Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach

Hao Li, Daniel Millimet and Punarjit Roychowdhury

No 1227, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: We examine economic mobility in India while accounting for misclassification to better understand the welfare e§ects 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 percent 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.

Keywords: Mobility; India; Measurement Error; Partial Identification; Poverty (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1227

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