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The role of health at birth and parental investment in early child development: evidence from the French ELFE cohort

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Abstract

This paper combines a theoretical and an empirical approach to address how health at birth affects child development. Using a simple theoretical model in which parents invest in their children, we identify the mechanisms through which better health at birth can improve child development. We also emphasise how parental socioeconomic status can shape the effects of health at birth. We perform an empirical analysis on a French cohort of children born in 2011, using a unique dataset ELFE. We identify the effect of birth weight and gestational age on child development at 1 year. The results indicate that only gestational age positively affects early development. We find no empirical evidence for the existence of a severity effect, according to which the adverse effects of poor health at birth are higher for children in low-income families or with poorly educated mothers.

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Fig. 1

This figure shows the distribution of birth weight and gestational age using Kernel density estimate

Fig. 2
Fig. 3

This figure represents box plots of mother’s education, household’s revenue, and family status over season of birth. The X axis represents the 4 cohort waves

Fig. 4

These figures plot the descriptive relationship between child development and birth weight or gestational age. The horizontal axis measures birth weight or gestational age. The vertical axis measures child development. Note that the cohort only interviewed mothers with babies from minimum 32 weeks of gestational age

Fig. 5

The horizontal axis shows deciles of revenue (Revenue). The vertical axis shows the estimated coefficient of the impact of gestational age or birth weight on early child development for each decile

Fig. 6

The horizontal axis shows four level of education with respect to the Meduc variable. The vertical axis shows the estimated coefficient of the impact of gestational age or birth weight on early child development for each subsample of education

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Notes

  1. Currie and Almond [19] provide a survey of empirical works that emphasise the long-term consequences for human capital of events occurring before age 5. The concept of dynamics complementarity presented in Cunha and Heckman [15], according to which the return on investment during childhood increases with early child development, provides an explanation.

  2. The Manuelli and Seshradi’ paper attempted to explain the income differences between countries. They focussed on the human capital in each country and provided a new way to measure it. They found that cross-country differences in average human capital per worker were much larger than that suggested by previous studies. They concluded that a large part of the cross-country differences in wealth could be explained by differences in human capital quality.

  3. Wehby et al. [44] considered very early child development, between 3 and 24 months, but they focussed on parental and household investment effects, not on health conditions at birth. They performed an empirical analysis with South American participants.

  4. Not that it is difficult to give an indisputable explanation for the differences observed across countries as outcomes, explanatory variables, and the periods analysed differ in each study. Nevertheless, their results may suggest less equitable access to child health care in the US.

  5. As the authors conducted their analysis using siblings conceived by the same mother at different times, differences in maternal characteristics as an explanation for seasonal differences in health at birth were excluded.

  6. The role of parents in this interplay is varied. For example, as revealed by O’Neill et al. [38], early public interventions aimed at supporting parents to address the child’s behavioural problems may favour economic returns in the long term.

  7. Our general theoretical predictions hold if we assumed that parents directly invest an amount of wage rather than a unit of time.

  8. Having a one-period model of childhood, we did not distinguish between early investment and late investment, and hence, we did not consider dynamic complementarity. This is relevant as our focus is not on the life-cycle profile of investment.

  9. There are several reasons why we do not have 18,300 children in this database. All families whose mothers had given their consent for their children’s follow-up in the maternity wards were eligible for the 1-year survey. However, not all mothers responded to the 1-year survey which led to a loss of many observations as we end up with 14 076. There are some missing observations as well. For example, the Child Development Inventory Index (CD) contains fewer than 14,000 observations, because we did not consider the absence of answers to the questions asked. However, the Pearson correlation coefficient between revenue and non-responses to the CD questions is very low, suggesting that non-responses do not bias our estimates.

  10. We did not consider the ones who do not respond to the questions. They are considered as “.” in the analysis.

  11. Note that these questions are included in the ELFE survey; we were not in a position to design them. They correspond to the methodology used by Ireton [33] and their validity is well established.

  12. It would be interesting to examine differences between maternal and paternal involvement. Nonetheless, this question cannot be addressed in our study as data for father are not well documented. We thus considered only maternal investment to construct our variable.

  13. Mothers could choose between three items to define housing type: 1—an individual house; 2—an apartment; and 3— other types.

  14. For mother education, we have: 1 “below high school” 2 “high school” 3 “bachelor” 4 “master.” They represent 8%;33%; 22% and 36% of the sample, respectively.

  15. For time spent in cleaning and to do prenatal exercise, mothers were asked to indicate on a six-point scale how frequently they performed the activity per week, ranging from 1 “never” to 6 “more than 3 h per week.”

  16. Respondent indicates whether the health of his child is: 1 “good,” 2 “somewhat good,” 3 “somewhat bad, and ” 4 “‘bad”.

  17. 1 “yes” 0 “no”.

  18. 1 means that the child is the firstborn.

  19. Time investment, CD, and Health perception were determined at 1 year of age. Other information was gathered at the maternity hospital. HEALTH PERCEPTION was also determined at 2 months.

  20. The unit of observation is the child.

  21. Several studies have highlighted the fact that season of birth affects long-term child development because of its effect on the age of pre-school entry. As we focussed on early child development at 1 year, this channel is not relevant for our study.

  22. The study of Del Boca et al. [22] provides some arguments in this sense.

  23. By definition, in the theoretical model, there is a positive relationship between parental time investment and a child’s development. This property is widely admitted and formalised in the literature. Our study did not examine this relationship, even if Table 7 in the Appendix provides some intuition about it.

  24. The impact of birth weight is significant in Column 3, but this result does not hold when adding relevant control variables.

  25. According to [17] or [31], children from lower socioeconomic households were found to be more vulnerable to pollution.

  26. [13] showed that long travel times, due to the closest maternity units in rural France being far, were associated with an increase in the risk of poor perinatal outcomes.

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Authors and Affiliations

  1. CEE-M, Univ. Montpellier, CNRS, INRAE, SupAgro, Montpellier, France

    Marion Davin & Emmanuelle Lavaine

  2. CEE-M, Univ. Montpellier, CNRS, INRAE, SupAgro, Montpellier, France

    Marion Davin

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The ELFE survey is a joint project between the French Institute for Demographic Studies (INED) and the National Institute of Health and Medical Research (INSERM), in partnership with the French blood transfusion service (Etablissement français du sang, EFS), Santé publique France, the National Institute for Statistics and Economic Studies (INSEE), the Direction générale de la santé (DGS, part of the Ministry of Health and Social Affairs), the Direction générale de la prévention des risques (DGPR, Ministry for the Environment), the Direction de la recherche, des études, de lévaluation et des statistiques (DREES, Ministry of Health and Social Affairs), the Département des études, de la prospective et des statistiques (DEPS, Ministry of Culture), and the Caisse nationale des allocations familiales (CNAF), with the support of the Ministry of Higher Education and Research and the Institut national de la jeunesse et de léducation populaire (INJEP). Via the RECONAI platform, it receives a government grant managed by the National Research Agency under the Investissements d’avenir” programme (ANR-11-EQPX-0038).

Appendix

Appendix

Child development

To select questions of the ELFE cohort relevant to build the child development index, we use the methodology of Ireton [33]. Ireton [33] provides a list of questions to appreciate the child development during the first year of life (Appendix Tables 6):

Table 6 Child development inventory based on the methodology of Ireton [33]
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Table 7 Robustness checks
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Table 8 Subcategories
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Davin, M., Lavaine, E. The role of health at birth and parental investment in early child development: evidence from the French ELFE cohort. Eur J Health Econ 22, 1217–1237 (2021). https://doi.org/10.1007/s10198-021-01332-x

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