CRISPR versus GMOs: Public acceptance and valuation

Global Food Security 19 (2018) 71–80

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Global Food Security journal homepage: www.elsevier.com/locate/gfs

CRISPR versus GMOs: Public acceptance and valuation a,⁎

a

a

Aaron M. Shew , L. Lanier Nalley , Heather A. Snell , Rodolfo M. Nayga Jr. a b

T a,b

, Bruce L. Dixon

a

University of Arkansas Department of Agricultural Economics, 217 Agriculture Building, Fayetteville, AR 72701, USA Norwegian Institute of Bioeconomy Research, NBER, Norway

A R T I C LE I N FO

A B S T R A C T

Keywords: CRISPR GMOs Public acceptance Willingness-to-pay Agricultural biotechnology Food regulation

CRISPR gene-editing has major implications for agriculture and food security. However, no studies have evaluated the public acceptance and valuation of CRISPR-produced food. As such, we conducted a multi-country assessment of consumers’ willingness-to-consume (WTC) and willingness-to-pay (WTP) for CRISPR-produced food compared to conventional and genetically modified (GM) foods, respectively. In the USA, Canada, Belgium, France, and Australia, 56, 47, 46, 30, and 51% of respondents, respectively, indicated they would consume both GM and CRISPR food. We also found that biotechnology familiarity and perceptions of safety were the primary drivers for WTC CRISPR and GM food. Moreover, respondents valued CRISPR and GM food similarly – substantially less than conventional food – which could be detrimental for meeting future food demand.

1. Introduction CRISPR (clustered regularly interspaced short palindromic repeats/ Cas9) gene-editing technology has been promoted widely by scientists for its promise in food and agriculture (Huang et al., 2016; Huesing et al., 2016; Ishii and Araki, 2016; Wolter and Puchta, 2017). CRISPR is being heralded as a promising technology because it can accurately insert and alter DNA with targeted specificity and relatively easy implementation. Hence, it has the potential to be a pivotal innovation in the drive to feed the current and growing world population. We struggle to feed the current population of seven billion people globally due to a myriad of challenges including yield gaps, post-harvest losses, market access, and nutritional diversity (Godfray and Garnett, 2014). Beyond these problems, population growth and climate change will create new challenges in coming decades. Some of these challenges include increased global demand for agricultural productivity and the simultaneous reduction of environmental degradation (Tilman et al., 2011; Wheeler and von Braun, 2013). As such, scientists must overcome these obstacles to meet global food demand, even while decreasing resource-use and preventing negative environmental impacts. CRISPR presents significant opportunities for improvements in crop production, both in reducing biotic and abiotic stresses as well as increasing yield potential (Doudna and Charpentier, 2014; Li et al., 2012, 2016). Moreover, these improvements could be obtained with little to no additional environmental pressure. However, many consumers have reacted negatively to previous agricultural applications derived from biotechnology (McFadden, 2017), namely Genetically Modified

⁎

Organisms (GMOs), particularly in the European Union (EU). Yet, CRISPR gene-editing can differ markedly from the genetic modification technologies introduced in the 1980s–90s. Specifically, CRISPR could be used to introduce changes to DNA intrinsic to the target species or cultivar, whereas traditional genetic modification introduces foreign DNA from a different species (i.e., transgenic) or from another cultivar of the same species (i.e., cisgenic; Ishii and Araki, 2016). Most GMOs in commercial production globally are products of transgenic modification (Huesing et al., 2016). Still, while CRISPR can differ in the type of change that can be introduced to a plant/crop in relation to the traditionally understood GMO, consumers may not distinguish them when purchasing or consuming food (Ishii and Araki, 2016; Lusk et al., 2018; McFadden and Lusk, 2016; Zerbe, 2004). Although CRISPR could be a significant boon for improving agricultural production, a lack of public acceptance might stifle further development of CRISPR crops before commercialization can become a reality (Huang et al., 2016; Ishii and Araki, 2016). Thus, we conducted a multi-country assessment of willingness-to-pay (WTP) for and willingness-to-consume (WTC) a hypothetical non-GMO CRISPR rice (Oryza sativa) compared to a transgenic GMO rice. The results of our study are relevant given the recent realization that CRISPR technology could be used to both increase food production and improve input-use efficiencies in agriculture. However, if consumers equate CRISPR to traditional GMO, CRISPR's full market potential may never be achieved.

Corresponding author. E-mail address: [email protected] (A.M. Shew).

https://doi.org/10.1016/j.gfs.2018.10.005 Received 5 July 2018; Received in revised form 20 September 2018; Accepted 31 October 2018 2211-9124/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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applications with the same desired genetic traits and physiological characteristics because rice is globally consumed and available.

1.1. Previous studies of GMO valuation In the last 20 years, numerous studies have measured consumers’ WTP for genetically modified (GM) food products and subsequently discussed market and policy implications for such products (Chern et al., 2002; Huesing et al., 2016; Huffman, 2003; Li et al., 2002; Lusk et al., 2005; McFadden and Lusk, 2016; Nayga et al., 2006; Wunderlich et al., 2017). The first GM crops were released for commercialization in 1994, and by 2014, more than 90% of the maize (Zea mays), soybeans (Glycine max), and cotton (G. hirsutum) produced in the United States were transgenic GMOs (Huesing et al., 2016). However, given the potential discounts and moratoriums on GM food products in many other countries (Delwaide et al., 2015; Frewer et al., 2013), traditional GM biotechnology applications have struggled to reach global commercialization potential, making them more expensive to produce. For example, although GM rice has been used in experimental test plots and could bring significant benefits to rice production, it has not been commercialized yet (High et al., 2004; Potrykus, 2012). Multinational agricultural companies have begun exploring different biotechnologies for crop improvements that do not involve transgenic modification as in many traditional GMOs. In part, this may be due to the widespread public skepticism associated with transgenic GMOs. It is also likely that more recent discoveries and technologies provide greater efficiency and higher profit margins. Previous studies found differences in consumers’ valuation of cisgenic GMO food compared to transgenic GMO food—valuing cisgenics over transgenics (Delwaide et al., 2015; Shew et al., 2016)—presumably because cisgenic (a within species transfer of DNA) is perceived as more “natural”. Similarly, Shew et al. (2017) investigated consumers’ valuation of topical RNAi insecticide compared to transgenic GMO insecticide and conventional insecticide and found that consumers differentiated between all three production methods when purchasing food. CRISPR is another innovative technology that may not result in the permanent introduction of foreign DNA into the host organism for some applications (Huang et al., 2016; Wolter and Puchta, 2017). Given the WTP differences found in other studies (Delwaide et al., 2015; Shew et al., 2017), it is possible that CRISPR will also be valued differently compared to conventional and transgenic GMO foods. As such, this study presents the first multi-country analysis of public valuation and acceptance of CRISPR food. The USA, Canada, Belgium, France, and Australia were specifically selected for this research in order to compare with the Shew et al. (2017) study of RNAi and to capture different approaches to regulating agricultural biotechnology applications and distinct consumer perceptions.

1.3. Traditional GM versus CRISPR application GM and CRISPR methods and applications are multi-dimensional, and not easily condensed into verbiage familiar to many consumers. As such, we selected a commonly used GM application in other row crops (maize, soybeans, and cotton)—glyphosate resistance—to compare with a hypothetical CRISPR-derived glyphosate resistant rice. By expressing the same desired trait, through two different mechanisms (CRISPR gene-editing and GM transgenic insertion of DNA), we can test if consumers view the two technologies differently from valuation and acceptance standpoints. Glyphosate is any herbicidally effective form of N-phosphonomethylglycine. In short, glyphosate inhibits the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) from converting acids essential to the biosynthesis of vital plant compounds (Barry et al., 1997), thus killing the plant. Glyphosate tolerance was first identified in the 1980s through the genetic engineering of plants to produce more EPSPS in the chloroplast of plant cells (Kishore and Shah, 1988; Shah et al., 1986). By inserting a wild variant of the enzyme, the plant produces 40–80 times more EPSPS to generate resistance to the effects of glyphosate, which would otherwise kill the plant (Kishore and Shah, 1988). On the other hand, CRISPR-derived glyphosate resistance differs from its transgenic GM counterpart described above in that no foreign DNA (e.g., wild variant of the EPSPS) remains in the host plant. Instead, scientists use CRISPR technology to identify intrinsic plant DNA sequences that can generate the same results as the GM counterpart (Li et al., 2012; Li et al., 2016; Huang et al., 2016)—in this case glyphosate resistance through increased production of EPSPS within the chloroplasts. Glyphosate resistance is one of the most commonly used GM applications, and although CRISPR could be applied to any number of agronomic problems, we selected glyphosate resistance so that respondents could make direct comparisons with a widely adopted, current biotechnology application. Currently, glyphosate is sprayed as a pre-emergence herbicide to kill existing weeds. A number of other herbicides are sprayed during post-emergence to control weeds. If glyphosate resistant rice were commercially available, it would likely reduce the amount and potency associated with herbicides in rice production (UAEX, 2018). 1.4. Information for valuation and acceptance In ideal circumstances, valuation and acceptance would be elicited in a non-hypothetical setting to mitigate possible hypothetical bias; e.g., the tendency of survey respondents to exaggerate responses (Carlsson et al., 2005; Silva et al., 2011). However, no CRISPR-derived staple foods are currently commercially available. Accordingly, any present investigation of CRISPR's public acceptance and valuation must use hypothetical techniques. Notably, the WTP results should be interpreted not based on magnitude; rather, based on the direction (discounts versus premiums) and statistical significance of results. This provides inference on the acceptance and valuation relative to conventional agricultural products and between technologies. To assess valuation and acceptance, we presented short and long information sets (discussed in Methods) to survey respondents to derive their WTP for glyphosate resistant CRISPR and GM rice versus a conventionally bred rice, respectively, when given more or less information. We then asked respondents if they were willing-to-consume CRISPR and GM glyphosate-resistant foods, and asked a sequence of questions about demographics, GM and CRISPR safety and familiarity, and environmental perceptions to estimate the effects of these factors on consumers’ WTP and WTC food produced with each technology. Interval regression and bivariate probit model procedures for

1.2. Potential benefits of CRISPR rice CRISPR technology has been successfully demonstrated in rice and wheat (Triticum aestivum; Shan et al., 2014), both of which are critical for global food security since they are the world's most dominant staple foods. Currently, one third of the world population consumes rice as their primary staple food, and more than half consume rice on a regular basis (Muthayya et al., 2014). Furthermore, while crops such as maize, soybeans, and cotton have experienced yield increases, input reductions, and lessening of physiological stresses due to genetic modification, crops such as wheat and rice have not experienced the same benefits due to domestic and international trade regulations and a lack of public acceptance (Davison, 2010). Rice and wheat are primarily field-to-table crops, consumed with little post-harvest processing. Presumably because of this field-to-plate characteristic, GM rice and wheat have been regulated more stringently and have not yet been commercialized. GM rice exists only in laboratories and experiment stations, having never been approved for commercialization (High et al., 2004; Potrykus, 2012). As such, rice serves as an appropriate medium to test the general public acceptance of hypothetical CRISPR applications compared to traditional GM 72

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rice plant does not have to compete with the weeds and can produce higher yields. However, weed control is limited because herbicides that kill grassy weeds can also injure or kill the rice plant. This may limit the ability of farmers to maintain higher yields when weed populations become prevalent in rice fields. The herbicides used in conventional weed control vary in human and environmental toxicity levels.”

estimating WTP and WTC parameters, respectively, may be found in the Methods section and in Supplementary Information. 2. Materials and methods To estimate respondents’ WTP for and WTC CRISPR compared to a transgenic GM rice, we conducted an “artefactual field experiment” using an online, multi-country survey through Survey Sampling International (SSI; Harrison and List, 2004). These methods have been extensively validated in the five countries included in this survey (SSI, 2014). We selected these countries since they represent a range of perspectives and regulatory protocols for GM biotechnology. The survey included 451, 463, 458, 499, and 444 participants in the US, Canada, Belgium, France, and Australia, respectively. Each respondent was first provided an introduction to the study and a “cheap talk script”. Cheap talk scripts are often incorporated into hypothetical studies to potentially reduce hypothetical bias (Carlsson et al., 2005; Silva et al., 2011). However, in recent literature (Howard et al., 2017; Vossler, 2016), there has been some controversy over the effectiveness of cheap talk scripts, which is important to note. This study followed the precedent of a number of other studies on similar topics to maintain possible comparisons with other hypothetical biotechnology studies (Delwaide et al., 2015; Shew et al., 2016; Shew et al., 2017). Following the cheap talk script, participants were randomly assigned to one of two treatments: half of the respondents in each country received two short, name-only descriptions of GM and CRISPR rice (i.e., the short treatment), while the other half of respondents received two long, full paragraph descriptions of GM and CRISPR rice (i.e., the long treatment). The long treatment included attributes commonly cited by consumers in their concern about GM foods (Baker and Burnham, 2001; Bredahl, 2001; Grunert, 2002; Kuiper et al., 2001). Thus, the long treatment in our study presents sciencebased statements with respect to each attribute in order to understand WTP given a scientific understanding of each attribute of the production technology.

CRISPR rice “Glyphosate-resistant CRISPR rice has been Genome-Edited, not Genetically Modified (non-GM), to be resistant to an herbicide called glyphosate. CRISPR technology re-sequences rice DNA so that glyphosate resistance is created. This is done without the insertion of DNA from any other organism. When CRISPR rice is sprayed with glyphosate, it remains unharmed while surrounding broadleaf and grass weeds are eliminated. Glyphosate-resistant CRISPR rice is naturally digested in the human gut. As an emerging technology, CRISPR rice is currently being evaluated for commercial production. Glyphosate has low toxicity levels for humans and the environment.” GM rice “Glyphosate-resistant GM rice has been Genetically Modified (GM) by the insertion of a bacterium gene into the rice genome. This gene, inserted from another organism, creates resistance to a specific herbicide called glyphosate. When the GM rice is sprayed with glyphosate, it remains unharmed while surrounding broadleaf and grass weeds are eliminated. Glyphosate-resistant GM rice is naturally digested in the human gut. As an emerging technology, Glyphosate-resistant GM rice is currently being evaluated for commercial production. Glyphosate has low toxicity levels for humans and the environment.”

2.2. Survey treatments and analysis In each treatment, the respondent first read a description of the conventionally produced rice and the description of the alternative rice (GM or CRISPR). This was followed by a Multiple Price List (MPL) format for WTP elicitation (Allcott and Taubinsky, 2015; Delwaide et al., 2015; Shew et al., 2016), which has been shown to reduce the impact of certain framing effects in contingent valuation studies (Anderson et al., 2007). Respondents were presented a binary choice between the conventional product and the alternative product with price levels fixed for the alternative products and decreasing for the conventional product. There were a total of 11 binary choices at different price levels. The alternative rice was presented in all 11 choices at a fixed price of $5.00 USD for a 5 lbs (2.5 kG) bag, while the conventionally bred rice was presented at 11 prices at the following descending intervals: $30.00, $20.00, $15.00, $10.00, $7.50, $6.25, $5.00, $4.00, $3.00, $2.00, and $1.00 USD. If the respondent selected the alternative variety for a given round, the price of the conventionally bred rice would descend one price level. The MPL questions terminated when (1) the respondent selected the conventionally bred rice, or (2) after the last interval (11th price level) was reached. Thus, each respondent answered a maximum of 22 binary price choices with 11 choices per biotechnology. Given the findings from previous discrete choice experiments (Anderson et al., 2007; Campbell et al., 2015; Hess et al., 2012), which tend to involve relatively more complex decisions across multiple attributes compared to this binary price choice MPL, we assume it would be unlikely for respondents in this survey to experience detrimental choice fatigue. Fig. 1 is an example binary choice from the Belgium survey in English: In a pretest of 40 surveys in the US and France, the highest price ($25.00) was selected based on the highest observed market price per pound (kg) of rice offered in a large chain, US-based grocery store at the

2.1. Biotechnology and product descriptions In each treatment, the separate descriptions of the GM and CRISPR rice were presented in random order to all participants to control for order effects. These information effects for short and long information treatments are critical to understanding the impact of labeling and the provision of more information on consumers’ valuation. Specifically, the estimated information effects could provide a foundation for identifying product characteristics that shift consumers’ WTP and WTC. In the short treatment, the brief, name-only descriptions given to the respondents are: Conventional rice “Non-Genetically Modified (non-GM) conventionally-produced rice variety with conventional weed control” CRISPR rice “Non-Genetically Modified (non-GM) CRISPR rice variety with glyphosate resistance for weed control” GM rice “Genetically Modified (GM) rice variety with glyphosate resistance for weed control” In the long treatment, the extended descriptions for each technology read as follows: Conventional rice “Conventionally-produced rice is not Genetically Modified (nonGM) and it is not Genome-Edited. Several herbicides are often used to control weed populations after the rice seed is planted so that the 73

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“NEP High”, for individuals scoring at or above 50. The choice of threshold includes about 67% of all survey respondents and indicates a pro-environmental worldview, particularly given the mean score of 52.0 for the WTP sample. Other questions were included for respondents to rate their strength of agreement with several statements on a scale from one to five, where (1) represents strong disagreement, (2) mild disagreement, (3) unsure, (4) mild agreement, and (5) strong agreement. The statements included: “CRISPR technology in rice production can help solve environmental problems”, “CRISPR rice is safe for human consumption”, “GM technology in rice production can help solve environmental problems”, and “GM rice is safe for human consumption”. The WTC questions were asked as follows: (1) Would you knowingly consume a food produced with GM technology? (2) Would you knowingly consume a food produced with CRISPR technology?

Fig. 1. An example question from the multiple price list survey.

time of the study. Prices were converted based on the following exchange rates in each of the countries surveyed: CAD 1.42, EUR 0.92, and AUD 1.42 rounded to the nearest 0.50 in all currencies. Prior to the full survey administration, these pretests help ensure prices and descriptions were appropriate. Due to some right censoring, the highest interval was increased to $30.00. Otherwise, no changes to the survey were made, and all respondents answered the WTP questions described above. At the end of the MPL questioning, i.e., the end of the comparison between GM/CRISPR and the conventionally bred variety, respectively, respondents rated their level of certainty about their choices. Participants provided a certainty rating between 1 and 10, with 1 being very uncertain and 10 being very certain. Answers to these questions provide a framework for understanding whether or not respondents’ choices are founded on strong beliefs, how belief intensity might impacted by more or less information, or by the CRISPR or GM biotechnologies, respectively. It has been shown that survey respondents self-report higher levels of knowledge when initially asked about GMOs, but this declines as more facts are presented (McFadden and Lusk, 2016). In this survey, the certainty rating for each information set may help explain heterogeneity in participants’ responses across treatments and could act as a proxy control for the impact of previous knowledge and beliefs. Following the WTP questions, participants were then asked a series of questions about their WTC GM and CRISPR-derived food, demographic information, GM and CRISPR familiarity, Glyphosate and Roundup® familiarity, and environmental perceptions. Environmental perceptions were obtained based on 15 New Ecological Paradigm (NEP) survey questions and the calculation of an NEP score (Dunlap et al., 2000) with the hypothesis that environmental perceptions can likely influence respondents’ WTC food produced with biotechnology. Each NEP item is measured on a 5-point Likert scale, where 5 is the highest pro-environmental response. The total NEP score is calculated by summing over the 15 items, so the value can range from 15 to 75. For ease of interpretation, we opted to create a binary indicator variable,

Based on responses to these survey questions and the MPL, several analyses were implemented to derive WTP and WTC in each of the five countries surveyed. See Supplementary Information for further details on model specification, estimation, and robustness checks (Cameron, Trivedi 2005; McNemar, 1947; Therneau, 2015; Yee, 2015). 3. Results In the USA, Canada, Belgium, France, and Australia, 56, 47, 46, 30, and 51% of respondents indicated they would consume both GM and CRISPR food, respectively. In all countries except for France, more people said they would consume both GM and CRISPR-derived food than those who said they would consume neither product. Moreover, among participants who would only consume food produced with one biotechnology, respondents were more willing to consume food produced with CRISPR compared to those who said they would eat food produced with GM. WTC CRISPR and GM were significantly different in all countries except for Australia (p < 0.01) where respondents’ WTC were not statistically different between CRISPR and GM. There was also a significant difference in between respondents WTC food produced with both CRISPR and GM technology and those who stated none in all countries (p < 0.05). In all countries except for France, more respondents were WTC both than none. In Fig. 2, respondent's WTC GM and CRISPR (both), GM only, CRISPR (CR) only, and neither GM nor CRISPR (none) are presented for each country. 3.1. Perception drivers of public acceptance In Tables 1 and 2, we present a list of variables with responses by country and a summary of the marginal effects from the bivariate probit models for WTC CRISPR and GM rice, respectively. The “CR (GM) helps” rows refer to a respondent's level of agreement with the statement: “CRISPR (GM) technology in rice production can help solve

Fig. 2. Respondents’ WTC CRISPR and GMderived food by country. Note: Respondents’ WTC both GM and CRISPR (both), CRISPR (CR) only, GM only, and neither GM nor CRISPR (none) are presented for each country. Consumers were more willing to consume GM and CR-derived food in all countries except for Australia where WTC GM and CR-derived food was statistically similar (p < 0.01). There was also a significant difference in between respondents WTC food produced with both CRISPR and GM technology and those who stated none in all countries (p < 0.05). In all countries except for France, more respondents were WTC both than none. These differences were identified with a Chi-squared test. 74

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CRISPR or GM WTC except for the case of CRISPR in the US and GM in Australia, possibly because these effects are represented in questions about safety and helpfulness. The main drivers of WTC were perceptions of safety and environmental helpfulness and previous experience (eaten GM), along with technology familiarity. Demographics had littleto-no effect on respondents’ WTC (see Supplementary Information SI Tables 3–8 for models including demographics).

Table 1 Percent of responses for each variable by country. Full sample percentages

USA (451)

Australia (444)

Belgium (458)

Canada (463)

France (499)

Never eat rice WTC GM WTC CR CR helps CR safe GM helps GM safe Eaten GM GM familiar CR familiar Roundup familiar Glyphosate familiar Male NEP high High certainty Age < 30 Age 30–39 Age 40–49 Age 50–59 Age > 60 Income < 20 K Income 20–34.9 K Income 35–49.9 K Income 50–69.9 K Income > 70 K High school or less Trade/some college Bachelor degree Higher than bachelor Long descriptions CRISPR first

3.3 64.3 71.4 5.5 40.1 8.2 5.5 65.2 59.9 20.0 66.5 25.9

2.9 57.7 59.9 4.3 49.8 8.6 6.3 48.4 68.2 12.6 68.2 40.1

3.5 52.0 56.8 5.5 48.5 8.5 8.3 38.6 51.7 19.2 46.1 29.5

1.9 52.1 59.8 3.7 51.0 8.0 6.9 64.6 73.7 15.8 57.2 26.8

0.4 33.3 50.3 15.6 34.9 20.8 29.5 15.8 53.3 10.2 68.7 30.3

46.6 55.2 60.8 26.8 23.7 13.7 13.1 22.6 15.1 12.2 14.6 19.1 39.0 25.1 31.9

56.1 62.4 50.9 25.0 21.4 17.8 16.0 19.8 17.8 17.3 16.2 21.8 26.8 27.0 33.3

47.4 62.0 34.9 19.7 13.5 15.3 24.0 27.5 17.2 35.2 25.5 12.0 10.0 45.0 23.1

52.1 66.3 52.7 25.5 23.3 17.5 17.5 16.2 21.2 17.9 20.7 20.3 19.9 27.6 32.4

50.7 76.4 33.9 15.8 17.8 22.2 20.8 23.2 21.4 39.9 23.8 7.8 7.0 49.1 19.8

25.9 17.1

24.8 14.9

16.4 15.5

28.5 11.4

16.6 14.4

49.2 51.2

49.8 49.3

47.6 50.0

49.2 50.1

48.7 49.1

3.2. Public valuation: CRISPR versus GMOs In Tables 3 and 4, we present results from the pooled and highly certain sub-sample interval regression models (suppressing demographics’ coefficients) for WTP. The full model parameter estimates can be found in SI Tables 9–20. WTP was estimated only for respondents willing to consume both GM and CRISPR food to ensure that only potential consumers are considered in the hypothetical market. Results indicate that consumers required a discount of $4.58, $1.17, $1.59, $2.12, and $2.24 per pound for CRISPR rice compared to conventionally bred rice in the USA, Canada, Belgium, France, and Australia, respectively, compared to a discount of $4.80, $0.92, $1.60, $2.11, and $2.21 per pound for GM rice compared to conventionally bred rice in the same countries, respectively. Within the white rice category, we found multiple observed price differences that are similar to our findings—150–400% premiums in some cases—in online grocery websites, which validates the potential discounts and premiums estimated in this model. In most WTP studies of GMOs, the conventional product receives a premium despite magnitudes differing by application (Baker and Burnham, 2001; Bredahl, 2001; Lusk et al., 2005; Shew et al., 2016). It is possible participants actually value non-biotechnology-derived rice at higher values given that these price differentials exist for specialty rice, e.g., organic versus conventional in actual markets, but perhaps the more impactful insight here is the relative discount between CRISPR and GM rice. In fact, the discounts for CRISPR and GM rice were not statistically different in any country (p < 0.05). This suggests that regardless of discount magnitudes CRISPR and GM rice would likely be valued similarly compared to conventional rice. Short treatments, as a neutral, attribute-independent description, provide a control for the long descriptions of CRISPR-produced food. The longer treatments warranted a lower discount than the short treatments in the USA and Australia (p < 0.05), possibly signifying that informing consumers about the science of each biotechnology can decrease the WTP gap with conventional rice. However, this was not the case in Belgium, France, or Canada. The WTP results for short and long treatments suggest that respondents needed a higher or equal discount without information about the production technologies.

Note: Based on SSI (2016), these survey responses should be representative of each country. The “CR (GM) helps” rows refer to a respondent's level of agreement with the statement: “CRISPR (GM) technology in rice production can help solve environmental problems.” The “CR (GM) safe” rows refer to similar questions about agreement with the statement: “CRISPR (GM) rice is safe for human consumption.” Consumers were also asked if they think they had eaten a GM food or ingredient in the past week (“Eaten GM”), and if they had heard of GM or Roundup®, respectively (“GM/Roundup® familiar”). Participants’ responses to the New Ecological Paradigm (NEP) questionnaire were also included as “NEP High” to show the effect of pro-environmental perceptions on WTC GM and CRISPR-derived food, respectively. Certainty is defined as the respondent's rank of their selections between 1 as not certain and 10 as highly certain, and High Certainty is defined as greater than seven.

environmental problems.” The “CR (GM) safe” rows refer to similar questions about agreement with the statement: “CRISPR (GM) rice is safe for human consumption.” Consumers were also asked if they think they had eaten a GM food or ingredient in the past week (“Eaten GM”), and if they had heard of GM or Roundup®, respectively (“GM/ Roundup® familiar”). Participants’ responses to the New Ecological Paradigm (NEP) questionnaire were also included as “NEP High” to show the effect of pro-environmental perceptions on WTC GM and CRISPR-derived food, respectively. Table 2 presents results for the marginal effects on WTC CRISPR and GM. Overall, consumers’ agreement with GM safety had a significant and positive influence on their WTC CRISPR and GM (i.e., likelihood of being WTC). Similarly, respondents’ WTC increased if they thought they had consumed a GM food or ingredient in the past week. Familiarity with GM had a positive effect on CRISPR and GM WTC in Australia, GM WTC in Canada and the US, and CRISPR WTC in France. Consumers with high certainty (> 7) of their WTP responses had a positive impact on WTC both CRISPR and GM in the US and Canada and on CRISPR in Australia. Notably, being pro-environment (NEP High) had no effect on

3.3. Certainty Impacts on biotechnology valuation Respondents’ levels of certainty on a scale of 1–10 were classified into highly certain (> 7) and less certain, given the frequency of responses to the certainty question. Because respondents certainty was highly significant (p < 0.01) in the pooled models for several countries, we estimated WTP for the “highly certain” sub-sample that included only individuals who were highly certain (> 7) of their choices in the MPL to test for heterogeneity of the model parameters (See Supplementary Information for details). In the highly certain model in Table 4 only Belgium has a reduced discount for CRISPR-derived rice compared to GM rice (p < 0.05). Although companies are investing in new biotechnologies, the results highlighted in the WTP models suggest consumers do not value CRISPRderived rice any more highly than they do GM-derived rice. While survey participants indicated differences in their WTC CRISPR, GM, and conventional rice, respectively, they valued CRISPR and GM with statistically similar discounts compared to conventionally bred rice. Importantly, no variables in the WTP models were statistically 75

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Table 2 Country-level marginal effects from probit estimates for WTC CRISPR and GM. Australia (444) CR CRISPR helps CRISPR safe GM helps GM safe Eaten GM GM familiar Roundup familiar Male High NEP High certainty Age: 30–39 Age: 40–49 Age: 50–59 Age: 60+ Income: $20–34.9K Income: $35–49.9K Income: $50–69.9K Income: $70K + Long description CRISPR first

GM **

0.087 (0.038) 0.037 (0.033) 0.076* (0.039) 0.097*** (0.033) 0.214*** (0.049) 0.158** (0.062) 0.126** (0.063) − 0.101* (0.052) 0.043 (0.057) 0.146*** (0.053) − 0.192** (0.085) − 0.116 (0.08) − 0.14 (0.089) − 0.176** (0.085) 0.204*** (0.07) 0.112 (0.079) 0.116 (0.075) 0.170** (0.07) 0.013 (0.052) − 0.086* (0.05)

− 0.091 (0.041) 0.032 (0.035) 0.171*** (0.042) 0.201*** (0.035) 0.218*** (0.052) 0.172*** (0.064) 0.073 (0.064) − 0.074 (0.055) 0.099* (0.056) 0.009 (0.055) − 0.231*** (0.088) − 0.162* (0.084) − 0.158* (0.088) − 0.121 (0.09) 0.114 (0.08) 0.008 (0.088) 0.13 (0.086) 0.048 (0.085) 0.034 (0.053) − 0.026 (0.054) **

Belgium (458)

Canada (463)

CR

CR

GM ***

0.146 (0.037) 0.082** (0.033) 0.03 (0.041) 0.108*** (0.034) 0.175*** (0.05) 0.065 (0.056) − 0.042 (0.058) 0.024 (0.055) 0.051 (0.054) 0.071 (0.053) − 0.028 (0.084) − 0.044 (0.089) − 0.049 (0.085) 0.002 (0.085) − 0.001 (0.106) − 0.013 (0.077) − 0.044 (0.082) 0.020 (0.089) 0.084* (0.05) 0.034 (0.05)

GM **

0.040 (0.04) 0.027 (0.035) 0.112** (0.044) 0.134*** (0.039) 0.118** (0.053) 0.065 (0.056) 0.01 (0.056) 0.017 (0.055) − 0.026 (0.055) 0.061 (0.053) − 0.023 (0.079) − 0.036 (0.091) − 0.018 (0.084) − 0.104 (0.086) 0.014 (0.105) − 0.14* (0.079) − 0.08 (0.084) − 0.081 (0.102) 0.060 (0.05) 0.024 (0.051)

France (499)

0.097 (0.038) 0.084** (0.033) 0.008 (0.037) 0.114*** (0.033) 0.246*** (0.054) 0.067 (0.061) 0.126** (0.055) − 0.024 (0.051) 0.041 (0.058) 0.13** (0.05) − 0.057 (0.087) − 0.046 (0.074) − 0.007 (0.078) − 0.088 (0.081) 0.003 (0.075) 0.039 (0.076) 0.102 (0.069) − 0.004 (0.072) − 0.026 (0.049) − 0.005 (0.049)

0.043 (0.04) 0.05 (0.037) 0.001 (0.038) 0.174*** (0.036) 0.256*** (0.056) 0.193*** (0.061) 0.116** (0.054) − 0.065 (0.053) − 0.036 (0.057) 0.18*** (0.051) − 0.083 (0.09) − 0.044 (0.075) − 0.107 (0.08) − 0.025 (0.085) − 0.034 (0.082) − 0.001 (0.078) − 0.031 (0.078) − 0.008 (0.082) 0.081 (0.052) 0.065 (0.051)

CR

USA (451) GM

***

0.170 (0.032) 0.072*** (0.027) 0.051 (0.031) 0.061** (0.029) 0.314*** (0.065) 0.108* (0.055) 0.068 (0.064) 0.028 (0.05) 0.072 (0.064) 0.065 (0.053) 0.007 (0.084) − 0.026 (0.089) − 0.034 (0.086) 0.092 (0.082) 0.35*** (0.074) 0.194*** (0.065) 0.108 (0.075) 0.056 (0.106) − 0.06 (0.05) 0.015 (0.05)

**

0.065 (0.027) − 0.029 (0.026) 0.067*** (0.025) 0.137*** (0.026) 0.484*** (0.062) 0.029 (0.047) − 0.056 (0.054) 0.015 (0.046) 0.018 (0.054) 0.074 (0.05) − 0.064 (0.075) − 0.057 (0.076) 0.056 (0.08) 0.05 (0.083) 0.225** (0.113) 0.067 (0.063) 0.05 (0.071) 0.01 (0.096) − 0.103** (0.044) − 0.014 (0.045)

CR

GM

0.050 (0.034) 0.049* (0.029) 0.019 (0.034) 0.073** (0.033) 0.2*** (0.05) 0.005 (0.047) 0.091* (0.052) − 0.098** (0.047) 0.095** (0.047) 0.16*** (0.047) − 0.04 (0.07) − 0.095 (0.075) − 0.132 (0.085) − 0.162* (0.092) 0.03 (0.064) 0.042 (0.075) 0.018 (0.073) − 0.047 (0.076) 0.016 (0.042) 0.027 (0.044)

0.000 (0.039) − 0.02 (0.036) 0.013 (0.035) 0.153*** (0.036) 0.144*** (0.054) 0.183*** (0.053) 0.116** (0.056) − 0.025 (0.052) − 0.014 (0.05) 0.132** (0.052) − 0.108 (0.073) − 0.064 (0.071) − 0.216** (0.09) − 0.06 (0.085) 0.056 (0.072) − 0.003 (0.09) 0.021 (0.085) − 0.067 (0.082) 0.045 (0.048) 0.043 (0.048)

Note: The above models show the marginal effects of the probability of WTC CRISPR (CR) and GM based on select variables of interest. The “CR (GM) helps” rows refer to a respondents’ level of agreement with the statement: “CRISPR (GM) technology in rice production can help solve environmental problems.” The “CR (GM) safe” rows refer to similar questions about agreement with the statement: “CRISPR (GM) rice is safe for human consumption.” Consumers were also asked if they think they had eaten a GM food or ingredient in the past week (“Eaten GM”), and if they had heard of GM or Roundup®, respectively (“GM/Roundup® familiar”). Participants’ responses to the New Ecological Paradigm (NEP) questionnaire were also included as “NEP High” to show the effect of pro-environmental perceptions on WTC GM and CRISPR-derived food, respectively. Certainty is defined as the respondent's rank of their selections between 1 as not certain and 10 as highly certain, and High Certainty is defined as greater than seven. The full country-level estimates, marginal effects, and significance levels can be found in the appendices 3–8. Standard errors for model estimates are in parentheses and model significance levels are represented as follows:. * p < 0.10. ** p < 0.05. *** p < 0.01.

other countries. GM and CRISPR technology familiarity were also significant in the US (p < 0.01 in the pooled model, and p < 0.05 in the highly certain model), but nowhere else was this significant. Interestingly, in the US, familiarity with GM led to an increase in the required discount, while familiarity with CRISPR led to a nearly equivalent decrease in the required discount.

significant across all countries. In the pooled country-level models, less certainty was highly statistically significant in Canada, Belgium, and France (p < 0.01), indicating that less certain individuals may err on the side of caution and require a higher discount. In the US, all significant variables in the pooled model remained significant in the highly certain sub-sample analysis, but this did not hold true for other countries. The next most robust result across countries was glyphosate familiarity, which was significant in the US pooled and highly certain models, the pooled Canada model, and the highly certain Belgium model (p < 0.05). In all four cases, more familiarity with glyphosate led to a decrease in the required discount. Notably, the long descriptions of the two technologies resulted in statistically lower discounts in both the pooled and highly certain subsets in the US (p < 0.01) and Australia (p < 0.1), but not in the

3.4. CRISPR versus GMOs based on price intervals To further evaluate WTP for CRISPR and GM rice, we examined the differences between respondent interval selections for the two technologies by subtracting the selected GM interval from the selected CRISPR interval. Respondents with identical interval selections are recorded as zero, while those negative (positive) have selected a price 76

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Table 3 Parameter estimates of WTP models by country for CRISPR and GM rice.

Sample (N) Intercept CRISPR CRISPR – first Long *

CRISPR CRISPR - first CRISPR* Long Eaten GM GM Familiar CR Familiar Glyphosate Familiar Roundup Familiar CR Helps environment GM Helps environment CR safe GM safe NEP high Less certain (< 8)

USA

Canada

Belgium

France

Australia

496 23.44 (6.42)*** − 1.06 (0.66) − 1.77 (1.56) − 4.19 (1.62)*** − 0.03 (0.96) 1.55 (0.96) 3.66 (2.03)* 4.75 (1.84)*** − 6.44 (2.22)*** − 3.98 (1.86)** 0.63 (1.67) 1.79 (1.44) − 0.56 (1.36) 0.94 (1.41) − 1.65 (1.47) − 0.91 (1.64) − 0.16 (1.66)

426 5.85 (5.36) 1.25 (0.81) 1.11 (1.25) − 0.82 (1.18) − 0.89 (0.83) − 0.67 (0.84) − 0.50 (1.27) 1.56 (1.31) 0.02 (1.64) − 2.85 (1.30)** 1.29 (1.53) − 0.57 (1.19) − 0.04 (1.01) − 0.14 (1.03) − 0.19 (0.95) − 1.00 (1.25) 6.09 (1.24)***

410 9.47 (5.43)* − 0.01 (0.73) 0.85 (1.60) 0.09 (1.58) 0.97 (1.02) − 0.15 (1.01) − 0.07 (1.34) 1.03 (1.51) 1.54 (2.11) − 1.51 (1.90) − 0.50 (1.57) − 2.04 (1.27) 1.17 (1.19) 0.14 (1.52) − 1.79 (1.32) − 2.62 (1.59)* 5.07 (1.34)***

298 11.52 (6.99)* 0.48 (0.80) − 2.40 (2.22) 2.24 (2.21) − 0.22 (1.16) − 2.24 (1.10)** 0.89 (2.11) − 2.28 (2.18) − 4.54 (4.04) 3.47 (2.57) − 2.01 (1.99) 0.40 (1.86) 0.23 (1.74) − 0.56 (1.54) − 0.76 (1.50) − 5.60 (2.62)** 2.73 (2.06)

442 10.96 (5.09)** 0.06 (0.87) − 1.70 (1.10) − 2.19 (1.14)* 1.04 (1.07) 0.99 (1.07) 0.49 (1.08) 0.90 (1.34) − 1.49 (1.82) − 1.95 (1.14)* 0.28 (1.25) − 0.87 (1.15) 0.47 (1.10) 1.88 (1.31) − 1.50 (1.34) − 2.19 (1.15)* 4.19 (1.07)***

Table 4 Parameter estimates of WTP by country for highly certain (> 7) respondents.

Sample (N) Intercept CRISPR CRISPR – first Long CRISPR* CRISPR – first CRISPR* Long Eaten GM GM Familiar CR Familiar Glyphosate Familiar Roundup Familiar CR Helps environment GM Helps environment CR safe GM safe NEP high

USA

Canada

Belgium

France

Australia

362 30.35 (8.15)*** − 0.74 (0.68 − 2.06 (1.86) − 5.55 (1.96)*** − 0.97 (1.06) 2.31 (1.09)** 2.60 (2.42) 5.49 (2.43)** − 6.55 (2.89)** − 4.90 (2.33)** − 0.07 (2.01) 0.09 (1.66) 0.15 (1.52) − 0.14 (1.94) 0.39 (1.97) − 1.80 (1.95)

270 − 0.97 (5.51) 0.97 (0.84) 0.37 (1.35) − 1.30 (1.20) − 0.78 (0.77) − 1.07 (0.77) 0.37 (1.15) 1.74 (1.29) 1.35 (1.75) − 2.11 (1.69) 1.38 (1.37) 1.84 (1.17) − 2.08 (1.00)** − 1.44 (1.24) 1.68 (1.09) − 1.11 (1.24)

172 11.25 (7.50) − 1.44 (0.66)** 1.56 (2.04) − 1.47 (1.64) − 0.22 (1.27) 2.71 (1.35)** − 2.35 (1.35)* 1.19 (1.67) 1.96 (2.03) − 4.96 (2.06)** 2.66 (2.06) 0.03 (1.17) 0.04 (1.43) − 0.17 2.20 − 1.98 (1.87) − 1.00 (1.83)

124 17.75 (10.80) 1.58 (1.68) − 5.03 (3.46) 1.46 (3.28) − 1.75 (2.07) − 1.17 (1.60) − 1.43 (3.06) − 5.66 (3.87)* − 7.38 (6.70) 8.87 (5.76) 0.25 (2.89) 2.54 (2.81) − 0.53 (2.38) − 2.21 (2.10) 1.05 (2.33) − 1.96 (4.11)

258 15.27 (6.29)** − 0.07 (0.75) − 1.90 (1.28) − 1.98 (1.31) 0.61 (0.74) − 1.00 (0.71) 0.32 (1.40) 1.08 (1.54) − 1.50 (2.26) − 2.76 (1.43)* 0.16 (1.36) − 0.63 (1.30) − 0.44 (1.38) 2.59 (1.42)* − 1.68 (1.48) − 2.28 (1.51)

Note: The “CRISPR – First” is the intercept for respondents receiving CRISPR information first. “Long” represents the intercept for longer information treatments with full descriptions. “CRISPR*Long” represents the interaction between respondents with CRISPR information and more information in long treatments. The “CR (GM) helps environment” rows refer to a respondent's level of agreement with the statement: “CRISPR (GM) technology in rice production can help solve environmental problems.” The “CR (GM) safe” rows refer to similar questions about agreement with the statement: “CRISPR (GM) rice is safe for human consumption.” Consumers were also asked if they think they had eaten a GM food or ingredient in the past week (“Eaten GM”), and if they had heard of GM or Roundup®, respectively (“GM/Roundup® familiar”). Participants’ responses to the New Ecological Paradigm (NEP) questionnaire were also included as “NEP High” to show the effect of pro-environmental perceptions on WTC GM and CRISPR-derived food, respectively. Some variables were omitted from this table for the sake of brevity. The full models can be viewed in Supplementary Information SI Tables 9–20. Standard errors for estimates are in parentheses and significance levels are represented as follows:. * p < 0.10. ** p < 0.05. *** p < 0.01.

Note: The “CRISPR – First” is the intercept for respondents receiving CRISPR information first. “Long” represents the intercept for longer information treatments with full descriptions. “CRISPR*Long” represents the interaction between respondents with CRISPR information and more information in long treatments. The “CR (GM) helps environment” rows refer to a respondent's level of agreement with the statement: “CRISPR (GM) technology in rice production can help solve environmental problems.” The “CR (GM) safe” rows refer to similar questions about agreement with the statement: “CRISPR (GM) rice is safe for human consumption.” Consumers were also asked if they think they had eaten a GM food or ingredient in the past week (“Eaten GM”), and if they had heard of GM or Roundup®, respectively (“GM/Roundup® familiar”). Participants’ responses to the New Ecological Paradigm (NEP) questionnaire were also included as “NEP High” to show the effect of pro-environmental perceptions on WTC GM and CRISPR-derived food, respectively. Some variables were omitted from this table for the sake of brevity. The full models can be viewed in Supplementary Information SI Tables 9–20. Standard errors for estimates are in parentheses and significance levels are represented as follows:. * p < 0.10. ** p < 0.05. *** p < 0.01.

proportion of individuals who had higher WTP (reduced discounts) for CRISPR compared to GM (p < 0.05).

interval lower (higher) than GM rice. A Wilcoxon Signed Rank test shows no difference in interval selections for CRISPR and GM rice overall (p < 0.05). This is further highlighted in the pie charts in Fig. 3. Sixty-three percent of individuals selected the same interval for both products. In Fig. 3, we present maps of country-level responses from the WTP sample, i.e., only those who would consume both technologies. Of those respondents who did not select the same price interval for both biotechnologies, more people had a reduced discount interval for CRISPR compared to GM, indicating a preference for CRISPR. This was true in all countries except Belgium. Overall, 21% showed a preference for CRISPR and 16% showed a preference for GM. In a binomial test of higher WTP for CRISPR versus higher WTP for GM rice in the pooled sample, there was a significant difference in the

4. Discussion Numerous recent scientific studies have described the potential benefits of CRISPR with particular emphasis on enhancing the ability to feed the growing global population. However, public acceptance and valuation will play an important role in the adoption of CRISPR technology in food production applications (Gao, 2018; Huang et al., 2016; Ishii and Araki, 2016; Jones, 2015; Wolt et al., 2016; Wolter and Puchta, 2017). Yet, no study has provided an empirical, multi-country elicitation of public valuation and acceptance until now. Although our results suggest the public remains skeptical of agricultural 77

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Fig. 3. Difference in WTP intervals by survey respondent in each country. Note: The spatial distribution of respondents aligns with population densities in each country. This is highlighted by the coastal responses in Australia, the southern responses in Canada, and the Flemish responses in Belgium. A few responses in Northern Australia, as well as Alaska and Hawaii in the US, were omitted from the maps. The dots represent the latitude and longitude of respondents who would consume both CRISPR and GM rice, and the pie charts show the proportion of respondents in each country with the same price interval (green), higher WTP GM (red), and higher WTP CRISPR (blue), respectively. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article).

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technologies and perspectives on their safety and potential environmental benefits suggest that efforts to increase consumer biotechnology knowledge may increase the subsequent consumer acceptance of CRISPR and similar biotechnology innovations. Given our finding that a simple provision of a longer description of the CRISPR or GM technology only shifted WTP in Australia and the US, more research on appropriate ways to educate or familiarize the public about the nature, safety, and environmental benefits of CRISPR technology is warranted. Given the nature of our stated preference study, the important characteristic in the WTP valuations is not the magnitude, but that CRISPR and GM rice are valued similarly and at a substantial discount compared to conventional rice, which suggests a potentially problematic foreground for approving and marketing CRISPR in the global marketplace. This study aimed to provide an initial market outlook for CRISPRderived foods based on the scientific knowledge surrounding some of the attributes that concern many consumers. It is possible, however, that the specific scientific descriptions used to describe these biotechnology-derived products influenced respondents’ decisions. Therefore, future research should be conducted with emphasis on product framing, both to test the robustness of our findings and to better understand potential influences of context on consumers’ WTP and WTC CRISPR-produced food. Without increased consumer acceptance—likely achieved by improved methods of education and public engagement—CRISPR agricultural applications may face the same regulations and challenges of traditional GMOs, hampering CRISPR's contribution toward feeding a growing global population.

biotechnology, 56, 47, 46, 30, and 51% of respondents in the USA, Canada, Belgium, France, and Australia, respectively, were willing to consume both GM and CRISPR foods. More importantly, respondents were more willing to consume CRISPR than GM food, which may indicate an opportunity to reduce the flow of skepticism about agricultural biotechnology in the realm of consumer demand. The WTP discounts for CRISPR gene-edited and GM-derived products are similar in our findings when compared to a conventional product. It could be that the respondents surveyed have a set amount of money they are willing to pay for rice regardless of production technology descriptions, or it may be that they reject any type of biotechnology-derived food without a substantial discount. Other studies (Delwaide et al., 2015, Shew et al., 2017) in similar countries found discounts for other biotechnologies as well, but the discounts were different for the alternative biotechnological application and the GM application (p < 0.05), with less of a discount required for the alternative. In our study, an herbicidal application was used as an example rather than an insecticidal application, and although not quantitatively tested, the general results reveal that higher discounts may be required for an herbicidal application of a biotechnology (GM or alternative) than an insecticidal application. Additionally, in the previous studies of biotechnological applications in food production, the new alternative technologies (topical RNA-interference or cisgenic GM) tended to result in the need for less of a discount than the GM product when compared to the conventional product (Shew et al., 2016, 2017). Yet, in the case of CRISPR gene-editing, the discounts from conventional products were statistically similar to GM-derived products (p < 0.01). While our results may suggest the need to differentiate the two technologies in the market, it is not entirely clear if a mandatory labeling policy would be appropriate at this point given that the results from our WTP analysis generally showed no significant difference in participants’ valuation of CRISPR and GMOs. Other studies (McFadden, 2017; Wunderlich et al., 2017) have shown consumers will almost always prefer to have more information, i.e., more labeling, but consumer valuation of products may or may not shift as a result of the specific information received and who is providing it (McFadden and Lusk, 2016). Moreover, consumers rarely consider the multiple costs of such labeling when lobbying for it. Regardless of what we might suggest for labeling based on the results of this study, the EU ruled in July 2018 that CRISPR gene-edited crops of all types and applications must be labeled and regulated in the same manner as transgenic GMOs. This signifies a potentially major blow to the commercial viability of CRISPR gene-editing in agricultural production and the market viability of food produced with such technology (Callaway, 2018; Stokstad, 2018). Our results, though elicited prior to the EU ruling, suggest that consumers who already choose not to eat GM food may hold the same negative view of CRISPR-derived food, which follows in sync with the EU ruling. Nevertheless, CRISPR may eventually be a more profitable and beneficial technology in other regions, notwithstanding production cost factors and a likely permanent loss of markets in Europe due to the recent ruling. Overall, we observe that more consumers in four of the five countries we tested are willing to consume food produced with the use of CRISPR gene-editing but show no WTP discount difference between CRISPR and GM rice. Additionally, we would be remiss not to note the omission of African and Asian countries from this study. CRISPR could be particularly beneficial in such countries where the potential development of greater resource-use efficiency and improved yields could ultimately play a role in decreasing food insecurity. More work on market viability in these regions is needed to better measure the truly global potential of CRISPR gene-editing in food production. Previous work has shown that these countries often follow the European precedent for regulating biotechnology in agriculture (Goochani et al., 2018; Shew et al., 2016), which could hinder CRISPR adoption in major rice producing and consuming areas. The significant influences of familiarity with CRISPR and GM

Acknowledgements This work was supported by the National Science Foundation Graduate Research Fellowship Program [Grant No. DGE-1450079]. Declaration of Interest None Authors Contribution 1) AMS, LLN, RMN; 2) AMS, RMN, HAS; 3) AMS, HAP, BLD; 4) AMS, RMN; 5) AMS, LLN, HAS, RMN, BLN; 6) AMS, RMN, HAS; 7) AMS, RMN; 8) RMN; 9) AMS, HAS; 10) LLN, RMN; 11) AMS, HAS; 12) AMS; HAS; 13) AMS, LLN, HAS, RMN, BLN; 14) AMS, LLN, HAS, RMN, BLN Appendix A. Supporting information Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gfs.2018.10.005. References Allcott, H., Taubinsky, D., 2015. Evaluating behaviorally-motivated policy: experimental evidence from the lightbulb market. Am. Econ. Rev. 105 (8), 2501–2538. Anderson, S., Harrison, G.W., Lau, M.I., Elisabet, R.E., 2007. Valuation using multiple price list formats. Appl. Econ. 39 (6), 675–682. Baker, G.A., Burnham, T.A., 2001. Consumer response to genetically modified foods: market segmentation analysis and implications for producers and policy makers. J. Agric. Resour. Econ. 26 (2), 387–403. Barry, G.F., Kishore, G.M., Padgette, S.R., Stallings, W.C., 1997. Glyphosate-tolerant 5enolpyruvylshikimate-3-phosphate synthases. ,633, 435 (U.S. Patent 5). Bredahl, L., 2001. Determinants of consumer attitudes and purchase intentions with regard to genetically modified food–results from a cross-national survey. J. Consum. Policy 24 (1), 23–61. Callaway, E., 2018. CRISPR plants now subject to tough GM laws in European Union. Nat. News Comments (Jul. 25, 2018). Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge, pp. 522–523. Campbell, D., Boeri, M., Doherty, E., Hutchinson, W.G., 2015. “Learning, fatigue and preference formation in discrete choice experiments. J. Econ. Behav. Org. 119, 345–363.

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