Capitalizing Data:

Theoretical Framework and Case Studies

By Rachel Soloveichik
Outline of Presentation

• Data are defined as easy-to-copy information – Storage methods: Servers, CD’s, paper, genetics, VHS tapes, and so on

• Theoretical framework

– Data can be sold or given free by the owner of complementary capital 

– Identify parameters where free data yield more value than sold data

– Argue that these identified parameters are common in the real world

• Case studies focus on four types of free data: 

tax, individual credit, driving, and marketing

– These four types alone had $2.1 trillion of free data creation in 2017

– Back-of-the-envelope calculations suggest that total privately funded free data creation may have been $6.7 trillion in 2017

• Recalculate GDP when data are capitalized

2
Theoretical Framework

• Firms 1 to n use v capital assets and w data 

types in a modified CES production function

– Parameters s1, s2, ..., sn determines each firms’ skill at using data

– Parameter ρ determines complementarity between data and capital

– Parameter σ determines complementarity between data types

– Parameters β1,1 β2,1,. , β1,v, ..., βw, v determine how specific each capital asset is to each data type

• V separate capital owners rent separate capital 

assets, K1 to Kv at fixed rental rates, r1 to rv

• Two ways to earn money from data:

– Data owners can sell data to firms 1 to n at a price p per unit

– Data owners can make their data free in return for a lump sum 

payment from either a capital owner or another data owner

3
Data Are Strong Complements to Data & 

Data are Weak Complements to Capital

4
Data Are Substitutes to Data &

Data are Weak Complements & Specific to Capital

5
Total Free Data Creation in 2017

Extrapolated from Case Studies and Model

6
Previous research studied 

complex datasets that are 

either used in-house or sold

I study simple free 

data which underly 

complex datasets

Data Pyramid: Free vs. Sold

7

Decisions made using data 

are not included in data
Data Creators

Impact of Capitalizing Data on GDP by Industry

• Almost every activity generates data

– Workers fill out tax forms when they start a job

– Borrowers, banks, and debt collectors create credit data

– Drivers, police officers, and insurers create driving data

• Data are sometimes primary output

– E.g. laboratories produce medical data but not treatment

• Data are generally secondary output

– Data given to governments are taxes in-kind

– Data given to workers are non-cash benefits

– Data given to customers are part of a bundled purchase

– Primary output ↓ by the value of data given to customers

• Household data creation doesn’t impact GDP

8
Platforms and Data Owners

Impact of Capitalizing Data on GDP by Industry

• Platforms organize data but don’t control data

– Neither inputs nor outputs change when data are capitalized

• Both sold and own-account data can be free – Sold data are owned by their purchaser

– Own-account data are owned by their creator

– This paper treats a government mandate to create data as a tax in-kind 

and therefore considers those data to be owned by the government

• Business data are tracked as intangible capital

– Intermediate inputs ↓ by payments for data

• Consumer data are tracked as durables PCE

– Nondurable PCE ↓ by payments for data

9
Data Users

Impact of Capitalizing Data on GDP by Industry

• When calculating value-added by industry, data sharing is treated as a barter transaction

– Customers who share their free data pay lower prices

– Workers who share their free data about earn higher wages

– Business owners who share their free data earn higher profits

• Businesses use data to target customers, hire 

workers, determine prices/wages, and so on

– Intermediate input ↑ by the value of free data services used

– Private output ↑ by the discounts given in return for customer data

• Governments use data to determine tax 

obligations, administer programs, and so on

– Government output ↑ by the value of data services used

10
Nominal GDP Revision

11

WW2
GDP Price Indexes in Case Studies

12

Fair Credit Reporting Act

Comprehensive 

Loss Underwriting 

Exchange
Conclusion

• Theoretical framework where data can either 

be sold or given free

– Identify plausible parameters where free data dominates sold data

– Argue that many important and expensive to create data types have 

parameters that fall in the free region

• Privately funded data creation in 2017

– Tax data: $0.4 trillion; individual credit data: $0.6 trillion; driving data: 

$0.4 trillion; marketing data: $0.6 trillion; other data: $4.6 trillion?

• Real GDP revisions in case studies

– Tax data: growth rose a total of 3.2 percentage point between 1929 

and 1948 due to Social Security and individual income taxes

– Credit data: growth fell a total of 1.5 percentage point around 1970 

due to the Fair Credit Reporting Act

– Driving data: growth rose a total of 1.5 percentage point around 1992 

due to the Comprehensive Loss Underwriting Exchange 13