What can we learn from administrative benefits data?

Juliet-Nil Uraz`, Mary-Alice Doyle, Magdalena Rossetti-Youlton
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 Drawing on our experience working with over 40 local authorities, we present the structure of three datasets: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. We show what variables are usually included, under which legal gateways this data can be shared and how the cohorts represented within the data compare with the low-income population. We discuss how these datasets can be linked at the household level with a number of other data held by local authorities such as social rent and Council Tax arrears, Housing Benefit overpayments and Discretionary Housing Payments (DHPs).
 Administrative benefits data provides a comprehensive snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external data processors to fulfil their statutory duties if a legal gateway allows. By identifying households at risk of cash shortfalls before they reach a crisis point, councils can target support when administering local welfare schemes and preventing homelessness. By assessing eligibility for benefits, they can run data-driven uptake campaigns. This data captures a proportion of the population on national and local benefits within a local authority at several points in time. Attrition is of concern since households may leave datasets over time. Some will see their income rise and no longer qualify for benefits. Others will move out of the constituency.
 Local authorities routinely process longitudinal data on households receiving means-tested benefits by administering housing benefits, council tax support, and discretionary support funds. This data provides a unique real-time insight into the socioeconomic situation of low-income households. Yet, we show that its promising potential for policy research remains largely untapped.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Population Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23889/ijpds.v8i2.2334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

We present the opportunities and limitations of administrative benefits data held by local authorities for data linkage projects. Whilst the richness of this data has been exploited by practitioners for administration, its potential remains little explored by researchers. We discuss data quality, sample selection and legal gateways for data sharing. Drawing on our experience working with over 40 local authorities, we present the structure of three datasets: the Council Tax Reduction Scheme, the Single Housing Benefits Extract and the Universal Credit Data Share. We show what variables are usually included, under which legal gateways this data can be shared and how the cohorts represented within the data compare with the low-income population. We discuss how these datasets can be linked at the household level with a number of other data held by local authorities such as social rent and Council Tax arrears, Housing Benefit overpayments and Discretionary Housing Payments (DHPs). Administrative benefits data provides a comprehensive snapshot of a household’s financial situation. Local authorities can proactively use and share this data with external data processors to fulfil their statutory duties if a legal gateway allows. By identifying households at risk of cash shortfalls before they reach a crisis point, councils can target support when administering local welfare schemes and preventing homelessness. By assessing eligibility for benefits, they can run data-driven uptake campaigns. This data captures a proportion of the population on national and local benefits within a local authority at several points in time. Attrition is of concern since households may leave datasets over time. Some will see their income rise and no longer qualify for benefits. Others will move out of the constituency. Local authorities routinely process longitudinal data on households receiving means-tested benefits by administering housing benefits, council tax support, and discretionary support funds. This data provides a unique real-time insight into the socioeconomic situation of low-income households. Yet, we show that its promising potential for policy research remains largely untapped.
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我们可以从行政福利数据中学到什么?
我们提出的机会和限制的行政效益数据由地方当局持有的数据联动项目。虽然这些数据的丰富性已被从业人员用于管理,但其潜力仍很少被研究人员探索。我们讨论了数据质量、样本选择和数据共享的法律网关。 根据我们与40多个地方当局合作的经验,我们提出了三个数据集的结构:理事会减税计划,单一住房福利摘录和通用信贷数据共享。我们展示了通常包括哪些变量,这些数据可以在哪些法律网关下共享,以及数据中所代表的队列与低收入人群的比较。我们讨论了如何在家庭层面将这些数据集与地方当局持有的许多其他数据联系起来,如社会租金和市政税拖欠、住房福利超额支付和可自由支配住房支付(dhp)。行政福利数据提供了一个家庭财务状况的全面快照。如果法律允许,地方当局可以主动使用这些数据,并与外部数据处理器共享这些数据,以履行其法定职责。通过在危机到来之前识别出有现金短缺风险的家庭,委员会可以在管理当地福利计划和防止无家可归时获得有针对性的支持。通过评估福利资格,他们可以开展数据驱动的宣传活动。这一数据记录了某一地方当局在几个时间点享受国家和地方福利的人口比例。损耗是一个值得关注的问题,因为家庭可能会随着时间的推移而离开数据集。一些人会看到他们的收入增加,不再有资格享受福利。其他人将会离开这个选区。 地方当局通过管理住房福利、议会税收支持和酌情支持基金,定期处理接受经济状况调查福利的家庭的纵向数据。这些数据为低收入家庭的社会经济状况提供了独特的实时洞察。然而,我们表明,它在政策研究方面的巨大潜力仍未得到充分开发。
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