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98 fortsat ... Model 1 Model 2 Model 3 Model 4 W/out H With H W/out H With H W/out H With H W/out H With H Para S.D Para S.D Para S.D Para S.D Para S.D Para S.D Para S.D Para S.D bl4 -11,10 2,19 -11,13 4,12 -12,88 4,93 -17,49 5,03 -14,81 3,37 -14,81 6,51 -10,43 6,18 -10,43 3,29 bl5 -10,63 2,19 -10,61 4,13 -12,40 4,94 -16,94 5,03 -14,32 3,40 -14,32 6,53 -9,94 6,16 -9,94 3,27 bl6 -9,83 2,20 -9,72 4,14 -11,61 4,94 -16,01 5,02 -13,53 3,41 -13,53 6,53 -9,19 6,16 -9,19 3,26 bl7 -9,97 2,20 -9,76 4,16 -11,75 4,95 -16,00 5,00 -13,66 3,41 -13,66 6,55 -9,79 6,14 -9,79 3,27 bl8 -9,78 2,21 -9,49 4,19 -11,58 4,97 -15,56 4,97 -13,50 3,40 -13,50 6,58 -9,66 6,16 -9,66 3,26 3. Heterogeneity Distribution p1 0,70 0,08 0,00 0,00 p2 0,00 0,15 0,00 0,00 p3* 0,30 – 0,77 1,00 1,00 v1 -0,36 0,40 -21,05 86,01 -2,93 182,68 -3,21 15,65 v2 -2,93 34,60 0,16 7,12 -2,90 174,25 -3,26 16,44 v3* 0,83 – 2,05 – 0,00 – 0,00 – NATIONALØKONOMISK TIDSSKRIFT 2005. NR. 1 4. Model Fit # of Para 37 41 36 40 42 46 48 52 Log Lik -916,41 -915,42 -920,50 -918,36 -917,875 -917,875 -911,35 -911,35 1. p3 and v3 are not freely estimated. They are subject two constraints defined in the paper.
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
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Nationaløkonomisk Tidsskrift 143 (
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OM LÆREPLADSSPØRGSMÅLET 3 serier
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OM LÆREPLADSSPØRGSMÅLET 5 90000
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OM LÆREPLADSSPØRGSMÅLET 7 0,60 0
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OM LÆREPLADSSPØRGSMÅLET 15 Dette
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OM LÆREPLADSSPØRGSMÅLET 17 Tabel
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OM LÆREPLADSSPØRGSMÅLET 21 Ikke
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OM LÆREPLADSSPØRGSMÅLET 23 didat
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OM LÆREPLADSSPØRGSMÅLET 25 Litte
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MILJØMODELLER TIL ADAM 27 ling i D
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MILJØMODELLER TIL ADAM 29 Tekniske
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MILJØMODELLER TIL ADAM 33 NH 3 54%
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MILJØMODELLER TIL ADAM 35 Tabel 4.
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MILJØMODELLER TIL ADAM 37 8%). End
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MILJØMODELLER TIL ADAM 39 portsyst
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MILJØMODELLER TIL ADAM 41 Konkurre
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BØR VIRKSOMHEDER OPLYSES OM SANDSY
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Page 126 and 127: 126 NATIONALØKONOMISK TIDSSKRIFT 2
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148 NATIONALØKONOMISK TIDSSKRIFT 2
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150 Dahl, C., H. Hansen og J. Smidt
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152 Autoregressiv model. (Ymax = 4)
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154 25 20 15 10 5 Official discount
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160 25 20 15 10 5 0 -5 Figure 3. Re
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162 ry of monetary regimes - some l
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164 Table A.1. Danish interest rate
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166 continued ... NATIONALØKONOMIS
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172 Average UIB rate pr. year 100 9
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180 Per cent 40 35 30 25 20 15 10 5
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182 Table 2. Trend adjusted differe
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186 den, Finland, Austria, Germany,
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188 continued ... NATIONALØKONOMIS
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190 1.000 kr 150 100 50 0 -50 -100
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196 Forsørgerkvote 150 140 130 120
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202 Timer 2200 2000 1800 1600 1400
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204 Pct. af BNP 5 0 2001 2011 2021
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206 Pct. 18 16 14 12 10 8 6 4 2 0 2
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210 Procent af BNP 8 4 0 2001 2021
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212 Pct. 18 16 14 12 10 8 6 4 2 0 2
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236 Bundskatteprocent 5,5 Figur 1.
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242 1999. Scandinavian Economic His
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278 Pct. 0,20 0,15 0,10 0,05 0,00 -
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298 rapport, København. Velfærdsk
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314 hold til individuelle handelspa
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316 ters, and a comprehensive cover
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318 tations. An understanding of th
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LABOR SUPPLY BEHAVIOR AND THE DESIG
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BOGANMELDELSE 449 Første del er en
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