Blog

Enriched colonies

A couple months ago, I discussed the book chapter I wrote on a different type of hen housing system: the enriched colony . Today, the Wall Street Journal ran a piece I wrote about this hen housing system and the costs of alternative housing systems.   

A few snippets:

A 2014 California voter initiative and subsequent state legislation ultimately led to a ban on sales of battery-cage eggs in the Golden State. Because eggs have few close substitutes, demand tends to be relatively insensitive to changes in price. When demand is inelastic, a small-percentage change in the quantity supplied causes an even greater increase in price.

Comparing the prices of eggs sold in California before and after the law with the prices of eggs sold in other states reveals that the legislation increased egg prices for Californians by at least 22%—or about 75 cents for a dozen. A related analysis using Agriculture Department wholesale price data indicates the California law increased prices between 33% and 70%. Poor Americans, who spend a larger share of their incomes on food, are disproportionately affected.

and

Rather than getting rid of the cages entirely, one answer is to use a relatively new type of housing: the enriched-colony cage system. Unlike the barren environment in the battery cages, the much larger, enriched-colonies have nesting areas for egg laying and a matted area that allows the hens to exercise their natural urge to scratch. Also available are perches that allow the hens to get up off the wire floor.

An enriched colony is not a Ritz-Carlton, and some animal advocates think the systems do not go far enough. However, they represent an innovative compromise that attempts to balance cost and the hens’ well-being.

Food Demand Survey (FooDS) - May 2016

The May 2016 edition of the Food Demand Survey (FooDS) is now out.  

Some noteworthy results from the regular tracking portion of the survey:

  • For the second month in a row WTP fell for all food products except hamburger, which increased 5.4% this month compared to last.  
  • Compared to one year ago, WTP is lower for all food products.
  • Consumers expect lower chicken and pork prices this month compared to last (and about the same prices for beef), and say they plan to buy more chicken, beef, and pork than they did last month.  
  • GMOs were less visible in the news this month; pink slime and LFTB were more visible.  
  • Concern for GMOs fell this month.

For the ad-hoc questions, we delved into consumers' beliefs about the use of added growth hormones in livestock and poultry production.  

First, participants were asked: “What percentage of the following types of farm animals in the United States are given added hormones to promote growth and muscle development?”.  The average answers were 60% for beef, 54% for pork, and 55% for broiler chickens.  These answers are quite wrong.

Virtually all feedlot cattle in the US are given added growth hormones but NONE of the hogs or broiler chickens are given added hormones.  Fewer than 2% of respondents knew this last fact. That is, 98% of respondents incorrectly think hormones are used in pork and chicken production.  

What impacts might these false beliefs have?  As it turns out, the impacts are non-trivial.  For example, consumers' responses to our initial choice questions that are used to derive WTP for each of the meat cuts depend on consumers perceptions about the prevalence of hormone use.  The larger the fraction of animals a consumer believes receives hormones, the less they're willing to pay for meat from that type of animal.  Here's a quick analysis I ran asking the question: how would consumers' WTP change if they went from having the current average level of false beliefs to knowing the truth?  

WTP for ground beef and steak would fall (because more cattle are given hormones than most people think) and WTP for pork and chicken would increase (because none of these animals are given added hormones despite the fact people think they are).  What this suggests is that demand for pork and chicken is depressed by false beliefs.

We can also see the impact of these sorts of false beliefs in a different way.  Participants were asked a second ad-hoc question on the survey: “If you walked into your local grocery store and saw a package of meat with the label ‘no added hormones’, what is the highest premium you would be willing to pay for the following meats with this label over meats without this label?

On average, respondents said they were willing to pay premiums between $1 and $2 for each of the meat cuts for ‘no added hormones.”  

The highest was for steak ($2.14/lb) and the lowest was for deli ham ($1.32). Of
course, paying a premium for chicken or pork labeled ‘no added hormone’ is superfluous because all pork and chicken production avoids the use of added growth hormones.

False beliefs tend to inflate WTP for ‘no hormone added’ labels. People’s beliefs about hormone use are correlated with their willingness to pay a premium for ‘no added hormone.’ For example, a person who thinks no hormones are used in pork is predicted to pay a premium of $1.44 for pork chops with a ‘no added hormone’ label, whereas a person who thinks 100% of pigs are given hormones is predicted to pay a premium of $1.81. For chicken breast, the same figures are $1.42 and $1.92.

All this perhaps explains why many pork and poultry producers add the claim "no added hormones" to the label.  These labels, however, while truthful, might also be misleading. Because, as our survey shows, people think there are high levels of hormone use in pork and poultry production.  

Does Diet Coke Cause Fat Babies?

O.k., I just couldn't let this one slide.  I've seen the results of this study in JAMA Pediatrics discussed in a variety of news outlets with the claim that researchers have found a link between mothers drinking artificially sweetened beverages and the subsequent weight of their infants.

I'm going to be harsh here, but this sort of study represents everything wrong with a big chunk of the nutritional and epidemiology studies that are published and how they're covered by the media.  

First, what did the authors do?  They looked at the weight of babies one year after birth and looked at how those baby weights correlated with whether (and how much) Coke and Diet Coke the mom drank, as indicated in a survey, during pregnancy.  

The headline result is that moms who drank artificially sweetened beverages every day in pregnancy had slightly larger babies, on average, a year later than the babies from moms who didn't drink any artificially sweetened beverages at all.  Before I get to the fundamental problem with this result, it is useful to look at a few more results contained in the same study which might give us pause.

  • Mom's drinking sugar sweetened beverages (in any amount) had no effect on infants' later body weights.  So drinking a lot of sugar didn't affect babys' outcomes at all but drinking artificial sweeteners did?
  • The researchers only found an effect for moms who drank artificially sweetened beverages every day.  Compared to moms who never drink them, those who drink diet sodas less than once a week actually had lighter babies! (though the result isn't statistically significant).  Also, moms drinking artificially sweetened beverages 2-6 times per week had roughly the same weight babies as moms who never drank artificially sweetened beverages.  In short, there is no evidence of a dose-response relationship that one would expect to find if there was a causal relationship at play.  

And, that's the big issue here: causality.  The researchers have found a single statistically significant correlation in one of six comparisons they made (three levels of drinking compared to none for sugar sweetened beverages and for artificially sweetened beverages).  But, as the researchers themselves admit, this is NOT a casual link (somehow that didn't prevent the NYT editors from using the word "link" in the title of their story).  

Causality is what we want to know.  An expecting mother wants to know: if I stop drinking Diet Coke every day will that lower the weight of my baby?  That's a very different question than what the researchers actually answered: are the types of moms who drink Diet Coke every day different from moms who never drink Diet Coke in a whole host of ways, including how much their infants weigh?  

Why might this finding be only a correlation and not causation? There are a bunch of possible reasons.  For example, moms who expect their future children might have weight problems may choose to drink diet instead of regular.  If so, the the moms drinking diet have selected themselves into a group that is already likely to have heavy children.  Another possible explanation: moms who never drink Diet Cokes may be more health conscious overall.  This is an attitude that is likely to carry over to how they feed and raise their children which will affect their weight in ways that has nothing to do with artificially sweetened beverages.

Fortunately economics (at least applied microeconomics) has undergone a bit of credibility revolution.  If you attend a research seminar in virtually any economist department these days, you're almost certain to hear questions like, "what is your identification strategy?" or "how did you deal with endogeneity or selection?"  In short, the question is: how do we know the effects you're reporting are causal effects and not just correlations.  

Its high time for a credibility revolution in nutrition and epidemiology.  

Lab grown meat

Quartz.com just ran a piece taken from one of the chapters of Unnaturally Delicious on lab grown meat.  Here's the start:

On Aug. 5, 2013, Mark Post went out to grab a hamburger. This was no drive-through Big Mac. Rather, Post bit into his $325,000 burger in front of an invitation-only crowd of journalists, chefs, and food enthusiasts in the heart of London.

The strangest part wasn’t the cost or the crowd but the meat. Post, a professor of vascular physiology at Maastricht University in the Netherlands, grew the burger himself. Not from a cow on his farm, mind you, but from a bovine stem-cell in a petri dish in his lab. Post’s research, partially funded by Sergey Brin, one of Google’s co-founders, has the potential to upend conventional wisdom on the environmental, animal welfare, and health impacts of meat eating.

Ironically enough, I first met Post at a meeting of some of the world’s largest hog producers.

The Quartz editors left out what I think is one of the most important points made in the chapter about relative inefficiencies of meat eating.  So, for sake of completeness, here's the segment they left out (long time readers will recognize that I've touch one this theme in previous blog posts).

****************************

More broadly, this line of argument – that meat production (inside the lab or out) is “wasteful” because it requires feed inputs that humans might use – is misplaced.  To see this, it is useful to consider a thought experiment – an imaginary story that might help us get to the bottom of things. 

Imagine a biologist on an excursion to the Amazon looking for new plant species.  She comes across a new grass she’s never before seen, and brings it back home to her lab.  She finds that the grass grows exceedingly well in greenhouses with the right fertilizer and soil, and she immediately moves to field trials.  She also notices that the grass produces a seed that is durable, storable, and extraordinarily calorie dense.  The scientist immediately recognizes the potential for the newly discovered plant to meet the dietary demands of a growing world population.

But, there is a problem.  Lab analysis reveals that the seeds are, alas, toxic to humans.  Despite the set-back, the scientist doesn’t give up.  She toils away year after year until she creates a machine that can convert the seeds into a food that is not only safe for humans to consume but that is incredibly delicious to eat.  There are a few downsides.  For every five calories that go into the machine, only one comes out.  Plus, the machine uses water, runs on electricity, burns fossil fuels, and creates carbon emissions. 

Should the scientist be condemned for her work?  Or, hailed as an ingenious hero for finding a plant that can inexpensively produce calories, and then creating a machine that can turn those calories into something people really want to eat?  Maybe another way to think about it is to ask whether the scientist’s new food can - despite its inefficiencies (which will make the price higher than it otherwise would be) - compete against other foods in the marketplace?  Are consumers willing to pay the higher price for this new food? 

Now, let’s call the new grass corn and the new machine cow. 

            This thought experiment is useful in thinking about the argument that corn is “wasted” in the process of feeding animals (or growing lab grown meat).  Yet, the idea that animal food is “wasted” is a common view.  For example, one set of authors in the journal Science wrote,

“Although crops used for animal feed ultimately produce human food in the form of meat and dairy products, they do so with a substantial loss of caloric efficiency. If current crop production used for animal feed and other nonfood uses (including biofuels) were targeted for direct consumption, ~70% more calories would become available, potentially providing enough calories to meet the basic needs of an additional 4 billion people. The human-edible crop calories that do not end up in the food system are referred to as the ‘diet gap.’”

The argument isn’t as convincing as it might first appear.  Few people really want to eat the calories that directly come from corn or other common animal feeds like soybeans.  Unlike my hypothetical example, corn is not toxic to humans (although some of the grasses cows eat really are inedible to humans), but most people don’t want to field corn.    

So if we don’t want to directly eat the stuff, why do we grow so much corn and soy?  They are incredibly efficient producers of calories and protein.  Stated differently, these crops (or grasses if you will) allow us to produce an inexpensive, bountiful supply of calories in a form that is storable and easily transported. 

The assumption seems to either be that the “diet gap” will be solved by convincing people to eat the calories in corn and soy directly, or that there are other tasty crops that can be widely grown instead of corn and soy which can produce calories as efficiently as corn and soy.  Aside from maybe rice or wheat (which also require some processing to become edible), the second assumption is almost certainly false.  Looking at current consumption patterns, we should also be skeptical that large swaths of people will want to voluntarily consume substantial calories directly from corn or soy.

What we typically do is take our relatively un-tasty corn and soy, and plug them into our machine (the cow or pig or chicken, or in Post’s case the Petri dish) to get a form of food we want to eat.  Yes, it seems inefficient on the surface of it, but the key is to realize that the original calories from corn and soy were not in a form most humans find desirable.  As far as the human pallet is concerned, not all calories are created equal; we care a great deal about the form in which the calories are delivered to us.

The grass-machine analogy also helps make clear that it is probably a mistake to compare the calorie and carbon footprint of corn directly with the cow.  Only a small fraction of the world’s caloric consumption comes from directly consuming the raw corn or soybean seeds.  It takes energy to convert these seeds into an edible form – either through food processing or through animal feeding.  So, what we want to compare is beef with other processed foods.  Otherwise we’re comparing apples and oranges (or in this case, corn and beef).

 The more relevant question in this case is whether lab grown meat uses more or less corn, and creates more or less environmental problems, than does animal grown meat.  

Restaurant Performance Index

I was recently made aware of the so-called Restaurant Performance Index (RPI) put out by the National Restaurant Association (NRA).  According to their website: 

The RPI is based on the responses to the National Restaurant Association’s Restaurant Industry Tracking Survey, which is fielded monthly among more than 400 restaurant operators nationwide on a variety of indicators including sales, traffic, labor and capital expenditures. Restaurant operators interested in participating in the tracking survey

and

Index values above 100 indicate that key industry indicators are in a period of expansion, while index values below 100 represent a period of contraction for key industry indicators. The Index consists of two components – the Current Situation Index, which measures current trends in four industry indicators (same-store sales, traffic, labor and capital expenditures), and the Expectations Index, which measures restaurant operators’ six-month outlook for four industry indicators (same-store sales, employees, capital expenditures and business conditions).

I was a bit curious. How well do some of the variables I'm tracking in my Food Demand Survey (FooDS) follow the NRA's RPI?  By looking back at past releases, I was able pull together  monthly data on the overall RPI, the current situation index, and the expectations index, and I merged these with data from FooDS on average reported spending on food away from home and changes in anticipated spending on food away from home each month.  

First, the good news.  Spending on food away from home (as measured by FooDS) seems to track closely with the RPI-current situation index.  

It probably isn't too surprising that the two are positive correlated since two of the measures of the RPI-current situation index are same store sales and traffic volume; however, it is still comforting to see my FooDS data roughly track this measure from the NRI.  One benefit of FooDS is that we release the data in a more timely manner.  Right now, the latest figures available from the National Restaurant Association (NRA) are for March.  However, I already have a measure of April's away from home food expenditures from FooDS (it's $55.43).  A simple linear regression predicts that NRA's current situation index will be 102.3 for April.  We're already fielding May's FooDS right now, so I can make an even more up-to-date forecast in a few days.

Now, the not-so-good news.  In FooDS, we track consumers' stated intentions to increase or decrease spending on food away from home in the following weeks.  One "anomaly" present in the FooDS data is that, every month, people say they plan to spend less on food away from home next month.  Over the three years we've been doing FooDS, the measure ranges from a -2.4% cutback in spending to a -1.05% cutback in spending (average is -1.59%).  I interpret this as people feeling guilty about spending on food away from home, and perhaps even evidence of a self-control problem: people plan to cut back on eating out but rarely actually do.  In any event, despite this "bias", trends in this variable might still be useful as there are some months people plan to cut back more than others.  

Here's a plot of the planned spending change on food away from home from FooDS alongside the expectations portion of the NRA's Restaurant Performance Index:  

While the correlation between the two is positive, it is pretty weak.  The two track each other pretty closely through about mid 2014 and then start moving in opposite directions.  The two measures are getting at slightly different things.  One is measuring how restaurant owners/managers are planning to change capital expenditures, staffing, and what they think sales will be in the future; the other is a measure of how much consumers think they'll change spending.  Maybe all this says is that restaurant operators' expectations are not the same as restaurant consumers' expectations.  

So, here's a little test.  How well do restaurant owner's expectations this month correlate with their own "acutal" (or current performance index) the following month?  The correlation between the lagged NRA expectation index and the current period NRA current situation index is 0.35.  O.k., so there is some accuracy associated with the NRA's expectation index.

Now, let's do the same thing with the FooDS data.  How well do consumers' expected spending changes this month correlate with their own "acutal" food away from spending next month? The correlation between the two is 0.68.  So, despite the fact consumers' expectations are downwardly biased as discussed above, changes in their expectations seem quite predictive of next month's spending on food away from home.    

So, it seems consumers (in aggregate) know their own futures a bit better than restaurant operators know theirs.