Official statistics: Language for public discourse (Part 2)
This is the second blog post of 2 contributions by Walter Radermacher, former Director-General of Eurostat and Chief Statistician of the European Union. Here, Walter employs the metaphor of transport infrastructures to highlight the importance of adequate policy frameworks for official statistics, including literacy, laws, rules, and the long-term maintenance of this key public infrastructure. Part 1 introduced the current social malaise around statistics.
Statistical information, in the sense of the ‘Économie des Conventions’, are artefacts that are designed and produced. The same rules apply to such informational products as to other products: their design must be suitable to provide factual answers to users’ questions; they must be produced with good quality; educated clientele with a sense for quality is a prerequisite. The public infrastructure that provides society, politics and the economy with elementary facts is official statistics. Where would we be without GDP, inflation rates, mortality tables, population figures, etc.? Facts on the basis of which momentous political decisions are made are based on international methodological standards (e.g., consumer price index for Central Banks’ monetary policy). Official statistics must perform their tasks with great efficiency and continuity [1]: they work with long lead times, industrial production lines, international standards and democratically decided work programmes. In this way, internationally, nationally, and temporally consistent indicators of high quality are provided.
Let us use the example of transport: data strategies aim at promoting and regulating individual (data) mobility. In addition, there is public rail transport with data and statistics, which must also be made fit for the future. This requires investments, because new areas are to be established on which modern high-speed trains are to run. Individual data use alone can be inefficient and ineffective. In the 1960s, we thought that promoting individual transport was the best option. Today we know that this one-sidedness has led us into congested cities and roads because rail expansion was not pushed with enough verve. If the infrastructure of public statistics is not modernised, geared to new technologies (high-speed statistics) and new terrains are not opened up (COVID-19, biodiversity, …), there will be parallel infrastructures in both the public and private sectors that will develop their own standards. Or, more precisely: We will have an outdated, unattractive public (data) railway with multiple rail widths (partly public, partly private) and incompatible industry standards; a setback for trust, transparency, and public discourse.
To prevent such a situation, the integration of a country’s different producers under one roof into a well-coordinated statistical system is crucial. Roles must be assigned, responsibilities defined, so that citizens can rely on the highest quality standards being met. To make it easy for users to obtain information, a certificate should be introduced that provides trustworthy information about the quality profile of an information product. Certification requires a neutral and trustworthy institution that sets and verifies quality standards.
The fact that initiatives to improve data literacy are gaining momentum, supported not only by business but also by politics and science, is very welcome. Data literacy serves to promote maturity in a modern digitalised world and is important for all people — not just specialists. This education, like other education, is about several competence dimensions: Knowledge, skills, and values [2].
However, a broad, balanced, and situational approach is rarely found in practice. Rather, the focus seems to be on teaching technical skills of data science, mathematics, and IT, reminiscent of the do-it-yourself wave of the 1970s, in which screwing, repairing, and constructing by anyone was propagated, sometimes even in cases where a good craftsman would have done the job better and cheaper than an amateur. For the citizen, the entrepreneur, the teacher, the student who wants to understand and apply the indicators of public statistics, sophisticated skills of data science (e.g., own analysis of raw data, knowledge of algorithms) are just as irrelevant as in-depth knowledge in the mathematical field of stochastics.
Rather, citizens should know enough about the informational product and its properties to be able to assess its quality regarding personal application goals and questions. This requires basic mathematical knowledge as well as experience in dealing with quantitative information; knowledge of descriptive statistics and its application in the processes of economic and social statistics is required. What the consumer price index says (or doesn’t say) about inflation should be taught in school and adult education; everyone should understand the indicators of sustainable development. For advanced users, microdata are also available [3] as ‘public use files’ to experiment with their own statistical evaluations and gain experience.
Official statistics require an adequate policy framework because they embody a public infrastructure maintained by public institutions with a public mandate financed by taxpayers’ money. Most countries have statistical governance, consisting of a body of laws, rules, principles, codifications, and work programmes. The European Statistics Code of Practice defines:
Institutional and organisational factors have a significant influence on the effectiveness and credibility of a statistical authority developing, producing and disseminating European Statistics. The relevant Principles are professional independence, coordination and cooperation, mandate for data collection, adequacy of resources, quality commitment, statistical confidentiality, impartiality and objectivity.[4]
For official statistics to develop successfully, the preconditions in terms of finances, personnel, organisation must be right. Beyond the canon of already existing criteria of the current Code of Practice, future demands arise, e.g., those regarding the introduction of quality labelling and certification of statistical information as well as with regard to initiatives to improve statistical education.
Compliance with these quality standards goes beyond the statistical institutions’ own sphere of influence. If there is a lack of political attention and will to address this issue, public statistics will sooner or later fall behind and will no longer be able to meet the requirements. The ‘Tragedy of the Commons’ particularly affects public infrastructure. If bridges, roads, sewers (and public statistics) are not maintained for a certain period, it is hardly noticeable at first. In the long term, however, the resulting damage and repair costs are all the higher.
End of Part 2. Part 1 is available here.
[1] W.J. Radermacher https://link.springer.com/book/10.1007%2F978-3-030-31492-7
[2] K. Schüller, Future Skills — A Framework for Data Literacy https://hochschulforumdigitalisierung.de/sites/default/files/dateien/HFD_AP_Nr_53_Data_Literacy_Framework.pdf
[3] E.g. https://ec.europa.eu/eurostat/web/microdata/public-microdata
[4] https://ec.europa.eu/eurostat/web/quality/european-statistics-code-of-practice
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