Child Stature, Maternal Education, and Early Childhood Development
This paper uses Multiple Indicator Cluster Surveys data from the Republic of Congo and São Tomé and Príncipe to study the relationships between child stature, mother's years of education, and indicators of early childhood development. The rela...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/714301600108705074/Child-Stature-Maternal-Education-and-Early-Childhood-Development http://hdl.handle.net/10986/34482 |
| Summary: | This paper uses Multiple Indicator
Cluster Surveys data from the Republic of Congo and São Tomé
and Príncipe to study the relationships between child
stature, mother's years of education, and indicators of
early childhood development. The relationships are
contrasted between two empirical approaches: the
conventional approach whereby control variables are selected
in an ad-hoc manner and the double machine-learning approach
that employs data-driven methods to select controls from a
much wider set of variables. Overall, the findings based on
the preferable double machine-learning approach differ
across the two countries depending on the measures of early
childhood development and child stature (height-for-age
Z-score and stunting) used in the analysis. Double
machine-learning estimates for the Republic of Congo suggest
that height-for-age Z-score and stunting have a direct
causal effect on early childhood development. In contrast,
for São Tomé and Príncipe, no relationship is found. Thus,
country-specific policy advice based on the relationships
observed from data in other countries may be quite risky, if
not misleading. Double machine-learning provides a practical
and feasible approach to reducing threats to internal
validity to derive robust inferences based on observational
data for evidence-based policy advice. |
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