SlideShare a Scribd company logo
1 of 24
Policy in Practice
Deven Ghelani
Using data to keep
children out of poverty
At the heart of prevention is
access to and use of data
Agenda
• About Policy in Practice (and the power of data)
• Who are the children at risk of being at risk?
• How can data help to keep children out of poverty?
• How can this approach help children in your area?
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 engaging
software that helps
people to increase their
income, reduce their
costs and helps them to
build their financial
resilience
There are 1.6m children from low
income families.
390,000 are recorded through
referral to care services.
Currently there is very little useful
data to support early intervention
for the 1,200,000 at risk of being
referred to Children’s Services.
“The social indicators of that
violence have remained identical
for almost 200 years.”
“Poverty, domestic abuse,
lack of education”
Who are the children at risk of being at risk?
Directors of Children’s
Services capture
information on vulnerable
children and their
interaction with statutory
services.
The two main datasets
they use are:
• The Children in Need
Census
• The Children Looked
After SSDA903 return
What do we know about vulnerable children?
No data is collected systematically about parents.
The current situation
73,000390,0001.6m10m
What we want to see happen
73,000390,0001.6m10m
Benefits data
The CCO Identified a number of ‘at risk’
indicators:
● Children of lone parents
● Children living in workless families
● Children living in TA / Homeless
● Children living in relative poverty
● Children at risk of food poverty
● Children at future risk of poverty
● Children in families with poor inter-parental
relationships
● Children of prisoners
● Children living with friends or wider family
Who can we help?
Local
authority
Children in
poverty
Children in
TA
B&D 22,467 2,518 11
Tower Hamlets 12,607 3,497 27
Coventry 12,005 494 4
Cornwall 9,602 207 2
Luton 8,297 2,719 32
Newcastle 8,174 21 0
Croydon 7,472 2,077 27
Barnet 6,533 2,327 35
WF 6,295 3,459 54
Lambeth 6,293 1,177 18
Camden 6,069 545 9
Greenwich 6,048 951 15
Haringey 5,655 4,577 80
Islington 4,781 838 17
Newport 4,693 107 2
Basingstoke 3,882 139 3
Denbighshire 2,257 83 3
Exeter 1,687 101 6
Wokingham 854 49 5
101010
Can we learn more about children
in care?
Benefits data + advanced analytics
Your Housing Benefit /
Council Tax data
+ Arrears
+ Support
Benefit and Budgeting
Analytics Engine
Who is impacted, How
much? What actions can
they take? Are they
better off? What are the
Council-wide effects?
The power of pooled data
25% - 33% of the total population
We bring multiple
datasets together.
To show the combined
impact of policy – both
now and in the future.
With financial resilience
and arrears risk through
household level income
and expenditure data.
Understand the cumulative impact of policies…
Mrs Jones near
Spring Lane is £5,009
in arrears with a
shortfall of £371 per
month, and will be
£54.38 per month
worse off as a result
of the benefit cap
and has high barriers
to work.
Drill down to each individual household….
Link the data directly into
our Benefit and Budgeting
support in single system.
Efficiencies avoiding
multiple or repeat data
capture.
Show the impact of
moving into work
alongside personalised
and preventative advice
on actions to increase
income, and reduce costs.
to engage residents with actions they can take
Understand the pathways
into and out of debt and
poverty.
Understand the journey
into and out of debt, or
see household in severe,
short term or persistent
debt.
Understand the
effectiveness of
interventions to get
people out of debt.
…to track households over time
Causal analysis:
Households affected by the
Benefit Cap are 21% more
likely to move into work,
versus a control group.
Deeper questions:
Is this because of the benefit
cap, or because of the
support local authorities give
to capped families?
The Benefit Cap through administrative data
For every child whose parents move into
work as a result of the cap, eight
children are ‘stuck’ on the benefit cap
5,772
Households
impacted by the
benefit cap over
the last 6 months
from Jan 2018
Borough
Percentage of households
currently impacted by the
benefit cap who were
impacted for a 6 month
period
Sutton 74.2%
Croydon 67.8%
K&C 66.6%
WF 65.7%
Southwark 65.3%
Greenwich 64.5%
Lambeth 64.3%
Haringey 63.3%
B&D 62.5%
Islington 62.0%
Brent 61.6%
Enfield 60.4%
Camden 60.3%
Hackney 60.1%
Ealing 56.3%
TowerHamlets 54.4%
Barnet 54.2%
Children with low Financial Resilience
Financial resilience as an indicator of
vulnerability
Understand the context of the families you
are working with
Use data to understand those who you
aren’t working with that may be at risk.
Give them tools to improve their living
standards and reduce the likelihood that
they need will need your help in future.
Link the data to understand the link
between living standards, poverty and
poor childhood outcomes.
Questions and Next Steps
Sign up to our newsletter to
read our analysis for the
CCO when its published
Follow us on twitter
@deven_ghelani
@policy_practice
Are your colleagues
already working with Policy
in Practice?
www.policyinpractice.co.uk
Contact us
Deven Ghelani
deven@policyinpractice.co.uk
07863 560677
Sam Tims
sam@policyinpractice.co.uk
07527 188797

More Related Content

More from Policy in Practice

AIM: Data protection, data governance, data management
AIM: Data protection, data governance, data managementAIM: Data protection, data governance, data management
AIM: Data protection, data governance, data managementPolicy in Practice
 
How viable is your council tax support scheme?
How viable is your council tax support scheme?How viable is your council tax support scheme?
How viable is your council tax support scheme?Policy in Practice
 
COVID-19: Who has fallen through the gaps?
COVID-19: Who has fallen through the gaps?COVID-19: Who has fallen through the gaps?
COVID-19: Who has fallen through the gaps?Policy in Practice
 
Reimagine Debt. A tale of two councils: Reimagine Debt Collection
Reimagine Debt. A tale of two councils: Reimagine Debt CollectionReimagine Debt. A tale of two councils: Reimagine Debt Collection
Reimagine Debt. A tale of two councils: Reimagine Debt CollectionPolicy in Practice
 
Embedding a data driven culture
Embedding a data driven cultureEmbedding a data driven culture
Embedding a data driven culturePolicy in Practice
 
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?Policy in Practice
 
Data science in safeguarding: an introduction to AIM
Data science in safeguarding: an introduction to AIMData science in safeguarding: an introduction to AIM
Data science in safeguarding: an introduction to AIMPolicy in Practice
 
London Councils: Identifying people at risk
London Councils: Identifying people at riskLondon Councils: Identifying people at risk
London Councils: Identifying people at riskPolicy in Practice
 
How viable is your council tax support scheme?
How viable is your council tax support scheme?How viable is your council tax support scheme?
How viable is your council tax support scheme?Policy in Practice
 
How councils can recover from COVID-19
How councils can recover from COVID-19How councils can recover from COVID-19
How councils can recover from COVID-19Policy in Practice
 
Roundtable - Who are the most vulnerable residents in London?
Roundtable - Who are the most vulnerable residents in London?Roundtable - Who are the most vulnerable residents in London?
Roundtable - Who are the most vulnerable residents in London?Policy in Practice
 
How to simplify the complexity of surplus earnings
How to simplify the complexity of surplus earnings How to simplify the complexity of surplus earnings
How to simplify the complexity of surplus earnings Policy in Practice
 
Is your post COVID-19 Council Tax Support Scheme sustainable?
Is your post COVID-19 Council Tax Support Scheme sustainable?Is your post COVID-19 Council Tax Support Scheme sustainable?
Is your post COVID-19 Council Tax Support Scheme sustainable?Policy in Practice
 
Coronavirus: stories from the front line
Coronavirus: stories from the front lineCoronavirus: stories from the front line
Coronavirus: stories from the front linePolicy in Practice
 
May recap of the major benefits changes and Coronavirus (COVID-19)
May recap of the major benefits changes and Coronavirus (COVID-19)May recap of the major benefits changes and Coronavirus (COVID-19)
May recap of the major benefits changes and Coronavirus (COVID-19)Policy in Practice
 
Recap of the major benefits changes and Coronavirus (COVID-19)
Recap of the major benefits changes and Coronavirus (COVID-19)Recap of the major benefits changes and Coronavirus (COVID-19)
Recap of the major benefits changes and Coronavirus (COVID-19)Policy in Practice
 
Coronavirus and tackling vulnerability
Coronavirus and tackling vulnerabilityCoronavirus and tackling vulnerability
Coronavirus and tackling vulnerabilityPolicy in Practice
 
The most underclaimed benefits and how to drive take up
The most underclaimed benefits and how to drive take upThe most underclaimed benefits and how to drive take up
The most underclaimed benefits and how to drive take upPolicy in Practice
 
Designing effective data-led local authorities
Designing effective data-led local authoritiesDesigning effective data-led local authorities
Designing effective data-led local authoritiesPolicy in Practice
 
LIFT Steering Group 23 January 2020
LIFT Steering Group 23 January 2020LIFT Steering Group 23 January 2020
LIFT Steering Group 23 January 2020Policy in Practice
 

More from Policy in Practice (20)

AIM: Data protection, data governance, data management
AIM: Data protection, data governance, data managementAIM: Data protection, data governance, data management
AIM: Data protection, data governance, data management
 
How viable is your council tax support scheme?
How viable is your council tax support scheme?How viable is your council tax support scheme?
How viable is your council tax support scheme?
 
COVID-19: Who has fallen through the gaps?
COVID-19: Who has fallen through the gaps?COVID-19: Who has fallen through the gaps?
COVID-19: Who has fallen through the gaps?
 
Reimagine Debt. A tale of two councils: Reimagine Debt Collection
Reimagine Debt. A tale of two councils: Reimagine Debt CollectionReimagine Debt. A tale of two councils: Reimagine Debt Collection
Reimagine Debt. A tale of two councils: Reimagine Debt Collection
 
Embedding a data driven culture
Embedding a data driven cultureEmbedding a data driven culture
Embedding a data driven culture
 
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?
IRRV virtual conference 2020: COVID-19 who has fallen between the gaps?
 
Data science in safeguarding: an introduction to AIM
Data science in safeguarding: an introduction to AIMData science in safeguarding: an introduction to AIM
Data science in safeguarding: an introduction to AIM
 
London Councils: Identifying people at risk
London Councils: Identifying people at riskLondon Councils: Identifying people at risk
London Councils: Identifying people at risk
 
How viable is your council tax support scheme?
How viable is your council tax support scheme?How viable is your council tax support scheme?
How viable is your council tax support scheme?
 
How councils can recover from COVID-19
How councils can recover from COVID-19How councils can recover from COVID-19
How councils can recover from COVID-19
 
Roundtable - Who are the most vulnerable residents in London?
Roundtable - Who are the most vulnerable residents in London?Roundtable - Who are the most vulnerable residents in London?
Roundtable - Who are the most vulnerable residents in London?
 
How to simplify the complexity of surplus earnings
How to simplify the complexity of surplus earnings How to simplify the complexity of surplus earnings
How to simplify the complexity of surplus earnings
 
Is your post COVID-19 Council Tax Support Scheme sustainable?
Is your post COVID-19 Council Tax Support Scheme sustainable?Is your post COVID-19 Council Tax Support Scheme sustainable?
Is your post COVID-19 Council Tax Support Scheme sustainable?
 
Coronavirus: stories from the front line
Coronavirus: stories from the front lineCoronavirus: stories from the front line
Coronavirus: stories from the front line
 
May recap of the major benefits changes and Coronavirus (COVID-19)
May recap of the major benefits changes and Coronavirus (COVID-19)May recap of the major benefits changes and Coronavirus (COVID-19)
May recap of the major benefits changes and Coronavirus (COVID-19)
 
Recap of the major benefits changes and Coronavirus (COVID-19)
Recap of the major benefits changes and Coronavirus (COVID-19)Recap of the major benefits changes and Coronavirus (COVID-19)
Recap of the major benefits changes and Coronavirus (COVID-19)
 
Coronavirus and tackling vulnerability
Coronavirus and tackling vulnerabilityCoronavirus and tackling vulnerability
Coronavirus and tackling vulnerability
 
The most underclaimed benefits and how to drive take up
The most underclaimed benefits and how to drive take upThe most underclaimed benefits and how to drive take up
The most underclaimed benefits and how to drive take up
 
Designing effective data-led local authorities
Designing effective data-led local authoritiesDesigning effective data-led local authorities
Designing effective data-led local authorities
 
LIFT Steering Group 23 January 2020
LIFT Steering Group 23 January 2020LIFT Steering Group 23 January 2020
LIFT Steering Group 23 January 2020
 

Recently uploaded

Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...limedy534
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Cantervoginip
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdfHuman37
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfgstagge
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改yuu sss
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfBoston Institute of Analytics
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsVICTOR MAESTRE RAMIREZ
 
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...dajasot375
 
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxCustomer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxEmmanuel Dauda
 
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024thyngster
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanMYRABACSAFRA2
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queensdataanalyticsqueen03
 
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degreeyuu sss
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptSonatrach
 
Call Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceCall Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceSapana Sha
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...Florian Roscheck
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]📊 Markus Baersch
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPTBoston Institute of Analytics
 

Recently uploaded (20)

Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
Effects of Smartphone Addiction on the Academic Performances of Grades 9 to 1...
 
ASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel CanterASML's Taxonomy Adventure by Daniel Canter
ASML's Taxonomy Adventure by Daniel Canter
 
E-Commerce Order PredictionShraddha Kamble.pptx
E-Commerce Order PredictionShraddha Kamble.pptxE-Commerce Order PredictionShraddha Kamble.pptx
E-Commerce Order PredictionShraddha Kamble.pptx
 
20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf20240419 - Measurecamp Amsterdam - SAM.pdf
20240419 - Measurecamp Amsterdam - SAM.pdf
 
RadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdfRadioAdProWritingCinderellabyButleri.pdf
RadioAdProWritingCinderellabyButleri.pdf
 
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
专业一比一美国俄亥俄大学毕业证成绩单pdf电子版制作修改
 
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdfPredicting Salary Using Data Science: A Comprehensive Analysis.pdf
Predicting Salary Using Data Science: A Comprehensive Analysis.pdf
 
Advanced Machine Learning for Business Professionals
Advanced Machine Learning for Business ProfessionalsAdvanced Machine Learning for Business Professionals
Advanced Machine Learning for Business Professionals
 
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
Indian Call Girls in Abu Dhabi O5286O24O8 Call Girls in Abu Dhabi By Independ...
 
Customer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptxCustomer Service Analytics - Make Sense of All Your Data.pptx
Customer Service Analytics - Make Sense of All Your Data.pptx
 
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
Consent & Privacy Signals on Google *Pixels* - MeasureCamp Amsterdam 2024
 
Identifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population MeanIdentifying Appropriate Test Statistics Involving Population Mean
Identifying Appropriate Test Statistics Involving Population Mean
 
Call Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort ServiceCall Girls in Saket 99530🔝 56974 Escort Service
Call Girls in Saket 99530🔝 56974 Escort Service
 
Top 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In QueensTop 5 Best Data Analytics Courses In Queens
Top 5 Best Data Analytics Courses In Queens
 
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
毕业文凭制作#回国入职#diploma#degree澳洲中央昆士兰大学毕业证成绩单pdf电子版制作修改#毕业文凭制作#回国入职#diploma#degree
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
 
Call Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts ServiceCall Girls In Dwarka 9654467111 Escorts Service
Call Girls In Dwarka 9654467111 Escorts Service
 
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...From idea to production in a day – Leveraging Azure ML and Streamlit to build...
From idea to production in a day – Leveraging Azure ML and Streamlit to build...
 
GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]GA4 Without Cookies [Measure Camp AMS]
GA4 Without Cookies [Measure Camp AMS]
 
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default  Presentation : Data Analysis Project PPTPredictive Analysis for Loan Default  Presentation : Data Analysis Project PPT
Predictive Analysis for Loan Default Presentation : Data Analysis Project PPT
 

Tackling Child Poverty: Building a Positive Future for Britain’s Youth

  • 1. Policy in Practice Deven Ghelani Using data to keep children out of poverty At the heart of prevention is access to and use of data
  • 2. Agenda • About Policy in Practice (and the power of data) • Who are the children at risk of being at risk? • How can data help to keep children out of poverty? • How can this approach help children in your area?
  • 3.
  • 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 engaging software that helps people to increase their income, reduce their costs and helps them to build their financial resilience
  • 5. There are 1.6m children from low income families. 390,000 are recorded through referral to care services. Currently there is very little useful data to support early intervention for the 1,200,000 at risk of being referred to Children’s Services. “The social indicators of that violence have remained identical for almost 200 years.” “Poverty, domestic abuse, lack of education” Who are the children at risk of being at risk?
  • 6. Directors of Children’s Services capture information on vulnerable children and their interaction with statutory services. The two main datasets they use are: • The Children in Need Census • The Children Looked After SSDA903 return What do we know about vulnerable children? No data is collected systematically about parents.
  • 8. What we want to see happen 73,000390,0001.6m10m Benefits data
  • 9. The CCO Identified a number of ‘at risk’ indicators: ● Children of lone parents ● Children living in workless families ● Children living in TA / Homeless ● Children living in relative poverty ● Children at risk of food poverty ● Children at future risk of poverty ● Children in families with poor inter-parental relationships ● Children of prisoners ● Children living with friends or wider family Who can we help? Local authority Children in poverty Children in TA B&D 22,467 2,518 11 Tower Hamlets 12,607 3,497 27 Coventry 12,005 494 4 Cornwall 9,602 207 2 Luton 8,297 2,719 32 Newcastle 8,174 21 0 Croydon 7,472 2,077 27 Barnet 6,533 2,327 35 WF 6,295 3,459 54 Lambeth 6,293 1,177 18 Camden 6,069 545 9 Greenwich 6,048 951 15 Haringey 5,655 4,577 80 Islington 4,781 838 17 Newport 4,693 107 2 Basingstoke 3,882 139 3 Denbighshire 2,257 83 3 Exeter 1,687 101 6 Wokingham 854 49 5
  • 10. 101010 Can we learn more about children in care?
  • 11. Benefits data + advanced analytics Your Housing Benefit / Council Tax data + Arrears + Support Benefit and Budgeting Analytics Engine Who is impacted, How much? What actions can they take? Are they better off? What are the Council-wide effects?
  • 12. The power of pooled data 25% - 33% of the total population
  • 13.
  • 14.
  • 15. We bring multiple datasets together. To show the combined impact of policy – both now and in the future. With financial resilience and arrears risk through household level income and expenditure data. Understand the cumulative impact of policies…
  • 16. Mrs Jones near Spring Lane is £5,009 in arrears with a shortfall of £371 per month, and will be £54.38 per month worse off as a result of the benefit cap and has high barriers to work. Drill down to each individual household….
  • 17. Link the data directly into our Benefit and Budgeting support in single system. Efficiencies avoiding multiple or repeat data capture. Show the impact of moving into work alongside personalised and preventative advice on actions to increase income, and reduce costs. to engage residents with actions they can take
  • 18. Understand the pathways into and out of debt and poverty. Understand the journey into and out of debt, or see household in severe, short term or persistent debt. Understand the effectiveness of interventions to get people out of debt. …to track households over time
  • 19. Causal analysis: Households affected by the Benefit Cap are 21% more likely to move into work, versus a control group. Deeper questions: Is this because of the benefit cap, or because of the support local authorities give to capped families? The Benefit Cap through administrative data
  • 20. For every child whose parents move into work as a result of the cap, eight children are ‘stuck’ on the benefit cap 5,772 Households impacted by the benefit cap over the last 6 months from Jan 2018 Borough Percentage of households currently impacted by the benefit cap who were impacted for a 6 month period Sutton 74.2% Croydon 67.8% K&C 66.6% WF 65.7% Southwark 65.3% Greenwich 64.5% Lambeth 64.3% Haringey 63.3% B&D 62.5% Islington 62.0% Brent 61.6% Enfield 60.4% Camden 60.3% Hackney 60.1% Ealing 56.3% TowerHamlets 54.4% Barnet 54.2%
  • 21. Children with low Financial Resilience
  • 22. Financial resilience as an indicator of vulnerability
  • 23. Understand the context of the families you are working with Use data to understand those who you aren’t working with that may be at risk. Give them tools to improve their living standards and reduce the likelihood that they need will need your help in future. Link the data to understand the link between living standards, poverty and poor childhood outcomes. Questions and Next Steps Sign up to our newsletter to read our analysis for the CCO when its published Follow us on twitter @deven_ghelani @policy_practice Are your colleagues already working with Policy in Practice?
  • 24. www.policyinpractice.co.uk Contact us Deven Ghelani deven@policyinpractice.co.uk 07863 560677 Sam Tims sam@policyinpractice.co.uk 07527 188797