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Does parents’ valuation of children’s health mimic their valuation of own health?

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Abstract

A single latent variable model of health status and therapeutic health care utilization is estimated for parents and own children of 6,557 US households. The equation system that identifies latent health status simultaneously determines a number of indicators of general health, including presence of morbidity symptoms, mobility limitations, medication needs, and utilization of therapeutic health care services. The main goal of the paper was to obtain an unbiased estimate of parents’ marginal substitution rate between own and child health. Results indicate that parents’ valuation of their children’s health exceeds their valuation of own health by almost twofold on average.

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Notes

  1. The use of preventive care to evaluate a parental tradeoff between own and child health is problematic. There often is no trade-off because good parental health can have a positive impact on child health. Consider for example the well-known Barker (1998) thesis regarding maternal health during pregnancy, infant health, and the infant’s health as an adult. Also, parental health may affect a parent’s ability to use and choice of preventive care (Archer et al. 2006). A healthy parent is in a better position to nurture, monitor, and teach her child.

  2. Viscusi et al. (1988) on household chemicals, Carlin and Sandy (1991) on seat belt use, Agee and Crocker (1996a,b) on body burdens of lead, Liu et al. (2000) on short-term morbidity symptoms, Jenkins et al. (2001) on bicycle helmets, Agee et al. (2004) on child abuse, and Dickie (2005) on acute illnesses appear to exhaust the list for children.

  3. Liu et al. (2000), Dickie and Messman (2004), and Agee and Crocker (2007) exhaust this list. In the absence of a scientific consensus on this relative value, US federal agencies have been using unadjusted adult health values to make their mandated assessments of the value of policy interventions upon the health and safety of children. See for example the benefit transfer procedures outlined in Kuchler and Golan (1999), US Environmental Protection Agency (2000), and in Dockins et al. (2001). Agee and Crocker (2004) discuss in detail the analytical and empirical issues associated with these procedures.

  4. r M in expression 2 assumes a single time cost for adult and child medical treatments and physician’s visits valued at the parent’s opportunity cost of time. We also assume a single market price vector for similar medical treatments/procedures, physician’s visits, drugs, insurance, and co-pays across individual household members. Given nearly 80% (17) of our sample households have private (Medicaid) coverage, we believe this assumption is plausible, as it is consistent with other empirical studies (e.g., Wolfe and van der Gaag 1981) which presume that individuals who reside together likely confront similar prices for similar medical goods/services via similar coverage.

  5. Note that our distance function formulation uses information derived from both the multiple input health production functions and the parents’ utility function. It can be contrasted with formulations in Gerking and Stanley (1986) and elsewhere wherein the demand for health is the product of the marginal cost (derived from the underlying health production function with a single input, e.g., medical care) of a health impact and an exogenous change in the source (e.g., pollution) of the impact.

  6. Monte Carlo results by Adamowicz et al. (1989) actually find the stability properties of linear-log forms of zero-bounded demand relations to exceed those of the more widely used linear and semilog forms.

  7. This sample is net of any missing observations. The gross sample consists of roughly 9,000 households.

  8. This information excludes normal health care use related to pregnancy and childbearing. Use due to problems experienced with pregnancy is included.

  9. While invariance of factor loadings is necessary to arrive at a meaningful parental marginal substitution rate estimate, reduced forms 15 and 16 show that “group non-invariance” of parameters in a and B is necessary for observed covariates in X to capture individual and group differences among observed health status indicators, I, referred to as “formative” or “cause” indicators on latent H*, as well as for parental differences in their observed choices of own and child health care utilization, \( M^{ * }_{P} \) and \( M^{ * }_{C} \), referred to as “reflective” or “effect” indicators due to latent H* (Bollen and Lennox 1991).

  10. All of these results are consistent with prior studies examining sociodemographic differences in general health measures (see, e.g., Burdine et al. 2000; Fleishman and Lawrence 2003).

  11. The significance of family income in the child health regression contrasts with some earlier studies that find little to no association between income and health when child health is measured using only one from a variety of proxy measures (see, e.g., Edwards and Grossman 1980).

  12. Model estimates using only the N = 1,450 subsample of families with children of ages two and younger indicate no added decrease in the marginal impact of child health status on child health care use. However, these estimates indicate a substantial increase in the adult health benefit of being male. Thus, the N = 1,450 subsample, which yields a mean substitution rate of 5.1, appears to be strongly influenced by a “parental youth” effect, i.e., younger parents with younger children have a higher estimated latent health status (particularly subsample males) and, given our Eq. 13, a higher coefficient estimate for \( H^{ * }_{P} \) results in a higher substitution rate.

  13. Some rudimentary observations are of course possible. For instance, if parents’ pure altruism is presumed not to decline as their children age, the higher substitution rates observed in Table 4 for younger children might reflect parents’ “excess uncertainty,” in view of the fact that younger children do not communicate health indicators to parents as effectively as older children.

  14. Probit estimates are available from the authors.

  15. Indeed, high multicollinearity among indicators in a MIMIC model reduces stability of indicator coefficient estimates (Diamantopoulos and Winklhofer 2001). As the absolute value of the coefficient estimates of the indicators are interpreted as indicator validity coefficients (Bollen 1989), high multicollinearity renders assessment of indicator validity problematic. From a theoretical perspective, the list of indicators should be sufficiently inclusive to capture fully the construct’s (latent variable) domain of content (Nunnally and Bernstein 1994, p 484). Similar to other surveys used to assess health status in general populations (e.g., the oft-used SF-12 survey; Ware et al. 1998), the NHIS indicators span the domain of self-assessed health, physical functioning, mental health, and presence of acute and/or chronic illnesses. The statistical significance of all our Table 3 indicator coefficients (including coefficients of STI when alternative normalizations are specified) suggests our construct is comprised of valid indicators.

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Acknowledgment

Insights and comments from two anonymous referees, Scott Atkinson, Karen Conway, and workshop participants at the University of Georgia are gratefully acknowledged. The US Environmental Protection Agency financially supported the research efforts through grant #R82871601. However, the research has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency.

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  1. Department of Economics, The Pennsylvania State University, Altoona, PA, 16601-3760, USA

    Mark D. Agee

  2. Department of Economics & Finance, University of Wyoming, Laramie, WY, 82071-3985, USA

    Thomas D. Crocker

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  1. Mark D. Agee
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  2. Thomas D. Crocker
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Correspondence to Mark D. Agee.

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Agee, M.D., Crocker, T.D. Does parents’ valuation of children’s health mimic their valuation of own health?. J Popul Econ 21, 231–249 (2008). https://doi.org/10.1007/s00148-007-0159-2

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