Distributional Implications of Climate Change on Indian Agriculture: A Quantile Regression Approach
Speaker: Chandra Kiran B Krishnamurthy
Center for Environmental and Resource Economics,Umea University.
Friday, 13th January 2012
Venue: Auditorium, COE Building
Madras School of Economics
(Behind Govt. Data Center &
Adjacent to Vidyasagar)
Gandhi Mandapam Road, Chennai 25.
Time: 3.30 pm
About the Speaker:
Having completed his PhD from Columbia University, School of International and Public Affairs, New York he is presently Post-Doctoral Fellow, at the Center for Environmental and Resource Economics, Umeå University. Dr. Chandra Kiran has presented seminars at MIT Sloan School of Management ,CERE, Umeå University, National Institute for Advanced Study (NIAS), Bangalore and Ashoka Trust for Research in Ecology and The Environment (ATREE), Bangalore. His primary research interests are: Environmental and Resource Economics, Applied Econometrics and Statistics, Secondary fields: Development Economics, Statistical Modeling of Climate and weather extremes. He has several publications in India and abroad in the broad area of Economics and Operations Research:
Using a 30-year dataset on district-level yields, with more than 250 districts, and pairing it with a newly available gridded weather dataset, this paper estimates the impact of climate change on major food crops of India. Unlike previous work, which focusses on impacts on the conditional mean, the current approach, using newly developed methods for fixed effects in the quantile regression context, evaluates the impact on yields at different locations along full condtional distribution of yield. This approach allows for a better understanding of the differential impacts of climate change on yield, even within a given region. Further, this paper allows for differential impacts of temperature and irrigation on crops grown in different seasons. This paper reports significantly reduced yields of wheat, of upto 12%, in all regions and at most quantiles, under scenarios with reasonable temperature increase. Further, the reductions are larger at the upper quantiles, indicating significant impacts on production. For rice however, under all scenarios considered, there is an increase, mostly modest but upto 10%, in yield at the lowest quantiles while at the upper quantiles there are minimal losses (of upto 2.5%). There are also significant regional differences in impacts at different quantiles. These estimates suggest significant likely loss in production of food grains under the scenarios considered and have significant implications for malnutrition in India.
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