Poverty and Famines: An Essay on Entitlement and Deprivation

Fuel stacking in India: Changes in the cooking and lighting mix, 1987–2010☆

In the developing world, households often stack multiple fuels. In the case of India, they use both kerosene and electricity for lighting while cooking with both LPG and traditional biomass. Existing scholarship on fuel stacking largely relies on small surveys and does not investigate change over time. We leverage the nationally representative National Sample Survey (NSS) of India in 1987 and 2010, finding that fuel stacking is decreasing in lighting, as people substitute electricity for kerosene, but increasing in cooking, as LPG does not replace traditional biomass. We also exploit a two-stage statistical model to analyze individual household's decision of fuel stacking. The most important finding is that, while a high household income reduces fuel stacking for lighting, it no longer does so in 2010 for cooking. The main policy implication of the study is that much more aggressive efforts are needed to deal with problems associated with biofuels, such as indoor air pollution, than to induce the switch from kerosene to electricity. The statistical model offers a considerable improvement over existing alternatives in the literature on household energy access.

Fuel Stacking in India: Changes in the Cooking and Lighting Mix, 1987-2010

Although many of us plead frequently for a period of stability so that we can \"settle down and get things done\", constant change should be intrinsic to an NHS responsive to evolving health, social and economic needs. This book, through a series of essays, explores the dynamics of change in the NHS and highlights some of the tensions in the present system, identifying a number of fundamental obstacles to innovation. In their introduction, the authors establish

https://jech.bmj.com/content/jech/47/2/163.4.full.pdf

Design and Inference in Finite Population Sampling

Macroeconomic indicators – especially inflation, gross domestic product growth, public deficits and unemployment – stand central in economic governance. Policy-makers use them to assess their economies’ health. Citizens evaluate politicians’ performance using them as yardsticks. But these indicators defy simple definition, and the formulae underlying them have varied across countries and over time. Particular choices have fundamental distributive consequences. This research agenda outlines how we might study macroeconomic indicators as powerful ideas and ask: why do we measure the economy the way we do? It illustrates the myriad ways in which macroeconomic indicators are embedded in contemporary social and political life, and it outlines how we can uncover both what power rests in these indicators and who has power over them. After path-breaking scholarship has demonstrated how consequential these indicators are, it is imperative to understand better which forces determine our choice for one indic...

https://www.tandfonline.com/doi/pdf/10.1080/13501763.2015.1115537

Studying macroeconomic indicators as powerful ideas

Summary

This is a brief history of the evolution of official and academic Statistics in India which focuses mainly on the period 1930 to 1960 but traces its origins in antiquity and recent history. We also comment on how Statistics has continued to evolve since the 1960's. This is a history of both institutions and people, who built and shaped them, and of ideas.

Resume

Ceci est une coute histoire de l'evolution de la Statistique officielle et acadeemique en Inde. Nous retracons l'hisoire a patir des origines de l'antiquite jusqu'a l'histoire plus recente mais potons plus d'atention sur la peiode des annees 1930 a 1960. Ceci et lhistoire d'institutions, des gens qui les batirent et les formerent eet d'idees.

https://www.jstor.org/stable/pdfplus/1403563.pdf

Evolution of Statistics in India

Considered is a linear 'interactive' model in the context of survey sampling. This situation arises when investi- gator and/or supervisor interventions are contemplated in the responses. An appropriate linear model is intro- duced to represent the response profile(s) arising out of each respondent-cum-investigator-cum-supervisor com- bination as per the planned 'design layout'. Two situations (blinded and unblinded submission of responses) are differentiated and corresponding data analysis techniques are discussed. Variance components are assumed to be known in this study. 1. Introduction to Survey Design and Interactive Linear Model Considered is the set-up of simple i.e., direct response on a quantitative response variable Y in the context of a finite labeled population of size N. It so happens that in actual surveys, we need investi- gators and often some supervisors as well. The instruction manuals are prepared for the investigators to maintain uniformity in data collection. The field level data are collected by the investigators. The scrutiny manual is prepared for scrutiny of the filled-in schedules by the supervisors. This is accom- plished independently of the investigators. We depict a situation wherein there are possibilities of investigator intervention effect and/or supervisor intervention effect on the response profiles before the same are finally received by the data collection agency. Of course, these intervention effects may be assumed to be random, having mean zero, non-interactive within and between the two sets of 'people'. The problem is to unbiasedly estimate the finite population total of the response vari- able Y by incorporating a fixed size (n) sampling design and by administering the sampling design in a situation wherein the above two types of random effects are likely to be present. Denote by i a respondent unit in the sample of size n and by S(i) the number of schedule-based observations collected on this particular unit. It is quite possible that a respondent unit is composed of more than one individual. In this article, we will deal with fixed-size non-overlapping clusters of individuals to represent such respondent units. These clusters are formed before the sampling

/pdf/interactive-linear-models-in-survey-sampling-667dd8sh72.pdf

Interactive Linear Models in Survey Sampling

There are now a number of poverty measures available in the literatures. Some of the measures are alternative to each other and some claimed to be superior in some sense to many others. While significant work has been done in developing the alternative measurts, not much attention has been paid to the problem of estimation of these indices. Estimation does not pose very serious problems in the large sample, but when one deals with a small sample, which may typically be the case in reality, situations become quite different. In fact usual estimators become biased for some of the indices. In this paper, alternative estimators for these cases have been proposed. Other properties of the estimators and some other relevant issues have also been examined.

On Some Estimates of Poverty Measures

Given paired observations, { } ii x yi n = on two variables x and y for a random sample s, from some bivariate non-normal population like bivariate gamma, beta-stacy which are of much use in modelling data obtained in Physical, Social and LifeSciences. This paper considers an improvement of the customary estimator of population variance. A mixture (i.e. a weighted combination) of the customary estimator of the variance and a suitably chosen statistic t is proposed. It is also indicated that under some conditions for a broad range of the values of the mixing constants, the improvement in the sense of having a smaller mean square error, over the traditional estimator is possible.

Some improved variance estimators from a bivariate non-normal population

The general models for variability in the literature are expressed either as a mean squared error decomposition model or as a linear model [Lessler and Kalsbeek (1992)]. The net bias is assumed to be zero, so that the models deal only with variability. The major difference between the two types of models is that the decomposition model approach often has a component attributable to the interaction between sampling and measurement errors, whereas the linear model approach omits this component. Here, we adopt a linear model approach, and we assess a direct response on a quantitative variable Y measured on selected units from a finite, labeled population of size N. Of course, in actual surveys, we need investigators and/or supervisors as well. We consider a situation wherein there are possibilities of investigators’ and/or supervisors’ intervention(s) on the response profiles ultimately received by the data collection agency. As in Sinha and Maiti (2014), we assume that these intervention effects are random with mean 0, and they are non-interactive both within and between the data collection and the data handling personnels. The problem is to unbiasedly estimate the finite population total of the response variable Y based on a two-stage sampling design which is administered in a situation wherein the above two types of random interventions are likely to be present. When the target population is not of the type discussed here, the mean squared error decomposition model can be utilized profitably. Journal of Statistical Theory and Applications, Vol. 16, No. 4 (December 2017) 467–475 ___________________________________________________________________________________________________________

/pdf/interactive-linear-models-in-the-context-of-two-stage-4qhcjtbgop.pdf

Pulakesh Maiti

Papers

Evolution of Statistics in India

Interactive Linear Models in Survey Sampling

On Some Estimates of Poverty Measures

Some improved variance estimators from a bivariate non-normal population

Interactive Linear Models in the Context of Two-Stage Sampling