Literacy in statistics for the public discourse

2.1What a good mix of topics could look like?

The following is obviously not about the (highly) specialised part of professional education. For this part, a corresponding academic education is required plus further training ‘on the job’ as well as corresponding specialised courses. Rather, it is about what a good mix of topics, skills and knowledge could and should look like, which the candidate statistician would have to compose as a basic prerequisite and preparation for employment in statistical organisations. One factor here is that the workflows, projects and tasks in a large organisation such as a national statistics institute are not (or only to a very limited extent) comparable with those in research, at universities or similar.

For this reason, the conceptual principles developed by W.E. Deming for management in industrial production are applicable to official statistics. In his later book, “The New Economics” [3], he developed his “System of Profound Knowledge” with four parts, all related to each other.33 These four parts are

The approach used in this paper follows Deming’s principles. It is all about profound knowledge for professionals working in official statistics. Obviously, there are some common basics that are vital to know and to teach as part of literacy. The most fundamental part is knowing how official statistics are organised and produced and what the brand identity is, i.e. the ‘DNA’ of official statistics [4]. The point is further to understand the possibilities and limits of measurement and knowledge creation, and what Deming calls ‘psychology’ in our case, accordingly, the sociology of the interactions between statistics and society.

2.2Understanding the statistical factory and the individual statistician’s role in it

In concrete terms, however, what knowledge and previous training is useful for understanding the statistical factory and its various interacting activities and organisational units? To begin with, the distinction introduced by A. Desrosières regarding the different stages of compiling statistics is followed, who separates three “aspects of statistics, (1) that of quantification properly speaking, the making of numbers, (2) that of the uses of numbers as variables, and finally, (3) the prospective inscription of variables in more complex constructions, models”  [5]. Following Desrosières’ categorisation, a common denominator of statistics education should include components from all these aspects, in other words an overview of the operation and important methodologies of survey statistics as well as an overview of the functioning and structure of national accounts or other model calculations and the specific requirements for statistical indicators [6]. This kind of general overview is a prerequisite for the individual to be able to place his or her role and work in the larger context and understand that official statistics embodies a comprehensive system that is more than its individual parts, processes or products. Some essential elements from the methodology of survey statistics, the representativeness of samples or the interaction of surveys and external, already existing data, should also be considered in the basic training. The same applies to the basic methodologies and theories of national accounts. Furthermore, it should contain an understanding of the business architecture, for example, by teaching the logic and structure of the Generic Statistical Business Process Model44 or similar modern standards.55 It also should include basic training in topics of information quality, quality management and communication. A difficult but important subject is that of defining the boundaries and scope of official statistics. Especially those areas where modelling is used to increase the relevance of information, for example by estimating preliminary results or by standardisation, filtering (e.g. seasonal adjustment) or interpolation, should be elementary components of the basic training to become a professional statistician. In particular, the shifting of relevant boundaries in historical retrospect also reveals what is actually at the core of public sector statistics: the common elaboration, understanding and agreement on conventions or even standards that fix methods and procedures for a certain period of time before they are modified in a process of revision.

2.3The experience of EMOS in training official statisticians

With the aim of promoting such broad-based training and preparation for employment in official statistics, the ‘European Master in Official Statistics EMOS’ programme was launched in European Statistics. “EMOS is a label awarded by the European Statistical System Committee. …  the EMOS network comprises 32 programmes in 18 countries and collaborating partners in statistical offices. The network builds on existing and nationally accredited Master programmes which, in line with the EMOS learning outcomes,66familiarise the graduates with the system of official statistics, production models, statistical methods and dissemination. EMOS labelled Master programmes also collaborate actively with the National Statistical Institutes or other producers of official statistics for relevant master thesis topics and internships in the sphere of official statistics.”77

EMOS impressively demonstrates that comprehensive scientific preparation of professionals for official statistics is possible when academic and applied training are combined and well synchronised. However, the experience of the first years has also shown the difficulties: a relatively small number of interested students per participating university, capacity problems at the partner statistical institutes, overemphasis on survey statistics versus a lack of accounting and indicators, to name just a few of the issues. Nevertheless, it is important to learn from the experience and draw appropriate conclusions for the coming periods of the EMOS programme. In this context, it will be necessary to take into account newer methods of data science and the corresponding data sources, as well as the interaction with citizens in the sense of citizen sciences, which are both becoming increasingly important for official statistics.

However, it might prove more difficult in practice to adequately fill other gaps, such as those especially concerning accounts, indicators or the epistemology of statistics. Here it would be necessary to involve representatives of academic disciplines other than statistics, such as macroeconomics or sociology, and to actively involve them in the further development of the course structure. As far as the attractiveness of this academic training programme is concerned, it may also be opportune to offer individual courses from it for those students who are not interested in the whole package and its degree. Finally, what is at stake is the (personnel) capacities at the partnered statistical institutions; without a permanent and appropriate allocation of resources, this education programme will not be able to flourish.

3.1Knowledge needed to assess the quality of the statistical product

The learning objective in this field of public statistics might become clear by comparison with the goal of healthy nutrition, where we would want people to eat (and live) better and promote appropriate education and culture for this. If I want to brew a good quality coffee, fairly and sustainably produced, I need a lot of knowledge about coffee as a raw material, the production process and value chain, the different brands and products (coffees and machines) on the market and finally the skills to handle my machine and the bought beans properly (which depends on my taste). Should I have a special interest in good coffee, I can learn a lot in so-called ‘Barista’ courses.1010 What I get for myself in terms of quality and coffee enjoyment, depends on my skills and knowledge (and values, such as my willingness to pay for good quality). What I don’t need, however, is the skill of growing coffee beans, roasting them, etc.; there are trained specialists for that. In short, one could compare training for the consumers of public statistics with such a Barista course. For statistical information, too, one needs to know a certain amount about the data sources, the preparation processes, quality standards, etc., but not at the level that would be required if one were to carry out this work oneself. In a value chain, it is not necessary to have the skills and abilities of the upstream process steps in detail. It is sufficient to be able to assess (and use) the delivered product and its quality.

There has been ample opportunity in the pandemic crisis of recent months to observe the lack of successful communication between data experts, policy makers and citizens [7]. Another prominent case is already somewhat older, but similarly revealing: In 2002, the euro was introduced as the currency in many European countries. Subsequently, there was massive criticism and mistrust against the Consumer Price Index because it did not correspond to the inflation perceived by customers in their daily shopping, where they observed incorrect conversion of currency to prices of services and consumer goods [8]. For someone to understand what the consumer price index indicates (or does not), courses in data analytics or in-depth knowledge of probabilities do not provide much help. Rather, some understanding is required of descriptive statistics, what an arithmetic mean is with its weighting scheme, how the basket of goods is composed, how price changes of representative commodities are determined. It is of great advantage to have gained some experience with these empirical methods, in our example by changing the weighting scheme and observing the corresponding effects, by comparing the consumer price index with another type of price comparison (e.g. between the cost of living in different regions). In this sense, it could also be helpful to involve citizens more in the production of statistics. Such co-production could – besides possible provision of data sources – contribute to bridging the gap between producers and users of statistical indicators of this kind [9].

3.2Make-or-Buy

The objective and tasks of data literacy initiatives are somewhat specific for managers and employees in public administrations, e.g. of municipalities. Under the impression of increased digitalisation requirements and corresponding expectations placed on them, employees may get the impression that a course in data analytics is nowadays part of their professional training programme if they want to be successful. Heads of administrative departments may feel called upon to set up their own data analytics units. Here, too, there is a danger of creating unrealistic incentives and misleading ambitions, organisationally as well as personally. If data analytics are propagated as part of the toolbox of modern administration, then the necessity of decisions between doing it oneself and cooperation with other parts of the same administration should also be pointed out, according to which a commissioning of a municipal statistics office could ensure the provision of adequate analysis and statistical monitoring for local politics. Wrong incentives will lead to the emergence of parallel information worlds here as well, which are ineffective and inefficient and will tend to end-up in poor information quality.