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

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.

Chess endgame news

The Endgame Tables Online initiative and p2p community have made available all published Nalimov DTM EGTs. It now looks forward to the 16 unpublished EGTs computed by August 9th and presumed to be still in existence, particularly as Marc Bourzutschky’s FEG results independently confirm all Nalimov’s published 3-3p DTM EGT statistics including those for KPPKPP.

Chess endgame news

A report of the new Bourzutschky/Konoval chess endgame depth record of DTC = 330 moves, and of the progress of the Kryukov peer-group to disseminate Nalimov's DTM EGTs. The question of data integrity and assurance is raised.

Chess endgame news

News that Bourzutschky confirms there is no sub-7-man pawnless zugzwang, and that Bourzutschky and Konoval are generating DTC EGTs with a new program. News also of Kristensen's work on EGTs using OBDDs, Ordered Binary Decision Diagrams.

Gender stereotyping and decision processes: extending and reversing the gender bias in fame judgements

M. R. Banaji and A. G. Greenwald (1995) demonstrated a gender bias in fame judgments—that is, an increase in judged fame due to prior processing that was larger for male than for female names. They suggested that participants shift criteria between judging men and women, using the more liberal criterion for judging men. This "criterion-shift" account appeared problematic for a number of reasons. In this article, 3 experiments are reported that were designed to evaluate the criterion-shift account of the gender bias in the false-fame effect against a distribution-shift account. The results were consistent with the criterion-shift account, and they helped to define more precisely the situations in which people may be ready to shift their response criterion on an item-by-item basis. In addition, the results were incompatible with an interpretation of the criterion shift as an artifact of the experimental situation in the experiments reported by M. R. Banaji and A. G. Greenwald. (PsycINFO Database Record (c) 2010 APA, all rights reserved)