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Using Data Science Methods to Learn International Trade
17: Political Sciences
In Song Kim
January 31, 2021
Hao Zhang <firstname.lastname@example.org>
Global production has become a standard mode of manufacturing in the era of globalization: Apple's iPhone is designed in the U.S., but its battery and processor are from Korea, camera from Japan, glass screen from Europe, touch ID from Taiwan, while the final output is manufactured in China. We study the rules that govern the country-origins in modern international trade agreements, i.e., Rules of Origin. Specifically, our project studies the impacts of rules of origin on regional trade flows and global production chains. To do so, we will construct and analyze the first comprehensive dataset on the rules of origin through natural language processing. We will then use various machine learning and data science methods to investigate the network effects of rules of origin on trade flows using product-level trade data. Our UROPs will further develop our algorithm that parses rules of origin from preferential trade agreements texts. He/she will read specific rules intensively and understand their substantial meanings. In addition, he/she may conduct preliminary statistical analysis with trade and tariff data. Our UROP will become familiar with the basics of international trade and develop skills in dataset building and statistical analysis.
The candidate should be familiar with Python or R/Stata. He/she can work remotely but should attend weekly Zoom meetings and share research progress on Github. Note that UROPs will work approximately 10 hours per week for IAP and Spring 2021. Interested applicants should send a short statement of interest indicating the preferred start date, along with a resume/CV to Hao Zhang <email@example.com>.