The Emotional Environment of Families Experiencing Chronic Poverty in India

The emotional environment of families living in chronic poverty in India was explored. We hypothesized that the poor (when compared to the nonpoor) would report more aversive interaction and would report less family and life satisfaction. Results showed partial support for our hypotheses. Poor husbands expressed love to wives in fewer ways than the nonpoor men, and they were more likely to use physical or verbal abuse with wives and children. Poor wives showed anger more frequently to husbands and children than the nonpoor wives. However, there were no differences between the poor and the nonpoor on family and life satisfaction. Thus, income alone was not a sufficient predictor of the family's emotional environment.     

An analysis of the supplier selection process

Customers select suppliers based on the relative importance of different attributes such as quality, price, flexibility, and delivery performance. This study examines the difference between managers' rating of the perceived importance of different supplier attributes and their actual choice of suppliers in an experimental setting. We use two methods: a Likert scale set of questions, to determine the importance of supplier attributes; and a discrete choice analysis (DCA) experiment, to examine the choice of suppliers. The results indicate that although managers say that quality is the most important attribute for a supplier, they actually choose suppliers based largely on cost and delivery performance.     

Fuzzy measures of the incidence of relative poverty and deprivation: a multi-dimensional perspective

In this paper we present a methodology for the study of multi-dimensional aspects of poverty and deprivation. The conventional poor/non-poor dichotomy is replaced by defining poverty as a matter of degree, determined by the place of the individual in the income distribution. The fuzzy poverty measure proposed is in fact also expressible in terms of the generalised Gini measure. The same methodology facilitates the inclusion of other dimensions of deprivation into the analysis: by appropriately weighting indicators of deprivation to reflect their dispersion and correlation, we can construct measures of non-monetary deprivation in its various dimensions. These indicators illuminate the extent to which purely monetary indicators are insufficient in themselves in capturing the prevalence of deprivation. An important contribution of the paper is to identify rules for the aggregation of fuzzy sets appropriate for the study of poverty and deprivation. In particular, we define a ‘composite’ fuzzy set operator which takes into account whether the sets being aggregated are of a ‘similar’ or a ‘dissimilar’ type. These rules allow us to meaningfully combine income and the diverse non-income deprivation indices at the micro-level and construct what we have termed ‘intensive’ and ‘extensive’ indicators of deprivation. We note that mathematically the same approach can be carried over to the study of persistence of poverty and deprivation over time.     

A statistical semantics for causation

We propose a model-theoretic definition of causation, and show that, contrary to common folklore, genuine causal influences can be distinguished from spurious covariations following standard norms of inductive reasoning. We also establish a sound characterization of the conditions under which such a distinction is possible. Finally, we provide a proof-theoretical procedure for inductive causation and show that, for a large class of data and structures, effective algorithms exist that uncover the direction of causal influences as defined above.     

Union and nonunion wages at the firm level: A combined institutional and econometric analysis

This paper presents an analysis of union-nonunion wage differences at the intrafirm level. At this level of analysis, modeling of the union effect can be informed by a historical overview of the organizational context of unionization, which is not as feasible at other levels of analysis. The findings suggest that a simultaneous equation (2-stage) model, in which union status is treated as an endogenous variable, is not appropriate in this case. Moreover, OLS estimations of the union effect on wages from a one-stage model in which union status is exogenous is found to be efficient and unbiased. The study suggests that combining institutional details of the organizational context with econometric analysis facilitates a better understanding of not only the magnitude of wage differences but also of how the differences may be interpreted in the context of industrial relations.     

The use of sampling in conjunction with population censuses

In Asia and the Pacific, the practice of governments is increasingly to collect information on their populations' size, age and sex composition, geographic distribution, and certain other basic demographic and socioeconomic characteristics on the basis of a complete (100%) enumeration, and to supplement this basic information by collecting information on a larger range of variables on a sample basis. The additional information, which is gathered as part of the census operation, may be related to such population characteristics as migration, employment, fertility, and health. A complete census is generally indispensable for obtaining information about small domains, and also for obtaining politically important data, which must be seen to be free from sampling variability. A complete census is typically confined to obtaining a detailed picture of the number and basic structural characteristics of the entire population, with as much detail as possible about local areas. Sample surveys can quickly obtain a wider variety of more complex data. Sampling applications for the census include 1) using sampling in the design and control of census operations, such as in planning, testing, controlling, and evaluating the census; 2) using sample enumeration to supplement items covered in the complete census; 3) sampling the census results for processing to make the results available more quickly and at lower cost; and 4) extracting samples of microlevel files of detailed census data so as to facilitate dissemination of primary data.