Estimating the Causal Impact of Non-Traditional Household Structures on Children's Educational Performance Using a Machine Learning Propensity Score

Over the past two decades, family structures have diversified. International migration has led to a rise in the number of families in which at least one parent is foreign-born. Increases have also been observed in both the rate of partnership separation, leading to a greater number of single-parent households and an increase in the number of families where grandparents have assumed caring responsibilities for their grandchildren. Evidence indicates a strong relationship between family structure and children's educational outcomes. Parental involvement is well documented as a key ingredient for the educational success of children. Drawing on Taiwanese multi-wave survey data (Taiwan Assessment of Student Achievement) and a machine-learning-based propensity score algorithm for multiple treatments, this paper aims to determine the various relationships between children from different household structures (two-parent households, skipped generation households, single-parent households, and immigrant households) and their cognitive knowledge (measured by test scores). Key findings reveal that children from skipped generation households achieve the lowest performance scores and that those from immigrant households tend to perform even better than children from traditional two-parent households in certain disciplines. Our results suggest that policy interventions targeted at providing remedial education and/or financial assistance are needed to support children from skipped generation families to redress existing educational disadvantages in Taiwan.