Food Spending

A colleague sent me a couple articles highlighting recent trends in spending on food at home and away from home.  Here's Ashley Lutz at Business Insider from back in July:  

“Restaurant sales growth has stalled in America.

McDonald’s, which had previously executed a huge turnaround, saw a slowdown in the most recent quarter. Other chains ranging from Chipotle to Buffalo Wild Wings also reported sales declines. ”

And, here's a story from a couple days ago by Bob Bryan also at Business Insider:

“Fast-food chains have watched their sales growth drop off a cliff.

Executives advance plenty of reasons for the slowdown — or, in the case of some brands, the outright decline in sales. Some blame politics, others blame the oil crash, and so on.

The real reason may be a bit simpler: It’s getting cheaper for Americans to eat at home.

The price of food at grocery stores has actually been on the decline since the end of 2015, based on the Consumer Price Index for food at home. In fact, in July (the most recent data available), the cost of food at home declined 1.55% from the same month a year ago.

On the other hand, the cost of food away from home — what you pay at restaurants — is still on the rise. In July, prices for food away from home rose 2.79% from same month last year.”

I thought I'd track down the numbers myself and see what's going on.  Here is the CPI data from the BLS, confirming the decline in prices of food at home and the increase in prices of food away from home.

The difference in trends looks even more dramatic if plotted in relative terms.  Here is the CPI for food at home divided by the CPI for food away from home.  Clearly, food at home has become relatively cheaper (compared to food away from home) over the past year.  

If the price of food at home is falling compared to the price of food away from home, does that that mean consumers are spending less on food at home or away from home?  Not necessarily.  Lower prices for food at home will induce consumers to buy more food at home.  Whether they ultimately spend more or less on food at home depends on the magnitude of the own-price elasticity of demand for food at home, and the cross-price elasticities of demand for food at home and away from home (I've previously estimated these parameters as discussed here).  

My data suggests these relative price changes have had a more complicated impact on food spending than suggested by the Business Insider articles.  Here is data from my monthly Food Demand Survey (FooDS) on estimated weekly food spending.  Interestingly, it appears there is a bit of an upward trend in spending on food away from home (spending on food away from home has increased 22% since January 2016), but by contrast spending on food at home has remained relatively more stable (spending is only 0.8% higher than in January, although it is about 5% higher than it was in June).  Thus, the increase in the relative price of food away from home hasn't led to a big drop off in spending on food away from home.  

As a consequence of the above trends, the share of food spending directed toward food away from home has risen from about 0.34 at the first of the year to about 0.38 today, as shown in the graph below.

All this might seem a bit counter-intuitive.  Prices on food at home fall, and yet people spend a smaller share of their food dollar on food at home.  Why?  The answer is because: spending = price paid * quantity purchased, and the two variables on the right hand side of the equation tend to work in opposite directions of each other.  When prices are lower, people tend to buy more quantity.  Whether the effect of changing prices has a bigger effect on spending than the effect of changing quantities, again, depends on the underlying demand elasticities.    

Real World Demand Curves

On a recent flight, I listened to the latest Freakonomics podcast in which Stephen Dubner interviewed the University of Chicago economist Steven Levitt about some of his latest research.  The podcast is mainly about how Levitt creatively estimated demand for Uber and then used the demand estimates to calculate the benefits we consumers derive from the new ride sharing service.  

Levitt made some pretty strong statements at the beginning of the podcast that I just couldn't let slide.  He said the following:

“And I looked around, and I realized that nobody ever had really actually estimated a demand curve. Obviously, we know what they are. We know how to put them on a board, but I literally could not find a good example where we could put it in a box in our textbook to say, “This is what a demand curve really looks like in the real world,” because someone went out and found it.”

As someone whose spent the better part of his professional career estimating consumer demand curves, I was a bit surprised to hear Levitt claim "nobody ever had really estimated a demand curve."  He also said, "we completely and totally understand what a demand curve is, but we’ve never seen one."  The implication seems to be that Levitt is the first economist to produce a real world estimate of a demand curve.  That's sheer baloney.  

The most recent Nobel prize winner in economics, Angus Deaton, is perhaps most well known for his work on estimating consumer demand curves.

In fact, agricultural economists were among the first people to estimate real world demand curves (see this historical account I coauthored a few years ago).  Here is a screenshot of a figure out of a paper by Schultz in the Journal of Farm Economics in 1924 who estimated demand for beef.  Yes - in 1924!  I'm pretty sure that figure was hand drawn!

Or, here's Working in a paper in the Quarterly Journal of Economics in 1925 estimating demand for potatoes.

Two years later in 1927, Working's brother was perhaps the first to discuss "endogeneity" in demand (how do we know we're observing a demand curve and not a supply curve?), an insight that had a big influence on future empirical work.

Fast forward to today and there are literally thousands of studies that have estimated consumer demand curves.  The USDA ERS even has a database which, in their words,  "contains a collection of demand elasticities-expenditure, income, own price, and cross price-for a range of commodities and food products for over 100 countries."   

Here is a figure from one of my papers, where the demand curve is cleanly identified because we experimentally varied prices.  

And, of course, I've been doing a survey every month for over three years where we estimate demand curves for various food items.

In summary, I haven't the slightest idea what Levitt is talking about.  

Food Insecurity is Down

The USDA just released their annual accounting of food security in the United States.  Good news!  Food insecurity fell to 12.7% in 2015 (down from 14.9% in 2011).  Here's a key graph from the report.

One could quibble with the USDA's method of computing food security (it is based on  responses to a variety of survey questions), but whatever "flaws" are inherent in the USDA methods, as long as they have remained constant over time, the trends should be informative.  

Of interest is how food insecurity measures change with participation in SNAP (aka "food stamps).  Using USDA data on SNAP participation, I calculated per-capita participation which is shown in the following graph.  Though the pattern is somewhat similar (i.e., food insecurity and SNAP participation both rose after the Great Recession and then declined in 2015), it isn't a perfect corollary.  In particular, food insecurity is higher in 2015 than in was in 1995, but today there are more participants per capita on SNAP than there were in 1995.  

Another variable which might relate to food insecurity and SNAP participation is the price of food.  Here is a graph of Bureau of Labor Statistics data showing the price (or CPI) of food relative to the price (or CPI) of non-food items from 1995 to 2015.  

Over at the US Food Policy blog, Parke Wilde notes that even though food insecurity has fallen, it hasn't fallen nearly enough to keep up with food insecurity targets.  The above graphs suggest one potential reason why: food is relatively more expensive today than was the case 20 years ago.  Of course, the overall story is surely much more complicated than that.  

Optimal fat tax

In the Washington Post article Catherine Rampell raises an important point with regard to the emerging debate over whether to tax soda.  

“Instead of arbitrarily singling out one category of bad foodstuff for taxation — and the categories of bad foodstuffs will always be somewhat arbitrary — a more effective route to reducing consumption of excessive sugar or calories might be a universal, graduated sugar or calorie tax.

But even that still doesn’t quite seem fair or, for that matter, efficient. After all, a calorie tax would also hit people who consume more calories because they are very active, such as marathoners. Besides being regressive, a tax on calories or sugar would also effectively, if unintentionally, make it more expensive for trim people to exercise.

In other words, a lot of inputs go into determining whether a person is obese. Taxing some of those inputs distorts the relative prices of those inputs, but it doesn’t necessarily change the desired output: obesity rates.

Which raises the question: Why not just target the output, rather than some random subset of inputs? We could tax obesity if we wanted to. Or if we want to seem less punitive, we could award tax credits to obese people who lose weight. A tax directly pegged to reduced obesity would certainly be a much more efficient way to achieve the stated policy goal of reducing obesity.”

Yet, people don't seem to like the idea of a fat-person tax.  Why not?

“Maybe it’s because they’re regressive (but so are soda taxes). Maybe it’s because it sounds like we’re shaming fat people (but arguably so does any policy aimed at reducing obesity). Maybe it just feels unfair to tax people based in any way on their genes, which, like diet and exercise, can also be a determinant of weight.

But if we assume it’s impossible for obese people to lose weight by any combination of inputs they do have control over, it’s hard to simultaneously argue that making one of those inputs more expensive could lead to some nationwide weight-loss miracle. Pop goes the pop-tax rationale.”

Cost Effectiveness of Soda Taxes

In a piece for Cato, Christopher Snowden discusses the effectiveness (or lack thereof) of soda taxes that seem to be gaining traction worldwide.  Snowden's views closely mirror my own.  I like the way he framed the relative effectiveness of soda taxes in this passage:

“Whilst the benefit remains forever on the horizon, the cost can be easily calculated; it is simply the amount of money squeezed from consumers by the tax. In New Zealand, for example, advocates claim that a 20 per cent tax on soda would save 67 lives per year and raise $40 million (NZ).[12] Leaving aside the reliability of the New Zealand forecast, this works out as a cost of $600,000 (NZ) for every life that is extended and does not represent good value for money.

Political action on public health grounds is often justified by the costs of unhealthy lifestyles to the healthcare system, and therefore to the taxpayer. The economic costs of obesity are often misrepresented and fail to account for savings to taxpayers, but even if they were more reliable it is far from obvious that additional taxes would relieve the economic burden.[13] For example, the UK’s Children’s Food Campaign recently claimed that a 20 per cent tax on sugary drinks would reduce healthcare costs in London by £39 million over twenty years, but their own figures suggest that the tax itself will relieve Londoners of £2.6 billion over the same period.[14] The cost of the tax will therefore exceed the savings by several orders of magnitude.”

By the way, if you want to see which (out of more than 100) action will produce the biggest bank for your buck, check out the work of the Copenhagen Consensus, which routinely conducts cost-benefit analysis on a whole set of issues.  See their list for the most cost-effective actions.