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LONG-TERM UNEMPLOYMENT AND THE GREAT RECESSION:
EVIDENCE FROM UK STOCKS AND FLOWS
ONLINE APPENDIX
Appendix A. Composition of the unemployment pool - data and methodology
The UK’s nationally representative Annual Population Survey (APS) combines responses from waves
one and five of the LFS, for the whole year, as well as incorporating local and regional boosts to the
sample to match its aim of providing representative data at the local authority level. To obtain more
reliable estimates of working-age (male 16-64, female 16-59) unemployment levels across the duration
distribution, I prefer this larger sample size dataset to the Quarterly Labour Force Survey. The increased
sample size is also useful when specifying heterogeneous types of unemployed individuals over multiple
levels (e.g. sex, age groups, duration of unemployment and industry of previous job). An indicative
reference for the APS datasets is as follows: Office for National Statistics. Social and Vital Statistics
Division. (2015). Annual Population Survey, January - December, 2004. [data collection]. 6th Edition.
UK Data Service. SN: 5334. Table A1 contains notes on the variables used and how these have been
transformed into the heterogeneous types used in the analysis. Table A2 gives the long-term shares of
unemployment across the various sets of personal characteristics for some of the years used here.
The counterfactual levels of unemployment by duration {˜
S,˜
M,˜
L}t, that would have occurred
had each type i’s distribution over unemployment duration remained constant relative to three years
previously, but allowing for the actual change over those years in the overall composition of types within
the total unemployment pool, where U=∑iUi, are similarly given by
˜
Lt=∑
iLi
Uit−3Ui
Ut
Ut.(8)
By definition the counterfactual is consistent with the realised total level of unemployment, i.e. ˜
St+˜
Mt+
˜
Lt=Ut. If there were no unemployed of type iat some duration three years previously, I simply retain
their current distribution over duration in the counterfactual: this will make little quantitative difference
since such types will have an insignificant weight.
Results for the counterfactual LTU share in 2007 relative to 2004, had each type’s duration shares
within unemployment remained constant, and likewise for 2010 and 2007, are given by Figures A1 &
A2. Each panel accounts for both the age and sex composition of the unemployed, as well as one other
level of heterogeneity. The small rise in the share of working-age unemployed who have been looking
for work for over twelve months between 2004 and 2007, 21% to 24%, cannot be explained by these
definitions of the composition. The change in composition with regards the occupation and industry of
an individual’s previous job marginally predicts a fall in the LTU share. The composition over when an
individual left their last job has no effect.
1
TABLE A1
Notes on variables used from the Annual Population Survey, 2004-2013
APS (2004)
variable Notes Transformations/categories
Age groups age
Groups as follows: 16-24, 25-34,
35-44, 45-54, 55-64/59.
Unemp.
duration durun
Minimum of the stated length of time looking for
work and length of time since respondent’s last job
(wnleft). From the APS the small share of the
weighted unemployed (less than 1%) who have no
duration response is dropped from the sample.
Three categories used: 0-3 months,
3-12 months, 12 months +
Region govtof Thirteen UK Government office regions - all
respondents.
Create ten categories by combining
North East and Yorkshire and
Humberside,North West and
Merseyside,East Midlands and West
Midlands.
Prev. job
industry inds92l
Standard Industrial Classification (SIC) 1992,
industry divisions . From 2008 onwards,
interviewers in the Labour Force Survey would
classify occupations using the SIC2007. Details of
how this differs from previous classifications can
be found on the ONS website. To generate a
consistent time series of employment by industry
sector I make use of the conversion variable in the
APS 2010, in0792sl. This was generated by the
ONS by matching SIC2007 sub-class to a higher
level of aggregation, i.e. division, in SIC1992, but
is not available for 2013.
Create new categories from 19
divisions and missing values: (D) -
Manufacturing, (F)- Construction,
(G) - Wholesale/retail, (H-I) -
Hotels, restaurants, transport,
comms, (J-K) - Finance & real estate
etc., (L-N) - Public sectors, (A-C, G,
O-Q, outside UK) - Others, Does not
apply (includes those with no
previous job).
Prev. job
occupation sc2klmj Standard Occupational Classification (SOC) 2000 -
major occupation groups.
Retain nine occupation groups and
include category for Does not apply
(includes those with no previous
job).
Reason left
prev. job redylft Applies to all respondents who are not working
and left job in 8 years before reference week.
Create five categories: (1-2) -
Redundant/dismissed, (3) -
Temporary job, (4-8) -
Resigned/gave up work/early
retirement, (9) - Other, (-9) - Does
not apply (includes those with no
previous job).
Type of
employment
sought
tyemps
Applies to all respondents looking for
employment. Large majority responded (2-3).
Other categories No preference, Self-employment
etc.
Create three categories: (2) - Full
time employee, (3) - Part-time
employee, (1, 4-11) - Other.
When left last
job (relative to
unemp. spell
starting)
wnleft
May differ from durun where there have been
spells since last job where an individual has not
looked for work, or where they have never had a
job.
Three categories: Same time -
wnleft=durun, Strictly longer -
wnleft>durun, Never had paid
employment.
Source: Author notes, but see also relevant dataset user guides held by the UK Data Service.
2
TABLE A2
Long-term shares of unemployment
2004 2007 2010
Sex Male 25.1 28.0 36.2
Female 15.5 18.2 24.5
Age groups 16-24 12.5 16.0 23.5
25-34 20.9 23.1 33.0
35-44 27.4 31.0 36.0
45-54 31.7 32.3 39.1
55-64/59 37.6 37.7 43.0
Region North East & Yorks. 41.9 37.9 42.8
North West & Mersey. 20.9 26.4 34.4
Midlands 22.6 24.4 30.2
Eastern 20.6 25.2 34.2
London 16.0 21.8 29.8
South East 23.8 26.0 33.0
South West 14.3 18.3 25.6
Wales 16.8 17.7 30.2
Scotland 20.1 22.3 30.4
Northern Ireland 24.6 22.8 29.2
Industry of prev. job Manufacturing 27.3 29.8 38.8
Construction 23.6 22.3 36.3
Wholesale, retail 15.6 17.4 29.8
Finance, real estate 15.9 17.4 27.6
Hotels, restaurants, transport,
comms. 19.0 20.3 29.0
Public sectors 13.3 18.4 25.1
Other 18.4 21.6 28.1
Does not apply 24.3 30.1 34.5
Occupation of prev. job Managers and Senior Officials 19.9 19.0 28.1
Professional occupations 17.3 16.1 22.3
Associate Professional and
Technical 15.6 18.2 24.4
Administrative and Secretarial 12.8 15.3 28.1
Skilled Trades Occupations 25.8 27.2 36.3
Personal Service Occupations 11.8 18.9 26.1
Sales and Customer Service
Occupations 9.8 14.5 20.9
Process, Plant and Machine
Operatives 26.0 27.8 39.2
Elementary Occupations 21.6 24.4 34.9
Does not apply 24.4 30.1 34.8
All 21.2 23.8 31.6
Source: Author calculations using UK Annual Population Survey, ages 16-64/59, January-December 2004, 2007 & 2010.
3
TABLE A2 (cont.)
Long-term shares of unemployment
2004 2007 2010
Reason left prev. job Redundant, dismissed 23.3 25.1 34.6
Temporary job 16.3 19.6 24.4
Resigned, gave up work, early
retirement 15.2 18.4 29.1
Other 21.4 20.2 29.3
Does not apply 24.5 30.3 34.5
Type of employment sought Full-time employee 23.2 24.9 33.9
Part-time employee 13.0 15.9 21.8
Other 23.1 29.8 35.2
When left last job (relative to
unemployment spell starting) Same time 26.1 27.8 38.9
Strictly before 16.6 18.7 23.4
Never had paid employment 20 24.4 28.4
All 21.2 23.8 31.6
Source: Author calculations using UK Annual Population Survey, ages 16-64/59, January-December 2004, 2007 &
2010.
4
Figure A1. Distribution of unemployment over duration in 2004 and 2007, and the role of composition
changes in between
(a) Region (b) Industry of last job
(c) Occupation of last job (d) Reason left previous employment
(e) Type of employment sought (f) When left last job
Notes: Counterfactual gives unemployment shares for 2007 holding constant the distribution over {S,M,L}for each stated
type of heterogeneity, interacted with sex and age groups, from 2004, and applying the overall distribution of types in the
unemployment pool from 2007.
Source: Author calculations using UK Annual Population Survey, ages 16-64/59, January-December 2004 & 2007.
5
Figure A2. Distribution of unemployment over duration in 2007 and 2010, and the role of composition
changes in between
(a) Region (b) Industry of last job
(c) Occupation of last job (d) Reason left previous employment
(e) Type of employment sought (f) When left last job
Notes: Counterfactual gives unemployment shares for 2010 holding constant the distribution over {S,M,L}for each stated
type of heterogeneity, interacted with sex and age groups, from 2007, and applying the overall distribution of types in the
unemployment pool from 2010.
Source: Author calculations using UK Annual Population Survey, ages 16-64/59, January-December 2007 & 2010.
6
Appendix B. Labour market flows - data & adjustments
Seasonal adjustment
Given quarterly gross flows between states ˆ
XY tfor 1997q2≤t≤2015q2, measured from the longitudinal
datasets, I first take the log difference from the series centred using a four quarter moving average,
i.e. ln ¯
XYt=ln ˆ
XY t−ln(0.125 ˆ
XY t−2+0.25 ˆ
XY t−1+0.25 ˆ
XY t+0.25 ˆ
XY t+1+0.125 ˆ
XY t+2).
I the regress this on a set of quarterly dummies, as well as additional dummies for t=2000q4,2001q1,
since there is a reduced sample of reported unemployment durations in the final quarter of 2000, which
can be accounted for at this stage. Using the residuals/predicted values εtfrom these regressions,
the seasonally adjusted gross flows series for 1997q4≤t≤2014q4 are then given by ˜
XY t=
ˆ
XY t/exp(ln ¯
XYt−εt).
Stocks-flows consistent adjustment for measured transition rates
To adjust the measured transition rates to be consistent with national labour market statistics measures
of the stocks I solve the following problem for each t:
min
φt
(φt−˜
φt)0˜
W−1
tφt−˜
φt(9)
s.t.∆zt=Zt−1φt,{µt}(10)
Rφt=0{νt}; (11)
i.e. I choose φt, a (20x1)vector of transition rates between states, to minimise its distance from the
equivalent ˜
φtestimated from the survey data, and where ˜
W−1
tis proportional to the covariance matrix
of ˜
φt. This is subject to (10), which states that the change in population rates should be equal to the
normalised gross flows, where Zt−1is a (4x20)matrix populated accordingly with population shares,
and (11), where Rcontains the restrictions pEM =pE L =pSL =pLM =pNM =pNL =0. The solution is
given by
φ
1/2µ
1/2ν
t
=
˜
W−1
tZ0
t−1R0
Zt−10 0
R0 0
−1
˜
W−1˜
φ
∆z
0
t
.(12)
‘Cleaned’ transition rates - specification (III)
As described in the main text, the primary assumption behind this robustness check is that an individual’s
employment status is most likely to have been recorded accurately. Starting from this strong assumption,
all employment to unemployment flows are then recoded to ES. Then, where it is unambiguous, allowing
for the possibility of unemployment restarts, if an individual is observed as unemployed up to three
quarters consecutively subsequent durations are recoded accordingly. Further, observed transitions to
a shorter duration between two quarters of LTU are reassigned to LLL, and the continuous observed
unemployment spell SMLL is reassigned to SMML. Table B1 details the number of observed transitions
reassigned as such.
7
Time aggregation bias correction - specification (V)
It is common in the literature to set out the stocks-flows decomposition in terms of continuous time
equivalent hazard rates instead of transition probabilities. This is intended to have the advantage of
accounting for time aggregation bias in measured transitions; i.e. movements between states, which
could be important in explaining the cyclical behaviour of labour market rates, are ignored due to the
frequency of data collection. Adjustments to account for this implicitly assume that hazard rates are
constant and identical for all workers within a state and period. However, given the analysis of limited
duration dependence of transition rates here, implicitly assuming that there is none within S,M,Lis
somewhat counter-intuitive. Nonetheless, since I isolate short-term unemployment as a separate state,
where the majority of the time aggregation bias would be expected to occur, it would be remiss not to
account for it in some way. As computed in EHS, the continuous time generator or hazard rate version of
Ptis its principal logarithm, lnPt=Ft. However, this only exists and is unique under certain conditions
on Pt.2Fortunately, these conditions are always met for the series of naïve transition rates estimated
here. The effect of the adjustment is substantial on the levels of transition rates to and from short-term
unemployment. The implied hazard rates for the E S and SE flows are both approximately doubled.
Given the much greater level of the latter transition rate, it follows mechanically that time aggregation
would bias the βshares of the variance decomposition downwards for unemployment exits.
The computed hazard rates can then be used to replace the steady-state (2) with its continuous time
equivalent,
¯
zt=−Λ−1
tλt
where Λtand λtare equivalents of Πtand πt. The derivation of (4) and (6) is then identical besides the
derivatives of the Taylor expansion taking a different analytical form.
2If Ptis ‘embeddable’ (non-singular) the only generator matrix is given by its principal logarithm when its eigenvalues are
real, distinct and positive.
8
TABLE B1
‘Clean’ flows - unweighted number of quarter to quarter transition observations changed on account of
reassignments, 1997q2-2015q2
Freq. Percent
Unchanged 3,093,122 99.69
EM ,EL,EU to E S 7,769 0.25
ESL to ESM 673 0.02
ESML to E SMM 188 0.01
SMLL to SMML 324 0.02
LSL,LML,LUL to LLL 510 0.02
Notes: U here refers to the small number of observations where no duration data was recorded.
Source: Author calculations using Two Quarter Longitudinal Labour Force Survey, ages
16-64/59. Information on how these values vary over time is available on request.
TABLE B2
‘De-NUN-ification’ - unweighted number of quarter to quarter transition observations changed on
account of reassignments, 1997q2-2015q2
Freq. Percent
Unchanged 3,097,667 99.84
NN LN or NNMN to NNNN 1,854 0.06
NLN N or NMN N toNNNN 1,536 0.04
LLNL to LLLL 420 0.02
LNLL to LLLL 288 0.00
MLNL MLLL 68 0.00
MMNL to MMLL 176 0.00
EN MN ENLN to E N NN 160 0.00
ESNM to E SMM 210 0.00
ESNL to E SMM 29 0.00
MNLL to MMLL (ambiguous)†178 0.00
†Although this small number of observation remains ambiguous, it was decided on the
balance of likelihood to reassign them.
Source: Author calculations using Two Quarter Longitudinal Labour Force Survey, ages
16-64/59. Information on how these values vary over time is available on request.
9
TABLE B3
Ratio of unweighted flows observations after adjustments to before, 1997q2-2015q2
‘Clean’ ‘Clean’ + ‘deNUN’
EE 1.00†1.00
EL 0.00 0.00
EM 0.00 0.00
EN 1.00 1.00
ES 1.25 1.25
EU 0.00 0.00
LE 0.94 0.94
LL 0.99 1.02
LM 0.78 0.78
LN 0.98 0.78
LS 0.90 0.90
LU 0.57 0.57
ME 0.93 0.93
ML 0.95 0.99
MM 0.97 0.98
MN 0.97 0.80
MS 0.93 0.93
MU 0.91 0.91
NE 1.00 1.00
NL 1.00 0.75
NM 1.00 0.87
NN 1.00 1.01
NS 1.00 1.00
NU 1.00 1.00
SE 1.06 1.06
SL 0.90 0.90
SM 1.11 1.12
SN 1.03 1.02
SS 1.02 1.02
SU 1.19 1.19
UE 0.72 0.72
UL 0.64 0.64
UM 0.83 0.83
UN 0.94 0.94
US 0.80 0.80
UU 0.94 0.94
†# of observations after / # of observations before.
Notes: U here refers to the small number of observations where no duration data was recorded.
Source: Author calculations using Two Quarter Longitudinal Labour Force Survey, ages
16-64/59. Information on how these values vary over time is available on request.
10
TABLE B4
Stocks-flows decomposition: including adjustments for classification errors, 1998q2-2014q4
(III)*(IV)**
∆e∆u∆urate †∆l∆e∆u∆urate †∆l
∆pEU 0.25§0.35 0.35 0.06 0.25 0.35 0.35 0.07
∆pEN 0.14 0.00 0.00 0.01 0.14 0.00 0.00 0.01
∆pUE 0.32 0.41 0.41 0.39 0.31 0.42 0.43 0.40
∆pUN 0.01 0.21 0.19 0.32 0.01 0.19 0.17 0.29
∆pNE 0.32 0.00 0.03 0.00 0.31 0.00 0.03 0.00
∆pNU 0.00 0.05 0.04 0.01 -0.01 0.05 0.04 0.01
∆pUU 0.01 0.02 0.02 0.10 0.01 0.02 0.02 0.10
Initial val. 0.01 0.02 0.02 0.05 0.01 0.02 0.02 0.05
Approx. err. -0.05 -0.06 -0.06 0.04 -0.04 -0.05 -0.05 0.07
*(II) and using classification error adjusted transition probabilities as ˜
φt.
** (III) and using ‘de-NUN-ified’ transition probabilities as ˜
φt.
†urate =u/(u+e)
§Interpretation: Share of variance in the quarterly change in the employment rate accounted for by past and present quarterly
changes in pE S (or hazard rate equivalent), i.e. βe
EU =cov(∆et,{cEU,t}1)
var(∆et).
Source: Author calculations using Two Quarter Longitudinal Labour Force Survey & Labour Market Statistics, ages
16-64/59.
TABLE B5
Stocks-flows decomposition: including time aggregation bias adjustment, 1998q2-2014q4
(I)*(VI)**
∆e∆u∆urate †∆l∆e∆u∆urate †∆l
∆pEU 0.27§0.36 0.36 0.10 0.28 0.26 0.27 0.10
∆pEN 0.16 0.00 0.00 0.00 0.12 -0.01 0.00 0.00
∆pUE 0.26 0.32 0.32 0.26 0.32 0.39 0.39 0.29
∆pUN 0.00 0.17 0.15 0.33 -0.01 0.29 0.26 0.41
∆pNE 0.30 0.01 0.03 0.00 0.28 0.01 0.03 0.00
∆pNU -0.03 0.14 0.12 0.16 -0.05 0.05 0.04 0.12
∆pUU 0.00 0.01 0.01 0.10 0.02 0.03 0.03 0.05
Initial val. 0.01 0.01 0.01 0.03 0.01 0.01 0.01 0.03
Approx. err. 0.02 0.01 0.01 0.01 0.02 -0.03 -0.02 0.00
*‘Naïve’ transition probabilities, i.e. with no zero value restrictions when adjusting ˜
φt.
** ‘Naïve’ hazard rates, i.e. with time aggregation bias adjustment.
†urate =u/(u+e)
§Interpretation: Share of variance in the quarterly change in the employment rate accounted for by past and present quarterly
changes in pE S (or hazard rate equivalent), i.e. βe
EU =cov(∆et,{cEU,t}1)
var(∆et).
Source: Author calculations using Two Quarter Longitudinal Labour Force Survey & Labour Market Statistics, ages
16-64/59.
11
Figure B1. Cumulative long-term unemployment contributions from exit rates and the share of gross
in-flows previously employed, 2008-2014
Notes: Series indexed to zero in 2007q4. Interpretation of series on left axis is the cumulative increase in long-term
unemployment’s population share since 2007 accounted for by past and present changes in transition rates.
Source: Author calculations using Two Quarter Labour Force Survey & Labour Market Statistics, ages 16-64/59. Transition
rates calculated using specification (I).
Appendix C. The potential role of labour market policy changes
It is possible that changes in UK Government labour market policy are responsible for some of the results.
Upon becoming unemployed in the UK, the typical process for many individuals is to first ‘sign on’ to
Jobseeker’s Allowance (JSA). This is an active benefit in so far as claimants must look for work and
be available to start at short notice, meaning that a LFS respondent receiving such payments would in
most cases be classified as ILO unemployed.3After signing on, eligibility for other alternative benefits
is considered, such as Income Support (primarily for lone parents) and allowances related to disability or
care. If eligibility is confirmed there is no monitored requirement to look for work. Although individuals
in receipt of such payments could be classified as ILO unemployed instead of inactive, this is less likely.4
If there was a tightening of eligibility criteria for inactive benefit payments since the Great Recession,
this could manifest itself in the aggregate flow rates as observed. Tightening criteria could immediately
lead to a reduction in UN flows, but the effect on the reverse flow would be drawn out as reviewing
eligibility and the fitness to work of those receiving disability or carer benefits is a slow process. There
was such a tightening in the UK, with the stricter Employment Support Allowance (ESA) gradually
replacing Incapacity Benefit towards the end of 2008. Furthermore, in November 2008, the age limit of
the youngest child for lone parents to be eligible for Income Support was lowered to twelve for all new
claimants. Out-of work parents would have had to claim JSA instead and actively look for work.5Panel
(a) of Figure C1 demonstrates the effect of these policy changes, using administrative data, through
the immediate downward shift concurrently in the share of all off-flows from JSA to either Income
Support, incapacity benefits or some other benefit, for both all claimants and those claiming for over
twelve months.6The majority of the fall is in off-flows to Incapacity Benefit. Although many of these
3Using the January-December 2007 Annual Population Survey, 21% of JSA recipients were classified as ILO inactive.
4Using the January-December 2007 Annual Population Survey, 13% of Income Support recipients were classified as active.
5The age limit was gradually lowered for existing claimants, starting the following year, to five by October 2011.
6Off-flows series accessed from NOMIS, ONS, 10/02/2016. Although these administrative flows data are detailed in terms
of the destinations following and reasons for a claim ending, they are incomplete in so far as the destination of some claimants
is unknown, and the rate at which claimants complete exit questionnaires is not constant over time, having reduced in recent
years.
12
claimants may have eventually moved to ESA, this is not recorded. To assess whether this could have
affected the estimated cyclical behaviour of transition rates, I imagine a counterfactual whereby all JSA
off-flows to other benefits are simply subtracted from the actual number of observed UN and LN gross
flows, not accounting for the introduction of the replacement ESA. Panel (b) of Figure C1 compares
actual transition rates with imagined ones which negate these JSA off-flows, pUN ∗and pLN∗. The policy
changes could account for a not insignificant amount of the cumulative decline in exit rates from 2008,
but the cyclical pattern remains. Given that this represents the absolute upper limit of any potential policy
effects occurring concurrently, the actual effect is likely to have been much smaller.
Figure C1. Share of JSA off-flows to inactive benefits and an estimate of the maximum potential policy
impacts on estimated flows series pUN and pLN
(a) JSA off-flow shares (b) Imagined counterfactual
Source: Author calculations using Two Quarter Labour Force Survey, ages 16-64/59, 1997q2 - 2015q2, and NOMIS, ONS
off-flows series from Jobseeker’s Allowance - using raw transition rates.
13