Explaining Human Capital Inequalities: Evidence from Linked Data from Millennium Cohort Study and the National Pupil Database

ObjectivesOur study has three objectives: (a) to document the educational achievement gap between children from richer and poorer backgrounds; (b) to show how much of the achievement gap is explained by school quality, parental investment and children’s own investment; and (c) to estimate the effect of parental investments, and children’s activities on achievement. MethodsUsing linked data from MCS and NPD at age 7, 11 and 16, we estimate the test score gap between children from advantaged and disadvantaged backgrounds. We use socio-economic status measured by eligibility to free school meals and family income. First, we estimate the test score gap between the two groups controlling for predetermined characteristics. Then, we progressively add the measures of school quality; parental time and material factors; and children’s time investments. We compare the coefficients that include school, parent and child factors with the ones estimated without to understand the role each played in explaining the achievement gap. ResultsAt age 7 children from poorer families score 0.58 of a standard deviation (SD) less in maths than their richer peers. When we control for school quality and parental investments, the gap declined to 0.35 SD- implying these factors account for 40% of the gap. Similarly, by age 16, poorer children score 1.33 of a grade less in their average attainment 8 GCSEs. Controlling for school quality, and parental and children investments reduced the gap to 0.82 of a grade. These factors explain about 38% of the attainment gap. We also find that school quality, educational activities by parents and children significantly and positively related with achievements. Children’s activities in unorganised leisure and parent’s leisure intensive material investments are negatively correlated with achievements. ConclusionUnderstanding the determinants of human capital inequality is very important to identify the role of different stakeholders and to design relevant policies. In this study, we document the role of schools, parents and children themselves in driving achievements through primary school outcomes to high-stakes exams.

Authors

Institutions

Publication Details

Journal
International Journal for Population Data Science
Published
2025-08-28
DOI
https://doi.org/10.23889/ijpds.v10i4.3290
Primary Topic
Intergenerational and Educational Inequality Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Explaining Human Capital Inequalities: Evidence from Linked Data from Millennium Cohort Study and the National Pupil Database

Bożena Wielgoszewska, Claire Crawford, Emla Fitzsimons, Habtamu Beshir
International Journal for Population Data Science
Intergenerational and Educational Inequality Studies
article

Explaining Human Capital Inequalities: Evidence from Linked Data from Millennium Cohort Study and the National Pupil Database

Bożena Wielgoszewska, Claire Crawford, Emla Fitzsimons, Habtamu Beshir
article en

Abstract

ObjectivesOur study has three objectives: (a) to document the educational achievement gap between children from richer and poorer backgrounds; (b) to show how much of the achievement gap is explained by school quality, parental investment and children’s own investment; and (c) to estimate the effect of parental investments, and children’s activities on achievement. MethodsUsing linked data from MCS and NPD at age 7, 11 and 16, we estimate the test score gap between children from advantaged and disadvantaged backgrounds. We use socio-economic status measured by eligibility to free school meals and family income. First, we estimate the test score gap between the two groups controlling for predetermined characteristics. Then, we progressively add the measures of school quality; parental time and material factors; and children’s time investments. We compare the coefficients that include school, parent and child factors with the ones estimated without to understand the role each played in explaining the achievement gap. ResultsAt age 7 children from poorer families score 0.58 of a standard deviation (SD) less in maths than their richer peers. When we control for school quality and parental investments, the gap declined to 0.35 SD- implying these factors account for 40% of the gap. Similarly, by age 16, poorer children score 1.33 of a grade less in their average attainment 8 GCSEs. Controlling for school quality, and parental and children investments reduced the gap to 0.82 of a grade. These factors explain about 38% of the attainment gap. We also find that school quality, educational activities by parents and children significantly and positively related with achievements. Children’s activities in unorganised leisure and parent’s leisure intensive material investments are negatively correlated with achievements. ConclusionUnderstanding the determinants of human capital inequality is very important to identify the role of different stakeholders and to design relevant policies. In this study, we document the role of schools, parents and children themselves in driving achievements through primary school outcomes to high-stakes exams.

International Journal for Population Data ScienceVol. 10(4)
Northern Ireland Housing Executive (GB), University College London (GB)
Openalex Percentile: Top 70%
Intergenerational and Educational Inequality Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.