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The Indian Official Statistical System Revisited
Abstract
Certain discrepancies in data sources for estimating the household consumption expenditure to derive Gross Domestic Product(GDP) of India are discussed in this paper. Simple implementable strategies are suggested to improve the estimation of GDP.
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In this article we shall trace the history and evolution of official statistics in India during the British period and immediately after independence. We shall also discuss briefly the teaching and training of statisticians in the country. In particular, the role played by P.C. Mahalanobis, C.R. Rao and P.V. Sukhatme among others, will be highlighted. Next, we shall look into the present scenario in the light of some of the recommendations, released in 2001, of the National Statistical Commission.
A new era of globalization, which began in the 1980s, brought about a significant decline in costs of transportation, communication, and production; considerably improved intercountry competitiveness; and broke down trade and cultural barriers among countries. The concept of a sovereign nation has been increasingly questioned in recent years. Some, indeed, have imagined a world without boundaries, without countries. Others who doubt the benefits of globalization have called for increased protectionism and greater regulation of economic activity.Has globalization made the world grow faster? Has poverty declined at a faster pace during globalization? If yes, why? If not, is it because the growth rate was lower, or because inequality worsened, or both? Who gained from globalization? Was it the elite in both the developed and developing world? What about the middle class? Who are they? How did they benefit from (or lose to) the forces of globalization?This comprehensive study firmly debunks several popular myths such as the belief that globalization has resulted in lower overall growth rates for poor countries, increasing world inequality, and stagnating poverty levels. Through rigorous, integrated methodologies and an enhanced dataset, the author, Surjit Bhalla, answers some of the most pressing policy issues confronting us today.
Within a Bayesian decision theoretic framework we investigate some asymptotic
optimality properties of a large class of multiple testing rules. A parametric
setup is considered, in which observations come from a normal scale mixture
model and the total loss is assumed to be the sum of losses for individual
tests. Our model can be used for testing point null hypotheses, as well as to
distinguish large signals from a multitude of very small effects. A rule is
defined to be asymptotically Bayes optimal under sparsity (ABOS), if within our
chosen asymptotic framework the ratio of its Bayes risk and that of the Bayes
oracle (a rule which minimizes the Bayes risk) converges to one. Our main
interest is in the asymptotic scheme where the proportion p of "true"
alternatives converges to zero. We fully characterize the class of fixed
threshold multiple testing rules which are ABOS, and hence derive conditions
for the asymptotic optimality of rules controlling the Bayesian False Discovery
Rate (BFDR). We finally provide conditions under which the popular
Benjamini-Hochberg (BH) and Bonferroni procedures are ABOS and show that for a
wide class of sparsity levels, the threshold of the former can be approximated
by a nonrandom threshold.
Prof. C. R. Rao’s Birth Centenary is being celebrated by statistical communities all over the world from 10 September 2019. In this paper, we shall present a brief biographical sketch of the living legend C. R. Rao and discuss some of his contributions in the areas of theory and practice of sample surveys which have resulted in certain important aspects.
In this article statistical inference is viewed as information processing involving input information and output information. After introducing information measures for the input and output information, an information criterion functional is formulated and optimized to obtain an optimal information processing rule (IPR). For the particular information measures and criterion functional adopted, it is shown that Bayes's theorem is the optimal IPR. This optimal IPR is shown to be 100% efficient in the sense that its use leads to the output information being exactly equal to the given input information. Also, the analysis links Bayes's theorem to maximum-entropy considerations.
In this article we shall briefly describe the main functions of the National Statistical Commission, the apex statistical body of the country, which was established in 2006 following the recommendations of the Rangarajan Commission. We shall then discuss the important role of the Commission in the Official Statistical System of India.
The National Sample Survey has been among the most robust and well respected national household surveys in the world for almost half a century. It is therefore natural that most observers accept the estimates thrown up by the NSS. However, since mid-1980s there is another large-scale survey, the Market Information Survey of Households (MISH) of the NCAER, which can provide consistent information on income trends in the country. Which of these two surveys is to be believed about the trends in poverty redressal during the reform era: MISH which suggests a marked decline or the NSS which points to stagnation in poverty ratios? To answer this question is the primary purpose of this paper.
A framework for comparing the maximum likelihood (ML) and maximum entropy (ME) approaches is developed. Two types of linear models are considered. In the first type, the objective is to estimate probability distributions given some moment conditions. In this case the ME and ML are equivalent. A generalization of this type of estimation models to incorporate noisy data is discussed as well. The second type of models encompasses the traditional linear regression type models where the number of observations is larger than the number of unknowns and the objects to be inferred are not natural probabilities. After reviewing a generalized ME estimator and the empirical likelihood (or weighted least squares) estimator, the two are compared and contrasted with the ML. It is shown that, in general, the ME estimators use less input information and may be viewed, within the second type models, as expected log-likelihood estimators. In terms of informational ranking, if the objective is to estimate with minimum a-priori assumptions, then the generalized ME estimator is superior to the other estimators. Two detailed examples, reflecting the two types of models, are discussed. The first example deals with estimating a first order Markov process. In the second example the empirical (natural) weights of each observation, together with the other unknowns, are the subject of interest.
If one replaces average consumption from India's National Sample Surveys with private consumption per capita from the National Accounts, while retaining the survey-based distributions, then one finds a faster rate of poverty reduction in the 1990s. However, the case made for this method of measuring poverty is questionable on many counts. There do appear to be problems in the poverty data for India in the 1990s, but this step is unlikely to solve them.
In the context of the controversy over the trends in poverty in India in the 1990s, this paper addresses the question of using the National Accounts Statistics (NAS) estimate of private final consumption expenditure (PFCE) in place of National Sample Survey (NSS) based estimate for calculating the proportion of the population below the poverty line or headcount ratio. A comparison is undertaken for the year 1993-94 between NAS estimate of PFCE and household consumer expenditure estimated from NSS disaggregated across item-groups of consumption and across selected fractile groups of the rural and urban population. Two alternative estimates from NAS - old series in NAS 1998 and the latest revision in NAS 1999 - are compared. Similarly, two alternative NSS estimates are considered - one directly available using a uniform 30-day reference or recall period and a synthetic one constructed to reflect the effect of using non-uniform reference period. The analysis of comparison of these four estimates suggests two major conclusions. One, the issue of accepting NAS estimate of PFCE as more correct and reliable than NSS estimate is far from settled. Two, the item-groups that accounted for a very large proportion of the aggregate discrepancy between NAS and NSS estimates had a much smaller budget share in the consumption basket of the bottom 30 per cent fractile group in the rural and urban areas, whereas in respect of item-groups which together accounted for over 75 per cent of the consumption of the bottom 30 per cent, the divergence between the two estimates was much smaller than on the average for all item groups and negative in some cases.
It is difficult to enumerate the people in India who are engaged in various small-scale industries in the unorganized sector because they are concentrated in small regional pockets. In estimating separately the total numbers of workers earning principally through ten respective single-industries in the unorganized small-scale sector in a specific district in rural India, through numerical illustrations we have two observations to report: (1) A traditional stratified two-stage sampling scheme is ineffective for some of the industries because of failures to capture the earners concentrated in priorly unknown locations. (2) An adaptive sampling scheme extending the initial sample by appropriate ‘network’ formations based on well-defined ‘neighbourhoods’ brings about dramatic improvements exploiting clustering tendencies of earners by different industries.
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.
Ceci est une coute histoire de l'évolution de la Statistique officielle et acadeémique en Inde. Nous retracons l'hisoire à patir des origines de l'antiquité jusqu'à l'histoire plus récente mais potons plus d'atention sur la péiode des années 1930 à 1960. Ceci et lhistoire d'institutions, des gens qui les batirent et les formèrent eet d'idées.
We study the properties of false discovery rate (FDR) thresholding, viewed as
a classification procedure. The "0"-class (null) is assumed to have a known
density while the "1"-class (alternative) is obtained from the "0"-class either
by translation or by scaling. Furthermore, the "1"-class is assumed to have a
small number of elements w.r.t. the "0"-class (sparsity). We focus on densities
of the Subbotin family, including Gaussian and Laplace models. Nonasymptotic
oracle inequalities are derived for the excess risk of FDR thresholding. These
inequalities lead to explicit rates of convergence of the excess risk to zero,
as the number m of items to be classified tends to infinity and in a regime
where the power of the Bayes rule is away from 0 and 1. Moreover, these
theoretical investigations suggest an explicit choice for the target level
of FDR thresholding, as a function of m. Our oracle inequalities
show theoretically that the resulting FDR thresholding adapts to the unknown
sparsity regime contained in the data. This property is illustrated with
numerical experiments.
The World Bank prepares and publishes estimates of the number of poor people in the world. While everyone knows that these numbers should be taken with a pinch of salt, the numbers are arguably important. In an institution where the reduction of poverty is the paramount objective, some overall yardstick of progress (or the lack of it) is required. The numbers are frequently quoted by politicians and by leaders of international organizations, including the Bank itself, who believe the numbers are effective for advocacy. Indeed, there is a long history of studies of poverty mobilizing support among the non-poor for anti-poverty policies. So it is important to know whether the world and national poverty counts are sound enough to support these uses. As recent discussions have made clear, the apparent lack of poverty reduction in the face of historically high rates of economic growth, both in the world as a whole, and in specific countries (most notably India), is providing fuel for the argument that economic growth does little to reduce poverty. How confident can we be that the data actually support these inferences? Are the changes in the poverty counts sufficiently well-measured to support conclusions about growth and poverty reduction? Should the World Bank stand ready to be judged by its success in reducing the current measures of world or even national poverty? If not, can better data collection or better methodologies improve the numbers?
In this article statistical inference is viewed as information processing involving input information and output information. After introducing information measures for the input and output information, an information criterion functional is formulated and optimized to obtain an optimal information processing rule (IPR). For the particular information measures and criterion functional adopted, it is shown that Bayes's theorem is the optimal IPR. This optimal IPR is shown to be 100% efficient in the sense that its use leads to the output information being exactly equal to the given input information. Also, the analysis links Bayes's theorem to maximum-entropy considerations.
This paper presents an approach to reconciling household surveys and national accounts data. The problem is how to use the information provided by the national accounts data to re-estimate the household weights used in the survey so that the survey results are consistent with the aggregate data. The estimation approach uses an estimation criterion based on an entropy measure of information. The survey household weights are treated as a prior. New weights are estimated that are close to the prior and that are also consistent with the additional information. This approach is implemented to reconcile household survey data and macro data for Madagascar. The results indicate that the approach is powerful and flexible, supporting the efficient use of information from a variety of sources to reconcile data at different levels of aggregation in a consistent framework. Copyright 2003 by the International Association for Research in Income and Wealth.
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