), which might be due to limited information about the admission process.
Croson and Gneezy (2004) suggests that this could be due to women’s lack of
competitiveness and social preferences. However, as Delaney and Devereux states in their 2021 article, this might be due to the fact that women are more risk averse than men. Another possibility is that women tend to take more mature choices than their male counterparts at any age (Lim, et.al., 2015).
2.2 The Norwegian Educational System
The Norwegian educational system consists of 10 years of mandatory schooling, including primary and middle school, and three to four years of high school. High school is voluntary, but if you want to, you have the right to attend high school. One can choose between taking a vocational path or an academic path. The vocational path usually consists of two years of preparation studies in school and two years of apprenticeship out of school. The academic path is a preparation for higher education and normally takes three years. 79% of the students that started the academic path in 2013 had completed their education by 2019 (Statistisk Sentralbyrå, 2020a)
There are multiple ways to qualify for higher education in Norway, but the most common way is by applying with a high school diploma. In Norway applications to all public and some private higher education institutions are submitted through a centralized application portal, The Norwegian Universities and College Admission Service (NUCAS). When applying for higher education, applicants can prioritize up to ten different programs from most educational institutions around the country. The main rule is that students are admitted based on their grades. Some studies do however have special requirements such as having chosen and
9 completed specific subjects. Most of these are STEM- Science, technology, engineering and mathematics programs that require you to have taken specific science classes in high school in order to get admitted. For example, some health related programs such as medicine,
pharmacy, dentistry and veterinary require specific science courses like chemistry. Other than these prerequisites, the admission is conducted based on the applicant’s grades. The program admits a specified number of students, and the candidates with the highest grades among those who have applied, get admitted.
In Norway applicants can also collect extra points that are added to their grade points when applying in NUCAS. They can be collected for instance by taking specific high school courses (mostly science courses) (Samordna Opptak, 2013a), or one can get gender points by applying to a field with dominance of the opposite gender, for instance nursing studies for men or engineering studies for women (Samordna Opptak , 2013b). You can also get two extra points by attending either military service, completing a year of higher education or attending folk high school (Samordna Opptak, 2013c). In addition, you get two extra points for every year you grow older until the age of 23. As a result, applicants are divided into two groups “førstegangsvitnemål”, which can be translated to the first time diploma (FTD). The group with first time diploma consists of the applicants only applying with their original grades from high school and bonus points obtained by taking specific high school courses.
One is categorized within this applicant group if one has not retaken any of the courses included in the high school diploma, while being under the age of 21. The second group is named “andregangsvitnemål”, and consists of everyone applying to higher education with their extra points and their grades from retaken courses (STD). 50% of available spots in a study program are reserved for the applicants with a first time diploma. The applicants with a first time diploma compete in both groups, the one for the first time and second time diploma (Samordna Opptak , 2013d).
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3 Data
The data used in this thesis is taken from the National Database for Education(NUD) from statistics Norway. NUD consists of Norwegian educational data back to the 1970s and contains information about the Norwegian populations´ education at all levels, from
kindergarten to PhD (Statistisk Sentralbyrå, 2020b). For the purpose of the present article the focus has been on higher education, and first time applicants between the age of 19 and 21.
This means that the maximum age of the people we analyze in this study is 21. The data spans from 1998 to 2018, as NUCAS was established in 1998. As we include information of
applicants’ attained education six years after their application the population of interest in this article is thus applicants for higher education in Norway through NUCAS from 1999 to 2012.
3.1 Data Source and Variables
The original data contains information from NUCAS in NUD, called so-soknalt consists of 937 379 individuals with a total of 2.14 M applications to 11.3 million observations. The number of applications are higher than the number of individuals, as it is possible to apply for higher education multiple times. This data gives us information about the applications,
regardless of whether or not the applicant is admitted. The data also contains information about grade points for both FTD and STD, the admitted program for each applicant, and if he or she accept his or her place of study or not. In order to obtain detailed information about the program names and institutions we include information from the dataset so-studium. As one of the main interests in this study is to look at how applicants are affected by their SES we focus on first time applicants who are between 19 and 21 years old. As more students were registered with a second time diploma than a first time diploma, we have chosen to use STD as our variable for grades in further analysis.
We include information about the applicant’s parents’ level of education and their field of education. We obtain this information from the dataset utd-person. The information about when the applicants were first registered in higher education was used to exclude applicants who had been enrolled into higher education prior to 1999. Parental education is used to
11 define the level of SES for each applicant, as we define SES based on the highest attained education for the applicants’ parents. We define SES as high if one or both parents have attained higher education, otherwise the SES of the applicant is low. Using the level of
education of parents as a parameter for SES is a common way to measure SES as social status and parental education correlates in similar studies (Bakken et.al., 2016).
We include data about the applicants’ attained education after six years, information is attained from the dataset bu-igang. We include information about the level of education for all of the applicants in our data. In order to use this information in analysis we are most interested in information about if the applicant has attained a degree of higher education six years after they applied, and not the level of the education. Six years is used as the time interval as the longest lasting programs one can apply to through NUCAS lasts for six years.
Information about the applicants´ gender, home municipality at the age of 16 and the first time they were registered into higher education were also included. The data is drawn from the dataset named faste-oppl. Based on data about study programs, a detailed and an aggregated classification on the field of education was made.6 The same classification was made for the parents' higher education, in addition to variables describing whethe they had taken higher education. As an example, a detailed field could be nursing, social work and physical therapy, while the aggregate field for all of them would be undergraduate health studies.
Classifications of place of study were made based on Norwegian municipalities. This was first done based on the location of the institution, but since many of the institutions are
geographically spread and certain institutions have merged over the years, some of the classifications had to be done by hand. As a result, some of the study programs has not been possible to classify to the right municipality, as the programs do not have location information in their name and the institution spans over multiple municipalities. These programs have
6 See appendix A.2 for classification of field
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been classified into as detailed locations as possible7. A classification by county was also made, largely on the basis of Norwegian regions8. The applicants’ home municipalities by the age of 16 were also classified into counties, and also into 6 regions (i.e. north, east.).9
Both the classification of field and place of study has made it possible to count the number of recurring fields and place of study in each application. The number of equal studies and locations makes the basis of the further analysis and make it possible to make the quantification of preferences. The way this is done, will be explained in section 4.1.
After multiple tests and investigation we excluded certain individuals from the dataset. Most of the applicants were excluded on the basis of reasons described previously, such as age, and prior applications or enrollments into higher education. Certain individuals were also
excluded based on missing or specious data, which was likely due to errors in the data set. For example, this could be applicants with grades that were higher than what is possible to attain or applicants that had not attained high school, but had the grades to apply for higher
education.
3.2 Descriptive Statistics
This section provides descriptive statistics on our data that are important for the following analysis in this paper. It is important to investigate the relationships and correlations between the different factors used in the analysis.
7 See appendix A.3 for classification of locations
8 Due to recent regional reform in Norway, an exception was made for the University of Sør-Øst Norge which is localized in the former counties Vestfold, Telemark and Buskerud. For simplicity's sake and due to the
difficulties of classifying study programs to the right municipality, Buskerud was included in Vestfold and Telemark, and Østfold and Akershus was kept alone. In all other cases, the classifications were based on Norwegian regions. (Kommunal- og moderniseringsdepartementet, 2019)
9 See appendix A.1 for classification of regions
13 As a result of the data managing process described in the former section, we are left with a comprehensive and unbalanced cross sectional data set consisting of 1.73 million observations of 271 346 individuals. The overall gender distribution has been stable at around 60% share of female applicants through the 13 years of observations. The number of applicants with higher socioeconomic status accounts for 57% of the total number of applicants. Table 1 shows that women tend to have lower SES than their male counterparts. In addition, males have on average lower grade points both on their FTD and STD, are marginally older and have fewer priorities in their application than their female co applicants. Table 1 shows that applicants tend to have more priorities to the same location than to the same field. This difference is larger for female applicants than male applicants. The standard deviation for all means given in table 1 are quite large, meaning that there is a lot of variation in the variables. The average number of priorities per applicant is 6.49. Figure 1 shows that approximately 22% of the applicants have 10 priorities in their application. Only 4% of the sample had more than 10 priorities in their application even though it was possible to apply with 15 priorities until 2003. Approximately 5% of the applicants in the sample applies with only one priority.
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TABLE 1PERSONAL CHARACTERISTICS FOR THE SAMPLE
Whole sample Per person Female Male
Age 19.53 19.55 19.53 19.59
(0.684) (0.698) (0.670) (0.740)
Female 0.63 0.62
(0.482) (0.485)
High SES 0.58 0.57 0.53 0.63
(0.493) (0.495) (0.499) (0.482)
Grades FTD 45.53 45.10 45.07 45.16
(7.448) (7.343) (7.181) (7.607)
Grades STD 46.68 46.27 46.16 46.46
(7.530) (7.437) (7.371) (7.627)
Maximum pri. 8.10 6.39 6.50 6.21
(3.128) (3.300) (3.286) (3.314)
Max fields 3.20 3.15 3.29
(1.928) (1.899) (1.972)
Max location 3.66 3.69 3.61
(2.299) (2.294) (2.307)
N 17332527 271346 168746 102602
Note: Max field is the number of priorities to the most listed field of study in the applicant’s application. Max
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location is the maximum number of priorities the applicant have to the same location in its application mean coefficients; SD in parentheses
FIGURE 1NUMBER OF PRIORITIES PER APPLICANT
The biggest share of applicants comes from the eastern part of the country, while the smallest share of applicants comes from the south of Norway. There are also some people that have a unknown place of origin. We do not know much about this population, but they are most likely applicants that lived outside of Norway when they were 16 years old. From figure 3a it is seen that the counties that most applicants prioritize in their applications are Oslo, Vestland and Trønderlag. These counties house the biggest10 and best (QS Top Universities , 2021) educational institutions. Nordland is prioritized by the fewest applicants. 11 Social science and
10 According to SSB NTNU which is mostly situated in Trønderlag is Norways biggest institution followed by UiO in second place, OsloMet in third that are both situated in Oslo and UiB in fifth place and Høgskulen på Vestlandet (HV) in 8th place, HV and UiB are situated in Vestland. (Nygård, 2020)
11 See appendix A.3 for locations within counties
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health are the aggregated fields that receive the most priorities, while graduate health studies (medicine) receives the fewest.12
FIGURE 2HOME REGION OF THE APPLICANTS
Note: Appendix 1 shows the number of applicants to each county within each regions
12 See appendix A.2 for detailed fields within the aggregated fields.
17 FIGURE 3NUMBER OF PRIORITIES TO THE DIFFERENT COUNTIES AND AGGREGATED FIELDS
Note: a) shows the total number of priorities to the given county. Appendix 3 shows the number of priorities to the locations within each county. b) shows the total number of priorities to the given aggregated field. Appendix 2 shows the number of priorities to each detailed field within each aggregated field.
According to table 3, 69% of the applicants have attained a lower higher education degree 6 years after their first application in NUCAS. 16% have obtained a higher-level education degree. We see that 35% of the applicants that have attained a lower degree of higher
education after they applied for the first time are male. In other words, there is a larger share of female applicants that have completed a lower higher education degree than in the whole sample population. Applicants that only have attained high school six years after they applied and for those that have attained a higher level degree, the share of male applicants is larger than it is for the whole population. The mean level of SES increases as the level of attained education increases, the same yields for the average grade points for both STD and FTD.
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TABLE 2PERSONAL CHARACTERISTICS FOR GROUPS WITH DIFFERENT
LEVEL OF ATTAINED EDUCATION
High school Lower HE high HE
Age 19.70 19.54 19.42
(0.760) (0.691) (0.630)
Female 0.54 0.65 0.56
(0.498) (0.475) (0.497)
High SES 0.46 0.56 0.71
(0.499) (0.496) (0.455)
Grades FTD 40.80 44.83 50.30
(7.002) (6.860) (6.636)
Grades STD 42.17 46.06 51.46
(7.048) (6.979) (6.817)
N 42391 187401 41554
Note: mean coefficients; SD in parentheses
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4 Empirical Approach
In order to see how gender, socioeconomic status, grades and which part of the country the applicant comes from affects the applicants’ preferences, we must quantify their preferences in terms of field of study and location in their application. In the following section we will provide information about how the quantification is done, why it is done and how it is used in further analyses.
We use a regression model to do the analysis - an approach used in several similar studies (Delaney & Devereux, 2021; Delaney & Devereux, 2020; Delaney & Devereux, 2019) Even though the study presented in this paper differs in important ways from previous studies as detailed in section 2, there are also important similarities, which makes these studies a relevant reference point. All papers examine applications to higher education, but we want to investigate specifically the effects of personal characteristics on the applicants´ preferences for field, and place of study.
4.1 Quantification of Preferences
In this article we will look at two significant factors that can affect the applicants preferences;
place and field of study. There are of course other factors that potentially can influence an applicant’s preferences, such as admission requirements or grade limits from previous years.
This is however not the focus of the present article, but this could be a fertile basis for further research. We use two different benchmarks for the preferences. First, the first priority of each applicant is used, as we assume that a candidates’ first priority represents the optimal and most favorable combination of field and place of study for the particular applicant. The second benchmark is the most listed field of study and location in each individual's
application regardless of their first priority. The most recurring field or place of study, also known as the mode of the application in terms of field of study or location, is used as it is as a field or location that the applicant has expressed a strong preference for through their
20
prioritizations. Both of the benchmarks are used for both field and place, and the quantification is done by using matrix norms.
As mentioned, matrix norms are used to weight the applicants´ applications in terms of field and place of study. In linear algebra a vector norm is also defined as the distance of a vector.
(Lindstrøm & Hveberg, 2015, p. 22-30) A matrix norm is the same, but in a finite defined vector space (Weisstein, 2021). The norm gives us the length of the matrix from a point of origin. In this case the point of origin is a zero matrix indicating an applicant's perfect preference, where all priorities go to the same field or place of study as the mode or the first priority in the application.
In this study a norm is a good way of indicating the preferences of the applicants as it
estimates the strength of the preference for attending a specific field of study or studying at a specific location. We find the weight of the preference of each applicant by normalizing the quantifications. By applying matrix norms to find the weight of the preferences we end up with equation 1.13
!!"# = 1 − $%!"#
√'() (1)
& = (1,2, … , +) , - = (./01, 2&345), 6 = (7/895&/:, ;&170) & : = (1,2, … , 15)
Where
!!"# = =
1 &; >!"# = 0
@!"# &; >!"# ≠ 0 0 &; >!"# = : − 1
, 0 < @!"# < 1 (2)
13 The way the matrix norms are applied to get to find equation 1 is found in appendix A.4, where equation 1 is equation 8.
21
> is the number of priorities in an application that is not equal to the first priority or mode of the applicant in terms of place or field of study, : is the number of priorities in applicant i’s application. I is the total number of applicants in the sample. - tells us if the mode or first choice is used as a benchmark and k indicates whether we weight the applicants preferences for field of study or location.
C!!" = !!"*+,-. − !!"/!.+0 (3)
C!!" ∈ (−1,1)
By subtracting the weight of place with the weight of field, we get the total preference of each applicant – if the applicant prefers place over field, is indifferent or prefers field over place (equation 3). C!!" has a value between -1 and 1 and a negative value indicates a stronger preference for field than location, 0 indicates that the applicant is indifferent and a positive value indicates a stronger preference for place of study than field of study. C!!"# will only be used to show some descriptive statistics, while !!"# will be used in further regression
analysis, where we estimate how factors such as gender, SES, grades and home region of the applicants affect their preferences. This is done by using an ordinary least square (OLS) regression model, but first we must look at some descriptive statistics of the applicants´
preferences.
4.1.1 Descriptive Statistics of the Preferences
We will now look at the relation between preference for field of study and the preference for location. It is seen from figure 4 below that the relation between the preferences are quite similar regardless of which benchmark we use. Thus, figure 4a shows that there are more applicants that have a no preference for neither field or place of study when the first choice is used as the benchmark than when the mode of the application is used. In other words there are
22
applicants that have a first priority that does not correspond to the rest of the priorities in the application.
FIGURE 4COMPARING THE PREFERENCES FOR FIELD OF STUDY AND LOCATION USING THE DIFFERENT
BENCHMARKS
Note: a) Compares the two preferences when first choice is used as the benchmark for the preferences. b) compares the two preferences of the applicants when the mode is used as the benchmark for the preferences. An preference of 0 has no preference for field of study/location, an applicant with preference of 1 has a perfect preference for field/location meaning that all the applicants priorities are to the same field/location.
In figure 5 the preferences we get using the different benchmarks are investigated. We see that the preference for both field of study and location when using the first choice as the benchmark for the preference is always smaller or equal to the preference when the most listed priority of the applicant is used as a benchmark. This is due to the fact that one cannot have more priorities similar to the first choice than the most listed. However, if the field or location of your first choice is the same as the most listed in the application, then the weight of the preference is equal for both the benchmarks. In addition it is seen that the stronger the preference is, the less variation there is between the two preferences.
23 FIGURE 5COMPARING THE DIFFERENT BENCHMARKS FOR BOTH PREFERENCES
Note: a) comparing the preference for field of study b) comparing the preferences for location. An preference of 0 has no preference for field of study/location, an applicant with preference of 1 has a perfect preference for field/location meaning that all the applicants priorities are to the same field/location.
In Figure 4, 5 and 6 it is seen that neither of the preferences have observations between 0.76 and 1. However, figure 6b and 6d indicates that the share of applicants that has no preference is higher when we use the first choice as the benchmark rather than the mode. This is quite intuitive as an applicant’s first choice does not have to be to the most listed field of study nor location. The share of applicants with a perfect preference, meaning that the all priorities in the application are to the same field of study or location, is the same regardless of which benchmark we use. Figure 6a and 6b shows that there is a bigger share of applicant with a strong preference for location than field.
24
FIGURE 6SHARE OF APPLICANTS WITH THE DIFFERENT PREFERENCES
Note: a) is the preference for location when the mode of the applicant is used as the benchmark for the
preference. b) is the preference for location when the first choice is used as the benchmark for the preference. c) is the preference for field of study when the mode is used as the benchmark for the preference. d) is the
preference for field of study when the first choice is used as the benchmark for the preference.
Now, when we use equation 3 to make some descriptive statistics. We see that the share boys that are indifferent between field and place or that only have one preference is bigger than the share of boys that has a preference for place or field. This result is found regardless of what is used as the benchmark for the preference. For both of the benchmarks we see that there are more applicants that have a stronger preference for place than field. We see that those that apply with only one priority on average have a lower SES than the rest of the sample. The share of men is also larger for the ones with one priority than what it is in the rest of the sample. In addition we see that those with stronger preference for field on average have a lower SES than those that have a stronger preference for place or are indifferent.
25 TABLE 3PERSONAL CHARACTERISTICS OF FIFFERENT PREFERENCES BY FIRST CHOICE
Full sample Place Field Indifferent One Pri.
Age 19.55 19.55 19.55 19.52 19.67
(0.698) (0.692) (0.700) (0.699) (0.757)
SES 0.57 0.59 0.53 0.59 0.54
(0.495) (0.492) (0.499) (0.492) (0.499)
Gender 0.62 0.65 0.64 0.54 0.55
(0.485) (0.477) (0.479) (0.498) (0.498)
Grades STD 46.28 46.83 45.21 46.81 45.37
(7.437) (7.383) (7.275) (7.615) (7.457)
Weight First Choice
0.11
(0.508)
Observations 271346 126461 81272 50269 13345
Note: This table is based on the results from equation 3, when the first choice is used as the benchmark for the preferences. Place are the applicants with !"$%> 0, place are the applicants where !"$%< 0, indifferent are the applicants where !"$%= 0 and more than one priority in their application, One Pri are the applicants that only have one priority in their application and therefore are indifferent. Mean coefficients; SD in parentheses
26
TABLE 4PERSONAL CHARACTERISTICS OF DIFFERENT PREFERENCES BY MODE
Full sample Place Field Indifferent One Pri.
Age 19.55 19.55 19.55 19.55 19.67
(0.698) (0.692) (0.701) (0.706) (0.757)
SES 0.57 0.59 0.53 0.58 0.54
(0.495) (0.492) (0.499) (0.494) (0.499)
Gender 0.62 0.66 0.62 0.55 0.55
(0.485) (0.474) (0.484) (0.498) (0.498)
Grades STD 46.28 46.84 45.14 46.60 45.37
(7.437) (7.423) (7.180) (7.616) (7.457)
Weight Mode 0.09
(0.485)
Observations 271346 124903 800945 65499 13345
Note: This table is based on the results from equation 3, when the most listed priority is used as the benchmark for the preferences. Place are the applicants with !"$%> 0, place are the applicants where !"$%< 0, indifferent are the applicants where !"$%= 0 and more than one priority in their application, One Pri are the applicants that only have one priority in their application and therefore are indifferent. Mean coefficients; SD in parentheses
4.2 Regression
We will now explain how an ordinary least square model is used to estimate the within group differences in the applicants´ preferences. First, we will estimate the differences for the
27 preferences found in section 4.1 for both field of study and location, for both benchmarks for preferences. Then we will see how the opposite preferences affect each other by including them in the regression model. Third, we will investigate the impact the preferences have on the applicants’ attained education after six years. At last we will use the number of priorities of each applicant to perform robustness checks.
We use OLS to estimate the between group difference in the applicants preferences for field and place of study. The first step in our analysis is to estimate how gender, SES, grades and home region of the applicant influence their preference for place and field of study. We start by investigating the differences on all of the preferences given in equation 1. By including our factors in the multiple regression model, we get equation 4.
EF!#"GH1:013, IEI, H39014, J/K1 31L&/:M= #1+ #)%&'(&r + #2*+S + #3%-.(&s +
#4012& -&341' (4)
6 = (7/895&/:, ;&170) & - = (./01, 2&345)
Equation 4 is the population regression line that estimates the conditional mean of the population in the sample. N5 = (N1, N), … , N4) is the slope coefficient of their associated factors. This means that N5 indicates the change in !#" by one unit change in N5‘s
corresponding independent variable, while holding the other independent variables constant.
When we examine the between group difference between the different home regions of the applicants, we use the applicants from the east of Norway as the default group and compare all other regions to the eastern Norway. The home region variable is a discrete variable, and will therefore not give us a proper result if we do not treat it as a binary variable.
The second step in our analysis is to include the preference for the opposite k in our
regression model, we will refer to this as the other preference. We do this in order to see the correlation of the other preference. It can be assumed that there is a negative correlation of
28
increasing the other preference, but we do not know if there actually are differences, nor how big they are. By including it we get equation 5 for preferences for field, and equation 6 for preferences for location. The interpretation of the coefficients and variables in equation 5 and 6 is the same as given for equation 4 above.
E[!",7!.+0|H1:013, IEI, H39014, P/Q:53R S935, !",+8-,9!8'] = #1 + #)%&'(&r + #2*+* +
#3%-.(&s + #46178-9 :.-t + #5<",$%&'()%* (5)
E[!",+8-,9!8'|H1:013, IEI, H39014, P/Q:53R S935, !",7!.+0] = #1 + #)%&'(&r + #2*+*)+
#3%-.(&=)+ #46178-9 :.-8)+ #5<",+),$- (6)
The third step of our analysis is that we see how the applicants’ preferences for field of study and location correlates with the applicants’ attained education six years after they applied.
This will be done by using a linear probability model. We will investigate the correlation if the applicant has obtained higher education or if it has not. As the level of education( UE)is a binary variables the slope coefficients now indicates the change in probability for that the applicant has attained higher education in equation 6.
Pr[UE = 1 | H1:013, IEI, H39014, P/Q:53R S935, !"#] = N1+ N)H1:013 + N2IEI + N3H39014 + N4P/Q:53R S935 + N:!",+8-,9!8' + N;!", ;&170 (7)
The linear probability model works the same way as an OLS regression model does, but instead that the slope coefficients N5 indicates change in UE for a one unit change in their corresponding dependent variable it indicates the change in probability.
In addition, we will perform robustness checks on our analysis in order to see if the estimated differences holds regardless of the number of priorities the applicant has in its application. If the preferences are equally distributed regardless of how many priorities there are in an
29 application, it should hold as the preferences are normalized by the number of priorities in each application. The robustness check will be performed on all of the regression models presented in equation 4-7. Unbiased standard errors are obtained by estimating robust standard errors.
As mentioned, an applicant’s preference is a result of multiple observed and unobserved factors. In our case the observed factors are gender, grades, SES and home region of the applicant. It is important to note that there are other factors affecting their preferences, such as influence from peers, guiding in school, previous grade limits and so on, but these factors will not be examined in this article. As we assume that there are other factors, we cannot fully avoid omitted variable bias. However, it is important to include as many explanatory factors as possible in order to avoid it as much as possible.
30
5 Results
Based on the method in the previous chapter, this section presents the results of the
regressions, where we present the within group differences on the preferences for our sample.
At last we will discuss our results and compare the two benchmarks up against each other.
The differences in preferences between applicants with an unequal number of priorities will also be discussed, when discussing the results from the robustness check described above.
5.1 Preferences
The estimates of equation 4 from section 4.2 are reported in table 6. The constant term in all of the models presented in table 6 represents the mean preference of the default group. While the estimates of equation 5 and 6 are reported in table 7.
31 TABLE 5REGRESSION RESULTS FOR PREFERENCES
Mode Field First Choice Field
Mode Location First Choice Location
b/se b/se b/se b/se
Female -0.042*** -0.036*** -0.020*** -0.021***
(0.001) (0.001) (0.001) (0.002)
High SES -0.014*** -0.018*** 0.015*** 0.014***
(0.001) (0.001) (0.001) (0.002)
East 0.000 0.000 0.000 0.000
(.) (.) (.) (.)
South -0.015*** -0.013*** -0.012*** -0.013***
(0.003) (0.003) (0.003) (0.003)
West -0.022*** -0.026*** 0.015*** 0.016***
(0.001) (0.002) (0.002) (0.002)
Middle -0.038*** -0.039*** 0.043*** 0.044***
(0.002) (0.002) (0.003) (0.003)
North -0.019*** -0.017*** -0.007** -0.005
(0.002) (0.002) (0.003) (0.003)
Grades STD -0.001*** -0.001*** 0.002*** 0.002***
(0.000) (0.000) (0.000) (0.000)
Constant 0.438*** 0.402*** 0.325*** 0.294***
(0.004) (0.004) (0.005) (0.005)
N 271346 271346 271346 271346
Note: this is the regression results for using equation 4 in section 4.2 for all of the quantified preferences. East is used as the default variable when controlling for the applicants home region. * p < 0.05, ** p < 0.01, *** p < 0.001
For all the four models in table 6, there is a negative estimated correlation between being female and the preferences for both field and location regardless of benchmark. This means
32
that female applicants have a weaker preference for both field and place of study regardless of which benchmark is used. The difference between the genders is larger for the preference for field than the preference for location, and highest when the most listed field is used as the benchmark for preference. In that case the preference for females is 0.04 lower than for male, which is quite high as male applicants have an average preference equal 0.4. In other words there is a 10% decrease in the preference for field of study by going from male to female, and keeping all other regressors constant. When using the first choice as the benchmark for the preference for field of study the estimated gender difference is also quite large. For location, the differences in preferences between the genders are smaller, meaning that female and male applicants have more equal preferences.
Table 5 shows that male applicants have a stronger preference for both field of study and location than their female counterparts. This difference in preference is stronger when the most listed field is used as the benchmark for the preference, than when the field of the first choice is used. Table 3 and 4 in section 4.1.1 indicates that there is a larger share of male that only have one priority in their applications than there are male in the whole sample. These findings taken together give a strong indication that male applicants on average have more specific applications than female applicants. An Irish study found that female applicants to higher education tended to be more risk averse when applying for higher education, and therefore listed programs they were overqualified for based on previous grade limits (Delaney
& Devereux, 2021). Adding programs to an application in case one is not admitted to ones preferred program could be a source of diversity in applications. This could thus help explain why men have more specific applications.
Looking at the differences between those with low and high socio-economic status, table 5 shows that applicants with low SES have a stronger preference for field of study than high SES applicants. The difference between the two groups is larger for the preference for
location, where applicants with high SES tend to have the strongest preference. When we use the applicant’s first choice as the benchmark for their preference, the preference for place of study is on average 5% stronger for applicants with high SES than those with low SES. The
33 difference in preference for field is on average 4% stronger for the ones that have a low SES than their high SES counterparts.
If we look at the differences in preferences for place of study, we find that there is a positive effect of having high SES on the preference for location. Previous studies have found that applicants with lower social class are less likely to attend and apply for universities than their high social class counterparts (Flannery & Cullinan, 2014; Delaney & Devereux, 2020). Our finding that applicants with high SES have a strong preference for location, seem to
substantiate these findings. There are few universities in Norway, so if applicants have a strong preference for attending a university rather than a university college, they have few locations to choose from. This might be one reason why applicants with high SES have a stronger preference for location, than students with low SES. Regardless of SES, table 6 shows that the better grades an applicant has, the stronger preference he or she has for place of study A limitation in this thesis is that how the preferences works in on the type of
institution has not been looked further into. Further studies can investigate this, by controlling for whether the institution is a university or a university college, or look more specifically into the locations with the highest preferences, and see what kind of institutions are present at the different locations.
Delaney and Devereux find that applicants from disadvantaged schools are more likely to have multiple priorities to the same field of study, while applicants from advantaged schools are more likely to apply to specific combinations of field and location (Delaney & Devereux, 2020). As seen in table 6 and 7 this is in accordance with the results from this study, which indicates that there is a negative effect of having a high SES on the preference for field of study.
Delaney and Devereux, find that women applicants with a lower SES tend to apply more like their male counterpart. Female applicants with high SES who have a tendency to apply for programs they are overqualified for, whereas female applicants with low SES do not have this tendency (Delaney & Devereux, 2021). As discussed in section 2.1.2 there is a correlation
