Deep Trade Agreements and UK Industries of the Future

研究建议摘要所有的现代工业都在某种程度上依赖于国际贸易和与伙伴国的贸易协定。减少贸易壁垒可以带来许多经济效益，包括增加福利和国民收入、创新进步、规模经济降低成本以及增加竞争。2017年，英国政府的产业战略白色文件强调了“未来产业”（IOTF），如人工智能、大数据、清洁能源和自动驾驶汽车，即，对英国经济具有重要战略价值的行业。谈判量身定制的贸易协议以最好地培育这些行业对于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削减关税外，还包含许多条款。现有研究在估计这些条款对贸易流动的影响时一直难以解决过度拟合和严重的多重共线性问题。最近的研究试图超越估计优惠贸易协定（PTA）的总体影响，并确定个别贸易协定条款在确定协定总体影响方面的相对重要性（例如，Kohl，Brakman和Garretsen，2016年，Mulabdic，Osnago和Ruta，2017年，Dhingra，Freeman和Mavroeidi，2018年）。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等人（2017）使用交易中的条款数量来衡量深度，Dhingra et al.（2018）而不是相对较小的捆绑组中的组条款。这两种克服共线性的方法都是以丢失关于具体条款贡献的重要信息为代价的。我将使用Breinlich，Corradi，罗查，Ruta，SantosSilva和Zylkin（2021）的技术。相反，他们使用完整的个人规定和机器学习技术，如LASSO（最小绝对收缩和选择运算符），以选择自适应影响双边交易的子集。myresearch的主要目标是使用按行业分类的数据（例如，Chen and Novy 2011）研究和确定条款对特定行业双边贸易流的影响，重点是IOTF。本文将基于机器学习和变量选择文献的最新发展，提出新的数据驱动方法，用于选择最重要的条款并量化其对这些特定部门贸易流量的影响。我的目标是通过将Breinlich et al.（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。本文的研究也将有助于用四层面板数据估计重力方程，在出口和进口国家，行业和时间的存在。考虑时间的作用和国家依赖的时间效应将不是微不足道的。我的目标是设计计算上可行的估计，扩展，例如，Larch et al.（2019）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PTA条款的综合数据集（Mattoo，罗查和Ruta，2020）。这组数据提供了贸易协定内容的证据，包括广泛的边际（涵盖的政策领域数量）和密集的边际（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，确定影响特定行业出口的条款可能会建议政府在谈判中签署哪些协议/条款。

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.