Disentangling Global Value Chains - Scholars at Harvard

particular, fears of supply chain disruptions stoked by GVC statistics that underplayed NAFTA integration should be heightened once it is revealed that ...
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Disentangling Global Value Chains∗ JOB MARKET PAPER Alonso de Gortari

[email protected] First version: August 12, 2016

This version: November 26, 2017 [latest version]

Abstract I present a new global value chain (GVC) framework in which intermediate input suppliers produce specialized inputs that are only compatible with specific downstream uses. This feature is confirmed by firm-level data and is at odds with the current GVC approach which assumes that all products within a given industry utilize the same inputs. For example, Mexican firm-level data shows that the manufacturing firms that export to the U.S. utilize relatively more U.S. inputs than those that export to other destinations. I show how the new GVC framework can combine bilateral trade data with firm-level data in order to obtain GVC flows that reflect the heterogeneity in the use of inputs observed in the latter. This reveals that 27% of the $118bn of Mexican final good exports to the U.S. is U.S. value-added returning home. In contrast, the current GVC approach yields a share of only 17% since it ignores the specialized inputs channel. This discrepancy has serious implications for the ongoing renegotiation of NAFTA as it suggests that the potential costs of supply chain disruption are being understated. Lastly, I show how to compute these counterfactuals with an extension of the influential sufficient statistics approach to specialized inputs models and highlight important areas for future data collection.

∗ I am extremely grateful to my advisors Pol Antràs, Elhanan Helpman, and Marc Melitz for their mentorship and guidance throughout this project. I thank Rodrigo Adão, Laura Blattner, Joaquín Blaum, Kirill Borusyak, Lorenzo Caliendo, Alfonso Cebreros, Davin Chor, Xiang Ding, Andrei Gomberg, Gita Gopinath, Matthew Grant, Yuxiao Huang, Oleg Itskhoki, Yizhou Jin, Taehoon Kim, Eben Lazarus, Jonathan Libgober, Kalina Manova, Dávid Nagy, Guillermo Noguera, Fernando Parro, Fernando Pérez, Esteban Rossi-Hansberg, Nihar Shah, Daniel Vélez, Nico Voigtländer, Maria Voronina, Chenzi Xu, Kei-Mu Yi, and seminar participants at Harvard, ITAM, the 1st CESC, the Imports and Global Value Chains conference, SAET Faro, and Banco de México for useful comments. I am eternally indebted to Gurobi and Odyssey. I gratefully acknowledge the hospitality of Banco de México, where part of this paper was written. All errors are my own.

Contents 1

Introduction

1

2

The Hunt for GVCs: The Challenge

6

2.1

Observable Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6

2.2

A Toy Roundabout Production Model . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

2.3

A Toy Specialized Inputs Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.4

The Perils of Roundabout Production Models . . . . . . . . . . . . . . . . . . . . . . . 12

2.5

Moving Beyond Roundabout Production: Specialized Inputs . . . . . . . . . . . . . . 14

3

4

5

Measurement Framework: Specialized Inputs 3.1

GVC Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3.2

Relation to Observable Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.3

The Fundamental GVC Estimation Problem . . . . . . . . . . . . . . . . . . . . . . . . 17

3.4

I-O Analysis: The Roundabout Solution . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.5

The Specialized Inputs Measurement Framework . . . . . . . . . . . . . . . . . . . . . 19 3.5.1

Relation to Specialized Inputs Toy Model . . . . . . . . . . . . . . . . . . . . . 22

3.5.2