Claudia Buch: Focus on micro data - potential benefits for the industry?
Speech by Prof Claudia Buch, Deputy President of the Deutsche Bundesbank, at the Eighth
ECB Statistics Conference "Central bank statistics: moving beyond the aggregates",
Frankfurt am Main, 6 July 2016.
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I would like to thank Robert Kirchner and Marit Wehner for their valuable contributions.
1. Focus on micro data: potential benefits for the industry?
This session addresses two issues that are closely interlinked. The first concerns the costs
and benefits of using micro data in statistics in general and in central bank statistics in
particular. The second deals with the benefits of micro data for the industry. These two
questions cannot be answered independently of each other. Central bank statistics are a
public good. Therefore, the costs and benefits of different types of statistics need to be
assessed from the perspective of society as a whole.
The financial industry is a key stakeholder in this process. The quality of the data reported
hinges on the reporting mechanisms employed by the industry, and data that are not
regularly kept up to date by financial institutions may be of limited value for central banks,
too. To a large extent, this aligns the interests of central banks and the reporting parties.
But, of course, public and private interests may differ when it comes to aspects such as
reporting costs. While reporting costs need to be balanced against the benefits of statistics,
some costs for the industry are inevitable in order to improve monitoring of the financial
system and reduce the likelihood of costly financial crises occurring in the future.
In the following, the traditional statistical compilation process is compared with the new,
microdata-based approach. This sheds light on the large potential of micro data, not only for
the creation of analytical benefits, but also for cost-savings.
2. Central banking statistics from the traditional "value chain" to micro data
In the system of official statistics that has evolved over time, reporting forms are used to
obtain information, which is used to compile specific statistics in a pre-defined aggregation
form. The reports are collected, checked for plausibility, supplemented by estimates for
missing data, extrapolated if need be, and then consolidated across pre-defined dimensions.
It is often neither legally nor technically possible to use the underlying micro data later on.
Firms whose reports have been included in these calculations can use the aggregated
statistics to analyse the macroeconomic environment, for instance. However, focused studies
on specific market segments are generally not feasible.
Micro data initiatives are based on a fundamentally different concept: data should only be
collected once and be used to compile various statistics. This approach entails initial
investment costs - for example, investment in information technology and training staff - as
well as current expenditure for data collection and program maintenance. However, this
information also has a much greater scope of use. Compared with traditional statistics, micro
data offer a range of advantages for various user groups.
3. What are the benefits of using micro data?
Over the past decade, the availability and use of micro data have revolutionized empirical
work on financial systems (Deutsch Bundesbank 2015a). Technological progress has
contributed to improved access to micro data and to improved handling of large, granular
datasets. Empirical methods that allow heterogeneity across suppliers and users of financial
services to be explored have become readily available. Researchers both inside and outside
policy institutions are highly skilled and use these improved infrastructures for their research
and day-to-day analytical work.
This work has shown that aggregate developments cannot be fully understood without
exploring the rich sources of heterogeneity shaping the response of market participants to
shocks and regulations. Systemic risk, for example, is defined as the contribution of the
distress of individual financial institutions (or a group of financial institutions) to overall stress
in the financial system, with adverse repercussions on the real economy. The contribution of
individual financial institutions to systemic risk is higher, the greater the risk of an individual
institution, the larger an institution is (too big to fail), the more connected an institution is (too
connected to fail), or the more financial institutions are exposed to common risk factors (too
many to fail). This definition shows that systemic risk cannot be analyzed without making use
of detailed and granular data on financial institutions (Deutsche Bundesbank 2015c).
Similarly, analysis of the transmission of monetary policy and of the effects of monetary
policy on risk taking has been changed fundamentally by the availability of micro data and
the parallel development of empirical models that allow these data to be analyzed (Deutsche
Bundesbank 2015b). These methods enable drivers of demand and supply to be identified
and the heterogeneous responses to monetary policy shocks of different types of financial
institutions (small versus large, domestic versus foreign) to be studied. New methods permit
a much more detailed insight into the workings of financial and money markets than analyses
using aggregate time series.
An improved understanding of (risks to) financial stability and of the transmission of monetary
policy is a clear benefit that the use of micro data brings to central banks. This enables
central banks to better perform their core tasks - maintaining price stability and contributing
to financial stability. In this sense, micro data benefit both, society as a whole as well as
financial institutions that operate in a more stable environment.
An additional advantage of an increased use of micro data is that these data can be used for
multiple purposes. Traditional statistics tend to serve specific purposes, such as the
construction of aggregate indicators for statistics on the financial account. If new analytical
questions arise, these (aggregate) data often do not fit the purpose. Analysis then has to rely
on information which is not really targeted, or additional surveys have to be conducted. In
addition to being costly, these surveys often have the disadvantage that they do not allow
developments to be tracked over time and that they survey only a sample of firms or banks
and are thus not very representative. Using micro data that have already been collected, it is
comparatively simple to respond to a new request by carrying out an appropriate aggregation
of the data.
Looking ahead, the microdata approach to compiling statistics may even replace the
traditional one, leading to cost-savings on the side of the reporting agents. In the traditional
system, data has to be aggregated and reported separately for each dataset. The cost of
aggregation is borne by the reporting agent. According to the microdata approach, reporting
agents have to send their data only once. Everything else is done by the central bank.
Hence, the costs of aggregation are passed on to the central bank.
That is a considerable advantage for the industry, but is that everything? Is it not the case
that the industry not only benefits from cost savings but can also make direct use of the
micro data? Can statistics be jointly compiled by the industry and the central bank on a
voluntary basis in order to increase benefits and reduce costs?
This is not a purely hypothetical question. The Bundesbank's corporate financial statement
statistics, which contains data on German non-financial corporations, are based on several
sources, a key one being a data pool with financial statements provided on a voluntary basis
by credit institutions and financial service providers. The individual institutions cannot access
the micro data of the other parties. However, they receive a very wide range of statistics on
key ratios, broken down by sector and size category, which they can use as part of their
credit assessment of firms or for sectoral analyses. For this reason, industry representatives
have started to voice concerns about the collection of statistics which are insufficiently
comprehensive and that cannot be used for detailed market analysis.
4. What are the challenges and the costs of using micro data?
The above examples may suggest that the advent of micro data has moved us into a brave
new world in which we have better information, better analyses, and a mutually beneficial
exchange of information. But any cost-benefit analysis must, of course, also address the
challenges and the costs that are associated with the use of micro data. Among those, data
confidentiality and the costs of upgrading and maintaining IT systems feature prominently.
a) Data confidentiality and data protection
The traditional "aggregate" statistics that central banks use and produce generally do not
raise data confidentiality issues. Whenever a sufficient number of observations (typically:
three) have been aggregated, the behavior of individual firms, households and banks cannot
be tracked from specific data points.
Micro data, by contrast, almost always raise confidentiality issues that may differ
substantially across jurisdictions. For the United States, information about the profitability and
the balance sheets of commercial banks has been available for many years in the "Call
Reports" that are published by the Federal Reserve Bank of Chicago. These data have been
used for many important studies on the US banking industry. It would not be possible to
publish such data in many other jurisdictions. "Feedback loops" to the industry or other users
- which would allow the behavior of individual firms or competitors to be identified - are not
permitted. Perhaps even more importantly, micro data cannot be merged across jurisdictions,
which complicates comparisons of analytical work for different countries.
In order to overcome this obstacle, new analytical and research networks have emerged that
allow information and research results to be exchanged across countries without sharing the
underlying (confidential) data. One example is the International Banking Research Network.
Established in 2012, the IBRN brings together researchers from more than 20 central banks
around the world - in addition to international organizations such as the BIS, the ECB and
the IMF - in order to analyze current issues pertaining to international banking. The different
country teams use comparable, country-level data on cross-border banking activities and
common methodological tools to analyze issues such as the transmission of liquidity risk,
spillovers of (macro)prudential policies, or the cross-border effects of monetary policy. As the
studies use a common research methodology, the results are comparable across countries
and can be analysed using meta-analyses (Buch and Goldberg 2015).
In each national jurisdiction, additional technical solutions are available that facilitate the use
of micro data without compromising on data confidentiality. The Bundesbank, for example,
has recently established a Research Data and Service Centre (RDSC), which provides
researchers inside and outside the Bank with access to its micro data.
In general, the more detailed the data are, the more restrictive is the data access offered by
the RDSC. Outside Bundesbank premises (off-site), only anonymised data specially
prepared for academic research purposes (scientific use files) from the Panel on Household
Finances study - a representative survey on the structure and composition of households'
wealth - may be used.
Besides the Panel on Household Finances, the RDSC provides anonymised datasets on
banks, securities, investment funds, enterprises and households, all of which can be
accessed at dedicated researcher workstations inside the Bundesbank. For some datasets,
remote execution is possible, too. External researchers have to send in their syntax files,
which will be processed by the RDSC staff. For both of these means of access, the RDSC
staff screen the output files, preserve anonymity, and send them to the external researcher.
The range of methods for access to data satisfies researchers' need for the rapid provision of
high-quality data, while at the same time keeping sensitive information confidential. The
Bundesbank is thus adhering to an established global standard: it is ensured that analytical
findings that are to be published are anonymised such that no inferences may be drawn
regarding particular individuals or enterprises. The international standard may be briefly
summarised as follows: "Safe People, Safe Projects, Safe Settings, Safe Outputs, Safe
Data".
b) Investment in (IT) infrastructure
Investment in IT infrastructure is a part of the construction of every new statistics (Schubert
2015). The procedures for processing micro data, which are only collected once, can be
standardised to a greater extent than is the case for the individual silo procedures used to
date. Accordingly, the multi-purpose approach will make it easier for both, data collecting
authorities and reporting parties, to develop integrated systems. Two initiatives of the ESCB
should be mentioned in this context:
1. The Banks' Integrated Reporting Dictionary (BIRD) is a document which helps to
develop a standardised model for organising the banks' internal data warehouses in an
integrated way, and for applying the transformation rules in the reports banks have to submit.
The dictionary can be used on a voluntary basis. The advantages are a higher data quality,
more efficient production of reports, more consistent data, a univocal interpretation, and
clarity of regulations.
2. The European Reporting Framework (ERF) should be of great significance for the
data collecting authorities in helping to ensure that data only have to be collected once, using
a harmonised procedure for different purposes. The framework will cover most reporting
requirements of the ECB and the European Banking Authority (EBA). The data reported in
this framework will form a basis for harmonised production of secondary statistics.
5. How to make better use of micro data
The use of micro data in central bank statistics has many potential upsides, but providing and
maintaining the necessary (IT) infrastructure also entails costs. As things stand, the optimal
point of balancing costs and benefits has probably not yet been reached, and there are four
key areas for improvement:
1. Realigning micro data with existing statistics: It will be possible to reap the full
benefits of micro data in terms of efficiency and effectiveness of reporting only if
there is close coordination between the scope of existing statistics and newly
collected micro data. This may, in some instances, require the scope of existing
statistics to be adjusted, and it requires detailed planning when designing new data
requirements.
2. Improving the technical infrastructure: Statistical reporting systems can be costly if
they are not adequately integrated and linked to existing IT systems. Moreover,
adjusting systems ex post is costly. The sooner potential uses for new statistics are
known and the better reporting systems are aligned with existing IT infrastructures,
the lower will be the costs of compliance and reporting.
3. Improving feedback loops: Setting up and improving feedback loops not only require
close coordination and communication between central banks and commercial
banks supplying statistical information. It also requires close cooperation within the
private sector, involving potential suppliers of data and users of information provided
in feedback loops. To compare the firms' own data to patterns in market data, firms'
reporting entities (for example, the accounting department) and analysts need to
exchange information. In particular, the quality of the statistics is likely to be
enhanced if the reported results are analysed by the enterprise itself.
4. Improving dialogue with the industry and academia: Efficient designs of data
requirements, IT systems, and feedback loops require good dialogue between
central banks and data providers in the industry. At the same time, fostering the
dialogue with academia can help improving analytical systems and exploit statistics
to the best possible degree.
References
 Buch, C.M., and L. Goldberg (2015), Mini-symposium on international banking, IMF
Economic Review, Vol 63, No 3, pp 375-376.
 Deutsche Bundesbank (2015a), Microdata - paradigm shift in central banks'
statistics, Annual Report 2015, Frankfurt a.M., pp 47-59.
 Deutsche Bundesbank (2015b), Effects of monetary policy on risk taking, Monthly
Report, March 2015, pp 50-54.
 Deutsche Bundesbank (2015c), Micro data and macroprudential policy, Financial
Stability Review 2015, pp 80-81.
 Schubert, A., (2015). Data as a core central banking asset - The strategy of the
ECB, Presentation at the Big data workshop, Sveriges Riksbank, 9 September
2015.
The statistical value chain: traditional system