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How to construct nationally Representative Firm Level Data from the ORBIS Global Database

机译:如何从奥比斯全球数据库构建具有全国代表性的企业级数据

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摘要

Firm-level data on productivity, financial activity and firms' international linkages have become essential for research in the fields of macro, international finance and growth. This paper describes the development of a firm-level global panel dataset for public and private companies based on the administrative micro-dataset ORBIS, provided commercially by Bureau van Dijk Electronic Publishing (BvD). The ORBIS database provides data on firms' financial and productive activities from balance sheets and income statements together with detailed information on firms' domestic and international ownership structure for over 130 million companies across the world. Researchers need to overcome several challenges before making the database usable for research. First, the database is not designed for large downloads that is essential for an econometric analysis. Second, there are several inherent biases in the database that affect the download process and lead to missing information. Third, the raw data may contain a number of irregularities which, if not dealt with, will result in data loss during a standard cleaning procedure. In combination, these issues cause minimal coverage of small firms, extensive missing data, and poor national representation. We give detailed instructions on the data gathering process from ORBIS in terms of downloading methodology and cleaning procedure so that a researcher can construct a database that is nationally representative with minimal missing information. We provide examples from several European countries to present the process and discuss the resulting dataset in detail.
机译:有关生产力,金融活动和企业国际联系的企业级数据对于宏观,国际金融和增长领域的研究已变得至关重要。本文介绍了基于局微数据集ORBIS的,面向上市公司和私营公司的公司级全球面板数据集的开发,该数据集由Bureau van Dijk Electronic Publishing(BvD)提供。 ORBIS数据库提供了资产负债表和损益表中有关公司财务和生产活动的数据,以及有关全球超过1.3亿家公司的国内外所有权结构的详细信息。在使数据库可用于研究之前,研究人员需要克服一些挑战。首先,该数据库不是为进行大型计量下载而设计的,这对于计量经济分析至关重要。其次,数据库中存在一些固有的偏差,这些偏差会影响下载过程并导致信息丢失。第三,原始数据可能包含许多不规则性,如果不加以处理,将导致在标准清洁过程中数据丢失。这些问题共同导致小型公司的覆盖面最小,数据缺失和国家代表性不足。我们从下载方法和清理程序方面对ORBIS的数据收集过程进行了详细说明,以便研究人员可以构建一个全国代表性的数据库,而其信息丢失最少。我们提供了几个欧洲国家/地区的示例,以介绍该过程并详细讨论所得的数据集。

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