Transcript Slide 1

Offshoring and Productivity:
A Micro-data Analysis
Jianmin Tang and Henrique do Livramento
Presentation to
The 2008 World Congress on National Accounts and Economic
Performance Measures for Nations
May 12–17, 2008
Outline
•
Introduction
•
Objective
•
Data
•
Offshoring and its Associated Factors
•
Offshoring and Productivity
•
Conclusion
2
What is offshoring?
Within countries
Between Firms
Domestic
Outsourcing
Between countries
International
Outsourcing
OFFSHORING
Within Firms
Domestic Supply
International
Insourcing
This paper covers
•
Material offshoring (both international outsourcing and insourcing)
•
R&D service offshoring (only international outsourcing)
3
Introduction
Firms are heterogeneous even in the same industry
•
Not all firms will participate in offshoring
•
Participants may pursue different offshoring models
•
They offshore to different geographical locations
•
They may obtain different productivity dividends from offshoring
•
Geographical locations of offshoring may matter for productivity
 Better to use micro data with information on geographical
location
4
Introduction (continued)
Offshoring may depend on many factors
•
Transaction costs
– relationship-specific investment,
– contractual incompleteness,
– search efforts,
– transportation costs, and
– coordination and communication costs
•
Market conditions of foreign suppliers
– trade liberalization,
– reduction in FDI restrictions,
– the availability of cheap skilled labour, and
– the ability to produce and supply high quality products and services
•
Firm-specific factors
– productivity level,
– the skill level of workers and managers, and
– size
 Offshoring is not for every firm and every foreign location
5
Introduction (continued)
Different offshoring business models
•
Simply replacing expensive domestic suppliers by cheap foreign suppliers
•
Offshoring low productive and low value added components,
•
Employing foreign expertise (e.g. R&D services) and using high-tech components
abroad for designing quality products, or
•
Offshoring due to adopting foreign technologies (e.g., investing in foreign M&E)
 Offshoring is associated with different business models or
organization, which leads to different geographical locations
6
Introduction (continued)
Possible productivity dividends from offshoring
•
The composition effect
- Moving up the value chain
 Focusing on high value added component
 Introducing high quality products
- Specializing and obtaining economy of scale
 Focusing on competency
•
The innovation effect
- Facing intense international competition
- Exposing to the world technology frontier and best management practices
•
The effects may depend on offshoring business models and geographical locations
7
 Geographical location of offshoring may matter for productivity dividend
Objectives
•
What factors are associated with material or R&D offshoring?
•
Is material or R&D offshoring associated with plants’ productivity?
Does the geographical location of offshoring matter for the
association?
8
Data
The Statistics Canada’s 2005 Survey of Innovation (SI)
•
6,143 plants in logging and manufacturing industries with at least 20 employees and
$250,000 in revenue
•
The response rate is 72%
•
One-time cross-sectional, covering the year of 2004 (for the purpose of this study)
•
Outward-oriented business activities with geographical locations
 Percentage of total expenditures on raw materials and components from overseas
(material offshoring)
 Percentage of R&D services contracted out to independent foreign firms
(R&D offshoring)
 Other plants and operations in the firm (multinationals)
 Percentage of investment on foreign M&E
 Percentage of revenue from exporting
•
Other important variables
 Percentage of workers with university education
 Plant size
9
Data (continued)
The SI was linked to 2002 and 2004 Annual Survey of
Manufacturers (ASM)
• 6,109 in-sample manufacturing plants, representing a
subpopulation of17,367 manufacturing plants.
• Production data
10
Offshoring and Its Associated Factors
11
Hypothesis 1
Offshoring is associated with:
•
outward-oriented business activities
-
•
being multinationals (establishing foreign affiliates through FDI)
adoption of foreign technology (investment in foreign M&E)
exporting
plant-specific factors
-
productivity level
skill level of workers and managers
plant size
12
outward-oriented business strategies
Being a multinational
•
•
intra-firm trade accounted for significant part of imports (47% in U.S. in 2005).
most of the intra-firm transaction is associated with intermediate inputs
Adopting foreign technologies
•
•
requiring specific materials or accessories from the foreign manufacturers
requiring the manufacturers’ expertise (R&D services)
Exporting (indirectly influencing)
•
•
•
forcing the firm to improve its cost-competitiveness by exposing a firm to
international competition
better understanding local markets
lower offshoring transaction costs due to established networks
13
Plant-specific factors
Productivity level
•
•
offshoring is often considered to be endogenous to productivity (Amiti and Wei (2006).
high-productivity firms more likely engage in offshoring activities than low-productivity firms
(Antràs and Helpman 2004)
Skill level
•
•
knowledge and skills are required to coordinate the complexity involved in offshoring
skilled workers are required to specify R&D projects for offshoring and to develop absorptive
capacity to benefit from R&D offshoring.
Plant size
•
Large firms are perceived to be more likely to engage in offshoring than small firms.
 the cost of financing offshoring projects is lower
 larger benefit from economies of scale
 lower risk of offshoring due to its large scope
14
Regression model 1
Oi , 04   0   1 M i   2 Ti , 04   3 Ei , 04   4 Pi , 02   5 U i , 04   6 S i , 02
3
6
20
j 1
k 1
m 1
   6 j Di , j    9 k Li ,k    15 m I i ,m   i ,
where Oi , 04 is the percentage of materials or R&D services in 2004 that are offshored;
M i is a dummy variable for plant i to be part of a multinational that has operation in
foreign location;
Ti , 04 is the percentage of plant i’s total expenditures on new M&E in 2004 that is
supplied from overseas;
Ei ,04 is the percentage of plant i’s total revenue in 2004 that come from abroad;
Pi ,02 is defined as value-added per worker in 2002;
Qi , 04 is a variable for skills, indicated by the percentage of workers with a university
education in 2004;
S i , 02 is a firm size dummy based on employment in 2002, taking the value one for
large firms and zero otherwise;
Di , j is a binary offshoring location dummy, taking the value one if plant i is
offshoring to a foreign country/region j and zero otherwise;
Li ,k is a binary operating location dummy, taking the value one if plant i is located k
in Canada and zero otherwise;
I i ,m is a binary industry dummy, taking the value one if plant i belongs to industry m
and zero otherwise; and
j
 i is the error term that is associated with geographical location j.
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Estimation results: material offshoring and the associated factors
Variable
Multinationals
Percentage of investment in foreign M&E in 2004
Share of revenue from exports in 2004
Productivity in 2002
Share of uni. educated workers
Dummy: large-sized plant
(1)
5.873***
(5.7)
0.168***
(15.6)
0.138***
(10.9)
-1.449
(-0.6)
0.112***
(3.4)
0.891
(0.6)
Dummy: material offshoring to U.S.
Dummy: material offshoring to Europe
Dummy: material offshoring to Asia Pacific
Dummy: plant located in Quebec
Dummy: plant located in Ontario
Dummy: plant located in Manitoba
Dummy: plant located in Saskatchewan
Dummy: plant located in Alberta
Dummy: plant located in British Columbia
Industry fixed effects
Adjusted R-squares
Number of observations
Yes
0.22
5073
(2)
4.126***
(4.6)
0.103***
(10.9)
0.071***
(6.3)
-2.289
(-1.1)
0.036
(1.2)
-0.593
(-0.5)
22.044***
(26.9)
11.886***
(12.5)
15.974***
(16.8)
1.023
(0.7)
2.485
(1.6)
1.025
(0.5)
-0.462
(-0.2)
-2.568
(-1.4)
1.711
(1.0)
Yes
0.41
5073
Note: t-statistics are in parenthesis. “*”, “**”, and “***” denote significance at 10%, 5% and 1%, respectively.
16
Estimation results: R&D offshoring and the associated factors
Variable
Multinationals
Percentage of investment in foreign M&E in 2004
Share of revenue from exports in 2004
Productivity in 2002
Share of uni. educated workers
Dummy: large-sized plant
(1)
-0.085
(-0.3)
0.014***
(4.7)
0.018***
(5.1)
0.382
(0.6)
0.067***
(7.3)
1.057**
(2.5)
Dummy: R&D offshoring to U.S.
Dummy: R&D offshoring to Europe
Dummy: R&D offshoring to Asia Pacific
Dummy: plant located in Quebec
Dummy: plant located in Ontario
Dummy: plant located in Manitoba
Dummy: plant located in Saskatchewan
Dummy: plant located in Alberta
Dummy: plant located in British Columbia
Industry fixed effects
Adjusted R-squares
Number of observations
Yes
0.04
5073
(2)
-0.104
(-0.6)
0.003*
(1.8)
0.002
(0.9)
0.141
(0.3)
-0.013**
(-2.2)
0.133
(0.5)
32.159***
(58.0)
24.675***
(29.3)
30.864***
(28.9)
-0.536*
(-1.7)
-0.828***
(-2.7)
-0.067
(-0.1)
-0.815
(-1.5)
-0.787**
(-2.1)
-0.722**
(-2.1)
Yes
0.62
5073
Note: t-statistics are in parenthesis. “*”, “**”, and “***” denote significance at 10%, 5% and 1%, respectively.
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Productivity and Offshoring
18
Hypothesis 2
•
Productivity (MFP) is associated with offshoring and the
association differs across geographical locations
-
The composition effect
 Moving-up the value chain
 Specializing and obtaining economy of scale
-
The innovation effect
 Increased competition
 Exposure to advanced technologies and business practices
-
These effects may differ across geographical locations
19
Regression model 2
EU
AP
OT
ln(Pi ,04 )   0  1 ln(Fi ,04 )   2 OiUS
, 04   3 Oi , 04   4 Oi , 04   5 Oi , 04   6 Ri , 04
6
20
k 1
m 1
  7 M i   8 Qi ,04   9 S i ,02    9 k Li ,k   15 m I i ,m   i ,
where ln(Pi ,04 ) is defined as value-added per worker in 2004;
ln(Fi ,04 ) is fuel and power consumption per worker in 2004, a proxy for capital intensity;
OiUS
, 04 is the percentage of total expenditure on materials that imported from the U.S. in
2004;
OiEU
, 04 is the percentage of total expenditure on materials that imported from Europe in
2004;
AP
Oi ,04 is the percentage of total expenditure on materials that were supplied from Asia
Pacific in 2004;
OT
Oi ,04 is the percentage of total expenditure on materials that were supplied from the rest
of countries including Mexico in 2004;
Ri,04 is the percentage of total expenditure on R&D services that were supplied from
overseas in 2004;
M i is a dummy variable for being a plant of a multinational, taking the value one if the
plant is part of a multinational and zero otherwise;
Qi ,04 is a variable for skills, indicated by the percentage of workers with a university
education in 2004;
Si,04 is a plant size dummy based on employment in 2002, taking the value one for large
firms and zero otherwise (from ASM);
Li ,k is a binary operating location dummy, taking the value one if plant i is located k in
Canada and zero otherwise;
I i ,m is a binary industry dummy, taking the value one if firm i belongs to industry m and
zero otherwise; and
 i is the error term.
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Control variables
Fuel and power consumption per worker, as a proxy for capital intensity
•
•
working capital stock is highly correlated with fuel and power consumption
industry differences in energy intensity are accounted for by industry dummies.
Being multinationals
•
multinationals are more productive than non-multinationals (Baldwin and Gellatly,
2007)
 scale, scope, diversified markets,
 unique technology, and superior business organizations
Skills
•
•
important for technology adoption and innovation
Forming and managing business organizations
Size, operating location and industry dummies
•
introduced to capture specific effects from differences in local business
environment, financial and technological opportunities across different size
groups, operating locations and industries.
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Estimation results: offshoring and productivity
Variable
Regression Regression Regression Regression Regression
(1)
(2)
(3)
(4)
(5)
Fuel and power consumption per 0.285***
0.286***
0.275***
0.270***
0.269***
worker
(40.0)
(40.0)
(38.2)
(37.5)
(37.4)
Material offshoring
0.155***
0.080***
(7.1)
(3.7)
to U.S.
0.151***
0.051*
0.039
(4.2)
(1.9)
(1.5)
to Europe
0.243***
0.153**
0.169***
(3.8)
(2.4)
(2.7)
to Asia Pacific
0.219***
0.153***
0.132**
(4.1)
(2.9)
(2.5)
to other countries
0.204***
0.195***
0.157**
(2.8)
(2.7)
(2.2)
R&D offshoring
0.159**
0.157**
0.031
0.053
0.054
(2.1)
(2.1)
(0.4)
(0.7)
(0.7)
Multinationals
0.158***
0.150***
0.149***
(9.5)
(9.1)
(9.1)
Share of university educated
0.484***
0.467***
0.470***
workers
(9.9)
(9.6)
(9.7)
Dummy: large-sized firms
0.058**
0.066***
0.066***
(2.3)
(2.7)
(2.6)
Dummy: plant located in Quebec
0.224***
0.225***
(7.6)
(7.6)
Dummy: plant located in Ontario
0.277***
0.275***
(9.7)
(9.6)
Dummy: plant located in Manitoba
0.174***
0.171***
(4.1)
(4.1)
Dummy: plant located in
0.226***
0.221***
Saskatchewan
(4.5)
(4.4)
Dummy: plant located in Alberta
0.302***
0.302***
(8.7)
(8.7)
Dummy: plant located in British
0.314***
0.315***
Columbia
(9.7)
(9.7)
Industry-fixed effects
Yes
Yes
Yes
Yes
Yes
Adjusted R-squares
0.36
0.37
0.39
0.40
0.40
Number of observations
5653
5653
5653
5653
5653
22
Concluding remarks
•
Material offshoring was highly associated with firms’ outward-oriented business
activities including foreign operation, investing in foreign M&E, and exporting,
after controlling for offshoring and operating locations advantages, and industryspecific effects.
•
For R&D offshoring (international outsourcing only), it is found that it was mainly
associated with investment in foreign M&E.
•
Material offshoring is positively associated with productivity and the association is
significantly larger for material offshoring to non-U.S. countries than for material
offshoring to the U.S. after controlling for other effects including being
multinationals, the education level of workers, and plant size.
23
Concluding remarks (continued)
•
The results should be interpreted with the understanding:
 The results are only about association not about causal relationship
 No delayed effects
 The result that material offshoring to non-U.S. countries tends to be associated
with higher productivity than material offshoring to the U.S may be justified by
higher transaction cost associated with material offshoring to non-U.S. countries.
24