samlet årgang - Økonomisk Institut - Københavns Universitet

THE EFFECT OF LABOUR MARKET CONDITIONS ON HIGHER EDUCATION COMPLETION 99
introduce the effect of financial aid variables and sabbatical year. All models are estimated
with and without the correction for unobserved individual heterogeneity. In each
case, a positive sign on a coefficient indicates that (education) duration decreases, i.e.
completion is quicker, while a negative sign indicates that the duration becomes longer.
Results of the first model show that the female indicator is not significant. This is
not a surprising result since studies have shown that the gender gap in education no
longer exists in countries such as US, Belgium, Spain, Canada and the Scandinavian
countries. According to the OECD study, women in the 25-34 year old age group in
Denmark even have a slight educational advantage over men (OECD Indicators,
1997). However, this model only allows for a gender difference through a shift of intercept
and there could potentially be gender differences in covariate effects, which are
not being allowed for here. The later the high school year of graduation, the longer is
the completion time, indicating the importance of cohort effects. Graduating from a
regular high school (gymnasium) is significant and negative, i.e. making duration
longer, even after allowing for program-specific baseline hazards. However, graduating
from technical high school is not significant. These findings indicate that the
individual’s type of high school is likely not a good proxy for underlying ability and
therefore we drop them in the next model. Age does not seem to be significantly
related to completion risk. The later is the calendar year, the lower the completion
hazard, which may reflect the effect of changing labour market conditions or increasing
generosity of the government-financed universal financial aid package (SU) over time.
The longer is the time between programs, the lower the risk that a subsequent spell will
be completed, suggesting depreciation of studying skills when not put to use. However,
if the spell is the first program, it has a lower hazard of completion. Both of these spellspecific
variables are significant. Cohabiter status speeds up degree completion but the
effect is not significant, while the presence of small children significantly reduces the
completion hazard as expected. The labour market variables are all strongly significant:
The higher the average wage of workers of similar educational background, the greater
is the completion hazard. The higher an individual's labour market earnings while
studying, the lower is the hazard of completion. Unexpectedly, unemployment upon
completion based on the unemployment experiences of workers holding a similar
degree significantly increases completion hazard. How do these results change when
we allow for unobserved heterogeneity with 3 discrete points of support? In column
(2), model 1, we see that the fit improves slightly (but not significantly so when doing
the likelihood ratio test) and although heterogeneity parameters themselves are not
statistically significant, probabilities p1 and p3 are non-zero indicating some type
of unobserved heterogeneity is present. Most of the estimated coefficients have the