Did the U.S BSE and EU27 HPAI Outbreaks Affect EU Meat Exports?

Transcription

1 Did the U.S BSE and EU27 HPAI Outbreaks Affect EU Meat Exports? By Fawzi A. Taha and William F. Hahn ERS-MTED-USDA, Washington, DC The authors are Economists with the Economic Research Service, USDA, 355 E Street, SW, Washington, DC Selected Paper prepared for presentation at the Agricultural & Applied Economics Association s 2013 AAEA & CAES Joint Annual Meeting, Washington, DC, August 4-6, The views expressed in this paper are the authors and do not represent those of the Economic Research Service or the Department of Agriculture. 1

2 Abstract This paper demonstrates that EU-27 meat exports underwent structural changes following the discovery of bovine spongiform encephalopathy (BSE) in the United States (2003) and highly pathogenic avian influenza (HPAI) outbreaks in the European Union (2006). The two diseases caused short-term (11-month) drops in beef meat and poultry demand but increased export demand for pork meat, beef offal, pork offal, and other meat. These results except for a decline in U.S. beef offal exports are similar to those found by modeling U.S. meat export demand after the BSE incident of December 23, By 2012, U.S. beef meat exports had recovered and exceeded their pre-bse levels, but EU beef meat exports were still 31 percent lower than in

3 Introduction The first case of bovine spongiform encephalopathy (BSE) in the United States was confirmed on December 23, Many governments imposed import bans on U.S. beef meat and beef offal exports, causing a sharp decline of 83 and 64 percent from December 2003 to January In the European Union countries (EU-27), which were free of BSE, exports of beef meat were down by 61 percent during the same period, whereas exports of pork meat, pork offal, and beef offal were up substantially during the same period. Another disease, the highly pathogenic avian influenza (HPAI), broke out in several EU countries in , causing millions of birds to be culled and exports of poultry to drop by 5 percent in 2006 and 12 percent in 2007 (from the 2005 level). By 2012, EU meat exports were all above their pre-outbreak volumes. However, export shares of beef meat and poultry meats were lower while meat export shares of beef offal, pork meat, and pork offal were higher. In comparison, the 2003 BSE incident in the United States caused a substantial drop in beef and beef offal exports due to imposed bans on U.S. shipments. However, as export markets reopened U.S. beef exports recovered and exceeded their pre-bse levels in Were the declines in EU beef exports in 2004 due to the U.S. BSE event? Were the poultry export declines in due to HPAI outbreaks in the EU? After examining the EU meat export market and how it has changed over time, we test these hypotheses using an econometric model. Data Sources Our analysis is based on monthly data from the World Trade Atlas covering January 1990 to June Data on EU-27 exports originated from the EuroStat customs data of the European 3

4 Commission for each country and for aggregate EU-27 trade, at the Harmonized System (HS) 6- digit level for each meat product. The data include volumes and unit values for beef meat, pork meat, beef offal, pork offal, poultry meat, and all other meats (including sheep, goats, horses, asses, salted, dried, other edible animal offal, and pig/poultry fat). Data for each category were aggregated, and a unit value in $ per metric ton (1,000 kilograms) was calculated as a weighted average for each of the six meat categories. The beef meat category consisted of fresh or chilled beef (Harmonized System (HS) 0201) and frozen beef (HS 0202). The pork meat category included fresh, chilled, and frozen pork (HS 0203). The beef offal category included fresh and frozen items with 6-digit levels (HS , HS , HS , and HS ). Similarly, the pork offal category included fresh and frozen items (HS , HS , and HS ). The poultry class consisted of fresh, chilled, and frozen poultry meat, whole and in parts (HS ). The class of all other meats consisted mainly of sheep, lamb, or goats (HS 0204); horses, asses and mules (HS 0205);, other frozen or fresh offal (HS 0208); pig and poultry fat (HS 0209); and salted, dried, etc. (HS 0210). Literature Review Bovine spongiform encephalopathy (BSE), commonly known as mad-cow disease was first discovered in the United Kingdom in Several authors have studied the economic impacts of animal disease outbreaks and food safety incidents on consumer demand for meat in several countries. Consumers fear of eating beef accelerated sharply after the British Government announced on March 20, 1996 the possible fatal link between BSE and a new variant, Creutzfeldt- Jakob disease (vcjd), in humans. This announcement had a substantial impact on beef consumption, production, prices, and trade around the world. In the UK, domestic sales of beef 4

5 dropped 40 percent and consumption fell 26 percent below the previous year s level (Atkinson, 2003). Sharp declines in fresh beef purchases and consumption were reported in many other countries, such as France (Latouche et al., 1998), the Netherlands (Mangen and Burrell, 2001), Belgium (Verbeke et al., 2000), and the United States (Schlenker and Villa-Boas, 2008). Similarly, after Japan s BSE outbreaks in September 2001, consumers substituted pork and chicken for both domestic and imported beef r (Clemens, 2003; Jin et al., 2003; Ishida et al., 2010). The HPAI (H5N1) virus was first isolated from a goose farm in Guangdong, China in The first HPAI outbreaks were reported on poultry farms and in live animals markets, with 18 human cases (6 fatal) in Hong Kong in In 2003 and early 2004, the virus infected poultry sectors of many East and Southeast Asian countries and spread to Russia, Kazakhstan, Turkey, the Middle East, Africa, and Western Europe in (WHO, 2011). Several studies addressed impacts of HPAI outbreaks on domestic and international meat markets and have reported substantial disruption in poultry production, consumption, prices, and/or trade in many countries. In Vietnam, 1 month after HPAI struck Hanoi in January 2004, 74 percent of consumers reported not eating poultry meat (Figuie and Fournier, 2008). In Taiwan, consumers substituted pork and aquatic products for poultry meat (Liu et al., 2007). Park et al. (2008) reported that the December 2003 HPAI outbreak in South Korea, which occurred simultaneously with the U.S. BSE case of 2003, caused poultry prices and consumption to decrease and demand for pork to increase. Ishida et al. (2010) reported that the 2004 HPAI outbreaks in Japan reduced domestic demand for chicken, increased demand for pork and fishery products, and had no impact on beef. 5

6 Jin et al. (2003) and Jin and Koo (2003) found that BSE outbreaks in Japan in September 2001 caused a structural change in Japan s domestic and international import markets. Also, Peterson and Chen (2005) found that demand for four types of beef (two domestic and two imported) was affected by BSE and that Japan s retail meat demand structure underwent a 2-month transition period following the 2001 BSE outbreaks. All the above-cited economic studies focused on the effects of BSE and HPAI outbreaks on consumer demand for meat and structural changes in importing countries. This study is focused on the effects of the same disease outbreaks on EU meat exports by measuring changing trade patterns and testing for structural changes. Changing Pattern of EU-27 Meat Exports EU total meat exports increased from 2.68 to 4.65 million metric tons over However, in 2004, beef meat exports fell by nearly 20 percent following the discovery of BSE in the United States in December They continued to drop until 2010 and 2011, when demand from eastern European countries, Russia, and the Middle East helped beef meat imports recover, before declining sharply in 2012 (table 1). 6

7 Table 1 EU27 meat exports 2000 to ,000 Metric tons Total 2,678 2,356 2,583 2,551 2,730 2,607 2,693 2,659 3,403 3,176 3,965 4,753 4,648 Pork meat ,236 1,009 1,260 1,590 1,603 Poultry meat , ,125 1,266 1,276 Pork offal ,043 1,009 Other Beef meat Beef offal Source: World Trade Atlas, May 2013 EU exports of poultry meat decreased over 10 percent in 2003 due to HPAI outbreaks in East and Southeast Asia, and continued to decline through Except for beef and poultry meat, EU-27 meat exports generally rose from 2003 to 2012 in absolute volumes and in percentage terms (fig. 1). By contrast, U.S. beef offal exports decreased sharply following the December 2003 BSE discovery, but U.S. poultry exports did not decrease because the United States was free of HPAI. The index of U.S. beef offal exports dropped sharply in 2004, whereas the EU index continued to increase (fig 2.). (Each meat export volume was indexed as 100 in 2003, the year prior to the BSE outbreak.) The EU poultry export index dropped substantially in 2007, but the U.S. index did not. Both indexes dropped slightly in 2004 due to HPAI outbreaks in East and Southeast Asia. 7

8 Percent Figure 1 : EU26 meat export shares by kind of meat, Pork Poultry Pork offal Other meat Beef Beef offal Source: World Trade Atlas, March Figure 2: EU 27 and US exports of beef offal and poultry meats, Index 100= EU Beef offal US Beef offal EU poultery US poultry Source: World Trade Atlas, March 2013 EU Major Meat Export Markets During , EU-27 meat exports increased 74 percent from 2.69 to 4.65 million tons. Most of the increase was in pork offal (335 percent) and beef offal (272 percent). Exports of other meat rose 268 percent, pork meat 78 percent, and poultry 33 percent, while beef meat exports declined 56 percent. The beef meat share of EU meat exports fell from 16 percent in 2000 to 4.1 8

9 percent in 2012, and the share of poultry meat dropped from 36 to 27 percent. EU markets for beef meat After the BSE outbreak in 2003, EU beef meat shipments decreased about 20 percent in 2004 from the year before. Shipments continued to wane before recovering temporarily in 2010 and Still, by 2012, total EU beef meat exports were 31 percent lower than in In 2003, EU-27 beef meat exports were concentrated in Russia (74 percent of total), Macedonia (5 percent), and Bosnia & Herzegovina (4 percent). By 2012, Russia was still the leading export market, at 28 percent, but shipments had expanded to Turkey, Switzerland, and Norway. EU27 export markets for beef offal EU-27 beef offal exports were also concentrated in a few countries in 2003, including Russia (29 percent), Liechtenstein (14 percent), Bosnia & Herzegovina (8 percent), Ukraine (4 percent), and Macedonia (3.3 percent). African markets totaled 12 percent, mainly to the Congo Democratic Republic of Congo and Gabon, while East Asian markets received 6 percent (mostly to Hong Kong, China, Vietnam, Philippines, South Korea, and Philippines). In 2004, EU beef offal exports were up 47 percent, with Russia receiving 51 percent, followed by Liechtenstein (11 percent). By 2012, the African markets were the EU s largest (37 percent), followed by East and Southeast Asia (22 percent) and Russia (21 percent). EU 27 export markets for pork meat In 2003, Japan was the largest EU largest pork meat market (36 percent), followed by Russia (19 9

10 percent). The United States received 7.6 percent, South Korea 7 percent, and Belarus 4 percent. African markets were small. In 2004, pork meat shipments rose about 14 percent, but export markets were nearly unchanged. By 2012, EU pork exports to Russia were still largest (19 percent), followed by China and Japan, at 14 percent each. EU export markets of pork offal In 2003, the bulk of EU pork offal exports went to East and Southeast Asian countries (50 percent), followed by Russia (29 percent), and Africa (10 percent). By 2012, most pork offal shipments went to Asia (76 percent), followed by Russia (12 percent), Africa (4.5 percent), and other eastern European countries (4 percent). China s export share rose from 14.5 percent in 2003 to 38.4 percent in EU export markets for poultry meat In 2005, the prior year of EU-HPAI outbreaks, EU poultry exports to Russia were 25 percent, followed by Saudi Arabia, (11 percent), Ukraine (7), and Benin (6 percent). In 2007, following the HPAI outbreaks, EU poultry exports declined about 12 percent from the 2005 level, but shipments went to the same destinations. By 2012, the largest EU market was Saudi Arabia (12 percent), followed by Benin (11 percent), and South Africa and Hong Kong (each 10 percent). EU export markets for other meat From 2000 to 2012, EU exports of other meat rose by 2.5 times, and were shipped mostly to Russia (62 percent) and Ukraine (9 percent), Belarus and Hong Kong each 3 percent. Other destinations included Switzerland Japan, United States, and Croatia 1 percent each in

11 METHODOLOGY We estimated a log-linear demand model for EU meat exports. The model s dependent variables are the quantities exported of the six meat classes. For price, we used the export unit values. Other independent variables include export scale, here measured as the total value of all six meat exports, and the $/ exchange rate. The model started with an intercept, trend, monthly dummies, and disease-shifting terms. The demand systems errors have a general, second-order VAR-type of autoregression. The inclusion of export scale variable follows Theil (1977), who noted the similarities between consumer demand functions and cost-minimizing input demand functions. We will not attempt to measure if the disease events had any effect on market scale. The use of conditional demand functions will change the interpretation of the elasticities of demand particularly the exchange rate elasticity. For example, we expect that increases in the value of the U.S. dollar relative to the Euro would tend to increase export demand for all EU meats, which would mean expanding the scale of the EU market. The model s form assumed scale as given, however. The exchange rate elasticities show the division of meat exports, given scale. Because of this, some of the exchange rate elasticities should be negative if others are positive. One of the reasons that we estimate a system of demand functions is that we expect that EU export demand would be driven by more than just disease effects. Changes in prices, scale, and exchange rates would also change demand. In 2004, as noted above, EU beef exports declined 20 percent for the year. On the other hand, EU beef exports had generally been declining since The declines between 1999 and 2003 cannot be attributed to BSE. By estimating price, scale, and 11

12 exchange rate elasticities, we can correct for the effects of price, scale, and exchange rates on export demand. The disease effects are those parts of export demand changes that are not driven by non-disease factors. The other factors can either reinforce the disease effects or counterbalance them. Modeling Disease Effects We model disease effects using non-stochastic state variables. This approach was used by Taha and Hahn in previous studies on the effect of disease events on import demand (Taha, 2007; Taha and Hahn, 2011a/b). We treat BSE and HPAI as intercept shifting effects. Each of the diseases is driven by a set of three dummy variables: an initial-shift effect for the first month of the event, an intermediate term and the full adjustment term. That makes six dummy variables, three for each disease. The pattern for BSE s three dummies is shown in table 2. HPAI s dummies have the same pattern starting in February Because the BSE case in the United States was confirmed on December 23, 2003, the starting month of the BSE event was considered January The dummy labeled dx0 is the firstmonth effect, the dummy dx1 allows some smooth transition between the initial and final effects, and the dummy dx2 determines the final effect (table 2). Table 2: Disease drivers for BSE event Month dx0 dx1 dx2 4-Jan 1 4-Feb 1 4-Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

13 13-Jan 0 1 We used a Koyck lag to turn these dummies into state variables: (1) s t,i = λ i s t 1,i + β i,0 dx0 t,i + β i,1 dx1 t,i + β i,2 dx2 t,i, where s t,i is the state variable in the month numbered t for the disease i ( either BSE or HPAI ). The dx0-dx2 are the disease driving dummy variables as defined in table 2 and the β is their respective coefficients to be estimated. The term λ i is a Koyck lag with a value between 0 λ i 1 1 for each disease. The closer λ i is to 1, the more that the last period s value for the state matters for this period. The BSE state variable is 0 before January 2004 and the HPAI state is 0 before February The long-term effect of a disease is driven by the interaction between its λ and β i,2 terms. To calculate the disease s effects on a particular meat, we multiplied each of the states (S t,i ) by a disease effect, C i,j, that varies by both export, the j subscript, and disease. Our approach causes two identification issues. First, we need an arbitrary restriction to identify the Cs and the βs for each disease. The net effect of the disease on a meat s exports is the C times the state. We can double the state by doubling its βs, then halve the Cs and get the same net disease effect for all the meats. We made βi,0 equal to 1 for both diseases. 2 With six meats, two free β and a λ, each disease adds nine coefficients to the model, so making a disease have no effect on any of the export demands might be a 9-degree-of-freedom restriction. We can make the disease have no effects on any of the export demands by making all its Cs equal to 0, a 6-degree-of-freedom 1 The fact that λ are bounded by 0 and 1 causes no econometric complications. Taha and Hahn (2011 a/b) found that the λ estimates were significantly different from both 0 and 1. 2 One small problem with making the initial dummy 1 would arise if there were a lagged response to the disease. We experimented with different normalization criteria for the β in our first runs. The freely estimated β 0 for both BSE and HPAI were non-0. Forcing β 0 =1 is a simple restriction for the programming software to handle. 13

14 restriction. The problem is that the 2 free βs and λ cannot be identified when the Cs are all 0. The long-term effects of the disease depend on the β i,2 and λ i. If λ i is less than 1, and β i,2 is non-0, the state variable will eventually converge over time to β i,2 1 λ i. If λ i is less than 1 and β i,2 is 0, then disease s effects will eventually disappear over time. If λ i is equal to 1, the disease can have permanent effects on demand even if the β i,2 is 0. A λ i equal to 1and non-0 β i,2 eventually turns the state variable into a trend that grows by β i,2 each month. The model s estimation form then is indicated below: (2) lnq t,x = a x,k d t,k + ε xj lnx t,j + s t,i C x,i +, where lnq t,x is the EU export of meat-class x in month t. The d t,k are intercepts, trends, and monthly dummies and the a x,k are the estimated coefficients. The ε xj are the elasticities with respect to prices, scale, and exchange rates. The s t,i are the disease-event states and the C x,i show what effect the disease (i) has on each meat export (x). Equation (2) is truncated and does not show the VAR-structure for the error term. A number of econometric issues about model structure were tested prior to estimating the major objective of the paper the impacts of the two disease outbreaks on EU export demand. These included testing the elasticities for local consistency with optimization (they passed), testing the VAR error term for unit roots-cointegration (there was 1 co-integrating relationship), and testing for the simultaneity of prices, quantities, and scale. These issues and their tests are discussed in the Appendix. 14

15 Estimating Disease Effects on EU Meat Exports All the tests we used for this study are likelihood ratio tests, with an imposed restriction or set of restrictions on the model, and comparing the likelihood with and without any restriction. The decrease in (twice) the likelihood is asymptotically chi-square under some conditions. Analysis started with estimating the effects, if any, of BSE and HPAI diseases on EU meat exports of each meat class, testing whether the disease had permanent or temporary effects, and assessing each disease s effect on EU meat export volume. Testing the disease outbreaks effects was conducted in two stages: the first stage tests the C coefficients in equation (2). In the least-constrained model, either disease outbreak can affect all six types of EU meat exports. In the first stage, we tested which, if any, of the meats an outbreak actually affected. Table 3 Double-loop tests for diseases effects on meat exports 1,2,3 Excluded step tests, 1 degree of freedom cumulative tests Meat event step test chi-square alpha HB 2,3 criteria test degrees of freedom chisquare alpha Beef meat HPAI % 5.00% % Poultry BSE % 2.50% % Pork offal HPAI % 1.67% % Pork meat BSE % 1.25% % Beef offal BSE % 1.00% % Pork offal BSE % 0.83% % Other meat HPAI % 0.71% % Beef offal HPAI % 0.63% % Beef meat and other Poultry and pork meat BSE % 0.56% % HPAI % 0.50% % 1 Source: ERS estimates based on World Trade Atlas data. 2 Holm Bonferroni criteria for multiple hypothesis tests. 3 Special formatting for the cells that are significant at the 5% level, corrected for position in the case of the step tests. 15

16 We programed a looped testing procedure in our estimation software. We first had the software test each D coefficient. The least significant D was set to 0 and the remaining terms retested. After the second loop, the least-significant D from that loop was set to 0 with the one from the first loop. Then there is another loop where remainders were tested. The computer looped until all the coefficients were set to 0. After the 4 th loop the least-significant terms were actually significant, see table 3. Table 3 shows that beef meat and pork offal were unaffected by HPAI while poultry and pork meat were unaffected by BSE. Table 3 has two sets of tests: the step tests show the statistical significance of dropping a term from the model, and the cumulative tests show the effect of dropping that term and all the previous terms. We decided to stop testing when either the step or cumulative test exceeded the 5-percent critical value for significance, which was reached in the 5 th step for the cumulative test. The step tests are adjusted by their position using the Holm-Bonferroni (HB) criteria namely, that the more tests one runs, the more likely one is to see a significant test (in our case, a 5-percent value). Do the diseases have permanent or temporary effects? The second-phase tests were designed to determine whether the disease outbreaks had permanent (long-term) or temporary (short-term) effects on EU meat export demand. Two things are required for the disease effects to go to 0 over time: (1) the coefficient on its dx2 term (see table 2) equal 0, and (2) its λ be less than 1. Tests indicated that neither of the diseases has statistically significant long-term effects on EU meat exports (table 4). Table 4: Testing long-term and intermediate variables coefficients 1 Long-term coefficient set to 0 test degrees of freedom chisquare alpha BSE % HPAI % 16

17 Both % 1 Source: ERS estimates based on World Trade Atlas data. After eliminating the long-term β from the model, we tested the λ i. (the Koyck term) in equation (1). unlike before, we cannot use a likelihood-ratio-type test because a chi-square test is inappropriate: bo an upper bound of 1 and a lower bound of 0. If the true value of the Koyck term is in fact 1, we wou to have a mixed distribution for this test and the estimated λ would likely hit its upper bound about h time. In this case, the test statistic would be 0. The times when the estimated λ was not 1 might have something like a normal-chi-square distribution. The same argument applies if the true λ is 0. Consequently, we concluded that the test distribution for forcing a λ to its bounds would be mix of 0 s and regular chi square. In that case the 5% critical value for this test would be the 10% value for the true chi-square distribution. To test our conclusion, we ran a single-equation model with a Koyck lag whose true coefficient is 1. We set the computer to run 10,000 iterations of the model, using a likelihood-ratio test of the coefficient equaling 1. Results showed that of these 10,000 test iterations 4,949 were 0, which is not statistically significantly different from 50 percent. In addition, 497 of these iterations were greater than or equal to chi-square s 1-degree-offreedom 10% value (or 2.71), which is not significantly different from 5 percent. We decided that we would reject the λ=1 or λ=0 hypothesis if it exceeded the 10% critical value for the chi-square. This is the equivalent of the 5% level for the mixed distribution. In addition to testing that the Koyck terms are 1, we also tested them against their lower bound, 0. Both of the Koyck are significantly different from 1; neither is significantly different from 0 (table 5). Given the structure of the disease shifters shown in table 2, the λ=0 case means that both diseases affected export demands for only 11 months, amounting to a short-term impact. Table 5 Hypothesis tests on the Koyck lag-λ terms The constrained Tests against The constraint disease free model Degrees of freedom Chi-square alpha 17

18 BSE % λ = 1 HPAI % Both % BSE % λ = 0 HPAI % Both % Source: ERS estimates based on World Trade Atlas data. Hypothesis tests show that both BSE and HPAI had statistically significant effects on some EU meat exports, albeit both for a limited time. The disease effects work as intercept shifters. In this log-linear form, an intercept of 0.01 is roughly a 1-percent increase in the demand for that product. Table 6 shows the disease-related shift terms and their standard errors. 3 The estimates in table 6 incorporate the insignificant restrictions from our disease pattern tests above. BSE affected only 4 types of exports: beef meat, beef offal, pork offal and other meats. BSE lowered beef meat exports but increases exports of the other 3. Like BSE, HPAI affected only 4 of the meat exports, although it is a different 4. HPAI decreased poultry exports while increasing the exports of beef offal, pork meat and other meats. BSE s β i,1 coefficient is (table 6),meaning that the disease had its largest seasonal effect in January 2004 and declined for the following 10 months. The HPAI coefficient, 3.067, means that it had it largest effect in March 2006, the month after the initial outbreak. Some of the estimated Z statistics in table 6 are insignificant. These standard errors are based on Monte-Carlo analysis of the model. These model iterations were generated using the coefficient estimates. The hypothesis tests using the real data suggested that these terms are mostly 3 The seasonal dummy and other estimates from the model are in the Appendix. 18

19 statistically significant. The insignificant Z statistics in the table are an example of a type II error. Our Monte-Carlo analysis has us rejecting true hypotheses. Table 6 Disease-shift coefficient estimates and standard errors 1,2,3 Disease-outbreak coefficient Estimate Z statistics 2,3 BSE HPAI BSE HPAI Poultry Beef meat Pork meat Beef offal Pork offal Other meat Intermediate effect multiplier Source: ERS estimates based on World Trade Atlas data. 2 Standard errors based on 5,000 Monte-Carlo iterations. 3 Special highlighting for insignificant Z statistics. Conditional demand and cross-price elasticities As expected, the own-price elasticities for all meat classes are negative (table 7) and inelastic. We generally expect unconditional export-import demands to be elastic. However, because these are conditional demands, we are not that surprised that the own-price elasticities are inelastic. Table 7 Conditional demand elasticity estimates for EU meat exports1,2,3 Poultry Beef Pork Beef Pork Other Meat Meat Offal Offal Meat Scale $US/ model estimates Poultry Beef Meat Pork Meat Beef Offal Pork Offal Other Meat z statistics 2 Poultry Beef Meat Pork Meat Beef Offal Pork Offal Other Meat Source, ERS estimates based on World Trade Atlas and USDA data 2 Standard errors based on 5,000 Monte-Carlo iterations. 19

20 3 Special highlighting for insignificant Z statistics. All the scale elasticities are positive. The cross-price terms are mixed. Recall that we imposed homogeneity on the price and scale elasticities. Since all of our own-price terms are between -1 and 0 and some of our scale elasticities are over 1, we need some negative cross-price effects for the elasticities to sum to 0. Poultry, which has the lowest scale elasticity, 0.427, has all positive crossprice elasticities. Beef meat and pork meat, with the highest scale elasticities, have all negative cross-price effects. A stronger U.S dollar lowers the beef and pork meat exports, given scale, and increases demand for the other meats. Table 7 also shows z-statistics for the coefficient estimates. These are generated using Monte-Carlo analysis where we used the estimated elasticities in table A1 to simulate the data. The insignificant terms in table 7 are better interpreted as low-power estimates. These terms likely would have been insignificant if tested against 0. Many of the cross-price elasticities are small to start with. Even if their standard errors are small, their z-ratios are going to be small as well. The exchange rate elasticities are generally the largest in absolute value of the whole set; their z-ratios are surprisingly small. EU Meat Export Shifts from BSE and HPAI Outbreaks The impacts of BSE and HPAI outbreaks were calculated by taking the model s demand shift coefficients and turning them into actual changes in EU meat export volume, assuming that the scale of exports was unaffected by either outbreak. EU export losses and gains were calculated for both diseases and for each meat over a period of 11 months, the duration of the short term as indicated by the model. Table 8 shows the implied losses/gains in terms of both tonnage and percentages Table 8: Implications of disease-shift estimates for EU exports Metric tons Percent of actual BSE HPAI BSE HPAI Poultry -234, % 20

21 Beef meat -12, % Pork meat 75, % 9.00% Beef offal 3,651 4, % 10.50% Pork offal 31, % Other meat 11,037 21, % 7.20% Source: ERS estimates based on World Trade Atlas data. The HPAI outbreaks had a much larger effect on EU poultry exports than the U.S. BSE discovery had on EU beef exports. Both diseases increased EU export demand for pork meat, pork offal, beef offal, and other meats. The estimated volumes of losses/gains might not coincide with actual volumes (table 1). For example, our estimates suggest that HPAI lowered poultry demand by 31 percent in the last 11 months of Actual EU poultry exports dropped about 5 percent between 2005 and Poultry exports dropped less than the HPAI effect because changes in prices, market scale, and exchange rates offset most of the disease-related drop. In other words, our estimates suggest that had it not been for HPAI, EU poultry exports would have grown by 26 percent in HPAI had a larger effect on EU exports than BSE. Summary A log-linear demand model was used to estimate the EU meat export demand system following the BSE-incident of December 23, 2003 in the United States and the PHAI outbreaks in the EU during 2006 and The model s dependent variables were the quantities exported of EU six meat classes, while independent variables were export unit values, scale variables, and the $/ exchange rate. The model started with an intercept, trend, monthly dummies, and disease-shifting terms. The demand systems errors were modeled using a general, second-order VAR-type of autoregression. The impacts of BSE and HPAI outbreaks were calculated by converted the model s demand shift 21

22 coefficients into EU meat export volume, assuming that the scale of exports was unaffected by either outbreak. Hypothesis tests indicated that both BSE and HPAI had statistically significant effects on some EU meat exports, albeit both for a limited time. Results showed that the BSE affected only 4 types of exports: beef meat, beef offal, pork offal, and other meats. BSE lowered beef meat exports but increased exports of the other 3. Similar to BSE, the outbreaks of HPAI in the EU 27 decreased poultry exports while increased exports of beef offal, pork meat, and other meats. As expected, own-price elasticities for all meat classes were negative and inelastic, scale elasticities were all positive, and cross-price terms were mixed. A stronger U.S dollar lowered beef and pork meat exports, given scale, but increased demand for the other meat class. EU export losses and gains were calculated for both diseases and for each meat class over a period of 11 months, the duration of the short term as indicated by the model. EU27 losses/gains in both tonnage and percentages indicated that the HPAI outbreaks had a much larger impact on EU poultry exports than the U.S. BSE-incident had on EU beef exports. Both diseases increased EU export demand for pork meat, pork offal, beef offal, and other meats. REFERENCES Atkinson, N. (2003). The Impact of BSE on the UK Economy, Head of Economics International Division, Ministry of Agriculture, Fisheries and Food, London, UK, Sept Burton, M., and Young, T. (1996). The Impact of BSE on the demand for beef and other meats in Great Britain, Applied Economics, Vol. 28, pp

23 Clemens, R. (2003). Meat Traceability and Consumer Assurance in Japan, MATRIC Briefing Paper 03-MBP 5, Midwest Agribusiness Trade Research and Information Center, Iowa State University, Ames, Iowa, September, Figuie, M. and Fournier, T. (2008). Avian Influenza in Vietnam: Chicken-Hearted Consumers? Risk Analysis, Vol.28. No. 2, pp Food and Agriculture Organization of the United Nations (2013). Rome, Italy. Fox, J., & Peterson, H.H. (2004). Risks and implications of bovine spongiform encephalopathy for the United States: insights from other countries, Food Policy 29, pp Henson, S., & Mazzocchi, M. (2002). Impact of Bovine Spongiform Encephalopathy on Agribusiness in the United Kingdom: Results of an Event Study of Equity Prices, American Journal of Agricultural Economics 84 (2), May, pp Ishida, T., Ishikawa N., & Fukushige, M. (2010). Impact of BSE and Bird Flu on Consumers' Meat Demand in Japan, Applied Economics, Vol. 42, pp Jin, Hyun J., and Koo, W.W. (2003) The Effect of the BSE Outbreak in Japan on Consumers Preferences, European Review of Agricultural Economics, Vol. 30 (2), pp Jin, H.J., Sun, C. & Koo, W.W. (2003). The Effect of Food-Safety Related Information on Consumer Preference: The Case of the BSE Outbreak in Japan, Agribusiness & Applied Economics Report No. 506, Center for Agricultural Policy and Trade Studies, North Dakota State University, Fargo, North Dakota, February Johansen, S. (1988). Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamic and Control, volume 12, number 2-3, June-September 1988, pages Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models, Econometrica, 59, pp Latouche, K., Rainelli P., & Vermersh D. (1998) Food Safety Issues and the BSE Scare: Some Lessons from the French Case. Food Policy, Vol. 23 No. 5, pp Leeming, J. & Turner, P. (2004). The BSE crisis and the price of red meat in the UK, Applied Economics, No 36, pp Liu, K. E., Huang, M-H., and Hsu, J.L. (2007). Consumer Awareness of the Avian Influenza Threat in Taiwan, Paper presented at the American Agricultural Economics Association Annual Meetings, Portland, OR, July 29-Aug.1. Mangen, M-J.J. & Burrell, A.M. (2001). Decomposing Preference Shifts for Meat and Fish in the Netherlands, Journal of Agricultural Economics 52 (2): Mathews, K. Jr., Bernstein, J. & Buzby, J. (2004). Food Safety Issues for Meat/Poultry Products and International Trade, Agricultural Information Bulletin No (AIB 789-4), U.S. Department of Agriculture, Economic Research Service, Washington, DC, February. McCluskey, J.J., Grimsrud, K. M., Hiromi Ouchi, & Wahl, T.I. (2003). Bovine Spongiform Encephalopathy in Japan: consumers food safety perceptions and willingness to pay for tested beef, The Australian Journal of Agricultural Economics, 49, pp Park, M., Jin Y. H. & Bessler D.A. (2008). The Impacts of Animal Disease Crises on the Korean Meat Market, Agricultural Economics, 39 (2), pp Peterson, H.H. & Chen, Y.J. (2005) The Impact of BSE on Japanese Retail Meat Demand. Agribusiness, Vol. 21, Issue 3, pp Schlenker, W. & Villas-Boas, S. (2008). Consumer and Market Responses to Mad-Cow Disease, paper No.1023, Department of Agricultural & Resource Economics, CUDARE working papers, University of California, Berkeley. 23

24 Taha, F. A. (2007). How Highly Pathogenic Avian Influenza (H5N1) Has Affected World Poultry- Meat Trade, Electronic Outlook Report from the Economic Research Service No LDP-M , U.S. Department of Agriculture, Washington, DC, p.27, August. Taha, F.A. and Hahn, W. F. (2011a). Highly Pathogenic Avian Influenza Impacts on Japan s Import Demand for Shell Eggs and processed egg-products, World s Poultry Science Journal, Vol. 67, March 2011, pp Taha, F.A. and Hahn, W. F. (2011b). Highly Pathogenic Avian Influenza Impacts on Japan s Import Demand for Cooked and Uncooked Poultry, Beef, Pork, and Other Meats, selected paper prepared for presentation at the Agricultural and Applied Economics Association Annual Meeting, Pittsburg, Pennsylvania, July Taha, F.A. & Hahn, W. F. (2013). The impact of BSE on U.S. exports of beef meat, beef offal, pork meat, and pork offal, Agribusiness Journal, forthcoming Fall Theil, H. (1977). The Independent Inputs of Production, Econometrica, Vol. 45, No. 6, pp Verbeke, W. & Ward, R.W. (2001). A Fresh Almost Ideal Demand System Incorporating Negative TV Press and Advertising Impact, Agricultural Economics 25, pp Verbeke, W., Ward, R.W., & Viaene, J. (2000) Probit Analysis of Fresh Meat Consumption in Belgium: Exploring BSE and Television Communication Impact, Agribusiness, Vol.16, No.2, pp USDA, Foreign Agriculture Service, Market and Trade Data, (November 2012). U.S. International Trade Commission. (2008). Global Beef Trade: Effects of Animal Health, Sanitary, Food Safety, and Other Measures of U.S. Beef Exports, Investigation No , USITC Publication 4033, September. World Trade Atlas (2013). Internet Edition, Global Trade Information Service, September. World Health Organization (2012). H5N1 avian influenza: Timeline of major events, accessed March Yang, S., & Koo, W.W. (1994). Japanese Meat Import Demand Estimation with the Source Differentiated AIDS Model, Journal of Agricultural and Resource Economics 19 (1994): Appendix Prior to checking for disease effects we looked at some other specification issues. First we tested whether or not the prices and scale terms are exogenous to EU export demand. The right-hand-side of our model has export unit values and total export scale. It is entirely likely that export scale and unit values are simultaneously determined within a larger supply and demand framework. To deal with the potential simultaneity of prices, quantities, and scale, we started our model estimation using Generalized-Method-of-Moments, GMM. We assumed that the exchange rate was exogenous to the meat exports. 24

25 After running an unconstrained model using GMM, we then tested the covariance between the changes right-hand-side (RHS) elasticity terms and the model s estimated error terms. If these correlations are statistically insignificant, then the variable may be treated as an exogenous rather than a jointly-determined variable. We used changes in the RHS variables because we used their lags as instruments. Table A1 shows the results of these tests. Many of the test statistics are insignificant at the 5% level. The export unit values for pork, other, and beef meats have statistics whose values exceed the 5% level. However, the more hypotheses one tests, the higher the chance one gets a seemingly significant test. One of the ways statisticians have developed to deal with this issue is the Holmes-Bonferroni (HB) criteria. The basic idea behind the HB criteria is that if you are testing 2 independent hypotheses the largest of the 2 will exceed the 5% value (for one test) about 10% of the time. The true 5% value for the largest of 2 tests is 5%/2=2.5% value and so on. To implement the HB correction, we sorted the results from the least to most significant and compare the nominal chi-square alpha 5% divided by the order of the tests. None of the three largest tests alphas exceed their HB criteria. It seems safe to treat the RHS variables in the demand system as exogenous to the model. Table A1 Testing the export unit values and scale for exogeneity1,2,3 Chi-square Right-hand-side variable 3 Test alpha 6 HB 2 statistic degrees of criteria Order 3 freedom Scale % 5.00% 1 Pork offal export unit value % 2.50% 2 Beef offal export unit value % 1.67% 3 Poultry export unit value % 1.25% 4 Pork meat export unit value % 1.00% 5 Other meat export unit value % 0.83% 6 Beef meat export unit value % 0.71% 7 1 Source, ERS estimates based on World Trade Atlas and USDA data 2 Holm Bonferroni criteria for multiple hypothesis tests. 3 The variables are sorted from least to most significant. 25

26 We are surprised that the price and scale are exogenous to EU export demand. Price-exogeneity is one of the things one would see in the small-country case. The EU seems a poor candidate to be a small country. Perhaps price setting is dominated by domestic conditions. Consistency with microeconomic restrictions We were interested in checking is the consistency of this data with optimization theory. International meat exports are generally intermediate products. They must be further processed before retail sale. Meat export demand is an example of the demand for inputs. Theil (1977, cited above) input demand will resemble consumer demand if we specify the firm s problem as maximization of output subject to a bound on input costs. The EU sells its meat to a wide range of firms and countries. It is well known that even if individual demands are consistent with theory, total market demand need not be. If the aggregate demands are consistent with theory, then we will improve our estimates by imposing the economic restrictions on them. Log-linear forms are not generically consistent with theory. However we can make log-linear demands consistent with theory at a point. The scale term in our model is exactly the same as the expenditure terms in consumer demand. Suppose that we call the elasticity of demand for good i with respect to price j ε ij and the elasticity with respect to scale ε ix. We use ε ix for the scale elasticity as it corresponds to the expenditure elasticity for consumers. We use the term s i to stand for the cost-share of good i. The cost share is the total expenditure for the good divided by the total expenditures for all goods in the system. These cost-shares sum to 1. 26

27 There are three basic sets of equality restrictions on the price and scale demand elasticities. Demands must be homogeneous of degree 0. For example, doubling all prices and scale will leave demands unchanged: j ε ij + ε ix = 0 for all i. We also have elasticities for the $/ exchange rate and the disease effects. Economic and business analysts often discuss the real exchange rate one where both currencies are corrected for inflation. For the demand theory point of view, the nominal exchange rate we use here is a real variable. The exchange rate only changes demand when the $/ ratio changes. The disease effects are also real. Another set of constraints is that the elasticities have to be consistent with the budget-total scale as shown in the following: s j = i s i ε ij, for all j prices 1 = i s i ε ix, for scale-expenditures 0 = i s i ε ik, for k equal to BSE, HPAI and $/. Finally, there is Slutsky symmetry: s i ε ij + s i s j ε ix = s j ε ji + s i s j ε jx for all i,j. Given that there are 6 meats in this system, there are 6 homogenous-of-degree-0 restrictions and 15 unique symmetry restrictions. It turns out that the budget constraint terms for the prices hold automatically given homogeneity and symmetry. There are 4 more budget constraint terms associated with the scale, two disease events, and exchange-rates. That is 25 restrictions. We can make these elasticities consistent at a point; that point will be determined by the share terms. So which shares should we use? We tried two sets of shares. For the first we used the 27

28 average shares for the sample. For the second set we allowed the computer to pick the shares subject to them being positive and summing to 1. With estimated shares we end up with a 20- degree-of-freedom restriction. We rejected the hypothesis when we used fixed shares. However, when we used estimated shares, the likelihood ratio test was and its alpha value is 46.7%. We accept the economic restrictions and imposed them for the rest of our analysis using the estimated shares. The VAR error-term tests Barnett and Serlittas (2008) wrote a review article on applied demand modeling. One of the things that noted was that it is common to find unit roots in the error terms of consumer demand systems. Our previous work on U.S. export demand found that U.S. exports are cointegrated with a single root=1. We estimated our demand system using a second-order VAR-type error term as follows: U t = Q t AZ t BX t V 1 [Q t 1 BX t 1 ] V 2 [Q t 2 BX t 2 ] In the equation above, U t is a vector of error terms in the month numbered t, Q t the vectors of logarithms of quantities, Z t a vector with an intercept, trend, and monthly dummies, and X t a vector of exogenous variables the logs of prices, scale, exchange rates and the disease-state variables. The matrix A has the coefficients for the intercepts, trends and dummies. The matrix B has the elasticities, and V1 and V2 are the 1 st and 2 nd -order parts of the VAR. We are not using lagged Z. The lagged intercepts and trends are poorly defined when there are roots=1 in the VAR (Johansen, 1991). The structure we have above can lead the intercept to induce non-stochastic trend and the trend to induce a non-stochastic squared trend when there are roots=1. When we estimated the model, we included all 12 monthly dummies and identified them by requiring that each meat s set of dummy coefficients sum to 0 over the year. 28

29 Johansen (1988, 1991) demonstrated that VAR estimates are in general consistent even when there are roots=1 and showed the asymptotic properties of various types of tests on the VAR 4. He proposed the use of likelihood ratio type tests for unit roots and in his 1988 paper found critical values for these tests. Johansen used an error-correction form and imposed roots=1 on this form using non-linear, cross-equation restrictions. We kept our model in VAR form, but imposed the equivalent restriction on the model. Allow us to strip down the equation above to a pure VAR by eliminating the elasticity and dummy terms Q t = V 1 Q t 1 + V 2 Q t 2 + U t To get this into error-correction form, we use the fact that Q t = Q t + Q t-1+ Q t-2 that Q t-1 = Q t-1+ Q t-2 Q t = [V 1 I] Q t 1 + [V 2 + V 1 I]Q t 2 + U t Johansen wrote his basic model in an error-correction form and did not keep VAR-type coefficients as we have above. If there are roots=1, the matrix [V 2 +V 1 -I] has less than full rank. In our case this matrix is 6 by 6. If there is one root=1, then the matrix has a rank of 5, two roots=1 gives the matrix a rank of 4 and so on. Johansen noted that one way to impose a root=1 on the errorcorrection form is to find a vector, call it W, such that: [V 2 + V 1 I]W = 0 W = V 1 W + V 2 W One way of interpreting the second part of the equation above is that if our Q=W in periods t-1 and t-2, then our forecast for period t is also W. We tested our VAR for roots=1 by imposing an estimated W or set of W on the VAR. Imposing 1 root=1 on the model gave us a test value of 1.65; Johansen s 5% value for this test is 4.2. We accept that first unit root. Imposing a second root on the VAR raised the test to 21.17, much larger than Johansen s 5% value for two roots=1. 4 Most of the tests are asymptotically chi-square-distributed with the exception of the unit root tests. 29