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Dhongde, Shatakshee

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    Development Economics Consortium
    ( 2019-04-26) Brummund, Peter ; Dhongde, Shatakshee ; Del Valle, Alejandro ; Filipski, Mateusz ; Liu, Xuepeng ; Magnan, Nicholas ; Moyano, Paloma ; O'Connell, Stephen ; Roy, Abhra ; Taylor, Laura ; Viceisza, Angelino ; Zimmermann, Laura
    This session will highlight frontiers of research in the area of development economics. The session will include research presentations on a wide range of topics, covering both micro and macro perspectives of development. This is the first time scholars who are actively conducting research in development economics, will come together in the hope of forming a professional network to enhance collaboration among higher educational institutes in Atlanta and nearby areas.
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    Technology, Development Economics, and Human Rights
    ( 2014-10-03) Dhongde, Shatakshee ; Kosal, Margaret E. ; Shemyakina, Olga
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    Poverty, Inequality, and Development
    (Georgia Institute of Technology, 2013-09-06) Bowman, Kirk ; Best, Michael L. ; Shemyakina, Olga ; Dhongde, Shatakshee ; Boston, Danny
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    Measuring Segregation of the Poor
    ( 2012-01) Dhongde, Shatakshee
    In this paper I propose a poverty segregation curve to measure inequality in the distribution of the poor. Axioms of relative income inequality are reformulated for the poverty segregation curve and a generalized segregation curve is proposed. The segregation analysis is applied to study regional concentration of the poor in India in the last two decades. Various measures of segregation indicate that although poverty has declined over a period of time in almost all regions, there is a significant increase in the segregation of the poor in some regions in India.
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    Global Poverty Estimates: A Sensitivity Analysis
    ( 2011-11) Dhongde, Shatakshee ; Minoiu, Camelia
    Current estimates of global poverty vary substantially across studies. In this paper we undertake a novel sensitivity analysis to highlight the importance of methodological choices in estimating global poverty. We measure global poverty using different data sources, parametric and nonparametric estimation methods, and multiple poverty lines. Our results indicate that estimates of global poverty vary significantly when they are based alternately on data from household surveys versus national accounts but are relatively consistent across different estimation methods. The decline in poverty over the past decade is found to be robust across methodological choices.