More on soda taxes

A few days ago, the World Health Organization (WHO) came out with report, suggesting the use of food taxes and subsidies to encourage healthy eating.  They were particularly in favor of soda taxes.  Soda taxes seems to be picking up steam in the U.S..  After passage in Berkeley, they will now be on the ballot in several locales in coming weeks.  

I've written so much on these topics, it's hard to know what more to say.  So, I thought I'd just, for the record, tell you what I had to say on a recent Food Sommelier podcast when the host asked me about this topic (and revealed her support for soda taxes, which came about in part because she said she felt guilty for having worked for PepsiCo earlier in her career).  

I'm in episode 38 and the discussion on soda taxes starts at about the 20 minute mark.  Here is my lightly edited discussion on the issue. 

“I’m not a fan of soda taxes for a whole host of reasons . . . but let me first say, though, that it’s really not that big a deal.  And that’s probably one of the reasons I’m against it . . .  As much as I’ve written about it, you’d think I’d get my feathers ruffled a lot [over things like the Philadelphia soda tax], but I don’t have a dog in the fight really one way or the other.  It’s not a big deal in the sense that, number 1, it’s just not going to have much of an effect on obesity rates if you look at the best available research.  We are talking about taxes that will have very, very small effects on people’s weight, and there a lot of reasons for that.  

There are substitutes for sugar sweetened beverages.  People can switch to juices or even non-beverage alternatives that may have calories in them.  I’ve seen a number of studies that suggest that.  Just because we put a tax on something doesn’t mean people are not going to consume calories; they might instead switch to something else equally caloric.  . . . 

We have these intuitions . . . and little thumb rules like for every 3,500 kcal we cut out, we lose a pound.  The reality is that relationship is not linear at all.  It’s nonlinear . . .  When we’re thinking in a linear way, each calorie I cut out will cause a constant reduction in weight, but it doesn’t really happen that way.  There are diminishing returns.  You may lose a little bit initially but then it will really level off.  . . .

When you look at the burden of the tax, and this is really true of almost any food tax, its going to tend to be borne relatively (at least relative to income) more by people in the lower economic strata of society.  The reason for that is that if you look at the share of spending on food, it tends to go down as we make more money.  What that means is that poor people are spending a larger proportion of their income on food. So, anything we do to make food more expensive, that burden or that tax, is going to tend to fall more heavily on lower income populations. . . .

I’ve got a paper actually coming out . . . where we compare very low income to regular income consumers.  What we tended to find there is that is that, especially in the case of subsidizing healthy foods, the richer consumers benefit the most because they’re already, first of all, consuming relatively health foods.  And, because we found . . . that the poor tended to want to stick to their original diets. They were more habit prone, so they didn’t change quite as much either when it was a tax . . . or a subsidy trying to get them to eat something a little healthier.  

I would say lastly, I’m going to bring up this sort of elitism. . . . This sort of paternalism argument.  We feel like we know how other people should be eating.  . . . I think it’s really hard to put ourselves in the shoes of other people, and so for us to take a step back and say ‘you should be doing something different; you should be eating more like me’ presumes that we know what it’s like to have their life and have their kind of income and know all the other sorts of things they’re facing. . . .  I have a problem philosophically with that. . . .

And I do think it’s different than . . . cigarette taxes.  With cigarette taxes there really was this externality – the second hand smoke.  When you’re smoking, that really does have an effect on the people around you.  With the drinking of soda, it’s really less clear there’s that same kind of phenomenon at play.  Most of what’s happening here is some kind of redistribution within our healthcare system because of Medicare and Medicaid.  But, that’s much more complicated than most people realize.  Most of what’s happening here are subsidies flowing from relatively wealthy people to relatively poor people because relatively wealthy people pay more in taxes. . . . It’s not a popular solution but part of the argument is that if people know their health care expenses are going to be taken care of, they’re going to eat in an unhealthy way . . . Economists call that a moral hazard.  The answer for a moral hazard is that people need to have some skin in the game.  We need people to pay a little bit of the costs of their health care.  It doesn’t have to be the same for everybody, and maybe it’s just a small amount for people who don’t have much income, but I think if the concern is that if people are going to behave “irresponsibly”, there needs to be a bit of a price for that in terms of their health care costs.  But, of course, there is a real price for being obese.  That’s something we tend to forget – that there is a lot of social shame associated with being obese, wages can be lower especially for women, and there are all the attendant medical costs, some of which are shielded from the consumer because of our health care system but a lot of those are borne directly.  There is a real cost to being overweight and obese, and people bear a lot of that just through their own daily lives.  

I’m running on here, but one last point that is really important because I hear so many people get this wrong.  What they say is, ‘well our farm policy system subsidizes food and makes sugar cheaper.’  That is absolutely false.  I’m not a fan of farm subsidies, but that particular argument is false for two reasons.  One is that if you look at cane sugar.  Cane sugar has a set of really convoluted policies, but they essentially restrict supply. . . . What [sugar producers via policy] are able to do is keep out foreign competition and keep other producers from growing sugar cane.  . . . World sugar prices are much higher than they would be if we didn’t have our US cane sugar policies.  The other thing is high fructose corn syrup (HFCS).  Right now roughly 40% of our corn supply goes to ethanol.  Ten years ago, that was mostly going to livestock and food processing. . . .  It’s not exactly a farm policy, but the energy policies we’ve had over the past 10-20 years have dramatically shifted corn production from going to places like HFCS to instead ethanol production and our cars, and as a result has made HFCS more expensive than it would be otherwise.  

Should we tax sugar?  In some ways, we already do, it’s just not very transparent that we’re doing it."                                       

Food Demand Survey (FooDS) - October 2016

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

From the regular tracking portion of the survey, a few items stood out.  First, there was a notable rise both in consumers' awareness of GMOs in the news over the past couple weeks and in concern that GMOs pose a food safety risk.  Here is the graphic on awareness of GMOs in relation to the other issues tracked.  I'm not aware of any major news items driving the uptick in awareness and concern for GMOs, but perhaps I've missed something.

In addition to this issue, consumers' said they expect somewhat lower beef, chicken, and pork prices this month as compared to last, and planned purchases of beef, chicken, and pork all rose as well.  In fact, compared to one year ago, planed purchases of all three meat products are markedly higher.  You can read the whole report to see changes in willingness-to-pay, etc.

Several ad hoc questions were added to the survey this month. The questions focused on consumers’ purchases of and beliefs about seven “niche” or “emerging” food products, and for comparison purposes, one conventional product, beef.

Participants were initially asked: “Have you consumed the following foods at least once in the past five years?” The question was followed by a list of eight food items and respondents simply answered “yes” or “no”. 

Approximately 95% have eaten beef in the last five years. Less than 25% of participants have consumed either goat, rabbit, kombucha, or emu in the last five years. A little over a quarter of respondents said they had eaten bison. About one third of participants stated they have eaten chia seed or quinoa. Less than 10% of participants stated they had eaten emu in the last five years.

I had a little bet running with some of my graduate students on the popularity of these items (and truth be told, some of the suggested items came from them).  One student - who will remain nameless - predicted half the population had tried kombucha.  I'd never heard of it (it's a kind of fermented tea which allegedly has a variety of health benefits).  My guess was less than 10%.  I suppose we were both wrong, as the answer was  14.1%.   

The survey proceeded to ask about the perceived health (1=very unhealthy; 5=very healthy), tasty (1=very untasty; 5=very tasty), and affordability (1=very unaffordable; 5=very affordable) of each of the eight items.  The results are below.  

On average, people thought chia seed and quinoa were the most healthy followed by beef and bison.  All foods averaged above a three, meaning they were perceived as more healthy than not.

Beef dominated the other items in terms of perceived taste. About 88% of respondents said beef was either “very taste” or “somewhat tasty”.  By contrast for the next most tasty item, only 49% of respondents said the same about bison.  On average, beef was perceived as most tasty followed by bison and then rabbit.   Kombucha was the only item for which the mean score was less than three - meaning it was perceived as more untasty than tasty.

Finally was the question on affordability.  Somewhat surprisingly, beef was rated as most affordable on average followed by quinoa and chia seed.  Bison and emu were seen as least affordable, and the mean rating indicates both were viewed as more affordable than affordable.    

Polling 101

I teach a graduate level course every spring semester on survey and experiment methods in economics and the social sciences.  In this election season, I thought it might be worthwhile to share a few of the things I discuss in the course so that you might more intelligibly interpret some of survey research results being continuously reported in the newspapers and on the nightly news. 

You've been hiding under a rock if you haven't by now seen reports of polls on the likelihood of Trump or Clinton winning the presidential election.  Almost all these polls will report (often in small font) something like "the margin of error is plus or minus 3 percent".  

What does this mean?

In technical lingo it means the "sampling error" is +/- 3% with 95% confidence.  This is the error that comes about from the fact that the polling company doesn't survey every single voter in the U.S.  Because not every single voter is sampled, there will be some error, and this is the error you see reported alongside the polls.  Let's say the projected percent vote for Trump is 45% with a "margin of error" of 3%.  The interpretation would be that if we were to repeatedly sample potential voters, 95% of the time we would expect to find a voting percentage for Trump that is between 42% and 48%.

The thought experiment goes like this: imagine you had a large basket full of a million black and white balls.  You want to know the percentage of balls in the basket that are black.  How many balls would you have to pull out and inspect before you could be confident of the proportion of balls that are black?  We can construct many such baskets where we know the truth about the proportion of black balls and try different experiments to see how accurate we are in many repeated attempts where we, say, pull out 100, 1,000, or 10,000 balls.  The good news is that we don't have to manually do these experiments because statisticians have produced precise mathematical formulas that give us the answers we want.  

As it turns out, you need to sample about 1,000 to 1,500 people (the answer is 1,067 to be precise) out of the U.S. population to get a sampling error of 3%, and thus most polls use this sample size.  Why not a 1% sampling error you might ask?  Well, you'd need to survey almost 10,000 respondents to achieve a 1% sample error and the 10x increase in cost is probably not worth a measly two percentage point increase in accuracy. 

Here is a key point: the 3% "margin of error" you see reported on the nightly news is only one kind of error.  The true error rate is likely something much larger because there are many additional types of error besides just sampling error. However, these other types of errors are more difficult to quantify, and thus, are not reported.

For example, a prominent kind of error is "selection bias" or "non-response error" that comes about because the people who choose to answer the survey or poll may be systematically different than the people who choose not to answer the survey or poll.  Alas, response rates to surveys have been falling quite dramatically over time, even for "gold standard" government surveys (see this paper or listen to this podcast).  Curiously, those nightly news polls don't tell you the response rate, but my guess is that it is typically far less than 10% - meaning that less than 10% of the people they tried to contact actually told them whether they intend to vote for Trump or Clinton or someone else.  That means more than 90% of the people they contacted wouldn't talk to them.  Is there something special about the ~10% willing to talk to the pollsters that is different than the ~90% of non-respondents?  Probably.  Respondents are probably much more interested and passionate about their candidate and politics and general.  And yet, we - the consumer of polling information - are rarely told anything about this potential error.

One way pollsters try to partially "correct" for non-response error is through weighting.  To give a sense for how this works, consider a simple example.  Let's say I surveyed 1,000 Americans and asked whether they prefer vanilla or chocolate ice cream.  When I get my data back, I find that there are 650 males and 350 females.  Apparently males were more likely to take my survey.  Knowing that males might have different ice cream preferences than females, I know that my answer of the most popular ice cream flavor will likely be biased if I don't do something.  So, I can create a weight.  I know that the true proportion of the US population is roughly 50% male and 50% female (in actuality, there are slightly more females than males but lets put that to the side).  So, what I need to do is make the female respondents "count" more in the final answer than the males.  When we typically take an average, each person has a weight of one (we add up all the answers - implicitly multiplied by a weight of one - and divide by the total).  A simple correction in our ice cream example would be to make a females have a weight of 0.5/0.35=1.43 and males have a weight of 0.5/0.65=0.7.  Females will count more than one and males will count less.  And, I report a weighted average: add up all the female answers (and multiply by a weight of 1.43) and add to them all the male answers (multiplied by 0.7), and divide by the total.  

Problem solved right?  Hardly.  For one, gender is not a perfect predictor of ice cream preference.  And the reason someone chooses to respond to my survey almost certainly has something to do with more than gender.  Moreover, weights can only be constructed using variables for which we know the "truth" - or have census bureau data which reveals the characteristics of the whole population.  But, in the case of political polling, we aren't trying to match up with the universe of U.S. citizens but the universe of U.S. voters.  Determine the characteristics of voters is a major challenge that is in constant flux.  

I addition, when we create weights, we could end up with a few people having a disproportionate effect on the final outcome - dramatically increasing the possible error rate. Yesterday, the New York Times ran a fantastic story by Nate Cohn illustrating exactly how this can happen.  Here are the first few paragraphs:

“There is a 19-year-old black man in Illinois who has no idea of the role he is playing in this election.

He is sure he is going to vote for Donald J. Trump.

And he has been held up as proof by conservatives — including outlets like Breitbart News and The New York Post — that Mr. Trump is excelling among black voters. He has even played a modest role in shifting entire polling aggregates, like the Real Clear Politics average, toward Mr. Trump.

How? He’s a panelist on the U.S.C. Dornsife/Los Angeles Times Daybreak poll, which has emerged as the biggest polling outlier of the presidential campaign. Despite falling behind by double digits in some national surveys, Mr. Trump has generally led in the U.S.C./LAT poll. He held the lead for a full month until Wednesday, when Hillary Clinton took a nominal lead.

Our Trump-supporting friend in Illinois is a surprisingly big part of the reason. In some polls, he’s weighted as much as 30 times more than the average respondent, and as much as 300 times more than the least-weighted respondent.”

Here's a figure they produced showing how this sort of "extreme" weighting affects the polling result reported:

The problem here is that when one individual in the sample counts 30 times more than the typical respondent, the effective sample size is actually something much smaller than actual sample size, and the "margin of error" is something much higher than +/- 3%.

There are many additional types of biases and errors that can influence survey results (e.g., How was the survey question asked? Is there an interviewer bias? Is the sample drawn from a list of all likely voters?).   This doesn't make polling useless.  But, it does mean that one needs to be a savvy consumer of polling results.  It's also why it's often useful to look at aggregations across lots of polls or, my favorite, betting markets.

Land Use in the United States

In our departmental seminar on Friday, we had a speaker with a background in forestry.  He showed some graphs related to the amount of forest land in the United States, and I have to say I was a bit surprised how much land is in forest.  

Here is some useful (if not slightly dated) figures on land use from the USDA Economic Research Service. The figure from a longer document shows the breakdown:

Of all the land in the U.S., only 14.8% is in cropland used for crops (it's 17.7% in the contiguous 48 states).  27.1% is in grassland or pasture (32.3% in the 48 contiguous states).  About a quarter of the land (both in the US as a whole and in the lower 48) is in forest that is not grazed, and another 5.6 to 6.7% is in grazed forest land.   By the way: Special uses includes: "rural transportation, national/ State parks, wilderness and wildlife areas, national defense and industrial areas, and farmsteads and farm roads" and miscellaneous land includes "marshes, open swamps, bare rock areas, desert, and tundra."

Also from the 2007 report:

“Total cropland increased in the late 1940s, declined from 1949 to 1964, increased from 1964 to 1978, and decreased again from 1978 to 2007. Between 2002 and 2007, total cropland decreased by 34 million acres to its lowest level since this series began in 1945 . . .”

These are useful statistics in light of the common sorts of things I read like "agriculture has more impact on the environment than any other human activity" or "agriculture is the biggest threat to the environment."  

Zilberman on the Slow and Natural Food Movment

David Zilberman, an agricultural economist at UC Berkeley, has an interesting blog post on the slow and natural food movements. The timing of his piece is impeccable given the long, aggressive defense of the food movement Michael Pollan just wrote in the New York Times Magazine. After a bit of praise for the movements, Zilberman gets to some critiques.

Here are the core criticisms:

“However, most of these bodies of thought emphasize advocacy and are short on analysis. In particular, they underemphasize several factors. First, they underemphasize tradeoffs and costs. There are tradeoffs on the demand side, where consumers choose food based on cost, taste, and convenience. Fast food is a huge industry for a reason. The development of ready-to-cook and ready-to-eat meals, modern equipment (electric stoves, refrigerators, and microwaves), and modern supermarkets have been contributors in enabling women to join the job market. At the same time, there are tradeoffs on the supply side between cost of production and technology. ”

and

“Second, the naturalized paradigms undervalue the importance of technology in production and distribution. Modern lifestyle is the result of immense innovations in medicine, biology, communication, etc. I am very aware of the risks that technologies pose, but when I see a poor farmer in Ivory Coast with a cell phone and bicycle, I realize the power of technology. ... The challenge is how to use it appropriately and spread its distribution broadly rather than giving up on it.”

and

“Third, the naturalist paradigm underestimates the importance of heterogeneity among people and regions. Differences in income lead to different food choices. ... There is a huge difference between farmers in Iowa that obtain more than 10 tons/Hectare of corn and farmers in Africa that may obtain 1.5 tons/Hectare. ... I don’t expect people to use the same techniques everywhere, and that different technologies are appropriate in different locations. ”

On his last point, I full agree:

“Heterogeneity brings me to a larger point. There is a place for both industrial and naturalized agricultural systems. The naturalization paradigm is leading to the emergence of higher-end restaurants and fresh food supply linking the farmer to the consumer, each of which have limited reach but are important source of income and innovation in agriculture. At the same time, the majority of people will be dependent on industrialized agriculture. The two can coexist and coevolve. ”