Geographic, Horizontal and Vertical
Diversification
Juan Alcacer, Harvard Business School
Milan, October 31st 2015
Agenda
n
Breaking up value chains
n
Geographic and vertical disaggregation
¨
¨
¨
n
Fort (2013)
Alcacer, Lecuona, Oxley (2015)
Delgado (2015)
Why within-firm collocation can be beneficial?
¨
¨
Alcacer & Delgado (2015)
Alcacer & Chauvin (2015)
2
Allocating Activities
Allocating Activities
Internal links
Dynamic links
External links
Allocating Activities
Internal links
Dynamic links
External links
Two geographic expansions
n
n
Wal-Mart
Target
6
Agenda
n
Breaking up global value chains
n
Geographic and vertical disaggregation
¨
¨
¨
n
Fort (2013)
Alcacer, Lecuona, Oxley (2015)
Delgado (2015)
Why within-firm collocation can be beneficial?
¨
¨
Alcacer & Delgado (2015)
Alcacer & Chauvin (2015)
7
Geographic fragmentation and outsourcing
Breaking up is hard to do: Why firms
fragment production across locations
Teresa Fort
Tuck Business School
Dartmouth College
8
Geographic fragmentation
n
n
Motivation
¨  Despite great interest in offshoring and multinational sourcing
little is known of its magnitude and drivers
Question
¨  How and why firms break up their productions process across
space?
n
n
n
Whether to fragment production
Conditional on fragmentation, whether to source domestically or
offshore
Empirical setting
¨  Novel micro-level dataset from US Census Bureau’s 2007
Census of Manufacturing., LBD and US custom import
transactions.
n
Contract Manufacturing Services (CMS): fragmentation of inputs
that are customized to meet a firm’s specific production criteria.
n
Analysis at plant and firm level.
9
Geographic fragmentation
n
Findings
No Purchases
Domestic Purchases
Offshore Purchases
Domestic & Offshore
¨
¨
Plants
0.71
0.27
0.02
na
Plant Shares
Sales
0.57
0.39
0.04
na
Emp
0.61
0.35
0.04
na
Firm Shares
Firms
Sales
Emp
0.69
0.31
0.42
0.28
0.42
0.39
0.02
0.03
0.03
0.01
0.24
0.16
The majority of plants do not fragment their production process for
customized inputs (even on sales or employment-weighted basis).
The majority of U.S> manufacturing sales and employment takes place
at firms with at least one plant that purchases CMS
10
Geographic fragmentation
n
Findings
Domestic Purchases
5-­‐10%
10-­‐20%
20-­‐35%
35-­‐50%
50-­‐60%
Total
¨
86 Manufacturing Industries (NAICS 4) (% of e stabs i n i ndustry)
Offshore Purchases
0%
0-­‐5%
5-­‐10% 10-­‐20% Total
0
2
0
0
2
1
22
2
0
25
1
31
6
2
40
0
13
4
1
18
0
1
0
0
1
2
69
12
3
86
Domestic fragmentation is more prevalent than offshoring, both across
and within industries
11
Geographic fragmentation
n
Findings
No Purchases
Domestic Purchases
Offshore Purchases
All Plants
¨
¨
¨
¨
Raw Means
Salesᵅ
Emp
ln(VAP)
19,487
51.3
4.51
37,077
79.8
4.63
51,457
137
4.74
24,686
60.4
4.55
Relative Ind. Means
Sales
Emp
ln(VAP)
0.87
0.91
-­‐0.03
1.28
1.2
0.07
2.17
1.69
0.2
1
1
0
Plants that fragment production are larger and more productive that
non-fragmenters
Plants that fragment production offshore are larger and more productive
than domestic fragmenters
22% of plants purchase CMS doemstically in low-wage states compared
to 25% and 30% in mid-wage and high-wage states,
Firms with no CMS purchases and domestic CMS purchase import 28
and 19 % of their manufactured goods from low-icnome countries. 48%
of offshores do.
12
Outsourcing and innovation
Forgetting by outsourcing: evidence form
the wireless handset industry
Ramon Lecuona
Joanne Oxley
Juan Alcacer
Duke University
Duke University
Harvard Business School
13
Outsourcing and innovation
n
Motivation
¨
Debate about effect of production outsourcing on technical innovation
and national competitiveness goes way back:
n
1980s: “Hollowing Out” (e.g.. Cohen & Zysman, 1987);
n
1990s: Dark side of ‘learning alliances’ (e.g. Hamel, 1991);
n
2000s: The debate continues… (Arrunada and Vazquez, 2006; Pisano and
Shih, 2009);
¨  Debate
has generated copious passionate rhetoric, but
limited systematic empirical study at firm level
n
Dearth of empirical research due to lack of extensive firm-level data
on outsourcing
n
Most prior studies on jobs at country/region level
n
Firm-level evidence based on cases / small-scale surveys
14
Outsourcing and innovation
n
Question
¨
¨
n
Empirical setting
¨
Comprehensive data of GSM handests introduced since 1992
Information on whether the handset was outsourced or manufactured
¨
Detailed technical information for each handset
¨
Patent data for each firm in the industry
¨
n
Does outsourcing manufacturing affect innovative capabilities of firms?
Introduce less advanced products
Findings (very preliminary)
¨
¨
¨
Firms that lag in technology are more likely to outsource, they start
outsourcing as the lag in market share
Firms that outsource are more likely to exit industry
Conditional on becoming and outsourcer
n
n
Firms are less likely to introduce a new feature, more likely to copy a feature
fast
Firms loose market share
15
Collocation of innovation and production clusters
The collocation of innovation and
production in clusters
Mercedes Delgado
Sloan- MIT
16
Collocation of innovation and production clusters
n
Question
¨
¨
n
How the co-location of innovation and production varies across different
clusters
The goal is to inform the debate and offer some implications for future
research on the innovation potential of regions and their firms.
Empirical setting
¨
¨
¨
US Cluster Mapping Project dataset to measure employment/patenting at the
region-cluster level.
U.S. Benchmark Cluster Definitions developed by Delgado, Porter, and Stern
(2015) grouping related industries based on input-output links, shared labor
occupations, and co-location patterns.
Data
n
n
spans the years 1998-2011, includes 177 mutually exclusive Economic Areas
(EAs), and incorporates 778 traded industries (6-digit NAICS), grouped into 51
clusters of related industries for each EA
Focus on 35 clusters with meaningful patenting
17
Por$olio of 51 Traded Clusters and their Connec6ons
2
Source: Delgado, Porter, and Stern (2014, 2015)
Collocation of innovation and production clusters
n
Methodology
¨
¨
¨
Innovation strength is measured by patent specialization of a region in the
cluster (location quotient of patent)
Production strength is measured by employment specialization (both mfg. &
services) of an economic area (location quotient of employment)
Using these measures, the paper develops indicators of the collocation of
innovation and production in clusters
n
n
Dual Specialization Correlation (DSCct) captures the dual specialization of
regional clusters in employment and patenting: i.e., the correlation btw regional
cluster specialization in employment and patenting
DSCct = Corr(Cluster SpecializationEmployment crt , Cluster SpecializationPatent
crt)
19
Changes in Collocation in Clusters in the US: 1998-2011
n
Findings
Year
Dual Spec
Correlation
1998
2011
Change98-11
DSCc,t [1,-1]
Mean (N=35)
0.24
0.22
-0.02
¨
¨
¨
Strong Clusters Patenting
SCPc,t (%)
Mean (N=35)
28
27
-1
SCPAll,t (%)
Total
40
44
4
Strong Clusters Employment
SCEAll,t
(%)
SCEc,t (%)
Mean (N=35)
Total
51
50
50
43
-1
-8
Dual specialization in patenting and employment has remained significant during the
whole period, with an annual average DSCct score of 0.20.
The average DSCct declined slightly from 1998 to 2011 (from 0.24 to 0.22). This
decline was accompanied by a small decrease in the average SCPct and SCEct (-1%)
The findings suggest that innovation concentration in strong clusters has increased by
4%, (from 40 to 44%), but employment in strong clusters declined by 8% during the
period
20
IT & Analytical Instruments
n
Findings
Year
1998
2011
Change98-11
¨
¨
IT & Analytical Instruments
DSCct
SCPct
SCEct
Score
Rank
Pat
Rank
Emp
Rank
[1,-1]
1-35
%
1-35
%
1-35
0.74
1
66
1
64
9
0.53
2
70
1
62
11
-0.22
4
-2
While the DSC is high as of 2011, it has declined by 0.22 points during the period
(from 0.74 in 1998 to 0.53 in 2011)
This large decline is due in part to an increase in the number of clusters with
employment strength but low patenting strength.
21
IT & Analytical Instruments, San Francisco EA 1998-2011
n
Findings
Year
Employment
1998
2011
EA Growth98-11
US Growth98-11
in 1k Shr Pctile
234 14% 100
121 11% 100
-48%
-35%
¨
¨
¨
¨
SpecializationEmploy
LQ
4.0
3.4
LQ-Pctile
98
98
Patents
No. Shr Pctile
3258 20% 100
7602 25% 100
133%
90%
SpecializationPate
nt
LQ
1.8
1.5
LQ-Pctile
97
97
Patents
per 1k
Employ
14
63
351%
197%
Wages
in 1k
$73
$144
97%
84%
Top IT and Analytical Instruments regional cluster by employment and patenting
High strength in employment and patenting in 1998/2011
It has outperformed other regional clusters in innovation/wages, but lost many jobs
during 2008-2011
The loss of employment may be associated with several factors:
¨  increased productivity,
¨  re-allocation of some of the production capacity to related clusters (e.g., Medical
Devices and Distribution and eCommerce clusters) in the region/nearby, and/or
¨  loss in production capacity (e.g., lost suppliers ?)
22
Agenda
n
Breaking up global value chains
n
Geographic and vertical disaggregation
¨
¨
¨
n
Fort (2013)
Alcacer, Lecuona, Oxley (2015)
Delgado (2015)
Why within-firm collocation can be beneficial?
¨
¨
Alcacer & Delgado (2015)
Alcacer & Chauvin (2015)
23
Why would same-firm collocation be beneficial?
Spatial organization of firms:
Location choices through the value
chain
Mercedes Delgado
Juan Alcacer
Sloan- MIT
Harvard Business School
24
Why would same-firm collocation be beneficial?
n
Motivation
¨
¨
n
Rich research on how external environment – location endowments and
agglomeration economies – impacts firm performance and location
choices …
…but there are many multiunit firms and tight coordination among
activities is often core for a firm’s competitive advantage (Porter 1996,
1985)
Question
¨
Is the location of firm activities influenced by where a firm’s existing
facilities are (internal agglomerations)?
¨
Do the internal agglomerations effects vary by activity (R&D,
manufacturing and sales)?
How internal agglomerations compares to external agglomerations?
¨
25
Conceptual framework
n
Firm location strategies are a function of two forces:
¨
External agglomerations
n
Geographically bounded inter-firm linkages that result in better firm performance
¨
¨
Internal agglomerations
n
Geographically bounded intra-firm linkages that result in better firm performance
¨
n
Collocation with other firms: Bringing firm activities apart?
Collocation with same-firm: Bringing firm activities together?
These two forces complement or oppose each other depending on firms’
previous locations.
¨
¨
For firms located in clusters (i.e. geographic concentrations of related
economic activity), internal and external agglomerations may work in the
same direction
Otherwise, external act as centrifugal forces that may drive firms to disperse
their activities geographically, and internal agglomerations as centripetal
forces that may drive within-firm collocation.
26
Why would same-firm collocation be beneficial?
¨
Collocation is beneficial because of internal agglomerations:
•
Within-firm Marshallian economies
•
Knowledge/information flows,
•
Specialized labor,
•
Specialized suppliers
•
Control and monitoring
•
Coordination
27
Defining the Value Chains
n
Bio-Value Chain
R&D
(SIC-8731)
n
Sales,
Distribution
(SIC-5120)
Other-Value Chains
¨
¨
Firms could be diversified into multiple business units – medical devices, downstream
chemical products, … (e.g., Johnson and Johnson)
Excludes Bio-Value Chain activities
R&D
(SIC-8732-8734)
n
Mfg
(SIC-2830)
Mfg
(SIC-20-39)
Sales,
Distribution
(SIC-50 to 59)
Support Activities for any Value Chain
n
n
E.g., Business services (HQs), medical labs, Financial/Insurance/Real State
Any industry codes excluded from Bio-Value Chains and Other-Value Chains.
28
Descriptive statistics: location frequency
External Agglomerations t-1:
Low
High
Top-10 EAs
EAs with zero firm employ
Low :
Internal
Agglomerationst-1: Medium: EAs with any firm employ
High:
Firm Base EA for Bio-VC
29
High External Agglomerations:
Top-10 EAs by share of US Bio-VC employment (and high specialization
Share US
Bio-­‐Value Chain employ
LQ Employ, Bio-­‐Value Chain
Employ Bio-­‐Value Chain
2.12
101972
Year
EA name
1992
New York-­‐Newark-­‐Bridgeport, NY-­‐NJ-­‐CT-­‐PA
18.5%
1992
Chi ca go-­‐Na pervi l l e-­‐Mi chi ga n Ci ty, I L-­‐IN-­‐WI
6.7%
1.58
36661
1992
Wa s hi ngton-­‐Ba l ti more-­‐Northern Vi rgi ni a , D C-­‐MD-­‐VA-­‐WV
4.7%
1.52
26010
1992
Bos ton-­‐Worces ter-­‐Ma nches ter, MA-­‐NH
4.7%
1.37
25678
1992
Phi l a del phi a -­‐Ca mden-­‐Vi nel a nd, PA-­‐NJ-­‐DE-­‐MD
4.4%
1.61
24149
1992
Sa n Jos e-­‐Sa n F ra nci s co-­‐Oa kl a nd, CA
4.3%
1.29
23858
1992
Ra l ei gh-­‐Durha m-­‐Ca ry, NC
1.7%
1.95
9362
1992
Indi a na pol i s -­‐Anders on-­‐Col umbus , I N
1.7%
1.36
9107
1992
Knoxvi l l e-­‐Sevi ervi l l e-­‐La F ol l ette, TN
1.5%
4.2
8274
1992
Sa n D i ego-­‐Ca rl s ba d-­‐Sa n Ma rcos , CA
1.5%
1.69
8262
50%
30
Descriptive statistics: location frequency
External Agglomerations t-1:
Low
High
Top-10 EAs
EAs with zero firm employ
372 (30%)
84 (7%)
Low :
Internal
419 (34%)
213(17%)
Agglomerations t-1: Medium: EAs with any firm employ
Firm Base EA for Bio-VC
54 (4%)
84 (7%)
High:
69%
31%
•
•
37%
52%
11%
100%
•
•
Few expansions in locations where both Internal and External are high (7%)
Many expansions (63%) in locations with same-firm presence (Internal drivers);
and the rest (37%) into new locations (External drivers)
Many expansions (69%) outside the top-10 EAs.
For Low-External, most expansions (38%) in locations with same-firm presence
•
Same patterns if we exclude Bio-Sales
Internal and External Agglomeration Matters
31
Alcacer & Delgado (2015): Findings
n
Internal agglomerations have a positive effect on location choices:
¨
¨
¨
They vary by activity in the value chain (lower for Sales),
They arise within activity (e.g., plants) and across activities (e.g., R&D & Mfg)
Even after controlling for factors that could influence same-firm collocation:
n
n
n
n
¨
n
Sociological factors (social capital),
Initial location of a firm,
Strategic location behavior, and
Different sources of firm heterogeneity.
They are positively associated with firm survival
External agglomerations have a positive effect on location choices, but
failing to consider internal agglomerations leads to an omitted variable
problem, that may bias estimates external agglomerations.
32
Same-firm collocation and survival
n
Explore the survival of the new establishments that were opened
during the period of analysis (i.e. the 1,226 new bio activities
examined in the baseline location models)
n
Estimating a cox proportional hazard model of the survival of each
new biopharmaceutical activity
n  Failure = Exit (death or acquisition)
n
We find a positive relationship between same-firm collocation in Bio
Value chain & survival: bio establishments opened in locations where
a firm was previously present are less likely to fail
33
Agglomeration and diversified firms
Agglomeration economies and the multibusiness firm
Jasmina Chauvin
Juan Alcacer
Harvrad Business School
Harvard Business School
34
Agglomeration and diversified firms
n
Motivation
¨
Most existing studies of agglomeration ignore the unique aspects of
location of MB firms and analyze firm location without regards to
organizational form
n
n
n
E.g. Ellison, Glaeser, Kerr (2010) calculate coagglomeration of U.S.
industries using the entire sample of manufacturing firms
Alcacer and Delgado (forthcoming) provide an exception by decomposing
location drivers into “internal” and “external” agglomeration economies in the
biopharmaceutical industry
Question
¨
¨
Do multi-business firms coagglomerate their disparate manufacturing
activities?
What drives coagglomeration in multi-business firms?
Coagglomeration-Stand alone
Coagglomeration - multibusiness
Coagglomeration - Chevron
Agglomeration and diversified firms
n
Empirical setting
¨
We focus on one value chain activity - manufacturing - at the level of
140 different (SIC3 level) industries
n
¨
We calculate measures of coagglomeration separately for
establishments belonging to MB firms and SA firms
n
n
We define a MB firm as any firm active in >1 SIC3 industry
Our sample includes 220,246 SA establishments and 173,889
establishments belonging to a MB firm from Dun & Bradstreet (2011
vintage)
Methodology
¨
We measure coagglomeration with the continuous Duranton-Overman
index at various distance thresholds
¨
This tells us the likelihood that two randomly picked establishments in
industries i and j are located within d miles of one another
n
n
¨
d can be any continuous distance, from 1 mile to >1,000 miles
n is the number of observations, h is the optimal bandwidth, and f is the
Gaussian kernel function
We also measure coagglomeration for discrete spatial units as the
share of establishments in industries i and j located in the same
county, CBSA, state
Kernel density: Stand-alone, multi-business, random at 50 miles
Industry coagglomeration by firm type
0
Number of observations
50
100
150
Stand-alone
Multi-business
Random
0
.02
.04
.06
.08
.1
Likelihood that two establishments are within distance of 50 miles
kernel = epanechnikov, bandwidth = 0.0005
Agglomeration and diversified firms
n
We use three main measures of agglomeration economies, all
symmetric for industry-pair ij:
¨
Direct input-output links (1992 U.S. IO tables)
max{max{InputReqij, InputReqji }, max{OutputShrij, OutputShrji}}
¨
Labor similarity (using occupational shares from NIOEM matrix from the
Bureau of Labor Statistics)
Vector correlation of occupational shares the 2001 industry-occupation matrix
from the U.S. Bureau of Labor Statistics (BLS
¨
Technology links (NBER patents database / EGK (2010))
max{max{PatInij, PatInji}, max{PatOutij, PatOutji}}
Preliminary takeaways
n
Multi-business co-locate their disparate activities more than
stand-alone firms in the same industries
¨
n
The drivers of agglomeration differ for multi-business and
standalone firms are somewhat distinct
¨
¨
¨
n
Suggests internal agglomeration economies potentially more important
than external ones
Labor sharing across establishments particularly important for multibusiness firms
Proximity due to tech linkages less important for MB firms suggests firm has greater ability to share knowledge at larger
distances
Proximity due to IO linkages less important at short distances suggests lower holdup concerns
For stand-alone firms, study confirms prior findings that labor
market pooling, access to inputs, and technology all
associated with greater coagglomeration
Departing thoughts
n
As a phenomenon, breaking up value chains is not new and
it has been studied by (and matters for) other communities
of scholars
¨  IB
scholars may be better equipped…but whatever is found
(conceptualized) should be shared
¨  Look at international economics (Pol Antras, Laura Alfaro),
organizational economics (Bob Gibbons, Eric Van den Steen)
¨  Still many facts that we are not aware of
n
Geographic disaggregation is a piece of overall strategy,
includes environment variation AND firm-variation
43