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Institutional Tracking and Achievement Growth: Exploring Difference-in-Differences Approach to PIRLS, TIMSS, and PISA Data

Abstract

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.

Keywords

  • Education
  • Tracking
  • International studies
  • Difference-in-differences
  • PISA
  • PIRLS
  • TIMSS

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Notes

  1. 1.

    For general discussion of the DD approach to cross-sectional and other types of data, see: Lee and Kang (2006) and Meyer (1995).

  2. 2.

    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.

  3. 3.

    Brunello and Checchi focused on mobility and measured how tracking changes the relation between family background and several outcomes.

  4. 4.

    Our experience with PISA tells that imputation variance inflates standard errors by several percents only.

  5. 5.

    For details of how plausible values in subscales were constructed, see: OECD (2002, 2005) and Martin et al. (2003, 2004).

  6. 6.

    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.

  7. 7.

    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.

  8. 8.

    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.

  9. 9.

    Regressions were also estimated with original scores to find almost identical results.

  10. 10.

    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.

  11. 11.

    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.

  12. 12.

    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.

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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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