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FICKLEFORMS Report Summary

Project ID: 637683
Funded under: H2020-EU.1.1.

Periodic Reporting for period 2 - FICKLEFORMS (Fickle Formulas. The Political Economy of Macroeconomic Measurement)

Reporting period: 2017-03-01 to 2018-08-31

Summary of the context and overall objectives of the project

Macroeconomic indicators are integral to economic governance. Measurements of growth, unemployment, inflation and public deficits inform policy, for example through growth targets and the inflation-indexation of wages. These indicators tell us “how economies are doing” and citizens often punish politicians who fail to deliver on them.
Their air of objectivity notwithstanding, it is far from self-evident how these indicators should be defined and measured. Our choice here have deeply distributional consequences, producing winners and losers, and will shape our future, for example when GDP figures hide the cost of environmental degradation. So why do we measure our economies the way we do?
Criticisms of particular measures are hardly new but their real-world effect has been limited. The project therefore asks: which social, political and economic factors shape the formulas used to calculate macroeconomic indicators? Extant research offers detailed histories of statistics, mostly in single countries. But we lack theoretical and empirical tools to describe and explain differences in measurement formulas between countries and over time.
FICKLEFORMS will provide such understanding through five sub-projects. The first systematically compares the evolution of four indicators in four central OECD countries: the United Kingdom, the United States, France and Germany. The second analyses the timing and content of statistical harmonization efforts through the United Nations, the IMF and the World Bank. The third constructs a new database of “measures of measures” to quantitatively test hypotheses emerging from the previous sub-projects. The final two sub-projects reach beyond the OECD and study the politics of macroeconomic measurement in China, India, Brazil and South Africa.
This project will promote public debate over meaningful measures, allow policy-makers to reflect on current practices, and sensitize academics who use macroeconomic data about their political roots.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Qualitative small-n historical comparison:
This subproject is conducted by the principal investigator, prof. dr. Daniel Mügge. Research on this subproject has been ongoing throughout the action. The research activities themselves have in the first instance focused on gathering the relevant data, both online and through interviews. The latter have included interviews with statisticians in all the countries studied here – Germany, France, the United Kingdom and the United States, in addition to statisticians here in the Netherlands.
Early on in the project, the PI has published an agenda-setting piece in the leading journal on European politics, the Journal of European Public Policy. Furthermore, the PI has signed a book contract with Harvard University Press and works on completion of the manuscript in 2018. At the same time, the PI has generated significant publicity for the project by disseminating early results through twelve lectures in Europe and North America.
The Politics of Top-Down Measurement Harmonization
PhD candidate Daniel DeRock’s research focuses on the work that international organizations such as the World Bank and the International Monetary Fund do to promote the dissemination of statistical standards around the world. These dissemination activities are interesting to us because they are not politically neutral. Often, DeRock has found, the standards in question are ill-fitting for developing countries in particular. Hence, we ask why the standards are promoted there nevertheless, who does so in particular, with what motivation, and what the response from the target countries is.
DeRock has so far interviewed a former director of the UN Statistics Division (UNSD), former head of national accounts branch at UNSD, chief economist of the Rwandan Central Bank, a former IMF statistician, and private sector statistical consultants in Amsterdam, Oxford, and at the University of Edinburgh. He has also gathered extensive data on the history of statistical standard setting and capacity building from online archives of the United Nations, World Bank, and IMF.
A large-n assessment of macroeconomic measurement reform
The project aims at revealing the politics that are hidden in macroeconomic statistics. One of the questions often posed to the team is whether the vagaries endemic to measurement formulas actually matter, or whether they are intellectually intriguing but of little real-world significance.
The other subprojects establish to what degree measurement uncertainties actually matter for example for pension indexation, economic policy or the distribution of unemployment benefits. This subproject, in contrast, tries to establish to what degree these uncertainties affect academic scholarship – for example in economics – and the conclusions that it draws.
We use trade mirror statistics to find that out. Every international transaction is recorded twice – once by the sending country, and once by the receiving country. In theory, these two values should match. In practice, we find significant and persistent differences, indicating that for example trade data is a lot less reliable than the official figures suggest.
Using such mirror statistics, we have built an alternative dataset, as outlined in the Description of the Action, and begun to replicate major academic studies. These replications show that academic insights are indeed sensitive to the kind of measurement error we research.
Measuring the BICS: India and China
PhD candidate Joan van Heijster has spent three months in Beijing, both to gather primary data on economic measurement in China (including through interviews) and to develop her Mandarin language skills further. Furthermore, she has spent one week in Washington DC, where she has consulted World Bank archives and conducted additional interviews. In total she has conducted seven interviews so far, with (former) statisticians, both Chinese and foreigners involved in developing the Chinese stat

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

Statistics as a tool of governance have a history going back to the 17th century. In the 19th century, the use of statistics blossomed in France and the United Kingdom in particular, taking root a little later in Germany and the United States. Statistics gained prominence in macroeconomic policy in the 1930s and 1940s, fueled by disillusion with laissez-faire policies, the rise of Keynesian ideas about macroeconomic steering, and the needs of wartime economic planning.
The growing prominence of statistics in government policy has triggered numerous critiques. Some have attacked the centrality of putatively reductionist statistics in technocratic economic and social governance, arguing that although the users of statistics frequently present numbers as objective knowledge, they actually serve to obfuscate the true motivations underlying political decisions. Others have questioned specific approaches to measurement. While formulas for GDP and economic growth have been the most prominent targets, measurements of inflation and public deficits have also repeatedly attracted skepticism.
Taken together, social scientists commonly acknowledge that statistics are central in contemporary governance, that they are far from objective in theory, that they are highly contested in practice, and that they are unlikely to disappear from the governance tool box anytime soon. Contemporary engagement must therefore begin with an understanding of how and why we use certain economic statistics and not others – or in other words, why we measure the economy the way we do. Answering this question is the project’s core mission.
Existing scholarship, broadly conceived, that speaks directly to this question falls into two categories. First, reports by for example the Organization for Economic Cooperation and Development, Eurostat and national agencies frequently highlight cross-country differences. The Atkinson Report, for example, commissioned by the UK government, revealed the problems and variation around measuring public services such as education and healthcare, which can constitute significant shares of GDP. When the UK switched to an alternative measurement, its annual growth figures were automatically lowered by roughly 0.25 percent.
Second, economic historians have focused on particular indicators, normally in a single country, including work on unemployment; inflation; economic growth and GDP; and public debt and deficits. While rich in detail, this work rarely involves systematic cross-national or cross-indicator comparison.
We thus know that historically and across countries, economies have been measured in very different ways and that our (unconscious) measurement choices matter greatly to individual citizens and our ability to govern our economies. But we have to connect the dots. If we find variation across countries, indicators, and time, and the extant literature does not account for them, we need to ask: what does?
In addition to shedding light on this question, the project aspires to create sensitivity among the users of macroeconomic statistics – citizens, policymakers, but also academics – about their limits and potentially unseen biases.

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