genetic modification and yield risk in corn: parametric and non-parametric analysis

1. genetic modification and yield risk in corn: parametric and non-parametric analysis Elizabeth Nolan University of sydney Paulo santos Monash university

2. Bt corn • First genetically modified traits approved at end of 1996 • Introduced commercially for 1997 • In 2012, corn hybrids with at least one GM trait were planted in over 88% of the crop area in the United States • Soil bacterium Bacillus thuringiensis is toxic to lepidopterous insects • European Corn Borer (CB) (1996) • Corn Rootworm (RW) (2002) • allows for an almost complete control of the European corn borer and corn rootworm • Superior to that of previously used techniques • Previous damage control technologies: • up to 80% against first generation corn borer • 67% against second generation borer • 63% in the case of corn rootworm.

3. Pesticides and risk • complete evaluation of the impact of new technologies requires explicit recognition of risk • Debate about how pesticides affect risk • Risk reducing (Feder) • Risk increasing if output uncertainty is the dominant cause of randomness (Pannell (1991) and Horowitz and Lichtenberg (1993)) • The impact of GM traits is, therefore, an empirical question

4. Objectives • Identify effect of GM traits on production risk 1. flexible moments approach of Antle to analyse higher moments • variance of a distribution does not distinguish between upside risk and downside risk • parametric 2. stochastic dominance • Can compare distributions of yields originated by different technologies • rank them in terms of desirability under minimal assumptions regarding decision makers’ preferences • non-parametric • Complementary methods • parametric approach allows quantification of the contribution of different input factors to differences in yield distributions , but requires distributional assumptions. • Stochastic dominance does not require distributional assumptions, not possible to quantify the influence of specific inputs on yield distributions.

5. Data • Results of experimental field trials of corn hybrids submitted by corn breeders to the Agricultural Extension Services of ten universities from 1997-2009 • Illinois, Indiana, Iowa, Kansas, Minnesota, Missouri, Nebraska, Ohio, South Dakota and Wisconsin • Range of locations important for results • Choice of period • Observations • 147,790 individual trials • 8,423 hybrids • 339 locations • 430 companies

6. CRDs represented in dataset compared with planted acres

7. Advantages of data • Advantages of experimental data • standardised trials • avoid problems of identification • but recognise that experimental yields higher than on farm yields • Provide • details of agronomic practices • information about the traits present in each hybrid • Information on weather conditions • wide variety of production conditions • over period since introduction of GM traits up to 2009 • Spatial variability compensates for relatively short temporal range

8. Independent variables • GM traits (combinations) • Site details and agronomic practices • plant density • soil type • cultivation type (conventional versus minimum or no till) • previous crop • early or late trial • irrigated or dryland • nitrogen application in lbs/ac • Weather conditions • monthly rainfall April to September • average minimum and maximum temperatures April to September • Year by location (CRD) interaction terms

9. Empirical method (parametric) • Large unbalanced panel dataset • Individual corn hybrids are the cross sectional elements • Use both fixed effects and the Hausman-Taylor random effects specification to estimate a linear production function • Square (cube) residuals to obtain variance (skewness) • Regress variance and skewness on the individual inputs (including combinations of GM traits) to find marginal variance and skewness for each input.

10. Effect of GM traits on yield variance and skewness (HT)

11. Margnal variance and skewness (FE)

12. Results of parametric analysis • Marginal variance for most GM traits (and their combinations) is positive • presence of GM leads to an increase in variance • HTO only weakly statistically significant • RWO not statistically significant • Most of the GM trait combinations have a statistically significant negative effect on skewness • Increase in downside risk. • RWO and HTO and RWHT not statistically significant effect.

13. Stochastic dominance • GM traits also have an important impact on mean yield • possible that decision makers are willing to trade the increase in variability and downside risk (which they may dislike) • with the increase in mean (which they may like) and still be better-off • Use stochastic dominance to take into account these simultaneous changes.

14. Ranking new technologies • If a new technology, for example GM traits, is superior according to first order stochastic dominance (FOD) criterion • will be selected by any risk-averse or risk neutral firm • will be chosen by farmers who always prefer higher expected return to lower • If the new technology is superior according to the second order stochastic dominance (SOD) criterion • will be selected by those farmers who prefer higher return to lower • and are also strictly risk averse

15. Trials by type of GM hybrids by year

16. SD for sub groups of GM hybrids • Divide data into subsets • analyse for each trait within specific CDFs for unconditional yield for the sub sets • CBHT hybrids first order dominate conventional hybrids • produce more than conventional hybrids under all conditions • effect of increased mean yield more than compensates for the increase in variance and downside risk in terms of producers’ welfare • CBO, CBRW, and CBRWHT hybrids • No dominant strategy

17. Stochastic Dominance Results

19. Results • Most combination of traits lead to increased variance • exception is rootworm resistance by itself, which has a negative coefficient, but is not statistically significant • Downside risk increases with the presence of CBO, CBHT, CBRW, and CBRWHT • RWO and HTO, by themselves and in combination have no statistically significant effect on downside risk • However, CBHT first order dominates conventional for the period 1999-2009

20. Conclusion • Pest resistance traits appear to lead to an increased variability of yield • Farmers may be more concerned about downside risk • our results show that in most trait combinations downside risk is increased for GM hybrids • Results differ from those of other recent studies • Data include observations from more marginal corn producing states

21. Number of observations below cross (SOD)

22. More detailed results

23. Summary statistics

24. Number of trials of hybrids by year and state

25. Hausman Taylor • Fits random effects model • Some covariates correlated with the unobserved effects • But not with idiosyncratic error • Estimate yit = x′1itβ1 + x′2itβ2 + z′1iγ1 + θi+ μit where x1it is a matrix of variables that are time varying and uncorrelated with θi,, x2itis a matrix of variables that are time varying and are correlated withθi andz1iis a matrix of variables that are time invariant and uncorrelated with θi (in this case, the various combinations of GM traits) • Hausman and Taylor show that we can use x1it, z1i , x2it - x2i and x1i as  instrumental variables

26. Stochastic dominance for subsets of traits and combinations of traits by period

32. Production function • When the disturbance h(X)ε enters the production function in an additive way • Allows for the possibility of increasing, decreasing or constant marginal risk (Just and Pope 1978). • Can express function as: yit = f(Xit) + uit = f(Xit) + h(Zit) εit • where • yit is output • we assume that E(εit) = 0, var(εit) = 1. • f(Xit) is the deterministic component of production (representing the conditional mean of production) as a function of the independent variables and • uit is the stochastic component (representing its conditional variance), which can be rewritten as a function of input use h(Zit).

33. Description of probability distributions • conditional mean and variance functions are not sufficient for a description of a stochastic production function. • risk not only equivalent to output variance (uit2) • Antle (1983) proposed the flexible moments approach • shows that consistent estimates of all central moments can be obtained econometrically without imposing arbitrary restriction on the moments of the distribution.