1

Disability Onset, Disability Exit, and Welfare Benefit

Receipt

Melanie K. Jones

Cardiff Business School

Cardiff University

Tel: +44 (0)2920875079

Email: jonesm116@cardiff.ac.uk

&

Duncan McVicar

Queen’s Management School

Queen’s University Belfast

Tel: +44 (0)2890974809

Email: d.mcvicar@qub.ac.uk

Preliminary Version: June 2016

Please do not quote

JEL: H51, H53, I38

Keywords: disability, disability onset, disability exit, welfare benefits, disability insurance,

local labour force survey, propensity score matching

Acknowledgements

This work is based on data from the Annual Population Survey which is produced by the

ONS and is accessed via special licence from the UK Data Archive, University of Essex,

Colchester. The usual disclaimer applies.

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Abstract

There is consensus in the dynamics of disability and labour market outcomes literature that

disability onset leads to a decline in employment and earnings and an increase in applications

for and receipt of disability insurance. But important questions remain. How does disability

onset impact on welfare benefit receipt beyond disability insurance? How does disability exit

impact on benefit receipt? To what extent do these relationships vary for individuals with

different socio-economic characteristics, in different labour-market contexts, and under

different disability benefit regimes? This paper addresses these questions exploiting rarely-

used longitudinal data constructed from the UK Local Labour Force Survey, combining

propensity score matching with difference-in-differences methods to draw plausibly causal

inferences under explicit assumptions. Disability onset is shown to (robustly) increase receipt

of sickness and disability benefits within one year (by between 3 and 8 percentage points),

although the impact on non-sickness benefits is small and non-robust. Onset effects are larger

for individuals with lower qualification levels, for those experiencing onset of disability

linked to mental health conditions or more severe disability onset, and for those under a

disability benefit regime with less rigorous screening and conditionality. Evidence on

disability exit effects is more mixed: some estimates suggest negative impacts on benefit

receipt, but this conclusion is fragile to different assumptions about diverging prior trends.

Arguably the most convincing exit estimates presented here – comparing those who report

disability exit with those who report disability exit one year later – suggest exit does not

impact on receipt of sickness and disability benefits two years on. There are few clear

differences in estimated disability exit effects by individual characteristics or labour market

context. The apparent asymmetry between benefit receipt impacts of disability onset and

disability exit provides further (indirect) evidence that even temporary disability can have

long lasting economic effects.

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1. Introduction

Disability benefits provide an essential safety net for many people of working age whose

health means that they are unable to carry out paid work. On the other hand disability benefits

may themselves contribute to low participation and employment rates among people with

disability (e.g. Parsons, 1980; Haveman and Wolfe, 1984; Bound, 1989; Autor and Duggan,

2003; Maestas et al., 2013). High and/or growing disability benefit recipiency rates in many

countries have also led to concerns about their fiscal sustainability (e.g. Autor and Duggan,

2006; Burkhauser et al., 2014).

The more complete our understanding of the relationships between disability, benefit receipt

and employment, the better able we will be to design effective policy concerning these trade-

offs. This paper addresses part of this wider picture concerning the relationship between

disability and benefit receipt. Specifically, in a parallel with the strand of the literature that

goes beyond cross-sectional analysis to examine the dynamic relationships between disability

and labour market outcomes (e.g. Charles, 2003; Jenkins and Rigg, 2004; Mok et al., 2008;

Garcia-Gomez, 2011; Meyer and Mok, 2013; Singleton, 2014; Polidano and Vu, 2015), this

paper uses previously unexploited British longitudinal data to examine the dynamic

relationships between disability and benefit receipt. Like this earlier literature, our focus on

dynamics is motivated by the dynamic nature of both disability (people flow into (experience

onset of) and out of (experience exit from) disability) and benefit receipt, leading to the

question: How does benefit receipt evolve with respect to changes in disability status? We

know time matters. For example, international experience of disability benefit reform

suggests that it is in the period immediately following disability onset that there is most scope

to constrain the growth of disability benefit rolls (by limiting inflows), and to support

employment among people with disability (Burkhasuer et al., 2014). Exploiting longitudinal

data also gives us additional tools for dealing with the endogeneity of self-reported disability.

We begin by examining the impact of disability onset on a set of four benefit receipt

outcomes ranging from the main income-replacement disability benefit – the UK version of

Disability Insurance (DI) known as Employment and Support Allowance (ESA) – to receipt

of any non-universal welfare payment. In doing so we build on the earlier work of Jenkins

and Rigg (2004) which finds a positive impact of disability onset on income from own

disability benefits and from other welfare benefits at the household level in both the onset

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year and the year after onset using British Household Panel Study (BHPS) data from 1991-

1998. In particular the period we study (2004-2012) follows or spans major reforms to

disability benefits in 1995 and 2008 and to unemployment benefits in 1996 which all

impacted disability benefit rolls (e.g. see McVicar, 2008; Banks et al., 2015). For the US

Singleton (2014) finds a positive impact of disability onset on DI receipt in the onset year and

the seven subsequent years using data from the Survey of Income and Program Participation.

Polidano and Vu (2015) also find a positive impact of disability onset on receipt of any

income replacement welfare payment in the onset year and the four subsequent years using

Household Income and Labour Dynamics in Australia (HILDA) survey data. Looking beyond

DI receipt or catchall measures of benefit receipt is important given that many working age

people with disability are not in receipt of disability benefits but may be in receipt of other

benefits (e.g. for the US see Meyer and Mok, 2013)1, and because flows onto and off

disability benefits are not only from and to employment but also from and to other benefit

payments including unemployment insurance (e.g. for the UK see Sissons et al., 2011; Beatty

and Fothergill, 2015). Taken together this suggests even the sign of disability onset effects on

receipt of welfare payments other than sickness and disability benefits is uncertain ex ante.

Our second contribution is that we are able to examine heterogeneous impacts of disability

onset on benefit outcomes across several dimensions because we have a larger sample, with

more disability onsets, than is typical in this literature. To date what we know in this regard is

limited to differences in DI application and receipt by severity of disability onset from

Singleton (2014) (those experiencing onset of work-preventing disability are much more

likely to apply for and receive DI than those experiencing onset of work-limiting disability)

and differences in receipt of any Income Support payment by broad education level from

Polidano and Vu (2015) (those with no qualifications are much more likely to receive Income

Support than those with vocational or higher-level qualifications). We re-examine both these

dimensions here using British data. There is more existing evidence on heterogeneous

impacts of disability onset on employment and other labour market outcomes. Polidano and

Vu (2015) finds variation by pre-onset employment status (impacts on employment are due

more to reduced inflows than to increased outflows from work) and by education level (larger

impacts for lower educated individuals). Exploiting the same Local Labour Force Survey

(LLFS) data we use here, Jones et al. (2013) finds stronger employment effects of disability

1Meyer and Mok (2013) present descriptive data from the Panel Study of Income Dynamics on recipiency rates

for various welfare benefit payments, including DI, between six and ten years after disability onset. They do not

present estimates of the impact of disability onset on these outcomes, however.

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onset for men, older individuals, those with more severe disability (proxied by multiple

conditions), but little difference by education level. Again we examine differences in

disability onset effects on benefit receipt along all these dimensions. A cross-country study

by Garcia-Gomez (2011) also suggests there are bigger employment impacts of disability

onset and negative health shocks in countries where disability (and other) benefits are more

generous, where they are conditioned on not working, and where employers do not have to

meet disability employment quotas. We too consider the institutional context for disability

onset, but rather than a cross-country approach we do so by examining variation in outcomes

within Britain either side of a major disability benefit reform introduced in 2008. Finally,

given evidence that disability rolls tend to be higher where and when labour markets are

weaker (e.g. Black et al., 2002; Autor and Duggan, 2003; McVicar, 2006), we also examine

whether the impacts of disability onset vary with local unemployment rates.

Our third contribution is to explicitly examine the impact of disability exit – no longer

reporting a disability – on benefit recipiency. This has been mostly overlooked by the

dynamics of disability literature, despite the fact that for many people disability is temporary

not permanent (e.g. see Burchardt, 2000; Meyer and Mok, 2013). One factor that may have

contributed to this is the perception that disability benefits are essentially an absorbing state

until either death or state pension age is reached, although this appears less the case in some

countries than others (see OECD, 2010). Further, even temporary disability can have long-

lasting effects on labour market outcomes through impacts on human capital accumulation

and through state dependence (e.g. Charles, 2003; Mok et al., 2008; Oguzoglu, 2012a; Meyer

and Mok, 2013). Note that an exception to the dearth of studies on disability exit effects is

Jones et al. (2013) which finds no overall impact of disability exit on employment, but a

small positive impact for women and young people. Here we build on this to examine

disability exit impacts on our range of benefit recipiency measures. As in the case of

disability onset, we also explore the extent to which disability exit effects vary across various

dimensions including gender, age, severity, type of impairment, and labour market context,

although smaller sample sizes for disability exits limits what we learn in this case.

The remainder of this paper is set out as follows. The following section provides a brief

overview of the British welfare system, and in particular disability benefits, pre and post

2008. Section 3 describes the LLFS data we use to estimate disability onset and exit impacts.

Section 4 sets out our approach to estimation and discusses identification. Sections 5 and 6

present and discuss the resulting estimates of disability onset and disability exit impacts,

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respectively. Section 7 concludes. The supplementary appendix presents additional data

details and the results and brief discussion of sensitivity analysis.

2. Disability and Other Benefits in Britain 2004-2012

Our data cover the period 2004-2012, and in this section we briefly describe the working-age

welfare system in Britain in place at that time, with particular emphasis on welfare payments

for people with disability. The main disability-related benefits in Britain are earnings

replacement benefits and additional costs benefits. In keeping with the disability and labour

supply literature, we concentrate primarily on earnings-replacement benefits here. These are

also the benefits that were most extensively reformed during the 2004-2012 period. The main

additional cost benefit throughout the period was called Disability Living Allowance (DLA),

and this only began to be reformed – with its gradual replacement by Personal Independence

Payments – subsequent to the period of interest here. The working-age recipiency rate for

DLA rose slowly but steadily over the period, from around 4% in 2004 to around 4.5% in

2012. Other major working-age benefit types that are covered by our broadest measure of

welfare recipiency – see Section 3 – include Jobseeker’s Allowance (JSA) (unemployment

benefit), Income Support (means tested social assistance, e.g. for single parents) and Housing

Benefit (for those with low income to help with housing costs). For further details on these

payments see Browne and Hood (2012).

From 2004 until the 27th

October 2008 the main earnings replacement disability benefit for

those unable to work on grounds of disability was called Incapacity Benefit (IB). This was a

contributory benefit, i.e. eligibility required a work history, and was (mostly) not subject to

means-testing. Incapacity for work was determined by government doctors by means of a

Personal Capability Assessment (PCA). IB was paid at one of three flat rates depending on

the length of time the individual had been unable to work: a short term lower rate for the first

28 weeks, a short term higher rate for the next 24 weeks, and a higher long-term rate

subsequently. Those who became sick or disabled while in work were generally ineligible for

IB during the first 28 weeks of a spell out of work and instead could claim Statutory Sick Pay

(SSP), for which employers were responsible.2 Those unable to meet the contributions based

eligibility criteria for IB were potentially eligible for Severe Disablement Allowance (SDA)

2 Note that unlike for DI in the US there is no mandatory waiting period for eligibility for IB (or for SSP for

those in work at the time of disability onset).

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(although no new claims for SDA were granted after 2001) or to have their National

Insurance credits – contributions towards the state pension – paid. ‘Credits only’ claimants

usually also received Income Support, often with a ‘disability premium’. Recipients of IB,

SDA and ‘Credits Only’ (but not SSP) were collectively referred to as incapacity benefits

claimants – note the practical equivalence in British welfare-speak between claiming and

receiving benefits, as opposed to applying for benefits – and made up the incapacity benefit

roll, which stood at around 6.7% in 2004, having hovered between 6% and 7% since the mid-

1990s.

From 2003 a new set of work-first reforms called Pathways to Work (PtW), aimed at slowing

the inflow to IB and boosting outflows for those having recently joined the roll, was

gradually rolled-out. It made movement onto the IB program (including credits only)

conditional on attendance at work-focused interviews, with the aim of steering at least some

recipients into employment support services and ultimately back into the labour market. It

also introduced a ‘back to work’ bonus payment, provided additional in-work condition-

management health support for those returning to employment from IB, and brought PCAs

forward so they took place three months into the IB claim rather than six months into the

claim. Evaluation evidence on the impacts of PtW has been mixed (see Adam et al., 2010;

National Audit Office, 2010), although the IB claimant rate fell steadily between 2004 and

2008 to around 6.2%. The unemployment rate hovered around 5% over this period.

In 2008, ESA replaced IB (and credits only IB) as the main earnings-replacement disability

benefit for new applicants. This new program of insurance-based benefit for those with

sufficient work history and means-tested social assistance benefit for those without sufficient

work history included a new tougher Work Capability Assessment (WCA), with fewer

exemptions, in place of the existing PCA. The requirement to attend work-focused interviews

introduced under PtW was extended into a requirement to engage in work-related activity for

all but the most severely disabled, linked explicitly to payments, with around one quarter of

the existing benefit payment made conditional upon compliance. There was also no longer a

higher rate of payment for longer-duration claims. Further, from April 2011 existing IB

recipients started to be reassessed under the new ESA eligibility criteria, although this

process was still not complete at the time of writing. Many were judged ineligible as a result

of medical re-screening under the stricter WCA, although some have since successfully

appealed these decisions (see Department for Work and Pensions Quarterly Official Statistics

Bulletin December 2014). Disability recipiency rates continued to fall slowly over the years

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from 2008-2012, reaching around 6% in 2012. Concurrently, the Great Recession led to a

rapid increase in the unemployment rate (and in the JSA claimant rate), rising to around 8%

by the second quarter of 2009, where it remained through to 2012. The fact that disability

recipiency rates did not increase during or in the years following the downturn, unlike in

earlier downturns, suggests the reforms outlined above may have impacted significantly on

flows onto and off IB/ESA. For more details of this reform and early estimates of its impacts

see Banks et al. (2015).

3. Data

The LLFS data have a number of desirable properties for our purposes. In particular, we are

able to draw on a large sample with sufficient numbers of disability onsets and exits to enable

examination of heterogeneous effects. The data also span a major disability benefit reform –

the switch from IB to ESA from 2008 for new applicants – which allows us to examine the

impacts of disability onset on benefit receipt under different benefit regimes within the same

country. The trade-offs are that the LLFS offers a relatively short longitudinal dimension with

respondents observed for a maximum of four years (so we cannot directly examine longer

term impacts of disability onset/exit), and that the longitudinal sample we end up using is not

fully representative of the wider working age population. The latter should be borne in mind

when drawing conclusions from the analysis presented here.

The LLFS is part of the Annual Population Survey (APS) which also contains observations

from the main Quarterly Labour Force Survey (QLFS) and the APS boost. Special Licence

LLFS data from the January to December APS are pooled from 2004 to 2012. We focus on

the LLFS because individuals are retained for four years with a 25% rotational panel element,

rather than the standard five quarters for the QLFS. We follow Jones et al. (2013) and use the

system variables, employed in the construction of the LLFS, to undertake a matching process

of individuals across time to construct a panel version of the LLFS. The LLFS covers Great

Britain but, as discussed by Jones et al. (2013), since it was designed to boost the sample size

of the main QLFS it is not geographically representative, although this has a limited effect on

the sample composition in terms of personal characteristics, albeit there is a slightly higher

proportion reporting disability and benefit receipt, and lower proportions reporting

employment than in the full APS sample pooled over the same period (see Table A1 in the

appendix).

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We restrict our analysis to respondents who provide valid information at four consecutive

waves between 2004 and 2012, creating a balanced panel, and who are of working age

throughout. We are left with a maximum sample of 49,071 individuals (196,284 person year

observations). Note that the LFS is not primarily designed as a panel survey and it is the

address rather than the individual that is traced across time. As a consequence observations in

the LLFS panel are restricted to households that did not move address and individuals who

remained resident within these households for four consecutive years. Our sample therefore

excludes individuals who experience disability onset/exit which is associated with selective

residential mobility, e.g. for formal or informal care purposes (Norman et al., 2005). These

are likely to be the most severe onsets or greatest recoveries. However, since migration more

generally is dominated by young and healthy individuals, attrition increases the prevalence of

disability in the LLFS panel relative to the unrestricted pooled LLFS by about two percentage

points. Overall, compared to the full APS sample, the balanced LLFS panel has slightly

higher rates of disability and disability benefit claiming, lower rates of non-sickness benefit

claiming, is slightly older, has fewer full-time students and singles, and more renters, in

addition to the aforementioned differences in geographical coverage (see Table A1).

Disability

The LLFS contains self-reported information on two alternative definitions of disability. Both

require a positive answer to an initial question on long-term health: “Do you have any health

problems or disabilities that you expect will last for more than a year?” This is then followed

by a series of questions: (i) “Does this health problem affect the kind of paid work you might

do? Does this health problem affect the amount of paid work you might do?” (ii) “Do these

health problems or disabilities, when taken singly or together, substantially limit your ability

to carry out normal day to day activities? If you are receiving medication or treatment,

please consider what the situation would be without the medication or treatment.” which

refer to work-limiting (WL) and Disability Discrimination Act (DDA) definitions of

disability, respectively (see Jones et al., 2006 for details). Individuals answering ‘no’ to the

first question on long-term health, or those answering ‘yes’ to the first question but ‘no’ to

the two follow-up questions, are classed here as non-disabled. The prevalence of WL and

DDA disability in the balanced panel is 17.48% and 18.11% respectively, with considerable

overlap. In what follows we present separate analysis for each definition, reporting analysis

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using the DDA definition in the main text for reasons which we set out below and analysis

using the WL definition in the Appendix.3

In relying on self-reported disability we acknowledge the possibility that measurement error

may bias estimated onset and exit effects towards zero. By focussing on consistent patterns of

disability reporting facilitated by longitudinal data, however, we reduce the scope for

measurement error compared to cross-sectional approaches. Specifically, we follow Jenkins

and Rigg (2004), Polidano and Vu (2015) and others by using two-period measures of

disability onset and exit where we define the onset group as those who experience two

periods reporting no disability followed by two periods of reporting disability (0011), and

those who experience consistent exit as those who report two periods of disability followed

by two periods of no disability (1100). For each we specify two alternative control groups.

Our first control group for onsetters are those who are continuously non-disabled (0000), i.e.

those at risk of onset who do not report onset. Similarly, for the exit group our first control

group are continuously disabled (1111), i.e. those at risk of exit who do not exit. Our second

control group in each case are those who do experience disability onset but one year later (i.e.

0001) and those who do experience disability exit but one year later (i.e. 1110). Table 1

provides the sample sizes for the various treatment and control groups, separately for the WL

and DDA definitions of disability. Note that using these definitions within our balanced

panel, onset or exit can occur at any time between 2006 and 2011, and there is a broadly

equal distribution of such events across this six-year window. Also note we observe fewer

disability exits than disability onsets.

Another potential issue with self-reported disability is justification bias – for a given degree

of disability, benefit recipients may be more likely than others to report themselves as

disabled – which may impart biases to our estimated onset and exit effects in the opposite

direction. The extent to which this is economically important, however, is not clear. Benitez-

Silva et al. (2004), for example, find that self-reported disability status is an unbiased

indicator of DI eligibility decisions. Bound (1991) suggests that justification bias may even

help to cancel out biases due to measurement error. Meyer and Mok (2013) also argue that

some alternative (more objective) measures may themselves be endogenous and are often too

3 The ONS has recently highlighted a discontinuity in the measures of disability in the LFS between 2009 and

2010. This relates to a minor change in the administration of the questionnaire where “I should now like to ask

you a few questions about your health. These questions will help us estimate the number of people in the

country who have health problems” was added to the survey. It is, however, thought to have increased the

prevalence of disability by about 1.5 percentage points.

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narrow, for example excluding conditions such as mental illness or pain which have no

physical marker (also see Benitez-Silva et al., 2004). Like Meyer and Mok (2013), we are

constrained by the absence of objective measures of disability in the LLFS which might

otherwise be used in place of, or to instrument, self-reported measures (e.g. see Disney et al.,

2006; Garcia-Gomez et al., 2013). But we do have a choice of self-reported definitions, and

our focus on the DDA definition rather than the WL definition of disability is intended to

reduce the potential for justification bias (see Oguzoglu, 2012b). We discuss potential biases

further in Section 4.

Benefit Receipt

All respondents are initially asked whether, in the reference week, they claimed any State

Benefits or Tax Credits (including State Pension, Allowances, Child Benefits and National

Insurance Credits). Those who respond positively are then asked ‘Which of the following

type of benefit or Tax Credits were you claiming?’ and are given a long list of options,

including ‘Sickness or disability benefits’; ‘Unemployment related benefits’; ‘Income

Support’; ‘State Pension’; ‘Family related benefits (excluding Child Benefit)’; ‘Child

Benefit’; ‘Housing/Council Tax rebate’; ‘Other’.4 We first generate a binary variable for

claiming any of the benefits listed excluding those who report only universal benefits (Child

Benefit, State Pension or both). This is our broadest, ‘any benefit’, measure and is reported

by 14.74% of the sample. We subsequently generate two narrower binary measures for

‘sickness benefit’ and ‘non-sickness benefit’. The latter is equal to 1 for those who report

‘Unemployment related benefits’, ‘Income Support’, ‘Family related benefits (excluding

Child Benefit)’, ‘Housing/Council Tax rebate’, ‘Other’, and is zero otherwise.

Those in receipt of ‘Sickness or disability benefits’ are asked to list which type of benefit

they claim and the responses include: ‘Incapacity Benefit’; ‘Severe Disablement Allowance’;

‘Statutory Sick Pay’; ‘Disability Living Allowance’; ‘Attendance Allowance’; ‘Industrial

Injuries Disablement Benefit’; and (from 2009) ‘Employment and Support Allowance’. We

exclude ‘Invalid Care Allowance’ (also reported) from our measure of sickness benefit since

this is claimed by a carer and not on the basis of own disability. In other words, our ‘sickness

benefit’ measure covers both income-replacement and additional costs benefits (the latter not

conditioned on being out of work). We also use this additional information to create a final

4 ‘Tax Credits’ is also listed among the full set of options but this is excluded from the measures available in the

APS datasets.

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narrower measure of receipt of IB or ESA which is equal to 1 for those who report receipt of

either benefit but 0 otherwise, and is reported by 4.92% of our sample. The percentage of the

working age population claiming each of the four measures is traced from 2004 to 2012 in

Figure 1. Note the decline in IB and rise in ESA recipients following the 2008 reforms, but

the overall stability of the series for IB or ESA and for sickness benefits, including through

the years of and following the Great Recession. In contrast, note the increase in receipt of

non-sickness benefits (driven primarily by increases in JSA receipt), and as a result in the any

benefit measure, from 2008 to 2009.

Table 2 reports sample proportions in receipt of benefits according to each of the four

measures by wave, split into (DDA) disability onset/exit treatment and control groups as

defined above. Note the stability of these sample proportions throughout for the main control

groups (0000 for onset and 1111 for exit), in contrast to increased (decreased) benefit receipt

for the onset (exit) treatment groups. Also note that for some outcomes benefit receipt rises

(falls) ahead of onset (exit) for the treatment group; although the biggest increase (decrease)

tends to be in the onset (exit) year. The alternative control groups are more similar to the

treatment groups in this respect. We return to this point in the following section. Further, note

that the uptake in IB/ESA for those reporting disability onset is modest; most of those

experiencing disability onset do not receive these benefits in the onset year or the year

following onset. Singleton (2014) similarly shows a relatively modest uptake of DI in the US

for those experiencing WL disability onset. Table 3 reports the proportion of the pooled

2004-2012 LLFS sample in receipt of benefits by employment status. Note that only half (one

quarter) of those describing themselves as DDA disabled receive any non-universal benefit

(IB/ESA). For non-employed DDA disabled – just over half of those reporting themselves as

DDA disabled – the corresponding proportions are 80% and 40% respectively.5

As in the disability case, the fact that these benefit receipt measures are self-reported means

we cannot rule out measurement error in our outcome variables. This seems most likely in

responses to questions about specific benefit types, so may affect the narrower measures

more than the broader ones. Note that the LLFS data for the narrowest IB/ESA measure track

the corresponding administrative data for the actual benefit roll very well over the 2004-2012

period, but at nearly two percentage points lower in all years (see Figure A1). The likeliest

explanation is that IB/ESA Credits Only recipients don’t tick the IB/ESA box because they

5 Tables A2 and A3 present the WL equivalents.

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don’t consider themselves IB/ESA recipients, although they are counted as such in the

administrative data. We may therefore underestimate both disability onset and exit effects on

IB/ESA receipt. For the broader definitions of benefit receipt, it seems reasonable to assume

that measurement error is random, in which case the magnitude of our estimated onset/exit

impacts will be unaffected although they may lose precision.

Control Variables

Like the QLFS the LLFS contains detailed information on personal and employment-related

characteristics using established definitions, measured consistently over time. In what

follows, we condition on a wide set of explanatory variables measured in wave 1, that is, two

years prior to onset (exit) for the treatment group. Following Polidano and Vu (2015) these

variables include age (age squared), gender, highest educational qualification, region of

residence, marital status, presence of dependent children under 16 in the household,

employment status, full-time student status, housing tenure and the local unemployment rate.

In order to further mitigate potential concerns over non-random selection into disability onset

or exit status, we also condition on benefit status in wave 1 and, given individuals may

experience onset in different years, on year first included in the survey to control for time

period effects.

As discussed above, the LLFS does not contain objective information on health. We are able,

however, to examine the sensitivity of our results not only to using different self-reported

measures of disability, but also to the inclusion of measures of self-reported health as

controls. Specifically, we examine sensitivity to conditioning on reporting a long-term health

condition in wave 1 and to reporting prior long-term health problems in wave 1. The relevant

question for the latter is: ‘Have you ever had any health problems or disabilities (apart from

the ones you have just told me about) that have lasted longer than one year?’ Note that the

sample size is reduced when conditioning on prior health because the relevant question is not

asked in proxy interviews.

We are also able to explore heterogeneity in the onset and exit effects by splitting our sample

by gender, age (older and younger defined as above or below age 45), highest qualification

(higher and lower qualifications defined as above or below GCSE grade C), disability type

(main health problem is classified as physical or mental condition at onset), disability severity

(single or multiple health conditions reported at onset), benefit receipt in wave 1, local

economic conditions (proxied by quartiles of the unemployment rate) and onset pre and post

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ESA. Note that the ESA option, although introduced on 27th

October 2008, was only included

in the questionnaire from 2009 onwards.

4. Approach to Estimation and Identification Issues

We want to estimate the impact of disability onset (exit) on a set of benefit receipt outcomes.

We cannot design a random experiment for this purpose, so we have to use observational data

and rely on econometrics to separate out treatment effects from differences in outcomes due

to selection (observable and unobservable differences between those experiencing onset/exit

and others, some of which may be time-invariant and some of which may be time-varying,

and which are themselves associated with benefit outcomes) and reverse causality (benefit

receipt may impact on disability as well as vice versa). Jenkins and Rigg (2004) showed that

individuals who experience disability onset tend to be more disadvantaged prior to onset than

those at risk of but who do not experience disability onset. The opposite is likely to be the

case for disability exit, i.e. exiters are likely to be less disadvantaged than those at risk of but

who do not experience disability exit. One potential mechanism for reverse causality is that

benefit receipt, or more specifically inactivity associated with benefit receipt, directly leads to

deteriorating health (e.g. Lindeboom and Kerkhofs, 2009; Deb et al., 2011; Coleman and

Dave, 2014). Another is justification bias outlined above: that for a given degree of disability,

benefit recipients may be more likely than non-recipients to report disability in order to

rationalise or justify their benefit status.

Following earlier papers including Garcia-Gomez (2011) and Polidano and Vu (2015), we

start by taking a propensity-score-matching (PSM) approach6 to identify disability onset

(exit) impacts separately from compositional differences between those experiencing onset

(exit) and those at risk but not experiencing onset (exit), under a standard conditional

independence assumption (CIA) (see Rosenbaum and Rubin, 1983). Specifically, we take the

sample of DDA 0011s and 0000s for onset (or 1100s and 1111s for exit) and match exactly

on receipt of any benefit in wave 1 and by year in wave 1 before estimating a probit model

for treatment status (disability onset/exit) regressed on an extensive set of wave 1 observables

as set out in the previous section (also see Table 4). For each individual experiencing onset

(exit) we then find the individual with the most similar probability of experiencing onset

(exit) between wave 2 and 3 given their characteristics but who did not do so. Note that

6 This is implemented using the Stata command psmatch2 (see Leuven and Sianesi, 2003).

15

before matching large and statistically significant differences between the treatment and

control groups exist in each case. After matching these differences in sample means disappear

(see Table 4).7 This also holds when using the WL definition of disability in place of the

DDA definition, and when using the alternative control groups (0001 and 1110) in place of

the standard control groups (see Tables A4-A6). Calculating how the treated individuals’

outcomes differ from their matched partners’ outcomes, and averaging these differences over

all treated individuals, yields our initial estimates of the impact of disability onset (exit) on

those who experience it.8 If the CIA holds this is interpretable as the average treatment effect

on the treated, or ATT. We report these differences separately for each of the two years

preceding onset (exit), for the year coinciding with onset (exit), and for the year following

onset (exit). The timing reflects the short LLFS panel and while we are unable to identify

longer-term trends we capture the period when the impact of onset is most pronounced (Mok

et al., 2008; Meyer and Mok, 2013).

How plausible is the (untestable) CIA in this case? Like Garcia-Gomez (2011) and Polidano

and Vu (2015) we can point to the availability of detailed individual characteristics on which

we match. Nevertheless, unobserved confounding factors are still likely to remain. To the

extent that such unobserved confounders are time-invariant (e.g. preferences, history),

however, we can recover the ATT by either exact matching on wave 1 outcomes (following

Garcia-Gomez, 2011) or by combining PSM with difference-in-differences (DID) (following

Garcia-Gomez et al., 2013; Polidano and Vu, 2015).9 Specifically, we difference the

differences in outcomes between the treatment and control groups between waves 1 and 2

(two and one years prior to onset/exit), waves 1 and 3 (two years prior to and the year of

onset/exit), and waves 1 and 4 (two years prior to and the year following onset/exit). Our

DID-PSM estimate of the ATT between wave 1 and the year of onset (exit) is therefore the

difference in differences between wave 1 and wave 3 outcomes for the matched sample. The

DID between wave 2 and 3 outcomes – not examined by Polidano and Vu (2015) – gives our

DID-PSM estimate of the ATT specifically for the onset (exit) year.10

7 There is one exception in each case: a higher proportion of the onset control group are from Rest of Yorkshire

& Humberside (significant at 95%) and a higher proportion of exit control group are from the East of England

(significant at 90%). 8 The analysis is performed over individuals within the region of common support (that is, where there is

overlap between propensity scores within the treatment and control group), so it’s not quite all treated

individuals. 9 This approach was originally proposed by Heckman et al. (1997).

10 An alternative is to additionally exact match on wave 2 outcomes and examine the difference in wave 3

outcomes.

16

Formally, the DID-PSM estimator can be expressed as:

where n denotes the number of individuals within the treatment group which are each denoted

l, whereas those in the control group are denoted j. The time period pre and post treatment is

denoted here as t’ and t respectively, and )( 'ltlt YY measures the change in benefit receipt

between wave 1 (or wave 2) and subsequent waves for treatment individual l. The change in

outcomes in the matched control is generated by weighting the difference in outcomes across

individuals j )( 'jtjt YY .11

Standard errors are calculated following Abadie and Imbens (2006)

and take into account that the propensity scores are estimated.

The DID-PSM approach outlined above, although it partially relaxes the CIA, rests on the

additional assumption of parallel trends between treatment and control groups. This is also

untestable but standard practice is to examine trends prior to treatment to provide some

indication of the assumption’s reasonableness. Given our definition of onset (exit) and the

four-wave length of the panel, our pre-treatment information is limited to waves 1 and 2 in

each case. Nevertheless we can still learn something from these data. We know from Table 2

that benefit receipt in the treatment groups tends to diverge from benefit receipt in the

(standard) pre-match control groups prior to onset/exit, i.e. between waves 1 and 2. The DID-

PSM estimate for the difference in difference in outcomes between waves 1 and 2 (defined

above) gives the post-match equivalent and, from one perspective, can therefore be

interpreted as a test of diverging prior trends. This interpretation assumes that any divergence

in outcomes prior to onset/exit is not itself part of the treatment effect of onset/exit, but

reflects selection on time-varying confounders (e.g. risk of job loss unrelated to health). For

‘shock’ treatments like acute hospitalizations (Lindeboom et al., 2006; Garcia-Gomez et al.,

2013) or heart attacks (Trevisan and Zantomio 2016) this is the most natural interpretation of

pre-treatment divergence in outcomes. For self-reported disability, however, pre-onset (exit)

divergence in outcomes may also reflect a gradual deterioration (improvement) in health

which occurs prior to reporting disability, in which case divergence in outcomes prior to

11

Although we report estimates based on NN1 matching in the main text, we also test sensitivity to other

commonly-used variants of the PSM matching method (see Table A13). The trade-off in selecting a matching

method is that the more neighbours one uses as matching partners, or the wider the gap in propensity scores one

allows, the more likely it is that the resulting estimate is biased because individuals who are not very similar to

each other are being compared; the fewer neighbours one uses or the smaller the gap in propensity scores one

allows, the less precisely the ATT will be estimated.

1l 1j

jtjtltlt YYjlWYYn

DiD ))(,()(1

''

17

reported disability onset (exit) may be more appropriately interpreted as part of the overall

treatment effect (Meyer and Mok, 2013). This may be even more the case for disability exit if

recovery is more gradual and labour market adjustment occurs prior to rather than after

disability exit (see Jones et al., 2013).

More generally this agnosticism about pre-onset effects is common to both PSM-type papers

(Polidano and Vu, 2015) and the individual fixed effects / event study papers of Mok et al.

(2008), Meyer and Mok (2013), Jones et al. (2013) and Singleton (2014), although this is not

always made explicit. Two exceptions are: Charles (2003), which takes an event study

approach but controls for differences in outcome trends at the individual level, albeit

specified over a long time horizon (10 years pre and post reported onset); and Garcia-Gomez

(2011) which exact matches on onset-year outcomes (see below). We adopt the treatment

effects approach here, but we also follow the event study papers in clearly setting out pre-

onset divergence in outcomes and presenting a range of estimates which recover the ATT

under different assumptions about pre-onset (exit) divergence. Specifically, if we assume that

pre-onset (exit) divergence following wave 1 is itself part of the treatment effect, then the

ATT corresponds to the DID-PSM estimates differenced between waves 1 and 3 (the onset

year) and waves 1 and 4 (the year following onset).12

In contrast if we assume pre-onset (exit)

divergence following wave 1 reflects confounding time-varying heterogeneity, then we have

a choice of two DID-PSM estimates depending on whether we assume the diverging trends

will continue beyond onset (exit) or stop at onset (exit). In the former case we can interpret

the DID-PSM estimate which double differences the difference in outcomes ((wave 3-wave

2)-(wave 2-wave 1)) as a conservative estimate of the ATT under the parallel growth

assumption (see Mora and Reggio, 2012).13

In the latter case the difference in differences

between wave 2 and 3 outcomes gives the ATT for the onset year.14

It is unclear (and is

untestable) which assumption is more reasonable, and we present both estimates in what

follows.15

12

In line with existing studies who find no evidence of pre-onset labour market adjustment more than one year

prior to onset (Charles, 2003, Jones et al., 2013) we use two years prior as our base period to maximise the panel

element of analysis. 13

We assume the trends are linear given we only have two waves of data pre-onset (exit). 14

In both cases corresponding estimates can be recovered for outcomes at wave 4. 15

One possible example of a time-varying confounding factor which could lead to ongoing divergence in

outcomes could be ongoing divergence in local labour markets throughout the 2004-2012 period, given the

association between local labour market tightness and benefit recipiency rates. An example of a time-varying

confounder which would likely not lead to ongoing divergence in outcomes, other than through impacts on

disability, could be job loss unrelated to health.

18

Table 2 shows that the standard parallel trends assumption looks much more defensible for

our alternative control groups for onset (0001) and exit (1110), i.e. those who do experience

onset or exit but one year later than those in the corresponding treatment groups. This is

confirmed by the post-match results presented in the following section. We therefore also

present DID-PSM estimates using these alternative control groups. In this case the DID-PSM

estimate of the ATT for the onset (exit) year is the difference in differences between wave 1

and wave 3 outcomes for the matched sample, which under the assumption of parallel trends

is equivalent to the difference in differences between wave 2 and 3 outcomes. The trade-off

in this case is increased chance of measurement error (classification into the control group is

based on disability onset/exit observed in a single year), that we wash out effects of gradual

deterioration (improvement) in health associated with but occurring in advance of disability

onset (exit), and the fact that it is not meaningful to consider differences in outcomes in wave

4.

Whichever estimator from the above discussion is considered, reverse causality within

onset/exit year remains a potential threat to identification. Specifically, given the annual wave

structure of the LLFS, we know only that onset / exit takes place between wave 2 plus one

day, and wave 3. Similarly, we do not know the benefit inflow date for those not receiving

benefits at wave 2 but receiving benefits at wave 3, and similarly for benefit outflows. If there

is reverse causality, either directly or as a result of justification bias, then if benefit receipt

(exit) precedes disability onset (exit) within year, we would expect estimated treatment

effects to be biased upwards in magnitude. This is not always explicitly discussed in the

papers cited above. An exception is Garcia-Gomez (2011) which uses exact matching on

onset year outcomes and only examines outcomes in the year after onset. The trade-off is that

you learn nothing of onset-year effects, and because you drop those with onset-year effects

the results end up being less generalizable.16

We report estimates from a Garcia-Gomez

(2011) style exact matching approach in the appendix. In the main text, however, we report

estimates under the assumption that any such reverse causality is negligible. In defence of

this assumption we are using a definition of disability (DDA, consistent onset/exit over two

years) which will reduce the scope for justification bias (see Charles 2003 on the latter point)

and we are looking at outcomes over a relatively short duration during which large direct

reverse causality effects are unlikely to have accumulated. Exact matching on benefit receipt

in wave 1 helps to reduce the scope for reverse causality other than that arsing within year.

16

In our application we would also need to match separately for each outcome.

19

Finally, in order to examine whether and how disability onset/exit impacts differently across

different groups of individuals and in different labour market and policy contexts, we split the

sample along a number of different dimensions prior to the matching procedure, using the

standard 0011 and 1100 control groups, and repeat. For conciseness we present just two

estimates in each case: the DID-PSM estimates for wave 3 minus wave 1 and for wave 3

minus wave 2 which include and exclude pre-treatment effects respectively. Likewise we do

not present separate balancing tests or separate discussion of identifying assumptions for the

estimates of heterogeneous effects.

5. The Benefit Recipiency Impacts of Disability Onset

Post-matching estimates of DDA disability onset treatment effects are reported in Table 5.

For each of the benefit measures we first present PSM estimates for the difference between

treatment and control group recipiency rates in each wave. Under the standard CIA, and

assuming negligible reverse causality within year, the wave 3 estimates give the ATTs in the

year of onset and the wave 4 estimates give the ATTs one year on. For all four outcome

measures the wave 3 estimates suggest large positive disability onset effects in the year of

onset, all of which are statistically significant at the 99% level. For IB/ESA and our more

general sickness benefits measures, those experiencing disability onset have wave 3 benefit

recipiency rates of 6.4% and 9.7% respectively, against control group recipiency rates that are

essentially zero or close to zero. For the any benefit measure those experiencing disability

onset have a recipiency rate almost double that for the controls (20.9% versus 11.5%). For

non-sickness benefits the difference (3.7%) is much smaller both in absolute magnitude and

relative to the wave 3 recipiency rate for the control group (10.2%), but still shows that

disability onset is associated with increased flows onto or decreased flows off non-sickness

benefits. Where we have outcome measures in common our findings are qualitatively

consistent with those of Jenkins and Rigg (2004), Singleton (2014) and Polidano and Vu

(2015), with effects of the same order of magnitude. The wave 4 estimates show further flows

onto IB/ESA and more generally onto sickness benefits over the subsequent year, but no

further change in overall benefit recipiency or receipt of non-sickness benefits. In other words

the additional inflows to disability benefits come from within the benefit system rather than

from outside the benefit system. That people with disability in Britain (and elsewhere)

sometimes move on to disability benefits following a spell on other benefits is well known

(e.g. Beatty et al, 2000; Sissons et al., 2011; Beatty and Fothergill, 2015). What we show

20

here, however, is that in the year after the onset year itself, all net flows onto disability

benefits appear to come from existing benefit claimants.

For one of the four outcomes (IB/ESA) there is a statistically significant difference between

the treatment and control groups even in wave 1, which makes us question the standard CIA.

Therefore Table 5 also presents the range of DID-PSM estimates discussed in the previous

section, under the weaker CIA assumption. First consider the difference between wave 1 and

2 recipiency rates. For all four outcome measures the gap between the treatments and controls

grows between waves 1 and 2, although the magnitude of this effect is small and in only one

case – sickness benefits – is this divergence statistically significant at the 95% level. The lack

of evidence for diverging trends suggests we can interpret the DID-PSM estimates

differencing waves 3 and 1 and waves 4 and 1 as ATTs, subject to the weaker CIA and

assuming negligible within-year reverse causality, at least for three of the four benefit

measures. In the light of this we interpret the pre-onset divergence for sickness benefits as

reflecting the effects of declining health in advance of reported disability, e.g. on receipt of

SSP, in which case we can similarly interpret the DID-PSM estimates differencing waves 3

and 1 and waves 4 and 1 as ATTs. For three of the four measures these estimates show large

and highly statistically significant treatment effects in the year of onset, increasing benefit

recipiency by 9.3 percentage points (any benefits), 8 percentage points (sickness benefits) and

5 percentage points (IB/ESA) respectively. The partial exception is for receipt of non-

sickness benefits, on which the impact of disability onset is relatively small (+3.8 percentage

points), although still statistically significant at 99%. The positive sign suggests that net flows

onto non-sickness benefits associated with disability onset exceed flows from non-sickness to

sickness benefits associated with disability onset. The DID-PSM estimates for wave 4-1 are

in line with the straight PSM estimates discussed above.

In terms of robustness, we still get positive, large (for three out of the four measures) and

statistically significant treatment effect estimates if we assume any divergence between

waves 1 and 2 is the result of time-varying confounders that do not drive ongoing divergence

post-onset (DID-PSM wave 3-2), although these estimates are generally smaller in

magnitude. If we assume all prior divergence is driven by confounders and that this

divergence would continue linearly post-onset (DID-PSM waves ((3-2)-(2-1)), then for all

four measures we obtain estimated treatment effects with positive signs, although they are

further reduced in magnitude than is the case under the more lenient assumptions, and only

for IB/ESA is the effect statistically significant at 95%.

21

If instead we compare the onset treatment group to the alternative control group then we can

reject diverging prior trends for all four outcome measures17

, and three of the four DID-PSM

estimates differencing wave 3 and 1 suggest positive, moderately sized or large treatment

effects which are statistically significant at 95% (see Table 6). The exception is non-sickness

benefits, for which the estimated treatment effect is zero according to all estimates, perhaps

casting some doubt on our earlier interpretation that net flows onto non-sickness benefits

associated with disability onset exceed flows from non-sickness to sickness benefits

associated with disability onset. Estimates are also robust to matching method and

additionally matching on prior health (see Tables A13 and A14).

To summarise, the range of estimates under the various assumptions discussed above suggest

that disability onset increases the probability of receipt of any benefit in the year of onset by

between 4.1 and 9.3 percentage points; receipt of any sickness benefit by between 3.0 and 8.0

percentage points; and receipt of IB/ESA by between 3.4 and 5.0 percentage points. Impacts

on non-sickness benefits are smaller and only significant under certain assumptions.

For the WL definition of disability, which is likely to be more closely linked with work-

contingent benefit outcomes, but also more susceptible to justification bias, all four outcome

measures show divergence between waves 1 and 2 which is statistically significant at 95%

(see Table A6). Estimated onset impacts that difference wave 3 and wave 1 outcomes are

larger partly as a result. So too are estimates that difference wave 3 and wave 2 outcomes,

and for three of the four outcomes, the estimates the difference wave ((3-2)-(2-1)) outcomes.

Again the exception is non-sickness benefits for which the DID-PSM estimate identified

under the parallel growth assumption is again zero. There are no such diverging prior trends

when the WL onset group are compared to the alternative WL control group, and in that case

estimated treatment effects are positive and statistically significant for all four benefit receipt

measures (see Table A7).

Finally, interpreting any of the estimates above as an ATT rests on the assumption that within

year reverse causality is negligible. We can get some indication of the reasonableness of this

assumption by following the approach of Garcia-Gomez (2011) and exact matching

additionally on outcomes in the onset year, thereby ensuring that disability onset precedes

any change in benefit status. Table A16 presents the resulting wave 4 PSM estimate for our

17

Again it is sickness benefits for which diverging prior trends looks more difficult to reject, consistent with

deterioration in health occurring in the year prior to reported disability onset.

22

four outcome measures, for both DDA and WL definitions of disability. For all four benefit

receipt measures the estimated onset effect remains positive, large and statistically significant

at 95%. Reverse causality does not drive our estimates.

5.1. Heterogeneous impacts of disability onset

In this section we discuss evidence for heterogeneous effects of disability onset by gender,

qualification level, age, broad disability type (mental/physical), severity (single/multiple

conditions), pre/post-2009, by local labour market unemployment rate quartile, by

employment status, by benefit status, by reporting a long-term health condition, and finally

by reporting a prior long-term health condition. Table 7 presents two estimates in each case:

the DID-PSM estimates for the change in each of the four measures of benefit between waves

1 and 3 and the equivalent estimates for the change in outcomes between waves 2 and 3. The

equivalent estimates using the WL definition of disability are reported in Table A8. The

results are robust to choice of disability definition.

We find big differences in onset effects by qualification level, type of disability, severity of

disability, and whether the individual reports a long term health condition in wave 1. We

discuss each in turn below.

First, consistent with Polidano and Vu (2015), we find a bigger impact of disability onset on

benefit recipiency for those with low qualifications than for those with higher qualifications.

A variety of factors are likely to contribute to this differential effect, including higher benefit

replacement rates for lower qualified workers (a labour supply effect), less ineligibility on

means-testing grounds e.g. related to partner income (an institutional effect), and the multiple

disadvantage of having a disability and few qualifications in labour markets at below full

employment (a labour demand effect).

Second, disability is not homogenous (Jones, 2011), and we find that the benefit impact of

onset of a mental health disability is much bigger than for the onset of a physical disability,

albeit our analysis of mental health disability onsets is based on a relatively small sample.18

That those with mental health impairments account for an increasing share of disability

benefit recipients in both the UK and elsewhere is well known (e.g. see Banks et al., 2015;

Burkhauser et al., 2014). In part this reflects increased prevalence or at least diagnosis and/or

18

That mental health impairment is more strongly associated with labour market outcomes than physical

impairments has also been reported in the wider cross-section literature on disability and labour markets (e.g.

see Kidd et al., 2000).

23

reporting of such conditions (e.g. Moncrieff and Pomerleau, 2000). Indeed, coupling the

increasing prevalence of such impairments with their larger benefit onset effects has

significant implications for disability benefit roll growth. We also find bigger onset effects on

benefit recipiency for those reporting multiple conditions than for those reporting a single

condition, which we interpret as a proxy for disability severity. There is also a very clear

contrast in disability onset effects by the pre-onset existence of a long-term health condition,

with much bigger impacts for those not reporting a long-term health condition in wave 1 than

for those reporting such a condition. This suggests that gradual disability onset has a lesser

impact on benefit outcomes than sudden onset, perhaps because it is easier to accommodate

the gradual onset of a disabling condition in work than a sudden onset condition.

In contrast, we find only small differences in onset effects by gender, age, local

unemployment, wave 1 employment status, and the presence of a long term health condition

prior to wave 1. Although it does appear that disability onset has a bigger effect on receipt of

non-sickness benefits in high unemployment areas than in low unemployment areas, the

mostly ‘null’ finding with regards to local unemployment rates is perhaps surprising given

evidence presented elsewhere of the impact of labour market context on disability benefit

claiming (e.g. Black et al., 2002; Autor and Duggan, 2003; McVicar, 2006). Similarly, the

mostly ‘null’ finding with regards to wave 1 employment status is also perhaps surprising

given evidence of big differences in employment effects of disability onset by prior

employment status found by Polidano and Vu (2015).

Our final sample split is for onset pre and post-2009, i.e. onset under the old IB regime in

place up to the end of October 2008 compared to onset under the new ESA regime in place

subsequently. In doing so we provide a within-country parallel to the cross-country study of

Garcia-Gomez (2011) who finds bigger employment impacts of negative health shocks in

countries where disability benefits are more generous and conditioned on not working. There

are also several examples in the wider disability benefits literature where benefit reforms

reducing payments, increasing the stringency of medical screening, and/or conditioning on

work-related activity – all of which are aspects of the shift from IB to ESA – have impacted

on program growth in the desired direction (e.g. Gruber, 2000; Adam et al., 2010; Staubli,

2011; de Jong et al., 2011). Having said that there are also contrasting findings where such

reforms appear to have had little impact (e.g. Campolieti, 2004; Karlstrom et al., 2008). The

estimates presented in Table 7, however, are consistent with Garcia-Gomez (2011) and the

former set of studies, i.e. we find bigger disability onset impacts on benefit recipiency under

24

the pre-reform regime than under the post-reform regime, with the biggest differences

unsurprisingly for the sickness benefits and IB/ESA measures. There is no apparent

countervailing positive impact of the reforms on receipt of non-sickness benefits which,

consistent with Banks et al., (2015), suggests only limited displacement onto non-sickness

benefits of those who might otherwise have claimed sickness and disability benefits. Of

course the introduction of ESA approximately coincided with the Great Recession – from

2007Q4 to 2009Q1 in the UK – and the post-recession labour market through to 2012 was

slacker than the pre-recession labour market from 2004. If anything, however, this would lead

us to underestimate the impact of the reform on the benefit receipt effects of disability onset

because we would expect higher recipiency rates across all income-replacement and means-

tested benefits in the 2009-2012 period than in the 2004-2008 period.19

6. The Benefit Recipiency Impacts of Disability Exit

Post-matching estimates of DDA disability exit treatment effects are reported in Table 8. As

in the case of onset, for each of the benefit measures we first present PSM estimates for the

difference between treatment and control group recipiency rates in each wave. Under the

standard CIA, and assuming negligible within-year reverse causality, the wave 3 estimates

give the ATTs in the year of exit and the wave 4 estimates give the ATTs one year on. For

three of the four outcome measures the wave 3 estimates suggest large negative disability exit

effects in the year of exit, all of which are statistically significant at the 99% level. Contrast

this with Jones et al. (2013) which finds no impact of disability exit on employment. For

IB/ESA and our more general sickness benefits measures, those experiencing disability exit

have wave 3 benefit recipiency rates of 3.5% and 6.1% respectively, against control group

recipiency rates of 21.7% and 31.2% respectively. For the any benefit measure those

experiencing disability exit have a recipiency rate half that for the controls (19.7% versus

37.9%). For these three outcomes, the gap in recipiency between the treatment and control

groups also grows between waves 3 and 4, in each case because recipiency increases for the

control group (likely reflecting further deterioration in health and falling income from other

sources) while it stays essentially flat for the treatment group. The suggestion is that receipt

of disability benefits may not be an absorbing state in the UK for all recipients: many people

19

On the other hand, the discontinuity in disability measurement between 2009 and 2010 referred to in footnote

3 may lead us to over-estimate the difference between pre-2008 and post-2008 if the latter period includes those

with less severe disabilities under the DDA definition than was the case prior to the discontinuity.

25

recover from temporary disabilities and flow off disability and sickness benefits; although

others do remain on IB/ESA one to two years after disability exit. Note there is no apparent

impact of disability exit on the recipiency rate for non-sickness benefits, either in wave 3 or

wave 4. If disability exit leads to moves from sickness to non-sickness benefits, then this

appears to be balanced out by moves off (or fewer moves onto) non-sickness benefits.

For three of the four outcomes there is a statistically significant difference between the

treatment and control groups even in wave 1, which again makes us question the standard

CIA. Table 8 therefore also presents the range of DID-PSM estimates discussed in Section 4.

First consider the difference between wave 1 and 2 recipiency rates. For all four outcome

measures the gap between the treatments and controls grows between waves 1 and 2, and in

contrast to the onset case the magnitude of this effect is large, and in all four cases

statistically significant at the 99% level. If we are prepared to interpret this pre-exit

divergence as part of the treatment, e.g. as a result of gradually improving health which leads

to reported disability exit once some subjective threshold is met, then DID-PSM estimates

differencing waves 3 and 1 and waves 4 and 1 can be interpreted as ATTs, subject to the

weaker CIA and assuming negligible within-year reverse causality. All are negative, large

and statistically significant at 99%.

Given the extent of pre-exit divergence, however, we cannot ignore the possibility that at

least some of this divergence is being driven by time-varying confounders. This points us

away from the initial DID-PSM estimates (and the PSM estimates discussed above) towards

the estimates differencing waves 3 and 2 (if we assume any divergence between waves 1 and

2 is the result of time-varying confounders that do not drive ongoing divergence post-onset)

or the estimates differencing waves ((3-2)-(2-1)) (if we assume confounding effects would

continue linearly post-onset). Whichever of these two assumptions we make, the evidence for

disability exit effects on benefit recipiency appears fragile. The estimates differencing waves

3 and 2 are all negative – suggesting disability exit is associated with lower benefit recipiency

rates in the year of exit – but none are large in magnitude and only one (sickness benefits) is

statistically significant at 95%. The second set of estimates are all positive, suggesting a fall

in the rate of divergence in benefit recipiency rates between the treatment and control groups

following disability exit, although again only one (any benefit) is statistically significant at

95%. These results point to the potential existence of long-lasting effects of temporary

disability, e.g. through impacts on human capital accumulation and through state dependence,

in line with those argued for labour market outcomes by Charles (2003), Mok et al. (2008),

26

Oguzoglu (2012a) and Meyer and Mok (2013). They are also more in line with the Jones et

al. (2013) zero impact of disability exit on employment. Note, however, that this conclusion

stems from the particular interpretation of the pre-treatment effects as being driven by time-

varying confounders and not by gradual improvements in health.

The evidence for exit effects continues to look fragile if we compare the exit treatment group

to the alternative control group, for which we can reject diverging prior trends for all four

outcome measures. Here both the DID-PSM estimates differencing waves 3 and 1 and

differencing waves 3 and 2 suggest large, negative treatment effects, which are statistically

significant at 95%, for the any benefits and non-sickness benefits measures (see Table 9). In

contrast, estimated exit impacts on sickness benefit recipiency and on the narrower IB/ESA

measure are small, of inconsistent sign, and statistically insignificant. Even if these are under-

estimates of the impact of disability exit (because they wash out the effect of gradual

improvements in health for exiters), again there is little here to support a conclusion that

disability exit per se has any impact on disability benefit receipt. Although it is not robust, the

non-sickness benefit result suggests that the impact of disability exit on benefit receipt is not

for those on disability benefits but for those who either did not apply for such benefits, who

applied but were denied such benefits, or who had moved off such benefits in advance of

reporting disability exit. We have no way of untangling the underlying reason for this

contrast here, but it is consistent with differences in exit effects by severity of disability (we

return to this below) and/or differences in exit effects by distance from the labour market.

As for disability onset, estimates of exit effects are robust to matching method (see Table

A15). The WL definition of disability, which is closer to eligibility criteria for IB/ESA but

may also be more susceptible to justification bias, similarly suggests diverging prior trends

and zero estimates according to the DID-PSM estimates which differences waves ((3-2)-(2-

1)). DID-PSM estimates differencing waves 3 and 2, however, suggest large, negative exit

effects for all but non-sickness benefits (see Table A10) as do estimates comparing WL

exiters to the alternative control group (Table A11). Again it appears difficult to draw any

firm conclusions regarding the benefit receipt impacts of disability exit from these data.

Finally, interpreting any of the estimates above as an ATT rests on the assumption that

reverse causality, specifically within-year reverse causality, is negligible. As for onset, we

can get some indication of the reasonableness of this assumption by following the approach

of Garcia-Gomez (2011) and exact matching additionally on outcomes in the exit year,

27

thereby ensuring that disability exit precedes any change in benefit status. Table A16 presents

the resulting wave 4 PSM estimate for our four outcome measures, for both DDA and WL

definitions of disability. For all four benefit receipt measures the estimated exit effect remains

positive, large and statistically significant at 95%. Reverse causality does not drive our

estimates.

6.1. Heterogeneous impacts of disability exit

As for disability onset, we split the sample along a number of dimensions to examine

heterogeneity in the benefit recipiency impacts of disability exit. The DID-PSM estimates

differencing waves 3 and 1 and waves 3 and 2 are reported in Table 10. The equivalent

estimates using the WL definition of disability are reported in Table A12. In contrast to the

estimates of heterogeneous disability onset effects, however, many of our estimates of

disability exit effects by subgroup appear sensitive to disability definition. Even just focusing

on the DDA definition, smaller sample sizes for the exit analysis means fewer differences are

statistically significant.

For onset we found large differences in benefit receipt effects by qualification level (bigger

effects for less qualified), type of disability (bigger effects for mental health) and severity of

disability (effects increasing with severity). For exit the picture is less clear. We might expect

higher benefit replacement rates and lower chances of finding work for lower qualified

workers to make the lower qualified less responsive to disability exit in terms of benefit

receipt. For three out of four outcomes (not IB or ESA), however, we find equal or bigger

exit effects (in absolute terms) on benefit claiming for those with low qualifications

compared to those with higher qualifications, which appear robust to disability definition at

least in direction. As for onset we also find bigger (absolute) exit effects on sickness benefit

and IB/ESA benefit receipt for those whose disability is related to mental health but we find

no robust differences in exit effects by disability severity.

In partial contrast to onset effects which appeared similar by age, we do find bigger disability

exit effects for younger compared to older exiters, although the differences are mostly not

large in magnitude. There are also no clear, robust differences in exit effects by local

unemployment rate quartile.

28

7. Conclusions

There is consensus in the dynamics of disability and labour market outcomes literature that

disability onset leads to a decline in employment and earnings and an increase in applications

for and receipt of disability insurance. But important questions remain. How does disability

onset impact on welfare benefit receipt beyond disability insurance? How does disability exit

impact on benefit receipt? To what extent do these relationships vary for individuals with

different socio-economic characteristics, in different labour-market contexts, and under

different disability benefit regimes? This paper addresses these questions exploiting rarely-

used longitudinal data constructed from the UK Local Labour Force Survey, combining PSM

with DID methods to draw plausibly causal inferences under alternative but explicit

assumptions.

We first show that disability does not imply benefit receipt. Many individuals with disability

work and few among them claim welfare benefits. Many of those reporting disability who do

not work also do not claim benefits, and fewer than half receive IB/ESA (the DI equivalent).

Having said that we show that disability onset (robustly) increases receipt of sickness and

disability benefits within one year, although the impact on non-sickness benefits is small and

non-robust. Onset effects are larger for individuals with lower qualification levels, for those

experiencing onset of disability linked to mental health conditions or more severe disability

onset, and for those under a disability benefit regime with less rigorous screening and

conditionality. This suggests a number of policy implications. First, disability benefit

characteristics – in this case the differences between IB and ESA – matter. Second,

interventions aimed at keeping more of those who experience disability onset (or whose

health gradually declines in advance of disability onset) in employment would help to reduce

inflows not only to disability benefits but also to other benefits, given that many individuals

take an indirect route to disability benefits via other benefits, or remain on such benefits after

disability onset. Third, assistance to help overcome disability–related barriers to employment

is likely needed across multiple benefit payments. Fourth, the estimates of heterogeneous

disability onset effects suggest targeting interventions at the low-skilled and those

experiencing onset of disability linked to mental health conditions or onset of multiple

conditions.

The evidence on disability exit effects presented here is more mixed: some estimates suggest

negative impacts on benefit receipt for some benefit measures; others suggest zero impacts.

29

Arguably the most convincing exit estimates presented here – comparing those who report

disability exit with those who report disability exit one year later – suggest exit itself does not

impact on receipt of sickness and disability benefits up to two years on. The existence of a

sizeable pre-exit decline in sickness benefit receipt among those who exit is, however, a

possible reflection of gradual recovery from disability and is consistent with the possibility

that labour market adjustment occurs pre-exit (Jones et al., 2013). There are also few clear

differences in estimated disability exit effects by individual characteristics or labour market

context.

Whilst we acknowledge that exiters are unlikely to representative of all those who experience

disability onset, the apparent asymmetry between the benefit receipt impacts of disability

onset and disability exit provides tentative evidence that disability benefits do have some

‘absorbing state’ characteristics in Britain, and further (indirect) evidence that even

temporary disability can have long lasting economic effects. Two explanations for such long

term effects of temporary disability put forward in the literature on disability and labour

market outcomes are declining human capital and state dependence. There are several

potential state-dependence type explanations for what we observe in this paper, albeit that we

cannot untangle here. First, some of those reporting disability exit may still qualify for

disability benefits even if rescreened immediately following the change in their self-reported

disability status. Second, some of those reporting disability exit in survey data may not report

disability exit to benefit administrators, or may delay doing so, and in the absence of regular

systematic rescreening, may therefore remain in receipt of such benefits. Third, even those

reporting changes in disability status may not be rescreened, or may not be rescreened

quickly.20

Human capital depreciation alone might suggest disability exit leading to

wholesale switching from disability benefits to other benefits, which we do not find here.

There is, however, an alternative interpretation, if one is willing to consider pre-exit effects as

part of the treatment itself, that is, the results reflect gradual withdrawal from welfare support

prior to rather than at disability exit.

Despite the insights afforded by these data we are unable to explore the alternative

interpretation of our findings in relation to disability exit without panel data spanning a

longer period. There are also limitations as to the representativeness of our sample which

limit the extent to which these results are fully generalisable even within the UK. These latter

20

Of course screening is costly and in recent years the UK has a rather poor track record in terms of screening

recommendations overturned on appeal.

30

points also apply to our conclusions regarding disability onset. While longitudinal

administrative data on benefit records exist and would overcome at least some of these issues,

such analysis would require that these data are linked to survey or medical data on health

which is not currently permitted in the UK.

31

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35

Figure 1: Proportion reporting receipt of welfare benefits, 2004-2012

Notes: LLFS working-age population (2004-2012).

0

.05

.1.1

5.2

Pro

po

rtio

n

2004 2005 2006 2007 2008 2009 2010 2011 2012

Year

Any benefit Non-sickness benefit

Sickness benefit IB or ESA

IB ESA

36

Table 1: Disability Onset and Exit Treatment and Control Groups

DDA WL

N % N %

Onset Control (0000) 33,216 67.74 35,486 72.37

Control (0001) 1,730 3.53 1,346 2.75

Treatment (0011) 905 1.85 596 1.22

Exit Treatment (1100) 420 0.86 431 0.88

Control (1111) 4,762 9.71 4,703 9.59

Control (1110) 579 1.18 486 0.99

Other 7,427 15.13 5,991 12.20 Notes: Sample is restricted to a balanced panel sample with a minimum of 4 waves within the LLFS. Onset and

exit groups are defined using a two wave definition of consistent disability onset or exit.

37

Table 2: Proportions receiving welfare benefit by wave and treatment status, DDA

disability

Any Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.058 0.064 0.064 0.063

Control (0001) 0.089 0.105 0.123 0.146

Treatment 0.135 0.158 0.211 0.232

Exit Treatment 0.267 0.242 0.196 0.200

Control (1110) 0.380 0.389 0.384 0.346

Control (1111) 0.700 0.708 0.724 0.724

Non-sickness Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.050 0.055 0.054 0.053

Control (0001) 0.075 0.084 0.095 0.098

Treatment 0.110 0.128 0.138 0.144

Exit Treatment 0.192 0.171 0.162 0.154

Control (1110) 0.209 0.218 0.237 0.223

Control (1111) 0.351 0.372 0.384 0.383

Sickness Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.007 0.008 0.007 0.008

Control (0001) 0.017 0.023 0.030 0.066

Treatment 0.023 0.039 0.099 0.132

Exit Treatment 0.113 0.093 0.051 0.068

Control (1110) 0.274 0.263 0.255 0.204

Control (1111) 0.589 0.608 0.625 0.633

IB or ESA Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.001 0.001 0.001 0.002

Control (0001) 0.003 0.007 0.016 0.036

Treatment 0.015 0.021 0.064 0.083

Exit Treatment 0.068 0.064 0.029 0.034

Control (1110) 0.165 0.182 0.169 0.123

Control (1111) 0.390 0.414 0.417 0.420 Notes: See notes to Table 1.

38

Table 3: Benefit Receipt by DDA Disability and Employment Status

Disabled Non-disabled

All Employed Non-

employed

All Employed Non-

employed

Any Benefits 50.6 11.9 79.7 9.5 3.1 33.4

Non-sickness Benefit 30.3 5.1 49.3 8.4 2.5 30.4

Sickness Benefit 37.3 7.9 59.5 1.2 0.5 3.8

IB or ESA 24.1 2.3 40.4 0.4 0.1 2.0

N 137,508 58,966 78,542 647,219 510,056 137,163 Notes: LLFS working-age population (2004-2012).

39

Table 4: Descriptive Statistics for Explanatory Variables by Control and Treatment

Groups

Onset Exit

Treatment

(0011)

Control

(0000)

(pre-

matching)

Control

(0000)

(post-

matching)

Treatment

(1100)

Control

(1111)

(pre-

matching)

Control

(1111)

(post-

matching)

Age 45.791 41.158*** 45.576 45.509 48.085*** 45.402

Gender

Male 0.495 0.476 0.474 0.497 0.515 0.506

Highest

qualification

Degree 0.192 0.236*** 0.182 0.116 0.082** 0.127

Other Higher

Education 0.123 0.113 0.098 0.116 0.081** 0.107

A level 0.218 0.231 0.231 0.223 0.185* 0.220

O level 0.193 0.227** 0.201 0.243 0.181*** 0.254

Other 0.112 0.086** 0.102 0.095 0.135** 0.121

None 0.163 0.107*** 0.185 0.208 0.335*** 0.171

Students

Full-time student 0.026 0.063*** 0.018 0.038 0.021** 0.020

Marital Status

Single 0.235 0.280*** 0.215 0.217 0.239 0.228

Married 0.618 0.613 0.640 0.613 0.539*** 0.621

Widowed/divorced 0.147 0.107*** 0.144 0.171 0.222** 0.150

Children

Dependent child in

household 0.316 0.442*** 0.329 0.309 0.224*** 0.286

Housing Tenure

Owned outright 0.238 0.213* 0.230 0.257 0.234 0.266

Mortgaged 0.549 0.651*** 0.522 0.494 0.352*** 0.503

Rented 0.214 0.137*** 0.248 0.249 0.414*** 0.231

Region

Tyne and Wear 0.021 0.030 0.029 0.032 0.037 0.035

Rest of North East 0.052 0.045 0.054 0.066 0.056 0.066

Greater Manchester 0.072 0.066 0.076 0.052 0.072 0.078

Merseyside 0.049 0.033** 0.047 0.029 0.043 0.029

Rest of North West 0.033 0.033 0.020 0.026 0.036 0.017

South Yorkshire 0.022 0.017 0.017 0.026 0.026 0.029

West Yorkshire 0.013 0.013 0.016 0.012 0.016 0.006

Rest of Yorkshire &

Humberside 0.028 0.033 0.047** 0.020 0.029 0.014

East Midlands 0.024 0.019 0.033 0.026 0.018 0.020

West Midlands

Metropolitan county 0.035 0.040 0.039 0.032 0.035 0.014

Rest of West

Midlands 0.030 0.031 0.033 0.017 0.028 0.023

East of England 0.041 0.030* 0.033 0.035 0.024 0.066*

Inner London 0.021 0.022 0.017 0.026 0.020 0.012

Outer London 0.029 0.030 0.030 0.020 0.021 0.020

South East 0.067 0.089** 0.080 0.069 0.052 0.052

South West 0.076 0.067 0.066 0.038 0.055 0.032

Wales 0.171 0.168 0.156 0.194 0.220 0.208

40

Strathclyde 0.094 0.101 0.077 0.113 0.096 0.116

Rest of Scotland 0.122 0.134 0.130 0.168 0.116*** 0.162

Economic

Conditions

Employed 0.751 0.833*** 0.752 0.668 0.269*** 0.671

Local area

unemployment rate 0.065 0.063*** 0.066 0.065 0.067* 0.064

Year of observation

2004 0.142 0.181*** 0.142 0.153 0.166 0.153

2005 0.133 0.172*** 0.133 0.199 0.158** 0.199

2006 0.199 0.186 0.199 0.171 0.186 0.171

2007 0.158 0.174 0.158 0.165 0.169 0.165

2008 0.199 0.146*** 0.199 0.159 0.166 0.159

2009 0.169 0.141** 0.169 0.153 0.156 0.153

Any benefit 0.134 0.058*** 0.134 0.269 0.706*** 0.269

N 762 27,762 737 346 4,173 303 Notes: All characteristics are measured at Wave 1. Treatment is defined by consistent onset/exit of DDA

disability and is estimated using a NN(1) matching algorithm. *,**,*** denote significance from the treatment

group at the 10%, 5% and 1% level respectively.

41

Table 5: DID-PSM, DDA Disability Onset Treatment Effects: Proportions Receiving

Benefits

Any Benefit

Onset Control (0000) Difference T stat

Wave 1 0.134 0.134 0.000 0.00

Wave 2 0.151 0.125 0.026 1.91

Wave 3 0.209 0.115 0.093 5.69

Wave 4 0.223 0.127 0.096 5.53

Difference (2-1) 0.017 -0.009 0.026 1.91

Difference (3-1) 0.075 -0.018 0.093 5.69

Difference (4-1) 0.089 -0.007 0.096 5.53

Difference (3-2) 0.058 -0.009 0.067 4.11

Difference (3-2)-(2-1) 0.041 0.000 0.041 1.61

Non-sickness Benefit

Onset Control (0000) Difference T stat

Wave 1 0.110 0.112 -0.001 -0.21

Wave 2 0.119 0.115 0.004 0.30

Wave 3 0.139 0.102 0.037 2.59

Wave 4 0.142 0.108 0.034 2.32

Difference (2-1) 0.009 0.004 0.005 0.39

Difference (3-1) 0.029 -0.009 0.038 2.61

Difference (4-1) 0.031 -0.004 0.035 2.38

Difference (3-2) 0.020 -0.013 0.033 2.25

Difference (3-2)-(2-1) 0.011 -0.171 0.028 1.16

Sickness Benefit

Onset Control (0000) Difference T stat

Wave 1 0.021 0.020 0.001 0.19

Wave 2 0.038 0.012 0.026 3.31

Wave 3 0.097 0.016 0.081 7.21

Wave 4 0.126 0.017 0.109 9.22

Difference (2-1) 0.017 -0.008 0.025 2.69

Difference (3-1) 0.076 -0.004 0.080 6.60

Difference (4-1) 0.105 -0.003 0.108 8.22

Difference (3-2) 0.059 0.004 0.055 4.87

Difference (3-2)-(2-1) 0.042 0.012 0.030 1.80

IB or ESA

Onset Control (0000) Difference T stat

Wave 1 0.014 0.004 0.010 2.41

Wave 2 0.021 0.003 0.018 3.45

Wave 3 0.064 0.004 0.060 6.86

Wave 4 0.083 0.003 0.080 8.34

Difference (2-1) 0.007 -0.001 0.008 1.36

Difference (3-1) 0.050 0.000 0.050 5.32

Difference (4-1) 0.068 -0.001 0.070 6.87

Difference (3-2) 0.043 0.001 0.042 4.85

Difference (3-2)-(2-1) 0.037 0.003 0.034 3.00 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

42

Table 6: DID-PSM, DDA Disability Onset Treatment Effects: Proportions Receiving

Benefits, Alternative Control Group

Any Benefit

Onset Control (0001) Difference T stat

Wave 1 0.134 0.134 0.000 0.00

Wave 2 0.151 0.147 0.004 0.23

Wave 3 0.209 0.170 0.039 1.98

Difference (2-1) 0.017 0.013 0.004 0.23

Difference (3-1) 0.075 0.035 0.039 1.98

Difference (3-2) 0.058 0.022 0.035 1.74

Non-sickness Benefit

Onset Control (0001) Difference T stat

Wave 1 0.110 0.118 -0.008 -0.93

Wave 2 0.119 0.122 -0.003 -0.16

Wave 3 0.139 0.139 0.000 0.00

Difference (2-1) 0.009 0.004 0.005 0.32

Difference (3-1) 0.029 0.021 0.008 0.46

Difference (3-2) 0.020 0.017 0.003 0.14

Sickness Benefit

Onset Control (0001) Difference T stat

Wave 1 0.021 0.029 -0.008 -0.87

Wave 2 0.038 0.024 0.014 1.34

Wave 3 0.097 0.035 0.062 4.70

Difference (2-1) 0.017 -0.005 0.022 1.84

Difference (3-1) 0.076 0.007 0.070 4.80

Difference (3-2) 0.059 0.012 0.047 3.35

IB or ESA

Onset Control (0001) Difference T stat

Wave 1 0.014 0.009 0.005 0.92

Wave 2 0.021 0.009 0.012 1.77

Wave 3 0.064 0.022 0.042 3.97

Difference (2-1) 0.007 0.000 0.007 0.90

Difference (3-1) 0.050 0.013 0.037 3.35

Difference (3-2) 0.043 0.013 0.030 2.97 Notes: See notes to Table 5.

43

Table 7: Heterogeneity in the DID-PSM (wave 3-1, wave 3-2), DDA Disability Onset

Treatment Effects: Proportions Receiving Welfare Benefits

DID

Any Benefit Non-sickness Benefit Sickness Benefit IB/ESA

DID_PSM 3-1 3-2 3-1 3-2 3-1 3-2 3-1 3-2

Male 0.077 0.074 0.024 0.023 0.077 0.066 0.056 0.048 Female 0.091 0.055 0.049 0.023 0.068 0.055 0.044 0.010

Low Qual 0.124 0.067 0.084 0.036 0.084 0.073 0.062 0.051

High Qual 0.067 0.054 0.020 0.017 0.054 0.035 0.039 0.032

Older 0.082 0.067 0.037 0.039 0.087 0.056 0.050 0.045

Younger 0.088 0.071 0.057 0.037 0.054 0.051 0.047 0.037

Mental (treatment n=73) 0.260 0.137 0.151 0.027 0.192 0.151 0.151 0.110

Physical 0.074 0.049 0.025 0.010 0.069 0.059 0.045 0.039

Single 0.070 0.035 0.044 0.006 0.047 0.041 0.021 0.018

Multiple 0.115 0.077 0.053 0.036 0.110 0.077 0.077 0.060

Pre-2009 0.069 0.064 0.030 0.039 0.080 0.055 0.055 0.053

Post-2009 0.062 0.060 0.017 0.013 0.072 0.057 0.042 0.032

Unemploy Q1 0.065 0.046 0.014 0.018 0.074 0.023 0.037 0.023

Unemploy Q2 0.074 0.051 0.045 0.040 0.080 0.057 0.045 0.040

Unemploy Q3 0.090 0.042 0.079 0.042 0.074 0.042 0.053 0.042

Unemploy Q4 0.072 0.072 0.006 0.006 0.100 0.094 0.072 0.067

Employed 0.080 0.049 0.030 0.009 0.073 0.051 0.049 0.040

Not employed 0.074 0.084 0.079 0.090 0.063 0.058 0.037 0.032

Any benefits 0.110 0.119 0.085 0.186 0.161 0.025 0.110 0.051

No benefits 0.071 0.071 0.030 0.022 0.059 0.062 0.033 0.037

Long-term health 0.043 0.030 0.030 0.023 0.020 0.023 0.015 0.028

No long-term health 0.121 0.101 0.058 0.055 0.112 0.074 0.082 0.060

Past health 0.167 0.106 0.091 0.045 0.152 0.091 0.045 0.045

No past health 0.106 0.075 0.065 0.032 0.063 0.059 0.055 0.051 Notes: See notes to Table 5. Samples are defined on the basis of information in wave 1 or at onset as

appropriate. Bold indicates statistically significant from zero at the 95% confidence level.

44

Table 8: DID-PSM, DDA Disability Exit Treatment Effects: Proportions Receiving

Welfare Benefits

Any Benefit

Exit Control (1111) Difference T stat

Wave 1 0.269 0.269 0.000 0.00

Wave 2 0.246 0.384 -0.139 -5.09

Wave 3 0.197 0.379 -0.182 -6.53

Wave 4 0.199 0.431 -0.231 -7.83

Difference (2-1) -0.023 0.116 -0.139 -5.09

Difference (3-1) -0.072 0.110 -0.182 -6.53

Difference (4-1) -0.069 0.162 -0.231 -7.83

Difference (3-2) -0.049 -0.006 -0.043 -1.60

Difference (3-2)-(2-1) -0.026 -0.121 0.095 2.04

Non-sickness Benefit

Exit Control (1111) Difference T stat

Wave 1 0.191 0.142 0.049 2.43

Wave 2 0.173 0.188 -0.014 -0.59

Wave 3 0.156 0.179 -0.023 -0.91

Wave 4 0.145 0.182 -0.038 -1.44

Difference (2-1) -0.017 0.046 -0.064 -2.72

Difference (3-1) -0.035 0.038 -0.072 -2.73

Difference (4-1) -0.046 0.040 -0.087 -3.35

Difference (3-2) -0.017 -0.009 -0.009 -0.33

Difference (3-2)-(2-1) 0.000 -0.055 0.042 1.32

Sickness Benefit

Exit Control (1111) Difference T stat

Wave 1 0.118 0.223 -0.104 -6.47

Wave 2 0.101 0.301 -0.199 -7.86

Wave 3 0.061 0.312 -0.251 -9.95

Wave 4 0.081 0.367 -0.286 -10.68

Difference (2-1) -0.017 0.078 -0.095 -3.76

Difference (3-1) -0.058 0.090 -0.147 -5.39

Difference (4-1) -0.038 0.145 -0.182 -6.43

Difference (3-2) -0.041 0.012 -0.052 -2.10

Difference (3-2)-(2-1) -0.023 -0.067 0.043 1.03

IB or ESA

Exit Control (1111) Difference T stat

Wave 1 0.072 0.145 -0.072 -3.86

Wave 2 0.066 0.217 -0.150 -6.33

Wave 3 0.035 0.217 -0.182 -8.38

Wave 4 0.040 0.257 -0.217 -9.64

Difference (2-1) -0.006 0.072 -0.078 -3.17

Difference (3-1) -0.038 0.072 -0.110 -4.43

Difference (4-1) -0.032 0.113 -0.145 -5.68

Difference (3-2) -0.032 0.000 -0.032 -1.47

Difference (3-2)-(2-1) -0.026 -0.072 0.046 1.18 Notes: See notes to Table 5.

45

Table 9: DID-PSM, DDA Disability Exit Treatment Effects: Proportions Receiving

Welfare Benefits, Alternative Control Group

Any Benefit

Exit Control (1110) Difference T stat

Wave 1 0.269 0.269 0.000 0.00

Wave 2 0.246 0.309 -0.064 -1.89

Wave 3 0.197 0.335 -0.139 -3.75

Difference (2-1) -0.023 0.041 -0.064 -1.89

Difference (3-1) -0.072 0.067 -0.139 -3.75

Difference (3-2) -0.049 0.026 -0.075 -2.55

Non-sickness Benefit

Exit Control (1110) Difference T stat

Wave 1 0.191 0.127 0.064 2.77

Wave 2 0.173 0.124 0.049 1.54

Wave 3 0.156 0.194 -0.038 -1.10

Difference (2-1) -0.017 -0.003 -0.015 -0.50

Difference (3-1) -0.035 0.066 -0.101 -3.06

Difference (3-2) -0.017 0.069 -0.087 -2.70

Sickness Benefit

Exit Control (1110) Difference T stat

Wave 1 0.118 0.214 -0.095 -4.62

Wave 2 0.101 0.231 -0.130 -3.88

Wave 3 0.061 0.208 -0.147 -4.63

Difference (2-1) -0.017 0.017 -0.035 -1.04

Difference (3-1) -0.058 -0.006 -0.052 -1.61

Difference (3-2) -0.041 -0.023 0.017 -0.82

IB or ESA

Exit Control (1110) Difference T stat

Wave 1 0.072 0.156 -0.084 -3.29

Wave 2 0.066 0.159 -0.093 -3.58

Wave 3 0.035 0.142 -0.107 -4.32

Difference (2-1) -0.006 0.003 -0.009 -0.40

Difference (3-1) -0.038 -0.014 -0.023 -0.98

Difference (3-2) -0.032 -0.017 -0.014 -0.78 Notes: See notes to Table 5.

46

Table 10: Heterogeneity in the DID-PSM (wave 3-1, wave 3-2), DDA Disability Exit

Treatment Effects: Proportions Receiving Welfare Benefits

DID

Any Benefit Non-sickness

Benefit

Sickness Benefit IB/ESA

DID_PSM 3-1 3-2 3-1 3-2 3-1 3-2 3-1 3-2

Male -0.174 -0.035 -0.064 0.017 -0.122 -0.047 -0.081 -0.029

Female -0.190 -0.098 -0.138 -0.098 -0.109 -0.012 -0.057 0.006

Low Qual -0.191 -0.085 -0.111 -0.053 -0.153 -0.069 -0.063 -0.021

High Qual -0.153 -0.064 -0.051 -0.013 -0.140 -0.038 -0.076 -0.032

Older -0.164 -0.056 -0.117 -0.037 -0.098 -0.014 -0.070 -0.009

Younger -0.182 -0.045 -0.061 0.000 -0.182 -0.076 -0.091 -0.030

Mental (treatment

n=35) -0.286 -0.114 0.000 0.057 -0.314 -0.171 -0.257 -0.143

Physical -0.256 -0.116 -0.097 -0.047 -0.217 -0.112 -0.173 -0.087

Single -0.228 -0.074 -0.142 -0.049 -0.142 -0.043 -0.136 -0.068

Multiple -0.170 -0.050 -0.050 -0.044 -0.187 -0.060 -0.121 0.000

Unemploy Q1 -0.226 -0.072 -0.060 0.012 -0.226 -0.131 -0.155 -0.107

Unemploy Q2 -0.217 -0.141 -0.141 -0.087 -0.130 -0.087 -0.130 -0.087

Unemploy Q3 -0.152 -0.098 -0.054 -0.054 -0.152 -0.043 -0.043 0.011

Unemploy Q4 -0.308 -0.115 -0.231 -0.077 -0.192 -0.115 -0.103 -0.090 Notes: See notes to Table 5. Samples are defined on the basis of information in wave 1 or at exit as appropriate. Bold indicates statistically significant from zero at the 95% confidence level.

47

Supplementary Appendix: Further Data Details and Additional Results

Figure A1: LLFS and WPLS Proportion reporting receipt of IB and ESA, 2004-2012

Notes: Data from the Work and Pensions Longitudinal Study (WPLS) and is access via NOMIS. Claimant rates

relate to Great Britain and are created using claimant numbers as of November each year and mid-year working-

age population estimates.

0

.02

.04

.06

.08

Pro

port

ion

2004 2005 2006 2007 2008 2009 2010 2011 2012

Year

IB ESA

IB WPLS ESA WPLS

48

Table A1: Representativeness of LLFS Balanced Sample

APS (2004-2012) LLFS (2004-2012)

All waves All waves 4 waves

WL disabled 15.13 15.49 17.48

DDA disabled 15.57 16.12 18.11

Long-term health problem 28.05 28.15 32.70

Past long-term health problem 7.76 7.59 8.39

Employment 72.44 71.54 76.34

Any Benefit 15.49 16.76 14.74

Non-sickness Benefit 11.68 12.70 9.58

Sickness Benefit 6.53 7.03 8.00

IB or ESA 3.89 4.27 4.92

Gender

Female 49.54 49.70 49.70

Male 50.46 50.30 50.30

Age 38.45 38.11 42.83

Highest qualification

Degree 19.84 18.74 20.00

Other Higher Education 8.62 8.97 10.99

A level 22.69 22.93 22.76

O level 22.73 22.62 21.83

Other 11.60 11.72 9.82

None 14.51 15.02 14.60

Students

Full-time student 8.70 8.74 4.83

Not full-time student 91.30 91.26 95.17

Marital Status

Single 39.79 40.88 25.49

Married 47.58 46.16 61.53

Widowed/divorced 12.63 12.95 12.99

Children

Dependent child in household 39.20 39.24 41.74

No dependent child in household 60.80 60.76 58.26

Housing Tenure

Owned outright 17.29 16.38 21.57

Mortgaged 50.18 49.38 59.87

Rented 32.53 34.24 18.56

Region

Tyne and Wear 2.66 3.67 3.19

Rest of North East 3.47 4.89 4.72

Greater Manchester 5.21 6.65 6.77

Merseyside 2.59 3.27 3.42

Rest of North West 4.36 3.54 3.43

South Yorkshire 2.16 1.97 1.91

West Yorkshire 2.95 1.42 1.36

Rest of Yorkshire & Humberside 2.99 3.31 3.23

East Midlands 5.46 2.62 1.93

West Midlands Metropolitan county 3.97 3.85 3.79

Rest of West Midlands 3.76 2.65 2.95

East of England 6.79 3.28 2.96

Inner London 4.28 4.24 2.08

Outer London 5.57 3.56 2.77

49

South East 11.69 9.18 7.96

South West 7.40 6.46 6.48

Wales 9.57 16.80 17.98

Strathclyde 5.47 8.15 9.83

Rest of Scotland 7.21 10.49 13.24

Northern Ireland 2.43 - -

Local Area Unemployment Rate 0.063 0.066 0.064

Year of observation

2004 19.72 22.45 20.07

2005 12.09 13.37 19.43

2006 10.18 9.17 16.80

2007 10.11 8.84 15.96

2008 9.86 8.79 14.03

2009 9.51 8.75 13.72

2010 9.47 9.07 -

2011 9.53 9.83 -

2012 9.52 9.72 -

Interview type

Face-to-face 78.82 74.42 85.59

Telephone 21.18 25.58 14.41

Sample

QLFS 59.89 - -

LLFS 40.11 100 100

N 1,099,439 440,947 49,071 Notes: All characteristics are measured at Wave 1. The APS sample excludes the APS boost.

50

Table A2: Proportions receiving welfare benefit by wave and treatment status, WL

disability

Any Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.054 0.060 0.060 0.059

Control (0001) 0.100 0.131 0.141 0.188

Treatment 0.137 0.196 0.308 0.318

Exit Treatment 0.251 0.260 0.199 0.184

Control (1111) 0.749 0.755 0.764 0.761

Control (1110) 0.477 0.480 0.470 0.432

Non-sickness Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.047 0.051 0.050 0.049

Control (0001) 0.085 0.098 0.105 0.120

Treatment 0.104 0.151 0.194 0.183

Exit Treatment 0.161 0.175 0.156 0.126

Control (1111) 0.379 0.402 0.410 0.412

Control (1110) 0.285 0.274 0.273 0.271

Sickness Benefit Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.006 0.007 0.007 0.008

Control (0001) 0.014 0.030 0.040 0.090

Treatment 0.030 0.055 0.142 0.182

Exit Treatment 0.124 0.106 0.053 0.068

Control (1111) 0.628 0.643 0.659 0.662

Control (1110) 0.303 0.316 0.312 0.259

IB or ESA Wave 1 Wave 2 Wave 3 Wave 4

Onset Control (0000) 0.001 0.001 0.001 0.002

Control (0001) 0.004 0.012 0.018 0.051

Treatment 0.009 0.028 0.093 0.113

Exit Treatment 0.078 0.070 0.034 0.034

Control (1111) 0.415 0.435 0.441 0.446

Control (1110) 0.180 0.237 0.202 0.147 Notes: See notes to Table 1.

51

Table A3: Benefit Receipt by WL Disability and Employment Status

Disabled Non-disabled

All Employed Non-

employed

All Employed Non-

employed

Any Benefits 57.0 15.4 80.5 9.0 3.0 32.4

Non-sickness Benefit 34.3 6.4 50.1 8.1 2.4 29.7

Sickness Benefit 41.8 10.4 59.6 1.1 0.5 3.2

IB/ESA 27.0 3.1 40.5 0.4 0.1 1.6

N 125,115 45,061 80,054 659,612 523,961 135,651

Notes: LLFS working-age population (2004-2012).

52

Table A4: Descriptive Statistics for Explanatory Variables by Control and Treatment

Groups, DDA, Alternative Control Groups

Onset Exit

Treatment

(0011)

Control

(0001) (pre-

matching)

Control

(0001)

(post-

matching)

Treatment

(1100)

Control

(1110) (pre-

matching)

Control

(1110)

(post-

matching)

Age 45.791 45.919 45.887 45.509 46.626 45.324

Gender

Male 0.495 0.472 0.501 0.497 0.520 0.541

Highest

qualification

Degree 0.192 0.176 0.170 0.116 0.150 0.176**

Other Higher

Education 0.123 0.131 0.129 0.116 0.092 0.095

A level 0.218 0.226 0.205 0.223 0.218 0.218

O level 0.193 0.212 0.212 0.243 0.146*** 0.231

Other 0.112 0.114 0.121 0.095 0.138* 0.098

None 0.163 0.142 0.164 0.208 0.257 0.182

Students

Full-time student 0.026 0.029 0.022 0.038 0.016* 0.026

Marital Status

Single 0.235 0.202* 0.231 0.217 0.273 0.1999

Married 0.618 0.654* 0.632 0.613 0.559 0.627

Widowed/divorced 0.147 0.144 0.137 0.171 0.168 0.173

Children

Dependent child in

household 0.316 0.340 0.309 0.309 0.263** 0.286

Housing Tenure

Owned outright 0.238 0.246 0.259 0.257 0.287 0.234

Mortgaged 0.549 0.563 0.523 0.494 0.421** 0.515

Rented 0.214 0.191 0.218 0.249 0.292 0.252

Region

Tyne and Wear 0.021 0.029 0.026 0.032 0.027 0.038

Rest of North East 0.052 0.052 0.043 0.066 0.068 0.075

Greater

Manchester 0.072 0.078 0.068 0.052 0.068 0.035

Merseyside 0.049 0.040 0.329 0.029 0.027 0.026

Rest of North

West 0.033 0.043 0.049 0.026 0.039 0.035

South Yorkshire 0.022 0.017 0.012 0.026 0.025 0.020

West Yorkshire 0.013 0.014 0.009 0.012 0.014 0.012

Rest of Yorkshire

& Humberside 0.028 0.027 0.024 0.020 0.043* 0.009

East Midlands 0.024 0.015 0.029 0.026 0.021 0.017

West Midlands

Metropolitan

county 0.035 0.0390 0.039 0.032 0.045 0.011*

Rest of West

Midlands 0.030 0.019 0.028 0.017 0.021 0.023

East of England 0.041 0.042 0.036 0.035 0.021 0.032

Inner London 0.021 0.016 0.014 0.026 0.021 0.026

Outer London 0.029 0.027 0.029 0.020 0.031 0.026

53

South East 0.067 0.075 0.087 0.069 0.084 0.121*

South West 0.076 0.065 0.087 0.038 0.066* 0.035

Wales 0.171 0.169 0.175 0.194 0.150* 0.202

Strathclyde 0.094 0.093 0.114 0.113 0.123 0.116

Rest of Scotland 0.122 0.141 0.108 0.168 0.109** 0.142

Economic

Conditions

Employed 0.751 0.824*** 0.735 0.668 0.532*** 0.685

Local area

unemployment

rate 0.065 0.063 0.065 0.065 0.066 0.064

Year of

observation

2004 0.142 0.142 0.142 0.153 0.142 0.153

2005 0.133 0.147 0.133 0.199 0.185 0.199

2006 0.199 0.168* 0.200 0.171 0.195 0.171

2007 0.158 0.228*** 0.158 0.165 0.154 0.165

2008 0.199 0.174 0.200 0.159 0.148 0.159

2009 0.169 0.141* 0.168 0.153 0.177 0.153

Any benefit 0.134 0.093*** 0.134 0.269 0.359*** 0.269

N 761 1,469 544 346 487 197 Notes: All characteristics are measured at Wave 1. Treatment is defined by consistent onset/exit of DDA

disability and is estimated using a NN(1) matching algorithm. *,**,*** denote significance from the treatment

group at the 10%, 5% and 1% level respectively.

54

Table A5: Descriptive Statistics for Explanatory Variables by Control and Treatment

Groups, WL

Onset Exit

Treatment Control

(pre-

matching)

Control

(post-

matching)

Treatment Control (pre-

matching)

Control

(post-

matching)

Age 45.233 41.698*** 45.528 44.218 47.892*** 46.008

Gender

Male 0.502 0.474 0.480 0.549 0.526 0.500

Highest

qualification

Degree 0.147 0.237*** 0.157 0.143 0.075*** 0.155

Other Higher

Education

0.092 0.116 0.064 0.098 0.077 0.130

A level 0.243 0.232 0.255

0.232 0.182** 0.235

O level 0.229 0.226 0.237 0.199 0.177 0.197

Other 0.108 0.085* 0.127 0.134 0.134 0.113

None 0.181 0.105* 0.161 0.193 0.356*** 0.169

Students

Full-time student 0.052 0.059 0.028* 0.036 0.019** 0.040

Marital Status

Single 0.267 0.266 0.233 0.249 0.260 0.218

Married 0.588 0.625* 0.618 0.599 0.507*** 0.649

Widowed/divorced 0.145 0.108*** 0.149 0.151 0.232*** 0.132

Children

Dependent child in

household

0.311 0.433*** 0.317 0.325 0.216*** 0.342

Housing Tenure

Owned outright 0.265 0.215*** 0.247 0.261 0.237 0.342**

Mortgaged 0.486 0.653*** 0.534 0.493 0.321*** 0.486

Rented 0.249 0.131*** 0.219 0.247 0.442*** 0.172**

Region

Tyne and Wear 0.024 0.031 0.010* 0.025 0.038 0.0283

Rest of North East 0.060 0.046 0.036* 0.064 0.057 0.040

Greater

Manchester

0.076 0.067 0.070 0.050 0.069 0.065

Merseyside 0.040 0.034 0.052 0.034 0.044 0.031

Rest of North

West

0.042 0.032 0.046 0.028 0.037 0.034

South Yorkshire 0.020 0.017 0.024 0.028 0.027 0.028

West Yorkshire 0.024 0.013** 0.018 0.014 0.015 0.025

Rest of Yorkshire

& Humberside

0.020 0.032 0.030 0.045 0.029* 0.071

East Midlands 0.016 0.018 0.014 0.025 0.019 0.014

West Midlands

Metropolitan

county

0.044 0.040 0.052 0.036 0.035 0.037

Rest of West

Midlands

0.026 0.030 0.018 0.028 0.029 0.028

East of England 0.034 0.031 0.030 0.022 0.026 0.045*

Inner London 0.016 0.021 0.006 0.017 0.022 0.023

Outer London 0.040 0.029 0.046 0.039 0.020** 0.017*

55

South East 0.082 0.087 0.108 0.010 0.051*** 0.096

South West 0.048 0.068* 0.054 0.048 0.053 0.037

Wales 0.195 0.168 0.219

0.171 0.219** 0.141

Strathclyde 0.090 0.100 0.076 0.101 0.097 0.097

Rest of Scotland 0.100 0.136** 0.088 0.123 0.114 0.144

Economic

Conditions

Employed 0.739 0.841*** 0.719 0.647 0.213*** 0.678

Local area

unemployment

rate

0.066 0.063*** 0.064 0.065 0.068*** 0.066

Year of

observation

2004 0.157 0.178 0.157 0.190 0.171 0.192

2005 0.171 0.171 0.171 0.176 0.157 0.178

2006 0.171 0.185 0.171 0.179 0.180 0.181

2007 0.159 0.175 0.159 0.149 0.175 0.150

2008 0.175 0.149 0.175 0.157 0.161 0.158

2009 0.169 0.142 0.169 0.149 0.157 0.141

Any benefit 0.139 0.054*** 0.139 0.241 0.755*** 0.243

N 498 29,778 483 354 4,117 294 Notes: All characteristics are measured at Wave 1. Treatment is defined by consistent onset/exit of WL

disability and is estimated using a NN(1) matching algorithm. *,**,*** denote significance from the treatment

group at the 10%, 5% and 1% level respectively.

56

Table A6: Descriptive Statistics for Explanatory Variables by Control and Treatment

Groups, WL, Alternative Control Groups

Onset Exit

Treatment Control

(0001)

(pre-

matching)

Control

(0001)

(post-

matching)

Treatment Control

(1110) (pre-

matching)

Control

(1110) (post-

matching)

Age 45.205 45.354 46.183 44.218 45.500 44.96

Gender

Male 0.501 0.473 0.497 0.549 0.512 0.546

Highest

qualification

Degree 0.147 0.158 0.165 0.143 0.117 0.120

Other Higher

Education

0.093 0.115 0.099 0.098 0.090 0.137

A level 0.242 0.208 0.2173

0.232 0.204 0.283

O level 0.229 0.2305 0.217 0.199 0.157 0.157

Other 0.109 0.116 0.092 0.134 0.147 0.132

None 0.181 0.172 0.179 0.193 0.286*** 0.171

Students

Full-time student 0.052 0.046 0.038 0.036 0.032 0.022

Marital Status

Single 0.268 0.232 0.243 0.249 0.318** 0.235

Married 0.588 0.617 0.591 0.599 0.525** 0.633

Widowed/divorced 0.145 0.151 0.165 0.151 0.157 0.132

Children

Dependent child in

household

0.311 0.332 0.264* 0.325 0.216*** 0.342

Housing Tenure

Owned outright 0.265 0.243 0.262 0.261 0.271 0.252

Mortgaged 0.487 0.547** 0.503 0.493 0.391*** 0.549

Rented 0.249 0.210* 0.235 0.247 0.338*** 0.199

Region

Tyne and Wear 0.024 0.029 0.026 0.025 0.042 0.028

Rest of North East 0.060 0.046 0.068 0.064 0.050 0.070

Greater

Manchester

0.076 0.074 0.062 0.050 0.057 0.081

Merseyside 0.040 0.028 0.032 0.034 0.037 0.050

Rest of North

West

0.042 0.044 0.046 0.028 0.045 0.025

South Yorkshire 0.020 0.013 0.012 0.028 0.027 0.025

West Yorkshire 0.024 0.014 0.014 0.014 0.017 0.014

Rest of Yorkshire

& Humberside

0.020 0.023 0.026 0.045 0.039 0.028

East Midlands 0.016 0.013 0.012 0.025 0.012 0.014

West Midlands

Metropolitan

county

0.044 0.042 0.030 0.036 0.044 0.022

Rest of West

Midlands

0.026 0.028 0.032 0.028 0.025 0.014

East of England 0.034 0.028 0.032 0.022 0.020 0.042

Inner London 0.016 0.020 0.006 0.017 0.015 0.020

57

Outer London 0.040 0.035 0.024 0.039 0.042 0.059

South East 0.082 0.082 0.087 0.010 0.082 0.112

South West 0.048 0.070* 0.052 0.048 0.057 0.056

Wales 0.195 0.184 0.233

0.171 0.201 0.129

Strathclyde 0.090 0.090 0.091 0.101 0.080 0.092

Rest of Scotland 0.100 0.135* 0.099 0.123 0.104 0.118

Economic

Conditions

Employed 0.739 0.772 0.744 0.647 0.458*** 0.689

Local area

unemployment

rate

0.066 0.063** 0.065 0.065 0.066 0.066

Year of

observation

2004 0.157 0.144 0.157 0.190 0.164 0.190

2005 0.171 0.138* 0.171 0.176 0.132* 0.176

2006 0.171 0.181 0.171 0.179 0.152 0.179

2007 0.159 0.213** 0.159 0.149 0.139 0.148

2008 0.175 0.168 0.175 0.157 0.211* 0.157

2009 0.169 0.156 0.167 0.149 0.202* 0.148

Any benefit 0.139 0.102** 0.139 0.241 0.473*** 0.241

N 497 1,128 365 357 402 194 Notes: All characteristics are measured at Wave 1. Treatment is defined by consistent onset/exit of WL

disability and is estimated using a NN(1) matching algorithm. *,**,*** denote significance from the treatment

group at the 10%, 5% and 1% level respectively.

58

Table A7: DID-PSM, WL Disability Onset Treatment Effects: Proportions Receiving

Welfare Benefits

Any Benefit

Onset Control (0000) Difference T stat

Wave 1 0.139 0.139 0.000 0.00

Wave 2 0.195 0.127 0.068 3.57

Wave 3 0.311 0.102 0.209 9.00

Wave 4 0.309 0.118 0.191 7.94

Difference (2-1) 0.056 -0.012 0.068 3.57

Difference (3-1) 0.173 -0.036 0.209 9.00

Difference (4-1) 0.171 -0.020 0.191 7.94

Difference (3-2) 0.117 -0.024 0.141 5.98

Difference (3-2)-(2-1) 0.060 -0.012 0.072 2.01

Non-sickness Benefit

Onset Control (0000) Difference T stat

Wave 1 0.104 0.116 -0.012 -1.46

Wave 2 0.151 0.104 0.046 2.62

Wave 3 0.197 0.082 0.114 5.88

Wave 4 0.183 0.104 0.078 3.74

Difference (2-1) 0.046 -0.012 0.058 3.35

Difference (3-1) 0.092 -0.034 0.127 6.36

Difference (4-1) 0.078 -0.012 0.090 4.16

Difference (3-2) 0.046 -0.022 0.068 3.47

Difference (3-2)-(2-1) 0.000 -0.010 0.010 0.32

Sickness Benefit

Onset Control (0000) Difference T stat

Wave 1 0.032 0.020 0.012 1.58

Wave 2 0.054 0.012 0.042 3.97

Wave 3 0.145 0.006 0.139 8.34

Wave 4 0.179 0.014 0.165 9.03

Difference (2-1) 0.022 -0.008 0.030 2.81

Difference (3-1) 0.112 -0.014 0.127 7.23

Difference (4-1) 0.147 -0.006 0.153 8.10

Difference (3-2) 0.090 -0.006 0.096 5.76

Difference (3-2)-(2-1) 0.068 0.002 0.066 3.01

IB or ESA

Onset Control (0000) Difference T stat

Wave 1 0.010 0.002 0.008 1.63

Wave 2 0.028 0.002 0.026 3.47

Wave 3 0.094 0.000 0.094 7.01

Wave 4 0.114 0.006 0.108 7.22

Difference (2-1) 0.018 0.000 0.018 2.41

Difference (3-1) 0.084 -0.002 0.086 6.48

Difference (4-1) 0.104 0.004 0.100 6.80

Difference (3-2) 0.066 -0.002 0.068 5.18

Difference (3-2)-(2-1) 0.048 -0.002 0.050 2.99 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

59

Table A8: DID-PSM, WL Disability Onset Treatment Effects: Proportions Receiving

Welfare Benefits, Alternative Control Group

Any Benefit

WL Onset Control (0001) Difference T stat

Wave 1 0.139 0.139 0.000 0.00

Wave 2 0.195 0.163 0.032 1.49

Wave 3 0.312 0.191 0.121 4.48

Difference (2-1) 0.056 0.024 0.022 1.49

Difference (3-1) 0.173 0.052 0.121 4.48

Difference (3-2) 0.117 0.028 0.089 3.21

Non-sickness Benefit

WL Onset Control (0001) Difference T stat

Wave 1 0.104 0.113 -0.008 -0.74

Wave 2 0.151 0.135 0.016 0.76

Wave 3 0.197 0.141 0.056 2.41

Difference (2-1) 0.046 0.022 0.024 0.92

Difference (3-1) 0.093 0.028 0.064 2.55

Difference (3-2) 0.046 0.006 0.040 1.68

Sickness Benefit

WL Onset Control (0001) Difference T stat

Wave 1 0.032 0.012 0.012 1.01

Wave 2 0.054 0.024 0.024 1.56

Wave 3 0.145 0.052 0.093 4.30

Difference (2-1) 0.022 0.010 0.012 0.91

Difference (3-1) 0.113 0.032 0.081 4.05

Difference (3-2) 0.091 0.022 0.068 3.26

IB or ESA

WL Onset Control (0001) Difference T stat

Wave 1 0.010 0.006 0.004 0.65

Wave 2 0.028 0.010 0.018 1.99

Wave 3 0.095 0.034 0.060 3.69

Difference (2-1) 0.018 0.004 0.014 1.75

Difference (3-1) 0.085 0.028 0.056 3.57

Difference (3-2) 0.066 0.024 0.042 2.70 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

60

Table A9: Heterogeneity in the DID-PSM (wave 3-1, wave 3-2), WL Disability Onset

Treatment Effects: Proportions Receiving Welfare Benefits

DID

Any Benefit Non-sickness Benefit Sickness Benefit IB/ESA

DID_PSM 3-1 3-2 3-1 3-2 3-1 3-2 3-1 3-2

Male 0.188 0.120 0.096 0.036 0.120 0.104 0.088 0.068

Female 0.214 0.113 0.145 0.060 0.113 0.085 0.081 0.060

Low Qual 0.240 0.155 0.143 0.062 0.136 0.120 0.093 0.074

High Qual 0.138 0.104 0.067 0.042 0.096 0.071 0.075 0.058

Older 0.211 0.147 0.107 0.060 0.120 0.090 0.077 0.054

Younger 0.186 0.121 0.121 0.055 0.126 0.095 0.106 0.085

Mental (treatment

n=56) 0.304 0.143 0.214 0.071 0.196 0.107 0.196 0.089

Physical 0.158 0.109 0.087 0.041 0.092 0.087 0.061 0.061

Single 0.123 0.057 0.090 0.024 0.052 0.043 0.038 0.024

Multiple 0.243 0.148 0.109 0.046 0.176 0.134 0.120 0.099

Pre-2009 0.178 0.101 0.073 0.020 0.162 0.024 0.134 0.117

Post-2009 0.156 0.104 0.108 0.032 0.056 0.044 0.044 0.024

Unemploy Q1 0.183 0.122 0.061 0.017 0.139 0.078 0.078 0.052

Unemploy Q2 0.187 0.151 0.086 0.072 0.101 0.115 0.094 0.086

Unemploy Q3 0.262 0.146 0.208 0.092 0.123 0.077 0.092 0.054

Unemploy Q4 0.140 0.105 0.105 0.070 0.105 0.088 0.079 0.070

Employed 0.201 0.133 0.109 0.054 0.133 0.106 0.092 0.079

Not employed 0.208 0.131 0.100 0.085 0.123 0.054 0.092 0.046

Any benefits 0.088 -0.059 0.103 -0.074 0.132 0.103 0.103 0.118

No benefits 0.214 0.131 0.114 0.056 0.121 0.089 0.084 0.061

Long-term health 0.166 0.103 0.108 0.040 0.072 0.067 0.040 0.036

No long-term health 0.257 0.173 0.151 0.088 0.147 0.107 0.121 0.096

Past health 0.163 0.184 0.102 0.122 0.122 0.061 0.102 0.061

No past health 0.212 0.111 0.127 0.029 0.117 0.101 0.091 0.081 Notes: See notes to Table 5. Samples are defined on the basis of information in wave 1 or at onset as

appropriate. Bold indicates statistically significant from zero at the 95% confidence level.

61

Table A10: DID-PSM, WL Disability Exit Treatment Effects: Proportions Receiving

Welfare Benefits

Any Benefit

WL Exit Control (1111) Difference T stat

Wave 1 0.243 0.243 0.000 0.00

Wave 2 0.254 0.328 -0.074 -2.39

Wave 3 0.201 0.410 -0.209 -6.45

Wave 4 0.172 0.415 -0.243 -7.47

Difference (2-1) 0.011 0.085 -0.073 -2.39

Difference (3-1) -0.042 0.166 -0.209 -6.45

Difference (4-1) -0.071 0.172 -0.243 -7.47

Difference (3-2) -0.054 0.082 -0.136 -4.36

Difference (3-2)-(2-1) -0.065 -0.003 -0.062 -1.18

Non-sickness Benefit

WL Exit Control (0000) Difference T stat

Wave 1 0.153 0.088 0.065 3.61

Wave 2 0.172 0.133 0.040 1.50

Wave 3 0.153 0.150 0.003 0.11

Wave 4 0.113 0.141 -0.028 -1.05

Difference (2-1) 0.020 0.045 -0.025 -1.04

Difference (3-1) 0.000 0.062 -0.062 -2.56

Difference (4-1) -0.040 0.054 -0.093 -3.46

Difference (3-2) -0.020 0.017 -0.037 -1.33

Difference (3-2)-(2-1) -0.040 -0.028 -0.011 -0.24

Sickness Benefit

WL Exit Control (0000) Difference T stat

Wave 1 0.119 0.203 -0.085 -5.30

Wave 2 0.107 0.266 -0.158 -5.84

Wave 3 0.059 0.333 -0.274 -9.15

Wave 4 0.071 0.359 -0.288 -9.55

Difference (2-1) -0.011 0.062 -0.073 -2.79

Difference (3-1) -0.059 0.130 -0.189 -6.10

Difference (4-1) -0.048 0.155 -0.203 -6.66

Difference (3-2) -0.048 0.068 -0.116 -4.24

Difference (3-2)-(2-1) -0.037 0.006 -0.042 -0.97

IB or ESA

WL Exit Control (0000) Difference T stat

Wave 1 0.073 0.127 -0.053 -3.12

Wave 2 0.067 0.166 -0.099 -4.52

Wave 3 0.037 0.234 -0.198 -8.08

Wave 4 0.034 0.232 -0.198 -7.95

Difference (2-1) -0.006 0.040 -0.045 -2.24

Difference (3-1) -0.036 0.107 -0.144 -5.81

Difference (4-1) -0.040 0.105 -0.144 -5.52

Difference (3-2) -0.031 0.068 -0.099 -4.41

Difference (3-2)-(2-1) -0.025 0.028 -0.054 -1.55 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

62

Table A11: DID-PSM, WL Disability Exit Treatment Effects: Proportions Receiving

Welfare Benefits, Alternative Control Group

Any Benefit

WL Exit Control (1110) Difference T stat

Wave 1 0.241 0.241 0.000 0.00

Wave 2 0.252 0.277 -0.025 -0.75

Wave 3 0.199 0.289 -0.090 -2.69

Difference (2-1) 0.011 0.036 -0.025 -0.75

Difference (3-1) -0.042 0.048 -0.090 -2.69

Difference (3-2) -0.053 0.011 -0.064 -1.81

Non-sickness Benefit

WL Exit Control (1110) Difference T stat

Wave 1 0.151 0.129 0.022 2.38

Wave 2 0.171 0.137 0.037 1.32

Wave 3 0.151 0.154 -0.003 0.09

Difference (2-1) 0.020 0.008 0.011 -1.02

Difference (3-1) 0.000 0.025 -0.025 -2.32

Difference (3-2) -0.020 0.017 -0.036 -1.13

Sickness Benefit

WL Exit Control (1110) Difference T stat

Wave 1 0.118 0.137 -0.020 -0.83

Wave 2 0.106 0.188 -0.081 -3.00

Wave 3 0.059 0.185 -0.126 -4.65

Difference (2-1) -0.011 0.050 -0.062 -2.09

Difference (3-1) -0.059 0.048 -0.106 -3.52

Difference (3-2) -0.048 -0.003 -0.045 -2.03

IB or ESA

WL Exit Control (1110) Difference T stat

Wave 1 0.073 0.087 -0.014 -0.63

Wave 2 0.067 0.118 -0.050 -2.08

Wave 3 0.036 0.120 -0.084 -3.54

Difference (2-1) -0.006 0.031 -0.036 -1.49

Difference (3-1) -0.036 0.034 -0.070 -2.67

Difference (3-2) -0.031 0.003 -0.034 -1.54 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

63

Table A12: Heterogeneity in the DID-PSM (wave 3-1, wave 3-2), WL Disability Exit

Treatment Effects: Proportions Receiving Welfare Benefits

DID

Any Benefit Non-sickness

Benefit

Sickness Benefit IB/ESA

DID-PSM 3-1 3-2 3-1 3-2 3-1 3-2 3-1 3-2

Male -0.201 -0.103 -0.072 -0.026 -0.180 -0.108 -0.082 -0.031

Female -0.119 -0.088 -0.006 0.006 -0.150 -0.100 -0.056 -0.044

Low Qual -0.155 -0.059 -0.032 -0.011 -0.193 -0.075 -0.128 -0.075

High Qual -0.226 -0.125 -0.048 -0.018 -0.202 -0.113 -0.131 -0.083

Older -0.213 -0.099 -0.079 -0.040 -0.178 -0.084 -0.094 -0.035

Younger -0.234 -0.058 -0.091 -0.019 -0.221 -0.045 -0.169 -0.052

Mental (treatment

n=40) -0.100 -0.050 -0.025 -0.125 -0.125 -0.050 -0.075 0.000

Physical -0.189 -0.096 -0.075 -0.039 -0.167 -0.089 -0.100 -0.050

Single -0.115 -0.028 -0.011 0.050 -0.126 -0.060 -0.093 -0.060

Multiple -0.231 -0.121 -0.064 -0.058 -0.179 -0.081 -0.121 -0.075

Unemploy Q1 -0.209 -0.174 -0.047 -0.058 -0.209 -0.174 -0.093 -0.070

Unemploy Q2 -0.228 -0.119 -0.020 0.010 -0.257 -0.168 -0.188 -0.109

Unemploy Q3 -0.186 -0.070 -0.023 -0.023 -0.186 -0.047 -0.151 0.012

Unemploy Q4 -0.203 -0.089 -0.203 -0.152 -0.215 -0.089 -0.101 0.000 Notes: See notes to Table 5. Samples are defined on the basis of information in wave 1 or at exit as appropriate. Bold indicates statistically significant from zero at the 95% confidence level.

64

Table A13: DID-PSM, DDA disability onset, sensitivity to matching method

Any Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) 0.027 2.20 0.026 1.72 0.035 3.03 Difference (3-1) 0.088 6.00 0.091 5.14 0.100 7.22 Difference (4-1) 0.102 6.43 0.105 5.55 0.116 7.74

Non-sickness Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) 0.018 1.57 0.019 1.34 0.026 2.54 Difference (3-1) 0.042 3.29 0.046 2.98 0.054 4.54 Difference (4-1) 0.041 3.05 0.046 2.81 0.056 4.51

Sickness Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) 0.019 2.55 0.018 2.04 0.019 2.63 Difference (3-1) 0.077 7.10 0.077 6.53 0.079 7.49 Difference (4-1) 0.109 8.86 0.106 7.75 0.108 8.94

IB or ESA

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) 0.007 1.25 0.007 1.16 0.007 1.38 Difference (3-1) 0.051 5.77 0.051 5.43 0.051 5.86 Difference (4-1) 0.070 6.89 0.069 6.53 0.069 6.88 Notes: Alternative matching estimators include using the 5 nearest neighbours NN(5), local linear regression

(LL) and kernel density (Kernel).

65

Table A14: DID-PSM, DDA disability onset, sensitivity to controls for health

Any Benefit Wave 1 Health Past Health

Control Exact Match Control Exact Match

Difference T stat Difference T

stat

Difference T

stat

Difference T

stat

Difference (2-1) 0.034 2.24 0.011 0.68 0.030 1.81 0.030 1.68

Difference (3-1) 0.099 5.65 0.074 4.15 0.084 4.25 0.102 5.00

Difference (4-1) 0.114 6.00 0.072 3.77 0.086 4.07 0.073 3.40

Non-sickness Benefit

Wave 1 Health Past Health

Control Exact Match Control Exact Match

Difference T stat Difference T

stat

Difference T stat Difference T

stat

Difference (2-1) 0.021 1.47

-0.005

-

0.38

0.020 1.24

0.025 1.47

Difference (3-1) 0.053 3.39 0.032 2.06 0.039 2.22 0.063 3.29

Difference (4-1) 0.053 3.10 0.007 0.41 0.029 1.52 0.029 1.49

Sickness Benefit

Wave 1 Health Past Health

Control Exact Match Control Exact Match

Difference T stat Difference T

stat

Difference T stat Difference T

stat

Difference (2-1) 0.018 2.09 0.026 2.79 0.018 1.76 0.023 2.12

Difference (3-1) 0.072 6.05 0.074 5.88 0.075 5.63 0.082 5.86

Difference (4-1) 0.108 8.25 0.111 7.82 0.093 6.21 0.097 6.08

IB or ESA

Wave 1 Health Past Health

Control Exact Match Control Exact Match

Difference T stat Difference T

stat

Difference T

stat

Difference T

stat

Difference (2-1) 0.005 0.92 0.009 1.57 0.007 0.99 0.013 1.62

Difference (3-1) 0.047 5.27 0.049 5.22 0.057 5.13 0.057 5.04

Difference (4-1) 0.070 6.86 0.070 6.59 0.063 5.19 0.061 4.90 Notes: See notes to Table 1. ATT are based on a NN(1) matching algorithm and are estimated over the region of

common support. T statistics are based on Abadie and Imbens (2006) standard errors.

66

Table A15: DID-PSM, DDA disability exit, sensitivity to matching method

Any Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) -0.127 -5.92 -0.120 -4.21 -0.116 -6.41

Difference (3-1) -0.191 -7.35 -0.199 -5.95 -0.194 -8.44

Difference (4-1) -0.206 -8.27 -0.204 -6.16 -0.201 -9.35

Non-sickness Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) -0.055 -2.77 -0.065 -2.62 -0.060 -3.22

Difference (3-1) -0.075 -3.16 -0.090 -3.12 -0.083 -3.74

Difference (4-1) -0.091 -3.94 -0.107 -3.70 -0.102 -4.78

Sickness Benefit

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) -0.092 -4.65 -0.090 -3.26 -0.089 -4.96

Difference (3-1) -0.158 -7.21 -0.162 -5.33 -0.161 -8.15

Difference (4-1) -0.162 -7.49 -0.154 -5.05 -0.155 -8.17

IB or ESA

NN(5) LL Kernel

Difference T stat Difference T stat Difference T stat

Difference (2-1) -0.068 -3.89 -0.063 -2.51 -0.064 -3.95

Difference (3-1) -0.099 -5.03 -0.102 -3.84 -0.105 -5.64

Difference (4-1) -0.115 -5.97 -0.101 -3.64 -0.105 -5.84 Notes: Alternative matching estimators include using the 5 nearest neighbours NN(5), local linear regression

(LL) and kernel density (Kernel).