The Effect of Family Background on Individual Wages and an Examination of Inequality of Opportunity in India

The paper attempts to explore the role of family background in contributing to the overall economic inequality in Indian society. Drawing on the recent work on equality of opportunity, and using the Indian Human Development Survey (IHDS, 2004–05), paper finds that the opportunity share of inequality in wage earnings in India ranges from 11% to 17% across different age based cohorts. Given the conservative view of what constitutes responsibility, the analysis is likely to identify only the lower bound of opportunity inequality. The paper also calls for redistributive policies favoring those who suffer from inferior circumstances.     

Book reviews

Abstract

Social Policy, Public Policy Meredith Edwards, Cosmo Howard, Robin Miller (2001), Allen & Unwin, Sydney ISBN 1 86448 9480 $35.00 RRP.

Spirituality in social work practice: Narratives for professional helping Sonia Abels (Ed) (2000), Love Publishing Company, Denver, Colorado 131 pp. $56.90 (paperback)

Family — centered policies and practices: International implications Katharine Briar-Lawson, Hal A. Lawson, Charles B. Hennon and Alan R. Jones, Columbia University Press, New York Chichester, West Sussex ISBN 0 231 12106 7 462 pp. (soft cover)

Lookin' after our own: Supporting Aboriginal families through the hospital experience Angela Clarke, Shawana Andrews and Neville Austin (2000), A report sponsored by the Victorian Health Promotion Foundation

Making stepfamilies work A course for couples Irene Gerrard & Margaret Howden (1998), Stepfamily Association of Victoria Inc. ISBN 0 646 3622 4 (spiral bound)

The empowerment approach to social work practice: Building the beloved community Judith AS Lee (2001), Columbia University Press, New York, ISBN 0 231 11548 2 517 pp. (hardback)     

Vector autoregression modelling and forecasting growth of South Korea

In this paper, we have estimated vector autoregression (VAR), Bayesian vector autoregression (BVAR) and vector error-correction models (VECMs) using annual time-series data of South Korea for 1950-94. We find evidence supporting the view that growth of real per-capita income has been aided by income, investment and export growth, as well as government spending and exchange rate policies. The VECMs provide better forecasts of growth than do the VAR and BVAR models for both short-term and long-term predictions.     

Likelihood-based Estimation of the Regression Model with Scrambled Responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator out-performs the classical estimators in all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.     

Tracking multiple moving objects in images using Markov Chain Monte Carlo

A new Bayesian state and parameter learning algorithm for multiple target tracking models with image observations are proposed. Specifically, a Markov chain Monte Carlo algorithm is designed to sample from the posterior distribution of the unknown time-varying number of targets, their birth, death times and states as well as the model parameters, which constitutes the complete solution to the specific tracking problem we consider. The conventional approach is to pre-process the images to extract point observations and then perform tracking, i.e. infer the target trajectories. We model the image generation process directly to avoid any potential loss of information when extracting point observations using a pre-processing step that is decoupled from the inference algorithm. Numerical examples show that our algorithm has improved tracking performance over commonly used techniques, for both synthetic examples and real florescent microscopy data, especially in the case of dim targets with overlapping illuminated regions.     

Estimation of a rare sensitive attribute for two-stage randomized response model in probability proportional to size sampling using Poisson probability distribution

This paper addresses the estimation of the mean number of individuals in the population who possess a rare sensitive attribute using Poisson distribution for the situations of (i) clustered population and (ii) stratified population with clusters are strata units. Properties of the proposed estimation procedures have been discussed when the proportion of a rare unrelated non-sensitive attribute is assumed to be known as well as unknown. Empirical studies are carried out to support the theoretical results which showed dominance over Lee et al. [Estimation of a rare sensitive attribute in probability proportional to size measures using Poisson distribution. Statistics (Ber). 2014;48(3):685–709] estimation procedures.     

Estimation of population mean in presence of random non response in two-stage cluster sampling

The present investigation addresses the problem of estimating a finite population mean in two-phase cluster sampling in presence of random non response situations. Utilizing information on an auxiliary variable, regression type estimators has been proposed. Effective imputation techniques have been suggested to deal with the random non response situations. The properties of the proposed estimation strategies have been studied for different cases of random non response situations in practical surveys. The superiority of the suggested methodology over the natural sample mean estimator of population mean has been established through empirical studies carried over the data sets of natural population and artificially generated population.     

Estimation of population mean in successive sampling under super-population model in presence of random non response situations

The present work is an attempt to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling in presence of random non response situations. The estimation strategy has been constructed under a super-population model design approach with the help of imputation technique. The estimators proposed on the current occasion cover the cases of occurrences random non responses on either of the occasions. Detail behaviors of the proposed class of estimators have been studied and its performance has been examined with the sample mean estimator. The results are demonstrated through empirical studies which establish the effectiveness of the proposed class of estimators. Suitable recommendations have been put forward to the survey statisticians for its practical application.