Analysis of the determinants of inflation in Ethiopia- Rift valley University

RIFT VALLEY UNIVERSITY BOLE CUMPUS
DEPARTMENT OF Developmental ECONOMICS
MA in Developmental Economics
ANALYSIS OF THE DETERMINANTS OF
INFLATION IN ETHIOPIA
BY
Mesfin Getu Biru
June, 2018
Addis Ababa, Ethiopia

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ANALYSIS OF THE
DETERMINANTS OF INFLATION IN ETHIOPIA
Mesfin Getu Biru
A thesis submitted to the Department of Economics in
Partial fulfillment of the requirements for the Degree of
Master of Art in Economics (Developmental Economics).
Advisor: Teferi Daba Lemma (PhD)
Rift Valley University
Addis Ababa
June, 2018

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Declaration
This is to certify that this thesis prepared by Mesfin Getu, entitled: Analysis of the
Determinants of Inflation in Ethiopia and submitted in partial fulfillment of the
requirements for Degree of Master of Art (MA) in Economics (Developmental
Economics) complies with the regulations of the Rift Valley University and meets
the accepted standards with respect to originality and quality.
Signed by the Examining Committee:
Examiner: ______________________ Signature: ___________ Date: __________
Examiner: ______________________ Signature: ___________ Date: __________
Advisor: _______________________ Signature: ___________ Data: __________
______________________________________________
Chair of Department or Graduate Program coordinator

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ACKNOWLEDGEMENT
It is not exaggeration to say that without insisting the Almighty God, and his Mom
St. Merry, would not have been in a place to complete successfully this thesis.
Thus, glory to him.
My gratitude and appreciation goes to my advisor Dr. Teferi Daba for his
constructive comments, technical support, welcoming approach in every step of
my work and helped me in shaped this study.
I would like to extend my gratitude to my families for their moral support; without
whose moral support, my achievement was not possible. And also I want to say
thank you all to my relatives and colleagues who have helped me while I was
writing this thesis.
Last, but not least, I want to thank National Bank of Ethiopia (NBE) worker
specially Mr. Bzuayehu Samuel for his support while I collect related data.

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Abstract
Inflation is a sustained rise in general price level of goods and services. The study carries out
long run as well as short run estimates of some factors or determinants that influencing inflation
in Ethiopia. The study used time series data for the period of 1972-2009 E.C. or 1979/1980-
2016/2017 using the data that are found from National bank of Ethiopia (NBE). The study reveal
there are stationarity of the variables at its first differenced which indicates the co-integration
between the variables, meaning that there a long-run relationship between the inflation and
consumer price index total, interest rate, money supply, exchange rate, government expenditure,
total export, import and GDP. The VAR estimated result revealed that in the long run, and
interest rate (IR), exchange rate (EX), Government expenditure (EXPE) and GDP are
contributed to decline in inflation rates, but only interest rates are statistically significant. The
rest variable like consumer price index (CPI), Money supply (MS), Export (EXPO) and import
have a positive contribution to raise inflation rate. The finding of this study also revealed a
bidirectional causality relationship between inflation and exchange rate, Government
expenditure, Export, imports and gross domestic product whereas the rest variable have a
unidirectional causality with inflation. Johansen co-integration test result indicates eight
cointegrating vectors which has evident for a long-run equilibrium relationship between the
variable inflation rate and its explanatory variables

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Table Of Content
Contents
Table Of Content.............................................................................................................................. vi
List of Table .....................................................................................................................................ix
List of figure.......................................................................................................................................ix
List of Acronyms..............................................................................................................................ix
CHAPTER ONE.............................................................................................................................. 1
1. INTRODUCTION.........................................................................................................................1
1.1 Background of the Study .....................................................................................................1
1.2 Statement of the Problem.....................................................................................................2
1.3. Research question...............................................................................................................4
1.4 Objective of the Study .........................................................................................................5
1.4.1. General objective of the study........................................................................................5
1.4.2. Specific objective of the study .......................................................................................5
1.5 Significance of the study ......................................................................................................5
1.6 Scope of the study................................................................................................................5
1.7 Limitation of the study ........................................................................................................5
1.8 Organization of the Paper....................................................................................................6
CHAPTER TWO............................................................................................................................. 7
LITRATURE REVIEW..................................................................................................................7
2.1. Theoretical Literature Review ............................................................................................7
2.1.1. Theories of Inflation.......................................................................................................7
2.1.2 Demand-Pull Theories of Inflation .................................................................................8
2.1.3 Monetarist Theory of Inflation........................................................................................9
2.1.4. Cost-Push Theories of Inflation .....................................................................................9

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2.1.5. Keynesian Theory of Inflation .....................................................................................10
2.1.6 New Neoclassical Synthesis (NNS) Theory of Inflation ..............................................11
2.1.7 New Political Macroeconomics Theory of Inflation.....................................................11
2.2 EMPIRICAL LITERATURE REVIEW.............................................................................12
CHAPTER THREE.........................................................................................................................16
METHODOLOGY OF THE STUDY............................................................................................. 16
3.1. Sources of data.................................................................................................................16
3.2. Model specification...........................................................................................................16
3.2.1. Expectations of the Variables in the Model .................................................................17
3.3. Stationarity Tests /Unit Root Test .....................................................................................18
3.3.1. The Augmented Dickey-Fuller (ADF) Test.................................................................18
3.4. Information Criteria (Lag length Selection).......................................................................19
3.5. Vector Auto Regression (VAR) Model...............................................................................19
3.6. The Granger Causality model ...........................................................................................20
3.7 Johansen Co-Integration Technique...................................................................................21
3.8 Vector Error Correction Model (VECM) ...........................................................................22
CHAPTER FOUR .........................................................................................................................24
RESULT AND DISCUSION ........................................................................................................24
4.1. Trends of Inflation Rate....................................................................................................24
4.1.1 Inflation, consumer Price index and Interest rate..........................................................25
4.2. Unit Root Test (Stationary Test)........................................................................................26
4.3 Lag Length Selection Process.............................................................................................27
4.4. Vector Auto regression (VAR) Estimation Results (Long run)............................................28
4.5. Granger causality test result .............................................................................................29
4.6. Analysis of Johansen co-integration test result...................................................................31
4.7 Test for serial Autocorrelation and normality of the disturbance.........................................32

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4.8 Vector Error Correction Model (Short run Results) ...........................................................33
CHAPTER FIVE........................................................................................................................... 35
CONCLUSION AND RECOMMENDATION...........................................................................35
5.1. CONCLUSION ................................................................................................................35
5.2. RECOMMENDATION ....................................................................................................36
References ......................................................................................................................................37
Annex..............................................................................................................................................40

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List of Table
Table 1: Unit Root Test…………………………………………………….………….….…. 26
Table 2: Lag length Selection…………………………………………………….….………. 28
Table 3: VAR estimation result………………………………………..………….…………. 28
Table 4: Granger causality test result………………………………………………..………. 30
Table 5: Unrestricted Cointegration Rank Test (Trace) ……………………….…….……… 31
Table 6: Unrestricted Cointegration Rank Test (Max) ……………………………………… 31
Table 7: LM Test for Residual Autocorrelation of VAR………………………….…………. 32
Table 8: Test for normally distributed disturbances…………………………………….…… 33
Table 9: Test for Vector Error Correction Model……………………………………….…… 34
List of figure
Fig 1: Inflation Rate in Ethiopia…………………………………………………………...… 25
Fig 2: Inflation Rate in Ethiopia…………………………………………………………...… 26
List of Acronyms
ADF: Augmented Dickey-Fuller
LM: Lagrange Multiplier
NBE: National bank of Ethiopia
NNS: New Neoclassical Synthesis
VAR: Vector auto-regressive
VECM: Vector Error Correction Model
OLS: Ordinary Least Square
AIC: Akaike’s Information Criteria
FPE: Final prediction error

CHAPTER ONE
1. INTRODUCTION
1.1 Background of the Study
The main target of every nation’s monetary and fiscal policies, whether a developed or less
developed nation has been the maintenance of a low and relatively stable rate of aggregate
inflation. Non-stationary price path introduces uncertainty in the objective function of economic
agents, reduces economic efficiency and consumer welfare. This is the reason why inflation as a
macroeconomic variable or phenomenon has received much attention in recent time. An
economy that is faced with moderate inflation (3 to 6 per cent) may experience positive
economic effect. Because it is believed this level of inflation encourages investment and
production and as such increase growth in wages and consumption. But, a high inflation rate in
the range of double digit may produce a negative economic effect. This will adversely affect
purchasing power of the consumer. It can lead to uncertainty of the value of gains and losses,
borrowers and lenders as well as buyers and sellers Abdul, Syed, & Qazi, (2007).
Inflation has been low in Ethiopia in the past due to various reasons. During the Derg regime the
price control by the government has kept prices stable. The government was also rationing goods
at fixed prices to the public which in turn has contributed to the lower inflation attained during
the Derg regime. In addition, the lower and pegged exchange rate has also helped to lower the
impact of international price hikes on Ethiopia; of course it also makes imports cheaper. During
the earlier years of the present regime inflation has been low despite the huge inflow of money
by the IMF and other donors. This happened because the displacement of former government
soldiers and layoffs of workers due to the structural adjustment policy (SAPS) followed by the
country had depressed demand. This depression of demand has counteracted the inflationary
impact of increased demand due to the inflow of aid. But in recent years’ inflation has been high
in Ethiopia. There is still no argument on the causes of the high inflation experienced in recent
years. The government state supply bottlenecks, market structure, increased income in the rural
sector and international price developments especially of petroleum to be the cause of inflation.
IMF and most economists argue that inflation is caused due to increased demand caused by
expansion in money supply, increased remittances. In addition, deficit is also regarded as a cause
of inflation. In short the government attributes inflation to supply factors while international
organizations and most economists attribute inflation to demand factors. Inflation occurs when
the total demand for goods and services in an economy exceeds the supply of the same. When

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the supply is less, the prices of these goods and services would rise. Inflation affects everyone in
the economy. When the price level rises, each unit of currency buys fewer goods and services;
inflation is also erosion in the purchasing power of money, a loss of real value in the internal
medium of exchange and unit of account in the economy (Walgenbach et al., 1973).
Furthermore, higher level of inflation creates uncertainty which discourages savings and
investment. Savings are discouraged as inflation reduces the real rate of return on financial
assets. This again leads to low investment and a declining economic growth. High inflation rate
disintegrates the gains from growth and leaves the poor worse off thereby increase the divide
between the rich and poor in the society. A high inflation rate result from increase in food prices,
it hurts the poor because of their high marginal propensity to consume.
Ethiopia’s devaluation of the birr by 15 percent at the end of October 2017, according to the
government, aims at revitalizing the country’s exports. It has put pressure on inflation, which
moved to double digits even before the devaluation and is expected to continue in 2018. The
government has put in place measures, such as restricting credit expansion to the non-export
oriented sectors, to address inflationary pressures from the devaluation African Development
Bank, (2018)
1.2 Statement of the Problem
Price stability is one of the major goals of monetary policy and the key indicators of
macroeconomic stability. Sustainable increase in general level of price may affect economic
conditions negatively. Economic growth of a country depends on the level of investment
resulting from the domestic saving and foreign saving of the economy. The level of investment,
in turn, depends on macroeconomic stability and investors’ expectation about the economy. Even
though countries have desire to achieve sustainable economic growth, its means of financing
may have series impact to macroeconomic stability. To achieve fast economic growth
governments may have exposed to budget deficits. Financing a persistent deficit by money
creation will lead to a sustained inflation Kibrom, (2008).
Ethiopia is one of Africa’s largest countries with an estimated 77 million people in 2008 but
currently estimated around 110 million. According to government data, about 38 percent of the

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population lived below the official poverty line in 2005, but it is likely that a larger proportion
experiences extended periods of poverty due to shocks Bigsten & Shimeles., (2008).
During the Derg regime, inflation has been low in Ethiopia for the reason that the price was
controlled by the government and the government itself was providing goods at fixed price to the
public. Further, the lower and fixed exchange rate has also contributed to the lower inflation rate.
Similarly, inflation rate has been low in the earlier years of the present government Sisay M. ,
(2008). However, in recent years’ inflation has been high in Ethiopia. Inflation rate in Ethiopia
averaged 18.69 percent from 2006 until 2015, reaching an all-time high of 64.20 percent in July
of 2008 and a record low of -4.10 percent in September of 2009 (www.Trading Economics).
Though Ethiopia has experienced a low inflation, recently, double digit inflation has become
worrisome for policy makers as well as the society. Emrta, (2013) has studied the optimal level
of inflation in Ethiopia around which inflation affect economic growth optimally. The study has
applied threshold approach. By doing so on the data from 1971-2010 inflation level of about 8%-
10% is optimal for Ethiopia. Any inflation level, greater or less than the estimated threshold
level, may not allow long-term and sustainable economic growth.
Since the level of income in Ethiopia is very low but expenditure on consumption items such as
food is very high, inflationary experience results in a low level of welfare. Evidence on the
welfare impacts of high food inflation on the rural population is somewhat inconclusive, but
there is evidence of a significant negative impact on the urban population Loening, Durevall, &
Birru, (2009).
The current inflation has a reducing effect on the current development of the export sector. This
is because inflation makes Ethiopian products dearer in the international market which in turn
makes them less competitive. In addition, inflation also adversely affects domestic industries.
This is because the increase in production cost of domestic industries results in higher product
price and it increase in the price of domestically produced products results in increased imports,
which also adversely affects the balance of payments, and in turn makes domestic industries to
be uncompetitive. By reducing savings and increasing uncertainty inflation reduces investment
and capital formation in Ethiopia in the long run. The balance of net export of the country still
indicates negative sign which still harsh the capability of the country to optimize this effect.

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Inflation in Ethiopia is also hampering Ethiopia from reducing poverty and hunger. The living
standard of urban dweller has been adversely affected by inflation in Ethiopia. Inflation also
redistributes wealth there by increasing the number of poor people in the county. Even if it is,
said by the government that farmers benefit from rising food prices, something that needs
empirical investigation, rise in food prices are causing many to be unable to feed themselves.
Most importantly inflation in Ethiopia may misallocate resources from productive to
unproductive sectors. Thus, it is essential that the government intervene to control the price trend
in the country. However, such intervention requires appropriate policies devised from careful
observation of the forces behind the price fluctuations. Therefore, studying the possibility of
controlling inflation and its dynamics is one of the themes to be addressed in Ethiopia.
This study has attempt to identify the short run and long run equilibrium causal relationship
between inflation and its determinants, and to ascertain the policy frame work within which
inflation can be reduced. This study has also proposed to analyze the main determinants of
inflation and its effects on countries growth based on data that was collected and its influence
variable based on the sign of respective variables based on empirical evidence or data that was
collected.
1.3. Research question
Based on the above problem of the study the study has expected to provide answer for the
following question.
➢ What are the main determinants of inflation in Ethiopia?
➢ What are the causal relationship between inflation and its determinants?
➢ What are the possible remedies to be taken to overcome this problem?

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1.4 Objective of the Study
1.4.1. General objective of the study
The general objective of this paper is to distinguish the main determinants of inflation in
Ethiopia.
1.4.2. Specific objective of the study
Specifically, this paper proposes to: -
➢ Identify the variables that have significant impact on inflation in Ethiopia
➢ Estimate the short run and long run direction and magnitudes of relationship between
inflation and its determinants
➢ Suggest the possible recommendation within which inflation can be reduced
1.5 Significance of the study
This study is expected to raise the interest of scholars to work on inflation. It serves as a
benchmark for the students who working on inflation. The study also serves as point of reference
for further studies and policy makers concerning the issue by providing some information about
the main determinants of inflation and its possible remedies to overcome this problem. This
study also gives some insight on inflationary effects on economy of the country and on the long
run and short run relationship between inflation and the stated macro-economic variables.
1.6 Scope of the study
Although, inflation have different future in different countries over all of the world; but this
study has focuses on determinants of inflation in Ethiopia due to lack of sufficient time, data and
other than financial constraints. For this reason, this study would try to addresses only on the
main determinants of inflation in Ethiopia.
1.7 Limitation of the study
A number of difficulties have been encounter during perform of this study. The most serious
problem is lack of finance, time constraint and the problem in relation to data collection. The
other great problem during this study has the problem of reference materials for both technical
like available document and STATA system estimation and respective locally published journal
and other data had been the limitation of this study.

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1.8 Organization of the Paper
This paper has organized under five chapters; the first chapter contains the introduction parts
with different sub section. Both theoretical and empirical literature review has categorized under
the second chapter. The third section of this study has methodology parts which contain data
source, model specification and data analysis method sub section. Chapter four has the result and
detail discussion of the study and the last fifth chapter covers the conclusion and
recommendation parts.

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CHAPTER TWO
LITRATURE REVIEW
2.1. Theoretical Literature Review
Inflation is a sustained rise in general price level of goods and services. The definition of
inflation concerns neither increase in price of particular commodity nor for particular period of
time. For an inflation to be happened, the rise in the general price of goods and services should
be sustained. Inflation takes a crucial role in the healthy functioning of a countries economic
performance. It is commonly recognized that an unpredictable fluctuation in the rate of inflation
is considered a major indicator of the instability of economic activity of a country Mishkin,
(2010).
There is different hypothesis as to the cause of inflation. According to the structuralist, inflation
is attributed to the structure of the developing countries economy. According to monetarist, the
expansion of money supply beyond the growth of real output is cause of inflation. Inflation may
also result from either increase in aggregate demand or a decrease in aggregate supply, these two
sources effect price level of an economy. An inflation resulting from increase in aggregate
demand is called demand-pull inflation. Demand-pull inflation arises due to many factors like
money supply, government expenditures, exports or gross domestic product, etc. Cost-push
inflation defined as an increase in general price level resulting from increase in cost of
production. The main sources of cost-push inflation may be decrease in aggregate supply that
may be due to cost of production, increasing wages, higher imports, rising taxes, budget deficit
or fiscal deficit Robert, (1982).
2.1.1. Theories of Inflation
The study of causes of inflation has probably given rise to one of the most controversial debates
in the field of economics. The debates differ in their hypotheses, mainly due to a range of
conventional views about the appropriate measure to control inflation. For example, Neoclassical
defined inflation as a rise in prices caused by excessive increase in the quantity of money. For
Keynesians true inflation happens when money supply increases beyond full employment level

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Jhingan, (1997). Though various economists define inflation in different ways a common general
agreement is that inflation is a sustained increase in the general price level.
Different schools of thought emphasize one or a combination of the possible sources of inflation.
For example, for monetarists inflation is always and everywhere purely a monetary phenomenon
whereas the proponents of the cost-push theory of inflation attribute inflation to a host of non-
monetary, supply-oriented influences that alter the unit cost and profit markup components of the
prices of individual goods Humphrey, (1998).
It is specifically difficult to identify the reasons for or factors that contribute to inflation. In
literature various schools of thought suggested different factors as the prime sources of inflation.
However, important variables such as monetary and fiscal developments may be crucial in
explaining inflationary processes. Yet, the sources of inflation in all countries need not be the
same. In the review of theories of inflation, the alternative theories are grouped in the following
categories
2.1.2 Demand-Pull Theories of Inflation
According to demand- pull theorists there is identical or equal relationship between national
income estimated at market prices and the velocity of circulation of the money supply. Based on
this theory, there is a positive relationship between price levels and the money supply. This
relationship is presented using the quantity equation.
MV = PY
Where: M is the stock of money in circulation, V is the velocity of circulation, P is the general
price level, Y is the total income.
Accordingly, there will be a proportionate positive relationship between the money supply and
the price levels of a given economy by assuming velocity is constant. That is, when the money
supply increases by a certain percentage the price levels will also increase by an equal
percentage.

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According to this theory it is believed that inflation is caused by an expansion in the money
supply of a given economy which is not supported by an increase in output levels of an economy
Aurora, (2010).
2.1.3 Monetarist Theory of Inflation
Monetarists say that “only money matters”, and as such monetary policy is a more important
instrument than fiscal policy in economic stabilization. According to monetarists the money
supply is the “dominant, though not exclusive” determinant of both the level of output and prices
in the short run, and of the level of prices in the long run. The long- run level of output is not
influenced by the money supply Jalil, (2011).
They further said that, when the money supply is increased in order to grow or increase
production and full employment it creates an inflationary situation within an economy.
Monetarist believes that increases in the money supply will only influence or increase production
and employment levels in the short run and not in the long run. Accordingly, there will be a
positive relationship between inflation levels and money supply. They further explain this
relationship by using the theory of natural rate of unemployment.
The theory of natural rate of unemployment suggests that there will be a level of equilibrium
output, employment, and corresponding level of unemployment naturally decided based on
features such as resources employment, technology used and the number of firms in the country
etc. the unemployment level decided in this manner will be identified as natural rate of
unemployment. However, in the short run, expansionary monetary policies will result in the
decline in the rate of unemployment and increase the production but the effectiveness of the
expansionary policies will be limited in the long run and lead to an Inflationary situation.
2.1.4. Cost-Push Theories of Inflation
For Cost-push inflation theorist’s inflation is a phenomenon in which the general price levels rise
due to increases in the cost of wages and raw materials Jalil, (2011). Cost-push inflation
develops because the higher costs of production factors decrease in aggregate supply (the amount
of total production) in the economy. Because there are fewer goods being produced (supply
weakens) and demand for these goods remains consistent, the prices of finished goods increase
Investopedia, (2011/01/12/).

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One of the proponents of cost push theories is James Steuart (1767) in his ‘Inquiry into the
principles of Political Economy,’ argues that “real forces derive individual and aggregate prices
alike” Humphrey (1998). Inflation is determined by forces that determine the prices of individual
goods. The forces governing the prices of specific goods are competition and cost. Competition
lowers prices as do falling costs.
According to Ibid Jhingan, (1997) the cause of cost push inflation is an increase in the price of
domestically produced or imported raw materials. The increase in raw material prices increases
production cost of firms. This in turn results in higher prices because firms pass the cost increase
to consumers.
2.1.5. Keynesian Theory of Inflation
J.A. Keynes (1940) is known as the father of modern economics in his theory of inflation he
argues that an increase in general price levels or inflation is created by an increase in the
aggregate demand which is over and above the increase in aggregate supply. If a given economy
is at its full employment output level, an increase in government expenditure (G), an increase in
private consumption (C) and an increase in private investment (I) will create an increase in
aggregate demand; Leading towards an increase in general price levels. Such an inflationary
situation is created due to the fact that at optimum or full employment of output (maximum
utilization of scarce resources) in a given economy is unable to increase its output or aggregate
supply in response to an increase in aggregate demand. According to Keynes, unexpected
increase in aggregate demand creates “inflationary gap” and leads to inflation under full
employment conditions. This in turn creates unanticipated profits for firms while nominal wages
remain temporarily constant. The rising profit creates excess demand in the goods market. The
rise in profit compels firms to expand their production there by creating excess demand in the
labor market. The competition for fully employed labor among firms pushes nominal wages until
real wage is restored at its initial level. The increase in real wage in turn produces excess demand
in the goods market and hence inflationary pressure. The interaction of the labor and goods
market produces wage-price spiral that can only be reversed by checks to aggregate demand
Kibritçioğlu, (2002).

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2.1.6 New Neoclassical Synthesis (NNS) Theory of Inflation
According to NNS monetary (Marvin & Robert, 1997) or demand, factors are a key determinant
of business cycles, because of the incorporated new Keynesian assumption of price stickiness in
the short run. At the same time, however, the NNS assigns a potentially large function to supply
shocks in explaining real economic activity, as suggested in the new classical real business cycle
theory. The highly complex model of the new neoclassical synthesis allows that Keynesian and
real business cycle mechanisms to operate through somewhat different channels. The so-called
new IS-LM-PC version of the NNS makes the price level an endogenous variable. In this model,
IS refers to Investment and saving i.e. equilibrium equation of goods and services market, LM
refers to demand for and supply of money i.e. equilibrium equation of money market and PC
refers to Philips Curve. The NNS also views expectations as critical to the inflation process, but
accepts expectations as amenable to manage by a monetary policy rule.
2.1.7 New Political Macroeconomics Theory of Inflation
The major important theories as mentioned above mainly focus on macroeconomic determinants
of inflation and simply ignore the role of non- economic factors such as institutions, political
process and culture in the process of inflation. Political forces, not the social planner, choose
economic policy in the real world. Economic policy is the result of a decision process that
balances conflicting interests so that a collective choice may emerge.
According to The new political economy theorist’s literature Alberto, (1988) provides fresh
perspectives on the relations between timing of elections, performance of policy maker, political
instability, policy credibility and reputation, and the inflation process itself. The case for Central
Bank independence is usually framed in terms of the inflation bias (deviation) present in the
conduct of monetary policies. However, the theoretical and empirical work suggests that
monetary constitutions should be designed to ensure a high degree of Central Bank autonomy.
They also overlook the possibility that sustained government deficits, as a potential cause for
inflation, may be partially or fully indigenized by considering the effects of the political process
and possible lobbying activities on government budgets, and thus, on inflation.

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2.2 EMPIRICAL LITERATURE REVIEW
Many researchers have carry out a variety of researches regarding to the determinants of
inflation. However, they did not agree on the specific variables that causes inflation in the
country. This suggests that the issue of inflation requires an intensive further study with sound
methodology so that it may be easy to investigate and predict it. In Ethiopia specifically there are
many researchers who conduct the captioned research (i.e. Analysis of determinants of inflation
in Ethiopia) or related topic. But most of the paper related to this topic were published before
five years ago and the behavior of inflation has changed through time needs further investigation
timely.
Anfofum, Andow, & Danpome, (2015) investigated the main determinants of inflation in Nigeria
for the period 1986 – 2011. The researcher uses the Augmented Dickey-Fuller to test unit root
test to examine the stationarity of the model and the statistics test revealed that all the variables
are stationary after first and second difference at 5% level of significance. The researcher
deployed Ordinary Least Square (OLS) method and Johansen co-integration test (Johansen 1991)
for vector autoregressive (VAR) test. The co-integration test was used to determine the long-run
relationship of the variables in the mode and result reveals long-run equilibrium relationship
between the rate of inflation and its determinants. The Granger causality test was also used in the
paper and the result revealed evidence of a feedback relationship between inflation and its
determinants.
Olatunji, Omotesho, & Ayinde, (2010) examined determinants of Inflation in Nigeria using Co-
Integration Approach using time series data that was sourced from the Central Bank of Nigeria
and National Bureau of Statistics. Descriptive statistics and co-integration analysis were the
analytical tools used. The paper observed that there were variations in the trend pattern of
inflation rate. Some of the variables considered were significant in determining inflation in
Nigeria. The paper indicates that the previous total export was found to have a negative impact
on current inflation while the previous total import exerts a positive effect likewise the food price
index. It has thus been recommended that policies that will set the interest rate to a level at which
it will encourage investment and increase in production level could be institutionalized;
importation should be reduced in Nigeria. But the researcher does not indicate the time in their
investigation to decide whether the data fulfill time series qualification.

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Ahmed, (2007) Examined the determinants of inflation in Ethiopia and concludes “structural
changes” such as increasing bargaining power of farmers and monetary expansion are the main
reasons of inflation in Ethiopia. He argues that monetary expansion is largely dictated by credit
expansion in both the public and private sector. Credit expansion is explained on the public side,
by decline in foreign finance flow, including a reduction foreign aid. At the same time, he points
out private sector credit expands substantially, which is supported by negative real interest rate
and increased investment demand.
Temesgen, (2013) investigate determinant and impacts of dynamic inflation in Ethiopia using
Ganger causality approach to investigate the determinants of inflation in Ethiopia by using four
testable hypotheses: i.e. (1) does the money supply growth Granger-cause inflation? (2) Does
currency devaluation Granger-cause inflation? (3) Does real GDP growth Granger-cause
inflation? And (4) does oil price fluctuation granger cause of inflation? The empirical finding on
this paper indicates that a bi-directional causality between money supply growth and inflation
and a unidirectional causality between currency devaluation, oil price volatility and inflation has
existed. However, the causality between inflation and economic growth was weak and
insignificant which shows that inflation by itself does not directly significantly affect the real
GDP growth in or economic growth does not Granger cause inflation.
Alemayehu & Kibrom, (2011) conduct the study on the galloping inflation in Ethiopia: a
cautionary tale for aspiring ‘developmental states’ in Africa to understanding the forces behind
the current inflationary experience in Ethiopia by developing synthesis monetarist and
structuralist model of inflation. The model is estimated using vector autoregressive (VAR)
formulation for the period 1994/95 to 2007/08 using quarterly data. The finding indicates that the
determinants of inflation are found to differ for food and non-food sectors and in the short and
long run as well. The most important forces behind food inflation in the long run are a sharp rise
in food demand triggered by an alarming rise in money supply/credit expansion, inflation
expectation and international food price hike. The long run determinants of non-food inflation,
on the other hand, are money supply, interest rate and inflation expectations. In the short run
model, wages, international prices, exchange rates and constraints in food supply are found to be
prime sources of inflation.

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On the other hands the Determinants of inflation in Pakistan: an econometric analysis using
Johansen co-integration approach has investigated by Furrukh, Shahbaz, Kalsoom, Usman,
Jahanzeb, & Muhammad, (2011) to examine demand side and supply side determinants of
inflation in Pakistan to investigate causal relationships among some macroeconomic variables.
The study used Johansen Co-integration and Vector Error Correction approaches for Long run
and short run estimation of determinants of inflation and Granger causality test was used for
Causal relationships investigation. The finding of the study indicates that in the long run
consumer price index has found to be positively influenced by money supply, gross domestic
product, imports and government expenditures on the other side government revenue is reducing
overall price level in Pakistan.
Fatukasi, (2004) investigates the determinants of inflation in Nigeria in the year between 1981
and 2003. Hence, the study conducted on an investigation into the multi-dimensional and
dynamic factors that affect inflation with the view to make appropriate recommendations to
decrease it. The finding of the study indicates that it was revealed that all explanatory variables
(fiscal deficits, money supply, interest and exchange rates) significantly and positively impacted
on the rate of inflation in Nigeria during the period under review. The explanatory variables
accounted for 72% of the variation in inflation during the period with the error terms capturing
28% of the variation.
Sisay M. (2008) examine the determinants of recent inflation in Ethiopia using quarterly data
from 1997/98 Q3 up to 2000/01 Q1. According to the paper 98.53 % of the variation in
consumer price index is explained by the independent variables since the researcher used CPI as
dependent variable to analyses the determinants of inflation and the adjusted R2 value has almost
the same with R2 (i.e. 98%). The Durbin –Watson was conducted to estimates absence of auto
correlation in the model and the result indicate the model has free of auto correlation. The paper
finding indicates the reason of Inflation in Ethiopia as structural problem, lending rates and
monetary phenomenon. The paper proposes monetary expansion by government as a solution to
overcome this problem. But, monetary expansion harsh the country’s inflation and not the
solution for one country as indicated by monetarists, the money supply is the “dominant, though
not exclusive” determinant of both the level of output and prices in the short run, and of the level
of prices in the long run. The long- run level of output is not influenced by the money supply

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Jalil, (2011). So proposing monetary expansion has not the only solution to overcome inflation in
short run and long run.
Recently in 2017, Teamrat, (2017) examine determinants of inflation in Ethiopia using time
series data for the period from 1975 to 2014 for analysis of demand and supply side determinants
of inflation in Ethiopia. The study employed the ordinary least square method to test for the
existence of a short-run and long-run relationship between inflation and its determinant variables
and co-integrating regression for long-run property of the model. The finding of the study
indicates that GDP is significantly and positively affect inflation rate both in the short and long-
run. The model variation in explanatory variable is 98 percent which indicates the fitness of the
model (depended on explanatory). The study recommends contractionary monetary policy gross
national saving to overcome the problem of inflation in Ethiopia. But monetary policy efficiency
has not the only solution for the problem and the ability of gross saving of the consumer is
basically based income. For instance, Ibid, Jhingan, (1997) indicates that, the price of imported
raw materials affects the consumer ability to save. So if consumer has depended on import goods
since the price imported good not only on domestic price situation the ability of saving may
affect.

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CHAPTER THREE
METHODOLOGY OF THE STUDY
3.1. Sources of data
In this study, time series data are used to analyze the determinants of inflation in Ethiopia for the
period of 1972-2009 E.C. or 1979/1980-2016/2017 using the data that are found from National
bank of Ethiopia (NBE). Macroeconomic forecasting model have traditionally been formulated
as simultaneous equation structural models. However, for a variety of reasons – such as the
inexact manner in which certain variables are excluded from the model’s equations and the need
to include future values of exogenous variables – structural models have proved unreliable for
forecasting (Busari, 2007). The vector autoregressive (VAR) model is one of the most
successful, flexible, and easy to use model for the analysis of multivariate time series. The VAR
model has proven to be useful for describing the dynamic behaviour of economic and financial
time series for policy making. Vector autoregressive (VAR) model offers alternative structural
macroeconomic model for forecasting purposes. In contrast to simultaneous structural model, a
VAR model is a set of dynamic linear equations in which each variable is determined by every
other variable in the model. Doan, Litterman and Sims (1984), and Busari (2007) have used
VAR model to explain the behaviour of inflation. Therefore, this study adopts a VAR model to
determine the variables that influence inflation in Ethiopia within the sample period of 1972-
2009 E.C. or 1979/1980-2016/2017.
3.2. Model specification
The analysis of short run dynamics is often done by first eliminating trends in the variable that is
making the variables to be at the same level by making non-stationary variable stationary. The
following Ordinary Least Square (OLS) semi-log multiple regression model have formulated as
indicated by (Anfofum, Andow, & Danpome, 2015), Olatunji, Omotesho, & Ayinde, (2010) and
Furrukh, Shahbaz, Kalsoom, Usman, Jahanzeb, & Muhammad, (2011) but with little
modification for this study to analyses the data that are collected with related the above specified
topic of study.

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The deterministic form of OLS model is given as:
𝐼𝑁𝐹𝑡 = 𝑓(𝐶𝑃𝐼, 𝐼𝑅, 𝑀𝑆, 𝐸𝑋𝐶𝐻, 𝐸𝑋𝑃𝐸, 𝐸𝑋𝑃𝑂, 𝐼𝑀𝑃, 𝐺𝐷𝑃)……………………. (1)
The multivariate stochastic form of equation 1 is of the following form:
𝐼𝑁𝐹𝑡 = 𝛽0 + 𝛽1 𝑙𝑛𝐶𝑃𝐼 + 𝛽2 𝑙𝑛𝐼𝑅 + 𝛽3 𝑙𝑛𝑀𝑆 + 𝛽4 𝑙𝑛𝐸𝑋𝐶𝐻 + 𝛽5 𝑙𝑛𝐸𝑋𝑃𝐸 +
𝛽6 𝑙𝑛𝐸𝑋𝑃𝑂 + 𝛽7 𝑙𝑛𝐼𝑀𝑃 + 𝛽8 𝑙𝑛𝐺𝐷𝑃 + 𝜀𝑡 ………………………… (2)
Where, 𝐼𝑁𝐹 = Inflation rate (percent) - Dependent variable
Independent variable
𝐶𝑃𝐼 = Consumer price index (2011= 100 & weight pattern)
𝐼𝑅 = Interest rate of average lending rate (percent)
𝑀𝑆 = Money Supply (In Millions of Birr)
𝐸𝑋𝐶𝐻 = Annual exchange rate (average Birr/USD)
𝐸𝑋𝑃𝐸= Government expenditure (In Millions of Birr)
𝐸𝑋𝑃𝑂 = Total export (In Millions of Birr)
𝐼𝑀𝑃= Total import (In Millions of Birr)
𝐺𝐷𝑃= Gross domestic product (In Millions of Birr)
𝑡 = time
𝜀 = error term
𝑙𝑛 = natural logarithm
3.2.1. Expectations of the Variables in the Model
Inflation rate (INF) is the dependent variable for this study. Variables like Consumer price index
(CPI), Interest rate of average lending rate (IR), Money Supply (MS), Annual exchange rate
(EXCH), Government expenditure (EXPE), Total export (EXPO), Total import (IMP) and Gross
domestic product (GDP) are the independent variables. Inflation rate is expected to have direct
relationship with CPI, EX, MS, and MP but GDP, EXPE, IR and EXPO are expected to have an

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indirect relationship with INF. U represents error term which stands for the omitted factors that
affect inflation but were not captured in the model. Subscript t indicates that the data for this
study is time series data. It is expected that as GDP of the economy improves inflation decreases.
Theories hold that increase in money supply and import price will increase inflation rate. In
general, as interest rates are lowered, more people are able to borrow more money. The result is
that consumers have more money to spend, causing the economy to grow and inflation to
increase. The opposite holds true for rising interest rates. With less spending, the economy slows
and inflation decreases. The theory also suggest that increased inflation means more
imports and less exports. But increased inflation should also increase the exchange rate (currency
depreciation). If you can trade foreign currency for more domestic currency, then exports should
increase and (conversely) imports should decrease. The variables in the model revealed that
inflation is a product of domestic and external factors.
3.3. Stationarity Tests /Unit Root Test
3.3.1. The Augmented Dickey-Fuller (ADF) Test
In econometric analysis when time series data are used the preliminary statistical step is to
determine the order of integration of each time series used. As indicated by Fekadu, (2012) a
time series Yt is stationary if its probability distribution does not change over time, that is, if the
joint distribution of (Ys+1, Ys+2…, Ys+T) does not depend on s; otherwise, Yt is said to be non-
stationary If the series is not stationary, then inference procedures are invalid. Results derived
from the regression models would produce spurious results if non stationary data is used.
Therefore, the first task is to check for the existence of stationarity property in the series of
inflation rate. To check the stationarity of the data the Augmented Dickey-Fuller (ADF) test is
applied.
The Augmented Dickey-Fuller (ADF) test for autoregressive unit root tests; the null hypothesis
H0: μ=0 against the one sided alternative H1: μ< 0 in the regression
 𝑌𝑡 = 𝛽0 + 𝜇𝑌𝑡−1 + 𝛿1∆𝑌𝑡−1 + 𝛿2∆𝑌𝑡−2+. . . +𝛿 𝜌∆𝑌𝑡−𝜌 + 𝑢 𝑡…………………… (3)
Under the null hypothesis μ=0, Yt has a unit root; under the alternate hypothesis, Yt is stationary.
The ADF statistic is the OLS t-statistic testing μ=0 in the equation above. If instead the alternate
hypothesis is that Yt is stationary around a deterministic linear time trend, then this trend t (the

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period number), must be added as an additional regressor in which case the Dickey-Fuller
regression becomes
 𝑌𝑡 = 𝛽0 + 𝛼𝑡 + 𝜇𝑌𝑡−1 + 𝛿1∆𝑌𝑡−1 + 𝛿2∆𝑌𝑡−2+. . . +𝛿 𝜌∆𝑌𝑡−𝜌 + 𝑢 𝑡 ….…………. (4)
Where α is an unknown coefficient and the ADF statistic is the OLS statistic testing μ=0 in the
above equation.
N.B. Here Yt is stands for inflation rates.
3.4. Information Criteria (Lag length Selection)
The lag length 𝜌 can be chosen using the Akaike’s Information Criteria (AIC) and final
prediction error (FPE) are used to estimates the lag length in this study. According to (Venus,
2004) Akaike’s Information Criteria (AIC) and final prediction error (FPE) are superior than the
other criteria under study in the case of small sample (60 observations and below), in the
manners that they minimize the chance of under estimation while maximizing the chance of
recovering the true lag length because it known as the best information criteria to use. Burnham
& Anderson, (2004) also argue that AIC has theoretical as well as practical advantage because it
is derived from principles of information criteria.
The general form for calculating AIC and FPE are
A) Akaike information criterion, 𝐴𝐼𝐶 𝑝 = – 2𝑇 [ln( 𝜎̂ 𝑝
2
)] + 2𝑝; and ………… (5)
B) The final prediction error, 𝐹𝑃𝐸 𝑝 = 𝜎̂ 𝑝
2 (𝑇 – 𝑝)−1(𝑇 + 𝑝) …………………….. (6)
Where,
𝜎̂ 𝑝
2
= (𝑇 – 𝑝 − 1)−1
∑ 𝜀̂𝑡
2
𝑇
𝑡=𝑝
𝜀𝑡 Is the model’s residuals, p is the number of parameters and T is number of observation?
Given a set of candidate values for the data, the preferred value is the one with the minimum
AIC and FPE value.
3.5. Vector Auto Regression (VAR) Model
A Vector Auto regression (VAR) expresses each variable as a linear function of its own past
values, the past values of all other variables being considered, and a serially uncorrelated error
term. It is a set of k time series regression in which the regressors are lagged values of all k

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series. When the number of lags in the equations is the same and is equal to p, the system of the
equation is called a VAR (p).
VAR with time series variables consists of equation Y1t and Y2t which represents the relationship
between inflation and its determinants can be formulated as; Johnston & Dinardo, (1997)
𝑌1𝑡 = 𝛽10 + 𝛽11 𝑌1 𝑡−1+. . . . + 𝛽1𝜌 𝑌1 𝑡−𝜌 + 𝛿11 𝑌2 𝑡−1+. . . +𝛿1𝜌 𝑌2 𝑡−𝜌 + 𝑢 𝑡…… (7)
Where the β’s are unknown coefficients and 𝑢 𝑡 are error terms
If the different variables are correlated with each other, as they typically are in macroeconomic
applications, then the error terms in the model will also be correlated across equations. One
application of VAR in time series forecast is to test whether the lags of included variable has
useful predictive content above and beyond others variables in the model. The claim that a
variable has a predictive content corresponds to the null hypothesis that the coefficients on all
lags of that variable are different from zero. Granger causality test is used to know the predictive
content of regressors.
3.6. The Granger Causality model
This study also provides a causality test to determine the causal relationship between inflation
and its determinants. In his view, Granger (1969) Y is said to “Granger-cause” X when only X is
better predicted by using the past values of Y than by not doing so with the past values of X
being used in either case. In this study, where only the lagged value of the inflation variable in
equation 8 is significant, it infers that inflation Granger causes inflation determinants (CPI, IR,
MS, EXCH, EXPE, EXPO, IMP and GDP). If the lagged independent variables in the two
equations are significant, then, it inferred a bi-directional causality between inflation and its
determinants, but where only the lagged value of the determinants of inflation equation 9 is
significant, it suggests that the determinants Granger causes inflation. To determine whether
there is Granger causality between inflation and its determinants.
The Granger causality model was adopted in line with Engle & Granger, (1987) with some
remarkable modification in the interest of this study.
Yt=α1+ ∑ δ1Yt-1 + ∑ β1DTYt-1 + ∑ ε1t ……………………………………………(8)
DTYt=α2+ ∑ β1DTYt-1 + ∑ δ1Yt-1 + ∑ ε2t …………………………………………(9)
Where

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α1 And α2 are constants, and ∑ ε1t and ∑ ε2t are the stochastic term; Yt is the rate of inflation
(INF) whereas DTYt represents the various inflation rate determinants CPI, IR, MS, EXCH,
EXPE, EXPO, IMP and GDP. The statement of hypothesis is;
𝐻01: Yt does not Granger cause DTYt
𝐻02: DTYt does not Granger cause Yt
3.7 Johansen Co-Integration Technique
(Johansen S. , 1988) And Johansen & Juselius, (1990) have given new technique for co-
integration for long run as well as short run relationships for multivariate equation as explained
below. As also indicated by Furrukh, Shahbaz, Kalsoom, Usman, Jahanzeb, & Muhammad,
(2011) assume that we have three variables, Yt, Xt and Wt which can all be endogenous, i.e. we
have that (using matrix notation for Zt = [Yt, Xt, Wt]).
𝑍𝑡 = 𝐴1 𝑍𝑡−1 + 𝐴2 𝑍𝑡−2 + … … … … … . + 𝐴 𝑘 𝑍𝑡−𝑘 + 𝜀𝑡 ……………………. (10)
It can be reformulated in a vector error correction model (VECM) as follows;
𝛥𝑍𝑡 = 𝑟1 𝛥𝑍𝑡−1 + 𝑟2 𝛥𝑍𝑡−2 + . . . . . . + 𝑟𝑘−1 𝛥𝑍𝑡−𝑘 + Π 𝑍𝑡−1 + 𝜀𝑡 … … … (11)
Where 𝑟𝑖 = (I – A1 – A2 – ….. – Ak) (I = 1, 2 …… k-1) and Π = – (I – A1 – A2 – …. – Ak). Here
we need to carefully examine the 3 Χ 3 Π matrix (The Π matrix is 3 Χ 3 due to the fact that we
assume three variables in Zt = [Yt, Xt, Wt]). The Π matrix contains information regarding the
long run relationship. In fact ∏ = 𝛼𝛽′ where 𝛼 will include the speed of adjustment to
equilibrium coefficients while 𝛽 will be the long run matrix of coefficients.
Therefore the 𝛽′ 𝑍𝑡−1 term is equivalent to the error correction term (𝑌𝑡−1 – 𝛽0– 𝛽1 𝑋𝑡−1) in the
single equation case, except that now 𝛽′ 𝑍𝑡−1 contains up to (n – 1) vectors in a multivariate
framework. For simplicity we assume that k = 1, so that we have only two lagged terms and the
model is then the following:
(
𝛥𝑌𝑡
𝛥𝑋𝑡
𝛥𝑊𝑡
) = 𝑟1 (
𝛥𝑌𝑡−1
𝛥𝑋𝑡−1
𝛥𝑊𝑡−1
) + ∏ (
𝛥𝑌𝑡−1
𝛥𝑋𝑡−1
𝛥𝑊𝑡−1
) + 𝜀𝑡 ……………………………………. (12)

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Or (
𝛥𝑌𝑡
𝛥𝑋𝑡
𝛥𝑊𝑡
) = 𝑟1 (
𝛥𝑌𝑡−1
𝛥𝑋𝑡−1
𝛥𝑊𝑡−1
)+(
𝛼11 𝛼12
𝛼21 𝛼22
𝛼31 𝛼33
) (
𝛽11 𝛽12 𝛽13
𝛽21 𝛽22 𝛽23
) (
𝛥𝑌𝑡−1
𝛥𝑋𝑡−1
𝛥𝑊𝑡−1
)+ 𝜀𝑡
Let us now analyze only the error correction part of the first equation (i.e. for ΔYt on the left
hand side) which gives;
Π1 𝑍𝑡−1 = ([𝛼11 𝛽11 + 𝛼12 𝛽21][𝛼11 𝛽12 + 𝛼12 𝛽22]) × [𝛼11 𝛽13 + 𝛼12 𝛽23] (
𝛥𝑌𝑡−1
𝛥𝑋𝑡−1
𝛥𝑊𝑡−1
) . . (13)
Where, Π1 is the first row of the Π matrix.
Which shows clearly the co-integrating vectors with their respective speed of adjustment terms
𝛼11 and 𝛼12. Johansen (1988) and Johansen and Juselius (1990) have proposed few steps for
reliable results discussed below.
1. For the application of Johansen Co-integration approach, all-time series variables involving in
the study should be integrated of order one [I (1)].
2. At second step, lag length would be chosen using VAR model on the basis of minimum values
of Final Predication Error (FPE), Akaike Information Criterion (AIC), and Hannan and Quinn
information criterion (HQ) but this study uses only AIC and FPE for the reason that have already
described under lag selection criterion.
3. At third step, appropriate model regarding the deterministic components in the multivariate
system are to be opted.
4. Johansen (1988) and Johansen and Juselius (1990) examine two methods for determining the
number of co-integrating relations and both involve estimation of the matrix Π. Maximal
eigenvalue statistics and trace statistic are utilized in 4th step for no of co-integrating
relationships and also for the values of coefficients and standard errors regarding econometric
model.
3.8 Vector Error Correction Model (VECM)
A vector error correction model is a restricted vector autoregressive (VAR) designed for use with
non-stationary series that are known to be co-integrated. It may be tested for co-integration using
an estimated VAR object.
The VECM has co-integration relations built into the specification so that it restricts the long run
behavior of the endogenous variables to converge to their co-integrating relationships while

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allowing for short run adjustment dynamics. The co-integration term is known as the error
correction term (speed of adjustment) since the deviation from long run equilibrium is corrected
gradually through a series of partial short run adjustments. The Short run equation from equation
2 above formulated as given below;
∆𝑌𝑡 =
[
𝛽0 + ∑ 𝛼1∆𝑌𝑡 𝑡−1
+ ∑ 𝛽1∆𝑙𝑛𝑥1 𝑡−𝑗
+
𝑞
𝑗=0
∑ 𝛽2∆𝑙𝑛𝑥2 𝑡−𝑗
+
𝑞
𝑗=0
+
𝑞
𝑗=0
𝑞
𝑗=1
+
𝑞
𝑗=0
+
𝑞
𝑗=0
+ ∑ 𝛽7∆𝑙𝑛𝑥7 𝑡−𝑗
+
𝑞
𝑗=0
+
𝑞
𝑗=0
𝑞
𝑗=0
𝛾1ECM 𝑡−1 + 𝜀𝑡
]
. . (14)
Where, Δ is difference operator, q is chosen lag length, β’s are parameters, 𝛾 is error correction
term or speed of adjustment term (calculated from long run results) and Ԑ is error term.

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CHAPTER FOUR
RESULT AND DISCUSION
4.1. Trends of Inflation Rate
Trends of inflation show moderate ups and downs from 1972 to 2009in E.C or from1979/80 to
2016/17 with exceptions of 1977, 2000, 2003 and 2004 in E.C. or 1984/85, 2007/08, 2010/11
and 2012/13. In 1984/85 there was a devastating drought which claims the life of many
Ethiopian and also created the current image of the country in the world. Since the country
depends on rain fed agriculture as a main source of income, the drought diminished output
growth which in turn has a significant influence on the increment of inflation by around 20.47
percent. In 1990/91 there was a political transition in country and high inflation was recorded
almost 45 percent. And these were because of political unrest of the country and hinder the
country’s economy to overcome the problem. 2007/08 there was Ethiopian millenniums’ and it
the time to most of the country stands for work also time transition partially from agri-based
economy to industry based economy and recorded the ever highest inflation in the country about
55.24 percent. During Ethiopian millennium CPI was climbed from 39.02 to 60.58 basically due
to food price rise at a time with some adjustments of nonfood price and national banks also made
an adjustment on interest rate from 3% to 4%. The data indicated also an increment of inflation
in 2010/11 and 2012/13 with amount of 38 percent and 20.81 percent due to government’s shift
of on monetary and fiscal policies and central bank continued to pursue gradual depreciation of
the birr by almost 20 percent in 2010/11(i.e. from 12.89 to 16.12).

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Figure 1: Inflation Rate in Ethiopia
Source: Computed from STATA-12 by the author (June, 2018)
4.1.1 Inflation, consumer Price index and Interest rate
The difference between the Consumer Price Index (CPI) and inflation is a source of confusion
for many. At its easiest level, the Consumer Price Index is used to calculate inflation. Thus, their
similarities are better understood based on that relationship even if the details of their differences
are not. CPI is defined as a measure of the average change over time in the prices paid by
consumers for a market basket of consumer goods and services whereas inflation is the overall
general upward price movement of goods and services in an economy. The behavior of inflation
and consumer price index in Ethiopia also indicates the same direction with each other but the
speed of CPI is more than inflation. On the other hand, inflation has inverse relationship with
interest rates which means the rise in interest rates reduces money supply form the economy and
this also affects the inflation rate inversely. The reverse is true for both variables; as indicated in
the graph

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Figure 2: Inflation, consumer Price index and Interest rate
4.2. Unit Root Test (Stationary Test)
Since this study employs a time series data, it is mandatory to test stationary of data. A unit root
test is conducted employing Augmented Dickey Fuller (ADF) test to prove whether the variables
in the model are stationary or not. The calculated values are compared with the critical values at
determined level of significance. If the calculated value is greater than any of the critical values,
then we reject the null hypothesis, which actually means the variables are stationary. Otherwise,
we do not reject the null hypothesis meaning that there is a nit root implying the variable is non-
stationary.
Table 1: Unit Root Test
Variables Tests for
Unit Root in
Include in
Test Equation
Test
Statistics
Critical
Value
Result
Inf
Level
Intercept 2.574 2.972
I(1)Trend and Intercept 2.775 3.560
1st Difference Intercept 9.306 3.675***
LnCpi
Level
I(1)
Trend and Intercept 1.086 3.552
LnIr Level Intercept 2.015 2.966 I(1)
0
50
100150200
1970 1980 1990 2000 2010
Year(Obs)
inf CPI
IR

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LnMs
Level
I(1)
1st Difference Intercept 3.349 2.969**
LnExch
Level
I(1)
LnExpe
Level
I(1)
LnExpo
Level
I(1)
LnImp
Level
I(1)
LnGDP
Level
I(1)
Note: * Significant at 10%, ** Significant at 5%, *** Significant at 1%
Table 1 presents the result of the unit root test from Augmented Dickey-Fuller test. All the
variables exhibit unit root at the level (at 5%level of significance), that is are non-stationary. But
at the first differencing, they all became stationary at 1% except money supply at 5% level of
significance. The differencing is needed in order to avoid having a spurious regression. Since the
differenced variables are stationary, there is co-integration between the variables, meaning that
there a long-run relationship between the inflation and consumer price index total, interest rate,
money supply, exchange rate, government expenditure, total export, import and GDP.
4.3 Lag Length Selection Process
The other step in estimation time series data analysis has the selection of appropriate lag length
using proper information criterions. The study employ Akaike’s Information Criteria (AIC) and
final prediction error (FPE) to estimates the lag length because they are preferable small (finite)
sample size. We can also decide based on the majority of estimated point that give the lowest
value during system equation estimation.

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Table 2: Lag length Selection
Lag FPE AIC HQ
0 1.1e-08 7.21679 7.15415
1 2.9e-15 -8.07438 -6.56399
2 5.7e-16* -10.6255* -7.95335*
* indicates lag order selected by the criterion calculated
using STATA-12
FPE: Final prediction error
AIC: Akaike information criterion
HQ: Hannan-Quinn information criterion
As we can see from Table 2, both proposed criteria indicated to use two lags. Therefore, two lags
will be considered in Johansson Cointegration model. Even if we cannot propose here to use it
HQ criterion also suggested using lag two to run the model.
4.4. Vector Auto regression (VAR) Estimation Results (Long run)
Table 3: VAR estimation result
Vector auto regression
Sample: 1974 – 2009 No. of obs = 36
Log likelihood = -124.0339 AIC = 7.501884
FPE = 108.2202 HQIC = 7.670762
Det(Sigma_ml) = 57.56391 SBIC = 7.985737
Equation Parms RMSE R-sq chi2 P>chi2
INF 11 9.10451 0.6907 80.40138 0.0000
INF Coef. Std. Err. z P>z [95% Conf. Interval]
Inf L1. -.4695021 .1206696 -3.89 0.000 -.7060102 -.2329939
L2. -.3064037 .1233623 -2.48 0.013 -.5481893 -.064618
lnCPi L1. 107.0965 16.86744 6.35 0.000 74.03696 140.1561
L2. -1.346362 1.851344 -0.73 0.467 -4.974928 2.282205
lnIR L1. -28.22816 7.283793 -3.88 0.000 -42.50413 -13.95219
L2. -.0281015 .1258978 -0.22 0.823 -.2748567 .2186538
lnMS L1. -39.62113 13.64701 -2.90 0.004 -66.36878 -12.87348
L2. -.5443819 .4371541 -1.25 0.213 -1.401188 .3124244
lnEXCH L1. -6.102696 10.38065 -0.59 0.557 -26.44839 14.243
L2. .207374 .2129722 0.97 0.330 -.2100439 .6247919
lnEXPE L1. -21.83381 12.69236 -1.72 0.085 -46.71037 3.042757
L2. -.0625225 .2088371 -0.30 0.765 -.4718358 .3467908

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lnEXPO L1. 2.243401 7.952808 0.28 0.778 -13.34382 17.83062
L2. .0517469 .1216148 0.43 0.670 -.1866137 .2901075
lnIMP L1. 6.817426 11.42823 0.60 0.551 -15.58149 29.21634
L2. .0678654 .2031548 0.33 0.738 -.3303106 .4660414
lnGDP L1. -19.90372 10.1636 -1.96 0.050 -39.82401 .0165734
L2. .0007754 .4672081 0.00 0.999 -.9149356 .9164863
_cons 400.4158 112.2226 3.57 0.000 180.4636 620.368
The result of the VAR model (as indicated above in table 3) provides useful information about
the response of a variable to innovations in another. The estimated VAR result revealed that only
four variables (i.e. CPI, IR, MS and GDP) are statistically significant at 5 per cent level of
significance. Furthermore, it is shown that there is negative relationship between the inflation
rate (Inf) and interest rate (IR), and GDP whereas the variable like consumer price index (CPI),
and Money supply (MS), have a positive relationship with inflation rate. This outcome clearly
demonstrates useful information about the response of a variable to innovations in another.
As the result indicates for instance inflation has positive relationship with consumer price index
because of CPI measures the overall general upward price movement of goods and services in an
economy. In the short run the amount of the money injected to the economy may trigger the
inflation of the country. According to demand- pull theorists there is identical or equal
relationship between national income estimated at market prices and the velocity of circulation
of the money supply. So expansion of money supply causes inflation. Inflation rates have
negative relationship with interest rate for the logic of rising interest rate creates money supply
contraction in the economy. The result of VAR estimation results indicates that the rise in
inflation rate affects the GDP of the country. This is basically due rising of inflation rates
without import substitution may harsh the purchasing power of low level income households and
that directly affects the GDP of the country.
4.5. Granger causality test result
Table 4 presents the estimated results of Granger causality test. The Granger causality results
revealed a bidirectional causality relationship between inflation and exchange rate, Government
expenditure, Export, imports and gross domestic product. This simply suggests that the Granger

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causality test is evidence of a feedback relationship between inflation and its determinants. But
consumer price index, interest rate and money supply have a unidirectional causality running
either from variables to inflation rate or vice versa. Therefore, the null hypothesis as stated under
methodology section for this result is rejected.
Table 4: Granger causality test result
Equation Excluded chi2 DF Prob> chi2 Remark
INF LNCPI 10.661 2 0.005 R
LNCPI INF 1.1398 2 0.566 A
INF LNIR .61747 2 0.734 A
LNIR INF 15.12 2 0.001 R
INF LNMS 14.259 2 0.001 R
LNMS INF 2.7058 2 0.258 A
INF LNEXCH 5.5257 2 0.063 A
LNEXCH INF 3.9506 2 0.139 A
INF LNEXPE 5.7557 2 0.056 A
LNEXPE INF 2.9931 2 0.224 A
INF LNEXPO .26034 2 0.878 A
LNEXPO INF .32995 2 0.848 A
INF LNIMP .34325 2 0.842 A
LNIMP INF 3.8111 2 0.149 A
INF LNGDP 4.3501 2 0.114 A
LNGDP INF 3.2396 2 0.198 A
HO1: Inflation does not cause its determinants
HO2: Inflation determinant (independent variable) does not cause inflation.
Note: A = Accepted, R= Rejection

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4.6. Analysis of Johansen co-integration test result
Table 5: Unrestricted Cointegration Rank Test (Trace)
Johansen tests for Cointegration
Trend: constant Number of obs = 35
Sample: 1975 – 2009 E.C Lags = 3
maximum
rank
Parms LL eigenvalue
Trace
Statistic
5% critical
Value
0 171 303.44803 . 2493.4942 192.89
1 188 857.09438 1.00000 1386.2015 156.00
2 203 1400.5854 1.00000 299.2195 124.24
3 216 1452.0127 0.94707 196.3649 94.15
4 227 1489.5313 0.88281 121.3276 68.52
5 236 1514.2883 0.75700 71.8137 47.21
6 243 1529.5802 0.58265 41.2298 29.68
7 248 1541.9344 0.50636 16.5215 15.41
8 251 1549.3605 0.34580 1.6693* 3.76
9 252 1550.1951 0.04657
Table 6: Unrestricted Cointegration Rank Test (Max)
Johansen tests for Cointegration
Trend: constant Number of obs = 35
Sample: 1975 – 2009 E.C Lags = 3
Maximum
Rank
Parms LL Eigenvalue
Max
Statistic
5% critical
Value
0 171 303.44803 . 1107.2927 57.12
1 188 857.09438 1.00000 1086.9820 51.42
2 203 1400.5854 1.00000 102.8546 45.28
3 216 1452.0127 0.94707 75.0373 39.37
4 227 1489.5313 0.88281 49.5139 33.46
5 236 1514.2883 0.75700 30.5838 27.07
6 243 1529.5802 0.58265 24.7084 20.97
7 248 1541.9344 0.50636 14.8522 14.07
8 251 1549.3605 0.34580 1.6693* 3.76
9 252 1550.1951 0.04657
*denotes rejection of the hypothesis at 0.05 level

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Table 5 and 6 above both showed the result of Johansen co-integration test of two likelihood
ratio test statistics: The Trace statistic and the Maximum Eigenvalue are commonly used to
determine the number of co-integrating vectors in a study. The Johansen co-integration test
reveals that there are at least eight cointegrating vectors in the series which was evident of the
presence of a long-run equilibrium relationship between the variable inflation rate and its
explanatory variables. Linear deterministic trend was assumed in the test.
The analysis indicates, we rejects the null hypothesis that there is no co-integrated vector
(None), there is at most 1 co-integrated vector (At most 1), there is at most 2 co-integrated
vectors (At most 2), there is 3 co-integrated vectors (At most 3), there is 4 co-integrated vectors
(At most 4), there is 5 co-integrated vectors (At most 5), there is 6 co-integrated vectors (At most
6), there is 7 co-integrated vectors (At most 7) and also there is at most 8 co-integrated vectors
(At most 8). It means that there are 8 co-integrated vectors in long run results which revealed as
there are eight vectors integrated at least suggests a long-term relationship between variables. It
shows high association between explanatory and dependent variables used in current study.
4.7 Test for serial Autocorrelation and normality of the disturbance
The LM test for residual autocorrelation is performed to test the behavior of residual at selected
lag. The result from system equation indicates here in the table below shows that we cannot
reject the null hypothesis of no autocorrelation in the residuals of the VAR model in both lags
and result indicates no autocorrelation residuals. As indicated in the table p value under both lags
are more than five percent level of significance which means we cannot reject the null hypothesis
rather we accept null hypothesis meaning that there is no serial correlation in this VAR model as
a whole.
Table 7: LM Test for serial Autocorrelation of VAR model
Lagrange-multiplier test
lag chi2 Df Prob > chi2
1 81.4820 64 0.06929
2 79.3723 64 0.09327
H0: no autocorrelation at lag order

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Table 8: Test for normally distributed disturbances
Jarque-Bera test
Equation chi2 df Prob> chi2
inf 7.986 2 0.01845
lncpi 7.057 2 0.02935
lnir 8.048 2 0.01788
lnms 7.189 2 0.02747
lnexch 9.523 2 0.00855
lnexpe 8.573 2 0.01375
lnexpo 9.128 2 0.01042
lnimp 9.130 2 0.01041
lngdp 1.781 2 0.41036
ALL 68.416 18 0.00000
H0: Residual are not normally distributed
The above table 8 indicates the VAR model residuals are normally distributed at five percent
level of significance. The model indicates that all variables are normal distribution at of the
residual; so we cannot reject the null hypothesis rather we accept the null hypothesis which
means residuals are normally distributed in this VAR model.
4.8 Vector Error Correction Model (Short run Results)
Table 6 discusses the short run results using vector error correction model. As specified in below
table we can use the ECM that is denoted _Cel on the first row of lag one coefficient to decide
whether the model have short run or long run causalities. If the value of Error correction model
(ECM) has positive value, we can decide that the model have short run causalities where as if the
value of the ECM has negative sign we conclude that the model have long run causalities (Sayed
Hossain, 2013). From the below table only consumer price index variables are significant at five
percent level of significance. But here our target is to know whether the model have the long run
or short run causalities based on the sign of error correction model. The most important thing in
the short run results is speed of adjustment term. It shows that how much time would be taken by
the economy to reach at long run equilibrium. Our model indicates the coefficient ECM (_ce1
0.6880422) implies that the process it not converging in the long run. Since there is no
autocorrelation as stated under table 7, we could also be suggesting as an indication of structural
changes. However, because of the value of the coefficient is statistically insignificant we cannot
decide the exact meaning of the sign.

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Table 9: Test for Vector Error Correction Model
Coef. Std. Err. Z P>z [95% Conf. Interval]
D_inf
L1_cel .6880422 .790669 0.87 0.384 -.8616406 2.237725
Inf
LD. -.0138646 .2852305 -0.05 0.961 -.5729061 .5451769
Lncpi
LD. -190.577 68.13262 -2.80 0.005 -324.1145 -57.03955
Lnir
LD. -7.581975 14.50947 -0.52 0.601 -36.02001 20.85606
Lnms
LD. 75.65814 56.83496 1.33 0.183 -35.73633 187.0526
Lnexch
LD. 28.23221 26.22015 1.08 0.282 -23.15835 79.62277
Lnexpe
LD. -16.13232 22.91574 -0.70 0.481 -61.04634 28.7817
Lnexpo
LD. -.9528218 15.97626 -0.06 0.952 -32.26571 30.36007
Lnimp
LD. 1.669817 24.18482 0.07 0.945 -45.73157 49.0712
Lngdp
LD. -9.222579 26.59511 -0.35 0.729 -61.34804 42.90288
_cons .0006195 6.642519 0.00 1.000 -13.01848 13.01972
The short run model of significant variable (i.e. lncpi) reveal that consumer price index of last
year (2008 E.C or 2015/16) are found to be negatively related with inflation rates of 2009 E.C or
2016/17 here since the value of inflation (Inf) is insignificant we cannot decide the direction of
the variable are the same or not.

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CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1. CONCLUSION
Inflation is a sustained rise in general price level of goods and services. The definition of
inflation commonly recognized as an unpredictable fluctuation which is considered a major
indicator of the instability of economic activity of a country rather than increase in price of
particular commodity or for particular period of time. For an inflation to be happened, the rise in
the general price of goods and services should be sustained. Inflation takes a crucial role in the
healthy functioning of a countries economic performance. A high inflation rate result from
increase in food prices, it hurts the poor because of their high marginal propensity to consume
thereby increase the divide between the rich and poor in the society. Recently, Ethiopia’s
devaluation of the birr by 15 percent at the end of October 2017, according to the government,
aims at revitalizing/stimulating the country’s exports. It has put pressure on inflation, which
moved to double digits even before the devaluation and is expected to continue in 2018.
Ethiopia has experienced a low inflation (i.e. During the Derg regime), but recently, double digit
inflation has become worrisome for policy makers as well as the society. Since the level of
income in Ethiopia is very low but expenditure on consumption items such as food is very high,
inflationary experience results in a low level of welfare. The current inflation has a reducing
effect on the current development of the export sector because of Ethiopian products has dearer
in the international market which in turn makes them less competitive.
The study carries out long run as well as short run estimates of some factors or determinants that
influencing inflation in Ethiopia. The study reveal there are stationarity of the variables at its first
differenced which indicates the co-integration between the variables, meaning that there a long-
run relationship between the inflation and consumer price index total, interest rate, money
supply, exchange rate, government expenditure, total export, import and GDP.
The estimated VAR result revealed that in the long run, and interest rate (IR), exchange rate
(EX), Government expenditure (EXPE) and GDP are contributed to decline in inflation rates, but
only interest rates are statistically significant. The rest variable like consumer price index (CPI),

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Money supply (MS), Export (EXPO) and import have a positive contribution to raise inflation
rate. Here the significant variable indicated positive influence as expected and theoretical
relationship between these variables.
The Granger causality results revealed a bidirectional causality relationship between inflation
and exchange rate, Government expenditure, Export, imports and gross domestic product
whereas the rest variable have a unidirectional causality with inflation. To know the
cointegrating vectors, Johansen co-integration test has run and the result indicates eight
cointegrating vectors which has evident for a long-run equilibrium relationship between the
variable inflation rate and its explanatory variables.
In the short run, the coefficient of error correction term is .688 suggesting 68.8% percent annual
adjustments towards long run equilibrium. Consumer price index of last year (2008 E.C or
2015/16) are found to be negatively related with inflation rates of 2009 E.C or 2016/17.
5.2. RECOMMENDATION
Even if most of the estimated variables are not significant, the sign of the variable gives us some
insight to suggest some policy action to control hyperinflation in the country. It is eminent that
export promotion proves the problem of Ethiopia still there are negative net export of the country
which indicates there are shortage of policy application practically rather than speaking and used
for political issues. The study also suggests the policy makers to control over monetary policy of
the country and promoting saving may have suggested as a solution as other researcher
recommendation.
Since agriculture is the main source of GDP, measures to boost and stabilize domestic
agricultural production and productivity, particularly production of major food staples, have
great importance because movement of inflation in the country is highly derived by price of food
staples. So increasing productivity of domestically consumed products must be given priority by
providing incentives to the agricultural sector and by transforming the sector from rain
dependent ways of production to commercial farming system.

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Sayed Hossain. (2013, Jul 6). You tube. Retrieved from Hossain Academy:
https://www.sayedhossain.com
Sisay, M. (2008). Determinants of Recent Inflation in Ethiopia. Munich Personal RePEc Archive: online at
https://mpra.ub.uni-muenchen.de/29668/, MPRA Paper No. 29668, posted 4. April 2011 06:17
UTC.
Sisay, M. (2008). Determinants of Recent Inflation in Ethiopia. Munich Personal RePEc Archive.
Teamrat, K. (2017). Determinants of Inflation in Ethiopia: A Time-Series Analysis. Journal of Economics
and Sustainable Development:ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online), Vol.8, No.19.
Temesgen, T. B. (2013). Determinant and Impacts of Dynamic Inflation in Ethiopia (A Granger causality
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Venus, K. L. (2004). Which Lag Length Selection Criteria Should We Employ? Economics bulletin, Vol. 3,
No. 33, PP 1-9.

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Annex
National Bank of Ethiopia (NBE) Annual DATA
Obs Inf CPI IR MS Exch Exp Export Import GDP
1972 3.90 9.04 6.00 1498.60 2.07 2122.00 857469.00 1432858.00 120934.34
1973 5.43 9.53 6.00 1715.30 2.07 2281.50 851509.00 1384234.00 121547.42
1974 5.23 10.03 6.00 1892.20 2.07 2629.71 778083.00 1641661.00 121051.43
1975 -0.18 10.01 6.00 2180.40 2.07 3786.10 809625.00 1752945.00 133494.35
1976 9.05 10.92 6.00 2379.30 2.07 3169.00 929625.00 2065005.00 123413.15
1977 20.47 13.15 6.00 2692.10 2.07 3823.40 744572.00 1770433.00 108860.64
1978 -11.82 11.60 6.00 3179.60 2.07 4062.20 923314.00 2201265.00 119719.43
1979 -4.66 11.05 4.00 3563.50 2.07 4003.10 754140.00 2236946.00 139098.99
1980 6.87 11.81 4.00 3910.80 2.07 4820.90 734319.00 2274651.00 137665.79
1981 11.05 13.12 4.00 4173.80 2.07 5725.80 848610.00 2110353.00 135994.08
1982 5.00 13.78 4.00 4990.00 2.07 5283.02 685909.00 1824119.00 141807.70
1983 45.00 19.98 4.00 6134.80 2.07 4854.20 536662.00 2130305.00 136760.36
1984 2.05 20.39 4.00 6845.30 2.07 4205.40 300267.00 1810897.00 130950.24
1985 4.71 21.35 10.00 7580.70 2.80 5219.40 932413.00 3618717.85 146054.40
1986 6.29 22.69 10.00 8373.20 5.77 7093.80 1404172.72 4739966.85 146521.18
1987 14.84 26.06 10.00 9922.40 6.25 8372.00 2737233.37 6546273.92 153976.83
1988 -9.00 23.71 10.00 9917.40 6.32 10194.00 2499515.15 7708246.47 171861.30
1989 -2.65 23.08 7.00 10024.00 6.50 10014.90 3635398.50 8505200.00 180779.06
1990 0.10 23.11 6.00 11094.00 6.88 10898.80 4019286.46 9338458.93 173376.45
1991 10.39 25.51 6.00 11378.90 7.51 14677.20 3437259.51 11702004.00 182061.26
1992 1.89 25.99 6.00 13050.30 8.14 17531.60 3754872.43 11438661.30 189080.89
1993 -10.77 23.19 6.00 13745.80 8.33 15737.30 3378925.67 12313956.15 203269.32
1994 -1.22 22.91 3.00 14ፐ152.5
2
8.54 17650.00 3373308.37 14485289.00 205133.45
1995 17.77 26.98 3.00 15416.77 8.58 20496.00 4137208.28 16067347.50 198999.64
1996 2.38 27.62 3.00 18036.01 8.62 20504.00 5178464.76 22295689.70 222679.36
1997 10.75 30.59 3.00 21291.08 8.65 24774.00 7331257.58 31434173.95 251008.44
1998 10.82 33.90 3.00 23811.87 8.68 29325.00 8685375.79 39873075.06 280790.29
1999 15.10 39.02 3.00 29617.68 8.79 35607.00 10457615.14 45126437.94 313190.93
2000 55.24 60.58 4.00 35350.36 9.24 46915.00 13643975.81 63146946.28 348316.41
2001 2.71 62.22 4.00 42112.66 10.42 57775.00 15217752.86 84677193.05 382384.30
2002 7.32 66.77 4.00 52434.63 12.89 71334.00 26115305.87 108956272.3 422094.37

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5
2003 38.04 92.18 5.00 76171.00 16.12 93831.41 44525565.04 129693361.8
7
478866.87
2004 20.81 111.36 5.00 94849.88 17.25 124416.80 54494767.31 191587138.7
1
519903.44
2005 7.39 119.59 5.00 114745.69 18.19 153928.68 56123591.72 196871016.1
1
571493.30
2006 8.46 129.71 5.00 134063.78 19.07 185471.78 62242999.54 261837358.0
8
630632.74
2007 10.45 143.26 5.00 154706.34 20.10 230521.18 59860381.12 330794232.9
1
696530.82
2008 7.50 154.05 5.00 178609.66 21.11 272930.09 59725752.81 353013855.6
6
1439981.73
2009 8.80 167.60 5.00 216769.62 22.41 329286.84 63685744.10 352453568.5
8
1597612.07
N.B: The time stated in the table has Ethiopian calendar year and when we convert it to
Gregorian it is from 1980 to June 2017.

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