19 October 2016 (updated on 9 January 2018)
Statistics – facts or pieces of data generated from a set of numerical data – are very important for the ECB and central banks in general. Much of the ECB’s work, be it related to our core monetary policy function, financial stability or banking supervision, depends on good quality statistics – a necessity for informed decisions.
The analysis of economic, monetary and financial developments prepared for each monetary policy meeting is a clear example of why statistics matter for the ECB – and also for the citizens of euro area countries. These statistics serve as a key input for the Governing Council when making monetary policy decisions, which in turn influence the interest rates people and businesses pay. So statistics can indirectly affect the lives of many.
Sound decisions require high-quality statistics. So we adhere to strict standards to ensure that ECB statistics are accurate, consistent, timely, and produced in keeping with international standards without any outside interference. Indeed, independence provides for trustworthy statistics, supporting the credibility of our policy decisions and trust in the ECB.
The recent financial crisis has highlighted the necessity of collecting relevant and more granular data. As a result, the scope of central bank statistics is moving beyond aggregates – which invariably involve averages – to also cover micro (more detailed) data, for example at the level of individual loans, transactions and banks.
Granular data can lead to a better understanding of how monetary policy is transmitted to different corners of the economy, it can help improve the design of future policy measures and, in the event of any outliers (data anomalies) or tail risks (events or outcomes that have a small probability of occurring but a big impact), it can enable a more timely policy response. The ECB’s AnaCredit project, which involves a new dataset with detailed information on individual bank loans in the euro area, is a noteworthy example of granular data.
Collecting detailed statistical information can be quite challenging, especially as regards choosing methodologies and calculation methods that ensure comparability across countries.
A further challenge is obtaining information from entities that operate outside the banking system which may have an impact on the financial system and therefore also on monetary policy. Two examples are hedge funds – investment partnerships that pool the money of a limited number of individual/institutional investors – and shadow banks – entities that provide services similar to banks but without explicit access to central bank liquidity.
There are also concerns about not overburdening banks as the task of gathering and reporting data requires adequate time, money and other resources.
Furthermore, the confidentiality of individual data must always be ensured, especially in the case of supervisory data used in activities conducted jointly with external parties.
Data harmonisation is also critical as it allows for meaningful results and reliable comparisons. For example, we can be confident about the accuracy of the inflation data used because they are derived from national data based on a common set of definitions and classifications, in other words the same sort of shopping basket of goods and services.
Naturally, the euro area is the main focus of the statistics collected, developed and prepared by the ECB. We also share key data.
You can obtain a wide range of statistics from either the Our Statistics website – which presents statistics visually and in formats that are easy to embed in digital media – or our comprehensive Statistical Data Warehouse.
The Statistical Data Warehouse includes:
The European System of Central Banks is proud to be one of two European sources of high-quality European statistics. This is possible thanks to the close cooperation between the ECB and the national central banks, as well as with EU institutions and national and international statistical offices, including the EU statistical office Eurostat.
Update: This explainer was updated on 9 January 2018 to provide more details on the topic.