The Impact of Foreign Aid on Government Expenditure in Ethiopia

Table of Content

Acknowledgment

Acronyms

List of Table

List of Figure

Abstract

CHAPTER ONE
1. INTRODUCTION
1.1. Background
1.2. Statement of the Problem
1.3. Objective of the Study
1.3.1. General Objectives
1.3.2. Specific Objectives
1.4. Research Questions
1.5. Significance of the Study
1.6. Scope of the Study
1.7. Limitation of the Study
1.8. Organization of the Paper

CHAPTER TWO
2. METHODOLOGY OF THE STUDY
2.1. Data Type and Sources
2.2. Description of Variables
2.3. Model Specification
2.4. Econometric Estimation Techniques
2.4.1. Testing for Unit Root
2.4.2. Test for Cointegration
2.4.3 Vector Error Correction Model (VECM)

CHAPTER THREE
3. ANALYSIS OF ECONOMIC PERFORMANCE IN ETHIOPIA
3.1. Aggregate Economic Growth in Ethiopia
3.2. Sectorial Economic Growth in Ethiopia
3.3. External Economic Sector and Its Financing
3.4. Government Expenditure and Means of Financing
3.5. Functional Classification of Capital Expenditure in Ethiopia
3.6. Classification of Current Expenditure in Ethiopia
3.7. Budget Deficit and Means of Financing in Ethiopia
3.8. Rate of ODA Flows to Ethiopia Relative to Economic Sectors
3.9. Composition of ODA Flows to Ethiopia

CHAPTER FOUR
4. EMPIRICAL RESULTS AND INTERPRETATION
4.1. Results for Unit-Root Test
4.2. Result of Cointegration Analysis
4.2.1. Tests for Co-integration and Long-Run Relationships
4.2.2. Test Result for Short Run Expenditure Equation

CHAPTER FIVE
5. SUMMARY, CONCLUSION AND POLICY IMPLICATION
5.1. Summary
5.2. Conclusion
6.3. Policy Implication

Reference

Appendixes

Acknowledgment

First of all I would thank the Almighty God for his an invaluable helps for all things that human beings can perform with his supreme power.

Then, I would like to express my sincere gratitude to instructor Amsalu Bedemo (PHD) who has tried to provide me valuable information in the course of my research work for their genuine and constructive ideas of providing their comments and their patience to go through and forward comments.

My special thanks would be extended to Mekonnen Bersisa (PhD Candidate), who has given me a genuine and constructive comments starting from the modification of the title by sharing from the meager time he has for his personal study and the application procedures of STATA in analysis of data.

Moreover, I want to extend more and more for those researchers and other professionals that are paved the road of research work in time series analysis.

Last but not list, I have a great full thanks to my wife S/r Deme Chemeda and others for their support and taking care of our children during my thesis work and reviewing of different sources for the interesting work.

Acronyms

illustration not visible in this excerpt

List of Table

Table 1 : Average growth rates of real GDP, per capita GDP and population

Table 2: Annual growth of industrial sectors value added (% of GDP)

Table 3: Annual growth rate of import and export (% of GDP)

Table 4: Current, capital, & external assistance share of GDP and total expenditure

Table 5: Five years average capital expenditure percent of total expenditure

Table 6: Five years average current expenditure percent of total expenditure

Table 7: Summary of budget deficit and means of financing (in million birr)

Table 8: Percentage shares of net ODA to economic sectors

Table 9: Three years average share of sectoral distribution of aid (% of total aid)

Table 10: ADF unit root test result for variables in the fungibility (expenditure) model

Table 11: Johansen tests for co-integration in developmental governmental spending

Table 12: Johansen tests for co-integration in non developmental government spending

Table 13: Long run β coefficients of developmental & non developmental expenditure

Table 14: Adjustment (α) coefficients of government expenditure

Table 15: Standardized (adjustment) coefficient government expenditure

Table 16: Test for zero restriction on β coefficient of developmental expenditure

Table 17: Test for zero restriction on β coefficient of non developmental expenditure

Table 18: Short run regression result of developmental expenditure equations

Table 19: Short run regression result of non developmental expenditure equations

List of Figure

Figure 1: Ethiopian and Sub-Sahara African GDP annual growth

Figure 2: Share of major industrial sectors value added (% of GDP)

Figure 3: Export, import, and trade balance trends (% of GDP)

Figure 4: Export, import, and trade balance trends (% of GDP)

Figure 5: General, social, economic & other current expenditure (% of total)

Figure 6: Total revenue, expenditure and deficit for the period of 1981-2012

Figure 7: Flow of Bilateral, multilateral and Non DAC aid to Ethiopia

Abstract

This study has examined the impact of foreign aid on government expenditure in Ethiopia over the period 1981 to 2012 using Multivariate Vector Auto Regression analysis. All the necessary time series tests such as stationary test, co-integration, weak exiguity, and other tests are conducted. The empirical result from the long run fungibility equation result indicates that sectoral aid has negative effect on its sector spending in developmental sectors except for agricultural sector government spending. The estimate of agricultural aid also support that a 1percent increase in agricultural aid leads to a 0.83percent increase in agricultural spending. Aid other than health aid also has positive impact on health spending. The positive coefficient of aid other than the health implies that there is an aid diversion towards health sector from the others. The negative coefficients of sectoral aid on the sector spending and the negative coefficients of aid other than sector-specific aid, indicate diversion of aid away from the specific sector. Negative coefficients of explanatory variables may arise when there is a diversion of categorical aid from developmental investment towards non developmental expenditure such as general service government expenditures.The result also shows education aid is fungible both in short and long run. Health aid is fungible in the long run but not in the short run. Agriculture aid is non fungible in both long and short run in Ethiopia. The coefficient of aid other than education aid has positive sign that implies the diversion of foreign aid to the education sector. Foreign aid have also negative impact on all of non developmental government spending In order to get the desired benefit from foreign aid, Ministry of Finance and Economic Development has to set sound financial management system which stimulates economic growth and mitigate any diversion of developmental sector aid to other non developmental expenditure particularly in education and health sectors. Therefore, effective and efficient monitoring system which was purpose oriented utilization of foreign aid is central to make sectoral spending non fungible in Ethiopia.

Key words: foreign aid, economic growth, fungibility, public expenditure, sector, Ethiopia

CHAPTER ONE

1. INTRODUCTION

1.1. Background

Foreign aid is a postwar World War II phenomenon (McGillivray, Feeny, Hermes, & Lensink, 2005). The origins of modern aid can be traced to the colonial period. Specifically, the British Colonial Development Act of 1929 provided for grants and loans to colonial governments to meet their infrastructural needs as well as enabling them to pay for imports. However, such aid was firmly subordinate to the economic and political interests of the “metropole”. The emphasis only began to change with the shift in international political and financial leadership from the old colonial powers, both at the global level and at the local level allowing aid to acquire a more purposeful development rationale (UNCTAD, 2006).

Foreign aid is the international transfer of public funds in the form of loans or grants either directly from one government to another (bilateral assistance) or indirectly through the vehicle of a multilateral assistance agency such as the World Bank (WB) (Abuzeid, 2009). Foreign aid should not include all transfers of capital to developing countries, particularly the capital flows of private foreign investors that represent normal commercial transactions, prompted by commercial considerations of profits and rates of return, and therefore should not be viewed as foreign aid (Bakare, 2011; Todaro & Smith, 2012; Bwire, Morrissey, & Lloyda, 2013).

Economists have defined foreign aid, as any flow of capital to a developing country that meets two criteria: (1) Its objective should be noncommercial from the point of view of the donor; and (2) it should be characterized by concessional terms; that is, the interest rate and repayment period for borrowed capital should be softer (less stringent) than commercial terms (Kanbur, 2003; Paul & Pistor, 2009). The concept of foreign aid encompasses all official grants and concessional loans, in currency or in kind, that are broadly aimed at transferring resources from developed to less developed nations on development, poverty, or income distribution grounds (Randhawa, 2012; Todaro & Smith, 2012).

Official Development Assistance (ODA) is a grants or loans to developing countries and multilateral agencies active in development that are undertaken by the official sector at concessional terms (if a loan, having a grant element of at least 25%), with the promotion of economic development and welfare as the main objective. Technical cooperation is also included in aid. Grants, loans, and credits to be used in military purposes are excluded (Radelet, 2006; UNCTAD, 2006) (OECD, 2009; Paul & Pistor, 2009; Randhawa, 2012).

The flow of foreign aid in the form of grants, concessional loans, and technical assistance from bilateral and multilateral institution was grown from an annual rate of under $5 billion in 1960 to $50 billion in 2000 and then to over $128 billion in 2008. However, the percentage share of developed country Gross National Income (GNI) allocated to ODA declined from 0.51 percent in 1960 to 0.23 percent in 2002 before improving to 0.33 percent by 2005 and to 0.45 percent in 2008 as part of a campaign to increase assistance in the wake of the continued lag in human development in Sub-Saharan Africa including Ethiopia (OECD, 2009; Paul & Pistor, 2009; Todaro & Smith, 2012).

From African countries, Sub-Saharan Africa receives a greater share of global aid than any other region in the world with East Africa receiving approximately 25 percent of all ODA to SSA. Within East Africa, Ethiopia receives the largest percentage (7%) of total ODA from all donors, followed by Tanzania (6%). According to OECD DAC statistics, aid to Ethiopia increased from US$1.1 billion in 1995 to US$3.5 billion in 2010 which is concentrated on core social sectors and infrastructure (Nganwa, 2013; Prizzon & Rogerson, 2013). In fact, foreign aid is considered to be a major supplement to government expenditure. As a result, foreign aid can have positive effect on economic growth, through public expenditure if properly channeled to the productive sectors of the economy (Odusanya, Abidemi, & Akanni, 2011; Marc., 2014).

Foreign aid has been the principal source of development finance for the majority of developing countries (Bhattarai, 2007). It is designed to stimulate economic growth through building infrastructure, supporting productive sectors such as agriculture, or bringing new ideas and technologies; to strengthen education, health, environmental, or political systems; to support subsistence consumption of food and other commodities, especially during relief operations or humanitarian crises; or to help stabilize an economy following economic shocks (WB, 1998; Radelet, 2006; Bakare, 2011). However, other researchers agree that recipient government divert aid away from donors’ desired target (fungible) (Burnside & Dollar, 2000; Collier & Dollar, 2004).

Earmarked aid is said to be fungible if total public spending in the targeted sector increases by less than the total amount of earmarked sectoral aid (Araral, 2008; Sijpe, 2010; Morrissey, 2012; Dieleman, Graves, & Hanlon, 2013). Fungibility is the diversion of aid away from its intended uses for investment and development. More precisely, targeted aid is fungible if it is transformed into pure revenue or income augmenting resource that can be spent whichever way the recipient government chooses (Bhattarai, 2007; Tiwari, 2007). It occurs when aid is not used for the purpose intended by donors (Mark & Oliver, 2004; Chatterjee, Giuliano, & Kaya, 2007; Sijpe, 2010; Morrissey, 2012; Marc, 2012).

Fungibility occurs when recipients respond to aid by changing the way they use their own resources. On the aggregate level, aid is fungible when one dollar of aid increases government expenditure by less than one dollar, and fully fungible when government spending does not rise at all. It can happen for at least two reasons: aid substitutes rather than complements the budget, therefore the government is able to decrease taxes, decrease borrowing needs or increase surplus; and aid is stolen and ends up in private pockets. Thus, if aid is fungible on the macro level, it finances on the margin private consumption and savings via taxes or corruption. Donors may favor different regions or sectors than recipients, and in response recipient's government may shift its own expenditures to other regions or sectors, or limit total government's spending, favoring private consumption. In the latter case, fungibility at the aggregate level will be recorded (Marc, 2012; Morrissey, 2012; Dieleman, Graves, & Hanlon, 2013).

1.2. Statement of the Problem

One of the main channels through which foreign aid influences development outcomes is through its impact on the recipient country’s public expenditures. This link between foreign aid and public expenditure is however, not straightforward, because some part of aid is fungible. For instance, an aid recipient country could allocate earmarked aid to specific sector however; the recipient country may reduce its own resource to sector receiving aid and transfer it to other sector budget (WB, 1998; Swaroop, Jha, & Rajkumar, 2000; Bwire, Morrissey, & Lloyda, 2013).

Researchers have also tried to address sectoral fungibility whether a higher foreign assistance for a particular sector rises spending for that sector. They tried to answer either by comparing spending over time within a country and comparing spending across countries. In some countries and sectors aid appears to be completely fungible across sectors, while in other countries and sectors the money seems to stick. It is important to note that, if aid is not supporting productive investment in recipient countries then it must be financing either unproductive investment or consumption. In this case, high proportion of aid, which goes to recurrent expenditure, is not being necessarily bad, since several components of such spending can have higher return than capital expenditure (Pettersson, 2007; Dieleman, Graves, & Hanlon, 2013; Marc., 2014).

Despite there is a tremendous increases in the follow of foreign aid to developing countries like Ethiopia from time to time, the study of foreign aid on a sectoral basis that is conducted by cross country analysis doesn’t allow us to clearly examine the sectoral impact of aid on sector’s spending. In this respect, different countries have different result for the impact of aid on sectors spending (Paul & Pistor, 2009). Hence, such work has to be conducted in the context of country specific, to capture the different impact of earmarked aid on the sectors.

For instance, (Jifar, 2002) has examined the impact of foreign aid on various public spending for a period covering 1966/67 to 1998/99 in Ethiopia using aid fungiblity model for an analyses on four developmental (agriculture, education, construction, and transport and communication) and three non-development(defense, general service and debt servicing) government spending. His findings revealed that in the long term aid given to each development sector has positive impact on the spending of the respective sectors and non-developmental foreign aid appeared to have positive impact on debt servicing expenditure while its impact is negative for expenditures on defense and general service. Also the short run result point that foreign aid allocated to transport and communication and construction sectors brought significant negative influence on the spending of the respective sectors. The rest, however, have positive roles on all non-development sectors spending. This study clearly shows the existence of fungiblity of foreign aid at sectoral level instead of considering at aggregate level.

However, his analysis fails to address the possibility of complementarities between sectors’ spending activity among developmental, social, and economic sectors. That is, an increase in one sector’s spending (following aid given to that particular sector) may also increase the spending of another sector due to the complimentarily nature of the two sectors being under similar categories. Also, in the current context of our country, most of the developmental expenditures are project related and are mostly hard to divert to other sector than non developmental sectors. Hence, ignoring such a case may tend to make aid appear fungible when it is actually not.

Therefore, the studies reviewed suffer from certain conceptual problems and hence the study is not strong enough to adequately identify fungibility of foreign aid. Analyzing the impact of foreign aid on government expenditure by aggregating the non developmental expenditure in line with the pool of the sectors with which there is chance of resource transfer and separately analyzing of developmental expenditures for which flow of resource among the sector is the appropriate system in detecting existence of foreign aid fungiblity in Ethiopia.

1.3. Objective of the Study

1.3.1. General Objectives

The major objective of the study is to analysis the impact of foreign aid on government expenditure in Ethiopia

1.3.2. Specific Objectives

The specific objectives of the study are to:

- Analyze the trend and structure of macroeconomic performance in Ethiopia as well as its linkage with government expenditure under the two regimes;
- Analysis the short and long run impact of foreign aid on government spending in Ethiopia;
- Determine whether aid fungibilty exists in Ethiopia across sectors of developmental and non developmental government expenditures.

1.4. Research Questions

Finally, this study is attempted to address the following research question. That is, to investigate sectoral analysis of the impact of foreign aid in Ethiopia.

- What is the impact of foreign aid on components of public expenditure among different sectors?
- Is foreign aid allocated at an aggregate level is fungible in Ethiopia?
- In which sectors of the economy does that fungibility of foreign aid exist?

1.5. Significance of the Study

The Fungibility analysis of impact of foreign aid in Ethiopia is important because foreign aid may be fungible in certain sectors while it cannot be in others. The impact of foreign aid at the sectoral level has not been given consideration in analysis of impact of foreign aid in Ethiopia in detecting whether it is used for the purpose intended. Therefore, is useful for improving policy design, institutional set up, implementation, monitoring and evaluation in the area of foreign aid allocation to public spending in general and sector wise in particular. Finally, the result of the study becomes the stepping stone for academicians, researchers, students, policy makers and other organization.

1.6. Scope of the Study

This study covers a period of thirty one years for a country level analysis in Ethiopia. The study uses aggregate aid, developmental and non developmental sector spending, GDP and other time series data for the period of 1981-2011 for government expenditure and 1981 to 2012 in the case of economic development of Ethiopia is used.

1.7. Limitation of the Study

The model of aid fungibility assumes two types of expenditures, i.e., developmental aid which is allocated only to developmental sector expenditure and non-developmental aid that is allocated for non developmental sector spending. However, the model assumption is strictly valid if these two types of expenditure separable in the government utility function. In other cases, it is assumed that all aid is allocated through government sector and none of it will be spend though other ways because in developing countries like Ethiopia, aid may have come through Non-governmental organizations or other sources that is understate the whole figure of aid that may be used in the study.

1.8. Organization of the Paper

The study of research is organized under five chapters. The first chapter deals with the problems and its approaches, which include background of the study, statement of the problem, research objectives, research question to be answered, significance of the study, scope and limitation of the study, and organization of the final research paper. The second chapter deals with methodology of the study that includes source and type of data, description of variables, and model specification and estimation techniques. Chapter three deals with analysis of macroeconomic performance of government expenditure and means of financing, and trends and sectoral compositions of ODA flow to Ethiopia. Chapter four presents the estimation results of time series characteristics of the data and tests for long-run relationships and short run model. Finally chapter five, the last chapter gives summary, conclusions, and policy implication of the study. All the reference materials used in the study are listed under reference.

CHAPTER TWO

2. METHODOLOGY OF THE STUDY

2.1. Data Type and Sources

Fungibility analysis of the impact of foreign aid is relevant in the context of Ethiopia due to an increasing per capita of foreign aid and the country’s dependence on it. To estimate the impact of foreign aid on central government’s spending, the study is based on a country level macro-data covering the period from 1981 to 2011. The choice of the period is based on the availability of relevant data for the study. The relevant data was collected from various sources: National Bank of Ethiopia, Ministry of Finance and Economic Development (MoFED), Ethiopian Economic Association, World Bank, World Development Indicator database, and OECD/CRS websites.

The total Ethiopian government expenditure is divided into development and non-development expenditures, with developmental expenditure having components of capital and recurrent categories. The development expenditure classification is done on account of economic and social services. In this case, expenditure on agriculture, education and health sectors are classified as developmental categories. In other case, expenditure on economic services, social services and general service are categorized as non-developmental expenditures. The AID and OAID categorical aid variables are the earmarked ODA to their respective sectors by OECD/CRS dataset. The other types of data that are described as percentage of GDP are obtained from World Bank world development indicator dataset.

2.2. Description of Variables

The major variables and concepts that are used in the equation of the impact of foreign aid in government expenditure in Ethiopia are described under the categories of developmental expenditure (D) also called capital expenditure and non developmental expenditures (ND) also called current expenditure are used. GDP is the real gross domestic product of an economy over the period 1981-2011 for Ethiopia at aggregate level. AID is the official development assistance as defined by the OECD as percentage of GDP.

For developmental sector, sector specific expenditure such as Government agricultural spending (AGGS), government educational spending (EDGS), and Government health spending (HEGS) are dependent variables whereas sector specific aid such as agricultural aid (AAID), educational aid (EAID), health aid (HEAID and aid other than sector specific aid are used as independent variables. Similarly, for non developmental sector, non developmental sector spending such as economic service government spending (ESGS), social service government spending (SSGS) and general service government spending (GSGS) are dependent variables whereas total aid (AID) and GDP are independent variables or explanatory variables that are used in the analysis.

D: is dummy variable for major political changes from Derg regime to Ethiopian peoples’ Revolutionary Democratic Front (EPRDF) that is taken in to account to see the effect of major shifts in political environment on the performance of government expenditure and foreign aid in the short run. The dummies are incorporated in to the fungibility model and thus D took 1 for 1992 to 2011 and 0 otherwise.

2.3. Model Specification

One of the main channels through which foreign aid influences development outcomes is through its impact on the recipient country’s public expenditures. This link between foreign aid and public expenditure is however, not straightforward, because some part aid is fungible (WB, 1998; Swaroop, Jha, & Rajkumar, 2000; Bwire, Morrissey, & Lloyda, 2013).

In this study, fungibility model developed by (Pack & Pack, 1990) and further modified and used by (Njeru, 2003; Pettersson, 2007) that distinguished between developmental and non-developmental expenditures was adopted. The model is described as follows:

Where: = Developmental expenditure at time t to sector i;

= Non Developmental expenditure at time t to sector i;

= Aid earmarked for sector i at time t;

= All other categorical aid to sectors other than i;

Time = Trend;

GDP = Gross Domestic Product.

Therefore, Pack and Pack (1990) incorporated important variable to analyze aid fungibility in a clearer manner. Hence, equation (1) and (2) are the basis of estimating the effect of sectoral aid on sectoral government spending. All variables are given in per capita natural logarithms (Pettersson, 2007; Batten, 2009). To control for political, economic and fiscal management of event and to determine whether it has had any structural effect on the expenditure of the Ethiopian economy, a dummy variable (D) is also included in the estimations. This variable takes the value of zero for the period of 1981 to 1991 and one for the period of 1992 to 2011 in all developmental and non developmental sectors. The above model can be disaggregated to developmental and non-developmental sectors as follows:

A. For developmental sectors;

Where: AGGS = Agricultural Government spending;

= Agricultural aid;

= Aid other than agricultural aid.

Where: EDGS = Educational Government spending;

= Educational aid;

= Aid other than educational aid.

Where: = Health Government spending;

= Health aid;

= Aid other than health aid.

Agricultural spending is expected to be positively related with GDP and its sector aid variable. However, in the analysis of fungibility the sign of the other aid variables is not pre-determined a priori. The same holds for all other developmental sectors.

B. For Non Developmental Sectors;

Where: = Economic Sector Government spending;

= Total foreign aid.

Where: SSGS = Social Sector Government spending

Where: GSGS = General Service government spending

In an empirical application of the model presented above, first, the impact of earmarked foreign aid to Ethiopia on its sectoral components of government spending was estimated from equation (3) to (5). Then, to inquire whether such assistance has funded specific non-development spending categories (e.g., social service, economic services, general services), the link between foreign aid and the various non-development spending activities of the central government was also be examined from equation (6) to equation (8).

2.4. Econometric Estimation Techniques

As the data used is time series, stationarity (unit root test), co integration test, and test for short run relation of expenditure are performed. A data series is said to be stationary if its error term has zero mean, constant variance and the covariance between any two time periods depends only on the distance or lag between the two periods and not on the actual time which it is computed. However in reality most macroeconomic variables are non stationary.

When variables entering into the estimation are non stationary, then the result obtained using OLS technique would be spurious in the sense that variables would seem to have promising diagnostic test (high R2 and low Durbin Watson test) result just because they have common trend over time rather than actual causation(Harris, 1995). Therefore hypothesis testing and inference using such results will be invalid. To avoid such wrong inferences from the non stationary regressions, the time series property of the data should be checked prior to the estimation of the long run model.

2.4.1. Testing for Unit Root

Unit root test has become a widely popular approach to test for stationary. Most time series data have a characteristic of stochastic trend (the trend is variable which cannot be predicted with certainty). In such cases, in order to avoid the problem associated with spurious regression, pre-testing the variables for the existence of unit roots (i.e. non-stationary) becomes compulsory. In general if a variable has stochastic trend, it needs to be differenced in order to obtain stationary. Such process is called difference stationary process (Gujarati D. , 1995). The number of unit roots a given variable posses determines how many times that variable should be differenced in order to attain stationary.

A commonly applied formal test for existence of a unit root in the data is the Dickey-Fuller (DF) tests. It’s simple extension being the Augmented Dickey Fuller (ADF) test. The augmentation is adding lagged values (p) of first differences of the dependent variable as additional regressors which are required to account for possible occurrence of autocorrelation. The Dickey-Fuller test starts with the following first order autoregressive model:

Subtracting Yt-1 from both sides gives

Then the test for stationarity is conducted on the parameter γ. If g=0 or δ=1 it implies that the variable Y is not stationary. The hypothesis to be tested is formulated as follows:

Ho: γ=0 or δ=1

H1: γ < 0 or δ< 0

Furthermore, the use of equation (10) is appropriate only when the series Yt has a zero mean and no trend term (Harris, 1995). If a variable has a zero mean, it implies that Yt= 0 when t=0 implying no constant term. A constant (drift) is included to the regression since it is difficult to know whether the true value of Y0 is zero or not. Including a constant (α) to equation (10) gives:

However, testing for stationarity using equation (11) is invalid if a series contains a deterministic trend. Because if g=0, the null hypothesis will be accepted that the series contains a stochastic trend when there exists deterministic trend. Thus to avoid such results, it is important to incorporate time trend in the equation above and gives:

Where, T the trend element

For the above equations (equation 11 and 12), the parameter g is used while testing for stationarity and the decision is made using t-statistics that is used for the regression without drift and trend. If the calculated value of t is less than the critical value the null hypothesis is accepted and not if otherwise. Accepting the null hypothesis implies the presence of unit root-i.e. the series is non stationary. However, the DF test has a series limitation in that it suffers from residual autocorrelation. Therefore to overcome this problem, the DF model is augmented with additional lagged first differences of the dependent variable. This is called Augmented Dickey-Fuller model (ADF).

The advantage of using ADF over that of DF model is that ADF model avoids the autocorrelation among the residuals. Therefore incorporating lagged first differences of the dependent variable to the above three equations (equations 10, 11 and 12) gives the corresponding ADF model as follows:

Where, Yt is any variable in the model to be tested for stationarity, a is a constant (drift), T is a trend element, k is the lag length, et is an error term, and D is the first difference operator.

The null hypothesis of ADF is d=0 against alternative hypothesis that d<0. Where d=g-1. A rejection of this hypothesis means that the time series is stationary or it does not contains a unit root while not rejecting means that the time series is non stationary(Enders, 1995). If the variable that is not stationary in levels appear to be stationary after dth difference, then the variable is said to be integrated of order d I(d).

2.4.2. Test for Cointegration

Cointegration means the regressions of one variable over the other is no meaning full (spurious). Economically speaking, two variables will be cointegrated if they have a long-term, or equilibrium, relationship between them (Gujarati D. , 2004). Despite variables are being individually non stationary, a linear combination of two or more time series can be stationary.

Cointegration among the variables reflects the presence of long run relationship among non stationary variables in the system. Testing for cointegration is the same as testing for long run relationship. In general, if variables that are integrated of order‘d’ produce a linear combination which is integrated of order less than ‘d’ say ‘b’, then the variables are cointegrated and hence have long run relationship(Gujarati D. , 2004). In order to determine whether or not a long-run equilibrium relationship exists among the unit root variables in a given model, we need to test empirically that the series in the model are cointegrated. To conduct test for co-integration, the study used Johanson (1988) maximum likelihood estimation procedure.

To conduct a test for co-integration in a multivariate framework using Johansen’s maximum likelihood procedure, first the general VAR model of relationship between the variables should have to be formulated. Thus a general VAR (p) of the following form is formulated:

Where Xt is a (mx1) vector of stochastic I(1) variables, Wt is a (qx1) vector of deterministic variables (for instance trend and dummy variables), and each i (i=1….p) and Ψ are (mxm) and (mxq) matrices of parameters. et is a (mx1) vector of normally and independently distributed disturbances with zero mean and non-diagonal covariance matrix (vector of white noise disturbance terms), and t=1….T (T is the number of observation).

Providing the variables are (at most) integrated of order one i.e. I(1) and co-integrated also has an equilibrium error correction representation that is observationally equivalent but which facilitates estimation and hypothesis testing, as all terms are stationary. The vector error correction model (VECM) is:

Simplifying equation (17) gives:

Where, i= 1-------p-1, and

From the above equation, the long run relationship among the variables is captured by the term pXt-p . The i coefficients estimate the short run effects of shocks on DXt and thereby allow the short and long run responses to differ.

In the Johansen (1988) procedure, determining the rank of p (i.e. the maximum number of linearly independent stationary columns in p) provides the number of cointegrating vector between the elements in x. In this connection, there are three cases worth mentioning. (i), If the rank of p is zero it points that the matrix is null which means that the variables are not co-integrated. In such case the above model is used in first difference, with no long run information,( ii), If the rank of p equals the number of variables in the system (say n) then p has full rank which implies that the vector process is stationary. Therefore the VAR can be tested in levels, (iii), If p has a reduced rank i. e. 1<r(p)<n it suggests that there exists r<(n-1) cointegrating vector where r is the number of co-integration in the system.

The matrix p is given by (p=αβ'), where β coefficients show the long run relationship between the variables in the system(Cointegration parameters) and α coefficients show the amount of changes in the variables to bring the system back to equilibrium i.e. it shows the speed with which disequilibrium from the long run path is adjusted. To identify the number of cointegrating vectors, the Johansen procedure provides n eigenvalues (λ) characteristic roots whose magnitude measures the degree of correlation of the cointegration relations with the stationary elements in the model.

Two test statistics (ltrace and lmax) are used to test the number of cointegrating vectors, based on the characteristic roots. The statistics are calculated from the following formula:

Where, T the sample size, li is the estimated eigenvalues

In Johanson procedure, the likelihood ratio (LR) test is used to test the significance of estimates of λi eigenvalues. The λtrace tests the null that the number of co-integrating vectors is less than or equal to r against an alternative of (r+1). The λmax statistics, on the other hand, tests the null that the number of co-integrating vectors is r against an alternative of (r+1). The distribution of both test statistics follows chi-square distribution.

Vector auto regression assumes that all variables in the system are potentially endogenous. Therefore, it is important to identify the endogenous and exogenous variables in the system. M’Amanja, Lloyd & Morrissey (2005) pointed that the weak exogeneity test gives an indication of the variables in the system with feedback effects on the long run levels of other variables but themselves are not influenced by these long run variables (M’Amanja, Lloyd, & Morrissey, 2005). This implies that if a variable is weakly exogenous its error correction term doesn’t enter the error correction model. As a result the dynamic growth equation for that variable depicts no information concerning the long run relationship in the system. Thus such variables should appear in the right hand side of the VECM. For this reason test for weak exogeneity is conducted by imposing zero restriction on the relevant adjustment parameters.

2.4.3 Vector Error Correction Model (VECM)

Finding long-run estimates of cointegration relationships is only a first step to estimating the complete model. The short-run structure of the model is also important in terms of the information it conveys on the short run adjustment behavior of economic variables. The analysis of short-run dynamics is often done by first eliminating trends in the variables, usually by differencing. This procedure, however, throws away potential valuable information about long-run relationships about which economic theories have a lot to say.

Vector error correction model enables to capture the short run dynamics of the model and formulated based on the identified long run relationships. The VECM has cointegration relation built into the specification so that it restricts the long run behavior of the endogenous variable to converge to their cointegrating relationships while allowing for short run adjustment dynamics. The cointegrating term is known as the error correction term since the deviation from long run equilibrium is corrected gradually through a series of partial short run adjustments. Thus cointegration implies the presence of error correcting representation and any deviation from equilibrium will revert back to its long run path.

The existence of co-integration allows for the analysis of the short run dynamic model that identifies adjustment to the long run equilibrium relationship through the error correction model representation. If the number of co-integrating vector(s) is/are determined and once the endogenous and exogenous variables are identified in the system, it is possible to formulate a VECM. Using the variables of our interest in the model a system of equations is developed that portray the VECM. Hence, assuming that Yt is endogenous variable(s) and Xjt representing weakly exogenous variables in the model, we can model Yt. Yt is modeled using the lagged first difference of Yt itself, the lagged first differences of the explanatory variables and the error correcting term which is designed to capture the speed of adjustment to the long run equilibrium. The equation is represented as:

Where ECTt-1 is the error correcting term, DXjt-1 is a vector of first differences of explanatory variables, DYt is a vector of first differences of endogenous variable(s) and D is a dummy variable for major political changes.

The general VECM model for government expenditure is represented below using the respective variables used in the estimation of the long run equilibrium equation. The VECM model for non developmental and developmental expenditure equations are specified as:

Where lag length of two is determined by Akakie Information Criterion (AIC), D and ECT represents a dummy for major political changes and error correction term respectively.

Using the above VECM specifications, a short run dynamic equation is estimated for expenditure. Dropping insignificant regressors from the specification (i.e. step-by-step elimination of insignificant regressors and lags from the general VECM model) following the general to specific modeling strategy, a parsimonious result for growth is estimated. In the estimation of the dynamic equation for expenditure, a dummy variable is incorporated to capture the influence of major political (government) changes on expenditure in the short run. In other words, dummy is used to see the immediate impact of major shifts in government expenditure from one regime to another.

In the next chapter that follows (chapter 3), descriptive analysis of the macroeconomic performance of Ethiopia and categories of government expenditure is described and it is followed by chapter four which states the results and discussion of econometric variables of the model specification described and test of the statistics are described. All the estimation of the empirical results is made by the use of STATA 10 software packages.

CHAPTER THREE

3. ANALYSIS OF ECONOMIC PERFORMANCE IN ETHIOPIA

3.1. Aggregate Economic Growth in Ethiopia

Ethiopia, with a population of about 82.9 million, is the second most populous country in SSA with an annual growth rate of 2.2 percent (World Bank, 2013). Ethiopia is also one of the world’s poorest countries. At USD 350 per year, Ethiopia’s per capita income was much lower than the SSA average of USD 1,077 in the financial year 2009 (Ruecker, 2011).

The performance of an economy in a given country is highly explained by the soundness of the macroeconomic policy environment, the political framework, and institutional setup of a given country. The design of macroeconomic policy is a reflection of the political process that the country follows. Ethiopia had two different regimes with different policies and ideologies since 1975: the Derg regime (1975 to 1991) and the Ethiopian peoples’ Revolutionary Front (EPRDF) since 1991. Economic performance in Ethiopia is highly correlated with the political framework. Before 1974, the macroeconomic policy was largely informed by a market-oriented economic system. The period 1974-1991 witnessed a centralized economic system, where the state played a major role in all spheres of economic activity. The post-Derg period (since 1991) is again taking us back to the market-oriented system of the Imperial regime.

The prevailing regime in Ethiopia shows an improvement of basic macroeconomic development of the country particularly in the last decades than that of the military government regime and the period during transition. Rebounded from the drought shock of 2002/03, Ethiopian economy has registered a sustainable double digit broad based growth between 2003/04-2008/09 indicating that the country is on the right path economic growth. The double digit growth has been sustained throughout five consecutive years averaging 11.7 percent which was higher than that of the performance of sub-Sahara African economies during the period under consideration.

There is a significant upward improvement in the annual real GDP of Ethiopian economy in the past three decades with annual average percentage growth of 1.4 percent between1981/82 to 1990/91, 4.5 percent for 1991/92 to 2000/01, and 8.4 percent for the year between 2001/02 to 2011/12 which was higher than sub-Sahara Africa total for the same period as shown by figure 3. Also, Ethiopian real GDP annual average performance of the economy shows a significant growth from 6.18 to 15.49 $billion in the past three decades.

Figure 1: Ethiopian and Sub-Sahara African GDP annual growth

Abbildung in dieser Leseprobe nicht enthalten

Source: Own computation based on data from WB, WDI, 2014

Despite the fact that the history of the growth performance of Ethiopia was poor in the past; the country has experienced strong economic growth in the then regime especially, since 2003/04 after recovery from drought of the country. That is the country has registered a significant upward growth of real GDP per capita from 111.9 to 253.1 $ million from 1991/92 to 2011/12 with an annual average growth from 129.1 to 188.9 $million in the post Derg regime and shows a slight decline during the military period.

Moreover, the growth rate of real GDP per capita of country was increased from an annual average of 1.3 to 5.5 percent and the growth records of real GDP per capita growth of Ethiopia was moderately better that that of SSA counties annual average growth rates for the period of 1981/82 to 2011/12.

Table 1 : Average growth rates of real GDP, per capita GDP and population

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Source: Own computation based on the data from World Bank, WDI, 2014

3.2. Sectorial Economic Growth in Ethiopia

Agriculture plays an important role in terms of employment and its contribution to gross domestic product in Ethiopia. Ethiopian economy is a subsistence one that is highly dependent on agriculture, which in turn depends on vagaries of nature. Over 80 percent of the population depends on agricultural sector for earning as the means of its livelihood. Agriculture accounts for almost more than half of the GDP and more than 90 percent of the export earnings. However, the share of agriculture value added is declining steadily and the declining rate is higher in the recent years around 2002/03 during severe drought shock in the country.

Despite agriculture remained the mainstay of the economy of the country, its share in total GDP was declined to 48.8 per cent in 2011/12 down from 68.9 per cent of GDP in 1991/1992 in the then government. In 2002/03, the country was under wide spread drought that led to food insecurity for over more than 14 million persons (USAID, 2003). As the country’s economy is highly dependent on agriculture (agricultural growth rate fall by 10.5 percent, from 2001/02) and the real GDP growth rate at that time has fallen to 2.2 percent. It is worth mentioning in this regard that much of the fluctuations in real GDP growth rate have been due to variation in rainfall and climatic condition that affects agricultural production as the economy is highly dependent on agriculture. In history of more than 30 years, the service sector become the dominant sector over agriculture and falls back to its position then after as shown by figure 2 described below.

Figure 2: Share of major industrial sectors value added (% of GDP)

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Source: Own computation based on the data from WB, WDI, 2014

The agricultural sector accounts for annual average share of GDP of 56.1percent and 45.8 percent of the country between 1990/91 to 2000/01 and 2001/02 to 2011/12 respectively. The annual average growth of agriculture during this period was 3.2 percent and 6.1 percent respectively. Table 2 summarizes major industrial sectors share of GDP and their annual growth rates.

Table 2: Annual growth of industrial sectors value added (% of GDP)

Abbildung in dieser Leseprobe nicht enthalten

Source: Own computation based on the data from WB, WDI, 2014

The industrial sector of Ethiopian economy is another sub-sector shows more or less stable type of economic growth. The share of industrial sector accounted for 10.1 per cent of GDP in 201/12, up from 7.2 percent of GDP of the year 1991/92. The growth performance of the sector also rose in 2011/12 to 12.5 percent from declining trend 19.9 per cent in 1991/92. The improved performance of the industrial sector reflected some success of the government’s privatization programme that has brought into production some hitherto dormant manufacturing and agro-processing industrial establishments. The improved industrial performance was, however, largely as a result of the improved agricultural production that increased the availability of raw materials especially for food processing industries. The service sector that includes education, health and other sectors refer to economic output of intangible commodities that may be produced, transferred, and consumed at the same time.

The services sector accounted for 23.9 per cent of GDP in 1991/92, increased on its share to 41.1 per cent in 2011/12. Growth in the services sector also increased to 11.3 per cent in 2011/12 from declining growth rate of 16.3 per cent of the year 1991/92. The main growth engine in the services economy during current regime was social services, especially education and health as the government diverted increased resources (including ODA) to these activities. Education service expanded to 21.2 per cent annual growth in 2006/07, up from 18.7 per cent in 1991/92, while the health services declined to 15.7 per cent in 2006/07, from the same magnitude (18.7%) in the year 1991/92.

3.3. External Economic Sector and Its Financing

In developing economies like Ethiopia, import of goods and services also plays an important role as it supplies capital goods that could not be produced locally, and it also augments local production through delivering necessary raw materials and intermediate goods (Tofik, 2012). Imports of goods and services comprise all transactions between residents of a country and the rest of the world involving a change of ownership from nonresidents to residents of general merchandise, nonmonetary gold, and services. It is the value of all goods and other market services received from the rest of the world. The value of imported goods and services was increased up to 7,471 million $USD from 404.6 million $ USD of the 1991/92 fiscal year in the post Derg period.

The annual average percentage share of import of goods and services to GDP during Derg regime shows rebounding around 11.3 percent whereas, in the post Derg regime there are significant increments owing to the demand of capital goods for economic development of the country. According to MoFED (2011) report, the highest value of import was on capital goods followed by consumer goods, raw materials, semi finished products, fuel, and others for the period after reform.

Despite the fact that, the total volume of import bill reached 32.2percent of the country’s GDP in 2011/12 up from 12.4 percent of the 1981/82, it requires enough exports to get the foreign exchange required to finance those imports. However, given the country’s heavy dependence on agricultural production, mainly subsistence which is backward in nature, it was not able to generate enough revenue from exporting enough commodities and has been suffering from continuous trade deficits.

The ten years annual average percentage share of import to GDP was also increased from 11.3 percent to 31.8 percent between the fiscal years of 1981/82-1990/91 and 2001/02-2011/12 respectively. The annual percentage growth of import of goods and services was also reached 8.7 percent in the year 2011/12 up from 4.7 percent growth of the year 1981/82. The ten year annual average percentage growth during the period of 1991/92-2000/1 was highly increased up to 18.6 percent and then started declining to 11.8 percent annual average growth between 2001/02-2011/12.

Table 3: Annual growth rate of import and export (% of GDP)

Abbildung in dieser Leseprobe nicht enthalten

Source: Own computation based on the data from WB, World Development Indicator, 2014

Furthermore, table 3 also depicts the value of trade performance of the country for the period under review. During these periods, the country has been facing a trade deficit, which has been getting wider and wider reaching almost USD 4.2 billion in 2011/12 from 319 million USD of 1981/82. The reason is that Ethiopia remained to be exporter of agricultural commodities, which has income inelastic demand and a have very volatile price in the international market. On the opposite side, its imports are manufactured goods, which are highly valuable and costly commodity to cover its value by the revenue generated from bulky and cheap agricultural products. Similarly, the ten year annual average of trade balance was also increased up to 2,837 million USD for the period of 2001/02-2011/12 from 319 million of the fiscal year between 1981/82-1991/92. The trade balance of the country shows increasing trends from time to time as shown by figure 3 shown below.

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