This study analyzes how parental investment responds to a low birth weight (LBW) outcome and finds important differences in investment responses by maternal education. High school dropouts reinforce a LBW outcome by providing less investment in the human capital of their LBW children relative to their normal birth weight children whereas higher educated mothers compensate by investing more in their LBW children. In addition, an increase in the number of LBW siblings present in the home raises investment in a child, which is consistent with reinforcement, but this positive effect tends to be concentrated among high school dropouts. These results suggest that studies analyzing the effects of LBW on child outcomes that do not account for heterogeneity in investment responses to a LBW outcome by maternal education may overestimate effects of LBW on child outcomes for those born to low-educated mothers and underestimate such effects for those born to high-educated mothers.
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For example, LBW children have been shown to achieve poor health, cognitive and behavioral development, education, employment, and earning outcomes relative to NBW children (Currie and Hyson 1999; Case et al. 2005; Black et al. 2007; Currie and Moretti 2007; Oreopoulos et al. 2008; Fletcher 2011; Datta Gupta et al. 2013; Chatterji et al. 2014a, b; Figlio et al. 2014; Cook and Fletcher 2015).
Most studies have found evidence that parents reinforce endowment differences among their children (Rosenzweig and Schultz 1982; Rosenzweig and Wolpin 1988; Behrman et al. 1994; Behrman and Rosenzweig 2004; Rosenzweig and Zhang 2009; Aizer and Cunha 2012; Datar et al. 2010; Frijters et al. 2013; Parman 2015). Several studies have found that parents compensate for differences in their children’s endowments (Griliches 1979; Behrman et al. 1982; Pitt et al. 1990; Ashenfelter and Rouse 1998; Ermisch and Francesconi 2000; Del Bono et al. 2012; Bharadwaj et al. 2014). And a few studies have found that parents neither compensate nor reinforce endowment differences (Royer 2009; Almond and Currie 2011a; Lynch and Brooks 2013). See Almond and Mazumder (2013) for an excellent review of the literature.
Using data on siblings from rural Ethiopia, Ayalew (2005) found evidence that parents reinforce endowment differences with respect to educational investments but compensate with respect to health investments. And using data on Chinese twins, Conti et al. (2015) found that the twin who experienced a negative early-life health shock receives more health investment later in life relative to her twin sibling who did not but receives less educational investment; they conclude that families behave so as to equalize investments across children who are differentially affected by early-life health shocks.
Recent studies find evidence that the quality of the home environment is important for the cognitive and noncognitive development of children (Blau 1999; Guo and Harris 2000; Brooks-Gunn et al. 2002; Aughinbaugh and Gittleman 2003; Todd and Wolpin 2007; Cunha and Heckman 2008; Cunha et al. 2010). For example, Todd and Wolpin (2007) find that 10–20 % of the math and reading test score gaps between whites and nonwhites can be closed if home inputs (as measured by the overall HOME score) were equalized at the average level observed for white children. Blau (1999) estimates the elasticities of child development outcomes with respect to cognitive stimulation and emotional support to be in the 0.07–0.14 range, which are much larger than the estimated elasticities with respect to other inputs such as child care.
See https://www.nlsinfo.org/content/cohorts/nlsy79-children/other-documentation/codebook-supplement/appendix-home-sf-scales (last accessed February 21, 2016) for a detailed list of the questions that contribute to HOME scores.
While I present results using the age-standardized HOME scores, an analysis of the raw scores yielded very similar results.
It is likely that mothers who care a lot about child quality have better-endowed children and also invest more in their children after birth. In this case, μ would be correlated with birth endowments and while OLS estimates would be biased, estimates using a within-family estimator would not be affected by the bias resulting from this correlation.
Since I rely on within-family variation in birth weight and HOME scores to identify parameters of interest, it is important to analyze the amount of within-family variation available to exploit in estimation. In an unreported analysis, I estimate the proportion of total variation in HOME scores and birth weight that can be explained by within-family variation, by estimating regression models that control only for a MFE. The proportion of total variation in HOME scores explained by within-family variation ranges between 36 and 50 %, and 39 % of the total variation in birth weight is explained by within-family variation. In addition, about 15.4 % of multiple-child families have at least one child who is LBW and at least one child who is NBW. And among high school dropouts and more educated mothers, the corresponding figures are 22.6 and 13.6 %, respectively.
Many studies in the literature do not estimate the impact of siblings’ endowment on investment in a child. In an unreported analysis, I omitted the measure of siblings’ endowments from regression models, which tended to slightly accentuate the estimated impact of a child’s own endowment on investment measures. This suggests that omitting a measure of siblings’ endowments from investment demand regression equations may cause a small bias in the estimated own endowment effects away from zero.
A possible explanation for the small estimated investment differences by income is measurement error in income, which tends to be exacerbated in specifications that identify parameters using within-family differences. In an alternate specification, I use average family income over all available observations for a child within a family to address the potential attenuation bias due to measurement error in income. The coefficient estimates of the interaction between LBW and average family income are similar in magnitude to those of the interaction between LBW and current family income, suggesting that measurement error in income is not causing a substantial amount of attenuation bias. In addition, coefficient estimates of the interaction between LBW and maternal education using this alternate specification are similar in magnitude to those found using the main specification. To the extent that maternal education is a proxy for “permanent income,” this unreported analysis also suggests that the interactive effects of LBW and maternal education on investment operate through channels in addition to the permanent-income channel.
I also explored the importance of several interactive effects that may drive the heterogeneity in investment responses to a LBW outcome by education and income. First, various aspects of the home environment may affect the ability of some mothers to make compensatory investments. For example, single motherhood or divorce, the lack of a child’s father in the home, and high or tightly spaced fertility could make mothers less able to compensate for their child being born with a LBW, since these children may require more time or resources than NBW children. Second, a mother’s cognitive and noncognitive skills could influence their investment response to a LBW outcome. For instance, high-educated mothers may be more productive at investing in poorly endowed children or may be better able to overcome the difficulties associated with caring for such children. In an unreported analysis, I examined these issues by adding interactions between birth endowments and the following variables to the main specification: indicator for whether a mother is married, indicator for whether the child’s father is present in the home, the total number of siblings in the home, birth order, birth spacing, and a mother’s performance on the AFQT, Rotter Locus of Control, Pearlin Mastery, and Rosenberg Self-Esteem Scales. I found no evidence that these factors drive the investment differences I observe by mother’s education and family income.
In an unreported analysis, I find that the differential investment response to a very low birth weight (VLBW)—defined as a birth weight below 1500 g—outcome by education is even larger. I estimate that high school dropouts invest about 17 % of a SD less in their VLBW children while mothers with at least 4 years of college invest 16 % of a SD more in their VLBW children.
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I would like to thank the editor Erdal Tekin and three anonymous referees for very helpful and detailed feedback. This paper previously circulated under the title “Who Compensates and Who Reinforces? Parental Investment Responses to Child Endowment Shocks” and is based on a dissertation chapter that was completed at The Ohio State University. I would like to give special thanks to my dissertation advisor David Blau and my dissertation committee members Audrey Light and Bruce Weinberg for their guidance and support during the early stages of this project. I would also like to thank Jérôme Adda, Christian Dustmann, Belton Fleisher, Trevon Logan, Derek Neal, Matthew Neidell, and Matthias Rieger for valuable comments and suggestions. This paper also benefitted from helpful comments by seminar participants at Cleveland State University, The Ohio State University, and European University Institute, and by conference participants at the 2011 GLASS Research Symposium and 2011 Southern Economic Association Annual Meeting.
Responsible editor: Erdal Tekin
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Restrepo, B.J. Parental investment responses to a low birth weight outcome: who compensates and who reinforces?. J Popul Econ 29, 969–989 (2016). https://doi.org/10.1007/s00148-016-0590-3
- Parental investment
- Human capital
- Low birth weight