Date: 13 January, 2020
Time: 3.30 – 4.30 pm
Seminar title: Ethnic-geographic continuum
Please find the details of the paper below:
Authors: Naveen Bharathi (IIM Bangalore), Deepak Malghan (IIM Bangalore) & Andaleeb Rahman (Cornell University)
The association between diversity and development – both negative and positive – has been empirically tested for a limited set of diversity variables despite its centrality to the political economy discourse. Using a unique census-scale micro dataset from rural India containing detailed caste, religion, language, and landholding data (n ~ 13:25 million households) in combination with administrative data on human development, satellite measurements of luminosity as proxy for sub-national economic development, we show that an association between social heterogeneity and economic development is tenuous at best, and is likely an artifact of geographic, political, and ethnic units of analysis. We formally de ne the “ethnic-geographic continuum” and develop a cogent theoretical framework for testing validity of theories across varying levels of ethnic and geographic aggregations. We show how our ethnic-geographic continuum framework accounts for the intersections between the Modi able Ethnic Unit Problem (MEUP) and the Modi able Areal Unit Problem (MAUP). We use seventeen different diversity metrics across multiple combinations of ethnic and geographic aggregations to empirically validate this framework, including the first ever census-scale enumeration and coding of elementary Indian caste categories (jatis) since 1931.
About the speaker:
Dr. Rahman is at the Charles H. Dyson School of Applied Economics & Management, Cornell University. He works on Development issues and has published in journals like the Journal of Development Studies, Food Policy, Economic & Political Weekly, Global Food Security, Climate Risk Management etc. His book “Transforming food systems for a rising India” has been identified as the Palgrave’s second most downloaded book for the year.
His homepage is below:
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