Global Value Chains: Spiders and Snakes - Economics...1 Spiders: Overview of Antr as, Fort and...

Global Value Chains: Spiders and SnakesStanford Department Seminar

Pol Antras

Harvard University

November 18, 2016

Pol Antras (Harvard University) Global Value Chains: Spiders and Snakes November 18, 2016 1 / 44

Introduction

Three Major Developments

Three major developments in the world economy in the last 30 years:

1 Information and communication technology (ICT) revolution

2 Deepening of trade liberalization and continuing transportation costreduction

3 Political developments expanding the reach of globalization

An implication: Gradual disintegration of production across borders

Topology of GVCs: Spiders and Snakes (Baldwin and Venables, 2013)


Introduction

A Spider: Boeing’s Dreamliner


Introduction

A Snake: Manufacturing a Chip


Introduction



Introduction



Introduction



Introduction

Why Should We Care?

Does it matter that about two-thirds of world trade is in intermediateinputs instead of final goods?

Does it matter that trade relationships are often initiated byimporters seeking to procure inputs from foreign suppliers?

Current workhorse trade models focus on the extensive and intensivemargin decisions of exporters selling finished products worldwide

Is this without loss of generality?


Introduction

Road Map

Today I want to highlight some novel features that arise whenanalyzing and estimating multi-country global sourcing models

1 Spiders: Overview of Antras, Fort and Tintelnot (2016)

2 Snakes: Preview of ongoing work with Alonso de Gortari


Spiders: Antras, Fort and Tintelnot (2016)

Spiders: Antras, Fort and Tintelnot (2016)


Spiders: Antras, Fort and Tintelnot (2016) Motivation

The Margins of Trade

Suppose that your interpret world trade flows (or U.S. imports morenarrowly) as the legs of spiders

Firms make decisions of where (extensive margin) to source inputsfrom and how much (intensive margin) to buy of each input

Fact: Extensive margin accounts for most of the cross-countryvariation in U.S. imports

Fact: Superior performance (size, labor productivity, TFP) ofimporters



Superior Performance of Importers

02

46

Pre

miu

m

1 3 5 7 9 11 13 15 17 19 21 23 25Minimum number of countries from which firm sources

Premium 95% CI



Challenges for a Multi-Country Global Sourcing Model

In canonical models of exporting, firms assumed to have constantmarginal costs unaffected by trade decisions

Easy to handle various margins of trade

But importing inputs naturally affects the marginal cost of the firm!

Import entry decisions are thus interdependent across markets

Interdependencies across markets complicate the firm’s decision

Which countries should a firm invest in importing from?

From which particular country should each input be bought?

How much of each input should be purchased?


Spiders: Antras, Fort and Tintelnot (2016) Contribution

Main Contributions of Antras, Fort and Tintelnot (2016)

Develop a quantifiable multi-country sourcing model

Characterization of intensive and extensive margins of global sourcing

Eaton-Kortum (2002) and multi-country Melitz (2003) are special cases

Develop methodology to solve firm’s problem with interdependencies

Apply theoretical insights and IO algorithm to estimate model

Counterfactual analysis of shock to China’s sourcing potential

Study effects of shocks to global sourcing

Distinguish net vs. gross changes in sourcing / employment

Reduced-form evidence consistent with these predictions


Spiders: Antras, Fort and Tintelnot (2016) Environment

Environment

J countries

Measure of Lj consumers / workers

Preferences: Dixit-Stiglitz over manufacturing varieties, elasticity ofsubstitution σ > 1 (later introduce non-manufacturing sector)

Final good sector produces these varieties:

Measure Nj of heterogeneous firms (pinned down by free entry)Firms characterized by core productivity ϕMonopolistic competitionNon-tradable final output

Intermediate good sector

Each firm uses a unit measure of intermediate inputs (next slide)

Trade cost τij to import from country j by country i

Perfect competition =⇒ Marginal-cost pricing of inputs



Production Technology

Marginal cost of final good producer ϕ based in i is:

ci

({j (v)}1

v=0 , ϕ)=

1

ϕ

1∫0

(pi (v , j(v)))1−ρ dv

1/(1−ρ)





ci

({j (v)}1

v=0 , ϕ)=

1

ϕ

1∫0

(τij(v )aj(v ) (v)wj(v )

)1−ρdv

1/(1−ρ)





ci

({j (v)}1

v=0 , ϕ)=

1

ϕ

1∫0


)1−ρdv

1/(1−ρ)

Tricky to characterize equilibrium in terms of aj ’s





ci

({j (v)}1

v=0 , ϕ)=

1

ϕ

1∫0


)1−ρdv

1/(1−ρ)


Productivity 1/aj (v) for a given location j drawn from Frechetdistribution:

Pr(aj (v) ≥ a) = e−Tjaθ, with Tj > 0.





ci

({j (v)}1

v=0 , ϕ)=

1

ϕ

1∫0


)1−ρdv

1/(1−ρ)


Productivity 1/aj (v) for a given location j drawn from Frechetdistribution:

Pr(aj (v) ≥ a) = e−Tjaθ, with Tj > 0.

Country-specific fixed cost of offshoring wi fij



Firm’s problem

Firm chooses:

Sourcing strategy Ji (ϕ) ⊆ {1, ..., J}Source country j(v) ∈ Ji (ϕ) for each intermediate v

Quantity of each input j(v) purchases

Price of final good

Sourcing strategy thus determines set of countries from which firmcan buy inputs


Spiders: Antras, Fort and Tintelnot (2016) Firm Behavior

Firm Behavior Conditional on Sourcing Strategy

Share of intermediate input purchases sourced from any country j :

χij (ϕ) =Tj (τijwj )

−θ

Θi (ϕ)if j ∈ Ji (ϕ)

Sourcing potential of country j (for firms in i): Tj (τijwj )−θ

Sourcing capability of firm ϕ in i :

Θi (ϕ) ≡ ∑k∈Ji (ϕ)

Tk (τikwk)−θ

Marginal cost:

ci (ϕ) =1

ϕ(γΘi (ϕ))−1/θ



Optimal Sourcing Strategy

Profit Function:

maxIij∈{0,1}Jj=1

ϕσ−1

(γ

J

∑j=1

IijTj (τijwj )−θ

)(σ−1)/θ

Bi − wi

J

∑j=1

Iij fij




Profit Function:

maxIij∈{0,1}Jj=1

ϕσ−1

(γ

J

∑j=1


)(σ−1)/θ

Bi − wi

J

∑j=1

Iij fij

Proposition 1. The solution Iij (ϕ) ∈ {0, 1}Jj=1 to the optimalsourcing problem is such that:

(a) a firm’s sourcing capability Θi (ϕ) =J

∑j=1

Iij (ϕ)Tj (τijwj )−θ

is nondecreasing in ϕ;

(b) if (σ− 1) /θ ≥ 1, then Ji (ϕL) ⊆ Ji (ϕH) for ϕH ≥ ϕL,where Ji (ϕ) = {j : Iij (ϕ) = 1} .




Profit Function:

maxIij∈{0,1}Jj=1

ϕσ−1

(γ

J

∑j=1


)(σ−1)/θ

Bi − wi

J

∑j=1

Iij fij

Proposition 2. Define the mapping Vj (ϕ, J) taking a value ofone whenever including country j in the sourcing strategy J

raises firm-level profits πi (ϕ, J) , and taking a value of zerootherwise. Then, whenever (σ− 1) /θ ≥ 1,Vj (ϕ, J′) ≥ Vj (ϕ, J) for J ⊆ J′.

This result will be instrumental for reducing the dimensionalityof the optimal sourcing problem



Interdependencies in Firm Sourcing Decisions

Proposition 3. Holding constant the the market demand level Bi ,whenever (σ− 1) /θ ≥ 1, an increase in the sourcing potential

Tj (τijwj )−θ or a reduction in the fixed cost fj of any country j ,

(weakly) increases the input purchases by firms in i not only from j ,but also from all other countries.

Corollary. There may exist complementarities between domestic andforeign sourcing


Spiders: Antras, Fort and Tintelnot (2016) Structural Estimation

Structural Estimation



Data

2007 data from the U.S. Census BureauEconomic CensusesImport transactions data

Sample is all manufacturing firms (around 250,000 firms)Include firms with non-manufacturing activity23% of employment and 38% of sales65% of (non-mining) importsA quarter of these firms imports

Structural EstimationLimit analysis to countries with 200+ U.S. importers66 countries and the U.S.

Reduced form evidence on interdependenciesBalanced panel of manufacturing firms in 1997 and 2007UN Comtrade data; 1997 BEA Input-Output tables



Some Firm-level Import Statistics

Count of distinct source locations and products imported by a firm

Mean Std. Dev. 25th Ptile Median 95th Ptile

Country Count 3.26 5.09 1 2 11Product Count 11.91 48.89 1 3 41

Although extreme, the continuum of inputs assumption helps a lot



Overview of Estimation

Step 1: Back out sourcing potential from firm-level input shares

Recovered from country fixed effects in normalized share regressions

Step 2: Estimate demand elasticity and productivity dispersion

Project fixed effect on human-capital adjusted labor cost

Step 3: Estimate fixed costs of sourcing and residual demand

Simulated method of moments + Jia’s (2008) algorithm

J,, fijn −1 ∑

j1

j∈JTj ijwj

−−1/

B − ∑j∈J

fijn

Step 1 Step 2Step 3



Sourcing Potential vs. Fixed Cost Estimates

10−3

10−2

10−1.9

10−1.8

10−1.7

10−1.6

10−1.5

10−1.4

10−1.3

10−1.2

CAN

MEX

GTM

SLV

HND

CRI

PAN

DOM

TTO

COL

VENECU

PER

CHL

BRA

ARG

SWE

NOR

FINDNK

GBRIRLNLD

BEL

LUX

FRA

DEU

AUT

CZESVK

HUN

CHE

POL

RUS

UKR

ESPPRT

ITA

SVN

GRC

ROMBGR

TUR

ISR

SAU

ARE

IND

PAK

BGD

LKA

THA

VNM

MYS

SGP

IDN

PHL

MAC

CHN

KOR

HKG

TWN

JPN

AUS

NZL

EGY

ZAF

Sourcing Potential

Med

ian

Fix

ed C

ost


Spiders: Antras, Fort and Tintelnot (2016) Counterfactual

Counterfactual: China Shock

Negative shock to China’s sourcing potential to match 1997 share ofChina importers (38% of its 2007 level)

Resolve for equilibrium price index and mass of new firms

Calculate impact from going back to 2007 sourcing potential values

Compare baseline model predictions to models with alternativeparameter values that imply:

Universal importing

Independent entry decisions

Common fixed costs


Spiders: Antras, Fort and Tintelnot (2016) Counterfactual

Baseline Results

Chinese Change sourcing Change Sourcing Shareimport status from US from other countries of firms

Entrants 1.008 1.015 0.066Continuers 1.002 1.002 0.019Others 0.994 0.986 0.915

Aggregate sourcing from the U.S. is reduced by 0.60 percent

For every 10 domestic manufacturing jobs destroyed, 2 new jobs arecreated (and we do not allow for exports!)

Manufacturing price index falls by 0.2%.


Spiders: Antras, Fort and Tintelnot (2016) Reduced-Form Evidence

Reduced-Form Comparison to the Data

Model predicts increased domestic and third market sourcing byChina importers

∆yn = β0 + βCh∆Chinan + εn

∆Chinan =ImportsChn2007−ImportsChn1997

(ImportsChn2007+ImportsChi1997)/2

∆yn is 1997 to 2007 change in firm n’s:

log domestic inputsDHS growth rate of non-China importslog number of non-China source countries

OLS estimates clearly problematic =⇒ use IV strategy similar toAutor, Dorn and Hanson (2013) but on the input side


Spiders: Antras, Fort and Tintelnot (2016) Reduced-Form Evidence

Estimates of the China Shock on Firm Sourcing

Dependent variable is change from 1997 to 2007 in firm n:

Domestic No. of Foreign Domestic No. of Foreigninputs countries inputs inputs countries inputs

OLS IV

China, DHS 0.084*** 0.255*** 0.360*** 0.934*** 0.553*** 0.654***(0.012) (0.007) (0.013) (0.258) (0.080) (0.197)

Constant 0.069*** 0.144*** 0.315*** -0.064 0.097*** 0.269***(0.023) (0.013) (0.026) (0.047) (0.017) (0.044)

Adj. R2 0.00 0.11 0.05N 127,400 127,400 127,400 127,400 127,400 127,400

First Stage Statistics Coeff (se) 2.691*** (0.504) KP Fstat 28.51

Notes: All variables are changes or growth rates from 1997 to 2007. Standard errors are in parenthe-ses and clustered by 439 NAICS industries. N rounded for disclosure avoidance.


Snakes: Antras and de Gortari (2016)






Snakes: Antras and de Gortari (2016) The Role of Trade Costs

Sequential Production and Trade Costs

Consider optimal location of production for the different stages in asequential GVC

Without trade frictions ≈ model with spiders

Conditional on a set of sourcing locations, minimize each input’ssourcing cost independently

With trade frictions, matters become trickier

Location of a stage takes into account upstream and downstreamlocations

Where is the good coming from? Where is it going to?

Need to solve jointly for the optimal path of production


Snakes: Antras and de Gortari (2016) Our Contribution

Main Contributions of Antras and de Gortari (2016)

Develop a general-equilibrium model of GVCs with a generalgeography of trade costs across countries

1 Characterize the optimality of a centrality-downstreamness nexus

Consistent with evidence from Factory Asia

2 Develop tools to solve the problem in high-dimensional environments

Reformulate problem so it is solvable with DP and LP techniques

Illustrate transition from domestic to regional to global value chains


Snakes: Antras and de Gortari (2016) Our Contribution

Main Contributions of Antras and de Gortari (2016)

Develop a general-equilibrium model of GVCs with a generalgeography of trade costs across countries

1 Characterize the optimality of a centrality-downstreamness nexus

Consistent with evidence from Factory Asia

2 Develop tools to solve the problem in high-dimensional environments

Reformulate problem so it is solvable with DP and LP techniques

Illustrate transition from domestic to regional to global value chains

3 Develop a tractable multi-stage variant of the Eaton-Kortum (2002)framework for an arbitrary number of countries and sequential stages

Facilitates quantitative analysis using world I-O tables


Snakes: Antras and de Gortari (2016) General Environment

Model: General Environment



Environment

There are J countries (indexed by i or j) where consumers deriveutility from consuming a set of final-good varieties (indexed by z)

Consumer goods produced combining N stages (indexed by n) thatneed to be performed sequentially using a unique factor (labor)

The last stage N can be interpreted as assembly

Countries can in principle differ in their:

Labor productivity: unit labor requirements ain (z)

Geography: J × J matrix of iceberg trade coefficients τij proportionalto value of good being shipped

Size: each country i is populated by Li consumers, each inelasticallysupplying one unit of labor



Sequential Production Technology

Stage n technology for good z :

yni (z) = f nz

(Lni (z)

ani (z), cn−1

i (z)

)for all n, i , z ,

where

cni (z) =J

∑j=1

δnji (z) ynj (z)

τji, for all n, i , z ,

with some initial vector of c0i ’s.

Applies also to assembly yNi (z) and final-good consumption cNi (z)

Can solve recursively to express final consumption in terms of valueadded in all stages


Snakes: Antras and de Gortari (2016) Example

Example: A Symmetric Cobb-Douglas Snake

Assume a Cobb-Douglas technology with a single source ofcomponents at each stage

yn`(n) =

(Ln`(n)

an`(n)

)1/n (cn−1`(n)

)1−1/n; cn−1

`(n)=

yn−1`(n)

τji

This delivers:

yN`(n) =N

∏i=1

(Ln`(n)

an`(n)

)1/N

×(

τ`(n−1)`(n)

)− n−1N

Unless τ`(n−1)`(n) = τ, can no longer minimize costs stage-by-stage

Incentive to reduce trade costs increases as one moves downstream


Snakes: Antras and de Gortari (2016) Isolating Trade Costs

Isolating Trade Costs


Snakes: Antras and de Gortari (2016) One-to-One Case

One-to-One Assignment with N = J

Consider the case with just one final good and log utility

Lemma 1 (Modified TSP)

In the even case N = J, the optimal one-to-one assignment of stages tocountries seeks to solve

min{`(n)}Nn=1

H (` (1) , ..., ` (N)) =N

∑i=1

ΛiN ln τ`(N)i +N−1

∑n=1

n ln τ`(n)`(n+1),

where Λi = λiLi/J

∑i=1

λiLi .

Connection to Traveling Salesman Problem

But ‘traveling salesman’ is getting increasingly tired



Optimal Pure Snake in Factory Asia: Production



‘Empirical Fit’

CHN

HKG

IDN

JPN

KOR

MYS PHLSGP

THA

TWN

VNM

1.7

1.8

1.9

2

2.1

2.2

2.3

2.4

2.5

0 1 2 3 4 5 6 7 8 9 10 11 12

Average Expo

rt Upstreamne

ss (ACFH)

Upstreamness in the Optimal Global Value Chain


Snakes: Antras and de Gortari (2016) A Probabilistic Approach

A Probabilistic Approach


Snakes: Antras and de Gortari (2016) A Probabilistic Approach

A Multi-Stage Eaton-Kortum Model

Assume preferences are

u

({cNi (z)

}1

z=0

)=

(∫ 1

0

(cNi (z)

)(σ−1)/σdz

)σ/(σ−1)

, σ > 1

Technology features CRS and Ricardian technological differences

yni (z) =

(Lni (z)

ani (z)

)1/n (cn−1i (z)

)1−1/n

If a production chain follows the path {` (1) , ` (2) , ..., ` (N)}, then

log

(Pr

(N

∏n=1

a`(n)n (z) ≥ a

))= −aθ

N

∏n=1

T`(n)

Randomness can be interpreted as uncertainty on compatibility


Snakes: Antras and de Gortari (2016) Some Results

Some Results

Likelihood of a particular GVC ending in i is

Pr (` (1) , ` (2) , ..., ` (N) ; i) =

N−1∏n=1

A`(n)

(τ`(n)`(n+1)

)−θn× A`(N)

(τ`(N)i

)−θN

Θi

where Aj = Tj (wj )−θ and Θi is the sum of the numerator over all

possible country permutations

Notice that trade costs again matter more downstream than upstream

Can compute final-good trade shares and intermediate input shares asexplicit functions of Aj ’s and trade costs


Snakes: Antras and de Gortari (2016) Centrality and Downstreamness

The Centrality-Downstreamness Nexus

Define the average upstreamness U (`; i) of production of a givencountry ` in value chains that seek to serve consumers in country i :

U (`; i) =N

∑n=1

(N − n+ 1)× Pr (` = ` (n) ; i)

∑Nn′=1 Pr (` = ` (n′) ; i)

Closely related to upstreamness measure in Antras et al. (2012)

If τij = (ρiρj )−1 for i 6= j and τii = ξ (ρiρi )

−1 with ξ < 1:

Proposition (Centrality-Upstreamness Nexus)

The average upstreamness U (`) of a country in global value chains isdecreasing in its centrality ρ (`).


Snakes: Antras and de Gortari (2016) Empirical Application

Empirical Application



Calibration to World-Input Output Database

We next map our multi-country Ricardian framework to worldInput-Output Tables

World Input Output Database: Released in 2012

35 sectors

40 countries (85% of world GDP) + ROW

Yearly: 1995-2011

Provides information on input and final output flows across countries



Calibration to World-Input Output DatabaseWhat a world IO table should look like

automotive production, illustrating its power in analyses of international productionfragmentation. Section 4 is more general and considers specific measurement issuesthat are important for prudent use of the data, and identifies areas that are most inneed for improvements. The WIOD is meant to serve as a dynamic resource that willbe expanded over time and section 5 considers future developments.

2. WIOD in Comparative Perspective

In this section we first outline the concept of a world input–output table, followed by abrief discussion of the contents of the WIOD compared with other database initia-tives. Through a comparison of value-added export measures based on the variousdatabases we conclude that empirical differences are relatively minor.

WIOD Characteristics

Central in the WIOD is a time-series of world input–output tables. A world input–output table (WIOT) can be regarded as a set of national input–output tables that areconnected with each other by bilateral international trade flows. This is illustrated bythe schematic outline for a WIOT involving three countries in Figure 1. A WIOT pro-vides a comprehensive summary of all transactions in the global economy betweenindustries and final users across countries. The columns in the WIOT contain informa-tion on production processes. When expressed as ratios to gross output, the cells in acolumn provide information on the shares of inputs in total costs. Such a vector of costshares is often referred to as a production technology. Products can be used as inter-mediates by other industries, or as final products by households and governments(consumption) or firms (stocks and gross fixed capital formation). The distribution ofthe output of industries over user categories is indicated in the rows of the table. Animportant accounting identity in the WIOT is that gross output of each industry(given in the last element of each column) is equal to the sum of all uses of the outputfrom that industry (given in the last element of each row).

In addition to a national input–output table, imports are broken down according tothe country and industry of origin in a WIOT. This allows one, for example, to tracethe country of origin of the chemicals used in the food industry of country A. Thecombination of national and international flows of products provides a powerful tool

…Country

1Country 1 …Country

MCountry M

Industry Industry1 … …

Industry1 N

Industry N… …

Industry 1…Industry N

……Industry 1…Industry N

Total use

Supply from country-

industries

Country 1

CountryM

Value added by labour and capital

Gross output

Use by country-industries Final use by countries

Figure 1. Schematic Outline of a World Input–Output Table (WIOT)

USER GUIDE TO WORLD INPUT–OUTPUT DATABASE 577

© 2015 John Wiley & Sons LtdFigure: Timmer et al. (2015)

Alonso de Gortari (Harvard University) Empirics of GVCs April 20, 2016 36 / 73



Targeting Final-Good Shares

We target final good shares, which are functions of Aj = Tj (wj )−θ

and ρij = (τij )−θ

Use gravity estimates (Head and Mayer, 2014) to back out trade costs

Distance, contiguity, common language, colonial link, RTAs, commoncurrency

Introduce additional wedge Z that magnifies foreign trade costsrelative to domestic ones

Can use (J − 1)× (J − 1) WIOD final-good shares and labor-marketclearing (or trade balance) to estimate Z , Tj and wj for j = 1, ..., J

We set θ = 5 and N = 3 to match an aggregate gross-output tovalue-added ratio of 2



Fit of the Model

10-8

10-6

10-4

10-2

100

WIOD

10-8

10-6

10-4

10-2

100

Estim

atio

n

Final Consumption Shares: All - LogLog Scale

10-8

10-6

10-4

10-2

100

WIOD

10-8

10-6

10-4

10-2

100

Estim

atio

n

Input Shares: All - LogLog Scale



Counterfactuals

A 50 percent reduction in foreign trade costs (wedge Z )

USA JP

NC

HN

CAN

AUS

RoW MEX IN

DBR

AID

NKO

RTW

NFR

AR

US

ESP

ITA

DEU

GBR TU

RPR

TFI

NPO

LSW

ER

OM

BEL

AUT

GR

CN

LDBG

RC

YP EST

CZE LV

AIR

LSV

KLT

UD

NK

HU

NSV

NLU

XM

LT

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

% o

f Exp

orts

GVC Participation Index

Benchmark50% Fall in Trade Costs



Counterfactuals

A 50 percent reduction in foreign trade costs (wedge Z )

CA

N

Ro

W

ME

X

CH

N

FR

A

AU

S

ES

P

JP

N

BR

A

RU

S

ITA

DE

U

IND

GB

R

IDN

0

0.02

0.04

0.06

0.08

0.1

0.12

% o

f E

xp

ort

sBenchmark Calibration: GVC Participation Index

Benchmark

50% fall in trade costs



Regional versus Non-Regional GVC Integration

As Z falls, GVCs first become more regional and then more global

0.75 1 1.25 1.5Z

0.25

0.26

0.27

0.28

0.29

0.3

Ratio

USA: NAFTA Participation / RoW Participation



Real Income Gains

Autarky Counterfactual: GVC Model vs. Eaton-Kortum with I-O loop

1 1.25 1.5 1.75

N=1 with I-O Loop

1

1.25

1.5

1.75

GV

Cs:

N=

3

Real Income Gains

1.7 1.75 1.8 1.85 1.9 1.95 2 2.05

Log(GDP)-Weighted Trade Cost to RoW

1

2

3

4

5

6

7

Co

rre

ctio

n t

o N

=1

with

I-O

Lo

op

Re

al In

co

me

Correction to Real Income Gains vs Remoteness


Snakes: Antras and de Gortari (2016) Conclusions

Conclusions

We have developed frameworks to study how technology andgeography shape the location of production along GVCs

Both for Spiders and for Snakes

Frameworks deliver novel qualitative insights, but can also be used toquantitatively assess the implications of the rise of GVCs

I view this work as a stepping stone for a future analysis of the role ofman-made trade barriers in GVCs

Should countries use policies to place themselves in particularlyappealing segments of global value chains?

What is the optimal shape of those policies?


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