Chief Pricing Officer

Meat Demand - The Big Picture

Every day billions of us make decisions on what we will buy, and how much of each item we will purchase.  Those decisions are tempered by how much money we have to spend, the relative prices of the goods and services available to us, and our individual preferences.  Our preferences are probably more determined by social norms and habit than we would like to admit, and do vary greatly by country.

Meat demand around the world is no different than any other good.  Depending on a wide variety of local conditions there are significant differences in meat diets at any point in time.  For example, in Australia, Brazil, Argentina and the U.S. we observe that beef has a much larger share of the diet than in Europe or China.  The major reason is that beef consuming countries need large areas of land suitable for grazing a beef cow herd.  China and Europe are crowded places, with limited land available for extensive grazing systems.  So, in Europe and China pork and poultry make up the vast majority of the meat diet.

When we look at total meat produced and consumed over time the spatial differences at a point in time tend not to matter.  What does matter though is total income and resulting total consumer buying power.  Funds available for consumer spending have driven almost all the growth in meat demand since at least 1961 (and probably before that if the data were available). 

The relationship between total consumer buying power and total meat production is one of the most remarkably consistent in all of agricultural economics.  The results of a simple regression between consumer spending and total meat consumption is shown below:

Goodness of fit:

Where:

Y = Total global meat production from FAO, FAOSTAT database, 1961-2007

Consumer Expenditures = Global final consumption expenditures, $2000, from the World Bank, World Development Indicators database, 1961-2007

Graphically, the relationship between the variables appears below.  The statistical fit is not quite as good in the 1960s as it was in later years, but the overall pattern is still quite consistent.

Several functional forms, including logarithms and exponential, were tried. The simple linear form had the best statistical fit and least amount of autocorrelation. 

At the data means the elasticity of production to a change in expenditures is 0.91.  That is, a 1% increase in expenditures results in a 0.91% increase in meat production (the elasticity from a natural log functional form was also about 0.90).  The coefficient for expenditures indicates that a $1 trillion increase in $2000 expenditures results in about a 9 million ton increase in total global meat production.

Not included as a separate variable, but also of critical importance, is global population.  From 1961 to 2007 population more than doubled.  All else equal, that factor alone should have doubled meat demand.  Looking forward, the rate of population increase is expected to slowly decline over the next 40 years.  Slower population growth will slow the rate of increase in global total real income and consumer spending.  Thus we should expect some slowing in the rate of increase in meat demand and production.

The equation tells us several things about 1961-2007.  There is a strong and consistent pattern of globally increasing real incomes that drove real consumption spending, and that drove meat demand, and then production.  The relationship has been consistent enough to tell us that the preference for spending for meat has been strongly ingrained for the almost 50 years of available data.  Despite some news about increasing societal preferences for avoiding meat consumption there has been no measurable change in global meat spending behavior.

Another pattern seen around the world is that as economies reach higher levels of real income and spending the demand for meat tends to respond less to increases in real income.  Market saturation may have an effect on future responses to increases in real income on a global scale, but there is no sign of any weakness in that response through 2007.  Outside of the richest countries of the world there are still well over 5 billion middle and low income people who would very likely increase meat consumption if they had more income.  Population growth in low income countries also tends to be higher than the global average.

It is implied by the equation that future gains in the volume of meat produced in the world will depend almost entirely on demand increases driven by increased incomes and spending.  However, one significant item that has not been statistically significant, meat prices, could derail this relationship, at least temporarily.

In fact, the astute reader should have already asked: “But what about meat prices, don’t they matter too?”  The answer is yes, they do.  But from 1961 to 2007 prices on a global scale did not vary enough to disturb what consumers do with their meat spending habits. 

Looking forward from 2009 we are seeing, on a global scale, real costs of meat production coming from grains and oilseeds markets that appear to have quickly moved to new price plateaus.  Those higher costs are in the process of being passed through as higher real retail meat prices.  Will those price increases be enough to cause a permanent change in consumer behavior?  History would argue that we might not see a significant effect.

We saw this same thing happen in the 1961-2007 data used in the regression.  From 1972 to 1976 there was a similar step-up in real feed costs and real meat prices.  If you look closely at the chart 1973 global meat production did actually fall below the long term trend, but then recovered in 1974-1975.  Increases in real income overcame real price increases, and the long term trend resumed.

Prices do matter in other important ways.  The relative prices of meats have had an important effect on the global market shares of the major meats.  Beef, generally the most expensive meat to produce, has seen its global share fall.  Chicken meat, the least expensive of the major meats, has seen major global share increases since 1961. 

Prices also matter enormously on the scale of short time periods and individual country and species markets.  Here in the U.S. were are currently seeing a price-cost squeeze coupled with a decline in real consumer spending that is leading a significant reduction in total meat production.  However, if history and the regression results above mean anything at all, when the global and U.S. economy begins its recovery meat demand will resume its long term growth path.

Market-driven price signals are also essential in determining decisions all the way down to the level of how producers process and market individual meat cuts.  At an a very low level of granularity price signals can determine, for example, whether chicken leg quarters are deboned, exported, or sold in the fresh market.  These decisions are critical to producer profitability and to supplying consumers with the optimal mix of the many products that can potentially be made from a live animal of any species.

Finally, freely moving, market-driven, prices are a major driving force behind the long term growth of global meat production and consumption.  Prices are the signaling mechanism that, at a very low level in time and space, efficiently tell producers what to produce and consumers what to buy.  Market prices are the mechanism though which we can approach an efficient and optimal product mix that simultaneously avoids waste, gives consumers the products they want, and allows producers to earn a profit.  Paradoxically, at a very low level, prices are almost all that matter.

pricing and margin in disassembly businesses

Back in March, the Chief Pricing Officer wrote about the concept of a pricing waterfall, which was introduced by McKinsey in “The Price Advantage”.  Price waterfall is an important method to determine the pocket price and margin for many businesses, but for disassembly businesses the challenges in calculating margin take a different form. 

For disassembly businesses, such as meat processing or lumber, the challenge (which is caused by the nature of the business) is that the organization needs to buy the supply as a whole entity (e.g. animals to process; trees to cut, etc) and then disassemble that entity into sellable parts (e.g. beef primal cut or ground meat for a beef processor; 2×4x12 or 4×4x12 for a lumber company).

Beef Production

Lumber Production

Part of what makes this even more complicated is the fact that not all the products that are produced have the same value.  For example, in the beef business, consumers will not pay the same price per pound for ground beef as they will for New York Strip steaks. Each cut has its value to the consumer and determining the right prices requires in-depth industry knowledge as well as the appropriate toolset.

The lumber industry, encounters similar challenges when pricing the different grades; the price of wood chips is not the same as a 2×4 plank.

So why is this such a critical challenge?  Well, at the end of the day, these companies need to ensure that the revenue gained from selling all the finished goods covers their expenses, of which, the raw material often comprises the largest portion.

These challenges are the reason that understanding the historical margin at the supply unit level (e.g. live cattle or trees) is crucial to a healthy business.  So what does this have to do with pricing? 

In the meat processing business, the overall revenue from a carcass is referred to as the cutout. This represents the total revenue obtained by virtually reassembling the carcass considering each cut’s yield and the price achieved in the marketplace.  Said another way, the cutout calculation takes into consideration the disassembly bill-of-material (BOM) as well as the revenue for the different cuts or pieces. When comparing this overall revenue to the corresponding costs (typically the cost of the whole supply unit – live animal; trees – is adjusted to consider the actual yield of the finished goods), you get a good idea of the gross margin achieved. Historical prices support the calculation of a historical cutout and gross margin picture, enabling disassemblers to understand how their business performed in the past.

Even more important however is being able to look forward, by applying the cutout calculation logic to projected costs and selling prices – allowing disassemblers to view forward looking margin.  This is critical to making effective business decisions.

To better illustrate the complexity, the following illustrates how carcasses are broken-down into different cuts based on the countries:

Reassemble Beef Carcasses to Calculate Revenue

The lumber industry has a similar challenge as trees are broken down into different finished goods:

Reassemble Trees to Calculate Revenue

And this is what is common between those businesses and the used part dealer – the dealer needs to make sure that when the revenue from the different parts is added up for the tires, windows, body, etc, this revenue exceeds the cost of buying the car and the labor used to disassemble it and market the parts.

Managing prices and margin in a disassembly business is different from those portrayed in the previous price waterfall discussion, however mechanisms like cutout help disassemblers better understand historical performance and take proactive, corrective actions going forward just as the margin waterfall helps manufacturers in traditional assembly businesses.

Professor Clive Granger, winner of 2003 Nobel Prize in economics, passed away on May 27, 2009. Few will argue that he revolutionized the field of economic time series forecasting. Professor Granger was particularly interested in prices and applied his theoretical ideas of causality and co-integration to financial stock market price time series.

He questioned bad econometric practices when he saw them….

“Before Dr. Granger’s studies, it was common practice for economists to take methods intended for stationary time series and use them to analyze nonstationary ones. But Dr. Granger — working closely with a colleague at the University of California at San Diego, Robert F. Engle — demonstrated that this approach could produce erroneous results” (http://www.nytimes.com/2009/05/31/business/31granger.html).

…and determined theoretically-sound concepts to overcome the underlying challenges.

“For want of better techniques, economists often applied statistics designed for stationary data to non-stationary data. But in 1974, Granger and his post-doctoral student Paul Newbold, building on the earlier work of the British statistician G Udny Yule, showed that pairs of non-stationary time series could frequently display highly significant correlations when there was no causal connection between them. For example, the US federal debt and the number of deaths due to Aids between 1981 and 2000 are highly correlated but are clearly not causally connected. Such “nonsense correlations” called into question the meaningfulness of many econometric studies.” (http://www.telegraph.co.uk/news/obituaries/finance-obituaries/5407598/Professor-Sir-Clive-Granger.html).

Professor Granger was awarded Nobel Prize in 2003 for his foundational work in the area of co-integration.

“His innovation has completely changed the way that economists estimate and build dynamic models of the macro economy,” Torsten Persson, an economist at the University of Stockholm who served on the Nobel Prize Committee for Economic Sciences, said at a ceremony honoring Dr. Granger in 2003. “Nowadays co-integration methods are literally used everywhere — by academically minded researchers in universities, as well as more practically minded investigators, be it in central banks or the private sector” (http://www.nytimes.com/2009/05/31/business/31granger.html).

As practitioners in the field of pricing, we owe a lot to Professor Granger and his lifetime of dedicated work.

SignalDemand customer, Hormel, was featured in The New York Times yesterday. In fact, the article was the 4th most emailed story on the NYTimes.com site. According to food reporter Andrew Martin, Hormel is working overtime to supply the nation with what many think of as the most economical of all proteins: Spam.

Though Martin doesn’t name prices or get into marketing strategy in the article, the gist is that, in times of economic belt-tightening, consumers are reaching for the foods they think of first when it comes to penny-pinching. In fact, the Times refers to Spam as “the most emblematic hard-times food in the American pantry.” Here’s a little more from the article, just for fun:

“Spam holds a special place in America’s culinary history, both as a source of humor and of cheap protein during hard times.

Invented duing the Great Depression by Jay Hormel, the son of the company’s founder, Spam is a combination of ham, pork, sugar, salt, water, potato starch and a ‘hint’ of sodium nitrite ‘to help Spam keep its gorgeous pink color,’ according to Hormel’s Web site for the product.

Because it is vacuum-sealed in a can and does not require refrigeration, Spam can last for years. Hormel says ‘it’s like meat with a pause button.’”

Though Hormel President Gary Ray wasn’t quoted in the New York Times story, he shared candid and upbeat “lessons learned” in pricing strategy with other major manufacturers at SIGNALS last month in Las Vegas.

So, we talk a lot here about food prices and what they mean to manufacturers and the folks at the start of the supply chain. But what about the folks at the other end, like you and me? As most people know first hand, food prices can be one of the biggest drains on families’ budgets – the bigger the family, the bigger the drain, and the lower a family’s income, the bigger bite food takes out of the monthly budget.

The United Nations’ Food and Agriculture Organization estimated that international food prices are up more than 60 percent since 2006, including a staggering climb during the first three months of 2008. Beyond the obvious bad news, other economic dangers stem from high food prices – a reverse “trickle-down” effect. Food and housing are a family’s two basic costs, expenses that cannot be forgone or significantly trimmed back – everyone needs to eat and needs a roof over their head. As those costs increase, consumers stop discretionary spending: restaurant visits slow down, people stop buying big ticket items, and generally stop spending money. Those at the bottom of the economic ladder also need more support, providing further strain.

Food prices are all encompassing, affecting broad swaths of the public and creating depressed conditions at the very base of the economy – consumers. Combine this with extreme pressure from the financial markets, and the country’s economy is squeezed from almost every angle.

This is a very simple illustration of why food producers need to intelligently determine prices. Smart prices throughout the supply chain benefit everyone in the long run.