College of Finance, Management, and Development
Department Development Economics
Master of Science Degree in Development Economics
Seminar Paper on the Effect of Budget Deficit on Economic Growth:
In the Case of Ethiopia
BY- Nigus Temare Agumas
February, 2021
Addis Ababa, Ethiopia
Abstract
The main objective of this study was to investigate the effect of budget deficit on economic
growth in Ethiopia. For this purpose, the study used time series secondary data, and the data
was extracted from the World Bank development indicators, Ministry of Finance, and National
Planning and Development Commission of Ethiopia. The data covered a period running from
1994 to 2020.The study employed the Autoregressive Distributed Lag (ARDL) co-integration
technique to determine the long and short-run relationship between budget deficit and economic
growth. The findings resulted from modeling and analysis of the study showed that there exists a
negative relationship between budget deficit and economic growth in Ethiopia and these results
are consistent with the neoclassical economist schools of thought. Besides, the inflation rate is
affecting the economic growth negatively and significantly whereas, government expenditure and
trade openness affect the economy positively and statistically significant in the long run. On the
other hand, the analysis in the short-run revealed that the budget deficit is positive but
statistically insignificant. This indicates that budget deficit changes have no immediate effect on
economic growth. The study suggested some policies which are important for the government of
Ethiopia to avoid certain levels of the budget deficit to achieve the desired level of growth.
i|Page
Table of Contents
Table of Contents ............................................................................................................................ i
List of Tables ................................................................................................................................ iii
Abstract .......................................................................................................................................... iv
Acronyms ........................................................................................................................................ v
CHAPTER ONE ............................................................................................................................. 1
1.
INTRODUCTION ................................................................................................................... 1
1.1.
Background of the Study .................................................................................................. 1
1.2.
Statement of the Problem ................................................................................................. 2
1.3.
Objective of the Study ...................................................................................................... 3
1.4.
Significance of the Study ................................................................................................. 3
CHAPTER TWO ............................................................................................................................ 4
2.
LITERATURE REVIEW ........................................................................................................ 4
2.1.
Theoretical Framework .................................................................................................... 4
2.2.
Empirical Literature ......................................................................................................... 5
CHAPTER THREE ........................................................................................................................ 7
3.
METHODOLOGY .................................................................................................................. 7
3.1.
Introduction ...................................................................................................................... 7
3.2.
Data Type and Source ...................................................................................................... 7
3.3.
Methods of Data Analysis ................................................................................................ 7
3.3.1.
Descriptive Statistics ................................................................................................. 7
3.3.2.
Econometrics Model ................................................................................................. 7
3.4.
Diagnostic Checking ...................................................................................................... 10
3.4.1.
Residuals Normality Test ........................................................................................ 10
3.4.2.
Vector Error Autocorrelation Test .......................................................................... 10
3.4.3.
Heteroscedasticity Test ........................................................................................... 10
CHAPTER FOUR ......................................................................................................................... 11
4.
RESULT AND DISCUSSION .............................................................................................. 11
4.1.
Trend Analysis of Budget Deficit and Economic Growth ............................................. 11
4.2.
Econometrics Analysis ................................................................................................... 13
4.2.1.
Unit Root Test ......................................................................................................... 13
ii | P a g e
4.2.2.
4.3.
Long run ARDL Bounds Tests for Co-integration ................................................. 14
Diagnostic Checking ...................................................................................................... 17
CHAPTER FIVE .......................................................................................................................... 18
5.
CONCLUSINON AND RECOMMONDATION ................................................................. 18
5.1.
Conclusion...................................................................................................................... 18
5.2.
Recommendations .......................................................................................................... 19
References
Appendix1
iii | P a g e
List of Tables
Table 4.1: Summary of all the variables………………………………………………………11
Table 4.2: Selection of maximum lag length ................................................................................ 13
Table 4.3: Stationarity tests of the variables at levels and first differences .................................. 14
Table 4.4. Bounds test for co-integration analysis ........................................................................ 15
Table 4.5. Long run Model ........................................................................................................... 16
Table 4.6. Short-run Error Correction Estimates .......................................................................... 16
iv | P a g e
Acronyms
ADF
Augmented Dickey-Fuller
AIC
Akaike Information Criteria
ARDL
Autoregressive Distributed Lag
BD
Budget Deficit
ECM
Error Correction Method
FPE
Final Prediction Error
GDP
Growth Domestic Product
SSA
Sub-Saharan Africa
GOV
Total Government Expenditure
OLS
Ordinary List Square
SCBIC
Schwartz-Bayesian information criterion
TRO
Trade Openness
VAR
Vector Auto-Regressive
v|Page
CHAPTER ONE
1. INTRODUCTION
1.1. Background of the Study
Budget is the amount of money reserved for a particular institution, activity, or time-frame;this is
a component of the financial plan.The budget can be either deficit or surplus depending on
planning. Keynes (1936), budget deficits were viewed as positive instruments to shore up
aggregate income to stimulate all sectors to spend more. Premchand, (1989) the increase in
expenditure that accompanied the more role assumed by the state brought within transformation
in the size of the budget and increasing the economic growth. Hagemann, (1999), the budget
balance was one of the most important macroeconomic factors that have an impact on economic
growth. However, the linkage between budget deficit and its relative contribution to economic
growth is a controversial issue from both theoretical and empirical perspectives.
According to the Keynesians Proposition, a standard theory of government budget deficit, the
decision to cover government spending due to tax cuts will help families respond to an increase
in current disposable income equal to the part of the tax cut. It stimulates high personal savings
and, in part, high consumer demand and economic growth. On the other hand, the neoclassical
economist argues that the congestion budget deficit harms economic growth, and they believe
that this relationship is negative. Besides, the Ricardian economists argue that the above
assumption is not correct and they believe that government budget deficits have no impact on
key macroeconomic variables. Thus, there is a neutral connection between the budget deficit and
economic growth (Abd Rahman, 2012).
The budget deficit is linked to the fiscal policy of the government; it has implications for the
monetary policy as well. Namely, to maintain the stability of the prices in the economy when
governments run a budget deficit, central banks must conduct restrictive monetary policy.
Contrary to the efforts to boost the economy, such limitations lead to a reduction in private
investments and private final consumption, in particular consumption of durable goods (Tobin &
Buiter, 1976). However, Government expenditure needs are unlimited, but financing resources
are not, the prevalence of budget deficit is evident. But unless the extent of the budget deficit and
its financing is not managed prudently, it has negative consequences, on macroeconomic
stability. In Ethiopia, the level and financing of the budget deficit are designed to promote the
1|Page
desired macroeconomic goals such as controlling inflation, boosting private investment and
growth, and maintaining external creditworthiness. Also, Budget deficit financing comes from
two sources which are tax revenue and central government debt. This debt consists of domestic
and foreign debt stocks. Domestic debt stock consists of the issue of treasury bills and
government bonds while foreign debt stock consists of foreign loans, and foreign loan
repayments (MoF, 2019).
Additionally, the Ethiopian economy has registered an average of 8.6 percent growth from
2015/16-2017/18 at a constant price. On the other hand, In 2017/18, the overall budget deficit of
Ethiopia including grants has become 3.0 percent of Gross domestic product at the current
market price (MoF, 2019). Hence, this study aims to investigate the effect of budget deficit on
economic growth in Ethiopia using annual data from 1994 to 2020 which is obtained from the
Ethiopian Ministry of Finance and National economic development, Ethiopian Ministry of
Revenue, and National Bank of Ethiopia. So this study helps to identify the relationship between
budget deficit and economic growth in the case of Ethiopia because it has different views by
economists and it has different relationship from country to country and from time to time in the
same country.
1.2. Statement of the Problem
A deficit policy plays a vital role in assisting countries to achieve macroeconomic stability,
poverty reduction, income redistribution, and sustainable growth. For this reason, most
governments use the budget as an effective tool in achieving their economic objectives. This
means that a large and accumulating budget deficit may not necessarily be a bad policy objective
if such deficits are effectively utilized to enhance economic growth. It is in line with this that an
appropriate operational definition and measure of budget deficit must be clearly stated (Antwiet
al. 2013).
The issue of the budget deficit has been one of the great debates among economists. Moreover,
economic theory indicates there was a debate that is deeply rooted in the theoretical controversy
between the Neoclassical Economists, Keynesian Economists, and the Ricardian economists due
to the observations that a lot of countries seem to run more budget deficits than a surplus.
Additionally, there is no consensus empirical agreement upon the relationship between budget
deficit and economic growth process even though voluminous empirical studies appeared
2|Page
1
worldwide. In this regard, studies carried out the relationship between budget deficits on
economic growth and their results showed a significant positive effect (Fatima etal.2012;
Onuorah&Nkwazema, 2013; Osoro, 2016). On the other hand, several studies surprisingly
indicated contradicting results that a significant negative effect (Haider et al, 2016; Aslam,
2016). Besides few studies do not have any effect on economic growth (Ghali, 1997; Dalyop,
2010; Abd Rahman, 2012).
Even though numerous studies have been done regarding the effect of budget deficit on
economic growth worldwide, but there was no enough research in Ethiopia particularly after the
downfall of the Derg regime1 in 1991. As a result, thisstudy aims to contribute to the
reconciliation of the effect of budget deficit on economic growth in Ethiopia. This study
employed the most famous econometric procedure of the ARDL approach which is developed
for this purpose and recently applicable in wide ranges. In general, the dynamic relationship
between budget deficitsand economic growths in Ethiopia has not been fully examined. Thus, the
study identified the effect of budget deficit on economic growth both in the short and long run in
Ethiopia from 1994 to 2020.
1.3. Objective of the Study
The main objective of this study is to investigate the effect of budget deficit on economic growth
in Ethiopia. Based on this basic objective, the study addresses the following specificquestions:
To analyze the trend of the budget deficit and economic growth;
To analyses the linkage between budget deficit and economic growth
1.4. Significance of the Study
This study aims to investigate the effect of budget deficit on economic growth in Ethiopia using
annual data from 1994 to 2020. So this study helps to identify the relationship between budget
deficit and economic growth in the case of Ethiopia, there are different points of view among
economists. Hence, this paper shows the relationship between two variables in Ethiopia. It helps
future researchers as a source of literature review and fills the gap of lack of literature on the
relationship between budget deficit and Economic growth of Ethiopia.
1
Derg regime stayed in power from1974 to 1991
3|Page
CHAPTER TWO
2. LITERATURE REVIEW
2.1.Theoretical Framework
Budgets are considered a very useful tool of control applied by companies. It can help set
developmental policies in the country. A budget is black and white about the earnings and
spending of an organization. The budget can be either deficit or surplus. Budget Deficit results in
situations where the expenditures of the country exceed its revenues, earned from the taxes and
other sources (Fatima etal 2012). The growing of budget deficits and its consequence has
created debate all over the world. Therefore, necessary to examine the relationship between
budget deficits and increasing government borrowing on the processes of sustainable economic
growth and development. Besides, there were three schools of thought concerning the economic
effects of budget deficits:
Neoclassical economists assume that each consumer belongs to a specific generation and the
lifespans of succeeding generations overlap. This school of thought also assumes that the market
will always be at equilibrium in all periods. Based on these assumptions, they argue that budget
deficits have detrimental effects on the economy and thus advocate for a balanced budget at all
times (Bernheim, 1989).
The Ricardian equivalences argued that the above seemingly sensible assumption is incorrect
and they believe that government deficits have no impact on key macroeconomic variables.
Hence, there is a neutral connection between the budget deficit and economic growth (Abd
Rahman, 2012).
Keynesian economists assume the existence of unemployed resources and credit constraints
individuals in an economy. They are of the idea that budget deficits are good due to their
multiplier effects on the economy. Increased government spending stimulates aggregate demand
which leads to the employment of idle resources and thus increases output. They advocate the
use of budget deficits during economic downturn periods to kindle aggregate demand and thus
reduce the period of recovery. Thus, they recommend that budget management should follow
anti-cyclical economic conditions (Barro, 1978). Mathematically, the equation
aggregate
4|Page
demand (Heijdra, B. J. 2017) relationship in the goods market in the Keynesian framework
is expressed as follows:
(
(
)
)
( )
,
then
substitute
them into equation (1) it gives the output at the equilibrium
(
(
(
)
(
(
) ) , SinceBudget deficit (BD)
(
)
, , ,
export, and import respectively,
and
)))
(
)
( )
Where Y=output, G= exogenous Government expenditure,
revenue, I=Investment=export, M= import,
(
=Disposable income, T=Tax
are exogenous consumption, investment,
Equation (2) implies that the budget deficit is positively related to the growth of any economy as
postulated by the Keynesian framework.
2.2.Empirical Literature
There are controversial arguments regarding the relationship between budget deficit and
economic growth. The Keynesian economies believed that there is a direct relation between these
two series, while the new classical economies argued that there exists a negative relationship. On
the other hand, the Ricardian equivalence hypothesis perceived that there is a neutral relationship
between budget deficit and economic growth.
In this regard, (Osoro, 2016) analyze the
relationship between budget deficit and economic growth in Kenya using time series data from
1980 to 2014 and he was employed Ordinary Least Squares (OLS) as a method of estimation. He
found that there a positive relationship between budget deficit and economic growth but as the
budget deficit increases, the impact on growth decreases.On the contrary, studies conducted by
(Fatima etal, 2012) using a similar model that identifying the effects of budget deficit on
5|Page
economic growth in Pakistan. They found a negative impact of budget deficit on the economic
growth.
Another study was conducted by (Nayab, 2015) to investigate the relationship between budget
deficit and economic growth for 30 developing countries from 1970 to 1990. By using panel data
analysis, they found that the budget deficit assists the economy to improve. Besides, Aslam,
(2016) dynamic relationship between the budget deficit and the economic growth of Sri Lanka
using annual time series data from 1959 to 2013 and employed Johansen co-integration
technique and Vector Error Correction Model, then he found a long-run dynamic relationship
during the study period but no short-run dynamic relationship. Also, the budget deficit had a
positive relationship with the economic growth of Sri Lanka.
In general, a lot of empirical studies were carried out on the effect of budget deficits on the
economy using a different model in different countries and concluded by agreeing with the
Keynesians proposition that the positive effects of budget deficit on economic growth (Kakar,
2011; Fatima et al. 2012; Maji& Achegbulu, 2012; Onuorah& Nkwazema, 2013; Osoro, 2016;
Fischer, 1993; Easterly, Nayab, H. 2015). On the other hand, some empirical studies with
different models (Aero& Ogundipe, 2016; Huynh, 2007, Keho, 2010; Nkrumah et al. 2016;
Hussain & Haque, 2017; Haider et al. 2016; Aslam, 2016) concluded and agreed with the
Neoclassical economists that budget deficits harm economic growth and development. Besides,
some empirical studies that support the Ricardian equivalence hypothesis, agree that budget
deficits do not have any impact on economic activities are the works of (Ghali, 1997; Dalyop,
2010; Abd Rahman, 2012).
Although there were numerous empirical studies on the effects of budget deficit on economic
growth in both developed and developing countries, this experience has been not enough well
documented for Ethiopia. Therefore this study aims to shed some light on reconciling the
relationship between budget deficits on economic growth in Ethiopia empirically.
6|Page
CHAPTER THREE
3. METHODOLOGY
3.1. Introduction
This chapter presents the methodology of the study, including the sources of the data and data
collection method, and econometrics model specification.
3.2.Data Type and Source
The study used time series secondary data and has been extracted from the World Bank
development indicators, Ministry of Finance, and National Planning and Development
Commission of Ethiopia. The data covered a period running from 1994 to 2020.
3.3.Methods of Data Analysis
The authors used both Descriptive statistics and econometric methods to analyze the data.
3.3.1. Descriptive Statistics
It is the method used to present, organize, and summarize the masses of the numerical data into
meaning full form.
3.3.2. Econometrics Model
3.3.2.1.Testing for Unit Roots
Before estimating a macroeconomic time-series model, it is necessary to identify the nature of
time series data whether it is stationary or non-stationary (trend). The model is said to be
stationary if the mean and variance are constant regardless of the actual time taken. A stationary
test makes sure that there will not exist spurious results. Thus, to test this we applied the
Augmented Dickey-Fuller test (ADF).
3.3.2.2. Autoregressive Distributed Lag test
This study employed the Autoregressive Distributed Lag (ARDL) co-integration technique or
bound test of co-integration (Pesaran et al, 2001) to determine the long and short-run relationship
between budget deficit and economic growth. The study considered real economic growth
(RGDP) as outcome variables and budget deficit as a share of a gross domestic product as our
interest explanatory variables (BD). Also, we considered other explanatory variables such as
7|Page
total government expenditure as a share of gross domestic products (GOV), Trade openness as a
share of Gross domestic products (TRO), and inflation rate (INF). However, the study used the
ARDL model to be suitable for our empirical exercise because there are numerous advantages to
using the ARDL approach to co-integration has over the Johansen approach to co-integration.
Firstly, it is can be used if the variables are integrated into a different order, I (0) and I (1), the
order of integration. Second, it is relatively more efficient in the cases of a small data finite
sample data size while the Johansen co-integration techniques require large data samples for
validity. This ARDL approach is also suitable when there is a combination of endogenous and
exogenous variables and it provides long-run estimates that are unbiased, unlike the VAR model
that is strictly for endogenous variables (Pesaranet al. 2001). Finally, it is also sufficient to
simultaneously correct for residual serial correlation.
As a result, we take a mathematical econometric function which used to evaluate the relationship
between budget deficit and economic growth presented as follow in the equation below:
(
)
( )
( )
Differencing equation (2) the growth equations becomes;
( )
The ARDL representation of the effect of budget deficit on the economic growth model is
specified as follows:
∑
∑
∑
∑
∑
( )
Where RGDP is real gross domestic product; GOV is total government expenditure as a share of
gross domestic products; BD is the budget deficit as a share of a gross domestic product; INF is
8|Page
inflation rate; TRO Trade openness as a share of Gross domestic products;
intercept;
are the short-run and long-run elasticity’s, respectively, ∆ is the
and
difference operator;
is the
white noise and p and q is the optimal lag length of the dependent and
exogenous variables, respectively.
The Wald test ( -statistics) derived from the above formulas is a critical part of the ARDL
procedure, which is helpful to assess the existence of a long-run relationship among the variables
included in the model. The null and alternative hypotheses for the Wald test are as follows:
(Null, i.e. the long-run relationship does not exist)
(Alternative, i.e. the long-run relationship exists)
The computed -test can be compared with the critical for the hypothesis testing. Accordingly to
them, the lower bound critical values are assumed that the explanatory variables are integrated of
order one. Therefore, if the computed -statistic is less than the lower bound value, the null is
not rejected. On the contrary, if the computed -statistics is greater than the upper bound value,
it implies the existence of a long-run relationship among the variables. Finally, if the computed
- statistics lie between the lower bound and upper bound, the long-run association between the
variables becomes inconclusive.
Finally, the short-run dynamic parameters may be obtained by estimating the error correction
representation of the ARDL model as given below:
∑
∑
∑
∑
Is the error correction term is lagged by one period;
∑
( )
is the coefficient of the
error correction term (ECM), and it is also expected to have a negative sign, which indicates the
convergence to its long-run dynamic equilibrium.
9|Page
3.4. Diagnostic Checking
3.4.1. Residuals Normality Test
The test of normality of the residuals is one of the important post-estimation diagnostic tests to
check the appropriateness of the model. To test the normality of residuals, Jarque-Bera (JB) test
will be used for the normal distribution. Rejection of the null hypothesis at the standard critical
values indicates the non-normality of the residuals.
3.4.2. Vector Error Autocorrelation Test
The other diagnostic test for evaluating the complete specification and robustness of the results
of an econometric model is the test of serial correlation of the residuals. Breusch-Godfrey
Lagrange Multiplier (LM) test, which is a multivariate test for residual serial correlation up to
some specified lag order is also used to test an autocorrelation between exogenous and error
terms. Rejection of the null hypothesis at the standard critical values indicates the existence of
serial correlation among the residuals.
3.4.3. Heteroscedasticity Test
Breusch-Pagantest will be used to evaluate the heteroskedasticity of the residuals. Breusch-Pagan
tests the null hypothesis that the residuals are both homoskedastic and that there is no problem of
misspecification.
10 | P a g e
CHAPTER FOUR
4. RESULT AND DISCUSSION
Before estimating the model it is important to know the properties and behaviors of study
variables. It is useful to take some correction measurements so that the variables are certainly
applicable in the estimation process. Data spanning the period of 1994 to 2020 is used in this
paper.
Table 4.1: Summary of all the variables
Variable
RGDP
Infl
GE
BD
TO
N
Mean
Std. Dev.
Min
27
27
27
27
27
7.832
9.955
20.413
3.689
27.371
4.341
10.963
3.600
2.091
5.692
-3.458
-8.484
15.070
0.945
16.552
Source: Compiled by Author’s Source using E-views 10
Max
Skewness
Kurtosis
13.573
44.391
28.172
9.034
37.253
-1.089
1.179
0.481
1.035
-0.077
3.568
5.350
2.269
3.265
2.195
From this table, it can be seen that the mean, standard deviation, a minimum and maximum value
of our study variables. The budget deficit (BD) has 0.9 and 9.0 percent a minimum and a
maximum respectively, and the average value was 3.7 percent. The economy has annually grown
by 7.8 percent on average showing a minimum of -3.4 percent and a maximum of 13.6 percent,
the standard deviation has been not very large indicates most of the value of real economic
growth has been around the mean growth rate. And also, the government expenditure as a share
of GDP has an average annual value of 20.4 percent, and the annual average inflation rate was 10
percent.
4.1.
Trend Analysis of Budget Deficit and Economic Growth
Figure (a) graphs budget deficit as a percentage of GDP against the real economic growth. This
is done to assess the effect of budget deficit on economic growth. Ethiopia is a country with fast
economic growth for the past two decades. The GDP growth rate was 10.4% per annum during
the years 2004- 2017. The most important factors that contributed to the economic growth of
Ethiopia are agricultural modernization, the development of new export sectors, strong global
commodity demand, and government-led development projects (WB, 2013). The big push of
public investment-led development has delivered positive returns but the development of a
strong and vibrant private sector is needed to sustain the high growth (WB, 2013), as it is cited in
11 | P a g e
(Feleke, 2013).The economic growth grew from 3.2 percent in 1994 to 12.4 percent in 1996 and
1998 the economy more decline due to the war with Eritrea. However, since 2004 it showed an
upward increment plus fluctuation trend due to rainfall shortage which affected the agricultural
production in 2016 and similarly declined to 6.1 percent in 2020 due to the effect of pandemic
covid 19.
Figure a: Trends of the budget deficit and economic growth. Figure b. government expenditure
vs. Revenue as a share of GDP.
Source: Compiled by Author’s Source using E-views 10
Generally, the analysis reveals in figure (a) since 1994, a clear relationship between the budget
deficit and economic growth is observable. Years of high economic growth were usually
followed by years of low deficits, and vice versa. Figure (b) depicts that the trend between total
government revenue including grant and total government expenditure as a share of gross
domestic product. As shown in the figure gap between total government expenditure and revenue
was the budget deficit. The figure shows that government expenditure always overweight
revenue means the budget balance of the Ethiopian government has been always negative.
Besides, In 2002 the gap is huge due to the government's need to increases its expenditure to
12 | P a g e
reallocate resources to basic social services (spending on roads, energy, education, and health
sectors) and economic infrastructure at a larger scale and believed that these are areas where
public investment is expected to facilitate overall economic performances.
4.2. Econometrics Analysis
We begin the analysis by considering the choice of maximum lag length. The number of lags to
be included in the stationary testis determined by the given information in the model. The
maximum lag length is automatically selected depending on the Akaike information criterion
(AIC), final prediction error (FPE), Hannan-Quinn information criterion (HQ), and SchwartzBayesian information criterion (SBIC) at the minimum value. Thus, the maximum lag length for
the model is one, which is automatically shown by an asterisk (*). The most important thing to
be noticed here is that it doesn’t necessarily mean each variable has a lag length of one. But, it
shows that the maximum lag length is one.
Table 4.2: Selection of maximum lag length
Lag
LR
FPE
AIC
SC
HQ
0
NA
0.012280
-1.594865
-1.366630
-1.615992
1
6.198611**
0.006701**
-2.226834**
-1.952952**
-2.252187**
2
0.048217
0.007985
-2.090865
-1.771336
-2.120443
3
0.188295
0.009458
-1.979390
-1.614215
-2.013194
Notes: The reported values indicate the lag order chosen by the criteria. LR: sequential modified LR test
statistic (each test at5% level).FPE: Final prediction error.AIC: Akaike information criterion.SC:
Schwarz information criterion.HQ: Hannan–Quinn information criterion.
Source: Compiled by Author’s Source using E-views 10
4.2.1. Unit root test
Since we have time-series variables, following the lag length determination of the model, before
conducting the empirical analysis, it is essential to test the existence of stationary in the variables
either at the level or at the first difference to undertake further analysis. The variables used in the
analysis need to be stationary and/or should be integrated to infer a meaningful relationship from
the regression because estimation of model using time series data of the model using time series
data techniques without testing for stationary may lead to spurious regression which leads to a
false conclusion. Besides, it is imperative to perform a unit roots test to verify whether the
13 | P a g e
variables are not integrated of an order higher than one so as ascertaining ARDL bounds
procedures. Thus, the empirical, Augmented Dickey-Fuller (ADF) unit root test technique is
normally used to examine the unit root characteristic of time series variables. This study has
employed this approach (ADF) to determine the stationary property of the variables and the lag
length in ADF is automatically selected by the Akaike information criterion (AIC).
Table 4.3: Stationarity tests of the variables at levels and first differences
Stationarity of all variables at levels Variables at first differences
Variables
At levels
At first difference
With trend
Without trend
With trend
Without trend
LRGDP
3.094
2.735
-4.592**
-7.446***
LBD
-2.721
-2.450
-4.980***
-5.086***
LINF
-1.881
-2.055
-4.163**
-4.616***
LGOV
-3.789**
-0.971
-2.691
-2.688
LTO
-0.664
-1.182
-5.038***
-4.149***
Note: AICtesting the stationarity of all variables,***and ** sign shows the rejection of the null
hypothesis of non-stationary at 1% and 5% significant level respectively.
Source: Compiled by the author using Eviews 10.
The hypothesis to be tested is:
Ho: The variable has a unit root
H1: The variable doesn’t have a unit root
The results of ADF test statistics show that four of the variables are non-stationary in their levels
while the null of nonstationarity is not rejected for one variable at a 5 % level of significance. On
the other hand, in the non-stationary series, it needs in their first differences; all of the variables
are stationary. These results indicate that, with intercept and trend, four of the variables are
integrated of order one, I (1), and one of them is integrated of order I (0).The results of such a
stationarity test do not allow us to apply the Johansen approach of co-integration. This is one of
the main justifications for using the ARDL approach developed by Pesaran, Shin, and Smith
(2001).
4.2.2. Long run ARDL Bounds Tests for Co-integration
Under the ARDL bounds procedure, the study tests the null hypothesis of no long-run
relationship (there is no co-integration) exist against the alternative hypothesis of the existence of
14 | P a g e
a long-run relationship in the model. The ARDL bounds test results indicate that, in general,
there is a co-integration among the variables given in the models since the F statistic is higher
than the given upper critical bounds values as is given in the table below.
Ho: The long-run relationship does not exist
H1: The long-run relationship does exist
Then F-test through the bound test is used to check the joint significance of the study variables.
The computed F-statistic value is compared with the lower bound and upper bound critical
values tabulated value (Pesaran et al. 2001).
Table 4.4. Bounds test for co-integration analysis
Pesaran (2001) critical values for K=4
Lower bound, I(0)
At 1% level
3.74
At 5% level
2.86
Function
LRGDP =(LBD ,LINF,LGOV ,LTO )
F-statistic
8.018129***
Upper bound I(1)
5.06
4.01
Co-integrated
*** Sign shows the rejection of the null hypothesis of long-run relationship at 1% significant level
respectively.
Source: Compiled by Author’s Source using E-views 10
The results of the bounds tests confirm the existence of a level relationship among the variables
for the models since their respective F-statistic is above the upper bound at all levels of
significance. This leads to the rejection of the null hypothesis of no co-integration (no long-run
relationship) exists in favor of the alternative of the variables share the long-run relationship. The
bounds F-test for co-integration so, indicate that the variables share a long-run relationship. So
the next stage involves estimating the long-run and short-run coefficients of each ARDL model.
The results (presented in Table 4.5) show a significantly negative relationship between budget
deficit and economic growth in the long run. This shows that a 10 percent increase in the budget
deficit, in the long run, would lead to a 0.6 percent decrease in real GDP, holding all other
factors constant. Besides, the inflation rate is affecting the economic growth negatively and
significantly in the long run whereas government expenditure and trade openness affect the
economy positively and statistically significant in the long run.In line with this, a one percent
increase in trade openness would lead to a 1.97 percent increase in the economy of Ethiopia in
the long run.
15 | P a g e
Table 4.5. Long run Model
Variables
C
LBD
LINF
LGOV
LTO
Coefficient
Std. Error
t-Statistic
Prob.
-0.62881
2.129603
-0.29527
0.7716
-0.06175
0.284993
-0.918427
0.0420**
-0.33191
0.099197
3.345934
0.0041***
1.011324
1.008728
1.002574
0.0310**
1.974851
0.62574
3.156025
0.0061***
R-squared
0.64315
Prob (F-statistic)
0.003140**
***and ** sign shows the rejection of the null hypothesis at 1% and 5% significant level respectively
Source: Compiled by Author’s Source using E-views 10
Table 4.6 presents the short-run relationship between budget deficit and economic growth. The
results show that the coefficient of the error correction term (ECT) is negative and highly
significant at 5 percent level. This confirms again the existence of a co-integrating relationship
among the variables in the model. The ECT represents the rate of adjustment to restore
equilibrium in the dynamic model following a disturbance. The coefficient of the error correction
term (ECT) is -0.25. This suggests that the speed of adjustment to long-run equilibrium is
approximately 25 percent per year. The size of the coefficient of the error correction term (ECT)
indicates a rate of adjustment to the equilibrium at 25 percent per annum in the next period in
case of a shock to economic growth in the current period. Moreover, the convergence to its
equilibrium is achieved in the long-term at a slow rate. The analysis in the short-run revealed that
the budget deficit is positive but statistically insignificant. This indicates that budget deficit
changes have no immediate effect on economic growth. This can be mainly becausethe
government spends mostly on long-term projects in poor sectors such as education, construction
of roads, and other infrastructural projects whose impacts are not observed in the short term.
Table 4.6. Short-run Error Correction Estimates
Variable
Coefficient
Std. Error
C
-0.0958
0.1139
∆LBD
0.0992
0.3539
∆LGOV
1.4663
2.7640
∆LINF
-0.1996
0.1076
t-Statistic
-0.8408
0.2803
0.5305
-1.8548
Prob.
0.4157
0.7837
0.1440
0.0164**
16 | P a g e
∆LTO
0.2825
1.1518
0.2453
0.0260**
ECM(-1)
-0.2489
0.8405
-0.2962
0.0341**
R-squared
0.5330
F-statistic
2.9680
0.0429**
** Sign shows the rejection of the null hypothesis at a 5% significant level respectively. Source:
Source: Compiled by Author’s Source using E-views 10
4.3. Diagnostic Checking
To check the verifiability of the estimated model, some diagnostic test is undertaken. The results
reported in appendix1 indicate that there is no error autocorrelation and heteroskedasticity, and
the errors are normally distributed. Besides, the stability tests as given by the cumulative sum of
recursive residuals (CUSUM) test indicate that it lays 5 percent bindery and confirms that the
model is specified well. Thus, the relationship between variables has been verified or validated.
17 | P a g e
CHAPTER FIVE
5. CONCLUSINON AND RECOMMONDATION
5.1. Conclusion
The main objective of this study was to investigate the impact of budget deficit on economic
growth in Ethiopia. For this purpose, the study used time series secondary data, and the data
were extracted from the World Bank development indicators, Ministry of Finance, and National
Planning and Development Commission of Ethiopia. The data covered a period running from
1994 to 2020.
The study has employed the Autoregressive Distributed Lag co-integration technique to
determine the long and short-run relationship between budget deficit and economic growth. The
study considered real economic growth as outcome variables and budget deficit as a share of a
gross domestic product as its interest is the explanatory variable. The study also considered other
explanatory variables such as inflation rate, total government expenditure, and Trade openness as
a share of Gross domesticproducts.
The findings resulted from modeling and analysis of the study showed that there exists a
negative relationship between budget deficit and economic growth in Ethiopia and these results
are consistent with the neoclassical economist schools of thought. Based on the dynamic growth
model, the study concludes that in the long run, budget deficits affect economic growth
negatively. This shows that a 10% percent increase in the budget deficit would lead to a 0.6
percent decrease in economic growth, holding all other factors constant. Besides, the inflation
rate is affecting the economic growth negatively and significantly whereas, government
expenditure and trade openness affect the economy positively and statistically significant in the
long run.
On the other hand, the analysis in the short-run depicts that the budget deficit is positive but
statistically insignificant. This indicates that budget deficit changes have no immediate effect on
18 | P a g e
economic growth. This can be mainly the fact that government spends mostly on long-term
projects in poor sectors such as education, construction of roads, and other infrastructural
projects whose impacts are not observed in the short term whereas inflation rate and trade
openness determine negatively and positively in the short run respectively.
5.2. Recommendations
The study recommends that Ethiopia should adopt and implement policies that could reverse the
short-lived budget deficit leading to reduction of economic growth but rather, put the economy
on a sustained path of growth and, development in the medium to long term. The optimal levels
of governments’ expenditure should be determined to avoid deficits and encourage as the
impetus to economic growth through increased capital expenditure. Besides,the Ethiopian
government could increase its revenue base by implementing appropriates tax policy and a good
administration system. Formulating and implementing suitable policies to encourage the rich to
pay their due shares of taxes, which should include some incentives for those, who pay taxes is
also important, this, in turn, creates more revenue sources to increase the income to reduce
dependence on developed countries.
Finally, these studies encourage future researchers to contribute by explainingin detail by
including more variables that affect economic growth such as trade deficit and public debt to
come up with reliable findings.
19 | P a g e
References
Abd Rahman, N. H. (2012, January). The relationship between budget deficit and economic
growth from Malaysia’s perspective: An ARDL approach. In 2012 International
Conference on Economics, Business Innovation (Vol. 38, pp. 54-58).
Aero, O., & Ogundipe, A. (2016). Fiscal deficit and economic growth in Nigeria: Ascertaining a
feasible threshold. Available at SSRN 2861505.
Antwi, S., & Atta Mills, E. (2013). Consequential effects of budget deficit on economic growth:
Empirical evidence from Ghana. International Journal of Economics and Finance, 5(3).
Aslam, A. M. (2016). Budget deficit and economic growth in Sri Lanka: An econometric
dynamic analysis. World Scientific News, (46), 176-188.
Barro, R. J. (1978). Comment from an unreconstructed Ricardian. Journal of Monetary
Economics, 4(3), 569-581.
Bernheim, B. D. (1989). A neoclassical perspective on budget deficits.Journal of Economic
Perspectives, 3(2), 55-72.
Chi-Chi, O. A., & Nkwazema, D. Deficit Financing and the Growth of Nigeria
Economy.International Journal of Management Sciences and Business Research, 3(2).
Dalyop, G. T. (2010). Fiscal deficits and the growth of domestic output in Nigeria.
Easterly, W., Rebelo, S. (1993) Fiscal Policy and Economic Growth: an Empirical
Investigation. Journal of Monetary Economics, 32, 417-57.
Fatima, G., Ahmed, M., & Rehman, W. (2012).Consequential effects of budget deficit on the
economic growth of Pakistan.International Journal of Business and Social Science, 3(7).
Fischer, S., (1993) "The Role of Macroeconomic Factors in Growth," Journal of Monetary
Economics, 32, 485-512.
20 | P a g e
Ghali, K. H. (1997). Government spending and economic growth in Saudi Arabia.Journal of
Economic Development, 22(2), 165-172.
Hagemann, R. (1999). The structural budgets balance the IMF's methodology.
Haider, A. S., Shaon, S. F., & Kabir, M. R. (2016). Impact of Budget Deficit on Growth: An
Empirical Case Study on Bangladesh
Heijdra, B. J. (2017). Foundations of modern macroeconomics.Oxford university press.
Hussain, M. E., & Haque, M. (2017). Fiscal deficit and its impact on economic growth: Evidence
from Bangladesh. Economies, 5(4), 37.
Kakar, Z. K. (2011). Impact of fiscal variables on economic development of Pakistan.Romanian
Journal of Fiscal Policy (RJFP), 2(2), 1-10.
Maji, A., & Achegbulu, J. O. (2012).The impact of fiscal deficits on economic growth in
Nigeria.International Business and Management, 4(2), 127-132.
Nayab, H. (2015). The relationship between budget deficit and economic growth of
Pakistan.Journal of Economics and Sustainable Development, 6(11), 85-90.
Nkrumah, K. O., Orkoh, E., & Owusu, A. M. (2016).Exploring the budget deficit-economic
growth nexus: new evidence from Ghana.
Onuorah, A. C., & Nkwazema, O. G. (2013).Deficit financing and the growth of Nigeria's
economy.International journal of management sciences and business research, 3(2).
Orji, U. O., Onyeze, C. N., & Edeh, L. (2014).The Keynesian-Ricardian Dichotomy on-budget
deficits in Nigeria.Journal of Economics and Finance, 5(2), 69-78.
Osoro, S. (2016).Effects of budget deficit on economic growth in Kenya (Doctoral dissertation,
University of Nairobi).
21 | P a g e
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001).Bounds testing approaches to the analysis of
level relationships.Journal of applied econometrics, 16(3), 289-326.
Premchand, A. (1989). Government budgeting and expenditure controls: theory and practice.
Tobin, J., & Buiter, W. (1976). Long-run effects of fiscal and monetary policy on aggregate
demand (pp. 273-309). Cowles Foundation for Research in Economics at Yale
University.
22 | P a g e
Appendix1
Diagnostic tests for ARDL models
Breusch-Godfrey
Testing methods
Serial Correlation
LM Test:
Critical Values
5%
0.597**
A normality test:
Jarquebera
Heteroskedasticity Test:
Breusch-Pagan-Godfrey
0.5744***
0.1014**
Plot of the cumulative sum of recursive residuals
Residuals Normality Test
23 | P a g e