Deep Trade Agreements and UK Industries of the Future

所有现代产业都在一定程度上依赖于国际贸易和与伙伴国家的贸易协定。减少贸易壁垒可以带来许多经济利益，包括增加福利和国民收入、促进创新、减少规模经济带来的成本以及增加竞争。2017年，英国政府的产业战略白皮书强调了“未来产业”（IOTF），如人工智能、大数据、清洁能源、自动驾驶汽车等，即对英国经济具有关键战略价值的产业。谈判量身定制的贸易协议，以最好地培育这些行业，对英国脱欧后的国际贸易至关重要。现代贸易协定除了在服务贸易、竞争政策、与贸易有关的投资措施或公共采购等领域降低关税外，还包含许多条款。在估计这些规定对贸易流动的影响时，现有的研究一直在与过度拟合和严重的多重共线性问题作斗争。最近的研究试图超越估计优惠贸易协定（pta）的整体影响，并确定个别贸易协定条款在确定协议整体影响中的相对重要性（例如，Kohl, Brakman, and Garretsen, 2016; Mulabdic, ossnago,and Ruta, 2017; dhinggra, Freeman, and Mavroeidi, 2018）。大多数条款共同出现在几个贸易协定中，因此在它们之间产生了高度的共线性。Mattoo et al.（2017）使用交易中的条款数量来衡量深度，dhinggra et al.（2018）而不是在相对较小的捆绑组中分组条款。这两种方法克服共线性的代价是失去关于具体规定的作用的重要信息。我将使用Breinlich， Corradi, Rocha, Ruta， SantosSilva和Zylkin（2021）的技术。相反，他们使用完整的单个条款集和机器学习技术，例如LASSO（最小绝对收缩和选择算子），以自适应地选择影响双边贸易的子集。我研究的主要目标是使用按行业分类的数据（例如Chen和Novy 2011）来研究和确定规定对特定行业双边贸易流动的影响，重点是物联网。本文将以机器学习和变量选择文献的最新发展为基础，提出新的数据驱动方法来选择最重要的条款，并量化它们对这些特定部门贸易流的影响。我的目标是通过将Breinlich等人（2021）的大数据/机器学习方法与Chenand Novy（2011）的部门分类方法相结合，为国际贸易文献做出贡献。我的研究也将有助于用四层面板数据估计重力方程，即在进出口国家、行业和时间存在的情况下。考虑时间和国家时间效应的作用不是微不足道的。旨在设计计算上可行的估计器，例如Larch等人（2019）将其扩展到行业分类数据。我将把这些方法应用于世界银行最近提供的关于优惠贸易区条款的综合数据集（Mattoo， Rocha和Ruta， 2020）。该数据集提供了贸易协定内容的证据，包括广泛范围（涵盖的政策领域数量）和密集范围（政策领域内的承诺）。重要的是，我的论文将在学术界之外产生影响。我认为，确定影响某一特定行业出口的条款，可能会给政府提供在谈判中应该签署哪些协议/条款的建议。

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.