Re-identification in the Absence of Common Variables for Matching

A basic concern in statistical disclosure limitation is the re-identification of individuals in anonymised microdata. Linking against a second dataset that contains identifying information can result in a breach of confidentiality. Almost all linkage approaches are based on comparing the values of variables that are common to both datasets. It is tempting to think that if datasets contain no common variables, then there can be no risk of re-identification. However, linkage has been attempted between such datasets via the extraction of structural information using ordered weighted averaging (OWA) operators. Although this approach has been shown to perform better than randomly pairing records, it is debatable whether it demonstrates a practically significant disclosure risk. This paper reviews some of the main aspects of statistical disclosure limitation. It then goes on to show that a relatively simple, supervised Bayesian approach can consistently outperform OWA linkage. Furthermore, the Bayesian approach demonstrates a significant risk of re-identification for the types of data considered in the OWA record linkage literature.     

Statistical Disclosure Control Methods for Census Frequency Tables

This paper provides a review of common statistical disclosure control (SDC) methods implemented at statistical agencies for standard tabular outputs containing whole population counts from a census (either enumerated or based on a register). These methods include record swapping on the microdata prior to its tabulation and rounding of entries in the tables after they are produced. The approach for assessing SDC methods is based on a disclosure risk–data utility framework and the need to find a balance between managing disclosure risk while maximizing the amount of information that can be released to users and ensuring high quality outputs. To carry out the analysis, quantitative measures of disclosure risk and data utility are defined and methods compared. Conclusions from the analysis show that record swapping as a sole SDC method leaves high probabilities of disclosure risk. Targeted record swapping lowers the disclosure risk, but there is more distortion of distributions. Small cell adjustments (rounding) give protection to census tables by eliminating small cells but only one set of variables and geographies can be disseminated in order to avoid disclosure by differencing nested tables. Full random rounding offers more protection against disclosure by differencing, but margins are typically rounded separately from the internal cells and tables are not additive. Rounding procedures protect against the perception of disclosure risk compared to record swapping since no small cells appear in the tables. Combining rounding with record swapping raises the level of protection but increases the loss of utility to census tabular outputs. For some statistical analysis, the combination of record swapping and rounding balances to some degree opposing effects that the methods have on the utility of the tables.     

Disclosure‐protected Inference Using Generalised Linear Models

Vast amounts of data that could be used in the development and evaluation of policy for the benefit of society are collected by statistical agencies. It is therefore no surprise that there is very strong demand from analysts, within business, government, universities and other organisations, to access such data. When allowing access to micro‐data, a statistical agency is obliged, often legally, to ensure that it is unlikely to result in the disclosure of information about a particular person or organisation. Managing the risk of disclosure is referred to as statistical disclosure control (SDC). This paper describes an approach to SDC for output from analysis using generalised linear models, including estimates of regression parameters and their variances, diagnostic statistics and plots. The Australian Bureau of Statistics has implemented the approach in a remote analysis system, which returns analysis output from remotely submitted queries. A framework for measuring disclosure risk associated with a remote server is proposed. The disclosure risk and utility of approach are measured in two real‐life case studies and in simulation.     

A Paradigm for Masking (Camouflaging) Information

This is an expository paper. Here we propose a decision-theoretic framework for addressing aspects of the confidentiality of information problems in publicly released data. Our basic premise is that the problem needs to be conceptualized by looking at the actions of three agents: a data collector, a legitimate data user, and an intruder. Here we aim to prescribe the actions of the first agent who desires to provide useful information to the second agent, but must protect against possible misuse by the third. The first agent is under the constraint that the released data has to be public to all; this in some societies may not be the case.
A novel aspect of our paper is that all utilities—fundamental to decision making—are in terms of Shannon's information entropy. Thus what gets released is a distribution whose entropy maximizes the expected utility of the first agent. This means that the distribution that gets released will be different from that which generates the collected data. The discrepancy between the two distributions can be assessed via the Kullback–Leibler cross-entropy function. Our proposed strategy therefore boils down to the notion that it is the information content of the data, not the actual data, that gets masked. Current practice of "statistical disclosure limitation" masks the observed data via transformations or cell suppression. These transformations are guided by balancing what are known as "disclosure risks" and "data utility". The entropy indexed utility functions we propose are isomorphic to the above two entities. Thus our approach provides a formal link to that which is currently practiced in statistical disclosure limitation.     

Using Multiple Imputation to Integrate and Disseminate Confidential Microdata

In data integration contexts, two statistical agencies seek to merge their separate databases into one file. The agencies also may seek to disseminate data to the public based on the integrated file. These goals may be complicated by the agencies' need to protect the confidentiality of database subjects, which could be at risk during the integration or dissemination stage. This article proposes several approaches based on multiple imputation for disclosure limitation, usually called synthetic data, that could be used to facilitate data integration and dissemination while protecting data confidentiality. It reviews existing methods for obtaining inferences from synthetic data and points out where new methods are needed to implement the data integration proposals.     

Corporate carbon risk exposure,voluntary disclosure,and financial reporting quality

The study examines whether corporate carbon risk exposure is associated with financial reporting quality and whether voluntary carbon disclosure mediates the relationship. We analyze data drawn from firms traded on the Johannesburg Stock Exchange (JSE), for the period 2011 to 2015. We document robust evidence that firms with higher carbon risk exposure tend to provide financial statements of poorer quality (i.e., direct effect) and this association is partially mediated through voluntary carbon disclosure (i.e., indirect effect). The overall negative association between corporate carbon risk exposure and the firm's financial reporting quality is partly explained by the quality of voluntary carbon disclosure.     

Corporate carbon risk,voluntary disclosure,and cost of capital: South African evidence

The study examines the interplay among corporate carbon risk, voluntary disclosure, and cost of capital within the context of South Africa, a “rising power” in the climate policy debate. We develop a system of simultaneous equations models and analyze data drawn from firms traded on the Johannesburg Securities Exchange (JSE), for the period 2010 to 2015, using the three‐stage least squares procedure. We find that voluntary carbon disclosure is associated with lower overall (and equity) cost of capital, after controlling for corporate carbon risk. We also find that firms with higher carbon risk tend to provide better quality carbon disclosure and signal the possibility of high carbon risk to avoid negative market reactions resulting from concealing carbon information. Although the capital market does not appear to incorporate individual firm's carbon risk exposure into the required cost of capital, we find that it generally requires higher returns for companies operating in carbon‐intensive sectors. These findings suggest that firms could exploit the virtues of voluntary carbon disclosure to reduce their overall (and equity) cost of capital. Our findings also imply that regulators and policymakers could point to the cost of capital reducing role of voluntary disclosure to lure firms into voluntarily providing superior quality carbon disclosures.     

Disclosure protection of microdata: problems and solutions

National statistical offices meet an increasing demand for the dissemination of data sets containing individual data on respondents. This trend may cause problems with respect to the care taken not to disclose data about individuals. Therefore the risk of disclosure of each microdata set to be released should be assessed. If this risk is too high, measures have to be taken to protect the data set. This contribution describes the disclosure problem, and explains why it is a real problem. Using the concept of uniqueness, some theory is developed which may help to establish the risk of identification. It turns out that useful microdata sets can only be released if some of the disclosure risks are dealt with by legal arrangements, rather than by restrictions on the data to be released.     

Water disclosure and firm risk: Empirical evidence from highly water‐sensitive industries in China

In this paper, we examine the relationship between water disclosure and firm risk. Specifically, based upon a panel dataset of 334 Chinese listed firms operating in highly water‐sensitive industries during 2010–2015, we use regression models to analyze the relationships between water disclosure and three types of firm risk (i.e., total risk, systematic risk, and idiosyncratic risk) and the moderating effects of media coverage on these relationships. Our empirical results show that (a) although there are no significant relationships between water disclosure and total risk and idiosyncratic risk, there is a significant negative relationship between water disclosure and systematic risk; (b) negative media coverage weakens the negative relationship between water disclosure and systematic risk, whereas nonnegative media coverage reinforces this negative relationship. Our cornerstone study examines the effect of a specific type of environmental disclosure (i.e., water disclosure) on firm risk, and our empirical findings are different from previous studies, which examined the effects of overall corporate social responsibility (CSR) disclosure on firm risk. We analyze the causes of the differences in detail. With this study, we make theoretical, empirical, and managerial contributions to CSR disclosure–firm risk research in business ethics literature.     

Public organizations and biodiversity disclosure: Saving face to meet a legal obligation?

The biodiversity disclosure practices of sub-national governments remain understudied and poorly understood. The present study investigates the biodiversity disclosure practices of the 101 public organizations in Québec, Canada. The study's purpose is to better understand the internal dynamics that may explain the various shortcomings observed in official documents. To meet this objective, Goffman's dramaturgical frontstage/backstage analogy is used. In particular, the current study employs a content analysis of the sampled organizations' action plans and annual reports (N = 505) as well as interviews (N = 35). The results highlight both significant gaps in Québec's public organizations' biodiversity disclosure practices and a tendency for these organizations to make symbolic rather than substantial commitments. Also, problematic behaviors that can affect transparency were identified among the organizations—specifically, bricolage of actions and the manipulation of figures to influence stakeholder perceptions.     

Environmental,social and governance disclosure and default risk

We investigate whether environmental, social and governance (ESG) disclosure is related to default risk. Using a sample of US nonfinancial institutions from 2006 to 2017, we find that ESG disclosure is positively related to Merton's distance to default and is negatively related to the credit default swap spread, which suggests that firms with a higher ESG disclosure have lower default risk. Our analysis further indicates that the inverse effect of ESG disclosure on default risk is through increased profitability and reduced performance variability and cost of debt. We also document that the negative impact of ESG disclosure on default risk is existent only for mature and older firms. These results are important for all stakeholders of firms, including shareholders and bondholders to consider firm's ESG disclosure in conjunction with life cycle stage before making their investment decisions.     

Litigation Risk and Corporate Voluntary Disclosure: Evidence from Two Quasi-Natural Experiments

Abstract

We examine the effect of litigation risk on corporate voluntary disclosure using two quasi-natural experiments, which have substantial and opposing impacts on the litigation risk of firms headquartered in the Ninth Circuit. We find that firms in the Ninth Circuit decrease (increase) the quantity and quality of their voluntary disclosure, relative to control firms, when their litigation risk is lowered (elevated). The pre-treatment test shows an indistinguishable trend between treatment and control firms. A battery of robustness checks indicates that our results are not driven by alternative explanations. We hypothesize and find that the impact of litigation risk is more pronounced when firms have bad news and that firms are more likely to preempt bad news through voluntary disclosures when litigation risk is elevated. Overall, results from both experiments suggest that litigation risk causally increases corporate voluntary disclosure.     

How cultural dimensions,legal systems,and industry affect environmental reporting? Empirical evidence from an international perspective

Past research has paid little attention to the impact of stakeholder engagement, cultural, legal, and industrial contexts on environmental disclosure. Thus, the aim of this paper is to explore how these three institutional factors affect the reporting of environmental information by companies in different countries. This research draws on institutional theory: normative isomorphism, coercive isomorphism, and mimetic isomorphism. This study uses the generalised method of moments procedure. The findings show that the legal system and certain cultural dimensions such as individualism, uncertainty avoidance, long‐term orientation, and indulgence are determinants of voluntary disclosure of environmental information (individualism and indulgence—negatively; uncertainty avoidance and long‐term orientation—positively), particularly when companies belong to industries with high environmental risk.     

Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database

In most countries, national statistical agencies do not release establishment‐level business microdata, because doing so represents too large a risk to establishments’ confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released establishment data are simulated from statistical models designed to mimic the distributions of the underlying real microdata. In this article, we describe an application of this strategy to create a public use file for the Longitudinal Business Database, an annual economic census of establishments in the United States comprising more than 20 million records dating back to 1976. The U.S. Bureau of the Census and the Internal Revenue Service recently approved the release of these synthetic microdata for public use, making the synthetic Longitudinal Business Database the first‐ever business microdata set publicly released in the United States. We describe how we created the synthetic data, evaluated analytical validity, and assessed disclosure risk.     

Disclosure risks for microdata

In this paper a model is developed for assesing disclosure risks of a microdata set. It is an extension of the one presented in Bethlehem et al. (1988). It is used to calculate (an upper bound of) the risk that an investigator is able to reidentify at least one individual in an anonimyzed data set, and hence discloses some sensitive information about him. This risk is shown to depend on, among other things, two variables which can be controlled by the statistical office which is disseminating such a data set: the 'coarseness' of the key variables and the size of the data set. The model yields guidelines as to the usage of these two instruments to control the disclosure risk.     

On identification disclosure and prediction disclosure for microdata

Two definitions of statistical disclosure - identification disclosure and prediction disclosure - are compared. Identification disclosure implies prediction disclosure but not vice versa. It is argued, however, that if sampling takes place then cases where prediction disclosure occurs and identification disclosure does not either have very small probability or do not present disclosure problems different from those normally met in the release of aggregate statistics. Finally the estimation of population uniqueness using the Poisson-Gamma model is considered.     

Statistical disclosure in general three-dimensional tables

Statistical offices are concerned with problems of protecting confidential information when publishing data in statistical tables. One method to avoid disclosure is the method of cell suppression, in which the values of the sensitive cells in the table are suppressed from publication. To prevent the values of the sensitive cells from being calculated from the tables totals, additional suppressions are necessary. The problem of minimizing the loss of information caused by the additional suppressions is a difficult optimization problem. We present and compare the performance of some heuristics for cell suppression for general three-dimensional tables.     

港口装卸企业安全交底措施的研究与应用

通过历年港口安全事故的详细资料,不难发现大部分港口安全事故都是责任事故,为了控制事故的发生,有必要在作业前进行安全交底工作,提示作业人员在作业过程中可能受到的安全风险。以港口企业为例,将JHA与HAZOP分析相结合进行危险源辨识形成安全交底信息库,以此为基础合理设计开发安全交底确认卡,并运用风险优先数筛选出需重点注意的风险信息,进而提高港口作业人员安全与责任意识,减少港口安全事故。     

基于可持续发展观下的环境会计信息披露

科学技术的日新月异使全球经济正以前所未有的速度向前发展,为人类创造了大量的财富。但经济快速增长的同时,随之而来的是自然资源的迅速耗竭和环境的严重污染。环境问题已由局部问题上升为人类生存性问题。要真正的让全社会认识到企业对环境的影响程度,必须加强企业对环境造成损害程度科学的披露,这就涉及到环境会计信息披露相关问题。文章从可持续发展观的视角入手,阐述了目前我国环境信息披露的现状和方式以及环境信息披露的局限性,并就目前的局限性提出了改进建议。     

论上市公司财务预测信息的披露

我国上市公司应披露的财务预测信息主要包括前瞻性信息、盈利预测、业绩预告和风险因素四大类。目前上市公司财务预测信息的披露尚存在许多问题,突出地表现为:编制与披露的法律、法规、规章、制度不完善,财务预测信息有效供给严重不足,财务预测信息披露不及时,没有建立类似美国的"安全港规则",没有形成财务预测信息披露的竞争机制,从而影响了财务预测信息的决策有效性。因此,必须采取相应措施来系统解决上述问题。