Deep Learning for Economists

TL;DR

This paper reviews how deep learning techniques can be applied by economists to analyze large-scale unstructured data like text and images, enabling new insights and scalable solutions.

Contribution

It introduces deep neural network methods tailored for economic data analysis, including practical applications and a resource-rich companion website.

Findings

Deep learning models can effectively analyze unstructured economic data.

Methods are scalable to large datasets involving millions or billions of data points.

The review provides practical tools and resources for economists.

Abstract

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 companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.