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O Measures of O Productivity for Companies The preceding chapters have examined the conceptual issues and measure- ment problems involved in measuring productivity for industries and sectors of the economy. This chapter considers measuring productivity for individual companies. It examines the special uses, data requirements, and other problems associated with company-level measures. One reason for considering measures of productivity for companies is to elaborate the uses to which these measures are particularly well suited. The use of productivity measures in finding particular ways to increase productive efficiency is probably most effective at the company level. The first section of this chapter describes how a company can improve its performance by developing its own measurement system. It also briefly describes general procedures and problems of establishing a measurement system and presents a few examples. Measures of productivity for companies and related ratios can also be used in research. Explaining the reasons for differences in productivity among companies helps in understanding the sources of productivity growth. Estimates of productivity at the establishment level in particular are needed to make estimates of economies of scale. The second section of this chapter reviews research based on company records collected by the Census Bureau. It also discusses how the confidentiality of company records prevents private researchers from gaining access to the data directly and so limits the scope and quality of this research. Another reason for narrowing our focus to the company level is to consider a special problem that arises from the lack of coordination of the 166

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Measures of Productivity for Companies 167 different data collection agencies. Different agencies collect the basic data on prices, output, hours, etc., that enter into the calculation of productivi- ty ratios at all levels of the economy, from the establishment to the private business sector. The third section discusses how productivity measures could be improved by better coordination of the agencies that make up the federal statistical system. MEASUREMENT TO IMPROVE COMPANY PRODUCTIVITY Since, in the long run, companies are basically concerned with profitability, why should they be concerned with measuring their produc- tivity? Profitability is the best overall indicator of company performance: it measures the outcome of all management decisions about sales and purchase prices, levels of investment and production, and innovation as well as reflecting the underlying efficiency with which inputs are converted into output. However, profits are often influenced by factors external to the firm, such as shifts in demand or inflation, and so profits may rise or fall for reasons beyond the control of management. Measures of productivity, on the other hand, are measures of the efficiency with which resources are used. They reflect directly management's efforts to improve profits and to remain competitive by improving efficiency (Kendrick and Creamer 1961~. How much a company can learn from a productivity measurement system depends on how extensively it is carried out. Productivity can be measured for each production facility within a company, for different levels of operation, such as fabrication and distribution, and for various divisional and organizational elements as well as for the entire company. When measures are available for several units, comparisons can be made that should reveal when one unit is less efficient than or not performing as well as others. Comparisons of production facilities are particularly widely used, but when data are also collected from support units such as sales divisions, the company takes a broader view of possible places for improvement in productivity. Many companies limit their measurement to labor productivity, but a complete set of multi-factor and partial productivity ratios is preferable, since it is advantageous to save any input. A company may nevertheless benefit greatly from even a single-factor measure such as labor productivi- ty because the company may produce a labor-intensive product, or it may want to compare its performance with average output per hour for its industry as published by BES.i A company can also use productivity measures to forecast labor requirements and capital investment needs when expansion of output is planned, and in collective bargaining

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168 REPORT OF THE PANEL situations, to help in preparing estimates of the impact of a wage settlement on costs. Company measures of labor productivity may be used by regulatory agencies to help determine by how much a company should be allowed to increase its prices. Public utility commissions have considered the use of productivity estimates in their "cost and efficiency adjustment" formulas by which rates are set. For example, in 1973 the New Jersey Bell Telephone Company was permitted to automatically pass certain cost increases on to customers. Rates could be increased in accordance with the rate of increase in average hourly earnings minus the past rate of increase in output per employee-hour. This adjustment provides an incentive for the utility company at least to equal its past productivity performance so as to be able to recoup increases in unit labor costs. There could be even more incentive for a utility or other regulated firm to improve its productivity if the regulatory agency extended the productivity adjustment to cover other inputs (Kendrick 1975~. The Price Commission planned to use individual company measures for this purpose during phase II of the Economic Stabilization Program, in the early 1970s, but found that it could not rely on the rather hastily and inaccurately prepared estimates it received.2 MEASUREMENT PROBLEMS AND DATA SOURCES In preparing productivity measures, a company faces many of the same data needs and measurement problems associated with aggregate mea- sures. Since the conceptual and other measurement problems are discussed elsewhere (see Kendrick and Creamer 1961), here we mention only briefly the issues most likely to arise in constructing company measures. We then discuss the sources of data particular to a company. What kinds of problems are likely to arise in measuring output? One of the first problems for a multi-product company is how to combine its diverse products. There are two ways to measure multi-product output: by deflated value or by a quantity index using base-year price or labor- requirement weights. If a company settles on value of sales deflated by a price index as a way to measure output, two other problems arise. First, it must adjust sales for additions to and subtractions from inventories of finished goods and from work in progress in order to calculate production for a given time period and to relate that production to the inputs used during the same period. Second, the value of inventories should be corrected for the arbitrary valuation methods used in standard accounting procedures, which often do not reflect market values. A non-financial company should usually exclude from its output

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Measures of Productivity for Companies 69 measure in a productivity ratio the income earned from financial assets because this income is not produced by labor input. However, the output of support activities to production, such as food services, maintenance, or force account construction should be included whenever the correspond- ing labor input is included. A number of problems are likely to arise in measuring inputs. The company must first determine whether it will be better served by a labor or a multi-factor productivity measure. If the multi-factor measure is chosen, labor and capital inputs must be measured and combined; for a more complete productivity accounting, materials inputs should also be includ- ed. Labor input may be a simple count of hours, but even in this case, a company should keep in mind that hours spent at the workplace are a better measure of actual labor input than hours paid for (see Chapter 6~. A slightly more complicated measure of labor input involves weighting hours by skills. When combining labor hours with other factors, it is conceptual- ly correct to use weighted hours. Materials input, like output, is usually a multi-product measure and presents the same measurement problems that output does, except that in the case of materials there may be more components for which collecting data on prices poses a problem. Capital input is usually the most difficult input to measure. The several steps of constructing a capital input measure are outlined in Chapter 6 and are not repeated here. However, it is worth emphasizing that a capital input measure is substantially more representative of actual input if assets are valued at replacement, not historic, costs and if depreciation is based on a reasonable service life of assets, not on arbitrary accounting life. After measuring individual inputs, the next task is to combine the factor inputs into a single index. The most obvious way is to use factor-share weights. There are other weighting schemes, but they are often more complicated and raise such issues as what is the appropriate production function, an issue that is incongruous at the company level. A company can find much of the data needed to measure productivity in its own cost-accounting, payroll, and other internal records. Supplementa- ry data can be found in the forms that many companies, especially large ones, send regularly to the Census Bureau, the BES, the IRS, and regulatory agencies. The Annual Survey of Manufactures (ASM) contains data on employment and hours and on costs of labor, capital, and materials inputs. See Table 8-1 for examples of productivity ratios that can be constructed with ASM data. There may be occasions when a company needs to collect data especially for productivity measurement. For example, a special collection program for prices might be established because the price data may be widely scattered among numerous invoices. As a shortcut, the company may go to external sources of data available from agencies that

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Measures of Productivity for Companies 171 produce economic statistics; for instance, it could use components of the BES consumer or producer price indexes. EXAMPLES Here we present two examples of companies that have developed productivity measurement systems. The first example is General Foods, a major food processor. The measurement program of General Foods has many of the features described in the preceding section. First, it has a comprehensive set of productivity measures, including measures for labor, capital, and materials, and a multi-factor measure that combines all three. A measure of materials productivity is important for General Foods because it is an intensive user of raw and packaging materials. Second, General Foods has measures for several of its plants. Thus, it can compare plant performances but with the awareness that productivity levels and rates of change diner for plants producing different items. Third, General Foods makes the program a company-wide effort, enlisting the participa- tion of management, accountants, engineers, and plant-level employees. A special feature in the construction of its productivity measures is the use of costs of materials lost instead of total purchased materials. Purchased materials include materials consumed in making a food item and materials lost, for example, grain spilled in unloading freight cars. General Foods concentrates on the materials lost about which something can be done over a short span of time. The second example is United Airlines, a major domestic air carrier. The controller's office has the responsibility for labor productivity measures for the entire company for major divisions. Labor productivity is measured in terms of equivalent passengers boarded per employee, and company-wide productivity is also measured in terms of revenue ton-miles per employee. The United Airlines program deals mostly in physical output measures, thus avoiding many of the problems associated with deflated-value measures. Its measures are akin to an efficiency concept of reducing labor input and are not designed for considering overall costs, as are those of General Foods. United Airlines uses internally collected data as well as the data provided to the Civil Aeronautics Board (CAB) to calculate its ratios. It also uses CAB data to construct productivity measures for other air carriers in order to have a basis for productivity #, comparison.

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Measures of Productivity for Companies ASSISTANCE FOR COMPANIES 173 A company can get help in preparing productivity measures from handbooks, from federal government agencies, or from productivity centers. Measuring Company Productivity by Kendrick and Creamer, although written in 1961, is still the most complete and informative treatment of the topic. The federal government is active in promoting productivity improvement at the company level. The U.S. Department of Commerce has conducted seminars for companies in trade-impacted industries to inform them about recent developments in production technology. The National Center for Productivity and Quality of Working Life was until 1978 the most active federal agency in encouraging and assisting companies to develop measurement programs. This function of the National Center has recently been transferred to the U.S. Department of Labor. In several countries there are private or public productivity centers that advise companies on how to improve their productivity.4 These centers often conduct a special type of program that allows a company to compare its performance with other competing companies that produce a similar product by similar techniques. Figure 8-1 illustrates the typical set of evaluation ratios provided to participants in a company comparison program. A company receives its own ratios and, in coded form, those for the other participants. The center, or perhaps a trade association or an accounting firsts, processes the company records. All the ratios in Figure 8-1 are components or derivations of what most companies think of as the key measure of performance, the rate of return on assets ratio 1. If a company shows a poor performance on that key ratio, it can follow the lines of Figure 8-1 to see if its labor costs as a percentage of sales (ratio 10) or some other factor deviates unusually from the average. The company comparison program as it is usually handled provides assessment mostly in terms of financial ratios and so functions as a means of evaluating the short-term performance of a company vis-a-vis its competition. For small- and medium-size firms, this kind of appraisal program is probably sufficient. A large company, especially one with unique production methods or a wide variety of products, may gain more from an independent measurement program.

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I74 REPORT OF THE PANEL Recommendation 21. The Panel recommends that companies investi- gate whether having measures of productivity would improve their performance, planning, and evaluation. It encourages the U.S. Department of Labor and the U.S. Department of Commerce to continue to inform companies of the potential benefits of productivity measurement programs. RESEARCH ON COMPANY PRODUCTIVITY Thus far we have discussed measures constructed by companies for the purpose of improving their own productivity. Now we turn to data collected from companies (or their establishments) by the Census Bureau and examine how these data are used to study productivity change in industries and in the economy. The Census Bureau data are collected in the quinquennial economic censuses and by other special surveys and reports for manufacturing, wholesale and retail trade, services, construc- tion, and agriculture. Most productivity research has been limited to manufacturing industries, probably because these data conform best to the underlying concepts of productivity measurement. From the study of productivity in manufacturing establishments, a great deal could be learned about how and why productivity change is taking place. Productivity change may result from the introduction of new establishments with higher productivity and the abandonment of high-cost marginal establishments. This result may often be associated with a shift in the location of an industry, such as the movement of the textile industry from the Northeast to the South. Similarly, productivity may increase through expansion of establishments with high productivity and contrac- tion of establishments with low productivity, without marked changes in the productivity of individual establishments. Alternatively, productivity increases may reflect changes within individual establishments from new techniques or learning through experience. To understand what is taking place, these different situations need to be distinguishable. A few studies have tried to investigate some of these possibilities and have met with varying degrees of success, often depending on the form in which the data were made available to the researcher. One approach to studying the causes of productivity change is to explain differences in productivity levels among establishments. Klotz (1973) attempted to analyze the causes of dispersion in labor productivity using data from the 1967 Census of Manufactures. He tried to explain the dispersion of value added per production-worker hour among establish- ments in terms of the dispersion of other ratios easily formulated from Census Bureau data, such as total gross assets per production worker, a

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Measures of Productivity for Companies 175 crude proxy for the capital/labor ratio. His measure of dispersion for any ratio was the difference between the quartile average and the industry average, with all quartiles formed by ranking establishments by value added per hour. Table 8-1 shows the industry and quartile means for value added per hour and a few other ratios for four selected industries. In all cases, the specialization ratio is high so that productivity differences are not due to substantial differences in the products manufactured by establishments.5 The higher value added per hour is associated in every industry example with higher assets per hour, as expected from production function theory (see Chapter 3~. Higher average production-worker wages are also associated with higher productivity. This association suggests that plants are more productive because they hire more skilled, and therefore more expensive, labor. Finally, the more productive plants are in two cases larger in terms of employment per establishment, so that economies of scale may be affecting their productivity. Based on the associations suggested in Table 8-1, Klotz tried to explain productivity dispersion within 200 4-digit manufacturing industries for which the specialization ratio was high, but he obtained no significant empirical results. A major weakness in Klotz's study is the level of aggregation of the basic data. Working with deciles or finer breakdowns might have improved his empirical results, but that degree of detail would make confidential company records public. Another approach to the study of productivity change is to explain movements in the productivity of establishments over time. In the 1960s the Census Bureau created a longitudinal file of establishments that would permit a time-series approach. However, it found many difficulties in constructing this file, such as tracing an establishment that is classified in different industries for different census years and dealing with the fact that even large establishments do not report every year between censuses. The result of the Census Bureau's effort was a data file for just three years, 1952-1954, which was the basis of very little research (Kallek 1975~. One of the more successful productivity research projects using time- series data on establishments is the one by Griliches (1980) relating R&D input to productivity (described in Chapter 7~. Part of the success of that project may be due to its more limited scope; it deals only with the large manufacturing companies responsible for most of R&D expenditures. There are two interesting points to note about the construction of the data set for Griliches's research. First, the study was possible because two micro-data sets held by the Census Bureau were merged (see Chapter 7~. This suggests other mergers that may open up new areas of research. Griliches has listed some possibilities: the merger of Census Bureau data

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176 REPORT OF THE PANEL with company records from the IRS, the Securities and Exchange Commission, or the patent once. Second, because of confidentiality restrictions, Griliches, like all private researchers, did not have direct access to company records. Following Griliches's instructions, the Census Bureau processed the basic data into moment matrices. This procedure hampered the research in two ways: first, it caused long delays 3 years lapsed from the conception of the project to the delivery of the first matrices; second, the data in their aggregated form were not flexible enough to allow other calculations, such as the social rate of return to R&D. Confidentiality restrictions have repercussions beyond their impact on research of private scholars; they also affect the quality of the basic data themselves, which are used in estimating price movements, GNP, employ- ment, and productivity. The issue of confidentiality of company records has two major aspects. First, confidentiality of data as it relates to the invasion of privacy of individuals has been a major issue over the last 15 years, as computerized files have been developed. There has been concern that sensitive records will be brought together to create dossiers on individuals without their knowledge, and that these dossiers will be used against the subjects by government agencies or by private businesses. This concern about the invasion of privacy has not been confined to government records. The computerized files of credit agencies, banks, schools, and the personnel records of employers have all been the subject of controversy. In such a climate, companies are naturally concerned about the confidentiality of records that the government obtains from them. The second aspect of the confidentiality of company records relates to the procedures under which information is collected. In order to get accurate, detailed, and valid information from companies about their operation, collecting agencies such as the Census Bureau have found it important to assure companies that the information provided will be kept confidential and will not be given to regulatory or administrative agencies concerned with such matters as tax collection, enforcement of antitrust laws, or monitoring wage and price behavior. Even in situations for which by law some reporting by companies is compulsory, collecting agencies believe that without such a promise of confidentiality, companies would be less cooperative and the information provided would be less complete. Where the provision of information is voluntary, the promise of confidentiality is even more important. As a result, almost all information collected from companies by federal agencies is kept confidential under rules that may be even stricter than those relating to data obtained from individuals.

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Measures of Productivity for Companies INTERAGENCY COOPERATION 177 The data relevant to productivity measurement are scattered among many different agencies. The Census Bureau conducts censuses arid annual surveys covering such important information as shipments, inventories, cost of materials, capital expenditures, employment, and employee-hours. The Bureau of Labor Statistics has the main responsibility for collecting price information, and it also collects information on wages, employment, hours, and earnings. The Federal Reserve Board compiles a production index from a wide variety of different sources. In addition to these sources, administrative and regulatory agencies collect much related information: the Internal Revenue Service obtains information in connection with corporate tax returns; the Social Security Administration obtains informa- tion from employers in connection with social security contributions; the Securities and Exchange Commission obtains quarterly income statements and balance sheets for all corporations listed on stock exchanges; and the Federal Trade Commission obtains line-of-business information from individual firms. There are also specialized sets of data collected by such agencies as the Federal Communications Commission, the Interstate Commerce Commission, the Civil Aeronautics Board, and the U.S. Departments of Energy, Agriculture, Housing and Urban [Development, Transportation, and the Interior. Although in some instances data at the individual company level are exchanged between agencies, this is the exception rather than the rule. The Census Bureau has authority to obtain company and establishment records from other federal agencies, and they use the records of the Internal Revenue Service and the Social Security Administration. But reports to the Census Bureau, by statute, cannot be given to other agencies. Among the other agencies, little or no exchange takes place. In some cases, interagency committees have been established to improve the comparability of data among agencies. For example, there is a "working group on the 1977 Census Production Indexes" composed of representatives of the Census Bureau, the Bureau of Labor Statistics, the Federal Reserve Board (FRB), and the Bureau of Economic Analysis. Each of these agencies prepares estimates of output for many 4-digit manufac- turing industries. The FRB estimates are published first as part of the monthly industrial production index, followed by annual estimates prepared by the BES and BEA and finally the Census Bureau quinquennial estimates. Even after all agencies have benchmarked their estimates to the Census Bureau data, discrepancies among the estimates may persist because each agency has its own set of procedures, deflators, and weighting schemes. For example, for recent benchmark years, considerable

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178 REPORT OF THE PANEL differences were found in the four output measures for the pulp and paper industries (see Myers and Nakamura in this volume). Also, sizable differences were found between the output measures of 50 manufacturing industries prepared by the BEA and by the FRB (see Popkin in this volume). These results suggest that agencies should develop a more integrated effort to measure real output. Recommendation 2a The Panel recommends that the relevant agencies try to reconcile their different output measures that cover the same industry or sector to improve the measures and to acquire a better understanding of measurement problems associated with weighting, deflation, and other procedures. This can be achieved by strengthening the existing mechanisms in government that bring the agencies together, such as committees formed by the Office of Federal Statistical Policy and Standards. A major problem with the comparability of the basic data has been that different agencies assign the same establishments to different industry classifications; as a consequence, aggregated data at the industry level are not in fact comparable from agency to agency. The Census Bureau has prepared a Standard Statistical Establishment List that indicates how it classifies different establishments by industry, but this listing has not been made available to other government agencies because it is felt that this would constitute a breach of confidentiality. The classifications assigned by the Census Bureau are based on its report, in which establishments list their products and services. Unless a uniform establishment list can be made available to all federal statistical agencies, standardization of information collected by different agencies is not possible.6 The result of the high degree of decentralization of federal statistics coupled with confidentiality requirements has been that the integration of data needed for productivity measurement has not been achieved even at the industry level. Recommendation 18. The Panel supports legislation that would allow the Census Bureau to share with other federal statistical collection agencies the Standard Statistical Establishment List, so that all those agencies could sample from a common universe, making the basic economic data more comparable. The President's reorganization project includes a statistical component, which is concerned with improving coordination among the statistical

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Measures of Productivity for Companies 179 agencies. The objectives of the improved coordination include reducing the reporting burden of companies and establishments, which may now report approximately the same information to several different federal agencies, and improving the utilization of existing information within the federal government, within the context of ensuring confidentiality. To achieve those objectives, procedures would have to be set up to make possible the interchange of data among statistical agencies with full protection of confidentiality. These would be similar to the procedures currently used for transferring administrative data from IRS and the Social Security Administration to the Census Bureau. Improved coordination would also promote cooperation among statistical and regulatory agencies in design- ing their statistical programs so that the data collected by different agencies would be complementary rather than conflicting or redundant. Under such conditions it should be possible to obtain production, labor input, and capital input information that would permit the computation of productivity measures at the establishment level. Recommendation 19. The Panel stresses the urgency of finding a solution to the problem of coordinating data collection and allowing data interchange among the federal statistical collection agencies for statistical and research purposes, in such a manner that the rights, benefits, or privileges of individual respondents are not violated. NOTES 1. A comparison of productivity in a single plant or company with the average productivity in an industry may not be valid if the plant or company produces an output mix very different from other plants or companies in the industry (see Chapter 2~. Also, the individual plant or company and the industry productivity measures should be prepared in the same way. (For an explanation of how to construct individual plant productivity measures akin to the BES measures, see Greenberg [19733.) 2. The discussion in Chapter 2 about the possible misuses of productivity measures in setting wages and prices and in forecasting in connection with the aggregate measures also applies to company-level measures. 3. For other case studies, see Kendrick and Creamer (1961) and National Center for Productivity and Quality of Working Life (1976~. 4. The American Productivity Center, a private productivity center supported by major corporations, has recently been established in Houston. 5. The specialization ratio is the ratio of industry primary product shipments to total industry shipments. It should be kept in mind that even a high

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180 REPORT OF THE PANEL specialization ratio is consistent with a variable mix of primary products among establishments. 6. Even though agencies may have their industry classification verified by the Census Bureau, all differences in assignment of establishments cannot be resolved in this way. Thus, there remains a need for sharing the Census Bureau list of establishments. It should be noted that several state governments compile lists of establishments in their states by industry and make them available to the public.