This podcast touches on the lack of innovation in pharma and criticism about outsourcing innovation. Do these criticisms ignore recent technological advances in biotech (and the convergence of AI and genomics) that have reduced the minimum efficient scale in drug discovery creating new opportunities for startups, small firms, and scientist entrepreneurs? When we think of therapeutics as dispensing knowledge packed into a capsule or syringe, knowledge that has properties of both a private and public good (i.e. non-rival and partially excludable) scientist entrepreneurs are better incentivized and able to capture greater value from their discoveries in a venture capital funded startup environment than a larger institution like pharmaceutical companies or universities (even with Bayh-Dole Act). Drug discovery is risky, but by combining option value and discovery of new information with staged investment VC firms can discover positive NPV projects that would otherwise be rejected under conventional financing models. The combination of technological change, the economics of knowledge, and venture capital seems to reduce the comparative advantage of innovating 'in-house.' Maybe it is the case that large pharmaceutical firms have more of a comparative advantage navigating the valley of death that lies between a discovery and a cure by focusing on the regulatory approvals and marketing efforts necessary to deliver those products than they have in drug discovery?
Saturday, April 24, 2021
Saturday, April 03, 2021
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
Saturday, February 06, 2021
The Convergence of AI, Life Sciences, and Healthcare
Several years ago I was writing about the convergence of AI and genomics in agriculture:
"The disruptions of new technology, big data and genomics (applications like FieldScripts, ACRES, MyJohnDeere or the new concept Kinze planters that switch hybrids on the go etc.) will require the market to continue to offer a range of choices in seeds and genetics to tailor to each producer's circumstances of time and place." (1)
We have also seen a similar convergence in healthcare:
"A series of breakthroughs in medical science and information technology are triggering a convergence between the healthcare industry and the life sciences industry, a convergence that will quickly lead to more intimate—and interactive—relationships among people, their doctors, and biopharmaceutical companies." (2)
This excellent segment on WBUR just a few years later picks up on the same themes:
Nobel Laureate and MIT Institute professor Phil Sharp has an even broader vision of this convergence: It’s not just computer science and biology that are converging, but engineering, physics, material science and agriculture too, he says.
“Life science is part of all of those processes and bringing physicists and engineering and information technology together to integrate life science with the translation to solving those problems is what convergence is about,” Sharp says. “It'll be decades of exciting science and exciting technology.” (3)
Outcomes and Value Based Pricing
Due to this convergence, better data and technology are creating new opportunities. Health insurance companies, healthcare providers, and seed companies are entering into value based contracts where payments are based on outcomes and quality.
In healthcare:
"By leveraging appropriate software tools, big data is informing the movement toward value-based healthcare and is opening the door to remarkable advancements, even while reducing costs. " (4)
"Value-based healthcare is a healthcare delivery model in which providers, including hospitals and physicians, are paid based on patient health outcomes. Under value-based care agreements, providers are rewarded for helping patients improve their health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way." (5)
(See below or https://healthinformatics.uic.edu/blog/shift-from-volume-based-care-to-value-based-care/ for an excellent infographic explaining this promising shift in healthcare)
In food and agriculture we are seeing risk sharing and outcomes based pricing contracts as well:
"...executives are touting their new pricing model, outcome-based pricing, as the potential pricing paradigm of the future. The model involves Bayer setting an expected yield outcome for a product or seed, based on a farm's data and history stored on the company's digital ag platform, FieldView, as well as the company's own research on their products. If a farmer's final yield falls below that expected value, the company will rebate a certain portion of the original price of the product. If the yield instead surpasses the initial set value, the farmer shares a pre-agreed portion of that additional income with the company." (6)
Precision Medicine and Precision Agriculture
Instead of one size fits all best practices for seed, pest management, tillage, and nutrient management recommendations driven by research from university and industry trials, growers can get individually customized prescriptions, not just at the farm or field level, but within field and moving closer and closer to the row foot level for some decisions. The combination of advanced genomics with big data generated from precision agricultural applications (remote sensing, IoT, automated steering, GPS/GIS) makes one size fits all obsolete.
As I quoted previously:
"That's also why the market has driven companies to treat hybrid selection like a 'big data' problem and they are developing multivariate recommender systems as tools to assist in this (like ACRES and FieldScripts). The market's response to each individual producer's unique circumstances of time and place also ensures continued diversity of crop genetics planted. There are numerous margins that growers look at when optimizing their seed choices and it will require a number of firms and seed choices to meet these needs as the industry's focus moves from the farm and field level to the data gathered by the row foot with each pass over the field." (1)
Similarly, in healthcare, the golden age of medicine driven by the 'omics' revolution and big data will allow us to move away from one size fits all generalizations of research and medicine allowing us to "tailor medical treatment to the specific characteristics of each patient involving the ability to classify individuals into subpopulations that are uniquely susceptible to a specific treatment, sparing expense and side effects and is derived from doubts on the results of subgroup analyses and on non responders in clinical trials" (7)
"Health systems will have to go rapidly from a one-size-fits-all model of treatment to a more customized model, which still uses mass-manufactured but where treatments are selected for patients based on specific biomarkers," Joshi said. "But we can now see the next advance in personalized medicine potentially going even further, something much more personalized, like a tailor-made suit...."Big data and advances in our understanding of genomics are providing us with the footholds into establishing and understanding, for the first time ever, the causal genetic factors that help us manage that golden triangle of treatment: the right target, the right chemistry, and the right patient." (2)
Venture Capital and Digital Platforms and Solutions
Monsanto's (now Bayer Crop Science) acquisition of The Climate Corporation occurred about the same time I was penning my first post on this convergence, and was the first major move in industry that solidified these potential synergies in my mind at least. This convergence has drawn the interest and has been fueled by a number of startups and venture capital firms. Farmer's Business Network (FBN) seems to be positioning itself as a disruptor, like the Amazon of agribusiness providing a platform that includes everything from purchasing inputs, crop analytics, finance and marketing, and more direct access to genetics. In the livestock space, companies like AAD (Advanced Animal Diagnostics) and Connecterra are building tools and services analogous to a Fitbit for cows. Body Surface Translations (BST) is a company whose image processing technology has targeted both problems in animal and human health. Tim Hammerich (the Future of Agriculture) and Sarah Nolet (AgTech So What?, AgThentic, Tenacious Ventures) have weekly discussions with innovators pioneering new solutions in this space covering a range of topics including automated irrigations systems, blockchain, regenerative agriculture, carbon sequestration and a range of companies from startups to larger players including Wal-Mart and Coca-Cola. Where Food Comes From is leveraging QR codes and mobile technology paired with their source verification processes to connect consumers to information about the people and processes behind the food they consume. IN10T is a digitally powered data driven company helping bridge the gaps between innovations and real world application of these technologies. Venture capital firm Foresite Capital even leverages data science to drive their investment strategy in therapeutics, diagnostics, and devices. This includes digital health apps like mindstrong which is leveraging AI for better diagnosis, monitoring, and treatment of behavioral health conditions and everlywell focused on actionable healthcare diagnostics and health engagement. Evidation is a company that leverages data from digital devices and sensors capturing, quantifying, and analyzing behavior, or mapping the 'behaviorome' in the context of human health (8). This is just a tiny survey of companies and products that I have encountered in just the last few years.
Addressing Society's Bigger Problems
This convergence is allowing us to address problems in healthcare like quality, cost, access and health equity. When it comes to the food we eat, AI, technology, and genomics is providing us the tools to combat issues like climate change, water quality, nutrition, safety, equity, and access.
It's obvious when you look at the big picture, this convergence is leading to progress that is both complimentary and synergistic across a range of industries related to food and healthcare. Better food and a healthier environment and planet led to better health outcomes. Healthcare payers and providers are realizing the importance of these issues in healthcare. Each is separately addressing key social determinants of health in ways that were not possible before:
"During the past several decades, it has become increasingly apparent that a person’s “health” is influenced by many more factors than health care alone. These other determinants are defined by the conditions and environment in which people are born, grow, live, work, and age, reaching beyond just what the delivery of acute care services can influence. These “social determinants of health” result in billions of dollars of additional costs annually. By working to mitigate the negative impacts of these factors, significant benefits can be achieved that improve both access and outcomes for individuals and lower overall costs." (9)
As I stated several years ago:
"as big data drives more diversity into every seed planted in every acre across every field, we may possibly begin to mitigate some of the risks and concerns traditionally associated with monoculture. So it is true, when you look across row after row and see only corn, you might technically call it 'monoculture' but it's not your grandparent's monoculture."
As a result of the convergence of AI and life sciences, it's not your grandparent's healthcare either.
References and Related Readings:
(1) Monoculture vs. the Convergence of Big Data and Genomics. Matt Bogard. October 13, 2017. https://www.linkedin.com/pulse/monoculture-vs-convergence-big-data-genomics-matt-bogard/ (previously published as: Big Data + Genomics != Your Grandparent's Monoculture. Economic Sense. December 22, 2014. http://ageconomist.blogspot.com/2014/12/big-data-genomics-your-grandparents.html
(2) Big Data Gets Personal as Healthcare and Life Sciences Converge. By Bob Evans, Senior Vice President, Oracle. https://www.oracle.com/industries/oracle-voice/big-data-gets-personal.html
(3) Next Chapter For Biotech? Many Say 'Convergence' With Data Science. WBUR. NPR. Bioboom June 8, 2018. https://wbur.fm/2MaaMkA
(4) Healthcare Big Data and the Promise of Value-Based Car. NEJM Catalyst. Brief Article. Jan 1, 2018
(5) What Is Value-Based Healthcare?. NEJM Catalyst. Brief Article. Jan 1, 2017
(7) Capurso L. Evidence-based medicine vs medicina personalizzata [Evidence-based medicine vs personalized medicine.]. Recenti Prog Med. 2018 Jan;109(1):10-14. Italian. doi: 10.1701/2848.28748. PMID: 29451516.
(8) Why Foresite Capital is Betting Big on the Convergence of AI and Biotech. August 23, 2018. https://soundcloud.com/levine-media-group/why-foresite-capital-is-betting-big-on-the-convergence-of-ai-and-biotech Check out their current portfolio of investments: https://www.foresitecapital.com/portfolio/
(9) Beyond the Boundaries of Health Care: Addressing Social Issues https://www.ahip.org/beyond-the-boundaries-of-health-care-addressing-social-issues/
Related:
What does the farmer say...about seed choices? (Channeling Hayek) http://ageconomist.blogspot.com/2013/12/what-does-farmer-say-about-seed-choices.html
Big Data: Causality and Local Expertise Are Key in Agronomic Applications. http://econometricsense.blogspot.com/2014/05/big-data-think-global-act-local-when-it.html
Modern Sustainable Agriculture Annotated Bibliography. http://ageconomist.blogspot.com/2011/02/modern-sustainable-agriculture.html
Sunday, October 04, 2020
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)
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
Using SNA in Predictive Modeling
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
Wednesday, July 08, 2020
Consumer Perceptions of Biotechnology: The Role of Information and Social Harassment Costs
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.
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
Thursday, April 09, 2020
Steak-umm Tweet Storm Tackles Coronavirus and Science Literacy
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:
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.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
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
Thursday, January 23, 2020
The Food Desert Mirage
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."
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)
Tuesday, January 21, 2020
Are Fruits and Vegetables Becoming Less Nutritious?
--> 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
Saturday, January 18, 2020
Addressing Gender Inequality in Developing Countries Through Crop Improvement
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
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.
Subramanian A, Qaim M. The impact of Bt cotton on poor households in rural India. Journal of Development Studies. 2010;46:295–311
GWP* Better Captures the Impact of Methane's Warming Potential
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
Some Beef Related Posts From the Incidental Economist
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:
Saturday, September 14, 2019
Welfare Analysis: Just Do It!
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."
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)
Sunday, September 01, 2019
Thinking Fast and Slow About Consumer Perceptions of Technology and Sustainability in Agriculture
From AgFunder News: https://agfundernews.com/farming-is-the-worlds-most-important-career-thats-why-it-needs-an-image-makeover.html
"Right now the field is in the midst of profound change as advanced technologies including green chemistries, robotics, artificial intelligence, IoT, autonomous vehicles, machine learning, regenerative agriculture and biomimetics transform how farms look and function. It might seem like the stuff of science fiction, but autonomous vehicles, indoor farming and drone pollination are becoming more common throughout the sector.Looking at, and more importantly, talking about farming as a part of the tech revolution has the potential to ignite the curiosity and imagination of the next generation.millennials want meaningful careers that help make the world a better place. Often that interest is funnelled towards jobs in CleanTech, non-profits, the environment or the arts. But farming is an overlooked industry with incredible potential to help improve the world."
I tend to agree.
From Drovers: https://www.drovers.com/article/consumers-speak-sustainable-farmers-wanted
"Consumers used to want farmers to be local, healthy or safe, but a new word is topping the chart this year, according to a new global study by Cargill. In a word, consumers want farmers to be sustainable."
However the theme above related to the need to promote the technological savy of farmers was echoed in this survey:
"Although 75% of the respondents thought technologically advanced farming was a good thing, very few respondents see farmers that way today. “Technologically savvy” was one of the terms least associated with farmers."
This explains why technological advancements in agriculture that actually improve sustainability (Bt, Glyphosate resistance, finely textured beef, etc.) are often rejected when in fact it delivers much of what they are asking for.
I've written before about some of the challenges related to consumer attitudes and perceptions about agriculture. See the links below. But along the lines of all of these themes I find a common thread in Daniel Kahneman's Thinking Fast and Slow:
"emotional attitude drives beliefs about benefits and risks and dominates conclusions over arguments."
Bad arguments and misleading intuition are driven by a number of biases mentioned in the book.
One of these biases is the 'affect heuristic' which "simplifies our lives by creating a world that is much tidier than reality. Good technologies have few costs in our imaginary world we inhabit, bad technologies have no benefits, and all decisions are easy. In the real world, of course we often face painful tradeoffs between costs and benefits."
I think this applies very well to food and agricultural technologies vs other kinds of technology.
Good Technology: Impossible Burger/Tesla
Bad Technology: Biotechnology
Easy Decisions: Meatless Monday/Ban Glyphosate
Real World Tradeoffs: U.S. beef contributes less than .5% of global greenhouse gas emissions, so going meatless on Mondays (or campaigning to replace beef with alternatives) likely won't have the impact many consumers believe. We also know that glyphosate is a low toxic herbicide that in combination with biotech traits has helped enable environmentally important farming practices including reduced tillage, reduced energy use, and has helped substitute away from more toxic chemistries(link see also Hybrid Corn vs. Hybrid Cars). Banning glyphosate (or creating a risk and litigation environment effectively banning its continued use) might seem like an easy 'costless' solution but there are definitely tradeoffs.
Additionally:
"System 1 is able produce quick answers to difficult questions by substitution, creating coherence where there is none....The question that is answered is not the one that was intended, but the answer is produced quickly and may be sufficiently plausible to pass the lax and lenient review of system 2"
There definitely seems to be a coherent story among consumers (and voters/politicians) about how good technologies and farming practices (local, natural, organic, non-GMO, vegan etc.) must be sustainable and virtuous while modern (high tech) 'industrialized' technologies and practices must be destructive, risky and harmful. Further, coherence and tidyness implies those advocating a different story with any strong or weak connection to companies producing and marketing these technologies must be biased and non-credible sources regardless of their expertise or what is found in the scientific literature.
It is very difficult to battle the 'coherence' and 'tidyness' of the stories and perceptions that is formed in the minds of consumers and critics of agriculture. This is definitely an area where some food marketers and the 'free from' approach to labeling seems to be most damaging (and profitable?). To say the least, after spending more than a decade studying consumer and voter preferences in relation to food and technology in the agriculture space, I think we are only beginning to scratch the surface. Maybe we have reached a critical mass or turning point in consumer interest in these topics, but can science communication and advocacy turn the tide?
Rational Irrationality and Satter's Hierarchy of Food Needs
The 'free from' Nash Equilibrium Food Labeling Strategy
Polarized Beliefs on Controversial Science Topics
An Economic Analysis of Preferences for Genetically Engineered Foods
Voter Preferences, The Median Voter Theorem, and Systematic Policy Bias
Friday, January 25, 2019
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