Measuring economic mobility in India using noisy data: a partial identification approach

Hao Li, Daniel Millimet, Punarjit Roychowdhury
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引用次数: 1

Abstract

Abstract We examine economic mobility in India while accounting for misclassification to better understand the welfare effects of the rise in inequality. To proceed, we extend recently developed methods on the partial identification of transition matrices. Allowing for modest misclassification, we find overall mobility has been remarkably low: at least 65% of poor households remained poor or at-risk of being poor between 2005 and 2012. We also find Muslims, lower caste groups, and rural households are in a more disadvantageous position compared to Hindus, upper caste groups, and urban households. These findings cast doubt on the conventional wisdom that marginalized households in India are catching up.
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用噪声数据衡量印度的经济流动性:部分识别方法
我们研究了印度的经济流动性,同时考虑了错误分类,以更好地理解不平等加剧对福利的影响。接下来,我们扩展了最近发展的关于转移矩阵的部分辨识的方法。考虑到适度的错误分类,我们发现整体流动性非常低:2005年至2012年期间,至少65%的贫困家庭仍然贫困或面临贫困风险。我们还发现,与印度教徒、高种姓群体和城市家庭相比,穆斯林、低种姓群体和农村家庭处于更不利的地位。这些发现让人们对印度边缘化家庭正在迎头赶上的传统观念产生了怀疑。
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