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-yearÌýproject started in October 2018.
This project is funded by the European Research Council.
- Background
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.Ìý
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), andÌý
- a reduced set of child outcomes (limited to educational outcomes and socio-behavioural outcomes during the early years).
PARENTIME aims to close this gap.Ìý
- Aims
PARENTIME will link large representative 24-hour diary survey data onÌýhow much time parents spend with their children with detailed information on child outcomes from administrative data.Ìý
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 Ìý(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.
- Methodology
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 techniquesÌýthat 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 andÌýheterogeneous effects - particularly across gender and class.
- Team
Principal Investigator
- ±Ê°ù´Ç´Ú±ð²õ²õ´Ç°ùÌýÌý
Co-investigators
Visitors
- (University of Zaragoza)
- (Leibniz Institute for Educational Trajectories)
Collaborators
- Ìý(University of Copenhagen)
- Nacho Gimenez-Nadal (University of Zaragoza)
- Cristina Borra (University of Seville)
- Afshin Zilanawala, Ïã¸ÛÁùºÏ²Ê
- Miriam Marcem (University of Zaragoza)
- Cheti Nicoletti (University of York)