Patrick Lim

Socioeconomic status and the allocation of government resources in Australia: How well do geographic measures perform?

Purpose - The aim of this paper is to compare the performance of area-based vs individual-level measures of socioeconomic status (SES). Design/methodology/approach - Using data from the longitudinal surveys of Australian youth (LSAY), a multidimensional measure of individual SES is created. This individual measure is used to benchmark the relative usefulness of socio-economic indexes for areas (SEIFA), a geographic set of measures often used in Australia to assess the SES of individuals. Both measures are compared in terms of classification bias. The effects of using the different SES measures on participation in post-compulsory education are examined. Findings - SEIFA measures perform satisfactorily with regard to the aggregate measurement of SES. However, they perform poorly when their use is aimed at channelling resources toward disadvantaged individuals. It is at the individual level that the analysis reveals the shortcomings of area-based SES measures. Research limitations/implications - While region based measures are relatively easy to collect and utilise, we suggest that they hide significant SES heterogeneity within regional districts. Hence, the misclassification resulting from the use of regional measures to direct support for low SES groups creates a risk for resource misallocations. Originality/value - The finding that region-based measures are subject to significant misclassification has important research and policy implications. Given the increasing availability of individual-level administrative data, the paper suggests that such data be used as a substitute for geographic SES measures in categorising the SES of individuals.

A Primer for Handling Missing Values in the Analysis of Education and Training Data.

Abstract Quantitative research in vocational education and training (VET) is routinely affected by missing or incomplete information. However, the handling of missing data in published VET research is often sub-optimal, leading to a real risk of generating results that can range from being slightly biased to being plain wrong. Given that the growing availability of data from large-scale surveys and administrative collections offers exciting new opportunities for quantitative VET research, it is important that researchers follow best-practice approaches when using such data in their own work. Against this backdrop, we: (1) provide a primer on the use of appropriate missing data methods for quantitative VET research; and (2) illustrate the detrimental effects of inefficient methods on research results via a simulation study using real-world education and training data from the Longitudinal Surveys of Australian Youth (LSAY).