DP12949 Big Data, artificial intelligence and the geography of entrepreneurship in the United States
|Author(s):||Tobias Ebert, Johannes C. Eichstaedt, Neil Lee, Martin Obschonka, Andrés Rodríguez-Pose|
|Publication Date:||May 2018|
|Keyword(s):||artificial intelligence, Big Data, Big Five, Counties, entrepreneurship, personality, psychological traits, social media, Twitter, U.S.|
|JEL(s):||L26, R11, R12|
|Programme Areas:||Labour Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=12949|
There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes and processes. But can artificial intelligence models, solely based on publicly available Big Data (e.g., language patterns left on social media), reliably identify geographical differences in entrepreneurial personality/culture that are associated with entrepreneurial activity? Using a machine learning model processing 1.5 billion tweets by 5.25 million users, we estimate the Big Five personality traits and an entrepreneurial personality profile for 1,772 U.S. counties. We find that these Twitter-based personality estimates show substantial relationships to county-level entrepreneurship activity, accounting for 24% (entrepreneurial personality profile) and 32% (all Big Five trait as separate predictors in one model) of the variance in local entrepreneurship and are robust to the introduction in the model of conventional economic factors that affect entrepreneurship. We conclude that artificial intelligence methods, analysing publically available social media data, are indeed able to detect entrepreneurial patterns, by measuring territorial differences in entrepreneurial personality/culture that are valid markers of actual entrepreneurial behaviour. More importantly, such social media datasets and artificial intelligence methods are able to deliver similar (or even better) results than studies based on millions of personality tests (selfreport studies). Our findings have a wide range of implications for research and practice concerned with entrepreneurial regions and eco-systems, and regional economic outcomes interacting with local culture.