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It is an open secret that economic policy is often based on shaky statistical foundations. Macroeconomic data, particularly from low-income countries, frequently falls short of international standards. But in fact, the problem runs much deeper than data quality: the measurement practices underlying economic indicators tend to be poorly suited to complex and shifting economic conditions. This is an issue of relevance, not merely reliability.

Macroeconomic indicators – from economic growth, to debt, to inflation – are produced according to international standards that prioritize comparability across countries. In other words, an indicator in one country should represent the same thing in any other. This harmonization, however, comes at the cost of reducing diverse socioeconomic realties into one-size-fits-all measurement formulas. Certain economic activities and actors are captured in headline indicators, while others are rendered invisible. These indicators, in turn, shape how national economies are made legible to policymakers and market actors.^[1]

Gross Domestic Product (GDP) – the linchpin of most macroeconomic analysis – offers some examples. The concepts and methodologies underlying GDP are defined in the System of National Accounts (SNA).^[2] The definitions contained within the SNA are masked behind final GDP figures, but they are consequential nonetheless. Take the concepts of the production boundary and the informal sector as examples. The production boundary in effect determines what counts as productive economic activity and what does not.^[3] The SNA definition of the informal sector, meanwhile, standardizes a highly contested concept and determines how certain forms of work are categorized in official statistics.^[4]

The production boundary is best understood as a theoretical line between economic and non-economic activity. While the concept is vague in many respects, it is particularly contentious with regard to unpaid household work. Based on SNA criteria, a vast amount of work – disproportionately performed by women – is excluded from GDP. This includes childcare, preparation of meals, and care for the sick and elderly.^[5] As feminist scholar and economist Marilyn Waring points out, market equivalents simply do not exist for a great deal of work performed primarily by low-income women; an entire day can be spent working and yet almost none of it counted as production.^[6]

Defining the “informal sector” is similarly problematic. The term commonly serves as a catch-all for a range of activities beyond the reach of regulation and taxation.^[7] The official definition, however, is much narrower and excludes some activities that might otherwise be perceived as “informal.” Among these exclusions are agricultural work, households producing goods for their own use, domestic housework, care work, and even paid domestic work.^[8] The category “paid domestic work” underscores the disconnect between perceptions of informality and the statistical definition: domestic workers are among the most exploited and abused workers on the planet and typically lack any kind of “formal” contract, but the output of their labor is not counted in informal sector statistics.^[9]

A quick look at debt statistics reveals similar issues. Even basic distinctions such as those between domestic and external debt, and similarly between public and private debt, are not as solid as they might seem. Again, these issues come down to definitions. The concept of residence is central to external debt,^[10] which is defined as debt owed by residents of a country to non-residents. But financial integration and capital account liberalization make it nearly impossible for most countries to know who actually holds their debt and whether the creditors are residents or not.^[11] As developing countries continue to open up to international investment, it is likely that at least some “domestic” debt is in fact held by foreign investors while “external” bonds can be held by residents.^[12] The result is that we cannot determine the debt composition of some countries with confidence.

The category of public debt is also subject to conceptual problems. For one thing, the increase in public-private partnerships in developing countries^[13] burdens governments with potentially large future liabilities. Importantly, public loans channeled to private companies do not appear on the government balance sheet, but come attached with guarantees to investors at some point in the future.^[14] This means that some governments are shouldering more sovereign debt than appears. As with GDP, the simple and seemingly objective nature of debt figures obscures these underlying issues.

Attention has recently been drawn to statistical capacity problems,^[15] and for good reason. World Bank economist Shantayanan Devarajan described the problem candidly: “In short, in presenting GDP per capita for many African countries, we cannot be sure of either the numerator or the denominator.”^[16] Yet in order to better understand policy challenges, the conversation needs to widen beyond these technical problems to address the more politically contentious conceptual problems inherent to macroeconomic indicators.

In doing so, the exercise of critiquing official statistics brings us into uncomfortable territory. After all, it is not only scholars raising questions about the trustworthiness of official statistics, but also a new wave of populists wary of expert explanation. So, where do we go from here? The solution lies neither in throwing numbers out of the window nor in an unquestioning faith in spreadsheets. Instead, we need to embed economic numbers in the messy social and historical contexts they are meant to describe.

In practical terms, there needs to be a greater awareness among analysts and policymakers of the conceptual biases built into economic indicators. This can start with incorporating a socially embedded perspective of statistics into economics and social science curricula. Most urgently, this perspective should inform development policy at the level of international organizations and national governments. A bit more modesty and tolerance for ambiguity could result in more just and effective strategies.

Turning our attention to these problems does not imply that we should simply abandon quantitative evidence. On the contrary, it can serve as a word of caution for those calling for the end of GDP and other indicators. Any effort to revise or replace these indicators will have to take the same kinds of issues into account. We can change the lenses, but we can never take off the glasses.

References

^[1] James Scott, Seeing like a State: How Certain Schemes to Improve the Human Condition Have Failed (New Haven: Yale University Press, 1998); André Broome and Leonard Seabrooke, “Seeing Like an International Organisation.” New Political Economy 17, no. 1 (2012): 1-16.

^[2] Intersecretariat Working Group on National Accounts. (2008) System of National Accounts 2008.

^[3] Intersecretariat Working Group on National Accounts. (2008) System of National Accounts 2008  (pp. 97-100).

^[4] Ibid.,  pp. 471-481.

^[5] Intersecretariat Working Group on National Accounts. (1993) System of National Accounts 1993  (p. 149).

^[6] Marilyn Waring, Counting for something! Recognizing women’s contribution to the global economy through alternative accounting systems. Gender and Development 11, no. 1 (2003): 35-43.

^[7] ILO, “Measurement of the Informal Economy,” in The Informal Economy and Decent Work: A Policy Resource Guide Supporting Transitions to Formality (Geneva: ILO, 2013), http://www.ilo.org/emppolicy/pubs/WCMS_212688/lang–en/index.htm.

^[8] Ibid.

^[9] Human Rights Watch, Claiming rights: domestic workers’ movements and global advances for labor reform, 2013, https://www.hrw.org/sites/default/files/reports/globaldw1013_brochure_LOWRES_SPREADS.pdf

^[10] International Monetary Fund, External debt statistics : guide for compilers and users / Inter-Agency Task Force on Finance Statistics, Washington D.C., 2014, http://www.tffs.org/pdf/edsg/ft2014.pdf.

^[11] Ugo Panizza, Domestic and External Public Debt in Developing Countries, United Nations Conference on Trade and Development Discussion Papers, 2008, no. 199 (June).

^[12] Ibid.

^[13] Independent Evaluation Group, “World Bank Group Support to Public-Private Partnerships: Lessons from Experience in Client Countries- FY02-12,” World Bank, 2014, http://ieg.worldbankgroup.org/sites/default/files/Data/Evaluation/files/ppp_eval_updated2.pdf.

^[14] Bodo Elmers, “The evolving nature of developing country debt and solutions for change,” Eurodad, July 2016, Discussion paper, http://www.eurodad.org/files/pdf/1546625-the-evolving-nature-of-developing-country-debt-and-solutions-for-change-1474374793.pdf.

^[15] Morten Jerven, Poor Numbers: How We Are Misled by African Development Statistics and What to Do about It (Ithaca: Cornell University Press, 2013).

^[16] Shantayanan Devarajan, “Africa’s Statistical Tragedy,” Review of Income and Wealth (2013): S11.