A composite index to explain variations in poverty, health, nutritional status and standard of living: Use of multivariate statistical methods

Antony, Grace; Rao, K. V. S. Rama

doi:10.1016/j.puhe.2006.10.018

Cited by 118 publications

(67 citation statements)

References 2 publications

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“…Acknowledging the inappropriateness of simple aggregation procedures, Lai (2003) modified the UNDP human development index by using PCA to create a linear combination of indicators of development. Several researchers have used PCA, especially since late 1990s, to implement socioeconomic indices (Antony & Rao, 2007;Fotso & Kuate-Defo, 2005). To our knowledge, the PCA algorithm has not yet been employed to develop educational indices.…”

Section: Methodsmentioning

confidence: 99%

Measuring teachers and learners’ perceptions of the quality of their online learning experience

2016

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This article explores the quality of the online learning experience based on the Sloan-C framework and the Online Learning Consortium's (OLC) quality scorecard. The OLC index has been implemented to evaluate quality in online programs from different perspectives. Despite this, the opinions of learners are ignored, and it is built using feedback from experts and panelists while ignoring the factors that teachers consider important during their lectures. We propose an alternative way of measuring the quality of online learning programs by analyzing the satisfaction of the learning experience and using teachers and students' perceptions. The 11 categories composing the index were weighted in the teacher and student indices using principal component analysis, and finally these two indices were linearly combined with a parameter that defines the importance of each body. Findings show that while teachers perceive collaborative learning variables as crucial, learners are more concerned with their own learning benefits.

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Section: Methodsmentioning

confidence: 99%

Measuring teachers and learners’ perceptions of the quality of their online learning experience

2016

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“…While the HPI measures poverty at the macro level, the MPI is unique as it identifies individuals (at the micro level) deprived in overlapping multiple dimensions and captures both the extent and intensity of poverty (Alkire and Santos 2010). Following the UNDP's work, several researchers have contributed towards the measurement and estimation of multidimensional poverty (Anand and Sen 1997;Chiappero-Martinetti 2000;Bourguignon and Chakravarty 2003;Gordon et al 2003;Qizilbash 2004;D'Ambrosio 2006: Alkire andFoster 2008;Antony and Rao 2007;Calvo 2008;Wagle 2008;Jayaraj and Subramanian 2010 ;Alkire and Santos 2010;Alkire and Foster 2011;Mohanty 2011;Mishra and Ray 2013;Alkire and Seth 2015). Most of these studies used the dimensions of education, health and standard of living and a few studies included subjective well-being such as fear of facing hardship (Calvo 2008) in defining multidimensional poverty.…”

Section: Introductionmentioning

confidence: 99%

Regional Estimates of Multidimensional Poverty in India

Dehury

Mohanty

2015

Economics

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This paper estimates and decomposes multidimensional poverty in 82 natural regions in India using unit data from the Indian Human Development Survey (IHDS), 2011-12. Multidimensional poverty is measured in the dimensions of health, education, living standard and household environment using eight indicators and Alkire-Foster methodology. The unique contributions of the paper are inclusion of a direct economic variable (consumption expenditure, work and employment) to quantify the living standard dimension, decomposition of MPI across the dimensions and the indicators, and estimates of multidimensional poverty at the sub-national level.Results indicate that 43% of India's population are multidimensional poor with large regional variations. The average intensity of poverty was 45.5% with a MPI value of 19.3. Six states in India-Bihar, Chhattisgarh, Jharkhand, Madhya Pradesh, Odisha and West Bengal who have a share of 45% of the total population-account for 58% of the multidimensional poor. Across regions, more than 70% of the population are multidimensional poor in the southern region of Chhattisgarh and the Ranchi plateau, while they comprise less than 10% in the regions of Manipur, Mizoram and Chandigarh. The economic poor have a weak association with health and household environment dimensions. The decomposition of MPI indicates that the economic dimension accounts for 22%, the health dimension accounts for 36%, the education dimension accounts for 11% and the household environment accounts for 31% of the deprivation. Based on these analyses, the authors suggest target based interventions in the poor regions to reduce poverty and inequality in India. JEL I J Z

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“…In what follows, we refer to Antony and Rao [2007] and we aggregate several indicators to construct a composite index of well-being for the entire 263 Tunisian municipalities. We retain 15 attributes grouped into three categories: education (net enrollment rate in primary and adult literacy rate), health (life expectancy at birth, infant mortality and fertility rates) and living conditions (1. raccordement housing to public networks: Access to drinking water, access to electricity, sewerage network and disposal of two rooms or less and 2. the possession of certain elements of comfort (TV, refrigerator, gas oven, Satellite TV, Phone, computer)…”

Section: Page111mentioning

confidence: 99%