数据是数字经济时代的重要资源和关键要素，数据流通是促进数据要素价值释放和数据要素市场建立的首要环节。“数据二十条”的出台和数字技术的发展分别为数据安全可信流通扫清了产权障碍和技术阻碍，如何为数据要素科学、合理定价仍是数据要素价值释放与市场建立的主要掣肘。因此，本文从数据要素特征角度出发，通过区分数据的信息性价值与知识性价值，尝试为数据要素提供定价方法。数据既能为企业提供生产经营、组织管理、内控风险等内部事实，也能帮助企业感知外部信息，这些均是数据的信息性价值。在数据要素的信息性交易场景中，数据API和汇编型数据为其主要产品。本研究通过爬取京东万象数据API商城的交易数据进行回归分析，结果表明隐私级别、数据来源、数据质量、数据范围和业务类型等是影响数据价格的关键特征。本研究发现了数据API价格会随着业务请求参数中个人信息的识别范围、敏感程度发生相对变化，呈现“对勾曲线”关系。同时，本研究还首次量化了数据API产品现行市场定价规律中蕴含的隐私相对价格，约为数据API市场均价的50%。进一步，本研究从数据的信息性特征出发，以前述实证结论为基础，通过构建隐私级别加权的信息熵公式，并结合收益法思想，为以用户画像为代表的数据信息提供了定价方法与计算案例。数据能够形成知识，促进新技术、新产品、新模式的产生与应用，给企业带来创新性价值。数据知识产权是对具有知识价值的数据要素的产权保护形式，国家正在着手开展相关试点工作。目前来看，数据知识产权主要包括蕴含商业秘密的数据知识和不含商业秘密的数据知识产品两类。本研究通过梳理知识产品与知识产权的价值评估与定价方法，综合考虑数据要素特征，为数据知识产权设计了包括投入质量、供方能力、产出结果3大层面，覆盖数据数量、数据质量，历史创新表现等18个维度的价值评估指标体系，并综合运用层次分析法、模糊综合评价法、专家打分法和公式法，结合计算案例说明了数据知识产权的定价方法。本研究为数据要素定价提供了一个区分信息性特征和知识性特征的理论框架，针对数据知识产权探索性地设计了价值评估指标体系与定价方法，并通过实证分析了影响数据API价格的特征，首次测度了隐私的相对市场价格。

Data is an important resource and key factor in the era of digital economy. Data circulation is a significant link to promote the release of data production factor value and the establishment of data production factor market. The introduction of “Opinions of the CPC Central Committee and the State Council on Building a Data Basis System to Better Play the Role of Data Production Factor” and the development of digital technology have respectively cleared away property rights and technical obstacles for the safe and reliable circulation of data. How to price data elements and products scientifically and reasonably is still the main constraint to release the value of data production factor and establish the market. Therefore, from the perspective of characteristics of data, this paper tries to provide pricing methods for data production factor by distinguishing information value and knowledge value of data.Data can not only provide enterprises with internal facts such as production, operation, organization, management, and internal risk control, but also help enterprises perceive external information. These are the information value of data. In the information trading scenario of data production factor, API and assembled data are the main products. In this study, the transaction data of Jingdong Vientiane API Mall is collected for regression. The empirical results show that privacy level, data source, data quality, data range and business type are the key characteristics affecting API price. This study finds that the API price will change with the identification range and sensitivity of personal information in the API request parameters, presenting a “tick curve” relationship. At the same time, this study also quantifies for the first time the relative price of privacy contained in the current market pricing law of API products, which is about 50% of the average market value of API products. Further, starting from the information characteristics of the data and based on the aforementioned empirical conclusions, this study provides pricing methods and a calculation case for the data information represented by the user profile by constructing the information entropy formula weighted by the privacy level and combining with the idea of income method.Data can produce knowledge, promote the generation and application of new technologies, new products and new models, and bring innovative value to enterprises. Data intellectual property is a form of property protection for data with intellectual value, and China is carrying out relevant pilot work. At present, data intellectual property mainly includes data knowledge containing business secrets and data knowledge products without secrets. By reviewing the value evaluation and pricing methods of intellectual products and intellectual property, and considering the characteristics of data factor comprehensively, this study designs a value evaluation index system for data intellectual property, which includes input quality, supplier ability and output result, and covers 18 dimensions, such as data quantity, data quality, innovation performance. The method of pricing data intellectual property is illustrated by analytic hierarchy process, fuzzy comprehensive evaluation, Delphi method and formula. A calculation case is also provided.This study provides a theoretical framework to distinguish information and knowledge characteristics for pricing data production factor, and designs an exploratory value evaluation index system and pricing method for data intellectual property rights. Through empirical proof, the characteristics affecting API price are analyzed, and the relative market price of privacy is measured for the first time.