The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit
Bibliographic record
Abstract
Past estimates of the effect of family income on child development have often been plagued by endogeneity and measurement error. In this paper, we use an instrumental variables strategy to estimate the causal effect of income on children's math and reading achievement. Our identification derives from the large, non-linear changes in the Earned Income Tax Credit (EITC) over the last two decades. The largest of these changes increased family income by as much as 20%, or approximately $2,100, between 1993 and 1997. Using a panel of roughly 4,500 children matched to their mothers from National Longitudinal Survey of Youth datasets allows us to address problems associated with unobserved heterogeneity, endogenous transitory income shocks, and measurement error in income. Our baseline estimates imply that a $1,000 increase in income raises combined math and reading test scores by 6% of a standard deviation in the short-run. Test gains are larger for children from disadvantaged families and are robust to a variety of alternative specifications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".