Master thesis I, 15 hp

Master’s Programme in Economics, 120hp

Spring term 2021

DETERMINANTS OF INFLATION IN

ETHIOPIA FROM 1980 to 2019.

Robera Gadisa Adugna

DETERMINANTS OF INFL

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Acknowledgement I praise God for giving me the wisdom, strength, support and for the guidance to surpass

all the trials that I have encountered to finish this thesis. Then I would like to express my

deepest gratitude and appreciation to my supervisor, Gauthier Lanot, for his advice and

guidance throughout this thesis. Finally, I want to thank My families for their love and

support. I would also like to acknowledge Tariku (lubbo) for his priceless help, Jonse

Bane for he’s guidance and Pastor Michael for he’s prayer.

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ABSTRACTS

This study examines the determinants of inflation in Ethiopia, using Vector Error

Correction Model (VECM) by using annual time series data from 1980 to 2019.

Augmented Dickey-Fuller unit root test indicated that the variables are integrated of order

one. However, the variables transformed to stationary by taking the first difference. The

Johansen co-integration test revealed that the existence of long-run relationship between

variables. Furthermore, the coefficients of VECM indicates that there is a positive and

significant relationship between inflation, budget deficit and national debt. However, the

effect of money supply on inflation is only on the short run. Finally, the model is stable

if a shock happens in the future.

Keywords: Inflation, VECM, Ethiopia.

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Table of Contents AFDB: African Development Bank. ............................................................................. 5

1.INTRODUCTION ....................................................................................................... 6

2.LITERATURE REVIEW ........................................................................................... 7

2.1 The theoretical framework .............................................................................. 7

3.RESEARCH METHODOLOGY ............................................................................. 11

3.1 Source of Data ................................................................................................. 11

3.2 Methods of Data Analysis .............................................................................. 11

3.3 Model specification and Variable description ............................................. 11

3.4 Augmented Dickey-Fuller Test...................................................................... 13

3.5 Lag order selection for VAR.......................................................................... 14

3.6 Cointegration Test .......................................................................................... 14

3.7 Vector Error Correction Model Specification ............................................. 15

4.EMPIRICAL RESULTS AND DISCUSSION ....................................................... 16

4.1 Trend and analysis ............................................................................................. 16

4.2 Unit root test ................................................................................................... 18

4.3 Choosing optimal lag length .......................................................................... 19

4.4 Cointegration Analysis ................................................................................... 19

4.5 Two Long run Equations ............................................................................... 20

4.6 Vector Error Correction Model .................................................................... 22

4.7 Model Checking ................................................................................................... 23

4.8 Impulse Response Function ........................................................................... 25

5. CONCLUSION ......................................................................................................... 26

6.REFERENCES .......................................................................................................... 28

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List of Tables and Figures

List of Tables

Table 4. 1 ADF tests for unit root with constant and trend ........................................... 18

Table 4. 2 Optimal lag length selection criteria.............................................................. 19

Table 4. 3 Johansen cointegration test result .................................................................. 20

Table 4. 4 Cointegrating Vectors .................................................................................... 21

Table 4. 5 Estimated short run coefficients .................................................................... 22

Table 4. 6 Autocorrelation Test ...................................................................................... 23

Table 4. 7 Jarque Bera test result .................................................................................... 24

Table 4. 8 Eigenvalue stability condition ....................................................................... 24

List of Figures

Figure 4. 1 The trends lnCPI, lnGND, lnGM2,lnBD n Ethiopia between 1980-2019 ... 16

Figure 4. 2 d.lnCPI, d.lnGND, d.lnGM2, d.lnBD in Ethiopia between 1980-2019 ....... 18

Figure 4. 3 Roots of companion matrix .......................................................................... 25

Figure 4. 4 Graph of Impulse Response Function .......................................................... 26

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Abbreviations

AFDB: African Development Bank.

BD: Budget Deficit

CPI: Consumer Price Index

CSA: Central Statistical Agency.

ECA: Economic Commission of Africa.

IMF: International Monetary Fund.

LDCs: Least Developing Countries.

NBE: National Bank of Ethiopia

ND: National Debt

VAR: Vector Autoregressive.

VECM: Vector Error Correction Model.

WB: World Bank.

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1.INTRODUCTION

Inflation measures a rise in the overall price level of goods and services in a given

economy. It is a decline of purchasing power of a given currency (Ethiopian Birr in this

case). A quantitative estimate of the rate at which the decline in purchasing power occurs

can be revealed in the increment of an average price level of a basket of selected goods

and services in the country during the study period. Individuals with tangible assets such

as property and stocked commodities may like to see some inflation as that raise the value

of their asset. But those holding cash may not like inflation, as it erodes the value of their

cash.

The majority of the developed countries have low rate of inflation and stable economy

over years. Sweden, UK and USA have registered 2.04%, 2.48%, 2.44% as inflation rate

in 2018, respectively. On the other hand, developing countries have unstable economy

and high inflation rate. Ethiopia, Sudan and Angola have registered 13.83%, 63.2% and

19.63% as inflation rate in 2018, respectively.

Ethiopia was ruled by the military junta between 1974 to 1991. During this regime, the

government was following a command market system and prices were controlled by the

government. The government was also rationing goods at a fixed price to the public which

in turn had contributed to attain lower inflation rate. The annual average inflation was 5.2

percent between 1980-2002 (Menji, 2008).

After the overthrow of the military Junta in early 1990s, the Ethiopian economy switched

to a market system. The first ten years of the new government was characterized by a low

inflation rate and a low economic growth. However, post 2002 Ethiopian economy is one

of the fastest growing economies in Africa and had been praised by World Bank (2010),

International Monetary Fund, Economic commission for Africa (Atilola, 2007). At the

same time, inflation began to emerge as a major issue following some policy changes

such as less conservative monetary and fiscal policy implemented by the current

government (Geda & Tafere, 2008).

The data from National Bank of Ethiopia indicates that the rate of inflation in Ethiopia

was 10.69%, 13.83%, 15.84% in 2017, 2018, 2019, respectively which is increasing in

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the past few years. Double digit inflation which is caused by several factors has become

worrying for the policy makers and for the citizens, especially for those who lives in the

capital city.

The determinants of inflation are different from one country to another. From the

economic perspective, these determinants have been classified as supply side and demand

side factors. Supply side factors are those economic factors which cause inflation by

increasing cost of production. Some important supply side factors are output growth,

capital formation, import prices, exchange rate, tax and wage. On the other hand, demand

side factors lead to inflation by decreasing the purchasing power of money. Some relevant

demand side factors are increment of money supply, private consumption and

government expenditure (Eftekhari Mahabadi & Kiaee, 2015).

Several studies have attempted to identify the determinants of inflation in Ethiopia. Based

on their result, authors give recommendation to policy makers to control high rate of

inflation. Similarly, the purpose of this study is to identify the determinants of inflation

in Ethiopia from 1980 to 2019 by using annual time series data.

This study is divided into five chapters which are introduction, literature review,

methodology, data analysis and conclusion.

2.LITERATURE REVIEW

2.1 The theoretical framework

2.1.1 Structuralist Theory of Inflation.

The concept of structural theory of inflation is discussed by Myrdal (1968) and Streeten

(1972) for the first time (Canavese, 1982). The theory explains the inflation in the least

developed countries (LCDs) in terms of the structural features of the countries. Both

Streeten and Myrdal (Canavese, 1982) have argued against the direct application of the

orthodox aggregative analysis to the LDCs. The orthodox aggregative analysis assumes

the existence of balanced and integrated structures in the economy where production,

consumption, backward and forward linkages in response to market signals are

reasonably smooth and fast, such that it is rational to talk in terms of aggregate demand

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and aggregate supply. However, the majority LDCs are characterized by unstable

economy, backward agriculture, weak institutions, underutilization of natural resource,

and frequent war etc. Because of this, it is difficult to apply aggregative analysis to the

LDCs.

Structuralists believe that inflation in LDCs is bound with developmental effort and

structural response to this effort is expressed through gaps of various kinds in these

countries. The gaps that have mentioned in literatures are Resource gap, food bottleneck,

foreign exchange bottleneck and infrastructural bottleneck. They suggest that, to

understand the true nature of inflation in LDCs one must identify the determinants that

force to generate bottlenecks of various kinds in the normal process of development, study

how the bottlenecks lead to price increases and how these increases spread to the whole

economy. Since Ethiopia is one of the least developed countries, the structuralist theory

and suggestion hold in Ethiopia too.

2.1.2 Cost push inflation

Cost-push inflation occurs when overall prices increase due to increases in the cost of

wages enforced by trade unions and cost of production. This type of inflation was

identified during the medieval period, but it was reviewed in the 1950s and 1970s as the

main cause of inflation. There are many causes of cost push inflation. High increment of

wages more rapidly than the productivity of labor is one of them. Trade unions push

employers to increase wage considerably, thereby raising the cost of production of

commodities. Consequently, employers increase the price of their commodities. Even if

the wage is increased, the workers would buy only as much as before because of the price

adjustment on products by the producer(Totonchi, 2011).

Once again, the trade unions demand higher wages, and the producer will set higher price.

This vicious circle process leads to cost-push or wage inflation. Another cause of cost-

push inflation is profit push inflation. To maximize their profits, Oligopolist and

monopolist firms charge high prices for their products to offset production and labor

costs. Because of the nature of oligopolist and monopolist market, firms are in charge of

setting price of their products: that is why profit- push inflation is also called price-push

inflation (Jalil, 2011).

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2.1.3 Demand-pull Inflation

Demand-pull inflation is the upward pressure on prices as a result of increase in aggregate

demand. John Maynard Keynes and his followers emphasized the increase in aggregate

demand as the source of demand-pull inflation (Totonchi, 2011). The aggregate demand

consists of investment, consumption and government expenditure. Inflation arises when

the value of aggregate demand exceeds the value of aggregate supply at the full

employment level. Keynesians did not deny this fact that even before reaching full

employment production factors and various constraints can cause increase public price.

According to Keynesians (Totonchi, 2011), policy that causes decrease in each

component of total demand is effective in reduction of pressure demand and inflation.

2.1.4 New Political Macroeconomics of Inflation

The theories discussed above mainly focus on macroeconomic determinants of inflation.

However, these theories ignored the role of non-economic factors such as political

instability and culture as a cause of inflation. The new political economy theory literature

provides a new perspective on the relations between timing of elections, performance of

policy makers, political instability and inflation (Totonchi, 2011).

Government officials use their power to increase government expenditure, especially at

the end of their term. Several studies have been done regarding the non-monetary

variables on inflation. For example, Khani Hoolari et al. studied the effect Governance

and political Instability on inflation in Iran by using annual time series data from 1959-

2010 (Seyed Morteza Khani Hoolari, Abbas Ali Abounoori, 2014). They compute polity,

cabinet change and government crisis variables as regressors and inflation as a response

variable. According to their result, these non-monetary variables significantly affect

inflation in Iran. Finally, we can conclude inflation is not only a monetary phenomenon.

2.1.5 Quantity Theory of Money

Quantity theory of money (QTM) states that money supply and price level in the economy

are directly proportional to one another. Irving Fisher showed the relationship between

them as follows:

M*V= P*T

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Where,

M is Money supply

V is Velocity of money

p is price level

T is Volume of the transactions.

In the above relationship the velocity of money is assumed to be constant. When there is

increment in money supply, the price will adjust by the same or lower proportion rate

immediately.

2.2 Empirical Literature Review

Empirical studies examine the determinants of inflation by using different econometric

tools such as cointegration, vector autoregressive and vector Error Correction Model etc.

A large number of studies have investigated the determinants of inflation across the world

(Ochieng et al., 2016; Olatunji et al., 2010).

When we think about the causes of inflation, two dimensions might be useful: domestic

and external factors. The study result of African development bank (AFDB, 2011)

indicates that the causes of inflation in Ethiopia, Uganda, Tanzania and Kenya are world

food and oil prices, domestic production and monetary, fiscal and exchange rate policies.

In the short run, external factors are outside the control of these countries, because of

production capacity constraints. The frequent drought in the region worsened the food

situation, causing a sharp increase in food prices. In addition to this, the rising world oil

prices have been transmitted to domestic inflation, aggravated rapid depreciation in

exchange rates across all four countries (AFDB, 2011).

Olatunji et al has examined the determinants of inflation in Nigeria by using annual time

series data from 1970-2006 (Olatunji et al., 2010). The total export, total import,

agricultural output, interest rate, government expenditure, exchange rate and crude oil

was included in the model as a determinant of inflation (Olatunji et al., 2010). The study

result reveals that the previous year total imports, government expenditure, and exchange

rate have negative influence on inflation rate (Olatunji et al., 2010). However, export,

agricultural output, interest rate and crude oil exports have negative impact on inflation

(Olatunji et al., 2010).

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Kahssay has investigated causes of inflation in Ethiopia using annual time series data

from 1975 to 2014 (Kahssay, 2017). He computed cointegration test and vector error

correction model by using broad money supply, gross domestic product, credit facility,

gross national saving, export and imports of goods and services as determinants of

inflation (Kahssay, 2017). He concluded that, in the long run broad money supply and

gross domestic product significantly affect inflation while other variables are

insignificant to affect inflation (Kahssay, 2017).

By using quarterly data from 1998Q1 to 2010Q4 and a Granger causality model approach,

Biresaw has studied determinants and impacts of dynamic inflation in Ethiopia (Biresaw,

2013). He included real growth domestic product, broad money supply, nominal

exchange rate and gas oil price as explanatory variables and inflation as a dependent

variable (Biresaw, 2013). He concluded that the causality running from inflation to broad

money growth was stronger than that from broad money supply growth to inflation

(Biresaw, 2013).

The empirical studies stated above have presented different results. The difference in

results depends on the economy of the country’s, period of the study, the method used,

and the variables included in the model.

3.RESEARCH METHODOLOGY

3.1 Source of Data

This study discusses Macroeconomic determinants of inflation in Ethiopia from 1980 –

2019 by using annual time series data. All the secondary data was collected from National

Bank of Ethiopia.

3.2 Methods of Data Analysis

After the required secondary data was collected, the researcher used both descriptive and

econometric analysis to identify determinants of inflation in Ethiopia from 1980-2019.

The descriptive analysis was used to analyze the trend of inflation in Ethiopia while

econometric analysis was applied to examine the relationship between inflation and the

explanatory variables by applying different tests.

3.3 Model specification and Variable description

The model used in this study includes Macroeconomic variables as a determinant of

inflation in Ethiopia. The variables computed in this model are the result of economic

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factors and governmental polices which are assumed to affect inflation. The dependent

variable is Consumer Price index as a proxy to inflation and it is the variable whose

behavior in a relation to explanatory variables has been investigated. According to

Ethiopian central statistics agency report in December 2016, about 54 % of the household

expenditure spent on food, beverages other goods and services. On the other hand,

Producer Price Index (PPI) is not a good measure in Ethiopian economy since the

proportion of income spent on the purchase of raw materials is low in the country.

Therefore, it is appropriate to use CPI as a measure of price change in Ethiopian economy.

The effect of money supply, budget deficit and National Debt on inflation are discussed

in this model. All variables measured in this model have been transformed into

logarithms, because of whenever logs are applied the empirical distribution behave better

and the variance would be stable (Wooldridge, 2015).

The empirical model used in this study can be specified as follows:

logCPIt = 𝛽0 + 𝛽1logGM2t + 𝛽2logBDt + 𝛽3logGNDt + ut

Where, the parameters 𝛽1 , 𝛽2 and 𝛽3 is the long run elasticities of the independent

variables and 𝛽0 is the value of the dependent variable when all independent variables are

zero.

Consumer price index (CPI): measures the average change in prices over time that

consumers pay for a basket of goods and services. Such changes in the prices of goods

and services have an effect on the real purchasing power of consumer's income and their

welfare.

Money Supply: traditionally, money supply is defined from its narrow and broader sense.

Narrow money supply is a measure of money stock proposed primarily for the use of

transactions. It consists of currency held by the public, traveler's checks, and demand

deposits. On the other hand, Broad money supply is a measure of domestic money supply

which includes narrow money plus Quasi-money (savings and time deposits), overnight

repurchase agreements, and personal balances in money market accounts. The National

Bank of Ethiopia takes the broader definition of money as a money supply. Similarly, this

study used the growth rate of broad money in local currency unit as a money supply. As

discussed earlier in the theoretical part, we expect positive relationship between inflation

and money supply.

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Budget Deficit: it occurs when government expenditure exceed revenue and indicate the

financial health of the economy. This gap between revenue and expenditure is

subsequently filled by government borrowing. Inflationary effect of budget deficits

depends upon the means by which the deficit is financed. The effect of budget deficit on

inflation has been controversial in the field of Economics. Metin has showed the existence

of a positive relationship between inflation and budget deficit (Metin, 1998).

However, Hondroyiannis and Papapetrou has analyzed the direct and indirect effects of

budget deficit on inflation in Greece for the period of 1957-1993 (Hondroyiannis &

Papapetrou, 1997). They conclude that the indirect effects of budget deficits on inflation

exists while the directs effects are not present (Hondroyiannis & Papapetrou, 1997).

Therefore, we expect either positive or negative effect of budget deficit on inflation.

National Debt: is the total outstanding borrowing of the central government comprising

of internal and external debt incurred to finance its expenditure especially from

International Monetary Fund (IMF) and World Bank in case of Ethiopia. Increment of

national debt will have a positive effect on inflation. Romero and Loaiza Marin supported

this idea by using panel data of fifty-two countries (Romero & Loaiza Marín, 2017). They

suggest that for countries whose public debt is already high, further increases in public

debt are inflationary (Romero & Loaiza Marín, 2017). Growth of national debt is used in

this model. Since, the National debt of Ethiopia has been increasing in the last two

decades we expect positive effect of national debt on inflation.

3.4 Augmented Dickey-Fuller Test

In time series data most of the variables are non-stationary, which means that they usually

exhibit unit root. When the variables exhibit a unit root, it indicates that their mean and

variance changes over time or non-constant. Because of this, a regression based on non-

stationary variables leads to spurious (misleading) result with high 𝑅 2 while there is no

meaningful relationship between variables.

In order to avoid the spurious regression problem, with its related non-stationary pattern

of the variables, differencing has become the most common method of converting non-

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stationary time series variable to stationary. When a variable is stationary at level, then it

is integrated of order zero. However, a variable is said to be integrated of order one or I

(1), if it is stationary after differencing once. Most of the time series variables become

stationary after the first difference (Stock H. & Watson W., 2019). This study used the

Augmented Dickey-Fuller (ADF) test, which follows the same features as the Dickey-

Fuller statistic by adding the lagged value of the dependent variable (Gujarati & Porter,

2009). ADF test aims at checking for the presence of unit root in a time series under the

null hypothesis that a unit root is rejected in favor of the alternative to be stationary.

3.5 Lag order selection for VAR

Johansen cointegration test and vector error correction model is usually preceded by a

test of determining optimal lag length due to the estimated results are affected by the

number of lags included. Therefore, it is mandatory to determine the number of lags

before estimating the model (Denbel et al., 2016). In practice determining the number of

lags requires balancing between choosing too few and too high lags. Choosing too few

lags omit potentially valuable information contained in the more distant lagged values.

On the other hand, if it is too high the model will estimate more coefficients than

necessary, which in turn introduces additional error in forecast (Stock H. & Watson W.,

2019).

Akaike information criterion (AIC), Hannan-Quin information criterion (HQIC) and

Schwarz and Bayesian information criterion (SBIC) and Likelihood Ratio (LR) can be

used to determine the optimal number of lags in time series analysis. By using information

criteria, the empirical issue is somewhat resolved since the information criteria with the

minimum value are the ones preferred (Basabose, 2020)

3.6 Cointegration Test

Once the number of lags in the model has been determined, cointegration test will be

performed. Two economic series are co-integrated if they have a long-run relationship or

equilibrium relationship between them (Gujarati & Porter, 2009). Engle-Granger

approach which is useful in a simple model with two variables and Johansen cointegration

approach which is suitable for a multivariate series can be applied for cointegration test

(Basabose, 2020). Therefore, Johansen cointegration test become convenient to this

study since the variables are multivariate series.

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3.7 Vector Error Correction Model Specification

Anoruo and Ahmad recommend using an ordinary VAR in the first difference if the

variables in a data set are not cointegrated (Anoruo & Ahmad, 2001). However, if they

are cointegrated, a VECM which combines levels and differences can be estimated

instead of VAR in levels (Maitra, 2019). Additionally, VECM allows analyzing the short-

run dynamics and long-run equilibrium relationships in the data set (Razaghi Khamsi,

2016).

VECM includes the lagged error correction term to measure the duration of the deviation

of the variables from the long run- equilibrium. In Practice most empirical applications

analyze multivariate systems. Since, there are four variables in this study, we can say that

it is multivariate analysis. Let us consider a VAR with P lags:

Yt = + ∑ 𝛾𝑖𝑝𝑖=1 Yt-1+ Ut

Where, Yt is a K × 1 vector of variables, is a K × 1 vector of parameters, 𝛾𝑖is K × K

matrices of parameters, and Ut is a K × 1 vector of error term. Since all variables have a

unit root and there is cointegration the best model is VECM. We can transform the VAR

model to Vector error correction model by taking the first difference of the variables.

Vector error correction model can be written as follows by taking the first difference of

any VAR(P):

∆Yt = + ∑ 𝛾𝑖Δ𝑝−1𝑖=1 Yt-1+ 𝜆Et-1+ Ut t= 1……T

Where, ∆Yt =Yt -Yt-1

If there is cointegration among variables in the long run, the error correction term ECT)

will adjust gradually the deviation from the long-run equilibrium through a series of

partial short-run adjustments. ECT is represented by Et-1 and the coefficient 𝝀 is the

speed of adjustment. In VECM the dependent variable is a function of its own lag, a

function of the lagged values of explanatory variables in the model, error correction term

and disturbance term (Stock H. & Watson W., 2019). After VECM is applied, the

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dynamic relationships among variables can be best understood by examining the impulse

response function (Dibooglu & Enders, 1995). Impulse response function measures the

effect of a shock caused by an endogenous variable on itself or another endogenous

variable (Kilian & Lütkepohl, 2017).

4.EMPIRICAL RESULTS AND DISCUSSION

4.1 Trend and analysis

Looking at the trends of variables would enable the readers to understand how the

variables changed over time. Trend analysis is a graphical illustration of the variables in

the model for a given period of time. It depicts the ups and downs (fluctuations) of the

interest variables due to several factors such as policy changes, increment in world price,

drought in the region and political instability in the region for example Ethio-Eritrea war

from 1998-2000, etc.

Figure 4. 1 The trends of lnCPI, lnGND, lnGM2, and lnBD in Ethiopia between 1980-

2019

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The above figure shows the individual trends for the variables of interest in this study.

Consumer price index in Ethiopia between 1987-2006 has a fluctuating behavior

characterized by a small ups and downs. But, after 2007 it keeps increasing at a high

speed because of expansionary monetary policy implemented in the country that caused

excess aggregate demand and the rising international food and oil price (Lindberg, 2014).

Despite the fact that Ethiopia is one of the least developed countries in the World, the

country's prudent fiscal and monetary policies have been praised both by the World Bank

and IMF (Bevan & Adam, 2001). Conservative fiscal and monetary policies combined

with some price controls during military regime (1974-1991) have been some of the silent

features of Ethiopia's macroeconomic policy (Wolde-Rufael, 2008) The budget deficit

was very small at the early stages of this study . However, due to many mega projects

initiated and resource misallocation in the country caused for the increment of

government expenditure in the last two decades. Prior to the present government most of

the deficit were financed locally by borrowing and money creation. However, the current

government has now reversed the process of money creation where there is a declining

trend in domestic borrowing but an increasing trend in external borrowing together with

massive inflows of external grant (Degefe & Nega, 2000)

Additionally, the graphical illustration showed above indicates the existence of a unit root

on all variables at their level form. Figure 4.2 present taking the first difference of the

variables will solve a unit root problem.

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Figure 4. 2 The d. lnCPI, d. lnGND, d. lnGM2, and d. lnBD in Ethiopia between 1980-

2019

4.2 Unit root test

Since the data used in this study is a time series data, it is mandatory to test the stationarity

of the variables. A regression based on non-stationary time series explains the relationship

of the variables for only one period. Because of this it is impossible to understand the

long run relationship of the variables. Additionally, regressing one non-stationary

variable on another non-stationary variable may cause spurious regression. Therefore, a

unit root test is conducted by employing Augmented Dickey Fuller (ADF) test to

demonstrate whether the variables in the model are stationary or not.

Table 4. 1 ADF tests for unit root with constant and trend

ADF t-statistic Value

Variables Level First Difference Lags P-value at first difference Order of Integration

lnCPI -0.746 -4.644*** 1 0.0009*** I (1)

lnGM2 -2.791 -6.495*** 2 0.0000*** I (1)

lnGDEBT -3.528 -6.006*** 2 0.0000*** I (1)

lnBD -2.605 -6.881*** 2 0.0000*** I (1)

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Note: ADF critical values at 5% are -3.548, -3.552, -3.552, -3.548 respectively. The

variables are non-stationary if the absolute value of ADF t-statistic is less than the critical

value at 5% respectively otherwise it is stationary. *** indicates 1% level of significance

and rejection of the null hypothesis of unit root respectively. The number of lags is

determined by Schwert criterion.

The result from the above table indicate that we accept the null hypothesis that the

existence of unit root for consumer price index, broad money supply growth rate, growth

rate of debt and budget deficit. This demonstrates all variables are non-stationary at level.

However, taking the first difference makes variables stationary.

4.3 Choosing optimal lag length

We have seen that in ADF test all variables are Integrated of order one or I (1).

Determining the number of lags in the VAR model is important before we proceed to

cointegration test and Vector Error Correction Model. Choosing few lags may lead to

systematic variation in the residuals whereas too many lags may cause loss of degrees of

freedom. The optimal lag length for Likelihood Ratio (LR) and Akaike information

criterion (AIC) is two. But, for Hannan-Quin information criterion (HQIC) and Schwarz

and Bayesian information criterion (SBIC) it is one. Gonzalo & Pitarakis and Aznar &

Salvador have shown that it is good to apply minimizing an information criterion to

choose optimal lag because it provides a consistent estimator of the number of

cointegrating equations (Aznar & Salvador, 2002; Gonzalo & Pitarakis, 1998). Therefore,

the lag length in VAR model is two according to Akaike information criterion (AIC).

Table 4. 2 Optimal lag length selection criteria

Lag LR AIC HQIC SBIC 0 -.357383 -.296022 -.179629

1 196.11 -5.04632 -4.73952* -4.15755*

2 36.406* -5.17222* -4.61997 -3.57243

3 23.699 -4.93504 -4.13735 -2.62424

4 22.041 -4.65051 -3.60738 -1.62869

Source: Stata output

Note: *Indicates the optimal lag length recommended by the listed criteria.

4.4 Cointegration Analysis

We have discussed that the variables in the model are integrated of order one and the

optimal lag length is two. when all variables are integrated of order one and multivariate,

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Johansen cointegration test is one of the tests that could be applied to check if there is a

long run relationship between the variables based on predetermined number of lags. The

decision criteria to determine the number of cointegration equation in the model is based

on comparing the trace statistic value with the 5% critical value. There is cointegration

equation in the model if the trace statistic value is greater than the critical value and vice

versa.

Table 4. 3 Johansen cointegration test result

Rank Parameters Eigenvalue Trace statistic 5% crit. Value

0 20 . 61.44 47.21

1 27 0.534 33.17 29.68

2 32 0.473 9.47* 15.41

3 35 0.188 1.74 3.76

4 36 0.046

Note: * indicates the selected rank.

The output of the above table indicates the trace statistics and their critical values of the

null hypotheses of no cointegration (rank 0), one or fewer cointegration equation (rank

1), and two or fewer cointegration equation (rank 2) etc. The eigenvalues are used to

compute the trace statistic. We strongly reject the null hypothesis of no cointegration.

Thus, we accept the null hypothesis that there is two cointegrating equation in the model.

4.5 Two Long run Equations

The long run coefficients are exactly estimated using VECM approach to show the long

run response of the dependent variable to changes in the independent variable. The

Johansen identification scheme has placed four restrictions on the parameters in both

cointegration vector. For the first cointegrating equation the parameters for lnCPI are

normalized to one and lnGM2 are normalized to zero. Consequently, this result is against

of our argument in the quantity theory of money. Which means increment of money

supply does not affect price in the long run in Ethiopia. This study finds a different result

from Kahssay regarding the effect of money supply (Kahssay, 2017). Similarly, for the

second cointegration equation the parameters for lnCPI and lnGM2 are normalized to

zero and one respectively.

Table 4.4 shows that there are two cointegration equations in the model.

lnCPI = - 2.74 + 2.38lnGND + .609lnBD………………………. (1)

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Since all variables are written in logarithm form and cointegrating vector is estimated,

the coefficients can be interpreted as a long-run elasticity. Thus, in the first long run

equation, a 1% increment in national debt and budget deficit will increase consumer price

index (inflation) by 2.38 % and 0.61% respectively. Both variables are statistically

significant at 1%. The result implies that the increment of budget deficit and growth of

national debt in the past few years caused increment of price in Ethiopia.

lnGM2 = - .044 +.243lnGND + .0155lnBD………………………..........(2)

The second long run equation can be interpreted as follows: a 1% increment in national

debt and budget deficit will increase consumer price index (inflation) by 0.24% and

0.015% respectively. Both variables are statistically significant at 1%. To sum up

everything that has been said so far this test results agree with economics theory that

increment of national debt and budget deficit has a positive relationship with money

supply.

Table 4. 4 Cointegrating Vectors

First cointegrating equation

Beta Coef. Std. Err. P-value

LnCPI 1 .

lnGM2 0 (omitted)

lnGND -2.38 .501 0.000

lnBD -.609 .053

0.000

Constant 2.74 . .

Second cointegrating equation

lnCPI 0 (omitted)

lnGM2 1 . .

lnGND -.243 .051 0.000

lnBD -.015 .005 0.005

Constant .044 . .

Note: This table present the estimates of the parameters, standard error and the p

value of the cointegrating vectors.

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4.6 Vector Error Correction Model

We have seen that the variables are cointegrated in the long run. This section reviews the

short-run behavior of the variables in the model. The error correction term in VECM

indicates the speed of adjustment to reach the equilibrium when there is a shock in the

short run. It is good when the coefficient of the error correction term is negative and

statistically significant.

Table 4. 5 Estimated short run coefficients

∆lnCPI

Coef. St. Err. t-value p-value

Et-1 -.197 .055 -3.60 0.00

Et-2 2.058 .48 4.28 0.00

∆lnCPI t-1 -.19 .153 -1.25 .213

∆lnGM2 t-1 -.973 .443 -2.19 .028

∆lnGND t-1 .114 .098 1.17 .243

∆lnBD t-1 .032 .057 0.56 .573

Constant .049 .025 1.99 .047

∆lnGM2 Et-1 -.003 .026 -0.11 .909

Et-2 -.633 .226 -2.80 .005

lnCPIt-1 .008 .072 0.11 .913

lnGM2 t-1 -.002 .209 -0.01 .994

lnGND t-1 -.115 .046 -2.51 .012

lnBD t-1 .003 .027 0.12 .907

Constant .022 .012 1.93 .053

∆lnGND Et-1 .284 .106 2.67 .007

Et-2 .542 .933 0.58 .562

∆lnCPI t-1 .239 .297 0.81 .42

∆lnGM2 t-1 .917 .861 1.07 .287

∆lnGND t-1 -.049 .19 -0.26 .796

∆lnBD t-1 .06 .11 0.55 .585

Constant -.071 .048 -1.49 .137

∆lnBD Et-1 .15 .164 0.92 .36

Et-2 -.241 1.436 -0.17 .867

∆lnCPI t-1 -.447 .457 -0.98 .327

∆lnGM2 t-1 -.476 1.325 -0.36 .719

∆lnGND t-1 .579 .292 1.98 .048

∆lnBD t-1 -.3 .17 -1.76 .078

Constant .2 .074 2.72 .007

Note: Et-1 and Et-2 indicates error correction term. ∆ is the lagged value of variables.

Consumer Price Index (CPI)

The coefficient Et-1 has a negative sign and statistically significant. The speed of

adjustment is approximately 20 % per year towards equilibrium. This means that it will

take 5 years for CPI to reach equilibrium when there is shock in the independent variables.

Similarly, the coefficient Et-2 indicates that speed of adjustment for CPI is approximately

23

205% per year. This means that it will take about six months to reach the equilibrium.

The coefficient has positive sign and statistically significant. In the short run, the lag of

growth in money supply will adjust the shock approximately by 97%.

Money Supply (GM2)

The coefficient Et-1 0.3% is negative and statistically insignificant. However, the speed of

adjustment for Et-2 is approximately 63% per year towards equilibrium. It is also negative

and statistically significant.

National Debt (GND)

The coefficient Et-1 is positive and statistically significant. The speed of adjustment when

the shock occur is approximately 28% per year. That means it takes over three years for

National debt to reach equilibrium. The coefficient Et-2 is negative and statistically

insignificant.

Budget Deficit (BD)

The error correction term for the first cointegration equation is positive and statistically

insignificant. Similarly, the error correction term for the second cointegration equation is

negative and insignificant. This imply that budget deficit does not adjust in the short run

when the shock happens.

4.7 Model Checking

In order to make sure that the model provides an appropriate representation, some

diagnostic tests should be performed.

4.7.1 Test of Residual Autocorrelation.

It is one of the most common tests after VECM is estimated. It is used to test the overall

significance of the residual autocorrelation at lag 2. The null hypothesis of the test is that

there is no autocorrelation up to lag 2.

Table 4. 6 Autocorrelation Test

Lag Chi2 DF Prob > chi2

1 12.6808 16 0.69594

2 10.4784 16 0.84048

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The above table suggest that, at 5% level of significance we cannot reject the null

hypothesis that there is no autocorrelation in the residuals for any of the orders tested.

Thus, this test finds no evidence of model misspecification.

4.7.3 Normality test.

As noted by Johansen , the log likelihood for the VECM is derived assuming the residuals

are independent and identically distributed normal (Johansen, 1995). The null hypothesis

is the residuals in the model are jointly normally distributed. Jarque -Bera test of

normality is reported in the table below.

Table 4. 7 Jarque Bera test result

Equation chi2 df Prob > chi2

∆lnCPI 0.461 2 0.793

∆lnGM2 5.253 2 0.072

∆lnGND 12.635 2 0.001

∆lnBD 0.325 2 0.850

ALL 18.674 8 0.016

The result from the table present tests statistic for each and all equations jointly against

the null hypothesis that the residuals in the model are normally distributed. However, the

null hypothesis is rejected at 5% level of significance for the whole model.

4.6.3 Model Stability condition Test

We should also evaluate the stability of the estimated VECM. Model stability is used to

check if the model stays stable or explode if a shock happens in the future. If a VECM

has K endogenous variables and r cointegrating vectors, there will be K − r unit moduli

in the companion matrix. For stability, the moduli of the remaining r eigenvalues should

be strictly less than unity.

Table 4. 8 Eigenvalue stability condition

Eigenvalue Modulus

1 1

-.410 + .382i .561

-.410 - .382i .561

.351 + 337i .486

.351 + .337i .486

-.073 - .096i .121

-.073 - .096i .121

The VECM specification imposes 2-unit Moduli.

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Figure 4. 3 Roots of companion matrix

The above table and graph show that the eigenvalues meet the stability condition.

Therefore, the model is stable.

4.8 Impulse Response Function

Impulse response function (IRF) can be used to show the change in the dependent variable

as a result of a shock in the independent variable. IRF also shows that the sign and how

long the shock stays. The presence of integrated order one variables in VECM implies

that the shocks can be permanent or transitory. Consequently, in this study the shocks are

positive and permanent.

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Figure 4. 4 Graph of Impulse Response Function

The result from the above graph indicates that a one-time positive shock in broad

money, National debt and budget deficit leads to a permanent increase in price.

5. CONCLUSION In the previous chapter we have discussed that budget deficit and growth of national debt

has a positive and significant effect on inflation in the long run. However, money supply

affects inflation only in the short run. Depending on the result of the model, we can

conclude that growth of budget deficit (Excess government expenditure than revenue)

and national debt are the causes of price increment in Ethiopia. The model stability test

suggested that the model will stay stable if a shock happens in the future.

Based on the result from the data analysis, some arguments and policy implications are

presented as follows:

The government has to cut its public expenditure to reduce its budget deficit.

However, it depends on the type of government expenditure reduction. If the

27

government cut pension spending (e.g., to make people work longer), then there

may be an actual increase productivity capacity. On the other hand, reduction in

public sector investment will have a bigger adverse effect on aggregate demand

and aggregate supply side of the economy. Additionally, increasing tax at the right

time (recession) could increase government revenue and help to reduce the budget

deficit. Therefore, decision has to be made comparing advantages and

disadvantages of implementing policies.

Several big projects launched by the government of Ethiopia consumes more time

and budgets than the allocated amount because of poor leadership and corruption

(Ethiopia: 50 Charged with Graft in Nile Dam Project, n.d.). For example, the

construction of the renaissance dam started in April 2011 and was planned to be

completed in 5 years at a cost of 5 billion USD, but the completion date has been

pushed forward to 2023. Since growth of national debt and budget deficit is

identified as a source of inflation in Ethiopia, the completion of these big projects

would generate more money to pay the national debt and to fill the gap of budget

deficit. Therefore, the government has to take measure when projects are delayed.

The theoretical part of this study argues that increment of money supply is one of the

main sources of inflation (increment of price) and inflation is not only a monetary

phenomenon. However, the long run model developed in this study revealed that money

supply does not affect price in Ethiopia in the long run. Additionally, due to limitation of

data for non-monetary variables only macroeconomic (monetary) variables are included

as determinants of inflation in Ethiopia for a given period of time. Finally, these two cases

can be considered as a limitation of this study and I recommend for further study.

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