Intra-Industry Foreign Direct Investment
Laura Alfaro
Andrew Charlton
Harvard Business School & NBER
London School of Economics
What Do We Do in This Paper?
• Study patterns of vertical and horizontal multinational activity: large
new data set of 650,000 multinational subsidiaries in 90+ countries
(close to population of MNCs).
• Traditionally, the literature has distinguished between two forms of—
and motivations for—multinational activity (different effects factor
incomes within and across countries).
– “Horizontal” FDI: locating production to be closer to customers
and avoid trade costs (Markusen, 1984; Brainard, 1993),
– “Vertical” FDI: firm’s attempts to take advantage of cross-border
factor cost differences (Helpman, 1984; Helpman and Krugman,
1985).
• Most research has found that the bulk of FDI is horizontal.
• Our results suggest that, due to data limitations, the literature has
systematically under-estimated vertical FDI.
Patterns: Firm Data
• Consistent with conventional wisdom,
– The bulk of multinational activity occurs between the rich nations.
– At the 2 digit industry level: horizontal FDI (subsidiaries in the
same industry as their parent) >vertical FDI (subsidiaries which
supply their parent with inputs).
• But …
– At the 4 digit level, more vertical activity.
→ Many of the foreign subsidiaries in the same 2 digit industry
as their parent are in fact producing highly specialized inputs
into their parents’ production.
– This pattern prevails even within developed countries.
Intra Industry FDI
•
We call these subsidiaries unveiled at higher levels: ‘intra-industry
vertical’ FDI.
–
•
Qualitatively different to vertical subsidiaries which cross twodigit industry codes (‘inter-industry vertical FDI’).
•
Supply parent firms with high-skill products
•
Mostly located in high-skill countries.
These facts are:
a.
At odds with conventional wisdom (more horizontal than vertical)
b.
And… cannot be fully explained by the traditional models of
comparative advantage (fragmentation: cost differences).
- But …. are consistent with trade data documenting large
flows of intra-firm trade in intermediate inputs between rich
countries, Bernard et al. (2006).
Discrepancy: Misclassification of Vertical FDI
• Significant amount of vertical FDI was misclassified as horizontal:
1. Most vertical FDI is north-north, it has been assumed to be market
seeking (horizontal)
• Firm level data indicates that these are vertical relationships:
parent firms sourcing inputs from their subsidiaries in other
northern countries.
2. Skill differences between parent and subsidiaries are small (even
within vertical FDI):
• This also lends support to horizontal motivations.
3. The vertical nature of these relationships is missed at the 2 digits:
• Many subsidiaries are supplying goods to their parents where
both the input and the final good are in the same 2 digit SIC.
Explaining Vertical FDI
• Both intra and inter industry subsidiaries: provide inputs to parent firms.
– But … intra-industry FDI is much harder to explain with the standard
theory of vertical FDI (factor cost differences).
• ‘Intra-industry vertical FDI’: north-north
– Differences between parent and child skill levels are small.
– Average proximity between two industries is higher for parent
subsidiary pairs:
• Proximity: proportion of the intermediate product used directly in
the final good ( i.e. raw materials have low proximity variables).
• Intra-industry FDI: tendency of multinational firms to own the later
stages of the production chain, and outsource the production of early
stages and raw materials.
Outline
• Introduction and Motivation
• Data
• Methodology:
– Vertical and Horizontal FDI
• Patterns and Results:
– Discrepancy between Aggregate and Firm Data
– Intra-Industry FDI
– Explaining Patterns: Comparative Advantage and Proximity
• Conclusions
MNC Activity
• MNC activities are best measured by firm-level data (Barba Navaretti
and Venables, 2005).
– Few countries have firm level data.
– Researchers tend to use FDI flows from the Balance of Payments
statistics as proxy for MNC activity.
• Characterization of world multinational activity using firm level data.
The D&B Data Set
• Worldbase data: database of both public and private companies in
more than 213 countries and independent territories in 2004. Complied
by Dun and Bradstreet.
• The unit of record is the ‘establishment’ rather than the firm.
• 4-digit SIC-1987 code of the primary industry in which firm operates;
for several countries, SIC codes of up to 5 secondary industries listed
in descending order of importance.
• Detailed ownership information: including information about the
firm’s family members (no of family members, its domestic parent and
its global parent).
• Information about the firm’s status (joint-venture, corporation,
partnership) and its position in the hierarchy (branch, division, head
quarters).
• Sales, employment, (some) export, age.
Foreign Ownership
• We describe an establishment as foreign owned if it satisfies two
criteria:
– Foreign owned: must report a global parent firm and that parent
firm must be in a different country.
– Parents are defined in the data as entities which have legal and
financial responsibility for another firm.
• Combining the location and ownership information it is possible to
identify 650 000+ firms in the database which are owned by a foreign
parent.
Comparisons with Other Data: UNCTAD
• UNCTAD’s World Investment Report 2004 reports that there are 61,582
parent firms with 926,948 affiliates operating in the world.
• In the D&B dataset there are 72,978 parent firms which have 658,188
affiliates in foreign countries reporting to them.
• Differences:
– Our data is at the plant level, while their data is at the firm level.
– UNCTAD data is inflated by a huge number of Chinese observations
(424 196): all approved FDI projects registered by the Chinese, but is
an overestimate of the number of actual foreign firms.
Comparisons with Other Data: US BEA
• BEA’s U.S. Direct Investment Abroad: Benchmark Survey, is a census
conducted every 5 years covering virtually the entire population of
U.S. MNC’s.
– In 2004, BEA reports that sales (employment) by foreign affiliates
of U.S. MNCs totaled $3,238 billion (10.02 million employees).
– In 2005 the DNB data : sum of all sales (employment) by foreign
establishments reporting US parents was $2,795b (10.01 million
employees).
– The distribution across countries is also consistent.
15
15
NGA
20
25
Dun and Bradstreet subsidiaries of US MNEs (log sales)
8
6
ARGMYS
KOR
CHN
THA
AUTNOR
TURISR
ZAF
VEN PRT
CHL
SAU
IDN
POL
PHL
COL
NZLHUN
IND
CZEFIN
GRC
LUX
EGY PER
ARE
CRI
GBR
CAN
4
20
ECU
GBR
CAN
JPN DEU
FRA
NLD
SGPMEX
BRAITA
AUS
CHE
BEL
IRL
ESP
HKG
NGA
DEU
NLD
MEX
JPN
AUS
ITA
BRA
ESPHKG
BEL
CHN CHE
SGP
ARG IRL
SWE
THA CHL
VEN KOR
INDMYS
ZAF
AUT
DNK
NOR
IDN
PHL COL PRT
POL
NZL
RUSTUR
ISR
CZE
HUN FIN
PAN
GRC
PER
LUX
SAU
BRB
EGYARE ECU
CRI
HND
FRA
DOM
2
RUS
BEA su bsidiaries of US MNEs (log # of firms)
25
30
10
Comparisons with US BEA
30
2
4
6
8
Dun and Bradstreet subsidiaries of US MNEs (log # of plants)
10
General Patterns
• The vast majority of our foreign owned subsidiaries are in richer
countries and services.
Measuring Horizontal versus Vertical FDI
• Data limitation: we do not observe intra firm trade.
• We infer it from information about the goods produced in each of the
firm’s establishments and the input-output relationship between those
goods.
– Hummels, Ishii, and Yi (2001): input-output tables to measure a
country’s vertical specialization.
• Advantages:
– Large amount of data for many countries and industries; value of
intra-firm trade not affected by transfer pricing.
– Using I-O tables avoids the arbitrariness of classification schemes
that divide goods into “intermediate” and other categories;
Hummels et al. (2001).
• But…identification of vertical subsidiaries as those which supply inputs
to their parents relies on a number of assumptions.
Measuring Horizontal and Vertical
• We calculate bilateral horizontal and vertical FDI using firm ownership
data and U.S. input output matrix.
– Horizontal FDI: activity of those foreign owned subsidiaries in the
same industry as their parent.
– Vertical FDI: activity of foreign owned subsidiaries in industries
which are upstream from their parent’s industry (according to the
US input output matrix).
– Complex FDI: firms satisfy both.
– None: If they satisfy neither of these criteria.
Horizontal
Complex
Vertical
Measurement
•
•
•
•
For each firm: up to six SIC codes for itself and its parent.
Let S be the set of SIC codes of the subsidiary, and let P be the set of SIC
codes of the parent.
We use notation x → z to denote any element x being an input into an
element z where x Є S and z Є P.
We define x → z if the input output coefficient from the US input output
matrix is greater than a threshold level which we vary. We define an owned
establishment as:
– Horizontal if S and P share any element (if x│x Є S ۷ x Є P) or if the
sets are identical (if S=P)
– Vertical if any element of S is an input into any element of P ( x│ x → z
where x Є S and z Є P) and if the sets are not identical (if S≠P)
– Complex if they share any element (if x│ x Є S ۷ x Є P) and if any
element of S is an input into any element of P ( x│ x → z where x Є S
and z Є P) and if the sets are not identical (if S≠P).
– Neither if none of these connections exist.
Methodology: Input-Output Analysis
• Threshold: determine the strength of the relationship required to
assume that a subsidiary is a supplier to its parent.
– Main results: threshold of 0.05 for the ‘total requirements’
coefficient (i.e. the use of a commodity directly and indirectly by
an industry).
• Robustness: 0.01 and 0.1.
• We use an alternative vector of input-output coefficients based on the
‘direct requirements’ (i.e. the use of a commodity directly by an
industry) with a threshold of zero.
• Analysis of the results: methodology is capturing a supply chain
relationship between parents and subsidiaries.
Vertical and Horizontal: Some Results
• Within manufacturing subsidiaries (188721), there are 112,939
vertical subsidiaries and 104,057 horizontal subsidiaries (15.8 million
versus 11.9 million employees).
– We exclude none and complex in the current analysis.
• Similar results excluding IO relation within same country.
• Results seem consistent related party trade data U.S.
Most Frequent Parent-Subsidiary Horizontal Industry
Combinations in DNB Data
Parent industry
Motor Vehicle Parts and Accessories
Pharmaceutical Preparations
Industrial Gases
Plastics Products, NEC
Motor Vehicles and Passenger Car Bodies
Computer Peripheral Equipment, NEC
Perfumes, Cosmetics, and Other Toilet Preparations
Periodicals: Publishing, or Publishing and Printing
Paints, Varnishes, Lacquers, Enamels, and Allied Products
Newspapers: Publishing, or Publishing and Printing
Books: Publishing, or Publishing and Printing
No. of Subsidiarys
1080
1042
1018
576
541
394
386
349
325
319
279
SIC
3714
2834
2813
3089
3711
3577
2844
2721
2851
2711
2731
Most Frequent Parent-Subsidiary Upstream Vertical
Industry Combinations in DNB Data
Parent industry
Medicinal Chemicals and Botanical Products
Speciality Cleaning, Polishing, and Sanitary Prep.
Orthopedic, Prosthetic, and Surgical App. and Supplies
Biological Products, Except Diagnostic Substances
Computer Storage Devices
Computer Peripheral Equipment, NEC
Computer Terminals
Pressed and Blown Glass and Glassware, NEC
In Vitro and In Vivo Diagnostic Substances
Motor Vehicles and Passenger Car Bodies
Periodicals: Publishing, or Publishing and Printing
Subsidiary industry
parent sic subsid sic No. of firms
Pharmaceutical Preparations
2833
2834
475
Soaps and Other Detergents, Except Speciality Cleaners
2842
2841
228
Surgical and Medical Instruments and Apparatus
3842
3841
201
Pharmaceutical Preparations
2836
2834
201
Computer Peripheral Equipment, NEC
3572
3577
167
Electronic Computers
3577
3571
165
Computer Peripheral Equipment, NEC
3575
3577
154
Flat Glass
3229
3211
146
Pharmaceutical Preparations
2835
2834
143
Motor Vehicle Parts and Accessories
3711
3714
134
Books: Publishing, or Publishing and Printing
2721
2731
128
Firm Level Example: General Motors
• General Motors Corporation: 2,248 entities which report it as their ‘global
ultimate parent’
– 455 are subsidiaries outside the United States
– 123 subsidiaries outside the United States in manufacturing industries.
• 68 are ‘horizontal’ subsidiaries (i.e. in the same primary 4 digit
SIC code as their parent firm, GM SIC 3711 Motor Vehicles and
Passenger Car Bodies)
• 42 subsidiaries as being ‘vertical’ FDI (i.e in industries which are
inputs in to the parent industry).
– The non-manufacturing subsidiaries are primarily dealerships, credit,
and insurance institutions.
General Motors: Characteristics of Vertical FDI
• Vertical subsidiaries top industries: Specialized Auto Parts (SIC 3714) e.g
GM Strasbourg which produces carburetors, pistons, rings, and valves in
France; GMI Engineering which produces diesel engine parts in Japan;
Vehicle engines (SIC 3519) e.g. Powertrain-Kaiserslautern in Germany.
• Average skill intensity of the industries of GM subsidiaries is not
significantly different.
• The set of GM’s foreign subsidiaries does not include any firms producing
what might be called the ‘raw materials’ or ‘low skill inputs’ into the
production of automobiles.
– GM’s ‘vertical FDI’ is focused on the “penultimate stages” in the
vertical production chain.
Vertical Activity: In Rich Countries
Firms
Employees ('000)
High income
countries
104,230
14,062
Low income
countries
8,709
1,738
Low income
countries (%)
9%
11%
Vertical: Level of Aggregations
• More than half of all vertical subsidiaries are in the same 2 digit industry as
their parent but a different 4 digit industry (for example, an automaker (SIC
3711) sourcing specialized auto parts (SIC 3714) from its foreign-owned
subsidiary).
– The vertical nature of these relationships is missed at the 2 digit level
since many subsidiaries are supplying goods to their parents where both
the input and the final good are in the same 2 digit SIC code
112,939
93,168
65,550
42,783
4 Digit
3 Digit
2 Digit
– Lower bound… but linked Input-Output relations.
1 Digit
Vertical FDI: In Close Activities
Vertical Activity: Skill Differences
FDI and Trade Facts
• FDI literature: multinational subsidiaries which supply their parents
with intermediate goods will be located in low factor costs countries.
– Evidence of comparative advantage considerations in MNCs
vertical location decisions, Yeaple (2003) and Hanson et al (2001).
– Implication: Intra-firm trade will be higher between rich and poor
countries than between rich countries.
• However …. Bernard, Jensen and Schott’s (2006) find that low income
countries have low shares of intra-firm exports to the US, while high
income countries generally report above average intra-firm imports to
the U.S.
– Implications: intra-firm trade data: lot of vertical FDI between rich
countries.
Inter and Intra Industry FDI and Trade Facts
• Distinction between intra and inter industry vertical FDI resolves this
contradiction.
• Analysis of FDI using data with industry information only at the 2
digit focuses exclusively on inter-industry FDI and misses intraindustry vertical FDI.
– Firms engaging in inter-industry FDI are more likely to be
sourcing low skill inputs from low skill countries:
• Validates the results of FDI studies at the 2 digit level.
• Including intra-industry vertical FDI (predominantly between rich
countries): high share of intra-firm trade flows between rich countries
observed in the trade data.
Vertical Patterns
• Analyze importance of comparative advantage and the inputs position in
the production process in the determination of vertical FDI.
• Following Brainard (1997), Yeaple (2003), Carr et al. (2001)
FDIijs = 2SumMktSizeij + 3Distanceij +4 CountrySkilli + 5
CountrySkilLij IndustrySkillInts + 6IndustrySKillInts + ijs (1)
– i and j: host and parent country, s: industry of the subsidiary.
– FDI : bilateral multinational activity in an industry (number of
subsidiaries, total sales, total employment).
– Distanceij: bilateral distance between the home and host country.
– Market size: sum of the GDPs in the host and parent economies.
– Country skill: average years of schooling.
– Industry Skill Intensity: ratio of non-production to total workers.
– Only manufacturing.
Determinants of Multinational Bilateral Activity
Multinational Activity in Each Bilateral Industry Pair--Tobit Regression
2 Digits
Dependent Variable
Log Distanceij
Log Sum of Market Sizeij
Country Skillj
Country Skill x Industry Skilljs
Industry Skills
# Observations
# Firms (US
parents only)
(1)
-27.006
[2.527]***
296.998
[30.897]***
-13.611
[1.782]***
20.582
[1.567]***
-320.695
[38.900]***
5668
# Firms
Sales
Empl.
(2)
(3)
(4)
-11.528
[1.602]***
42.555
[2.252]***
-7.383
[1.224]***
16.710
[1.295]***
-36.906
[25.677]
13553
-0.409
[0.054]***
1.520
[0.073]***
-0.059
[0.038]
0.246
[0.037]***
-3.328
[0.860]***
13553
-0.820
[0.164]***
3.236
[0.223]***
-0.547
[0.117]***
0.597
[0.114]***
-2.348
[2.616]
13553
Determinants of Multinational Bilateral Activity
Multinational Activity in Each Bilateral Industry Pair--Tobit Regression
2 Digits
Dependent Variable
Log Distanceij
Log Sum of Market Sizeij
Country Skillj
Country Skill x Industry Skilljs
Industry Skills
# Observations
# Firms (US
parents only)
(1)
-27.006
[2.527]***
296.998
[30.897]***
-13.611
[1.782]***
20.582
[1.567]***
-320.695
[38.900]***
5668
4 Digits
# Firms
Sales
Empl.
# Firms
Sales
Empl.
(2)
(3)
(4)
(5)
(6)
(7)
-11.528
[1.602]***
42.555
[2.252]***
-7.383
[1.224]***
16.710
[1.295]***
-36.906
[25.677]
13553
-0.409
[0.054]***
1.520
[0.073]***
-0.059
[0.038]
0.246
[0.037]***
-3.328
[0.860]***
13553
-0.820
[0.164]***
3.236
[0.223]***
-0.547
[0.117]***
0.597
[0.114]***
-2.348
[2.616]
13553
-1.900
[0.139]***
5.096
[0.187]***
-0.303
[0.163]*
0.302
[0.403]
10.079
[3.707]***
106914
-7.175
[0.630]***
18.222
[0.903]***
-0.676
[0.715]
0.812
[1.755]
39.982
[16.293]**
106914
-2.152
[0.162]***
5.294
[0.235]***
0.005
[0.186]
0.117
[0.457]
11.916
[4.292]***
106914
Comparative Advantage and Proximity
• Interaction between the relative skilled-labor abundance of countries
with the skilled-labor intensity of industries.
– 2 digits: strong evidence that vertical FDI is driven by comparative
advantage, i.e. low skill activities tend to be located in low skill
countries.
– 4 digit: we find significantly less evidence that vertical FDI is
driven by comparative advantage.
• Proximity: the position of intermediate inputs in the chain of production
contributes to the understanding of the patterns of intra-industry FDI:
– Goods closer to raw materials are less likely to be the subject of FDI
than intermediate goods which are proximate to the final good.
Measuring Proximity: Requirements
• Requirements: ratio of direct/total requirements coefficients.
– Direct requirements coefficient, i.e., the amount of the output of
industry i used directly as an input into industry j
– Total requirements coefficient, i.e, the total amount of industry i used
either directly or indirectly in the production of industry j.
• The more of the intermediate product used directly in the final
good the higher the proximity variable, i.e. raw materials have
low proximity variables.
• Closeness: absolute difference between the four digit SIC codes of the
two products.
– Closeness variable takes advantage of the fact that the 1987 SIC
groups similar industries together.
Determinants of Multinational Bilateral Activity
Multinational Activity in Each Bilateral Industry Pair--Tobit Regression
4 digit
Dependent Variable
Log Distanceij
Log Sum of Market Sizeij
Country Skillj
Country Skill x Industry Skilljs
Industry Skills
# Firms
Sales
Sales
Empl
Empl
(1)
(2)
(3)
(4)
(5)
-1.908
[0.139]***
5.116
[0.188]***
-0.309
[0.160]*
0.315
[0.397]
9.323
[3.650]**
Proximityps
(Direct/Total IO Coefficient)
Closenessps
(Abs. Difference in 1987 4 digit SIC)
-0.009
[0.001]***
# Observations
106914
-7.193
[0.631]***
18.221
[0.903]***
-0.680
[0.718]
0.818
[1.761]
40.791
[16.363]**
6.660
[2.469]***
-7.174
[0.629]***
18.222
[0.901]***
-0.697
[0.702]
0.856
[1.721]
37.082
[15.967]**
-2.158
[0.162]***
5.291
[0.235]***
0.007
[0.187]
0.108
[0.459]
12.264
[4.311]***
1.980
[0.631]***
-0.033
[0.004]***
106914
106914
-2.150
[0.162]***
5.292
[0.234]***
-0.009
[0.183]
0.140
[0.449]
11.052
[4.208]***
-0.009
[0.001]***
106914
106914
Rationales for Proximity
• Information advantages associated with the co-ownership of later
stages: activities involved in producing proximate inputs may have
more in common with the production of the final good than do the
activities involved in the production of raw materials,
• Firms may be more worried about their intellectual property when the
good is closer to their final good.
• Monitoring advantage over the penultimate stages of the production.
• Maximize quality control over later stages of production.
• Contractability characteristics later stages of production (skill intensity,
capital intensity, product characteristics…)
(Spencer (2006) overview…. Bernard et al. (2006), Antras (2003),
Aghion and Tirole (1995))
Conclusions
• Firm level data: close to a comprehensive picture multinational
activities.
• The firm level data: vertical FDI is larger than commonly thought
(Hanson et al. 2001, 2005).
• Discrepancy: Significant amount of vertical FDI was misclassified.
– North-north FDI between parent
– Subsidiaries in similarly skilled activities,
– More than half of all vertical subsidiaries are only observable at
the four-digit level because the inputs they are supplying are so
proximate to their parent firm’s final good.
Conclusions
• ‘Intra-industry’ vertical subsidiaries: qualitatively different to the interindustry vertical FDI visible at the two-digit level.
– Produce inputs with skill intensity overwhelmingly producing in
high skill countries.
• Activities not readily explained by the comparative advantage models.
• Pattern of intra-industry north-north vertical FDI: decision to outsource
versus own the production of intermediate inputs.
– Multinationals source raw materials and inputs in early stages of
production from outside the firm,
– but tend to own the stages of production proximate to their final
production giving rise to a class of high-skill intra-industry vertical
FDI.
Conclusions: Implications
• Level of aggregation: important elements of the pattern of foreign
direct investment are missed at the 2 digit level and are not observable
without industry data.
– Echoing results by Schott (2003) for trade—highlight the
importance of shifting away from industry analysis towards more
disaggregated data to understand the location decisions firms.
• Analysis suggest that foreign activity may be better explained by more
complex production processes involving several stages which
incorporate both arms length and in sourcing decision.
• Future research…