CIGI Papers No. 280 — September 2023

Valuing Data
Where Are We, andWhere Do We Go Next?
Tim Sargent and Laura Denniston

CIGI Papers No. 280 — September 2023

Valuing Data
Where Are We, andWhere Do We Go Next?
Tim Sargent and Laura Denniston

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Table of Contentsvi

About the Authorsvi

Acronyms and Abbreviations

1

Executive Summary

1

Introduction

2

Current Treatment of Data in the National Accounts

3

Current Treatment of Data in the SNA

4

Experimental Estimates of the Value of Data by Statistical Agencies

8

Next Steps for National Accounts

9

Alternate Methods for Data Valuation

12

Conclusions and Directions for Future Research

14

Works Cited

About the Authors
Tim Sargent is a CIGI distinguished fellow with
28 years of experience with the Governmentof Canada. He has held senior roles at Global
Affairs Canada, the Privy Council Officeand the Department of Finance, giving him
policy-making experience at the highest
level, in particular in the areas of trade policy,
international finance and macroeconomics.

vi

Acronyms andAbbreviationsAI

artificial intelligence

BEA

Bureau of Economic Analysis

CSLS

Centre for the Study of Living Standards

GFCF

gross fixed capital formation

Tim earned a Ph.D. in economics at the
University of British Columbia, an M.A. at the
University of Western Ontario and a B.A. ineconomics at the University of Manchester.

IP

intellectual property

NAICS

North American Industry
Classification System

Laura Denniston is an honours economics student
at the University of Ottawa. She was a coop student
at the Centre for the Study of Living Standards
in fall 2022 when this paper was drafted.

PIM

perpetual inventory method

R&D

research and development

SNA

System of National Accounts

TFP

total factor productivity

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

Executive Summary

Introduction

There is widespread agreement on the importanceof data in advanced economies, but no consensus
on exactly how to value the magnitude of thiscontribution. The authors of this paper look atrecent attempts to value both the stock and flow ofdata in the national accounts and attempt to assess
the way forward. They begin with separating out
three different categories of data-related assets —data itself, databases and data science — andthen outline some of the key national accounting
concepts related to incorporating them asassets in the national accounts framework. They
then examine in detail three recent studies bystatistical agencies in Canada, the Netherlands
and the United States, each of which tries tovalue data-related assets using a cost-based
methodology. While each of these studies findsthat data is a significant asset — investment indata-related assets is estimated at anywherefrom one to three percent of output — there are
significant differences in the results, which are,in turn, driven by some arbitrary assumptions
that the authors were required to make.

In 2017, The Economist (2017) announced that “theworld’s most valuable resource is no longer oil,but data,” arguing that data is fuelling the modern
economy just as oil did a century earlier. In thesame year, Jonathan Haskel and Stian Westlake(2017) published Capitalism without Capital: The Riseof the Intangible Economy, arguing that intangible
capital, which includes data but also research anddevelopment (R&D), patents and other intellectual
property (IP), is now the main driver of advanced
economies, rather than physical capital such asmachinery and buildings. Since then, the perceived
importance of data to business and the economy
at large has only grown, especially given theimportance of data as the raw material in artificial
intelligence (AI) systems, which promise to be a
new “general purpose technology” as consequential
and wide ranging as computers or electricity.

Going forward, a key question is whether data
will be treated as an asset in the 2025 System
of National Accounts (SNA). The authors argue
that it likely will, but that more work willneed to be done before statistical agencies will
start including data as an asset in individual
countries’ national account systems.
The authors then go on to examine two other waysof valuing data: the income-based method and themarket-based method, as well as a hybrid approach.
The authors find that while conceptually superior,these methods are hard to implement in practice.
Finally, the authors conclude with suggestions forfurther work. The scope of efforts to value data
could be broadened to include the public sector,
which has significant holdings of data. Also, thesocial and not just the private value of data needs
to be calculated — this will mean consideringboth the positive and negative externalities
of data-related investment and use. Finally,
given these broader social implications of dataacquisition and use, more work needs to be done
on appropriate data governance models to ensurethat data is being used for the benefit of all.

The importance of data to the economy, andparticularly its increasing importance as an asset,
poses a challenge to statistical agencies. Whilethe current SNA standard recognizes the costs ofdatabase management software, it does not treat
data as an asset, and does not count the costs ofacquiring the data. This absence reflects someof the very real problems that exist in valuing
a commodity that is very situation-specific,
is usually not bought and sold in transparent
markets (or at all), and, consequently, is noteasily obtainable from firms’ balance sheets.
Despite these problems, several national
statistical offices have risen to the challenge ofattempting to measure the value of data in a
way that is consistent with national accounts
concepts for capital. In this paper, the authors
will take stock of these attempts, assess how
plausible their estimates are, examine otherpossible methods of valuation and suggest
potential ways forward for the valuation of data,
both in an SNA context and more broadly.

Valuing Data: Where Are We, and Where Do We Go Next?

1

Current Treatment of Datain the National Accounts
Fundamental Concepts
and Definitions
The authors begin by exploring the key concepts
associated with valuing data as an asset in the SNA.
The first is the definition of data, and how it relates
to adjacent concepts such as databases and data
science. One useful way to address this issue is tosituate data in what Statistics Canada (2019a) calls
the “information value chain,” shown in Figure 1.

Figure 1: Information Value Chain

Data
Science
Databases
Data
Observations
Source: Statistics Canada (2019a).

“Observations” (or “observed phenomena”) are
the first step in the information value chain.
Observations can be practically anything.
For example, observations include what anindividual ate for lunch, the weather, or thetime someone gets home from work. They are
emitted constantly by people, objects and theenvironment, and are usually transitory andintangible. However, observations are notinherently recorded and stored. In most cases,
someone must decide to capture and digitize
them. Only when observations are recorded
in a digital format do they become “data,” thesecond step of the information value pyramid.
However, data is merely stored and digitizedobservations. It is not organized or processed; itis still essentially raw material. It cannot yet beinterpreted or used to gain insights. To be useful,it needs to be entered into the third tier of the2

information value chain: a database, defined
as “an organized store of data that can bereadily retrieved and manipulated” (ibid., 8).1
The highest tier of the pyramid is “data science.”
This tier includes the analysis done to gain
knowledge from the data to inform future
activities. Data science looks at the data set
as a whole to reveal trends and patterns thatcannot be gleaned from individual data points.

National Accounts Concepts
Appropriately categorizing the elements of theinformation value chain in the SNA requires
assessment along two different dimensions. The
first is whether the element is an asset or not. If it
is, then any expenditures count as an investment.
The SNA defines assets as “entities that must be
owned by some unit…and from which economic
benefits are derived by their owner(s) by holding
or using them over a period of time” (European
Commission et al. 2009, 7). Thus, a factory is anasset because it is durable and provides benefits
over many years, whereas the car parts it produces
are intermediate consumption because they arequickly transformed into something else and so are
not durable. Similarly, an oil field for which rights
have been assigned is an asset because it is owned
and provides benefits over time, whereas once theoil is pumped out, it is subsequently refined and sowould be counted as intermediate consumption.
Assets, of course, need not last forever, and if
something is determined to be an asset, then a viewneeds to be taken on an appropriate depreciation
rate, which would need to consider both physicaldecay and reduction in economic value.
The second dimension along which elements of theinformation chain need to be assessed is whether
the element is produced or not. In the SNA,
“Production is understood to be a physical process,carried out under the responsibility, control andmanagement of an institutional unit, in which
labour and assets are used to transform inputs ofgoods and services into outputs of other goods andservices. All goods and services produced as outputs
must be such that they can be sold on markets or
at least be capable of being provided by one unit
to another, with or without charge” (ibid., 6).

1This distinction between databases and raw data would disappear to
the extent that AI applications are able to draw insights from raw data
without the need for these data to be “organized” in databases.

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

In the examples above, a factory is a produced asset,
whereas car parts and barrels of oil are produced
goods; however, the oil field is a non-produced asset
because it has not been created by human agency.

Current Treatment of Datain the SNA
While the concepts of asset and produced good are
relatively clear and well accepted, their application
to the information chain is not, and discussions on
how to value data-related assets have been ongoingfor more than 30 years. Originally, expenditureson collecting and producing data, entering it
into a database and analyzing the results, weretreated as intermediate expenditures and so not
as investment. Despite these expenditures, data,
databases and data science were essentiallytreated as a non-produced asset, along with otherforms of IP. This changed partially with the 1993
SNA, which recommended the inclusion of large
databases as produced assets (Commission of theEuropean Communities et al. 1993). However, thischange was largely made to ensure consistency
with the proposed inclusion of software in the
SNA: it was (and remains) impractical to separate
database management software from the database
itself (Statistics Canada 2001). Thus, the purchaseand development costs of database software were
capitalized, but not database content, creation or
updating. Data and data science therefore remained“outside the asset boundary” in statisticians’
parlance — not treated as produced assets.
A key innovation of the 2008 SNA was the creationof an asset class of IP products, which includes theexisting asset classes of software and databases,
but also R&D. In the buildup to the 2008 SNA,
the Canberra II Group re-examined the issue of
how best to include data and databases in the
SNA (Rassier, Kornfeld and Strassner 2019). This
group considered two definitions for databases.
The first included “the value of the information…
stored on the databases” (the value of the data)
and the second did not (Ahmad 2005, 2). The groupultimately recommended the second definition,
excluding the value of data from the database.
This was done to not risk “the capitalization ofknowledge” (ibid.): the fear was that by including

the value of data, one would ultimately be
driven to include the value of all knowledge,
which would risk overwhelming the rest of thenational accounts (Ahmad and van de Ven 2018).
Ultimately, the SNA 2008 does not come down
completely on one side or the other; instead,whether data should be included as an asset or not
depends on the practical issue of how the value ofthe database is determined. For those databasesthat are developed for in-house purposes andnot intended for sale, which is most databases,“the cost of preparing data in the appropriate
format is included in the cost of the database
but not the cost of acquiring or producing thedata” (European Commission et al. 2009, 10.113).
However, those databases intended for sale“should be valued at their market price, which
includes the value of the information content”(ibid., 10.114). Essentially, the authors of the SNA
prefer to exclude data from the list of assets but
recognize that this would be impractical whendatabases are valued at market price, as it would
be very difficult to disentangle the value of thedata from the rest of the value of the database.
This approach is somewhat different from theapproach used to capitalize R&D in the 2008
SNA where R&D, unless its market price isobserved directly, is valued as the sum of all
costs associated with activity — acquisitionand production costs are not excluded.
Where does this leave data science? In principle,
data science meets the criterion for R&D: “Researchand [experimental] development consists of thevalue of expenditures on creative work undertaken
on a systematic basis in order to increase thestock of knowledge, including knowledge ofman, culture and society, and use of this stockof knowledge to devise new applications”(ibid., 10.103). However, Peter Goodridge, Jonathan
Haskel and Harald Edquist (2022) argue that,in practice, R&D statistical agencies generally
measure only traditional science laboratories,
which, by and large, excludes data science. The
latter might get categorized under softwaredevelopment but would then be assigned tosoftware. Data science therefore remains effectively
uncapitalized in the current version of the SNA.

Valuing Data: Where Are We, and Where Do We Go Next?

3

Experimental Estimatesof the Value of Data by
Statistical Agencies
The increasing importance and prominence of datain the economy, as well as the above-mentioned
inconsistencies in the current treatment of data,
have led a number of researchers to consider
how data could be capitalized in the SNA, andto develop experimental estimates of the asset
value of data in advance of the next versionof the SNA, which is expected in 2025. In thissection, the authors will evaluate the threestudies, all from national statistical agencies, thatdevelop experimental estimates of the value of
data: Statistics Canada (2019b); Hugo de Bondt
and Nino Mushkudiani (2021) from Statistics
Netherlands; and José Bayoán Santiago Calderónand Dylan G. Rassier (2022) from the Bureau of
Economic Analysis (BEA) in the United States.

Concepts and Methods
The three papers all use essentially the same
approach. In each case, estimates are presented
for data, databases and data science (the US
study aggregates the three, whereas the othertwo present aggregates for the three different
concepts). Estimates are for own account data; data
for sale is excluded. Databases already included inexisting measures of software are also excluded.
To compute the value of the stock and flow ofdata-related assets, all three studies used thesum-of-costs approach, whereby the value of
assets is measured by the labour, capital andcosts incurred in production. This is a common
approach in the SNA when no market price isobserved — it is also used for valuing own accountsoftware and R&D, as well as many government
activities. Since businesses are unlikely to pay
more for producing an asset than what it is worth
to them, this method at least provides a lower
bound on the value of the asset to a company.
The value of data according to this approachis calculated as follows: For each occupation
where there is data production, one calculates
the proportion of labour time devoted to data
production and multiplies this by the average

4wage.2 Summing this over all relevant occupations
gives the total labour cost, which is then marked
up to account for capital and intermediate
consumption costs to provide an overall value
for production costs of data for a given year. This
gives a value for nominal investment in data.
To calculate real investment, a price deflator isconstructed using wages, the price of intermediate
consumption and capital (if available), andestimates of the rate of technological change.
To calculate the stock of data assets, the Canadianand US studies (the Dutch study did not estimate
the value of the stock) both use the perpetualinventory method (PIM). The stock of data is
calculated by cumulating over time the flows ofinvestment minus the flows of depreciation anddiscards. This method requires assumptions about
the useful life of data and its depreciation rate but,given these considerations, is straightforward to
implement. The PIM is a standard method usedby statistical agencies when direct measures ofthe value of a capital asset are not available.

Differences in Methodology
Despite a common methodology, there aresome important differences between the threestudies, largely driven by data availability.
→ Aggregation: The Canadian and Dutch studies
both disaggregate data-related assets into data,
databases and data science in their estimates; theUS study does not.
→ Occupations: Each study uses its own methodfor determining relevant occupations. The Dutch
study tries to be as close as possible to the
Canadian categorization; however, the US study
uses machine learning applied to job descriptions
from online job advertisements to determine thedata content of an occupation.
→ Sectoral scope: The US and Dutch studies only
cover the business sector, whereas the Canadianstudy covers government data assets as well.
→ Time period: The Canadian study covers theperiod 2005–2018; the Dutch study 2001–2017;
and the US study 2002–2021.

2

This approach assumes that wages are equal to the worker’s marginal
product. To the extent that workers earn a wage premium, this approach
will overestimate that value of data. Large firms often pay significant
wage premia in order to motivate and retain workers.

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

→ Adjustment for overlap with existing measuresof capital formation: The US study makes two
adjustments for potential overlap between
estimates of production of data-related assets
and existing measures of production of R&D andsoftware, which are already incorporated intomeasures of investment in the national accounts.
The study reduces the estimates of labour incomeby the proportion of employees engaged in
R&D, removes occupations such as computer
programmer and software developer that arelikely engaged in developing software, andfurther assumes that 50 percent of labour input
is already included in capital formation. The
Canadian and Dutch studies note the problem
but do not address it.
→ Adjustment for own account data: The
Canadian and Dutch studies do not have an
explicit adjustment factor for the portion oflabour input that is used to produce own accountdata rather than data for sale. Instead, thesubjective estimates of data-related input foreach occupation are meant to exclude data forsale. The US study assumes that only 50 percentof the activities of one of the key occupations(data processing and hosting) is for own accountdata.
→ Markup: The Canadian study assumes a markup
on wage costs of 50 percent to cover “nondirect salary and other costs”; the Dutch study
assumes a markup of 60 percent. Both assume
an additional markup of three percent to covercapital costs. The US study assumes a markup of
153 percent — much higher than the other two
studies.
→ Depreciation rates and useful lives: The
Canadian study assumes a useful life for data of
25 years, databases of five years (to be consistent
with software in the Canadian SNA) and data
science of six years. A geometric depreciation
rate is assumed. The US study, which does notmake a distinction between the three categories
of information, assumes a service life of
five years and a geometric depreciation rate of
0.33 (this is the same treatment as software in the
US SNA). There is therefore a very large difference
between the US and Canadian treatment of data:
25 years versus five years.
→ Price indices: The Canadian study bases its
estimates of prices solely on labour costs,
adjusted down by one percent per year toaccount for assumed productivity growth. The
Dutch study uses labour costs, consumption
costs and capital costs to produce a price index,
which is then (as with the Canadian study)
adjusted down by one percent per year to reflect
productivity growth. The US study uses labourcosts and intermediate consumption costs (butnot capital costs) to construct an input priceindex that is then adjusted down by estimates of
total factor productivity (TFP). This index is then
combined with the index for products of datarelated industries.

Comparison of Results
Table 1 compares estimates of the share of
GDP and of gross fixed capital formation(GFCF) represented by data-related investment
and its three components from the threestudies. For Canada and the Netherlands,
both upper and lower bounds are shown.
By and large, the estimates are somewhat different
across the three countries. Data-related assets
are smallest as a share of output in the UnitedStates (1.1 percent) and largest in the Netherlands(2.1–2.7 percent), with Canada in the middle(1.3–1.8 percent). Similarly, data-related assets
as a percentage of GFCF range from 5.1 percent
in the United States to 5.8–7.9 percent in Canada
to 12.7–16.3 percent in the Netherlands.
Looking at the three different subcategoriesof data-related assets for the two studies thatprovided this breakdown, these statistics wereof similar magnitude, with data science having
the largest share of GDP (0.5–0.6 percent),
closely followed by data (0.4–0.6 percent) anddatabases a little further behind (0.4–0.5 percent).
In the Netherlands, the data category(1.0–1.4 percent) was well ahead of data science(0.6–0.7 percent) and databases (0.5–0.6 percent).
This significantly higher contribution of data
can explain most of the discrepancy between
the Dutch and Canadian estimates.
Table 2 compares nominal growth rates fordata-related investment for the three countries.
Investment in data-related assets grewfastest in Canada (6.5 percent) and slowest
in the Netherlands (3.5–3.6 percent), with theUnited States in the middle (4.7 percent).
Looking at components, growth in both countries
was fastest in data science (8.9 percent in Canada

Valuing Data: Where Are We, and Where Do We Go Next?

5

Table 1: Share of Nominal GDP and Nominal GFCF Represented by Data-Related Investment
Share of GDP and GFCF (%)

Canada2018

Netherlands
2017

United States
2020

Lower

Upper

Lower

Upper

Data-related

1.3

1.8

2.1

2.7

1.1

Data

0.4

0.6

1.0

1.4

-

Database

0.4

0.5

0.5

0.6

-

Data science

0.5

0.6

0.6

0.7

-

Share of GDP/BVA

Total

PrivatePrivateData-related

5.8

7.9

12.7

16.3

5.1

Data

1.9

2.8

5.9

8.5

-

Database

1.6

2.3

3.2

3.6

-

Data science

2.4

2.8

3.6

4.2

-

Share of GFCF

Sources: Statistics Canada (2019b) (Canada); de Bondt and Mushkudiani (2021) (Netherlands); Calderón and Rassier (2022)(United States); Centre for the Study of Living Standards (CSLS) calculations.
Notes: BVA = business value added. US GDP share is a share of BVA; US and Dutch GFCF is private GFCF.

Table 2: Growth of Investment in Data-Related Assets in Current Prices
Compound Average Growth Rates (%)

CanadaNetherlands

United States

Investment
Lower

Upper

2010-2018

Lower

Upper

2010-2017

2012-2021

Data-related

6.5

6.5

3.6

3.5

4.7

Data

2.8

3.5

2.7

2.4

-

Database

8.7

8.4

-3.4

-3.3

-

Data science

8.9

8.9

21.4

21.2

-

Sources: Statistics Canada (2019b) (Canada); de Bondt and Mushkudiani (2021) (Netherlands); Calderón and Rassier (2022)(United States); CSLS calculations.

6

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

and 21.2–21.4 percent in the Netherlands). Growthin data was similar (2.8–3.5 percent in Canada and2.4–2.7 percent in the Netherlands). However, avery different pattern emerges for databases. In
Canada, database investment grew 8.4–8.7 percent,almost as fast as investment in data science;
however, in the Netherlands, database investment
fell by an average of 3.3–3.4 percent annually. The
authors of the Dutch study speculate that this
could be due to offshoring by Dutch firms, or anunderestimate of productivity growth in this sector.
We can compare growth in the net stock of datarelated assets in current prices for the Canadian and
US studies. The Canadian study finds a net stockof data-related assets of between $157 billion and$217 billion — 5.7–7.7 percent of total non-residential
fixed assets. (For context, IP products are aboutnine percent of total assets.) Two-thirds of thisstock is in data, 12 percent in databases and about
20 percent in data science. This preponderanceof data is very much driven by assumed service
lives: 25 years for data as opposed to five years
for databases and six years for data science. In
the US study, data-related assets are calculated atonly 1.3 percent of private fixed assets — a verylarge difference. (IP products were 14 percent of
total US non-residential fixed assets in 2021.)
Each of the three studies calculates a price index
for data-related investment, using input costs(particularly wages) and assumptions about
productivity growth. The Canadian study finds
an annual average increase of 0.9 percent fordata and databases and 0.6 percent for data
science (2010–2018). Overall, the price increase is
1.8 percent; presumably, this overall price growthis higher than that of its individual components
because of compositional shifts (recall data grew
more slowly than databases and data science
over this period). The Dutch study calculates anincrease of zero percent for data and databases and
0.4 percent for data science (2010–2017), while the
US study finds a decline in the price of data-related
assets of more than one percent (2012–2020).
Finally, the Canadian and US results permit some
sectoral breakdowns. The Canadian study finds thatin 2018, non-financial corporations accounted forabout half of all investment in data-related assets,with financial corporations accounting for a thirdof investment and government about a fifth. The
US study breaks down investment by industry
sector and finds that for the period 2002–2021, theindustries with the largest investments were

professional, scientific and technical services(25 percent), followed by manufacturing(14 percent), and finance and insurance (13 percent).

Reasons for the Disparities
It is notable that, according to the three studies, therelative importance of data assets is much lower
in the United States, which seems to have much
lower rates of investment and a much lower stockof assets. Given the importance of the information
economy in the United States, which domiciles
information powerhouses such as Google and Meta,
this is very unlikely. The more likely explanation isdifferences in methodology. The first candidate isdifferences in the adjusted markup. The Canadianand Dutch studies multiply the wage data by a ratioof 1.53–1.63 percent to reflect non-wage costs. The
United States assumes a markup ratio of 2.52 but
halves this number to 1.26 to adjust for overlap withexisting measures of capital formation, and halves
it again to 0.63 for industries in North American
Industry Classification System (NAICS) code 518(data processing, hosting and related services) toadjust for overlap with data-related products thatare sold rather than used for a firm’s own account(this is the industry where data sales largely occur).
As a result, the adjusted wage data is anywherefrom 25 to 60 percent lower for the United States,
depending on whether the industry is in NAICS
code 518. It is likely that many industries do, in fact,
fall into the NAICS code as it includes industries
that have data as a primary business activity.
The other factor that will likely have an important
impact on the difference between the two setsof results is the method the US study uses toallocate occupations and the proportion of time
dedicated within occupations to data-related
production, which is much more sophisticatedthan the method adopted by the other two
studies. Assessing the extent of this differenceis challenging, as the machine-learning process
by which the US study assigns labour input
to occupations is, by its very nature, opaque;nonetheless, it is likely to be significant given
how very different it is from the manual approach
adopted by the Canadian and Dutch studies.
For prices, a key difference is assumptions about
the pace of productivity growth and the mixof inputs. As noted above, the Canadian study
simply assumes a productivity growth rate ofone percent annually and uses only labour tocalculate input prices. The Dutch study makes

Valuing Data: Where Are We, and Where Do We Go Next?

7

the same assumption about productivity growth,
but allows for intermediate inputs and capitalas well as labour, and thus uses price data on
these two inputs as well. This would explain
the somewhat lower price growth than the
Canadian study, as wages tend to grow fasterthan intermediate input costs or capital costs.
The US study uses actual estimates of TFP growthfrom the two relevant industries (data processing
and hosting, and computer systems design), alongwith input cost data to construct an input costindex. This is then combined in a simple averagewith an average industry price index. According to
published data from the Bureau of Labor Statistics,
TFP growth in data processing and hosting was
3.3 percent annually over the 2010–2020 period,
whereas TFP growth in computer systems design
was -0.9 percent. Using, as the US study does,
industry gross output to weight these two indices,
we arrive at a TFP growth for the two industries
combined of 1.5 percent annually, half a percentage
point higher than the one percent assumed by theother two studies. This difference is not enoughto explain most of the difference in price growth
between the Dutch and US studies. The other
difference between the two studies is that the US
study combines the input index with an average
price index. However, as industry prices weregrowing (at least for data processing — about one
percent per year),3 it seems that the key reason why
prices for data decline in the United States but notin the Netherlands would be faster price declines
in the United States for intermediate inputs.

Weakness of the Studies
Interesting as the three studies are, there are someclear challenges in moving forward with any ofthese methodologies to produce estimates for
the SNA. The most obvious are in assessing the
proportion of own account production versus
production for sale, and in assessing the amounts
of non-labour inputs, particularly capital, in the
production process. The studies either sidestep
the issue or make very arbitrary assumptions.
As we have seen, those assumptions seem tocontribute to quite large differences in the share of

3

8

The authors were not able to find producer price data for the computer
systems design industry, but as this industry is part of professional
services, the other components of which did see significant priceincreases, it is implausible that there were price decreases in this industry
large enough to lead to a significant price decrease for the two industries
combined.

data-related investment in GDP, and so statisticalagencies would need to survey producers to get abetter sense of what these proportions should be.
Another area where quite arbitrary assumptions
are made is the asset life of data. As we haveseen, the Canadian study assumes 25 years for
data, whereas the US study assumes five years.
The Canadian study bases the 25-year assumption
on the idea that data would be useful for about ageneration, and this is how long firms would store
the data. However, it is likely that a lot of datais quickly replaced by more timely data, which
would argue for a much shorter service life. It is
plausible, therefore, that asset lives for data arequite short, which would significantly reduce
the estimated size of the net stock of data assets
from what is estimated in the Canadian study.
Finally, there are some fairly arbitrary assumptions
made to calculate the price data. Both the Canadianand Dutch studies use an assumed productivity
growth rate; using an actual productivity growthseries, as the US study does, would obviously bebetter if the data is available. Furthermore, basing
output prices on input prices, as the Canadian andDutch studies do, is not ideal; if the data were sold,the firm would presumably make some return.
The US approach, as noted above, is to combine
an input price index with an output price index
from the industry that is sold: this seems a better
approach, even if the data produced for own
account is likely different from that produced forsale. However, the equal weight of the two indicesseems quite arbitrary, and more work should ideallybe done to determine the appropriate weights.

Next Steps for National
Accounts
Given these experimental studies, how likely is
it that data will indeed be included in the 2025
SNA? A good indication is given in a guidancenote prepared for the United Nations Economic
and Social Council in 2022 by its Digitalization
Task Team (United Nations Economic andSocial Council 2022). The note covers the main
issues raised in recording data in the SNA.

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

Data as a Produced Good
The task team essentially accepts the taxonomyof the information chain outlined above, withobservations (following John Mitchell, Molly
Lesher and Marion Barberis [2022], it uses
the term “observed phenomena”) outside theproduction boundary, and data, databases anddata science (it uses the term “insights,” again
following Mitchell, Lesher and Barberis [2022])
within the production boundary. The rationale forincluding data is that it is the result of production,with labour and capital being used to transforma non-produced good (observations) into a
digital format, which is the produced good.

Data as an Asset
The SNA recommends that a produced good thatis used in production but is consumed within ayear be treated as intermediate consumption,
whereas a good that is used in production for
longer than a year be treated as capital. The
guidance note recommends this approach fordata that is purchased: if the data is intended
to be used for more than a year, it should becapitalized and the resulting asset form part of
the IP class of assets. For own account data, theguidance note argues that, in practice, it will bedifficult for statistical agencies to get informationon the expected service life of a data set when it
is produced; the note therefore recommends thatall data be capitalized in order to be consistent
with the way some other IP assets are treated in
the SNA, particularly mineral exploration. The note
recognizes that because of its time-sensitive nature,
a lot of data is in fact used within a year; however,the note recommends that this issue be dealt withby assuming appropriately short service lives.

Valuation of Data andData Science
The guidance note recommends that own
account data be valued according to the sumof-costs approach, which is consistent withhow other own-account IP assets are valued.
While the guidance note recognizes data scienceas part of the production chain, downstream
from data and databases, it does not argue
for its inclusion in the asset boundary.
Given these conclusions, which build upon thework done by the three experimental studies, it
seems likely that the 2025 SNA will recommend

the inclusion of data, but not data science, in
the asset boundary (unless it is purchased dataintended to be used within a year). In many ways,
data-related assets are following the same welltrodden path as other IP assets such as softwareand R&D, which are now capitalized in the SNA.
The main issues at this point are practical: how
best to measure service lives and prices, and how
to avoid duplication with other IP assets. On theformer, it seems likely that the guidance will be
for a much shorter service life than the 25 years
contemplated by the Canadian experimental
study, and perhaps less than the five years in the
US study, in order to offset the inclusion of datawith a relatively short life in the asset boundary.
The note recognizes that a significant amountof testing may be required before an acceptableapproach to determining service life is established.
It could therefore be several years after the releaseof the 2025 SNA before statistical agencies start
capitalizing data in the national accounts.

Alternate Methods for
Data Valuation
The cost-based methodology used in thethree studies examined above has numerous
advantages that explain its popularity in datavaluation exercises. Costs usually occur in thepast and are easy to measure: for data, mostof the costs are salary costs for employees
engaged in data acquisition, manipulation andanalysis, which are picked up in regular surveys
of firms and workers conducted by statisticalagencies. The cost-based method is alreadyused by statistical agencies to value intangibles
such as software and R&D expenditures thatare produced by firms as intermediate inputs
into the production process and are not soldto third parties. It is also used in the academic
literature: Goodridge, Haskel and Edquist (2022)
use this method to estimate the impact of dataon productivity growth in the European Union.
However, while conceptually straightforward,
the cost-based method is, at best, only a lower
bound on the value of data. Given how profitabledata-driven firms are, it is likely that the returns
on investments in data-related assets are well

Valuing Data: Where Are We, and Where Do We Go Next?

9above the actual costs of the investments. Also,
as we have seen, assumptions about service
lives and depreciation rates need to be madeto arrive at a capitalized value for data.
These problems have led researchers to consider
other approaches to valuing data. These are
the income-based method, which attempts tovalue the flow of revenues from investing in
data; the market-based method, which uses
market prices for data or for the value of thefirm; and a hybrid model, which combines theincome-based and cost-based approaches.

Income-Based Methods
The income-based method uses direct estimatesof expected revenue streams that derive from thedata asset. If reliable estimates of revenue streams
are available, then this is a straightforward method
to implement as long as one is willing to specify
an appropriate discount rate. This method is a
type of “value in use” method, which is alreadyfamiliar to most accountants as it is defined in theInternational Accounting Standard 36. Value-inuse methods are any methods where the asset’svalue is derived as “a function of the future value
streams the asset creates or enables” (Girard,
Lionais and McLean 2021, 6). An example of theuse of this approach for other types of intangible
assets is for patents and trademarks, where
streams of payments are directly observable overa significant period (Coyle and Manley 2022).
However, in practice, this method has only limited
application for valuing data. First, it is only
really feasible when data is being produced for
sale, so that the income stream can be directly
observed, rather than being used as an input
further along the information value chain to
produce insights and analytics that are used
elsewhere in the business or sold to clients. In thelatter case, it is difficult — if not impossible — todisentangle the contribution of the data itself
from other inputs to the production process.
Second, even if data is produced for sale,
identifying the revenue from this activity is notstraightforward. Daniel Ker and Emanuele Mazzini(2020) attempt to use business survey data from anumber of Organisation for Economic Co-operation
and Development countries to measure the outputof activities related to compiling and selling
databases. They find that for many industries,
sales of data are not the main product, and10

so data on revenue by industry significantly
overestimates revenues from data sales.
Unfortunately, the detailed product line data, which
would be required for more reliable estimates,
is simply not available for most countries.
Finally, even if reliable revenue data were available,
there would still be the problem of valuing
future flows in order to arrive at a valuation of
the data asset. While for some assets, such as
natural resources, one might be able to forecast
future revenues with some confidence, the paceof technological change in applications of data,such as AI, makes forecasting revenue streams
from current revenues a very difficult endeavour.

Market-Based Methods
An alternative to the income-based method of
valuing data is the market-based method, whichuses the current observed price of the asset, rather
than the revenue stream, to make the valuation.
In principle, this is an ideal solution as there is noneed to make assumptions about discount rates
or depreciation: the market takes care of all this.
However, for this approach to work in practice,
one requires transparent markets with enoughtransactions to establish the market pricefor a specific type of data at any given time.
Unfortunately, there is very little observable
information on prices for buying and selling
data sets (Coyle and Manley 2022). This is partly
because, as discussed above, firms usually produce
data for their own use rather than sell it directlyon the open market. However, even for thosefirms that are engaged in selling data directly, thetransactions are kept private. Part of the challenge,
as noted by Pantelis Koutroumpis, Aija Leiponen
and Llewellyn D. W. Thomas (2020), is that data
is an “experience good”: its value is only known
once it is purchased. This implies an inherent
asymmetry of information between buyer andseller, which makes the market prone to failure. The
private nature of transactions also makes it easierfor sellers to price discriminate, so that different
buyers may pay a very different price dependingon their willingness to pay, with the result that
there is no one market price for a given data set.
One way around the lack of observable transactionsin the market for data is to turn to another market,
the stock market. In principle, if a company isprimarily engaged in selling data, its market
valuation should reflect the net present value of

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Denniston

that data, along with the other assets it owns,
both tangible (buildings and equipment) andintangible (organizational strengths, customer
knowledge, patents, proprietary research andso forth). The attraction of this method is that,at least for publicly traded companies, market
valuations are readily available. Furthermore,
the value of a company’s assets is available
from the information companies are obliged
to make public (even if data is not mentioned
explicitly, other more conventional assets are).
Ker and Mazzini (2020) look at stock market
valuations for 64 data-driven firms whosebusiness model relies primarily on data collecting
and data analytics (for example, Amazon,
Cognisant, Microsoft and Oracle). They findthat the market capitalization of these firms
in 2020 was more than US$5 trillion (about
12 percent of total US market capitalization) andequivalent to about one-quarter of US GDP.
There are numerous challenges to this approach,as Ker and Mazzini admit. First, not all data-driven
firms are listed on public markets: social media
company X (formerly known as Twitter) is one
example (Murphy 2022). Second, it is very difficult
to separate out the value of data from the valueof other intangible assets such as proprietary
research or organizational know-how. Companies
such as Amazon or Microsoft may be data-driven,
but their primary product is not data, and there
are many assets, both tangible and intangible, that
contribute to the production process. Attributing
all or even most of these companies’ stock
market valuations to data is likely to produce
a large overestimate, even when the value oftangible assets such as information technology
infrastructure and buildings is netted out.

Hybrid Method
Wendy C. Y. Li, Makoto Nirei and Kazufumi Yamana(2019) use a hybrid of the cost-based and revenuebased methods to value data, using a method
developed by Wendy Li and Bronwyn H. Hall (2018)
for valuing R&D expenditures. They argue that thevalue of data for data-driven firms can be calculated
by measuring the value of these firms’ businessmodels, or what is referred to in the literature
as “organization capital” (originally coined by
Edward C. Prescott and Michael Visscher [1980]).
Following earlier studies, Li, Nirei and Yamana(2019) reported selling, general and administrative

expenses from annual income statements forfour online platform companies as a proxy for
those firms’ investment in organization capital.
These expenses include “employee training costs,
brand enhancement costs, consulting fees, andthe installation and management costs of supply
chains” (ibid., 22–23). The authors then use thisinvestment data along with revenue data (alsofrom annual income statements) to estimate
the capital value of organization capital using aperpetual inventory model. The key identifying
assumption is that the (unobserved) rate of
return on organization capital is the same as thefirm’s overall (observed) rate of return — which,in a standard model, would be so as the firm
would seek to ensure that the marginal rate of
return on all its investments was the same.
It should be noted that, like the market
capitalization-based method, this hybrid method
can only really be used for firms whose businessmodel is focused on using data. To the extent that,say, Amazon, has organizational knowledge about
logistics that is not simply a result of data holdings,
this method, which ascribes all expendituresthat are not spent on production or R&D to data,
will overestimate the value of data per se.
That said, it is interesting to note that the estimatedvalues for data are well below what would beobtained from a market capitalization approach.
The authors calculate that Amazon, for example,
had organization capital of US$125 billion in2017, about 16 percent of its total valuation.
For Google, the figure was US$48.2 billion,
about eight percent of its total valuation.
One way to refine these figures is to estimate
the impact of a data-driven firm that enters amarket where incumbents are not data-driven.
The decline in the value of organization capitalof these incumbent firms then becomes a proxy
for their willingness to pay for the data that the
new entrant possesses. This method is adopted
by Diane Coyle and Wendy Li (2021), who look at
the impact of the arrival of Airbnb on the hotel
market and, in particular, the impact on the
Marriott hotel chain. The authors find that there
was a significant drop in the value of Marriott’s
organization capital following Airbnb’s entry, andbased on that drop, they estimate the market sizefor data in the global hospitality sector to have been
US$43 billion in 2017, about seven percent of theapproximately US$600 billion global hospitality

Valuing Data: Where Are We, and Where Do We Go Next?

11market in 2018. Of course, this approach relieson the natural experiment of a data-driven new
entrant, and so has limited wider applicability.

Conclusions andDirections for Future
Research
Since the publication of The Economist article
referenced at the beginning of this paper, there
has been considerable work on valuing data,
both quantitative and theoretical. National
statistical agencies, propelled in part by theforthcoming revision of the SNA in 2025, havedone a significant amount of experimental work,
using a traditional cost-based methodology that
may well enable data (if not data science) to follow
other intangibles, such as databases, software andR&D spending, and be capitalized in the SNA.
Nonetheless, the relatively wide variation inestimates of the value of data flows and stocks
shows that there are still some significant problems
to be sorted out. First, national statistical agencies
will need to decide on an appropriate methodology
for identifying data-related occupations: themachine-learning approach of the BEA, while
likely more accurate than conventional methods ofdetermining data-related occupations, may prove
too opaque for some agencies to be comfortablewith. Second, there will need to be agreement on
appropriate depreciation rates and service lives,
and the proportion of production that is for own
account rather than for sale: this will require
actual surveys of firms rather than the arbitrary
assumptions used by the studies outlined in thispaper. Third, more work will need to be done on
developing appropriate price indices, particularly
agreeing on the right measure of productivity
growth by which to deflate price indices; twoof the three studies use an entirely arbitrary
approach to estimating productivity growth.
What will be the likely magnitude of estimates ofdata-related assets? At this point, the authors of
this paper regard the US study as the most reliable
guide, given that it has the most sophisticatedapproach to determining occupations, and moreconservative assumptions for depreciation rates.

12

This study calculates the value of data-related
assets in 2021 at US$421 billion, equivalent
to 1.3 percent of total private fixed assets.
Substantial though this number is, it issignificantly less than the value of total IP products(US$4.18 billion in 2021), and very significantly
below the combined market capitalization ofjust two major data-related companies (Alphabet
and Meta), which was US$2.9 billion in 2020. This
discrepancy arises, in part, from the cost-based
methodology used by statistical agencies: giventhat data-related companies may be earning
very large rents from their ownership of data,
costs and revenues of data assets will not be as
closely aligned as they would be in a competitivemarket where competition would eliminate theserents over time. (Part of this rent comes fromthe non-rival nature of data, which means it canbe sold to many customers simultaneously.)
Unfortunately, alternative and conceptually
superior methods of calculating the value ofdata are difficult to implement in practice. One
problem is the absence of open and transparent
markets for data. While it is possible to get aroundthis by estimating measures of organization
capital, the problem is that this is very much a
residual category, which includes not just databut other forms of hard-to-measure intangible
capital such as management expertise andaccumulated knowledge. While one could argue
that the latter is ultimately founded on data, oreven data science, these forms of capital are still
not what the authors mean by data-related assets
and are conceptually separate. Ultimately, it may
well be the case that the bulk of the value of acompany such as Alphabet or Meta is bound up
in what one might call “intangible intangibles”
rather than “tangible intangibles” such as data
and data science, or patents and software.
What are the next steps for research in thisarea? From an SNA perspective, the mostpressing is probably to establish reasonable
depreciation rates and service lives for data.
Without international agreement on this issue,
we are likely to get dramatically different capitalstock estimates for different countries.
The next issue is one of scope. Measures of datarelated assets will need to be developed for the
public sector, as they have been for other IP
products. This could be done, as it was in the
Canadian study, using the cost-based method: as

CIGI Papers No. 280 — September 2023 • Tim Sargent and Laura Dennistonmost public data is free, the revenue or marketbased approaches are unsuitable. However, aswith private sector data, the challenge is that thevalue of public sector data is likely well above
what it costs to produce. One way of getting
this information would be through survey orexperimental data on users’ willingness to pay: theUK Office for National Statistics has done significant
work along these lines (see Cope, n.d., quoted in
Slotin [2018], and Office for National Statistics[2021]), and this approach could be taken further.
This leads to the broader issue of the social
valuation of data: Are there positive or negative
externalities associated with data that are notaccounted for by private sector actors? On thepositive side, investment in data and data science,such as R&D, may generate new insights andways of doing things that cannot be entirely
hidden from competitors, thereby generating
positive spillovers to the rest of the economy. In
this case, the social value of data would exceed
the private value. It would be useful to estimatea model of economic growth that would capture
the extent of these spillovers by estimating theimpact of investment in data by one firm on
the productivity of other firms in the industry,
just as studies have attempted to do for R&D.
On the other hand, there may also be negative
spillovers associated with data. When an
individual’s personal data is correlated with otherindividuals’ personal data, then if the individual
shares their data, they compromise not only
their own privacy but also the privacy of all theother individuals with correlated data. Daron
Acemoglu et al. (2019) argue that this negative
externality leads to excessive data sharing(individuals are less conservative with allowing
their personal data to be collected) and results inan underpricing of personal data. They explainthat this externality is why individuals are often
willing to divulge their data without any sort offinancial remuneration (ibid.). They also argue that
this makes individuals more willing to share theirdata: if their personal information can already beassembled from other people’s data, what incentive
do they have to protect their own privacy?
Assessing the costs of this negative externality
would be another direction for future research.

role for government in trying to influence thecurrent “market” for data (for individuals, thismarket is largely implicit: they give up their datain exchange for services provided by free apps). It
is not sufficient to simply try to create a market
for data by, for example, clearly establishing
individuals’ property rights over data, because theresulting prices will be suboptimal if they do nottake into account externalities. This is an area wheremore policy work is clearly required: to quote
Dan Ciuriak (2019), “there is no good historicalanalogue for a regulatory framework to control thenegative externalities to which [data] gives rise orto equitably share the benefits that it generates.”
Depending on the nature of the externalities,
regulatory tools could include restricting the kindsof data individuals can share, requiring open
data access by companies that collect data, andcreating ways for individuals to band together inorder to bargain on an equal footing with firmsthat collect their data. Assessing the social valueof data will be an essential underpinning of thepolicy work that will be required to effectively
govern the data space in the years to come.

Acknowledgement
The authors would like to thank CIGI Managing
Director of Digital Economy Robert Fay,
CSLS Executive Director Andrew Sharpe
and two anonymous reviewers for helpfulcomments. Comments are welcome andmay be sent to tim.sargent@csls.ca.

Discussion of externalities raises the question ofdata governance. As Diane Coyle and Stephanie
Diepeveen (2021) note, if the private and public
valuations of data differ, then there is a potential

Valuing Data: Where Are We, and Where Do We Go Next?

13

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pub/13-605-x/2019001/article/00009-eng.htm.
The Economist. 2017. “The world’s most valuable resource
is no longer oil, but data.” The Economist, May 6.
www.economist.com/leaders/2017/05/06/the-worldsmost-valuable-resource-is-no-longer-oil-but-data.
United Nations Economic and Social Council. 2022.
“DZ. 6 – Recording of data in the National Accounts.”
https://unstats.un.org/unsd/nationalaccount/RAdocs/
DZ6_GN_Recording_of_Data_in_NA.pdf.

Valuing Data: Where Are We, and Where Do We Go Next?

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