We compare the trajectories of families who have a child with poor neonatal health compared to those who do not, using administrative birth record data merged with longitudinal household survey data. We apply entropy balancing and weighting methods to enhance comparison between the two types of families. We find that children with poor neonatal health are more likely to be diagnosed with a disability and to receive Supplemental Security Income. Mothers who have children with poor neonatal health are more likely to reduce labor force participation at both the intensive and extensive margins when the child is young. Further, they are more likely to receive benefits from the Supplemental Nutrition Assistance Program and the Temporary Assistance for Needy Families program. We find no significant effects of poor neonatal health on maternal mental health or household income. Parents who have children with poor neonatal health are significantly less likely to remain married or cohabiting.

This paper adopts an actuarial approach to identify the risk factors of government-funded maternal out-of-hospital costs in Australia, with a focus on women who experience adverse birth outcomes. We use a two-phase modelling methodology incorporating both classification and regression trees and generalised linear models on a data set that links administrative and longitudinal survey data from a large sample of women, to address maternal out-of-hospital costs. We find that adverse births are a statistically significant risk factor of out-of-hospital costs in both the delivery and postnatal periods. Furthermore, other significant cost risk factors are in-vitro fertilisation, specialist use, general practitioner use, area of residence and mental health factors (including anxiety, intense anxiety, postnatal depression and stress about own health) and the results vary by perinatal sub-period and the patient’s private health insurance status. We highlight these differences and use the results as an evidence base to inform public policy. Mental health policy is identified as a priority area for further investigation due to the dominance of these factors in many of the fitted models.