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Data and Code for: Text as Data in Economic Analysis

Principal Investigator(s): View help for Principal Investigator(s) Tarek A. Hassan, Boston University; Stephan Hollander, Tilburg University; Aakash Kalyani, Federal Reserve Bank of St. Louis; Laurence Van Lent, Frankfurt School of Finance and Management; Markus Schwedeler, NL Analytics Inc.; Ahmed Tahoun, London Business School

Version: View help for Version V1

Name	File Type	Size	Last Modified
Replication package			08/07/2025 10:33:AM

Project Citation:

Data and Code for: Text as Data in Economic Analysis. Nashville, TN: American Economic Association [publisher], 2025. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-08-07. https://doi.org/10.3886/E228102V1

Project Description

Summary: This is data and code accompanying the article, which discusses how to apply computational linguistics techniques to analyze largely unstructured corporate-generated text for economic analysis. As a core example, we illustrate how textual analysis of earnings conference call transcripts can provide insights into how markets and individual firms respond to economic shocks, such as a nuclear disaster or a geopolitical event: insights that often elude traditional non-text data sources. This approach enables extracting actionable intelligence, supporting both policy-making and strategic corporate decision-making. We also explore applications using other sources of corporate-generated text, including patent documents and job postings. By incorporating computational linguistics techniques into the analysis of economic shocks, new opportunities arise for real-time economic data, offering a more nuanced understanding of market and firm responses in times of economic volatility.

Funding Sources: Deutsche Forschungsgemeinschaft (403041268-TRR 266); Institute for New Economic Thinking

Scope of Project

Subject Terms: Text as data; Computational linguistics; Corporate text; Economic analysis

JEL Classification:
      B40 Economic Methodology: General
      C80 Data Collection and Data Estimation Methodology; Computer Programs: General
      D80 Information, Knowledge, and Uncertainty: General
      E60 Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: General
      H32 Fiscal Policies and Behavior of Economic Agents: Firm

Geographic Coverage: United States of America (US), Global

Time Period(s): 1/1/2002 – 5/31/2023

Collection Date(s): 1/1/2002 – 5/31/2023

Universe: Firms, corporate representatives, and capital market participants (e.g., financial analysts)

Data Type(s): other; text

Collection Notes: Data included in the replication package are obtained from https://www.nlanalytics.tech/, Hassan et al. 2024 (Review of Economic Studies), Kalyani et al. 2025 (Quarterly Journal of Economics), Bloom et al. 2021 (NBER Working Paper No. 28999)

Methodology

Response Rate: Not applicable

Sampling: Not applicable

Data Source: Data included in the replication package are obtained from https://www.nlanalytics.tech/, Hassan et al. 2024 (Review of Economic Studies), Kalyani et al. 2025 (Quarterly Journal of Economics), Bloom et al. 2021 (NBER Working Paper No. 28999)

Collection Mode(s): other

Scales: Not applicable

Weights: Not applicable

Unit(s) of Observation: Firms

Geographic Unit: Headquarters state

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Published Versions

V2 [2025-08-12]

V1 [2025-08-07]

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