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4 Measures of Government Productivity: Concepts, Methods, and Sources Previous chapters have discussed the concepts of productivity. We turn now to the way in which these concepts have been defined and measured in practice. This chapter describes the data sources and methods used by government agencies, particularly the Bureau of Labor Statistics (BES), in measuring productivity. It also includes brief comments on the adequacy and reliability of the official measures; major evaluation of these measures is included in Chapters 5 and 6. Productivity measures by private researchers that are almost as well known and widely used as the government measures are also covered in later chapters. Following a brief overview of government measures, the rest of this chapter is divided into sections on each of the major classifications of the economy for which official productivity measures are prepared: economy- wide, industry divisions, selected detailed industries, and the federal government. The last section, "Omissions from Existing Measures," deals with sectors and external effects not covered in any official Pleasures of productivity. Although this survey points up some gaps and weaknesses in the current official programs, we do not mean to imply that the current measures are not useful or that the agency personnel responsible for the series are not well aware of the weaknesses and gaps. Indeed, in many cases it was agency personnel who called our attention to the problems. For example, much of our analysis of weaknesses in the measurement of labor input came from a report of the Bureau of Labor Statistics Task Force on Hours Worked (1976~. 50

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Government Measures of Productivity OVERVIEW OF GOVERNMENT MEASURES 51 BES iS the major producer of government productivity measures.) It publishes or maintains measures for a wide array of sectors and industries from aggregate measures covering almost the whole economy to measures for detailed industry classifications. However, BES publishes only measures of labor productivity. Measures of output per unit of labor and physical capital combined, although widely estimated by private investigators, are not now part of the official program to measure productivity.2 Our review considers the various sector and industry measures separately since issues about data and concepts diner somewhat for the aggregate economy, industry divisions, and detailed industries. Two basic considerations apply to the official measures at all levels of aggregation, however, and it is useful to set them out in advance. One is the use of many secondary data sources in measuring productivity. The other is the difficulty of measuring real output for some sectors and industries. DATA SOURCES Unlike the government's series of measures of unemployment and prices, official measures of productivity are made up of data components that come from separate and independent surveys that are conducted for purposes other than measuring productivity. Even in measuring productiv- ity for detailed industry categories (e.g., cereal breakfast foods), BES must usually combine data from two independent surveys of the establishments in the industry. Data for labor input and the value of output come from one survey (taken by the Census Bureau); data on prices, which are needed to convert the value data into measures of real output change, come from another survey (usually taken by BES). For measuring productivity at high levels of aggregation (e.g., the private business sector), many different data sources must be combined. Not only do the data on value of output, price change, and labor input come from different sources, but also for each of the three components a number of different sources must be used (especially for value of output). Moreover, the data sources are not all governmental; trade associations and large companies are important sources of data for some industries. Neither are all the sources statistical surveys; some data are by-products of activities of regulatory agencies. This situation makes it difficult to develop objective measures of the margin of error in productivity estimates, especially the more aggregate ones. The well-established methodologies for estimating sampling error that are applied to other official statistics, like the unemployment rate,

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52 REPORT OF THE PANEL cannot be applied to the official productivity measures. Holland and King (in this volume) attempt to measure overall sampling error of output-per- hour measures from the sampling error in individual data series that are used in constructing these measures. Recommendation 3. The Panel recommends that the Bureau of Labor Statistics and the Bureau of Economic Analysis explore methods for estimating the implications of error reduction in component measures for the reduction of overall error in productivity measures beyond that corrected by routine revisions. The Panel recognizes that BES must continue for the present to use many sources of data because of the very high cost of carrying out a large- scale special survey to collect all the data components needed for productivity measures.3 The full expense of such a survey would involve not only direct outlays by the federal government but also the cost to firms to respond. (Ways to improve indirect measures of output per hour and other government statistics through cooperation among the many statisti- cal collection agencies are explored in Chapter 8.) PROBLEMS OF MEASURING REAL OUTPUT The difficulty of measuring changes in real output varies greatly across sectors and industries. At one extreme is an industry like copper whose output is a simple standardized product that does not change either over time or from place to place. At the other extreme are firms and agencies in the not-for-profit and government sectors, for which output measurement is extremely di~cult.4 Along a spectrum between these extremes are products and services for which good measures of output are available but only with increasing degrees of difficulty: apparel, consumer durables, new construction, producer durables, prescription drugs, the services of doctors and dentists, and the services of financial intermediaries are some examples of the more difficult ones. The current official program only publishes measures for specific industries when the concepts and data permit relatively good measures. Because there is also a need for productivity measures that cover broad sectors of the economy, BES has a program of measures covering the private business sector and major industry divisions. However, the data for constructing these measures vary in quality. In making aggregate measures, the better data are combined with weaker data, and the resulting measures necessarily suffer from some of the drawbacks of the weaker

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Government Measures of Productivity - c' .o _ 8 An IL y J 1 LO o Cry 5 o I to LL ~ 4 J o 53 9 7 6 Productivity at constant rate of growth (3.3 percent per year) '' ' ~Actual productivity / 3LI 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1948 1 1952 1956 1960 1964 1968 1972 1976 YEAR FIGURE 4-1 Productivity in the private business sector. (U.S. Department of Labor.) components. Our presentation will try to allow the reader to identify these weak spots. ECONOMY-WIDE MEASURES The most inclusive productivity measure regularly published quarterly and annually by BES iS real output of the private business sector per unweighted hour of all workers. Figure 4-1 shows the trend since World War II in this measure. This measure undoubtedly has an important influence on public policy formation. Its movements over long periods have given rise to the current concern over the productivity slowdown, while its movements from quarter to quarter and year to year are closely watched as an important cyclical indicator.5 The productivity measure in Figure 4-1 is composed of a numerator that measures real output produced and a denominator that measures the labor input used in producing that output. Table 4-1 shows the value of labor

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54 REPORT OF THE PANEL TABLE 4-1 Components of the Bureau of Labor Statistics' Productivity Measure for the Private Business Sector, 1''73-1977 Labor Inputb Output per Annual Change in Outputa (billions (billions of Hour (billions Output per Hour Yearof 1972 dollars) hours) of 1972 dollars) (id) 1973974.5 134.0 7.27 2.0 197495 1.3 134.6 7.07 .-2.7 1975929.1 129.1 7.20 2.4 1976993.6 132.6 7.49 4.0 19771,053.4 137.1 7.68 2.5 aFrom last row of Table 4-2. bUnpublished data from the Bureau of Labor Statistics, U.S. Department of Labor. input, output, and the ratio of the two for the private business sector for 1973-1977. BLS generates its own measure of the denominator, relying largely on data it collects itself. For the numerator, BES uses, with minor modifications, measures produced by the Bureau of Economic Analysis (BEA) in the U.S. Department of Commerce as part of the program of national income and product accounts. BEA uses data from a large number of sources to estimate income and product. REAL OUTPUT The numerator of the private business sector series is derived from measures of the gross national product (GNP) and its components. The GNP measures the market value of the output of all capital and labor resources that generate observable income transactions, plus the imputed product of some resources for which there are no observable market transactions, such as the services of owner-occupied homes and food produced and consumed on farms. The GNP includes the output of domestic workers hired by households (although not that of people who do the same work for themselves). The GNP also includes the output of employees of local, state, and federal government agencies and of not-for-profit organizations. However, BES subtracts the measures of output for these three sectors- household employment of domestic workers, employees hired directly by government, and not-for-profit organizations-from the GNP because at present the output measures for these sectors are based only on the amounts of labor inputs that are used. In the absence of any observable transactions involving output, BEA uses changes in inputs to measure changes in real output. When BES measures changes in labor productivity, it excludes these sectors from the output measure because, by definition,

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Government Measures of Productivity TABLE 4-2 Components of the Difference Between the Gross National Product and the Numerator of the Bureau of Labor Statistics' Productivity Measure for the Private Business Sector, 1973-1977 (billions of 1972 dollars) 55 Component1973a1974al97sa1976a 1g77b ._ . Gross national product 1,235.0 1,217.8 1,202.1 1,274.71,337.5 Less: rest of world 7.6 6.8 4.9 6.77.4 Gross domestic product 1,227.4 1,211.0 1,197.2 1,268.01,330.1 Less: government 138.9 141.9 144.6 145.8147.5 Gross domestic private product 1,088.5 1,069.1 1,052.6 1,122.21,182.6 Less: households and institutions 38.1 38.0 38.9 40.241.4 Gross domestic business product 1,050.4 1,031.1 1,013.7 1,082.01,141.2 Less: Gross product of owner-occupied housings 71.0 74.5 79.0 83.286.1 Less: "residual" 4.9 5.3 5.6 5.21.7 Bureau of Labor Sta tistics numerator (private business sector) 974.5 951.3 929.1 993.61,053.4 aBureau of Economic Analysis (1977) Survey of Current Business, July, Table 1.8. bBureau of Economic Analysis (1978) Survey of CurrentBusiness, February, Table 3. CUnpublished estimates by the Bureau of Economic Analysis, U.S. Department of Commerce. productivity change is zero.6 BES also excludes from output the imputed rental value of the services of owner-occupied homes. This is done because there is no way of obtaining good measures of the labor input (e.g., maintenance activities) associated with this output. Finally, BLISS deducts the output of resources owned by U.S. citizens but located in foreign countries and adds the output of resources owned by foreigners but located in the United States. This is done because the data for measuring labor input include only labor at work in the United States. In BEA terminology the numerator of the BES measure is equal to gross domestic business product in constant dollars minus the imputed value (in constant dollars) of the services of owner-occupied homes. Table 4-2 shows the components of the difference between GNP and the numerator of the BES productivity measure. BEA estimates gross domestic business product in constant dollars in two ways. One involves measuring the flow of real goods and services to the final-demand categories, and the other involves measuring the real

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S6 REPORT OF THE PANEL gross product originating in the industries that make up the private business sector.7 If all the data used in the two approaches were without error, the two measures would be equal. In practice, of course, the data have errors that lead to a difference between the two measures. The difference, called the residual, has been between 0.1 and 0.5 percent, with the final-demand measure always exceeding the product-originating measure. The rest of this section describes the BEA method based on the sales to final-demand approach. The following section, which is devoted to measures for industry divisions, describes the data and procedures used by BEA to measure real gross product originating by industry.8 The BEA procedure for estimating domestic business product in constant dollars involves two steps. First, BEA estimates output in current dollars from data on business sales to the major categories of final demand: personal consumption expenditures (PCE, 72.2 percent), producers' dura- ble equipment (PDE, 7.5 percent), structures (7.7 percent), government purchases of goods and services from private business (11.0 percent), change in inventories (0.8 percent), and net exports (0.9 percent).9 Second, it converts the current-dollar measure to one in constant dollars by a detailed deflation procedure. i. . r . ~ . BEA eStlmaleS OI current-aollar components come in a sequence ot preliminary and revised estimates for any given year (called a reference year) with each subsequent revision being based on more comprehensive and more direct sources of data. Table 4-3 shows some of the data sources used by BEA for making the annual current-dollar estimates. The table is arranged by final-demand category and revision sequence.l The data sources listed in Table 4-3 are a subset of all the sources used; the other data used are more specialized and are usually collected either by a trade association or by a government regulatory agency. The data sources diner in terms of sample size and the randomness of sampling procedures as well as in the degree to which they directly measure the magnitudes of interest. Some detail on the estimation of current-dollar PCE in 1974 will illustrate the characteristics of some of the data sources. In 1974, 42.8 percent of current-dollar PCE was based on data from the current-dollar sales of goods (durable and nondurable) reported in the monthly and annual Census Bureau Surveys of Retail Trade (see Once of Federal Statistical Policy and Standards 1977, Chapter 5~. For the remainder of PCE goods-new and used cars and trucks, and gasoline and oil (13 percent of PCE - BEA uses data from business and trade association sources. For new cars, four data sources are used. One trade source provides data every 6 months on auto production and list prices broken down by detailed categories of nameplate, model, and options. These are used to establish the average list price. Another trade source provides data

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Government Measures of Productivity 57 on overall auto sales (i.e., sales to consumers, businesses, and govern- ments) in quantity units. BES provides data on auto price discounts from the consumer price index Icy. Finally, a third industry source provides data used to estimate what part of the unit sales went to consumers. For consumer expenditures on gasoline and oil, BEA uses data provided by the Ethyl Corporation on the quantity of gasoline sold in a sample of gasoline stations and the data from the BES consumer price index for gasoline prices. For the services component of PCE (44 percent of PCE), a number of data sources are used. For hotel, personal, recreational, medical, and legal servicesii (6 percent of PCE), the data come from the Census Bureau's Monthly Selected Services Receipts Survey (see Once of Federal Statisti- cal Policy and Standards 1977, Chapter 5~. For the rents paid for rented dwellings (4 percent of PCE), BEA combines information on the number of tenant-occupied housing units from the Annual Housing Survey (of the Census Bureau and the U.S. Department of Housing and Urban Development) with information on rental prices from both the latest decennial census and the consumer price index. To estimate expenditures on auto repair services (2 percent of PCE), BEA combines trade source data on the number of car registrations with cat information on prices of auto repairs. For household utilities gas, electricity, telephone, etc.-(6 percent of PCE), BEA uses either company or industry sources. For example, the American Telephone and Telegraph Company supplies data on residential local and long distance telephone calls. The discussion above relates to estimation of output in current dollars; to measure output in constant dollars, BEA deflates the current-dollar data. For the large part of PCE that involves direct and explicit money transactions between consumers and private business firms (about 80 percent of PCE), BEA uses price change information from the cat for deflation purposes. The deflation is carried out at a fairly detailed level of product and service disaggregations.~3 The remainder of private business PCE iS made up almost entirely of the services of life insurance companies and banks and other financial intermediaries. For this sector, because of conceptual and data gaps, BEA measures real output change by measuring input change. For life insurance companies, the current value of operating expenses is deflated;~4 for financial institutions, changes in unweighted employee-hours between the base year and the current year are used to extrapolate the base-year value of operating expenses. For deflating PDE, which involves direct money transactions between business firms, BEA relies mainly on the price change information in the producer price index UPPED.

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58 o 50 C,3 V7 ._ as ._ V3 o o C,3 Cal ._ Cal 5 a: Cal - C~ ._ ._ - o Cal Cal Cal 3 o V) a' C) _I Cal C) _ .= 3 C ._ '~ V) V) C) Ct ._ =' _ 3 c: ._ ._ _ O A_ C: ~ - _ . ._ ,. _ O _. '- 3 C.) .= ~ Cal ~ Ct O O 3 3 V, r' C: Cal Cal Cal ._ Cat o Cal in: C) =~ ~ U ~ C) ~= ._ ~ U) ~ Ct C) . ~ _ ~ q Cal ~ C) :> 3 So: Cal ~ ~ C~ ;> tt I_ 3 -~ O O C~. ~ ~ - o ._ _ Cal 3 ~ .'' As c,5 Cat Cal .= C) C) at: C) Cal ~ ._ C) ~ Cal Cal U) C) _ ~ ~ V) ~ :^ _ _ _ ~ _ O O V: C) ._ V, - 5 - '-_ ~ O cr lo, _ .= ~ _ ~ O 3 ~ a: 3 Cal - Cal o - - C) Cal 3 ,~ 3 Ed o :^ - U) - Ct 3 ~ ~ C: U - o ._ 3 - V) o o 3 V) C) C: - _) o _1 C) G.) Cat ~ Can Cal O ~ ;^ C) ~ 3 (A If ~ - o , O) Cal sit O ^ ~ . ~ - Lea ~- V) ~> - ~. _ V'^ ~- C) ~ ~-Cal Ax ~ C: ~ O - - - Is: _ - ~ - O - o _ =3 _ ~ (t E - ' V: - C~: Cs V, ' ~ ' ~ := ~, c~ ~ ~ C~ o ~ o O v) C) 3 - C~ o :^ 3 - Ct 3 ' ~; C~ - ._ :t C) ~ C) o C:} V3 ~ C~ C) ~ :> ~ _ C) V) - C~ ~ C) - _ ~ O ~ - o 3 - V) C) 3 - I ~Q O X . . - O _ ~, ~ C~: ~ C) .o O ~- ) ~= . _ ~ ~.- .) ~r O ~ O _ 3 V) - ~: - ~: 0- o ~ , C~ o - X C) z C~ ~ , , .C ~ o Q~, ~ C~ _ ~ o O ._ e~ _ ;> t, cn D - - c'3 D ~ ~ C ~ O - O ~ ~ C ~ ~ C~ ~ , O.Q C ~ _. - Ce O .o ~, C,0 t .~ O V) ,_, ~ O ~ C :D 1- c,, O 0 t4 ~ bc - ~ '~ _ ~ ,~> 3 ~_ ~, ~ t_ _ t'; -0 X - _ ~ O ~ ~ = ~c' 3 _ :e ~ ~ ._ ~ , _ ~ _,_= o :^ o oo _ - C~ ~ , ~ ~ _ ~ _ ~_ _ ~ ~ o _ - _ tC ^ 3 Am - ~ ; ~ ~ ~ ~_ ~ ~ ~ 00 ~ ~ Q 1 1 ~ ~ ~ e~ ~ ~ _ ~ _ _ W ~ _ ~ _ _ ~ ~ - ~ ' .' .' _ I I c,) O C ~_ ,D ~ ~) ~ ~ :t ~ ~ ~ ~ ~ ~ CQ

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Government Measures of Productivity 59 The remaining major components of current-dollar business product shown in Table 4-3 are structures and government purchases from private firms, for both of which there is very little coverage of prices by government statistical agencies. For deflation, BEA uses price indexes from a wide variety of trade associations, regulatory agencies, and other governmental and private sources. When the constant-dollar measures of change in inventories and net exports are added to the estimates described above, the result is BEA'S official gross domestic business product in constant dollars (see Office of Federal Statistical Policy and Standards 1977, Chapters 5 and 8~; BES subtracts the constant-dollar value imputed to the services of owner- occupied dwelling units to arrive at the numerator of its measure of productivity for the private business sector. LABOR INPUT The concept of labor input used by BLS iS the unweighted sum of the hours of all workers engaged in the production of the output measured in the numerator of the productivity ratio. The data used to measure labor input come from different sources and sometimes involve imputations or assumptions. Unlike the output data, which come from many sources, the hours data are based primarily upon sample data from two sources: the current employment statistics (CES) program and the Current Population Survey (cPs). The CES covers all wage and salary workers in the nonagricultural business sector, who account for about 80 percent of total hours of the private business sector. For workers not covered by the CES, primarily those in agriculture and the self-employed, BES relies largely on the cPs. Table 4-4 summarizes the sources and imputations of hours of labor input for major components of the private business sector.~5 Like the output data, the input data come from programs not specifically designed to measure productivity. The CES iS designed to provide timely information on employment, hours, and earnings for the nonagricultural economy and also for detailed industries and for metropol- itan areas and state. The cPs, conducted by the Census Bureau for BES, iS designed primarily to provide timely information about the labor force, employment, and unemployment. The CES data are collected in a mail survey from the payroll records of about 165,000 sample establishments. State employment agencies, cooper- ating with BES, choose the state samples and survey the establishments that participate in the program, which is voluntary. The data cover 1 week, the survey week containing the twelfth day of the month, and become the basis of monthly estimates of employment, average hourly

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60 REPORT OF THE PANEL TABLE 4-4 Source Data for Hours in the Private Business Sector, 1977 Percentage of Components' Average Weekly Hours to Total Total and Components Hours (AWH) Employment Hours (Jo) Private business sectora 100.0 Manufactunngb 29.3 Production workers C CES CES 20.8 Nonproduction workersC assumed constantd CES 8.0 Self-employment CPS CPS 0.5 Nonmanufactunuge 63.8 NonsupervisoryC'f CES CES 46.0 Supervisory workersC same as nonsupervisory CES 7.9 Self-employed CPS CPS 9.2 Unpaid family workers CPS CPS 0.7 Government enterprises CPS BEA 2.2 Farm-all employees CPS CPS 4.7 CES = Consumer Expenditure Survey, CPS = Current Population Survey, BEA = Bureau of Economic Analysis. aNot-for-profit institutions are excluded from measures of the private business sector, so the Bureau of Labor Statistics subtracts their hours from those of the entire sector. Esti- mates of not-for-profit institutions" hours are calculated by dividing compensation in not-for-profit institutions by the compensation per hour figure for the entire industry. BEA is the source of the compensation data, collected annually. bBureau of Labor Statistics assumes no unpaid family workers in manufacturing. CEstimates of employment are benchmarked to unemployment insurance, Social Se- curity, regulatory and other government agencies, and private sources. dBureau of Labor Statistics has held AWH for nonproduction workers in nondurable manufacturing constant at 39.1 and for durable at 39.7 since 1962. Prior to 1962, it estimated these hours from its Area Wage Surveys but has found them changing very gradually and so has not changed estimates in recent years. eNonmanufacturing includes mining, construction, transportation, wholesale and retail trade, finance, insurance and real estate, and public utilities. Construction workers in the construction industry. SOURCE: Derived from unpublished data provided by the Bureau of Labor Statistics, U.S. Department of Labor. earnings, and average weekly hours. (When a survey week contains a holiday, BES makes adjustments so that data for that week correspond to a regular 5-day week.) The cPs sample consists of about 56,000 households. Since it is a probability sample, reliable estimates can be constructed from the sample data for the entire labor force. The sample households are visited by a Census Bureau enumerator or are interviewed by telephone: a respondent is asked to report hours at work and where worked for each member of the household who is 16 years of age and older. Like the CES data, the monthly cPs data are based on 1 week, the survey week containing the twelfth day

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77 ~ ~ o _ ~ CO oo . . . . . . . _ ~ ~ _ _ _ ~ ~ ~ C~ ~ ~ o o . . . . . . . o~ ~ ~ o o ~ ~ _ oo _ _ _ _ _ _ _ ~o ~ _ o ~ ~ . . . . . . . O ~ ~ ~ ~D - ~- _ ~ _ o oo _ ~ _ _ _ _ _ _ ~ ~ ~ C~ ~ o ~ . . . . . . . o~ oo oo ~ ~ oo ~ o C~ _ o oo o _ _ _ _ _ _ _ 00 ~D - <7\ -, v~ _~ . . . . . . .. o _ ~' ', ', ~ Cr~ _ ~ ~ o oo o o_ _ _ _ _ _ __ _ ~ _ ~ ~ oo oo . . . . . . . oo oo oo _ - - _ O oo cr _ _ _ _ _ _ ~ oo o ~ ~ U~ ~ . . . . . . . o ~ CO ~ ~ U) ~ _ _ _ o oo ~ _ _ _ _ _ _ ~ o oo ~ oo o oo . . . . . . . ~ ~ oo ~ ~ ~ _ o _ _ o oo ~ o _ _ _ _ _ ~ o ~ ~ o _ ~_ . . . . . . . . _ o ~ ~ ~ ~ o o o ~ Cr~ oo o o _ _ _ _ _ ~ ~ ~ C~ ~ ~ ~ . . . . . . . o _ _ oo ~ ') ~ 0 0 0 ~ ~ cr. 0 _ _ _ _ C~ ~ ~ o C~ o _ CO ~D oo ~ ~ 00 O O O cr ~ cr o~ _ _ _ o o o o o o o . . . . . . . o o o o o o o o o o o o o o _ _ _ _ _ _ _ o . ~ - ~ ~ o o ~ fi ~ ~ t - ~ hO ~ Ce C) ~ ~_ Ce _ _ ~3 ~ ~ a bo _ - , ~ oo _ ~ ~ ~ . . . . . . . . ( ~C~ ~ _ ~ 0 ~ ~ ~ 0 . . . . . . . . 0 C~ ~ 0 ~ oo ~ 0 ~ _ _ _ _ _ _ _ _ _ ~ ox ~ ~ ~ _ ~ . . . . . . . . _ ~ ~ ~ V~ ~ 0 ~ _ _ ____ __ ~ ~ ~ - 0 ~ ~ . . . . . . . . ~ ~ cr ~ ~ ~ 0 _ ~ ~ _ ~ ON ~ _ _ ____ __ . . . . . . . ~ o~ 0 ~ ~ ~ oo ~ _ ~ - C~ ~ O ____ __ ~ ~ a~ ~ ~D ~ c~ . . . . . . . . ~ - 0 ~ ~ ~ ~ _ ~ ~ ~ _ cr~ ~ 0 _ ____ __ oo _ ~ - ~ 0 0 ~ . . . . . . . . _ ~ ~ C~ 0 oo _ C~ - ~ - 0 - O _ _ _ ~ _ _ _ _ O ~ ~ ~ -~ ~D . . . . .. . _ o ~ 0 ~41 ~ ~ _ ~ _ ~ __ 0 _ ______ ~ _ - C~ O . . . .. . ~ oo oo 01 ~ e~ _ 0 _ _- 0 ______ _ 0 ~ C~ ~ ~ . . . . .. . \~) a~ ~ _ oo1 v~ ~ 0 0 0 - -- 0 _ ______ _ e' O oO ~1 o - 0 0 0 0 0_ 0 _ ______ 0 0 0 0 00 0 . . . . .. . o 0 0 0 0 1 o 0 0 0 0 0 0 0 0 _ ____ __ - o C,4 ~ .~: 0 ~ _ ^` .9 ~ O c, E.= ~a.O t4 ~ ,:' ~ ~ - - o CL ~ ~ ~ O ~ ~ ~ ~ ~ ~ ~C~ V~ V) ~ ~ ~ 5 Ct .0 Ct ~ ~ ~ o C~ ~, ~, ~ ~ r c~ 0 _ 0 - ~ \~) 11 C~ 11 ~ oo ~ C~ ~ ~ ~1 r ~ C~ Ct o~ Ct _ ~ _ ~ _ Ct _ 5) Ct ~ e~ ;^ ~ >, Ct---= .~ .U) .U~ .C~ ~ t.0 V) ba Ct ,_ Ct ~ D ct 5: ct ~ S C.) ' C) ~ C) C ~ O ~ O t ~ C5 ~ ~ X 13 ~ ~ bo S o U, O ~o C ~Ct 5 ~ O V, ~ Ct ~ C> C) _ Ct , C CL _ . ~ < ~r 0 r _ 0 ,-] ~ x "a b4, ~ u, ~ C ~ ON --- ~ ~ _ .`,, D ~ X =~,- Ce . ,_ ~ ~ O O-- ~o V:1 ~ O S Ct ~ ~ ~ .. S ~ O ~ ._ C) ~ ~ ~ _ ~ 0~ ~ ~

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78 REPORT OF THE PANEL TABLE 4-8 Selected Activities and Output Indicators, by Selected Functional Groups, from the Bureau of Labor Statistics' Federal Government Productivity Program Finance and accounts Regulation-inspection and enforcement and rulemaking and li- censlng Commissary store operations; laundry and dry cleaning Dining facilities operations; recruit training Flight training Active duty personnel, pay ac- counts (Navy); civilian time cards (shipyard, Norfolk) Invoices and travel processing (shipyard, Norfolk); billing, bill and collect for GSA Accounting obligations and expenditures (USIA); pay- ment and reconciliation of of checks (Treasury) Background checks on persons assuming sensitive positions in Department of Defense; develop and publish rail safety standards (DOT) Inspections of manufacturing plants, retail stores, etc. (Consumer Product Safety Comm.); consumer com- plaints (CAB) New animal drug review and approval process (FDA); patent application exami- nation. Functional Grouping Military base services and military training Activity Output Indicator Deflated dollar value of sales; pieces processed Number of meals served; students enrolled Student-years trained Number of active duty ac- counts maintained; num- ber of time cards processed Number of invoices and travel claims processed; bills mailed Obligation and expendi- ture documents processed; checks paid and reconciled and tax deposit forms processed Number of cases closed Number of new standards and modifications com- pleted Number Inspections made Number of consumer com- plaints processed Number of new animal drug applications; ap- plication disposed DOT = Department of Transportation; GSA = General Services Administration; CAB = Civil Aeronautics Board; USIA = U.S. Information Agency; FDA = Federal Drug Ad- ministration. SOURCE: Productivity Programs in the Federal Government. Supplement to Vol. 1. The Measurement Data Base. Annual Report to the President and the Congress by the Joint Financial Management Improvement Program.

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Government Measures of Productivity 79 pool garage, categories such as number of tune-ups, number of oil changes, etc., are preferable to the single category "number of cars serviced." In addition, as with other productivity measures, even the most detailed output can change in quality over time. The main approach used to minimize these difficulties is to collect data with as much output detail as possible. There are differences of opinion about whether productivity measures can be used to improve the current measure of real output for the federal government in the national accounts. BEA'S current method of measuring government output assumes that no productivity change takes place in the public sector; therefore, BES must exclude this sector in calculating its aggregate productivity measure. Some analysts note that almost all of the output measures are of intermediate activities that, although needed if the government sector is to produce anything, do not relate directly to the final services that taxpayers expect for their tax dollar national security, improved allocation of resources, etc.28 When any agency provides its intermediate services more efficiently, this only means that the potential for increasing overall government productivity has improved: we say "potential" because there is no direct link between increases in productivity at the intermediate level and at the level of final output. This may be shown by a hypothetical example. One agency produces its output faster than before-getting out a report for the use of another agency. But the second agency cannot implement the report any faster. Therefore a chain of events that might have reduced the amount of labor needed to produce the same level of national defense, or improved resource allocation, is choked oh. There is no way to confirm such a situation because there is no measure of final output. Other analysts stress that, for some programs, activities and output measures are closer to the flow of final services than for others, and that with further research and development the measures might be made worth using (see Searle and Waite 1980~. EXCLUSIONS FROM EXISTING MEASURES Several sectors of the economy are omitted from the official measures of output per hour. Some sectors included in GNP are excluded from productivity measures because BEA estimates their output by labor (and sometimes other) inputs. This results in essentially no measured productiv- ity change for these sectors (see the first part of Chapter 5~. General government, including federal, state, and local governments, is the largest of the excluded sectors. (As discussed above, the BES has a program to measure the productivity of the federal government that could, in

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80 REPORT OF THE PANEL principle, be used to extend the coverage of the aggregate productivity measures.) Other sectors omitted for this reason include not-for-profit institutions, like private hospitals and universities, and domestic services purchased by households. Still other sectors are excluded both from GNP and from productivity measures. The largest of these is the household sector, which produces such output as child care, using nonpurchased inputs. The GNP measure also does not include, as a deduction from output, the effects of environmental pollution, congestion, or noise. This section describes measurement efforts by BEA and by private investigators for the omitted sectors; for further discussion of sectors and effects not included in GNP, see Moss (in this volume). STATE AND LOCAL GOVERNMENT There is evidence of widespread interest in and use of productivity measures by state and local governments throughout the country. One of the main activities of the National Center for Productivity and Quality of Working Life (see its report 1975) was to help make state and local government managers aware of organizational and other innovations, including the use of productivity measurement, that would lead to cost savings and enhance productivity. There is an important difference between state and local governments and the federal government in the potential for developing productivity measures that could be used to expand the economy-wide productivity measures, namely, that most activities of state and local governments deliver final services directly to taxpayers. This makes it possible to measure quantities of those final services. Activities such as garbage collection, recreation services, library services, public transportation, water supply, and elementary and secondary schooling deliver a service of value to final consumers. Some kind of quantity measure, such as tons of garbage collected, number of visitors to the zoo, or number of passenger- miles traveled, is conceptually straightforward and possible to develop. There remains the problem of quality change. Work currently under way at the Urban Institute seeks to develop objective measures of the quality dimensions of the services of state and local governments (see Hatry et al. 1977~. That work suggests that two quality characteristics for garbage collection, "spillage of garbage collections" and "damage to private property by collection crews," could be measured by means of household and business sample surveys. Changes in tons of garbage collected is a good measure of real output change if the responses to the questions about these characteristics do not change. If the responses to the

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Government Measures of Productivity 81 quality questions do change, the problem remains of how to value the change in quality in terms of the appropriate amount of adjustment to the quantity measure. Of course, there is an analogous problem in the private sector whenever there are changes in the performance charactistics of goods and services sold. Private sales in current dollars are deflated by price change data that may not adequately allow for changes in the quality of the service sold. (For further discussion of measuring quality change in output, see Chapter 5.) NOT-FOR-PROFIT SECTOR Hospitals and colleges and universities are the two major industries in the not-for-profit sector. These organizations, although not-for-profit, have become increasingly concerned about cost and efficiency. There is an extensive literature by educators, medical and operations research special- ists, and economists on output, input, and productivity measurement and analysis for many aspects of hospital and university operations (see Froomkin et al. 1976, Institute of Medicine 1976, Stanford Center for Health Care Research 1976, Feldstein and Taylor 1977~. Much of the work (like that of the BES program for federal agencies, described above) is aimed at helping not-for-profit organizations improve their own efficiency rather than at developing measures that would permit comprehensive comparisons of productivity change between these sectors and the private business sector. One exception in higher education has been the use of weighted indexes of courses taken, using credit hours to aggregate the various kinds of course enrollments, such as full-time, part-time, degree credit, and nondegree credit (see O'Neill 1976~. The use of such output measures (and analogous measures, such as patient days, for hospitals) is similar to using tons of garbage collected to measure the output of garbage collectors; it is satisfactory as long as the quality of the basic unit of quantity does not change. This is particularly important for schools and hospitals because changes in the output quantity measure can themselves be associated with quality changes. If improve- ments in surgical procedures shorten the length of a hospital stay, they might result in lower output as measured by average patient load, and the productivity measure based on this output could decrease. If class size is significantly increased to reduce expenditures and the quality of instruc- tion falls, the output measured by credit hours would not change and the productivity ratio would increase. Indicators of changes in various quality dimensions (e.g., mortality rates adjusted by case mix, length of stay by type of ailment, class size, etc.) are needed to supplement pure quantity

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82 REPORT OF THE PANEL measures. (For further discussion on measuring output in hospitals, see Scott in this volume.) HOUSEHOLDS In recent years there has been much work by economists analyzing the family as a producing unit. A behavioral model is used in which the goods purchased from business firms are considered intermediate inputs that are then combined within the family with other "inputs" the non-market time of family members to produce the final services of ultimate interest (good health, vacations abroad, dinner or theatre evenings out, etch. This approach is an outgrowth of earlier work by labor economists who, in analyzing trends and cyclical movements in the labor force participation of women and teenagers, were forced to expand their models of labor supply to include the interrelationships of the work and leisure choices of all family members. A related topic involving the use of consumers' time is the time used in obtaining market goods and services. For many goods and services, such as medical care, repair services, travel, banking, and shopping, the input of the time of the consumer is an important factor of production. Entrepre- neurs are aware of this in making decisions about resource allocation, location, and technical change. Despite the widespread interest in non-market uses of time, there has as yet been no systematic effort by government agencies to develop data series that measure the utilization and productivity of non-market time, either of time used in conjunction with purchased goods and services or of time used to purchase goods and services. There is one nongovernmental project of sufficient size and scope that it may produce information about the productivity of non-market time although it is not directly aimed at measuring productivity change. This is a project by researchers at six institutions called "Cooperative Research in Goals Accounting.''29 Its primary objective is to measure changes in various broad aspects of"well-being" (such as life expectancy, mental health, and safety) and to relate these changes to a number of categories of input: purchased consumer goods and services, expenditures and services of government, and the non-market time input of individuals. The project also seeks to explore exogenous factors that influence such broad goals as mental and physical health, including factors involving demographic and mobility trends. (These objectives go beyond the usual province of analysis of household productivity the saving of time and market goods in such activities as doing housework or in shopping.) Some of the work of the project, which combines survey data on the uses of non-market time by

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Government Measures of Productivity 83 type of activity with national accounts data on purchased goods and services, may produce measures of secular changes in the time devoted to various activities. Moss (in this volume) discusses traditional measures of national income and broader measures of welfare. EXTERNAL EFFECTS AND OUTPUT The erects of production on the environment and on worker health and safety are now of great concern. Regulation of these erects is now pervasive and is widely believed to have had a significant erect on the change in measured productivity. To incorporate these effects fully into a productivity measure would require estimates of how much of every industry's activities has changed the quality of the environment at different times. This erect could be positive, as when regulations require firms to reduce their level of air or water pollution, or it could be negative or zero. The information could then be used to adjust the traditional measure of the industry's real output, which measures only its output of conventional goods.30 The change in the ratio of an index of adjusted output to an index of inputs-those devoted solely to producing conventional output and those being used specifically to influence the effects on the environment would yield a measure of productivity that fully reflected the ability of the industry to produce environmental erects in addition to conventional goods and services. It is not possible to perform this type of analysis with data now available. However, BEA iS planning to develop such information as part of its new Environmental and Nonmarket Economics Division. The data currently available primarily cover the resource costs that firms have incurred in meeting specific environmental standards imposed by regulatory agencies. Although these data alone do not allow for the computation of a comprehensive productivity ratio of the kind discussed above, they can be used to correct conventional productivity measures for the erects of requiring the firm to shift partially into the production of some favorable environmental output. These corrected ratios measure the trends in productivity that would have occurred if resources had all been used to produce conventional output and none had been used to reduce external environmental effects. Some researchers have used the available data to make such adjustments and thus to improve understanding of the trends in measured productivity in the period since 1966, which was one of rapidly increasing environmental and health and safety regulation. Chapter 7, which reviews the sources of measured productivity change, discusses the data and methods used in estimating these erects.

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84 REPORT OF THE PANEL Recommendation 5. The Panel recommends that research on the measurement of the output and productivity of the resources in excluded sectors be expanded. However, there should be no prema- ture selection or foreclosing of any of the alternative measures of output for such systems as health care and higher education. Although progress has been made in measurement in these areas, we do not yet know enough about the operation of such systems either to measure precisely their salient outcomes or to assume that we understand the processes that account for them. NOTES 1. The Economics, Statistics, and Cooperatives Service of the U.S. Depart- ment of Agriculture is the only other major government producer of productivity measures. It publishes both labor and multi-factor productivity measures for the farm sector. Since a task force of the American Agricultural Economics Association is currently assessing the data and methodology underlying USDA'S measures, our study did not include a review of those measures. 2. BES does develop measures of physical capital input (and other inputs, such as weighted labor input) on a periodic basis in order to analyze the factors underlying changes in labor productivity (see Norsworthy and Fulco 1977), but such measures are not published on an ongoing basis. 3. BES experimented with collecting all the productivity data from a single survey; the cost and complexity of the statistical design made the approach infeasible (see Mark 1961~. 4. BES does not publish any economy-wide productivity measures that include the not-for-profit and government sectors because there are no independent measures of output for those sectors (see Chapter 5~. 5. BES also publishes series on other, less-inclusive, aggregate measures: for the private nonfarm economy and for all non-financial corporations. Our discussion focuses on the measures for the private business sector. 6. This statement is not precisely true in all cases. The output measure used by BEA for employees of government involves a weighted sum in which senior and higher-grade employees count for more labor. Since the BES labor input measure is an unweighted sum, the resulting productivity ratio would show increases whenever the experience/skill mix increased. However, it is generally recognized that using inputs, even with this kind of adjustment, to measure real output is of limited value. There is interest in finding more direct measures of government output (see the section below on measuring productivity in the federal govern- ment). 7. The final-demand categories include sales of goods and services by business firms to households, other business firms on capital account (including the net changes in inventories of all firms), government, and the rest of the world (exports minus imports). The only business sales that are excluded are those that go to another firm on current account, so-called intermediate purchases. Gross product originating for an industry is defined as the value of its sales (adjusted for net

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Government Measures of Productivity 85 change in inventories) minus all purchases from other firms on current account. If this quantity is summed over all industries, then all intermediate purchases and sales will drop out and what is left are the sales (net of inventory change) by firms to the final-demand categories. 8. There is a third way of measuring business product based on the accounting identity between total value of production and the sum of payments to the factors of production (including any indirect taxes and including a profit component defined so that the two sides will always balance). When these factor payments are summed over all finals, they will be equal, conceptually, to current-dollar business product measured as the sum of sales to the final-demand categories. In practice, they differ by an amount called the "statistical discrepancy." Thus, the factor- payments data could be used to estimate current-dollar business product, and real product can be derived by deflating current-dollar product by the ratio of current- to constant-dollar business product derived from the data on sales to final-demand categories. In effect, one first estimates real business product via the sales to final- demand approach and then uses this information to form a deflator to apply to the current-dollar product estimate derived from the income side. 9. The percentages in the text show the share of each category in final demand after subtracting estimates of the contribution to these categories of the non- business sectors and of the resources located in the rest of the world. Thus, total purchases by government in 1976 were $264.4 billion (in 1972 dollars) of which $145.8 billion was for direct hire of employees and $1 18.6 billion was for purchases of goods and services from private business. The percentage above for government reflects only the $1 18.6 billion, since we are excluding sectors with no independent measure of output (Survey of Current Business 1977, Tables 1.2 and 1.8~. 10. The most comprehensive data sources are quinquennial censuses, and although they are listed at the end of the sequence, they also play an important role in making the preliminary estimates for many subsequent years. For example, the data available from monthly and annual surveys to estimate current-dollar PCE and PDE do not contain enough product detail for carrying out the deflation process for conversion to real product. These more detailed current-dollar components are estimated using information contained in the large input-output (I-O) table maintained by BEA. The detailed cells of the I-O table, showing product shipments and purchases by detailed industry, are revised on the basis of information in the quinquennial censuses. Thus, when the data from the 1967 censuses became available in 1976, it meant that all the product estimates in the period 196~1976 were subject to revision. 11. For the hotel, personal, and recreational categories, the monthly Census Bureau survey is used for all the non-benchmark estimates-quarterly, and the first, second, and third July revisions. For medical and legal services the monthly survey is used only for the quarterly and first July revisions. The second and third revisions are based on the business receipts data in the Internal Revenue Service's Statistics of Income. 12. BEA uses a deflation procedure to obtain what it calls "output in constant dollars." This terminology is somewhat misleading because the procedure used by BEA iS a mixture of a constant-dollar quantity index (with base-year price weights) and a deflated-value index using price indexes with base-year quantity weights. BEA divides the current-dollar value of expenditures into individual product classes and

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86 REPORT OF THE PANEL deflates each one by a price index. Each of these indexes is weighted by the relative quantities of individual products within the product class in the base year. If there was no change in product mix, the deflated value for each product class would precisely equal the value, in base-year prices, of the current-year mix of individual products, and the sum over all product classes would be equal to current output at base-year prices, i.e., "output in constant dollars." In general, however, the product mix of product classes changes, the amount of change being larger the less detailed the deflation categories. BEA iS aware of this problem and uses the most detailed categories. 13. Information on the actual detailed product breakdown is not in the quarterly and annual surveys that underlie the various July revisions. These detailed components are estimated using information contained in the large I-O table maintained by BEA (see note 10~. 14. The operating expense categories are paper, printing, telephone and telegraph, utilities, rent, computer leasing, advertising, medical service, postage, business travel and expense, and wages. Price change data for each of these categories are obtained from the producer price index (PP~), the cat, and other sources. The price change data are then used to deflate the current-dollar value in each category. 15. Some government services are sold directly to consumers, for example, those of the U.S. Postal Service. These activities, called "government enterprises" by BEA, are treated like private firms in the national accounts and are included by BES in the input and output of the private business sector. 16. The divisions of the private business sector are farming, mining, construc- tion, manufacturing, transportation, communications, utilities, wholesale and retail trade, finance, insurance and real estate, and services. 17. BES does not publish measures for the other divisions because the available measures of output either are based primarily on inputs, as in finance and life insurance, or are not considered reliable enough to justify publication on a regular basis. 18. Industry measures of labor productivity based on a value-added concept of industry output have some desirable properties compared with industry measures based on total output. Value-added measures do not change when the vertical integration structure of the industry changes, and they move in the right direction when firms in the industry economize on purchased inputs per unit of output. Neither of these properties is shared by measures based on total output. However, for purposes of analyzing the sources of an industry's output growth, its cost and price behavior, etc., a multi-factor productivity measure relating total output to all inputs (both intermediate purchases and labor and capital) is more relevant. 19. This description focuses on the internal logic of the double-deflation method and on how suitable the available data breakdowns (e.g., level of product detail available on a year-to-year basis) are for this methodology. The ability of the price change data to allow for changes in the performance characteristics of products and services within the deflation categories used is discussed in Chapter 5. 20. Primary products are the 5-digit product classes or 7-digit products listed under an industry in the Census Bureau's Numerical List of Manufactured Products (Bureau of the Census 1972; see Table 4-5~. Products of an industry not on this list are shown as secondary products.

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Government Measures of Productivity 87 21. Wherever-made shipments include those from plants where the products are primary and from plants where they are secondary. Deflators calculated with these shipment data are called "wherever-made deflators" and are based on the product mix of all industries where the products are made. The use of these deflators for industries where the products are primary and where the mix may differ can result in some error. 22. For some 4-digit industries, shipments of secondary products are sig- nificant. For these industries, the 5-digit product class mix of their secondary products has to be estimated from the latest available quinquennial census. Also, if an industry has a low "coverage ratio" of its primary products (e.g., the industry's shipments of its primary products are less than 75 percent of the wherever-made shipments), the industry deflator, which is based on wherever-made weights, may have a large error if price behavior differed between primary producers and other producers (see Myers and Nakamura in this volume). 23. There are 20 2-digit industry categories in manufacturing, such as food and kindred products (sac 20), tobacco manufactures (sac 21), and textile mill products (sac 22). 24. In connection with output measures based on the quinquennial censuses, it should be noted that the Census Bureau publishes a volume showing real output indexes for all 4-digit manufacturing industries (U.S. Bureau of the Census, Census of Manufactures, Volume IV, Indexes of Production). See Myers and Nakamura (in this volume) for a discussion of the methods used by the Census Bureau. 25. Very often, the match between a Census Bureau 7-digit product line and the product specification used by the PP~ is approximate (Ruggles 1977~. 26. See note 15. 27. A total of about 1,600 individual output indicators are aggregated using their respective base-year unit labor requirements for weights. 28. See the dissenting comment by Denison in Office of Federal Statistical Policy and Standards (1977, p. 8~. 29. The project participants are Nestor Terleckyj, National Planning Associa- tion; Abraham Charnes, University of Texas; William Cooper, Carnegie-Mellon University; F. Thomas Juster, University of Michigan; Michael Levy, The Conference Board; and Milton Moss, Stanford Research Institute. 30. Such an adjustment would require some judgment about the value to consumers of the increment in environmental quality.