O¤shoring as Process Innovation
Mitsuru Igami
Yale

August 2013

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Industry Dynamics of O¤shoringO¤shoring
Labor-market impact: Feenstra & Hanson (’96, ’97, ’99, ’03), Autor et al.
(’03), Hsieh & Woo (’05), Feenstra (’10), Ottaviano et al. (’10), Burstein &Vogel (’11), Hummels et al. (’11)
Product-market impact: ?

From product-market perspectives, o¤shoring is:
Cost-reducing investment (“process innovation”)
Possibly “drastic” (Arrow ’62)
Location change

Theory predicts fundamental (yet ambiguous) relationships between:Incentives to o¤shore
Market structure (i.e., how many rivals & where)

So what?
Life & death of …rms & industries
Job destruction 2 creative destruction

This paper

Study strategic industry dynamics of o¤shoring
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This PaperQuestions
How does market structure a¤ect o¤shoring incentives?
How does o¤shoring shape market structure evolution?

Model: Dynamic oligopoly with radical process innovation
Dynamic game, …nite horizon, non-stationary
Decision to stay North or go SouthAs more rivals o¤shore...
Competitive pressure on global output priceBusiness stealing from home …rms

Approach: Dynamic & structural
Estimate
1. Demand (global)
2. Production costs (north & south)
3. Sunk cost of o¤shoring (& entry/exit)

Why bother?
Simultaneous evolution
What if no o¤shoring?
Welfare analysis of government interventions (in future)

Data
Universe of Hard Disk Drive makers in the world (1976–98)
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Why Study Hard Disk?
Relevant

Figure 1: Market Structure and O¤shoring

FeasibleLong panel (23 years)
Global coverage (178 …rms)
Details on technology, products, & plant locations
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4 / 22Data (1 of 4): Why Singapore?
Why not California?
Seagate relocated entire assembly from Scotts Valley to Singapore due to the“high cost, marginal quality and poor availability of labor” in US.
Co-founder: “We had too many surfers.”

Labor-cost advantage of o¤shoring
Table 1: Hourly Wage Rate for Manufacturing (US$)
Year
U.S.
Singapore
Malaysia
ThailandPhilippines
Indonesia

1983
8.83
1.49
–
0.43
0.59
0.13

19859.54
2.47
1.41
0.54
0.55
0.3

1988
10.19
2.67
1.34
0.62
0.74
0.38

1990
10.83
3.78
1.39
1.03
1.02
0.60

1993
11.74
5.38
1.74
1.25
1.07
0.92

1995
12.37
7.33
2.01
1.41
–
–

Note: Current USD. *, **, and *** indicate data in 1994, 1986, and 1992, respectively.

No-nonsense government
Tax incentives
Market-friendly industrial policy
Pool of electronics managers, engineers, technicians, & operators.
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Data (2 of 4): Entry & Exit

Figure 2: Entry, Exit, and O¤shoring

Massive entry & exit
Non-o¤shorers exit more often

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Data (3 of 4): Price, Quantity, & Market Share

Figure 3: Price, Output, & Market Share

Falling priceRising output
Growing market share of o¤shorers
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Data (4 of 4): North vs South

Figure 4: Average Output by LocationO¤shorers sell more than non-o¤shorersMore output after o¤shoring

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8 / 22Model (1 of 2): Overview
Dynamic discrete game

Nt …rms in North8<9
φβE Vt +1 (st +1 ) jst + ε0it , =βE Vt +1 (st +1 ) jst + ε1it ,Vt (st ) = π t (st ) + max:;
βE Vt +1 (st +1 ) jst
κ + ε2itNt …rms in SouthVt (st ) = π t (st ) + max
Igami (Yale)φβE Vt +1 (st +1 ) jst + ε0it ,βE Vt +1 (st +1 ) jst + ε1itO¤shoring Dynamics

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9 / 22Model (2 of 2): Timeline
In each year t
1. Potential entrants (∞):
Observe market structure st = (N t , N t )Sequentially decide whether to enter: free entry
max V t (st )κ entt ,0Actual entrants become active in North

2. Each active …rm i (incumbents + actual entrants):
Observes updated st & private cost shocks ε0it , ε1it , ε2it
Decides whether to: fexit, stay North, go South g
If already in South, whether to exit

3. Active …rms earn period pro…ts
π lt (Nt , Nt )4. Decisions implemented & state evolves

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10 / 22Estimation (1 of 4): Demand
Steps: (1) demand ! (2) supply ! (3) dynamics
Industry demand: Di¤erentiated products
lnmsjtms0tModel:
Estimation method:
Price ($000)
Nests of Diameters
Diameter = 3.5-inch
Log Capacity (MB)Adjusted R 2
Num. obs.

= α1 pjt + α2 gj + α3 xj + ξ jt ,Logit
OLS
(1)
.93

IV
(2)
3.28

1.75
.04
.50
405

.91
1.20.27405

Nested Logit
OLSIV(3)
(4)
.05
1.63
.98.492.24
1.70.08.65
.80
.67
405
405

Note: ***, **, and * indicate signi…cance at the 1%, 5%, and 10% levels, respectively.

IVs for pjt
Prices in other region/user (Hausman-Nevo)
Num. of product models/…rms (Bresnahan-BLP)
Years since standard established
Unpredictable changes in unobserved quality (Sweeting)Igami (Yale)O¤shoring Dynamics

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Estimation (2 of 4): Supply
Cost of production
Invert the estimated demand system
Firm i ’s FOC (Cournot with cost=location heterogeneity)
qit : Pt +d∂P
q = mcdlt∂Q it

Figure 5: Estimated Cost Advantage of O¤shore Production
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Estimation (3 of 4): Dynamic Game
Cost of o¤shoring (& entry/exit)
Algorithm: Nested Fixed Point (c.f., Rust ’87)
1. Try some (κ, φ)
2. Solve for Equilibrium
Perfect Bayesian Equilibrium
Backward induction, from year 1998
For each state-year, …nd a …xed point of strategies & beliefs
Simultaneous-move vs Sequential-move
3. Pick (κ, φ) with maximum likelihood
6 V t (N t 1, N t )4. Free entry: V t (N t , N t ) 6 κ̂ entt

Data variation: Time-series of entry/exit/o¤shoring
Table 2: Estimated O¤shoring Cost, Entry Cost, and Sell-o¤ Value
Parameter
Sunk Cost of O¤shoring (κ )Sunk Cost of Entry (κ ent )Sell-o¤ Value (φ)

UnitBillion $Billion $Fraction of …rm value

ML Estimate
3.20
5.47
.48

Note: * annual average over the sample period.

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Estimation (4 of 4): Equilibrium Pro…ts & Values

Figure 6: E¤ects of Market Structure on Pro…ts & Values

Pro…tsDrop fast as N " (faster for non-o¤shorers)
Due to P # & business stealing

ValuesDecreasing in N =) Pr (exit ) " in N
Gap (VV ) " in N =) Pr (o¤shore ) " in N
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Finding (1 of 3): How Market Structure A¤ects O¤shoring

Pr (o¤shore ) initially " then # in N

“Replacement e¤ect” (Arrow ’62) dominates when N = 1“E¢ ciency e¤ect” (Gilbert & Newbery ’82) dominates when N > 1

Pr (o¤shore ) monotonically " in N

Disproportionate competitive pressure: “‡y or die”
Hence Pr (o¤shore ) " in N /N when N > 1

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Finding (1 of 3): How Market Structure A¤ects O¤shoring

How does Pr (o¤shore ) change with N /N?
Fix total N̄ = N + N and vary N (& hence N /N)O¤shoring breeds o¤shoring

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Finding (2 of 3): How O¤shoring Shapes Market Structure
World without Singapore
O¤shoring cost prohibitively high: κ = 4κ̂

Relative to “no-o¤shoring” scenario, the possibility of o¤shoring:
Discourages entry & encourages N # “‡y or die”
Accelerates “shake-out” (i.e., mass exits in maturing industry)
Yet pro-competitive: P #, Q ", SW " (due to innovation race)
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Finding (3 of 3): O¤shoring in Industry Life Cycle
Incentives to o¤shore vary with (endogenous) life cycle
Initially low (* still small market)
Mid ’80s: more (* demand growth & competitive pressure)
Mid ’90s: ‡y or die (* N /N keeps rising)Table 3: Evolution of Market Structure and O¤shoring/Innovation Incentives
Phase
I. EarlyII. Middle
III. Later

Pr (o¤shore )Pr (exit )Low
MediumHighEntryMany
Few
NoneN"##N""!

N +N"!
#N /N!
""

Note: Based on estimates and descriptive statistics.

Is o¤shoring “drastic” innovation? (Arrow ’62)
No, in the static sense
Yes, in the dynamic & strategic sense

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Finding (4 of 3): Anti-O¤shoring Policy

Ban on o¤shoring
Same as “No Singapore” simulation

Evaluating government interventions
Timing matters (* o¤shoring incentives change with life cycle phase)
Table/Figure, coming soon

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ConclusionO¤shoring as process innovationO¤shoring breeds o¤shoring: strategic complementarity
Explains labor-market …ndings: “displacement from a …rm with rising o¤shoring
generates larger and more persistent wage and earnings losses” (Hummels et al.
’11)

Dynamically pro-competitive & accelerates shake-out
Dynamically “drastic” innovation
One innovator/o¤shorer may not drive out others, but
Pressure on others to “‡y or die”
Eventually & collectively “drastic”

Planner’s dilemmaO¤shoring accelerates itself
Timing matters
Stop o¤shoring early?
Home industry will die (or survive on expensive life support)
Think creative destruction, not just job destruction

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Appendix: Persistent Firm Heterogeneity? (1 of 2)

Firm size dynamics

Figure 7: Seemingly Random Patterns of Firm Heterogeneity

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Appendix: Persistent Firm Heterogeneity? (2 of 2)

Self-selection
Table 4: Do Better Firms Self-Select into O¤shoring?
Quartile based on
1976–85 market share
1st quartile
2nd quartile
3rd quartile
4th quartile

Igami (Yale)

Number ofFirms
1111
11
11% o¤shored by 1991

O¤shoring Dynamics

36.4
27.3
36.4
18.2

% exited by 1991
(without o¤shoring)
36.4
63.6
36.4
63.6

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