Sabine Lautenschläger: Central bank statistics - moving beyond the
aggregates
Speech by Ms Sabine Lautenschläger, Member of the Executive Board of the European
Central Bank and Vice-Chair of the Supervisory Board of the Single Supervisory Mechanism,
at the Eighth ECB Statistics Conference "Central bank statistics: moving beyond the
aggregates", Frankfurt am Main, 5 July 2016.
* * *
Ladies and gentlemen,
I'd like to warmly welcome you all, both on behalf of the Executive Board of the European
Central Bank (ECB) and personally, to our Eighth biennial ECB Statistics Conference. I am
very pleased to see that so many distinguished participants from all over the world have
gathered here in Frankfurt today.
The title of this year's conference is "Central bank statistics: moving beyond the aggregates".
Beyond the aggregates lies a whole world of facts and observations that feed into granular
statistics - namely, statistical data that need to be collected, checked, disseminated and,
eventually, analysed by policymakers.
In fact we would like, this year, to discuss with you the paradigm shift that central banking
statistics are currently undergoing: the move from macro to micro statistics, or from
aggregate to granular statistics, if you like.
That move is nothing short of a big bang for central bank statistics. In a sense, it resembles
what happened in the 1930s, when the Great Depression moved the focus of economic
analysis from equilibrium to fluctuations and downturns, eventually fostering the development
of national income accounts.
Similarly, in the aftermath of the recent financial crisis, we saw a huge and sudden increase
in heterogeneity, or "fragmentation", in several dimensions: across and within economic
sectors, geographic areas and market segments. We have learnt that aggregate statistics,
although of high quality and internationally standardised, do not suffice any more as a basis
for good decision-making. What we need are high-quality and timely granular datasets and
indicators. I will elaborate more on this in a minute.
So, once again, big changes in statistics have been triggered by dramatic economic events
which challenged the status quo and added to policymakers' demands for information.
Moving beyond the aggregates - impetus and initiatives
For the European System of Central Banks (ESCB), there are at least two reasons for
moving "beyond the aggregates".
First, we had to change the way in which we fulfil our mandate, that is, maintaining price
stability through monetary policy. Following the dramatic change in the economic
environment since the onset of the financial crisis, we have, for instance, adopted a number
of unconventional monetary policy measures, such as targeted liquidity provision and an
asset purchase programme. These measures are quite different from the traditional
approach of setting an interest rate. As you can imagine, conducting this kind of
unconventional monetary policy is rather difficult when decisions have to be taken on the
basis of conventional data, i.e. traditional aggregate statistics. Mitigating systemic risk in very
turbulent times on that same basis is equally difficult.
Second, the ECB has been assigned additional functions as a result of the crisis.
We became responsible for banking supervision, of course, but we also received a mandate
on macroprudential policy. In addition, the ECB now has to support the statistical needs of
the European Systemic Risk Board.
Not surprisingly, these new functions and new data users have led to new challenges and a
remarkable development in the field of statistics: in the last few years, the ESCB has
enriched its datasets in several dimensions. Let me recall some of the most recent ones in
the area of granular data, which is the theme of tomorrow's conference.
As a result of the money market statistical reporting, we started collecting in April this year up
to 35,000 transaction-by-transaction data on a daily basis from 52 large banks in four
different segments of the euro money market.
We have gathered security-by-security data on issuances and on corresponding holdings by
euro area residents. Worldwide holdings of securities by 26 individual banking groups based
in the euro area are also available, and are planned to be extended to all banking groups
under the ECB's direct supervision by 2018.
Using the AnaCredit dataset, we will provide policymakers, as of the second half of 2018,
with a considerable amount of harmonised loan-by-loan information collected from all euro
area banks by the respective national central banks.
Credit data have of course a special role for the ECB. The euro area is a bank-based
economy: loans are the main source of financing for companies and almost the only one for
small and medium-sized enterprises. Symmetrically, loans are an important asset in banks'
balance sheets. Therefore detailed and high-quality information on credit is essential both for
monetary policy and for financial stability.
This is why I am convinced that AnaCredit will play a central role in supporting our key
central bank functions.
So, as you can see, within the ESCB we are quickly moving "beyond the aggregates", and I
am sure that this move will bring huge benefits. Let me explain why.
Lost in aggregation - the benefits of granular data
By asking for aggregate data, which is pre-organised and aggregated by the reporting agents
or by the national central banks, we miss lots of valuable information. After all, it is not only
the average that matters, but also the underlying distribution. And in order to analyse the
distribution we need the "basic" (granular) data.
Let me give you a simple example. Assume that we see credit to businesses accelerating in
a given country. We can think of several different developments underlying this "aggregate"
fact.
It might be that solid companies, with low debt and good economic perspectives, are taking
out more or larger loans. Or it could be that fragile and highly indebted companies are
borrowing more or are restructuring their debt just to survive. Credit might also be growing
because more companies have access to bank financing. In turn, this might reflect better
economic prospects and a greater appetite for investment - which is good - or just a
deterioration of credit standards - which is not so good.
All these possibilities (and I didn't mention the possible factors on the credit supply side!) are
consistent with growth in aggregate credit. But, clearly, they might have very different
implications in terms of the monetary policy stance and risks to financial stability.
Granular data will help us look beyond aggregates and reveal the underlying developments.
By having granular loan-by-loan data we will know, abstracting from the respective identities,
the characteristics of specific groups of counterparties involved in each transaction. Then we
can assess the "driving forces" behind any aggregate development and distinguish genuine
and "healthy" growth from potential bubbles. This is very important for policymakers.
And the benefits of going granular also extend to those who have to provide the data in first
place: the reporting agents. After the initial high set-up costs resulting from the large
volumes, reporting information at a granular level brings significant savings. This is because
statistical requirements are going to be more stable over time and ad hoc data requests for
special or urgent policy needs are minimised.
Also, the more we ask banks to provide information which resembles what they already have
in their internal systems, the easier it will be for them to comply with the reporting
requirements.
Last but not least, in those countries where feedback loops are established by the respective
national central bank, reporting agents will benefit from much more complete and
harmonised information on the creditworthiness of their counterparts, in particular when they
are foreign residents.
On our side, even when policy decisions are taken on the basis of aggregate statistics, as is
usually the case, moving towards granular data offers the big advantage of timeliness and
flexibility: raw information can be organised and aggregated in different ways depending on
the specific policy question at hand.
This is key, given that it takes three to five years to develop new aggregate statistics, while
policymakers usually want the data "yesterday". Granular data allow us to provide the
necessary statistics in a flexible and timely manner - so probably not "yesterday" but not too
far from "today".
Allow me, for a moment, to compare the compilation of statistics with the making of a cake,
say, a Sachertorte. Going granular is like asking banks for the basic ingredients such as
chocolate, cream, flour, eggs, butter and sugar instead of asking them for a slice of
Sachertorte.
Our clients - policymakers, analysts and researchers - are always hungry, and they need
different cakes depending on the policies they have to decide on.
Once we have all the basic ingredients, we can bake a variety of cakes just by combining
these ingredients in different ways. So we don't have to go shopping every time, which
makes it easier for us, and which makes it easier for those who have to provide the
ingredients, that is, the banks.
New challenges ahead
Ladies and gentlemen, I hope that I have convinced you all of the benefits of moving "beyond
the aggregates" - including those of you who do not fancy baking cakes. I should add though
that granular statistical information also poses new challenges.
First and foremost, granular datasets need to be standardised and well integrated. We have
to prevent any build-up of disconnected "silo" data collections. We should no longer request
the same data twice, and whenever we cannot avoid requesting similar information twice we
have to explain why. Once collected, we have to make sure we can combine the data from
different granular datasets as necessary.
Second, granular datasets must be multi-purpose: they need to serve different users and
support different analyses, all at the same time. AnaCredit is a great example of this; it is
designed to support different key central bank functions.
Third, keeping in mind the need for integration and versatility, we must provide financial
institutions with a unified reporting framework - a framework that is based on a consistent
and stable set of rules and definitions. This is precisely what we are doing with the current
work to define an ECB Single Data Dictionary and an integrated European Reporting
Framework.
Fourth, we need to adopt and manage state-of-the-art IT solutions that can process huge
data volumes and ensure the necessary data confidentiality.
Last, but not least, I have already said that granular data allow us to satisfy our customers'
craving for "cakes" of all different kinds. So the remaining question is: can our customers
digest all the cakes they request? Can they handle large sets of granular data? I am pretty
sure they can!
So, as you can see, moving "beyond the aggregates" entails quite a few challenges, but I am
confident that we will master all of them.
Conclusion
Ladies and gentlemen, we are currently in the middle of a paradigm shift in which granular
data is rapidly gaining relevance.
And one thing is obvious: good statistics - both micro and macro - are essential tools for
policymakers, helping them to take decisions and to assess the impact of those decisions on
the economy.
But let's be realistic. Granular information will not save us from any future financial crisis. It
will, though, put policymakers in a better position to mitigate the risks ex ante and to limit
their potential impact ex post by taking the appropriate corrective measures and by
monitoring their effectiveness in a timely manner.
Besides offering extraordinary opportunities, this move beyond the aggregates brings big
challenges, as I have already said.
New questions arise. For instance, will granular data ever replace aggregate statistics? Let
me put it another way: is it possible to reduce and simplify the collection and production of
aggregate statistics once sufficient individual information is available? Or do we need to
collect and compile both granular and aggregate data for the purpose of cross-checking?
These two kinds of statistics nicely complement each other, in my view. Micro data can be
used to fill important data gaps. At the same time, aggregate statistics provide a useful
benchmark for granular data, especially while these are still at an early stage of
development.
Nevertheless, keeping the reporting burden to a minimum is an important priority for us,
therefore micro and macro data collections as we know them now cannot continue in parallel
forever. As statistical reporting inevitable creates costs for third parties, we will regularly
reassess our requirements, taking account of the information needs of policymakers, and at
the same time constantly striving for the best possible balance between merits and costs.
I am very confident that the conference tomorrow will help us to find the right answers to
these and many other questions relevant for the future of central bank statistics.
Thank you very much for your attention.