Software firms dataset about diversification and interdependence

We start by identifying U.S.-based software organizations in the computer programming and data processing industry (SIC 737), as a knowledge-intensive high-growth setting. We integrate two main data sources. First, to collect the knowledge-based measures, we use publicly available data provided by the U.S. Patent and Trademark Office (USPTO). Using the General Architecture for Text Engineering (GATE) software, we design queries that retrieve the complete class and subclass information for each patent, as well as citations, inventors, and total patents granted between 1998 and 2011 inclusive. We aggregate the data by organization-year observation at the class and subclass levels and use these aggregated measures to compute the knowledge-based predictors and covariates. To compute moving averages for some variables, we collect five years of additional USPTO data which makes our knowledge dataset span between 1993 and 2011. Second, we use Compustat to collect organization-level control variables such as assets, number of employees, market valuation, R&D expenditures, intangibles, solvency, and slack. The integration of the two datasets yields a final sample panel of 100 organizations with 3.2 years of observations on average per organization from 1998 to 2011.本研究首先以标准产业分类（Standard Industrial Classification, SIC）737所涵盖的计算机编程与数据处理行业内的美国本土软件企业为研究对象，将其作为知识密集型高增长的研究场景。本研究整合了两类核心数据源： 首先，为获取知识相关测度指标，我们使用由美国专利商标局（United States Patent and Trademark Office, USPTO）公开提供的数据。我们借助文本工程通用架构（General Architecture for Text Engineering, GATE）软件设计检索语句，获取1998年至2011年（含首尾两年）内每件专利的完整类别与子类信息，同时获取其引用关系、发明人信息以及授权专利总量。我们以企业-年度为观测单元，按专利类别与子类层级对数据进行聚合，并基于这些聚合后的测度指标计算知识相关的预测变量与协变量。为计算部分变量的移动平均值，我们额外获取了五年的USPTO数据，由此使得知识类数据集的时间跨度覆盖1993年至2011年。 其次，我们通过Compustat数据库获取企业层面的控制变量，包括资产总额、员工数量、市场估值、研发（Research and Development, R&D）支出、无形资产、偿债能力以及组织冗余。 两类数据集整合后，最终得到1998年至2011年间的面板样本，包含100家企业，每家企业平均拥有3.2个年度观测值。