Firm Heterogeneity and Imperfect Competition in
Global Production Networks∗
Hanwei Huang†
Kalina Manova
City University of Hong Kong and CEP UCL, CEPR and CEP
Oscar Perelló
UCL

Frank Pisch
TU Darmstadt, CEP

March 2022
Abstract
We study the role of firm heterogeneity and imperfect competition for global production networks and the gains from trade. We develop a quantifiable trade modelwith two-sided firm heterogeneity, matching frictions, and oligopolistic competition
upstream. More productive downstream buyers endogenously match with more upstream suppliers, thereby inducing tougher competition among them and enjoying
superior sourcing outcomes. We then present consistent empirical evidence using
highly disaggregated data on firms’ production and trade transactions for France,
Chile and China. Downstream French and Chilean buyers import higher values
and quantities at lower prices as upstream Chinese markets become more competitive over time, with stronger responses by larger buyers. Chinese suppliers set
lower prices and mark-ups to buyers that source from more suppliers. Counterfactual analyses indicate that lower barriers to entry upstream, lower matching costs,
and lower trade costs amplify firm productivity and aggregate welfare downstream,with differential effects across firms. These effects operate through a combinationof improved buyer-seller matches, gains from variety, and lower mark-ups. Global
production networks thus generate greater impacts and cross-border spillovers fromindustrial policy and trade liberalization.

Keywords: global value chains, buyer-supplier production networks, firm heterogeneity,
matching frictions, imperfect competition, gains from trade
JEL Codes: D24, F10, F12, F14, L11, L22∗

We thank our discussants for their valuable feedback. Thanks also to audiences at various seminars
and conferences. The work described in this paper was partially supported by a grant from the Research
Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 21504720).
Manova acknowledges support from The Leverhulme Trust and ERC Horizon 2020 Grant Agreement724880.
†Hanwei Huang:huanghanwei@gmail.com.
Kalina Manova (corresponding author):k.manova@ucl.ac.uk.
Oscar Perelló: oscar.perello.19@ucl.ac.uk.
Frank Pisch: frank.pisch@tudarmstadt.de.

1

1

Introduction

Global value chains have fundamentally transformed international trade and firm operations in recent decades. Firms increasingly rely on imported inputs sourced from foreign
suppliers and in turn sell to both final consumers and downstream producers at home andabroad (e.g. Antras et al. 2017, Bernard and Moxnes 2018). At the same time, there
has been dramatic and growing heterogeneity in productivity, size and trade participation
across firms within countries and sectors. Large firms dominate global trade and transact
with the greatest number of buyers and suppliers (e.g. Bernard et al. 2012, Bernard etal. 2019). The skewness and granularity of the firm size distribution affect aggregate
productivity and the gains from trade (e.g. Gabaix 2011, di Giovanni et al. 2014, Melitz
and Redding 2015, Gaubert and Itskhoki 2016). The rise of superstar firms has also beenaccompanied by higher industry concentration and mark-ups (e.g. Autor et al. 2017,
De Loecker and Eeckhout 2017, 2018, Yeaple 2019). These phenomena raise importantquestions about the optimal design of trade and industrial policies.
This paper examines the role of firm heterogeneity and imperfect competition for
global production networks and the gains from trade. We develop a quantifiable trademodel with two-sided firm heterogeneity, matching frictions, and oligopolistic competition
upstream. More productive downstream buyers endogenously match with more upstream
suppliers, thereby inducing tougher competition among them and enjoying superior sourcing outcomes. We then present consistent empirical evidence using highly disaggregateddata on firms’ production and trade transactions for France, Chile, and China. Downstream French and Chilean buyers import higher values and quantities at lower prices as
upstream Chinese markets become more competitive over time, with stronger responses bylarger buyers. Chinese suppliers set lower prices and mark-ups to buyers that source from
more suppliers. Counterfactual analyses indicate that lower barriers to entry upstream,
lower matching costs, and lower trade costs amplify firm productivity and aggregate welfare downstream, with differential effects across firms. These effects operate through a
combination of improved buyer-seller matches, gains from variety, and lower mark-ups.
Global production networks thus generate greater impacts and cross-border spillovers
from industrial policy and trade liberalization.
Our first contribution is theoretical. We develop a general-equilibrium model of global
sourcing in which heterogeneous buyers transact with heterogeneous suppliers in thepresence of trade costs, matching frictions, monopolistic competition downstream, andoligopolistic competition upstream. At a higher fixed cost, downstream buyers can meet
more potential suppliers and choose suppliers with higher buyer-supplier match quality.
The number and identity of a firm’s suppliers determines its suppliers’ optimal buyerspecific price and mark-up.
The combination of matching frictions and imperfect competition implies that endogenous network formation amplifies the underlying heterogeneity among firms. Moreproductive firms source from more suppliers (with their marginal supplier being less productive), and thereby induce tougher competition within their larger pool of suppliers.
More productive firms thus enjoy lower input costs because of higher input variety, bettermatched input providers, and lower input mark-ups, even though their average supplier isless productive. Respectively, more productive input suppliers sell more to more buyers2(with their marginal buyer being less productive) and earn higher sales revenues.
A distinctive prediction of this framework is that a fall in upstream entry costs makes
the market for input suppliers more competitive. As a result, sufficiently productive
downstream firms optimally match with more suppliers and benefit from lower input
costs, with the latter effect increasing with buyer productivity. By contrast, reductions in
trade or matching costs enable buyers in the middle of the productivity distribution thatare not yet sourcing from all potential suppliers to match with more suppliers. While all
input buyers enjoy a lower input price index, those that expand their supplier set gain
more.
Our second contribution is empirical. We combine firm-level production data andtransaction-level customs data for the universe of French, Chilean and Chinese firms in
2000-2006 to validate key predictions of the model. This allows us to examine how thedramatic expansion in firm entry and trade activity in China has affected downstream
producers in two economies at different segments of the global value chain and at different
levels of economic development, Chile and France. For Chile, we exploit rich data on firm
imports from China that identify the seller, HS 6-digit product, value, price and quantity
at the transaction level, matched to indicators for broad firm size bins. For France,
we access analogous transaction-level import data without the seller identity, but insteadmatched to detailed firm balance sheets. Finally, for China, we use matched data on firms’
export transactions and balance sheets to characterize the Chinese market structure andthe set of suppliers to France, Chile and the rest of the world (ROW) by HS-6 product.
Guided by the model, we quantify the upstream market structure relevant to individual
producers in Chile and France with the number of potential Chinese suppliers by productand year. We present baseline results using the number of Chinese exporters to ROW
excluding respectively Chile or France. We provide robust evidence using instead theactual number of Chinese exporters to Chile or France, which is arguably exogenous
from the perspective of atomistic buyers. We also instrument the latter with the numberof Chinese exporters either to ROW or to a larger yet comparable market: the Pacific
Alliance countries in the case of Chile, and the US in the case of France. Given theimportance of Chinese inputs to Chilean and French firms and the insignificance of theChilean and French markets to China, our identification strategy allows us to draw causal
conclusions. In turn, examining both Chile and France presents an opportunity to assess
whether similar economic forces operate in environments with very different market size,
economic development, and economic geography.
We first empirically establish that market structure upstream and buyer heterogeneity downstream shape the pattern of global production networks in line with the model’s
predictions. Downstream French and Chilean firms import greater quantities, pay lower
unit prices, and spend more on imported inputs from China when there are more upstream Chinese producers. Bigger buyers benefit more from tougher competition amongsuppliers. These results are robust to controlling for firm, product and year fixed effects,
as well as product-specific time trends. They are also not driven by other supply conditions upstream, such as the distribution of supplier productivity and quality, the rolesof intermediated or processing trade, and the presence of multi-product or multinational
sellers. The patterns are also independent of product-specific trade costs (import tariffs)
and the downstream market structure.
3

Exploiting the richer information on trade partner identity in the Chilean data, we thenprovide additional evidence that Chinese suppliers systematically vary sales prices across
Chilean firms in a way consistent with the market structure in the model. In particular,
upstream sellers offer lower unit prices for the same HS 6-digit product to downstreambuyers that source that product from more Chinese suppliers. This result obtains instringent specifications that account for marginal costs and quality with seller-productpair fixed effects and for downstream demand with buyer-product pair fixed effects.
Our third contribution is methodological. The model permits structural estimationand quantification despite its rich economic environment. We are able to characterizefirms’ optimal sourcing strategy, even though it is the outcome of a high-dimensional
combinatorial discrete-choice optimization problem. We develop a solution method that
extends tools from the prior literature (such as convexification, sequential supplier entry
into matching markets, and Fréchet-distributed match quality) to endogenous production
networks with two-sided heterogeneity and imperfect competition (Antràs et al. 2017,
Arkolakis et al. 2021, Taschereau-Dumouchel 2019). The model also delivers gravity
expressions for trade flows at the firm-to-firm and firm levels. This makes it possible
to estimate key model parameters from the available data, while calibrating others toestimates from the literature that are consistent with the model.
Our last contribution is quantitative. We use the model to evaluate the effect oftrade policy, industrial policy, and technological progress on global sourcing and the gains
from trade. We find that lower barriers to entry upstream, lower matching costs, andlower trade costs amplify firm productivity, firm size dispersion, and aggregate welfare
downstream. These effects operate through a combination of improved matching of buyers
and suppliers, gains from greater input variety, and lower mark-ups. Shutting down one
model feature at a time, we establish that two-sided firm heterogeneity, matching frictions
and imperfect competition all play a large quantitative role.
These counterfactual exercises have important policy implications. Existing studies
that evaluate trade policies rely on Computable General Equilibrium (CGE) models or
New Quantitative Trade Models (NQTM), which typically ignore global production networks, firm granularity, and/or market power.1 Our results indicate that taking theseforces into account can generate significantly higher gains from trade. In addition, ouranalysis illustrates the benefits associated with reductions in buyer-supplier matchingcosts as distinct from trade costs. This may provide justification for policies that decrease matching costs through trade promotion, information technology or international
contract enforcement. Finally, our conclusions reveal how imperfect competition in global
value chains gives rise to cross-border spillovers of national industrial policies such as
deregulation and other reforms that encourage firm entry.
We advance several strands of literature. Most directly, we contribute to research on
the determinants of global sourcing and its implications for firm performance. Accessto foreign inputs has been shown to increase aggregate welfare and firm productivity,
product quality, innovation, and profitability (Amiti and Konings, 2007; Goldberg et al.,2010; Halpern et al., 2015; Yu, 2015; Bøler et al., 2015; Manova et al., 2015; Blaum
1

Ottaviano (2014) discusses the difference between these two approaches, while Costinot andRodrı́guez-Clare (2014) provide a synthetic treatment of NQTM.

4

et al., 2018). Recent theory emphasizes the role of firm productivity and trade costs
in driving these outcomes (Antras, Fort, and Tintelnot, 2017; Furusawa et al., 2017;
Boehm and Oberfield, 2018; Bernard et al., 2019). This literature assumes perfect ormonopolistic competition upstream and typically abstracts from matching frictions, suchthat heterogeneous downstream firms source promiscuously from anonymous upstream
suppliers.
A growing research stream examines the role of firm heterogeneity in buyer-supplierproduction networks (see Bernard and Moxnes, 2018 for a recent survey). Bernard etal. (2019) study the impact of domestic supplier connections on firms’ marginal costs
and performance in Japan, whereas Bernard et al. (2018), Eaton et al. (2016), andEaton et al. (2018) explore the matching of exporters and importers using data on firmto-firm trade transactions for Norway, US-Colombia, and France, respectively. Bernard,
Dhyne Magermann, Manova, Moxnes (2019) find that two-sided firm heterogeneity andmatch-specific shifters are important in explaining the variation in firm-to-firm sales in
the complete domestic production network in Belgium. Recent models of buyer-seller
networks feature monopolistically competitive markets that imply constant mark-ups,
with generally one-sided firm heterogeneity (Chaney, 2014; Eaton et al., 2015; Bernard,
Moxnes, and Ulltveit-Moe, 2018; Lim, 2018; Oberfield, 2018).
Extending these two literatures, we consider global production networks with (i) twosided firm heterogeneity, (ii) matching frictions, and (iii) imperfect competition upstream.
The interaction of these three forces gives rise to novel theoretical insights, and is thus
necessary and sufficient to rationalize empirical patterns in the data that other frameworks
cannot. On necessity, models without (i) or (ii) cannot simultaneously account for thevariation in sourcing patterns across downstream firms, across suppliers within buyers,
and across buyers within suppliers. Frameworks that feature (i) and (ii) but omit (iii)
rule out heterogeneous pricing across buyers within a supplier and differential effects ofchanges in the market structure upstream across downstream firms; this includes a large
class of models in the prior literature. On sufficiency, ours is the first within a potential
class of data-consistent models that accommodate the complexity of (i), (ii) and (iii), yetremains parsimonious and highly tractable.
Indirectly, we also contribute to the literature on imperfect competition in international trade. Classic paradigms with monopolistic competition have attractive theoreticaland empirical properties (Krugman 1980, Melitz 2003). Without assumptions of CES
demand and Pareto-distributed productivity, however, such models typically cannot generate gravity expressions for aggregate trade flows, which restricts their use in structural
estimation (Melitz and Ottaviano, 2008; Arkolakis et al., 2018; Combes et al., 2012; Headet al., 2014; Head and Spencer, 2017). Recent advances accommodate the role of superstar firms in tractable oligopolistic environments with data-inspired strategic interactions
among firms (Bernard et al., 2003; Atkeson and Burstein, 2008; Edmond et al., 2015;
Neary, 2016; Amiti et al., 2018). Our contribution is to develop a tractable model ofimperfect competition in produciton networks that can both match micro-level empiricalpatterns and generate gravity trade flows conducive to counterfactual analysis.
At a broader level, we shed more light on the implications of production networks forthe firm size distribution. Prior work indicates that not only own characteristics, but alsocharacteristics of input suppliers contribute to the large and growing firm size dispersion
5

(Melitz, 2003, Sutton, 2007, Bernard, Dhyne Magermann, Manova, Moxnes, 2019). We
show that endogenous match formation with imperfect competition upstream is one mediating force through which produciton networks augment the productivity advantage ofmore efficient firms and thereby amplifies the firm size dispersion.
Our work also informs the transmission of idiosyncratic and macro-economic shocks.
Input-output linkages in asymmetric networks have been found to generate aggregate
shocks from firm-specific shocks and enhance long-run growth (Acemoglu et al., 2012;
Baqaee, 2018; Baqaee and Farhi, 2019; Acemoglu and Azar, 2017; Magerman et al.
2016). Global production networks can in turn transmit shocks across countries (Carvalho et al., 2016; Boehm et al., 2019; Lim, 2018; Tintelnot et al. 2017). Our analysis
suggests imperfect competition and two-sided heterogeneity can strengthen these transmission mechanisms, while global sourcing can generate international externalities fromdomestic industrial and trade policies.
The paper is organized as follows. Section 2 presents the model of global sourcing with
two-sided firm heterogeneity, endogenous buyer-supplier match formation, and oligopolistic competition upstream. Section 3 introduces the production and trade data for France,
Chile and China, and provides reduced-form empirical evidence in line with the model’s
predictions. Section 4 develops and implements a structural estimation strategy for operationalizing the model. Section 5 performs counterfactual exercises within the model toevaluate the impact of industrial policies, trade reforms, and technological progress. The
last section concludes.

2

Theoretical Framework

We first develop a quantifiable, general-equilibrium model of global sourcing in whichheterogeneous buyers match with heterogeneous suppliers in the presence of trade costs.
We examine the impact of matching frictions and oligopolistic competition upstream on
the sourcing behavior of monopolistically competitive firms downstream. We characterize
the endogenous formation of the global production network and key outcomes at the firmand firm-to-firm transaction levels. We relegate detailed proofs to an online Appendix.

2.1

Final Demand

There are J countries in the world. Consumers have Cobb-Douglas preferences acrosshomogeneous and differentiated goods. In each country i, wages wi are pinned down in
the freely tradable, homogeneous-good sector produced under constant returns to scale.
Consumers exhibit CES preferences for available varieties ωϵΩi of the non-tradable differentiated final good:Ui = Q1−α
0

Z
q(ω)

σ−1
σ

ασ
 σ−1

dω

, σ > 1,ω∈Ωiwhere σ is the elasticity of substitution across varieties. Given aggregate expenditure Ei
and price index Pi for differentiated goods, demand for variety ω with price pi (ω) is:qi (ω) = Ei Piσ−1 pi (ω)−σ .
6

(1)

2.2

Downstream Production

In each country, a continuum of monopolistically competitive firms assemble domestic
and imported inputs to manufacture final goods. Firms optimally set a constant mark-up
above their marginal production cost ci (ω) to maximize profits:max (pi (ω) − ci (ω))qi (ω), s.t. qi (ω) = Ei Piσ−1 pi (ω)−σ ,pi (ω)pi (ω) =σci (ω).
σ−1

Downstream firms draw core productivity φ from distribution Gi (φ) with support[φi , ∞), upon paying an entry cost of wi fi . They manufacture by combining varieties v
produced by upstream suppliers s in countries jϵJ and sectors kϵK. The elasticities ofsubstitution across varieties from the same country-sector and across varieties from different country-sectors are λ > 1 and η > 1, respectively. The marginal cost of downstream
firm φ is given by:1
ci (φ) =
φ

J X
K
X

1
! 1−η

Iijk (φ)cijk (φ)1−η

Z 1

1−λ

zijk (φ, v), cijk (φ) =

1
 1−λ

dv

,

(2)

0

j=1 k=1

where Iijk (φ) is an indicator equal to 1 if the firm sources sector k inputs from countryj, and cijk (φ) is the composite cost index of jk inputs.
The input cost index cijk (φ) aggregates the cost to φ of input varieties v, zijk (φ, v).
It is specific to downstream firm φ for two reasons: oligopolistic competition upstream andmatching frictions. Imperfect competition upstream will imply that the price pijks (Sijk (φ))
offered by supplier s to buyer φ will depend on the endogenous, discrete set of φ’s suppliersin country j and sector k, Sijk (φ). A supplier may thus charge different prices to different
buyers. In addition, matching frictions will lead downstream firms to endogenously choose
different sets of suppliers based on their productivity.
After matching with supplier s and observing s’s price for variety v, firm φ receives amatch-specific cost shock ξijks (φ, v) from a Fréchet distribution with dispersion parameter
θ.2 This shock can be thought of as the unexpected cost of adapting an input to thefirm’s production process, or alternatively, as the cost equivalent of an expected quality
defect. Thus equally productive buyers matched with the same set of suppliers may choose
different suppliers for the same input variety.
Conditional on sourcing inputs from a given country-sector, the downstream firm
optimally buys variety v from the lowest-cost upstream supplier it has matched with:zijk (φ, v) =θmin {τijk pijks (Sijk (φ)) ξijks (φ, v)} , Pr(ξijks (φ, v) ≥ t) = e−t , θ > 0, (3)s∈Sijk (φ)

where τijk is the iceberg trade cost of shipping sector-k inputs from country j to i.
2

Higher θ corresponds to higher input substitutability across suppliers.

7

2.3

Upstream Production

A discrete number of upstream suppliers Sjk produce differentiated inputs in country jand sector k at a constant marginal cost cjks .3 In order to sell to downstream buyers inU(U for upstream), which can be thoughtcountry i, they have to pay a fixed cost of wj fijkof as the supplier registration fee to attend a trade fair in a convention center. This fixed
cost will imply that only the most productive suppliers select into exporting.
Upstream suppliers sϵSijk (φ) matched to downstream buyer φ in country i compete
oligopolistically amongst themselves.4 They set their optimal match-specific prices toU(φ) separately from each relationship:
maximize profits πijks
Umax πijks(φ) = Qijks (φ)(pijks (φ) − cjks )(4)pijks (φ)

where Qijks (φ) is the expected residual demand of buyers with productivity φ.

2.4

Buyer-Supplier Matching

Let Sijk suppliers in country j be productive enough to export to country i in sector k.
We assume there are Sijk rooms in the convention center where upstream and downstream
firms can meet. These rooms can hold bidding games with seats for 1, 2, ···, Sijk suppliers,
respectively. Each buyer from country i can choose which room to enter, but it has to pay
DDa higher fixed cost to hold a bidding game in a bigger room, i.e. fijk
(Sijk ) ≥ fijk
(Sijk −1) ≥D· · · ≥ fijk (1) ≥ 0 (D for downstream). These matching costs can be thought of as theregistration fees and sourcing managers that scale up with the number of suppliers.
Upstream firms enter each bidding room sequentially in increasing order of marginal
costs, until the room reaches capacity. This assumption will ensure a unique matchingequilibrium and grant significant tractability: Instead of facing a high-dimensional choiceover 2Sijk possible sets of jk suppliers, a downstream firm has to consider only Sijk opD′
tions.5 At the cost of wi fijk
(Sijk), a buyer from country i can therefore match with the′
top Sijk suppliers of sector k inputs in country j.

2.5

Firm Sourcing Problem

In this environment, downstream firms optimize their global sourcing strategy in two
steps. First, they select the optimal set of countries and sectors from which to purchase
inputs, Ii (φ) = {j ⊗ k : Iijk (φ) = 1}, and the optimal number (and hence identity) of
input suppliers from each origin country-sector, Si (φ) = {j ⊗ k : Sijk (φ) ∈ {0, 1, ..., Sijk }}.
Second, they determine their optimal sourcing patterns across suppliers given Ii (φ) andSi (φ). We characterize these problems in reverse order.
3

Upstream suppliers can produce all varieties in a given sector and can operate in multiple sectors.
Their marginal cost is constant across varieties within a sector, but it can vary across sectors.
4
In the spirit of Neary (2016), upstream suppliers are large to an individual downstream buyer, but
small to the downstream sector as a whole.
5This assumption also underlies the solution concept in Eaton et al. (2012) and Gaubert and Itskhoki(2016). It can be rationalized for example with room-specific fixed costs, whereby more productive
suppliers expect higher profits and are more likely to be profitable in bigger, more competitive rooms.

82.5.1

Sourcing Pattern Conditional on Supplier Set

Within origin country-sector jk, buyer φ will source variety v from the lowest-cost of its
matched suppliers Sijk (φ) by solving the sourcing problem in equation (3). The probability
that supplier s is this lowest-cost supplier is:χijks (φ) = PS

pijks (Sijk (φ))−θ

ijk (φ)s=1

pijks (Sijk (φ))−θ.

(5)With a continuum of varieties and iid cost shocks across matches, the law of large numbers implies that χijks (φ) is also the market share of supplier s in the buyer’s expenditureon jk inputs. Buyers’ composite cost index for jk inputs is thus:i−1/θhX
Sijk (φ)−θ
cijk (φ) = γτijk
pijks (φ)
,
(6)
s=1

 1
where γ = Γ( θ+1−λ) λ−1 is a constant given by the gamma function Γ(·).6 Downstreamθfirms’ total input costs, Ci (φ), and demand for jk inputs, Qijk (φ), can be expressed as:
Ci (φ) = qi (φ)ci (φ) = (σ−1 σ) Ei Piσ−1 ci (φ)1−σ ,
σ

(7)

σ−1 σ
) Ei Piσ−1 φη−1 ci (φ)η−σ cijk (φ)−η .
(8)σFrom the perspective of upstream supplier s, residual demand by buyer φ is Qijks (φ) =Qijk (φ)χijks (φ), such that the supplier’s problem (4) becomes:
Qijk (φ) = (Umax πijks(φ) = Qijk (φ)χijks (φ)(pijks (φ) − cjks ), s = 1, ..., Sijk (φ).

(9)

pijks (φ)

While a higher price boosts a seller’s profit margin, pijks (φ) − cjks , it reduces its market
share χijks (φ) and residual demand Qijk (φ) by raising the buyer’s marginal cost ci (φ).
Proposition 1 summarizes suppliers’ optimal pricing strategy:
Proposition 1 There exists a unique Nash Equilibrium with supplier s prices
pijks (φ) =εijks (φ)
cjks ,
εijks (φ) − 1(10)where εijks (φ) = [σδijk (φ) + η (1 − δijk (φ))] χijks (φ) + θ [1 − χijks (φ)] is the elasticity ofresidual demand, and δijk (φ) is the share of country-j sector- k inputs in buyer φ’s input
purchases.
Upstream firms are able to price discriminate and charge buyer-specific mark-ups,
εijks (φ) 7µijks (φ) = εijks. Suppliers set higher mark-ups when they have a bigger market(φ)−16

As in Eaton and Kortum (2002), we need λ < θ + 1 for the price index to be well-defined.
In Atkeson and Burstein (2008), the presence of market-specific iceberg trade costs is necessary to
generate pricing to market. In our model, markets are further segregated across firms due to matching
frictions.
79

share in their buyer’s input basket, if ρijk (φ) ≡ θ − η + (η − σ)δijk (φ) > 0; we assume
this condition holds given evidence in the prior literature (Amiti. et al. 2019, Kikkawa et
al. 2019).8 This implies that downstream firms with more diversified sourcing portfolios
and lower average χijks (φ) enjoy lower mark-ups. Suppliers also have less market power(higher input demand elasticity εijks (φ)) and charge lower mark-ups when buyers face
more elastic final demand (higher σ) and inputs are more substitutable across and withincountries and sectors (higher η and θ).9
Proposition 2 describes the benefits to a buyer associated with sourcing inputs from
more suppliers:
Proposition 2 An increase in the number of country-j sector-k suppliers to a buyer
Sijk (φ)(a) reduces the market shares χijks (φ), mark-ups µijks (φ) and prices pijks (φ) of all inframarginal jk suppliers to the buyer;(b) lowers the buyer’s input cost index across jk inputs cijk (φ).
These results reflect several effects: Along the extensive margin, higher Sijk (φ) increases the probability that the buyer finds a better-matched and therefore lower-costsupplier for any input variety. It also generates cost savings from gains to input variety.
Along the intensive margin, higher Sijk (φ) intensifies competition among matched suppliers and lowers the price and mark-up of each incumbent variety. These three beneficial
effects outweigh a final counteracting effect on the extensive margin: Given sequential
supplier entry into bidding rooms, expanding the set of matched suppliers means that thebuyer adds some less productive suppliers on the margin.
These effects can be illustrated by decomposing the fall in the buyer’s input price indexµ(φ)′
following a rise in the number of suppliers from Sijk (φ) to Sijk (φ)′ . If µbijks (φ) = µijks
ijks (φ)
PSijk (φ)′
denotes mark-up changes and s=Sijk (φ)+1 χijks (φ)′ is the market share of new suppliers,
the change in cijk (φ) is:
−1/θPSijk (φ)′−θ
cijk (φ)χijks (φ)bµijks (φ) bcijk (φ) ≡=  s=1P
⇒
(11)
′
S
(φ)
ijk
cijk (φ)
1−χ (φ)′
s=Sijk (φ)+1log bcijk (φ)−θ = log|

X

Sijk (φ)χijks (φ)bµijks (φ)−θ
s=1{zintensive margin

ijks



X S (φ)′ijk′− log 1 −χ(φ).
s=Sijk (φ)+1 ijks
} |
{z
}



extensive margin

The fall in the input price index combines changes on the intensive margin (lower
mark-ups by incumbent suppliers) and the extensive margin (market share reallocation
across suppliers). 10
8

We show that ρijk (φ) > 0 implies strategic complementarities in price setting across upstream firms.
With no match-specific shocks and θ → ∞, the model collapses to classical Bertrand competition
with pjks (φ) = cjks . With a continuum of suppliers and no matching frictions, the model collapses toθ.
monopolistic competition with ubiquitous sourcing, Sijk (φ) → ∞, χijks (φ) → 0 and µijks (φ) → θ−1
10
If there is secular productivity growth and suppliers’ marginal costs fall, this would also lower buyers’
input price index. The expressions for b
cijk (φ) would then always multiply incumbent suppliers’ market
shares χijks (φ) with their supplier-specific cost shocks b
c−θ
jks .
9

10

2.5.2

Optimal Supplier Set

Downstream firms optimally choose their set of country-sector origins Ii (φ) and suppliers
Si (φ) by maximizing total profits:
J X
K
X

D
Iijk (φ)wi fijk
(Sijk (φ)),

(12)

J X
K
hX
i− 1−η
X
1γ
θ
Sijk (φ)
1−η−θΘi (φ) 1−η , Θi (φ) ≡,Iijk (φ)τijk
p
(φ)
ijks
s=1
φ
j=1 k=1(13)

max

Iijk (φ)∈{0,1}J,K
j=1,k=1
Sijk (φ)∈{0,1,2,...,Sijk }J,K
j=1,k=1ci (φ) =πiD (φ) = Bi ci (φ)1−σ −j=1 k=1σ 1−σ) Ei Piσ−1 is the final demand shifter in country i, and the firm’s
where Bi = σ1 ( σ−1marginal cost ci (φ) decreases with its sourcing capability Θi (φ) since η > 1. Although
there is no explicit solution to this high-dimensional discrete-choice problem, the following
proposition characterizes the optimal sourcing strategy:

Proposition 3 Downstream buyers’ optimal sourcing strategy is such that:(a) the set of input suppliers is non-contracting in φ under sourcing complementarity
σ > η and ρijk (φ) > 0, Iijk (φH ) ≥ Iijk (φL ) and Sijk (φH ) ≥ Sijk (φL ) for φH ≥ φL ;(b) buyers’ sourcing capability Θi (φ) is non-decreasing in φ.
Result (a) implies that downstream firms observe a pecking order of input sourcing
both across country-sector origins jk and across potential jk suppliers. This holds when
final goods are closer substitutes to each other than intermediate inputs in production,
σ > η. This condition is intuitive: a laptop and a desktop are certainly more substitutable than the motherboards, soundcards, software etc. than go into the production ofthese devices. We assume this condition is satisfied, as it gives rise to negative degree
assortative matching between buyers and suppliers on the extensive margin, in line withprior evidence in the literature (Bernard and Moxnes, 2018). In particular, more productive firms purchase inputs from more countries in more sectors. They also transact
with more suppliers in each country and sector, including less productive suppliers on themargin. By contrast, less productive firms use inputs from fewer origins and suppliers
within origins, and match with only the more productive suppliers. Symmetrically, moreproductive upstream firms sell to a wider range of downstream buyers, including to lessproductive buyers, compared to their less productive competitors.
Figure 1a illustrates the negative degree assortative matching between upstream suppliers (top row of circles) and downstream buyers (bottom row of circles) under sourcing
complementarity, with circle sizes indicating firm productivity. For reference, Figure 1b
shows one possible counteractual network that would emerge under sourcing substitutability. 11[Insert Figure 1 about here]
11

Therefore, our model shows that the sourcing technology faced by downstream firms determines theassortativity of the buyer-supplier network.

11

Together with Proposition 2, result (b) implies that endogenous sourcing in global
production networks amplifies the productivity advantage of more efficient downstream
firms. This prediction is also consistent with earlier evidence in the literature for thecontribution of production networks to the firm size dispersion (Bernard et al. 2019).

2.6

Trade Flows

We next characterize trade flows at different levels of aggregation. The gravity expressions
we derive here underlie our structural estimation methodology.
2.6.1

Firm-to-Firm Trade

Imports of sector-k inputs by downstream firm φ in country i from an upstream suppliers in country j are:−θ
cijk (φ)1+θ−η pijks (φ)−θ
Xijks (φ) = Ai φη−1 ci (φ)η−σ τijk−θ −θ
= γ η−σ Ai φσ−1 cjks
τijk Ψijk (φ, cjks , Sijk )(14))σ Ei Piσ−1 . The second equality obtains from equations (2), (6) andwhere Ai = γ −θ ( σ−1σσ−η(10) for ci (φ), cijk (φ) and pijks (φ), where Ψijk (φ, cjks , Sijk ) ≡ Θi (φ) η−1 µijks (φ)−θ cijk (φ)θ+1−ηvaries across downstream firms φ because matching frictions and imperfect competition
upstream affect their sourcing capability Θi (φ) and input mark-ups µijks (φ).
Firm-to-firm sales Xijks (φ) increase with the productivity of the supplier. A higher
marginal cost cjks reduces the supplier’s market share in the buyer’s input purchases andadditionally drives down the buyer’s overall input demand.
How firm-to-firm sales vary with buyer productivity depends on the net effect of two
opposing forces. On the one hand, more productive downstream firms face higher output
demand and need more intermediate inputs. This scale effect is amplified by the endogenously higher sourcing capability of more productive firms. On the other hand, moreproductive buyers source from more suppliers, and this competition effect reduces input
demand from an individual supplier. If the scale effect dominates, Xijks (φ) increases withthe productivity of the buyer. We assume this holds as it implies positive assortative
matching between buyers and suppliers on the intensive margin, consistent with evidence
in the literature (Sugita et al., 2014; Benguria, 2015; Bernard and Moxnes 2018). This is
illustrated by the thickness of the arrows in the network map in Figure 1a.
2.6.2

Firm-Level TradeAt the firm level, imports by downstream firm φ in country i of sector-k inputs fromcountry j are:σ−1 σ
) Ei Piσ−1 φη−1 ci (φ)η−σ cijk (φ)1−η =
σ
X
 η−1
θ
Sijk (φ)σ−1 η−1
1−η σ − 1 σ
η−σ 1−η−θ−θ= γ () Ei Pi φ ci (φ) τijk
µijks (φ) cjks
(15)
,
s=1
σ

Xijk (φ) = (12where the last equality follows from equations (6) and (10) for cijk (φ) and pijks (φ).
Matching frictions and imperfect competition upstream both influence firm-level imports:
Xijk (φ) increases with the endogenous choice of suppliers Sijk (φ) both directly and indirectly through lower mark-ups µijks (φ).
2.6.3

Sector-Level Trade

Aggregating across firms, total sector-k imports from country j into country i are:
Z ∞Xijk (φ)dGi (φ),(16)
Xijk =
φijk

where φijk is the least productive downstream buyer in i that sources jk inputs.

2.7

Industry and General Equilibrium

In equilibrium, free entry into downstream production implies that expected profits fromentry must equal the fixed cost of entry, such that only firms above a threshold productivity φi begin operations:Z ∞πiD (φ)dGi (φ) = wi fi .
(17)
φi

In the upstream sector, all country-j input producers below a certain marginal cost
cut-off will be able to sell to downstream buyers in country i. This selection results fromthe combination of fixed export costs per destination and sequential entry into bidding
rooms. The number of suppliers from j to i, Sijk , is determined by the profits of the least
productive, marginal supplier sijk , ΠsUijk , Sijk :ΠUsijk , Sijk = ∆i

Z ∞
φijks

U
U
πsUijk , Sijk (φ)dGi (φ), ΠUsijk , Sijk ≥ wj fijk
, ΠUsijk +1, Sijk < wj fijk,

(18)

where φijks is the least productive downstream buyer in country i that buys sector-k inputs
from the marginal upstream supplier in country j. It can be shown that the equilibrium
mass of downstream firms in i, ∆i , is given by:
αLi

∆i =
σ

2.8

hR

∞P
D
j⊗k∈Ii (φ), Sijk ∈Si (φ) fijk (Sijk )dGi (φ) + fi
φi

i.

(19)

Comparative Statics

How do industrial policy, trade policy and technological progress affect firms in a global
production network?
Consider first an exogenous increase in the number of upstream suppliers in country jand sector k from Sijk to Sijk ′ . This could for example result from deregulation that lowers
entry costs or encourages more firms to export. Since the marginal upstream entrants areless productive, the impact on downstream buyers combines (positive) pro-competitive13

and love-of-variety effects and a (negative) selection effect of entry. From Proposition
2, sourcing from more suppliers Sijk (φ) does reduce buyer φ’s input price index cijk (φ)
on net. However, not all buyers will match with more suppliers following firm entry
upstream: From Proposition 3, more productive downstream firms will be more likely toenter a bigger bidding room, expand their pool of suppliers, and benefit from deregulation
upstream. We summarize the effect of a rising number of suppliers in the sourced country
on downstream sourcing in the following proposition.
Proposition 4 Under sourcing complementarity, a rise in the number of country-j sectork suppliers Sijk(a) weakly increases the number of jk suppliers to a buyer;(b) weakly reduces buyers’ input price index cijk (φ) and weakly increases quantities Qijk (φ)
and purchases Xijk (φ) of jk inputs;(c) exerts bigger effects on more productive buyers.
Figure 2 (a) visualizes the effect of result (a). As we can see, only firms productive
enough will be able to include the new suppliers. More productive buyers weakly include
more new suppliers.
D(Sijk ). Intuitively,
We next consider a reduction in trade costs τijk or matching costs fijk
both would make it more profitable for more downstream buyers to source inputs from
more country-sector origins and from more suppliers within country-sectors.
Proposition 5 Under sourcing complementarity σ > η and a fixed market demand shifterD
Bi , a fall in country-j sector-k trade costs τijk or matching costs fijk
(Sijk )(a) weakly expands buyers’ sourcing strategy Ii (φ) and Si (φ);(b) weakly reduces buyers’ input price index cijk (φ) and weakly increases input purchases
Xijk (φ) of jk inputs;(c) exerts bigger effects on more productive buyers.
The effect is demonstrated in Figure 2 (b). A reduction of trade costs or matchingcosts reduces the productive cut-offs to source from certain number of suppliers. For
the most productive firms, they would have sourced from every suppliers anyway. For
the least productive firms, the cost reduction is not enough to induce them to buy fromforeign suppliers. Therefore, the effect is bigger for the mid productive buyers.
We define the supply potential of country j in sector k for buyer φ as ϕijk (φ) =hi− 1−η
θ1−η P Sijk (φ)−θ, which captures country-sector jk’s potential to supply lowp
(φ)
τijk
ijks
s=1
cost inputs to φ and is thus related to φ’s global sourcing capability in equation (13).
Countries’ supply potential can improve if bilateral shipping costs fall or if a secular
productivity shock lowers input producers’ marginal costs. One can show that positive
shocks to upstream productivity or supply potential in jk would benefit downstreambuyers as a decline in trade or matching costs in Proposition 5. Note that any reductionsin the country-sector input price index cijk (φ) of a downstream producer translate into
reductions in its marginal cost ci (φ) in equation (2).
[Insert Figure 2 about here]

14

2.9

Numerical Analysis

Our model does not permit an analytical solution. To explore the model properties further,
we resort to numerical analysis. Building on the recent literature on methods to solve optimization problems of combinatorial binary discrete choice (Jia 2008, Antràs et al. 2017,
and Arkolakis et al. 2021), we develop a new method to solve an optimization problem
with interdependent combinatorial multinomial discrete choices for heterogeneous firms.12
To demonstrate the numerical solution, we solve a model of two countries, home andforeign, and with one sector. In the baseline, we assume that the home buyers can sourcefrom 3 suppliers in each market at most. Sourcing from the foreign country entails iceberg
trade costs, while domestic sourcing does not. Moreover, we assume that the fixed costof matching with suppliers is a linear function in terms of the number of suppliers, i.e.,f (S) = α × S. In the baseline, we assume that α = 1. As for the upstream suppliers, we
assume that their costs are drawn from the discrete Pareto distribution. The distribution
is specified in Appendix Table A1, along with other parameters of the model.
Figure 3 depicts the baseline solution. The sourcing strategy from the domestic andforeign market is a step-function: firms with higher productivity tend to buy from more
suppliers. Moreover, due to additional iceberg trade costs when sourcing from the foreign
market, the productivity cut-offs for sourcing from a given number of suppliers tend tobe higher for the foreign market.
[Insert Figure 3 about here]
To further explore the effect of other model parameters and other outcomes, we conduct numerical comparative statics in Figure 4. Specifically, we examine three sets ofcomparative statics: trade liberalization, which reduces the iceberg trade costs; improvement in the matching technology, which reduces the fixed cost of matching; and upstream
entry with additional suppliers, which increases input market competition. We study theeffects on a firm’s marginal costs and revenue.
Trade liberalization leads to lower productivity cut-offs for sourcing from the foreign
market. However, the sourcing complementarity also lowers the productivity cut-offs forsourcing from the domestic market. It thus enables firms with low productivity to adjust
the extensive margin of their sourcing choice by buying from more domestic and foreign
suppliers, which reduces their marginal costs. Since high productivity firms have alreadysourced from all potential suppliers, they only adjust intensive margins, which helps boost
their revenues.
For an improvement in matching technology we find that it induces firms to adjust
their sourcing strategy in both markets. Firms that buy from more suppliers enjoy a
reduction in their marginal costs and an increase in revenues. These tend to be buyers in
the middle of the productivity distribution. Firms with very high or very low productivity
leave their sourcing strategies unchanged.
12

The method can be applied to solving other similar problems with interdependent multinomial discrete choice, e.g., a firm considering how many stores to set up in each market, or a manager thinking
about how many workers to have for each team. The algorithm and code will be posted online.

15

[Insert Figure 4 about here]
Finally, we conduct an experiment on input market structure by having one more
foreign potential supplier enter the upstream market. Consistent with theoretical results,
only buyers at the top end of the productivity distribution buy from the new supplier,while firms with lower productivity do not adjust their sourcing strategies. Upstream
entry therefore amplifies inequality in marginal costs and revenues between high- andlow-productivity firms.

3

Empirical Analysis

3.1

Institutional Context

We evaluate the empirical relevance of the model by examining the relationship between
upstream market structure in China and downstream sourcing behavior in Chile andFrance over the 2000-2006 period. All three countries have high trade openness and are
active on global markets in almost all product categories. At the same time, they occupy
different segments of the global value chain, with China known as factory of the world that
supply intermediate inputs and assembly services to manufacturers in both developed anddeveloping economies. In turn, Chile and France exemplify economies with very different
market sizes, levels of economic development and institutional strength, and internal andexternal economic geography. Finding consistent evidence for two contrasting downstream
markets can arguably reveal the ubiquity and significance of the mechanisms of interest.
China experienced dramatic export growth after joining the WTO in 2001, graduallyrelaxing various barriers to entry, developing trade-oriented special economic zones, andshoring up physical and institutional infrastructure to support trade activity. This madeChina an important new input supplier from the perspective of French and Chilean firms,
accounting for 5.7% of total French imports and 9.9% of total Chilean imports in 2006.
By contrast, France and Chile are not key export markets for Chinese producers, with
their respective market shares of around 1% and 0.3%. This makes French-Chinese andChilean-Chinese tarde relationships ideal contexts in which to identify the role of upstream
entry on downstream sourcing.

3.2

Identification Strategy

The theoretical framework in Section 2 delivers sharp predictions for the impact of the
upstream market structure in China on the sourcing of Chinese inputs by downstream
French and Chilean firms. We evaluate these predictions for the value, quantity and unit
price of imports from China by firm f of HS 6-digit product p in year t with variants ofthe following log-linear specification:{ln Xf pt , ln Qf pt , ln cf pt } = β ln SCHN →ROW,pt + ΓΩCHN,pt + δf + δp + tδp + δt + εf pt . (20)
We proxy unit prices with the average unit value across multiple input purchasesfrom China at the f pt level in the baseline, and present robust results using instead
16

model-consistent CES import price indices that weight unit values across multiple input
transactions by import value, scaled by Broda-Weinstein elasticities of substitution.
Proposition 4 in the model indicates that the total number of Chinese exporters of
product p to Chile in year t, SCHN →CHL,pt , is the relevant metric for the upstream market
structure that will determine the value, quantity, and price of Chinese inputs bought by
Chilean buyer f . The analogous metric for France is the total number of Chinese exportersof product p to France, SCHN →F RA,pt . Conceptually, if SCHN →CHL,pt or SCHN →F RA,pt endogenously responded to aggregate import demand in Chile and France respectively, thiswould be consistent with our general-equilibrium model of global sourcing with imperfectcompetition and not invalidate causal interpretations at the level of individual firms. However, SCHN →CHL,pt and SCHN →F RA,pt may fail to capture the set of prospective upstream
suppliers, or their correlation with downstream sourcing outcomes may in principle bedriven by forces outside our model.
To alleviate such concerns, we quantify the number of potential Chinese suppliers to
Chile (to France) with the number of Chinese exporters to the rest of the world excluding
Chile (France), by product p and year t. With some abuse of notation, we label this proxy
SCHN →ROW,pt for both Chile and France. Guided by the model, we provide consistentevidence using the actual number of Chinese exporters to Chile SCHN →CHL,pt (to France
SCHN →F RA,pt ), which is arguably exogenous from the perspective of atomistic buyers.
We also instrument the latter either with SCHN →ROW,pt or with the number of Chinese
exporters to a larger yet comparable market: the Pacific Andes countries in the case of
Chile, and the US in the case of France.
Given that Chile and France is a small export destination and individual French firms
are small buyers from the perspective of Chinese suppliers, we believe this reduced-form
estimation strategy allows us to isolate causal effects of interest. In particular, we condition on a full set of firm, product, and year fixed effects, as well as on product-specifictime trends, δf , δp , δt , and tδp . We also guard against omitted variable bias by including
a large vector of product-year specific controls, ΩCHN,pt . These ensure that the market
structure indicators do not capture trade costs or other supply conditions in China, as
discussed below. We therefore identify coefficient β purely from the impact of changes in
the Chinese market structure within firms over time. We cluster errors εf pt by productyear - at the level of the main variable of interest - to account for common supply anddemand shocks across firms within product-years.
The theoretical model also characterizes the variation in trade activity across buyers
from the perspective of suppliers. In particular, a distinctive prediction of the model withendogenous network formation and oligopolistic competition upstream is that a Chinese
seller will price discriminate across its customers depending on the number of suppliers
they source from. We confront this prediction with data using variants of the following
regression equation:ln psf pt = β ln SCHN →f pt + δsp + δf p + δpt + εsf pt ,(21)

where ln psf pt is the log unit value Chinese supplier s charges when selling HS-6 productp to downstream firm f in year t, and ln SCHN →f pt is the log number of Chinese suppliers
that f buys varieties of input p at time t. We estimate this specification only on the17transaction-level data for Chile which identifies foreign suppliers, unlike the French customs registry. We condition on seller-product pair fixed effects that account for variationin marginal costs and quality at that level, such that we implicitly identify the variationin mark-ups across buyers within a seller-product, on the assumption of minimal productcustomization across trade partners. In progressively more stringent specifications, we
further add year fixed effects; or both product-year and buyer-product fixed effects. We
conservatively cluster standard errors εsf pt at the product-year level, but the results are
virtually identical when alternatively clustering by buyer-product-year.

3.3

Data

We exploit micro-level production and trade data for the near universe of Chilean, French
and Chinese firms. For Chile and France, we use information on the value, quantityand price (unit value) of all import transactions at the firm - origin country - HS 6-digit
product level from the Chilean and French Customs Agencies, respectively. In the case of
Chile, these records additionally report the identity of the foreign supplier, which makes it
possible to identify the bi-partite network of seller-buyer matches. For France, we obtain
detailed accounting statements and the main industry of activity for all tax-registeredfirms from FICUS, and match these to the customs declarations based on unique firm
identifiers. For Chile, we likewise observe the primary output industry of each firm, but
are able to access only information on which of 13 broad size categories a firm belongs tobased on total firm sales, from the Chilean Tax Authority.
For China, we use comprehensive information on the universe of export transactions
at the firm - destination country - HS 8-digit product category from the Chinese Customs Trade Statistics (CCTS), which we aggregate up to the HS 6-digit level. CCTS
reports additional information that we employ in robustness checks. For example, CCTS
distinguishes between processing-trade and ordinary-trade transactions, where the former entail exports produced on behalf of a foreign party using imported inputs that are
exempt from import duties. CCTS also contains firm names, which makes it possible
to identify direct traders from trade intermediaries based on a common word filter fromthe literature. It further identifies private domestic enterprises, state-owned enterprises(SOEs), joint ventures, and affiliates of foreign multinationals. Where we need information on additional firm characteristics, we exploit the Chinese Annual Survey of Industrial
Enterprises (ASIE) which covers non-SOEs with sales above 5 million Yuan and all SOEs.
We match ASIE and CCTS using standard algorithms in the literature based on firmnames, zip code and phone number.
Since import transactions are recorded inclusive of cost, insurance freight, we arecareful to consider changes in trade duties over time. For Chile, regular MFN import tariffs
applied on Chinese products and remained unchanged throughout the 2000-2006 period
of interest. These will therefore be subsumed by product fixed effects in the analysis.13
For France, we account for the gradual relaxation in import barriers on Chinese goods
with time-varying EU import tariffs from UN WITS. We use applied ad-valorem tariffs
at the HS 6-digit level, and take the maximum value whenever there are multiple tariff
13

Chile and China signed a Preferential Trade Agreement only in 2007, after the end of our sample
period.

18

lines within a product code. All results are fully robust to using simple averages instead.
Throughout the sample period, China was subject to the EU’s GSP program and faced
very low tariffs for most of its goods. Consequently, the vast majority of products carry
zero tariffs, and there is little variation over time. As standard in the literature, we
compute the tariff measure as lmaxtarif fpt = ln (1 + max rate/100).
[Insert Table 1 about here]
Panel A in Table 1 overviews the variation in market structure upstream across tradedproducts, and illustrates the dramatic trend in upstream entry over time. In 2000, China
exported 2,139 HS 6-digit products to France. The average number of Chinese suppliers
to France was 16.9, with a median of 5 and a standard deviation of 38.3. By 2006, China
shipped 2,954 distinct products to France, with an average number of suppliers of 37.7,
a median number of 8 and standard deviation of 92.3. Similar expansion is observed in
China’s exports to Chile over this period. The total number of HS-6 products it shipped
to Chile grew from 1,431 to 2,388, while the average number of exporters per products
jumped from 12.4 (standard deviation 23.5) to 21.4 (standard deviation 43.8).
Panel B demonstrates that the dramatic firm entry in China during 2000-2006 changed
the composition of Chinese exporters in some respects. In particular, China experienced
secular productivity growth, with a steady increase in measured average value added per
worker and average TFP, along with a rise in productivity dispersion. Average productquality remained relatively stable, as proxied by input price indices constructed fromfirm-product-level import data in the customs records. At the same time, Chinese export
activity exhibited stable features in other dimensions, such as the share of trade performedby trade intermediaries, multinational affiliates, or multi-product exporters. Effectively
applied EU tariffs on Chinese goods fell from 3.9% to 2.8% for the average product, while
the overall share of processing trade declined from 36% to 26%.
Panel C summarizes the extent of downstream firm heterogeneity in Chile and France.
Between 2000 and 2006, the number of producers sourcing inputs from China increased
from 12,571 to 25,737 for France and more than doubled for Chile (from 2,525 to 6,519).
Worldwide firm imports also increased on average, and this partly reflects China’s growing
share in their import portfolio. Consistent with productivity-driven selection into global
sourcing on the extensive margin and growing frim-level imports on the intensive margin,
the median sales per worker across firms importing from China remained stable as thenumber of importers grew.
Panel D overviews the bi-partite network of seller-buyer links that we observe for
Chilean firms sourcing inputs from China. Between 2000 and 2006, the average numberof Chilean buyers per product remained stable for Chinese sellers. Similarly, the average
number of Chinese suppliers per product shows little variation for Chilean producers.
This is consistent with both a significant entry of Chinese suppliers per product and a
sharp increase in the number of Chilean producers sourcing from China, as shown inPanel A. Yet, Chilean buyers widened the range of import values and unit prices across
suppliers, while the dispersion of trade values and unit prices across buyers also increased
for Chinese sellers.
19

3.4

Upstream Market Structure and Downstream Sourcing

Table 2 presents baseline results for the impact of the upstream market structure in
China on the sourcing behavior of downstream firms in Chile (Columns 1-2) and in France(Columns 3-4), based on estimating equation (20). Panel A examines how the log numberof Chinese exporters of an HS 6-digit product to the rest of the world in a given year,
ln SCHN →ROW,pt , affects the log value of imports from China by a Chilean or French firm
for that product and year, ln Xf pt . Panels B and C decompose ln Xf pt , and repeat theanalysis for the log quantity and log unit value of imports from China at the downstream
firm - product - year level. Trade quantities are systematically recorded in kilograms
across all products in the French customs data, and are available instead in natural unitsof accounting that vary across products in the Chilean records. Any heterogeneity in thisrespect is inconsequential for the analysis in the presence of product fixed effects.
[Insert Table 2 about here]
We consistently find that more competition upstream induces downstream buyers
in both Chile and France to expand their input expenditure and purchase higher input quantities, while enjoying lower input prices. Through the lens of the model, thepro-competitive effect of upstream competition on input prices lowers downstream firms’
marginal production costs, which raises final demand for their output and in turn boosts
their input demand. Together, these pro-competitive and scale effects result in higher
import values. From the perspective of Chinese suppliers, the evidence is implicitly consistent with tougher competition incentivizing them to lower mark-ups and cut prices.
These findings obtain both when we adopt a very flexible specification with year fixed
effects only in Columns 1 and 3, and when we consider in Columns 2 and 4 the moststringent variant of specification (20) with a full set of buyer firm, year and product fixed
effects, along with product-specific time trends and additional controls. The results canthus not be attributed to intransient buyer characteristics, global shocks, stable or trending product features. They also do not reflect the role of other product-year specific supply
conditions in China, as we control for the average and the variance of the productivity ofChinese exporters (based on log value added per worker in the matched ASIE-CCTS data)
and a proxy for the average output quality of Chinese exporters (based on the average unit
value of each exporter’s imported inputs). Columns 2 and 4 also include the log numberof Chilean or French importers in the same output industry that import from the rest of
the world, to capture potentially relevant differences in downstream demand and market
structure. We further conditions on five value shares of Chinese exports conducted respectively by trade intermediaries, under the processing regime, by foreign-owned exporters,by state-owned enterprises, and by multi-product exporters. Finally, the specification for
French firms controls for the ad-valorem EU import tariff on Chinese goods as a proxyfor changing trade costs.
Quantitatively, we estimate economically significant effects of the upstream market
structure on downstream outcomes. For illustration, suppose the (log) number of potential
upstream suppliers in China were to increase by one SD. Our results imply that Frenchfirms’ import value would increase by 11.8% of a SD, total quantity would grow by 13.3%
20

of a SD, and prices would fall by 6.4% of an SD. The corresponding numbers for Chilean
buyers would be 4.9%, 10.3% and -8.9%. Alternatively, the total rise in the number ofChinese exporters to ROW over the sample period would trigger an adjustment in Frenchfirms’ import values, quantities and prices by 22%, 28.1% and -6.1%, respectively, while
Chilean producers would experience analogous changes of 10.9%, 26.8% and -15.9%.
[Insert Table 3 about here]
Table 3 confirms that these baseline results survive a series of robustness checks. We
first explore different sub-samples of firms. In Column 1, we drop upstream suppliers
identified as wholesalers. This lowers all point estimates and makes the results for French
import prices weakly insignificant, suggesting that large wholesalers play an importantrole in the context of imperfect competition upstream. In Column 2, we remove insteadwholesale buyers downstream. If anything, this magnifies coefficient magnitudes in thecase of Chile and slightly dampens those for France. Together, these results are consistent
with interdependencies in price setting across suppliers within a buyer but not acrossbuyers within a supplier, such that dropping important suppliers can underestimate theimpact of upstream competition. In contrast, dropping individual buyers reveals does not,with the caveat that the model predicts bigger effects of the upstream market structure
on larger, more productive downstream firms - a prediction we formally evaluate below.
In Column 3, we consider alternative measures of import prices and quantities. In
particular, we construct CES indices (instead of simple averages) of unit values andquantities at the firm-product-year level from the underlying transaction-level data, using product-specific elasticities of substitution from Broda and Weinstein (2006). Thesemodel-consistent measures can in principle more accurately capture the impact of the
upstream market structure, as they recognize that downstream firms can reallocate purchase shares across inputs based on input prices. Indeed, using these CES indicators
produces highly significant estimates of higher magnitude than the baseline. We haveconfirmed that all other robustness checks likewise deliver stronger results with CES priceand quantity measures. Since computing CES measures requires additional parametricassumptions, however, we have opted to remain conservative and use simple averages inthe baseline.
In Colums 4-6, we explore instead alternative proxies for the upstream market structure in China. From the perspective of individual buyers in Chile or France, the total
number of Chinese exporters to their country can be considered exogenous. It may moreover better reflect the set of potential suppliers to their market, given differences in market
size, proximity and institutional context across countries that drive the export decisionsof Chinese sellers. Column 4 establish that ln SCHN →CHL,pt and ln SCHN →F RA,pt indeed
generate robust results in line with the baseline estimates. Column 5 provides additional
corroborative evidence when instrumenting the actual number of Chinese exporters to
Chile or France with the baseline number of Chinese exporters to ROW, excluding Chileor France respectively. Column 6 applies a more fine-tuned instrument that is based on
Chinese export entry into markets with similar characteristics as Chile and France rather
than ROW. We consider Chile’s neighbors and co-signatories to the Pacific Andes Coun21

tries organization (Colombia, Mexico, Peru), and use the US as a benchmark for France.
The results remain qualitatively unchanged.
Finally, we present several additional specification checks in Appendix Table A2. First,
in Column 1, we restrict the sample to a balanced set of Chilean or French firms that are
active in every period in the 2000-2006 panel. This reduces the number of observations
significantly, but the estimates remain stable. Second, in Column 2, we define quantities
and unit values in the French data based on supplementary information on different unitsof accounting (instead of kilos), available for a subset of products. This exercise does not
apply to the Chilean data which enters with natural unit of accounting already int hebaseline.
Third, although we control for the number of Chilean or French importers in any HS
4-digit downstream industry throughout, this may not be sufficient to rule out potential
effects of other aspects of the downstream market structure. We therefore include industry
by year fixed effects in Column 3. In Columns 4 and 5, we ensure instead that changes in
upstream competition in other products that a firm sources do not confound our estimates:
We control alternatively for the log (import value weighted) average number of Chinese
suppliers in a buyer’s products other than p, or for the log total number of Chinese
exporters to the ROW in the HS 4-digit category p belongs to.
Finally, in Column 6 we restrict the French sample to importers who do not sourcefrom Eastern Europe throughout our sample period. The findings confirm that we havenot falsely assigned the effects of structural change in Eastern Europe that took place
during our sample period to increased competition in China.

3.5

Downstream Firm Heterogeneity

Table 4 demonstrates that bigger downstream buyers adjust their sourcing behavior morein response to greater competition upstream, in line with the theoretical predictions. We
use two different proxies for downstream firm size - total sales and total imports - andgroup buyers into three terciles based on each size distribution, separately for Chile andFrance. We then construct indicator variables for buyers that belong in the middle or in
the top tercile, and add interactions of these two dummies with the baseline measure ofmarket competition upstream in specification (20). The main effect of ln SCHN →ROW,pt
now identifies the impact on the bottom tercile, while the interaction terms pick up
differential effects on mid-size and large buyers. The level effects of the size dummies are
subsumed by the firm fixed effects. We report results using either simple unit values andquantities as in the baseline or the CES price and quantity indices.
[Insert Table 4 about here]
The evidence in Table 4 indicates that bigger downstream buyers benefit more fromtougher upstream competition than their smaller peers: They enjoy even lower input
prices, source even higher input quantities, and have even higher imported input purchases
overall. Through the lens of the model, these patterns are consistent with bigger buyers
being able to incur higher matching costs, transact with more suppliers, and benefit fromlower mark-ups induced by the greater competition among their suppliers.
22

The results are economically and statistically more significant when using worldwide
imports to measure buyers’ size, compared to using firm sales. The role of total buyer
imports is consistent with price setting from the perspective of upstream sellers in themodel: Total input purchases by a downstream buyer determine the seller’s expected
profits from the relationship and therefore the optimal input price. The buyer’s own sales
are only relevant to the extent that they are monotonic in firm productivity and thereby
in total input purchases. This raises the possibility that global sourcing decisions mayvary across firms for unobserved reasons that are not fully captured by total sales. To theextent that there are economies of scale in overcoming matching costs, the total amountof imported inputs may therefore be a more accurate indicator of firms’ ability to matchwith more suppliers and benefit from stiffer competition among them.

3.6

Upstream Price Discrimination

The findings above documents the impact of the upstream market structure on downstream buyers. We conclude by complementing this analysis with evidence on the pricing
strategy of Chinese exporters across buyers. The results strongly support the model’s prediction that sellers charge buyers with more suppliers systematically lower mark-ups andprices, even within finely disaggregated product categories. This is consistent with sellers
engaging in price discrimination across buyers depending on the extent of competition
they face from other suppliers to that buyer.
[Insert Table 5 about here]
Table 5 presents results from estimating specification (21) on the full set of Chinese
supplier - Chilean buyer - HS6 product - year observations in the data. Column 1 includes
seller-product and year fixed effects, such that the impact of the buyer’s portfolio ofsuppliers is identified from the variation within a supplier across buyers of the same
product. Colmn 2 then replaces the year fixed effects with product-year fixed effects that
more flexible control for changes in supply and demand conditions by product over time.
Column 3 then further adds a stringent set of buyer-product fixed effects, such that themain coefficient of interest is now identified from changes in sourcing strategie withinbuyer-product input lines over time. Finally, Column 4 additionally controls for the log
number of non-Chinese suppliers of the relevant product that a buyer sources from. Thisimplicitly accounts for changes in supply conditions in other origin countries but China.
The evidence consistently points to upstream Chinese suppliers offering lower prices todownstream buyers that maintain a more diversified portfolio of Chinese suppliers, product by product. This lends strong empirical support to the role of imperfect competition
upstream in the model and thereby to the pro-competitive effects that upstream entry
can exert on sourcing outcomes downstream.

4

Structural Estimation (in progress)

In this section, we estimate the model primitives using micro-level production and tradedata. Our strategy is to first estimate each country’s sourcing potential ϕijk (φ) for buyers
23

φ using French and Chilean firm-level input purchases. Then we will estimate the elasticity
parameters (θ, η, and σ) using price variations due to exogenous shifters such as tariffs.
D(Sijk (φ)) and other parameters
In the last step, we will estimate the matching friction fijk
using the simulated method of moments and the numerical method that we have developed
to solve the combinatorial multinomial discrete choice optimization problem. To do so, weDwill have to make functional assumptions for fijk
(Sijk (φ)) and exploit Chilean firm-to-firmtrade data.

5

Counterfactual analysis (in progress)

Having structurally estimated the model, we perform three counterfactual analyses to assess the role of industrial policy, trade policy and technological progress on global sourcing
and the gains from trade. We find that lower barriers to entry upstream, lower matchingcosts, and lower trade costs amplify firm productivity, firm size dispersion, and aggregate
welfare downstream. These effects operate through a combination of improved matchingof buyers and suppliers, gains from greater input variety, and lower mark-ups. Shutting down one model feature at a time, we establish that two-sided firm heterogeneity,
matching frictions and imperfect competition all play a large quantitative role.
These counterfactual exercises have important policy implications. Existing studies
that evaluate trade policies rely on Computable General Equilibrium (CGE) models or
New Quantitative Trade Models (NQTM), which typically ignore global production networks, firm granularity and/or market power. Our results indicate that taking these forces
into account can generate significantly higher gains from trade. In addition, our analysis
illustrates the benefits associated with reductions in buyer-supplier matching costs as distinct from trade costs. This may provide justification for policies that decrease matchingcosts through trade promotion, information technology or international contract enforcement. Finally, our conclusions reveal how imperfect competition in global value chainsgives rise to cross-border spillovers of national industrial policies such as deregulation andother reforms that encourage firm entry.

6

Conclusion

This paper has examined for the first time the role of firm heterogeneity and imperfectcompetition for global production networks and the gains from trade. We develop aquantifiable trade model with (i) two-sided firm heterogeneity, (ii) matching frictions,and (iii) oligopolistic competition upstream. Combining highly disaggregated data on
firms’ production and trade transactions for China and France, we present empiricalevidence in line with the model that cannot be rationalized without features (i)-(iii).
Downstream French buyers import higher volumes and quantities at lower prices when
upstream Chinese markets become more competitive. These effects are stronger for larger,
more productive buyers and weaker when input suppliers are more heterogeneous.
Our analysis indicate that global production networks amplify the gains from tradeliberalization. Moreover, they can generate international spillovers from national industrial and trade policy. In particular, lower barriers to entry upstream, lower matching24

costs, and lower trade costs amplify firm productivity, firm size dispersion and aggregate
welfare downstream.
Our work opens several promising avenues for future research. Incorporating imperfectcompetition both upstream and downstream could provide valuable insights into sourcing
patterns and gains from trade. While we have studied matching frictions and imperfectcompetition in a bipartite network of buyers and suppliers, future work could broaden theanalysis to complete production networks. Studying the role of reputational contracts andarm’s-length vs. intra-firm off-shoring would improve our understanding of rent sharing
and shock transmission in global value chains.

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29

Figures and Tables
Figure 1: Network Structure

Figure 2: Comparative statics on firms’ sourcing strategy
(a)

(b)

Sijk (φ)

Sijk (φ)

′
Sijk

Sijk

Sijk
φ

φ

30

Figure 3: Baseline Sourcing Strategy

3

number of suppliers

Domestic Sourcing Strategy
Foreign Sourcing Strategy

2

1

0
0

2

4

6

8

10

12

buyer productivity
This figure plots the sourcing strategy from domestic market (the solid line), andsourcing from the foreign market (the dashed line) in terms of the number of suppliers
for buyer firms with different level productivity. We assume that there are at most
3 suppliers that a buyer can buy from in each market. Other model parameters are
specified in Table A1.

31

Figure 4: Trade Liberalization, Matching Technology, and Market Structure(a) Trade Liberalization
3

50

1.2
Domestic Sourcing Strategy =2.2
Foreign Sourcing Strategy =2.2
Domestic Sourcing Strategy =1.5
Foreign Sourcing Strategy =1.5

Baseline
= 1.5

1.1

40

1

0

35
0.9

buyer revenue

buyer marginal cost

number of suppliers

1

2

0.8
0.7
0.6

2

4

6

8

10

30
25
20
15

0.5

10

0.4

5
0

0.3

0

Baseline
= 1.5

45

12

5

6

7

8

buyer productivity

9

10

11

0

12

2

4

6

8

10

12

buyer productivity

buyer productivity(b) Matching Technology
3

30

1.2
Domestic Sourcing Strategy f=s
Foreign Sourcing Strategy f=s
Domestic Sourcing Strategy f=0.7 s
Foreign Sourcing Strategy f=0.7 s

Baseline
f = 0.7 S

Baseline
f = 0.7 S

1.1

25

1

20

buyer revenue

buyer marginal cost

number of suppliers

1

2

0.9
0.8
0.7

15

10
0.6

5
0.5

0

0

0.4

0

2

4

6

8

10

12

6

7

8

buyer productivity

9

10

11

0

12

2

4

6

8

10

12

buyer productivity

buyer productivity(c) Upstream Market Structure
4

Baseline
S =4Baseline
S =4

1.1

3

30

1

2

25

buyer revenue

buyer marginal cost

number of suppliers

35

1.2
Domestic Sourcing Strategy S =3
Foreign Sourcing Strategy S =3
Domestic Sourcing Strategy S =4
Foreign Sourcing Strategy S =4

0.9
0.8
0.7

20

15

10

1

0.6

5

0.5

0

0

0.4

0

2

4

6

buyer productivity

8

10

12

6

7

8

9

buyer productivity

10

11

12

0

2

4

6

8

10

12

buyer productivity

This figure plots how the buyer firms’ sourcing strategy, marginal cost function, andrevenue function respond to reduction in iceberg trade costs (fig a), reduction in matching costs (fig b), and increase in the number of suppliers in the foreign market (figc). The baseline model parameters are specified in Table A1. In figure (a), iceberg
trade cost τ reduces from 2.2 to 1.5. In figure (b), the fixed cost of matching changes
from f (S) = S to f (S) = 0.7S. In figure (c), the number of potential suppliers in theforeign goes up from 3 to 4.

32

Table 1: Summary Statistics
N

Mean

2000
St DevMedian

N

Mean

2006
St DevMedianPanel A. Market Structure (by HS-6 product)# CHN exporters to CHL# CHN exporters to ROW w/o CHL# CHL importers from CHN# CHN exporters to FRA# CHN exporters to ROW w/o FRA# FRA importers from CHN

1,431
1,952
1,954
2,139
2,865
2,863

12.4
353
14.8
16.9
272
28.6

23.5
488
29.8
38.3
426
72.1

5
183
4
5
124
6

2,388
3,030
3,034
2,954
3,695
3,671

21.4
868
22.9
37.7
729
56.6

43.8
1,577
46.8
92.3
1,452
142.1

7
313
6
9
231
9

Panel B. Control Variables (by HS-6 product)applied EU import tariff (%)
mean VA / worker CHN exporters (log)variance VA / worker CHN exporters (log)mean TFP CHN exporters (log)variance TFP CHN exporters (log)mean input unit value CHN exporters (log), de-meaned
share CHN processing tradeshare CHN trade intermediares
share CHN foreign-owned exporters
share CHN multi-product exporters

2,899
2,699
2,546
2,699
2,546
2,863
2,865
2,865
2,865
2,865

3.9
4.16
7.23
6.93
13
4.17
0.36
0.41
0.17
0.95

7.5
0.82
2.23
0.89
2.22
1.4
0.32
0.24
0.12
0.11

1.5
4.09
7.31
6.85
13.2
4.22
0.29
0.40
0.15
0.99

3,600
3,576
3,454
3,576
3,454
3,689
3,695
3,695
3,695
3,695

2.8
5.01
9.30
7.57
14.7
4.29
0.26
0.43
0.17
0.94

7.1
0.88
2.27
0.97
2.25
1.48
0.27
0.22
0.12
0.11

0
4.94
9.35
7.50
14.7
4.30
0.16
0.44
0.14
0.99

Panel C. Importer Characteristics (Firm-level)
CHL sales (1m CHL Pesos)
CHL total imports (USD 1,000)
FRA sales (EUR 1,000)
FRA total imports (EUR 1,000)
FRA sales / worker (EUR 1,000)

2,164 20,681
2,525
730
11,319 59,600
12,571
785
10,679460

55,141
3,532
609,900
7,088
2,854

1,050
74
4,000
43
215

6,488 16,173 48,987
6,519 1,193
7,511
22,790 48,400 574,300
25,737864
7,631
20,860
466
3,530

1,050
71
3,200
32
222

67.5
1,848
37.4
1.5
106
10
2.0
180
9.9

5
37.2
0.005
1
2.9
0.004
1
1.8
0.003

3,034
3,034
3,034
89,71489,71489,71469,542
69,542
69,542

8
99.3
0.005
1
3.78
0.005
1
2.4
0.005

Panel D. Chilean Sourcing Network with China# CHL importer - CHN exporter pairs (by HS-6 product) 1,954
trade value (by HS-6 product, USD 1,000)
1,954
unit value (by HS-6 product, USD 1,000)
1,954# CHL importers (by exporter-HS-6 product)
37,954
trade value (by exporter-HS-6 product, USD 1,000)
37,954
unit value (by exporter-HS-6 product, USD 1,000)
37,954# CHN exporters (by importer-HS-6 product)
28,940trade value (by importer-HS-6 product, USD 1,000)
28,940unit value (by importer-HS-6 product, USD 1,000)
28,940

26.1
439
1.1
1.3
22.6
0.14
1.8
29.7
0.14

37.3
1,122
3.6
1.3
37.9
0.38
1.6
48.9
0.46

91.5
5,124
120
1.3
272
23.1
1.8
378
28.4

This table provides summary statistics for the variation in upstream Chinese market structure across HS-6 products in Panel A, other upstream control
variables across HS-6 products in Panel B, downstream Chilean and French firm characteristics in Panel C, and characteristics of the sourcing network of
Chilean buyers and Chinese suppliers in Panel D.

33

Table 2: Baseline Results

Chile

France

(1)

(2)

(3)

(4)

0.028**(0.014)0.003

0.095**(0.039)0.553

0.085***(0.010)0.008

0.222***(0.029)0.585

Panel B. (log) Import Quantityf pt(log) # CHN → ROW Exporterspt 0.209***(0.021)
R2
0.011

0.232***(0.066)0.558

0.140***(0.013)0.006

0.285***(0.032)
0.605

Panel C. (log) Import Unit Valuef pt(log) # CHN → ROW Exporterspt -0.181***(0.017)R2
0.037-0.137***(0.053)0.727-0.055***(0.010)0.005-0.063***(0.015)0.714

N306,857

306,857

897,091

897,091

Year FE
HS-6 Product FE
HS-6 Product Trend
Firm FE
Product × Year Controls

YES

YES
YES
YES
YES
YES

YES

YES
YES
YES
YES
YES

Panel A. (log) Import Valuef pt(log) # CHN → ROW Exporterspt
R2This table examines the effect of upstream market structure on downstream sourcing. The dependent variable is the log value, quantity or unit value of imports from China by Chilean or French
firm, HS 6-digit product and year. The upstream market stucture is measured with the (log) number of Chinese exporters to ROW by HS 6-digit product and year. The product × year controls
include the (log) number of Chilean or French importers from ROW; the EU ad-valorem importtariff on Chinese exports in Columns 3 and 4; the average productivity of Chinese exporters,
the variance of the productivity of Chinese exporters, the average quality of Chinese exporters;
the value shares of processing trade, intermediated trade; and the share of foreign-owned, multiproduct, state-owned firms in Chinese exports. Singletons are dropped and standard errors are
clustered by HS-6 product × year. *** p < 0.01, ** p < 0.05, * p < 0.1.

34

Table 3: Robustness
Reported Regressor:

(1)

(2)

CES
ImportPrice Index(3)

Panel A. Chile(log) Import Valuef pt(log) Import Quantityf pt(log) Import Unit Valuef pt
N
KP-Stat

0.063**
0.133***-0.070*306,857

0.160***
0.315***-0.155**
154,226

0.274***-0.189***306,762

Panel B. France(log) Import Valuef pt(log) Import Quantityf pt(log) Import Unit Valuef pt
N
KP-Stat0.129***
0.124***
0.005
897,091

0.136**
0.186***-0.050*134,482

0.296***-0.082***
897,091

YES
YES
YES

YES
YES
YES

YES
YES
YES

(log) # CHN→ROW Exporterspt

Firm, Year, HS-6 Product FE
HS-6 Product Trend
Product × Year Controls

No Wholesalers
Upstream

Downstream

Regressor: CHN→CHL/FRA Exporters
IV: # CHN→ROW IV: # CHN→PA/US
Exporters
Exporters
(4)
(5)
(6)

OLS

0.055***
0.069***-0.014
296,957

0.071
0.256**-0.185*0.101
0.425***-0.324***

130

162

0.116***
0.150***-0.034***
887,062

0.268***
0.359***-0.091***
887,062606

0.124***
0.219***-0.095***879,879
350

YES
YES
YES

YES
YES
YES

YES
YES
YES

This table reports estimates for the effect of upstream market stucture—measured by the (log) number of Chinese exporters to ROW by HS 6-digit productand year—on the outcomes in the rows. Coefficients in Columns 1 and 2 exclude Chinese wholesale exporters and Chilean or French wholesale importers,
respectively. Column 3 considers CES price indices of unit values and quantities across a firm’s import transactions within an HS-6 digit product andyear, using Broda-Weinstein (2006) elasticities of substitution. In Columns 4 and 5, the main regressor is the actual number of Chinese exporters to Chileor France, instrumented with the number of Chinese suppliers to the Pacific Alliance or the US, and with the number of Chinese suppliers to ROW. The
product × year controls include the (log) number of Chilean or French importers from ROW; the EU ad-valorem import tariff on Chinese exports inPanel B; the average productivity of Chinese exporters, the variance of the productivity of Chinese exporters, the average quality of Chinese exporters;
the value shares of processing trade, intermediated trade; and the share of foreign-owned, multi-product, state-owned firms in Chinese exports. Singletons
are dropped and standard errors are clustered by HS-6 product × year. *** p < 0.01, ** p < 0.05, * p < 0.1.

35

Table 4: Downstream Heterogeneity
Importer Size Measure

Panel A. (log) Import Valuef pt(log) # CHN→ROW Exporterspt
× 2nd Down Size Tercile Dummy× 3rd Down Size Tercile Dummy
R2

Chile
Sales
Total Imports
Baseline CES Index Baseline CES Index
(1)
(2)
(3)
(4)
0.088**(0.039)0.007**(0.003)0.007(0.005)0.553-0.040(0.039)0.088***(0.002)0.153***(0.003)
0.557

FranceSales
Total Imports
Baseline CES Index Baseline CES Index
(5)
(6)
(7)
(8)
0.196***(0.030)0.019***(0.005)0.049***(0.006)0.588

0.122***(0.029)0.027***(0.007)0.105***(0.008)0.590

Panel B. (log) Import Quantityf pt(log) # CHN→ROW Exporterspt 0.215***(0.066)× 2nd Down Size Tercile Dummy 0.016***(0.004)× 3rd Down Size Tercile Dummy 0.021***(0.005)R2
0.558

0.255***(0.069)0.018***(0.004)0.023***(0.006)
0.527

0.090
(0.065)
0.096***(0.003)0.161***(0.004)
0.561

0.104
(0.069)0.114***(0.003)0.193***(0.004)0.531

0.268***(0.033)0.015***(0.005)0.048***(0.007)0.607

0.271***(0.034)0.021***(0.006)
0.059***(0.008)0.598

0.172***(0.033)0.036***(0.007)0.119***(0.008)0.609

0.168***(0.033)0.044***(0.007)0.138***(0.009)0.601

Panel C. (log) Import Unit Valuef pt(log) # CHN→ROW Exporterspt -0.128**(0.053)× 2nd Down Size Tercile Dummy -0.009***(0.002)× 3rd Down Size Tercile Dummy -0.013***(0.003)R2
0.727-0.175***(0.057)-0.011***(0.002)-0.018***(0.003)0.688-0.130**(0.053)-0.009***(0.002)-0.008***(0.002)0.727-0.144**(0.057)-0.032***(0.002)-0.050***(0.003)0.688-0.071***(0.015)0.003(0.002)0.001(0.003)0.713-0.079***(0.016)-0.004(0.003)-0.014***(0.003)0.693-0.050***(0.015)-0.009***(0.003)-0.014***(0.003)0.714-0.047***(0.016)-0.020***(0.004)-0.041***(0.004)0.694

NFirm, Year, HS-6 Product FE
HS-6 Product Trend
Product × Year Controls

306,762
YES
YES
YES

306,857
YES
YES
YES

306,762
YES
YES
YES

836,678
YES
YES
YES

836,678
YES
YES
YES

893,300
YES
YES
YES

893,300
YES
YES
YES

306,857
YES
YES
YES

This table explores heterogeneity in the effect of upstream market structure on downstream sourcing across downstream firms of different sizes. French firm
size tercile dummies are based on total sales in Columns 1, 2, 5, and 6, and on total imports in Columns 3, 4, 7, and 8. Columns 2, 4, 6 and 8 consider CES
price indices of unit values and quantities across a firm’s import transactions within an HS-6 digit product and year, using Broda-Weinstein (2006) elasticities
of substitution. The product x year controls include the (log) number of Chilean or French importers from ROW; the EU ad-valorem import tariff on Chinese
exports in columns 5-8; the average productivity of Chinese exporters, the variance of the productivity of Chinese exporters, the average quality of Chinese
exporters; the value shares of processing trade, intermediated trade; and the share of foreign-owned, multi-product, state-owned firms in Chinese exports.
Singletons are dropped and standard errors are clustered by HS-6 product × year. *** p < 0.01, ** p < 0.05, * p < 0.1.

36

Table 5: Upstream Price Discrimination(log) UVsf pt
(1)

Chile
(log) UVsf pt (log) UVsf pt(2)(3)-0.033***(0.003)-0.029***(0.003)-0.017***(0.004)

R2
N

0.860
330,3810.892
326,594

0.928
285,335-0.019***(0.004)0.019***(0.006)
0.928
285,335

Year FE
Supplier × HS-6 Product FE
HS-6 Product × Year FE
Buyer × HS-6 Product FE

YES
YES

YES
YES

YES
YES
YES

YES
YES
YES

(log) # CHN Suppliersf pt(log) # ROW Suppliersf pt(log) UVsf pt(4)This table explores upstream price discrimination and pro-competitive effects. The dependent variable
is the (log) unit value charged by a Chinese supplier to a Chilean importer for a given HS 6-digit
product and year. The level of competition faced by the Chinese supplier is measured by the (log)number of Chinese suppliers selling the same HS 6-digit product to that buyer in a given year. Column
1 includes seller-product and year fixed effects, so the impact of the number of Chinese suppliers is
identified from the variation within a supplier across buyers of the same product. Column 2 replaces
the year fixed effects with product-year fixed effects that control for supply and demand conditions byproduct over time. Column 3 adds buyer-product fixed effects, so the coefficient is now identified fromchanges in sourcing strategies within buyer-product pairs over time. Column 4 also controls for the(log) number of non-Chinese suppliers that a buyer has for the same product. Singletons are dropped
and standard errors are clustered by HS-6 product × year.*** p < 0.01, ** p < 0.05, * p < 0.1.

Table A1: Baseline Parameters for Simulation
Parameters
Definition
σelasticity of final-good demandη
elasticity of substitution between country-sectorθFréchet parameters capturing differentiation between upstream firms
Cmupper bound of Pareto distribution for suppliers’ marginal costkshape of Pareto distribution for suppliers’ marginal costτiceberg trade costs sourcing from foreign

37

value
5
2
3.5
2
4
2.2

Table A2: Additional Robustness
Balanced
Sample(1)NaturalQuantity
Units
(2)

(3)

(4)

(5)

0.089
0.231***-0.140***294,1490.089**
0.241***-0.152***
301,3700.129***
0.317***-0.188***306,857

No Eastern
EuropeImporters(6)

Panel A. Chile(log) Import Valuef pt(log) Import Quantityf pt(log) Import Unit Valuef pt
N

0.021
0.200***-0.179***169,436

Panel B. France(log) Import Valuef pt(log) Import Quantityf pt(log) Import Unit Valuef pt
N0.148***
0.194***-0.045***486,849

0.277***
0.356***-0.078***308,7180.126***
0.159***-0.033***
829,3080.125***
0.160***-0.036***803,363

0.114***
0.145***-0.030***887,0620.094***
0.126***-0.033***319,098

YES
YES
YES

YES
YES
YES

YES
YES
YES
YES

YES
YES
YES

YES
YES
YES

YES
YES
YES

Firm, Year, HS-6 Product FE
HS-6 Product Trend
Product × Year Controls
Downstr. Industry x Year FE(log) # CHN→ROW
Exporterspt other products(log) # CHN→ROW
Exporterspt in HS-4
Sample

YES
YES
(1)

(2)

This table confirms the robustness of the results in Columns 2 and 4 of Table 1. Columns 3 and includes the (log)number of Chinese exporters to the rest of the world in all products of a firm other than p as a control. Columns 4
and 12 include the (log) number of Chinese exporters to the rest of the world in the HS 4 product which p belongs
to. Sample (1) includes trade flows of firms that are present in all years under consideration. Sample (2) includes
trade flows of firms that never trade with an Eastern European country during our sample period. The product × year
controls include the (log) number of French importers from ROW; the EU ad-valorem import tariff on Chinese exports
in Columns 8-16; the average productivity of Chinese exporters, the variance of the productivity of Chinese exporters,
the average quality of Chinese exporters; the value shares of processing trade, intermediated trade; and the share offoreign-owned, multi-product, state-owned firms in Chinese exports. Singletons are dropped and standard errors are
clustered by HS-6 product × year. *** p < 0.01, ** p < 0.05, * p < 0.1.

38