How longitudinal analysis can help prevent poverty
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The document discusses how longitudinal analysis can be utilized to better understand and prevent poverty, particularly in the context of the UK's changing economic landscape. It highlights the impacts of universal credit, welfare reforms, and changing employment patterns on low-income households, emphasizing the need for local authorities to adopt dynamic analysis approaches. The author advocates for a focus on financial resilience rather than solely on relative poverty to effectively address the needs of affected families.
How longitudinal analysis can help prevent poverty
- 1. How longitudinal analysis can help prevent poverty Juan Alvarez Villanova Policy in Practice Public Policy Exchange Preventing Further Poverty in the UK
- 2. Agenda 1. Introductions 2. The context: JAMs hardest hit by 2020 3. A dynamic approach to poverty: longitudinal analysis & financial resilience 4. Croydon Council 5. Final thoughts on poverty: a. Investing in Universal Credit b. Local support / Discretionary Housing Payments c. Low productivity / high rents
- 3. We make the welfare system simple to understand, so that people can make the decisions that are right for them
- 4. 444
- 5. From one policy on many people, to many policies on one person www.policyinpractice.co.uk
- 6. The new normal: working families increasingly at risk
- 7. Policy in Practiceâs approach Family Resources Survey 3 x FRS Our Benefit and Budgeting Calculator Rich, detailed impact assessment 1. 2012-2013,2013-2014,2014- 2015 FRS data 2. Our policy modelling tool â the Universal Benefit and Budgeting Calculator 3. Who is impacted and what are the country-wide effects?
- 8. Real incomes fall significantly by 2020 Who are these people?
- 9. ⌠and working families most affected
- 10. Work allowances cut for these households in 2016 2015 2016 Change Single person ÂŁ1,332 ÂŁ0 -ÂŁ1,332 Lone parent (w ith housing support) ÂŁ3,156 ÂŁ2,304 -ÂŁ852 Lone parent (no housing costs) ÂŁ8,808 ÂŁ4,764 -ÂŁ4,044 Couple w ithout children ÂŁ1,332 ÂŁ0 -ÂŁ1,332 Couple w ith children (w ith housing support) ÂŁ2,664 ÂŁ2,304 -ÂŁ360 Couple w ith children (no housing costs) ÂŁ6,432 ÂŁ4,764 -ÂŁ1,668 Disabled people (w ith housing support) ÂŁ2,304 ÂŁ2,304 - Disabled people (no housing costs) ÂŁ7,764 ÂŁ4,764 -ÂŁ3,000 Reductions to work allowances under Universal Credit
- 11. The impact of the benefits freeze â larger families more affected Average income loss by 2020, working households
- 13. What to do? ⢠Universal Credit roll-out well under way ⢠Some measures still to come fully into effect: ⢠Increases in National Minimum Wage, Personal Tax Allowance ⢠Doubling of free childcare hours for 3-4 year olds ⢠Freezing of LHA rates â accentuating poverty in private rented sector ⢠Changes to Child Tax Credit ⢠A complex and changing picture for individual households â requires a new approach
- 14. 141414 A dynamic approach: using longitudinal analysis to understand and prevent poverty
- 15. ⢠27% of the total population in participating boroughs A partnership with Trust for London & 14 London boroughs
- 16. The static picture ⢠Working-age households in work: 42% ⢠Average number of hours worked: 25 ⢠80% of households earn below living wage. 1. There is a constant churn of low-income households Dynamic analysis ⢠12% of households moved into or out of work in the last 12 months. ⢠8% of out of work households moved into work. By shifting the focus from the aggregate figures to dynamic analysis, a picture of constantly changing employment patterns emerges.
- 17. 2. From poverty to financial resilience In relative poverty At financial risk Ove Total number of households 79,252 93,042 1. Social rent (59.8%) 1.Social rent (58.9%) 1. S 2. Owner- occupiers (25.5%) 2. Private rent (27.6%) 2. P Percentage in work 17.0% 24.7% Average rent £547.70 £822.49 Highly impacted by welfare reform 6.6% 19.9% Receiving sickness or disability benefits 20.3% 23.6% Tenure types most affected Living standards (Jan 2017)Taking needs into account is essential to identifying households living day to day. It captures more households ⢠In work ⢠Renters ⢠Impacted by welfare reform and is arguably a better assessment of resilience than relative income. Local authorities are using this to target support, such as DHPs.
- 18. Hackney and Enfield are hardest hit under both measures. H&F, Lambeth & Camden have a higher % of people lacking financial resilience, than households living in poverty Relative poverty vs financial risk indicators, by borough
- 19. Relative poverty vs financial resilience indicators, by ward
- 20. Croydon Council: mapping financial resilience
- 21. LAs can look at poverty differently ⢠The face of poverty is changing - no longer a single group stubbornly stuck in poverty ⢠JAMs look set to be struggle significantly in next few years ⢠This necessitates a new approach that takes into account 1. Churn among low-income households 2. Financial resilience vs absolute poverty ⢠Councils have the data resources to carry out a dynamic analysis ⢠⌠and understand many other factors: changes to UC, local support/DHP, policy changesâŚ
- 22. www.policyinpractice.co.uk Thank you Juan Alvarez Vilanova juan@policyinpractice.co.uk 07969425508
Editor's Notes
- #2: Understanding what households are highly impacted by WR gives us a view on what households may face financial distress, and in turn what households may face a debt problem. This helps local governments act now, before it becomes a crisis
- #5: Deven Ghelani, our founder and director, was a member of the team at Centre for Social Justice who developed Universal Credit and, when the policy was adopted by government, he left there to set up Policy in Practice. He was keen to ensure that the policy intent was actually put into practice.  Since then, and together with the team he's built at Policy in Practice, he's facilitated conversations between leading local authorities and the Prime Minister's office to ensure frontline feedback about welfare reform policy has been heard.  In addition, Deven and the team have helped local organisations to understand the aggregate and cumulative impact of welfare reform changes on their customers so that they can accurately target support programmes.  And finally, to close the loop, the software that Policy in Practice has developed simplifies the conversations that frontline advisors can have with customers by clearly showing what benefits they can get under the current system and when they move to Universal Credit, comparing the two side-by-side using data visualisation.
- #6: Allows us to focus on the individual and what his/her circumstances are really like; act on the individual with more certainty. âŚWhile also aggregating this to show you cumulative impact, and the patterns that lie within
- #8: 3 yearsâ worth of FRS data for Great Britain (2013,2014,215) Weighted in favour of previous years Sampled survey data rather than household-level data Data cleaned and ran through welfare policy modelling engine
- #9: Despite the governmentâs mitigation measures, the combined impact of welfare reform, UC and rising cost of living is high
- #10: These are often Why are working families worse hit? A combination of: Cuts to UC Benefits freeze / cost of living Limited impact of mitigation measures⌠etc
- #12: One fifth of highly impacted households have two or more chidlren
- #13: Red is negative, green is positive â everything affecting different households in different way. The picture is not static â things will still change.
- #14: The information we have up to now is static. What if a household receives a DHP? Or transitional protection? Or there is a behaviour change?
- #17: Under the surface, a lot more changes that there seems to be Low income households and their situations are not static â but dynamic They are not a group of stubbornly poor households â but rather many are fluctuating into and out of employment Poverty is precarity
- #18: The new face of poverty suggests a new way to measure poverty We took needs into account from ONS data, the results It captures more of the JAMs that we know will be struggling â otherwise they might fly under the radar We have been doing this on a household level, which allows us to track households and see how their circumstances change (e.g., see if a DHP has been effective)
- #19: Illustrating what this difference means
- #21: â9/10 hhs that apply for DHPs get them, but only 1/5 that need them actually applyâ