# Foundational cognitive skills in developing countries: early-life nutritional, climatic and policy determinants and impacts on adolescent education, socio-emotional competencies and risky behaviors

> **NIH NIH R21** · UNIVERSITY OF PENNSYLVANIA · 2020 · $157,605

## Abstract

PROJECT SUMMARY:
Early undernutrition is highly prevalent in low- and middle-income countries (LMICs) and it is widely claimed that
unless undernutrition is addressed in the first 1,000 days, it has long-lasting and almost irreversible
consequences. Climatic variation also is considerable in many LMICs and widely claimed to have substantial
impacts on child development, with long-run consequences. However, there is little population-based evidence
about mechanisms through which early-life undernutrition and climatic variations lead to poorer adolescent and
adult outcomes and whether early-life deficits may be mitigated. This project will investigate the potential impact
of undernutrition and climatic variations on a set of foundational cognitive skills (FCS), importantly including
executive function (EF), which remains malleable throughout childhood and adolescence. EF increasingly has
been found to be a key domain of child development and a key predictor of educational success and possibly
correlated with non-cognitive skills development and risky behaviors. However, most evidence comes from high-
income countries (HICs) and there is very little evidence about the determinants of and the impacts of FCS in
LMICs, where conditions are much different (e.g. greater early-life undernutrition and climatic variations). The
Specific Aims (SA) are to investigate: (SA1) the determinants of FCS in late childhood, including (SA1a) early-
life nutrition; (SA1b) climatic variations; and (SA1c) social policies; and (SA2) the impacts of late childhood FCS
on adolescent educational achievements, socio-emotional competencies and risky behaviors. Each of these
specific aims will examine differences for boys versus girls. The project will use unique data on a set of FCS
measures collected in Ethiopia and Peru as part of the Young Lives Study (YLS), the largest multi-country cohort
dataset on childhood poverty and wellbeing in LMICs. That the YLS data are longitudinal and comparable
measures have been collected across very different countries make these data uniquely well-suited for
examination of longer-term impacts of early-life deficits on a variety of outcomes measured at ages 5,8,12 and
15 years. The data collected in 2013 for ~4,000 children in Ethiopia and Peru and ~2,000 of their immediate
siblings included RACER (Rapid Assessment of Cognitive and Emotional Regulation), a novel touch-screen
computer application designed to obtain measurements of a set of FCS: working memory, inhibition (both of
which are considered EF), declarative memory and implicit learning. These data are unique, rarely available from
LMICs and not previously available for large samples. The analysis promises significant contributions for (1)
deeper understanding of how early-life nutrition, climatic variations and other events affect FCS, (2) how policy
interventions can help mitigate the effects of early childhood poverty through affecting EF in contexts of two
countries at very differ...

## Key facts

- **NIH application ID:** 10013277
- **Project number:** 5R21HD097576-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** JERE R BEHRMAN
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $157,605
- **Award type:** 5
- **Project period:** 2019-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10013277

## Citation

> US National Institutes of Health, RePORTER application 10013277, Foundational cognitive skills in developing countries: early-life nutritional, climatic and policy determinants and impacts on adolescent education, socio-emotional competencies and risky behaviors (5R21HD097576-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10013277. Licensed CC0.

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