Abstract
We examine the role that parental engagement with child’s education plays in the lifecourse dynamics of locus of control (LOC), one of the most widely studied non-cognitive skills related to economic decision-making. We focus on parental engagement as previous studies have shown that it is malleable, easy to measure, and often available for fathers, whose inputs are notably understudied in the received literature. We estimate a standard skill production function using rich British cohort data. Parental engagement is measured with information provided at age 10 by the teacher on whether the father or the mother is very interested in the child’s education. We deal with the potential endogeneity in parental engagement by employing an added-value model, using lagged measures of LOC as a proxy for innate endowments and unmeasured inputs. We find that fathers’, but not mothers’, engagement leads to internality, a belief associated with positive lifetime outcomes, in both young adulthood and middle age for female and socioeconomically disadvantaged cohort members. Fathers’ engagement also increases the probability of lifelong internality and fully protects against lifelong externality. Our findings highlight that fathers play a pivotal role in the skill production process over the lifecourse.
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Notes
A landmark study by Heckman et al. (2006) showed that a summary non-cognitive skill measure derived from self-efficacy and self-esteem personality questionnaires was at least as important as cognitive skills in determining a range of life outcomes including educational and labor market outcomes. A series of studies that followed and the role of non-cognitive skills in shaping lifetime opportunities were elegantly summarized in Almlund et al. (2011) and in Cobb-Clark (2015).
We have explored rigorously the possibility of alternative data to study this question. To date, the BCS1970 is the only available data set that provides LOC data in both childhood and at least some consistent LOC measurements in young adulthood and middle age. The National Child Development Study (NCDS) has data available on four adulthood LOC measures, but has no measures in childhood. The Avon cohort—the so-called ALSPAC study—provides LOC data in childhood, but the participants are only young adults in the follow-up. Our own previous research has exploited available LOC data to study the malleability of LOC in adolescence (over 8 years) and in adulthood (over 4 years) using the Household Income and Labour Dynamics in Australia survey (Cobb-Clark and Schurer 2013; Elkins et al. 2017).
They argue that a reversed trend occurs because the youngest sample members (age 14) were lowest in internality in the first measurement period and therefore able to experience the largest increase.
The findings in Specht et al. (2013) may be driven by the utilization of LOC measures that were differently coded in the two measurement periods. Thus, changes in LOC may be the result of coding differences and not of differences in personality change.
Early work in the 1970s found that a socioeconomic gradient in internal control beliefs already existed among young school children (see Stephens and Delys (1973) for a review of this literature). Stephens and Delys (1973) found that pre-kindergarteners from disadvantaged backgrounds attending Head Start schools were more likely to report external control tendencies than middle class children from Montessori and cooperative nursery schools. In contrast, Bartel (1971) found that control perceptions did not differ between socioeconomic groups before entering first grade, but reported that substantial differences emerged by the sixth grade, an effect they suggest is driven by differences in the social control exerted by schools.
These findings are in line with previous studies suggesting that highly educated parents do not only spend more time with their children but spend their time on activities believed to be more productive or “developmentally effective” (Kalil et al. 2012).
Hofferth (2006) discusses the evidence on the positive association of non-traditional family structures—families that are not composed of married-biological-parents—and children’s behavior problems.
Note: The full CARALOC questionnaire contains 20 items, with five “distractors”. We have retained distractor item 12 based on a factor analysis because it improves the scale’s Cronbach’s alpha (see Ogollah, 2010).
Practically, such a large dimension is not possible, especially if a third predictor variable is added. To aid the data reduction process, the algorithm needs to categorise the continuous variables such as age 10 LOC. We ex ante specify to categorize age 10 LOC into three terciles. As we will demonstrate in the empirical section, the key conclusions of the analysis are not sensitive to this split.
Practically, the algorithm does this by including only levels of the predictor variable that are significantly different from each other. To reduce the b × a table to the most significant k × a with k = 2(1)b. Then choose the k × a table that has the most significant chi-squared statistic. The null hypothesis of the independence of the predictor variable age 10 LOC and the dependent variable age 42 is tested using the Pearson’s chi-square statistic. We use the—chaid—program for STATA written by Joseph N. Luchman at Behavioral Statistics Lead. The algorithm considers three steps: preparing predictors, merging categories, and selecting the split variable.
Cluster analysis is another data reduction technique which is designed to group similar observations in a data set, such that observations in the same group are as similar to each other as possible, and similarly, observations in different groups are as different to each other as possible. The “K-means” cluster analysis method groups observations by minimizing Euclidean distances between them. Euclidean distances are similar to measuring the hypotenuse of a triangle, where the differences between two observations on two variables, let’s say age 42 LOC and age 10 LOC, are plugged into the Pythagorean equation to solve for the shortest distance between the two points. This approach requires that all variables used to determine clustering using k-means must be continuous, which we will assume in our data setting. In order to perform k-means clustering, the algorithm randomly assigns k initial centers, a number which needs to be chosen by the user ex ante. We use the standard algorithm, the Hartigan-Wong algorithm, which aims to minimize the Euclidean distances of all points with their nearest cluster centers, by minimizing within-cluster sum of squared errors (SSE). K-means clustering also requires a priori specification of the number of clusters k, a choice that can be facilitated empirically with the data. We use a screeplot to graph within-group SSE against each cluster solution (Aldenderfer and Blashfield 1984).
When categorizing age 10 LOC ex ante into four quartiles or five quintiles, we obtain nine maturation pathway types. These are almost identical to the eight types described above, but we are able to identify a slightly more nuanced maturation profile (Fig. 7, Supplement).
A supplement, Table 4 reports the underlying sample numbers
Examples of items include “children should not be allowed to talk at the meal table”, “unquestioning obedience is not a good thing in a young child”, and “a well-brought up child is one who does not have to be told twice to do something.”
Optimally, we would like to use the same control variables for fathers and mothers. However, we rely on information about the father as reported by the mother in the interview. Father and mother roles in the family were very different in the 1970s and 1980s; therefore, work- and occupation-related information for mothers is less predictive in our models than for fathers. We have experimented with different specifications, among others specifications where we perfectly align the available control variables for fathers and mothers. Our estimation results and conclusions are not sensitive to the concern that paternal and maternal control variables are not perfectly symmetric.
In a further robustness check, we add also age 10 cognitive skills for children to further control for the possibility that parental engagement at age 10 reflects only unobserved skills.
For an overview of these standard models and how to calculate marginal probability effects, see Cameron and Trivedi (2005).
Our conclusions are robust to adding additional control variables for unobserved abilities at age 10. Parental engagement with the education of the child at age 10 could be the result of cognition difficulties that were not present at age 5, which caused especially fathers to engage with the child’s schooling. We added cognitive ability tests scores from age 10 into model (4) such as the BAS Word Definitions test BAS Recall of Digits test, BAS Similarities test, BAS Matrices test. The MPE for father’s interest in the child changes from 0.036 to 0.035 and remains statistically significant at the 5% level. These results are provided upon request.
For children where both parents were interested in their education, the MPEs for both father’s and mother’s interest are, respectively, .056 with a standard error of .016 (significant at the 1% level) and .019 with a standard error of .036 (not significant).
This calculation is based on a MPE of 4%-points and a base probability of 4%, which yields a percent decrease of 100.
This calculation is based on a MPE of 5%-points and a base probability of 25%, which yields a percent increase of 25.
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Acknowledgement
We would like to thank three anonymous referees and recognize their help and guidance in the review process. We also thank Deborah A. Cobb-Clark, Jana Mareckova, Michael A. Shields, David W. Johnston, and participants of the IZA/OECD/World Bank Workshop on Cognitive and Non-Cognitive Skills and Economic Development in Bertinoro, Italy, 3-4 October 2014 for valuable comments. We acknowledge financial support from an Australian Research Council Discovery Early Career Award (DE140100463) and the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027). All errors are our own.
Funding
Schurer acknowledges financial support from an Australian Research Council Discovery Early Career Award (DE140100463) and the Australian Research Council Centre of Excellence for Children and Families over the Life Course (project number CE140100027).
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Appendices
Appendix 1
Appendix 2 Robustness checks
2.1 2.1 Sample of cohort members who lived with their biological fathers
Relationship between parental interest in child’s education (mother, father) and the probability of a specific permanent control belief type (childhood-age30-age42). Reported are marginal probability effects obtained from a multinomial logit model estimated on 5465 observations with a full set of control variables. Types 7 and 8 have high lifelong internality; types 1 to 3 have lifelong externality; types 5 and 6 demonstrate a relative reversal whereby they are above-average in childhood but below-average in adulthood; and type 4 individuals exhibit the opposite pattern indicating low childhood internality and high adulthood internality. Types 4, 7, and 8 constitute 78% of the sample. Horizontal gray bars are 95% confidence intervals
2.2 2.2 Relaxing the number of percentiles in age 10 LOC to split the groups
CHAID: 9 clusters (allowing for four or five quintiles to dichotomize childhood LOC)
2.3 2.3 Choice of number of clusters with k-mean clustering
Optimal number of clusters: standard clustering method
2.4 2.4 k-mean clustering: ex ante five clusters
k-mean clustering: 5 clusters
2.5 2.5 k-mean clustering: ex ante eight clusters
k-mean clustering: 8 clusters
2.6 2.6 Heterogeneity: by sex
Type estimation based on CHAID with eight clusters-heterogeneity for female (estimation did not converge for male)
2.7 2.7 Heterogeneity: by socioeconomic status
Type estimation based on CHAID with eight clusters-heterogeneity by SES
Appendix 3 Determinants of age 10 locus of control beliefs
To understand the initial conditions of locus of control, we present in Table 7 estimation results from a regression model in which we regress a measure of age 10 internality on a set of standard early-life factors, including parental involvement in the education of the child. The dependent variable is a standardized version of our continuous childhood control belief measure, and parameter estimates are obtained using ordinary least squares (OLS). We allow for heteroskedastic standard errors (Huber-White). Results are presented by sex and socioeconomic status (according to father’s occupational class). High SES is defined as professional or manager occupations, while low SES is defined as low- or no-skilled or service occupational class. To reduce the high-dimensionality of estimation results, we present and limit our discussion to the estimated coefficients of interest. Full estimation results are provided upon request.
Parental interest in the education of the child predicts childhood internality independent of the influence of family structure; maternal, paternal, and individual childhood factors; and important socioeconomic indicators including parental occupational status and education. Overall, we find that children of parents very interested in their education are more internally oriented relative to children of parents who are not very interested. The magnitude of this association varies by sex and SES for mother’s involvement (standardized coefficients range between 0.07 and 0.15 SD and drop from significance among high SES cohort members), although the differences across groups are not statistically significant. In contrast, father’s involvement is a significant and stable predictor of internality across every group (standardized coefficients range between 0.17 and 0.22 SD), and the magnitude of this association with LOC is stronger than for mother’s involvement, especially for boys and children from privileged backgrounds. The estimates on father’s involvement are stronger in magnitude when focusing on families with biological or adoptee fathers (Panel B).
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Elkins, R., Schurer, S. Exploring the role of parental engagement in non-cognitive skill development over the lifecourse. J Popul Econ 33, 957–1004 (2020). https://doi.org/10.1007/s00148-020-00767-5
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DOI: https://doi.org/10.1007/s00148-020-00767-5
Keywords
- Non-cognitive skills
- Locus of control
- Father school involvement
- Lifecourse dynamics
- British Cohort Study 1970