This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

« Back to Results

Big Data: Value, National Accounts, and Public Policy

Paper Session

Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)

Manchester Grand Hyatt, Mission Beach B
Hosted By: Society of Government Economists
  • Chair: Gideon F. Lukens, U.S. Office of Management and Budget

Value of Data: There’s No Such Thing as a Free Lunch in the Digital Economy

Wendy Chuen-Yueh Li
,
U.S. Bureau of Economic Analysis
Makoto Nirei
,
University of Tokyo and RIETI
Kazufumi Yamana
,
Kanagawa University

Abstract

The Facebook-Cambridge Analytica data scandal demonstrates that there is no such thing as a free lunch in the digital world. Online platform companies exchange “free” digital goods and services for consumer data, reaping potentially significant economic benefits by monetizing data. The proliferation of “free” digital goods and services pose challenges not only to policymakers who generally rely on prices to indicate a good’s value but also to corporate managers and investors who need to know how to value data, a crucial input for the innovation of digital goods and services. In this research, we first examine the data activities for seven major types of online platforms based on the underlying business models. We show how online platform companies take steps to create the value of data, and present the data value chain to show the value-added activities involved in each step. We find that online platform companies can vary in the degree of vertical integration in the data value chain, and the variation can determine how they monetize their data and how much economic benefits they can capture. Unlike R&D that may depreciate due to obsolescence, data can produce new values through data fusion, a unique feature that creates unprecedented challenges in measurements. Our initial estimates indicate that data can have enormous value. Online platform companies can capture most benefits of the data, because they create the value of data and because consumers lack knowledge to value their own data. As trends such as 5G and the Internet of Things are accelerating the accumulation speed of data types and volume, the valuation of data will have important policy implications for innovation, investment, trade, and growth.

Measuring the Digital Economy in Macroeconomic Statistics: The Role of Data

Marshall Reinsdorf
,
International Monetary Fund
Jennifer Ribarsky
,
International Monetary Fund

Abstract

The strategic focus of businesses in the modern knowledge-based economy has been to generate and control traditional intellectual property (IP) assets, such as patents and copyrights. Nowadays, the strategic focus of businesses is increasingly to generate and control data. As the economy becomes more knowledge-based and data-driven it is increasingly difficult to measure. National statistical compilers often rely on business accounting records or surveys of businesses to derive estimates, yet business accounting methods have not evolved to measure the value of data. This creates difficulties for national statistical compilers. This paper explores what data is, what’s its value, what’s its role in the modern economy and whether it is an asset in the national accounts– the perimeter of what could be capitalized while considering what is already included in R&D, software and databases– and potential estimation methods.

Measuring the Economic Value of Data and Data Flows

David Nguyen
,
U.K. National Institute of Economic and Social Research
Marta Paczos
,
U.K. National Institute of Economic and Social Research

Abstract

The process of collecting, aggregating and analysing data for the purpose of successful operation is nothing new for companies. However, the amount and variety of data they use has increased dramatically in recent years. In fact, data have often become a central element in business models, posing fresh challenges to researchers and policymakers alike. In this paper, we investigate how the economic value of data can be conceptualised and measured from a business perspective. We first discuss data monetisation as a strategy for developing new business models, as well as enhancing “traditional” business models. Secondly, we review taxonomies for data and propose a new taxonomy with a specific focus on measuring the business value of data. Here our discussion is centred on four stylised ‘data monetisation strategies’ that are commonly used by companies to generate new streams of revenue, or to improve current business processes or products. We also discuss how different data characteristics and types affect economic value. Next, we examine the role of cross-border data flows as a key enabler of our global economy. We discuss how and why businesses transfer data across borders, as well as the broad scale and value of cross-border data flows. To do so we present the concept of a ‘global data value chain’, based on the idea that digitalisation enables the physical detachment of data collection, analysis, storage and monetisation. Finally, we summarise and discuss the most promising avenues for measuring the economic value of data and consider their feasibility in the short and long-term.

The Value of Data: Implications for Policy

Diane Coyle
,
University of Cambridge

Abstract

Business and governments see data as an opportunity to drive economic growth. The rapidly growing use of data in the economy, and its potential untapped uses, raises a range of important policy questions: how much is it worth, who should control it, who should have the right to use it, how should it be governed? Addressing these questions will require an understanding of what drives the value of data of different types, and the scope of the positive externalities due to its non-rival character and other distinctive features such as varying marginal returns and rates of depreciation. This paper presents a categorisation of data types and of the different public good aspects which will mean some types or combinations of data will be under-provided by the private sector. It considers some policy challenges in capturing and creating social value in the data driven economy, such as inter-operable standards, data infrastructures, terms of governance and access, and models of supply such as public provision or data trusts.
Discussant(s)
Diane Coyle
,
University of Cambridge
Laura Veldkamp
,
Columbia University
Michael Mandel
,
Progressive Policy Institute and University of Pennsylvania
Louise Sheiner
,
Brookings Institution
JEL Classifications
  • O3 - Innovation; Research and Development; Technological Change; Intellectual Property Rights
  • H0 - General