Fertility differences between the family of orientation and the family or procreation in Barbados

Data from an island-wide probability sample of 4119 Barbadian females aged 16-50 were used to study whether there has been an intergenerational fertility decline between the respondents and their mothers. The fertility of the respondents, all from the low or lower middle class, was significantly lower than that of their mothers. However, the size of the family of procreation was seen to be positively related to the size of the family of orientation; i.e., those from large families tended to have large families and vice versa. There was, however, a regression to the mean. There were no differences between women from small and large families as to fertility norms, age at 1st use of contraceptives, or actual practice of contraception. Women from small families did tend to enter sexual relationships and get pregnant at a later age. The women from small families were better educated, earned higher incomes, and had higher status occupations, all factors which might have influenced their fertility. Women from larger families cited higher numbers for both small and large families than did the women from small families. This indicates a perceptual difference which was, in turn, related to fertility differences.     

APPLICATION AND IMPLEMENTATION

Organizations are in constant interaction with their environment. This phenomenon has been termed in the literature as the environment-organizational interface. An organization acquires information concerning changes in its environment via some scanning system. This paper presents the results of an empirical investigation of the scanning process. The relationships of scanning to different environmental states is discussed and various propositions advanced. Different environmental states (stable versus dynamic) are moderated by the determinants of hierarchical levels and functional specialties in the firm.     

METHODS FOR QUANTIFYING SUBJECTIVE PROBABILITIES AND MULTI-ATTRIBUTE UTILITIES*†

In many situations where normative decision-aiding techniques could be usefully applied, historical data are inadequate for estimating the required outcome probabilities, and economic methodologies are inadequate for estimating the aggregate utility derived from the several outcome attributes. In such cases it is often useful to obtain the required estimates in the form of expert judgments, i.e. to obtain subjective probabilities and multi-attribute utilities. Similarly, in many situations where behavioral decision processes are to be studied, it is necessary to scale the expectations and perceived values of the decision makers. This article describes the methods for eliciting subjective probabilities and multi-attribute utilities whose usefulness has been empirically studied and reported in the research literature. It also contains summary guidelines concerning the elicitation and use of such judgments.     

Cost/quality tradeoffs for control procedures in information systems

An analytical framework is presented to study the cost/benefit tradeoffs of alternative internal control scenarios designed to ensure quality of outputs from information systems. A mathematical model is used to evaluate the impact that various alternatives have on system performance and cost. The model can be used to compare the benefits gained from enhanced processing with those arising from more effective internal control procedures. Model variables under the control of system designers include placement and effectiveness of internal control procedures, cost and quality of processing activities, both manual and computerized, and cost and quality of correction procedures. The model incorporates a penalty cost incurred by failure to detect and correct errors. The analytical process is illustrated through application to a specific information system.     

Correcting missing-data bias in historical demography

Studies on population history are often based on incomplete records of life histories. For instance, in studies using data obtained from family reconstitution, the date of death is right censored (by migration) and the censoring time is never observed. Several methods for the correction of mortality estimates are proposed in the literature, most of which first estimate the number of individuals at risk and then use standard techniques to estimate mortality. Other methods are based on statistical models. In this paper all methods are reviewed, and their merits are compared by applying them to simulated and to seventeenth-century data from the English parish of Reigate. An ad hoc method proposed by Ruggles performs reasonably well. Methods based on statistical models, provided they are sufficiently realistic, give comparable accuracy and allow the estimation of several other quantities of interest, such as the distribution of migration times.     

On predictive distributions and Bayesian networks

In this paper we are interested in discrete prediction problems for a decision-theoretic setting, where the task is to compute the predictive distribution for a finite set of possible alternatives. This question is first addressed in a general Bayesian framework, where we consider a set of probability distributions defined by some parametric model class. Given a prior distribution on the model parameters and a set of sample data, one possible approach for determining a predictive distribution is to fix the parameters to the instantiation with the maximum a posteriori probability. A more accurate predictive distribution can be obtained by computing the evidence (marginal likelihood), i.e., the integral over all the individual parameter instantiations. As an alternative to these two approaches, we demonstrate how to use Rissanen's new definition of stochastic complexity for determining predictive distributions, and show how the evidence predictive distribution with Jeffrey's prior approaches the new stochastic complexity predictive distribution in the limit with increasing amount of sample data. To compare the alternative approaches in practice, each of the predictive distributions discussed is instantiated in the Bayesian network model family case. In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure. In the empirical part of the paper the predictive distributions are compared by using the simple tree-structured Naive Bayes model, which is used in the experiments for computational reasons. The experimentation with several public domain classification datasets suggest that the evidence approach produces the most accurate predictions in the log-score sense. The evidence-based methods are also quite robust in the sense that they predict surprisingly well even when only a small fraction of the full training set is used.     

The perils of father-reported fertility data for household labour supply models

Many panel data-sets contain father-reported fertility data. Yet, since men tend to underreport past fertility--especially daughters or children from previous marriages--using such data can lead to significantly biased results when estimating household labour supply models. For example, when analysing fertility data from the Panel Study of Income Dynamics, which has a significant retrospective component, fathers' labour supply appears more responsive to the births of sons than to daughters (Lundberg and Rose, Review of Economics and Statistics 84(2): 251-268, 2002). However, no evidence for this differential can be found in a much larger sample of Current Population Survey data from the same population. It is important for researchers to consider the provenance of data on fertility variables and to undertake robustness checks with data reported by women whenever possible.