Valuing the U.S. Data Economy Using
Machine Learning and Online Job Postings
José Bayoán Santiago Calderón and Dylan G. Rassier
U.S. Bureau of Economic Analysis∗
March 17, 2022

Abstract
With the recent proliferation of data collection and uses in the digital economy, theunderstanding and statistical treatment of data stocks and flows is of interest among
compilers and users of national economic accounts. In this paper, we measure the valueof own-account data stocks and flows for the U.S. business sector by summing the production costs of data-related activities implicit in occupations. Our method augments
the traditional sum-of-costs methodology for measuring other own-account intellectual
property products in national economic accounts by proxying occupation-level time-use
factors using a machine learning model and the text of online job advertisements. In our
experimental estimates, we find that annual current-dollar investment in own-accountdata assets for the U.S. business sector grew from $82.6 billion in 2003 to $159.5 billion
in 2020, with an average annual growth rate of 3.9 percent. Cumulative current-dollar
investment for the period 2003–2020 was $2.1 trillion. In addition to annual currentdollar investment, we present historical-cost net stocks, real growth rates, and effects
on value-added by industrial sector.
Keywords: Data Assets, System of National Accounts, Digital Economy,
Intangible Capital, Measurement
JEL Codes: E22, O3, O51∗

Author e-mail addresses: Jose.Santiago-Calderon@bea.gov and Dylan.Rassier@bea.gov. The views expressed in this paper are those of the authors and do not necessarily represent the U.S. Bureau of Economic
Analysis (BEA) or the U.S. Department of Commerce. We thank Christopher Blackburn, former researcheconomist at BEA, for developing the machine learning approach we use in the paper (Blackburn 2021).
We also thank Marshall Reinsdorf, Jon Samuels, Brian Sliker, Brenda Bugge, and John Haltiwanger andother participants at the NBER-CRIW Pre-conference on Technology, Productivity, and Economic Growthfor early comments.

1

1

Introduction

With the recent proliferation of data collection and uses in the digital economy, the understanding and statistical treatment of data stocks and flows is of interest among compilers
and users of national economic accounts. During the last revision of the System of National
Accounts (SNA) (United Nations 2010), which is the international standard for national
economic accounts, the treatment of data stocks and flows was a topic of discourse among
statistical agencies and international organizations, who ultimately settled on the treatment
of databases as a subcategory of software in capital formation (Ahmad 2004; Ahmad 2005).
Current global efforts for the next revision of the SNA, expected to be published in 2025,
have a renewed focus on the valuation and recording of the information content of databases– i.e., the embedded data – in response to the presumed rapid increase in data stocks andflows over the last decade and longer.
The value of data is implied in the profits and market values of some firms. In 2020, two of
the largest global data firms – Alphabet Inc. (Google) and Meta Platforms Inc. (Facebook)– had a combined net income before tax of $81.3 billion, which amounted to 3.7 percent of
U.S. corporate profits before tax. In November 2021, the combined market capitalization of
the two firms was $2.9 trillion, which amounted to 7.5 percent of the market capitalization of
all S&P 500 firms.1 While the value of data may be implied in these measures, data stocks
and flows are not visible in national economic accounts under the current SNA treatment.
Moreover, understanding and measuring the value of data presents challenges to economic
statisticians.
In this paper, we measure the value of own-account data stocks and flows for the U.S.
business sector by summing the production costs of data-related activities implicit in occupations. Production costs include labor costs, capital costs, and intermediate consumption. To
estimate production costs, we apply a markup to an estimate of the wage bill for data-related
1

Net income before tax comes from each firm’s 10-K filings with the U.S. Securities and Exchange Commission for year-end December 31, 2020. Corporate profits come from BEA’s National Income and ProductAccounts table 6.17D. Market capitalizations come from YCHARTS (GOOG, FB) as of November 19, 2021.

2

activities, which is consistent with the Bureau of Economic Analysis (BEA) methodology for
own-account software. Our method augments the traditional sum-of-costs methodology formeasuring other own-account intellectual property products (IPPs) in national economic accounts by proxying occupation-level time-use factors using an a machine learning model andthe text of online job advertisements.
The occupation-level time-use factors can be decomposed into two components: 1) the
fraction of jobs in an occupation engaged in qualifying activities based on data-relevant skills
revealed in the job advertisement and 2) the average share of time allocated to the datarelevant activities. Using online job advertisements from Burning Glass Technologies (BGT),
we identify which skills in the BGT taxonomy are relevant to data-related activities, including
data entry, storage, analysis, and management. The fraction of jobs in an occupation engaged
in qualifying activities is given by the fraction of BGT job advertisements that contain atleast one of the data-relevant skills. The average time allocation is based on the distanceof an occupation to known data-intensive occupations that serve as “landmark” occupations(e.g., data entry keyers or statisticians). The landmark occupations are selected based on
the top 20 occupations by highest rates of job openings listing data-relevant skills. We
then train a doc2vec model on the job advertisement text for each occupation to obtaina numerical representation of what the occupation-level job postings convey. Using thenumerical representation, we obtain occupation-level pair-wise distances to measure how“close” or similar an occupation is to the landmark occupations. The product of the similarity
to a landmark occupation and the ratio of job openings with identified data-relevant skills
serves as the proxy for the occupation-level time-use factor. We then apply the time-use
factors to the product of average annual wages and annual number of employees by occupation
at the 3-digit North American Industry Classification System (NAICS) level available fromthe U.S. Bureau of Labor Statistics (BLS) Occupational Employment and Wage Statistics(OEWS) to calculate the wage bill for data-related activities.
The main challenges for measurement of own-account data stocks and flows that we
3

address in the paper are similar to challenges imposed by other own-account IPPs that are
already included in capital formation in the SNA and the U.S. National Income and ProductAccounts (NIPAs). First, the scope of capital formation is not yet well-defined for ownaccount data. Second, own-account data and other own-account IPPs are at risk of multiple
counting from sources such as overlap among categories of IPPs and non-rival use of data.
A third challenge is what proportions of the sum-of-costs should be accounted for by laborcosts, capital costs, and intermediate consumption in light of the role that capital services
play in the collection, storage, analysis, and management of data. Finally, own-account data
are not transacted in active markets, which means there are no observed transactions thatare useful for measuring prices and depreciation. We discuss each of these challenges and ourapproach to addressing them in section 4 of the paper.
Our experimental results indicate that annual current-dollar investment in own-accountdata assets for the U.S. business sector grew from $82.6 billion in 2003 to $159.5 billion in
2020, which yields an average annual growth rate of 3.9 percent. Cumulative current-dollar
investment for the period 2003–2020 was $2.1 trillion. Annual current-dollar investment in
own-account data for the period averaged 0.9 percent as a share of business sector valueadded, 4.4 percent as a share of investment in private fixed assets, and 15.6 percent as ashare of investment in IPPs. Likewise, the historical-cost net stock of data assets grew from
$201.0 billion in 2003 to $375.3 billion in 2020, which yields an average annual growth rate of
3.7 percent. The average annual growth rate in real data investment over the period was 4.1
percent, which yields an average annual increase in real business sector value-added growthof only 1 basis point. In contrast, growth in real data investment is lower than growth inreal investment in IPPs and software, which yields a decline in average annual growth of real
investment in IPPs and software of 21 basis points and 120 basis points, respectively. For
NAICS sectors, the largest dollar investments were made in Finance and Insurance (NAICS
52) and Manufacturing (NAICS 31-33). The largest increase in average real value-added
growth by NAICS sector shows up for Management of Companies (NAICS 55).
4

The next section summarizes the SNA background on the treatment of own-account data
stocks and flows and related literature. Section 3 provides details on our measurement,
including source data and methodologies. Section 4 identifies challenges associated withmeasurement of own-account IPPs and how we address those challenges for own-accountdata. Section 5 reports our core experimental results and some additional experimental
results of interest. Section 6 concludes.

2

National Accounts Background and Related
Literature

2.1

System of National Accounts

The 1993 version of the SNA includes only a brief paragraph on the inclusion of “large
databases that the enterprise expects to use in production over a period of time of more than
one year” as part of the computer software category of capital formation (United Nations
1993, paragraph 10.93). There is no mention of embedded information content (i.e., data)
in SNA 1993. Leading up to the 2008 version of the SNA, an SNA group of statisticalagencies and international organizations considered the inclusion of embedded data in capitalformation. To guide the discussions, Ahmad (2004) outlined two components of databases –supporting software and data stored in the database – and summarized practical challenges
that countries encounter while trying to implement the vague SNA 1993 recommendation.
In light of the challenges, Ahmad (2005) described two definitions for databases consideredby the SNA group. One definition included the value of the information content to be stored
in databases as long as the information had a useful life of more than one year, and one
definition did not include the value of the information content. The group recommended
that the latter definition is preferable because the former definition would “open the door
to the capitalization of knowledge” (Ahmad 2005, p. 2). Based on the summary outlined in

5

Ahmad (2005), the group primarily considered databases maintained by statistical agencies.
The recommendation that was ultimately written into SNA 2008 includes databases andcomputer software as separate categories of intellectual property products in capital formation(United Nations 2010, paragraphs 10.109–10.114). If a database is developed for own use,
SNA 2008 recommends a sum-of-costs approach to value the database. The sum-of-costs
includes the cost of preparing data in a format that conforms to the database but excludes
the cost of acquiring or producing the data. In addition, the sum-of-costs excludes thevalue of the database management system (DBMS), which is included instead with computer
software. If a database is developed for sale or for license, the value should be determinedby the market price, which includes the value of the information content. Thus, SNA 2008
recommends an inconsistent treatment for data in capital formation depending whether adatabase is developed for own use or for sale or license. In addition, the value of data acquiredor produced for inclusion in databases is not to be treated as intermediate consumption in thesum-of-costs approach for own-account databases, which is inconsistent with the inclusionof intermediate consumption in the usual sum-of-costs measurement. The overall conclusiondrawn by the SNA group was that if data is an asset, it is a non-produced asset whose value
should be limited in national accounts to measures of purchased goodwill. Thus, there should
be no value of data reflected in production measures.

2.2

U.S. National Accounts

The U.S. national accounts are consistent with the SNA recommendations on intellectual
property products, including computer software (U.S. Bureau of Economic Analysis 2020).
Similar to general practice in other countries, the U.S. accounts do not include a separation
between software and databases (i.e., the software that houses data) in published capitalstock and flow measures. The value of any data included in purchased software is included
in measures of investment and capital stock. The value of any data in own-account software
is excluded from measures of investment and capital stock.
6

BEA estimates three categories of software: 1) prepackaged, 2) custom, and 3) ownaccount. Benchmark estimates of prepackaged and custom software are determined using acommodity flow method based on receipts reported in the U.S. Economic Census for Software
Publishers (NAICS 5112), Data Processing, Hosting and Related Services (NAICS 5182), andComputer Systems Design and Related Services (NAICS 5415). For non-benchmark years,
estimates are based on receipts reported in the Census Bureau’s Service Annual Survey.
Benchmark estimates of own-account software are determined using a sum-of-costs methodology based on wage data in the BLS OEWS for four occupations - Computer Programmers,
Computer System Analysts, Software Developers (applications), Software Developers (system software) - and based on the Economic Census. For non-benchmark years, estimates areprimarily based on the OEWS data. In addition to labor costs and intermediate consumption,
own-account software includes a cost for capital services (Chute et al. 2018).

2.3

Related Literature

Two strands of literature provide additional context for this paper. The first is literatureon the value of data, which not only suggests that data has value but also suggests thatthe value of data may, at least in part, be a result of a production process rather than nonproduced, which is the current perspective in national economic accounts. The second is
emerging literature from statistical agencies, international organizations, and other sourcesthat is beginning to recognize the likelihood that some data are produced assets and to
generate renewed focus for recording and valuing data stocks and flows.
2.3.1

Value of Data

Varian (2018) describes a data pyramid that is a variation of the data-information-knowledgewisdom (DIKW) hierarchy introduced by Ackoff (1989) and subsequently used in information
science and economics (Rowley 2007; Boisot and Canals 2004). The data pyramid is used toillustrate the relationships among data that is stored as bits, information that is stored in7

documents, and knowledge that is stored in the human mind (Mokyr 2013). Related to thedata pyramid is the data value chain presented in OECD (2013) and subsequently expanded
in Moro Visconti et al. (2017). The data value chain illustrates a production process fordata from an unstructured form that has very little value to a structured form that can beleveraged in a business model or other usage (Bakhshi et al. 2014). The stages of the chain
include collection, storage, processing, distribution, and usage. The Moro Visconti et al.
(2017) version of the data value chain focuses on business users at the last stage of the chain
with the monetization of data via a business model, which may be data-dependent or dataneutral. Data-dependent business models, such as online platforms, rely heavily on data forsources of revenue and profits (Li et al. 2019; Nguyen and Paczos 2020). Data-neutral firms
do not depend on data for revenue but can still realize benefits from data that help improveexisting products or offer new products. The OECD (2013) version of the data value chain
includes business users and also includes government, non-profit, and household users.
Hughes-Cromwick and Coronado (2019) outline the value of U.S. government data tobusiness decision-making. Likewise, the value of household data is evident in literature on
the economics of personal privacy, which has re-emerged as an area of interest as summarizedin Acquisti et al. (2016).
Farboodi and Veldkamp (2021) construct a growth model of the data economy in whichdata is an information asset that contributes to growth by reducing uncertainty and helping
firms choose better production techniques via forecasts. The model demonstrates short-run
increasing returns due to a feedback loop within the firms. However, long-run diminishingreturns result in the absence of traditional technological progress in the Solow (1956) growthmodel because better forecasts are not a tool that can logically sustain long-run growth.
In contrast to Farboodi and Veldkamp (2021) and Jones and Tonetti (2020) model data
contributing directly to productivity and generating long-run growth.

8

2.3.2

Recording and Valuing Data Stocks and Flows

Under the second strand of literature, statistical agencies and international organizations are
renewing efforts to introduce guidelines for recording and valuing data as a produced asset
in national economic accounts. Ahmad and van de Ven (2018) and Reinsdorf and Ribarsky(2020) provide background on the historical treatment of data stocks and flows and offer
preliminary thoughts on moving forward with changes to the SNA. Rassier et al. (2019)
also summarize considerations for treatment of data stocks and flows and present cursory
estimates of data-related flows based on official statistical sources for the U.S. economy.
Statistics Canada (2019b) carefully defines and categorizes data, databases, and data scienceand presents experimental estimates for each category using a sum-of-costs measurement
methodology for the Canadian economy. Each of these previous efforts have resulted inpreliminary guidance that is currently being considered by the SNA community.
Goodridge et al. (2021) are the first to provide a harmonized set of cross-country estimatesfor data assets in European Union countries and then estimate the contribution of data
capital deepening to growth in productivity. They find that about 43 percent of employment
engaged in capital formation of software and data is unaccounted for in measured own-accountsoftware and databases, and the missing piece of capital formation is growing faster thanthe measured piece. Goodridge et al. (2021) also provide a summary of previous economic
literature on information and knowledge.

3

Data and Methods

The SNA recommendation for valuing IPPs that lack an observable market transaction isa cost-based approach including labor costs, capital costs, and intermediate consumption.
For market producers, capital costs include a net return to fixed assets (or “normal profit”)
in addition to consumption of fixed capital. Our strategy builds on BEA’s sum-of-costs
methodology for own-account software. Estimates of aggregate production costs for data9

related output have the general form

𝐶𝑖,𝑡 = 𝛼 ∑ 𝜏𝜔 𝑊𝜔,𝑖,𝑡 𝐻𝜔,𝑖,𝑡(1)

where for each occupation 𝜔, industry 𝑖, and year 𝑡, we calculate the wage bill by multiplying the annual number of employees (𝐻𝜔,𝑖,𝑡 ) by the average annual wage (𝑊𝜔,𝑖,𝑡 ) andan occupation-specific time-use factor (𝜏𝜔 ) that reflects the time-effort that the occupation
allocates to data-related activities. The parameter 𝛼 is a markup that reflects employee
benefits (not included in the wage bill), capital costs, and intermediate consumption, whichyields the full production costs. The rest of this section discusses our estimation of each of
the elements in equation 1.

3.1

Employment and Wage Data

The U.S. Bureau of Labor Statistics (BLS) has 12 survey programs that provide information on pay and benefits. We prioritize having wage data at an occupational level specificenough to capture the activities of interest.2 The BLS Occupational Employment and Wage
Statistics (OEWS) program produces employment and wage estimates annually for around
800 occupations and is well-suited for our purpose. In addition, the historical data temporal
coverage dates back to the early 2000s, allowing us to generate a longer time series.
Occupational data collected by the U.S. federal statistical system is generally collected,
calculated, and disseminated based on the Standard Occupational Classification (SOC) system. Employees are assigned to an occupation based on the work they perform and not on
their education or training. The OEWS data are an SOC-based occupational system thatuses SOC codes to assign occupations at levels for which the data are published. Some SOC
codes are aggregated into a single OEWS occupational code for reporting purposes. The
OEWS system allows the data to be linked to other systems such as the U.S. Department of2

https://beta.bls.gov/comparison-matrix

10

Labor (DOL) Occupational Information Network (O*NET) (Hopson 2021).
The OEWS program reports data using the North American Industry Classification System (NAICS). We use estimates at the NAICS 3-digit subsector levels that are representativeof privately-owned business establishments, which excludes government and nonprofit institutions serving households (NPISH).3
To generate data-related employment and wage estimates for 2003–2020, we use modelbased estimates (Dey et al. 2019) for 2015–2019 and official estimates for the period 2003–2014
and for 2020 (U.S. Bureau of Labor Statistics 2021). We use estimates of the average annual
wage and annual number of employees for privately-owned establishments at the NAICS
3-digit level.
The wage series use several versions of the underlying occupational and industry systems
across time. In order to obtain a consistent time series, we use the OES 2019 hybrid structure
crosswalk and rely on the OES 2010–2011 classification crosswalk to account for the temporary codes in those two years. For example, if multiple occupations are aggregated in thelatest taxonomy (OES 2019), the estimated average annual wage is an average of available
wage data weighted by the employment estimates for the corresponding occupations. In cases
when an occupation is attributed to multiple 2019 codes, which may occur due to special
hybrid-code estimates, those are equally distributed among the corresponding 2019 codes.
The resulting series provide us the employment and wage numbers by year and industry.

3.2

Time-Use Factors

Time-use factors are important to the sum-of-costs methodology as they provide measuresof time-effort allocated to the activities of interest such as data-related activities. Time-use
factors have been used to examine quality of service, job satisfaction, and other outcomes invarious domains such as education, health, and security. For example, the OECD Teaching
3

The sectors and subsectors identified as NPISH include Educational Services (NAICS 61), Health Careand Social Assistance (NAICS 62), Arts, Entertainment and Recreation (NAICS 71), and Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813).

11and Learning International Survey (TALIS) collects data on the time-use allocation of teachers among categories: (1) Administrative and leadership tasks and meetings, (2) Curriculum
and teaching-related tasks and meetings, (3) Student interactions, (4) Parent or guardian
interactions, and (5) Interactions with local and regional community, business and industry.
In the area of health, studies have collected information on the share of time physicians spend
on direct care with patients or administrative work (Woolhandler and Himmelstein 2014).
While most data on time-use allocations is collected in surveys, other methods include direct
observation of the work performed such as rides with police officers (Parks et al. 1999).
For data as an asset, we estimate time-use factors using a methodology developed inBlackburn (2021) for data-relevant skills, which are reflected in activities including entry,
storage, analysis, and management of data. Examples of specific activities include data
cleaning, data wrangling, data manipulation, and data science.
Following Blackburn (2021), the time-use factor 𝜏 can be decomposed as follows

𝜏𝜔 =

𝑙𝜔 ∗
𝑠 = 𝜌𝜔 𝑠∗𝜔
𝐿𝜔 𝜔

(2)

where the time-use factor for occupation 𝜔 is the product of the fraction of employees thatengage in activities of interest (𝜌𝜔 ) and an estimate of how much time the occupation allocatesto the activities (𝑠∗𝜔 ). Without time-use factors, the sum-of-costs methodology relies on
identifying specific occupations assumed to best embody the activities of interest.
3.2.1

Online Job Advertisements

The method uses online job advertisement data from Burning Glass Technologies (BGT) toestimate time-use factors for a broad range of occupations.4 The data not only contain the job
advertisement text but also enhancements, including deduplication, identified skills, degree4

Online job advertisements have been used in applications related to labor, education, and credential
research. Examples of online job advertisement providers include Burning Glass Technologies, Indeed, andthe National Labor Exchange (NLx) Research Hub. These providers collect job postings in “real-time” fromvarious websites.

12requirements, location, and information on the employer (e.g., industry). BGT also uses theirown-developed occupational auto-coders to identify the occupation of the job advertisement.5
Lancaster et al. (2021) look at the data quality, suitability, and representativeness of
BGT job advertisement data for research purposes. The study confirms several findingsin the literature such as the over-representation of certain occupation groups in the BGTjob advertisement data. One aspect of the BGT data that distinguishes them from otheralternatives is the focus on skills rather than occupations as the main unit of analysis forunderstanding the job market (Burning Glass Technologies 2019). The BGT skills taxonomy
includes over 17,000 skills as well as various skill properties such as whether a skill refers todata or software (or both).
The strategy uses the BGT job advertisement data to estimate equation 2. The firstcomponent refers to the fraction of employees in an occupation who engage in data-related
activities. The concept is operationalized with the following definition:
𝐿

𝜌𝜔̂ =

𝜔
∑𝑗=1
𝟙 (𝑦𝑗̂ )𝐿𝜔

(3)

where 𝟙 (𝑦𝑗̂ ) denotes ∃ 𝑠 ∶ 𝑠 ∈ 𝑆 such that 𝑠 is a subset of skills identified for job advertisement 𝑦𝑗 , and 𝑆 is the set of skills identified as data relevant. In other words, a ratio iscomputed as the fraction of job advertisements with at least one of the data-relevant skills outof all job advertisements (𝐿𝜔 ) for each occupation. Blackburn (2021) manually identified 203
BGT skills that are data relevant, which excludes any skill deemed a software skill as a way
to minimize potential overlap with capital formation in software. The top 80 data-relevant
skills by frequency are presented in table 1.
A two-step approach is used to estimate (𝑠∗𝜔 ). The first step identifies the occupations
with the highest rate of employees engaged in data-related activities (𝜌𝜔 ) based on the BGTskills. The top 20 occupations are denoted “landmark” occupations and assigned full time5

The NLx Research Hub uses the O*NET SOC Code AutoCoder™ (https://www.onetsocautocoder.
com).

13

Table 1: BGT skills identified as data relevant
3D Seismic Data
Accenture Data Governance Framework
Advanced Data Entry
Assessment Data
Big Data
Big Data Analytics
Billing Data Analysis
Biological Database SearchBusiness Intelligence Data ModelingCascading Big Data Applications
Climate Data Analysis
Clinical Data Abstracting
Clinical Data Analysis
Clinical Data Exchange
CDISC
Clinical Data Management
Clinical Data Review
Clinical Data Understanding
Clinical Database Development
Clinical Research Data Accuracy and Integrity
Cloud Security Data Protection And Privacy
Columnar Databases
Conceptual Data Models
Customer Data Integration
Customer Service Database
Data Acquisition
Data Acquisition Systems
Data Analysis
Data and Safety Monitoring Board
Data Architecture
Data Archiving
Data Buffers
Data Capture
Data Center Hardware
Data Cleaning
Data Collection
Data Communications
Data compression
Data Conversion

Data Dictionary System
Data Documentation
Data Encryption
Data Engineering
Data Entry
Data Entry Prioritization
Data Evaluation
Data Exploitation
DFHSM
Data Flow Diagrams (DFDs)
Data Governance
Data Integration
Data Integrity
Data Lakes / Reservoirs
Data Loss Prevention
Data Management
Data Management Platform (DMP)
Data Manipulation
Data Mapping
Data Migration
Data Mining
Data Mining Industry Knowledge
Data Modeling
Data Modeling Star / Snowflake Schema
Data Multiplex System (DMS)
Data Munging
Data Operations
Data Platform as a Service
Data Pre-Processing
Data Privacy
Data Protection Industry Knowledge
Data Protection Planning
Data Protection Strategy
Data Quality
Data Quality Assessment
Data Reports
Data Science
Data Security
Data Security Classification

Note: Top 80 skills by frequency out of 203 data relevant skills identified manually.

14

Table 2: Landmark occupations
O*NET SOC 2010

Description

Time-use factor

43-9021.00
15-1111.00
15-1141.00
15-1199.06
19-1029.01
19-4061.00
15-2041.00
15-1199.07
15-2041.01
15-1199.08
53-7073.00
19-3022.00
43-9111.01
43-9111.00
29-2092.00
15-2041.02
43-3021.01
51-8099.02
15-1199.05
33-3021.06

Data Entry Keyers
Computer and Information Research Scientists
Database Administrators
Database Architects
Bioinformatics Scientists
Social Science Research Assistants
Statisticians
Data Warehousing Specialists
Biostatisticians
Business Intelligence Analysts
Wellhead Pumpers
Survey Researchers
Bioinformatics Technicians
Statistical Assistants
Hearing Aid Specialists
Clinical Data Managers
Statement Clerks

Methane/Landfill Gas Generation System Tech.

Geographic Information Systems TechniciansIntelligence Analysts

0.94
0.77
0.75
0.72
0.68
0.67
0.66
0.63
0.63
0.61
0.60
0.59
0.58
0.54
0.54
0.54
0.50
0.47
0.44
0.43

Note: For landmark occupations, the similarity to the nearest landmark is one, and thus the timeuse factor 𝜏𝜔̂ is the same as 𝜌𝜔̂ .

effort. Table 2 shows the occupations determined to be “landmark” occupations. Manyof the occupations are obvious landmark candidates such as data entry keyers and various
research and analyst occupations. Some of the occupations are less obvious as landmarks. For
example, wellhead pumpers may not be an occupation immediately associated with data, but
their job activities rely on monitoring and assessing data in order to act on that information
promptly.
For non-landmark occupations, 𝑠∗𝜔 is estimated as the cosine similarity to the closest
landmark occupation. Mathematically,𝑠∗𝜔̂ = max {𝑤 ∈ 𝕄A𝜔 ⋅ A𝑤}‖A𝜔 ‖‖A𝑤 ‖

15

(4)

where 𝕄 is the set of landmark occupations, 𝐴𝜔 (i.e., omega) is a numerical representationof occupation 𝜔, and 𝐴𝑤 (i.e., double-u) is the mathematical representation of occupation 𝑤(i.e., one of the landmark occupations). The effective time-use factor then becomes𝜏𝜔̂ = 𝜌𝜔̂ 𝑠∗𝜔̂ =𝜔
∑𝐿
𝟙 (𝑦𝑗̂ )
𝑗=1

𝐿𝜔

max {

𝑤 ∈ 𝕄

Â𝜔 ⋅ Â𝑤}.
‖Â𝜔 ‖‖Â𝑤 ‖(5)

The estimate for the full production costs of data-related activities for a given industry 𝑖
at time 𝑡 is then as follows:̂ = 𝛼∑⎡
𝐶𝑖,𝑡
⎢
𝜔∈Ω ⎣
3.2.2

𝜔
∑𝐿
𝟙 (𝑦𝑗̂ )𝑗=1

𝐿𝜔( max {𝑤 ∈ 𝕄

A𝜔̂ ⋅ Â𝑤}) 𝑊̂ 𝜔,𝑖,𝑡 𝐻̂𝜔,𝑖,𝑡 ⎤⎥.
‖Â𝜔 ‖‖Â𝑤 ‖⎦(6)

Sample Design and Description

The BGT data enable observation of over 239 million job advertisements in the United Statesfor 2010–2019. The data include over a thousand O*NET SOC 2010 occupations. The data
are used to train an auto-coder model for O*NET SOC 2010 using job text to obtain the
numerical representation for each occupation such that we can compute pair-wise similarity
between occupations.
The sample design uses the occupation distribution across NAICS 3-digit subsectors
based on the annual number of employees in privately-owned establishment from OEWS
for 2015–2019. If observations available at the occupation-industry level are below the target (1,500 × percentage of occupations in a NAICS 3-digit subsector), all observations areincluded. Otherwise, we sample from the valid observations a targeted sample of 1,500 observations equally distributed based on the sequential order of the job posting dates. We only
include occupations for which at least 100 job ads are available, meaning there is complete
information for the job text, job posting date (ISO 8601), O*NET SOC 2010, and NAICS
subsector. The sample design aims to be representative of the business sector in terms of
the occupation-industry interactions and composition as well as temporal changes, while also

16

maintaining desirable properties for modeling, such as avoiding extreme unbalanced classes or
making inferences based on limited data. Our goal is different from traditional auto-coders,which may emphasize some metric like accuracy and may prefer to focus on having better
performance for frequent occupations over having acceptable performance for less frequent
occupations.
Considering the observations with complete information (O*NET SOC 2010 and NAICS
3-digit codes are not always available), we sample occupations for which at least 100 observations are available and limit the observations included per occupation to around 1,500. We
also exclude military occupations (SOC major group 55) and those that are exclusive to the
public sector like legislators and postal service employees. The sample includes 959 O*NETSOC 2010 occupational codes that have between 101 and 1,548 observations.
The final sample comprises 1.12 million observations for which we recover the job advertisement text as well as the skills for each of the job advertisements. We split the dataset by
applying a clustered sampling design based on occupation-industry subsector with 90 percentof the sample being used as the training set and 10 percent as the test set.
3.2.3

Machine Learning Model

The modeling approach uses the doc2vec (Le and Mikolov 2014) implementation in gensim
v4.0.1 (Řehůřek and Sojka 2010) for Python (van Rossum and Drake 2009) v3.8.5. The
modeling technique uses paragraph vectors to capture the semantics of documents as analternative to “bag-of-words” or “bag-of-n-grams” representations. In our case, each job
advertisement is represented not just by applying a tokenization to the text but by also
including indicators for each job advertisement. After training the model, each occupationis represented by a multidimensional numerical abstraction. Once the model has been fitted,
we extract the features that are a 1,000-dimensional numerical representation of each O*NETSOC 2010 code. The pair-wise cosine distance is then computed to obtain the occupation
similarity matrix. The similarity matrix allows us to find the most similar “landmark”
17

occupation.
3.2.4

Validation

Model Validation. Occupation auto-coders are automated models that can predict occupation codes based on inputs such as job advertisements and job titles. One common
autocoder regarded as a gold standard is the O*NET-SOC AutoCoder™ developed by R.M.
Wilson Consulting, Inc. for the U.S. Department of Labor. There are various ways to evaluate the performance of a multi-class classifier. For example, the classifier accuracy claims
for the O*NET-SOC AutoCoder™ are stated based on the inputs whether it operates on a
job advertisement and job title or on just a job title. For job titles and job advertisement
text, the model accuracy for predicting O*NET SOC 2019 codes is 85 percent based on theirinternal test results. Since the model is using just the unstructured job advertisement text,
we expect the performance to be lower than the gold standard using additional information.
Another consideration is that the labeled data are also based on the BGT auto-coder for
O*NET SOC 2010 codes which in turn contains errors from the BGT auto-coder. Given
the licensing terms and industry practices, properly assessing the performance of these tools
is difficult. For example, a measure of accuracy can be significantly driven by unbalanced
classes. Performing well for common occupations in a sample will result in better accuracy
even if the performance for many occupations is not as good if those occupations are lesscommon. For the purpose of obtaining a good numerical representation for each occupation,
it is more valuable to have good performance for many occupations rather than great performance for a select few. One metric we compute is the unweighted average F1 score on our
test set (out-of-sample) of around 120,000 observations for which the model predictions yieldan estimate of 0.5 on a 959-dimensional classification problem.
Validation of Landmark Occupations.

An alternative strategy for obtaining represen-tative occupations and similarity between occupations is to use the O*NET program. The
O*NET tasks file provides estimates of how frequently certain occupations perform job work18

activities. For example, for the occupation data entry keyers (43–9021.00) in the O*NET
v26 data, there is a task relevant to data-related activities: “Compile, sort, and verify theaccuracy of data before it is entered”. For this occupational task, the O*NET data provide
a breakdown of how frequent the task is performed: Yearly or less (1.49), More than yearly(0), More than monthly (2.67), More than weekly (41.69), Daily (18.6), Several times daily(18.6), Hourly or more (35.54). Each task can then be assigned an indicator of whether it
is considered data-relevant or not and estimate a time-use factor for each occupation. Someof the heuristics we employ include assigning the frequency for each task per occupation to
the category with the highest value (daily, several times daily, or hourly). At each of thedifferent frequency categories, we compute the fraction of tasks deemed data-relevant. We
then collapse the ratios assigning relative values of 10 percent, 20 percent, and 70 percent
to daily, several times daily, and hourly, respectively. This back-of-the-envelope heuristic isused as an additional check on our rankings. To assess whether the landmark occupations
are reasonable, we compute the Spearman’s rank correlation coefficient and obtain a value
around 0.22. The correlation coefficient reveals a negligible correlation, but the machinelearning model seems to provide more reasonable rankings than the O*NET-based model.
3.2.5

Mapping Occupational Codes

Using equation 5, we obtain O*NET SOC 2010 level estimates for the time-use factors.
However, employment and wage data are collected and prepared with OEWS Employment
Projections (EP) 2019 codes. We use the crosswalk files to map the O*NET SOC 2010 codes
to the SOC 2010 codes and then to OEWS 2019 codes (see subsection 3.1 for additional
information on the process and data files). If multiple occupations are mapped to a single
OEWS-EP 2019 code, the time-use factor for that occupation is the mean value for all mapped
occupations. We then obtain time-use factor estimates for 736 OEWS 2019 occupation codes.
These estimates tend to be quite low with a mean lower than 10 percent and a 95 percent
quantile of 8 percent. The larger values are almost exclusive to the landmark occupations
19

shown in table 2.

3.3

Markup and Additional Adjustments

Markup.

We estimate the full production costs by multiplying the data-related wage billby a markup (i.e., the 𝛼 in equations 1 and 6) that is designed to reflect employee benefits,
capital costs, and intermediate consumption, none of which are included in the wage bill.
Observing publicly available information, firms engaged in data aggregation, data sales, andother data-related activities appear generally to be classified to industries in Information(NAICS 51), Professional, Scientific and Technical (PST) Services (NAICS 54), or Administrative & Support Services (NAICS 56).6 Based on these observations, we use NAICS 51,
NAICS 54, and NAICS 56 as the representative sectors for the markup in the full production cost estimates. In particular, we calculate 𝛼 as a composite ratio of compensation,intermediate consumption (exluding materials), consumption of fixed capital (CFC), and net
operating surplus to wages and salaries using data summed for select industries from BEA’s
annual industry accounts. In other words, the composite ratio is weighted by the size of thechosen industries.
We consider calculating the composite ratio using BEA industry accounts data at the
NAICS 2-digit sector level - i.e., all industries in NAICS 51, 54, and 56 - or at a more detailed subsector level of industries. The sector level yields a ratio that reflects a broader
representation of industries and may be driven by the production functions of products thatare not data-related. The latter feature assumes the production function of data assets resembles the production functions of primary products for the representative industries. The
subsector level allows us to choose industries that are more explicitly engaged in the production of data-related products and, thus, yields a ratio that more closely reflects the production
6

For example, Axciom, Facebook, Localeze, and Burning Glass Technologies appear to be classified to
NAICS 51 industries. In addition, Alphabet, Foursquare, Infogroup, Nielsen, Automatic Data Processing Inc.
(ADP), The NPD Group, and International Data Corporation (IDC) all appear to be classified to NAICS
54 industries. Exceptions are the credit bureaus, which are classified to Administrative & Support Services(NAICS 56) industries.

20

functions of data-related products. The industries we choose from BEA’s industry accounts
include Data Processing, Internet Publishing and Other Information Services (NAICS 518),
Computer Systems Design and Related Services (NAICS 5415), and Administrative & Support Services (NAICS 561).
At the sector level, the annual ratios range from 2.91 to 3.11 for 2003–2020, with a simple
average of 3.03. At the subsector level, the annual ratios range from 2.22 to 2.61, with asimple average of 2.42. For our core experimental results, we use the subsector average of 2.42
as a time-invariant lower bound markup for each year 2003–2020. In addition, we present
results using the sector average of 3.03 as a time-invariant upper bound markup for eachyear.
Additional Adjustments. We do not have an estimate of own-account data output thatis used up in current production versus own-account data output that becomes capital formation. Thus, we reduce the full production cost estimate by 50 percent to capture capitalformation.
We also adjust for potential overlap between data assets and R&D assets by reducing thecapital-formation-adjusted production cost estimates by 50 percent for R&D intensive industries (Galindo-Rueda and Verger (2016)), including industries in Chemical Manufacturing(NAICS 325), Computer and Electronic Product Manufacturing (NAICS 334), Transportation Equipment Manufacturing (NAICS 336), Publishing (NAICS 511), and PST Services(NAICS 541). Likewise, we adjust for overlap between own-account data and purchased data
by further reducing the adjusted production cost estimate for PST Services (NAICS 541) by
50 percent. Since we exclude software skills in our estimates of time-use factors, no additional
adjustment is required for potential overlap between data assets and software assets.
We choose 50 percent as a placeholder for each of the additional adjustments to reflect our
acknowledgement of the adjustments until future empirical evidence comes available. Table
3 summarizes by NAICS subsector the effective factors that are applied to the wage bill to
calculate investment in data assets for our core experimental results. Section 4 has more
21

Table 3: Effective factors applied to the wage bill
NAICS

MarkupCapital formation

R&D

Purchased data

Effective factor

325
334
336
511
541
All other

2.42
2.42
2.42
2.42
2.42
2.42

0.50
0.50
0.50
0.50
0.50
0.50

0.50
0.50
0.50
0.50
0.50
N/A

N/A
N/A
N/A
N/A
0.50
N/A

0.605
0.605
0.605
0.605
0.3025
1.21

Note: The table summarizes by NAICS subsector the effective factors that are applied to the wage
bill to calculate the full production costs. All other excludes NPISH and general government: Educational Services (NAICS 61), Health Care and Social Assistance (NAICS 62), Arts, Entertainment
and Recreation (NAICS 71), Religious, Grantmaking, Civic, Professional, and Similar Organizations (NAICS 813), and Public Administration (NAICS 92).

discussion on the markup and additional adjustments.

4

Addressing Measurement Challenges

The main challenges for measurement of own-account data stocks and flows that we addressin the paper are similar to challenges imposed by other own-account IPPs that are alreadyincluded in capital formation in the SNA and the U.S. NIPAs. We identify here four challenges
and how we address the challenges for own-account data.

4.1

Scope of Costs in Capital Formation

The scope of costs to include in capital formation is important for own-account data andother own-account IPPs.7 The stages of the data value chain (OECD 2013; Moro Visconti
et al. 2017) yield insights into activities that underlie the production process for data –including collection, storage, processing, distribution, and usage – some of which are reflectiveof costs that embody capital formation. The scope that has been recently introduced by the7

For example, OECD (2010) identifies eight stages of activities in the production of own-account software:feasibility analysis, functional analysis, detailed analysis, programming, tests, documentation, training, andmaintenance. Of the eight stages, only costs for functional analysis, detailed analysis, programming, tests,
and documentation are within scope for capital formation.

22

OECD (2021) is whether capital formation should include both recording and processing
costs and the costs of procuring access to data or only include recording and processing
costs. Procurement costs may include either explicit purchases or the value of “free” digitalproducts exchanged for access.8 OECD (2021) considers recording and processing costs to be
production activities that could result in capital formation. However, they call into question
whether procurement costs should be considered production activities or simply costs toimprove the quantity or quality of access for a more valuable data asset, and thus, outside
the production boundary.
We address this challenge by identifying skills in the BGT taxonomy that are relevant
to data-related activities, including data entry, storage, analysis, and management. Theseskills are embodied by the occupations that underlie our estimates of the data wage bill.
In addition, we reduce the full production cost estimate for data by 50 percent to accountfor data-related output that is used up in the current period and never becomes capitalformation.9

4.2

Multiple Counting

Own-account data and other own-account IPPs are at risk of multiple counting from several
sources.
4.2.1

Overlap Among Categories of IPPs

One source of multiple counting is overlap among categories of IPPs that are separately
measured, such as data, software, and research and development (R&D).
8

The latter procurement costs are identified in Farboodi and Veldkamp (2021) as a measurement challenge,
and their model moves beyond price-based valuation by assigning a value to goods and data that have a zerotransaction price.
9
Another approach would be to use a depreciation rate for data that reflects a more rapid decline in valueof investment flows as a result of obsolescence. However, such an approach would disregard the accounting
difference between intermediate consumption and capital formation.

23

Software. We address overlap between data and software by estimating the time-use factors
for data, which are intentionally based on unique data-relevant skills that are distinguishable
from software-relevant skills.10 For comparison with data-relevant skills presented in table 1,
table 4 presents the top 80 BGT skills identified as software relevant. Likewise, table 5 shows
the top time-use factors for occupations engaged in software-related activities to comparewith landmark occupations for data reported in table 2.
While tables 1 and 4 demonstrate no overlap for BGT skills by design, there is overlapof seven occupations with high time-use factors in tables 2 and 5. The occupations thatshow up in both tables include Business Intelligence Analysts, Computer and Information
Research Scientists, Database Administrators, Database Architects, Data Warehousing Specialists, Geographic Information Systems Technicians, and Statisticians. In addition, thetime-use factors sum to more than 100 percent for the first five occupations, which implies
the occupations may be used to measure either data- or software-related activity but not
both. However, there is no overlap between landmark occupations for data and the four
occupations BEA currently uses to estimate own-account software.
Overlap is expected when activities are intertwined. One example is software and R&D.
Based on the 2018 Business Enterprise Research and Development Survey (BERD) table 19,$165.6 billion (37.5 percent) of domestic R&D was software products and embedded software
R&D.11 In the NIPAs, this overlap is included with R&D investment and excluded fromsoftware investment. There may also be job activities at the intersection of data, software,
10

We also considered completely excluding from the data wage bill the wages of the four occupations that
BEA uses to estimate own-account software–Computer Programmers, Computer System Analysts, Software
Developers (applications), and Software Developers (system software). However, the time-use factors forthese four occupations are modest - each less than 20 percent - and we think the occupations may reasonably
embody some data-related activities that accompany own-account software development.
11
According to guidance in the BERD survey, R&D activity in software includes 1) software development or
improvement activities that expand scientific or technological knowledge and 2) construction of new theories
and algorithms in the field of computer science. Likewise, the BERD survey requires that R&D activity
in software exclude 1) software development that does not depend on scientific or technological advance,such as supporting or adapting existing systems, adding functionality to existing application programs, androutine debugging of existing systems and software, 2) creation of new software based on known methods
and applications, 3) conversion or translation of existing software and software languages, and 4) adaptationof a product to a specific client, unless knowledge that significantly improved the base program was addedin that process.

24

Table 4: BGT skills identified as software relevant.NET
Microsoft Sql Server Integration Services (SSIS)
Active Server Pages (ASP)
Microsoft Visio
Agile Development
Middleware
AJAX
MySQL
Amazon Web Services (AWS)
Object-Oriented Analysis and Design (OOAD)
AngularJS
OracleApache Hadoop
Oracle PL/SQLApache Tomcat
PERL Scripting Language
Apache Webserver
Platform as a Service (PaaS)
Application Design
PythonASP.NET
Relational DataBase Management System (RDBMS)
Atlassian JIRA
Ruby
C++Salesforce
COBOL
SAP
Computer EngineeringSAS
Crystal Reports
Scrum
Debugging
Shell Scripting
EclipseSoftware ArchitectureEnterprise Resource Planning (ERP) Software as a Service (SaaS)
Extensible Markup Language (XML) Software Development
Extensible Stylesheet Language XSL Software Engineering
Firmware
Software Testing
Git
SQL
HTML5
SQL Server
Hypertext Preprocessor (PHP)SQL Server Reporting Services (SSRS)IBM WEBSPHERE
Systems Analysis
Informatica
Systems Development Life Cycle (SDLC)Java
Teradata DBA
Java Server Pages (JSP)
Transact-SQL
JavaScript
Unified Modeling Language (UML)
JavaScript Object Notation (JSON) UNIX
jQueryUNIX Shell
JUnit
User Acceptance Testing (UAT)Linux
User Interface (UI) Design
Microsoft Access
Visual Basic
Microsoft Azure
Visual Studio
Microsoft C#
VMwareMicrosoft Operating Systems
Waterfall
Microsoft Project
WebLogic
Microsoft SQLWindows Server
Note: Top 80 software skills by frequency identified from landmark occupations.

25

Table 5: Top time use factors for software
O*NET SOC 2010

Description

Time use factor

15-1133.00
15-1132.00
15-1131.00
15-1121.00
15-1134.00
15-1199.02
15-1199.08
15-1199.06
15-1141.00
15-1199.01
15-1199.09
15-1143.00
13-1111.00
15-1199.07
15-1111.00
15-1142.00
15-1199.00
15-1122.00
11-3021.00
17-2061.00
15-1199.04
15-1199.03
15-1152.00
15-1151.00
15-2041.00
13-2099.01
15-1199.11
15-1143.01
17-3029.07
17-1011.00
13-2051.00
15-1199.10
13-1081.02
17-2199.08
27-1021.00
27-3042.00
17-3031.02
15-1199.05

Software Developers, Systems Software
Software Developers, Applications
Computer Programmers
Computer Systems Analysts
Web Developers
Computer Systems Engineers/Architects
Business Intelligence Analysts
Database Architects
Database Administrators
Software Quality Assurance Engineers and Testers
Information Technology Project Managers
Computer Network Architects
Management Analysts
Data Warehousing Specialists
Computer and Information Research Scientists
Network and Computer Systems Administrators
Computer Occupations, All Other
Information Security Analysts
Computer and Information Systems Managers
Computer Hardware Engineers
Geospatial Information Scientists and Techs
Web Administrators
Computer Network Support Specialists
Computer User Support Specialists
Statisticians
Financial Quantitative Analysts
Video Game Designers
Telecommunications Engineering Specialists
Mechanical Engineering Technologists
Architects, Except Landscape and Naval
Financial Analysts
Search Marketing Strategists
Logistics Analysts
Robotics Engineers
Commercial and Industrial Designers
Technical Writers
Mapping Technicians
Geographic Information Systems Technicians

0.95
0.94
0.85
0.82
0.65
0.60
0.55
0.55
0.52
0.52
0.50
0.49
0.48
0.46
0.45
0.41
0.40
0.40
0.39
0.38
0.38
0.37
0.37
0.36
0.34
0.32
0.32
0.32
0.30
0.30
0.28
0.28
0.28
0.27
0.27
0.27
0.27
0.27

Note: Bold occupations denote landmark occupations that are by design the same occupations
BEA currently uses to estimate own-account software.

26

and R&D, such as software programming to prepare a data set for research. Observing
independently-estimated time-use factors that together exceed 100 percent is not problematicin itself for our estimates because we know that own-account software estimation is limited
to four specific occupations with relatively low time-use factors. However, the outcome doessuggest an incentive to jointly estimate own-account data and own-account software to ensure
consistency and prevent double counting if the scope of own-account software is expanded toinclude more occupations in the future.
Research & Development. In contrast to software, the BGT skills dataset does notinclude R&D-relevant skills to distinguish them from data-relevant skills, which may yieldsome overlap between data assets and R&D assets. For example, Goodridge et al. (2021)
and Statistics Canada (2019a) both argue that data science activities meet the SNA andOECD (2015) definition of R&D. However, Goodridge et al. (2021) suggest that data science
is not included in measured R&D in practice by some countries because R&D is generally
measured based on surveys designed for known performers of traditional scientific R&D
activities. Likewise, Statistics Canada (2019a) suggests that the survey they use to measure
R&D needs to be examined for updates because the survey was developed years ago and isbiased toward the selection of firms engaged in more traditional forms of R&D activities,
such as pharmaceutical firms, and biased away from a growing number of firms in diverse
industries engaged in data science activities.
For R&D measures in the U.S. national accounts, the survey used to collect informationon performance of business R&D is the BERD survey.12 The 2019 BERD survey explicitly
includes software development or improvement activities that expand scientific or technological knowledge and construction of new theories and algorithms in the field of computer
12

The 2019 BERD survey defines research and development as follows: “Research is defined as experimentalor theoretical work undertaken primarily to acquire new knowledge or understanding of phenomena andobservable facts. Research may be either “basic”, where the goal is primarily to acquire new knowledge orunderstanding of a given topic without a specific commercial application in mind, or “applied”, where thegoal is to solve a specific problem or meet a specific commercial objective. Development is defined as thesystematic use of research and practical experience to produce new or improved goods, services, or processes.
In simple terms, the intended output of research is ideas and the intended output of development is products.”

27

science. In addition, the 2019 BERD survey asks respondents to report the percentage of domestic R&D expenditures paid for and performed by them that was for artificial intelligence(AI), which includes speech recognition, machine vision, machine learning, text analytics,
and natural language generation and processing. Thus, data science may not be absent inpractice from the BERD survey and may be included in BEA’s measures of R&D based on
the BERD survey.
To adjust for potential overlap between data and measured R&D in our estimates, wereduce the capital-formation-adjusted production cost estimate by 50 percent for R&D intensive industries (Galindo-Rueda and Verger 2016), including Chemical Manufacturing (NAICS
325), Computer and Electronic Product Manufacturing (NAICS 334), Transportation Equipment Manufacturing (NAICS 336), Publishing Industries (NAICS 511), and Professional, Scientific, and Technical Services (NAICS 541).13 Consistent with the suggestions of Goodridge
et al. (2021) and Statistics Canada (2019b), the resulting dollar adjustments for these industries are modest.14
4.2.2

Non-Rival Use of Data

Another source of multiple counting is overlap between own-account data and purchased data,
which is also a source of multiple counting for software and R&D. We address this challenge
by further reducing the adjusted production cost estimate by 50 percent for PST Services(NAICS 541), which we determine to be the NAICS code used by most data aggregators andbrokers and is the NAICS code with the highest data wage bill (prior to adjustment) under13
We also consider completely excluding from the data wage bill the wages of four SOC 2010 occupations
that appear to be R&D occupations based on “research” in the descriptions: Computer and Information
Research Scientists (SOC 15–1111), Operations Research Analysts (SOC 15–2031), Survey Researchers (SOC
19–3022), and Social Science Research Assistance (SOC 19–4061). However, we think the occupations may
reasonably embody some data-related activities that accompany R&D, and we are not able to determine if
excluding these four occupations based on “research” is as comprehensive as adjusting the R&D intensive
industries.
14
The resulting dollar adjustment for 2020 is $29.4 billion or approximately 5.75 percent of business R&D
investment in the U.S. national accounts.

28

our methodology.15
Finally, a source of multiple counting that has been cited as a concern specifically for dataas an asset is that the same piece of information can be used in multiple databases. However,a single piece of information is transformed when it is added to a record with other pieces
of information. Likewise, we are not valuing pieces of information but rather the activities
associated with making the information available in a record, which is unique to every firm
for own-account measures.

4.3

Measuring Capital Costs

A challenge that may be more relevant (but not unique) to data stocks and flows is how
to measure the capital cost component of the sum-of-costs. There is a question of what
proportions of the sum-of-costs should be accounted for by labor costs, capital costs, andintermediate consumption, given the role of capital services in the collection, storage, analysis,
and management of data.
For our core experimental results, we apply a conservative markup of 2.42 to the wage
bill as explained in section 3.3. In addition, we apply an alternative markup of 3.03 to
the wage bill, which we treat as an upper bound result. Since the markups are weighted
composite ratios of compensation, intermediate consumption (excluding materials), CFC,
and net operating surplus, we can decompose the markups into the proportionate share ofeach component in the sum-of-costs. The decomposition is presented in table 6.
Aside from the overall numeric difference between the two markups - i.e., 2.42 and 3.03
- table 6 demonstrates two important differences between the two markups that result fromthe NAICS levels chosen for each. First, the ratios for labor costs (i.e., compensation) are
the same between the two markups (i.e., 1.17). Second, the ratios for capital costs (i.e., net
operating surplus plus CFC) and intermediate consumption are both higher for the upperbound markup, which yields higher proportionate shares of capital costs and intermediate15

The reduction for PST Services (NAICS 541) to adjust for non-rival use of data is in addition to the
reduction of 50 percent to adjust for R&D overlap.

29

Table 6: Weighted composite ratios for full sum-of-costs
Subsector (lower bound)
Ratio
Share (%)
Compensation
Intermediate consumption
Consumption of fixed capital
Net operating surplus

1.17
0.78
0.18
0.29

Markup

2.42

48
32
8
12

Sector (upper bound)
Ratio
Share (%)
1.17
1.07
0.29
0.50

38
35
10
17

3.03

Note: All data are from BEA’s annual industry accounts. Intermediate consumption excludes
materials. The lower bound column reports the simple average for 2003-2020 of each annual measuresummed for NAICS 518, 5415, and 561 divided by annual wages and salaries summed for the same
subsectors. The upper bound column reports the simple average for 2003–2020 of each annual
measure summed for NAICS 51, 54, and 56 divided by annual wages and salaries summed for thesame sectors.

consumption and a lower proportionate share of labor costs for the upper bound markup.
A gap of 10 percentage points exists in the proportionate shares of labor costs between thelower- and upper-bound markups, and the gap is offset by 7 percentage points attributableto capital costs and 3 percentage points attributable to intermediate consumption.

4.4

Prices and Depreciation

Own-account data are not transacted in active markets, which means there are no observed
transactions that are useful for measuring prices and depreciation. While measuring prices
and depreciation for own-account data is beyond the scope of this paper, we utilize international guidelines and U.S. practice for deflating and depreciating own-account softwareand databases as a starting point for deflating and depreciating own-account data, whichreflects the inclusion of own-account data as a subcategory of software and is consistent withpreliminary guidance emerging from the SNA community’s current work on revising the SNA.
Prices.

In the absence of deflators for own-account software, OECD (2010) recommends

that deflators for custom software be used as a proxy until own-account software deflators
are developed. In addition, OECD (2010) suggests three options for own-account database30

deflators. One option is to use a price index of a related activity for which there is a priceindex of reasonable quality. The other two options are an input cost index, one with zeroproductivity growth and another with an adjustment based on productivity growth of asimilar industry.
BEA does not compile a price index for own-account databases because own-accountdatabases are not measured separately from own-account software in the U.S. NIPAs. For
own-account software, BEA currently compiles a price index from a weighted average ofthe prepackaged software price and a BEA input cost index (U.S. Bureau of Economic
Analysis 2020). The input cost index is compiled from BLS data on wage rates for Computer
Programmers and Computer System Analysts and the intermediate consumption associatedwith the production of software. The input cost index reflects an explicit adjustment forchanges in productivity that is based primarily on a BLS multifactor productivity index.16
To address the lack of prices for own-account data, we propose using BEA’s current priceindex for own-account software as a proxy to deflate current-dollars. In the future, an input
cost index with a productivity adjustment could be developed specifically for data.
Depreciation.

OECD (2009) recommends several options for determining depreciation

parameters for an asset class, which generally requires information on prices or service lives.
One option uses information on the service life and makes an additional assumption about
the functional form of the depreciation pattern. A functional form that is commonly used
for pragmatic reasons is the geometric model of depreciation, which yields a pattern ofconstant percentage decline in an asset’s value. In the absence of econometric estimatesof the geometric depreciation rate, the geometric depreciation rate can be estimated using asimple declining balance method as 𝛿 = 𝑅 / 𝑆, where 𝑅 is an estimated declining balance
rate and 𝑆 is an average service life (Hulten and Wykoff 1996). The declining balance rate
16

When BEA first introduced capital measures of own-account software into the NIPAs in 1999, an input
cost index was compiled from a weighted average of compensation rates for Computer Programmers andComputer System Analysts and the intermediate consumption associated with their work (Moulton and
Sullivan 1999). No productivity adjustment was made.

31

may be either estimated econometrically or is sometimes assumed to be 2. For softwareand databases, OECD (2010) recommends the average service life be obtained by surveying
software users, surveying software suppliers, or hiring software consultants.
BEA does not compile a measure of depreciation for own-account databases but does
compile a measure of depreciation for own-account software in the U.S. Fixed Assets Accounts(FAAs). To estimate depreciation for own-account software, BEA uses a 5-year service life
and a declining balance rate of 1.65 to determine the geometric depreciation rate. The
service life is based on estimates of the relationship between computer expenditures andsoftware expenditures, anecdotal evidence about how long software is used before replacement(including an informal survey of business uses of software), and tax-law-based service lives ofsoftware.17 The declining balance rate is borrowed from the Hulten-Wykoff methodology thatis used for most equipment in the FAAs (Hulten and Wykoff 1981; Hulten, McCallum, et al.
1981; Wykoff and Hulten 1979; Fraumeni 1997). To address the lack of depreciation rate for
own-account data, we use BEA’s current geometric depreciation rate of 0.33 for own-accountsoftware as a proxy.

5

Results

5.1

National Aggregates

5.1.1

Experimental Current-Dollar and Historical-Cost Estimates

Experimental estimates for aggregate business sector current-dollar investment in data assets
for 2003–2020 are presented in figure 1. The band in the figure reflects current-dollar levels
and the bars reflect current-dollar growth rates. The lower bound uses a markup of 2.42, andthe upper bound uses a markup of 3.03. With the lower markup, estimated current-dollar
investment in 2003 is $82.6 billion and in 2020 is $159.5 billion. Growth of current-dollar
17

A summary of BEA’s depreciation estimates is available at: https://apps.bea.gov/national/pdf/
BEA_depreciation_rates.pdf.

32

Figure 1: Current-dollar annual investment in data assets
200

10.0

190

9.5
9.0

180

8.5
8.0

160

7.5

150

7.0

140

6.5
6.0

130

5.5
120

5.0

110

4.5

100

4.0

90

3.5
3.0

80

2.5
70

2.0

60

1.5

50

1.0

40

0.5

Growth Rate (%)

Current Dollars ($B)

170

0.0

30

0.5
20

1.0

10

1.5

0
20

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
03 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020

2.0

Year

Note: The band in the figure reflects current-dollar levels and the bars reflect current-dollar growth rates.
The lower bound uses a markup of 2.42, and the upper bound uses a markup of 3.03.

investment is as high as 8.9 percent for 2004 and as low as −1.7 percent for 2010. The
average annual growth rate in current-dollar investment for 2004–2020 is 3.9 percent. Prior
to the recession of 2007–2009, the average annual growth rate for 2004–2007 was 5.9 percent,which declined to 3.2 percent for 2008–2020. Average annual growth for the ten-year period
2011–2020 was 4.3 percent.
Current-dollar investment in data assets as a share of current-dollar business sector valueadded is reported in figure 2. With the lower markup, the share of data investment for theperiod averages 0.9 percent of business sector value-added. With the higher markup, theshare of data investment averages 1.2 percent of business sector value-added.
We also report current-dollar investment in data assets as a share of current-dollar investment in IPPs in figure 3 and as a share of current-dollar investment in private fixed assets infigure 4. In figure 3, lower bound data investment as a share of investment in IPPs declines
over the period from 17.2 percent in 2003 to 13.4 percent in 2020 - the share averages 15.6
percent for the period. In figure 4, lower bound data investment as a share of investment inprivate fixed assets is 4.3 percent in 2003 and 4.5 percent in 2020 with some variation overthe period - the share averages 4.4 percent for the period.
33

Figure 2: Investment in data assets as a share of business sector value-added
1.30
1.25
1.20
1.15
1.10
1.05
1.00
0.95
0.90

Share (%)

0.85
0.80
0.75
0.70
0.65
0.60
0.55
0.50
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20

Year

Note: Numerator is the current-dollar annual investment in data assets. Denominator is the current-dollar
annual investment in data assets plus business sector value-added from line 2 of U.S. Bureau of Economic
Analysis, “National Income and Product Accounts: Table 1.3.5. Gross Value Added by Sector”. The lower
bound uses a markup of 2.42, and the upper bound uses a markup of 3.03.

Figure 3: Investment in data assets as a share of investment in IPPs
22
21
20
19
18
17
16
15

Share (%)

14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
20

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
03 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020

Year

Note: Numerator is the current-dollar annual investment in data assets. Denominator is the current-dollar
annual investment in data assets plus investment in IPPs from line 1 of U.S. Bureau of Economic Analysis,“Fixed Assets Accounts: Table 3.7I. Investment in Private Intellectual Property Products by Industry”,
adjusted to exclude NPISH by subtracting lines 66, 67, and 72. The lower bound uses a markup of 2.42, andthe upper bound uses a markup of 3.03.

34

Figure 4: Investment in data assets as a share of investment in private fixed assets
7.0
6.5
6.0
5.5
5.0

Share (%)

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20

Year

Note: Numerator is the current-dollar annual investment in data assets. Denominator is the current-dollar
annual investment in data assets plus investment in private fixed assets from line 1 of U.S. Bureau of Economic
Analysis, “Fixed Assets Accounts: Table 3.7ESI. Investment in Private Fixed Assets by Industry”, adjusted
to exclude NPISH by subtracting lines 66, 67, and 72. The lower bound uses a markup of 2.42, and the upperbound uses a markup of 3.03.

Experimental estimates for aggregate business sector historical-cost net stocks of data
assets for 2003–2020 are presented in figure 5. The historical-cost net stocks are calculatedusing a perpetual inventory method (PIM) with geometric depreciation. The PIM is a sumof annual depreciated historical-cost investment in data assets. Annual historical-cost investment in data assets prior to 2003 is backcast using the annual growth in current-dollar
investment in own-account software as an indicator. Depreciation of annual historical-cost
investment is calculated using BEA’s depreciation rate for own-account software of 0.33, assuming new investment in data assets is placed in service at midyear. The calculation forannual depreciated historical-cost investment, i.e., net stock 𝑁, in year 𝑡 for investment 𝐼
placed in service in year ℎ can be summarized as follows
𝛿𝑁𝑡,ℎ = 𝐼ℎ (1 − ) (1 − 𝛿)𝑡−ℎ .
2(7)

The band in figure 5 reflects historical-cost levels and the bars reflect historical-cost
35

Figure 5: Historical-cost annual net stock of data assets
500
480
460
440
420
400
360
340
320
300
280
260
240
220
200
180
160
140
120
100
80
60
40
20
0
20

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
03 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020

Growth Rate (%)

Historical Dollars ($B)

380

5.4
5.2
5.0
4.8
4.6
4.4
4.2
4.0
3.8
3.6
3.4
3.2
3.0
2.8
2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

Year

Note: The band in the figure reflects historical-cost levels and the bars reflect historical-cost growth rates.
The lower bound uses a markup of 2.42, and the upper bound uses a markup of 3.03.

growth rates. The lower bound uses a markup of 2.42, and the upper bound uses a markup
of 3.03. With the lower markup, estimated historical-cost net stock in 2003 is $201.0 billion
and in 2020 is $375.3 billion. Growth of historical-cost net stocks is as high as 5.5 percent for2007 and as low as 0.9 percent for 2010. The average annual growth rate in historical-cost
net stocks for 2004–2020 is 3.7 percent.
We report the historical-cost net stocks of data assets as a share of the historical-cost
net stocks of IPPs in figure 6 and as a share of the historical-cost net stocks of private fixed
assets in figure 7. In figure 6, lower bound data stocks as a share of IPP stocks decline overthe period from 12.8 percent in 2003 to 10.1 percent in 2020 - the share averages 11.5 percent
for the period. In figure 7, lower bound data stocks as a share of private fixed assets stocks
is 1.3 percent in 2003 and 1.2 percent in 2020 with some variation over the period - the share
averages 1.2 percent for the period.
5.1.2

Real Growth

We compare the log growth in real U.S. aggregate measures with and without our experimental estimates of business sector investment in data assets for 2004–2020. Real aggregate
36Figure 6: Stock of data assets as a share of aggregate IPPs
16
15
14
13
12
11

Share (%)

10
9
8
7
6
5
4
3
2
1
0
20

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
03 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020

Year

Note: Numerator is the historical-cost annual net stock of data assets. Denominator is the historical-costannual net stock of data assets plus historical-cost net stock of IPPs from line 1 of U.S. Bureau of Economic
Analysis, “Fixed Assets Accounts: Table 3.3I. Historical-Cost Net Stock of Private Intellectual Property
Products by Industry”, adjusted to exclude NPISH by subtracting lines 66, 67, and 72. The lower bounduses a markup of 2.42, and the upper bound uses a markup of 3.03.

Figure 7: Stock of data assets as a share of aggregate private fixed assets
1.8
1.7
1.6
1.5
1.4
1.3

Share (%)

1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20
03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20

Year

Note: Numerator is the historical-cost annual net stock of data assets. Denominator is the historical-costannual net stock of data assets plus historical-cost net stock of private fixed assets from line 1 of U.S. Bureau
of Economic Analysis, “Fixed Assets Accounts: Table 3.3ESI. Historical-Cost Net Stock of Private Fixed
Assets by Industry”, adjusted to exclude NPISH by subtracting lines 66, 67, and 72. The lower bound uses
a markup of 2.42, and the upper bound uses a markup of 3.03.

37

Table 7: Growth in real measures with and without investment in data assets (%)
Average

Cumulative

With data

W/o data

Δ

With data

W/o data

Δ

4.07
1.74
4.84
6.38

1.73
5.06
7.58

0.01
−0.21
−1.20

69.13
27.92
77.50
102.0827.74
80.90
121.300.18−3.40−19.23

4.07
1.75
4.80
6.19

1.73
5.06
7.58

0.02
−0.26
−1.40

69.13
27.99
76.80
98.96

27.74
80.90
121.300.25−4.10−22.34

Markup of 2.42
Data
Value-added
IPPs
SoftwareMarkup of 3.03
Data
Value-added
IPPs
SoftwareNote: The table reports average and cumulative log growth rates in real data investment alongwith changes in growth for business sector real value-added, private sector real investment in IPPs,
and private sector real investment in software with and without data investment for 2004–2020.
Price indexes for own-account software are used as a proxy for own-account data and aggregate
price indexes are recalculated using Törnqvist expenditure shares.

measures include business sector value-added, private sector investment in IPPs, and private
sector investment in software. We use the price index for own-account software as a proxyfor own-account data and recalculate measured aggregate prices using Törnqvist expenditure
shares. We report average and cumulative annual growth in table 7.
With the lower markup, the largest annual growth rate in real data investment is 10.6
percent for 2004 and the smallest is -1.1 percent for 2009. The average annual growth inreal data investment for 2004–2020 is 4.1 percent and the cumulative annual growth for theperiod is 69.1 percent. When data investment is added to business sector value-added, theaverage annual change in real value-added over the period is an increase of 1 basis point, andthe cumulative annual change is an increase of 18 basis points. Results are similar with thehigher markup.
In contrast to business sector value-added, growth in real investment in IPPs and insoftware are both higher than real data investment. When data investment is added, the38average and cumulative growth in real measures decline. With the lower markup, the decline
in average annual growth in real investment in IPPs for 2004–2020 is 21 basis points (i.e.,
0.21 percentage point) and in cumulative average annual growth is 340 basis points (i.e., 3.4
percentage points). The decline in average annual growth in real investment in software forthe period is 120 basis points (i.e., 1.2 percentage points) and in cumulative average annual
growth is 1,923 basis points (i.e., 19.2 percentage points). The declines are steeper with thehigher markup.
The changes in growth of business sector real value-added and private sector real investment in IPPs and software all reflect the modest growth in measured own-account data
investment for the period.18

5.2

NAICS Sector Aggregates

5.2.1

Experimental Current-Dollar Estimates

Experimental estimates for current-dollar investment in data assets by NAICS sector for
2003–2020 are presented in table 8. Table 9 reports the average annual growth in currentdollar investment in data assets by NAICS sector for 2004–2020. The current-dollar estimatesas a share of current-dollar value-added by NAICS sector for 2003–2020 are shown in figure 8.
Since our methodology starts with an estimate of the occupation-based wage bill, results by
NAICS sector reflect industries that employ occupations engaged in data-related activities.
For the period 2003–2020, table 8 shows the total current-dollar investment in data was$2.1 trillion. The largest dollar investments were made in Finance and Insurance ($354
billion), Manufacturing ($283 billion), Professional, Scientific and Technical (PST) Services($214 billion), and Administrative Services ($214 billion). The smallest dollar investments
were made in Agriculture, Mining, Utilities, and Other Services.
18

Goodridge et al. (2021) report labor productivity with and without data investment for 13 European
Union countries and find a modest average increase in labor productivity of only 5 basis points with data
investment for 2011–2016. The largest average annual increase by country is 9 basis points attributable toGermany, and largest average annual decrease by country is 9 basis points attributable to Estonia.

39

Table 8: Current-dollar investment in data assets by NAICS sector 2003–2020
NAICS

Description

($B)

11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
72
81

Agriculture, Forestry, Fishing and Hunting
Mining, Quarrying, and Oil and Gas Extraction
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate and Rental and Leasing
Professional, Scientific, and Technical Services
Management of Companies and Enterprises

Administrative & Support and Waste Management & Remediation Services

Accommodation and Food Services
Other Services (except Public Administration)

Total

3
27
27
80
283
194
132
76
207
354
46
214
195
214
31
27
2,110

Note: Current-dollar estimates summed for 2003–2020 by NAICS sector. Estimates for NPISH
and general government are excluded: Educational Services (NAICS 61), Health Care and Social
Assistance (NAICS 62), Arts, Entertainment and Recreation (NAICS 71), Religious, Grantmaking,
Civic, Professional, and Similar Organizations (NAICS 813), and Public Administration (NAICS
92).

Table 9 demonstrates that average annual growth in current-dollar investment in data
assets was as low as 2.2 percent for Retail Trade and as high as 6.4 percent for Managementof Companies. Average annual growth rates by NAICS sector exceeded the aggregate averageannual growth rate of 3.9 percent for Mining (5.1 percent), Construction (4.1 percent), Transportation and Warehousing (4.2 percent), Finance and Insurance (4.1 percent), Real Estate(4.3 percent), PST Services (4.9 percent), and Management of Companies (6.4 percent).
In figure 8, the three largest shares of NAICS sector value-added show up for Managementof Companies (NAICS 55), Administrative Services (NAICS 56), and Finance and Insurance(NAICS 52). Shares of sector value-added above 1.0 percent also show up for Information(NAICS 51), Wholesale Trade (NAICS 42), and PST services (NAICS 54). The smallest shares of NAICS sector value-added show up for Agriculture (NAICS 11), Real Estate40

Table 9: Average annual growth in current-dollar investment in data assets by NAICS sector
2004–2020
NAICS

Description

(%)

11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
72
81

Agriculture, Forestry, Fishing and Hunting
Mining, Quarrying, and Oil and Gas Extraction
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate and Rental and Leasing
Professional, Scientific, and Technical Services
Management of Companies and Enterprises

3.6
5.1
2.8
4.1
2.3
3.0
2.2
4.2
3.8
4.1
4.3
4.9
6.4
3.6
2.7
3.1

Administrative & Support and Waste Management & Remediation Services

Accommodation and Food Services
Other Services (except Public Administration)

Note: Annual log growth rates for current-dollar investment averaged for 2004–2020 by NAICS
sector.

(NAICS 53), Accommodation and Food Services (NAICS 72), and Other Services (NAICS
81).
5.2.2

Real Growth

We compare the log growth in real value-added for NAICS sectors with and without ourexperimental estimates of business sector investment in data assets for 2004–2020. We use
the price index for own-account software as a proxy for own-account data and recalculate
measured NAICS sector prices using Törnqvist expenditure shares. We report average andcumulative annual growth in real value-added growth by NAICS sector in table 10.
Consistent with the result for business sector real value-added in table 7, the change inaverage annual growth is positive for each NAICS sector except Information (NAICS 51).
When data investment is added, the three largest increases in average real value-added growthshow up for Management of Companies (NAICS 55, 13 basis points), Administrative Services
41

Figure 8: Investment in data assets as a share of value-added by NAICS sector
4.0
3.8
3.6
3.4
3.2
3.0
2.8

Share (%)

2.6
2.4
2.2
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

11

21

22

23 31-3

3

42 44-4 48-4 51
9
5

52

53

54

55

56

72

81

NAICS

Note: Numerator is the current-dollar annual investment in data assets summed for 2003–2020. Denominatoris the current-dollar annual investment in data assets plus NAICS sector value-added summed for 2003–2020
from the U.S. Bureau of Economic Analysis, “Interactive Access to Industry Economic Accounts Data: Table
1 Value Added by Industry”.

(NAICS 56, 4 basis points), and Finance and Insurance (NAICS 52, 4 basis points). Increases
in cumulative real value-added growth are also largest for those three NAICS sectors. The
smallest increases in average and cumulative real value-added growth show up for Agriculture(NAICS 11), Mining (NAICS 21), Information (NAICS 51), and Real Estate (NAICS 53).
5.2.3

Non-profit Institutions Serving Households

While core experimental results for the paper are limited to the business sector, table 11
reports supplemental experimental results for current-dollar investment in data assets for
NAICS sectors that represent the NPISH sector.
For the period 2003–2020, table 11 shows the total current-dollar investment in data
by NPISH was $551 billion. The largest dollar investments were made in Health Care andSocial Assistance ($326 billion), which is second only to Finance and Insurance in the businesssector. The second largest dollar investments by NPISH were made in Educational Services($157 billion).
42

Table 10: Growth in real value-added with and without investment in data assets by NAICS
sector (%)
Average

Cumulative

NAICS

With data

W/o data

Δ

With data

W/o data

Δ

11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
55
56
72
81

2.26
3.36
1.85
−0.84
1.40
1.30
0.92
1.29
5.51
1.75
1.91
3.00
2.46
2.43
−0.69
−1.07

2.25
3.35
1.84
−0.88
1.39
1.28
0.91
1.26
5.53
1.71
1.90
2.97
2.33
2.39
−0.70
−1.09

0.00
0.00
0.01
0.03
0.01
0.02
0.01
0.03
−0.02
0.04
0.00
0.03
0.13
0.04
0.01
0.02

38.35
57.08
31.44
−14.33
23.77
22.15
15.66
21.92
93.66
29.81
32.44
51.00
41.78
41.24
−11.69−18.18

38.33
57.02
31.32
−14.93
23.62
21.79
15.46
21.46
94.01
29.15
32.38
50.49
39.53
40.64
−11.91−18.49

0.02
0.06
0.12
0.59
0.15
0.36
0.20
0.46
−0.35
0.66
0.06
0.52
2.25
0.60
0.21
0.31

Note: The table reports average and cumulative log growth rates in real value-added by NAICS
sector with and without data investment for 2004–2020. Price indexes for own-account software areused as a proxy for own-account data and NAICS price indexes are recalculated using Törnqvist
expenditure shares.

Table 11: Current-dollar investment in data assets for NPISH 2003–2020
NAICS

Description

($B)

61
62
71
813

Educational Services
Health Care and Social AssistanceArts, Entertainment, and Recreation
Religious, Grantmaking, Civic, Professional, and Similar Organizations

157
326
21
47

Total

551

Note: Current-dollar estimates summed for 2003–2020.

43

6Conclusions

In this paper, we measure the value of own-account data stocks and flows for the U.S. businesssector by summing the production costs of data-related activities implicit in occupations. In
our experimental estimates, we find that annual current-dollar investment in own-accountdata assets for the U.S. business sector grew from $82.6 billion in 2003 to $159.5 billion
in 2020, which yields an average annual growth of 3.9 percent. Cumulative current-dollar
investment for the period 2003–2020 was $2.1 trillion. Overall, our results indicate thatbusiness sector investment in own-account data grew only a bit faster than other businesssector economic activity and slower than business sector investment in other IPPs.
The method we use in the paper augments the traditional sum-of-costs methodology formeasuring other own-account intellectual property products in national economic accounts
by proxying occupation-level time-use factors using a machine learning model and the text of
online job advertisements. The method appears to be a feasible method for identifying occupations engaged in data-related activities and for estimating the time-effort that occupations
allocate to data-related activities. The time-use factors we develop for occupations engagedin own-account data appear to have some overlap with the time-use factors we develop for
occupations engaged in own-account software, which suggests an incentive to jointly estimate own-account data and own-account software to ensure consistency and prevent double
counting if the scope of own-account software is expanded to include more occupations in
the future.
In the future, we plan to expand the scope of estimation to include the NPISH andgovernment sectors. In addition, this paper focuses on estimating current-dollar values anduses the price index and depreciation rate for BEA’s measures of own-account software asproxies to calculate real growth rates and net stocks, respectively. Thus, an important area ofdevelopment in the future will be a price index and depreciation rate specific to own-accountdata. Likewise, an important area for further development will be estimates of purchased data
assets and more precise adjustments for capital formation and overlap with R&D investment.
44

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