Abstract
This Selected Issues paper on the United States analyzes problems in the measurement of output and prices. The paper examines income versus expenditure measures of national output. Sources of consumer price index and findings of the Boskin Commission are discussed, and mismeasurement of output and productivity is analyzed. Developments in productivity across industries in the United States are described. In particular, the paper focuses on the slowdown in aggregate productivity growth that began in the mid-1970s and examines whether this slowdown has continued in recent years and is common across industries.
I. Problems in the Measurement of Output and Prices1
1. In recent years, confidence in the ability of economists to clearly analyze developments in the U.S. economy has been questioned by growing awareness and perhaps growing problems in measuring output and prices. In particular, substantial statistical discrepancies have emerged in the national income and product accounts (NIPAs) in the alternative measures of the value of output. At the same time, considerable attention has been focused on problems in the measurement of prices, especially the consumer price index (CPI). In turn, these difficulties in measuring nominal output and in measuring key price deflators can create errors in the measurement of real output and other important statistical indicators, such as productivity, which are derived from real output estimates.
A. Income Versus Expenditure Measures of National Output
2. The value of national output can be measured as the sum of expenditures on currently produced final goods and services, or as the sum of incomes (wages and salaries, profits, proprietors’ income, interest, and rental income) paid to factors of production used in the current production of goods and services. In principle, the two approaches represent different ways of measuring essentially the same thing, since the value of final production of goods and services is ultimately distributed as wages, profits, interest, and rent. Some minor definitional differences exist between the two approaches that principally relate to the coverage of transactions (most notably, indirect business taxes and business transfer payments, which are captured in the expenditure estimates but not in the income estimates since they do not represent returns to factors of production). Adjusting for these differences, the two measures should yield the same results. However, in practice, measurement difficulties typically result in these approaches yielding different estimates for the value of national output. The difference between the two estimates is referred to as the “statistical discrepancy” in the NIP As. A negative (positive) statistical discrepancy indicates that the income estimate exceeds (is less than) the expenditure estimate.
3. Since the third quarter of 1994, the income measure of GDP has grown significantly faster than the expenditure measure. Consequently, since that time, gross national income has increased at an annual rate of 5.6 percent through the first quarter of 1997, while gross national expenditure increased at a 4.8 percent annual rate.2 The statistical discrepancy was positive during most of the period since 1970, but it has become increasingly negative since the third quarter of 1995 (see Chart 1).
UNITED STATES: ALTERNATIVE MEASURES OF PRODUCTIVITY, GDP, AND THE STATISTICAL DISCREPANCY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.UNITED STATES: ALTERNATIVE MEASURES OF PRODUCTIVITY, GDP, AND THE STATISTICAL DISCREPANCY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.UNITED STATES: ALTERNATIVE MEASURES OF PRODUCTIVITY, GDP, AND THE STATISTICAL DISCREPANCY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.4. Although both types of estimates suffer from measurement problems, until recently economists had generally considered the expenditure estimates to be more reliable. On the expenditure side, measuring consumption of services presents the greatest difficulties, while on the income side certain components of nonwage income are especially difficult to measure, particularly proprietor’s income. A number of analysts now suspect that the expenditure estimates of GDP have been seriously understated, and have suggested several reasons why recent income estimates might present a more reliable picture of developments in the value of national output.3 In particular, the income estimates of nominal output are more consistent with the higher-than-expected personal income tax revalues collected in 1996 and 1997.4 At the same time, the growth of real GDP over the last several years, as derived from the expenditure estimates of nominal output, has been roughly in line with potential, yet (hiring this period, the unemployment rate has declined, counter to what would have been expected according to Okun’s Law.5 On the other hand, real GDP growth derived from the income estimates of nominal output has been higher than potential, which is consistent with the prediction of a falling unemployment rate under Okun’s Law. In addition, labor productivity growth derived from the income estimates is more in line with developments in the real product wage than is productivity growth measured on the expenditure side. The real product wage is defined as hourly compensation divided by the prices received by producers. Typically, changes in the real product wage track labor productivity growth. Since 1995, the average annual rate of growth of the real product wage has been 1.5 percent, which is about equal to productivity growth derived from the income estimates, while it is well above the 0.5 percent annual rate of productivity growth derived from the expenditure estimates.
B. Sources of CPI Bias and Findings of the Boskin Commission
5. A commission was established at the behest of the U.S. Senate Finance Committee to investigate the issue of how well the CPI measures changes in the cost of living. The December 1996 release of the Final Report of the Advisory Commission on the Consumer Price Index (the Boskin Commission) has heightened interest in understanding the principal economic and policy implications of potential biases in the CPI.
6. The shortcomings of the CPI as a cost of living index (COLI) can largely be attributed to the simple fact that the CPI was not intended to measure changes in the cost of living. A cost of living index (COLI) would measure changes in the minimum cost of consuming a basket of goods today at currant market prices that would provide the same level of satisfaction (standard of living) as a representative basket of goods purchased in a base period.6 In contrast, the CPI measures how much it would cost at current market prices to purchase the same basket of goods that was consumed in the base period. Hence, the composition and relative weights of the basket of goods measured by a COLI would change over time to reflect the effects of movements in relative prices on the basket of goods that inviduals consume, while the CPI’s market basket remains fixed. As a result, relative to a COLI, the CPI has a commodity substitution bias, since it fails to pick up the effort of consumers to minimize the effects of price changes on their “level of satisfaction” by substituting relatively less expensive products for those goods whose prices haverisen.7 Also, in response to changes in relative prices, consumers will adjust their shopping patterns, shifting from higher-priced to lower-priced retail outlets. An outlet substitution bias in the CPI may result if such shifts are not picked up in the price surveys, or if new types of retail outlets develop and these are not surveyed. Both of these types of substitution bias would result in increases in the CPI overstating increases in the cost of living.
7. Other factors contribute to errors in the CPI (and its use in attempting to measure changes in the cost of living) owing to the mismeasurement of prices. Quality bias may arise because the attributes of goods change over time. When this occurs, unless the measured price changes properly reflect underlying quality changes, the price changes will be mismeasured. The Bureau of Labor Statistics (BLS) attempts to control for quality changes in the calculation of the CPI; however, because of the difficulties involved, these adjustments are generally regarded as insufficient to capture quality changes fully.8 The direction of the overall quality bias in the CPI is not clear a priori. The Boskin Commission’s report maintains that this bias tends to overstate inflation (i.e., quality improvements are not folly accounted for and thus price increases are overstated), but other analysts have suggested that the reverse could be true, owing to reductions in the quality of some goods.
8. Along somewhat similar lines, the introduction of new goods may lead to a mismeasurement of prices. Over time, items in the CPI market basket may cease to be produced as new substitute products are introduced. Matching discontinued hems in the market basket with tile new products replacing them can introduce a bias, since the attributes of the two products are not likely to be identical, and the direction of this bias cannot be known a priori. In addition, another source of new goods bias associated with the CPI arises from the introduction of entirely new goods (for example, video tape recorders and cellular phones). These new goods tend to be included in the CPI market basket some time after they have been introduced, and as a result, the CPI routinely misses the reductions in their prices that typically takes place soon after their introduction.
9. The Boskin Commission’s efforts to quantify these sources of CPI bias suggest that, while the bias may vary from year to year, the CPI has overstated the change in the cost of living on average by 1.1 percentage points per year. The total bias includes: (1) 0.6 percentage point owing to new products and quality changes; (2) 0.4 percentage point owing to commodity substitution effects; and (3) 0.1 percentage point owing to new outlet substitution bias. The Commission also identified a “plausible range” for the total CPI bias of 0.8 to 1.6 percentage points per year.9 This plausible range is consistent with the range of estimates derived from other studies of the CPI bias (Table 1).
United States: Recent Estimates of Bias in the U.S. Consumer Price Index
(Percentage points)
United States: Recent Estimates of Bias in the U.S. Consumer Price Index
(Percentage points)
| Source | Point Estimate | Interval Estimate |
|---|---|---|
| Boskin Commission: Final Report (1996) | 1.1 | 0.8-1.6 |
| Boskin Commission: Preliminary Report (1995) | 1.0 | 0.7-2.0 |
| Boskin (1995) | 1.5 | 1.0-2.0 |
| Congressional Budget Office (1994) | — | 0.2-0.8 |
| Darby (1995) | 1.5 | 0.5-2.5 |
| Diewert (1995) | — | 1.3-1.7 |
| Gordon (1995) | 1.7 | — |
| Greenspan (1995) | — | 0.5-1.5 |
| Griliches (1995) | 1.0 | 0.4-1.6 |
| Jorgenson (1995) | 1.0 | 0.5-1.5 |
| Klumpner (1996) | — | 0.3-0.5 |
| Lebow, Roberts, and Stockton (1994) | — | 0.4-1.5 |
| Pakes (1995) | 0.8 | — |
| Shapiro and Wilcox (1996) | 1.1 | 0.7-1.6 |
| Wynne and Sigalla (1994) | <1.0 | — |
United States: Recent Estimates of Bias in the U.S. Consumer Price Index
(Percentage points)
| Source | Point Estimate | Interval Estimate |
|---|---|---|
| Boskin Commission: Final Report (1996) | 1.1 | 0.8-1.6 |
| Boskin Commission: Preliminary Report (1995) | 1.0 | 0.7-2.0 |
| Boskin (1995) | 1.5 | 1.0-2.0 |
| Congressional Budget Office (1994) | — | 0.2-0.8 |
| Darby (1995) | 1.5 | 0.5-2.5 |
| Diewert (1995) | — | 1.3-1.7 |
| Gordon (1995) | 1.7 | — |
| Greenspan (1995) | — | 0.5-1.5 |
| Griliches (1995) | 1.0 | 0.4-1.6 |
| Jorgenson (1995) | 1.0 | 0.5-1.5 |
| Klumpner (1996) | — | 0.3-0.5 |
| Lebow, Roberts, and Stockton (1994) | — | 0.4-1.5 |
| Pakes (1995) | 0.8 | — |
| Shapiro and Wilcox (1996) | 1.1 | 0.7-1.6 |
| Wynne and Sigalla (1994) | <1.0 | — |
10. The Commission observed that procedural changes at the BLS have helped to reduce the substitution and quality biases in the CPI over the years in a number of important product categories.10 In addition, changes scheduled to be implemented over the next three years are expected to reduce the CPI bias by roughly 0.3 percent per year beginning in 2000. These changes reflect steps to reduce substitution bias, including the introduction of an updated market basket and new computational techniques for aggregating the prices of the individual items in the various categories in the CPI market basket.11
C. Mismeasurement of Output and Productivity
11. Difficulties in measuring nominal expenditure or income and in measuring prices will lead to commensurate problems in measuring real output and, in turn, productivity. When the NIPAs ware revised to report real GDP data based on chain-type weights in 1995, much of the substitution bias associated with fixed-weighted measurements was eliminated (box below).12 However, biases in the measurement of prices remained, for example, reflecting quality changes and the introduction of new products. Because units of output, quality changes, and new products are less easily identified in service sectors, measuring output in these sectors is likely to be subject to greater measurement errors than output in goods-producing sectors. As the service sector has grown in importance over the past few decades, the potential for mismeasurement of output has thus increased significantly, and has given rise to speculation that some of the productivity slowdown that began in the mid-1970s may be attributable to measurement problems.
12. A paper by Slifman and Corrado (1996) provides indirect evidence of the mismeasurement of prices and real output.13 The authors note that labor productivity growth in the U.S. private business sector averaged about 1¼ percent per year over the period from 1973–94. During the same period, labor productivity in the nonfarm, nonfinancial corporate business sector rose at a faster average annual rate of 1¾ percent. Since the latter subsector accounts for about two-thirds of the private business sector, this suggests that labor productivity growth in the rest of the private business sector was nearly flat on average for more than 20 years. Slifman and Corrado attempt to identify which industries account for the sluggish measured labor productivity growth in this part of the economy, and to see if the measured labor productivity performance in these industries appears plausible. They find that measured productivity growth in most service sectors has been flat to negative since the mid-1970s, and they conclude that, in view of the maintenance of profitability in these sectors, measured productivity growth (and hence output growth) must be understated.14
Fixed-Versus Chain-Weighted Measures of Real GDP
In principle, measuring real economic activity requires separating price changes from quantity changes and aggregating these quantity changes. One way of approching this problem is to select prices from a single base year and then to value future production in each sector at these base year prices. The aggregate value of production of final goods and service across all sectors at base-year prices provides a “fixed-weighted” measure of real GDP. A problem with this approach is that, especially during periods of significant economic change, the resulting calculations are generally sensitive to the selected base year. When NIPAs are periodically moved to a new base year, the history of real GDP growth is then effectively rewritten. This occurs, for example, because those sectors in which production is rising relatively quickly also tend to be associated with prices that rise relatively slowly. Thus, when earlier base-year prices are selected those commodities experiencing relatively strong output growth tend to be assigned relatively greater weight than if later base-year prices were selected. Under such conditions, measured real GDP growth based on earlier base years will be higher than measured GDP growth based on later base years.
While this problem had bean viewed as relatively insignificant, more recent developments (including, in particular, the secular decline exhibited in computer prices and the rising share of computers in expenditures) have led to increasing differences in fixed-weighted measures of real GDP depending on the choice of base year. To deal with this problem, a chain-weighted method of calculating real GDP was introduced in the NIPAs in December 1995. The chain-weighted procedure involves making two calculations of real GDP growth for each year and using an average of them as the estimate of real GDP growth. Specifically, real GDP growth for each period is calculated based on both previous period prices and current period prices. By using this method for every period, the effects of changes in relative prices on the measure of real GDP growth is eliminated.
13. Among the estimates of annual average growth in labor productivity across various sectors of the economy derived by Slifman and Corrado, legal and health services have experienced the greatest measured reductions in productivity since 1977 (Table 2). While it may be difficult to agree on an a priori judgement regarding productivity in legal services, continuously declining labor productivity in the health care industry would appear to be implausible, in view of the rapid technological advances that have been achieved in this sector.
United States: Real Gross Product Originating per Hour, 1977–94
(Percent change at an annual rate over period indicated)
United States: Real Gross Product Originating per Hour, 1977–94
(Percent change at an annual rate over period indicated)
| Industry | 1977–94 | 1980–90 | 1990–94 | |
|---|---|---|---|---|
| Nonfarm business sector, excluding housing (BLS) | 1.0 | 1.1 | 1.1 | |
| Nonagricultural private industries (excluding housing) | 0.9 | 1.2 | 1.0 | |
| Mining | 2.6 | 5.0 | 4.6 | |
| Construction | −1.0 | −0.7 | 0.8 | |
| Manufacturing | 2.5 | 3.2 | 2.2 | |
| Durables | 2.8 | 3.6 | 3.1 | |
| Nondurables | 2.0 | 2.7 | 1.0 | |
| Transportation and utilities | 1.5 | 1.3 | 2.6 | |
| Transportation | 0.6 | −0.1 | 2.3 | |
| Communications | 4.5 | 4.0 | 5.2 | |
| Public utilities | 0.7 | 1.2 | 2.8 | |
| Trade | 2.1 | 2.5 | 2.4 | |
| Wholesale trade | 3.3 | 3.3 | 5.4 | |
| Retail trade | 1.3 | 2.0 | 0.6 | |
| Finance, insurance, real estate (excluding housing) | 0.2 | 0.1 | 0.9 | |
| Services | −0.6 | −0.5 | −1.1 | |
| Hotels and lodging | −1.5 | −1.5 | 0.4 | |
| Personal services | −0.9 | −0.5 | −0.7 | |
| Business and other services | −0.4 | −0.2 | −1.1 | |
| Auto repair | −1.3 | −1.0 | −1.9 | |
| Miscellaneous services | −0.2 | −1.2 | −3.5 | |
| Motion pictures | 1.7 | 1.7 | −1.1 | |
| Amusement services | 1.0 | 2.6 | −4.8 | |
| Health services | −1.8 | −1.8 | −2.5 | |
| Legal services | −2.8 | −2.6 | −3. | |
| Education services | 0.0 | −0.5 | 0.2 | |
| Membership organizations and social services | −0.2 | −0.1 | 0.5 | |
| Private households | 2.2 | 3.7 | 2.1 | |
United States: Real Gross Product Originating per Hour, 1977–94
(Percent change at an annual rate over period indicated)
| Industry | 1977–94 | 1980–90 | 1990–94 | |
|---|---|---|---|---|
| Nonfarm business sector, excluding housing (BLS) | 1.0 | 1.1 | 1.1 | |
| Nonagricultural private industries (excluding housing) | 0.9 | 1.2 | 1.0 | |
| Mining | 2.6 | 5.0 | 4.6 | |
| Construction | −1.0 | −0.7 | 0.8 | |
| Manufacturing | 2.5 | 3.2 | 2.2 | |
| Durables | 2.8 | 3.6 | 3.1 | |
| Nondurables | 2.0 | 2.7 | 1.0 | |
| Transportation and utilities | 1.5 | 1.3 | 2.6 | |
| Transportation | 0.6 | −0.1 | 2.3 | |
| Communications | 4.5 | 4.0 | 5.2 | |
| Public utilities | 0.7 | 1.2 | 2.8 | |
| Trade | 2.1 | 2.5 | 2.4 | |
| Wholesale trade | 3.3 | 3.3 | 5.4 | |
| Retail trade | 1.3 | 2.0 | 0.6 | |
| Finance, insurance, real estate (excluding housing) | 0.2 | 0.1 | 0.9 | |
| Services | −0.6 | −0.5 | −1.1 | |
| Hotels and lodging | −1.5 | −1.5 | 0.4 | |
| Personal services | −0.9 | −0.5 | −0.7 | |
| Business and other services | −0.4 | −0.2 | −1.1 | |
| Auto repair | −1.3 | −1.0 | −1.9 | |
| Miscellaneous services | −0.2 | −1.2 | −3.5 | |
| Motion pictures | 1.7 | 1.7 | −1.1 | |
| Amusement services | 1.0 | 2.6 | −4.8 | |
| Health services | −1.8 | −1.8 | −2.5 | |
| Legal services | −2.8 | −2.6 | −3. | |
| Education services | 0.0 | −0.5 | 0.2 | |
| Membership organizations and social services | −0.2 | −0.1 | 0.5 | |
| Private households | 2.2 | 3.7 | 2.1 | |
14. Slifman and Corrado also point out that BLS data indicate that profitability in the nonfarm, noncorporate sector has remained in line with historical levels, even though labor productivity in the sector has been continuously declining (Chart 2). Firm-level profits are heavily determined by developments in unit labor costs, and rising real unit labor costs, ceteris paribus, would tend to imply falling profits. Thus, the coexistence of rising real unit labor costs implied by the declining indicators of productivity and favorable profitability figures would appear to be inconsistent.
UNITED STATES: PRODUCTIVITY, PRICE INFLATION, AND PROFITABILITY BY INDUSTRY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.1/ Derived from nominal and real GDP (chained-1992 dollar) by industry.UNITED STATES: PRODUCTIVITY, PRICE INFLATION, AND PROFITABILITY BY INDUSTRY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.1/ Derived from nominal and real GDP (chained-1992 dollar) by industry.UNITED STATES: PRODUCTIVITY, PRICE INFLATION, AND PROFITABILITY BY INDUSTRY
Citation: IMF Staff Country Reports 1997, 097; 10.5089/9781451839494.002.A001
Sources: Bureau of Labor Statistics, U.S. Department of Labor; Bureau of Economic Analysis, U.S. Department of Commerce, and Fund staff estimates.1/ Derived from nominal and real GDP (chained-1992 dollar) by industry.15. This anomaly can be explained in one of three ways: (1) output prices in the nonfarm, noncorporate sector have risen, in fact, relatively rapidly;15 (2) growth in nominal output in this sector has been continuously understated;16 or (3) the increase in output prices in this sector have been overstated and the growth in output concomitantly understated.17 Slifman and Corrado conclude that it is unlikely that nonfarm, noncorporate output prices rose significantly faster than in the rest of the nonfarm business sector. They also argue that it is not likely that there are significantly greater errors in measuring nominal output and hours worked in the nonfarm, noncorporate sectors than in other sectors. They conclude, therefore, that the mismeasurement of price inflation (and, hence, output and labor productivity) is the most compelling explanation for the apparently incompatible data on productivity, profitability, and price trends in the nonfarm, noncorporate sector. If instead of declining over the last two decades, labor productivity had remained flat in those two-digit service-producing industries with measured negative productivity growth, Slifman and Corrado calculate that aggregate labor productivity growth would have beat nearly half a percentage point higher per year than the published data indicate; inflation, of course, would have been correspondingly lower.18
List of References
Armknecht, Paul A., 1996, “Improving the Efficiency of the U.S. CPI,” International Monetary Fund Working Paper, WP/96/103 (September).
Boskin, Michael J., et al., 1996, “Toward a More Accurate Measure of the Cost of Living,” Final Report to the Senate Finance Committee from the Advisory Commission to Study the Consumer Price Index (December).
Boskin, Michael J., 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C, pp. 109–15.
Bureau of Economic Analysis, 1995, “Preview of the Comprehensive Revision of the National Income and Product Accounts: BEA’s New Featured Measures of Output and Prices,” Survey of Current Business (July).
Bureau of Labor Statistics, 1996, “Briefing on the Consumer Price Index,” (December).
Congressional Budget Office, 1994, “Is the Growth of the CPI a Biased Measure of Changes in the Cost of Living?” Congressional Budget Office, Washington, D.C. (October).
Congressional Budget Office, 1997, An Analysis of the President’s Budgetary Proposals for Fiscal Year 1998, (March).
Council of Economic Advisers, 1997, “Annual Report of the Council of Economic Advisers,” U.S. Government Printing Office, Washington, D.C. (February).
Darby, Michael R., 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C, pp. 173–76.
Diewert, W. Erwin, 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Semite, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C, pp. 115–18.
Gordon, Robert J., 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C, pp. 122–26.
Greenspan, Alan, 1995, “Prepared Statement,” in U.S. Congress, “Review of Congressional Budget Cost Estimating: Joint Hearing before the House of Representatives Committee on the Budget and the Senate Committee on the Budget, Serial No. 104-1, U.S. Government Printing Office, Washington, D.C, pp. 130–49.
Griliches, Zvi (ed.), 1992, Output Measurement in the Service Sectors, University of Chicago Press.
Griliches, Zvi, 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C., pp. 129–32.
Jorgenson, Dale W., 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C., pp. 36–41.
Klumpner, Jim, 1996, “Fact and Fancy: CPI Biases and the Federal Budget,” Business Economics, (April), pp. 31:2, 22–29.
Lebow, David E., John M. Roberts, and David J. Stockton, 1994, “Monetary Policy and ‘The Price Level,’” mimeo, Federal Reserve Board, Washington, D.C.
Moulton, Brent R., 1996a, “Bias in the Consumer Price Index: What is the Evidence?” Journal of Economic Perspectives (Fall), pp. 159–77.
Moulton, Brent R., 1996b, “Bias in the Consumer Price Index: What is the Evidence?” United States Bureau of Labor Statistics Working Paper no.294, Washington, D.C. (October).
Pakes, Ariel, 1995, “Prepared Statement,” In Consumer Price Index: Hearings Before the Committee on Finance, United States Senate, Senate Hearing 104–69, U.S. Government Printing Office, Washington, D.C., pp. 44–48.
Peach, Richard W., “The Statistical Discrepancy, Productivity, and Inflation,” unpublished paper, Federal Reserve Bank of New York, (December 13) 1996.
Shapiro, Matthew D., and David W. Wilcox, 1996, “Causes and Consequences of Imperfections in the Consumer Price Index,” mimeo, University of Michigan, forthcoming in NBER Macroeconomics Annual 11.
Slifman, Lawrence and Carol Corrado, 1996, “Decomposition of Productivity and Unit Costs,” Federal Reserve Board Occasional Staff Studies, No. 1 (Washington, DC: Board of Governors of the Federal Reserve System).
Wynne, Mark A., and Fiona D. Sigalla, 1994, “The Consumer Price Index,“ Federal Reserve Bank of Dallas Economic Review (Second Quarter), pp. 1–22.
Prepared by Michael Leidy and Yutong Li.
In real terms, gross national income increased at an annual rate of 3.4 percent from the third quarter of 1994 through the first quarter of 1997, while gross national expenditure increased at a 2.6 percent annual rate (Chart 1).
The strength of these tax receipts, however, suggest that even the income estimates might understate the value of national output.
According to Okun’s Law the unemployment rate remains stable what GDP grows at its potential rate, rises when GDP grows below potential, aid fells when GDP grows faster than potential.
For a more detailed overview of problems associated with the CPI as an indicator of inflation and as a cost of living index see Armknecht (1996).
Implicitly, the fixed-weighted CPI assumes a price elasticity of substitution of zero. Under the assumption that the price elasticity of substitution is one (implying expenditure shares across goods are constant), a well known solution to the problem of commodity substitution bias is to use a geometric mean index of the following kind:
where So,i is the base-period expenditure share and Po,i and Po,i are current-period and base-period prices, respectively. The BLS began releasing an experimental CPI using a geometric mean in April 1997.
See, for example, the discussion in Moulton (1996a). To give a sense of the extent to which quality changes already enter CPI calculations, Moulton (p. 171) points out that the change in the new car component of the CPI from 1967 to 1994 would have been 80 percent higher if adjustments for quality improvements had not been made.
This “plausible range” is not a statistical confidence interval The “plausible range” as used in the Boskin Commission report amounts to a view on the reasonableness of the assumptions needed to generate estimates of the various types of CPI bias.
The Commission observed, for example, that changes in BLS methodology “largely or entirely eliminated an upward bias in the CPI for new automobiles prior to the mid-1960s and a downward bias for apparel after the mid-1980s” (p.32). The Commission’s report also discusses the various methods employed by the BLS to deal with quality changes for existing products (pp. 36–38).
For additional information on computation changes in the CPI that are being implemented, see Bureau of Labor Statistics (1996).
This methodological change affected estimates of the magnitude of the productivity slowdown that began in the early 1970s. While the fixed-weight measure showed the average annual rate of real GDP growth slowing from 3.7 percent in 1959–72 to 2.4 percent in 1973–94, the chain-type weight measure shows the average annual growth in real GDP growth slowing from 4.1 percent to 2.5 percent over these two periods (BEA, 1995, p.35).
The issue of downward bias in measuring real output and labor productivity, particularly in service-producing industries, is also addressed in Griliches (1992).
It was necessary for Slifman and Corrado to deduce the sectoral decomposition of labor productivity estimates because the Bureau of Labor Statistics (BLS) does not produce such figures.
If prices in the nonfarm, noncorporate sector have risen relatively rapidly, continuing relative price increases could have been sufficient to maintain profitability even in the face of declining productivity.
If nominal output has been underestimated, then the associated output and productivity estimates would also be underestimated. Given this mismeasurement, the fact that profitability has been maintained would no longer be surprising.
If output prices have been overstated, then nominal output is over deflated and real output consequently is underestimated. Higher productivity figures based on accurate price measurement would be consistent with maintained profitability.
Interestingly, although Slifman and Corrado approached the issue of price and output mismeasurement from a macroeconomic perspective, the overstatement of inflation implied by their hypothetical scenario is roughly in line with the Boskin Commission’s estimate of the share of inflation bias attributable to difficulties in controlling for quality changes and the introduction of new products.
