The price difference between farm and retail levels is called price spread, which is constituted mostly by marketing costs and profits. From the price spread, this paper intends to estimate elasticities of price transmission for pork in Malaysia via different empirical model specifications of markup pricing model. Using data from January 1997 to December 2007, a quantitative analysis of farm-to-retail price spreads was undertaken for pork in Malaysia. It was found that retail price is the only variable which is significant. The farm-retail price transmission for pork is very elastic.Price spread, elasticity of price transmission, pork

Randy, Stringer

Tey, (John) Yeong-Sheng

Wendy, Umberger

English

Research Papers in Economics

FARM-RETAIL PRICE SPREAD FOR PORK IN MALAYSIA 
 
by 
 
Tey (John) Yeong-Sheng
1, Randy Stringer
2 and Wendy Umberger
2 
 
 
ABSTRACT 
 
 
The price difference between farm and retail levels is called price spread, which is constituted 
mostly by marketing costs and profits. From the price spread, this paper intends to estimate 
elasticities of price transmission for pork in Malaysia via different empirical model specifications 
of  markup  pricing  model.  Using  data  from  January  1997  to  December  2007,  a  quantitative 
analysis of farm-to-retail price spreads was undertaken for pork in Malaysia. It was found that 
retail price is the only variable which is significant. The farm-retail price transmission for pork is 
very elastic. 
 
 
Keywords: Price spread, elasticity of price transmission, pork  
 
JEL code: Q11, Q13 
 
 
1.0 INTRODUCTION 
 
There has been a spate of changes precipitated by the frequent increase in retail price of 
pork despite a declining trend of per capita consumption of pork in Malaysia since late 1990’s. 
Foremost amongst these are the doubling of feed, production, and marketing costs over the years. 
All these costs are embedded in the price changes of pork which are fully transmitted through the 
whole  chain  to  the  consumers.  The  declining  trend  of  per  capita  consumption  of  pork  is 
attributed mostly to disease, environmental, and health issues over the years in the country. The 
bulk of the new measures as well as stepping up of on-going efforts are directed at increasing 
food safety and environmental friendly pig production. All these are potential ‘extra costs’ in 
supply as well as price changes at retail level. The price difference between farm and retail levels 
is called price spread, which is constituted mostly by marketing costs and profits. From the price 
spread, this paper intends to estimate elasticities of price transmission for pork in Malaysia via 
different empirical model specifications of markup pricing model.  
 
Markup  pricing  model  has  been  notably  applied  in  previous  studies  (Heien,  1980; 
Kinnucan  and  Forker,  1987;  Ferris,  1998).  However,  Gardner  (1975)  and  Wohlgenant  and 
                                                             
1 Corresponding author 
Email: tyeong.sheng@gmail.com 
Address: Institute of Agricultural and Food Policy Studies, Putra Infoport, Universiti Putra Malaysia, 43400 UPM 
Serdang, Selangor, Malaysia.  
2 School of Agriculture, Food and Wine, University of Adelaide, Australia. Mullen (1987) found the inferior performance of the markup pricing model compared to the 
relative price spread specification. This is because the farm-retail price spread changes when 
retail  food  demand,  farm  product  supply,  or  the  supply  of  marketing  services  shifts. 
Encountering  such  issues  in  policy  applications,  Wohlgenant  and  Mullen  (1987)  suggest  a 
relative price model. The relative price model was compared with the markup pricing model in 
Dickerson (2003). As Dickerson (2003) intended to derive elasticity of price transmission, the 
better performance of the markup pricing model is consistent with the suggestion by Wohlgenant 
and Mullen (1987) that the markup pricing model is more plausible for deriving elasticities. 
 
 
2.0 DATA AND METHOD 
 
Monthly data from January 1997 to December 2007 for pork production, farm price, and 
retail price were collected from the Federation of Livestock Farmers’ Associations of Malaysia 
(2009). Farm-retail price spread can  be  further seen as  an aggregate of  marketing costs and 
profits. Ferris (1998) suggests that the price spread is equal to the equilibrium of demand and 
supply of marketing services and materials per unit of product, where marginal value of the 
marketing services and  materials per unit of product (addressed as  marketing  margin  in this 
study) is equal to marginal cost. The marketing margin is in fact the price spread between farm 
and retail levels. By simplifying the equilibrium procedure, the markup pricing function can be 
expressed as: 
 
) , , ( C P Q f MM r                    (1)   
where   MM  = the price spread,  
Q = the quantity of the agricultural commodity processed, and 
r P = the price of the retail product, and  
C is a vector of marketing input prices (wage rates, transport costs, and others). 
 
Using the function as a basis, the following empirical model specifications of markup 
pricing model are estimated via Ordinary Least Square: 
 
3 2 1 5 4 3 2 1 S S S FC P c MM t rt t                      (2) 
t t rt t Q S S S FC P c MM 6 5 4 3 2 1 3 2 1                      (3) 
where  t MM  = Retail price minus farm price in month t (Ringgit/kilogram), 
rt P = Retail Price in month t (Ringgit/kilogram), 
FCt = Fuel Cost (Diesel) in month t (Ringgit/litre), 
S1 = Season 1, 
S2 = Season 2, 
S3 = Season 3, and 
Qt = Pork Production in month t (kilogram). 
 
The results from Equations (2) and (3) are tested with Breusch-Godfrey test for serial 
correlation  in  the  residuals.  With  the  presence  of  serial  correlation  in  the  residuals  of  the 
equations,  it  will  lead  to  incorrect  estimates  of  the  standard  errors,  and  invalid  statistical inference for the coefficients of the equations. To handle this, Ferris (1998) suggests first order 
autoregressive error specification that can be expressed as: 
 
) 1 ( 3 2 1 6 5 4 3 2 1 AR S S S FC P c MM t rt t                      (4) 
) 1 ( 3 2 1 7 6 5 4 3 2 1 AR Q S S S FC P c MM t t rt t                      (5) 
where  other variables are as described earlier and 
) 1 ( AR   =  First  order  autoregressive  error  specification  to  correct  for  serial 
correlation in the residuals. 
 
Based on its performance, parameters in a better model are used to yield elasticities of 
price transmission for pork. The formula for the elasticity of price transmission is: 
 
rt
t
t P
FP
CRP
EPT *
) 1 (
1

                 (6) 
where  CRP = Coefficient Associated with Retail Price, 
t FP = Farm Price in month t (Ringgit/kilogram), and 
rt P = Retail Price in month t (Ringgit/kilogram). 
 
 
3.0 RESULTS 
 
Table  1  presents  the  parameter  estimates  of  initial  specifications  of  markup  pricing 
model.  Without  interpreting  the  parameter  estimates, the  attention  is  to  look  at  the  Durbin-
Watson statistics. The low values of the Durbin-Watson statistics reported above are indicative 
of the presence of serial correlation in the residuals of the estimated equation. Thus, Breusch-
Godfrey test were conducted for serial correlation in the residuals. 
 
Table 1: Parameter estimates of initial specifications of markup pricing model 
  Equation (2)  Equation (3) 
Variable  Coefficient  (Std. Error)  Coefficient  (Std. Error) 
Constant  0.4742  (0.3586)  0.1799  (0.3885) 
Retail Price  0.5457  (0.0348)***  0.5268  (0.0359)*** 
Quantity of Production  -  -  0.0000  (0.0000)* 
Fuel Cost  -0.4523  (0.3139)  -0.4078  (0.3118) 
Season 1  0.0481  (0.1389)  0.0445  (0.1376) 
Season 2  -0.1588  (0.1388)  -0.1604  (0.1374) 
Season 3  -0.1811  (0.1389)  -0.1783  (0.1376) 
Durbin-Watson statistics  0.4684  0.5030 
 
The results of the test are reported in Table 2. The statistic labeled “Obs*R-squared” is 
the  LM  test  statistic  for  the  null  hypothesis  of  no  serial  correlation.  The  (effectively)  zero 
probability value strongly indicates the presence of serial correlation in the residuals in both 
models.  
 Table 2: Results of Breusch-Godfrey test 
  Equation (2)  Equation (3)    Equation (2)  Equation (3) 
F-statistic  197.3874  176.7524  Prob. F  0.0000  0.0000 
Obs*R-squared  80.8193  77.5765  Prob. Chi-Square(1)  0.0000  0.0000 
 
Relatively, first order autoregressive error specifications of markup pricing models were 
estimated. Table 3 presents the comparisons of performance for the markup models expressed in 
Equations (4) and (5) respectively. The slightly bigger R-square value of Equation (4) gave a 
first indication that the goodness of fit of Equation (4) is better than Equation (5). To reaffirm 
such  indication, the  best specification was chosen  based on the  lowest Schwarz and  Akaike 
criteria. The markup pricing model of Equation (4) was determined to be the better model for 
pork.  
 
Table 3: Comparisons for the markup models 
  Equation (4)  Equation (5) 
R-square  0.9156  0.9154 
Akaike info criterion  0.5839  0.5961 
Schwarz criterion  0.7375  0.7717 
 
With the selection of Equation (4), the parameter estimates of first order autoregressive 
error  specifications  of  markup  pricing  model  are  presented  in  Table  4.  While  most  of  the 
variables are not statistically significant, the coefficient of retail price indicates that retail price 
has a positive and statistically significant effect on marketing margin for pork.  
 
Table 4: Parameter estimates of first order autoregressive error specifications of markup pricing 
model 
Variable  Coefficient  (Std. Error) 
Constant  -0.9269  (1.1942) 
Log(Retail price of pork)  0.7879  (0.0469)*** 
Log(Price of fuel)  -1.1796  (0.7969) 
Season 1  0.0235  (0.0905) 
Season 2  0.0758  (0.1021) 
Season 3  0.0171  (0.0881) 
AR(1)  0.8902  (0.0447)*** 
 
Figure 1 illustrates the changes of the elasticity of price transmission for pork over the 
years. It is obvious that the elasticities vary from month to month. This is because the ratio of the 
farm price to retail price varies from month to month. Hence, the elasticity of price transmission 
is defined as the percentage change in retail price due to a one percent change in farm price. For 
instance, the average elasticity of price transmission over the years (2.25) can be interpreted as 
one percent increase in farm price is likely to see 2.25 percent increase in retail price of pork.  
 
Seemingly, the price transmission has been very elastic even at its lowest and highest 
levels  at 1.35 and 3.64 in  April and June 1999 respectively. The  sudden change within this 
period is due to the Nipah outbreak that saw astonishing shift away from pork consumption where retailers were forced to sell at as low as RM0.80/kg marketing margins in June 1999. 
After the recovery, the market has corrected itself to reward the retailers with commensurate 
marketing  margins  between  RM4.50/kg  and  RM6.50/kg,  regardless  of  increasing  production 
costs which implies directly in the farm price of pork. Hence, the latter years see the elasticity of 
price transmission is within the range of 1.50-2.50. 
 
Figure 1: Changes of the elasticity of price transmission for pork over the years 
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4.0 CONCLUSION 
 
Using data from January 1997 to December 2007, a quantitative analysis of farm-to-retail 
price  spreads was undertaken  for pork  in Malaysia. Based on the R-square and  Akaike and 
Schwarz Information Criterion, a better first order autoregressive error specifications of markup 
pricing  model  was  estimated.  It  was  found  that  retail  price  is  the  only  variable  which  is 
significant. Should data  is available,  future studies  may want to incorporate other  marketing 
costs (labor, packaging, advertising, and other costs) to build a better model. The farm-retail 
price transmission for pork is very elastic, which is consistent with the priori observation where 
there has been frequent increase in retail price of pork despite a declining trend of per capita 
consumption  of  pork  in  Malaysia.  This  also  tells  that the  consumers  have  no  choice  but  to 
consume expensive pork or they seek for substitutes or they simply give up consuming pork. 
When having this in sight, it is a business opportunity for those who can offer cheaper pork to 
the country.    
 
 
 
 ACKNOWLEDGEMENT 
 
We would like to say thank you to Federation of Livestock Farmers’ Associations of Malaysia 
for providing the data used in this study.  
 
 
REFERENCES 
 
Dickerson, M.L. (2003). Analysis of Farm-to-Retail Price Spreads for Whole and Two Percent 
Milk in Seven Selected Cities [M.S. thesis]: Texas A&M University, Texas. 
 
Federation  of  Livestock  Farmers’  Associations  of  Malaysia  (2009). 
http://www.flfam.org.my/main.php?section=statistic&page=statistic_index.    (Accessed  on  23 
January 2009. 
 
Ferris,  J.  (1988).  Agricultural  Prices  and  Commodity  Market  Analysis.  WCB  McGraw-Hill, 
Michigan State University Press. 
 
Gardner B.L. (1975). The Farm-Retail Price Spread in a Competitive Food Industry, Journal of 
Agricultural Economics, 57: 399–409  
 
Heien,  D.M.  (1980).  Markup  Pricing  in  a  Dynamic  Model  of  the  Food  Industry,  American 
Journal of Agricultural Economics, 62: 10-18. 
 
Kinnucan, H.W. and Forker, O.D. (1987). Asymmetry in Farm-Retail Price Transmission for 
Major Dairy Products, American Journal of Agricultural Economics, 69: 285-292. 
 
Wohlgenant,  M.  and  Mullen,  J.  (1987).  Modeling  the  Farm-Retail  Price  Spread  for  Beef, 
Western Journal of Agricultural Economics, 12: 119-125. 
 

Farm-retail price spread for pork in Malaysia

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