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Universal Credit's hidden hardships: case studies of councils intervening early

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Policy in Practice as invited to present at the IRRV Scotland's IRRV Universal Credit Conference 2019 on Thursday 18 April in Grangemouth.

Peter Carter and Megan Mclean presented Universal Credit's hidden hardships: case studies of councils intervening early.

To find out more please visit http://policyinpractice.co.uk/, email hello@policyinpractice.co.uk or call 0330 088 9242.

Published in: Data & Analytics
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Universal Credit's hidden hardships: case studies of councils intervening early

  1. 1. Peter Carter Megan McLean Policy in Practice Universal Credit's hidden hardships: case studies of councils intervening early
  2. 2. Agenda 1. Introduction to Policy in Practice 2. Universal Credit – Who is falling off the cliff? 3. Homelessness • What is predictive analytics? • Case studies 4. Measuring successful preventions
  3. 3. 333 About Policy in Practice
  4. 4. A team of professionals with extensive knowledge of the welfare system who are passionate about making social policy work We help local authorities use their household level data to identify vulnerable households, target support and track their interventions We develop software that engages people. We identify the actions people can take to increase their income, lower their costs and build their financial resilience
  5. 5. 555 Universal Credit: Identifying households who are falling off the cliff
  6. 6. National case study "The worst is yet to come for those who will be most severely hit, namely low income families with children. Local authorities need to plan ahead for the impact." Professor Christina Beatty, Centre for Regional Economic and Social Research source: Centre for Regional Economic and Social Research
  7. 7. A new Living Standards Index for London tracks the financial security of 550,000 low-income families to reveal the pockets of poverty in our nation’s capital. • Study of 550,000 families shows 15% of London’s low-income households can’t pay the bills week to week • 42% of low income families will be worse off under Universal Credit – this represents 231,000 households across 18 London Boroughs • 15% of low income households face a cash shortfall. This is 82,500 of 550,000 households. • This has increased by 21% since 2016 and is expected to increase by a further 61% by 2021 Living Standards Index for London
  8. 8. Drill down to one local authority
  9. 9. Easy segmentation
  10. 10. Household level insights
  11. 11. Transition to Universal Credit
  12. 12. 121212 Homelessness: what do we know?
  13. 13. The triggers that immediately precede a homelessness application are shown in the table opposite. Causes include: • An increase in demand for affordable homes, not matched by supply • The impact of welfare reforms • Personal factors that can cause homelessness The drivers of homelessness
  14. 14. 141414 Predictive analytics: Advanced analytics which are used to make predictions about unknown future events. Our predictive analytics uses many techniques such as data mining, statistics, modelling, to analyse current data in order to make predictions about the future.
  15. 15. Identifying those at risk of homelessness
  16. 16. 161616 Early engagement: how do you engage early?
  17. 17. Identifying early predictors Effective use of data can identify struggling households and offer proactive, tailored support to help boost their incomes before they hit crisis point. This can accelerate employment, prevent homelessness, improve health and alter the trajectory of low-income families. Mark Fowler is director of community solutions at Barking & Dagenham LBC and Sue Nelson is director for revenues, benefits and customer services at Luton BC Article in Municipal Journal 27 March 2019
  18. 18. Luton Council: context • Luton Council has received funding for the Homelessness Trailblazer • As part of a service re-design, a new homelessness prevention team has been created • The use of household level data a key feature of new team • Identifying homelessness risk early through data to drive prevention at 56+ days • New service estimated to be three times as effective in preventing homelessness
  19. 19. How is Luton Council using data? • Tracking pathways into homelessness to understand biggest local risk factors • 78 households identified as at risk but not yet presenting as homeless • All households contacted early and offered support through Early Action Network; 22 agreed • Early support included benefits/budgeting support, rent deposits, income maximisation etc. • Prevention team staff designed a manual for process post data-led identification • Outcomes of 22 households tracked through data to determine impact
  20. 20. Engaging households early
  21. 21. 212121 Examples of councils intervening early
  22. 22. Target support where most needed and most effective Corin Hammersley of The Royal Borough of Greenwich has led a benefits take- up campaign using this screen. Her strategy has been to focus on those who are eligible but are not yet claiming Severe Disability Premiums. This benefit is worth £64.30 per week for a single claimant and £128.60 for a couple who are both eligible, which could make a significant impact on household finances.
  23. 23. Target support where most needed and most effective
  24. 24. Croydon Council • Croydon’s award winning Gateway programme focused on using data tackle hardship proactively • As a result of the programme, 2,003 families at high risk of homelessness have avoided being moved into temporary accommodation. • 217 of these were previously unemployed households helped into stable work • Overall, Croydon Council estimates that using the LIFT Dashboard to guide early intervention has achieved costs avoidance savings of over £4m in one year
  25. 25. Swansea City Council action plan
  26. 26. Smaller scale projects • Cornwall Council: Undertaking a small scale project to support fewer than 100 families across the private rented sector identified as being at risk or in crisis. Support provided by Cornwall Housing and external employment support partners. • Exeter Council: Preventing households that are currently in rent arrears from accruing further arrears when they move to Universal Credit. 66 households in Exeter that are council tenants with rent arrears and would be worse-off under Universal Credit. • Islington Council: 113 lone parent households with their youngest child turning 5 within 6 months. Fliers sent to these households informing them about the support network available for UC through the council and the voluntary sector.
  27. 27. 272727 Measuring successful prevention
  28. 28. Tracking outcomes and evidencing prevention
  29. 29. Tracking outcomes
  30. 30. www.policyinpractice.co.uk Thank you Peter Carter peter@policyinpractice.co.uk 07805 254371 Megan McLean megan@policyinpractice.co.uk 0330 088 9242 hello@policyinpractice.co.uk

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