Science and Society: A Reflexive Approach to Official Statistics
3.3 Statistics and Society
3.3.2 The Co-construction of Statistics and Society—History in Fast Motion
3.3.2.4 The Audit Society, Neo-liberalism and Populism
Fig. 3.8 Innocent Questions, Arnold Dreyblatt. Photos courtesy of the artist Arnold Dreyblatt
The following section reflects on the significance of an ‘evidence-based decision- making’ or, more recently, a ‘data-driven decision-making’ environment for official statistics. The analogy to a medicine is used, the effect of which crucially depends on the fact that a medicine is applied with the right dose and the right timing. Deviations from the prescribed dosage may cause side effects, short-term and long-term damage;
permanent misuse may lead to dependencies.
The benefits generated by information-based, rational decision-making are immense. Instead of vague opinions and incomprehensible subjective impressions, objectively measured and quantified facts occur and are an important element in the knowledge base. This shortens the decision-making process, prevents or reduces con- flicts, increases transparency both internally and externally, and allows for controls in the implementation of the decision. More targeted actions are possible, improving their effectiveness and efficiency. Benchmarking opens the door to an exchange of good practices and mutual learning. Management by objectives enables a modern and emancipated approach between superiors and employees. Quality improvements are realised through constant measurement and learning.48
As far as decision-making in the public sector is concerned, the claim is formulated in the so-called Data Manifesto of the Royal Statistical Society (Royal Statistical Society2014): “Evidence must be taken more seriously in policy formulation and evaluation, and statistics should be at the heart of the policy debate. Making policy when resources are tight is difficult but choices should take into account the probable quantified consequences of alternatives. … Government should publish the data and evidence that underpin any new policy it announces and should also commit to regular and long term evaluation policies. Where we lack the data to inform choices between options in important policy areas, we should invest in getting it”.
That better data means better decisions is not a new discovery. Nevertheless, if this motto is used by the UK Statistical Authority,49it is an indication that, surprisingly, it seems necessary to promote statistics—apparently a paradox. There seems to be a contradiction between the principle that evidence is essential and the interest that this evidence is and can be produced with good quality.
A snapshot from a practitioner’s blog gives an impression of what the problem is and how the contradiction can be explained: “The good news is that we manage these unmeasurables perfectly well without any need for yardsticks. ‘If you can’t measure it, you can’t manage it’ won its place in the Big Book of Business Dogma because the business world, or at least the bureaucratic edifice it relies on, the one we call Godzilla, is all about measurement. Measurement is a religion in the business world! If we can slap a metric on something, by God, we’re going to do it. We love to measure things, because it makes us feel as though we’re really doing something.
… Measurement is our drug in the business world…” (Ryan2014).
48One of the currently leading quality management approaches Six Sigma defines the concept as follows: ‘Six Sigma is a systematic approach to process improvement using analytical and statis- tical methods. The special feature of Six Sigma compared to other process improvement methods is the mathematical approach. It is assumed that every business process can be described as a mathematical function’http://www.six-sigma.de/en/six-sigma-definition.
49https://www.statisticsauthority.gov.uk/better-statistics-three-years-on/.
It is worth remembering the theorems of W. E. Deming, which were explained at the beginning of this chapter. Deming has warned of a measuring machinery.
Excessive measurements jeopardise the positive effects of the medicine ‘evidence’
and create (of course unwanted) side effects that are ultimately detrimental to the goal of good data and statistics. Appreciation of information, awareness of limits of measurability and unconditional prioritisation of the quality of statistics are key factors. If these factors are not sufficiently strong, one is only guided by the presence of data; quantity goes before quality.
However, there is a larger context that is important in understanding the current status of statistics in Western societies. Ever since Michel Foucault dealt with the forms of governing, the relationship between knowledge, power and techniques of governance has been explored by scholars of different disciplines. Michel Foucault:
“The theory of the art of government was linked, …to the whole development of the administrative apparatus of the territorial monarchies, the emergence of governmen- tal apparatuses; it was also connected to a set of analyses and forms of knowledge
…which were essentially to do with knowledge of the state, in all its different elements, dimensions and factors of power, questions which were termed precisely ‘statistics’, meaning the science of the state…” (Foucault1991, p. 96).
Further, Foucault relates ‘economy’ with ‘governance’ and with ‘statistics’ in the following manner: “It was through the development of the science of government that the notion of economy came to be re-centred on to that different plane of reality which we characterize today as the ‘economic’, and it was also through this science that it became possible to identify problems specific to the population; but conversely we can say as well that it was thanks to the perception of the specific problems of the population, and thanks to the isolation of that area of reality that we call the economy, that the problem of government finally came to be thought, reflected and calculated outside of the juridical framework of sovereignty. And that ‘statistics’ … now becomes the major technical factor, or one of the major technical factors, of this new technology” (Foucault1991, p. 99).
Finally, Foucault introduced the famous term ‘Governmentality’, which since then has become a scientific term that inspired an entire branch of sociological studies and research.50‘La Gouvernementalité’,51in his view, means three things:
1. The ensemble formed by the institutions, procedures, analyses and reflections, the cal- culations and tactics that allow the exercise of this very specific albeit complex form of power, which has as its target population, as its principal form of knowledge political economy, and as its essential technical means apparatuses of security.
2. The tendency which, … has steadily led towards the pre-eminence over all other forms (sovereignty, discipline, etc.) of this type of power which may be termed government, resulting, on the one hand, in the formation of a whole series of specific governmental apparatuses, and, on the other, in the development of a whole complex of saviors.
50A small sample of references might be sufficient here (Bröckling et al.2000; Burchell et al.1991;
Ewald1991; Hacking1991; Zamora and Behrent2014; Fried2014; Hammer2011; Jasanoff2004b;
Brown2015; Sangolt2010b; Lupton2013; Davies2016; Power1997).
51Foucault (1978).
3. The process, or rather the result of the process, through which the state of justice of the Middle Ages, transformed into the administrative state during the fifteenth and sixteenth centuries, gradually becomes ‘governmentalized’.” (Foucault1991, pp. 102–103) The so-called neo-liberal form of governance that Foucault describes is controver- sial in many aspects, particularly in terms of the penetration of life through economic principles and the focus on economic goals such as efficiency and competition. How- ever, this is not of direct relevance to this work. Rather, the point here is to show the quantitative turn that results from the fact that in modern Western societies, it is a supposed normality to do a “systematic effort to delineate and measure the objects and results of governance quantitatively for the purpose of demonstrating competi- tive edge and superiority at the individual and/or collective level” (Sangolt2010a, p. 75).
When Linda Sangolt speaks of the ‘Age of Quantification and Cold Calculation’, she refers to the twentieth century and its waves, fashions and policies (such as Taylorism and Thatcherism), which have contributed to accelerate and intensify the use of metrics for governance purposes in the private and the public sectors (Sangolt 2010a, p. 77). In particular, its impact on the public sector, for example in ‘New Public Management’,52is of interest to this work. As mentioned earlier, it cannot be ruled out that the same policy calls for data, but questions its production. The example of Sir Derek Rayner’s activities in Margaret Thatcher’s public sector reform53shows that statistical offices are among the first targets of austerity policies, despite the fact that they provide much needed political data. It can be concluded that statistics have power, but statisticians do not.
Desrosières describes the history of statistics54 in relation to that of economic theories and that of economic policies in a very condensed overview of crucial phases and forms of governance as well as their impact on statistics (see Fig.3.9). The history of statistical methods is linked “with the history of issues placed on the agenda for official decisions which themselves subsume: (1) ways of conceptualizing society and the economy, (2) modes of public action, and (3) different forms of statistics and of their treatment” (Desrosières2011). From this overview of the historical stages of development, one can also deduce how the current official statistics programme has developed over the different forms of governance.
According to this overview, we are currently in the phase of neo-liberal gover- nance, which seeks to achieve self-regulation of actuaries using measures and indi- cators rather than instructing them with regulations. This approach promises no less
52For a critical review see (Lægreid and Christensen2007).
53For the review of the UK Statistical Service under M. Thatcher see GreatBritain (1981), Thomas (1984).
54“Statistics is not only, as a branch of mathematics, a tool of proof, but is also a tool of governance, ordering and coordinating many social activities and serving as a guide for public action. As a general rule, the two aspects are handled by people of different specializations, whose backgrounds and interests are far apart. Thus, mathematicians develop formalisms based on probability theory and on inferential statistics, while the political scientist and sociologist are interested in the appli- cations of statistics for public action, and there are some who speak of ‘Governing by indicators’.
The two areas of interest are rarely dealt with jointly” (Desrosières2011, p. 41).