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Manufacturer’s Outsourcing to Employment Services
Matthew Dey, BLSSusan Houseman, Upjohn Institute
Anne Polivka, BLS
Presentation for 2008 World Congress on National Accounts
Views are those of the authors’ and do not necessarily reflect official BLS positions
May 15, 2008
2
Outline of Presentation
• Why Concentrating on Employment Services (ES) and Manufacturing
• OES Data and Construction of OES Panel
• OES Trends in Occupational Structure of Employment Services
• Imputing Employment Service Workers to Manufacturing
• Implications of ES Outsourcing for Productivity Trends
3
• Employment Services Sector Includes:
3 Industries
○ Temporary Help Agencies (THS) – 71% in 2005
○ PEOs – 21% in 2005
○ Employment Placement Agencies – 8% in 2005
Concentration on Employment Services and Manufacturing • Employment Services is the best measured form of
domestic contracting out
• Employment Services exhibited rapid employment growth during the 1990’s
• Manufacturing exhibited marked employment decline during the 1990s
• Employment Services exhibited dramatic occupational shift
5
1990s Period of Rapid Employment Growth in U.S.
• Aggregate payroll employment grew by 21.3% from 1989-2000
• Employment Services ○ Accounted for 10.6% of aggregate employment
growth
○ Increased share of aggregate employment from 1.3% to 3.0%
• Manufacturing employment decreased by 4.1%
6
Employment Trends, Aggregate and Employment Services, 1989-2005
(Index 1992=100)
50
100
150
200
250
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Em
plo
yme
nt i
nd
exe
s, 1
99
2=
10
0
Aggregate Employment Services
7
Outline of Presentation
• Why Concentrating on Employment Services (ES) and Manufacturing
• OES Data and Construction of OES Panel
• OES Trends in Occupational Structure of Employment Services
• Imputing Employment Service Workers to Manufacturing
• Implications of ES Outsourcing for Productivity Trends
8
OES Data and Construction of Panel • Occupational Employment Survey program
– Operated in current form since 1988, most recently available data 2004
– Provides wage and employment data for detailed occupations, industries, and geographic areas
– Surveys ≈ 400,000 establishments annually – Change in survey structure in 1996:
• Pre-1996, surveyed 1/3 of industries each year
• 1996-present: every industry surveyed every year• We have access to establishment level data 1996-2004
OES Data and Construction of Panel Data
• Constructing Panel Data on Occupation by Industry from OES– To handle changes in occupation and industry classification
systems, aggregate data into 18 occupation groups and 16 sectors
– Benchmark employment levels to CES– Pre-1996: assume occupation structure of employment same for
three-year cycle– Jack-knife methods to compute standard errors of occupation by
industry employment estimates from 1996-2004
1,,1,ˆ
tttsE
EEE
ojs
oijsc
jtijt
10
Outline of Presentation
• Why Concentrating on Employment Services (ES) and Manufacturing
• OES Data and Construction of OES Panel
• OES Trends in Occupational Structure of Employment Services
• Imputing Employment Service Workers to Manufacturing
• Implications of ES Outsourcing for Productivity Trends
Dramatic Shift in ES Occupational Structure in 1990s
• Dramatic growth in production and other manual occupations in ES
○ Equivalent to almost all of employment growth in these occupations economy wide
○ Trend first noted by Segal and Sullivan, 1997
• Shift towards higher-skilled production occupations
○ Accounted for almost a quarter of ES growth
12
Trends in Occupation Shares, Employment Services
1989
1989
1989
2000
2000
2000
2004
2004
2004
0
10
20
30
40
50
Blue Collar Office & Administrative Support All Other Occupations
(per
cent
)
Occupation’s share of ES
employmenta
Occupation’s share of ES growthb
1989 2000 20041989–2000
1989–2004
2000–2004
Office and administrative support
41.8(n.a.)
29.9(0.9)
23.7(0.8) 22.8 11.1 93.7
Production6.3(n.a.)
17.2(1.4)
14.8(0.9) 23.6 20.7 41.3
Helpers, laborers, material movers (hand)
16.0(n.a.)
18.0(0.8)
21.9(1.3) 19.1 25.9 −22.1
Total growth rate 168.8 133.1 −8.9aReported figures are percentage of employment services employment in the indicated occupation. Standard errors of this percentage are in parentheses.bReported figures are the percentage of employment services growth over the period accounted for by growth in the indicated occupation.
Trends in Occupational Distribution of Employment within ES, Selected Occupations
Occupation Employment services Manufacturing
share of occupationa share of occupationa
1989 2000 2001 2004 1989 2000 2001 2004
Office and administrative support
2.8(n.a.)
5.0(0.2)
3.5(0.2)
3.7(0.1)
8.6(n.a.)
6.9(0.1)
6.2(0.1)
6.1(0.1)
Production 0.9(n.a.)
5.9(0.5)
4.1(0.3)
5.9(0.3)
76.6(n.a.)
71.0(0.8)
71.9(1.0)
72.7(0.7)
Helpers, laborers, material movers (hand)
6.3(n.a.)
15.8(0.7)
17.2(1.1)
17.6(1.0)
35.0(n.a.)
26.2(0.7)
25.0(0.7)
24.5(0.5)
Total 1.3 3.0 2.5 2.7 16.3 12.9 12.0 10.8
aShares are written as a percentage. Standard errors are in parentheses.
Share of Total Employment in ES and Manufacturing, Selected Occupations and Years
15
Outline of Presentation
• Why Concentrating on Employment Services (ES) and Manufacturing
• OES Data and Construction of OES Panel
• OES Trends in Occupational Structure of Employment Services
• Imputing Employment Service Workers to Manufacturing
• Implications of ES Outsourcing for Productivity Trends
Imputing Employment Service Workers to Manufacturing
• Methodology and Assumptions
• Results
• Robustness Checks
Imputing ES Workers to Manufacturing
• Previous Studies – Segal and Sullivan, 1997 – Estavão and Lach (1999a, 1999b)
– Hampered by lack of data and inconsistencies
Imputing ES Workers to Manufacturing
• Our Approach
Use proportion of THS workers in each occupation assigned to manufacturing from CWS with OES estimates of number of ES workers in each occupation
ˆ ˆ Ht i it
i
M PE
Key Assumptions Underlying Our Approach
• Within occupations, CWS provides an unbiased estimate of industry of assignment for THS workers
• Probability of THS workers in occupation being assigned to manufacturing = Probability of other ES workers (PEOs) working in manufacturing
• Within occupations, assignment probabilities do not change over time○ Estimated number assigned to manufacturing changes
over time due to changes in OES total number of ES workers and changes in occupational composition of ES workers
20
Key Results of Imputation
• Rapid Increase in Outsourcing to Employment Services by Manufacturing during the 1990s○ ES employment adds an estimated 2% to
manufacturing employment in 1989, 8% in 2000, 9% by 2004
○ Manufacturing employment without ES workers shrank 4% from 1989 to 2000
○Adding ES workers employment in manufacturing actually grew by 1%
Figure 2: Trends in Manufacturing Employment and Employment Service Workers Assigned to Manufacturing
11
12
13
14
15
16
17
18
19
20
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Manufa
ctu
ring a
nd
Adju
ste
d M
anu
factu
ring
Em
plo
ym
ent (m
illions)
0.3
0.8
1.3
1.8
2.3
2.8
3.3
ES
Assig
ned to
Ma
nufa
ctu
ring (
millions)
Adjusted manufacturing employment
Manufacturing employees
ES Assigned to manufacturing
Notes: Shaded area represents 95% confidence interval for employment adjusted for ES workers assigned to manufacturing.
As a percentage of manufacturing employees within the occupation
1989 1996 2000 2001 2004
Office and administrative support
6.3 13.7 14.2 10.9 11.6
Production 1.0 4.2 7.2 4.9 7.0
Helpers, laborers, material movers (hand)
9.0 16.4 30.0 34.4 35.9
All 2.3 5.3 8.2 6.9 8.7
ES Workers Assigned to Manufacturing, by Occupation, Selected Years
23
Robustness Checks
• Occupational assignment not changing over time ○ Test using CWS data indicates holds for 1995-2005
period ○ Single year imputations look similar
• Probability of THS assignment = Probability of Other ES assignment ○ THS workers can be distinguished from other ES
workers in OES data beginning in 1999○ Imputation with just THS workers, number is lower,
but pattern and implications remain
24
Outline of Presentation
• Why Concentrating on Employment Services (ES) and Manufacturing
• OES Data and Construction of OES Panel
• OES Trends in Occupational Structure of Employment Services
• Imputing Employment Service Workers to Manufacturing
• Implications of ES Outsourcing for Productivity Trends
25
Labor Productivity
Definition in Manufacturing
○ Output/worker or Output/labor hour
○ Manufacturing uses gross output
▪ constant dollar value of shipments▪ Not a value-added concept ▪ Inputs purchased from outside
manufacturing not subtracted out
26
Labor Productivity
• Given Definition
○ Measures could be significantly impacted by changes in contracting out to ES
○ Use of employment services by manufacturers accounted for 14% of labor productivity growth from 1989 to 2000
Manufacturing Labor Productivity Growth Adjusted for Employment Services
Annual growth rate of output per worker
Time period
Measured growth rate of
output per worker
Adjusted for use of employment
services
Contribution of
employment services to
productivity growth
1989–2000 3.63 3.12 0.51
1989–2000,adj. for hours 3.63 3.16 0.47
1989–1996 3.70 3.29 0.41
1996-1999 5.09 4.28 0.80
1996–2000 3.98 3.31 0.67
2000–2001 2.14 3.33 −1.19
2001–2004 6.14 5.60 0.54
28
Multifactor Productivity (KLEMS)
• Should account for outsourcing to ES○ Tries to account for shifts of inputs, in this
case from “workers” to “purchased services”
• Dependent on the accuracy estimated use of employment services by manufacturing
• Derived from BEA benchmark input-output tables
29
Multifactor Productivity (KLEMS)
• BEA benchmark I-O table estimates of manufacturers use of ES
○ 1992 15%, 1997 5%, 2002 5%
• Our estimates ○ 1992 29%, 1997 36%, 2002 34%
• OES production workers share of ES employment
○ 6% 1989, 17% in 2000• BEA estimates inconsistent in both magnitude
and trend
30
Conclusion
• Outsourcing by manufacturers to Employment Services substantial
• Affects estimates of sectoral employment trends • Affects measure of labor productivity, and
evidences suggest not adequately reflected in KLEMS
• Affects on labor productivity vary cyclically • Has potential implications for studies of skill-bias
technological change and wage trends in manufacturing