Bilateral Relations and Exports: Evidence from Google Big Data

• Published date: 01 January 2023
• Author: Jing Li,Hongkui Liu,Qian Xie
• Date: 01 January 2023
• DOI: http://doi.org/10.1111/cwe.12463

©2023 Institute of World Economics and Politics, Chinese Academy of Social Sciences

182 China & World Economy / 182–210, Vol. 31, No. 1, 2023

*Jing Li, Assistant Professor, School of International Trade and Economics, University of International

Business and Economics, China. Email: jingli@uibe.edu.cn; Hongkui Liu, Associate Professor, Institute

of Economics, Chinese Academy of Social Sciences, China. Email: 450822729@qq.com; Qian Xie

(corresponding author), Associate Professor, Institute of Economics, Chinese Academy of Social Sciences,

China. Email: hbuxq@163.com. This research was financially supported by the National Natural Science

Foundation of China (Nos. 72173132 and 72003193).

Bilateral Relations and Exports:

Evidence from Google Big Data

Jing Li, Hongkui Liu, Qian Xie*

Abstract

This paper investigates the effect of bilateral relations on exports using data from

Google Global Data. It fi nds that bilateral relations signifi cantly reduced the negative

effect of cultural distance on exports, indicating that they can promote exports by

reducing trade costs. The paper finds that higher average Goldstein scores of events

correlated with more exports and that bilateral relations had a larger eff ect on trust-

intensive products, indicating that positive relations built trust and decreased the

emotional distance between trading partners. The results also show that bilateral

relations promoted exports at both the intensive and extensive margins but with a

greater eff ect on the latter. Finally, bilateral relations had a greater positive eff ect on

developing countries than on developed ones. The results were qualitatively unchanged

when endogeneity issues and robustness concerns were considered.

Keywords: bilateral relations, cultural distance, export, Google big data

JEL codes: D24, F1, P45

I. Introduction

Theoretical and empirical studies document that exporters usually face considerable

uncertainty when they start to export (Hausmann and Rodrik, 2003). Self-learning,

experimentation, learning from neighbors and friends, and establishing export networks

are common ways for exporters to overcome information asymmetry, deal with

uncertain environments, and open up new markets (Albornoz et al., 2012; Estrada et al.,

2012; Chaney, 2014; Fernandes and Tang, 2014). Many studies highlight the importance

of informal institutions on exports, such as political connections (Ding et al., 2018;

©2023 Institute of World Economics and Politics, Chinese Academy of Social Sciences

183

Bilateral Relations and Exports

Sharma et al., 2020), bilateral trust (Guiso et al., 2009; Nunn and Wantchekon, 2011),

and linguistic factors (Melitz and Toubal, 2014). For domestic institutions, research

shows that exporters can reduce uncertainty in periods of economic transition by

developing relationships with state bureaucrats to learn how state bureaucracies

operate and engender trust between exporters and bureaucrats (Haveman et al., 2017;

Ding et al., 2018). For international institutions, studies show that lower bilateral trust

can lead to less trade between two countries (Guiso et al., 2009), linguistic proximity

can significantly stimulate trade (Melitz and Toubal, 2014), and exporters start

with higher volumes and sell for longer periods in countries with better contracting

institutions (Araujo et al., 2016). In essence, learning, networks, and other forms of

bilateral informal institutional links enable exporters to reduce information asymmetry

and uncertainty and shorten the bilateral distance, thereby promoting the growth of

bilateral trade.

This paper examines a new form of informal institution, bilateral relations, and their

effect on international trade. The key challenge is how to measure bilateral relations

between two trading partners. We use a new large-scale Confl ict and Mediation Event

Observations (CAMEO)-coded dataset – Global Data on Events, Location, and Tone

(GDELT) – constructed by Google to measure bilateral relations. This dataset contains

data about more than 200 million geolocated events1 with global coverage through news

reports from a variety of international news sources coded using the Tabari system for

events and additional software for location and tone. GDELT event records capture two

actors (Actor1 and Actor2) and the action performed by Actor1 in one country on Actor2

in another country, providing technical possibilities for studying bilateral relations.

Bilateral relations can increase mutual understanding, prevent prejudice, and

reduce geographic and cultural distance. When one country initiates a positive action

toward another country (an action that promotes the stability of the recipient country),

the recipient country forms an emotional connection with the exporting country, which

is likely to be reflected in changes in international trade. Indeed, a few studies have

confirmed that bilateral trust and cultural proximity are important determinants of

bilateral trade volumes (Guiso et al., 2009; Felbermayr and Toubal, 2010).

1Leetaru and Schrodt (2013) provided a detailed introduction to the GDELT dataset. The Event Database

records individual events, aggregating data from many diff erent news articles. Entries in the Event Database

are made at the level of an individual event following the format of who did what to whom and how many

news articles are talking about it. An event captures aspects of a news article; thus, the same news article can

be referenced in several events with diff erent aspects highlighted. There are two actors, Actor1 and Actor2,

in an event. Actor1 can be an individual, an organization, or even a country. It indicates “who” is involved in

doing something to Actor2. For more information and to download the event data fi les, please see the main

GDELT website at http://gdeltproject.org/ [online; cited October 2021].