Brookings: Experts - Jack E. Triplett

Health System Productivity

Jack E. Triplett — Sun, 22 Nov 2009 00:00:00 -0500

INTRODUCTION

Productivity is the ratio of outputs to inputs, and productivity growth is the growth of the ratio-that is, it is a shift in a production function. In concept, health system productivity differs little from productivity in any other industry or sector. Though the economics of medical care may in some respects appear unique, production in the sector is still described by a production function, a relation between medical care inputs and output. Productivity change in medical care is a shift in that medical care production function.

However, productivity in the medical care sector has behaved very differently from other industries, even other services industries: Measured productivity growth in medical care has typically been negative. Murray [1] reported negative labor productivity growth in Swedish hospitals. Triplett and Bosworth [2], [3] found negative productivity growth in U.S. medical care between 1987 and 2005, at a rate of about one percent per year, a finding confirmed by Harper, et al. [4]. When the U.K. statistical office added the output of medical care to the country's national accounts, the negative productivity growth implied by the new measure provoked an outcry in Parliament and the appointment of a special commission on public sector services productivity measurement [5] to determine what was wrong.

Few industries have experienced more innovation, so medical care's negative productivity growth is highly suspect. Economists generally believe that measured productivity growth in the sector is biased downward because of difficulties in measuring medical care output accurately and also that measurement errors are pervasive in some of the inputs, particularly in pharmaceuticals and medical devices and in the high-tech portions of medical equipment. In all productivity measurement, the most essential tasks are getting the data right, which provides the agenda for section III of this chapter. Data on inputs and outputs-indeed, economic data generally-for the health care sector are much less well developed than for many other sectors of the economy, which is bizarre considering the size of health care in most industrialized countries and the importance of the sector. Many studies of medical care productivity employ data from the national accounts [6], but microdata studies face the same measurement problems.

Medical care is not the only determinant of health. In section IV, I discuss some of the economic implications of the fact that medical care, though it is demanded to improve health, does not, by itself, produce health.

[1] Murray, Richard. 1992. “Measuring Public Sector Output: The Swedish Report.” In Output Measurement in the Service Sector, Zvi Griliches, ed. National Bureau of Economic Research Studies in Income and Wealth Vol. 56, pp. 517-42. Chicago: University of Chicago Press.
[2] Triplett, Jack E. and Barry P. Bosworth. 2004. Productivity in the U.S. Services Sector: New Sources of Economic Growth. Brookings Institution Press.
[3] Triplett, Jack E. and Barry P. Bosworth. 2007. The Early 21st Century U.S. Productivity Expansion is Still in Services. International Productivity Monitor. Number 14, Spring 2007, pp. 3-19.
[4] Harper, Michael J., Bhavani Khandrika, Randal Kinoshita, and Steven Rosenthal. 2008. Multifactor Productivity Contributions of U.S. Non-Manufacturing Industry Groups: 1987-2005. Paper presented at the World Congress on National Accounts and Economic Performance Measures for Nations, Arlington, Virginia, USA, May 13-18.
[5] Atkinson, Tony. 2005. Atkinson Review: Final Report: Measurement of Government Output and Productivity for the National Accounts. Palgrave McMillan.
[6] Commission of the European Communities—Eurostat, International Monetary Fund, Organisation for Economic Cooperation and Development, United Nations and World Bank. 1993. System of National Accounts, 1993.

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Jack E. Triplett

Publication: Oxford Handbook of Health Economics

Output and Productivity Measures for Medical Care and Education

Jack E. Triplett — Wed, 01 Apr 2009 00:00:00 -0400

Of services output generally, “…it is not exactly clear what is being transacted, what is the output and what services correspond to the payments made to their providers (Griliches, 1992). Medical care and education are services sectors where output measurement is ambiguous or difficult, both for conceptual and empirical reasons. Much recent progress has been made on methods for measuring medical care output. Far less progress is evident on education output, for reasons I will explain in section II of this paper.

In the U.S., concern for improving data on medical care output and productivity extends well beyond the national accountants. A National Academy of Sciences panel is currently reviewing measures of medical care output and accounts for medical care and for health. In the U.K. as well, concern for these topics extended outside the rarified circle of national accountants: Is there any other country where health system productivity figured in Parliamentary debate?

In the last section of this paper, I contend that measuring the output of medical care should be a concern, not just for the national accountants, but also for politicians and managers of health care systems: The data needed by national accountants to measure health care output appropriately are data that are also necessary for analysis and understanding of advancing health care costs and for improving health care delivery. The public policy analysis need is a more urgent one than improving the national accounts, but fortunately the two agendas coincide.

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Jack E. Triplett

Is the 21st Century Productivity Expansion Still in Services? And What Should Be Done About It?

Barry P. Bosworth and Jack E. Triplett — Sat, 06 Jan 2007 12:00:00 -0500

The 20th century ended with an unexpected surge in U.S. productivity growth. The 21st century has opened with another. Labor productivity grew two and a half percent per year during 1995-2000, nearly double its growth rate over the previous two decades, and has exceeded three percent per year since 2000, in Bureau of Labor Statistics data.

In Triplett and Bosworth (2006), Bosworth and Triplett (forthcoming) and Triplett and Bosworth (2004),² we advanced an interpretation of the post-1995 U.S. productivity expansion that differed in several respects from previous research (Oliner and Sichel, 2000; Jorgenson, Ho and Stiroh, 2000; and Gordon, 1999). Earlier studies focused on impressive multifactor productivity (MFP) growth in computer and semiconductor production, its resulting feedback into information technology (IT) investment in the rest of the economy, and the subsequent labor productivity (LP) growth in “IT-using” industries because of IT capital deepening.

Unlike previous researchers, we examined productivity in services industries. The post-1995 IT investment boom did create a capital deepening effect on services industry LP (IT investment goes overwhelmingly to services industries). In this, our services industry results parallel aggregate results of others.

We also showed that strong MFP growth in the services sector transformed American economic performance after 1995. During the previous (1973-1995) years of slow aggregate productivity growth, the services industries were marked by productivity stagnation, in both LP and MFP, as Griliches (1992, 1994) pointed out. After 1995, services productivity accelerated strongly. In the revised Bureau of Economic Analysis/ Bureau of Labor Statistics (BEA/BLS) data used for this paper, services sector LP and MFP growth rates more than doubled after 1995 (Table 1). Services sector acceleration substantially exceeded the more modest productivity accelerations in the goods-producing sector.

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Publication: Presented at the 2007 Meeting of the American Economic Association

Productivity in the U.S. Services Sector : New Sources of Economic Growth

Barry P. Bosworth and Jack E. Triplett — Fri, 01 Oct 2004 00:00:00 -0400

Brookings Institution Press 2004 401pp.

Recipient of the 2005 Harry Freeman Award for its exceptional contribution to knowledge of the U.S. service economy.

The services industries—which include jobs ranging from flipping hamburgers to providing investment advice—can no longer be characterized, as they have in the past, as a stagnant sector marked by low productivity growth. They have emerged as one of the most dynamic and innovative segments of the U.S. economy, now accounting for more than three-quarters of gross domestic product. During the 1990s, 19 million additional jobs were created in this sector, while growth was stagnant in the goods-producing sector.

Here, Jack Triplett and Barry Bosworth analyze services sector productivity, demonstrating that fundamental changes have taken place in this sector of the U.S. economy. They show that growth in the services industries fueled the post-1995 expansion in the U.S. productivity and assess the role of information technology in transforming and accelerating services productivity. In addition to their findings for the services sector as a whole, they include separate chapters for a diverse range of industries within the sector, including transportation and communications, wholesale and retail trade, and finance and insurance.

The authors also examine productivity measurement issues, chiefly statistical methods for measuring services industry output. They highlight the importance of making improvements within the U.S. statistical system to provide the more accurate and relevant measures essential for analyzing productivity and economic growth.

ABOUT THE AUTHORS

Barry P. Bosworth

Jack E. Triplett

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Services Productivity in the United States: Griliches' Services Volume Revisited

Barry P. Bosworth and Jack E. Triplett — Fri, 19 Sep 2003 00:00:00 -0400

Zvi Griliches (1992) reviewed services sector productivity trends, as well as issues in measuring services productivity, as these matters stood in the early 1990's (see also his American Economic Association presidential address, Griliches, 1994). In this paper, we analyze the rapid post-1995 productivity growth in services industries, which as we show have contributed greatly to the strength of U.S. productivity growth in recent years. We also review some of the major measurement issues that Griliches addressed, from roughly a dozen years on.

The contexts of the early 1990's and early 2000's are very different, yet at the same time similar. Griliches wrote in the context of the post-1973 U.S. productivity slowdown, which was the big puzzle of that day. He pointed out that services were crucial to the post-1973 slowdown, because productivity in services industries grew much more slowly than productivity in goods-producing industries after the late 1960s. Services, therefore, acted as a brake on U.S. productivity growth, a conclusion that was particularly unsettling because services have represented an increasing share of U.S. economic activity, a pattern that is also evident in Europe and other advanced economies.

The post-1973 puzzle was never resolved, just abandoned by economists when they were confronted with a new problem—the acceleration of U.S. productivity after about 1995. We find, in this paper and in our previous one (Triplett and Bosworth, 2002), that accelerating productivity in services industries played a crucial part in post-1995 U.S. productivity growth. Indeed, in recent years services industry labor productivity has grown as fast as labor productivity in the rest of the economy, which is why we have previously said that "Baumol's disease has been cured." In this, our findings are a mirror image of the conclusions emphasized by Griliches: Both the post-1973 slowdown and the post-1995 acceleration in U.S. productivity growth—both labor productivity and multifactor productivity—is located disproportionately, though not entirely, in services.

In Griliches' time and now, services industries are the industries that are the most intensive users of information and communication technology (IT) capital equipment. But unlike Griliches, who complained that the IT effect on services productivity was invisible in the data of his day, we find that IT investments now make a substantial contribution of labor productivity growth in services-producing industries. This, of course, is another change from the early 1990's, when lagging services productivity seemed a stifling problem for economic growth.

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Productivity Measurement Issues in Services Industries: "Baumol's Disease" Has been Cured

Barry P. Bosworth and Jack E. Triplett — Mon, 01 Sep 2003 00:00:00 -0400

It is now well known that after 1995, labor productivity (LP, or output per hour) in the United States doubled its anemic 1.3 percent average annual growth between 1973 and 1995 (see chart). Labor productivity in the services industries also accelerated after 1995.

As we documented in a longer version of this paper (Triplett and Bosworth forthcoming), labor productivity growth in the services industries after 1995 was a broad acceleration, not just confined to one or two industries, as has sometimes been supposed. Using the 1977-95 period as the base, we showed that fifteen of twenty-two U.S. two-digit services industries experienced productivity acceleration. Both the rate of LP improvement in services after 1995 and its acceleration equaled the economywide average. That is why we said "Baumol's Disease has been cured."

We also examined the sources of labor productivity growth. The major source of the LP acceleration in services industries was a great expansion in services industry multifactor productivity (MFP) after 1995. It went from essentially zero in the earlier period to 1.4 percent per year, on a weighted basis. As MFP is always a small number, that is a huge expansion. Information technology (IT) investment played a substantial role in LP growth, but its role in the acceleration was smaller, mainly because the effect of IT in these services industries is already apparent in the LP numbers before 1995. Purchased intermediate inputs also made a substantial contribution to labor productivity growth, especially in the services industries that showed the greatest acceleration. This finding reflects the role of "contracting out" in improving efficiency.

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Alternative Measures of Corporate Profits

Jack E. Triplett — Fri, 01 Nov 2002 00:00:00 -0500

The Sydney (Australia) Morning Herald for Tuesday, February 12, 2002 (Business Section, page 21) contained a short note on the measurement of corporate profits.

"On the adults-only hot, hot, hot topic of…accounting, [a] study…compares earnings reports from companies listed on the New York Stock Exchange with statistics from the U.S. Bureau of Economic Analysis…our spies tell us that the report concludes that U.S. profits in 2001 have been overstated by $130 billion or 27%."

Others have made similar comparisons. William Nordhaus (2002) observed that Standard & Poor's earnings grew 15% per year over the 1992-2000 interval, nearly twice as fast as the 8% per year for corporate profits in the national accounts. Paul Krugman wrote in his New York Times column (Friday, October 11, 2002, page A31): "In the last three years of the bubble reported profits soared, but the overall measure of profits calculated by the [BEA]...grew hardly at all." And in an August 7, 2002, National Association for Business Economics (NABE) teleconference on alternative measures of corporate earnings, Richard Berner, past president of NABE, reported the "sentiment in Wall Street that, because they are based on tax records, the national accounts based earnings measures may be conservative and therefore the best gauge of corporate profits" (taken from a recording of the conference—NABE, 2002).

I. Alternative Measures of Accounting Profits

For an economist, getting an aggregate measure of accounting profits has become more complicated. In "as reported" earnings (the traditional S&P profits measure), profits are stated according to generally accepted accounting principles (GAAP). Blitzer et al. (2002) note that "operating earnings" is a popular alternative but that it lacks a consistent definition. Partly in response to concerns about reported profits, and partly because operating earnings lacks an agreed-on definition, Standard & Poor's introduced a "core earnings" measure of operating profits that was offered as a new reporting standard.

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Jack E. Triplett

Publication: Prepared for Brookings Workshop on Economic Measurement: Two Topics in Finance

Some Information on the (Nonbank, Noninsurance) Financial Services Industries

Barry P. Bosworth and Jack E. Triplett — Fri, 01 Nov 2002 00:00:00 -0500

Finance and insurance account for over 8 percent of gross domestic product (GDP) and the share of these rapidly expanding industries is growing—it was only 6 percent 15 years ago (Table 1). In the old SIC, finance and insurance was composed of six industries: depository institutions (banks, SIC 60), nondepository credit institutions (SIC 61), security and commodity brokers, dealers, exchanges and services (SIC 62), insurance carriers (SIC 63), insurance agents, brokers and service (SIC 64), and holding and other investment offices (SIC 67).

Brookings Measurement Workshops have previously covered banking and insurance. Banking and insurance account for about three-quarters of the finance total, but the other two industries (nondepository financial institutions and financial and commodity brokers) have far higher output growth rates (Table 2). Additionally, these are the industries where new financial products arise. For these reasons, it is important to examine the data for "nonbank, noninsurance financial services," which is the topic of this workshop.

I. Recent growth and productivity trends.

Table 3 presents an overview of the three financial services industries. As the table indicates, all three have experienced very rapid output growth in recent years. For example, securities industry real output in 2000 was over seven times as high as it was in 1987 (an index number of 287.1, compared with an index of 39.3)

Financial services industries not only have high output growth in recent years, they have very high productivity growth rates. Indeed, their productivity growth rates are at or near the top of all services industries (Triplett and Bosworth, 2002). Table 4 and Charts 1 and 2 present the data. We also present in Table 4 data for banks (depository institutions, SIC 60), partly for comparison, and partly because in our previous paper we combined depository and nondepository financial institutions because of certain data problems.

Though much recent analysis of productivity acceleration in the U.S. uses 1995 as the "break year" (Jorgenson and Stiroh, 2000, Oliner and Sichel, 2000, Stiroh, 2001), examination of the data for the financial industries suggests that 1992 is a better year to mark productivity acceleration.3 The choice of break year has no important substantive implications: Numerical magnitudes of the average annual growth rates are affected by it, but the qualitative results and conclusions are not.

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Publication: Prepared for Brookings Economic Measurement Workshop Two Topics in Finance

Integrating Cost-of-Disease Studies into Purchasing Power Parities (PPP)

Jack E. Triplett — Fri, 21 Jun 2002 00:00:00 -0400

The OECD aging-related diseases (ARD) study contains information on the cost of specific treatments for heart disease, strokes, and breast cancer. I have been asked to review whether such data on cost of disease treatments are appropriate for improving Purchasing Power Parity (PPP) measures for medical care. The short answer is: cost of treatment information is exactly the information that is needed, in principle. However, the information in the ARD studies might need a little refinement. The rest of this paper explains.

Health Care Expenditures and Health.

I begin by considering a contention that frequently arises in discussions of the relation between medical expenditures and health. One frequently hears statements such as: U.S. spending on health care, which amounts to around 13% of GDP, must not be productive (says the speaker), because life expectancy in the U.S. is lower than it is in some other countries that spend a smaller amount on health care. What is the relationship between medical care expenditures and health?

There is little disagreement that health is produced by many factors, and not solely by the activities of the medical sector. Diet, lifestyles, environmental factors, genetic endowments, and other influences determine an individual?s, or a society?s, level of health. It is sometimes asserted that nonmedical influences on health are more important than the medical ones (McKeown, 1976; Mokyr, 1997), and for the major, long-run changes in health, there is much to be said for that position.

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Jack E. Triplett

Should the Cost-of-Living Index Provide the Conceptual Framework for a Consumer Price Index?

Jack E. Triplett — Fri, 01 Jun 2001 00:00:00 -0400

Abstract

In some countries, the cost-of-living (COL) index serves as the conceptual framework for the CPI, but it has been rejected in others. This paper reviews issues that have arisen in the statistical agency debate and in the economics literature on the COL index, including rhetorical matters which have influenced the debate. I contend that COL theory is useful as a decision-making framework in estimating components of the CPI, and that COL index theory provides appropriate guidance for measuring consumer inflation, contrary to the view that has been adopted for the European HICP and expressed by an advisory committee for the RPI.

(The complete article is available using the View Full Article link above.)

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Jack E. Triplett

Publication: Economic Journal

What's New About the New Economy?: IT, Economic Growth and Productivity

Barry P. Bosworth and Jack E. Triplett — Fri, 20 Oct 2000 00:00:00 -0400

Abstract

An unexpected surge in economic growth and productivity in the United States since 1995 has stimulated much discussion about a "new economy." This paper examines the effect of information and communication technology (IT) on economic growth and productivity. Contrary to views that have appeared in the press and elsewhere, we emphasize that the effect of IT on recent U.S. labor productivity growth is more like a continuation of past trends than any great departure, and that it is not unique to the U.S. An equally unexpected acceleration in multifactor productivity (MFP) is also a major contributor to the acceleration in U.S. labor productivity growth. Although available industry-level data cloud the behavior of IT users' MFP, we see no strong evidence that IT is the generator of U.S. MFP growth among users of IT. Finally, we contend that what is really different about the U.S. economy in the late 1990's is the behavior of its labor market, a factor that has been given too little consideration in the "new economy" explanation of the late '90's U.S. economic success story.

Introduction

The U.S. economy performed extraordinarily well in the 1990s. Unemployment has dropped to historically low rates; the federal government is awash with revenues, and after a quarter century of near stagnation, productivity growth is soaring. The unexpected economic strength has stimulated much discussion about the 'new economy,' and what the emergence of a new economy implies for the sustainability of the economic expansion in future years.

The 'new economy' discussion has been inconclusive, in part because the term 'new economy' means different things to different people. Some definitions of the new economy embrace a very broad notion—that the fundamental economic concepts that guided economic policy in the past have become irrelevant in an age of global competition and rapid technological change. Others have a more narrow focus—the role of information processing and communications technology (IT) in accelerating the economy's trend rate of output and productivity growth

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Numbers Matter: The U.S. Statistical System and a Rapidly Changing Economy

Barry P. Bosworth and Jack E. Triplett — Sat, 01 Jul 2000 00:00:00 -0400

Economic statistics play a vital role in public policy decisions. However, the U.S. statistical system is struggling to keep up with the rapidly evolving nature of the economy.

There are many examples of gaps between information needs and data availability. For example, over the past few months, economists inside and outside government have debated whether the economy has the capacity to satisfy the strong surge in aggregate demand that the United States has experienced in the last year or so without a sharp acceleration of inflation. The critical statistic upon which the debate hinges is output per hour, or labor productivity, which has also surged since 1996.

If this higher productivity growth represents a new trend in supply, monetary policy can accommodate the expansion of demand without inflation. But the lack of available statistical data leaves policymakers without adequate guidance, particularly in the case of services. Federal Reserve Board Chairman Alan Greenspan has recently remarked on "disquieting problems with the measurement of productivity, especially in the noncorporate sector," and suggested the need for more detailed productivity data to provide answers to the following questions:

Is productivity growth sustainable, or is it a cyclical or temporary development that will not persist?
What are the sources of the recent gains in productivity?
Are gains evenly dispersed across the American economy, or are they concentrated in just a few sectors?

A second gap between policy needs and statistical availability arises in medical care. Between 1985 and 1995, the medical care component of the Consumer Price Index (CPI) rose 6.5 percent per year, but the overall CPI rose only 3.6 percent annually. When this picture of rapid medical inflation is coupled with existing statistics showing that measured productivity in the medical care industry has actually fallen over the same decade (by 2.2 percent per year), the statistical picture that emerges is one of a troubled industry with an overwhelming need for new policies to correct its problems.

However, new analysis and statistical information is radically altering our view of the medical care industry. Many economists now suspect that the available statistics do not present an accurate economic picture of the industry's performance. As a result, medical price increases are probably lower and productivity higher than the numbers suggest.

POLICY BRIEF #63

The Brookings Institution has been conducting a program of workshops and research on measurement issues in the services sectors to focus attention on the problems of measuring productivity in those industries. The workshops have brought together academics, researchers, government policy analysts, and program managers at the major statistical agencies to compare approaches and to assess problems and progress in improving the nation's statistics for productivity analysis. In this policy brief, we discuss some of the difficulties in measuring output of these services sectors, review recent research on individual sectors of the services economy, and summarize the commendable progress on these difficult issues that statistical agencies in the U.S. and Canada have made in recent years.

The Statistical Agencies and the Problems They Face

Economies are continually evolving, which requires that the statistical system also change and adapt to new circumstances. The United States has become a predominantly service-based economy, as goods-producing industries account for only about one-fifth of the nation's output and employment. Yet our statistical system remains heavily focused on goods production.

Identifying the sources of productivity growth, for example, requires accurately measuring output at the level of major sectors and individual industries, and linking that information to corresponding data on the quantities of material and services inputs, capital, and workers that are used to produce it. The U.S. statistical database for manufacturing has the required data, for the most part, but the statistics for the services industries are far less adequate.

The primary statistical database for computing productivity in manufacturing industries comes from the Quinquennial Censuses and Annual Surveys of Manufactures, which the Census Bureau has conducted for decades. These data collections contain a large amount of integrated and useful data on the outputs of manufacturing industries, their employment, investment in capital plant and equipment, and their purchases of goods and services from other industries. Price indexes for deflating the outputs of most manufacturing industries are compiled and published by the Producer Price Index (PPI) program of the Bureau of Labor Statistics. Thus, there is a relatively good database for productivity analysis. But even in manufacturing, the recent growth has been concentrated in the newer high-tech industries where the statistical coverage is least strong.

For services, however, the databases are less satisfactory. The Census Bureau's Annual Services Surveys only began in the 1980s, and still often contain only a minimal amount of information on individual services industries. In contrast to its good coverage of manufacturing, the PPI price index program had covered only a small fraction of non-goods producing industries by 1990. The information databases for the services and "high-tech" manufacturing industries, which have shown the greatest growth in the past several decades, are insufficient for economic analysis, and are generally inferior to those for the older "smokestack" industries that once accounted for the bulk of U.S. economic activity.

These statistical gaps in the services sector inhibit the analysis of the U.S. economy. For example, the large gains in the efficiency with which we produce computers seems evident in the rapid decline in their price. But have there been similar gains in the computer-using industries? Some analysts have argued that the benefits of the computer are underestimated precisely because computer use is concentrated in services industries, such as finance and insurance, where output is not well-defined or measured.

The medical care industry employs high technology, especially in the form of pharmaceuticals and new technologies. This is an industry that has experienced substantial innovations in medical treatments, introduced many new medical procedures, and undertaken substantial investment in complex medical equipment. Although it is not normally thought of as a computer intensive industry, some kinds of medical equipment are computers in all but name. None of these facts is characteristic of industries with declining productivity, so the present statistical profile of medical care is puzzling. Recently, a number of studies suggest that price inflation in medical care may be overstated through a failure to capture changes in medical outcomes. The statistical problems arise from the difficulty of measuring the output from improved medical procedures and assigning them an economic value.

For a significant number of services industries, such as education and business services, the current statistical system provides no meaningful measure of price changes or real output growth. Output is simply assumed to grow in parallel with employment, which is equivalent to assuming that there is no labor productivity growth.

A Shortage of Resources

These gaps in the statistical system are well-known among users of the economic data. However, despite encouragement from industry representatives and analysts, the statistical agencies have found it difficult to gain the financing to develop measurement programs for newly-emerging industries. For example, the Bureau of Economic Analysis (BEA), which produces both the national accounts and the inter-industry statistics that are used for productivity analysis, has had its budget frozen in nominal terms in recent years. Even the Census Bureau's proposals to collect new data on the digital economy and expand its e-commerce statistics have not met with congressional approval.

To an extent, statistical agencies can gain resources for services sector measurement by shifting them out of less vital programs. For example, BEA discontinued publication of the "leading economic indicators" to concentrate on its core statistical programs. But shifting their resources from one area to another can only do so much. It is relatively cheap—and still a priority, especially in the high-tech sectors—to maintain existing surveys and measurement methods for the old economy, but it has proved difficult and expensive to develop the tools for measuring the output of the new, services-oriented economy.

Despite budgetary stringency, there are many recent examples of evolutionary and innovative changes in the U.S. statistical system. The Bureau of Labor Statistics' price index research program has contributed to a series of important methodological changes in the CPI that, when brought into the Gross Domestic Product (GDP), raised the estimated growth rates of output and productivity. The Producer Price Index has been expanded since 1990 to cover the output of a growing number of services industries. The national income and product accounts were recently changed to include measures of investment in computer software and a new measure of the output of the banking system that begins to reflect the diverse nature of financial services. The Census Bureau recently released its first estimate of consumer sales over the Internet, reflecting a rapid reorientation of its retail trade surveys to accommodate new forms of retailing.

However, there are also a substantial number of situations where the statistical information is incomplete. For example, within the national accounts, about 12 percent of industry output is still measured on the basis of changes in inputs with no role for potential productivity change. This is done because the required output data do not exist. Nearly all of those problem industries are within the services sector.

Measuring The Output of the Services Sector

The available data suggest that services played a disproportionate role in the slowing of productivity growth after 1973, because measured productivity growth is lower in services than in the manufacturing sector. Some analysts argue that productivity improvements are harder to achieve in services. Others allege that the disparity of performance between services and goods-producing industries is simply a result of miscalculating output. Even if the measurement of each services industry is as reliable as it ever was, mismeasurement is inevitably a greater problem in economic statistics than before, because service-based industries now account for a much larger share of the economy.

What is needed to improve services statistics? If there is an overall message that has emerged from the Brookings workshops, it is that there is no central theme to the problem of output and productivity measurement in services. Each of the industries that we examined involved unique problems. In many cases, the problems involve developing a clearer concept of the output of an industry and deciding what should be measured.

Conceptual Problems in Estimating Services Sector Outputs

It is often not easy to measure the output of complex goods, like cars or computers, because the quality of what is produced changes from year to year. But the statistical agencies can start by counting the numbers of cars, or computers, or by collecting information on the prices of cars or computers ("deflating" sales with a price index is generally how agencies estimate output growth for most industries). After collecting the initial data, statistical agencies must figure out how to adjust for improvements in quality, which is always the most difficult part of measuring output.

In services, it is sometimes difficult to take the first step of determining what the output is. For example, business services contain a diversity of activities, including professional and consulting services (other than legal and financial), advertising, data processing and building maintenance. Management consultants, lawyers, architects, and economists often bill by the hour. It is simple just to count billing hours, or the hourly billing rate. But what is done in that hour? That is the output. Surely, what is done in an hour of business services is greater today than it was in the past, but it is not easy, even for the professionals themselves, to specify what they do in a way that provides a measure of a unit of output.

Another type of conceptual problem is illustrated by financial services. A bank cashes checks and it operates Automatic Teller Machines (ATMs). But the transaction services provided to account holders represent only a small portion of the bank's activities, and they are provided mainly to get deposits that banks lend. Is a bank loan part of its output? If so, is it the number of loans, the size of them, or a combination of both? If ATM transactions are part of banks' output because they are part of the services produced and provided by banks, how should statistical agencies put value on ATM services when they are mostly provided to account holders for free in exchange for the banks' use of their deposits? Statistical agencies encounter similar problems defining and valuing the output of insurance and securities firms.

Resolving conceptual problems like these takes thought and experiment with alternative approaches, and not a simple survey. Often, it requires an iterative process of collecting information, analyzing the results, consulting with industry representatives and others, and then modifying the survey and collecting a new set of statistics. The statistical agencies need the resources to maintain and promote a research agenda in addition to their data collection programs.

Measuring Output in a Rapidly Changing World

In other industries, the problems are centered around the collection of the right kinds of data, and resolving issues that arise from rapid changes in industry structure. For example, much of the innovation in retail trade is reflected in shifts in the distribution of sales among stores with different formats. Yet these shifts are explicitly ruled out in the Consumer Price Index because the survey only tracks the price of a specific product in a specific store. In effect, the shift of sales from department stores to lower-priced discount outlets is treated as a reduction in quality (and therefore output), not a reduction in price. But the growth in the popularity of discount and outlet stores suggests that they provide a net gain to consumers, and at least a portion of the price reduction should be reflected in increased real output. The statistical agencies lack a solid means of distinguishing the real gain from reductions in customer service and increased travel time.

The changing structure of trade has been even more dramatic in the case of e-commerce. The growth of this new industry has been extraordinary, even though it remains very small when measured against the scale of the total economy or retail trade. Some confusion results from the tendency to report the total volume of transactions that were touched by the Internet, rather than focusing on value added or final sales. Moreover, the Census Bureau has already expanded its survey activity to measure retail sales over the Internet. If resources are made available, it appears that e-commerce sales will be fully integrated into the existing statistical system before they get very large. However, less has been done to measure prices of those sales in ways that are comparable to the price of the products in traditional commerce.

More difficult problems arise with the potential for e-commerce to alter business practices and competition among firms, in ways that may impact on productivity and the structure of existing industries. It is less evident what type of information should be collected to evaluate those issues. Many of these firms also emphasize forms of employee compensation, such as stock options and bonuses, that are not well-reported in the existing statistics.

Similar problems arise in medical care. Within the last decade, substantial innovations have been made in measuring the prices for medical care. In 1992, BLS introduced new price indexes for health care in its Producer Price Index program which incorporated a new methodology for measuring the price of medical care. Rather than pricing the cost of a day in the hospital, as did the historical Consumer Price Index, the BLS attempts to measure the cost of treatment for specific medical conditions. Overall, the new PPI indexes present a picture of lower medical care inflation, compared to the CPI for the period where the two overlap. BLS subsequently introduced similar methodology into the CPI.

Even with the new methodology, it has been difficult for BLS to find data to adjust for changes in the efficacy of treatment. There is some controversy on how far statistical agencies should go in incorporating measures of treatment efficacy into price and output measures, but we doubt if anyone seriously disagrees that the price index should be "adjusted" or corrected in some fashion for improvements in medical outcomes. Because medical economists generally believe that progress has been made in medical technology in the way of better prognoses, shorter hospital stays, and less painful treatments, they believe that inadequate adjustment for changes in medical technology creates upward biases in price indexes for medical care.

There is less agreement, however, on how to value those changes. In the PPI, the Bureau of Labor Statistics looks for information on the change in costs that are associated with improvements in medical efficacy. In contrast, some economists argue for a greater focus on the value of those improvements to the patient. For example, the greater utility to the patient of less unpleasant treatments, and the value of reductions in unwanted side effects (a less painful medical procedure, a less onerous regimen, or a shorter recovery time) are all elements that might go into a measure of medical outcomes. But some of the improvements in outcomes go outside the traditional "market boundary" of economic measurement. Should they go into an economic accounting for medical care? If so, how should they be valued? Stating the problem in this way underscores the difficulties the statistical agencies face in producing price indexes for medical care.

What to do?

The United States still has one of the world's best statistical systems. But it is evident that the system is faced with severe challenges in its efforts to keep up with the evolving nature of the economy. Much of the recent growth of the economy has been in sectors that are poorly measured by the existing reporting system. In some cases, such as electronic commerce, the primary need is to expand the existing network of surveys to capture the new firms and products in a timely fashion. But in other cases, the growth of the digital economy is highlighting an old set of problems—growth in industries for which output was never well defined or an acceleration of changes in the structure of industries and products that complicates the measurement of quality change.

The accelerating pace of economic change suggests a need to restructure the existing statistical system, expand its resources, and refocus it toward better reporting of the services sectors of the economy. While some of the change will have to be in the form of new surveys of the services sector, there is an equal or greater need to expand the research on what type of questions to ask. How should we define the output of these industries and how should we distinguish between changes in prices and the quality of service? This suggests the need to expand research by the statistical agencies to resolve the myriad measurement issues. That research must also be coordinated with the affected industries whose cooperation is critical to arriving at a common set of definitions and agreement on a feasible program of measurement.

Finally, it is becoming more and more apparent that the decentralized structure of the U.S. statistical system is contributing to the problem of inadequate economic measurement. U.S. statistical agencies, in contrast to those of most other industrialized countries, cannot exchange and share data collected in closely-related programs. For example, both the Census Bureau and the Bureau of Labor Statistics collect industry employment, mostly from the same firms, which is not only wasteful in terms of agency resources, but imposes unnecessary and duplicative reporting burdens on cooperating firms. Bringing at least some of the major U.S. statistical agencies into a single, integrated, partly "centralized" statistical agency (which exists in most other countries with high quality statistical systems) might free up resources for attacking problem areas and lead to more efficiencies and better integration of economic statistics. It might also increase respondents' voluntary cooperation with important U.S. data collection activities.

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Publication: Brookings Policy Brief #63

Productivity in the Services Sector

Barry P. Bosworth and Jack E. Triplett — Sat, 01 Jan 2000 00:00:00 -0500

Overview and Introduction

From 1949 to 1973, the Bureau of Labor Statistics (BLS) estimates that U.S. non-farm multifactor productivity grew at 1.9% per year. After 1973, multifactor productivity grew only 0.2% per year (table A). Despite a 20-year intensive research effort to find the cause, no convincing explanation of the post-1973 productivity slowdown exists.

Whatever the ultimate cause, circumstantial evidence suggests that services industries play some important role in the slowdown. In the first place, the aggregate numbers indicate that the slowdown is greater in the non-goods producing portions of the economy. While no official estimate of productivity in services is published by the BLS, nonfarm multifactor productivity slowed by 1.7 percentage points (from 1.9% per year to 0.2%), and manufacturing productivity fell by 0.6 percentage points (from 1.5% per year to 0.9%). Because manufacturing accounts for about 22% of non-farm business, this implies a 2 percentage point slowdown in the non-manufacturing sector.

If the data are right, one might infer, as did Baumol (1967) many years ago, that productivity improvements in services are harder to achieve than in goods producing industries. If so, the shift of the economy toward a larger share of services implies a reduction in the national rate of productivity improvement.

But this view of manufacturing and services is undoubtedly too simple. Substantial disparities exist among productivity growth rates within the manufacturing sector and also within the non-manufacturing sector. It simply is not true that all individual services industries have productivity growth rates that are lower than all individual manufacturing industries, or even below the average for manufacturing industries.

But more importantly, perhaps, the data may not be right. One popular hypothesis about the productivity slowdown is that it is a product of mismeasurement. According to this hypothesis, the mismeasurement of output contributes to the productivity slowdown because an increasing portion of output is not captured in the basic statistics.

Again, circumstantial evidence points to the services industries. Griliches (1994) pointed out that some of the services industries whose productivity growth rates in the 1947-1973 era were as high or higher than productivity growth in manufacturing industries had, since 1973, much lower productivity improvements. Additionally, the productivity slowdown has been particularly intense in services industries where output is hard to measure—health services, for example, have the greatest labor productivity slowdown of any industry in table 1, and both banking and health services have large multifactor productivity slowdowns (see table 2). This points again to possible mismeasurement.

Another puzzle involves computers. The 1992 capital flow table shows the purchases, by industry, of computer equipment (Bonds and Aylor, 1996). The five industries that are the largest purchases of computers are all services—in order, financial services, wholesale trade, business services, insurance, and communications. Those five services industries account for more than 50 per cent of US investment in computers. Within these industries computers have created new forms of service output that may not be fully captured in the statistics. An example is the growth of ATM machines in banks that reduce the time spent waiting in line for teller transactions, make the transactions available on weekends, and have, with computer-assisted verification systems for credit card purchases, virtually eliminated the need to carry traveler's checks on foreign travel to many countries. Prior to the 1999 revisions to GDP, ATM usage was not reflected in the measure of banking output in the national accounts.

In all of these services industries, conceptual and empirical problems in measuring output and prices are notorious: For example, an economic consulting firm is part of the business services industry. How do we measure the output of an economic consulting firm? How would we construct a price index for economic consulting? And how would we compute the productivity of economists? The science of economics is no closer to developing methods for measuring the output of economists' own activities than it is for measuring the output of banks, law firms, and insurance agents. All of these services poses difficult problems for constructing price indexes and real output measures and therefore for measuring productivity.

This paper gives a progress report of a project we are conducting, with collaborators, on service sector output and productivity. Its major message is that there is no central theme to the problem of services measurement. Each industry we have examined contains unique problems. If quality change is, as Shapiro and Wilcox (1996) put it, the "house-to-house fighting" of price indexes, measuring service output requires, at least, a hedgerow-by-hedgerow assault.

In the next section, we present some measures of the growth in labor and multifactor productivity within the services industries in a form that is consistent with the published measures for the aggregate economy. This allows us to document the wide dispersion of productivity growth rates across industries and the pervasiveness of the post-1973 slowdown. Section III summarizes recent research on individual sectors that have been subjects of Brookings workshops on measurement issues in services industries.

Paper will be published as Chapter Two in the forthcoming book "Services in the International Economy" edited by Robert M. Stern.

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Research in Price, Quality, and Quantity Measurement: What Agenda for the Next Twenty Years?

Jack E. Triplett — Wed, 21 Jul 1999 00:00:00 -0400

Remarks Presented at the Panel Discussion on Research in Price, Quality, and Quantity Measurement: What Agenda for the Next Twenty Years?

NBER Productivity Program
Ernst Berndt & Zvi Griliches, Organizers
July 21, 1999

Cambridge, MassachusettsMembers of the panel were invited to discuss improvements in the U.S. Consumer Price Index (CPI) and to suggest an agenda of research issues in price measurement for the future.

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Jack E. Triplett

Publication: NBER Productivity Program

Measuring the Prices of Medical Treatments

Jack E. Triplett — Tue, 06 Jul 1999 00:00:00 -0400

Brookings Institution Press 1999 276pp.

How serious is medical care inflation in the United States? For many years, price indexes for medical care have outstripped the overall rate of inflation. For example, between 1986 and 1996, the medical care component of the Consumer Price Index (CPI) rose 6.5 percent per year, roughly exceeding the annual increase in the overall CPI during this period by 75 percent.

Many economists, however, believe that economic statistics on medical care do not accurately measure medical care price changes because it is especially difficult to construct accurate price indexes for medical markets. Some very recent research, reported in this volume, suggests that-contrary to the usual presumption of runaway medical inflation-prices for at least some medical care interventions are not rising rapidly and may even be falling.

Understanding medical care inflation is important for policy issues such as medical care cost containment. Medical care price indexes also affect other economic statistics on medical care, including national accounts and the national health accounts. Understanding economic trends in the medical care sector is vitally dependent on accurate medical care price measures.

This volume, the result of a conference cosponsored by the Brookings Institution and the American Enterprise Institute, brings together state-of-the-art methodological and empirical work on the measurement of medical outcomes and prices. It will be a useful tool for anyone concerned about medical inflation, medical outcomes, the quality of medical treatments, and public policy toward medical cost containment.

ABOUT THE AUTHOR

Jack E. Triplett

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Economic Statistics, the New Economy, and the Productivity Slowdown

Jack E. Triplett — Thu, 01 Apr 1999 00:00:00 -0500

From 1949-1973, the Bureau of Labor Statistics (BLS) estimates that non-farm multifactor productivity grew at 1.9 per cent per year. Following 1973, the comparable number is 0.2 per cent, despite the economy's substantial investment in computing equipment, the growth of the information economy, and the many innovations that have come to be known as the "new economy." Many economists believe that productivity must be growing more rapidly than the government numbers suggest. This article reviews two reasons—one wrong, I contend, and one more plausible—for believing that inadequate measurement of output in our economic statistics may be hiding essential developments in our economy.

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Jack E. Triplett

Publication: Business Economics

The Solow Productivity Paradox: What Do Computers Do to Productivity?

Jack E. Triplett — Mon, 01 Mar 1999 00:00:00 -0500

You can see the computer age everywhere but in the productivity statistics.
Robert Solow (1987)

Abstract

Solow's aphorism, now more than ten years old, is often quoted. Is there a paradox? And if so, what can be said about it? This paper reviews and assesses the most common "explanations" for the paradox. It contains separate sections evaluating each of the following positions.

(1) You don't see computers "everywhere," in a meaningful economic sense. Computers and information processing equipment are a relatively small share of GDP and of the capital stock.

(2) You only think you see computers everywhere. Government hedonic price indexes for computers fall "too fast," according to this position, and therefore measured real computer output growth is also "too fast."

(3) You may not see computers everywhere, but in the industrial sectors where you most see them, output is poorly measured. Examples are finance and insurance, which are heavy users of information technology and where even the concept of output is poorly specified.

(4) Whether or not you see computers everywhere, some of what they do is not counted in economic statistics. Examples are consumption on the job, convenience, better user-interface, and so forth.

(5) You don't see computers in the productivity statistics yet, but wait a bit and you will. This is the analogy with the diffusion of electricity, the idea that the productivity implications of a new technology are only visible with a long lag.

(6) You see computers everywhere but in the productivity statistics because computers are not as productive as you think. Here, there are many anecdotes, such as failed computer system design projects, but there are also assertions from computer science that computer and software design has taken a wrong turn.

(7) There is no paradox: Some economists are counting innovations and new products on an arithmetic scale when they should count on a logarithmic scale.

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Jack E. Triplett

Publication: The Canadian Journal of Economics

New Developments in Measuring Medical Care

Jack E. Triplett — Mon, 01 Feb 1999 00:00:00 -0500

Abstract

How serious is "medical care" inflation in the United States? For many years, price indexes for medical care have outstripped the overall rate of inflation. For example, between 1986 and 1996, the medical care component of the Consumer Price Index (CPI) rose 6.5 per cent per year, which far exceeds the 3.6 percent annual increase in the overall CPI during this period.

However, constructing accurate price indexes for medical markets is especially difficult, and many economists believe that economic statistics on medical care do not accurately measure medical care price changes. Some very recent research, reported in this volume, suggests that—contrary to the usual presumption of runaway medical inflation—prices for at least some medical care interventions are not rising rapidly and may even be falling.

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Jack E. Triplett

Accounting For Health Care: Integrating Price Index And Cost-Effectiveness Research

Jack E. Triplett — Mon, 01 Feb 1999 00:00:00 -0500

Abstract

Personal health care expenditures in the National Health Accounts increased at an average annual rate of 10.2 percent per year between 1985 and 1992. Were health care prices also rising rapidly between 1985 and 1992? That is less clear.

Health expenditures are the product of price and quantity. Determining whether expenditure increases are caused by price increases requires a methodology for separating health care spending into price and quantity changes—into medical price inflation and increases in the output of health care. Economic statisticians lack an adequate methodology for this task.

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Jack E. Triplett

Price Indexes for Medical Care Goods and Services: An Overview of Measurement Issues

Jack E. Triplett — Tue, 01 Sep 1998 00:00:00 -0400

Abstract

We review in considerable detail the conceptual and measurement issues that underly construction of medical care price indexes in the U.S., particularly the medical care consumer price indexes (MCPIs) and medical-related producer price indexes (MPPIs). We outline salient features of the medical care marketplace, including the impacts of insurance, moral hazard, principal-agent relationships, technological progress and organizational changes. Since observed data are unlikely to correspond with efficient outcomes, we discuss implications of the failure of transactions data in this market to reveal reliable marginal valuations, and the consequent need to augment traditional transactions data with information based on cost-effectiveness and outcomes studies.

We describe procedures currently used by the BLS in constructing MCPIs and MPPIs, including recent revisions, and then consider alternative notions of medical care output pricing that involve the price or cost of an episode of treatment, rather than prices of fixed bundles of inputs. We outline features of a proposed new experimental price index—a medical care expenditure price index—that is more suitable for evaluation and analyses of medical care cost changes, than are the current MCPIs and MPPIs. We conclude by suggesting future research and measurement issues that are most likely to be fruitful.

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Jack E. Triplett