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Empirical Research in Vocational Education and Training

Table 1 Random effects estimations—dependent variable: average cantonal requirements profile in chosen VET occupations (mean: 37.80; sd within: 0.39)

From: Brain drain from vocational to academic education at upper-secondary level? An empirical analysis for Switzerland

Variables

(1)

Without industry controls

(2)

With industry controls

Academic education rate

2.476

1.765

(2.171)

(2.448)

Percentage of immigrants

2.038

− 1.351

(2.248)

(4.039)

Percentage of school-based VET (entries)

2.140**

3.314***

(0.874)

(0.973)

Percentage of graduates from compulsory school

60.45

7.139

(77.49)

(106.1)

Percentage of employees in agriculture

 

3.350

 

(17.66)

Percentage of employees in production

 

0.674

 

(10.27)

Percentage of employees in other services

 

− 5.864

 

(10.65)

Percentage of employees in construction

 

− 7.942

 

(13.61)

Percentage of employees in retail

 

13.27

 

(11.39)

Percentage of employees in financial services and insurance

 

17.30

 

(11.71)

Percentage of employees in public administration and education

 

− 0.785

 

(15.86)

Constant

34.75***

35.77***

(1.301)

(9.066)

Language region

Yes

Yes

Percentage of imputed values

Yes

Yes

Year dummies

Yes

Yes

Observations

182

156

R2

0.278

0.292

Number of cantons

26

26

  1. This table shows coefficients from a random effects regression. Standard errors in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. The reference category for the share of employees in the different sectors is the health sector
  2. To show that the results are not driven by outliers, two different bivariate illustrations of the relationships between the average cantonal requirements profile and the academic education rate, as well as the percentage of school-based VET appear in Appendix A, Figs. 3 and 4
  3. We additionally tested these regressions with FE models, with very similar results
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