Does Firm Size Comfound the Relationship Between Corporate Social Performance and Firm Financial Performance?

There has been some theoretical and empirical debate that the positive relationship between corporate social performance (CSP) and firm financial performance (FFP) is spurious and in fact caused by a third factor, namely large firm size. This study examines this question by integrating three meta-analyses of more than two decades of research on (1) CSP and FFP, (2) firm size and CSP, and (3) firm size and FFP into one path-analytic model. The present study does not confirm size as a third factor which would confound the relationship between CSP and FFP. That is, even if firm size is controlled for across studies (comprising, on average, over 15 000 observations), CSP and FFP remain positively correlated, showing a "true-score" corrected path coefficient p of 0.37.     

Orthogonal main effect plans for two and three factors in small blocks

Orthogonal main-effect plans for two and three factors in small blocks are obtained from the dual of adjusted orthogonal row-column designs. The method for constructing efficient plans is presented, and a relationship between the average efficiency factors of the row-column design and the corresponding main effects is given for the two-factor case. Orthogonal Main Effect.     

Deconfounding and Causal Regularisation for Stability and External Validity

We review some recent works on removing hidden confounding and causal regularisation from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts on the issue of concept drift, raised recently by Efron, when the data generating distribution is changing.     

On the effect of obesity on employment in the presence of observed and unobserved confounding

The aim of this paper was to estimate the effect of obesity on the employment probability for Italian men and women accounting for both observed and unobserved confounding. We use microdata collected by the Italian National Statistical Office for the year 2009 during a multi‐scope survey of Italian households. The employment–obesity relationship is estimated after controlling for observed confounding by using probit regression and a propensity score weighting approach. To control for both observed and unobserved confounding (endogeneity), a semiparametric recursive bivariate probit model is employed instead. Our findings suggest that obesity has a significant negative effect on the employment probability and that endogeneity might not be an important issue.     

Adjustments and their Consequences—Collapsibility Analysis using Graphical Models

We review probabilistic and graphical rules for detecting situations in which a dependence of one variable on another is altered by adjusting for a third variable (i.e., non‐collapsibility or non‐invariance under adjustment), whether that dependence is causal or purely predictive. We focus on distinguishing situations in which adjustment will reduce, increase, or leave unchanged the degree of bias in an association that is taken to represent a causal effect of one variable on the other. We then consider situations in which adjustment may partially remove or introduce a bias in estimating causal effects, and some additional special cases useful for case‐control studies, cohort studies with loss, and trials with non‐compliance (non‐adherence).     

sk−p Fractional factorial designs in sb blocks

Draper and Guttman (1997) shows that for basic 2^k−p designs, p≥0, k − p replicates of blocks designs of size two are needed to estimate all the usual (estimable) effects. In this work, we provide an algebraic formal proof for the two-level blocks designs results and present results applicable to the general case; that is, for the case of s ^k factorial (p=0) or s ^k−p fractional factorial (p >0) designs in s ^b blocks, where 0<b<k− p, at least replicates are needed to clear up all possible effects. Through the theoretical development presented in this work, it can provide a clearer view on why those results would hold. We will also discuss the estimation equations given in Draper and Guttman (1997).  Research supported in part by the National Science Council of Taiwan, R.O.C., Grant No. NSC 89-2118-M110-010. Acknowledgement. The authors would like to thank the referee for very helpful comments.