This blog is based on the new paper “Well-being in metrics and policy” published in Science.
What we know depends on what we measure. Traditional income-based metrics, such as GDP and poverty headcounts, tell a story of unprecedented economic development, as seen by improvements in longevity, health, and literacy. Yet, well-being metrics, which are based on large-scale surveys of individuals around the world and assess their daily moods, satisfaction with life, and meaning and purpose in life, among other things, provide a very different picture of what is happening to people within and across countries—stories that economic numbers often do not tell. Deep poverty and frustration persist in the most fragile countries, and income inequality and unhappiness are increasing in some of the richest ones. Remarkably, some of the most worrisome trends are in countries with rapid economic growth and falling poverty.
The world’s most recent success story in rapid growth and poverty reduction is India, with extreme poverty falling from 38 percent (2004) to 21 percent (2011). Still, life satisfaction dropped significantly—10 percent or a full point on a 0-10 scale—from 2006-2017. That is roughly equivalent to the unprecedented drop in life satisfaction in the U.S. during the first six months of the financial crisis. Optimism about the future, which typically remains steady even when life satisfaction drops, also fell at the same rate.
Another stark marker is suicide. By 2016, India had 18 percent of the world’s population but over a quarter of all global suicides, an increase of 40 percent from 1990 (these numbers may be low due to underreporting). Indian women account for 36 percent of the world’s total suicides for females and Indian men account for 24 percent of the male total. There were large differences across ages and states—with young and elderly women and elderly men having much higher rates. This was also the case for poorer states. (Because of the high rate of population growth and large population, suicide rates actually went down slightly during this period, even though absolute numbers of suicide increased significantly.)
While both genders show a stark downward trend in life satisfaction, they do not reflect the gender differences in suicide rates. In part, this is a sad artefact of construction: life satisfaction surveys do not have scores for those who have taken their lives. Strong norms may also affect the response scales. Recent research, based on vignettes asking respondents to report their life satisfaction under alternative scenarios, suggests that women in places with strong gender discrimination report to be happier than they actually are due to low expectations.
This story of ill-being juxtaposed against the positive story told by standard economic indicators is not unique to India. Indeed, these trends mirror what happened in China a decade earlier. In the 1990s, China experienced perhaps the most rapid growth and poverty reduction in modern history. GDP per capita and household consumption increased fourfold between 1990 and 2005, and life expectancy increased to 75.3 years from 67 years in 1980. Nevertheless, in that period, life satisfaction fell dramatically and reported depression and suicides increased, the latter reaching one of the highest rates in the world in the 1990s.
Another example is the U.S., where booming stock markets and record low unemployment rates coincide with falling life expectancy—unique among rich countries—due to preventable deaths—suicide, drug overdose, alcohol poisoning, and other preventable causes—among non-college-educated whites, the traditional bastion of the American working class. An increasing proportion of this group—15 percent of prime-age males—is out of the labor force altogether, a figure projected to reach 25 percent by 2025.
Income-based indicators do not provide good explanations for these paradoxes. Our research in the U.S. finds that poor whites report much less hope for the future and more stress than do poor African Americans and Hispanics, despite the latter groups facing higher levels of poverty, disadvantage, and discrimination. Yet minorities have also been making gradual if hard-fought progress at a time that the white working class is shrinking in size and fears losing its identity. These markers of ill-being link closely to the individual and locational patterns in the “deaths of despair”, and minorities are much less likely to die of these deaths.
Hope is a factor. New studies show that interventions that provide hope for the future lead to more effort or investments producing better outcomes. Research based on panel data for the U.S. finds that individuals born in the 1930s and 1940s, who reported to be optimistic in their twenties, were much more likely to be alive in 2015. The one population cohort that experienced drops in hope beginning in the 1970s, meanwhile, was non-college educated whites. The above trends show that lack of hope, even amidst prosperity, can have high negative costs for societies as a whole.
In China, the unhappiest cohorts were educated workers in the private sector, precisely those who were benefiting the most in income terms from China’s “new” economy. Long working hours and lack of sleep and leisure time were important drivers of this trend. In India, the explanations range from inequality across genders and regions, to working conditions and hours, to rising expectations driven by very visible extreme wealth juxtaposed against large pockets of deep poverty.
The well-being approach
Are these paradoxes a sign that our models of growth—and the lifestyles they result in—are out of touch with the realities and desires of the average human being? Are they sustainable going forward? At a minimum, they suggest that complementing standard income-based indicators of progress, such as GDP, with different markers, such as well-being metrics, will provide us with a fuller picture of what is happening to human welfare in our complex and changing global economy. As we write, along with Kate Laffan, this month in Science, using both sets of metrics will help in the design of policies that are inclusive and politically and socially sustainable.
The well-being approach gives us new insights into how humans experience and assess economic processes, helping to explain progress paradoxes such as those above. For example, economics informed by well-being helps us understand individuals whose conceptions of happiness or a good life reflect low expectations or strong norms (such as gender discrimination in the case of India), preferences for equity and altruism rather than income gains, and the negative role that stigma plays in schools, the workforce, and in homes.
Well-being metrics concord with income measures in showing that income is necessary to human well-being. Yet, depending on the specific metric used, they also show that factors such as health, meaningful work, and fairness and friendships can be as important. Very rapid economic change tends to disrupt many of these things. Well-being studies also find that smoking is bad for well-being and exercise and volunteering is good for it. In the U.S., the least happy places with the highest rates of deaths of despair have higher percentage of smokers and a lower one of those who exercise, as well as less civic organizations. Existing research also highlights the negative well-being costs of air pollution, traffic, and airplane noise—prevalent factors in India and China. They also suggest that pro-environmental behaviors, such as recycling, while often entailing time and money costs, are associated with higher levels of well-being, as most people seem to benefit from contributing to a better community.
Well-being metrics, like income-based metrics, also have limitations and methodological challenges. These include adaptation (very poor people with low expectations reporting to be very happy) and scale interpretation (we cannot assume, for example, that all individuals interpret the 10 point life satisfaction scale the exact same way). Innovations in the measurement science, though, are providing reasonable ways to adjust for these problems, such as via the vignette strategy described above.
Individuals with higher levels of well-being tend to have better long-term outcomes, in part because they believe in their futures and are therefore more likely to invest in them. While the environment people live in undoubtedly plays a role, so do innate character traits. An early study found that the individual-level life satisfaction that is not explained by observable demographic and environmental factors is associated with better income, health, and social outcomes later on. Sibling studies show that those with genetic and other markers of higher well-being (who grow up in the same households and environments) have better future outcomes.
There is still much more that we need to know, including how genes and the environment interact to generate better well-being over the life course, and if hope, resilience, and other markers of well-being can be learned and fostered beginning early in life. More immediately, though, the costly progress paradoxes above, as well as the positive linkages between well-being, productivity, and health, make a strong case for how and why it should matter to policy today and in the future.