Distribution-sensitivity of rank-dependent poverty measures

We propose a criterion to rank poverty measures on the basis of distribution-sensitivity. The criterion compares reactions to ‘lossy’ transfers among the poor. We focus on the class of rank-dependent poverty measures and provide distribution-sensitivity rankings of the poverty gap ratio, the first and second Sen measures, the Thon measure, the Shorrocks measure, and the Thon, Kakwani and S-Gini classes of measures. Moreover, we discuss the relationship between the proposed criterion and two alternative distribution-sensitivity criteria based on the Arrow–Pratt theory of risk aversion. Finally, we provide an empirically tractable necessary and sufficient condition for unanimous poverty rankings by all continuous and replication invariant rank-dependent poverty measures that exhibit a predetermined minimum degree of distribution-sensitivity.     

A general procedure of estimating the population variance when coefficient of variation of an auxiliary variable is known in sample surveys

This paper deals with the problem of estimating population variance ${{S}_{\rm y}^2}$ of the study variable y. We have suggested a family of estimators of population variance ${{S}_{\rm y}^2}$ using the transformations on both the study variable and the auxiliary variable when coefficient of variation of an auxiliary variable x is known. The suggested family of estimators is very wide from which we can generate many estimators by putting the suitable values of scalars. The bias and mean squared error have been obtained upto the first order of approximation. The empirical study is carried out to the support of the suggested family of estimators.     

Institutional quality and poverty measures in a cross-section of countries

We provide cross-country empirical evidence on the link between measures of institutional development and of poverty over the period 1960-1990. We find that the more efficient the institutions, the lower the degree, severity, and incidence of poverty. We provide both ordinary least squares and two-stages least squares estimates, and show that our results are robust for an overall measure of institutional development as well as for an index of risk of expropriation and quality of the bureaucracy.     

Estimating a distribution function in the presence of auxiliary information

For estimating the distribution functionF of a population, the empirical or sample distribution functionF _n has been studied extensively. Qin and Lawless (1994) have proposed an alternative estimator for estimatingF in the presence of auxiliary information under a semiparametric model. They have also proved the point-wise asymptotic normality of . In this paper, we establish the weak convergence of to a Gaussian process and show that the asymptotic variance function of is uniformly smaller than that ofF _n . As an application of , we propose to employ the mean and varianceŜ _n ² of to estimate the population mean and variance in the presence of auxiliary information. A simulation study is presented to assess the finite sample performance of the proposed estimators , andŜ _n ² .     

The adequacy of measures of gender roles attitudes: a review of current measures in omnibus surveys

The measures of attitudes toward gender roles included in many representative international and national omnibus surveys were developed mostly in the 1970s and 1980s with a focus on the male breadwinner model. This article deals with the issue of whether the measures provided in these omnibus surveys need to be adjusted to specific social changes. A review of these measures has found that adjustments have occurred in a limited way that focused on the role of women and disregarded the role of men. Furthermore, most of these measures only examined the traditional roles of men and women. More egalitarian role models have not been considered sufficiently. In addition, most items that have been measured are phrased in a general form and, for example, do not specify parents’ employment or the ages of children. A specification of these aspects of measurement would help to clarify the conceptual meaning of the results and increase the possibility of more accurately analyzing gender role attitudes over time.

     

Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence

We address the problem of the estimation of the population mean and the distribution function using nonparametric regression. These methods are being used in a wide range of settings and areas of research. In particular, they are a good alternative to other classical methods in the survey sampling context, since they work under the assumption that the underlying regression function is smooth. Some relevant nonparametric regression methods in survey sampling are presented. Data on breast cancer prevalence derived from 40 European countries are used to study the application of the nonparametric estimators to the estimation of cancer prevalence. Result derived from an empirical study show that nonparametric estimators have a good empirical performance in this study on cancer prevalence.     

Estimation of a finite population total in varying probability sampling for multi-character surveys

The problems of estimating population total in multi-charter surveys are considered in a unified set up. Alternative estimators for Rao-Hartley-Cochran (1962), Midzuno-Sen (1952,53) and other varying probability sampling schemes are proposed when the measure of size is not well related to the study variables. Some of the proposed estimators are found superior to the existing alternatives. A numerical study is carried out to investigate the performances of the proposed alternatives.     

Towards a typology of information to aid reliability of response in social surveys

Covariance structure modelling in the form of a multi-method multitrait model is employed to analyse the reliability of responses to a mail questionnaire sent to young people. A typology of the type of information sought is derived and the model used to test hypotheses about the nature of the reliability of different types of information obtained in the survey.     

Estimation of Fisher information using model selection

In the paper the problem of estimation of Fisher information I _f for a univariate density supported on [0, 1] is discussed. A starting point is an observation that when the density belongs to an exponential family of a known dimension, an explicit formula for I _f there allows for its simple estimation. In a general case, for a given random sample, a dimension of an exponential family which approximates it best is sought and then estimator of I _f is constructed for the chosen family. As a measure of quality of fit a modified Bayes Information Criterion is used. The estimator, which is an instance of Post Model Selection Estimation method is proved to be consistent and asymptotically normal when the density belongs to the exponential family. Its consistency is also proved under misspecification when the number of exponential models under consideration increases in a suitable way. Moreover we provide evidence that in most of considered parametric cases the small sample performance of proposed estimator is superior to that of kernel estimators.     

A general class of estimators for estimating population mean using auxiliary information

Summary A general class of estimators for estimating the population mean of the character under study which make use of auxiliary information is proposed. Under simple random sampling without replacement (SRSWOR), the expressions of Bias and Mean Square Error (MSE), up to the first and the second degrees of approximation are derived. General conditions, up to the first order approximation, are also obtained under which any member of this class performs more efficiently than the mean per unit estimator, the ratio estimator and the product estimator. The class of estimators in its optimum case, under the first degree approximation, is discussed. It is shown that it is not possible to obtain optimum values of parameters “a”, “b” and “p”, that are independent of each other. However, the optimum relation among them is given by (b−a)p=ρ C _y/C _x. Under this condition, the expression of MSE of the class is that of the linear regression estimator.     

Sub additive measures of relative information and inaccuracy

Summary In the study of information theoretic measures, additivity has been the basic requirement. However it is quite interesting to investigate the sub-additive measures. Starting from subadditivity for measures associated with a pair of distributions of a discrete random variable, it has been changed into an equality relation using another function of a pair of distributions., Under the sum property of the function and the measures, the relation is expressed in terms of a functional equation of which the most general complex solutions have been obtained. In terms of the real continuous solutions of the functional equation, the sub-additive measures of Relative information and Inaccuracy have been defined and characterized. Particular cases and simple properties particularly the convexities of some of these new measures have also been studied.     

The value of imperfect sample separation information in mixtures of normal distributions

This paper conducts a Monte Carlo study in order to evaluate the value of information in a normal mixture model when an imperfect sample separation indicator is available. For a variety of cases, computations are done on the ratios of asymptotic variances of the parameter estimators when sample separation is known versus when only the indicator is available. This study is patterned closely on a paper by Schmidt (1981), and obtains similar results when parameters appearing in Schmidt's model are allowed to vary. The new results here focus on the importance of the probabilities that the regime indicator is correct, for each of the two possible regimes.     

On axiomatic characterization of some non-additive measures of information

An axiomatic characterization of non-additive measures of information associated with a pair of probability distributions having the same number of elements has been given. This quantity under additional suitable postulates leads to the non-additive Entropy, Directed-Divergence and Inaccuracy of one or more parameters.     

会计信息失真的原因及对策探讨

文章从实际出发,论述了会计信息失真的概念、行为性质及其表现形式,着重探讨了会计信息失真的原因和解决会计信息失真问题的对策。     

Estimation of the threshold stochastic frontier model in the presence of an endogenous sample split variable

Heterogeneity among firms has been an important issue in studying firms’ technical efficiencies. If firms do not randomly fall into different groups with different technologies but by self-selection, statistically it implies the data are subject to the sample selection bias. In this paper, we generalize the stochastic frontier (SF) model to accommodate heterogeneous technologies among firms by considering the threshold SF model with an endogenous threshold variable. We discuss the econometric techniques appropriate for the threshold SF model with panel data. To determine the optimal number of regimes, we use modified the model selection criteria of Gonzalo and Pitarakis (J Econom 110(2):319–352, 2002) and investigate their finite sample performance by some Monte Carlo experiments. Finally, we also demonstrate our approach by an empirical example.     

The effects of sampling on measures of association between variables based on sample means

Many studies that involve people's perceptions or behaviors focus on aggregate rather than individual responses. For example, variables describing public perceptions for some set of events may be represented as mean scores for each event. Event mean scores then become the unit of analysis for each variable. The variance of these mean scores for a variable is not only a function of the variation among the events themselves, but is also due to the variation among respondents and their possible responses. This is also the case for the covariances between variables based on event mean scores. In many contexts the variance and covariance components attributable to the sampling of respondents and their responses may be large; these components can be described as measurement error. In this paper we show how to estimate variances and covariances of aggregate variables that are free of these sources of measurement error. We also present a measure of reliability for the event means and examine the effect of the number of respondents on these spurious components. To illustrate how these estimates are computed, forty-two respondents were asked to rate forty events on seven risk perception variables. Computing the variances and covariances for these variables based on event means resulted in relatively large components attributable to measurement error. A demonstration is given of how this error is removed and the resulting effect on our estimates.     

信息化时代流通业的变革及深圳应采取的对策

本文论述了信息化与流通业的关系;信息化时代流通业发生的变革：１,信息化使商品流通的方式发生变革;２,信息化使流通过程不断地向“虚拟化”的方向演进;３,信息化使许多实物商品流通范畴之外的物流活动由手工向自动化、同步化发展;对深圳目前流通业信息化水平进行了深刻的剖析,并在此基础上提出了深圳流通业对信息化主动适应策略。