To keep up with the recent trends in consumer demand for food products, an understanding of the associated customer characteristics is needed. This study identifies significant characteristics and factors of households who are likely to purchase basic household food when prices are high. The analysis was performed using logit model. The results of the study imply that the following households are more likely to purchase foods when prices are high: those with large sizes of land for farming and for home garden, those who frequently purchase the basic food stuffs, those who receive some advice on prices of food, and those with large household sizes. Educated females with information sources on food prices, and those household who stay far away from the nearest food shops do not appear to be likely to purchase food when prices are high.Food prices, consumer characteristics, logit model, Consumer/Household Economics, Demand and Price Analysis,

Anim, F.D.K.

Mandleni, B.

English

Research Papers in Economics

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Consumer Characteristics 
Associated with Food 
Prices 
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B. Mandleni and FDK Anim 
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Contributed Paper presented at the Joint 3rd African Association of Agricultural Economists 
(AAAE) and 48th Agricultural Economists Association of South Africa (AEASA) Conference, 
Cape Town, South Africa, September 19-23, 2010.   2 
 
CONSUMER CHARACTERISTICS ASSOCIATED WITH FOOD PRICES 
 
B. Mandleni
1 and FDK Anim
2
 
 
Abstract 
To keep up with the recent trends in consumer demand for food products, an understanding 
of the associated customer characteristics is needed.   This study identifies significant 
characteristics and factors of households who are likely to purchase basic household food 
when prices are high. The analysis was performed using logit model. The results of the study 
imply that the following households are more likely to purchase foods when prices are high: 
those with large sizes of land for farming and for home garden, those who frequently 
purchase the basic food stuffs, those who receive some advice on prices of food, and those 
with large household sizes. Educated females with information sources on food prices, and 
those household who stay far away from the nearest food shops do not appear to be likely to 
purchase food when prices are high. 
 
Keywords: Food prices, consumer characteristics, logit model. 
 
Introduction 
Food prices in South Africa, as measured by the CPI-Food, increased by 15.7% from April 
2007 to April 2008. According to NAMC, this is 0.4% higher than the increase in food prices 
reported for March 2008 (NAMC, 2008). The food categories that made the largest 
                                                 
1Lecturer, UNISA, College of Agric. & Environmental. Sciences; Dept. of Agric., Animal Health & Human Ecology; Florida Campus, 
Private Bag X6 Florida 1710, South Africa, Tel: 011-471-2252; Fax: 011-471-2260; E-mail: mandlb@unisa.ac.za 
 
2 Associate Professor, UNISA, College of Agric. & Environmental. Sciences; Dept. of Agric., Animal Health & Human Ecology; Florida 
Campus, Private Bag X6 Florida 1710, South Africa, Tel: 011-471-3231; Fax: 011-471-2260; E-mail: animfdk@unisa.ac.za 
   3 
contribution to this increase included grain products; milk, cheese and eggs; fats and oils; 
fruits and nuts; and vegetables. From a food security point of view prices of staples continued 
to increase. The increase in brown and white bread prices were 21.58% and 26.37%, 
respectively, year-on-year. The prices of a 5kg bag of super and special maize meal increased 
by 5.23% and 5.66%, respectively, year-on-year (this is within the inflation target band), 
bringing some relief to the poor consumers that relied mainly on staple food such as maize 
meal. Dairy product prices increased, on average, by 24.65%, while meat prices on average 
continued to increase at a much slower rate than most other food products. The prices of the 
main vegetables showed year-on-year increases of 32.85%. The increase in grain prices was 
mainly due to pressure on international and domestic grain stocks, while the increases in 
vegetables prices could be largely attributed to climate events. Input cost pressures were 
playing an increasingly important role in pushing prices higher. 
 
These trends point to a growing concern for purchasing household food items when prices are 
high. Although the agricultural sector has been actively responding to consumer demand by 
attempts to produce more food, the failure rate of an increase in food supply still remains 
high. The agricultural sector with its myriad problems has been criticized in this regard for 
not knowing their customers. To fill this gap, this study examines the consumer 
characteristics associated with the purchase of food at high prices. Knowledge of these 
characteristics and factors can be used as guide in developing marketing programmes for 
products with high prices. The study examines not only consumers’ demographic factors but 
also external factors using a cross sectional data of 390 households in 24 rural communities 
in the Limpopo province of South Africa. 
 
Methodology   4 
A model is estimated to examine the likelihood that a household will purchase food products 
that experienced higher than the inflation target increases in prices as at the end of April 
2008.  The logit framework is used for this purpose. The logit technique is preferred over 
other categorical variable estimation technique (Fox, 2000), and is a better procedure for 
capturing the magnitude of the independent variable effects for quantitative dependent 
variables that are probit models (Pampel 2000)). The logit model is estimated using 
maximum likelihood estimation as it results in large-sample properties of consistency and 
asymptotic normality of the parameter estimates. The model is based on the cumulative 
logistic probability function and is specified as (Pindyck and Rubinfield, 1991): 
( )
z i e
X F z F P
− +
= ′ = =
1
1
) ( ) ( β  
Where Z is the theoretical index determined by a set of explanatory variables X; F(Z) is the 
cumulative logistic function; e represents the base of natural logarithms (approximately equal 
to 2.718); and P is the probability that a household will make a choice, given the knowledge 
of X , and ß represents the estimated coefficients. 
 
The logit model centres on the hypothesis that a set of variables influences the decision to 
purchase food products that experienced higher prices. The logit model is specified as 
follows: 
Prob = bo + b1GEN + b2EDUC + b3SIZEF + b4SIZEH + b5FOOD + b6INFO +  
 b7EXT + b8DIST + b9HHS 
where Prob is  a binary variable equal to 1 if household purchases food products and 0 
otherwise. The description and the means of the independent variables are exhibited in Table 
1.  The  GEN  variable is hypothesised to have negative or positive effect on decision to 
purchase, depending on whether the household’s purchasing behaviour is determined by 
males or females. On the other hand, the variable EDU is expected to have a negative sign in   5 
the model indicating that the more a household head is educated the less likely that he/she 
will purchase food at high prices. The size of land for farming variable, SIZEF and size of 
land for home garden variable, SIZEH are both expected to be positively and negatively 
related to the likelihood of purchasing food at high prices. The decision to purchase will 
depend on whether the land in each case is productive or not.  
 
The type of food commonly eaten by the family FOOD, and household size, HHS are likely 
to have negative or positive influence on the decision to purchase at high prices. Exposure to 
advice on food prices, EXT is likely to impact positively on the decision to purchase when 
prices are high. Long distances from household dwelling to the nearest shop, DIST are also 
hypothesised to have negative impact on the decision to purchase. 
 
Data description 
Data in this study were collected by in-person interviews from a total of 24 rural households 
in the Limpopo province between February and March 2008. In all, a total of 390 household 
heads interviewed at random constituted the sample for the survey. The period of study was 
chosen purposely to include the time when food products in rural areas experienced higher 
than the inflation target increases in prices (NAMC, 2008). 
 
The survey contained various socio-economic variables. Sample statistics for the variables 
used in the model are presented in Table 1. The table shows that households were more 
educated and female headed. Sizes of farm for farming and home garden were 1.14 and 
1.02ha respectively. The small sizes are indications of small scale farming typical in the study 
areas. Household food commonly purchased appeared to be mostly wheat and maize 
products. Sources of information on food prices appeared to be between good and average,   6 
while difficulty in getting advice from extension officers ranged between difficult and easy. 
The average distance from household dwelling to the nearest shop for food purchases was 
4.44 km and average household size was estimated to be 5 persons. 
 
Table 1: Description and means of independent variables used in the analysis 
Variable name    Description          Mean 
GEN    Gender of household head:  1= male; 2=female    1.57 
EDUC    Level of education: 1=more than std 8; 2=less than std 8  1.47 
SIZEF    Size of land for farming (ha)        1.14 
SIZEH    Size of land for home garden (ha)        1.02 
FOOD    Household’s frequent food purchases       1.84 
(1= wheat products; 2= maize products….11=sorghum meal) 
INFO    Information sources on food prices       2.34 
(1=very good; 2= good; 3=average 4=poor) 
EXT  Difficult to get advice on food prices from extension officers    2.15 
  (1=very difficult; difficult=2; 3=easy; 4=don’t know) 
DIST    Distance from house to nearest shop      4.44 
  (1= >50km; 2=20-50km; 3= 10-20km; 4= <10km; 5=don’t know) 
HHS    Household size (persons)          5.00 
Number of cases, n=390 
 
Results and discussion 
The maximum likelihood estimates of the model are presented in Table 2. Based on the 
statistical significance of the estimated coefficients, the results indicat that gender, education, 
source of information and distance from household to nearest shop are negatively related to 
the likelihood of purchasing household food commodities at high prices. On the other hand,   7 
size of land for farming and household garden, frequent food purchases, information of 
prices, and household size are positively related to the purchase of high food prices.  
Contrary to expectations, the study suggests  that  household with large sizes of land for 
farming and for home garden are more likely to purchase food for household consumption 
when prices are high.  A plausible explanation is that these households may have enough 
funds raised from income  on  farm operations to purchase food compared with those 
households that do not have funds from farm operations. 
 
Households who make frequent purchases of basic food commodities are also more likely to 
purchase food when prices are high.  It could be  explained  that  these households have 
developed tastes and preferences for such food, hence they  cannot do without them. 
Households with large household sizes and receive advise on prices from extension officers 
are also more likely to purchase food at high prices. 
 
The perceived importance of information and education are included in the model to test the 
hypothesis that the two variables negatively affect the likelihood of purchasing food at high 
prices.  Deaton (1990) alluded to the importance of these variables in consumer’s food 
purchase decisions. Educated household heads that have adequate information on food prices 
are less likely to purchase food for home consumption when prices are high due to their 
understanding of the effects on the household budget. 
 
Gender differences are included as independent variables because of possible differences in 
decision making of the household or habits (Deaton and Grosh, 2000). For instance, females 
other than males have lower financial control rates than their male counterparts in rural areas.   8 
It is therefore possible that females are less aware of the effects of high food prices on the 
household budgets. 
 
Table 2: Estimated probit model 
Variable name    Estimate  Std.  Error   Z    Sig.   
GEN      -0.300    0.051    -5.882    0.000 
EDUC      -0.107    0.052    -2.072    0.038 
SIZEF      0.097    0.029    3.316    0.001 
SIZEH      0.047    0.026    1.828    0.068 
FOOD      0.199    0.027    7.312    0.000 
INFO      -0.075    0.028    -2.692    0.007 
EXT      0.070    0.024    2.948    0.003 
DIST      -0.039    0.009    -4.526    0.000 
HHS      0.024    0.012    2.050    0.040 
Intercept      -0.795    0.147    -5.398    0.000 
PROBIT (P) = Intercept + BX 
 
The study also recognises that long distance from the household dwelling to the nearest shop 
is likely to negatively affect the decision to purchase at high food prices.  
 
Conclusions and recommendations 
To keep up with the recent trends in consumer demand for food products, an understanding of 
the associated customer characteristics is needed. Due to increasing concerns about high food 
prices, the food and agribusiness industry is continually interested in creating a vast array of 
concepts that appeal to specific consumer tastes and preferences. It is therefore imperative 
that the profile of households who are more likely to purchase food when prices are high be 
known to the food and agribusiness industry  for future planning. This information is   9 
particularly useful for processors and producers who want to anticipate future market changes 
in prices and derived demand for their products. 
 
The results of the study imply that the following households are more likely to purchase 
foods when prices are high: those with large sizes of land for farming and for home garden, 
those who frequently purchase the basic food stuffs, those who receive some advice on prices 
of food, and those with large household sizes. However, educated females with information 
sources on food prices and those households who stay far away from the nearest food shops 
do not appear to be likely to purchase food at high prices. 
 
The identification of households likely to purchase foods at high prices is essential in 
analysing consumption patterns and developing specific marketing programmes. Food 
processors and manufacturers selling food at high prices should tailor their products to the 
taste of these households who are most likely to purchase products to boost sales. Based on 
the empirical findings, for example, high food prices may be targeted to those households 
with large sizes of land for farming and for home garden, those who frequently purchase the 
basic food stuffs, those who receive some advice on prices of food, and those with large 
household sizes (Gibson and Rozelle, 2002). Conversely, educated females with information 
sources on food prices, and those households who stay far away from the nearest food shops 
may be less receptive to marketing campaigns that promote high food prices. Considering the 
fact that consumer preferences and high prices are related, the findings in this study might be 
of interest to market analysts not only in South Africa but elsewhere as well. 
 
 
   10 
 
 
 
References 
Deaton, A. (1990), “Price Elasticities from Survey Data. Extensions and Indonesian 
Results”, Journal of Econometrics, vol. 44, pp. 281-309. 
 
Deaton, A. and Grosh, M. (2000), “Consumption”, in M. Grosh and P. Glewwe, (eds.) 
Designing Household Questionnaires for Developing Countries: Lessons from 
Fifteen Years of the Living Standard Measurement Study. The World Bank, 
Washington D.C., pp. 91-133. 
 
Fox, John (2000). Multiple and generalized nonparametric regression. Thousand            Oaks, 
CA: Sage Publications. Quantitative Applications in the Social Sciences Series No.131.  
 
Gibson, J. and Rozelle, S. (2002), “Prices and Unit Values in Poverty Measurement and Tax 
Reform Analysis,” mimeo, Department of Economics, University of Waikato. 
 
National Agricultural Marketing Council (NAMC) (2008). Quarterly Food Price Monitor, 
May 2008, Media Release, Food Price Trends: April 2007 to April 2008. 
 
Pampel, Fred C. (2000). Logistic regression: A primer. Sage Quantitative Applications in the 
Social Sciences Series #132. Thousand Oaks, CA: Sage Publications. Pp. 35-38.  
 
Pindyck, R.S., and Rubinfield, D. (1991). Econometric models and economic forecasts. New 
York; McGraw-Hill.   11 
 

Consumption”,

Econometric models and economic forecasts.

Logistic regression: A primer. Sage Quantitative Applications in the Social Sciences Series #132. Thousand Oaks, CA: Sage Publications.

Multiple and generalized nonparametric regression. Thousand Oaks, CA: Sage Publications.

National Agricultural Marketing Council (NAMC)

Price Elasticities from Survey Data. Extensions and Indonesian Results”,

Prices and Unit Values in Poverty Measurement and Tax Reform Analysis,” mimeo,

CONSUMER CHARACTERISTICS ASSOCIATED WITH FOOD PRICES

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