THE NATIONAL ACCOUNTS IN A CHANGING ECONOMY: HOW BEA MEASURES E-COMMERCE

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1 THE NATIONAL ACCOUNTS IN A CHANGING ECONOMY: HOW BEA MEASURES E-COMMERCE BARBARA M. FRAUMENI, ANN M. LAWSON, and G. CHRISTIAN EHEMANN Bureau of Economic Analysis Brookings Program on Output and Productivity Measurement in the Service Sector WORKSHOP ON MEASURING E-COMMERCE September 24, 1999 BROOKINGS INSTITUTION WASHINGTON, DC

2 Brief Summary < The size of the digital economy is unknown, although some estimates do exist, they vary widely. < The digital economy and E-commerce are embedded in the national accounts, not distinguishable from other economic activities, yet the level of activities is reflected in GDP. < BEA methodology is designed to quickly incorporate information from new surveys and censuses, such as the Annual Retail Trade Survey and the quinquennial economic censuses. < Some activities may be missed or misallocated among intermediate goods or services and GDP, for example in some instances when manufacturers sell directly to individuals. < There are a number of statistical agency E-commerce initiatives, including those involving supplements to existing Census surveys, a proposed BEA initiative in conjunction with other agencies to focus on E-commerce, and BLS research into CPI-related effects. < In some cases E-commerce measurement may be difficult as many activities occur in difficult to measure industries such as banking, a number of web goods and services such as information are free, the dividing line between goods and services is often fuzzy, and product prices and quality characteristics may differ from those of conventional outlet products. < There are additional data needs to measure E-commerce beyond currently planned data collection efforts, such as information on cross-border flows, tracking of new goods and services, including new investment goods, and new E-business formation, and information on changes in distribution brought on by E-commerce. < BEA and other U.S. statistical agencies are rising to the measurement challenge. These challenges include more accurately reflecting the nature of E-commerce and separating E-commerce from other activities.

3 I. Introduction The National Accounts in a Changing Economy: How BEA Measures E-Commerce Barbara M. Fraumeni, Ann M. Lawson, and G. Christian Ehemann 1 A frequently asked question is What is the size of the digital economy? Although many raising this question focus on the future growth of the digital economy, its current size is also debated. A June 1999 Commerce Department report The Emerging Digital Economy II cited widely varying although not directly comparable E-commerce estimates for 1998, as well as forecasts 2 for 2002 and The Commerce report concludes that estimates made only a year earlier in 1998 were too conservative. The BEA national accounts, by measuring the level of market activities in the economy, are an essential element in formulating an answer to the size of the digital economy question. This paper uses the term digital economy as used in the Commerce report and the terms E- business and E-commerce as used by the Census Bureau to develop their strategy for collecting new data on theses areas. By the digital economy, the Commerce report refers to E-commerce and Information Technology (IT) industries, the latter a concept similar to the Census concept Ebusiness infrastructure. Census considers E-business as any process that a business organization 3 conducts over a computer-mediated network. Census currently defines E-commerce as any transaction completed over a computer-mediated network that involves the transfer of ownership or rights to use goods and services. The definitions for these terms can be expected to change and evolve at the same time as the digital economy itself is changing and evolving. The focus of this paper is on E-commerce. Although E-commerce is a transactions-based definition, the activities that are classified by Census as E-commerce will be classified within existing classification systems. This approach will be consistent with the national accounts approach. The extent to which E-commerce is and will be included in the national accounts is explored. No attempt is made to describe how BEA might separate E-commerce activities from other 1 This paper represents views of the authors and is not an official position of the Bureau of Economic Analysis or the Department of Commerce. 2 U.S. Department of Commerce, Economics and Statistics Administration, Office of Policy Development, The Emerging Digital Economy II, p At the May 25-26th, 1999 Commerce organized conference Understanding the Digital Economy: Data, Tools and Research, the term E-business was used more generally to include changes in the organization of business brought about by the digital economy. 1

4 activities. The emphasis is on how E-commerce activities are included over time using BEA methodologies such as those employed in the generation of quarterly and annual estimates. These methodologies underlie the relationships between the National Income and Product Accounts (NIPA s) and the Input-Output (I-O) Accounts. Much of the discussion of E- commerce in this paper applies as well to other activities. However, the pace of change associated with the emerging digital economy accentuates the measurement challenges. The paper begins with a section on methodology. The discussion more specific to E-commerce includes subsequent sections on coverage in the national accounts, data initiatives by the statistical agencies, and estimated time lines reflecting the incorporation of NAICS and other new initiatives, measurement challenges, and additional data needs. II. Methodology Quarterly and annual estimates of Gross Domestic Product (GDP) The data used to prepare current estimates of quarterly and annual GDP consist of a variety of economic measures, such as sales or shipments, wages and salaries, insurance premiums, and housing stock. Although GDP is a product side concept, concurrent estimates are made of the components of the income side of the national accounts. For most product and income components, the source data are value data ; that is, they encompass both the quantity and price dimensions that are required for current-dollar estimates. For those estimates not derived from value data, a combination of data with separate quantity and price dimensions is used to derive the required value estimate; for example, the estimate for new autos is calculated as unit sales times expenditure per auto. The source data, as listed in Appendix Table 1, vary depending upon whether estimates are being prepared for a comprehensive (benchmark) NIPA revision, a non-benchmark year, or a current quarterly estimate. The source data for a NIPA comprehensive revision can vary depending upon whether a new benchmark I-O Account has become available since the last NIPA comprehensive revision, e.g., currently the 1992 I-O Accounts, whether preliminary data are available from the more recent quinquennial census, e.g., currently the 1997 economic census, and the extent of newly available information from the annual surveys. There are three releases of the current quarterly estimates of GDP: Advance, preliminary, and final. The advance estimate is released approximately one month after the end of the quarter; the preliminary estimate at the end of two months; and the final at the end of three months, at which point the estimate is not subject to further revision until additional information is incorporated at the time of an annual estimate or during a NIPA benchmark revision. During the two months after the release of the advance estimate, more source data become available. Accordingly, the advance data sources are the most limited, but are expanded in successive estimates. Appendix Table 1 lists sources for NIPA estimates in benchmark versus non-benchmark (other) years and for advance quarterly GDP estimates.. 2

5 The current quarterly estimates of the annual rate of growth of GDP, along with the other estimates of product and income, are of crucial importance to policy-makers, forecasters, and other analysts. When the NIPA benchmark process occurs, such as in this year, historical revisions of annual time series of all components of the accounts are be introduced to reflect definitional, classificational, and statistical changes, including the incorporation of information 4 from the most recent benchmark I-O Accounts. GDP can be measured either as the sum of final expenditures or as the sum of costs incurred and the incomes generated in production. Final expenditures exclude expenditures for intermediate goods and services. Final expenditures (called final uses in the I-O Accounts) include personal consumption expenditures (PCE), gross private domestic investment, exports less imports (net exports), and government consumption expenditures and gross investment. Two methods that are used to prepare current dollar estimates of many PCE and PDE categories 5 are the retail-control method and the abbreviated commodity-flow method. The retail-control method provides the indicator series used to extrapolate and interpolate the total of most goods from their benchmark levels and the control total to which the PCE categories and residential PDE included in this group must sum. An abbreviated form of the commodity-flow method is used to prepare most current dollar estimates of PDE. This method updates benchmark levels with data on product shipments, imports, exports, and limited information on government gross 6 investment expenditures. The fiscal year-analysis method provides the framework for the estimates of Federal government consumption expenditures and gross investment. The estimates of expenditures are prepared by program. For most programs, the fiscal (budget) year analysis begins by adjusting budget outlays for coverage and for netting and grossing differences between these outlays and NIPA expenditures. The expenditures total as adjusted for a program is then classified by type of NIPA expenditure with nondefense consumption expenditures and gross investment determined residually. 4 The release of the NIPA benchmark is scheduled for October 28th of this year. A summary of the definitional and classificational revisions appeared in the August Survey of Current Business (SCB). Two summaries are forthcoming in future SCBs: A summary of the table revisions in the September SCB and a summary of the statistical revisions in the October SCB. below. 5 The full-blown commodity flow method used for I-O benchmark years is described 6 The main benchmark relationships that are maintained for the abbreviated commodity flow method include the products in the category being updated, the rates used to estimate trade margins and transportation costs, and the split between final and intermediate uses. 3

6 7 The source data for net exports are the balance of payments accounts prepared by BEA. For some NIPA components, the balance of payment accounts estimates are adjusted to conform to NIPA concepts and definitions. Real (chained 1992-dollar) GDP is computed as the product of the GDP chain-type quantity 8 index and current-dollar GDP for To prepare quantity indexes for each component of real GDP, one of three alternative methods is used. These include deflation, quantity extrapolation, and direct valuation. (Appendix Table 2 provides a summary of the data sources and estimating methods used in preparing real estimates of final uses.) In deflation, the quantity index is obtained by dividing the current-dollar index by an appropriate price index that has the reference year currently 1992 equal to 100 then by multiplying the result by 100. The quantityextrapolation and direct-base-year valuation methods both use explicit quantity data. In quantity extrapolation, quantity indexes are obtained by using a quantity indicator to extrapolate from the reference year value in both directions. In direct valuation, actual quantity data and a price index set equal to 100 in the reference year is used. Comprehensive revision (benchmark) estimates of GDP The source data used to prepare estimates of GDP are most complete and the estimating methods 9 used are most detailed in I-O benchmark years. The I-O benchmark forms the basis for establishing the level, general composition, and component detail of the NIPA s in the benchmark year, and from which GDP is extrapolated in the non-i-o-benchmark years. The I-O benchmark estimates are based primarily on data collected from the quinquennial economic censuses conducted by the Bureau of the Census every five years for years ending in 2" and 7". The economic censuses provide comprehensive and consistent data including information on industry and commodity production, materials consumed, and operating expenses that are not available on a more frequent basis. The same quinquennial economic censuses form the basis for the benchmark I-O Accounts. The 1992 benchmark I-O Accounts were completed in November 1997, and the 1997 benchmark accounts are slated to be completed in late The 1992 benchmark I-O Accounts used data from economic censuses covering the following industries: Agriculture; mining; construction; manufacturing; wholesale trade; retail trade; transportation; 7 See U.S. Department of Commerce, Bureau of Economic Analysis (May 1990). Revisions to the methodologies described in this report are typically introduced as part of the annual revisions of the balance of payment accounts. These revisions are described in the SCB articles that describe the most recent data, most recently in Bach (1999). 8 With the release of the NIPA benchmark revision on October, 28, 1999, the reference year will change from 1992 to The word benchmark is used in this paper in two ways: To refer to the I-O benchmark and to the NIPA benchmark, also called the NIPA comprehensive revision. The relationship between these two benchmarks is explained in the text. 4

7 communications; utilities; finance, insurance and real estate; and services. In addition, the I-O Accounts also used data from the 1992 Census of Governments. As part of the current NIPA benchmark process, the 1992 benchmark I-O Accounts are being incorporated into the NIPA s, as well as preliminary estimates from the 1997 economic censuses. The NIPA benchmark process has been used as an opportunity to introduce several major 10 innovations that contribute to our understanding of the digital economy. The BEA qualityadjusted a computer price index, which was introduced into the national accounts in late 1985, 11 helped to clarify the impact of computers on the economy. Since 1985, the work on qualityadjusted price indexes has been extended to several other information technology products, such 12 as semiconductors and telephone switching equipment. In the forthcoming benchmark 13 revisions, computer software will be treated as investment. In addition, the contribution of ATMs to bank output will be recognized for the first time in the new deflation procedure for 14 unpriced bank services. Relationship between the NIPA and the I-O Accounts For quinquennial economic census years, the I-O Accounts play a crucial role in the generation of current estimates of GDP. The I-O Accounts present a much more detailed look at the economy than the NIPA s. This is made possible by the quinquennial economic censuses which are more comprehensive and detailed than the annual surveys. A substantial amount of the information in the I-O Accounts is not directly used in the NIPA s. Rather, the I-O Accounts form a baseline for determining the level and composition of the product and income sides of the NIPA s. The basic conceptual relationship between the NIPA s and the I-O Accounts is highlighted in Figure A. The NIPA s, shown on the left side of Figure A, measure the production of the Nation within its boundaries, both in terms of final uses and in terms of the costs incurred and the incomes generated in production. Final uses consist of PCE, gross private domestic investment, net exports of goods and services, and government consumption expenditures and gross investment. The sum of the final uses equals GDP. The same total may be derived by summing the costs incurred and incomes generated in production (gross domestic income) plus the statistical discrepancy. 10 See Moulton (1999) for a more detailed description of these accomplishments. 11 The computer price index was the result of a BEA and IBM collaboration. See Cole et. al (1986) See Grimm(1998) and Parker and Seskin (1997). See Moulton, Parker and Seskin (1999). See the forthcoming October 1999 SCB. 5

8 Figure A.--Gross Domestic Product In the National Income and Product Accounts In an Input-Output Format Compensation of employees Personal consumption expenditures INDUSTRIES FINAL USES (GDP) Indirect business tax and nontax Gross private domestic investment liability Gross Government Net exports of goods and services COMMOD- private consumption Other income: ITIES Personal domestic expenditures Proprietors' income with IVA Government consumption investconsumption Net and gross GDP and CCAdj expenditures and gross expenditures ment exports investment Rental income of persons investment with CCAdj Compensation Corporate profits with IVA of and CCAdj employees Net interest Business transfer payments Less: Subsidies less current VALUE Indirect surplus of government ADDED business enterprises tax and Consumption of fixed capital nontax Less: Receipts of factor income liability from the rest of the world Plus: Payments of factor income to the rest of the world Other value added* GROSS DOMESTIC INCOME Plus: Statistical discrepancy TOTAL VALUE GROSS DOMESTIC PRODUCT GROSS DOMESTIC PRODUCT ADDED CCAdj Capital consumption adjustment IVA Inventory valuation adjustment * Other value added consists of the following national income and product accounts components of gross domestic income: Proprietors' income with IVA and CCAdj, rental income of persons with CCAdj, corporate profits with IVA and CCAdj, net interest, business transfer payments, current surplus of government enterprises less subsidies, consumption of fixed capital, and net payments of factor income to the rest of the world. U.S. Department of Commerce, Bureau of Economic Analysis

9 The right side of Figure A shows the final use components of GDP and value added components, arranged in the format of an I-O matrix, that is, a table in which information is presented in rows and columns. The row labeled commodities shows the final uses that make up GDP. The column headed industries shows the incomes that make up value added. The equality on the left side of the chart between GDP and the costs incurred and the incomes generated in production is maintained on the right side, where the total of the commodities row equals the total of the industries column. The empty box represents intermediate consumption of commodities by industry. The detailed information in this box is not directly used in the NIPA s. However, an understanding of its role in commodity flow calculations and in the column and row equality constraints of the I-O framework brings into sharper focus the effect of business-tobusiness (B-to-B) and business-to-consumer (B-to-C) transactions on the composition of the product and income sides of the NIPA s and their relationship to one another. I-O Benchmarks Two methods the direct estimation method and the commodity-flow method are used to prepare estimates of final uses in the I-O Accounts for benchmark years. Estimates are made at the detailed level for approximately 2,000 different products consumed by final users; these detailed estimates are then aggregated to about 500 commodities for the published estimates. Direct estimation is used for some PCE, some structures in gross private domestic investment, Federal government consumption expenditures and gross investment components, and exports and imports of goods and services. (Direct estimation is also used to determine totals of State and local government spending by category, using compilations from budget documents.) Generally, direct estimation is used when the final user is defined by the data source, such as exports and imports from the balance of payments accounts, or when by definition the commodity is sold only to a particular final user, such as the rental value of owner-occupied dwellings, which is attributed exclusively to persons. Direct estimation is also used when the method results in a more accurate and reliable estimate; for example, estimates of gasoline and oil purchases by persons are based on unit sales and average prices for these commodities. For benchmark estimates, the commodity-flow method is used to derive most estimates of PCE and producers durable equipment (PDE) in gross private fixed investment, as well as the commodity detail for State and local government consumption expenditures and gross investment. The commodity flow method begins with the identification of products that should be included in a final use spending category. Judgements are made on the basis of the nature of the product from the product titles contained either in the detailed tabulations in the quinquennial economic censuses or in the Standard Industrial Classification Manual, 1987 for the 1992 and earlier benchmarks. An estimate of output for each product is then made. The commodity flow method then converts the value of these outputs produced by domestic establishments, referred to as domestic output, to the value of production available for sale to domestic purchasers, referred to as domestic supply. The domestic supply is then allocated to domestic 7

10 15 purchasers that is, to persons, businesses, and government. As noted earlier, an abbreviated commodity flow method is used to produce current estimates of PDE. Gross Product Originating (GPO) by Industry BEA views its annual gross product originating (GPO) by industry estimates as preferred to the 16 benchmark I-O estimates of value added by industry. However, the GPO by industry estimates 17 are not as detailed as the I-O estimates. The current-dollar GPO is derived as the sum of industry distributions of the detailed components of the NIPA measure of gross domestic 18 income. This methodology, which conceptually is equivalent to measuring GPO for an industry as the difference between its gross output and intermediate inputs, is used for all GPO 19 industries. The GPO series were used as input to the recent Commerce report on the digital economy, however, components of IT infrastructure had to be broken out as the GPO estimates do not provide sufficient detail. Real (chained 1992-dollar) GPO is computed as the product of the GPO chain-type quantity index and the current-dollar GPO for For most private industries, the GPO chain-type quantity index is computed using the double-deflation method, in which the GPO quantity index is derived from quantity indexes for gross output and for intermediate inputs. This method is preferred because it requires the fewest assumptions about the relationships among industry outputs, inputs, and prices. The double-deflation method, however, requires detailed price indexes and current-dollar values for both gross output and intermediate inputs by industry. 20 Currently, price indexes are not available for all industries. When the double-deflation method 15 See U.S. Department of Commerce, Bureau of Economic Analysis (1997), p. M-5. Output refers to gross output or the sum of intermediate outputs and final uses. 16 See Parker (1997). 17 The GPO series is published for 62 industries and four government categories, whereas the I-O series are published for close to 500 industries. 18 For private industries, these components consist of wage and salary accruals, supplements to wages and salaries, indirect business tax and nontax liability less subsidies, corporate profits, proprietors income, rental income of persons, net interest, capital consumption allowances, and business transfer payments. 19 See Yuskavage (1998). 20 These industries include the following: Water transportation, transportation services, depository institutions, nondepository institutions, other real estate, holding and other investment offices, business services, social services, membership organizations, other services, and private 8

11 cannot be used, the GPO quantity index is either a measure of labor input or derived by deflating the current-dollar GPO series. III. Data Coverage of E-Commerce for Preparing the National Accounts The previous section discussed the relationship between the NIPA s and the benchmark I-O Accounts, as well as the data sources and estimating methods used to prepare current and benchmark estimates for these accounts. This section begins the discussion that is more specific to E-commerce. Its focus is data coverage of E-commerce in preparing the national accounts. An overview will be provided of the current SIC-based data used to estimate E-commerce in the accounts, followed by a brief discussion of changes that are being introduced by the NAICS, beginning with the 1997 economic census conducted by the Bureau of the Census. The NIPA s and the most recent set of completed benchmark I-O Accounts are both based on the 1987 Standard Industrial Classification (SIC) system. The original SIC system was developed in the 1930's as an establishment-based industry classification system, based on the primary production of the establishment. Over time, this system has been revised periodically to reflect changes in the economy of the United States. Although its last revision was in 1987, two years 21 prior to the origination of the World Wide Web, the SIC system has shown remarkable adaptability to capture these and other changes over the years. On the other hand, although the SIC-based data collection system has been able to incorporate these new economic activities within its overall estimates, generally these new activities are not readily separable but are instead embedded with other economic activities. Generally, E-commerce can be viewed as a channel for conducting transactions that prior to its advent in the mid-1990's would have been completed in more traditional ways, for example, by face-to-face contact at retail stores and by telephone, mail or facsimile for catalog sales. Regardless of the distribution channel, in the case of the purchase of a good, its purchasers price value in the national accounts is the sum of the value of the good in producers prices and the wholesale and retail trade margins, as well as transportation costs. In the case of the purchase of a service, whatever the channel used to complete the transaction, its output is measured as the value of the commodity receipts. In the benchmark I-O Accounts, retail trade and wholesale trade costs are estimated separately from the value of the underlying goods and their transportation costs. From the detailed information collected by the economic censuses, estimates are made separately for retail and household services. 21 In 1989, a Britisher researcher, Tim Berners-Lee, working at the European Particle Physics Lab in Geneva, Switzerland, completed and circulated for comments, Information Management: A Proposal ; this paper is credited for providing a conceptual basis for the World Wide Web. 9

12 wholesale trade sales by establishments that have trade as their primary activities. Those retail establishments that sell through E-commerce channels as their primary activity are included with catalog, mail-order houses, and other similar types of establishments as SIC In the case of establishments for which selling through E-commerce channels is only a secondary activity for example, a manufacturer of computers (SIC 3571) that is secondarily also involved in the direct selling of some of its computers through the Internet additional estimates for retail and wholesale trade costs are not needed because these transactions are bypassed. E-commerce-based transactions for services for example, the purchase of airline tickets through the Internet are similarly embedded in the benchmark estimates. As noted earlier, typically the purchase of a service as an E-commerce-based transaction is a substitute for an earlier similar transaction by telephone, facsimile, or other traditional channel. Although the current data collection system does not distinguish among these channels for selling services, estimates of services sold through the new channels are embedded in the total. IV. E-Commerce Related Data Initiatives In general, BEA is not the source for the data used in the national accounts with the exception of some international statistics. As a result, the data initiatives of the Bureau of the Census (Census) and the Bureau of Labor Statistics (BLS) are of crucial importance to BEA. BEA can develop new statistical estimates of E-commerce, but the extent of these efforts depends on data obtained from surveys normally undertaken by others. 22 Initiatives to help fill in the E-commerce data gaps are being undertaken primarily by Census. Census short-term strategy is to use existing surveys and to focus primarily on E-commerce. E- commerce coverage initially will be selective. For example, the 1999 Annual Retail Trade Survey will collect on-line and mail order E-commerce data for 8-10 selected commodities. There are no plans in the short-run to cover business-to-business E-commerce, which represents the majority of E-commerce activity, with the exception of computer retailers sales to business. Census long-term strategy is to initiate special E-commerce surveys and to primarily focus on understanding E-business processes, as proposed in a FY2001 budget initiative. Data from the short-term strategy to collect expanded information from the 1999 Annual Retail Trade Survey will be available at soonest in Spring of 2000, at latest under current plans by mid As these surveys are annual surveys rather than a quinquennial census, they will be used as indicator series to extrapolate from the 1992 benchmark I-O Accounts. (Information from the 1997 economic censuses is only now becoming available.) While activity levels would clearly be reflected in the NIPA s, changes in the distribution of activities across final demand categories and between intermediate output and final demand might not be reflected until completion of the 22 The source of information is a handout of xeroxed copies of transparencies used in the Digital Economy Working Group presentation by Thomas L. Mesenbourg, Jr. of the Census Bureau on July 15,

13 1997 I-O Account in A BEA initiative is being formulated in coordination with Census and BLS to incorporate E- commerce more fully into its economic accounts. This initiative, which is in its early planning stages and requires interagency cooperation, tentatively calls for BEA to develop detailed and consistent data on E-commerce, including the volume of activity, E-commerce s effect on GDP and the U.S. economy, its cross-industry, regional, income and price effect. Specific E- commerce concerns are the effect on service industries, output, prices and productivity of E- commerce users, the level of wholesale versus retail activity, method of delivery of goods and services, investment in computers and communication equipment, tax collections, the domestic and international location of production, worker compensation, the number of self-employed workers, and overall growth and productivity. BLS data will be incorporating E-commerce and the digital economy mainly through the adoption of NAICS, which is a interagency and international effort, and a new Standard Occupational Classification (SOC) system. The 2002 NAICS, a continuation of the NAICS interagency and international effort, will include more E-commerce and digital economy-related industry detail. The new 1998 SOC system has more occupations than the previous 1988 SOC. Notably among the expanded detail are more computer-related occupations. Internet service providers (ISPs) may be more easily recognizable as an industry in the 2002 NAICS and a new producer price index will be constructed by BLS for information retrieval services, which contains ISPs, in FY2000. Attention is also being paid in the 2002 NAICS to the design of classifications encompassing information retrieval or search services such as that of yahoo.com. As the nascent North America Product Classification System (NAPCS) is designed, other opportunities will arise to incorporate E-commerce and the digital economy. Currently, research is being conducted at BLS to determine how E-commerce might fit into the Consumer Price Index (CPI). This research is primarily concerned with assuring that CPI initiation procedures (drawing samples of retail outlets and consumer items in those outlets) give the proper chance of selection to E-commerce outlets and items. Additional research concerns ongoing pricing activities (following the price movement of the selected items in the selected outlets over time), since the E-commerce prices may move differently from those in conventional outlets. Incorporation of the 1997 economic censuses is already underway. As BEA depends upon 23 information from other government agencies to construct its accounts, BEA s I-O timetable is contingent upon the timetables of other agencies, notably Census and BLS. Some parts of the 1997 economic censuses are already available. It is expected that 1997 census releases will continue through mid BEA plans to complete the 1997 I-O Accounts by the end of Complicating the process is the transition from the Standard Industrial Classification (SIC) system to the NAICS system. At least one government agency, BLS, will not fully convert to 23 The 1992 I-O Accounts were released in November

14 24 the NAICS system until NAICS 2002 is in place. If history is a guide, the 2002 I-O Accounts, which will be based on the 2002 economic censuses, will not be available until at soonest V. Some E-Commerce Measurement Challenges There are several reasons why E-commerce output measurement may be difficult even if the additional data described in the next section is collected. A number of E-commerce activities occur in industries whose output is difficult to measure even if the goods and services are provided through a brick-and-mortar outlet. These industries include finance, banking, insurance, and business services. Examples of E-commerce activities in these industries include on-line brokerage services, on-line bill-paying and bank account services, ATMs, and on-line 25 insurance purchase and customer services. In the case of banks it is difficult to measure output directly as many services are paid for by the difference between interest rates paid by borrowers and those received by depositors. Also, in the case of business services, it is difficult to identify standard products. The growth of E-commerce will increase the importance of activities whose output is difficult to measure just as did the growth of service industries generally. When services are provided free, output is difficult to measure. Information is commonly provided free. There may be less of a connection between information provision and business sales on the web than there is with other forms of information provision. For example, airlines provide information for free over the telephone on flights and fares in the hope of making a sale. The same thing occurs on the web. However, web search engines are not necessarily used to find information related to a prospective purchase. The web has become a virtual encyclopedia of information. Expenses of web search engines are supported largely by advertising, which is a business expense. The purchase of information by an individual would not be allocated to PCE in the same way that a purchase of an encyclopedia would be allocated to books under PCE. Instead, the purchase is indirectly included in a variety of PCE categories to the extent the associated costs are embedded in the prices of goods and services purchased by consumers from 26 businesses who advertise on the web. Census is not planning to attempt to measure or document the frequency and type of services provided free in its E-commerce supplements to existing surveys. 24 See Murphy summary (April 20, 1999). 25 Note that under the Census transactions definition of E-commerce, customer service will not be counted as E-commerce. 26 Currently, free goods are not included in GDP except by businesses passing on their costs to purchasers of priced goods and services included in final demand. The definition of what is a free good is fairly tricky. For example, own-bank ATM use is free to consumers having deposit accounts. Note that ATM transactions are included in bank transactions which affect the new NIPA price for unpriced banking services. 12

15 The dividing line between goods and services becomes fuzzier with E-commerce. If you receive a newspaper on-line, is it a good or service? When a printed copy of the newspaper is delivered to your home, it is counted as a good. When a newspaper is delivered on-line, it could be counted as a good or service. Our perception of the changing composition of economic activity can be affected by the classification of such goods or services. E-commerce prices and goods and services quality are frequently different from brick-and-mortar outlet prices and goods and service quality. Do we need price indexes for E-commerce goods and services that are different from price indexes for brick-and-mortar outlet goods and services? Is a good or service delivered electronically or sold over the web really the same in all its dimensions as a product not delivered or sold in that manner? An electronic airline ticket is a good example. Electronic tickets currently cannot be transferred between airlines, but paper tickets normally can be transferred. A substitution exists between time spent by an airline reservation representative inputting information and the time spent by an individual purchasing a ticket on the web inputting information. In addition, as is true with ATMs versus teller transactions, the convenience attributes of E-commerce goods and services are not well captured in the national accounts; therefore, prices may not accurately reflect what is being delivered. VI. Additional Data Needs to Measure E-Commerce Additional data are needed to measure E-commerce. Survey efforts are needed beyond those currently planned or funded. The data collected should include more E-commerce related information on changes in methods of distribution, point-of-sale, new products, including new investment goods, new businesses, and the level and composition of exports and imports. Notably on the household side, E-commerce may be bringing about a significant change in distribution methods. Goods formerly shipped in bulk to brick-and-mortar outlets are now being delivered by courier services and smaller trucks to homes. Consumers who had participated in the distribution system by bringing goods home from brick-and-mortar outlets now receive these goods without leaving home. For households, the effect of the growth of E-commerce on distribution is similar to the effect of the growth of mail-order business. However, the size of the effect is expected to be significantly larger. Distribution methods may be affected in other ways. B-to-B E-commerce is the primary form of E-commerce today. It is unclear if B-to-B E commerce has or will in the future change the nature and form of distribution. Some goods, particularly software, are being delivered electronically, e.g., over the internet. For these goods there is not a conventional transportation component. It is difficult to predict the full ramifications of the E-commerce revolution on distribution methods. At least some information will be forthcoming from the Census surveys on point-of-sale. Of specific concern is the documentation of direct sales to consumers from manufacturers and wholesalers as these represent PCE, a component of GDP. If these sales are wholly captured as 13

16 direct sales by existing and planned surveys, then it is a matter of a shift from a B-to-B transaction, representing intermediate inputs, followed by a B-to-C transaction, representing final demand, to a B-to-C transaction only sequence, resulting in the elimination of the intermediate step. If sales to consumers are wholly captured by the existing surveys, but not as a direct sales, then GDP is correctly estimated. However, the intermediate input part of the commodity flow of goods and services through the economy, a crucial part of the input-output accounts, is misestimated. If any sales to consumers, regardless of the source, are not wholly captured by the existing surveys, then GDP is underestimated. In any E-commerce direct sale situations, traditional retail prices might be applied to determine real PCE as opposed to internet manufacturer or wholesale prices. The B-to-B flow of goods and services may be affected by E- commerce in other ways, for example if small businesses switch purchases from retailers and small wholesalers to manufacturers and larger wholesalers. The B-to-B flow does not enter into GDP as it is not final demand as previously noted. Whether these changes arise from B-to-C or B-to-B transactions, the pattern of growth and the industrial composition of the economy can be affected. Growth in E-commerce and the supporting infrastructure has been accompanied by many new products, including new investment goods. An example of a new product is internet service provision. Clearly internet service providers (ISPs) did not exist before the internet. ISPs offer heterogeneous products that are typically bundled. The variety of ISP products and their sales 27 have both been increasing, adding to the measurement difficulty. How fast can ISPs and other new E-commerce related goods and services be incorporated into the accounts? In addition, how will the prices (and quantities) of new products be estimated? Supporting infrastructure frequently involve new types of investment goods, some of which may not be picked up by existing surveys, therefore compounding the problem. The new product problem is not a new problem. However the apparent high rate of growth of E-business and E-commerce and the associated rate of new product creation increases its importance. The level of economic activity may be underestimated if E-commerce or E-business related new business formation is missed. If the amount of activity is significant and not well-represented by those businesses surveyed, it is a problem. Tracking the creation and activity levels of new virtual businesses may be more difficult than tracking new brick-and-mortar businesses. The pace of new virtual business creation may be fast and their potential growth very high. Stock market expectations for such businesses are high, but may be incorrect. With E-commerce it is more difficult to track the origins and destinations of goods and services than with brick-and-mortar businesses. This can be a problem for international statistics, consequently, for GDP estimates, as the latter include net exports. The internet does not have customs agents. E-commerce facilitates transactions between parties that are geographically distant from each other. Digital products can move long distances at little or no cost subsequent to the initial set-up cost. E-commerce contributes to a global economy without national boundaries, but creates additional headaches for national accountants. 27 For a discussion of ISPs, see Greenstein (1999). 14

17 VII. Conclusion BEA and other U.S. statistical agencies are rising to the challenge of measuring E-commerce. E- commerce is only the most recent of a continuum of changes in the economy addressed by the statistical agencies. E-commerce is already included in BEA s national accounts, but not separated out from other activities. The two-fold challenge for all statistical agencies is to improve E-commerce estimates. The first challenge is to more accurately reflect the nature of E- commerce. This first challenge requires new measures of output and prices, a more complete picture of distribution methods and commodity flows through the economy and across our borders, and a greater understanding of how information technology infrastructure contributes to economic growth and productivity. The second challenge is to separate E-commerce from other activities. Separation of E-commerce activities from other activities will not change estimates of GDP, but will facilitate analysis of the E-commerce phenomenon. The current planned and proposed initiatives by BEA and Census, supplemented by the efforts of BLS, will go a long ways towards meeting these challenges. Additional surveys and efforts are needed as E- commerce and the digital economy evolve and grow. 15

18 Bibliography Bach, Christopher L. (1999), U.S. International Transactions, Revised Estimates for , Survey of Current Business, Vol. 79, No. 7, pp Cole, Rosanne, Y.C. Chen, Joan A. Barquin-Stolleman, Ellen Dulberger, Nurhan Helvacian, and James H. Hodge (1986), Quality-Adjusted Price Indexes for Computer Processors and Selected Peripheral Equipment, Survey of Current Business, Vol. 66, No. 1, pp Greenstein, Shane, Framing Empirical Research on the Evolving Structure of Commercial Internet Markets, written for Understanding the Digital Economy: Data, Tools and Research, Washington, DC, May 25-26, Grimm, Bruce T. (1998), Price Indexes for Selected Semiconductors, , Survey of Current Business, Vol. 78, No. 2, pp Haltiwanger, John and Ron S. Jarmin, Measuring the Digital Economy, conference draft for Understanding the Digital Economy: Data, Tools and Research, Washington, DC, May 25-26, Mesenbourg, Thomas, L., Jr., Measuring Electronic Business, presentation to the Digital Economy Working Group, July 15, 1999, Washington, DC. Moulton, Brent R., GDP and the Digital Economy: Keeping Up with the Changes, presented at the Digital Economy Conference, sponsored by the U.S. Department of Commerce, on May 25, Moulton, Brent R., Robert P. Parker and Eugene P. Seskin (1999), A Preview of the 1999 Comprehensive Revision of the National Income and Product Accounts, Definitional and Classificational Changes, Survey of Current Business, Vol. 79, No. 8, pp Murphy, Summary BLS draft for program office review, April 20, Parker, Robert P. and Eugene P. Seskin (1997), Annual Revision of the National Income and Product Accounts: Annual Estimates, ; Quarterly Estimates, 1993:I-1997:I, Survey of Current Business, Vol. 77, No. 8, pp Parker, Robert P. (1997), Note on Alternative Measures of Gross Product by Industry, Survey of Current Business, Vol. 77, No. 11, pp Taub, Leon W. and Robert P. Parker (1997), Preview of Revised NIPA Estimates for 1992 From the 1992 I-O Accounts, Survey of Current Business, Vol. 77, No. 12, pp Yuskavage, Robert E.(1996), Improved Estimates of Gross Product by Industry, , 16

19 Survey of Current Business, Vol. 76, No. 8, pp U.S. Department of Commerce, Bureau of Economic Analysis, (1990), Personal Consumption Expenditures, Methodology Paper, Methodology Paper Series M-5 (Washington, DC: U.S. Government), pp and U.S. Department of Commerce, Bureau of Economic Analysis (May 1990), The Balance of Payments of the United States, (Washington, DC: U.S. Government). U.S. Department of Commerce, Bureau of Economic Analysis, (1998), Benchmark Input-Output Accounts of the United States, 1992 (Washington, DC: U.S. Government). U.S. Department of Commerce, Bureau of Economic Analysis (1998), Updated Summary NIPA Methodologies, Survey of Current Business, Vol. 78, No. 9, pp U.S. Department of Commerce, Economics and Statistics Administration, Office of Policy Development, (June 1999), The Emerging Digital Economy II (Washington, DC: U.S. Government). U.S. Department of Commerce, Bureau of the Census, Development of NAICS, (accessed on August 18, 1999). 17

20 APPENDIX A 18

21 14 SURVEY OF CURRENT BUSINESS September 1998 Updated Summary NIPA Methodologies THIS REPORT PRESENTS summary descriptions of the principal source data and methods used to prepare the current-dollar estimates of gross domestic product (GDP) and the estimates of real GDP. 1 These descriptions have been updated to reflect the methodological improvements that were introduced in the annual revision of the national income and product accounts (NIPA s) that was released in July Current-dollar estimates Table 1 lists the components of current-dollar GDP starting with the components on the product side and proceeding to those on the income side. The subcomponents, with their dollar values for 1997, are grouped according to the methodology used to prepare them. The column for the annual estimates covers the revision cycle for those estimates and notes the major differences in methodology as the estimates move through the three annual revisions to a comprehensive, or benchmark, revision. 3 Forexample,for most goods in personal consumption expenditures (the first item on the product side), the table indicates one methodology for benchmark years and another for all other years. The column for the quarterly estimates covers only the advance estimate for the current quarter that is, the estimate prepared about a month after the end of the quarter. That estimate, rather than the preliminary or final quarterly estimate, is described because more attention focuses on the first look at the quarter. In addition, the column lists only the source data and methods; it does not indicate how many months of source data are available or whether the data are subject to revision by the source agency. Information on the key monthly source data appears each month in the Business Situation in the SURVEY OF CURRENT BUSINESS. Additional information on the monthly source data used for the advance estimate is available online from the Department of Commerce s Economic Bulletin Board. 4 The source data listed consist of a variety of economic measures, such as sales or receipts, wages and salaries, unit sales, housing stock, insurance premiums, expenses, interest rates, mortgage debt, and tax collections. For most components, the source data 1. BEA has prepared a series of papers that provide detailed descriptions of NIPA concepts and methodologies. The methodologies described in these papers are subject to periodic improvements, which are typically introduced as part of annual and comprehensive revisions; these improvements are described in the articles in the SURVEY OF CURRENT BUSINESS that cover these revisions. For more information, see appendix B at the back of this issue. 2. See Annual Revision of the National Income and Product Accounts, SURVEY OF CURRENT BUSINESS 78 (August 1998): For additional details on the release schedule for the NIPA estimates, see A Guide to the NIPA s, SURVEY 78 (March 1998): For additional information about the Economic Bulletin Board, call STAT USA at are value data ; that is, they encompass both the quantity and price dimensions that are required for current-dollar estimates. In these cases, the methodology indicated in table 1 covers only the adjustment of the value data to derive estimates consistent with NIPA definitions and coverage. For those estimates not derived from value data, the table indicates the combination of data with separate quantity and price dimensions that is used to derive the required value estimate and the major adjustments needed to derive estimates consistent with NIPA definitions and coverage. On the product side, a physical quantity times price method is used for several components. For example, the estimate for new autos is calculated as unit sales times expenditure per auto (the average list price with options, adjusted for transportation charges, sales tax, dealer discounts, and rebates). On the income side, an employment times earnings times hours method and variations of a stock of assets/liabilities times an effective interest rate method are used for several components. Some of the source data shown in table 1 for the annual estimates are used as indicators to interpolate and extrapolate the levels established by source data that are more comprehensive, and all of the source data shown for the advance quarterly estimates are used to extrapolate the level of the preceding quarter. In addition, extrapolation and interpolation may be based on trends, as is the case when judgmental trend is listed in the table. 5 Estimating methods. Table 1 refers to four methods commodity flow, retail control, perpetual inventory, and fiscal year analysis used by BEA for estimating specific components. The commodity-flow method is used to obtain the value of final users purchases of goods and services (that is, commodities) for BEA s benchmark input-output accounts. These values serve as the benchmark for the NIPA estimates of personal consumption expenditures (PCE), of producers durable equipment (PDE), and of the commodity detail for State and local government consumption expenditures and gross investment. 6 This method is also used for PDE in nonbenchmark years, but it is implemented in an abbreviated form. An even more abbreviated commodity-flow method is used for current quarterly estimates of PDE. 5. For a few components, the final quarterly estimates are based on newly available source data that replace judgmental trends. 6. For additional information on the commodity-flow method, see U.S. Department of Commerce, Bureau of Economic Analysis, Personal Consumption Expenditures, Methodology Paper MP 6 (Washington, DC: U.S. Government Printing Office, 1990): 31 34; and U.S. Department of Commerce, Bureau of Economic Analysis, GNP: AnOverviewofSourceDataand Estimating Methods, Methodology Paper Series MP 4 (Washington, DC: U.S. Government Printing Office, 1987):

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