Human capital is vital to long-term well-being, since it addresses the future health, knowledge, competencies and skills of society. The pandemic has adversely impacted current well-being, but it has also weakened the stocks of human capital that will shape future well-being and resilience. In OECD countries, COVID-19 has led to millions of potential years of life lost and exacerbated the dangers of certain health risk factors, including obesity and smoking. As more young people drop out of school due to learning disruptions from the pandemic, long-term educational attainment could fall. Record numbers of people are finding themselves unemployed, underemployed or marginally attached to the labour market – particularly those in the youngest age cohort – which could lead to scarring effects that persist well into the future. For young adults who began their careers in the midst of the Global Financial Crisis, the pandemic is set to compound their disadvantages.
COVID-19 and Well-being
9. Human capital and COVID-19
Abstract
The OECD defines human capital as the knowledge, skills, competencies and attributes embodied by individuals that coalesce to create personal, social and economic well-being at a societal level (OECD, 2013[1]; OECD, 2020[2]). While different institutions may have different ways of defining human capital, all definitions encompass outcome measures of knowledge, skills and health, all of which influence the total stock of human capital in a society at a given point in time, and the broad range of benefits that this stock could deliver in the future (OECD, 2013[1]). Human capital can be nurtured in many ways – be it through formal schooling, job training, parenting, social interactions, individual health choices and more – many of which are covered in this chapter, and some others were considered in previous chapters (see especially Chapters 3 and 4).
9.1. Health risks and premature deaths
Data from 81 countries show that, as of January 2021, more than 20 million years of life may have been lost due to COVID-19 since the start of the pandemic…
The COVID-19 pandemic has led to tens of millions of years of life lost across more than 80 countries worldwide (Pifarré i Arolas et al., 2021[3]). While excess mortality is a useful indicator to measure the short-term impact of COVID-19 on mortality rates (see Chapters 3 and 6), measures of premature mortality are useful in framing COVID’s impact on human capital loss. Potential years of life lost (PYLL) is a measure of premature mortality that more heavily weighs deaths that occur at younger ages.1 Although older age groups experienced higher mortality rates, especially in the early waves of the pandemic, younger age cohorts have suffered a substantial loss of potential years of life. In addition to the human tragedy of losing so many young lives, these losses deprive societies of the human resources and competencies that they will need to thrive in the future. A study focusing on countries with high incidence of COVID-19 concluded that years of life lost to the pandemic, using different thresholds per age group, exceeded four million by July 2020 (Oh et al., 2020[4]).2 A further study covering over 81 countries, using country/age-specific thresholds, found that 20.5 million years of life had been lost to COVID-19 as of January 2021 (Pifarré i Arolas et al., 2021[3]); three-quarters of this loss occurred in the under-75 population.3 In the United States, although only one-fifth of recorded COVID-19 deaths occurred in the under-65 population, this group accounted for almost half of the years of potential life lost (Wu, 2020[5]; Elledge, 2020[6]).4
The years of life lost to COVID-19 dwarf those from the seasonal flu, even when focusing on the oldest age cohorts who are most susceptible to severe outcomes from the flu (Figure 9.1). Annual data indicate that the years of life lost from the pandemic in 2020 were at least two to nine times higher than the average from seasonal influenza (Pifarré i Arolas et al., 2021[3]). Data from England and Wales illustrate this point: in a bad flu year, an average of 30 000 people die from the flu or pneumonia, for a total of around 250 000 years of life lost. As of March 2021, 146 000 had died from COVID-19 for a total of 1.5 million potential years of life lost, i.e. 4.9 and 6 times more, respectively (The Health Foundation, 2021[7]; Krelle and Tallack, 2021[8]). The gaps in life lost between COVID-19 and the flu are large for both women and men, though more so for men (Figure 9.1).
Figure 9.1. Years of life lost due to COVID-19 dwarf those from flu or pneumonia in England and Wales, even among the elderly
Note: Data refer to the population aged 75 or over. Flu and pneumonia data refer to the 2018 flu season. Actuarial life expectancy tables are used to calculated years of life lost for each age group.
Source: Krelle and Tallack (2021[8]), One year on: Three myths about COVID-19 that the data proved wrong, The Health Foundation, https://www.health.org.uk/publications/long-reads/one-year-on-three-myths-about-COVID-19-that-the-data-proved-wrong.
…and these years of life lost are unequally distributed across the population
Men have lost more years of life than women (The Health Foundation, 2021[7]; Pifarré i Arolas et al., 2021[3]; Wu, 2020[5]) (Figure 9.1), despite having lower life expectancies on average than women, because men had higher fatality rates from the virus. However, as a measure based on mortality, PYLL does not take into account the long-term debilitating effects of an illness; measures such as disability-adjusted life years (DALYS) and quality-adjusted life-years (QALY) are therefore better able to provide the full picture of the COVID-19 disease burden (Pifarré i Arolas et al., 2021[3]). As women are more likely to suffer severe symptoms from long COVID (Torjesen, 2021[9]; Jackson, 2021[10]) (see also Box 6.1), data referring only to years of life lost do not provide the full gender picture.
Certain population groups have been more adversely affected than others, especially those with underlying medical conditions, those from low-income households and those belonging to racial and ethnic minority groups. Those with underlying health conditions, such as obesity, diabetes and heart disease, are more likely to die of COVID-19 were they to contract the virus, and hence have higher rates of years of life lost (Wu, 2020[5]). Data from England show that those living in the most deprived parts of England were twice as likely to die from COVID-19, and to die at younger ages, than those from wealthier areas (Krelle and Tallack, 2021[8]). In the United States, evidence referring to the spring of 2020 illustrates stark racial/ethnic inequalities (Figure 9.2). Black Americans lost around 127.6 years of life before age 65 per 100 000 population, compared to only 18.9 years per 100 000 population for white Americans (Bassett, Chen and Krieger, 2020[11]). The gaps in COVID-19 outcomes are widest at lower age groups: Black Americans aged 35-44 have seven to nine times higher mortality rates than white Americans (Resnick, 2020[12]).5
Figure 9.2. Striking racial/ethnic inequalities in years of life lost due to COVID-19 in the United States
Note: Potential years of life lost are calculated using a threshold of 65-years-old. The choice of a relatively low threshold (i.e. compared to the OECD Health Statistics practice of using a threshold of 75-years-old) was done purposefully by the researchers to highlight the burden of lives lost on younger people and on those still in the labour market.
Source: Bassett, Chen and Krieger (2020[11]), “The unequal toll of COVID-19 mortality by age in the United States: Quantifying racial/ethnic disparities”, HCPDS Working Paper Series, No. 3, Harvard Center for Population and Development Studies, https://cdn1.sph.harvard.edu/wp-content/uploads/sites/1266/2020/06/20_Bassett-Chen-Krieger_COVID-19_plus_age_working-paper_0612_Vol-19_No-3_with-cover.pdf.
Despite initial disagreements, experts now agree that smoking is a risk factor for COVID-19
Public health officials warn that smoking may put individuals at risk of more severe COVID-19 symptoms (WHO, 2020[13]; Galiatsatos, 2020[14]; CDC, 2021[15]). Initial meta-analyses identified links between regular smoking or vaping and the severity of COVID-19, including in terms of rates of hospitalisation and death (WHO, 2020[16]; Galiatsatos, 2020[14]; HCSP, 2020[17]). These patient-based findings have been confirmed by population-based studies, such as the Zoe COVID-19 Symptom Study. This UK study used a mobile phone app to provide longitudinal data on over 2.4 million people, tracking their health and COVID-19 symptoms from 24 March to 23 April 2020. The study showed that regular smokers were at an increased risk of developing COVID-19 symptoms, including cough, fever and breathlessness (Hopkinson et al., 2020[18]).6
Smoking rates vary substantially across OECD countries, though they have been falling almost everywhere over the last 10 years (OECD, 2020[2]; OECD, 2019[19]). An average of 18% of adults across OECD countries smoked tobacco daily in 2017, down from an average of 23% in 2007 (OECD, 2019[19]). However, significant cross-country differences in tobacco usage remain: Greece, Turkey and Hungary have some of the highest rates, at 25% or over, while Costa Rica, Mexico and Iceland have rates below 10%. In general men are more likely to smoke than women, and those with lower levels of education are more likely to be smokers than those with higher educational attainment (OECD, 2019[19]); these groups have heightened risk profiles, and indeed experienced higher mortality rates from COVID-19 (Chapter 6).
While some people may have used the pandemic as an opportunity to quit smoking, some non-smokers also took up smoking, and tobacco use increased in some countries. Survey data collected by Ipsos Global Advisor in October/November 2020 showed that individuals in eight OECD countries were more likely to report having given up smoking than having started smoking since the pandemic began (Bailey et al., 2021[20]) (Figure 9.3, Panel D).7 In some of the countries in which a relatively high share of respondents have reported stopping smoking (Mexico, Sweden), overall smoking rates were already relatively low; however, in others (Chile, Turkey) higher rates of quitting may be linked to higher smoking prevalence when the pandemic started (OECD, 2020[2]). Only four OECD countries included in the research – Chile, Turkey, Mexico and Israel – had a higher share of respondents indicating that they had taken up smoking since the start of the COVID-19 pandemic relative to those who reported quitting smoking. A cross-sectional study of 6 870 smokers in Australia, Canada, England and the United States found that while 46.7% considered quitting during the pandemic, the vast majority (70.2%) made no changes to their behaviour (Gravely et al., 2021[21]).
Evidence from individual countries shows that for those who already smoke, and did not quit in 2020, the pandemic led to an increase in tobacco consumption. A nationally representative survey in the United Kingdom conducted from 27 April to 24 May 2020 found that 25.5% of current smokers reported smoking more during the pandemic, compared to 51% who reported no change in smoking levels and 20% who reported smoking less: young women aged 16-29 were most likely to increase their smoking behaviours (Chen, 2020[22]). Data from Ireland found similar patterns for young women (Box 9.1). Consumption data from the United States show that the years-long decline in cigarette sales flattened in 2020 (Fakuade, 2020[23]), while calls to state “quitlines” – call centres that provide advice to those who want to quit smoking – fell 27% from 2019 to 2020 (North American Quitline Consortium, 2021[24]). Those who reported smoking more than usual had higher rates of anxiety, stress and isolation and poorer mental health in general (Chen, 2020[22]; The Lancet Respiratory Medicine, 2021[25]).
Obesity worsens individuals’ COVID-19 outcomes, including death…
Those who are obese are at greater risk of more severe outcomes from COVID-19, including death. Unlike smoking, the evidence surrounding obesity and negative health outcomes from the virus has been clear-cut from the start. As a risk factor for COVID-19 mortality, obesity is second only to age (World Obesity Federation, 2021[26]). Studies from Brazil, China, France, Italy, Mexico, Spain, Sweden, the United States and the United Kingdom show that obesity increases the risk of having severe symptoms of COVID, including risks of hospitalisation, admission to intensive care units (ICUs), the need for ventilators and death (CDC, n.d.[27]; Public Health England, 2020[28]; Yang, Hu and Zhu, 2021[29]; Simonnet et al., 2020[30]; Popkin et al., 2020[31]; World Obesity Federation, 2021[26]). These findings hold even when controlling for demographic and socio-economic factors (Public Health England, 2020[28]) and can be distinguished from COVID-19 deaths stemming from other cardiometabolic conditions closely related to obesity, such as diabetes mellitus, hypertension and heart failure (O’Hearn et al., 2021[32]). It is not just those who are obese (i.e. with a BMI of 30 or above) who are at an increased risk: simply being overweight (with a BMI of 25 or above) also puts one at risk for worse COVID-19 outcomes (Kompaniyets et al., 2021[33]). Stigma and discrimination against the obese may play a role: stigma results in obese patients avoiding the healthcare system, which can exacerbate pre-existing conditions, hinder the prevention of chronic disease and delay COVID treatment (Wadman, 2020[34]; Public Health England, 2020[28]).
Among OECD countries, rates of overweight and obesity vary substantially and have been rising in the large majority of them over the last 10 years (OECD, 2019[19]). Of the 27 OECD countries with time-series data, none showed a decline in obesity rates from 2005 to 2017, though there is wide variation in prevalence – from 30% in the United States, Chile, Mexico, New Zealand and Australia to less than 5% in Japan and Korea (OECD, 2020[2]). Countries with higher obesity rates therefore have a higher share of the population at risk of severe COVID-19 complications and potentially face a higher burden on the health care system for a given rate of infection. A report by the World Obesity Federation concluded that countries with higher rates of overweight or obese adults had higher mortality rates from COVID-19, even when accounting for the age structure of the population, GDP per capita and the quality of COVID-19 data reporting (World Obesity Federation, 2021[26]; Boseley, 2021[35]).
…and the risk posed by obesity may have been exacerbated by weight put on during pandemic lockdowns and confinement
Survey data show that more respondents have gained weight, as opposed to lost weight, over the course of 2020 (Figure 9.3, Panel A). Ipsos survey data from late 2020 show that on average in 19 OECD countries, 31% of respondents had gained weight since the start of the pandemic, compared to only 18% who had lost weight (Bailey et al., 2021[20]). Women were more likely than men to gain weight, as were younger people (Figure 9.4, Panels A and B). Another international survey of 7 800 participants conducted online from April to May 2020 found that 27% of respondents had gained weight since lockdowns began, compared to 17% who had lost weight (Flanagan et al., 2021[36]).8 Weight gains were largest for those who were already classified as obese (33%), likely driven by a reduction in exercise and time spent outside (O’Connor, 2020[37]). Survey data from individual countries support the finding that more people have gained rather than lost weight in 2020. In the United States, a survey by the American Psychological Association found that 42% experienced unwanted weight gain, versus 18% with unwanted weight loss. Women (45%), young adults aged 18-25 (52%), parents (51%) and essential workers (50%) were more likely to experience unwanted weight gain (APA, 2021[38]). An online poll in Canada found that 33% had gained weight compared to 15% who had lost weight (Alhmidi, 2020[39]); 55% of Israelis reported weight gain (I24news, 2020[40]); and in Italy, overweight boys experienced more weight gain compared to girls (Maltoni et al., 2021[41]) (see Box 9.1 for data from Ireland). While these numbers appear striking, baseline data provide a sense of how much they deviate from a normal, non-pandemic, year. Using data from the 2007 to 2010 National Health and Nutritional Examination Surveys, the US Center for Disease Control estimates that the average adult gains 1-2 pounds (0.45-0.91 kg) a year from early adulthood to middle-age, with the highest weight gain concentrated in the early 20s (Fryar CD, Gu Q and Ogden CL, 2012[42]; Ingraham, 2016[43]). By way of comparison, a longitudinal study of 269 participants in the United States from 1 February to 1 June 2020, which collected weight data from Bluetooth-connected scales, concluded that respondents gained 1.5 pounds (0.68 kg) per month, on average (Lin et al., 2021[44]).
Weight gain is in part driven by higher consumption of alcohol (Figure 9.3, Panel C) and unhealthy foods (see Box 9.1 for data from Ireland). People consume unhealthy “comfort” foods in response to stressful situations (Gapper, 2021[45]; O’Connor, 2020[37]). 26.8% of Canadians surveyed by Statistics Canada between 29 March and 3 April 2020 reported they had increased eating junk food or sweets, compared to 14.7% who reported eating less; the numbers were higher for younger people aged 15 to 49 (37.1% of them increased consumption) (Statistics Canada, 2020[46]). Alcohol is also used as a means to cope (Grossman, Benjamin-Neelon and Sonnenschein, 2020[47]). Data from Australia, Belgium, the United Kingdom and France found that women, parents of young children, those with higher income and/or those with worse mental health were more likely to increase their alcohol consumption during lockdown (OECD, 2021[48]) (see also Chapter 3). In mid-March, Nielsen (a US data and market measurement company) reported that sales of alcohol in the United States had increased 54% from the same week in the previous year (Grossman, Benjamin-Neelon and Sonnenschein, 2020[47]). A study of 1 540 Americans conducted by the RAND corporation showed that alcohol consumption increased by 14% from 29 April – 9 June 2019 (the baseline period) to 28 May – 16 June 2020 (Pollard, Tucker and Green, 2020[49]). Similarly, 14% of Canadians reported they had increased their alcohol consumption during the COVID crisis, with a stronger rise (20%) among younger people (aged 15-49) (Statistics Canada, 2020[46]).9 Men were more likely to increase their consumption of alcohol than women (Figure 9.4, Panel A); a US survey found that 48% of fathers reported drinking more to cope with stress, compared to 29% of mothers (APA, 2021[38]).
Figure 9.3. In most OECD countries with data, more people adopted unhealthy, rather than healthy, behaviours since the start of the pandemic
Note: The healthy behaviours considered refer to weight gain/loss; exercise increase/decrease; increase/decrease in alcohol consumption; and start/stop smoking. Data are rounded to the nearest percent. The OECD average includes only those 19 countries shown.
Source: Bailey et al. (2021[20]), Diet and health under COVID-19, Ipsos, https://www.ipsos.com/sites/default/files/ct/news/documents/2021-01/diet-and-health-under-covid-19.pdf.
Box 9.1. Innovation: The Ireland Social Impact of COVID-19 Survey
The Irish Central Statistics Office (CSO) has implemented a series of surveys since the start of the pandemic to better understand Irish residents’ experiences (CSO, 2021[50]). The Social Impact of COVID-19 is one such survey, conducted in April 2020. The questionnaire contained modules relating to well-being, concerns about COVID-19, and changes in consumption patterns during the pandemic. The sampling frame was based on respondents between the ages of 18 and 75 who had participated in the Labour Force Survey (LFS) since the first quarter of 2019 and provided up-to-date contact information (i.e. email address). In total, 1 362 respondents were surveyed.
Key findings from the changes in consumption module, which measures behavioural changes since the introduction of pandemic-related restrictions, include:
30.5% of respondents increased smoking, 8.6% reduced it. Women (34.7%) were more likely than men (26.2%) to have increased smoking behaviour.
45.4% increased their consumption of junk food and sweets, while 12.3% reduced it.
22.2% increased their consumption of alcohol, while 17.2% reduced it.
37.1% increased the frequency with which they exercise, while 33.2% reduced it. Those aged 70 or over were most likely to decrease exercise (53.4%), followed by the youngest (18 to 34) age group (36.4%).
Source: CSO (2020[51]), Social Impact of COVID-19 Survey April 2020: Changes in consumption, Central Statistics Office, https://www.cso.ie/en/releasesandpublications/ep/p-sic19/socialimpactofcovid-19surveyapril2020/changesinconsumption
Weight gain is also a reflection of the reduction in exercise over the same period (Figure 9.3, Panel B) (Bailey et al., 2021[20]), stemming from lockdowns and the closing of recreational sporting activities such as gyms, yoga, dance, sports clubs and exercise classes. A survey of 221 Canadians from 24 September to 8 December 2020 found that physical exercise decreased by 3.1 percentage points during the second wave of the pandemic (beginning in October 2020), compared to the first wave (mid-March to May 2020) (Gupta et al., 2021[52]). Ipsos survey respondents with higher levels of education, and those from households with higher levels of income, were more likely to increase their amount of exercise (Figure 9.4, Panels C and D); this may be because these groups are more able to telework (see Chapter 5), meaning they have greater flexibility to take time to exercise during the day. Data from the United Kingdom Office for National Statistics (ONS) Time Use Survey (see Box 7.2) also showed that those with higher incomes exercised more frequently during the week than those with lower incomes (ONS, 2020[53]).
Figure 9.4. On average in 19 OECD countries, changes in health behaviour were unequally distributed across the population
Note: Health behaviours include weight gain/loss; exercise more/less; more/less alcohol consumption; and start/stop smoking. The OECD average includes Australia, Belgium, Canada, Chile, France, Germany, Hungary, Israel, Italy, Japan, Korea, Mexico, the Netherlands, Poland, Spain, Sweden, Turkey, the United Kingdom and the United States. Data are rounded to the nearest percent.
Source: Bailey et al. (2021[20]), Diet and health under COVID-19, Ipsos, https://www.ipsos.com/sites/default/files/ct/news/documents/2021-01/diet-and-health-under-covid-19.pdf.
9.2. Knowledge and skills among youth and young adults
When extended over time, remote schooling may increase dropout rates and lower educational attainment
The pandemic and ensuing lockdowns have critically impacted schooling, leading to fears of increased dropout rates, lower graduation numbers and long-term impacts on educational attainment. While the learning disruptions caused by remote schooling at all levels (primary, secondary and tertiary) is of immediate concern for the acquisition of knowledge and skills (Chapter 3), time spent away from employment, education and training implies an important loss of human capital. The closure of schools, workplaces and training programmes during the pandemic has taken a toll on everyone but has particularly affected young adults.10 This in turn carries risks of long-term harm, due to the formative nature of learning at this stage in the life course and the important role that youth skills play in future well-being (OECD, 2020[2]; OECD, 2015[54]). Evidence from previous crises, such as the Asian financial crisis (Cameron, 2009[55]), showed that these shocks can lead young people to drop out of education, with lifelong impacts on educational attainment (Iqbal et al., 2020[56]). A study of the US education system by McKinsey & Company estimates that between 2% and 9% of current high school students (i.e. between 230 000 and 1.1 million students) may drop out because of the pandemic and subsequent school closures (Dorn et al., 2020[57]); data from Canada show that attendance in high schools dropped by 2-3 percentage points between the 2019-20 and 2020-21 school years (Statistics Canada, 2021[58]). Evidence from the OECD shows how secondary schooling assessments were heavily impacted by COVID-19 in 2020: in many countries, secondary school examinations were postponed, rescheduled or had their criteria amended in light of the disruptions due to the pandemic. Though country level data are not yet available, it is clear that these shifts in criteria will have a significant impact on graduation rates (OECD, 2021[59]).11
Those who do not complete secondary education will have lower lifetime earnings than their peers who complete their degrees (Dorn et al., 2020[57]; OECD, 2020[2]; OECD, 2021[59]). Countries that already have lower rates of secondary school attainment among the young (25- to 34-year-old) population may be most at risk for the detrimental impacts of longer school closures during the pandemic (see the top left-hand quadrant of Figure 9.5).
The gender gap in educational attainment is changing in many OECD countries, as girls outpace boys, and COVID-19 may accelerate this trend. In most if not all OECD countries, girls are overtaking boys in terms of secondary school graduation and enrolment in tertiary institutions (OECD, 2020[60]; OECD, 2021[59]). In the United States, the high school graduation rate for girls was higher than that for boys in the 2017-18 school year in all 37 states for which data were available (Reeves, Buckner and Smith, 2021[61]). Furthermore, among males, graduation rates were lowest for Hispanic/Latino and Black boys. Data for university enrolment show a similar trend. Gender gaps in university enrolment have been widening (in favour of women) over the past five years. In 2020, the gap increased further, as university enrolment for men fell by 5.1%, while the drop for women was only 0.7% (Bassok et al., 2021[62]).
Figure 9.5. OECD countries with low educational attainment for young adults and more school closures due to COVID-19 may be most at risk of losing human capital
Note: Data on school closures refer to the share of days during which schools were fully or partially closed due to COVID-19, as a share of the total number of all instruction days (i.e., excluding school holidays, public holidays and weekends), as a pooled average from March 2020 to June 2021. “Fully closed” is defined as government-mandated closures of schools (pre-primary through upper secondary, ISCED levels 0 through 3) because of the COVID-19 pandemic. “Partially closed” refers to instances in which schools are closed in certain regions only, and/or are closed for some grade levels/age groups but not others, and/or are employing a hybrid (in-person combined with distance learning) approach. Educational attainment data refers to 2020 for all countries aside from Chile (2017), Denmark (2019) and Turkey (2019). The OECD average excludes Japan, which is missing educational attainment data.
Source: UNESCO (n.d.[63]), Global Monitoring of School Closures, COVID-19 (database), United Nations Educational, Scientific and Cultural Organization, https://en.unesco.org/covid19/educationresponse; and OECD (n.d.[64]), OECD Education Statistics (database), https://doi.org/10.1787/edu-data-en.
For those who stay in school, learning losses may affect their ability to access higher education and lower lifetime earnings
For students who remain in education, the disruptions associated with remote schooling may lead to learning losses, especially for the most vulnerable (see Chapters 3 and 6). A wide evidence base shows that remote schooling leads to a reduction in instruction time (OECD, 2020[65]) and that children who do not have sufficient digital tools perform worse on schoolwork and standardised tests as compared to their better-equipped peers (The Pew Charitable Trusts, 2020[66]). The learning gap between high- and low-income groups has likely grown, as a consequence. Lacking quality equipment can make it more difficult for students to follow online schooling, making it more likely for them to be absent. Chronic absenteeism is associated with worse academic performance – increasing with the number of school days missed – and is a large risk factor for eventual dropout (OECD, 2019[67]; García and Weiss, 2020[68]).
Learning losses today foreshadow lost income and lower economic growth in the future. An OECD study estimates that students in primary and secondary education may experience 3% lower income over the course of their lifetime as a result of pandemic disruptions to learning, which in turn could translate to an annual loss of 1.5% of GDP for the rest of the 21st century. Students from disadvantaged backgrounds will be particularly at risk. It may be difficult to make up for these learning losses, even if school systems are able to return to pre-pandemic performance levels relatively quickly (Hanushek and Woessmann, 2020[69]).
9.3. Labour underutilisation
Labour market underutilisation skyrocketed during the pandemic, reaching levels close to those seen during the Great Financial Crisis in 2008-09…
Labour market underutilisation increased in 2020 in all OECD countries, compared to 2019 (see Figure 2.8), raising concerns of human capital loss. The loss of knowledge and skills while people are not employed, or when these skills are underutilised due to underemployment or inactivity, is much higher than when being at work (OECD, 2020[2]; OECD, 2020[70]). Unemployment rates rose drastically in some countries, though remained stable in others (Figure 2.7), reflecting both differences in policy response to the pandemic and in the measurement of unemployment (see Chapter 2). The labour underutilisation rate includes, in addition to the unemployed, the underemployed (full-time workers who were working less than usual during the survey reference week for economic reasons, and part-time workers who wanted but could not find full-time work) and the marginally attached (persons not in the labour force who did not actively seek work during the previous four weeks but who wish to and are available to work). On average in 32 OECD countries in 2020, labour underutilisation was more than twice as high as unemployment (17% and 7%, respectively), due to the large number of underemployed (6%) and marginally attached (4%) workers (Figure 9.6).
The rise in labour underutilisation from 2019 to 2020, for 32 OECD countries on average, was almost five times higher than the rise in unemployment over the same period. This is because, in contrast to trends in unemployment rates that differed across countries, the share of underemployed and marginally attached workers increased in all OECD countries in 2020 (with the exceptions of underemployment in Chile and marginally attached workers in Latvia) (Figure 9.7, Panels A and B). The large increase in labour market underutilisation underscores that the loss of knowledge and skills stemming from COVID may affect many more people than those who are conventionally counted as unemployed.
The increases in labour market underutilisation are large and reached levels comparable to those seen during the Great Financial Crisis in 2008-09 (Figure 9.8). Time-series data of 17 OECD countries show that the year-on-year increase in the OECD average labour market underutilisation was higher from 2019-20 (4.4 percentage points) than for all previous years aside from 2008-09 (5.3 percentage points).
Figure 9.6. Labour underutilisation increased in all OECD countries, reflecting higher numbers of people underemployed and marginally attached to the labour market
Note: The figure shows overall labour market underutilisation in 2020, decomposed into each component: the unemployed, the underemployed (full-time workers working less than usual during the survey reference week for economic reasons, and part-time workers who wanted but could not find full-time work) and the marginally attached (persons not in the labour force who did not actively seek work during the previous four weeks but who wish to and are available to work), expressed as a ratio of the total labour force. The OECD average excludes Colombia, Costa Rica, Germany (data are not available due to the introduction of the new German system of integrated household surveys since the beginning of 2020), Israel, Korea and Mexico (data are not available due to changes in survey collection during the year).
Source: OECD calculations based on OECD (n.d.[71]), OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH.
…and unemployment among the young is of particular concern
Across the OECD, the unemployment rate for young people has long been higher than for other age cohorts, and the pandemic has further widened this gap (Figure 9.9, Panel A) (Figure 5.7). Young adults were particularly affected by rising unemployment in the aftermath of the Great Financial Crisis and are again most at risk in the current pandemic (Figure 9.9). This is in part because they had yet to recover from the previous recession, and in part because they were more likely to work in service industry jobs that were the first to lay off workers during the first wave of the pandemic (Aaronson and Alba, 2020[72]). Young people aged 25-34 with an upper secondary education experienced a larger increase in unemployment from 2019 to 2020 (2 percentage points) than did young people with a tertiary degree (1 percentage point) (OECD, 2021[59]). Youth unemployment impacts not only short- to medium-term career prospects, but also lifelong earnings, access to training, skills improvement and mental health (Reuters, 2020[73]; Douine, 2020[74]; OECD, 2020[75]; OECD, 2020[70]).
Figure 9.7. Higher numbers of people underemployed and marginally attached to the labour market drove labour underutilisation increases in 2020 in all OECD countries
Note: Panel A shows the percentage point change in 2020 compared to 2019 for rates of underemployment (full-time workers working less than usual during the survey reference week for economic reasons, and part-time workers who wanted but could not find full-time work); Panel B shows the same for the marginally attached (persons not in the labour force who did not actively seek work during the previous four weeks but who wish to and are available to work), expressed as a ratio of the total labour force. Positive values mean the indicator has increased from the year prior, whereas negative values indicate the indicator has declined. In both panels, the OECD average excludes Colombia, Costa Rica, Germany (data are not available due to the introduction of the new German system of integrated household surveys since the beginning of 2020), Israel, Korea and Mexico (data are not available due to changes in survey collection during the year).
Source: OECD calculations based on OECD (n.d.[71]), OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH.
Figure 9.8. The sharp jump in labour market underutilisation in 2020 is close to that experienced during the Great Financial Crisis
Note: The OECD average includes Australia, Austria, Canada, Spain, Estonia, Finland, the United Kingdom, Hungary, Iceland, Italy, Lithuania, Latvia, the Netherlands, Norway, New Zealand, Slovenia and the United States. Other OECD countries are omitted due to breaks in time series.
Source: OECD calculations based on OECD (n.d.[71]), OECD Household Dashboard (database), http://stats.oecd.org/Index.aspx?DataSetCode=HH_DASH.
While the number of young people not in education, employment or training (NEET) had been declining over the past few years, the pandemic reversed this trend (Figure 9.9, Panel B) (Figure 5.8). NEET rates are particularly high for migrants: across OECD countries, 19% of young migrants are NEET compared to 14% of native-born youths (OECD, 2021[59]). Traditionally, young people with lower educational attainment are more likely to be NEET than those with tertiary attainment; however in 2020 the share of NEETs with some amount of tertiary education rose sharply in many OECD countries (OECD, 2021[76]).12 Quarterly data for some OECD countries showed large changes in early 2020: in Canada, the share of young people (aged 15 to 29) neither in employment nor in education or training increased from 12% in February to 18% in March 2020 and 24% in April 2020 (Brunet, 2020[77]).13 However in the United Kingdom, NEET rates fell continuously throughout the pandemic, reaching historic lows by June 2021 (ONS, 2021[78]). This is likely due to young people choosing to stay in education, rather than enter the labour market during a period of instability (ONS, 2021[78]; Adcock, 2020[79]). OECD data show a similar pattern among youths aged 18-24 in Austria, France, Poland, Portugal and Slovenia (OECD, 2021[59]).
Figure 9.9. The pandemic has exacerbated poor labour market outcomes for younger age cohorts
Note: In Panel A, the OECD average excludes Costa Rica, Germany, Mexico, Switzerland and the United Kingdom due to breaks in the time series. In Panel B, the OECD average includes Australia, Austria, Belgium, Canada, the Czech Republic, Estonia, Finland, France, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Mexico, the Netherlands, New Zealand, Norway, Poland, Portugal, the Slovak Republic, Slovenia, Spain, Sweden, the United States and the United Kingdom.
Source: OECD (n.d.[80]), Unemployment rate by age group (database), https://doi.org/10.1787/997c8750-en; and OECD (n.d.[64]), OECD Education Statistics (database), https://doi.org/10.1787/edu-data-en.
Box 9.2. Further reading
OECD (forthcoming), Health at a Glance 2021: OECD Indicators, OECD Publishing, Paris
OECD (2021), Preventing Harmful Alcohol Use, OECD Health Policy Studies, OECD Publishing, Paris, https://dx.doi.org/10.1787/6e4b4ffb-en
OECD (2021), Education at a Glance 2021: OECD Indicators, OECD Publishing, Paris, https://dx.doi.org/10.1787/b35a14e5-en
OECD (2020), Education and COVID-19: Focusing on the long-term impact of school closures, OECD Publishing, Paris, https://doi.org/10.1787/2cea926e-en
OECD (2020), OECD Employment Outlook 2020: Worker Security and the COVID-19 Crisis, OECD Publishing, Paris, https://dx.doi.org/10.1787/1686c758-en
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Notes
← 1. The OECD calculates Potential Years of Life Lost (PYLL) by summing up the deaths that occur at each age and multiplying this by the number of remaining years up to a selected threshold: 75 years. PYLL are standardised to allow for cross-country and cross-year comparisons (OECD, 2019[19]; OECD, 2015[54]). PYLL is a measure of premature mortality if the threshold is around 75-years-old or below. PYLL may be calculated somewhat differently across studies. Some studies use a different threshold year (65, 70, 80, etc.). Other statistical offices, such as the UK Office for National Statistics (ONS), use actuarial life expectancy tables to assign a threshold value for each year of death. For example, a man aged 80 in England or Wales has an expected life expectancy of an additional 8.2 years, meaning that if he were to die at age 80 his potential years lost would be 8.2 (Krelle and Tallack, 2021[8]).
← 2. This study calculated Years of Life Lost (YLL) attributable to the virus in 30 countries with the highest rates of COVID-19 as of July 2020: Australia, Austria, Brazil, Belgium, Canada, China, Chile, Ecuador, France, Germany, India, Iran, Ireland, Israel, Italy, Japan, Korea, the Netherlands, Norway, Peru, Poland, Portugal, Romania, the Russian Federation, Sweden, Switzerland, Spain, Turkey, the United Kingdom and the United States. COVID-19 incidence and mortality data were collected from official statistics within each country. YLLs were calculated using life expectancy tables for Japanese females at one-year age intervals, who have the longest life expectancy globally. United Nations demography data were used to calculate YLL per 100 000 population (Oh et al., 2020[4]).
← 3. The Pifarré i Arolas et al. (2021[3]) study uses country-specific life expectancy tables to calculate years of life lost for deaths at a given age (YLL). YLL is calculated as the difference between an individual’s age at time of death from COVID-19 and their life expectancy at that age in their country.
← 4. The Elledge (2020[6]) study uses actuarial life expectancy tables to set thresholds for each age.
← 5. The disparity in outcomes may be in part a result of the higher poverty rates experienced by racial and ethnic minority groups, which can limit access to quality health care services.
← 6. Lung cancer and COVID-19 share some symptoms – including cough, shortness of breath and lowered oxygen levels – leading to fears that the pandemic may result in delayed diagnoses of lung cancer (The Lancet Respiratory Medicine, 2021[81]).
← 7. The Ipsos Global Advisor survey ran from 23 October to 6 November 2020 and collected data from 22 008 individuals across 30 countries: Argentina, Australia, Belgium, Brazil, Canada, Chile, China, France, Germany, Great Britain, Hong Kong, Hungary, India, Israel, Italy, Japan, Malaysia, Mexico, the Netherlands, Peru, Poland, the Russian Federation, Saudi Arabia, Singapore, South Africa, Spain, Sweden, Turkey, the United Kingdom and the United States. Only findings from OECD countries are used in this chapter. Surveys were conducted online via the Ipsos Online Panel system. Data are weighted to be nationally representative; however, Ipsos notes that the samples in certain countries, such as Brazil, may be more urban, educated and/or affluent than the general population (Bailey et al., 2021[20]).
← 8. This survey was administered to 7 753 respondents between 3 April and 3 May 2020. Participants were invited to participate in the survey via paid advertisements on Facebook. Respondents from any country could participate; however, the researchers paid for advertisements to target participants from the United States, Australia, Canada, Ireland and the United Kingdom. The survey was also accessible via the research team’s website, through email invites and word of mouth (Flanagan et al., 2021[36]).
← 9. However a subsequent study from Statistics Canada, using data through January 2021, found that – of the Canadians aged 15 to 29 who consumed alcohol in the past month – 33% reported decreasing their consumption since the start of the pandemic, compared to only 18% of those aged 30-64 (Statistics Canada, 2021[83]). Therefore there is some variability in alcohol consumption rates by age, depending on the study and the time frame.
← 10. In the early stages of the pandemic, education institutions of all levels shifted to remote learning in the majority of OECD countries. However, as the pandemic wore on, many OECD countries prioritised keeping primary schools open, while in a number of countries remote learning in secondary and tertiary institutions continued (see Chapter 3, as well as (OECD, 2021[85])).
← 11. Graduation ratios (defined as the ratio of those who graduate from upper-secondary institutions compared to those who attended) for 2020 are not yet available in full. Early evidence suggests falls in some OECD countries, stability in others, and increases in graduation rates primarily stemming from vocational students (OECD, 2021[82]).
← 12. One facet not captured by NEET or youth unemployment rates is the fall in student employment during the pandemic, which, in the case of Canada, had unequal gendered impacts: the employment rate for young women aged 15-24 attending school full-time declined by 10.6 percentage points from 2019 to 2021, compared to a 4.2 percentage point decline for men (Statistics Canada, 2021[84]).
← 13. Statistics Canada notes that sharp increases in the NEET rates of the youngest age cohorts (aged 15 to 19) in the first two months of the pandemic may have been affected by measurement issues, such as young people reporting they were not in schooling due to remote learning, or changes to education delivery. However, the large increases among older age cohorts (20 to 24, and 25 to 29) were most likely due to increases in unemployment, rather than measurement issues (Brunet, 2020[77]).