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Offshoring

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REV: JULY 11, 2005

Offshoring at Global Information Systems, Inc.
The Opportunity
Early in the first quarter of 2004, Jane Harding was considering a proposal to shift 3,000 wellcompensated computer programming jobs from the United States to existing company locations in
China, India, and Brazil. Harding was the senior vice president for Human Resources in the Global
Services Division (GSD) of Global Information Systems, Inc. (GIS). GIS had 2003 revenues of nearly
$90 billion (Exhibit 1). GSD accounted for about half of GIS’s total revenues and an equal fraction of its total profits. It also included more than half of the company’s 315,000 worldwide employees.

Background
A significant portion of GSD’s business came from customers outsourcing their business-process needs. GSD signed multiyear contracts with customers. Most of these long-term contracts were won through highly contested competitive proposals against firms such as Accenture, Ltd., Electronic
Data Systems Corp., Computer Sciences Corp., and Perot Systems (Exhibit 1). The multiyear billings of some contracts totaled in excess of $1 billion. Pretax profit margins at the division level were close to 10%.
In order to present the most attractive value proposition to potential customers, GSD sought to cut costs and improve performance by “offshoring” certain activities. The combination of huge capacity expansion and similarly large price reductions in telecommunications made it highly efficient to establish customer service/call centers, software development centers, engineering design centers, and back-office accounting centers in low-cost areas of the world that had been or were now developing politically and socially reliable infrastructures.
In India, for example, exports of information technology services grew fivefold between 1997 and
2002.

________________________________________________________________________________________________________________
Professor William E. Fruhan, Jr. prepared this case. This case was developed from published sources. HBS cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2004 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545-7685, write Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of Harvard Business School.

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Offshoring at Global Information Systems, Inc.

Table A

India’s IT Boom

Exports of Software, IT Services
In billions

20.4%

$ 10
17%

8
13.8%

6
Share of
India’s exports

10.6%

4
7.6%

2

4.9%

0
1997
Source:

1998

1999

2000

2001

2002*

India’s National Association of Software & Services Companies.

*Fiscal year ends in March.

Multinational firms headquartered in many of the developed areas of the world were participating in this trend. In February 2004, Siemens announced that it was moving 15,000 software programming jobs from Western Europe and the United States to India, China, and Eastern Europe.1
Of the 30,000 programmers Siemens employed worldwide, 3,000 were already employed in India when the announcement was made.
The competitive landscape for some of the larger business-process outsourcing companies in the
United States and Europe is shown below.

1 The Boston Globe, February 17, 2004, p. C2.

2
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Offshoring at Global Information Systems, Inc.

Table B

Business-Process Outsourcing Providers

Business Service
Providers, Vertical
Service Providers
Employease
NetLedger
Marlborough Stirling

Source:

204-144

Consultants
PwC
Cap Gemini Ernst
& Young
Accenture
KPMG
Hewitt

IT Service
Providers

Process
Specialists

Pure-Play
Business-Process
Outsourcers

Support
Services

IBM
EDS

Paychex
ADP

Xchanging
Exult

Hays
Serco

CSC
SBS
Accenture
Xansa

Cerdian Arinso
Spherion
Convergys

Capita
Liberata

Adapted from “Business Process Outsourcing,” Schroder Salomon Smith-Barney, November 8, 2002.

Company Economics of Offshoring
In order to accommodate growth in its business, GSD had previously established its own programming centers in Bangalore, India; Shanghai and Dalian in China; and Sumare, Brazil. Now
GSD would be going a step further—instead of taking business growth offshore, the new proposal would begin reducing the U.S. headcount for programmers involved in this activity and moving existing U.S. jobs offshore.
The cost savings from the proposed move promised to be dramatic (Exhibit 2). A U.S. programmer with a total employment cost (salary and all benefits) of $112,000 per year would be replaced by an offshore programmer with a total employment cost of $25,000 per year (line 3,
Exhibit 2). Grossed up for 3,000 people, the cost saving would amount to $261 million per year. Of course, other costs associated with running this activity offshore would consume a significant fraction of the savings. Additional telecommunications and management cost would consume over 35% of the savings (line 7, Exhibit 2), and severance costs in the United States would amount to slightly over
$25,000 per person displaced. Transition costs (line 9, Exhibit 2) would result in a loss of $19 million in 2004, the first year of the program (line 10, Exhibit 2). By 2005, however, the program would show a net savings of $40 million. By 2006, once all the transition costs had been absorbed, the savings would be about $168 million per year.

Political Sensitivity to Offshoring
While the numbers were very compelling, indeed staggering, Harding understood that a decision of this kind would be extremely sensitive. For example, some of the foreign programmers would come to the United States for several weeks to be trained by the very people they were replacing!
There was also a strong debate under way in the developed world economies about both the politics and the economic implications of offshoring. In the United States, for example, presidential candidate
John Kerry described executives who outsourced U.S. jobs as “Benedict Arnold CEOs.” 2

2 Benedict Arnold was an American general in the U.S. Revolutionary War who became a traitor.

3
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A newspaper article published in November 2003 captured the tone of the debate. The article noted an arrangement made by Massachusetts General Hospital (a Harvard University teaching hospital) to relieve an acute shortage of radiologists. “The hospital would beam images electronically from some scans to India, to be worked on by radiologists there.” 3 Knowledge of the deal set off a minor furor among 30,000 U.S. radiologists. “Who needs to pay us $350,000/year if they can get a cheap Indian radiologist for $25,000/year?"4
Indeed, why would anyone in the developed world invest in highly specialized training in the future if the skill sets generally associated with higher-level training were becoming commoditized?
(Exhibit 3.)
A backlash against offshoring was beginning to get under way by late 2003. At the state level a contract in New Jersey was awarded to an Indian company to relocate a call center for unemployment services. The contract was cancelled at an added cost of $900,000 to save 12 U.S. jobs.5 At the federal level in the United States “the Thomas-Voinovich amendment to H.R. 2673 would prohibit any private company awarded a federal contract under OMB Circular A-76 to perform any of the work outside the United States.”6

Job Losses in Manufacturing
On the economic front, job losses in the United States had previously been heavily focused in manufacturing. After hitting a peak in 1979, nearly 5 million manufacturing jobs had been lost to the
U.S. economy (Exhibit 4). Employment levels in many industries ranging from primary metals manufacturing to computer manufacturing had shrunk (Exhibit 5) as both production capacity and well-paid jobs moved offshore.
Net imports of nonagricultural goods as a percent of manufacturing gross domestic product in the
United States exceeded 30% by the end of 2002 (Exhibit 6).
While manufacturing had been the focal point of job losses in prior periods, now it appeared that job losses would move to the services sector. Forrester Research Inc., an economic forecasting company, suggested that over 3 million service jobs would disappear offshore from the United States between 2000 and 2015.

3 Andrew Pollack, “Who’s Reading Your X-Ray,” The New York Times, November 16, 2003, p. 1.
4 Ibid.
5 Stuart Anderson, “Creeping Protectionism: An Analysis of State and Federal Global Sourcing Legislation,” The National

Foundation for American Policy, December 2003, p. 1.
6 Ibid.

4
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Table C

Number of U.S. Jobs Moving Offshore by Job Category, 2000 to 2015
2000

Management
Business
Computer
Architecture
Life sciences
Legal
Art, design
Sales
Office
Total number of U.S. jobs going offshore

Source:

204-144

2005

2010

2015

0
10,787
27,171
3,498
0
1,793
818
4,619
53,987

37,477
61,252
108,991
32,302
3,677
14,220
5,576
29,064
295,034

117,835
161,722
276,954
83,237
14,478
34,673
13,846
97,321
791,034

288,281
348,028
472,632
184,347
36,770
74,642
29,639
226,564
1,659,310

102,674

587,592

1,591,101

3,320,213

“Tech Strategy Research Brief,” Forrester Research, Inc., November 11, 2002, p. 4.

Craig Barrett, CEO of Intel Corp., stated, “The structure of the world has changed. The U.S. no longer has a lock on high tech white collar jobs.” 7
Nandan Nilekani, CEO of Infosys Technologies, stated, “Everything you can send down a wire is up for grabs.”8
The new environment of job losses created a whole new set of issues for individual workers, companies, and countries. In many prior situations when manufacturing jobs went offshore, they went to companies that were headquartered offshore. U.S. companies in the declining industries of
Exhibit 5, for example, went out of business as their customers acquired the products offshore. In this round of job losses in the service sector, however, companies were not losing their customers. In fact they were striking preemptively by offshoring and were getting financially and competitively stronger even as their U.S. workforces experienced job losses.

The Global Economics of Offshoring
In thinking about who won and who lost in the process of offshoring, several factors needed to be considered. According to estimates by McKinsey and Company, offshoring created a “win-win” situation for both the “job transferor” economy and the “job transferee” economy. Using the United
States and India as examples, McKinsey calculated a $1.47 value to the global economy from $1.00 of
U.S. spending offshored. The gain of $0.47 was allocated $0.33 to India and $0.14 to the United States
(Exhibit 7).
A key component of the value created for the United States in Exhibit 7 is the $0.47 value of labor reemployed. Exhibit 7 assumes that most of the service employees losing jobs to imports were eventually reemployed. This is in line with the data in Exhibit 8 showing that about 70% of U.S. workers in service industries who lost their jobs between January 1999 and December 2001 were
7 Steve Lohr, “Many New Causes for Old Problem of Jobs Lost Abroad,” The New York Times, February 15, 2004, p. 25.
8 Ibid.

5
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Offshoring at Global Information Systems, Inc.

reemployed in January of 2002. The balance of the workers who were not reemployed were either unemployed or had left the workforce (retired or stopped looking for work).
Exhibit 9 shows the new wages and salaries earned by those employees who were able to find new jobs after being displaced from their old jobs. If new jobs paid 95% of the wages of the old jobs, this value of U.S. labor reemployed of $0.47 per $1.00 of spending offshored can be calculated as follows: (1) Wage component of $1.00 of spending offshored equals

$ 0.72

(2) Fraction of offshored employees finding new jobs

x 0.70

(3) Wage recovery if new job pay equals old job pay

$ 0.50

(4) Mean wage of new job versus old job

x 0.95

(5) Value from U.S. labor reemployed

$ 0.47

While the arithmetic of how offshoring could produce a win-win was clear, the validity of some of the assumptions going forward was less so.
Historically the benefits of free trade had been premised on the logic of comparative advantage— an idea of economist David Ricardo.
However, when Ricardo said that free trade would produce shared gains for all nations, he assumed that the resources used to produce goods—what he called the “factors of production”—would not be easily moved over international borders. Comparative advantage is undermined if the factors of production can relocate to wherever they are most productive: in today’s case, to a relatively few countries with abundant cheap labor. In this situation, there are no longer shared gains—some countries win and others lose.
...
[There has been]… a seismic shift in the world economy brought on by three major developments. First, a new political stability is allowing capital and technology to flow far more freely around the world. Second, strong educational systems are producing tens of millions of intelligent, motivated workers in the developing world, particularly in India and
China, who are as capable as the most highly educated workers in the developed world but available to work at a tiny fraction of the cost. Last, inexpensive, high-bandwidth communications make it feasible for large work forces to be located and effectively managed anywhere.9 Harding wondered whether the concept of comparative advantage was losing its relevance as an argument in favor of free trade, and what argument, if any, could replace it. If free trade merely transferred jobs, where would the new jobs come from to utilize the skills of highly trained displaced developed-world workers who had grown accustomed to high levels of pay? In 2004 new jobs were not appearing as the economy improved in the United States as they had following the recession of the early 1990s (Exhibit 10), and Europe and Japan appeared to be in worse shape than the United
States in terms of unemployment trends (Exhibit 11).

9 Paul Craig Roberts, “The Jobs Problem…or Is It?” The Washington Times, January 18, 2004, p. B3.

6
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Advice for Jason
After throwing her support behind the offshoring proposal, Harding arrived at home to welcome her son Jason. Jason was home for spring break from his college studies in upstate New York. He was a freshman and on his first day back was interested in discussing both his summer job opportunities and his course of study for his remaining three undergraduate years. Jason wanted some advice on whether he should pursue a degree in the humanities or whether he should have a more “preprofessional” focus to his studies such as on engineering, computer science, or premed
(Exhibit 12).

7
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Exhibit 1

Financial Data for Information Technology Business-Process Outsourcing Firms (1999–2003)
1999

Sales
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Net Profit
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Net Worth
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Pretax Profit/Sales
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Net Profit/Net Worth
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Market Value/Book Value
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Enterprise Valuea/EBITb
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Price/Earnings Ratio
GSD
Electronic Data Systems Corp.
Accenture, Ltd.
Computer Sciences Corp.
Perot Systems
Source:

87,548
18,731
9,550
8,111
1,152
7,712
658
2,023
356
76
20,511
4,535
2,208
2,589
391
13.4
3.5
22.5
6.6
10.9

2000
2001
2002
––––––––––––––– $ millions –––––––––––––––
88,396
83,067
81,186
18,856
21,141
21,502
9,752
11,444
13,105
9,371
10,493
11,379
1,106
1,205
1,332
8,093
1,762
2,464
403
55

7,723
2,148
1,057
233
-3

3,579
1,525
245
344
78

2003
89,131
21,476
13,397
11,347
1,462
7,583
-389
498
440
18

20,624
23,614
22,782
27,864
5,139
6,446
7,022
6,198
2,368
690
958
1,678
3,044
3,215
3,624
4,606
501
531
677
713
–––––––––––––––––– % ––––––––––––––––––
13.1
13.8
9.3
12.2
9.3
10.2
7.1
-1.8
27.7
9.2
8.1
12.0
6.5
3.2
4.4
5.4
8.3
1.1
9.1
5.6

39.0
8.1
15.5
28.3

39.7
23.6
107.7
14.3
12.4

35.1
23.5
79.8
7.5
-.5

15.4
16.7
68.0
10.1
13.0

30.0
-26.7
80.8
10.8
2.6

9.5
6.9
5.5
4.5

7.4
5.2
3.1
1.8

8.8
5.1
2.4
3.9

5.9
1.3
30.6
1.5
1.7

5.7
2.1
24.6
1.6
2.2

18.1
22.0
NA
16.3
13.3

15.0
23.3
22.6
17.3
16.1
16.8
6.6
29.5
NA
NA
9.8
NA
20.1
11.7
16.8
11.1
11.4
8.1
–––––––––––––––––– Times –––––––––––––––––
29.1
19.1
26.3
19.9
21.3
34.9
25.2
25.6
8.3
36.1
19.3
25.1
38.9
21.8
31.6
14.2
16.0
32.5
17.3
32.9
15.8
24.5

Company annual reports.

aEnterprise value equals the market value of all outstanding debt and equity securities minus the value of cash and cash and

cash equivalents. bEBIT equals earnings before interest and taxes.

8
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$261,000,000

Differences

(3) = (1)–(2)

bBefore any adjustment for inflation.

2005

(5)

2006

(6)

Thereafter

(7)

——————— $ millions ——————
261.0
261.0
261.0
93.0
93.0
93.0
30.6
47.4
156.4
80.6
(19.0)
40.0
168.0
168.0b

2004

(4)

aThree to five years’ experience in writing software code and other programming tasks for applications software. No customer contact as part of job.

Source: Casewriter.

Net programmer salary and benefits savings post-transition
Other incremental distance costs (telecommunications and management)
Severance costs in U.S. @ $25,000/person displaced
Savings delays, overlapping costs and other transition expenses
Net savings

$12.50 x 2,000
$ 25,000 x 3,000
$75,000,000

6
7
8
9
10

$ 56.00 x 2,000
$112,000
x 3,000
$336,000,000

Offshore

(2)

Total salary and benefits/persona/hour
Hours worked/year
Cost/person/year
Number of people displaced
Total cost/year

U.S.

(1)

Global Systems Division, GIS Corp.: Annual Benefit from Offshoring 3,000 Applications Programming Jobs

1
2
3
4
5

Line
#

Exhibit 2

204-144

-9-

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This document is authorized for use only by Yixiao Liang in 2015.

n
0.4
n

1.0%
1.9
1.2 n 0.2 n 0.7 n 0.1
0.3
0.3 n 0.1
0.1
0.2
0.3
0.1
0.3
0.3 n 0.6
0.2
n
0.1
0.1
0.1
0.3
0.7

Private Sources

1.1
.8
.6

2.5%
2.4
2.3
1.8
1.7
1.6
1.5
1.5
1.5
1.4
1.3
1.2
1.1
1.1
1.1
1.1
1.1
1.1
1.1
1.0
1.0
1.0
1.0
0.9
0.8
0.8
1.3
1.6

Total

13,567
5,798
n

$15,211
5,356
19,220
8,114
14,222
10,657
11,725
12,070
12,096
9,673
12,285
17,997
7,867
10,393
5,861
4,789
4,802
5,707
9,554
4,260
10,278
3,912
4,328
5,688
7,552
5,321
9,210

Expenditure per Student in U.S.
Dollars Converted Using PPPs

“Education at a Glance—2002,” Organization for Economic Cooperation and Development (OECD).

1.1
0.5
0.6

Brazil
China
India

Source:

1.6%
0.5
1.1
1.8
1.5
1.5
0.8
1.4
1.4
1.1
1.0
1.2
1.0
1.0
0.8
0.8
1.0
0.9
0.8
1.0
0.5
0.8
1.0
0.8
0.7
0.8
1.0
0.9

Public Sources

Spending as Percentage of GDP from Public and Private Sources

University-Level Educational Spending and Degree Holding

Canada
South Korea
United States
Finland
Sweden
Denmark
Australia
Austria
Norway
Ireland
Netherlands
Switzerland
France
Germany
Hungary
Mexico
Portugal
Spain
United Kingdom
Greece
Japan
Poland
Turkey
Czech Republic
Italy
Slovakia
Country mean
OECD Total

Country

Exhibit 3

7
2
n

25
25
30
18
20
11
24
7
32
20
24
16
18
14
15
15
11
24
21
17
24
15
10
11
12
11
18

25–34

9
1
n

20
20
28
16
16
8
19
8
26
14
22
18
11
15
15
15
7
18
18
14
25
11
8
13
11
11
16

35–44

% of Population by Age
Holding Degree

9
1
n

20
11
30
13
17
6
19
6
23
11
20
15
10
15
14
11
5
13
18
12
17
11
9
11
10
10
14

45–54

204-144

-10-

For the exclusive use of Y. Liang, 2015.

100.0
96.5
94.9
94.8
96.2
97.4
97.4
98.4
99.2
97.9
97.6
92.9
86.5
83.1

(%)
10,736
10,219
9,945
9,900
10,131
10,372
10,485
10,704
10,910
10,830
10,876
10,335
9,517
9,093

(000s)
100.0
95.2
92.6
92.2
94.4
96.6
97.7
99.7
101.6
100.9
101.3
96.3
88.6
84.7

(%)
6,959
6,849
6,854
6,873
6,890
6,869
6,752
6,716
6,650
6,492
6,388
6,107
5,789
5,608

(000s)

100.0
98.4
98.5
98.8
99.0
98.7
97.0
96.5
95.6
93.3
91.8
87.8
83.2
80.6

(%)

Nondurable Goods
Manufacturing Total

This document is authorized for use only by Yixiao Liang in 2015.

p = Preliminary.

aIncludes wholesale trade, transportation and warehousing, and utilities, not shown separately .

Data are based on reports from employing establishments and relate to full- and part-time wage and salary workers in nonagricultural establishments who received pay for any part of the pay period that includes the 12th of the month. Not comparable with labor force data, which include proprietors, self-employed persons, unpaid family workers, and private household workers; which count persons as employed when they are not at work because of industrial disputes, bad weather, etc., even if they are not paid for the time off; which are based on a sample of the working-age population; and which count people only once—as employed, unemployed, or not in the labor force. In the data shown here, people who work at more than one job are counted each time they appear on a payroll.

17,695
17,068
16,799
16,774
17,021
17,241
17,237
17,419
17,560
17,322
17,263
16,441
15,306
14,701

(000s)

Durable Goods
Manufacturing Total

Note:

100.0
99.0
99.3
101.2
104.4
107.1
109.3
112.1
115.0
117.8
120.4
120.4
119.1
118.8

(%)

Manufacturing
Total

Department of Labor, Bureau of Labor Statistics.

109,487
108,374
108,726
110,844
114,291
117,298
119,708
122,776
125,930
128,993
131,785
131,826
130,376
130,045

(000s)

All Industriesa
Total

U.S. Employment Levels: Employees on Nonagricultural Payrolls, by Major Industry, 1990–2003

Source:

2003p

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002

Exhibit 4

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487.4

500.3

500.3

501.6

505.3

491.9

490.0

446.9

397.0

369.7

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

70.4

75.6

85.1

93.3

93.7

96.2

95.5

95.3

95.3

92.8

90.1

675.8

744.1

875.8

949.3

932.9

964.7

951.1

915.2

890.3

863.9

856.4

876.3

925.6

68.9

75.9

89.3

96.8

95.2

98.4

97.0

93.4

90.8

88.1

87.4

89.4

94.4

319.7

351.9

402.2

433.1

433.2

431.8

427.7

433.9

438.4

434.7

421.8

425

435.6

465.2

All 2003 figures are preliminary.

473.3

1993

91.2

94.6

100.0

Note:

478.7

1992

980.2

Bureau of Labor Statistics, National Employment, Hours and Earnings.

496.9

1991

100.0

(000s)

68.7

75.6

86.5

93.1

93.1

92.8

91.9

93.3

94.2

93.4

90.7

91.4

93.6

100.0

(%)

Electrical
Equipment
and Appliances

Source:

525.1

(000s)

(%)

(000s)

Primary Metals

(%)

Durable Goods
Computer and
Electronic
Products

216.0

242.2

275.8

315.2

333.7

357.2

367.1

371.7

393.2

403.3

403.9

406

407.2

417.9

(000s)

51.7

58.0

66.0

75.4

79.9

85.5

87.8

88.9

94.1

96.5

96.6

97.2

97.4

100.0

(%)

Textile Mills

U.S. Employment Levels: Production Workers on Manufacturing Payrolls for Selected Industries

1990

Exhibit 5

248.9

294.3

351.2

415.4

471.8

549.9

611.5

650.2

719.3

763.1

788

809.8

805.1

830

(000s)

30.0

35.5

42.3

50.0

56.8

66.3

73.7

78.3

86.7

91.9

94.9

97.6

97.0

100.0

(%)

Nondurable Goods
Apparel and
Other Textile
Products

525.5

531.9

562.2

587.7

595.2

600.6

593.3

595.1

598.4

595.6

590.1

586.2

599.7

620.3

(000s)

84.7

85.7

90.6

94.7

96.0

96.8

95.6

95.9

96.5

96.0

95.1

94.5

96.7

100.0

(%)

Chemicals and Allied

204-144

-12-

For the exclusive use of Y. Liang, 2015.

Source:

This document is authorized for use only by Yixiao Liang in 2015.

1967

-5

0

5

10

15

20

25

30

35

Percent
40

1977

1982

Net Import of Goods (% of mfg. GDP)

1972

1992

1997

Net Import of Goods (% of total GDP)

1987

U.S. Net Imports of Nonagricultural Goods as a Percent of Manufacturing GDP and Total GDP, 1967–2002

United States Economic Report of the President—2004, Chart 2-11.

Exhibit 6

2002

204-144

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For the exclusive use of Y. Liang, 2015.
204-144

Offshoring at Global Information Systems, Inc.

Exhibit 7

VALUE POTENTIAL ACCRUED TO U.S.
Value potential to the U.S. from $1 of spending offshored to India (2002 dollars)
1.14b
0.47
0.67
0.04
0.58

Savings accrued to U.S. investors and/or customers

0.05

Import of U.S. goods and services by providers in
India

Transfer of profits by U.S. providers in low-wage country to parent

Total direct benefit retained in the U.S.

Current direct benefita

Value from U.S. labor reemployed (conservative estimate)

Potential for total value creation in the U.S. economy
(conservative
estimate)

Potential future benefit

aEstimated based on historical reemployment trends from job loss through trade in the U.S. economy. bFurther value creation potential through




Increased global competitiveness of U.S. business
Multiplier effect of increased national savings

VALUE POTENTIAL ACCRUED TO SUPPLY COUNTRY—INDIA EXAMPLE
Value accrued from $1 of U.S. spending offshoreda (2002 dollars)
0.33
0.09

0.10

0.10
0.01

State governmentb 0.03

Central governmentc Labor

Profits retained in India

Suppliersd

Total value accrued to India

Offshoring sector
Source:

“Offshoring: Is It a Win-Win Game?” McKinsey Global Institute, August 2003.

aEstimated using the India offshored services industry example. bIncludes sales tax on the supplier industries and revenue from the sale of power to offshored service providers. cIncludes income tax from labor employed in the offshored services sector and the supplier industries and corporate tax on the

supplier industries. dIncludes revenue accrued to the supplier industries less sales taxes, income taxes to employees, and corporate taxes.

14
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This document is authorized for use only by Yixiao Liang in 2015.

100.0
100.0
100.0

100.0

100.0
100.0
100.0
100.0
100.0
100.0
100.0

100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

100.0%
100.0
100.0
100.0
a

21.0
21.4

63.9
63.7

23.7
29.1
a

a

17.2
63.7
53.0

62.5

18.5
17.1
20.8
17.7
18.4
13.8
20.7

62.0
55.7
52.9
60.9
61.6
67.2
53.6
68.4
74.6
65.6
71.2
70.3

68.2
70.9
63.6
67.1
63.5
71.2
64.7

33.3
25.5
27.8
21.2
23.6
23.6
23.7
17.3
18.9
16.6
16.6
16.4

a

a

21.2%

a

63.6%

a

12.5
17.9

20.2

13.3
11.9
15.6
15.2
18.2
15.0
14.6

4.7
18.8
19.3
17.9
14.8
9.2
22.7
14.3
6.6
17.8
12.3
13.3

a

15.1
14.9

a

15.2%

insufficient work, or the abolishment of their positions or shifts.

bData refer to people who had three or more years of tenure on a job they had lost or left between January 1999 and December 2001 because of plant or company closings or moves,

aData not shown where base is less than 75,000.

Bureau of Labor Statistics, Current Population Survey, Worker Displacement Survey, August 21, 2002.

571
745
37

Precision production, craft, and repair
Operators, fabricators, and laborers
Farming, forestry, and fishing

Source:

229

1,200
751
449
1,133
136
446
551

35
256
1,318
862
456
295
173
122
723
227
495
284
858

Total
(thousands)
3,969
45
3,886
3,769

Service occupations

Managerial and professional specialty
Executive, administrative, and managerial
Professional specialty
Technical, sales, and administrative support
Technicians and related support
Sales occupations
Administrative support, including clerical

Occupation of worker of lost job

Mining
Construction
Manufacturing
Durable goods
Nondurable goods
Transportation and public utilities
Transportation
Communications and other public utilities
Wholesale and retail trade
Wholesale trade
Retail trade
Finance, insurance, and real estate
Services

Total, 20 years and overb
Agricultural wage and salary workers
Nonagricultural wage and salary workers
Private wage and salary workers

Industry of worker of lost job

Percent Distribution by Employment Status in January 2002
Not in the
Total
Employed
Unemployed
Labor Force

Exhibit 8 Displaced U.S. Workers (1) by Industry and (2) Occupation of Workers Who Lost Jobs Between January 1999 and December 2001 and Employment Status in January 2002

204-144

-15-

For the exclusive use of Y. Liang, 2015.

This document is authorized for use only by Yixiao Liang in 2015.

188
432
192
539
288
251

Transportation and public utilities
Wholesale and retail trade
Finance, insurance, and real estate

Services
Professional services
Other service industries
13

439
236
204

151
335
157

620
373
248

22
135

1,893

-

77
47
29

35
72
28

185
114
71

7
38

450

Bureau of Labor Statistics, Current Population Survey, Worker Displacement Survey, August 21, 2002.

4

63
33
30

26
60
20

52
35
17

2

234

Totalb

20 Percent or More
Below

3

81
42
39

31
51
24

143
80
62

6
17

356

Below, but within
20 Percent

6

124
73
52

36
107
51

119
66
53

5
35

483

Equal or Above, but within
20 Percent

4

70
34
36

16
42
27

65
39
26

4
27

255

20 Percent or More
Above

-

37
19
18

12
37
16

46
39
7

4
17

171

Selfemployed and Unpaid
Family
Workers

bIncludes about 349,000 people who did not report earnings on lost job.

work, or the abolishment of their positions or shifts.

aData refer to people who had three or more years of tenure on a job they had lost or left between January 1999 and December 2001 because of plant or company closings or moves, insufficient

Source:

17

719
447
271

Manufacturing
Durable goods
Nondurable goods

Public administration

26
154

2,298

Total who lost full-time wage and salary jobsa

Mining
Construction

Total

Industry of lost job

Part
Time

Reemployed in January 2002
Wage and Salary Workers
Full Time
Earnings Relative to Those of Lost Job

Exhibit 9 Displaced U.S. Workers (1) Who Lost Full-Time Wage and Salary Jobs Between January 1999 and December 2001 and Were Reemployed in
January 2002 by Industry of Lost Job and (2) Earnings Level of New Job (in thousands)

204-144

-16-

For the exclusive use of Y. Liang, 2015.

For the exclusive use of Y. Liang, 2015.
Offshoring at Global Information Systems, Inc.

Exhibit 10

204-144

Net Monthly Additions to Nonfarm U.S. Jobs, 1994–2004 (000s)

Year

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004

266
123
171
94
262
335
–120
52
307
270
321
–18
229
268
113
194
–53
–165
94
97(p)

124
107
232
452
258
253
–300
–71
237
192
211
435
294
185
396
146
104
–90
–159
21(p)

346
93
249
276
192
221
–156
59
–46
468
220
255
317
148
124
493
15
43
–110

195
188
338
245
173
34
–217
156
307
357
163
165
291
278
382
308
–271
–68
–20

274
125
227
227
118
149
–122
127
273
339
–9
329
262
401
214
231
1
2
–28

145
–93
171
363
117
19
80
56
164
310
227
271
246
205
257
–25
–150
25
–14

189
318
346
223
39
–47
–50
74
300
359
71
228
276
121
295
160
–115
–111
–45

193
113
170
121
47
–204
19
137
159
303
278
203
–7
355
193
–28
–141
11
–25

204
346
229
340
249
–84
33
37
240
354
239
218
506
221
187
89
–267
–47
67

187
187
492
268
111
–164
8
173
279
206
149
238
339
192
422
30
–361
93
88

209
186
231
339
277
–146
–58
144
263
425
144
292
307
285
295
183
–332
–37
83

168
204
294
289
95
–57
26
213
302
270
140
177
298
344
294
124
–212
–209
8

Source:

U.S. Department of Labor, Bureau of Labor Statistics, March 7, 2004.

(p) = Preliminary.

17
This document is authorized for use only by Yixiao Liang in 2015.

For the exclusive use of Y. Liang, 2015.
204-144

Offshoring at Global Information Systems, Inc.

Exhibit 11 Unemployment Rates in Five Countries, Approximating U.S. Concepts, Seasonally
Adjusted, 1990–2004

United States

Japan

France

Germanya

United
Kingdom

1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001

5.6
6.8
7.5
6.9
6.1
5.6
5.4
4.9
4.5
4.2
4.0
4.7

2.1
2.1
2.2
2.5
2.9
3.2
3.4
3.4
4.1
4.7
4.8
5.1

9.1
9.5
9.9
11.3
11.8
11.3
11.9
11.8
11.3
10.6
9.1
8.4

5.0
5.6
6.7
8.0
8.5
8.2
9.0
9.9
9.3
8.5
7.8
7.9

6.8
8.4
9.7
10.4
9.7
8.7
8.1
7.0
6.3
6.0
5.5
5.1

2002
Qt. I
II
III
IV

5.8
5.7
5.8
5.7
5.9

5.4
5.4
5.4
5.5
5.4

8.7
8.5
8.6
8.8
8.9

8.6
8.4
8.6
8.7
8.9

5.2
5.1
5.2
5.3
5.1

2003
Qt. I
II
III
IV
August
September
October
November
December

6.0
5.8
6.1
6.1
5.9
6.1
6.1
6.0
5.9
5.7

5.3
5.4
5.4
5.2
5.1
5.2
5.2
5.2
5.2
4.9

9.2
9.0
9.2
9.3
9.3
9.2
9.4
9.3
9.3
9.3

9.4
9.3
9.4
9.4
9.3
9.4
9.4
9.3
9.3
9.3

January

5.6

5.0

9.3

9.1

Period

5.1
5.0
5.0
5.0
5.0
4.9
4.9

2004

Source:

U.S. Department of Labor, Bureau of Labor Statistics, March 7, 2002.

aGermany (unified) for 1991 onward. Prior to 1991, data relate to the former West Germany.

Note on adjustments: The foreign country data are adjusted as closely as possible to U.S. concepts, with the exception of age limits and the treatment of layoffs, for which no adjustments are made. In addition, for some countries, no adjustment is made for deviations from U.S. concepts in the treatment of unpaid family workers, people waiting to start a new job, passive job seekers (for example, people only reading newspaper ads as their method of job search). In the United States, job search must be “active,” such as placing or answering advertisements, and simply reading ads is not enough to quantify as active search.
Except for the inclusion of passive job seekers in Canada (for which an adjustment is made), these “unadjusted” differences are believed to have a negligible effect on the comparisons. For further information on comparability issues, see Constance
Sorrentino, “International unemployment rates: how comparable are they?” Monthly Labor Review, June 2000, pp. 3–20.
Acknowledgment: Data used to calculate these unemployment rates come mainly from national statistical sources but also from OECD and EUROSTAT.

18
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Offshoring at Global Information Systems, Inc.

Exhibit 12

204-144

U.S. Employment and Wages by Occupation for High-Wage Occupations
Employment
(000s)

Management Occupations
Chief executives
Engineering managers
Architecture and Engineering Occupations
Petroleum engineers
Life, Physical, and Social Science
Physicists
Legal Occupations
Lawyers
Judges, magistrate judges, and magistrates
Education, Training, and Library Occupations
Law teachers, post-secondary
Health-care Practitioners and Technical Occupations
Dentists
Optometrists
Anesthesiologists
Family and general practitioners
Internists, general
Obstetricians
Pediatricians
Psychiatrists
Surgeons
Podiatrists
Transportation and Material-Moving Occupations
Airline pilots, copilots, and flight engineers
Air traffic controllers
Source:

Mean Annual Wages
($ 000s)

455.9
214.8

107.7
88.9

11.4

81.8

10.9

83.7

490.0
27.9

91.9
79.5

9.7

79.1

87.8
24.2
24.7
135.3
53.3
17.2
24.2
21.6
48.9
7.6

110.8
88.0
131.7
110.0
126.9
133.4
116.6
113.6
137.0
94.5

88.8
23.0

99.4
83.4

Handbook of U.S. Labor Statistics, Seventh Edition, 2004, pp. 229–239.

19
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