Battling Vaccine Hesitancy: Asking and Not Telling?

We often hear that science and evidence rarely will change minds when it comes to biotechnology or climate change,  (or vaccine hesitancy). But some think maybe there is a strategy to get out in front of misinformation. In a previous post I discussed an article 'Finding a Vaccine for Misinformation.' The authors discuss 'inoculating' consumers through gamification so that they are less susceptible to misinformation. 

"Introne believes that he can use this approach to target the weakest links in false narratives and bring people closer to changing their minds. He says that if he can deliver information that doesn’t conflict with a person’s belief state but still brings them around to a more accurate point of view, “then I’ve got a pretty powerful thing.”

Thinking more about this I was reminded of an article in the Journal of the Federation for American Societies for Experimental Biology (FASEB) where Jayson Lusk and Brandon McFadden observed the following:

1) consumers, as a group, are unknowledgeable about GMOs, genetics, and plant breeding and, perhaps more interestingly

2) simply asking these objective knowledge questions served to lower subjective, self-assessed knowledge of GMOs (i.e., people realize they didn't know as much as they thought they did) and increase the belief that it is safe to eat GM food. 

So essentially, just asking skeptics the right questions appeared to mitigate the Dunning Kruger effect and decreased resistance to evidence based views on the safety of genetically engineered foods. Asking, rather than telling in this scenario seems consistent with the strategy of innculating consumers against misinformation and disinformation. 

References: 

News Feature: Finding a vaccine for misinformation.Gayathri Vaidyanathan. Proceedings of the National Academy of Sciences Aug 2020, 117 (32) 18902-18905; DOI: 10.1073/pnas.2013249117 https://www.pnas.org/content/117/32/18902

McFadden, B.R. and Lusk, J.L. (2016), What consumers don't know about genetically modified food, and how that affects beliefs. Faseb, 30: 3091-3096. https://doi.org/10.1096/fj.201600598

Rational Irrationality and Behavioral Economic Frameworks for Combating Vaccine Hesitancy

Background and Introduction

Some previous work on vaccine hesitancy related to childhood vaccinations inspired by Caplan's notion of rational irrationality indicates parents willing to bear costs as high as $8,000 in order to avoid vaccinating their children. What is the associated willingness to pay (WTP) in order to avoid COVID vaccination? What kinds of intervention strategies are supported by various behavioral economic frameworks for combating vaccine hesitancy in the case of COVID19?

Social Harassment Costs and Imperviousness to Evidence

In a previous post, I discussed the role of social harassment as it relates to one’s worldview and the disutility associated with changing one’s mind or updating one’s prior about that worldview as a result.

 

 

(Figure 1)

 

Depending on one's peer group, and the level of social harassment, consumers might express preferences that otherwise might seem irrational from a scientific standpoint. For example, based on peer group, a consumer might embrace scientific evidence related to climate change, but due to strong levels of social harassment, reject the views of the broader medical and scientific community related to the safety of genetically engineered foods. 

Near Neoclassical Demand Curve and Rational Irrationality

Bryan Caplan's notion of rational irrationality might also explain these kinds of preferences:

"...people have preferences over beliefs. Letting emotions or ideology corrupt our thinking is an easy way to satisfy such preferences...Worldviews are more a mental security blanket than a serious effort to understand the world."

This means that:

"Beliefs that are irrational from the standpoint of truth-seeking are rational from the standpoint of utility maximization."

This can be modeled in the form of a kinked ‘near’ Neoclassical demand curve as depicted below. Caplan hypothesized that in many cases, the cost of holding irrational beliefs can be low or near zero and people make tradeoffs with regard to the level of rationality they consume.

(Figure 2)

Below Pa, people are willing to make tradeoffs between price and the ‘amount’ of irrationality they consume. It might then follow that if members of society are opposed to genetically engineered foods, they may be willing to pay up to Pa to avoid foods with GMO ingredients.

Implications for Combating Vaccine Hesitancy

So if the cost of holding antivax views (we can think of costs in terms of perceived risk of serious illness, loss of work, risk of quarantine, and other opportunity costs associated with not getting vaccinated) is below some level Pa, then one may be willing to make tradeoffs with regard to the level of irrationality they want to consume (we can think of the level of consumption as level of vaccine hesitancy or probability of getting vaccinated). This is represented by the portion Pa - Qa of Caplan’s near neoclassical demand curve representing rational irrationality.

Murphy (2016) extends Caplan’s model of rational irrationality to include applications where demand for irrationality occurs at prices greater than zero. In his paper he applies this theory to things like willingness to pay for ‘fair trade goods’ and vaccine hesitancy. 

 

For example, he calculates the willingness to pay (WTP) to avoid childhood vaccinations from the parent’s perspective as follows:

 

WTP = WTP to avoid 1 yr sickness × premium for child × increase in probability of 1yr of sickness

 

He looks at vaccines for pertussis, invasive pneumococcal disease, and varicella and based on certain assumptions calculates the WTP. 

 

“Using the median assumption that parents value the statistical life of their children 50% more than they value their own lives, the cost implicit in not vaccinating for these three diseases is US$8,420. As pointed out earlier, as many as ten percent of families exhibit this willingness-to-pay—and they do so repeatedly should they have more than one child. Under the influence of retracted evidence, thousands of parents retreat to the naturalistic fallacy, rejecting modern medicine they deem to be artificial.”

 

How might we apply this to combating vaccine hesitancy related to COVID? For instance, if these parents were offered more than their WTP to avoid vaccination then they may be willing to get their children vaccinated for the three conditions mentioned above. But what is the WTP to avoid COVID vaccination? $8000 is really high on an individual basis, and given the resistance and hesitancy we have seen to this point I would not expect a very low price point among the most staunch resistors. This may explain why some have concluded that offering lottery tickets to induce vaccinations may not have been as impactful as hoped:

“Our results suggest that state-based lotteries are of limited value in increasing vaccine uptake. Therefore, the resources devoted to vaccine lotteries may be more successfully invested in programs that target underlying reasons for vaccine hesitancy and low vaccine uptake”

 

The hope of the lottery was based on the idea that people tend to either overestimate or completely ignore low probability events. From a behavioral economic perspective we were hoping that those that were ignoring the risk of COVID might overestimate their chances of winning the lottery and be induced to get vaccinated. Apparently the expected values of the lottery did not exceed WTP in the case of COVID vaccination. There has been some recent interest in offering direct payments to people to get vaccinated. For instance the idea of paying up to $100 or even $1000 has been floated in the last year. 

Some have pushed back on this. Recently the Biden administration has apparently endorsed $100 payments.

  

Depending on an individual's relative WTP, paying folks to be vaccinated may or may not be promising. If social harassment costs (based on my framework) are an important factor, then this could also impact the optimal WTP to target with such an outreach.

 

We also have to recognize the role of externalities. While there are negative externalities associated with not getting vaccinated, the positive externalities are great. For instance, if we were doing a WTP calculation to encourage vaccination, we should add some premium to that payment to account for positive externalities associated with getting vaccinated to justify the costs and increase the effectiveness of the incentive. It is an important question if the WTP for the vaccine hesitant on average is so large that this becomes prohibitively expensive.

 

Alternatively, targeting WTP does not have to be shouldered by government. Private employers can also offer similar incentives to get vaccinated either in the form of direct payments or penalties.

But it is hard to say if these sorts of WTP calculations would be useful for targeting 'the right' payment to incentivize vaccination if that is even something we should be doing, or if it is cost effective especially given the heterogeneity of preferences and attitudes toward risk in the population. We would need to know more. But the concept itself is probably most useful as a way to think about and quantify the level of resistance and think of what kinds of communication strategies and nudges would work best to increase vaccinations. What things could we do to reduce WTP or increase the opportunity costs of refusing the vaccine. More below.

 

Other Approaches

 

In 'Finding a vaccine for misinformation'  (Vaidyanathan, 2020) authors address the challenges of misinformation as it relates to vaccine hesitancy and leverage some of the same behavioral economic frameworks.

 

They go on to explain that our worldview (pre-existing internal stories based on our our mental tapestry of culture, knowledge, beliefs, and life experiences) determines which gist is stored and resonates (and impacts our resistance to updating our priors with new evidence).

 

Part of their strategy for dealing with this is 'inoculating' consumers through gamification so that they are less susceptible to misinformation. 

 

"Introne believes that he can use this approach to target the weakest links in false narratives and bring people closer to changing their minds. He says that if he can deliver information that doesn’t conflict with a person’s belief state but still brings them around to a more accurate point of view, “then I’ve got a pretty powerful thing.”

 

In the rational irrationality and social harassment frameworks above, by circumventing or inoculating against misinformation and getting out in front of it you can combat hesitancy or dampen the the extent to which adopting worldview  v* lowers utility. Similarly, if we can deliver information in a way to get people to adopt v* without lowering utility, that would be a very impactful communication strategy. 

 

BE works has applied behavioral economics to understanding and responding to vaccine hesitancy. They identified four cognitive factors driving hesitancy including one factor related to valuing personal beliefs over evidence (which sounds a lot like the rational irrationality framework we have been discussing here). (See “COVID-19 Vaccine Hesitancy: A Behavioral Lens on a Critical Problem” ) They followed up with a report and recommendations on combating hesitancy based on these findings (BEACON: A Strategic Framework for Overcoming Vaccine Hesitancy). These reports and more are available from the BE Works website (https://beworks.com/covid-19/ )

 

One of the strategies highlighted in this work consistent with the rational irrationality and social harassment frameworks laid out above includes leveraging social capital:

 

"Communications aimed at sharing relevant vaccination-positive stories of people within the recipients’ peer groups, or other groups to whom an individual feels a strong social connection, could foster a positive view of vaccination and help them see it as a routine and valued step within their identified community...."

 

Communications from key people in a social network (i.e. figure 1 above) may accomplish this. So an outreach targeted at these people or enlisting their efforts could be an impactful way to get members to accept vaccinations without increasing related social harassment costs and lowering utility. These people might also be a way to deliver information that doesn’t conflict with a person’s belief state as discussed above (having the same impact on social harassment and utility).

 

Conclusion

 

While we may not be able to guess everyone's WTP to get vaccinated, we should diversify our approach to targeting it or equivalently raising the opportunity costs of not getting vaccinated such that P > Pa in the context of near neoclassical demand curve above. The most effective approach may be private sector initiatives such as vaccine requirements or bonus payments for getting vaccinated. There are also ways we might leverage behavioral economics and behavioral design frameworks to magnify our impact. Most of our learnings from combating misinformation and disinformation indicate that this takes more than communicating accurate information, it requires communicating with intent and influence. But in the case of vaccinations, even small wins can result in great improvements as Murphy points out in his article.

 

 “Economic education may struggle to sway the median voter; it may only move those with roughly correct priors regarding economic questions towards a more consistent, factual worldview. On the other hand, for each and every person convinced to vaccinate their children, the world is made better off. Some may dismiss these prescriptions, but they may have far more practical effect. Convince 300 individuals that free trade is good, the chance this changes trade policy is vanishingly small. Convince 300 individuals to vaccinate their children, and society tangibly improves. When it comes to persuasion on private markets, unlike politics, every- one is the marginal decision-maker for the irrationality present in their own lives.”

 

Similar to the swiss cheese model of non-pharmacological interventions in absence of a vaccine, a layered approach based on several behavioral economics frameworks seems most pragmatic for dealing with vaccine hesitancy.

 

See also:

Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs

Using Social Network Analysis to Understand the Influence of Social Harassment Costs and Preferences Toward Biotechnology

Consumer Perceptions, Misinformation, and Vaccine Hesitancy

Perceptions of GMO Foods: A Hypothetical Application of SEM

Fat Tails, The Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

References:

 

Borland,Melvin V. and Robert W. Pulsinelli. Household Commodity Production and Social Harassment Costs.Southern Economic Journal. Vol. 56, No. 2 (Oct., 1989), pp. 291-301

 

The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Bryan Caplan. Princeton University Press. 2007

Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature 582, 230–233 (2020). https://doi.org/10.1038/s41586-020-2281-1

 

Murphy RH. The willingness-to-pay for Caplanian irrationality. Rationality and Society. 2016;28(1):52-82. doi:10.1177/1043463115605478

 News Feature: Finding a vaccine for misinformation.Gayathri Vaidyanathan. Proceedings of the National Academy of Sciences Aug 2020, 117 (32) 18902-18905; DOI: 10.1073/pnas.2013249117 https://www.pnas.org/content/117/32/18902

Can Capitalism Be A Force For Good When it Comes to Food?

Great discussion at the AgTech So What podcast about capitalism and food innovation. Probably an innovation that gets the most headlines these days, and discussed in the headlines is related to plant based proteins and companies like Impossible Foods. But to answer the question more broadly, can capitalism be a force for good in the food and agricultural sector, we can look at previous ag tech innovations to get some kind of answer. 

For example, positive benefits associated with the development of biotech crops include non-trivial decreases in greenhouse gas emissions equivalent to the removal of nearly 12 million cars from America's roads on an annual basis (this is roughly 50% of the number of new cars purchased annually). Additionally, we see benefits in terms of improved health and safety related to decreased levels of mycotoxins, reduced pesticide exposure, reduced groundwater pollution, and improved biodiversity to name some of the health and environmental benefits as well as social benefits related to gender equity.

In the livestock sector we've also seen incredible improvements in the health and environmental benefits related to beef. Thanks to advances in economic development, technological change, innovations in management, marketing, and pricing (for just a few examples see here, here, here, here, and here), we've seen gains in beef production and quality. For instance, consider Brad Johnson's work at Texas Tech related to increasing marbling and healthy fats without increasing unhealthy backfat while also reducing time on feed. Or like the research in beef genetics and air quality and emissions at U.C. Davis.

In 2007 compared to 1977 we were able to produce the same amount of beef using roughly 30% fewer cattle and 30% less land. Feed and and water usage were down between 15-20% with a 16% lower carbon footprint (Capper, 2007). All in all, based on full lifecycle analysis, U.S. beef consumption accounts for less than .5% of global greenhouse gas emissions. Additionally when compared to beef produced and consumed in other parts of the world, the carbon footprint of beef produced and consumed in the U.S. is 10 times or more lower (Herrero et al., 2013).

While not realized yet, with technological advancements like blockchain and IoT, the potential to exploit innovative ideas like animal welfare units discussed by economist Jayson Lusk could be another unexploited opportunity given the right strategy.

And these technologies don't require scaling up 100 fold or doubling every year for the next 16 years the way some analysts project for cell cultured meat. Nor do they require drastic dietary or lifestyle changes. These positive benefits are driven by capital investment and consumer and producer driven choices in the marketplace without the requirement of coercive mitigating policies or significant behavior change. That's not to say more can't be done or that the last mile won't be difficult, but it is a testament to the role markets and technological innovation have played in the last few decades that is often overlooked or even shunned in many contemporary conversations.

References:

C. Alan Rotz et al. Environmental footprints of beef cattle production in the United States, Agricultural Systems (2018). DOI: 10.1016/j.agsy.2018.11.005

https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions 

Lusk, J.L. The market for animal welfare. Agric Hum Values 28, 561–575 (2011). https://doi.org/10.1007/s10460-011-9318-x

Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions
Graham Brookes & Peter Barfoot
GM Crops & Food Vol. 8 , Iss. 2,2017
Link: http://www.tandfonline.com/doi/full/10.1080/21645698.2017.1309490

The environmental impact of recombinant bovine somatotropin (rbST) use in dairy production Judith L. Capper,* Euridice Castañeda-Gutiérrez,*† Roger A. Cady,‡ and Dale E. Bauman* Proc Natl Acad Sci U S A. 2008 July 15; 105(28): 9668–967

Texas Tech University. "Increasing marbling in beef without increasing overall fatness." ScienceDaily. ScienceDaily, 5 May 2016. <www.sciencedaily.com/releases/2016/05/160505223115.htm>.

J. L. Capper, The environmental impact of beef production in the United States: 1977 compared with 2007, Journal of Animal Science, Volume 89, Issue 12, December 2011, Pages 4249–4261, https://doi.org/10.2527/jas.2010-3784

Herrero M, Havlík P, Valin H, Notenbaert A, Rufino MC, Thornton PK, Blümmel M, Weiss F, Grace D, Obersteiner M. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc Natl Acad Sci U S A. 2013 Dec 24;110(52):20888-93. doi: 10.1073/pnas.1308149110. PMID: 24344273; PMCID: PMC3876224.

The Economics of Innovation in Biopharma

This podcast touches on the lack of innovation in pharma and criticism about outsourcing innovation. Do these criticisms ignore recent technological advances in biotech (and the convergence of AI and genomics) that have reduced the minimum efficient scale in drug discovery creating new opportunities for startups, small firms, and scientist entrepreneurs? When we think of therapeutics as dispensing knowledge packed into a capsule or syringe, knowledge that has properties of both a private and public good (i.e. non-rival and partially excludable) scientist entrepreneurs are better incentivized and able to capture greater value from their discoveries in a venture capital funded startup environment than a larger institution like pharmaceutical companies or universities (even with Bayh-Dole Act). Drug discovery is risky, but by combining option value and discovery of new information with staged investment VC firms can discover positive NPV projects that would otherwise be rejected under conventional financing models. The combination of technological change, the economics of knowledge, and venture capital seems to reduce the comparative advantage of innovating 'in-house.' Maybe it is the case that large pharmaceutical firms have more of a comparative advantage navigating the valley of death that lies between a discovery and a cure by focusing on the regulatory approvals and marketing efforts necessary to deliver those products than they have in drug discovery?

Consumer Perceptions, Misinformation, and Vaccine Hesitancy

In graduate school I focused on how consumer consumption patterns signal social viewpoints, and the role of information and misinformation in the process. Particularly interesting was the observation that some consumers had strongly held science based views related to some issues while simultaneously holding other views that were inconsistent with views of the larger scientific community. What could explain this? I hypothesized a utility maximizing model that involved world views and social harassment costs consistent with the idea that viewpoints that may be irrational based on an objective related to scientific truths and evidence can be rational from the standpoint of personal utility maximization. This isn't so different from the idea of coherence, from Kahneman's Thinking Fast and Slow, where they argue that the coherence of the story matters more than the quality of the evidence. 

In 'Finding a vaccine for misinformation' authors address the challenges of misinformation as it relates to vaccine hesitancy and leverage some of the same behavioral economic frameworks. They explain:

"A coherent story works because our minds don't just encode facts and events into memory...we also store bottom line meaning or 'gist' and it is the stored gist, not the facts, that typically guides our beliefs and behaviors"

They go on to explain that our worldview (pre-existing internal stories based on our our mental tapestry of culture, knowledge, beliefs, and life experiences) determines which gist which is stored and resonates.

Part of their strategy for dealing with this is 'inoculating' consumers through gamification so that they are less susceptible to misinformation. I'm not sure gamification is the answer, but at the least what can be learned from this research definitely could lead to progress on this front:

"Introne believes that he can use this approach to target the weakest links in false narratives and bring people closer to changing their minds. He says that if he can deliver information that doesn’t conflict with a person’s belief state but still brings them around to a more accurate point of view, “then I’ve got a pretty powerful thing.”

This reflects a lot of what we have learned over the years. Simply presenting facts and evidence, telling people they are wrong on the internet so to speak, isn't going to change minds or behavior. Our communication has to be much more strategic with laser like intent. 

References:

News Feature: Finding a vaccine for misinformation.Gayathri Vaidyanathan. Proceedings of the National Academy of Sciences Aug 2020, 117 (32) 18902-18905; DOI: 10.1073/pnas.2013249117 https://www.pnas.org/content/117/32/18902

Related References:

Information Avoidance and Image Concerns. Exley, Christine L and Kessler, Judd B. National Bureau of Economic Research. Working Paper No. 8376 January 2021. doi. 10.3386/w28376. http://www.nber.org/papers/w28376

Related Reading:

Using Social Network Analysis to Understand the Influence of Social Harassment Costs and Preferences Toward Biotechnology

Perceptions of GMO Foods: A Hypothetical Application of SEM

Fat Tails, The Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

The Convergence of AI, Life Sciences, and Healthcare

Several years ago I was writing about the convergence of AI and genomics in agriculture:

"The disruptions of new technology, big data and genomics (applications like FieldScripts, ACRES, MyJohnDeere or the new concept Kinze planters that switch hybrids on the go etc.) will require the market to continue to offer a range of choices in seeds and genetics to tailor to each producer's circumstances of time and place." (1)

We have also seen a similar convergence in healthcare:

"A series of breakthroughs in medical science and information technology are triggering a convergence between the healthcare industry and the life sciences industry, a convergence that will quickly lead to more intimate—and interactive—relationships among people, their doctors, and biopharmaceutical companies."  (2)

This excellent segment on WBUR just a few years later picks up on the same themes:

Nobel Laureate and MIT Institute professor Phil Sharp has an even broader vision of this convergence: It’s not just computer science and biology that are converging, but engineering, physics, material science and agriculture too, he says.

“Life science is part of all of those processes and bringing physicists and engineering and information technology together to integrate life science with the translation to solving those problems is what convergence is about,” Sharp says. “It'll be decades of exciting science and exciting technology.” (3)

There are a number of parallels I want to discuss below including outcomes and value based pricing, precision medicine and precision agriculture, venture capital and digital solutions, and how these trends are leading to products and solutions that can address some of society's biggest problems like healthcare quality and cost, social determinants of health, and climate change.

Outcomes and Value Based Pricing

Due to this convergence, better data and technology are creating new opportunities. Health insurance companies, healthcare providers, and seed companies are entering into value based contracts where payments are based on outcomes and quality. 

In healthcare:

"By leveraging appropriate software tools, big data is informing the movement toward value-based healthcare and is opening the door to remarkable advancements, even while reducing costs. " (4)

"Value-based healthcare is a healthcare delivery model in which providers, including hospitals and physicians, are paid based on patient health outcomes. Under value-based care agreements, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way." (5)

(See below or  https://healthinformatics.uic.edu/blog/shift-from-volume-based-care-to-value-based-care/ for an excellent infographic explaining this promising shift in healthcare)

In food and agriculture we are seeing risk sharing and outcomes based pricing contracts as well:

"...executives are touting their new pricing model, outcome-based pricing, as the potential pricing paradigm of the future. The model involves Bayer setting an expected yield outcome for a product or seed, based on a farm's data and history stored on the company's digital ag platform, FieldView, as well as the company's own research on their products. If a farmer's final yield falls below that expected value, the company will rebate a certain portion of the original price of the product. If the yield instead surpasses the initial set value, the farmer shares a pre-agreed portion of that additional income with the company." (6)

Precision Medicine and Precision Agriculture

Instead of one size fits all best practices for seed, pest management, tillage, and nutrient management recommendations driven by research from university and industry trials, growers can get individually customized prescriptions, not just at the farm or field level, but within field and moving closer and closer to the row foot level for some decisions. The combination of advanced genomics with big data generated from precision agricultural applications (remote sensing, IoT, automated steering, GPS/GIS) makes one size fits all obsolete. 

As I quoted previously: 

"That's also why the market has driven companies to treat hybrid selection like a 'big data' problem and they are developing multivariate recommender systems as tools to assist in this (like ACRES and FieldScripts). The market's response to each individual producer's unique circumstances of time and place also ensures continued diversity of crop genetics planted. There are numerous margins that growers look at when optimizing their seed choices and it will require a number of firms and seed choices to meet these needs as the industry's focus moves from the farm and field level to the data gathered by the row foot with each pass over the field." (1)

Similarly, in healthcare, the golden age of medicine driven by the 'omics' revolution and big data will allow us to move away from one size fits all generalizations of research and medicine allowing us to "tailor medical treatment to the specific characteristics of each patient involving the ability to classify individuals into subpopulations that are uniquely susceptible to a specific treatment, sparing expense and side effects and is derived from doubts on the results of subgroup analyses and on non responders in clinical trials" (7)

"Health systems will have to go rapidly from a one-size-fits-all model of treatment to a more customized model, which still uses mass-manufactured but where treatments are selected for patients based on specific biomarkers," Joshi said. "But we can now see the next advance in personalized medicine potentially going even further, something much more personalized, like a tailor-made suit...."Big data and advances in our understanding of genomics are providing us with the footholds into establishing and understanding, for the first time ever, the causal genetic factors that help us manage that golden triangle of treatment: the right target, the right chemistry, and the right patient." (2)

Venture Capital and Digital Platforms and Solutions

Monsanto's (now Bayer Crop Science) acquisition of The Climate Corporation occurred about the same time I was penning my first post on this convergence, and was the first major move in industry that solidified these potential synergies in my mind at least. This convergence has drawn the interest and has been fueled by a number of startups and venture capital firms. Farmer's Business Network (FBN) seems to be positioning itself as a disruptor, like the Amazon of agribusiness providing a platform that includes everything from purchasing inputs, crop analytics, finance and marketing, and more direct access to genetics. In the livestock space, companies like AAD (Advanced Animal Diagnostics) and Connecterra are building tools and services analogous to a Fitbit for cows. Body Surface Translations (BST) is a company whose image processing technology has targeted both problems in animal and human health.  Tim Hammerich (the Future of Agriculture) and Sarah Nolet (AgTech So What?, AgThentic, Tenacious Ventures) have weekly discussions with innovators pioneering new solutions in this space covering a range of topics including automated irrigations systems, blockchain, regenerative agriculture, carbon sequestration and a range of companies from startups to larger players including Wal-Mart and Coca-Cola. Where Food Comes From is leveraging QR codes and mobile technology paired with their source verification processes to connect consumers to information about the people and processes behind the food they consume.  IN10T is a digitally powered data driven company helping bridge the gaps between innovations and real world application of these technologies. Venture capital firm Foresite Capital even leverages data science to drive their investment strategy in therapeutics, diagnostics, and devices. This includes digital health apps like mindstrong which is leveraging AI for better diagnosis, monitoring, and treatment of behavioral health conditions and everlywell focused on actionable healthcare diagnostics and health engagement. Evidation is a company that leverages data from digital devices and sensors capturing, quantifying, and analyzing behavior, or mapping the 'behaviorome' in the context of human health (8). This is just a tiny survey of companies and products that I have encountered in just the last few years.

Addressing Society's Bigger Problems

This convergence is allowing us to address problems in healthcare like quality, cost, access and health equity. When it comes to the food we eat, AI, technology, and genomics is providing us the tools to combat issues like climate change, water quality, nutrition, safety, equity, and access. 

It's obvious when you look at the big picture, this convergence is leading to progress that is both complimentary and synergistic across a range of industries related to food and healthcare. Better food and a healthier environment and planet  led to better health outcomes. Healthcare payers and providers are realizing the importance of these issues in healthcare. Each is separately addressing key social determinants of health in ways that were not possible before:

"During the past several decades, it has become increasingly apparent that a person’s “health” is influenced by many more factors than health care alone. These other determinants are defined by the conditions and environment in which people are born, grow, live, work, and age, reaching beyond just what the delivery of acute care services can influence. These “social determinants of health” result in billions of dollars of additional costs annually. By working to mitigate the negative impacts of these factors, significant benefits can be achieved that improve both access and outcomes for individuals and lower overall costs." (9)

As I stated several years ago:

"as big data drives more diversity into every seed planted in every acre across every field, we may possibly begin to mitigate some of the risks and concerns traditionally associated with monoculture. So it is true, when you look across row after row and see only corn, you might technically call it 'monoculture' but it's not your grandparent's monoculture." 

As a result of the convergence of AI and life sciences, it's not your grandparent's healthcare either. 

References and Related Readings:

(1) Monoculture vs. the Convergence of Big Data and Genomics. Matt Bogard. October 13, 2017. https://www.linkedin.com/pulse/monoculture-vs-convergence-big-data-genomics-matt-bogard/ (previously published as: Big Data + Genomics != Your Grandparent's Monoculture. Economic Sense. December 22, 2014. http://ageconomist.blogspot.com/2014/12/big-data-genomics-your-grandparents.html

(2) Big Data Gets Personal as Healthcare and Life Sciences Converge. By Bob Evans, Senior Vice President, Oracle.  https://www.oracle.com/industries/oracle-voice/big-data-gets-personal.html

(3) Next Chapter For Biotech? Many Say 'Convergence' With Data Science. WBUR. NPR. Bioboom June 8, 2018. https://wbur.fm/2MaaMkA

(4) Healthcare Big Data and the Promise of Value-Based Car. NEJM Catalyst. Brief Article. Jan 1, 2018

(5) What Is Value-Based Healthcare?. NEJM Catalyst. Brief Article. Jan 1, 2017

(6) Q&A With Bayer on Outcome-Based Pricing. By Emily Unglesbee. DTN Progressive Farmer. 10/2/2019 

(7) Capurso L. Evidence-based medicine vs medicina personalizzata [Evidence-based medicine vs personalized medicine.]. Recenti Prog Med. 2018 Jan;109(1):10-14. Italian. doi: 10.1701/2848.28748. PMID: 29451516. 

(8) Why Foresite Capital is Betting Big on the Convergence of AI and Biotech. August 23, 2018. https://soundcloud.com/levine-media-group/why-foresite-capital-is-betting-big-on-the-convergence-of-ai-and-biotech   Check out their current portfolio of investments: https://www.foresitecapital.com/portfolio/ 

(9) Beyond the Boundaries of Health Care: Addressing Social Issues https://www.ahip.org/beyond-the-boundaries-of-health-care-addressing-social-issues/ 

Related: 

What does the farmer say...about seed choices? (Channeling Hayek) http://ageconomist.blogspot.com/2013/12/what-does-farmer-say-about-seed-choices.html 

Big Data: Causality and Local Expertise Are Key in Agronomic Applications. http://econometricsense.blogspot.com/2014/05/big-data-think-global-act-local-when-it.html

Modern Sustainable Agriculture Annotated Bibliography. http://ageconomist.blogspot.com/2011/02/modern-sustainable-agriculture.html

University of Illinois at Chicago

Using Social Network Analysis to Understand the Influence of Social Harassment Costs and Preferences Toward Biotechnology

In a previous discussion I described how social harassment costs (Borland and Pulsinelli, 1989) might explain why some consumers could hold seemingly contradictory views about science (i.e. accepting certain scientific views related to global warming but rejecting other scientific views related to genetically modified foods). 

In my graduate school research I hypothesized that consumers adopt a worldview v (regarding climate change, food preferences, religious beliefs, public policy, etc.) that gives them the greatest level of utility seemingly invariant to evidence supporting some alternative worldview v'.

U(v) > U(v')  (1)

One way to to explain this would be to model utility as a function of social harassment  'c'. 

U(v, c) > U(v', c)  (2)

for c > k

U(v, c) < U(v', c)  (3)

for c < k

In this formulation social harassment provides disutility, and would enter the utility function as a negative term. If social harassment is great enough to exceed some threshold 'k', consumers with preferences like those above may choose to ignore scientific evidence that lowers utility by conflicting with their vision or the vision of their peers.  The level of 'k' may vary depending on the consumers sensitivity to social pressure.

Some of the implications of this model were that consumers might increase utility and reduce social harassment by avoiding information that conflicts with their world views, they might also seek information that supports utility maximizing views regardless of weight of evidence. 

This also seemed to align with a number of ideas supported by findings from behavioral  and public choice economics (Caplan, 2007; Kahneman, 2011). For example the idea that beliefs that are irrational from the standpoint of truth-seeking are rational from the standpoint of utility maximization (Caplan, 2007).

In graduate school I attempted to investigate this empirically by developing a survey instrument to measure preferences toward genetically modified foods as well as attitudes toward abortion, climate change, embryonic stem cell research, animal welfare as well as political ideology, education levels, and science knowledge. I found that respondents with a positive view of embryonic stem cell research and those that were more concerned about the impacts of climate change were less likely to accept the safety of genetically modified foods. This is in spite of evidence of the safety of biotechnology or its potential for mitigating the impacts of climate change. However, the sample size was very small and as noted elsewhere a better instrument and structural equation modeling approach might offer a much richer and more rigorous understanding of the latent factors shaping consumer perceptions.

Additionally, the behavioral theoretical utility model above is very general. While this model's predictions could be loosely supported by the empirical work, many untested assumptions remain. For instance, the level of social harassment 'c' and the threshold 'k'. These are abstract latent factors hard to estimate and validate empirically. 

However, if we think of social harassment being a function of our exposure to media, social media, and peers, we can begin to frame up an analytical strategy for better understanding these phenomena in the context of social network analysis (SNA). For example, assume two actors, 'A' and 'B' who have preferences similar to (2) and (3) above. And assume a simple network of connections with peers as depicted below:

Each node (depicted as black, white or grey dots above) represents a peer's sentiment toward genetically modified foods. For subject A, strongly influenced by peers with negative sentiments, we might hypothesize that the social harassment costs associated with believing in the safety of biotech crops could be high even in the face of strong scientific evidence (which they may not be aware of, discount highly, or avoid in order to maximize utility). For subject B, social harassment costs in relation to these beliefs might be much lower and likely be imposed rarely by a few peripheral connections. This is just a toy example, but this framework helps motivate a number of questions:

• How exactly should these networks be defined and constructed to properly frame the question/hypothesis I have? Who/what entities should each node represent (people, media outlets, websites, celebrities, scientists, etc.)?
• Connections between nodes are referred to as edges and represent pathways through which information and social harassment costs might flow - should different edges be given different weights as a function of the entity represented by each node? Are there interactions between the type of node and the type of information flowing from it? 
• Is there any correlation between network metrics (i.e. degree centrality, eigenvector centrality) and influence on preferences/perceptions? 
• What can we learn from previous research in SNA in the area of viral marketing? Are there key nodes that can be influenced? 
• What role does network architecture play in information diffusion, influence, and ultimately the level of social harassment costs of a given node (ultimately this is what I would want to quantify to empirically support the theoretical model above)?
• Are there causal inferential approaches with the necessary identification properties allowing us to interpret these effects causally? (see perhaps Tchetgen et al., 2020)

Applications could extend beyond perceptions of genetically modified foods to include climate change, food preferences, religious beliefs, or vaccines. Johnson et al.(2020) has made a lot of progress using a similar framework to study the spread of disinformation across social networks as it relates to attitudes toward vaccination:

"Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change and highlight the key role of network cluster dynamics in multi-species ecologies."

A recent discussion of this paper can be found via the Data Skeptic podcast: https://podcasts.apple.com/sg/podcast/the-spread-of-misinformation-online/id890348705?i=1000491199543 

References:

Borland,Melvin V. and Robert W. Pulsinelli. Household Commodity Production and Social Harassment Costs.Southern Economic Journal. Vol. 56, No. 2 (Oct., 1989), pp. 291-301

The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Bryan Caplan. Princeton University Press. 2007

Johnson, N.F., Velásquez, N., Restrepo, N.J. et al. The online competition between pro- and anti-vaccination views. Nature 582, 230–233 (2020). https://doi.org/10.1038/s41586-020-2281-1

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

Eric J. Tchetgen Tchetgen, Isabel R. Fulcher & Ilya Shpitser (2020) Auto-G-Computation of Causal Effects on a Network, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1811098

SNA and Related Posts at EconometricSense:

Perceptions of GMO Foods: A Hypothetical Application of SEM

An Introduction to Social Network Analysis with R and NetDraw

GMM, Endogeneity, SNA, Viral Marketing, and Causal Inference

SNA & Learning Communities

Using SNA in Predictive Modeling

The Robustness of SNA Metrics

All SNA Posts at EconometricSense

Related Posts and Background at EconomicSense

Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs

Fat Tails, the Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods 

Comments of Rule for Rules on Gene Editing Technology

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs

Agricultural biotechnology offers tremendous benefits to farmers and to society as it provides tools for mitigation of a number of environmental externalities related to water quality and food safety (USDA, 2000; Munkvold, 1999). However, perceptions of the safety of recombinant DNA technology (a.k.a. genetically engineered foods) on the part of consumers can shape the policy environment in ways that may inhibit expanded use of biotech traits in agriculture.

As a graduate student I found it particularly interesting that some consumers had strongly held science based views related to climate change, but might at the same time have views related to the safety of genetically modified foods that were at the time inconsistent with the larger scientific community. What could explain this?

In my work I hypothesized that consumers adopt a worldview v  (regarding climate change, food preferences, religious beliefs, public policy, etc.) that gives them the greatest level of utility.

U(v) > U(v')  (1)

One way to to explain this would be to model utility as a function of  social harassment  'c'. 

U(v,c) > U(v',c)  (2)

for c > k

U(v,c) < U(v',c)  (3)

for c < k

In this formulation social harassment provides disutility, and would enter the utility function as a negative term (I later found out you could alternatively model this similarly introducing a 'bliss' point in a utility model such that consumers might obtain utility from holding a certain viewpoint up to some level of saturation beyond which disutility sets in).

If social harassment is great enough to exceed some threshold 'k', consumers with preferences like those above may choose to ignore scientific evidence that lowers utility by conflicting with their vision or the vision of their peers.  The level of 'k' may vary depending on the consumers sensitivity to social pressure.

Those that are sensitive to social pressure and whose preferences are impacted strongly by the veracity of a particular vision may be resistant to conflicting evidence. 

If they were to accept the alternative (perhaps scientifically supported) viewpoint v*, and peers find these views distasteful, social harassment would lower utility. This could give the appearance of holding conflicting views related to scientific issues. An example would be accepting scientific consensus in some areas like evolution (where social harassment may be lower) but rejecting it in other areas like the safety or benefits of genetically engineered (GE) foods (where social harassment  may be higher in many circles). 

What do I mean by 'social harassment' and how might this relate to preferences toward genetically engineered foods? We might view them as a form of peer pressure, political correctness, or social norming. Consumers may choose a certain worldview (or express it through consumption patterns signaling their social viewpoints) based on their desire to be accepted by others. As a result they may discard any conflicting information or evidence and maximize utility by holding onto their world view 'v.'

U(v,c) > U(v',c) (2)

for c > k

I attempted to explore this theory empirically leveraging a data set containing demographics and survey responses related to political and religious views, food consumption preferences (organic/natural etc.), attitudes toward animal welfare, views on other scientific advances like stem cell research, scientific literacy, and attitudes toward genetically engineered foods. 

I found that respondents with a positive view of embryonic stem cell research and those that were more concerned about the impacts of climate change were less likely to accept the safety of genetically modified foods. This is in spite of evidence of the safety of biotechnology or its potential for mitigating the impacts of climate change. My theory of social harassment would be consistent with those empirical findings. (much more advanced work has been done in the last 15 years - see links and references below)

While I was aware at the time of previous work empirically estimating consumer attitudes toward genetically engineered foods (see references below) I was not aware of other related work in behavioral and public choice economics.

In retrospect, this not so different from the concept of 'rational irrationality' discussed in Bryan Caplan's 'The Myth of the Rational Voter':

"...people have preferences over beliefs. Letting emotions or ideology corrupt our thinking is an easy way to satisfy such preferences...Worldviews are more a mental security blanket than a serious effort to understand the world."

This means that:

"Beliefs that are irrational from the standpoint of truth-seeking are rational from the standpoint of utility maximization."

And in application:

"Support for counterproductive policies and mistaken beliefs about how the world works normally come as a package. Rational irrationality emphasizes this link."

From Kahneman's Thinking Fast and Slow:

"emotional attitude drives beliefs about benefits and risks and dominates conclusions over arguments."

Borland and Pulsinelli's Social Harassment Costs and Abatement

My idea of social harassment was inspired by Borland and Pulsinelli's work, although their formulation was in the context of household production (inspired by Gary Becker, 1965) with social harassment built into a budget constraint and utility maximization framework. Their discussion of social harassment costs as 'guilt trips' for driving gas guzzlers in the face of shortages and price controls was the motivating example for my thinking. 

A key idea from their theory is the concept of purchasing social abatement. Society will permit individuals to purchase goods or services that it finds distasteful if they also purchase abatement for that good.

While their paper predates contemporary notions of carbon offsets, the idea of hollywood stars or politicians buying carbon offsets to avoid social harassment from peers because of their jet setting lifestyles could be one application of social abatement. It follows from their logic that there is a market for goods that can be purchased for the purpose of social abatement. 

Perhaps some goods are 'bundled' with social abatement. For instance, a consumer that believes that beef consumption contributes excessively to climate change might be willing to buy beef if it has socially abating attributes like being grass fed, organic, or hormone free. Never mind that the carbon footprint of U.S. beef represents less that .5% of global greenhouse emissions and 3% of total U.S. GHG emissions, or the fact that these products may not actually lower beef's carbon footprint (and could possibly increase it - see also Rotz et al 2018). Another example, parents might be OK with a sugary cereal or drink if it is at least made with non-GMO ingredients (where the social points gained from being 'non-GMO' outweighs or abates social points lost from being a bad parent giving their kids sugary foods)

Social harassment can be explicit, in terms of negative comments from friends or colleagues, or they can simply be internal perceptions. But either way the purchase of socially abating goods or goods with socially abating characteristics could be explained this way. And food marketers are capitalizing on that in various ways in the form of free-from food labeling among other things.

Both my formulation and Borland and Pulsinelli's concept of social harassment costs would fit these trends in food preferences. My perspective differs in that social harassment impacts the views we adopt and the role of information in changing or shaping those views. These views either translate directly to consumption choices or indirectly as a means of signaling our views on food systems etc. In this formulation, consumer views are invariant to new information if it conflicts with their adopted view, because if others found out they had strayed in thought or conviction, they would incur social harassment disutility. So we might adopt a worldview that is unsupported by the wealth of scientific evidence. Purchases that signal socially desirable preferences help guarantee the higher level of utility and lower levels of social harassment. For those with the most strongly held views, they may evangelize others to adopt them (imposing additional social harassment on others). This signals that not only do I conform to the socially acceptable worldview, but I'm practicing my religion at the highest level.

Additional Implications

Social harassment may also be interacting with social media by making us aware of socially acceptable and unacceptable behaviors. This plays a role in influencing our worldviews as well as providing a platform for signaling our world views. This might explain the proliferation of increased attention paid to food in the last decade. 

While consumers might increase utility and reduce social harassment by avoiding information that conflicts with their world views, they might also seek information that supports utility maximizing views regardless of weight of evidence. 

In their paper "Monetizing disinformation in the attention economy: The case of genetically modified organisms (GMOs)" Ryan, Schaul, Butner and Swarthout provide an in depth background on the attention economy, disinformation, the role of the media and marketing as well as socioeconomic impacts. They articulate how how rent seekers and special interests are able to use disinformation in a way to create and economize on misleading but coherent stories with externalities impacting business, public policy, technology adoption, and health. These costs, when quantified can be substantial and should not be ignored:

"Less visible costs are diminished confidence in science, and the loss of important innovations and foregone innovation capacities"

Merchants of misinformation and disinformation can exploit consumers (and voters) with preferences sensitive to social harassment. 

Conclusion

The idea that facts alone often fail to change consumers minds is not novel. And the concept I leveraged related to social harassment and social harassment costs might not be so different from ideas related to social norming or social proof. However, I think it is a useful exercise to think through the implications of different formulations of these concepts because they can help us better understand the role of science and evidence in consumer perceptions and decision making. Better understanding may improve science communication. This could have implications for climate change, food sustainability, as well as vaccines and other impacts on public health.

Related Reading:

Perceptions of GMO Foods: A Hypothetical Application of SEM

Fat Tails, The Precautionary Principle, and GMOs

Defining Consensus Regarding the Safety of Genetically Modified Foods

Rational Irrationality and Satter's Hierarchy of Food Needs

The 'Free From' Nash Equilibrium

The Challenging Tradeoff of Weighing Biased Consumer Preferences Against Marketing Food with Integrity

Polarization of Controversial Science and Limitations of Science Literacy

Voter Irrationality and Systematic Bias: Applications in Food and Biotechnology

Left vs Right Science vs Risk vs Propensity to Regulate

Additional References:

Abdulkadri, Abdullahi O, Simmone Pinnock, and Paula Tennant. " Public Perception of Genetic Engineering and the Choice to Purchase Genetically Modified Food." Paper presented at the American Agricultural EconomicsAssociation Annual Meeting. 2004

Adulaja, Adesoji, et al. "Nutritional Benefits and Consumer Willingness to Buy Genetically Modified Foods." Journal of Food Distribution Research . Volume 34, Number 1. 2003 p. 24-29.

Baker, Gregory A. "Consumer Response to Genetically Modified Foods: Market Segment Analysis and Implications for Producers and Policy Makers." Journal of Agricultural and Resource Economics" Vol 26, No. 2. 2001. p.387-403.

Becker, G.S. (1965). ‘A theory of the allocation of time’, ECONOMIC JOURNAL, vol. 75(299), pp. 493–517

*Borland,Melvin V. and Robert W. Pulsinelli. Household Commodity Production and Social Harassment Costs.Southern Economic Journal. Vol. 56, No. 2 (Oct., 1989), pp. 291-301

Camille D. Ryan, Andrew J. Schaul, Ryan Butner, John T. Swarthout, Monetizing disinformation in the attention economy: The case of genetically modified organisms (GMOs), European Management Journal, Volume 38, Issue 1, 2020, Pages 7-18, ISSN 0263-2373

The Myth of the Rational Voter: Why Democracies Choose Bad Policies. Bryan Caplan. Princeton University Press. 2007

Chiappori, P.‐A. and Lewbel, A. (2015), Gary Becker's A Theory of the Allocation of Time. Econ J, 125: 410-442. doi:10.1111/ecoj.12157

Russell Golman, David Hagmann, George Loewenstein. Information Avoidance. Journal of Economic Literature, 2017; 55 (1): 96 DOI: 10.1257/jel.20151245

Hine, Susan and Maria Loureiro. "Understanding Consumers' Perceptions Toward Biotechnology and Labeling."Paper presented at the American Agricultural Economics Association Annual Meeting . 2002.

Jones, Gerald M., Anya McGuirk and Warren Preston. "Introducing Foods Using Biotechnology: The Case of Bovine Somatotropin." Southern Journal of Agricultural Economics. Vol 24, No. 1 1992. p 209-223

Lacey Wilson, Jayson L. Lusk,Consumer willingness to pay for redundant food labels,Food Policy, 2020,101938, ISSN 0306-9192,

Jayson L. Lusk, Brandon R. McFadden, Norbert Wilson, Do consumers care how a genetically engineered food was created or who created it?,Food Policy,Volume 78,2018,Pages 81-90,ISSN 0306-9192

Nicholas Kalaitzandonakes, Jayson Lusk, Alexandre Magnier, The price of non-genetically modified (non-GM) food,Food Policy,Volume 78,2018,Pages 38-50,ISSN 0306-9192

B. R. McFadden, J. L. Lusk. What consumers dont know about genetically modified food, and how that affects beliefs. The FASEB Journal, 2016; DOI: 10.1096/fj.201600598

National Academies of Sciences, Engineering, and Medicine. 2016. Genetically Engineered Crops: Experiences and Prospects. Washington, DC: The National Academies Press. https://doi.org/10.17226/23395.

C. Alan Rotz et al. Environmental footprints of beef cattle production in the United States, Agricultural Systems (2018). DOI: 10.1016/j.agsy.2018.11.005

The New Food Fights: U.S. Public Divides Over Food Science. Differing views on benefits and risks of organic foods, GMOs as Americans report higher priority for healthy eating DECEMBER 1, 2016 https://www.pewresearch.org/science/2016/12/01/the-new-food-fights/