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

Hao Li, Daniel Millimet and Punarjit Roychowdhury ()
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Hao Li: Nanjing Audit University
Punarjit Roychowdhury: Indian Institute of Management

No 12505, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: We examine economic mobility in India while rigorously accounting for measurement error. Such an analysis is imperative to fully understand the welfare effects of the rise in inequality that has occurred in India over the past few decades. To proceed, we extend recently developed methods on the partial identification of transition matrices and apply this methodology to newly available panel data on household consumption. We find overall mobility has been markedly low: at least 7 out of 10 poor households remain 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 in terms of escaping poverty or transitioning into poverty compared to Hindus, upper caste groups, and urban households. These findings suggest inequality in India is likely to be chronic and also challenges the conventional wisdom that marginalized households are catching up on average.

Keywords: mobility; India; measurement error; partial identification; poverty (search for similar items in EconPapers)
JEL-codes: C18 D31 I32 (search for similar items in EconPapers)
Pages: 116 pages
Date: 2019-07
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Citations: View citations in EconPapers (2)

Published - published in: Journal of the Royal Statistical Society, Series A, 2023, 186 (1), 84-109

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Working Paper: Measuring Economic Mobility in India Using Noisy Data: A Partial Identification Approach (2023)
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