2. Material conditions in the first year of COVID-19

Abstract

COVID-19’s impact on households’ material conditions has been significant. Although government support ensured that average household income did not decrease as markedly as GDP in 2020, many households did nevertheless face financial difficulties. The pandemic led to the closure, both permanently and temporarily, of activities and businesses, and for many jobs teleworking is simply not an option. Despite unprecedented government action to support workers and employers, labour underutilisation rates nearly doubled, and there has been a steep reduction in hours worked. Overcrowded and poor-quality housing conditions increase vulnerability to COVID-19, while the lack of Internet access still prevents some people from working, studying or accessing services remotely. Housing cost overburden and sharp rises in rents and house prices have added to the difficulties faced by poorer households. These impacts have tended to hit vulnerable people and places the hardest, threatening to widen pre-existing inequalities.

2.1. Income and Wealth

While the COVID-19 pandemic has put large numbers of households in financial difficulty, the impact has been mixed within and across countries. Across OECD countries, government measures are being implemented with the aim of supporting households facing a considerable loss of labour income. As a result, real household income per capita did not decrease as markedly as GDP per capita, on average, in the OECD area in 2020. Nevertheless, many households are struggling financially.

Crisis measures taken by governments helped to cushion the impact on incomes…

Government measures shielded average household per capita income from the direct economic impacts of COVID-19 across the OECD area. As a result, while OECD real GDP per capita decreased by 5.1% cumulatively between 2019 and 2020, average real household disposable income per capita (i.e. after direct taxes and transfers) increased by 2.9% cumulatively (Figure 2.1 and Figure 2.2 Panel A).

GDP and household disposable income per capita followed divergent trends in 2020. Average household disposable income per capita reached its peak in Q2 2020 (with a 3.9% increase over the previous quarter) and progressively declined until Q4 2020. By contrast, after reaching its trough in Q2 2020 (with a 10.5% decline over the previous quarter), average GDP per capita increased up to Q4 2020 (Figure 2.1 and Figure 2.2 Panel B). Canada and the United States experienced a particularly marked increase in household disposable income per capita between 2019 and 2020, of 7.9% and 6.1% respectively, in both countries peaking in Q2 2020.

Government transfers to the household sector in response to the pandemic played a significant buffering role. Net cash transfers to households increased in most OECD countries between 2019 and 2020 (Figure 2.3, Panel A). In Canada, government net cash transfers to households increased by 20.7% between Q1 and Q2 2020, which explains why Canada experienced a larger increase in household disposable income per capita compared to other countries (Figure 2.3, Panel B). Over 8.9 million Canadians received income assistance from the Canada Emergency Response Benefit or enhanced Employment Insurance between 16 March and 26 September 2020, which helped many families to avoid low income. According to Statistics Canada’s experimental estimates of weekly household income, the proportion of individuals living in families that had earnings below the Low-Income Measure – the Canadian poverty line – in April 2020 was 16 percentage points lower when pandemic benefits were taken into account than otherwise (22% versus 38%) (Government of Canada, 2021[1]; Beck et al., 2020[2]). Similarly, in the United States, government support through the April 2020 CARES Act contributed to the significant increase in household disposable income during the first two quarters of 2020 (OECD, 2020[3]).

The decrease in real household disposable income per capita between Q2 and Q4 2020 reflected the reduction of government transfers to households after the unprecedented support provided at the beginning of the pandemic (Figure 2.3, Panel B). Nevertheless, the United States enacted additional fiscal stimulus throughout the Coronavirus Response and Relief Supplemental Appropriations Act of 2021 and the American Rescue Plan Act of 2021, which is reflected in the large increase in household disposable income in Q1 2021. Local and regional actors have also played an increasingly important role in providing support to vulnerable businesses and households. They often implemented emergency support policies on behalf of national governments, complementing them with local actions to fill gaps for specific sectors or populations and helping local workers and firms navigate the sometimes-complex patchwork of schemes (OECD, 2021[4]).

Figure 2.1. In 2020, GDP decreased by 5.1% across the OECD area

Figure 2.2. Government support measures shielded household disposable income during the pandemic

Figure 2.3. Net cash transfers to households increased in most OECD countries in 2020

… but the most vulnerable households have been facing financial difficulties

There is preliminary evidence for a small number of countries that government support to households during the pandemic led to a decrease in income inequality. While official data on income inequality and poverty are not yet available for 2020, a study conducted in five European countries (France, Germany, Italy, Spain and Sweden) (Clark, D’Ambrosio and Lepinteur, 2021[6]) and one conducted in Germany (Grabka, 2021[7]) revealed a decline in income inequality during the first year of the pandemic. According to both studies, while income at the top of the distribution decreased slightly, lower-income households were effectively protected by government emergency packages, leading to lower income inequality. Estimates from Finland, meanwhile, suggest that inequality as measured by the Gini index increased, but only marginally (at one-digit level), owing to existing social support mechanisms (e.g. unemployment insurance, income transfers, housing benefits), which successfully shielded lower-income deciles (Kyyrä, Pirttilä and Ravaska, 2021[8]). Eurostat estimates for EU 27 countries also show that the compensatory effects of government support 1 to households were more pronounced for households belonging to lower income quintiles, which experienced higher increases in equivalised disposable income (1.9% for households in the bottom quintile versus 0.5% for households in the top quintile) (Figure 2.4) (Eurostat, 2021[9]).

Figure 2.4. Government schemes compensated employment income losses, particularly for lower income quintiles

Despite strong government support, lower-income households are struggling financially. High rates of financial insecurity before the pandemic left many households vulnerable as they entered the crisis. On average across 28 OECD countries, OECD wealth distribution data indicate that 36% of people who were not income poor in 2016 were nevertheless at risk of falling into poverty within three months in the event of a sudden loss of income (OECD, 2020[10]).2 When the pandemic hit, higher-income households were, on average, able to accumulate wealth by cutting discretionary spending on activities that were restricted during the lockdowns (e.g. restaurants, culture, travel) while lower income households – whose spending is largely not discretionary – were not able to reduce their spending to the same extent (refer to Chapter 5 for more information about financial difficulties across the income distribution). Data collected in September-October 2020 in 25 OECD countries as part of the OECD Risks That Matter Survey indicate that close to one third (31%) of respondents reported that they or their household experienced at least one form of financial difficulty since the pandemic began (Figure 2.5 – the figure note provides the full list of financial difficulties included). Countries with higher GDP per capita and with higher spending on social programmes show lower levels of financial difficulties in September-October 2020 (OECD, 2021[11]).

Figure 2.5. An average of 31% of respondents in 25 OECD countries have been struggling financially since the outbreak of COVID-19

Across 22 European OECD countries the share of households reporting difficulties to make ends meet increased in 2020. Questions on financial difficulty and savings have been included in the Eurofound Living, working and COVID-19 e-survey (see Box 2.1, below). These data indicate that in April-June 2020, just over one-fifth (21%) of people in 22 European OECD countries reported difficulty or great difficulty in making ends meet – a level that was broadly sustained in a second and third round of data collection in June-July 2020 and February-March 2021. These 2020-21 averages are well above the 14.5% level observed by the European Quality of Life Survey in 2016 (Figure 2.6).

Figure 2.6. Across European countries, financial struggles increased as a result of the pandemic

The most vulnerable households are facing food insecurity. In 25 OECD countries, 3.9% of respondents reported that they had “gone hungry because they could not afford to buy food” in September-October 2020 (OECD, 2021[11]). Data collected in 22 European OECD countries in April-June 2020 indicate a much higher level of food insecurity: 13.6% of respondents reported that they went without fruit and vegetables, and 28.5% that they bought cheaper cuts of meat or bought less than wanted in the previous two weeks because money was needed for other essentials (Eurofound, n.d.[14]). Separate national studies also show worrying figures. A study from the United Kingdom revealed that between August 2020 and January 2021, 4.7 million adults experienced food insecurity (i.e. 9% of all households, compared to 7.6% in 2019). This includes 1.6 million adults who reported having had to go a whole day without eating due to not being able to afford or access food (Goudie and McIntyre, 2021[15]).3 In Chile, 19.4% of households suffered from moderate/severe food insecurity in July 2020, and 11.5% in November 2020 (Ministerio Desarrollo Social y Familia, n.d.[16]) (Box 5.3).4 In the United States, 20% of households indicated that they often or sometimes ran out of food before having enough money to buy more between 1 and 8 June 2020, slightly improving from April (23%) and May (22%) (Wozniak et al., 2020[17]) (Box 3.1). In Canada, between 4 and 10 May 2020 one in seven (14.6%) people reported that they lived in a household experiencing some form of food insecurity – ranging from food not lasting before there was money to buy more, to going hungry because there was not enough money for food – in the previous thirty days (Statistics Canada, 2020[18]).5

Box 2.1. Methods: The Eurofound Living, working and COVID-19 e-survey

The Eurofound Living, working and COVID-19 e-survey was launched in April 2020. It aims to investigate well-being, work, telework and people’s financial situations across the European Union during the crisis.

Fieldwork and sampling

As of June 2021, three rounds of survey fieldwork had been completed: Round 1 from 9 April to 11 June 2020; Round 2 from 22 June 2020 for panel respondents and 25 June 2020 for public respondents, closing for both on 27 July 2020; and Round 3 from 15 February 2021 to 29 March 2021.

The survey was conducted online via the SoSciSurvey platform. It was open to respondents from all countries, but only promoted in the 27 European Union countries. In both Round 1 and Round 2 the recruitment of respondents was carried out via uncontrolled convenience sampling, by publishing the link to the survey on social media and distributing it among Eurofound’s contacts and stakeholders, complemented by social media advertising targeting hard-to-reach groups. In Round 2 an additional panel element was introduced: Round 1 collected email addresses from respondents interested in participation in further survey rounds, who then received an invitation to complete Round 2 a few days before the questionnaire was launched to the public.

Cleaning and weighting, effective sample size

The final sample size after cleaning was of 53 918 for Round 1, 19 987 for Round 2 and 38 708 for Round 3. The weighting methodology was the same in all rounds. To produce country level and EU 27 averages, all individual responses were re-weighted to be representative of the demographic of each respondent’s country. Data were weighted by age crossed with gender (in 12 age-gender combinations), urbanisation level (2 categories) and education level (2 categories). Weighting targets for each country included 2019 population estimates from Eurostat by age and gender, self-defined urbanisation levels by age and gender as measured in the 4th European Quality of Life Survey, and education levels by age and gender from the 2018 Labour Force Survey.

Source: Eurofound (n.d.[14]), Living, working and COVID-19 e-survey (database), http://eurofound.link/covid19data

2.2. Work and Job Quality

The COVID-19 crisis had an impact on both the quantity and quality of jobs. Early government measures aimed at containing the spread of the virus led to the closure of many activities and businesses. The relatively small increase in the unemployment rate in most OECD countries does not fully reflect the extent of the job crisis. As a large number of workers went on job retention schemes or were temporarily laid off, and as people who were out of work stopped actively looking for jobs (meaning that they are no longer counted in official unemployment rates),6 overall labour underutilisation rates rose sharply across the OECD area. Teleworking reduced the immediate job consequences of physical-distancing measures in some sectors, but in practice it remains restricted to jobs that can be performed remotely – around 25% of all jobs in the EU 28 (Fana, Pérez and al., 2020[19]).

The pandemic’s impact on unemployment varied across countries

The pandemic’s impact on OECD labour markets has been significant. Between 2019 and 2020, OECD average unemployment rose by 1.7 percentage points (Figure 2.7, Panel A). In particular, following the onset of the crisis, the OECD unemployment rate rose from 5.4% in Q1 2020 to 8.6% in Q2 2020. This surge was largely driven by substantial increases in countries such as the United States and Canada (of 9.3 and 6.7 percentage points respectively), where large numbers of workers on temporary layoff increased unemployment figures (OECD, 2021[20]). Costa Rica, Colombia and Chile also experienced very large increases in unemployment between 2019 and 2020 (respectively of 7.9, 5.6 and 3.6 percentage points). By contrast, elsewhere in the OECD area – particularly in European countries, which made large use of job retention schemes – unemployment rates were relatively steady or even fell slightly in the first months of the pandemic (Figure 2.7, Panel B). The Q2 2020 falls in some European countries such as France and Italy do not generally indicate that labour market conditions improved, but rather that some jobless individuals stopped actively seeking work and therefore no longer met the international definition of unemployment.7 This is borne out by the very substantial rises in the labour underutilisation rate in the second quarter of 2020 (Figure 2.8). The divergent patterns between the United States, Canada and other G7 countries continued as the pandemic progressed: while in Q3 and Q4 of 2020, unemployment rates were falling in North America, they were rising in Japan, Italy, the United Kingdom and France.

Differences in unemployment rates partly reflect differences in policy responses. Most OECD countries, and particularly those in Europe, relied on job retention schemes, which allow companies to reduce or entirely halt employees’ work while keeping them employed with (part of) their salaries covered from government funds (OECD, 2021[20]). By contrast, the United States relied on temporary layoffs and unemployment insurance benefits: many companies released their workers when the crisis hit, often with the intention of hiring them back once economic activity resumed.8 Although unemployment insurance benefits replaced a portion of earnings for many workers on temporary layoff, many lost their salary and sometimes their health insurance. These factors explain why the unemployment rate rose so much more in the United States and Canada compared to other OECD countries, as well as why it decreased substantially by Q3 2020, as businesses started to reopen and many workers on temporary layoff went back to work (OECD, 2021[20]). There are also differences in how national statistical offices classify people on “temporary layoff” – differences which normally have little impact on the comparability of unemployment statistics, but in times when layoffs affect larger numbers of people, the impact can be larger (OECD, 2021[20]; OECD, 2020[21]).

Job losses due to COVID-19 disproportionally affected vulnerable population groups. Indeed, younger, lower-income, less-educated individuals, as well as women, and people belonging to racial/ ethnic minorities and LGBTQ+ communities, who are over-represented in the industries most exposed to government closures and containment measures (e.g. leisure and hospitality, tourism, retail), were less often able to telework and were most likely to lose their jobs (see Chapter 5 for more information on how COVID-19 impacted labour market outcomes for different population groups).

These impacts also varied strongly across regions and types of employment. The regions most affected by the pandemic were those with a strong focus on tourism, leisure or cultural services, as well as those with many independent, temporary or informal workers, who are also the least likely to be covered by safety nets (OECD, 2021[4]). In particular, self-employed workers registered a 19% decrease in total hours worked between Q2 2019 and Q2 2020, a fall that was 12 percentage points larger than among dependent employees (OECD, 2021[20]). Small-and-medium enterprises (SMEs) are also over-represented in the sectors that have been more impacted by COVID-19. On average across OECD countries, SMEs are estimated to account for 75% of employment in the most affected sectors (see also Box 5.4) (OECD, 2021[4]).

Figure 2.7. The impact of COVID-19 on unemployment has varied across OECD countries

Unemployment does not fully reflect the extent of the job crisis: labour underutilisation rates increased substantially, and job insecurity levels are high

Unemployment figures do not capture the full extent of the job crisis, while labour underutilisation provides a more comprehensive measure of labour market slack. Indeed, in addition to the unemployed, the underutilisation rate includes marginally attached workers (i.e. those who wish to and are available to work, but who did not actively seek work within the last four weeks) and people who are underemployed (i.e. those who are involuntarily working part-time because they could not find full-time work, or full-time workers working less than usual during the survey reference week for economic reasons). Due to the exceptional nature of the COVID-19 crisis and the government response measures, marginal attachment and underemployment levels increased, resulting in a large rise in the labour underutilisation rate. On average in 32 OECD countries, the underutilisation rate increased from 12.3% to 16.8% between 2019 and 2020 (Figure 2.8). This increase was largely driven by rises in underemployment, which accounted for 6.1 percentage points of the labour underutilisation rate in 2020, compared to only 3.7 in 2019. In 2020, unemployment and marginal attachment made up 7.0 and 3.7 percentage points of the underutilisation rate respectively (versus 6.0 and 2.6 in 2019) (refer to Chapter 9).

The number of marginally attached workers increased, as government lockdown measures and the fear of contracting the virus disrupted job search activity (OECD, 2021[20]). In 32 OECD countries, the average number of marginally attached workers increased from 2.6% in 2019 to 3.7% in 2020. The pandemic caused some people who were out of work to pause their job search activity – meaning they were counted as “outside of the labour force” or “marginally attached” rather than “unemployed” (since to be counted as unemployed in labour market statistics, an out-of-work individual needs to have been actively seeking work within the last four weeks, and available for work within the next two weeks). This created counter-intuitive decreases in unemployment statistics in several OECD countries, especially in some European countries such as Italy and France.

Underemployment also increased, as those who kept their jobs were working reduced – or even zero – hours. On average across 32 OECD countries, underemployment increased from 3.7% to 6.1% between 2019 and 2020. Job retention schemes aim to minimise job losses by allowing firms experiencing a temporary drop in business activity to receive support for a significant share of the wages of employees working reduced hours (OECD, 2021[20]). Across the OECD, about 60 million workers have been included in company claims for job retention schemes, thereby preventing a massive surge in unemployment. At the same time, the large number of employees working reduced – or even zero – hours led to an increase in underemployment (refer to Figure 2.10 for information on hours worked) (OECD, 2021[20]).

The COVID-19 crisis led many workers to fear for their jobs in the near future. Even in the early stages of the pandemic when job retention schemes were offering workers some protections, 14% of employees in 19 European OECD countries felt it was “likely” they would lose their job within three months (Figure 2.9). Feelings of job insecurity may be heightened among those with only temporary employment contracts, a group that represented 10.1% of all employed people of working age (15–64) in the second quarter of 2020 across the same European OECD countries (Eurostat, n.d.[22]). On average, feelings of job insecurity in these European countries improved slightly as the pandemic progressed, falling to 10% by June-July 2020 and to 9% by February-March 2021 (Figure 2.9).

Figure 2.8. Labour underutilisation in the OECD area almost doubled across the first two quarters of 2020

Figure 2.9. In 19 European countries, nearly 1 in 7 workers feared for their jobs in April-June 2020

Total hours worked decreased substantially, and reduced working time among those who remained in employment caused much of the initial fall

Decomposing the reduction in total hours worked provides a clearer picture of the pandemic’s impacts on the labour market, and of the channels through which these impacts operated (i.e. employees working reduced hours versus joblessness) (OECD, 2021[20]). In Q2 2020, total hours worked decreased by 15.3% compared to the second quarter of 2019. Most of the decline (8.7 percentage points) was accounted for by workers who, although remaining in employment, were working zero hours. By contrast, joblessness accounted for only 4.2 percentage points of the reduction in hours worked on average (OECD, 2021[20]). This was largely driven by European countries making extensive use of job retention schemes. In the United States, where many workers were laid off, the majority of unworked hours was channelled through joblessness (Figure 2.10) (OECD, 2021[20]). In the third quarter of 2020, as many employees went back to work after the first lockdown, total hours worked recovered in most OECD countries (just 4.3% below the third quarter of 2019), with joblessness accounting for the majority of the decrease. Nevertheless, in the fourth quarter of 2020, as a new wave of the virus brought further closures, working hours dropped again across the OECD (a 5.6% decrease compared to Q4 2019), with reduced working time once again accounting for the biggest share of this reduction (OECD, 2021[20]).

Figure 2.10. Across the OECD, total hours worked decreased by 15.3% in Q2 2020 year-on-year

Hours decreased the most in sectors such as hospitality, tourism, arts and leisure. For instance, in accommodation and food services, total hours worked more than halved in Q2 2020 compared to a year earlier on average across the OECD. In the arts sector, total hours worked fell by over 42% in Q2 2020 year-on-year (OECD, 2021[20]). By contrast, the financial and insurance activities and the information and communication sectors experienced an increase in hours worked in Q2 2020 compared to the previous year, probably due to the larger number of jobs that can be performed remotely in these sectors (Figure 2.11; see also (OECD, 2021[20]) for further detail).

Meanwhile, there is also evidence that those who could telework are working longer hours, on average. While the available data suggest that people whose work can be performed remotely were less likely to lose their jobs (Box 2.2), they have been working longer hours, on average, in many countries across Europe and North America (DeFilippis et al., 2020[23]). In June-July 2020, employees working exclusively from home in the EU 27 were most likely to report that their number of hours worked had increased, or increased a lot (Eurofound, 2020[24]). Similarly, in the United States in October 2020, 33% of those working entirely from home said they worked longer hours than before the pandemic, compared to 21% of those whose work cannot be done from home (Parker, Menasce Horowitz and Minkin, 2020[25]).

Figure 2.11. Working hours decreased the most in the accommodation and food services and arts sectors

Box 2.2. The relationship between teleworking opportunities and change in employment in the United States

A study from the US Bureau of Labor Statistics relied on employment estimates from the Current Population Survey (CPS, see Box 2.3) to assess how the possibility to telework acted as a protective factor for employment during the pandemic. Overall, from February to April 2020 employment in the United States decreased by 15.6%. In all industries, the drop in employment in occupations in which telework is not feasible (21.2%) was considerably larger than in occupations where telework is feasible (7.9%). Over the same time period, unemployment increased by 14.3 percentage points in occupations in which telework is not feasible, but only by 6.2 percentage points in occupations in which telework is feasible (with overall unemployment rising by 10.8 percentage points). This points to a strong relationship between employment loss and teleworking possibilities: in every industry except agriculture, workers in occupations in which telework is feasible experienced a smaller percentage decline in employment. The difference is particularly large in the information sector and the services industry, where employment fell by 37.3% and 35.9%, respectively, in occupations in which telework is not feasible, but by only 2.1% and 8.4% in occupations in which telework is feasible (Table 2.1).

Table 2.1. In the United States occupations where teleworking is possible registered a smaller decrease in employment
Difference in employment between occupations able to telework and those not able to telework, by employment sector, Feb-Apr 2020
Industry	Percentage of employed able to telework (Apr 2020)	Labour market outcomes		Percentage change in employment rate (Feb-Apr 2020)			Percentage point change in unemployment rate (Feb-Apr 2020)
Industry	Percentage of employed able to telework (Apr 2020)	Percentage change in employment rate (Feb-Apr 2020)	Percentage point change in unemployment rate (Feb-Apr 2020)	Able to telework	Not able to telework	Difference	Able to telework	Not able to telework	Difference
Financial activities	81.1	-6.1	3.7	-5.8	-7.2	1.4	2.8	7.2	-4.4
Information	80.4	-11.8	9.3	-2.1	-37.3	35.2	5.8	21.1	-15.3
Professional and business services	71.6	-9.6	5.5	-6.4	-16.8	10.4	3.5	10.0	-6.5
Public administration	57.0	-3.8	3.4	-1.5	-6.7	5.1	3.2	3.8	-0.6
Education and health services	47.9	-13.9	9.4	-12.5	-15.2	2.8	8.8	9.9	-1.1
Manufacturing	41.0	-13.7	9.2	-3.9	-19.5	15.5	4.3	12.3	-8.0
Mining, quarrying, and oil and gas extraction	40.3	-14.9	4.2	5.5	-24.8	30.3	4.2	5.1	-0.8
Other services	39.9	-27.2	19.4	-8.4	-35.9	27.5	10.6	24.3	-13.6
Transportation and utilities	32.7	-10.9	8.7	4.7	-16.9	21.6	4.9	10.4	-5.5
Wholesale and retail trade	26.5	-16.4	12.6	-9.4	-18.6	9.2	7.6	14.2	-6.6
Construction	20.7	-16.6	10.2	-11.9	-17.8	5.8	5.1	11.3	-6.2
Leisure and Hospitality	20.3	-42.0	32.1	-25.5	-45.1	19.6	22.9	34.1	-11.2
Agriculture, forestry, fishing and hunting	8.1	-1.2	-1.7	-4.3	-1.0	-3.3	-5.9	-1.3	-4.5
Total	45.8	-15.6	10.8	-7.9	-21.2	13.3	6.2	14.3	-8.1
Note: Calculations based on Feb-Apr 2020 Current Population Survey (CPS) data and O*NET job-content data.
Source: Dey et al. (2020[26]), Ability to work from home: Evidence from two surveys and implications for the labor market in the COVID-19 pandemic, US Bureau of Labor Statistics, https://www.bls.gov/opub/mlr/2020/article/ability-to-work-from-home.htm.

On average, earnings increased slightly in 2020, with large cross-country differences

Across the OECD area, average annual wages increased by 0.5% between 2019 and 2020. The pandemic’s impact on wages varied however across countries, ranging from a 7.1% increase in Lithuania to a 9.6% decline in Chile (Figure 2.12). One reason for higher average wages is the change in the composition of employment. Indeed, during the COVID-19 crisis, low-paid workers were more likely to become unemployed (refer to Chapter 5), so average wages reflect the wages of higher-paid workers who experienced fewer job losses (ILO, 2020[27]). This composition effect was most powerful in countries with a high number of temporary layoffs, such as the United States. By contrast, in other countries, including European countries, where unemployment did not increase as much owing to job retention schemes, average wages remained relatively steady or declined, as working time was reduced or the nominal wages of workers were frozen or reduced (ILO, 2020[27]). Eurostat estimates show that, in EU 27 countries, losses in employment income were almost halved by government compensation schemes (Eurostat, 2020[28]).

It is estimated that, in the EU 27, the loss in labour income between 2019 and 2020 was particularly strong for some sectors and vulnerable population groups, such as low-income and young workers. The food and accommodation sector was hit the hardest, registering losses of almost 20% in labour income (Eurostat, 2020[28]). Meanwhile, labour income losses for workers in the bottom income quintile were estimated to be four times higher than those for earners in the top income quintile in the EU 27 (see Chapter 5) (Eurostat, 2021[9]).

Figure 2.12. Average annual wages increased marginally in 2020, but trends diverged strongly across OECD countries

Teleworking has become the normal way of working for around one-third of all employees, but not all jobs can be done remotely

With the closure of many workplaces as the pandemic hit, many companies and employees transitioned to working from home. In April-June 2020, survey data suggest that 39% of employees in 21 European OECD countries started teleworking as a result of COVID-19 (Figure 2.13) (Eurofound, n.d.[14]). In four countries (Finland, Luxembourg, Belgium and the Netherlands) the share was above 50%. Separate data from the United Kingdom Office for National Statistics indicate that 47% of people did some work from home in April-May 2020, 86% of whom did so as a result of the pandemic (see Box 2.3) (ONS, 2020[30]). In the United States, according to the Bureau of Labor Statistics, 35% of employees worked from home in May, and 31% in June (see also Box 2.3) (BLS, n.d.[31]).

A sizeable portion of employees also worked from home in June-July 2020 and February-March 2021. A second Eurofound survey wave conducted in June-July 2020 asked where respondents worked during the pandemic, with 46% saying that they worked from home across 22 European OECD countries. In February-March 2021, the share of respondents reporting this decreased to 41% (Eurofound, n.d.[14]).9 Reflecting the changing nature of government restrictions, the share of people working exclusively from home decreased in the United Kingdom from a peak of 38% in June to 24% in July 2020 but then rebounded to 47% in early February 2021 (ONS, 2020[32]; Shine, 2021[33]). In the United States, the share of people working from home fell to 26% in July 2020, and continued decreasing to 23% in February and 17% in May 2021 (BLS, n.d.[31]). In Canada, a study by Statistics Canada indicates that, at the beginning of 2021, 32% of employees aged 15 to 69 worked most of their hours from home, compared to only 4% in 2016 (Mehdi and Morissette, 2021[34]). The figures presented above could, however, overestimate the share of people who teleworked during the pandemic. Data from the 2020 European Union Labour Force Survey indicate that, on average across five European OECD countries, 18.9% of employees worked from home (OECD, 2021[20]).10

Figure 2.13. Over a third of workers in 21 European OECD countries began working from home due to COVID-19

The shift to teleworking was not equally possible within and across countries. Indeed, teleworking has been restricted to employees working in jobs that could be performed remotely, mainly in sectors such as education, most of public administration, finance, insurance and telecommunications (Fana, Pérez and al., 2020[19]). It is estimated that, in the EU 28, only 25% of jobs are “teleworkable” (refer to Chapter 5 for more information on teleworking opportunities for different population groups) (Fana, Pérez and al., 2020[19]). In addition, the propo rtion of jobs with tasks amenable to remote working appears to be much higher in cities and capital regions than elsewhere. Compared to rural areas, the share of jobs amenable to remote working in cities is 13 percentage points higher (OECD, 2020[35]).

At the national level, several factors explain the different take-up of telework. For instance, prior teleworking experience supported the transition to working from home in countries such as the Netherlands, Sweden and Finland, where over 30% of employees worked from home at least sometimes in 2019 – well above the 10% share observed in over half of EU member states. Other explanatory factors include differences in countries’ industrial structures (e.g. in 2019 and during the pandemic, telework was more common in countries with more employees in knowledge- and ICT-intensive services, such as Denmark, Sweden and Finland); the distribution of employment by firm size (e.g. in Sweden and Finland, firms with more than 50 employees account for a larger share of total employment in knowledge-intensive business services, where the prevalence of teleworking before the pandemic was more common); and workers’ digital skills (e.g. workers with low or no digital skills number just 10% in the Netherlands, compared to an average of 20% in the EU 27) (JRC, 2020[36]). Access to teleworking is also affected by countries’ regulations and firms’ management cultures. Prior to the COVID-19 crisis, teleworking was higher in countries where workers had an enforceable right to request teleworking (e.g. the Netherlands, the United Kingdom), and highest in countries where this right was granted through collective bargaining (e.g. Denmark, Sweden) (see Chapter 5 in (OECD, 2021[20])).

A large share of employees wish to continue working from home. Between June 2020 and March 2021, almost half of all employed people in 22 European OECD countries (46%) would like to work from home at least several times a week after COVID-19 subsides; 15% of them would like to do so daily (Eurofound, n.d.[14]). In June-July 2020, most workers (60%) across 22 European OECD countries reported to be “overall satisfied with the experience of working from home”, with a positive assessment of both the quality (67%) and the amount of work performed (55%) (Eurofound, n.d.[14]). Data from the United States and the United Kingdom tell a similar story. In the United States, in October 2020, 80% of teleworkers reported no particular difficulties in meeting their deadlines and completing their projects on time, while 86% of them reported that it had been very easy or somewhat easy to have the equipment they need to do their job remotely (Parker, Menasce Horowitz and Minkin, 2020[25]).11 In addition, if their employer required a full return to business premises 36% of workers currently working from home in June 2021 would start looking for another job and 6% of them would quit their job (Barrero, Bloom and Davis, 2021[37]).12 In the United Kingdom, in May 2021 more than 70% of employees said they would like to work from home at least 2 days a week (Bloom, Minzen and Taneja, 2020[38]).13

Nevertheless, teleworking has also been challenging for some categories of workers – especially young workers and parents. Indeed, teleworking is blurring the boundaries between work and private life (refer to Chapter 4 for more information on work-life balance), and the pandemic often brought significant new burdens for those with caring responsibilities (e.g. due to school or nursery closures, or the need to provide additional support to people who were shielding from the virus), which had to be juggled alongside teleworking duties. In addition, some workers, including teleworkers, have been feeling unmotivated and unsatisfied, as well as isolated and stressed (see Box 5.5 for more information on COVID-19 and teleworking).

Box 2.3. Innovation: Adapting data collections to capture labour market outcomes during the crisis

United Kingdom: Office for National Statistics’ Online Labour Market Survey

As part of a wider response to the COVID-19 pandemic, the Office for National Statistics (ONS) has explored new ways to measure changes in the labour market, including through more rapid access to government data sources and the introduction of new online surveys. At the end of March 2020, the ONS launched the Online Labour Market Survey (LMS). The LMS has a mixed-mode design, online and face-to-face (but online by default). The current LMS design is still a prototype in development. The survey includes around 18 000 households per quarter. Respondents are asked questions on employment, unemployment and economic inactivity relating to a reference week one to two weeks prior to the interview. In addition, respondents are also asked if they did any work at home, and if their main reason for doing this was the COVID-19 pandemic. The LMS is based on random sample of households (addresses) drawn from the Postcode Address File. The geographical ordering of the frame implicitly stratifies the sample, ensuring a geographic spread of addresses. The quarterly sample is computed across 13 weeks, with each week containing a representative proportion of addresses, for each nation within the United Kingdom, as well as for large English regions.

According to the LMS, in April 2020, 47% of people in employment did some work at home, 86% of whom did so as a result of the COVID-19 pandemic. Working from home resulted in a reduction of hours worked for 34% of respondents, while 30% of them reported working more hours than usual. Women were slightly more likely than men to do some work at home (48% and 46% respectively), and young people (aged 16 to 24) were less likely to telework than older age groups. Homeworking was more frequent among occupations requiring higher educational attainment (such as managers, directors, senior officials) and more professional experience, relative to elementary or manual occupations (such as sales and customer service occupations). Lastly, white and ethnic minority groups had around the same proportion of people doing some work from home.

United States: Bureau of Labor Statistics Current Population Survey

The Bureau of Labor Statistics (BLS) added questions to the Current Population Survey (CPS) to help gauge the effects of the COVID-19 pandemic on the labour market. These questions were launched in May 2020 and will remain in the CPS until further notice. The CPS is a monthly sample survey of 60 000 eligible households conducted using a combination of live telephone and in-person interviews with household respondents. The newly added questions ask whether people teleworked or worked from home because of the pandemic; whether people were unable to work because their employers closed or lost business due to the pandemic; whether they were paid for that missed work; and whether the pandemic prevented job-seeking activities. All of these supplemental questions refer to activities preformed at any time during the “last 4 weeks” and follow the monthly labour force questions.

In June 2020, 31% of workers teleworked due to COVID-19, down from 35% in May. Women, workers over 25, full-time workers, and workers with higher educational attainment were more likely to have teleworked due to the COVID-19 pandemic. Workers in educational services, finance and insurance, or professional and technical services were more likely to work from home than workers employed in the accommodation and food services or in agriculture.

In addition, in June 2020, 16% of the civilian non-institutional population said that they had been unable to work at some point in the previous 4 weeks because their employer closed or lost business due to COVID-19 – that is, they did not work at all or worked fewer hours. Part-time workers were twice as likely as full-time workers to report not being able to work at some point in the previous 4 weeks due to the pandemic. Among those unable to work at some point in the last 4 weeks because of pandemic-related closures or lost business, 15% received at least some pay from their employer for the hours not worked. Those who usually work part-time were about half as likely as full-time workers to report being paid by their employer for the hours they did not work. People employed in personal care and service occupations were the least likely to be paid by their employer for the hours they missed (9% in June). Those employed in education, training or library occupations were the most likely to be paid (54%) by their employer.

Sources: ONS (2020[30]), Coronavirus and homeworking in the UK: April 2020, Office for National Statistics, https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/bulletins/

coronavirusandhomeworkingintheuk/latest and BLS (n.d.[31]), Supplemental data measuring the effects of the coronavirus (COVID-19) pandemic on the labor market, US Bureau of Labor Statistics, https://www.bls.gov/cps/effects-of-the-coronavirus-covid-19-pandemic.htm

2.3. Housing

Housing conditions play an important role in people’s experience of COVID-19. While only limited data are available on housing conditions in 2020, pre-pandemic data highlight a number of risks and challenges in this area. Overcrowding and poor access to sanitation make it harder for vulnerable households to stay safe and contain the spread of the virus. A lack of electronic devices and/or home Internet access exclude people from teleworking and homeschooling as well as from receiving essential services and supports virtually (i.e. mental health services, services for students with disabilities, etc.). Poorer households are also more likely to be overburdened by increasing housing costs. Over the course of the pandemic, governments across the OECD enacted a number of housing measures to support households at risk (see, for example (OECD, n.d.[39]; OECD, 2021[40]).

Poor housing conditions put people’s well-being at greater risk…

People living in poor housing conditions are more vulnerable to the physical and mental health effects of COVID-19. Pre-pandemic data indicate that 10.6% of households, on average, live in crowded conditions in OECD countries with this share exceeding 30% in Latvia and Mexico (Figure 2.14) (OECD, n.d.[39]). People living in overcrowded housing are at greater risk of infection, since it is harder to isolate symptomatic individuals and the same basic facilities are being shared by a large number of people. During lockdown periods, both people in overcrowded housing and people living alone face elevated risks to mental health. In addition, lack of access to basic sanitation (i.e. an indoor flushing toilet for the sole use of the household) – which is still an issue for 6.2% of poor households across OECD countries – makes it harder to contain the spread of COVID-19 between households living in close proximity (OECD, n.d.[39]).

Homeless people face huge difficulties in protecting themselves from the virus. They have no means of self-isolating, and where they do have shelter available it is typically in hostels with limited means of isolation or protection for at-risk individuals (such as those with existing health conditions). For example, data from the Australian Bureau of Statistics indicate that 44% of those considered “homeless” were living in severely crowded dwellings in 2016 (ABS, 2018[41]).14 While country comparisons are challenging due to different definitions and methods of data collection, it is estimated that around 2.1 million people are homeless across OECD countries for which data are available (OECD, n.d.[39]).

Lack of Internet access or electronic devices is a challenge for the most vulnerable households. COVID-19 has accelerated the digital transition, making connectivity (both in terms of quantity and quality) even more critical. Individuals who do not have a sufficient number of computers or access to high-speed Internet at home could not telework, home-school, video-call friends and relatives, or reach remote services such as medical consultations or community support (e.g. delivery of groceries and medicines). Across 33 OECD countries, 87.4% of households had access to the Internet at home in 2019, but this share is less than 60% in Colombia and Mexico (OECD, n.d.[42]). Even where Internet access is available, variable connection quality can still pose challenges to efficient and effective teleworking, remote schooling, etc. (refer to Chapter 5 for more information on digital divides).

Figure 2.14. Over one third of households in Mexico and Latvia live in overcrowded conditions

… and rising house and rent prices may threaten vulnerable households’ finances

Reduced earnings and working hours threaten people’s ability to meet housing costs. Workers who have lost their jobs, are temporarily laid off, or are working reduced hours for reduced pay may struggle to cover their monthly rent, mortgage or utilities payments. This is especially true for those households that are already overburdened by housing costs (OECD, 2021[40]). On average across OECD countries, 27.2% of the population in the bottom income quintile spent more than 40% of their disposable income on housing (i.e. rent and mortgage costs) before the COVID-19 crisis.

During the pandemic, many struggled to pay housing expenses. In April-June 2020, 10% of people in 22 European OECD countries reported being in arrears for their utility bills, a share that rose to 12% in February-March 2021 (Eurofound, n.d.[14]). In the same countries, 8% of people were in arrears for their rent/mortgage payments in both April-June 2020 and in February-March 2021 (Eurofound, n.d.[14]). In the United States, as of 16-21 July 2020, 12.5% of respondents were experiencing housing distress – i.e. they were late on their rent or mortgage payments, or their payments were deferred. Housing hardship in the United States was most common among families that identify as Black or Hispanic/Latino; those who lack a four-year college degree and renters (US Census Bureau, n.d.[43]).

A general rise in house and rent prices is worsening housing affordability, especially for poorer households. In 2018, housing costs accounted for around 20% of household disposable income in 34 OECD countries (OECD, 2020[10]). In a vast majority of OECD countries, house prices have been growing faster than general inflation since 2012, and data show that this continued to be the case in 2020 during the pandemic. On average, house prices rose by 4.7% from 2019 to 2020 across the OECD area. Prior to the COVID-19 crisis, rent prices had been systematically increasing in all but two OECD countries. Between 2019 and 2020, rental prices grew to a lesser extent than house prices, but still increased by 1.8% (Figure 2.15). This may reflect caps on rent prices and other artificial rent suppression measures that were implemented in response to the COVID-19 pandemic.

Governments across the OECD enacted measures to alleviate the negative consequences of COVID-19 on tenants and mortgage-holders. The most common measures included mortgage forbearance and eviction bans, introduced in 20 and 18 countries respectively. At least 10 countries took action to provide shelter and/or services to the homeless, while 11 countries allowed at least some households to defer payment of utility payments and/or required continuity of services even when payments were missed (OECD, 2021[40]; OECD, 2021[44]).

Figure 2.15. COVID-19 is threatening households’ financial stability and housing affordability

Box 2.4. Further reading

OECD (2020), OECD Economic Outlook, Volume 2020 Issue 2, OECD Publishing, Paris, https://dx.doi.org/10.1787/39a88ab1-en
OECD (2020), OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://doi.org/10.1787/1686c758-en
OECD (2020), Housing Amid Covid-19: Policy Responses and Challenges, OECD Publishing, Paris, https://doi.org/10.1787/cfdc08a8-en
OECD (2021), Building for a better tomorrow: Policies to make housing more affordable, Employment, Labour and Social Affairs Policy Briefs, OECD Publishing, Paris, http://oe.cd/affordable-housing-2021
OECD (2021), Brick by Brick: Building Better Housing Policies, OECD Publishing, Paris, https://doi.org/10.1787/b453b043-en
OECD (no date), Affordable Housing database, www.oecd.org/social/affordable-housing-database.htm
OECD (2021), OECD Employment Outlook 2021: Navigating the COVID-19 crisis and recovery, OECD Publishing, Paris, https://doi.org/10.1787/5a700c4b-en

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Notes

← 1. Temporary policies enacted by governments as a response to COVID-19 have been simulated via the microsimulation model Euromod, and include actions such as wage compensation schemes, transfers from government to firms and households, lump-sum benefits, and reductions or exemptions on taxes.

← 2. Namely, equivalised liquid financial assets below 25% of the national median income poverty line.

← 3. Since the beginning of the pandemic, the Food Foundation has conducted seven nationally representative surveys on food insecurity in the United Kingdom through YouGov. Each survey included three questions on food insecurity sourced from the US Department of Agriculture’s (USDA) Adult Food Security Survey Module 20: 1): Did (you/you or other adults in your household) ever cut the size of your meals or skip meals because there wasn't enough money for food? 2) Were you ever hungry but didn’t eat because there wasn’t enough money for food? 3) Did (you/the other adults in your household) ever not eat for a whole day because there wasn’t enough money for food? YouGov survey respondents are drawn from a large pool of potential respondents. Active Sampling ensures that the right people are invited in the right proportions. In combination with statistical weighting, this ensures that results are representative of the country as a whole. Samples of at least 2 000 respondents (aged 18 or over) are weighted to match the adult UK population by age, gender and region, social class and highest education level.

← 4. People who face uncertainties in their abilities to acquire food or who are forced to buy less/lower-quality food than usual are said to face moderate food insecurity, and people who often run out of food or that go one or more day without eating are said to face severe food insecurity.

← 5. Between May 4 and 10, Statistics Canada collected the second wave of its new web panel survey, the Canadian Perspectives Survey Series (CPSS). 4 600 respondents from all 10 provinces participated in the CPSS during that time period. Food insecurity is based on a scale of six food experiences: food didn’t last and no money to get more, sometimes or often; couldn’t afford balanced meals, sometimes or often; adults in household skipped or cut size of meals; adults in household skipped or cut size of meals, 3 days or more; personally ate less because not enough money to buy food; and personally was hungry but didn’t eat because couldn’t afford food.

← 6. The standard ILO classification of unemployment (followed by the OECD) defines unemployed persons as those who did not perform any paid work in the survey reference week, actively searched for work within the last 4 weeks, and would be available to start work within the next 2 weeks. In the context of the pandemic, some jobless individuals who want to work would nevertheless not meet this definition, either due to halting their job search (e.g. if in sectors closed by government restrictions) or due to caring responsibilities that span more than 2 weeks (e.g. home-schooling).

← 7. The term "visible minority" is used here because it is the official demographic category defined by the Canadian Employment Equity Act and is used by Statistics Canada in their surveys. The Employment Equity Act defines visible minorities as "persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour". The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Latin American, Arab, Southeast Asian, West Asian, Korean and Japanese. The question of appropriate terminology is currently being reviewed in Canada, in the context of a task force on modernising the Employment Equity Act (Department of Finance Canada, 2021[81]).

← 8. The term temporary layoff refers to a worker whose employment contract is terminated (temporarily or permanently, severing employer obligations), but where there is an expectation that the employee may be recalled back to the same job in future. The term job-retention scheme covers a worker whose employment contract is maintained, but where his/her work is reduced or entirely halted, and (part of) his/her salary is covered from government funds.

← 9. Although these data are higher than in April-June 2020, this could be due to the change in how the question was formulated – since data from the later two survey rounds also include respondents who worked from home routinely, even prior to the pandemic.

← 10. The average includes the following five European countries: Austria (24.6%), France (26.1%), Germany (18.5%), Italy (12.1%) and Poland (13.3%).

← 11. Data collected as part of a larger survey conducted between 13 and 19 October 2020. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. The survey is weighted to be representative of the United States adult population by gender, race, ethnicity, partisan affiliation, education and other categories. It included 5 858 respondents.

← 12. Data are from the June 2021 Survey of Working Arrangements and Attitudes. 2 232 people who were currently working from home at least one day a week were asked the following question: How would you respond if your employer announced that all employees must return to the worksite 5+ days a week starting on 1 August 2021? (1) I would comply and return to the worksite; (2) I would start looking for a job that lets me work from home at least one or two days a week, but return to the worksite if I don't find one by 1 August. (3) I would quit my job on or before 1 August, regardless of whether I got another job. Responses were population-weighted to match population shares in the 2010 to 2019 Current Population Survey.

← 13. Data is from a survey of 2 500 working-age employees conducted in May 2021 in the United Kingdom. The sample was re-weighted to match the Labour Force Survey figures by age, gender and education.

← 14. In the context of the elements developed for the Australian Bureau of Statistics definition of homelessness, people living in “severely” crowded dwellings are considered to be homeless because they do not have control of, or access to, space for social relations.

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https://doi.org/10.1787/1e1ecb53-en

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