Data and Code for : Local Heterogeneity in Artificial Intelligence Jobs Over Time and Space

Principal Investigator(s):  View help for Principal Investigator(s) LEFTERIS ANDREADIS, Bank of Greece; ELENI KALOTYCHOU, Cyprus University of Technology; MANOLIS CHATZIKONSTANTINOU, Georgetown University Qatar; CHRISTODOULOS LOUCA, Cyprus University of Technology; CHRISTOS MAKRIDIS, Arizona State University

Version:  View help for Version V1

Name File Type Size Last Modified
  data_code_ai 05/01/2025 08:20:PM

Project Citation: 

ANDREADIS, LEFTERIS , KALOTYCHOU, ELENI, CHATZIKONSTANTINOU, MANOLIS, LOUCA, CHRISTODOULOS, and MAKRIDIS, CHRISTOS . Data and Code for¿: Local Heterogeneity in Artificial Intelligence Jobs Over Time and Space. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-05-13. https://doi.org/10.3886/E228344V1

Project Description

Summary:  View help for Summary
In this paper, we try to understand the spatial and temporal predictors of AI adoption between 2014 and 2023. Specifically, we run a horse-race among demographic,innovation, and industry factors.

Scope of Project

JEL Classification:  View help for JEL Classification
      O33 Technological Change: Choices and Consequences; Diffusion Processes
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 2014 – 2023
Collection Date(s):  View help for Collection Date(s) 2024 – 2024
Universe:  View help for Universe All counties in the United States
Data Type(s):  View help for Data Type(s) aggregate data


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V1 [2025-05-13]

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