Lessons from Nike

Improving Labor Standards
in Global Supply Chains:
Lessons from Nike
Richard M. Locke

Road Map
Broader Project Goals
Research Questions
Data Analysis
Case Studies
Follow­Up Research, Engagement with
Stakeholders
© 2007 MIT Sloan School of Management

Motivation
Globalization of production has provoked fierce debate
over labor standards
Child labor, excessive work hours, hazardous working
conditions, poor wages rampant in 3 rd world factories
In absence of functioning international organizations
capable of promoting global justice and/or nation­states
willing or able to enforce domestic labor codes, codes of
conduct and other forms of private voluntary regulation
have become dominant method MNCs and NGOs address
problems with labor standards
Does Monitoring Work?
© 2007 MIT Sloan School of Management

Million US$
Why Nike?: Success Through Global
Sourcing
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
1978
1979
1980
1981
Total Revenue ­ Net Income 1978 ­ 2001
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
Total Revenue Net Income (Million US$'s)
Figure 1 ­ Net Income
Sources:
a) 1978­97: HBS Case #9­299­084 "Nike, Inc.: Entering the Millennium," March 31,
1999.
b) 1998­2001: Company financial information
1995
1996
1997
1998
1999
2000
2001
© 2007 MIT Sloan School of Management

Why Nike?
© 2007 MIT Sloan School of Management

Why Nike?: Suppliers Engaged in
“Poor” Working Conditions
© 2007 MIT Sloan School of Management

Why Nike?
# of mentions
180
160
140
120
100
80
60
40
20
0
1992
1993
Unfavorable media mentions
Major World new spapers
1994
1995
1996
1997
1998
1999
2000
Sw eatshop Child Labor Exploitation
2001
2002
© 2007 MIT Sloan School of Management

Why Nike?: Doonesbury
© 2007 MIT Sloan School of Management

Research Questions
What are working conditions actually like among
Nike’s suppliers?
What accounts for the variation in working conditions
among Nike’s suppliers?
Are working conditions improving over time in these
factories?
Does Monitoring Improve Working Conditions?
© 2007 MIT Sloan School of Management

Research Design and Data
Data Analysis of Factory Inspection Reports
– (over 800 factories in 51 countries since 1998)
Field Research in China, Turkey, Mexico,
U.S., and Europe
© 2007 MIT Sloan School of Management

Data
SHAPE
FLA audit
Management Audit (M­Audit)
Compliance Rating (CR rating)
© 2007 MIT Sloan School of Management

Factory Conditions Vary Across
Sectors & Countries
First M­Audit Scores across all factories (Nov. 2002 to Jan. 2005)
Density
0 1 2 3
Histogram:
First Maudit score
.2 .4 .6 .8 1
Maudit score
Density kdensity maudit
Number of Observations: 575
Average M­Audit Score: 65%
Similar patterns of variation within sectors (footwear, apparel,
equipment) and within countries.
© 2007 MIT Sloan School of Management

What Explains Variation?
Factory characteristics
– Factory size, ownership, type of product, etc.
Interaction with the buyer
– Length of relationship, percentage of capacity dedicated to
Nike, frequency of visit, strategic partnership
Contextual factors
– Legal and regulatory environment
© 2007 MIT Sloan School of Management

What Explains Variation? (Cont’d)
M­audit = a0 +
a1* total employees +
a2* total employees^2 +
a3*ownership +
a4 * number of visit by Nike +
a5*strategic partnership +
a6* duration of relationship with Nike +
a7* percentage for Nike +
a8* rule of law +
a9* aprl +
a10*ftwr +
ε (1.1)
© 2007 MIT Sloan School of Management

Results
© 2007 MIT Sloan School of Management

Results
1) Country Effects
A significant proportion of the variation is at the
country level
Generally, factories located in counties with higher
rule of law index, proxy for strong regulatory and
institutional environment, do better in compliance
performance based on M­Audit score
© 2007 MIT Sloan School of Management

Results
2) Factory Level Effects
Factory size matters: generally, smaller factories are
doing better in compliance
Ownership does not have a significant impact on
compliance performance
© 2007 MIT Sloan School of Management

Results
3) Relationship Between Nike and Suppliers
The number of visits by Nike personnel and whether
or not a factory is strategic partner are positively
connected with M­audit scores.
The duration of the relationship with Nike and the
percentage of capacity dedicated to Nike are
negatively correlated to the M­audit scores.
© 2007 MIT Sloan School of Management

Are Things Getting Better?
A) Change in M­Audit Scores
First M­audit Score
Second M­audit Score
Third M­audit Score
Mean
0.65
0.70
0.82
Standard Deviation
0.16
0.16
0.07
© 2007 MIT Sloan School of Management

Are Things Getting Better?
Test­1 Nike randomly chose which firms to
do the second M­audit.
Factories with only
one M­audit
Factories with more
than one M­audit
Mean
0.65
0.64
First M­audit Score
Standard Deviation
0.16
0.16
458
117
Test­2 Second M­audit scores are on
average better than first scores.
# of observations
© 2007 MIT Sloan School of Management

Are Things Getting Better?
M­audit(i,t) = a0 +
a1*num_employee(i,t) +
a2*num_employee(i,t)^2 +
a3*ownership(i, t) +
a4 * shape_visit(i,t) +
a5*strategic(i,t) +
a6*month_Nike(i,t) +
a7*Nike_percentage(i,t) +
a8*rule of law(i,t) +
a9* aprl(i,t) +
a10*ftwr(i,t) +
b*effort(i,t) +
ε(i,t)
© 2007 MIT Sloan School of Management

Are things getting better: CR rating
60
50
40
30
20
10
0
01­­
02
02­­
03
03­­
04
04­­
05
A
B
C
D
© 2007 MIT Sloan School of Management

Are Things Getting Better?
B) Change in Compliance Rating Inspections
Change in CR Rating
­3 (Down by 3 degrees)
­2 (Down by 2 degrees)
­1 (Down by 1 degree)
0 (No change)
1 (Up by 1 degree)
2 (Up by 2 degrees)
3 (Up by 3 degrees)
Total
Freq.
323
116
763
0.92
100
Note: A is 4, B is 3, C is 2, and D is 1, and the change in CR rating is the score in the most recent
audit minus the score from the earliest audit, ranging from –3 to 3. For example, if a factory has a
score C in the earliest audit and a score A in the most recent audit, then it has a change of +2.
20
74
181
42
7
Percent
2.62
9.70
23.72
42.33
15.20
5.50
© 2007 MIT Sloan School of Management

Are things getting better?
How do we interpret apparently contradictory findings
between M­Audit and CR rating analyses?
– M­Audit privileges documentary evidence, whereas CR­rating is
more subjective.
– Suppliers are “learning” to go through the M­Audit, whereas Nike
compliance staff are not fooled.
– Audits based on different samples of firms.
© 2007 MIT Sloan School of Management

Qualitative Analysis
– A Tale of 2 Factories
Average Weekly Wage
Team Work
Job Description
Job Rotation
Worker Participation in
Work­Related Decisions
Nationality
Overtime
Workplace Characteristics
Managers
Supervisors
Production Workers
Plant A
$ 86.00 USD
Yes
Multi­Tasks
Yes
Yes
Mexican
Mexican
Mexican
Voluntary and
Within Limit
Plant B
$ 67.80 USD
No
Single Task
No
No
Chinese
Chinese
Mostly Mexican
Mandatory and
Over Limit
© 2007 MIT Sloan School of Management

Qualitative Analysis
– A Tale of 2 Factories continued
Comparison of Production Systems
Total # of Workers
in one line or cell
T­Shirts per Day
per line or cell
Daily Wage per Worker
(Fixed Salary + Bonuses)
T­Shirts per Worker
Cost per T­Shirt
Plant A
6
900
$ 17.20 USD
150
$ 0.11 USD
Plant B
10
800
$ 13.60 USD
80
$ 0.18 USD
© 2007 MIT Sloan School of Management

Qualitative Analysis
– A Tale of 2 Factories continued
Comparison between Old and New
System of Production in Plant A
Total # of Workers
T­Shirts per Day
per module or cell
Productivity per Worker
Average Weekly Salary
Old System
(module)
10
1200
120
$ 67.80 USD
New System
(cell)
6
900
150
$ 86.00 USD
© 2007 MIT Sloan School of Management

On­Going Research/Outreach
Outreach
Other Nike Research:
• Up­Stream Business Processes
• CR/Business Outcomes
• NOS Effect
Research on Other Companies
(PVH, Coke)
Stakeholder Engagement
(FLA, WRC, Joint Initiative, World Bank, ILO)
© 2007 MIT Sloan School of Management

Doonesbury Again
© 2007 MIT Sloan School of Management