For Good Measure Read online

  9. https://www.slideshare.net/StatsCommunications/hleg-thematic-workshop-on-measuring-inequalities-of-income-and-wealth-ravi-kanbur.

  10. https://www.slideshare.net/StatsCommunications/hleg-thematic-workshop-on-measurement-of-well-being-and-development-in-africa-rashad-cassim.

  11. https://www.slideshare.net/StatsCommunications/hleg-thematic-workshop-on-measurement-of-well-being-and-development-in-africa-daniel-masolwa.

  12. https://www.slideshare.net/StatsCommunications/hleg-thematic-workshop-on-measurement-of-well-being-and-development-in-africa-chukwudozie-ezigbalike.

  13. On tools, see OECD (2017) for development and application of SDG measurement tools to OECD countries.

  14. The need for internationally agreed statistical standards also applies to indicators for monitoring targets that are primarily under the responsibility of the individual countries. Comparing countries and, above all, combining country information to obtain a global picture requires comparable data.

  15. https://www.slideshare.net/StatsCommunications/hleg-thematic-workshop-on-measurement-of-well-being-and-development-in-africa-dean-jolliffe.

  16. For an introduction to decomposition methodology, see Kanbur (2007).

  References

  Alkire, S. and J.E. Foster (2011), “Understandings and misunderstandings of multidimensional poverty measurement,” Journal of Economic Inequality, Vol. 9, pp. 289–314.

  Alkire, S. et al. (2015), Multidimensional Poverty Measurement and Analysis, Oxford University Press, Oxford.

  Atkinson, A.B. (2016), Monitoring Global Poverty: Report of the Commission on Global Poverty, World Bank, Washington, DC.

  Bourguignon, F. et al. (2010), “Millennium Development Goals at Midpoint: Where Do We Stand?,” in Kanbur, R. and A.M. Spence (eds.), Equity in a Globalizing World, World Bank for the Commission on Growth and Development, pp. 17–40.

  CONEVAL (2010), Methodology for Multidimensional Poverty Measurement in Mexico, www.3ieimpact.org/media/filer_public/2014/02/19/methodology_poverty_measurement_mexico.pdf.

  Deaton, A. (2010), “Price indexes, inequality, and the measurement of world poverty,” American Economic Review, Vol. 100, pp. 5–34.

  Deaton, A. and V. Kozel (2005), “Data and dogma: The great Indian poverty debate,” World Bank Research Observer, Vol. 20(2), pp. 177–200.

  Doyle, M.W. and J.E. Stiglitz (2014), “Eliminating extreme inequality: A sustainable development goal, 2015–2030,” Ethics and International Affairs, Carnegie Council, www.ethicsandinternationalaffairs.org/2014/eliminating-extreme-inequality-a-sustainable-development-goal-2015-2030/.

  Ferreira, F. et al. (2015), “A global count of the extreme poor in 2012: Data issues, methodology and initial results,” Policy Research Working Paper, No. 7432.

  Guio, A.C. and E. Marlier (2016), “Amending the EU material deprivation indicator: Impact on the size and composition of the deprived population,” in Atkinson, A.B., A.C. Guio, and E. Marlier (eds.), Monitoring Social Europe, Publications Office of the European Union, Luxembourg.

  Jolly, R. (1976), “The world employment conference: The enthronement of basic needs,” Development Policy Review, Vol. A9(2), pp. 31–44.

  Kanbur, R. (2007), “The policy significance of inequality decompositions,” Journal of Economic Inequality, Vol. 4(3), pp. 367–374.

  Kanbur, R. (1990), “Poverty and development: The Human Development Report and the World Development Report, 1990,” in van der Hoeven, R. and R. Anker (eds.), Poverty Monitoring: An International Concern, St. Martin’s Press, New York.

  Kanbur, R. and J. Zhuang (2012), “Confronting rising inequality in Asia,” in Asian Development Outlook 2012, Asian Development Bank.

  Lakner, C. and B. Milanovic (2015), “Global income distribution: From the fall of the Berlin wall to the Great Recession,” World Bank Economic Review, Advanced Access, www.gc.cuny.edu/CUNY_GC/media/LISCenter/brankoData/wber_final.pdf.

  McCarthy, J. (2013), “Own the goals: What the Millennium Development Goals have accomplished,” Brookings Institution, www.brookings.edu/research/articles/2013/02/21-millennium-dev-goals-mcarthur.

  Morris, M.D. (1980), “The physical quality of life index (PQLI),” Development Digest, Vol. 18(1), pp. 95–109.

  OECD (2017), Measuring Distance to the SDG Targets: An Assessment of Where OECD Countries Stand, OECD, Paris, www.oecd.org/std/OECD-Measuring-Distance-to-SDG-Targets.pdf.

  Ravallion, M. (2014), “An exploration of the international comparison program’s new global economic landscape,” NBER Working Paper, No. 20338, National Bureau of Economic Research.

  Ravallion, M. (2011), “On multidimensional indices of poverty,” Journal of Economic Inequality, Vol. 9(2), pp. 235–248.

  Reddy, S.G. and T.W. Pogge (2010), “How not to count the poor,” in Anand S., P. Segal, and J.E. Stiglitz (eds.), Debates on the Measurement of Global Poverty, Oxford University Press, Oxford, pp. 42–85.

  Sachs, J.D. (2005), The End of Poverty: Economic Possibilities for Our Time, Penguin Press, New York.

  Sen, A. (1985), Commodities and Capabilities, Elsevier, North-Holland, Amsterdam.

  Stiglitz, J.E., A. Sen, and J.-P. Fitoussi (2010), Mismeasuring Our Lives: Why GDP Doesn’t Add Up, The New Press, New York.

  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.

  Talberth J., C. Cobb, and N. Slattery (2006), The Genuine Progress Indicator 2006—A Tool for Sustainable Development, Redefining Progress, Oakland, CA, http://rprogress.org/publications/2007/GPI%202006.pdf.

  UNDP (2016), Human Development Report 2016—Human Development for Everyone, United Nations Development Programme, New York, http://hdr.undp.org/sites/default/files/2016_human_development_report.pdf.

  UNDP (1990), Human Development Report 1990, United Nations Development Programme, New York, http://hdr.undp.org/sites/default/files/reports/219/hdr_1990_en_complete_nostats.pdf.

  United Nations (2015a), The Millennium Development Goals Report 2015, https://www.un.org/millenniumgoals/2015_MDG_Report/pdf/MDG%202015%20rev%20(July%201).pdf.

  United Nations (2015b), “Transforming our world: The 2030 agenda for sustainable development,” Resolution 70/1 of the UN General Assembly, www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E.

  World Bank (1990), World Development Report 1990: Poverty, Oxford University Press, New York, https://openknowledge.worldbank.org/handle/10986/5973.

  3.

  Measuring the Distribution of Household Income, Consumption, and Wealth

  Nora Lustig

  This chapter addresses the challenges posed by measuring vertical inequalities in household income, consumption, and wealth. It takes stock of international databases on economic inequality, highlighting the fact that they often display not only different levels of inequality but, for some countries, diverging trends as well. The chapter also discusses the challenges in measuring inequality because of under-coverage and underreporting of top incomes (the “missing rich”) and approaches to correct for the problem. The shortcomings of typical welfare metrics used to measure economic inequality in international databases (disposable income and/or consumption expenditures) are also discussed, stressing the need of a more comprehensive metric, using an income variable that includes social transfers in kind (especially for education and health care) and adds the effect of consumption taxes and subsidies. The chapter makes several recommendations to address the existing shortcomings in the measurement of income and wealth inequality.

  Nora Lustig is Samuel Z. Stone Professor of Latin American Economics at Tulane University. The author is very grateful to members of the High-Level Expert Group on the Measurement of Economic Performance and Social Progress (HLEG) for their comments and suggestions. In particular, the author wishes to acknowledge Facundo Alvaredo for invaluable discussions and inputs throughout the preparation of this text, as well as François Bourgu
ignon, Marco Mira d’Ercole, and Sharon Christ for their comments on a previous draft. The author is also grateful to Angus Deaton, Jacob Hacker, Joseph E. Stiglitz, Jean-Paul Fitoussi, and Martine Durand for their comments on earlier versions of this chapter, and to the participants of the Workshop on Measuring Inequalities of Income and Wealth, hosted by the Bertelsmann Foundation in Berlin on September 15–16, 2015. Last but not least, the author is grateful to Xavi Recchi for his research assistance. The section titled “Measuring Economic Inequality: Scope and Limitations of International Databases” draws largely on Ferreira, Lustig, and Teles (2015). “The ‘Missing Rich’ in Household Surveys” is based on Lustig’s “The ‘missing rich’ in household surveys: Causes and correction methods,” CEQ Working Paper (forthcoming), Tulane University. The opinions expressed and arguments employed in the contributions below are those of the author and do not necessarily reflect the official views of the OECD or of the governments of its member countries.

  Introduction

  After decades of relative neglect, the issue of how household economic resources (income, consumption, and wealth) are distributed is back on the agenda. We have moved from “bringing distribution in from the cold,” as Tony Atkinson wrote in 1997, to putting it in the political and research spotlight.1 The rising prominence of distribution can be readily observed in the UN Sustainable Development Goals, which, in contrast to the previous Millennium Development Goals, now include a specific goal—Goal 10—to reduce inequality within and among countries. Similarly, multilateral organizations such as the IMF, the OECD, UN agencies, and the World Bank as well as global nongovernmental groups have been paying unprecedented attention to the causes and consequences of economic inequality.2 This growing prominence is, in large measure, the product of significant changes in the distribution of income and wealth—in particular, rising inequality in advanced countries—and their implications for political outcomes.3 It is also the consequence of developments in economic theory and improvements in the available data.4

  Why do we care about the distribution of economic resources across individuals and households? This is an issue charged with value judgments, where different authors have arrived at very different conclusions. A conventional view in economics has long argued that incentives are needed to promote economic growth, and that these incentives imply some degree of inequality in material rewards (Mirrlees, 1971). Higher inequalities may also result from a historical process whereby some people escape from destitution before others, as the benefits from improved technologies, higher living standards, and better policies reach some people and communities first before spreading elsewhere (Deaton, 2013).

  From a normative standpoint, the interest in inequality is related to considerations about justice and, as emphasized by Rawls (1971), about fairness. Rawls suggested that citizens blocked by a “veil of ignorance”—unknowing about their lot in life—would choose a social arrangement that maximizes the level of welfare achieved by the less well-off person (the maximin principle) as the accepted social contract. This principle sets up the basic notion of justice as equality of ex ante opportunity (World Bank, 2006). Equality of opportunities, in this way, entails that individuals’ achievements in life—including their income—are independent from initial circumstances (see the discussion by François Bourguignon in the present volume). However, inequality of outcomes may be unpalatable as such, too. High inequality in both opportunities and outcomes are perceived to be problematic in most societies.5

  In addition to normative concerns, there are instrumental reasons to care about inequality. A more unequal distribution of economic resources lowers the impact of economic growth on reducing absolute poverty (Bourguignon, 2003; Ravallion, 2001). Economic inequalities may also translate into inequalities in health and education, which, by lowering productive opportunities, may dampen the overall productivity of the economy and economic growth. Economic inequality manifests itself also as misallocation and inefficiency in the use of resources. Since some economic disparities arise from market failures, reducing them can have important payoffs in terms of productivity and efficiency, boosting individuals’ capacity to generate income and contribute to aggregate economic growth.

  Economic inequalities may also promote social and political inequality and breed social conflict, disaffection, and violence. Very high levels of wealth and income concentration at the top are associated with a disproportionate amount of influence by certain actors and lead to state capture and policy distortions, whereby the interests of those at the top are systematically favored (Esteban and Ray, 2006). Inequality, in this way, can shape not only the bargaining power of actors today, but those of the next generation as well. In sum, high inequality may be associated with lower inter-generational mobility, in which the poor are trapped in a state of permanent deprivation. High concentration of capital is also likely to generate persistent inequalities of income in a vicious circle (Piketty, 2014). Finally, the distribution of household economic resources has implications for macro-economic policies (Alvaredo, Atkinson, and Morelli, 2017). For example, the size and distribution of assets and liabilities has implications for macro-economic stability, while differences in household savings rates and wealth-to-income ratios across the distribution have implications for demand management, and may explain the weakness of the recovery from the global financial crisis.

  Given its prominence and far-reaching consequences, measuring the level and evolution of economic inequality accurately is of utmost importance. This chapter focuses on the data challenges encountered while measuring vertical economic inequality, i.e., inequality of income and consumption, and—whenever feasible—wealth among households or individuals ranked by the level of their economic resources.6 The World Bank’s Monitoring Global Poverty: Report of the Commission on Global Poverty (Atkinson, 2016) complements the issues discussed here in a number of ways—for instance, on how to tackle under-reporting and noncoverage at the bottom of the distribution; on the limitations of available data on purchasing power parities; and on how to address the shortcomings of price indexes. This chapter is not meant to be exhaustive concerning all topics relevant to economic inequality: it focuses on some of the areas that, in the view of the author, require greater investment by the statistical and research communities. In particular, the chapter does not present an overview of inequality trends or discuss the advantages and disadvantages of specific summary inequality indicators. These topics have been thoroughly covered elsewhere.7

  The chapter is organized as follows. The next section presents a critical assessment of international databases on inequality. As we shall see, among the worrisome facts is that international databases not only show different levels of inequality but also, for some countries (especially in sub-Saharan Africa), diverging trends. A key factor behind the limitations of these databases is the quality of the underlying data: that is, of the household surveys (micro-data) used as inputs for their construction. The challenges encountered when running household surveys are the topic of the third section. Among the salient challenges is that household surveys suffer from undercoverage and under-reporting of top incomes, i.e., the “missing rich.” Given its importance for inequality measurement, the missing rich problem is taken up in the fourth section, which presents an analytical taxonomy of approaches to correct for this population. As discussed in the section immediately following this, the typical welfare metrics used to measure inequality in international databases are disposable income and consumption expenditures; these, however, take into account only part of the effect that taxes and transfers have on people’s economic well-being. The fifth section suggests that a more comprehensive assessment needs to use an income or consumption variable, or a pairing of both, that includes social transfers in kind (especially for education and health care) and adds the effect of consumption taxes and subsidies as well. The concluding section presents a number of recommendations to improve the quality of statistics in this field.

  Measuring
Economic Inequality: Scope and Limitations of International Databases

  As a result of multiple efforts by academics, statistical offices, and international organizations to improve and harmonize inequality data, there has been an increase in the number of publicly available databases providing measures of economic inequality covering a broad range of countries, ranging from specific world regions (e.g., Latin America, OECD countries) to the world as a whole. All these databases contain summary statistics (the most common being the Gini coefficient) that describe (with very few exceptions) national-level inequality in incomes or consumption expenditures in multiple countries over multiple years. These cross-national inequality databases are being used by researchers, with increasing frequency, to document global or regional inequality trends (e.g., Atkinson and Bourguignon, 2015a; Atkinson, 2015; Bourguignon, 2015a; and Piketty, 2014), as well as by scholars interested in including inequality measures in cross-country regression analyses, either as dependent or independent variables (e.g., Acemoglu et al., 2015; Ostry, Berg, and Tsangarides, 2014). Yet these different databases are often designed for different purposes, and constructed in very different ways. Given that results could be sensitive to the choice of data set, a special issue of the Journal of Economic Inequality, edited by Ferreira and Lustig (2015), was devoted to an assessment of the merits and shortcomings of eight such databases. Some of its conclusions are summarized below.

  Depending on the source of the summary inequality statistics they report, there are four types of databases among those that rely directly or indirectly on household surveys.8