Panel on Measuring the Transformation of
 

Retail Trade and Related Activities
 

Committee on National Statistics
 

Division of Behavioral and Social Sciences and Education
 

A Consensus Study Report of 
 

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 

(2021). A Satellite Account to Measure the Retail Transformation: Organizational, 

Conceptual, and Data Foundations. Washington, DC: The National Academies 

Press. https://doi.org/10.17226/26101. 
 

 

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please visit www.nationalacademies.org/about/whatwedo. 
PANEL ON MEASURING THE TRANSFORMATION 

OF RETAIL TRADE AND RELATED ACTIVITIES 

J. STEVEN LANDEFELD (Chair), Consultant and former director, 

Bureau of Economic Analysis, Washington, DC 

CAROL A. CORRADO, The Conference Board, Washington, DC 

GREGORY DUNCAN, Amazon and University of Washington 

TERESA C. FORT, Dartmouth College 

JOHN C. HALTIWANGER, University of Maryland, College Park 

DALE W. JORGENSON, Harvard University 

MICHAEL MANDEL, Progressive Policy Institute, Washington, DC 

KELLY MCCONVILLE, Reed College 

LEONARD I. NAKAMURA, Federal Reserve Bank of Philadelphia 

WESLEY YUNG, Statistics Canada, Ottawa, Ontario 

STUART ELLIOTT, Co-study Director 

NANCY KIRKENDALL, Co-study Director 

ANTHONY MANN, Program Associate 

ELLIS GRIMES, Senior Program Assistant 

v 
COMMITTEE ON NATIONAL STATISTICS 

ROBERT M. GROVES (Chair), Office of the Provost, Georgetown 

University 

LAWRENCE D. BOBO, Department of Sociology, Harvard University 

ANNE C. CASE, Woodrow Wilson School of Public and International 

Affairs, Princeton University 

MICK P. COUPER, Institute for Social Research, University of Michigan 

JANET M. CURRIE, Woodrow Wilson School of Public and 

International Affairs, Princeton University 

DIANA FARRELL, JPMorgan Chase Institute, Washington, DC 

ROBERT GOERGE, Chapin Hall at the University of Chicago 

ERICA L. GROSHEN, School of Industrial and Labor Relations, Cornell 

University 

HILARY HOYNES, Goldman School of Public Policy, University of 

California, Berkeley 

DANIEL KIFER, Department of Computer Science and Engineering, 

The Pennsylvania State University 

SHARON LOHR, School of Mathematical and Statistical Sciences, 

Arizona State University, Emerita 

JEROME P. REITER, Department of Statistical Science, Duke University 

JUDITH A. SELTZER, Department of Sociology, University of California, 

Los Angeles 

C. MATTHEW SNIPP, School of the Humanities and Sciences, Stanford 

University 

ELIZABETH A. STUART, Department of Mental Health, Johns Hopkins 

Bloomberg School of Public Health 

JEANNETTE WING, Data Science Institute and Computer Science 

Department, Columbia University 

BRIAN HARRIS-KOJETIN, Director 

CONSTANCE F. CITRO, Senior Scholar 

vi 
Acknowledgments
 

This report reflects the contributions of many colleagues who generously gave their time and expert advice in helping prepare the panel’s report 

on measuring the transformation of retail trade and its activities. 

The panel thanks Bureau of Labor Statistics (BLS) Commissioner 

William Beach and Associate Commissioner Lucy Eldridge for their help in 

understanding the challenges confronting measurement of retail trade and 

in shaping the scope of the study. We also thank BLS staff, in particular 

the panel’s main contacts at BLS—Jenny Rudd, Chris Sparks, and Chris 

Manning—who worked within BLS to find answers to questions the panel 

raised throughout the study; Chet Myers, Jenny Rudd, Kandi Miller, and 

Brian Chansky for their background briefings at the first panel meeting; 

and Ken Robertson, Matthew Russell, Brian Chansky, Brendan Williams, 

Bonnie Murphy, and Dominic Smith for their presentations and discussions 

at the panel’s workshop, which helped us to understand key conceptual, 

methodological, and source-data issues. 

The panel also benefited greatly from presentations on topics central 

to the panel’s charge in measuring the transformation of retail trade, made 

by experts from the Bureau of Economic Analysis (BEA) (Jon D. Samuels, 

Tina Highfill, and Ana Aizcorbe) and the Census Bureau (Emek Basker, 

Ian Thomas, and Edward Watkins). A number of other experts at BEA 

and Census also helped in providing important insights in understanding 

current and alternative estimates of retail trade; in particular, I was greatly 

helped by Erich Strassner, Edward Morgan, and Thomas Howells at BEA. 

We are also grateful for the presentations by, and discussions with, 

experts from academia, business, international organizations, and the 

vii 
 

 

 

 

viii ACKNOWLEDGMENTS 

Federal Reserve Board. These experts provided perspective on researchers’ 

and other data users’ views of the forces transforming retail trade, the statistical challenges in measuring the change, and suggestions for addressing 

needed changes in measurement: Chad Syverson (University of Chicago), 

Steve Noble (McKinsey and Company), Jack Kleinhenz (National Retail 

Federation), Drew Spata (Macy’s), David Glick (FLEXE), Richard Phillips 

(Yale Divinity School and former CEO of Pilot Freight Services), Anne 

Goodchild (University of Washington), Leland Crane (Federal Reserve 

Board), Phillip Smith (Statistics Canada), Marshall Reinsdorf (International 

Monetary Fund), and Robert Feenstra (University of California, Davis). 

The panel could not have conducted its work efficiently without 

the capable staff of the National Academies of Sciences, Engineering, 

and Medicine, including Brian Harris-Kojetin, director of CNSTAT, and 

Constance Citro, senior scholar, who helped in framing the task for the 

panel, advising on specific issues during the panel study, and making 

important suggestions on the presentation of the panel conclusions and 

recommendations. Nancy Kirkendall and Stuart Elliott did an outstanding job in working with the panel to distill and summarize a broad range 

of complex topics to support the conclusions and recommendations presented in the report. 

Finally, and most importantly, I would like to add a note of appreciation for my fellow panel members who formed the core of the study team. 

Panel members helped in developing the focus, identifying and prioritizing 

the challenges and solutions, making presentations, chairing sessions, acting 

as discussants, drafting materials, providing detailed information in their 

areas of expertise, and reviewing and commenting on multiple rounds of 

workshop summaries and drafts of the report. 

This Consensus Study Report was reviewed in draft form by individuals 

chosen for their diverse perspectives and technical expertise. The purpose of 

this independent review is to provide candid and critical comments that will 

assist the National Academies in making each published report as sound as 

possible and to ensure that it meets the institutional standards for quality, 

objectivity, evidence, and responsiveness to the study charge. The review 

comments and draft manuscript remain confidential to protect the integrity 

of the deliberative process. 

We thank the following individuals for their review of this report: Emek 

M. Basker, Center for Economic Studies, U.S. Census Bureau; Thomas J. 

Holmes, Department of Economics, University of Minnesota; Thomas L. 

Mesenbourg, Jr., former U.S. Census Bureau; Colm A. O’Muircheartaigh, 

Harris School of Public Policy, NORC at the University of Chicago; Ariel 

Pakes, Department of Economics, Harvard University; Richard G. Phillips, 

Yale Divinity School, former chair and CEO of Pilot Freight Services; and 

Chad Syverson, Booth School of Business, University of Chicago. 
ACKNOWLEDGMENTS ix 

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the conclusions 

or recommendations of this report nor did they see the final draft before 

its release. The review of this report was overseen by Charles F. Manski, 

Department of Economics, Northwestern University. He was responsible 

for making certain that an independent examination of this report was carried out in accordance with the standards of the National Academies and 

that all review comments were carefully considered. Responsibility for the 

final content rests entirely with the authoring committee and the National 

Academies. 

J. Steven Landefeld, Chair 

Panel on Measuring the Transformation 

of Retail Trade and Related Activities 

Contents
 

Summary	 1
 

1	 Introduction 9
 

The Panel’s Approach, 11
 

2 Transformation of the Retail Sector 13
 

How Recent Retail Changes Relate to the Basic Definition
 

of the Sector, 20
 

The Retail Transformation as Seen in Existing Statistical
 

Series, 23
 

Recent Changes in the Retail Sector, 14
 

3 Measuring Retail Employment and Labor Productivity 29
 

Measuring Retail Employment and Labor Productivity:
 

The High-Level Task, 30
 

Defining the Retail Sector, 32
 

Measuring Output, 43
 

Adjusting Nominal Output for Changes in Prices, 55
 

Measuring Input, 62
 

Additional Data Sources, 65
 

4 Toward a Retail Satellite Account 73
 

What Are Satellite Accounts and How Are They Used?, 74
 

Audiences for a Retail Related Satellite Account and Measures
 

of Employment and Productivity, 76
 

xi 
xii CONTENTS 

Defining a Retail-Supporting Sector, 78
 

Existing Satellite Accounts with Potentially Useful Features, 84
 

5 Recommendations for a Retail Satellite Account 89
 

Motivation and Overarching Recommendations, 90
 

Design of a Satellite Account (Specifications), 92
 

Studying and Solving Data Issues, 97
 

References 103
 

Appendixes 

A Agenda for the Panel’s Workshop 107
 

B Retail Output, Hours, and Labor Productivity, 1997-2018 111
 

C Biographical Sketches of Panel Members 113
 
Boxes, Figures, and Tables
 

BOXES 

1-1	 Statement of Task, 10
 

3-1 	 Labor Productivity for Trade-Related Industries: How It Is
 

Measured by the Bureau of Labor Statistics, 31
 

3-2 	 Measuring the Economy: The North American Industry
 

Classification System (NAICS), 33
 

3-3 	 NAICS Classification and Designation of Auxiliaries, 36
 

3-4 	 Alternative Measures of Nominal Output, 45
 

3-5 	 The Census Bureau’s Annual Economic Surveys, 49
 

3-6 	 The Census Bureau’s 5-Year Surveys, 51
 

3-7 	 BLS Data on Employment and Hours for Trade-Related
 

Industries, 63
 

3-8 	 Census Bureau’s Other Important Annual Survey, 65
 

4-1 System of National Accounts, 75
 

4-2 Evaluation Criteria, 77
 

4-3 Four Alternative Definitions of a Retail-Related Sector, 79
 

FIGURES 

2-1 	 Average annual change in labor productivity, 1997-2018,
 

by type of output measure, 24
 

xiii 
xiv BOXES, FIGURES, AND TABLES 

4-1a Comparison of distributional and retail-supporting industry 

codes, 80 

4-1b Comparison of retail-controlled and enterprise-based industry 

codes, 80 

TABLES 

2-1 Several Indicators of the Retail Transformation, 1997-2019 

(percentage), 26 

3-1a Comparison of the Number of Establishments by NAICS Codes, 

as Measured by Three Programs, 2017, 42 

3-1b Comparison of the Estimated Number of Employees by NAICS 

Codes, as Measured by Three Programs, 42 

3-2 Number of Auxiliary Establishments That Supported Retail Trade, 

2012 Economic Census, 54 

App B Retail Output, Hours, and Labor Productivity, 1997-2018, 112 
Summary
 

The Bureau of Labor Statistics (BLS) asked the Committee on National 

Statistics (CNSTAT) of the National Academies of Sciences, Engineering, 

and Medicine to evaluate changes in the retail trade sector since the 1990s, 

assess measures of employment and labor productivity for the sector, and 

discuss the value and specifications for a satellite account to measure retailrelated employment and labor productivity that would better capture the 

transformation. The request was motivated in part by shifts in the ways that 

warehouses, transportation, and delivery services are now supporting retail, 

which are not reflected in retail employment and labor productivity statistics. 

The panel’s primary information-gathering activity was to hold a workshop that provided input from researchers, industry representatives, data 

users, and relevant statistical agencies. The workshop supplemented the 

panel’s expertise on the economics and statistics of the retail sector with 

the expertise of additional economists who have studied the retail sector, 

experts who understand the details of current government statistical programs, and industry representatives. The panel took a broad approach in 

reviewing options for a retail-related satellite account, considering both 

pragmatic immediate steps and aspirational longer-term goals. It also identified ways to progress toward the aspirational goals by carrying out specific 

analyses, collecting additional data, and conducting case studies. 

TRANSFORMATION OF THE RETAIL SECTOR 

The retail sector has experienced a number of important changes over 

the past few decades in both the way it is structured and the nature of the 

1
 
2 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

goods and services it provides. These changes include the rise of warehouse 

clubs and supercenters; the rise of e-commerce; the digital transformation of 

some retail goods, such as books, music, and video; the increase in imports 

of retail goods and services; the role of large firms in driving the transformation in retail; increased product variety and the role of retail firms 

in presenting and organizing products; and recent changes in response to 

COVID-19 that have in turn heightened some longer-term trends. 

As a result of these changes, the cost structure of large retailers is now 

often quite different from that of small retailers (Conclusion 2-1). Large 

retailers often provide wholesale, warehousing, and transportation services 

directly, whereas small retailers usually purchase these services. In addition, 

large retailers sometimes outsource some traditional retail services, such 

as customer service and order fulfilment, whereas small retailers usually 

provide these services directly. This difference in cost structures between 

large and small retailers heightens the importance of using measures of 

employment and labor productivity that can be meaningfully compared 

across retailers that are structured differently. 

The recent transformation in the retail sector has also shifted some 

retail services outside the traditional definition of the sector, for example 

shifting videos from sales to leasing. It has also brought some services into 

retail that were formerly outside the traditional definition, such as providing delivery services for e-commerce purchases (Conclusion 2-2). Where 

this has taken place, an understanding of the employment and productivity 

effect of the changes will require analyses that compare services inside and 

outside the traditional retail sector. 

Beyond these specific changes, the dynamic nature of the retail sector 

ensures that new—and as yet unknown—changes will regularly appear in 

the years ahead to challenge available measures of employment and labor 

productivity. This dynamic nature raises an additional challenge to efforts 

to track the ongoing transformation in retail and continue to adapt retailrelated measures over time. 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVTY 

The U.S. statistical programs that collect retail-related data provide a 

framework for measuring retail employment and labor productivity, but 

they also have some notable constraints. Calculating labor productivity 

involves estimating the real output of the retail sector and dividing it by 

the hours worked in the sector. All aspects of this simple definition pose 

conceptual and practical measurement difficulties. 

Federal economic data are collected by industry according to the North 

American Industry Classification System (NAICS), which classifies establishments hierarchically according to their business processes. Although this 
 

SUMMARY 3 

system usefully groups together similar business establishments in providing 

data, it specifically separates wholesale, warehousing, and transportation 

services into their own industries, even though large retailers are now increasingly integrating these functions into their retail operations. Similarly, 

retail transactions that take place through leasing rather than sales or 

through digital products appear in entirely different industries. As a result, 

the way the data are currently collected makes it difficult to identify the 

portion of wholesale, warehousing, and transportation services, or the portion of leasing or digital transactions that are closely related to retail trade 

and could be usefully analyzed as part of a broader retail-related sector. A 

study of a broader retail sector will require estimates of the retail-related 

portion of industries—such as warehousing—where the relevant NAICS 

codes are only partially related to retail (Conclusion 3-1). 

Labor productivity measures are calculated with data provided by two 

different agencies—BLS and Census—that use separate business registers 

with separate classifications of business establishments as sampling frames 

for their surveys to estimate output (Census), price deflators (BLS), and 

labor input (BLS). The differences between these sampling frames likely 

contribute to error in the labor productivity estimates (Conclusion 3-2). 

This error could be investigated and a reconciliation could be undertaken 

between the two business lists (Conclusion 3-3), and that in turn could be 

used to develop factors to adjust for the effects of any systematic differences between them (Conclusion 3-4). The ideal long-term solution would 

be for the federal government to develop and use a single common business 

register (Conclusion 3-5). 

The nominal output of the retail sector is defined in four different ways 

in the federal statistical system: (1) as total sales revenue; (2) as the difference between sales revenue and the cost of goods sold (gross margin); (3) as 

the difference between sales revenue and the cost of all purchased inputs 

(value added); and (4) as the difference between sales revenue and the cost 

of all inputs purchased within the sector (sectoral output). For narrowly 

defined sectors, the sectoral output measure is effectively sales revenue, but 

as the scope of a sector becomes increasingly broad the sectoral output 

measure moves toward a value-added measure.1 

A sales revenue measure of output is the simplest to produce, but it does 

not reflect changes in a retailer’s cost structure when additional functions— 

like warehousing—are integrated into the business. A value-added measure 

of output is theoretically preferred for measuring labor productivity in 

retail, capturing the difference between gross output and intermediate 

1The term “gross output” is used across sectors by the U.S. Bureau of Economic Analysis 

(BEA) to refer to a gross margin measure for retail and wholesale trade and a sales revenue 

measure for all other sectors. 
 

 

4 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

inputs. However, it requires estimating all noncapital purchased inputs, 

not just goods purchased. Comprehensive measures of value-added at the 

industry level rely on input-output accounts that have limitations in source 

data, including the frequency of updates. A gross margin measure of output for retail and wholesale trade reflects the value of the most important 

input for a retailer—the cost of goods sold—while sidestepping problems 

related to estimating other inputs (Conclusion 3-6). For retail-supporting 

services that might be combined with retail trade in a broader retail-related 

sector, similar choices are necessary concerning which measure of nominal 

output to use, although the gross margin concept applies only to retail and 

wholesale trade. 

The Economic Census and the Economic Surveys provide limited data 

on purchases and operating expenses for computing gross margin and 

value-added output measures, respectively. These data limitations limit 

the level of industry detail and frequency for gross margin and valueadded measures of retail output. They also offer limited data for estimating 

which support establishments in a firm (“auxiliaries”) support its retail 

establishments and to what extent (Conclusion 3-7). Private-sector data 

could potentially provide more timely information about economic output 

(Conclusion 3-12). 

Nominal output must be adjusted by price changes to identify the real 

changes in the output of the sector. The price adjustment step is crucial, 

because price changes can accentuate or mask any real changes that are 

occurring, particularly during a period of rapid change when goods and 

services are evolving and are hard to compare over time. Conceptually, 

the key price adjustment that needs to take place for the retail sector itself 

relates to the services the sector provides, with respect to changes in the 

prices of those services and adjustment for changes in their quality. This 

differs from price adjustment related to the products the retailer sells, which 

focuses on the characteristics of the goods themselves. Price deflation in the 

retail sector needs to consider, for example, the shifts in services in moving 

from a traditional department store to a warehouse store to e-commerce, 

and these shifts involve changes related to such things as product variety 

and the process for identifying and obtaining goods (Conclusion 3-9). 

The federal statistical system collects two different types of price indices 

that can be used for deflation: the producer price index (PPI), which looks 

at changes in the prices of producer goods for a variety of inputs and at 

changes in margin prices for retail trade; and the consumer price index 

(CPI), which looks at changes in prices of consumer goods and is used to 

deflate sales revenue measures of output. Although the existing price indices 

provide a way of describing price changes that occur for the services and 

products provided by individual retail outlets, they do not capture the 

aggregate price changes that result as consumers move from one type of 
SUMMARY 5 

retail outlet to another. For example, the price indices do not reflect the 

change in the price and quality of retail services as consumers move from 

a traditional department store to a warehouse store to e-commerce, except 

when consumers move between outlets classified in different NAICS codes 

(Conclusion 3-8). Private-sector data could potentially be used to estimate the price effect of consumers moving between retail outlets (Conclusion 3-11) and provide more timely estimates of price changes in general 

(Conclusion 3-12). 

Finally, employment is measured by estimating hours worked in the sector. The simple quantity of work hours should also be adjusted to reflect the 

different qualities of work provided by workers with different skill sets. In 

practice, this is done by looking at pay differences across groups of workers 

defined by difference in educational attainment, age, and gender. However, 

the retail transformation is substantially changing the workforce among 

some of the large retailers that are driving the biggest changes, with large 

increases in the number of workers with high-end programming and data 

analysis skills that support e-commerce (Conclusion 3-10). Private-sector 

data on payrolls could potentially be used to provide more timely estimates 

of quality-adjusted work hours (Conclusion 3-12). 

BLS currently develops measures of employment and labor productivity 

in retail that focus on the retail sector as specifically defined by NAICS, use 

a sectoral output measure of nominal output that is deflated by the CPI, 

and reflect hours worked in retail establishments that are not adjusted for 

labor quality. 

CONSIDERATIONS FOR CREATING A
 

RETAIL SATELLITE ACCOUNT
 

A satellite account provides a framework to explore a specific aspect of 

the economy that is linked to the System of National Accounts while deviating in ways that help address important questions about that aspect of 

the economy. These deviations may involve grouping or valuing economic 

activities in ways that differ from those that the national accounts use or 

providing more detailed statistics than are provided in the national accounts 

(Conclusion 4-1). 

There are several ways a retail satellite account might be defined to incorporate some of the related activities currently being integrated with retail 

services, such as wholesale, warehouse, and delivery functions. Including 

all establishments in these other industries would be feasible, but it would 

include many establishments with no relation to retail. Including only those 

establishments in these other industries that are part of retail enterprises 

would also be feasible, but that would exclude many relevant establishments simply because they are not owned by a retail enterprise. A “retail 
 

6 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

supporting” scope for a satellite account could include all establishments 

in transportation, warehousing, wholesale trade, and business services that 

serve retail trade firms, in addition to retail trade establishments themselves 

(Conclusion 4-2). If a retail satellite account’s scope is limited to only those 

retail-supporting establishments that are part of larger retail enterprises, 

it will miss aspects of the sector’s transformation that are taking place 

between rather than within firms. 

The implementation of a retail-supporting satellite account would require 

estimating the portion of establishments in transportation, warehousing, 

wholesale trade, and business services that support retail (Conclusion 4-4). 

This split between retail-supporting and nonretail-supporting pieces would 

likely be different for the outputs of these sectors than for their labor input. 

It would be necessary to explore a variety of approaches for carrying out this 

split, including the use of alternative data sources. The input-output tables 

provide some information for estimating the split in output, but not the split 

in labor input. A collaborative effort across agencies could use microdata to 

explore issues related to a retail satellite account, including structural changes 

in firms and the role of auxiliary establishments (Conclusion 4-3). 

The definition of the broader retail sector for a satellite account could 

be developed initially by using several definitions that are each simple to 

implement and that together provide lower and upper bounds for the included activities. A lower-bound definition could include all NAICS codes 

for retail establishments and for industries that are focused on supporting 

retail. An upper-bound definition could include all NAICS codes for industries that at least partially support retail. The range between these estimates 

would then indicate the potential benefit of developing careful approaches 

for splitting the input and output of industries that only partially support 

retail (Conclusion 4-5). 

Several existing satellite accounts developed by the Bureau of Economic 

Analysis (BEA) may provide useful models for developing a retail satellite 

account, given the measurement challenges posed by the retail transformation. The digital economy satellite account includes e-commerce and digital 

services, which are both important aspects of the retail transformation. 

The health care satellite account involves a reconceptualization of health 

care spending, which might suggest novel ways to reflect the changing cost 

structure of retail. The outdoor recreation satellite account addresses the 

challenge of dividing up statistics from several industries to combine some 

of them in a new grouping that is useful to the field. The small business 

satellite account addresses the challenge of identifying establishments of 

different sizes, which may also be an important way to divide the data for 

the retail sector (Conclusion 4-6). 

One approach to constructing a retail satellite account would be to create a central account with modules for experimentation and exploration. 
 

 

 

SUMMARY 7 

This would allow it to reflect the current consensus in the central account 

while identifying areas where new information and further research are 

needed for a consensus to emerge. The modules might address issues such as 

alternative output measures and deflators; alternative aggregations and classifications of retail-related industries or inputs; experimental price indices 

that might better reflect new retail services; integrated analyses of retail 

products that cross the boundary between goods and services or between 

physical and digital goods; and alternative ways of measuring and allocating productivity gains. 

RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 

The panel endorses the creation of a satellite account to study the transformation in retail trade. Such an account would be an appropriate and 

useful vehicle for BLS to use to study the impact on employment and productivity of the transformation in retail trade and to develop exploratory 

measures that describe that transformation (Conclusion 5-1). 

Given the distribution of data and expertise across government agencies, 

BLS should develop a satellite account for an expanded retail trade sector 

in collaboration with BEA and Census. Such a team could be formed under 

the Evidence Based Policy Act to facilitate administrative and collaborative 

efforts (Recommendation 1). 

The team developing the retail satellite account should solicit input and 

advice from industry and academia, with a special focus on collaboration 

with industry (Conclusion 3-13). Government statistics need input to ensure 

that the concepts being measured are relevant and keep up with the rapid 

pace of change in industry (Recommendation 2). 

In implementing a satellite account, BLS and its partners should adopt 

an iterative and modular approach, starting with feasible options that draw 

upon the BEA industry account and the BLS-BEA integrated industry-level 

production account to see what insights these might provide about the sector and about feasible fixes. The modular approach should include a set 

of estimates in a central module, with a set of submodules to investigate 

important side questions or alternative measures and a set of studies to 

carry out over time to investigate relevant questions (Recommendation 3). 

The satellite account should cover all retail and retail-supporting 

establishments, identifying these by combining available information from 

existing and enhanced data. The retail-supporting establishments should 

encompass all establishments supporting the distribution of retail goods to 

the consumer, but excluding their manufacturing and importing (Recommendation 4). 

The satellite account should examine multiple measures of output, 

price deflators, and labor input in order to support comparisons that lead 
8 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

to informed decisions. Output measures should include gross sales and 

gross margins for trade industries, gross sales/revenues for other industries, and value-added for all industries. Deflators should include current 

margin deflators and new options that capture the changing characteristics 

of retail trade. Labor input measures should include simple hours worked 

and quality-adjusted hours worked to capture the changes in workforce 

quality. Modules should also be used to evaluate alternative approaches 

to estimating the split between retail-related and nonretail-related for both 

output and input (Recommendation 5). 

The modules could also address more specialized issues that contribute 

to understanding the transition in retail trade, such as (1) international 

trade and global value chains, (2) digitization, (3) labor quality, and (4) providing real-time and subsector analyses. Over time, the central module 

would incorporate improvements developed in the submodules and in new 

data collection (Recommendation 6). 

Given the errors introduced by the separate business registers used by 

BLS and Census, measures should be taken immediately to facilitate the reconciliation of business lists across agencies. This will require changes to be 

enacted by Congress or implemented by the Treasury Department to modify 

the relevant IRS regulations (Recommendation 7). BLS and Census should 

establish an interagency task force, potentially including other relevant 

agencies, to develop a plan for implementing a consolidated business register to use as the sample frame for all business surveys (Recommendation 8). 

Developing a retail-related satellite account will require considerable 

effort to acquire and use data and to address data gaps in existing data. 

Individual projects include: Filling data gaps in the Economic Census and 

Economic Surveys that relate to the calculation of gross margins, value 

added, and the contribution of auxiliaries; identifying data to estimate the 

split in hours worked between retail-related and nonretail-related for retailrelated service industries; correcting for differences in the numerator and 

denominator of productivity caused by the use of different business registers 

and classifications; and exploring the use of private-sector data to improve 

the timeliness and detail provided in the account. Some of these efforts are 

best accomplished by a team with access to the Census Bureau’s economic 

microdata (Recommendation 9). 
1
 

Introduction
 

Retail trade has experienced dramatic changes over the past several 

decades in the United States, with changes in the types of outlets where 

goods are sold, the nature of the transactions that provide goods to consumers, and the structure of retail operations behind the scenes. The recent 

changes include the rise of warehouse stores and e-commerce, and the 

further growth of import and large retail chains. These changes highlight 

and typify many aspects of the broader evolution of the economy as a 

whole in recent years—with the growing role of large firms and information technology—while taking place in a sector that directly serves the vast 

majority of the American population and provides substantial employment. 

Despite the everyday experience of these dramatic changes in retail, 

there is concern that the most transformational aspects of those changes 

may not be captured well by the economic indicators relied on to understand the sector. In particular, the dynamic restructuring of retail should be 

accompanied by substantial improvements in productivity as retail firms 

innovate, but the sector’s economic indicators do not tell a story of large 

productivity increases. This mismatch between everyday experience and 

economic indicators is more than an idle curiosity. It goes to the heart of 

our ability to understand the changes taking place in the U.S. economy and 

to develop appropriate policies in response. 

In this context, the Bureau of Labor Statistics (BLS) asked the Committee on National Statistics (CNSTAT) of the National Academies of Sciences, 

Engineering, and Medicine to evaluate changes in the retail trade sector, 

assess measures of employment and labor productivity for the sector, and 

discuss the value of, and specifications for, a new satellite account that 

9
 
10 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

could measure retail-related employment and labor productivity in ways 

that would better capture the transformation. The request was motivated 

in part by shifts in the ways that warehouses, transportation services, and 

delivery services are now supporting retail, shifts that are not reflected in 

retail employment and labor productivity statistics because those statistics 

classify such services under industries separate from retail. The request was 

also motivated, more broadly, by a sense that the economic impacts of a 

range of retail innovations—highlighted by the growing and pervasive role 

information technology and e-commerce play in the sector—may not be 

well measured by the available indicators of retail employment and labor 

productivity. 

To respond to this request from BLS, the National Academies formed 

the Panel on Measuring the Transformation of Retail Trade and Related 

Activities. The panel’s statement of task is provided in Box 1-1. 

The panel was just beginning its work to respond to the BLS request 

in 2020 when the COVID-19 crisis shocked the U.S. and world economies. 

This shock powerfully accelerated many of the longstanding retail trends 

that provided the motivation for the study, including the rise of e-commerce 

and the increasing role played by large retail chains, while pointedly demonstrating the sector’s ability to innovate with the rapid evolution of shopping 

services and curbside delivery. The COVID-19 experience has provided a 

BOX 1-1
 

Statement of Task
 

The Committee on National Statistics of the National Academies of Sciences, 

Engineering, and Medicine shall appoint an expert panel to review the issues

related to measuring employment and productivity in retail-related industries for

the Bureau of Labor Statistics (BLS) in the U.S. Department of Labor. The expert

panel shall evaluate changes in the retail trade landscape and assess how they 

are impacting measures of employment and productivity in retail-related industries

and determine if, and how, a satellite account can be designed to capture this

retail transformation. The panel shall carefully review the existing measures as

well as the methodological issues surrounding measurement of these concepts.

As part of its information-gathering activities, the panel shall hold a public workshop to discuss the views of industry experts, academics doing work in related

fields, and data users. The panel shall produce a consensus report, which shall

include conclusions and recommendations for BLS on (1) the value and specifications for a satellite account for the retail-related sector, (2) ways to identify

the proportion of output, employment, and hours outside of retail trade that are

directed toward supporting retail trade, and (3) ways to maintain a retail-related

satellite account. 
INTRODUCTION 11 

dramatic example of the types of retail innovation that need to be reflected 

in the indicators of retail employment and labor productivity. 

THE PANEL’S APPROACH 

The panel’s primary information-gathering activity consisted of holding 

a workshop that provided input from researchers, industry representatives, 

data users, and relevant statistical agencies. The workshop supplemented 

the panel’s expertise related to the economics and statistics of the retail 

sector with additional expertise from economists who have studied the sector, government experts who understand the details of current government 

statistical programs, and industry representatives who are helping drive the 

sector’s transformation. 

The agenda for the workshop is provided in Appendix A. The workshop included sessions on the changes in retail, from the perspective of 

researchers and members of the industry; key measurement and data challenges for developing measures of employment and labor productivity; 

options for developing a satellite account for the retail sector; qualityadjusted prices for retail; uses of bottom-up measures in measuring employment and productivity for retail; and global value chains and the role of 

imports in the sector. 

In deliberating on the workshop input, the panel took a broad approach to the options for a retail-related satellite account, considering both 

pragmatic immediate steps and aspirational longer-term goals and identifying ways to progress toward the aspirational goals by carrying out specific 

analyses and collecting additional data. 

The next chapter provides an overview of the transformations occurring 

in the retail sector and the ways these are reflected in available indicators. 

Chapter 3 provides an overview of conceptual and data issues related to 

retail employment and labor productivity statistics, including brief descriptions of the government programs that collect relevant statistics. Chapter 4 

discusses satellite accounts and key options that are relevant to establishing a satellite account for the retail sector. Finally, Chapter 5 provides the 

panel’s recommendations for developing such a satellite account. 

2
 

Transformation of the Retail Sector
 

This chapter describes recent changes in the retail sector, highlighting 

several important shifts since the 1990s in the way the sector is structured 

and the nature of the goods and services it provides. The chapter then 

considers these sectoral shifts and the way they extend or challenge the 

traditional definition of the retail sector. Finally, it turns to look at trends 

related to the retail sector from current statistical series to see what picture 

they provide of this sectoral transformation. 

During the panel’s workshop, four of the seven sessions provided an 

overview of important recent changes in the retail sector.1 The first session 

considered the transformation from the perspective of researchers who 

study the sector. The second session considered the transformation from 

the perspective of industry representatives, focusing on participants who 

could provide a detailed understanding of the way the retail supply chain 

is being restructured. The sixth session looked at some of the trends in the 

sector that are revealed in analyses of firm-level data. Finally, the seventh 

session considered the importance of global value chains in the production 

of goods and services related to retail and the insights these provide about 

the way retail is changing. 

1See Appendix A for the workshop agenda, including the panelists and moderators who 

participated in each session. 

13
 
 

14 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

RECENT CHANGES IN THE RETAIL SECTOR
 

This section describes seven different but related changes in the retail 

sector, discussing each in turn. These changes capture the ways the sector 

is transforming that could be reflected in the government measures of the 

retail sector. 

Collectively, these seven changes also illustrate a fundamental characteristic of the retail sector, which is its intensely dynamic nature. Without 

knowing in advance what new changes may appear in the future, it is 

a near-certainty that further changes of this magnitude will continue to 

appear to further challenge available measures of retail employment and 

labor productivity. So, while the seven changes discussed are important 

for the sector in the recent past and highlight important measurement 

challenges to address, they also illustrate the types of far-reaching change 

that will continue into the future in new and unexpected ways beyond the 

specific changes discussed here. 

Rise of Warehouse Clubs and Supercenters 

Since the 1990s, there has been a large shift toward warehouse clubs 

and supercenters (NAICS 452311)2 at the expense of department stores 

(Hortaçsu and Syverson, 2015). Both formats are classified as general merchandise stores (NAICS 452), which represent one-fifth of total employment 

in the retail sector, 3.0 million out of 15.7 million employees.3 Over the 

first two decades of this century, while employment in retail overall and in 

general merchandise stores stayed roughly constant,4 there was a large shift 

in employment within general merchandise: department store employment 

decreased by 0.7 million while employment in general merchandise stores, 

including warehouse clubs and supercenters, increased by 0.9 million.5 This 

shift moved department stores from 62 to 36 percent of employment in the 

2Industry statistics in the United States are classified according to the North America 

Industry Classification System (NAICS). In the 2017 version of NAICS, there are 12 retail 

trade industries at the 3-digit level, 27 at the 4-digit level, and 66 at the 6-digit level. Warehouse clubs and supercenters are coded as 452311 in the 2017 release of NAICS and as 45291 

in the 2012 release. 

33.0 million employees for general merchandise stores (NAICS 452) compared to 15.7 million for retail overall (NAICS 44-45), seasonally adjusted data for January 2020 [August 5, 

2020] from https://data.bls.gov. 4Seasonally adjusted employment in January 2000 was 2.8 million for general merchandise 

stores and 15.2 million for retail overall. 

5Seasonally adjusted employment in department stores (NAICS 4522) decreased from 

1.73 million to 1.08 million from January 2000 to January 2020, while the corresponding employment in general merchandise stores, including warehouse clubs and supercenters 

(NAICS 4523), increased from 1.08 to 1.97 million. 
 

TRANSFORMATION OF THE RETAIL SECTOR 15 

general merchandise sector. The shift appears even more strikingly in sales 

figures, with department stores declining from 60 percent of total sales in 

the general merchandise sector in January 2000 to 18 percent in January 

2020.6 Warehouse clubs and supercenters are now the largest subindustry 

in general merchandise stores (NAICS 452). 

The warehouse club and supercenter format moves some of the traditional functions of the warehouse into the store itself, providing inventory 

storage directly in the store. In addition, the shift moves toward a format 

that provides a lower level of customer service than is provided by traditional department stores and also includes food sales, which are not part 

of traditional department stores. 

Rise of E-Commerce 

The shift toward e-commerce is another important change in the retail 

sector (for a recent overview see Lafontaine and Sivadasan, forthcoming). 

Until recently, however, e-commerce remained a small part of retail overall, despite high growth rates. For example, nonstore retailers (NAICS 

454) represented only 2.9 percent of total retail employment and only 

7.9 percent of total retail sales in January 2010.7 Total sales in nonstore 

retailers surpassed sales in general merchandise stores, including warehouse 

clubs and superstores, only in 2015.8 According to the Census Bureau, 

e-commerce as a percentage of total sales grew from 0.6 percent in 1999 

to 16.1 percent in the second quarter of 2020.9 

As e-commerce has grown in recent years, it has become increasingly 

difficult to separate out the e-commerce portion of the industry. Most 

e-commerce could be identified within the nonstore retailer category as of 

2013 (Hortaçsu and Syverson, 2015, p. 96), but e-commerce is becoming so 

pervasive that it is now not only difficult to clearly identify individual firms 

as predominantly e-commerce firms, but also often impossible to clearly 

classify individual retail sales as either e-commerce or not. The focus now 

for many retail firms is to adopt an “omni-channel” strategy, whereby they 

provide both e-commerce and in-store “channels” for customers to learn 

about and buy products. Customers frequently combine channels within a 

single purchase, sometimes reading online descriptions and reviews before 

6Seasonally adjusted sales data for January 2000 and 2020. See https://www.census.gov/ 

retail/mrts/historic_releases.html for department stores (2012 NAICS 4521). 7Seasonally adjusted employment of 0.416 million out of 14.4 million in January 2010 and 

seasonally adjusted sales of $27.3 billion out of $346 billion for the same month. 

8See https://www.census.gov/retail/mrts/historic_releases.html. 9U.S. Census Bureau. (2020). Census Bureau Provides Data on Fast-Growing Retail 

E-Commerce. November 24. See https://www.census.gov/library/stories/2020/11/share-ofonline-retail-sales-soaring.html. 
 

16 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

inspecting a product in a store and purchasing it, but other times first seeing the product in a store and then later ordering it online after making 

comparisons across vendors. 

The rise of e-commerce can be thought of as extending retail services 

in two different ways. First, e-commerce incorporates a set of warehouse 

services into the online store itself, providing customers with access to a 

vast range of inventory that goes far beyond the range of inventory that 

a brick-and-mortar store can physically stock. Second, e-commerce also extends the services provided by the retailer into a consumer’s home, replacing 

some of the shopping and delivery services that consumers have until only 

recently provided for themselves. Providing these extended retail services 

has required the creation of substantial computing and demand analysis 

functions that are associated with the headquarters of large retailers and 

that produce substantial intangible assets that are essential to the success 

of these firms. 

While e-commerce extends retail services, the digital technology that 

makes e-commerce possible also takes away a number of key retail services 

from the physical store itself, including providing information to customers 

about the products that are available, accepting the customer’s payment to 

execute the purchase, and providing the product to the customer upon purchase. Those services are not only removed from the physical store, but also 

sometimes no longer directly provided by the retailer at all. For example, a 

freight company may contract with a retailer to manage inventory, interact 

with customers to execute purchases, and deliver products, operating under 

the retailer’s own brand. 

The section further below on responses to the COVID-19 crisis describes 

the substantial acceleration of e-commerce that has occurred in response to 

the pandemic. 

Digital Transformation of Retail Goods 

Digital technology has not only allowed e-commerce but also transformed the form of many retail goods themselves. Books, music, and video 

provide clear examples of this transformation, with products that used to be 

sold as physical goods now largely transformed into digital downloads that 

may be either sold or rented. Of course, the renting of retail products has 

existed for a long time, notably for car leasing and formal wear. However, 

digital technology has made it increasingly feasible to expand the rental 

markets for other consumer goods, such as a much broader range of clothing (e.g., Rent the Runway). 

A few statistics provide an illustration of the range of these changes. 

Revenue from e-books and downloaded audio books totaled $3.25 billion 

in 2019, representing 12.5 percent of total publishing industry revenue, 
TRANSFORMATION OF THE RETAIL SECTOR 17 

up from 7.3 percent in 2015. Between 2015 and 2019, e-book and downloaded audio book revenue increased by 61 percent.10 Expenditures on 

video and audio streaming and rental, as a proportion of total expenditures 

on video and audio, have increased from 22.3 percent in 2000 to 32.5 percent in 2010 to 66.5 percent in 2019.11 For comparison, expenditures on 

motor vehicle rental and leasing as a proportion of total expenditures 

on new motor vehicles, including sales, rentals, and leasing, have fluctuated 

between 13 and 23 percent since 1994 without a clear trend, after increasing from below 2 percent in the 1980s.12 

Imports of Retail Goods and Services 

Imports of retail goods and services are also transforming the sector. 

For example, four large retailers (Walmart, Target, Home Depot, and 

Lowe’s) accounted for almost 10 percent of U.S. imports by volume in 

2018.13 These large firms import directly, providing their own import 

distribution centers. Some product categories—such as toys, furniture, 

clothing, and electronics—are heavily dependent on imports. Imports of 

consumer goods totaled $1.19 trillion in 2007, having grown 3.7 times 

from the value of $319 billion in 1992 (Smith, 2019).14 Consumer imports 

in 2007 represented almost a quarter of total retail and food services sales 

of $4.4 trillion.15 

Given the complexity of global supply chains, it can be difficult to 

identify the domestic and imported portions of a good’s value, whether it 

comes directly from a domestic or foreign manufacturer. Complex products, 

such as motor vehicles and consumer electronics, often include components 

sourced from several different countries. In addition, the value of many 

imports includes intellectual property, which may actually be owned by the 

importing U.S. firm. Despite the complexity of many products, a number 

10Revenue figures provided by the American Association of Publishers. See https://publishers.org/ 

news/aap-statshot-annual-report-book-publishing-revenues-up-slightly-to-25-93-billion-in-2019. 

11BEA, nominal Personal Consumption Expenditures, comparing nominal expenditures on 

video and audio streaming and rental with total nominal expenditures on video and audio 

streaming and rental along with recording media. Calculated from the underlying detail tables 

for Personal Consumption Expenditures, see https://apps.bea.gov/iTable/index_nipa.cfm. 12BEA, nominal Personal Consumption Expenditures, comparing nominal expenditures 

on motor vehicle leasing, motor vehicle rental, and new motor vehicles. Calculated from the 

underlying detail tables for Personal Consumption Expenditures, see https://apps.bea.gov/ 

iTable/index_nipa.cfm. 

13See https://www.joc.com/maritime-news/top-100-us-importer-and-exporter-rankings-2018_ 

20190530.html, cited by Dominic Smith at the panel’s workshop. 14Both figures reported in 2007 dollars. 

15See https://www.census.gov/retail/mrts/www/mrtssales92-present.xls. 
18 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

of international organizations and researchers have developed global inputoutput estimates of their global value chains.16 

In addition to imports of the retail goods for sale in the United States, 

some portion of retail services can also be outsourced by retail firms. For 

example, L.L. Bean carries out much of its back-office work in Costa 

Rica.17 

Role of Large Firms in the Retail Transformation 

The four changes previously mentioned have been driven by national 

and regional multi-unit retail firms, which lead productivity growth in the 

industry and represent most of the sector’s growth in sales and employment 

(Foster et al., 2016). Sales of the eight largest retail firms as a percentage of 

all retail sales rose from 11.7 percent to 19.5 percent from 1997 to 2012.18 

Single-unit retail firms still account for roughly 60 percent of retail establishments, but only 30 percent of retail sales.19 

Large firms pose a challenge to developing statistics by industry, since 

their level of integration indicates clear economies of scale and scope that 

go across their divisions. Fundamentally, this means that some inputs—at 

a minimum, each firm’s management—are contributing to multiple outputs 

in a way that cannot easily be apportioned for statistics or reproduced by 

single-unit firms. For firms that have retail divisions in addition to other 

divisions—such as manufacturing, warehousing, or transportation—it is 

hard to appropriately attribute the common inputs that are contributing to 

the retail portion of the firm. 

Despite the increasing role played by large firms over the past several 

decades, the digital systems that have become pervasive over this same 

period now allow retailers to outsource many retail functions to other providers, including customer interaction and order fulfillment. These systems 

increasingly allow any retailer to reproduce the same quality and speed in 

customer interaction and order fulfillment that a company like Amazon 

can provide. This suggests that the transformation that has been driven by 

16See work by the Organisation for Economic Co-operation and Development with the 

World Trade Organization, https://www.oecd.org/sti/ind/measuring-trade-in-value-added.htm; 

by the Global Trade Analysis Project, see https://www.gtap.agecon.purdue.edu/; and by the 

World Input-Output Database, http://www.wiod.org/home. 17Cited by Marshall Reinsdorf at the panel’s workshop. 

18Census Bureau, Economic Census of Retail Trade: Establishment and Firm Size (Including 

Legal Form of Organization), 1997 Economic Census, Retail Trade, Subject Series, Issued October 2000, EC97R44S-SZ, Table 6, page 197, see https://www2.census.gov/library/publications/ 

economic-census/1997/retail-trade/97r44-sz.pdf, for 1997; and https://data.census.gov/cedsci/ 

table?q=EC1244&tid=ECNSIZE2012.EC1244SSSZ6&hidePreview=true for 2012. 19John Haltiwanger at the panel’s workshop. 
See

TRANSFORMATION OF THE RETAIL SECTOR 19 

the large retail firms over the past couple decades is now being extended 

in a way that smaller retailers can use, with implications throughout the 

entire sector. 

Increased Product Variety 

A number of changes in the retail sector already discussed have contributed to a general change reflected across the sector in making more 

products available to consumers and providing new retail services to help 

consumers navigate and take advantage of growing product variety. This 

can be seen in the sheer number of products offered at warehouse clubs and 

supercenters, which has increased again with the rise of e-commerce, where 

nonstore retailers can offer a huge range of products beyond the limits of 

a physical store. At the same time, inventory management software and 

online search and recommendations systems allow consumers to identify 

and obtain specific products from the vast array offered for sale, powerfully 

extending the service that retailers have always provided in presenting and 

organizing products for consumers to consider purchasing. These services 

include the ability of consumers to easily compare prices and customer 

reviews and to have their purchases delivered directly and quickly to their 

homes, making it possible for them to access a larger variety of products 

while simultaneously reducing shopping time, travel costs, and prices. 

Changes in Response to COVID-19 

This project was carried out virtually while the United States, along 

with much of the rest of the world, was struggling to find a successful response to the COVID-19 pandemic. That context highlighted changes that 

the pandemic had already brought to the retail sector and might bring in 

the future, beyond the temporary closure of many retail stores during the 

initial response, which in many cases may become permanent. 

Dramatic changes in e-commerce occurred during the first two quarters 

of the COVID-19 crisis. E-commerce sales increased by 31.9 percent from 

the first quarter to the second quarter of 2020, and e-commerce sales in the 

third quarter of 2020 were 36.7 percent larger than in the third quarter of 

2019.20 E-commerce in the second and third quarters of 2020 represented 

16.1 percent and 14.3 percent of total U.S. retail sales, respectively, compared to 11.8 percent in the first quarter of 2020. Relatedly, there has been 

a surprising surge in applications for new businesses during the COVID-19 

crisis, dominated by an increase in applications for nonstore retailers.21 

20 https://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf.
 21See https://www.census.gov/econ/bfs/projects.html.
 
See

20 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

These patterns suggest that the COVID-19 crisis has accelerated the longrun trend toward a greater role for e-commerce in retail trade. 

HOW RECENT RETAIL CHANGES RELATE TO
 

THE BASIC DEFINITION OF THE SECTOR
 

The different changes that have occurred in the retail sector sometimes 

raise the question of whether the transformed activities should still be classified as belonging to retail. This question is raised with particular urgency 

by e-commerce, where a growing number of firms now provide some aspects of retail services without identifying themselves as retail firms. Does a 

freight company that subcontracts with a retailer to provide customer service and order fulfillment services effectively become a retail establishment 

in some sense? The digital transformation of some traditional retail goods 

poses similar questions. Should books or videos that are now provided as 

digital downloads or as part of a larger subscription service be included 

as part of the retail sector? We discuss these two questions in turn. 

Relation of Retail to Other Industries 

The potential difficulty in deciding whether some firms belong to the 

retail classification leads to a consideration of the essential characteristics 

that define the retail sector. Retail is often identified as facing the final consumer and distributing “retail-like products” without transforming them. 

The definition used by NAICS notes that retail provides services that are 

“incidental to the sale of merchandise” and that retail “is the final step in 

the distribution of merchandise.”22 The NAICS definition further notes 

that “the buying of goods for resale is a characteristic of retail trade establishments that particularly distinguishes them from establishments in the 

agriculture, manufacturing, and construction industries.” 

Thus, a farm, manufacturer, or housing developer that sells directly to 

the public is not considered to be a retailer because each of these businesses 

produces what it sells, rather than buying products for resale. Wholesale 

trade is also distinguished from retail trade, because wholesalers “are not 

usually organized to serve the general public.” The NAICS definition notes 

that “dealers of durable nonconsumer goods, such as farm machinery and 

heavy-duty trucks, are included in wholesale trade even if they often sell 

these products in single units” and even though they are often sold to the 

final (business) purchaser. The NAICS definition also provides examples 

of “incidental” services that are sometimes provided by retailers, including 

22 https://www.census.gov/naics/?input=44&chart=2017&details=44. 
 

 

TRANSFORMATION OF THE RETAIL SECTOR 21 

“the provision of after-sales services, such as repair and installation” in 

the cases of “new automobile dealers, electronics and appliance stores, 

and musical instrument and supplies stores.” Some processing activities 

are also considered “incidental” to the retailing function such as “optical 

goods stores that do in-store grinding of lenses, and meat and seafood 

markets.” Thus, the existing definitions identify retailers as those who 

purchase goods for resale to the general public with limited transformation of those goods. 

Traditionally, a relatively stable wholesale sector moved goods between 

manufacturers and retailers without directly interfacing with final consumers. The warehousing sector stored goods in the transition from manufacturer to final consumer, and the transportation sector moved goods 

between manufacturer, wholesaler, retailer, and final consumer. As noted 

above, large retailers today often directly provide wholesale services (including direct importing), along with related warehousing and transportation 

functions. Retailers may outsource some of the services related to retail, such 

as customer service or order fulfillment, either to specialized firms operating invisibly under the umbrella of the retailer’s brand or to other retailers 

like Amazon. In some cases, a retailer may outsource order fulfillment to a 

manufacturer, who may deliver directly to the consumer without the retailer 

ever taking possession of the good and holding it in inventory. 

As a result of these transformations, large retailers have often added 

functions performed by the wholesale, warehousing, and transportation 

industries, and they may also have outsourced some retail services to specialized providers. Nevertheless, they still largely provide the defining function of purchasing goods for resale to the general public with limited 

transformation. In that sense, the traditional definition of retail trade still 

applies to large retailers, even after these transformations, as much as it 

applies to traditional single-unit retailers that use the wholesale, warehousing, and transportation sectors in the traditional ways. However, the internal cost structure of large and small retailers and the functions provided 

by their employees are likely to be quite different. A large retailer may 

obtain goods at a lower cost directly from the manufacturer (domestic or 

foreign), but then provide various wholesale, warehousing, and transportation services internally that a traditional single-unit retailer would have to 

pay for. At the same time, a large retailer may outsource some traditional 

retail services, like customer service, that a traditional single-unit retailer 

provides directly. These different arrangements of purchased and produced 

services need to be reflected to provide meaningful comparisons of employment and productivity across large and small retailers that can apply as the 

sector continues to evolve. 
See

22 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

CONCLUSION 2-1: The traditional definition of retail trade applies 

to the large retailers that have become increasingly important over 

the past few decades as well as it applies to more traditional small 

retailers. However, the cost structures of these two types of firms 

can be quite different. Large retailers often provide wholesale, warehousing, and transportation services directly, whereas small retailers 

usually purchase these services. In addition, large retailers sometimes 

outsource some traditional retail services, such as customer service and 

order fulfilment, whereas small retailers usually provide these services 

directly. Defining retail output as the quantity of goods sold will understate the contribution of retailers that provide high levels of service. 

Range of Goods and Services Included in the Retail Sector 

One aspect of the transformation on the goods side involves digital versions of products, such as books and videos, that were previously provided 

physically. In cases where the digital versions are provided by a retailer 

(such as Amazon) as well as a publisher, the standard retail definition would 

classify the sales as occurring in the retail sector, recognizing that goods can 

be considered to be intangible (Reinsdorf and Slaughter, 2009). Of course, 

if the digital books were sold by the publisher, the sale would be counted 

within the information industry (NAICS 51), but that is no different from 

other sales occurring directly from manufacturers. However, if digital goods 

are leased rather than sold, then they no longer fall under the definition of 

retail, instead moving to rental and leasing services (NAICS 532), which 

includes automobile leasing and various other types of leasing, such as 

formal wear, home health equipment, and office machinery. 

On the retail services side, e-commerce has replaced some of the shopping and delivery services that consumers have until recently provided 

for themselves (Mandel, 2017). This type of shift is not novel; in earlier 

periods, deliveries of this type were sometimes standard, as when retailers 

provided home delivery of milk. However, this shifting in the types of services provided by the retail sector can make it difficult to interpret changes 

that are occurring in measured employment and productivity. Specifically, 

the extra services can produce an increase in employment and therefore 

possibly suggest a decrease in productivity unless the output measure recognizes that a greater level of service is being provided, as reflected in the 

significant reduction in unpaid shopping hours reported by the American 

Time Use Survey.23 

These two changes, which involve shifts from activities inside the 

traditional retail definition to activities outside that definition or vice 

23 https://www.bls.gov/tus. 
TRANSFORMATION OF THE RETAIL SECTOR 23 

versa—movement of some products from sales to leasing, and shifts in 

the direct labor provided by consumers—underline potential challenges 

to collecting meaningful statistics during a period of sector change. These 

types of changes are not novel and occur in other sectors as well as retail, 

but their effects may be extensive enough in retail that it is worth the extra 

effort to understand their role in affecting employment and productivity. 

Since both involve shifts related to what is included in the retail category, 

understanding the effects of these changes on employment and productivity 

would require analyses that combine or compare information from inside 

and outside the traditional retail category. 

CONCLUSION 2-2: In cases where the recent transformation in the 

retail sector has shifted some retail services outside the traditional 

definition of the sector (e.g., by moving from sales to leasing of some 

products like clothes or movies) or brought some services into retail 

that were formerly outside the traditional definition (e.g., by delivering 

purchases that consumers previously purchased at a store), an understanding of the employment and productivity effects of the changes 

will require analyses that compare services inside and outside the retail 

sector. 

Beyond these examples, there are further expansions in the types of 

goods and services offered by large retailers that go well beyond the traditional retail sector, such as the provision of cloud computing by Amazon 

and of consumer health services by Walmart. Under the standard NAICS 

classification system, these products would be classified in other industries 

(NAICS 518 and 621, respectively). The only difficulty that might arise concerns the extent to which large firms operating in multiple industries necessarily comingle work related to those different industries, at a minimum 

with respect to the contribution of the firms’ management. This situation 

necessarily requires some approximation in allocating inputs to different 

industries, though that is a challenge throughout the economic statistical 

system and is not specific to the retail transformation. 

THE RETAIL TRANSFORMATION AS SEEN
 

IN EXISTING STATISTICAL SERIES
 

As a starting point in considering the statistical challenge of portraying 

and understanding the retail transformation, Figure 2-1 illustrates the sector’s labor productivity as measured by three different statistical series. In 

each case, the series looks at the average annual change in labor productivity over the 21-year period from 1997 to 2018, where labor productivity 
24 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

FIGURE 2-1 Average annual change in labor productivity, 1997-2018, by type of 

output measure. 

SOURCES: Output measured by sales revenue from U.S. Bureau of Labor Statistics 

(BLS), Division of Industry Productivity Studies. Labor productivity calculated by 

dividing change in output by change in hours worked, using hours data from BLS 

(BLS data from https://www.bls.gov/lpc/lpc_by_industry_and_measure.xlsx using 

the “Output” and “Hours” fields for the sale revenue and hours indices, respectively). Output measured by gross margin and value added from U.S. Bureau 

of Economic Analysis, Industry Data webpage (using the “Chain-Type Quantity 

Index” for both gross output and value added). All series provided in Appendix B. 

is defined as the real output in the sector divided by the hours worked.24 

The three series define retail output in different ways: (1) as total sales 

revenue; (2) as the difference between sales revenue and the cost of goods 

24Appendix B, which contains the underlying data, also breaks the series at 2007, which 

shows the slowdown in retail labor productivity growth that occurred from the first decade 

(1997-2007) to the second (2007-2018). 
 

TRANSFORMATION OF THE RETAIL SECTOR 25 

sold (gross margin); and (3) as the difference between sales revenue and the 

cost of all purchased inputs (value added), with nominal values deflated 

by appropriate price indices in each case. Labor productivity growth rates 

are provided for the retail sector overall, along with the major three-digit 

retail subindustries, and they are provided as well for two other sectors for 

comparison: wholesale trade and warehousing. 

The data underlying these series are discussed in Chapter 3, but the 

focus in this chapter is simply on comparisons of the qualitative picture of 

the sector that emerges from the changes discussed above and the quantitative picture that emerges from the different statistical series 

The changes experienced by retail over the past few decades suggest 

that the sector is highly competitive and is undergoing substantial change 

and reorganization. As discussed earlier, the changes described involve 

warehouse clubs and superstores (a part of general merchandise, NAICS 

452), e-commerce (concentrated in NAICS 454 in earlier periods and 

expanded to NAICS 493 in more recent periods), digital goods, imports, 

and large firms, along with some more recent changes brought about by 

COVID-19. 

By contrast, the three statistical series describe a sector where annual 

labor productivity growth averaged 2.1 to 3.1 percent per year over the 

two decades from 1997 to 2018. This growth in labor productivity was not 

substantially different from the 2.1 percent growth in labor productivity for 

the nonfarm business sector over this period.25 Despite the similarity across 

the three estimates, the cumulative differences in these growth rates over 

the 21-year period are substantial, with the sales revenue measure reflecting 

a cumulative labor productivity increase of 92.5 percent, compared to the 

56.5 percent increase that emerges from the value-added measure.26 The 

range across these three figures reflects meaningfully different pictures of 

the labor productivity growth in the retail sector. 

The one retail subindustry that clearly stands out for its labor productivity performance is the nonstore sector, where labor productivity 

was more than twice as large as for the retail sector as a whole. However, 

for general merchandise stores, which saw substantial restructuring in the 

decline of department stores and the rise of warehouse clubs and supercenters, productivity changes look no different than for the rest of the retail 

sector in the three statistical series. 

Another observation of note in Figure 2-1 is the cases where there is 

a clear divergence in the picture provided by the different statistical series: 

Food and beverage stores (NAICS 445) and gasoline stations (NAICS 447) 

25Haver Analytics database, nonfarm business sector, real output per hour of all persons, 

Bureau of Labor Statistics, annualized percent growth based on annual data, 1997-2018. 

26See Appendix B. 
26 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

both show labor productivity falling when measured using value added, 

but rising when using sales revenue or gross margin. Similarly, health and 

personal care stores (NAICS 446) show zero labor productivity when measured using value added, compared to rising productivity when measured 

using sales revenue or gross margin. 

There is no way for the other changes discussed above to be directly 

reflected in Figure 2-1. The subindustry breakdowns do not align with 

changes in digital goods and services, imports, or the role of large firms. 

Table 2-1 provides several indicators besides labor productivity that 

show the extent of some of the changes discussed in the retail sector. The 

TABLE 2-1 Several Indicators of the Retail Transformation, 1997-2019 

(percentage) 

1997 2002 2007 2012 2017 2019 

Warehouse and supercenter share 

of total retail sales 

4.6 7.3 9.3 10.9 10.8 10.6 

Nonstore retailer share of total 

retail sales 

5.1 6.1 7.7 9.5 12.5 14.6 

Employment share in firms with 

<500 employees 

42.9 39.0 35.9 35.2 

Employment share in firms with 

10,000+ employees 

44.6 49.7 52.3 53.5 

Share of total retail sales for 

8 largest retail firms 

11.7 15.3 17.5 19.5 

E-commerce share of sales: 

Music and video 12.3 41.2 76.5 

Books and magazines 9.1 22.1 41.1 

Computers and software 18.7 30.3 32.9 

Food and beverages 0.2 0.7 0.9 

SOURCES: Sales data from BLS, https://www.bls.gov/lpc/lpc_by_industry_and_measure.xlsx, 

using the Value of Production field for NAICS 45231 (General merchandise, including warehouses and supercenters), NAICS 454 (Nonstore retailers), and NAICS 44, 45 (Retail trade). 

Employment data from Census Bureau, Statistic of US Business (SUSB), https://www.census. 

gov/programs-surveys/susb/data/tables.html, for Retail Trade. Revenue share of 8 largest firms 

from Census Bureau, Economic Census of Retail Trade, https://www.census.gov/programssurveys/economic-census/data/tables.html. E-commerce share of sales calculated in Hortaçsu 

and Syverson, 2015, Table 1, “by dividing the sum of the product category’s e-commerce sales 

within and outside Electronic Shopping and Mail-Order Houses (ESMOH) by the sum of total 

ESMOH sales of the product and total sales of the product’s corresponding retail industry.” 

Chad Syverson generously provided the underlying data points for the series. The 2012 value 

for computers and software is missing in the series and is calculated in the table as the average of 2011 and 2013. 
TRANSFORMATION OF THE RETAIL SECTOR 27 

first pair of figures shows the shift in the share of total retail sales in the 

subset of general merchandise stores that includes warehouse clubs and 

supercenters (NAICS 45231) and in nonstore retailers (NAICS 454). In 

both cases, the share of total retail sales in these types of outlets more than 

doubles over two decades, and the shift is large in relation to overall retail 

sales. The second set of figures shows the shift in the employment share 

in small (< 500 employees) and large (10,000+ employees) firms, with a 

shift of 8 percentage points in overall retail employment from small to 

large firms over a 15-year period. It also shows the shift in revenue of the 

largest eight retail firms over an earlier (but overlapping) 15-year period. 

Finally, the third set of figures shows estimates of the e-commerce share of 

sales in different product categories over a single decade, with substantial 

shifts to e-commerce in some product categories—music and video, books 

and magazines—and very small shifts in others, especially in food and 

beverages. 

3
 

Measuring Retail Employment
 

and Labor Productivity
 

This chapter discusses the task of measuring retail employment and 

productivity, addressing both the conceptual elements that need to be 

measured and the available data for doing so. Inevitably, the available data 

fall short of capturing the concepts, which leads to the discussion in the 

next chapter of possible ways of addressing those shortfalls in the context 

of a retail satellite account. 

As a starting point for discussion, this chapter first clarifies two highlevel concepts essential to measuring productivity and its components: 

output (including price deflators) and input. It then considers the definition 

of the retail sector, which structures the way data related to retail are collected, expanding on the relevant concepts and the data provided by U.S. 

federal statistical programs to measure output, including deflators, and 

employment. The chapter ends with a discussion of alternative data sources. 

The concepts and data presented here derive from two main sources: 

documentation provided by the U.S. federal statistical agencies and the 

information-gathering workshop organized by the panel. The panel’s workshop included three sessions related to measurement issues. The workshop’s 

third session focused on key measurement and data challenges, with particular attention to the data collected and the measures produced by the 

Bureau of Labor Statistics (BLS), the U.S Census Bureau, and the Bureau 

of Economic Analysis (BEA). The workshop’s fifth session focused on 

quality-adjusted prices, a particularly difficult measurement issue related to 

deflation of output. The sixth session discussed improvements in the measurement of retail trade productivity that might be gained using microdata 

from the statistical agencies. Beyond those three sessions, the workshop’s 

29
 
 

 

30 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

other sessions prompted a number of exchanges on the conceptual and 

data issues related to measuring retail employment and labor productivity. 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY: 

THE HIGH-LEVEL TASK 

The project focuses on the concepts and data needed to measure employment and labor productivity in the retail sector. This scope immediately 

raises questions about the ways such terms as “employment,” “labor productivity,” and “retail sector” should be defined. Answering those questions is the task of this chapter, and it is also the main focus of government 

economic statistics programs that must implement data-gathering processes 

to produce a set of measures of the economy. 

A key point to note is that the productivity concept at issue is labor 

productivity, not multifactor productivity. Labor productivity concerns the 

amount of output produced per unit of labor input. At heart, this involves 

the division of industry output by labor input, once the appropriate measures have been defined. Box 3-1 summarizes the current labor productivity 

measures produced by BLS for the trade industries (retail and wholesale) 

and retail-related services. For these industries, the output measure currently used by BLS is gross sales deflated with a price index. That is, the 

sales figure is adjusted to convert dollars to a base year, in order to remove 

apparent changes in output that are actually due to price changes. Input is 

measured as hours worked. 

Alternative approaches to measuring labor productivity for retail-related 

industries also use the equation shown in Box 3-1 but may define output 

(deflated using an appropriate deflator) as gross margin (sales revenue minus 

cost of goods sold), sectoral output1 (gross output minus all inputs originating from firms within the industry being measured), or value added (gross 

output minus the value of all inputs originating as the output of other firms). 

BLS measures labor productivity by deflating detailed revenues with 

corresponding price indexes, using either its own Consumer Price Index 

(CPI) or Producer Price Index (PPI) or else Merchant Wholesale Deflators 

from BEA. 

BLS determines the revenue obtained from specific product classifications within each industry. Revenues for detailed product classes are 

deflated with corresponding price indexes. For about 97 percent of retail 

sales, BLS uses price indexes from CPI. For about 3 percent of the sales the 

Bureau uses a PPI, because pricing data for those products or services are not 

1The BLS labor productivity measures for the manufacturing sector, individual manufacturing industries, and NIPA-level nonmanufacturing industries are calculated under a sectoral 

output approach. 
 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 31 

BOX 3-1
 

Labor Productivity for Trade-Related Industries:

How It Is Measured by the Bureau of Labor Statistics
 

Representation: Business establishments in the United States, by industry (NAICS 

code).

When released: Up to 8 months after the close of the reference year for preliminary values; up to 20 months after the close of the reference year for revised

values. 

Key variables: Labor productivity; output; hours worked; and implicit price deflator.

Level of detail: 2-, 3-, and 4-digit NAICS codes, with some 5- and 6-digit detail. 

Measurement: Annual productivity growth is derived as the annual percentage

change in real output minus the annual percentage change in hours worked: 

(DOt/DOt–1) – (Ht/Ht–1) 

where Ot represents output (sales, revenue, or value of shipments) from the

Census Bureau’s annual (revised values) and monthly or quarterly (preliminary

values) economic surveys at time t plus revenue from the Census Bureau’s 

Nonemployer Statistics at time t. DOt = Ot/Dt where Dt is a deflator (or price

index) that converts dollars to a base year to remove any change in output

due to price changes. Ht represents input (hours worked) at time t from BLS. 

(See Box 3-7.) 

available from the CPI. For services, BLS employs a mix of PPIs and CPIs, 

using a larger portion of the former depending on the industry. For wholesale 

output, BLS uses PPIs for the manufacturers’ sales branches and offices and 

merchant wholesale deflators from BEA. For industries for which BLS possesses revenue detail from the Economic Census (conducted every 5 years) to 

break up the annual sales, BLS applies available product- or service-specific 

deflators to the detailed portion of the total revenue. When the Bureau does 

not have the revenue detail, as for several services industries, it uses the totalindustry deflator from PPI.2 Alternatives would use a price deflator appropriate for the selected output measure. 

The input measure currently used by BLS to estimate labor productivity is hours worked. Alternative formulations adjust hours worked for 

differences in labor composition (typically, education or skill). An example 

of a potential alternative formulation3 is used by BLS in its formulation of 

multifactor productivity, as described next. 

2From e-mails with Jenny Rudd, BLS, December 14-16, 2020. 

3Another potential alternative way of accounting for labor composition is used by BLS/BEA 

in their joint Industry-Level Production Account. 
32 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Multifactor productivity involves multiple inputs in addition to labor— 

including various forms of capital and other purchased inputs such as 

energy, materials, or purchased services. In addition, the BLS multifactor 

productivity measure adjusts hours worked to account for differences in 

labor composition. As described in BLS (2020): 

At the major sector level, measures of hours worked are supplemented to 

account for changes in so-called ‘labor composition’. This is a measure 

of the overall level of skill of the labor force. To compute the change in 

labor composition, the labor force is sorted into types of workers, defined 

by combinations of age, education, and gender. For each of these worker 

types (a.k.a. ‘cells’), total hours worked and median hourly wage are calculated in each year. Wages are assumed to be a proxy for worker skill, 

with more skilled workers receiving greater compensation. 

The hours and wage data are used to calculate each type of worker’s 

share of total wages. The labor composition adjustment is calculated 

as the difference between the percent change in total hours worked and 

the weighted sum of the percent changes of hours worked by each age/ 

education/gender worker type. 

Because multifactor productivity involves multiple inputs, computations are more complex. This report does not address the additional conceptual and data issues related to measuring multifactor productivity. 

DEFINING THE RETAIL SECTOR 

This discussion starts with the last of the three terms that need to be 

defined—“retail sector”—because of its centrality to the motivation for the 

project. Specifically, the project seeks to answer the question, Has the transformation in retail affected the definition of the sector in ways that would 

require a different, perhaps broader, definition of the sector? The statement 

of task for the project asks about the creation of a satellite account that 

could address a “retail-related” sector that would go beyond the businesses 

included in retail alone. 

This section first considers how the definition of industries in economic 

data affects the ability to identify a retail-related sector. It then turns to an 

important practical problem in the way industry classification is carried out 

in the United States across multiple statistical agencies. 

Defining Retail-Related Establishments in Federal Data 

One of the key organizing frameworks for federal economic data is 

provided by the North American Industry Classification System (NAICS), 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 33 

which classifies establishments in a hierarchical coding system according to 

their primary activity. Box 3-2 provides more detail about NAICS. NAICS 

was implemented in 1997 to replace the Standard Industrial Classification 

BOX 3-2
 

Measuring the Economy

The North American Industry Classification System (NAICS)
 

The Great Depression of the 1930s spawned many new federal mechanisms 

for tracking the economy. One of them was the Standard Industrial Classification

(SIC) system, developed when manufacturing was the dominant industry. While

there were many modifications to the SIC over the years, by the 1990s it was

clear that major shifts in the American economy mandated major change in how

its industries were classified. The result: the North American Industry Classification System (NAICS), adopted in 1997.a 

NAICS is now the standard used by federal statistical agencies in the United

States, as well as those in Canada and Mexico, to classify business establishments, that is, economic units at a single location that produce and/or sell goods

or services. The classification is valuable for collecting, analyzing, and publishing

statistical data related to the business economy. Revisions to NAICS are considered every 5 years in calendar years ending with 2 and 7 through international

collaborations. For 2022, the main items under consideration were released for 

comment in the Federal Register in February 2020.b Of particular relevance to

the study of retail trade are the discussions in sections III and IV concerning

NAICS 454111, Electronic Shopping, and NAICS 519130, Internet Publishing and

Broadcasting and Web Search Portals. These codes delineate industries based

on mode of delivery, the internet, rather than by product as most NAICS codes

within the retail and wholesale sectors are delineated. 

Under NAICS, establishments that have similar production processes are

classified in the same industry, and support establishments are designated as

auxiliaries during the classification process. Business establishments are identified with individual locations and may be part of a larger firm (“enterprise”) that

may have establishments working in a number of different industries. Each statistical agency implements the classification of business establishments based on its

own available data.c 

The major NAICS designations of interest to this project are these three sectors: retail trade (NAICS 44-45), wholesale trade (NAICS 42), and transportation

and warehousing (NAICS 48-49); as well as the more detailed codes within those

sectors. Elements of other sectors may also be included in the analysis to fully

account for the transformation of retail trade. 

aSee http://www.incontext.indiana.edu/2002/july-aug02/details.asp. bSee https://www.federalregister.gov/documents/2020/02/26/2020-03797/2017-northamerican-industry-classification-system-naics-updates-for-2022-update-of-statistical. c See https://www.census.gov/eos/www/naics/2017NAICS/2017_NAICS_Manual.pdf (p. 3);

and https://www.census.gov/eos/www/naics/history/history.html, third file on NAICS classification memos. 
 

See

34 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

System (SIC), which classified establishments by sector using different concepts, such as production- or demand-based definitions. 

A key difference between the SIC and NAICS classifications is in their 

treatment of auxiliary establishments. As Fort and Klimek (2018, p. 8) 

explain, 

Auxiliary establishments are defined as those establishments primarily 

serving other establishments of the same enterprise. Examples of auxiliary 

establishments include management, warehousing, data processing, and 

R&D. Under SIC, auxiliary establishments were classified in the primary 

industry of the establishments that they served. In contrast, NAICS classifies these establishments in a number of different industries and sectors, 

depending upon the types of services the establishments actually provide. 

Hence, under NAICS, additional information needs to be used to identify 

whether an establishment is an auxiliary that primarily supports retail trade. 

The 1992 Economic Census, the last such census that relied solely on 

SIC classifications, showed more than 840,000 auxiliary employees assigned 

to retail trade out of a total of 18 million retail trade employees. Also in 

1992, BLS payroll data showed 13 million retail trade employees. In 1997, 

the Economic Census collected data with sufficient detail so that it could be 

categorized under both SIC and NAICS. That year the number of retail trade 

employees fell to 13 million, close to the count from BLS payroll data. Today, 

under the NAICS system, the auxiliary employees who had been listed in 

retail trade under the SIC classifications are most likely to have been moved 

to one or more of the following sectors or subsectors: Management of Companies and Enterprises; Administrative Support, Waste Management and 

Remediation Services; Warehousing and Storage; Computer Systems Design 

and Related Services; and Accounting, Tax Preparation, Bookkeeping and 

Payroll Services.4 

Ding and colleagues (2020, p. 1) illustrated the impact of auxiliaries on 

the manufacturing sector, observing that 

firms with in-house professional service establishments are larger, grow 

faster, are more likely to survive, and are more likely to open plants in 

other sectors than firms without such plants. These trends motivate a 

model of within-firm structural transformation in which non-manufacturing workers complement physical production, and where physical input 

price reductions induce firms to reallocate toward services. 

The changes in the retail trade sector discussed in the preceding chapter 

suggest that a broader definition of the sector than provided by NAICS 

4 http://www.incontext.indiana.edu/2002/july-aug02/details.asp. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 35 

might be required to be able to understand the shifts that are occurring. In 

particular, the restructuring that started first with the warehouse clubs and 

superstores and then moved on to e-commerce has begun to blur the lines 

between the retail industry and several other sectors, including wholesale 

trade, warehousing and storage, different types of transportation, and some 

other types of business services. 

The ability to analyze these changes in an integrated way is directly 

affected by the structure of the economic data in the U.S. federal statistical 

system and, therefore, by the NAICS classification. 

CONCLUSION 3-1: Given the structure of economic data in the U.S. 

federal statistical system, a study of the retail-related sector will require 

identifying those North American Industry Classification System codes 

that can be defined as either retail related or partially retail related. For 

those that are partially retail related, estimates will be needed for the 

portion that is related to retail. 

Classification 

Until this point, the discussion has described the NAICS classification 

of businesses in the abstract. However, the classification scheme needs to 

be applied to a specific set of businesses using a set procedure to determine 

the classification for each business unit. See Box 3-3 for a brief introduction 

to guidelines for classification. 

Each statistical agency independently uses NAICS guidelines to classify establishments into industries on the basis of their primary activity, as 

measured in that agency’s data, and updates that classification on its own 

agency schedule. Generally, for an establishment engaging in more than one 

activity, the entire employment of the establishment is included under the 

industry indicated by the primary activity.5 Because business registers rely 

on different underlying source data, the Census Bureau and BLS may assign 

the same establishment to different industries or record the establishment 

with a different employment level. 

There is even less agreement concerning the assignment of a code to 

enterprises, because such classification is not required under NAICS. In fact, 

BLS does not assign NAICS codes to enterprises. Some agencies choose to 

assign NAICS codes to enterprises based on their own internal data, and 

5Some large companies report different activities at the same location as separate profit 

centers. The Census Bureau’s County Business Patterns and Statistics of US Businesses (SUSB) 

program treats each profit center as a separate establishment. The Economic Census reporting 

may combine the profit centers into one establishment. This results in establishment count 

differences due to differences in how the data are collected. See https://www.census.gov/ 

programs-surveys/cbp/technical-documentation/methodology.html#par_textimage_36648475. 
See

36 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

BOX 3-3
 

NAICS Classification and Designation of Auxiliaries
 

The NAICS classification is based on a production-oriented or supply-based

conceptual framework. It groups and classifies establishments according to similarities in the processes they use to produce goods or services. NAICS makes no

distinction between auxiliary and operating establishments, and it recognizes the

unique nature of corporate, subsidiary, and regional managing offices by including an industry code for Corporate, Subsidiary, and Regional Managing Offices

(NAICS 551114, classified under sector 55).

Under NAICS, an establishment is classified under an industry when its

primary activity meets the definition of that industry. Because establishments may

perform more than one activity, there are procedures for identifying the primary

activity of an establishment. Ideally, the principal product or service should be

determined by its relative share of current production costs and capital investment. In practice, however, it is often necessary to use other variables, such as

revenue, shipments, or employment as proxies for measuring significance. The

most commonly used proxy measure for production in determining primary activity 

has been receipts or sales.

A NAICS industry may include both establishments that produce output for

sale to others (market transactions) and establishments that produce output

for other establishments of the same company (support activities) without a fee.

Some establishments may be engaged in both support and market activities,

and when this is the case their classification is based on the establishment’s 

primary activity. Receipts reported by establishments on surveys are for their

market activity and exclude the contribution of support activities; nevertheless,

receipts for such secondary activities are becoming more prevalent as support

facilities attempt to maximize capacity utilization.

Support activity is considered a primary activity only when it takes place in

a separate establishment of a multi-establishment firm where the market activity

(if any) is secondary or unrelated to the primary objective of the enterprise. Such

establishments, where support is a primary activity, are designated auxiliaries if

they are classified in one of six industries in the services sector: NAICS 48-49

(Transportation and Warehousing), NAICS 51 (Information), NAICS 54 (Professional, Scientific and Technical), NAICS 55 (Management of Companies or Enterprises), NAICS 56 (Administration and Support and Waste Management and

Remediation), and NAICS 81 (other services except public administration). Data

used for classification and designation are maintained in the Census Bureau’s

Business Register. 

SOURCE: Based on Clarification Memo no. 3, “Classifying SIC Auxiliary Establishments in

NAICS.” https://www.census.gov/eos/www/naics/history/history.html. 
See

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 37 

some may ask respondents to report the code that best describes their primary business activity. The Census Bureau’s Statistics of U.S. Businesses 

classifies enterprises in this way: 

An enterprise may have establishments in many different industries. For the 

purpose of classifying an entire enterprise into a single industry, the classification methodology starts by excluding nonoperating establishments— 

establishments classified as manufacturers’ sales branches and offices, 

establishments engaged in management of enterprises and enterprises 

(NAICS 55), and auxiliary establishments. The enterprise is then classified into the 2-digit NAICS sector in which it paid the largest share of its 

payroll. Then, within this 2-digit NAICS sector, the enterprise is classified 

into the 3-digit NAICS subsector in which the enterprise paid the largest 

share of payroll. Finally, within the assigned 3-digit NAICS sub-sector, 

the enterprise is classified into the 4-digit NAICS industry group with the 

largest share of payroll.6 

One of the challenges with enterprise classification is that the “primary 

sector” is likely to change over time as business lines evolve and establishments are bought and sold. 

Ideally, the NAICS coding of establishments would be applied uniformly, with all federal statistical agencies using the same code for each 

business unit. However, this is effectively impossible in the U.S. context, 

due to laws that restrict the sharing of individually identifiable information, even across federal statistical agencies. As a result, the two agencies 

that provide data related to business output and employment, the Census 

Bureau and BLS, each develop their own address lists and classifications of 

business establishments, with limited ability to share and compare them.7 

6 https://www.census.gov/programs-surveys/susb/technical-documentation/methodology. 

html. An enterprise may consist of groups of establishments that operate in different sectors. 

Each such group may be referred to as a “firm” for purposes of reporting to annual, quarterly, 

and monthly surveys described later in this document. 

7As a result of the Confidential Information Protection and Statistical Efficiency Act of 2002, 

BLS now receives annually the list of enterprises and the Employer Identification Numbers 

(EINs) that are associated with them as well as business names, addresses, and industry codes 

from the Census Bureau’s Business Register. However, the panel was told that merging the two 

lists is a time-consuming, resource-intensive exercise, because in many cases different EINs 

are recorded on the two lists. In an email from Ken Robertson on September 2, 2020, BLS 

observed that an enterprise can and often does have multiple EINs. Consider a large enterprise with 60 subcomponents. The company might register one EIN with IRS for the entire 

enterprise, or it might register one EIN for each subcomponent. It might report 20 of those 

60 EINs to the Census Bureau, aggregating 3 subcomponents each into each of 20 reports, 

and list a different 20 when reporting on employment to BLS, or report all 60 to one agency 

but not the other. Even within the Census Bureau, an enterprise may use one EIN to report 

payroll and another to report revenue. So, even with Census and BEA data we have incom­
 

38 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Business Registers 

A long-standing issue for the U.S. federal statistical system has been 

the fact that the economic and business surveys conducted by the Census 

Bureau and those conducted by BLS rely on samples drawn from separate 

business registers (sampling frames), with different strengths and weaknesses (Fairman et al., 2008; Fixler and Landefeld, 2006). More recent 

updates are described in National Academies of Sciences, Engineering, and 

Medicine ([NASEM], 2017, p. 41): 

The Census Bureau is able to access federal tax information from the IRS 

for a specified set of purposes (Internal Revenue Code 6103(j)) . . . [The] 

Census Bureau uses those data to create the Census Business Register; 

however, BLS does not currently have access to those data and so has to 

base its frame on a different source. Because BLS and the Census Bureau 

both conduct different surveys of businesses using different frames, there 

have been long-standing issues in comparing and reconciling the different statistics that describe the economy from the two agencies (National 

Research Council, 2007). The Bureau of Economic Analysis (BEA) has 

acknowledged the differences and cannot resolve them. Being able to use 

the same business list and synchronize the existing lists would both reduce 

the burden on businesses and improve the quality of economic statistics, 

and it is likely that it would also result in cost savings (National Research 

Council, 2007). The situation is particularly frustrating since BLS and 

the Census Bureau have had explicit legal authority to allow them to 

share business information for statistical purposes since 2002 (PL 107-347 

Title V, Subtitle B). The required change to the IRS legislation that would 

permit BLS to have access to limited business tax information has not been 

passed, despite numerous efforts.8 

More recently, in November 2020, the American Economic Association 

provided a letter to the incoming Biden-Harris transition team regarding 

“Necessary Improvement in the U.S. Statistical Infrastructure.”9 Under the 

seventh bullet in that letter, the association makes these points: 

plete information about enterprises. An article that BLS has published profiling these data is 

available at https://www.bls.gov/opub/mlr/2016/article/establishment-firm-or-enterprise.htm. 8The Obama administration pushed for this legislative authority (see, e.g., U.S. Department of the Treasury, 2014; U.S. Office of Management and Budget, 2016), but despite 

support from previous administrations and broad support from the statistical and research 

community no action has been taken for this limited data sharing of business tax information for exclusively statistical purposes by Census, BEA, and BLS (see http://www.copafs. 

org/UserFiles/fle/FederalBusinessRegistryLetterSenatewithAttach.pdf). 

9See https://www.aeaweb.org/content/file?id=13507. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 39 

The Executive Branch and the Congress need to resolve critical problems 

resulting from the decentralized nature of the Federal Statistical System, 

which confounds accuracy and consistency. For example: The Treasury 

Department must support, and the Congress must revise, Title 26, the 

Internal Revenue Code, to codify data sharing among BEA, Census, and 

BLS as routine practice. The consequential reconciliation of currently differing BLS and Census Bureau business registers will substantially improve 

the accuracy and comparability of major economic statistics used for business and public policy decision-making. 

As described in the National Academies’ report (NASEM, 2017, p. 41), 

“the Census Bureau’s Business Register is a listing of all legal business 

entities—incorporated businesses, partnerships, and sole proprietorships— 

operating in the United States and its territories (island areas) as identified 

by the U.S. Internal Revenue Service (IRS). It lists businesses that have paid 

employees (i.e., employer businesses), of which about 5 million have only 

one location and 160,000 have more than one location. It also lists nonemployer businesses, of which there are about 25 million.” 

The National Academies’ report (NASEM, 2017, pp. 42, 43) goes on 

to say that 

this Business Register combines data from multiple sources with the goal 

of providing comprehensive, accurate, and timely coverage of business 

units. Administrative records are the foundation of the Business Register. 

The primary data for identifying businesses come from the IRS, which 

provides information from its Business Master File, income tax returns, 

and quarterly payroll tax returns. The IRS provides updates to the Census 

Bureau for each of these types of records on a weekly basis. The Business 

Master File records are a source of information on name, address, and 

legal form of organization for all of the Employer Identification Number 

(EIN) entities of which the IRS is aware. Tax records provide information on revenues, assets, inventories, payroll, employment, and industry. 

EIN applications filed with the IRS and processed by the Social Security 

Administration are shared with the Census Bureau on a monthly basis and 

provide NAICS codes for new businesses.10 

The Business Register is updated with information from the IRS, the 

Economic Censuses, the Company Organization Survey (see Box 3-8), the 

Census Bureau’s Business and Professional Classification Survey, and the 

Annual Survey of Manufacturers (but no other annual economic surveys). 

The Business Register for BLS is designed to support BLS’s Quarterly 

Census of Employment and Wages. Establishments are classified into industries on the basis of their primary activity. For an establishment engaging 

10BLS does not have access to IRS data. 
 

40 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

in more than one activity, the entire employment of the establishment is 

included under the industry indicated by the principal activity. Industry 

information is also collected on a supplement to the quarterly unemployment insurance tax reports filed by employers. 

The Quarterly Census of Employment and Wages, which consists of 

a monthly count of employment and quarterly counts of wage levels and 

business establishments, covers greater than 95 percent of the jobs available 

in the United States. The primary source for this census is administrative 

data from state unemployment insurance programs. These data are supplemented by data from two BLS surveys: the Annual Refiling Survey and the 

Multiple Worksite Report. 

As reported by the National Academies (NASEM, 2017, p. 43), 

each quarter, the Census Bureau prepares a listing of unclassified or partially classified EINs to refer to the BLS for comparison with its Business 

Register. The BLS provides approximately 30 percent of industry codes for 

EINs that appear on the Census Bureau’s list, mostly for small employers. 

In addition to providing data that would otherwise be missing, this operation helps to make the Census Bureau’s Business Register more consistent 

with the separate BLS register. 

In 2004, BLS and the Census Bureau reinitiated a project to compare 

and contrast the two registers. Preliminary results, reported by Becker and 

colleagues (2005), used an aggregate analysis comparing 2001 data from 

the Census Bureau’s County Business Patterns to data from BLS’s Quarterly 

Census of Employment and Wages. They observe that there are four main 

types of known differences between the two lists: differences in collection 

(the surveys used to maintain the lists), differences in scope (the parts of the 

economy covered by the data), data definitions (e.g., agencies use different 

definitions—for payroll and for designation of a company as “active”), 

and differences in reference period. If adjustments are made for these 

known differences, establishment counts and wages at the national level are 

similar, but employment differences remain. One of the challenges encountered in making comparisons at the sectoral level was that in 2001, BLS 

data were classified based on 2002 NAICS codes, while Census data were 

classified based on 1997 NAICS codes. The sectors impacted most by this 

difference were retail trade (NAICS 44-45), wholesale trade (NAICS 42), 

and information (NAICS 51). 

Fairman and colleagues (2008) report on the follow-on study, a microdata match between the two registers. Of the 6.1 million unique EINs in 

the BLS and Census registers in 2003, only about 75 percent matched. They 

concluded: “while it seems likely that differences in establishment classification by Census and BLS at the same companies may explain a substantial 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 41 

part of the industry differences between the two lists, the lack of a common 

establishment-level identifier makes matching individual establishments and 

comparing their industry codes very difficult” (pp. 5, 6). 

One of the industry differentials they observed was an apparent misclassification of the headquarters operations for mining companies in Texas. 

These tended to be assigned by BLS to the category of Mining and by 

Census to Management (a category that includes auxiliaries). The paper 

concluded that “it is difficult to determine the best way to classify these 

establishments” (Fairman et al., 2008, p. 4). In general, BLS has limited 

information as a basis for designating auxiliaries. 

Impact of Differences 

Even when the concepts being measured are the same, some differences 

will emerge in the estimates by the two agencies due to their separate sources 

of data, separate processes for maintaining business registers, and the fact 

that different classifications may be assigned to the same enterprise by the 

two agencies. See Tables 3-1a and 3-1b for a comparison of the number of 

establishments and the number of employees estimated to fall under different 

NAICS codes as measured by three programs: the Economic Census (under 

the Census Bureau), the Statistics of US Businesses (also under the Census 

Bureau), and the Quarterly Census of Employers (under BLS). 

The largest percent differences are in the establishment counts, between that of the Economic Census and that of the Quarterly Census of 

Employers, with the latter generally having larger counts and with percent 

differences ranging from –2.1 to 33.3 percent. The percent differences in 

employment are smaller, ranging from –5.5 to 10.6 percent. These latter 

percentages may provide a clue as to the impact of differences in classification on the numerator (output data collected by Census) and denominator 

(input data collected by BLS). 

CONCLUSION 3-2: Labor productivity is measured as the ratio of 

change in output divided by change in input. Given that nominal output is measured by Census Bureau surveys while labor input and price 

deflators are measured from the Bureau of Labor Statistics’ surveys, 

and that the two agencies use separate business registers with separate 

classifications of business establishments by the North American Industry Classification System code as sampling frames for surveys, estimates 

of productivity are bound to contain errors. The resulting differences 

in statistics produced by the two agencies likely contribute to this error because different establishments may contribute to the numerator 

and denominator. The error most likely has a time-varying component 

because each agency updates its business list on a different schedule. 
42 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

TABLE 3-1a Comparison of the Number of Establishments by NAICS 

Codes, as Measured by Three Programs, 2017 

Statistics 

of US 

Businesses 

(SUSB) 

Quarterly 

Census of 

Employers 

(QCEW) 

Percent 

Difference 

(SUSB – 

EC) / EC 

Percent 

Difference 

(QCEW – 

EC) / EC 

Economic 

Census 

(EC) 

NAICS 

Description 

42 Wholesale trade 408,333 409,656 612,359 0.3% 33.3% 

44-45 Retail trade 1,064,087 1,064,449 1,042,096 0.0% –2.1% 

48-49 Transportation 

and 

warehousing 

237,095 237,308 242,932 0.1% 2.4% 

481 Air 

transportation 

4,450 4,441 5,784 –0.2% 23.1% 

483 Water 

transportation 

1,643 1,668 2,063 1.5% 20.4% 

484 Truck 

transportation 

126,803 126,986 127,366 0.1% 0.4% 

492 Couriers and 

messengers 

14,467 14,359 17,407 –0.7% 16.9% 

493 Warehousing 

and storage 

16,956 16,901 17,389 –0.3% 2.5% 

TABLE 3-1b Comparison of the Estimated Number of Employees by 

NAICS Codes, as Measured by Three Programs 

Statistics 

of US 

Businesses 

(SUSB) 

Quarterly 

Census of 

Employers 

(QCEW) 

Percent 

Difference 

(SUSB – 

EC) / EC 

Percent 

Difference 

(QCEW – 

EC) / EC 

Economic 

Census 

(EC) 

NAICS 

Description 

42 Wholesale trade 6,242,335 6,115,476 5,898,637 –2.0% –5.5% 

44-45 Retail trade 15,938,821 15,705,808 15,854,454 –1.5% –0.5% 

48-49 Transportation 

and 

warehousing 

4,954,931 4,866,282 4,947,369 –1.8% –0.2% 

481 Air 

transportation 

508,300 470,353 493,349 –7.5% –2.9% 

483 Water 

transportation 

62,745 61,762 64,276 –1.6% 2.4% 

484 Truck 

transportation 

1,480,107 1,465,040 1,452,674 –1.0% –1.9% 

492 Couriers and 

messengers 

633,108 641,572 666,600 1.3% 5.3% 

493 Warehousing 

and storage 

921,320 913,559 1,018,613 –0.8% 10.6% 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 43 

CONCLUSION 3-3: The Bureau of Labor Statistics (BLS) annually 

receives a file containing Census Bureau Firm IDs, Employee Identification Numbers (EINs), and establishment detail. However, BLS does 

not use the Census file on a regular basis because the reconciliation of 

EINs between Census and BLS is labor intensive and time consuming. 

It would be beneficial to be able to quantify all of the activity under 

firm IDs that have some establishments classified as retail and for which 

linking BLS and Census firm and establishment data might help in identifying retail-related auxiliaries in BLS data, for example, something 

that is not currently possible. This has the potential for helping in the 

development of a satellite account on activities supporting retail trade. 

CONCLUSION 3-4: A study using the Census Bureau’s firm-level 

microdata and other relevant information could be designed to develop 

factors to adjust for systematic differences between numerator and 

denominator to improve productivity estimates. 

CONCLUSION 3-5: The ideal long-term solution to the issue of separate business registers being developed, maintained, and used by the 

Bureau of Labor Statistics and the Census Bureau would be to remove 

the obstacles to data sharing noted in National Academies of Sciences, 

Engineering, and Medicine (2017) and National Research Council 

(2007) and for the federal government to develop and use a single 

common business register. 

MEASURING OUTPUT 

Conceptual Issues in Measuring Nominal Output 

A central part of measuring labor productivity is measuring the real 

output of an industry and the way it changes over time. This discussion is 

divided into two sections, because there are two substantial issues that need 

to be addressed: the concept of output that forms the basis of the measurement and the price indices that are used to deflate nominal measures of 

output to adjust for inflation. This section addresses the output concept and 

its measurement; the next section addresses the price indices. 

As noted in the previous chapter and above in this chapter, output in 

the retail-related sector is defined in four different ways11 in the federal 

11Note that none of these output measures addresses household tastes, so they have no 

way of measuring the effect of increased product variety on consumer welfare, as explored 

by Neiman and Vavra (2019). It is not clear how the increase in product variety could be 

appropriately reflected in the output measure. 
44 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

statistical system: (1) as total sales revenue; (2) as the difference between 

sales revenue and the cost of goods sold (gross margin); (3) as the difference 

between sales revenue and the cost of all purchased inputs (value added); 

and (4) as the difference between sales revenue and the cost of all inputs 

purchased within the sector (sectoral output). This conceptual discussion 

focuses on the contrasts in the first three definitions (see Box 3-4), since 

sectoral output varies with the definition of the sector, ranging from sales 

revenue (for narrow definitions of the sector) to value added (when the 

sector encompasses the entire economy). 

BLS has said12 that for most service industries, and in particular the 

retail-related service industries, sectoral output nearly equals the gross sales 

or revenue of the industry, because intra-industry transfers are tiny or even 

nonexistent. Hence, for purposes of measuring the output of retail-related 

industries, sectoral output is approximately equal to gross sales but is more 

complex to compute. Hence, gross sales provides a reasonable simple approximation to nominal output for the service industries. However, the 

observation that intra-industry transfers are tiny for the service industries 

may be a reflection of data gaps. 

Conceptually, a sales revenue measure of retail output uses the retail 

sales price of a product as the measure. That price reflects the entire chain 

of processes that goes into the product, from its initial design to the raw 

materials to the manufacture to the multiple steps involved in providing the 

product for sale and delivering it to the final customer. In other words, it 

includes the contributions of the entire value chain in the production and 

distribution of the good, not just the value added by the retailer. In contrast, 

a value-added measure of retail output focuses on the portion of the product’s value directly provided by the retailer, subtracting the wholesale cost 

of the product and any other purchased inputs that the retailer does not 

provide. Thus, the value-added measure of output directly isolates the portion of the value chain that is produced by the retail firm’s own labor and 

capital. Between these two concepts, the gross margin measure removes the 

wholesale cost of the product, which reflects the value related to its design 

and manufacture, but includes the value added by other factors besides the 

retailer’s own labor and capital, such as the value provided by leasing a 

store or paid to another vendor who handles customer service. 

Strictly speaking, the value-added measure of output is the one that is 

associated with the services provided by the retailer that derive from the 

12Email from Jenny Rudd, BLS October 21, 2020: “In most cases the sectoral output of a 

service industry nearly equals the gross output of the industry. Because intra-industry transfers 

are tiny or even non-existent, the values for the two output concepts overlap. We have looked 

into the possibility of adding intra-industry adjustments to service industries. The problem is that 

the data to do so from the input-output (IO) tables are generally not at a detailed enough level.” 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 45 

BOX 3-4
 

Alternative Measures of Nominal Output
 

Gross sales are often used as a measure of gross output for retail-related 

services. 

Gross margins, or as they are also called, trade and transport margins, are used

by the U.S. National Accounts, and by the international handbook, the System of

National Accounts, as the appropriate measure of the gross output of the trade

and transport industries (wholesale, retail, and transportation of goods). Unlike

other industries, these trade industries do not transform goods from intermediate

materials into finished goods. Rather, they buy finished goods for resale, with little

or no transformation of the product. Hence, for these industries output is best

measured by the services they provide—including advertising, information and

display of products, inventorying, and delivery—which can be measured by their

sales less their cost of goods sold. 

Value-added estimates exclude all inputs purchased by the industry and are

therefore the only output measure that does not double count some outputs

across industries. As a result, the sum of value-added by industry equals GDP.

Gross margins, while useful, are not a pure measure of trade industry output or

productivity, because like gross sales for other industries, they still contain double

counting for other intermediate inputs, like energy and purchased services. The

appropriate measure of the unduplicated output of any industry is value added,

measured as gross sales less all intermediate inputs (or the sum of labor compensation, profits, proprietor’s income, and rents and other capital income). 

capital used and the activities of the retailer’s employees who provide the 

labor input used to calculate labor productivity. Of particular importance, 

the value-added measure of output is the only measure that makes the adjustments necessary to compare the labor productivities of large national 

retail chains and small mom-and-pop stores on an equal footing, in each 

case removing the contributions to total sales revenue that are provided by 

the workers at other firms. However, not all analysts are persuaded that 

the value-added measure is superior; some analysts are concerned that a 

value-added measure of output can be distorted by monopoly pricing (e.g., 

Walter Oi, cited by Manser, 2005). In fact, all three measures are potentially influenced by varying markups. Value-added measures are in a sense 

residual measures, reflecting the effect of business cycles, shifts in demand, 

and input cost changes, and this can make them quite volatile. 

Despite the conceptual differences across these three output measures, 

they are clearly related, and under some conditions they will produce similar measures of labor productivity change. Specifically, in cases where the 

contributions of the different inputs are fixed, the three output measures 
46 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

will move proportionally. For example, if the cost of the goods represents 

one-third of the sales price, and the cost of the store lease and other purchased services represents one-third of the sales price, then the gross margin 

will be two-thirds of total sales revenue and value-added will be one-third 

of total sales revenue. In this simple fixed case, the three different output 

measures will all increase by the same percentage. As a result, if the industry 

is relatively stable, all three measures will show roughly the same change. 

However, if the industry is experiencing change, with different parts of 

the value chain growing more or less quickly than others, then the three 

different output measures are likely to show different percentage changes. 

It would be reasonable to expect the retail sector to show such differences 

at this time, given the kinds of transformation discussed in the previous 

chapter. And indeed, Figure 2-1 in that chapter shows that the three different measures of retail output produce three different estimates of labor 

productivity growth for the 1997-2018 period. That implies three different 

estimates of output growth, since all three estimates use the same measure 

of the change in labor input. Figure 2-1 also shows that sales revenue for 

the retail sector overall grew faster than gross margin, which in turn grew 

faster than value-added. Given the relationships between these measures, 

these inequalities imply that the cost of goods sold grew faster than the 

gross margin, and that the contribution of other purchased factors grew 

faster than the value added by retailers’ own labor and capital. These different growth rates in the other sectors related to retail—particularly those 

that produce other services purchased by retail firms—provide some hints 

of the restructuring occurring.13 

CONCLUSION 3-6: The nominal output of the retail and related 

sectors is measured in several different ways in the federal statistical 

system. A sales revenue measure of output is the simplest to produce 

but does not reflect changes in a retailer’s cost structure when additional functions—like warehousing—are integrated into the business. 

It does not focus on the services the retail sector provides, either. A 

value-added measure of output is theoretically preferred for measuring labor productivity in retail, capturing the difference between gross 

output and intermediate inputs, but there are limits in the ability to obtain the data needed to produce value-added measures. A gross margin 

measure of output reflects the value of the most important input for a 

retailer—the cost of goods sold—while sidestepping problems related 

to estimating other inputs. Because the extra data on purchases and 

13This paragraph slightly oversimplifies the comparison, since the labor productivity comparisons in Figure 2-1 show changes in real—not nominal—labor productivity, so differences 

in both nominal output measures and price deflation will affect the comparison. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 47 

other inputs are not published for as many detailed North American 

Industry Classification System codes as for sales, gross margin and 

value-added measures are available for fewer detailed retail industries. 

For retail-supporting services that might be combined with retail trade 

in a broader retail-related sector, similar choices would need to be made 

concerning which measure of nominal output to use, and those choices 

would entail tradeoffs between simplicity of data and conceptual focus. 

When the retail sector experiences significant change, the different output measures will give different pictures of labor productivity growth, 

depending on the extent to which the change is occurring for the retail 

services themselves, the various retail-supporting services provided by 

other suppliers, or the products provided for sale. 

Federal Data and Issues for Measuring Output 

The following sections discuss the statistical programs of the U.S. 

Census Bureau that collect retail-related data for measuring output (sales, 

revenue, or value of shipments; purchases; detailed expenses; and transfers 

between establishments) including the Annual, Quarterly, and Monthly Economic Surveys, the Economic Census, and other every-5-year collections. 

The Census Bureau, like other statistical agencies, collects information 

in a time sequence ranging from simple statistics published frequently to 

more detailed statistics published with longer delays. The quality of the 

early estimates is lower, because samples are smaller and respondent-provided data may consist of estimates made by businesses. There is typically 

more detail provided with later estimates, because they have larger sample 

sizes, and there is more time to clean the data. This time sequence and 

the use of benchmarking mean that there may be very long delays before 

final data are available. For example, the detailed data from the Annual 

Retail Trade Survey (ARTS) for 2018 were released in February 2020 and 

were benchmarked to the 2012 Economic Census (because 2017 Economic 

Census data were not yet available.) 

The Census Bureau’s Monthly, Quarterly, and Annual Economic Surveys 

The Monthly Retail Trade Survey and Advance Monthly Retail Trade 

Survey collect sales data from a sample of retail firms14 that report for their 

retail establishments. Data from the former are published within 50 days of 

the close of the reference month, while data from the latter are published 

14We use the term “firm” to distinguish a group of establishments within an enterprise, all 

of which are either classified in (say) retail trade or classified as supporting establishments for 

retail trade. An enterprise may contain many such firms. 
48 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

within 20 days. Both surveys provide estimates with less NAICS detail than 

ARTS provides. 

The Monthly Wholesale Trade Survey collects data from a sample 

of U.S. merchant wholesalers (excluding manufacturers’ sales branches 

and offices) on monthly sales, end-of-month inventories, and number of 

enterprises reported for. Data are released about 40 days after the close 

of the reference month and are provided with less NAICS detail than for 

the Annual Wholesale Trade Survey (AWTS). 

The Quarterly Services Survey collects total sales, receipts, revenue, 

and total operating expenses from a sample of firms with establishments in 

selected services industries. Estimates are released about 50 days after the 

close of the reference quarter, and the data are provided with less NAICS 

detail than for the Services Annual Survey (SAS). 

The surveys summarized in Box 3-5 collect economic detail for retail 

and retail-related industries in the three annual surveys mentioned above: 

ARTS (retail), AWTS (wholesale), and SAS (services). ARTS provides data 

on nominal gross sales, purchases, and gross margins for establishments 

classified in the retail sector. AWTS provides nominal gross sales, operating 

expenses, gross margins, and purchases for establishments classified in the 

wholesale sector.15 And SAS provides revenue and total expenses for establishments classified in the services sector, which includes transportation 

and warehousing. Except as noted, for all three surveys data are provided 

at the 4-digit NAICS level, with some 5- and 6-digit detail. The exceptions 

are that ARTS provides estimates of purchases, gross margins, and total 

expenses only at the 3-digit NAICS level, with some 4- and 5-digit NAICS 

level detail; for transportation industries in SAS, 3-digit NAICS level data 

are also provided for detailed expenses and commodity-level revenue. 

For the margin industries (retail and wholesale), there is a data gap 

in integrating the Economic Census data with the annual surveys. In the 

annual surveys, gross margins are measured at the industry level of detail 

(mostly 3-digit). In the Economic Census data, gross revenue is collected 

at a detailed product-group level, but no information is collected on gross 

margins. This implies that gross margins at the product-group level are 

never directly measured but only inferred by combining this disparate 

15To create the sampling frame for the Monthly Retail Trade Survey and ARTS (same 

approach used for the Monthly Wholesale Trade Survey and AWTS) all employer establishments located in the United States and classified in the retail trade and accommodation and 

foot-services sectors are sorted by EINs or firm identifiers. The establishment data for the 

EIN/firm (potentially only part of an enterprise) are aggregated to become potential sampling units. The sample is selected by a stratified design and selected firms/EINs are asked to 

report for the aggregate of their retail establishments and retail auxiliaries. Thus, the survey 

data statistically represent retail establishments. See https://www.census.gov/retail/mrts/how_ 

surveys_are_collected.html. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 49 

BOX 3-5 

The Census Bureau’s Annual Economic Surveys 

Annual Retail Trade Survey (ARTS) (NAICS 44, 45) 

Representation: Retail trade establishments in the United States; no establishment detail collected. 

When released: About 15 months after close of reference year. 

Variables: Sales, e-commerce sales, end-of-year inventories, purchases, gross 

margins, and total operating expenses.

Level of detail: Sales and operating expenses are reported at the sector,

subsector, and industry levels, with some 5- and 6-digit detail. End-of-year

inventories, purchases, and gross margins are reported at the sector and

subsector levels with some 4- and 5-digit detail. E-commerce sales are

reported at the sector and subsector levels and one 4-digit industry detail. 

Annual Wholesale Trade Survey (AWTS) (NAICS 42) 

Representation: Wholesale trade establishments in the United States; no 

establishment detail collected. 

When released: About 14 months after close of reference year. 

Variables: Sales, e-commerce sales, end-of-year inventories, purchases, gross

margins, total operating expenses, and commissions.

Level of detail: Sales, purchases, gross margins, e-commerce sales, endof-year-inventories, and operating expenses are reported at the sector,

subsector, and industry levels, with some 5-digit detail. Commissions are

reported at the industry level for electronic markets, agents, and brokers. 

Services Annual Survey (SAS) (NAICS 22, 48-49, 51, 52, 53, 54, 56, 61, 62, 

71, 72, and 81)

Representation: Service industry establishments in the United States; no

establishment detail collected. Of special interest to the study of retailrelated industries are warehousing and transportation (48, 49).

When released: No later than 13 months after close of reference year. 

Variables: Revenue, sources of revenue, total and detailed operating expenses

by product.

Level of detail: Total revenue and total expenses are reported at the 2-, 3-, and

4-digit NAICS code levels, with some 5- and 6-code detail. For NAICS 48

and 49, detailed operating expenses are published for most 2- and 3-digit

NAICS codes (for transportation this includes Purchased Freight Transportation), sources of revenue are provided for a number of transportation

categories, and revenue sources by commodity handled are provided for

Truck Transportation (484). 
 

 

50 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

information measured at different frequencies. This is a potential source of 

measurement error, including mismatches between the price deflators at the 

product-group level and the measured nominal gross margins. 

The sampling units for ARTS, AWTS, and SAS are enterprises that 

are asked to report for the aggregates of their retail, wholesale, or services 

establishments (respectively) plus the auxiliaries that support those industries. The only exception is that enterprises reporting on ARTS are asked 

to exclude auxiliaries from their retail sales aggregates. The enterprises are 

asked to break out the industry detail if, for example, an enterprise with 

retail activity has establishments in multiple retail industries. If enterprises 

do not provide this detail, their allocation to an industry category is based 

on administrative data and the Economic Censuses. 

The Census Bureau’s Economic Census and Related Surveys 

As illustrated in Box 3-6, the Economic Census provides measures of 

gross sales, payroll, first quarter payroll, and number of employees every 

5 years. Data are collected at the establishment level and are available at the 

U.S. level with 6-digit NAICS code detail as well as at the product code level 

(the Business Expense Supplement to ARTS and AWTS provides detailed 

expense data at the enterprise level every 5 years). The Commodity Flow 

Survey, sponsored by the Bureau of Transportation Statistics and collected 

by the Census Bureau, is also conducted every 5 years to describe domestic 

freight shipments by establishments in the mining, manufacturing, wholesale, auxiliaries, and selected retail and services trade industries located in 

the 50 states and the District of Columbia. The Commodity Flow Survey 

is of potential importance to this project because data reported on a transported commodity may indicate whether the shipment is for retail (e.g., TVs 

or clothing) or wholesale (e.g., jet engines or elevator assemblies). 

Differences in the unit of observation and the range of data collected 

in ARTS, relative to that collected in the Census for Retail Trade (CRT), 

create limitations in estimating gross margins at detailed industry levels at 

an annual frequency. The data in ARTS are collected at the enterprise level, 

which limits the level of detail directly available from the survey data (although the Census Bureau requests large multi-units in multiple industries 

within retail to break out industry detail). Given these limitations, the data 

released from ARTS combines information directly from the survey data 

with adjustments from the Economic Census.16 This is an imperfect process 

in a number of ways. For example, the release of the 2018 Annual Retail 

Trade data in February 2020 uses adjustments from the 2012 Economic 

Census, because the 2017 Economic Census tabulations were not yet 

16See https://www.census.gov/data/tables/2018/econ/arts/annual-report.html. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 51 

BOX 3-6 

The Census Bureau’s 5-Year Surveys

(conducted in years ending in “2” and “7”) 

Economic Census 

Representation: Establishments covering most industries and all geographic

areas of the United States. 

When released: First release typically 18-24 months after the close of the

reference year.

Key variables: Number of firms, number of establishments, sales, annual payroll, first quarter payroll, and number of employees. These are provided for

most industries, including retail (NAICS 44-45), wholesale (NAICS 42), and

transportation and warehousing (NAICS 48-49). Wholesale trade tables

also include total operating expenses.

Level of detail: At the U.S. level, tables provide six-digit NAICS code detail and

select 7- and 8-digit details. 

Business Expense Supplement (questions added to ARTS and AWTS during 

economic census years)

Representation: Establishments in retail and wholesale trade industries in the 

United States. No establishment detail collected. 

When released: About 15 months after close of reference year with ARTS

data and about 14 months after close of reference year with AWTS data.

Key variables: Detailed operating expenses.

Level of detail: Reported at about the same level of detail as purchases and

gross margins for ARTS. Reported at the sector, subsector, and industry

levels, with some 5-digit detail for AWTS. 

Commodity Flow Survey (sponsored by the Bureau of Transportation Statistics

and conducted by the U.S. Census Bureau)

Representation: Establishments engaged in domestic freight shipping. 

When released: 2017 data tables first released July 2020. 

Key variables: Type of commodity shipped, origin, destination, value, weight,

mode of transportation, distance shipped, and ton/miles.

Level of detail: Data provided for 48 industry groups, including 18 in wholesale,

2 in retail, and 1 in services. Some detail is also offered for auxiliaries that 

are also shippers. 

available for these tabulations.17 In addition, while ARTS collects information on gross margins, the Census for Retail Trade does not, which makes 

combining information from that census and ARTS more complicated. A 

17This discussion is related to the construction of the input-output accounts by BEA. The 

2012 Economic Census is the most recent Economic Census data in the BEA input-output 

accounts used to produce 2020 statistics on economic activity. 
52 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

related problem is the computation of margin prices from the PPI to match 

the gross margin measure of output by industry. The changing product mix 

within different types of retailers is captured only once every 5 years via the 

Census for Retail Trade. Margin prices on different products and by outlet 

type vary, but the changing product mix and outlet type are not well captured in the annual, quarterly, and monthly surveys of retail trade activity. 

Auxiliaries 

The Economic Census collects information on auxiliaries, also called 

enterprise support establishments, for six industries in the services sector: 

NAICS 48-49 (Transportation and Warehousing), NAICS 51 (Information), 

NAICS 54 (Professional, Scientific and Technical), NAICS 55 (Management 

of Companies or Enterprises), NAICS 56 (Administration and Support 

and Waste Management and Remediation), and NAICS 81 (other services 

except public administration). Information about auxiliaries in these six 

industries was collected in 2012 and 2017. Included are data by 3-digit 

sector served, number of establishments, number of employees, payroll, and 

external sales or revenue receipts. In the Economic Census an auxiliary is 

tabulated in two places: under its own establishment NAICS code and in 

the tabulation of auxiliaries.18 

The Economic Census auxiliary questionnaire is sent to establishments 

that are marked as auxiliaries in the Business Register. New auxiliaries 

are so designated based on analyst research and other information. The 

questionnaire obtains information for each auxiliary establishment about 

the main industry that it serves, classified according to 3-digit NAICS code. 

Using this classification, Fort and Klimek (2018) determined that 20 percent of the payroll related to the retail industry in 1997 was represented 

by employment in nonretail auxiliary establishments that primarily served 

the retail sector.19 Most of this auxiliary payroll—15 percentage points— 

relates to management (NAICS 551114). The other significant industries 

represented in auxiliaries are Warehousing and Storage (NAICS 493, 2% 

of payroll), Truck Transportation (NAICS 484, 1%), Accounting Services 

(NAICS 5412, 1%), and Unclassified (1%). 

BEA includes the Census Bureau’s information about auxiliaries in 

its national accounts and other products. Because an auxiliary serves its 

18Information on Census Bureau data on auxiliaries provided by Edward Watkins at the 

workshop. 

19When calculated using employment rather than payroll, the auxiliary portion represents 

only 7 percent of employment related to the retail industry in 1997. This auxiliary portion of 

employment is substantially lower than the auxiliary portion of payroll because employees in 

the establishments related to firm management are compensated at a higher rate than those 

in many other establishments. 
 

 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 53 

enterprise, it does not report output data related to its support functions 

on the census. Any profits or earnings that accrue because of the contribution of auxiliaries to their enterprise are not captured in current data. BEA 

measures the service outputs of auxiliaries using data on expenses.20 

Table 3-2 shows the Census 2012 data on auxiliaries in the six sectors 

that report that they serve the retail trade (by 3-digit NAICS code). The 

table shows that in 2012, 73 percent of the auxiliaries serving retail trade 

were in Administration and Support and Waste Management and Remediation, and 23 percent were in Transportation. 

Those auxiliaries reporting that they serve a retail firm seem to be exactly the type of establishments that should be included in a retail-related 

satellite account, regardless of where they are classified. However, data 

on auxiliaries or support establishments are currently collected and available through the Economic Census only for establishments classified in six 

industries. Other information about such service establishments may be 

available in the microdata available at the Census Bureau. 

The panel proposes that a study should be undertaken, using the 

Census Bureau’s microdata21 that include firm IDs as well as establishment 

data, to gain insights into how enterprises are structured and how support 

establishments might be identified. One could imagine a new public domain 

data product that uses this information to quantify support services. Additionally, the study could inform further work on improving the collection 

of data on auxiliaries and possibly improving information to estimate the 

value they provide to their enterprises. 

CONCLUSION 3-7: Data available from the Economic Census and the 

Economic Surveys for the retail trade-related industries limit the ability 

to estimate output for retail-related industries in important ways: 

•	 Purchase data are needed to compute gross margins, but the 

only purchase data for retail are collected on the Annual Retail 

Trade Survey (ARTS), not the Economic Census. As a result, 

purchase data are not available at the establishment level for 

retail establishments, so benchmarking to the Economic Census 

requires assumptions that likely affect the quality of estimated 

gross margins. 

•	 ARTS does not have the same product detail for sales as in the 

retail trade component of the Economic Census, and it does 

not request any industry breakdown of activity. However, such 

detailed data are needed to accurately and separately allocate 

20Reported by Jon Samuels during the workshop.
 

21Through a Federal Statistical Research Data Center.
 
54 

TABLE 3-2 Number of Auxiliary Establishments That Supported Retail Trade, 2012 Economic Census
 

NAICS Code of Auxiliary 

NAICS 48 51 54 55 56 81 

Served Title # Est # Est # Est # Est # Est # Est 

44-45 Retail trade 3,296 53 224 10,222 238 316 

441 Motor vehicle and parts dealers 197 0 8 492 6 16 

442 Furniture and home furnishings stores 506 2 49 357 7 5 

443 Electronics and appliance stores 92 17 10 407 41 19 

444 Building material, garden equipment and supplies dealers 191 3 7 460 13 4 

445 Food and beverage stores 710 4 10 950 6 4 

446 Health and personal care stores 129 3 5 966 12 2 

447 Gasoline stations 65 11 7 845 4 6 

448 Clothing and clothing accessories stores 254 3 40 1,717 9 161 

451 Sporting goods, hobby, book, and music stores 111 0 1 553 8 6 

452 General merchandise stores 594 8 43 2,447 30 80 

453 Miscellaneous store retailers 266 1 25 579 72 5 

454 Nonstore retailers 181 1 19 449 30 8 

Percentage of total 23.0 0.4 1.6 71.2 1.7 2.2 

NOTES: Data on auxiliaries in 2017 Economic Census not available until September 2021. 
 

 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 55 

sales and purchases to codes; their absence may affect the quality 

of estimated gross margins. 

•	 Data on operating expenses are needed to compute value added. 

Operating expenses for retail and wholesale trade establishments 

are collected as an aggregate of an enterprise’s establishments on 

ARTS22 and Annual Wholesale Trade Survey once every 5 years 

during Economic Census years. Data on expenses are not collected at the establishment level in the Economic Census. 

•	 Auxiliaries are a key concept for quantifying the impact of vertical integration in a retail-related satellite account. Though some 

data are available from the Economic Census, there are limited 

ways to estimate the value an auxiliary establishment provides 

to its enterprise, and the Bureau of Labor Statistics currently has 

limited information to designate auxiliaries that support retail. 

ADJUSTING NOMINAL OUTPUT FOR CHANGES IN PRICES 

After obtaining a measure of the nominal output of the retail sector, 

that estimate must be adjusted to remove the effect of price changes to 

identify the real changes in the output of the sector. This step is crucial for 

determining productivity, because nominal output figures can be strongly 

affected by inflation or deflation, particularly in a sector undergoing rapid 

change. 

The price adjustment required will depend on the type of nominal 

output measure used: a sales revenue measure of retail output is deflated 

using the CPI. A gross margin measure of retail output is deflated using the 

PPI for the retail margin. A value-added measure of retail output is deflated 

by using the PPI for the retail margin component and other PPI indices 

for the other inputs. Similarly, the price adjustment for the various retailsupporting industries, such as warehousing and transportation, uses the PPI 

indices for those industries for a gross output measure and the analogous 

PPI indices for the key inputs for a value-added output measure. 

This section addresses both quality adjustment, which is implemented 

in current U.S. price indices, and the problem of outlet substitution bias, 

which has been discussed in the research literature but is not reflected in 

U.S. economic statistics. It then provides a brief overview of the CPI and 

PPI indices.23 

22See https://www.census.gov/programs-surveys/economic-census/data/bes.html. 23This section draws on workshop presentations by Ana Aizcorbe on the conceptual issues 

related to retail price indices, Brendan Williams on the CPI, and Bonnie Murphy on the PPI. 
56 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Quality Adjustment in Price Indices 

Adjusting for quality is a key issue in developing price indices because 

potential quality differences across similar products or services make it difficult to know whether a price difference reflects a difference in the price level 

(indicating inflation) or a difference in quality. In the context of retail trade, 

“quality” refers specifically to the quality of the retail services themselves— 

not the quality of the products sold by retailers—and relates to the kinds 

of shifts the retail sector has experienced over the past few decades. As discussed in the previous chapter, the recent changes in retail have introduced 

different kinds of retail outlets—including warehouse stores, e-commerce, 

and large retailers—that provide greater product variety and different ways 

of obtaining and learning about products. These changes reflect changes 

in the quality of the services that the sector provides, and adjusting for 

them is necessary to determine the real output of retail services. If a gross 

margin or value-added measure of output is used, the quality of retail services will be reflected in a price index for the retail margin, like the PPI. If 

sales revenue is used to represent retail output, the quality of retail services 

will be reflected as one part of a price index for the product’s overall sales 

price, like the CPI. Both types of price index are addressed here. The CPI 

is discussed first—because it is currently used by BLS and provides a more 

concrete example of the underlying concepts—but the PPI is more directly 

related to quality adjustment of retail services. 

The simplest way to account for potential quality differences is to try 

to eliminate them by focusing price comparisons on products or services 

that are identical. When feasible, this strategy eliminates the confounding 

of price and quality because quality has been fixed. 

The strategy of focusing on price changes for identical products is the 

starting point for the price comparisons used to construct the CPI itself, 

which collects price information across a broad range of consumer goods. A 

sample of products with specific characteristics is selected at a specific retail 

outlet, and the prices of those sampled products are collected monthly for a 

4-year period and then rotated. To the extent possible, the exact same items 

are sampled over the entire period. If a selected item becomes unavailable, 

it is replaced with a comparable item, to the extent possible. 

A similar approach to control for quality is used for the price information collected for the PPI for retail, which looks at changes in the average 

margin price (the average difference between the sales revenue and cost of 

goods per product) within “comparable product lines” of “related products that are distributed under a similar set of conditions.”24 The standard 

24The quotes in this paragraph come from Bonnie Murphy’s presentation at the workshop 

describing the guidelines for the PPI for retail. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 57 

guidelines for identifying the products that will be considered together call 

for products that are “classified within a single product category,” “displayed and/or marketed in a similar manner,” and “located in the same 

area or department of the store.” By controlling these different factors that 

can affect the nature of the retail services offered, the PPI for retail aims to 

provide margin price comparisons that essentially hold constant the quality 

of the retail services provided. 

Of course, there are cases when it is not possible to keep quality fixed 

and it is necessary to explicitly adjust for quality differences when comparing prices. These problems are not new conceptually: there are welldeveloped techniques—specifically, hedonic price indices25—that derive the 

price differences associated with different product features related to quality. 

When it is not possible to find an identical or comparable item for a 

sampled product, BLS generally either imputes the prices of the missing 

items from other available observations in the sample or uses hedonic 

methods to estimate the previous period price for a replacement product.26 

In two important cases—computers and motor vehicles—the price adjustment is carried out using information on wholesale component costs and 

markups to derive a comparable price for the now-unavailable sampled 

product from the price of a similar but noncomparable product that is 

available. 

Explicit quality adjustments using hedonic price techniques are not a 

standard feature of the PPI program that collects average margin prices 

for retail, although there was a program that developed a hedonic margin 

price model to adjust for the quality of retail services at beer, wine, and 

liquor stores.27 Over a 12-year period, the program collected data related 

to the retail services of individual stores, including square footage, number 

of checkouts, number of full-time employees, number of products carried, whether product testing was conducted, whether classes were offered, 

and whether local deliveries were offered. This information was used to 

develop a model that related changes in margin prices to these characteristics. Unfortunately, the model did not show a meaningful relationship 

between these indicators of the quality of retail services and the change in 

the margin prices. Despite this failure to use hedonic price techniques to explain retail service quality changes within individual retail outlets, hedonic 

techniques have significantly improved the measures of important product 

25Hedonic price indices analyze price changes for changing consumer products by estimating 

prices associated with each product’s different characteristics. The price changes for the different characteristics can then be used to estimate the overall change in price for the product 

while controlling for the shifting characteristics. 

26Aghion et al. (2019) argue that using prices from available observations is likely to overstate the price of missing (discontinued) items and substantially bias the CPI. 

27This program was described by Bonnie Murphy in her workshop presentation. 
58 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

classes—such as computers (Berndt, Griliches, and Rappaport, 1995)—and 

they could be important in understanding the differences in the quality of 

retail services across retail outlets. However, that shift raises the challenge 

of estimating price indices when there are shifts in consumption across 

retail outlets, which is addressed in the next section. 

Outlet Substitution Bias 

As noted above, the techniques for estimating price indices for both 

retail products and services—CPI and PPI, respectively—use samples of 

product or margin prices for individual outlets that are combined using 

fixed weights across outlets. However, one important aspect of the economy 

is the ability of consumers to move from one supplier to another, and the 

dynamic nature of the retail sector suggests that this type of change is 

clearly important in retail. When consumers shift their purchases in this 

way, the resulting changes in aggregate price indices for the retail sector 

are particularly important to reflect. The bias introduced in the price index 

when this shift is ignored is known in the research literature as “outlet 

substitution bias” (Reinsdorf, 1993).28 

Outlet substitution bias was originally studied in the context of the 

earlier wave of retail transformation involving the rise of warehouse clubs 

and supercenters, which was discussed in the preceding chapter. These discount stores often sell the same products as more expensive stores but at 

generally lower prices. The effect of these stores on price level comes from 

the opportunity for consumers to switch from a regular store to a discount 

store to buy the same products for less. The major effect on average prices 

comes from the “outlet substitution”—consumers moving from one retail 

outlet to another—and not from the smaller price changes occurring at the 

discount stores themselves. To the extent that e-commerce products are 

cheaper than their in-store counterparts, e-commerce would pose a similar 

outlet substitution effect on the average price level. 

Outlet substitution poses a conceptual challenge for the price indices 

currently collected by BLS, which focuses on price changes for similar goods 

and services at individual retail outlets. These BLS price indices weight 

the price changes from different outlets by their relative share at different 

outlets, but the change in relative share across outlets caused by consumers 

switching from one outlet to another is not reflected in the price indices. 

This is true for both the CPI used for sales prices and the PPI used for retail 

margin prices. As a result, neither the CPI nor the PPI can describe how 

prices change—either for the overall product or for the margin price of the 

28This section draws on the workshop presentation by Ana Aizcorbe on the conceptual 

issues related to retail price indices. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 59 

retail services themselves—when consumers systematically move from regular stores to discount stores or from discount stores to e-commerce, except 

when consumers move between outlets classified in different NAICS codes. 

This problem of outlet substitution bias poses a direct conceptual 

challenge to understanding the labor productivity effects of the transformation in retail involving warehouse clubs, supercenters, and e-commerce. 

If the potential price decreases from outlet substitution are not reflected in 

the price indices, then the estimated price increases will be too large, which 

will in turn make the estimates of increases in real output too low. Since 

price indices fail to correct for outlet substitution, they effectively miss the 

important productivity effects of the recent transformation in retail. 

One solution to the problem of outlet substitution bias involves the use 

of a different kind of price index—a “unit value index”—that specifically 

looks at the change in the average price of a product or service over time 

(Nakamura et al., 2015).29 This price index explicitly reflects changes in 

the distribution of sales across different outlets, allowing it to capture the 

effect of consumers moving to less expensive stores. To do this, however, 

a unit value price index requires information about the quantity of sales 

of each sampled product at each outlet at each time period, in addition to 

information about prices. 

Transaction data from retailers could be used as a way of providing 

information about both prices and quantities of sales. (See the discussion 

below about private sector data sources.) The available transaction data 

are incomplete, with data from aggregators such as Nielsen, IRI, NPD, 

Affinity, and Palantir including aggregations from both scanners and credit 

cards but often missing key types of outlets, such as the warehouse clubs, 

supercenters, and e-commerce outlets of particular interest.30 

A new data collection effort by the PPI division of BLS is collecting 

more detailed margin price data directly from large wholesale trade companies that would provide transaction-level data on a monthly basis electronically.31 If this approach proves workable, it could provide a model for 

expanding data collection in the retail sector in a way that is easier for large 

29Another solution to the problem of outlet substitution bias would involve estimating the 

consumer utility related to the different services that retailers offer. This potential approach 

would have the benefit of also providing an analysis of the consumer benefits resulting from 

the increased variety offered by retailers. Techniques that have been developed to analyze the 

consumer utility from consumer goods (e.g., Diewert and Feenstra, 2019; Feenstra, 1994; 

and Redding and Weinstein, 2016) could be used to do this. The panel thanks an anonymous 

reviewer for pointing this out. 

30This overview of the coverage of private data sources was provided in Ana Aizcorbe’s 

presentation at the workshop. 

31This new data collection effort was described in Bonnie Murphy’s presentation at the 

workshop. 
60 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

companies to provide and with additional detail for price index estimation. 

However, the more limited coverage of this effort—likely limited to large 

companies that routinely make extensive use of electronic data systems— 

would limit its potential use for broader price indices like the CPI and PPI, 

which sample from all types of outlets. 

The lack of the necessary quantity of information in regular reporting 

raises the question of understanding the size of the bias caused by outlet 

substitution.32 This in turn raises the question of quality adjustment, which 

was discussed in the previous section. In constructing the CPI and PPI 

margin price indices, quality needs to be controlled or adjusted within an 

individual retail outlet for the products or services sampled in that outlet. 

However, to construct a unit value index across retail outlets, it is necessary 

to control or adjust quality across products or services across the full range 

of retail outlets, which could raise substantial comparability problems. 

The difficulties of adjusting for quality raise the pragmatic question 

of the extent to which the size of the bias from outlet substitution could 

be bounded by an approach that did not attempt to adjust for quality differences. Most studies agree that outlet substitution bias generally causes 

price increases to be overestimated (ILO/IMF/OECD/UNECE/Eurostat/The 

World Bank, 2004), although Nakamura and colleagues (2015) show that 

the bias can go in either direction, in principle. Moulton (2018) estimates 

that the outlet substitution effect caused by the introduction of warehouse 

clubs and supercenters in the 1990s led to an overestimate of price increases 

by only 0.08 percentage points per year. The effect of outlet substitution 

bias from the growth of nonstore retailers in recent years is likely to be 

much smaller (Hatzius, 2017), given their relatively small share of retail 

sales. In any case, it is important to remember that outlet substitution 

bias is a problem only during the transition itself, when market shares are 

changing across outlets. Of course, there is likely to be the most interest in 

productivity statistics for the retail sector precisely when market shares are 

changing across outlets. 

Federal Price Deflators 

BLS produces the primary price deflators that are used to estimate real 

output from the nominal measures of output derived from Census data. 

The PPI is a family of indexes that measure the average change over 

time in selling prices received by domestic producers of goods and services. 

Most useful in measuring productivity is the set of indices that uses NAICS 

classifications, measuring changes in prices received for an industry’s output 

32This discussion of the problem of estimating the bias caused by outlet substitution was 

provided by Ana Aizcorbe’s presentation at the workshop. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 61 

sold outside the industry (that is, its net output). The prices included in the 

PPI are from the first commercial transaction for many products and some 

services. The PPI is the primary deflator source for manufacturers’ sales and 

branch offices’ sales, for truck transportation, couriers, and messengers, 

and for warehousing. For retail and wholesale trade, the PPI measures average margin prices for narrow product groups, calculated as the difference 

between the sales price and the cost of goods sold. Calculation of the PPI 

first captures the changes in the average margin prices for individual outlets, 

and then weights those individual-outlet changes according to the share of 

each outlet. For the PPI retail and wholesale margins, the outlet weights 

are based on the margin revenue for each outlet. These outlet weights are 

updated every 5 years using margin revenue data from the Census Bureau.33 

The CPI is a measure of the average change over time in the prices paid 

by urban consumers for a market basket of consumer goods and services. 

Indexes are available for major groups of consumer expenditures (food and 

beverages, housing, apparel, transportation, medical care, recreation, education and communications, and other goods and services), for items within 

each group, and for special categories, such as services. The CPI-U-RS is 

the primary deflator source for retail trade industry sales used by the BLS 

industry program to obtain its labor productivity measures. Price surveys 

capture the changes in price of a particular item at a particular store, and as 

such do not capture price changes when a new retail outlet enters, nor when 

a service is added to the sale. The weights used to sample outlets and choose 

the number of price quotes for each outlet are determined by the reported 

expenditure shares for each item category at each outlet. These weights are 

updated when the outlet sample is revised in a staggered rotation of eight 

sampled cohorts, each lasting 4 years with one cohort being revised every 

6 months. However, any implicit shift across outlets that occurs when a 

sample is revised to reflect current sales is not reflected in average prices, 

because price changes are based on only the matched model that compares 

current and previous period prices of the same item at the same outlet.34 

In addition to these BLS price deflators, annual data from BEA’s 

National Income and Product Accounts on implicit price deflators for 

manufacturing and trade sales are used to deflate merchant wholesale 

sales. An implicit price deflator is the ratio of the current-dollar value 

of a series, such as gross domestic product (GDP), to its corresponding 

chained-dollar value, multiplied by 100. 

CONCLUSION 3-8: Although the existing price indices provide a 

way of describing price changes that occur for services and products 

33Bonnie Murphy, BLS, personal communication, October 28, 2020
 

34Brendan Williams, BLS, personal communication, October 27, 2020.
 
 

 

62 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

provided by individual retail outlets, they do not typically capture the 

aggregate price changes that result when consumers move from one 

type of retail outlet to another. For example, the price indices do not 

reflect the change in the price and quality of retail services as consumers 

move from a traditional department store to a warehouse store or to 

e-commerce, except when consumers move between outlets classified in 

different North American Industry Classification System codes. 

CONCLUSION 3-9: The price deflator for retail-sector industries 

should relate to the change in services the sector provides and changes 

in the prices and quality of those services. This differs from price 

adjustment related to the products the retailer sells, which is focused 

on the characteristics of the goods themselves. Price deflation in the 

retail sector needs to consider, for example, the shifts in services in 

moving from a traditional department store to a warehouse store to 

e-commerce, which involves changes related to such things as product 

variety and the process for identifying and obtaining goods. 

MEASURING INPUT 

Compared to the difficulties involved in measuring nominal output and 

prices, the conceptual issues related to measuring employment are more 

straightforward. There are two primary factors to address, one related to 

the quantity of labor input and the other related to its quality. Although it 

is convenient to refer to “employment” as the denominator of labor productivity, the correct concept is actually quality-adjusted hours of labor, 

reflecting the fact that different workers provide different numbers of hours 

of work (because of differences in standard hours, overtime, and paid time 

off) and labor of different quality (because of varying skill levels). During 

periods of increasing labor quality, labor inputs that are not quality-adjusted 

could be understated and could lead to overstatements of changes in labor 

productivity. Alternatively, advanced technology, such as automation or the 

use of scanner technology, may substitute for more skilled workers in some 

components of retail trade, so that there is declining labor quality. 

Obtaining hours of labor requires making adjustments to convert information on employment into hours worked. These adjustments need to 

reflect data related to standard hours, overtime, and paid time off, in addition to information related to part-time employees. Box 3-7 describes BLS 

employment data. 

Adjusting for labor of differing quality could be done in a variety of 

ways, using different empirical approaches to account for different skill 

levels. In practice, however, only crude measures of skill are available 

across the entire labor force—primarily the proxies of education, age, and 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 63 

BOX 3-7
 

BLS Data on Employment and Hours

for Trade-Related Industries
 

Current Employment Statistics (CES)

Representation: Monthly survey of U.S. establishments covered by Unemployment Insurance. Data are collected for the pay period that includes the

12th of the month. 

When released: Generally, the third Friday following the week that includes

the 12th of the month. 

Key variables: Nonfarm employment series for all employees and production

and nonsupervisory employees. CES also produces average weekly hours

(AWH) for all employees and nonsupervisory employee hours; these are

hours for which pay was received.

Level of detail: Aggregation to CES-defined major industry sectors with detail

at the 3- or 4-digit NAICS code level, with some 5- and 6-digit detail. 

National Compensation Survey

Representation: Private industry and state and local government workers

in U.S. establishments with nonfarm payrolls covered by Unemployment

Insurance. Data are collected for the pay period that includes the 12th day

of the month for the reference periods of March, June, September, and

December. 

When released: At the end of the month following the reference month. 

Key variables: Hours worked (excludes leave, etc.); hours paid.

Level of detail: 3-digit industry level. 

Current Population Survey

Representation: Monthly survey of U.S. households. Data are usually collected

for the week that includes the 12th of the month. 

When released: Generally, the third Friday following the week that includes

the 12th of the month. 

Key variables: Employment and hours worked for supervisors, nonsupervisors,

the self-employed, and unpaid family workers.

Level of detail: A Census-defined industry coding system with 270 categories

that maps to NAICS codes or aggregates of NAICS codes. 

Computing Key Employment Variables from Survey Data

Hours worked for nonsupervisory payroll employees (total annual) = nonsupervisory AWH paid (CES) x [hours worked/hours paid ratio (NCS)] x

nonsupervisory employment (CES) x 52 weeks.

Hours worked for supervisory payroll employees (total annual) = nonsupervisory AWH paid (CES) x [supervisor/nonsupervisory hours worked ratio

(CPS)] x [hours worked/hours paid ratio (NCS)] x supervisory employment

(CES) x 52 weeks.

Total hours worked = hours worked for payroll (nonsupervisory + supervisory)

employees + hours worked self-employed (CPS) + hours worked unpaid

family workers (CPS). 
 

64 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

compensation.35 As noted above, BLS measures labor quality for the multifactor productivity program using data on average wage rates for different 

groups of workers defined by differences in educational attainment, age, 

and gender. The shift in the labor force distribution across these different 

categories, resulting in a shift in the weighted average wage rate across 

the labor force, is then used to adjust the number of hours worked by the 

change in quality. 

One challenge in implementing a broader retail satellite account is 

likely to be in allocating employment (or hours worked) into retail-related 

and nonretail-related for some NAICS codes. It is likely that this will 

require new data. 

A final issue regarding the measurement of employment concerns properly accounting for outsourcing, that is, an establishment’s use of workers 

from temporary agencies or a professional employer organization that 

has its own NAICS code. Professional employer organizations are under 

NAICS 56–Administrative and Support—one of the codes related to auxiliary establishments. Outsourcing makes it difficult to link the workforce 

to the sector in which the work is being done. Outsourcing has become 

common for some firms. For example, there are some Walmart distribution 

centers that are in-house warehouses with all workers outsourced. Although 

some data are available through the Census Bureau, this is another potential 

data gap that will require investigation. 

Federal Data and Issues 

Employment Statistics 

The primary federal statistics for input (employment or hours worked) 

are collected by the BLS on an establishment basis through its Current 

Employment Statistics Survey and the National Compensation Survey (see 

Box 3-7). The Census Bureau collects data on employment and payroll (not 

hours worked) by establishment through its annual Company Organization 

Survey (see Box 3-8) and through the Economic Census. 

CONCLUSION 3-10: While hours worked is considered to be the 

appropriate measure of input for measuring labor productivity, it is 

improved when work hours are adjusted to reflect the quality of work 

provided by workers with different skill sets. Current Bureau of Labor 

Statistics’ approaches adjust for worker quality by looking at pay 

35New research by Acemoglu and Autor (2011), Autor (2013), and Acemoglu and Restrepo 

(2018) suggests that task-based measures of job-specific skill may be more useful measures of 

labor quality, which would require additional data sources. 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 65 

BOX 3-8 

Census Bureau’s Other Important Annual Survey

(data not directly published) 

Company Organization Survey

Representation: Multi-establishment firms that report for their establishments.

The main purpose is to maintain the Census Bureau’s Business Register.

When released: For use within the Census Bureau. 

Key variables: Details about firm ownership, whether firm leases 50% or more

of its workforce from a professional employer organization, lists of establishments with establishment-level data on payroll, number of employees

by pay period, activity code that “best describes” the activity, and principal

products or services. The data are used to maintain the Business Register

and provide key source data for County Business Patterns reports and

other statistical series. 

differences across groups of workers defined by difference in educational attainment, age, and gender. However, the retail transformation 

is bringing substantial changes to the workforce, with large increases 

in workers with high-end programming and data analysis skills that 

support e-commerce. New research in labor economics is investigating 

ways to measure the skill shifts related to such changes by looking at 

changes in the tasks involved rather than the educational attainment, 

age, and gender of the workforce. 

ADDITIONAL DATA SOURCES 

In addition to the primary data sources described previously, federal 

data sources also include secondary data products: estimates prepared by 

federal agencies to illuminate economic concepts such as productivity and 

the national accounts. Two of these, the BEA Industry Accounts and the 

BLS BEA Integrated Industry-Level Production Account, are summarized in 

the first two sections below. The third section describes private and other 

non-federal data sources, including commercially produced data for purchase, data derived from web-scraping, and credit card data. 

BEA Industry Accounts 

BEA’s industry economic accounts, which are presented both in 

an input-output framework and as annual output by industry, provide 

a detailed view of the interrelationships between U.S. producers and 
 

See

See

66 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

users and the contribution made to production across industries.36 Data 

products include 

•	 GDP by industry, which measures industries’ performance and 

their contributions to GDP; 

•	 gross output by industry, principally a measure of sales or revenue 

from production for most industries, although it is measured as 

sales or revenue less cost of goods sold for margin industries like 

wholesale and retail; 

•	 input-output accounts, a data set showing how industries interact 

with each other and with the rest of the economy; and 

•	 employment by industry, which measures the nation’s number of 

full- and part-time workers as well as the self-employed. 

BEA provides further detail about its input-output accounts:37 Supply 

tables show the total value of goods and services available in the domestic economy, including those produced by foreign as well as domestic 

industries. Use tables show how the supply of goods and services is used, 

including domestic purchases by industries, individuals, and government 

and exports to foreign purchasers. Requirements tables summarize the full 

supply chain by showing how production relies on both direct and indirect 

inputs. For example, flour is a direct input for a baker, while wheat (used 

in the production of flour) is an indirect input for the same baker. The requirements tables can be used to analyze the economic repercussions of a 

natural disaster or other event that changes spending patterns. 

Of special interest to this study are the input-output tables, released 

in November of each year. The input-output accounts are represented in 

detailed tables showing how industries interact with each other and the rest 

of the economy. The input-output data, which provide information on 71 

industry categories (including 4 in retail, 1 in wholesale, 7 in transportation, and 1 in warehousing), are updated each year. Detailed benchmark 

input-output statistics, which are further subdivided into 405 industries, are 

produced roughly every 5 years. 

BLS-BEA Integrated Industry-Level Production Account (KLEMS) 

During the panel’s workshop, Jon Samuels (BEA) told the panel that 

the new BLS-BEA Integrated Industry-Level Production Accounts (ILPAs)38 

36 https://www.bea.gov/data/economic-accounts/industry. 37 https://www.bea.gov/resources/learning-center/what-to-know-industries. 38A reference for the official BEA-BLS ILPA work is here: https://www.nber.org/papers/ 

w22453.pdf. BEA-BLS have done research work to extend the account back to 1947. See 

https://www.sciencedirect.com/science/article/pii/B9780128175965000111. 
See

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 67 

were designed with the intention of capturing innovation and the importance of the trade sectors in aggregate productivity growth (Eldridge et al., 

2020; Jorgenson, Ho, and Samuels, 2016). These productivity measures use 

as the numerator the BEA output measures (gross output and value added), 

which are consistent with GDP. They make use of the KLEMS approach 

to compute the input measures. Samuels cited work by Jorgenson, Ho, 

and Samuels (2016) as describing the approach that has been incorporated 

into the new BLS-BEA account, but noted that the paper only covers 1987 

forward. At a minimum, these accounts may provide information useful to 

this project, with any proposed satellite account building on this prior joint 

work, rather than duplicating it. 

As described by BEA,39 

the ILPA is an ongoing collaboration between BEA and BLS to measure 

disaggregated prices and quantities of industry outputs and inputs consistent with accounts that measure GDP by industry. The ILPA account 

includes information on 63 industries that span the total economy. One of 

its main advantages is that on the input side it is based on disaggregated 

measures, including about 170 different types of workers by industry (to 

account for skill mix across industries) and about 100 types of capital 

assets, including inventories and land (to account for differences in marginal productivities of capital assets). It also uses all the detail on intermediate inputs that underlies BEA annual GDP by industry accounts. This 

input detail allows for more accurate measures of multifactor productivity 

growth by industry.40 

Growth accounting (Jorgenson and Griliches, 1967) provides a method 

for using the estimates in the integrated industry-level production account 

to estimate how factors of production contribute to aggregate economic 

growth. Gollop, Fraumeni, and Jorgenson (1987) showed how to do this 

at the industry level, and this account uses that basic approach. 

The integrated industry-level production account decomposes growth 

in industry gross output into contributions from growth in intermediate 

inputs, capital, labor, and multifactor productivity. Similarly, the account 

decomposes growth in aggregate economy value added into the separate 

contributions from industries’ growth in capital, labor, and multifactor 

productivity. Data on gross output and intermediate inputs by industry are 

drawn from BEA statistics on GDP by industry, while data on capital and 

labor inputs come primarily from the BLS productivity program. Total capital and labor compensation by industry are controlled to match estimates of 

value added by industry from BEA. Labor, capital, and intermediate inputs 

39 https://www.bea.gov/resources/learning-center/what-to-know-industries. 40See https://apps.bea.gov/scb/2020/04-april/0420-integrated-industry-level-production.htm. 
68 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

are adjusted to account for changes in composition over time. Growth in 

multifactor productivity is defined residually as the difference between 

industry output growth and the sum of the share-weighted growth in industry inputs of intermediates, capital, and labor. 

Data are provided under the following headings: sector (21 NAICS 

codes), summary (71 codes), underlying summary (138 groups), and detailed (405 groups, but only available every 5 years). For retail, summary 

includes three 3-digit codes plus one aggregate for retail and six 3-digit 

codes for transportation. The underlying summary tables include 11 additional codes for wholesale, 6 additional 3-digit codes for retail, and 2 

additional 3-digit codes for transportation. The most recent data release 

occurred on March 2, 2020. 

During the workshop, Samuels noted that under the current NAICS 

classifications, retail and services are comingled; for example, 30 percent 

of Motor Vehicle Retailing is under services, as is 12 percent of Restaurant 

Food and Beverages. He noted that the total factor productivity accounts 

can be used to analyze these things, and can also be used to split the output 

of industries by commodity when there is joint production. 

Private-Sector and Nonfederal Data 

The Census Bureau’s Economic Surveys and Economic Census and 

BLS’s statistical collections, some of which were described earlier in this 

chapter, form the building blocks of the federal economic statistics program. Because of their limitations, however, particularly concerning timeliness and granularity, these surveys and collections are being augmented 

with private-sector and alternative data sources. Some of the most promising of these alternative sources are scanner data (available for purchase 

from the private sector), credit card transactions or bank data, and web 

scraping, each discussed below. 

Private retailers and manufacturers have a long history of collecting 

consumer data, often for market research purposes. Proprietary data are 

collected, owned, and made available by commercial firms. Granularity 

is among the strengths of scanner data, and some data are available on a 

weekly basis. At the same time, these data are collected for marketing or 

other purposes, are not nationally representative, and are not well documented, and store coverage is not equal across all geographic areas. 

Data originating from commercial and alternative sources provide 

information assets not available elsewhere. Attributes may include timeliness, 

granularity, geographic distribution, longitudinal information, and cross-time 

measures. At the same time, hurdles to the use of commercial and alternative 

data sources include access issues; bias in coverage and representation; perpetually dynamic algorithms; lack of documentation and transparency; fake 
MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 69 

data and bots; limited scope of organic data sources; and privacy concerns. 

One of the challenges with the use of any outside data source is determining 

its quality and coverage, which are key to understanding how the data can 

best be used. (See NASEM [2020], pp. 76-79, for more detail on challenges.) 

Scanner Data 

Horrigan (2013) reported that BLS has explored the use of scanner 

data for many years. The most extensive use he reported was undertaken 

as comparative research between the CPI and the scanner and associated 

household panel data from Nielsen. There are two types of scanner data 

available: data that originate from retail establishments (retail scanner 

data, such as InfoScan, IRI Worldwide) and data that originate from 

consumers (household panel and scanner data, such as IRI Worldwide’s 

Consumer Network and Nielsen’s Homescan). 

The following discussion of scanner data draws heavily on NASEM 

(2020, pp. 68-70). Retail “scanner data capture transactions for purchased 

products with a Universal Product Code (UPC) on their labels, as well as 

random-weight products such as fruits and vegetables” (NASEM, 2020, 

p. 68). The retail data include store information, including store name and 

corporate parent, address, and retail outlet type. “Granularity is among 

the strengths of scanner data” (NASEM, 2020, p. 69), some of which are 

available on a weekly basis. For each consumer store purchase, “scanner 

devices can detect and record exactly which products are purchased, the 

number of items, total dollars spent after discounts (if any), and total gross 

amount (before discount)” (NASEM, 2020, p. 68). With this information, 

“researchers can infer the average price paid as the ratio between dollars 

spent and units purchased, since many retailers do not share individual-level 

purchase prices with the data aggregators (Nielsen and IRI) but prefer to 

share average prices within a store or across geographic areas. This means 

the price data are not individual prices but are averages” (NASEM, 2020, 

p. 68). 

“From the perspective of the firms IRI and Nielsen, store data are seen 

as a census. Whether or not this is accurate, their methods do not treat these 

sales data as a sample, and data available for purchase may include only 

those stores that have agreed to share their data. Infoscan, for example, 

does not include all large retailers (e.g., Costco is not included)” (NASEM, 

2020, p. 68, footnote 32). 

“The National Consumer Panel, a joint venture by Nielsen and IRI, is 

used by both these firms in their household panel data products. It comprises 

more than 120,000 households, which provide information on their demographic characteristics in addition to purchase information (NASEM, 2020, 

p. 70). “Unlike the retail scanner data collected at check-out, household 
From

70 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

scanner data are collected using hand-held scanning devices provided to 

participating households or using a mobile cellphone app. In this way, 

purchases can be captured for the panel of households. Again, this source 

includes products with barcodes” (NASEM, 2020, p. 70). 

The Economic Research Service (ERS) uses the Nielsen and IRI data 

(both retail scanner data and household consumption and scanner data) in 

addition to other purchased data as an integral part of its Consumer Food 

Data Program (NASEM, 2020). Brent Nieman and Joseph Vavra (2019) use 

Nielsen Homescan data to investigate changes in consumer shopping over 

the last 15 years. Scanner data and credit card transactions data have the 

potential to improve price indices by adjusting for the long lags between 

incorporation of the Economic Census data into the Census Bureau’s data 

program. Scanner data, in particular, are especially rich and available for 

the retail trade sector. 

Credit Card Transactions, Bank Data, and Payroll Processer Microdata 

One of the more exciting new applications of credit card data to 

improve the timeliness of estimates is BEA’s advance estimates of GDP 

for 2020, undertaken to try to capture the impact of COVID-19 on the 

economy. As noted in news releases, advance estimates are based on source 

data that are subject to updates. Much of the data used in these advance 

estimates is from the monthly and quarterly surveys that are part of the 

Census Bureau’s Economic Surveys. However, BEA also reported that its 

“assumptions were based on a variety of sources, most notably: private 

high-frequency credit card transactions data to better capture shifts in 

consumer spending, news reports on reopenings, and industry and trade 

association reports, that include volume data, such as health care patient 

visits and traveler throughput. More information on the source data and 

BEA assumptions that underlie the second-quarter estimate is shown in the 

‘Key Source Data and Assumptions’ table on the BEA Website.”41 

Cajner and colleagues (2018) show that high-frequency private payroll microdata can help forecast labor market conditions, noting that 

payroll employment is the most reliable real-time indicator of the business 

cycle. In their example, they demonstrate that using payroll microdata 

substantially improved forecast accuracy for current month employment 

and revisions to the BLS Current Employment Statistics. 

Using anonymized transactions data from a large electronic payments 

technology company, Aladangady and colleagues (2019) created daily estimates of retail spending at detailed geographies. When aggregated to the 

national level, they found that these estimates had a pattern of monthly 

41 https://www.bea.gov/sites/default/files/2020-07/tech2q20_adv.pdf. 
See 

MEASURING RETAIL EMPLOYMENT AND LABOR PRODUCTIVITY 71 

growth rates similar to that found in the official Census statistics. The daily 

estimates were available a few days after the transactions, and the authors 

provided historical estimates from 2010. They suggested that such daily 

estimates might be particularly useful during times of stress, such as hurricanes. Other examples include Mian, Rao, and Sufi (2013) using credit 

card company data, and Farrell and Grieg (2015) using accounts from a 

large bank. 

In most of these studies, the source data were purchased or use of the 

data was granted through agreements. Collaborations between the government and the larger Internet-related companies in private industry, many of 

which have assembled massive data sets, might fruitfully expand the data 

available for the study of the retail trade transformation. 

Web Scraping 

Web scraping is the practice of extracting data from websites, typically 

through the use of a software program that simulates human exploration. 

One of the best-known examples of web scraping is the Billion Prices 

Project at MIT,42 which constructed daily price indexes for several countries 

using web scraping techniques to convert posted Internet prices of products 

to create a new daily version of a consumer price index for 22 countries 

(Cavallo and Rigobon, 2016). 

The Bureau of Justice Statistics conducted a pilot project using webscraped data from online articles to try to improve estimates for arrestrelated deaths, finding that the “open-source methodology alone identifies 

the majority of law enforcement homicides, but agency surveys aid in 

identifying deaths by other causes (e.g., accidents, suicides, and natural 

causes).”43 

While web-scraping has great potential, it also creates policy challenges for federal statistical agencies. Unresolved questions include whether 

information on a company’s website can be considered publicly available 

data and how confidentiality protections should be applied. Policy to date 

has guided statistical agencies to secure permission from companies before 

web-scraping data from their websites. 

Other Sources 

Other alternative data sources include information from trade associations and commercial data on establishments. The National Establishment 

Time Series (NETS) is extracted from Dun and Bradstreet and available 

42 http://bpp.mit.edu.
 43See https://www.bjs.gov/index.cfm?ty=pbdetail&iid=6626.
 
72 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

from Wold Associates.44 The NPD Group provides retail tracking data 

including firm and store/product-level sales data.45 

CONCLUSION 3-11: Private-sector data, such as scanner data, might 

support capturing both quantities and prices of purchases to estimate 

the price effect of consumers moving between retail outlets. 

CONCLUSION 3-12: Private-sector, credit card, and payroll processing data have been used to provide more timely information about 

economic output, prices, and input that could potentially be used 

to provide more timely estimates for labor productivity in the retailrelated sector, though issues about the representativeness of those data 

will need to be addressed. 

CONCLUSION 3-13: A collaboration between the government and 

larger Internet-related private companies has the potential to vastly 

expand the types of data available to study the transformation in retail 

trade and may support detailed analysis by population subgroup. 

44See http://maryannfeldman.web.unc.edu/data-sources/longitudinal-databases/nationalestablishment-time-series-nets. 45See https://www.npd.com/wps/portal/npd/us/solutions/tracking-services/retail-tracking. 
4
 

Toward a Retail Satellite Account
 

This chapter starts with a summary of what satellite accounts are and 

then describes optional approaches that might be considered for a satellite 

account to describe the broader retail sector. It goes on to describe existing satellite accounts and how some of their features might illuminate the 

development of a retail-related satellite account. 

Information concerning satellite accounts was derived from the relevant 

literature and, most importantly, from the workshop organized by the 

panel to gather information. This included a background paper on satellite accounts in Canada produced by Philip Smith, Satellite Accounting in 

Canada (Smith, 2020). The fourth workshop session, titled, “Toward a BLS 

Satellite Account for Retail,”1 was of key importance to this discussion. 

Other sessions relevant to the discussion were the third session, “Data: 

Availability, Needs, Discrepancies and Gaps,”2 and the fifth session, “Uses 

of Bottom-up in Measuring Employment and Productivity.”3 The latter two 

sessions were also discussed in Chapter 3. 

1Discussants included Brian Chansky, BLS; Tina Highfill, BEA; Philip Smith, Statistics 

Canada (retired); and Marshall Reinsdorf, International Monetary Fund; as well as panel 

members Leonard Nakamura and Carol Corrado. 

2Discussants included Ken Robertson, BLS; Jon D. Samuels, BEA; Matthew Russell, BEA; 

Ian Thomas, Census Bureau; and Edward Watkins, Census Bureau. The moderator was panel 

member Wesley Yung, Statistics Canada. 

3Presentation by panel members Teresa Fort, Dartmouth College; and John Haltiwanger, 

University of Maryland. 

73
 
 

 

 

 

74 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

WHAT ARE SATELLITE ACCOUNTS AND HOW ARE THEY USED? 

Satellite accounts are described by Eurostat as accounts that provide “a 

framework for exploring some aspect of the economy that is linked to the 

System of National Accounts (SNA), allowing attention to be focused on a 

certain field or aspect of economic and social life in the context of national 

accounts.”4 Eurostat cites as common examples satellite accounts that focus 

on the environment, tourism, or unpaid household work. As Philip Smith 

details in his background paper, over the last three decades this method of 

accounting has gradually become popular around the globe. It first emerged 

in the 1980s as an idea, was set out formally in 1993, and was fully established in 2008 (Smith, 2020, p. 1). It is described more fully in Box 4-1. 

Smith regards the SNA as “an enormously successful framework for describing the world economy and the national and sub-national economies,” 

while noting that “no system can be all things to all people and the SNA 

certainly has its limitations” (Smith, 2020, p. 1). Its central concept, gross 

domestic product (GDP), is perhaps the best known and most widely used 

economic statistic available. Smith goes on to say: 

Satellite accounts offer a means of borrowing some of the best features 

of the international SNA while giving freedom to depart from some of its 

restrictions. Often people want to know the size of a particular activity, 

such as tourism or the digital economy, in relation to the total market 

economy. Satellite accounts provide a way of determining this. Alternative 

valuations can be adopted and the production boundary can be redefined. 

Product and industry classes can be recombined in other ways that may be 

more convenient for some purposes. Alternative (more familiar) vocabulary 

can be adopted. And all of this can be done while linking directly into the 

large, internally consistent and carefully curated databases offered by 

the SNA. (Smith, 2020, p. 2) 

Smith also cautions that “there are costs as well as benefits from moving away from the international standard SNA into satellite accounts. One 

country’s satellite account may not be easily comparable to another’s, and it 

may not be possible to aggregate satellite accounts from different countries. 

The Organisation for Economic Co-operation and Development (OECD) 

and the United Nations are working to encourage some standardization, 

but standardization takes time and also conflicts with one of the major benefits of satellite accounting: its flexibility. In addition, satellite accounts are 

4See https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Satellite_account# 

:~:text=Satellite%20accounts%20provide%20a%20framework,tourism%2C%20or%20 

unpaid%20household%20work, page 1. This link is to introductory lecture notes on satellite 

accounts by Eurostat. 
 

TOWARD A RETAIL SATELLITE ACCOUNT 75 

BOX 4-1
 

System of National Accounts
 

The System of National Accounts (SNA) is the internationally agreed standard set of recommendations on how to compile measures of economic activity.

The SNA describes a coherent, consistent, and integrated set of macroeconomic

accounts in the context of a set of internationally agreed concepts, definitions

classifications, and accounting rules. In addition, the SNA provides an overview of

economic processes, recording how production is distributed among consumers,

businesses, government, and foreign nations.

Consequently, the national accounts are one of the building blocks of macroeconomic statistics, forming a basis for economic analysis and policy formulation.

The SNA is intended for use by all countries. 

SOURCE: See https://unstats.un.org/unsd/nationalaccount/sna.asp#:~:text=. 

more vulnerable to political influence, since they often depend on outside 

financial and other support from their main clients” (Smith, 2020, p. 21). 

A recent survey administered by Statistics Canada for the United 

Nations Economic Commission for Europe (UNECE) covered more than 

80 countries and identified 241 satellite accounts. It was part of the work 

program of the Conference of European Statisticians, and its main objectives were to determine the extent of satellite accounting around the world 

and explore why and in what directions satellite accounting studies are 

increasing. According to Smith, 

The UNECE survey found that the most common topics addressed were 

tourism, environment and health. Primary reasons mentioned for developing these accounts were (1) giving greater prominence to a particular 

activity, (2) bringing more detailed statistics to an activity than are directly 

available in the core national accounts, and (3) extending the conceptual 

boundaries in the core national accounts for production, consumption 

and/or assets. (Smith, 2020, p. 2) 

It may also be useful to consider how Eurostat, the statistical agency of 

the European Union, defines satellite accounts in its online glossary: 

Satellite accounts are one way in which the SNA may be adapted to meet 

differing circumstances and needs. They are closely linked to the main 

system but are not bound to employ exactly the same concepts or restrict 

themselves to data expressed in monetary terms. Satellite accounts are 

intended for special purposes such as monitoring the community’s health 
See

76 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

or the state of the environment. They may also be used to explore new 

methodologies and to work out new accounting procedures that, when 

fully developed and accepted, might become absorbed into the main system over time. Satellite accounts can meet specific data needs by providing 

more detail, by rearranging concepts from the central framework or by 

providing supplementary information. They can range from simple tables 

to an extended set of accounts in special areas like [for example,] environment or education.”5 

An important question in setting up such an account is how to ensure 

that it is a place to experiment with new data and methodology while 

maintaining acceptable levels of error from a data user’s perspective. It is, 

by definition, a derived data product measuring selected economic concepts. 

The proposed development of a retail-related satellite account has a data 

quality objective: to improve the relevance of employment and productivity 

measures for retail. 

Another critical aspect of data quality is transparency. This argues 

for keeping users of the satellite account engaged and informed, and for 

providing them with information so that they can determine whether the 

data are fit for their use and provide feedback to further the development 

of the account. 

At the workshop, Steve Landefeld, former director of BEA, which has a 

long history of developing satellite accounts, noted that it is most important 

to address the underlying data from a statistical viewpoint, such as standard 

errors and replicability, as part of preparing such an account for production. 

In BEA’s experience, useful questions to ask in addressing this include the 

following: Can this be done in the new account with the same data quality 

as the current employment, productivity, or GDP? Does the new account 

get the trend right? If yes, does it get whether growth is high or low right? 

In its own development of satellite accounts, BEA employs the following criteria to assess data quality (see Box 4-2). 

AUDIENCES FOR A RETAIL RELATED SATELLITE ACCOUNT 

AND MEASURES OF EMPLOYMENT AND PRODUCTIVITY 

A primary concern among potential users of employment and productivity data is to be able to capture the transformational shifts in the sector, 

which is the same concern that has motivated BLS to consider a satellite 

account. Primary users of current statistics on the retail sector want to 

understand how retail productivity is changing and how it contributes 

5 https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Satellite_account#: 

~:text=Satellite%20accounts%20provide%20a%20framework,tourism%2C%20or%20unpaid 

%20household%20work, p. 1. 
 

 

TOWARD A RETAIL SATELLITE ACCOUNT 77 

BOX 4-2
 

Evaluation Criteria
 

• How close are they to BEA’s mission and expertise? 

• How relevant and useful are they to customers, experts, policy makers, business

users, investors, Congress, and the Administration? 

• How often do they need to be produced? 

• What are the resource costs to begin regular production? (And benefits relative

to core research) 

• What is the methodology and what is the availability of source data?

—Accuracy, timeliness, relevance, objectivity, and use of economic theory and

methods 

• Why should BEA rather than others produce these accounts? 

• How successfully can BEA vet and roll out the alternative accounts? 

to overall productivity; a retail-related satellite account could provide a 

perspective on these questions that better reflects the transformational shifts 

in the sector. 

These primary potential users of newly formulated estimates for employment and productivity include the following: 

•	 Monetary, administration, and congressional authorities, who rely 

on data tracking changes in retail trade productivity. Monetary 

authorities use them in making projections of sustainable growth 

when formulating monetary policy. Administration and congressional authorities use them to assess sources of growth in the 

economy for making budget projections. For example, Triplett and 

Bosworth (2004, abstract) analyzed services sector productivity, 

including communications, transportation, and the wholesale and 

retail trade, demonstrating the role of information technology in 

accelerating services sector productivity. The authors also highlighted the importance of making improvements within the U.S. 

statistical system to provide the more accurate and relevant measures essential for analyzing productivity and economic growth. 

•	 Federal, state, and local government officials, who use federal data 

to assess issues related to tax policies and government regulations. 

For example, retail trade data are key to understanding the impact 

of COVID-19 and policies to assist afflicted workers. 

•	 Professional and trade associations, which use federal data to represent the retail industry in discussions about such areas as taxation 

and regulation. 
78 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

•	 Institutional investors, which use federal data to provide a reality 

check on the short- and long-term prospects for an industry. 

•	 Individual companies, which use federal data on retail trade to provide information on industry trends as a reality check for expansions, mergers and acquisitions, and other long-term investments. 

In addition to these primary uses of the newly formulated employment 

and productivity measures, a satellite account could support more detailed 

analyses that would benefit particular users and potentially lead to longterm improvements in the relevant employment and productivity statistics. 

•	 Researchers use federal data to identify economic relationships 

that are not directly reflected in the reported statistics, such as contrasts related to firm size or between domestic and foreign-owned 

establishments. 

•	 Macroeconomists are interested in measures that capture the 

dynamics of broad sectors of the economy. 

•	 Individual retail companies may be interested in using the newly formulated measures in benchmarking themselves against the industry. 

In summary, a satellite account provides a useful and flexible mechanism for studying the expanded retail-related sector. 

CONCLUSION 4-1: A satellite account provides a framework to explore a specific aspect of the economy that is linked to the System of 

National Accounts while deviating in ways that help address important 

questions about that aspect of the economy. These deviations may 

involve grouping or valuing economic activities in different ways than 

the national accounts or providing more detailed statistics than are 

provided in the national accounts. 

DEFINING A RETAIL-SUPPORTING SECTOR 

Box 4-3 summarizes the options that were provided in advance of the 

workshop to participants in the session titled, “Toward a BLS Satellite 

Account for Retail.” These options were initially presented in BLS (2020) 

to stimulate thought about how a satellite account might be structured. BLS 

observed that other options should also be considered. 

Figure 4-1a is a diagram with a comparison of the distributional and 

retail-related sectors. It illustrates that these two options are related only 

to those NAICS codes that best describe the establishments within each 

option, not to the classification of their enterprises. Distributional is the 

simpler concept since it includes only major-sector NAICS designations. 
 

TOWARD A RETAIL SATELLITE ACCOUNT 79 

BOX-4-3 

Four Alternative Definitions of a Retail-Related Sector 

The fundamental issue raised by the transformations in retail and the BLS

charge to the panel involves the increasing integration of a range of wholesale,

warehouse, and delivery functions into the services provided by retailers: here are

four options for defining a retail-related satellite account. 

1. Distributional retail would include much or all of wholesale, retail, warehousing, and freight transportation. It might be defined as follows: All

establishments engaged in the business of distributing goods from

manufacturers, agriculture, resource extraction, and importers to users

(including both firms and final consumers). It might include the following

NAICS sectors: retail (44-45), wholesale (42), and transportation and

warehousing (48-49).

2. A retail-supporting sector would include retail trade plus some elements

of transportation, warehousing, wholesale trade, and business services

that serve retail trade firms. It could be defined as all establishments in 

retail trade and establishments in specific NAICS codes in other sectors

that primarily serve retail activities or customers. It might include retail

(44, 45) plus the more detailed NAICS codes from 42, 48, 49, and 56

that are specifically related to retail. It would likely require splitting some

NAICS codes into retail-trade supporting and other.

3. A retail-controlled sector would include retail trade establishments and 

other establishments, regardless of how classified, in enterprises primarily 

engaged in retail trade. Includes retail trade establishments (NAICS 42)

as well as other establishments in enterprises classified into retail trade.

4. Finally, an enterprise-based retail trade sector would include all establishments that are part of enterprises or firms primarily engaged in retail

trade. Includes all enterprises classified in retail trade and their establishments, regardless of how classified. 

SOURCE: Bureau of Labor Statistics (2020). 

Figure 4-1b compares the retail-controlled and enterprise-based sectors. 

Both of these options make use of the classification of an enterprise as part 

of the definition. Here enterprise-based is the simpler concept, since it includes only those establishments located within a retail enterprise. 

Discussants observed that a distributional retail definition may be 

overly broad even though it would be the most straightforward to implement from a data point of view. However, starting with a distributional 

account and moving toward a version of retail-supporting may be a good 

strategy, because starting with the simplest option helps to inform next 

steps and develop expertise. It could be called a case study to get initial 
 

 

80 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

FIGURE 4-1a Comparison of distributional and retail-supporting industry codes.

FIGURE 4-1b Comparison of retail-controlled and enterprise-based industry codes. 
TOWARD A RETAIL SATELLITE ACCOUNT 81 

results out so that users could provide input. One difficulty of this may be 

separating passenger transportation services from goods transportation, 

particularly for air travel. 

There are three major disadvantages of retail-controlled and retailenterprise-based options. First, BLS does not have the information to 

develop enterprise-based statistics comparable to those produced by the 

Census Bureau. Second, an enterprise-based retail trade account would be 

outside the NAICS framework. Third, the sector definition would depend 

on vertical integration. Enterprise classifications, especially among complex firms, can change from year to year. This alone would make defining accounts based on enterprise classification undesirable for tracking 

trends. Additionally, discussants observed that the retail-controlled definition seemed too narrow, even though an enterprise-based definition might 

be of interest to data users. 

Data issues with these two options are due to the fact that the data 

on enterprise classifications and links to their establishments are available only for approved projects through the Census Bureau at Federal 

Statistical Research Data Centers (FSRDCs). While data are available to 

approved outside researchers, obtaining a new data product for use in a 

satellite account would require collaboration, interagency agreements, 

and time. 

CONCLUSION 4-2: None of the four options on which to base a 

satellite account is perfect as it stands; however, a definition based on 

retail supporting is closest to what is needed according to the statement 

of work and most practical. Elements of the broader option, distributional, will also need to be incorporated into the newly defined retailsupporting sector, such as auxiliary establishments and parts of other 

industries, such as computing, intangibles, leasing, and importing. 

Identifying the precise definition(s) to be used for the retail-supporting 

sector will require exploration and experimentation. 

CONCLUSION 4-3: To better understand the changes in retail-traderelated industries, a collaborative effort between the Bureau of Labor 

Statistics, Bureau of Economic Analysis, and Census Bureau staff could 

make use of microdata as a laboratory to better understand many of 

the complicated aspects of developing a retail-related satellite account. 

The purpose would be to use the concepts and data to gain a better 

understanding of key issues, such as assessing the structural changes 

associated with the retail trade transformation by size of enterprise; 

understanding the role of auxiliaries and other nonretail establishments 

within retail trade enterprises; and assessing data gaps and approaches 

to solving them. 
82 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Splitting NAICS Codes into Retail- and Nonretail-Supporting 

As described under “retail-supporting” in Box 4-3, implementing a 

satellite account will likely require estimating the portion of the output 

and the portion of the input under some detailed NAICS codes in wholesale, transportation, warehousing, and others that are retail-related versus 

nonretail-related. 

In some cases, the contrasts between detailed NAICS codes can provide an indication of whether an industry is likely to support the retail 

sector. For industries that support several sectors, such as wholesale trade 

and transportation, there is some information available about the portion 

of output directed to retail in the NAICS classification system. Four-digit 

NAICS codes for wholesale trade often differentiate between consumer 

products (e.g., groceries, furniture and home furnishing, motor vehicles, 

apparel, beer, and wine) and producer products (e.g., chemicals, farm raw 

materials, and raw metals and materials). 

A NAICS designation alone is not always sufficient to identify the 

contribution of retail-related establishments that partly support retail and 

partly support something else. For example, NAICS 481112 comprises 

establishments primarily engaged in providing air transportation of cargo 

(not passengers) over regular routes and on regular schedules. However, 

this cargo transportation may also support retail, wholesale, mail delivery, 

or something else. 

Beyond the NAICS codes of the establishments themselves, data from 

the Census Bureau’s Economic Surveys provide some information about 

the commodities an establishment deals in as part of its sources of revenue. 

Canada’s satellite accounts usually rely on Statistics Canada’s inputoutput tables. These satellite accounts typically rearrange information from 

those tables and add in additional detail from other sources. Statistical 

products based on input-output tables may have substantial delays in 

publication, so satellite accounts also need to be supplemented with more 

current indicators and projections. 

Bureau of Economic Analysis (BEA) satellite accounts are also typically 

developed from BEA input-output tables.6 These accounts are developed by 

first looking through the list of 5,000 goods and services to determine which 

are in-scope for the new project. Then BEA seeks to determine whether the 

whole commodity is within scope or only part of the commodity is. As 

6See https://www.bea.gov/resources/learning-center/what-to-know-industries. Input-output 

data are updated each year and provide information on 71 industry categories. Detailed 

benchmark input-output statistics, produced roughly every 5 years, are further subdivided into 

405 industries. Data sources include the Economic Census, including a special tabulation on 

auxiliaries, the Annual Retail Trade Survey, and other sources. The manual for input-output 

accounts is found here: https://apps.bea.gov/papers/pdf/IOmanual_092906.pdf. 
 

 

 

TOWARD A RETAIL SATELLITE ACCOUNT 83 

described in the next section (“Existing Satellite Accounts”), one example is 

bicycles for the outdoor recreation account. As determined from an outside 

source survey, 93 percent of people who buy bicycles purchase them for 

outdoor recreation, the others may be purchasing them for business use, 

such as courier work. 

Input-output tables (in particular, the use tables) provide estimates of 

the proportion of output in services such as wholesale and trucking that are 

attributable to retail and the shares of intermediate inputs of goods and services purchased by the retail trade. They also provide estimates for changes 

in these proportions over time. The BEA input-output table starts with a 

gross output measure (gross margins for trade industries and gross sales for 

other industries) and uses the relationships found in the input-output table 

to determine a value-added number and then an employment number. This 

distribution of intermediate goods and services purchased by the retail trade 

could be used to develop a definition of retail-supporting industries, though 

it might not have the full NAICS code detail needed. 

While this provides one way to identify the split between retail and nonretail output, the proportions are unlikely to be the same for the labor that 

goes into those categories. It was observed that questions have been raised 

about the net impact of e-commerce on jobs and employment. Estimating 

how many people are working in retail and retail-supporting sectors, as 

well as estimating the net change in jobs and pay, would be very helpful by 

itself and useful for measuring productivity. 

Estimation of the split between retail-related and nonretail-related inputs will likely require some creative use of alternative data sources. Some 

companies7 may have internal worker-level data about what workers were 

doing—such as handling aircraft engines versus clothing. There may also 

be trade associations of delivery firms that collect this type of information. 

CONCLUSION 4-4: The retail-supporting sector definition will likely 

require splitting the currently measured input and output for some 

North American Industry Classification System codes into retail-related 

and “other.” Options are available for splitting retail-related outputs. 

Using existing Bureau of Economic Analysis’ input-output tables as 

well as those available in the Bureau of Labor Statistics/BEA Integrated 

Labor Productivity Account may provide a start, and approaches that 

use existing data on commodities transported from the Commodities 

Flow Survey are also likely to be useful. However, this account will also 

need to develop new methods and data to estimate the split in input 

between retail-related and other, which will likely require experimentation and the development of new data sources. 

7Reported by Richard Phillips at the workshop. 
84 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

CONCLUSION 4-5: It is important to start with a relatively simple 

sector definition to develop expertise and communicate with users. 

The distributional option was mentioned as a possible starting point, 

but it was viewed as overly broad. Another useful starting point might 

be to start from the list of North American Industry Classification 

System codes to be included in the expanded retail-supporting option, 

identifying those that are entirely retail-supporting or partly retailsupporting. An account that includes only those codes that are entirely 

retail-supporting and an account that includes all codes with some 

retail-supporting activity provide upper and lower bounds for what 

might be gained by a careful development of estimates for splitting the 

input and output of industries that are partly retail-supporting. The 

distribution of intermediate goods and services purchased by retail 

trade as measured in the Bureau of Economic Analysis input-output 

tables could also be used to start the development of a definition of 

retail-supporting industries. 

The discussion of satellite accounts reinforced the importance of filling 

the data gaps identified in Chapter 3. The major new data gap identified 

during the discussion in Chapter 4 is the need to identify the proportion of 

input and output in selected NAICS codes that is retail-related, along with 

the key observation that the proportion is likely not the same for input and 

output. Developing the split for input will likely require new data sources 

and approaches. 

It is possible to start the project using the simple assumption that the 

input and output can be split using the same proportion, but it will be 

important to develop data to either confirm that assumption or to replace 

it with better estimates. 

EXISTING SATELLITE ACCOUNTS WITH
 

POTENTIALLY USEFUL FEATURES
 

BEA has prepared many of the satellite accounts in the United States.8 

Similarly, Statistics Canada has prepared most of the satellite accounts in 

Canada (which are summarized in Smith, 2020). The workshop discussion 

identified the BEA satellite accounts discussed next as having features that 

may prove valuable in the design and implementation of a retail-related 

satellite e-account. 

8See https://www.bea.gov/data/special-topics for links to the BEA satellite accounts. 
 

 

 

See

TOWARD A RETAIL SATELLITE ACCOUNT 85 

BEA states9 that it 

... developed the digital economy satellite account to better capture the 

effects of fast-changing technologies on the U.S. economy and on global 

supply chains. The project calculates the digital economy’s contribution to 

U.S. GDP and improved measures of high-tech goods and services, and it 

offers a more complete picture of international trade. Other goals are to 

advance research for digital goods and services, the sharing economy, and 

free digital content, and to explore economic measures beyond GDP to 

better understand Americans’ well-being. BEA includes in its definition of 

the digital economy three major types of goods and services: 

1.	 infrastructure, or the basic physical materials and organizational 

arrangements that support the existence and use of computer networks 

and the digital economy; primarily information and communications 

technology (ICT) goods and services; 

2.	 e-commerce, or the remote sale of goods and services over computer 

networks; and 

3.	 priced digital services, or those services related to computing and communication and that are performed for a fee charged to the consumer. 

The BEA digital economy satellite account already addresses some 

retail areas and illustrates a flexible use of sources to allocate different 

industries into digital and nondigital pieces. This account required the organization of a new sector from the ground up. The OECD has guidance for 

developing a digital economy satellite account that may be worth reviewing 

in developing a satellite account for retail trade.10 

As summarized by BEA,11 

BEA developed a set of supplemental statistics called the health care satellite account to better measure spending trends and treatment prices. This 

satellite account measures U.S. health care spending by the diseases being 

treated (for example, cancer or diabetes) instead of by the types of goods 

and services purchased (such as doctor’s office visits or drugs). At the same 

time, BEA continues to produce the traditional goods-and-services health 

care estimates that are part of its core statistics, such as GDP. Within this 

satellite account, there are two different sets of disease-based statistics. 

One version uses data from the Medical Expenditure Panel Survey, the 

only nationally representative survey that contains detailed expenditure 

information by disease. BEA calls this the MEPS Account. Because of its 

relatively small sample size, the MEPS Account produces more volatile 

estimates across years. To address this issue, BEA also produces a ‘Blended 

9 https://www.bea.gov/data/special-topics/digital-economy. 10See https://www.oecd.org/sdd/its/Handbook-on-Measuring-Digital-Trade-Version-1.pdf. 11See https://www.bea.gov/data/special-topics/health-care. 
 

86 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Account,’ which blends together data from multiple sources, including 

large claims databases that cover millions of enrollees and billions of 

claims. 

The BEA outdoor recreation satellite account “measures the economic 

activity as well as the sales or receipts generated by outdoor recreational 

activities, such as fishing and vacation travel by recreational vehicle. These 

statistics also measure each industry’s production of outdoor goods and 

services and its contribution to U.S. GDP. Industry breakdowns of outdoor 

employment and compensation are also included.”12 

The BEA outdoor recreation satellite account had to apportion many 

commodities between “recreation” and “something else,” in ways similar 

to what will be needed in the retail-related satellite account. BEA used 

about two dozen data sources to do this, including a survey that measured 

whether purchased bicycles were for recreational or business use. This 

satellite account may be especially relevant because there was no precedent 

about how an outdoor recreation sector should be defined, but there were 

strong views. BEA ended up having two definitions, one narrow and one 

broad. The outdoor recreation account might be a guide in how to allocate 

output according to input-output relationships versus outside sources. 

Finally, a new satellite account is being developed by BEA, the 

Small Business Administration, Statistics Canada, and the University of 

Pennsylvania (Highfill et al., 2020) to better track the overall growth and 

relative contributions of small business in the U.S. economy. A main challenge with this account is identifying the portion of gross output from 

manufacturing firms attributable to very small businesses. Tina Highfill, of 

BEA, highlighted this account because some users wanted enterprise-level 

statistics, which pose data challenges. While some data are available, data 

on enterprises and their establishments are only available in the Census 

Bureau’s business register, which makes them challenging to access except 

for approved projects through an FSRDC. 

Another set of satellite accounts that may provide guidance on splitting transportation NAICS codes into retail-supporting versus other is the 

Bureau of Transportation Statistics (BTS) Transportation Satellite Accounts, 

prepared by BTS in collaboration with BEA.13 

CONCLUSION 4-6: Several existing Bureau of Economic Analysis 

satellite accounts may provide useful models for developing a retail 

satellite account, given the measurement challenges posed by the 

retail transformation. The digital economy satellite account includes 

12See https://www.bea.gov/data/special-topics/outdoor-recreation.
 13See https://www.bts.gov/satellite-accounts.
 
TOWARD A RETAIL SATELLITE ACCOUNT 87 

e-commerce and digital services, which are both important aspects of 

the retail transformation. The health care satellite account involves a 

reconceptualization of health care spending, which might suggest novel 

ways to reflect the changing cost structure of retail. The outdoor recreation satellite account addresses the challenge of dividing up statistics 

from several industries to combine some of them in a new grouping 

that is useful to the field. The small business satellite account addresses 

the challenge of identifying establishments of different sizes, which may 

also be an important way to divide the data for the retail sector. 

5
 

Recommendations for a
 

Retail Satellite Account
 

In this chapter we review the information provided in the previous 

chapters and present the panel’s recommendations. The statement of task 

requested the following: a review of the issues related to measuring employment and productivity in retail-related industries, an evaluation of changes 

in the retail trade landscape and an assessment of how they are impacting 

measures of employment and productivity, and a review of the existing 

measures as well as the methodological issues surrounding measurement 

of these concepts. The panel was asked to determine “if, and how, a satellite account can be designed to capture this retail transformation”; and to 

comment on (1) the value and specifications for a satellite account for the 

retail-related sector, (2) ways to identify the proportion of output, employment, and hours outside of retail trade that are directed toward supporting 

retail trade, and (3) ways to maintain a retail-related satellite account. 

The chapter points out how this report addresses the original statement 

of task by referring back to previous chapters and providing the panel’s 

recommendations. 

The overarching recommendation is for the Bureau of Labor Statistics 

(BLS) to develop a labor productivity satellite account that will enable 

a fuller understanding and better measurement of the transformation of 

retail trade. Within this supplementary satellite account, BLS can begin 

to develop and experiment with new measures, which could feature the 

following: 

•	 Alternative concepts of output for retail trade, such as gross margins 

and value-added, that better measure the output and productivity 

89
 
 

90 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

of retail trade by focusing on the services that retail trade provides 

rather than on the products they sell; 

•	 Price indexes that better measure changes in the quality of the 

services retail trade provides rather than the quality of the goods 

they sell; 

•	 Quality-adjusted measures of labor input, beginning with those 

already in use in the integrated BLS and Bureau of Economic 

Analysis (BEA) multifactor productivity estimates; 

•	 Estimates for both the retail sector and the retail-related sector that 

capture the changing organizational structure of retail trade; and 

•	 Parallel estimates featuring currently used measures based on existing methodologies and source data (gross sales, final goods deflators, 

and unadjusted labor hours). 

The last set of estimates will be key to assessing the new indicators 

against the existing indicators in their ability to decompose and identify 

changes in labor productivity due to changes in the organization of retail 

trade, changes in the services provided by retail trade, changes in the quality 

of the goods, and changes due to increased labor quality versus labor hours. 

MOTIVATION AND OVERARCHING RECOMMENDATIONS 

The statement of task asks the panel to evaluate changes in the retail 

trade landscape, assess how they are impacting measures of employment 

and productivity in retail-related industries, and determine whether and 

how a satellite account could be designed to capture this retail transformation. The panel’s evaluation of the changes in retail trade industries is presented in Chapter 2, along with a discussion of the measurement challenges 

this transformation poses (Conclusions 2-1 and 2-2). Conclusion 4-1 summarizes some of the attributes of a satellite account that make it well suited 

to evaluate the impact of the changes observed in retail trade industries. 

One key advantage of a satellite account is its potential to enable 

experimentation with alternative concepts and more detailed and alternative definitions. In addition to providing a mechanism to study the continuing transformation of retail trade, a satellite account can suggest important 

additions to data collection and analysis to resolve data gaps. A valuable 

output of the satellite account might be an updated approach to measuring 

labor productivity for the main accounts published by BLS, one that could 

more clearly illustrate the impact of the transformation in retail trade. 

CONCLUSION 5-1: A satellite account would be an appropriate 

and useful vehicle for the Bureau of Labor Statistics to use to study 

the impact of the transformation in retail trade on employment and 
RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 91 

productivity and to develop exploratory measures that describe that 

transformation. 

Development of a retail-related satellite account is best conducted by an 

interagency team comprising staff from those agencies that have the widest 

range of expertise and skills needed to address this challenge. As described 

in Chapter 3, the needed expertise, skills, and data are spread across three 

separate agencies, including coverage of economic analysis (BEA, BLS, 

and Census), the development and use of satellite accounts (BEA), and 

the development and use of data systems and surveys that measure output 

(those of the Census Bureau) or that measure employment and prices (BLS). 

The study of the transformation in retail trade is important for a variety of 

reasons, including the fact that many of the changes in retail trade are also 

seen in other sectors. It is critically important for the government statistical system to adapt information collection and data systems to measure 

changing industries. 

Models for such a collaborative interagency approach already exist, 

including the BLS’s Collaborative Micro Productivity Project, which developed new data products (Dispersion Statistics on Productivity [DiSP]),1 and 

BEA’s Integrated BEA GDP-BLS Productivity Account.2 

RECOMMENDATION 1: The Bureau of Labor Statistics should 

develop a satellite account for an expanded retail trade sector in 

collaboration with the Bureau of Economic Analysis and the Census 

Bureau. Such a team could be formed under the Evidence-Based Policymaking Act to facilitate administrative and collaborative efforts. 

Like many ongoing systems, the U.S. Federal Statistical System has not 

been as nimble in keeping up with industry change as would be desirable. 

While keeping up with change is never easy, mechanisms are needed to help 

identify what is important to change and how. As noted in Chapter 2, the 

retail-related industry is changing fast at the margins, and the near-term 

future is likely to see substantially new changes beyond those that are currently well-known in the industry. 

Due to the COVID-19 pandemic, the diffusion of innovations like 

e-commerce is accelerating and evolving. Somehow, retail experts from the 

industry need to be involved to make sure that the measures developed 

are not just of academic interest but instead are of interest to industry as 

a whole and are designed to be sensitive to new trends. Industry also has 

access to new types of data that may provide important new measures 

1See https://www.bls.gov/lpc/productivity-dispersion.htm. 2See https://www.bea.gov/data/special-topics/integrated-bea-gdp-bls-productivity-account. 
92 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

(Conclusions 3-11, 3-12, and 3-13). Public-private partnerships or outside 

technical advisory committees might help decide how some of these measurement challenges can be addressed. One such industry-collaborative 

project was undertaken under the Federal Economic Statistics Advisory 

Committee. An older successful example was a collaboration on hedonics 

for computer products, which was developed with key inputs and analysis 

from IBM (Cole et al., 1986). The question is how to best involve retail 

industry experts to help the interagency team understand what data to 

collect, what to present, and how to incentivize industry to share its data. 

RECOMMENDATION 2: The Bureau of Labor Statistics, in collaboration with the Bureau of Economic Analysis and the Census Bureau, 

should pursue approaches to soliciting input and advice from industry and academia, with a special focus on collaboration with industry. 

Government statistics require input concerning the data and measures 

needed, both to ensure the relevancy of concepts being measured and, 

most importantly, to help government statistics keep up with the rapid 

pace of change in industry. 

DESIGN OF A SATELLITE ACCOUNT (SPECIFICATIONS) 

Road Map to a Retail Trade Satellite Account 

The panel proposes that the satellite account be based on the concept 

of a central account with modules for experimentation to address important 

side questions, data issues, and subjects on which it is difficult coming to a 

consensus. The first step is to find consensus on a central module that BLS 

(with the help of BEA and Census) could develop quickly. 

One of the first questions to answer is, “What is retail?” Though retail 

may have a current definition, it is important to consider how it should 

be defined in the future. These future-oriented ideas may be addressed as 

modules. Examples of such ideas include digital goods, such as e-books, and 

the impact of off-shoring. Adapting to the future might require ongoing case 

studies that involve firms and industry organizations as well as confidential 

studies of microdata at Federal Statistical Research Data Centers (FSRCDs). 

Several existing satellite accounts created by BEA may provide useful 

models for developing a retail satellite account, given the measurement 

challenges posed by the retail transformation. The digital economy satellite 

account includes e-commerce and digital services, which are both important aspects of the retail transformation. The health care satellite account 

involves a reconceptualization of health care spending, which might suggest novel ways to reflect the changing cost structure of retail. The outdoor 

recreation satellite account addresses the challenge of dividing up statistics 
 

RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 93 

from several industries to combine some of them in a new grouping that is 

useful to the field. The small business satellite account addresses the challenge of identifying establishments of different sizes, which may also be 

an important way to divide the data for the retail sector (Conclusion 4-6). 

The project should begin with aspects of retail that can be defined 

through a broad consensus, and should then incorporate additions and 

adaptations as new information becomes available and research is completed. 

RECOMMENDATION 3: In implementing a satellite account, the 

Bureau of Labor Statistics (BLS) and its partners should adopt an 

iterative and modular approach, starting with feasible options that 

draw upon the Bureau of Economic Analysis (BEA) industry accounts 

and the BLS-BEA Integrated Labor Productivity Account to see what 

insights these might provide about the retail sector and about feasible 

fixes. The project should provide a set of estimates in a central module, 

but also a set of submodules to investigate important side questions 

or alternative measures. It should also outline a set of studies to carry 

out over time to investigate different questions—assessing importance/ 

relevance, resources required, feasibility, accuracy, need for further 

research, source data, and benefit versus cost—and suggest possible 

improvements. 

Defining the Retail-Supporting Sector 

Of the four options discussed in Chapter 4 for defining retail— 

distributional, retail-supporting, retail-controlled, and retail enterprise— 

retail-supporting is closest to what is needed according to the statement of 

work, and it is the most practical as the basis for a satellite account. The 

retail-supporting definition will also need to be augmented with retailsupporting auxiliaries (support establishments), as well as with other 

retail-supporting industries (defined by North American Industry Classification System [NAICS] code) such as computing, intangibles, and leasing 

(Conclusion 4-2). 

It is important to start with a relatively simple sector definition to 

develop expertise and communicate with users. The distributional option 

was mentioned at the workshop as a possible starting point,3 but it was 

viewed as too broad to satisfy the statement of task. Another useful starting point might be to start from the list of NAICS codes to be included in 

the expanded retail-supporting option, identifying those that are entirely 

3Leonard Nakamura of the Federal Reserve Bank of Philadelphia observed that the distribution sector definition would be welcomed by macroeconomists who seek a streamlined view 

of the economy. 
94 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

retail supporting and those that are partly retail supporting. An account 

that includes only those codes that are entirely retail supporting alongside 

another account that includes all codes with some retail-supporting activity 

would together provide lower and upper bounds for what might be gained 

by a careful development of estimates for splitting the input and output of 

industries that are partly retail supporting (Conclusion 4-5). 

RECOMMENDATION 4: The satellite account should cover all retail 

and retail-supporting establishments, identifying these by combining 

available information from existing and enhanced data. This group 

encompasses all establishments supporting the distribution of retail 

goods to the consumer, excluding the manufacturing and importing of 

retail goods. 

Outputs, Deflation, and Inputs for Measuring Labor Productivity 

Chapter 3 reviews existing measures, commenting on their conceptual 

attributes and the methodological issues surrounding their measurement. Important aspects of conceptual and measurement issues are summarized below. 

The three definitions of nominal output considered most appropriate 

for a study of retail-related industries are gross sales for service-related 

industries, gross margins for trade-related industries, and value-added for 

all industries. Gross sales and purchases are measured on the economic 

survey appropriate to the sector. For trade industries, gross margin is equal 

to gross sales less purchases (the cost of goods sold). Because purchases are 

not published for as many detailed NAICS codes in retail trades as are sales, 

gross margin is similarly available for fewer detailed NAICS codes. Valueadded, the purest measure of output, is the most complex to compute, and 

is more limited in industry detail because it relies on measures of intermediate inputs that are less broadly available (Conclusion 3-6). 

Nominal output needs to record the changing organization of retail 

trade and supporting industries and to measure the output of the services it 

provides, not the value of the goods it sells. Gross sales in service industries 

and gross margins in trade industries are good measures of industry output, 

but they produce a misleading double-counted total over all industries. If 

a consistent total is the goal, the value-added measures of industry output, 

consistent with GDP, should be used. 

Existing price indices provide a way of describing price changes that 

occur for services and products provided by individual retail outlets. However, these indices do not typically capture the aggregate price changes that 

result as consumers move from one type of retail outlet to another (Conclusion 3-7). The price deflator for retail-sector industries should relate to the 

change in the services the sector provides and to changes in the prices and 
RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 95 

quality of those services. This differs from price adjustment related to the 

products the retailer sells, which focus on the characteristics of the goods 

themselves. Price deflation in the retail-related sector needs to consider, for 

example, the shifts in services consumers receive when they move from a 

traditional department store to a warehouse store to e-commerce. Those 

shifts, in turn, involve changes related to such things as product variety and 

the process for identifying and obtaining goods (Conclusion 3-9). 

Real output needs to be measured with a deflator that captures the 

transformation of the services that retail trade provides, including greater 

variety, efficiency of shopping (ease of price comparisons, quick and lowcost home delivery, etc.), not the increase in productivity coming from the 

goods that retail trade provides. Conceptually, the Producer Price Index 

(PPI) gross margins deflator is appropriate for deflating gross margins. 

However, ideally it needs to be adjusted for outlet bias, variety increase, 

and changes in the services provided by retailers. 

While “hours worked” is considered to be the appropriate measure of 

input for measuring labor productivity, it is improved when work hours are 

adjusted to reflect the quality of work provided by workers with different 

skill sets. Current BLS approaches adjust for worker quality by looking 

at pay differences across groups of workers defined by differences in educational attainment, age, and gender. However, the retail transformation 

is bringing substantial changes to the workforce with large increases in 

workers with high-end programming and data analysis skills that support 

e-commerce. New research in labor economics is investigating ways to 

measure the skill shifts related to such changes by looking at changes in 

the tasks involved rather than the educational attainment, age, and gender 

of the workforce (Conclusion 3-10). 

Labor input should reflect changes in education and skills accompanying the transformation in retail trade. These issues are key not only to 

measuring labor productivity but to understanding the impact of the retail 

trade transformation on productivity, automation, employment, the distribution of income, and the offshoring of jobs. However, additional work is 

needed to better evaluate the changes in the retail-related labor force and 

the skills needed. 

In summary, there are multiple potential measures of output, deflators, 

and inputs; some are currently available, whereas others will require future 

enhancements. The account should be developed with the goal of studying 

the impact of the different choices. 

RECOMMENDATION 5: The satellite account should focus on 

examining multiple measures of output, deflators, and labor input. 

Output measures should include gross sales and gross margins for trade 

industries, gross sales/revenues for other industries, and value-added 
96 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

for all industries. Deflators should include current margin deflators and 

new options that capture the changing characteristics of retail trade. 

Labor input measures should include both simple hours worked and 

quality-adjusted hours worked to capture the changes in workforce 

quality. Modules should also be used to evaluate alternative approaches 

to estimating the split between retail-related and nonretail-related for 

both output and input. 

Potential Experimental Submodules 

One of the features of a satellite account with the greatest value is its 

potential to allow experimentation with alternative concepts and more 

detailed and alternative definitions. The experimental projects noted in 

the following are just some of the studies that could be conducted using a 

satellite account. 

Alternative output measures and deflators should be compared in modules. Some of the key decisions to be made in developing a satellite account 

will be to select output measures, deflators, and input measures. Each of 

these decisions should be carefully evaluated by incorporating the alternative measures in modules. Hence, for example, there should be modules for 

comparing output measures: gross sales, gross margins for trade industries, 

gross sales for service industries, and value-added. 

Modules could be used to provide alternative aggregations, classifications, and details of interest to researchers interested in better understanding 

productivity, foreign direct investment, and wages. (Examples include data by 

size of firm, more detailed breakouts by occupation or wages, and foreignowned versus domestic-owned.) Special attention could be given to apparent 

divergences between apparently high-productivity big firms and the official 

statistics for their industries. Modules might also be used to experiment with 

new measures by making more assumptions or using uncertain data. 

Modules could be used to experiment with quality-adjusted price indexes 

that provide a measure of the real output and productivity of retail trade 

based on the characteristics of today’s “transformed” retail trade industry. 

A satellite account might incorporate modules to address products that 

cross the boundary between goods and services. It could do this by integrating statistics for related retail products that are now classified in multiple 

industries, like books, newspapers, movies, and games (which come in 

physical, audio, and digital form), music (including CDs, digital sales, and 

streaming), and cars (both sales and leases). The digital economy satellite 

account might provide a reference. 

Modules might be useful for better measuring and allocating productivity gains due to various sources, such as imported inputs, domestic IT 

products sold by retailers, and nonretail-trade support industries, such as 
RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 97 

transport. Work on extended input-output accounts and global value chains 

at the United Nations, Organisation for Economic Co-operation and Development (OECD), and other international and national statistical agencies, 

including BEA, could be helpful in understanding the role of international 

trade and investment in measuring the source of productivity chains. 

An account that could capture the many services provided by today’s 

retail trade firms and the firms that support them would be invaluable. 

These services include the broad diversity of products available at one site/ 

location; the ability to compare prices and product characteristics; and 

rapid and low-cost home delivery. A satellite account could incorporate 

estimates of consumer shopping time that would allow an integrated analysis of the labor productivity implications of the increased shopping and 

delivery options being provided by many retailers. 

Modules that help to assess and illuminate the accuracy and utility 

of employment and productivity data could be used to both update and 

identify needed improvements to these data through new research, new 

methods, and new source data. For example, new source data might include NETS, NPD, and credit card information. NPD and credit card data 

could potentially provide high-frequency data related to sales revenues and 

purchases to help prepare more timely estimates. 

RECOMMENDATION 6: Experimental submodules may address 

more specialized issues that contribute to the transition in retail trade, 

such as (1) international trade and global value chains; (2) digitization; 

(3) labor quality; and (4) providing real-time and subsector analyses. 

Over time, the central module would incorporate improvements developed in the submodules and in new data collection. 

STUDYING AND SOLVING DATA ISSUES 

Identifying and filling data gaps, correcting for errors in data, using 

data to help define the scope of the retail-related sector, and exploring the 

use of new data sources will be a major part of the effort to design and 

build a retail-related satellite account. There are data gaps and data issues 

associated with the Census Bureau’s economic surveys, with the BLS employment surveys, and some errors in productivity result from the use of 

separate business registers by BLS and Census. On a more forward-looking 

note, a study using microdata at the Census Bureau could help define the 

scope of the project, and identifying and using alternative new data sources 

might help improve timeliness and detail. These data sources are primarily 

discussed in Chapter 3 and are summarized below. 

While these are the key issues identified by the panel, they are not the 

only data deficiencies that will be identified during the construction of a 
98 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

retail-related satellite account. Identifying data gaps and data needs and 

working to improve accuracy when the data are deficient will become a 

major effort going forward. Improved source data are needed to make 

substantive progress on measuring the transformation of retail trade. 

Data Gaps for Output-Related Data (Census Bureau) 

The data available from the Census Bureau’s Economic Census and 

surveys are the foundation of U.S. economic statistics. However, data available for retail-trade-related industries are less extensive than information 

collected for other industries and significantly less extensive than the data 

available for manufacturing. Given that retail trade has become a key driver 

of the economy, it would be prudent to expand on the data available to 

measure the retail-related sector more accurately. Examples of deficiencies 

include the following. 

•	 Purchase data are needed to compute gross margins, but the only 

purchase data for retail are collected in the Annual Retail Trade 

Survey (ARTS), not in the Economic Census. As a result, purchase 

data are not available at the establishment level for retail establishments, and benchmarking to the Economic Census requires assumptions that likely affect the quality of estimated gross margins. 

•	 Product detail for retail sales is not covered by ARTS, though it 

is covered in the Economic Census of retail trade. ARTS does not 

request any industry breakdown of sales activity, and it offers no 

information on gross margins by product class. However, these 

missing data are needed to accurately and separately allocate sales 

and purchases to codes. This lack of detail may affect the quality 

of estimated gross margins. Changes in measured gross margins in 

ARTS likely reflect compositional changes in product mix that are 

impossible to detect under the current system. 

•	 Data on operating expenses are needed to compute value-added. 

Operating expenses for retail and wholesale trade establishments 

are collected as an aggregate of an enterprise’s establishments on 

ARTS4 and the Annual Wholesale Trade Survey (AWTS) once every 

5 years during Economic Census years. Data on expenses are not 

collected at the establishment level on the Economic Census. 

•	 Auxiliaries are a key concept for quantifying the impact of vertical integration in a retail-related satellite account. Although some 

data are available from the Economic Census, there are limited 

ways to estimate the value an auxiliary provides to its enterprise. 

4See https://www.census.gov/programs-surveys/economic-census/data/bes.html. 
 

RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 99 

In addition, BLS currently has limited information to designate 

auxiliaries (Conclusion 3-4). 

Including new questions in ARTS and in the retail trade census could 

result in better integration between ARTS (which provides gross margins 

and operating expenses at the enterprise level with little industry/product 

detail) and the retail trade census (which provides industry/product detail 

of sales at the establishment level). However, this is only a feasible solution if survey reporting entities have access to the needed data so they can 

report it. The panel understands that this is a key data issue. There may be 

administrative data from IRS that could address expense data gaps, if they 

were available for statistical uses within the federal government. Identifying solutions to data gap issues is important to making sure government 

statistics evolve to measure a changing industry. An interagency team led by 

BLS and including representatives from BEA, Census, and potentially IRS 

could identify critical data gaps and address solutions. Collaboration with 

industry could help to ensure that industry could provide the requested new 

information without an undue burden. 

Data Gaps for Employment (BLS)—Splitting Input 

The statement of task asks for ways to identify the proportion of output, employment, and hours outside of retail trade that are directed toward 

supporting retail trade. Options are available for splitting retail-related 

outputs. For output this can be achieved initially by building upon and 

disaggregating elements of the BLS-BEA integrated industry-level production account, BEA industry accounts, and detailed Census Bureau survey 

data. Approaches that use existing data on commodities transported are 

also likely to be useful (Conclusion 4-4). 

The employment side of this project is very important. There are many 

questions about the net impact of e-commerce on jobs and employment. 

Estimating how many people are working in retail and retail-supporting 

industries, as well as the net change in jobs and pay, would be very helpful by itself as well as useful for measuring productivity (Conclusion 4-2). 

Solving the problem of splitting hours worked in retail-related industries 

will likely require new methods, creative use of alternative data sources, 

and potentially augmenting existing surveys. Some approaches proposed 

during the workshop included evaluating data that might be available from 

trade associations and identifying data items that companies might be able 

to report, such as the commodity employees worked with (e.g., handling 

aircraft engines versus clothing). 
100 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Business Registers/Classification 

Labor productivity is measured as the ratio of change in output divided 

by change in input. Nominal output is measured through Census Bureau 

surveys. Labor input and price deflators are measured through BLS surveys. 

The two agencies use separate business registers with separate classifications 

of business establishments by NAICS code as sampling frames for their surveys. The resulting differences in statistics produced by the two agencies 

likely contribute to errors in the estimation of productivity, because different establishments may contribute to the numerator and denominator. This 

error most likely has a time-varying component, because each agency also 

updates its business lists on a different schedule (Conclusion 3-2). 

The challenges concerning the use of multiple business registers by the 

U.S. statistical system has been a topic of concern for years, with solutions 

recommended in reports by the National Academies of Sciences, Engineering, and Medicine. This panel proposes a multistep process to address 

this issue, although some steps/projects can be addressed simultaneously 

because they involve different groups of people. The process might include 

the following: 

Shorter-term efforts would focus on specific projects to support the 

development of a satellite account for retail. For example, a detailed evaluation of linked microdata at an FSRDC could be targeted toward developing 

adjustment factors to account for differences in concept between output 

and input in the retail-related satellite account (Conclusion 3-4). 

BLS annually receives a file containing Census Bureau Firm IDs, employer identity numbers (EINs), and establishment detail. However, BLS 

does not use the Census file on a regular basis, because the reconciliation 

of EINs between Census and BLS is labor-intensive and time-consuming. 

It would be beneficial to be able to quantify all of the activity under firm 

IDs that have some establishments classified as retail and for which linking BLS and Census firm and establishment data might help in identifying 

retail-related auxiliaries in BLS data, for example, something that is not 

currently possible. This has the potential for helping in the development of 

a satellite on retail-trade-supporting activities (Conclusion 3-3). 

The ideal long-term solution to the issue of separate business registers 

being developed, maintained, and used by BLS, BEA, and Census would be 

to remove the obstacles to data sharing noted in the National Academies 

(NASEM, 2017) and National Research Council (2007) reports and for the 

federal government to develop and use a single, common business register 

(Conclusion 3-5). 

RECOMMENDATION 7: Measures should be taken immediately to 

facilitate the expansion of the Confidential Information Protection and 
RECOMMENDATIONS FOR A RETAIL SATELLITE ACCOUNT 101 

Statistical Efficiency Act to increase the kinds of information that may 

be shared among statistical agencies for the purpose of reconciling 

the business lists and for the design of special surveys. This expansion of data sharing can be accomplished by (1) Congress acting to 

enact legislation that revises the Internal Revenue Service (IRS) Code 

Section 6103(j) to extend authorized access to IRS tax information 

to the Bureau of Economic Analysis and Bureau of Labor Statistics; 

(2) the Treasury Department initiating an update of the IRS regulations 

that clarify purpose and detail specific items that can be shared with 

authorized agencies; or (3) a combination of the preceding two activities5 (National Research Council, 2007, p. 111, Recommendation 15). 

The panel is hopeful that these legislative hurdles to development of a 

single business register may be resolved. The semi-final step would be the 

actual development and maintenance of a single consolidated business register for use by BLS, BEA, and the Census Bureau. A longer-term goal is a 

business register that could also be used as a sampling frame by other government agencies. This would be a significant undertaking and might require 

resolving additional legal issues. In addition, it would require addressing 

operational issues, such as coordinating survey feedback when two (or more) 

organizations use the same business register; agreeing on the classification of 

establishments; agreeing on the linkage between establishments, including 

auxiliaries, and their enterprise; and identifying roles and responsibilities, 

such as keeping structures up-to-date and approving changes. Maintaining 

a common business register would mean that BLS, BEA, the Census Bureau, 

and IRS would have to work together very closely to ensure coherence. 

RECOMMENDATION 8: The Bureau of Labor Statistics, Bureau 

of Economic Analysis, Census Bureau, and Internal Revenue Service 

should establish an interagency task force, potentially including other 

relevant agencies, to develop a plan for implementing a single consolidated business register to use as the sample frame for all business 

surveys. The task force should scope out the problem and identify what 

needs to be done and what is required to get it done. 

Better Defining the Retail-Related Sector 

To better understand the changes in retail-related industries, a collaborative effort between BLS, BEA, and Census Bureau staff could make use 

5Changes in access to tax data are required for BEA and BLS, not because BEA or BLS needs 

direct access to tax data, but because the Census business register is built on IRS data and some 

of the Census data directly use tax data or are considered to be “comingled” with tax data. 
 

 

102 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

of microdata as a laboratory to better understand many of the complicated 

aspects of developing a retail-related satellite account. The purpose would be 

to use the concepts and data to gain a better understanding of key issues, such 

as assessing the structural changes associated with the retail trade transformation by size of enterprise and understanding the role of auxiliaries and other 

nonretail establishments within retail trade enterprises (Conclusion 4-3). 

Data Gaps for Timeliness and Detail 

As described in Chapter 3, examples of private-sector data sources 

include proprietary/commercial data, web-scraped data, data from trade 

associations or other private groups, data from credit card companies or 

banks, data from individual stores or loyalty programs, and so on. Typical 

challenges with proprietary data include inadequate representation, lack of 

documentation, and challenging nondisclosure agreements. 

Private-sector data such as scanner data might support capturing both 

quantities and prices of purchases to estimate the price effects of consumers 

moving between retail outlets (Conclusion 3-10). Additionally, privatesector, credit card and payroll processing data have been used to provide 

more timely information about economic output, prices, and input that 

could potentially be used to provide more timely estimates for labor productivity in the retail-related sector (Conclusion 3-12). One key challenge 

in using private-sector data is understanding how well they represent all 

businesses, both large and small. For all their challenges, private-sector data 

have some key advantages, including timeliness and detail. 

RECOMMENDATION 9: Developing a retail-related satellite account 

will require considerable effort to acquire and use data and to address 

data gaps in existing data. The panel has identified the following 

data issues that need to be addressed, but others will arise during the 

course of the study. Individual projects include: Filling data gaps in 

the Economic Census and Annual Economic Surveys that relate to the 

calculation of gross margins, value added, and the contribution of auxiliaries; identifying data to estimate the split in hours worked between 

retail-related and nonretail-related for retail-related service industries; 

correcting for differences in numerator and denominator of productivity caused by the use of different business registers and classifications; 

and exploring the use of private-sector data—such as scanner data, 

bankcard data, and payroll processing data—to improve the timeliness and detail provided in the account. Some of these efforts are best 

accomplished by a team that is granted access to the Census Bureau’s 

economic microdata from the business register and from its Economic 

Census and to surveys at a Federal Statistical Research Data Center. 
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Appendix A
 

Agenda for the Panel’s Workshop
 

Consensus Panel Study on Measuring the Transformation of
 

Retail Trade and Related Activities
 

Meeting 2: June 22-23, 2020
 

Virtual workshop to discuss the transformation,
 

data challenges, and potential solutions
 

Monday, June 22, 2020 

10:00-10:25 Welcome and Overview of the Workshop 

10:00-10:05 Welcome, Brian Harris-Kojetin, director, CNSTAT 

10:05-10:10 Welcome, Lucy Eldridge, BLS 

10:10-10:25 Workshop overview, J. Steven Landefeld, panel chair 

What Is Retail and How Is It Changing? 

10:25-11:25 Research on Retail Changes. Moderator: Gregory Duncan, 

panel member 

10:25-10:45 Statements by participants. Emek Basker, US Census 

Bureau; Chad Syverson, University of Chicago; and 

Steve Noble, McKinsey 

10:45-11:05 Panel discussion
 

11:05-11:25 Discussion
 

107
 
 

 

 

 

 

 

 

 

 

108 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

11:25-11:40 Break 

11:40-12:50 Industry Perspectives of Industry Changes: Past, Present, 

Future Moderator: Jack Kleinhenz, Retail Trade Federation 

11:40-11:45 Overview: Jack Kleinhenz, Retail Trade Federation 

11:45-12:05 Statements by panelists. Drew Spata, Macy’s; David 

Glick, FLEXE (formerly Amazon); Richard Phillips, 

Yale Divinity School (former chairman and former CEO 

of Pilot Freight Services); Anne Goodchild, University 

of Washington 

12:05-12:25 Panel discussion 

12:25-12:50 Discussion 

12:50-2:00 	 Lunch break 

Key Measurement and Data Challenges 

2:00-4:00	 Data: Availability, Needs, Discrepancies, and Gaps. A panel 

discussion. Moderator: Wesley Yung, panel member 

2:00-2:35 Initial statement by panelists. Panelists: Ken Robertson, 

BLS; Jon D. Samuels, BEA; Matthew Russell, BLS; 

Leland Crane, Federal Reserve; Ian Thomas, Census 

Bureau; Edward Watkins, Census Bureau 

2:35-3:15 Panel discussion 

3:15-3:30 Break 

3:30-4:00 Discussion 

4:00	 Adjourn 

Tuesday, June 23, 2020 

Potential Improvements to Measuring Employment and Productivity in 

Retail-related Sectors 

10:00-11:30 Towards a BLS Satellite Account for Retail: Moderator: 

Carol Corrado, panel member 

10:00-11:00 Panel discussion. Panelists: Brian Chansky, BLS; Tina 

Highfill, BEA; Philip Smith, Statistics Canada (retired); 

Marshall Reinsdorf, International Monetary Fund 

11:00-11:10 Discussant: Leonard Nakamura, panel member 

11:10-11:30 Discussion 

11:30-11:45 Break 
 

 

 

 

 

APPENDIX A 109 

11:45-12:30 Quality-adjusted Prices for Retail. Moderator: Dale 

Jorgensen, panel member 

11:45-12:00 Ana Aizcorbe, BEA 

12:00-12:15 Brendan Williams and Bonnie Murphy, BLS 

12:15-12:30 Discussion 

12:30-2:00 Lunch break 

2:00-3:00 Uses of Bottom-Up in Measuring Employment and 

Productivity. Moderator: Kelly McConville, panel member 

2:00-2:30 Teresa Fort and John Haltiwanger, panel members 

2:30-3:00 Discussion 

3:00-3:20 Break 

3:20-4:20 Global Value Chains. Moderator: Michael Mandel, panel 

member 

3:20-3:40 Dominic Smith, BLS 

3:40-4:00 Robert Feenstra, UC Davis 

4:00-4:20 Discussion 

4:20 Adjourn 

Appendix B
 

Retail Output, Hours, and
 

Labor Productivity, 1997-2018
 

111
 
App B-1

Appendix B

Retail Output, Hours and Labor Productivity, 1997-2018

SOURCES: Output measured by sales revenue (“Sales”) from U.S. Bureau of Labor Statistics 

(BLS), Division of Industry Productivity Studies 

(https://www.bls.gov/lpc/lpc_by_industry_and_measure.xlsx using the "Output" field). Output 

measured by gross margin (“Margin”) and value added (“Value Added”) from U.S. Bureau of 

Economic Analysis, Industry Data webpage (using the "Chain-Type Quantity Index" for both 

gross output and value added). Hours data (“Hours”) from BLS 

(https://www.bls.gov/lpc/lpc_by_industry_and_measure.xlsx using the "Hours" field). All four 

series described using indices where 2007=100 (Sales, Hours) or 2012=100 (Margin, Value 

Added). Labor productivity calculated by dividing change in output by change in hours worked, 

using hours data.

112 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

 

 

 

 

 

 

 

 

Index Change Productivity Change 

Year 1997-2018 Total Annual 

Industry Measure 1997 2007 2018 1997-2018 Total Annual 1997-2007 2007-2018 1997-2007 2007-2018 

44,45 - Retail trade Sales 64.916 100.000 124.940 92.5% 89.0% 3.1% 50.9% 25.2% 4.2% 2.1% 

Margin 75.329 103.21 125.78 67.0% 64.0% 2.4% 34.3% 22.1% 3.0% 1.8% 

Value Added 76.897 105.14 120.33 56.5% 53.7% 2.1% 34.0% 14.7% 3.0% 1.3% 

Hours 97.987 100.000 99.785 1.8% 

441 - Motor vehicle and parts dealers Sales 70.863 100.000 120.501 70.0% 52.6% 2.0% 32.9% 14.8% 2.9% 1.3% 

Margin 101.77 104.82 155 52.3% 36.6% 1.5% -3.0% 40.9% -0.3% 3.2% 

Value Added 95.839 109.44 151.98 58.6% 42.3% 1.7% 7.5% 32.3% 0.7% 2.6% 

Hours 94.166 100.000 104.962 11.5% 

444 - Building material and garden supply stores Sales 61.031 100.000 110.475 81.0% 68.8% 2.5% 34.9% 25.1% 3.0% 2.1% 

Margin 76.671 115.58 121.92 59.0% 48.3% 1.9% 24.1% 19.5% 2.2% 1.6% 

Value Added 67.394 108.69 109.26 62.1% 51.2% 2.0% 32.8% 13.9% 2.9% 1.2% 

Hours 82.319 100.000 88.284 7.2% 

445 - Food and beverage stores Sales 94.752 100.000 114.421 20.8% 34.0% 1.4% 21.6% 10.2% 2.0% 0.9% 

Margin 100.54 104.41 102.22 1.7% 12.9% 0.6% 19.6% -5.7% 1.8% -0.5% 

Value Added 120.6 112.74 95.773 -20.6% -11.9% -0.6% 7.7% -18.2% 0.7% -1.8% 

Hours 115.207 100.000 103.795 -9.9% 

446 - Health and personal care stores Sales 62.581 100.000 108.074 72.7% 39.1% 1.6% 37.4% 1.2% 3.2% 0.1% 

Margin 76.623 93.07 117.96 53.9% 24.0% 1.0% 4.4% 18.7% 0.4% 1.6% 

Value Added 92.852 95.913 116.56 25.5% 1.1% 0.1% -11.2% 13.8% -1.2% 1.2% 

Hours 85.972 100.000 106.769 24.2% 

447 - Gasoline stations Sales 89.167 100.000 102.647 15.1% 26.0% 1.1% 29.4% -2.6% 2.6% -0.2% 

Margin 100.86 108.93 98.78 -2.1% 7.2% 0.3% 24.6% -14.0% 2.2% -1.4% 

Value Added 132.34 130.25 74.198 -43.9% -38.6% -2.3% 13.5% -46.0% 1.3% -5.4% 

Hours 115.357 100.000 105.401 -8.6% 

448 - Clothing and clothing accessories stores Sales 55.427 100.000 116.855 110.8% 118.6% 3.8% 73.4% 26.1% 5.7% 2.1% 

Margin 59.241 103.56 114.14 92.7% 99.8% 3.4% 68.0% 18.9% 5.3% 1.6% 

Value Added 69.112 106.33 113.8 64.7% 70.8% 2.6% 47.8% 15.5% 4.0% 1.3% 

Hours 96.087 100.000 92.657 -3.6% 

452 - General merchandise stores Sales 54.865 100.000 121.999 122.4% 76.7% 2.7% 52.2% 16.1% 4.3% 1.4% 

Margin 61.229 108.4 114.25 86.6% 48.3% 1.9% 47.8% 0.3% 4.0% 0.0% 

Value Added 61.185 112.98 119.22 94.9% 54.9% 2.1% 54.2% 0.4% 4.4% 0.0% 

Hours 83.509 100.000 105.060 25.8% 

454 - Nonstore retailers Sales 34.660 100.000 231.360 567.5% 482.3% 8.8% 198.2% 95.3% 11.5% 6.3% 

Margin 41.42 81.045 177.01 327.3% 272.8% 6.5% 102.2% 84.3% 7.3% 5.7% 

Value Added 34.5 72.103 159.62 362.7% 303.6% 6.9% 116.0% 86.8% 8.0% 5.8% 

Hours 103.360 100.000 118.494 14.6% 

42 - Wholesale trade Sales 66.058 100.000 109.230 65.4% 66.5% 2.5% 49.4% 11.4% 4.1% 1.0% 

Margin 57.984 88.297 114.24 97.0% 98.3% 3.3% 50.2% 32.0% 4.2% 2.6% 

Value Added 68.023 102.07 113.09 66.3% 67.4% 2.5% 48.0% 13.0% 4.0% 1.1% 

Hours 98.660 100.000 98.009 -0.7% 

493 - Warehousing and storage Sales 60.027 100.000 156.161 160.2% 0.7% 0.0% 15.2% -12.5% 1.4% -1.2% 

Margin 28.37 62.763 119.72 322.0% 63.4% 2.4% 52.9% 6.9% 4.3% 0.6% 

Value Added 48.383 80.867 144.62 198.9% 15.8% 0.7% 15.5% 0.2% 1.5% 0.0% 

Hours worked 69.125 100.000 178.501 158.2% 

SOURCES: Output measured by sales revenue (“Sales”) from U.S. Bureau of Labor Statistics 

(BLS), Division of Industry Productivity Studies (https://www.bls.gov/lpc/lpc_by_industry_ 

and_measure.xlsx using the “Output” field). Output measured by gross margin (“Margin”) 

and value added (“Value Added”) from U.S. Bureau of Economic Analysis, Industry Data 

webpage (using the “Chain-Type Quantity Index” for both gross output and value added). 

Hours data (“Hours”) from BLS (https://www.bls.gov/lpc/lpc_by_industry_and_measure.xlsx 

using the “Hours” field). All four series described using indices where 2007=100 (Sales, Hours) 

or 2012=100 (Margin, Value Added). Labor productivity calculated by dividing change in 

output by change in hours worked, using hours data. 
 

Appendix C
 

Biographical Sketches of Panel Members
 

J. STEVEN LANDEFELD (Chair) was director of the U.S. Bureau of Economic Analysis (BEA) for nearly 20 years. Since his retirement, Landefeld 

has served as a distinguished visiting professor at the U.S. Naval Academy 

and as an adviser and consultant to various organizations, including the 

United Nations, the Committee on National Statistics, and BEA. His current research focuses on the development of “satellite” accounts that better 

measure economic well-being by measuring the distribution of production 

and income, the sustainability of growth, and auxiliary accounts on topics 

such globalization, energy, the environment, health, human capital, and 

household production. As director of BEA, he led the Bureau in a number 

of measurement improvements including regular updates using new methodologies and source data to provide more timely and relevant data. He is 

a recipient of the President’s Distinguished Executive Award, the National 

Association for Business Economics and American Statistical Association 

joint Julius Shiskin Award, and other national and international awards. 

He has regularly published works on economic measurement throughout 

his career. He has B.A., M.A., and Ph.D. degrees in economics, all from the 

University of Maryland, College Park. 

CAROL A. CORRADO is research director at the Conference Board in 

Washington, DC. She also works with the Conference Board’s China Center 

for Research on Economics and Business. Corrado is a member of the executive committee of the National Bureau of Economic Research’s (NBER’s) 

Conference on Research on Income and Wealth and is an organizer of 

a workshop on economic measurement at the NBER’s annual Summer 

113
 
 

114 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

Institute. She has authored key papers on the macroeconomic analysis of 

intangible investment and capital, including the winner of the International 

Association of Research on Income and Wealth’s 2010 Kendrick Prize and 

a paper in the NBER volume on Measuring Capital in the New Economy. 

Corrado received the American Statistical Association’s prestigious Julius 

Shiskin Award for Economic Statistics in 2003 in recognition of her leadership in these areas and was a recipient of a Special Achievement Award 

from the Board of Governors of the Federal Reserve System in 1998. She 

has a B.S. degree in management science from Carnegie Mellon University, 

and a Ph.D. in economics from the University of Pennsylvania. 

GREGORY DUNCAN is senior principal economist, technologist, and 

machine learning scientist at Amazon and affiliate professor of economics 

at the University of Washington. At Amazon, he has worked on addressing numerous econometric and statistical issues throughout the company, 

including projects related to policy and competition, forecasting, the use of 

machine learning, and supply chain research. He is also a co-founder of the 

Amazon Machine Learning University. He has a B.A. degree in economics 

from the University of Washington, and an M.A. degree in statistics and a 

Ph.D. in economics, both from the University of California, Berkeley. 

TERESA C. FORT is an associate professor of business administration at 

the Tuck School of Business at Dartmouth College. She conducts research in 

international trade and industrial organization. Her current work analyzes 

how technology affects firm-level offshoring and production fragmentation 

decisions, and the impact of these decisions on domestic employment and 

innovation. Fort is a faculty research fellow at the National Bureau of Economic Research and a research affiliate at the Centre for Economic Policy 

Research. She holds a Ph.D. in economics from the University of Maryland, 

and a B.A. degree from the University of Virginia. 

JOHN C. HALTIWANGER is a distinguished university professor in the 

Department of Economics at the University of Maryland, College Park. 

He also serves as research associate at the National Bureau of Economic 

Research, senior research fellow at the Center for Economic Studies at 

the U.S. Census Bureau, and fellow of the Society of Labor Economics 

and the Econometric Society. His research increasingly uses the data and 

measures on firm dynamics from a substantial number of advanced, emerging, and transition economies. Haltiwanger has published more than 

100 academic articles and numerous books, including Job Creation and 

Destruction. He has a Sc.B. in applied mathematics-economics from Brown 

University and a Ph.D. in economics from the Johns Hopkins University. 
 

 

APPENDIX C 115 

DALE W. JORGENSON is the Samuel W. Morris university professor 

in the Department of Economics at Harvard University. Jorgenson was 

awarded the John Bates Clark Medal by the American Economic Association and served as its president in 2000. He was a founding member of 

the Board on Science, Technology, and Economic Policy of the National 

Research Council and served as its chair. Jorgenson has conducted groundbreaking research on information technology and economic growth, energy 

and the environment, tax policy and investment behavior, and applied 

econometrics. He is the author of more than 300 articles on economics and 

the author and editor of 37 books. He has a B.A. degree in economics from 

Reed College and a Ph.D. in economics from Harvard. 

MICHAEL MANDEL is chief economic strategist at the Progressive Policy 

Institute in Washington, DC, senior fellow at Wharton’s Mack Institute 

for Innovation Management at the University of Pennsylvania, and fellow 

at the Manufacturing Policy Initiative at Indiana University. With experience spanning policy, academics, and business, Mandel has helped lead the 

public conversation about the economic and business impact of technology 

for the past two decades. He taught at New York University’s Stern School 

of Business and his introductory economics textbook, Economics: The 

Basics, is currently in its fourth edition. Mandel holds an A.B. in applied 

mathematics and a Ph.D. in economics from Harvard. 

KELLY MCCONVILLE is assistant professor of statistics at Reed College, 

specializing in survey sampling. In her work, she develops survey estimation 

techniques that combine data collected under a complex sampling design 

with auxiliary data sources. McConville has collaborated with the U.S. 

Forest Service’s Forest Inventory and Analysis Program and the U.S. Bureau 

of Labor Statistics. She co-chairs two national programs: the Undergraduate 

Statistics Project Competition and the Electronic Undergraduate Statistics 

Research Conference. McConville has a background in establishment surveys and has previously worked as an American Statistical Association/ 

National Science Foundation/Bureau of Labor Statistics research fellow. She 

has a B.A. degree in mathematics from Saint Olaf College and an M.A. and 

a Ph.D. in statistics from Colorado State University. 

LEONARD I. NAKAMURA is emeritus economist of the Federal Reserve 

Bank of Philadelphia, after having served as vice-president and economist 

for more than 30 years. His research addresses financial economics and 

economic measurement issues, including intangibles, information flows, 

and free products. Previously, Nakamura led the research team responsible 

for producing the Business Outlook Survey, a regional manufacturing survey, and the State Coincident Indexes, as well as other economic indicators. 
116 A SATELLITE ACCOUNT TO MEASURE THE RETAIL TRANSFORMATION 

He also served as economist at Citibank and as senior economic consultant 

for The Conference Board. He has taught courses at the Wharton School 

of the University of Pennsylvania, Swarthmore College, and Bryn Mawr 

College, and was previously a faculty member at Rutgers University. He 

has a B.A. degree in social sciences from Swarthmore College and an M.A. 

and a Ph.D. in economics from Princeton University. 

WESLEY YUNG is director of the Economic Statistics Methods Division of 

Statistics Canada. In this role, he manages a division of 110 methodologists 

who provide support to the Economic Statistics Field of Statistics Canada. 

Prior to this, he was assistant director of the division, where he managed 40 

methodologists who provided support to annual and sub-annual business 

surveys and to the Tax Data Division. Yung also served as section chief and 

senior methodologist at Statistics Canada. While currently in a management 

position, he continues to remain active in survey methods research touching 

on variance estimation and, more recently, collection research. He has B.Sc. 

and M.Sc. degrees in statistics from Dalhousie University in Nova Scotia 

and a Ph.D. in statistics from Carleton University in Ontario. 
COMMITTEE ON NATIONAL STATISTICS 

The Committee on National Statistics was established in 1972 at the 

National Academies of Sciences, Engineering, and Medicine to improve 

the statistical methods and information on which public policy decisions are 

based. The committee carries out studies, workshops, and other activities to 

foster better measures and fuller understanding of the economy, the environment, public health, crime, education, immigration, poverty, welfare, 

and other public policy issues. It also evaluates ongoing statistical programs 

and tracks the statistical policy and coordinating activities of the federal 

government, serving a unique role at the intersection of statistics and public 

policy. The committee’s work is supported by a consortium of federal agencies through a National Science Foundation grant, a National Agricultural 

Statistics Service cooperative agreement, and several individual contracts.