Bias in aggregate productivity trends revisited

Monthly Labor Review

Volumn 125, Issue 3, 2002, Pages 32-40

  Gullickson, William ^a   Harper, Michael J ^b  

^a Division of Productivity Research

^b BUREAU OF LABOR STATISTICS   (United States)

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EID: 11244325052     PISSN: 00981818     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (17)

1. This earlier article is available at http://stats.bls.gov/opub/mlr/1999/02/art4full.pdf.
2. Solow, Robert M., "Technical Change and the Aggregate Production Function," Review of Economics and Statistics. (1957) Vol. 39, No. 3, pp. 312-20.
3. Evsey D. Domar, "On the Measurement of Technological Change," Economic Journal December 1961, pp. 709-29.
4. In an unpublished paper supplementing the earlier article, we described an "ideal" set of data and also a model that could use these data to calculate industry and aggregate productivity in a consistent manner. Ideally we would have (a) an annual set of nominal input-output tables defined consistently over time, (b) a complete set of price indexes for each product, and (c) complete data on real capital and labor inputs used by each industry. All of this would be consistent with published aggregate data on output and inputs. Of course, the available data fall short of this ideal. Construction of this data-set would be expensive, because it would involve reconciling considerable amounts of conflicting information and estimating much incomplete information. In the economic censuses, in Bureau of Economic Analysis input-output and National Income and Product Accounts work and in Bureau of Labor Statistics multifactor productivity (MFP) work, however, much of what would be needed to construct this ideal data-set is already effectively estimated. To get the results in the Monthly Labor Review article and the new results here, we have made assumptions and adjustments to reconcile various data with the framework we have in mind. By using many shortcuts, we have attempted to infer what MFP trends might emerge if the ideal data-set were really constructed. The unpublished paper also spelled out the rationale for the model used in terms of production functions. We had this model and these "ideal" data in mind in formulating the industry exercise. It is important to be able to relate real economic growth measurement procedures to formal production theory. It is not the case that describing the link to theory forces a lot of assumptions onto the data. To the contrary, we are assuming much in any event, and careful links to theory help us understand what it is we are assuming, and by doing so, to guide the way to less rigid assumptions.
5. On a quinquennial basis, we estimate a full set of inputs and outputs in both nominal and real terms. These are based in part on input-output tables and on industry gross output and output price series that the authors obtained from the BLS Office of Employment Projections (OEP). The OEP starts with Bureau of Economic Analysis benchmark input-output tables and makes adjustments for "time series consistency." At this point, the most recent benchmark table available is for 1992. The tables in the current paper reflect new OEP work that, in turn, reflects the 1999 comprehensive revisions to Bureau of Economic Analysis National Income and Product Accounts.
6. This involves substituting the Bureau of Economic Analysis output trends for BLS output trends and using the input-output data to adjust the input trends estimates for compatibility with the Bureau of Economic Analysis output levels. Bureau of Economic Analysis-based trends are based on adjustments to BLS trends, and we never develop a full set of inputs and outputs consistent with Bureau of Economic Analysis-output based MFP trends.
7. For those unfamiliar with the Monthly Labor Review article, we will note the major data sources used to estimate these MFP.trends in table 4. These are the 1977 and 1992 input-output tables from the Bureau of Economic Analysis, adjustments to these tables for consistent definitions made by the BLS Office of Employment Projections (OEP), an estimate of the 1997 table made by the OEP and the authors using data from the 1997 Economic Censuses; Bureau of Economic Analysis data on nominal and real gross output associated with their gross product originating measures; and data on capital and labor from the data-set supporting the published BLS MFP measures for the private business sector.
8. Gordon, Robert J., "Does the New Economy Measure Up to the Great Inventions of the Past?" Journal of Economic Perspectives 14 (2000): pp. 49-74.
9. To simplify processing, the "point contributions" of specific input categories to "input growth" were computed by multiplying each category's input quantity trend, for 1977-97, by the arithmetic mean of its cost shares in 1977 and 1997. Since the aggregate input growth trends are built up by chaining quinquennial trends, the sum of point contributions sometimes differs from the input growth rate by several tenths of a percent.
10. The ratio is the nominal value of the industry's sectoral output divided by the nominal value of private business gross product originating. The contribution of industries to aggregate productivity growth by Domar's method is independent of the form of the MFP measures used for each. Industry measures can be alternatively based on gross output, "sectoral output," or a net value-added output and, as long as the nominal values used for weights and the inputs and outputs underlying the MFP measure are defined consistently, industrial contributions are unaffected. The term "sectoral output" is attributable to Frank Gollop. It expresses Domar's preferred concept of an industry's output: it includes all sales to final demand plus all sales to other industries, but deducts from that the value of intermediate inputs purchased from within the industry in question. Note that the scope of the measure depends on the degree of aggregation: as we examine progressively more aggregate industrial sectors, successively more intermediate inputs are excluded. As we noted in our Monthly Labor Review article, the sum of these ratios is more than one because the industries sell intermediate products to one another. The intuition as to why the weights would add up to more than one can be illustrated by the following example. If the productivity of steel makers improved by 1 percent, and the productivity of automakers improved by 1 percent, then the productivity with which the economy created cars would have increased by more than 1 percent.
11. When contributions are added up (with more precision than shown), these new calculations approximately replicate the published MFP trends for the private business sector (compared in two lines near the bottom of the table). For 1977-97 the new detailed contributions are consistent with an aggregate MFP trend of 0.7.
12. Note that the category we refer to as "banks" includes private, forprofit financial institutions within SICs 60, 61, and 67. Among the more important types of institutions in these industries are commercial banks, savings and loans, credit agencies, bank holding companies, certain trusts, and royalty administrators. Commercial banks accounted for about 57 percent of the employment in this category in 1995.
13. In table 4, the trend is -1.6 percent per year from 1977-97. We also calculated the trend for 1987-97 and this was -0.8 percent per year.
14. Oliner, Stephen D. and Daniel E. Sichel, "The Resurgence of Growth in the Late 1990s: Is Information Technology the Story?" Journal of Economic Perspectives, Fall 2000, pp. 3-22.
15. These results are based on MFP trends that BLS publishes, and so they are not subject to quite so many qualifications as the nonmanufacturing estimates.
16. Oliner and Sichel found similarly large effects from both the making and the use of high tech items.
17. Both of Oliner and Sichel's effects are directly dependent on the measured growth rate of high tech quality change.