Saturday, August 20, 2016

GMOs and QR Codes: Consumers need more than a label they need a learning path

I recently came across an article in national geographic about the new GMO labeling compromise that I thought was well written:

http://www.nationalgeographic.com/people-and-culture/food/the-plate/2016/07/gmo-label/  

The article asks:

"But what good is a label if people don't know what it means?"

That's the point...a blatant politically charged label with direct language or terms like "genetically engineered" is meaningless. I've discussed before how this can increase information asymmetry (i.e.consumer confusion).

One thing the article discusses in the huge gap in the science related to biotechnology and consumer knowledge and perceptions (something I have been studying since graduate school):

"Despite thousands of scientific studies, support from the World Health Organization, the American Medical Association, and the National Academy of Sciences, and, most recently, the concerted advocacy of 107 concerned Nobel laureates, the bulk of the public remains firmly convinced that GMOs are at best undesirable and at worst, downright dangerous. In other words, to the majority of Americans, a GMO label on a can of corn might as well be a skull-and-crossbones. What we’ve got here is a gaping divide between reputable scientific research and public perception. Unfounded GMO fear-mongering is doing us, as a planet, more harm than good."


There is huge burden on the consumer in terms of understanding complex modern agriculture. Earlier in the article there is some criticism of the currently proposed labeling paradigm:

"The federally approved warning label can consist of a QR (Quick Response) code, accessible by smartphone, or an 800 number that customers can call for information. These alternatives are not immediately helpful, and require time and effort on the part of consumers, many burdened with long grocery lists and fractious toddlers."

But given the huge gap in information I'm not sure there is any label that can be immediately helpful. The last thing we need is a shortcut label with confusing language like "genetically modified" that information economizing consumers will just interpret as a skull and cross-bones and move on. That approach is no better. It may in fact be the case that the QR code, if implemented properly may be the best way to attempt to fill that gap. It accomplishes a couple important things:

1) It can provide full disclosure and transparency
2) For consumers that truly want to understand what is in their food, it *can* potentially provide a learning path that helps fill this gap of knowledge from farm to fork

As I understand it, the details around the content and format of information related to QR codes is yet to be decided. I think a few things are necessary to make this work.

First, if this issue is important enough to be addressed FEDERALLY with a national labeling standard, then lets make this work for all food. Maybe require in some format that all foods that fall under this legislation have a "more information" section and a QR code, not just foods that contain so called "genetically engineered" ingredients.  If a label with a QR code becomes a proxy indicator for GMOs, that will defeat the whole purpose of an effort genuinely designed to inform the consumer. After all there are lots of approaches to food production out there- conventional breeding and hybridization, recombinant DNA, mutagenic approaches, and on the horizon CRISPR cas technology (HT: John Phipps).

Second, what should the 'landing page' look like for a QR code? What kind of information should it contain and how should it be presented? This is where the government needs to elicit the help of experts in science and communication. I am not sure, but I propose a learning path. Before saying anything about how a specific food product was produced, the consumer should quickly and effectively be exposed to a summary or survey of the many ways plants and animals are modified in agriculture to produce the foods we have today. (again conentional breeding/selection/hybridization/mutagenic/recombinant/CRISPR cas9/fermentation/cheese cultures etc.). Also they should be informed about the safety, regulations etc. about these technologies and the consensus views of groups like World Health Organization, the American Medical Association, and the National Academy of Sciences etc.

Finally they should be informed about the specifics of the food they are considering to purchase. All of this info can be standardized and used as stock for all food products, with more specific information for each food product detailed at the end of the 'learning path.' Maybe this could all be accomplished with a video or interactive infographic. But I firmly believe that a more comprehensive universal learning path approach like this is the most honest and transparent way to inform consumers about current and new technologies on the horizon and their safety and benefits. Not some politically loaded unscientific term like "genetically engineered" or "genetically modified."

Tuesday, August 16, 2016

An Econometric and Game Theoretic Analysis of Producer and Consumer Preferences Toward Agricultural Biotechnology

It is no secret these days that there are anti-biotech activists that reject the science related to the safety and benefits of biotechnology but yet have no issues accepting the science related to climate change or other fields. In my early days, back in graduate school I hypothesized that beliefs about the safety of biotechnology were more related or driven by political constructs than knowledge or acceptance of science itself. This was crude (I wish I had the floppy with the actual paper...and a drive to read it) but my general findings were that those that believed in climate change or were supportive of stem cell research were less likely (45-50% less using the divide by 4 rule for marginal effects) to believe in the safety of biotech foods. 

Of course this work had some drawbacks, including small sample size and power. But also, after a few years on the job and working on a limited basis with structural equation modeling, there are more powerful methods I could have used looking at these effects. But I think it was an interesting preliminary finding that seems to still hold true almost a decade later.

See also:

Perceptions of GMO Foods: A Hypothetical Application of SEM 

Left vs Right Science vs Risk vs Propensity to Regulate 

Monsantophobia Explained

Reference:

Matt Bogard. "An Econometric and Game Theoretic Analysis of Producer and Consumer Preferences Toward Agricultural Biotechnology" Western Kentucky University (2005)
Available at: http://works.bepress.com/matt_bogard/31/ 

 Abstract:

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, food safety, and climate change. However, perceptions of the safety of recombinant DNA technology on the part of consumers and management decisions by producers can shape the policy environment in ways that may inhibit expanded use of biotech traits in agriculture. This presentation presents a summary of results from an econometric and game theoretic analysis of consumer perceptions and producer decisions as they relate to agricultural biotechnology.

Submitted in partial fulfillment of AGRI 597 Independent Study/Special Problems in Agriculture.





Wednesday, July 06, 2016

CRISPR Technology

A nice article related to CRISPR technology and an application with waxy corn in a recent DTN article:

https://www.dtnpf.com/agriculture/web/ag/news/article/2016/06/17/gene-editing-comes-agriculture 

A very nice description of CRISPR technology:

"The letters CRISPR stand for "clustered regularly interspaced short palindromic repeats," that is, snippets of DNA….They work as part of the bacteria's defense system, in partnership with a group of special, DNA-cutting Cas ("Crispr-associated") proteins and RNA molecules….When viruses invade, the bacterial CRISPR-Cas copies DNA sequences from the virus and saves this information as a short CRISPR repeat -- a sort of molecular mug shot. When that virus invades again, these repeats are remobilized as RNA molecules, which recognize the virus DNA sequence and guide the CRISPR complex to it. There, the Cas protein snips the offending DNA sequence out, disabling the virus."

"Using a specific protein, Cas9, researchers are now using this CRISPR complex to target specific genes in the genome of plants, animals and even humans. The RNA guides the CRISPR complex to the gene sequence in question, and Cas9 cuts it out. Researchers can leave the DNA to heal on its own or they can insert a desired gene in its place."


The article goes on to discuss the regulatory environment and applications related to a new variety of waxy corn in the development pipeline for Pioneer.

Saturday, June 04, 2016

Left vs Right Science vs Risk vs Propensity to Regulate

Jayson Lusk has an interesting post on his blog related to an article in the Journal of Agricultural and Resource Economics finding an interesting relationship between left leaning voters and their willingness to support GMO labeling initiatives:



“One distinction, which I think is missing, is the greater willingness of those on the left to regulate on economic issues, such as GMOs, than those on the right. Stated differently, there are questions of science: what are the risks of climate change or eating GMOs. And then there are more normative questions: given said risk, what should we do about it? Even if the left and the right agreed on the level of risk, I don’t think we should expect agreement on political action.”


If I understand this correctly, I think this implies that if both those on the left and right agreed that there was some 'day after tomorrow' scenario (in terms of climate change) that warranted some type of government intervention, and they agreed that the science says there is a 3% chance of it happening without the intervention, then those on the right might object to the intervention for that given level of risk while a more left leaning person would support it. A right leaning person might suggest more market based alternatives or taking the gamble. But perhaps if the risk were higher, they might support doing more. In other words there might be different thresholds for the level of risk required to support a given policy interventions across the political spectrum.

Of course, the scientific consensus on climate change may not really even be strong enough to know for sure, i.e. the science isn't settled on exactly what scenarios are likely to play out and the probabilities that they will occur. There's a lot of science to support a wide range of probabilities and scenarios based on a number of assumptions. (see herehere, here, and here). So really, I think even the science, risk, and potential outcomes or scenarios are largely based on perceptions and these might actually differ significantly across the political spectrum. Maybe its really about perceived risk.

Just thinking about this a little more what if we specified a model of preferences toward government intervention like that below (this is more an illustration than a serious attempt to look at this empirically):

Pr(SUPPORT POLICY) =  B0 + B1 PERCEIVED RISK + B2 KNOWLEDGE

So if we estimated simple linear probability models as specified above for democrats and republicans (as short hand for political preferences) according to the story line above B1 would be higher for democrats than republicans. (I'm ignoring the use of interaction terms on purpose for simplicity) I wonder if this would also be true for B2, for a given level of knowledge, would B2 be higher for democrats/liberals? I also wonder if PERCIEVED RISK is really a function of KNOWLEDGE? Maybe a different specification would look something like:

Pr(SUPPORT POLICY) = B0 + B1 PERCEIVED RISK(KNOWLEDGE)

where PERCEIVED RISK = f(KNOWLEDGE)

So in this case perhaps B1 would still be higher for those with more left leaning politics. Still I wonder, besides this effect, what if its the case that the level or mean of PERCEIVED RISK is in general higher for those on the left? So you have this effect of a greater inclination for a preference for government intervention given a level of PERCEIVED RISK (via B1) but also a population of left leaning voters with a PERCEIVED RISK levels that are on average some magnitude higher. Both of these effects would likely increase the propensity of supporting government intervention.

Consider also....if PERCEIVED RISK = f(KNOWLEDGE), is the level of KNOWLEDGE about GMOs or climate change the same for those on the left and right and is this really what is partly determining different levels of PERCEIVED RISK?  I'm not sure....how often do we hear arguments from the left that drastic actions or mitigating policies to combat climate change are necessary because of the scientific consensus on climate change when in fact the consensus as it is is pretty weak. Too weak to offer much guidance on actions, or very precise estimates of actual risks. (again see herehere, here, and here). And even some of the world's leading experts in risk modeling tend to have some ideas about GMO risks that can be seriously questioned (see here). There was a really good book a few years back discussing voter preferences and systemic bias regarding economic policy that addressed similar issues (see The Myth of the Rational Voter).

If preferences toward policy can be modeled in this way, an interesting and maybe promising feature is that perhaps the level of knowledge feeding into PERCEIVED risk can be altered. We often hear that science and evidence rarely will change minds when it comes to biotechnology or climate change, however, in a paper recently published by the Journal of the Federation for American Societies for Experimental Biology (FASEB) Jayson and Brandon McFadden observed the following:

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

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

I'm not  a PhD Economist or Psychometrician but I would think an approach similar to the structural equation modeling framework I discussed before (depicted below) might get closer to specifying and measuring all of the causal paths and connections between latent constructs around risk perception and the policy environment for GMOs or climate change. Of course that would also require a solid data set and valid survey instruments. Jayson's work seems to be leading the way. These are just my initial thoughts prior to even reading the Jayson and McFadden article or the JARE article mentioned above and honestly I have not reviewed much of the actual literature or survey analysis related to risk and perceptions or policy preferences since graduate school. Maybe a lot of this has been done already.

(click to enlarge)



Sunday, November 01, 2015

The Cult of Statistical Significance...or...no bacon is not really as bad or worse than cigarettes!

 In their very well known article "The Cult of Statistical Significance", Ziliak and McCloskey write:

"William Sealy Gosset (1876-1962)  aka “Student,” working as Head Experimental Brewer at Guinness’s Brewery, took an economic approach to the logic of uncertainty. Fisher erased the consciously economic  element, Gosset's "real error."  We want to bring it back….Statistical significance should be a tiny part of an inquiry concerned with the size and importance of relationships."

For those unfamiliar with the history of statistics, Gosset was the one that came up with the well known t-test so many of us run across in any basic statistics class. A lot of issues are addressed in this paper, but to me one related theme is the importance of 'practical' or what I might call 'actionable' statistics. And context matters. Are the results relevant for practical consideration? Is the context realistic?  Are there proper controls? What about identification? For instance, not long ago I wrote about a study that attempted to correlate distance from a farm field and exposure to pesticides and autism that has been criticized for a number of these things, even though the results were found to be statistically significant.  As well as this one attempting to claim that proteins from Bt (read "gmo") corn were found in the blood of pregnant women. And...not to forget, the famous Serelini study that claimed to connect roundup herbicide to cancer in rats, that was so bad that it was retracted. Context, and economics (how people behave in the context of real world decision making scenarios) really matter. Take for instance, California's potential consideration to put roundup on a list of known carcinogens that might actually cause environmental harms in a number of ways magnitudes worse than roundup itself ever could.

So what does this all have to do with bacon? Well recently you might have heard a headline like this: “Processed meats rank alongside smoking as cancer causes – WHO.” 

This is a prime example of the importance of putting science and statistical significance, effect sizes, context (like baseline risks in the case of WHO quote above) and practical significance into perspective. Millions of people have heard this headline, taken the science at face value, and either acted on it or given it way more credence and lip service than it deserves. At a minimum every time for the rest of their life they have a piece of bacon they might think, wow, this could be almost as bad or worse than smoking.

Economist Jayson Lusk has a really nice post related to this with several quotes from a number of places, and I'm going to borrow a few here. From an article he links to in the Atlantic:

"the practice of lumping risk factors into categories without accompanying description—or,  preferably, visualization—of their respective risks practically invites people to view them as like-for-like. And that inevitably led to misleading headlines like this one in the Guardian: “Processed meats rank alongside smoking as cancer causes – WHO.”

“One thing rarely communicated in these sorts of reports is the baseline level of risk.  Let's use Johnson's example and suppose that eating three pieces of bacon everyday causes cancer risk to increases 18%.  From what baseline?  To illustrate, let's say the baseline risk of dying from colon cancer (which processed meat is supposed to cause) is 2% so that 2 out of every 100 die from colon cancer over their lifetime (this reference suggests that's roughly the baseline lifetime risk for everyone including those who eat bacon).  An 18% increase means your risk is now 2.36% for a 0.36 percentage point increase in risk.  I suspect a lot of people that would accept a less-than-half-a-percentage point increase in risk for the pleasure of eating bacon….studies that say that eating X causes a Y% increase in cancer are unhelpful unless I know something about my underlying, baseline probably of cancer is without eating X.”

The real cult of statistical significance (and in effect all of the so called science that follows from it) is a cult like believing and following by multitudes that hear about this study or that, overly dramatized by media headlines, (even if it is a solid study, potentially taken out of context and misinterpreted to fit a given agenda or emotive response), and then synthesized into corporate marketing campaigns and unfortunately public policies. Think gmo labeling, gluten free, antibiotic free,  climate change policy, ad naseam.

Thursday, October 29, 2015

Applied Microeconomics: The Normative Representative Consumer and Welfare Analysis

In a previous post, I pondered some questions related to using market demand functions to make welfare statements, following broadly Microeconomic Theory by Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green (MWG).

Making welfare statements about aggregate demand revolves around a few key concepts including a positive representative consumer, a wealth distribution rule, and a social welfare function. At a high level, these concepts seem to represent the technical assumptions and characteristics that need to hold in order to make most of the basic analysis of an intermediate microeconomics course mathematically sound or tractable for applied work. Here is a shot at some high level explanations:

positive representative consumer- at a high level, a hypothetical consumer who's UMP facing society's budget constraint generates a market or economy's aggregate demand function

wealth distribution rule - for every level of aggregate wealth, assigns individual wealth.
This rule or function is what allows us to write aggregate demand as a function of prices and wealth in order to move forward with the rest of our discussion about welfare analysis.

Examples given in MWG include wealth distribution rules that are a function of shareholdings of stocks and commodities which make wealth a function of the market's price vector

social welfare function (SWF) - this assigns utility to the vector of utilities for all 'I' consumers in an economy or market. W(u1,.....,uI) or can be written in terms of indirect utilities W(v1,....vI). 

Maximizing Social Welfare and Defining the Normative Representative Consumer

The wealth distribution rule is assumed to maximize society's social welfare function subject to a given level of aggregate wealth. The optimal solution indicates a particular indirect utility function v(p,w)

Normative Representative Consumer- a positive representative consumer is a normative representative consumer relative to social welfare function W(.) if for every (p,w) the distribution of wealth maximizes W(.). v(p,w) in the optimum is the indirect utility function for the normal representative consumer. 

For v(p,w) to exist, we are assuming a SWF, and assuming it is maximized by an optimal distribution of wealth according to some specified wealth distribution rule.

An example from MWG: When v(p,w) is of the Gormon form, and the SWF is utilitarian, then an aggregate demand function can always be viewed as being generated by a normative representative consumer.  

Tuesday, October 27, 2015

Applied Microeconomics: The Strong Axiom of Revealed Preference,Aggregation, and Rational Preferences

Professionally most of my focus has been empirical and to a great extent, has been agnostic when it comes to micro theory. Take data mining and machine learning, social network analysistext mining or issues related to big data for instance. Or many of the other issues I have taken up at EconometricSense. A lot of what I have worked on has been more about data, algorithms, and experimental design than the nuts and bolts of microeconomic theory.

However, there are some theoretical issues in microeconomics that I either have forgotten, or never really understood that well.

Particularly these issues have to do with the strong axiom of revealed preference, the market aggregated demand function, and welfare analysis as discussed in one of my graduate texts (Microeconomic Theory by Andreu Mas-Colell, Michael D. Whinston, and Jerry R. Green).  From that text  (MWG) I basically get the following:
  • The strong axiom (SA) of revealed preference is equivalent to the symmetry and negative semi-definiteness of the slutsky substitution matrix
  • Violations of the SA mean cycling of choices or violations of transitivity
  • If observed demand follows the SA, then preferences that rationalize demand can always be recovered
  • It is impossible to find preferences that rationalize a demand function when the slutsky matrix is not symmetric
That means, for an individuals' observed demand function, if the slutsky matrix is not symmetric, you can't make welfare statements based on the area beneath the demand curve.

What happens when we aggregate individual demand to get a market demand function? It seems to me that the data of interest in most applied work is going to be related to an aggregate market demand curve. Based on Green et al:
  • the chances of the SA "being satisfied by a real economy are essentially zero"
  • If we allow individuals in an economy to have different preference relations/utility functions, when we aggregate to get a market demand function, the negative semi-definiteness of the slutsky matrix (equivalent to the weak axiom of revealed preference) might hold, but "symmetry will almost certainly not." 
  • While positive effects of an equilibrium might hold, without symmetry the SA does not and we therefore cannot make statements about consumer welfare based on the area beneath an observed empirical market demand function
This last conclusion leaves me with a lot of questions to ponder:
  1. What does that imply with regard to empirical work? It seems to not matter for positive effects (for instance a conclusion that a wage set above equilibrium causes a surplus of labor). 
  2. But, how much does it matter that I can't use an empirical demand function to calculate changes in consumer surplus for a change in prices? Maybe it only matters if I am interested in calculating some amount? 
  3. For any individual, if the SA might holds (which is possible), we certainly know a price increase would reduce consumer surplus, put them on a lower indifference curve and make them worse off. Regardless of the conclusions above, wouldn't that hold for all consumers represented by the aggregate market demand curve? Can't we make a normative statement  (in  terms of a qualitative directional sense even if we can't calculate total surplus) about all consumers even if the SA fails in the aggregate but holds for each individual?
Now,  the MWG text mentioned above does go on in later chapters to discuss the notions of a positive and normative representative consumer as well as a social welfare function and wealth distribution mechanisms and implications for welfare analysis. But I'd really like to know about #3. Can we make directional statements about changes in welfare as long as we know that any attempt at quantification or calculation of surplus would be invalid due to violations of the SA?

Is this a case where one should just take the example of Milton Freidman's pool players who behave as if they know physics? Maybe all of the assumptions (like the SA) fail to hold for a market demand function, but we still feel confident making directional or qualitative welfare statements about price changes because everything else about the model predicts so well?

Any thoughts from readers?

I found it interesting, that the issues in the bulleted statements related to the MWG text were never addressed that I can tell in any of my undergraduate principles or intermediate micro texts, nor even in Nicholson's more advanced graduate text. It just seems like these texts jump from individual demand to market demand as a horizontal summation of individual's demand curves and go straight to welfare analysis and discussions about consumer surplus without discussing these issues related to the SA.

Note: I definitely spent some time with the issue of consumer surplus calculations based on compensated vs uncompensated demand curves. I don't think that is the issue here at all.

****updated modified on October 29, 2015