INFLUENCE FUNCTIONS IN FUNCTIONAL MEASUREMENT ERROR MODELS WITH REPLICATED DATA

We consider the construction and properties of influence functions in the context of functional measurement error models with replicated data. In these models estimates of the parameters can be affected both by the individual observations and the means of replicated observations. We show that influence function of the means of replicates on the estimate of regression coefficients can be only derived under the assumption that the variances of the errors are known, while one for the individual observations can be only derived simultaneously with their influence function on the estimators of the variances of the errors.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

Strategic corporate sustainability performance of Chinese state-owned listed firms: A meta-frontier generalized directional distance function approach

Although sustainability recently became a key factor in social science, little progress has been made toward improving the measurement of sustainability performance. This paper proposes composite corporate sustainability performance indicators using a meta-frontier generalized directional distance function. This advanced approach can measure the efficiency of corporate social responsibility activities by benchmarking, while simultaneously considering industrial heterogeneities, using the meta-frontier approach. First, we propose the concept of a meta-frontier generalized directional distance function. Second, several standardized composite indicators related to corporate sustainability performance are developed. The meta-frontier directional distance function is estimated by solving a series of data envelopment analysis models. Chinese state-owned listed enterprises are then empirically examined using the proposed model. We find significant group heterogeneities in terms of corporate sustainability performance. We also derive some policy implications using the empirical results.     

Using data envelopment analysis for the assessment of technical efficiency of units with different specialisations: An application to agriculture

In this paper we consider the use of data envelopment analysis (DEA) for the assessment of efficiency of units whose output profiles exhibit specialisation. An example of this is found in agriculture where a large number of different crops may be produced in a particular region, but only a few farms actually produce each particular crop. Because of the large number of outputs, the use of conventional DEA models in such applications results in a poor efficiency discrimination. We overcome this problem by specifying production trade-offs between different outputs, relying on the methodology of Podinovski (J Oper Res Soc 2004;55:1311–22). The main idea of our approach is to relate various outputs to the production of the main output. We illustrate this methodology by an application of DEA involving agricultural farms in different regions of Turkey. An integral part of this application is the elicitation of expert judgements in order to formulate the required production trade-offs. Their use in DEA models results in a significant improvement of the efficiency discrimination. The proposed methodology should also be of interest to other applications of DEA where units may exhibit specialization, such as applications involving hospitals or bank branches.     

Carbon Emissions Abatement (CEA) allocation and compensation schemes based on DEA

As environment constraints on economic growth are strengthening, Carbon Emissions Abatement (CEA) allocation becomes a significant issue that draws academia׳s attention. In the literature, the Data Envelopment Analysis (DEA) technique has been applied to obtain CEA allocation with centralized models. Nevertheless, a centralized allocation plan suffers from an implementation difficulty in persuading decision-making units (DMUs) into an agreement. In this paper, we propose a new two-step method to mitigate this side effect. In the first step, we provide improved DEA-based centralized allocation models under the assumptions of constant returns-to-scale (CRS) and variable returns-to-scale (VRS) respectively and in the second step, two compensation schemes are developed for centralized allocation plans. An empirical application to the countries in Organization for Economic Co-operation and Development (OECD) is presented to elaborate the main idea.     

Does state ownership really matter? The dynamic alignment of China's resource environment and firm internationalization strategies

Recent research suggests that unequal access to home country institutional resources affects firm internationalization strategies. We add to this debate, based on an analysis of state-owned (SOEs) and non-state-owned (NSOEs) Chinese mining firms, by developing a more dynamic and multi-layered understanding of this interplay. We find that home institutional support can be ownership-based or performance-based, whereby the former benefits SOEs and the latter favors high-performing NSOEs. Combined, these support structures serve as institutional control mechanisms in promoting competition, performance and loyalty to national policies. In addition, they establish a permanent link between firm- and home country-specific assets and incentivize firms to develop dynamic resource access capabilities at home and abroad. Our findings inform our understanding of the firms-institutions nexus and Chinese foreign direct investment dynamics, especially in industries of strategic importance to the state.     

大数据与政府治理能力提升:机遇、挑战与展望

随着社会问题复杂程度的不断增加,大数据应用为政府解决棘手问题、提升治理能力、应对挑战提供了新思路,但也带来了一定程度的适应难题。因此,探析大数据在我国的政府治理实践中如何得到合理应用、有效推动治理能力提升需要结合国内外成功经验,从机遇和挑战两个维度进行理解。分析发现,大数据可以推动政府治理协同化、政府决策科学化、数据管理规范化,并会遇到数据壁垒、专业能力欠缺以及政策法规滞后等方面的阻碍。研究意义在于分析大数据对政府治理能力提升的作用,为未来我国政府在治理实践中更好地利用大数据技术提供参考。     

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques. To address this problem, we developed a novel statistical obfuscation method (DataSifter) for on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). DataSifter provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Simulation results suggest that DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.     

互联网免费服务价值核算研究

近年来,越来越多的企业选择以低廉或免费的价格为居民提供互联网服务,但产出价值和消费行为却无法在GDP核算中体现。服务业产出被低估一直为学术界所讨论,在此背景下,互联网经济下创新的免费商业模式对传统核算理论的挑战成为本文研究切入点。本文在“互联网免费服务与顾客价值”的易货交易框架下对其价值核算展开研究,将互联网免费服务价值核算与数据资产核算联系在一起,使数据成为连接互联网免费服务与国民经济核算中生产、消费、收入核算的桥梁。文章最后通过模拟核算案例表现了互联网免费服务核算对不同账户的影响,建议对住户部门的生产、消费与收入各增加一笔虚拟处理,对企业的生产、收入及资产也各增加一笔虚拟处理,且这6笔虚拟价值相等。