Factors Driving South African Poultry and Meat Imports 1

Transcription

1 Internatonal Food and Agrbusness Management Revew Volume 18 Specal Issue A, 2015 Factors Drvng South Afrcan Poultry and Meat Imports 1 Fawz A. Taha a and Wllam F. Hahn b a Economst, Food Securty & Development Branch, Economc Research Servce, Unted States Department of Agrculture, 355 E Street SW, Washngton, DC 20024, USA. b Economst, Anmal Products and Cost of Producton, Economc Research Servce, Unted States Department of Agrculture, 355 E Street SW, Washngton, DC 20024, USA. Abstract Import demand for poultry has made t South Afrca s fastest growng meat product, whle demand for beef, sheep, and goat meat s generally declnng. Poultry was found to be a statstcally sgnfcant substtute for pork and other meat (sheep, goat, and offal), but nsgnfcant wth respect to beef. Pork tends to be complementary to beef, though statstcally nsgnfcant. The artcle nvestgates whch of three crucal factors are most affectng South Afrca meat mports: (1) changes n consumer tastes and/or meat processng technology, (2) prces, or (3) scales ndcatng the total sze of the mported meat market. Major fndngs showed that changes n taste-technology had a greater mpact on ncreasng poultry and pork mports than changes n prces, even though poultry prces tended to ncrease less than the prces of beef, pork, and other meats, makng t a better bargan. Keywords: mport demand system, taste-technology, scale, and prce, South Afrca, poultry, beef, pork, sheep & goat Correspondng author: Tel: Emal: F.A. Taha: faha@ers.usda.gov W.F. Hahn: whahn@ers.usda.gov 1 The vews expressed here are those of the authors, and may not be attrbuted to the Economc Research Servce or the U.S. Department of Agrculture Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 165

2 Introducton Anmal products are by far the largest agrcultural sector n South Afrca (SA), contrbutng 46.4 percent to the total gross value of agrcultural producton n 2012/13, followed by feld crops (28.6 percent), and hortcultural products (25.0 percent) (Trends n the Agrcultural Sector 2013). Nonetheless, the defct n the SA balance of trade for meat rose from a low of US $3.8 mllon n 2002 to US $428 mllon n 2013 n nomnal terms, after reachng a record hgh of US$546 mllon dollars n Although statstcal data for 2013 and 2014 showed that the SA meat trade defct s slghtly declnng, poultry mports have contnued to grow, accountng for over 81 percent of SA total meat mports. The goal of ths research s to explore the key drvers of the South Afrcan meat mports, focusng specfcally on whch of three factors have the most effects on meat mports: prces, scale, or taste-technology. Intally, we estmated the demand shft among varous meats n relaton to ther own prces, cross-prces, and scale elastctes to determne the patterns of consumer demand for dfferent meats and the substtuton effects. These elastctes have a number of potental uses. The Unted States s a major exporter of these four types of meats; the more we know about how the world s meat buyers respond to prces, the better U.S. forecasts and analyss of U.S. meat exports wll be. Estmated elastctes could also be used to analyze mplcatons of SA polcy changes and reforms affectng the meat trade. In addton, we would expect that South Afrca s growng economy wll normally lead to expandng consumer demand for meat, and lkely to hgher mports. Of course, expandng supply could have offset expandng demand. On the other hand, attempts to expand SA s corn and soybean markets could fal and lead to hgher domestc feed prces, hgher cost of producton for domestc meat producers, and hgher meat mports from more compettve world markets. Consequently, formal analyss of the role of government polces affectng domestc supply and exports of crtcal feed commodtes requres more focus n future research work. Over the past few decades, authors n a number of countres have nvestgated the demand for meat mports n those countres, among them Hayes, Wahl, and Wllams (1990) n the Unted States; Kawashma and Sar (2010) and Yang, and Koo (1994) n Japan; Lopez (2009) n Mexco; Ablayeva et al. (2004) n Russa; and Pantzos and Fouseks (1999) n Greece. To the authors knowledge, however, no study has nvestgated whch of three crucal factors are most affectng South Afrca meat mports: (1) changes n consumer tastes and/or meat processng technology, (2) prces, or (3) scales. The followng sectons descrbe the SA lvestock sector, meat producton, meat consumpton, and meat mports from 1997 to It also ncludes a dscusson of the economc lterature on SA mport demand for meats, documentng a smlar shft n consumer preferences from red meat toward poultry n other countres. Followng the dscusson of economc lterature are the methodology, emprcal results, and conclusons Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 166

3 South Afrca s Meat Producton and Consumpton Total SA meat producton nearly trpled from 1,009 mllon Kg n 1975/76 to over 2,752 mllon Kg n 2012/13. Bovne was consstently the most produced meat untl 1995/96, when poultry outpaced red meats (beef, pork, sheep, and goats). Both red and poultry meats made strkng ncreases over the perod, but poultry producton was the most mpressve, accountng for 56 percent of SA s total meat producton. Table 1 shows the shft n total meat consumpton from red meats toward poultry. Based on South Afrca s offcal data, per capta consumpton of poultry meat surpassed that of the four-red meats n 1997/98 (20.7 Kg versus 20.2 Kg per year). As Table 1 shows, the gap contnued to ncrease n favor of poultry, rsng to 35.1 kg n 2010/11, and slghtly thereafter to 36.3 Kg n 2012/13. On the other hand, most of the declne n per capta red-meat consumpton occurred versus 24.9 kg for red-meat n 2012/2013. Table 1. Producton, Consumpton Whte and Red Meat Poultry Red Meat Poultry Red Meat Prod. Cons. Prod. Cons. Per capta Cons. Mllon Kg Klogram Source. Abstract of Agrcultural Statstcs, South Afrcan consumers shft to poultry from red meats seems smlar to the shft among European and U.S. consumers more than 40 years ago, and there are common drvers of change. Generally, these have ncluded rsng consumer awareness of healthy and unhealthy eatng e.g., of detary fat causng hgh levels of cholesterol as well as safety concerns followng outbreaks of anmal dseases, ncreased emphass on convenence (tme to cook and prepare) for housewves as ther partcpaton n the labor market rses, and changes n relatve meat prces. These concerns are seemngly correlated wth ncreasng per capta ncome. For example, n the Unted States, health ssues (lnkng fat content and hgh cholesterol levels and strokes) ntated a change n meat demand from beef to poultry/chcken, lean pork, and fsh products (Pggott and Marsh 2004; Moschn and Melke, 1989; and McGrurk et al. 1995). Smlarly, Huston (2000), n dscussng reasons for the demand shft from red-meat to poultry, ncluded the factors mentoned above: safety, health ssues, convenence (tme to cook and prepare), and relatve prces. In the Unted States, some of these factors, f not all, contrbuted to a decrease n U.S. per capta consumpton of beef from 84 to 62.5 pounds per year, and a remarkable ncrease n chcken from 40 to over 80 pounds, durng the perod (Davs and Stewart 2002) Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 167

4 In the UK, the declne n beef and veal consumpton durng was attrbuted to safety concerns about beef as a food, anmal welfare and envronmental ssues, outbreaks of anmal dseases, changes n demographcs, changes n relatve prces, health concerns (fat content), and the demand for convenence (Resurreccon 2003). In other parts of the world, authors reported that changng lfestyles led to the shft toward more convenence n meat and food preparaton. Anderson and Shugan (1991) and Grunert (2006) found that consumer demand for convenence boosted demand for poultry relatve to beef. Grunert called convenence the most sgnfcant trend contrbutng to rsng chcken sales. Increasngly, fat content n meat s generally seen as negatve. Ths was confrmed n four European countres; France, Germany, Span, and the UK (Grunert 1997), as well as n South Afrca (Shongwe et al. 2007). Also n South Afrca, Neuwoudt (1998) and Louw et al. (2010) explaned changng demand for meat n general to growth n populaton and per capta ncome, urbanzaton, and food preferences among dfferent racal groups. Louw et al. also explaned the rse n demand for poultry to global derved factors, ncludng health concerns, expanson of fast food outlets, and demand for convenence. Taljaard, et al. (2006) also examned the factors drvng South Afrca s demand for meat. Ther econometrc models ncluded prces, ncome, and a group of taste effects measurng consumer demands for health and safety, convenence, qualty, anmal welfare and the envronment. Taljaard et al. splt ther sample nto two separate perods: and Prces and ncome were the most mportant drvers of demand n the frst perod; the group of taste factors was the most mportant n the later perod, when taste-technology was one among a group of several factors but was not the standout factor. Economsts wdely beleve that demand for poultry s rsng manly because t has the lowest relatve prces of all major meats. Does ths apply to South Afrca, where poultry mports averaged over 81 percent of total meat mports over the last fve years? Ths artcle concentrates on ths queston, namely, nvestgatng whch of three factors s most strongly effectng changes n mport demand for meats n South Afrca; prces, scale, or taste-technology. Commercal Meat Trade Total meat mports to South Arca ncreased 124 percent, but poultry rose the most: 281 percent from 1997 to Durng the last fve years ( ), the rse n poultry meat mports brought them to over 81 percent of SA s total mports, followed by other meat (9 percent), beef (2 percent), and pork (6 percent) (Table 2). In 2014, about 48 percent of the SA poultry mports orgnated from the EU-27, 43 percent from Brazl, 5 percent from Argentna, and only 1.3 percent from the Unted States. Ths was manly due to the Ant-dumpng dutes case aganst U.S. poultry products and the mposton n 2000 of ant-dumpng tarffs, amountng from Rand 2.24/ kg to 6.96/kg (US $0.32 to US $1.00), n addton to an mport duty of Rand 2.20/kg (US $0.31/kg) (USDA-FAS Gan Report, Republc of South Afrca 2011) Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 168

5 Table 2. South Afrca's Meat Imports, Poultry Beef Pork Other Total Mllon Klogram Source. World Trade Atlas, February 2015 Meat exports from South Afrca, albet small, more than trpled from $US mllon n 2010 to $US mllon n 2014, mostly shpped to neghborng Afrcan countres n Exports n 2014 conssted of 45 percent beef and 34 percent poultry, wth the remanng 21 percent consstng of other,.e., pork, sheep, and goat meat and offal. Statstcal Analyss of South Afrcan Import Demands The analyss of South Afrcan meat mports has two basc goals: 1. To determne what factors are drvng SA poultry mports. As noted, prevous studes have typcally agreed that the low relatve prce of poultry has been a drver of ncreased poultry demand, as would be expected, whle several of the studes found that taste shfts have also contrbuted to the ncrease. In ths artcle, we develop a model smulaton to determne whch of the followng three factors has the most effect on South Afrcan meat mports (1) taste-shft, (2) prces, or (3) scales. 2. To estmate South Afrca s meat mport demand system to provde support for future work. We can use these model estmates to calculate South Afrca s meat mport demand elastctes. These elastctes have a number of potental uses. The Unted States s a major exporter of these 4 types of meats; the more we know about how the world s meat buyers respond to prces, the better U.S. forecasts and analyss of U.S. meat exports wll be. In addton, these elastctes could also be used to analyze mplcatons of SA polcy changes and reforms affectng the meat trade Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 169

6 Modelng SA Meat Import Demand System The study used the Central Bureau of Statstcs (CBS) model developed by Keller and Van Drel n The name refers to the authors employer, the Dutch Central Bureau of Statstcs. The CBS model s essentally condtonal on the scale of meat mports, where scale s a measure of the total outputs produced usng the mported meats. The CBS model was augmented by addng a trend varable to cover changes n consumer demand, trggered by consumers rsng preference for certan meat. The demand for nputs can be changed by both shfts n consumer demand for outputs and by changes n the technology used to produce those outputs. Economc analysts often use tme trends to model taste and/or technology shfts. Trend terms mply constant, straght-lne growth over tme. Addtonally, consumer demand for meat products could change the demand for the meat mports used to make them. Changes n meat processng technology could also encourage meat mporters to shft among the types of meat. Ths sort of straght-lne change n taste or technology effects s rather smplstc; however, we allowed for a more complex pattern by addng a squared-trend term to the model. A squared trend gves us a more flexble taste-technology shft. A pure trend wll ether constantly ncrease or decrease the demand for a product. By addng a squared-trend n the model, we can have reversals n the taste and technology shfts. For example, demand for one of the mports could grow early on, then reverse and begn to declne later on durng a certan tme perod. Together, the trend and squared-trend terms measure the effect of changes n tastes and/or technology 2. Keller and Van Drel orgnally developed the CBS system to model consumer demands. Meat mports are ntermedate goods: they must be further processed before sale to consumers. Pror to the development of the CBS model, Thel (1977) showed that consumer demand models are also consstent wth cost-mnmzng nput demand. All dfferental demand systems use the total dfferental of the budget constrant: (1) q p = x w ln p + w ln q = ln x, where q and p are the quantty and prce of good, x s the total expendture, or n the case of derved demand, the total cost of nputs, and ln stands for the change n the natural logarthm of the term. The term w s the budget share for product, defned as: 2 The CBS model also ncluded seasonal demand shfters. These seasonal shfters measure how one month s demand for a type of meat dffers from others. It seems more lkely that these would be drven by shfts n consumer tastes rather than technology. As we set these up, these monthly shfters only cause mports to vary month-to-month and not over longer perods of tme. Our dscusson of these s lmted gven our focus on the long-term drvers of mport demand Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 170

7 (2) pq w =. x The summaton terms n equaton (1) are often replaced wth dvsa prce and quantty ndces, defned as: (3) P = w ln p, (4) Q = w ln q. Equatons (3) and (4) can be nserted n (1) and rearranged to produce: (5) ln x P = Q. In ther development of the Central Bureau of Statstcs (CBS) model, Keller and Van Drel (1985) used equaton (6) below, whch s a more appealng verson from the standpont of consumer theory. In a derved demand context of the current analyss, equaton (6a) s more convenent. Actually the forms n (6) and (6a) are equvalent; one smply uses dfferent sdes of equaton (5) n the specfcaton. (6) w [ q Q] = c ln p + b [ ln x P] ln,, or j (6a) w [ q Q] = c ln p + b Q ln,. j j j Thel (1977) showed that changes n the dvsa quantty ndex corresponded to changes n total output. The coeffcent b, whch multples the quantty ndex n (6a), shows how demand for an nput responds to changes n the total output. As s common n appled demand analyss, we wll refer to the b as scale terms. If all the b s are 0, the technology has constant returns to scale. Negatve b mples that the cost share (the w) for nput decreases as the total scale of output ncreases, and vce versa. The cross-prce elastctes (c j coeffcents) show how quantty reacts to changes n the prce of j. The c j also nclude c. These coeffcents can be used wth the budget share to derve prce and scale elastctes of demand usng the followng formulas: cj (7) ε j = w b (8) η = 1+ w. In (7) and (8), ε j s the elastcty of demand for nput and prce j and η s nput s scale elastcty of demand. Keller and Van Drel demonstrated that the CBS model s a locally flexble functonal form. One can take any set of demand elastctes and fnd a set of CBS coeffcents consstent wth these 2015 Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 171

8 elastctes. In order to be consstent wth optmzaton, the coeffcents have to be homogenous of degree 0, consstent wth the budget or total-cost constrant defned n equaton (1) and symmetrc. Ths wll be true f the followng two equatons hold: (9) cj = 0 j, (10) b = 0. Equatons (9) and (10) mply that the c j and the b coeffcents sum to 0 when added over all the nputs. Other constrants requre demand to be homogeneous of degree 0 n prces-expendtures and symmetrc, as shown n equaton (11) and (12), respectvely: (11) cj = 0, j (12) c = c j. j j, All constrants n equatons (9-12) are lnear equalty restrctons. Optmal cost-mnmzng demand dervatves also have to be negatve sem-defnte (NSD) n prces. The CBS system s globally NSD when the matrx of c j, s tself NSD, whch could be acheved by mposng economc restrctons of equatons (9-12) on all CBS estmatons. One mplcaton of demands beng NSD s that ther own-prce elastctes of demand are negatve. The economc drvers for ths demand system are the mport prces of the 4-meats, and scale. Scale s an economc measure of the fnal amount of products meat mporters make from a certan quantty of mported meat. The scale of the market s gong to be determned by a larger set of nternal and external economc factors; nternally, they nclude supply, demand, and polcy condtons, and externally, the prces pad for meat mports. Estmaton Forms for the CBS Model and Prelmnary Tests The CBS and other dfferental models of demand start wth demand dervatves. Demand dervatves are not observed; prces and quanttes are. The CBS models are estmated under the assumpton one can approxmate the dfferental equatons wth fnte dfferences. A general way to wrte the dfference equaton would be: (13) q wt Ln q j, t, b p cj Ln p j, t j, b j q w j Ln q X b + j Ln X j, t j, b t b = y j t = k H p w jt Ln p k, t j, t j, b * d, k + e t. The terms q,t p,t and X t are actual quanttes, prces and expendtures for a specfc month. The q,b p,b and X b are the baselne values; the baselne expendture s consstent wth the baselne prces and quanttes. The term e, t s a random error term. By vrtue of the model s constructon, the error terms n each tme perod sum to Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 172

9 The H k,t are a set of other exogenous varables that act as taste-technology shfters; the d,k are estmated coeffcents, whch have to sum to 0 over for each k f the budget constrant s to hold. The H varables nclude an ntercept, a trend, a squared trend, and 12 monthly dummes. The ntercept and monthly dummes are perfectly collnear, so the dummes coeffcents are dentfed by makng each meat s set of monthly dummes sum to 0 when summed over the year. Unlke the other terms n (13), the H k,t are not explctly dfferenced from a baselne. The nterpretaton of the ntercept and error depends on what one uses as a baselne. The most common baselne used n ths type of demand analyss s last perod s value; for example q b,t would be q,t-1. In ths type of non-lnear frst-dfference approach, the ntercept would represent the dfference of a trend, and the ntercept s coeffcent s generally nterpreted as a taste-change varable. Ths analyss follows Hahn and Mathews (2007), who used a non-lagged baselne to calculate average prces and quanttes to create baselne prces, quanttes, shares, and expendtures. Ths type of formulaton s based on the assumpton that average quanttes are optmal gven average prces, whch mght not be the case. The ntercept terms n ths case can be nterpreted as correctng the baselne quanttes or measurng the shft necessary to make them optmal. The monthly dummes allow for some monthly varaton n ths correcton. The ntercept can be nterpreted as the average correcton for the year; the dummes are the seasonal varance from that average. If the monthly dummy coeffcent for a product s 0, that product s demand s at ts yearly average level n that month. The trend and trend-squared terms allow shfts n demand 3. Because these are derved demands, the demand shfts can be due to both changes n consumer tastes and meat-processng technology. If the trend and squared trend coeffcents are all 0, there are no taste-technology shfts. Also, lke Hahn and Mathews, we specfed our error term as a vector autoregressve (VAR) process: (14) ee,tt = 3 kk=1 vv,jj,kk ee jj,tt kk + uu,tt In (14), the term v,j,k s the estmated effect of the k-lag of demand j on the current error for demand, whle u,t stands for an dentcally, ndependently dstrbuted random component 4. The ntal runs used a 3 rd -order VAR. Because of the constructon of the endogenous varables, the errors, e, and u, also sum to 0 over equatons n each tme perod. The sum of the v,j,k over s also 0 for each j,k par. The current and lagged errors are perfectly collnear. To dentty the v,j,k the j subscrpt s defned for only three of the four quanttes. It has long been determned (Barten 1969) that the soluton to estmatng systems wth sngular errors s to estmate the model usng all but one of the equatons, then usng the economc restrctons to estmate the parameters assocated wth the dropped equaton. If one uses Full Informaton Maxmum Lkelhood estmaton (FIML), the estmates are ndependent of the excluded equaton. 3 Usng both a trend and ts square gves us more flexblty n modelng the pattern of shfts. 4 The u,,t s are ndependently dstrbuted over tme; they wll have covarance over equatons Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 173

10 Model Structure Testng Pror to developng the fnal-form model on whch ths analyss s based, we dealt wth a number of sde ssues. For example, were the taste-technology shfters statstcally sgnfcant? Results showed that all four types of meats had statstcally sgnfcant trends. Addtonally, beef and pork had statstcally sgnfcant squared-trends (Table 5). Ths ndcated that taste-technology changes were experenced n all four meat types, and squared-trends were experenced n beef and pork. The mplcaton of these two changes was a steady rse n the mports of poultry and a steady declne n other meats. Imports of pork were ntally ncreasng, but later on declned, as a result of the statstcally sgnfcantly squared-trend. For the same reasons, beef mports were ntally declnng, then changed course n the last few years. The emprcal results presented next are based on a smpler model than that outlned n equatons (13) and (14), whch have a large number of parameters n t. We were aggressve n restrctng the model and elmnatng terms from the model. We have already mentoned that we restrcted the quadratc parts of the shfters; poultry and other-meat quadratc terms were statstcally nsgnfcant. The followng restrctons were mposed on the model: We were able to elmnate the 2 nd and 3 rd lags from the VAR error terms and greatly restrct the 1 st -order terms. The pork, poultry, and other meat s VAR had only ther own lagged error; beef had all three lagged errors. The most common method for puttng auto regresson n these demand models was developed by Berndt and Savn (1975), and t uses a dagonal type structure, as we found wth a common set of autoregressve coeffcents. We were able to reject ths dagonal structure; the lagged-error coeffcents for pork, poultry, and other meats are statstcally sgnfcantly dfferent from each other. We started wth 12 monthly dummes and managed to elmnate most of them from the model. Only January, August, September, November, and December have sgnfcant dummes, and each of these matters for only 2 of the meats. The two meats that matter vary by the monthly dummy. Data Sources South Afrca s monthly data were compled from the World Trade Atlas coverng the perod January 1997-October 2010.Data ncluded volumes and prces (unt-values) of each meat category. Meat mports were dvded nto four categores: poultry; beef; pork; and all other meats. Other meat s category ncluded sheep, goat, edble anmal offal, horses, asses, and mules; salted, dred or cured meat; and anmal fat. A unt value ($US per klogram) was calculated as a weghted average for each of the four categores. Emprcal Results Table 3 has the R-square, a measure of how well the model explans the data. An R-square of 100% means that the model fts the data perfectly. There are two versons of the R-square, shown n Table 3. The frst column shows the ft relatve to the CBS endogenous varable as defned n equaton (13). As seen n (13), ths endogenous varable s a complcated functon of the actual 2015 Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 174

11 quanttes. We used the smulaton model that created Fgures 1 to 4 to turn the predcted CBS endogenous nto predcted quanttes. The fts for the actual quanttes are qute good for a nonlnear model of ths type. Table 3. R-squares n Percent s Relatve the Naïve Model CBS Endogenous Varable Quantty Va Smulaton Beef Pork Poultry Other meat Demand shft among varous meats, ther own prces, cross-prce, and scale elastctes were estmated and are used to examne patterns of consumer demand for the dfferent meats and substtuton effects n the South Afrcan meat demand system (Table 4). Because the prce terms are symmetrc, we show only the upper trangular part of ther matrx. Note that our use of Z statstcs s somewhat msleadng. For example, the own-prce term for beef appears to be statstcally nsgnfcant, but n fact t s not, because we requred the c,j coeffcent matrx to be negatve-sem-defnte (NSD) n the model. However, all of the Monte Carlo teratons of the beef own-prce term were negatve and statstcally sgnfcant. Poultry was found to be a statstcally sgnfcant substtute for pork, sheep/goat, and offal meats, but not sgnfcant wth respect to beef. Also, pork tends to be complementary to beef, but was statstcally nsgnfcant. Most lkely, the nsgnfcant relatonshps are due to small beef mports, averagng 2.4 percent of all meat mports over the last 10 years. Table 5 has the ntercept, trend, squared trend, and monthly dummy terms. Table 6 shows the cost-mnmzng elastctes of demand. These are calculated usng the coeffcent estmates from Table 4 and equatons (7) and (8). The prce-elastctes of demand show how each mport wll respond to changes n each prce. Most of these elastctes are small. Our prce-based smulatons can only have large mpacts on demand f there are large shfts n the relatve prces. Import demand would appear to be senstve to changes n total market scale. Table 4. The Prce and Scale Estmates for the SA Meat Import Demand System Own- and Cross-Prce Estmates Scale Beef Pork Poultry Other or b Beef Estmate Z-stat Pork Estmate Z-stat Poultry Estmate Z-stat Other Estmate Z-stat Note. 1 Standard devatons and Z statstcs are a based on 5,000 Monte Carlo teratons. 2 Z-stat. means Z-Statstcs 2015 Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 175

12 Table 5. Intercepts, Demand Shfters, and Seasonal Terms 1 Meat Beef Pork Poultry Other Estmate Z-stat. Estmate Z-stat. Estmate Z-stat. Estmate Z-stat. Intercept Trend Trend squared January August September November December Note. 1 Only 5 of the monthly dummes are statstcally sgnfcant. All of the 5 monthly dummes sum to 0 for each meat-knd. 2 Standard devatons and Z statstcs 3 Z-stat. means Z-Statstcs Model Smulaton: Whch of the Three Varables Are Drvng SA Meat Imports the Most? The CBS has a number of desrable features one lkes to see n a demand system, but t has one major drawback: t uses a rather convoluted functon of quanttes. We used a smulaton model that allowed us to turn the changes n prces, scale, and the taste-technology shfters nto changes n quanttes mported. When runnng these smulatons, we started wth a baselne mport level. The baselne level for each meat s approxmately the amount mported at the average prces and scale. The smulatons keep all other parameters constant (ceters parbus) and then estmate mport demand usng each month s actual (1) taste-shft, (2) prces, or (3) scales. Results were depcted for each meat knd n comparson to SA s actual mport demand levels. Mllon Kg Q97 3 Q98 actual 1 Q00 3 Q01 1 Q03 taste and technology 3 Q04 1 Q06 Scale changes 3 Q07 1 Q09 prce changes 3 Q10 Fgure 1. Beef Imports, Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 176

13 Fgure 1 shows that actual beef mports were hghest early n the sample and lowest n Beef had both the lnear and quadratc taste and technology shfters. Its shfter effect s a smoothly curved lne that declnes throughout the early-year perods, turnng up slghtly sometme n As shown n Table 6, beef has the hghest scale elastcty of all the meats and generally the smallest changes n own and cross-prce elastctes. Ths ndcates that prce changes have small effects on beef mports, whle scale changes have larger effects. Between 2001 and 2002 when beef mports were at ther lowest levels, both the taste-technology and scale effects were contrbutng to lowerng beef mports. However, n 2003, beef mports started to ncrease and were largely drven by the ncreasng scale of South Afrcan total meat mports. Table 6. Cost-Mnmzng Import Demand Elastctes 1 Prce Elastctes Meat Beef Pork Poultry Other Scale Beef Pork Poultry Other meats Note. 1 Estmates based on World-Trade Atlas Data, February Pork also has a quadratc taste-technology term. The remanng two meats have only lnear effects. Pork s quadratc has to offset beef s quadratc. As llustrated n the case of beef (Fgure 1), beef s quadratc trend caused ts taste-technology shfter to have a valley pattern; pork s quadratc trend made t looks lke a hll (see Fgure 2). Pork s much less senstve to scale changes than beef but more senstve to prce changes. Mllon Kg Q97 3 Q98 1 Q00 3 Q01 1 Q03 3 Q04 1 Q06 3 Q07 1 Q09 3 Q10 actual taste and technology Scale changes prce changes Fgure 2. Pork mports, Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 177

14 Lke beef, poultry mports are relatvely responsve to changes n scale. South Afrcan poultry mports have ncreased, albet rregularly, throughout the sample perod (Fgure 3). Poultry s taste-technology effect s purely lnear and mples ncreasng poultry mports. Changes n scale over tme have ncreased poultry demand as well, snce Whle poultry s more prceresponsve than beef, as wth beef, prce changes have had small effects on poultry mports. Mllon Kg Q97 3 Q98 1 Q00 3 Q01 1 Q03 3 Q04 1 Q06 3 Q07 1 Q09 3 Q10 actual taste and technology scale changes prce changes Fgure 3. Poultry mports, Fgure 4 shows the results for other meats. Its mport pattern mrrors beef: relatvely hgh at the begnnng, low n the mddle, wth some recovery toward the end. Lke beef, both the declnng tastes effect and the scale effect work together early-on to lower ts demand. Toward the end of our sample, the shfter and scale effects go mostly n the opposte drectons. Mllon Kg Q97 3 Q98 1 Q00 3 Q01 1 Q03 3 Q04 1 Q06 3 Q07 1 Q09 3 Q10 actual taste and technology Scale changes prce changes Fgure 4. Other meat Imports, Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 178

15 As noted n our lterature revew, poultry consumpton has grown rapdly worldwde. Studes of poultry demand nvarably fnd that ts relatvely low prce s a major factor n ts expanson; poultry prces have tended to ncrease less than the prces of red meats, makng t a better bargan. In contrast and unlke all prevous studes, our estmates show that poultry prces have had small effects on the long-term trends n South Afrcan meat mports, whle taste shfts have the largest effects on SA rsng demand for poultry meat mports. Of all meat, only pork s extremely senstve to prce changes, whch make pork mports more volatle. The fact that taste or technology changes have ncreased the mports of poultry s consstent wth the results of demand studes n other countres, as noted n our lterature revew. Moreover, we found smlar, f not dentcal explanatons of what s drvng these changes. As n several countres cted n the lterature revew, poultry n South Afrca s perceved to be healther than all other meats. Increasng health concerns are an example of a taste shft. Other studes have found that more convenent, ready-to-cook cuts of poultry have also ncreased ts demand. Part of the added convenence appeals to consumers, shftng ther tastes; much of the added convenence s the result of changng processng technology. The total scale of meat mports s an mportant drver for at least three of the four types of meats. Although no formal analyss of factors drvng meat mport scale has been dscussed here, we would expect that South Afrca s growng economy wll normally lead to expandng consumer demand for meat. Ths expandng consumer demand could lead to hgher mports. Of course, expandng supply could offset expandng demand. Attempts to expand and stablze SA s corn and soybean markets faled, whch led to hgher domestc feed prces and hgher costs of producton for domestc meat producers, makng them less compettve n world markets. More analyss of the agrcultural supply, demand, and polcy condtons n South Afrca would be valuable. Fnally, a complete structural model would be able to analyze SA s feed-meat complex, one that would nclude agrcultural supply and demand for feed grans such as yellow corn, sorghum, and mllet, olseeds or olseed-meals, ncludng soybean seeds, sunflower seeds, cotton seeds, rapeseeds, and flaxseeds. The model mght usefully nclude SA agrcultural polces, especally domestc polces relatng to trade n general and specfcally to export party prcng and mport party prces. However, ths artcle s based on a statstcal model (CBS model), and s based on tme-seres meat mport statstcal data snce Conclusons Ths artcle used the Central Bureau of Statstcs (CBS) model developed by Keller and Van Drel n 1985, due to several advantages entaled n the CBS model. A demand model s lnear n ts parameters and restrctons. The CBS model was used to estmate acceleraton n meat mports, the patterns of consumer demand for meats by knd, own-prce, cross-prce, scale elastctes, and substtuton and/or complementary effects. We also augmented the analyss usng a condtonal demand model, addng a trend and trend-square varables to capture possble changes n meat mports snce We specfcally focused on three possble shfts that mght be trggered by consumers rsng preference for certan meat, due to changes n tastetechnology, n meat prces, and n market scales to explore whch of these s the most nfluental n South Afrca Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 179

16 Poultry was found to be a statstcally sgnfcant substtute for pork, sheep/goat, and offal meats, but not sgnfcant wth respect to beef. Beef and pork tend to have a complementary relatonshp, albet statstcally nsgnfcant. Most lkely, the nsgnfcant relatonshps are due to beef small mports, averagng 2.4 percent of all meat mports over the last 10 years. All the model s scale elastctes are postve, ndcatng that demand for all 4 products grows as the market expands. Increases (decreases) n scale are the only parameters that may expand (contract) the mports of all products smultaneously. The smulaton model also shows that taste-technology and market scale had greater mpact on SA meat mports specfcally poultry mports than changes n meat prces. More specfcally, taste-technology effect was purely lnear that ncreases poultry mports and decreases other meat mports. Beef had both lnear and quadratc taste-technology shfters, and as a result beef mports were trendng downward up to one pont n later years (see fgure 1). Pork has only a quadratc taste-technology term, makng ts mports trend upwards. Pork s quadratc effect has to offset beef s quadratc effect, leavng pork up slghtly. The smulaton model also shows that taste-technology had a greater mpact on rsng SA mport demand for poultry than changes n poultry prces. In part, however, the growth n poultry mports s drven by the fact that ts prce has ncreased less than the prce of red meats, makng t a better bargan. The fact that taste and/or technology changes have ncreased the mports of poultry s consstent wth the results of demand studes n other countres, as mentoned n the lterature revew. Ths s because worldwde, poultry s perceved to be healther than other meats, and ncreasng health concern s an example of a taste shft. Lastly, the smulaton model showed that prce changes have small effects on poultry, beef, and other meat mports, whle pork s more senstve to prce changes and poultry s more prceresponsve than beef. Therefore, prce changes have had smaller effects on poultry mports than on mports of beef, pork, and other meat. In general, poultry prces have tended to ncrease less than the prce of red meats. The analyss helps to explan why South Afrca s poultry mports grew from 48 percent of South Afrca s meat mports n 1997 to 81 percent n References Ablayeva, B., Ames, G. W., L. F. Gunter, and J. E. Houston U.S. Market Share for Poultry Meat n Russa: Trade Polcy and Exchange Rate Effects. Journal of East-West Busness 10 (2): Anderson, E.W. and S.M. Shugan Repostonng for Changng Preferences: The Case of Beef versus Poultry. Journal of Consumer Research 18(2): Abstract of Agrcultural Statstcs Department of Agrculture, Forestry and Fsheres (DAFF), Pretora, Compled by Drectorate Statstcs and Economcs Analyss. The Republc of South Afrca. Pretora: Internatonal Food and Agrbusness Management Assocaton (IFAMA). All rghts reserved. 180

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