论文标题
映射汽车领域的公司内部贸易：一种网络方法
Mapping intra firm trade in the automotive sector: a network approach
论文作者
论文摘要
公司内部贸易描述了附属公司之间的贸易，并且随着全球生产分散而越来越重要。但是，关于全球企业内贸易模式的统计和数据广泛不可用。这项研究提出了一种新型的多层次方法，将公司和国家级别的数据结合在一起，以构建一套国家内部贸易网络，以为汽车生产链的各个细分市场。多级网络由宏观层面的国际贸易网络，微型级别的公司所有权网络以及在中索级别连接两者的公司所有权网络。一种主题检测方法用于过滤这些网络，以在国家之间提取潜在的公司内部贸易关系，其中图案（或子结构）是通过贸易联系的两个国家，每个国家都隶属于一家公司，而这两家公司则由所有权链接。该基序检测用于提取潜在的国家水平内部贸易关系。指数随机图模型（ERGM）应用于国家级内部贸易网络，该网络是汽车生产链的每个部分的一个，以告知该国级别贸易的决定因素。
Intra-firm trade describes the trade between affiliated firms and is increasingly important as global production is fragmented. However, statistics and data on global intra-firm trade patterns are widely unavailable. This study proposes a novel multilevel approach combining firm and country level data to construct a set of country intra-firm trade networks for various segments of the automotive production chain. A multilevel network is constructed with a network of international trade at the macro level, a firm ownership network at the micro level and a firm-country affiliation network linking the two, at the meso level. A motif detection approach is used to filter these networks to extract potential intra-firm trade ties between countries, where the motif (or substructure) is two countries linked by trade, each affiliated with a firm, and these two firms linked by ownership. The motif detection is used to extract potential country level intra-firm trade ties. An Exponential Random Graph Model (ERGM) is applied to the country level intra-firm trade networks, one for each segment of the automotive production chain, to inform on the determinants of intra-firm trade at the country level.
