Supplement to Forster, Bol & van de Werfhorst (2016) Sociological Science
Summary
An online supplement to the 2016 Sociological Science article Vocational Education and Employment over the Life Cycle by Andrea G. Forster, Thijs Bol and Herman G. van de Werfhorst, dated June 2016, Volume 3. Appendix A reproduces the article's main figures with an added four-way interaction between age, age squared, vocational education (VET) and measures of vocational systems, and states the results support the article's main findings. Appendix B gives country-by-country logistic regressions of employment for men (Table B1) and women (Table B2) across 22 countries, plus predicted employment probabilities for women by country (Figure B1). The analyses cite PIAAC 2012 data, release March 2015.
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“Supplement to Forster, Bol & van de Werfhorst (2016) Sociological Science.”
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Supplement to:
Forster, Andrea G., Thijs Bol, and Herman G. van de
Werfhorst. 2016. “Vocational Education and Em-
ployment over the Life Cycle” Sociological Science
3: 473-494.
Supplement to “Vocational Education and Employ-
ment over the Life-Cycle”
Appendix A
One could argue that we should also interact age squared with the characteristics of the
educational system. E↵ectively, this then becomes a four-way interaction between age,
age, VET, and one of the two variables that we use to measure vocational systems. Since
this would produce tables that are very hard to interpret, we have decided to not show
these in the main text. The analyses below, however, reproduce our main figures using
this four-way interaction.
The main conclusion is that the overall findings are not that di↵erent. For the pooled
regressions (Figure A1), we find a very similar pattern where there is an initial benefit of
having a vocational degree which later turns into a penalty. The average marginal e↵ect
plots (Figure A2 and Figure A3) show a slightly di↵erent picture. For men the results are
still very much the same: an initial benefit of having a vocational degree that is larger in
more vocationally oriented educational systems, or systems with a larger dual system. For
women, however, we see that the life-cycle decrease in more vocationally oriented systems
tends to be stronger than in non-vocationally oriented systems, although the di↵erence
is not significant. At the same time, we do not find this when we look at the results for
women with the dual system variable as the country level characteristic. All in all, these
results support the main findings as presented in the article.
S1
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Figure A1: Predicted Probabilities of Employment by Type of Education (Voca-
tional/General) with Additional Interaction between Age squared and VET (Pooled
model)
(a) Men
Men
1.00
Predicted probability (pr=Employed)
0.75
0.50
0.25
0.00
20 30 40 50 60
General Vocational
(b) Women
Women
1.00
Predicted probability (pr=Employed)
0.75
0.50
0.25
0.00
20 30 40 50 60
Age
General Vocational
Note: The estimates of the predicted probabilities are based on a model similar to the one of Model 3 in
Table 2 with the full set of predictors but now an additional interaction between age squared and VET
is included. Age is used in its original form with years of age corresponding to values of the variable.
Source: PIAAC 2012, release March 2015, own calculations.
sociological science | www.sociologicalscience.com S2 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Figure A2: Average Marginal Effects of VET on Employment for Countries with Low and
High Vocational Enrollment (Including Interactions with Age Squared)
(a) Men
0.2
Average Marginal Effect of VET
0.1
0.0
−0.1
20 30 40 50 60
Low % Voc. Enrollment High % Voc. Enrollment
(b) Women
0.10
Average Marginal Effect of VET
0.05
0.00
−0.05
−0.10
20 30 40 50 60
Age
Low % Voc. Enrollment High % Voc. Enrollment
Note: These figures are based on a model with a full four-way interaction between age, age (i.e. age
squared), VET and the Vocational Enrollment indicator. Age is used in its original form with years of
age corresponding to values of the variable.
Source: PIAAC 2012, release March 2015, own calculations.
sociological science | www.sociologicalscience.com S3 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Figure A3: Average Marginal Effects of VET on Employment for Countries with Low and
Dual System Enrollment (Including Interactions with Age Squared)
(a) Men
0.3
Average Marginal Effect of VET
0.2
0.1
0.0
−0.1
20 30 40 50 60
Low % Dual System High % Dual System
(b) Women
0.10
Average Marginal Effect of VET
0.05
0.00
−0.05
−0.10
20 30 40 50 60
Age
Low % Dual System High % Dual System
Note: These figures are based on a model with a full four-way interaction between age, age (i.e. age
squared), VET and the dual system indicator. Age is used in its original form with years of age
corresponding to values of the variable.
Source: PIAAC 2012, release March 2015, own calculations.
sociological science | www.sociologicalscience.com S4 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Appendix B
This appendix contains the regression tables for the separate country analyses for men
(Table B1) and for women (Table B2). Furthermore, the predicted probabilities of being
employed for women with vocational and general education in the single countries are
shown in Figure B1.
sociological science | www.sociologicalscience.com S5 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Table B1: Country-by-Country Logistic Regressions with Employment as the Dependent Variable (Selected Coefficients for Men)
Austria Belgium Canada Czech Rep. Denmark Estonia Finland France Germany Ireland Italy
Age (age 16 = value 0) 0.155† 0.398† 0.208† 0.208† 0.141† 0.063 0.247† 0.293† 0.134† 0.173† 0.228†
(0.037) (0.041) (0.023) (0.058) (0.031) (0.033) (0.038) (0.032) (0.049) (0.035) (0.039)
Age squared (divided by 10) −0.050† −0.075† −0.035† −0.057† −0.036† −0.028† −0.049† −0.061† −0.027† −0.032† −0.061†
(0.004) (0.005) (0.003) (0.007) (0.004) (0.004) (0.004) (0.004) (0.005) (0.004) (0.005)
VET 0.340 0.226 0.503 0.027 1.036† −0.055 0.403 0.006 1.669† −0.059 0.281
(0.471) (0.386) (0.312) (0.722) (0.375) (0.288) (0.343) (0.313) (0.491) (0.309) (0.458)
VET × Age −0.024∗ −0.024∗ −0.014 0.009 −0.032† 0.001 −0.014 −0.004 −0.045† −0.004 −0.022
(0.011) (0.011) (0.008) (0.016) (0.009) (0.008) (0.009) (0.008) (0.013) (0.011) (0.015)
Constant −0.401 −3.133∗ −2.909† −2.275 −1.751∗ −0.868 −4.265† −2.935† −2.876∗ −2.466† −0.605
(1.110) (1.303) (0.632) (1.453) (0.816) (0.861) (1.109) (0.886) (1.334) (0.809) (0.965)
Controls yes yes yes yes yes yes yes yes yes yes yes
N 2,043 1,886 9,009 2,046 2,927 2,616 2,026 2,301 1,987 2,044 1,846
Japan Rep. of Korea Netherlands Norway Poland Russia Slovak Rep. Spain Sweden UK US
Age (age 16 = value 0) 0.312† 0.227† 0.229† 0.099† 0.131† 0.246† 0.256† 0.195† 0.214† 0.259† 0.082∗
(0.048) (0.048) (0.047) (0.038) (0.030) (0.063) (0.034) (0.040) (0.043) (0.046) (0.035)
Age squared (divided by 10) −0.055† −0.049† −0.044† −0.028† −0.035† −0.041† −0.047† −0.043† −0.045† −0.039† −0.025†
(0.006) (0.005) (0.005) (0.005) (0.004) (0.007) (0.004) (0.004) (0.005) (0.006) (0.005)
VET 1.603∗ 0.606 1.082∗ −0.006 0.695∗ 0.428 −0.222 0.081 0.424 0.200 1.091∗
(0.663) (0.334) (0.511) (0.398) (0.291) (0.442) (0.287) (0.488) (0.433) (0.619) (0.537)
VET × Age −0.038∗ −0.013 −0.009 0.003 −0.023† −0.017 −0.001 −0.006 −0.010 −0.011 −0.040†
(0.016) (0.011) (0.012) (0.011) (0.009) (0.014) (0.009) (0.016) (0.012) (0.017) (0.015)
Constant −1.725 −0.801 −2.207 −0.799 −1.496 −3.139 −5.362† −3.019† −2.855† −4.549† −0.538
(1.485) (1.331) (1.616) (0.972) (0.765) (1.702) (0.923) (1.012) (1.058) (1.350) (0.817)
Controls yes yes yes yes yes yes yes yes yes yes yes
N 2,014 2,436 1,875 2,000 3,029 890 2,264 1,785 1,728 2,192 1,733
Note: Coefficients are logged odds. Standard errors in parentheses. Age squared is divided by 10 to improve the readability of coefficient and standard error. ∗ p < 0.05, † p < 0.01
Source: PIAAC 2012, release March 2015, own calculations
sociological science | www.sociologicalscience.com S6 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Table B2: Country-by-Country Logistic Regressions with Employment as the Dependent Variable (Selected Coefficients for Women)
Austria Belgium Canada Czech Rep. Denmark Estonia Finland France Germany Ireland Italy
Age (age 16 = value 0) 0.201† 0.315† 0.174† 0.371† 0.212† 0.170† 0.258† 0.308† 0.209† 0.187† 0.137†
(0.035) (0.043) (0.024) (0.054) (0.039) (0.030) (0.042) (0.033) (0.039) (0.033) (0.046)
Age squared (divided by 10) −0.043† −0.058† −0.025† −0.055† −0.039† −0.037† −0.043† −0.046† −0.033† −0.017† −0.031†
(0.004) (0.004) (0.002) (0.005) (0.004) (0.003) (0.004) (0.003) (0.004) (0.004) (0.004)
Type of Education (VET) 1.305† 0.126 0.435∗ 0.383 0.277 −0.072 0.778∗ 0.723† 1.766† 0.343 −0.132
(0.362) (0.342) (0.222) (0.488) (0.345) (0.248) (0.340) (0.266) (0.364) (0.256) (0.521)
VET × Age −0.040† −0.014 −0.009 −0.001 −0.013 0.000 −0.032† −0.019∗ −0.043† −0.018∗ −0.006
(0.009) (0.010) (0.006) (0.011) (0.009) (0.007) (0.010) (0.008) (0.010) (0.009) (0.017)
Constant −2.490† −3.859† −4.073† −6.058† −4.198† −2.430† −5.028† −5.563† −4.383† −4.679† −2.412∗
(0.935) (1.284) (0.695) (1.515) (1.056) (0.839) (1.241) (0.876) (1.083) (0.824) (1.132)
Controls yes yes yes yes yes yes yes yes yes yes yes
N 2,086 1,866 10,329 2,514 2,924 3,169 1,963 2,323 2,029 2,473 1,833
Japan Rep. of Korea Netherlands Norway Poland Russia Slovak Rep. Spain Sweden UK US
Age (age 16 = value 0) 0.046 0.110† 0.166† 0.081∗ 0.282† 0.155† 0.354† 0.098† 0.296† 0.209† 0.060∗
(0.035) (0.041) (0.046) (0.039) (0.034) (0.054) (0.037) (0.036) (0.046) (0.033) (0.029)
Age squared (divided by 10) −0.008∗ −0.021† −0.027† −0.020† −0.055† −0.038† −0.054† −0.023† −0.042† −0.030† −0.019†
(0.003) (0.003) (0.004) (0.004) (0.004) (0.005) (0.004) (0.004) (0.005) (0.004) (0.004)
Type of Education (VET) −0.002 0.246 0.816∗ 0.779 −0.076 −0.117 −0.916† −0.343 0.185 0.075 0.936∗
(0.248) (0.238) (0.404) (0.521) (0.284) (0.344) (0.308) (0.410) (0.422) (0.396) (0.403)
VET × Age −0.002 −0.003 −0.009 −0.020 −0.005 0.001 0.008 −0.001 −0.007 −0.019 −0.024∗
(0.008) (0.009) (0.011) (0.014) (0.008) (0.011) (0.009) (0.015) (0.012) (0.012) (0.012)
Constant −0.265 −0.986 −3.348∗ −2.105∗ −4.314† −2.636 −7.161† −0.968 −6.174† −4.162† −0.847
(0.991) (1.064) (1.416) (0.991) (0.921) (1.548) (0.958) (0.826) (1.249) (0.909) (0.729)
Controls yes yes yes yes yes yes yes yes yes yes yes
N 2,281 2,689 1,930 1,781 2,819 1,817 2,475 1,867 1,617 3,141 2,029
∗ †
Note: Coefficients are logged odds. Standard errors in parentheses. Age squared is divided by 10 to improve the readability of coefficient and standard error. p < 0.05, p < 0.01
Source: PIAAC 2012, release March 2015, own calculations
sociological science | www.sociologicalscience.com S7 June 2016 | Volume 3
Forster, Bol, and van de Werfhorst Vocational Education and Employment
Figure B1: Predicted Probabilities of Employment per Country (Women)
Austria Belgium Canada Czech Rep. Denmark Estonia
1.00 1.00 1.00 1.00 1.00 1.00
0.75 0.75 0.75 0.75 0.75 0.75
0.50 0.50 0.50 0.50 0.50 0.50
0.25 0.25 0.25 0.25 0.25 0.25
0.00 0.00 0.00 0.00 0.00 0.00
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
Finland France Germany Ireland Italy Japan
1.00 1.00 1.00 1.00 1.00 1.00
0.75 0.75 0.75 0.75 0.75 0.75
0.50 0.50 0.50 0.50 0.50 0.50
Predicted probability (pr=Employed
0.25 0.25 0.25 0.25 0.25 0.25
0.00 0.00 0.00 0.00 0.00 0.00
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
Korea Rep. Netherlands Norway Poland Russia Slovakia
1.00 1.00 1.00 1.00 1.00 1.00
0.75 0.75 0.75 0.75 0.75 0.75
0.50 0.50 0.50 0.50 0.50 0.50
0.25 0.25 0.25 0.25 0.25 0.25
0.00 0.00 0.00 0.00 0.00 0.00
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
Spain Sweden United Kingdom United States
1.00 1.00 1.00 1.00
0.75 0.75 0.75 0.75
0.50 0.50 0.50 0.50
General Vocational
0.25 0.25 0.25 0.25
0.00 0.00 0.00 0.00
20 30 40 50 60 20 30 40 50 60 20 30 40 50 60 20 30 40 50 60
Age
Source: PIAAC 2012, release March 2015, own calculations.
sociological science | www.sociologicalscience.com S8 June 2016 | Volume 3
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