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.

In graduate school I was interested in consumer preferences toward biotechnology. Particularly interesting was the observation 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/

Steak-umm Tweet Storm Tackles Coronavirus and Science Literacy

Have you ever heard of the company Steak-umm or their thin sliced frozen steak products (think Philly cheesesteak) found in a number of grocery stores across the country? If you have a twitter account you may have come across a seemingly random tweet or retweet by folks a bit perplexed by why this company was sharing tips about misinformation related to the coronavirus epidemic sweeping the country.

I've been historically a bit of a critic of a number of companies and brands for their often deceptive approaches to food marketing. In Thinking Fast and Slow About Consumer Perceptions of Technology and Sustainability in Agriculture and The 'free from' Nash Equilibrium Food Labeling Strategy I discuss how food marketing efforts leverage consumer behavioral biases to promote their products at the expense of science literacy and possibly in direct contradiction to consumer preferences related to healthy and sustainable food systems.

There are big costs to these marketing tactics (which borderline misinformation and disinformation campaigns). In their research "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"

See additional links that follow for more background and context around behavioral economics and food marketing tactics. But in a world where deceptive advertising has often often been the norm and even praised (Chipotle comes to mind see here and here), out of nowhere comes this viral storm of tweets from Steak-umm pushing back against misinformation related to coronavirus:

friendly reminder in times of uncertainty and misinformation: anecdotes are not data. (good) data is carefully measured and collected information based on a range of subject-dependent factors, including, but not limited to, controlled variables, meta-analysis, and randomization

— Steak-umm (@steak_umm) April 7, 2020

In explaining 'why' they think their messaging was so effective they state:

people think it's bizarre, ironic, and funny when a frozen meat company points out the importance of critical thinking, but chances are the same message would never "go viral" if it was from a person. our society values entertainment over truth and that's a huge problem

— Steak-umm (@steak_umm) April 8, 2020

They clearly get that evidence doesn't necessarily move the needle when it comes to science communication and persuasion. As discussed in a number of the posts below consumers tend to believe the things that maximize utility, not necessarily their science or policy literacy. How emotional attitude (system 1) drives beliefs about benefits and risks and overrides careful thinking about the strength of actual evidence.

The heroes of the day, @steak_umm have clearly figured this out and demonstrate that in addition to the coherence of the story, entertainment value goes a long way getting folks to pay attention.

Related Links

Thinking Fast and Slow About Consumer Perceptions of Technology and Sustainability 

Rational Irrationality and Satter's Hierarchy of Food Needs 

The 'free from' Nash Equilibrium Food Labeling Strategy

Polarized Beliefs on Controversial Science Topics

Voter Preferences, The Median Voter Theorem, and Systematic Policy Bias

The Food Desert Mirage

If you build a new supermarket in a food desert, will low income households go there to buy healthier food? Are Dollar Stores cornering the market in poor neighborhoods reducing options for healthy food choices?

There is a misconception, a mirage if you will, related to the relationship between proximity of super markets that sell healthy foods and actual consumption and health effects. As discussed in this New Food Economy article 'Is it time to retire the term food desert':

"The idea that supermarkets enter into food deserts and all of a sudden provide access to healthy food is a little bit of a misconception"

Public Health literature provides evidence that households in lower income neighborhoods tend to eat less healthy food. These neighborhoods are often characterized as being food deserts due to the lack of access to healthy groceries for a given geography. Policy and discussion involving food deserts is often colored by an implicit or assumed causal relationship between food deserts (lack of supply of healthy food options) and nutrition and health outcomes. Failure to better understand this causal relationship can lead to potentially bad policy decisions. According to this City Journal article 'Unjust Deserts'  some communities have essentially banned or greatly restricted Dollar General from operating their stores which provide a variety of low priced products. However, some research questions a relationship between food choices and the presence or absence of a Dollar General store.

In a Health Economics Review article (Drichoutis, 2015), using a combination of difference-in-difference and propensity score matched analysis authors looked at the relationship between BMI in children and the proximity of Dollar General Stores and failed to find a relationship.

The authors conclude:

"Combatting the ill effects of a bad diet involves educating people to change their eating habits. That’s a more complicated project than banning dollar stores. Subsidizing the purchase of fresh fruits and vegetables through the federal food-stamp program and working harder to encourage kids to eat better—as Michelle Obama tried to do with her Let’s Move! campaign—are among the economists’ suggestions for improving the nation’s diet. That’s not the kind of thing that generates sensational headlines. But it makes a lot more sense than banning dollar stores."

A paper from the National Bureau of Economic Research this past year took a very exhaustive look at the relationship between food deserts, poverty, and nutrition. "THE GEOGRAPHY OF POVERTY AND NUTRITION: FOOD DESERTS AND FOOD CHOICES ACROSS THE UNITED STATES." Working Paper 24094 (http://www.nber.org/papers/w24094).

This paper helps provide a very rigorous empirical understanding of these relationships that can be leveraged for more effective policy and interventions to improve nutrition and health.

They used a very rich dataset consisting of:

1) Nielsen Homescan data - 60,000-household panel survey of grocery store purchases

2) Nielsen’s Retail Measurement Services (RMS) data - 35,000-store panel of UPC-level sales data (this covers 40% of all U.S. grocery store purchases)

3) Nielsen panelist survey data on nutrition knowledge

4) Entry and location data for 1,914 new supermarkets by zip code

Among the many findings uncovered in this data source was the following:

"over the full 2004-2015 sample, households with income above $70,000 purchase approximately one additional gram of fiber and 3.5 fewer grams of sugar per 1000 calories relative to households with income below $25,000."

Their data reflects what has been found in the public health literature in relation to low income households and nutritional health. In addition, household food purchase data was transformed using a modified version of the USDA's Healthy Eating Index (HEI) based on dietary recommendations. These various sources were brought together to give a very rich picture of household choice sets, retail environment, consumption patterns, and nutritional quality.

Using a regression based event study analysis and a structural demand model they examine the impact of supermarket entry on the nutritional quality of changes in food purchases. They also are able to separate the main drivers explaining the differences in the measured nutritional quality index (HEI) of food purchases between low and high income groups.

They model household and income group preferences using both constant elasticity of subsitution (CES) and Cobb-Douglass utility specifications. They apply this model to the rich data sources mentioned above using a Generalized Method of Moments (GMM) framework and use the model estimates to simulate policies that allow households of different incomes to be exposed to similar prices and product availability. (i.e. to make apples to apples comparisons and determine what's driving healthy vs. unhealthy food choices among low income households in food deserts vs. wealthier households).

Key Findings:

1) When new supermarkets open in what was formally a food desert, they find most of the changes in consumption are related to shifting purchases from more distant super markets to the new local super market. The change in the healthy eating index or substitutions away from unhealthy purchases from convenience and drug stores to more healthy food was minimal. This is because even in food deserts among low income households, willingness to travel was quite substantial and mitigated the lack of access to local healthy food.

" households in food deserts spend only slightly less in supermarkets. Households with income below $25,000 spend about 87 percent of their grocery dollars at supermarkets, while households with incomes above $70,000 spend 91 percent. For households in our “food deserts,” the supermarket expenditure share is only a fraction of a percentage point lower"

"one supermarket entry increases Health Index by no more than 0.036 standard deviations for low-income household"

They conclude that access to supply of healthy food or lack thereof explains only about 5% of the difference in the healthy eating index between low and high income households. Access does not appear to be driving the nutrition-income relationship.

2) Most of the differences in healthy vs unhealthy food choices by income group are driven by demand factors...i.e. preferences. When faced with the same choices and same prices, lower income households simply made purchases with a lower HEI.

"The lowest-income group is willing to pay $0.62 per day to consume the healthy bundle instead of the unhealthy bundle, while the highest-income group is willing to pay $1.18 per day."

They find that wealthier households value fruit three times the rate of lower income households and twice the rate for vegetables compared to lower income households.

Policy Implications

The authors reference studies by Montonen et al (2003) and Yang et al (2014):

"consuming one additional gram of fiber per 1000 calories is conditionally associated with a 9.4 percent decrease in type-2 diabetes" and consuming "3.5 fewer grams of sugar per 1000 calories is conditionally associated with a ten percent decrease in death rates from cardiovascular disease."

Improvements of the HEI definitely could be a driver for better health. However focusing on access may not be the greatest way to lever change. Certainly the correlations between income, food deserts, and healthy eating hold in this study and can be great flags to predict or identify which populations may need intervention. However, as this study points out the intervention should be based on theoretical and causal relationships that go beyond the supply of healthy foods and focus on aspects related to food preferences and demand. The authors conclude:

"For a policymaker who wants to help low-income families to eat more healthfully, the analyses in this paper suggest an opportunity for future research to explore the demand-side benefits of improving health education—if possible through elective interventions—rather than changing local supply."

References:

Drichoutis, A.C., Nayga, R.M., Rouse, H.L. et al. Food environment and childhood obesity: the effect of dollar stores. Health Econ Rev 5, 37 (2015). https://doi.org/10.1186/s13561-015-0074-2

NBER. "THE GEOGRAPHY OF POVERTY AND NUTRITION: FOOD DESERTS AND FOOD CHOICES ACROSS THE UNITED STATES." Working Paper 24094 (http://www.nber.org/papers/w24094)

Are Fruits and Vegetables Becoming Less Nutritious?

Here are some highlights from research on this topic:

--> Mineral nutrient composition of vegetables, fruits and grains is not declining.

--> Allegations of decline due to agricultural soil mineral depletion are unfounded.

--> Some high-yield varieties show a dilution effect of lower mineral concentrations.

--> Changes are within natural variation ranges and are not nutritionally significant.

--> Eating the recommended daily servings provides adequate nutrition.


Reference:

Robin J. Marles, Mineral nutrient composition of vegetables, fruits and grains: The context of reports of apparent historical declines, Journal of Food Composition and Analysis, Volume 56, 2017,
Pages 93-103, ISSN 0889-1575, https://doi.org/10.1016/j.jfca.2016.11.012.
(http://www.sciencedirect.com/science/article/pii/S0889157516302113)

HT: James Wong https://twitter.com/Botanygeek

Addressing Gender Inequality in Developing Countries Through Crop Improvement

A lot of production related benefits of biotechnology have been discussed in the literature, for instance decreased greenhouse gas emissions (Brookes and Barfoot, 2017), reduction in exposure to toxic chemicals (Kouser & Qaim, 2011), and food safety(Munkvold et. al, 1999). However additional research indicates that there may also be social benefits related to gender equality as discussed in
Social and Economic Effects of Genetically Engineered Crops (National Academies of Science, 2016).

Below are some highlights from this research:

• Women comprise a significant proportion of agricultural related labor in developing countries (~43%)
• Women in developing countries face significant challenges related to access to education, information, credit, inputs, assets, extension services, and land 

The adoption of biotechnology in developing countries has had some mitigating effects:

• In India biotechnology adoption (Bt cotton) resulted in increased work hours and income for women (Subramanian and Qaim, 2010)
• Reduced exposure and freeing women from spraying toxic chemicals and related labor (Bennett et al., 2003; Zambrano et al., 2013; Zambrano et al., 2012; Smale et al., 2012)
• Increased importance of women in decision making within households (Yorobe and Smale, 2012; Zambrano et al., 2013; Rickson et al., 2006

References:

National Academies of Sciences, Engineering, and Medicine; Division on Earth and Life Studies; Board on Agriculture and Natural Resources; Committee on Genetically Engineered Crops: Past Experience and Future Prospects. Genetically Engineered Crops: Experiences and Prospects. Washington (DC): National Academies Press (US); 2016 May 17. 6, Social and Economic Effects of Genetically Engineered Crops. Available from: https://www.ncbi.nlm.nih.gov/books/NBK424536/

Graham Brookes & Peter Barfoot (2017) Environmental impacts of genetically modified (GM) crop use 1996–2015: Impacts on pesticide use and carbon emissions, GM Crops & Food, 8:2, 117-147, DOI: 10.1080/21645698.2017.1309490

Bennett R, Buthelezi TJ, Ismael Y, Morse S. Bt cotton, pesticides, labour and health: A case study of smallholder farmers in the Makhathini Flats, Republic of South Africa. Outlook on Agriculture. 2003;32:123–128.

Kouser, S., Qaim, M., Impact of Bt cotton on pesticide poisoning in smallholder agriculture: A panel data analysis,Ecol. Econ. (2011), doi:10.1016/j.ecolecon.2011.06.008

Comparison of Fumonisin Concentrations in Kernels of Transgenic Bt Maize Hybrids and Nontransgenic Hybrids. Munkvold, G.P. et al . Plant Disease 83, 130-138 1999.

Rickson ST, Rickson RE, Burch D. Women and sustainable agriculture. In: Bock BB, Shortall S, editors. Rural Gender Relations: Issues and Case Studies. Wallingford, UK: CABI Publishing; 2006. pp. 119–135.

Smale M, Zambrano P, Paz-Ybarnegaray R, Fernández-Montaño W. A case of resistance: Herbicide-tolerant soybeans in Bolivia. AgBioForum. 2012;15:191–205.

Subramanian A, Qaim M. The impact of Bt cotton on poor households in rural India. Journal of Development Studies. 2010;46:295–311

Yorobe JM Jr, Smale M. Impacts of Bt maize on smallholder income in the Phillipines. AgBioForum. 2012;15:152–162

Zambrano P, Smale M, Maldonado JH, Mendoza SL. Unweaving the threads: The experiences of female farmers with biotech cotton in Colombia. AgBioForum. 2012;15:125–137.

Zambrano P, Lobnibe I, Cabanilla DB, Maldonado JH, Falck-Zepeda J. Hiding in the plain sight: Women and GM crop adoption. Paper presented at the 17th ICABR Conference: Innovation and Policy for the Bioeconomy, June 18–21. Ravello, Italy: 2013.

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!

Some Beef Related Posts From the Incidental Economist

I'm a big fan of the Incidental Economist blog where I have learned a lot about healthcare economics. Recently healthcare economist Austin Frakt has shared some video monologues discussing meat, fake meat, health and the environment.

In the first video he discusses some recent research related to meat consumption and health, mainly there is no evidence that red meat presents a major health concern. And the challenge of observational data and research related to this:



However, in this  next video, I think the facts being referenced are making some assumptions that need clarification. Mainly, there seems to be an assumption that beef produced and consumed in the U.S. is exchangeable with beef produced in developing countries or that land devoted to beef production is exchangeable for land that could be used for food production purposes. Reducing consumption of beef in the U.S. likely won't have the impacts on consumption in other countries in the simplified way this story is often told.  U.S. beef accounts for .5% or less of global greenhouse gas emissions accounting for fossil fuel and grain consumption, as well as land use alternatives. And most of the land used for beef production isn't suitable for any other type of food production. Ruminants are able to convert inedible plant and fiber on marginal lands to highly palatable nutrient dense food sources. Adding a little grain (accounting for ~ 7% of the U.S. corn crop) can shorten the time grazing and increase production actually decreasing lifecycle greenhouse gas emissions.



I
n this final video, Dr. Frakt discusses how alternative/fake meat products are in fact NOT a healthier alternative to real beef: