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A number of multidimensional poverty measures that respect the ordinal nature of dimensions have recently been proposed within the counting approach framework. Besides ensuring a reduction in poverty, however, it is important to monitor distributional changes to ensure that poverty reduction has been inclusive in reaching the poorest. Distributional issues are typically captured by adjusting a poverty measure to be sensitive to inequality among the poor. This approach, however, has certain practical and conceptual limitations. It conflicts, for example, with some policy-relevant measurement features, such as the ability to decompose a measure into dimensions post-identification and does not create an appropriate framework for assessing disparity in poverty across population subgroups. In this chapter, we propose and justify the use of a separate decomposable inequality measure – a positive multiple of “variance” – to capture the distribution of deprivations among the poor and to assess disparity in poverty across population subgroups. We demonstrate the applicability of our approach through two contrasting inter-temporal illustrations using Demographic Health Survey data sets for Haiti and India.

The authors analyze a model for N different measurements of a persistent latent time series when measurement errors are mean-reverting, which implies a common trend among measurements. The authors study the consequences of overdifferencing, finding potentially large biases in maximum likelihood estimators (MLE) of the dynamics parameters and reductions in the precision of smoothed estimates of the latent variable, especially for multiperiod objects such as quinquennial growth rates. The authors also develop an R² measure of common trend observability that determines the severity of misspecification. Finally, the authors apply their framework to US quarterly data on GDE and GDI, obtaining an improved aggregate output measure.

Many studies, motivated by concerns for activity-based costing (ABC) implementation efforts being less than successful, have suggested that the lack of success in this area stems more from behavioral, as opposed to technical, factors. This concern for the behavioral aspects of systems implementation has also emerged from much of the more general information systems research examining determinants of implementation success. Accordingly, the purpose of this study is to determine if a popular process theory of motivation, expectancy theory, would be useful in explaining the motivation of managers to incorporate ABC information into their job. Data obtained from two experiments employing a judgment modeling methodology support the relevance of both the valence and force models of expectancy theory in this context. Further, the judgments provided by the subject managers suggest they perceive improved product cost accuracy as the most beneficial outcome of ABC use, followed by an equivalent appreciation for both an enhanced ability to communicate the underlying economics of the firm and to identify non-value-added activities. Additionally, subject managers exhibited a greater concern for the possibility that obtaining the data to maintain the ABC system would be difficult and costly than they did for concerns that the ABC information would increase the level of complexity of the information that they use.

This chapter analyzes the properties of an alternative least-squares based estimator for linear panel data models with general predetermined regressors. This approach uses backward means of regressors to approximate individual specific fixed effects (FE). The author analyzes sufficient conditions for this estimator to be asymptotically efficient, and argue that, in comparison with the FE estimator, the use of backward means leads to a non-trivial bias-variance tradeoff. The author complements theoretical analysis with an extensive Monte Carlo study, where the author finds that some of the currently available results for restricted AR(1) model cannot be easily generalized, and should be extrapolated with caution.

After a vigorous debate in the late 1970s, the sociology of emotion put aside most discussion of whether or not the physiological arousal associated with emotion labels is differentiated. Since this early period, scholars have made great progress on two fronts. First, theories about the interrelationship of identity, action and emotion have specified a family of new concepts related to emotion. Second, a large corpus of research on the physiological correlates of emotional experience emerged. In this chapter, we review the well-developed control theories of identity and emotion, and focus on the key concepts that might relate to different physiological states. We then review the general classes of physiological measures, discussing their reliability, intrusiveness and other features that might determine their usefulness for tracking responses to social interaction. We then offer a highly provisional mapping of physiological measures onto the concepts that they might potentially measure, given past research about how these physiological processes relate to environmental stimuli. While any linkage between concepts and measures must be speculative at this point, we hope that this review will serve as a stimulus to theoretically guided research that begins to assess the validity of these new measures for sociological use.