印度北方邦粮食不安全发生率的小区域估计

Hukum Chandra, Bhanu Verma
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引用次数: 0

摘要

联合国第二个可持续发展目标(SDG)试图到2030年消除潜在的饥饿和粮食不安全问题,但在2019冠状病毒病大流行的困境中,这已成为全球和印度更为关键和持久的问题。印度国家抽样调查办公室(NSSO)的全国社会经济调查旨在分别对农村和城市部门在邦和国家一级的重要粮食不安全参数进行可靠和有代表性的估计,但这些调查不能直接用于产生可靠的地区一级估计。鉴于,对粮食不安全流行程度(或发生率)进行有效和具有代表性的分类水平估计,对制定有效的政策计划和监测消除粮食不安全方面的进展具有直接影响。在此背景下,本文通过将NSSO 2011 - 12年家庭消费者支出调查数据与2011年印度人口普查数据联系起来,概述了估算印度北方邦农村地区粮食不安全发生率的小区域估计方法。绘制了一幅空间地图,显示了北方邦粮食不安全发生率的空间差异。这些分类水平估计与可持续发展目标指标2.1.2 -粮食不安全严重程度相关且有目的。预计粮食不安全发生率的估计数和地图将为政策分析人员和决策者提供宝贵的信息。
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Small area estimation of food insecurity prevalence for the state of uttar pradesh in India
The 2nd Sustainable Development Goal (SDG) of the United Nations attempt to eliminate the potential hunger and food insecurity issues by 2030, but in the plight of COVID19 pandemic it has become far more critical and persistent issue globally as well as in India. The nation-wide socio-economic surveys of National Sample Survey Office (NSSO) in India are designed to produce reliable and representative estimates of important food insecurity parameters at state and national level for both rural and urban sectors separately but these surveys cannot be used directly to generate reliable district level estimates. Whereas, efficient and representative disaggregated level estimates for the extent (or incidence) of food insecurity prevalence has direct impact on strategizing effective policy plans and monitoring progress towards eliminating food insecurity. In this backdrop, the paper outlines small area estimation approach to estimate the incidence of food insecurity across the districts of rural Uttar Pradesh in India by linking data from the 2011–12 Household Consumer Expenditure Survey of NSSO, and the 2011 Indian Population Census. A spatial map has been generated showing spatial disparity for the incidence of food insecurity in Uttar Pradesh. These disaggregated level estimates are relevant and purposeful for SDG indicator 2.1.2 – severity of food insecurity. The estimates and map of food insecurity incidences are expected to deliver invaluable information to the policy-analysts and decision-makers.
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Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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