Disparities in children’s vocabulary and height in relation to household wealth and parental schooling: A longitudinal study in four low- and middle-income countries
Bibliographic record
Abstract
Children from low socio-economic status (SES) households often demonstrate worse growth and developmental outcomes than wealthier children, in part because poor children face a broader range of risk factors. It is difficult to characterize the trajectories of SES disparities in low- and middle-income countries because longitudinal data are infrequently available. We analyze measures of children's linear growth (height) at ages 1, 5, 8 and 12y and receptive language (Peabody Picture Vocabulary Test) at ages 5, 8 and 12y in Ethiopia, India, Peru and Vietnam in relation to household SES, measured by parental schooling or household assets. We calculate children's percentile ranks within the distributions of height-for-age z-scores and of age- and language-standardized receptive vocabulary scores. We find that children in the top quartile of household SES are taller and have better language performance than children in the bottom quartile; differences in vocabulary scores between children with high and low SES are larger than differences in the height measure. For height, disparities in SES are present by age 1y and persist as children age. For vocabulary, SES disparities also emerge early in life, but patterns are not consistent across age; for example, SES disparities are constant over time in India, widen between 5 and 12y in Ethiopia, and narrow in this age range in Vietnam and Peru. Household characteristics (such as mother's height, age, and ethnicity), and community fixed effects explain most of the disparities in height and around half of the disparities in vocabulary. We also find evidence that SES disparities in height and language development may not be fixed over time, suggesting opportunities for policy and programs to address these gaps early in life.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 0.000 |
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".