*Direct correspondence to Daniel P. Aldrich, Department of Political Science, Purdue University, 100 N. University St., West Lafayette, IN 47907 〈email@example.com〉. Daniel P. Aldrich will share all data and coding information with those wishing to replicate the study. Mr. M. Louis and People's Watch Tamil Nadu worked tirelessly to procure much of the data used in this analysis; the author gratefully acknowledges their efforts and generosity in sharing their data. The author conducted the relevant fieldwork, archival research, and interviews in Tamil Nadu, India for this article while on an Abe Fellowship from the Center for Global Partnership and the Social Science Research Council during 2007–2008. He thanks Janki Andharia, Lokesh Gowda, Jacquleen Joseph, and Sunil Santha with the Tata Center for Disaster Management within the Tata Institute of Social Sciences, Annie George with the Nagapattinam Coordination and Resource Centre, and Hari Ayyappan for their help while in the field. Finally, Jay McCann and four anonymous reviewers provided valuable advice.
Separate and Unequal: Post-Tsunami Aid Distribution in Southern India*
Article first published online: 26 OCT 2010
© 2010 by the Southwestern Social Science Association
Social Science Quarterly
Special Issue: Inequality and Poverty: American and International Perspectives
Volume 91, Issue 5, pages 1369–1389, December 2010
How to Cite
Aldrich, D. P. (2010), Separate and Unequal: Post-Tsunami Aid Distribution in Southern India. Social Science Quarterly, 91: 1369–1389. doi: 10.1111/j.1540-6237.2010.00736.x
- Issue published online: 26 OCT 2010
- Article first published online: 26 OCT 2010
Objective. Disasters are a regular occurrence throughout the world. Whether all eligible victims of a catastrophe receive similar amounts of aid from governments and donors following a crisis remains an open question.
Methods. I use data on 62 similarly damaged inland fishing villages in five districts of southeastern India following the 2004 Indian Ocean tsunami to measure the causal influence of caste, location, wealth, and bridging social capital on the receipt of aid. Using two-limit tobit and negative binomial models, I investigate the factors that influence the time spent in refugee camps, receipt of an initial aid packet, and receipt of 4,000 rupees.
Results. Caste, family status, and wealth proved to be powerful predictors of beneficiaries and nonbeneficiaries during the aid process.
Conclusion. While many scholars and practitioners envision aid distribution as primarily a technocratic process, this research shows that discrimination and financial resources strongly affect the flow of disaster aid.