Reseña educativa sobre el papel de las políticas monetarias en la resiliencia
económica de Irán y su vulnerabilidad para el auge de la producción
Educational Examination on the Role of Monetary Policies in Iran's Economic Resilience
and Vulnerability to Booming Production
Mohsen Yadegari
1
, Farid Asgari
2a
, Farzaneh Khalili
3
Abhar Branch, Islamic Azad University, Abhar, Iran
123
Orcid ID: https://orcid.org/0000-0003-4311-1555
1
Orcid ID: https://orcid.org/0000-0003-0621-4686
2
Orcid ID: https://orcid.org/0000-0001-9715-621X
3
Recibido: 25 de mayo de 2020 Aceptado: 12 de enero de 2021
Resumen
El objetivo de este estudio fue examinar el papel de las políticas monetarias (MP) en la resiliencia
económica (RE) y la vulnerabilidad de Irán para el auge de la producción. Al hacerlo, se utilizaron
los datos de Irán para el período 2017-2018. Se utilizó un método generalizado de momentos
(GMM) para analizar los datos, y todos los análisis se realizaron con el software Eviews10. Los
resultados indican que el PM y la política fiscal han afectado el índice ER de Irán, por lo que se
sugiere que el gobierno allane el camino para fortalecer la resiliencia del país mejorando la
eficiencia del sistema monetario y financiero del país. Los resultados indicaron que el MP y la
política fiscal habían afectado a Irán, por lo que se sugiere que los responsables de la política
económica del país intenten diseñar mecanismos de alerta para tomar medidas en la primera
oportunidad para resolver la discrepancia en caso de cualquier inconsistencia en las políticas
monetarias y fiscales que hacer al país más vulnerable. Finalmente, los resultados indicaron que el
PM y la política fiscal habían afectado el índice ER neto de Irán; por lo tanto, se recomienda que
los funcionarios monetarios y financieros del país especifiquen índices financieros como el estado
presupuestario del gobierno e índices monetarios como la liquidez y tengan en cuenta las
consecuencias de estas políticas en la RE neta del país.
Palabras clave: Resiliencia Económica, Vulnerabilidad Económica, Política Monetaria.
Abstract
The objective of this study was to examination of the role of monetary policies (MP) in Iran's
economic resilience (ER) and vulnerability for the production boom. In doing so, Iran's data was
a
Corresponding Author: Email: asgari@aftermail.ir
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Apuntes Universitarios
, 2021: 11(
2
), abril-junio ISSN:
2304-0335 DOI: https://doi.org/10.17162/au.v11i2.660
apuntesuniversitarios.upeu.edu.pe
used for the period 2017-2018. A generalized method of moments (GMM) method was used to
analyze the data, and all the analyses were done using Eviews10 software. The results indicate that
MP and fiscal policy have affected the Iran ER index, so it is suggested that the government pave
the way for strengthening the country's resilience by enhancing the efficiency of the country's
monetary and financial system. The results indicated that MP and fiscal policy had affected Iran,
so it is suggested that the country's economic policymakers try to design warning mechanisms to
take action at the earliest opportunity to resolve the discrepancy in the event of any inconsistency
in monetary and fiscal policies that make the country more vulnerable. Finally, the results indicated
that MP and fiscal policy had affected net ER index of Iran; thus, it is recommended that the
country's monetary and financial officials specify financial indices like the government's budget
status and monetary indices like liquidity and bear in mind the consequences of these policies on
the country's net ER.
Keywords: Economic Resilience, Economic Vulnerability, Monetary Policy
Introduction
The global shocks due to oil prices, international financial crises, and global recessions in
recent decades resulted in the entry of a concept called EV into the economic literature. Indeed,
several countries suffered serious losses and faced economic and political shocks face with the
stated international shocks and crises, making the degree of economic vulnerability to shocks
significant. In spite of the existence of the above shocks, countries' ability to face shocks and their
recovery, which shows their economies' resilience, were different: the ones with acceptable
resilience levels could quickly digest shocks and return to their previous path. Hence, one can state
that the opposite counterpart to the countries' EV is their ER (Taherpour, 2018).
Bates et al. (2014) divide resilience-vulnerability calculation indices into two categories.
One is economic indices and governance, affecting other indices used as control dimensions. The
second class of the indices was environmental, social, and peripheral indices that could not affect
other indices independently, considered as conditional dimensions. Accordingly, four conditions
have been classified for countries from worst to best: uncontrolled vulnerability, limited
vulnerability, unsustainable resilience, and sustainable resilience. In the case of uncontrolled
vulnerability, the values of the control and conditional dimensions will be negative, and thus, the
net value of the vulnerability-resilience index will be negative. Limited vulnerability shows a state
where the values of the control dimensions are greater than zero, and the values of the conditional
dimensions are less than zero so that the net resilience-vulnerability index will be negative. In the
case of unsustainable resilience, the values of the positive control dimensions, and the values of
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the conditional dimensions are negative, so that the net resilience-vulnerability index becomes
more than zero. Sustainable resilience shows the ideal situation, where the values of control and
conditional dimensions are positive, and thus the net value of the resilience-vulnerability index is
positive (Bates et al., 2014).
The issue seen in both the resilience and vulnerability debates is the focus on
macroeconomic indices and their effect on resilience and vulnerability indices. Thus, examining
the relationship between macroeconomic indices and resilience and EV indices carefully is
necessary. Among these indices is the MP (Bruneckiene et al, 2019). Regarding this, the study
examines the effect of MPs on ER and vulnerability in Iran. The literature of the study will be
presented. Later on, the theoretical foundations and empirical background will be presented. The
model, variables, research method, and data analysis results will be presented, and finally, the
conclusion will be presented.
Literature review
Economic resilience
ER can be defined differently, yet the term refers to the ability to recover or mitigate the
negative effects of external economic shocks in this study. Resilience is defined as the ability to
recover fast from the effects of an adverse event. In economic literature, the term refers to at least
three types of abilities: a) rapid recovery from a shock, b) resistance to the effects of a shock, and
c) preventing exposure to a shock (Ghiyashvannd and Abdolshah, 2015).
Economic vulnerability
Vulnerability is one of the structural features of a country that increases the weak points of
the economy against exogenous shocks, and the vulnerability will deter long-term development.
Vulnerability is defined as the capacity of a country to recover from a shock or to resist the effect
of a shock (Angion and Bates, 2015).
Monetary policy
The policies that are used by the central bank to control liquidity are called MPs. The bank
tries to affect the pattern of household consumption and enterprise production and finally, inflation
by applying these policies. The most significant purpose of macroeconomic policies in general,
and MPs are price stability, economic growth, and the desired level of employment. As realizing
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the ultimate goals is not readily achievable for policymakers, it is necessary to introduce
intermediate goals and appropriate tools. In the MP case, the choice of intermediate target is often
summed up in the choice between interest rate controls and money supply. In Iran, by pursuing an
MP based on the control of monetary aggregates, it is tried to prevent disproportionate monetary
expansion for liquidity and inflation contained in development programs besides providing the
cash needed by manufacturing and investment sectors (Ehsani et al., 2017).
Theoretical foundations
Overall, the factors affecting EV and ER can be divided into four main areas based on the
theory of economic resilience. To Briguglio et al (2003), these four areas are macroeconomic
stability, market efficiency, proper management and supervision, and social development. The
issue of financial policy is placed in the realm of macroeconomics, which is the focus of the present
study.
Macroeconomic stability is the interaction between economic needs and demands. If the
total cost of supply and demand is in equilibrium, the economy has an internal equilibrium, a stable
financial position, a low inflation rate and an unemployment rate close to the normal rate, and at
the same time an external equilibrium, where the volume of debt in the economy is low. These
variables are strongly affected by the economic policies of governments and show how the
government can cope with economic crises and sanctions (Jahanian, 2016). Overall,
macroeconomics is based on three variables:
Financial policies as budget deficits
The status of the state budget is one of the most significant features as the result of financial
and budgetary policies and one of the important tools for governments that can deal with the crises.
A healthy financial status helps the government adjust its spending and finance policies when faced
with crises. Iran is unable to comply with external shocks and sanctions, given its weak budget
and tax policies. On the other hand, one of the biggest problems of Iran's economic system is the
severe and long-term budget deficit caused by lack of proper planning, and the government has
spent more than its income (Ardakani, 2017). For instance, during the lifetime of various
governments, especially the ninth and tenth governments, the country's economic growth
fluctuated drastically, which shows the lack of stability in the national economy. In 2005, at the
beginning of the ninth government, economic growth reached 3.6%, and the growth rate targeted
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for the implementation of the Fourth Development Plan was 8%, but the previous trend was
stopped with severe mismanagement, and the country's economy gradually moved backward. Even
the increase in prices could not help economic stability, and gradually the country's export volume
reduced, and even economic growth turned negative (Yumarni, 2018).
High inflation and unemployment
These two issues are significant indices, and these two rates show a budget deficit. This is
because inflation and unemployment are affected by other economic policies and include monetary
and supply policies. If the economy has a high rate of inflation and unemployment, in times of
crisis like sanctions or economic shocks, these two indices increase, and in contrast, the economy
where these two variables are low, is resistant to economic crises and shocks and does not increase
welfare costs. In Iran, one of the main crises it has always faced is the control of inflation and the
fight against unemployment, so that there are no suitable jobs for many graduates.
In other words, weaknesses in inflation and unemployment have weakened Iran's economy
and mismanaged the resistance economy. According to the results obtained in 2016 from the
statistical plans of the Statistics Center, the unemployment rate of the population for ten years and
more in the whole country is 12.4%, the growth rate of the active population (supply of labor)
1.1%, the employed population (labor demand) 84.0%, the unemployed population was 7.3%, and
the unemployment rate increased from 10.3% to 12.12%. Concerning inflation, although it
increased to 40% in the ninth and tenth governments and reduced in the eleventh government, it is
still considered high. The main cause of the increase in inflation is the high volume of liquidity
and the adoption of inappropriate MPs by the relevant authorities (Ardakani, 2017).
Lack of knowledge-based economy
A stable economy has to be able to create and generate knowledge, and on the other hand,
all activities in the macroeconomics have to be transparent to provide common policies for
competition in the international arena and to counter rising prices. Knowledge-based economics is
composed of three main sections: 1) Economic stimuli and the structure of the institutions (dealing
with customs barriers and tariffs, law enforcement), 2) matching new technologies and creating
innovation, and 3) infrastructures of information and communication technology (ICT) (Rahrovani
and Khosroshahi, 2016).
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Unfortunately, statistics show that Iran has made very little advancement in the cases stated
from 2008 to 2017, ranking 95 out of 145 countries. The existence of many customs formalities,
cumbersome rules, weakness in customs structures, and lack of supervision over the performance
of this sector has created many problems for exporters and importers. In 2017, Iran ranked 78 in
the knowledge-based economy, which has improved compared to previous statistics in previous
years; however, there are still problems in this section (Heydari and Ahmadian, 2018).
Inefficiency of markets in microeconomics
Four main components are very effective in financial vulnerability among several
components regarding the efficiency of markets in microeconomics:
The breadth and dominance of the government. The dimensions and size of a government are
determined according to various factors like subsidies and transfers, and the percentage in GDP,
the investment and tax revenue, and so on. Among the 157 countries in the world, Iran has ranked
99 in terms of government size. The government's share of the economy through consumption,
investment and subsidies affects the private sector and can reduce the economy's resistance to
crisis and sanctions by weakening the private sector and reducing free trade (Ardakani et al., 2014).
Lack of free trade. This means that the government interferes in the international trade sector and
renders the economy to lose its resilience against economic crises or shocks and fail to adapt to
international trade patterns. Moreover, the sanctions prevent Iran from entering the global markets;
for instance, 95% of the world's saffron is produced in Iran, but the lack of branding and the
presence of Iranian companies in international markets have enabled the Spanish brand to replace
Iran in this sector (Rahrovani et al., 2016).
Lack of proper supervision and management. Proper supervision and management are essential in
an economic system to operate properly and be resilient at the same time. In this section, the
management of intellectual property laws and rights is discussed. The absence of proper
mechanisms in times of economic crisis and shock leads to social or economic chaos or makes it
vulnerable. Indeed, the status of legal structures and creating security in intellectual property laws
are the critical components of proper supervision and management, along with judicial
independence, the impartiality of courts, protection of intellectual property rights, and the integrity
of legal and political systems. Nonetheless, the gaps in the mentioned cases in Iran, as well as the
long-term and persistent bureaucracy and the inability to reduce long-term administrative
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processes, have led to the collapse of the resistance economy. Regarding this, studies indicate that
Iran has made no significant progress or changes, and not enough has been done in this regard
from 2004 to 2014 (Pajouhandeh, 2015).
Low social development. Another component of resilience is social development, and its focus is
on building social relationships properly and being able to operate the economic system. Social
solidarity in such an economy means that social discourse occurs effectively in the economy. There
are many indices for determining social development like income and its distribution, poor
population rate, long-term unemployment rate, which shows that a part of a country's population
has low skills or no permanent employment, or their level of education is low (Moghari, 2016).
Research background
Based on the studies done up to now, no studies have been done in Iran or other countries
to study the effect of monetary and fiscal policies on resilience and EV to be presented in the
previous studies section. Thus, only new studies on resilience and EV and related concepts are
presented in this section.
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Table 1
Summary of previous studies
Researcher
(year)
Results
Amiri et al.
(2018)
Both the vulnerability and resilience have increased
in recent years, but the vulnerability more than the
resilience, showing an increase in the degree of
vulnerability in the Iranian economy.
Moghari (2016)
EV is inversely related to ER and directly to GDP,
with the effect of ER more among the countries
studied.
Ghiasvand and
Abdolshah
(2015)
A social system is resilient when it can absorb
temporary or permanent risks and adapt to rapidly
changing conditions without losing its function.
Bruneckiene et
al. (2019)
The results indicate that economic shocks have
affected the resilience of the socio-economic system.
Oliva and Lazarti
(2018)
The paper tries to strengthen the debate on regional
economic flexibility against natural disasters by
building resilience and recovery indices for Japanese
prefect affected by major earthquakes.
Bastamnia et al.
(2018)
Social resilience increases with length of stay in the
neighborhood, the number of educated family
members, and the level of higher education of the
head of the household, the number of unemployed
people, and the number of physically and mentally
disabled people in the family, and the ownership of a
house compared to being a tenant.
Angion and Bates
(2018)
Developed countries have high resilience and less
developed countries have high vulnerability
Bates et al. (2017)
Singapore resilience is greater than its vulnerability.
Moreover, it has been observed that resilience is the
result of good government and the benefits of global
integration
Sabatino (2017)
Ultimately, this study can provide a new
management culture according to the principles of
adaptation, resilience, and innovation
According to the points stated in the previous sections, the following hypotheses are
presented in the study:
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Hypothesis 1: The effect of MP on the Iran ER index is significant.
Hypothesis 2: The effect of MP on the Iran EV index is significant.
Hypothesis 3: The effect of MP on the Iran net ER index is significant.
Methodology
The study was applied in terms of purpose since its results can be used in the decisions of
major national authorities. Furthermore, it was descriptive-correlational regarding inference on
research hypotheses, as the correlation coefficient of regression techniques were used to find the
relationships between the variables. Moreover, as we concluded by testing the available data, it
was cross-sectional. The data collection method was the library method.
The following regression models were used to examine the effect of MP on ER and EV
indices. The modeling process for resilience and vulnerability indices is as follows:
RES
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 Deficit
t
+ c7 DEBT
t
+ c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c12 OPEEN
t
+ c13 DUM1
t
+ c14
DUM2
t
+ c15 DUM3
t
+ e
t
VUL
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 Deficit
t
+ c7 DEBT
t
+ c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c12 OPEEN
t
+ c13 DUM1
t
+ c14
DUM2
t
+ c15 DUM3
t
+ e
t
(RES – VUL)
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 GDP
t
+ c7 INF
t
+ + c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c12 OPEEN
t
+ c13 DUM1
t
+ c14
DUM2
t
+ c15 DUM3
t
+ e
t
Dependent variables:
ER Index (RES): Considering the data limitation for Iran, as well as following Briguglio et al.
(2006), the weighted average variables of the budget deficit, government size, economic freedom
index, and education index were used as indices for ER.
EV Index (VUL): Considering data limitation for Iran, as well as following Briguglio (2003),
economic openness was used as an index of EV, calculated by dividing the total exports and
imports by GDP.
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Independent variables
MP index includes interest rates (IR), legal deposit rate (LR), and liquidity (LIQ), which are
interest rates on one-year deposits of state-owned banks, legal deposit rates of banks, and
banknotes and coins and visuals are defined and calculated.
Control variables
Financial policy index includes government expenditures (GEXP), consumption and development
expenditures, government tax revenues (TR), the ratio of budget deficit to GDP (Deficit), and the
ratio of debt to GDP (DEBT).
Gross domestic product (GDP) is defined as the country's annual economic growth rate.
Governance index as a rule of law index published annually by the International Transparency
Organization, as well as the Social Development Index as a literacy rate, are also independent
research variables.
Inflation rate (INF) defined and calculated as the annual inflation rate of the country
The degree of trade openness (OPEEN) defined as the sum of exports and imports divided by the
country's GDP
DUM1 shows the virtual variable related to Donald Trump's presidency, which will be zero for
2016, which is before his presidency, and one for the time later.
DUM2 shows the dummy variable of the Joint Comprehensive Plan of Action (JCPOA), which is
zero for 2015 (the year of signing JCPOA) and one for later on.
DUM3 shows the dummy variable of the resistance economy, which is one for 2013 when the
general policies of the resistance economy were first proposed by the Leader of the Revolution,
and zero for previous years.
Parameter c0 shows the constant value or y-intercept in the model
The database of the Central Bank, the Statistics Center of Iran, the World Bank, and the
UNCTAD database were used to measure the indices. A 37-year period from 1982 and 2018 was
selected to examine hypothesis testing. It has to be noted that all calculations and analyses were
done in Excel and 10Eviews. GMM was used to analyze the information.
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Results
Variables stationary
In this section, the stationarity of the variables and their tests are discussed. Augmented
Dickey–Fuller test (ADF) was used for this purpose.
Table 2
The results of variables stationarity test
Result
Probability
t statistic
Variables
Stationary
0.0248
-2.39
RES
Stationary
0.0001
-4.33
VUL
Stationary
0.0000
-3.39
RESVUL
Stationary
0.0168
-4.10
IR
Stationary
0.0113
-4.18
LR
Stationary
0.0022
-4.29
LIQ
Stationary
0.0173
-2.28
GEXP
Stationary
0.0000
-5.58
TR
Stationary
0.0372
-2.08
DEFICIT
Stationary
0.0295
-2.22
DEBT
Stationary
0.0000
-2.71
GOVERNENCE
Stationary
0.0369
-3.79
DEVELOPMENT
Stationary
0.0002
-5.71
GDP
Stationary
0.0548
-3.49
INF
Stationary
0.0000
-8.35
OPEEN
Source: Research Findings
As the significance level of the test for all variables is less than 0.10, we reject the
assumption that there is a single root in the series, and the data are stationary at 90% significance.
Testing research hypotheses
RES
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 Deficit
t
+ c7 DEBT
t
+ c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c12 OPEEN
t
+ c13 DUM1
t
+ c14
DUM2
t
+ c15 DUM3
t
+ e
t
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Table 3
The results of regression analysis of resilience model
Probability
t statistic
Coefficient
Abbreviation
Index
Variable
0.1682
-1.425090
-131.5875
IR
Interest rate
0.6983
0.392746
9.848780
LR
Legal deposit rate
0.0128
-2.709416
-339.9687
LIQ
Liquidity
0.0209
-2.488184
-1143.449
GEXP
Government expenses
0.0063
3.019756
1416.916
TR
Tax revenue
0.1503
1.490442
0.951980
DEFICIT
Deficit
0.2630
-1.148715
-42.04341
DEBT
Debt ratio
0.3244
1.007971
135.3936
GOVERNENCE
Good governance
0.7423
-0.332981
-61.12493
DEVELOPMENT
Social Development
0.1038
1.696976
26.09257
GDP
Economic Growth (gross domestic
production)
0.4217
0.818854
19.63872
INF
The inflation rate
0.6119
0.514737
14.36035
OPEEN
Commercial openness
0.0432
-2.145338
-130.8927
DUM1
President Trump
0.5133
0.664501
27.98722
DUM2
JCPOA
0.0027
3.387224
100.3062
DUM3
Economic Strength
0.92
2
R
Model determination coefficient
2.46
Durbin-
Watson
0.87
Adj-R2
Modified determination coefficient
of the model
0.0649
Sig.
5.46
J statistic
Model goodness of fit
Source: Research findings
The above table shows that as J statistic has a probability less than the significance level
10%, the significance of the whole regression model and the instrumental variables used in the
model are confirmed. Moreover, the value of the coefficient of determination shows that about
92% of the dependent variable changes are explained by independent and control variables,
showing a high explanatory power. In sum, the results of estimating the main research model show
that:
- IR has an effect of 131.58 units on the dependent variable (ER index), which has been
negative and insignificant so that 131.58 units of changes in the ER index is explained by
changes in the interest rate.
- LR has an effect of 9.84 units on the dependent variable (ER index), which has been
positive and insignificant so that 9.84 units of changes in the ER index are explained by
the changes in the LR variable.
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- LIQ has an effect of 339.96 units on the dependent variable (ER index), which has been
negative and significant so that 399.96 units of changes in the ER index are explained by
changes in liquidity.
VUL
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 Deficit
t
+ c7 DEBT
t
+ c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c13 DUM1
t
+ c14 DUM2
t
+ c15
DUM3
t
+ e
t
Table 4
The results of regression analysis of vulnerability model
Probability
t statistic
Coefficient
Abbreviation
Index
Variable
0.0000
7.269187
2.508130
IR
Interest rate
0.0028
-3.350777
-0.453217
LR
Legal deposit rate
0.0001
-4.861450
-1.979378
LIQ
Liquidity
0.0000
5.948372
7.982122
GEXP
Government
expenses
0.0021
-3.465976
-4.413954
TR
Tax revenue
0.1468
-1.501522
-0.003515
DEFICIT
Deficit
0.0193
-2.515820
-0.628119
DEBT
Debt ratio
0.0000
12.83667
3.276419
GOVERNENCE
Good governance
0.0000
-9.702942
-4.254920
DEVELOPMENT
Social
Development
0.9633
0.046518
0.002851
GDP
Economic Growth
(gross domestic
production)
0.0396
-2.181888
-0.146429
INF
The inflation rate
0.2410
-1.204937
-1.016630
OPEEN
Commercial
openness
0.9267
0.093019
0.015917
DUM1
President Trump
0.0000
-10.12906
-1.294871
DUM2
JCPOA
0.0051
3.093559
0.419100
DUM3
Economic Strength
0.88
2
R
Model
determination
coefficient
2.14
Durbin-
Watson
0.82
Adj-R2
Modified
determination
coefficient of the
model
0.0597
Sig.
5.63
J statistic
Model goodness of
fit
Source: Research Findings
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The table above shows that as J statistic is less than the significance level 10%, the
significance of the whole regression model and the instrumental variables used in the model are
confirmed. Moreover, the value of the determination coefficient shows that about 88% of the
dependent variable changes are explained by the independent and control variables, showing the
high explanatory power of the study. In sum, the results of estimating the main research model
show that:
- IR has an effect of 2.50 units on the dependent variable (EV index), which has been a
positive and significant effect so that 2.50 units of changes in EV are explained by IR.
- LR has an effect of 0.45 units on the dependent variable (EV Index), which has been
negative and significant so that 0.45 units of changes in EV are explained by changes in
LR.
- LIQ has an effect of 1.97 units on the dependent variable (EV index), which has been
negative and significant so that 1.97 units of changes in EV are explained by changes in
liquidity.
(RES – VUL)
t
= c0 + c1 IR
t
+ c2 LR
t
+ c3 LIQ
t
+ c4 GEXP
t
+ c5 TR
t
+ c6 GDP
t
+ c7 INF
t
+ + c8
Governence
t
+ c9 Development
t
+ c10 GDP
t
+ c11 INF
t
+ c12 OPEEN
t
+ c13 DUM1
t
+ c14
DUM2
t
+ c15 DUM3
t
+ e
t
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Table 5
The results of regression analysis of net resilience model
Probability
t statistic
Coefficient
Abbreviation
Index
Variable
0.1883
-1.355886
-97.03280
IR
Interest rate
0.9744
0.032480
0.708321
LR
Legal deposit rate
0.0064
-2.996463
-438.8801
LIQ
Liquidity
0.0223
-2.449481
-1063.889
GEXP
Government expenses
0.0029
3.330843
1476.544
TR
Tax revenue
0.0360
2.227461
1.161836
DEFICIT
Deficit
0.0996
-1.716113
-54.49715
DEBT
Debt ratio
0.0150
2.628891
211.0262
GOVERNENCE
Good governance
0.2231
-1.252226
-151.0234
DEVELOPMENT
Social Development
0.0179
2.551220
32.30506
GDP
Economic Growth (gross
domestic production)
0.2527
1.173361
19.89985
INF
The inflation rate
0.0000
6.584471
3.118377
OPEEN
Commercial openness
0.0232
-2.432103
-144.0142
DUM1
President Trump
0.8100
-0.243237
-6.886529
DUM2
JCPOA
0.0019
3.496818
117.7937
DUM3
Economic Strength
0.92
2
R
Model determination
coefficient
2.41
Durbin-Watson
0.87
Adj-R2
Modified determination
coefficient of the model
0.0702
Sig.
5.31
J statistic
Model goodness of fit
Source: Research Findings
The table above shows that as J statistic is less than the significance level 10%, the
significance of the whole regression model and the instrumental variables used in the model are
confirmed. Moreover, the value of the determination coefficient shows that about 92% of the
dependent variable changes are explained by the independent and control variables, showing the
high explanatory power of the study. In sum, the results of estimating the main research model
show that:
- IR has an effect of 97.03 units on the dependent variable (ER index), which has been a
negative and significant effect so that 97.03 units of changes in ER are explained by IR .
- LR has an effect of 0.70 units on the dependent variable (ER Index), which has been
positive and insignificant so that 0.70 units of changes in ER are explained by changes in
LR.
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- LIQ has an effect of 438.88 units on the dependent variable (ER index), which has been
negative and significant so that 438.88 units of changes in ER are explained by changes in
liquidity.
- GEXP has an effect of 1063.88 units on the dependent variable (ER index), which has
been negative and significant so that 1063.88 units of changes in ER are explained by
changes in GEXP.
- TR has an effect on the dependent variable (net ER index) by 1476.54 units, which has
been a positive and significant effect so that 1476.54 units of changes in the net ER are
explained by the changes in TR.
Diagnostic tests
Test of the normality of the error component
Table 6 shows the statistical value and significance of the Jarque-Bera statistic for the error
component of the models estimated above.
Table 6
The results of Jarque-Bera test
Model
Jarque-Bera
statistic
Probability
Results
Resilience
0.49
0.7805
Normal
Vulnerability
0.46
0.7915
Normal
Net resilience
0.49
0.7805
Normal
Source: Research findings
In the normality test, the null hypothesis states that the expression is part of the error of the
models estimated is normal. As Jarque-Bera statistic for the expression is an error component of
the models estimated in the study greater than 0.10, the null hypothesis stating the normality of
error component in the models estimated in confirmed.
Error component collinearity test
The results of collinearity of LM test are presented in the table below:
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Table 7
The results of LM collinearity of error component
Model
Breusch–Pagan
test
Probability
Results
Resilience
0.16
0.8528
Collinearity
Vulnerability
0.11
0.7476
Collinearity
Net resilience
0.16
0.8528
Collinearity
Source: Research findings
In the collinearity test, the null hypothesis states that the residual components of the
regression model lack strong collinearity. As the LM statistic has a probability of more than 0.10,
it suggests that the assumption that there is no sharp linearity for the remaining components of the
regression model is confirmed.
Variance heterogeneity test of the error component
Table 8 shows the results of the analysis of variance similarity using Breusch–Pagan test.
Table 8
Test results of variance heterogeneity of the research models
Source: Research findings
The results in Table 4-6 show that the probability of the statistical calculation calculated in
the heterogeneity variance test is inconsistent for the larger research models greater than 0.10, so
the H
0
hypothesis of this test based on the variance similarity is not rejected.
Model
Statistics
Probability
Results
Resilience
1.56/1
0.1680
Heterogeneity of
variance
Vulnerability
1.24
0.3139
Heterogeneity of
variance
Net resilience
1.56
0.1680
Heterogeneity of
variance
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Structural failure test
CUSUM test is used to diagnose a structural failure in the study. The results of this test are
presented in the following diagrams:
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1396 1397
CUSUM of Squares 5% Significance
Figure 1
Structural failure test for resilience model
Source: Research findings
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1396 1397
CUSUM of Squares 5% Significance
Figure 2
Structural failure test for vulnerability model
Source: Research findings
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0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1396 1397
CUSUM of Squares 5% Significance
Figure 3
Structural failure test for net resilience model
Source: Research findings
The results in the graphs above show that the estimated models are stable as the probability
of CUSUM (blue line) is within a significant range of 5% (red lines).
Discussion
The purpose of this study was to examination of the role of monetary policies (MP) in Iran's
economic resilience (ER) and vulnerability for the production boom. Among the limitations that
we had in this study are the lack of available statistics and information and its confidentiality, lack
of transparency in the Iranian monetary and banking system, lack of access to classified data, lack
of cooperation of related organizations.
In relation to the hypothesis 1, it was observed that the effect of MP on the Iran ER index
is significant. The results showed that from among the three indices - IR, LR, and liquidity - the
liquidity has significant effects on the resilience index in Iran during the study period (coefficient
of 339.96 with a probability of 0.0128). Thus, the null hypothesis is rejected, and the opposite
hypothesis is confirmed: there is a significant relationship between MP index and ER. Thus, the
first hypothesis was not rejected. Regarding this, it is stated that indices like IR, LR, and liquidity,
which are the main tools of MP, will have a significant effect on the fragility of the country's
financial and banking system, which can thus affect ER of the country. Thus, the existence of a
significant relationship between MP and ER is theoretically confirmed.
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This conclusion is in line with those of Amiri et al. (2018), Bakhtiari, and Sajjadieh (2018).
In relation to the hypothesis 2, it was observed that the effect of MP on the Iran EV index
is significant. The results showed that three indices - IR, LR, and liquidity - have significant effects
on the vulnerability in Iran during the study period (respectively, coefficient 2.50 with a probability
of 0.0000, coefficient 0.45 with a probability of 0.0028 and a coefficient of 1.97 with a probability
of 0.0001). Thus, the null hypothesis is rejected, and the opposite assumption - there is a significant
relationship between MP index and vulnerability - is confirmed. Thus, the third hypothesis is not
rejected. Regarding this, it is stated that as the government has a critical and varied role in the
Iranian economy, government policies as MP have a great role in the real sector of the economy
that can make the country more vulnerable to external damage. Hence, the existence of a
significant relationship between MP and EV is theoretically confirmed.
This is in line with those of Amiri et al. (2018), Bakhtiari and Sajjadieh (2018).
Finally, as for the hypothesis hypothesis 3, the effect of MP on the net economic index of
Iran resilience is significant. The results showed that from among the three indices - IR, LR, and
liquidity - liquidity has significant effects on the net resilience index in Iran during the study period
(coefficient 438,88 with a probability of 0.0064). Thus, the null hypothesis is rejected, and the
opposite assumption - there is a significant relationship between MP index and net ER - is
confirmed. Hence, the fifth hypothesis is not rejected. Regarding this, it is stated that the more the
central bank has created more credibility through measures like successful inflation control,
injecting money into the market during the recession, and so on, these measures will create more
space to facilitate MP or support economic activities. Thus, the country's resistance to the crisis
will increase during the recession, and net resilience will increase. Therefore, the existence of a
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significant relationship between MPs and net resilience is theoretically confirmed. This is in line
with those of Amiri et al. (2018), Bakhtiari and Sajjadieh (2018).
Conclusion
All these results indicate that MP and fiscal policy have affected the Iran ER index, so it is
suggested that the government pave the way for strengthening the country's resilience by
enhancing the efficiency of the country's monetary and financial system. The results indicated that
MP and fiscal policy had affected Iran EV, so it is suggested that the country's economic
policymakers try to design warning mechanisms to take action at the earliest opportunity to resolve
the discrepancy in the event of any inconsistency in monetary and fiscal policies that make the
country more vulnerable.
Also, the results indicated that MP and fiscal policy had affected net ER index of Iran; thus,
it is recommended that the country's monetary and financial officials specify financial indices like
the government's budget status and monetary indices like liquidity and bear in mind the
consequences of these policies on the country's net ER.
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