Gender discrimination and efficiency in marriage: the bargaining family under scrutiny

We consider a repeated family bargaining model that links the topics of employment and households. A key aspect of the model is that marital bargaining power is determined endogenously. We show that: (1) the efficiency of household decisions is sometimes inversely related to the prevailing degree of gender discrimination in labor markets; (2) women who are discriminated against have difficulty enforcing cooperative household outcomes because they may be extremely limited to credibly punish opportunistic behavior by their male partners; (3) the likelihood that sharing rules such as “equal sharing” are maintained throughout a marriage relationship is highest when men and women face equal opportunities in labor markets. Responsible editor: Deborah Cobb-Clark     

Structural analysis and strategic research design: Studying politicized interorganizational networks

Network analysis and structural sociology have given relatively little attention to questions of research design and data collection. This paper discusses the substantive implications of conducting structural analysis with more intensive work on research design and data collection. Based on evidence from a case study of a politicized social structure, the major features of strategic research design are presented in a discussion of cognitive and behavioral data, types of ties, multiplexity, and access restrictions.     

Editorial

Helmut Anheier is Assistant Professor of Sociology at Rutgers University, New Brunswick, New Jersey, and Research Associate at the Institute for Policy Studies, The Johns Hopkins University, Baltimore, Maryland, USA.     

A statistical method to convert published response rates into marginal distributions with an example application in psoriasis

Assessment of severity is essential for the management of chronic diseases. Continuous variables like scores obtained from the Hamilton Rating Scale for Depression or the Psoriasis Area and Severity Index (PASI) are standard measures used in clinical trials of depression and psoriasis. In clinical trials of psoriasis, for example, the reduction of PASI from baseline in response to therapy, in particular the proportion of patients achieving at least 75%, 90%, or 100% improvement of disease (PASI 75, PASI 90, or PASI 100), is typically used to evaluate treatment efficacy. However, evaluation of the proportions of patients reaching absolute PASI values (eg, ≤1, ≤2, ≤3, or ≤5) has recently gained greater clinical interest and is increasingly being reported. When relative versus absolute scores are standard, as is the case with the PASI in psoriasis, it is difficult to compare absolute changes using existing published data. Thus, we developed a method to estimate absolute PASI levels from aggregated relative levels. This conversion method is based on a latent 2‐dimensional normal distribution for the absolute score at baseline and at a specific endpoint with a truncation to allow for baseline inclusion criterion. The model was fitted to aggregated results from simulations and from 3 phase III studies that had known absolute PASI proportions. The predictions represented the actual results quite precisely. This model might be applied to other conditions, such as depression, to estimate proportions of patients achieving an absolute low level of disease activity, given absolute values at baseline and proportions of patients achieving relative improvements at a subsequent time point.     

Locally robust correlation coefficients

Robustness of correlation coefficients is studied in the framework of testing for independence, robustness being measured by the minimum asymptotic power over a suitable class of local alternatives. A minimax result is derived; the robust correlation coefficient is computed from suitably transformed and truncated observations. Moreover, the related approach of Huber (1977) is analyzed, and inadmissibility of his minimax solution is shown.     

A general robustness property of rank correlations

Rank correlations are shown to be generally robust in the sense that the tests for independence, which they naturally define, have weakly equicontinuous error probabilities. The ordinary sample correlation coefficient is not robust in this respect.