In or out? Poverty dynamics among pensioner
households in the UK.
Ricky Kanabar1
1 Institute
for Social and Economic Research
University of Essex
Family finance surveys user conference, Royal Statistical
Society (July 2016)
Introduction and motivation
11.4m individuals or 17.7% of the UK population is at or
above SPA (DWP, 2015)
14% females (13% males) in 2011/12 were living in poverty
(DWP, 2013)
Fiscal implication of pensioner poverty is non-trivial. In the
tax year 2013/14, £6.233 billion pounds was spent on
Guarantee Credit; a specific benefit available to pensioners on
low incomes (DWP, 2015)
Policymakers need to understand low income dynamics in
order to reduce current and future pensioner poverty. How
much volatility is there in pensioner incomes?
More importantly, does a dichotomous measure (poverty)
adequately reflect actual pensioner living standards?
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Preview: main findings
Benefit income is an important determinant of poverty status,
specifically
Disability and incapacity income significantly reduces the
probability of being in poverty
However this does not accurately reflect an individual’s
standard of living, especially if one accounts for care costs
Whilst there is a relatively high degree of state persistence,
pensioner incomes exhibit volatility, specifically there is
regression toward the mean
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Literature: pensioners poverty in the UK
Bardasi, Jenkins and Rigg (2002): Onset of retirement is
correlated with becoming poor
Jenkins and Rigg (2001): Dynamics of poverty in Britain
DWP: HBAI annual report (various), Kotecha et al. (2013)
IFS: Brewer et al. (2007); Bozio et al. (2010, 2011)
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This paper
Analyse poverty dynamics among British pensioners
accounting for initial conditions and non-random survey
attrition.
Highlight the importance of:
labour market history (proxied by pension receipt/type) and
the implications it has for pensioner poverty.
Educational attainment
benefit income, in particular disability/incapacity income in
determining poverty status.
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Income wave 3 (£)
400
600
800
1000 1200 1400 1600 1800 2000 2200
How much does income actually change in retirment?
400
600
800
1000
1200
1400
1600
1800
2000
Income wave 2 (£)
Notes: Income refers to net monthly household equivalised income after
housing costs which has been adjusted to account for inflation (2010
prices).
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Usually think of retirement coinciding with income being
stable, however this is not (entirely) true.
Estimation strategy and data
Strategy
Endogenous switching model (first order Markov model
developed by Cappellari and Jenkins (2004))
Analyse initial poverty, survey attrition and conditional
poverty status simultaneously
Methodological framework can account for left censored
observations
Data/sample
Data: Pool waves 2-4 of Understanding Society (most
variables defined at HoH level)
Restrict attention to pensioner households1
N=12,904 of which 1,796 attrit (14%)
1I
restrict the sample to individuals who (at wave 2) are aged at least 60 if logo.png
they are female and 65 if they are male and report being in retirement and live
in a household which is defined as a ’single’ or ’couple’ pensioner household.
Econometric framework
∗
0
1.Initial poverty: pi,t
+ι
−1 = β xi,t −1 + ζ
| i {zi,t −}1
ε i,t −1
2. Survey retention:
∗
ri,t
= γwi,t −1 + ωi + ei,t
| {z }
κi,t
3. Conditional
poverty status:
h
i
0
0
∗
pi,t = (Pi,t −1 )θ1 + (1 − Pi,t −1 )θ2 si,t −1 + τi + ς i,t
| {z }
ϑi,t
I estimate the most general version of the model (all three
equations simultaneously).
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Cross equation correlations
Unobserved heterogeneity is summarised by the cross equation
correlation coefficients.
Initial poverty and survey retention:
ρ1 ≡ corr (ε i,t −1 , κi,t ) = cov (ζ i , ωi )
Initial and conditional poverty status:
ρ2 ≡ corr (ε i,t −1 , ϑi,t ) = cov (ζ i , τi )
Conditional poverty status and survey retention:
ρ3 ≡ corr (κi,t , ϑi,t ) = cov (ωi , τi )
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Econometric framework
Model is identified using suitable exclusion restrictions:
In order to address biases which arise due to initial conditions
I include a control for paternal educational attainment in the
initial poverty equation (1.) but exclude this from conditional
poverty equation (3.)
In the case of survey retention (2.), I use information collected
by survey inteviewer about whether the respondent raised queries
regarding the survey e.g. What’s the point? How long is this
going to take? Is there a financial incentive? Will you come
back again? Who has access to my responses?
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Poverty status, transitions and attrition
T-1
Poor
Non-poor
Total
Poverty status and attrition.
T
Poor
Non-poor
Missing
58.75% (2,024) 27.08% (939)
14.17% (488)
8.18% (774)
78% (7,377) 13.83% (1,308)
2,798
8,310
1,796
Total
3,445
9,459
12,904
Attrition rate is not significantly different conditional on
poverty status.
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Descriptive statistics poor versus non-poor
Characteristic
Individual
Age
Male
Married
HoH
Father educated
Degree
Excellent health
In receipt of incapacity benefit
In receipt of disability benefit
State pension (& pension credit) or no pensions
Two or more non-state pensions
Poort −1
Non-poort −1
75
0.35
0.44
74
0.42
0.59
0.13
0.03
0.03
0.05
0.09
0.34
0.08
0.16
0.14
0.07
0.14
0.25
0.19
0.17
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Estimation results: Initial poverty statust −1
Variable
M.E.
Coefficient(S.E)
Father educated
-0.026**
-.148(0.05)
In receipt of incapacity/severe disability benefit
-0.157***
-.86(.07)
In receipt of attendance or carers allowance
-0.165***
-.95(.07)
Cares for another in household
-0.068***
-.27(0.09)
1 Employer/occupational pension
-0.11***
-.48(0.04)
>1 Employer/occupational pension
-0.16***
-.93(0.06)
Individual
Other contols: age, gender,Marital status
HoH
Other controls: Age, gender, education, housing tenure, health, longstanding illness, health*longstanding illness,
region, subjective financial situation, income sources (investment income, private benefit income, misc income)
Log-likelihood: -15205.078, χ222 : 314.27, N: 12904
Notes: Bold text indicates characteristic is significant at at least the 10% level.
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Estimation results: Survey retentiont
Variable
M.E.
Coefficient (S.E.)
-0.033***
-.031(.006)
Individual
Age
Other controls: marital status, gender
HoH
Health: Fair
-0.0473***
-.27(.08)
Health: Poor
-0.105***
-.51(.08)
Raised 1 query
-0.036***
-.21(.04)
-0.029*
-.18(.10)
Raised >1 query
Other controls: age, gender, housing tenure, caring, longstanding illness,
health status, occupational/employer pension. Notes: Bold text indicates
covariate is significant at least the 10% level.
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Estimation results: Conditional poverty statust
Poor
Variable
Non-poor
M.E.
β(σ)
M.E.
β((σ)
-0.058
-0.12(.14)
-0.0177
-.181(.087)
Individual
Other controls: age, gender, marital status.
HoH
In receipt of Incapacity/severe disablement benefit
In receipt of attendance or carers allowance
-0.208
-0.48(.18)
-0.0156
-.164(.098)
1 Employer/occupational pension
-0.015
-.0007(.06)
-0.002
-.017(.046)
>1 Employer/occupational pension
-0.039
.009(.11)
-0.016***
-.22(.08)
Other controls: age,gender, education, housing tenure, health status, Longstanding illness/disability,
Longstanding illness/disability, health status*Longstanding illness/disability,region, cares,
sources of income (investment income), subjective financial situation
Notes: Bold text indicates characteristic is significant at at least the 10% level.
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Model correlations and test statistics
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Income at t − 1 versus change in income
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Change in individual income components between t − 1and
t
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Additional checks: Instrument validity
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State dependence
Study
Me
Cappellari and Jenkins (2004)
Buddelmeyer and Verick (2009)
Fusco and Islam (2013)
ASD
0.587
0.526
0.57
0.65
Test for absence of Genuine State Dependence H0 : θ1 = θ2 (not
rejected at conventional levels of significance)
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Stylised examples
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Conclusions
Specific factors are important in determining pensioner
poverty.
benefit receipt, which suggests ’poverty’ may not (by itself) be
suitable for measuring pensioner living standards (perhaps also
use material deprivation, wellbeing etc).
educational attainment (individual and paternal)
housing tenure, health, having a limiting illness
presence of an occupational or private pension
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Conclusions
Large degree of variation in poverty persistence, entry rates
and also predicted time spent in poverty- even for individuals
with the same characteristics highlights the importance of
controlling for unobserved heterogeneity.
Hence, when analysing pensioner poverty dynamics one should
account for initial conditions.
Mean reversion driven by particular subcomponents of
individual income: investment, pension and benefit income.
No evidence of correlation between initial and conditional
poverty status with non-random attrition.
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