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Final author version accepted for publication in the Journal of Social Policy in 2021.
Plumbing the Depths: The Changing (Socio-Demographic) Profile of
UK Poverty
Daniel Edmiston, University of Leeds
D.Edmiston@leeds.ac.uk
Abstract
Official statistics tend to rely on a headcount approach to poverty measurement, distinguishing
‘the poor’ from the ‘non-poor’ on the basis of an anchored threshold. Invariably, this does little
to engage with the gradations of material hardship affecting those living, to varying degrees,
below the poverty line. In response, this paper interrogates an apparent flatlining in UK poverty
to establish the changing profile of poverty, as well as those most affected by it. Drawing on
the Family Resources survey, this paper reveals an increasing depth of poverty in the UK since
2010, with bifurcation observable in the living standards of different percentile groups below
the poverty line. In addition, this paper demonstrates substantial compositional changes in the
socio-demographic profile of (deep) poverty. Since 2010, the likelihood of falling into deep
poverty has increased for women, children, Black people, larger families and those in full-time
work. Within the context of COVID-19, I argue there is a need to re-think how we currently
conceptualise poverty by better attending to internal heterogeneity within the broader analytical
and methodological category of ‘the poor’. Doing so raises important questions about the
prevailing modes of poverty measurement that tend to frame and delimit the social scientific
analysis of poverty, as well as the policies deemed appropriate in tackling it.
Keywords: poverty gap; depth of poverty; relative poverty; poverty profile; social security;
destitution
Introduction
Social enquiry into relative poverty is currently faced with an ostensive contradiction. Research
suggests we are witnessing increased material hardship and destitution amongst those most
vulnerable to socio-economic and welfare state restructuring in liberal welfare regimes such as
the UK (Bassel and Emejulu, 2017). This is perhaps unsurprising given that such contexts have
typically been characterised by residual public social assistance, high commodification and
market-based poverty alleviation strategies. Whilst there is non-trivial diversity in the varieties
of liberalism at play, English-speaking democracies have converged on increasingly restrictive
social transfers that have undermined the living standards of low-income citizens over the last
decade (Deeming, 2017). Despite this, official statistics tell a story of remarkable stability in
2
relative poverty rates across the same contexts with only minor or temporary upticks for
particular groups in recent years (DWP, 2020c).
How then can the acute financial hardships engendered through welfare revisioning and late
capitalist transformation be squared with a relative stasis in rates of relative poverty? In great
part, minor fluctuations or declining rates of relative poverty are an artefact of methodological
limitation. The relative poverty line is a somewhat arbitrary one that counts all those falling
below a given threshold (usually 50% or 60% of median incomes), which reveals little about
the changing income dynamics, socio-demographics or concentrations of poverty (Sen, 1981;
Veit-Wilson, 1998). Equally, it runs the risk of obscuring fluctuations in the substantive living
standards of those falling below the poverty threshold by anchoring this to (potentially
stagnating) median incomes. Within the context of COVID-19 and stagnant or falling median
incomes, these ongoing limitations are all the more acute. Despite these cautionary axioms,
there is still a tendency within academic and policy analysis to characterise this threshold
approach as a definition of poverty, rather than measuring a particular distribution of below
average incomes. Such slippage between conceptual definition and methodological
categorisation leads to an attenuated analysis of poverty, reduced to technical thresholds and
subject to limitations of both methodology and data (Lister, 2021). If the methods we choose
to measure poverty come to determine how we understand what it is (and who counts as ‘poor’)
then researchers find themselves in a situation where the methodological tail is wagging the
conceptual dog. This matters because dominant measures of poverty invariably guide how we
come to appraise and understand ‘the problem’, as well as the policies deemed appropriate or
necessary in tackling it
1
.
In this paper, it is not my intention to detract from the measurement of relative poverty and its
widespread utility in poverty analysis or policy evaluation. Rather, I aim to explore
heterogeneity within this category to establish changes in the composition and depth of poverty
witnessed since 2010. Doing so demonstrates the distinctive merits of different approaches to
poverty measurement and how these can be combined to gain a fuller picture of low-income
realities and trends. In many respects, a unitary theory of poverty is problematic in light of its
multi-dimensional nature and any approach to poverty measurement should also reflect and
respect this reality. There is a rich history of doing so that can be traced back to the first
systematic surveys of poverty in the UK. Charles Booth pioneered a poverty line approach but
also deployed statistical methods and ethnographic observations to identify degrees of
privation, including its correlates and underlying determinants. Criticising the lack of empirical
and policy attention being given to the changing structure and determinants of ‘extreme
poverty’ within advanced capitalist economies, the United Nations Special Rapporteur on
‘extreme poverty’ and human rights recently recommended such an undertaking during an
official country visit to the UK (Alston, 2018: 15).
In response, this paper seeks to refine our conceptual, methodological and policy
understanding of ‘the poor’ in terms of measurement and composition. Three lines of enquiry
highlight the importance of doing so, exposing gaps in what we currently know about the
profile and depth of poverty. First, research into the ‘poverty gap’ – the average distance low-
income households fall from the poverty threshold – comes some way to track aggregate
changes in the depth of poverty and evidence suggests that the poverty gap for families and
children has increased since 2010 (Lee, 2020). That said, this measure relies on an overall
1
Similarly, poverty (alleviation) policy routinely frames our particular understanding of financial hardship and
its underlying determinants.
3
average anchored to the poverty threshold and is therefore unable to fully capture inequality
‘below the line’. Second, academic and policy analysis has examined the systematic over-
representation of women, children, Black, Asian and minority ethnic (BAME) groups and
disabled people in poverty. However, less attention has been given to how these social
differences intersect with concentrations of poverty. This seems particularly important if we
are to better understand how ‘social difference’ is articulated in relation to the material social
locations of people across the entirety of the income distribution, not just on either side of a
given threshold. Third, recent efforts have been made to estimate the extent of ‘destitution’ in
particular regions by drawing on local surveys to construct national estimates (Bramley et al.,
2016). The findings of these studies suggest there are hidden degrees of hardship that are not
being captured through conventional methods of poverty measurement associated with
household income surveys. Given the uneven impacts of COVID-19 on livelihoods, these gaps
highlight a need to go beyond poverty measures currently dominant in the UK and beyond.
Whilst attempts have recently been made to explore heterogeneity below the poverty line,
‘urgent research is needed to better understand the experiences and outcomes of people who
are measured as being more than 50% below the poverty line’ (SMC, 2019: 23). With that in
mind, this paper seeks to answer the following questions: How has the profile and depth of
poverty changed in the UK since 2010? And how are these trends related to the changing socio-
demographic characteristics of ‘the poor’? Drawing on data from the Family Resources survey,
I demonstrate that there has been a splintering in the economic fortunes of different percentile
and socio-demographic groups falling towards the bottom of the income distribution. The
findings demonstrate the need to deploy a plurality of approaches to poverty measurement, to
better understand how and why people move, not just ‘in’ and ‘out’ of (extreme) financial
hardship, but also through it along a continuum of disadvantage. The paper concludes by
reflecting on what this means for poverty analysis and alleviation.
Background and Policy Context
Depending on the particular dimension foregrounded, the concept of relative poverty can be
an imperfect but common proxy for identifying (A) a lack of financial means for human
welfare (B) systemic exclusion from mainstream social practices; (C) material or resource
deprivation whereby individuals lack access to certain items or activities deemed necessary; or
(D) a combination of all three (Bradshaw and Finch, 2003). Each of these traditions within
poverty studies is rooted in a conception of disadvantage that seeks to foreground a particular
feature of its character, cause or effect. All nonetheless attempt to capture the relative nature
of poverty and better understand this in relation to the ‘rest of society’. In doing so though,
binary demarcations between those above and below a given ‘threshold’ (usually 60% of
median incomes) risk glossing over much of the heterogeneity that exists below the relative
poverty line (Figure 1). Such an approach risks ascribing a unilateral condition or experience
to the entirety of the broader category of ‘the poor’ (a sizeable portion of the income
distribution), without recognising how income poverty functions in a scalar (or even vector)
relationship with questions of ‘human welfare’, ‘participation’ and ‘inclusion’. Indeed, it might
be reasonably assumed, but is subject to empirical investigation, that households furthest from
the relative poverty threshold are not only likely to be experiencing (D) but in a qualitatively
different sense to those closer to the poverty line.
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Figure 1. Conceptualising relative poverty: categorical vs. continuum
Since the 2007-08 global financial crisis, a great deal of attention has been paid to the changing
profile of income inequality with efforts made to explore gradations of advantage amongst ‘the
rich’. However, we have not witnessed the same efforts to explore degrees of disadvantage.
Whilst it is beyond the remit of this paper to examine the qualitative significance of a changing
poverty profile, I do explore the relativity of financial hardship that bears on the lives of those
living, to varying degrees, below the poverty line
2
. It is hoped doing so affords fuller insight
into the distributional effects of socio-economic and welfare state restructuring.
Since 2010, reforms to the tax-benefit system have reduced the coverage and generosity of
low-income, working-age social security in the UK. Over the last decade, Crisis Loans, the
Child Trust Fund, Education Maintenance Allowance, as well as elements of Child Tax Credit
and Working Tax Credit have been abolished. The phased introduction of Universal Credit
(UC) is gradually consolidating six working-age benefits into a single payment to ‘incentivise’
work and streamline the claims-making process. Much less generous than its original
formulation, those towards the bottom (10%) of the income distribution have lost most (Brewer
et al., 2019). Delayed payments and sanctioning have also presented significant risks to the
economic security of low-income households. Between 2014-16, uprating for certain benefits
(including elements of Tax Credits (TC) and Child Benefit) moved from the Retail Price Index
to Consumer Price Index. In 2016, the majority of working-age benefits were frozen for four
years, along with tax credits and local housing allowances. Local rent limits and under-
occupancy penalties have been introduced for certain groups in social housing. A benefit cap
has been introduced (and subsequently lowered) with the national main rate currently set at
£20,000 (£23,000 in London). In 2017, a two-child limit was introduced which means UC will
only provide support for a maximum of two children for certain families. The full impact of
this is yet to be felt with the effects being cumulative for larger families. Breaking the link
between entitlement and need, the result of these changes is a substantive decline in ‘the
adequacy of income support’ for many low-income households with benefit levels falling
2
As argued by Summers (2020: 594), there are methodological and ethical risks in not delineating
between individual research subjects and the ‘the wider social groups(s) to which they are understood to
belong’ (and indeed further challenges in understanding what it means to ‘belong to’ a given social group).
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further away from average earnings and minimum income standards since 2010 (Hirsch, 2020:
218).
Particularly for larger low-income families, these changes are ‘guaranteed to increase poverty
depth’ (Bradshaw and Keung, 2019: 11; Lee, 2020). The distributional effects of this are, in
many respects, well-rehearsed. Academic and grey literature demonstrates the highly
regressive nature of welfare recalibration whereby changes have damaged the living standards
of those toward the bottom of the income distribution most
3
. For example, distributional
analysis demonstrates that those in the 1st and 2nd deciles of the income distribution are likely
to lose around 20% of their incomes as a result of tax and benefit changes (Resolution
Foundation, 2020: 50). Many have also highlighted how these cuts are heavily gendered and
racialised in ways that are routinely glossed over (Bassel and Emejulu, 2017; Hall et al., 2017).
Since 2010, many have forecast that ‘the likely outcome is an increase in poverty and
inequalities in the next few years’ (Taylor-Gooby, 2012: 78). And yet, rates of relative poverty
for the whole population have flatlined (with isolated upticks for smaller population subgroups)
(DWP, 2020c). There are a number of reasons for this that do not exclusively concern the
working-age population or indeed the economic resources available to low-income households
(Bourquin et al., 2019). However, one possibility is that those worst affected by welfare
reforms may already have been below the poverty line before tax-benefit changes took effect.
This is all the more likely given that those disproportionately exposed to the negative effects
of social security cuts (very low-income households, working-age adults and children) are most
prone to ‘persistent poverty’ (SMC, 2019). Looking ahead, there is evidence to suggest that
those furthest away from the poverty line are worst affected by COVID-19 in terms of
employment and pay (SMC, 2020). Crisis social security measures have helped temper some
of the worst effects for the ‘poorest families’ through uprating, easements and additions.
However, many of these changes are temporary, partial or ineffectual for households claiming
legacy benefits or affected by the benefit cap and two child-limit. If the uprating to UC and TC
standard allowance is not made permanent, the poorest households stand to lose 7% of their
annual income on average in 2021 (Handscomb, 2020).
Against this backdrop, threshold approaches to poverty analysis that focus exclusively on rates
as opposed to degrees of financial hardship will (continue to) fail to fully capture the changing
living standards of low-income households in the years to come. Situating more recent
developments within their historical context, this limitation is clearest looking at rates of
‘absolute’ and ‘relative’ poverty over time. In 1994/95, 24% of individuals were living on less
than 60% of contemporary median household incomes (after housing costs) (DWP, 2020c).
Following a widespread expansion in means-tested benefits for low-income families in the
early years of New Labour, this fell to 22% in 2002/03. However, it has not really moved since
and currently stands at 22% according to the latest available figures (DWP, 2020c). As we
might expect, rates of ‘absolute’ poverty fell considerably between the mid 1990s and mid
2000s: from 41% of individuals living on less than 60% of 2010/11 median household incomes
(held constant in real terms) in 1994/95, to 22% in 2002/03 suggesting an improvement in the
living standards of low-income individuals. However since then, rates of progress have been
limited and stood at 20% in 2018/19 (DWP, 2020c).
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Some of these trends have been partially offset by real terms increases in the minimum wage and labour
market participation.
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In the UK and further afield, reforms have either been introduced or proposed to develop
alternative indicators of poverty within official statistics to more effectively capture realities,
causes and effects believed to shape low-income dynamics (Francis-Devine, 2020). In part,
this is motivated by a long-standing concern about the validity of dominant methods of
measurement and, in particular, their capacity to capture extreme poverty (Bradshaw and
Movshuk, 2019). In response, there are a range of approaches seeking to better understand the
intensity of poverty. The most obvious approach is to use a lower poverty threshold but this
ultimately suffers from the same methodological limitations as a higher threshold measure.
Alternatives include the World Bank $x per day indicator, severe deprivation indices and
thresholds set relative to social assistance, minimum income standards or reference budgets.
Each of these measures have distinctive benefits and limitations but have all been used to
measure aspects of extreme poverty across time and space (Bradshaw and Movshuk, 2019,
Lister 2021). The most widely used indicator of poverty intensity across advanced capitalist
economies is currently the ‘poverty gap’ indicator which measures how far people fall, on
average, from the poverty line. Bradshaw and Keung (2019) evidence increases in the absolute
and relative poverty gap for households with children in the UK since 2010 and argue cuts to
social security entitlement have undermined the minimum income scheme previously
available. More recently, poverty depth has also been assessed through the ‘low income gap’
which measures the average distance people fall from a socially agreed minimum income
standard (Hirsch et al., 2020). Whilst instructive, these approaches tend to track aggregate
trends which, as demonstrated later in this paper, presents particular difficulties if bifurcation
in the living standards of low-income households is observed.
Finally, an increasingly popular approach is to combine multiple indicators. For example, the
Europe 2020 ‘at risk of poverty or social exclusion’ indicator is a composite measure of income
poverty, severe material deprivation of items and activities, and work intensity, that seeks to
capture the multidimensional character, causes and effects of disadvantage. Composite
indicators such as this are most effective as capturing multiple dimensions and degrees of
poverty at the same time. However, this paper focuses exclusively on income indicators to
assess changing living standards. I do so mindful of the limitations associated with such an
approach (discussed later in this paper). I am nonetheless keen to focus on the proxies of
disadvantage currently used in official poverty statistics that are perhaps less contested and
have greatest (apolitical) currency across research, policy and practice. Policies such as the UK
Welfare Reform and Work Act that sought to move away from income-based measures have
been widely criticised for conflating definitions of poverty with some of the causes and
correlates of it (e.g. Stewart and Roberts, 2018). Such developments nonetheless reflect
growing political and policy concern that welfare interventions are failing to identify and target
‘hard-to-reach’ groups, most in need of public assistance. As a result, there has been a broader
movement to better understand and target ‘vulnerable’ low-income households—especially
workless families with children—through improvements to data collection, analysis and policy
intervention (DWP, 2017). Lessons from (and shortcomings in) these initiatives have fuelled
applied and theoretical interest in the dominant methods of poverty measurement that tend to
frame and delimit the social scientific analysis of poverty.
Data and Methodology
This paper presents analysis based on data from the Family Resources Survey (FRS) which is
used to produce national statistics on poverty and inequality in the UK (DWP, 2020a). This
survey offers a uniquely rich dataset on levels and sources of income, as well as household and
socio-demographic characteristics. The FRS has been conducted continuously since 1992, with
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a large representative sample of around 19,000 households for each year. I draw on the
Households Below Average Income (HBAI) dataset which includes derived variables drawing
on underlying data from FRS (DWP, 2020b). All findings concerning ‘income’ refer to weekly
net disposable income after housing costs: as a better approximation of living standards.
Nominal values are presented in 2018/19 prices. Unless otherwise stated, the unit of analysis
is based on individual level data. However, incomes are measured at the household level and
equivalised using the OECD modified equivalence scale (cf. Karagiannaki and Burchardt,
2020).
Bivariate analysis is undertaken to establish the changing economic security and characteristics
of individuals in the bottom 5th, 10th, 20th, 30th percentiles of the income distribution between
2010/11 and 2018/19 (the latest available year). These percentiles are not used as alternative
‘thresholds’ or ‘definitions’ of poverty, but as instructive indicators to establish varying degrees
of privation in two ways. First, to explore real terms nominal and relative changes in the living
standards of individuals falling within and between different interval (percentile) groups over
time. Second, to examine how socio-demographic characteristics are related to changes in the
resources and position of different groups across the low-income distribution. The results
outline trends in the living standards of different interval groups, and the changing depth of
poverty for social groups most exposed to it.
The term ‘deep poverty’ is used here to refer to those falling in the bottom 10% of the income
distribution. Such categorisation is obviously subject to the same limitations as any other cut-
off point but is used here as part of a broader strategy to explore compositional changes within
and across low-income interval groups. Whilst the term deep poverty is often inconsistently
applied, it is anticipated that individuals falling within this group are not only likely to be
experiencing (D), but in a qualitatively different sense to those closer towards the relative
poverty threshold (Figure 1). This is of course subject to empirical investigation, but if true
stands to nuance a categorical understanding of relative poverty currently in widespread. There
are also a number of methodological motivations for focusing on the bottom 10% that concern
the accurate measurement of living standards towards the very bottom of the income
distribution. Previous research suggests household survey data on the very bottom (1-3%) of
the income distribution are susceptible to biases in non-response, sample attrition and benefit
under-reporting (Bramley et al., 2016; Brewer et al., 2017; Corlett et al., 2018). For example,
inadequate correspondence between incomes and expenditure and numerous risks associated
with measurement error (Meyer et al., 2009). For this reason, the inclusion of the lowest-income
cases in FRS analyses has previously been brought into question (Bramley et al., 2016: 10).
That said, the FRS does offer the best available data: growth in ‘benefit and tax credit incomes
are similar in administrative and FRS data over time’ (Bourquin et al., 2019: 7). In addition,
Brewer et al. (2017) find that incomes and expenditure monotonically increase from the 2nd
percentile of the income distribution and begin to better reflect actual living standards. With
this in mind, a conservative approach has been taken to exclude the bottom 3% of cases when
measuring nominal and relative income changes for the bottom 5% of the income distribution
unless otherwise stated. This follows the convention of the Department for Work and Pensions
and makes it possible to measure outcomes of those that are a significant distance from the
poverty line, without compromising on data quality and thus inferences possible (DWP, 2020c).
The analysis is comprised of three parts. First, I present descriptive statistics on the changing
incomes of different interval groups towards the bottom of the income distribution. Second, I
present bivariate analysis on differences in the socio-demographic characteristics of those
falling in different interval groups. Third, results from multiple logistic regression are discussed
8
to compare the risk of falling into deep poverty across different socio-demographic groups over
time. Model 1 includes time period only for the two years under consideration: 2010/11 and
2018/19. In Model 2, I add socio-demographic predictor variables in a stepwise fashion to
assess the extent to which period differences could be the result of an altered composition of
the overall population. In Models 3-8, I explore interaction terms with period to establish
whether the socio-demographic composition and labour market engagement of those falling
into deep poverty has changed since 2010/11. To enhance interpretability and to provide a more
parsimonious model, sociodemographic predictor variables such as age, ethnicity and labour
market engagement have been collapsed from more detailed categorisations so that there are
fewer parameters to model interaction effects. BAME data in the bivariate analysis are based
on three-year averages (2009/10-2011/12) because single year estimates are considered too
volatile for smaller ethnic minority groups (DWP, 2020b). However, logistic regressions only
draw from single-year estimates as the models are based on larger ethnic minority categories
for the two years under consideration. To address the problem of unobserved heterogeneity in
logistic regression, y-standardisation of the logit coefficient (B) has been undertaken in
Appendix 1 (Mood, 2010). Here, odds ratios (Exp(B)) have been rescaled to make them more
comparable within and across nested models. In light of ongoing debate about whether such
rescaling is necessary (Kuha and Mills, 2018), unstandardized summary results are also
available in Table 4. All findings draw on analyses of weighted estimates.
Results
How has the profile and depth of poverty changed since 2010?
The equivalised poverty line (after housing costs) increased from £253 to £268 between
2010/11 and 2018/19. However, the very poorest have seen their average incomes fall further
away from this line since 2010. For individuals in the bottom 5% and 10% of the income
distribution, the nominal gap between median incomes and the poverty line has grown by 17%
and 15% respectively (Figure 2)
4
. For those closest to the poverty line (in the 2nd and 3rd decile
of the income distribution) their incomes have kept better pace with real terms increases in the
poverty threshold. As a ratio, this means that the average (median) value of incomes for the
bottom 5% has fallen from 39% to 32% of the poverty line between 2010/11 and 2018/19 and
from 45% to 41% for those in the bottom 10% over the same period (Figure 3). By contrast,
the income ratios for those in the 2nd and 3rd decile have remained relatively stable. The result
is a nominal and relative reduction in the resources available to those towards the very bottom,
with those closer to the poverty line seeing their incomes remain reasonably stable in
comparison.
4
As discussed in the methodology, reported incomes for the bottom 5% exclude cases for the bottom 3% and
this applies to Figures 2-4.
9
Situating these trends within a broader context, Figure 4 summarises the percentage change in
average incomes of different interval groups and demonstrates varied fluctuation in low-
income living standards over time. Since 2007/08, those towards the bottom (5% and 10%) of
the income distribution have experienced a substantive reduction in average incomes, whilst
those closer to the poverty line (2nd and 3rd deciles) have experienced modest increases.
However, these trends are particularly pronounced from 2011 onwards. In part, this is
explained by a staged divergence in the economic fortunes of different income interval groups
in the wake of the 2007/08 global financial crisis. Average incomes of the lowest income
groups initially fell but subsequentlty recovered whilst average incomes for those closer to the
poverty line stagnated but then began to catch up with median incomes in earnest. As a result,
155
146
150
151
159
166
179
182
182
138 130 133 136 142 148 159 162 159
38 35 35 37 41 42 44 44 47
-21 -22 -20 -21 -22 -18 -17 -19 -19
-50
0
50
100
150
200
2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18 2018/19
Figure 2: Nominal gap between median income of interval groups and the
relative poverty line (AHC, £pw equivalised 2018/19 prices)
0.1%-5% 0.1%-10% 10.1%-20% 20.1%-30%
39% 41% 39% 39% 38% 37% 33% 32% 32%
45% 47% 46% 45% 45% 44% 41% 40% 41%
85% 86% 86% 85% 84% 84% 84% 84% 83%
108% 109% 108% 108% 109% 107% 106% 107% 107%
0%
20%
40%
60%
80%
100%
120%
2010/11 2011/12 2012/13 2013/14 2014/15 2015/16 2016/17 2017/18 2018/19
Figure 3. Median incomes of interval groups as a ratio of relative poverty line
(AHC, £pw equivalised 2018/19 prices)
0.1%-5% 0.1%-10% 10.1%-20% 20.1%-30%
10
a more dramatic bifurcation in the living standards of low-income individuals is observable
from 2011. Between 2011/12 and 2018/19, those in the bottom 5% and 10% of the income
distribution witnessed a reduction in their average incomes by 15% and 7%, whilst average
incomes for those in the 2nd and 3rd deciles increased by 4% and 7% respectively. These trends
co-incide with a series of changes to the tax-benefit system (implemented from 2011 onwards)
that many have previously argued regressively impact on the livelihoods of low-income
households (e.g. Resolution Foundation, 2020: 50). However, the trends presented here
demonstrate that the very poorest are losing most with other low-income individuals
experiencing relative increases that come closer to keeping pace with median incomes.
Alongside differential labour market engagement trends, those in the 2nd and 3rd decile have
been less exposed (at least in relative terms) to social security changes implemented since 2010,
compared to those in the bottom 10% of the income distribution (Bourquin et al., 2019;
Gardiner, 2019).
Between 2010/11 and 2018/19, equivalised median incomes for the overall population grew
from £422 to £446. At the same time, average (mean) disposable incomes fell considerably
for the lowest income groups (Table 1). This means that those in the bottom 5% of the
income distribution saw their annual incomes fall by £548 on average between 2010/11 and
2018/19. By contrast, those in the 2nd and 3rd deciles saw their annual incomes increase by
£269 and £667 on average respectively during the same period.
-8%
-4%
5% 6% 5%
-12%
-5%
3%
5% 6%
-15%
-7%
4%
7%
9%
-20%
-15%
-10%
-5%
0%
5%
10%
10.1%-20% 20.1%-30% Population Median
Figure 4: Percentage change between median incomes of interval
groups, 2007/08-2018/19 (AHC, £pw equivalised 2018/19 prices)
% change from 2007/08 to 2018/19 % change from 2010/11 to 2018/19
% change from 2011/12 to 2018/19
0.1%-5% 0.1%-10%
11
Table 1. Mean and Standard Deviations of Equivalised Disposable Income by Interval Groups (AHC, £pw
equivalised 2018/19 prices)
2010/11
Mean
2010/11
SD
2018/19
Mean
2018/19
SD
Mean Difference
(Xi - X j)
Effect Size
Cohen’s d
Annual
change in
incomes
20.1%-30%
274.4
16.2
287.2
18.5
12.8
0.7
£667
10.1%-20%
214.0
19.2
219.2
22.2
5.2
0.2
£269
0.1%-10%*
134.9
29.7
129.4
33.2
-5.4
0.2
£-284
0.1%-5%*
96.5
13.2
86.0
15.9
-10.5
0.7
£-548
*The bottom 3% of the income distribution are excluded due to data volatility.
A consequence of these trends is that an increasing proportion of low-income households are
falling further away from the poverty line. Returning to a broader view, the proportion of
households falling more than 50% below the poverty line grew from 28.4% to 32.8% between
2007/08 and 2018/19 (Table 2). Over the same period, the proportion of households falling
more than 75% below the poverty line increased from 15.1% to 17.1%. Since 2010, the
proportion of low-income households falling more than 50% below the poverty line has grown
by an estimated 2.6 percentage points.
Table 2. Distribution of households falling varying proportions below the relative poverty line (AHC)
Households
Below Poverty
Line
0.1%-
10%
below line
10.1%-
25%
below line
25.1%-
50%
below line
50%+
below line
50.1%-75%
below line
75%+
below line
2007/08
5,738,963
19.2%
24.0%
28.4%
28.4%
13.3%
15.1%
2010/11
5,646,957
18.5%
24.8%
26.4%
30.3%
15.3%
14.9%
2018/19
6,145,623
18.1%
22.7%
26.4%
32.8%
15.8%
17.1%
% point change 2010/11-
2018/19 [Confidence
Intervals]
-0.4%
[-1.1% to
0.2%]
-2.1%
[-2.8% to -
1.4%]
0.0%
[-0.8% to
0.7%]
2.6%
[1.8% to
3.4%]
0.5%
[-0.2% to
1.1%]
2.1%
[1.5% to
2.8%]
In sum, there has been a splintering in the economic fortunes of different interval groups falling
towards the bottom of the income distribution. Thus far, these trends have not been fully
captured because current attempts to measure poverty depth rely on average incomes for all
those falling below a given threshold to compare this against nominal or ratio trends. Measures
that focus exclusively on these aggregate changes inevitably gloss over dispersion within the
broad category of ‘the poor’. Often used as a measure of poverty depth, the ‘poverty gap’ is
one such indicator that risks underestimating the full extent to which there has been a deepening
of poverty because it does not account for potential bifurcation in the economic resources
available to low-income households at different interval groups. Whilst this may result in an
underestimation of current trends, other approaches seeking to capture poverty depth that fail
to account for this may actually lead to a misrepresentation of current trends. For example, in
2019 the UK Social Metrics Commission, a key proponent of alternative and additional poverty
measures, argued that the depth of poverty had changed very little since 2000 (SMC, 2019).
An official briefing from the House of Commons library, reporting on poverty depths
concluded that ‘on average, people living below the poverty line have moved closer towards
it’ (Francis-Devine, 2020: 39). Such conclusions underline the need to a) better measure and
12
report on heterogeneity ‘below the line’ in poverty analysis, and b) refine existing poverty
depth indicators to capture low-income dynamics over time.
How has the socio-demographic profile of (deep) poverty changed since 2010?
The systematic over-representation of particular social groups in poverty is well-documented.
However, examination of this phenomenon has often treated poverty as a categorical condition
which particular social groups are more or less likely to fall into. Such an approach to both
material and social axes of division can be productive for establishing how certain groups fare
relative to others, and the wider standards prevailing. However, as observed in the previous
section, economic disadvantage is neither unilateral nor linear, and methods that treat it as such
risk obscuring or misrepresenting trends. This does not render a systemic examination of the
relationship between poverty and social difference impossible. However, it does require a more
fine-grained conceptualisation of financial hardship: one that avoids relying on or reproducing
a binary understanding of disadvantage. With this in mind, I now explore how degrees of
poverty intersect with markers of social difference across the entirety of the low-income
distribution, not just on either side of a given threshold.
In Tables 3 and 4, I present an overview of how the socio-demographic composition of (deep)
poverty has changed since 2010. These tables detail the proportion of particular social groups
represented in the whole population, in relative poverty, and in the bottom three deciles of the
income distribution in 2010/11 and 2018/19. Through simple cross-tabulations, I explore
nominal and ratio changes in the incidence of (varying degrees of) poverty accounting for
broader demographic and labour market trends. Results summarised in Tables 3 and 4 confirm
that the incidence of relative poverty is much higher amongst children, women, BAME groups,
those affected by a disability, and those that are unemployed, working part-time and living in
households with 2 or more dependent children. However, bivariate analysis also reveals
significant non-linear variation in the exposure of particular social groups to gradations of
financial hardship since 2010. In Table 5, I present a summary of binary logistic regression
analyses undertaken to compare the influence of socio-demographics on the likelihood of being
in deep poverty over time. All of the socio-demographic and economic variables included had
a statistically significant effect on the likelihood of being in deep poverty. However, only a
selection of observations are discussed here for the sake of interpretability: I examine trends
for women, children, those affected by a disability, ‘race’ and ethnicity, and economic status
in turn.
13
Table 3. Socio-demographic change by low-income interval groups
(% of) Total
Population
% in
Poverty
0.1%-10%
10.1%-20%
20.1%-30%
Total number of people
2010/11
6,145,687
6,153,115
6,165,910
61,559,704
21.1%
2018/19
6,589,901
6,602,698
6,524,601
65,478,229
22.1%
Total number of households
2010/11
2,828,423
2,408,506
2,620,145
26,319,048
21.0%
2018/19
2,947,803
2,606,734
2,844,246
27,828,599
21.9%
Women (as % of interval group in parentheses)
2010/11
(49.5%) 3,045,058
(53.3%) 3,281,663
(53.3%) 3,287,650
(50.9%) 31,352,143
21.4%
2018/19
(49.9%) 3,290,170
(51.8%) 3,421,672
(54.6%) 3,565,291
(50.6%) 33,163,886
22.4%
% ±
Group
2.1%
-1.4%
2.5%
-
4.7%
% ±
Interval
0.8%
-2.8%
2.5%
-0.6%
-
Children (as % of interval group in parentheses)
2010/11
(23.8%) 1,460,367
(31.5%) 1,937,885
(27.8%) 1,711,275
(21.5%) 13,207,025
27.3%
2018/19
(25.9%) 1,709,688
(31.6%) 2,083,393
(27.3%) 1,783,788
(21.3%) 13,924,893
30.0%
% ±
Group
11.0%
2.0%
-1.1%
-
9.9%
% ±
Interval
9.2%
0.2%
-1.5%
-0.9%
-
Households with 2+ children (as % of interval group in parentheses)
2010/11
(14.9%) 420,129
(24.5%) 589,642
(19.7%) 516,836
4,057,125
26.4%
2018/19
(17.6%) 518,195
(25.2%) 656,931
(20.3%) 576,965
4,360,408
29.8%
% ±
Group
14.8%
3.7%
3.9%
-
13.00%
% ±
Interval
18.3%
2.9%
2.8%
1.6%
-
Households with 3+ children (as % of interval group in parentheses)
2010/11
(5%) 141,469
(8.3%) 200,566
(7.9%) 206,783
(4.1%) 1,069,759
34.5%
2018/19
(7%) 207,583
(10.6%) 276,054
(8%) 226,570
(4.7%) 1,301,143
41.4%
% ±
Group
20.6%
13.2%
-9.9%
-
19.9%
% ±
Interval
40.8%
27.2%
0.9%
15.0%
-
Affected by a disability (as % of interval group in parentheses)
2010/11
(31.4%) 1,926,759
(35.3%) 2,171,831
(39.1%) 2,411,172
(29.6%) 18,198,126
23.9%
2018/19
(36.6%) 2,411,541
(43.5%) 2,871,646
(45.2%) 2,946,723
(34.3%) 22,478,329
26.2%
% ±
Group
1.3%
7.0%
-1.1%
-
9.3%
% ±
Interval
16.7%
23.2%
15.5%
16.1%
-
Black, Asian and Minority Ethnic people (BAME)* (as % of interval group in parentheses)
2009-12
(23%) 1,419,230
(18.4%) 1,137,105
(13.5%) 834,952
(11.1%) 6,854,178
39.4%
2016-19
(24.3%) 1,575,676
(23%) 1,500,819
(16.6%) 1,073,288
(13.2%) 8,575,714
38.6%
% ±
Group
-11.3%
5.5%
2.74%
-
-2.0%
% ±
Interval
5.6%
24.8%
23.0%
19.0%
-
* Figures are based on 3-year averages (2009/10-2011/12).
14
Table 4. Socio-demographic change by low-income interval groups
(% of) Total
Population
% in
Poverty
0.1%-10%
10.1%-20%
20.1%-30%
Pakistani* (as % of interval group in parentheses)
2009-12
(4%) 249,858
(4.6%) 281,136
(2.6%) 163,082
(1.7%) 1,044,576
52.3%
2016-19
(4%) 261,780
(5.6%) 368,301
(3.8%) 246,849
(2.2%) 1,428,589
48.1%
% ±
Group
-23.4%
-4.2%
10.7%
-
-8.1%
% ±
Interval
-0.3%
23.9%
44.8%
30.1%
-
Bangladeshi* (as % of interval group in parentheses)
2009-12
(1.8%) 113,890
(1.4%) 89,213
(1.2%) 71,346
(0.6%) 399,230
54.7%
2016-19
(1.8%) 116,802
(3%) 196,886
(1.6%) 101,358
(1.0%) 634,470
52.3%
% ±
Group
-35.5%
38.9%
-10.6%
-
-4.4%
% ±
Interval
-2.4%
108.7%
35.9%
51.2%
-
Black/African/Caribbean/Black British* (as % of interval group in parentheses)
2009-12
(5.3%) 330,408
(3.9%) 243,544
(3.8%) 235,726
(2.6%) 1,632,283
38.6%
2016-19
(6.9%) 444,826
(5.2%) 339,675
(4.2%) 272,429
(3.1%) 1,982,350
42.5%
% ±
Group
10.9%
14.8%
-4.8%
-
10.1%
% ±
Interval
28.1%
31.9%
10.6%
15.5%
-
Self-employed** (as % of interval group in parentheses)
2010/11
(15.4%) 945,829
(9.2%) 564,797
(6.9%) 423,857
(9.9%) 6,120,166
25.5%
2018/19
(14.9%) 984,665
(10.7%) 706,755
(8.8%) 576,642
(10.2%) 6,647,727
27.0%
% ±
Group
-4.2%
15.2%
25.2%
-
5.9%
% ±
Interval
-2.9%
16.6%
28.6%
2.1%
-
Working part-time** (as % of interval group in parentheses)
2010/11
(13.1%) 803,870
(12.6%) 774,267
(12.5%) 773,301
(9.2%) 5,652,247
29.5%
2018/19
(15.1%) 996,261
(15.3%) 1,012,794
(13.1%) 852,017
(9.8%) 6,447,740
33.8%
% ±
Group
8.64%
14.67%
-3.41%
-
14.6%
% ±
Interval
15.58%
21.90%
4.12%
7.25%
-
Full-time work** (% of interval group in parentheses)
2010/11
(5.5%) 340,369
(8.1%) 500,030
(10.1%) 624,432
(26.2%) 16,154,585
5.6%
2018/19
(8.8%) 580,110
(8.2%) 538,223
(13.1%) 852,730
(28.9%) 18,893,006
6.6%
% ±
Group
45.7%
-8.0%
16.8%
-
17.3%
% ±
Interval
58.9%
0.3%
29.1%
10.0%
-
Workless** (as % of interval group in parentheses)
2010/11
(41.3%) 2,538,722
(34.5%) 2,122,382
(20.1%) 1,240,474
(13.3%) 8,171,804
58.9%
2018/19
(31.7%) 2,087,558
(22.7%) 1,499,728
(14.0%) 916,377
(10.6%) 6,951,268
56.0%
% ±
Group
-3.3%
-16.9%
-13.2%
-
-5.0%
% ±
Interval
-23.3%
-34.1%
-30.2%
-20.0%
-
* Figures are based on 3-year averages (2009/10-2011/12). ** Economic status of benefit unit (ECOBU); "Self-employed"
= One or more self-employed "Part-time" = No one in full-time work and 1 or more part-time work; "Full-time Work" =
Single, couple all in full-time work; "Workless" = Workless head or spouse unemployed and Workless, other inactive.
15
Since 2010, there has been an increase (4.7%) in the proportion of women in relative poverty
and a slight growth (2.1%) in the proportion in deep poverty, but women are still relatively
‘under-represented’ in this group, compared to other low-income interval groups and the rest
of the population (Table 3). Variation in the gendered composition of low-income interval
groups highlights the importance of complicating a standard feminisation of poverty thesis
(Dermott and Pantazis, 2014). Specifically, there is a need for a gendered analysis of poverty
that does not assume women’s exposure to the risk of financial hardship functions in a unilinear
fashion. Such an approach reveals that the increase in rates of relative poverty amongst women
since 2010, is principally driven by an increasing proportion of women in deep poverty. In line
with the bivariate results, Model 3 presented in Table 5, shows that the likelihood of being in
deep poverty (as opposed to not being in deep poverty) is slightly lower for women than men
(reference category) (odds ratio [OR]=0.907) but this gender gap is smaller in 2018/19
(OR=1.022) than it was in 2010/11 (reference category). This is, perhaps, unsurprising given
that low-income (particularly BAME) women have been some of those worst affected by
changes to the tax-benefit system since 2010 (Hall et al., 2017).
Between 2010 and 2019, the proportion of children in relative poverty increased from 27.3%
to 30.0%. During the same period, the proportion of children in deep poverty increased by
11.0%. As a result, more than a quarter (25.9%) of those in deep poverty are currently children
(Table 3). The increased incidence of relative and deep poverty amongst children is particularly
pronounced for children in larger families who are more likely to be affected by the two-child
limit and benefit cap. Since 2010, the proportion of larger families in deep poverty has
increased significantly: by 14.8% for households with 2+ children, and by 20.6% for
households with 3+ children (Table 3). These results demonstrate an increasing risk and depth
of poverty witnessed amongst children in the UK. Compared to pensioners (reference
category), all other age groups are more likely to be in deep poverty (
!"!"#$%
=5.713;
!"&$#!%
=6.544;
!"'#&(
=5.665). However, the likelihood of falling into deep poverty has
increased most for children since 2010 (OR=1.225) (Model 4, Table 5). Depending on the
number of dependent children in it, households differ in terms of their likelihood of being in
deep poverty (
!"&)*+,-.
=1.054,
!"/).01*+,-.
=0.922,
!"!2.01*+,-.
=0.938) (Model 7, Table 5).
Since 2010, the likelihood of being in deep poverty for households with one dependent child
has decreased (
!"&)*+,-.
=0.909), and the lower likelihood of being in deep poverty for larger
families has weakened, particularly for those with 3+ dependent children (
!"/).01*+,-.
=1.125,
!"!2.01*+,-.
=1.421) (Model 7, Table 5). Despite additional COVID-19 provisions and
temporary uprating, these trends are likely to continue with the benefit cap and two-child limit
still in place.
Between 2010-2019, the proportion of households affected by a disability living in relative
poverty increased from 23.9% to 26.2% (Table 3). Looking at low-income interval groups,
there has also been a slight increase (1.3%) in the depth of poverty amongst those affected by
a disability (Table 3). The likelihood of falling into deep poverty is lower for those affected by
a disability (OR=0.846) and this is slightly more pronounced in 2018/19 than it was in 2010/11
(OR=0.989) (Model 6, Table 5). However, the extent of poverty depth for those affected by a
disability is likely to be underestimated given that additional disability-related costs are not
reflected in calculations of disposable household income in the FRS (SMC, 2019). Since 2010,
a shrinking category of disability in social security administration has reduced the coverage
and generosity of benefits. In part, we can see this reflected in the disproportionate growth
(+23.2%) of those affected by a disability represented in the 2nd decile of the income
distribution (Table 3).
16
Table 5: Summary results of logistic regression for likelihood of being in deep poverty, 2010/11 and 2018/19, Odds Ratios and (Standard Errors)
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant
0.111 (.000)
0.004 (.002)
0.004 (.002)
0.004 (.002)
0.004 (.002)
0.004 (.002)
0.004 (.002)
0.003 (.002)
Period (Ref. 2010/11)
1.009 (.001)
1.095 (.001)
1.083 (.001)
0.985 (.002)
1.017 (.002)
1.023 (.002)
1.034 (.002)
1.429 (.003)
Sex (Ref. Male)
0.917 (.001)
0.907 (.001)
0.906 (.001)
0.907 (.001)
0.907 (.001)
0.909 (.001)
0.91 (.001)
Age (Ref. Pensionable)
Older working-age (35-64)
6.185 (.001)
6.185 (.001)
5.713 (.002)
5.612 (.002)
5.618 (.002)
5.705 (.002)
5.742 (.002)
Younger working-age (16-34)
6.353 (.001)
6.352 (.001)
6.544 (.002)
6.205 (.002)
6.22 (.002)
6.278 (.002)
6.404 (.002)
Children (0-19)
6.289 (.001)
6.289 (.001)
5.665 (.002)
5.432 (.002)
5.441 (.002)
5.88 (.002)
5.952 (.002)
Ethnicity (Ref. White)
A Mixed/Multiple Ethnic Group
1.691 (.002)
1.691 (.002)
1.676 (.002)
2.194 (.003)
2.195 (.003)
2.197 (.003)
2.215 (.003)
B Asian/Asian British
2.199 (.001)
2.199 (.001)
2.189 (.001)
3.066 (.001)
3.066 (.001)
3.118 (.001)
3.139 (.001)
C Black/African/Caribbean/British
2.252 (.001)
2.251 (.001)
2.242 (.001)
2.109 (.002)
2.109 (.002)
2.12 (.002)
2.137 (.002)
D Other Ethnic Group
2.897 (.002)
2.897 (.002)
2.884 (.002)
2.919 (.003)
2.92 (.003)
2.917 (.003)
2.941 (.003)
Disability (Ref. family not affected)
0.849 (.001)
0.849 (.001)
0.848 (.001)
0.841 (.001)
0.846 (.001)
0.852 (.001)
0.848 (.001)
Dependent Children (Ref. none)
1 Dependent Child in Household
1.007 (.001)
1.008 (.001)
1.005 (.001)
1.01 (.001)
1.01 (.001)
1.054 (.001)
1.054 (.001)
2 Dependent Children
0.975 (.001)
0.975 (.001)
0.974 (.001)
0.979 (.001)
0.979 (.001)
0.922 (.001)
0.914 (.001)
3+ Dependent Children
1.135 (.001)
1.135 (.001)
1.131 (.001)
1.139 (.001)
1.139 (.001)
0.938 (.002)
0.924 (.002)
Employment Status (Ref. Full-time)
A One or more self-employed
6.83 (.001)
6.829 (.001)
6.83 (.001)
6.872 (.001)
6.872 (.001)
6.876 (.001)
9.352 (.002)
B Full-time and part-time work
2.461 (.001)
2.461 (.001)
2.454 (.001)
2.457 (.001)
2.458 (.001)
2.45 (.001)
2.777 (.002)
C Only part-time work
7.174 (.001)
7.173 (.001)
7.157 (.001)
7.202 (.001)
7.204 (.001)
7.185 (.001)
8.558 (.002)
D Workless (Inactive/Unemployed)
14.959 (.001)
14.958 (.001)
14.976 (.001)
15.105 (.001)
15.105 (.001)
15.124 (.001)
18.309 (.002)
Interaction Terms
Sex
Age
Ethnicity
Disability
Children
Employment
Period × Independent Variable
(Female)
1.022 (.001)
(35-64)
1.164 (.002)
(A)
0.597 (.005)
(Affected)
0.989 (.001)
(1)
0.909 (.002)
(A)
0.583 (.003)
(16-34)
0.935 (.002)
(B)
0.530 (.002)
(2)
1.125 (.002)
(B)
0.817 (.003)
(0-19)
1.225 (.002)
(C)
1.101 (.003)
(3+)
1.421 (.002)
(C)
0.745 (.003)
(D)
0.966 (.004)
(D)
0.722 (.002)
"!#(Nagelkerke)
.000
.178
.178
.178
.180
.180
.181
.182
$!(Degrees of freedom)
230.625
(1)
11307702.6
(17)
11308018.8
(18)
11337819.6
(21)
11463655
(25)
11463718.5
(26)
11501609.3
(29)
11547796.9
(33)
Note. Ref. = reference category, all odds ratios presented are significant p < 0.001, N = 100,739, Weight = G_INDPP
17
Rates of relative poverty amongst BAME individuals remain particularly high: 39.4% in 2010
reducing to 38.6% in 2019 (Table 3). Alongside this, there have been compositional changes
in the ethnic profile of the low-income population that are not in line with those observed in
the wider general population. There has been an increase in the representation of BAME
individuals in the bottom 3 deciles of the income distribution, but relative increases are
particularly pronounced closest to the poverty line in the 2nd and 3rd deciles (+24.8% and
+23%). Despite only making up 13.2% of the overall population, almost a quarter (24.3%) of
those in deep poverty were BAME in 2019 (Table 3). Model 5 in Table 5 shows that the
likelihood of being in deep poverty is significantly lower for White groups (reference category)
than it is for Mixed (OR=2.194), Asian (OR=3.066), Black (OR=2.109) and Other Ethnic
(OR=2.919) groups.
However, interaction terms show that such racial and ethnical inequalities became less
pronounced for those identifying as Mixed (OR = 0.597), Asian (OR = 0.530) or Other Ethnic
group (OR = 0.966) between 2010 and 2019 (Model 5, Table 5). A particularly pronounced
periodic effect witnessed amongst Asian individuals is also reflected in descriptive trends. In
Table 4, rates of relative poverty are shown to have fallen amongst Pakistani and Bangladeshi
individuals (albeit from a very high place) since 2010. Accounting for changes in the ethnic
composition of the overall population, there has been a substantial reduction in the proportion
of Pakistani and Bangladeshi individuals in deep poverty: falling by 23.4% and 35.5%
respectively. The extent of this improvement differs across the low-income distribution though
with the representation of Pakistani individuals increasing (+44.8%) closer to the poverty line
(in the 3rd income decile) and representation of Bangladeshi individuals increasing
considerably (+108.7%) in the 2nd income decile
5
. Compared to all other ethnic groups, Black
individuals have fared worst in terms of their economic security since 2010. Beyond an
increased rate of relative poverty (from 38.6% to 42.5%), the proportion in deep poverty has
grown considerably by 10.9% (Table 4). As a result, over a fifth (22.4%) of all Black people
are currently in deep poverty. Model 5 in Table 5 confirms that the likelihood of being in deep
poverty for Black people is stronger (OR=1.101) in 2019 than it was in 2010.
For employment status, the results from Model 8 in Table 5 demonstrate there are significant
differences in the likelihood of being in deep poverty depending on the type and degree of
labour market engagement. Those living in households comprising full-time work (reference
category) are much less likely to be in deep poverty than those whose household economic
status is self-employed (OR=9.352), a mixture of full-time and part-time employment
(OR=2.777), only part-time work (OR=8.558), or workless (OR=18.309). That said, full-time
work appears to have become less effective at protecting against deep poverty. Since 2010, the
likelihood of being in deep poverty has fallen for other types of employment compared to those
in full-time work (e.g.
!"!"#$%"&'#()"*
=0.583,
!"+(,-#"!!
=0.722).
In part, this is explained by an intrinsic increase in working poverty in the UK whereby the
poorest have seen the biggest increase in employment rates in recent years
6
. For example, rates
of relative poverty amongst for those living in full-time working households grew slightly from
5.6% to 6.6% between 2010 and 2019 (Table 4). During the same period, the proportion of
people living in full-time working households in deep poverty jumped considerably by 45.7%
5
To speculate on the drivers behind this trend, the employment rate for Pakistani and Bangladeshi individuals
grew by 23.9% compared to by 7.1% for the overall population between 2010-2018 lifting many out of deep
poverty but not low incomes (GOV.UK, 2020).
6
There are also a number of exogenous reasons for this (cf. Bourquin et al., 2019).
18
(Table 4). In addition to this, there has been a substantial reduction (-23.3%) in the
representation of workless households in deep poverty between 2010 and 2019 (Table 4).
However, the most substantial reduction has been the representation of workless households
that fall in the 2nd and 3rd deciles of the income distribution: -34.1% and -30.2% respectively.
Since 2010, the risk and depth of poverty has increased significantly for those living in part-
time working households: a third are currently in relative poverty and the proportion in deep
poverty has grown by 8.6% (Table 4). Overall, it appears there are not just limits to which work
provides a meaningful route out of poverty (Hick and Lanau, 2018), but also a route out of
deep poverty.
Discussion and Conclusion
In this paper, I have outlined trends in the changing economic security of those living, to
varying degrees, below the poverty line. Since 2010, there has been a bifurcation in the living
standards of low-income citizens. Those closest to the poverty line have seen relative
improvements in their incomes. At the same time, there has been a deepening and
intensification of financial hardship for those towards the very bottom of the income
distribution with the proportion of people falling 75+% below the poverty line growing by
14.8% since 2010 (Table 2). These changes in the economic profile of poverty have also
occurred alongside substantive shifts below the poverty line that are reconfiguring the socio-
demographic composition of (deep) poverty. Despite ostensive progress in reducing rates of
relative poverty amongst particular social groups, markers of social difference are intersecting
with gradations of financial hardship in ways that are non-linear but nonetheless systematic.
The likelihood of being in deep poverty has increased for women, children, larger families,
Black people, and those in full-time work since 2010
7
.
In light of the findings, it is worth reflecting on a key limitation of using household income
surveys to measure changes in the depth and (socio-demographic) composition of poverty.
Namely, that the increasing depth and socio-demographic diversity of poverty is likely to be
underestimated. This is for three reasons. First, Corlett et al. (2018: 50) warn ‘of the UK’s
population of 66 million, 1.2 million are outside the scope of the FRS’ because only those in
private households are sampled. Trends then, suggesting an increasing depth of poverty, are
likely to be underestimated as ‘some groups at high risk of destitution may also be over-
represented in the categories of non-response, missing data and sample attrition’ (Bramley et
al., 2016: 10). Second, the full impact of tax and benefit changes will not yet be reflected in
available FRS data. The four-year freeze to working-age benefits began in 2015/16 so the
cumulative significance of this is only currently observable for three years of available data:
we can therefore reasonably anticipate an increasing depth of poverty in future data releases.
Third, the depth of poverty is likely to be more pronounced once (gendered) intra-household
inequalities are taken into account (Karagiannaki and Burchardt, 2020).
Despite these limitations, evidence on the changing poverty profile and socio-demographic
composition of those below the poverty line can be brought to bear on established debates
concerning poverty categorisation, measurement and analysis in four ways. First, to encourage
critical reflection on how and what we should measure when it comes to researching ‘the poor’
and what this means for dominant analytical and methodological frameworks within poverty
studies. Second, to refine our theoretical understanding of poverty and the extent to which
7
A notable exception to this, is the significant reduction in relative and deep poverty experienced by BAME
groups overall and Pakistani and Bangladeshi people in particular.
19
‘human welfare’, ‘participation’ and ‘inclusion’ can be understood as either present or absent
in the lives of (extremely) low-income households. This is particularly important for fleshing
out a conception of poverty that accounts for the relational gradations of ‘agency’, ‘autonomy’
and ‘welfare’ that mediate the lives of low-income households. Such an approach makes it
possible to establish how and why people move, not just ‘in’ and ‘out’ of financial hardship,
but also ‘through’ it along a continuum of disadvantage. This should encourage poverty
researchers to examine what, if anything, is distinctive about deep poverty and the wider
continuum of disadvantage it sits within. A third, and related avenue of research, is to capitalise
on the additional dimensions of description and social explanation that a non-categorical
measure of financial hardship brings. Doing so makes it possible to identify what underlies a
clustering or dispersion of low incomes and what affects the likelihood of falling into deep
poverty over time through the use of panel data. Fourth, to better understand how ‘social
difference’ is articulated in relation to the material social locations of people, across the entirety
of the income distribution, not just on either side of a given threshold. Within the current
context, these agendas for poverty research present an opportunity to better understand, explain
and address the full distributional effects of tax-benefit changes implemented since 2010 and
the COVID-19 pandemic.
The findings of this paper also raise questions for policy and practice when it comes to poverty
alleviation. In terms of official statistics, the results demonstrate how dominant methods of
poverty measurement are not currently fit for purpose. That is, they fail to fully capture the
(socio-demographic) composition and depth of poverty which invariably constrains the
capacity of social policy to both understand and tackle it. In particular, it is clear that the
‘poverty gap’ indicator that is often used to analyse poverty depth needs to be used alongside
measures such as those presented in this paper to account for and track potential bifurcation in
the living standards of low-income households below the poverty line. It is also clear that a
pluralistic approach to poverty measurement is needed: one that recognises the distinctive
merits and limitations of particular indicators of financial hardship. To gain the fullest picture
of changes in the profile and depth of poverty, a range of measures need to be used in tandem
to capture nominal and relative dimensions across the entirety of the low-income distribution.
In an era of constrained public social spending, this matters for identifying which tax-benefit
changes can most effectively and efficiently mitigate against (deep) poverty. This presents a
number of challenges for how policy evaluation and design (e.g. equality impact assessments
and targeted means-testing) might respond to varying degrees and dimensions of financial
hardship whilst implementing poverty alleviation measures that are practicably feasible. In the
wake of COVID-19, this will become increasingly important given that the very poorest
individuals are likely to be worst affected by the pandemic (SMC, 2020) and certain key groups
(such as those with No Recourse to Public Funds) have been overlooked by government
protections and provisions.
Acknowledgments
Parts of this paper were made possible by funding received through the UKRI rapid response
grant: ES/V003879/1. Many thanks to the editors and two reviewers for their comments on
earlier drafts of this paper. Thanks also to Ruth Lister, Jonathan Bradshaw and Albert Varela
for their helpful guidance on aspects of this paper.
20
Appendix 1: Summary results of logistic regression for likelihood of being in deep poverty, 2010/11 and 2018/19 (Standardized)
Odds Ratios
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
Constant
0.461
0.140
0.141
0.143
0.142
0.142
0.142
0.133
Period (Ref. 2010/11)
1.003
1.033
1.029
0.995
1.006
1.008
1.012
1.134
Sex (Ref. Male)
0.970
0.966
0.966
0.966
0.966
0.967
0.967
Age (Ref. Pensionable)
Older working-age (35-64)
1.901
1.901
1.849
1.837
1.838
1.847
1.852
Younger working-age (16-34)
1.919
1.919
1.939
1.903
1.905
1.911
1.924
Children (0-19)
1.912
1.912
1.843
1.816
1.817
1.868
1.876
Ethnicity (Ref. White)
A Mixed/Multiple Ethnic Group
1.203
1.203
1.200
1.319
1.319
1.320
1.323
B Asian/Asian British
1.320
1.320
1.318
1.484
1.484
1.493
1.497
C Black/African/Caribbean/British
1.331
1.331
1.329
1.301
1.301
1.304
1.307
D Other Ethnic Group
1.455
1.455
1.453
1.459
1.459
1.458
1.463
Disability (Ref. family not affected)
0.944
0.944
0.943
0.941
0.943
0.945
0.944
Dependent Children (Ref. none)
1 Dependent Child in Household
1.002
1.003
1.002
1.004
1.004
1.019
1.019
2 Dependent Children
0.991
0.991
0.991
0.992
0.992
0.972
0.969
3+ Dependent Children
1.046
1.046
1.044
1.047
1.047
0.978
0.973
Employment Status (Ref. Full-time)
A One or more self-employed
1.968
1.968
1.968
1.973
1.973
1.973
2.200
B Full-time and part-time work
1.374
1.373
1.372
1.373
1.373
1.371
1.433
C Only part-time work
2.003
2.003
2.001
2.006
2.006
2.004
2.132
D Workless (Inactive/Unemployed)
2.595
2.595
2.596
2.604
2.604
2.605
2.787
Interaction Terms
Sex
Age
Ethnicity
Disability
Children
Employment
Period × Independent Variable
(Female) 1.008
(35-64) 1.055
(A) 0.833
(Affected) 0.996
(1) 0.967
(A) 0.827
(16-34) 0.976
(B) 0.799
(2) 1.042
(B) 0.931
(0-19) 1.074
(C) 1.034
(3+) 1.132
(C) 0.902
(D) 0.988
(D) 0.891
"!#(Nagelkerke)
.000
.178
.178
.178
.180
.180
.181
.182
$!(Degrees of freedom)
230.625
(1)
11307702.6
(17)
11308018.8
(18)
11337819.6
(21)
11463655
(25)
11463718.5
(26)
11501609.3
(29)
11547796.9
(33)
Note. Ref. = reference category, all odds ratios presented are significant p < 0.001, N = 100,739, Weight = G_INDPP
21
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