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Baskaran, T., Min, B., & Uppal, Y. (2015). Election cycles and electricity provision: Evidence from a quasi-experiment with Indian special elections. Journal of Public Economics, 126, 64–73.
Abstract: We present evidence from India showing that state governments induce electoral cycles in electricity service provision. Our data and research strategy allow us to build on models of political business cycles and targeted distribution in two important ways. First, we demonstrate that by manipulating the flow of critical inputs into economic activity like electricity, elected leaders can influence economic outcomes even in contexts where they have constrained fiscal capacity. Second, we identify the effect of elections on electricity provision by focusing on special elections held for exogenous reasons. Our results show that state governments induce substantive increases in electricity service to constituencies that hold special elections. Manipulation of the power supply is stronger in contested constituencies and during special elections held in states where the government commands only a small majority. Overall, we find no evidence of positive welfare effects from the electoral manipulation of electricity supply.
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Johnson, A., Phadke, A., & de la Rue du Cann, S. (2014). Energy Savings Potential for Street Lighting in India. Lawrence Berkely National Laboratory report, .
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Mann, M., Melaas, E., & Malik, A. (2016). Using VIIRS Day/Night Band to Measure Electricity Supply Reliability: Preliminary Results from Maharashtra, India. Remote Sensing, 8(9), 711.
Abstract: Unreliable electricity supplies are common in developing countries and impose large socio-economic costs, yet precise information on electricity reliability is typically unavailable. This paper presents preliminary results from a machine-learning approach for using satellite imagery of nighttime lights to develop estimates of electricity reliability for western India at a finer spatial scale. We use data from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar Partnership (SNPP) satellite together with newly-available data from networked household voltage meters. Our results point to the possibilities of this approach as well as areas for refinement. With currently available training data, we find a limited ability to detect individual outages identified by household-level measurements of electricity voltage. This is likely due to the relatively small number of individual outages observed in our preliminary data. However, we find that the approach can estimate electricity reliability rates for individual locations fairly well, with the predicted versus actual regression yielding an R2 > 0.5. We also find that, despite the after midnight overpass time of the SNPP satellite, the reliability estimates derived are representative of daytime reliability.
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Roychowdhury, K., Jones, S., Arrowsmith, C., & Reinke, K. (2011). Indian census using satellite images: Can DMSP-OLS data be used for small administrative regions? Urban Remote Sensing Event (JURSE), 2011 Joint, , 153–156.
Abstract: India conducts its census every ten years. Census data is collected manually in India with enumerators visiting every household in the country. Being such a vast country (in terms of area) and with a population of more than 1 billion, manual data collection is a laborious and expensive process. In response, this paper proposes a surrogate census method using DMSP-OLS night-time images. The study focuses on smaller administrative regions such as sub-districts (or taluks as they are known in the country) in the state of Maharashtra. Models are proposed using selected census metrics, and mean and standard deviation of stable lights and brightness information as obtained from the satellite images. The adjusted r2 values range from 0.2 to 0.8 at 95% confidence interval, with the majority of the metrics being moderately correlated (with r2 between 0.4 and 0.7). Generally it was found that the observed lights and brightness of big rural settlements from DMSP-OLS images have the potential for predicting certain census metrics. However, unlike larger areas such as districts where DMSP-OLS night-time images adequately predict census metrics, at the sub-district level the results need to be supplemented and validated with other information sources such as survey reports.
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Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. Applied Geography, 63, 292–303.
Abstract: Over the last quarter of a century, analyzing the pace of urbanization and urban economic growth in South Asia has become increasingly important. However, a key challenge relates to the absence of spatially disaggregated national accounts data â in particular, the absence of GDP data for sub-national administrative units and individual cities. The absence of such data limits the scope for detailed empirical analysis of spatial patterns of economic growth, particularly across individual urban settlements or cities. This paper aims to test the suitability of DMSP-OLS Nighttime Lights (NTL) data as a proxy for GDP to analyze detailed spatial patterns of urban economic growth across South Asia over the period 1999â2010. It will help to build an understanding of the nature and heterogeneity of spatial patterns of urban economic growth within the region and contribute to the development of a framework for the usage of NTL to investigate such patterns. Geographic Information System (GIS) is employed to identify the cities and urban agglomerations together with their NTL data in South Asia, and spatial statistics are used to analyze the spatial and temporal patterns of NTL growth. This paper adopts descriptive and inferential statistics to determine the quantitative relationship between NTL and population, urban size, and proximity to the coast. This paper reveals that the inter-annually calibrated NTL data is a good proxy for changes in national and sub-national GDP. In South Asia, the urban NTL hot spots are around major cities with populations between 1.3 and 2.6 million in 1999 and 0.5 to 1.3 million in 2010. Cities in the region have also become more clustered and connected forming urban agglomerations. NTL per unit of land in such clusters tends to be higher than in single cities in South Asia. India, Pakistan, and Sri Lanka tend to have higher NTL (economic) growth on average, while Nepal and Bangladesh have lower growth or declining NTL. There exists a very strong positive linear relation between distance to the coast and the total NTL within that distance, which leads to similar NTL growth rates among inland and coastal cities.
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