Suchergebnisse

Though the Census of India and large-scale demographic surveys have bridged the data gap on key demographic and health parameters, estimates on poverty and inequality remain deficient for the districts of India. The estimates on poverty and inequality indices across the states of India conceal large variations among districts. We use an innovative approach to provide consumption-based estimates of poverty and inequality indices in the districts of India by pooling the 66th and 68th rounds of consumption expenditure carried out by the National Sample Survey. The new official poverty line of 2009–2010 and 2011–2012 as recommended by the Rangarajan Committee and adopted by the Government of India is used in the estimation of poverty. A set of poverty and inequality indices, the poverty head count ratio, poverty gap square, the Gini index, Theil index and mean log deviation (MLD) are used to estimate poverty and inequality indices for 623 of the 640 districts in India. Estimates of poverty are obtained by pooling the estimates of 2009-10 and 2011-12. Results suggest wide variations in the level, depth and incidence of poverty among the districts of India irrespective of size, stage and governance in the states. The pattern of inequality is different from that of poverty; it is higher in districts with a higher level of development. Estimates of poverty are consistently correlated with wealth index, agricultural labour and female literacy. Among various factors, the fertility level, wealth index and the proportion of agricultural worker are significant predictors of poverty. Based on the findings, we suggest to increase the sample size to estimate consumption poverty in every alternate quinquennial survey and undertake a special round of survey in multidimensional poverty. Districts ranked low in poverty head count ratio should be accorded high priority in planning and program implementation.

Cover -- Front Matter -- Acknowledgments -- Contents -- Executive Summary -- 1 Introduction -- 2 Needs for Small-Area Income and Poverty Estimates -- 3 Current SAIPE Models -- 4 Future Model Development: The Role of Surveys -- 5 Future Model Development: The Role of Administrative Records -- 6 Using Estimates in Allocation Formulas -- 7 Recommendations for Producers and Users -- APPENDIX Interactions Between Survey Estimates and Federal Funding Formulas -- References and Bibliography -- Biographical Sketches of Panel Members and Staff.

Specification counts in the formulation of any economic problem, estimation of its magnitude and its assessment. This is particularly so for problems in the context of economic development. Solutions for poverty alleviation in developing countries like India are often formulated under misspecified premises. This results in wrong choice and design of strategies and policies. Faulty evaluation due to specification errors in estimates of poverty only compounds the error. This paper discusses such an in issue in the context of strategies for and estimates of poverty reduction in the developing countries. The results are general. They are illustrated with reference to India. India has pursued a strategy for growth with redistribution with little appreciation of its limited potential. What have been the consequences? There is a consensus that incidence of poverty with reference to the calorie intake criterion has declined in India from about 55 percent in the mid 1970s to about 25 percent of the population today. The paper lists the implicit assumptions underlying the application of the conventional income/consumption based poverty estimates. It examines how institutional and structural changes during the development process could lead to specification errors in such estimates and hence, faulty evaluation of the consequences. The study concludes that the estimates of poverty with reference to a time-invariant calorie-intake-based norm do not show a real reduction in poverty but only a reduction in overestimation of poverty for the initial years followed by its underestimation for the later years. Even today about 75 percent of the population is calorie deficient. – growth ; redistribution ; poverty ; specification error

This paper aims to clarify the confusion over estimates of poverty in Pakistan1. The paper highlights the root causes of the confusion in the existing literature, which are based on estimates from the "nationally representative" data collected by the Federal Bureau of Statistics Household Integrated Economic Surveys (HIES). The paper uses the latest available HIES 2010-11 to illustrate and clarify these issues. ; Non-PR ; IFPRI1; CRP2; PSSP ; DSGD; PIM ; CGIAR Research Program on Policies, Institutions, and Markets (PIM)

Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across different estimation methods. The decline in poverty over

Not Available ; Despite having long term efforts, poverty is an important and persistent social issue in India. Existing data based on socio-economic surveys produce state and nationally representative poverty estimates but cannot be used directly to generate reliable disaggregate or local level estimates. The state and national level estimates often mask the variations at the local level which in turn restricts the effective implementation of policies related to poverty alleviation locally within and between administrative units. This paper uses the Household Consumer Expenditure Survey data of NSSO and link with the Population Census data to produce the reliable district-level estimates of poverty incidence in the rural areas of West Bengal in India. In particular, small area estimation (SAE) method is explored to generate reliable district-level poverty estimates. The results clearly indicate that the district-level estimates generated by model–based SAE method are precise and representative. A map showing how poverty incidence varies by district across the State of West Bengal is also produced. The estimates generated from this research are useful for meeting the data requirements for policy research and strategic planning by different international organizations and by Departments and Ministries in the Government of India. ; Not Available