obesity and low-carb diets in the united states: a herd behavior model

Obesity and Low-Carb Diets in The United States:A Herd Behavior Model

Dragan Miljkovic1

Department of Agribusiness and Applied Economics, North Dakota StateUniversity, Fargo, ND 58105–5636. E-Mail: [email protected]

Daniel MostadConAgra Trade Group, Omaha, NE 68102. E-mail:[email protected]

ABSTRACT

We propose that consumer herding is a plausible explanation of the popularity of low-carb diets inthe United States+ This proposition was empirically tested using per capita consumption of bothbroilers and eggs as proxies of the popularity of low-carb diets+ Results confirm that people do notalways make ~perfectly! rational choices, even when a good signal or correct information is avail-able to them+ Instead, they choose to do what everyone else is doing+ In addition, we could notconclusively determine that an increase in media reports about low-carb diets led to further increasein the popularity of low-carb diets+ @JEL: D12, D82, Q18# + © 2007 Wiley Periodicals, Inc+

1. INTRODUCTION

According to the Centers for Disease Control ~CDC! Behavioral Risk Survey ~2003!,61% of adults in the United States were overweight or obese+ Obesity is measured com-monly by the body mass index ~BMI!, which is weight in kilograms divided by height inmeters squared+ The convention is that overweight people have a BMI above 25, whileobese people have a BMI above 30+ Thirteen percent of children aged 6 to 11 years and14% of adolescents aged 12 to 19 years were overweight in 1999+ This prevalence hasnearly tripled for adolescents in the past 2 decades+ The increases in being overweightand obese cut across all ages, racial and ethnic groups, and both genders+ Three hundredthousand deaths each year in the United States are associated with obesity+ Being over-weight and obese is associated with heart disease, certain types of cancer, type 2 diabetes,stroke, arthritis, breathing problems, and psychological disorders, such as depression+

Considering the fact that the majority of Americans ~61%! today are either overweightor obese, it comes as no surprise that many of them are on a weight-reducing diet programof some sort+ Cutler, Glaeser, and Shapiro ~2003! argue that people are willing to spendlarge amounts of money to try to lose weight+ They present survey evidence that desiredBMI rises much more slowly than actual BMI, indicating that most overweight peoplewould like to weigh less than what they do+ If their finding is correct, there are two waysto accomplish the goal of losing weight+ Considering the basic relationship of calories in

1Corresponding author+

Agribusiness, Vol. 23 (3) 421–434 (2007) © 2007 Wiley Periodicals, Inc.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/agr.20131

421

versus calories out, people get heavier if they consume more calories or expend fewercalories+ But many people are unwilling or unable to make the sacrifice of eating lessand0or exercising more, and the actual question they are asking is: How can I lose weightwithout eating less and0or exercising more? This is the point where many dietary “experts”come into place with proposed diets, often based on questionable scientific studies,whichwill supposedly resolve the problem of obese and overweight people+ The solution theyoften propose is to change the diet+ The same foods have often been “healthy” at one timeand “unhealthy” at a different time+ While the early 1990s brought a low-fat, low-cholesterol bonanza, the New Diet Revolution of Dr+ Robert Atkins has certainly been themost popular diet in the United States during the last 5– 6 years+ This diet is one of theso-called low-carb diets+ More than 17% of Americans have been on the Atkins or someother low-carb diet in 2004 ~www+acnielsen+com!+ The low-carb diet limits the intake ofcarbohydrates ~primarily grains and vegetables! while promoting the increase in con-sumption of other foods ~primarily meat and dairy!+

The theory of herd behavior is advanced here as a possible explanation for the rise inpopularity of low-carb diets in the United States+ Herd behavior is defined as decisionmaking that is influenced by what others around us are doing+ Using simple language,herding is when everyone is doing what everyone else is doing+ This kind of behavior isobserved in various social and economic situations including, among others, voters’ behav-ior being influenced by opinion pools to vote in the direction that the poll predicts willwin ~e+g+, Cukierman, 1991!; fertility decision making, including deciding whether to usecontraception or how many children to have ~Cotts Watkins, 1990!; or investors’ behaviorin financial markets ~e+g+, Bulow & Klemperer, 1994; Devenow & Welch, 1996; Jain &Gupta, 1987; Veldkamp, 2004!+

The objective of this article is to develop and use a model of consumer herd behaviorto explain the popularity of the low-carb diets in the United States+ In addition, the effectof media frenzy and low-carb food prices on the popularity of the low-carb diets is esti-mated in empirical analysis as well+

The article is organized as follows+ The Economics of Obesity and Low-Carb Dietssection contains the information on the economic importance of the obesity problem inthe United States and the nature of low-carb diets that became a popular tool in fightingthe obesity epidemics+ In the Model section, an economic model of consumer herdingbehavior in the low-carb diet market is developed+ Empirical testing of the herding behav-ior hypothesis is conducted in the Method, Data, and Results section+ Finally, the Impli-cations and Conclusions section contains implications and conclusions+

2. ECONOMICS OF OBESITY AND LOW-CARB DIETS

The economic cost of obesity in the United States was about $117 billion in 2000 alone~e+g+, Anderson, Butcher, & Levine, 2003; Cutler et al+, 2003; Lakadawalla & Philipson,2002!+ In 2000, the direct cost of obesity-related disease was estimated at $61 billion,while indirect costs were estimated at $56 billion+ Direct costs, for instance, are health-care costs associated with physician visits and hospitalizations+ Indirect costs are the valueof lost wages by those who cannot work due to sickness or disability and foregone earn-ings due to premature death+

Overweight and obese people receive lower wages than those without weight prob-lems+ This may be because obesity-related illness reduces productivity or because of

422 MILJKOVIC AND MOSTAD

Agribusiness DOI 10.1002/agr

employer discrimination ~Averett & Korenman, 1996; Cawley, 2000!+ Next, Cutler et al+~2003! argue that there might be “internalities,” the costs borne by individuals themselvesbecause of their higher weights+ These internalities exist in the presence of self-control oraddiction problems: People would like to eat less than they do, but they have difficultylimiting their consumption+ They are similar to externalities because they result fromindividuals consuming food and not internalizing the impact on their future happiness+

In spite of this well-documented cost that society incurred due to the epidemics ofoverweightness and obesity, very little has been done in the domain of public health ~andpublic policy in general! in order to deal with this problem+ There is no consensus amongthe public that would explain why that is the case+ One can always assume lack of rec-ognition, ignorance, or inertia among policymakers as possible causes for the epidemicsof obesity+ Nestle ~2003! presented ample evidence that “food politics” is the commondenominator that actually explains the lack of public policy action in the arenas of publichealth and the food industry+ She considers effective lobbying by the multi-billion dollarfood industry the main reason for the lack of action in public health arena+ That leavesindividuals affected by obesity and overweightness to deal with the problem on theirown+ Low-carb diets are the latest most popular resort for them+

Low-carb diets have been around for many years+ There are reports that the first low-carb diet was implemented in 1864 ~Nestle, 2003!+ They have recently gained popularity+Dr+ Robert Atkins was the first to commercialize a low-carb diet+ He also was the first tocriticize the USDA’s Food Pyramid for containing 6–11 servings of carbohydrates+ Dr+Atkins’ diet plan was first marketed in 1972, but it took off in popularity in 1992 when hereleased his booked entitled, Dr. Atkins New Diet Revolution+

There are many different types of low-carb diets+ Some of the most successful low-carb commercial diets are the Atkins Diet, Somerizing, Protein Power Diet, CarbohydrateAddict’s Diet, and the Zone+ The list of low-carb diets is long, but the one thing that issimilar in most of these diets is the increased level of protein and decreased level ofcarbohydrates+ These proteins come from many different places+ Red meat, poultry, pork,fish, nuts, and eggs are a few of the sources of protein+ Many of these diets also includethe consumption of healthy oils such as fish and olive oils+ Carbohydrates come frommany sources: grains, potatoes, bananas, and sugars, to name the few+

There are benefits and drawbacks to low-carb diets, just like any other diet+ These prosand cons have been studied heavily since the popularity of the low-carb diet started toincrease+ However, there are no long-term studies that show how these diets will affectthe human body+ The benefits and drawbacks presented are only from short-term studies+These studies have all taken place in less than one-year’s time+ The major benefit of thesediets is that they limit the number of foods one can eat and therefore limit the food intake~Willett, 2004!+ The reduction in carbohydrates reduces the carbohydrate load insulinresponse+ Finally, the low-carb diet,when used properly and when weight loss does occur,reduces resistance to insulin+

There are also a few drawbacks to the low-carb diet+ The first is that it is a hard diet tomaintain and, therefore, it is a poor long-term weight control diet+Also, the diet limits thenumber of macronutrients, dietary fiber, and phyto-chemicals that participants would intakedue to the lack of certain foods+ The diet is not good for those who are athletically activebecause of the lack of whole foods that the body needs when under a lot of high activity~Eisenstein, Roberts, Dalla, & Saltzman, 2002!+ A big concern for dieticians about thediet is the lack of calcium intake due to a lack of dairy products+ This has a negative effecton the health of human bones+

OBESITY AND LOW-CARB DIETS 423


Many nutritionists and dieticians have been against low-carb diets from the start+ Thereasons for this are usually based on conventional wisdom that the best diets for heartdisease are diets low in fat and high in starch ~found in foods with high carbohydrates!+This has been somewhat disproved, as diets high in fat have not been shown to prove thiswisdom+ There are studies that prove that fat intake cannot predict risk of cancer, fatintake does not predict coronary heart disease, and the intake of some fatty acids is veryimportant to a person’s health+ The low-carb diet developed by Dr+Atkins works well, butnutritionists do not think an unlimited amount of meat and butter is good for their over-weight patients ~Willett, 2004!+

In a one-year study, low-carb diets have been compared to conventional diets to seehow each performs on a similar group of participants ~Stern et al+, 2004!+ This resulted inseverely obese persons losing a greater amount of weight and having improvements inother health measures on the low-carb diets in the first six months+ After the year wascomplete, the weight loss between the two groups was comparable, but the effects oncertain health measures such as triglyceride level, HDL cholesterol level, and glycemiccontrol favored the low-carbohydrate diet+ This was a short-term study, so a long-termcomparison is needed to show the true effectiveness of low-carb diets compared to con-ventional diet plans+ The resulting higher weight loss from the low-carb diets was alsothought to be a result of the lower caloric intake of this diet+

In spite of the uncertainty surrounding the effectiveness and healthiness of low-carbdiets, they became and remain a popular way of dealing with extra weight+ The lack of ascientific consensus and public policy action regarding this issue only further encouragedpeople in choosing some easier and more convenient solutions, such as adopting the low-carb diets as opposed to radically changing their life style+ In the absence of other obviousor acceptable options, a large portion of the population affected by obesity and over-weight may have chosen to follow what others are doing+

3. MODEL

The model is based on Banerjee ~1992!+A sequential decision model is analyzed in whicheach overweight or obese individual or decision maker looks at the decisions made byprevious ~obese and overweight! decision makers in making her own decision about los-ing weight+ This is rational because these previous decision makers may have some infor-mation that is important for the current decision maker+ However, it is further shown thatthe decision rules chosen by the optimizing individuals are characterized by herd behav-ior+ In other words, people will be doing what others are doing rather than using their owninformation even though the resulting equilibrium is inefficient+

There is a population of ~overweight! agents of size N+ Each of them maximizes theidentical utility function defined on the space of weight loss due to various diet programs+The set of diets is indexed by numbers in @0,1# + Thus, the ith diet is d~i !+ The weight lossdue to the ith diet to the nth person starting that diet is w~i !� R+Assume there is a uniquei * such that w~i ! � 0 for all i � i * and w~i * ! � w, where w . 0+ This is basically theassumption that the weight loss the people experience due to being on one ~specific! dietis strictly greater than the weight loss from being on any other diet+ Given this assump-tion, everyone would want to be on diet i * + The problem is that no one knows which onediet it is+ Furthermore, uniform priors are assumed which means that there is not even alikely candidate for i * + However, some people have an idea of which diet it might be+More formally, there is a probability a that each person receives a signal telling her that



the true i * is i ' + The signal may or may not be true+2 The probability that it is false is 1—b+If the signal is false, we assume that it is uniformly distributed on @0,1# and, therefore, itdoes not give any information about what i * really is+

The decision making in this model is sequential+ This means that one person chosen atrandom makes her decision first+ She cannot decide to delay her decision+ The next per-son who is chosen at random as well makes her decision next+ She is allowed to observethe choice made by the previous person and potentially benefit from the information con-tained in the previous person’s choice+ Notice that second person does not know whetherthe person before her actually got a signal+ The rest of the game proceeds in the same way+Each new decision maker makes her decision on the basis of the history of the past deci-sions and her own signal if she has one+ After everyone has made their choice, all thealternatives that have been chosen are tested; and, if any of these turn out to work, thosewho have chosen it are rewarded ~by losing their weight!+ If no one has chosen an optionthat works, the truth about the best or most effective diet remains undiscovered+

Several assumptions are made in order to make the model functional+ First, the struc-ture of the game and Bayesian ~ex post! rationality are common knowledge+ Each person’sstrategy is a decision rule that tells us for each possible history what that person willchoose+ In these strategies, we are looking for Bayesian Nash equilibrium, i+e+, the bestchoice-response for every next decision maker given the choice of the previous decisionmaker~s!+ The nature of the equilibrium will depend on some tie-breaking assumptionsthat are introduced here+ The assumptions are as follows: ~a! whenever a decision makerhas no signal and everyone else has chosen diet i � 0, she always chooses i � 0, i.e+, sheremains on the current diet which by default does not lead to any weight loss; ~b! whendecision makers are indifferent between following their own signal and following some-one else’s choice, they always follow their own signal; ~c! when a decision maker isindifferent between following more than one of the previous decision makers, she alwayschooses to follow the one who chose the diet i that has the lowest cost requirements interms of both money and time+ Each of these assumptions is also made with the idea ofminimizing the possibility of herding+

The unique Nash equilibrium decision rule that everyone will adopt, under the set ofassumptions previously mentioned, is as follows:

1+ The first decision maker follows her signal if she has one and chooses i �0 otherwise+2+ If the second decision maker has no signal, then she will imitate the first decision

maker and choose the same diet+A more complex situation arises when the seconddecision maker has a signal but the first person has not chosen diet i � 0+ Sheknows that the first decision maker had a signal and this signal is as likely to beright as her own signal+ She is, therefore, indifferent between following the firstdecision maker’s signal and following her own signal+ In this situation, our assump-tion ~b! becomes relevant, i+e+, the second person will, by invoking this assumption,follow her own signal+

3+ For kth decision maker when k . 2, when she has no signal:a!a! Choose diet i � 0 if everyone else has chosen i � 0+b! Choose the diet that has the lowest cost requirements in terms of both money

and time if one person has chosen all options already chosen+

2The signal, for instance, may be in the context of this problem, a newspaper article, or other media infor-mation about the popularity or potential effectiveness of a certain diet+



c! If only one diet other than the current diet i � 0 has been chosen by more thanone person, choose that diet+

d! If two diets other than current diet i � 0 have been chosen by more than oneperson, choose the diet with lower cost and time requirements+

4+ For kth decision maker when k � 2, when she has a signal ik:a! Choose diet i � ik if some other person chose i � ik +b! Choose diet i � ik if no one else has chosen i � ik and no diet other than the

current diet i � 0 has been chosen by more than one person+c! If no other person has chosen diet i � ik but one diet other than i � 0 has been

chosen by more than one person, then choose that diet+d! If no other person has chosen diet i � ik but two diets other than i � 0 have been

chosen by more than one person, choose the diet with lower cost and timerequirements+

The uniqueness of the decision rule comes from the fact that each person’s weight lossw~i ! is completely independent of the choices made by everyone coming after her in thedecision process+ Thus, there are no strategic elements in the game+ One can solve thisgame by moving forward in the game tree+ The uniqueness of the solution is then auto-matically guaranteed+3

The consequences of the above-mentioned decision rule are as follows+ The equilib-rium decision rule in this model is characterized by extensive herding+ In other words,people decide to abandon their own signals and follow others even when they are not sureabout the other person being correct! The first person always follows her own signal ifshe has one, and so does the second person+ However, we cannot guarantee that even thethird person will follow her own signal+ If the first person chooses a diet i Þ 0 and thesecond person follows her, the third person will always follow them+ Moreover, all sub-sequent decision makers will also choose the same diet+ Herding can also happen wheneach of first k people chooses a different diet+After k different options have been chosen,if the next decision maker does not have the signal, she will choose the lowest cost0timediet from the diets already chosen+ Following this, all subsequent individuals will choosethe same diet unless one of their signals matches one of the options already chosen+ Thiscan happen, however, only if the correct option has already been chosen+ In other words,there will be herding at an incorrect option ~diet!, unless the first decision maker to havea signal, or someone coming after her but before the first subsequent decision makerwithout a signal, made the choice+

4. METHOD, DATA, AND RESULTS

In the absence of consistent and long enough time series on the actual number of indi-viduals on low-carb diets,4 the consumption per capita of typical low-carb diet foodsseems to be the best alternative available to proxy the popularity of the diet+ The rational

3The proof for this decision rule is the proof for Proposition 1 in Banerjee ~1992!+ Both notation and contextof the model are changed+ Also, assumption ~c! is modified to suit the problem addressed here+ However, nosubstantial difference in modeling terms exists+

4There are only a few ~5– 6! surveys done by A+C+ Nielsen and Gallup that provide evidence about theprevalence of low-carb diets in the United States+ Data from these surveys are insufficient and inadequate foruse in a scientific study+ Thus, using proxies in the analysis was inevitable+



herding model described in the previous section implies that current consumption of typ-ical low-carb foods is the function of increased consumption of these same foods in pre-vious time periods+ It is reasonable to assume that some other variables also may be playinga role in explaining the popularity of low-carb foods+ Two possible candidates seem to bethe price of low-carb foods and the frequency of media reports on low-carb diets+ Veld-kamp ~2004! suggested how media frenzies may further exacerbate herding behavior+While media frenzies themselves may be modeled as herding behavior within their indus-try, it is reasonable to assume that they may induce or exacerbate the consumption oflow-carb foods+

The nature of the herding model suggests that the vector autoregression ~VAR! wouldbe appropriate methodology to use in testing the theory empirically+5 VAR is commonlyused for estimating or forecasting systems of interrelated time series+ The VAR approachsidesteps the need for structural modeling by treating every endogenous variable in thesystem as a function of the lagged values of all of the endogenous variables in the system+6

The mathematical representation of a VAR is

yt � A1 yt�1 � {{{� Ap yt-p � Bxt � «t ~1!

where yt is a k vector of endogenous variables, xt is a d vector of exogenous variables,A1, + + + ,Ap and B are matrices of coefficients to be estimated, and «t is a vector of inno-vations that may be contemporaneously correlated but are uncorrelated with their ownlagged values and uncorrelated with all the right-hand side variables+ Because only laggedvalues of endogenous variables appear on the right-hand side of the equations, simulta-neity is not an issue and OLS yields consistent estimates+ Moreover, even though theinnovations «t may be contemporaneously correlated, OLS is efficient and equivalent toGLS since all equations have identical regressors ~Enders, 1995; Hamilton, 1994!+

The VAR approach is appropriate if the time series under consideration is stationary+However, analysis in levels is inappropriate with cointegrated, nonstationary series+ TheVector Error Correction ~VEC! model is appropriate in this case because it has cointe-gration relations built into the specification, and so it restricts the long-run behavior ofthe endogenous variables to converge to their cointegrating relationships while allowing

5One of the reviewers suggested how the inertia theory would yield the same reduced-form specification+Wedo not share his or her view for the following reason+ Inertia is a repetitive buying process of the same brand toavoid making a decision+ The consumer is passively involved in the buying process with little information andthen evaluates after the purchase+ The learning theory for inertia is described by classical conditioning in whichthe consumer’s need,which results from a stimulus-and-response function, is generated through repetitive adver-tising+Hoyer & Brown ~1990! posited, in their classical study, that subjects indicated inertia by simply selectingthe brand they knew best, even if it was low in quality+ This would lead to a different pattern from one describedin our herd behavior model because people would continue consuming what they have been consuming ratherthan adopting what others have been consuming+

6Estimating demand for eggs and broilers is traditional approach used in agricultural economics to describeconsumer behavior ~e+g+, Eales & Unneveher, 1988;United States Department of Agriculture Economic ResearchService, 1997–2005!+ Such approach imposes more rigid structure and involves a number of socio-economicand demographic variables+ However, demand analysis is a product of perfect rationality and the theory ofrational choice+ If one is to go back to the basics of how demand is derived from preferences and choices, analtogether different behavioral ~perfect rationality! model is underlying demand analysis+ It has been recog-nized for a long time now that the rational choice model, albeit still frequently used in economics and especiallyin agricultural economics, describes the actual consumer behavior very poorly ~e+g+, Kahneman & Tversky,1979; Simon, 1982; Tversky & Kahneman, 1992!+



for short-run adjustment dynamics+ The cointegration term is called the error correctionterm because the deviation from long-run equilibrium is corrected gradually through aseries of partial short-run adjustments+ We present here a simple case of a two variable~ y1 and y2! system with one cointegrating equation and lagged difference terms+

Dy1, t � a1,0 � a1~ y2, t�1 � by1, t�1!� Sa1,1~i !Dy1, t�i � Sa1,2~i !Dy2, t�i � «1, t ~2!

Dy2, t � a2,0 � a2~ y2, t�1 � by1, t�1!� Sa2,1~i !Dy1, t�i � Sa2,2~i !Dy2, t�i � «2, t + ~3!

Again, «1, t , «2, t , and all terms involving Dy1, t�i and Dy2, t�I are stationary+ Thus, thelinear combination of two variables ~ y2, t�1 � by1, t�1! must also be stationary+ In thissimple model, the only right-hand side variable is the error correction term+ In long-runequilibrium, this term is zero+ However, if y1 and y2 deviate from the long-run equilib-rium, the error correction term will be nonzero and each variable adjusts to partially restorethe equilibrium relation+ Finally, the coefficient ai measures the speed of adjustment ofthe i-th endogenous variable towards the equilibrium+

In empirical testing of the herd behavior nature of the popularity of low-carb diets inthe United States, the representative foods used were broilers and eggs+7 Monthly datacover the time period from January 1997 to December 2004+ Consumption per capita ofbroilers and eggs and per unit prices are obtained from the Economic Research Service ofthe US Department of Agriculture ~ERS-USDA! and the United States Census Bureau~http:00www+census+gov0popest0national0NA-EST2004-01+html!+ The frequency of arti-cles published in newspapers or popular magazines related to low-carb diets are used toanalyze the effect of media on the popularity of these diets+ The assumption here is thatthe newspapers and magazines will report and inform about the low-carb diets only if thatrepresents news defined as current information and happenings or new information aboutspecific and timely events ~Merriam-Webster’s Collegiate Dictionary, 2004!+ NewsBank,Inc+ is the source of this information ~http:00nl+newsbank+com!, which is considered to bethe world’s largest news archive+ Approximately 600 major newspapers and magazinespublished in the United States were searched for articles and features related to low-carbdiets published between January 1997 and December 2004+ Only articles with referenceto low-carb diets in its title or the first paragraph have been accounted for in the analysis+

4.2 Broilers Consumption Results

Augmented Dickey-Fuller test ~Dickey & Fuller, 1979! is used in order to test if the timeseries under consideration ~i+e+, per capita broilers consumption, price, and monthly fre-quency of articles published! is stationary or not+ The null hypothesis is one of nonsta-tionarity or the variable having a unit root+We were able to reject the null hypothesis for

7The reason for red meats ~beef and pork!, which are also considered fairly typical low-carb diet foods, to beomitted from the analysis is that their consumption is driven to a great extent by the supply of livestock andhogs+ It is known that long cycles in the livestock industry prohibit instantaneous or even quick supply response~Marsh, 2003!+Moreover, most new diets are perceived to by “trendy” or short-lived phenomena and, as such,the popularity of low-carb diets may not be a sufficient incentive to livestock farmers to make major capitalinvestment in enlarging their herds+ Hence, consumption of broilers and eggs is deemed to proxy more accu-rately the popularity of low-carb diets+



all three variables when tested at a 1% significance level+ Thus, each variable is I~0!+Notice that in all three cases, exogenous variables were constant and linear trend+ Theunit root test results indicated that the VAR analysis would be appropriate in this case+

The VAR was then run allowing for up to 12 lags+ Based on both Akaike ~AIC! andSchwarz ~SC! model selection criteria, one lag was selected as the model with optimallag structure+ Some additional residual tests were conducted as well+ Lagrange multiplier~LM! tests for the presence of autocorrelation up to the 12th order and Jarque-Bera testsof normality both indicate that the specification with one lag is satisfactory+ The nullhypotheses of no serial correlation at lag order 1–12 and the residuals being multivariatenormal, respectively, could not be rejected at a 5% significance level+ Estimated coeffi-cients are presented in Table 1+

We are primarily interested in the broiler equation+ The dependent variable is currentper capita consumption of broilers, while the explanatory variables are the lagged con-sumption per capita, lagged price, and lagged number of articles published+ The resultsvery strongly support the hypothesis of herding behavior:An increase in consumption ofbroilers by 10% in the previous month leads to an increase in current broiler consumptionby 3%+ This result is statistically significant at a a� 0+01 significance level+ The articlesvariable is also statistically significant but the coefficient is very small+ An increase incoverage of low-carb diets in newspapers and magazines by 10% in the previous periodincreases the broiler consumption by only 0+1%+ Price plays no role in consumers’ deci-sions to increase the consumption of broilers+ Indeed, some of the record-high broilerprices observed in 2004 did not seem to deter consumers from their pursuit of healthylow-carb chicken meat+

While media effect on consumption of broilers is statistically significant but small,media frenzy seems to be the reason behind newspapers’ decisions to write about low-carb diets+ This is yet another example of herding behavior, only this time by the media+In the media-articles equation, the only variable that explains newspapers’ decisions towrite about low-carb diets is the fact that everyone else is writing about it! An increase incoverage of low-carb diets in newspapers and magazines by 10% in the previous periodresults in an increase of articles published in the current period by 8+6%+

TABLE 1+ The VAR Model—Broiler Consumption

Equation:

Explanatory variables Broiler Price Media-articles

Broiler ~�1! 0+304* 6+045** 1+456~3+011! ~2+193! ~1+039!

Price ~�1! 0+002 �0+034 �0+019~0+444! ~�0+325! ~�0+358!

Articles ~�1! 0+013* 0+055 0+863*~3+139! ~0+509! ~15+575!

Constant 4+829* 114+726* �6+269~5+513! ~4+794! ~�0+515!

R2 0+31 0+08 0+79Adj+ R2 0+28 0+06 0+78

Note+ t-test values are in parentheses+* and ** denote statistical significance at 1% and 5%, respectively+



4.2 Eggs Consumption Results

Once again, augmented Dickey-Fuller test ~Dickey & Fuller, 1979! is used in order to testif the time series under consideration ~i+e+, per capita eggs consumption, price, and monthlyfrequency of articles published! are stationary or not+ The null hypothesis is one of non-stationarity or the variable having a unit root+We were unable to reject the null hypoth-esis for price and per capita egg consumption at the levels at both the 1% and 5%significance levels+ However, the first differences of these variables are stationary, i+e+,they are I~1!+ As reported previously, monthly frequency of articles published is I~0!+ Inall three cases, exogenous variables were constant and linear trend+

After establishing that two out of three time series under consideration are I~1!, wecould pursue the cointegration analysis+ The multivariate cointegration test ~Johansen,1991, 1995! was carried out with one lag in differences ~two lags in levels!+ Based on theresults of both trace statistics and maximum eigenvalue statistics, we can conclude thatthe three variables are cointegrated with p-values being below 0+01 considering one cointe-grating vector and below 0+05 considering two cointegrating vectors+

Given the presence of unit roots and variables being cointegrated, the appropriate methodto estimate the herding behavior model would be the VEC procedure+ But before analyz-ing the short-run parameters from the VEC model, we test whether any of the variablesare influenced by other variables in the long run+ This amounts to a test of each of thevariables for weak exogeneity and can be tested on the speed of adjustment coefficients~Johansen & Juselius, 1994!+With two cointegrating vectors, the null hypothesis of weakexogeneity is H0 : ai1 � ai2 � 0, for all i , where i is, respectively, eggs consumption,price, and articles+ The tests are AQ14: format change okay? changed numbers to lettersbecause they are not procedures or steps and also not to confuse them with the numberedlist that follows+Also corrected to reflect this change where cited assumption distributedas chi-squared with 2 degrees of freedom and are reported in Table 2+ The null hypothesesof weak exogeneity are clearly rejected for eggs ~per capita egg consumption! and arti-cles published, while we cannot reject the null hypothesis that egg prices are weaklyexogenous+ Hence, it seems that, in the long run, the price of eggs determines the con-sumption of eggs, as one would expect+

According to the classic article by Hall ~1994!, using only the model selection criteriato choose the optimal lag structure may not be the most appropriate way to proceed inVEC analysis due to the presence of the long-run adjustment parameters from the cointe-gration analysis+ He suggested that a reasonable starting point be the maximum numberof lags based on economic theory, prior expectations, or common sense+ One, then, maydecrease gradually the number of lags by simultaneously looking into the model selectioncriteria and maintaining the original rationale ~i+e+, economic theory, prior expectations,

TABLE 2+ Weak Exogeneity Tests

Potentially exogenous test

Variable Statistic p-value

Eggs ~consumption per capita! 46+159* 0+000Price 3+624 0+163Articles 30+071* 0+000

*indicates significance at a 1% level+



or common sense! until the most satisfactory model is selected+ Following this procedure,we started out with a lag length of 12 in all equations to account for full calendar year+However, lags 9–12 were insignificant both separately and jointly and were thereforedeleted, and the model with 8 lags was selected+Again, the same additional residual testswere conducted as well+ Lagrange multiplier ~LM! tests for the presence of autocorrela-tion and Jarque-Bera tests of normality both indicate that the specification with eight lagsis satisfactory+ Estimated coefficients from the VEC model are presented in Table 3+

We are primarily interested in the eggs equation+ The dependent variable is current percapita consumption of eggs, while the explanatory variables are the lagged consumptionper capita, lagged price, and lagged number of articles published+ The results very stronglysupport the hypothesis of herding behavior:An increase in consumption of eggs in 6 outof 8 previous months led to an increase in current egg consumption+ These results arestatistically significant for some lags at a� 0+05 and for others at the a� 0+1 significancelevel+ The articles variable is significant at only one lag and has the negative sign+ Basedon this result, one cannot conclude that media reporting induced an increase in eggs con-sumption+ Positive and statistically significant coefficients on price indicate that an increas-ing price of eggs did not seem to lower ~as one may have expected! the consumption ofeggs+Also, in the media-articles equation, the results do not indicate the presence of mediafrenzy unlike in the broiler analysis+ Finally, low values of speed of adjustment coeffi-cients indicate minimum adjustment taking place within the first month for both egg priceand per capita consumption+

5. IMPLICATIONS AND CONCLUSIONS

The results of this study are interesting for several reasons+ First, herding seems to explainrather well the popularity of low-carb diets in the United States+ This was empiricallyproven using per capita consumption of both broilers and eggs as a proxy for the popu-larity of low-carb diets+ This result implies that people do not make ~perfectly! rationalchoices, even when a good signal or correct information is available to them; instead,they choose to do what everyone else is doing+

Secondly, a typical consumer choice or demand study involves consumers who reactto price, i+e+, lower price implies an increase in consumption+ Considering the short run,our findings are completely the opposite:A positive relationship between egg prices andconsumption is statistically significant, while no statistically significant relationship wasdetermined between price and consumption of broilers+ It seems that producers wererational and were able to quickly adjust-increase production to accommodate this changein food preferences among consumers and capture the premium associated with it+ Itneeds to be repeated that the nature of the production process for both broilers and eggsallowed for this quick adjustment, unlike in the long-cycle characterized cattle and hogindustries that may have benefited from high prices but could not make the quick pro-duction adjustment+ In the long run however, based on weak exogeneity test results,price does have a significant impact on consumption of eggs, i+e+, popularity of low-carb diets, as one might expect+

The relationship between the popularity of low-carb diets and media coverage of thelow-carb diets is also an interesting one+ Based on our results, it seems that increasingmedia reports about low-carb diets are a product of two factors: ~a! the trend in actual~observed! dietary behavior of the American population that is worthy of being news, and~b! media frenzy or reporting the information everyone else is reporting+ This second



TABLE 3+ The VEC Model—Egg Consumption

Equation:

Explanatory variables Eggs Price Media-articles

Eggs ~�1! 0+905*** 3+768 3+756~1+907! ~0+828! ~0+638!

Eggs ~�2! 0+789*** 3+570 3+941~1+864! ~0+879! ~0+739!

Eggs ~�3! 0+917** 6+072*** 2+738~2+447! ~1+691! ~0+580!

Eggs ~�4! 0+894** 3+980 2+364~2+480! ~1+152! ~0+520!

Eggs ~�5! 0+885* 1+934 2+307~2+702! ~0+616! ~0+559!

Eggs ~�6! 0+469 �2+035 2+338~1+632! ~�0+738! ~0+645!

Eggs ~�7! 0+370*** �3+000 2+378~1+756! ~�1+483! ~0+894!

Eggs ~�8! 0+032 �1+817 0+813~0+246! ~�1+431! ~0+487!

Price ~�1! 0+045** �0+342*** 0+078~2+425! ~�1+912! ~0+334!

Price ~�2! 0+008 �0+252 0+413**~0+495! ~�1+595! ~1+987!

Price ~�3! 0+015 �0+117 0+286~0+959! ~�0+770! ~1+424!

Price ~�4! 0+041* �0+077 0+359***~2+759! ~�0+540! ~1+907!

Price ~�5! 0+046* �0+319** 0+275~2+911! ~�2+102! ~1+378!

Price ~�6! �0+016 �0+274*** �0+013~�0+964! ~�1+691! ~�0+064!

Price ~�7! �0+017 �0+269*** 0+166~�1+233! ~�1+960! ~0+920!

Price ~�8! �0+027** �0+257** �0+029~�2+029! ~�2+019! ~�0+173!

Articles ~�1! �0+040** 0+730* �0+143~�2+444! ~4+643! ~�0+695!

Articles ~�2! �0+019 0+456* �0+212~�1+249! ~3+032! ~�1+072!

Articles ~�3! �0+008 0+590* 0+076~�0+582! ~4+343! ~0+431!

Articles ~�4! 0+011 0+251*** �0+001~0+812! ~1+822! ~�0+004!

Articles ~�5! �0+011 0+224*** �0+012~�0+818! ~1+735! ~�0+073!

Articles ~�6! �0+014 0+234*** �0+196~�1+106! ~1+842! ~�1+174!

Articles ~�7! ~�0+004! 0+449* �0+428*~�0+391! ~3+681! ~�2+674!

Articles ~�8! 0+014 0+303** 0+273~1+049! ~2+260! ~1+550!

ECM1 ~Eq+1! 0+010* �0+003 0+009~4+282! ~�0+151! ~0+319!

ECM ~Eq+2! 0+003* 0+003 0+005~4+235! ~0+514! ~0+650!

Constant �0+071 �0+956 �0+390~�0+874! ~�1+221! ~�0+379!

R2 0+82 0+64 0+58Adj+ R2 0+74 0+48 0+40

Note+ t-test values are in parentheses+1The speed of adjustment coefficient+*, **, and *** denote statistical significance at 1, 5, and 10 percent, respectively+



factor is just another example of herd behavior+Most importantly, however, we could notconclusively determine that the increase in media reports led to further increase in thepopularity of low-carb diets+

It is generally believed that popularity of different diets is short-lived+ Based on that,many believe that the popularity of low-carb diets passed its peak in 2004+ While thismay be true, the issue of popularity of low-carb diets was a big cultural, economic, andsocial issue in the United States for several years and as such deserves to be addressed byacademics in agriculture, food, health and nutrition, business, and economics disciplines+We are making our modest contribution to that effect+ Also, the fact remains that sev-eral agricultural industries in the United States were affected greatly during the lastseveral years by the popularity of low-carb diets+ The industries that were able to makequick adjustments were also able to capture the most benefits ~or prevent more seriouslosses!, while other industries that could not or chose not to react quickly assumed therole of bystanders who benefited or lost in the process purely by accident+ While mosteconomic studies are concerned with strategic decision making and give advice aboutadjustment in the medium run to long run, short-term phenomena in real life often rep-resent the difference between being a successful operation or disappearing from the scene+Thus, understanding the actual consumer behavior rather than using the fictional approx-imation called perfect rationality may help producers in better managing the risk associ-ated with consumer behavior+

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Dragan Miljkovic is Associate Professor of applied economics at North Dakota State University.Dr. Miljkovic obtained his Ph.D. in agricultural economics from the University of Illinois at Urbana-Champaign in 1996. His current research interests are in the areas of consumer behavior and appliedagricultural trade and price analysis.

Daniel Mostad is Commodity Merchant with ConAgra Trade Group. He obtained his M.S. degreein agribusiness and applied economics from North Dakota State University in 2005.



obesity and low-carb diets in the united states: a herd behavior model

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