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.     

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.     

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.     

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.     

Estimation of a linear regression under microaggregation with the response variable as a sorting variable

Microaggregation is a popular statistical disclosure control technique for continuous data. The basic principle of microaggregation is to group the observations in a data set and to replace them by their corresponding group means. However, while reducing the disclosure risk of data files, the technique also affects the results of statistical analyses. The paper deals with the impact of microaggregation on a multiple linear regression in continuous variables. We show that parameter estimates are biased if the dependent variable is used to form the groups. Using this result, we develop a consistent estimator that removes the aggregation bias, and derive its asymptotic covariance matrix.     

The endogeneity bias in the relation between cost-of-debt capital and corporate disclosure policy

Abstract

The purpose of this paper is twofold. First, we provide a discussion of the problems associated with endogeneity in empirical accounting research. We emphasize problems arising when endogeneity is caused by (1) unobservable firm-specific factors and (2) omitted variables, and discuss the merits and drawbacks of using panel data techniques to address these causes. Second, we investigate the magnitude of endogeneity bias in Ordinary Least Squares (OLS) regressions of cost-of-debt capital on firm disclosure policy. We document how including a set of variables which theory suggests to be related with both cost-of-debt capital and disclosure and using fixed effects estimation in a panel data-set reduces the endogeneity bias and produces consistent results. This analysis reveals that the effect of disclosure policy on cost-of-debt capital is 200% higher than what is found in OLS estimation. Finally, we provide direct evidence that disclosure is impacted by unobservable firm-specific factors that are also correlated with cost of capital.     

Measuring Identification Risk in Microdata Release and Its Control by Post‐randomisation

Statistical agencies often release a masked or perturbed version of survey data to protect the confidentiality of respondents' information. Ideally, a perturbation procedure should provide confidentiality protection without much loss of data quality, so that the released data may practically be treated as original data for making inferences. One major objective is to control the risk of correctly identifying any respondent's records in released data, by matching the values of some identifying or key variables. For categorical key variables, we propose a new approach to measuring identification risk and setting strict disclosure control goals. The general idea is to ensure that the probability of correctly identifying any respondent or surveyed unit is at most ξ, which is pre‐specified. Then, we develop an unbiased post‐randomisation procedure that achieves this goal for ξ>1/3. The procedure allows substantial control over possible changes to the original data, and the variance it induces is of a lower order of magnitude than sampling variance. We apply the procedure to a real data set, where it performs consistently with the theoretical results and quite importantly, shows very little data quality loss.     

非财务信息与财务危机预警——来自我国上市公司的证据

本文从我国上市公司中,选取30家财务危机公司,与30家财务健康公司作为样本。首先对两组公司的13个财务变量,进行均值的t检验和Wilcoxon秩检验。选择合适的财务变量集,再将此财务变量集分别与董事会高管人员持股比例、股权集中度、股票价格变动趋势,及是否更换会计师事务所等,四个非财务变量一起作为解释变量,建立Logistic回归模型。实证研究结果表明,股票价格变动趋势与财务危机风险显著相关,加入非财务变量的预测模型效果更优。     

The impact of voluntary corporate disclosures on the ex-ante cost of capital for Swiss firms

The relationship between disclosure quality and cost of equity capital is an important topic in today's economy. In general, economic theory and anecdotal evidence suggest a negative association. Empirical work on this link, however, is confronted with major methodological drawbacks – neither disclosure level nor cost of capital can be observed directly – and has documented somewhat confounding results so far. Adopting a finite horizon version of the residual income model, I provide evidence on the nature of the above relationship and try to quantify the effect of a firm's voluntary disclosure policy on its implied cost of capital. Switzerland seems especially suited for an analysis of this kind given that Swiss firms have considerable reporting discretion and the mandated level of disclosure is low. For a cross-sectional sample of seventy-three non-financial companies I show a negative and highly significant association between the two variables. The magnitude is such that the most forthcoming firms enjoy about a 1.8 to 2.4% cost advantage over the least forthcoming firms. The findings persist even after controlling for other potentially influential variables, e.g. risk characteristics and firm size. Furthermore, adjusting for self-selection bias – a major concern in disclosure studies – the marginal effect remains of the same direction and even increases in magnitude, although at lower levels of statistical significance. One reason for the strong relationship might be found in differing institutional factors between the US and Swiss capital markets.     

Statistical Disclosure Risk: Separating Potential and Harm

Statistical agencies are keen to devise ways to provide research access to data while protecting confidentiality. Although methods of statistical disclosure risk assessment are now well established in the statistical science literature, the integration of these methods by agencies into a general scientific basis for their practice still proves difficult. This paper seeks to review and clarify the role of statistical science in the conceptual foundations of disclosure risk assessment in an agency’s decision making. Disclosure risk is broken down into disclosure potential, a measure of the ability to achieve true disclosure, and disclosure harm. It is argued that statistical science is most suited to assessing the former. A framework for this assessment is presented. The paper argues that the intruder’s decision making and behaviour may be separated from this framework, provided appropriate account is taken of the nature of potential intruder attacks in the definition of disclosure potential.     

Information Measures in Perspective

Information-theoretic methodologies are increasingly being used in various disciplines. Frequently an information measure is adapted for a problem, yet the perspective of information as the unifying notion is overlooked. We set forth this perspective through presenting information-theoretic methodologies for a set of problems in probability and statistics. Our focal measures are Shannon entropy and Kullback–Leibler information. The background topics for these measures include notions of uncertainty and information, their axiomatic foundation, interpretations, properties, and generalizations. Topics with broad methodological applications include discrepancy between distributions, derivation of probability models, dependence between variables, and Bayesian analysis. More specific methodological topics include model selection, limiting distributions, optimal prior distribution and design of experiment, modeling duration variables, order statistics, data disclosure, and relative importance of predictors. Illustrations range from very basic to highly technical ones that draw attention to subtle points.     

A non-parametric index of corporate governance in the banking industry: An application to Indian data

This paper presents a methodological framework for constructing a non-parametric index of corporate governance for banks. The index is constructed by aggregating six distinct dimensional indices capturing different dimensions of corporate governance, namely board effectiveness, audit function, risk management, remuneration, shareholder rights and information, and disclosure and transparency. For aggregation, a tailored version of data envelopment analysis (DEA) approach which is popularly known as constrained ‘Benefit-of-the-Doubt (BoD)’ model is employed. This approach is unique and distinctive in the sense that it requires no a priori knowledge of weights, and assigns endogenous weights obtained from actual data to individual dimensions of bank governance in order to construct a composite index of corporate governance. This methodological framework has illustrated by applying it for a data set of 40 Indian banks operating in the year 2017. The data set has been compiled using 58 governance regulations as defined by relevant jurisdictions.     

List augmentation with model based multiple imputation: a case study using a mixed-outcome factor model

This study concerns list augmentation in direct marketing. List augmentation is a special case of missing data imputation. We review previous work on the mixed outcome factor model and apply it for the purpose of list augmentation. The model deals with both discrete and continuous variables and allows us to augment the data for all subjects in a company's transaction database with soft data collected in a survey among a sample of those subjects. We propose a bootstrap-based imputation approach, which is appealing to use in combination with the factor model, since it allows one to include estimation uncertainty in the imputation procedure in a simple, yet adequate manner. We provide an empirical case study of the performance of the approach to a transaction data base of a bank.     

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.     

Estimation strategies for the regression coefficient parameter matrix in multivariate multiple regression

We consider improved estimation strategies for the parameter matrix in multivariate multiple regression under a general and natural linear constraint. In the context of two competing models where one model includes all predictors and the other restricts variable coefficients to a candidate linear subspace based on prior information, there is a need of combining two estimation techniques in an optimal way. In this scenario, we suggest some shrinkage estimators for the targeted parameter matrix. Also, we examine the relative performances of the suggested estimators in the direction of the subspace and candidate subspace restricted type estimators. We develop a large sample theory for the estimators including derivation of asymptotic bias and asymptotic distributional risk of the suggested estimators. Furthermore, we conduct Monte Carlo simulation studies to appraise the relative performance of the suggested estimators with the classical estimators. The methods are also applied on a real data set for illustrative purposes.     

上市公司审计委员会与年报风险信息披露——基于管理层信息操纵视角

上市公司年报风险信息是了解公司经营现状与预测未来发展的重要信息来源,它具有信息增量价值,但容易受到管理层信息操纵的影响。审计委员会作为公司治理的独立机制,对管理层具有内部监督作用。基于此,利用2010—2021年上市公司风险信息披露数据,实证检验了审计委员会对管理层信息操纵的治理作用。研究发现,审计委员会独立性、勤勉性和专业性越强,年报风险信息披露水平越高,且在不同性质市场环境中审计委员会的治理作用存在差异。机制检验结果表明,内部控制发挥了部分中介作用;此外,在内部治理环境较弱与外部监督压力较大时,审计委员会的治理作用更加显著。结论拓展了年报风险信息披露的影响因素研究,并通过构建不同维度的风险信息指标来综合衡量披露水平,证实了审计委员会对管理层信息操纵的治理效应,为审计委员会更好发挥监督效果以及监管部门进一步完善上市公司风险信息披露制度提供了重要支撑。     

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.     

基于核心竞争力的信息披露管理研究

上市公司的信息披露是一种战略管理行为.信息披露战略管理不仅能增加公司信息披露的可理解性,而且有利于树立公司形象,并最终有利于实现公司竞争战略目标.信息披露战略管理要求依据竞争对手、上下游公司以及竞争环境的变化及时调整公司的信息披露策略,上市公司对信息披露的管理可以跨越公司边界,向上下游公司以及竞争对手延伸.     

Models for the Valuation of International Convertible Bonds

This paper presents a family of three models for the valuation of international convertible bonds which are denominated in a currency different from that of the country of the issuing corporation. The first model is two-state, with the value of the underlying stock and the value of the currency of the parent country as state variables. The second model is one-state. It is derived from the first one, with the two state variables collapsed into one, listed share price times exchange rate. The third model is an extension of the second one, in that it includes the risk of devaluation of the currency of the country of the issuing corporation. One specific Euroconvertible bond, issued by the Swedish corporation SCA, is used as illustration throughout.     

The effect of microaggregation by individual ranking on the estimation of moments

Microaggregation by individual ranking (IR) is an important technique for masking confidential econometric data. While being a successful method for controlling the disclosure risk of observations, IR also affects the results of statistical analyses. We conduct a theoretical analysis on the estimation of arbitrary moments from a data set that has been anonymized by means of the IR method. We show that classical moment estimators remain both consistent and asymptotically normal under weak assumptions. This theory provides the justification for applying standard statistical estimation techniques to the anonymized data without having to correct for a possible bias caused by anonymization.