From Twitter to GDP: Estimating Economic Activity From Social Media

Indaco, Agustín (2019): From Twitter to GDP: Estimating Economic Activity From Social Media.

Abstract

Using all geo-located image tweets shared on Twitter in 2012-2013, I find that the volume of tweets is a valid proxy for estimating current GDP in USD at the country level. Residuals from my preferred model are negatively correlated to a data quality index, indicating that my estimates of GDP are more accurate for countries with more reliable GDP data. Comparing Twitter with more commonly-used proxy of night-light data, I find that variation in Twitter activity explains slightly more of the cross-country variance in GDP. I also exploit the continuous time and geographic granularity of social media posts to create monthly and weekly estimates of GDP for the US, as well as sub- national estimates, including those economic areas that span national borders. My findings suggest that Twitter can be used to measure economic activity in a more timely and more spatially disaggregate way than conventional data and that governments’ statistical agencies could incorporate social media data to complement and further reduce measurement error in their official GDP estimates.

• Item Type: MPRA Paper
• Language: English
• Keywords: National Accounts, Big Data
• Subjects: C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; E - Macroeconomics and Monetary Economics > E0 - General > E01 - Measurement and Data on National Income and Product Accounts and Wealth ; Environmental Accounts; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q11 - Aggregate Supply and Demand Analysis ; Prices

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