Measure formulas between countries and over time

The FickleFormulas project examines macroeconomic indicators. But the political economy drivers underlying them are not easy to pin down. They play out in national statistical offices, government ministries, and sprawling international organizations. They have roots in academic debates that stretch back more than a century. They are fought out between unions and employers, between savers and depositors, across gender divides, or simply between national capitals. We need to be attentive to the long arches of intellectual history as much as to mundane office politics in statistical offices.

A plethora of approaches and methods

A research object that has so many different facets calls for a broad range of lenses to bring it into focus. Above all, we are pragmatic: the questions we ask determine our methodologies. With so many white spots on our intellectual map, the FickleFormulas projects feature a plethora of approaches and methods.

Chronicle the evolution of formulas

Many of our projects try to explain why we measure our economies the way we do. Our dependent variable are the choices for or against particular formulas to measure a concept. Our first step is to chronicle the evolution of formulas over time and to discover key junctures – moments when officials adjusted these formulas or rejected forceful reform initiatives.

The places we study

When and where we study these formulas varies. The first subproject compares the indicator politics over the past decades in a handful of West-European and North American countries, where global indicator debates in the post-War period surfaced first. A systematic comparison will reveal which factors are determinant in pushing countries towards one or the other formula.

Beyond the North Atlantic, data becomes scarcer

Venture beyond the North Atlantic, and data becomes much scarcer. Needing to dig deeper, two of our projects will focus on India and China, and South Africa and Brazil to understand what journey indicators have taken in these countries. 

National politics are not enough

National dynamics are key to indicator politics, but so is what happens in organizations that go beyond the nation state – the OECD, the IMF, the World, and of course the EU. These organizations do not just coordinate international harmonization efforts but work hard to convert other countries to international statistical conventions and to make them legible. And in the case of the EU a single, supranational entity – Eurostat – enmeshes formerly standalone national agencies in a dense European web of official statistics.

Focus on key players

FickleFormulas studies indicators that stand at the heart of macroeconomic statistics: those measuring growth, inflation, public debt and unemployment. But not all of them deserve equal attention in each project. Subproject 2 focuses on EU initiatives that have been most important for employment and public debt statistics. The World Bank and UN are the anchor for subproject 3 as they have been pushing for harmonized statistics, especially GDP calculation. Also in these choices, our projects take a pragmatic approach.

Where do we get our data?

We can find a lot of data on the internet: official reports, minutes of working group meetings, consecutive editions of statistics manuals. Depending on the case at hand, we can also draw on existing scholarship that sheds light on selected episodes in indicator politics. Every now and then, battles over for example unemployment statistics even end up in the news, allowing us to reconstruct part of the story from there.

Talking to the people who devise the formulas

Interviews remain our most important qualitative data source: talking to the people who actually devise the indicator formulas we study. It is only when we triangulate their historical knowledge and insight with the other data we have, that we are able to reconstruct the political history of macroeconomic indicators and trace the processes through which they have evolved.

Going quantitative

Once we have distilled the core insights from our subprojects, we subject them to quantitative tests. We will code variation in countries' measurement approaches: for example, do countries treat military spending as production or as expenditure? Do they use the input or the output model when adding public services to GDP? This database will allow us, for the very first time, to understand how national biases have coloured past and present measurement practices across a broad range of countries. The results of this sub-project will offer the broader scholarly community that works with macroeconomic time-series data a completely novel perspective on the data they use.