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Claire Mackevicius

C. Kirabo Jackson, Claire Mackevicius.

We use estimates across all known "credibly causal" studies to examine the distributions of the causal effects of public K12 school spending on test scores and educational attainment in the United States. Under reasonable assumptions, for each of the 31 included studies, we compute the same parameter estimate. Restricted maximum likelihood estimates indicate that, on average, a $1000 increase in per-pupil public school spending (for four years) increases test scores by 0.044 standard deviations, high-school graduation by 2.1 percentage points, and college-going by 3.9 percentage points. The pooled averages are significant at the 0.0001 level. When benchmarked against other interventions, test score impacts are much smaller than those on educational attainment -- suggesting that test-score impacts understate the value of school spending. The benefits to capital spending increases take about five-to-six years to materialize, but after this, one cannot reject that the average marginal effects differ across capital and non-capital spending types. The marginal spending impacts are much less pronounced for economically advantaged populations. Consistent with a cumulative effect, the educational attainment impacts are larger with more years of exposure to the spending increase. Average impacts are similar across a wide range of baseline spending levels -- providing little evidence of diminishing marginal returns at current spending levels.

To speak to generalizability, we estimate the variability across studies attributable to effect heterogeneity (as opposed to sampling variability). This heterogeneity explains about 40 and 70 percent of the variation across studies for educational attainment and test scores, respectively, which allows us to provide a range of likely policy impacts. A policy that increases per-pupil spending for four years will improve test scores 92 percent of the time, and educational attainment even more often.  We find suggestive evidence consistent with small possible publication bias, but demonstrate that any effects on our estimates are minimal.

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