This chapter analyzes the impact of early institutional tracking on achievement measured through international educational surveys. Building on the seminal work of Hanushek and Woessmann (Economic Journal, 116(510), C63–C76, 2006), the difference-in-differences approach was applied to assess whether tracking students into distinct types of schools has any impact on achievement growth. The growth is estimated based on achievement in primary school measured in PIRLS 2001 (reading) or TIMSS 2003 (mathematics, science) and achievement in secondary school tested in PISA 2000 and 2003 (all three subjects). The chapter presents several robustness checks to test the validity of this assumption in the case of Hanushek and Woessmann approach. It was argued that the official country-level results published by survey organizers are not directly comparable and using them was not valid in their case. Hanushek and Woessmann approach was repeated on more comparable samples obtained from PIRLS, TIMSS, and PISA micro-data. Additionally, a new difference-in-differences method was proposed. It was found that while the seminal approach was not robust to sample and method modifications, the newly proposed method partially supports earlier findings that early tracking negatively affects achievement growth, especially for unprivileged students.
- International studies
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In regression we adjust for mean differences in covariates (all of them were coded as categorical variables). We employed also matching methods, which balanced the distribution of covariates in the samples. The results were nearly identical and we do not present them here. Additional results are available upon request from the author.
Brunello and Checchi focused on mobility and measured how tracking changes the relation between family background and several outcomes.
Our experience with PISA tells that imputation variance inflates standard errors by several percents only.
It could be that migrants are forced into vocational tracks, which makes tracking effects even more negative. However, even in this case, tracking effects should be observable for native students if some of them are in vocational tracks.
The language criterion could be criticized if multilingual countries are considered. However, nearly the same results were obtained using the place of birth criterion only.
In fact, PIRLS and TIMSS are testing the upper of the two adjacent grades that contain the largest proportion of 9-year-olds at the time of testing (see Martin et al., 2003, p. 54). Thus, in some countries, students are in different grades than 4th. However, it doesn’t change the fact that population of students is differently defined than in PISA. That has obvious consequences on age and grade distributions.
Regressions were also estimated with original scores to find almost identical results.
The number of countries, which track 8th grade students is much lower. These are mainly countries with a system similar to that in Germany and share other common characteristics, which could additionally bias the results.
We couldn’t find theoretically valid expectation of how difference in mean age should affect score distribution. While in the case of mean performance or quantiles interpretation is clear (more or less time for learning), there is no theory or robust evidence that score dispersion should increase or decrease with time.
Standard errors were computed analytically correcting for clustering at the school level. We tried also bootstrap and jackknife estimators of standard errors but they were only slightly different and did not change any of the conclusions.
Ammermueller, A. (2005). Educational opportunities and the role of institutions (ZEW Discussion Paper, No. 05–44).
Brown, G., Micklewright, J., Schnepf, S., & Waldmann, R. (2005). Cross-national surveys of learning achievement: How Robust are the findings? Southampton Statistical Sciences Research Institute, S3RI Applications and Policy Working Papers, A05/05.
Brunello, G., & Checchi, D. (2007, October). Does school tracking affect equality of opportunity? New international evidence. Economic Policy, 22, 781–861.
Brunello, G., & Giannini, M. (2004). Stratified or comprehensive? The economic efficiency of school design. Scottish Journal of Political Economy, 51, 173–193.
Duflo, E., Dupas, P., & Kremer, M. (2008). Peer effects and the impact of tracking: Evidence from a randomized evaluation in Kenya (Working Paper 14475). National Bureau of Economic Research
Dustmann, C. (2004, April). Parental background, secondary school track choice, and wages. Oxford Economic Papers, Oxford University Press, 56(2), 209–230.
Eisenkopf, G. (2007). Tracking and incentives. A comment on Hanushek and Woessmann. Research Paper Series, Thurgau Institute of Economics.
Gruber, J. (1994, June). The incidence of mandated maternity benefits. American Economic Review, 84(3), 622–641.
Hanushek, E., & Woessmann, L. (2006). Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries. Economic Journal, 116(510), C63–C76.
Lee, M. J., & Kang, C. H. (2006). Identification for difference in differences with cross-section and panel data. Economics Letters, 92, 270–276.
Martin, M., Mullis, I., & Chrostowski, S. (Eds.). (2004). TIMSS 2003 Technical Report. Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College.
Martin, M., Mullis I., & Kennedy A. (Eds.). (2003). PIRLS 2001 Technical Report. Chestnut Hill, MA: Boston College.
Maurin, E., & McNally, S. (2007). Educational effects of widening access to the academic track: A natural experiment (CEE Discussion Papers 0085). LSE: Centre for the Economics of Education.
Meier, V., & Schütz, G. (2007). The economics of tracking and non-tracking (Ifo Working Paper No. 50). Ifo Institute for Economic Research at the University of Munich.
Meyer, B. (1995, April). Natural and quasi-experiments in economics. Journal of Business & Economic Statistics, 13, 151–162.
Mühlenweg, A. M. (2007). Educational effects of early or later secondary school tracking in Germany (ZEW Discussion Papers 07–079).
Mullis, I., Martin, M., & Gonzalez, E. K. (2001). PIRLS 2001 International Report. International Study Center, Lynch School of Education, Boston College.
OECD. (2002). PISA 2000 Technical Report. Paris: OECD.
OECD. (2005). PISA 2003 Technical Report. Paris: OECD.
OECD. (2006). Education at a glance. Paris: OECD.
Pekkarinen, T. (2005). Gender differences in educational attainment: Evidence on the role of the tracking age from a Finnish quasi-experiment (IZA Discussion Papers 1897). Institute for the Study of Labor (IZA).
Schnepf, S. (2002). A sorting hat that fails? The transition from primary to secondary school in Germany (Innocenti Working Papers, 92). The United Nations Children’s Fund Innocenti Research Centre.
Waldinger, F. (2006). Does tracking affect the importance of family background on students’ test scores? Mimeo: London School of Economics.
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Jakubowski, M. (2010). Institutional Tracking and Achievement Growth: Exploring Difference-in-Differences Approach to PIRLS, TIMSS, and PISA Data. In: Dronkers, J. (eds) Quality and Inequality of Education. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3993-4_3
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Online ISBN: 978-90-481-3993-4