Governing by numbers?

Thomas Völker & Zora Kovacic

Governing by numbers?

Thomas Völker & Zora Kovacic

The use of quantification in governance processes is widespread and rarely questioned. Several authors have attributed the attractiveness of numbers to their ability to travel. In this way, quantification can be seen as a positive tool or technology that enables policy actors to govern and understand phenomena from a distance (Scott, 1998). Although it may seem trivial at first glance, this issue reaches the very core of how European society works and is governed – because how one calculates and knows something is just as important as the what that one calculates. To make governance through numbers possible considerable investment in capacity is needed to establish infrastructures of ‘knowing’ – the processes of knowledge gathering and sharing. This includes technical infrastructures and institutional environments, but it also refers to the development of methods and training a skilled workforce.

On the other hand, once quantification infrastructures, or “machineries” (Edwards, 2010) are put in place, they become reactive. In other words, the processes and choices inherent in quantification become performative and can influence society and the way that it is ordered. For example, the way national surveys are conducted affects the way  nations are seen (Porter, 1995). Environmental metrics such as ecological indicators, carbon stocks and CO2 emissions create particular natures that may emphasize some environmental aspects over others and, in turn, become the basis upon which decisions are made (Turnhout, Hisschemöller, & Eijsackers, 2007).

What’s more, this performativity of measurement leads to further complications down the line. This is due to the difficulties in introducing novelties and making changes to long-established accounting procedures. The critical question here is: Is it the policy goals that define which indicators are needed or do the available indicators define which goals can be attained? The assumption would be that the goals that determine the choice of indicators, but in practice, often the direction of causality between policy goals and indicator development is more complex. One can think for example about difficulties of measuring waste and the role of waste in Circular Economy indicators.

Armed with an understanding of how particular ideas of quantification become stabilized and reinforced through institutional frameworks, the next question then becomes, what elements of quantification do policy-makers take for granted that they should not? Processes of measurement and categorization have been associated with outcomes of both emancipation and persecution throughout history. Today, the enlightenment ideals of rationality, human agency, planning, control and progress are deeply inscribed into modern institutions of governance. They are central to evaluative practices, to processes of categorization, legitimation, standardisation and the production of hierarchies or ‘grammars’ or ‘orders’ of worth (Boltanski & Thévenot, 2006; Welch, Keller, & Mandich, 2017). For example, studies have shown that school administrators adapt their curriculums to the rating and ranking systems of schools in order to achieve high scores (Espeland & Sauder, 2007). In this case, quantification practices are transformed in ordering practices.

When it comes to policymaking, research has found that the use of evidence for decision making is a process that is far less linear then commonly assumed. In fact, in many cases the link between evidence and policymaking may be disconnected (Waylen & Young, 2014). Evidence does not necessarily inform policy making. Instead, evidence may be used after policy priorities have already been established, through target setting negotiations and in monitoring progress towards targets. Quantified evidence plays an important procedural role in policymaking. Numbers as quantified evidence give a sense of authority and legitimacy. In a situation in which the authority of politicians is questioned, trust is transferred to the ideal of “mechanized objectivity” (Porter, 1995). In other words, quantification can render issues apolitical and therefore non-debatable.

A principal challenge that surrounds nexus policymaking is described as methodological and disciplinary “silos” through which policy relevant evidence is produced (Stirling, 2015). Silos in this respect denote a lack of communication and coordination among institutions, much like a sealed-off concrete silo where grains are kept untouched for long periods of time. Although there are active channels and interconnections among different sectors - for example, water and energy departments work together in the cases of desalination and hydropower -  there are great challenges in understanding the nature of these connections on a systemic level. The challenge of modelling the nexus as a whole is not just one of changing the models used. This then requires changing the procedures and technologies of producing quantitative evidence. In this respect, we query how might institutional and organizational orderings change with a nexus approach?

Using these observations as a starting point, the MAGIC project explores – through focusing on narratives and their quantitative basis - how alternative modes of environmental governance might open up paths for a more productive and effective use of quantification in governance.


Boltanski, L., & Thévenot, L. (2006). On justification: Economies of worth: Princeton University Press.

Edwards, P. N. (2010). Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming, 1-518.

Espeland, W. N., & Sauder, M. (2007). Rankings and reactivity: How public measures recreate social worlds. American journal of sociology, 113(1), 1-40.

Jasanoff, S. (2003). Technologies of Humility: Citizen Participation in Governing Science. Minerva, 41(3), 223-244.

Jasanoff, S. (2007). Technologies of humility. Nature, 450(7166), 33.

Kinsey, A. C., Pomeroy, W. B., Martin, C. E., & Sloan, S. (1948). Sexual behavior in the human male (Vol. 1): Saunders Philadelphia.

Porter, T. M. (1995). Trust in Numbers. The Pursuit of Objectivity in Science and Public Life. Princeton: Princeton University Press.

Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed: Yale University Press.

Stirling, A. (2015). Developing ‘Nexus Capabilities’: towards transdisciplinary methodologies. University of Sussex, Brighton, UK.

Turnhout, E., Hisschemöller, M., & Eijsackers, H. (2007). Ecological indicators: between the two fires of science and policy. Ecological indicators, 7(2), 215-228.

Waylen, K. A., & Young, J. (2014). Expectations and experiences of diverse forms of knowledge use: the case of the UK National Ecosystem Assessment. Environment and Planning C: Government and Policy, 32(2), 229-246.

Welch, D., Keller, M., & Mandich, G. (2017). Imagined futures of everyday life in the circular economy. interactions, 24(2), 46-51.