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Demography
Published in: Demography 3/2016

12-05-2016

The (Conditional) Resource Dilution Model: State- and Community-Level Modifications

Authors: Benjamin G. Gibbs, Joseph Workman, Douglas B. Downey

Published in: Demography | Issue 3/2016

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Abstract

One of the most consistent patterns in the social sciences is the relationship between sibship size and educational outcomes: those with fewer siblings outperform those with many. The resource dilution (RD) model emphasizes the increasing division of parental resources within the nuclear family as the number of children grows, yet it fails to account for instances when the relationship between sibship size and education is often weak or even positive. To reconcile, we introduce a conditional resource dilution (CRD) model to acknowledge that nonparental investments might aid in children’s development and condition the effect of siblings. We revisit the General Social Surveys (1972–2010) and find support for a CRD approach: the relationship between sibship size and educational attainment has declined during the first half of the twentieth century, and this relationship varies across religious groups. Findings suggest that state and community resources can offset the impact of resource dilution—a more sociological interpretation of sibship size patterns than that of the traditional RD model.
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Appendix
Available only for authorised users
Footnotes
1
The roots of resource dilution could be traced to Dumont’s “law of capillary action” (Dumont 1890), but Blake (1986) is widely considered the first scholar to use the term.
 
2
Note the overlap between SRD and the quantity-quality model of fertility (Becker and Tomes 1976). The proponents write, “An increase in the quantity of children raises the cost or shadow price of the quality of children” (p. 143).
 
3
A less common phasing of the RD model is “resource depletion theory” (e.g., Fingerman et al. 2009).
 
4
Sibship size associations are generally stronger for educational outcomes, such as years of education attained and high school and college graduation, but are weaker for cognitive skills (Steelman et al. 2002).
 
5
Even without disconfirming evidence, concerns about spuriousness—at least for sibship and intelligence—have been debated (Guo and VanWey 1999b; Rodgers et al. 2000; see Steelman et al. 2002).
 
6
Of course, pro-fertility communities likely support parents of both large and small families, but this help may matter more for children in large families, for whom parental resources are stretched thin.
 
7
Interestingly, as the relationship between education level and expected number of children is negative for the average American, the expected number of children increases modestly as education levels increase among Mormons (Heaton et al. 2004). Specifically, using the General Social Survey, Heaton et al. (2004) found a slight increase from 3.5 expected children among Mormon high school graduates (2.5 U.S. average) to about 4.0 among individuals with a graduate degree (2.0 U.S. average). Tests for statistical significance were not performed.
 
8
Curtis et al. (2015) found that tithing contributions are more likely among lifelong Mormons than among converts.
 
9
Unlike other denominations, the LDS congregation size is capped at approximately 600 members with membership in a given ward determined by preset “ward boundaries” (Chaves 2006). This results in two potentially advantageous outcomes for families in need. First, there are no large Mormon “megachurches,” which would likely limit interaction with leadership. Second, because ward boundaries are typically drawn to include a socioeconomically diverse membership, wards are more socioeconomically diverse than they might otherwise be if members were to choose their own congregations.
 
10
As with most studies of Mormons, these statistics are generated from a small number of cases (n ≈ 50).
 
11
Analyses performed with and without these restrictions reveal little change in the estimates. For the sibling measure, the cut point at 24 siblings is admittedly arbitrary but does represent a slight drop in the number of cases moving from 23 (n = 14) to 24 siblings (n = 4) reported. We also analyzed the sibling variable as categorical (0, 1, 2, 3, or 4 compared with 5 or more), finding similar declining associations of educational attainment across decades of birth.
 
12
This measure does not allow us to distinguish siblings living in the household from those who are not, or to determine precisely how long a particular sibling lived in the home. For example, an individual may have a stepsibling who did not become part of the family until after the respondent completed his or her education.
 
13
We also considered separating Utah Mormons from other Mormons, but there were too few cases in Utah and surrounding states to conduct reliable analyses.
 
14
Because the wording for this question changed after 1993, we include a binary variable (Mother Employment Flag) indicating whether the survey was pre- or post-1993.
 
15
When comparing analyses with categorical versus continuous treatment of the family background measures, we found that these transformations did not meaningfully impact our results.
 
16
When these measures were analyzed as categorical variables (i.e. suburb, city, and so on; East South Central, Middle Atlantic, and so on), our substantive results were relatively unchanged.
 
17
Birth years of 1900–1904 and 1978–1979 had too few cases for independent regression analyses. Also, given that the data begin in 1905, Fig. 1 starts in 1915, an artifact of the 10-year smoothed averages.
 
18
Of course, one potential challenge for this analysis is the fact that the dependent variable is a moving target: years of education attained increased in significant ways over the century. The relationship between sibship size and years of education attained might be sensitive to this overall change. In supplemental analyses, we addressed this possibility by predicting the deviation from the average years of education attained for those born in the same year, thereby normalizing the dependent variable by each year. We also did this for sibling size. Normalizing the dependent variable in this way or the independent variable of siblings did not change the overall patterns (see Tables 5, 6, and 7 in the appendix). Also, in supplemental models, we found little evidence that these patterns vary by urban/rural status.
 
19
Including the most recent wave of GSS data (2014) reveals that the reversing trend line for cohorts in the 1970s only increases for cohorts in the 1980s. In other words, the more recent association between sibship size and educational attainment appears to be returning to pre-1960s cohort levels. Analysis available upon request.
 
20
The coefficient for sibship size from Model 3 in Table 3 is –0.25 (–0.25 × 6 = –1.5).
 
21
Results indicate that the relationship between family background and educational attainment became weaker during the twentieth century. Family income (0.62), father’s occupational prestige (0.34), and parental education (1.11) all have positive coefficients for the main effect. All three measures have a negative coefficient for the interaction with cohort, indicating that the relationship with educational attainment became weaker (i.e., less positive).
 
22
The analyses require access to the GSS sensitive data, which contain information on the state in which the respondent was raised at age 16. We link the GSS sensitive data with data from the U.S. Census of Governments, which captures all spending on higher education from state and local sources every five years. State spending is measured the decade after the respondent’s birth. Because of sample size limitations, we also have to restrict analyses to those born in the 1950s, 1960s, and 1970s.
 
23
In supplemental analyses we also explored whether the Mormon pattern also changed over time. We found little evidence that the religion-based patterns also interacted with the historical changes, but sample sizes became small, limiting our confidence in this analysis.
 
24
We also attempted to explain the Mormon interaction with indicators in the GSS of “community support.” We relied on past research that gauged “social capital” (Paxton 1999) using the GSS data with indicators such as trust in individuals and voluntary associations. In supplemental analyses, these indicators did not reduce the interaction to nonsignificance. We are uncertain about the value of these analyses, however, because our measures of social capital were taken among adults, and we are most interested in community-level investments respondents received while growing up.
 
25
We find evidence of this pattern with the inclusion of the 2014 GSS data. Results available upon request.
 
26
In supplemental analyses, we attempted to move in this direction but confronted data obstacles. For example, to understand mechanisms for the Mormon interaction coefficient, we performed supplementary analyses of the relationship between sibship size and educational outcome with the National Longitudinal Study of Adolescent Health (Add Health) (Harris et al. 2003). We found that the estimate for the Mormon–sibling size interaction was in the right direction but not statistically significant. This may be partly due to the Add Health data, which had a relatively small sample of Mormons (n = 210).
 
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Metadata
Title
The (Conditional) Resource Dilution Model: State- and Community-Level Modifications
Authors
Benjamin G. Gibbs
Joseph Workman
Douglas B. Downey
Publication date
12-05-2016
Publisher
Springer US
Published in
Demography / Issue 3/2016
Print ISSN: 0070-3370
Electronic ISSN: 1533-7790
DOI
https://doi.org/10.1007/s13524-016-0471-0

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