Peter H. Egger Katharina Erhardt Gerard Masllorens
30/10/2020
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Overview
2 Model
3 Data
4 Results
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Motivation
Production, trade and investment are increasingly organised within
so-called global value chains (GVCs).
Different stages of the production process are located across
national borders.
Organizational structure of global production networks is of
crucial importance.
Any given firm can be integrated in the value chain in three
forms:
Backward integration: e.g., a manufacturing firm integrating a
commodity producer. Forward integration: e.g., a manufacturing firm
integrating a wholesale firm. No integration: input demand and
supply at arm’s length.
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Motivation
The literature on topic has focused on the decision between no
integration and backward integration.
Nevertheless both data and anecdotal evidence suggest that forward
integration is very prevalent.
In ORBIS dataset covering 2007-2013 we see:
Unique shareholders: 571,636 Unique subsidiaries: 999,531
Firm-to-firm links in average year: 12,229,737 Backward integration
for 52% of links (subsidiary is in a top-5 supplying
country-sector). Forward integration for 52% of links (subsidiary
is in a top-5 buying country-sector). Clearly there is some but not
a complete overlap between top5- buying and supplying.
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Literature
Seminal property rights framework for the boundaries of the firm by
Grossman and Hart (1986).
Focus on the importance of ownership rights as a source of power
when contracts are incomplete. Core result: Residual rights of
control should be assigned to the party whose investment
contributes most to the value of the final output.
Vertical organization of the firm (Nunn and Tre er, 2008; Alfaro
and Charlton, 2009; Alfaro et al., 2016)
Organization of value chains within and across national borders
(McLaren, 2000; Antras and Helpman, 2004; Antras and Chor, 2013;
Alfaro et al., 2019)
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Literature
The most recent advances focus mainly on backward
integration:
Models of global sourcing (Grossman and Helpman, 2003; Antras and
Helpman, 2004).
Organizational structure of global production networks (Antras,
2005).
Sequential multi-stage production networks (Antras and Chor, 2013;
Alfaro et al., 2019)
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Contribution
We build on model of forward and backward integration (Acemoglu et
al., 2010).
Extending the model to include fixed integration costs.
Empirically assessing hypotheses regarding both integration
directions.
Providing evidence for the property rights framework approach on
forward integration.
We run our empirical analysis on a panel covering the
country-sector pairs of the entire world.
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Model setup
Backward integration (B) Forward integration (F) Independence
(I)
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Model setup
o S ) = xS (peo
P + 1) (1)
xS ∈ (0, 1) is a customized input.
indicates to what extent the final output relies on the provision
of the customized input; (1− ) is importance of standard
input.
eo P ≥ 0 and eo
S ≥ 0 are the (endogenous) investment levels of P and S ,
respectively.
p and s are the respective marginal products of investment.
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Timing
1 P offers organizational form o ∈ {F , I ,B} and transfers, T
o
P + T o S = 0.
2 S decides whether to accept the offer.
3 S and P simultaneously decide on their investment levels eo
P ≥ 0, eo S ≥ 0.
4 After investments are realized, S and P bargain over shared
revenues.
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Outside options
In order to determine the outcome of the Nash bargaining, we have
to define the respective outside options V o
i in case of disagreement for player i under organizational form o,
where {λS , λP} are retained investment fractions and θ is the
outside-marketability share of a customized input in case of
disagreement:
Producer Supplier
F S )
No Integration Y (xS = 0, e I P , 0) θ(se I
S + 1) Backward Y (xS = 1, eB
P , (1− λS )eB S ) 0
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Profits
We can then compute the gross revenue accruing to each party under
each organizational form,
yo i (eo
i + 1
P , e o S )− V o
S − V o P ) (2)
which, in turn, we can use to calculate profits,
πo i = yo
i + T o i (3)
where F o i denotes fixed costs of integration and Ci (e
o i ) is a simple
quadratic form for the costs of investments. Optimal
investment
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Total surplus
Equilibrium organizational form maximizes total surplus So = πo S +
πo
P at optimal investment levels and noting that T o
P + T o S = 0:
SF = 1
2 s2 +
2
) s2 − F (6)
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Total surplus
Which organizational form will be chosen depends on the joint
surplus. Let:
F = SF − S I (7)
B = SB − S I (8)
γ = p
s (9)
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Graphical representation
Forward integration Independence Backward integration
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Predictions I
∂γ ≥ 0
A relatively higher investment intensity of the producer vis-a-vis
the supplier induces backwards integration (γ > γB∗)
A relatively higher investment intensity of the supplier vis-a-vis
the producer induces forward integration (γ < γF∗)
Corollary: The shareholder has relatively higher investment
intensity than the subsidiary.
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Predictions II
∂ < 0.
Marketability of the customized input (θ). Larger θ ⇒ Higher
outside option for S ⇒ Larger [γF∗, γB∗] ⇒ any form of integration
becomes less likely.
Dependence on the input for final production (). Larger ⇒ Lower
outside option for P ⇒ Smaller [γF∗, γB∗] ⇒ any form of integration
becomes more likely.
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Predictions III
∂F > 0.
Fixed costs of integration (F ). Larger F ⇒ Integration more costly
⇒ Larger [γF∗, γB∗] ⇒ any form of integration becomes less
likely.
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Predictions IV
∂F∂θ < 0.
The cross-derivative of fixed integration costs and input
marketability on {γB∗, γF∗} indicates that an increase in the
marketability of inputs reduces the detrimental effect of fixed
integration costs on backward integration but increases it on
forward integration.
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ORBIS data on ownership
annual firm data for 2007-2013.
J = 199 countries (i ,j).
S = 38 ISIC Revision-4 one-digit (two-digit for manufacturing)
sectors (r ,s).
1992 · 382 = 57, 183, 844 country-sector-pair cells of potential
ownership links per year.
For the period 2007-2013 this yields a panel data-set of
400,286,908 observations.
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ORBIS data on ownership
CF rs ij as the number of firms in country i and sector r that are
owned by
firms from sector s in country j (number of connected firms).
Figure: Number of Connected Firms
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World Input-Output Table
Figure: World Input-Output Table - Antras and Chor (2017)
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Input-output coefficients
ij,t
): normalized inputs of sector-r
output (regardless of its geographic origin) as used by country j
in its production of sector-s output in year t.
Output coefficient (brs i ,t =
∑J j=1 Z rs
ij,t
): which sectors (regardless of
the country) are the main users for country i ’s sector-r output at
year t.
(ars j , b
rs i ) are the average coefficient over the years in the
sample
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Backward and Forward variable
0 otherwise.
Indicates whether sector r of the affiliates is among the top-5
supplying sectors of shareholders in j and s.
Forwardrs j =
0 otherwise.
Indicates whether sector r of the affiliates is among the top-5
using sectors of shareholders in j and s.
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Investment intensity (γ)
We proxy γ with R&D intensity (expenditures on R&D over
total sales of a firm).
We calculate γs for every shareholder sector and γs for every
affiliate sector.
Finally we define the following indicator on whether the
shareholder has relatively higher investment intensity than the
subsidiary
γrs =
{ 1 if γs ≥ γr , 0 otherwise.
According to the corollary of Prediction 1 the coefficient on γrs
should be positive for the forward and backward integration.
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Investment intensity (γ). Assessing Prediction 1
Table: R&D Investment Intensity
Number of Firm-to-Firm Connections (CF rs ij ,t) (1) (2) (3)
Rel. high shareholder R&D intensity (γrs t ) 0.689∗∗∗ 0.745∗∗∗
0.541∗∗∗
(0.065) (0.069) (0.067) Backwardrs
(0.075) (0.079) Forwardrs
j 0.327∗∗∗ 0.339∗∗∗
(0.051) (0.057) Forwardrs
(0.056) (0.068)
Country-pair FE X X X Shareholder-country-industry-year FE X X X
Subsidiary-country-industry-year FE X X X Domestic-year FE X X
X
Obs. 28,484,832 28,484,832 28,484,832 R2 0.92838 0.92813
0.93018
Standard errors are clustered at country-industry pairs level and
reported in parentheses.
* p < 0.1, ** p < 0.05, *** p < 0.01
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Competition (θ)
We proxy θ as the ratio of the total number of firms in downstream
sector over the total number of firms in upstream.
θrs ij ,t =
for forward integration.
According to Prediction 2 the coefficient on θrs ij ,t should be
negative for the
forward and backward integration.
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Competition (θ). Assessing Prediction 2
Table: Competition Effects
Number of Firm-to-Firm Connections (CF rs ij ,t) (1) (2) (3)
Market thickness of shareholder industry rel. to subsidiary
industry (θB rs ij ,t) −0.000023∗∗∗ −0.000011∗∗∗
(0.000) (0.000) Backwardrs
j 0.915836∗∗∗ 0.765719∗∗∗
(0.000) (0.000)
Market thickness of subsidiary industry rel. to shareholder
industry (θF rs ij ,t) 0.000012∗∗∗ 0.000015∗∗∗
(0.000) (0.000) Forwardrs
j 0.802499∗∗∗ 0.624699∗∗∗
Country-pair FE X X X Shareholder-country-industry-year FE X X X
Subsidiary-country-industry-year FE X X X Domestic-year FE X X
X
Obs. 28,600,089 28,600,089 28,600,089 R2 0.92432 0.92314
0.92713
Standard errors are clustered at country-industry pairs level and
reported in parentheses.
Column (3) also includes Output coef. × θB and Input coef. × θF as
controls.
* p < 0.1, ** p < 0.05, *** p < 0.01
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Input-consumption effects ()
We proxy as the share of total input consumption over
production
sB j ,t =
R∑ r=1
ars j ,t
rF i ,t =
S∑ s=1
asr i ,t
According to Prediction 2 the coefficient on s j ,t should be
positive for the
forward and backward integration.
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Input-consumption effects (). Assessing Prediction 2
Table: Total Input-consumption Effects
Number of Firm-to-Firm Connections (CF rs ij ,t) (1) (2) (3)
Backwardrs j −0.031 −1.109∗∗∗
Backwardrs j × Rel. importance of inputs for shareholder (B s
j ,t) 1.985∗∗∗ 1.636∗∗∗
Forwardrs j × Rel. importance of inputs for subsidiary (F r
i ,t) 1.933∗∗∗ 0.941∗∗∗
Country-pair FE X X X Shareholder-country-industry-year FE X X X
Subsidiary-country-industry-year FE X X X Domestic-year FE X X
X
Obs. 28,576,343 28,560,530 28,536,807 R2 0.92498 0.92368
0.92921
Standard errors are clustered at country-industry pairs level and
reported in parentheses.
Column (3) also includes Output coef. × B and Input coef. × F as
controls.
* p < 0.1, ** p < 0.05, *** p < 0.01
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Fixed integration costs (F )
We proxy a reduction of Fixed Integration Costs (F−1) as Bilateral
Investment Treaty (BIT) coming into force.
F−1 ij ,t = BITij ,t =
{ 1 if a BIT is in force between i and j at year t, 0
otherwise.
According to Prediction 3 the coefficient on F−1 ij ,t should be
positive for the
forward and backward integration.
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Fixed integration costs (F ). Assessing Prediction 3
Table: Fixed-cost Effects
Number of Firm-to-Firm Connections (CF rs ij ,t) (1) (2) (3)
BIT (F−1 ij ,t ) −0.036 −0.005 −0.053
(0.030) (0.031) (0.034) Backwardrs
Country-pair FE X X X Shareholder-country-industry-year FE X X X
Subsidiary-country-industry-year FE X X X Domestic-year FE X X
X
Obs. 28,600,089 28,600,089 28,600,089 R2 0.92433 0.92314
0.92711
Standard errors are clustered at country-industry pairs level and
reported in parentheses.
* p < 0.1, ** p < 0.05, *** p < 0.01
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Cross Effects of Key Parameters. Assessing Prediction 4
Table: Competition and Fixed-cost Effects
Number of Firm-to-Firm Connections (CF rs ij ,t) (1) (2) (3)
BIT (F−1 ij ,t ) −0.050309 −0.022309 −0.074162∗∗
(0.031) (0.032) (0.034) Rel. high shareholder R&D intensity
(γrs
t ) 0.688817∗∗∗ 0.743656∗∗∗ 0.539057∗∗∗
(0.064) (0.069) (0.067)
Market thickness of shareholder industry rel. to subsidiary
industry (θB rs ij ,t) −0.000013∗∗ −0.000006
(0.000) (0.000) Backwardrs
j 0.324891∗∗∗ 0.351065∗∗∗
(0.061) (0.064) Backwardrs
(0.075) (0.079)
(0.000) (0.000)
Market thickness of subsidiary industry rel. to shareholder
industry (θF rs ij ,t) 0.000013∗∗∗ 0.000012∗∗∗
(0.000) (0.000) Forwardrs
j 0.320228∗∗∗ 0.334782∗∗∗
(0.053) (0.058) Forwardrs
(0.056) (0.068)
(0.000) (0.000)
Country-pair FE X X X Shareholder-country-industry-year FE X X X
Subsidiary-country-industry-year FE X X X Domestic-year FE X X
X
Obs. 28,484,832 28,484,832 28,484,832 R2 0.92844 0.92818
0.93028
Standard errors are clustered at country-industry pairs level and
reported in parentheses.
* p < 0.1, ** p < 0.05, *** p < 0.01
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Robustness
Different definitions of Forward/Backward creating TopH Inputrs
j
for Backward and TopH Outputrs j for Forward with H measuring
whether a sector is among the H most-important ones with H ∈ {1,
..., 10} Different measure of investment intensity (γ) using
physical investment intensity (expenditures on physical investment
over total sales).
Subsamples of the data keeping in the sample only those countries
that are reported in the WIOT.
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Thank You!
Acknowledgement: This project has received funding from the
European Union’s
Horizon 2020 research and innovation programme under the Marie
Sklodowska-Curie
agreement No 721916.
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Investment level
Using (3) we can compute the optimal level of investment under each
organizational form taking the other party’s investment as
given:
eF∗ S = s, eF∗
back
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Motivation and literature