04.11.2020
Statistical Production and Economic Policymaking: A View from a
Central Bank
2020 International Conference of Statistics for Society - INE
Pablo Hernández de Cos
Governor
General considerations
Let me first congratulate the Instituto Nacional de Estadística on this anniversary. Since its
creation in the very difficult times after the Spanish civil war, INE has continuously grown in
stature. Today, it boasts the highest standards in terms of professionalism and credibility.
We live in a society overcrowded with data. Every decision we take and every transaction
or movement we make are reflected in some or another database. But this per se has not
necessarily increased our real information or our knowledge. Many of those data are not
structured, lack a precise definition or do not follow a specific methodology. This is why
statistics and, in particular, official statistics are so important, as they transform data into
real information. And we need this to make sound decisions. Policymaking thus heavily
relies on timely, exhaustive and detailed information provided by official statistics.
Unsurprisingly, statistical production plays a prominent role in central banks. Several factors
are at play here, such as a long-standing tradition in producing and analysing monetary and
financial information, staff who are highly experienced in these tasks and good "raw
materials", in part benefitting from the key role of the central bank as the banking regulator
and supervisor. I should stress that the autonomy of central banks has also greatly helped
enhance the credibility of their statistical output.
Indeed, as part of the Eurosystem, as a participant in the Single Supervisory Mechanism
and given its responsibility for the proper functioning and stability of the financial system,
the Banco de España regularly collects a wealth of financial and supervisory information
from credit institutions, which is key to performing all our duties. This information is also
necessary for strengthening the performance of other functions of the Banco de España,
such as the conduct of monetary policy, the oversight of the stability of the financial system
and the compilation of statistics. We are also responsible for designing financial reporting
as efficiently as possible and, in this endeavour, both the Banco de España and the
European authorities have some substantial challenges ahead. I shall refer to these later.
Some of the key statistics produced by the Banco de España are present in the Spanish
National Statistical Plan (the PEN, as we refer to it). They include some macroeconomic
statistics, such as the balance of payments and the financial accounts, which complement
and have to be consistent with those compiled by INE. In this regard, I think we can be
proud of the long-standing tradition of close and candid cooperation between our two
institutions. Joint discussion continuously enriches our professionals, and their efforts to
ensure the consistency and complementarity of the results are a guarantee of the quality of
the statistical system. This is a clear example of how the quality of the overall statistical
system is higher than what the mere sum of independent products would be, were such
products not well-connected.
Micro data as the Holy Grail
We can all agree that statistics have been undergoing a revolution in the last few years.
Important technological advances have increased the ability to process huge amounts of
data and productivity in this area has surged: policymakers and analysts have more and
more information and details at their fingertips and now have the possibility of applying
more sophisticated models, designing more refined economic policy responses and
answering more complex questions.
Some of these changes stem from the experience of the Great Financial Crisis of the last
decade. Then it was felt that only the availability of detailed information on economic agents'
positions (including cross-border ones) would allow the relevant policy authorities to
thoroughly analyse the exposures of an increasingly interconnected global financial system
and to better understand the monetary policy transmission mechanism and the massive
deleveraging that followed. To close these gaps, the Eurosystem promoted a number of
projects based on granular information. These included AnaCredit (to build up a Central
Credit Register at the European level), the Securities Holding Statistics Database (which
contains exhaustive security-by-security information on European portfolios) and Money
Markets Statistical Reporting, where daily information at the transaction level is collected
from financial institutions.
In Spain, we also learned the lesson. In 2013 we overhauled the information compiled by
the Central Credit Register, switching more towards a very complete granular database in
order to strengthen our micro- and macro-prudential supervisory mechanisms. We are also
fortunate to have, since the eighties, an exhaustive database of the annual balance sheets
and the profit and loss accounts of Spanish firms (the so-called Central de Balances), whose
inception was one of the landmarks as far as the set of statistics produced by Banco de
España is concerned. The database also contains a very valuable sample of firms voluntarily
providing quarterly information. Moreover, the balance of payments and the international
investment position have been historically produced by relying on mainly micro information.
And from the outset of this century we have conducted every three years (which will be
every two years in the future) a wave of the Survey of Household Finances, which offers a
very rich picture of some of the key economic and financial decisions taken by Spanish
households. All this micro information helps the Banco de España perform our functions
and allows us to address flexibly both analytical work and statistical compilation, under our
multi-purpose remit.
Moving towards a statistical landscape broadly relying on micro data requires deep changes
in a number of processes. As automation is of the essence, new techniques need to be used
to manage these data (including the necessary quality checks) and staff skills have to be
upgraded accordingly.
This can prove very demanding, particularly since data quality management in micro data
is a very cumbersome process. Let me be clear here: micro data per se do not ensure
highquality statistics. But it is true that if you have micro data you can check the quality of the
data, pinpoint where the problems are and potentially revise them. In the case of aggregate
data, you may not have the adequate means at hand to check their quality and correct them.
An important issue for ensuring data quality is the proper identification of economic agents.
In a complex and globalised world (and in particular in a monetary union where domestic
transactions correspond to an area larger than your own country), the identification of
counterparts to maintain high-quality statistics is simply essential. Since the European
National Central Banks can only properly pursue their functions by previously identifying the
financial corporations under their scrutiny and the non-financial counterparts of financial
market transactions, they have created their own register: the Register of Institutions and
Affiliates Database, (RIAD).
A key step forward to ensure there is adequate identification of agents at the European level
would be to enhance cooperation in this field between the European Statistical System and
the Eurosystem. This could be done, for example, by integrating their reference data
directories (such as the ESCB's RIAD and the ESS's Euro Group Register). That would afford
us better knowledge of the relations between subsidiaries and their parent companies,
which are so important for understanding the effects of globalisation.
Moreover, the ESCB has contributed to the creation of a worldwide network to set up a
global identifier for legal entities, the Legal Entity Identifier (or LEI). In Spain, some
institutions, including INE and the Banco de España, are promoting the use of the LEI and
facilitating its implementation in our country. I think there is a broad consensus that the
widespread use of the LEI among institutions and firms globally would greatly favour the
monitoring of financial stability and the compilation of statistics.
Globalisation also poses significant challenges for the compilation of macroeconomic
statistics, on which our institutions cooperate very closely. Multinational enterprises (MNEs)
organise their activity and finances globally and the complexity of allocating them correctly
across countries can only be afforded by data-sharing -nationally and internationally- and
perhaps even by resorting to centralised data reporting and quality checking. Obtaining
information from MNEs as groups would allow reporting to be simplified, while at the same
time ensuring a consistent cross-country recording of transactions, assets and liabilities.
I have mentioned before how important it is for official statistics to offer a complete and
consistent picture of the whole economic situation. In this regard, it is crucial that users
know the links between the different products, even when they are produced by different
institutions. And this is something which should hold not only for aggregates but also for
micro data.
Opening up our data for analysis / transparency
The current statistical revolution has gone hand in hand with substantial progress in
empirical economics and modelling. Some years ago, macro literature often relied on
representative agent models. That was a natural consequence of computational limitations.
However, the aggregation of individual behaviour does not in general coincide with the
behaviour of one average individual. That's why this literature is now moving towards
models with heterogeneous agents (each with distinct behaviour and a distinct reaction to
policies), which allows not only for a more accurate representation of the economy, but also
for the analysis of distributional issues, such as inequality.
For such sophisticated analysis, detailed micro information is key. This is also true for
empirical economics, and in particular for the evaluation of economic policies. Many policies
target very specific groups of agents according to some individual- or location-specific
characteristics. Moreover, one must take into account that the effects of policies depend
on many individual features as well as on other contextual issues. Those two factors notably
increase the heterogeneity of the effects of such measures. As a consequence, access to
massive data that do not limit the scope of the analysis on account of small sample biases
is key for the evaluation of economic policies.
As I have stressed many times recently, and in particular over the last few months during
the COVID crisis, the key to understanding the degree of success of policies or the
occurrence of potential unexpected consequences is to proceed with proper and timely
evaluations based on sufficient background information. This is the only way to ascertain to
what extent policies are working or need adjustment in terms of their timing, magnitude and
focus. Global authorities are now in uncharted waters in terms of the dimension of the crisis
and its multiple ramifications, and it is very difficult to calibrate the response of economic
policy. Only good and timely data can allow us to design an optimal policy reaction in such
a complex context.
The present crisis has also stressed the importance of having data in real time: in the last
few months, information on the use of payment cards, traffic on roads and at airports,
electricity consumption, mobility, Google searches and so on has been fundamental for
assessing the magnitude of the economic slump and the subsequent timid recovery. These
indicators will hardly join the compact and methodologically sound realm of official
statistics; but I think we should also pay attention and invest some resources in
understanding them, familiarising ourselves with their use and incorporating them into our
forecasting models. Probably, some of these indicators could be temporarily considered
"experimental statistics", a label statistical institutions may use to explore new fields and to
convey to users the as yet not fully sound nature of these data. At the same time, I think we
should improve the timeliness of official statistics as much as possible. In this connection,
the recent shorter time lag in the production of INE's CPI and GDP flash estimates are good
examples.
In the case of the Banco de España, we have also been able to obtain some real-time
granular information that is very useful for assessing the current difficult conditions. As
regards information from banks, the data provided by the Central Credit Register is now
more necessary than ever for assessing the risks of granting credit and the level of
aggregated risk. For us and other economic authorities, this information has also helped in
the design and assessment of the effectiveness of the measures adopted during this period
(moratoria on loans, public guarantees on lending, etc.). Last April we also made use of the
Central Balance Sheet Data Office database to conduct a quick survey of a sample of
nonfinancial companies in order to capture their initial reactions to the lockdown. This
information proved very helpful in guiding our assessment of the initial impact of the
pandemic on Spanish firms.
And, of course, the challenge posed by the implications of climate change for the economy
and the financial system also requires that new databases be built and that the key signals
to keep the authorities' policies focussed be extracted from those we already have.
In all these tasks I believe that the role of external analysts and researchers can be pivotal
In this regard, I see only advantages from making information produced by authorities public
to the scientific community. Evaluation of public policies should not be the sole preserve of
analysts from policymaking institutions. Data availability paves the way for evaluations by
independent researchers with a clear potential for enriching our knowledge on the impact
of public policies. This in turn increases the number of cross-checks to ensure more robust
results by looking at the same question from different angles. My impression is that the more
ambitious we are in this field, the better the assessment by researchers we are going to
have. Indeed, it is a fact that countries with more open data typically enjoy larger than
average high-quality economic evaluations.
That's why we have created a data laboratory at the Banco de España (BELab) and intend
to gradually open up our data to the scientific community. We have started offering the
annual financial statements of about one million companies per year, made available thanks
to the agreement signed with the Mercantile Register Association (Colegio de Registradores
Mercantiles). As part of our medium-term strategic plan, we are currently analysing the legal
and technical conditions to broaden the databases disseminated at this laboratory and to
ease access to the data. Currently, it is only possible to access the data lab using the
"dataroom" located on our premises. But we are preparing the tools to make such access also
possible by a remote and secure means.
Naturally, a dilemma arises between the advantages of opening up our micro data to society
(in particular for scientific purposes and for improving the design of policies) and the
statistical confidentiality that reporting agents wish to keep. I think this is something that in
the past has been biased on the side of excessively restrictive access to data. Given modern
anonymisation techniques, my impression is that the picture should now become more
balanced in favour of opening up our institutions and data to society.
INE's own laboratory is a good example of this movement. And I can in fact affirm that some
Banco de España analysts are already enjoying access to INE's data lab. Their experience
shows the potential advantages and benefits of cooperation between institutions and
researchers.
Once public institutions provide the appropriate technology ensuring data confidentiality,
analysts can undertake relevant policy evaluations and analyses that until now were not
available for policymakers. This opens the way towards improving the policymaking process
by conferring a greater weight on evidence and evaluation techniques. In this connection,
the role of policymakers is to decide on public policy, but this role can be conducted more
effectively the greater the information they have on economic conditions and on the
consequences of each choice made. Useful evidence-based inputs to policymaking
crucially require good and accessible datasets.
Conceivably, we could think about a further step forward, linking and federating our data
labs. Or we could think about the possibility of establishing "safe hubs" of granular
information where data from different institutions are shared with every guarantee of
retaining statistical secrecy and confidentiality. By way of example, in some Nordic
countries there is a consortium of public institutions which share their data not only with
their own analysts but also with the scientific community.
It would also be worth strengthening the possibilities of having access to administrative
registers of other institutions and to allow the matching of different databases. The value of
micro datasets increases exponentially when they can be combined, including the granular
information of one dataset in the observations of another. The amount of research
possibilities here is really huge. Therefore, we should make every effort to achieve these
types of dataset combinations, through the above-mentioned "safe hubs", through a
consortium or by any other means.
Another related issue is the importance of providing all the observations making up a given
register. A large amount of observations is by itself important, as it allows the study of
specific groups of agents with sufficient accuracy. But it is also crucial when it comes to
merging different datasets. If an administrative register is reduced to some sub-set of
observations, then the chances of finding the same agents in a different dataset decrease
dramatically. This is of particular importance in the case of surveys containing important
information to be merged with some registers. Surveys usually have a much smaller sample
size. Therefore, the number of observations in common with an artificially reduced register
could be very low or even zero.
The path to striking a balance between giving access to the research community to the more
interesting databases while ensuring confidentiality and anonymisation could much benefit
from the experience of other institutions. To this end, a few years ago several central banks
set in motion the so-called INEXDA network. At present this network comprises central
banks, including the participation of the ECB Statistical Directorate, and other statistical
institutes, like Eurostat. In this forum participants from the statistical areas and datalabs
exchange experiences in the management of micro data, the aim being to guarantee the
harmonisation of certain procedures and share best practices in this domain.
Access to the information in the hands of private sources is also a very interesting field
where we can make further progress. INE had a very good experience this year in exploiting
data from mobile phones to analyse citizens' mobility in different cities and regions, easing
and lessening the cost of developing such statistics, with the help of new technologies. The
need to better understand and organise the transition from customer data, privately used
by service corporations, to citizens' micro data, of interest for public analysis, has to be
studied thoroughly. That is why I welcome and congratulate INE on recently launching a
Business to Government Working Group (B2G WG), under the name of "WG about the role
of official statistics on data stewardship". In this WG, key players will reflect on ways to
provide access to private data while maintaining and promoting ethical principles.
In fact, I consider there is a need to break the quasi-monopoly which some big
techcompanies have on personal data, something which requires a co-ordinated international
effort. The European data strategy announced by the European Commission a few months
ago, aimed at public/private-sector collaboration in data-sharing, is a good example in this
respect.
It is also important to recall the usefulness of statistical information for public opinion.
Central banks' information is quite often of a very technical nature. But, at the same time,
some variables -such as interest rates- have a strong impact on household finances and
SMEs, whereas information about recent developments on key economic and financial
variables are also very relevant for public opinion. Therefore, accessible (transparent, clear)
statistics are also needed.
In our case, we are continuously striving to improve how statistics are conveyed to society
and to adapt our communication to different types of users. A couple of years ago we
conducted a survey of the users of our statistical information. The results offered a useful
guidance about some key issues. Following the input from this survey, we have in recent
years tried to exploit new technologies to display data, including more charts, interactive
menus, dashboards, etc. And just a few days ago we launched an application which will
allow users to have our statistical information readily available on their mobile devices.
The quest for efficiency in statistical reporting and compilation
As we have seen, data - whether micro data or aggregate variables - are an essential
ingredient for analysing and designing economic policies. But at the same time, reporting
data are not free. This always means a burden on reporting agents. And I think that the
responsibility of all of us as authorities is to minimise that burden and establish the
mechanisms which ensure data collection is as efficient as possible and smooth the ensuing
interactions with agents to check data quality.
In a sense, collecting micro data may help reduce the reporting burden on economic agents
(such as banks, investment funds, non-financial companies, etc.) if they are able to replace
other aggregate requirements imposed by the authorities. Moreover, these requirements
may sometimes change over time, thus demanding a continuous and costly adaptation of
the agents' reporting schemes, including complex IT developments.
But, in any case, data collection imposes short-term costs on agents. Public institutions
should be sensitive to their demands to establish an efficient system to collect information,
avoiding overlaps and harnessing synergies.
In order to tackle this issue there are already some data-sharing mechanisms in place across
statistical institutions. Indeed, the special relationship between INE and the Banco de
España in the national accounts and balance of payments fields is a good example. Yet
more can probably be done both at the national and international level.
On the international front there are good practices we can explore. A case in point is that in
Portugal, where non-financial companies have a sort of single window to report the data
required by the main Portuguese authorities (Tax Agency, National Statistical Institute,
Mercantile Register, Central Bank). In particular, our neighbour has been able to build a
single data platform to which every authority can subsequently resort to obtain the
information they are entitled to access. I have seen that in the Spanish National Statistical
Plan (PEN) for next year there is a reference to a project setting up a "Sistema Integrado de
Información de las Empresas", which I think is along the same lines. It could then contribute
to significantly reducing firms' reporting burden in the future.
As concerns financial information, we should also be open to more efficient financial
reporting and statistical production at the Banco de España and the ESCB level. The Banco
de España is very much committed to this goal. In fact, we already have a strategy to simplify
and rationalise regulatory reporting that is starting to bear results. The main aim is to
alleviate the reporting burden and to improve data management by institutions and by the
Banco de España itself.
On top of that, various initiatives have been taken as part of the Banco de España's Strategic
Plan (which runs to 2024) dealing with data management and transparency. The goal here
is to have a better knowledge of the overall datasets available at the institution (which may
point to some synergies, overlapping and gaps), and to establish good data governance
procedures and a technically sound platform to host and exploit all that information.
At the European level, several initiatives are in motion as well. First, the European
Commission has asked the European Banking Authority (EBA) to conduct a feasibility study
to explore the possibilities for statistical, supervisory and resolution reporting to converge,
thus reducing the reporting costs of financial institutions. The ESCB proposal to the EBA
feasibility report -published last September- is based on three main avenues of work: a) a
common standard data dictionary; b) the harmonisation of reporting formats, the removal
of duplications and improved data-sharing; and c) increased cooperation between
authorities and reporting agents.
Linked to this project, the ESCB is developing an Integrated Reporting Framework whose
aim is to reduce the reporting burden for banks and to ensure the effort made by any credit
institution does not depend on the particular euro area country in which a bank is resident.
To this end, the purpose is to integrate the existing ESCB statistical data requirements for
banks into a single, standardised reporting framework, making it homogeneous across the
EU.
There are also initiatives to explore the role played by national central banks as producers
of statistics at the euro area level. For many statistical products and for different stages of
the statistical process, central banks mainly rely on their own capabilities, running different
IT systems and developing autonomous projects in each country. Here, some synergies can
probably be harnessed and costs reduced if collaboration between central banks is further
strengthened in that statistical process.
Concluding remarks
Allow me to conclude. Modern technologies offer a wide range of possibilities for obtaining
and assessing information at much lower costs than in the past. Here we have a
technological challenge, but another we face is to recruit our staff and to provide them with
the skills needed to address these new issues. This may be a long process but we need to
tackle it now. As I have tried to explain today, the potential benefits for economic
policymaking are very significant.
At the same time, there are other obstacles in the way of improving our knowledge of the
rapidly changing world. These have more to do with institutional factors and even some
cultural traits. They are not easy to remove, either; but we cannot deny we have a major role
to play in gradually changing our mind-set and overcoming the barriers which today hamper,
for example, data-sharing among institutions and open data for scientific purposes. My
impression is that we need to be very ambitious if we do not want to miss that train. We also
have more possibilities of succeeding if we join forces with other institutions, both at the
national and the international level.