Measuring Regulatory Barriers Using Annual Reports of Firms

TL;DR

This paper introduces a novel method to quantify regulatory barriers at the country-year level by analyzing annual reports of U.S. firms using neural language models and dynamic item response theory, providing less biased measurements for political science research.

Contribution

It develops a new approach combining neural language processing and item response theory to measure regulatory barriers from corporate reports, reducing confounding biases.

Findings

Estimated regulatory barriers for 40 countries from 2006 to 2015.

The method reduces bias from international politics in barrier measurement.

Provides a scalable, data-driven tool for future research on regulation.

Abstract

Existing studies show that regulation is a major barrier to global economic integration. Nonetheless, identifying and measuring regulatory barriers remains a challenging task for scholars. I propose a novel approach to quantify regulatory barriers at the country-year level. Utilizing information from annual reports of publicly listed companies in the U.S., I identify regulatory barriers business practitioners encounter. The barrier information is first extracted from the text documents by a cutting-edge neural language model trained on a hand-coded training set. Then, I feed the extracted barrier information into a dynamic item response theory model to estimate the numerical barrier level of 40 countries between 2006 and 2015 while controlling for various channels of confounding. I argue that the results returned by this approach should be less likely to be contaminated by major confounders such as international politics. Thus, they are well-suited for future political science research.