Claudia M. Buch
Vice-President of Deutsche Bundesbank
Can Technology and Innovation Help?
New Data Generating Possibilitiesi
Speech prepared for the
European Central Bank Conference on Statistics
Frankfurt, 12 July 2018
Seite 1 von 18
Deutsche Bundesbank, Directorate General Communications
Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany, Tel: +49 (0)69 9566 3511 or 3512, Fax: +49 (0)69 9566 3077
presse@bundesbank.de, www.bundesbank.de
Reproduction permitted only if source is stated.
New data, technologies, and IT developments significantly alter production processes and
distribution channels in the economy. Comparative advantage depends, to an increasing
degree, on the capability to select the right data and to take "data driven" decisions. At the
same time, regulators are challenged in the areas of consumer protection, competition
policy, and data confidentiality.
Central banks are affected as well - although they do not "compete" on markets. Man-
dates of central banks comprise the provision of public goods: price and financial stability.
This, in turn, requires analytical strength and making best use of data and methodologies.
For policy analysis and research, statistics and reliable data based on standardization and
quality assurance are crucial inputs. New technologies can contribute to a better quality of
data and better analytical work.
In this talk, I argue that technology and innovation affect the production of central bank
statistics and the production of knowledge. I outline how existing and new sources of data
can be used more efficiently and how new analytical tools can improve production pro-
cesses (Section 2). At the same time, challenges need to be managed (Section 3). The
demand for skills changes away from manual to analytical and communication tasks,
cost-benefit analysis needs to be improved, and knowledge sharing within and across
central banks is crucial.
1 What's new?
Digitalization has the potential to overhaul the traditional system of compiling (central
bank) statistics. In the traditional system, reporting forms are used to obtain targeted in-
formation meeting a specific purpose as, for instance, the compilation of balance of pay-
ments statistics. Interest is in aggregated data, not the underlying micro- or granular data.
This philosophy has changed: Digitalization lowers the costs of processing and storing da-
ta due to general technological advancement in hard- and software components. Digital
storage and data processing power has increased substantially throughout the past dec-
ades, and costs have fallen. As a result, central banks are in a position to collect larger
amounts of data which can be organized and processed automatically. Given the ever in-
creasing amounts of data, some degree of automation is inevitable. Granular, micro- data
sets, which comprise security-by-security or loan-by-loan can be processed and used for
multiple purposes. Such data can be tailored to specific needs of users. All this funda-
mentally alters the statistical value chain (Figure 1).
Figure 1: The statistical value chain
The following trends are not new - but their speed has accelerated:
1. Granularity of data: Central banks increasingly move away from the collection of
aggregate data, compiled according to pre-defined frameworks and designed for
specific purposes. Instead, reporting agents transmit microdata, which are then
compiled at the central bank, according to the specific needs of users.
2. New sources of data: New sources of data and in particular "Big Data" have be-
come a buzzword. "Big Data" feature characteristics of Volume, Velocity, Variety,
Variability, and Veracity. New datasets differ from traditional sources of statistics
because they are unstructured or because they are provided by private sources.
3. New analytical technologies: Machine learning (ML) and artificial intelligence (AI)
date back to the late 1950s.2 AI research deals with the study of "intelligent agents"
- a system which reacts to changing circumstances and learns from experience.3
ML methods are used to learn about patterns in data and to use this information in
order to make predictions for other, similar cases.4
4. New IT developments: Significant progress has been made in terms of dealing with
large volumes of data. Moore's law, which was formulated in 1965, predicts that
the complexity of integrated circuits doubles every one to two years.5 Not only has
the hardware infrastructure become more efficient, distributed computing and cloud
computing also allows for the decentral processing of tasks. Already in the early
2000s, commentators claimed that "IT doesn't matter"6 due to the rapid price defla-
tion of computer chips, data storage, and data transmission.
Why have all these trends accelerated and reinforced each other in recent years? In
1934, Schumpeter argued that innovations basically result from the "changes of the com-
binations of the factors of production [...] They consist primarily in changes in methods of
production [...]"7 Innovation thus happen as innovators recombine existing productive
components throughout a "creative" process. In this context, "General Purpose Technolo-
gies" are crucial because they potentially impact several sectors or the economy and al-
low for new methods of production by re-combining factors of production. For example,
the combination of electricity with other means of production made innovations in the en-
tire economy possible. Similarly, the internet boom of the late 1990s has been described
as an example of "combinatorial innovation", resulting in a situation with relatively little
scarcities in input factors and very low marginal costs.8
New data, IT-Infrastructure and analytical tools may bring about a new wave of "combina-
torial innovation". Most input factors are of immaterial nature as are statistical methods
and software. This could potentially explain the enormous speed with which new applica-
tions occur.
Given these trends, how do central banks generate knowledge? According to Boos
(2018), data are the starting point. Connecting different data points generates "infor-
mation", bringing this information into context generates "knowledge", using it generates
"intelligence", and reflecting on the use of data generates "wisdom".9 Each of these stag-
es requires the input of different production factors, reminiscent of a production function:
Y = f (A, K, L, D)
where Y can represent statistics, knowledge, or intelligence.
The IT-infrastructure is captured by (K), i.e. any hardware related component that is nec-
essary for the processing of data. The costs for IT-Infrastructure have fallen tremendously
throughout the years which, viewed in isolation, would reduce the costs of producing
knowledge. Data (D) are collected by the central banks from reporting agents, according
to regulations, or bought from commercial providers. Technological progress often aug-
ments the inputs of labor (L) as it gives the statistician tools at hand that make her work
more efficient. Either more data can be dealt with or the quality of data could be improved,
while holding working-hours constant. Technology (A) represents software-tools, algo-
rithms and statistical methods needed for the handling and processing of data.
Knowledge produced by central banks is essentially the result of a combination of the in-
put factors data, labor, capital, and technology. Importantly, technology leverages any unit
of labor that is invested into the production of knowledge. Machine learning algorithms are
an example of how technology (A) can augment the efficiency of labor (L) because it low-
ers the quantity of routine tasks that need to be performed manually. This, in turn, allows
the statistician to focus on non-routine tasks, e.g. interpretation or communication with
users. ML-algorithms can support checks of data quality. Algorithms can provide predic-
tions of the results that manual data quality checks would have produced by checking
each data point for plausibility.
Record linkages are another example for how algorithms can support statistical work:
Matching of large, granular data sets without unique identifiers manually is cumbersome,
time-consuming, and prone to produce errors. Machine learning approaches can be used
11
to match data sets based on probabilistic scores.10,
Using new technologies cannot only allow using existing factors of production more effi-
ciently, thus potentially generating returns to scale, it may also enhance the division of la-
bor. For example, within a team of data scientists, each team member could focus on a
specific subset of methods, say, in the field of machine learning, for example one in su-
pervised and one in unsupervised learning. Cooperation with colleagues, each one bring-
ing in "expert knowledge" at a particular stage of the project, creates a process of mutual
learning with the potential to increase productivity.
Reaping synergies along the production process is another potential benefit of technolog-
ical progress. Increases in quantities of data, particularly if collected and stored according
to well-defined information models, allow for more standardized procedures. Certain tasks
can be centralized along the production chain. The "statistical value chain" changes: the
diverse requirements and objectives that statistics faces are not treated in isolation any-
more. Rather, uniform concepts of data collection, processing and dissemination make it
possible that each of these heterogeneous needs is dealt with by a specialized unit dedi-
cated to one step of the production chain.
2 What are the implications for the production of statistics?
Making better use of (existing) data
Traditionally, central banks collect a large number of datasets which are then stored in dif-
ferent business areas. In the Bundesbank, for example, bank-level data are collected by
the Statistics Department and the Banking Supervision Department, information on pay-
ments transactions are collected by the Payments System Department, and additional mi-
cro-data are available in the Markets Department.
These data contain potentially useful information for analytical policy work in different
business areas. However, combining the data has traditionally not been feasible due to
legal restrictions because of confidentiality requirements or data protection rules. Very
practically, company identifiers or software tools often differed. The fixed costs of using
these data have, therefore, been high, which severely constrained their use in day-to-day
policy work. Merging different micro-level datasets requires, for example, identifiers, cum-
bersome matching algorithms and/or decisions to be taken for matching reporting entities.
Studies using micro data are often not readily comparable because analysts made differ-
ent choices when compiling datasets. As a result, existing micro data have often not been
used efficiently in the past for policy work.
In order to address these issues and making best use of existing data, Deutsche Bundes-
bank initiated, in 2013, its project IMIDIAS - the Integrated MIcroData Information and
Analysis System. IMIDIAS focuses on the cross-sectional microdata storage, integration
and usage of relevant microdata sets. The initiative consists of different components:
• an in-house steering committee representing all owners and users of data across
the business areas of the Bundesbank,
• the House of Microdata (HoM) as an integrated data warehouse, the content of
which are datasets which are relevant for internal analysis and research,
• the Research Data and Service Centre (RDSC) as a facilitator for internal and ex-
ternal researchers to get access to selected microdata of high quality and as a con-
tact point for data providers and data users.12 The RDSC offers access for non-
commercial research to (highly sensitive) micro data of the Bundesbank. All re-
searchers (internal and external) have the same transparent access to microdata
of the Bundesbank. The RDSC has been accredited by the German Data Forum.13
Currently, 20 employees are working at the RDSC, which provides 12 work places for ex-
ternal guest researchers. Currently, over 300 active projects of more than 160 institutions
are affiliated with the RDSC. In 2017 alone, the RDSC received about 130 project applica-
tions, of which 73 were realized. Reasons why projects are not realized range from inad-
equacy of the available data, the project not being classified as independent research, to
idiosyncratic factors. In order to preserve confidentiality of sensitive microdata, 250 output
controls for research projects were conducted. By now, the first papers of "RDSC users"
have been published.
Developing new sources of data
Analytical work in central banks traditionally focused on macroeconomic aggregates like
GDP, inflation, credit, or interest rates. The advantage of these data is that they can easily
be shared and combined within and across institutions. The fixed costs of using the data
are thus relatively small. Yet, the analytical insights based on aggregate statistics can be
limited.
For example, understanding the build-up of risks in financial systems and the transmis-
sion of monetary policy shocks requires sufficiently granular data. Micro-level heterogene-
ities within and across sectors and countries, play a crucial role in determining macro-
level aggregates.14 Therefore, the analysis of macroeconomic measures alone proved to
be insufficient as they cannot account for the relevant heterogeneities and frictions.
The analysis of micro-level, granular data has made significant progress over the past two
decades. Availability of new data sources has improved, not least through research data
centers, and new empirical methodologies have been developed. Many of these im-
provements have taken place in the areas of labor economics, international trade and in-
vestment, or in the identification of effects of monetary policy.
Within the European System of Central Banks (ESCB), there has been a paradigm shift
away from the production of aggregates towards more granular statistics:
• Since 2005, granular data on securities have been collected and proceeded in the
Centralised Securities Database (CSDB). This dataset contains information on 6.8
million actively traded securities (1.9 million from Germany) issued by approximate-
ly 670.000 issuers (30.000 from Germany).
• Since 2014, the Eurosystem has been collecting quarterly data on securities hold-
ings with the Securities Holdings Statistics Database (SHSDB). 6 million observa-
tions on sector-by-sector holdings are reported from 25 national central banks,
roughly 2 million observations by the Bundesbank.
• From September 2018, the Analytical Credit Datasets (AnaCredit) will provide in-
formation on loans provided to legal entities and credit risk, providing crucial inputs
for monetary policy analysis and the surveillance of financial stability risks. The
novelty of AnaCredit is that it provides information on banks and borrowers in a
harmonized way at the European level, unlike existing national credit registries
which often differ in scale and scope.
• For all reporting systems, reference data are collected in the Register of Institutions
and Affiliates Data (RIAD), including almost all financial but also non-financial
firms. High quality reference data is a necessary condition for the production of sta-
tistics as they allow to uniquely identifying the reporting population and, for exam-
ple, to derive group structures of reporting agents. Hence, information stored in
RIAD will allow matching information across different data sets.
Collecting and processing these data is costly. Costs arise for the reporting firms which
need to maintain IT infrastructures based on in-house information systems which provide
the relevant data for external users. But costs arise also within central banks as new in-
frastructures have to be set up. Planning new micro-data projects and balancing costs
and benefits thus requires a longer-term horizon. The AnaCredit project, for example, has
been initiated in the year 2011 and data will become available in 2018 - i.e. after seven
years. This shows that careful planning, cost-benefit analysis, and consultation with the
industry must be crucial elements of any new data project.
In parallel, researchers and statisticians at central banks have also tested other external,
sometimes less structured and more "unconventional" data sources. Matching internal
with external data is often useful. At the Bundesbank, pilot projects make use of rather
unstructured, external data stemming from internet sources. For example, Google search
data are used in several research projects to analyze the behavior of depositors during
the financial crisis15, of developments on the German residential mortgage market16, or
17
potential improvements in forecasting models.
New analytical tools
The increasing number of microdata sets also poses challenges for data production and
analysis. New tools are needed for handling these data and for the extraction of the rele-
vant knowledge. Therefore, several projects in Statistics at Bundesbank use machine
learning techniques:
• Quality assurance and checking for errors in the data is, typically, a manual task.
Such routine tasks are labor intensive and may not detect all errors. In order to ver-
ify implausible securities data for the Securities Holdings Statistics, a random forest
algorithm is thus used. This algorithm is based on decisions taken in the past by
compilers to flag reported securities as erroneous. A machine learning algorithm
approximates the human decision making process.
• The Register of Institutions and Affiliates Data (RIAD) system develops a prototype
for record linkages based on machine learning tools.
• The Bundesbank's Research Data and Service Center (RDSC) makes use of rec-
ord linkage methods that are based on supervised machine learning to automati-
cally link several data sets on companies. The result is a mapping table with com-
pany identifiers.
• Seasonal adjustments are improved by using random forests of conditional infer-
ence trees. This approach assesses the seasonal nature of a given time series
with a high degrees of accuracy and can be used to improve tests for seasonal pat-
terns in the data.
Given the increasing importance of European statistical projects and the potential to learn
from experience, international cooperation is crucial. Under the auspices of the ECB's
Statistical Committee (STC), a Task Force on machine learning has been established with
the aim to exchange experiences and ideas for machine learning applications in central
banks statistics.
3 What are the implications for the production of
knowledge?
The production of central bank statistics is not an end in itself but a central element in the
creation of knowledge - knowledge about the structure and functioning of the financial
system, knowledge about its interaction with the real economy, and, not least, knowledge
about the effects of policy measures affecting the financial system. The last step - the ex-
post evaluation of policy effectiveness - is a crucial element of the transparency and the
accountability of central banks.
Data alone do not provide the necessary information. Data need to be combined with ap-
propriate models - be they formal or informal - and with appropriate empirical techniques
to generate knowledge. Only then can we make statements about causes and effects of
policy measures. In short, we need to go beyond correlations and should aim at establish-
ing causalities.
Improving identification through heterogeneity
Identification of policy effects using aggregate data is challenging and often not possible.
The identification of causal effects thus requires sufficiently detailed data. Heterogeneity
across institutions, individuals, or countries is often the key to identification. Seminal theo-
retical work on the functioning of credit markets, relevant frictions, and implications for
monetary transmission dates back 20 years.18 For empirical research in this field, loan-
level microdata comprising detailed information on lenders, financing conditions, and the
provision of collateral is of utmost importance. Throughout the past years, studies based
on micro- (firm-level) data have provided important insights:
• Analyzing the allocation of bank credit to firms and the implications for productivity
requires detailed micro-level data. Such data show that, in OECD countries, lend-
ing to weakly performing firms has tended to be stronger since the mid-2000s, with
negative consequences for the performance of stronger firms. Performance is
measured in terms of investment and employment.19 In a related approach, using
European data, this mechanism is linked to the use of unconventional monetary
policies.20
• Work with micro-level data on banking has a long tradition in Italy. Using granular
data for a period from 2004-2013, one result of this research is that weaker banks
have been lending to weaker firms during the Eurozone financial crisis.21 Yet, this
misallocation of credit at the firm level does not seem to be a key driver of the ag-
gregate volume of credit. But "distress lending" slowed down the exit of weak firms.
This adjustment along the "extensive margin" may be a channel through which
credit allocation also affects overall performance.
• A potential misallocation of credit is, however, not confined to countries with weak
growth performance. Small and mid-sized firms with negative returns on asset and
net investment increased their leverage in the period from 2010 to 2014. These
patterns are observable, both in stressed economies (such as Greece, Ireland,
Portugal, Slovenia, and Spain) and in non-stressed economies (such as Germany
and France).22
• Since 2014, the Nederlandsche Bank has been collecting loan level data on mort-
gages which provide important information for the analysis of risks building up on
mortgage markets.23 These detailed data contain information on the individual
mortgage lenders portfolio at quarterly frequency. The variety of attributes that are
recorded allow for the computation of various descriptive statistics as (joint) distri-
butions of interest rates, outstanding amounts, the ratio of performing loans, or the
age of borrowers. Similar data for the German mortgage market a not available, as
has recently been highlighted by the IMF.24
4 Managing the challenges
New, granular sources of data, new analytical technologies, and new IT-developments
promise significant improvements for the production and enhancement of (existing) statis-
tics, the generation of new information, and ultimately for enhancing knowledge. Yet,
reaping these benefits and coping with the challenges, requires managing the changing
demand for skills, improved cost-benefit analysis, and improved knowledge sharing and
international cooperation.
Managing the demand for skills
New data and technologies alter the demand for skills in the production of statistics and
knowledge. The use of "algorithms" does not lead to a reduced demand for labor due to
the "replacement" of employees by machines. Quite to the contrary: Tasks now being per-
formed by humans will move away from manual routine tasks towards the provision of
services, including the explanation of statistics to users. New technologies will comple-
ment, not substitute workers, enabling them to be more productive and to enhance their
existing skills.
To give an example: The arrival of the personal computer or new spreadsheet software
profoundly altered the way finance and accounting departments work. But this did not
make the affected employees disappear or obsolete at all.25 Another example in this con-
text is the introduction of ATMs and employment in banking. Contrary to initial expecta-
tions, the rapid spread of ATMs in the U.S. from the 1970s on did not lead to a significant
decrease of bank teller jobs.26
The key to making best use of new technologies and managing the transition is training -
at school, universities, but also continuous learning on the job. Many universities already
alter their curricula by introducing study programs that better prepare future graduates for
the change in required skills. One example is the European Master in Official Statistics
(EMOS), a network of postgraduate master degree programs fostering the cooperation
between universities and official statistics as data producers. The main goal is to train
postgraduate experts in order to be able to cope with the rapid changes that statistical of-
fices are facing today.
As regards the production of knowledge, skill sets focusing on the use of aggregate statis-
tics and empirical methodologies using macro data will increasingly be complemented by
skills for the use of micro data and identification mechanisms based on micro-econometric
tools. Many promising developments in empirical economics bridge the gap between
macro- and micro-analytical tools. These include factor-augmented vector-autoregressive
(FAVAR) models, which combine traditional structural VAR-models with factor analysis,
summarizing large numbers of time series to a smaller number of "factors". This, in partic-
ular, allows to condition VAR models on a richer set of information which are likely to bet-
ter represent actual decision makers' information sets. Making best use of such methodo-
logical developments, which are particularly useful in "data-rich" environments, may
require explicit training programs for central bank analysts traditionally working with more
aggregate datasets.
There are several initiatives within the Bundesbank as well as in cooperation with other
institutions which allow employees to acquire the necessary skills for e.g. working with
microdata. The Bundesbank is represented in the Task Force Big Data of the European
Statistical System, in order to exchange views and experiences on how new data sources
could be implemented into official statistics. At the national level, Deutsche Bundesbank
and the Federal Statistical Office have conducted a joint workshop on the use of machine
learning techniques for data production. Further, internally, the Bundesbank runs several
training activities that allow statisticians to improve their technical (programming) and me-
thodical skills.
Improving cost-benefit analysis
At first sight, granular data seem to increase the reporting costs for industry. However, in
the medium to long term, granular reporting frameworks have the potential to reduce
costs for the reporting population and central banks, where at the same time the analytical
value of the data increases. Ultimately, these benefits may also accrue to industry as bet-
ter analytical tools in central banks improve policymaking. In addition, feedback loops can
provide reporting firms with input for their own analysis and benchmarking.
Improving the production of statistics and knowledge requires sound cost-benefit analysis,
industry consultation, and long-term strategic planning. Costs of implementing new report-
ing systems should amortize over time. As stability of new reporting frameworks has ma-
terialized, aggregate statistics and indicators can be calculated from underlying microda-
ta. This, however, requires consistency between different data sets. To achieve this
objective, the long-term approach of the European System of Central Banks (ESCB) is to
collect data from banks in a standardized way to integrate existing ESCB statistical
frameworks, as far as is possible, across domains and countries. The main objective of
this approach is to increase the efficiency of reporting and to lower reporting costs, while
providing users with high-quality data. This approach relies on clear concepts, definitions
and requirements, which help automate data processing and enhance data quality. The
banking industry and other reporting agents are involved in this process already today
through consultations in the course of the ECB Merits and Costs Procedure and in public
consultations on new or enhanced ECB Regulations on European statistics.27
One element of the strategic approach of the ESCB is the ESCB Integrated Reporting
Framework (IReF).28 This projects aims at integrating existing ESCB statistical data re-
quirements for banks into a single framework. It consists of two parts: First, an integrated
set of reports for banks, which can ultimately replace national reporting templates. Sec-
ond, transformation rules compiling the statistics required by authorities, possibly to be
shared with the stakeholders involved.
The other element is the Banks' Integrated Reporting Dictionary (BIRD).29 One key objec-
tive of BIRD is to both, lower the costs of reporting statistics and enhance data quality.
BIRD consists of a harmonized data model which describes data to be extracted from
banks' internal IT systems to derive reports required by authorities, together with rules on
how to transform the data. In this sense, BIRD is a "public good" that can be used by
banks and other data providers. Participation is voluntary. At this moment, BIRD covers
the reporting requirements of AnaCredit and securities holdings statistics (SHS), while
coverage of the Financial Reporting for supervisory purposes (FinRep) is under develop-
ment.
Enhancing knowledge sharing
Sharing knowledge and information within and across central banks can contribute signifi-
cantly to making best use of new technological developments. Three initiatives can serve
as examples:
• The International Network for Exchanging Experience on Statistical Handling of
Granular Data (INEXDA) shares information on methods for harmonizing access
procedures and metadata structures, thus fostering the efficiency of statistical work
with granular data.30 Its ultimate aim is to facilitate the use of granular data for ana-
lytical purposes, and it provides a platform also for users outside the participating
institutions, of course observing confidentiality regulations. Beside knowledge shar-
ing, INEXDA promotes the G20 Data Gaps Initiative II (DGI-2), in particular rec-
ommendation 20, addressing the accessibility of granular data.
• The International Banking Research Network (IBRN) is a global network of central
bank researchers which aims at making better use of micro data in central banks
without having to share the (confidential) data. Teams of researchers across cen-
tral banks collectively develop analytical models, apply these models to their na-
tional datasets, and the findings are compiled in a meta analysis. Research in the
network contributes to an improved understanding of the role of global banking in
the transmission of (policy) shocks across countries.31
• Internal processes may need to be adjusted in order to benefit from new technolo-
gies and data. Procedures for the governance of data need to be improved with re-
gard to structuring and organizing knowledge. Currently, information that is availa-
ble within organizations may not always be systematically collected and may be
stored in a decentralized manner. As a consequence, the accessibility, i.e.
"searchability" of these data is not always granted for all potential users. In order to
structure such information and to make it available, the Bundesbank is working on
a dynamic and adaptable repository which connects the relevant stakeholders like
data producers or researchers in an interactive and learning knowledge map.
5 Summing up
Overall, technology and innovation can contribute to improvements in the production of
statistics and, ultimately, knowledge in central banks. Reaping these benefits requires
good planning, commitment of leadership, and good dialogue with all stakeholders.
At this point in time it seems useful to "start small" and learn from experience. Different
input factors need to be combined to generate "innovative processes", and it is not clear a
priori which combination generates the best results. Experimenting with different models
can thus be useful.
New approaches and ideas are needed and welcome. The "statistical value chain" is
changing profoundly given to the increased demand for microdata. This in turn is likely to
have implications - for required skills, data-governance, and the organization of work. We
need to "think outside the box" to best management these challenges.
In this sense, the establishment of units which think and organize work across the tradi-
tional "silos", which establish a culture of knowledge sharing can be useful to experiment
with and implement useful, innovative ideas.
i
I would like to thank Stefan Bender, Jürgen