Data and Code for: Deep Learning for Economists

Principal Investigator(s):  View help for Principal Investigator(s) Melissa Dell, Harvard University

Version:  View help for Version V1

Name File Type Size Last Modified
  figure-7 12/20/2024 01:20:PM
  table-2 12/20/2024 01:21:PM
LICENSE.txt text/plain 18.2 KB 12/20/2024 08:13:AM
README.html text/html 29.2 KB 12/20/2024 08:13:AM
README.md text/x-web-markdown 16.4 KB 12/20/2024 08:13:AM
README.pdf application/pdf 142.6 KB 12/20/2024 08:13:AM
jel-dl.yml text/plain 341 bytes 12/20/2024 08:13:AM
requirements.txt text/plain 81 bytes 12/20/2024 08:13:AM

Project Citation: 

Dell, Melissa. Data and Code for: Deep Learning for Economists. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-01-27. https://doi.org/10.3886/E210922V1

Project Description

Summary:  View help for Summary Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a regularly updated companion website, https://econdl.github.io/}{EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications. 

Scope of Project

Subject Terms:  View help for Subject Terms Deep learning
JEL Classification:  View help for JEL Classification
      C45 Neural Networks and Related Topics
Geographic Coverage:  View help for Geographic Coverage United States, United Kingdom
Time Period(s):  View help for Time Period(s) 1877 – 2012
Data Type(s):  View help for Data Type(s) text

Methodology

Data Source:  View help for Data Source Comparative Agendas; American Stories, Newswire
Unit(s) of Observation:  View help for Unit(s) of Observation news articles, legislative bills/acts

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Published Versions

V1 [2025-01-27]

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