The Ethics of Dietary Nudges and Behavior Change Focused on Climate and Sustainability

As discussed in McFadden, et al., 2022, when focusing on policies and behaviors related to climate change and sustainability we should consider actual abatement potential and plasticity.   

 "Policy interventions are likely to provide the best return on investment when they target choices and behaviors for which abatement potential and plasticity are high enough to lead to meaningful reductions in GHG emissions"

A lot of important work needs to be done to understand which solutions are technically correct but also the most impactful at scale from a behavior change perspective. And importantly - ethical and honest.  It is important when designing for behavior change that choice architectures reflect the science and honestly represent tradeoffs that are relevant to the context and particulars of circumstances and place. Often behavioral designers are challenged by the fact that nudges are sensitive to context, so may be less impactful when context changes in different environments.  Good science and good business practices and good ethics mandate testing in different contexts to understand the impact. We must also recognize, as I discuss below, that the science and tradeoffs that can be implicitly baked into choice architecture also depend on context and must be considered. When we design choice architectures it should reflect this in ways that are honest and transparent, and not let politics and personal biases get mixed into the batter. If nudges are successful, we want to make sure we are not doing more harm than good at scale. We don't want to unintentionally embed misinformation into product design, and certainly don't want to do this willfully (see here and here for posts discussing misinformation getting been baked into 'GMO' labeling). 

Recent Work Related to Dietary Nudges Focused on Climate and Sustainability

Blondin et al. (2022) investigate adding descriptive messages to nudge consumers to choose plant based food choices. They find that the most impactful framing was related to a 'small changes big impact' frame: 

"Each of us can make a positive difference for the planet. Swapping just one meat dish for a plant-based one saves greenhouse gas emissions that are equivalent to the energy used to charge your phone for two years. Your small change can make a big difference."

This nudge more than doubled vegetarian selections compared to the control group (25.4 percent versus 12.4 percent). They conclude that these types of descriptive messages represent a low cost and scalable intervention that could be adapted and tailored to a variety of retail contexts.  But as I will discuss below - context matters. 

De-loyde et al. (2022) investigated the impacts of eco-labelling and social nudging on sustainable food choices using randomized online experiments. These learnings are important because of the relative cost and difficulty of leveraging eco-labeling vs. more easily scalable social nudges. The costs of eco-labeling are related to the cost of information involved in creating eco-labels that are accurate and transparent (something I will discuss further) as well as the logistical costs of having to provide that information via menus or other media. Understanding the impact on consumer choice relative to cost is important for informing business decisions concerning the use of these nudges. 

Social nudges applied a simple indication on the menu (a star) annotated as 'most popular.'  Eco nudges were more elaborate:

"The three burrito types were displayed alongside a traffic light system, with a scale of 1–5, which was circled at the appropriate sustainability level for that burrito: beef burrito – unsustainable, chicken burrito – neither sustainable nor unsustainable and vegetarian burrito – sustainable (Figure 1). This is consistent with research measuring the CO2 emissions (Espinoza-Orias & Azapagic, 2012), water usage (Mekonnen & Hoekstra, 2010) and impact on biodiversity from the different burrito ingredients (Crenna Sinkko & Sala, 2019)."

Authors conclude:

"This study suggests that future policy could include eco-labelling and/or a social nudge to reduce meat consumption and meet global climate change targets."

Challenges Related to Dietary Nudges Focused on Climate and Sustainability

Metrics Matter

When it comes to measuring climate impact the important human, sustainability, and nutrition tradeoffs related to the metrics we use matter. In Blondin et al. (2022) it was not clear to me how they defined the relationship between food choices and greenhouse gas emissions.  In the article above De-loyde et al. (2022) they cite Espinoza-Orias & Azapagic (2012) in relation to greenhouse gas emissions. While that analysis was super detailed and rigorous, I'm not sure it represents to most accurate or transparent information when it comes to making these choices as it relates to CO2 equivalents. 

When considering beef and dairy specifically, understanding the differences in the way CO2 vs. methane behaves is fundamental to understanding their respective roles impacting climate change, and personal and policy decisions related to mitigating future warming.  Understanding this can help direct attention to those areas where we can make the biggest difference in terms impacting climate change over the long term. As discussed in Allen et al. (2018):

"While shorter-term goals for emission rates of individual gases and broader metrics encompassing emissions’ co-impacts remain potentially useful in defining how cumulative contributions will be achieved, summarising commitments using a metric that accurately reflects their contributions to future warming would provide greater transparency in the implications of global climate agreements as well as enabling fairer and more effective design of domestic policies and measures."

It's not clear that the work cited from 2012 cited in  De-loyde et al. (2022) or the work in Blondin (2022) appropriately accounts for the biogenic carbon cycle and the role of methane as a flow vs. stock gas in their estimates of GHG emissions and warming potential.

Are consumers scanning QR codes on their smartphones (pumping new permanent sources of GHG into the atmosphere) to read a menu nudging them away from beef being distracted from focusing on other seemingly arbitrary choices they could be making to positively impact the climate in more meaningful ways? (This post compares GHG emissions and warming potential of internet use vs. beef consumption)

Additionally, even an accurate measure of GHG emissions and warming potential isn't enough to transparently relate all of the tradeoffs involved. 

In a 2010 Food and Nutrition Research article, authors introduce the Nutrient Density to Climate Impact (NDCI) index. Metrics like this could add some perspective. According to their work:

"the NDCI index was 0 for carbonated water, soft drink, and beer and below 0.1 for red wine and oat drink. The NDCI index was similar for orange juice (0.28) and soy drink  (0.25). Due to a very high-nutrient density, the NDCI index for milk was substantially higher (0.54) than for the other beverages. Future discussion on how changes in food consumption patterns might help avert climate change need to take both GHG emission and nutrient density of foods and beverages into account."

Authors Drewnowski, Adam et al. apply this more nuanced approach to 34 different food categories including meat and dairy:

"Efforts to decrease global GHGEs while maintaining nutritionally adequate, affordable, and acceptable diets need to be guided by considerations of the ND [nutrient density] and environmental impact of different foods and food groups. In a series of recent studies, the principal sustainability measure was carbon cost expressed in terms of GHGEs (8, 14, 15). Testing the relation between nutrient profile of foods and their carbon footprint can help identify those food groups that provide both calories and optimal nutrition at a low carbon cost."

While it does not impact the major findings related to how to influence consumer choices about food and sustainability, it is not clear to me that Blondin et al. (2022) or De-loyde et al. (2022) sufficiently consider these tradeoffs in the choice architectures that they proposed for nudging consumers to make decisions about food consumption. 

If appropriately accounted for in their specific contexts, it does not necessarily imply that these tradeoffs would be appropriately accounted for in different contexts which is what I turn to next.

Context Matters

When it comes to behavior change, context and environment matter immensely.

As discussed in Kanemoto et al. (2019)

"most global-scale models have an important shortcoming; they do not consider subnational variations in food production and consumption. This lack of subnational detail is significant because subnational detail could be more important than global coverage and may show the opportunity to promote and use different subnational policy"

Using combined microdata on 60,000 households collecting information about diet, income, and demographics and a subnational input-output model for production and trade across 47 prefectures in Japan they find: 

"higher-CF  [carbon footprint] households are not distinguished by excessive meat consumption relative to other households but rather have higher household CF intensity because of elevated consumption in other areas including restaurants, confectionery, and alcohol."

De-loyde et al. (2022) start off their article with a global framing of the impact of livestock on GHG emissions: 

"Livestock production contributes an estimated 14.5% of human-induced global greenhouse-gas emissions."

Despite some of the questions above about the metrics used to relate food to climate impact and the nutritional tradeoffs involved when presenting consumers with options, maybe this global framing is appropriate for consumers in the U.K. but is this the most honest framing to use if we try to transport these results to other consumers like U.S. consumers? Are the tradeoffs the same? 

Kanemoto (2019) was based on Japanese consumers who consume relatively low amounts of beef compared to U.S. consumers. When considering U.S. consumers, there are important differences we should consider when attempting to adopt choice architectures used in other contexts designed to influence consumer choice.

Ignoring change in context ignores important differences between technological capabilities and production practices but also differences in incomes, tastes, and preferences. As stated in a recent article in Foreign Policy: 

"Generalizations about animal agriculture hide great regional differences and often lead to diet guidelines promoting shifts away from animal products that are not feasible for the world’s poor....A nuanced approach to livestock was endorsed in the latest mitigation report of the U.N. Intergovernmental Panel on Climate Change (IPCC)." 

When we are designing choice architectures to influence dietary choices related to climate and sustainability, a nuanced approach is also necessary. 

A lot of the criticism of U.S. beef misappropriates or conflates the environmental footprint of beef produced and consumed in the U.S. with beef from other parts of the world. These criticisms may not be appropriate when applied to U.S. consumers. When we drill into agriculture and focus on beef in the U.S. we find that it accounts for about 4% of total emissions. But on a global scale, which matters most to climate change, overall, total GHG emissions related to U.S. beef consumption are 10X lower in the U.S. than beef produced in other places in the world and accounts for less than 1/2 of 1% (i.e. .5%) of global GHG emissions.  (EPA GHG Emissions Inventory, Rotz et al, 2018). See also Allen, M.R., Shine, K.P., Fuglestvedt, J.S. et al., 2018. 

Similar to Kanemoto et al. (2019) and McFadden, et al., 2022 when it comes to U.S. consumers it may be the case that the real meat on the bone so to speak (what has the most potential impact given consumer plasticity and realistic assessments of climate impact) as it relates to climate may have more to do with where the food is consumed than what is consumed. It may be the case that once you are already in the restaurant reading the menu that having a salad vs. steak sourced in the U.S. is inconsequential when looking at the global impact. It could even be the opposite case, considering nutritional density and climate tradeoffs, when appropriately quantified, the salad in the restaurant may not be the best choice. 

Scaling and Ethics

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"

One of the big challenges presented by misinformation and disinformation is its clever use of our fast thinking system 1, its use of social proof and confirmation bias, magnified by technology and social media that focuses on capturing attention and dissemination vs. communication and persuasion. To say the least it can be dangerous at scale. In today's information age and polarized political climate we need to be careful about the things we do at scale. Low cost scalable interventions can be dangerous.

If lots of businesses used nudges and choice architectures that did not accurately reflect the tradeoffs for a given context, or government became involved as an enforcer, or it became a common strategy for corporations to juice up their ESG reporting, these issues could have negative impacts at scale. The harm would be greatest if this becomes a distraction and diverts attention and resources away from more impactful behavior change or reduces investment in greener food technologies. 

We can look at what has happened to the use of rBST in milk production for instance. According to Perkowski (2013) "more than two-thirds of dairy farmers who have ever treated their cows with rBST have stopped using it" despite the fact that rbST in dairy (just one technology) has the equivalent impact of removing 300,000 cars from the road annually (see Capper, 2008). Would consumers favor milk produced from cows supplemented by rBST (leading to an economic premium to encourage its adoption by producers) if they were made more aware of these tradeoffs? Are current labels related to rBST doing consumers justice by providing them better information or are they reinforcing false negative perceptions? 

We could ask similar questions regarding other technological advances in food production and manufacturing like finely textured beef which was so maligned by social media that its developer Beef Products Incorporated (BPI) forced a settlement with NBC for spreading misinformation characterizing it as unsafe pink slime (Mclaughlin, 2017). 

Do current GMO labeling regulatory requirements and food packaging accurately reflect the tradeoffs involved? Would consumers view GMOs differently if they were presented with knowledge that their adoption currently has offset the emissions equivalent to 15 million cars annually. That is more than the total new cars typically sold in the U.S. every year, and about 20% of all new cars sold globally. Or 3X the number of EVs currently being sold in the U.S. annually. 

Whether intentionally designed or not we are always nudging. Are we nudging consumers in the wrong direction (in terms of environmental impact) with labels claiming 'no rBST' on milk cartons and non-GMO labels on other food products? 

Damage can be done when attention and perceptions are manipulated and consumer sentiment leads us to abandon promising technologies that could really make a difference when it comes to climate. 

Misinformation and disinformation have played a major role driving vaccine hesitancy and may have even been accelerated during the COVID19 pandemic. See Johnson et al. (2020) & Vaidyanathan (2020). Both misinformation and disinformation play large roles in other areas like climate denialism and GMO hesitancy and act as bottlenecks to adopting better technologies and policies.  Behavioral designers often strive to identify nudges that are both as effective and scalable. In the research above, De-loyde et al. (2022) and Blondin et al. (2022), social nudges seem to fit the bill here.  But misinformation and disinformation unfortunately represent some of most effective and scalable social nudges you will find. Even with the best of intentions, we need to be careful in the design and deployment of social nudges. 

When it comes to developing nudges and choice architectures related to food choices, we want to be careful that what we do is based on sound science and transparently reflects tradeoffs vs. simply manipulates consumers to act according to our own biases and preferences that may not necessarily represent the optimal choice in every context.

A Guide for Ethical Nudging

The Observatory of Public Innovation has recently published a document titled Good Practice Principles For Ethical Behavioural Science In Public Policy that includes checklists and questions that may speak to many of the issues above. 

They provide a checklist and prompting questions focused on four areas: Scope, Design, Research and Evaluation, and Policy Implementation. Below are some relevant prompting questions based on the discussion above: 

 - Did you establish clear criteria for why the behavioural change has a positive outcome for the affected population? Are these criteria monitored and evaluated regularly? (related to Scope)

-Set up protocols to identify and mitigate ethical risks (such as unintended negative side-effects, both in general and to particular groups (related to Design)

- Have you considered new ethical concerns resulting from scaling and adapting in new contexts?

The criteria for the nudges mentioned above seem to be based on measures of CO2e. But we know that those metrics don't necessarily reflect the latest science and don't necessarily encompass all the relevant tradeoffs implied in the choice architectures being presented to consumers. As a consequence, if these nudges were scaled, we might nudge people to make choices that may not be as optimal as advertised when the goal is climate impact, especially for certain groups in given contexts. These are important side effects to consider. 

Perhaps in the research and design phase when there is debate in the literature or the metrics are complex (as they are in climate science and nutrition and health) it is hard to claim that any of the papers discussed above represent a marked violation of ethics. No standard of ethics should require human infallibility or perfect knowledge - especially when the goals are learning. I don't think this guide is necessarily meant to catch all of the nuances discussed in this post. But when it comes to adoption and scaling of interventions or policies, the ethics involved probably merit greater attention. I think the checklist and prompting questions as they are may be quite useful and can be refined to better reflect certain domains. 

Conclusion

Learnings from studies like Blondin et al. (2022) and De-loyde et al. (2022) are very important for understanding how choice architecture and nudges can be used to influence behaviors that may represent impactful solutions for important societal problems like climate change. We should take these learnings and test them in other contexts to confirm they work in different environments before scaling. We also want to make sure that as we learn about nudging and behavior change to improve choices related to food and sustainability in non-coercive ways, that we do not inadvertently do more harm than good on a grander scale in terms of loss of trust in the science, institutions, and innovations that can have the greatest impact. We need to make sure that we are using choice architecture responsibly, and the ingredients are based on sound science and transparent in the tradeoffs they represent and sensitive to the context in which choices are being made. 

Additional and Related Reading

More recent related posts and updates: 

Nudging Back: Turning off your Zoom Camera May Be Good for the Climate. https://ageconomist.blogspot.com/2023/02/nudging-back-turning-off-your-camera.html 

Picture This: Putting Beef and Climate into Perspective. https://ageconomist.blogspot.com/2023/03/putting-beef-and-climate-into.html

Rational Irrationality and Behavioral Economic Frameworks for Combating Vaccine Hesitancy https://ageconomist.blogspot.com/2021/08/rational-irrationality-near.html

Facts, Figures, or Fiction: Unwarranted Criticisms of the Biden Administration's Failure to Target Methane Emissions from Livestock. https://ageconomist.blogspot.com/2021/12/facts-figures-or-fiction-unfair.html 

The Limits of Nudges and the Role of Experiments in Applied Behavioral Economics. https://ageconomist.blogspot.com/2022/06/the-limits-of-nudges-and-role-of.html 

Will Eating Less U.S. Beef Save the Rainforests? http://realclearagriculture.blogspot.com/2020/01/will-eating-less-us-beef-save.html

Can Capitalism Be A Force For Good When it Comes to Food? https://ageconomist.blogspot.com/2021/07/can-capitalism-be-force-for-good-when.html

GMOs and QR Codes: Consumers need more than a label they need a learning path https://ageconomist.blogspot.com/2016/08/gmos-is-just-any-label-enough.html

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

References

Allen, M.R., Shine, K.P., Fuglestvedt, J.S. et al. A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Clim Atmos Sci 1, 16 (2018) doi:10.1038/s41612-018-0026-8

Blondin, Stacy & Attwood, Sophie & Vennard, Daniel & Mayneris, Vanessa. (2022). Environmental Messages Promote Plant-Based Food Choices: An Online Restaurant Menu Study. World Resources Institute. 10.46830/wriwp.20.00137. 

Graham Brookes & Peter Barfoot (2020) Environmental impacts of genetically modified (GM) crop use 1996–2018: impacts on pesticide use and carbon emissions, GM Crops & Food, 11:4, 215-241, DOI: 10.1080/21645698.2020.1773198

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): 9668–9673

De-loyde, K., Pilling, M., Thornton, A., Spencer, G., & Maynard, O. (2022). Promoting sustainable diets using eco-labelling and social nudges: A randomised online experiment. Behavioural Public Policy, 1-17. doi:10.1017/bpp.2022.27

Drewnowski, Adam et al. “Energy and nutrient density of foods in relation to their carbon footprint.” The American journal of clinical nutrition 101 1 (2015): 184-91 .

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

Keiichiro Kanemoto, Daniel Moran, Yosuke Shigetomi, Christian Reynolds, Yasushi Kondo,Meat Consumption Does Not Explain Differences in Household Food Carbon Footprints in Japan, One Earth, Volume 1, Issue 4, 2019,Pages 464-471, ISSN 2590-3322, https://doi.org/10.1016/j.oneear.2019.12.004.

Private costs of carbon emissions abatement by limiting beef consumption and vehicle use in the United States. McFadden BR, Ferraro PJ, Messer KD (2022) Private costs of carbon emissions abatement by limiting beef consumption and vehicle use in the United States. PLOS ONE 17(1): e0261372. https://doi.org/10.1371/journal.pone.0261372

ABC TV settles with beef product maker in 'pink slime' defamation case. By Timothy Mclaughlin https://www.reuters.com/article/us-abc-pinkslime/abc-tv-settles-with-beef-product-maker-in-pink-slime-defamation-case-idUSKBN19J1W9 

Dairymen reject rBST largely on economic grounds. Mateusz Perkowski Dec 10, 2013 Updated Dec 13, 2018 .https://www.capitalpress.com/dairymen-reject-rbst-largely-on-economic-grounds/article_335ce36b-ec75-5734-9aad-80f11cae1d43.html

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

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 

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

The Role of Identify Protective Cognition in the Formation of Consumer Beliefs and Preferences

Background

People pick and choose their science, and often they do it in ways that seem rationally inconsistent. One lens through which we can view this is Bryan Caplan's idea of rational irrationality along with a Borland and Pulsinelli's concept of social harassment costs. 

According to Caplan:

 "...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."

In a previous post, I visualized social harassment costs that vary depending on one's peer group. If these costs exceed a certain threshold (k), 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. 

See here, and here.

Identity Protective Cognition

In Misconceptions, Misinformation, and the Logic of Identity-Protective Cognition (Kahan, 2017) the concept of identity protective cognition adds more to the picture. Below are three aspects of identify protective cognition:

1. What people accept as factual information is shaped primarily by their values and identity
2. Identity is a function of group membership, i.e. it's tribal in nature
3. If people choose to hold beliefs that are different from what the 'tribe' believes, then they risk being ostracized (i.e. they face social harassment costs)
4. As a result, individual thinking and thought patterns evolve to express group membership and what is held to be factual information is really an expression of 'loyalty to a particular identity-defining affinity group.

Additionally Kahan discusses some important implications of this sort of epistemic tribalism. Additional education and more accurate information aren't necessarily effective tactics for addressing the problems of misinformation and disinformation. In fact, what Kahan's and others research have shown is that it can actually make the problem worse. 

'those highest in science comprehension use their superior scientific-reasoning proficiencies to conform policy-relevant evidence to the position that predominates in their cultural group....persons using this mode of reasoning are not trying to form an accurate understanding of the facts in support of a decision...with the benefit of the best available evidence....Instead they are using their reasoning to cultivate an affective stance that expresses their identity and their solidarity with others who share their commitments.' 

In this way, identity protective cognition creates a sort of spurious relationship between what may be perceived as facts and the beliefs we adopt or choices we make. It gives us the impression that our beliefs are being driven by facts when the primary driver may actually be cultural identity. 

This sort of tribalism can result in a sort of tragedy of the science communication commons - similar to the tragedy of the commons in economics where what seems rational from an individual standpoint (adopting the beliefs of the group to avoid punishment) is irrational from the standpoint of accuracy of beliefs and has negative consequences for society at large. As a result:

'citizens of a pluralistic democratic society are less likely to converge on the best possible evidence on threats to their collective welfare.'

Of course this has consequences for elections, the regulatory environment, and decisions by businesses and entrepreneurs in terms of what products to market and where to invest capital and resources. Ultimately this impacts quality of life and our ability to thrive in a world with a changing climate and bitter partisanship and social unrest. 

The Problem and the Solution

As discussed above, this sort of tribal epistemology is not easily corrected by providing correct information or education. In fact it drives one to seek out misinformation in support of one's identity while ignoring what is factually correct. The authors speak broadly about the role that 'pollutants' or 'toxins' in the science communication environment play in promoting this tribal mentality. One form of social harassment cost that may be driving this is cancel culture or call out culture. Cancel culture works like an immune system that scans the network of believers and seeks out non-conforming views, and tags it to be attacked by others in the group. This drives even the brightest to seek out misinformation instead of avoiding it. 

Another pollutant to the science communication environment is troll epistemology and related efforts to produce a 'firehose of falsehood' (see Paul and Matthews, 2016). Whether intentional or not, modern media technology provides the infrastructure to produce an effect similar to modern propaganda techniques pioneered in Russia.  This emphasizes flooding the science communication environment via high-volume and multichannel, rapid, continuous, and repetitive false or unsubstantiated claims with no commitment to objective reality or logical consistency. 

Authors conclude:

'the most effective manner to combat the effect of misconceptions about science and outright misinformation is to protect the science communication environment from this distinctive toxin.'

Related Posts and 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

Frimer, J. A., Skitka, L. J., & Motyl, M. (2017). Liberals and conservatives are similarly motivated to avoid exposure to one another's opinions. Journal of Experimental Social Psychology, 72, 1–12. https://doi.org/10.1016/j.jesp.2017.04.003

Kahan, Dan M., Misconceptions, Misinformation, and the Logic of Identity-Protective Cognition (May 24, 2017). Cultural Cognition Project Working Paper Series No. 164, Yale Law School, Public Law Research Paper No. 605, Yale Law & Economics Research Paper No. 575, Available at SSRN: https://ssrn.com/abstract=2973067 or http://dx.doi.org/10.2139/ssrn.2973067

Paul, Christopher and Miriam Matthews, The Russian "Firehose of Falsehood" Propaganda Model: Why It Might Work and Options to Counter It. Santa Monica, CA: RAND Corporation, 2016. https://www.rand.org/pubs/perspectives/PE198.html.

The Limits of Nudges and the Role of Experiments in Applied Behavioral Economics

In a recent article in Nature, Evidence from a statewide vaccination RCT, authors found that eight different nudges previously shown to be effective for encouraging flu and COVID vaccination failed to show impact when tested on more reluctant populations. I think a knee jerk reaction is that maybe nudges aren't effective ways to increase vaccination after all. But that completely misses a very important aspect of nudges. Nudges work in most cases because humans can be sensitive to context. And applied behavioral design processes work to understand this context and test the impact of interventions to know if they are effective in a given context. At the highest level I think this paper is less about the ineffectiveness of nudges per say and more about the important role of changing context on behavior. 

I'd like to try to unpack more by focusing on the following: 

• The importance of testing. You can’t blindly chuck nudges over the fence at your customers and simply assume they will be effective just because they worked in prior published studies or in other businesses. In this paper they tested 8 different nudges. If they had just scaled any or all of these without testing we would not have learned anything about effectiveness or the other lessons that follow relating to why they may not have worked. And vice versa - just because something failed to replicate in one context doesn't invalidate prior work or imply it won't work in yours. We just know findings are not generalizable across all contexts. The only way to really know about your context is to test. That is part of the value of business experiments.

• Context matters. In the paper they discussed important differences in context between late stage COVID vaccination and vaccination earlier in the pandemic, as well as differences between flu and COVID.

• The utility of behavioral personas and behavioral mapping to guide our thinking about why a given nudge may work or not. To take context a bit further, authors discussed differences in populations (age) and different challenges to flu vs COVID vaccinations and the differential impact related to how both logistical and psychological barriers may have been addressed in different populations and different contexts with different designs. All of these are things that we can point to or think about in the framework of behavioral mapping. Other issues related to 1) different kinds of hesitancy and changing norms over time, 2) whether some participants may have already been vaccinated (and not mentioned perhaps how prior infection may have changed the sense of effectiveness or urgency). These things may relate more to the kinds of personas that any given nudge may speak to. Although the paper doesn't discuss behavioral mapping or developing personas their utility here seems palpable.

• Behavioral design frameworks. Additionally, authors discussed the impact of things like message saturation and novelty effects in addition to timing. These are things that I tend to think about in the context of Stephen Wendel’s CREATE action funnel as a design framework that speaks to issues like the importance of Cue and Timing. (Actually every aspect of CREATE speaks to almost all of the aspects of this messaging in some way).

• The importance of operationalizing applied behavioral science through repeatable iterative cycles of learning. Even if one constructed behavioral maps and personas in the design of these nudges, the findings in this paper (and in many instances where we leverage experiments to test impact) dictate that we go back and revise our maps and personas based on learnings like these.

There has also been some recent discussion about the failure of nudges because they focus too much on individual behavioral (i-frame) vs. larger systemic issues  (s-frame). It seems to me that best practices in the 'diagnosis' phase of behavioral design process would be helpful in both of these areas if the behavioral lens is widened to include deeper thinking about the broader system (s-frame). As discussed in The Consitution of Knowledge: A Defence of Truth Jonathan Rouch discusses the challenges of changing behavior when beliefs and identity become tightly braided together. Sometimes people first have to be moved to a 'persuadable place emotionally' and their 'personal opinions, political identities, and peer group norms' have to be 'nudged and cajoled simultaneously, which is a long slow process.' To quote Jim Manzi, you can't test your way out of a bad strategy. It does not mean that we should give up on leveraging applied behavioral science to make a positive change in society, but it does make understanding of the larger ecosystem in the implementation of nudges all the more critical. 

As discussed in a recent article in The Behavioral Scientist:

"Our efforts at this stage will determine whether the field matures in a systematic and stable manner, or grows wildly and erratically. Unless we take stock of the science, the practice, and the mechanisms that we can put into place to align the two, we will run the danger of the promise of behavioral science being an illusion for many—not because the science itself was faulty, but because we did not successfully develop a science for using the science." 

The authors follow with 6 guidelines echoing some of the above sentiments above that are well worth reading. 

Reference: 

Rabb, N., Swindal, M., Glick, D. et al. Evidence from a statewide vaccination RCT shows the limits of nudges. Nature 604, E1–E7 (2022). https://doi.org/10.1038/s41586-022-04526-2

Chater, Nick and Loewenstein, George F., The i-Frame and the s-Frame: How Focusing on Individual-Level Solutions Has Led Behavioral Public Policy Astray (March 1, 2022). Available at SSRN: https://ssrn.com/abstract=4046264 or http://dx.doi.org/10.2139/ssrn.4046264

Agricultural Economics in the Healthcare Space

During the pandemic, it wasn't too uncommon to hear the criticism that economists should stay in their lane when it comes to issues related to health. So I thought I would write a short piece discussing what role I have had as an applied (agricultural) economist working in the healthcare space for almost a decade now. 

Economics is the study of people's choices and how they are made compatible. At a high level, agricultural economics focuses on choices related to food, fiber, natural resources, and energy production and consumption. This makes the intersection of food, health, and the environment an interesting space in agricultural economics. 

How do choices in this space impact health? What factors lead individuals to make healthy choices? In graduate school I specifically focused on why people seem to pick and choose their science and the role of evidence in food choices and attitudes toward food technology. What is the role of information and disinformation in the formation of consumer preferences and the choices they make? How can we design better policies, products, services, interventions, or choice architectures for better outcomes? How can we communicate science and risk more effectively? And, what are the best approaches in experimental design and causal inference to measure the impact in these areas? How do we bring this all together to make better decisions as individuals, business leaders, and as a society? At an applied level, which is where I work, this is not so much about making a novel contribution to the literature or advancing the field as much as it is about implementation - applying the principals of economics to develop solutions or provide frameworks to solve or better understand questions and problems in this space.

This line of reasoning has value not just in the context of food choices but for a myriad of behaviors related to healthcare at both the patient and provider level. From a business perspective, this is about how to identify opportunities to move resources from a lower to a higher valued use, and how we monetize behavior change. Of course applying this economic lens also requires bringing an ethical perspective to the table as well, which is important when we consider all of the tradeoffs involved in human decision making. 

When we are faced with wicked problems that may have alternative solutions, we can't just jump directly form the science to a cure, better policy, or product or service.  We learned from the pandemic the difference between having a vaccine and having people get vaccinated. At the end of the day there are no solutions really, only tradeoffs, and we need a framework for understanding those tradeoffs so we can make better decisions about food and health. That is squarely in the lane of theoretical and applied economists.

Related Posts and Readings

Why Study Economics / Applied Economics 

The Convergence of AI, Life Sciences, and Healthcare

The Economics of Innovation in Biopharma

Science Communication for Business and Non-technical Audiences 

The Value of Business Experiments

Statistics is a way of thinking not a toolbox

Causal Decision Making with Non-Causal Models

Rational Irrationality and Behavioral Economic Frameworks for Combating Vaccine Hesitancy 

Consumer Perceptions, Misinformation, and Vaccine Hesitancy

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

Fat Tails, The Precautionary Principle, and GMOs

Innovation, Disruption and Low(er) Carbon Beef

Examining Changes in Healthy Days After Health Coaching. Cole, S., Zbikowski, S. M., Renda, A., Wallace, A., Dobbins, J. M., & Bogard, M. American Journal of Health Promotion. (2018)

Intrapersonal Variation in Goal Setting and Achievement in Health Coaching: Cross-Sectional Retrospective Analysis. Wallace A.M., Bogard M.T., Zbikowski S.M. J Med Internet Res 2018;20(1):e32

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

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

GWP* Better Captures the Impact of Methane's Warming Potential

Understanding the differences in the way CO2 vs methane behaves is fundamental to understanding their respective roles impacting climate change, and personal and policy decisions related to mitigating future warming. A practical example, properly accounting for these differences, the global impact of U.S. beef consumption (or other ruminant food sources) over time in terms of carbon footprint (related to enteric emissions) could be even less than previously understood. Understanding this can help direct attention to those areas where we can make the biggest difference in terms impacting climate change.

 From:

 Allen, M.R., Shine, K.P., Fuglestvedt, J.S. et al. A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation. npj Clim Atmos Sci 1, 16 (2018) doi:10.1038/s41612-018-0026-8


"While shorter-term goals for emission rates of individual gases and broader metrics encompassing emissions’ co-impacts2,6,31 remain potentially useful in defining how cumulative contributions will be achieved, summarising commitments using a metric that accurately reflects their contributions to future warming would provide greater transparency in the implications of global climate agreements as well as enabling fairer and more effective design of domestic policies and measures."

https://www.nature.com/articles/s41612-018-0026-8#Sec1

See also:

A Green New Deal for Agriculture?

Religiousity, Beef, and the Environment

EconTalk: Matt Ridley, Martin Weitzman, Climate Change and Fat Tails

Hybrid Corn vs. Hybrid Cars

Welfare Analysis: Just Do It!

Welfare Analysis: Just Do It!

Some time ago I wrote a couple posts discussing some of the issues in microeconomics that perplexed me the most in graduate school.

In  Applied Microeconomics: The Strong Axiom of Revealed Preference,Aggregation, and Rational Preferences I discussed some of the properties of consumer preferences that were required to rationalize a demand function. This came down to properties of what is known as the Slutsky substitution matrix which was require to be symmetric and negative semi-definite. These properties satisfy the strong axiom (SA) of revealed preference. As stated in the widely adopted graduate micro text by Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green (MWG) chances of the SA "being satisfied by a real economy are essentially zero."

In a follow up post Applied Microeconomics: The Normative Representative Consumer and Welfare Analysis I discussed the idea of a 'normative representative consumer.'  In order to have a normative representative consumer, we have to assume a social welfare function, and assume it is maximized by an optimal distribution of wealth according to some specified wealth distribution rule.

Making more 'impossible' assumptions didn't seem to help. And in fact, as I eventually found out according to Arrow's Impossibility Theorem, they really were practically impossible. So....when it comes to policy analysis (like for instance policies related to climate change) how do economists include social welfare in a cost benefit analysis?

There was a really great discussion about this in a Macro Musings podcast with James Broughel hosted by David Beckworth.

James Broughel: "And the welfare measure that they use is a social welfare function that they derive from the Ramsey neoclassical growth model, which is a famous economic growth model. So they take a welfare function from that model, they say this is society's preferences or this is the social planner's preferences or something along those lines. And then their goal is to maximize that....Well, the most obvious problem with this approach is that it relies on this social welfare function, which is supposed to describe the aggregated preferences of everyone in society. And aggregating the time preferences of everyone in society is really just a special case of aggregating the preferences in general, which runs into this issue of Arrow's Impossibility Theorem."

Arrow's theorem* requires that in order for any social welfare function to represent society's preferences (which are an aggregation of individual preferences) it must obey six axioms:

1) It must rank all social states
2) It must obey transitivity (see my previous post about symmetry of the Slutsky substitution matrix)
3) The ranking must be positively related to individual preferences
4) New social states should not affect the ranking of original social states - also referred to as independence of irrelevant alternatives
5) The ranking should not be based on customs overriding individual preferences
6) Rankings are not made by a dictator

Arrow's theorem states that there is no social welfare function that can aggregate preferences or a social decision rule that can satisfy all six axioms. Like I mentioned in my previous posts, it seems like based on 'the math' and the theory, welfare analysis for applied policy work isn't feasible. Maybe we should just limit ourselves to positive analysis (focusing on efficiency). So how do economists approach normative welfare related policy questions?

James Broughel: "they just say, well, that's society's preferences. And this has become a convention in economics, it's done all over the place."

David Beckworth: "Because it's tractable, right? It's easy to do. The math is easy."

James Broughel: "Yeah, you can do the math. But, there really isn't any basis for it. I think that they would, the advocates of this approach would acknowledge that. They would say, our approach is normative, but hey, lots of economists agree on it."

So the tongue in cheek answer is how do you do welfare analysis despite all of the challenges I have discussed? You make some impossible assumptions and 'just do it' because the math is easy....sort of. But reflecting on this over the years I have come to accept there are a number of problems that require these kinds of simplifying assumptions to motivate more critical thinking about the alternatives we face in a policy and decision making environment, as imperfect as that may be.

Most of the pocast was actually about two major schools of thought regarding the appropriate discount rate for doing cost benefit analysis for policies with long term impacts (again like climate change).  Even if we are able to achieve scientific consensus on the impacts of climate change, the actual policy solutions have to be evaluated in terms of the costs today vs. the benefits of mitigating future climate events. That requires a discount rate, which as David and James discuss, there is no solid consensus on what is appropriate. That merits a future post!

*Microeconomic theory:basic principles and extensions. 8th Edition
Walter Nicholson (2002)

Economics, Evidence, and High Causal Density

How do we form our beliefs about the solutions to societies most complex problems? Do we trust data? Theory? Both? What does it mean to base policy on science and evidence?

According to Manski:

"Social scientists and policymakers alike seem driven to draw sharp conclusions, even when these can be generated only by imposing much stronger assumptions than can be defended. We need to develop a greater tolerance for ambiguity. We must face up to the fact that we cannot answer all of the questions that we ask."

I think Russ Roberts puts it well in his EconTalk Episode with Noah Smith:

"Can you think of a study that was so decisively performed in terms of the crossing of t's and dotting of i's that the identification and all the econometric challenges were met with such impressiveness that people on the other side of the debate had to throw up their hands and say, 'Well, I guess I was wrong. I've got to change my view.' Because I can't think of one. I can't think of one. And if that's true, then I would suggest that economics has some serious problems in claiming it's a science."

When it comes to evidence there are lots of challenges. For the most part, in economics and the social sciences it's often impossible to implement randomized controlled trials to identify treatment effects related to policy changes. For the most part we have to leverage observational data using quasi-experimental designs. The challenge for both approaches as Jim Manzi discusses in his book ''Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society'  is the issue of 'high causal density.'

In an environment of high causal density "the number of causes of variation in outcomes is enormous, and each has significant potential effects compared with those of the potential cause of interest. We don't know enough to list each of them and hold them constant, but if we randomly assign patients to the test and control groups, then these hidden conditionals won't confound our estimate of treatment causality."

Unfortunately in the social sciences, causal pathways are extremely complex. There are always hidden conditionals we may not be able to measure or don't have sufficient knowledge to even consider. Given that hidden conditionals are always present, a well entrenched proponent of a given policy can always find a reason to explain why it has failed to prove itself out in the face of evidence.

But Jim does more than offer criticisms of theory and methods. He introduces the concept of 'Liberty as Means.'  Embracing the concepts of evolutionary economics, he promotes a flexible system of government that sounds a lot like federalism. As he discusses, the mistake we often see from both the right and the left is enforcement of social norms at the national level vs. fostering numerous experiments at the local level.

While economic theory and applied econometrics are useful and powerful tools for policy analysis,  these tools will not necessarily help provide clear cut  always defensible evidence to improve public policy. These methods will never discover a 'Polio vaccine' for policy. It is in fact their shortcomings that provide the strongest argument for our constitutional republic and federalism that our founders envisioned.

Reference: Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society, by Jim Manzi https://www.amazon.com/Uncontrolled-Surprising-Trial---Error-Business/dp/046502324X/ 

See also: EconTalk: Manzi on Knowledge, Policy, and Uncontrolled

Tariffs and the Corn-Soybean Industrial Complex

Recent trade policy talks and tariffs imposed by the Trump administration have had an impact on soybean prices (see: Trump's And China's Tariffs Could Do Permanent Damage To Soybean Farmers). As a result, one time payments have been proposed to help farmers but going into the next marketing year nothing is on the table.

An interesting observation is that some folks typically critical of the Trump administration have found this to be a silver lining. Their story goes something like this: Not all that is Trump is bad because hopefully he's breaking down the corn-soybean industrial complex. The trade war is overpowering the effects of the subsidies that usually keep the machine churning out the kinds of crops that are harming the planet and making us sick at the expense of more sustainable and healthy fruits and vegetables.

This isn't really new, its just another version of the same criticisms we often hear from the politically correct food activist crowd (i.e. the pro organic, pro-heirloom/nostalgic market,anti meat, anti-grain, anti-commodity anti-biotech agriculture folks)

Subsidies (primarily crop insurance) can impact marginal changes in the mix and total acres of corn and soybeans each year, but they are not a primary driver in the decision to grow those crops vs. vegetables etc. The difference has more to do with biology than policy.

Economist Jayson Lusk discusses the impacts of reducing or removing these subsidies: 

"complete removal of crop insurance subsidies to farmers would only increase the price of cereal and bakery products by 0.09% and increase the price of meat by 0.5%, and would also increase the price of fruits ad vegetables by 0.7%.  So, while these policies may be inefficient, regressive, and promote regulatory over-reach, their effects on food prices are tiny"

When we try to connect this to food consumption and the impacts on obesity, the evidence is very weak.

Alston (2010) found:

“Eliminating U.S. grain subsidies alone would lead to a small decrease in annual per capita caloric consumption—simulated to be 977 calories per adult per year, which would imply a 0.16% per year reduction in average body weight assuming 3,500 calories per pound. In contrast, removing all farm subsidies, including those provided indirectly by trade barriers, would lead to an increase in annual per capita consumption in the range of 200 to 1,900 calories—equivalent to an increase in body weight of 0.03% to 0.30%.”


The difference in prices between grains and vegetables won't change much with changes in subsidies and the impact on obesity is less than trivial. One reason commodities and grains are favored over other crops is they are more affordable, cost less to produce, and store much better. As Tamar Haspel notes in a previous Washington Post article:

"Factor in that corn delivers 15 million calories per acre to broccoli’s 2-ish million, and the cost to grow broccoli (25 cents per 100 calories) is 50 times larger than corn (half a cent per hundred calories). And that’s just the difference on the farm. After harvest, that broccoli needs to be refrigerated and transported to where it’s going before it spoils. Broccoli has nutrients that corn doesn’t, of course, so it’s a good thing that we eat some. But an all-vegetable, or mostly vegetable, diet is prohibitively expensive for most people"

At the end of the day, labeling the complex network of producers, scientists, retailers, merchandizers, processors, and traders involved in feeding a hungry global population as the 'corn-soybean industrial complex' may have dramatic appeal. But the biology and economics involved tell another story.

References:

Choices. 3rd Quarter 2010 | 25(3)
FARM POLICY AND OBESITY IN THE UNITED STATES
Julian M. Alston, Bradley J. Rickard, and Abigail M. Okrent
JEL Classifications: I18, Q18

The 'free-from' Nash equilibrium

Recently I was reading an article, "The big Washington food fight" in Politico discussing challenges of bringing diverse interests and perspectives on food issues under one roof through the Grocery Manufacturers Association. 

There are a couple things influencing my thinking about this. One is the idea that voters and consumers may have systemic biases in their knowledge and preferences in general and specifically about food and technology. The other thing is related to recent research showing a divergence between public perceptions and science driven by political leaning....a divergence that widens *with* more education and science knowledge (see http://www.pnas.org/content/114/36/9587 ).

So in this context, what does it mean to say 'the customer is always right' and how do you give the customer what they want, but do it with integrity? There seem to be two dominant approaches or paradigms followed by food companies for dealing with this. 

One approach is going all in with 'negative' advertising or 'free from' labeling regardless of scientific justification. This paradigm feigns or fakes transparency in the sense it acknowledges consumer preferences related to knowing 'what is in their food' but adds lots of confusion about substantial differences related to food safety and sustainability. The other paradigm takes a 'less is more' approach in terms of honest disclosure about these technologies.

A more generous interpretation of the first behavior is that these food retailers and manufacturers are cognizant of consumer preferences, and assume great deal of consumer ignorance. Take for instance consumers attempting to avoid gluten (reasons why merit a separate discussion). Retailers may assume consumers are extremely ignorant of the fact that gluten originates from wheat based ingredients. A number of food products (like apples, raisins, ground beef, potatoes, carrots etc.) generally would not possibly contain gluten unless they were highly processed or prepared using some wheat based ingredient. As an extreme example for illustrative purposes, we might imagine an uninformed consumer choosing between a bag of carrots and a loaf of bread. A manufacturer would feel they are helping the consumer simplify the decision by adding a 'gluten free' label to the bag of carrots. Additionally, if competitor brands don't have the label, they may risk losing a sale to the 'gluten free' labeled product if a large enough number of uninformed consumers are trying to avoid gluten. We end up with a Nash equilibrium strategy to employ 'free from'labels that really make no sense from a nutritional or scientific standpoint. Gluten is just one example, the logic easily carries over to a number of food products and ingredients (i.e. free-from labels related to added hormones in pork and poultry, GMO free tomatoes etc.)

Helping consumers avoid what they *think* or *perceive* to be harmful to themselves or the environment using this strategy may help increase or defend sales, but it does very little to truly educate consumers about food choices. It likely perpetuates ignorance and myths about food, nutrition, health, and sustainability. Worse, in some cases it may directly or indirectly lead to consumers actually choosing marginally less healthy or less sustainable products or technologies either directly or indirectly because of decreased viability of marketing alternatively sustainable foods (i.e. the loss of rBST from the milk supply).

Whichever paradigm becomes the most dominant (both in the marketplace and the ballot box) may ultimately influence the types of products we see on the shelves and reduce the potential for healthier and more environmentally sustainable solutions to challenging worldwide problems. Efforts to escape this less than optimal Nash equilibrium position could include restrictions limiting free from labels to only non-obvious food products but I am not sure this is a viable solution.

Sometimes people devise cooperative ways to escape from a Nash equilibrium without resorting to taxes or regulation. Nobel prize winning economist Elinor Ostrom's work speaks to this:

"Predictions that individuals will not devise, precommit to, and monitor their own rules to change the structure of interdependent situations so as to obtain joint benefits are not consistent with evidence that some individuals have overcome these problems, although others have not." - Governing the Commons: The Evolution of Institutions for Collective Action. By Elinor Ostrom

But how would this work? The recent backlash by the scientific community regarding StonyField Organic's portrayal of young girls in a Facebook video and the Peel Back the Label movement are two examples of social harassment costs or other monitoring behaviors within the industry that may give some hope. Will this continue or become a large enough force for change?