4490.0.55.001 - Microdata: Australian Priority Investment Approach to Welfare Research Dataset, 5 percent sample, 2001 to 2015  
Latest ISSUE Released at 11:30 AM (CANBERRA TIME) 29/11/2017  First Issue
   Page tools: Print Print Page Print all pages in this productPrint All RSS Feed RSS Bookmark and Share Search this Product

INTRODUCTION

This product provides information about the release of microdata from the Australian Priority Investment Approach to Welfare research dataset (PIA) TableBuilder dataset. Information about how to use the data in TableBuilder and a detailed data item list are also provided.

The Priority Investment Approach to Welfare helps to identify groups at risk of long-term welfare dependency. The approach was established as part of the 2015-16 Budget, following a comprehensive review of Australia’s welfare system, A New System for Better Employment and Social Outcomes, released on 25 February 2015. It uses data analysis to provide insights into how the system is working and uses those insights to find innovative ways of helping more Australians live independently of welfare. As part of the PIA policy initiative, in September 2016 the Minister for Social Services announced a plan to allow limited public access to PIA data.

The Department of Social Services (DSS) is providing three levels of controlled access to the PIA data, in line with differing levels of user need and experience:

  1. TableBuilder is a data analytics tool for creating tables and graphs automatically confidentialised for the user. TableBuilder is provided by the Australian Bureau of Statistics. The PIA data contains a limited number of variables and is suitable for research access while maintaining the privacy and confidentiality of income support recipients.
  2. Secure Unified Research Environment (SURE) enables users to analyse unit record level data provided via a secure, password-protected online Remote Access Research Gateway. Access is only granted to researchers who possess high-level statistical programming skills and are accredited to work in SURE, and
  3. Synthetic Data, an initiative developed by CSIRO’s Data 61, provides access to data that mimics characteristics of a subset of PIA data.

ABOUT THE DATA

PIA data is administrative data that was collected from Department of Human Services (DHS) forms and online data systems for the purpose of recording eligibility for benefits, service delivery activities and payments.

The dataset contains a series of static, point-in-time quarterly snapshots over 14 years, rather than a continuous data series. The reference periods available in TableBuilder include all periods from quarter ending September 2001 to quarter ending June 2015. The dataset only captures payment activity at the end of the quarter, so will not necessarily reflect changes that occur at other times during the quarter. Each individual recipient has a separate entry for each payment they receive.

The data has been de-identified so that names, addresses and identification numbers used for providing Australian Government payments are not included. Unusual characteristics have also not been included so as to prevent an individual from being identified accidentally due to their unique circumstances, in addition to the removal of names and addresses from the dataset.

Some variables have been reduced in detail to further confidentialise the data. For example, all date-related variables, such as Date of birth, have been changed from MMYYYY to YYYY format.

ABOUT THE SAMPLE

This subset contains a limited number of variables suitable for research access, while maintaining the privacy and confidentiality of income support recipients. A 5% sample of the full PIA dataset has been selected and is presented in TableBuilder for use by researchers. A 5% sample is the level chosen to maximise the utility of the data while providing sufficient identification protection.

To create the sample for use in TableBuilder, 5% of people in the population file were randomly selected. These recipients were then included in the TableBuilder dataset for all reference periods where they received a benefit.

In any sample, sampling error occurs because only a small proportion of the total population is used to produce estimates that represent the whole population. Sampling error refers to the fact that for a given sample size, each sample will produce different results, which will usually not be equal to the population value. There are two common ways of reducing sampling error - increasing sample size and utilising an appropriate selection method. Given the large sample size for the PIA dataset (1 in 20 persons), and simple random selection, sampling error is minimal.

APPLYING FOR ACCESS TO PIA

To apply for access to PIA data in TableBuilder, see How to Apply.

USER RESPONSIBILITIES

The Census and Statistics Act, 1905 includes a legislative guarantee to respondents that their confidentiality will be protected. This is fundamental to the trust the Australian public has in the ABS, and that trust is in turn fundamental to the excellent quality of ABS information. For more information, see 'Avoiding inadvertent disclosure' and 'Microdata' on our web page How the ABS keeps your information confidential.

The release of microdata must satisfy the ABS legislative obligation to release information in a manner that is not likely to enable the identification of a particular person or organisation. Therefore, in accordance with the Census and Statistics Act, a confidentiality process is applied to the data in TableBuilder to avoid releasing information that may lead to the identification of individuals, families, households, dwellings or businesses.

Prior to being granted access to TableBuilder users must agree to the following ABS Terms and Conditions of TableBuilder Access.

CONDITIONS OF SALE

All ABS products and services are provided subject to the ABS Conditions of Sale. Any queries relating to these Conditions of Sale should be emailed to intermediary.management@abs.gov.au.

PRIVACY

The ABS Privacy Policy outlines how the ABS handles any personal information that you provide to us.