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Using Data Science Methods to Study International Trade
17: Political Sciences
In Song Kim
Feb 7, 2020
Hao Zhang, email@example.com
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 UROP will apply regular expression to extract the key information about rules of origin from various preferential trade agreements. He/She will read specific rules intensively and code exceptions. In addition, he/she may merge product-level rules of origin with trade and tariff datasets and conduct preliminary statistical analysis. Our UROP will become familiar with the basics of international trade and develop skills in dataset building and statistical analysis. UROPs will work approximately 10 hours per week for the Spring 2020. Interested applicants should send a short statement of interest indicating the preferred start date, along with a resume/CV to Hao Zhang <firstname.lastname@example.org>.
The candidate is expected to be familiar with regular expression and python/R programming in general. He/She should also be attentive to details.