Using data analytics to understand child vulnerability

USING DATA ANALYTICS TO
UNDERSTAND CHILD
VULNERABILITY
Early intervention and data
linkage for childrens’ services
Wed 17 April 2019
Policy in Practice
webinar

HOUSEKEEPING
www.policyinpractice.co.uk
● Audio check
● Please ask questions
● Downloads available
● Polls and a survey
● Aim to finish by 11:30

AGENDA
1. Introduction to administrative data and child vulnerability
2. The CCO’s perspective
3. Policy in Practice’s research: method and findings
4. Audience Q&A
5. Next steps

TODAY’S SPEAKERS
Dr Ben Fell
Senior Analyst
Policy in Practice
Haroon Chowdry
Head of Analysis
Children's Commissioner's
Office
Deven Ghelani
Director
Policy in Practice

Over to Deven

Poll: How important is a preventative
approach in your local authority?

THE CHILDREN AT RISK OF BEING AT RISK
There are 1.6m children from low income
families
390,000 are recorded through referral to
care services
There’s currently very little useful data to
support early intervention for the
1,200,000 at risk of being referred to
Children’s Services

Children’s Services capture information on
vulnerable children’ interaction with statutory
services
Two main datasets:
1. The Children in Need Census
2. The Children Looked After SSDA903 return
No data is systematically collected about family
context
WHAT WE KNOW ABOUT VULNERABLE
CHILDREN

TRAJECTORIES OF VULNERABILITY

Poll: What data does your authority use to
identify people at risk?

Over to Haroon

ABOUT THE CHILDREN’S COMMISSIONER
Anne Longfield OBE
● Independent role established to protect and
promote the rights and views of children
● Special responsibility for rights of:
○ Children in care
○ Children receiving social care services
○ Children living away from home (e.g. in
secure settings)
● Statutory powers to:
○ Enter any establishment where children are
living (except private dwellings)
○ Access data held by public bodies and
services on children
● Special helpline for children: ‘Help at Hand’

OUR VULNERABILITY FRAMEWORK
Programme of work which aims to:
● Assess the overall numbers of children who
have some form of vulnerability
● Provide all the data in one place (an
aggregation tool)
● Provide a common framework for others to use
● Support local and national assessments of
need and priorities
2018 update available at: https://www.childrenscommissioner.gov.uk/publication/childrens-commissioner-
vulnerability-report-2018

GROUPS IN OUR VULNERABILITY
FRAMEWORK

Over to Ben

OUR APPROACH

VULNERABILITY INFORMATION IN
BENEFITS DATA
● Children of lone parents
● Children living in workless families
● Children living in Temporary Accommodation / Homeless
● Children living in relative poverty
● Children at risk of food poverty
● Children at future risk of poverty
● Children in families with recent parental relationship breakdown

RESEARCH QUESTION
What impact will Universal Credit and associated welfare reforms have on children in
low-income households?

● Administrative data from 19 UK local authorities (latest extract from the last 6
months)
● 274,579 children in 138,793 households claiming either HB or CTS
● Demographics:
● Average monthly income £1,568
● Average savings £634
● 67% lone parents
● 51% in work
● 32% private renters, 48% social/council renters, 5% temporary
accommodation
● 54% London residents
METHOD: THE DATA

All benefits data processed through our in-house policy modelling engine:
METHOD: POLICY MODELLING
4,000+ parameters capturing all elements of
the benefit system, four government
departments + macroeconomic trends
Housing benefit and CTS extracts from ~140,000 households
Bottom-line impact on individuals
+
Universal Credit
UC Advance
-
2 Child Limit
Benefit Cap

Coping - Income > Costs by more than £100 (or Savings > 3 months
Costs)
Struggling- Income > Costs by less than £100
At Risk - Costs > Income
In Crisis - Costs > Rent
Key outcome measure:
versus
METHOD: FINANCIAL RESILIENCE
TOTAL INCOME
Earnings
+
Benefits
TOTAL COSTS
Housing costs (rent)
+
Household costs (ONS estimates)
Income
surplus
Income
shortfall

● 42% of households facing a cash shortfall under the Two Child Limit would no
longer face a cash shortfall if the policy were removed
● By removing the Two Child Limit, 17,950 children who were ‘at risk of being at risk’
would avoid a family income shortfall
RESULTS: TWO CHILD LIMIT*
*Financial Resilience (TCL applied to all families with 2+ children) vs. Financial Resilience (no TCL)

● 37% of households and 12,904 children facing a cash shortfall under the current
system would no longer face a cash shortfall under Universal Credit
● 4% of households and 7,680 children with a cash surplus under the current
system will face a cash shortfall under Universal Credit
RESULTS: UNIVERSAL CREDIT*
*Financial Resilience (legacy benefits system) vs. Financial Resilience (Universal Credit)

RESULTS: FIVE WEEK WAIT*
*Financial Resilience (legacy benefits system) vs. Financial Resilience( loss of Tax Credits, DWP benefits
and three weeks’ Housing Benefit)
● 155,800 children and 70% of households with a cash surplus under the current
system would experience a cash shortfall during the Five Week Wait
● If households dip into their savings to cover costs during the Five week wait, their
savings will be reduced by £681 on average
● This would completely exhaust the savings of 46,527 households, placing 98,075
children severely ‘at risk of being at risk’

● Instead of dipping into savings, households can take an advance which will then
be recouped from subsequent Universal Credit payments
● When the advance is removed from UC awards, the number of families moving
from shortfall to surplus under Universal Credit is reduced by 24%, meaning that
3,064 children remain ‘at risk of being at risk’
● 3x more households move from surplus to shortfall under reduced UC vs. full UC,
with an additional 15,765 children placed ‘at risk of being at risk’ following
migration
*Financial Resilience (legacy system) vs. Financial Resilience (Universal Credit - advance repayment)
RESULTS: UNIVERSAL CREDIT ADVANCE
PAYMENT*

● Produce macro-level statistics on the impact of individual policy elements as well
as cumulative impact of all policies combined
● Support individual households to make financial decisions (to migrate or not, to
take advance or not)
● Give councils and support organisations operational information about the
impact of policies on their residents
APPLICATIONS: HOW TO USE THESE
FINDINGS

1. Individuals and advisors: Benefit and Budgeting Calculator
https://www.betteroffcalculator.co.uk
1. Councils: LIFT Dashboard
http://policyinpractice.co.uk/policy-dashboard
1. Policymakers: Living Standards Index for London
http://policyinpractice.co.uk/lsi-london
THREE USEFUL LINKS

Over to you for questions

NEXT STEPS FOR US
Children’s Commissioner
● Upcoming report on child homelessness and homelessness risk (including Policy
in Practice analysis)
● Follow us twitter.com/ChildrensComm
Policy in Practice
● Develop predictive modelling (machine learning) capability - “Which children this
month are likely to end up in TA next month?”
● Follow us twitter.com/policy_practice

Identify vulnerable children now:
● Build early intervention projects using your data and the LIFT Dashboard
Call us on 0330 088 9242 or email us on hello@policyinpractice.co.uk
Link benefits data with children’s service data:
● Track interventions outcomes
● Predict service demand and plan resource allocation
● Track care leaver trajectories
Read our blog post How benefits data can help keep children out of poverty
Email ben@policyinpractice.co.uk
NEXT STEPS FOR YOU

View downloads:
● Children's Commissioner’s vulnerability report
● Universal Credit roadmap
● Flyer: About us
● Flyer: LIFT Dashboard
● Flyer: Benefit and Budgeting Calculator
Complete our very short survey:
● Immediately after the webinar ends
● Ask questions and request a follow up conversation
● Tell us how we did
● Auto register for our next webinar on Wed 15 May: How frontline organisations
can prepare for managed migration
THANK YOU

THANK YOU
hello@policyinpractice.co.uk
0330 088 9242

Using data analytics to understand child vulnerability

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Using data analytics to understand child vulnerability