The Consumer Price Index and its Role in Influencing Exports, Food
Imports, and the Local Output of the Jordanian Agricultural Sector
THIABAT ADNANa,*, ABDUL BAQI REEM, AL-NABULSI MANWAc,
BATAINEH ASHRAFd
Department of Financial and Administrative Sciences,
Al-Balqa' Applied University,
JORDAN
*Corresponding Author
Abstract: - Jordan is in the east of Asia, with 91971 km2 of land and water of 329 km2. The study examined the
consumer price index as an independent factor and its impact on exports, food imports, and the agricultural sector's
local output as dependent factors in the Jordanian economy. The study took the period from 2006 to 2016 as a
sufficient period for measurement, as the agricultural sector is important in the process of economic development,
so it was necessary to study the factors affected by the process of changing the price structure represented in the
index of the consumer price, as this factor is important in decision-making by businessmen and government alike.
To express these variables, statistical measures had to be taken in the analysis, based on finding the simple linear
regression of the dependent and independent factor by least squares and testing the estimated equation to avoid
measurement errors. The relationship between the different variables influenced by the consumer price index,
which the study was taken into, represents the column of this sector of production, export, and import. Under
globalization, a country cannot be satisfied with its production and self-sufficiency. Still, there is an external world
that carries out open international trade according to each country's comparative advantage, and we believe that
Jordan possesses this comparative advantage in the agricultural sector due to its land and work resources. Still, the
circumstances surrounding the rise in prices affect this sector. As for the trade balance and Jordan's entry into the
International Trade Organization, the door has been opened for external work in intra-trade with external
knowledge until the price increase affects the exported products' prices. Imported goods can enter at lower prices,
which affects the sector. The study found a strong direct relationship between the consumer price index and the
agricultural sector's domestic product, with the addition of economic justifications for these results and the study
reached the results and recommendations, the most important of which was finding direct support for citizens in
light of the conditions of rising prices and increasing immigrants.
Key-Words: - Consumer Price Index, Exports, Imports, GDP, Agricultural Sector, Jordanian economy.
Received: September 14, 2023. Revised: April 17, 2024. Accepted: May 16, 2024. Published: May 31, 2024.
1 Introduction
The change in prices, inflation, and its relationship
with the consumer price index greatly impact
economic activity in general. The study deals with
the effect of the consumer price index on the
agricultural sector, which is represented by its
domestic product, exports, and imports of
agricultural products. The importance of this lies in
the importance of food at the global level, especially
in bad economic conditions. Note that foodstuffs'
prices increased globally during the period 2007-
2008 and 2010-2011, [1].
The noticeable rise in global prices impacted
Jordan, as it is part of the Third World, which is
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1428
Volume 21, 2024
aORCID: https://orcid.org/0000-0003-0701-300X
bORCID: https://orcid.org/0000-0002-2932-0812
cORCID: https://orcid.org/0000-0001-5853-0643
dORCID: https://orcid.org/0000-0002-9249-9472
b
quickly affected by the surrounding conditions. A
study is needed to find solutions for the awakening in
this vital sector, especially what this sector possesses
of unique features in the ability to produce, with a
decline in some sectors during the same duration of
the study and a marked increase in productivity.
1.1 Study Problem
International exchange is one of the principles of the
scarcity of materials because no country has all the
necessary resources, [2].
The study problem can be put in the form of
questions:
1. Is there a relationship between changing prices for
food products and the number of exports from them?
2. Is there a relationship between changing prices for
food products and the number of imports from them?
3. Is there a relationship between changing prices for
food products and food production?
4. Is there a relationship between the price index and
the increase in demand for food commodities?
5. Do high prices have positive effects on the
economy?
1.2 Study Objectives
The purpose of this topic is about trying to achieve
the following goals:
1. Clarify the consumer price index and methods of
its measurement.
2. Clarify the materials that the agricultural sector
exports and imports.
3. Clarify the concept of the trade balance and the
role of the agricultural sector in supporting it.
4. Clarifying the extent of the agricultural sector's
participation in local production.
1.3 The Importance of the Study
The study's importance lies in knowing how to
advance in the agricultural sector and provide the
national economy with production while researching
the causes of decline or progress in this sector. And
work to enhance the positive factors and get rid of
obstacles to progress.
1.4 Study Variables
The first equation
Independent variable: The consumer price index for
food commodities
Dependent variable: Food exports
The second equation
Independent variable: The consumer price index for
food commodities
Dependent variable: Food imports
The third equation
Independent variable: The consumer price index for
food commodities
Dependent variable: The GDP of the agricultural
sector.
1.5 The Hypothesis of the Study
They are all testable parameters of society, [3].
The study is based on three hypotheses:
First: Null hypothesis H0: There is a positive
relationship between the consumer price index for
food commodities and food exports.
Alternative hypothesis H1: There is a negative
relationship between the consumer price index for
food commodities and food exports.
Second: Null hypothesis H0: There is a positive
direct relationship between the consumer price index
for food commodities and food imports.
Alternative hypothesis H1: There is a negative
relationship between the consumer price index for
food commodities and food imports.
Third: Null hypothesis H0: There is a positive direct
relationship between the consumer price index for
food commodities and the agricultural sector's GDP.
Alternative hypothesis H1: There is a negative
relationship between the consumer price index for
food commodities and the agricultural sector's GDP.
1.6 Tools and Scales of the Study
To establish or deny the validity of the research
hypotheses, a descriptive statistical analysis method
will be used to display the statistical tables for all
variables during the study period from 2006 to 2016;
then, we will use the standard census method to
estimate simple linear regression and perform the
necessary tests to prove the estimated equation and
determine the nature of the relationship between the
variables and its strength using the EViews 10
program until we reach the aim of the study.
1.7 Data Collection Method
The researchers relied on the general statistics reports
of the Ministry of Industry and Trade and the
Ministry of Agricultural Statistics (2006-2016).
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Thiabat Adnan, Abdul Baqi Reem,
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E-ISSN: 2224-2899
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2 Previous Studies
The study examined Nigeria's inflation rate of 10%
and how it was affected by interest rates that
contributed to the increase in inflation. The study
found that the work of the Central Bank of Nigeria
must change its approach because its monetary policy
hurts Nigeria's inflation and must control the supply
of cash, knowing that monetary policy alone is
unable to combat inflation, [4].
The study is based on an analysis of food price
fluctuations in Indonesia's inflation and a correlation
between staple prices and inflation, especially during
the coronavirus period, while monetary policy
indirectly affected prices of agricultural products and
inflation and low-income earners in developing
countries, [5].
The Study's overarching objective is to assess the
influence of Saudi Arabia's and Jordan's respective
monetary and fiscal policies on agricultural output
and GDP expansion in several Arab countries, with a
focus on Iraq. The study adopted a descriptive
method mixed with quantitative analysis performed
in a statistical package (Eviews10). Thirty-one years
(1990-2020) were analyzed in the study's time series.
The autoregressive vector model was selected to
estimate the correlation between the long- and short-
term variables. This study employed a model to
examine the correlation between macro policies
(financial and monetary) in the agricultural sector
and the economic growth rates of various countries in
the sample. The findings revealed that the policies
implemented by these countries were ineffective,
leading to a decline in the agricultural sector's
contribution to economic growth. Notably, Iraq and
Saudi Arabia, being oil-dependent nations,
experienced particularly low added value in the
agricultural sector, resulting in reduced economic
growth. Conversely, Jordan witnessed an increase in
added value, which positively impacted its economic
growth. It was suggested that the sample countries'
economic growth rates might be increased if fiscal
and monetary policy relied more heavily on
mechanisms that increased agricultural added value,
[6].
The study is based on the function of Cobb-
Douglas in blending the element of work and capital
in the production of the Jordanian agricultural sector,
which contributes to domestic production and
achieves food security, especially in developing
countries, including Jordan. The study found that
Jordan's agricultural sector is on a decline. The
intensity of the capital used in the Jordanian
agricultural sector is below the required level. One of
the most important recommendations was to bring
investments to the agricultural sector and focus on
scientific research in the development of production.,
[7].
While agroecology has been criticized for its
wasteful use of land, it has also been proposed as a
means of extending the life of the food chain. Five
exploratory stories were compared to stakeholder-
developed scenarios for the food system in the EU in
2050. We projected a range of biophysical (such as
food production and land usage), environmental
(such as glasshouse gas emissions), and social
indicators by 2050, including the possibility of local
food self-sufficiency. There were two opposing
stories about how to expand agroecological practices.
One nation has used agroecology to produce high-
value goods for high-income consumers through
trade, while only meeting two of the eight
environmental policy criteria of the EU and 40
percent of its agricultural land being under, [8].
The study used standard analysis of the existence
of causal relationships in the long run. The study
concluded that exports had the highest impact on
inflation in the Pakistani economy. The study
recommended encouraging local investment,
especially in the textile, fish, and agricultural
products sectors, [9].
The study examined the price that is one of the
most important factors influencing the purchase
decision. Producers and retailers developed pricing
methods, and there were more appropriate ways to
deal with consumers, From the study's questions is
whether these methods influence consumer
purchasing decisions and the perception of the
quality of the product and build a clear perception of
the consumer in comparing prices and selecting the
product that makes the highest gain under the other
variables as the price directly affects the intention to
buy, [10].
The study aims to clarify inflation and the
consumer price index, the reasons for the rise, and
the proposed solutions. The study found that
reducing consumption and pushing towards saving
and developing exports are proposed solutions to
Egypt's inflationary situation. It also aimed to restrict
external imports of goods and services and overcome
the deficit in the balance of payments, [11].
The study aims to show the importance of
exports in agricultural output growth and the
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Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
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importance of economic blocs in foreign trade; the
study mentioned the obstacles that affect exports.
Egyptian agricultural exports, export decisions,
foreign trade relations, and the deficit in the trade
balance. The study also determined the importance of
prices and their determination, economic blocs and
trade relations, and the opening of new markets, [12].
The study aims to show inflation and the rise in the
consumer price index for Yemeni economic activity
from 1990 to 2000 where. Yemen was suffering from
inflation, which reached 482.6% at the end of 2000,
and the study found that the effect of this was
different from one city to another in Yemen. The
study also found that the increase in consumer
demand and lower production were the main factors
for inflation, which led to a decrease in per capita
income and the currency's purchasing value, [13].
The study stated that considering globalization,
the sectors had become highly influenced by the
factors affecting them in light of globalization; the
third chapter in the study indicated an analysis of the
role of foreign trade in the Jordanian economy,
exports and imports, and the researcher used the data
in the study from 1969 to 1996.
One of the most important results was the
apparent effect of foreign trade, exports, and imports
on the gross domestic product and its components
and the interdependence between countries in trade
relations, [14].
3 Descriptive Framework
The price index's primary use is to convert the face
value into a real value, [15].
The consumer price index is known as a
statistical measure of changes in the prices of a
basket of goods and services and the comparison of
prices with prices for the base year.
Goods differ in their response to change and size
according to the weights for the relative importance
of each commodity and the relative importance of
goods and services: The percentage of the share of
expenditures on a good or service from the total
expenditures for all goods and services.
"The Relative Importance of the Consumer Price
Index, Basis 2010 (2006-2019)" according to Table 1
in the Appendix section.
Shows the relative importance of goods in Jordan
from 2006 to 2016 divided into 12 groups where the
largest share was for food and non-alcoholic
beverages, with a percentage of 30.51 for food and
2.86 for non-alcoholic beverages and they combined
in one group and took 33.36 of relative importance,
followed directly by the house with a percentage of
21.92 and it was the least relative importance of the
share of restaurants and hotels, reaching 1.83, and the
index measures public consumer prices by the
following formula, [16].
First, we calculate the high or low prices of
materials according to the groups by the following
equation, [17].
PN\PO*100=PI
Where:
PI: Price Index
PN: Price New
PO: Purchase Order
Then, we calculate the general index according to the
relative importance of each group.
CPI=PIn*Wn +Pin+1+Wn+1……..Σ
Where
CPI: Consumer Price Index
PI: Group Standard Price
W: Relative importance of the set
N: Group
Thus, the general consumer price index is
measured.
Due to the great importance of food
commodities, we reduced the study to the index's
effect as an independent factor for the rest of the
dependent variables.
"Consumer price index for food and non-
alcoholic beverages from 2006-2016 for the base
year 2010 for each month"
From Table 2 (Appendix), which includes the
price index of food and non-alcoholic beverages from
2006 to 2016 and from every month in the year, from
1 to 12, then the full year is averaged, we note that
the price index was less than 100, before 2010 and
this is normal because 2010 is a base year and it
became 100 in the base year 2010 and then rose to
115.2 in 2015 at its highest level and returned to
111.2 in 2016.
We also note that the monthly index fluctuated in
one year and reached 117.43 in the month of 10,
2015, [16].
"Quantities of food commodities exported
according to food groups 2006-2016 in tons. "
As for the value of food exports, they were
shown in Table 3 (Appendix) and divided into 19
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Thiabat Adnan, Abdul Baqi Reem,
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food groups from 2006 to 2016. The table shows the
quantities exported per ton of different materials. The
largest export number of vegetables was 812,897
tons in 2013, and the least was fresh milk, which was
0 tons from 2006 to 2016. They always came in first
place in export quantities, followed by fruits. As for
the total and total exports in tons of all food
commodities, the largest amount was in 2013,
amounting to 1,105,191 tons, and the lowest exported
quantity was in 2006, at 798,611 tons, [16].
"Quantities of imported food commodities
according to food groups 2006-2016 in tons as for the
value of imports of food", were also shown in Table
4 (Appendix) and they were divided into 19 food
groups from 2006 to 2016. The table shows the
quantities imported per ton of different materials.
The largest amount of imported cereals and their
products was 3,349,609 tons in 2014, and the least
was fresh milk, which was 0 tons from 2006 to 2016.
The grains and their products were always ranked
first in the quantities imported, followed by sugar and
sweeteners. As for the total and total imports per ton
for all food commodities, the largest amount was in
2016 and reached 4,717,830 tons, and the lowest
imported amount was in 2010 at 2,602,362 tons, [16].
"GDP at current prices from 2006-2016
Production of the agricultural sector" in developing
countries in the third world is a way of earning a
living and constitutes 25% of the value-added of the
GDP, [18].
In Jordan, the GDP is at the current prices of the
agricultural sector, and from Table 5 (Appendix), we
notice that the GDP started to increase from 2006 to
2016. Likewise, the agricultural sector's GDP
increased from the lowest value in 2006, amounting
to 3719 million dollars in 2016, and reached 14127
million dollars, [16].
4 Results of the Study
And to measure the study hypotheses.
We take the first hypothesis and convert it to the next
linear equation.
First Equation
EX= C * PI
Where
EX: Food exports
C: The constant factor
PI: Food Price Index
We take the second hypothesis and convert it to the
next linear equation.
Second Equation
IM= C *PI
Where
IM: food imports
C: The constant factor
PI: Food Price Index
We take the third hypothesis and convert it to the
next linear equation.
Third Equation
Q= C * PI
Where
Q: The GDP of the agricultural sector
C: The constant factor
PI: Food Price Index
We will enter and analyze the data in EVIEWS
10 first equation, second equation, and third
equation.
4.1 The First Equation
EX= C * PI
Table 6. "Dependent Variable: EX"
*Program eviews10
Notes from the Table 6
H0: The model is inappropriate.
H1: the model is appropriate.
From the Table 6 Prob (F-statistic) (0.032524) is
below the significant level (0.05). This means that
the F-statistic has significant significance, and we
take it in the alternative hypothesis. Thus, we say that
Dependent Variable: EX
Method: Least Squares
Date: 11/30/23 Time: 22:46
Sample: 2006 2016
Included observations: 11
Coefficient
Std. Error
t-Statistic
Prob.
527828.7
169854.7
3.107531
0.0126
4219.743
1671.472
2.524568
0.0325
0.414575
Mean dependent var
952565.1
0.349528
S.D. dependent var
96046.64
77463.41
Akaike info criterion
25.51596
5.40E+10
Schwarz criterion
25.58831
-138.3378
Hannan-Quinn
criteria.
25.47036
6.373442
Durbin-Watson stat
2.308367
0.032524
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41% of the change in exports is due to the
independent factor Pl.
The Prob for the independent variable (0.0325)
(PI) is less than the significance level (0.05); that is
(PI) has a significant significance, and the alternative
hypothesis was taken.
The fixed Prob (0.0126) (c) is less than the
significant level (0.05); that is, (c) has a significant
significance, and the alternative hypothesis was
taken.
In conclusion, the model is appropriate in
estimating the relationship between the consumer
price index for foodstuffs and food exports according
to the following formula:
EX = 527828.749581 + 4219.74305871*PI
We test the Breusch-Godfrey Serial Correlation
LM Test.
Table 7. "Breusch-Godfrey Serial Correlation LM
Test"
*Program eviews10
Notes from the Table 7
H0: If Prob> (0.05), there is no self-correlation.
H1: If Prob <(0.05), there is a self-correlation.
From the Table 7 F-statistic Prob (0.4341)>
(0.05) we take the null hypothesis. There is no self-
link.
Obs * R-squared Prob (0.3114)> (0.05) We take the
null hypothesis, no self-correlation
Heteroskedasticity Test: ARCH
Table 8. "Heteroskedasticity Test: ARCH"
*Program eviews10
Notes from the Table 8
H0: Homogeneity of error variance Prob> 0.05.
H1: There is no uniformity in the error variance Prob> 0.05.
From the Table 8 F-statistic Prob (0.2916)>
(0.05) we take the null hypothesis that the error
variance is homogeneous.
Obs * R-squared Prob (0.2196)> (0.05) We take the
assumption that there is no homogeneity in the error
variance.
The First Hypothesis
We take the null hypothesis H0. There is a direct
relationship between the price index and exports, but
we note that the R-squared was 0.414575, which
indicates that only 41% represents the change in the
price index for the change in exports.
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
0.942313
Prob. F(2,7)
0.4341
Obs*R-squared
2.333343
Prob. Chi-Square (2)
0.3114
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 11/30/23 Time: 22:49
Sample: 2006 2016
Included observations: 11
Pre-sample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-38719.52
174532.2
-0.221847
0.8308
PI
469.6127
1737.184
0.270330
0.7947
RESID (-1)
-0.628641
0.483412
-1.300423
0.2346
RESID (-2)
-0.061871
0.492554
-0.125612
0.9036
R-squared
0.212122
Mean dependent var
-4.23E-11
Adjusted R-squared
-0.125540
S.D. dependent var
73488.25
S.E. of regression
77964.76
Akaike info criterion
25.64119
Sum squared resid
4.25E+10
Schwarz criterion
25.78588
Log-likelihood
-137.0265
Hannan-Quinn criteria.
25.54998
F-statistic
0.628208
Durbin-Watson stat
1.721226
Prob(F-statistic)
0.619486
Heteroskedasticity Test: ARCH
F-statistic
1.523809
Prob. F (2,6)
0.2916
Obs*R-squared
3.031578
Prob. Chi-Square (2)
0.2196
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 11/30/23 Time: 23:04
Sample (adjusted): 2008 2016
Included observations: 9 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
1.08E+10
3.66E+09
2.937161
0.0260
RESID^2(-1)
-0.610042
0.516267
-1.181641
0.2821
RESID^2(-2)
-0.797143
0.507416
-1.570984
0.1672
R-squared
0.336842
Mean dependent var
5.78E+09
Adjusted R-squared
0.115789
S.D. dependent var
7.05E+09
S.E. of regression
6.63E+09
Akaike info criterion
48.32853
Sum squared resid
2.64E+20
Schwarz criterion
48.39428
Log-likelihood
-214.4784
Hannan-Quinn criteria.
48.18666
F-statistic
1.523809
Durbin-Watson stat
1.672579
Prob(F-statistic)
0.291643
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4.2 The Second Equation
IM= C * PI
Table 9. "Dependent Variable: IM"
*Program eviews10
Notes from the Table 9
H0: The model is inappropriate.
H1: The model is suitable.
From the Table 9 Prob (F-statistic) (0.023203) is
below the significant level (0.05). That is, the F-
statistic has a significant sign, and the alternative
hypothesis was accepted. Thus, we say that 45.3% of
the change in imports is due to the independent
operator, Pl.
The Prob for (PI) is less than (0.05); that is, (PI)
has a significant significance, and the alternative
hypothesis was taken.
The fixed Prob (0.0277) (c) is less than (0.05),
meaning that there is a significant significance for
(c), and the alternative hypothesis was taken.
In conclusion, the model is appropriate in estimating
the relationship between the consumer price index for
foodstuffs and food exports according to the
following formula:
IM = 282317.823055 + 33848.36881*PI
Table 10. "Breusch-Godfrey Serial Correlation LM
Test"
*Program eviews10
Notes from the Table 10
H0: If Prob> (0.05), there is no self-correlation.
H1: If Prob <(0.05), there is a self-correlation.
From the Table 10 F-statistic Prob (0.3177)>
(0.05) we take the null hypothesis. There is no self-
correlation.
Obs * R-squared Prob (0.2151)> (0.05), and the null
hypothesis was taken, that there is no self-correlation.
Heteroskedasticity Test: ARCH
Table 11. "Heteroskedasticity Test: ARCH"
*Program eviews10
Notes from the Table 11
H0: Homogeneity of error variance Prob> 0.05.
H1: No homogeneity in error variance Prob> 0.05.
Dependent Variable: IM
Method: Least Squares
Date: 11/30/23 Time: 23:47
Sample: 2006 2016
Included observations: 11
Variable
Coefficient
Standard
Error
t-Statistic
Prob.
C
282317.8
1259693.
0.224116
0.0277
PI
33848.37
12396.13
2.730559
0.0232
R-squared
0.453085
Mean dependent var
3689310.
Adjusted R-squared
0.392317
S.D. dependent var
736962.4
S.E. of regression
574491.8
Akaike info criterion
29.52332
Sum squared resid
2.97E+12
Schwarz criterion
29.59567
Log-likelihood
-160.3783
Hannan-Quinn criteria.
29.47772
F-statistic
7.455952
Durbin-Watson stat
0.794550
Prob(F-statistic)
0.023203
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
1.356929
Prob. F(2,7)
0.3177
Obs*R-squared
3.073181
Prob. Chi-Square(2)
0.2151
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 11/30/23 Time: 23:49
Sample: 2006 2016
Included observations: 11
Pre sample missing value lagged residuals set to zero.
Variable
Coefficient
Standard
Error
t-Statistic
Prob.
C
-225664.2
1234916.
-0.182737
0.8602
PI
2623.781
12219.94
0.214713
0.8361
RESID(-1)
0.556684
0.403290
1.380355
0.2099
RESID(-2)
0.052753
0.418778
0.125969
0.9033
R-squared
0.279380
Mean dependent var
9.52E-11
Adjusted R-squared
-0.029457
S.D. dependent var
545010.8
S.E. of regression
552979.7
Akaike info criterion
29.55932
Sum squared resid
2.14E+12
Schwarz criterion
29.70401
Log-likelihood
-158.5762
Hannan-Quinn criteria.
29.46811
F-statistic
0.904620
Durbin-Watson stat
1.780356
Prob(F-statistic)
0.485530
Heteroskedasticity Test: ARCH
F-statistic
0.309220
Prob. F(2,6)
0.7451
Obs*R-squared
0.840978
Prob. Chi-Square(2)
0.6567
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 11/30/23 Time: 23:51
Sample (adjusted): 2008 2016
Included observations: 9 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
4.13E+11
2.06E+11
2.005335
0.0917
RESID^2(-1)
-0.235012
0.394000
-0.596477
0.5727
RESID^2(-2)
-0.243094
0.401249
-0.605842
0.5668
R-squared
0.093442
Mean dependent var
2.89E+11
Adjusted R-squared
-0.208744
S.D. dependent var
3.60E+11
S.E. of regression
3.96E+11
Akaike info criterion
56.50666
Sum squared resid
9.39E+23
Schwarz criterion
56.57240
Log likelihood
-251.2800
Hannan-Quinn criteria.
56.36479
F-statistic
0.309220
Durbin-Watson stat
2.260531
Prob(F-statistic)
0.745052
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DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
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Volume 21, 2024
From the Table 11 F-statistic Prob (0.7451)>
(0.05) We take the null hypothesis that the error
variance is homogeneous.
Obs * R-squared Prob (0.6567)> (0.05) We take the
null hypothesis that the error variance is
homogeneous.
The Second Hypothesis
We take H0 null imposition. There is a direct
relationship between the price index and imports, but
R-squared is 0.453085; in other words, only 45%
represents the change in the price index and the
change in exports.
4.3 The Third Equation
Q= C * PI
Table 12. "Dependent Variable: Q"
*Program eviews10
Notes from the Table 12
H0: The model is inappropriate.
The model is appropriate.
From the Table 12 Prob (F-statistic) (0.000134)
is below the significant level (0.05). That is, F-
statistic has an important sign, and we take in the
alternative hypothesis, and thus we say that 81.7 (%)
of the change in output is due to the independent
factor Pl.
The Prob for the independent variable (0.0001)
(PI) is less than the significance level (0.05),
meaning that there is a significant significance for
(PI), and we take the alternative hypothesis.
The fixed Prob (0.0027) (c) is less than the
significance level (0.05), meaning that there is a
significant significance for (c), and we take the
alternative hypothesis.
In conclusion, according to the following formula,
the model is appropriate for estimating the
relationship between the consumer price index for
foodstuffs and food exports.
Q = -2243.5584134 + 34.0983946418*PI
Table 13. "Breusch-Godfrey Serial Correlation LM
Test"
*Program eviews10
Notes from the Table 13
H0: If Prob> (0.05), there is no self-correlation.
H1: If Prob < (0.05), there is a self-correlation.
From the Table 13 F-statistic Prob (0.0423) <
(0.05) Taking the alternative hypothesis. There is a
self-correlation.
Obs * R-squared Prob (0.0379) < (0.05) Take the
alternative hypothesis. There is a self-correlation.
Table 14. "Heteroskedasticity Test: ARC"
Dependent Variable: Q
Method: Least Squares
Date: 11/30/23 Time: 23:53
Sample: 2006 2016
Included observations: 11
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-2243.558
546.3529
-4.106427
0.0027
PI
34.09839
5.376439
6.342190
0.0001
R-squared
0.817160
Mean dependent var
1188.600
Adjusted R-squared
0.796844
S.D. dependent var
552.8127
S.E. of regression
249.1680
Akaike info criterion
14.03710
Sum squared resid
558762.5
Schwarz criterion
14.10944
Log-likelihood
-75.20404
Hannan-Quinn criteria.
13.99149
F-statistic
40.22337
Durbin-Watson stat
0.551199
Prob(F-statistic)
0.000134
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
5.139866
Prob. F(2,7)
0.0423
Obs*R-squared
6.543912
Prob. Chi-Square(2)
0.0379
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Date: 11/30/23 Time: 23:55
Sample: 2006 2016
Included observations: 11
Pre sample missing value lagged residuals set to zero.
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
-79.95997
445.1188
-0.179637
0.8625
PI
0.860745
4.559511
0.188780
0.8556
RESID (-1)
1.213297
0.380475
3.188898
0.0153
RESID (-2)
-0.711773
0.520409
-1.367718
0.2137
R-squared
0.594901
Mean dependent var
5.68E-14
Adjusted R-squared
0.421287
S.D. dependent var
236.3816
S.E. of regression
179.8229
Akaike info criterion
13.49711
Sum squared resid
226354.0
Schwarz criterion
13.64180
Log-likelihood
-70.23411
Hannan-Quinn criteria.
13.40590
F-statistic
3.426577
Durbin-Watson stat
1.652928
Prob(F-statistic)
0.081420
F-statistic
1.138083
Prob. F (2,6)
0.3810
Obs*R-squared
2.475240
Prob. Chi-Square (2)
0.2901
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Date: 11/30/23 Time: 22:55
Sample (adjusted): 2008 2016
Included observations: 9 after adjustments
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
68735.97
51676.45
1.330122
0.2318
RESID^2(-1)
0.713737
0.963903
0.740466
0.4870
RESID^2(-2)
-1.208072
1.074231
-1.124593
0.3037
R-squared
0.275027
Mean dependent var
58634.34
Adjusted R-squared
0.033369
S.D. dependent var
79712.20
S.E. of regression
78370.96
Akaike info criterion
25.63750
Sum squared reside
3.69E+10
Schwarz criterion
25.70324
Log-likelihood
-112.3687
Hannan-Quinn criteria.
25.49563
F-statistic
1.138083
Durbin-Watson stat
0.975076
Prob(F-statistic)
0.381036
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1435
Volume 21, 2024
*Program eviews10
Notes from the Table 14
H0: Homogeneity of error variance Prob> 0.05
H1: No homogeneity in error variance Prob> 0.05
From the Table 14 F-statistic Prob (0.3810)>
(0.05) we take the null hypothesis. The error variance
is homogeneous.
Obs * R-squared Prob (0.2901)> (0.05) Taking the
null hypothesis that the error variance is
homogeneous.
The Third Hypothesis
We take the null hypothesis H0, a direct relationship
between the price index and imports, but R-squared
reached 0.817160. 82% of the change in GDP at
current prices was responsible for the change in the
price index.
5 Discuss the Results of the Study
5.1 The First Hypothesis
We take the null hypothesis H0. There is a direct
relationship between the price index and exports, but
we note that the R-squared was 0.414575, which
indicates that only 41% represents the change in the
price index for the change in exports.
There are 59% represented by other factors,
including the availability of rain and water reserves,
the lack of harvest yields, or factors related to
commercial relations and the conditions of
neighboring countries.
5.2 The Second Hypothesis
We take H0 null imposition. There is a direct
relationship between the price index and imports, but
R-squared is 0.453085. In other words, only 45%
represents the change in the price index, the change
in exports, and 55% represents other factors,
including foreign trade, the increase in the population
as a result of wars in neighboring countries, the
increase in IDPs, which required an increase in food
and other factors.
5.3 The Third Hypothesis
We take the null hypothesis H0, a direct relationship
between the price index and imports, but R-squared
reached 0.817160. 82% of the change in GDP at
current prices was responsible for the change in the
price index. This result indicates that the output was
increasing due to the increase in prices. As for the
quantities, the increase was 19%.
This is an interpretation that corresponds to the
GDP's economic logic at current prices, where each
year of production is estimated at prices for that year,
which leads to a strong direct relationship between
them.
6 Recommendations
1-Finding direct support for citizens considering the
conditions of rising prices and increasing immigrants.
2-Focusing on production and quantities of
foodstuffs, with an increase in the rate of imports.
3-Supporting the agricultural product in the
production of vegetables and fruits.
4-The use of modern technologies in agriculture.
5-Consolidating foreign trade relations and opening
new markets.
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
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E-ISSN: 2224-2899
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Volume 21, 2024
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Countries for the Period, 1990-2020,
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
Thiabat Adnan is the main author, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf are co-authors
who contribute to the consultation, methodology,
data analysis, final solution, and overview of
research.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
No funding was received for conducting this study.
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
https://creativecommons.org/licenses/by/4.0/deed.en_
US
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DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1437
Volume 21, 2024
APPENDIX
Table 1. The Relative Importance of the Consumer Price Index, Basis 2010 (2006-2019)
Relative
importance
Relative
importance
Home maintenance services
0.39
1)Food and non-alcoholic Beverages
33.36
Water and Sanitation
1.11
Food Items
30.51
Fuels and Lighting
4.85
Cereals and Products
4.99
5)Household Furnishings and
Equipment
4.19
Meat and Poultry
8.24
Furniture, Rugs, and Bedspreads
0.97
Fish and Sea Products
0.82
Home Textiles
0.1
Dairy Products and Eggs
4.23
Household appliances
0.72
Oils and Fats
1.92
Housewares
0.27
Fruits and Nuts
2.73
Home Maintenance
2.13
Vegetables and Legumes Dry and Canned
3.89
6) health
2.21
Sugar and its Products
2.77
7) Transportation
13.58
Spices and food additives, other food
0.91
8) Communication
3.5
Non-alcoholic beverages
2.86
9) Culture and Recreation
2.27
Tea, Coffee, and Cocoa
1.42
10) Education
5.41
Drinks and Refreshments
1.44
11) Restaurants and Hotels
1.83
2) Alcohol and Tobacco and Cigarettes
4.43
12) Other Goods and Services
3.75
Alcoholic beverages
0.03
Personal Care
2.67
Tobacco and Cigarettes
4.4
Personal Effects
0.4
3) Clothing and footwear
3.55
Insurance connected with
Transport
0.26
Clothing
2.79
Contribute to the Unions
0.02
Footwear
0.76
Other Services
0.39
4) housing
21.92
All Items
100
Rents
15.57
Source: General Statistics. Indices
Table 2. Consumer price index for food and non-alcoholic beverages from 2006-2016 for the base year 2010 for
each month
Average
1
2
3
4
5
6
7
8
9
10
11
12
Year
71.7
69.35
68.27
68.28
69.73
72.02
71.29
70.81
71.4
73.46
74.17
74.93
76.77
2006
78.5
78.05
77.96
78.69
78.33
77.08
76.32
75.76
76.34
79.1
79.62
81.41
83.81
2007
94.1
85.07
89.95
91.89
93.33
91.91
90.51
93.59
96.3
101.02
99.47
99.06
96.89
2008
95.7
96.5
96.13
95.84
94.82
94.49
92.89
92.75
97.13
98.16
95.84
96.47
97.11
2009
100.0
98.38
99.24
99.2
99.66
97.59
97.45
97.53
99.12
101.66
103.42
102.51
104.36
2010
104.2
103
101.63
102.7
104.69
104.59
103.61
103.52
104.97
105.1
105
105.42
106.57
2011
109.0
106.59
105.24
105.98
109.42
108.64
107.33
109.24
111.14
111.2
111.65
110.61
110.78
2012
113.6
111.83
112.72
112.96
114.12
112.2
112.79
114.18
114.15
114.45
115.13
114.03
114.62
2013
114.0
114.62
114.75
114.97
114.77
112.3
111.18
112.2
113.35
114.48
114.66
114.87
115.41
2014
115.2
116.3
114.66
115.1
114.63
114.76
114.82
113.16
115.51
116.96
117.43
114.73
114.87
2015
111.2
113.48
111.08
111.16
111.65
110.85
108.88
112.91
113.71
111.41
110.5
108.99
110.24
2016
Source General Statistics, Price Indices
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1438
Volume 21, 2024
Table 3. Quantities of food commodities exported according to food groups 2006-2016 in tons
Source: General statistics, agricultural food budget, quantities of food commodities, * Includes exports and re-exports.
No
Food article
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
1
Cereals and
Products
20,845
118,492
40,955
34,590
29,805
18,119
35,782
57,154
18,179
33,765
33,844
2
Starchy
Roots
15,543
20,190
29,964
11,750
13,135
7,805
16,217
18,300
15,578
26,137
17,404
3
Sugar and
Sweeteners
23,556
7,017
5,969
6,030
4,035
3,204
8,211
14,698
11,984
6,364
7,732
4
Pulses
5,803
13,221
12,697
7,163
8,541
12,191
9,335
9,714
6,612
5,224
4,482
5
Nuts
597
509
487
649
511
374
387
586
452
212
383
6
Oil Crops
15,007
15,651
12,292
17,973
8,396
10,556
10,112
18,792
10,783
15,763
7,568
7
Vegetable
Oils
3,800
4,650
8,320
8,247
13,481
14,037
6,292
34,631
14,002
20,870
113,984
8
Vegetables
609,527
706,096
776,744
812,897
702,462
760,922
697,886
751,879
681,116
687,975
540,229
9
Fruits and
Products
86,085
85,134
39,521
33,539
50,796
44,686
49,062
46,068
36,575
34,325
32,302
10
Stimulants
7,482
4,354
3,423
4,826
3,938
4,302
3,748
4,487
2,673
2,834
2,983
11
Spices
1,151
5,145
615
758
887
455
2,405
347
2,712
2,270
2,053
12
Non-
Alcoholic
Beverages
34,747
48,593
53,513
123,374
50,263
24,682
12,085
27,079
51,846
36,699
2,664
13
Animal
Meats
17,910
26,807
22,276
31,979
31,378
29,190
43,541
37,207
23,976
7,443
11,021
14
CarassOffals
Edible
311
102
74
375
355
355
189
505
0
16
0
15
Animal Fats
235
845
80
78
30
287
39
97
266
93
225
16
Milk
0
0
0
0
0
0
0
0
0
0
0
17
Milk
Products
11,211
10,174
9,208
7,867
4,718
7,063
13,442
16,447
12,864
6,410
18,871
18
Eggs
1,569
2,148
1,326
1,133
1,484
4,183
9,377
6,321
6,355
3,582
1,904
19
Sea Foods
434
1,318
724
1,963
4,532
2,668
1,672
3,811
1,931
354
962
Total
855,812
1,070,446
1,018,188
1,105,191
928,747
945,078
919,781
1,048,123
897,904
890,336
798,611
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1439
Volume 21, 2024
Table 4. Quantities of imported food commodities according to food groups 2006-2016 in tons
N.
Food
Material
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
1
Cereals
and
Products
3,206,3
93
2,975,9
00
3,349,6
09
2,615,5
17
2,459,9
69
2,322,5
23
1,475,8
74
2,022,6
28
2,082,6
29
2,275,9
05
2,313,8
61
2
Starchy
Roots
106,456
99,496
111,814
139,303
85,432
64,489
61,849
45,958
62,704
50,983
44,180
3
Sugar and
Sweetener
s
360,841
336,814
368,306
343,462
337,937
279,205
307,784
238,753
321,514
281,827
263,442
4
Pulses
63,779
69,601
64,727
63,204
59,133
60,296
52,233
51,139
55,847
49,920
49,584
5
Nuts
16,642
18,028
12,408
14,866
12,662
13,493
14,157
13,952
11,337
12,296
10,285
6
Oil Crops
63,422
55,742
47,693
44,498
37,245
35,951
34,408
32,426
25,356
29,729
29,556
7
Vegetable
Oils
165,582
149,409
146,341
136,537
133,571
122,663
103,950
123,908
77,314
91,171
165,313
8
Vegetable
s
82,825
63,039
102,423
101,823
86,496
77,892
84,641
86,327
75,174
67,004
58,715
9
Fruits and
Products
198,311
201,002
215,710
216,831
183,357
179,500
142,456
144,251
118,347
115,224
87,664
10
Stimulants
51,000
35,978
35,514
48,181
37,277
33,377
34,784
35,974
33,258
29,597
24,777
11
Spices
10,031
10,399
7,355
8,199
7,014
5,860
5,360
4,896
5,026
6,136
5,199
12
Non-
Alcoholic
Beverages
147,844
144,967
117,683
109,207
89,774
83,289
78,553
74,845
77,314
70,439
57,526
13
Animal
Meats
131,722
158,198
145,299
148,922
129,678
115,240
111,244
96,489
85,568
73,438
66,268
14
CarassOff
als Edible
1,484
1,777
2,519
2,440
1,662
1,354
1,477
1,124
706
656
735
15
Animal
Fats
15,787
13,894
14,348
16,952
14,195
9,691
4,791
5,158
4,423
4,271
6,924
16
Milk
0
0
0
0
0
0
0
0
0
0
0
17
Milk
Products
61,625
68,259
58,807
55,968
51,634
48,317
63,910
67,251
43,748
43,528
42,261
18
Eggs
1,061
1,513
336
1,946
1,465
872
1,146
824
698
33
0
19
Sea Foods
33,026
32,827
29,120
20,701
32,678
38,589
23,745
28,839
26,633
20,746
21,497
Total
4,717,8
30
4,436,8
40
4,830,0
13
4,088,5
58
3,761,1
79
3,492,6
01
2,602,3
62
3,074,7
42
3,107,5
96
3,222,9
03
3,247,7
86
Source: General statistics, agricultural food budget, quantities of food commodities
Table 5. GDP at current prices from 2006-2016
Agricultural sector output
Gross domestic product
Year
In a million dollars
In a million dinars
In a million dollars
In a million dinars
371.9
264.0
15,036
10,675.4
2006
477.3
338.9
17,086
12,131.4
2007
745.1
529.0
22,192
15,756.0
2008
908.5
645.0
23,944
17,000.0
2009
1109.9
788.0
26,520
18,829.0
2010
1184.5
841.0
28,907
20,524.0
2011
1195.8
849.0
30,935
21,964.0
2012
1412.7
1,003.0
33,617
23,868.0
2013
1673.2
1,188.0
36,051
25,596.0
2014
1939.4
1,377.0
37,923
26,925.0
2015
2056.3
1,460.0
39,197
27,830.0
2016
Source: General Statistics, National Accounts, Annual Estimates of the Fourth Revision (ISIC4) Base year 2016
The dollar = 0.71 / dinar
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.117
Thiabat Adnan, Abdul Baqi Reem,
Al-Nabulsi Manwa, Bataineh Ashraf
E-ISSN: 2224-2899
1440
Volume 21, 2024