Measurng the determnants of pork consumpton n Bloemfonten, Central South Afrca OA Oyewum & A Jooste 1 Abstract The man objectve of ths study s to nvestgate the determnants of households pork consumpton usng a logstc regresson procedure. The model was ntally ftted wth ten varables, selected from factors dentfed by precous studes, that affect meat consumpton n South Afrca. Sx of these varables were found to be sgnfcant at the 10 per cent sgnfcance level and all had the expected sgns. These nclude household monthly ncome, current household monthly expendture on meat, relatve prce of pork, preference for value-added pork products, prce of substtutes (the most preferred household meat type), and response of household to change n pork qualty. The result obtaned was further analyzed to compute partal effects and to conduct smulatons for sgnfcant factors. Analyss of partal effects revealed that qualty assurance and value-addng lead to much greater probablty of pork consumpton by households. Smulatons conducted on the base category of pork-consumng households revealed that qualty assurance and value-addng have relatvely hgh potental to almost double and more than double household pork consumpton respectvely. 1. Introducton In 2003, the prmary pg sub-sector contrbuted 4.63 per cent to the gross value of lvestock producton (R26 925 308 000) n South Afrca, whle sheep and goats, and cattle contrbuted 5.99 and 21.3 per cent respectvely (NDA, 2005). Accordng to Eskort (2005) the pork processng ndustry s estmated to be worth n excess of R900 mllon rand per annum. South Afrca slaughters around 1.7 mllon pgs per annum; ths accounts for less than 0.2 per cent of world pork producton. South Afrcan pork exports to other Afrcan natons, the Far East and the European Unon reached 0.35 tonnes n 2004, whle over 20,000 tonnes of pork were mported n the same year, wth rbs consttutng close to 60 per cent of mports (SAMIC, 2005). 1 Graduate Researcher and Assocate Professor respectvely, Department of Agrcultural Economcs, Unversty of the Free State, Bloemfonten, South Afrca. E-mal: oyewumoa.sc@mal.uovs.ac.za; joostea.sc@mal.uovs.ac.za; Tel: +27 51 401-2824; Fax: +27 51 401-3473. 185
In 1970 pork was the second-most consumed meat n the world after beef. However, n 1980 pork overtook beef as the most consumed meat. Pork s share of world consumpton of meat ncreased from 34.6 per cent n 1970 to 43.4 per cent n 2003 (Barnard, 2005). Pork consumpton s on average 15 kg per capta (NPPC, 2004). Per capta consumpton of pork n South Afrca, on the other hand, s relatvely low and has shown a decreasng trend snce the early 1970s, from 3.5 kg per capta to 2.7 kg per capta currently (NDA, 2005). A concern n ths regard s that () the real prce of pork has experenced a declnng trend snce the early 1970s, () real prces of substtutes have declned over the same tme and () real ncome per capta, although movng sdeways for most of the perod snce the early 1970s to the early 1990 s, has ncreased snce the early 1990s; gven ths one would expect pork per capta consumpton to ncrease, but t has not. A queston that arses s whether economc factors are stll the only factors that determne the consumpton of pork n South Afrca. In ths regard t s mportant to take note of the work of Bansback (1995), Huston (1999) and Dcknson et al (2003), who showed that non-economc factors (.e. non prce/ncome factors) are becomng more mportant n determnng consumers' purchasng decsons. For example, n a study by Bansback (1995) on the demand for meat n the EU, he showed that, for the perod 1955 to 1979, prce and ncome factors accounted for a hgher proporton of the explanaton of changes n meat consumpton than the perod 1975 to 1994 2. Huston (1999) argues that, by focusng only on product consstency and qualty, food safety, health and nutrton concerns and convenence, snce 1998 the US beef ndustry was able to stablze beef demand. Dcknson et al (2003) concludes that many, but not all, Canadan and Amercan consumers would be wllng to pay for red-meat traceablty, transparency, and enhanced qualty assurances n red-meat products. The objectve of ths study s to examne whether economc factors alone are stll the man drvers of pork consumpton among households n central South Afrca. 2. Survey procedure Due to cost constrants, ths study covered a lmted geographcal area (randomly selected households wthn the Bloemfonten area n South Afrca). There s however a hgh degree of smlarty between the racal composton and ncome dstrbuton of South Afrcan households n general and that of the 2 See Appendx A,Table A1 for the result obtaned by Bansback (1995). 186
Free State n partcular (see Appendx A, Tables A.2 and A.3). The meat preferences and pork consumpton survey was carred out by personal ntervews wth meat shoppers representng 333 households. Table 1 shows the preferences of households for pork products n the survey area by race and product preference. Whle whtes consume the most pork products, the percentage of blacks who consume hgh-value pork s close to that of whtes. Asans consume the least amount of pork products, whch could most probably be traced back to relgous belefs. Table 1: Household preferences for pork products Race Fresh meat Value-added product Pre-prepared pork foods Blacks 48.4% 70% 46.2% Whtes 76.9% 78% 57.1% Coloureds 53.8% 48% 35% Asans 25% 37.5% 25% Source: Authors computaton based on survey data. 3. Methodology 3.1 Descrpton of varables Ten explanatory varables dentfed by prevous studes (as reported by Vsser, 2004) as the major determnants of meat (pork) consumpton n South Afrca were ntally used n ths study. These nclude four contnuous varables (ncome, relatve prce of pork, prce of other meat types, and expendture on meat) whle sx are dscrete varables (race, gender, relgon, qualty, place of purchase and value addng). Sx were found to be sgnfcant and all had the expected sgns. The sgnfcant varables nclude household monthly ncome, relatve prce of pork, current household monthly expendture on meat, preference for value-added pork products, prce of substtutes (the most preferred household meat type) and pork qualty. The result obtaned was further analyzed to compute partal effects and to conduct smulaton analyss on sgnfcant varables. Two further aspects need menton,.e.: In ths study pork consumers are aggregated. The aggregaton of all pork consumers s motvated by the study conducted by McGugan and Neuwoudt (2002), whch projected that pork consumpton between 187
hgh and low ncome groups of the South Afrcan populaton s closely related (whch s hghly correlated wth racal composton). Resurreccon (2003) regards vsble pork fat as the strongest vsual cue for consumers consderng the purchase of meat at the retal level. Ths study therefore consders appearance and health as the dmensons of qualty affectng purchase motves of consumers. 3.2 The model Ths study uses smlar theoretcal and methodologcal approaches as used by Knnucan et al (1993) (cted n Hanson et al, 1995) and Gempesaw et al (1995) who analyzed consumer preferences and household choces of food products. Households derve utlty from food consumpton; hence they make decsons regardng food choces on the bass of a set of percepton characterstcs that translate to preferences. The model used n ths study to determne factors affectng household choce of pork consumpton s gven below. 1 φ = E( y = 1/ X ) = k (1) ( β1 + β j x j ) j 1+ e Where: φ stands for the probablty that household consumes pork, y s the observed pork consumpton status of household, x j are factors determnng household pork consumpton, and β j stands for parameters to be estmated. k n Denotng β + Σ = βj as Z, equaton 1 can be wrtten to gve the probablty of j= 1 pork consumpton of household as: 1 φ = E( y = 1/ X ) = Z 1+ e (2) Gven equaton 2, the probablty that a household wll not consume pork can be wrtten as (1-φ ). Ths s expressed n equaton 3 as follows: 1 ( 1 φ ) = z 1+ e (3) From the two equatons above, the odds rato,.e., φ / (1- φ ), s gven by equaton 4 as 188
φ 1+ e = 1 φ 1+ e z z = e z (4) The natural logarthm of the odds rato n equaton 4 gves rse to equaton 5 k n φ Ln = β + βj + ε φ 1 Σ = j =1 (5) Rearrangng equaton 5, wth the dependent varable (pork consumpton) n log odds, the logstc regresson can be manpulated to calculate condtonal probabltes as k = n β ο + β j xj = 1 e = k = n βο + β j xj j = 1 φ (6) 1+ e Once the condtonal probabltes have been calculated for each sample household, the partal effects of the contnuous ndvdual varables on household pork consumpton can be calculated by the expresson: φ = φ ( 1 φ ) β j x j (7) The partal effects of the dscrete varables are calculated by takng the dfference of the probabltes estmated when value of the varable s set to 1 and 0 ( x 0, x = 1), respectvely. = 4. Results 4.1 Descrptve results 3 Ths secton reports the descrptve results of the relatonshp between pork consumpton and the major determnants (sgnfcant varables) of pork consumpton. Table 2 shows that average ncome, monthly expendture on meat and prce of substtutes for pork-consumng households are hgher than for non-pork-consumng households. Furthermore, the percentage of households who regard qualty assurance as the determnng factor for pork 3 Only the descrptve statstcs of the sgnfcant determnants are reported n ths secton. Results of non-sgnfcant factors can be provded upon request. 189
consumpton s hgher for non-pork consumers than for pork consumers. More pork-consumng households show a preference for value-added products than non-pork-consumng households. The relatve prce of pork s lower for porkconsumng households than for non-pork-consumng households,.e. nonpork consumng household regard pork as beng more expensve than the meat they usually consume. The results confrm the fndngs of prevous surveys (as dscussed n Vsser, 2004) regardng the relatonshp between pork consumpton and the major determnants of pork consumpton examned n ths study. Table 2: Household pork consumpton rates for sgnfcant varables Varable Pork consumer Non-pork consumer Income (R) 9678.29 3492.11 Monthly expendture on meat (R) 616.54 257.25 Value-addng (%) 0.56 0.24 Prce of substtute (R) 24.05 13.82 Relatve prce of pork (unt) 0.96 1.01 Qualty (%) 0.46 0.83 Note: Source: Pork consumers are regarded as those consumers who consume fresh, value added and pre-prepared pork foods. Non-pork consumers are regarded as those consumers who do not consume pork at all or who ndcated that they would purchase other types of meat before they would purchase pork. Authors computaton based on survey data. 4.2 Emprcal results - determnants of household pork consumpton Table 3 shows the results of the test of sgnfcance of the determnants of pork consumpton examned n ths study and the predctve effcency of the model. Two varables, namely prce of substtute and value addng, were found to be sgnfcant at the 1 per cent probablty level. Income, qualty and the relatve prce of pork were sgnfcant at the 5 per cent probablty level, whle expendture was sgnfcant at the 10 per cent probablty level. In addton, the LR statstc value 4 of 117.26, wth p<0.001 ndcates the overall sgnfcance of the model. The result shows that all the parameters of the determnants of pork consumpton shown n equaton 1 are jontly sgnfcant. Also, the predctablty effcency of the model s 76.3 per cent. 4 Calculated on the bass of the formula LR=2(ULLF-RLLF) where ULLF and RLLF are, respectvely, unrestrcted log-lkelhood functon and restrcted log-lkelhood functon. It s ch-square dstrbuted wth 6 degrees of freedom. 190
Table 3: Parameter estmates of the logstc regresson Varable Coeffcent Standard error Z-statstc Probabltes Constant -1.087924 0.447917-2.428854 0.0151** Prce of substtute 0.046843 0.017020 2.752277 0.0059* Income 0.000158 6.81E-05 2.316510 0.0205** Expendture 0.001713 0.001029 1.663800 0.0962*** Value addng 1.725280 0.352074 4.900336 0.0000* Qualty 0.700903 0.337475 2.076903 0.0378** Relatve prce of pork -1.048767 0.428097-2.449834 0.0143** Percentage of correct predcton 0.763 LR statstc 117.2635 <0.001 *Sgnfcant at the 1% level; **sgnfcant at the 5% level; and ***sgnfcant at the 10% level. Source: Authors computaton based on survey data. 4.3 Parameter estmates of determnants of pork consumpton The margnal effects of a unt change n the contnuous varables, computed at sample means, on the probablty of pork consumpton were estmated. Tables 4 and 5 gve results of partal effects of contnuous and dscrete varables, respectvely. Accordng to Table 4, the margnal effect of a unt change n the prce of substtutes, computed at the sample mean of prce of substtutes, s 0.01. Ths means that the probablty of pork consumpton ncreases by 0.01 (1%) for a one rand ncrease n the prce of substtutes. The probablty of pork consumpton for a rand ncrease n both monthly ncome and expendture (computed at ther sample means) are however less than 1 per cent. The relatve prce relatonshp of pork wth other types of meat appears to be a much stronger determnant of pork consumpton,.e. the probablty of pork consumpton decreases by 21 per cent for a unt ncrease n the relatve prce of pork. Table 4: Partal effects for contnuous determnants Determnant Partal effect Prce of substtute 0.01 Income 0.00003 Expendture 0.0004 Relatve prce -0.21 Source: Authors computaton based on survey data. Table 5 shows that addng value to pork ncreases the probablty of a household consumng pork from 0.22 to 0.43,.e. by 21%. In addton, 191
household response to qualty satsfacton yelds an ncrease n the probablty of consumng pork by 2% (.e. from 0.155 to 0.175). Table 5: Change n probabltes between X=0 and X=1 for dscrete determnants Determnants Probabltes Change n probabltes Value Non value-added pork 0.22141 Value-added pork 0.43132 Qualty Other-factor 0.15535 Qualty-factor 0.17523 Note: other factor s mostly prce. Source: Authors computaton based on survey data. 0.21 0.02 Smulatons were conducted wth reference to a base group of households representng non-pork-consumng households. The results are reported n Table 6. The base group represents non-pork-consumng households wth an average monthly ncome of R3,492.11 and monthly expendture on meat of R257.25. The base group of households also pays an average of R13.82 for ther choce meat per kg and a relatve prce of 1.01 for pork. In addton, the dummy varables for value addng and qualty were set to zero. Table 6: Smulated mpact of determnants on the probablty of household pork consumpton Varable Predcted probablty Base 0.38 Monthly ncome ncreased by R500 0.40 Monthly expendture on meat ncreased by R35 0.39 Average prce of substtutes ncreased R2.5 per kg 0.40 Relatve prce of pork ncreased by one unt 0.17 If value s added 0.77 If pork qualty s assured 0.55 Source: Authors computaton based on survey data. Accordng to Table 6, the condtonal probablty of pork consumpton for the base group of households s 0.38. Ths means that, of 100 meat-consumng households, 38 consume pork. However, f a group of households wth characterstcs smlar to that of the base group of households s assured of pork qualty, the number of pork-consumng households wll ncrease to 55. Addng value to the pork consumed by the base group of households wll 192
ncrease the number of pork-consumng households to 77. Table 6 shows further that nether an ncrease of R500 n ncome, a R2.5 per kg ncrease n the prce of a substtute, nor a R35 ncrease n expendture on meat results n a substantal ncrease n the number of households that consume pork. However, f the relatve prce of pork ncreases by one unt (.e. a substantal prce gap between pork and substtutes) the number of pork-consumng households wll reduce. 5. Concluson The analyss shows that non-economc factors are becomng ncreasngly more mportant when consumers have to make purchasng decsons regardng pork. Ths corresponds wth trends nternatonally. The analyss furthermore shows that the prce of pork relatve to other types of meat remans mportant. Note should however be taken that a unt change n the relatve prce s very unlkely, snce pork meat prces, as well as that of other meats, tend to more or less follow that of beef (Van Heerden et al, 1989). The results of ths study may have mportant mplcatons for the South Afrcan pork ndustry, especally f also consderng the fndngs by Neuwoudt (1998) who estmated future demand for meat n South Afrca based on economc factors, and concluded that the demand for pork n South Afrca s unlkely to ncrease at the same rate as other meats over the next 15 years. Frstly, t s mperatve that the pg/pork ndustry, especally at prmary level, undergo a substantal paradgm shft to move closer to the end consumer of pork n an effort to better understand changng consumer preferences and, through ths, make the requred changes to stmulate and grow pork consumpton. In addton, t would allow prmary producers, as well as other role players to capture greater value from consumers spendng on meat. Relatonshp management wll form the essence of movng closer to end consumers of pork and pork products. Relatonshp management entals that producers change ther strategc poston from an arms-length relatonshp wth clents focused on product exchange, to one of partnerng (Goldsmth and Gow, 2001). Ths would allow role players, especally producers and processors, to ntegrate themselves nto the supply chan wthout the manageral burden of vertcal ntegraton. Closer collaboraton also creates the scale to enable dedcated or sourced expertse to brdge the gap between producton competences and suppler needs, as well as releasng the captal to nvest n servce related assets, lke product research (Goldsmth and Gow, 2001). 193
Secondly (and related to the prevous remark), product research and nnovaton should be a second core mperatve n the South Afrcan pork ndustry. Moreover, determnaton of consumer needs and adaptng to meet these needs s vtal for the long-term sustanablty and proftablty of the pork ndustry n South Afrca. Grunert et al (2004) argue that the level of correlaton between qualty expectaton and experenced qualty determnes, to a great extent, the predctablty of the purchase decson by consumers. Therefore the challenge facng producers/processors of pork and pork products n South Afrca s ensurng that the end product meets hgh qualty expectatons as well as consumers experenced qualty demands. Thrdly, communcatng the varous qualty attrbutes of pork and pork products to consumers should receve ncreasng attenton. Promotonal actvtes focusng only on economc factors wll only provde part of the requred ncentve for consumers to purchase more pork and pork products, and may be short lved. Internatonal experence n Australa and the US (Barnard, 2005; Huston, 1999) has clearly shown the value of (prmarly) focusng on the non-economc attrbutes of red meat n recent years to turn around the downward trend n red meat per capta consumpton. Wthn the framework of ths paper, areas of further research nclude nvestgaton of consumers wllngness to pay for specfc non-economc attrbutes of pork and pork products and the geographcal characterstcs assocated wth ths. Acknowledgements Ths materal s based upon work supported by the Natonal Research Foundaton under Grant Number 2053338. Opnons, fndngs and conclusons or recommendatons expressed n ths materal are those of the authors and do not necessarly reflect the vews of the Natonal Research Foundaton. References Bansback B (1995). Towards a broader understandng of meat demand. Journal of Agrcultural Economcs 46(3):287-308. Barnard P (2005). Global trends n the beef ndustry. Presentaton at the Northern Cape Red Meat Producers' Organsaton, Upngton, South Afrca, March 8. Dcknson DL, Hobbs JE & Baley D (2003). A comparson of US and Canadan consumers wllngness to pay for red-meat traceablty. Paper prepared for 194
presentaton at the Amercan Agrcultural Economcs Assocaton Annual Meetngs, Montreal, Canada, July 27-30. Eskort Lmted (2005). Avalable onlne at: www.eskort.co.za. Accessed on 20 Aprl. Gempesaw II CM, Bacon JR, Wessels CR & Manalo A (1995). Consumer perceptons of aquaculture products. Amercan Journal of Agrcultural Economcs 77:1306-1312. Goldsmth P & Gow H (2001). Strategc postonng under agrcultural structural change: A crtque of long-jump co-operatve ventures. Paper prepared for the annual meetng of the AAEA, Chcago, Illnos, August. Grunert KG, Bredahl L & Brunso K (2004). Consumer percepton of meat qualty and mplcatons for product development n the meat sector: A revew. Meat Scence 66:259-272. Hanson GD, Hermann RO & Dunn JW (1995). Determnants of seafood purchase behavour: Consumers, restaurants and grocery stores. Amercan Journal of Agrcultural Economcs 77:1301-1305. Huston JL (1999). Beef marketng challenges n the US. IMS Newsletter No 207 31/3/99. Kane-Berman J (2004). South Afrca Survey 2002/2003. South Afrcan Insttute of Race Relatons, Braamfonten. Creda Communcatons. Knnucan HW, Nelson RG & Harey J (1993). Unted States preferences for fsh and seafood: An evoked set analyss. Marne Resource Economcs 8:273-291. McGugan SM & Neuwoudt WL (2002). The expected consumpton of proten feed n South Afrca by 2020. Agrekon 41(1):1-23. Natonal Department of Agrculture (NDA) (2005). Abstract of Agrcultural Statstcs. Natonal Department of Agrculture, South Afrca, Pretora. Neuwoudt WL (1998). The demand for lvestock products n South Afrca for 2000, 2010 and 2020: Part 1. Agrekon 37(2):130-142. NPPC (2004). Pork s world s most-eaten meat. Natonal Pork Producers Councl of USA. Avalable onlne at: http://www.nppc.org/. Accessed on 20 May. 195
Rantho L (2003). Demographcs of South Afrcan households 1995. Background Paper 2003 (3):3, The Provncal Decson-makng Enablng Project (PROVIDE), Elsenburg. Resurreccon Ava (2002). Sensory aspects of consumer choces for meat and meat products. Meat Scence 66:11-20. SAMIC (2005). Import and export statstcs for beef, sheep, pork and offal. South Afrcan Meat Industry Company. Avalable onlne at: www.samc.co.za. Van Heerden AF, Van Zyl J & Vvers FL (1989). Prce nter-relatonshps n the South Afrcan meat market II: An emprcal applcaton. Agrekon 28(1):73-79. Vsser DP (2004). Structurng of breedng objectves n the pork supply chan n South Afrca. Unpublshed PhD Thess, Unversty of Pretora, South Afrca. 196
Appendx A Table A1: Importance of economc and non-economc factors n meat demand 1955 1979 1975-1994 Product Economc Non-Economc Economc Non-Economc Percentage Beef 95 5 68 32 Pork 98 2 55 45 Sheep meat 84 16 58 42 Source: Bansback, 1995. Table A2: South Afrca s populaton by provnce and racal composton 5 Provnce Afrcan (%) Coloured (%) Indan (%) Whte (%) Western Cape 17.28 57.92 1.99 22.81 Eastern Cape 81.04 12.33 0.38 6.24 Northern Cape 30.05 50.31 0.56 19.07 Free State 79.15 7.39 0 13.46 KwaZulu-Natal 79.08 3.25 10.29 7.38 North-West 83.75 5.26 1.92 9.07 Gauteng 56.47 9.67 6.83 27.03 Mpumalanga 86.69 1.99 1.45 9.86 Lmpopo 94.4 0.7 0.26 4.64 Natonal 71.77 13.27 3.27 11.69 Source: Rantho, 2003. Table A3: Co-effcent of ncome dstrbuton by provnce and race, 2003 Provnce Afrcan Coloured Indan Whte Eastern Cape 0.63 0.55 0.48 0.47 Free State 0.60 0.56 0.52 0.49 Gauteng 0.60 0.52 0.46 0.42 KwaZulu-Natal 0.62 0.52 0.51 0.45 Lmpopo 0.63 0.59 0.48 0.47 Mpumalanga 0.61 0.56 0.52 0.46 North-West 0.57 0.55 0.50 0.48 Northern Cape 0.61 0.58 0.51 0.48 Western Cape 0.60 0.52 0.48 0.46 South Afrca 0.62 0.55 0.51 0.46 Source: Kane-Berman, 2004. 5 The report tself refers to the fact that although the 1996 Census show that Asans/Indans resde n the Free State, the OHS/IES survey faled to capture them due to small sample sze. 197