J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 64
Demand for Imported Staple Food Commodities in the Kingdom of Saudi Arabia
Sadiq, M. S.
*1
, Singh, I. P.
2
and Ahmad, M. M.
3
1
Department of Agricultural Economic & Extensions, FUD, Dutse, Nigeria
2
Department of Agricultural Economics, SKRAU, Bikaner, India
3
Department of Agricultural Economics, BUK, Kano, Nigeria
*Corresponding author: sadiqsanusi30@gmail.com
Received: March 23, 2020 Accepted: October 19, 2020
Abstract: This research empirically estimated the demand for imported staple food commodities in the
Kingdom of Saudi Arabia using dated data of 38 years (1980 to 2017) sourced from Food and Agriculture
Organization and United Nations Conference on Trade and Development databases. The collected data covered
the consumer price index; import quantities and expenditures of fifteen staple food commodities. The collected
data were analysed using descriptive statistics and linear expenditure system almost ideal demand system
(LES/AIDS) model. The empirical evidence showed the diversification on food spending to be very low as one
commodity (barley) had a dominant influence on the consumers’ budget expenditure. Furthermore, it was
observed that the dietary diversity of consumers is low. Income effect had strong influence than the substitution
effect in determining the demand for the selected imported commodities. It also showed that as consumers’
income increase and consumers’ diversify their diets, the consumption of non-staple foods rather than the staple
foods would increase. Therefore, the study recommends that people should be encouraged to engage in optimal
dietary diversification in order to enhance their diet nutritional quality and health status. However, since almost
all of the commodities are important given that they fulfilled the needs of the people, especially the poor who
face tight budgetary constraints. Thus, it becomes imperative for the policymakers to enhance their home-grown
economy so as enhance the economy, foreign exchange reserve and protect the health status of the country
population.
Keywords: Food commodity, imported food, LES/AIDS model, Saudi Arabia
This work is licensed under a Creative Commons Attribution 4.0 International License
1. Introduction
The Gulf Cooperation Council (GCC) countries,
with an approximate population of 40 million are
among the world‟s richest countries in respect to
oil and gas reserves, and per capita wealth (Adam
et al., 2019). However, about 90% of their food
requirements are imported as the domestic
production is inadequate to meet their current
demand. Therefore, the food imports in the GCC
region stud at $25.8 billion in 2010 (FAO et al.
2019). High dependence on imports makes the
GCC food supply very vulnerable and highly
dependent on the world food market (Vasileska and
Rechkoska, 2012; FAO, 2019).
In the last four decades, countries of the Arab Gulf
region experienced a rapid and drastic change in
their socio-economic situation, patterns of food
consumption, lifestyle and health status. This was
mainly attributed to the sharp increase in income
due to oil revenue accumulations. Nevertheless,
under-nutrition and micro-nutrient deficiencies still
exist among vulnerable groups; diet-related chronic
diseases have become the main health problems
while communicable diseases have diminished
(FAO, 2017; Adam et al., 2019).
Adam et al. (2019) opined that qualitative and
quantitative changes in food diets represent the
main characteristics of the dietary changes and
diversification in transitional nutrition. Thus, the
growth, consumption patterns and outlook of the
food sector is of substantial importance for these
countries. Many factors interact in different and
complex ways to influence and shape dietary
consumption patterns; and diet composition and
content. These factors include income, prices,
individual preferences and beliefs, culture,
traditions and geographical location,
environmental, social and economic factors. Major
shifts in dietary patterns are occurring, such as a
shift in consumption of basic staple foods towards
more diversified diets.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 65
Therefore, Kingdom of Saudi Arabia being an
epicentre of tourism and rapid population growth in
the GCC region was chosen as a pilot country to
examine the demand pattern of imported staple
food commodities so as to chart a realistic narrative
that will enhance homegrown economy and the
health status of its populace in particular and the
region in general. Thus, it is in view of the
foregoing that the present study aimed at
determining the demand elasticity for imported
foods in The Kingdom of Saudi Arabia. The
specific objectives were to examine the Average
budget and Marginal budget shares of the
consumers; and, to determine the expenditure and
price elasticities of the selected food items
2. Research Methodology
The study used time-series data that spanned from
1980 to 2017; which covered Consumer price
index(CPI), import quantities and expenditures of
fifteen staple food commodities viz. barley, wheat,
rice, maize, millet, dry beans, potatoes, root and
tubers, coffee, tea, vegetables, spices, beef, mutton
and chicken meat. The data were sourced from the
database of FAO and UNCTAD and the collected
data were analysed using descriptive statistics (first
objective) and LES/AIDS model (second
objective).
Empirical model
The budget share form of the LA/AIDS model is
indicated below following Anwarul-Huq et al.
(2004), Awal et al. (2008) and Babar et al. (2011).


 

  
[1]


[2]




 

  
[3]
The restrictions on the parameters of the AIDS in
equation (1) are:




 [4]

 [5]


 [6]
Where,
= budget share of the i
th
commodity (i.e.
);
= is the price of the j
th
commodity;
X = total household expenditure on all the food
items considered for the study;
= stone price index;
= stochastic term and it is assumed to be zero
and has constant variance;
= intercept;

= price coefficient; and,
= expenditure coefficient
According to Blanciforti and Green (1983) and
Awal et al. (2008) the model that uses Stone‟s
geometric price index is referred than the “Linear
Approximate Almost Ideal Demand System
(LA/AIDS)”. The demand elasticities are calculated
as the functions of the estimated parameters and
they have standard implications.
The expenditure elasticity (
) which measures the
sensitivity of demand in response to changes in
consumption expenditure is specified as follow:
  
[7]


[8]
Where,
MBS = marginal budget share
ABS = average budget share
Price elasticity is estimated in two ways viz.
uncompensated (Marshallian) elasticity that
contains both price and income effects, and the
compensated (Hicksian) elasticity which contains
only price effect.
The uncompensated own-price elasticity (

) and
the cross-price elasticity (

) measure how a
change in the price of one product affects the
demand of itself and that of the other products,
respectively, with the total expenditure and other
prices being held constant, that is, ceteris paribus.
The Marshallian own and cross-price elasticities
are calculated using the model indicated below
(Babar et al., 2011).


  
  [9]


  

[10]
The Hicksian own and cross-price elasticities
(



), which measure the price effects on
the demand assuming the real expenditure (
)
is constant is given below following Babar et al.,
(2011).


  
  [11]
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 66


  
[12]
Besides, the compensated price elasticity can be
estimated by using
,

and

, and the
permutation as indicated below.




 
[13]
Babar et al. (2011) reported that the sign of the
estimated

indicates the substitutability or
complementarily between the destinations under
consideration. A commodity pair is denoted as a
complement or substitute if their compensated
cross-price elasticity is negative or positive,
respectively.
Based on the value of expenditure elasticity, a food
item is classified as a necessity/necessary
commodity ( 
 , a luxury commodity
(
 or a Giffen / inferior commodity (

(Babar et al. 2011). In absolute term, the demand
for a particular commodity is price elastic
(inelastic) if the elasticity value of its own-price is
larger than unity (less than unity).
The Hicksian elasticity indicates the change in
demand for a commodity due to a price variation.
The real expenditure change caused by the
aforementioned price variation is compensated by
an expenditure variation so that satisfaction/utility
is kept constant.
When the objective is to use a tax instrument to
limit consumption of a certain item by raising its
price to consumers, the value of the price elasticity
of demand is the key (Clements and Si, 2015)
which was calculated following the formula below.



[14]
Where,
RPI = Required price increase
3. Results and Discussion
3.1. Average and marginal budget shares of the
food items
Table 1 showed imported barley to have the highest
budget share (0.328), followed by imported rice
(0.202) and then imported chicken meat (0.201)
(Table 1). Thus, the average budget share of these
three household food commodities had an
overwhelming dominance in the average annual
expenditure budget of consumers of imported
commodities in the study area. This showed that on
the average consumers of imported commodities in
the study area expended $0.328, $0.202, $0.201
and $0.269 on barley, rice, chicken meat and the
rest of the considered food items respectively from
a $1.00 budget on imported food items. On the
average, with the exception of the three food
commodities viz. barley, rice and chicken, all the
remaining imported food items each accounted for
less than 10% from the average annual budget
expenditure of consumers of imported food
commodities, thus indicating a very little diversity
in their diet. Thus, the diversity of barley, rice and
chicken in the diet of the consumers in the studied
area is high. This did not come as a surprise as the
major food items consumed in the studied area are
these three items.
On the average, the quantity consumed per year
was highest for barley (4.9 million MT), followed
by maize (1.23 million MT), then rice (0.78 million
MT), wheat (0.59 million MT), chicken meat
(0.372 million MT) and the least been the dry
beans (4741.03MT) (Table 2). Therefore, it can be
suggested that these food items had more patronage
with respect to consumption in the study area,
possibly attributable to the low price of these food
items when compared to their relative substitutes.
The coefficient of variations in price for vegetables,
barley, coffee, potatoes, root and tubers and mutton
were high; dry beans, maize, rice, spices, tea,
wheat, beef and chicken meat were moderate; while
it was low for millet. This indicates that the first
and the second categories of the food items are sold
based on grades, thus the reason for the high and
moderate variations in their prices (Table 3 and 4).
However, millet having low variation in its price
indicates that the imported commodity is not
graded, thus the low price variation may be
attributed to spatial and temporal marketing costs.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 67
Table 1: Summary statistics of the budget share
Items
Mean
SD
Minimum
Maximum
CV

0.327605
0.099883
0.076819
0.529767
0.30489

0.001255
0.000385
0.000174
0.002177
0.30673

0.00103
0.000762
2.01E-05
0.002429
0.73995

0.083133
0.027056
0.019045
0.153566
0.32546

0.000679
0.000399
0.000138
0.001609
0.58714

0.010361
0.008971
0.002383
0.038199
0.86589

0.202216
0.047743
0.12945
0.317712
0.23610

0.000403
0.000279
0.000106
0.001704
0.69334

0.010207
0.004139
0.003628
0.019648
0.40549

0.044805
0.019863
0.012004
0.09232
0.44331

0.002
0.001544
0.000368
0.006037
0.77207

0.037014
0.03784
7.98E-06
0.129633
1.0223

0.031759
0.029376
0.001473
0.10089
0.92496

0.20125
0.055069
0.082444
0.379701
0.27363

0.046284
0.013125
0.024795
0.074463
0.28358
= budget share; SD = standard deviation; CV= coefficient of variation
Source: Authors‟ own computation, 2020
Table 2: Summary statistics of the volume/quantity of import (metric ton)
Item
Mean
SD
Maximum
CV
Barley
4858060
2429596
10546312
0.50012
Dry Beans
4741.026
2975.481
12541
0.62760
Coffee
1325.447
1587.314
5028
1.1976
Maize
1233816
830609.1
3732787
0.67320
Millet
5710.361
3535.816
15263
0.61919
Potatoes
66663.5
33436.72
135225
0.50157
Rice
780329
381636.2
1591875
0.48907
Root & Tubers
2753.526
2754.039
11269
1.0002
Spices
12235.97
9183.778
36596
0.75056
Tea
20071.13
8367.374
37454
0.41689
Vegetables
8676.684
11295.13
51808
1.3018
Wheat
590047.6
943802.7
3237739
1.5995
Beef
24173.08
16123.56
55090
0.66700
Chicken meat
372282
229361.7
885386
0.61610
Mutton
40643.71
13091.61
62504
0.32211
SD = standard deviation; CV= coefficient of variation
Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 68
Table 3: Summary statistics of commodity prices
Items
Mean
SD
Minimum
Maximum
CV

186.5777
78.32663
68.95066
389.9718
0.41981

708.9445
236.8246
423.8317
1368.845
0.33405

3195.471
1480.827
1484.146
8112
0.46341

190.3024
67.7005
105.8637
370.7296
0.35575

283.2722
55.05606
185.0571
457.9321
0.19436

325.6181
144.4897
172.2278
725.8683
0.44374

677.956
225.0707
475.177
1244.162
0.33198

601.1391
287.3596
222.2222
1250
0.47803

2314.497
802.0513
1390.879
4381.5
0.34653

5109.302
1161.581
3397.967
7563.966
0.22735

1108.999
1023.671
189.7847
6038.06
0.92306

311.2782
79.80295
124.1135
587.6289
0.25637

2649.263
824.6798
1692.308
4566.913
0.31129

1438.393
413.9402
993.1615
2382.056
0.28778

2831.693
1226.683
1650
5612.594
0.43320
P = Price; SD = standard deviation; CV= coefficient of variation
Source: Authors‟ own computation, 2020
Table 4: Summary statistics average annual expenditure ($)
Items
Mean
SD
Maximum
CV
Barley
984046.4
807048
3249587
0.82013
Dry Beans
3746.868
3527.074
13928
0.94134
Coffee
3899.421
5339.664
17230
1.3693
Maize
256057.6
216867.5
692611
0.84695
Millet
1683.876
1211.133
4920
0.71925
Potatoes
19721.37
8711.995
37066
0.44175
Rice
592581.6
474844.6
1769426
0.80132
Root & Tubers
1516.5
1953.348
9976
1.2881
Spices
32959.82
36156.71
128534
1.0970
Tea
107652.8
65120.8
254004
0.60492
Vegetables
8167.026
11794.35
39490
1.4441
Wheat
165767.9
269553.3
1023886
1.6261
Beef
61680.08
40860.09
148254
0.66245
Chicken meat
613473.1
553171.6
1978438
0.90170
Mutton
121838.6
75295.27
283536
0.61799
Expenditure
2974793
2370725
8785074
0.79694
$ = Dollar
Source: Authors‟ own computation, 2020
Furthermore, barley, rice and chicken meat had the
highest marginal budget shares with an estimate of
48.34%, 18.80% and 14.91% respectively (Table
5). Therefore, it can be inferred that there is low
diversification in food spending with one single
commodity dominating the consumers‟ food diet.
.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 69
Table 5: Marginal budget share (marginal propensity
to consume) of the selected foods
Commodity
ABS
MBS

0.327605
0.48341

0.001255
0.000728

0.00103
0.000475

0.083133
0.113263

0.000679
3.07E-05

0.010361
-0.00155

0.202216
0.188036

0.000403
0.000335

0.010207
0.006945

0.044805
0.002881

0.002
0.001578

0.037014
0.027195

0.031759
-0.00988

0.20125
0.149045

0.046284
0.037515
Total
1
1
ABS = Average Budget Share
MBS = Marginal Budget Share
Source: Authors‟ own computation, 2020
3.2. Parameter estimates of demand function
The results of the parameter estimates for the
demand function proved that the functional form
(semi-logarithm) best fit the specified linear
approximate almost ideal demand system
(LA/AIDS) model as indicated by the most
pertinent diagnostic statistics viz. Langrage
Multiplier test statistic for serial correlation; Arch
effect test statistics for auto-covariance and
Koenker test statistics for heteroscedasticity which
were within the acceptable margin of less than 10%
degree of freedom(Table 6). Also, it was observed
that the OLS estimated model is devoid of spurious
correlation and regression as evidenced from their
respective coefficient of multiple determinations
(R2) which were within the plausible region and
less than the D-W statistics respectively. The
estimated results were found to be consistent as
they did not violate the homogeneity and symmetry
restrictions implied by consumption theory. Thus,
these indicate that the parameter estimates are
efficient, consistent and reliable for future
predictions with certainty and accuracy.
A cursory review of the results showed the R2 for
the estimated demand functions ranges from 0.589
to 0.926, with imported vegetables having the
lower limit while imported beef had the upper
limit. The R
2
estimates showed the percentage
contribution of the price and income to the
household demand for a particular commodity. For
instance, the R
2
values of 0.589 and 0.926 for
vegetables and beef respectively, imply that 58.9%
and 92.6% variations in consumers‟ demand for
imported vegetables and beef respectively were
determined by own-price, substitute prices and
income that were captured in the model while the
disturbing economic reality accounts for the
remaining percentages. With the exception of
coffee, root and tubers, and vegetables, the
intercept coefficient of all the remaining
commodity demand functions was different from
zero at 10% degree of freedom (Table 6).
Commodities viz. barley and wheat had negative
significant intercept coefficients while the
remaining selected food items had positive
significant intercept coefficients. The significance
of the intercept implies an exogenous growth in the
demand for a commodity independent of the
movements in prices and income. Thus, it can be
inferred that the exogenous growth in the share of
the commodities with positive intercepts have
increased while that of barley and wheat have
declined as evident by the negative sign. The
observed decline in the demand for barley and
wheat may be attributed to changes in tastes (Table
6).
The results showed that the budget share of barley
increased and decreased with an increase in the
prices of millet, wheat and chicken; and, potatoes
respectively. Also, the budget share of dry beans
increased with an increase in its own-price and
price of chicken meat while it decreased with an
increase in the prices of maize, roots & tubers, tea,
vegetables and wheat. The budget share of coffee
decreased with an increase in its own-price and
prices of roots and tubers, tea and vegetables; while
it increased with an increase in the price of mutton.
The budget share of maize increased and decreased
with an increase in its own-price, prices of dry
beans, tea and beef; and, prices of millet, potatoes,
wheat and chicken meat, respectively. The demand
for millet increased with an increase in its own-
price, prices of potatoes and chevron, while it
decreased with an increase in the prices of maize
and tea. The demand for potatoes increased and
decreased with an increase in its own-price, prices
of cocoa, spices and wheat; and, millet, tea,
vegetables and mutton, respectively.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 70
The budget share of rice increased with an increase
in its own-price and price of tea, while it decreased
with an increase in the prices of barley, coffee,
spices and wheat. The consumption share of roots
and tubers increased and decreased with an
increase in the prices of potatoes and mutton; and,
chicken meat respectively. The budget share of
spices increased with an increase in its own-price
and the price of wheat; and decreased with an
increase in the prices of roots and tubers, tea and
vegetables. The budget share of tea decreased with
an increase in the prices of coffee, roots and tubers
and chicken meat; while it increased with an
increase in the prices of barley, millet, potatoes and
mutton. The budget share of vegetables was found
only to increase with an increase in the prices of
potatoes and mutton. The budget share of wheat
decreased and increased with an increase in the
prices of chicken meat; and, coffee, maize,
potatoes, rice, roots and tubers, vegetables and
mutton, respectively.
The budget share of beef decreased with an
increase in its own-price and the prices of maize,
rice and tea; while it increased with an increase in
the prices of millet, potatoes, spices and vegetables.
The budget share of chicken meat increased with an
increase in the price of potatoes and decreased with
an increase in the price of tea. The budget share of
mutton increased with an increase in the prices of
dry beans and tea; and, decreased with an increase
in the prices of millet, spices, vegetables and beef.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 71
Table 6: Parameter estimates of the LA/AIDS
Item
Barley
Dry Bean
Coffee
Maize
Millet
Potatoes
Rice
Root& Tuber
Intercept
-1.4825
0.008049
0.004745
0.150580
0.003445
0.07889
0.69629
0.00121
3.25***
4.80***
1.44
NS
1.65*
2.84***
3.05***
4.75***
1.01
NS

0.034242
5.88E-05
-0.00015
-0.01604
0.000312
0.007059
-0.05774
0.000244
0.42
NS
0.19
NS
0.25
NS
0.97
NS
1.43
NS
1.52
NS
2.19**
1.13
NS

-0.04718
0.001451
-0.00033
0.038913
-0.00034
0.004903
0.000424
4.27E-05
0.57
NS
4.80***
0.56
NS
2.36**
1.56
NS
1.05
NS
0.02
NS
0.20
NS

-0.04532
7.15E-05
-0.00068
0.007904
1.84E-05
0.009904
-0.02947
7.22E-05
1.02
NS
0.44
NS
2.12**
0.88
NS
0.16
NS
3.92***
2.06**
0.62
NS

-0.11493
-0.00077
-0.00026
0.067582
-0.0007
0.004867
-0.03883
-0.00029
1.04
NS
1.89*
0.33
NS
3.06***
2.38**
0.78
NS
1.10
NS
0.99
NS

0.192687
3.68E-05
0.000733
-0.08316
0.000925
-0.01074
-0.05015
5.13E-06
2.02**
0.10
NS
1.06
NS
4.35***
3.65***
1.99**
1.63*
0.02
NS

-0.12727
0.00021
0.000412
-0.02769
0.000532
0.00624
-0.01729
0.000452
1.99**
0.89
NS
0.89
NS
2.16**
3.13***
1.72*
0.84
NS
2.69***

-0.22287
0.000305
-0.0005
-0.022
-4.7E-05
0.004506
0.261195
2.88E-05
1.53
NS
0.57
NS
0.47
0.75
NS
0.12
NS
0.55
NS
5.59***
0.08
NS

0.01181
-0.00042
-0.0005
-0.00361
-5.6E-05
0.003151
0.002895
-3E-05
0.34
NS
3.25***
1.96**
0.51
NS
0.59
NS
1.58
NS
0.26
NS
0.33
NS

-0.00349
0.000178
0.000193
-0.00691
0.000201
0.008953
-0.09417
-8.3E-05
0.04
NS
0.51
NS
0.28
NS
0.36
NS
0.80
NS
1.66*
3.09***
0.33
NS

0.051931
-0.0012
-0.00267
0.055286
-0.00101
-0.01282
0.092091
-7.8E-05
0.53
NS
3.37***
3.79***
2.84***
3.93***
2.33**
2.95***
0.31
NS

-0.01463
-0.00029
-0.00052
0.00602
9.59E-06
-0.00255
0.000471
0.000027
0.54
NS
2.92***
2.66***
1.11
NS
0.13
NS
1.66*
0.05
NS
0.38
NS

0.097943
-0.00053
0.000211
-0.01705
0.000149
0.004942
-0.0596
-0.00014
2.15**
3.15***
0.64
NS
1.87*
1.23
NS
1.91**
4.07***
1.14
NS

0.109392
-0.00012
0.000758
0.049257
0.000139
-0.00671
-0.03691
-3.5E-06
1.01
NS
0.31
NS
0.97
NS
2.27**
0.48
NS
1.09
NS
1.06
NS
0.01
NS

0.296361
-0.00015
0.001045
-0.08838
-6.9E-05
-0.00157
-0.07867
-0.00116
1.69*
0.23
NS
0.82
NS
2.52**
0.15
NS
0.16
NS
1.40
NS
2.52**

-0.15017
0.000772
0.002573
-0.02611
0.000557
-0.00999
0.019517
0.000864
1.59
NS
2.23**
3.77***
1.38
NS
2.23**
1.87*
0.64
NS
3.49***
Expenditure
0.155805
-0.00053
-0.00055
0.03013
-0.00065
-0.01191
-0.01418
-6.8E-05
4.30***
3.96***
2.12**
4.16***
6.74***
5.81***
1.22
NS
0.71
NS
***, **, * = significant at 1%, 5% and 10%, respectively; NS = non-significant; Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 72
Table 6: Continued
Item
Spices
Tea
Vegetable
Wheat
Beef
Chicken meat
Mutton
Intercept
0 .02972
0.367059
-0.001872
-0.327625
0.371889
0.865466
0.234753
1.79*
6.52***
0.26
NS
3.13***
6.44***
3.15***
4.55***

-0.00197
0.019208
-0.0014
-0.006
0.053963
-0.0415
0.009704
0.66
NS
1.89*
1.08
NS
0.32
NS
5.18***
0.84
NS
1.04
NS

0.002665
0.008845
-0.0018
-0.02371
0.016899
-0.01804
0.017267
0.89
NS
0.87
NS
1.40
NS
1.26
NS
1.62*
0.36
NS
1.86*

-0.00071
-0.01446
-0.00016
0.031585
-0.00823
0.043288
0.006196
0.44
NS
2.63***
0.22
NS
3.08***
1.46
NS
1.61
NS
1.23
NS

-0.00182
-0.02171
0.001798
0.083487
-0.02659
0.054712
-0.00654
0.45
NS
1.59
NS
1.04
NS
3.29***
1.90**
0.82
NS
0.52
NS

0.002509
0.039756
-0.00042
-0.00615
0.030466
-0.09382
-0.02267
0.72
NS
3.37***
0.28
NS
0.28
NS
2.52**
1.63*
2.10**

0.002979
0.04369
0.002168
0.031332
0.021099
0.065399
-0.00227
1.28
NS
5.53***
2.16**
2.13**
2.60**
1.70*
0.31
NS

-0.00818
-0.01497
0.000305
0.081148
-0.06075
-0.01929
0.001118
1.54
NS
0.83
NS
0.13
NS
2.43**
3.30***
0.22
NS
0.07
NS

-0.00339
-0.00874
0.000105
0.019345
-0.00183
-0.02099
0.002263
2.64***
2.01**
0.19
NS
2.39**
0.41
NS
0.99
NS
0.57
NS

0.008775
0.001095
0.000767
0.01253
0.064409
0.02778
-0.02022
2.54**
0.09
NS
0.51
NS
0.57
NS
5.36***
0.49
NS
1.88*

-0.01553
-0.0131
-0.00226
-0.01792
-0.05183
-0.10367
0.022785
4.39***
1.09
NS
1.48
NS
0.80
NS
4.22***
1.77*
2.07**

-0.00191
-0.00502
0.000339
0.017416
0.0079
-0.00193
-0.00533
1.93**
1.50
NS
0.80
NS
2.80***
2.30**
0.12
NS
1.74*

0.002936
-0.00536
0.000442
0.012864
0.000227
-0.03044
-0.00659
1.77*
0.95
NS
0.62
NS
1.23
NS
0.04
NS
1.11
NS
1.28
NS

0.003943
0.007457
0.000284
-0.03115
-0.03252
-0.04344
-0.02037
1.00
NS
0.56
NS
0.17
NS
1.25
NS
2.37**
0.67
NS
1.66*

-0.00351
-0.04205
-0.00232
-0.16388
0.015722
0.083615
-0.01499
0.55
NS
1.94**
0.84
NS
4.07***
0.71
NS
0.79
NS
0.76
NS

0.014275
0.021394
0.003623
0.056917
-0.00694
0.056908
0.0158
4.16***
1.84*
2.45**
2.63**
0.58
NS
1.00
NS
1.48
NS
Expenditure
-0.00326
-0.04192
-0.00042
-0.00982
-0.04164
-0.05221
-0.00877
2.47**
9.39***
0.74
NS
1.18
NS
9.09***
2.40**
2.14**
***, **, * = significant at 1%, 5% and 10%, respectively; NS = non-significant; Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 73
Table 6: Continued …
Items
Barley
Dry Bean
Coffin
Maize
Millet
Potatoes
Rice
Root& Tuber
R2
0.6020
0.6386
0.6424
0.7827
0.8239
0.8419
0.8204
0.6493
F-stat
1.98***
2.31***
2.35***
4.72***
6.14***
6.98***
5.99***
2.42***
D-W stat
1.80(0.02)**
1.95(0.05)**
1.67(0.007)***
2.24(0.26)
NS
2.84(0.91)
NS
1.59(0.003)***
2.27(0.28)
NS
2.15(0.17)
NS
Autcr. Test
0.198(0.95)
NS
0.37(0.82)
NS
0.49(0.49)
NS
0.50(0.48)
NS
0.97(0.49)
NS
1.62(0.21)
NS
0.63(0.43)
NS
1.81(0.17)
NS
Hetero (LM)
35.1(0.32)
NS
36.8(0.25)
NS
34.2(0.35)
NS
32.9(0.41)
NS
32.2(0.45)
NS
34.7(0.33)
NS
29.2(0.60)
NS
37.7(0.22)
NS
Arch test(LM)
6.12(0.29)
NS
3.99(0.40)
NS
15.3(0.35)
NS
0.007(0.93)
NS
4.19(0.52)
NS
4.26(0.37)
NS
0.14(0.69)
NS
19.1(0.16)
NS
Norm. test (
0.49(0.78)
NS
1.68(0.43)
NS
0.60(0.73)
NS
3.57(0.16)
NS
4.12(0.12)
NS
0.87(0.64)
NS
2.58(0.27)
NS
20.5(3.5e-5)***
RESET test
2.06(0.16)
NS
1.41(0.26)
NS
1.03(0.37)
NS
1.03(0.32)
NS
0.95(0.40)
NS
5.16(0.16)
NS
1.59(0.22)
NS
10.2(0.96)
NS
CUSUM test
3.91(0.85)
NS
1.34(0.19)
NS
0.98(0.33)
NS
1.13(0.26)
NS
1.10(0.28)
NS
0.95(0.35)
NS
2.03(0.54)
NS
1.17(0.25)
NS
Chow test
6.14(0.57)
NS
2.87(0.15)
NS
3.17(0.13)
NS
2.46(0.19)
NS
1.08(0.52)
NS
2.38(0.20)
NS
2.46(0.19)
NS
0.34(0.94)
NS
Items
Spices
Tea
Vegetable
Wheat
Beef
Chicken meat
Mutton
R2
0.6927
0.8470
0.5899
0.8532
0.9264
0.5181
0.7048
F-stat
2.95***
7.26***
1.88***
7.66***
16.5***
1.45***
3.13***
D-W stat
1.53(0.002)***
2.01(0.08)*
2.15(0.17)
NS
1.91(0.04)**
2.07(0.11)
NS
1.77(0.01)***
1.75(0.01)***
Autcr. Test
0.59(0.70)
NS
0.004(0.94)
NS
0.15(0.69)
NS
0.01(0.89)
NS
0.08(0.77)
NS
0.27(0.61)
NS
0.53(0.47)
NS
Hetero (LM)
31.4(0.49)
NS
29.6(0.58)
NS
34.9(0.32)
NS
32.1(0.45)
NS
36.0(0.28)
NS
33.0(0.41)
NS
32.8(0.42)
NS
Arch test(LM)
0.02(0.87)
NS
1.02(0.31)
NS
7.31(0.19)
NS
2.31(0.12)
NS
0.12(0.72)
NS
0.50(0.47)
NS
0.76(0.38)
NS
Norm. test (
0.87(0.64)
NS
7.06(0.02)**
13).3(0.001)
NS
0.19(0.91)
NS
0.47(0.79)
NS
1.07(0.58)
NS
0.08(0.95)
NS
RESET test
2.29(0.12)
NS
8.22(0.26)
NS
0.43(0.51)
NS
1.06(0.31)
NS
0.56(0.46)
NS
4.77(0.41)
NS
0.89(0.42)
NS
CUSUM test
1.60(0.12)
NS
0.80(0.43)
NS
0.29(0.77)
NS
1.18(0.24)
NS
-1.12(0.27)
NS
3.80(0.11)
NS
0.31(0.75)
NS
Chow test
5.68(0.52)
NS
1.58(0.35)
NS
7.70(0.03)**
1.27(0.45)
NS
19.7(0.005)***
3.95(0.09)*
7.74(0.03)**
***, **, * = significant at 1%, 5% and 10%, respectively; NS = non-significant
Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 74
3.3. Expenditure and own-price elasticities
Displayed in Table 7 are the expenditure and both
uncompensated and compensated own-price
elasticities for the considered food items. A good
can be identified as a necessity, luxury or Giffen
(inferior) based on the signs and size of the degree
of fluctuation of the elasticity value for a particular
commodity to a change in income. Generally, the
expenditure elasticities for the selected imported
food items in the country were high and this can be
explained by the economic situation in the country.
This revealed that most of the households
especially the poor face tight budgetary constraints
and all of the selected commodities are considered
to be very important because they fulfil their
fundamental needs.
The estimated expenditure elasticities of barley,
maize, millet, rice and wheat were 1.48, 1.36, 0.05,
0.93 and 0.74 respectively, indicating that a
10%increase in consumers‟ income would increase
the demand for the aforementioned commodities in
respective order by 14.8%, 13.6%, 0.5%, 9.3% and
7.4% (Table 7). The expenditure elasticity
estimates for coffee, tea, vegetables and roots and
tubers were 0.461, 0.064, 0.789 and 0.832
respectively, implying that a 10% increase in
consumers‟ income would increase the demand for
the above-mentioned commodities in respective
order by 46.1%, 0.64%, 5.89% and 8.32%. Besides,
the expenditure elasticity estimates of chicken meat
and mutton were 0.741 and 0.811 respectively,
imply that an increase in consumers‟ income by
10% would increase the demand for the former and
latter commodities by 7.41% and 8.11%
respectively.
However, the empirical evidence showed the
expenditure elasticity estimates of potatoes and
beef to be -0.150 and -0.31 respectively, indicating
that if consumers‟ income increased by 10% the
demand for the former and latter goods would
decrease by 1.50% and 3.1%. Therefore, for most
of the food items, any policy aimed at raising the
per capita income in the country, diversity towards
high-quality diet is likely to be enhanced.
A cursory review of the results showed a positive
expenditure elasticity estimates for all the food
items with the exception of potatoes and beef, thus
implying that potatoes and beef are non-normal
goods while the remaining are normal goods. The
empirical evidence showed potatoes and beef to be
income inelastic and negatively signed, that is, less
than zero, indicating they are inferior or Giffen
commodities. Furthermore, barley and maize were
found to be income elastic, that is, greater than
unity, implying they are luxury commodities, while
all the remaining food items have positive income
inelastic, that is, less than unity but greater than
zero, indicating that these food items are
necessities. However, it is worth to mention that
the expenditure elasticity values of millet and tea
were close to zero, indicating that these
commodities are near or close to inferior
commodity, that is, not as such sensitive to change
in expenditure.
The expenditure elasticity values for potatoes and
beef and all the remaining food items revealed that
if the consumers‟ income increased the demand for
the former would be decreased while that of the
later commodities would increase. Thus, it is
expected that these commodities will witness an
increase or decrease (potatoes and beef) in demand
when the increase in the income is in tandem with
the overall economic growth in the study area.
However, in relative terms, if the real per capita
income plummets, necessary commodities would
have less income allocated to them. Given a fixed
supply for normal goods, the upward shift of
demand curves will imply that the equilibrium
market prices will increase. For those commodities
whose own-prices are less than unity (inelastic), it
is anticipated that the increase in their respective
prices due to the shift in their respective demand
curves would lead to a decrease in the demand by
less than the proportionate change in price. Also,
for those food items whose own-elasticity values
are elastic i.e. greater than unity, it is anticipated
that the rise in their respective prices due to the
shift in their respective demand curves would lead
to a decrease in the demand by more than the
proportionate change in price. Thus, results show
that as consumers‟ income rise and consumers‟
diversify their diets, their consumption of non-
staple foods rather than staple foods tends to
increase.
An interesting observation was that coffee and tea
tend to have less expenditure elasticity. The
consumption of this commodity class (coffee and
tea) is relatively less affected by income changes
and can be inferred that it is a staple food in the
country, thus occupying a special place in the
consumers‟ diet.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 75
Table 7: Expenditure (Income), Uncompensated and Compensated own-price elasticities
Items
Expenditure
Uncompensated
Compensated
Income effect
PP(%PR)
Barley
1.475588
-1.05128
-0.56787
4.834096
23.780443
Dry Beans
0.579977
-0.156865
-0.157593
0.00728
159.373
Coffee
0.46133
-1.66402
-1.66355
0.004752
15.023843
Maize
1.362437
-0.21719
-0.10392
1.132628
115.10864
Millet
0.045291
-0.363108
-0.363138
0.000307
68.85011
Potatoes
-0.14978
-0.38582
-0.38737
0.0155183
64.796833
Rice
0.929875
-0.305846
-0.493881
1.880356
81.74056
Root &Tubers
0.831554
-1.07521
-1.07488
0.003347
23.251244
Spices
0.680451
-0.13697
-0.13002
0.069451
182.52435
Tea
0.064302
-1.25035
-1.24747
0.02881
19.994371
Vegetables
0.78921
-0.83014
-0.82856
0.015785
30.115525
Wheat
0.734709
-0.64264
-0.61545
0.271948
38.902041
Beef
-0.31121
-1.98219
-1.99208
0.0988379
12.612299
Chicken meat
0.740596
-0.53232
-0.38327
1.490448
46.964561
Mutton
0.810533
-0.64986
-0.61235
0.375147
38.469824
PP = Protectionist Policy; PR = Price Rise
Source: Authors‟ own computation, 2020
3.4. Response of demand to price changes
According to economic theory, own-price elasticity
is expected to be negatively signed, implying that
the demand curve is negatively sloped. The
Marshallian (uncompensated) elasticity of demand
refers to change in the demand for a commodity
due to the change in price without any
compensation for the change in price or income
(Table 7). While the Hicksian (compensated)
elasticity of demand for a commodity refers to that
portion of a total change in demand which is
compensated by a change in price. Once the change
in total demand is compensated by a changing in
price, the remaining left is an income effect. In
other words, compensation is meant to sustain
consumers at the same level of utility as before the
change in price. Thus, the price effect plus income
effect equals a total effect.
The empirical evidence showed both the
uncompensated and compensated own-price
elasticities for all the selected food items to be in
conformity with the prior expectation (negative
sign), indicating an inverse relationship between
the price of a normal commodity and its demand
(Table 7). Between the uncompensated and
compensated own-price elasticities, a substantial
difference was observed, thus indicating a
substantial income effect. In addition, most of the
uncompensated own-price elasticities in absolute
term were higher than their respective
corresponding compensated own-price elasticities,
implying that price effect wax more influence than
the income effect.
The uncompensated own-price elasticity estimates
for barley, coffee, root and tubers, tea and beef
were greater than unity while that of the remaining
selected food items were less than unity. This
implies that the demand for the former goods react
elastically to change in their respective own-prices
while the demand for the latter goods reacts
inelastic to change in their respective own-prices.
Thus, for barley, coffee, root and tubers, tea and
beef, change in their respective own-price affects
their demand to a greater extent when compared to
other commodities. It was observed that the
uncompensated own-price elasticity estimates for
all the selected food items were lower than their
respective corresponding income elasticities,
indicating that the responsiveness of demand to
own-price changes is smaller than to the variations
in the total expenditure.
The Marshallian own-price elasticity is composed
of price or substitution effect and income effect.
The uncompensated own-price elasticity estimate
of barley indicates that if the price of barley
plummets by 10% the demand for barley would
increase by 10.51%. Of this total increase in
demand, compensated own-price elasticity posited
that 5.68% was purely due to price effect. The
income effect of the price fall accounts for 4.823%
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 76
(10.51-5.68) increase in barley demand due to the
increase in the real per capita income if the nominal
or money income remains unchanged. If the per
capita income increased by 10% and subsequently
accompanied by a decline in the price of barley by
10%, the demand for barley would increase by
25.27% (10.51+14.76). The rise in the per capita
income represents a shift in the demand curve of
barley which normally would lead to an increase in
the price of barley; this is not desirable for an
importing economy which depends on an external
market for supply. However, a decrease in the
supply (importation) of barley by 25% would
increase the price of barley by 23.78%, thus
resulting in a decrease in the demand for barley.
Any international trade protectionist measures
taken to protect Saudi‟s economy from external
economic incursion will aid in stimulating growth
and development of domestic industries, thus
increasing the GDP of the country. However, for
the estimation of the resulting equilibrium level of
barley consumption, information on the supply
elasticity of barley would be required.
The results showed the income effect of change in
price to be moderate for barley while it was small
for the remaining selected food items. These were
so because the barley had a moderate share in the
household income while the remaining selected
food items each had a small share in the annual
consumers‟ expenditure budgets. Thus, change in
barley price had a moderate effect on the real
income which owed to its moderate share in the
consumers‟ annual budget while for the remaining
food items, income effect due to change in their
respective prices are small and it owes to their
small share in the annual consumers‟ budget. It was
observed that the compensated own-price elasticity
values were lower than the uncompensated own-
price elasticity estimates, thus indicating the
predominant effect of income effect over the
substitution effect. Also, it implies that the price
responsiveness of all the selected food items was
income-dependent, such that if income is held
constant i.e. ceteris paribus (that is, income is not
constant in the decision making process),
consumers would tend to be less responsive to food
prices.
3.5. Cross-price elasticity
The matrices of cross-price elasticity estimates for
the uncompensated and compensated are shown in
Table 8 and 9. The uncompensated cross-price
elasticity shows the „gross‟ cross-price effect that
includes both the price and income effects while
the compensated cross-price elasticity represents
the pure price effect, that is, only the substitution
effect or the net effect of a price change on
demand. The cross-price elasticity characterized a
pair of goods as complements or substitutes
depending on the signs of the elasticity estimate. If
the elasticity estimate is positive, the commodity
pair is referred to as „substitute‟; if negative, the
commodity pair is referred to as „complement‟. For
the uncompensated cross-price elasticity, out of the
105 estimates, 52 commodity pairs were found to
be complementary commodities as evidenced by
their respective elasticity values which were
negatively signed while the remaining 53
commodity pairs were substituted commodities, as
indicated by their respective cross-price elasticity,
were positively signed. Besides, for the
compensated cross-price elasticity, it was observed
that 55 commodity pairs were „net‟ substitutes
while fifty commodity pairs were „net‟
complements as indicated by their respective
positively and negatively signed elasticities
respectively. For the food items which were found
to be substituted, it implies that an increase in the
price of one commodity would lead to an increase
in the demand of its pair, ceteris paribus. While in
the case of a commodity pair which complement, it
implies than an increase in the price of one
commodity would lead to a decrease in the demand
of its pair, ceter is paribus. For substitute
commodities, it means two commodities can
substitute each other if there is a change in the price
of one of the commodity; while for the
complementary commodities, it means that the
consumption of one implies the consumption of the
counterpart, thus an increase in the price of one
will lead to a decrease in the demand for its
complementary counterpart.
The negative sign of the uncompensated cross-
price elasticity of demand for barley due to change
in the price of vegetables indicates that the two
goods are complements. The estimated elasticity of
the commodity pair being 0.046 implies that if the
price of vegetables increase by 10% the demand for
barley would decrease by 0.46%, ceteris paribus,
The compensated cross-price elasticity of
vegetables to barley, that is, the net effect of
vegetables price change on demand for the barley;
indicates that if the price of vegetables increase by
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 77
10%, the consumption of barley would decrease by
0.43%, ceteris paribus.
The positive sign of the uncompensated cross-price
elasticity of wheat to barley implies that the two
commodities are substitutes. The elasticity estimate
being 0.281 means that if the price of wheat
increase by 10% the demand for barley would
increase by 2.81%,ceteris paribus, that is,
consumers‟ would shift to the alternative (barley in
this case). For the compensated cross-price
elasticity, the net effect of wheat price change on
the demand for barley implies that if the price of
wheat surged by 10% the demand for barley would
increase by 3.36%, ceteris paribus.
Therefore, it can be inferred that the decrease and
increase in the demand for barley by 0.46% and
2.81%, is due to the increases in the prices of
vegetables and wheat respectively, and an increase
in the real per capita income. In addition, the
decrease and increase in the demand for barley by
0.43% and 3.36% are due to pure price effect
arising from the increase in only the prices of
vegetables and wheat respectively. Thus, the
relationship between vegetable and barley are net
complements while that of wheat and barley are not
substitutes.
It was observed that the signs of the
uncompensated and compensated cross-price
elasticity estimates for some particular
commodities were contrary. The negativity of the
uncompensated cross-price elasticity of demand for
beans (-0.032) due to the fall in the price of chicken
meat, that is, the total effect of a change in the price
of chicken on the demand for beans indicated that
the two commodities were „gross‟ complements.
However, the compensated cross-price elasticity
estimate was positive (0.085), indicating that the
two commodities are „net‟ substitute. The
uncompensated cross-price elasticity is more
ambiguous as reported by Awal et al. (2008). They
postulated that in change, a strong income effect
plays a role. Furthermore, they suggested that
compensated cross-price elasticity is the most
appropriate when information on substitution
possibilities are needed.
Generally, it can be inferred that there is low
diversity in the Saudi Arabians‟ dietary
composition. It is important that a number of
different food sources should be consumed, thus
consumers should be encouraged to consume a
wide variety of foods to improve their nutritional
quality and health condition. Aziz et al. (2011)
reported that dietary diversity is one of the most
pertinent ways to ensure a balanced diet for people
across all the age categories.
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 78
Table 8: Uncompensated cross-price elasticity for the selected food items
Items
















-1.0513
0.1845
0.0316
-0.3117
0.7727
1.0580
-0.2626
0.6619
-0.0881
0.7352
-0.6290
-0.0751
2.1287
-0.1212
0.2717


-0.1446
-0.1569
-0.3229
0.4676
-0.5004
0.4747
0.0022
0.1063
0.2615
0.1986
-0.9010
-0.6402
0.5337
-0.0893
0.3733

-0.1388
0.0574
-1.6640
0.0947
0.0281
0.9571
-0.1457
0.1796
-0.0697
-0.3218
-0.0781
0.8536
-0.2579
0.2154
0.1341

-0.3904
-0.5752
-0.208
-0.2172
-0.9469
0.5654
-0.1862
-0.6988
-0.1520
-0.4068
0.9162
2.2776
-0.7283
0.2934
-0.1256

0.5879
0.0296
0.7119
-1.0005
-0.3631
-1.0363
-0.2479
0.0129
0.2459
0.8879
-0.2112
-0.166
0.9602
-0.4660
-0.4897

-0.3934
0.1716
0.4059
-0.3369
0.7942
-0.3858
-0.0848
1.1242
0.2952
0.9848
1.0862
0.8492
0.6779
0.3277
-0.0470

-0.7765
0.3283
-0.3740
-0.3379
0.1232
0.6674
-0.3059
0.1056
-0.7369
-0.1449
0.1952
2.2459
-1.6476
-0.0434
0.0625


0.0359
-0.3355
-0.4852
-0.0435
-0.0817
0.3046
0.0144
-1.0752
-0.3319
-0.1947
0.0527
0.5227
-0.0572
-0.1042
0.0489

-0.0155
0.1463
0.1924
-0.0869
0.3060
0.8758
-0.4649
-0.2038
-0.1369
0.0339
0.3855
0.3412
2.0414
0.1407
-0.4350

0.1372
-0.9399
-2.5636
0.6488
-1.4504
-1.1858
0.4586
-0.1872
-1.5074
-1.2504
-1.1222
-0.4723
-1.5732
-0.5035
0.5008


-0.0456
-0.2308
-0.5046
0.0717
0.0160
-0.2436
0.0025
0.0674
-0.1861
-0.1102
-0.8301
0.4711
0.2514
-0.0091
-0.1147

0.2814
-0.4049
0.2251
-0.2185
0.2545
0.5195
-0.2922
-0.3331
0.2995
-0.0850
0.2287
-0.6426
0.0557
-0.1416
-0.1354

0.3188
-0.0839
0.7531
0.5810
0.2349
-0.6111
-0.1803
-0.0035
0.3965
0.1962
0.1485
-0.8332
-1.9822
-0.2076
-0.4341


0.8089
-0.0319
1.1232
-1.1361
0.0901
0.0799
-0.3749
-2.8416
-0.2798
-0.7502
-1.1159
-4.3741
0.7589
-0.5323
-0.2858

-0.4804
0.6345
2.5229
-0.3308
0.8653
-0.9105
0.0998
2.1534
1.4134
0.5208
1.8213
1.5499
-0.1579
0.2948
-0.6499
Own-price elasticities are written in bold letters; R & T = Roots & Tubers; veg = vegetables; ChM = Chicken meat
Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 79
Table 9: Compensated cross-price elasticity for the selected food items
Items
















-0.5679
0.3745
0.1827
0.1347
0.7875
1.0089
0.0421
0.9343
0.1348
0.7563
-0.3705
0.1656
2.0267
0.1214
0.5373

-0.1428
0.1576
-0.3223
0.4693
-0.5003
0.4745
0.0034
0.1073
0.2624
0.1987
-0.9001
-0.6393
0.5333
-0.0884
0.3743

-0.1373
0.0579
-1.6636
0.0961
0.0281
0.9569
-0.1447
0.1804
-0.0689
-0.3218
-0.0773
0.8543
-0.2582
0.2161
0.1349

-0.2677
-0.5269
-0.1697
-0.1039
-0.9432
0.5529
-0.1089
-0.6297
-0.0955
-0.4015
0.9818
2.3387
-0.7542
0.3549
-0.0582

0.5889
0.0299
0.7122
-0.9996
-0.3631
-1.0364
-0.2473
0.0134
0.2465
0.8879
-0.2107
-0.1655
0.9599
-0.4655
-0.4891

-0.3781
0.1776
0.4107
-0.3227
0.7946
-0.3874
-0.0751
1.1328
0.3022
0.9855
1.0944
0.8569
0.6747
0.3353
-0.0387

-0.4781
0.4455
-0.2807
-0.0624
0.1324
0.6371
-0.4939
0.2738
-0.5993
-0.1319
0.3548
2.3946
-1.7106
0.1064
0.2264

0.0365
-0.3353
-0.4849
-0.0429
-0.0816
0.3046
0.0147
-1.0749
-0.3317
-0.1947
0.053
0.5230
-0.0573
-0.1039
0.0493

-0.0005
0.1523
0.1971
-0.0729
0.3065
0.8743
-0.4555
-0.1953
-0.1300
0.0347
0.3935
0.3487
2.0382
0.1482
-0.4268

0.2033
-0.9139
-2.5429
0.7098
-1.4483
-1.1925
0.5002
-0.1499
-1.4769
-1.2475
-1.0868
-0.4394
-1.5872
-0.4703
0.5371

-0.0427
-0.2297
-0.5037
0.0744
0.0161
-0.2439
0.0043
0.0691
-0.1848
-0.1101
-0.8286
0.4725
0.2507
-0.0076
-0.1131

0.3359
-0.3834
0.2421
-0.1681
0.2562
0.5139
-0.2577
-0.3024
0.3247
-0.0827
0.2579
-0.6155
0.0442
-0.1142
-0.1054

0.3657
-0.0655
0.7678
0.6243
0.2364
-0.6158
-0.1508
0.0229
0.4181
0.1982
0.1736
-0.8098
-1.9921
-0.1841
-0.4084

1.1059
0.0849
1.2161
-0.8619
0.0992
0.0498
-0.1878
-2.6743
-0.1429
-0.7372
-0.9571
-4.2262
0.6963
-0.3833
-0.1227

-0.4121
0.6613
2.5442
-0.2677
0.8674
-0.9175
0.1428
2.1919
1.4449
0.5238
1.8579
1.5839
-0.1723
0.3291
-0.6124
Own-price elasticities are written in bold letters; R & T = Roots & Tubers; veg = vegetables; ChM = Chicken meat
Source: Authors‟ own computation, 2020
J. Agric. Environ. Sci. Vol. 5 No 2 (2020) ISSN: 2616-3721 (Online); 2616-3713 (Print)
Publication of College of Agriculture and Environmental Sciences, Bahir Dar University 80
4. Conclusions
Based on the findings of the study, it can be
inferred that there is low diversification in food
expenditure with barley having an overwhelming
influence on the consumers‟ food budget. In
addition, rice and chicken with appreciable portions
trailed behind barley in the consumers‟ budget
share. Of the fifteen considered food items, one
commodity was found to be a luxury and the other
one inferior; while the remaining eleven
commodities were necessities. Thus, it indicates
that as consumers‟ expenditures increase and
consumers‟ diversify their diets, the consumption
of non-staple foods rather than the staple foods tend
to increase. For approximately two-third of the
selected commodities, the responsiveness of their
demand to own-price changes is less than the
responsiveness to total expenditure (income).
Furthermore, income effect dominates in
determining the demand for imported commodities
as evidenced from the uncompensated own-price
elasticity estimates been greater than the
compensated own-price elasticity estimates. The
cross-price elasticity estimates showed the
substitution effects of prices to be very weak.
Therefore, the research advised people to consume
a wide variety of foods to improve their diet
nutritional quality and health status. However,
since almost all of the commodities are important
given that they fulfilled the needs of the people,
especially the poor who face tight budgetary
constraints. Thus, it becomes imperative for the
policymakers to enhance their homegrown
economy to enhance the economy, foreign
exchange reserve and protect the health status of
the country populace.
Conflict of Interest
The authors declare that there is no conflict of
interest.
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