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Effectiveness of Vocational High Schools in Students’ Access to and Persistence in Postsecondary Vocational Education

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Abstract

Vocational education is increasingly seen as a viable path to higher education and not simply a direct route to the labor market. This paper studies the relationship between the secondary school track attended by Chilean students (vocational or academic) and their subsequent outcomes in access to and persistence in postsecondary vocational programs. Although it is expected that vocational students will display lower access to higher education programs, due to their less intense curriculum and the generally non-academic environment of their schools, their performance in post-secondary vocational programs is unclear. Field-specific knowledge and vocational maturity could improve the performance of students from secondary vocational education backgrounds. Our analysis uses propensity score matching to reduce selection bias and determine causal relationships. It also utilizes sensitivity analysis to check for the robustness of results. The findings reveal that, indeed, vocational students have lower rates of access and persistence than academic ones. Students who change fields between secondary and postsecondary education have the lowest persistence levels. We also found that vocational students have lower probabilities of access to higher education funding, and higher probabilities of enrolling in evening classes. Thus, when controlling for these two characteristics, we found that students who continue in the same vocational field between secondary and postsecondary levels had a better performance than students with an academic background. These findings may suggest that vocational students require greater support in order to increase their probabilities of success in higher education.

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Notes

  1. Tech Prep, Career Cluster, dual enrollment, youth apprenticeships and school-to-work.

  2. The “shared financing” law in Chile allows privately subsidized schools to charge monthly fees, a “parents’ copayment”, in addition to the per-pupil subsidy.

  3. Usually the nine differentiated hours and the six electives are also used for math, sciences and language.

  4. The State Guaranteed Loans (Crédito con Aval del Estado) and the New Millenium Scholarship (Beca Nuevo Milenio).

  5. These programs focus on practical, technical or occupational skills for direct entry into the labor market. Although some theoretical foundations are covered in the respective programs, they can be classified as 5B, according to the revised International Standard Classification of Education (ISCED, 1997). In contrast, NDPs correspond to 5A in the same classification system.

  6. The study followed almost the complete cohort of students in 10th grade 2006 to 12th grade 2008, excluding those in private non-subsidized schools (about 7 % of the population).

  7. Math and Language are the two common subjects used in Chile for research (see Mizala and Torche 2010); they also usually have higher response rates than Science and Social Sciences, which are the other subjects in the test.

  8. Note that students do not know their performance in the SIMCE test before choosing between VESL and AESL.

  9. The SIMCE test score and the parents’ survey are applied at the end of the school year (about three months before the new school year starts). As we only had socioeconomic data from the parents’ survey, we assumed that students maintain the same characteristics between the survey period (2004) and the beginning of the new school year (2005).

  10. We did not include students from private non-subsidized schools where enrollment in VESL was almost nonexistent and the socioeconomic status is the highest (24,089 cases).

  11. These cases were outside of the common support zone.

  12. Management includes all programs related to trade, such as accounting, secretarial, and so on. Technology includes programs such as metalworking, electricity and construction. Other programs include the maritime industry, agriculture, tourism, and clothing design.

  13. In the matching model (1), we kept the variables that seemed to be most important for explaining test score results in Chile (see, for instance, Mizala and Torche 2010).

  14. A caliper is defined as the maximum distance or ratio in the propensity score axis for which possible counterfactual cases are considered in each case to be matched.

  15. We tried different calipers (0.01; 0.001; 0.0001) searching for a reduction in bias, and thus losing the less possible cases. We chose the smallest one because the sample is large, and this caliper ensured the best balance between the treatment and control groups, losing the same number of cases than in the second best alternative (0.001).

  16. Many students choose to work and study at the same time after finishing secondary education. These students tend to choose programs that exclusively offer evening classes. Some VESL schools foster this combination (working during the day and studying in the evening).

  17. To estimate the probability of graduating from secondary education of all students we use a model similar to Eq. (3), based on the following procedure: a) estimate the parameters, including the weights from the matching procedure; b) estimate the probability of graduation; and c) estimate the inverse of the probability of graduation. A similar procedure is followed with the probability of enrollment in VETL (explained later on). Our final weights are a composition of the different weights previously estimated.

  18. In the results section, we also present a table with examples for a better understanding of the methodology used.

  19. The conditional independence assumption may not completely hold.

  20. In recent years, third level scholarships have not been allocated due to the low number of applications by students.

  21. Most scholarships or loans have cut-off points linked to a student’s academic performance.

References

  • Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.

    Google Scholar 

  • Altonji, J. G., Elder, T. E., & Taber, C. R. (2005). Selection on observed and unobserved variables: Assessing the effectiveness of catholic schools. Journal of Political Economy, 113(1), 151–184.

    Article  Google Scholar 

  • Arum, R., & Shavit, Y. (1995). Secondary vocational education and the transition from school to work. Sociology of Education, 68(3), 187–204.

    Article  Google Scholar 

  • Baker, T. L., & Vélez, W. (1996). Access to and opportunity in postsecondary education in the United States: A review. Sociology of Education, 69, 82–101.

    Article  Google Scholar 

  • Beattie, I. R. (2002). Are all “adolescent econometricians” created equal? Racial, class, and gender differences in college enrollment. Sociology of Education, 75(1), 19–43.

    Article  Google Scholar 

  • Boesel, D., & McFarland, L. (1994). National Assessment of Vocational Education Final Report to Congress: Vol. I. Summary and recommendations. Washington DC: U.S Department of Education.

    Google Scholar 

  • Brown, A., Moerkamp, T., & Voncken, E. (1999). Facilitating progression to higher education from vocational paths. European Journal of Education, 34(2), 219–235.

    Article  Google Scholar 

  • Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72.

    Article  Google Scholar 

  • Carbonaro, W. (2005). Tracking, students’ effort, and academic achievement. Sociology of Education, 78(1), 27–49.

    Article  Google Scholar 

  • Carbonaro, W. J., & Gamoran, A. (2002). The production of achievement inequality in high school English. American Educational Research Journal, 39(4), 801–827.

    Article  Google Scholar 

  • Cellini, S. R. (2006). Smoothing the transition to college? The effect of Tech-Prep programs on educational attainment. Economics of Education Review, 25(4), 394–411.

    Article  Google Scholar 

  • Choy, S. (2002). Access and persistence: Findings from ten years of longitudinal research on students. Washington, DC: Center for Policy Analysis, American Council on Education.

    Google Scholar 

  • Dare, D. E. (2006). The role of career and technical education in facilitating student transitions to postsecondary education. In D. D. Bragg & E. A. Barnett (Eds.), Academic pathways to and from the community college (Vol. 135, pp. 73–80). New Directions for Community Colleges. San Francisco: Jossey-Bass.

  • Duflo, E., Glennerster, R., & Kremer, M. (2007). Using Randomization in Development Economics Research: A Toolkit. Handbook of development economics, ed. T. Paul Schultz and John Strauss, (Vol. 4, pp. 3895–3962). Amsterdam, Oxford: Elsevier, North-Holland.

  • Elffers, L., Oort, F. J., & Karsten, S. (2012). Making the connection: the role of social and academic school experiences in students’ emotional engagement with school in post-secondary vocational education. Learning and Individual Differences, 22(2), 242–250.

    Article  Google Scholar 

  • Farias, M. (2014). School choice and inequality in educational decisions. Multidisciplinary Journal of Educational Research, 4(1), 1–34. doi:10.4471/remie.2014.01.

    Google Scholar 

  • Farias, M., & Carrasco, R. (2012). Diferencias en resultados académicos entre la Educación Media Técnico Profesional y Humanista-Científica en Chile. Revista Calidad en la Educación, No 36.

  • Field, S., Hoeckel, K., Kis, V., & Kuczera, M. (2010). Learning for jobs: Synthesis report of the OECD reviews of vocational education and training. Paris: OECD.

    Google Scholar 

  • Gamoran, A. (1987). The stratification of high school learning opportunities. Sociology of Education, 60(3), 135–155.

    Article  Google Scholar 

  • Gamoran, A., & Mare, R. D. (1989). Secondary school tracking and educational inequality: Compensation, reinforcement, or neutrality. American Journal of Sociology, 94(5), 1146–1183.

    Article  Google Scholar 

  • Gury, N. (2011). Dropping out of higher education in France: a microeconomic approach using survival analysis. Education Economics, 19(1), 51–64.

    Article  Google Scholar 

  • Hallinan, M., & Kubitschek, W. N. (1999). Curriculum differentiation and high school achievement. Social Psychology of Education, 3(1–2), 41–62.

    Article  Google Scholar 

  • Hayward, G., & Hoelscher, M. (2011). The use of large-scale administrative data sets to monitor progression from vocational education and training into higher education in the UK: possibilities and methodological challenges. Research in Comparative and International Education, 6(3), 316–329.

    Article  Google Scholar 

  • Hoelscher, M., Hayward, G., Ertl, H., & Dunbar-Goddet, H. (2008). The transition from vocational education and training to higher education: a successful pathway? Research Papers in Education, 23(2), 139–151.

    Article  Google Scholar 

  • Holm, A., Jæger, M. M., Karlson, K. B., & Reimer, D. (2013). Incomplete equalization: The effect of tracking in secondary education on educational inequality. Social Science Research, 42(6), 1431–1442.

    Article  Google Scholar 

  • Imbens, G., & Wooldridge, J. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.

    Article  Google Scholar 

  • Kirst, M., & Venezia, A. (2004). From high School to College. Improving opportunities for success in postsecondary education. San Francisco: Jossey-Bass.

    Google Scholar 

  • Larrañaga, O., Cabezas, G., & Dussaillant, F. (2014). Trayectorias educacionales e inserción laboral en la enseñanza media técnico profesional. Estudios Públicos, 134, 7–58.

    Google Scholar 

  • Levesque, K., Lauen, D., Teitelbaum, P., Alt, M., Librera, S., & Nelson, D. (2000). Vocational education in the United States: Toward the year 2000. National Center for Education Statistics.

  • Metz, M. H. (1978). Classrooms and corridors: The crisis of authority in desegregated secondary schools. Berkeley, CA: University of California Press.

    Google Scholar 

  • MINEDUC (2011). Educación Técnica Profesional en Chile: Antecedentes y claves de Diagnóstico. Centro de Estudios. Retrieved September 25, 2014, from http://www.mineduc.cl/usuarios/mineduc/doc/201204241130130.DiagnOsticoEducaciOnTPCentrodeEstudiosMINEDUC.pdf

  • Mizala, A., & Torche, F. (2010). Bringing the schools back in: The stratification of educational achievement in the Chilean voucher system. International Journal of Educational Development, 32(1), 132–144.

    Article  Google Scholar 

  • Oakes, J. (1985). Keeping track: How schools structure inequality. New Haven, CT: Yale University Press.

    Google Scholar 

  • Ono, H. (2001). Who goes to college? Features of institutional tracking in Japanese higher education. American Journal of Education, 109(2), 161–195.

    Article  Google Scholar 

  • Person, A., & Rosenbaum, J. (2006). Education outcomes of labor-market linking and job placement for student at public and private 2-year colleges. Economics of Education Review, 25(4), 412–429.

    Article  Google Scholar 

  • Plank, S., DeLuca, A., & Estacion, A. (2008). High school dropout and the role of career and technical education: A survival analysis of surviving high school. Sociology of Education, 81(4), 345–370.

    Article  Google Scholar 

  • Reardon, S. F. (2010). Applied quasiexperimental research in education. Lecture notes. Stanford.

  • Reardon, S. F., Cheadle, J. E., & Robinson, J. P. (2009). The effect of Catholic schooling on math and reading development in kindergarten through fifth grade. Journal of Research on Educational Effectiveness, 2(1), 45–87.

    Article  Google Scholar 

  • Reynolds, C. L., & DesJardins, S. L. (2009). The use of matching methods in higher education research: Answering whether attendance at a 2-year institution results in differences in educational attainment. Higher education: Handbook of theory and research (Vol. 24, pp. 47–97). Netherlands: Springer.

    Chapter  Google Scholar 

  • Rosenbaum, P. R. (1986). Dropping out of high school in the United States: An observational study. Journal of Educational Statistics, 11(3), 207–224.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33–38.

    Google Scholar 

  • Rouse, C. (1998). Do two-year colleges increase overall educational attainment? Evidence from the states. Journal of Policy Analysis and Management, 17(4), 595–620.

    Article  Google Scholar 

  • Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.

    Article  Google Scholar 

  • Stone, J. R., & Aliaga, O. A. (2003). Career and Technical Education, Career Pathways, and Work-Based Learning: Changes in Participation, 1997-1999. National Research Center for Career and Technical Education.

  • Stone, J. R., & Lewis, M. V. (2012). College and career ready in the 21st century: Making high school matter. Teachers College Press.

  • Wang, X., & McCready, B. (2013). The effect of postsecondary coenrollment on college success initial evidence and implications for policy and future research. Educational Researcher, 42(7), 392–402.

    Article  Google Scholar 

  • Zimmer-Gembeck, M., & Mortimer, J. T. (2006). Adolescent work, vocational development, and education. Review of Educational Research, 76(4), 537–566.

    Article  Google Scholar 

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Acknowledgments

We thank the data provided by the Centro de Estudios from the Ministry of Education-Chile. We also thank all the comments received from different sources, in particular from an anonymous reviewer. Mauricio Farias was in part supported by CONICYT, Programa de Inserción de Capital Humano Avanzado, project number 781204034. Typical disclaimers apply.

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Authors and Affiliations

  1. Inspecting Division, Superintendence of Education in Chile, Santiago, Chile

    Mauricio Farías

  2. Diego Portales University, Santiago, Chile

    María Paola Sevilla

Authors
  1. Mauricio Farías
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  2. María Paola Sevilla
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Correspondence to Mauricio Farías.

Appendices

Appendix 1

See Fig. 5.

Fig. 5
figure 5

Structure of the Chilean educational system (source: Farias 2014)

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Appendix 2

See Table 8.

Table 8 List of variables used in the study
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Farías, M., Sevilla, M.P. Effectiveness of Vocational High Schools in Students’ Access to and Persistence in Postsecondary Vocational Education. Res High Educ 56, 693–718 (2015). https://doi.org/10.1007/s11162-015-9370-2

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