Text processing of trade agreements as data can help us find better answers to a large number of policy questions, such as: textual methods include a large number of research instruments that allow us to gain new knowledge about trade agreements. For example, textual similarity measures are able to identify fine-grained differences in contract design. So-called dimensionality reduction techniques, which compress textual information contained in a text into a number of abstract variables, can help predict the impact of trade agreements more accurately than the measures available so far. How similar are trade agreements between countries and regions? This makes it increasingly difficult to analyse and compare the content of trade agreements, which is necessary to assess their impact on international trade and welfare. Big data and textual methods can help researchers, policymakers, and other stakeholders systematically extract information (and data) from trade regulatory texts. . . .
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