Social Security Policy Branch – Random Sample Surveys

Began in 98/99 Continues as an ongoing project Methodology tested, improved and ratified by External Consulting Actuaries Audited by the Australian National Audit Office (ANAO) Recommendations have been implemented Random Sample Surveys Background

Social Security Policy Branch – Random Sample Surveys Service level agreement FaHCSIA and Centrelink have an SLA in place to manage the RSS SLA is a set of agreed processes and procedures to be followed by both parties Living Document – can be modified to meet changing political or departmental requirements

Social Security Policy Branch – Random Sample Surveys Sample Design Define Sample Population (Age Pension, Carer Payment) Make Exclusions where Necessary Define Strata – Population Groups defined by characteristics (eg Gender, Rate, Marital Status) Consideration for Remote Customers (Clustering) Allocate Sample (operational requirements and strata)

Social Security Policy Branch – Random Sample Surveys Exclusions and Substituons In an Ideal sample ALL customers have a chance of selection RSS Population is not a closed system Need to balance randomness of the sample against burden to the customers Exclusion Rules ensure that: customers that have an outstanding or recently completed review; customers that are seriously ill or out of the country; Are not selected

Social Security Policy Branch – Random Sample Surveys Stratification The target population is divided into discrete, non-overlapping sub populations This assists in ensuring a Representative Sample is selected Exploits the depth of administrative information held on populations under review Sharpens the final estimates around variables associated with compliance status Allows for unequal probability selection

Social Security Policy Branch – Random Sample Surveys

Clustering - Background Centrelink’s most remote Areas (North & Central QLD, North Australia, Western Australia) have smaller customer populations over wider geographic areas Need to balance the requirement to sample in these locations with the cost of travel and distance Clustering concentrates the selections to a smaller location within the remote Area

Social Security Policy Branch – Random Sample Surveys

Selection Design Delivered to Centrelink Random Selection of Customers within Allocation Boundaries by Centrelink FaHCSIA views selection Centrelink proceeds after approval

Social Security Policy Branch – Random Sample Surveys Reviews Selected Customer attends an Interview in Centrelink Office (CSC or ASO) Full Re-Assessment of Entitlement conducted via Questionnaire (Income, Assets, Residency, POI, Qualification, Accommodation) More Detailed information from Review filled out by Centrelink Officer after review concluded and entered into RRRS Quality Assurance is performed by Centrelink on a sample of the results

Social Security Policy Branch – Random Sample Surveys Review Results Assessment of Current and Prior Entitlement can have the following results Cancellation of Payment Variation (Upward or Downward) of Current Fortnightly Rate Debt Raised for an overpayment in an earlier period Arrears Paid for an underpayment in an earlier period These Results are not mutually exclusive. A Cancellation/Variation can happen in conjunction with a Debt and/or Arrears. Supplementary Components (Eg RA, PHA, TAL) are also reviewed.

Social Security Policy Branch – Random Sample Surveys FaHCSIA Processing Centrelink Review Results Stored on ISIS/IRS/RRRS Data is Transmitted from Centrelink to FaHCSIA FaHCSIA tests the data integrity Validates a sample (10%) of the Reviews - (Centrelink QAs this sample) Tabulates and Analyses the Results

Social Security Policy Branch – Random Sample Surveys

Results Results used by FaHCSIA to: Provide an estimate FaHCSIA Programme Outlay Accuracy Identify Emerging Risks Assess effectiveness of Compliance Budget measures Evidence Base for Compliance Policy Latent Debt Measure – Debt KPI Improve predictors for Service Profiling

Social Security Policy Branch – Random Sample Surveys Outlay Accuracy Modelled Estimate of the Mispayed Amount / Outlayed Amount subtracted from 100% Does not discriminate between customer and Centrelink errors Weighted by $ Value – a $450 cancellation has a greater effect than a $0.05 upward variation Only counts current episodes of mispayment ie. is not affected by debts for prior periods of mispayment Sourced from Dataset 1

Social Security Policy Branch – Random Sample Surveys Payment Correctness Modelled Estimate of the Number of Customers with an Administrative Error that affected Payment / Total Population subtracted from 100% Only counts customers where the error was made by Centrelink which caused a dollar impact Does include dollar impact from debts (ie prior periods of mispayment) Does not count multiple errors – if any 1 of up to 4 Errors meets the criteria the customer is counted once Not weighted by dollar value - Counts customers not amounts – a $35,000 debt contributes the same as a $0.05 variation Sourced from Dataset 4

Social Security Policy Branch – Random Sample Surveys Latent Debt Measure of the level of undetected indebtedness in the benefit population Key Prevention Indicator for Debts is the Latent Measure of Indebtedness to Outlays Make the distinction between “Current Year” Indebtedness and New Debts

Social Security Policy Branch – Random Sample Surveys

Addressing emerging risk top 10 reasons for mispayment Random Sample Results are further subdivided by reason for mispayment These indicate the Risks to Outlays to be addressed by Compliance and Prevention measures Mispayments in debt and variation can be for the same or different reasons Risks are ranked by number (incidence) and value (magnitude) Charts observe the Higher risks to outlays

Social Security Policy Branch – Random Sample Surveys