For Good Measure Read online

• Recommendation 9: Statistical offices should provide the information needed to aggregate across quality-of-life dimensions, allowing the construction of different indexes.

  • Recommendation 10: Measures of both objective and subjective well-being provide key information about people’s quality of life, and statistical offices should incorporate questions to capture people’s life evaluations, hedonic experiences, and priorities in their own survey.

  • Recommendation 11: Sustainability assessment requires a well-identified dashboard of indicators, whose elements should be interpretable as variations of some underlying “stocks.”

  • Recommendation 12: The environmental aspects of sustainability deserve a separate follow-up based on a well-chosen set of physical indicators.

  Source: Stiglitz, J.E., A. Sen, and J.-P. Fitoussi (2009), Report by the Commission on the Measurement of Economic and Social Progress, http://ec.europa.eu/eurostat/documents/118025/118123/Fitoussi+Commission+report.

  Sustainable Development Goals and the Measurement of Economic and Social Progress

  As Ravi Kanbur, Ebrahim Patel, and Joseph Stiglitz argue in Chapter 2, the process leading to the SDGs reveals the tension between the desire for completeness and thoroughness on one side, and the need for clarity on the other. This was a central tension discussed in the Stiglitz, Sen, and Fitoussi report. Obviously, the more that detailed information and data are disaggregated, the more complete picture one has of what is going on. The 169 SDG targets and 232 indicators provide a useful platform and have the virtue that they are agreed to internationally. But their implementation will need to be sensitive to national needs and priorities, as well as limited resources. Accountability and sovereignty lead to the recommendation that this streamlining and selection of indicators takes place in the context of a national dialogue informed by international frameworks. The international dimension is important because there is a trade-off with comparability across countries; countries themselves need to be mindful of comparability as, to know how well one is doing, one needs to know how well other similarly situated countries are performing.

  In order to pursue the agenda of the SDGs, and the larger agenda of measuring social and economic progress, National Statistical Offices (NSOs) must have the governance and financial resources necessary to provide an independent and credible statistics to nourish the national policy dialogue and enable accountability. In low-income countries, statisticians have to have the means to resist not only the political pressures any NSO is subject to, but also pressures coming from powerful international organizations that may inadvertently harm the autonomy of NSOs by imposing an agenda that takes insufficient account of national needs and capacities.

  When considering global and transnational issues, such as world inequality and poverty or climate change, harmonization of measurement over countries is of key importance. International organizations have a large and important role to play to support such harmonization, and the international community should commit resources to supporting the production of those national statistics that are critical for assessing global issues.

  Measuring the Distribution of Household Income, Consumption, and Wealth

  Stiglitz, Sen, and Fitoussi emphasized the importance of inequality. Even if average income per capita was increasing, a majority of citizens could be experiencing a decline. One of the original motivations for the Commission was the concern, expressed by President Sarkozy, that our indicators were presenting a picture that was inconsistent with individuals’ own perceptions. Even though the government could boast that GDP was increasing, most individuals could still feel worse off.

  In Chapter 3, Nora Lustig addresses the challenges posed by measuring vertical inequalities in household income, consumption, and wealth. The issue is important from a normative standpoint in relation to social justice, but there are instrumental reasons to care about these inequalities too. Inequality in the distribution of household resources has come to the fore of the political debate in recent years, partly as it has become more extreme and partly as the economic, social, and political costs have become clearer.

  While there have been notable improvements in the availability of data (including more extensive use of administrative data), substantial challenges remain in measuring inequality in economic circumstances through the joint analysis of income, consumption, and wealth. These analyses are often based on databases relying on household surveys: micro-based data sets, which calculate inequality measures directly from these surveys; secondary sources data sets, which combine inequality indicators from a variety of other sources; data sets that generate inequality measures through a variety of imputation and statistical inference methods instead of relying directly on unit-record data sets; and WID.world, described below. Unfortunately, different international databases show not only different levels of inequality but also, for some countries (especially in sub-Saharan Africa), diverging trends.

  These different data sets all suffer from the fact that household surveys suffer from under-coverage and under-reporting of incomes at both ends of the distribution. The under-reported top incomes are sometimes referred to as “the missing rich” problem. The factors embedded in the data collection process that may explain the missing rich problem in household surveys are many, ranging from under-reporting of their income or a refusal to answer by very rich people, to the fact that very few rich people are likely to be included in the sampling frame of the survey. Approaches to address the missing rich problem can be classified into three broad groups: using alternate data (such as using tax records instead of surveys); operating within survey, making inferences about the missing data using parametric and nonparametric methods; and correcting survey data (or inequality estimates) by combining surveys and administrative data.

  The bottom incomes are not being covered sufficiently either—for example, the incomes of the homeless or others with no fixed address. And many low-income people often report levels of consumption expenditures well in excess of their declared income, suggesting that they are consuming out of savings or experiencing a temporary drop in income or that they may be simply under-reporting their material living standards. This underscores the importance of joint analysis of income, consumption, and wealth; such an analysis would enable us to ascertain the extent to which the poor are “eating up” their assets.

  There are also large differences in the nature of data sets between advanced and developing countries, and the extent to which the data provided correspond to appropriate definitions of income or consumption. For advanced economies, economic inequality is typically measured based on equivalized income (where adjustments are made for family size) while in the rest of the world, per capita consumption or income is used. While in principle the income variable that should be the focus of attention is disposable income—what individuals can spend, after paying their taxes and receiving any transfer—the income concept used in developing countries’ data is often not clear. Likewise, while many argue that income or consumption should include consumption of own production (goods and services produced within the household) and imputed rent of owner-occupied housing (the rent that individuals would have had to pay if they were renting their house), in practice this is generally not the case.

  Moreover, the analysis of the “true” level of economic inequality is typically hindered by the fact that standard measures of income exclude free in-kind services (especially, education and health care) provided by governments and nonprofit institutions. Valuing social transfers in kind raises both conceptual and measurement challenges. There are difficulties in ascertaining the appropriate range of services to be considered; the monetary valuation of the services provided; and their allocation to various beneficiaries. In practice, the most frequently used approach is to value in-kind transfers at the costs incurred by the government in producing them. This approach, however, does not take into account variations in needs across income or age groups, nor does it consider service quality, and may not refle
ct the actual valuation by beneficiaries. Imputation to individual users is particularly complex in the case of health care. The allocation of benefits is done following either the “actual consumption approach” or the “insurance value approach”—which assigns the same per capita spending to everybody sharing the same characteristic such as age or gender, irrespectively of their actual use of these services. The choice of methods has a large influence on the results obtained.

  The impacts of consumption taxes and subsidies on household resources are often neglected too. While it is acknowledged that household consumption possibilities are reduced or increased by, respectively, consumption taxes or production subsidies passed on to the prices that households pay for goods and services, taking this impact into account has not been part of the conventions typically used for analyzing disparities in households’ economic well-being.

  In addition, there are many technical issues affecting the comparability of data, which in turn affect the ability to make cross-country comparisons. Databases differ on whether adjustments (and which ones) are made to the micro-data to correct for under-reporting, to eliminate outliers, or to address missing responses. Inconsistencies mean that different data sets frequently produce different results about the level of inequality and whether there is convergence in levels of inequality among countries, and this is so even when the same metric is employed.

  Timeliness is another problem, with estimates of economic inequalities in many countries lagging behind GDP data by years.

  A further issue is that, with exceptions, household surveys collect data on only income or only consumption, which significantly limits the possibility of undertaking the joint analysis of both variables and rigorous cross-country comparisons. Even when measures exist on the distribution of household income, consumption, and wealth, very few countries collect data in ways that would allow the joint distribution of household income, consumption, and wealth to be analyzed in a coherent way; doing so was one of the key recommendations of the Stiglitz-Sen-Fitoussi report.

  An additional challenge is that, for most countries in the world, totals for house hold income and consumption from surveys do not match the equivalent totals from national accounts; not even their growth rates match. (This is a topic discussed more extensively in Chapter 6.)

  As in other areas of the measurement of economic performance, greater international efforts should be devoted to assess the availability and quality of data on wealth distribution, and to ensure that the data collected provides information that is comparable across countries and over time.1 Accurate measurement of economic inequality will require a political commitment. Governments, international organizations, and the academic community need to be committed to transparency and to make information publicly available in ways that facilitate the measurement and analysis of economic inequality while protecting the identity of respondents to preserve confidentiality.

  Horizontal Inequalities

  Inequality in income, consumption, and wealth among individuals, sometimes called “vertical inequality,” ignores systematic inequities among population groups, leaves out nonincome dimensions of inequality, and assumes that each individual in a household receives the mean income of that household. In Chapter 4, Carmen Diana Deere, Ravi Kanbur, and Frances Stewart discuss the importance of “horizontal inequalities,” inequalities among groups with shared characteristics in both income and nonincome dimensions, intra-household inequality, and the gender wealth gap. The three issues are important in their own right, but they also link with each other in important ways. For example, a key aspect of intra-household inequality is inequality between men and women within the household, and this relates to the broader question of horizontal inequality in society.

  While these inequalities are of great policy relevance, notably because of their implications for justice and social stability, there are no systematic efforts to collect the necessary data and publish the appropriate indicators. This is due, in part, to the conceptual and practical challenges that their measurement entails. However, much more could be done to standardize the practice of collecting the relevant information and broadening the diagnostic indicators used for social progress assessments.

  People are members of many groups (age, gender, ethnicity, religion, etc.) so multi-dimensionality is an essential feature of horizontal inequality and its measurement. Three prime dimensions are socio-economic, political, and cultural recognition, each with an array of elements. For example, socio-economic inequalities include inequalities in access to basic services and inequalities in economic resources, including income, assets, employment, and so on. In the political dimension, it is a matter of representation in government, the upper levels of the bureaucracy, the military, the police, and local administrations. On the cultural side, relevant inequalities include those in recognition, use of, and respect for language, religion, and cultural practices.

  The measurement of horizontal inequalities raises the question of which group classification to adopt. And given that group size varies, it may be desirable to weight any aggregate measure by the size of each group.

  An inequality measure that is silent as to the relationship of inequality to the overall structure of a society (for example, economic inequality between ethnic groups or between men and women) is of limited value, since a concern about inequality is rooted in a concern for justice and overall societal health.

  In addition, when intra-household inequality is ignored, overall inequality will be underestimated. Quantifying intra-household inequality is a first step toward getting a more accurate measure of the overall level of inequality in society and of the responsiveness of poverty reduction to economic growth. It can also be an important part of an investigation of inequality across gender and across age groups, both of which are aspects of horizontal inequality. But, as we have seen, so far as the headline money-metric measures of inequality are concerned, most household surveys collect information only at the household level, so that understatement of inequality is endemic to official statistics.

  It is unlikely that all official household surveys can be turned to collecting individual-level information. But there are alternatives. Structural econometric methods can be used to estimate intra-household inequality parameters by modeling distribution at the household level. Systematic investigation of other indicators available at the individual level in some surveys (for example, individual earnings, or individual anthropometrics) could be analyzed to develop a sense of the under statement of overall inequality in situations where individual information is not available. Finally, small specialized surveys can also be mounted.

  The level of detail of traditional surveys is usually not sufficient to explore certain types of inequalities. A case in point is that of within-household inequalities in terms of wealth. When data on asset ownership is collected in household surveys, for example, it has tended to be at the household rather than the individual level, constraining gender analysis; some assets may also be held in joint ownership, and in some cases this may not be well defined and depend on the specific legal provisions of each country. Methodological constraints are one of the reasons that progress on measuring individual level wealth has been slow, such as whether reliable data on the valuation of assets can be elicited from respondents. Other issues include the questions of who should be interviewed in an asset survey, how ownership should be defined, how the value of assets should be measured, and whether all assets need to be included in wealth estimates.

  Several questions could be added to household surveys to help in this respect, such as those seeking to understand the relevant marital regime and those collecting data on who in the household owns its immovable property.

  Inequality of Opportunity

  One key dimension of inequality is inequality of opportunity. While the Stiglitz, Sen, and Fitoussi report emphasized the difficulties in measuring inequalities of income and wealth, those presented by inequality of opportunity are far greater.
r />   In Chapter 5, François Bourguignon looks at how the circumstances involuntarily inherited or faced by individuals (such as gender or ethnicity, or the income or education of one’s parents) affect their economic chances, opportunities, and achievements. Inequality of opportunity is often presented as the truly unfair part of the inequality of income, as opposed to that part of income inequality that results from free individual decisions. Apart from this basic question of fairness, inequality of opportunity matters because it is a key determinant of inequality of income and also because it may reduce the aggregate efficiency of an economy, or the average outcome, by weakening incentives. People who get off to a bad start in life due to circumstances beyond their control, or face discrimination in the economic system because of particular personal traits, may see little point in trying hard since they will be left behind anyway. Likewise, those who are favored have less incentive too, since they know they are more likely to succeed. Moreover, inequalities of opportunity imply that many individuals will not be able to live up to their potential.

  Measuring the inequality of opportunity is practically and conceptually challenging. It will never be possible to observe differences among individuals across all the circumstances that may shape their economic success independently of their will. Besides, the distinction between what is not under the control of individuals, i.e., circumstances, and what is, often referred to as “efforts,” may often be extremely ambiguous. However, it is possible to measure some observable dimensions of inequality of opportunity and, most importantly, their impact on inequality of outcomes. Data on specific outcomes, some circumstances, and, possibly, some types of efforts are available in household surveys or from administrative sources. It is also possible to measure directly some dimensions of inequality of opportunity independently of their impact on economic outcomes—for example, cognitive ability or health status. The most obvious example of inequality of opportunity in a specific dimension is inter-generational mobility of earnings, i.e., the relationship between the earnings of the parent and those of the child.