Official Statistics—Public Informational Infrastructure
2.3 Quality in Official Statistics
2.3.5 Evolution and Continuous Adaptation
2.3.5.1 The Learning Cycle, Continuous Improvement
The great diversity and complexity of the subject ‘quality’ in official statistics have become clear in the preceding sections. Simple and quick solutions to these questions are therefore not expected and nor would they be appropriate. Rather, it becomes very clear that the wide range of processes and products in the statistical factory can only develop over longer periods of time, and that this feature of continuous improvement will continue to be inseparable from official statistics in future.
It is therefore important to make a virtue of this fact by finding a dynamic devel- opment and balance between preserving and innovating in terms of W. E. Deming’s quality management (see Fig.2.13):
The PDSA Cycle (Plan-Do-Study-Act) is a systematic process for gaining valuable learning and knowledge for the continual improvement of a product, process, or service. … The cycle begins with the Plan step. This involves identifying a goal or purpose, formulating a theory, defining success metrics and putting a plan into action. These activities are followed by the Do step, in which the components of the plan are implemented, such as making a product.
Next comes the Study step, where outcomes are monitored to test the validity of the plan for signs of progress and success, or problems and areas for improvement. The Act step closes the cycle, integrating the learning generated by the entire process, which can be used to adjust the goal, change methods, reformulate a theory altogether, or broaden the learning – improvement cycle from a small-scale experiment to a larger implementation Plan. These four steps can be repeated over and over as part of a never-ending cycle of continual learning and improvement. (Deming Institute2018)
Fig. 2.13 PDSA cycle
Plan
Do
Study
Act
In the spirit of this continuous and systematic process of learning and improving official statistics, dialogue with users and interaction with science is crucial. The planning of official statistics at all levels (variable, product and programme) has to be understood and organised as an evolutionary process, as a sequence of learning cycles and feedback loops.
Over time, changes might be started from all three angles. New demands and political issues trigger new statistical developments, as new data sources or new methodologies do. Historically, it can be observed that these driving forces are also mutually influencing each other, thus stimulating new episodes in official statistics (Desrosières1998).
It is therefore essential to link the communication process of today with the devel- opment of statistics for tomorrow. Partly, this loop could be a short one, if user feed- backs can lead to quick fixes and improvements in services. Partly, however, it might take time, since changes in a programme need profound preparations and even more profound developments (Fig.2.14).
This evolutionary development of statistics is confronted with several limiting factors, which could be practical limitations, such as:
• Clandestine, non-observable phenomena
• Statistical items in future and elsewhere, relevant for decisions now and here (e.g., capital goods, depreciation, trade chains, Sustainable Development)
• Values and prices for non-market-goods (can we simulate non-existing markets?)
• Limitations by resource or time constraints; in cases, where only a limited amount of information and data is available or where limited time is given for the decision- making process
• Limitations could also relate to the understanding and use of data and information
• Innumeracy, statistical and data illiteracy Fig. 2.14 Statistical
learning process
• High and too high expectations
• (No) Appetite for high-quality information (Davies2017).
Since the beginning of official statistics in the nineteenth century, the boundaries have been substantially stretched. Continuous improvement has opened new oppor- tunities so that today many subjects (e.g., quality of life) that were impossible to observe only a few years ago are fully integrated elements of the standard statistical programme. Nevertheless, it is crucial to understand that basic principles must be respected, if the fundament on which trust in official statistics is built is not to be dam- aged. This is, for example, the reason to refrain from monetising natural resources and their services, if they are not valued by market transactions.
2.3.5.2 Planning and Decision Procedures, Consultation of Users, Governance
Official statistics is a special application and form of statistics that belong to the public infrastructure of (modern) states. Working methods in official statistics reflect both their political and administrative position as well as the status and development of societies (i.e. the specific relationship between state and citizens).
How is public infrastructure planned and decided? This is generally an important issue, as it is about providing the greatest possible value and return on taxpayers’
money. The way the statistics programme is decided reflects what is called gover- nance (and what Foucault would have calledgouvernementalité32) and the status of the relationship between the state and its institutions on the one side and civil society (the citizens, interest groups) on the other.
Public statistics in a modern and democratic state has to be benchmarked against principles of good governance in the public sector, out of which the overarching and most important ones are (Fig.2.15).
Behaving with integrity, demonstrating strong commitment to ethical values, and respecting the rule of law
Public sector entities are accountable not only for how much they spend, but also for how they use the resources under their stewardship. This includes accountability for outputs, both positive and negative, and for the outcomes they have achieved. Public sector entities are accountable to legislative bodies for the exercise of legitimate authority in society. This makes it essential that each entity as a whole can demonstrate the appropriateness of all of its actions and has mechanisms in place to encourage and enforce adherence to ethical values and to respect the rule of law.
Ensuring openness and comprehensive stakeholder engagement
As public sector entities are established and run for the public good, their governing bodies should ensure openness in their activities. Clear, trusted channels of communication and consultation should be used to engage effectively with all groups of stakeholders, such as individual citizens and service users, as well as institutional stakeholders.
Fig. 2.15 Overarching principles of good governance. Adapted from IFAC (2014)
32See for example Foucault (1991).
At least for European statistics, it can be stated that the first criterion is already taken very seriously. Both the statistical programmes and the single statistical acts go through cumbersome legislative procedures, which place extremely high demands on ex-ante impact assessment and consensus-building prior to final decision-making.
Of course, such ambitious legal procedures also have a price, namely the lack of adaptability and dynamics of the statistical system. It should also be mentioned that important control mechanisms (quality reports to the European Parliament, review by the European Court of Justice, etc.) have been set up and are in place for the implementation of European statistics.
For the second criterion (stakeholder engagement), however, the status quo leaves some room for improvement: In a modern and democratic definition, official statistics is no longer a knowledge tool in the hands of the powerful and mighty. Rather, it must follow principles of neutrality and impartiality, whereby this information infrastruc- ture becomes an important democratic pillar, equally available and accessible for everyone.
The interaction with stakeholders must be governed by principles of transparency, democratic control/supervision and public/legislative conventions. In particular, the programme of work must emerge from a democratic decision-making process, at the end of which a choice is made in favour of the ‘Pareto-optimal’ composition of statistical tasks. Priority setting in this context has an important role to play, as it must facilitate the annual adaptation of the programme following changes in user needs.
The way in which this consultation and decision-making have been organised so far relies mainly on the functioning of ‘official’ procedures concerning the prepara- tion of legislation and political decisions. Modern societies, however, ask for more—
more in terms of wider consultation (more room for all active contributions from civil societies), new forms (collection of user needs through social media) and speed (quicker adaptation of the programme).