Productivity Analysis at the Federal Reserve: The Role of the BEA’s Annual Industry Accounts Paul Lengermann Industrial Output Section Federal Reserve Board of Governors BEA Industry Accounts Users’ Conference, October 26, 2007

2 Outline Background and motivation FRB Productivity System Conceptual Framework Industry Measures & Empirical Challenges Results for Basic Industry Results FRB Custom Aggregates Construction Key Findings Extensions Concluding Remarks

3 Background & Motivation Late-1990s – Sectoral Productivity Project –alternative, largely income based approach for studying productivity High-tech Measurement– work on IP index extended into links to aggregate productivity Following this: Detailed industry level analysis – emphasis on role of high tech and nonfinancial corporations Work on industry origins of statistical discrepancy

4 Background & Motivation (cont’d) More recently, focus shifted to combining the industry results to illuminate sectoral trends Coincided with 2004 comprehensive revision to Industry Accounts Key challenges: Customizing aggregates to provide a more meaningful view of productivity Traversing breaks and noise in the data Resulting “six sector” estimates are: Flexible in terms of composition & construction Readily updatable

5 FRB Productivity System Developed by Eric Bartelsman & Joe Beaulieu, it is both: a collection of SAS macros designed to automate routines commonly used in productivity research and a data repository –Hierarchical databases & Meta-data –Stored data files include: BEA: Annual Industry Accounts (AIAs), Benchmark IO, selected NIPA data BLS: MFP data, Current Population Survey Census County Business Patterns FRB Industrial production

6 Conceptual Framework We adopt the Domar or “deliveries-to-final demand” framework to examine the role of our sectors: MFP growth at any level of aggregation can be decomposed into contributions from underlying sectors or industries Framework has nice theoretical properties: Maps productivity growth into the rate of expansion of the social production possibilities frontier Production modeled using the concept of “sectoral output” Equals gross output less amount produced and consumed within the sector For detailed industries, close to gross output; for aggregates, closer to value added

7 Growth Accounting (Bolded letters denote real growth rates)

8 Industry Measures Nominal Sectoral Output: Use the annual IO accounts to subtract own-industry intermediate use from gross output Real sectoral output growth: Built from quantity indexes that assume the price index for own industry intermediates equals the published gross output deflator Real growth of intermediates from other industries: Derived by stripping real value of own intermediates from total intermediates Real capital input growth : Two broad measures of capital services constructed from BEA’s detailed fixed assets: –IT Capital (computers, software, comm. equipment) –Other capital (equipment, structures, inventories, land)

9 Industry Measures (cont’d) Labor Input: Captures changes in hours worked of all persons Implicit labor quality adjustment made using the very detailed industry-level data from Census County Business Patterns BEA’s NAICS-based employment begin in 1998 and 2000 respectively: –Using Census data, we developed a concordance between the 1997 SIC and the 1997 NAICS –Concordance applied to unpublished BEA SIC-based data on hours

10 Source Data Discrepancies Application of concordance complicated by large differences in industry classification between Census & BLS Especially pronounced for Census employment in Management of Companies (N55) These inconsistencies complicate productivity analysis: Industry output data (Census) are classified differently than labor input (BLS). Integrated annual IO and GDPI data inherited the BLS compensation levels Also apparent in the CPS which complicates labor quality adjustments

11 Source Data Discrepancies (cont’d) What do we do? Ignore the problem for compensation shares Adopt Census NAICS industry composition in 1998 and treat the BEA/BLS employment measures as indicators going forward Re-balance our concordance to be consistent with the “adjusted” labor data before applying it to the pre-1998 SIC-based data Goal: labor input changes which are consistent over time

12 Each row is a sources of growth decomposition for period : Estimates generated in Dec with Industry Accounts release In progress: “preliminary” estimates following July’s NIPA annual revision

13 FRB Custom Aggregates

14 FRB Custom Aggregates (cont’d) Key challenge: Disaggregate several BEA industries to define our sectors High-tech industries: Publishing –> Software & Publishing Broadcasting –> Telecommunications & Radio/TV/Cable Broadcasting Information –> Information Services & Data Processing Services Nonprofits & Owner Occupied Housing: Real Estate Health Care Other Services

15 Each row is a decomposition of the change in growth during the late-1990s: Acceleration in aggregate MFP driven by high-tech and distribution Pickup in IT capital services driven by finance and business services and distribution

16 Each row is a decomposition of the change in growth during : Faster aggregate MFP has been the major driver High-tech and distribution are no longer the main drivers. MFP accelerated in finance & business services, the industrial sector, and personal services

17 Key Findings By 2005, the productivity resurgence that began in mid-1990s was relatively broad based Late 1990s: U.S. productivity resurgence was a sectoral story Increases for high tech and distribution partly offset by step- downs elsewhere Since 2000: MFP the major contributor to economic growth Strong MFP gains in the finance & business services MFP growth for players in late-1990s remained robust

18 Extension: Modeling Aggregate Trend Productivity Six sectors clearly have very different trends Separate accounting and forecasting may intuition behind estimate of current trend in aggregate MFP Sectoral trends can be linked to other elements of staff projections

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20 Extension: The Role of Imported Intermediates Imported intermediates derived from detailed measures obtained from the BEA Price and quantity measures from other domestic industries derived residually In progress: combine with BEA’s industry level KLEMS data Total Purchased Inputs (Mk) Own Industry (Xk) Other Industries (Rk + Nk) Foreign (Rk)Domestic (Nk)

21 Each row is a decomposition of the change in growth during : While import contribution has accelerated in in most sectors Domestic inputs spurred growth in construction and distribution but subtracted from it elsewhere Industrial sector growth due entirely to faster MFP and increased reliance on foreign inputs

22 Concluding Remarks Annual Industry Accounts are an essential input to FRB work on productivity Sufficiently detailed to provide a rich—and highly customizable—assessment of recent trends Measurement: we know the least about the sectors with the best productivity performance High-tech –Necessary to break out high-tech industries to work with the industry accounts. Finance and Business Services –How reliable are the data for this sector? –Proposed Improvements to AIAs would really help (broader SAS coverage; expanded PPIs)

23 Concluding Remarks (cont’d) Industry classification differences: Analysis compromised when industry output and inputs classified differently Proposed reconciliation effort is important Flexibility: To group industry-level results into vertically-integrated sectors further disaggregation of BEA industry data was necessary (high tech, nonprofits) Plans to publish VA for more detailed industries would help a lot