Local contexts and the transition to university

The transition from secondary school to university or vocational training marks a crucial point in the individual life course and it is known that students’ chances of entering university are not equally distributed. Enrolment chances are influenced by previous education; better school performance increases a student’s probability of attending university (e.g.

Müller et al. 2009, Heine & Lörz 2007). However, even when students perform equally, enrolment in higher education largely depends upon social class origin. Higher parental education and occupational status increase the probability of entering higher education, whereas students from lower class origin are more likely to attend vocational training

programmes instead (Watermann, Daniel & Maaz 2014, Lörz 2012, Müller et al. 2009, Becker & Hecken 2008, Lauer 2002). Aside from social class origin, previous research indicates that women respond more strongly to institutional and contextual characteristics and changes (Lörz 2013, Helbig et al. 2011). Despite their lower school performance, students of immigrant origin are more likely to enter higher education as they are highly motivated to succeed in the receiving country (Kristen, Reimer & Kogan 2008).

Research that explicitly focuses on the influence of regional contexts on the transition to university is predominantly concentrated on two groups of explanatory variables:

characteristics of regional labour markets and the regional supply of higher education institutions. While analyses of enrolment rates typically find a positive effect of regional unemployment (e.g. Hillman & Orians 2013, Betts &

McFarland 1995), results for individual level data are less clear cut. Higher regional youth unemployment seems to increase students’ chances of entering university (Giannelli &

Monfardini 2003, Rizzica 2013, Flannery & O' Donoghue 2009, Albert 2000). It would appear that general unemployment rates have a positive impact on individuals’

participation in higher education (Lauer 2002). However, other studies find no significant effect (Becker & Hadjar 2013 for Eastern Germany, Rephann 2002) or the results indicate that enrolment is comparably high where unemployment rates are low (Helbig, Jähnen & Marczuk 2015, Becker & Hadjar

2013 for Western Germany, Heine & Lörz 2007). Fernández and Shoiji (2001) argue that these ambiguous findings are caused by different unemployment effects. Unemployment influences the costs of and returns to higher education (investment effect), i.e. high unemployment decreases the costs of higher or further education. On the other hand, unemployment also influences parental wealth and intergenerational transfers (wealth effect), which makes investments in further or higher education less likely when unemployment is high. Results indicate that both effects are present (Fernandez & Shoiji 2001, Micklewright, Pearson &

Smith 1990).

Another potential explanation of differing results refers the modifiable areal unit problem (MAUP). This describes the sensitivity of analytical results to the definition of spatial units. It could repeatedly be shown that different spatial units can lead to differences in results even when the same indicators are used (e.g. Fotheringham & Wong 1991).

Moreover, research suggests that the influence of regional unemployment on enrolment in university is group-specific.

Students with low social status are particularly affected, while there is no significant impact on the enrolment decisions of students from a higher social class origin (Sievertsen 2014, Bozick 2009, Beattie 2002). Reimer (2011) finds that this holds true only for the enrolment of women from lower social class origin, whereas other authors conclude that men are more strongly influenced by labour-market characteristics

(Bruckmeier, Fischer & Wigger 2013, Casarico, Profeta &

Pronzato 2012, Beattie 2002).

A second line of research on the impact of regional contextual conditions focuses on the role of the regional supply with higher education institutions (Reimer 2013). This supply is typically operationalised as distance to college or university.

The underlying theoretical assumption is that distance influences the monetary and emotional costs of higher education. Monetary costs relate to expenses for transportation or renting. Emotional costs evolve from the distance to peers, parents and the familiar living environment.

A large number of studies for various countries conclude that distance has a negative effect on enrolment chances (e.g.

Spieß & Wrohlich 2010, Frenette 2004, 2006, Sá, Florax &

Rietveld 2004, Rouse 1995, Tinto 1973). Moreover, distance also affects the type of college or university attended (Gibbons & Vignoles 2012, Ordovensky 1995, Rouse 1995).

Students with a lower social class origin and lower-ability students seem to be particularly disadvantaged by distance (Cullinan et al. 2013, Eliasson 2006, Frenette 2006). The same seems to apply to women (Helbig, Jähnen & Marczuk 2015, Heine & Lörz 2007). Turley (2009) criticises the distance approach for not taking the number of colleges in proximity into account as a greater selection should increase the chances of finding a college that matches one’s individual interests. Her findings show that each additional college within commuting distance has a positive effect.

Beyond quantitative aspects of the university infrastructure (e.g. distance and number of accessible universities) aspects of quality should play a role. Research on university rankings suggests that the perceived quality of a university is of relevance in educational decision-making (Weiss, Schindler

& Gerth 2015). This line of research does, in turn, not take regional contexts and the accessibility of universities into account.

Further problems might be caused by factors which influence the choice of residence and higher education decisions simultaneously (residential sorting, Gibbons & Vignoles 2012). To avoid this problem, some research focuses on the expansion of higher education (Rizzica 2013) and the founding of new universities in a specific regional context (Frenette 2009). The results suggest that the distance effect is indeed causal.