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Testing and correcting sample selection in academic achievement comparisons

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  • Onil Boussim

Abstract

Country comparisons using standardized test scores may in some cases be misleading unless we make sure that the potential sample selection bias created by drop-outs and non-enrollment patterns does not alter the analysis. In this paper, I propose an answer to this issue which consists of identifying the counterfactual distribution of achievement (I mean the distribution of achievement if there was hypothetically no selection) from the observed distribution of achievements. International comparison measures like means, quantiles, and inequality measures have to be computed using that counterfactual distribution which is statistically closer to the observed one for a low proportion of out-of-school children. I identify the quantiles of that latent distribution by readjusting the percentile levels of the observed quantile function of achievement. Because the data on test scores is by nature truncated, I have to rely on auxiliary data to borrow identification power. I finally applied my method to compute selection corrected means using PISA 2018 and PASEC 2019 and I found that ranking/comparisons can change.

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  • Onil Boussim, 2023. "Testing and correcting sample selection in academic achievement comparisons," Papers 2309.10642, arXiv.org, revised Oct 2023.
    • Handle: RePEc:arx:papers:2309.10642
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    3. Hanushek, Eric A. & Woessmann, Ludger, 2011. "Sample selectivity and the validity of international student achievement tests in economic research," Economics Letters, Elsevier, vol. 110(2), pages 79-82, February.
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    5. Patrick Mcewan & Jeffery Marshall, 2004. "Why does academic achievement vary across countries? Evidence from Cuba and Mexico," Education Economics, Taylor & Francis Journals, vol. 12(3), pages 205-217.
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    7. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-694, July.
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