Bailey, DeeVon

Dustin, Al

Vickner, Steven S.

English

Research Papers in Economics

Western Economics Forum, Fall 2006 
 
 
  36
University-Retail Industry Research Partnerships as a Means to 
Analyze Consumer Response: The Case of Mad Cow Disease 
 
Steven S. Vickner, DeeVon Bailey and Al Dustin
1 
 
Introduction 
 
Quality data are vital to any empirical inquiry into market behavior. Often the econometrician is 
unable to investigate natural occurring experiments in real-world markets given limited access to 
data and must resort to experimental or contingent markets for hypothesis testing and statistical 
inference. Actual market transactions are preferred as they capture what consumers did, not 
what they might claim to do in a contrived, sterile setting. Establishing an ongoing partnership 
with food retailers is a means to work with actual market transactions for a variety of research 
initiatives. In this paper, we cooperated with Salt Lake City-based Associated Food Stores, Inc. 
to explore the impact of three separate information shocks related to bovine spongiform 
encephalopathy (BSE), or Mad Cow Disease, on retail meat consumption.  
 
Markets and Mad Cow Disease 
 
The noun ‘market’ is so familiar to the economist’s vocabulary that it is often ambiguously used 
in expressions like ‘market impact’ or ‘market response’. But with an issue as headline-worthy 
and far-reaching as BSE, determining exactly what market is impacted and to what extent 
requires some careful thought and consideration. Several recent publications (Mathews et al. 
2006; Blayney et al. 2006) overview and chronicle the events associated with BSE on import 
markets, export markets and trade flows. Other markets, such as futures, equities, food away-
from-home, institutional and food at-home have different buyers and sellers, and hence one 
might expect different market outcomes. We analyze the food at-home or grocery store market. 
  
On a Tuesday afternoon, December 23, 2003, the U.S. Secretary of Agriculture, Ann M. 
Veneman and her advisors met with the media in a historic press conference to discuss the test 
results of a nonambulatory Holstein cow (USDA Transcript 0433.03). At that time it was thought 
the cow came from a farm in the state of Washington, but was later traced back to a farm in 
Canada. The cow tested presumptive positive for BSE using immuno-histo-chemistry. The 
USDA quarantined the farm in Washington and began to investigate the meat packer, Midway 
Meats, also located in Washington. Despite federal officials emphasizing the safety of the 
domestic meat supply, the media frenzy began. News broadcasts, talk shows and newspaper 
headlines covered the topic exhaustively over the ensuing weeks. 
 
Six months later, Dr. John Clifford, the Deputy Administrator for the USDA’s Animal Plant Health 
Inspection Service (APHIS) held a similar press conference on the evening of June 25, 2004 
(USDA Transcript 0263.04). He announced that an inconclusive BSE test result was obtained 
and re-emphasized the safety and quality assurance of the domestic beef supply. Four days 
                                                 
1 The authors are Associate Professor and Professor, Department of Economics, Utah State University, and Head, 
Department of Farm and Ranch Management, Bridgerland Applied Technology College.  This project was funded in 
part by the USDA Rural Business Service Value-Added Producer Grant program and the Utah Agricultural 
Experiment Station. 
 Western Economics Forum, Fall 2006 
 
 
  37
later, on the evening of June 29, 2004, Dr. Clifford announced another inconclusive BSE test 
result (USDA Transcript 0266.04). Again the public was assured that the animal did not enter 
the food supply. On the morning of November 18, 2004, the Associate Deputy Director of the 
USDA-APHIS, Andrea Morgan, announced yet another inconclusive BSE test result (USDA 
Transcript 0501.04). Compared to the December 23, 2003 press conference, the media 
coverage of the three inconclusive BSE test results was virtually nonexistent. That was not 
surprising as nearly a year had passed and federal officials underscored the safety of the beef 
supply in that time. Inconclusive BSE test results do not sell newspapers. 
 
Empirical Model and Estimation 
 
The principal empirical objective of this project is to determine how BSE information affects the 
demand for fresh meats while controlling for their retail prices, real per capita meat expenditure 
and seasonality in the grocery store distribution channel. Using detailed, representative point-of-
purchase scanner data supplied by Salt Lake City-based Associated Food Stores, Inc. we 
estimate this consumer demand system. The data spanned the weeks beginning May 9, 2004 to 
May 1, 2005 for twenty of the stores they own. The twenty stores were spatially dispersed 
throughout their Utah selling region and well-represent the major population centers in the state 
(i.e., Logan, Ogden, Layton, Salt Lake City, Orem, Provo and Saint George). The data were 
then aggregated across store into a time series data set resulting in T = 52 weekly observations. 
Finally, the individual meat items were aggregated to investigate the retail demand for only fresh 
beef, pork, chicken and seafood. Within this time frame, the three separate USDA-APHIS 
inconclusive tests were announced on June 25, 2004, June 29, 2004 and November 18, 2004. 
The media coverage of each event was the same and practically non-existent based on a 
detailed analysis of the word count frequency of BSE and related terms in the media. Actually, 
within two months of the December 23, 2003 event the media coverage was effectively over 
from a word count perspective. The information regarding those three 2004 announcements will 
be included in the model using dummy shift variables to assess if BSE news influences 
purchasing patterns for fresh meats. 
 
We build a theoretically consistent empirical model of demand using an Almost Ideal Demand 
System (Deaton and Muelbauer 1980; Moschini 1995). It is theoretically consistent because we 
impose symmetry and homogeneity in the demand equations. There is a separate demand 
equation for beef, pork and chicken, and we control for the effect of relative prices and real per 
capita expenditure in each equation. Since expenditure shares sum to one, the parameter 
estimates in the seafood equation are recovered with the adding up restrictions. In each 
equation we also incorporate non-price and non-income information shift variables for both BSE 
and seasonality using a framework similar to that proposed by Teisl, Roe and Hicks (2002). 
These variables allow the demand curves to shift. For example, ceteris paribus, one might 
expect the demand curve for beef to shift inward or leftward during the week of one of the three 
USDA press conferences. Our model allows for that possibility. This test involves restricting 
parameters across all three equations in the nonlinear demand system so we must use a 
likelihood ratio test (Gallant 1987). The error terms across demand equations are likely 
contemporaneously correlated given the interrelated nature of the retail meats so estimation is 
performed using nonlinear seemingly unrelated regression using PROC MODEL in SAS. 
Autocorrelation correction in the demand system is given by the Berndt-Savin methodology. 
There are MT – Ku = (3)(51) – 28 = 125 degrees of freedom in the unrestricted model, since 
one time series observation was lost due to the Berndt-Savin methodology. 
 Western Economics Forum, Fall 2006 
 
 
  38
Results and Discussion 
 
The conditional demand system is given in Table 1. It exhibits reasonable properties for the data 
set and application. Again the parameters in the seafood equation are not presented but may be 
recovered using the adding up restrictions in the demand system. The fact that only six price 
parameters are presented reflects symmetry is imposed on the model. Four of the six price 
parameters are statistically significant (p<0.10). It is noted that these parameters relate price 
and expenditure share, not quantity demanded, and, as such, may not be interpreted as 
elasticities. They are assembled with other demand parameters and expenditure shares to 
obtain price elasticities of demand (Moschini 1995). Two of the three parameters on real per 
capita meat expenditure are statistically significant (p<0.05), and all three intercepts are 
statistically significant as well (p<0.05).  
 
As for the non-price and non-expenditure shift variables in the model, four of the six seasonality 
parameters are statistically significant (p<0.10) indicating expenditure shares exhibit seasonal 
patterns. However, all nine of the parameter estimates on the BSE shift variables are 
statistically insignificant (p>0.10). This finding is not surprising given the lack of media attention 
to the three inconclusive BSE tests during the study period. While parameter by parameter 
inspection of asymptotic t-tests is telling, a more thorough test of this hypothesis will involve a 
system-wide likelihood ratio test as discussed in the next section. The Durbin Watson statistics 
indicate the parsimonious version of the Berndt-Savin autocorrelation correction procedure (i.e., 
the same rho parameter in each equation) is successful in purging positive serial correlation 
from the model. Stability or robustness of the parameter estimates and significance of the 
parameter estimates are quite good for this model. 
 
Table 1.  Retail Meat Demand Model Parameter Estimates
 
  Beef Pork Chicken 
Prices     
   Beef  -0.0675** 
(0.0284)
1 
-0.0128 
(0.0250) 
0.0677** 
(0.0299) 
  Pork   -0.1945*** 
(0.1042) 
0.0260 
(0.0316) 
  Chicken    -0.1141** 
(0.0437) 
Expenditure  -0.0854** 
(0.0386) 
-0.0252 
(0.0287) 
0.1068** 
(0.0452) 
Intercept  0.3700* 
(0.0928) 
0.1518** 
(0.0645) 
0.5230* 
(0.1120) 
Seasonality1  -0.0968** 
(0.0437) 
0.1365* 
(0.0299) 
-0.0400 
(0.0540) 
Seasonality2    -0.1077** 
(0.0441) 
0.0163 
(0.0283) 
0.0943*** 
(0.0544) 
BSE1 -0.0003 
(0.0455) 
0.0434 
(0.0307) 
-0.0602 
(0.0560) 
BSE2 0.0351 
(0.0450) 
0.0093 
(0.0292) 
-0.0289 
(0.0555) 
BSE3 -0.0133 
(0.0437) 
0.0034 
(0.0282) 
0.0163 
(0.0539) 
     
Autocorrelation 0.2503** 0.2503** 0.2503** 
  (0.1002) (0.1002) (0.1002) 
Durbin  Watson  1.8435 2.3560 2.2230 
Log Likelihood  53.7386     
     
1 Standard error in parentheses. Note: *, ** and *** denote statistical significance  
at the 0.01, 0.05 and 0.10 levels respectively. Western Economics Forum, Fall 2006 
 
 
  39
Given the model in Table 1, we remove the effects of the BSE announcements by removing 
those explanatory variables from the demand equations. The model in Table 1 is the 
unrestricted model whereas the second regression, without the BSE dummies, is the restricted 
model. Gallant (1987) outlines a procedure to compare the likelihood surface from the 
unrestricted nonlinear demand system to that of the restricted system. The test is called a 
likelihood ratio test and under the null hypothesis it is distributed asymptotically chi-square with 
nine degrees of freedom in this case (i.e., since nine parameters were removed in the restricted 
model). The likelihood ratio statistic is 4.4199 and the chi-square critical value is 14.6837 for a 
10% level of alpha, so we fail to reject the null hypothesis of no BSE announcement effects.  In 
fact, we find no statistical difference between the unrestricted and restricted models at the 1%, 
5% and 10% levels of significance (only the 10% level is reported). This test is considered to be 
far superior to a simple inspection of the parameter by parameter asymptotic t-statistics, 
especially in small samples. Using any single-equation approach, it is not possible to 
comprehensively test the BSE announcement effects on the demand system overall. We can 
conclude for this data set and application, the BSE announcements collectively had no impact 
on consumer response. 
 
Finally, the uncompensated or Marshallian own and cross price elasticities exhibit reasonable 
direction and magnitude with the only exception being the cross price effect of pork in the beef 
equation (i.e., indicating complementarity); own price elasticities are negative and all cross price 
elasticities but one are positive (Table 2). For example, a 1% increase in the price of beef leads 
to a 1.0305% decrease in the quantity demanded of beef. Similarly, a 1% increase in the price 
of chicken leads to a 0.1490% increase in the quantity demanded of beef. The Hicksian 
elasticities too are quite reasonable and similar too. The conditional expenditure elasticities 
each show the rates of segment growth as the fresh meat category expenditures rise; beef and 
pork rise proportionally slower, while chicken and seafood rise proportionally faster. 
 
Table 2.  Estimated Price and Expenditure Elasticities. 
 
 
 
Beef 
 
Pork 
 
Chicken 
 
Seafood 
Marshallian      
   Beef  -1.0305 -0.0011 0.1490  0.0291 
   Pork  0.0130 -2.3402 0.2218 1.2820 
   Chicken  0.0244 0.0480 -1.6172 0.0670 
   Seafood  0.2039 3.5260 0.3829 -5.1846 
      
Hicksian      
   Beef  -0.5332 0.1205 0.3398 0.0729 
   Pork  0.4928 -2.2230 0.4059 1.3243 
   Chicken  0.8855 0.2585 -1.2868 0.1427 
   Seafood  0.8284 3.6787 0.6226 -5.1297 
      
Expenditure 0.8535  0.8234 1.4778 1.0719 
 
 
Conclusion 
 
In 2004, the USDA made three press releases regarding inconclusive BSE test results. Based 
on the data collected by Associated Food Stores, Inc. we were able to isolate our attention on 
just one retail market, the grocery store distribution channel, which could have been affected by 
that information. For our data set, the results definitively show that there was no change to the 
retail demand for fresh meats and those results were not surprising. The media covered the 
presumptive positive test result from December 23, 2003 in great detail but did not address the Western Economics Forum, Fall 2006 
 
 
  40
inconclusive tests in 2004. Over the course of 2004, the USDA also took many steps to assure 
the quality and safety of the domestic beef cattle supply and make these efforts known to 
domestic consumers. 
 
References 
 
Berndt, E.R. and N.E. Savin. 1975.  Estimation and Hypothesis Testing in Singular Equation 
Systems with Autoregressive Disturbances. Econometrica 43: 937-958. 
 
Blayney, D.P., J. Dyck and D. Harvey.  2006 (April). Economic Effects of Animal Diseases 
Linked to Trade Dependency.  Amber Waves.  
 
Deaton, A.  and J. Muelbauer. 1980.  An Almost Ideal Demand System.  American Economic 
Review 70: 312-326. 
 
Gallant, A.R. 1987.  Nonlinear Statistical Models. New York: John Wiley and Sons. 
 
Mathews, K.H., M. Vandeveer and R.A. Gustafson.  2006 (June).  An Economic Chronology of 
Bovine Spongiform Encephalopathy in North America.  USDA-ERS, LDP-M-143-01. 
 
Moschini, G.  1995.  Units of Measurement and the Stone Index in Demand System Estimation. 
American Journal of Agricultural Economics 77: 63-68. 
 
Teisl, M.F., B. Roe and R.L. Hicks.  2002. Can Eco-Labels Tune a Market? Evidence from 
Dolphin-Safe Labeling.  Journal of Environmental Economics and Management  43: 339-359. 
 
USDA Transcripts, Office of Communications, various releases. 
 
  

An Almost Ideal Demand System.

Can Eco-Labels Tune a Market? Evidence from Dolphin-Safe Labeling.

Economic Effects of Animal Diseases Linked to Trade Dependency. Amber Waves.

Estimation and Hypothesis Testing in Singular Equation Systems with Autoregressive Disturbances.

Nonlinear Statistical Models.

Units of Measurement and the Stone Index in Demand System Estimation.

University-Retail Industry Research Partnerships as a Means to Analyze Consumer Response: The Case of Mad Cow Disease

University-Retail Industry Research Partnerships as a Means to Analyze Consumer Response: The Case of Mad Cow Disease

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