Abstract
This study examines the relationship between religious affiliation, importance of religion, and frequency of church attendance and the reported overall health status and psychological health of children and adolescents by age group (6–11, 12–15, and 16–19 years old), using national data from the Child Development Supplement to the Panel Study of Income Dynamics. Controlling for child’s initial health, individual and family demographic characteristics, and socio-economic status, differences were found by age and measures of religion and health. Probit analysis revealed a generally positive and statistically significant association between religion and health, especially for the psychological health of children ages 12–15. Mitigating the issue of selection bias on observable characteristics, the Propensity Score Matching analysis generated similar positive associations between religion and child health. These findings are consistent with the corresponding literature on adults.
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Notes
For simplicity, this paper refers to any house of worship as “church.”
State religious support, religious regulation measures, and religious pluralism were used as instruments.
Because of missing values for one or more of the variables included in the analysis, only 4.9 % (135 records) of the original CDS/PSID sample of biological/step/adoptive/foster children ages 6–19 had to be dropped. Since a larger number of observations of the mother’s education were missing, a missing mother’s education variable was included in the analysis. The children in the sample used in this study (N = 2,604) were statistically not different from the children in the comparable CDS/PSID sample (before dropping records for missing variable values) (maximum N = 2,739), except for mother’s education (a difference of 0.23 years of education in favor of the regression sample). A summary statistics table is available from the authors on request.
Among children ages 12–19, parental report was used for 16.9 % of children to construct the variables on religious affiliation and denomination, 9.8 % for church attendance, and 9.2 % for importance of religion.
The literature refers to Mainline Protestants also as “Liberal Protestants,” and Conservative Protestants also as “Fundamentalist Protestants” or “Evangelical Protestants.” Due to small sample sizes, Jews, Orthodox, and Mormons, were included under the category “other religion.” The Appendix includes a detailed description of the denominational groups.
Due to concerns on how to define other race categories given small sample size (Thornton and White-Means 2000), other races included Asian/Pacific Islander, American Indian or Alaskan Native, and Multi-racial.
The square terms for maternal education and family income were included to capture the potential non-linear relationship between these variables and child health, expecting diminishing returns of education with each year of schooling, as the general health production function suggests.
The regressions including health insurance are available from the authors upon request.
A Cronbach’s alpha, the statistic typically used for measuring the internal consistency or index reliability, of 0.70 or higher is considered reliable (Streiner and Norman 1989).
The traditional family index was based on questions, such as “If a husband and a wife both work full-time, they should share household tasks equally”; “Women are much happier if they stay at home and take care of their children”; “It is more important for a wife to help her husband's career than to have one herself” (1 = strongly disagree to 4 = strongly agree), whereas the index indicating the degree of child’s independence comprised 5 questions on the frequency, in the last 6 months, that the child made his/her own bed, cleaned his/her own room, helped manage his/her own time, etc. (1 = never/almost never to 5 = almost always). Detailed information of the construction of the health, religion, and control variables is available on request from the authors.
The IV technique also attempts to provide an alternative to account for selection into religion. However, finding a suitable instrument for religion proved difficult. State prevalence of religion (both from external data and based on the PSID), namely belief in God or religious affiliation, importance of religion, and weekly church attendance as well as indices for parental child-rearing attitudes and family values were tested as instruments, but the first stage results showed very low explanatory power of the model introducing extra noise in the estimation. Bound et al. (1995) provided a detailed discussion of problems with IV estimation.
Ali and Ajilore (2011) provided an excellent discussion of the PSM technique.
A correlation matrix not shown here indicated that even though the religious measures of affiliation, attendance and importance were statistically significantly correlated, the correlations were not strong, the highest correlation coefficient being (−0.5) between religion not important and affiliated. As expected, a negative correlation was found between having an affiliation and religion not important and a positive correlation was found between having an affiliation and frequency of church attendance.
Consistent with the proposed theory for differences in the religion-health relationship by age, a Wald test indicated that separating the analysis by age group was the preferred approach as compared to a pooled sample using interaction terms by age. In particular, the Wald test for psychological health indicated that separate models should be run by age groups, whereas the results of Wald test for overall health were just shy of the conventional level of significance. For consistency purposes, for both health outcomes the results were presented for the full sample and by age group.
The full regression equations are available on request from the authors.
The probit equations presented in Tables 3, 4 5 were also estimated in two stepwise manners: (1). First-order regressions where a health outcome was regressed on a religious variable (without the control variables); and (2). Regressions of the health variable on the control variables (without the religion variables). The results were not sensitive to the order in which the religion and control variables were entered in the equations. Those regressions are available from the authors upon request.
In the first step in implementing the matching method the propensity score for the treatment group (dichotomous variables for affiliation, religion being very important, and weekly/more frequent church attendance) was estimated as a function of all of the variables included in the health equation, as well as the presence of health insurance and indices for the degree of traditional family, child independence, and behavioral problems. Following the algorithm proposed by Becker and Ichino (2002), we found that in each of the blocks (8 blocks for affiliation, 7 blocks for importance, and 6 blocks for attendance) the propensity score for the treatment group satisfied the balancing property, i.e., the score was balanced across the treated units and controls.
We tried using the IV technique to address the endogeneity of religion, but the technique was not successful because of the absence of appropriate identifying instruments.
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Acknowledgments
The authors would like to thank Susan Averett, Laurence Iannaccone, and three anonymous referees for their helpful comments. The paper also benefited from feedback when it was presented at the meetings of the Association for the Study of Religion, Economics, and Culture (ASREC) and the Eastern Economic Association (EEA).
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Chiswick, B.R., Mirtcheva, D.M. Religion and Child Health: Religious Affiliation, Importance, and Attendance and Health Status among American Youth. J Fam Econ Iss 34, 120–140 (2013). https://doi.org/10.1007/s10834-012-9312-5
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DOI: https://doi.org/10.1007/s10834-012-9312-5