DETERMINANTS OF THE CHANGES IN DEGREE PROGRAMS

5.5. DETERMINANTS OF THE

The data used in the exercises are for 69 of the 72 universities included in the 9^th edition of U- Ranking³⁰, since their performance index is used as one of the explanatory variables. The rest of the variables come from the sources described in this chapter.

Results: differences between universities Table 5.6 shows the results of the multivariate re- gressions for the two dependent variables: the number of new degrees as a percentage of the total number of degrees (including double de- grees) offered in the 2020-2021 academic year (columns 1 and 2); and the number of new single degrees (excluding double degrees) as a percent- age of the total number of single degrees offered in that academic year (columns 3 and 4). New de- grees means degrees first opened for enrollment in the 2013-2014 academic year, as this is the first year for which information on student employ- ment rates is available. The analysis does not in- clude new double degrees separately because, as shown in previous sections of this chapter, most of the new double degrees were created by the older universities and are already included in the overall change in the offering.

Regarding the first variable analyzed, columns 1 and 2 of table 5.6 show the effects of a number of determinants when the autonomous community is, respectively, disregarded and controlled for.

The most significant results are as follows:

a) Private ownership has a marked positive ef- fect on the intensity of the changes that is statistically significant at 1% and larger than the effect controlled for by university age.

Age shows a positive and significant effect when the university was created after the 1983 LRU, that is, when it is middle-aged, compared to those classified as old. This could be because the more established uni- versities have a broad offering which they may not need to improve if it is already at- tractive; but it could also be that they find it more difficult to change because they have a more rigid structure. After controlling for ownership, a young age does not seem to be relevant for explaining the proportion of new degrees, possibly because the seven

30 The European University of Valencia, the International University of Valencia and the European University of the Canary Islands are not included in the multivariate

universities classified as young are all pri- vately owned.

b) The quality of a university, as reflected by the U-Ranking overall performance index is very relevant for explaining the offering of new degrees, since the effect is significant at 1%.

The interpretation is that the best universi- ties pay more attention to their results and their environment and so react earlier by changing their offering of degrees more sub- stantially, so as to maintain and improve their teaching and research quality.

c) Both the percentage of a university’s faculty with a PhD and the employment rate of its graduates show a negative effect on the in- tensity of change, although the coefficient is higher for the employment rate. The mean- ing is different, however. For faculty with a PhD, the interpretation is that stability can make structures more rigid and thus foster inertia. For the employment rate, the inter- pretation is that the worse the labor market outcome for graduates, as measured by the employment rate, the greater the risk for the university of not revising its policies. And adapting the offering by creating new de- grees is a way of reacting to that risk.

d) In the second model, when we control for the effect of the autonomous community in which each university is located, only Castile- La Mancha and the Valencian Community show significant regional effects (compared to Andalusia, which is taken as the refer- ence). In both cases the intensity of the changes is greater than in the reference com- munity, though with a different sign (positive in Castile-La Mancha and negative in the Va- lencian Community). However, including the regional dummies does not improve the fit of the model. The results of the other variables mentioned above are robust to the introduc- tion of the regional dummies, although the size of the effect of private ownership is re- duced and age loses significance. The effect of the employment rate and the percentage of faculty with a PhD is intensified and re- mains significant, though at a lower level.

regression analysis because no data on graduate employ- ment rates are available.

The results are very similar if we take as our de- pendent variable the number of new single de- grees (excluding double degrees) as a percentage of the total number of degrees offered in the last academic year (columns 3 and 4). In this case the regional variables are significant in quite a few cases and improve the overall significance of the model, according to the adjusted R2. With this de- pendent variable, the employment rate, besides

showing a larger effect, is only significant when we control for the autonomous community. The private ownership coefficient is also somewhat higher when we consider only new single degrees, which may reflect the fact that private universities have tended to create more new single degrees, whereas public universities have shown a prefer- ence for new double degrees.

Table 5.6. Determinants of bachelor’s degree offerings: number of new degrees as a percentage of the total num- ber of degrees. 2020-2021

Total degrees Degrees

(1) (2) (3) (4)

Ref: Public Private 22.880 *** 19.318 ** 25.187 *** 20.009 ***

(5.041) (7.556) (4.865) (6.625)

Ref: Andalucía Aragón -7.638 -0.028

(5.182) (4.672)

Principado de Asturias -0.146 6.767 *

(4.434) (3.614)

Illes Balears -9.431 2.416

(7.081) (6.862)

Canarias 0.893 9.819 *

(8.106) (5.760)

Cantabria -2.821 -0.497

(5.726) (4.898)

Castilla y León 5.551 9.356

(6.643) (5.991)

Castilla-La Mancha 16.427 *** 22.830 ***

(5.105) (4.182)

Cataluña 3.542 14.020

(9.302) (8.532)

Comunidad Valenciana -11.463 * -6.148

(6.328) (5.738)

Extremadura 1.998 9.571 **

(5.077) (3.983)

Galicia 4.457 6.954

(8.237) (7.530)

Comunidad de Madrid 8.760 13.740 **

(5.918) (5.265)

Región de Murcia 3.916 12.882 *

(7.982) (7.331)

Comunidad Foral de Navarra 8.977 15.089

(11.442) (10.807)

País Vasco -1.706 5.308

(10.229) (7.916)

La Rioja 0.093 12.803

(13.487) (12.519)

Ref: Universidad antigua Average age 5.798 * 5.809 6.483 * 7.101 **

(3.389) (3.821) (3.334) (3.458)

Young 4.665 11.201 5.296 11.206

(6.729) (7.125) (7.494) (7.023)

Global index 34.323 *** 38.440 *** 33.031 *** 33.066 ***

(6.966) (12.904) (7.295) (10.915)

Faculty member with PhD (%) -0.509 *** -0.562 ** -0.391 ** -0.415 *

(0.163) (0.220) (0.173) (0.214)

Employment rate -0.757 *** -0.789 ** -0.543 -0.867 **

(0.228) (0.298) (0.326) (0.353)

Constant 75.968 *** 75.961 * 44.724 62.550

(24.071) (39.957) (29.940) (39.533)

R² 0.583 0.691 0.595 0.730

R² adjusted 0.543 0.543 0.556 0.600

Log. Likelihood -267.889 -257.597 -265.489 -251.562

Observaciones 69 69 69 69

Note: *p<0,1; **p<0,05; ***p<0,01. The table offers the standarized coefficients and robust standard errors.

Source: Spanish Ministry of Universities (2019, 2021c, 2021f), BBVA Foundation-Ivie and own elaboration.