Last post on the estimated effects of Mississippi school reforms

2026-01-05 · Source: Statistical Modeling, Causal Inference, and Social Science · Field: Education & Learning — Academic Research & Higher Education, K-12 Education & Child Development · Depth: Intermediate, short

Summary

Noah Spencer responds to critiques regarding his analysis of Mississippi's Literacy-Based Promotion Act (LBPA) and its impact on National Assessment of Educational Progress (NAEP) scores. Spencer refutes the claim that observed gains are a mechanical consequence of weaker 3rd-grade students being retained and thus not taking the NAEP. He explains that retained students typically proceed to 4th grade the following year, preventing a mass exclusion of weaker students from NAEP cohorts. Spencer's paper also found no statistically significant changes in the demographic composition of NAEP-takers, such as percentages of White, male, English language learners, or students with disabilities. He estimates that only about 22% of the 2016-2017 treatment effect is attributable to retention, and notes that effects persist until at least grade 7 on state-level tests, with some fadeout. Spencer also corrects a claim about Mississippi's 2024 NAEP math rankings, stating the state ranked 16th in 4th grade and 35th in 8th grade, not 50th, and clarifies his synthetic difference-in-differences methodology.

Key takeaway

For education policy analysts evaluating the impact of retention policies, your analysis should account for the typical progression of retained students into subsequent grades. Do not assume that retention mechanically removes lower-performing students from future test-taking cohorts, as this can lead to an overestimation of selection bias. Instead, model the re-entry of these students to accurately assess policy effects.

Key insights

Mississippi's education reforms show genuine gains, not just selection bias from student retention.

Principles

• Retention policies do not necessarily remove weaker students from future test cohorts.
• Educational interventions often exhibit some fadeout over time.

Method

The synthetic difference-in-differences method generates a control group from a weighted average of states with similar pre-treatment test score trends.

In practice

• Consider student progression post-retention when analyzing policy impacts.
• Verify state-level test score rankings against official data sources.

Topics

• Mississippi Education Policy
• NAEP Test Scores
• Student Retention
• Selection Bias
• Synthetic Difference-in-Differences

Best for: Research Scientist, Data Scientist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by Statistical Modeling, Causal Inference, and Social Science.