A Case Study of Consumer’s Risk Attitudes to the Use of Plant Biotechnology in Food, Industrial and Medical Application

1. A Case Study of Consumer’s Risk Attitudes to the Use of Plant Biotechnology in Food, Industrial and Medical Applications Michele Veeman, YulianDing, Yu Li,WiktorAdamowicz Department of Resource Economics and Environmental Sociology, University of Alberta, Edmonton Alberta T6G 1X1, Canada Contact: michele.veeman@ualberta.ca

2. Why is PMF of interest? • The use of plants as food, medicines, and industrial products dates from prehistory • Modern biotechnological methods to use plants as production platforms for vaccines & pharmaceutical drugs, specialized industrial products & functional foods date from 1992 • Plant molecular farming (PMF) promises both potentially important benefits and potential risks and costs……..public acceptance is necessary!

3. Some factual examples: • Production of plant-based pharmaceutical drugs (eg, insulin expressed in safflower plants or animal and human vaccines produced in tobacco plants) continues under development • Initiatives to improve nutritional content of particular foods (eg, Golden Rice)--continue to face considerable regulatory and commercialization challenges. • Industrial products such as biofuel and other biomaterials obtained from modified plants.

4. Potential costs include: • Contamination by plants modified to express medical or industrial compounds may lead to accidental contamination of food and feed crops, • Associated potential issues of food and environmental safety • Very considerable financial costs from accidental contamination of non-GM crops

5. Rationale for study of individuals’ risk perceptions Better understanding of public/consumers’ attitudes to these bio-economy innovations can aid development of effective policy, risk management, and risk communication

6. Conceptually, the study of risk perceptions: • recognizes the influence of family and society on individual’s risk preferences and beliefs • recognizes that risk attitudes may change with new information and experience (Branson et al. 1996, Viscusi 1989) • trust is also increasingly viewed as an important influence on people’s views and behavior relative to risky situations and actions (eg., Uslaner 2002) • early psychometric studies established the importance for risk perceptions of whether a risk is undertaken voluntarily, involves lack of choice, and /or is poorly understood or invokes dread, amongst other features (eg., Slovic 1987, 2000, 2010)

7. Empirical model: We apply ordered probit models based on individual’s risk assessments where: y*mnis an unobserved continuous dependent latent variable (the extent of concern attributed by the nth individual to the mth risk situation), Xnspecifies the socio-economic and demographic characteristics of individual n, and βis the parameter vector.

8. Following Greene (2003), for estimation: • y * mnis replaced by observed categorical values of respondent’s risk ratings (ymn). • Parameters µ are to be estimated and specify thresholds between category rankings (0 ˂ μ₁˂ μ₂) . Considering four risk rating levels, which we give respective values of 0, 1, 2, and 3, gives:

9. Again, following Greene (2003): Assuming that ϵmnare normally distributed, the probabilities of ymn= 0,1,2,3 are calculated, as are the marginal effects. Marginal effects indicate the probabilities of change from one risk rating level to another, based on the significant estimated parameters associated with respondents’ characteristics

10. DATA: Two large scale cross-Canada consumer surveys, conducted in 2009 and 2005 Each survey focused on consumers’ attitudes and stated choices re agricultural biotechnology innovations. Each was developed &tested with randomly recruited focus groups. Eachsurvey (in English and French) wasadministered to sampled respondents drawn from large internet-based consumer panels.For the 2009 survey sample N= 1009; the 2005 survey sample N= 1575. By design, features of each sample are reasonably consistent with major demographic characteristics of the Canadian population;this may not apply for unobserved population characteristics.

11. Each survey queried risk ratings for four biotechnology innovations & seven other food risk issues Innovations/risk issues were random ordered: “Use of genetic modification/engineering in crop production” “Drugs (i.e. medicines) made from plant molecular farming through genetic modification/engineering” “Genetically modified/engineered crops to produce industrial products like plastics, fuel or industrial enzymes” “Genetically modified/engineered crops to increase nutritional qualities of food.”  Respondents were asked to rated each of these as: “High Risk”, “Moderate Risk”, “Slight Risk”, “Almost No Risk” or “Don’t Know/Unsure”

12. Explanatory variables: socioeconomic and demographic information Much similar data are available foreach sample. But data on different measures of trust attitudes & family health status differ 2005: no family health information; trust in information from different sources is simply queried (Y/N) 2009: family health is queried two concepts of trust are queried--- generalized trust (GSS) “Most people can be trusted” ---institutional trust (following De Jong 2008) we develop a measure of system trust for each respondent, based on ratings for different dimensions of trust in food producers, processors, retailers and government)

13. Results and Discussion, Qualitative Issues:Table1. Summary statistics of risk ratings for four plant biotechnology applications, 2009 survey (Number of respondents=1009)

14. TTable 2. Summary statistics of risk rating rank order, by percentage of respondents citing issue as “high risk” and associated percentages of respondents choosing this response, 2009 and 2005

15. Results and Discussion, Quantitative Models: The following two tables give estimated coefficients for two different versions of the ordered probit models for each of the four biotechnology innovations: generalized trust (table 3) and food system trust (table 4) for 2009. Estimated coefficients based on the 2005 data are in the paper. Marginal effects based on significant coefficients from these estimates are in Tables 5 and 7

16. Table 3. Coefficients and standard errors of ordered probit models for four plant biotechnology applications based on generalized trust, 2009 survey data

17. Table 4. Coefficients and standard errorsofordered probit models for four plant biotechnology applications based on trust in the food system, 2009 survey data

18. Table 5. Marginal effects of significant coefficients (!%, 5%, 10%) for“high risk”and “almost no risk” ratings, 2009 data

19. Table 7. Marginal effects of significant coefficients (1% and 5% levels) for “high risk’ and “almost no risk” ratings, 2005 data

20. Summing Up • Quebec residents are more averse to GM/GE in crop/food production than are other Canadians • Women see more risk than do men in these GM/GE applications. Older people perceive more risk than others. This is consistent with results commonly found in numbers of studies of food risk perceptions • Our most striking results are the influence of trust (ie those who trust & those who trust the food system) in mitigating high risk perceptions. Implications: need to maintain trust & need for gender awareness in risk communication!

21. Extrapolating from our previous work and other studies regarding food bio-fortification: Although many individuals place value on nutritive or environmental benefits, this value componentis typicallyless than the discount in WTPto accept identified GM/GE-based foods. Stigmatization and regulatory lags and costs hinder approval of food bio-fortification through GM/GE (eg Golden rice) &, with targeting by activists, these have heightened barriers to research and commercialization of such products. Thus, where genetic diversity allows, efforts to produce foods with nutritionally improved components are much more readily accepted if these are not directly based on GM/GE techniques.

22. As regulatory barriers have grown, what other effects have these influences tended to have on bio-economy innovation? Dramatic reductions in costs to identify, sequence and analyze genes enablesplant scientists and breeders touse of new molecular biological tools to identify molecular “markers” of desired plant traits such as drought resistance and some—but not all---desired nutritional components. Thus “traditional” breeding techniques, allied with molecular biology techniques may be pursued for many (not all) crop innovations. This approach involves: # Capital costs and genetic diversity in target plants, and # Considerable commitment to both basic and applied research, But these areunder majorfinancial pressuresin public and university research centers. The implications of these pressures surely deserve further assessment by economists and other policy analysts.