Submitted Article The Effect of H1N1 (Swine Flu) Media Coverage on Agricultural Commodity Markets

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1 Applied Economic Perspectives and Policy Advance Access published May 18, 2011 Applied Economic Perspectives and Policy (2011) volume 00, number 00, pp doi: /aepp/ppr008 Submitted Article The Effect of H1N1 (Swine Flu) Media Coverage on Agricultural Commodity Markets Witsanu Attavanich, Bruce A. McCarl, and David Bessler* Witsanu Attavanich is a Ph.D. candidate in the Department of Agricultural Economics, Texas A&M University, and is an instructor in the Department of Economics, Kasetsart University, Thailand. Bruce A. McCarl is Distinguished and Regents Professor in the Department of Agricultural Economics, Texas A&M University. David Bessler is Regents Professor in the Department of Agricultural Economics, Texas A&M University. *Correspondence to be sent to Attavanich.Witsanu@gmail.com. Abstract The authors estimate the market impact of media coverage related to the name swine flu, relabeled subsequently as 2009 H1N1 flu, on the future prices of lean hogs, live cattle, corn, and soybeans. They then quantified the revenue loss, employing the subset vector autoregressive model. The results indicate that the media coverage was associated with a significant and temporary negative impact on the futures prices of lean hogs, but with little impact on the other futures prices. The impact persisted for about four months, yielding an April December 2009 market revenue loss of about $200 million (about 2.51 percent). Key words: 2009 H1N1 flu, swine flu, media coverage, futures markets, agricultural commodity markets, risk communication, price analysis, subset vector autoregression. JEL codes: C32, Q13, G13. In April 2009, an influenza-like illness causing severe respiratory infections in humans was reported in the United States. It was later confirmed to be a strain of influenza A (H1N1) that was initially referred to as the swine flu and subsequently relabeled the 2009 H1N1 flu. 1 Because of the wide range of infection and rate of spread, in June the World Health Organization (WHO) raised the worldwide pandemic alert level to Phase 6, the highest level since the 1968 Hong Kong flu. 1 The 2009 H1N1 flu was first called "swine flu because laboratory testing showed that many of the genes in this new virus were very similar to influenza viruses that normally occur in pigs (swine) in North America (see The outbreak appeared to be a new strain of H1N1 which resulted when a previous triple reassortment of bird, pig, and human flu viruses further combined with a Eurasian pig flu virus. For those who are interested in details related to the transmission of the outbreak between swine and humans, see Richt (2009). # The Author(s) Published by Oxford University Press, on behalf of Agricultural and Applied Economics Association. All rights reserved. For permissions, please journals.permissions@oup.com. 1

2 Applied Economic Perspectives and Policy Initial labeling and widespread publicity regarding swine flu caused a downturn in domestic and international pork markets. Within one week of the April 24, 2009 announcement the Chicago Board of Trade nearby futures price for lean hogs dropped about 15 percent, which is equivalent to the reduction in sales value of about $6.9 million (Meyer 2009). Blendon et al. (2009) indicate the drop may have occurred because of consumer fears that eating pork might result in infection. 2 Several pork-importing countries officially imposed bans on swine and pork products. In two of these countries, Russia and China, 2009 annual imports fell by 28 and 56 percent, respectively, compared to The effect also rippled throughout other agricultural markets, such as feed grains, since lower pork consumption would reduce grain demand (Johnson 2009). Upon recognition of this negative association between the virus and consumers reactions to pork and pork products (in May), the WHO reaffirmed that pork was safe to eat and changed the name of the malady from swine flu to 2009 H1N1. Although the illness s official name was changed, its impact remained, as many media, even a year later, continued to use the swine flu label and consumers did not fully grasp the fact that H1N1 and pork consumption were unrelated. For example, survey results from Chinese consumers compiled by the U.S. Meat Export Federation (USMEF) 3 showed that: 64 percent of respondents stopped eating pork in the early stages of the outbreak one-fifth of those surveyed believed that they could catch the virus by eating pork 54.7 percent of those who feared the connection between pork and the flu virus responded that this connection was because the virus had been labeled swine flu. Given these demand shifts and opinion survey results, we seek in this article to address the following questions: (1) Did media coverage impact on the futures prices of lean hogs, live cattle, corn, and soybean? (2) How persistent was the impact by the media coverage? (3) What were the revenue losses from the media-induced price changes? Although there is a rich literature investigating the impact of food safety events and related media coverage on meat products (see for example Schlenker and Villas-Boas 2009; Lloyd et al. 2006; Piggott and Marsh 2004; Burton and Young 1996), to our knowledge, no one has estimated the impact of media coverage related to the H1N1 (swine flu) on meat and related product prices, and quantified the revenue losses across the meat and related markets. The following section presents a literature review related to the impact of the media coverage on consumer demand. This is followed by an account of the empirical methodology used; the empirical results; with conclusions and policy implications discussed in the last section. 2 They conducted a survey to 1,067 US consumers on 29 April 2009 and found that US consumers were confused by how humans can get the virus. Some of them believed that humans can get the virus from eating pork. 3 Questionnaires were issued to 1,200 Chinese consumers in six Chinese cities during August 6 10,

3 The Effect of H1N1 Review of the Literature The literature contains several estimates of the impact of the H1N1 outbreak on the pork industry, but without methodological details. An estimate in the National Hog Farmer (2009) indicated that producers would lose $ million over the following twelve months based on market conditions in the first few days after the virus was identified. An estimate in the United States Trade Representative (2009) indicated that the U.S. pork industry may face losses of about $270 million in the second quarter of 2009 alone. The news media has been found to play a crucial role in determining the market consequences of a food safety event, as news coverage has been seen to be a key factor in consumer response in many studies. For example, Brown and Schrader (1990) found that information on the links between cholesterol and heart disease was linked to decreased per capita shell egg consumption of percent by the first quarter of In their changing coefficient model, they found that the falling egg price and rising income increased egg consumption less than they otherwise would have. Burton and Young (1996) found media indices related to bovine spongiform encephalopathy (BSE) had significant effects on both short-and long-run consumer expenditure allocations among meats. Lloyd et al. (2006) found that a media scare index related to BSE event coverage was negatively correlated with both retail and producer prices of beef in the U.K. market. Piggott and Marsh (2004) found, however, that the average demand response in the U.S. meat market to food safety events is small in comparison to price effects. Moreover, such events tend to have minor long-run impacts. Several papers focus attention on food safety events and futures markets. Robenstein and Thurman (1996) found that cattle futures did not adjust when information was released on the negative health effects of red meat. Lusk and Schroeder (2002) found that, in general, daily futures prices of live cattle and lean hogs were not significantly affected by beef and pork recall announcements. However, medium-sized beef recalls of severe health consequence were found to influence negatively live cattle futures prices, but the potential impact was economically small. Marsh, Brester, and Smith (2008) examined cattle futures price changes after the 2003 BSE event and concluded that the demand for beef was predominantly impacted on by the trade ban from foreign countries and not by household consumption changes. Schlenker and Villas-Boas (2009) found cattle futures showed a pattern of abnormal price drops, with contracts with longer maturity showing smaller drops, suggesting that the market anticipated the impact to be transitory. Many studies quantify the welfare effect and the revenue loss from major food scares. Foster and Just (1989) examined the welfare losses associated with information regarding contamination of milk with heptachlor in Hawaii during Their study found a very sharp increase in consumer welfare losses initially and then a rather rapid and, finally, more prolonged decline in losses. Applying the framework developed by Foster and Just (1989), Mazzocchi, Stefani, and Henson (2004) measured the consumer welfare losses associated with information about a BSE scare in Italy. They found that the estimated loss per person per month ranged from percent of total expenditure on the meat group, depending on 3

4 Applied Economic Perspectives and Policy the period assumed to embody correct beliefs about the safety level of beef. Thomsen, Shiptsova, and Hamm measured sales losses experienced by frankfurter brands following a recall for a food borne pathogen. They found that sales of the recalled brands decreased about 22 percent after the recall and took about two to three months to begin to recover and about four to five months to reach pre-recall levels. In the risk communication literature, several studies have evaluated how effective governments are in communicating safety information relative to animal disease outbreaks. Ringel, Trentacost, and Lurie (2009) evaluated how effectively state and local health departments communicated information regarding the 2009 H1N1 outbreak in terms of timeliness, accessibility, and thoroughness. 4 They found that only about one-third of local health departments websites satisfy these three criteria. Leiss and Nicol (2006) assessed the effectiveness of risk communication by the Canadian governments of risk related to BSE and salmon contaminants. They found that the communications failed to express accurately the nature and scope of the risk in the case of BSE; while, in the case of farmed salmon, there was a failure to provide a clear message. Empirical Methodology We now turn to a study of how information measures affect demand and supply. To estimate the relationship between media coverage related to the H1N1 outbreak and market prices, our empirical model uses information of the equilibrium prices in the lean hogs, live cattle, corn, and soybeans markets. Using fundamental results from the theory of supply, we allow interdependence between factors of production (corn and soybeans) and supply of meat products, since they are the major sources of feed. We also consider trade bans from other countries on U.S. meat related to H1N1 (swine flu) as a supply shifter. Following demand theory, we use an index of media coverage as a demand shifter. This index is constructed following widely used practices (Smith, van Ravenswaay, and Thompson 1988; Brown and Schrader 1990; Burton and Young 1996; Piggott and Marsh 2004; Lloyd et al. 2006). Namely we construct a daily count of English newspaper articles mentioning H1N1 and associated key words in four world leading newspapers (the New York Times, thejapan Times, the (London) Times, and the China Daily). The specific key words searched for were swine flu, H1N1 and swine flu, 2009 H1N1 and swine flu, influenza A (H1N1) and swine flu. The search strategy was tested for accuracy by reviewing the content of the sources. Clearly our media index is crude, in that it does not discriminate between negative and positive articles. However, other authors, including Smith, van Ravenswaay, and Thompson (1988), have found that negative media coverage often has a larger impact than positive coverage. Moreover there also exists a high correlation between the negative and positive coverage, which discouraged us from trying to discriminate between the two. Finally, during food scares, even positive news may 4 Timeliness: Was the information posted within twenty-four hours of the nationwide alert issued by the federal government? Accessibility: Was the information easy to locate and understand? Thoroughness: Did the information cover key topics, such as what was happening and how government was responding? 4

5 The Effect of H1N1 induce negative effects, due to lack of trust and the fact that consumers may still recall a potential risk. 5 Following Bomlitz and Brezis (2008), who find a significant relationship between the number of deaths caused by an outbreak and the intensity of the media coverage, this study uses the daily cases of confirmed deaths caused by the H1N1 outbreak as a factor determining the index of media coverage. Model estimation approach Our study employs time series methods, mainly the subset vector autoregressions (subset VARs). We use subset VARs because of their superior forecasting ability relative to unrestricted VARs (see Kling and Bessler 1985; Brüggemann and Lütkepohl 2001). For subset VARs, a Hsiao-search with Schwarz s Bayesian information criterion (BIC) 6 is utilized to place zero restrictions on the right-hand-side variables of each equation in the system. 7 Therefore we can write our model, similar to the unrestricted VARs, except that, in the subset VARs, zero restrictions are placed on some of the coefficients associated with the right-hand-side variables of each equation in the matrix coefficients a(k) and C(s), as shown in equation 1. X t = c 0 + K k=1 a(k)x t k + S s=0 C(s)W t s + e t (1) where e t N iid(0, S) and a(k) is the autoregressive matrix of dimension (5x5) at lag k which connects X t and X t k. K is the maximum lag in the VAR. X t denotes a vector that includes the log of nearby future prices of commodities under consideration and the log of the index of media coverage. 8 W is a vector of exogenous variables consisting of the variable of the number of confirmed deaths caused by H1N1 with a lag length (DEATH t 1,...,DEATH t s ) and a dummy variable for the imposition of the trade ban (TB t ). 9 To control for seasonal influences, we also add quarterly dummy variables (Q2, Q3, and Q4) in the futures prices equations. We can solve for the moving average representation of the VARs, where the vector X t can be written as a function of the infinite sum of past innovations as shown in equation 2. X t = 1 k=0 H k e t k (2) 5 We thank the referee for suggesting this concept. 6 The BIC statistic is calculated as follows.bic = log +(mx k)(log T)/T where is the error covariance matrix estimated with k regressors in each equation, T is the total number of observations on each series, denotes the determinant operator, and log is the natural logarithm. 7 Generally, received theory is not rich enough to do this task. The BIC loss function has demonstrated good performance in Monte Carlo studies (see Lütkepohl 1985). 8 X t = ( X 1t, X 2t, X 3t, X 4t, X 5t ) in this study, where the subscripts 1, 2, 3, 4, and 5 represent the price of lean hog, live cattle, corn, soybean, and the index of media coverage, respectively. 9 The variable reflecting the trade ban (TB t ) is equal to one from May 4 to October 29, 2009 and zero otherwise, reflecting that China, the second largest U.S. pork export market, banned all U.S. pork products on May 4, 2009 and continued this ban until October 30, The variable for the number of confirmed deaths is used only in the media coverage equation following Bomlitz and Brezis. 5

6 Applied Economic Perspectives and Policy where H k is a 5 x 5 matrix of moving average parameters which map historical innovations at lag k into the current position of the vector X t. The effects of the dummy variables are set to zero for this representation, although they were not set to zero in the estimation stage. To conduct innovation accounting, the contemporaneous structure of the error covariance must be orthogonal, which is usually not the case with economic data. To obtain orthogonal innovations, this study employs a Bernanke ordering instead of Choleski ordering to convert e t into the orthogonal innovations 1 t. The Choleski decomposition may not reflect the true causal patterns among a set of contemporaneous innovations (Park, Jin, and Bessler 2008). This study applies a directed acyclic graph (DAG) representation found with PC algorithm to place zeros on the A matrix. 10 Recently several papers have employed such a representation, along with recent inductive inference methods for VAR innovation identification (Swanson and Granger 1997; Hoover 2005). We use these inductive methods as they are less ad hoc in modeling contemporaneous causal orderings, being based on conditional correlations found in the data, rather than the more arbitrary ordering in traditional Choleski decompositions. In a DAG, arrows represent the direction of information flow between variables. For example, X Y indicates that variable Y is caused by variable X. A line connecting two variables, say A B, indicates that A and B are connected by information flows, but the algorithm cannot determine if A causes B or vice versa. Moreover, no arrow is allowed to direct from one variable all the way back toward itself. The causal inference method used here (PC algorithm with a DAG representation) is able to inform us about such contemporaneous relationships under some generally mild conditions (Markov probability factorization, causal sufficiency, and faithfulness; see Pearl 2000 and Spirtes, Glymour, and Scheines 2000 for discussions of these conditions). Results of our application of this causal inference algorithm can be compared to a priori knowledge of a structural model suggested by economic theory or subjective intuition (Park, Jin, and Bessler 2008). Accordingly our methods do not preclude interactions between theorists and empiricists. While we do not engage in such here, Awokuse and Bessler (2003) do compare a theorist s a priori structure and the output of this algorithm with generally favorable results. (See also the work of Wang and Bessler (2006) on the use of goodness of fit tests for overidentifying structural representations.) With the above method, the dynamic interactions among variables in the model can be explored by various types of innovation accounting analysis, consisting of the impulse response function, the forecast error variance decomposition, and the historical decomposition. Initially we expect that the future price for lean hogs will exhibit abnormal drops when the media coverage referring to swine flu is revealed; further, we expect that the influence of the media will decline over time as discussed in 10 The PC algorithm is an ordered set of commands that begins with a general unrestricted set of relationships among variables. Neyman-Pearson type statistical tests of partial correlation are used to remove edges between variables and direct causal flow. For more details about this algorithm see Spirtes, Glymour, and Scheines. 6

7 The Effect of H1N1 Beardsworth and Keil (1996). 11 However, it is more difficult to hypothesize what happens to the live cattle, corn, and soybean price. Data The data used for analysis are daily nearby future prices, collected from DataStream, on lean hogs, live cattle, corn, and soybeans, for the period from December 17, 2007 to December 16, 2009 and traded on the Chicago Board of Trade. The data thus are comprised of 523 daily observations. This study constructs the index of media coverage as a demand shifter as discussed in the section of supply and demand specifications. For the supply shifter, the data on trade bans from various countries on U.S. meat related to H1N1 is collected from the USMEF. The daily cases of confirmed deaths caused by the H1N1 outbreak are provided by the European Centre for Disease Prevention and Control (ECDC). To quantify the revenue loss created by the media coverage, we collected the daily hog slaughter quantity and its average carcass weight statistics from the United States Department of Agriculture (USDA). Plots of data are given in figure 1. Notice that the price of lean hogs dropped, while the price of corn and soybean increased on April 26 when the U.S. Government declared a public health emergency related to H1N1. The price of live cattle seems to be more stable than the other prices. The media index shows a rapid initial rise in the number of articles, which finds a peak between the last weeks of April and the beginning of May. Between March 24 and December 16 there were 765 articles, of which 55.03, 28.50, and percent were published in the second, third, and fourth quarters of 2009, respectively. The number of daily confirmed deaths fluctuates around several local peaks. Empirical Results Empirical results from the subset VAR All series are studied in their natural logarithm form, excepting the number of confirmed deaths. As an initial step, the data were tested for the order of integration using the Augmented Dickey-Fuller (ADF) test. The null hypothesis is that each series is itself nonstationary. Here we fail to reject the null hypothesis for each variable. This confirms that the series are nonstationary in levels and stationary in first difference, as visual inspection of the data suggests in figure 1. We studied each nonstationary series in its levels, following Sims (1980), and Nerlove, Grether, and Carvalho (1979). These authors allow the nonstationary in one series to cancel the nonstationary in the other series. (We report evidence on residuals to support this approach below.) Following Hsiao (1979), this study ranks each series as to its importance as a manipulated variable in the multivariate expression of each other 11 Beardsworth and Keil (1996) classify public reaction in the following five stages: (1) initial equilibrium characterized by a lack of concern about the potential food risk factor; (2) news about a novel and potential risk factor; (3) public concern increases as the risk factor becomes a major element of interest in the media coverage; (4) public response begins, usually with avoidance of the suspect food item; (5) public concern gradually decreases, leading to the new equilibrium. 7

8 Applied Economic Perspectives and Policy Figure 1 Plots of historical data on the nearby future prices, the media index, and number of daily confirmed deaths, March 24 December 16, 2009 Notes: Prices of lean hog and live cattle are in dollars per hundredweight; prices of corn and soybeans are in dollars per 100 bushels; the index of media coverage is illustrated by the number of daily articles; and the confirmed deaths are shown by the number of cases per day. Source: 1 Thomson Reuters Datastream; 2 Author s calculation; 3 the European Centre for Disease Prevention and Control series. The subjective rankings on the importance of each series are provided in table 1. We then apply the BIC to each series, where manipulated variables are considered in the specified order of importance and the univariate specification of each series (found previously) is taken as given using OLS regression. A final step involves fixing the representation of lagged variables determined last and reexamining the BIC for variables whose lags were determined earlier. 12 The final models selected were estimated with the near VAR in Regression Analysis of Time Series (RATS). Estimated coefficients of the subset VAR are shown in table 2. Overall each equation has a high coefficient of determination (Adjusted R-squared). The Ljung-Box statistic (Q-stat) with a lag length five in each equation is not statistically significant, suggesting that the null hypothesis of white noise residuals cannot be rejected. 13 Further, ADF test results on 12 We also checked for robustness by performing experimentation on different orderings and found that the multivariate specifications were unchanged. 13 We performed a robustness check by recalculating the Q-statistic for lags between 1 and 10 in each equation and found the Q-statistic in each equation remained statistically insignificant. 8

9 The Effect of H1N1 Table 1 Rankings on order of importance in multivariate specifications on each series Rank Lean hog Live cattle Corn Soybean First Live cattle Lean hog Live cattle Corn Second Corn Corn Lean hog Live cattle Third Soybean Soybean Soybean Lean hog Fourth Media Media Media Media Table 2 Estimated results of the subset VAR ln LH t ln LC t ln CN t ln SOY t ln MEDIA t ln LH t *** n.a n.a. n.a. (0.0131) (0.0149) ln LC t ** *** n.a. (0.0199) (0.0148) (0.0321) (0.0299) ln CN t 1 n.a ** *** n.a. (0.0049) (0.0176) (0.0161) ln SOY t 1 n.a. n.a *** n.a. (0.0172) (0.0454) ln SOY t 2 n.a. n.a. n.a n.a. (0.0441) ln MEDIA t ** n.a. n.a. n.a *** (0.0021) (0.0432) ln MEDIA t 2 n.a. n.a. n.a. n.a *** (0.0442) DEATH t 1 n.a. n.a. n.a. n.a (0.0003) DEATH t 2 n.a. n.a. n.a. n.a (0.0003) DEATH t 3 n.a. n.a. n.a. n.a * (0.0003) 1(TB t ) ** *** (0.0047) (0.0018) (0.0050) (0.0041) (0.0586) Q *** n.a. (0.0041) (0.0017) (0.0046) (0.0035) Q *** ** n.a. (0.0041) (0.0020) (0.0048) (0.0048) Q n.a. (0.0029) (0.0015) (0.0036) (0.0033) Constant *** *** (0.0952) (0.0475) (0.1274) (0.1056) (0.0191) Q-stat(5 0) Adj R * Significant at the 10 percent level; ** significant at the 5 percent level; *** significant at the 1 percent level; n.a. ¼ not applicable; and standard errors in ( ) Notes: LH ¼ lean hog; LC ¼ live cattle; CN ¼ corn; SOY ¼ soybean; MEDIA ¼ media coverage; TB ¼ trade ban; DEATH ¼ number of confirmed deaths from H1N1; Q-stat ¼ the Ljung-Box statistic; Q2, Q3, and Q4 ¼ the quarterly dummy variables in the second, third, and fourth quarters, respectively. 9

10 Applied Economic Perspectives and Policy Figure 2 Directed acyclic graph on innovations on future prices Notes: MEDIA, CN, SOY, LC, and LH are innovations of the media coverage and the futures prices of corn, soybeans, live cattle, and lean hogs, respectively. We used the knowledge command in the TETRAD software. observed residuals reveal that no unit root behavior in the residuals is present. We find that a one period lag over each left-hand-side variable is significantly correlated with itself in the current time period with 1 percent statistical significance, as shown in table 2. We find the one period lag of the media coverage index, and the dummy variable associated with the trade bans, are negatively correlated with the nearby lean hog futures price with 5 percent statistical significance, although their magnitudes are small. The small negative magnitude of the trade ban may reflect efforts by the USDA and others to reopen pork export markets. The trade ban and the third lag of the number of confirmed deaths enhance the number of H1N1 related articles with 1 and 10 percent significance, respectively. Finally the lean hog prices in the second and third quarters, and the live cattle price in the second quarter, are statistically higher than in the first quarter. Directed graph and innovation accounting analysis To answer the three questions stated above, the analysis of directed graphs is carried out using the software TETRAD IV (Spirtes et al.), which uses the PC algorithm. Based on Spirtes, Glymour, and Scheines (2000), and since our sample size is greater than 300 observations, we derive the causal structure using the PC algorithm, employing a statistical significance level of 10 percent for local independence tests applied to the contemporaneous time period. Figure 2 illustrates the directed acyclic graph on the causal relationship between innovations on future prices and the index of media coverage. The results in figure 2 suggest, in contemporaneous time, the innovations of soybean futures prices directly affect innovations in the futures prices of corn, and the innovations of live cattle futures prices are directly affected by innovations in the futures prices of corn. Finally innovations in the media coverage, the soybean price, and the lean hog price are exogenously determined. The dynamic response of each future price series to one-time shocks in each series is demonstrated in figure 3. We find that the response of each series to a shock in itself is positive and strong (diagonal elements in figure 3). We also find a one-time shock in media coverage decreases the lean hog price with a very small positive effect on the other futures prices. 10

11 The Effect of H1N1 Figure 3 Response of each future price series to a one-time-only shock in each series Notes: Each graph gives the response of each series listed in the row heading (left-hand-side identifier) to a one-time only shock in the series listed in the column heading. The responses are normalized; each response is divided by the standard error of the innovations for that series, which allows the series responses to be compared. Table 3 Forecast error variance decomposition of four future prices Horizon (days ahead) Lean hogs Live cattle Corn Soybeans Media Lean hogs Live cattle Corn Soybeans Notes: Error decompositions are partitions based on observed innovations from the estimated error correction model (Doan 2006). The entries are percentages summing to one hundred (within rounding error) for any particular row. The interpretation of each row is as follows: looking ahead at the horizon given in the far left-hand column (0, 1, 5, or 10 days), the uncertainty in the future prices of agricultural commodities for the series given in the centered subcategory (e.g. lean hogs) is attributable to variation in each series labeled as the column heading. We also find that these price impacts are temporary and decreasing over time and finally converge to zero. A more precise measure of the dynamic interactions can be developed through forecast error variance decomposition. We decomposed the forecast error variance in each series at various horizons (table 3). In the short run, each future price series is principally explained by the information 11

12 Applied Economic Perspectives and Policy arising in its own market (there is little dynamic influence of other series at horizons of 0 and 1 day), excepting the corn price. In the longer run, other factors enter with: Soybeans becoming important in explaining corn prices Corn and soybeans becoming important in explaining live cattle prices Media coverage accounting for 2.54 and 5.27 percent of the variation in the lean hogs futures price after five and ten days, respectively Media coverage has little effect on other futures prices. From the error variance decompositions, we see lean hog market price is affected primarily by itself, the live cattle price, and the media reports in both the very short- and the longer-run horizons. Further we find that the lean hog price has little impact on other prices. These results have direct implications for revenue losses, which we turn to in the next section. Finally the forecasting ability of the above specification was studied to give us a sense of how well the model performed with a reasonable environment of usage. We fit our subset VARs from December 17, 2007 to May 5, 2009 and forecasted recursively a one-step horizon (one day ahead with recursive parameter estimate updating) through the out-of-sample dataset, following Haigh, Nomikos, and Bessler (2004). At each data point in the out-of-sample period, we reestimated the subset VARs before forecasting the new 1-step ahead horizon. We find that, in the main model, the root mean squared error is very small, which is equal to , and the Theil U-statistic is equal to This last result suggests our model does not perform better than a random walk; however, we are able to attain this threshold by including forecast information on the explanatory variable (media behavior), indicating that our model has relatively fair forecasting ability. 14 Put another way, the knowledge of media behavior does not deteriorate forecast performance, relative to an efficient market forecast (a random walk), and provides us with information relevant to policy analysis. Revenue loss measurement Here we address the reduction in the revenue associated with the media coverage regarding the name swine flu. We focus on the revenue loss in the lean hogs market, since the previous results indicate that there was no meaningful effect of the media coverage on other markets. To do this we use a historical decomposition method on the estimated VAR innovations (see Park, Jin, and Bessler 2008 for the algebraic details). Figure 4 illustrates the decomposition for the lean hog future price caused by the shock in the media index. The difference between the media effect dashed line and the base line projection (LH_trend) of the 14 Theil U s greater than 1.0 indicates relatively poor forecasting ability; whereas values less than 1.0 indicate improved forecasting ability (relative to the random walk). We also quantify the root mean square error and the Theil U-statistic of the exact model, in which the variable of the media coverage is dropped out. We find that both root mean square error and the Theil U-statistic are greater than that of the model with the media coverage index, indicating that incorporating the media coverage index in the model enhances the model s forecasting ability. We thank a reviewer for the useful suggestion of examining this aspect of the model. 12

13 The Effect of H1N1 Figure 4 Plot of historical decomposition on the lean hog future price caused by the shock of the media coverage index Note: The x-axis gives the number of trading days before (negative) and after the event where the event occurred on the day numbered as zero (April 26, 2009). Figure 5 Contribution of each future price series and the media scare index on the lean hog future price when responding to the H1N1 outbreak Notes: Each stacked bar illustrates the ( positive or negative) contribution of four price series and the index of the media coverage to the innovation of the lean hog prices. The solid line represents the deviation of the actual lean hog price from the base projection. The x-axis gives the number of trading days before and after the event, where the event occurred on the day numbered as zero (April 26, 2009). lean hog future price implies that there is a strong influence exerted by the media coverage index on the future price of lean hogs for much of the first half of the selected period. The difference between the actual line (LH_actual) and the base line projection (LH_trend) is accounted for by the sum of contributions from innovations from the index of media coverage and innovations for all future prices. This reveals that, during about the first fifty trading days after the H1N1 outbreak, the future hog market was negatively affected by the H1N1 outbreak media coverage. Figure 5 demonstrates the estimated contributions from each future price series and the index of media coverage to the market prices of lean hogs. The media coverage contribution reaches a maximum negative influence of $15.10 per head on May 20, 2009, about eighteen trading days after the announcement date, and remains negative for most of the first half of the study period. This temporary or transitory shock supports the 13

14 Applied Economic Perspectives and Policy stages of public reaction as discussed in Beardsworth and Keil (1996). Moreover a major factor that helps to reduce the impact of this media coverage comes from the efforts of many organizations, including the USDA, sending the clear message that eating pork is safe and convincing the mass media to change the label they used for the malady from swine flu to 2009 H1N1. The results also show that the live cattle future price makes a significant, positive contribution to the lean hog future price in the first half of the study period, which implies that there exists demand switching back to the lower-price lean hogs. Corn and soybean future prices exhibit a small contribution across the study period. To develop a revenue loss estimation, we assume that the future price today can signal slaughtered hogs in the future. To calculate the revenue loss, we sum the product of the average change of future price in each month (from April to December) and the monthly slaughtered hogs of the next period when the next deliverable contract begins. 15 From April 27 to December 16, 2009, we find that the revenue loss associated with the media coverage amounts to about $200 million or 2.51 percent of the total value of sales. Considering as well other futures price series, we find that total revenue loss, summation of the revenue loss generated by shocks in each series, is equal to about $140 million during the same period. Live cattle, corn, and soybeans actually help to improve the revenue in the lean hogs market. Robustness checks We pursued three alternative approaches for checking the robustness of our results. Overall we find that the results from these three approaches support our main findings. The first approach employs that of a standard event study (as done in Mackinlay 1997, for example). This approach offers us an alternative quantification of the impact of the H1N1 outbreak, in general. Here we find, at the 1 percent level of significance, that the outbreak negatively influences the lean hog future price and creates a calculated revenue loss in the lean hog market of about $460 million. This approach serves as an upper bound on revenue loss since there are factors beyond H1N1 that influence futures prices, such as global economic conditions. We also note this approach cannot measure the impact of media coverage directly. For the second approach, we removed the quarterly dummy variables, ignoring seasonal influences, and narrowed the period of study using data from March 24 to December 16, 2009 to capture the effect of the outbreak just before it began. Here we find that the media index still significantly influences the lean hog price (at the 10 percent level). The total revenue loss and the revenue loss generated from the media coverage in the lean hog market are equal to $50 and $73 million, respectively. This indicates that seasonality influences the results. 15 Based on the definition of the continuous future prices from the Datastream data source, the nearest deliverable contract forms the first value in the series. For example, during May, prices for the June contract are taken for the continuous series. Since the future lean hog contract months consist of February, April, May, June, July, August, October, and December, we calculate our new revenue loss by summing the product of the average change of future price in each month, say May, and the monthly slaughtered hog quantity, in the month when the contract month begins (June in this case). 14

15 The Effect of H1N1 For the third approach, we tried alternative media index related specifications. Namely we employ the estimation and analysis using the cumulative number of news releases, not the daily media index, taking into account the fact that fear and behavioral response could result from cumulative exposure (although we note using the daily media index in our model naturally allows us to take into account both the wear-out effects and cumulative effects as recommended by a reviewer). We find similar results to those before, where the effect of cumulative media coverage is negatively correlated with the futures price of lean hogs with 5 percent statistical significance and that the estimated revenue losses with and without seasonal adjustment from the media coverage are equal to $121 and $156 million, respectively. 16 Comparison with other studies We compared our results with other available estimates. We found that our estimated revenue losses from the main and alternative approaches are lower than what were reported from the USTR and National Hog Farmer (2009). 17 The main difference comes from the fact that both of those studies employ price and quantity changes based on market conditions in the first few days after the outbreak and initial media coverage, which were somewhat more severe than the eventual market implications. Our estimated revenue losses are also in the neighborhood of the losses estimated by the National Pork Producers Council (NPPC). Their request was for $250 million in financial assistance from the USDA, to recover losses from the pork crisis in August 2009 (NPPC 2009). 18 Conclusions and Policy Implications We have estimated the market impact associated with media coverage related to swine flu on the nearby future prices for lean hogs, live cattle, corn, and soybeans traded on the Chicago Board of Trade, then quantified the revenue loss by employing the subset VAR model along with a set of procedures combining the directed acyclic graphs (DAGs) and Bernanke s (1985) methods of structural vector autoregression (SVAR) modeling. We studied daily data from various sources such as DataStream, the ECDC, and the USDA. These methods allow us to view the dynamic evolution of responses of prices of related agricultural commodities in a neighborhood of the H1N1 event. The DAG representation of contemporaneous structure on innovations (new information in each daily observation) allows research workers to be quite specific about underlying time series structure. While we did not provide comparisons between our PC-generated structure and subjective, a priori, structural representations, researchers can easily make such comparisons if questions on underlying structure are points of debate (see the discussion offered above on comparisons 16 Due to the limited space, details of the three alternative approaches are not included, but they can be provided by the authors upon request. 17 Compared to USTR, our revenue loss is $ million in the second quarter of The NPPC asked the USDA to (1) purchase an additional $50 million of pork for various federal food programs in each fiscal year (2009 and 2010); (2) use $50 million to purchase pork for the program, which uses customs receipts to buy non-price-supported commodities for school lunch and other food programs; (3) use $100 million for addressing the H1N1 virus for the swine industry. 15

16 Applied Economic Perspectives and Policy of a priori structural representations versus inferred structure from PC-Algorithm). Our results indicate that the media coverage was associated with a significant but temporary negative impact on the lean hog futures prices, but with little impact on the other nearby futures prices. The impact persisted for about four months, reaching a peak price influence of a $15 per head reduction, yielding an April December 2009 market revenue loss of about $200 million (about 2.51 percent). We also find that trade bans negatively affected the hog futures prices despite the World Organization for Animal Health (OIE 2009) statements that they were not justified. This is also likely related to the swine flu label. Several policy implications can be drawn. First the results indicate that it would be desirable if the public policy and health communities exercised care in labeling outbreaks, avoiding names related to commodities not directly involved with an outbreak, as such actions can cause food safety concerns and can be inadvertently costly to the industries associated with those commodities. Many media even a year later continue to use the swine flu label, although the illness s official name was changed. Policymakers dealing with risk communication might find it beneficial to follow the tenets of the risk communication literature and get training in media skills, work to establish long-term relationships with members of the media, and include a media relations person on any crisis response team (FAO and WHO 1999). Second, the results imply the appropriateness of the one-health (Kaplan, Kahn, and Monath 2009) approach which incorporates human and animal health agencies in agriculturally related disease response. This approach could well bring an animal effect consideration into disease management decision making, avoiding unnecessary commodity market effects. Third, preplanning and information generation is desirable with respect to possible zoonotic disease outbreaks regarding food safety effects. This would involve both research activities on food safety consequences and mitigation plus associated educational efforts/information creation. Fourth, it appears that agencies like USDA might need to form a group that reviews real-time disease related developments, looking for those that might have commodity market implications. Such a group could raise warnings about possible undue food safety concerns. Fifth, our results indicate that commodity market effects lagged behind the outbreak by about a week. Thus pre-emptive action might have been able to allay fears regarding food safety. Thus, watchdog groups or a onehealth approach could move to soothe food safety fears sometime after an improper labeling occurs. Such actions, in combination with the OIE statements that trade bans were not justified on a disease spread basis, could potentially have limited commodity market effects. Sixth, our findings indicate that pork industry compensation may be appropriate as the swine flu label and resultant publicity appears to have damaged their markets. Perhaps favorable action should be taken on the NPPC (2009) request for $250 million in financial assistance. Finally, we cannot conclude without a word of caution. Past events have shown that public statements regarding food safety have on occasion been misleading or excessively optimistic and, obviously, a balance needs to be struck between reassuring the public and knowing that the reassurance is truly accurate. 16

17 The Effect of H1N1 Acknowledgments The authors would like to thank two anonymous reviewers for their valuable comments, which greatly improved the quality of our work. Funding This study was partially funded by the Foreign Animal and Zoonotic Disease Defense Center, a Department of Homeland Security National Center of Excellence at Texas A&M University. References Awokuse, T., and D.A. Bessler Vector Autoregressions, Policy Analysis and Directed Acyclic Graphs: An Application to the U.S. Economy. Journal of Applied Economics 6: Beardsworth, A., and T. Keil Sociology on the Menu: An Invitation to the Study of Food and Society. London, UK: Routledge Press. Bernanke, B.S., Alternative Explanations of the Money-Income Correlation. Carnegie-Rochester Conference Series on Public Policy 25 (1): Blendon, R.J., G.K. SteelFisher, J.M. Benson, K.J. Weldon, and M.J. Herrmann Swine Flu (H1N1 Virus) Survey. Harvard Opinion Research Program, Harvard School of Public Health. Bomlitz, L.J., and M. Brezis Misrepresentation of Health Risks by Mass Media. Journal of Public Health 1: 1 3. Brown, D.J., and L.F. Schrader Cholesterol Information and Shell Egg Consumption. American Journal of Agricultural Economics 72: Brüggemann, Ralf, and Helmut Lütkepohl Lag Selection in Subset VAR Models with an Application to a U.S. Monetary System. In Econometric Studies: A Festschrift in Honor of Joachim Frohn, ed. Ralph Friedmann, Lothar Knüppel, and Helmut Lütkepohl, London: LIT VERLAG Münster. Burton, M., and T. Young The Impact of BSE on the Demand for Beef and Other Meats in Great Britain. Applied Economics 28: Doan, T., RATS: User s Guide Version 6.2. Evanston, IL: Estima. FAO and WHO The Application of Risk Communication to Food Standards and Safety Matters. Report of a Joint FAO/WHO Expert Consultation. Rome, Italy. Foster, W., and R.E. Just Measuring Welfare Effects of Product Contamination with Consume Uncertainty. Journal of Environmental and Resource Economics and Management 17: Haigh, S.M., N.K. Nomikos, and D.A. Bessler Integration and Causality in International Freight Markets: Modeling with Error Correction and Directed Acyclic Graphs. Southern Economics Journal 71 (1): Hoover, K.D Automatic Inference of the Contemporaneous Causal Order of a System of Equations. Econometric Theory 21: Hsiao, C Autoregressive Modeling of Canadian Money and Income Data. Journal of the American Statistical Association 74: Johnson, R Potential Farm Sector Effects of 2009 H1N1 Swine Flu: Questions and Answers. CRS Report for Congress. Congressional Research Service. Kaplan, B., L.H. Kahn, and T.P. Monath The Brewing Storm: One Health - One Medicine Linking Human, Animal and Environmental Health. Veterinaria Italiana 45 (1):