Big data in economics

TL;DR

Big data has transformed economic research by providing vast, diverse, and real-time data sources that enable faster, cost-effective analysis of economic phenomena and indicators.

Contribution

This paper offers a taxonomy of big data sources and demonstrates their application in empirical economic analysis and indicator construction.

Findings

Big data sources are diverse and include social media, sensors, and satellites.

Big data enables rapid and cost-effective economic analysis.

New data sources improve measurement of economic activities.

Abstract

The term of big data was used since 1990s, but it became very popular around 2012. A recent definition of this term says that big data are information assets characterized by high volume, velocity, variety and veracity that need special analytical methods and software technologies to extract value form them. While big data was used at the beginning mostly in information technology field, now it can be found in every area of activity: in governmental decision-making processes, manufacturing, education, healthcare, economics, engineering, natural sciences, sociology. The rise of Internet, mobile phones, social media networks, different types of sensors or satellites provide enormous quantities of data that can have profound effects on economic research. The data revolution that we are facing transformed the way we measure the human behavior and economic activities. Unemployment, consumer price index, population mobility, financial transactions are only few examples of economic phenomena that can be analyzed using big data sources. In this paper we will start with a taxonomy of big data sources and show how these new data sources can be used in empirical analyses and to build economic indicators very fast and with reduced costs.