International economic statistics: biased arbiters in global affairs?

Autor: Daniel Mügge
Přispěvatelé: Political Economy and Transnational Governance (PETGOV, AISSR, FMG)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Fudan Journal of the Humanities and Social Sciences, 13(1), 93-112. Springer
Fudan Journal of the Humanities and Social Sciences
ISSN: 1674-0750
DOI: 10.1007/s40647-019-00255-5
Popis: International economic statistics play central roles in global economic governance. Governments and international organizations rely on them to monitor international economic agreements; governments use them to understand potential imbalances in bilateral relationships; and international investors build their country assessments on such data. These statistics increasingly suffer from serious defects, however, due to globalization, the digitization of our economies, and the prominence of secrecy jurisdictions and multinational corporations. For that reason, economic data is not a neutral arbiter in international affairs. Instead, it suffers from four kinds of bias: expert attention bias means that the objects of measurement – what they are meant to capture – depends on the preoccupations of the small circle of statistical experts. Countability bias skews economic figures in favor of countable objects and away from for example unremunerated labor a nd production as well as ephemeral economic process, such as knowledge production. Capitalist bias emerges because economic statistics naturalize unequal power relations in the global economy: they mistake a country’s inability to fetch high prices for its products for low productivity and a lack of added value. Stealth-wealth bias, finally, means that statistics naturalize the distorted image we have of the global economy as corporations and individual hide profits and wealth in secrecy jurisdictions. This article cautions against an insufficiently critical use of statistics in international affairs. And it encourages policymakers to “know thy data” lest biases in the numbers generate skewed policies, unnecessary disputes and a gradual delegitimization of statistics in general.
Databáze: OpenAIRE
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