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Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 71
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 72
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 73
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 74
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 75
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 76
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Page 77
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
×
Page 78
Suggested Citation:"5 Recommendations for a Retail Satellite Account." 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. doi: 10.17226/26101.
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Prepublication copy, uncorrected proofs 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 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; 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 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 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 BEA MFP 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 vs. labor hours. 69

Prepublication copy, uncorrected proofs 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 if 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 BLS to use to study the impact of the transformation in retail trade on employment and 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),88 and BEA’s Integrated BEA GDP-BLS Productivity Account.89 RECOMMENDATION 1: BLS should develop a satellite account for an expanded retail trade sector in collaboration with BEA and the Census Bureau. Such a team could be formed under the Evidence Based Policy Act to facilitate administrative and collaborative efforts. 88 See https://www.bls.gov/lpc/productivity-dispersion.htm. 89 See https://www.bea.gov/data/special-topics/integrated-bea-gdp-bls-productivity-account. 70

Prepublication copy, uncorrected proofs 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 (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 (FESAC). 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 BLS, in collaboration with BEA 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 FSRDCs. 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 71

Prepublication copy, uncorrected proofs 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) 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, BLS and its partners should adopt an iterative and modular approach, starting with feasible options that draw upon the 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 retail-supporting auxiliaries (support establishments), as well as with other retail-supporting industries (defined by 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,90 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 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. 90 Leonard 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. 72

Prepublication copy, uncorrected proofs 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. Value-added, 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 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 low-cost 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 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. (Conclusion 3-10) 73

Prepublication copy, uncorrected proofs 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 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 foreign-owned vs. 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, 74

Prepublication copy, uncorrected proofs 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 non-retail- trade support industries, such as transport. Work on extended input-output accounts and global value chains at the United Nations, 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 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. 75

Prepublication copy, uncorrected proofs 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 ARTS91 and the Annual Wholesale Trade Survey (AWTS) once every five 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. 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. 91 See https://www.census.gov/programs-surveys/economic-census/data/bes.html. 76

Prepublication copy, uncorrected proofs 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 ILPA, 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 vs. clothing). 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, 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) The BLS annually receives a file containing Census Bureau Firm IDs, EIN’s 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 77

Prepublication copy, uncorrected proofs 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 NASEM (2017) and NRC (2007) 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 Statistical Efficiency Act (CIPSEA) 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 IRS Code Section 6103(j) to extend authorized access to IRS tax information to BEA and BLS; (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 activities92 (NRC, 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 the 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 BLS, BEA, Census Bureau, and IRS 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 of microdata as a laboratory to better understand many of the complicated aspects of developing a retail-related satellite 92 Changes 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. 78

Prepublication copy, uncorrected proofs 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, private sector, credit card, and payroll processing data have been used to provide more timely information about economic output, prices, and input which 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 an FSRDC. 79

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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 imports 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 about the sector. In order to develop appropriate economic policies, we need to be able to capture more detailed data, including data about changes to productivity.

At the request of the U.S. Bureau of Labor Statistics, this report evaluates changes in the retail trade sector, assesses measures of employment and labor productivity for the sector, and recommends a new satellite account that could measure retail-related employment and labor productivity in ways that would better capture the transformation.

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