Deep Trade Agreements and UK Industries of the Future

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Research Proposal Abstract All modern industries rely, to some degree, on international tradeand trade agreements with partnering nations. Reduced trade barriers can have manyeconomic benefits, from increased welfare and national income, advances in innovation,reduced costs from economies of scale, and increased competition. In 2017, the UKgovernment's industrial strategy white paper emphasised the "industries of the future"(IOTF), such as artificial intelligence, big data, clean energy, and self-driving vehicles, i.e.,industries having key strategic value to the UK economy. Negotiating tailored trade deals tobest nurture these industries is critical for post-Brexit international trade. Modern tradeagreements contain many provisions besides tariff reductions in areas as diverse as servicestrade, competition policy, trade-related investment measures, or public procurement. Existingresearch has struggled with overfitting and severe multicollinearity problems whenestimating the effects of these provisions on trade flows. Recent research has tried to movebeyond estimating the overall impact of Preferential Trade Agreements (PTAs) andestablishing the relative importance of individual trade agreement provisions in determiningan agreement's overall impact (e.g., Kohl, Brakman, and Garretsen, 2016, Mulabdic, Osnago,and Ruta, 2017, Dhingra, Freeman, and Mavroeidi, 2018). Most provisions jointly appear inseveral trade deals, thus creating a high degree of collinearity among them. Mattoo et al.(2017) use the number of provisions in a deal to measure the depth, Dhingra et al. (2018)instead of group provisions in relatively small groups of bundles. Both approaches toovercome collinearity come at the cost of losing important information about the contributionof specific provisions. I will be using the techniques of Breinlich, Corradi, Rocha, Ruta, SantosSilva and Zylkin (2021). They instead use the complete set of individual provisions andmachine learning techniques, such as LASSO (Least Absolute Shrinkage and SelectionOperator), to choose the subset that affects bilateral trades adaptively. The main goal of myresearch is to use data disaggregated by industries (e.g., Chen and Novy 2011) to study andidentify the effects of provisions on bilateral trade flows in specific industries, with anemphasis on the IOTF. This paper will build on recent developments in the machine learningand variable selection literature to propose novel data-driven methods for selecting the mostimportant provisions and quantifying their impact on trade flows in these specific sectors. Iaim to contribute to the international trade literature by merging the big data/machinelearning approach of Breinlich et al. (2021) to the sectorial disaggregated approach of Chenand Novy (2011). My research will also contribute to estimating the gravity equation withfour-layer panel data, i.e., in the presence of export and import countries, industries, andtime. To consider the role of time and country-dependent time effect would not be trivial. Iaim at devising computationally feasible estimators, extending, e.g., Larch et al. (2019) toindustry disaggregated data. I will apply these methods to a comprehensive data set recentlymade available by the World Bank of PTA provisions (Mattoo, Rocha and Ruta, 2020). Thisdata set provides evidence on the content of trade agreements both at the extensive margin(number of policy areas covered) and the intensive margin (commitments within a policyarea). Importantly, my dissertation will have an impact outside of academia. I believe thatidentifying the provisions that affect exports in a given industry may advise a governmentabout which agreements/provisions it should subscribe to in negotiations.