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

Author:

Li Hao1,Millimet Daniel L23,Roychowdhury Punarjit45

Affiliation:

1. School of Economics and Management, Southeast University , Nanjing , China

2. Department of Economics, Southern Methodist University , Dallas, Texas , USA

3. IZA - Institute of Labor Economics , Bonn , Germany

4. Department of Economics, Shiv Nadar University , Delhi NCR , India

5. Global Labor Organization , Essen , Germany

Abstract

AbstractWe 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.

Publisher

Oxford University Press (OUP)

Subject

Statistics, Probability and Uncertainty,Economics and Econometrics,Social Sciences (miscellaneous),Statistics and Probability

Reference54 articles.

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