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This paper examines geographic variations in illiteracy in India, both at the district and state levels, based on 1991 Census data. Multilevel regression modelling techniques are applied in order to establish whether variations relate to the type of people in particular places (compositional effects) or to the characteristics of the places themselv...
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... on standard deviations, the former is divided into low, average and high, whilst the latter is classified as steep, average and shallow. Table 4 reports the distribution of districts for the resulting nine types which, in effect, show different forms of age-based inequalities. The purpose of developing the typology, therefore, is to make conditional place assessments on the basis of age. ...
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... Además, subraya la interconexión entre la educación y otros problemas sociales. Esto es contrario a la visión teórica del desarrollo, pues la educación representa un aspecto intrínseco del bienestar humano (Subramanian et al., 2000). Terán Arce (2022) realizó una investigación sobre analfabetismo en áreas rurales de San Pablo, Cajamarca, Perú. ...
El analfabetismo impide el desarrollo personal y limita la participación de las personas en la sociedad, porque la lectura y la escritura son habilidades que fortalecen la calidad humana. En México, el analfabetismo se atiende con un programa de extensión educativa. El objetivo del estudio fue determinar las afectaciones que ocasionan la falta de habilidades en la lectura y la escritura en las personas pertenecientes a comunidades rurales de Camino Real, Campeche, México. Esta investigación fue descriptiva, no experimental y transversal con un enfoque mixto. Para obtener los datos se utilizó una encuesta. Los resultados señalaron que el 31% de los encuestados se sienten frustrados por no saber leer y escribir. Además, el 62% comentó que para obtener información importante conversa con otras personas. El analfabetismo es causa de varios problemas sociales como dificultades para conseguir un empleo que brinde una buena calidad de vida, problemas para manejar ingresos, requerimiento de ayuda para realizar trámites personales y discriminación, por no saber leer ni escribir.
... The fundamental premise in these frameworks is that there intrinsically exists one ideal unit of analysis and inference; be it individual or one particular geographic scale 4,5 . With the advent of multilevel modeling 3,6-8 , while there has been substantial efforts to simultaneously consider, especially the scales or units of individual and certain geographic aggregation 4,5,9 , efforts to consider multiple geographic units/scales has been limited 10,11 . ...
There has been limited effort to consider multiple areal units or scales in understanding spatial and geographic processes. Treating observed differences in the results by choice of geographic unit of analysis simply as a nuisance is conceptually problematic and can be empirically misleading. We consider the existing research on geographic variations in life expectancy in the United States to demonstrate that prior county-level studies have overestimated the importance of the county level by omitting states. Future investigations should critically assess the relative importance of multiple geographic, spatial, and non-geographic contexts, including an assessment of what units/scales have been omitted.
... Moreover, while previous studies have accounted for clustering at the village (primary sampling unit (PSU)) level, none have attempted to empirically quantify the extent to which this level contributes to the geography of poverty in India. Although states, regions, and districts each represent important political, agro-ecological and administrative units, treating each independent of other levels may result in an incomplete and misleading understanding of the geography of poverty for several reasons (Goldstein, 2005;Moellering & Tobler, 1972;Raudenbush & Bryk, 2002;Subramanian, Duncan, & Jones, 2000;Subramanian, Duncan, & Jones 2001). ...
Since the economic reforms in India in 1991, there has been a proliferation of studies examining trends of economic development and poverty across the country. To date, studies have used single-level analyses with aggregated data either at the state level or, less commonly, at the region and district levels. This is the first comprehensive and empirical quantification of the relative importance of multiple geographic levels in shaping poverty distribution in India. We used multilevel logistic models to partition variation in poverty by levels of states, regions, districts, villages, and households. We also mapped the residuals at the state, region and district levels to visualize the geography of poverty. We used data on 35 states, 88 regions, 623 districts, 25,390 villages and 202,250 households from the National Sample Survey in years 2009–10 and 2011–12. Our study found that geography of poverty in India cannot be fully explained by clustered distribution of poor households, and that there may be important contextual factors operating at the state and village levels. We found 13% of the variation in poverty to be attributable to states, 12% to villages, 4% to districts and 3% to regions, after accounting for important household characteristics. Similar variance partitioning was observed for rural and urban sample. The relative importance of one contextual level was highly sensitive to other levels simultaneously considered in the model. Findings from this study suggest that further explorations using multilevel modeling are warranted to identify specific contextual determinants of poverty at the state and village levels to reduce poverty and promote balanced regional development in India.
... Our example consists of data from the 1991 Indian census on levels of literacy in the population that were originally analysed by Subramanian et al. (2000Subramanian et al. ( , 2001. The data set has two geographical levels-state and district with 442 districts nested within 29 states. ...
A common application of multilevel models is to apportion the variance in the response according to the different levels of the data. Whereas partitioning variances is straightforward in models with a continuous response variable with a normal error distribution at each level, the extension of this partitioning to models with binary responses or to proportions or counts is less obvious. We describe methodology due to Goldstein and co-workers for apportioning variance that is attributable to higher levels in multilevel binomial logistic models. This partitioning they referred to as the variance partition coefficient. We consider extending the variance partition coefficient concept to data sets when the response is a proportion and where the binomial assumption may not be appropriate owing to overdispersion in the response variable. Using the literacy data from the 1991 Indian census we estimate simple and complex variance partition coefficients at multiple levels of geography in models with significant overdispersion and thereby establish the relative importance of different geographic levels that influence educational disparities in India. Copyright 2005 Royal Statistical Society.
... Studies showing spatial variation across regions are somewhat limited in South Asia, except in India. Researchers in India are recently paying more attention to spatial inequality, compared to other South Asian countries (e.g., Sastry 1997;Tagade 2005;Nair 2007;Subramanian et al. 2000;Rao et al. 2004;Ladusingh and Singh 2006;Bawdekar and Ladusingh 2008). This is probably due to a surging regional disparity on economic growth among various regions/states in India (Kurian 2007;Dev et al. 2007 important contextual variables reported with respect to child nutrition were road accessibility, health facilities, and community literacy. ...
... The issue of contextual and compositional effect was also interestingly discussed by Subramanian et al. (2000), although their paper was not on malnutrition itself. Taking a case of illiteracy variances across the district and state levels, they employed multi-level analysis on Census 1991 data. ...
With nearly 42% of children below age five nutritionally stunted, child malnutrition is a social, economic, and public health issue in Nepal. Even more disheartening is the wide variation of malnutrition across sub-regions within country, which seems to disproportionately disadvantage children in certain regions as opposed to others. This dissertation aims to understand the extent and causes of child stunting from a regional inequality perspective. Household data from the Nepal Demographic and Health Surveys (NDHS) 1996, 2001, and 2006 are used to analyze national and regional trends of stunting of children age 6-59 months. Various data sources including the Nepal Census and the Health Management and Information System are used for regional level data. Both household and regional data are then analyzed using two-level Hierarchical Linear Modeling (HLM). The results show that stunting is declining albeit very slowly in Nepal and across all thirteen regions. But there are significant and consistent disparities across regions that are not decreasing over time.
Since most census data are released for spatial aggregates, the microscale of people and the macroscale of places are confounded in analyses. Although regrettable, this situation is usually tolerated owing to the other obvious attractions of census data. In this paper, we consider how multilevel statistical procedures offer a solution to this problem. Importantly, we show how they allow places to be considered in terms of several different scales simultaneously. As we demonstrate, this provides important connections with recent moves towards performance review in several areas of public policy. An analysis of data on illiteracy from the 1991 Indian Census provides an illustration of multilevel approach and its usefulness.
This paper examines the role of observed contextual factors like topography, development and literacy on severe malnutrition among social groups in rural Maharashtra based on the Reproductive and Child Health District Level Household Survey (RCH-DLHS) Round II (2002-04) data. Multilevel modelling techniques were applied in order to examine the district-wise variations in severe malnutrition associated with the characteristics of the places (contextual effects), as the relationships with the type of people (compositional effects) have already been well established. The results show that developmental aspects such as road connectivity, community literacy, toilet facilities and household standard of living contribute positively to the status of severe malnutrition. Also, the scheduled tribe, aboriginal underprivileged group are more at risk of severe malnourishment due to a lack of proper development, poor awareness about maintaining and enhancing the nutritional value of food and lack of hygiene and sanitation as compared with the scheduled castes, another aboriginal group.
