Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique

The National Sample Survey Organisation (NSSO) surveys are the main source of official statistics in India, and generate a range of invaluable data at the macro level (e.g. state and national levels). However, the NSSO data cannot be used directly to produce reliable estimates at the micro level (e.g. district or further disaggregate level) due to small sample sizes. There is a rapidly growing demand of such micro-level statistics in India, as the country is moving from centralized to more decentralized planning system. In this article, we employ small-area estimation (SAE) techniques to derive model-based estimates of the proportion of indebted households at district or at other small-area levels in the state of Uttar Pradesh in India by linking data from the Debt–Investment Survey 2002–2003 of NSSO and the Population Census 2001 and the Agriculture Census 2003. Our results show that the model-based estimates are precise and representative. For many small areas, it is even not possible to produce estimates using sample data alone. The model-based estimates generated using SAE are still reliable for such areas. The estimates are expected to provide invaluable information to policy analysts and decision-makers.     

On Bounds for Probability Generating Functions

Brook (1966) gave an upper bound for the moment generating function (m.g.f.) of a positive random variable (r.v.) in terms of its moments, and used this to obtain an upper bound for the probability generating function (p.g.f.) and hence the extinction probability of a simple branching process. Agresti (1974) rederived this bound of the p.g.f. and used it to obtain a lower bound of the expectation of extinction time of a branching process. In both of these applications the random variable is integer valued, and for this class we improve on Brook's bound by deriving the best upper bound of the p.g.f. Our method, which is a variant of Brook's (1966) is used later to obtain the lower bound of the p.g.f. when the third moment is also known.     

The Identifiability of Dependent Competing Risks Models Induced by Bivariate Frailty Models

In this paper, we propose to use a special class of bivariate frailty models to study dependent censored data. The proposed models are closely linked to Archimedean copula models. We give sufficient conditions for the identifiability of this type of competing risks models. The proposed conditions are derived based on a property shared by Archimedean copula models and satisfied by several well‐known bivariate frailty models. Compared with the models studied by Heckman and Honoré and Abbring and van den Berg, our models are more restrictive but can be identified with a discrete (even finite) covariate. Under our identifiability conditions, expectation–maximization (EM) algorithm provides us with consistent estimates of the unknown parameters. Simulation studies have shown that our estimation procedure works quite well. We fit a dependent censored leukaemia data set using the Clayton copula model and end our paper with some discussions. © 2014 Board of the Foundation of the Scandinavian Journal of Statistics     

A Development Delivery Institution for the Tribal Communities: Experience of the National Rural Employment Guarantee Scheme in India

This article examines the varied impacts of the National Rural Employment Guarantee Scheme (NREGS) as a development delivery institution for the tribal communities vis‐à‐vis other social groups across the Indian States, using the framework of new institutional economics. A number of State‐specific, socio‐economic institutional factors seem to be responsible for these variations. The article therefore suggests institutional reforms and convergence of the development initiatives of the Ministry of Tribal Affairs with the NREGS in order to realise the optimal potential of the scheme, and, in particular, to ensure greater livelihood opportunities for these marginalised groups and their entitlement to productive resources with greater socio‐economic and political empowerment.     

Determinants of Individual Giving Behavior in Urban India

This paper, based on a primary sample survey over 1925 earning individuals in the cities of Kolkata, Cuttack and Bengaluru, examines how the individual and household characteristics influence the acts of giving in urban India. The regression results indicate income, family size and property ownership affecting likelihood and extent of giving. Likelihood to give is more with females, though males tend to donate more. There exists threshold income beyond which likelihood to donate is less. Characteristics like age, education, dependency ratio and marital status influence certain acts of giving. As the opportunity cost of non-cash giving increases with the rise in income, cash donations substitute non-cash giving. There also prevails complementarity in the acts of giving. On behavioral front, in addition to work–life balance and pledging, the notion of rational choice seems to be gaining ground.

     

A low complexity semidefinite relaxation for large-scale MIMO detection

Many wireless communication problems is based on a convex relaxation of the maximum likelihood problem which further can be cast as binary quadratic programs (BQPs). The two standard relaxation methods that are widely used for solving general BQPs such as spectral methods and semidefinite programming problem (SDP), each have their own advantages and disadvantages. It is widely accepted that small and medium sized SDP problems can be solved efficiently by interior point methods. Albeit, semidefinite relaxation has a tighter bound for large scale problems, but its computational complexity is high. However, Row-by-Row method (RBR) for solving SDPs could be opted for an alternative for large-scale MIMO detection because of low complexity. The present work is a spectral SDP-cut formulation to which the RBR is applied for large-scale MIMO detection. A modified RBR algorithm with tighter bound is presented to specify the efficiency in detecting massive MIMO.     

Post-randomization for controlling identification risk in releasing microdata from general surveys

Before releasing survey data, statistical agencies usually perturb the original data to keep each survey unit''s information confidential. One significant concern in releasing survey microdata is identity disclosure, which occurs when an intruder correctly identifies the records of a survey unit by matching the values of some key (or pseudo-identifying) variables. We examine a recently developed post-randomization method for a strict control of identification risks in releasing survey microdata. While that procedure well preserves the observed frequencies and hence statistical estimates in case of simple random sampling, we show that in general surveys, it may induce considerable bias in commonly used survey-weighted estimators. We propose a modified procedure that better preserves weighted estimates. The procedure is illustrated and empirically assessed with an application to a publicly available US Census Bureau data set.     

Changes in Diarrheal Disease and Treatment Among Brazilian Children from 1986 to 1996

We examined changes in diarrhea prevalence and treatment in Brazil between 1986 and 1996. Over this 10-year period there was a small decline in diarrhea prevalence but treatment with oral rehydration therapy (ORT) increased greatly. Deaths due to dehydration were thus averted, although the costly burden of morbidity remained high. The decline in diarrhea prevalence was largely due to changes in the effects of several key covariates, such as breastfeeding, with only a modest role played by socioeconomic change, infrastructure improvements, and other behavioral factors. ORT treatment of diarrhea was essentially unrelated to child and family characteristics, suggesting that the large increase was due to the success of public health efforts to promote its use widely. Our results suggest that the most effective policies for reducing diarrhea prevalence are likely to be further increases in education and the promotion of breastfeeding. Persistent disparities in diarrhea prevalence mean that policies to prevent the disease should be targeted at disadvantaged socioeconomic groups.