Accessibility navigation


Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

Crost, B., Shankar, B., Bennett, R. and Morse, S. (2007) Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data. Journal of Agricultural Economics, 58 (1). pp. 24-36. ISSN 0021-857X

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1111/j.1477-9552.2007.00076.x

Abstract/Summary

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Item Type:Article
Divisions:Faculty of Science > School of Archaeology, Geography and Environmental Science > Department of Geography and Environmental Science
Faculty of Science > School of Archaeology, Geography and Environmental Science > Human Environments
ID Code:3441
Uncontrolled Keywords:biotechnology development economics genetic modification productivity analysis BT COTTON INSTRUMENTAL VARIABLES ECONOMIC-PERFORMANCE ADOPTION TECHNOLOGIES IMPACT CHINA RICE RISK
Additional Information:

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation