Measuring economic activity in China with mobile big data

Lei Dong,Sicong Chen,Yunsheng Cheng,Haishan Wu,Chao Li,Zhengwei Wu

doi:10.1140/epjds/s13688-017-0125-5

Abstract

Emerging trends in the use of smartphones, online mapping applications, and social media, in addition to the geo-located data they generate, provide opportunities to trace users’ socio-economic activities in an unprecedentedly granular and direct fashion and have triggered a revolution in empirical research. These vast mobile data offer new perspectives and approaches to measure economic dynamics, and they are broadening the social science and economics fields. In this paper, we explore the potential for using mobile data to measure economic activity in China from a bottom-up view. First, we build indices for gauging employment and consumer trends based on billions of geo-positioning data. Second, we advance the estimation of offline store foot traffic via location search data derived from Baidu Maps, which is then applied to predict Apple’s revenues in China and to accurately detect box-office fraud. Third, we construct consumption indicators to track trends in various service sector industries and verify them with several existing indicators. To the best of our knowledge, this is the first study to measure the world’s second-largest economy by mining such unprecedentedly large-scale and fine-granular spatial-temporal data. In this way, our research provides new approaches and insights into measuring economic activity.

Highlights

The mobile internet, especially location-aware services, is ubiquitous in our everyday lives: each time a user opens an application, searches for a nearby restaurant, takes a car using a ride-hailing app, or uses mobile map navigation services, the user’s location is detected via Global Positioning System (GPS) technology and logged on a server, generating massive mobility trace data
China, with approximately million smartphone users [ ], has been profoundly affected by the mobile internet, even as it struggles to transform its economy from investment led to consumer driven
The pace of layoffs may accelerate when that company faces difficulties in business operations or a weakening market. These phenomena are closely bound to the macro-economy

Summary

Introduction

The mobile internet, especially location-aware services, is ubiquitous in our everyday lives: each time a user opens an application (app), searches for a nearby restaurant, takes a car using a ride-hailing app, or uses mobile map navigation services, the user’s location is detected via Global Positioning System (GPS) technology and logged on a server, generating massive mobility trace data. But most importantly, the changing economic structure raises new challenges for measuring the emergence of service industries such as retail, restaurants, entertainment, and finance, which increasingly compose a considerable proportion of the economy but have been difficult to quantify Facing these challenges, researchers have previously turned to new data sources, such as search queries [ – ], social media [ – ], satellite images [ – ], online commodity pricing [ , ], financial transactions [ – ], check-in data [ ] and mobile phone data [ – ], to build socio-economic indicators or to study economic behaviours from different perspectives. Supplant traditional surveys but to supplement such indicators in order to achieve more complete measurements

Data We use four datasets in this study:

Methods

Results