Happiness, health
and good jobs

Previous editions of the Transition Report (and other special reports produced by the EBRD) have examined subjective well-being and its cross-country variation in the EBRD regions using the Life in Transition Survey (LiTS) conducted by the EBRD and the World Bank. Early waves of the LiTS showed a severe happiness deficit in the EBRD regions, as did other research using data from the World Values Survey.4 However, an important finding emerged in the third survey round (LiTS III) in 2016: the happiness gap between post-communist countries in the EBRD regions and their comparators had closed at last.5 In other words, once differences in gross domestic product (GDP) per capita had been controlled for, people in transition countries were, on average, no longer less satisfied with their lives than people in Germany and Italy (the two western European comparator countries included in LiTS III). Indeed, they were in fact more satisfied than people in Cyprus, Greece and Türkiye. Research using the annual Gallup World Poll and a broader range of comparator countries suggests that the gap closed around 2012.6

This chapter revisits the topic of satisfaction with life in the EBRD regions using early data from the fourth round of the Life in Transition Survey (LiTS IV), which is in the process of being conducted across the EBRD regions and in selected comparator countries. The analysis includes preliminary data for all economies where at least 250 interviews have been completed (out of a total of 1,000). That survey round started in October 2022 and is expected to conclude later in 2023.

Average life satisfaction levels have risen further relative to 2016 in almost all countries. Certain countries in Central Asia continue (as in previous rounds) to score very highly, notwithstanding their relatively low levels of GDP per capita, while south-eastern Europe (SEE) and eastern Europe and the Caucasus (EEC) have seen notable increases in life satisfaction. Importantly, the increase in life satisfaction is broadly based overall, covering all age cohorts, both males and females, and people in both urban and rural areas.

Why are the EBRD regions doing so well in terms of satisfaction with life? One possibility is that people’s answers have been influenced by growing prosperity. LiTS IV was launched at a time when most economic aggregates were moving in a favourable direction, with supply chains reopening and household demand bouncing back following the relaxation of Covid restrictions. However, two other issues are probably also influencing the results: improvements in the health of the population and favourable developments in labour markets (including a shift towards more pleasant and higher-skilled jobs).

A comparison of different rounds of the LiTS shows that people’s assessments of their own health have also improved significantly over time. Those improvements are broadly based across all age groups, but the decline in health as respondents age is still steeper in the EBRD regions than it is in the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom and the United States of America), particularly for older women. The prevalence of some specific health problems – particularly vision loss and anaemia – is also higher in the EBRD regions than in the G7.7

Importantly, self-assessed health includes not only physical aspects, but also mental health, which is itself closely linked to satisfaction with life. The World Health Organization (WHO) estimates that 12 billion working days are lost globally each year because of depression and anxiety,8 with many economists arguing strongly that increased resources need to be devoted to improving mental health.9

This chapter uses data from LiTS IV to construct a new index of mental distress, and the results show that poor mental health is not only associated with lower satisfaction with life, but also tends to be more prevalent in poorer countries and among individuals who are financially insecure. Partly for these reasons, mental distress is particularly prevalent in the southern and eastern Mediterranean (SEMED) region. It is also more common among women than men – a finding that can be seen in all countries in the EBRD regions.

Lastly, there has been an important shift in the labour markets of some EBRD economies since 2019: more men and women now have high-skilled jobs, with correspondingly fewer having medium and low-skilled occupations. Furthermore, there have also been changes to working practices and a reduction in the average time spent commuting to work.

At the same time, there is substantial variation in the level of life satisfaction across the economies of the SEMED region. Barely one in four people in Lebanon say that they are satisfied, the lowest percentage of any country covered by the survey, reflecting the prolonged socio-economic crisis seen in that country in recent years. In contrast, Jordan, Morocco and the West Bank and Gaza all show happiness levels comparable to some of the best performers in central Europe and the Baltic states (CEB).

The rise in happiness over time is broadly based, rather than being concentrated in certain groups. Across all age groups, levels of life satisfaction are, on average, significantly higher than they were in 2006 (see Chart 1.2), as well as being higher than in 2010 and 2016. For both genders, happiness drops steadily as age increases, before rising slightly in later years. Thus, the data conform to the U-shaped pattern, which is found in most of the literature on happiness, although the turning point comes a bit later than usual in the EBRD regions. Levels of life satisfaction have also risen steadily in both urban and rural areas.

The results confirm that a negative and statistically significant happiness gap was present in post-communist countries in 2010, and that this gap had been eliminated by 2016 (with the relevant coefficient turning positive, without being statistically significant; see Table 1.1). In LiTS IV, the coefficient on the post-communist dummy variable is again positive (and slightly larger than in 2016) but still not significant at conventional levels.

Thus, people in former communist countries are, on average, just as happy as those in Germany and Türkiye, taking into account differences in income and other variables. Similar results can be obtained using an ordered probit model for various degrees of satisfaction with life or using an unbalanced panel of countries.

We can also see that happiness is positively correlated with better levels of education and higher household income. The coefficient on national income is negative when included together with individual income, perhaps suggesting that higher income relative to the national average is a particularly strong correlate of satisfaction with life.

The familiar U-shaped relationship with age can also be observed, with a turning point reached at the age of 49 – somewhat later than is typically found by other researchers. This finding of a U-shape also holds when using age group dummy variables rather than controlling for age and age squared. Women tend to be happier than men, although the differences are not always statistically significant. People living in a rural area, married individuals and people who trust others also tend to report greater satisfaction with life. The finding regarding trust echoes one of the core points of the World Happiness Report: average levels of happiness are higher when governments and businesses can generally be trusted to be free from corruption.11

Happiness is also strongly linked to good physical and mental health. On average, people with a self-reported health status of “good” or “very good” (as opposed to “fair”, “bad” or “very bad”) are more than 10 percentage points more likely to be satisfied with life. LiTS IV data allow us, for the first time, to explore the issue of mental health and its impact on well-being, and it is noticeable that an index of mental distress derived from various answers to the survey is negatively correlated with satisfaction. The next section of this chapter contains an in-depth assessment of health trends and disabilities across the EBRD regions.

A strongly negative correlation between being unemployed and happiness is one of the most robust findings in the literature on happiness. Many countries in the EBRD regions have seen high levels of job creation in recent years, along with a shift towards higher-skilled jobs and, in the post-Covid era, greater flexibility and less commuting. These trends and their implications for well-being are explored in depth in the last section.

Since including variables for health and labour-market status increases the share of total variation in life satisfaction that is explained by the econometric model by around 15 per cent, the next two sections take a detailed look at trends in those areas.

In LiTS IV, the highest level of self-assessed health can be found in Greece. Meanwhile, in most other EBRD economies in the EU and the Western Balkans, levels of self-assessed health are comparable to – or even higher than – the level observed in Germany. Eurostat data paint a similar picture. While higher-income economies tend to be healthier overall, at a certain level additional GDP per capita no longer consistently translates into better health. Middle-income countries can achieve gains through public health infrastructure, proper sanitation and water supply, and access to nutritious foods, while richer countries may struggle to overcome health inequality.

People’s assessment of their own health tends to decline steadily as they age. However, the improvement seen in self-assessed health since 2006 encompasses all age groups. The LiTS data also show that there has been further progress since 2016, suggesting that the Covid-19 pandemic has not halted the steady improvement in health outcomes over time. These trends are further corroborated by the European Social Survey (with some countries being surveyed in the same year that they were covered in the LiTS).

Self-assessed health can also be tracked using the Gallup World Poll – a representative household survey with a strong focus on satisfaction with life and well-being. That annual survey includes a variety of questions about physical and mental health, five of which have been aggregated here to construct an index of overall health. Those five questions ascertain whether respondents feel healthy, well-rested, in physical pain, worried or sad (see Chart 1.4).

Data from the Gallup World Poll indicate that perceived health in the relatively rich EBRD economies in the EU is generally comparable to that seen in the industrial nations of the G7. This is true for both genders up to the age of 70, at which point the EBRD economies start to fall behind the G7, especially in the case of women. In other EBRD economies (excluding the SEMED region), perceived health falls significantly short of G7 levels after the age of 50 for men and after the age of 40 for women. The low level of self-reported health among 15 to 19 year-old women in the G7 reflects low self-assessments of both physical and mental health, particularly in the United States and Canada. In SEMED economies, perceived health declines sharply from an early age.

Some debilitating conditions are more prevalent in the EBRD regions than in the G7, but others are not (see Chart 1.5). Across all countries, the impairments with the highest prevalence are anaemia and hearing loss. Vision loss and anaemia are more common in the EBRD regions than in the G7 and are especially prevalent among women. Globally, anaemia is typically associated with nutritional deficiencies (and that is true even in middle-income EBRD economies).13 Anaemia particularly affects people in lower socio-economic groups, as well as menstruating adolescent women, pregnant women and those who have recently given birth. Symptoms include fatigue, impaired concentration and weakness, among others. It is also associated with reduced productivity and lower household incomes.14 Meanwhile, men are more likely to suffer heart failure and hearing loss. In the absence of a hearing aid, hearing loss can lead to increased fatigue, the need to take sick leave more often and earlier retirement.15 Hearing loss is more common among men across all age groups, but that is particularly true of men over the age of 45.

Against that background, LiTS IV asks respondents whether and how often they experience sadness, depression, anxiety and apathy. Those answers have been aggregated here to construct an index of self-assessed mental distress.

Levels of mental distress vary considerably, both across economies and by gender (see Chart 1.6). In Slovenia, mental distress is almost as infrequent as it is in Germany, whereas in Algeria, Jordan and Tunisia, around 90 per cent of respondents report feeling some form of mental distress at least once a week. The average incidence of distress is higher for women than for men in all countries. This difference is statistically significant in most cases and holds across age cohorts, consistent with global trends as reported in the annual Global Burden of Disease Study. Other signs of mental distress that are not covered by the survey, such as substance abuse and lack of impulse control, are typically more common among men. At the same time, men may under-report symptoms that could indicate weakness, such as low mood or illness, especially if they adhere more strongly to traditional norms regarding masculinity.18

Mental distress differs from disabilities and overall health assessments in that the prevalence tends to be broadly similar across different age groups (see Chart 1.7). The percentage of LiTS IV respondents reporting that they experience some form of mental distress at least weekly stands at around 40 per cent for the young, the middle-aged and the elderly alike. Globally, the severity and prevalence of mental illness are estimated to peak in people’s mid-thirties and remain high in their prime working and child-rearing years.19

In LiTS IV, respondents also reported their ability to perform sensory-related, mobility-related and cognitive tasks. Physical disabilities include problems seeing, hearing and walking/climbing steps, while cognitive ability is about being able to remember things and concentrate and communicate well. Not surprisingly, the percentages of respondents reporting a disability (or describing their health as “bad” or “very bad”) increase with age. Just over 20 per cent of people aged 35 or younger report being limited by a disability, compared with 80 per cent of respondents over the age of 65.

Differences between men and women in terms of the prevalence of health problems mostly arise in middle age (30-54), with only minimal gender differences in disability and poor health among those aged 18-29. In the EBRD regions, however, there is also a gender difference in mental distress among the young.

Countries with lower GDP per capita tend to have higher levels of mental distress. However, other factors are also relevant. Regression analysis can be used to link mental distress as reported in LiTS IV to a number of other self-reported measures of health, as well as other individual characteristics such as labour-market status (see Chart 1.8).

First, all other things being equal, people who have disabling impairments such as problems with their memory, vision or hearing are the most likely to experience frequent mental distress. This probably reflects the psychological challenge of living with impairments in social and work settings that do not allow for full participation in normal activities. People with a disabling impairment are also likely to have fewer opportunities to work, perhaps as a result of unsuitable workspaces and inflexible processes. Across all age groups, people with a disability are 11 percentage points less likely to report that they work than those with no disability. Meanwhile, the percentage of people with a disability who are not in the labour force is about 12 percentage points higher than the equivalent figure for people without a disability.

Second, there is a clear association between mental distress and having a household member who died from Covid-19. This is a poignant reminder of the lingering and lasting impact of the pandemic.

Third, financial fragility is clearly associated with mental distress (see Chart 1.9). Approximately 65 per cent of those who are unable to save experience distress at least weekly, compared with about 35 per cent for those who are able to save. This echoes the results of recent studies, which have found that economic crises have a negative effect on mental health through the impact of sudden unemployment and the associated decline in living conditions.20

Furthermore, being in work seems to have a positive impact. Looking only at individuals who are drawing on savings/running into debt, the percentage of people in work who report experiencing frequent mental distress is about 10 percentage points lower than the equivalent figure for people who are not in work, with the same differential being observed among individuals who are able to save and among individuals who are just making ends meet. These differentials are statistically significant at the 1 per cent level.

The results show that employees do indeed tend to value the possibility of working from home at least part of the time: a 1 standard deviation increase in teleworkability (equivalent to the difference between teachers and production managers) corresponds to a 1.3 percentage point increase in the probability of being satisfied with life today (see Chart 1.11). This equates to roughly 9 per cent of the gap between the satisfaction of employed and unemployed respondents. Similarly, reduced commuting time and a shift to a higher-skilled job are also associated with higher levels of satisfaction. The link between moving to a higher-skilled occupation and happiness also holds when focusing specifically on workers who have shifted to a higher-skilled job more recently (that is to say, since 2000).

The pandemic is now over, but the shift to working from home will probably endure.25 In the light of this development, several questions arise: How prevalent is remote work across EBRD countries? What are the main working arrangements at present? And what are the primary benefits of hybrid work?

In order to answer those questions, the analysis in this section draws on the results of a comprehensive recent survey conducted in 34 countries (including six economies in the EBRD regions: the Czech Republic, Greece, Hungary, Poland, Romania and Türkiye) in April and May 2023. The survey, which was conducted online, covered full-time workers between the ages of 20 and 64 with a secondary, tertiary or postgraduate education. The samples used were broadly representative with respect to age, gender and education; however, the results largely reflect trends among workers who are able to use smartphones, computers or tablets and are therefore able to take part in online surveys (rather than being applicable to the population as a whole).

People greatly value the opportunity to work from home. To elicit information on people’s willingness to pay for the option of working from home, the survey first asked: “How would you feel about working from home two or three days a week?” If the response was “neutral”, the willingness to pay was coded as 0. Those who answered positively (“I would view it as a benefit or extra pay”) were asked: “How much of a pay rise (as a percentage of your current pay) would you value as much as the option to work from home two or three days a week?”

On average, the option of working from home two or three days a week was deemed to be worth 8 per cent of earnings (see Chart 1.13), ranging from 1 per cent of pay in Austria and France to about 10 per cent of pay in Greece, Poland and Romania, and nearly 20 per cent in Türkiye. The results also show that, in most economies, women value the option of working from home more highly than men. This is true even when taking into account the presence of children and other observable demographic characteristics. Indeed, this gender difference is also observed among single persons without children, suggesting that the result is not fully driven by childcare responsibilities. It may be that women are also more likely to take on other care-giving and household responsibilities, leading them to place greater value on the flexibility and time savings offered by the option of working from home (see also Box 1.1 for a discussion of the role that gender and gender norms play in labour force participation decisions).

Across the EBRD regions, the average score is 2.8. Economies in central Europe tend to have relatively gender-equal norms (especially Estonia, Slovenia and Croatia), while economies in Central Asia and the SEMED region have more gender-stereotypical views (especially Uzbekistan, the West Bank and Gaza, and Jordan). Within countries, men and people living in rural locations tend to have more gender-stereotypical views than women and people living in urban centres (with these differences being statistically significant at the 1 per cent level).

Cross-country analysis shows that less equal gender norms are associated with lower levels of female labour force participation across the EBRD regions (see Chart 1.1.1).30 Moreover, even within economies, people with conservative gender norms are less likely to be in the labour force themselves. Strikingly, this effect is driven entirely by women – even when controlling for other individual characteristics such as age, age squared, marital status, an indicator for children in the household and an urban dummy, as well as country of residence fixed effects. A 1 point increase in the gender norms index is associated with a 3 percentage point reduction in the probability of a woman being in the labour force. This equates to 5 per cent of the total female labour force participation rate.

Gender norms may also influence the ways in which individuals sort into occupations. As part of LiTS IV, respondents were asked whether they believed that certain occupations – nine in total – were suitable for (i) men, (ii) women or (iii) both. Across all EBRD regions, people regarded “engineer” and “surgeon” as relatively male occupations, while regarding “nurse” and “primary school teacher” as stereotypically female occupations. For each respondent, one can count the number of times that they indicate that an occupation is “definitely/somewhat more suitable for men” or “definitely/somewhat more suitable for women”. The absolute occupational skewness is then a simple count of the number of occupations for which there is perceived unequal suitability.

The gender views that individuals hold correlate strongly with how much they perceive occupations as gendered (see Chart 1.1.2). Similarly, a regression framework that controls for country fixed effects and individual characteristics shows that a 1 point increase in the extent to which someone’s gender norms are unequal is associated with an increase of 0.56 in absolute occupational skewness. This amounts to 16 per cent of the mean extent to which occupations are seen to be gender-specific. This suggests that conforming to stereotypical gender norms may influence the ways in which men and women sort into occupations.31

Individual occupations (defined at the two-digit level using ISCO codes) can be classified as (i) STEM (science, technology, engineering and mathematics) occupations; (ii) HEAL (health, education, administration and literacy) occupations;32 or (iii) neutral occupations. In contrast to STEM occupations, HEAL occupations tend to be more people-focused than technology-oriented and tend to require literacy skills rather than numeracy.

As Chart 1.1.3 shows, there is considerable variation across economies in the importance of HEAL and STEM occupations, with men tending to have more of the STEM jobs and women tending to have more of the HEAL jobs. Strikingly, across the EBRD regions as a whole, the percentage of women who report having HEAL jobs is 11 percentage points higher than the equivalent figure for men, whereas the percentage of female respondents with STEM jobs is only 2 percentage points lower than the figure for men. A similar pattern can be observed for a sub-sample of countries covered by European Labour Force Surveys.

The EBRD Just Transition Initiative is one example of the ways in which the Bank helps countries to harness the capacity of their workforce, paving the way for more women to join STEM sectors. In Tunisia, the EBRD helped the national electricity and gas utility company to improve women’s access to technical and STEM roles by strengthening its equal opportunity policies and practices (see also Box 3.3).

One reason for encouraging men to take up HEAL jobs is to tackle labour shortages, particularly when it comes to healthcare. The WHO estimates that countries need at least 4.5 healthcare professionals per 1,000 people in order to provide universal healthcare and achieve health-related Sustainable Development Goals. In the EBRD regions, Egypt, Morocco and Tunisia are not expected to meet that criterion by the end of 2023.33

Strong gender norms – and, consequently, strong views about occupations that are only suitable for men or women – may contribute to a misallocation of human capital.34 In particular, they may prevent men from applying for jobs in HEAL professions, such as nursing, thus making it more difficult for countries to achieve health-related development goals.

Even before the invasion, Ukraine’s population was experiencing a rapid decline owing to low fertility rates. There is concern that if a significant number of Ukrainian refugees choose to remain abroad, this could hinder post-war reconstruction efforts and make Ukraine more vulnerable to future military aggression. Against that background, this box investigates Ukrainian refugees’ future intentions using Kantar’s online “Voice of Ukraine” panel surveys, which cover Ukrainian refugees across Europe. In the first wave of the survey, respondents were recruited primarily through Facebook ads. They were then invited by email to take part in follow-up waves. The survey asks individuals about their plans as regards returning to Ukraine, with the possible answers being: “I intend to go back very soon”; “I intend to go back at some point later when I feel it is safe to return”; “I do not intend to go back and plan to settle outside Ukraine”; “I don’t know”; and “I prefer not to answer”.

In conclusion, recent surveys indicate that the vast majority of Ukrainian refugees in Europe intend to return to Ukraine. This determination to return to their homeland contrasts with the strong desire to emigrate that tended to be expressed in surveys before the war. It may be that they now have a stronger national identity, which is acting as a powerful counterpoint to the push and pull factors that typically drive cross-border migration (such as differentials in per capita income or the quality of economic institutions). The actual level of return migration remains to be seen, and that return migration will play a pivotal role in future reconstruction efforts.

How significant has the loss of human capital been? The fog of war means that reliable data are hard to come by, but the numbers involved are certainly large.

First, human capital is directly reduced by the loss of human life, as well as debilitating physical or mental injuries. Tens of thousands of civilians have been killed, with 80 per cent of Ukrainians estimated to have at least one family member or close friend who has been killed or injured in the war.41 At least 8 million have become refugees, seeking sanctuary across Europe and beyond, another 8 million have been displaced internally within Ukraine, and millions more are under Russian occupation.

Second, the education of many Ukrainian children continues to suffer severe disruption as a result of those children being displaced or stranded in war-torn regions, with access to schools limited. Indeed, more than 3,000 educational institutions have been destroyed or severely damaged.42 Evidence from previous wars shows that such disruption to schooling reduces the productivity and future earnings of affected individuals. We know, for instance, that childhood exposure to war during the Second World War and the Vietnam War had long-lasting detrimental effects on education, health and labour-market outcomes that extended far beyond the conclusion of those conflicts.43 Residents of Austria and Germany who were ten years old during the Second World War experienced reduced educational attainment and were still facing significant earnings gaps four decades after the war.44

Third, adults can lose human capital even without being directly affected by the war. For example, a year without work is estimated to reduce human capital by between 4 and 8 per cent.45 This is a major concern, given that survey evidence suggests that 40 per cent of workers employed in Ukraine before the war were out of work in April 2023 (see Chart 1.3.1) and unemployment stood at approximately 30 per cent in 2022.

Recent surveys reveal that many reallocated workers have had to switch sectors and accept jobs for which they are over-qualified. For example, 20 per cent of the Ukrainian refugees living in Poland had pre-war jobs that required specialist skills, but only 3 per cent have found comparable high-skilled jobs in Poland.46 At the same time, between 500,000 and 1,000,000 men and women are estimated to have been drafted into the armed forces.47 In addition to the risk that such people could lose some of the skills required for their pre-war jobs, members of the armed forces also risk suffering physical or mental injuries, which could reduce their productivity.48

Thus, a substantial amount of resources will need to be committed to the rebuilding of human capital if Ukraine’s post-war recovery is to be successful. It is vital, in that regard, that children have continuous access to education – both during and after the war. This can be achieved by offering classes for Ukrainian refugees, providing tutoring online or in person, and adapting curriculums to enhance learning, especially for refugee children. Universities and colleges should also facilitate the transfer of credits in order to foster continuity of education.

Further investment in the quality of education remains crucial. Ukraine’s average scores in standardised international comparisons of students’ ability (such as the Programme for International Student Assessment (PISA) run by the Organisation for Economic Co-operation and Development (OECD)) have been modest, despite relatively high levels of spending on education in relative terms (5.4 per cent of GDP in 2019, compared with 3.6 per cent of GDP in Greece, which enjoyed better PISA results).

The war will exacerbate skill mismatches between labour demand and labour supply.49 The post-war recovery will require new skills, as some jobs may disappear, while new ones will emerge. Construction, civil engineering, health, IT and agricultural export industries may all gain in importance. Identifying the skills that are needed in the post-war economy and establishing retraining centres for displaced workers will be essential. At the same time, retraining programmes can also support veterans’ reintegration into civilian life after their military service.

Given the enormous negative effects that the war will inevitably have on health (including post-traumatic stress disorder), it is vital to develop disability-inclusive infrastructure and workplace policies that make it easier for people with disabilities to return to their previous roles and help to rebuild Ukraine. The Ukrainian Ministry of Veterans’ Affairs estimates that the total number of war veterans in Ukraine could triple to about 3 million.

Lastly, the war can be expected to exacerbate Ukraine’s demographic challenges, including its relatively low fertility rate. In this context, childcare could be made more affordable by establishing subsidy programmes, introducing targeted incentives to encourage private providers to enter the market, and having childcare facilities offer extended opening hours.

In summary, rebuilding human capital will be at the very heart of Ukraine’s reconstruction efforts and will represent a huge challenge.