Agribusiness Profitability Impacts of Highly Pathogenic Avian Influenza Outbreak. Jada M. Thompson, University of Tennessee,

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1 Agribusiness Profitability Impacts of Highly Pathogenic Avian Influenza Outbreak Jada M. Thompson, University of Tennessee, Carlos Trejo-Pech, University of Tennessee, Dustin Pendell, Kansas State University, Selected Paper prepared for presentation at the 2018 Agricultural & Applied Economics Association Annual Meeting, Washington, D.C., August 5-7, 2018 Copyright 2017 by Jada Thompson, Carlos Trejo-Pech, and Dustin Pendell. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.

2 Agribusiness Profitability Impacts of Highly Pathogenic Avian Influenza Outbreak Jada M. Thompson 1, Carlos Trejo-Pech 1, and Dustin Pendell 2 1 University of Tennessee, Dept. of Agricultural and Resource Economics, Knoxville, Tennessee 2 Kansas State University, Dept. of Agricultural Economics, Manhattan, Kansas INTRODUCTION Publicly traded firms are subject to market fluctuations and price changes in response to a number of events including public policy changes, changes in overall market health, or events affecting production or branding. Agribusiness firms face additional risks associated with agricultural production that are beyond firm control such as weather or disease. While there are best practices that attempt to limit the effects of these risks, by their nature they can substantially affect the supply chain and consequently, firm value. The length of the price effects can be shortlived or, in devastating cases, can lead to firm closures. In terms of production, animal disease events specifically can create disruptions along the supply chain that may lead to changes in a firm s value by creating risk and uncertainty in operations which affect investor decisions. While apparently separated from animal agriculture, the market effects from an animal health events could lead to a depression on affected firm values due to changes in forecast cash flows as a result of changes in the costs of production or supply limitations. Depending on the severity of the disease event, such as the disease being prolific and/or having substantial implications for production, consumption, or trade, the firm impacts could be great. For example, an individual outbreak of bovine spongiform encephalopathy (BSE) in 2003 led to long term international trade restrictions beyond the official conclusion of the disease. While the effects of a disease could be industry wide, there are internally diversified firms that could be less affected than a single product firm. The result of 1

3 the changes in firm value could be an increase in the favorability for proactive and preventative measures to reduce disease occurrence and expedite its conclusion. The policy effects of animal diseases on firm values can potentially generate motivation and funding to support increased biosecurity, surveillance, and best management practices to limit the potential market disruptions and firm value fluctuations. This work is the first to analyze the effects of the highly pathogenic avian influenza (HPAI) outbreak in U.S. poultry on stock values for agribusinesses food processors using an event study methodology. This contributes to the literature by estimating the effects of HPAI events on firm values for meat processors, which provides firms more robust information in determining the effects of disease events. Results can also provide the additional industry costs of a disease event not previously considered in policy making and management practice determination. DISEASE STUDIES AND BACKGROUND Studies consistently show the negative effects disease outbreaks have on consumers, producers, industries, and trade in the affected industry (Costello and McAusland 2003; Schlenker and Villas-Boas 2009; Johnson et al. 2015). From the local community to international markets, the effect of a highly contagious animal disease like HPAI can be far reaching. Local communities can be impacted depending on regional characteristics, production practices, and disease specific factors (Pendell et al. 2007). Diseases affect trade in both the exporting and importing markets where these effects are greater for countries that rely heavily on export markets such as Brazil (Costa, Bessler and Rosson 2015) or the United States (Crowley and Shimazaki 2005). Agribusiness firm profitability may also be affected directly through supply shocks or indirectly 2

4 through consumer perceptions or trade changes. HPAI outbreaks are expected to change agribusiness firm profitability through both mechanisms. Highly Pathogenic Avian Influenza Highly pathogenic avian influenza, or historically known as fowl plague, is a disease reportable to the World Organization for Animal Health (OIE) of highly pathogenic strains of influenza A viruses (OIE 2015). HPAI is considered a highly contagious disease of poultry causing acute clinical disease leading to high morbidity and high mortality. HPAI is differentiated from low pathogenic avian influenza (LPAI) with mortality rates, where mortality rates above 75% within 10 days are considered highly pathogenic strains. Most HPAI viruses occurring in poultry have been H5 and H7 subtypes, named based on their hemagglutinin (H) antigens. The virus is spread through direct contact with sick or infected poultry as well as through fomites. Additionally, HPAI can be zoonotic with high species specific transmission barriers, and can be fatal to humans which can cause social and economic effects as a result regardless of whether transmission has occurred (USDA:APHIS 2015). HPAI is a global concern and has been reported in countries all over the world since the first formal record of it in 1878, even though many believe that it predates those records (Lupiani and Reddy 2009). Global incidences have been increasing driven by many factors including increased globalization and travel, cultural shifts in production practices, and urbanization as well as advancements in detection, testing, and reporting. In the United States, HPAI was first detected in 1924 in live bird markets, and later in 1983 the first large-scale outbreak in commercial poultry occurred (Lupiani and Reddy 2009). The largest animal health emergency in U.S. history to date was a HPAI outbreak in commercial and backyard flocks from December 2014 to June 2015 leading to $879 million in federal expenditures and the destruction of 7.5 3

5 million turkeys and 42.1 million egg-layer and pullet chickens (Johansson, Preston and Seitzinger 2016). The total costs to the federal government were minimized with the use of proactive risk assessments that provided scientific basis for movement permits to aid in business continuity during the outbreak (Thompson and Pendell 2016). However, no estimate of the effects of the outbreak had on agribusinesses have been assessed. This work aims to provide an indication of the effect of the outbreak of HPAI on agribusiness firm values. Disease Event Studies Event studies have a long history of providing valuable insights to the effects of market shocks such as disease outbreaks, food safety recalls, or changing regulatory practices (Thomsen and McKenzie 2001; Mazzocchi, Ragona and Fritz 2009; Pozo and Schroeder 2016). The first event study published by Dolley (1933) used common stock prices to estimate the effects of stock splits. While methodologically basic, Dolley provided a foundation that was later improved and culminated in the seminal works by Ball and Brown (1968) and Fama et al. (1969). These analyses introduced the methodology used in today s event studies. There have been many modifications to the methodology to address issues or specific hypotheses, but the fundamentals have remained the same. MacKinlay (1997) and Binder (1998) provide historical review of event studies in economics, noting the methodological evolution, testing, and issues related in estimation and evaluation. Event studies continue to be useful in economic literature as reliable and consistent estimators of the impacts of shocks to security prices. Event studies provide estimates of the changes in firm value during an event. An event affecting food and food supply is expected to change the cost structure for a firm through either physical or perception-based drivers such as a food safety concern or a perceived human or animal health risk (Thomsen and McKenzie 2001; Attavanich, McCarl and Bessler 2011). 4

6 However, it is often not economically feasible or possible to collect sufficient data to estimate these cost structures. In order to estimate the impact of an event on a firms profitability a proxy for changes in firms cost structures can be used. Stock prices provide a readily and publicly available source for firm profitability since they represent the net present value of firms future profits (i.e., cash flows) discounted by a risk-adjusted cost of capital. Event study methodology assumes that due to the rationality of the market, stock prices immediately reflect changes in business profitability. These profitability changes manifest through abnormal market shocks as a reflection of inherent changes in private cost changes by the affected firms (Pruitt and Peterson 1986; Salin and Hooker 2001; Pozo and Schroeder 2016). Event studies have been useful in estimating the effect on firm profitability during disease outbreaks. Jin and Kim (2008) estimated the effect of the bovine spongiform encephalopathy (BSE) in the United States on agribusiness and food processing firms. They compared the effects to the beef sector as well as substitute sectors to show that while beef sector security prices were negatively affected, substitute sectors had greater positive gains during the outbreak. Pendell and Cho (2013) similarly estimated the impact on agribusiness and food processing firm profitability for five food-and-mouth disease (FMD) outbreaks in South Korea finding negative reactions for industries with negative outlooks as a result of the outbreak. Allied industries that were not affected by the FMD outbreaks were positively affected, except in the case when simultaneous events were also present in that industry, e.g., highly pathogenic avian influenza found in Korean poultry. EVENT STUDY METHODOLOGY The event study methodology is generally comprised of several stages including establishing the event to be studied, the time periods to be estimated, the firms to be included in the analysis, and 5

7 finally estimating abnormal stock returns, if any. Abnormal returns provide an estimate of the effect of the event (e.g., HPAI) on firm profitability. Researchers may further implement objective-specific modeling. For instance, they may want to model abnormal stock returns during the particular event as a function of firm specific characteristics. The observations T are separated into mutually exclusive sub-periods: the estimation window and the event window. The estimation window, 255 days in our study, precedes the detection announcement, (tî[t1 + 1, T2]). The event window contains the actual announcement period (t = 0) and extends until the end of the event study window (tî[t2 + 1, T3]). To determine which firms should be included in the analysis some selection criteria should be used. These criteria can be based on data availability, predominant industry of firm, or a financial threshold, but all criteria should minimize any biasness in sample selection. To estimate the effects of an event on firm profitability, a measure for abnormal returns should be used. Abnormal returns are the actual daily stock returns of a firm during the event window minus the expected (i.e., normal) returns of the firm. Expected returns are estimated with an asset pricing model by using stock prices of the firm and market index prices during the estimation period. For the firm i the abnormal return is:!" #$ = " #$ '(" #$ * $ ) (1) where!" #$, " #$, and '(" #$ * $ ) are the abnormal, actual, and normal returns subject to conditioning information, * $, in time period t. The normal returns predict what the expected returns would have been in the absence of the event which provides a counterfactual to estimate the abnormal returns. Traditionally there are two models that have been used to estimate normal returns: constant mean return model and the market model. The constant mean return model limits the 6

8 normal returns to a function of a constant mean return for firm i plus a disturbance term to account for volatility, but in doing so ignores any factors the market plays in determining firm value. The market model estimates the firm values as a function of a market portfolio assuming that the firm value deviates from this relationship during event periods. The market portfolio can be represented by a global index like Standards and Poor s (S&P) Global, a national based stock index such as the S&P 500, or exchange specific like the NASDAQ-100 or NYSE US 100. The market model for time t can be expressed as: " #$ =, # +. # " /$ + 0 #$ (2) where " #$ is the stock return for firm i at day t, " /$ is the return of the market portfolio,, # and. # are estimable parameters, and 0 #$ is the error term assumed to be independent and normally distributed with a mean of zero. In order to make inferences on the effects of an event, the abnormal returns must be aggregated over the event window in a measure called the cumulative abnormal returns (CAR). For this study, these windows varied by 3, 5, and 10 days prior to the event and 3, 5, and 10 days after the event to better understand the short term abnormal returns. Each window is tested with the Standard Cross-Sectional Z and the Portfolio Time-Series (CDA) T test to determine statistical significance. DATA We analyze three HPAI events that occurred on December 18, 2014; January 23, 2015; and March 4, These are the dates when the HPAI disease outbreaks were officially confirmed by the U.S. Department of Agriculture Animal and Plant Health Inspection Service (2016). The first date represents the initial detection of HPAI in an Oregon backyard flock. The January date represents the movement of HPAI to commercial layer farm in California. For both of these 7

9 events, the cases remained relatively isolated. The HPAI was suspected to be spread by wild bird migratory patterns through North American flyways. The third date, March 4, 2015 signified the movement of HPAI from the Pacific flyaway to the heavy poultry concentrated Mississippi flyway. The majority of the birds affected during the HPAI event were concentrated in the Mississippi flyway. These three dates represent three milestones in the HPAI event progression and provide a rich understanding of how these events affect meat processing firm values. Since confirmation announcements are preceded by pre-announcements (e.g., starting dates, when the disease is first detected) and are followed by reports that continue throughout the disease event and may signal the magnitude of economic damage, we evaluate event windows rather than specific event dates. In general, the event study methodology suggests the use of event windows to account for potential leaking of information before announcement dates and subsequent reactions the days following announcements as investors adjust their portfolios. We define and evaluate the following event windows, with 0 representing the announcement date: - 10 to 0, 0 to +10, -5 to 0, 0 to +5, -3 to 0, and 0 to 3. In addition, we use 255 trading days as the estimation period (i.e., the period to estimate expected returns), with the estimation period ending 15 days before the event announcement. Firms in the sample are taken from two lists, the 2013 top 100 meat and poultry firms listed in Meat and Poultry (Crews 2013) and the Investsnips list of publicly traded meat producers (2015). Since these lists contain both public and private agribusinesses, we selected only publicly traded firms, for which an event study can be conducted. We searched for Central Index Key (CIK) identifiers of publicly traded agribusinesses on the U.S. Securities and 8

10 Exchange Commission (SEC) s EDGAR website 1. CIKs are used to retrieve financial data needed for the event study. We use Eventus to conduct the event study. Eventus is a software by Cowan Reseach, L.C., which uses stock prices and index prices provided by The Center of Research for Security Prices (CRSP) at the University of Chicago (Cowan 2005). We accessed Eventus via Wharton Research Data Services (WRDS). Eventus has been used in studies published recently in highquality journals (Cicon 2017; Janney and Gove 2017). Our final sample contains the following seventeen publicly traded firms in the lists referred above and with available data in CRSP-Eventus: (1) Sanderson Farms, (2) Alico, (3) Aoxin Tianli Group, (4) Kraft Foods, (5) Bob Evans Farms, (6) Leucadia National, (7) Cal Maine Foods, (8) Hormel Foods, (9) Seaboard, (10) Sysco, (11) ConAgra, (12) Tyson Foods, (13) Cresud S A C I F y A, (14) Industrias Bachoco, (15) B R F S A, (16) Mondelez International, and (17) Pilgrims Pride. Since each company is evaluated with three events, our sample contains 51 (17x3) firm-events. Table 1- Market model parameter estimates and estimation period statistics Mean Median Mean Total Returns % of Positive Raw Returns 52.6% 52.8% Alpha Beta Market Model Positive Residuals 49.6% 50.2% Residual Standard Deviation Table 1 provides mean and medians of selected statistics for the estimation period (255 trading days up to 15 days before the events). The aggregate systematic risk of agribusinesses in 1 Accessed on April

11 the sample is below the risk of an average American public company (i.e., below 1.0). As expected, alpha is around zero, suggesting no abnormal returns during the estimation period. Around half of the firms-days yield positive daily raw returns. RESULTS AND DISCUSSION Overall the results show significant effects on firm values as a result of the HPAI event in U.S. poultry. The results are separated into aggregate a composite of all events together similar to Pozo and Schroeder (2016) and individual results for each event. The disaggregation shows the finer detail in estimates of the changes in firm values over the varying event windows where the aggregation speaks to the conglomerative effects of the events on firm prices. Aggregate Results The overall effects of HPAI on firm values vary by day. Table 2 shows the daily mean abnormal return for all companies and events for the event window. The abnormal returns vary on a range of -1.27% up to 0.49%, which implies that the impacts on firms can vary between firms and fluctuate over the event window. The heterogeneity between changes in firm value is evident in the number of firms with a positive versus negative effect, shown in the third column of Table 2. For many of the days there is a seemingly equitable split between positive and negative mean abnormal returns, often leading to statistically insignificant abnormal returns. Many of these days have insignificant abnormal returns. Alternatively, there are several days with significant difference in directional abnormal returns including days 1, 4, 5, 7, and 10 prior to and days 2, 4, 5 and 9 after the event date. In each of these days, there are significantly more firms with negative than positive abnormal returns. For example, on day 4 prior to the event, there were 38 firms with negative abnormal returns compared to 13 which had positive abnormal returns. This disparity shows that there are firms that may benefit (have a positive abnormal return) to the 10

12 HPAI event. These benefiting firms are more likely firms that do not market poultry products. The more common response for meat processing firm values tended to have a negative response. Table 2- Market model daily abnormal returns for aggregated event dates (N=51) Day Mean Abnormal Positive: Negative Standard Cross- Portfolio Time- Series (CDA) t Generalized Sign Z Return Sectional Z % 28: $ % 23: % 29: % 18:33< ** $ * % 22: % 28: * 1.449$ % 13:38<<< *** ** *** % 34:17>> ** % 21: % 26: $ % 18:33< * * % 23: % 18:33< $ * % 28: % 17:34< ** $ * % 13:38<<< *** *** *** % 30:21) $ % 25: % 28: % 16:35<< ** * ** % 20:31( $ The symbols $, *, **, and *** denote statistical significance at the 0.10, 0.05, 0.01 and levels, respectively, using a generic one-tail test. The symbols (, < or ), > correspond to $,* and show the direction and significance of a generic one-tail generalized sign test. Understating the daily effects provides a granular view of the abnormal changes the studied firms observed, but the broader question whether there were abnormal returns overall for the outbreak should be studied through the use of event window analysis. This provides a cumulative approach which will account for significant changes over the window rather than a daily significant abnormal return. The market model results from the event aggregated study for various study windows are presented below in Table 3. 11

13 Table 3- Market model estimates for various estimation windows (N=51) Day Mean Cumulative Abnormal Return Precision Weighted CAAR Positive: Negative Standard Cross- Sectional Z Portfolio Time- Series (CDA) t Generalized Sign Z (-10,0) -0.98% -0.25% 23: (-5,0) -0.58% -0.18% 23: (-3,0) -0.17% 0.17% 27:24: (0,+3) -1.10% -0.91% 16:35<< $ $ ** (0,+5) -2.81% -2.45% 13:38<<< *** *** *** (0,+10) -2.95% -2.84% 13:38<<< *** ** *** Using the various event windows, results indicate significant cumulative effects of HPAI on meat processor firm value. The significant windows all proceeded the event date: (0,+3), (0,+5), and (0,+10) days. The stock returns are also all significantly negative. For example, from (0,+5) days, there is a highly significant CAR of -2.81%, which would imply that for meat processors their values decrease during this event window. During that same window, there are 38 firms that are negatively affected. Contrarily, there are 13 firms which had a positive effect. The CAR reported represents the effect across all firms, which implies the directional impact reported are conservative for those companies which are negatively affected. Individual Event Results The overall effects of HPAI on agribusiness firm values provides valuable insight. However, analyzing the effects by event can provide additional information on the timing and magnitude of firm value responses to create a more complete understanding of the studied events. For this analysis each event was individualized. The market model estimates for each of the three distinct event windows are presented in Table 4. 12

14 Table 4- Market model estimates for various estimation windows (N=17 per event) Event December 18, 2014 January 23, 2015 March 4, 2015 Day Mean Cumulative Abnormal Return Precision Weighted CAAR Positive: Negative Standard Cross- Sectional Z Portfolio Time- Series (CDA) t Generalized Sign Z (-10,0) -1.40% -0.45% 6: (-5,0) -1.07% -0.38% 7: (-3,0) 0.73% 0.82% 9:08: (0,+3) 1.35% 1.58% 9: $ (0,+5) -0.41% 0.21% 7: (0,+10) -2.40% -1.88% 5:12< $ * (-10,0) 0.50% 1.34% 10: $ (-5,0) 2.12% 2.25% 13:4> 2.093* 1.395$ 2.216* (-3,0) 1.06% 1.36% 12:5> 2.049* * (0,+3) -1.41% -1.57% 3:14<< ** ** (0,+5) -3.80% -3.61% 2:15<<< *** ** *** (0,+10) -3.85% -3.82% 3:14<< *** * ** (-10,0) -2.05% -1.67% 7: $ (-5,0) -2.80% -2.47% 3:14<< ** * ** (-3,0) -2.29% -1.74% 6:11: * * (0,+3) -3.22% -2.84% 4:13< ** ** * (0,+5) -4.22% -4.06% 4:13< *** ** * (0,+10) -2.59% -2.84% 5:12< * * As described above, December was the initial HPAI exposure to a backyard flock. While a serious concern, this had not yet affected commercial production and the market response is minimal. There is a response of -2.40% CAR, but the statistical significance of this result is not strong (e.g., 0.1 according to the standardized cross-sectional test in Table 4, and not significant according to the time-series standard deviation test, CDA). Similar to the aggregate analysis, this post-event effect speaks to the reactionary nature of investors to the first official detection announcement. The first movement of HPAI to commercial flocks was reported in January. The event analysis around this specific announcement shows both a pre and post event investors response. 13

15 Pre-event firm value responses are found in the (-5,0) and (-3,0) windows at the 0.05 level and (- 10,0) at the 0.10 level of significance. These each show a positive response, 2.12% and 1.06% respectively. In comparison, post event windows (0,+3), (0,+5), and (0,+10) all show significant negative responses of higher magnitudes and stronger statistical significance levels compared to the pre event responses. For example, (-5,0) showed a 2.12% CAR whereas the (0,+5) was estimated to have a -3.80% CAR. These magnitudinal differences are consistent across each pre and post event window pairings. These results could show an expectation of a negative test of HPAI in the commercial flock or as a result of limited exposure and rapid conclusion of the December HPAI outbreak, investor expectations that firms would be able address and limit the effects of a positive test. In either case, the expectations prior to the announcement could lead investors to an attempt to arbitrage the market. However, as a result of the official announcement which could insinuate potential changes to production costs, there was a swift reduction in firm value. A conjectural explanation could be that the market expectation was weighted down by the previous event in December. In order to address any potential change in updated market expectations, we re-estimated the January event backing up the estimation period prior to the December event. We found that the positive CAR (2.06% and -3.08% for the (-5,0) and (0, +5) event windows) still held, even accounting for the adjusted event period which excludes this as an explanation for the positive CAR. The first two event dates were still predominantly isolated. The March event differs in that it was the first move to commercial flocks in the highly concentrated Mississippi flyway. The results show estimate negative CARs for all event windows. The range of CAR for all studied windows are -2.05% to -4.22%, a much deeper range than previous events. The highest magnitude and most significant response was for the (0, +5) event window which was estimated 14

16 to be -4.22%. This change in magnitude of response may speak to the updated expectations by investors. The January event had potential to remain isolated where investors could maintain a more positive outlook, but by the March event they updated these expectations which were more pessimistic. There were two prior events and many reasons to expect continued outbreaks throughout the flyway. The changes in firm values reflect the changing expectations on the effect the HPAI outbreak would have on source supply for the studied meat processing firms. CONCLUSION Animal diseases can have a significant impact on producers, consumers, and markets. A highly pathogenic disease can lead to international trade restrictions, limiting market access abroad. Disruptions in production through mortality or morbidity can lead to changing expectations of firm value and profitability outlooks. Highly pathogenic diseases such as HPAI have been shown to have caused substantial economic losses globally due to their potential for zoonosis, transboundary spread, and pathogenic nature. As such, publicly traded firms can face additional effects of a disease event through reactionary abnormal changes in stock value. This additional economic impact of disease events in U.S. animal protein firms has not been previously studied. This work focused on the effects of the outbreak of HPAI on agribusiness firm values using an event study methodology. It was shown that meat processing firms were significantly affected as a result of the USDA announcements of HPAI both in backyard and commercial flock over various event windows. We uniquely traced the event announcements to show how the magnitude of the abnormal returns differed by the sequence of disease detection as well as the factors of the outbreak, including the commercialization of the affected birds and the geographical risk factors associated with disease spread. By understating these sequential 15

17 impacts, industry and animal health agencies are provided a more complete understanding of the effects of a disease event. The results from this work are beneficial to firms that were affected by HPAI, but more broadly this work shows how the methodology can be applied to animal health events as a means of extrapolating some of the temporal effects during the ongoing event. This method could be applied to other animal health events or other events that require more than an aggregate analysis. Additional future work could build on this framework to estimate the factors that contribute to the heterogeneity of firms and the magnitude of the abnormal returns. Future research could also expand this study to estimate the impacts of other pathogenic animal disease events. Animal health events are disruptive and tragic for those affected, but their occurrence globally has been increasing and the use of event studies provide additional understanding of the true economic costs of a disease outbreak and potentially helps to provide economic incentives for proactive preventative measures to reduce the burden to producers, consumers, trading partners, and agribusiness firms. 16

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