UNITE and the benefit cap: building a new data platform

Daniel Brown & William Baxter - DWP

Background

"This is the biggest reform of the welfare system in a generation,"

“Universal Credit is at the heart of the Government’s reforms”

Universal Credit

Income related JSAIncome related ESAIncome Support Working Tax CreditsChild Tax CreditsHousing Benefit

Key question:

“ Can we use existing administrative data to create an analytical dataset that will bring together all the key elements of Universal Credit”.

Data structure

UNITE

DWP HMRC LA

Source systems

UNITE population?

• Includes Tax Credit, DWP and LA customers

• Over 18 million benefit claims

• Transformed to simulate Universal Credit

UNITE population?

• But….

• Wider than Universal Credit

• 29 Million benefit spells

• Covers most DWP benefits

Challenges

• Household level

• Linking large amounts of data

• New benefits

• Quality assurance

Population

HMRC

DWP

HB

Universal Credit timeseries

Jan-11 Feb-11 Mar-11 Apr-11 May-11 Jun-11 Jul-11 Aug-11

Household

Individual

• Universal Credit Caseloads/ migration

• Geographical analysis

• Interactions between legacy benefits

• Impact of UC on pensioners

• Benefit Cap

Benefit Cap: Background

• Introduced from April 2013 by a reduction to HB payments

• Cap (maximum limit) on the total amount of benefits that people can get ensures that unemployed families will not receive more in benefits than the average income for working families

• Expected limits:• £500 per week for households comprising couples and lone

parents

• £350 per week for single persons

Benefits in and out of scope

• LA administered benefits (Housing Benefit)

• DWP benefits (inc. JSA, ESA and Income Support)

• HMRC benefits (Child Tax Credit and Child Benefit)

• Exemptions will apply (Working Tax Credit, disability related benefits)

Building the Cap population dataset

HMRC DWP

HB

HB claimants only

Identify those who are in scope for the cap:

Apply exclusions - people with disablities, workers.

Identify families whose benefit income exceeds

the maximum cap amount.

63,000 families identified as being in scope for the benefit cap

Benefits Claimed Number of Families

%

All 52,900 84.1

LA & DWP 5,300 8.4

LA only 1,500 2.3

UniteFamilies claiming HB

Building the cap population dataset

• Use of a common identifier throughout (all data sources have already been matched to NINOs)

• Use of WTC to identify employment, CB data to identify number of dependent children and DLA to identify people with disabilities

• All benefit rates increased to allow for annual budget increases in the run up to April 2013

Supporting operations

• address matching from CIS to generate mailshot which was sent to over 80,000 individuals

• Provision of personal information to enable JCP offices and LA’s to offer employment support and housing advice

Amount capped based on benefit data to November 2011 compared to the Impact Assessment

Average Amounts Of Benefit Reduced For Assessment Units Affected By The Benefit Cap Policy

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

UC Data Extract (percentage*) Impact Assessment

Loss By £50 Band

Proportion of Capped Households, in 2013-

14

Over £150

£101 to £150

£51 to £100

Up To £50

43.40%

23.18%

13.22%

20.21% 17%

12%

26%

45%

Legend

PERCENT

up to 0.10

0.11 - 0.20

0.21 - 1.00

1.01 - 1.50

1.51 +

England and WalesHouseholds over the benefit cap, as a percentage of total households(excluding CTB)

Map produced by

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Regional Impacts

Limitations

It’s good but…as with any administrative data, not perfect:

• not real-time data• relies on data sharing gateways across

Government (not in place for MOD war pensions exemption)

• does not allow for modelling all exemptions (e.g. 12 months employment spells)

Summary

• Built a new data platform across Government benefits

• Used for operational support for the introduction of the benefit cap

• Benefit data allows us to model many family characteristics

• Some limitations – e.g. timeliness • Will support evaluation, analysis and statistics

as well as operations