Understanding how parental time influences educational and socio-behavioural outcomes of children.
PARENTIME will look at the mechanisms driving the inter-generational transmissions of inequalities by looking at the effect of parents and children interactions on their children's later life outcomes.
This 5-year project started in October 2018.
It is evident that high socioeconomic status parents consistently produce high socioeconomic status children - the question is how.
The objective of PARENTIME is to develop new socio-economic theories that unpack the detailed mechanisms driving the inter-generational transmission of inequality.
Because of data limitations and theoretical traditions, the literature has focused on:
- a narrow conceptualisation of parental time (limited to the quantity of time spent with children in different kinds of activities), and
- a reduced set of child outcomes (limited to educational outcomes and socio-behavioural outcomes during the early years).
PARENTIME aims to close this gap.
PARENTIME will link large representative 24-hour diary survey data on how much time parents spend with their children with detailed information on child outcomes from administrative data.
The aims are twofold:
- First, to go beyond the quantity of parental time to explore the inter-connections between family members in the child’s acquisition of skills (i.e., the timing and sequence, co-presence, multi-tasking, and instantaneous parental enjoyment).
- Second, to establish long-term effects of parental time investments by looking at a comprehensive set of child human capital measures all the way into the child’s adult life.
To understand how parental time investments influence child outcomes, we will first separate the contribution of other confounders such as:
- prenatal factors
- parental income and wealth
- parental employment
In some cases, individuals are linked across families and in networks. This allows us to compare:
- contributions from parental time inputs net of other factors
- school and neighbourhood characteristics.
These issues will be addressed through many state-of-the-art techniques that take care of selection and causation such as:
- treatment effects models
- siblings' fixed effects models
- instrumental variables
- propensity score matching.
To understand the factors at play we will:
- move forward statistical estimations of structural models of the technology of skills formation
- take advantage of the multi-level and longitudinal nature of the linked data to run sub-group analysis and heterogeneous effects - particularly across gender and class.
- Professor Almudena Sevilla
- Valentina Tonei
- Marina Morales (University of Zaragoza)
- Anika Schenck-Fontaine (Leibniz Institute for Educational Trajectories)
- Mette Goertz (University of Copenhagen)
- Nacho Gimenez-Nadal (University of Zaragoza)
- Cristina Borra (University of Seville)
- Afshin Zilanawala, UCL
- Miriam Marcem (University of Zaragoza)
- Cheti Nicoletti (University of York)