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Parental limit-setting decisions and adolescent subject grades

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Abstract

Too much decision-making freedom in adolescence might discourage academic effort at the level parents desire since children tend to be less patient and risk-averse. Therefore, many parents limit their child’s choices in order to achieve optimal effort. In this paper, we analyze how limits on autonomy affect a child’s academic effort, gauged by both official transcript and child-reported grades in four core subjects. One empirical challenge is that parents might allow more independent decisions when a child exerts more academic effort, creating a downward bias. Our approach is to employ recursive bivariate models in which community differences in conservative Protestant market share produce external variations in the number of limits. We find US parents limit independent decision-making primarily to reinforce grades in high school English and math, with gains that diminish with the number of limits.

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Notes

  1. Some parents might set limits on autonomy allowing for negotiations and discussion of their policy with their child. Others set limits without the child’s input. Note our measure that counts the number of limits (i.e., parent-directed decisions) does not distinguish the two approaches.

  2. This model nests bivariate ordered probit models in which no endogenous variable features in another equation (Greene & Hensher, 2010). It also generalizes probit or ordered probit models with a binary endogenous covariate (Wooldridge, 2010). It does so only by partitioning both latent continuous endogenous variables into more than two ordered groups, but still having one equation per latent variable (with coefficients assumed to be invariant across groups). Whereas Li and Tobias (2006) rely on a Bayesian method to estimate this model, Roodman (2011) uses the classical maximum likelihood method. Based on the latter method, we use Stata’s eoprobit for estimation.

  3. Mourifié and Méango (2014) show that pinning down the value of the correlation parameter, using exclusion restrictions, achieves point identification in the special case of recursive bivariate probit models.

  4. While we exploit county conservative market share as an exogenous source of variation in number of limits, it is likely similar across proximal counties, especially within the Bible Belt or the Mormon corridor. Thus, as suggested by MacKinnon et al. (2022), we adjust standard errors for a coarser level of clustering (i.e., intra-state) to be prudent. Still, we cluster at the county level and find that standard errors are somewhat smaller for our coefficients of interest. Note that Angrist and Pischke (2009) advise clustering at the level that produces the largest standard error.

  5. Since we use a 1994-95 survey, the data are not current. We acknowledge that since 1995 the number of activities over which parents can apply limits increased, chief among them are children’s use of internet, social media, and mobile devices in their spare time. Notwithstanding this, we argue the data we use to assess monitoring effort has current relevance. Same as 1995, parents still set limits across key domains, for instance, to promote safety (e.g., via curfews), to encourage healthy habits with self-care (e.g., via healthy eating, good sleeping/bedtime habits) and with spare time (e.g., monitoring electronic devices), and to enhance social skills (e.g., via choice of friends). Our data gauges the extent of monitoring effort along those domains. Our focus is not on the effect of any specific type of limit.

  6. UNICEF and others define adolescents as those age 10–19, with the first 5 years classified as early adolescence and the second as late adolescence.

  7. Still, we acknowledge potential reporting issues, as children might vary in how they perceive the concept of independent decision-making across activity type. Here, extracting community-level variations in the extent of limit-setting based on conservative market share becomes important.

  8. The falsification tests in Section 4, however, suggest this variable does not violate the exclusion restriction.

  9. Excluded denominations are 23 moderate (e.g., Disciples of Christ, Catholics) and liberal ones (e.g., Episcopal Church, United Church of Christ), in addition to Black Baptist and Jewish adherents.

  10. In fact, the conservative share of the religious market has 78% of its variance in common with the conservative share of county residents. Thus, the former measure not only strongly reflects conservative practice in a county, but also the dominance of conservatives among religious adherents. At any rate, when we use the conservative share of county residents as the exclusion variable, our main findings in Table 7 are similar.

  11. Figure 3 shows no estimate for age 11 since there are only seven students of that age.

  12. Results from an ordered probit model, as in Eq. (1), are even more significant (see Table 9 in the Appendix).

  13. We adjust specifically for child religiosity (i.e., attending services, religious salience, praying, strict conservative beliefs, religious affiliation, and attending religious schools). Further adding parental religiosity (not reported in table) hardly changes the estimated effects, since reports of religiosity are largely consistent within a family.

  14. Note that at 7 limits the CDF difference is trivially equal to zero, since the right-hand limit of a CDF function is 1.

  15. Based on Table 3, column 1, the F-statistic = (0.547/0.117)2 = 21.9.

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Authors and Affiliations

  1. Southern Illinois University Edwardsville, Edwardsville, IL, 62025, USA

    Marlon R. Tracey

  2. Virginia Tech, Blacksburg, VA, 24061, USA

    Chanita C. Holmes

  3. George Mason University, Fairfax, VA, 22030, USA

    Marvin G. Powell

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  1. Marlon R. Tracey
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  2. Chanita C. Holmes
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Correspondence to Marlon R. Tracey.

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Appendix

Appendix

Tables 9 and 10

Table 9 Regression models of number of limits
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Table 10 Recursive bivariate ordered probit model—full child-reported subject grade equation
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Tracey, M.R., Holmes, C.C. & Powell, M.G. Parental limit-setting decisions and adolescent subject grades. Rev Econ Household 22, 143–171 (2024). https://doi.org/10.1007/s11150-023-09655-9

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