Escalating health care costs . . . for pets

I was intrigued by this article by Stanford and MIT professors, Liran Einav, Amy Finkelstein, and Atul Gupta in the most recent issue of the American Economic Review (ungated version here).  We are all aware that Americans spend a lot on health care, but there seems to be a lot of disagreement as to why.  Some say it's too much government regulation.  Others say too little.  These authors point out, however, that spending on pet health care roughly mirrors that on humans despite the fact that this is a largely unregulated industry. 

The authors write:

“The fact that despite these differences—often mentioned as potential explanations for the large and rapidly growing health-care sector in the United States—some pet health-care patterns appear qualitatively quite similar to the analogous human health-care pattern, strikes us as noteworthy. It should give us pause before attributing the large and rising health-care costs in the United States solely to the prevalence of insurance and government involvement. The similar growth patterns in US human and pet health care may also suggest that technological change in human health care may have spillover effects on related sectors, including perhaps pet health care or human care in other countries.”

A couple figures from the paper.

Food Demand Survey (FooDS) - May 2017

The results from the May 2017 edition of the Food Demand Survey (FooDS) are now in.

Some observations from the regular tracking portion of the survey:

• Willingness-to-pay for the "premium" cuts from each meat species (steak, chicken breast, and pork chop) all increased this month compared to last; exactly the opposite was true for the the lesser-valued cuts (ground beef, wings, and deli ham).  Willingness-to-pay for non-meat items declined significantly.
• Awareness and concern for a list of 17 items all fell this month compared to last. Concern for antibiotic use rose to the top three behind E Coli and Salmonella.
• Compared to last month, consumers increased expenditures on food at home but reduced expenditures on food away from home.  
• Fewer people declared vegetarian status or indicated suffering from a food borne illness this month than has been the case for more than a year.

Several new ad hoc questions were added to the survey this month.  

The first set of questions was added in response to some queries by Ranjith Ramanathan who is a meat scientist at Oklahoma State. He was interested in some issues related to how consumers buy and cook ground beef.  To focus in on ground beef eaters, we first asked: “Do you eat ground beef patties (i.e., hamburgers)?” About 88% of the participants answered “yes”.  Those who answered yes were then asked several questions related to cooking and buying ground beef patties. 

Ground beef eaters were asked: “How do you determine the doneness of ground beef patties when cooking hamburger?”  Choice options were: A) By using a meat thermometer, B) By visual observation (i.e., looking at the color of meat in the center of the patty), C) By cooking a certain length of time, or D) Other ways.  

Approximately two-thirds of the participants who said they eat ground beef patties, stated they determine doneness by visual observation.  Next most common, selected by about 18% of respondents, was determining doneness by length of cooking time.  Only about 13.5% said they used a meat thermometer to determine doneness.   

The next question asked: “What is your preference for the cooked internal color of ground beef patties?”  Response options were: Red, Pink, Brown, or Another color.

The majority of participants, about 69%, stated they prefer the internal color of ground beef patties to be brown.  About 26% of participants stated pink as their preferred cooked internal color. Only 5% of participants stated they would want a red center in their ground beef patties.  Less than 1% stated they would want another color. 

Participants were then asked: “To what internal temperature (degrees Fahrenheit) does the USDA recommend cooking ground beef patties?”  Respondents could answer on a slider scale that ranged from 100 to 200 in one degree increments.

The average temperature stated by participants was 162 degrees Fahrenheit (the median was 161).  The figure is remarkably close to the actual USDA recommendation of 160F.  Nonetheless, a large share of participants were incorrect in their assessment.   Thirty one percent stated a temperature less than 160 and 54.5% stated a temperature higher than 160.  Even providing a five-degree margin of error, 28% stated a temperature less than 155 and 37% stated a temperature greater than 165.  Thus, 28%+37%=65% of respondents gave an answer that was at least 5 degrees higher or lower than the USDA recommendation.  Below is a histogram showing the distribution of responses.

Next, participants were asked: “How is the ground beef you normally buy packaged?”  Response categories included text and photos of six different packaging options including: vacuum sealed, in a box as frozen patties, in butcher wrapped paper, as a chub, film wrapped, and in a tray.

Of those who eat ground beef, about one third stated they buy packaged ground beef in a tray.  Ground beef in a film wrapped packaged was selected by about 28% of participants.  About 18.7% of respondents stated they buy ground beef packaged as a chub.  8.5% said they normally buy ground beef in a box as frozen patties.  Only 5.6% of participants said they normally purchase ground beef in a vacuum sealed package. 

Finally, as I was grading final projects from one of my classes, I noticed one team, comprised of Ph.D. students Bernadette Chimai and Pedro Machado, asked an interesting question on a survey they'd posed to students.  I modified it an included it on FooDs.  Here is a screenshot of the question asked:

Participants most frequently stated that free range chickens were the most efficient (i.e., used the least amount of feed to produce a pound of meat) followed by grass fed cattle and grain fed chickens.  However, response patterns were not necessarily symmetric.  Thirty percent of participants believed feedlot cattle were least efficient (i.e., used the most feed to produce a pound of meat) followed by 25%, who indicated grass fed cattle as most inefficient.  About an equal number of respondents thought free range pigs were both most and least efficient.  
 

To help summarize the results, I calculated the difference in the percent of respondents who viewed an animal and production system as most efficient and subtracted it from the percent who viewed it as least efficient.  Overall, free range and grain fed chickens were ranked highest in perceived efficienciency followed by free range pigs.  Grain fed pigs and feedlot cattle were perceived as least efficient.  

Productivity and Sustainability

Last week, I noted the possibility of using measures of farm productivity and efficiency as a means of measuring sustainability.  It seems I wasn't the only one thinking along these lines.  In the newest issue of the journal Applied Economic Perspectives an Policy, three professors at Wageningen University in the Netherlands, Daniel Gaitan-Cremaschi, Miranda Meuwissen, and Alfons Lansink, offer a more fully developed proposal along the same lines.  Here's the abstract from the paper.  

“Sustainable agricultural commodities should be favored in international trade negotiations to meet the growing demand for food in a context of environmental conservation, population growth, and globalization. There is a need for a metric that allows for the differentiation of traded agricultural commodities according to how sustainably they were produced. In this context, this paper develops two single metrics, based on a Total Factor Productivity indexing approach, for benchmarking products in terms of their sustainability performance. Both metrics are adjusted to internalize the social and environmental externalities of food production, and to account for the sustainability effects of stages along agri-food supply chains. Key aspects such as data availability, the selection of variables, and the selection of sustainability standards and targets are discussed.”

Measuring Sustainability

This article in Beef Magazine by Bryan Weech notes the growing interest in beef sustainability but he also emphasizes the challenge for producers in knowing what sustainability means or how to measure it.  He writes:

“Often these events either focus on comparing the sustainability of beef industry of 1970 versus today or discuss the developing definition of sustainable beef, which seems to be coalescing on beef that is environmentally friendly, socially responsible, and economically viable.

That is a broad sentence that nicely sets the boundaries of sustainability. However, it leaves most of us wondering what it means from a management perspective and how exactly is environmentally friendly, socially responsible, and economically viable beef accomplished from a day-to-day, on-the-ground management perspective?”

He then provides a nice table listing 14 different approaches advocated by different parties to promote sustainability including approaches such as "reduced consumption/vegan", organic, and grass fed.  The only approach he lists as having a potential meaningful effect on sustainability is a Verified or Certified Sustainable program.  However, few details were mentioned as to what exactly such a program would entail.  

I'll throw an idea out here.  I've been arguing for the past several years that productivity growth is an often forgotten cornerstone of sustainability.  Enhanced productivity means being able to produce more (or the same amount of) beef using fewer resources.  That sounds like a pretty important part of being sustainable.  The challenge is, however, that the way productivity growth is often measured is in aggregate terms over long periods of time, such as the comparison to the 1970s that Weech mentioned above.  It is also the sort of comparison I used in this New York Times piece.  But, to put such measures of productivity into practice (or to form a basis of a certification program), one would have to measure the productivity and efficiency of individual farms over time and as compared to each other.

Fortunately, economists have a well established set of tools to conduct precisely these sorts of calculations.   This suite of methods go by a variety of names such as technical efficiency analysis, frontier analysis, data envelopment analysis, and more.  The basic idea is take data on a set of farms and figure out which ones are on the "frontier" of the production function or cost function.  That is, for a given level of inputs, which farm produces the highest level of output?  Once the farms along this frontier are determined, then one can measure the distance of every other farm from the frontier to establish measures of relative efficiency.  It is also possible to use multiple farms over time to see whether the frontier is shifting over time or to measure how far a farm was from the frontier in, say, 1980 compared to, say, 2017.  This sort of approach has been widely used to compare relative efficiencies of organic and conventional dairy farms, Guatemalan corn farmers,  hog farms in Hawaii, US agricultural cooperatives, and much much more.  There have been so many studies conducted on these topics that there are even meta analyses attempting to quantitatively summarize the vast literature and identify which types of farms are more or less likely to be efficient.  

Just to give an example, consider this 1997 study by Allen Featherstone, Michael Langemeier, and Mohammad Iset that compared 192 Kansas cow-calf operations in the year 1992.  Here is one of their main findings:

“Technical efficiency ranged from 0.37 to 1, with an average measure of 0.78 (table 2). Thus, the output of the farms potentially could be increased by roughly 22% if each farm were purely technical y efficient (i.e., if each farm operated on the production frontier). Forty-nine out of the 195 farms were technically efficient.”

So, there were 195-49 = 146 cow calf operations that were not as sustainable as they could be in the sense that they could have produced more output for the particular amount of resources (or inputs) they were using as compared to other farms in the sample.  

This sort of analysis also allows one to easily visualize how farms compare to each other in terms of efficiency.  Here is a figure from the paper which shows scale efficiency of all 192 operations (each farm is represented as an asterisk in the figure).  The solid dark line is the frontier showing operations with the lowest costs for a given amount of revenue.  

The authors write:

“The results generally indicate that a greater proportion of overall inefficiency was due to farms producing above the cost frontier than to farms being of an inefficient scale. The individual analyses of the farms showed that 62 farms were operating in the region of increasing returns to scale, one farm was producing at constant returns to scale, and 132 farms were operating in the region of decreasing returns to scale.”

I'm not claiming that this sort of efficiency analysis is a perfect measure of sustainability or that it is capable of capturing all the dimensions of sustainability consumers might find important.  However, it strikes me a productive way to help move forward the debate on how one might attempt to start quantifying an important aspect of sustainable production.

What if we forced food to be more local?

I recently ran across this paper in Food Policy published back in 2011 by Charles Nicholson, Miguel Gomez, and Oliver Gao.  The paper asks an interesting question: what would happen if we required food (or in this case, milk in particular) to be more local?   This is a policy proposal that has been seriously put forth by prominent food writers.  

The authors took data on current location of milk production, processing plants, and consumers and created a mathematical model to minimize the cost of supplying various dairy products to consumers.  Here's their description of the spatial dimensions of the data:

“The model uses 231 multiple-county milk supply regions, each represented with a single centrally-located point. Dairy processing plant locations are specified based on observed plant locations observed in 2005, and vary in number from 319 possible locations for fluid plants (Fig. 3) to 11 for milk protein concentrate products. Demand locations are represented as a single point for 424 major population centers and aggregations of multiple-county regions”

Given this set-up, what is the effect of cost of reducing the number of miles traveled - or the weighted average source distance (WASD) - by 10% or 20%?  

The authors find that (in the month of May), requiring a 10% or 20% reduction in WASD would increase total costs by about $1 million and $18 million per month (0.1% and 1.7% cost increases), respectively.  All this is a way of saying that milk production and dairy processing is located in particular regions for a reason, and forcing a different spatial configuration will increase costs.  The authors write:

“These relatively small reductions in overall costs contrast with more marked shifts in the allocation of costs within the supply chain. In each case, the costs for assembling milk from farms to plants decreases, as it is optimal to ship milk shorter distances to processing facilities. Costs for interplant shipments increase by about the same magnitude of the increase in total costs. The largest increase in costs occurs in product distribution; increases in distribution costs range from 2% to 25%–6 to 24 times as large as the overall increase in costs.”

In other words: the effects are complicated and impose much larger costs on some portions of the supply chain than others.  In terms of the impacts on consumers:

“The increases in the value of a gallon of milk due to reduced WASD vary from less than $0.50 (which is often more than 10% of the retail price) in the western US to more than $4.00 per gallon in the southeastern US, but the average for all demand locations is $1.66.”

Another interesting result is that even though WASD is reduced over all by 10% or 20%, some dairy products, such as cheese, end up having to be transported even further.  

The authors consider another interesting scenario in which people just want to reduce the distance traveled by fluid milk by 10%.  In this case, total costs increase a whopping 12%, and the WASD for all products actually increases by 98 miles (a 31% increase in distance traveled). This remarkable result shows the unintended result of, for example, local schools requiring their milk be purchased locally without considering what happens to the yogurt, butter, cheese, and nonfat dry milk that will also be consumed by someone.  

The authors conclude as follows:

“The primary conclusion is that developing a cost effective strategy to localize a multi-product supply chain is complex. Such complexity accrues to the multiple links that exists in a multi-product supply chain including the relationships across supply chain segments, the dependency of the various products, the diversity in supply and demand across geographic regions, and the seasonality of the production process. Therefore, decision makers should adopt a systems approach to anticipate the consequences of industry wide or public policy initiatives to increase localization in the food industry.”