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

Agricultural Modernisation in Rural Orissa: Land Transfer and Ownership Pattern:

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.

On monitoring development using high resolution satellite images.

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.

On monitoring development indicators using high resolution satellite images

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Hiring, training and managing a field team

Investigating biases in census questions on mortality using Agincourt heath and demographic surveillance system data

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)

Vemuri

Papers

Data collection in census: a survey of census enumerators.