Where have all the flowers gone? The Impact of COVID-19 on UK

Households’ Economic Well-Being

DEMETRIO PANARELLO1,2,a, GIORGIO TASSINARI2,b

1Prometeia,

Piazza Trento e Trieste 3, 40137 Bologna,

ITALY

2Department of Statistical Sciences “P. Fortunati”,

University of Bologna,

Via Belle Arti 41, 40126 Bologna,

ITALY

aORCiD: https://orcid.org/0000-0003-1667-1936

bORCiD: https://orcid.org/0000-0002-5161-7989

Abstract: - The United Kingdom introduced a national lockdown in March 2020, as a means to curb the rising

pace of COVID-19 infections in the country. Since then, the various restrictions imposed on citizens have

produced enormous social and economic consequences. However, full awareness of the mid-term and long-

term impacts of such restrictive measures is still lacking. In this paper, by making use of longitudinal data from

the Understanding Society COVID-19 study, consisting of nine survey waves administered to a representative

sample of UK citizens from April 2020 to September 2021, we analyze the potential determinants of lack of

employment and poor economic conditions, considering individuals’ length of stay in an economic hardship

context and the differential effects related to their socio-demographic characteristics.

Key-Words: - Job loss, Bill payments, Subjective financial situation, Employment, Minority ethnic groups,

Health, Loneliness, Persistence, Pandemic crisis, Understanding Society.

Received: March 27, 2023. Revised: January 29, 2024. Accepted: February 21, 2024. Published: March 22, 2024.

1 Introduction

The first national lockdown in the United Kingdom

was carried out on 23 March 2020, as a response to

the rapid spread of COVID-19 cases in the country.

Since then, the price of the implemented restrictions

has been incredibly high, with strong consequences

on the population as a whole and, in particular, on

its most vulnerable segments, in terms of mass

unemployment, isolation of the people, and

widespread financial difficulties. Indeed, among

European countries, the United Kingdom was one of

the hardest hit by the pandemic, [1] and three

lockdowns have been implemented in the country so

far.

The economic consequences at the aggregate

level have been extremely strong. In 2020, GDP

decreased by 9.7% compared to the previous year,

followed by a growth of 6.9% in 2021, [2]; the

unemployment rate in 2020 increased by only 1.5%,

mostly as a result of government support, but a great

heterogeneity among different groups of households

was pointed out for what concerns the COVID-19

effects on unemployment, [3]. In 2021, despite the

strong recovery in the economic activity level, the

forecasts predicted a further increase in

unemployment of 1.8%, until reaching a level of

7.1%, [4]. Moreover, households’ consumption fell

by 10.6% in 2020 but recovered by 6.2% in 2021

(+6.2%), [5].

Recent evidence has shown that the COVID-19

pandemic and the related social and economic

interventions, such as physical distancing and

closure of production activities, have had different

impacts on the various social groups, [6], [7]. It

must be underlined that the regressive impact of

COVID-19 comes after three decades of increase in

inequality in household income, [8]. Furthermore,

cuts in benefits in the years immediately preceding

the COVID-19 pandemic left low-income

households with a poor degree of protection: as

highlighted in [9], in the two-year period 2017-

2018, the wage growth was lower than inflation and

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the reductions in benefits pushed down the poorest

households’ incomes.

In the UK, females and parents are among those

who have experienced the largest reduction in

subjective well-being, [10], [11]. Black, Asian, and

minority ethnic groups (BAME) exhibited a higher

COVID-19-related mortality rate than the White

population, [12] and suffered harsher economic

consequences, [13].

The available evidence concerning the impact of

COVID-19 identifies the existence of immediate

effects; nevertheless, our understanding of the mid-

term and long-term consequences of the pandemic

and its related measures remains incomplete. To this

end, we analyze a representative sample of the UK

population, the UK Household Longitudinal Study

(UKHLS), making use of data from the

Understanding Society COVID-19 Study, [14], [15].

It consists of nine survey waves administered to a

representative sample of UK citizens from April

2020 to September 2021. The analysis of numerous

survey waves gives a reliable picture of the

phenomenon and allows us to investigate not only

the immediate effects of the lockdown (as done,

e.g., by [3], [16]) but also its mid-term effects.

Through this study, we aim to contribute to the

comprehension of the effects of COVID-19 by

focusing on three outcome variables: job loss,

difficulties in paying bills, and subjective financial

situation.

These outcome variables were chosen as they

highlight three different but interconnected faces of

economic and social malaise. Employment is

obviously at the core of social life, both as a way for

individuals to integrate into society and fulfill

themselves. A scarce ability to pay bills is an index

of material deprivation. As underlined by [17], [18],

[19], among others, it is reductive to use income or

monetary consumption as a proxy of living

standards. In [20], realizations are defined as the

various activities or goods that an individual

performs or uses in order to lead a satisfactory life;

individuals’ basic abilities mirror the different

combinations of the realizations they can achieve,

given what they are able to choose. This concept

opened the path to the direct measurement of living

standards and is at the basis of official surveys such

as Understanding Society and EU-SILC, [21], [22],

[23], [24], that aim at giving a comprehensive

picture of economic and social conditions in

different countries.

Moreover, the perception of one’s financial

situation responds to the principle of subjective

measurement of living standards. The subjective

approach leads to measures based on the opinions of

interviewees and the relationship between their

subjective opinions and their welfare, [25], [26].

The subjective approach has been criticized by [20].

In brief, people can get used to their situation,

following a behavior that marketing researchers

describe as cognitive dissonance, [27]. In our study,

we use the subjective approach as we agree with the

position of [28], which assumes that individuals are

responsible for their preferences.

It should be mentioned that government

interventions to support families and businesses

accounted for 5.5% of GDP in 2020, [4]. As regards

households, the Coronavirus Job Retention Scheme

took shape. It guaranteed transfers to companies to

pay 80% of furloughed workers’ wages up to a

maximum of 2,500 British pounds if they did not

work while receiving the subsidy. In addition, self-

employed workers received a taxable subsidy equal

to 80% of the average income of the previous three

months, as part of the Self-Employment Income

Support Scheme. Furthermore, the government

temporarily increased the unemployment benefits.

Indeed, the United Kingdom pursued a policy of

protecting jobs during the crisis.

The impact of the COVID-19 crisis on

unemployment in the UK has been small. This is

due to the policy measures enforced by the

government, especially those concerning furloughed

workers. Comparing the UK with other Western

countries, [29], we can see that its furloughing

scheme has been more efficient in protecting jobs.

For instance, in the United States of America,

unemployment rose steeply (+16% in April 2020),

mainly as a consequence of the lack of a

furloughing scheme.

In what follows, we will be highlighting the

differential effects on the three outcome variables,

related to the structural characteristics of individuals

and households – such as gender, age, ethnic group,

and family size. Our main hypothesis (as in [3]) is

that the economic effects of the COVID-19

pandemic have been regressive, and the negative

consequences of the economic crisis have had the

greatest impact on the socio-demographic groups

that were already in the worst situation before the

onset of the pandemic.

From the methodological side, the main

novelties of our study reside in the

conceptualization and operationalization of the

dependent variables: we consider the degree of

permanence of each respondent in each condition of

interest, to measure the intertemporal patterns of the

analyzed phenomena. In doing so, we take

inspiration from [30], [31].

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For a review of the different methods for

expressing the persistence of a statistical unit in a

specific state (such as poverty or unemployment),

[32]. For aggregate data, the usual time-series

methods can be applied, [33].

From a substantive point of view, we aim to

highlight the segmentation of individual responses

to the COVID-19-driven economic crisis, by using

social and demographic variables. The next Section

illustrates the data and techniques put to use to reach

our aims, while the main findings are presented in

Section 3. Then, some final remarks are given in

Section 4.

2 Data and Methods

The Understanding Society COVID-19 study is a

longitudinal survey aimed at capturing the

experiences of UK individuals during the COVID-

19 pandemic, covering the changing impact of the

pandemic on the welfare of the UK population, [14],

[15]. It is part of the UK Household Longitudinal

Study (UKHLS) and includes all members of the

main Understanding Society sample who

participated in waves 8 or 9 (2017-2018), selected

through a probability sampling of postal addresses

in the UK. The COVID-19 survey was conducted

during the first lockdown (April, May, and June

2020), in its immediate aftermath (July and

September 2020), during the last two lockdowns

(November 2020, January 2021, and March 2021),

and finally in September 2021, for a total of nine

survey waves so far.

The pattern of our investigation concerns the

longitudinal effects of the COVID-19 pandemic on

households’ economic well-being. This goal can be

achieved by analyzing intertemporal aggregations of

indicators that reflect economic malaise, [30],

measured by using variables such as income,

unemployment, ability to make ends meet, and

economic subsidies received. The degree of

economic malaise also depends on the length of

time during which an individual or household

lingers in a difficult situation, [34]: therefore, we

can assume that a challenging time is such if there is

a minimum length of persistence in a state of

difficulty.

The economic and statistical literature has

focused mainly on the permanence in two states:

unemployment and poverty, [35], [36]. Here, we

measure the persistence in a state of lack of

employment, difficulty in paying bills, and poor

subjective financial situation, by using the approach

drawn in [30].

Such approach is grounded on the length of the

period in which an individual lingers in a condition

of economic malaise. Their proposal consists of

computing, for each statistical unit (individual or

household), the weighted average of the indicators

linked to economic discomfort for each interval. The

weights are represented by the length of the period

in which the disease is present (for an alternative

scheme, [35]). To give a simple example, referred to

unemployment (0 = employed; 1 = unemployed),

the sequence (1, 0, 1, 0) is considered to be better

than the situation (1, 1, 0, 0), as the latter implies

that the individual has been unemployed for two

consecutive periods rather than two distinct ones.

Using a formal approach, the proposal in [30],

can be described as follows.

Let   



For each   , an individual profile of size T is a

vector  , where  is the experience of

economic malaise characterizing the individual i in

the period t = (1, 2, …, T).

An intertemporal measure of economic malaise is

given by the function:

  

where, for each  , 󰇛󰇜 is the individual

condition affecting the i-th unit.

Let us consider each

   

  󰇛󰇜 

 

Let 󰇛󰇜 as the maximum number of consecutive

intervals (t included) in which the indicator of

economic malaise is zero.

The measure of intertemporal uneasiness is defined

as:

󰇛󰇜  

 



 󰇛󰇜

(1)

for each    and for each  

.

The indicator specified above treats persistence

coherently to our aims. Ceteris paribus, longer spells

between two periods in which economic malaise is

captured decrease the intertemporal index, while

shorter periods between two discomfort episodes

make the intertemporal index greater. When the

number of consecutive periods during which the

individual remains in a state of economic malaise

becomes greater, the weight  increases, which

implies the growth of the individual measure. For a

review of the algebraic properties of the indicator,

[30].

Even if the measure proposed in [30], – just as

any other statistical index – holds a conventional

component, we regard a representation of economic

malaise that considers the persistence in the

situation of interest as preferable to one in which

duration has no role.

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We run a set of panel regression models, using

three different dependent variables, each of which

reflects a condition of economic malaise (not being

employed or self-employed, being behind with some

or all bill payments, considering the own financial

situation as quite or very difficult). The value of

each dependent variable is adjusted for its duration,

according to the method described in [30] (formula

1 supra). The estimated models include several

regressors related to the socio-demographic

characteristics of the individuals and incorporate

wave fixed effects.

Specifically, along with individual demographic

characteristics (ethnic background, gender, and age),

we consider household structure (family size and

presence of a partner) and health disorders as

proxies of households’ needs, as well as subjective

experiences of loneliness since the lack of social

networks may undermine the ability to cope with

adverse economic circumstances, [37].

Since some relevant variables were not captured

in waves 3, 5, and 7 of the Understanding Society

COVID-19 study, we only used six survey waves (1,

2, 4, 6, 8 and 9).

Descriptive statistics of the employed variables,

computed for the estimation sample, are presented

in Table 1.

Table 1. Descriptive statistics of the variables

employed in the models

Variable

Obs.

Min

Median

Max

Mean/%

Std.

Dev.

Subjective

financial

situation:

Finding it quite

or very

difficult

(persistence)

34525

-1

0.112

0.541

Not employed

nor self-

employed

(persistence)

34525

-1

0.732

1.546

Behind with

some or all bill

payments

(persistence)

34525

-1

0.105

0.566

White (1: yes)

34525

89.1%

Age

34525

47.75

12.11

Male (1: yes)

34525

38.6%

Household size

34525

2.815

1.285

Living with a

partner (1: yes)

34525

71.6%

Long-term

health

condition (1:

yes)

34525

45.6%

Feeling lonely

(1: Hardly ever

or never; 2:

Some of the

time; 3: Often)

34525

As expected, those who are not employed are

the ones who most frequently report issues with bill

payments (Table 2). In particular, while almost

4.4% of the observations under consideration are

classified as being behind with all or some bill

payments, this proportion increases to 8.05% when

only considering the respondents who are currently

not employed nor self-employed, while it decreases

to 3.27% when looking at the employed or self-

employed individuals.

Table 2. Bill payments and employment status

Not behind will

bill payments

Behind with some or all

bill payments

Total

Employed or

self-employed

96.73%

3.27%

100%

Not employed

nor self-

employed

91.95%

8.05%

100%

Total

95.63%

4.37%

100%

Table 3, Table 4 and Table 5 show the matrixes

of transition probability (i.e., the likelihood of

change between categories over time) between t and

t+1 for the given sample, as regards employment

status (Table 3), issues with bill payments (Table 4),

and reported financial situation (Table 5). Being the

diagonal values always the highest, the matrix

clearly shows that these variables are particularly

stable over time. However, while employment status

looks to be highly stable from one wave to another,

reported issues with bill payments and individual

perceptions of financial conditions appear to be

more volatile.

Table 3. Transition probability of employment

status between t and t+1

Employed or

self-employed

Not

employed nor

self-

employed

Total

Employed or self-

employed

97.52%

2.48%

100%

Not employed nor

self-employed

7.76%

92.24%

100%

Total

76.79%

23.21%

100%

Table 4. Transition probability of issues with bill

payments between t and t+1

Not behind

will bill

payments

Behind with some or

all bill payments

Total

Not behind

will bill

payments

98.50%

1.50%

100%

Behind with

some or all bill

payments

32.13%

67.87%

100%

Total

95.66%

4.34%

100%

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Table 5. Transition probability of subjective

financial situation between t and t+1

Subjective

financial

situation: Not

finding it

difficult

Subjective

financial

situation:

Finding it

quite or very

difficult

Total

Subjective financial

situation: Not

finding it difficult

97.78%

2.22%

100%

Subjective financial

situation: Finding it

quite or very

difficult

42.40%

57.60%

100%

Total

94.98%

5.02%

100%

Figure 1, Figure 2 and Figure 3 show the

average sample values of the three dependent

variables across waves. It appears to be increasingly

more likely to be non-employed as time goes by,

while the probability of being behind with bill

payments is especially high during the first period of

the pandemic. Respondents’ reports of their

perceived financial situation are the worst during the

first survey period, measured in April 2020.

Fig. 1: Proportion of respondents not employed nor

self-employed across waves

Fig. 2: Proportion of respondents behind with some

or all bill payments across waves

Fig. 3: Proportion of respondents with a quite or

very difficult subjective financial situation across

waves

3 Main Results

Table 6 shows the results from the first model, in

which the outcome is a continuous variable

calculated according to formula (1).

White people, compared to Black, Asian, and

minority ethnic groups, have a lower probability of

not being employed. The same applies to females

compared to males. The regressive effects of

COVID-19 are thus absolutely evident. Living with

a partner decreases the probability of being non-

employed while being part of a larger household

increases this probability.

Older individuals have a higher probability of

not being employed, as well as those with a long-

term health disorder and those who are experiencing

loneliness. Besides, the lack of employment is

positively associated with difficulties in making

ends meet, proxied by being behind with bill

payments and by perceiving the own financial

situation as quite or very difficult. The effects of

time (expressed by the wave coefficients) are

progressive and signal a worsening of employment

conditions throughout the pandemic.

Table 7 shows the results from the second

model, in which the outcome is a continuous

variable expressing the persistence in a state of

economic hardship reflected by the inability to pay

some or all bills.

The results from this model are in line with

those we got in the previous one, which examined

the persistence in a non-employment status. White

people have a lower probability of being behind

with bill payments. The same is true for older

individuals, for which the probability of being

behind with payments is lower than that of younger

respondents. Gender, on the other hand, does not

show a significant association with the dependent

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variable. For what concerns household structure,

being part of a larger family is associated with an

increase in persistence in a state of not being able to

pay bills on time; living with a partner, on the other

hand, appears to be helpful in this respect. As we

could easily expect, also not being employed and

perceiving the own financial situation as difficult

are positively associated with material difficulties.

Feeling lonely is negatively associated with being

behind with payments while being affected by a

long-term health disorder appears to increase the

probability of not making ends meet.

Table 6. Results from Model 1 – Panel regression of

being not employed nor self-employed (persistence)

Variable

Coefficient

Std.

Error

Behind with some or all bill payments

(persistence)

0.200***

0.0155

Subjective financial situation: Finding

it quite or very difficult (persistence)

0.149***

0.0161

White

-0.253***

0.0558

Age

0.021***

0.0015

Male

-0.240***

0.0352

Household size

0.027***

0.0091

Living with a partner

-0.178***

0.0254

Long-term health condition

0.115***

0.0259

Feeling lonely: Some of the time

0.052***

0.0130

Feeling lonely: Often

0.099***

0.0251

Time 2

0.202***

0.0136

Time 3

0.395***

0.0136

Time 4

0.565***

0.0137

Time 5

0.742***

0.0138

Time 6

0.910***

0.0139

Intercept

-0.463***

0.0917

Observations

34525

0.210

0.070

R-squared (within)

R-squared (between)

R-squared (overall)

0.102

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

Turning now to the examination of wave

coefficients, it should be underlined that, from one

wave to another, the coefficients do not show large

differences, meaning that the value of the

persistence variable is likely steady across time.

This can be interpreted as evidence that individuals

continuously enter and exit from this state during

the COVID-19 period, maybe due to intermittent

financial assistance received by the government.

From a comprehensive point of view, the effects

of the COVID-19 crisis on households’ well-being

highlight a regressive effect, that hits the weaker

segments of the population – such as BAME and

younger citizens – the hardest.

Table 7. Results from Model 2 – Panel regression of

being behind with some or all bill payments

(persistence)

Variable

Coefficient

Std.

Error

Subjective financial situation: Finding it quite or very

difficult (persistence)

0.369***

0.0052

Not employed nor self-employed (persistence)

0.024***

0.0019

White

-0.147***

0.0192

Age

-0.001*

0.0005

Male

-0.018

0.0121

Household size

0.010***

0.0031

Living with a partner

-0.020**

0.0088

Long-term health condition

0.065***

0.0090

Feeling lonely: Some of the time

0.002

0.0045

Feeling lonely: Often

-0.017**

0.0087

Time 2

0.022***

0.0047

Time 3

0.037***

0.0048

Time 4

0.044***

0.0049

Time 5

0.050***

0.0050

Time 6

0.057***

0.0051

Intercept

0.153***

0.0316

Observations

34525

R-squared (within)

0.144

R-squared (between)

0.222

R-squared (overall)

0.205

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

We now move on to the subjective perception of

respondents’ financial situation (Table 8). It is

interesting to compare the results from this

regression with the previous ones: indeed, while the

first two models employ objective indicators of

economic hardship as dependent variables, this

model uses a subjective one, for which some caveats

may be in order, [20].

White people, compared to BAME, have a

lower probability of reporting a bad financial

situation. This is in line with the results of the

previous models (Table 6 and Table 7), in which

White respondents were shown to have a lower

probability of suffering economic hardship. A

negative association with an unenthusiastic

perception of own financial situation can also be

pointed out for those living with a partner, which is

also in line with the results from the previous

models. Being behind with bill payments and being

not employed, as expected, are also positively

linked with own perceptions of a bad financial

situation. Finally, reporting feeling lonely is

positively associated with the perception of a bad

financial situation, and the deeper the feeling of

loneliness, the worse the perceived financial

situation.

Contrary to our expectations, we do not get

significant coefficients for age, gender, household

size, and long-term health issues, which could be

seen as an indirect confirmation of the criticism of

[20].

Finally, the wave coefficients show little

variation across time, but there is a clear

monotonical trend that confirms an increase in the

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persistence of a difficult financial status throughout

the pandemic.

Table 8. Results from Model 3 – Panel regression of

quite or very difficult subjective financial situation

(persistence)

Variable

Coefficient

Std.

Error

Behind with some or all bill payments

(persistence)

0.341***

0.0048

Not employed nor self-employed

(persistence)

0.017***

0.0018

White

-0.095***

0.0181

Age

0.000

0.0005

Male

-0.003

0.0114

Household size

-0.002

0.0030

Living with a partner

-0.028***

0.0084

Long-term health condition

-0.005

0.0085

Feeling lonely: Some of the time

0.026***

0.0044

Feeling lonely: Often

0.096***

0.0084

Time 2

0.013***

0.0046

Time 3

0.021***

0.0046

Time 4

0.030***

0.0047

Time 5

0.036***

0.0048

Time 6

0.043***

0.0049

Intercept

0.117***

0.0299

Observations

34525

R-squared (within)

0.136

R-squared (between)

0.232

R-squared (overall)

0.210

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

4 Conclusions

In this study, we analyzed the consequences of the

pandemic on the UK population, using data from the

Understanding Society COVID-19 Study, [14], [15].

We estimated three panel regression models,

focusing on three outcome variables: job loss,

difficulties in paying bills, and subjective financial

situation. Our models considered several socio-

demographic characteristics and controlled for the

effect of time, as policy measures and their impact

on the population have greatly changed throughout

the pandemic. Moreover, we took into account the

persistence of an economic-hardship status over

time.

Among other things, our outcomes highlight

the vulnerability of some social groups. It appears

that the ethnic component plays a key role in

determining the probability of employment loss:

Black, Asian, and minority ethnic groups result to

be more at risk of losing their jobs. Moreover, such

citizens are also more likely to be behind will bill

payments and to report a bad financial situation. The

same can be pointed out for women, compared to

men. Nevertheless, older adults are more likely to

lose their jobs than younger individuals but less

likely to report being behind with bill payments: this

is probably due to a higher monetary wealth that

they might be able to tap into, making them more

capable than younger individuals to absorb income

shocks. However, this may not be true for all, and

policymakers should ensure that older individuals

who are financially affected by the pandemic are

adequately assisted. We also show that, as time goes

by, it becomes increasingly likely for citizens to

suffer economic hardship.

As regards future research, further waves of the

survey will enable scholars to assess whether the

presented results represent longer-term trends.

As uncertainty increases while the world adapts to

the pandemic, understanding how people react is

crucial to help those in need. By doing so, we will

be able to build back better in a post-COVID-19

world.

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed to the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflicts of interest to declare.

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