Nontraditional Data & Statistical Learning with Applications
to Macroeconomics
Welcome address by Daniele Franco
Senior Deputy Governor of the Bank of Italy
Rome, 11 November 2020
Ladies and Gentlemen,
I am glad to open this virtual conference jointly organized by Banca d'Italia and the
Federal Reserve Board. I would like to welcome all the participants from more than 60
countries.
1. This conference is about the impact of digital activities on economic analysis.
An astonishing amount of digital activity is occurring at any given moment. This
explosion stems from the aggregate action of 4.5 billion internet users, a number that is
projected to rise even further in the coming years.
Since the outbreak of COVID-19, technology has played an even greater role in our
daily lives.
This development has increased the production of data. In fact, data are constantly
being generated by our clicks, reactions on social media, shares, streaming of videos,
digital transactions, digital recordings of our personal and work activities, digital
circulation of scientific texts, and so on and so forth.
These data can give us a better understanding of the state of the economy at both
the micro and macro level, provided - obviously - that we have access to them.
Data ownership is therefore extremely important.
As recently highlighted in The Economist ('Who owns the web's data?', 22 October
2020), we are now experiencing a sort of 'digital feudalism', where a handful of big tech
companies have full control over data.
Tech companies such as Facebook, Amazon, and Google have reached an impressive
position, becoming some of the biggest companies in the world. In this process, they
have caught up with longer-standing titans like Apple and Microsoft.
The use of data is actually one of the world's biggest businesses. We see this in the
market capitalization of some companies, like Alphabet (the owner of Google) and
Facebook. Even once the value of their cash, physical and intangible assets, and
accumulated R&D has been stripped out, their capitalization remains huge.
These developments are causing increasing concerns among policymakers, from
both a political and economic point of view. As to the former, the big techs' business
model depends on network effects to influence users and sell more advertising, which
may result in collective behaviour that can harm public discourse. Economic concerns
stems from the fact that the size of big tech firms may shrink competition.
2. Banca d'Italia has always based its decisions on the data. Micro-level statistical
information on firms has been collected since the early 1950s. Back then, only a few
aggregate statistics were available. We began by surveying the balance sheets of a small
group of firms, an activity that was progressively expanded until it became what is now
the Survey on Industrial and Service Firms. These days, we collect detailed data on about
5,000 firms.
At the beginning of the 1960s, the Bank began surveying household finance.
Nowadays, this survey allows us to evaluate income and wealth distribution at the micro
level in order to analyse individual preferences and the determinants of their economic
decisions. In recent years, we have interviewed a sample of more than 8,000 households
in each survey; about 50 per cent of them were part of a panel.
In 2016, Banca d'Italia started its project on Big Data and Machine Learning
applications. We wanted to fine-tune the accuracy of our economic analysis by combining
all available skills ranging from economics to statistics, from computer science to
engineering, and from mathematics to applied physics.
This year we have stepped up our action by strengthening the team with new
resources from universities and by widening our data collection from private companies.1
3. The COVID-19 pandemic has radically transformed our lives in a number of
different ways. Direct personal contacts have been partly replaced by online events. Most
meetings around the world are now organized via multiple IT applications. This has
allowed many of us to keep working in safety. It has also reduced both travelling cost
and carbon emissions.
These changes have been made possible by the progress of IT in many areas, from
core technology advances - like further doublings of basic computer power every 18
months - to successful investment in complementary innovations, like cloud infrastructure.
One of the most important developments is the wave of recent improvements in
artificial intelligence, especially machine learning.
Machine learning represents a paradigm shift from the first wave of computerization.
Historically, most computer programs were created by accurately codifying human
knowledge, mapping inputs to outputs as prescribed by the programmers.
In contrast, machine-learning systems use categories of general algorithms (e.g.
neural networks, random forest and gradient boosting) to figure out mappings and are
typically driven by voluminous amounts of data. By employing huge data sets and big
data processing resources, machines have made impressive progress. For instance, error
rates in labelling the content of photos on ImageNet, a dataset of over 10 million images,
have fallen from over 30 per cent in 2010 to as low as 2.2 per cent in 2019. This has, inter
alia, big implications for the identification of fake banknotes, which is part of our central
bank work.
The exponential growth of digital data, such as images, videos and speeches, from
numerous sources (e.g. social media, Internet-of-Things, etc.) is driving the search for
tools that allow us to extract relevant information from disparate kinds of data.
Private companies have played an important role in collecting and organizing
behavioural microdata on firms and households. In this respect, we can mention a recent
paper on the economic impact of COVID-19 by Professor Raj Chetty and his team at
Using anonymized data from a large number of private companies,
Opportunity Insights.2
Professor Chetty and his co-authors develop a publicly accessible platform, which
measures some important macroeconomic variables such as spending, employment, and
other outcomes both at a very high frequency, and at a very granular level.
Their work shows how new nontraditional data can be used to obtain quasi-real-
time insights into the effects of the COVID-19 pandemic. Along the same lines, a recent
study by colleagues of Banca d'Italia, Banque de France and the Bundesbank points to
the role of nontraditional data and indicators for a rapid assessment of the implications
of the pandemic.3
4. Banking and financial industries have been profoundly impacted by these
developments. Central banks are now using big data to improve their capacity to carry
out their missions: monetary policy, banking supervision and payment system oversight.
Over the next two days, we will hear about several studies applying new data sources
to different issues relevant to central bank activities.
Some papers focus on the new data and tools available to improve the trade- offs
between the objectives for inflation, employment/growth and financial stability. Others
show the advantages provided by machine learning methods for estimating and
forecasting macroeconomic variables and the relevance of nontraditional sources of
information, especially when it is impossible to run a classical survey, as was the case
during the COVID-19 pandemic.
Besides its long history of cooperation with commercial banks, with Istat (our
National Statistical Institute) and other public institutions, Banca d'Italia has already
started to collect nontraditional data from social media, newspapers, payment platforms
and on real estate and home mortgages.
The harnessing of larger data sets, faster computational power, and more advanced
analytical techniques is spurring progress on a range of micro- and macroeconomic
problems.
At Banca d'Italia, we approached the data deluge by building a multidisciplinary
team of economists, statisticians, econometricians, mathematicians, engineers and IT
specialists. By connecting these data experts with their frontline colleagues, we began to
identify projects that can increase both the accuracy and performance of our policy
action.
5. The Big Data revolution poses some fundamental challenges. Let me briefly
mention two of them.
First, big data can lose their statistical relevance if they are not used in a
methodologically sound way. Such data typically target only a particular layer of the
population; increasing the sample size will not improve the accuracy of estimates if the
estimation methods do not correct for this kind of distortion. Social media data for
example, like many other internet-based sources of big data, are usually based on a
biased sample of the population.
Second, the availability of a huge amount of data raises the importance of its
integrity, confidentiality and privacy. Personal data protection is central to our societies.
The sheer amount of personal data now available, and the growing ease with which
individual information can be connected across databases, have far-reaching implications
for privacy and freedom. Indeed, this has prompted the development of regulations
concerning the treatment of digital data (think of the GDPR or the CCPA in California).4
Rules on data management are sometimes different across jurisdictions and data
domains. Ideally, one would like to have global standards. Therefore, international
cooperation is to be encouraged as far as possible.
Furthermore, the adoption of machine learning and artificial intelligence requires as
input huge amounts of granular and unstructured data, which should be collected
through close collaboration between public institutions and private companies. We also
need specific platforms or data lakes to store and analyse such data so as to preserve
privacy and security. This can be done through a collectively trusted cloud where all
actors, both public and private, can operate to gain insights for their core business.
The availability of privacy-preserving algorithms or the use of federated learning,
recently suggested by researchers at Google, could provide the enabling technology.
Federated learning allows machine learning algorithms or artificial intelligence on data
to be distributed on different servers, avoiding any data exchange. This will require
further investment from both the public and the private sector. It will also require tighter
cooperation between the private sector, which typically owns most of the new data, and
the public sector, which uses such data for policy reasons and for the common good.
6. The challenges generated by big data and machine learning technologies require
continuous organizational and technological innovation in the institutions that want to
use them effectively and fairly. Banca d'Italia has taken several steps in recent years to
enhance its ability to collect, store and exploit huge amounts of data, using state-of-the-
art hardware and software. Other institutions are doing the same.
Let me conclude my talk by thanking, once again, all the speakers and participants
for joining us today. We hope to welcome you here in person in the future. Special thanks
go to those who have contributed to organizing the workshop, which brings together
leading economists, statisticians, artificial intelligence and machine-learning specialists,
data scientists from about 60 central banks, 57 universities, 24 government agencies, 7
multilateral organizations, and 20 private companies. This will ensure a broad variety of
perspectives and a lively discussion.
I am sure that you can count on having two interesting and productive days.
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