Will Big Data Keep Its Promise?
Speech given by
Andrew G Haldane
Chief Economist
Bank of England
Data Analytics for Finance and Macro Research Centre, King's Business School
19 April 2018
The views expressed here are not necessarily those of the Bank of England or the Monetary Policy
Committee. I would like to thank Shiv Chowla for his help in preparing the text. I would like to thank
David Bholat, David Copple, Andi Joseph, Perttu Korhonen, Katie Low, Clare Macallan,
Paul Robinson, Michael Saunders, Misa Tanaka, Silvana Tenreyro, Ryland Thomas, Arthur Turrell,
Arzu Uluc and Michalis Vasios for their comments and contributions.
I am delighted to be here to launch the Data Analytics for Finance and Macro (DAFM) Research Centre at
King's College Business School. I would like to congratulate Professors George Kapetanios and Georgios
Chortareas, as Co-Directors (as well as former colleagues), for getting the centre to the launch pad and
primed for take-off.
To get my punchline in early, I believe the application of data analytic techniques to the many pressing
questions in finance and macro holds great promise. That is the reason the Bank of England, around four
years ago, set up its own data analytics division. And that is why I very much welcome the setting up of this
new centre, as a means of realising that promise.
But will Big Data keep its promise? I want to try and illustrate some of that promise of Big Data, as well as
the potential pitfalls, by drawing on examples from recent Bank of England research on the economic and
financial system. I will conclude with some, more speculative, thoughts on future Big Data research.1
The Path Less Followed
The first thing to say is that Big Data and data analytic techniques are not new. Nonetheless, over recent
years they have become one of the most rapidly rising growth areas in academic and commercial circles.
Over that period, data has become the new oil; data analytic techniques have become the oil extraction and
refining plants of their time; and data companies have become the new oil giants.2
Yet economics and finance has, to date, been rather reticent about fully embracing this oil-rush. For
economics and finance, the use of data analytic techniques has been the path less followed, at least relative
to other disciplines. One simple diagnostic on that comes from looking at the very different interpretations
put on the expression "data mining" by those inside and outside of economics and finance.
For economists, few sins are more heinous than data-mining. It is the last resort of a scoundrel to engage in
"regression-hunting" - reporting only those regression results which best fit the hypothesis the researcher
first set out to test. It is what puts the "con" into econometrics.3 For most economists, such data-mining has
unfortunate similarities with oil-drilling - a dirty, extractive business which comes with big health warnings.
For data scientists, the situation could not be more different. For them, the mining of data is a means of
extracting valuable new resources and putting them to use. It enables new insights to be gained, new
products to be created, new connections to be made, new technologies to be promoted. It provides the raw
material for a new wave of productivity and innovation, an embryonic Fourth Industrial Revolution.4
1
Coeuré (2017) offers an excellent summary of the potential for Big Data to improve policymaking, in particular in central banks.
2
For example, the Economist (2017), Henke et al (2016).
3
Leamer (1983).
4
See, for example, Schwab (2017).
What explains some economists' caution about Big Data? The answer lies, in part, in methodology.5
A decent chunk of economics has followed in the methodological footsteps of Karl Popper in the 1930s.
Popper championed a deductive approach to scientific advance.6 That started with axioms, moved from
axioms to theory and then and only then took hypotheses to the data. Theory, in other words, preceded
measurement.
There is an alternative, inductive, approach. This has even deeper roots, in the work of Francis Bacon from
the early 1600s.7 This turns the telescope around. It starts with data, unconstrained by axioms and
hypotheses, and then uses this to inform choices about models of behaviour. Data, in other words, precedes
theory. Indeed, some data scientists have suggested such an approach could signal the "End of Theory".8
So where some economists have tended to see the pitfalls in Big Data, data scientists have seen promise.
Where some economists have tended to see the ecological threat it poses, data scientists have seen the
economic potential. I am caricaturing a little, but only a little. So who is right? And does the era of Big Data
signal an oil-rush or an oil-spill?
The truth, as often, probably lies somewhere in between. Both deductive and inductive approaches can offer
insights into understanding the world. They are better seen as methodological complements than as
substitutes. Put differently, using one approach in isolation increases the risk of making faulty inferences,
and potentially serious mistakes, in understanding and policy. Let me give a couple of examples to illustrate.
During the global financial crisis, it is now fairly well accepted that the workhorse Dynamic Stochastic General
Equilibrium (DSGE) model of the macro-economy fell at the first fence.9 It was unable to account for
business cycle dynamics, during or after the crisis. While theoretically pure, it proved empirically fragile.
That empirical fragility arose, I would suggest, from a methodological over-reliance on deductive methods.
Or, put differently, from too little focus being placed on real-world data from the past, including on crises.
As a counter-example, in 2008 Google launched a predictive model of flu outbreaks based on searches for
phrases such as "indications of flu".10 This did a terrific job of tracking flu outbreaks in the US in 2009-10.
But that model's predictions broke down spectacularly in the following years.11 That empirical fragility arose, I
would suggest, from an over-reliance on empirical regularities and an over-adherence to inductive methods.
Or, put differently, from too little focus being placed on the deep medical causes of past flu outbreaks.
In the first case, empirical fragility arose from too tight a set of axioms and restrictions, from placing too great
an emphasis on theory over real-world correlations and historical experience. In the second, empirical
5
Haldane (2016).
6
Popper (1934) and Popper (1959).
7
Bacon (1620).
8
Anderson (2008).
9
For example, Stiglitz (2018).
10
Ginsberg et al (2009).
11
Lazer et al (2014).
fragility arose from too loose a set of axioms and restrictions, from observed empirical correlations being
given too great a role relative to theory and causality.
In both cases, these mistakes might have been reduced had inductive and deductive approaches been used
in a complementary, or iterative, fashion. This iterative approach has a strong pedigree in other disciplines.
The history of advance in many scientific disciplines has involved a two-way process of learning between
theory and empirics, with theory motivating measurement at some times, and measurement motivating
theory at others, in a continuous feedback loop.12
One example of this approach, discussed by Governor Carney at the time the Bank's own data analytics
programme was launched, concerns the dynamics of planetary motion.13 It was Sir Isaac Newton (a fellow
money-printer, as former Master of the Royal Mint) who developed the physical theory of celestial motion.
But this theory was built on the empirical shoulders of another scientific giant, Johannes Kepler. When it
comes to planetary motion, empirics first led theory, the inductive led the deductive.
The same has been true, at times, when understanding the motion of economies and financial markets.
Keynesian and Monetarist theory were both built on empirical experience during the Great Depression.
The Phillips curve began life as a Kepler-esque empirical regularity, which was only subsequently given a
Newtonian theoretical basis. Many puzzles in finance, which have taxed theorists for decades, began as
empirical anomalies in asset markets.14 In each case, empirics led theory, the inductive led the deductive.
My lesson from all of this is clear. If that iterative learning process between empirics and theory is to
continue to bear fruit in economics, then deductive and inductive approaches may need to share a broadly
equal billing. If so, I think there are high returns to economics and finance making a further intellectual
investment in Big Data and accompanying analytical techniques in the period ahead, the path less followed.
The Definition of Big Data
If Big Data holds promise, it is probably useful to start by defining just what it is. This is not entirely
straightforward. Like beauty, what counts as Big Data lies in the eye of the beholder. It is also a fluid
concept. For example, it is clear that data no longer means just numbers; it means words too. Indeed,
growth in research on semantics has taken off over recent years, including in economics and finance.
What is less contentious is that there has been the most extraordinary revolution in the creation, extraction
and capture of data, broadly defined, over the course of the past decade or so. This has been, in part, the
12
Bacon (1620) summarises this well: "Those who have handled sciences have been either men of experiment or men of dogmas.
The men of experiment are like the ant, they only collect and use; the reasoners resemble spiders, who make cobwebs out of their own
substance. But the bee takes a middle course: it gathers its material from the flowers of the garden and of the field, but transforms and
digests it by a power of its own."
13
Carney (2015).
14
Obstfeld and Rogoff (2001) discuss six major puzzles in international macroeconomics, such as the excess volatility of exchange
rates relative to fundamentals.
result of Moore's Law and accompanying advances in information technology.15 Unlike oil, whose resources
are finite, new data is being created at an unparalleled rate and has almost limitless supply.
It is estimated that 90% of all data ever created occurred in the past two years.16 A good chunk has come
courtesy of social media. Around 1.5 billion people use Facebook daily and 2.2 billion monthly. In 2017,
there were 4.4 billion smartphone subscriptions, more than one for every second person on the planet.
By 2023, there are projected to be 7.3 billion smartphone subscriptions, almost one for every person.17
An estimated 1.2 trillion photos were taken in 2017, as much as 25% of all photos taken ever.18
A different window on this information revolution is provided by looking at numbers of data scientists. Using
vacancies data from the job search website Reed, there were recently over 300 UK job adverts for data
scientists.19 As recently as 2012, there were hardly any. Estimates based on self-identification on social
networking site Linked-In suggest there may be upwards of 20,000 data scientists globally.20
There has, at the same time, been rapid growth in new techniques for handling, filtering and extracting
information from these data. Machine learning techniques are developing rapidly. So-called "deep learning"
techniques are complementing existing approaches such as tree-based models, support vector machines
and clustering techniques.21 Within text mining, dictionary techniques, vector space models and semantic
analysis are rapidly gaining traction.22
All of these methods offer different means of teasing out information, and making robust inferences, in
situations where empirical relationships may be complex, non-linear and evolving and where data may be
arriving at different frequencies and in different formats. These approaches differ significantly from classical
econometric techniques for inference and testing often used in economics and finance.
This revolution in data provision, and in techniques to understand it, offers analytical riches. Mining those
riches requires, however, considerable care. For example, issues of data privacy loom much larger with
granular, in some cases personalised, data. These issues have, rightly, risen in prominence recently. At the
same time as putting it to use, safeguarding Big Data is a key preoccupation of the Bank in its research.
The Promise of Big Data
To the extent Big Data can be characterised, this is usually done using the "three V's": volume, velocity and
variety. Using the three V as an organising framework, let me discuss some examples of how these data
15
Moore (1965) noted the annual doubling in the number of components per integrated circuit.
16
SINTEF (2013).
17
Ericsson Mobility Report (2017)
18
See https://www.statista.com/chart/10913/number-of-photos-taken-worldwide/
19
Using dataset in Turrell et al (forthcoming).
20
Dwoskin (2015). The true number of data scientists worldwide is highly uncertain. Many individuals work on data science without
necessarily using that job title, but the opposite is also true.
21
Chakraborty and Joseph (2017).
22
Bholat et al (2015).
and techniques have been used in recent Bank research to improve our understanding of the functioning of
the economy and financial system.
Volume
The statistical bedrock of macro-economic analysis, since at least the middle of the 20th century, has been
the National Accounts. The National Accounts have always relied on an eclectic range of data.23 In the
past, manor accounts on land use, crops and livestock were used to estimate agricultural output. Industrial
production was measured by sources as varied as numbers of iron blast furnaces and books listed by the
British Library. And services output was estimated based on merchant shipping tonnage.24
With more data than ever coming on stream, that use of new and eclectic data sources and methods is, if
anything, gaining further ground within statistical agencies. In the area of consumer price measurement,
MIT's "Billion Prices Project" uses data from over 1,000 online retailers in around 60 countries to collect
15 million prices on a daily basis. This approach has been found to offer a timelier (and cheaper) reading on
consumer prices than traditional surveys.25 Online price data have also been found to improve short-term
forecasts for inflation in certain markets.26
In the same spirit, the UK Office for National Statistics (ONS) is exploring the use of 'web scraping' to
complement existing price collection methods. To date, they have focussed on items such as groceries and
clothing. Although early days, the potential benefits in terms of increased sample sizes and granularity seem
to be considerable. For example, the ONS have so far collected 7,000 price quotes per day for a group of
grocery items, which is larger than the current monthly collection for those items in the CPI.27
For GDP measurement, new sources and methods are also gaining ground. One recent study used satellite
imaging to measure the amount of non-natural light emitted from different regions of the world. This has
been found to have a statistically significant relationship with economic activity.28 This approach could
potentially help in tracking activity in regions that are geographically remote, where statistical surveying
techniques are poor or where mismeasurement problems are acute.
A more down-to-earth example, used by the UK's ONS and other statistical agencies, is so-called
administrative data. This includes data collected by government agencies as part of their activities - for
example, on tax revenue and benefit payments. In the UK, some of these data have recently become
available for wider use through the government's Open Data initiative, albeit subject to important checks.
23
Coyle (2014).
24
Fouquet and Broadberry (2015).
25
Cavallo and Rigobon (2016).
26
Coeuré (2017).
27
See https://www.ons.gov.uk/economy/inflationandpriceindices/articles/researchindicesusingwebscrapedpricedata/august2017update
28
Henderson, Storeygard and Weil (2011).
As one example, VAT data from SMEs in a subset of industries have recently been used by the ONS when
constructing output-based estimates of GDP. As with prices, the gains in sample size and granularity from
using such administrative data are potentially large. The ONS's Monthly Business Survey of activity typically
relies on a sample of around 8,000 firms to represent this subset of SMEs. This is now being complemented
by VAT returns from around 630,000 reporting units.29
These new data augment, rather than replace, existing survey methods. They have the potential to improve
the timeliness and accuracy of National Accounts data on aggregate economic trends. The ONS has its own
Data Science Campus to spearhead these efforts. And new research organisations, such as the Alan Turing
Institute, are doing excellent work applying new data and techniques to economic measurement.
Another potentially fruitful area of exploration, when tracking activity flows in the economy, is financial data.
Almost all economic activity leaves a financial footprint on the balance sheet of some financial institution.
Tracking the flow of funds between financial institutions can help in sizing that footprint and thus, indirectly,
in tracking economic activity.
At the Bank, we have over recent years drawn on the Financial Conduct Authority's Product Sales Database
(PSD). This is a highly granular source of administrative data on owner-occupier mortgage products taken
out in the UK. It contains data on close to 16 million mortgages since mid-2005. The PSD has provided the
Bank with a new, higher resolution lens on household and housing market behaviour.
For example, in 2014 the PSD was used by the Bank's Financial Policy Committee (FPC) to help inform and
calibrate its decisions on setting macro-prudential restrictions on high loan-to-income mortgages to UK
households.30 Since then, we have used these data to track the characteristics of existing mortgagors with
high loan-to-income and high loan-to-value mortgages over time.31 PSD data have been used to understand
pricing decisions in the UK housing market.32 And they have also been used to calibrate a multi-sector,
agent-based model of the UK housing market.33
The Bank and the ONS have over recent years been developing a more comprehensive set of data on flows
of funds between institutions. The hope is that these data help in tracking not only portfolio shifts but also
how these might affect financial markets and the wider economy. For example, do portfolio reallocations by
institutional investors affect asset markets and have knock-on effects for spending?34 Answering questions
like this helps, for example, when assessing the efficacy of quantitative easing.35
29
https://www.ons.gov.uk/economy/grossdomesticproductgdp/articles/vatturnoverinitialresearchanalysisuk/december
30
June 2014 Financial Stability Report.
31
Chakraborty, Gimpelewicz and Uluc (2017).
32
Bracke and Tenreyro (2016) and Benetton, Bracke and Garbarino (2018).
33
Baptista et al (2016).
34
Bank of England and Procyclicality Working Group (2014).
35
For example, Albertazzi, Becker and Boucinha (2018) show evidence of the portfolio rebalancing channel from the ECB's asset
purchase programme.
New, highly granular data are also coming on stream on payment, credit and banking flows. Some of these
have been used to help predict, or track, movements in economic activity. They have had some success.
For example, in the US a dataset of over 12 billion credit and debit card transactions over a 34 month period
has recently been used to analyse consumption patterns by age, firm size, metropolitan area and sector.36
In time, it is possible these sorts of data could help to create a real-time map of financial and activity flows
across the economy, in much the same way as is already done for flows of traffic or information or weather.
Once mapped, there would then be scope to model and, through policy, modify these flows. This is an idea I
first talked about six years ago. Today, it looks closer than ever to being within our grasp.37
These are all areas where DAFM could contribute importantly to efforts to improve the quality and timeliness
of data on the macro-economy and financial system. As is well-recognised, the scope for improvements in
the quality of National Accounts data is considerable.38 And those measurement challenges will only
become greater as we move towards an increasingly digital and service-oriented economy.
Velocity
A second dimension of the Big Data revolution is its greater frequency and timeliness. Higher-frequency
data can give new or timelier insights into trends in financial markets and the economy. It can also
sometimes help with thorny identification problems, which otherwise plague both Big Data (as the Google flu
example demonstrated) and classic econometric methods (as the DSGE example demonstrated).
The crisis revealed that, in situations of stress, some of the world's largest and deepest financial markets
could be drained of liquidity. This resulted in some of these markets seizing up. In response, as one of their
first acts, the G20 agreed in 2009 to collect a far greater amount of data on transactions in these markets, to
help better understand their dynamics in situations of stress.39 These data are stored in Trade Repositories.
Over recent years, these trade repositories have begun collecting data on a highly granular, trade-by-trade
basis. This has meant they have quickly accumulated a large stockpile of data. For example, around
11 million reports are collected in the foreign exchange market each working business day. These provide a
rich data source when making sense of high-frequency financial market dynamics and dislocations.
One example of such a dislocation came when the Swiss franc was de-pegged in January 2015. This
unexpected move caused large shifts in asset prices. The franc exhibited a sharp V-shaped movement in
36
Farrell and Wheat (2015).
37
Ali, Haldane and Nahai-Williamson (2012).
38
For example, Bean (2016).
39
See, for example, FSB (2010).
the hours immediately after the de-pegging. By analysing trade repository data on forward contracts for the
Swiss franc-euro exchange rate, some of the drivers behind these moves could be detected.40
For example, high-frequency movements in the Swiss exchange rate can be compared to the volume of
trades in forward contracts. These trades can be further decomposed by counterparty - for example, large
dealer banks versus end-investors. This type of decomposition technique shows that it was the withdrawal
of liquidity by the large dealer banks that caused the overshoot of the franc - a classic sign during times of
market turmoil.41 This move partially reversed once dealers resumed market-making.
The trade repository data can also be used to assess whether the franc de-pegging had any lasting impact
on market functioning. The Bank's research found it did, with a persistent fragmentation in the franc
forwards market. Liquidity and inter-dealer activity was structurally lower, and market volatility persistently
higher, after this episode.
The extra granularity of these data makes it possible to tell a quasi-causal story about the drivers behind the
V-shaped movement in asset markets after the de-pegging. Using tick-by-tick and trade-by-trade data
side-by-side allows identification of the triggers and amplifiers, in a way which would not otherwise be
possible.
A second example of research using higher-velocity data to improve our understanding of economic
dynamics comes from the labour market. Understanding the joint behaviour of employment and wages
remains one of the central issues in modern macro-economics. These dynamics have been complicated
recently by changes in the world of work, with automation changing both the nature and structure of work.
Recent Bank research has used granular data on advertised job vacancies to shed light on these
dynamics.42 The research analyses around 15 million job vacancies over a ten year period. Instead of
classifying jobs by sector, occupation or region, it uses machine learning techniques on the text describing
jobs to classify and cluster vacancies. What results is a more "job description-based" classification scheme
for labour demand.
This approach provides a different way of classifying and describing how the world of work is evolving - for
example, the types of skills required in the face of automation. The classification scheme has also been
useful when identifying the relationship between labour demand and wages. Using the job description-based
classification helps identify a clearer link between labour demand and offered and agreed wages.
40
Cielinska et al (2017). Other recent research papers using trade repository data include Abad et al (2016) and Bonollo et al (2016).
41
See, for example, Duffie, Gârleanu and Pedersen (2005) and Lagos, Rocheteau and Weill (2011).
42
Turrell et al (forthcoming).
Variety
One of the potentially most productive avenues for Big Data research, in the macro and finance sphere,
involves using not numbers but words as data. Semantic data and semantic search techniques have a rich
pedigree in other social sciences, such as sociology and psychology. But, so far, their application in
economics and finance has been relatively limited.43
Like other social sciences, economics and finance involves human choice. And we know humans often rely
on heuristics or stories, rather than statistics, when making sense of the world and when making decisions.
Capturing these stories, semantically, is thus important for understanding human behaviour and decisions.
As an example, the Bank has recently begun looking at the language it uses when communicating externally,
whether to financial firms or the public at large. For example, Michael McMahon at the University of Oxford
and I have recently assessed how the simplification of the Monetary Policy Committee's (MPC) language in
the Inflation Report at the end of last year boosted public understanding of monetary policy messages.44
A second example considers a far less well-explored aspect of the Bank's decision-making - its supervision
of financial firms.45 This draws on a text-based analysis of the Bank's confidential Periodic Summary
Meeting (PSM) letters to financial firms. These are arguably the single most important letters the Prudential
Regulation Authority (PRA) regularly sends to firms, setting out supervisors' assessment of firms' risks and
requested actions to mitigate these risks. Using a machine learning technique called random forests, the
research analyses these letters and extracts data on their tone and content.
This type of analysis has a number of policy applications. It can be used to assess whether the letters
convey a clear and consistent supervisory message to firms. For example, it is possible to compare the
strength and content of these letters with the Bank's internal assessment of firms' strengths and shortfalls.
Are the two, and the Bank's supervisory messaging, consistent? In general, the research found they are.
This approach can also be used to assess how the style of supervision has evolved over time. For example,
how has it changed since the transition in supervisory models from the Financial Services Authority (FSA) to
PRA? The research finds that supervisory messaging has become more forward-looking, formal, and
content-rich comparing the two regimes, consistent with the PRA's new supervisory model.
This exercise is, I think, a good example of a new technique (random forests) being applied to an entirely
new database (the Bank's supervisory assessments) in a policy area virtually unexplored previously by
researchers (financial firm-specific supervision). It reaches conclusions that have a direct bearing on policy
questions. As such, I think it highlights nicely the promise of Big Data.
43
Notable examples include Schonhardt-Bailey (2013) and Goldsmith-Pinkham, Hirtle and Lucca (2016).
44
Haldane and McMahon (forthcoming).
45
Bholat et al (2017).
My final example uses not newly-created but old data. Nonetheless, I think it provides a good illustration of
how new techniques can also be used to understand the past. Long before it was responsible for monetary
policy and financial stability, one of the Bank's key roles was the provision of last resort lending to
commercial banks facing liquidity pressures.
It is hard to date precisely, but the Bank began undertaking such operations in earnest probably around the
time the UK faced a steady sequence of banking panics in 1847, 1857 and 1866. The Bank responded to
these panics by making liquidity available to support banks. Last resort lending, as Bagehot came
subsequently to call it, was born.46 Indeed, Bagehot later defined principles for such lending: it should occur
freely, at a penalty rate against good collateral.
An interesting historical question, with relevance to the present day, is whether in fact the Bank abided by
these principles in its last resort lending during the 1847, 1857 and 1866 panics. To assess that, we took
data from the giant paper ledgers recording changes to the Bank's balance sheet where these interventions
were recorded on a loan by loan, counterparty by counterparty, interest rate by interest rate basis.47
The transcription of these data benefitted from the fact that the hand-writing in the ledgers was from a small
number of clerks across the three crises - one of the indirect benefits of job continuity. While the data was
mostly transcribed manually, the project developed an image recognition system using a neural network
algorithm which we will use in future to turn historic ledger activities into 21st century machine readable data.
The data on the Bank's historic last resort lending is new and highly granular, Big Data from a bygone era.
It shows that the Bank's approach to last-resort lending evolved considerably during the mid-19th century
crises. That meant, by the time of the 1866 crisis, the Bank was more or less following the principles for
last-resort lending subsequently set out by Bagehot. It is another example of empirics leading theory.
Machine learning techniques are being applied to the statistics regularly collected and reported by the Bank.
In particular, these techniques are being used to spot errors or anomalies in the raw data provided to the
Bank. This makes cleaning the data much more systematic and efficient than is possible using manual
processes. Data science methods can also be used to match new sources of granular data. This not only
provides another means of checking the plausibility of data, but can offer insights that individual data sources
cannot reveal by themselves.48 At the Bank of England, as elsewhere, the robots are on the rise.
46
Bagehot (1873).
47
Anson et al (2017).
48
Bahaj, Foulis and Pinter (2017), for example, match firm-level accounting data, transaction-level house price data and loan-level
residential mortgage data to show how the house price of the director of an SME can affect their firm's investment and wage bill.
Looking to the Future
Looking to the future, there are many potential areas where these new sources and new techniques could be
expanded to improve the Bank's understanding of the economic and financial system. From a long list, let
me discuss one which I think holds particular promise.
Behavioural economics has, rightly, made a big splash over the past several years in re-shaping economists'
thinking about how human decisions are made. Human decisions and actions deviate, often significantly and
consistently, from the rational expectations norm often assumed.49 Rules of thumb and heuristics dominate
human decision-making. And the expectations formed by people are often shaped importantly by
history, emotion and others' actions, every bit as much as by rational calculation.
These behaviours appear to be important both for individuals (micro-economic) and for societies
(macro-economic). For example, the popular narratives which develop in financial markets and in everyday
public discourse have been found to be important empirical drivers of fluctuations in asset prices and in
economic activity.50 These narratives may be particularly important at times of economic and financial
stress, when emotions run high and social stories take on added significance.
Yet when it comes to measuring these behaviours, at either the micro- or macro-economic level, our existing
methods are often poorly-equipped. Capturing people's true sentiments and preferences is devilishly
difficult. Traditional surveys