Journal Article•
Data collection in census: a survey of census enumerators.
TL;DR: In this paper, a framework for analysing measurement error in the Indian census is presented based on the results of a survey of 1981 Census enumerators, which is focused on the primary demographic data collected using two individual (universal and sample) forms.
Abstract: In this paper a framework for analysing measurement error [in the Indian census] is presented. Based on the framework the results of a survey of 1981 Census enumerators are discussed. This exploratory survey is focused on the primary demographic data collected in the census using two individual (universal and sample) forms. While some errors are committed by enumerators other errors occur independent of enumerator characteristics. (EXCERPT)
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TL;DR: In this paper, an examen des changements agraires a l'oeuvre dans l'Etat indien d'Orissa montre une tendance a la polarisation de la structure fonciere, i.e., les mesures de modernisation profitent aux proprietaires les plus riches, qui consolident la valorisation de leurs terres, elles poussent les plus pauvres a vendre des terres qui n'assurent plus la securite economique.
Abstract: Un examen des changements agraires a l'oeuvre dans l'Etat indien d'Orissa montre une tendance a la polarisation de la structure fonciere. Tandis que les mesures de modernisation profitent aux proprietaires les plus riches, qui consolident la valorisation de leurs terres, elles poussent les plus pauvres a vendre des terres qui n'assurent plus la securite economique. Il ne s'agit pas d'un mecanisme de libre concurrence, car il est lourdement biaise par les structures de pouvoir des villages, influences directement par la segregation dans l'acces au credit et aux services gouvernementaux, ainsi que par le monopole exerce par les elites villageoises sur l'organe juridique local, le panch
12 citations
Posted Content•
06 Dec 2017
TL;DR: A machine learning based tool for accurate prediction of development and socio-economic indicators from high resolution day-time satellite imagery and shows that the direct regression of asset indicators gives superior R2 scores compared to that of transfer learning through night light data, which is a popular proxy for economic development used world wide.
Abstract: We develop a machine learning based tool for accurate prediction of development and socio-economic indicators from high resolution day-time satellite imagery. The indicators that we use are derived from the Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and the NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data. We use a deep convolutional neural network to build a model for regression of asset indicators from satellite images. We show that the direct regression of asset indicators gives superior R2 scores compared to that of transfer learning through night light data, which is a popular proxy for economic development used world wide. We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images, or are not always well correlated with each other. The tool can be extended to monitor the progress of development of a region over time, and to flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output.
10 citations
Cites background from "Data collection in census: a survey..."
...Census is also error prone and noisy due to the large variability in the data collection processes across the geography, and there is often no validation [Brown, 1971; Vemuri, 1994; Bose, 2008]....
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Posted Content•
TL;DR: A machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery that can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies.
Abstract: We develop a machine learning based tool for accurate prediction of socio-economic indicators from daytime satellite imagery. The diverse set of indicators are often not intuitively related to observable features in satellite images, and are not even always well correlated with each other. Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies. Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India.
10 citations
Cites background from "Data collection in census: a survey..."
...Census is also error prone and noisy due to the large variability in the data collection processes across the geography, and there is often no validation [Brown, 1971; Vemuri, 1994; Bose, 2008]....
[...]
10 Apr 2011
2 citations
Additional excerpts
...2007), websites (for example, NASDA, 2010; WCSRM, 2010), older books (for example, Devereux and Hoddinott, 1992; Poate and Daplyn, 1993; Puetz, 1993; Vemuri, 1997) or more rarely in peer-reviewed papers (for example, Ross,...
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...…(for example, FAO, 1995; Hoare, 1999; PEN, 2007), websites (for example, NASDA, 2010; WCSRM, 2010), older books (for example, Devereux and Hoddinott, 1992; Poate and Daplyn, 1993; Puetz, 1993; Vemuri, 1997) or more rarely in peer-reviewed papers (for example, Ross, 1984; Whittington, 2002)....
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Dissertation•
01 Jan 2013
2 citations
References
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Journal Article•
10 citations
TL;DR: The paper presents principles governing quality control in the population census and explains a series of quality control procedures adopted to reduce possible errors originating in census planning, enumeration and data processing.
Abstract: The author reports on experiences with Chinas 1982 census which covered approximately one billion people and 19 census items. It is noted that "the postenumeration sample survey indicates that the quality of the census enumeration is: 0.071% of overcounts and 0.056% of undercounts. The paper presents principles governing quality control in the population census and explains a series of quality control procedures adopted to reduce possible errors originating in census planning enumeration and data processing." (EXCERPT)
3 citations