Data linkage for service targeting and policy design: brave new world or more of the same?

Stream: Big data and social policy
Date: Wednesday, 11 September 2019
Time: 1.25 pm – 2.25 pm


The linking of data sets to improve targeting, reduce fraud, track outcomes and promote system integrity has long been a key feature of Australia’s targeted social policy framework. This paper provides a perspective on these ‘big data’ developments by reviewing the role of data linkage in combating social security fraud and improving systemic integrity and examining recent efforts to implement improved data matching in a range of other Commonwealth and State social policies. In addition to reviewing official documents, the study is conducting a series of high-level interviews to explore how different agencies perceive the risks and benefits associated with these developments and identify what actions they are taking to ensure that big data is not used to further disadvantage vulnerable groups. The role of data matching within DSS has traditionally been threefold: to ensure that program administration was paying the correct benefits to eligible individuals; to protect and enhance system integrity internally and to demonstrate its integrity externally; and as a way of making cost savings. Several other jurisdictions are using data matching initiatives to examine the impact of past policies and model future policy impacts, although there is currently no overarching strategy to manage this process, including on the key issue of the challenge of exploiting the potential to identify individuals. The paper will review these current developments in data matching, examine how and where they are new, identify what constraints and safeguards exist (including for users), and highlight key future challenges.


Peter Saunders (Presenter), SPRC
Peter Saunders is a Research Professor in Social Policy at the SPRC

Ilan Katz (Presenter), SPRC
Ilan Katz is a Professor in the SPRC

Sheila Shaver, SPRC
Sheila Shaver is an Adjunct Professor at SPRC