University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice,...

128
University Choice, Equality, and Academic Performance

Transcript of University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice,...

Page 1: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

University Choice, Equality, and Academic Performance

Page 2: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 3: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Acta Wexionensia No 188/2009 Economics

University Choice, Equality and Academic Performance

Susanna Holzer

Växjö University Press

Page 4: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

University Choice, Equality, and Academic Performance. Thesis for the de-gree of Doctor of Philosophy, Växjö University, Sweden 2009. Series editor: Kerstin Brodén ISSN: 1404-4307 ISBN: 978-91-7636-681-3 Printed by: Intellecta Infolog, Göteborg 2009

Page 5: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Abstract Holzer, Susanna (2009). University Choice, Equality, and Academic Perform-ance. Acta Wexionensia No 188/2009. ISSN:1404-4307, ISBN: 978-91-7636-681-6. Written in English. This thesis consists of three essays that examine issues on university attendance behavior, factors of university completion, and the labor market value of a uni-versity diploma in Sweden.

Essay [I] analyzes how the rapid expansion of higher education that increased the geographical accessibility to higher education in the 1990s affected univer-sity enrollment decisions among various socioeconomic groups of young adults in Sweden. The empirical findings show that the probability of enrollment in uni-versity education increases with accessibility to university education. The results also indicate that accessibility adds to the likelihood of attending a university within the region of residence. Access to higher education more locally seems to have decreased the social distance to higher education, meaning that the option of attending higher education, as compared to entering the local labor market af-ter upper secondary school, has become a more common and a more natural al-ternative for more socioeconomic groups in society.

Essay [II] compares the performance of students in universities built before and after the large decentralization and expansion of the higher educational sys-tem in Sweden, starting in the late 1970s. Two outcome measures are used: (i) whether or not the student has obtained a degree within seven years after she ini-tiated her studies; and (ii) whether or not she obtained 120 credit points (the re-quirement for most undergraduate degrees) within seven years. Controlling for several background variables as well as GPA scores in a binomial probit model, we show that students at old universities are about 5 percentage points more likely to get a degree and about 9 percentage points more likely to obtain 120 credit points. However, in an extended bivariate model where we consider selec-tion on unobservables into university type, we cannot reject the possibility of no difference in performance between the two university types.

Essay [III] analyzes the labor market value of a university diploma (sheep-skin) in Sweden. In contrast to previous studies, this study only focuses on Swedish university students who have three years of full time university educa-tion or more − where some have obtained a university degree, others not. The re-sults show that for male students, the wage premium of possessing a degree, i.e. the sheepskin effect, is roughly 5-8 percent. For women, it is about 6-7 percent for those who have completed four years of fulltime or more. For students who attended a more prestigious university in the metropolitan areas in Sweden and majored in the natural sciences, a sheepskin effect of roughly 13 percent for men and 22 percent for women is traced. However, this result did not hold among stu-dents who attended a newer university outside the metropolitan reas and/or ma-jored in the social sciences.

Keywords: Higher education, university enrollment; university choice; accessi-bility; university completion; selection bias; propensity score matching, sheep-skin, human capital.

Page 6: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 7: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

To my parents,

Irena & Franz Holzer

Page 8: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 9: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Contents

Acknowledgements

Introduction

Essay I: The Expansion of Higher Education in Sweden

and the Issue of Equality of Opportunity

Essay II: University Choice and Academic Success in

Sweden

Essay III: Are there Sheepskin Effects in the Return to

Higher Education in Sweden?

Svensk sammanfattning

Page 10: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 11: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Acknowledgments Less than two hours after I got my acceptance call from Karlstad University (I was on their reserve list), I sat on the train to Karlstad – little did I know that this was the start of one of the longest journeys in my life. With a short stop at Karl-stad University, followed by several years at Lund University, to finally end up at Växjö University with this dissertation … There are so many people to whom I am grateful who have crossed my path over the years that I hardly know where to start… First of all, there are two people that have my deepest gratitude – my supervi-sor Professor Mårten Palme and co-supervisor Dr. Håkan Locking. The German word for supervisor is Doktor Vater (doctor father). The parent metaphor em-braces, I think, so much more in terms of patience, support and humanity, than just a supervisor’s professional guidance in how to write a thesis. Just like ordi-nary parents you have shown a tremendous amount of patience and given me in-valuable support in both my thesis writing and in boosting my self-confidence when needed. For putting up with my crazy ideas, yet kindly guiding me back on track again… Mårten, your sharp insights and comments have been the key ele-ment when turning my thoughts on higher education in Sweden into the three es-says in this dissertation. Håkan, for ALWAYS being there for me and always finding the time to answer me whenever I needed to ask you for help – on almost anything and at almost any hour of the day. Thank you both for putting up with me and all the versions of this thesis, and for patiently guiding me to this final product. To Professor Inga Persson and Professor Curt Wells at Lund University, who both encouraged me to consider graduate school in the first place. A special thanks to Professor Harald Niklasson and Professor Jan Ekberg – who offered me a position at Växjö University and who encouraged me and al-lowed me to find my own way in research. To all my friends and colleagues at the School of Management and Econom-ics at Växjö University – especially at the Department of Economics and Statis-tics – for putting up with me for so many years, for sharing the joy and the ag-ony of economic sciences, and for reading and commenting on all versions of my papers in thesis − here it goes: Zangin Ahmad, Lars Andersson, Lina Andersson, Dominique Anxo, Abdullah Almasri, Lars Behrenz, Mårten Bjellerup, Lennart Delander, Jan Ekberg, Mats Hammarstedt, Hans Jonsson, Gösta Karlsson, Joel Karlsson, Yushu Li, Thomas Lindh, Håkan Locking, Monika Hjeds Löfmark, Andreas Mångs, Jonas Månsson, Maria Mikkonen, Maria Nilsson, Harald Nik-lasson, Mikael Ohlson, Osvaldo Salas, Klas Sandén, Ghazi Shukur, Carl-Erik Sjödahl, Jonas Söderberg, and Lars Tomsmark – THANK YOU ALL!

Page 12: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

To my beloved fiend Karin Olsson, for whom I am so grateful. Through you I met my other dear friends in political sciences, Johanna Jormfeldt, Tobias Bro-mander, and Otto Petterson. Guys − what would these last few years have been without our breakfast club and lunch meetings? – Thanks for bringing out the laughter in me! Through our tight friendship over the years, I have gotten to know and have almost become a part of your department/school too – so thank you all at the School of Social Sciences! To my beloved friend Marie Eriksson, for our energizing power walks/talks around Växjösjön – I do not know how I would have survived without it! To Nick Barr, Peder Pedersen, Chris Taber and Jeff Smith – inspiring Profes-sors that I have stumbled across on conferences and seminars – thanks for taking the time to comment on early versions of my papers and inspiring me to keep on working! To the participants at the meetings of EEA in Budapest (2007), and Barcelona (2009), EALE in Prague (2006), SOLE in Boston (2009), the Arne Ryde Symposium in Lund (2006), Higher Education Systems, Decentralization and Educational Outcomes in Novara (2008), and seminars at Aarhus University – thank you all for your insightful discussions and comments on my work. To Professor Anders Björklund, for fruitful and helpful comments on my li-centiate thesis in 2006, and to Professor Mikael Lindahl, who was the appointed opponent at my final seminar of this thesis earlier this year, whose insightful comments have helped me improve this final version of the thesis. To Christina Lönnblad for improving my English. Financial support by Växjö University, Jan Wallander and Tom Hedelius’ Research Foundation, the Swedish Research Council, and the Sveriges Riksbank is gratefully acknowledged. Graduate studies can make the most social person asocial, which is why the presence of stubborn and beloved friends that refuse to give up on you becomes even more important. For this, Maria Wennerbo with family (my long lasting childhood friend), Annika and Per Lundfors, Paula Hallonsten and Ingvar Lind-holm, Marie and Magnus Eriksson, Mark Vooreveld, Monika and Martin Löf-mark – I am humble and grateful. An meine Familie in Polen und in Österreich – die Dorner, Glaner, Holzer, Leitner, Palme, Riebler, und die Trzcinski – Ich bin stolz ein Teil von euch zu sein! Last but not least – my beloved parents: Irena and Franz Holzer. –Hoppa inte i sjön bara för alla andra gör det, brukar du pappa säga. Exakt vad det betyder vet nog bara du, men min tolkning är att jag ska våga gå min egen väg och följa min egen övertygelse. En sak är säker, även om jag vågat och bitvis lyckats med di-verse galenskaper i mitt liv, är det för att ni båda funnit vid min sida med ert ovillkorliga stöd och kärlek – och för detta är jag er för evigt tacksam! For those I have forgotten to mention and for all of you that I have men-tioned: – Thank you all for crossing my path, for being such a source of inspiration, giv-ing me support and faith – and most of all for giving me joy. Susanna Holzer Stockholm, September 23, 2009.

Page 13: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Introduction When several reports showed that Sweden had been falling behind in relevant comparisons concerning national levels of higher education in the 1980s, strong criticism was raised towards the Swedish government for its centrally monitored and supply-side oriented higher education being under dimensioned.1 Sweden risked losing competence within several academic professions, if the sector of higher education did not expand its undergraduate education to compensate for large scale retirements in the 1990s.2 As a result, the sector of higher education became the target of a very substantial and state-funded expansion that started in the early 1990s.3 In less than 15 years, the student body grew from 150,000 stu-dents at the end of the 1980s, to more than 330,000 students in early 2000 – cor-responding to roughly 50 percent of all the younger birth cohorts in Sweden. The point of departure for this dissertation is this rapid expansion of the sector of higher education in Sweden in the 1990s. This thesis consists of three self-contained essays, each of which has a different approach to investigate the out-come of this expansion. The economic literature on higher education mainly examines the relationship between college (university) quality and labor market outcomes.4 The dominat-ing part of the literature is based on data for the United States; see e.g. Brewer and Ehrenberg (1996), Brewer et al. (1999), Berg Dale and Krueger (2002) and

––––––––– 1 See af Trolle (1990), UHÄ (1989), OECD (1993) and Hammarström (1996). 2 See The National Agency for Higher Education (HSV) (1998, p15f) for a brief discussion and over-

view. 3 The economic recession that Sweden and some other industrialized countries suffered in the early

1990s which made the labor market harsh, for especially for young adults, did have an impact on the speed of the expansion.

4 The theory that in basic all research on higher education rely on is the theory of human capital, see seminal work by Becker (1964), Schultz (1961) and Mincer (1974). The theory says that the deci-sion whether or not to participate in an education and for how many years is equivalent to making an investment, i.e. an investment in one’s own human capital. The rational individual has to decide if it is worth the forgone earnings he/she will suffer while studying, instead of participating in the labor market, in order to enhance his/her future chances of better labor market opportunities and la-bor market outcomes. In a computer age where individuals’ educational histories and wage devel-opments are well recorded, labor market economists have been using this information at an escalat-ing speed to provide overwhelming empirical evidence of the years invested in education being positively correlated with an increasing economic return (see Card (1999) and Harmon et al. (2003) for an overview of the literature.) However, a common feature of most of this research is that edu-cation is treated as a homogeneous good and the schools as “black boxes” where the educational production is taking place. Only in the last 15 years have empirical researchers started to pay more attention to institutional characteristics and other factors that might affect educational outcome – which, in turn, might affect later labor market outcomes.

Page 14: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Black and Smith (2004, 2006). Their overall conclusion is that not only does col-lege education have an impact on labor market outcomes, college quality (col-lege choice) is also of importance. Although they all have different ways of reaching their empirical findings, they all end up with similar results − that an investment in college studies has a positive effect on an increased labor market outcome up to about 20-50 percent, and attending high-quality colleges rather than low-quality colleges generally increases wages in the range of 5−15 percent. When classifying universities into quality groups in a Swedish context , the most common way of proceeding is to use the fact whether the university was estab-lished before or after the reform of the sector of higher education in 1977 – the universities are then commonly referred to as old and new universities, respec-tively. Sweden has had a similar development of the sector of higher education as the United States in terms of expansion and decentralization, in establishing new smaller (and for the majority of the population) more local regional univer-sities outside the metropolitan areas as a complement to the fewer old universi-ties (mostly situated in the metropolitan areas in Sweden).5 The newer establish-ments were initially offering shorter and a more limited choice of educational programs or courses. Over the years, however, there has been an increase in the amount of educational choices and their length. In contrast to the US higher edu-cational system, there are four important traits of the Swedish higher educational system that we must consider when interpreting outcomes of higher education in Sweden in the 1990s and later. Higher education is i) centrally monitored and quality controlled by the government; ii) state funded; iii) free of charge for the student; and iv) the new universities are, like the old universities, allowed to award students bachelor’s and master’s degrees on a regular basis. For Swedish evidence on estimating the labor market outcomes of a university education in general or a specific university choice, see e.g. Wadensjö (1991), Gartell and Regnér (2004), Lindahl and Regnér (2005), and Eliasson (2006). The studies that group universities into quality groups based on old versus new uni-versities find that graduates from old universities on average receive an earnings premium that is about 0-7 percent higher than that of their fellow peers at the new universities. When controlling for specific universities (and in some cases also the subject majors) the earning effects are in the range up to 20 percent or more. A common feature of most (if not all) of these empirical studies on Swedish data is that they only consider university graduates. In Sweden, roughly 40 percent of all students that enter higher education end up with a degree after seven years or more. That less than half of the students body ends up with a degree is not typi-cal for Sweden alone – on the contrary. Manski and Wise (1983) reported in-creasing drop out rates from US colleges already in the early 1980s, and OECD ––––––––– 5 Throughout this text and this thesis, I will refer to all Swedish establishments of higher education as

universities, based on the fact that all of them are offering educations up to the master’s level – which is not the case for some of the colleges in the US.

Page 15: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

(2008) shows this to be quite common across most industrialized countries. What is special for Sweden, though, is that more than one-third of all students who ne-ver obtain a university degree are recoded as having three years or more worth of higher education, and most of it at intermediate or higher levels (see SCB (2007)). In the worst case scenario, this means that prior empirical research has underestimated the real labor market outcome of an investment in a university education, since these highly educated individuals with no formal degree are put in the control group together with upper secondary graduates. This raises the question of what is being measured in these empirical studies; labor market out-comes due to an educational investment − or the labor market value of a formal university degree? These issues are discussed and some of them are analyzed in Essay III of this thesis. The second drawback of only considering graduates, especially in terms of esti-mating the labor market outcomes associated with university qualities, is that the research made does not control for where the student has actually obtained his/her education – only which university that has issued the actual diploma is considered. About one third of all students in Sweden move between universi-ties.6 It is not uncommon that students that attend a regional (and in some opin-ions less prestigious) university move to an older (and more prestigious) univer-sity in their fourth year, in order to receive a master’s diploma from the latter university. What do any differences between labor market outcomes measure in this case? Part of this could, of course, be due to university quality, but any dif-ferences in wages could just as well be due to unobserved individual characteris-tics that made some students move – and others not. To analyze the institutional impact on educational performances – which labor market economists argue to be reflected in wage differences or other labor market outcomes later on – the in-stitutional impact on actual educational performances by university students is discussed and analyzed in Essay II. Besides the labor market motive for expanding higher education, egalitarian mo-tives are often used to motivate the state funding of the entire educational system in Sweden (from elementary education to tertiary education).7 The egalitarian idea of improving educational opportunities8 is often interpreted in terms of im-

––––––––– 6 See raw LINDA with LADOK information and Essay II in this thesis. Similar results are reported

on US students by Light and Strayer (2000). 7 In Sweden, the powerful Swedish labor movement in association with the Social Democratic Party

did already in the 1940s stress the importance of providing a nation-wide education. One of the lar-gest threats to a democratic society and the sustained wealth of a nation was, according to them, differences in educational standards among its population (see e.g. Erikson and Jonsson (1993:31ff)).

8 Equality of opportunity, or in this case equality of educational opportunity, relates to the extensive economic literature on intergenerational mobility, which looks at the relation between parents’ eco-nomic outcomes and the future economic outcome of their children. A high intergenerational mo-bility indicates a low relation between parents’ economic outcomes and the future economic out-come of their children. A weak relation between the child and parental characteristics indicates a high equality of opportunity in society, saying that children from different social origins have simi-

Page 16: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

proving access for the population.9 A political action by Brint and Karabel (1989) and Rouse (1995) is referred to as democratization. That is, nearby easy-access would give all who want to participate in an educational program the op-portunity to do so or encourage those who might otherwise not have participated. This egalitarian motive for state funding of the expansion of the sector of higher education in Sweden in the 1990s and how increased access has affected differ-ent socioeconomics enrollment behavior before and after the expansion consti-tute the point of departure in Essay I. The empirical material used in all three essays in this thesis is based on regis-tered data provided by Statistic Sweden (SCB). The core database is the Longi-tudinal INdividual DAta for Sweden (LINDA), which is a representative sample of three percent of the population in Sweden and their household members (see Edin and Fredriksson (2000) for a description). LINDA goes back to 1968 and the years up until 2006 are used here. In addition, information from the Swedish upper secondary school register which contains information on final grades from upper secondary school, and from the Swedish higher education register, which reveals any activities within the higher educational system, has been attached to the main subjects in LINDA.

Essay I The empirical focus of this essay is to investigate three questions: (a) to what ex-tent did individuals living in the municipalities of the new universities of the late 1970s and later become more inclined to attend higher education in the 1990s; (b) to what extent did the choice of attending a university in general become less dependent on individual and family background characteristics in the 1990s (i.e. was there an increase in intergenerational educational mobility?); and (c) did the new universities divert students from lower socioeconomic backgrounds from at-tending older universities? With mostly shorter vocational-oriented programs be-ing offered at the new universities, did they divert potential students who might otherwise have attended an old university, i.e. who would otherwise have in-vested in longer educations? This effect is referred to as a diversion effect by Rouse (1995, 1998), and Leigh and Gill (2003, 2004).

lar chances in life; see seminal work such as Becker and Tomes (1979, 1986). Increased access more locally is considered to improve this intergenerational mobility.

9 In philosophical economics, there is an ongoing discussion on whether or not opportunity and ac-cess are two different things; see Roemer (2006). The main issue in modern egalitarian policy is, however, how to divide and obtain welfare, but the way of doing this differs. The difference in views was often on how much responsibility we can put on the individual alone, i.e. the cause of her actions that leads to a certain welfare outcome, and how much responsibility we can put on so-ciety as a whole, i.e. the circumstances/milieu in which she makes her decisions. This, in turn, can affect the welfare outcomes of her educational choices.

Page 17: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

A sample of 299,944 individuals is extracted from LINDA (1968-2001), which is divided into 25 cohorts of 18-year-olds for the years 1977-2001. The cohorts of 1977-1989 represent the 1980s and the remaining cohorts represent the 1990s. All youths are conditioned on having at least one parent in LINDA, which pro-vides relations between parents and their youths. Two probit models are esti-mated, one on the democratization and one on the diversion effect. The results show that living and growing up in the area of a rapidly growing educational institution seems to have a strong positive effect on overall univer-sity attendance. With an average impact of roughly six percentage points, this indicates that the expansion of the higher educational sector in Sweden has had an overall democratization effect (the local effect at the new universities is roughly 41 percentage points). A more local access to higher education seems to have decreased the social distance to higher education, meaning that the option of attending higher education, as compared to entering the local labor market af-ter upper secondary school, has become a more common and possible alternative for more socioeconomic groups in society. The fact that more individuals chose to attend higher education further away in the 1990s among those living in the areas of new universities could be interpreted as the choice of moving being con-sidered less costly (a smaller risk) once the decision of attending higher educa-tion has been made in the first place and that the new universities do not have any diversion effect on overall university attendance. The relatively largest growth occurred among students whose parents had upper secondary school as their highest education, which can bee seen as some indications of political suc-cess in equalizing educational opportunities.

Essay II In this essay, a comparison is made between the academic performance of stu-dents that attended a university that was established before or after the large de-centralization and expansion of the higher educational system in Sweden which started in the late 1970s. Two outcome measures are used: (i) whether or not the student has obtained a degree within seven years after she initiated her studies; and (ii) whether or not she obtained 120 credit points (the requirement for most undergraduate degrees) within seven years. To model how university type might affect educational outcomes, two probit models are employed. First, we use a binomial probit model, where we control for possible heterogeneity by including observed characteristics as regressors. Second, the effect of selection on unobservables that might affect the educational outcome which is correlated with initial university choice is estimated with a bivariate probit model. The empirical material used in this study is a sample of 5,565 individuals that are extracted from LINDA. All those individuals entered a Swedish university for the very first time in the years 1996-1999, i.e. approxi-mately three percent of all new enrolled students at the time.

Page 18: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Controlling for several background variables as well as GPA scores in a bino-mial probit model, students who attended the old universities are about 5 per-centage points more likely to get a degree and about 9 percentage points more likely to obtain 120 credit points. However, when controlling for selection on unobservables with a bivariate probit model, we found that on the probability of completing 120 credit points or more, the selection parameter turned out to be significantly different from zero and the coefficient for an old university was not significantly different from zero. This means that we cannot rule out the possibil-ity that the higher probability of obtaining 120 credit points at older universities is attributed to selection on unobservables.

Essay III Although the positive relationship between educational investments and earnings is one of the most established relationships in the social sciences, we still argue about what exactly in the educational investment affects earnings – is it years of schooling, credentials, or perhaps a mixture of them both? Mincer (1974) argued that earnings are mainly affected by the number of years of education, while other researchers point at the importance of the acquisition of credentials by me-ans of formal degrees. In the latter case, an accredited worker earns more than its non-accredited counterparts, a phenomenon referred to as a sheepskin effect; see e.g. Hungerford and Solon (1987), Jaeger and Page (1996), and Flores-Lagunes and Light (2007). Two questions are raised in this study: First, is there a general difference in the economic outcome for former university students with a degree, as compared to those with no formal degree, i.e. is there a sheepskin effect? Second, does the sheepskin effect vary within groups of university types, subject majors, and edu-cational programs? Altonji (1993), Altonji et al. (2005), Arcidiacono (2004), Brewer et al. (1999), and Dale and Krueger (2002) all point out that information on school quality and educational programs, i.e. on university choice and univer-sity major, is of importance when the objective is to value the returns to univer-sity education. In contrast to most previous studies on sheepskin effects on the labor market, this study only focuses on university students in Sweden with about the same number of years of higher education (three years or more) and differences in labor mar-ket outcome due to whether or not they have a degree (bachelor’s or higher). In addition to university choice and within-university choices, individual and fam-ily characteristics and ability are also considered. A random sample of 2,363 in-dividuals was extracted from the cross section sample of LINDA 2006. Tradi-tional OLS-models are employed and complemented with models based on pro-pensity score matching. The results show that men face a wage-premium of possessing a degree, i.e. the sheepskin effect, of roughly 5-8 percent for those who have obtained 120 credit

Page 19: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

points or more (i.e. three years or more). For women, this is about 6-7 percent for those who have obtained 160 credit points (i.e. four years or more) or more. For students who attended a more prestigious old university in the metropolitan areas in Sweden, and majored in the natural sciences, a sheepskin effect of roughly 13 percent for men and 20 percent for women is traced. However, this result did not hold among students who attended a newer university outside the metropolitan areas. Controlling for specific occupational programs for economists, engineers and teachers did not, regardless of gender, give any significant estimates of sheepskin effects.

References af Trolle, Ulf (1990), Mot en internationellt konkurrenskraftig AKADEMISK

UTBILDNING, Lund: Studentlitteratur. Altonji, Joseph G. (1993), The Demand for and Return to Education When Edu-

cation Outcomes are Uncertain, Journal of Labor Economics, 11(1):48-83.

Altonji, Joseph G., Todd E. Elder and Christopher R. Taber (2005), Selection on Observed and unobserved Variables: Assessing the Effectiveness of Catholic Schools, Journal of Political Economy, 113(1):151-184.

Arcidiacono, Peter (2004), Ability sorting and the returns to university major, Journal of Econometrics, 121(1-2):343-375.

Becker, Gary S. (1964[1993]), Human Capital, 3 ed. Chicago: University of Chicago Press.

Becker, Gary S. and Nigel Tomes (1979), An Equilibrium Theory of the Distri-bution of Income and Intergenerational Mobility, Journal of Political Economy, 87:1153–89.

Becker, Gary S. and Nigel Tomes (1986), Human capital and Rise and Fall of the Families, Journal of Labor Economics, 4(3):1-39.

Björklund, Anders, Mårten Palme and Ingemar Svensson (1995), Tax Reforms and Income Distribution: An Assessment Using Different Income Con-cepts, Swedish Economic Policy Review, 2:229–266.

Black, D. and Smith, J. (2004), How Robust is the Evidence on the Effects of College Quality? Evidence from Matching, Journal of Econometrics 121 (1−2):99−124.

Black, D. and Smith, J. (2006), Estimating the Returns to College Quality with Multiple Proxies for Quality, Journal of Labor Economics, 24(3):701-728.

Brewer, D. and Ehrenberg, R. (1996), Does it Pay to Attend an Elite Private Col-lege? Evidence from the Senior High School Class of 1980, Research in Labor Economics 15:239−271.

Brewer, D., Eide, E. and Ehrenberg, R. (1999), Does it Pay to Attend an Elite Private College? Cross-Cohort Evidence on the Effects of College Type on Earnings, Journal of Human Resources, 34 (1):104−123.

Brint, Steven and Jerome Karabel (1989), The Diverted Dream: Community Col-leges and the Promise of Educational Opportunity in America 1900-1985, New York: Oxford University Press.

Page 20: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Card, David (1999), The casual effect of education on earnings, in Handbook in Labor Economics, Vol. 3A, (red) Orley C. Ashenfelter and David Card, Amsterdam: North-Holland: Elsevier Science Publishers.

Dale, Stacy Berg and Allan Krueger (2002), Estimating the Payoff to Attending a More Selective University: An Application of Selection on Observables and Unobservables, Quarterly Journal of Economics, 117(4):1491-1527.

Edin, Per-Anders and Peter Fredriksson (2000), LINDA - Longitudinal INdivid-ual DAta for Sweden, Working paper 2000:19, Uppsala, Sweden: Depart-ment of Economics, Uppsala University.

Eliasson, Kent (2006), University choice and earnings among university gradu-ates in Sweden, Umeå Economic Studies No. 693.

Erikson, Robert and Jan O. Jonsson (1993), Ursprung och utbildning, in SOU:1993:85, Stockholm: Fritzes.

Gartell, Maria and Håkan Regnér (2004), Inkomstpremier av lärosäten för män och kvinnor som tog en examen under 1990-talet, Institutet för framtids-studier: 2004:1

Government-bill. (2000), Vuxnas lärande och utvecklingen av vuxenutbildning-en, Stockholm: Proposition 2000/01:72.

Hammarström, Margareta (1996), Varför inte Högskola?, Göteborg: Universita-tis Gothoburgensis.

Harmon, C., Oosterbeek, H. and Walker, I. (2003), The Returns to Education: Microeconomics, Journal of Economic Surveys,17(2):115−155.

HSV (1998), The Current Swedish Model of University Governamnce - Back-ground and Description, Raport : 1998:10S, Stockholm: National Agency for Higher Education.

Hungerford, Thomas and Garry Solon (1987), Sheepskin effects in the returns to education, Review of Economics and Statistics, 69:175–177.

Flores-Lagunes Alfonso and Audrey Light (2007), Interpreting Sheepskin Effects in the Returns to Education, Econ Working paper 0707, Department of Economics, University of Arizona

Jaeger, David. A. and Marianne E. Page (1996), Degrees Matter: New Evidence on Sheepskin Effects in the Returns to Education, Review of Economics and Statistics 78:733-740.

Kane, Thomas J. and Cecilia E. Rouse (1995), Labor Market Return to Two- and Four Year College, American Economic Review,. 85(3):600–14

--- --- (1999), The community college: educating students at the margin between college and work. Journal of Economic Perspectives 13(1):63–84.

Light, A., Audrey and Wayne Strayer (2000), The determinants of university completion: school quality or student ability?, Journal of Human Re-sources 35(2):299–332.

Lindahl, Lena and Regnér, Håkan, (2005), College Choice and Subsequent Earn-ings: Results Using Swedish Sibling Data, Scandinavian Journal of Eco-nomics, 107 (3), 437−457.

Leigh, Duane E. and Andrew M. Gill (2003), Do community college really di-vert the students from earning a bachelor’s degree?, Economic of Educa-tion Review, 22(1):23-30.

Page 21: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Leigh, Duane E. and Andrew M. Gill (2004), The effect of community colleges on changing students’ educational aspirations, Economic of Education Re-view, 23(1):95-102.

Manski, Charles F.and D. A. Wise (1983), University Choice in America. Cam-bridge, Massachusetts: Harvard University Press.

Mincer, Jacob. (1974), Schooling, Experience, and Earnings, NY Columbia University Press.

MIS (2000), Utbildningsklassificering, vol 1, Statistic Sweden. OECD (1993), Education at a Glance 1993, Paris: OECD. OECD (2008), Education at a Glance 2005, Paris: OECD. Rouse, Cecilia E. (1995), Democratization or Diversion? The effect of commu-

nity colleges on educational attainment, Journal of Business and Eco-nomic Statistics, 3(2):217–224.

--- (1998), Do Two-Year Colleges Increase Overall Educational Attainment? Evidence from the States, Journal of Policy Analysis and Management, 17(4): 595-620.

Roemer, John E. (2006), Democracy, Education, and Equality – Graz Schum-peter Lecture, Economic Society Monographs, Cambridge University Press.

SCB (2007), Universitet och högskolor: Genomströmning och resultat i högsko-lans grundutbildning rom 2005/06; Statistiska Meddelanden UF 20 SM 0702.

Schultz, Theodore W. (1961), Investment in Human Capital, The American Eco-nomic Review, 51(1):1-17

Solon, Gary (1999), Intergenerational Mobility in the Labor Market, in Orley Ashenfelter and David Card (eds.), Handbook of Labor Economics, Vol. 3A, Elsevier, Amsterdam.

SOU (1972), U68 – Högskolan, SOU1972:3, Stockholm: Fritzes. Light, A., Audrey and Wayne Strayer (2000), The determinants of university

completion: school quality or student ability?, Journal of Human Re-sources, 35(2):299–332.

UHÄ (1989) Högskoleutbidlningens framtida dimensionering, Stockholm: UHÄ1989:17.

Wadensjö, Eskil (1991), Högre utbildning och inkomster, in E. Wadensjö (ed.), Arbetskraft, arbetsmarknad och produktivitet, SOU1991:82 Expertrapport 4, Fritzes, Stockholm.

Page 22: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 23: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

I

Page 24: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 25: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

The Expansion of Higher Education in Sweden 

and the Issue of Equality of Opportunity 

 

Susanna Holzer 

Abstract

This paper analyzes to what extent the political means of democratization have decreased educational inequalities (i.e. the choice of attending higher education has become less dependent on family background in the 1990s than before). Especially the new universities were heavily exposed to the expansion. The results show that living and growing up in the same area as rapidly growing educational institutions seems to have a strong overall positive effect on university attendance of roughly six percentage points, which indicates that the expansion of the higher educational sector in Sweden has had an overall democratization effect. Having more local access to higher education also seems to have decreased the social distance to higher education, meaning that the option of attending higher education, as compared to entering the local labor market after upper secondary school, has become a more common and possible alternative for more socioeconomic groups in society. The fact that more individuals chose to attend higher education further away in the 1990s among those living in the area of new universities, could be interpreted as the choice of moving being considered less costly (a smaller risk) once the decision to attend higher education has been made in the first place and the new universities do not have a clear diversion effect on overall university attendance. The relatively largest growth in attendance occurred among students whose parents had upper secondary school as their highest education, which can bee seen as an indication of a political success in equalizing educational opportunities in Sweden.

JEL Classification:  I22, I28, J24 

Keywords: Higher Education, Intergenerational Educational Mobility, Regionalization  

Correspondence address: Susanna Holzer, School of Management and Economics, Växjö University, SE-351 95 Växjö, Sweden. Phone: +46 470 70 85 79. E-mail:[email protected]. I am grateful to Mårten Palme, Håkan Locking, Anders Björklund, Chris Taber, Nicholas Barr, Jan Ekberg, and Ghazi Shukur for helpful comments and suggestions. I have benefited from many useful comments from seminar and conference participants at Växjö University, Aahrhus University, SSGPE in Linköping, ECEO in Antwerpen, EALE in Prague and the Arne Ryde Symposium in Lund. I acknowledge financial support from Växjö University and Jan Wallander and Tom Hedelius’ Research Foundation.

Page 26: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

2

 1   Introduction 

One central institution for implementing political views of social and distributive justice is

education. Swedish policy makers are by far no exception and by decreasing educational

inequalities in society they have, for the last century, used the egalitarian motive to reform the

entire education system. An important means of educational equalization has been to improve

access, both in terms of increasing the amount of slots at the existing institutions of education,

but also to geographically increase and spread the amount of institutions in the country that

could offer educational training. The egalitarian idea of improving educational opportunities

in terms of improving access for the population is referred to as a democratization by Brint

and Karabel (1989) and Rouse (1995). That is, nearby easy-access would give all who want to

attend an educational program the opportunity to do so or encourage those who might

otherwise not have attended.

The supply-side oriented and centrally monitored higher education in Sweden was

concentrated to six universities in the early 1970 (mainly located in the metropolitan areas). In

the late 1970s, these universities were complemented with an additional 12 smaller and

geographically more dispersed regional universities.1 However, improved geographical access

did not initially have any major impact on the size of the student body. A change toward the

end of the 1980s, though, led to a rapid expansion in the institutions of higher education. In

less than ten years, there was an increase from approximately 150,000 students during the

entire 1980s to about 330,000 students in 1999. The new universities, which in some cases

grew by 400 percent in terms of the number of students, were a main contribution to this

expansion.2

The empirical focus in this paper is to investigate three questions: (a) to what extent did

individuals living in the municipalities of the new universities of the late 1970s and later

become more inclined to attend higher education in the 1990s; (b) to what extent did the

choice of attending a university in general become less dependent on individual and family

background characteristics in the 1990s (i.e. did intergenerational educational mobility

1 Through the reform of the higher educational system in Sweden in 1977, there was a change in the entire definition of higher education. This makes any before and after comparisons of the higher educational system almost pointless, since it would be comparing apples to pears. Universities established in 1977 or later are throughout the paper referred to as new universities, universities established before 1977 are referred to as old universities. Since both universities and university colleges in Sweden are allowed to provide educations on master level, both institutions are throughout the text referred to as universities. 2 See SOU (1972:3) for the political motives for the educational expansion.

Page 27: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

3

increase?); and (c) did the new universities divert students from lower socioeconomic

backgrounds from attending older universities? With mostly shorter vocational-oriented

programs being offered at the new universities, did they divert potential students who might

otherwise have attended an old university, i.e. who would otherwise have invested in longer

educations? An effect referred to as a diversion effect by Rouse (1995), and Leigh and Gill

(2003, 2004).

Increased access and shorter traveling distances decrease the investment cost, which should

make the investment more appealing to more groups with limited economic resources. This

supports the democratization effect. Access to higher education nearby might decrease the

physical traveling distance and the social distance. If higher education becomes a natural

alternative for more groups in society, the decision to enroll in alternative higher education

programs than what is offered by the local university might become more interesting. The

decision to move in order to attend a higher education elsewhere could be interpreted as less

costly, since the actual decision to attend higher education in the first place has been less

dramatized by having local access to some higher education.

The empirical data used in this study is a sample of roughly 300,000 individuals extracted

from the Swedish Longitudinal INdividual DAta panel (LINDA). The sample is divided into

25 cohorts of 18-year-olds for the years 1977-2001, where the cohorts of 1977-1989 represent

the 1980s and the remaining cohorts the 1990s. All youths are conditioned as having at least

one parent in LINDA, which provides associations between parents and their youths. Two

models, one on the democratization and one on the diversion effect, are estimated.

The results here show that living and growing up in the area of a rapidly growing educational

institution seems to have a strong positive effect on overall university attendance. With an

impact of roughly six percentage points, this indicates that the expansion of the higher

educational sector in Sweden has had an overall democratization effect (the local effect at the

new universities is roughly 41 percentage points). Access to higher education more locally

seems to have decreased the social distance to higher education, meaning that the option of

attending higher education, as compared to entering the local labor market after upper

secondary school, has become a more common and possible alternative for more

socioeconomic groups in society. The fact that more individuals chose to attend higher

education further away in the 1990s among those living in the areas of new universities could

Page 28: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

4

be interpreted as the choice of moving being considered less costly (a smaller risk) once the

decision of attending higher education has been made in the first place and that the new

universities do not have any diversion effect on overall university attendance. The relatively

largest growth occurred among students whose parents had upper secondary school as their

highest education, which can bee seen as some indications of political success in equalizing

educational opportunities.

The paper is organized as follows: The next section gives an overview of Sweden’s higher

educational policy, followed by a brief overview of the literature in Section 3. Section 4

presents the empirical specifications and Section 5 presents the data management. Section 6

reports the results and Section 7 concludes the paper.

2 Brief History of Sweden’s Higher Educational System

A fast growing industry and a rapidly increasing demand for a larger and more highly

educated labor force obliged Sweden, like most industrialized countries after World War II, to

put political and economical resources into improving the entire educational system. The

education offered was, and still is, almost entirely financed and monitored by the government

and offered free of charge (i.e. from compulsory education to the university level). This

increasing demand for education, together with an egalitarian vision of equality of opportunity

by the leading Social Democratic Party in Sweden, brought about a substantial reform of the

entire education system in the 1950s and 1960s. By both improving and extending the years

spent in compulsory school and by rapidly increasing the access to upper secondary school,

more people than ever before qualified and chose to attend more schooling beyond the

mandatory level.3 This, together with the introduction of a student aid program that provided

university students with grants and generous student loans (that were independent of the

financial status of the parents), triggered the development of the few Swedish universities

becoming literally overcrowded. The chaotic situation suffered by the institutions of higher

education in the late 1960s called for drastic measures for solving the future increasing

demand for higher education.4

3 See Meghir and Palme (2005) on effects of an earlier reform in the Swedish educational system on educational attendance, and Erikson and Jonsson (1993) for an overview of the Swedish higher educational system and issues concerning educational opportunities in Sweden. 4 During the 1960s alone, the amount of newly enrolled students rose from 7,800 during the academic year 1960/61, to 27,100 in 1969/70 – see SOU 1972:3, p 84.

Page 29: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

5

Whereas the Swedish population was offered compulsory and upper secondary schools in

their local region, higher education in the early 1970s was mainly concentrated to six

university (mostly metropolitan) areas in Sweden.5 In terms of regional policy, local access

was sought to favor those who would like to stay, live and work locally. A successful regional

policy in offering higher education nearby would not only increase the overall educational

level, it would most likely also increase the number of job opportunities in the region and

promote local economical growth (see SOU (1972:3)). Hence, the future goal of Swedish

higher education could be interpreted both as egalitarian and as a form of regional

redistribution.

There was a dramatic change in the Swedish university structure as a result of the 1977 Act of

Higher Education. Besides the six old universities, 12 new regional universities were

established.6 The new institutions of higher education were in most cases former schools for

teacher training, military training, and nursing schools that were granted an upgraded status as

tertiary educations due to the reform. Therefore, the majority of the programs initially offered

at those institutions had a vocational character and were shorter (less than three years). Most

of the new institutions of higher education were only to conduct undergraduate education

without any research connections. The geographical locations of all institutions of higher

education from 1977 and onwards are presented in Figure 1.1.

Initially, there were no limitations in the admissions to higher education in the 1960s, which

was also a contributing factor for the institutions becoming overcrowded. As a result, overall

restrictions and limitations in admissions to higher education were implemented in 1977 to

1979. However, to encourage new student groups to attend higher education, the limited

admission was softened, with alternative ways of qualifying for higher education. Besides the

traditional and most common way of qualifying for higher education (i.e. by having a degree

from an upper secondary school with Grade Point Average scores (GPA)), degrees from adult

5 Göteborg, Linköping, Lund-Malmö, Stockholm, Umeå and Uppsala. In the same geographical areas as the universities were three large institutes in Stockholm: the Royal Institute of Technology, the Karolinska Institute of Medicine, and Stockholm School of Economics; and two others: the Chalmers Institute of Technology in Gothenburg, and the Institute of Agriculture in Uppsala. All institutions of higher education in these six university municipalities are included in the definition of old universities in this paper. 6 The new universities of 1977 were: Borås, Eskilstuna/Västerås, Falun/Borlänge, Gävle/Sandviken, Jönköping, Kalmar, Karlstad, Kristianstad, Sundsvall/Härnösand, Växjö, Örebro, Östersund, Luleå. Later university establishments were: 1983 – Halmstad and Skövde, 1988 – Ronneby/Karlskrona, 1990 - Uddevalla/Trollhättan, 1995 - Södertörn, and 1998 – Gotland and Malmö. In this paper, Malmö is never separated from Lund.

Page 30: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

6

schooling (folkhögskola), four years of labor market experience, or good results from the

Swedish Scholastic Aptitude Test (högskoleprovet) became new means of qualifying for

higher education.7 Note that Swedish universities cannot choose freely among eligible

students. The qualifications of the presumptive student are the only means for the individual

of competing for a student slot in a certain education. The centrally monitored admission

system from 1977 remains more or less the same today.

Figure 1.1  The geographical location of the institutions of higher education in Sweden. Note: The old universities (established prior to 1977) are in capitals, the rest are new universities that received the status of independent institutions of higher education in 1977 or later. Source: Statistic Sweden. 

Despite the increased amount of institutions of higher education and the overall increased

geographical access, there was a very modest development in the sector of higher education in

the first ten years after the reform. In fact, the number of students at the old universities and

the new universities was roughly the same in 1987 as it had been in 1977.

The reforms of the lower educational levels resulted in Sweden having one of the highest

population rates with upper secondary qualifications in an international comparison in the

7 See Kim (1998) and Öckert (2001) for a description and discussion about the admission rules of 1977.

Page 31: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

7

1980s. Yet only a smaller proportion of the Swedish population made the transition to higher

education. In the late 1980s, several reports stated that Sweden had fallen behind in relevant

comparisons concerning national levels of higher education.8 Sweden received strong

criticism for its higher education, centrally monitored by the government, being under-

dimensioned with respect to the demand. Reports stated that Sweden risked losing

competence within several academic professions if the sector of higher education did not

expand its undergraduate education to compensate for large scale retirements in the 1990s.9

To meet the present and future demand of higher education, the sector of higher education

became the target of a very massive expansion in the 1990s. Especially the new universities

became heavily exposed, where in some cases the enrollment grew by 400 percent in less than

ten years. The growth in enrollment into the universities in Sweden is presented in Figure1.2.

Figure 1.  Students enrolled in Swedish higher education 1977‐2001 Note: The figure illustrates the enrollments at old and new universities for the years 1977‐2001. Source: Statistic Sweden.  

The strict diversion between establishments mainly conducting undergraduate education and

establishments conducting both undergraduate education and research was softened in the

1990s. This made it possible for the new universities to conduct research on a larger scale

than before. The increased research activities at the new universities, together with overall

structural changes of educational programs in the early 1990s, made it possible for more of

8 See af Trolle (1990), UHÄ (1989), OECD (1993) and Hammarström (1996). 9 See HSV (1998, p 15f) for a brief discussion and overview.

Page 32: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

8

the new universities to offer longer educational programs than before. However, most of the

prestigious educational programs in law, medicine and art, for which the competition among

students is the highest, are until the present day restricted to the old universities.

3 Previous Literature

Equality of educational opportunity relates to the extensive economic literature on

intergenerational mobility, which looks at the association between parents’ economic

outcomes and the future economic outcome of their children. A high intergenerational

mobility indicates a low association between parents’ economic outcomes and the future

economic outcome of their children. A weak association between the child and parental

characteristics indicates a high equality of opportunity in society, saying that children from

different origins have similar chances in life (see e.g. Becker and Tomes (1979, 1986) and

Solon (1999) for a survey of the literature on intergenerational mobility).10

On the issue of expanding and increasing access to higher education in Sweden, only modest

economic research has been performed on how the expansion as a political means has affected

intergenerational mobility. One of the few examples is Holm and Häggström (1972) who

conducted cost-benefit analysis on early pilot projects to relocate higher education into new

areas in Sweden in the late 1960s. They argued that they could detect some positive effects on

recruiting youth from the new regions with less traditional family backgrounds. This was seen

as early indications of increased local access increasing educational mobility and promoting

social mobility in the region. However, Fasth (1980), who did research similar to that of Holm

and Häggström, found no support for the hypothesis that having more local access encouraged

more people from different socioeconomic groups in these new geographical areas to invest in

higher education than in other areas in Sweden.

The effect of traveling distance as part of a student’s investment cost was investigated by

Kjellström and Regnér (1999). They assumed that the distance to an institution of higher

education was to be positively correlated with the cost of attending higher education – the

10 See Björklund and Jäntti (1997) and Björklund, Lindahl and Sund (2003) for some Swedish examples. An alternative track in the economic literature of intergenerational mobility is also found in the economics of philosophy; see e.g. Roemer (1996, 2006) on issues of democracy, equality and distributive justice when discussing the impact of educational policies.

Page 33: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

9

longer the distance, the higher the cost. When examining the probability of attending higher

education among 10,000 individuals in Sweden born in 1967, the authors found that traveling

distances had a significant negative effect on the probability of attending higher education.

However, the effect was so small that their conclusion was that this was most likely not the

strongest determinant for the individual in deciding whether to attend higher education.

More extensive research on the effects of the expansion of higher education on educational

inequalities in Sweden has been conducted by sociologists like Erikson and Jonsson (1993,

1994, 1996, 2006) and Dryler (1994, 1998).11 Dryler (1994, 1998) used aggregated data from

the population census and data from the Higher Education Register in order to follow 17-24

year olds from 1968-1990 in three geographical areas that received new establishments of

higher education in the 1970s. She found no support for the hypothesis that new

establishments had increased the probability of enrollments of individuals from lower

socioeconomic backgrounds.

Erikson and Jonsson (2006) present an individual-educational-choice model, which they use

to analyze to what extent expanding higher education has reduced inequalities in education in

Sweden. They address three angles on the issue of higher education: how GPA from upper

secondary school and social origin affect higher education attainment; how increased access

has affected the association between social origin and the educational outcome of an

individual over the last century; and how these intergenerational associations were affected by

the higher educational reform in the late 1970s. The last question was divided into two parts.

First, how did the increased access affect individuals with different social origins? Second,

did the increased access to shorter, tertiary programs divert individuals from lower

socioeconomic groups to attend the shorter educational programs, instead of attending the

traditional, longer university educations?

To answer their questions, they used data from several registers, the population census for

several years and interviews with several students from several years, covering cohorts from

the period 1892-1970. Their key finding is that the association between parents and their

children has weakened during the second half of the twentieth century. Nonetheless, they state

that there may have been an increase in social mobility, but the political means of expanding

11 See also Broady, Börjesson and Palme (2002).

Page 34: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

10

and increasing access to higher education had a very modest equalizing affect on educational

opportunities in Sweden.

Comparisons between the political outcomes of how expansion has affected inequality in

Swedish educational opportunities and the outcome of similar expansion in other countries

should be made with caution, since educational systems differ among countries. Differences

in educational and financial aid systems give individuals different conditions in each country.

Therefore, the effect of the educational expansion and increased access may vary from

country to country. One of few examples is Rouse (1995) which could be taken as a good

comparison to the Swedish development. She analyzes the possible effect on higher

educational attainment of the implementation of several regional two-year colleges, as a

complement to four-year colleges, in the United States. Two-year colleges were established to

increase local access to higher education and were considered to influence more people from

lower social origins to consider investing in higher education, i.e. the democratization effect.

She also discusses how increased access to shorter tertiary educational tracks at the two-year

colleges nearby might divert able individuals from lower socioeconomic backgrounds to settle

for shorter educations instead of investing in a longer educational program at four-year

colleges. Even though she found some tendencies of a diversion effect of the two-year

colleges on higher educational investment, the overall democratization effect was so much

stronger, giving the overall expansion and increased educational access a positive effect on

increasing educational attainment in the United States.12

Although some examples from the United States indicate that an increased access raises the

democratization effect, most of the literature on the effect of expanding and increasing the

access to higher education in Sweden provides no or very modest support for its being an

effective means of educational equalization.

12 See Kane and Rouse (1999) for an overview of the higher educational system in the United States; see also Kane and Rouse (1995) and Leigh and Gill (2003, 2004) for more examples of research.

Page 35: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

11

4 Empirical Specification

This study applies a differences-in-differences methodology to investigate how higher

educational attendance choice behavior and school choice behavior for individuals living in

the geographical areas of the new universities differ from the Swedish population in general.13

This kind of methodology allows us to estimate differences in choice behaviors before and

after the expansion of higher education in Sweden during the 1990s. It also allows us to

estimate how individuals living in the areas of the new universities may differ in their choice

behavior from the rest of the population. The areas of the new universities will henceforth be

referred to as the areas of NEW.

The following latent variable specification is used both for modeling the individual propensity

for attending higher education and the propensity for new university colleges of diverting

students from attending old traditional universities:

(1) NEWuZA jijij 3210*

90*90* 210 DuDZYear jij

,210 90**90**90* ijjij DNEWuDNEWZDNEW

where *ijA is a latent variable measuring attendance to higher education, defined as:

(2)

,0

1 *

otherwise

cAifA ij

ij

where ijA is the binary outcome variable for student i living in county j that reveals if the

student attends higher education or not. c is a threshold, Zij is a vector of personal and family

background characteristics, Year is a vector of year dummies, NEW is a dummy variable for

living in a municipality where a new university started in the late 1970s or later, uj is the

county youth unemployment rate in the county labor market in which the individual lived at

the time of entrance, D90 is a dummy variable taking the value of zero for the years 1977-

1989, and unity for the years 1990-2001. ij is a random error term representing all omitted

variables that might affect individual choice behavior and it is assumed to be approximated by

a normal distribution.

13 See Angrist and Krueger (1999) for an overview of differences-in-differences methodology.

Page 36: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

12

The key policy parameters in Equation (1) are α3, γ0, γ1, and γ2. They all measure how

individual choice behavior changes in the 1990s and how this behavior differs for individuals

living in the areas of NEW compared to the top rest of the Swedish population.

4.1  The democratization effect of the expansion 

The effects of increased access to higher education, the democratization effects, are modeled

in a latent variable specification described in Equation (2). Here, the binary response variable

takes the value of 1 if an individual attends higher education, and zero otherwise. The control

group here is individuals who have never attended higher education.

According to standard human capital theory on intergenerational mobility, both educational

level and income level are to some extent to be transmitted across generations within families,

i.e. children from homes of more highly educated parents are more likely to attend higher

education themselves.14 The impact of individual and family characteristics (such as gender,

level of parental education, and parental income) on higher educational attendance in the

1980s is measured by α1.

More local access to higher education should make it possible for more people from different

social origins to attend universities without moving or making use of long distance

communications. This is a motivation for regionalization and educational equalization that to

a high extent motivated the rapid expansion, especially at the new universities in the 1990s.

The overall effects of increased access to universities on attendance behavior in the 1990s,

based on the same set of individual and family characteristics as before, are measured by β1.

The differences-in-differences effect of the individual and family characteristics on

attendance in the 1990s for those individuals who lived and grew up in the area of NEW, as

compared to the population in the rest of Sweden, is measured by γ1.

The county youth unemployment rate, uj, is included as a factor of a macro economic

externality. Besides parental influence, local youth unemployment is assumed to have a

positive influence on the educational investment choice by the youth. Poor job opportunities

should decrease the alternative cost of educations; see e.g. Freeman (1980) and Rouse (1995).

Yet it is not easy to make any assumptions about the effect of local youth unemployment in

14 See Becker and Tomes (1986) and Becker (1993) for an example, or Solon (1999) for an overview of the literature.

Page 37: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

13

the 1980s, measured in α2.15 Sweden enjoyed a huge economic boom in the 1980s, where

unemployment was to a very large extent due to individual characteristics, rather than a lack

of job opportunities. As for the 1990s, unemployment was very much caused by the sustained

loss of job opportunities due to the dramatic crisis in the early 1990s, an impact on higher

educational attendance that is represented by β2, and is assumed to be positive. A majority of

the areas of the new universities were the parts of Sweden most affected by the economic

crisis in the 1990s. Parameter γ2 measures how youths living and growing up in the area of

NEW were differently affected by unemployment in their region, as compared to Sweden as a

whole in the 1990s.

The difference in the propensity to attend higher education for individuals living in the areas

of NEW during the 1980s, as compared to the Swedish population in general, is measured by

α3. How this propensity changes due to the overall [är det vad du menar?] population in the

1990s is measured by γ0. Yearly changes in attendance from the years 1977-2001 are

measured by β0, using 1989 as a base year. Since the supply of student slots increases over

time and the impact of family background probably weakens, we should expect that trend of

attendance to stand out as positive. Since the dependent variable is binary, a probit model is

used to estimate the model of higher educational attendance.

4.2  The diversion effect of new university colleges 

To study how the new universities divert students from attending old universities, Equation

(1) is once more used. However, the binary response variable A now takes the value of 1 if an

individual attends an old university and zero otherwise. The control group here contains both

individuals who have never attended higher education and those attending a new university.

The possible diversion effect of the new universities in Sweden on higher educational

attendance from old universities in the 1980s, based on individual and family characteristics,

is measured by α1. How the increase of student slots at the new university colleges may affect

a diversion in the 1990s is measured by β1. However, the increased and improved educations

offered at the new universities in the 1990s should have attracted more youths from higher 15 Sweden was badly hurt by an economic crisis at the beginning of the 1980s. However, after the devaluation of the Swedish krona in 1982, the economy experienced a rapid recovery and the unemployment rate became historically small. If a person was unemployed in the late 1980s, the reason for this was most likely not a lack of job opportunities. Rather, to a considerable extent, it had to be looked for in individual characteristics. The labor market of the 1990s did not display such a corresponding characteristic. Rather, the 1990s became marked by a dramatic loss of job opportunities.

Page 38: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

14

socioeconomic groups to the new universities than before. This, in turn, would give a

contractive effect on the diversion in attendance between some socioeconomic groups.

Living in an expansive NEW area in the 1990s should have encouraged more individuals from

these areas to attend their local institutions of higher education (i.e. this should have an

expected negative effect on attendance at old universities). How the propensity to attend an

old university differs for youths living in the area of NEW from youths overall in Sweden,

based on individual and family characteristics, is measured in the differences-in-differences

coefficient γ1. The impact of just living in NEW in the 1980s, measured by α3, is expected to

have a negative effect on attendance at an old university. The expansion of higher education

in the 1990s, in which were the most exposed in the areas of NEW, is expected to manifest

itself in an even higher negative effect on attendance at old universities in the 1990s.

Finally, β0 measures if and how the trend in attendance changes over the years and whether

increased access to the new university colleges in the 1990s had a negative effect on

attendance at the old universities.

5 Data and Measurements

The empirical analysis is based on data from Longitudinal INdividual DAta for Sweden

(LINDA). LINDA is a random sample of approximately three percent of the population of

Sweden, where the information is based on income-tax registers, population censuses and

other register based data (see Edin and Fredriksson (2000) for a description). In addition to

the main subjects in LINDA, family members and cohabits that belong to the same household

as the main subjects are also included in the data. In total, the dataset contains information

regarding nearly one million individuals.

Information from the Swedish Higher Education Register has been added, which reveals if

and where any of the individuals in LINDA have attended an institution of higher education in

Sweden. This information is only available from 1977 and onwards, which gives this analysis

the natural starting point of 1977. The last year considered is 2001.

Page 39: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

15

5.1  The Sample 

At the age of 18, most youths in Sweden still live at home and attend the last year of upper

secondary school. Conditioning the 18-year-old to have at least one parent in LINDA allows

for intergenerational connections in the dataset.16 All in all, 299,944 18-year-olds divided into

25 cohorts were extracted from LINDA between the years 1977 and 2001, i.e. roughly 10

percent of the entire cohorts of 18-year-olds in Sweden for the same time period. Table 5.5

shows descriptive statistics of all cohorts. The sample of 18-year-olds extracted from LINDA

is either main subjects in LINDA (i.e. one of the randomly sampled three percent of the

Swedish population) or in the LINDA-data as a family member of a main subject. Due to this,

the amount of 18year-olds can exceed three percent of the year cohort.

Extracted information about the 18-year-olds includes gender and the geographical location of

the household in which the youths were registered at the age of 18, both at the county and the

municipality level. The only information available about the youths after the age of 18 is if

and where they attend higher education (at which university) before the age of 26.17

Family background information is based on information about the parents registered in

LINDA. The financial status of the household is represented by parental income, measured by

disposable income after taxes and received benefits. The nominal income of the household

has been transformed into a relative income of the household as compared to all households in

the year their youth turns 18.18 To have some indicator of whether the parents have economic

problems, two financial aid forms, social welfare and unemployment benefits, are included.

Both social welfare and unemployment benefits are transformed into dummy variables,

indicating if any of the parents received one or both financial aid forms during the year their

youth turned 18.

16 An intergenerational connection based on the multi-generation-register of Sweden Statistic over the population. 17 There has been an outspoken wish by policymakers to encourage young adults to attend higher education at an early age. In fact, the political goal is to encourage 50 percent of an age-cohort to enter higher education before the age of 26; see Government Bill (2000/01:72). In line with this goal, this study will be restricted to studying youth up to the age of 26. 18 In the two-parent household case, household income has been divided by 1.7 in order to compare the economical support capacity of a two-parent household to that of a one-parent household (see Björklund, Palme and Svensson (1995)).

Page 40: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

16

Table 5.1.  The sample of 18‐year‐olds in LINDA 1977‐2001. (The proportion of women in the sample is presented in parentheses.) 

Cohort

�1977 11,594 (0.47)  1,996 (0.52)  696 (0.69)  8,902 (0.44) 

1978 11,118 (0.47)  1,929 (0.52)  686 (0.67)  8,503 (0.45) 

1979 11,541 (0.47)  2,001 (0.53)  718 (0.65)  8,822 (0.45) 

1980 11,554 (0.47)  1,952 (0.53)  741 (0.61)  8,861 (0.44) 

1981 12,157 (0.48)  2,204 (0.54)  769 (0.64)  9,184 (0.45) 

1982 12,804 (0.48)  2,281 (0.54)  883 (0.63)  9,640 (0.46) 

1983 13,217 (0.48)  2,262 (0.54)  935 (0.62)  10,020 (0.45) 

1984 13,254 (0.48)  2,284 (0.54)  1,006 (0.62)  9,964 (0.45) 

1985 13,110 (0.48)  2,299 (0.55)  996 (0.60)  9,815 (0.46) 

1986 12,285 (0.47)  2,211 (0.53)  1,027 (0.58)  9,047 (0.45) 

1987 11,601 (0.48)  2,201 (0.55)  1,164 (0.55)  8,236 (0.45) 

1988 11,978 (0.47)  2,388 (0.54)  1,283 (0.51)  8,307 (0.45) 

1989 12,253 (0.48)  2,650 (0.54)  1,481 (0.53)  8,122 (0.45) 

1990 11,906 (0.48)  2,754 (0.54)  1,683 (0.55)  7,469 (0.45) 

1991 12,240 (0.47)  2,982 (0.54)  1,922 (0.54)  7,336 (0.42) 

1992 12,550 (0.48)  3,138 (0.55)  2,064 (0.54)  7,348 (0.43) 

1993 11,943 (0.48)  3,021 (0.55)  2,067 (0.54)  6,855 (0.43) 

1994 11,691 (0.47)  3,057 (0.54)  2,113 (0.55)  6,521 (0.41) 

1995 11,508 (0.48)  2,740 (0.55)  2,012 (0.55)  6,756 (0.43) 

1996 11,167 (0.48)  2,479 (0.55)  1,899 (0.55)  6,789 (0.43) 

1997 11,505 (0.48)  2,393 (0.56)  1,745 (0.56)  7,367 (0.43) 

1998 11,647 (0.48)  2,160 (0.57)  1,573 (0.58)  7,914 (0.44) 

1999 11,493 (0.48)  1,641 (0.55)  1,015 (0.61)  8,837 (0.45) 

2000 11,567 (0.48)  707 (0.54)  477 (0.65)  10,383 (0.479 

2001 12,261 (0.48)  22 (0.59)  4 (0.75)  12,232 (0.48) 

N  299,944 (0.47)  55,752 (0.54)  30,959 (0.57)  213,230 (0.45) 

Number of

observations

Attends

OLD*

Attends

NEW*

Control

group

 Note:  *Attendance  is divided  into  attendance  at  an old university  and  attendance  at  a new university.  The control group is the 18‐year‐olds who never attended a Swedish university before the age of 26. The majority of  the  attending  youths  in  the  sample  attend  higher  education  between  ages  20‐22.  It may  appear  that attendance decreases for the cohorts after 1994. However, this is more due to the fact that the study period of this paper ends in 2001, so the cohorts after 1994 are not followed up to the age of 26. 

Moreover, the parents’ highest educational level is also included, divided into four

educational levels:19 compulsory school of a maximum of nine years, upper secondary school,

19 The educational history is based on the SUN-code and it is a standard used in classifying individual educational programs (see MIS (2000)). In this study, all forms of compulsory schooling have been merged into one level of elementary education, the same goes for upper secondary school. In case the parents have a university degree as their highest education, they are separated into two categories; one in which the degree is taken after passing a shorter higher educational program (shorter than three years), and one in which the degree is worth three years or more of higher education. I have kept the two university levels separated due to the fact

Page 41: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

17

a shorter university education of less than three years and a longer university education of

three years or more.

County youth unemployment rates have been attached to this data in order to incorporate the

possibility of external influences in this analysis. The youth unemployment rate corresponds

to the year and county in which the individual lived at the age of 18. All explanatory variables

are presented in the Appendix.

5.2  Descriptive Statistics 

In the descriptive statistics regarding the explanatory variables presented in Table 5.2, we can

see that the sample of 18-year-olds is presented in three panels, showing the mean value of all

explanatory variables of the cohorts summarized into 1980s and 1990s, respectively.

The first panel in Table 5.2 shows the entire sample of 18-year-olds used in this analysis. We

can see that the data contains slightly more men than women. Comparing the mean values of

educational levels of the parents in the 1980s with the values of the 1990s, the share of

parents only having compulsory schooling declined by roughly 12-16 percentage points. The

share with upper secondary schooling increases, as does the share of parents with one of the

two university levels.

In terms of family finances, relative income has increased over time. Sweden went through a

turbulent macroeconomic period in the early 1990s with high unemployment and low

economic growth. That households were affected by harsh financial times in the 1990s can be

seen by there being an increase in the amount of households receiving social welfare or

unemployment benefits or both.

In the second panel of Table 5.2, the youths attending higher education before the age of 26

are compared to youths never attending higher education, for each decade respectively. In

section three, we can compare youths who attend an old university with youths who attend a

new university, for each decade respectively.

that they give a relatively good signal of what sort of university education the parents have. As briefly mentioned in the policy section, numerous vocational educations (with less than three years of duration) received university status in the late 1970s. Longer university educations (i.e. three years or more) are dominated by traditional university degrees in law, medicine and art.

Page 42: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

18

As can be seen in both Tables 5.1 and 5.2, women are in the majority among the youths who

attended higher education. This result is totally in line with the overall trend in Sweden for the

same time period.

Furthermore, we can see that the parents of attending youths are, on average, more highly

educated than the parents of non-attending youths. Those who attend an old university on

average have more highly educated parents than those who attend a new university. The

overall educational level of parents rose in the 1990s. An intergenerational pattern can be

traced from the 1980s into the 1990s: youths who never attend higher education are those

who, on average, have the lowest educated parents, followed by the parents of youths that

attend a new university.

Patterns can also be traced in the variables of the household economy. The households of

youths who never attend higher education on average have the lowest incomes. Family

finances are, on average, lower in households whose 18-year-olds attend a new university, as

compared to households whose youths attend an old university.

Page 43: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table 5.2 

Descriptive statistics: the sam

ple of 18‐year‐olds 

The table is divided

 into three sections: the first describes the m

ean values of all characteristics of the entire sam

ple, also divided

 into 1980s and 1990s; the second section describes the 

characteristics of the youths who Attend higher education in

 the 1980s or 1990s, compared to youths who never atten

d higher education (the Control group); and the third section describes 

the characteristics of the youth who atten

d an Old university and those atten

ding a New

 university, also divided

 into the 1980s and 1990s. 

All

1980s

1990s

1980s

1990s

1980s

1990s

Attent

Control

Attent

Control

Old

New

Old

New

Mean

Mean

Mean

Mean

Mean

Mean

Mean

Mean

Mean

Mean

Mean

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

(Std.dev)

The 18‐year‐old:

Female 

0.48 

0.47 

0.48 

0.57 

0.45 

0.54 

0.44 

0.54 

0.63 

0.54 

0.55

(0.50) 

(0.50) 

(0.50) 

(0.50) 

(0.50) 

(0.50) 

(0.50) 

(0.50) 

(0.48) 

(0.50) 

(0.50)

Living in NEW 

0.22 

0.21 

0.22 

0.23 

0.21 

0.24 

0.20 

0.18 

0.38 

0.15 

0.37

(0.41) 

(0.41) 

(0.41) 

(0.42) 

(0.41) 

(0.43) 

(0.40) 

(0.38) 

(0.49) 

(0.36) 

(0.48)

Father’s education:

Compulsory 

0.41 

0.47 

0.35 

0.28 

0.52 

0.21 

0.45 

0.24 

0.37 

0.17 

0.26

(0.49) 

(0.50) 

(0.48) 

(0.45) 

(0.50) 

(0.41) 

(0.50) 

(0.43) 

(0.48) 

(0.38) 

(0.44)

Upper secondary 

0.39 

0.38 

0.40 

0.40 

0.38 

0.39 

0.41 

0.40 

0.40 

0.36 

0.43

(0.49) 

(0.49) 

(0.49) 

(0.49) 

(0.48) 

(0.49)

(0.49) 

(0.49) 

(0.49) 

(0.48) 

(0.50)

University < 3 years 

0.08 

0.06 

0.09 

0.10 

0.05 

0.13 

0.07 

0.10 

0.09 

0.13 

0.13

(0.27) 

(0.24) 

(0.29) 

(0.30) 

(0.22) 

(0.33) 

(0.25) 

(0.30) 

(0.29) 

(0.33) 

(0.33)

University ≥ 3 years 

0.12 

0.09 

0.15 

0.22 

0.05 

0.27 

0.07 

0.26 

0.14 

0.33 

0.18

(0.32) 

(0.28)

(0.36) 

(0.42) 

(0.21) 

(0.44) 

(0.25) 

(0.44) 

(0.34) 

(0.47) 

(0.38)

Mother’s education:

Compulsory 

0.40 

0.48 

0.32 

0.31 

0.52 

0.17 

0.41 

0.29 

0.38 

0.15 

0.20

(0.49) 

(0.50) 

(0.47) 

(0.46) 

(0.50) 

(0.38) 

(0.49) 

(0.45) 

(0.48) 

(0.36) 

(0.40)

Upper secondary 

0.40 

0.38 

0.42 

0.36 

0.38 

0.40 

0.44 

0.35 

0.39 

0.36 

0.45

(0.49) 

(0.48) 

(0.49) 

(0.48) 

(0.49) 

(0.49) 

(0.50) 

(0.48) 

(0.49) 

(0.48) 

(0.50)

University < 3 years 

0.10 

0.08 

0.13 

0.14 

0.06 

0.19 

0.09 

0.15 

0.12 

0.20 

0.18

(0.30)

(0.27) 

(0.34) 

(0.35) 

(0.24) 

(0.39) 

(0.29) 

(0.36) 

(0.32) 

(0.40) 

(0.39)

University ≥ 3 years 

0.09 

0.07 

0.12 

0.18 

0.04 

0.23 

0.06 

0.20 

0.11 

0.28 

0.16

(0.29) 

(0.25) 

(0.33) 

(0.38) 

(0.19) 

(0.42) 

(0.23) 

(0.40) 

(0.32) 

(0.45) 

(0.37)

Economy of

the household:

Income A 

1.04 

1.03 

1.05 

1.20 

0.99 

1.15 

0.99 

1.24 

1.10 

1.20 

1.06

(0.99) 

(0.49) 

(1.31) 

(0.54) 

(0.46) 

(0.86) 

(1.51) 

(0.60) 

(0.35) 

(0.99) 

(0.62)

Social welfare 

0.05 

0.03 

0.06 

0.01 

0.04 

0.03 

0.09 

0.01 

0.01 

0.03 

0.03

(0.21) 

(0.17) 

(0.24) 

(0.08) 

(0.19) 

(0.16) 

(0.28) 

(0.07) 

(0.09) 

(0.16) 

(0.16)

Unemployment benefit 

0.10 

0.06 

0.14 

0.04 

0.07 

0.11 

0.17 

0.03 

0.06 

0.09 

0.13

(0.30) 

(0.24) 

(0.35) 

(0.19) 

(0.25) 

(0.31) 

(0.37) 

(0.17) 

(0.23) 

(0.29) 

(0.33)

Single parent

0.24

0.22

0.26 

0.14

0.24

0.19

0.30

0.15 

0.13 

0.20

0.18

(0.42) 

(0.42) 

(0.44) 

(0.35) 

(0.42) 

(0.39) 

(0.46) 

(0.35) 

(0.33) 

(0.39) 

(0.38)

Macro variable:

Youth unemployment 

4.10 

2.54 

5.60 

2.60 

2.52 

5.39 

5.72 

2.48 

2.92 

5.11 

5.77

(2.55) 

(1.10) 

(2.63) 

(1.09) 

(1.11) 

(2.88) 

(2.47) 

(1.05) 

(1.13) 

(2.85) 

(2.88)

Number of

Observations: 

299,944

147,001

153,943

29,578

117,423

57,133

95,810

21,142

8,436

34,634

22,549

Note:  A) Household income is defined

 as relative net‐income the year the youth turns 18. See the Appendix for a description. The case of a one‐paren

t‐household is considered

 in the 

estimation. This is presented in

 the Appendix.   

Page 44: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

6 Results

The main results are presented in Tables 6.1 and 6.2.20 The first panel of each table tells us

how the covariates affect the probabilities of attending higher education in the 1980s; the

second panel tells us how the marginal effect of the explanatory variables changed in its

influence on this probability in the 1990s. To see how the impact of the covariates in the

model may differ depending on where in Sweden the youths grew up, the model is estimated

with and without interaction with a dummy variable NEW, in the tables referred to as the Base

model and the Model with interactions. The results in the third panel in both tables tell us how

the differences-in-differences in the marginal effect of the explanatory variables of individuals

who grew up in the area of NEW differ from those of youths who grew up elsewhere in

Sweden in the 1990s. All results are transformed into marginal effects, and the marginal

effects are to be read additively from panel to panel.21

6.1  The democratization effect of the expansion 

The democratization effect is here measured by estimating how the probability of attending

higher education in general has changed due to the large expansion in the 1990s. As described

in the brief history of the higher educational sector in Sweden, no major changes occurred in

the 1980s – which could explain why the effect of growing up in the area of NEW in the

1980s on the probability of attending higher education, as shown in Table 6.1, had no

significant impact. In the 1990s, however, the effect of NEW on the probability of university

attendance tells us a somewhat different story. In the Base model, where we do not consider

any interaction between individual and family covariates with the dummy variable NEW, the

impact of living in the same municipally as a rapidly expanding university shows an average

positive effect of roughly six percentage points in Sweden. The difference-in difference 20 The outcome of the estimated models rests upon the assumption that all 18-year-olds who desire to attend higher education before the age of 26 are qualified to do so. Through the Act of Higher Education of 1977, alternative ways of qualifying for higher education (besides having a degree from upper secondary school – which is the most frequent way of qualifying for higher education) were introduced; a scholastic aptitude test was introduced, offering a chance of qualifying for higher education for applicants with poor degrees, or for applicants who were 25 years or older, with more than four years of work experience, work experience could be accounted for as a qualifications (see Kim (1998)). Moreover, according to Statistic Sweden, roughly 80-85% of the cohorts before 1990 had upper secondary school qualifications at the age of 26 and, correspondingly, 90% of the cohorts from the 1990s had upper secondary school qualifications. For adults who did not attend or perhaps did not finish upper secondary school in their early youth, Sweden had and still has very generous possibilities of attending local adult education (”komvux”) to help them receive the qualifications of upper secondary school. Those attending local adult education were also given financial support by the government. All in all, this strengthens the assumption about the university qualifications of 18-year-olds made above. 21 Complete regressions from both models are presented in the Appendix.

Page 45: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

21

results of living in the area of a new university, showed in the Model with interaction, show

that living in the area of NEW has a local average effect of about 41 percentage points in the

1990s. Compared to the impacts of any other observables accounted for in the model, this is

the largest by far. Thus, increased local access has a greater impact on encouraging youths

(especially men from low educated parents) to enter higher education overall than what is the

impact of their socioeconomic background or local labor market conditions.

In the 1980s, it only made a small difference whether the parents had elementary or upper

secondary schooling as their highest education for their youth’s probability of enrolling in

higher education. However, if the parents had lower university degrees, it had a considerable

larger positive impact on the probability of attending higher education. The impact rose even

more if the parents had a higher university degree. This indicates that intergenerational

mobility (i.e. that students attend more and higher education as compared to their parents) in

Sweden seems to have been very low in the 1980s. This is in line with previous findings by

Erikson and Jonsson (1993, 1996) and Dryler (1998). Notably, the impact of the mother’s

education on the probability of her child attending higher education is significantly different

and larger as compared to the impact of the father’s education. These findings are comparable

to those of Currie and Moretti (2003) when analyzing the intergenerational transmission

power of education and health between a mother and her child.

The impact of parental education changed somewhat in the 1990s. All levels above

compulsory schooling as the highest level of schooling for the parents became more

important. The probability of attending higher education tripled for youths whose parents had

upper secondary schooling as their highest education, as compared to those whose parents

only had compulsory schooling as their highest education. This might indicate that the

increased number of student slots in the 1990s has improved the intergenerational educational

mobility for at least one of two groups of youths from less educated parents. The impact of

high-educated parents on their youths’ educational choices grew even stronger in the 1990s,

thus to a smaller extent than for parents with a somewhat lower education.

The differences-in-differences effect of living in the area of NEW, presented in the third panel

in Table 6.1 under the Model with interaction, all interacted covariates show a negative effect.

But remember that the marginal effects are to be read additively through panels, meaning that

the negative values tell us that the impact of individual and parental covariates and local youth

Page 46: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

22

unemployment has a smaller, yet overall positive, impact on the probability of university

attendance among youths who grew up in the area of NEW, as compared to youths who grew

up elsewhere in Sweden in the 1990s.22

In the 1980s, most income factors seem to affect higher educational attendance, as could be

expected. If any of the parents in the household received social welfare, the impact on the

attendance probabilities were strongly negative. If any or both of the parents received

unemployment benefits, this had a slightly negative effect on attendance. The 1990s was a

turbulent macro economic decade, however. There was a dramatic increase in the rate of

unemployment and more people were declared as poor than ever before in modern time. This

may account for why social welfare and unemployment benefits became more or less

naturalized in the 1990s (i.e. circled around a zero effect). Parental income, on the other hand,

rose from a small negative income effect on the probability of attending higher education in

the 1980s to a small positive effect in the 1990s. The differences-in-differences effect of

household income for youth living in the areas of NEW is almost zero.

According to the descriptive data in Table 5.2, more women than men attended higher

education, both in the 1980s and the 1990s. The marginal effects on higher educational

attendance indicate that the probability of attending higher education is larger for women than

men. In fact, the probability of attending higher education for women tripled in the 1990s as

compared to the 1980s. For individuals in the areas of NEW, the increase . doubled from the

1980s. This means that the expansion of the new universities affected males who grew up in

the area of NEW to a larger extent than the rest of the country.

The marginal effect of a macro externality, here represented by county youth unemployment

rates, on the probability of attending higher education is negative in the 1980s. A possible

explanation for this somewhat odd result could be that the existing unemployment during the

booming economic cycle Sweden in the 1980s was, to a relatively large extent, probably

caused by individual characteristics rather than a lack of job opportunities. In the 1990s, on

the contrary, much of the observed high unemployment was caused by the sustained loss of

job opportunities due to the dramatic crisis in the early 1990s. Now, the local youth 22 For instance, the probability of a youth whose mother only had upper secondary school attending higher education in the 1980s was 2.1 percentage points. In the 1990s, the corresponding probability was (2.1) + (14.9) = 17 percentage points and (2.1) + (14.9) + (– 7.6) = 9.4 percentage points if the youth lived in NEW in the 1990s.

Page 47: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

23

unemployment rate has a positive marginal effect of roughly three percentage points on the

probability of attending higher education. In the area of NEW, this local effect is roughly two

percentage points lower, i.e. about one percentage point.

To illustrate how the chances of attending higher education changed in the 1980s to the 1990s

for various socioeconomic groups in Sweden, four groups of youths are illustrated in Figure

6.1a for Sweden and Figure 6.1b for youth living in the municipalities of NEW. The figures

illustrate the changes in the probability of youths attending higher education based on the

mean characteristics of the parents, given that they have the same highest educational level.23

It is clearly shown in both figures that all groups, except one, have considerably increased

their probabilities of attending higher education in the 1990s. The exception is youths whose

parents have elementary education as their highest education. According to the presented

results, their situation has become worse. Yet is notable that this lower socioeconomic group

has become a more selective group during the 1990s than what was previously the case. This

can be seen in the descriptive statistics presented in earlier sections of this paper.

23 Alternative combinations of household incomes and educational levels by the parents show similar developments as what is shown in Figures 6.1a and 6.1b.

Page 48: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

24

Table 6.1  Democratization Effects The table shows the probability of attending higher education  in Sweden  in the 1980s, changes  in the 1990s, and  the differences‐in‐differences  in  change  for  those  living  in NEW  in  the  1990s,  as  compared  to  Sweden overall. The effects are presented as marginal effects. 

Variables Model with interaction

Std. dev.  Std. dev. 

Effects on democratization: From living in NEW   0.008 0.014 0.014 0.013

Female   0.050 *** 0.004 0.053 *** 0.005

Upper secondary~father 0.041 *** 0.004 0.044 *** 0.004

University < 3 years~father   0.145 *** 0.008 0.147 *** 0.008

University ≥ 3 years~father   0.295 *** 0.009 0.295 *** 0.008

Upper secondary~mother  0.018 *** 0.004 0.021 *** 0.004

University < 3 years~mother    0.167 *** 0.007 0.169 *** 0.007

University ≥ 3 years~mother    0.301 *** 0.010 0.301 *** 0.009

Household income   ‐0.044 *** 0.009 ‐0.033 *** 0.008

Social welfare   ‐0.163 *** 0.007 ‐0.160 *** 0.007

Unemployment benefit   ‐0.059 *** 0.006 ‐0.058 *** 0.006

Single parent ‐0.090 *** 0.005 ‐0.086 *** 0.005

Youth unemployment   ‐0.055 *** 0.007 ‐0.050 *** 0.006

Changes in the 1990s:From living in NEW   0.056 *** 0.020 0.412 *** 0.026

Female   0.094 *** 0.005 0.103 *** 0.005

Upper secondary~father 0.107 *** 0.007 0.118 *** 0.007

University < 3 years~father   0.101 *** 0.010 0.118 *** 0.009

University ≥ 3 years~father   0.047 *** 0.009 0.066 *** 0.010

Upper secondary~mother  0.133 *** 0.007 0.149 *** 0.007

University < 3 years~mother    0.111 *** 0.008 0.128 *** 0.008

University ≥ 3 years~mother    0.079 *** 0.009 0.096 *** 0.010

Household income   0.070 *** 0.009 0.064 *** 0.008

Social welfare   0.132 *** 0.016 0.129 *** 0.017

Unemployment benefit   0.052 *** 0.008 0.051 *** 0.008

Single parent 0.154 *** 0.010 0.165 *** 0.011

Youth unemployment   0.088 *** 0.009 0.088 *** 0.008

Interacted effects; Changes in the 1990s and of living in NEW: Female   ‐0.050 *** 0.007

Upper secondary~father ‐0.064 *** 0.008

University < 3 years~father   ‐0.068 *** 0.010

University ≥ 3 years~father   ‐0.070 *** 0.010

Upper secondary~mother  ‐0.076 *** 0.008

University < 3 years~mother    ‐0.071 *** 0.009

University ≥ 3 years~mother    ‐0.069 *** 0.009

Household income   ‐0.025 *** 0.005

Social welfare   ‐0.021 0.015

Unemployment benefit   ‐0.004 0.009

Single parent ‐0.076 *** 0.010

Youth unemployment   ‐0.022 *** 0.002

Base model

Marg. eff. Marg. eff. 

Note:  The  standard  errors  are  robust  and  are  adjusted  by  283  clusters  –  equivalent  to  the municipalities accounted  for  in the paper. ***, **, * denote significance at 1, 5, and 10 percent, respectively. The cases of one‐parent and two‐parent households are considered  in the estimations. Not presented  in this paper, some other variables have been considered and analyzed as well; e.g. the municipalities adjoining the municipalities of  the new universities have been  included  in  the dummy NEW,  resulting  in  somewhat weaker  results  than those presented here;  variables of  family  and  individual wealth have  also been  estimated  –    showing non‐significant  results;  kilometer  distances  to  the  nearest  old  university  and  the  nearest  new  university  gave significant estimations – but the marginal effect was extremely small. 

Page 49: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

25

Figure 6.1a  The probability of attending higher education in the 1980s and 1990s. 

0

10

20

30

40

50

60

70

80

90

100

1980s 1990s

Perc

ent

ED1 ED2 ED3 ED4 Figure 6.1b  The probability of attending higher education in municipalities of NEW 

0

10

20

30

40

50

60

70

80

90

100

1980s 1990s

Perc

ent

ED1 ED2 ED3 ED4 

Note: The probabilities shown in Figure 6.1a and Figure 6.1b. are based on the average value of the covariates given  the highest educational  level of  the parents. ED1 = elementary, ED3= upper  secondary, ED3=  shorter tertiary education, ED4 = longer tertiary education. 

 

Page 50: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

26

6.2  The diversion effect of new universities   

The diversion effect is here measured by estimating how the probability of attending an old

university changed due to the huge expansion of the higher educational sector in Sweden in

the 1990s.

Let us initially consider the Base model, where the covariates are not interacted with the

variable NEW. In the 1980s, the chances of attending an old university for those youths that

grew up in the area of the new universities were about 4 percentage points lower as compared

to youths who lived elsewhere in Sweden. A possible explanation for this is that the

regionalization effect of higher education in the late 1970s did succeed in encouraging youths

in the areas of NEW to consider attending higher education in their region instead of moving.

This could, of course, be interpreted as the new universities diverting the youths in their areas

from attending an old university, i.e. a longer university education. Considering the massive

expansion of the universities in the area of NEW in the 1990s (of which some grew by 400

percent), the probability of attending an old university in the 1990s only fell by 0.4 percentage

points. Yet, living in the area of NEW has an average negative effect for attending an old

university, indicating that we can see a small diversion effect among individuals living in the

area of NEW.

Looking at the impact of parental education on the probability of attending an old university,

the effects are pretty much what could be expected. The higher is parental education in the

1980s, the higher is the probability of their children attending an old university – all compared

to youths whose parents only have elementary education as their highest education. Even

though the parental impact on the probability of attending an old university grew stronger in

the 1990s, the relatively largest increase is among students whose parents have upper

secondary education as their highest education. In some sense, this result contradicts the

diversion effect, since at least one of two low socioeconomic groups did respond positively to

the overall expansion of higher education in Sweden.

Turning to the Model with interaction, the story becomes slightly different, but only for the

effects in the 1990s. The marginal effects of the covariates in the 1980s are similar to those in

the Base model. Allowing the covariates to interact with the dummy variable NEW, however,

shows that the impact of living in the area of NEW goes from a small negative effect to a

small positive effect in the 1990s. This does not necessarily mean that the local institutions

Page 51: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

27

failed to absorb the youths in their region. In fact, it could just be the other way around. Local

access to higher education has most likely decreased the social distance to higher education

overall, meaning that the option of attending higher education has become a more common

and possible alternative for more socioeconomic groups in society. The fact that more

individuals chose to attend higher education further away in the 1990s among those living in

the area of NEW, could be interpreted as the choice to move being considered less costly (a

smaller risk) once the decision to attend higher education at all has been made.

Compared to the youths whose parents only have compulsory education as their highest

education, the trend in the 1990s seems to be that there is an overall small positive significant

increase in the probability of going to an old university among all other groups. Once more,

the overall largest relative increase is among students whose parents only have upper

secondary education as their highest education.

For youths growing up in the area of NEW, however, the differences-in-differences effect

presented in the third panel does not indicate that the expansion of the new universities has

caused (or increased) a diversion among socioeconomic groups in Sweden. In fact, the

increased local access in the area of NEW seems to have weakened the importance of family

background across all socioeconomic groups in these regions for attending an old university.

Page 52: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

28

Table 6.2   The Diversion Effects 

The table shows the probability of attending an old university  in Sweden  in the 1980s, changes  in the 1990s, and the differences‐in‐differences in the change for those living in NEW in the 1990s, as compared to Sweden overall. The effects are presented as marginal effects. 

Variables Model with interaction

Std. dev.  Std. dev. 

Effects on diversion: From living in NEW   ‐0.043 ** 0.018 ‐0.039 ** 0.017

Female   0.020 *** 0.003 0.023 *** 0.003

Upper secondary~father 0.041 *** 0.003 0.044 *** 0.003

University < 3 years~father   0.116 *** 0.008 0.119 *** 0.008

University ≥ 3 years~father   0.247 *** 0.010 0.249 *** 0.010

Upper secondary~mother  0.015 *** 0.003 0.017 *** 0.003

University < 3 years~mother    0.126 *** 0.006 0.128 *** 0.006

University ≥ 3 years~mother    0.232 *** 0.010 0.233 *** 0.010

Household income   ‐0.002 0.003 0.002 0.003

Social welfare   ‐0.100 *** 0.005 ‐0.098 *** 0.005

Unemployment benefit   ‐0.046 *** 0.005 ‐0.045 *** 0.005

Single parent ‐0.038 *** 0.004 ‐0.035 *** 0.005

Youth unemployment   ‐0.048 *** 0.005 ‐0.044 *** 0.005

Changes in the 1990s:From living in NEW   ‐0.004 *** 0.029 0.058 *** 0.004

Female   0.052 *** 0.004 0.063 *** 0.006

Upper secondary~father 0.057 *** 0.006 0.060 *** 0.009

University < 3 years~father   0.051 *** 0.009 0.037 *** 0.007

University ≥ 3 years~father   0.025 *** 0.007 0.080 *** 0.007

Upper secondary~mother  0.075 *** 0.006 0.060 *** 0.009

University < 3 years~mother    0.052 *** 0.008 0.048 *** 0.008

University ≥ 3 years~mother    0.041 *** 0.007 0.013 *** 0.003

Household income   0.014 *** 0.003 0.108 *** 0.014

Social welfare   0.121 *** 0.014 0.038 *** 0.007

Unemployment benefit   0.037 *** 0.007 0.119 *** 0.010

Single parent 0.116 *** 0.009 0.047 *** 0.005

Youth unemployment   0.049 *** 0.006 0.197 *** 0.053

Interacted effects; Changes in the 1990s and of living in NEW: Female   ‐0.033 *** 0.005

Upper secondary~father ‐0.048 *** 0.008

University < 3 years~father   ‐0.049 *** 0.007

University ≥ 3 years~father   ‐0.052 *** 0.007

Upper secondary~mother  ‐0.041 *** 0.006

University < 3 years~mother    ‐0.045 *** 0.007

University ≥ 3 years~mother    ‐0.043 *** 0.008

Household income   ‐0.010 *** 0.003

Social welfare   0.014 0.011

Unemployment benefit   ‐0.008 0.008

Single parent ‐0.045 *** 0.014

Youth unemployment   ‐0.009 *** 0.002

Base model

Marg. eff.  Marg. eff. 

Note: The standard errors are robust and adjusted by 283 clusters – equivalent to the municipalities accounted for  in the paper. ***, **, * denote significance at 1, 5, and 10 percent, respectively. The cases of one‐parent and  two‐parent  households  are  considered  in  the  estimations.  Not  presented  in  this  paper,  some  other variables have been considered and analyzed  as well; e.g. the municipalities adjoining the municipalities of the new universities have been  included  in  the dummy NEW,  resulting  in  somewhat weaker  results  than  those presented here; variables of family and individual wealth have also been estimated –  showing non‐significant results.   

Page 53: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

29

7 Conclusions and Discussions

Is the expansion of higher education a successful political means of decreasing educational

inequalities? The results here show that living and growing up in the same area as a rapidly

growing educational institution seems to have a strong positive effect on overall university

attendance. With a positive average marginal effect of six percentage points in Sweden, this

result supports the hypothesis that nearby-easy access encourages more people to attend

higher education (the local average effect is even higher, 41 percentage points). Local access

to higher education has most likely decreased the social distance to higher education,

meaning that the option of attending higher education rather than entering the local labor

market after upper secondary school has become a more common and possible alternative for

more socioeconomic groups in society. The fact that more individuals chose to attend higher

education further away in the 1990s among those living in the area of NEW, could be

interpreted as the choice of moving being considered less costly (a smaller risk) once the

decision to attend higher education at all has been made.

The relatively largest growth occurred among students whose parents had upper secondary

school as their highest education, which can be seen as some indications of political success

in equalizing educational opportunities. On the other hand, for youths whose parents only had

elementary education as their highest education and where the family overall had a low family

income – their probabilities of entering higher education seem to have become even lower in

the 1990s than before. This results can, however, be attributed to the fact that over the 25

years accounted for in this study, this group has become increasingly selective, as parental

generations have become increasingly educated over time.

References af Trolle, Ulf (1990), Mot en internationellt konkurrenskraftig AKADEMISK UTBILDNING,

Lund: Studentlitteratur. Angrist, Joshua and Allan B. Krueger (1999), Empirical Strategies in Labor Economic, in

Handbook in Labor Economics, ed. Orley C. Ashenfelter and David Card. Vol. 3A Amsterdam: North-Holland: Elsevier Science Publishers.

Becker, Gary S. (1964[1993]), Human Capital, 3 ed. Chicago: University of Chicago Press. Becker, Gary S. and Nigel Tomes (1979), An Equilibrium Theory of the Distribution of

Income and Intergenerational Mobility, Journal of Political Economy, 87:1153–89.

Page 54: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

30

--- (1986), Human Capital and Rise and Fall of the Families, Journal of Labor Economics, 4(3):1-39.

Björklund, Anders, Mårten Palme and Ingemar Svensson (1995), Tax Reforms and Income Distribution: An Assessment Using Different Income Concepts, Swedish Economic Policy Review, 2:229–266.

Björklund, Anders and Mikael Jäntti (1997), Intergenerational Income Mobility in Sweden Compared to the United States, American Economic Review, 87:1009–1018.

Björklund, Anders, Mikael Lindahl and Krister Sund (2003), Family background and school performance during a turbulent era of school reforms, Swedish Economic Policy Review 10(2):111–136.

Brint, Steven and Jerome Karabel (1989), The Diverted Dream: Community Colleges and the Promise of Educational Opportunity in America 1900-1985, New York: Oxford University Press.

Broady, Donald, Mikael Börjesson and Mikael Palme (2002), Det svenska högskolefältet under 1990-talet: Den sociala snedrekryteringen och konkurrensen mellan lärosäten, in Perspektiv på högskolan - i ett förändrat Sverige, Stockholm: Högskoleverket.

Currie, Janet and Enrico Moretti (2003), Mother’s education and the intergenerational transmission of human capital: Evidence from college openings, The Quarterly Journal of Economics, 118(4): 1495-1532.

Dryler, Helen (1998). Educational Choice in Sweden: Studies on the Importance of Gender and Social Context, Stockholm University, Stockholm: Swedish Institute for Social Research No 31.

Dryler, Helene (1994), Etablering av nya högskolor - ett medel för minskad snedrekrytering, in Skola och Sortering - Studier av snedrekrytering och utbildningens konsekvenser, ed. Robert Eriksson and Jan O. Jonsson. Stockholm: Carlssons Förlag.

Currie, Janet and Enrico Moretti (2003), Mother’s education and the intergenerational transmission of human capital, The Quarterly Journal of Economics, 1998(4):1495-1523.

Edin, Per-Anders and Peter Fredriksson (2000), LINDA - Longitudinal INdividual DAta for Sweden, Working paper 2000:19, Uppsala, Sweden: Department of Economics, Uppsala University.

Erikson, Robert and Jan O. Jonsson (1993), Ursprung och utbildning, in SOU:1993:85, Stockholm: Fritzes.

Erikson, Robert and Jan O. Jonsson (1994), Sortering i skolan, Stockholm: Carlssons Bokförlag.

Erikson, Robert and Jan O. Jonsson (1996), Can Education be Equalized: The Swedish Case in Comparative Perspective, Boulder and Oxford: Westview Press, Social Inequality Series.

Erikson, Robert and Jan O. Jonsson (2007), Why educational expansion is not such a great strategy for equality: Theory and evidence for Sweden, in Stratification in Higher Education, ed. Adam Gamoran Yossi Shavit, Tichard T. Aurum and Gila Menahem. Stanford, CA: Stanford University Press.

Fasth, Eva (1980), Aspects on Relocalization of Higher Education. Göteborg: UHÄ National Board of Universities and Colleges.

Freeman, Richard B. (1980), The Facts about the Declining Economic Value of College, Journal of Human Resources, 15:124–142.

Government-bill (2000), Vuxnas lärande och utvecklingen av vuxenutbildningen. Stockholm: Proposition 2000/01:72.

Page 55: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

31

Hammarström, Margareta (1996), Varför inte Högskola?, Göteborg: Universitatis Gothoburgensis.

Holm, Einar and Nils Häggström (1972), Högre utbildning - regional rekrytering och samhällsekonomiska kalkyler, SOU:1972:23, Stockholm: Fritzes.

HSV (1998), The Current Swedish Model of University Governamnce - Background and Description, Raport: 1998:10S, Stockholm: National Agency for Higher Education.

Kane, Thomas J. and Cecilia E. Rouse (1995), Labor Market Return to Two- and Four Year College, American Economic Review, 85(3):600–14.

--- (1999), The community college: educating students at the margin between college and work, Journal of Economic Perspectives, 13(1):63–84.

Kim, Lillemor (1998), Val och urval till högre utbildning: en studie erfarenheterna av 1977 års tillträdesreform, Uppsala: Uppsala Universitet.

Kjellström, Christian and Håkan Regnér (1999), The Effect of Geographical Distance on the Decision to Enroll in University Education, Scandinavian Journal of Education Research. 43(4): 335-348.

Leigh, Duane E. and Andrew M. Gill (2003), Do community college really divert the students from earning a bachelor’s degree?, Economic of Education Review, 22(1):23-30.

Leigh, Duane E. and Andrew M. Gill (2004), The effect of community colleges on changing students’ educational aspirations, Economic of Education Review, 23 (1):95-102.

Meghir, Costas and Mårten Palme (2005), Educational Reform, Ability and Parental Background, American Economic Review, 95(1):414–424.

MIS (2000), Utbildningsklassificering, vol 1, Statistic Sweden. OECD (1993), Education at a Glance, Paris: OECD. Moulton, Brent R (1986), Random group effects and the precision of regression estimates,

Journal of Econometrics, 23(3): 385-397. Rouse, Cecilia E. (1995), Democratization or Diversion? The effect of community colleges on

educational attainment, Journal of Business and Economic Statistics, 3(2):217–224. Roemer, John E. (2006), Democracy, Education, and Equality – Graz Schumpeter Lecture,

Economic Society Monographs, Cambridge University Press. Roemer, John E. (1996), Theories of Distributive Justice, Harvard University Press. Solon, Gary (1999), Intergenerational Mobility in the Labor Market, in Handbook in Labor

Economics, ed. Orley C. Ashenfelter and David Card. Vol. 3A Amsterdam: Elsevier Science Publishers.

SOU (1972), U68 – Högskolan, SOU1972:3, Stockholm: Fritzes. UHÄ (1989), Högskoleutbildningens framtida dimensionering, Stockholm: UHÄ1989:17. Öckert, Björn (2001), Effects of Higher Education and the Role of Admission Selection,

Stockholm University, Stockholm: Swedish Institute for Social Research No 52.

Page 56: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

32

APPENDIX Table A.1   Description of the explanatory variables  

Variable name Description

The 18‐year‐old:

Female  1 if female, 0 otherwise

NEW* 1 if the youth lives in the municipality that received

local access to a higher education through a new university

in 1977 or later, 0 otherwise.

Father’s highest education**:

Compulsory School ≤ 9 years  1 if it is compulsory schooling, 0 otherwise.

Upper Secondary ≤ 3 years  1 if it is upper secondary school, 0 otherwise.

University  < 3 years  1 if it is less than 3 years of university, 0 otherwise.

University ≥ 3 years 1 if it is 3 years or more of university, 0 otherwise.

Mother’s highest education**:

Compulsory School ≤ 9 years  1 if it is compulsory schooling, 0 otherwise.

Upper Secondary ≤ 3 years  1 if it is upper secondary school, 0 otherwise.

University  < 3 years  1 if it is less than 3 years of university, 0 otherwise.

University  ≥ 3 years  1 if it is 3 years or more of university, 0 otherwise.

Economy of the Household:

Income***  Relative net‐income of the household.

Social Welfare  1 if the household receives social welfare, 0 otherwise.

Unemployment Benefit  1 if the household receives unemployment benefits, 0 otherwise.

Single parent 1 if the household consists of one adult (one parent), 0 otherwise.

Macro variable:

Youth Unemployment  County unemployment rate for the age group 16‐24 years.

Year/Cohort dummies:

L1977  1 if  the 18‐year old is 18 years in 1977, 0 otherwise.

L1978  1 if  the 18‐year old is 18 years in 1978, 0 otherwise.

....

L2001 1 1 if  the 18 year old is 18 years in 2001, 0 otherwise.

D90  1 for the years 1990‐2001, 0 for the years 1977‐1989. Note1:  (*) Municipalities  represented  in  NEW:  Boden,  Borlänge,  Borås,  Eskilstuna,  Falun,  Gotland,  Gävle, Halmstad,  Helsingborg,  Härnösand,  Jönköping,  Kalmar,  Karlskrona,  Karlstad,  Kristianstad,  Luleå,  Ronneby, Skövde,  Sundsvall,  Uddevalla,  Vänersborg,  Västerås,  Växjö,  Örebro,  Örnsköldsvik  and  Östersund.  For municipalities that obtained a university after 1977, the dummy variable shifts from zero to one the year the university was officially established there. An example: the University College of Blekinge (in the municipalities Karlskrona and Ronneby) was established in 1990, and the variable NEW has the value of zero before 1990, and one  in  1990  and  after  for  the  municipalities  Karlskrona  and  Ronneby.  Note  2:  (**)  Information  about individuals’ educational history in LINDA starts in 1990. Due to this lack of information for the cohorts prior to 1990, all  information on parental educational  level  is based on the educational  level that  is registered  in the census  of  1990.  Note3:  (***)  Family  income  is  presented  as  relative  net‐income  (after  tax  reduction  and received benefits) for the household to which the student belonged at the age of 18. 

Z

i

Z

i itit

itit

HousholdFAMincome

FAMincomeincomeFamily

1 1/

_  

where  itincomeFamily _ stands for the nominal income of the household of student i at time t. t = 1968, ..., 

2001)  indicates the year the student turned 18. The sum of all nominal incomes in year t is divided by all households in the same year. In the two‐parent household case, the nominal  family income has been divided by 1.7  to compare income levels with  one‐parent households (see Björklund, Palme, and Svensson (1995)). Note 4: All individual data is based on the database LINDA. Youth unemployment is taken from the open database over unemployment in Sweden. The intergenerational connection between parents and their youths is based on the multigenerational register of Statistics Sweden over the population in Sweden. All information on activities within the higher educational system in Sweden is based on the Swedish Higher Education Register. All data has been provided by Statistics Sweden. 

Page 57: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table A2 

Probit estim

ates of ‘The democratization effect of the expan

sion’ 

Estimating the probability of attending higher education in

 Swed

en in

 the 1980s, changes in the probabilities in

 the 1990s, and the differences‐in‐differences effect of living 

in a m

unicipality where a new

 university college was established

 in the late 1970s or later. 

OLS

PROBIT

Coeff. 

Std. D

ev.

Coeff. 

Std. D

ev.

Coeff. 

Std. D

ev.

Marg. Eff. 

Std. D

ev. 

Coeff. 

Std. D

ev.

Marg. Eff. 

Std. D

ev. 

Effects on dem

ocratization:

From living in NEW

  0.006

0.012

0.011

0.011

0.027

0.044

0.008

0.014

0.046

0.040

0.014

0.013

Female  

0.036

***

0.004

0.039

***

0.004

0.159

***

0.015

0.050

***

0.004

0.171

***

0.015

0.053

***

0.005

Upper Secondary~father

0.029

***

0.004

0.031

***

0.004

0.129

***

0.012

0.041

***

0.004

0.139

***

0.013

0.044

***

0.004

University < 3 years~father  

0.124

***

0.007

0.126

***

0.007

0.418

***

0.021

0.145

***

0.008

0.425

***

0.021

0.147

***

0.008

University ≥ 3 years~father  

0.278

***

0.009

0.278

***

0.008

0.813

***

0.022

0.295

***

0.009

0.815

***

0.021

0.295

***

0.008

Upper Secondary~mother 

0.008

**0.004

0.011

***

0.004

0.056

***

0.013

0.018

***

0.004

0.067

***

0.013

0.021

***

0.004

University < 3 years~m

other   

0.143

***

0.006

0.145

***

0.006

0.482

***

0.018

0.167

***

0.007

0.488

***

0.017

0.169

***

0.007

University ≥ 3 years~m

other   

0.272

***

0.009

0.273

***

0.009

0.827

***

0.025

0.301

***

0.010

0.828

***

0.024

0.301

***

0.009

Household income  

‐0.038

***

0.010

‐0.031

***

0.009

‐0.141

***

0.031

‐0.044

***

0.009

‐0.106

***

0.026

‐0.033

***

0.008

Social W

elfare  

‐0.097

***

0.004

‐0.094

***

0.004

‐0.684

***

0.036

‐0.163

***

0.007

‐0.672

***

0.036

‐0.160

***

0.007

Unem

ploym

ent Ben

efit  

‐0.045

***

0.005

‐0.044

***

0.004

‐0.201

***

0.020

‐0.059

***

0.006

‐0.197

***

0.020

‐0.058

***

0.006

Single Paren

t‐0.079

***

0.004

‐0.075

***

0.004

‐0.310

***

0.017

‐0.090

***

0.005

‐0.292

***

0.017

‐0.086

***

0.005

Youth Unem

ploym

ent  

‐0.051

***

0.006

‐0.047

***

0.006

‐0.177

***

0.021

‐0.055

***

0.007

‐0.161

***

0.020

‐0.050

***

0.006

Changes in

 the 1990s:

From living in NEW

  0.051

***

0.016

0.270

***

0.019

0.172

***

0.058

0.056

***

0.020

1.133

***

0.071

0.412

***

0.026

Female  

0.093

***

0.004

0.099

***

0.005

0.289

***

0.013

0.094

***

0.005

0.314

***

0.015

0.103

***

0.005

Upper Secondary~father

0.103

***

0.006

0.108

***

0.006

0.322

***

0.020

0.107

***

0.007

0.353

***

0.019

0.118

***

0.007

University < 3 years~father  

0.109

***

0.010

0.119

***

0.009

0.299

***

0.028

0.101

***

0.010

0.346

***

0.026

0.118

***

0.009

University ≥ 3 years~father  

0.037

***

0.011

0.050

***

0.012

0.144

***

0.028

0.047

***

0.009

0.201

***

0.030

0.066

***

0.010

Upper Secondary~mother 

0.117

***

0.005

0.122

***

0.005

0.398

***

0.019

0.133

***

0.007

0.444

***

0.019

0.149

***

0.007

University < 3 years~m

other   

0.115

***

0.008

0.119

***

0.008

0.327

***

0.023

0.111

***

0.008

0.375

***

0.023

0.128

***

0.008

University ≥ 3 years~m

other   

0.070

***

0.009

0.076

***

0.010

0.239

***

0.026

0.079

***

0.009

0.287

***

0.027

0.096

***

0.010

Household income  

0.053

***

0.013

0.047

***

0.013

0.227

***

0.030

0.070

***

0.009

0.206

***

0.026

0.064

***

0.008

Social W

elfare  

0.031

***

0.008

0.034

***

0.008

0.382

***

0.042

0.132

***

0.016

0.374

***

0.044

0.129

***

0.017

Unem

ploym

ent Ben

efit  

0.035

***

0.005

0.036

***

0.005

0.161

***

0.022

0.052

***

0.008

0.157

***

0.024

0.051

***

0.008

Single Paren

t0.118

***

0.008

0.125

***

0.008

0.450

***

0.028

0.154

***

0.010

0.479

***

0.030

0.165

***

0.011

Youth Unem

ploym

ent  

0.083

***

0.007

0.083

***

0.007

0.282

***

0.027

0.088

***

0.009

0.283

***

0.024

0.088

***

0.008

(Model with inteaction)

(Base model)

(Base m

odel)

(Model with inteaction)

Page 58: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table A2 cont.  

Changes in the 1990s and from living in NEW: 

Female  

‐0.046

***

0.008

‐0.172

***

0.026

‐0.050

***

0.007

Upper Secondary~father

Upper Secondary~father***

0.010

‐0.224

***

0.030

‐0.064

***

0.008

University < 3 years~father  

‐0.058

***

0.013

‐0.239

***

0.041

‐0.068

***

0.010

University ≥ 3 years~father  

‐0.065

***

0.012

‐0.246

***

0.040

‐0.070

***

0.010

Upper Secondary~mother 

‐0.050

***

0.008

‐0.270

***

0.030

‐0.076

***

0.008

University < 3 years~mother   

‐0.042

***

0.011

‐0.250

***

0.036

‐0.071

***

0.009

University ≥ 3 years~mother   

‐0.045

***

0.010

‐0.242

***

0.036

‐0.069

***

0.009

Household income  

‐0.011

0.012

‐0.079

***

0.015

‐0.025

***

0.005

Social W

elfare  

‐0.033

***

0.012

‐0.071

0.052

‐0.021

0.015

Unemploym

ent Benefit  

‐0.013

0.008

‐0.013

0.029

‐0.004

0.009

Single Parent

‐0.071

***

0.011

‐0.271

***

0.039

‐0.076

***

0.010

Youth Unemploym

ent  

‐0.020

***

0.002

‐0.072

***

0.007

‐0.022

***

0.002

Year dummies : 

L1977

0.071

***

0.007

0.080

***

0.006

0.367

***

0.023

0.126

***

0.008

0.414

***

0.023

0.143

***

0.009

L1978

0.097

***

0.008

0.104

***

0.008

0.448

***

0.027

0.157

***

0.011

0.488

***

0.026

0.172

***

0.010

L1979

0.092

***

0.007

0.099

***

0.007

0.431

***

0.026

0.150

***

0.010

0.473

***

0.025

0.166

***

0.010

L1980

0.077

***

0.007

0.085

***

0.007

0.381

***

0.025

0.131

***

0.009

0.424

***

0.026

0.147

***

0.010

L1981

0.118

***

0.009

0.123

***

0.008

0.518

***

0.030

0.183

***

0.012

0.552

***

0.029

0.196

***

0.012

L1982

0.155

***

0.013

0.158

***

0.012

0.645

***

0.047

0.233

***

0.019

0.668

***

0.044

0.242

***

0.018

L1983

0.163

***

0.014

0.164

***

0.014

0.677

***

0.051

0.245

***

0.020

0.694

***

0.048

0.252

***

0.019

L1984

0.137

***

0.012

0.140

***

0.011

0.584

***

0.043

0.209

***

0.017

0.606

***

0.041

0.217

***

0.016

L1985

0.108

***

0.010

0.111

***

0.009

0.471

***

0.035

0.165

***

0.013

0.495

***

0.033

0.174

***

0.013

L1986

0.087

***

0.009

0.089

***

0.009

0.380

***

0.035

0.131

***

0.013

0.402

***

0.035

0.139

***

0.013

L1987

0.052

***

0.006

0.055

***

0.006

0.245

***

0.023

0.082

***

0.008

0.267

***

0.024

0.089

***

0.009

L1988

0.026

***

0.004

0.028

***

0.005

0.135

***

0.019

0.044

***

0.006

0.152

***

0.021

0.049

***

0.007

L1990

‐0.228

***

0.007

‐0.245

***

0.008

‐0.738

***

0.022

‐0.170

***

0.004

‐0.828

***

0.028

‐0.183

***

0.004

L1991

‐0.251

***

0.009

‐0.267

***

0.008

‐0.823

***

0.029

‐0.183

***

0.004

‐0.905

***

0.028

‐0.193

***

0.004

L1992

‐0.320

***

0.015

‐0.333

***

0.013

‐1.056

***

0.054

‐0.210

***

0.006

‐1.128

***

0.048

‐0.217

***

0.005

L1993

‐0.408

***

0.024

‐0.418

***

0.022

‐1.352

***

0.092

‐0.234

***

0.006

‐1.415

***

0.082

‐0.237

***

0.006

L1994

‐0.385

***

0.024

‐0.397

***

0.022

‐1.279

***

0.090

‐0.229

***

0.007

‐1.346

***

0.081

‐0.233

***

0.006

L1995

‐0.409

***

0.024

‐0.420

***

0.022

‐1.350

***

0.090

‐0.233

***

0.006

‐1.417

***

0.080

‐0.237

***

0.006

L1996

‐0.443

***

0.025

‐0.453

***

0.024

‐1.461

***

0.095

‐0.239

***

0.006

‐1.523

***

0.087

‐0.242

***

0.005

L1997

‐0.479

***

0.024

‐0.489

***

0.022

‐1.572

***

0.092

‐0.245

***

0.005

‐1.634

***

0.083

‐0.247

***

0.005

L1998

‐0.474

***

0.019

‐0.485

***

0.017

‐1.549

***

0.072

‐0.244

***

0.004

‐1.616

***

0.065

‐0.247

***

0.004

L1999

‐0.537

***

0.015

‐0.548

***

0.015

‐1.776

***

0.059

‐0.253

***

0.004

‐1.843

***

0.054

‐0.255

***

0.004

L2000

‐0.452

***

0.015

‐0.469

***

0.014

‐1.601

***

0.057

‐0.247

***

0.003

‐1.696

***

0.053

‐0.250

***

0.003

L2001

‐0.520

***

0.011

‐0.538

***

0.011

‐3.318

***

0.089

‐0.283

***

0.004

‐3.429

***

0.091

‐0.284

***

0.004

_cons

0.233

***

0.009

0.206

***

0.011

‐0.890

***

0.023

‐1.022

***

0.035

Observations: 

2,999,944

2,999,944

2,999,944

2,999,944

2,999,944

2,999,944

R‐squared

0.235

0.238

Pseuda R2

0.209

0.213

Log pseudolikelihood

‐142,563.63

‐141,888.29

Note: R

obust standard errors (shown in paren

thesis) are adjusted

 by 283 clusters – eq

uivalen

t to the municipalities accounted for in the paper. ***, **, * den

ote 

significance at 1, 5, and 10 percent, respectively. 

Page 59: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table A3 

Probit estim

ates of ’The diversion effect of new university colleges’ 

Estimating the probability of attending an

 old university in the 1980s, changes in probabilities in

 the 1990s, and the differences‐in‐differences effect of living in a m

unicipally 

where a new

 university was established

 in the late 1970s or later.   

OLS

PROBIT

Coeff. 

Std. D

ev.

Coeff. 

Std. D

ev.

Coeff. 

Std. D

ev.

Marg. Eff. 

Std. D

ev. 

Coeff. 

Std. D

ev.

Marg. Eff. 

Std. D

ev. 

Effects on dem

ocratization:

From living in NEW

  ‐0.044**

0.020

‐0.041**

0.018

‐0.205**

0.092

‐0.043**

0.018

‐0.188**

0.084

‐0.039**

0.017

Female  

0.013***0.003

0.015***0.003

0.092***

0.012

0.020***0.003

0.102***0.013

0.023***0.003

Upper Secondary~father

0.031***0.003

0.033***0.003

0.180***

0.012

0.041***0.003

0.192***0.013

0.044***0.003

University < 3 years~father  

0.102***0.007

0.103***0.007

0.434***

0.024

0.116***0.008

0.443***0.024

0.119***0.008

University ≥ 3 years~father  

0.254***0.009

0.254***0.009

0.829***

0.023

0.247***0.010

0.834***0.023

0.249***0.010

Upper Secondary~mother 

0.007***0.003

0.009***0.003

0.067***

0.014

0.015***0.003

0.078***0.015

0.017***0.003

University < 3 years~m

other   

0.115***0.006

0.117***0.006

0.471***

0.018

0.126***0.006

0.478***0.018

0.128***0.006

University ≥ 3 years~m

other   

0.233***0.008

0.234***0.008

0.786***

0.024

0.232***0.010

0.790***0.024

0.233***0.010

Household income  

‐0.011***0.004

‐0.006

0.004

‐0.010***

0.016

‐0.002

0.003

0.009

0.013

0.002

0.003

Social W

elfare  

‐0.062***0.005

‐0.059***0.005

‐0.629***

0.039

‐0.100***0.005

‐0.617***0.039

‐0.098***0.005

Unem

ploym

ent Ben

efit  

‐0.036***0.004

‐0.035***0.004

‐0.228***

0.025

‐0.046***0.005

‐0.224***0.025

‐0.045***0.005

Single Paren

t‐0.041***0.005

‐0.037***0.005

‐0.180***

0.021

‐0.038***0.004

‐0.164***0.022

‐0.035***0.005

Youth Unemploym

ent  

‐0.051***0.005

‐0.048***0.005

‐0.215***

0.021

‐0.048***0.005

‐0.201***0.021

‐0.044***0.005

Changes in

 the 1990s:

From living in NEW

  ‐0.005***0.030

0.177***0.032

‐0.018***

0.133

‐0.004

0.029

0.700***0.156

0.058***0.004

Female  

0.064***0.004

0.072***0.004

0.222***

0.015

0.052***0.004

0.243***0.016

0.063***0.006

Upper Secondary~father

0.060***0.006

0.069***0.005

0.235***

0.023

0.057***0.006

0.260***0.021

0.060***0.009

University <  3  year s~ fa the r  

0.062***0.009

0.079***0.010

0. 206***

0.031

0.051***0.009

0.241***0.032

0.037***0. 007

University ≥ 3 ye ars~father  

0.031***0.009

0.054***0.010

0.108***

0.028

0. 025***0.007

0.156***0.029

0.080***0.007

Upper Se condary~mothe r 

0.071***0.006

0.077***0.006

0. 309***

0.023

0.075***0.006

0.326***0.025

0.060***0. 009

University < 3 years~m

other   

0.062***0.008

0.076***0.010

0.211***

0.028

0.052***0.008

0.241***0.031

0.048***0.008

University ≥ 3 years~m

other   

0.050***0.009

0.064***0.010

0.172***

0.028

0.041***0.007

0.199***0.030

0.013***0.003

Household income  

0.023***0.008

0.018**

0.009

0.063***

0.016

0.014***0.003

0.057***0.013

0.108***0.014

Social W

elfare  

0.038***0.005

0.030***0.006

0.446***

0.042

0.121***0.014

0.406***0.044

0.038***0.007

Unem

ploym

ent Ben

efit  

0.024***0.005

0.024***0.005

0.156***

0.028

0.037***0.007

0.160***0.028

0.119***0.010

Single Paren

t0.096***0.008

0.100***0.008

0.445***

0.030

0.116***0.009

0.453***0.031

0.047***0.005

Youth Unemploym

ent  

0.055***0.007

0.055***0.006

0.219***

0.025

0.049***0.006

0.210***0.023

0.197***0.053

(Base model)

(Model with inteaction)

(Base model)

(Model with inteaction)

Page 60: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table A3  cont.  

Changes in the 1990s and from living in

 NEW: 

Female  

‐0.046***0.008

‐0.160***0.028

‐0.033***0.005

Upper Secondary~father

‐0.065***0.009

‐0.248***0.043

‐0.048***0.008

University < 3 years~father  

‐0.081***0.015

‐0.253***0.042

‐0.049***0.007

University ≥ 3 years~father  

‐0.095***0.018

‐0.275***0.040

‐0.052***0.007

Upper Secondary~mother 

‐0.049***0.008

‐0.205***0.032

‐0.041***0.006

University < 3 years~mother   

‐0.073***0.018

‐0.232***0.039

‐0.045***0.007

University ≥ 3 years~mother   

‐0.071***0.024

‐0.216***0.047

‐0.043***0.008

Household income  

‐0.006

0.010

‐0.045***0.014

‐0.010***0.003

Social  W

elfare  

0.012

0.008

0.060

0.047

0.014

0.011

Unemploym

ent Benefit  

‐0.005

0.006

‐0.039

0.036

‐0.008

0.008

Single Parent

‐0.053***0.016

‐0.229***0.079

‐0.045***0.014

Youth Unemploym

ent  

‐0.012***0.003

‐0.041***0.010

‐0.009***0.002

Year dummies : 

L1977

0.078***0.006

0.085***0.006

0.457***

0.027

0.124***0.009

0.495***0.026

0.137***0.009

L1978

0.102***0.008

0.108***0.008

0.552***

0.032

0.156***0.011

0.584***0.031

0.167***0.011

L1979

0.095***0.007

0.101***0.007

0.527***

0.027

0.147***0.009

0.561***0.026

0.158***0.009

L1980

0.080***0.007

0.086***0.006

0.458***

0.028

0.125***0.009

0.493***0.028

0.136***0.009

L1981

0.120***0.008

0.124***0.007

0.621***

0.030

0.179***0.011

0.648***0.029

0.189***0.011

L1982

0.153***0.012

0.154***0.011

0.754***

0.044

0.227***0.016

0.771***0.042

0.233***0.016

L1983

0.159***0.013

0.160***0.012

0.781***

0.048

0.236***0.018

0.792***0.046

0.240***0.017

L1984

0.132***0.011

0.133***0.010

0.654***

0.040

0.190***0.014

0.670***0.039

0.196***0.014

L1985

0.109***0.009

0.111***0.009

0.544***

0.034

0.152***0.011

0.560***0.032

0.158***0.011

L1986

0.089***0.009

0.090***0.008

0.430***

0.034

0.116***0.011

0.444***0.034

0.120***0.011

L1987

0.049***0.006

0.051***0.006

0.244***

0.024

0.061***0.007

0.258***0.025

0.065***0.007

L1988

0.024***0.005

0.026***0.005

0.132***

0.023

0.031***0.006

0.141***0.024

0.034***0.006

L1990

‐0.145***0.008

‐0.160***0.007

‐0.429***

0.025

‐0.076***0.005

‐0.462***0.023

‐0.080***0.004

L1991

‐0.146***0.012

‐0.160***0.011

‐0.417***

0.042

‐0.074***0.007

‐0.441***0.039

‐0.077***0.007

L1992

‐0.159***0.019

‐0.170***0.018

‐0.434***

0.078

‐0.076***0.011

‐0.447***0.075

‐0.078***0.011

L1993

‐0.172***0.030

‐0.180***0.029

‐0.438***

0.134

‐0.077***0.018

‐0.439***0.129

‐0.077***0.018

L1994

‐0.166***0.031

‐0.176***0.029

‐0.422***

0.134

‐0.075***0.019

‐0.425***0.130

‐0.075***0.018

L1995

‐0.189***0.030

‐0.199***0.029

‐0.508***

0.130

‐0.086***0.017

‐0.512***0.126

‐0.086***0.016

L1996

‐0.207***0.030

‐0.216***0.029

‐0.570***

0.130

‐0.093***0.015

‐0.571***0.127

‐0.093***0.015

L1997

‐0.225***0.030

‐0.234***0.028

‐0.641***

0.129

‐0.100***0.014

‐0.642***0.125

‐0.100***0.014

L1998

‐0.244***0.024

‐0.254***0.023

‐0.745***

0.099

‐0.110***0.010

‐0.751***0.096

‐0.110***0.010

L1999

‐0.283***0.021

‐0.293***0.020

‐0.931***

0.082

‐0.123***0.007

‐0.942***0.079

‐0.124***0.007

L2000

‐0.219***0.017

‐0.234***0.015

‐0.830***

0.063

‐0.117***0.006

‐0.858***0.058

‐0.119***0.006

L2001

‐0.273***0.018

‐0.289***0.017

‐2.312***

0.096

‐0.161***0.006

‐2.354***0.097

‐0.161***0.006

_cons

0.159***0.010

0.137***0.012

‐1.246***

0.041

‐1.351***0.051

Observations: 

2,999,944

2,999,944

2,999,944

2,999,944

2,999,944

2,999,944

R‐squared

0.173

0.177

Pseuda R2

0.1744

0.1769

Log pseudolikelihood

‐118,938.56

‐118,583.54

Note: R

obust standard errors (shown in paren

thesis) are adjusted

 by 283 clusters – eq

uivalen

t to the municipalities accounted for in the paper. ***

 ,**, * den

ote 

significance at 1, 5, and 10 percent, respectively. 

Page 61: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

II

Page 62: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 63: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

University choice and academic success in Sweden 

By Susanna Holzer 

 

   Abstract  We compare the performance of students in universities built before and after the large decentralization and expansion of the higher educational system in Sweden that started in the late 1970s. Two outcome measures are used: (i) whether or not the student has obtained a degree within seven years after initiating her studies; and (ii) whether or not she obtained 120 credit points (the requirement for most undergraduate degrees) within seven years. Controlling for several background variables as well as GPA scores in a binomial probit model, we show that students in old universities are about 5 percentage points more likely to get a degree and about 9 percentage points more likely to obtain 120 credit points. However, in an extended bivariate model, where we consider selection on unobservables into university type, we cannot reject the possibility of no difference in performance between the two university types.

JEL Classification:  I23, I28, J24 

Keywords: Higher Education, Government Policy, Human Capital 

Correspondence address: Susanna Holzer, School of Management and Economics, Växjö University, SE-351 95 Växjö, Sweden. Phone: +46 470 70 85 79. E-mail: [email protected]. I am grateful to Mårten Palme, Håkan Locking, Thomas Lindh, Lennart Delander, Harald Niklasson, Abdullah Almasri, Ghazi Shukur and seminars participants at Växjö University, and conference participants at the conference Higher Education Systems, Decentralization and Educational Outcomes, Novara, Italy, November 2008, for helpful comments and suggestions. I acknowledge funding from Växjö University, Jan Wallander and Tom Hedelius’ Research Foundation, and the Swedish Research Council.

Page 64: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

2

1 Introduction

Providing and ensuring access to education is one of the strongest political concepts of

democratization in most Western democracies.1 Increased access is considered to encourage

more socioeconomic groups in society to invest in education and thus, enhance (especially

young) people’s chances of equality of opportunity in life. Focusing only on the publicly

financed and centrally monitored higher education in Sweden in the following, it is shown

that policies of democratization and regionalization have lead to a dramatic change in the

geographical and physical access to higher education during the last fifty years. For instance,

in the 1960s, only a very select group in society, amounting to roughly 25,000 students,

attended higher education at the six (mostly metropolitan located) universities. Fifty years

later, more than 360,000 students attend higher education in more than 40 university areas

that are widely spread across Sweden.

Although the student body has grown to cover roughly half of all younger birth cohorts in

Sweden, the composition of the student body is still highly skewed. Among students born in

the 1970s and the 1980s, about 22 percent of the children with parents with compulsory

education as their highest education attended higher education, whereas the corresponding

attendance rate among children from parents with a post-graduate education was 86 percent.2

Once this skewed (but more heterogeneous as compared to the composition of the student

body in the 1960s) student body has entered the higher educational system, only modest

attention has so far been given to educational outcome. This issue of equality of opportunity

becomes even more essential knowing that less than half of all students who attended higher

education in the 1990s and the early 2000s managed (or chose) to complete their education

with a university degree, which should be compared to a completion rate of more than 80

percent in the 1960s.3

Just like in Sweden, there has been an increase in the number of institutions providing higher

education in the United States, which has resulted in a rapid growth of the student body. Also

in the US has there been a sharp fall in the relative completion rates. When Manski and Wise

1 By enhancing the access to postsecondary education the hypotheses is that community colleges may increase the years of schooling completed - an effect known as a democratization effect; see Karabel and Brint (1989), and Rouse (1995) for a longer discussion of the concept. 2 SCB (2008). 3 See SCB (1975, 2007).

Page 65: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

3

(1983) first brought attention to this development in the US, they explained the increasing

drop-out rates as simply a matter of ability and taste for education. However, later studies

argue that especially high tuition costs at the older, more prestigious, and fewer four-year

colleges, as compared to the newer local community (two-year) colleges, inhibit the

enrollment of students from lower socioeconomic backgrounds; see Empty Promises (2002).

This should be kept in mind when considering studies by Dougherty (1994), Rouse (1995),

Kane and Rouse (1995, 1999) and Leigh and Gill (2003, 2004) who all find college choice to

be of importance for an individual’s chances for academic success. They argue that

community colleges have a diversion effect on overall educational attainment, in that they

show that students who first enter the local community colleges on average complete fewer

years of schooling than for students starting at four-year colleges.4 In terms of university

degrees, students who first attend a four-year college have a higher likelihood of ending up

with a bachelor’s degree, as compared to students who first attend a two-year college.

Turning back to the case of Sweden, smaller regional universities were introduced at the end

of the 1970s in order to complement the six older universities that are often regarded as more

prestigious. (Henceforth, the universities established prior to 1977 are referred to as old

universities and the rest as new universities.5) In contrast to the US higher educational system,

there are four important traits of the Swedish higher educational system that we must

consider: higher education in Sweden is i) centrally monitored and quality controlled by the

government; ii) publicly funded; iii) free of charge for the student (no fees!); iv) and the new

universities, like the old universities, are allowed to award students bachelor’s and (often)

master’s degrees.6

Keeping these traits of the Swedish higher educational system in mind, the following

questions are investigated; a) does academic success depend on university type in Sweden, i.e.

if the student attended an old or a new university? Since all institutions of higher education

are equally funded per student by the government, this action is interpreted as the assumption

4 A student who first enters a four-year college has a greater chance of ending up with at least a bachelor’s degree than a student that first enters a two-year college – even if they initially have similar abilities, and even if the student at the two-year college were given the chance of transferring to a four-year college. 5 In the following, both universities and university colleges are refereed to as universities, since both are allowed to proved educations at the master’s level in Sweden. 6 There are some examples of colleges in Sweden that do not have the Swedish government as their superior, e.g. Stockholm School of Economics and University College of Jönköping for example. However, they all rely on public funding for their undergraduate education and, like other colleges, they are subordinated Swedish law and regulations.

Page 66: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

4

by the government being that educational outcome across institutions is homogeneous7; b)

does university choice affect a student’s educational prospects differently if we control for

his/her socioeconomic background? Performance accountability at the two university types is

explored as a mechanism for examining detailed student outcomes in order to analyze equity

issues.

Two probit models are employed in order to model how university type might affect

educational outcomes. First, we use a binomial probit model, where we control for possible

heterogeneity by including observed characteristics as regressors and second, as mentioned

above, the composition of the student body is skewed, so it is fair to assume that we face a

self-selection into universities.8 The effect of selection on unobservables that might affect

educational outcome that is correlated with initial university choice is estimated with a

bivariate probit model. The empirical material used in this study is a sample of 5,565

individuals that are extracted from the Swedish Longitudinal INdividual DAta (LINDA).

They all entered a Swedish university for the very first time during the years 1996-1999, i.e.

they constitute approximately three percent of all new enrolled students in that period of time.

When we do not control for selection on unobservables into university types, the results from

the binomial probit model show that we have an average university type effect of 5 and 9

percentage points on completing university with a degree or 120 credit points, respectively,

where attending an old university is more favorable for any of these. In this first case, family

background has no impact on educational outcome. When controlling for selection on

unobservables with a bivariate probit model, however, we found that on the probability of

completing 120 credit points or more, the selection parameter turned out to be significantly

different from zero and the coefficient for an old university was not significantly different

from zero. This means that we cannot rule out the possibility that the higher probability of

obtaining 120 credit points at older universities is attributed to selection on unobservables.

Once more, family background seems to have no impact on educational outcomes.

The paper is organized as follows: In Section 2, the institutional settings of higher education

in Sweden are described in brief. Section 3 provides an overview of the literature, Section 4 7 Student slots in the same subject major are equally funded across universities; however, the funding differs across the subject majors. More on this issue in the next section of this paper. 8 Also studied and discussed by Manski (1989), Altonji (1993), Light and Strayer (2000), and Arcidiacono (2004).

Page 67: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

5

shows the empirical specification of the paper and Section 5 views the data and measurements

used. The empirical findings are presented in Section 6 and Section 7 concludes the paper.

2 Brief facts about Swedish Higher Education In 1977, there was a dramatic change in Swedish higher education, which is supply-side

oriented and centrally monitored by the Swedish government. The six older universities,

which constituted the sector of higher education and most of which were located in the

metropolitan areas in Sweden, were now complemented with (initially) 12 new and more

vocationally-oriented regional universities located all over Sweden. The main difference

between an old and a new university, i.e. a university established prior to and after 1977

is that only the old universities are entitled to award postgraduate degrees on a regular

basis and all prestigious educations in medicine, law and art are concentrated to the old

universities.9 In contrast to the US university system, all new universities in Sweden have

the right to provide educations up to the bachelor’s level and, in most cases, up to the

master’s level.

All student slots are restricted by the government which, in most cases, also sets the eligibility

requirements for a certain educational program or course. The students may, of course, choose

the education and the university they desire, but they have to compete for the limited amounts

of students slots, most often with their Grade Point Average (GPA) from upper secondary

school as the only means of competing. The selection of students is mostly made centrally by

the National Admissions Office to Higher Education (VHS), meaning that the universities

cannot choose their students themselves.10

9 The author is aware of the upgraded status that Luleå Technical University received in 1998, and Karlstad University, Växjö University, and Örebro University in 1999, where they all were granted the right to conduct research and offer graduate educations on a more permanent basis than what was previously the case. 10 The Swedish Agency for Higher Education (VHS). Since the outlined rules of admission are the only tools that must be considered by VHS in its selection process, the admission system can be said to be fairly transparent in the sense that the institutions of higher education cannot freely choose among eligible students. The admission rules currently in force in Sweden have been subjected to an intense debate since they were introduced in 1977/1979, but apart from some minor modifications, they still remain the same today. To be admitted to undergraduate education, the applicant must fulfill the basic eligibility requirements, which are the same for all courses and programs of education. Basic eligibility is obtained by having a degree from upper secondary school or if the applicant is 25 years of age and has at least four years of work experience and possesses a knowledge of English and Swedish comparable to that obtained at upper secondary school. In addition to the basic requirements of a degree from upper secondary school, the applicant can improve his/her chances by adding good scores from the Swedish Scholastic Aptitude Test (see Öckert (2001) for a thorough overview).

Page 68: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

6

Basically all student performances are registered in the national system for documentation

of academic performances (Ladok).11 The system was jointly started by a majority of all

universities in Sweden in 1993. Although Ladok has been available since 1993/1994, the

administrative routines did not become stable until 1996/1997, which is the reason why

all aggregated input factors of Swedish higher educational production, Table 2.1, are only

presented for the years 1997 to 2005. Besides total production for all universities in

Sweden, educational production is also divided into the two university types of interest here.12

University is free of charge for the student and alternative funding is rare, so all universities

depend on public funding for their undergraduate education.13 A student slot in a specific

subject major is funded equally regardless of university, but educations in the natural

sciences, medicine, and technology (a group henceforth referred to as natural sciences)

receive more than twice the funding of educations in the humanities, law, and the social

sciences (a group henceforth referred to as social sciences). The proportion of educations in

the natural sciences is larger at the older universities as compared to the newer universities,

which could explain why the older universities in Table 2.1 on average receive slightly more

funding per student.

Table 2.1 also reports the input factor teachers. Here, all sorts of teacher resources are

included, i.e. from all levels of professors to graduate students acting as teaching

assistants – all transformed into full year equivalents. The older universities supply

roughly two-thirds of the higher educational production in Sweden and employ more

teachers than the newer universities. If we break down the teacher-resource to a teacher-

student ratio, we can see that students at the older universities on average have almost

twice the teacher resources per student than students at the new universities. The

additional funding received by the older universities could, of course, be a possible 11 ADB-baserat studiedokumentationssystem (Ladok). At the time of the introduction of Ladok, 21 of 40 universities were connected to the system, but by the end of the 1990s, 36 out of 40 universities were connected. Ladok is the largest source of information for the government on higher educational production. Universities that are not connected to Ladok are mainly universities specializing in art, drama, and music, but basically all universities that offer more general educations in the social sciences, the natural sciences, technology, the humanities, medicine etc. are connected to Ladok (see www.ladok.se). 12 Note that the sum of the two university types is slightly lower that the total for Sweden, since only facts based on universities that are included in the present study are reported, i.e 24 of 39 universities. More on this in the section on sample selection. 13 Even though not all universities in Sweden are officially subordinated the Swedish government, they are all dependent on public funding to survive and all educations must be in accordance with the Higher Education Act and the Higher Education Ordinance and thus, basically all universities are monitored by the government – directly or indirectly.

Page 69: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

7

explanation for why they have more staff. Another explanation is that the older

universities have more access to cheaper teacher resources, e.g. graduate students and

research assistants. Since the new universities (given some exceptions) are not allowed to

conduct research on the same regular basis as old universities, they do not have any

automatic access to these possible additional teacher resources. Table 2.1 Average input factors of Swedish higher education for the years 1997-2005

All UniversitiesA Old UniversitiesB New UniversitiesB

Input factors

FundingC 17,083 9,235 4,689

Students 272,191 162,157 91,524

    Funding per studentD 63 57 51

Teachers 24,861 17,526 5,766     Teacher Student Ratio 0.08 0.11 0.06

Note: A) All universities during 1997 to 2005. B) Only universities that are accounted for in this study. C) Funding is presented in billion SEK. D) Funding per student is presented in thousand SEK.   Source: Statistics  Sweden and the Agency for Higher Education (2008). 

Educational performances by the students are measured in credit points, where one week

of successful full time study corresponds to one credit point. A student who studies full

time is assumed to obtain 40 credit points in one year. Most academic degrees require at

least 120 credit points (equivalent to three years of full-time study) – which is the lowest

credit point level requirement for receiving a bachelor’s degree. Table 2.2 shows student-

specific performances for students who entered higher education in Sweden during the

academic years 1993/94–2005/06. These are then followed for up to seven years or up to

the academic year 2005/06. Only about 16 percent have obtained at least 120 credit

points within three years, 54 percent within five years, and 61 percent within seven years.

After seven years, only marginal changes in educational performances occur. Therefore,

the follow up is restricted to seven years in the following.14

Given all sorts of academic degrees, roughly 6 percent had an academic degree after

three years, 29 percent after five years, and 45 percent after seven years. This leaves

about 20 percent of all students who have obtained 120 credit points or more with no

formal degree. A possible explanation for this is that the students themselves must apply

for a degree from the university they attended, and will obtain it if they fulfill the 14 A full-time student is granted student grants and loans for up to twelve semesters (= six years). This is most likely the reason why there is a rapid decrease in university activities after six years.

Page 70: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

8

requirements set by the government and, to some extent, the university, i.e. they have passed

the required combination of courses. Among these 20 percent who lack a formal degree,

some of the students might lack one or more of the required courses to fulfill the degree

requirements, or may simply not value a formal degree so much as to consider it worth

applying for. A side effect of this is that it may make the Swedish official registers of the

educational level of the population in Sweden somewhat skewed and thus underestimate the

actual education level, since these registers often only consider the highest completed

degree.15

Table 2.2 Student performances for the academic years 1993/34 to 2005/06

Academic University Ladok 160 ≤ creditC

Year Entrants (in %) (in %)

3 years  5 years  7 years 3 years  5 years  7 years 7 years

Sweden ‐ All UniversitiesA

1993/94  59,490 79 10 32 46 17 52 59 35

1995/96  61,920 88 8 28 43 16 51 58 33

1997/98  58,930 93 6 28 44 16 54 61 35

1999/00  63,032 97 6 29 47 19 58 65 37

2001/02  69,514 98 5 29 20 57

2003/04  71,308 98 6 17

Old Universities B

1993/94  30,853 96 6 25 44 16 54 61 40

1995/96  32,022 97 4 20 38 16 53 60 39

1997/98  29,770 97 4 22 42 15 54 62 41

1999/00  30,654 97 5 26 47 18 60 60 38

2001/02  36,583 96 4 26 19 59

2003/04  37,508 96 6 17

New UniversitiesB

1993/94  20,496 78 10 35 45 19 49 55 26

1995/96  21,434 97 7 34 46 17 50 56 25

1997/98  22,892 95 6 30 44 18 52 59 28

1999/00  27,093 97 5 32 46 19 56 72 37

2001/02  29,007 94 5 33 21 64

2003/04  29,652 94 6 18

Awarded DegreeC

Passed 120 ≤credit pointsC

(in %) (in %)

Note: A) All universities in Sweden. B) Only universities that are accounted for in this study. This will be described in the next section. C) The credit point rate is only based on performances by students at universities affiliated with Ladok. An Academic Year starts at the end of  August  in  one  year  and  ends  in  early  June  the  following  year.  Entrants  refer  to  all  first  time  enrolled  students  in  higher education  in Sweden. Here, only university entrants who are permanent residents  in Sweden are accounted for. Ladok refers to how  many  of  the  students,  expressed  in  percent,  that  entered  a  university  that  was  affiliated  with  the  Ladok‐system.  All educational performances are presented as awarded degrees and credit points in percent of all university entrants. Observe that all  sorts  of  degrees  are  accounted  for  here,  i.e.  they  have  a  theoretical  production  time  of  two  to  five  and  a  half  years.  The degree rate and the credit point rate are  measured after 3, 5 and 7 years. Source: Statistic Sweden (2007).  

Table 2.2 shows that new universities are better at producing degrees within three years,

as compared to older universities. However, it should be kept in mind that the table

shows all sorts of degrees, i.e. even degrees that require less than 120 credit points

15 Only in some cases are 120 credit points or more accounted for; see Statistic Sweden (2007).

Page 71: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

9

(although they are few). Compared to the new universities, the older universities have a

larger share of educational programs that, in theory, require more time, i.e. three years or

more, which can most likely explain why most of the differences between the old and

new universities’ educational production have disappeared after seven years. In terms of

credit points, the older universities show a slightly higher rate of production than the new

universities and their share of students with at least 160 credit points (equivalent to four

years of full-time study) is also higher, when measured after seven years.

 

3  Previous literature   

As in most fields in economics, the existing evidence on what determinates higher educational

completion is very much dominated by US research and the empirical research can be

considered from three angles.

From the first angle, we have the issue of the democratization effect of new establishments in

the US, i.e. the institutional impact on college completion of first attending a two-year college

instead of a four-year college. The empirical research is entirely based on National

Longitudinal Youths Surveys (NLYS) and the Longitudinal Study of High School Class of

1972 (NLS72). The advocates of geographically spreading higher education nationwide argue

that having access to a college nearby decreases the overall cost of attending higher education

– both in terms of lower tuitions fees , which is usually the case for the community (two-year)

colleges, and in terms of lower commuting costs. This does in particular encourage students

from non-academic backgrounds to invest in higher education to a larger extent than what was

previously the case (see e.g. Dougherty (1994), Rouse (1995)) and Leigh and Gill (2003,

2004)). The critics, on the other hand, argue that two-year colleges “channel” students into

vocational-oriented educations, away from studies for a bachelor’s degree – i.e., they divert

potential students towards settling for less education than what they have the capacity for (see

e.g. Karabel and Brint (1989), Rouse (1998) and Kane and Rouse (1995,1999)). In the US

system, students who initially entered a two year-college and who desire a bachelor’s degree

or an even higher degree must transfer to a four-year college. According to the critics, this

transfer decision is associated with high costs, which is the reason why students at the margin

between pursuing their studies at a four-year college or entering the local labor market (often

Page 72: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

10

students from less fortunate backgrounds), may be lost to the latter. Rouse (1998, p. 602)

expresses the benefits for college completion of attending a four-year college already at the

start where “the four-year college environment helps keeping students ‘on-track’ and focused

on the bachelor’s degree” which, according to her, is not the case for students who first attend

a two-year college.

From the second angle, Light and Strayer (2000) try to answer the question of whether college

completion depends on college quality or student ability, when only controlling for quality

levels among four-year colleges.16 They use the NLYS, who all graduated from high school in

1978. In total, 780 four-year colleges were categorized into four quality groups. Their main

findings are that the more able (measured by pre-college educational performances) is an

individual, the more likely he/she is to attend higher education – regardless of the rank level

of the educational institution. However, in terms of attendance and completion decisions,

students tend to sort themselves by ability, i.e. the matching between student ability and

college quality is of importance for the likelihood of a student completing college. A low-

ability student has a higher likelihood of graduating if he/she attends a less select college –

compared to if he she attends a more highly ranked college; whereas a high-ability student is

more likely to graduate if he/she attends a high-quality college – compared to if he/she attends

a weaker school.

From the third angle, Altonji (1993) (followed by Arcidiacono (2004)) showed evidence of

within-university choices being far more important determinants of college completion than

college choice and family background. Based on NSL72 data, he found that within university

choices, like university majors (especially in the natural sciences), had a far greater impact on

university completion and on later economic outcomes as compared to the impact of

individual and family background characteristics and, in some cases, college choice.

A common criticism of most studies on college choice and college performances is that they

often fail to control for initial differences in student characteristics that may cause self-

selection to be inherent in the educational outcomes. As in the Light and Strayer (2000) case

above, better performing students "self-select" into better quality schools, and students either

16 See also Dale and Krueger (1998) and Brewer, Eide and Ehrenberg (1999) for more US examples.

Page 73: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

11

choose to pursue their education or choose to drop out (see also e.g. Keane and Wolpin (1997)

and Cameron and Heckman (1998, 2001)).

4 Empirical specifications

4.1 Model 1: A binomial probit model on university completion

Two different models are used in the empirical analysis. The first is a binomial probit model:

(1) *iD = 0 + 1 OLDi + iX2 + iZ3 + iCohort4

iX5 * OLDi + iZ6 * OLDi + iCohort7 * OLDi + ,i

where the *iD is a latent variable measuring student performances and is defined as:

(2)

,0

1 *

otherwise

cDifD i

i ,

where Di is the binary educational outcome for student i, c is a threshold defining a degree or

a certain credit point amount, OLD is a dummy variable for attending an old university, Xi is a

vector of personal-specific characteristics and family background information, Zi is a vector

of within university choices (e.g. course majors, program participation, transfer decisions) and

Cohort is a vector of year dummies indicating to which of four ‘university-entry-cohorts’ the

student belongs. Xi, Zi, and Cohort are interacted with the dummy variable OLD in order to

study how the impact of the independent variables differs between students who attended an

old university and those who attended a new university.

1 is the key parameter, measuring what is the impact of attending an old university on the

probability of university completion, as compared to attending a new university. The politics

concerning the higher educational sector in Sweden assumes it to be homogenous and that the

university type is of no importance for the likelihood of educational success. For this to hold,

1 , 5 , 6 , and 7 should not differ from zero. i is a random error term representing all

omitted variables that might affect individual completion behavior and it is approximated by a

normal distribution.

Page 74: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

12

4.2 Model 2: A bivariate probit model on university choice and university completion

University choice can be seen as a search process made by people in their discovery of what

they like and dislike in terms of education.17 By modeling this process into sequences, we

capture how possible effects of self selection into one university type endogenously affect the

probability of a positive educational outcome. Endogeneity is also assumed to be correlated

with unobservables that might affect the educational outcome of the students, e.g. teacher

quality, educational preferences, work preferences, peer group quality etc.

To model choice of university type and how possible endogeneity affects educational

outcomes, a recursive bivariate probit model is used (see e.g. Greene (2008)).18 The latent

variable specification for the bivariate probit is:

(3a) *iD = 0 + 1 OLDi + iX2 + iZ3 + iCohort4

iX5 *OLDi+ iZ6 * OLDi + Cohort7 * OLDi + ,i

(3b) *iOLD = 0 + iX1 + iCOM2 + Cohort3 + ,i

where both *iD and *

iOLD are latent variables measuring educational performances and

university choice, respectively, and they both follow the assumption made about *iD in

equation (2). Vectors Xi, Zi, and Cohort are the same as in the binomial probit model case. 17 This is inspired by Manski (1989), Altonji (1993), Light and Strayer (2000) and Arcidiacono (2004) who model the educational choices in sequences and the final outcome of an investment is assumed to be uncertain, but it is endogenously affected by earlier educational choices. 18 Some simplifications of the model are necessary. First, the sequential process of student application, university admission, and student acceptance decisions is collapsed into a single choice. Second, only attendance and graduation decisions at the first university are considered. Although one third of the students in the sample used in this study have recorded activities at other universities, besides the one to which they were first admitted, university-transfer or parallel studying decisions are beyond the scope of this study. Third, according to Altonji (1993) and Arcidiacono (2004), university majors affect the probability of graduating from university. They categorized university majors into two categories; mathematics (including technology and natural sciences) and humanities (including social sciences) and find that students in the mathematics category had a higher probability of completing university, as compared to the other category. This broad definition of university majors will be considered in this study, but only as an individual-specific factor that might influence the outcome in the second choice. Fourth, students tend to enroll and reenroll quite frequently at all universities, which is why continuous time-spells for the educational production are hard to come by. For Cameron and Heckman (1993) and Light (1995), the timing of the educational decision was in focus, in terms of education completion. Official statistics of educational performances at Swedish universities reveals that the activities at the universities after seven years are small, which is why the second decision – whether to complete university or not – will be collapsed into one single (person-specific) time period. A possible explanation for why the activity decreases so rapidly after six years is the financial support system for students which (given some exceptions), stops after twelve semesters (= six years).

Page 75: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

13

According to Kjellström and Regnér (1999), the distance to an institution of higher education

is positively correlated with the cost of attending higher education – the longer the distance,

the higher the cost.19 So, in the present case, we can assume that if a student lives close to an

old university, it is more likely that this student will attend this institution instead of a new

university further away. The other way around is assumed to hold for students living close to

a new university. To account for possible effects of having one university type in the students’

direct neighborhood where they lived/grew up prior to university entry, the vector iCOM is

included in the model. iCOM contains two dummy variables which indicate if a student’s

residence one year prior to university entry was in the same or in a neighboring municipally to

a new and old university, respectively. and are parameters and i , and i are random

error terms that are assumed to be approximated by a normal bivariate distribution with

E( i )=E( i )=0, and var( i )=var( i )=1.

The identification of this model relies on the assumption that Zi is unrelated to the

student’s ability to complete a university education. Simultaneously estimating equation

(3a) and equation (3b), we allow the respective outcomes to be dependent on each other.

There are two core features of this model; one is that OLD in equation (3a) is

endogenous (i.e. directly an outcome of equation (3b)), the second is that the

simultaneous estimations allow the error terms to be correlated, i.e. cov( i , i ) = . The

basic assumption is: if there is an endogenous effect of choice of university type on

educational outcome that is not accounted for by the covariates in the model, i.e. if there

is a selection into university that indirectly affects educational outcome, this will be

caught by the covariance, , and it will differ significantly from zero.

5 Data and measurement The primary data source is the Longitudinal INdividual DAta for Sweden (LINDA), a random

sample of approximately three percent of the population in Sweden. The core data is based on

income-tax registers of 1994 and it contains a sample of roughly 300,000 individuals. In

19 Higher education is free of charge for students in Sweden. The costs referred to here are mainly those associated with room, board, and books. I assume that the overall cost for a student is lower if he/she can attend higher education within a commuting distance, while living with his/her parents, as compared to if the student has to move in order to attend higher education and explicitly pay for room and board.

Page 76: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

14

addition, population censuses and other register based data are added (see Edin and

Fredriksson (2000) for a description). The data is traced back to 1968 and up to 2006, and

individuals who for some reason leave the registers (die or leave the country) are replaced,

leaving the data to be used as longitudinal and as a representative annual cross-section sample

of the population in Sweden. In case the LINDA subject is a part of a family, the family

members are also registered, but only as long as they share the same household as the main

subject. Intergenerational relations among the individuals in LINDA are controlled for

through the intergenerational register.

Information about the highest level of completed education, based on highest degree, is to be

found in LINDA. However, more detailed information about education prior to university and

any activities made by a LINDA subject within the higher educational system in Sweden is

added: grade point average (GPA) of the final grades from upper secondary school and its

track character are added through the Swedish upper secondary school register (that dates

back to 1973); the Swedish Higher Education Register reveals activities within the higher

educational system such as first time entrance, reenrollments, choice of university, choice of

subjects, choice of courses, length of courses, courses passed, if the course was part of an

educational program etc. The main information in the Swedish Higher Education Register is

based on Ladok. Only students who attended a Ladok-connected university are considered,

meaning that the education offered was of a more ‘general’ kind (i.e. the remaining

universities have no outspoken specialization in music, drama, art, agriculture or sports).

Observe that in 1996, the 24 Ladok-connected universities (as compared to 36 today) hosted

roughly 90 percent of all students.20

5.1      The Sample 

The basic LINDA sample used here is a cross-section sample of 1996, conditioned on having

GPA from upper secondary school, at least one parent in the LINDA-data base, and being a

20 Old universities: Chalmers University of Technology, Göteborg University, Karolinska Institutet, Linköping University, Lund University, Stockholm University, The Royal Institute of Technology, Umeå University and Uppsala University. New universities: Blekinge Institute of Technology, Halmstad University, Högskolan Dalarna, Jönköping University, Kalmar University, Karlstad University, Kristianstad University, Luleå University of Technology, Mid Sweden University, Mälardalen University, University of Borås, Skövde University, Växjö University, University West and Örebro University.

Page 77: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

15

first time university entrant between the years 1996 and 1999, which leaves a sample of 5,565

students.21 The final sample is presented in Table 5.1.

All individuals in the sample are aged 18 to 37, with an average age of 22 years, containing

slightly more women than men and with a proportion of 95 percent that were born in Sweden.

The socioeconomic background is here entirely based on the information on the subjects’

parents. A large number of the parents in LINDA are in the data as a family member and once

their child moves away from home, information about the parents is not followed any further.

Data on family background is therefore collected at the age of 18, an age at which the vast

majority of youths attend their last year of upper secondary school, at which preferences for

university are taking form, and at which most youths still live at home with their parents. Data

on parents is collected between the years 1977 and 1996. Table 5.1 shows that more than half of the parents have elementary or upper secondary school

as their highest education. Only about one percent of all students only have one parent.

Family income is measured as a relative income, i.e. the income of one family is set relative

to all households that had an 18-year old in the LINDA-data base the same year, i.e. roughly

11,000 households per year (about 10 percent of all 18 year-olds in Sweden) since 1977.22

The household income, by which the students was affected at the age of 18, shows that, on

average, students at the older universities come from households with a higher income as

compared to students at the new universities. The GPA is notably lower for students at the new universities as compared to students at the

old universities. As for socioeconomic background, there are also obvious differences among

the subsamples. Students at older universities have more highly educated parents and their

family income is higher on average. Both in terms of educational level and income, some of

the differences can be explained by the fact that the average population in Sweden is more

educated and have a higher income in the metropolitan areas, where most of the old

universities are located.

21 In order to avoid the risk that a student is recorded twice as a new student in the higher educational system in Sweden, the oldest students considered here are aged 37. They were 18 years in 1977, date of the creation of the Register of Higher Education and the likelihood of they having been enrolled prior to 1977 is very small. 22 See the Appendix for an explanation.

Page 78: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

16

Table 5.1 Sample description

Variables Mean (Std. Dev). Mean (Std. Dev). Mean (Std. Dev).

Individual characteristicsAge 22.35 (4.12) 22.07 (4.67) 22.75 (4.35)

Woman* 0.56 (0.50) 0.55 (0.50) 0.58 (0.49)

Swedish* 0.95 (0.21) 0.95 (0.22) 0.96 (0.20)

Child* 0.05 (0.23) 0.04 (0.20) 0.07 (0.26)

Married* 0.06 (0.23) 0.04 (0.20) 0.08 (0.27)

GPA 14.69 (2.82) 15.13 (2.94) 14.10 (2.53)

   ‐ Natural Science track 0.33 (0.47) 0.39 (0.49) 0.26 (0.44)

   ‐ Social Sciences track 0.41 (0.49) 0.41 (0.49) 0.42 (0.49)

Lived in an area of NEW 0.41 (0.49) 0.26 (0.43) 0.62 (0.49)

Lived in an area of OLD 0.32 (0.46) 0.45 (0.49) 0.13 (0.33)

Family background at age 18

Father’s  highest education:Elementary* 0.18 (0.38) 0.14 (0.35) 0.22 (0.41)

Upper Secondary* 0.32 (0.47) 0.29 (0.45) 0.36 (0.42)

University* 0.31 (0.46) 0.37 (0.48) 0.23 (0.48)

Missing* 0.18 (0.38) 0.19 (0.39) 0.18 (0.41)

Mother's highest education:Elementary* 0.18 (0.38) 0.15 (0.36) 0.21 (0.41)

Upper Secondary* 0.40 (0.40) 0.35 (0.50) 0.43 (0.50)

University* 0.38 (0.49) 0.44 (0.50) 0.29 (0.46)

Missing* 0.04 (0.20) 0.04 (0.20) 0.04 (0.19)

Income quota 1.02 (0.57) 1.06 (0.60) 0.96 (0.52)Single parent* 0.01 (0.10) 0.01 (0.09) 0.01 (0.10)

SAMPLE

New UniversitiesAll Universities

A

Old Universities

5,565 3,211 2,354

Note:  A)  All 24 universities accounted for in this paper. *Dummy variables. All variables are explained in the Appendix.  

  

As mentioned in earlier sections, all student slots at all universities are restricted and subject o

competition. The most common way of competing is with GPA. GPA takes a value between 0

and 20, where 10 equals pass and 15 pass with distinction. The average GPA of the sample

presented in Table 5.1 of (14.69) is well above pass. That the older universities have a higher

GPA can partly be explained by the fact that many of students with high GPA apply to more

prestigious educational programs in medicine and law that are only offered at the old

universities. But only about 6-8 percent of all student slots at the older universities are

occupied by medical students and students in law, meaning that these prestigious educations

Page 79: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

17

just constitute a small proportion of the total educational production at these universities and

can only partly explain why their GPA is higher.23

5.2   Educational performances 

Turning to educational performances, Table 5.2 shows student performances after seven years

and they are broken down into subgroups based on individual and family characteristics and

by old and new universities. In the traditional case, only completed degrees are measured as a

performance. Since so many students obtain a considerable amount of credit points but lack a

formal degree, educational outcomes are measured both in terms of degree rates (Degree), and

in terms of obtained credit points (120 credits), i.e having obtained 120 credit points or more.

In the degree rate case, the rates of the old universities are slightly higher compared to those

of the new universities. The difference, however, increases more if we compare the rates of

students that have obtained 120 credit points or more. On average, a student at an old

university obtains 113 credit points and 57 percent of all its students manage to obtain at least

120 credit points. Comparable performances at the new universities are 95 credit points on

average, and 50 percent have obtained at least 120 credit points.

From the descriptive statistics in Table 5.2, there are no obvious differences as concerns

university types among any of the personal characteristics displayed. Students at the old

universities follow the descriptive statistics in Table 2.2 over Sweden, where they on average

take more credit points compared to students at the new universities. Once more, it must be

kept in mind that the proportion of longer educations is larger at the old universities as

compared to the new ones.

Men prefer studies in the natural sciences (Nat), including mathematics, technology etc., over

the social sciences (Soc), which include humanities, political science, law, economics, nursing

etc. Women seem to have preferences toward the social sciences. A higher proportion of

women attends educations that lead to a profession where the degree equals a license ( i.e a

professional degree, (License), nursing school for example. More than two thirds of all

courses for which both genders register are included in an educational program (Program)24 –

the proportion is slightly larger at the new universities as compared to the old universities.

23 Phone interview with Olof Nelsson, director at the Office of Evaluation, Lund University, December 1, 2008. Lund University is the largest university in Sweden. 24 A preset course package.

Page 80: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

18

According to Light and Strayer (2000), roughly one third of all students in the United States

transferred to other universities. The Swedish transfer rate is about the same (Transfers).

However, although some of the students presented in the transfer rate do change universities,

the majority of the students stay at the university of first choice and study in parallel or get a

second degree at other universities while or after studying at the university of first choice.

This behavior is prevalent for both university types.

Page 81: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table 5.2   

Educational perform

ances within seven years after first entering a  university 

Stud

ent

Cred

it point cha

racter (>

50%) :

Tran

sfers

Entrants

Soc

Nat

Program

License

Degree

120 ≤

Cred

it points

Variables

Total

Mean

(Std.D.)

(in %)

(in %)

(in %)

(in %)

(in %)

(in %)

(in %)

All (24

) Universities

5,56

510

5.50

(69.11)

6333

728

4354

33

Old Universities

3,21

111

2.92

(72.96)

6135

698

4457

33Individu

al cha

racteristics:

Man

1,456

114.34

(75.13)

4650

734

4257

32Wom

an1,752

111.73

(71.09)

7323

6711

4557

34Sw

edish bo

rn3,041

111.23

(73.07)

6135

708

4457

34Not Swed

ish bo

rn170

107.37

(70.80)

6232

6811

3456

26Family backgroun

d at age 18

Parents'  highe

st edu

catio

n:Elem

entary*

603

106.56

(72.03)

6530

6811

3754

32Upp

er Secon

dary*

1,104

105.50

(69.56)

6530

676

4454

29Co

llege*

1,504

120.91

(74.95)

5640

729

4760

37

New

 Universities

2,35

495

.37

(62.07)

6630

749

4150

33Individu

al cha

racteristics:

Man

994

95.34

(63.44)

4848

802

3547

34Wom

an1,360

94.67

(61.06)

7816

7113

4553

33Sw

edish bo

rn2,251

95.74

(61.92)

6629

748

4151

33Not Swed

ish bo

rn103

87.29

(65.01)

5837

7711

3741

43Family backgroun

d at age 18

Parents'  highe

st edu

catio

n:Elem

entary*

472

91.46

(62.21)

6729

748

3745

32Upp

er Secon

dary*

1,097

94.59

(60.91)

6728

748

4252

28Co

llege*

785

98.83

(63.48)

6332

759

4151

41

Educationa

l outcome:

Passed

Cred

it points

 Note:   *Here highest ed

ucation among both paren

ts is steering the categorization. Source: LINDA  and Svenska Högskoleregistret . 

Page 82: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

20

6 Results

Table 6.1 presents the results of the binomial probit model on the probability of university

completion transformed into marginal effects, for both the educational outcome “degree”

(Degree) and obtaining 120 credit points or more (P120). The model is estimated with and

without interaction with a dummy variable OLD (1 equals attending an old university, zero

equals attending a new university), and is referred to the Base Model and the Interacted

Model in the table. The first model allows us to measure the average effect of the various

covariates in the model and through the interacted model, we can see if the effects of the

covariates differ in impact depending on choice of university type.

The results of key interest here are if university choice is of importance for educational

outcome. In the Base Model, we can see that attending an old university increases the

students’ chances of university completion with on average 5 and 9 percentage points, for the

educational outcomes Degree and P120, respectively. In the Interacted Model, we control for

the interacted effect of the covariates and university choice and besides some differences in

the interacted covariates, there is no additional university type effect on university

completion.

If we look at how individual and family background characteristics affect university

completion, almost none of the covariates of family background show any impact whatsoever.

The impact of age is overall significantly negative on university completion, which sounds

reasonable. The older the students are, the less likely they will be to finish, due to factors of

family formation and the outside labor market that affect a student’s chances and willingness

to complete a university education. However, the age impact is so small that it can hardly be

considered a huge determinant of university completion. Other factors like having a child or if

the student is married seem to have a far greater impact on P120 than age. Having a child

increases the probability by roughly 17 percentage points, and being married decreases the

probability by about 10 percentage points. That especially children have a positive impact

could be interpreted as parents on maternity leave combining child care with taking self-

contained courses at a university. This may also explain why the child effect (and the

marriage effect) is not to be found for the Degree-outcome, i.e. their incentive when studying

may not be to take a degree, just the courses themselves.

Page 83: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

21

Heckman and Cameron (1993) argued that the value of the GPA is one of the greatest means

of forecasting whether a student will succeed at university or not. The higher the GPA, the

higher the likelihood of success. According to the Swedish results in Table 6.1, we can see

that this also seems to hold for Sweden, where the impact of GPA on university completion is

on average 2 percent, i.e. the higher the GPA, the greater the chances of university

completion. If we also take into account what track the student took in upper secondary

school, having a background in theoretical tracks like Social Sciences or Natural Sciences

increases the impact by roughly 5-10 percentage points on the outcome P120, as compared to

a more vocational-oriented track in upper secondary school.

Vocationally oriented programs at the universities, like the nurse-training program, are among

the largest educational programs. They are dominated by women and it is a profession in

which a degree is equivalent to a professional degree. This is most likely the reason why the

probability of obtaining a degree is higher if the student is a Woman, the university education

is oriented towards the Social Sciences (which here include the caring professions), if the

degree is equivalent to a License, and if the education is part of a preset course program,

Program. Here, upper secondary educational background seems to be of less importance.

Other huge educational programs at the universities are in engineering and teaching, for none

of which a license is required to practice in a corresponding profession. This could explain

why the pattern is not quite the same if we look at the educational outcome for P120. The

gender impact is gone, and the impact of License is roughly one-third in size compared to its

impact on a Degree. But, once more, participating in a set course program, Program,

increases the student’s chances of university completion by roughly 50 percentage points.

Page 84: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

22

Majoring in the social sciences enhances the chances of university completion by roughly 9

and 14 percentage points, for educational outcome Degree and P120, as compared to

majoring in the natural sciences among those who study at a new university. Only in the

Degree-case does the impact of university subject major differ between the two university

types; the impact of studying in the social sciences is roughly 16 percentage points for

students attending a new university. At the old universities, on the other hand, we cannot see

that a subject major in the natural sciences or in the social sciences differs in impact on

university completion.25

The results from the bivariate probit model are shown in Table 6.2. Under Y1 in Table 6.2, the

outline is similar to that presented for the binomial probit estimations in Table 6.1, where the

probabilities of university completion are expressed as marginal effects. OLD is now

endogenous, however. If there are other factors associated with university choice that are not

accounted for in the model, e.g. selection on observables, this will be absorbed by the

covariance ρ. The probabilities of initial university choice, expressed as marginal effects, are

presented under Y2 in Table 6.2.

First of all, there are two outcomes that we want to highlight. By allowing the impact of

university type, OLD, on university completion to be endogenous under Y1 in Table 6.2, we

can see that this weakens its precision and its impact on university completion for both

educational outcomes, Degree and P120. The effect of university type is no longer significant.

Furthermore, in the P120-case, we can see that the covariance ρ is significant. This significant

and positive covariance means that we have a selection effect on observables of attending an

old university that is positively correlated with the probability for a student of completing

university with at least 120 credit points.

In the degree-outcome case, this effect of the selection on observables is not significant, nor

can we see any effect of choice on university type. A possible explanation is that the outcome

Degree only reflects the more vocational-oriented programs at the universities – and in some

cases, the students are more homogeneous across university types, whereas the educational

outcome P120 absorbs and reflects the entire educational production at both university types. 25 The university effect of studying in the social sciences instead of the natural sciences at the old universities in Sweden is roughly zero. This is traced by looking at the interacted models. In the Degree case, the marginal effect of attending an old university and subject majoring in the social sciences is 0.165-0.160=0.005 – i.e. almost no difference in the effect .

Page 85: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

23

For all other covariates under Y1, the results are pretty much the same as in the binomial

probit models presented in Table 6.1. 

In Table 6.2 under Y2, we can see that the covariates explaining the probability of attending

an old university act pretty much as expected. Having highly educated parents increases the

probability of attending an old university by roughly 8 percentage points. GPA has an overall

positive impact of on average 3 percentage points on attending an old university. Moreover,

knowing that the proportion of educations in the natural sciences is larger at the older

universities explains why having a background in the natural sciences from upper secondary

school increases the possibility of attending an old university by roughly 16 percentage

points, as compared to those choosing a more vocational-oriented education. A theoretical

background in the social sciences has a positive impact of about six percentage points on

attending an old university. This is a somewhat weaker impact, which can most likely be

explained by that fact that the younger universities to a larger extent constitute a possible

alternative for more students in the social sciences.

Controlling for several individual and family background variables as well as GPA scores in

the bivariate probit model when estimating the probability of attending an old university

allows us to exclude them in the interpretation of the covariance ρ. However, although we can

see that individual characteristics and family background are obviously of importance for

university choice, there are other unobserved factors, correlated with university choice that

seem to be of importance for university completion.

Page 86: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

24

Table 6.1  Binomial probit model on university completion  

Two educational outcomes are accounted  for: Degree and P120. The  results are  transformed  into marginal effects 

Y1( Degree) Y1( P120)

    Marg. Std. Err.     Marg. Std. Err.     Marg. Std. Err.     Marg. Std. Err.

OLD 0.054 *** 0.015 0.040 0.188 0.091 *** 0.016 0.141 0.188

Age ‐0.005 ** 0.002 ‐0.006 0.004 ‐0.011 *** 0.003 ‐0.009 ** 0.004

Woman 0.059 *** 0.016 0.051 ** 0.025 0.017 0.016 0.031 0.025

Swedish 0.070 ** 0.033 ‐0.001 0.057 0.044 0.036 0.060 0.057

Child 0.049 0.047 0.019 0.065 0.175 *** 0.042 0.154 ** 0.061

Married ‐0.001 0.044 0.026 0.063 ‐0.101 ** 0.044 ‐0.060 0.063

GPA 0.022 *** 0.003 0.019 *** 0.005 0.024 *** 0.003 0.018 *** 0.005

‐Nature Science 0.012 0.022 0.046 0.034 0.097 *** 0.023 0.103 *** 0.034

‐Social Science ‐0.040 ** 0.020 ‐0.036 0.028 0.054 *** 0.020 0.042 0.029

Father Upper Sec. 0.034 ** 0.018 0.029 0.026 0.002 0.018 0.030 0.027

Father UNI 0.029 0.019 0.041 0.031 0.018 0.020 0.055 0.032

Mother Upper Sec. ‐0.002 0.019 ‐0.025 0.028 ‐0.003 0.020 ‐0.002 0.029

Mother UNI ‐0.017 0.021 ‐0.047 0.032 0.000 0.022 ‐0.043 0.034

Fam_inc 0.000 0.014 0.020 0.023 ‐0.004 0.014 0.015 0.024

Single ‐0.121 0.074 ‐0.025 0.122 ‐0.046 0.081 ‐0.002 0.124

Social Science 0.088 *** 0.017 0.165 *** 0.022 0.142 *** 0.019 0.142 *** 0.024

Program 0.437 *** 0.013 0.468 *** 0.020 0.503 *** 0.014 0.542 *** 0.021

Transfers ‐0.172 *** 0.015 ‐0.199 *** 0.023 ‐0.263 *** 0.015 ‐0.244 *** 0.024

License 0.225 *** 0.028 0.273 *** 0.043 0.073 *** 0.028 0.049 0.042

Cohort 1997 0.031 0.020 0.039 0.033 0.027 0.021 0.037 0.033

Cohort 1998 0.050 ** 0.021 0.018 0.032 0.065 *** 0.021 0.046 0.033

Cohort 1999 0.039 * 0.021 0.007 0.032 0.067 *** 0.021 0.076 ** 0.032

Interaction with OLD: 

Age 0.000 0.005 ‐0.003 0.005

Woman ‐0.007 0.032 0.025 0.033

Swedish 0.110 0.072 ‐0.024 0.074

Child 0.072 0.095 0.040 0.094

Married ‐0.059 0.083 ‐0.086 0.089

GPA 0.004 0.006 0.010 0.006

‐Nature Science 0.004 0.040 ‐0.013 0.046

‐Social Science ‐0.061 0.043 0.018 0.041

Father Upper Sec. 0.008 0.036 ‐0.048 0.037

Father UNI ‐0.018 0.040 ‐0.059 0.041

Mother Upper Sec. 0.046 0.040 0.001 0.040

Mother UNI 0.057 0.044 0.070 0.044

Fam_inc ‐0.036 0.030 ‐0.034 0.031

Single ‐0.203 0.124 ‐0.089 0.163

Social Science ‐0.160 *** 0.029 0.001 0.033

Program ‐0.097 ** 0.041 ‐0.086 * 0.041

Transfers 0.039 0.034 ‐0.035 0.034

License ‐0.119 ** 0.050 0.043 0.057

Cohort 1997 ‐0.010 0.041 ‐0.016 0.043

Cohort 1998 0.058 0.043 0.035 0.043

Cohort 1999 0.058 0.042 ‐0.013 0.043

Log likelihood ‐3046.0197 ‐3020.1037  ‐2975.8287  ‐2965.2944

(Base Model) ( Interacted Model) (Base Model) ( Interacted Model)

Note: *, **, *** indicates a significance level at 10, 5, and 1 percent, respectively. Complete regressions are presented in the APPENDIX. 

Page 87: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table 6.2 

Recursive bivariate probit m

odel on university choice and university completion 

Two educational outcomes are accounted for: Deg

ree  and P12

0. The results are transform

ed into m

arginal effects.

Y1( Degree)

Y1( P120)

Y2(OLD)

Y2(OLD)

when Y1(Degree)

when Y1(P120)

   M

arg.

Std. Err.    Marg.

Std. Err.   M

arg.

Std. Err.   M

arg.

Std. Err.

   M

arg.

Std. Err.   M

arg.

Std. Err.

OLD

0.026

0.038

0.012

0.192

0.022

0.039

0.064

0.193

Age

0.009***

0.002

0.009***

0.002

Age

‐0.005*

0.003

‐0.006

0.004

‐0.010***0.003

‐0.008**

0.004

Woman

0.001

0.015

0.001

0.015

Woman

0.059***0.016

0.051**

0.025

0.017

0.016

0.031

0.025

Swedish

‐0.041

0.034

‐0.040

0.034

Swedish

0.068**

0.033

‐0.005

0.057

0.039

0.036

0.049

0.057

Child

‐0.079*

0.046

‐0.078*

0.046

Child

0.046

0.047

0.017

0.065

0.169***0.043

0.148**

0.061

Married

‐0.078*

0.043

‐0.079*

0.043

Married

‐0.004

0.044

0.022

0.063

‐0.107**

0.044

‐0.069

0.063

GPA

0.032***

0.003

0.032***

0.003

GPA

0.023***0.003

0.020***0.005

0.026***0.003

0.020***0.005

‐Nature Science

0.158***

0.020

0.158***

0.020

‐Nature Science

0.017

0.023

0.052

0.034

0.109***0.023

0.118***0.034

‐Social Science

0.059***

0.019

0.059***

0.019

‐Social Science

‐0.037*

0.020

‐0.034

0.028

0.060***0.021

0.048*

0.029

Father Upper Sec.

‐0.009

0.017

‐0.009

0.017

Father Upper  Sec

0.034*

0.018

0.029

0.026

0.001

0.018

0.029

0.026

Father UNI

0.079***

0.019

0.080***

0.019

Father UNI

0.031

0.020

0.043

0.032

0.023

0.020

0.061*

0.032

Mother Upper Sec.

0.008

0.019

0.008

0.019

Mother Upper Se

‐0.002

0.019

‐0.024

0.028

‐0.003

0.020

‐0.001

0.029

Mother UNI

0.080***

0.020

0.080***

0.020

Mother UNI

‐0.014

0.021

‐0.044

0.033

0.006

0.022

‐0.035

0.034

Fam_inc

0.012

0.013

0.012

0.013

Fam_inc

0.000

0.014

0.021

0.023

‐0.002

0.014

0.018

0.024

Single

0.016

0.077

0.015

0.077

Single

‐0.121

0.074

‐0.026

0.122

‐0.047

0.081

‐0.003

0.123

Cohort 1997

0.005

0.020

0.005

0.020

Social Science

0.089***0.017

0.165***0.022

0.142***0.018

0.142***0.024

Cohort 1998

‐0.022

0.020

‐0.022

0.020

Program

0.436***0.013

0.468***0.020

0.501***0.014

0.543***0.022

Cohort 1999

‐0.041**

0.020

‐0.041**

0.020

Transfers

0.172***0.015

‐0.198***0.023

‐0.262***0.015

‐0.239***0.024

Live near Old

0.240***

0.017

0.239***

0.017

License

0.225***0.028

0.272***0.043

0.074***0.028

0.048

0.042

Live near New

‐0.255***

0.016

‐0.256***

0.016

Cohort 1997

0.031

0.020

0.040

0.033

0.027

0.021

0.039

0.033

Cohort 1998

0.049**

0.021

0.018

0.032

0.063***0.021

0.046

0.033

Cohort 1999

0.038*

0.021

0.006

0.032

0.064***0.021

0.073**

0.032

Interaction with OLD: 

Age

0.001

0.005

‐0.002

0.005

Woman

‐0.007

0.032

0.026

0.033

Swedish

0.112

0.072

‐0.017

0.073

Child

0.073

0.095

0.039

0.094

Married

‐0.058

0.083

‐0.083

0.089

GPA

0.004

0.006

0.009

0.006

‐Nature Science

‐0.063

0.043

‐0.017

0.046

‐Social Science

0.004

0.040

0.019

0.041

Father Upper Sec.

0.008

0.036

‐0.049

0.037

Father UNI

‐0.018

0.039

‐0.060

0.041

Mother Upper Sec.

0.046

0.040

0.001

0.040

Mother UNI

0.057

0.044

0.069

0.044

Fam_inc

‐0.037

0.030

‐0.036

0.031

Single

‐0.203

0.124

‐0.088

0.161

Social Science

‐0.160***0.029

0.000

0.032

Program

‐0.099**

0.041

‐0.092**

0.041

Transfers

0.036

0.034

‐0.043

0.034

License

‐0.118**

0.050

0.046

0.057

Cohort 1997

‐0.011

0.041

‐0.019

0.043

Cohort 1998

0.057

0.043

0.032

0.043

Cohort 1999

0.058

0.042

‐0.013

0.043

Roh (coefficient)

0.051

0.064

0.055

0.065

0.122**

0.064

0.141**

0.065

Log likelihood

‐6160.964 

‐6134.9958 

‐6089.2398 

‐6078.1525

(Base M

odel)

(Interacted M

odel)

(Base M

odel)

(Interacted M

odel)

Note: *, **, ***

 indicate a significance level at 10, 5, and 1 percent, respectively.  Y1=1

 if the studen

t has obtained

 120 credit points or more or has taken

 a degree, and zero 

otherwise. Y2=1

 if an OLD

 university, zero if a new

 university. The outcome for Y2

 is the same, given

 both the base and the interacted

 model in

 Y1, so only one is presented 

here from the respective educational outcome. Complete regressions are presented in the APPEN

DIX. 

Page 88: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

26

7  Conclusions 

Is university type of any importance for university completion and is university success

affected by family background in Sweden? In a binomial probit model, where we do not

control for selection on observables that might affect the students’ choice of university type

which, in turn, might affect educational outcomes, the empirical results show that we have an

average university type effect of 5 and 9 percentage points, respectively, on a student’s

chances for educational success, where attending an old university increases the chances. In

this first case, family background has no impact on educational outcome.

In addition, we also estimate a bivariate model, where selection on unobservables into the

two types of universities is considered. For one of the outcomes, i.e. completing more than

120 credit points, the selection parameter turned out to be significantly different from zero

and the coefficient for an old university was not significantly different from zero. This means

that we cannot rule out the possibility that the higher probability of obtaining 120 credit

points in older universities is attributed to selection on unobservables.

References  

Altonji, Joseph G. (1993), The Demand for and Return to Education When Education Outcomes are Uncertain, Journal of Labor Economics, 11(1): 48-83.

Arcidiacono, Peter (2004), Ability sorting and the returns to university major, Journal of Econometrics, 121(1-2): 343-375.

Björklund, Anders, Mårten Palme and Ingemar Svensson (1995), Tax Reforms and Income Distribution: An Assessment Using Different Income Concepts, Swedish Economic Policy Review, 22(2):267–269.

Brint, Steven and Jerome Karabel (1989), The Diverted Dream: Community Colleges and the Promise of Educational Opportunity in America 1900-1985, New York: Oxford University Press.

Cameron, Stephen, and James Heckman (1993), The Nonequivalence of High School Equivalents, Journal of Labor Economics, 11(1): 1-47.

--- (1998), Life Cycle Schooling and Dynamic Selection Bias: Models and Evidence for Five Cohorts of American Males, Journal of Political Economy, 106(2): 262-333.

--- (2001), The Dynamics of Educational Attainment for Black, Hispanic, and White Males, Journal of Political Economy, 109(3): 455-499.

Dale, Stacy Berg and Alan B. Krueger (2002), Estimating the Payoff to Attending A More Selective University: An Application of Selection on Observables and Unobservables, The Quarterly Journal of Economics, 117(4):1491-1527.

Dougherty, Kevin James (1994), The contradictory community university: the conflicting origins, impacts and futures of the community university, Albany, NY: State University of New York Press.

Page 89: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

27

Edin, Per-Anders and Peter Fredriksson (2000), LINDA - Longitudinal INdividual DAta for Sweden.Working Paper 2000:19, Uppsala, Sweden: Department of Economics, Uppsala University.

Eliasson, Kent (2006), College Choice And Earnings Among University Graduates In Sweden, Umeå Economic Studies 693, Umeå University, Department of Economics.

Empty promises: The myth of university access in America (2002), Washington, DC: Advisory Committee on Student Financial Assistance.

Eherenbergh, Ronald. G. (2004), Econometric studies of higher education, Journal of Econometrics, 121(1-2):19-37.

Greene, William H. (2008), Econometric Analysis, sixth edition, Prentice Hall. Kane, Thomas J. and Cecilia E. Rouse (1995), Labor Market Return to Two- and Four Year

College, American Economic Review, 85(3):600–614. --- (1999), The community university: educating students at the margin between university

and work, Journal of Economic Perspectives, 13(1):63–84. Kjellström, Christian and Håkan Regnér (1999), The Effect of Geographical Distance on the

Decision to Enroll in University Education, Scandinavian Journal of Education Research, 43(4): 335-348.

Keane, Michael P. and Kenneth I. Wolpin (1997), The Career Decisions of Young Men, Journal of Political Economy, 105(3):473-522.

Leigh, Duane E. and Andrew M. Gill (2003), Do community universities really divert students from earning bachelor's degrees?, Economics of Education Review, 22: 23–30.

Leigh, Duane E. and Andrew M. Gill (2004), The effect of community universities on changing educational aspirations, Economics of Education Review, 23:95–102.

Light, Audrey A., and Wayne Strayer (2000), The determinants of university completion: school quality or student ability?, Journal of Human Resources, 35(2):299–332.

Light, Audrey (1995), Hazard Model Estimates of Decision to Reenroll in School, Labour Economics, 2(4):381-406.

Manski, Charles F., (1989), Schooling as experimentation: a reappraisal of the postsecondary dropout phenomenon, Economics of Education Review, 8(4):305-312.

Rouse, Cecilia E. (1995), Democratization or diversion? - The effect of community universities on educational attainment, Journal of Business and Economic Statistics 13(2):217–224.

--- (1998), Do Two-Year Colleges Increase Overall Educational Attainment? Evidence from the States, Journal of Policy Analysis and Management, 17(4): 595-620.

Statistic Sweden (SCB) (1975), Högskolestatistik I. Nyinskrivna, närvarande och examinerade vid universitet och högskolor 1962/63 – 1971/72. (Promemoria från SCB 1975:2) Stockholm: Statistiska centralbyrån.

--- (2007): Universitet och Högskolor. Genomströmning och resultat i högskolans grundutbildning t.o.m.2005/06. Statistiska meddelanden UF 20 SM 0702.

--- (2008), Universitet och högskolor - Högskolenybörjare 2007/08 och doktorandnybörjare 2006/07 efter föräldrarnas utbildningsnivå [Higher education. Level of parental education], Statistiska meddelanden UF 20 SM 0802.

--- (2009): Befolkningens utbildning 2008[Educational attainment of the population 2008]. Statistiska meddelanden UF 37 SM 0901.

Öckert, Björn (2001), Effcets of Higher Education and the Role of Admission Selection, Stockholm University, Stockholm: Swedish Institute for Social Research No 52.

Alternative sources: Internet: www.ladok.se (December 1, 2008) Phone interview: Olof Nelsson,, Director of the Office of Evaluation, Lund University, (December 1, 2008), Lund University is the largest university in Sweden.

Page 90: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

28

APPENDIX    

A.  Variable descriptions Table A1 1.   Variable description 

Variables Description

120 credits  1 if the students obtains has least 120 credit points, 0 otherwise

Age  Numerical, 18‐46 years

Child  1 if the student has at least 1 child, 0 othertwise

Cohort 1996  1 if the student was enroled for the first time in 1996, 0 otherwise

Cohort 1997  1 if the student was enroled for the first time in 1997, 0 otherwise

Cohort 1998  1 if the student was enroled for the first time in 1998,, 0 otherwise

Cohort 1999  1 if the student was enroled for the first time in 1998, 0 otherwise

Degree  1 if the students obtains a degree, 0 otherwise

Lived in an area of NEW  1 if the student lived in the same or in the direct neighbor municipally of a new 

university the year before university entrance otherwiseLived in an area of OLD  1 if the student lived in the same or in the direct neighbor municipally of an old 

old university the year before university entrance, 0 otherwise

Fam_inc  A Family income, measured as relative income

Father Elementary  1 if the father has elementary education as highest education, 0 otherwise

Father Missing  1 if information about father's education is missing, 0 otherwise

Father UNI  1 if the father has a university education, 0 otherwise

Father Upper  1 if the father has upper secondary school as highest education, 0 otherwise

GPA  Grade Point Average from upper secondary School with a value between 0 and 20.

‐ Natural Sciences track  1 if a theoretical track in natural or technology track in upper secondary school, 0 otherwise

‐ Social Sciences track  1 if a theoretical track in social studies, economics or humanities in upper secondary school, 0 othe

License  1 if the education results in an occupation where the degree equals a license to practice it, 0 other

Male  1 if the students is a man, 0 otherwise

Married  1 if the student is married/cohabiting, 0 otherwise

Mother Elementary  1 if the mother has elementary education as highest education, 0 otherwise

Mother Missing  1 if information about mother's education is missing, 0 otherwise

Mother UNI  1 if the mother has a university education, 0 otherwise

Mother Upper 1 if themother has upper secondary school as highest education, 0 otherwise

Nature  1 if university the major is in mathematics, technology, chemistry, biology, etc. , 0 otherwise

Old  1 if an old college, zero equals a new college

Program  1 if the student is enrolled in an educational program, 0 otherwise

Single  1 if the family is a single‐parent household

Swedish  1 if the student was born in Sweden

Transfers  1 if Ladok has records of activities at other colleges, 0 otherwise Note: A) Family income is presented as relative net income (after tax reduction and received benefits) for the household to which the student belonged at the age of 18. 

Z

i

Z

i itit

itit

HousholdFAMincome

FAMincomeincomeFamily

1 1/

_  

where  itincomeFamily _ stands  for  the  nominal  income  of  the  household  of  student  i  at  time  t.  t  = 

(1968,...,1996)  indicates  the year  in which  the student  turned 18. The sum of all nominal  incomes year  t  is divided by all households  the  same year.  In  the  two‐parent household  case,  the nominal  income has been divided by 1.7 to able to compare one‐parent households with two‐parent households incomes of the 18‐year old (see Björklund, Palme, and Svensson (1995). 

Page 91: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

29

B.  Regression results Table: B1: Binomial probit model – Educational outcome: Degree 

Y1 (Degree) Coef. Std. Err. Coef. Std. Err.

OLD 0.141 *** 0.040 0.105 0.492

Age ‐0.012 ** 0.006 ‐0.015 0.010

Woman 0.154 *** 0.041 0.133 ** 0.065

Swedish 0.187 ** 0.092 ‐0.003 0.148

Child 0.125 0.119 0.050 0.167

Married ‐0.003 0.113 0.066 0.160

GPA 0.057 *** 0.007 0.049 *** 0.012

‐Nature Science 0.032 0.058 0.119 0.087

‐Social Science ‐0.104 ** 0.051 ‐0.094 0.074

Father Upper Sec. 0.089 ** 0.046 0.075 0.068

Father UNI 0.075 0.050 0.105 0.081

Mother Upper Sec. ‐0.004 0.050 ‐0.064 0.074

Mother UNI ‐0.043 0.054 ‐0.123 0.085

Fam_inc ‐0.001 0.036 0.052 0.060

Single ‐0.335 0.221 ‐0.067 0.325

Social Science 0.232 *** 0.046 0.441 *** 0.062

Program 1.341 *** 0.052 1.474 *** 0.088

Transfers ‐0.462 *** 0.042 ‐0.539 *** 0.066

License 0.572 *** 0.071 0.697 *** 0.114

Cohort 1997 0.081 0.053 0.101 0.084

Cohort 1998 0.129 ** 0.053 0.046 0.083

Cohort 1999 0.101 * 0.053 0.018 0.082

Interaction with OLD: 

Age 0.001 0.013

Woman ‐0.019 0.083

Swedish 0.288 0.189

Child 0.184 0.240

Married ‐0.158 0.229

GPA 0.011 0.015

‐Nature Science 0.010 0.104

‐Social Science ‐0.162 0.116

Father Upper Sec. 0.021 0.092

Father UNI ‐0.047 0.104

Mother Upper Sec. 0.119 0.101

Mother UNI 0.147 0.112

Fam_inc ‐0.095 0.078

Single ‐0.605 0.454

Social Science ‐0.429 *** 0.080

Program ‐0.255 ** 0.110

Transfers 0.101 0.086

License ‐0.326 ** 0.147

Cohort 1997 ‐0.026 0.108

Cohort 1998 0.148 0.109

Cohort 1999 0.150 0.108

Constant ‐2.244 *** 0.240 ‐2.098 *** 0.374

Log likelihood ‐3046.0197 ‐3020.1037  Note:  *,  **,  ***  indicates  a  significance  level  at  10,  5,  and  1  percent,  respectively.  

Page 92: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

30

Table: B2: Binomial probit model – Educational outcome: P120 

Y1(P120) Coef. Std. Err. Coef. Std. Err.

OLD 0.228 *** 0.040 0.355 0.479

Age ‐0.027 *** 0.006 ‐0.023 ** 0.009

Woman 0.043 0.041 0.078 0.064

Swedish 0.111 0.090 0.150 0.144

Child 0.455 ****0.118 0.399 ** 0.166

Married ‐0.254 ** 0.113 ‐0.150 0.159

GPA 0.060 *** 0.007 0.045 *** 0.012

‐Nature Science 0.246 *** 0.058 0.259 *** 0.086

‐Social Science 0.137 *** 0.051 0.105 0.073

Father Upper Sec. 0.005 0.046 0.074 0.067

Father UNI 0.046 0.050 0.137 * 0.080

Mother Upper Sec ‐0.008 0.050 ‐0.005 0.073

Mother UNI ‐0.001 0.055 ‐0.108 0.084

Fam_inc ‐0.010 0.036 0.037 0.060

Single ‐0.116 0.204 ‐0.005 0.311

Social Science 0.358 *** 0.047 0.357 *** 0.061

Program 1.399 *** 0.049 1.545 *** 0.081

Transfers ‐0.673 *** 0.041 ‐0.623 *** 0.065

License 0.186 *** 0.072 0.123 0.107

Cohort 1997 0.067 0.053 0.093 0.083

Cohort 1998 0.164 *** 0.054 0.117 0.082

Cohort 1999 0.168 *** 0.053 0.191 ** 0.081

Interaction with OLD: 

Age ‐0.007 0.013

Woman 0.062 0.083

Swedish ‐0.060 0.185

Child 0.101 0.239

Married ‐0.217 0.227

GPA 0.024 0.015

‐Nature Science ‐0.032 0.116

‐Social Science 0.046 0.104

Father Upper Sec. ‐0.121 0.092

Father UNI ‐0.147 0.104

Mother Upper Sec. 0.002 0.101

Mother UNI 0.176 0.112

Fam_inc ‐0.087 0.079

Single ‐0.225 0.414

Social Science 0.003 0.082

Program ‐0.215 ** 0.103

Transfers ‐0.089 0.084

License 0.109 0.144

Cohort 1997 ‐0.041 0.108

Cohort 1998 0.088 0.109

Cohort 1999 ‐0.032 0.108

Constant ‐1.760 *** 0.234 ‐1.885 *** 0.362

Log likelihood ‐2975.8287  ‐2965.2944  Note: *, **, *** indicates a significance level at 10, 5, and 1 percent, respectively.    

Page 93: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

31

Table: B3: Recursive bivariate probit model – Educational outcome: Degree 

Y1(Degree) Coef.     Std. Err. Coef.     Std. Err.Y2(OLD) Coef.     Std. Err. Coef.      Std. Err.

OLD 0.069 0.099 0.031 0.499 Age 0.023 *** 0.006 0.023 *** 0.006

Age ‐0.012 * 0.007 ‐0.015 0.010 Woman 0.002 0.040 0.002 0.040

Woman 0.154 *** 0.041 0.133 ** 0.065 Swedish ‐0.106 0.089 ‐0.106 0.089

Swedish 0.181 ** 0.092 ‐0.012 0.149 Child ‐0.201 * 0.117 ‐0.201 * 0.117

Child 0.119 0.119 0.043 0.168 Married ‐0.199 * 0.109 ‐0.199 * 0.109

Married ‐0.011 0.114 0.058 0.161 GPA 0.083 *** 0.007 0.083 *** 0.007

GPA 0.059 *** 0.008 0.051 *** 0.013 ‐Nature Science 0.420 *** 0.054 0.420 *** 0.054

‐Nature Science 0.044 0.060 0.135 0.088 ‐Social Science 0.154 *** 0.049 0.154 *** 0.049

‐Social Science ‐0.098 * 0.052 ‐0.087 0.074 Father Upper Sec ‐0.024 0.044 ‐0.024 0.044

Father Upper Se 0.089 * 0.046 0.075 0.068 Father UNI 0.208 *** 0.049 0.208 *** 0.049

Father UNI 0.080 0.050 0.112 0.081 Mother Upper Se 0.022 0.049 0.021 0.049

Mother Upper S ‐0.004 0.050 ‐0.064 0.074 Mother UNI 0.208 *** 0.053 0.208 *** 0.053

Mother UNI ‐0.037 0.055 ‐0.115 0.086 Fam_inc 0.032 0.034 0.032 0.034

Fam_inc 0.001 0.036 0.054 0.060 Single 0.040 0.201 0.040 0.201

Single ‐0.334 0.221 ‐0.068 0.324 Cohort 1997 0.014 0.052 0.014 0.052

Social Science 0.233 *** 0.046 0.441 *** 0.062 Cohort 1998 ‐0.058 0.053 ‐0.058 0.053

Program 1.340 *** 0.052 1.476 *** 0.088 Cohort 1999 ‐0.105 ** 0.052 ‐0.105 ** 0.052

Transfers ‐0.462 *** 0.042 ‐0.535 *** 0.067 Live near Old 0.654 *** 0.050 0.654 *** 0.050

License 0.572 *** 0.071 0.696 *** 0.114 Live near New ‐0.661 *** 0.043 ‐0.661 *** 0.043

Cohort 1997 0.081 0.053 0.103 0.084 Constant ‐1.651 *** 0.222 ‐1.651 *** 0.222

Cohort 1998 0.127 ** 0.053 0.046 0.083

Cohort 1999 0.098 * 0.053 0.016 0.082

Interaction with OLD: Age 0.001 0.013

Woman ‐0.018 0.083

Swedish 0.294 0189

Child 0.186 0.239

Married ‐0.156 0.228

GPA 0.011 0.015

‐Nature Science 0.010 0.104

‐Social Science ‐0.167 0.116

Father Upper Sec. 0.020 0.092

Father UNI ‐0.048 0.104

Mother Upper Sec. 0.119 0.101

Mother UNI 0.147 0.111

Fam_inc ‐0.096 0.078

Single ‐0.604 0.453

Social Science ‐0.429 *** 0.080

Program ‐0.262 ** 0.110

Transfers 0.093 0.087

License ‐0.324 ** 0.147

Cohort 1997 ‐0.029 0.108

Cohort 1998 0.145 0.109

Cohort 1999 0.149 0.108

Constant ‐2.251 *** 0.240 ‐2.111 *** 0.374

Roh 0.051 0.064 0.055 0.065

Log likelihood

(Base Model) ( Interacted Model)

‐6160.964  ‐6134.9958 

(Base Model) ( Interacted Model)

 Note: *, **, *** indicates a significance level at 10, 5, and 1 percent, respectively.    

Page 94: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

32

Table: B4: Recursive bivariate probit model – Educational outcome: P120 

Y1(P120) Coef.     Std. Err. Coef.     Std. Err. Y2(OLD) Coef.     Std. Err. Coef.     Std. Err.

OLD 0.055 0.098 0.161 0.485 Age 0.023 *** 0.006 0.023 *** 0.006

Age ‐0.025 *** 0.006 ‐0.021 ** 0.009 Woman 0.002 0.040 0.002 0.040

Woman 0.042 0.041 0.078 0.064 Swedish ‐0.105 0.089 ‐0.104 0.089

Swedish 0.097 0.090 0.124 0.144 Child ‐0.199 * 0.116 ‐0.199 * 0.117

Child 0.439 *** 0.119 0.382 ** 0.166 Married ‐0.200 * 0.109 ‐0.200 * 0.109

Married ‐0.270 ** 0.113 ‐0.173 0159 GPA 0.083 *** 0.007 0.083 *** 0.007

GPA 0.065 *** 0.007 0.051 *** 0.012 ‐Nature Science 0.419 *** 0.054 0.419 *** 0.054

‐Nature Science 0.274 *** 0.060 0.298 *** 0.087 ‐Social Science 0.154 *** 0.049 0.154 *** 0.049

‐Social Science 0.151 *** 0.052 0.121 * 0.073 Father Upper Sec. ‐0.024 0.044 ‐0.023 0.044

Father Upper Sec 0.004 0.046 0.073 0.067 Father UNI 0.208 *** 0.049 0.209 *** 0.049

Father UNI 0.059 0.051 0.154 * 0.080 Mother Upper Sec. 0.020 0.049 0.020 0.049

Mother Upper Se ‐0.007 0.050 ‐0.003 0.073 Mother UNI 0.208 *** 0.053 0.207 *** 0.053

Mother UNI 0.015 0.055 ‐0.089 0.084 Fam_inc 0.031 0.034 0.031 0.034

Fam_inc ‐0.006 0.036 0.044 0.060 Single 0.040 0.201 0.040 0.201

Single ‐0.117 0.204 ‐0.008 0.309 Cohort 1997 0.013 0.052 0.013 0.052

Social Science 0.358 *** 0.047 0.357 *** 0.061 Cohort 1998 ‐0.057 0.053 ‐0.058 0.053

Program 1.392 *** 0.049 1.546 *** 0.081 Cohort 1999 ‐0.106 ** 0.052 ‐0.106 ** 0.052

Transfers ‐0.671 *** 0.041 ‐0.610 *** 0.065 Live near Old 0.650 *** 0.050 0.650 *** 0.050

License 0.188 *** 0.072 0.120 0.107 Live near New ‐0.665 *** 0.043 ‐0.665 *** 0..043

Cohort 1997 0.068 0.053 0.099 0.083 Constant ‐1.649 *** 0.222 ‐1.651 *** 0.222

Cohort 1998 0.160 *** 0.054 0.116 0.082

Cohort 1999 0.161 *** 0.053 0.185 * 0.081

Interaction with OLD: Age ‐0.006 0.013

Woman 0.064 0.083

Swedish ‐0.043 0.184

Child 0.099 0.237

Married ‐0.209 0.225

GPA 0.024 0.015

‐Nature Science ‐0.042 0.115

‐Social Science 0.047 0.103

Father Upper Sec. ‐0.123 0.092

Father UNI ‐0.149 0.103

Mother Upper Sec. 0.001 0.101

Mother UNI 0.174 0.111

Fam_inc ‐0.090 0.078

Single ‐0.221 0.411

Social Science 0.000 0.081

Program ‐0.232 ** 0.103

Transfers ‐0.109 0.084

License 0.117 0.144

Cohort 1997 ‐0.048 0.107

Cohort 1998 0.080 0.108

Cohort 1999 ‐0.033 0.107

Constant ‐1.775 *** 0.233 ‐1.911 *** 0.361

Roh 0.122 ** 0.064 0.141 ** 0.065

Log likelihood ‐6089.2398  ‐6078.1525

(Base Model) ( Interacted Model) (Base Model) ( Interacted Model)

 Note: *, **, *** indicates a significance level at 10, 5, and 1 percent, respectively.   

Page 95: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

III

Page 96: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 97: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Are there sheepskin effects in the return 

to higher education in Sweden?  

  

By Susanna Holzer    

Abstract In contrast to previous studies on sheepskin effects (diploma effects), this study only focuses on Swedish university students with about the same number of years of education. A sample of 2,363 individuals is extracted from the Swedish Longitudinal INdividual DAta (LINDA). Individual characteristics, family background, ability, university type, and university major are controlled for, and the students are conditioned to have obtained at least 120 credit points (corresponding to three years of full-time study). Traditional OLS-models (log-wage models) are complemented with models based on propensity score matching to minimize possible bias due to self selection. Forcing the empirical material to become as homogeneous as possible, the idea here is to isolate possible sheepskin effects from other impacts that might be caused by pure heterogeneity in data. The results show that for male students, the wage-premium of possessing a degree, i.e. the sheepskin effect, is roughly 5-8 percent. For women, it is about 6-7 percent for those who have obtained 160 credit points or more. For students who attended a more prestigious university in the metropolitan areas in Sweden and majored in the natural sciences, a sheepskin effect of roughly 13 percent for men and 22 percent for women is traced. However, this result did not hold among students who attended a newer university outside the metropolitan areas and/or majored in the social sciences. JEL Classification: I23, J24, J31 Keywords: Returns to education, Sheepskin effects, Propensity Score Matching, Higher Education, Human Capital  

Correspondence address: Susanna Holzer, School of Management and Economics, Växjö University, SE-351 95 Växjö, Sweden. Phone: +46 470 70 85 79. E-mail: [email protected]. I am grateful to Mårten Palme, Håkan Locking, Thomas Lindh, Lennart Delander, Harald Niklasson, Mikael Lindahl and seminars participants at Växjö University and the SOLE-meeting in Boston, for helpful comments and suggestions. Financial support by Växjö University, Jan Wallander and Tom Hedelius’ Research Foundation, and the Swedish Research Council is acknowledged . All remaining errors are my own.

Page 98: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

2

1  Introduction  

 

Although the positive relationship between educational investments and earnings is one of the

most established relationships in the social sciences, we still argue about what exactly in the

educational investment affects earnings—is it years of schooling, credentials, or perhaps a

mixture of them both? Human capital theorists like Mincer (1974) argued that earnings are

mainly affected by the number of years of education, while other researchers point at the

importance of the acquisition of credentials by means of formal degrees.1 In the latter case, an

accredited worker earns more than its non-accredited counterparts, a phenomenon referred to as a

sheepskin effect; see e.g. Hungerford and Solon (1987), Jaeger and Page (1996), and Flores-

Laguunes and Light (2007).2 Understanding the impact and the importance of credentials as well

as of years of education is important for shaping educational policies. Empirical studies based on

competing views on the role of educational investments for labor market outcomes may help us

better characterize the relationship between such investments and their economic returns.

Once a university student in Sweden has passed and fulfilled a set of course-requirements for a

certain university degree, he or she can apply for a formal degree issued by the university he/she

attended. For the vast majority of degrees, the examination requirements are stipulated by

Swedish law and the formal certificate (the diploma) itself is free of charge for the student, except

for the costs in terms of time and effort spent on preparing the formal application. So, the basic

dilemma faced by all students is to value if the possible revenue of a formal degree on the labor

market exceeds the cost of applying for it. In 2005, less than 50 percent of all students who

entered university in the 1990s had a formal university degree (e.g. a bachelor’s, master’s or a

higher degree), another 20 percent were recorded to have passed university courses

corresponding to at least three years of full time study (the theoretical time length of study for a

bachelor’s degree), yet they had no formal degree. Some of them might not value a degree, and

therefore not worth applying for. A more common explanation, though, is the absence of one or

more of the courses that are required to fulfill the degree requirements. However, in terms of

years of education (or the amount of university credits points), they can be compared to other

students who have obtained their degree.

1 On human capital theory, see seminal work by Becker (1964 [1993]) and Schultz (1961). 2 Also referred to as a diploma effect or a credential effect.

Page 99: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

3

Two questions are raised in this study: First, is there a general difference in the economic

outcome for former university students with a degree, as compared to those with no formal

degree, i.e. is there a sheepskin effect? Second, does the sheepskin effect vary within groups of

university types, subject majors, and educational programs? Altonji (1993), Altonji et al. (2005),

Arcidiacono (2004), Brewer et al. (1999), and Dale and Krueger (2002) all point out that

information on school quality and educational programs, i.e. on university choice and university

majors, is of importance when the objective is to value the returns to university education.

Adding more information should make us divide students into various groups that, in turn, will

make the students within each group more homogeneous, which should make it possible to

isolate sheepskin effects on the labor market from other heterogeneity in the data. We also

control for whether a student attended an old (and often regarded as more prestigious) university,

if he/ she majored in mathematics, humanities, social sciences etc., and if the student was

enrolled in a vocational educational program such as a teacher training program, a program in

engineering, or a program in economics – in order to see how possible sheepskin effects may

vary depending on student group.

In contrast to most previous studies on sheepskin effects on the labor market, this study only

focuses on university students with about the same number of years of education. In addition to

university choice and within-university choices, individual and family characteristics and ability

are also considered. A random sample of 2,363 individuals was extracted from the Swedish

Longitudinal INdividual DAta (LINDA). The issue of whether the labor market values students’

educational performances differently if they have a formal degree is evaluated by controlling for

individual characteristics, family background, prior educational background, and by conditioning

the students in the sample to have obtained at least 120 credit points (corresponding to three years

of full-time study). Traditional OLS-models are employed and are complemented with models

based on propensity score matching.

The results show that men face a wage-premium of possessing a degree, i.e. the sheepskin effect,

of roughly 5-8 percent. For women, it is about 6-7 percent for those who have obtained 160 credit

points or more. For students who attended a more prestigious old university in the metropolitan

areas in Sweden, and majored in the natural sciences, a sheepskin effect of roughly 13 percent for

Page 100: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

4

men and 20 percent for women is traced. However, this result did not hold among students who

attended a newer university outside the metropolitan areas. Controlling for specific occupational

programs for economists, engineers and teachers did not, regardless of gender, give any

significant estimates of sheepskin effects.

The paper is organized as follows: Section 2 gives a description of how higher educational

performances are measured in Sweden. Section 3 briefly outlines previous research. Section 4

describes the data and measurements. Section 5 presents the econometric strategy that is

employed. Section 6 presents the estimated results and Section 7 concludes the paper.

2 Measurement of higher educational performances in Sweden The most common forms of higher education offered to the students at Swedish universities are

program based and normally result in students being eligible for a formal university degree (e.g. a

bachelor’s or master’s degree, a nursing diploma etc.). Attending the university is free of charge

for the student. The students may freely choose university and education, but they must compete

for the student slots available in their desired education, since these are limited by the

government.3 The universities may set certain requirements, but they are not allowed to choose

among eligible students. For the vast majority of students, the Grade Point Average (GPA) from

upper secondary school is the only characteristic with which they compete.

As a consequence of a structural reform of the sector of higher education in Sweden in 1993, a

majority of all universities jointly introduced a system for documentation of the academic

performances by all their students – called Ladok.4 Educational performances are measured in

3 The sector of higher education in Sweden is to a very large extent centrally monitored by the Swedish government and it is supply-side oriented. Since no universities are allowed to charge any students a university fee, and alternative income sources are rare, all universities are dependent on public funding to finance their educational production. The government, in turn, sets stipulations of performance funding, performance budgeting, and outcome assessment on each university, and thereby steers the entire production of higher education in Sweden. 4 An ADB-based system for documentation of university studies. At the time of the introduction of Ladok, 21 of 40 universities were connected to Ladok. By the end of the 1990s, that number had grown to 36. This means that it has become the government’s largest source of information on higher educational production. Colleges that are not connected to Ladok are, mostly, universities that specialize in art, drama, and music, but basically all universities that offer more general educations in social sciences, natural sciences, technology, humanities, medicine, etc. are connected to Ladok (see www.ladok.se).

Page 101: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

5

credit points, where one week of successful full-time study by a student corresponds to one

credit point (given that the student has passed the required examinations and fulfilled all

other requirements). A student who studies full time is assumed to obtain 40 credit points in

one year.

At least 120 credit points (three years of full-time study) are usually required to qualify for

an academic degree - which is the lowest requirement to qualify for a bachelor’s degree.5

Table 2.1 shows that a good 15 percent have obtained at least 120 credit points within three

years, about 55 percent within five years, and about 60 percent within seven years. After

seven years, only marginal changes in educational performances occur.6

Table  2.1  Student performances for the academic years 1993/34 to 2003/04

Academic College Earned degrees in percent 120 ≤ credit points in precentB

160 ≤ credit pointsB

YearA

Entrats3 years  5 years  7 years  Ladok  3 years  5 years  7 years  7 years 

Sweden

1993/94  59,490 10 32 46 47,070 17 52 59 35

1995/96  61,920 8 28 43 54,475 16 51 58 33

1997/98  58,930 6 28 44 54,955 16 54 61 35

1999/00  63,032 6 29 47 61,384 19 58 65 37

2001/02  69,514 5 29 68,054 20 57

2003/04  71,308 6 70,183 17

Note: A) An academic year starts at the end of August and ends in early June the following year.  B) New university entrants only include students who were enrolled at a university that was connected to the Ladok‐system at the time of entrance.  Source: Statistics Sweden (2007).  

   

Once the student has obtained enough credit points and has fulfilled the requirements for a certain

degree, he/she can apply for a formal degree at the university he/she attended. Table 2.1 shows a

lower frequency of degrees as compared to credit point production; about 6 percent had an

academic degree after three years, 29 percent after five years, and 45 percent after seven

years.7 This leaves roughly 20 percent of the students without a formal degree; they have,

5 There are more requirements, but they will not be further discussed here. See www.hsv.se for more information. 6 A possible explanation for the lower activities after seven years is that students are only allowed student grants and student loans for twelve semesters at most, i.e. six years. 7 In Sweden, a student must apply for a degree from the university that he or she attends, and will obtain it if he or she fulfills the requirements set by the government and, to some extent, the university, i.e. he/she has obtained the required combination of courses. This procedure in itself seems to undermine the incentives by the students to apply for a degree. As long as the degree is not equivalent to a license, e.g. a degree from a nursing school which is required for a nurse to be allowed to practice in the desired profession, employers seem to accept university transcripts of passed courses as proof of competence. This makes the Swedish official registers over the educational

Page 102: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

6

however, obtained 120 credit points or more.8 Possible explanations for this could, of course,

be that some of these students lack one or more of the required courses to fulfill the degree

requirements, or they may simply not value a formal degree sufficiently high to consider it

worthwhile applying for.

3 Previous research in brief As mentioned above, the most commonly used application of human capital theory is the one

associated with the work of Mincer (1974). He argues that accreditations have no impact on labor

market earnings. In brief, human capital theories argue that an investment in education adds to an

individual’s productivity and therefore increases the labor market value of his labor, reflected in

labor market earnings. Screening theories, like the theory of the sheepskin effect, argue that the

returns of an educational investment are due to specific educational credentials rather than

accumulated years of education. In accordance with the strict screening theory, years of education

only work as a filter/signal to sort more able students from less gifted ones. A degree works as an

additional signal of a student’s potential and endowments with characteristics that are desired by

the labor market, which is why we should observe higher labor earnings associated with degree

completion.9

Empirical studies by Hungerford and Solon (1987) found that the wage premium rose for

individuals in the year when they were assumingly awarded a university diploma. They used a

population survey from 1978 of roughly 16,500 individuals and found that the wage increase was

significantly larger between the 15th and 16th year of education (the year of a university diploma

from a four-year college), as compared to the difference between the 14th and 15th year of

education. Their conclusion was that about 10 percent of the additional wage increase in the year

of a diploma was caused by a screening effect by the labor market due to the individual holding a

formal diploma. These findings, in particular the discontinuity at 16 years of education, have

level of the population in Sweden somewhat askew since they only consider highest completed degree or, in some cases, whether the student has obtained 120 credit points. 8 Statistics Sweden does not provide this information divided into genders. 9 See seminal work on screening theories by Arrow (1973), Spence (1973) and Stiglitz (1975). Arrow (1973) argued that going to university could bee seen as a filter. The fact that some students are accepted for an education and others not provide a signal of a person’s traits that might be relevant on the labor market, and a university degree is only assumed to make this signal stronger.

Page 103: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

7

been confirmed in studies by Belman and Haywood (1991), Card and Kreuger (1992) and

Heckman, Layane-Farrar and Todd (1996).

Kane and Rouse (1995) analyze the returns to two-year community colleges and four-year

colleges using data that provides information on completed course credits for graduates and

dropouts. They found that course credits at two-year and four-year colleges have similar payoffs

on the labor market. The strongest evidence for potential sheepskin effects was found for

associate’s degrees10 for women (approximately 12 percent) and bachelor’s degrees for men

(approximately 24 percent).

Jaeger and Page (1996) used a population survey from 1992 of roughly 18,700 individuals in

which they controlled for both actual years of education and whether a person had received a

formal degree or not. In addition, Jaeger and Page tried to see if there were any differences in

possible sheepskin effects on the labor market besides the number of years of education if they

controlled for individual characteristics such as gender and race. They could confirm that there

was a sheepskin effect of receiving a bachelor’s degree of roughly 25 percent for white men, 30

percent for black men, 22 percent for white women, and 39 percent for black women. The authors

could only statistically verify that there was a sheepskin effect, the differences between races and

sexes were not statistically significant.

After having obtained employment, we expect the value of a diploma to decrease since the

employer now has more direct information about an individual’s labor market qualities and

abilities as a basis for assessing his/her productivity (mirrored in the wage premium). This is

confirmed by Belman and Heywood (1997) on basis of a comparison between wage incomes for

five different age-cohorts, which showed that there was a decrease in the wage premium of a

formal degree as the individual became older and gained work experience. They concluded that

the sheepskin effect on the labor market is to be seen as a short-term effect.

One of few Swedish attempts at detecting possible sheepskin effects on the Swedish labor market

was made by Antelius and Björklund (2001). For the purpose of analyzing the quality value of

10 An associate’s degree can be obtained in the US after two years of college studies, and is most frequently rewarded at the two-year colleges.

Page 104: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

8

using Swedish educational register data when estimating possible economic returns to an

educational investment, they compared the quality of the data in this register data with more

precise information based on the Swedish Level of Living Surveys (SLLS). Whereas the register

data mainly reveals highest completed education, based on highest completed degree, the SLLS

also gave information on partial educational performances. Partial educational performance was

defined as having some years of upper secondary school or university. The authors found that

partial education has a significant impact on wage income and thus, they concluded that

sheepskin effects are a minor/rare occurrence in Sweden.

4 Data and measurement The primary data source is Longitudinal INdividual Data for Sweden (LINDA), which is a

random sample of approximately three percent of the Swedish population. The core data is based

on income tax registers of 1994, and it contains a sample of roughly 300,000 individuals which in

addition was merged with population censuses and other register based data (see Edin and

Fredriksson (2000) for a description). The data is traced back to 1968 and up to 2006. In case the

LINDA subject is a part of a family and as long as he or she belongs to the same household, the

family members are registered as well — which makes it possible to trace intergenerational

relationships in the data. Such relationships in LINDA are controlled for by matching them

against the Swedish intergenerational register.

The vast majority of empirical research on education is based on data on self-reported

educational levels, of which most data only has one measure of educational attainment; years of

completed education or the highest degree obtained. The problem with self-reported data, as

pointed out by Card (1999) and Kane et.al. (1999), is that people with a university degree tend to

report this to a higher extent than individuals who only have a partial university education and, in

some cases, people tend to lie. This might cause traditional OLS estimates to understate the

returns per year of education.

To minimize the problem of self-reported data in this study, detailed information on subjects’

educational history, both prior to university and during a university education, is based on official

public records: the Swedish upper secondary school register (dates back to 1973) provides the

Page 105: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

9

GPA of the final grades from upper secondary school,11 and the Swedish Higher Education

Register reveals activities within the higher educational system, such as first-time entrance,

reenrollments, choice of university, choice of subjects, choice of courses, length of courses,

passed courses, if the course was part of an educational program, etc.

The main information in the Swedish Higher Education Register is based on Ladok, which dates

back to the school year 1993/1994. However, the information on where and when a student was

first enrolled goes back to 1977, which makes it possible for older individuals with university

experiences prior to 1977 to appear as ”new” students if they reenrolled in 1977 or later. To avoid

this, the oldest students that will be considered were born in 1959, i.e. those who were 18 years of

age in 1977, which is the age at which students on enter university at the earliest.

Moreover, only universities that became connected to the Ladok system no later than in 1993

are considered, meaning that 21 of today’s 40 universities are accounted for. However, they

roughly hosted 80 percent of the entire student population in 1993.12 These are all

universities that offer general and similar educations in social sciences, natural sciences,

technology, medical care, teacher’s training, etc.

4.1  Sample selection and descriptive statistics The basic sample consists of individuals who were first-time students at a Swedish Ladok

connected university during the years 1994-1996. If there are any sheepskin effects on the

Swedish labor market, the effect is expected to be strongest during the first years after the student

has left university. But to give as many students as possible the chance to fulfill their educational

aspirations and the chance to settle down on the labor market, the cross-section sample is taken

from the year 2006, and consists of 4,025 individuals. 11 Due to a reform of the entire upper secondary school system in Sweden in the mid 1990s, the system and rules for all grades changed as well. However, the Agency for Higher Education and the National Admissions Office to Higher Education (VHS) use a translation key in order to compare students with older and students with newer grades from upper secondary school; a method which is also employed in this study. 12 By restricting the universities to have become connected to Ladok no later than 1993, new establishments are excluded from the sample of universities, and the students’ study time is considered to be equal at all universities. The 21 universities accounted for are: Blekinge Institute of Technology, Chalmers University of Technology, Göteborg University, Halmstad University, Dalarna University, Kalmar University, Karlstad University, Karolinska Institutet, Kristianstad University, Linköping University, Luleå University of Technology, Lund University, Mid Sweden University, Mälardalen University, Stockholm University, The Royal Institute of Technology, Umeå University, Uppsala University, University West, Växjö University and Örebro University. See Holzer (2007) for a longer discussion. Observe that the vast majorities of schools in healthcare and nursing were not incorporated into Ladok until the late 1990s, which is why most of them are not accounted for in this study.

Page 106: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

10

Using annual wage instead of hourly wage could, according to Card (1999), cause an

overestimation of the educational impact on wage income. However, according to Antelius and

Björklund (2000), a wage-income level of at least 100,000 SEK in 1991 gave fair and comparable

results to using hourly wage income. The 100,000 SEK were based on three base amounts in

1991, and the comparable value of three base amounts in 2006 is 130,000.13 Furthermore,

regional labor market effects on wage-incomes are deflated, i.e. differences, if any, in average

wage structure caused by local labor markets are removed.14 Conditioning on university entry

years and an annual income of 130,000 SEK in 2006, this leaves a sample of 3,536 individuals.

Due to the fact that men and women tend to have different wage structures on the labor market,

which is reflected in labor market earnings, the individuals in the present study are grouped

according to gender.

Moreover, the sample is restricted in that only students that have obtained at least 120 credit

points, i.e. equivalent to three years of full-time study and the minimum number of credit points

required to qualify for a bachelors degree. Part of the obtained credit points are, furthermore,

controlled for to be at an intermediate and master level. In addition, all students in the sample are

conditioned by having at least one parent in LINDA and having GPA from upper secondary

school, which leaves a final sample of 2,363 individuals. The final sample is presented in Table

4.1, where the data is grouped into gender and whether the individual possessed a degree in 2006.

The sample of students contains slightly more women than men, and 97 percent of the individuals

were born in Sweden. In 2006, the individuals in the sample were aged between 28 and 44 years,

with an average age of 32-33 years. More than one third had a parent with a university education.

The average income of the household in which the students lived at the age of 18 was slightly

higher than that of comparable households in Sweden at that time.

13 Various income cut-off points of 100,000 SEK, 120,000 SEK,140,000 SEK and 160,000 SEK have been controlled for, but 130,000 SEK are used in this paper. If I go any lower, I tend to include part time workers, PhD students and other less representative labor market participants which just add more noise into the data. 14 In accordance with definitions by the EU and Statistics Sweden, I have divided Sweden into eight labor market regions. The average wage in each of these regions is used to calculate a ‘regional-wage-deflator’ that is used in order to clear the wage-data from regional differences. The regions are all described in the Appendix.

Page 107: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

11

GPA from upper secondary school takes a value between 0 and 20, where 10 equals pass and

qualification for university. According to Table 4.1, the average GPA of the sample is well above

a pass. There is a higher rate of female students with an educational background in the social

sciences from upper secondary school, whereas there is a higher rate of male students with an

educational background in the natural sciences. Students that are awarded a degree on average

have a slightly higher GPA as compared to those who are not awarded a degree.

Of all students in the final sample, 60 percent of the women and 73 percent of the men had 160

credit points or more in 2006, and 81 percent of the women and 72 percent of the men had a

university degree. The educational patterns from upper secondary school are followed by similar

choices of university majors: more women are majoring in the social sciences as compared to

men who to a higher degree major in the natural sciences. A higher share of women studied to

become teachers as compared to men, and more men than women chose to study to become

engineers.

Page 108: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

12

Table  4.1    Descriptive data   Women Men

Degree No Degree Degree No Degree

Variables Mean Std. Dev. Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.

Individual and family characteristics:

Age 32.248 3.121 32.372 2.985 32.509 3.097 32.785 3.131

Swedish 0.968 0.175 0.957 0.202 0.972 0.166 0.979 0.143

College fatherA 0.307 0.462 0.306 0.462 0.361 0.481 0.336 0.473

College motherA 0.373 0.484 0.438 0.497 0.448 0.498 0.356 0.480

Single parent A 0.243 0.429 0.240 0.428 0.187 0.390 0.221 0.416

Relative income of the household A 1.023 0.391 1.074 1.199 1.065 0.552 1.050 0.373

Upper Secondary School: 

Grade Point Average 15.923 2.224 15.280 2.227 15.389 2.481 14.517 2.488

     Natural SciencesB 0.242 0.428 0.225 0.418 0.568 0.496 0.457 0.499

     Social SciencesB 0.599 0.490 0.671 0.471 0.278 0.448 0.370 0.484

University: 

Metropolitan university 0.478 0.500 0.477 0.500 0.529 0.500 0.498 0.501

Educational achievements: 

Average credit points 179.983 47.195 173.493 39.697 189.697 52.298 166.602 34.887

     120 ≤ credit points 1.000 0.000 1.000 0.000 1.000 0.000 1.000 0.000

     160 ≤ credit points 0.604 0.489 0.566 0.497 0.734 0.442 0.547 0.499

Majors and programs:      Natural SciencesC 0.228 0.426 0.217 0.413 0.585 0.493 0.574 0.495

     Social Sciences C 0.695 0.491 0.767 0.423 0.379 0.485 0.351 0.478     Teacher's program 0.293 0.455 0.140 0.347 0.101 0.301 0.073 0.260     Economist program 0.100 0.300 0.105 0.307 0.089 0.284 0.104 0.306     Engineer program 0.076 0.265 0.054 0.227 0.286 0.452 0.228 0.421

Economic outcome: 

Annual Wage 2006 D 277,994 93,811 267,796 93,543 360,839 12,2997 341,022 131,863

Number of observations:  1071 258 745 289

Note:  All  students  have  obtained  at  least  120  credit  points,  i.e.  three  years  of  full  time  study.  A)  The  family background variables relate to conditions when the students were 18 years of age. B) Omitted are the vocational oriented tracks that are not to be classified into the two presented tracks in natural sciences and social sciences. C) Omitted are university majors that are vocational oriented and not to be classified as natural sciences and social sciences. D) Wage in Swedish kronor (SEK), where 8 SEK ≈ 1 US Dollar (March 25, 2009). All variables are described in the Appendix. 

Page 109: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

13

5 Econometric strategy Two probit models will be used in the following, first a log-wage model in the form of a

traditional Mincer equation and then a binary model where propensity score matching methods

will be employed.

According to the Mincer equation:

(1) uDXEXPEXPSLnW 542

3210 ,

where W stands for wages, S for years of schooling, EXP for years of labor market experience,

and X is a vector of additional individual and other covariates that may influence earnings, such

as age, gender, nationality, place of residence, etc. Hungerford and Solon (1987) and most of

their followers changed the numerical variable S into a vector of year dummies in order to isolate

possible sheepskin effects between years of schooling. Jaeger and Page (1996) followed this

strategy, but added an extra dummy variable, D, into the equation that indicated whether the

individual had formal educational credentials. is a parameter vector that measures the marginal

effect of each variable on the logarithm of a wage. The models were regressed with a traditional

OLS.

A drawback in comparing a treatment group with a non-experimental comparison group

could be that the results are biased due to self-selection (see Dehejia and Wahba (1999,

2002)). For instance, is the group of individuals with a degree self-selected so that these students

possess different traits as compared to their non-accredited counterparts? A possible solution to

minimize this possible bias is, according to Rosenbaum and Rubin (1983), to use a propensity

score matching method in which the first step is to estimate each individual’s probability of being

awarded a degree, i.e. each individual’s propensity score (p(X)). This is preferably done with a

binary model, e.g. a binomial probit model. The effect on the labor market of possessing a degree

can then be estimated by comparing the economic outcomes between individuals who have a

degree with those lacking one, given that they have similar propensity scores. (In this section,

individuals with a degree will henceforth be referred to as treated, and their counterparts with no

degree as untreated.)

Page 110: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

14

The propensity score matching method relies on the assumption of selection on observables, i.e.

that treatment participation and treatment outcome are independent conditional on observable

characteristics of students (see Heckman and Robb (1985). But for the matching to hold, it is

necessary to condition on the support common to both treated and untreated (see e.g., Heckman

et al. (1997, 1998) and Dehejia and Wahba (1999, 2002)). The common support assumption rests

on matches being made when individuals are similar on a certain amount of comparison attributes

(e.g. age, gender, GPA from upper secondary school), which constitutes the core in minimizing

the possible impact of the problem of self-selection on potential sheepskin effects. This

assumption forces the characteristics and the propensity scores of the treated and the untreated to

be as equal as possible. Furthermore, when using propensity score matching, only matches are

used, i.e. observations that lack common support are excluded.

It is important to note that when using matching methods, all empirical information must either

be time invariant or conditioned to hold prior to the treatment. Unlike the Mincer equation above,

no information after the fact that the students were awarded a degree or left school is added, e.g.

labor market experience is excluded. For the OLS estimates to be comparable with the results

based on matching, the covariate conditions that hold when using the matching methods will be

used when employing the OLS. Since all subjects are university students with a similar number

of years, years of education are excluded from the log-wage model as well. However, each

individual credit point obtained will be included.

In the ideal case, we would find a one-to-one match, but that often requires a very large (infinite)

sample size. Instead, we could match each person in the treatment group with the person among

the untreated whose propensity score is closest to that of the treatment group observation—

nearest neighbor matching. Considering that in the current case we have a small sample and that

the untreated are fewer than the treated, it would be necessary to implement a method that allows

the same comparison observation to be repeatedly used. Using replacement could, however,

result in large standard errors (since few of the observations of the untreated may be heavily

used). According to Monte Carlo simulation studies by Frölich (2004) and a recommendation by

Black and Smith (2004), density matching, using Epanechnikov kernel matching, performs much

better and gives more robust matching estimates than nearest neighbor matching, which is why

the former will be used in this study. Kernel density matching also allows for conditioning on

Page 111: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

15

covariates, yet we set bandwidths on how far apart a match is allowed to be in terms of its

propensity score, and still be considered a match.

Standard errors are obtained by bootstrapping. Here I allow 1000 iterations.

6 Results The results from employing OLS and propensity score matching are presented in Table 6.1 for

men and in Table 6.2 for women.15 The results are all conditioned on individual characteristics,

family background, GPA from upper secondary school, if the student has an upper secondary

education in the natural sciences or the social sciences, and if the student attended a metropolitan

university or not, studied in the field of natural sciences or social sciences and how many credit

points the student has obtained.

In the first panel of Table 6.1, the male students are conditioned to have obtained at least 120 or

160 credit points. In the 120 credit-point case, all students have just enough credit points to

qualify for a bachelor’s degree, and the estimations do show some indications that may be

interpreted as a sheepskin effect on the labor market. Here, an average diploma effect according

to the OLS results is roughly five percent, and according to the results obtained by propensity

score matching, the average treatment effect (ATE) of possessing a degree is roughly eight

percent. Increasing the restrictions to having obtained at least 160 credit points increases the

average diploma effect to eight percent, an effect which is not found in the results obtained by

propensity score matching.

In the second panel of Table 6.1, all male students have obtained at least 120 credit points and are

now sampled into groups based on university choice, university majors and a combination of

these two aspects. Among students that attended a metropolitan university, the estimates indicate

that having a degree increases the labor market earnings by almost 11 percent according to the

OLS, and by 15 percent according to the matched result. Among students who have attended less

prestigious universities outside the metropolitan areas in Sweden, no sheepskin effects are traced.

15 All matching estimates are obtained by using PSMATCH2 for STATA, by Leuven and Sianesi (2003).

Page 112: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

16

Only among students who majored in the natural sciences does the OLS results indicate a

diploma effect of six percent. These are results that cannot be traced in the social sciences or in

any of the results obtained by propensity score matching for both majors.

Conditional on both university choice and university major, the only combination in which any

sheepskin effects are found is among male students who attended a metropolitan university and

majored in the natural sciences, where the labor market value of a formal degree manifests itself

in an increase in labor market earnings by 14 percent according to the OLS, and by 15 percent

according to the results obtained by propensity score matching.16 No degree effects are found for

students studying at universities outside the metropolitan areas and/or majoring in the social

sciences or the natural sciences.

According to the results for women shown in Table 6.2, the only significant sheepskin effects are

found among students who have obtained 160 credit points or more. The OLS results indicate an

average diploma effect of six percent and the matched result of roughly seven percent. When

controlling for choice of university type and university major in the second panel of the table, no

conclusive and significant indications of sheepskin effects are obtained. One exception, however,

is that majoring in and having a degree in the natural sciences seems to be more highly valued on

the labor market, due to the fact that a degree gives a reward of roughly eight percent according

to the OLS and 13 percent additional wages according to the results obtained by propensity score

matching.

Sampling female students on both university choice and university major gives some additional

indications of sheepskin effects. Among students that majored in the natural sciences and

attended a metropolitan university, the possession of a degree gives an average value added on

the labor market of 19-23 percent.17 No such effect can be traced among any other combinations

of university type and university majors.

16 Similar results are to be traced if we narrow down the sample to only include students who have obtained 160 credit points or more. The results also hold if we exclude students in medicine. 17 The results also hold if we exclude students in medicine.

Page 113: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

17

Comparing the results for men and women, it seems most likely that students studying the natural

sciences at metropolitan universities are those driving the positive results obtained when only

controlling for the amount of credit points in the first panels of both tables.

For both men and women, three specific educational programs that are associated with a certain

occupation group are singled out; economists, engineers, and teachers. The matched average

effect of possessing a degree is all negative for men. Excluding engineers, this also holds for

women. However, note that these results are based on extremely small samples, which is why any

interpretations should be made with some caution and since no results are significant, no

conclusions on possible sheepskin effects can be drawn from these results.

The results obtained by propensity score matching are overall somewhat larger than the OLS

results, but at the price of weaker precisions.

Page 114: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table 6.1  

Propensity score estim

ates of the effect of possessing a university degree on the Swedish labor market – the case of men

 

Credit Points

Conditioning on college choice, college m

ajor, and a combination of these 

BOccupationsB

120 ≤

160 ≤

Metro.

Country

Nature

CSocial

CMet+N

atC

Met+SocC

Cou+N

atC

Cou+SocC

Engineer

Teacher

Economist

ATE

0.081**

0.088

0.148**

0.021

0.064

0.056

0.154**

0.120

‐0.049

‐0.005

‐0.013

‐0.0547

‐0.064

St. D

ev. 

0.042

0.056

0.061

0.042

0.048

0.054

0.080

0.093

0.092

0.049

0.075

0.053

0.109

Bandwidth

0.0006

0.001

0.002

0.002

0.001

0.002

0.001

0.004

0.0015

0.02

0.0025

0.1

0.007

OLS

0.047**

0.080***

0.107***

‐0.002

0.064**

0.052

0.135***

0.118***

0.014

‐0.011

‐0.020

‐0.0464

‐0.105

Std. D

ev 

0.022

0.029

0.034

0.029

0.031

0.033

0.048

0.051

0.040

0.044

0.039

0.0406

0.0674

Pseudo R2 before

0.079

0.095

0.132

0.048

0.116

0.062

0.153

0.128

0.081

0.030

0.113

0.071

0.105

Pseudo R2 after

0.026

0.055

0.054

0.041

0.082

0.026

0.053

0.035

0.055

0.037

0.131

0.015

0.080

Treated on support 

321

238

206

223

320

183

215

57

65

142

83

72

58

Untreated on support

223

119

106

132

115

137

58

67

38

79

40

19

30

Treated off support 

424

309

188

128

116

99

28

69

127

4130

18

Untreated off support

66

39

38

13

829

129

27

126

20

Sample

1,034

705

538

496

559

448

302

222

257

226

279

94

96

Note: *, *, and ***

 indicate a significance level o

f 10, 5, and 1 percent. Bandwidths are selected

 after using a minim

um root mean squared criterion, w

hich im

plies 

that the differences in covariates between treated and control group used in the matching procedure are non‐significantly different from zero. Bootstrap

ped

 estimates for matching estimates are based

 on 1,000

 replications, and standard errors are presented ben

eath the ATE results. Robust standard errors from the OLS 

estimates are presented ben

eath. A) Covariates: age, born Swed

ish, university father, university m

other, household relative income, upper secondary school: GPA, 

mathem

atics or social scien

ce directions, atten

ding a  metr opol ita n

  uni versit y. B) All studen

ts  have obtained at  least  120  credit point s. Excluded by  the covariate s 

are  the metr opolita n

  uni ver sity  indi cator  and  university m

a jor s.  C)  Excluded here ar e  studen

ts that  are m

a joring in  fields  that could  not  be  class ifie d

  int o  either  the 

social  or the  na tural  scie n

ces: 14 studen

ts at  the metropol itan

 universities (Metro)  and 13  student s  at the universities outside  the metr opolitan

  (Country) areas  in 

Swed

en. 

Page 115: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Table 6.2  

Propensity score estim

ates of the effect of possessing a university degree on the Swedish labor market – the case of women

  

Credit Points 

OccupationsB

120 ≤

160

 ≤Metro.

Country

Nature

CSocial

CMet+N

atC

Met+SocC

Cou+N

atC

Cou+SocC

Engineer

Teacher

Economist

ATE

0.029

0.072**

0.029

0.037

0.127*

0.022

0.225**

‐0.008

0.040

0.034

0.058

‐0.038

‐0.014

St. D

ev. 

0.027

0.035

0.049

0.034

0.058

0.030

0.113

0.045

0.078

0.033

0.206

0.056

0.114

Bandwidth

0.001

0.0008

0.001

0.003

0.005

0.0025

0.01

0.02

0.04

0.2

0.1

0.0025

0.01

OLS

0.030

0.057**

0.031

0.025

0.086*

0.012

0.190***

‐0.014

‐0.008

0.029

0.011

‐0.007

0.1011

Std. D

ev 

0.021

0.029

0.032

0.029

0.050

0.024

0.072

0.036

0.076

0.033

0.102

0.041

0.0771

Pseudo R2 before

0.028

0.051

0.042

0.038

0.163

0.022

0.220

0.028

0.298

0.030

0.072

0.056

0.212

Pseudo R2 after

0.014

0.035

0.030

0.030

0.093

0.019

0.104

0.025

0.110

0.010

0.062

0.068

0.137

Treated on support 

801

510

265

501

174

683

95

332

79

409

81171

46

Untreated on support

242

141

114

126

46

195

23

91

20

106

934

20

Treated off support 

270

153

247

58

70

61

46

318

0143

18

56

Untreated off support

16

59

910

312

07

12

09

Sample

1,329

809

635

694

300

942

176

426

124

516

95350

131

Conditioning on college choice, college m

ajor, and a combination of these 

B

Note: *, *, and ***

 indicate a significance level o

f 10, 5, and 1 percent. Bandwidths are selected

 after using a minim

um root mean squared criterion, w

hich im

plies 

that the differences in covariates between treated and control group used in the matching procedure are non‐significantly different from zero. Bootstrap

ped

 estimates for matching estimates are based

 on 1,000

 replications, and standard errors are presented ben

eath the ATE results. Robust standard errors from the OLS 

estimates are presented ben

eath. A) Covariates: age, born Swed

ish, university father, university m

other, household relative income, upper secondary school: GPA, 

mathem

atics or social scien

ce directions, atten

ding a metropolitan

 university. B) All studen

ts have obtained at least 120 credit points. Excluded by the covariates 

are the metropolitan

 university indicator and university m

ajors. C) Excluded here are studen

ts m

ajoring in fields that could not be classified

 in the social or the 

natural scien

ces: 14 students at metropolitan

 universities and 13 students at universities outside the metropolitan

 areas in Swed

en. 

Page 116: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

7 Conclusions

In contrast to most studies on sheepskin effects, the focus of this study is only on university

students that have invested in a similar number of years and fields of education. The idea is to

make the empirical sample as homogenous as possible in order to isolate possible sheepskin

effects of having obtained a formal university degree from other heterogeneities in the data. For

male students with 120 credit points or more (corresponding to three years of full-time study), the

wage-premium of possessing a degree, i.e. the sheepskin effect, is roughly 5-8 percent. For

women who have obtained 160 credit points or more, it is about 6-7 percent. These results on

Swedish students on the Swedish labor market are comparable to the US findings on US data

made by Hungerford and Solon (1987), Belman and Haywood (1991), Card and Kreuger (1992),

Heckman, Layane-Farrar and Todd (1996) and Kane and Rouse (1995).

When controlling for university type and university majors, we found that students (both genders)

who attended a more prestigious university in the metropolitan areas in Sweden and majored in

the natural sciences gained a sheepskin effect of roughly 13-22 percent. These results are most

likely driving the overall results found when only controlling for obtained credit points. No

diploma effects were traced for students who attended a newer university outside the

metropolitan areas, or who majored in the social sciences or for a combination of them both.

Controlling for specific occupational programs for economists, engineers and teachers did not,

regardless of gender, give any significant estimates of sheepskin effects. However, these results

are based on extremely small data samples and thus, alternative outcomes in future studies on

sheepskin effects cannot be ruled out.

Page 117: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

21

References 

Altonji, Joseph G. (1993), The Demand for and Return to Education When Education Outcomes are Uncertain, Journal of Labor Economics, 11(1):48-83.

Altonji, Joseph G., Todd E. Elder and Christopher R. Taber (2005), Selection on Observed and unobserved Variables: Assessing the Effectiveness of Catholic Schools, Journal of Political Economy, 113(1):151-184.

Antlius, Jesper and Anders Björklund (2000), How Reliable are Register Data for Studies of the Return on Schooling? An examination of Swedish data, Scandinavian Journal of Educational Research, 44(4):341-355.

Arcidiacono, Peter (2004), Ability sorting and the returns to university major, Journal of Econometrics, 121(1-2):343-375.

Arrow, Kenneth (1973), Higher education as a filter, Journal of Public Economics, 2:193-216. Becker, Garry. S. (1964 [1993]), Human Capital, 3 ed, Chicago: University of Chicago Press. Belman, Dale and John S. Heywood (1991), Sheepskin effect in the Return to Education: An

Examination of Women and Minorities, The Review of Economic and Statistics, 73:720-724.

Belman, Dale, and John S. Heywood (1997), Sheepskin Effects by Cohort: Implications of Job Matching in a Signaling Model, Oxford Economic Papers, New Series, 49(4):623-637.

Björklund, Anders, Mårten Palme and Ingemar Svensson (1995), Tax Reforms and Income Distribution: An Assessment Using Different Income Concepts, Swedish Economic Policy Review, 2: 229–266.

Black, Dan and Jeffery Smith (2004), How Robust is the Evidence on the effect of University Quality? Evidence from Matching, Journal of Econometrics, 121(1-2):99-124.

Brewer, Dominic J and Ronald G. Ehrenberg (1996), Does it pay to attend an élite private university? Evidence from the senior class of 1980, Research in Labor Economics, 15:239-72.

Brewer, Dominic J., Eric Eide, and Ronald G. Ehrenberg (1999), Does It Pay To Attend An Elite Private University? Cross Cohort Evidence on the Effects of University Quality on Earnings, Journal of Human Resources, 34(1):104-123.

Dale, Stacy Berg and Allan Krueger (2002), Estimating the Payoff to Attending a More Selective University: An Application of Selection on Observables and Unobservables, Quarterly Journal of Economics, 117(4):1491-1527.

Card, David. (1999), The casual effect of education on earnings, in Handbook in Labor Economics Vol. 3A, (red) Orley C. Ashenfelter and David Card, Amsterdam: North-Holland: Elsevier Science Publishers.

Dehejia, Rajeev, and Sadek Wahba, (2002), Propensity score matching methods for nonexperimental causal studies, Review of Economics and Statistics, 84:1, pp 151–161.

Dehejia, Rajeev, and Sadek Wahba (1999), Causal effects in nonexperimental studies: reevaluating the evaluation of training programs, Journal of the American Statistical Association 94(448):1053–1062.

Edin, Per-Anders and Peter Fredriksson (2000), LINDA - Longitudinal INdividual DAta for Sweden.Working Paper 2000:19, Uppsala, Sweden: Department of Economics, Uppsala University.

Flores-Lagunes Alfonso and Audrey Light (2007), Interpreting Sheepskin Effects in the Returns to Education, Econ Working paper 0707, Department of Economics, University of Arizona

Frölich, Markus (2004), Finite sample properties of propensity score matching and weighting estimators, Review of Economics and Statistics, 86:(1): 77–90.

Page 118: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

22

Heckman, James and Richard Robb (1985), Alternative Methods for Evaluating the Impact of Interventions, in J. Heckman and B.Singer (Eds.), Longitudinal Analysis of Labor Market Data, Econometric Society Monograph, No. 10 (Cambridge, UK: Cambridge University Press, 1985: 63–113.

Heckman, James Heckman, Anne Layne-Farrar and Petra Todd (1996), Human Capital Pricing Equations with an Application to Estimating the Effect of Schooling Quality on Earnings, The Review of Economics and Statistics, 78(4), 562-610.

Heckman, James, Hidehiko Ichimura, and Petra Todd (1997), Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Program, Review of Economic

Studies, 64(4): 605–654. Heckman, James, Hidehiko Ichimura and Petra Todd (1998), Matching as an Econometric

Evaluation Estimator, Review of Economic Studies, 65: 261-294. Holzer, Susanna (2007), The Expansion of Higher Education in Sweden and the Issue of Equality

of Opportunity, CAFO working paper series No 5, Växjö University. Hungerford, Thomas and Garry Solon (1987), Sheepskin effects in the returns to education”,

Review of Economics and Statistics, 69: 175–177. Jaeger, David. A. and Marianne E. Page (1996), Degrees Matter: New Evidence on Sheepskin

Effects in the Returns to Education, Review of Economics and Statistics, 78:733-740. Kane, Thomas J. and Cecilia Elena Rouse (1995), Labor Market Return to Two- and Four Year

College, American Economic Review, 85(3):600–14. Kane, Thomas J., Cecilia Elena Rouse and Douglas Staiger (1999), Estimating Returns to

Schooling when Schooling is Misreported, NBER Workin Paper, No. 7235. Mincer, Jacob (1974), Schooling, Experience, and Earnings, NY Columbia University Press. Rosenbaum, P. and D. Rubin, 1983, The central role of the propensity score in observational

studies for causal effects, Biometrica, 70:41-55. Schultz, Theodore W. (1961), Investment in Human Capital, The American Economic Review,

51(1):1-17. Spence, Michael, (1973), Job market signaling, Quarterly Journal of Economics, 87:355-374. Stiglitz, Joseph E (1975), The Theory of ‘Screening,’ Education, and the Distribution of Income,

American Economic Review, 65: 283-300.

Page 119: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

23

APPENDIX 

A1.   Variable description 

Table  A1.1   Variable description 

Variable name Description

Age Age in 2006Age Squared Age in 2006, squaredSwedish 1 if born in Sweden,  0 otherwise College parents 1 if the father has a college degree, 0 otherwise 

Relative income A relative income for the households when the student was 18 years old

Upper Secondary School: Grade Point Average clusters ‐11.99; 12‐13.99: 14‐15.99: 13‐17.99; 18‐20‐ Mathematics 1 if the direction  was towards the natural sciences, 0 otherwise‐ Social Sciences 1 if the direction  was towards the social sciences, 0 otherwise

University type: 

Metropolitan college B 1 if Chalmers University of Technology, Göteborg University, Karolinska institutet,  Lund University, Uppsala University,   Stockholm University, The Royal Institute of Technology,  0 otherwise

Natural Sceinces C 1 if the direction  was towards the natural sciences, 0 otherwise

Social Sciences C 1 if the direction  was towards the social sciences, 0 otherwiseCredit points cluster Clustered as:  120‐139; 140‐159; 160‐179; 180‐199; 200‐229; 230‐160 ≤credit points 1 if the student have achieved 160 credit points or more, 0 otherwise120 ≤credit points 1 if the student have achieved 120 credit points or more, 0 otherwiseTeacher program 1 if the student has 120 credit points or more at the program, 0 otherwiseEconomist program 1 if the student has 120 credit points or more at the program, 0 otherwiseEngineer program 1 if the student has 120 credit points or more at the program, 0 otherwiseDegree 1 if the student has a college degree, 0 otherwise 

Economic outcome: 

Wage D Labor market wage in 2006

Individual and family characteristics:

Note: A) Family income is presented as the relative net‐income (after tax reduction and received benefits) of the household to which the student belonged at the age of 18. 

Z

i

Z

i itit

itit

HousholdFAMincomeFAMincomeincomeFamily

1 1/

_ , 

where  itincomeFamily _ is  the nominal  income of  the household of student  i at  time  t.  t =  (1968,...,1996)  indicates  the 

year in which the student turned 18. The sum of all nominal incomes in year t is divided by all households the same year. In the two‐parent household case, the nominal income has been divided by 1.7 in order to compare the household income by a one‐parent with the ones by a two‐parent (see Björklund, Palme, and Svensson (1995)). B) These are all universities situated in the metropolitan  areas  in  Sweden,  they  are  somewhat older  and normally  looked upon as more prestigious universities. C) The social sciences  include: Humanities, Social Sciences, Economics, History and Law. The natural sciences  include Technology and Medicine. Observe that the caring profession  is excluded  in both groups, yet  it  is part of the total student sample. D) Annual wage  is “regional deflated”, meaning  that  the direct  impact on  regional  labor market  impact  is deflated away. According  to Statistic  Sweden  and  the  EU,  Sweden  is  divided  into  eight  labor  market  regions  (NUTS‐2  regions):  Stockholm,  östra Mellansverige,  Småland med öarna, Sydsverige, Västsverige, norra Mellansverige, mellersta Norrland, and övre Norrland. 

Page 120: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 121: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 122: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic
Page 123: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

1

Svensk sammanfattning Föreliggande sammanläggningsavhandling består av tre fristående artiklar röran-de svensk högskolepolitik, huvudsakligen under 1990-talet och början av 2000-talet. Avhandlingens empiriska delar bygger på statistiskt material från Statistis-ka Centralbyrån. De representativa urval av individer som används i samtliga studier har hämtats ur databasen LINDA. I avhandlingens två första artiklar delas de svenska högskolorna in i gamla respektive nya högskolor beroende på om de var etablerade som självständiga högskolor före respektive efter högskolerefor-men 1977. Artikel [I] analyserar hur den kraftiga och snabba expansionen av den svenska högskolan under 1990-talet påverkade ungdomars antagningsbeteenden, med be-toning på deras socioekonomiska bakgrund. Expansionen skedde vid samtliga högskolor i Sverige, men var särskilt markant vid de yngre lärosätena utanför storstadsregionerna – där några av dem ökade sitt studentintag med upp till 400 procent på tio år. De empiriska resultaten antyder att den ökade tillgången till högre studier i den lokala och regionala närmiljön har ökat sannolikheten för unga individer att studera - och då inte bara i närheten av individens bostadsort. Den ökade geografiska tillgängligheten verkar ha minskat det ”sociala avståndet” till högre utbildning. Mer precist betyder det att det har blivit mer allmänt accep-terat bland fler socioekonomiska grupper (även icke-akademiska) att betrakta högre utbildning som ett minst lika självklart val efter gymnasiala studier, som att inträda på arbetsmarknaden. Den relativt största ökningen av tillströmmande studenter har skett bland ungdomar från icke-akademiska hem, mer precist; gruppen ungdomar vars föräldrar har högst gymnasial utbildning. Artikel [II] i avhandlingen analyserar högskolevalets betydelse för individers chanser att klara sina studier. I undersökningen används två utfallsvariabler: (i) huruvida studenten har tagit en examen (motsvarande kandidatexamen eller hög-re) inom sju år efter högskoleinträdet eller ej; (ii) huruvida studenten uppnått minst 120 högskolepoäng eller mer (minsta poängkrav för att erhålla kandidatex-amen) inom sju år efter högskoleinträdet eller ej. Genom en binom probit-modell – där individ- och familjebakgrundsspecifika variabler kontrollerats, samt ge-nomsnittligt avgångsbetyg från gymnasiet inkluderats – visar resultaten att studi-er vid en äldre högskola ökar individens chanser att klara sina stunder med 5 procentenheter om utfallet är examen, och med 9 procentenheter om utfallet är 120 poäng eller mer. Med en utökad bivariat probit-modell – där vi tar hänsyn till att det kan förekomma en selektion in till respektive högskolekategori på grund av individers ickeobserverbara egenskaper – visar resultaten dock att det

Page 124: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

2

inte kan uteslutas att skillnader i prestation inte kan bindas till respektive hög-skolekategori. Artikel [III] i avhandlingen analyseras värdet av en examen (motsvarande minst kandidatexamen) på den svenska arbetsmarkanden. Till skillnad från tidigare studier fokuserar föreliggande studie endast på de studenter som har motsvaran-de minst tre års (heltids) högskolestudier – varav delar på avancerad nivå – där en del tagit en formell examen och andra inte. Resultaten visar att lönepremien av att ha en formell examen är cirka 5-7 procent för män. För kvinnor som har motsvarande minst fyra års heltidsstudier visar resultaten att examenspremien är 6-7 procent. För studenter som studerat vid de äldre lärosätena (Lund, Göteborg, Stockholm och Uppsala) och inom naturvetenskapliga ämnen, visar resultaten att lönepremien av att ha en formell examen är nästan 13 procent för män, respekti-ve 22 procent för kvinnor, jämfört med studenter från samma lärosäten som sak-nar en examen. Motsvarande lönepremie av en formell examen kunde inte esti-meras bland studenter vid andra lärosäten i Sverige, eller bland studenter som läst i huvudsak samhällsvetenskapliga ämnen.

Page 125: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

Acta Wexionensia Below please find a listing of publications in the Acta Wexionensia series. For more in-formation, please see www.vxu.se Series III (ISSN 1404-4307). From 2007 and onward. 106. Ann-Charlotte Larsson 2007, Study of Catalyst Deactivation in Three Different In-

dustrial Processes (doktorsavhandling), ISBN: 978-91-7636-533-5. 107. Karl Loxbo, 2007,Bakom socialdemokraternas beslut. En studie av den politiska

förändringens dilemman - från 1950-talets ATP-strid till 1990-talets pensionsuppgörel-se (doktorsavhandling), ISBN: 978-91-7636-535-9.

108. Åsa Nilsson-Skåve, 2007, Den befriade sången. Stina Aronsons berättarkonst (dok-torsavhandling), ISBN: 978-91-7636-536-6.

109. Anne Haglund Morrissey, Daniel Silander (eds.), 2007, The EU and the Outside World - Global Themes in a European Setting, ISBN: 978-91-7636-537-3.

110. Robert Nyqvist, 2007, Algebraic Dynamical Systems, Analytical Results and Nume-rical Simulations (doktorsavhandling), ISBN: 978-91-7636-547-2.

111. Christer Fritzell, Lena Fritzén, 2007, Integrativ didaktik i olika ämnesperspektiv. ISBN: 978-91-7636-548-9.

112. Torgny Klasson, Daniel Silander, 2007. Hot och hotbilder i globaliseringens tid – en studie av den svenska säkerhetspolitiska debatten. ISBN: 978-91-7636-550-2

113. Olof Eriksson (red.), 2007. Översättning och Kultur. Föredrag från ett symposium vid Växjö universitet 17-18 november 2006, ISBN: 978-91-7636-552-6

114. Henrik Tryggeson, 2007. Analytical Vortex Solutions to the Navier-Stokes Equation (doktorsavhandling), ISBN: 978-91-7636-555-7.

115. Sofia Ask, 2007. Vägar till ett akademiskt skriftspråk (doktorsavhandling), ISBN: 978-91-7636-557-1.

116. Cesar Villanueva Rivas, 2007 Representing Cultural Diplomacy: Soft Power, Cos-mopolitan Constructivism and Nation Branding in Mexico and Sweden. (doktorsav-handling), ISBN: 978-91-7636-560-1.

117. Elisabet Frithiof, 2007. Mening, makt och utbildning. Delaktighetens villkor för per-soner med utvecklingsstörning (doktorsavhandling). ISBN: 978-91-7636-554-0.

118. Mats Johansson, 2007. Product Costing for Sawmill Business Management (dok-torsavhandling). ISBN: 978-91-7636-564-9.

119. Rune Svanström, 2007. När väven blir skör och brister – erfarenheter av att leva med demenssjukdom (doktorsavhandling). ISBN: 978-91-7636-565-6

120. Sofia Almerud, 2007. Vigilance & Invisibility. Care in technologically intense envi-ronments (doktorsavhandling). ISBN: 978-91-7636-569-4.

121. Urban Ljungquist, 2007. Core Competence Matters: Preparing for a New Agenda (doktorsavhandling) . ISBN: 978-91-7636-567-0.

122. Jimmy Engren, 2007. Railroading and Labor Migration. Class and Ethnicity in Ex-panding Capitalism in Northern Minnesota, the 1880s to the mid 1920s (doktorsav-handling). ISBN: 978-91-7636-566-3.

123. Susanne Källerwald, 2007. I skuggan av en hotad existens – om den onödiga striden mellan biologi och existens i vården av patienter med malignt lymfom (doktorsavhand-ling). ISBN: 978-91-7636-568-7.

124. Gunilla Härnsten, Britta Wingård, 2007. Högskoleutbildning – Javisst, men med vem och för vad? ISBN: 978-91-7636-570-0.

Page 126: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

125. Thérèse Eng, 2007. Traduire l´oral en une ou deux lignes – Étude traductologique du sous-titrage français de films suédois contemporains (doktorsavhandling). ISBN : 978-91-7636-570-0.

126. Andreas Jansson, 2007. Collective Action Among Shareholder Activists (doktorsav-handling). ISBN: 978-91-7636-573-1.

127. Karl-Olof Lindahl, 2007. On the linearization of non-Archimedean holomorphic functions near an indifferent fixed point (doktorsavhandling) ISBN : 978-91-7636-574-8.

128. Annette Årheim, 2007. När realismen blir orealistisk. Litteraturens ”sanna historier” och unga läsares tolkningsstrategier (doktorsavhandling). ISBN: 978-91-7636-571-7.

129. Marcela Ramírez-Pasillas, 2007. Global spaces for local entrepreneurship: Stret-ching clusters through networks and international trade fairs (doktorsavhandling). ISBN: 978-91-7636-577-9.

130. Daniel Ericsson, Pernilla Nilsson, Marja Soila-Wadman (red.), 2007. Tankelyft och bärkraft: Strategisk utveckling inom Polisen. ISBN: 978-91-7636-580-9.

131. Jan Ekberg (red.), Sveriges mottagning av flyktingar – några exempel. Årsbok 2007 från forskningsprofilen Arbetsmarknad, Migration och Etniska relationer (AMER) vid Växjö universitet. ISBN: 978-91-7636-581-6.

132. Birgitta E. Gustafsson, 2008. Att sätta sig själv på spel. Om språk och motspråk i pe-dagogisk praktik (doktorsavhandling). ISBN: 978-91-7636-589-2.

133. Ulrica Hörberg, 2008. Att vårdas eller fostras. Det rättspsykiatriska vårdandet och traditionens grepp (doktorsavhandling). ISBN: 978-91-7636-590-8.

134. Mats Johansson, 2008. Klassformering och klasskonflikt i Södra och Norra Möre hä-rader 1929 – 1931 (licentiatavhandling). ISBN: 978-91-7636-591-5.

135. Djoko Setijono, 2008. The Development of Quality Management toward Customer Value Creation (doktorsavhandling). ISBN : 978-91-7636-592-2.

136. Elisabeth Björk Brämberg, 2008. Att vara invandrare och patient i Sverige. Ett indi-vidorienterat perspektiv (doktorsavhandling). ISBN: 978-91-7636-594-6.

137. Anne Harju, 2008. Barns vardag med knapp ekonomi. En studie om barns erfarenhe-ter och strategier (doktorsavhandling). ISBN: 978-91-7636-595-3.

138. Johan Sjödin, 2008. Strength and Moisture Aspects of Steel-Timber Dowel Joints in Glulam Structures. An Experimental and Numerical Study (doktorsavhandling). ISBN: 978-91-7636-596-0.

139. Inger von Schantz Lundgren, 2008. Det är enklare i teorin… Om skolutveckling i praktiken. En fallstudie av ett skolutvecklingsprojekt i en gymnasieskola (doktorsav-handling). ISBN: 978-91-7636-600-4.

140. Lena Nordgren, 2008. När kroppen sätter gränser – en studie om att leva med hjärt-svikt i medelåldern (doktorsavhandling). ISBN: 978-91-7636-593-9.

141. Mirka Kans, 2008. On the utilisation of information technology for the management of profitable maintenance (doktorsavhandling). ISBN : 978-91-7636-601-1.

143. Christer Fritzell (red.), 2008. Att tolka pedagogikens språk – perspektiv och diskur-ser. ISBN: 978-91-7636-603-5.

144. Ernesto Abalo, Martin Danielsson, 2008. Digitalisering och social exklusion. Om medborgares användning av och attityder till Arbetsförmedlingens digitala tjänster. ISBN: 978-91-7636-608-0.

145. Patrik Wahlberg, 2008. On time-frequency analysis and pseudo-differential opera-tors for vector-valued functions (doktorsavhandling). ISBN: 978-91-7636-612-7.

146. Morgan Ericsson, 2008. Composition and Optimization (doktorsavhandling). ISBN: 978-91-7636-613-4.

Page 127: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

147. Jesper Johansson, 2008. ”Så gör vi inte här i Sverige. Vi brukar göra så här.” Retorik och praktik i LO:s invandrarpolitik 1945-1981 (doktorsavhandling). ISBN: 978-91-7636-614-1.

148. Monika Hjeds Löfmark, 2008. Essays on transition (doktorsavhandling). ISBN: 978-91-7636-617-2.

149. Bengt Johannisson, Ewa Gunnarsson, Torbjörn Stjernberg (red.), 2008. Gemensamt kunskapande – den interaktiva forskningens praktik. ISBN: 978-91-7636-621-9.

150. Sara Hultqvist, 2008. Om brukardelaktighet i välfärdssystemen – en kunskapsöver-sikt. ISBN: 978-91-7636-623-3.

151. Jaime Campos Jeria, ICT tools for e-maintenance (doktorsavhandling). ISBN: 978-91-7636-624-0.

152. Johan Hall, Transition-Based Natural Language Parsing with Dependency and Con-stituency Representations (doktorsavhandling). ISBN: 978-91-7636-625-7.

153. Maria Fohlin, L’adverbe dérivé modifieur de l’adjectif. Étude comparée du français et du suédois (doktorsavhandling). ISBN: 978-91-7636-626-4.

154. Tapio Salonen, Ernesto Abalo, Martin Danielsson, 2008. Myndighet frågar medbor-gare. Brukarundersökningar I offentlig verksamhet. ISBN: 978-91-7636-628-8.

155. Ann-Christin Torpsten, 2008. Erbjudet och upplevt lärande i mötet med svenska som andraspråk och svensk skola (doktorsavhandling). ISBN: 978-91-7636-629-5.

156. Guillaume Adenier, 2008. Local Realist Approach and Numerical simulations of Nonclassical Experiments in Quantum Mechanics (doktorsavhandling). ISBN: 978-91-7636-630-8.

157. Jimmy Johansson, 2008. Mechanical processing for improved products made from Swedish hardwood (doktorsavhandling). ISBN: 978-91-7636-631-8.

158. Annelie Johansson Sundler, 2008. Mitt hjärta, mitt liv: Kvinnors osäkra resa mot häl-sa efter en hjärtinfarkt (doktorsavhandling). ISBN: 978-91-7636-633-2.

159. Attila Lajos, 2008. På rätt sida om järnridån? Ungerska lantarbetare i Sverige 1947-1949. ISBN: 978-91-7636-634-9.

160. Mikael Ohlson, 2008. Essays on Immigrants and Institutional Change in Sweden (doktorsavhandling). ISBN: 978-91-7636-635-6

161. Karin Jonnergård, Elin K. Funck, Maria Wolmesjö (red.), 2008. När den professio-nella autonomin blir ett problem. ISBN: 978-91-7636-636-3

162. Christine Tidåsen, 2008. Att ta över pappas bolag. En studie av affärsförbindelser som triadtransformationer under generationsskiften i familjeföretag (doktorsavhand-ling). ISBN: 878-91-7636-637-0

163. Jonas Söderberg, 2009. Essays on the Scandinavian Stock Market (doktorsavhand-ling). ISBN: 978-91-7636-638-7

164. Svante Lundberg, Ellinor Platzer (red.), 2008. Efterfrågad arbetskraft? Årsbok 2007 från forskningsprofilen Arbetsmarknad, Migration och Etniska relationer (AMER) vid Växjö universitet. ISBN: 978-91-7636-639-4

165. Katarina H. Thorén, 2008 “Activation Policy in Action”: A Street-Level Study of Social Assistance in the Swedish Welfare State. ISBN: 978-91-7636-641-7

166. Lennart Karlsson, 2009. Arbetarrörelsen, Folkets Hus och offentligheten i Bromölla 1905-1960 (doktorsavhandling). ISBN: 978-91-7636-645-5.

167. Anders Ingwald, 2009. Technologies for better utilisation of production process re-sources (doktorsavhandling) ISBN: 978-91-7636-646-2.

168. Martin Estvall, 2009. Sjöfart på stormigt hav – Sjömannen och Svensk Sjöfarts Tid-ning inför den nazistiska utmaningen 1932-1945 (doktorsavhandling). ISBN: 978-91-7636-647-9.

Page 128: University Choice, Equality, and Academic Performance275129/FULLTEXT01.pdf · University Choice, Equality, and Academic Performance. ... University Choice, Equality, and Academic

169. Cecilia Axelsson, 2009. En Meningsfull Historia? Didaktiska perspektiv på historie-förmedlande museiutställningar om migration och kulturmöten (doktorsavhandling). ISBN: 978-91-7636-648-6.

170. Raisa Khamitova, 2009. Symmetries and conservation laws (doktorsavhandling). ISBN: 978-91-7636-650-9.

171. Claudia Gillberg, 2009. Transformativa kunskapsprocesser för verksamhetsutveck-ling – en feministisk aktionsforskningsstudie i förskolan (doktorsavhandling). ISBN: 978-91-7636-652-3.

172. Kina Hammarlund, 2009. Riskfyllda möten. Unga människors upplevelser av sexu-ellt överförbara infektioner och sexuellt risktagande (doktorsavhandling). ISBN: 978-91-7636-653-0.

173. Elin K. Funck, 2009. Ordination Balanced Scorecard – översättning av ett styrin-strument inom hälso- och sjukvården (doktorsavhandling). ISBN: 978-91-7636-656-1.

174. Ann-Kari Sundberg, 2009. Le poids de la tradition. La gestion professorale de l’altérité linguistique et culturelle en classe de FLE (doktorsavhandling). ISBN : 978-91-7636-657-8.

175. Peter Bengtsson, 2009. Development towards an efficient and sustainable biofuel drying (doktorsavhandling). ISBN: 978-91-7636-659-2.

176. Linda Reneland-Forsman, 2009. A changing experience – communication and meaning making in web-based teacher training (doktorsavhandling). ISBN: 978-91-7636-660-8.

177. Anders Andersson, 2009. Numerical conformal mappings for waveguides (doktorsavhan-dling). ISBN: 978-91-7636-661-5.

178. Rune Svanström, 2009. När livsvärldens mönster brister – erfarenheter av att leva med demenssjukdom (doktorsavhandling). ISBN: 978-91-7636-662-2.

179. Mats Anderberg och Mikael Dahlberg, 2009. Strukturerade intervjuer inom missbruks-vården – en grund för kunskapsutveckling (doktorsavhandling). ISBN: 978-91-7636-663-9.

180. Arianit Kurti, 2009. Exploring the multiple dimensions of context: Implications for the design and development of innovative technology-enhanced learning environments (doktorsavhandling). ISBN: 978-91-7636-665-3.

181. Joakim Krantz, 2009. Styrning och mening – anspråk på professionellt handlande i lärarutbildning och skola (doktorsavhandling). ISBN: 978-91-7636-671-4.

182. Hans Lundberg, 2009. Kommunikativt entreprenörskap: Underhållningsidrott som totalupplevelse före, under och efter formeringen av den svenska upplevelseindustrin 1999-2008 (doktorsavhandling). ISBN: 978-91-7636-673-8.

183. Jens Nilsson, 2009. Transformation and Combination in Data-Driven Dependency Parsing (doktorsavhandling). ISBN: 978-91-7636-674-5.

184. Uffe Enokson, 2009. Livspusslet: Tid som välfärdsfaktor (doktorsavhandling). ISBN: 978-91-7636-676-9.

185. Karin Olsson, 2009. Den (över)levande demokratin. En idékritisk analys av demo-kratins reproducerbarhet i Robert Dahls tänkta värld (doktorsavhandling). ISBN: 978-91-7636-677-6.

186. Rüdiger Lincke, 2009. Validation of a Standard- and Metric-Based Software Quality Model (doktorsavhandling). ISBN: 978-91-7636-679-0.

187. Lina Andersson, 2009. Essays on economic outcomes of immigrants and homosexu-als (doktorsavhandling). ISBN: 978-91-7636-680-6.

188. Susanna Holzer, 2009. University Choice, Equality, and Academic Performance (doktorsavhandling). ISBN: 978-91-7636-681-3.

Växjö University Press S-351 95 Växjö www.vxu.se, [email protected]