samlet årgang - Økonomisk Institut - Københavns Universitet

100
NATIONALØKONOMISK TIDSSKRIFT 2005. NR. 1
same signs and significance, except that calendar year and the expected unemployment
degree are no longer significant.
In Model 2, we drop previous high school type and add the squared term to time between
programs. The first column without heterogeneity does not fare especially well,
and only future expected wages, current earnings and the effect of children show up as
being statistically significant. In the second column when unobserved heterogeneity
is allowed, time between programs and the effect of first program are significant. In
general, Model 2 is worse than Model 1 in terms of fit. Note that for Models 1 and 2,
unobserved heterogeneity can be said to be present, because although the heterogeneity
distribution parameters (v’s) are not all statistically significant, the distribution
at least is not degenerate, with some variation and non-zero probabilities (p’s).
In Model 3, we relax the assumption of no gender difference in covariates and
allow a full set of gender interactions. The fact that even some of the gender-specific
interactions are significant indicates that men and women do have different hazard
functions and covariate effects which a single intercept term could not adequately capture.
In Model 3, the labour market variables continue to exert a significant influence
on time to completion, with higher expected wages speeding up the time to completion
and higher current earnings while studying slowing down completion times for both
men and women. However, only men experience a speeding up due to higher expected
unemployment rates, a result that appears contrary, but may be a result of the confounding
effects of different types of higher educational courses that have been aggregated
within each program type (short, medium, long) in the present analysis. Calendar year
and time between programs are significant for women, but weakly so, but both with a
negative sign indicating that completion times have been lengthening over time and
are longer, the longer the time that elapses between programs.
When controlling for heterogeneity in column 2, results do not move and the
heterogeneity distribution collapses at unit mass at P(V=0) =1, i.e. a degenerate
distribution. All the labour market factors have the same signs and significance as in
column 1. For women, time between programs remains weakly significant.
In the final model, Model 4, we try adding the effect of policy-related variables to the
previous model, i.e. financial aid variables (GRANT and WINDOW) and the effect of
taking a sabbatical year (BREAK). In general, these factors are not statistically significant,
except for men, being within the 70 months window means significantly longer
completion times, giving some evidence that financial aid policy does seem to work as
intended, but for this group only. The labour market variables continue to exert the same
signs and significance as in Model 3, and just as in Model 3, calendar year and time
between programs tends to slow down completion for women, and high-school year and
time between programs the same for men, but these effects are moderate. In general,