If progress has been made lately in measuring some aspects of the inequality of opportunity and in making international comparisons, monitoring them over time at the country level is still infrequent and often imprecise. Few consensual estimates are available about whether inter-generational mobility has increased, remained the same, or decreased in recent decades. Progress has been made in monitoring mean educational achievements in many countries, most notably under the OECD PISA initiative, but no systematic reporting or discussion takes place on the evolution of their dispersion. Also, if the mean earnings gap across gender is reported regularly in most advanced economies, the same cannot always be said of the earnings gap adjusted for changes in the educational attainment of women and men (a measure which suggests that most of the narrowing in the gender wage gap observed in recent years mainly reflects higher education of women, rather than lower gender gaps between women and men of similar education); or the gap across ethnic groups or between natives and first- and second-generation migrants. Yet, in most countries, data to evaluate these and other indicators on a regular basis either are available, or could often be made available at little cost.
The data required to improve the situation and monitor observable dimensions of inequality of opportunity in a systematic way include data on family background, wealth, and students’ skills. Three basic statistics should receive priority attention and should be harmonized as much as possible across countries and over time: inequality of economic outcomes (earnings, income) arising from parental background and its share in total inequality of outcome; variance analysis of scores in PISA and analogous surveys at earlier ages, including pre-school, the share of that variance explained by parental/social background, or the gaps in scores between students from different families; and gender inequality in earnings, unadjusted and adjusted for differences in education, age, job experience, types of occupation, etc.
Distributional National Accounts
In Chapter 6, Facundo Alvaredo, Lucas Chancel, Thomas Piketty, Emmanuel Saez, and Gabriel Zucman discuss the limits of the System of National Accounts (SNA) for looking at disparities within the household sector. The focus of the SNA has been on the main sectors in the economy, only distinguishing results for the household sector as a whole. Partly as a result, the discrepancies between income levels and growth rates displayed in national accounts and the ones displayed in micro-statistics and underlying distributional data have been growing in all dimensions: income, consumption, wealth. Scholars have been aware of the discrepancies for some time (see, for instance, Anand, Segal, and Stiglitz, 2010), and have proposed ideas to explain the reasons behind them, but systematic and coordinated action to put national accounts and micro-economic data in a consistent framework started only in 2011, when the OECD and Eurostat launched a joint Expert Group to carry out a feasibility study on compiling distributional measures of household income, consumption, and saving within the framework of national accounts, on the basis of micro-data.
The World Inequality Database (WID.world) project presents a renovated approach to the measurement of economic inequality consistent with macro aggregates, aiming to rebuild the bridges between distributional data available from micro sources and national accounts aggregates in a systematic way through Distributional National Accounts (DINA). In some cases, this may require revising central aspects of key national accounts concepts and estimates. The two main data sources used in DINA income series are income tax data and national accounts, as in earlier versions of the approach. However, these two core data sources are now used in a more systematic and consistent manner, with fully harmonized definitions and methods, and together with other sources such as household income and wealth surveys, inheritance, estate, and wealth tax data, as well as wealth measures for those at the top of the distribution provided by “rich lists” compiled by the press.
The DINA initiative aims to provide annual estimates of the distribution of income and wealth using concepts that are consistent with the macro-economic national accounts. In this way, the analysis of GDP growth and economic inequality can be carried over in a coherent framework. The long-run goal of DINA is to release income and wealth synthetic micro-files for many countries on an annual basis. Such data can play a critical role in the public debate, and can be used as a resource for further analysis by various actors in civil society and the academic, business, and political communities.
A comparison between the United States, China, and France (broadly representative of Western Europe) illustrates how DINA can be used to analyze the distribution of economic growth across income groups. National income per adult increased in the three countries between 1978 and 2015: by 811% in China, 59% in the United States, and 39% in France.2 In China, the top earners experienced very high growth rates, but average growth was so large that the average income of the bottom 50% also grew markedly, by around 400%. In contrast, the bottom 50% of adults in the United States experienced a small drop. In France, very high incomes grew more than average, but their numbers are too small to affect the overall average, while the bottom 50% income group enjoyed the same growth as average growth (39%).
Statistics on the distribution of wealth are highly imperfect, but they show substantial variations in their size and trends across countries, suggesting that country-specific policies and institutions matter considerably. High GDP growth rates in emerging countries reduce between-country inequality, but this in itself does not guarantee acceptable within-country inequality levels and ensure the social sustainability of globalization. Access to more and better data (administrative records, surveys, more detailed and explicit national accounts, etc.) is critical to monitor global inequality dynamics, as this is a key building block both to properly understand the present as well as the forces that will dominate in the future, and to design potential policy responses.
Understanding Subjective Well-Being
Stiglitz, Sen, and Fitoussi argued that traditional metrics need to be supplemented with indicators of subjective well-being, i.e., measures of how people perceive their own well-being and experience their life. Advances in psychology have led to the development of replicable indicators that are systematically related to other aspects of economic performance and social conditions, and which themselves could be at least partially explained by other objective indicators. In Chapter 7 Alan B. Krueger and Arthur A. Stone discuss the potential of subjective well-being as an indicator of the “health” of a community and the individuals that compose it. There is an increasing consensus that broader measures of societal progress should take into account how people feel about and experience their own lives, alongside information about their objective conditions. At a social level, subjective well-being measures are powerful indicators that can signal wider problems in people’s lives, capture prevailing sentiment, and predict their behavior.
The availability of survey data on subjective well-being, including panel data, has increased rapidly since the 2009 Stiglitz, Sen, and Fitoussi report. National Statistical Offices are increasingly including subjective well-being questions in their surveys, and a majority of OECD countries now collect at least some subjective well-being data. Continued methodological progress would be facilitated by the collection and dissemination of long time series in large, high-quality data sets. Collection of such data will also facilitate the generation of policy-relevant insights.
Advances have been made on many of the methodological and interpretive issues that caused concern about using subjective well-being measures in 2009. While a deep examination of these issues is important to improving the measurement of subjective well-being, it is equally important to avoid setting a uniquely high standard for subjective well-being in contrast to other indicators, such as income, consumption, or wealth inequality, which can also be difficult to calculate or are similarly derived from self-reported measures that are equally sensitive to survey methodology. We have come to accept these other measures, and gloss over their methodological problems, simpl
y because they have been used for so long.
There have also been other advances, such as the wider implementation of time-use surveys for collecting detailed information on subjective well-being connected to daily activities.
Applications of subjective well-being have also begun to appear—for example, in assessing the impact of the global financial crisis. Other innovative but early work is experimenting with the incorporation of subjective well-being into standard cost-benefit analysis. Several harmonized international data sets now exist, allowing comparison of subjective well-being levels over time.
An area with great potential for development is the examination of different types of subjective well-being. Existing research generally focuses on life evaluation (how satisfied one is with one’s life) but less on emotion (happiness or depression) and eudaemonia (meaning and purpose in one’s life). While these types of subjective well-being are related, they are not the same, and each yields different insights that can be helpful for policies and research.
Achieving a better understanding of the direction of causality between subjective well-being and people’s objective circumstances (e.g., does better health increase happiness, or does happiness help people engage in healthier behaviors?) is one of the issues that needs to be explored further for a more complete understanding of subjective well-being. It is difficult to reach strong conclusions about causality on the basis of on much of the subjective well-being research that is currently available, which relies mainly on observational and self-reported data. Heterogeneity across individuals also needs to be addressed: just as focusing on the simple average income gives an incomplete picture, so too does focusing on the average level of subjective well-being. For example, life-cycle patterns of income are important to understand, and the same applies for subjective well-being. One wants to understand inequalities in subjective well-being—what drives them and how they are related to inequalities in income.
Although data collection on subjective well-being has expanded enormously, there remain two important areas where there is still a lack of data, and where the inclusion of subjective well-being questions in surveys is likely to be relatively low cost. The first is to expand high-quality data collection on subjective well-being to less developed countries—for example, by including a life satisfaction and experiential well-being module in household surveys. Second, in order to increase our understanding of experiential well-being, subjective well-being measures should be included in official time-use surveys.
Economic Security
People’s economic security has both observed (objective) and perceived (subjective) dimensions. In Chapter 8, Jacob S. Hacker reminds us that even before the financial crisis, citizens of advanced democracies and their leaders perceived that economic security was declining. Various observed measures provide an indication of the likely scale of the problem. For example, while around 12% of people in developed countries are classified by the OECD as “income poor,” the share of those having financial assets insufficient to cover more than 3 months of (poverty level) living standards is typically three times as high. Similarly, around 12% of adults will typically experience an income loss of 25% or greater in any given year.
In developing countries, governments have also grappled with economic insecurity, as citizens move into wage labor, health care grows more costly, and the traditional risk-spreading role of the family declines. In both developed and developing countries, public debate has centered on the changing character of the economy and society, and on the relative roles of governments, markets, and households in coping with the related economic risks.
Still, the definition and measurement of economic security have continued to pose serious difficulties. This is in part because of the multiplicity of definitions and measures proposed; indeed, even the boundary between economic security and other forms of security remains hazy. It is also because of the relative scarcity of high-quality data, particularly panel data in comparable form across a significant number of countries. Despite the difficulties, it is possible to identify a common definition of economic security that is implicit or explicit in much existing literature: individuals’ (or households’) degree of vulnerability to economic loss. Three elements are inherent in this definition: some probability of an adverse event; some negative economic consequence if this event in fact occurs; and some set of protections (from formal insurance to informal risk-sharing, self-insurance through savings, and the like) that potentially offset or prevent these losses.
Within that definition, two distinctions are important when talking about economic security. The first is between observed security and perceived security. Observed security describes measures that use economic data to determine whether an individual or household is insecure (for example, because they are at risk of a large reduction in income or consumption). Perceived security describes measures based on individuals’ own reports of their subjective response to their economic situation (whether through surveys, experiments, or some other revelation technique).
The second distinction is between scoreboards or indices of economic security based on (weighted) multiple measures, and integrated measures, which try to capture individual or household security in a single statistic. The main class of integrated measures looks at income volatility in some form, particularly large drops in income from one period to the next. For many reasons, integrated measures are preferable to weighted indices measures, which are less transparent and more sensitive to analysts’ choice of components and weights.
Since 2009, thinking has greatly advanced on how to conceptualize a lack of economic security as distinct from (but related to) poverty, as well as how to understand the role of psychology, the voluntary or nonvoluntary nature of income losses, and the role of buffers that reduce those losses. The development of new indices, as well as new and improved measures, has expanded our understanding of how these metrics perform.
Considerable additional work is required, however, to select the best types of measure and understand their properties. The availability of reliable and cross-nationally comparable data has been a crucial constraint on the development of improved measures of economic security. Three shortcomings of existing statistics stand out: the limited pool of long-term and cross-nationally comparable panel data; the weaknesses of most administrative data for tracing individuals over time; and the lack of regular questions about perceived security in conventional random-sample surveys, much less in panel data.
Nonetheless, these data have been rapidly improving, catalyzed by the extensive and increasingly sophisticated literature on volatility. In addition to offering crucial conceptual and methodological guidance, the literature on volatility also provides many valuable clues about the evolution of citizens’ economic security. It is increasingly clear, for example, that volatility is particularly high in the United States. Moreover, high volatility suggests that, since an individual’s circumstances change often over time, many more people turn to social benefits to cushion them from shocks at some point over their lives than a survey at one point in time would suggest. This was particularly true during the crisis, which not only directly reduced economic security in many countries, but also created pressures for policy changes that could further reduce the risk-protecting role of government.
Measuring Sustainability
The SDGs framework recognizes that progress has to be considered in a holistic manner to take account of the inevitable trade-offs, spill-overs, and possible unintended consequences of policy and investment decisions. In Chapter 9, Enrico Giovannini, Marleen De Smedt, and Walter J. Radermacher argue that complex systems theory provides a powerful complement to the capital approach for integrating the analysis of the different types of capital involved in sustainability, and for dealing with the many interactions that determine sustainability. A systems approach could also more adequately capture the extent to which a production and consumption path is sustainable, safe, and resilient.
The capital
approach implies that a sustainable community should keep capital intact for the next generation. It will not consume more than it can produce, so that the level of capital that it leaves for the future is greater than that which it inherited. Sustainability requires taking a broad view of capital, including economic, natural, human, and social capital. Measuring changes in capital thus requires adopting a balance sheet to record changes in each of the components. In such a framework, extraction of natural resources is not counted only as a gain (due to the revenue from selling the resources) but also as a loss (since the natural resources have been depleted).
Although it is difficult in practice to build such a measurement framework, there have been substantial advances in advancing our understandings of different elements of the capital approach since 2009. For example, the System of Environmental-Economic Accounting—Central Framework (United Nations et al., 2014), formally adopted in 2012, extends standardized national accounting practices to include a broader set of environmental assets such as fish stocks.
The G20 Data Gaps Initiative3 is working toward comprehensive measures of economic sustainability, and the Guide on Measuring Human Capital (UNECE, 2017) provides a systematic overview of methods for measuring human capital.
At the same time, many issues remain open, with unresolved controversies over the best way, for instance, of accounting for the depletion of natural resources, the degradation of the environment, and the loss of biodiversity. There are also disputes on the best way of improving and expanding measures of human and social capital.
Measuring the sustainability of the systems (sets of processes working together and interacting) that contribute to human society—including our eco-system in particular—also requires accounting for transboundary issues, uncertainties, instabilities, tipping points, and other issues related to complexity. For example, our eco-system clearly interacts with our economic system, stretches across international boundaries, and is likely to be vulnerable to tipping points that we do not yet understand well. Indeed, a common flaw of economic analysis is that it does not take into account the planetary boundaries within which our economic system operates. While some progress has been achieved on the environmental aspects of our overall global “system,” notably with respect to emissions of greenhouse gases (through global input-output tables), the quantification of uncertainties, instabilities, and tipping points has mostly remained confined to scientific journals and has not yet translated into statistical practice or even standard economic analysis.
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