Unpacking the links between household income and child academic achievement in Australia through the development of the PICAA microsimulation systems model

Abstract

Driven by the internationally observed phenomenon that poorer children perform worse on standardised tests than their more affluent counterparts, this study attempts to understand the key links between household income and educational outcomes at a system level, through the development of the Parental Income and Child Academic Achievement (PICAA) microsimulation model. This model was developed with the goal of being used by policymakers to test the magnitude of expected outcomes of educational and welfare interventions for student outcomes. To do so, this study develops and employs a novel targeted review method to identify key pathways in existing education literature. Following this, it shows how this information can be mapped into a systems diagram, which can then be quantified and operationalised as a microsimulation model based on academic literature and aggregate national data. Finally, it outlines how this model can be validated with de-identified individual data from nationally representative samples, and finally how it can generate results when applied to policy questions. This study represents one of the first applications of modelling to investigate educational inequality as a function of income in an Australian context, one of the first applications of microsimulation to the question of income and educational outcomes, and the first application to the Australian education system. The completion of the validated PICAA model represents the key contribution of this work. This model has high potential to be used for the evaluation of education policy interventions, which is illustrated through a series of income-based policy intervention scenarios. However, the development process of the PICAA microsimulation model has also resulted in a number of key findings and contributions. This study reinforces the importance of the early years as a period of significant developmental growth and a time period in which income-based interventions may provide the best value-for-money for policymakers looking to increase achievement scores or reduce inequality in achievement. This study has also identified an increasing correlation between income and achievement with increasing time between measurements, first identified using the PICAA model but also identified in analysis of NAPLAN achievement data. This newly identified trend has implications for both the interpretation of results of policy analysis and for the importance of longitudinal studies in the study of inequality. Additionally, this study has identified a likely rise in formal care costs in real terms within the Australian context over the past twenty years. This increase has made formal care as much as three times more expensive in real terms, after accounting for inflation and allowing for increases in real wages and hours of use. Quantifying the scale of the cost increase over this period is a further contribution of this study, especially in relation to potential policy interventions to address income and educational inequality. The key recommendations for policy arising from this study include that: there is likely to be greater potential to reduce inequalities in educational outcomes through interventions implemented earlier in life; sustained income interventions (for example, increases to welfare payments for low-income households with young children) are more likely to have impact than one-off income interventions (e.g. cash injections); and that interventions that reduce the negative effects of poverty in early childhood are likely to have significant positive effects on later educational inequalities. In summary, this study has illustrated the value of modelling as an interdisciplinary synthesis tool, and specifically the value in the application of microsimulation to the modelling of education systems, with a particular emphasis on the generation of policy-relevant insights.