Productivity Analysis at the Federal Reserve: The Role of the BEA’s

Annual Industry Accounts

Paul LengermannIndustrial Output SectionFederal Reserve Board of Governors

BEA Industry Accounts Users’ Conference, October 26, 2007

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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

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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

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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

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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

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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

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Growth Accounting (Bolded letters denote real growth rates)

Productivity growth can be expressed either as weighted average of

underlying industries:

(1) MFPk = ki

i k

d MFPi , where “Domar weights” are ik

ik

Sd

S

Or it can be calculated residually:

(2) MFPk = Sk• I k• .

We calculate industry-level MFP with (2) and sectoral MFP with (1). Finally, we conduct our sectoral growth accounting using:

(3) Sk• = Nks Nk• + k

ii k

d [ MFPi + L

is Li + Kis Ki + R

is Ri] .

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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)

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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

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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

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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

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• 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

Sectoral IT Other PurchasedOutput MFP Capital Capital Labor Inputs

(1) (2) (3) (4) (5) (6)A. 1987 to 2005

1. Private industries 3.4 1.2 .6 .7 .7 .32. Goods-producing industries 2.4 1.2 .2 .2 -.2 1.03. Agriculture, forestry, fishing, and hunting 1.6 1.4 .0 .0 -.2 .34. Mining .4 -.2 .1 .0 -.2 .75. Construction 1.9 -.4 .1 .1 .8 1.46. Manufacturing 3.7 2.4 .2 .1 -.5 1.49. Services-producing industries 3.6 .8 .6 .8 .9 .6

10. Utilities 1.5 1.1 .2 .4 -.2 .011. Wholesale trade 4.0 1.7 .4 .2 .5 1.312. Retail trade 4.4 2.2 .2 .2 .3 1.413. Transportation and warehousing 3.3 1.2 .4 .1 .5 1.114. Information 6.1 1.6 1.0 .5 .6 2.415. Finance, insurance, real estate, rental, and leasing 3.6 .0 .7 1.4 .3 1.216. Professional and business services 4.6 .2 .7 .3 1.5 1.917. Educational services, healthcare, and social assistance 3.4 -.8 .2 .4 1.7 1.918. Arts, entertainment, recreation, accomodation, and food services 3.0 .1 .1 .3 .8 1.619. Other services 2.4 .1 .1 .1 .2 2.0

Sources of growth for U.S. private industry and major industry groups

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FRB Custom Aggregates

Initial (based on NAICS) Primary Producing Function

High-tech Computer and electronic product mfg. (NAICS 334)

Software publishing (NAICS 5112)

Telecommunications services (NAICS 5133)

Information services (NAICS 5141)

Computer systems design and related

services (NAICS 5415)

Housing Construction (NAICS 23)

Real Estate (NAICS 53pt)

Industrial Manufacturing (NAICS 31, 32, 33, excl. 334)

Mining (NAICS 21) Production of Goods

Utilities (NAICS 22)

Forestry, fishing and related (NAICS 113-5)

Distribution Wholesale trade (NAICS 42)

Retail trade (NAICS 44, 45) Merchandisers and Transportation of

Goods

Transportation (NAICS 48)

Warehousing (NAICS 49)

Finance and Finance and insurance (NAICS 52)

business Real estate and rental and leasing (NAICS 53pt)

Professional, scientific, and technical services

(NAICS 54, excluding 5415) Services to Businesses

Management of companies (NAICS 55)

Admin, waste and related (NAICS 56)

Data processing services (NAICS 5142)

Personal and Health care and social assistance (NAICS 62pt)

cultural Arts, entertainment, and recreation (NAICS 71)

Accommodation and food services (NAICS 72)

Other services (NAICS 81pt) Services and Cultural Products to

Persons

Newspaper, book, etc. publishing (NAICS 5111)

Motion pictures and sound recordings (NAICS 512)

Radio/TV broadcasting (NAICS 5131-2)

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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

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• 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

Sectoral IT Other PurchasedOutput MFP Capital Capital Labor Inputs

(1) (2) (3) (4) (5) (6)C. Difference in Annual Averages, (1995 to 2000) vs. (1987 to 1995)

1. Private nonfarm business 2.3 .3 .5 .1 .7 .62. Excl. high-tech 2.0 -.1 .4 .1 .5 .93. Construction and real estate 2.4 -.2 .1 -.2 .9 1.74. Industrial .8 -.4 .1 .1 .0 1.05. Distribution 1.2 .8 .3 .2 .2 -.36. Finance and business 4.0 -.3 .8 .2 1.0 2.37. Personal and cultural .8 .1 .1 .0 -.2 .88. High-tech 8.0 2.7 .9 .0 2.1 2.4

Sources of growth in sectoral output for major and "intermediate" sectorsof the U.S. economy1

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•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

Sectoral IT Other PurchasedOutput MFP Capital Capital Labor Inputs

(1) (2) (3) (4) (5) (6)

D. Difference in Annual Averages, (2000 to 2005) vs. (1995 to 2000)

1. Private nonfarm business -3.0 1.3 -.6 -.5 -2.3 -1.02. Excl. high-tech -2.6 1.5 -.5 -.4 -1.8 -1.44. Construction and real estate -.9 1.8 -.1 -.5 -1.2 -1.03. Industrial -2.2 1.1 -.2 -.2 -1.2 -1.75. Distribution -2.6 -.3 -.3 -.3 -1.2 -.56. Finance and business -4.5 2.1 -.9 -.7 -2.4 -2.67. Personal and cultural -1.4 .9 -.2 -.1 -.5 -1.68. High-tech -13.7 -1.0 -1.2 -.4 -4.8 -6.3

Sources of growth in sectoral output for major and "intermediate" sectorsof the U.S. economy1

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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

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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

Core Inflation: Trend MFP increases 3/4 ppt

0

0.5

1

1.5

2

2.5

3

4Q98 1Q99 2Q99 3Q99 4Q99 1Q00 2Q00 3Q00 4Q00

MFP Decrease MFP Increase MFP Base

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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)

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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

Sectoral IT Other Purchased Purchased

Output MFP Capital2 Capital3 Labor Domestic Inputs Foreign Inputs(1) (2) (3) (4) (5) (6) (7)

D. Difference in Annual Averages, (2003 to 2005) vs. (1997 to 2002)

1. Private nonfarm business -.6 1.6 -.6 -.4 -.5 -1.1 .32. Excl. high-tech -.4 1.5 -.4 -.3 -.3 -1.1 .33. Construction 1.0 .6 -.1 -.1 -.4 .7 .34. Industrial .7 1.2 -.2 -.1 .0 -.6 .55. Distribution -.2 -.5 -.3 -.2 -.2 .7 .16. Finance and business .0 2.6 -.7 -.6 -.4 -.9 .07. Personal and cultural -.2 1.1 -.2 -.1 -.2 -.7 .18. High-tech -3.0 2.1 -1.3 -.4 -2.1 -1.7 .4

Table 3

Sources of growth in sectoral output for major and "intermediate" sectorsof the U.S. economy1

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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)

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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