Structural Change in U.S. Consumer Response to Food Safety Recalls

Transcription

1 Structural Change in U.S. Consumer Response to Food Safety Recalls Mykel R. Taylor Department of Agricultural Economics Kansas State University H. Allen Klaiber Department of Agricultural, Environmental and Development Economics The Ohio State University Fred Kuchler Economic Research Service U.S. Department of Agriculture Selected Paper prepared for presentation at the Agricultural and Applied Economics Association s 213 AAEA & CAES Joint Annual Meeting, Washington, DC, August 4-6, 213 The opinions expressed here are those of the authors and not those of the U.S. Department of Agriculture or of the Economic Research Service.

2 Consumer response to food safety information is of great interest to agribusinesses, commodity organizations, and policy makers. The interest stems from a desire to more fully understand how a typical consumer will react to information on the quality, safety, or composition (e.g. presence of allergens) of their food. Will consumers avoid the food product when it is declared unsafe? How long will the avoidance behavior last? Will consumers switch to substitute products and subsequently return to the original product? The size and scope of impacts on demand influence whether the food industry has a financial incentive to implement food safety procedures. The wellbeing of consumers gives government agencies, such as the U.S. Department of Agriculture s Food Safety and Inspection Service (FSIS) and the U.S. Food and Drug Administration (FDA), reason to monitor industry activities. As FSIS and FDA do not communicate directly with consumers, the impact of media coverage of recalls has been an area of interest to researchers for many years, covering a wide variety of foods and types of food safety events (Smith, van Ravenswaay, and Thompson 1988; Burton and Young 1996; Dahlgran and Fairchild 22; Piggott and Marsh 24; Marsh, Schroeder, and Mintert 24). Analysis has primarily focused on the immediate impact of food safety information, with consideration of residual effects up to a few months following the initial recall or event. For the most part, studies have found little compelling evidence for long-term, structural changes in consumer behavior; rather impacts have been transitory. This study presents evidence of a structural change in the way consumers respond to recalls of ground beef made by the Food Safety Inspection Service (FSIS) after the December 23, 23 announcement confirming the discovery of a dairy cow infected with Bovine Spongiform Encephalopathy (BSE) in the United States. While BSE had been previously discovered in the European Union, Japan, and Canada, this was the first confirmed case of a BSE-positive animal 2

3 in the United States. This event was heavily covered by U.S. media outlets and the U.S. Department of Agriculture implemented new regulations to reduce consumer exposure to BSE through animals intended to be part of the food supply. Consumers had access to a large amount of media-supplied information regarding the nature of the disease and possible ramifications if they consumed the meat. The magnitude of information related to this event may have affected consumers perceptions of beef safety and their subsequent reaction to recalls over a longer period of time than was previously measured in the literature. We employ household-level data on purchases of fresh ground beef purchases over a biweekly period as recorded by the Nielsen Company for the years 2 through 25. This dataset allows for investigation of consumer behavior in the market place while controlling for demographic characteristics, time and location effects, and the presence of FSIS recalls. The results of the estimation of a household-specific fixed effects model indicate that while recalls of ground beef do not have a measurable impact on the probability of a household purchasing ground beef in a given two week period prior to January 24, there is a statistically significant negative effect after that date. By analyzing both the short-run impacts and longer-term structural changes in demand, we contribute to the existing literature as well as industry understanding of consumer behavior in response to food safety recalls. Finding evidence of a structural change in behavior suggests that previous estimates of economic impacts from food safety recalls may be understated if only the short term behavior is considered. A more complete measure of the impact from a large food safety event would account for subsequent changes in behavior not observed previous to the major food safety event. The FSIS recall associated with the BSE-positive cow is a good example of a large impact event in terms of food safety information and the availability of 3

4 purchase data for fresh ground beef both before and after the event provide the opportunity to test for structural changes. Recognition of the potential existence of these structural shifts may inform future analysis methods of food safety recalls. Literature Review The body of literature addressing food safety and consumer demand is comprised of several different analysis methods and data sources. Studies have focused on changes in either consumer demand (e.g. Schlenker and Villas-Boas 29, Piggott and Marsh 24) for a product implicated in a food safety event or the impact on the value of firms selling an implicated product via stock (Thomsen and McKenzie 21) and commodity futures prices (Schlenker and Villas-Boas 26, Lusk and Schroeder 22). The definition of a food safety event has varied across studies, with measures such as media indices and federal recall counts serving as proxies for food safety information available to consumers. Magnitude and duration of impact is always of interest to the researchers and has proven to vary widely, depending on the data sources employed. One approach found in the literature employs aggregate disappearance data at the species level (beef, pork, poultry) to estimate a demand system based on the representative consumer. Examples of studies using aggregate data to determine consumer behavior include Piggott and Marsh (24), Marsh, Schroeder, and Mintert (24), and Burton and Young (1996). The advantages of using aggregate disappearance data to model food safety impacts on consumer demand include the availability of a long time series, consumption measures that include both food-at-home and food-away-from-home purchases, and the opportunity to measure crossspecies impacts on demand. 4

5 Piggott and Marsh (24) analyzed the impact of food safety information on demand using quarterly U.S. disappearance data to estimate a generalized AIDS model. Their media index of food safety information measured bundles of contaminants reported individually for beef, pork, and poultry by newspapers. Findings indicated that effects of food safety information on meat demand were statistically significant, but with no lagged effect, implying a relatively small economic impact. Marsh, Schroeder, and Mintert (24) analyzed both media indices composed of newspaper articles and Food Safety and Inspection Service (FSIS) recall data as proxies for food safety information. Quarterly disappearance data from 1982 to 1998 on beef, pork, poultry, and other consumption goods was used to estimate an absolute price version of the Rotterdam model. Their findings indicated that while FSIS recall events significantly affect demand, media reports do not. The length of an effect on demand from recall events for beef and pork dies out within three periods and affects demand in the contemporaneous period only for poultry recall events. Evidence that food safety information can have substantial impacts on food demand comes from Great Britain following the discovery of BSE in that country. Burton and Young (1996) analyzed the impact in on meat demand using media indices and aggregate disappearance data to estimate a dynamic AIDS model. The model considered publicity on BSE to be a form of negative advertising and measured its effect using an index of media coverage. The index included both the number of news articles per quarter and the cumulative number of articles to date for each quarter. The results of this study differ from the others in that BSE publicity was shown to have both significant short-run and long-run effects on consumer expenditures on beef. By the end of 1993, three years after the initial discovery of BSE, 4.5% decline in market share for beef had occurred. The authors note that a longer post-bse data 5

6 series would allow for further exploration, including dissipation over time, of the market share decline. Our study makes several contributions to the food safety and consumer demand literature. First, we use household-level data on consumption of fresh ground beef, rather than aggregate disappearance data for beef. This dataset is preferable if we are interested in knowing how foodat-home consumption is affected by food safety information. The dataset allows us to specify shorter time periods of consumption impact and recovery (biweekly rather than quarterly) and thus potentially reveal some impacts that would be masked in lower frequency data. We control for household-specific characteristics that are likely to impact both ground beef consumption and reaction to food safety information, and being able to match FSIS recalls of a specific product (ground beef) with consumption of that exact product. These fundamental differences in the data employed in this study will offer new insights into consumer behavior and provide evidence of structural changes in demand that can occur as a result of major food safety events. Model Following notation found in Piggott and Marsh (24), the household's utility function can be generally represented by,, where is the quantity of fresh ground beef consumed by household and is the amount of public information available to the consumer concerning the safety of beef. Rather than focusing on changes in the quantity of ground beef purchased, the consumer's decision to avoid ground beef entirely is modeled as a discrete decision to enter the market and make a purchase or remain out of the market for a given period of time. The derivation of the model begins by specifying a random utility model where an individual, n, faces J alternatives. The utility a person gets from choosing one of the J 6

7 alternatives is decomposed into an observed portion (i.e. known by the researcher),, and an unobserved portion,, that is treated as random (Train, 23). In this study, the observed components of the utility function include biweekly purchases of fresh ground beef, the per pound price of ground beef, FSIS recalls of ground beef by region of the United States, annual and biweekly fixed effects, ground beef purchase patterns specific to the individual household, and demographic characteristics of the household. Unobserved components of the utility function include, but are not limited to, previous experience with foodborne pathogens and personal health conditions that influence diet (e.g. high cholesterol, hypertension). The utility of choosing a particular alternative is, where is distributed independently and identically as extreme value. Using Train s notation, the probability that individual chooses alternative is: (1) Prob, Prob,. The portion of utility that is observable,, is specified as a linear function of parameters as follows: (2), where is an alternative-specific constant term for alternative, is a vector of containing both household- and alternative-varying characteristics, and the corresponding vector of estimated coefficients is. If the utility of alternative is greater than all other alternatives, then that will be the alternative that is chosen. McFadden (1974) shows that if the error terms of the unobserved utility model are independent and identically distributed as Type I extreme value, then the probability of household n choosing any alternative from J = 2 alternatives is: 7

8 (3). The model estimated for this study has two alternatives: ground beef is purchased or ground beef is not purchased. The log likelihood function used in model estimation is as follows:, (4) ln ln where is an indicator vector with value equal to one if household n chose alternative j and zero otherwise. The bivariate logit model follows the linear in parameters form shown in equation (2) and is specified as follows (5), where is the intercept term; is the price of ground beef paid by household n in time t; is a parameter measuring the number of FSIS recalls of ground beef occurring within household n s region during time period t, which period t occurring in the years prior to December 23 when BSE was discovered in the United States; is a similarly defined measure of FSIS recalls occurring during the period after the discovery of BSE in the United States;,,, and are a set of variables measuring the impact of purchase behavior and inventory effects for the household; and are binary variables representing the year and biweek, respectively;,, and are vectors of coefficients to be estimated; and is a component error structure where is household-specific and does not vary over time and is iid. The variable Price used in the model includes both unit prices of ground beef and predicted prices for the non-consuming households. The expected impact of price on the 8

9 probability of purchasing a commodity is negative. The ground beef recall variables, and, measure the number of ground beef recalls within a specific region and biweekly time period for the pre- and post-bse time periods, respectively. Our expectation of the impact of recalls on the probability of a household purchasing ground beef is negative. Differences in the impact of recalls in the pre- and post-bse time periods are not known a priori. Variables measuring purchase decisions made in previous time periods, referred to as state-dependent variables, are included in the model to capture both inventory and purchase habit effects. The variables are specified following Moeltner and Englin (24) and consist of total numbers of purchases,, and total numbers of consecutive purchases,. There are also corresponding totals for non-purchases,, and consecutive non-purchase,. The total purchase variables allow us to test the hypothesis that households who consistently purchase ground beef on a biweekly basis are likely to purchase again in the next period, whereas households who rarely purchase ground beef are unlikely to purchase in the next period. The total purchases variables are specified to represent a household s propensity to purchase (not purchase) due to their habits over time. The consecutive purchase variables are included to account for the possibility that a household having made many repeated purchases (nonpurchases) may have sufficient inventory to stop (begin) purchasing in the next period. Data The binomial logit model is specified using four different types of variables: household purchases of ground beef, prices, recalls, and household characteristics. The data needed to form these variables come from two sources. Data on household purchases and expenditures on ground beef were obtained from the Fresh Foods component of the Nielsen Homescan panel. For 9

10 this study, we used data that covers households from all across the United States during the time period January 1999 to December The Nielsen panel data also contain the information used to construct several demographic characteristics variables for the participating households. Summary statistics and descriptions of all the variables used in the binary logit model are presented in table 1. The product of interest for this article is fresh beef. No processed ground beef products were included to keep the product of interest relatively homogeneous. In the Nielsen Homescan panel data, each record is a separate product purchase and includes the total quantity purchased in pounds, the total amount spent on the item in dollars, and the date of purchase. 2 A biweekly purchase periodicity was chosen for the empirical analysis to avoid excessive censoring rates, but still allow for short-run food safety effects. This frequency also reflects households' tendency to make fresh meat purchases twice a month, which corresponds to the commonly used two-week pay period. 3 Unobserved Prices Prices per unit of product were calculated by dividing total expenditure on ground beef by total quantity purchased. This results in retail prices being available only for the households that actually made purchases. For the households that chose not to purchase a product in a given two-week period, the price they faced for that product is not recorded. Therefore, the missing 1 The Nielsen Homescan panel is a nationwide survey of households and their retail food purchases. Households record purchase data by scanning the universal product codes (UPCs) of the items they purchase. Data include detailed product information, date of purchase, total quantity, total expenditure, and the value of any coupons used for every item purchased. The household sample is selected to correspond with the U.S. Census demographic distribution. 2 If multiple purchases were made on a given day, each purchase is recorded as a separate observation in the raw dataset. 3 Earlier models estimated using a monthly time period did not reveal any change in behavior with regard to probability of purchase. Given the lack of statistically significant parameter estimates in a monthly model and a biweekly average number of shopping trips recorded in this panel for fresh meat and poultry purchases, it is important to consider time periods in the data aggregation that correspond to observed behavior. 1

11 prices must be imputed for households without positive purchases in order to have a complete dataset for estimation purposes. Imputation of the missing prices is based on the linear price model found in Cox and Wohlgenant (1986). The regression is specified using the average price of the good during a given time period from the consuming households in the panel. Household income is also used to capture hypothesized increases in quality that may be demanded from increased income. A variable for household size is used to account for economies of size in purchasing meat and poultry products. Quadratic terms for both income and household size are also included in the regression to capture non-linear effects of these variables. Other demographic variables were considered for the price equations, including region, race, and education, but are not used in the final specification of the price imputation model. The final specification of the linear price regression is as follows: (6) γ r, where is the observed price of fresh ground beef in period t for consuming household n, is the sample average biweekly price for ground beef in period t, r is a vector of binary variables indicating the region in which the household is located, is a binary variable indicating if the household is located in an urban area, is household income, is household income squared, is the size of household, is the squared size of household, is an iid error term, and, γ,,,,, and are the corresponding coefficients to be estimated. 4 The regression is estimated without a constant term so that all the regional binary variables can be included and standard errors are estimated using the robust sandwich estimator (Huber, 1967; White, 198). The regression coefficients for each good were subsequently used to 4 Total household income is recorded as an interval in this dataset. Therefore, the midpoint of the interval is the value used in the price regression. To calculate the midpoint of the highest income range, an upper bound of $15, was used. 11

12 predict prices for the non-consuming households. Predicted prices were obtained by using the sample biweekly average prices and the geographic and demographic characteristics of the nonconsuming households. Recalls of Ground Beef FSIS recall announcements include a large amount of information on the nature of a recall. Detailed product information, company name and contact information, the exact type of contamination and its potential effects on consumers, the level at which consumers would come in contact with the contaminated product (retail, wholesale, or institutions), and the affected states. Data on all the recalls of fresh ground beef occurring from 1998 to 25 were collected from the FSIS website. Of the 123 ground beef recalls initiated during the study period, 113 were due to E. coli O157:H7, one was due to Salmonella, and the remaining recalls were due to labeling errors or unsanitary processing. 5 Figure 1 presents the total number of ground beef recalls, by year, for the study period. The largest number of recalls occurred in 22 (32 recalls), while 24 and 25 saw the lowest numbers (6 recalls each year). The recall process begins with the discovery of potentially unsafe or mislabeled products by the manufacturer, FSIS testing or field inspection, or by epidemiological data submitted by a state or local public health department. In period between 1998 and 22, the frequency of recalls was relatively high, prompting a change in the procedures of testing for contamination. Through 22, samples of product were taken at the processing facility and sent off for testing. In the meantime, the product was released into the food supply chain. If contamination was discovered, the product was recalled. Starting in 23, 5 Recalls due to E. coli and Salmonella are labeled as Class 1 recalls, where there is a reasonable probability that consumption of the product will cause serious health risks or death. The other recalls in the dataset are either classified as having a remote probability of adverse health consequences (Class 2) or are recalls due to mislabeling or unidentified allergens (Class 3). 12

13 the process was modified and tests of samples were conducted prior to releasing the product. This dramatically reduced the number of FSIS recalls of ground beef from an average of 2.2 per year between 1998 and 22 to an average of 7.3 recalls in the years 23 to 25. This change in procedure does not imply fewer (or greater) numbers of contaminated samples, only the number of resulting FSIS recalls that were initiated. FSIS recalls are specific to certain products as well as the geographic areas in which those recalled products were distributed. Therefore, it is possible to determine which region(s) may have been affected by a certain recall. Recalls are classified as nationwide by FSIS if more than 13 states are affected. The number of recalls, by region, is given in table 2. Nationwide recalls average approximately 12% of all recalls that occurred in a given year. The FSIS recalls enter the model as region-specific rolling averages of the number of recalls that occurred in the previous two weeks for each day of the biweek period. This specification gives equal weight to each day within the biweekly period, but allows for the impacts of recalls occurring further back in time to decrease in importance for current purchase decisions. 6 Results The estimation results for the conditional, fixed-effects logit model specified in equation 5 are presented in table 3. The model is estimated by identifying the households within the panel dataset. By controlling for the unobserved heterogeneity of the individual households, the results may be interpreted as occurring within, rather than across, households with similar demographics. The model is estimated over 16,97 unique households, with a total of 1,513,92 observations representing separate and household-specific purchase occasions. 6 Model specifications using lags three to four and five to six weeks after the current period were also estimated. Variables lagged beyond the most recent two weeks were not statistically different from zero at or below the 1% level. 13

14 The primary parameters of interest are and, which measure the impact of an additional FSIS recall of ground beef in a household s region on the probability of that household making a purchase of ground beef in a given biweekly time period. The recall variables enter the model as pre-bse (recalls occurring prior to December 23, 23) and recalls occurring post-bse. For the time period prior to the BSE case, there is no statistically measurable impact of an additional recall of ground beef on the probability of a household making a fresh ground beef purchase. However, there is a statistically measureable response to recalls in the post-bse period at the 6% level. The post-bse response is brief, not lasting longer than the two-week purchase period. It is possible that the observed structural change in consumer behavior with regard to ground beef is correlated with, or possibly driven by, either the type or number of the recalls before and after January 24 rather than the discovery of a BSE-positive cow. However, a closer look at the historical record of FSIS recalls for ground beef does not suggest this type of relationship. First, no recall in the two year period following January 24 was issued due to another BSE-positive cow. Rather the recalls were for the presence of E. coli or other foodborne pathogens. Second, the average number of ground beef recalls declined sharply in the post-bse period, as shown in figure 1. This suggests that the increased sensitivity of consumers to recalls of ground beef occurring after the discovery of a BSE-positive cow was not driven by the nature or frequency of FSIS recalls. Our finding that consumers response to food safety-related recalls is short-lived is similar to the results of other studies in the United States (Piggott and Marsh 24). The unique finding is that the discovery of a BSE-positive cow in the United States marked a turning point in U.S. consumers perceptions and subsequent responses to recalls of ground beef. While recalls 14

15 did not measurably change consumers probability of purchasing in the pre-bse period, there is a statistically significant and negative impact on their response in the post-bse era. This result suggests that previous research findings of short-lived impacts from food safety information (including media reports and recalls) has understated the scale of influence the BSE event had on consumer behavior. The impact of an increase in price on the probability of making a purchase of ground beef within a given biweekly time period is negative. The coefficient is statistically significant at less than 1% level. This result is consistent with demand theory. The coefficients of the state-dependent variables, which provide a measure of purchase habits and inventory effects, are all statistically different from zero at less than the 1% level. The positive sign on the total purchase variable indicates that households who commonly purchase ground beef are more likely to make another purchase in the subsequent period. However, households with ever increasing periods of non-purchase are also more likely to make a purchase in the next period. This is an unexpected result for the total non-purchase coefficient and may indicate that the variable is controlling for something other than habit or inventory effects. The repeated purchases variables indicate a strong habit effect when purchases are measured consecutively. The positive sign for the repeated purchases variable suggests that habitual purchases of fresh ground beef increase the probability of making another purchase in the next period. The opposite is the case for households with ever-increasing numbers of consecutive non-purchases; they are less likely to make a purchase in the next period. The vast majority of the annual and biweekly fixed effect variables are statistically significant at a level of less than 1%. These variables do not necessarily reveal impacts on purchases that can be interpreted, but they do add a great deal of rigor to the model results with 15

16 regard to food safety recalls. By controlling explicitly for unobservable factors that are time- and household-specific, the impact of recalls on purchase behavior is interpreted as emanating from within the household and not from other factors that cross households. Conclusion The objective of this research is to determine if FSIS recalls of fresh ground beef change the purchase behavior of consumers. Using a national panel of U.S. households over an 8-year period, a binary logit model of ground beef purchases was estimated. The model explicitly controlled for factors affecting purchases such as price of the product, habit and inventory effects, annual and biweekly time effects, and the impacts of the number of region-specific FSIS recalls occurring in close time proximity to the biweekly purchase period. The FSIS recall parameters entered the model separately as occurring prior to the December 23, 23 discovery of a BSE-positive cow in the United States and after the BSE discovery. Results of the model indicate that while, direct impacts from FSIS recalls are short-lived lasting only about two weeks after they occur, a large structural shift in consumer perceptions occurred. FSIS recalls of ground beef did not have a measurable impact on consumer behavior, as modeled by the probability of making a purchase of fresh ground beef. However, after the discovery of a BSE-positive cow, consumers of ground beef were less likely to make a purchase in the two week period following an FSIS recall. This structural change due to the BSE incident has not been detected in previous studies of consumer response to food safety information in the United States, thereby understating the overall effects of food contamination on consumers. Future research must account for the possibility of a structural and long-lasting change in consumer behavior, not just the length of 16

17 short-run purchase decisions. Arguably, another incident of a similar nature to the first discovery of BSE in the United States could be expected to shift consumers perceptions of the safety of their food supply. An interesting area for future research would be to determine if there are substitution effects between fresh meat products. For example, the negative impact of FSIS recalls on ground beef purchases may impact the probability of purchasing poultry or pork. These substitution effects have been considered in other research, but not in the structural change model presented here. 17

18 References Burton, M. and T. Young. The Impact of BSE on the Demand for Beef and Other Meats in Great Britain. Applied Economics 28(1996): Cox, T. and M. Wohlgenant. Prices and Quality Effects in Cross-Sectional Demand Analysis. American Journal of Agricultural Economics 68(1986): Dahlgran, R. and D. Fairchild. The Demand Impacts of Chicken Contamination Publicity: A Case Study. Agribusiness 18(22): Huber, P. The Behavior of Maximum Likelihood Estimates Under Nonstandard Conditions. In Proceedings of the Fifth Berkeley Symposium in Mathematical Statistics, Vol. 1. Berkeley: University of California Press, Lusk, J.L., and T.C. Schroeder. Effects of Meat Recalls on Futures Market Prices. Agricultural Resource Economics Review 31(22): Marsh, T.L, T.C. Schroeder, and J. Mintert. Impacts of Meat Product Recalls on Consumer Demand in the USA. Applied Economics 36(24): McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior. In Frontiers in Econometrics, ed. P. Zarembka, New York: Academic Press, Moeltner, K. and J. Englin. Choice Behavior Under Time-Variant Quality: State Dependence Versus Play-It-By-Ear in Selecting Ski Resorts. Journal of Business and Economic Statistics 22(24): Piggott, N. and T. Marsh. Does Food Safety Information Impact U.S. Meat Demand? American Journal of Agricultural Economics 86(24): Schlenker, W., and S. Villas-Boas. Consumer and Market Responses to Mad Cow Disease. American Journal of Agricultural Economics 91(29): Smith, M., E. van Ravenswaay, and S. Thompson. Sales Loss Determination in Food Contamination Incidents: An Application to Milk Bans in Hawaii. American Journal of Agricultural Economics 7(1988): Thomsen, M. and A. McKenzie. Market Incentives for Safe Foods: An Examination of Shareholder Losses from Meat and Poultry Recalls. American Journal of Agricultural Economics 83(21): Train, K. Discrete Choice Methods with Simulation, New York: Cambridge University Press,

19 White, H. A Heteroscedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroscedasticity. Econometrica 48(198):

20 Figure 1. Annual FSIS Recalls for Ground Beef, 1998 too 25 Table 1. Summary Statistics of Binary Logit Model Variables P R prebse Variabl le R postbse T purch T nopurch C purch C nopurch Υ 1998 Υ 1999 Υ 2 Υ 21 Υ 22 Υ 23 Υ 24 Υ 25 Ψ y Description Mean Price of ground beef Number of recalls in pre-bse.256 period Number of recalls in post-bse.43 period Total number of purchase periods by household Total numb ber of non-purcha ase periods by household Consecutiv ve number of purchase.53 periods by household Consecutiv ve number of non purchase periods by househo old Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for the year Binary variable for each biweek of 13.5 the year Standard Deviation Minimum.3 1 Maximum

21 Table 2. FSIS Recalls of Ground Beef by Region of the United States Region Year Central Northern Southern Western Nationwide Total:

22 Table 3. Binary Logit Model Estimated Coefficients Variable Coefficient Standard Standard Standard Standard P> z Variable Coefficient P> z Variable Coefficient P> z Variable Coefficient Error Error Error Error P> z P Ψ Ψ Ψ R prebse Ψ Ψ Ψ R postbse Ψ Ψ Ψ T purch.13.. Ψ Ψ Ψ T nopurch.7.. Ψ Ψ Ψ C purch.6.1. Ψ Ψ Ψ C nopurch Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Υ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ Ψ