# Presentation: 323112

## 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*

## 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

##  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

##  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

## 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

## 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

##  Growth Accounting (Bolded letters denote real growth rates) 

## 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)

## 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

## 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

## 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

## Each row is a sources of growth decomposition for period 1987-2005:
Estimates generated in Dec with Industry Accounts release
In progress: “preliminary” estimates following July’s NIPA annual revision

- Estimates generated in Dec with Industry Accounts release
- In progress: “preliminary” estimates following July’s NIPA annual revision

## FRB Custom Aggregates

## 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

##  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

## Each row is a decomposition of the change in growth during 2000-2005:

-  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

## 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

## 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

## Slide 19

## 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

## Each row is a decomposition of the change in growth during 2003-2005:

-   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

## 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)

## 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