Do fathers who take a parental leave and stay involved raise smarter children? Evidence from the Millennium Cohort Study (MCS1-MCS6)

According to Lamb (2011, p.23) the major motif of father`s involvement in childcare 'has shifted in turn from moral guidance to breadwinning to sex-role modelling, marital support and finally nurturance'. Shaw and Lohan (2012, p.2) in their policy brief indicate a shift of the concept of fatherhood across cultures towards 'an ideal of a nurturing father'. They also note that fathers are nowadays strongly expected to be involved in children`s lives, and this change is propelled globally by government policies. These policies are, in turn, stimulated by growing evidence on a positive effect of father`s involvement on child`s development.

Huerta et al (2013, p.8) found that taking paternity leave positively affects father`s involvement in childcare and house chores. In turn, the results showed an association between fathers` involvement in childcare and better children's cognitive test scores, though this association was of small size. The research is based on datasets from four OECD countries, including three waves from the UK's MCS.

Elliot and Norman (2015) attempts to measure paternal involvement in childcare and housework. They state that previous research on father's involvement is lacking quantitative tools to accurately measure it (Elliot and Norman, 2015, p.1). Using cross-sectional data from the 1st sweep of MCS they conceptualize father's involvement and produce two quantitative measures of paternal involvement, 'engagement' and 'responsibility', using CFA. The measurement is based on frequencies of performing child- and housework-related tasks. Produced latent variables are then applied to look into employment and socio-demographic features of involved fathers. The authors assert that measuring paternal involvement is still a matter of wide debate, and call for further research on the topic.

Norman et al (2014) investigates predictors of father's involvement. The paper is mainly interested in parents' employment as a predictor of paternal involvement in childcare. Using the MCS, they provide evidence of higher importance of a mother's employment mode than father's working hours to father's involvement. Though focussed on the role of parents' employment, the authors consider a class, race, and household composition, gender of a child, parents' qualifications and age among important predictors shaping father's involvement in childcare.

Applying Latent Structural Equation Modelling, the proposed research aims to explore power of father's leave-taking around childbirth as a predicator of his participation in childcare; causal relationships between father's involvement, child's early cognitive outcomes and child's later cognitive outcomes. An advantage of the approach is flexibility of adding paths between covariates (such as father's leave around childbirth, employment and ethnicity) and factors (father's involvement, child early cognitive development) and among factors themselves (direct and mediated effect), which leaves less unanswered questions than multiple GLMs would.

An abundant supply of variables in the MCS provides us with a wide choice of indicators for building latent constructs and predictors for running regressions of interest. An amount of waves available gives us freedom to apply longitudinal analysis (as inspired by Steele, 2008, and Wickrama et al, 2016) and be ambitious about time-series analysis.

Some authors avoid longitudinal analysis because of its complexity, even when they have the data. Taking advantage of longitudinal data, the project will produce highly original results and interest a wide audience. As better methods of analysis emerge, which allow the latent factors (e.g. indices of development) to be tested for their shapes (distributions) and how they change over time, a wider range of applications will be found for Multilevel Longitudinal Structural Equation Modelling.