Download Studying the Neutrality of Money: An Evidence of OPEC Member

International Journal of Applied Economic Studies
Available online at http://sijournals.com/IJAE/
ISSN: 2345-5721
Vol. 3, Issue 4, August 2015
Studying the Neutrality of Money: An Evidence of OPEC Member States
Mohsen Mehrara
Faculty of Economics, University of Tehran, Tehran, Iran
[email protected]
Sarah Katanchian
M.A Student of Economics, Aras International Campus, University of Tehran, Iran
[email protected]
Abstract
The main fundamental issue in developing oil exporting countries is that there is a disagreement between monetary
policy makers and producers. Producers claim that the contraction of monetary policies reduces the output. In contrary
with this, policymakers claim that the problem is low efficiency of producers. This paper studies the neutrality of
money in OPEC member countries using panel approach during the period 1960-2013. Gross domestic product,
liquidity, oil price, exchange rate, gross fixed capital formation and consumer price index are variables which were used
in this investigation. The results of this research implied that there is a negative and significant relationship between
liquidity and economic growth. A positive and significant relationship between inflation and economic growth is also
founded. The results support that money is not neutral in mentioned countries. So, economists and policy makers ought
to use appropriate monetary policies for reducing macroeconomic volatilities.
JEL Classification: E4, E5
Keywords: Gross Domestic Product, Liquidity, Neutrality of Money, Oil Price, Organization of the Petroleum
Exporting Countries.
1. Introduction
A popular field of debate in Economics literature is the neutrality of money in oil exporting countries (OPEC1
members). A clear understanding of the relationship between these variables is important, especially to the
policymakers to guarantee effectiveness of macroeconomic stabilization policies. Money and liquidity have low
influence on real economic variables in these countries. However, they have dramatically influenced nominal economic
variables. Policymakers use monetary policies for controlling quantity of output. However, it might cause inflation.
Hence, using or not using monetary policies is a well-known field of debates between economists.
These facts guided to an expanding number of researches studying neutrality of money in oil exporting countries. Some
of them believe in money neutrality and some do not. Some theories are represented about effectiveness of monetary
policy in short-run by monetarists. On the other hand, real business cyclists, in contrary with monetarists, believe that
money is neutral in short- and long-run (Abbasinejad, 2006). Classics believe that money is neutral in long-run and it’s
movements do not have any effects on real economic sector. They separated nominal and real sectors of economy and
overall they believe in neutrality of monetary policies (Snowdon and Wynareczyk, 1994). Real business cyclists believe
in neutrality of money in short-run and also long-run and there is a positive correlation between money and output. It
means that money has influenced output. They believe that money demand increased in prosperity condition and it is
with positive responses from money supply, particularly if policymakers used exchange rate targeting. Literman and
Weiss (1985); Barro (1993) and Sims (1980, 1983) give some evidence in order to support real business cycle that
indicates their tendency for non-monetary approach for modeling business cycle.
In this paper, we are going to study neutrality of money in oil exporting countries which are member of OPEC. Unlike
the previous individual-country level researches, according to the authors' knowledge, this paper is one of the rare
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Organization of the Petroleum Exporting Countries
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Studying the Neutrality of Money: An Evidence of OPEC Member States
Mohsen Mehrara, Sarah Katanchian
studies in this subject. Lack of data is one of the main causes of scarcity of studies in this field. Panel approach allows
us to pool large number of observations from small number of countries and get more accurate results (Mehrara and
Mohagheghç 2011).We will study the macroeconomic dynamics between economic output (GDP), domestic price level
(CPI), money supply (M2) as a % of GDP, real effective exchange rate (ER), gross fixed capital formation (GFCF) as a
% of GDP and oil price over a group of main oil exporting countries which are OPEC members. A panel approach is
used for estimating the nexus between these variables. Our sample covers data from 1960 to 2013 in all OPEC member
states, namely Algeria, Angola, Ecuador, Iran, Iraq, Kuwait, Nigeria, Qatar, Saudi Arabia, The United Arab Emirates
and Venezuela.
This paper organized in 5 sections. In section 2, a brief review of literature on the neutrality of money in oil exporting
countries is presented. Section 3, introduces the data and methodology. Section 4 reports empirical findings and finally,
section 5 concludes the paper.
2. Literature review
Studying the role of oil price in macroeconomic dynamics came in to the focal point of researches since 1970s. Here,
we review the related literature regarding in neutrality of money.
Blanchard and Quah (1989) want to investigate effects of dynamic movements of demand and supply on output and
unemployment rate in America during 1950 to 1987. They used a VAR model indicating that variance decomposition
has dramatic standard deviation. Demand disturbances in output fluctuations are greatly affected by trend and structural
break. Demand disturbances in short-run are improved. However, demand and supply disturbances in unemployment
fluctuations are not influenced by trend and structural break. Altogether, demand shocks have more impact on
unemployment fluctuations.
Cover (1992) evaluates the effects of monetary shocks on real economic variables in the United States of America. The
results indicated that negative monetary shocks have significant effects on real output. However, positive monetary
shocks have not any significant effects on real output and also negative shocks have more effects on real output that
positive ones.
Kireyev (2000) using the mean-group estimator in a PVAR approach, analyzed the effects of both internal shocks on
macroeconomic movements in 18 Arab countries. Kireyev (2000) compare growth performance, fiscal and current
account developments in these countries.
Bonato (2007) estimates the relationship between nominal variables and inflation in Iran. This paper answered
questions about decline in inflation which took place at the first half of 2006. The outcome implies that there is a strong
relationship between money and inflation when M1 is used with no proof of a structural change.
Mehrara and Mohaghegh (2011) focus on developing net oil exporters (unlike with most other studies which focuses on
developing net importers) and investigate macroeconomic fluctuations, supplying fresh penetration into the impacts of
oil shocks on macroeconomic variables. Their findings indicate that money is not neutral in mentioned countries.
Lashkary and Kashani (2011) investigate the neutrality or non-neutrality of the money in Iran during the period 1959 to
2008. This study has been investigated by the monetarists’ approach and analyzes events by experimental observation in
several statistical models. The results showed that there is not any significant relationship between money volume and
real economic variables, production and employment. There is not any intensive fluctuation in gross domestic products
in Iran except in recent years and it has a normal direction. However, there is a large fluctuation which is not natural in
unemployment rate.
Boyoiyour and Selmi (2013) study the relationship between real oil price and real effective exchange rate in three GCC
countries namely Qatar, Saudi Arabia and United Arab Emirates. This study has been investigated by employment of
wavelet decomposition and nonlinear causality test. The results showed that Qatar and UAE should improve the
downward effect of oil price on real exchange rate by amending diversification policy and also they implied that the
behavior of Saudi Arabia as a price maker may let it to keep a fast recovery under oil shocks.
Damette and Seghir (2014) empirically investigate dynamic relationship in 12 oil exporting countries between two
variables during 1990 to 2010. This research has been investigated by recently developed panel econometric techniques.
This paper accounts for cross-section dependence when analyzing the energy–income nexus. The observation outcome
means that there is a long-run equilibrium relationship between energy consumption and economic growth. Moreover,
the result of a dynamic panel error–correction model reveals a short–run unidirectional causality from energy
consumption to economic growth. But, in the long-run, it is the economic process that determines in energy
consumption trend.
Alavinasab (2014) investigates the factors affecting the rate of inflation in Iran. In this paper, augmented Dickey Fuller
(ADF) test used for determining the existence of unit root and also stationary of the series. Moreover, Johansen cointegration test is used for evaluating the existence of long-run relationship among the series and also for capturing the
convergence of the inflation determining factors for achieving long-run equilibrium. An error correction mechanism
(ECM) is also used. The results showed that there is a long-run nexus among variables of money supply, gross domestic
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International Journal of Applied Economic Studies
Vol. 3, Issue 4, August 2015
product, oil export revenue, and inflation. Findings of ECM implies short-run equilibrium happen to equalize the model
in long-run.
3. Data and Methodology
In this section, for the sake of estimating neutrality of money in oil exporting countries, we concentrate on these 12
countries: Algeria, Angola, Ecuador, United Arab Emirates, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia and
Venezuela during the period 1960-2013.
In an unbalanced Panel framework, our variables which were involved in this paper are logarithm of gross domestic
product (GDP), money supply (M2) as a % of GDP, logarithm of consumer price index (CPI), real effective exchange
rate (ER), gross fixed capital formation (GFCF) as a % of GDP and yearly average of crude oil price (OILP). The whole
data obtained from World Development Indicators (WDI)’s online database. Using logarithm forms can trim down the
problem of heterosckedasticity (Gujarati, 2004). Thus, in this model, we use logarithmic form of our variables which
are not % of GDP themselves.
Since most of empirical studies in the subject of neutrality of money use non-stationary in levels, it is fundamental to
examine for unit roots, co-integration and cross-section dependence for avoiding spurious results. Existence of unit root
test in panel data like time series led to spurious regression. Hence, testing for unit root tests has become an standard
procedure not also in time series but also in panel data analyzes. Panel unit root tests have been declared by Levin, Lin
and Chu (1992); Im, Pesaran and Shin (1997); Harris and Tzavalis (1999); Madala and Win (1999).
Panel co-integration tests can be motivated by the search for more powerful tests than those obtained by applying
individual time series co-integration tests. Pedroni (1999, 2004) proposed several tests for the null hypothesis of cointegration in a panel data model that allows for considerable heterogeneity.
Based on a panel approach our modified model is specified as below:
D(log(GDP))it=ß0+ß1D(M2)it+ß2D(log(CPI))it+ß3D(ER)it+ß4(GFCF)it+ß5 D(oilprice)it+uit
Equation (1)
Where: i represents countries and t represents the year and u is stochastic error term. D implies the first difference.
log (GDP) represents the logarithm of gross domestic product, (M2) represents money and quasi money as a % of GDP,
CPI represents consumer price index (2010=100), ER represents official Exchange rate (2010=100), GFCF represents
gross fixed capital formation as a % of GDP, oilprice represents yearly average of crude oil price of OPEC members.
Gross Domestic Product (GDP) is the value of all final goods and services produced in the economy in a given time
period (quarter or year). GDP is the basic measure of economic activity (Dornbusch, 1977).
Consumer Price Index (CPI) is the cost of a given basket of goods, representing the rate of increase of prices
(Dornbusch, 1977).
Money plus Quasi Money (M2) consists of M1 plus savings accounts and small-denomination time deposits plus
balances in retail money market mutual funds (Walsh, 2006). M1 consists of currency held by the nonbank public,
travel checks, and demand deposits. M0 is a narrow definition of the money supply, consisting of total reserves held by
the banking system plus currency in the hands of the public.
Gross Fixed Capital Formation (GFCF) is investments in land improvements (fences, ditches, drains, etc.); plant,
machinery, and equipment purchases; and the construction of roads, railways and the like, including commercial and
industrial buildings, offices, schools, hospitals and private residential dwellings (Krkoska, 2002 ).
Exchange Rate (ER) is an exchange rate between two currencies is the rate at which one currency will be exchanged for
another. It is also regarded as the value of the one country’s currency in terms of another currency (Sheffrin, 2003).
Oil Price is the price of oil, or the oil price, generally refers tosspot price of a barrel of benchmark crude oil. A
benchmark crude or marker crude is a crude oil that serves as a reference price for buyer and sellers of crude oil. There
are three primary benchmarks, west Texas intermediate (WTI), Brent Blend, and Dubai crude. Benchmarks are used
since there are many different varieties and grades of crude oil. Using benchmarks makes referencing types of oil easier
for sellers and buyers. (International Crude Oil Market Handbook, 2011)
4. Empirical results
In this section, we are going to report the empirical evidence regarding in the model specified in section 3.
First of all, the descriptive statistics are calculated and performed for each variable in this section. All the variables have
positive mean values and CPI has greater mean, median, maximum and minimum among all variables. Eventually
statistics express the highest standard deviation for oil price.
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Studying the Neutrality of Money: An Evidence of OPEC Member States
Mohsen Mehrara, Sarah Katanchian
Table 1: Descriptive Statistics
Median
Maximum
25.70296
27.34127
46.87477
79.14567
86.02780
136.1297
26.14848
46.87646
68.38000
114.2100
72.06065
9281.152
Variables
Mean
25.66557
GDP
43.63253
M2
84.46570
CPI
24.72356
GFCF
71.75577
OILP
670.5233
ER
Source: Authors’ findings
Minimum
23.37573
16.25266
28.01355
5.467015
26.43000
1.223562
Std.Dev.
0.919845
15.99909
24.26639
10.90772
27.80356
2268.963
It is well established that the non-stationarity of the variables in standard OLS regression can cause spurious regression
(Granger and Newbold, 1974). Thus, it is important to know if our variables are stationary or not. In this paper, we
apply Im, Pesaran and Shin W-stat unit root tests for the panel data. Table (2) indicates the results of unit root tests
showing that all variables are non-stationary at level except GFCF.
Variables
LOG(GDP)
2.56408
Statistic
0.9948
Prob.
Source: Authors’ findings
Table 2: Unit Root Test Results
M2
LOG(CPI)
GFCF
-0.11021
7.76087
-5.63823
0.4561
1.0000
0.0000
ER
2.86973
0.9979
OILP
0.62851
0.7352
As it is obvious, our variables are non-stationary at level except GFCF. So, it is necessary to obtain first difference of
them. Results of Table (3) indicated that after getting first difference, all of them are stationary.
Table 3: Unit Root Test Results after Considering First Difference of Variables.
Variables
D(LOG(GDP))
D(M2)
D(LOG(CPI))
D(ER)
D(OILP)
8.56399
12.7741
2.12016
10.5683
4.04764
Statistic
0.0000
0.0000
0.0170
0.0000
0.0000
Prob.
Source: Authors’ findings
Pedroni residual co-integration test is conducted in Table (4) to analyze the existence of long-run relationship among
the variables. As the results imply, all the probability of variables are greater than 5%. Hence, we accept null-hypothesis
of no co-integration among these variables at 5%. Thus, there is no co-integration among the variables.
Table 4: Pedroni Residual Co-integration Test
Statistic
Prob.
Weighted Statistic
Prob.
-0.861462
0.8055
-0.786171
0.7841
Panel v-Statistic
3.676122
0.9999
3.669977
0.9999
Panel rho-Statistic
-0.845957
0.1988
-1.978583
0.0239
Panel PP-Statistic
1.558620
0.9405
-0.134533
0.4465
Panel ADF- Statistic
Source: Authors’ findings
Due to the redundant likelihood test results (for choosing best method between pooled and panel), we chose fixed
effects method.
Table5: Redundant Fixed Effects Test
Effects Test
Statistic
d.f.
Prob.
2.702414
(7,50)
0.0186
Cross-section F
Source: Authors’ findings
Variables
C
D(M2)
D(LOG(CPI))
GFCF
D(OILP)
D(ER)
R-squared
0.986710
Adjusted R-squared
0.959229
Coefficient
0.090562
-0.009744
0.562417
0.006609
0.005574
-0.000160
Table 6: Cross-Section Fixed Effects
Std. Error
t-Statistic
0.020493
4.419155
0.002295
-4.246569
0.196424
2.863282
0.003448
1.916820
0.000924
6.034477
0.000173
-0.926457
Prob(F-statistic)
0.000000
Durbin-Watson stat
1.965052
4
Prob.
0.0000
0.0001
0.0059
0.0603
0.0000
0.3581
S.E of regression
0.096327
International Journal of Applied Economic Studies
Vol. 3, Issue 4, August 2015
Source: Authors’ findings
According to Table 7, Hausman specification test (to choose the best method between fixed and random) indicates that
since the probability is greater than 5%, we should use Random Effects.
Test summary
Cross-section random
Source: Authors’ findings
Table 7: Correlated Random Effects-Hausman Test.
Chi-Sq. Statustic
Chi-Sq. d.f.
7.982493
5
Prob.
0.1572
Note: H0 estimates by Random are not different from those from fixed effects. Random should be accepted. H 1
estimates by Random are not different from those from fixed effects. Random should be rejected.
Table 8: Cross-Section Random Effect tests
Variables
Coefficient
Std. Error
t-Statistic
Prob.
0.099689
0.23207
4.295610
0.0001
C
-0.009915
0.002093
-4.738341
0.0000
D(M2)
0.458591
0.206282
2.223127
0.0302
D(LOG(CPI))
0.007194
0.003175
2.265736
0.0273
GFCF
0.005506
0.000843
6.531274
0.0000
D(OILP)
-0.000163
0.000187
- 0.867907
0.3891
D(ER)
R-squared
Prob(F-statistics)
0.907770
0.0000
S.E of regression
0.090051
Adjusted R-squared
Durbin-Watson stat
0.882136
1.949938
Source: Authors’ findings
We find that all variables are significant at 5% except inflation which is significant at 10%. As it obvious from results,
there is a positive and significant (0.365593) relationship between economic growth and inflation and also a negative
and significant relationship between economic growth and liquidity. By comparing between five effective factors on
economic growth, we discover taht the effect of inflation among all factors is the most. The result implies that an
increase of 1% in inflation causes 4.5% increase in economic growth and an increase of 1% in liquidity 0.099%
decreases economic growth. Findings imply that money is not neutral in mentioned countries.
Table 9: Cross-Section Random Effects after Omitting ER.
Variables
Coefficient
Std. Error
t-Statistic
Prob.
0.102245
0.027287
3.746994
0.0004
C
-0.009941
0.002100
-4.732856
0.0000
D(M2)
0.365593
0.214603
1.703582
0.0938
D(LOG(CPI))
0.007622
0.003188
2.391153
0.0201
GFCF
0.005511
0.000846
6.515511
0.0000
D(OILP)
R-Squard
Prob(F-statistic )
0.914314
0.0000
S.E of regression
0.087078
Adjusted R-squared
Durbon-watson stat
0.894611
1.939628
Source: Authors’ findings
5. Conclusions
The main purpose of present study was to investigate neutrality of money in oil exporting countries which is OPEC
members namely: Algeria, Angola, Ecuador, United Arab Emirates, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi
Arabia and Venezuela during the period 1960 to 2013. One of the fundamental issues in these counties is that there is a
disagreement between monetary policy makers and producers. Producers claim that the contractionary monetary
policies reduce the output. In contrary, policymakers argue that the problem is low efficiency of producers. Unit Root
Test for investigating stationarity of variables has been used and findings implied that some variables were non–
stationary in sample. So, first difference of those variables is used. Moreover, co-integration test has been used and
showed that there was no co-integration among variables. Hausman test indicated that Random Effect test should be
used. Eventually, there was a positive and significant relationship between economic growth and inflation and also a
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Studying the Neutrality of Money: An Evidence of OPEC Member States
Mohsen Mehrara, Sarah Katanchian
negative and significant relationship between economic growth and liquidity. Findings imply that money is not neutral
in these countries. So, any increase in liquidity would affects prices and also it affects Gross Domestic Product.
Monetary policies can be used to stimulate national output. If money is neutral, it means that money just affects prices.
Economists and policy makers ought to use appropriate monetary policies for controlling macroeconomic volatilities.
We suggest examining each country as individual, so that maybe other results will get, and also money can be neutral in
some countries individually.
Acknowledgment
Hereby I would like to thank Dr. Ehsan Shafeie and Reza Mohammadpour for their useful help and comments.
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