How to measure Corporate Social Responsibility

Compliance with Corporate Social Responsibility (CSR) standards may require capacity that varies from one aspect to the other and companies in different industries may encounter different difficulties. Since CSR is a multidimensional concept, latent variable models may be usefully employed to provide a unidimensional measure of the ability of a firm to fulfil CSR standards. A methodology based on Item Response Theory has been implemented on the KLD sustainability dataset. Results show that companies in the industries Oil & Gas, Industrials, Basic Materials and Telecommunications have a higher difficulty to meet the CSR standards. Criteria based on Environment, Community relations and Product quality have a large capacity to select the firms with the best CSR performance, while Governance does not exhibit similar behavior. A stock selection based on the ranking of the firms according to our CSR measure outperforms, in terms of risk-adjusted returns, stock selection based on other criteria.Socially Responsible Investment, CSR ability, latent variable model, item response theory

Identification of discrete concentration graph models with one hidden binary variable

Conditions are presented for different types of identifiability of discrete variable models generated over an undirected graph in which one node represents a binary hidden variable. These models can be seen as extensions of the latent class model to allow for conditional associations between the observable random variables. Since local identification corresponds to full rank of the parametrization map, we establish a necessary and sufficient condition for the rank to be full everywhere in the parameter space. The condition is based on the topology of the undirected graph associated to the model. For non-full rank models, the obtained characterization allows us to find the subset of the parameter space where the identifiability breaks down.Comment: Published in at http://dx.doi.org/10.3150/12-BEJ435 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Parameter identifiability of discrete Bayesian networks with hidden variables

Identifiability of parameters is an essential property for a statistical model to be useful in most settings. However, establishing parameter identifiability for Bayesian networks with hidden variables remains challenging. In the context of finite state spaces, we give algebraic arguments establishing identifiability of some special models on small DAGs. We also establish that, for fixed state spaces, generic identifiability of parameters depends only on the Markov equivalence class of the DAG. To illustrate the use of these results, we investigate identifiability for all binary Bayesian networks with up to five variables, one of which is hidden and parental to all observable ones. Surprisingly, some of these models have parameterizations that are generically 4-to-one, and not 2-to-one as label swapping of the hidden states would suggest. This leads to interesting difficulties in interpreting causal effects.Comment: 23 page

Missing data: a unified taxonomy guided by conditional independence

Recent work (Seaman et al., 2013; Mealli & Rubin, 2015) attempts to clarify the not always well-understood difference between realised and everywhere definitions of missing at random (MAR) and missing completely at random. Another branch of the literature (Mohan et al., 2013; Pearl & Mohan, 2013) exploits always-observed covariates to give variable-based definitions of MAR and missing completely at random. In this paper, we develop a unified taxonomy encompassing all approaches. In this taxonomy, the new concept of ‘complementary MAR’ is introduced, and its relationship with the concept of data observed at random is discussed. All relationships among these definitions are analysed and represented graphically. Conditional independence, both at the random variable and at the event level, is the formal language we adopt to connect all these definitions. Our paper covers both the univariate and the multivariate case, where attention is paid to monotone missingness and to the concept of sequential MAR. Specifically, for monotone missingness, we propose a sequential MAR definition that might be more appropriate than both everywhere and variable-based MAR to model dropout in certain contexts

Item Response Models to measure Corporate Social Responsibility

Corporate Social Responsibility (CSR) is a multidimensional con- cept that involves several aspects, ranging from Environment, to Social and Governance. Companies aiming to comply with CSR standards have to face challenges that vary from one aspect to the other and from one industry to the other. Latent variable models may be use- fully employed to provide a unidimensional measure of the grade of compliance of a firm with CSR standards that is both understand- able and theoretically solid. A methodology based on Item Response Theory has been implemented on the multidimensional sustainability rating as expressed by KLD dataset from 1991 to 2007. Results sug- gest that companies in the industry Oil & Gas together with firms in Industrials, Basic Materials and Telecommunications have a higher difficulty to meet the CSR standards. Criteria based on Human rights, Environment, Community and Product quality have a large capacity to select the best performing firms, as they are very discriminant, while Governance does not exhibit similar behavior. A stock selection based on the ranking of the firms according to the proposed CSR measure supports the hypothesis of a positive relationship between CSR and financial performanc

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