数据要素市场培育：法律难题、域外经验与中国方案

数字经济时代,数据继土地、劳动力、资本、技术之后成为第五大生产要素。目前我国数据权属确定的法律制度不够完善,数据要素市场交易的法律制度尚未建立,数据要素市场监管的法律制度不够健全,这都制约了我国数据要素市场发展。为了培育数据要素市场,应以数据生产者与数据处理者为划分标准,建立“数据生产者（扩展权利)—数据处理者（有限产权)”的数据产权制度;以“降低制度成本—减少交易成本”的思路完善数据要素市场交易相关规则;以“事前预防与事后规制相结合”的方式完善数据要素市场监管法律制度。     

企业数据赋能实现路径研究——一个资源编排案例

在大数据时代,数据赋能价值逐渐受到实践界与理论界的广泛关注。现有研究主要基于赋能和能力视角,探讨如何获取数据赋能价值,未充分考虑数据独特的资源属性。基于案例企业数据赋能实践,从资源视角出发,采用探索性单案例研究方法,利用资源编排模型打开数据赋能实现路径的“黑箱”。结果发现：第一,基于资源编排模型,企业可通过数据组合、数据与其它企业资源捆绑、数据利用与价值创造3个过程实现数据赋能。第二,处于不同业务发展阶段,企业数据赋能实现形式包括支持型数据赋能、驱动型数据赋能和使能型数据赋能,其取决于数据信息与数字技术发挥的作用及价值。结论既可为数据赋能实现路径研究提供新的视角,对企业数据赋能价值获取也具有一定的指导意义。     

数据要素价值化生态系统建构与市场化配置机制研究

数据要素作为推动经济高质量发展的新动能，如何应用整体观和系统观理论构建数据要素价值化生态系统，成为推进数据要素市场化配置、释放数据要素价值的关键议题。针对数据要素价值化面临的理论与实践挑战，引入创新生态系统理论，系统探究多元主体参与数据要素价值化过程的生态系统联动机制，深入剖析数据要素市场化配置动态过程机制，有助于深化数据要素价值化和市场化配置理论研究，为探索数据要素价值化过程中多元主体协同共创、优化数据要素市场化配置机制、加强数据要素监管与治理、打造数字驱动型区域创新生态系统、激活数字创新生态活力与成效、加快数字中国建设提供理论和决策启示。     

经济数据分析:一种基于数据的函数性视角的分析方法

在经济数据分析中,经常会见到融合时间序列和横截面两者的数据。虽然20多年来,计量经济学大量研究了具有时间和横截面两维特征的面板数据,并提出了许多行之有效的分析方法,但是面板数据只是函数性数据的一种类型,且其分析方法太过于依赖模型的线性结构和假设条件等。本文基于经济数据的函数性特征,引入一种对数据进行分析的全新方法,并利用基于MATLAB编写的程序,率先对经济函数性数据进行分析,拓展了函数性数据分析的应用范围,填补了我国在此方面研究的空白。实例分析结果表明,函数性数据分析方法,较之计量经济学和其它统计方法具有更多的优越性,尤其能够揭示其它方法所不能揭示的数据特征。     

宏观统计数据质量规范研究

根据国内外宏观统计数据质量规范演变的历程,在界定宏观统计数据质量新内涵的基础上,依据国际货币基金组织评价数据质量的规范,本文对我国宏观数据质量的规范进行了实证研究。我国宏观数据质量在金融统计数据、社会人口统计数据上与国际货币基金组织制定的数据公布通用系统(GDDS)一致,而财政统计数据与对外经济数据与GDDS尚存在差距;我国数据公布系统在金融统计和人口统计上与GDDS一致,在对外统计与财政统计上与GDDS存在一定差异。     

主体间性视阈的科学数据共享存续机制研究

首先，以主体间性为基本理论视阈论述科学数据共享存续机制，挖掘科学数据共享活动中可能存在的各类主体间关系，为科研主体的科学数据共享行为赋予选择权，在尊重参与人主体间性的基础上，对参与人主体间性发展建构共生共存空间；其次，从发生前提和内在动力两个维度论述科学数据共享产生的基础，基于主体间性内涵，从内在动机和实践机制等层面剖析主体间性作为科学数据共享行为产生的原因，基于预期建立和预期获益两个阶段阐释科学数据共享产生的内在动力；最后，从个体层面参与约束和整体层面获益分配论证科学数据共享可持续机制，指出个体保留效用的满足和公平合理的获益分配能够满足个体层面参与约束动机及主体持续参与科学数据共享的正向心理预期。基于主体间性的科学数据共享理念能够孕育、激发和强化科研主体参与科学数据共享的内在动机，为科研主体数据共享嵌入行为提供契机，并为科研主体动态关系的建构与存续提供实践启示。     

数据信托参与数据治理:理论逻辑与实现机制

基于数据流通场景的数据治理和数据权利保护,以个人对个人信息/数据的绝对控制权为理论基础;这种个人绝对赋权模式忽视信息/数据的社会属性和公共属性,不能充分发挥信息/数据的价值。传统信托制度参与数据治理与信托当事人理论和信托财产理论等方面存在抵牾。当前实在法体系下,综合运用法律解释等法律方法能够为数据信托参与数据治理提供制度可能。由传统信托理论出发,对未来数据信托制度的建构必须基于数据收集阶段和数据流通阶段的具体区分,数据流通阶段是数据信托发生的主要场景;可以采取修改法律以及出台司法解释等方式更新传统信托理论,使之更加契合数据时代对数据权利保护的根本目的。     

对开放数据新态势的观察与思考

数据已被视为一种重要的战略资源,是全球经济中新的资本要素。开放数据可给世界各国带来巨大的经济效益,催生大量新企业的诞生,并有助于新产品和新服务的创造,推动一些科技领域飞速发展。美国在开放数据方面走在世界最前列,奥巴马政府出重拳推动开放数据以促进创新创业和经济增长,并将科研数据开放视为数据政策和重要组成部分。我国应进一步控掘开放数据对于创新创业及经济增长的推动作用,充分认识开放数据有助于进一步推动企业成为创新主体,同时要注重开放数据的制度化。     

对统计数据质量的几点认识

统计数据是收集加工数据的重要工作,中国统计数据质量中存在着诸如数据不实、信息滞后、分析不够等问题。加 强统计数据质量管理、提高数据质量,必须加强统计管理体制和统计制度改革,加强监督职能,建立健全数据质量评价机制。     

基于空间聚类的区域经济差异分析方法

在对传统的区域经济差异分析方法分析的基础上,提出了基于空间聚类、分级地图和类轴分析的区域经济空间差异分析的新方法,并以东北经济区2000年人均GDP数据分析为例,开展了应用试验,分析结果表明与实际的区域经济差异发展情况相一致。该方法不仅可以揭示经济数据的宏观分布规律,而且可以挖掘其中微观的空间分布特征,可以更好地帮助分析和解释大量空间经济统计数据所蕴涵的经济特征和规律。     

Using paradata to collect better survey data: Evidence from a household survey in Tanzania

Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system (GPS) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.     

数据挖掘技术在企业薪酬设针中的应用研究

现代企业薪酬设计过程中要面对数量众多、关系复杂的信息和数据,而数据挖掘技术在有效处理大量数据方面具有独特的优势。本文以薪酬设计中的数据分析过程为主线,探讨如何应用分类、聚类以及关联规则等数据挖掘技术来分析和处理数据,为薪酬设计提供强有力的决策支持。     

大数据价值来源、价值内容与价值创造机理——基于2011—2021年管理类和商业类SSCI期刊分析

大数据是收集、管理和分析“5V”数据的技术方法,逐渐成为管理和商业研究领域的热门主题。以2011—2021年管理类和商业类SSCI期刊文献为研究样本,在文献计量分析的基础上,围绕“来源—内容—机理”框架,系统回顾管理和商业领域大数据价值主题。首先,从主客体两方面切入,梳理大数据内涵、研究对象及测量方法;其次,聚焦“大数据能带来什么样的价值”这一问题,从效率性价值和创新性价值两类范畴着手,回顾大数据价值内容相关研究成果;第三,系统梳理大数据价值创造过程机理和边界条件。在此基础上,构建管理和商业领域大数据价值研究分析框架,并讨论该领域未来发展方向。研究结论可为管理和商业领域大数据价值研究提供更系统、深入的研究框架与思路。     

Incorporating biometric data in models of consumer choice

The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.     

The Willingness to Sell Personal Data

We assess the willingness of individuals to sell personal data in laboratory experiments. Our experiments are novel in that they are incentivized, the focus on privacy issues is salient, and the use of the data is transparent and unambiguous. We find considerable heterogeneity in the data. Roughly one in six participants refuse to sell personal data at all and a similar fraction sell their data for 2.50 euros or less. Our results contrast with those from hypothetical questionnaires. Those willing to sell, request, on average, 15 euros for their contact details and 19 euros for their Facebook data.     

DSGE models,detrending, and the method of moments

One important question in the DSGE literature is whether we should detrend data when estimating the parameters of a DSGE model using the moment method. It has been common in the literature to detrend data in the same way the model is detrended. Doing so works relatively well with linear models, in part because in such cases the information that disappears from the data is usually related to the parameters that also disappear from the detrended model. Unfortunately, in heavy non‐linear DSGE models, parameters rarely disappear from detrended models, but information does disappear from the detrended data. Using a simple real business cycle model, we show that both the moment method estimators of parameters and the estimated responses of endogenous variables to a technological shock can be seriously inaccurate when detrended data are used in the estimation process. Using a dynamic stochastic general equilibrium model and U.S. data, we show that detrending the data before estimating the parameters may result in a seriously misleading response of endogenous variables to monetary shocks. We suggest building the moment conditions using raw data, irrespective of the trend observed in the data.     

知识产权运营领域数据要素市场化配置路径研究

基于马克思经济学资源配置理论,探讨知识产权运营与数据要素市场化配置的关联性。在梳理数据要素市场化配置基础政策、关联政策和专门政策的基础上,对知识产权大数据平台系统、大数据金融产品、大数据中心建设和大数据产业应用联盟进行比较。结果发现:第一,我国知识产权运营领域数据要素市场化配置改革主要存在3大瓶颈：数据要素产权界定不明、交易流转不畅及安全监管不易;第二,知识产权运营领域数据要素市场化配置路径需从加快知识产权运营数据汇聚共享、推动知识产权运营数据产业化应用和强化知识产权运营数据安全治理3个方面予以完善。     

A PROTOTYPE MICRO-MACRO LINK FOR THE CANADIAN HOUSEHOLD SECTOR

This paper seeks to forge a link between Canadian macro and micro data relating to the household sector. The analysis is in three parts. The first part begins with National Accounts data on the personal sector. These data are adjusted to remove transactions relating to non-biological persons, so that the result is income and expenditure for the household sub-sector. The second part starts with the annual household survey used to collect income distribution data. These survey data are augmented in various ways to account for under-reporting and to add information from other micro data sets particularly the periodic survey of household expenditure patterns and a sample of individual income tax returns. The result is a comprehensive, albeit partially synthetic, household micro data set. In the final part of the paper these two largely independent data sets are compared, and the general quality of the results is discussed.     

Poverty Mapping with Aggregate Census Data: What is the Loss in Precision?

Spatially disaggregated maps of the incidence of poverty can be constructed by combining household survey data and census data. In some countries (notably China and India), national statistics agencies are reluctant, for reasons of confidentiality, to release household‐level census data, but they are generally more willing to release aggregated census data, such as village‐ or district‐level means. This paper examines the loss in precision associated with using aggregated census data instead of household‐level data to generate poverty estimates. The authors show analytically that using aggregated census data will result in poverty rates that are biased downward (upward) if the rate is below (above) 50%, and that the bias approaches zero as the poverty rate approaches zero, 50%, and 100%. Using data from Vietnam, it is found that the mean absolute error in estimating district‐level poverty rates is 2.5 percentage points if the census data are aggregated to the enumeration‐area level means, and 3–4 percentage points if the data are aggregated to commune or district level. Finally, the authors propose a method for reducing the error using variances calculated from the census. When this approach is applied to the Vietnam data, this method can cut the size of the aggregation errors by around 75%.     

Should Mobile Marketers Collect Data Other Than Geo‐Location?*

We take today's mobile marketing data landscape as a starting point and consider a duopoly model of third‐degree price discrimination in which firms can complement geo‐location information with data on consumer flexibility of varying quality. We show that, depending on consumer heterogeneity, higher‐quality flexibility data affect profits according to three different patterns. In equilibrium, both firms tend to acquire data if the data are of high quality, while only one acquires data if the data quality is low. Firms are likely to gain from additional data if consumers have similar preferences and/or when data are precise. Although social welfare (weakly) improves, consumers can be harmed.