Offending employment andOffending, employment and benefits – emerging findings from the MoJ / DWP / HMRC datathe MoJ / DWP / HMRC data share

RSS Event – 21 February 2012

M li C St ti ti i i M JMelissa Cox – Statistician in MoJ

Nick Murphy - Economic Adviser in DWP

This presentation will cover:

- Background to the data-share

- Approval for the data-share- Approval for the data-share

- Data matching methodology and results

- Introduction to the linked data

- Cleaning and validating the matched data. g g

- Initial findings from analysing linked data

- Benefits of data-share: supporting policy development

- Next steps

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Glossary

W ’ll t t id b t j t iWe’ll try to avoid acronyms, but just in case…..:

CJS – Criminal Justice SystemyJSA – Jobseeker’s Allowance (key out-of-work benefit)ESA - Employment and Support AllowanceIB Incapacity BenefitIB – Incapacity BenefitIS – Income SupportP45 – P45 employment spellNBD – National Benefit Database PNC – Police National ComputerDPA – Data Protection ActDPA – Data Protection ActMoU – Memorandum of UnderstandingPIA – Privacy Impact Assessment

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Background to the data share

Joint analytical data sharing project between DWP and MoJ which shares administrative data between MoJ and DWP / HMRC on offending, benefits and P45 employment to:

i th ( thi ) id b ff d l t• improve the (very thin) evidence base on offender employment and benefit outcomes;

• get a better understanding of the links between re-offending, employment and benefits; and

• use that evidence to develop effective policies to reduce re-ff di d lf d doffending and welfare dependency.

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Coverage of data share

MoJ data on offending (primarily Police National Computer (PNC) data) linked to DWP / HMRC data on benefits and P45 employment.p y

3.6 million offenders who have at least one caution/conviction between 2000 and 2010 and have at least one P45 employment or benefit spell2000 and 2010, and have at least one P45 employment or benefit spell.

The data covers all offending, benefit and P45 employment spells over thi ti i l di t f ff d t t f b fitthis time – including types of offences and sentences, types of benefits claimed.

The employment data included in the data-share is derived from P45 forms sent to HMRC by employers. P45 employment spells do not usually record employment paid at levels below tax thresholds, or self-employment or cash-in-hand informal economy work but should provide a useful proxy of employment. No information included on earnings, type of employment.

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g yp p y

Our two year data sharing journey

Nov 2009 Nov 2010 Nov 2011

Data Sharing approval

DataData

matching

Data

prep

Analysing linked data

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Approval for the data-share– why?

•Full consideration was given to relevant legal and ethical issues before a decision was taken to proceeda decision was taken to proceed.

•Data sharing needs to be:- lawful, - fair,- justified andj- proportionate

•Approval needed by MoJ DWP HMRC and PIAP (data owners)•Approval needed by MoJ, DWP, HMRC and PIAP (data owners)

•Agreement reached and full approval for a one-off data-share was i i D b 2010given in December 2010.

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Legal basisMoJ, DWP and HMRC lawyers agreed legal basis for data sharing:

Section 14 4 (c) of the Offender Management Act 2007Section 14, 4 (c) of the Offender Management Act 2007

Which enables disclosure of information for offender management purposes - including the development or assessment of policies relating to matters connected with the management of offenders.management of offenders.

To avoid multiple flows of data, DWP will act as Data Processor for HMRC and process and transfer HMRC data that DWP holds under other gateways.

Condition that shared data can be used for analytical purposes only.

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Ethical approval – safeguards

DWP did t hi ti t• DWP did matching - more proportionate• Only shared a limited number of variables - justification for each• Excluded offenders aged under 16• Anonymised at earliest opportunity and personal data deleted• Shared data has restricted access and securely stored• Approval from Ethics Committee • Approval from operational security to do secure data transfers• Approval from operational security to do secure data transfers - personal delivery• Bound by an Memorandum of Understanding and Privacy y g yImpact Assessment•Joint moderation group set up to ensure compliance

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Challenges in getting approval

A i l l- Agreeing on legal power

- Building relationships across Departments and data owners – multi-Building relationships across Departments and data owners multidisciplinary and cross-Government team

- Change in personnel through project

Persevering with the necessary paperwork- Persevering with the necessary paperwork

- Dependent on successful feasibility studyp y y

- DWP Ethics Committee

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Method of data transfer

Data transfer 1 Dataset 1 – MoJ dataPersonal identifiers of 4.2 million offendersfrom Police National Computer extract – all adults who have offended from 2000

Data transfer 1

MoJ DWP DWP successfully matched

PNC d t t

Data transfer 2- Personal identifiers removed

PNC data to Master

Index and P45 according to

Dataset 2 – matched data

DWP MoJ- Personal identifiers removed.

- Agreed DWP / HMRC variables added.

- MoJ and DWP anonymous identifiers

according to agreed matching

algorithm

retained.

Data transfer 3 Dataset 3 – anonymised datasetDataset 1 destroyed by DWP once data

MoJ DWP MoJ added on agreed MoJ variables to Dataset 2 (removed DWP/HMRC variables).

Dataset 3 anonymised dataset by DWP once data successfully

matched

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Data matching - Variables

No unique identifier in common between MoJ and DWP / HMRC data so data matching techniques needed.

Identified common variables across administrative data sources:

ForenameSurnameDate of birthGenderP t d (f ll t d hi t )Postcode (full postcode history)•Also included alias names

Reference ID (MoJ, DWP)

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Data matching - methodology

Matching rules developed using common variables (including initial of forename and fuzzy matching on names to get best y g gmatch)

37 t t hi l ith i i ll d i i37 step matching algorithm originally agreed using a scoring system and combinations of at least 3 of the 5 variables (forename, surname, date of birth, postcode, gender). ( , , , p , g )

Ranging from:

Exact matching on all 5 variablesExact matching on all 5 variables

3 out of 5 variables: surname, date of birth, gender

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Final matching algorithm • Quality assurance process to try to minimise matching errors:

• False positives: an identified but incorrect match• False positives: an identified but incorrect match • False negatives: an unidentified but correct match

C t t d i i i i f l iti if thi l t• Concentrated on minimising false positives even if this lost some additional true matches.

• QA process: - Match data using all 37 rules- Sample of matches found at each stagep g- Manually examine personal details - If estimate more than 5% false positive – rule abandoned

• Algorithm simplified/improved from 37 to 20 rules – following quality assurance

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Final matching processMOJ data

DWP data (Master Index)

20 matching rule algorithm:)

Matching algorithm-1st matching

rule – match or no match?

HMRC data (P45)

Matched data – 66%

Unmatched data 34%

no match?

- Unmatched records thendata (P45)data 66% data – 34%

Matching algorithm

records then used 2nd

matching rule etc

Matched data Unmatched

etc…….86% matc Matched data

– 20%Unmatched data – 14%h

rate

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Data matching results

• 86% match rate: 3.66 m offenders matched to DWP /HMRC data

M h b tt th t d t h t f f ibilit t d j t 70%•Much better than expected - match rate of feasibility study was just over 70%

• Quality of matches:- 40% of matches = exact match on 5 personal identifiers- 40% of matches = exact match on 5 personal identifiers- Over 75% of matches was an exact match on all 5 variables, or exact

match on 4 variables (all excluding postcode)

•Representativeness of matched data: - distributions of key variables between the matched data, un-matched data, and the total.

l l diff th i it ( li ht d t ti f th i- only real differences are ethnicity (slight under representation for ethnic minority groups) and disposal category (higher proportion of cautions in the un-matched data)

Expertise of developing and running matching algorithm

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Introduction to matched dataCriminal Justice System information (MoJ data): - basic offence details (date of offence and offence type);

b i d t il f t i d ll f i d b ti- basic details of sentence received; spell of prison and probation where known;

B fit P45 l t d i f ti (DWP/HMRCBenefit, P45 employment and programme information (DWP/HMRC data): - benefit spells (start and end dates, benefit type);

P45 l t ll ( t t d d d t )- P45 employment spells (start and end dates); -programme spells (start and end dates, programme type) - date of death- ICD code (identifies type of illness for incapacity benefit claim)-geographic level data; and - necessary variables to use data including extract dates, details of y g ,match strength, anonymous identifiers and so on. - Data matched internally for further analysis e.g. to look at specific interactions with the benefit or criminal justice systems etc.

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Introduction to the data

Combined spells dataset has approx. 40 million rows;

3.6 m offenders

7 6 t di l t7.6 m non-custodial sentences

1 0 m custodial sentences1.0 m custodial sentences

13.8 m benefit spellsp

18.8 m employment spells

2.2 m programme spells

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Cleaning and validating the data

Considerable time spent cleaning and understanding data due to scale d l i f h d dand complexity of matched data.

Statistical QA procedures applied to protect integrity of matched dataStatistical QA procedures applied to protect integrity of matched data including;

- removal of duplicated entries, - checks for completeness, and- cleansing of inconsistent data based on business intelligence.

•Worked closely together:• Set up and maintained an issues logp g• Weekly video conference meetings

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Any questions or comments so far?

15 mins15 mins

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Initial findings

f fInitial findings from analysing linked data to support policy development in specific areas and are intended to demonstrate the potential of the improved evidence base.the potential of the improved evidence base.

Published in November: http://www.justice.gov.uk/publications/statistics-and-data/ad-hoc/index.htm

These statistics do not imply causality between benefit or employment status and proven offendingemployment status and proven offending.

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Initial findings – visualising data

Example 1 – Person 1338Male, Born mid 1960sJuly 2006 sentenced to 4 years prison for “violence against the person”July 2006 sentenced to 4 years prison for violence against the person

EmploymentP45

Prison

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Initial findings – visualising dataExample 2 – Person 534Male, Born mid 1970sSentenced several times for theft once for burglary once for violenceSentenced several times for theft, once for burglary, once for violence and many times for other indictable offences

Basic Skills

ESA

Emp. Zones

Incap. ben

Income sup

JSA

Non-custodial spellNon-custodial spell

Prison

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Initial findings – visualising dataExample 3 – person 1773Male, born early 1980sSentences for (in order) theft robbery summary offences exclSentences for (in order) theft, robbery, summary offences excl. motoring and violence.

P45Employment

JSA

Non-custodial spell

PrisonPrison

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Initial findings–descriptive statistics

Th i f ff d i h h f dThe proportions of offenders with each type of record are:

86 % have at least one P45 employment spell;86 % have at least one P45 employment spell;76 % have at least one DWP benefit spell; and28 % have at least one DWP programme spell.

26 % of all 4.9 million out-of-work benefits being claimed on 1 December 2010 in England and Wales were claimed by offenders inDecember 2010 in England and Wales were claimed by offenders in the data-share. - 33 % of JSA claims were by offenders. - 20%+ of Incapacity Benefit, Employment and Support Allowance or

Income Support claims

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Benefit and P45 employment status around time of sentence

Proportion of offenders claiming benefits or in P45 employment at

Offenders sentenced during year ending 30 November 2010

benefits or in P45 employment at some point in the month before

sentence

Cl i i b fi ( b fi ) 4%Claiming benefits (any benefits) 54%

Claiming out of work benefits 51%Jobseeker's Allowance 24%Jobseeker s Allowance 24%Incapacity benefits 13%Employment and Support Allowance 9%Income Support 14%Income Support 14%

Other benefits 3%

N b fit l i d 46%No benefits claimed 46%

In P45 Employment 33%

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Benefit and P45 employment status around time of sentence – by sentence type

Benefit and P45 employment status for offenders in the monthbefore sentence by sentence type for offenders in the matched data who were sentenced in the year ending 30 November 2010 and recorded on the PNC

Proportion of offenders

Any out-of-work

benefits

In P45 employment

Offenders sentenced to a caution less likely to be claiming benefits

All disposals 51% 33%

Caution 40% 46%

gbenefits, and more likely to be in P45 employment

Caut o 0% 6%Fine 1 47% 39%Community Sentence 57% 29%Suspended Sentence Order 57% 30%Immediate Custody 51% 13%

Only 13% of offenders sentenced to immediate custody are in P45 employment in month beforeDischarges (Absolute / Conditional) 64% 27%

Other 60% 24%

employment in month before sentence

1. Care should be taken with the analysis on fines. The PNC data largely covers 'recordable' offences where the coverage of fines in the matched data only includes fines that are given for the more serious summary offences. The PNC includes less than a fifth of all fines given by the courts so these findings must not be interpreted as representative of all fines.

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Benefit, P45 employment and prison status for all prisoners released in 2008

• 47% claiming out-of-work benefits 2 years after release from prison

•15 % in P45 employment

• 11% back in prison

50%

60%

s

• 11% back in prison

40%

50%

JSA,ESA,IB,ISP45of

fend

ers

30%

5PRISONAny NBD Benefit

ortio

n of

10%

20%

Prop

0%0 13 26 39 52 65 78 91 104 117 130 143 156

Number of weeks since release from prison

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Number of weeks since release from prison

Benefit status for all prisoners released in 2008 –by benefit type

30%

35%

20%

25%

30%

nder

s

10%

15%

20%

on o

f offe

n

0%

5%

10%

Prop

ortio

0%0 26 52 78 104 130 156

JSA P45 PRISON IB ESAIS ICA AA RP DLA

P

IS ICA AA RP DLASDA WB PC PIB BB

Number of weeks since release from prison

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Whether ex-prisoner had any benefit, P45 or prison spell in weeks following release in 2008

Cumulative proportion:Cumulative proportion:• 75% of offenders made at least one claim to an out-of-work benefit within 2 years of release from prison

• 29% started at least one P45 employment spell

80%

• 46% had at least one prison spell at some point in 2 years following release

50%

60%

70%

ffend

ers

30%

40%

50%

rtio

n of

of

10%

20%

%

JSA+IB/ESA+ISEMPPr

opor

0%0 13 26 39 52 65 78 91 104

PRN

Number of weeks since release from prison

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Number of weeks since release from prison

Whether ex-prisoner claimed certain benefits in weeks following release in 2008 – by benefit type

Cumulative proportion:Cumulative proportion:• 60% of offenders made at least one claim to Jobseeker’s Allowance within 2 years of release from prison

• Just under 30% made at least one Incapacity Benefit or Employment and Support Allowance claim

70%JSA

• Around 15% made at least one Income Support claim

50%

60% IB/ESAIS

ende

rs

30%

40%

on o

f offe

10%

20%

Prop

ortio

0%0 13 26 39 52 65 78 91 104

Number of weeks since release from prison

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Number of weeks since release from prison

JSA survival rate comparisonDWP: May 2011

• Ex-prisoners seem to perform pretty much the same as the average JSA claimant – only 10% of claims last for a year. But.......

90%100%

JSA Prisonersy y

60%70%80%

min

g JS

A

30%40%50%

Still

Cla

im

0%10%20%%

S

0 13 26 39 52Duration (weeks)

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JSA survival rate comparisonDWP: May 2011

....ex-prisoners spend almost 40% more time on benefits in the 3 years following a new JSA claim than the JSA average

Whether on benefits in weeks following new JSA claim100%

Average JSA

60%

80% Ex-Prisoner

laim

ants

20%

40%

rtio

n of

cl

0%

20%

0 13 26 39 52 65 78 91 104 117 130 143 156

Prop

or

0 13 26 39 52 65 78 91 104 117 130 143 156

weeks since JSA claim start

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Analysis informing Policy (1):Prisoner Work Programme development

Two Key Findings:1. Offenders spend more time on benefits from “day 1” of a JSA claim th th JSA l i t d f th “12 th” i tthan the average JSA claimant spends from the “12 month” point.2. A little over 30,000 offenders leave prison each year and claim JSA within 13 weeks.

Maximum time onTime on benefit

over 2 yearsTime in prison over 2 years

Maximum time on benefit or in

prison

JSA25+ 66% 1% 67%

JSA E l A 83% 2% 85%JSA Early Access 83% 2% 85%

JSA Prison Leaver 59% 10% 69%

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Analysis informing Policy (2):The Policy

From March 2012 all offenders leaving prison and claiming JSA within 13 weeks will be mandated to the Work Programme.

Extensive reforms are taking place within the benefit system to enable prisoners to start the JSA claim process prior to release.

- Minimise “prisoner finance gap” andM i i l t t- Maximise employment support

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Some Other Findings in brief

- 44% of 500,000 Community Care Grant (CCG) applications made in England and Wales in 2009/2010 were made by people on the DWP/MoJ dataset.

This includes 15% that were recorded on the dataset as having been in prison at some- This includes 15% that were recorded on the dataset as having been in prison at some point.

-9% of 3 million Disability Living Allowance claims by offenders1% f 11 6 illi R i P i l i b ff d- 1% of 11.6 million Retirement Pension claims by offenders

-Over the three year period, it is estimated that, per individual, the ex-prisoner population receive an extra £1,500 (or 38 per cent more) in benefits than the average JSA claimant.( p ) g

- Response to August public disorder:-35 % of adults were claiming an out-of-work benefit at the time of the August 2011 public disorder (compared to 12 per cent of the working age population in England)disorder (compared to 12 per cent of the working age population in England). -45 % of all offenders who were sentenced for an indictable offence in 2010 were claiming benefits.

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Benefits of shared data - uses

Breaking the cycle: rehabilitation of offenders and reducing welfare dependencywelfare dependency

Analysis from linked data already playing important role inAnalysis from linked data already playing important role in helping DWP and MoJ to produce better evaluations, monitoring information on interventions and in targeting resources and

(developing implementation plans (such as on the Work Programme extension to Prison Leavers claiming JSA).

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Press coverageFURY AS JUNKIES GET £1BN BENEFITS – Express, May 2011“A further £162million a year is being handed to convicted y gcriminals who go straight on to jobless benefits after they are released from prison.”

THIRD OF UNEMPLOYED ARE CRIMINALS – Telegraph, Mail, Metro + others December 2011Metro + others, December 2011

TOO SICK TO WORK BUT NOT TOO SICK TO RIOT: 1 in 8TOO SICK TO WORK BUT NOT TOO SICK TO RIOT: 1 in 8 defendants were on incapacity or disability benefits – Mail, October 2011

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Next steps

• Continue to analyse shared data, e.g:- Evaluate the links between employment and re-offending- Link shared data to other datasets (ensuring compliance with MoU) - Grateful for your ideas after we’ve finished presentation!

• We intend to move to an ongoing data share (pending approval being given) given the value from the shared data

• Exploring data shares with other Government Departments to support policy development and evaluationssupport policy development and evaluations

• Keen to exploit the shared data as much as possible. p pConsidering ways to provide anonymised access to data –possibly through a Datalab – we will keep you posted!

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• Questions and discussionQuestions and discussion

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