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Policy spillover effects on student achievement: evidence from PISA


National education reforms do not occur in isolation. Countries look towards each other to identify ways that improve the quality of their education systems. When evaluating the effect of an education policy, it is worth considering both local effects of the policy and its spillover effects on other countries. Ignoring spillover effects between countries can lead to biased estimates of policy effects and suboptimal decision making. This paper examines spillover effects of one widespread education policy, school autonomy, on student achievement using three waves of data from the Programme for International Student Assessment (PISA). The spatial autoregressive model is applied to capture both spillover and local effects of school autonomy. Overall, school autonomy raises student achievement in Reading, Mathematics, and Science. We confirm the existence of positive and statistically significant average spillover effects; thus, estimates based on linear regression underestimate the impact of school autonomy. Our findings indicate that there is spatial dependence in student achievement across countries linked to the geographic proximity between countries. Possible extensions of this work are discussed.

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Data used in this project are publicly available at

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All estimation was conducted using the open source software R. All codes are available from the authors.


  1. LeSage and Pace (2010) refers to local and spillover effects as direct and indirect effects—see also Fischer et al. (2009), LeSage and Pace (2010), and LeSage and Pace (2013).

  2. Teltemann and Windzio (2018) takes a first step in this direction by testing and controlling for spatial proximity between countries in an analysis of student performance. The authors do not, unlike this paper, quantify spillover effects.

  3. The process by which countries influence each others’ policy decisions is defined as policy borrowing, transfer, or diffusion—see, e.g., Meseguer and Gilardi (2009).

  4. Due to push-back from parents and teachers, the 2007 reform was revoked in 2013.

  5. In the online appendix, we explore other conditional factors such as GDP per capita, OECD membership, and income categories.

  6. Country-level summary statistics are provided in the online appendix.

  7. For variables included in our analyses, this constitutes less than 5% of student observations.

  8. Private school status values for Sweden and Israel were missing in 2015. These two missing values were imputed using the respective country averages of 2009 and 2012.

  9. For example, countries either have nationwide exit exams or they do not.

  10. To avoid perfect collinearity with the constant term, \(\eta _N\) and \(\delta _T\) are dropped.

  11. Correlated shocks arise when factors, e.g., a “PISA shock" or global economic shocks, cause countries to react similarly.

  12. \({\varvec{w}}_n\) is intransitive when a neighbour’s neighbour is not a neighbour.

  13. This is the type of interpretation found in Teltemann and Windzio (2018), and Gaku and Tsyawo (2021) as the authors consider spatially-lagged outcome as a control.

  14. A feedback effect occurs when an impact from a country passes through its neighbour back to the country of origin.

  15. The standard deviations of PISA test scores are given in Table 1.

  16. Partial effects are highly non-linear functions of the asymptotically normally distributed parameter estimates—see Sect. 3.2.

  17. E.g., for Reading, this value is computed as \(\frac{113.977/10}{44.004} \times 100\% \approx 25\%\) where the total effect is 113.977 and the standard deviation of reading scores is 44.004 (see Table 1).

  18. The results in Table 4 is based on Reading. See the online appendix for results by subdomain on Mathematics and Science.

  19. Robustness analyses for Mathematics and Science give similar results and are thus not provided in the main text—see the online appendix.

  20. This is likely attributable to the smaller sample size.


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Correspondence to Emmanuel S. Tsyawo.

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We thank Michael Rovine, Thanh Lu, Keisha Solomon, Eric Kadio, and Ashley Mcfarlane for valuable comments.

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Gerstner, CC.E., Tsyawo, E.S. Policy spillover effects on student achievement: evidence from PISA. Lett Spat Resour Sci (2022).

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  • PISA
  • Student achievement
  • School autonomy
  • Spillover effect
  • Spatial autoregressive model

JEL Classification

  • C21
  • I20