The Effect of Foreign Aid on Economic Growth in Developing Countries

Evidence Sub Saharan Africa, Panel Data Analysis 1999-2017


Master's Thesis, 2019

66 Pages, Grade: 2


Free online reading

Table ofContents

Abstract

Acknowledgement

List of Tables

List of Figures

List of Abbreviations

1. Introduction

2. Literature Review
2.1 Definition and Concept of Foreign Aid
2.2 Empirical Literature Review
2.3 Empirical Literature Review in Sub-Sahara Arica

3. Theoretical Summary of Aid-Growth Relationship
3.1 The Solow-Swan Model
3.1.1 The Effect of Saving on investment and Economic Growth in the Neoclassical Growth Model Analysis
3.1.2 The Role of Foreign Aid on Economic Growth in the Neoclassical Growth Model Analysis
3.2 The Two-Gap Model

4. Methodology and Data
4.1. Data source and Data Measurement
4.2. Specification of Model
4.3. Estimation Methods

5. Foreign aid and Economic Growth in sub-Sahara Africa
5.1 Trends in Foreign Aid Inflows into sub-Sahara Africa
5.2 Trends in Regional shares of total Foreign Aid (1990-2017)
5.3 Trend in Net ODA growth rate and GDP growth rate of SSA

6. Econometrics Analysis
6.1 Foreign Aid and Economic Growth
6.2 The Impact of Economic Policies on the Aid-Growth Relationship
6.3 The Role of Institutional Factors on the Aid-Growth Relationship
6.4 The Role of Foreign aid in the Two-Gap Model

7. Conclusion

References

Appendixes

Abstract

The paper aims to analyze the aid-growth relationship in sub-Saharan Africa (SSA) using a panel data which ranges over the period of 1999-2017. In addition to its main objective, this paper also examines the role of macroeconomic and institutional factors in understanding the linkage between aid and growths. A huge aid inflow to sub-Saharan Africa and persistent economic growth as well as extreme poverty in the regions causes a heated debate on the success of foreign financial sources in achieving economic development in SSA. Accordingly, this paper devoted to evaluating the effect of aid on the economic growth of SSA’s twelve countries. The result shows that aid has a positive effect on growth but statistically insignificant. However, the empirical findings reveal that the interaction terms of aid with inflation and government consumption are statistically significant. This implies that aid will be more effective in a sound macroeconomic environment. Consequently, the result shows that “institutional quality” has a negative but statistically insignificant effect on growth. Whereas its interaction term with aid found to be positive but also statistically insignificant this implies that the effectiveness of aid on growth is independent to the institutional environment. The paper also evaluated the impact of aid on growth through the “Two-Gap” model which tests that aid accelerate growth through improves saving and trade. The result reveals that aid has a positive and significant effect on investment, but an insignificant role in enhances trade. The overall result shows that the relationship between aid and growth is conditional on the macroeconomic factors, but irrespective to the institutional environment.

Acknowledgement

Firstly, I would like to express my sincere gratitude to my first supervisor Professor Dr. Xenia Matschke for her continuous guidance and valuable comments during the process of writing my thesis. Besides my advisor, my great appreciation also goes to my second supervisor Daria Suprunenko for her valuable guidance and information for my thesis. I wish to express my deepest gratitude to Open Society Foundation (OSF) for technical training and financial support during my study.

I also would like to give sincere thanks and recognition to my friends Tibebu Aragie, Fenet Jima, and Anteneh Tamirat who read part of my thesis and gave me their constructive comments and corrections. Finally, I wish to acknowledge the support and great love of my family, and also my beloved friend and sister Eden Guesh for her support and encouragements since the initial stage of my study.

List of Tables

Table 4. 1 Descriptions of the Variables

Table 5.1 Top Ten Bilateral Donor Countries to Africa in Million USD (2014-2016)

Table 5.2 Top Ten Multilateral Donors to Africa in Million USD (2014-2016)

Table 5.3 Top Ten ODA Recipients in Africa from all Donors in Millions of USD (2014-2016)

Table 6.1 Model Estimation using Macroeconomic Indicators and AID

Table 6.2 Institutional Quality Index

Table 6.3 Model Estimation Using Institutional Quality Index and AID 39

List of Figures

Figure 5.1 ODA Inflows into SSA in Millions of USD, (1990-2017)

Figure 5.2 Regional shares of total ODA (1990-2017)

Figure 5.3 Annual Growth Trends for Net ODA Inflows and GDP growth rate in SSA (1990-2017)

Figure 5.4 Scatterplot of economic growth (GDP) and foreign aid (ODA)

List of Abbreviations

Abbildung in dieser Leseprobe nicht enthalten

1. Introduction

Developing countries receive foreign to fill their financial gap. Accordingly, there has been massive inflow of foreign aid to developing countries. However, the effectiveness of aid on the economic growth of receiving countries differs as the country varies. As a result of this ambiguity, there has been growing research interest in evaluating the effect of foreign aid on the economic growth in developing countries. The first serious consideration on the subject of foreign aid dates back to the end of the WWII, where the USA started its first international aid initiative (the so-called "Marshal-Plan") for the period of 1948-1951. In the year of 1948 alone, the USA provided over $12 billion for the program in the form of aid to rebuild Western European Economy from its destruction caused by the War. Moreover, the program was successful (Eichengreen, 2011). Since then, many countries got a lesson and showed interest to replicate it. Following the success of “Marshal-Plan,” then after, the concept of foreign aid or official development assistant (ODA, hereafter) have been extensively used and accepted in many countries. One of the active receivers of foreign aid is the Sub-Saharan Africa region for instance only in the year 2015 over 51 billon USD (29% of the total ODA) flowed into the reign.

SSA region comprises of 49 countries, and most of these countries are categorized as low-income countries. It is one of the poorest and least developed in the world where more than half of its population lives under extreme poverty (below the poverty threshold of USD 1.90) World Bank (2017). Consequently, the region is one of the higher recipients of foreign aid in the world, every year billions of dollars flow to the region to promote economic growth and reduce poverty. However, the effectiveness of aid in the SSA is still doubtful since the region is lagged both in economy and good governance.

According to the OECD (2017), for the last two decades, SSA is one of the biggest receivers of foreign aid, between the years 1999-2017 the region received over 723.3 billion USD. Likewise, in 2016, out of the total world ODA, which is $157.7 billion, SSA received $50 billion (31.6%). As a comparison, South Asia region received only 10% of the total world aid. Despite such huge amount of aid flows, however, the region is still characterized by having low level of living standard, more than 50 percent of the world’s poor lives in this region alone. Therefore, it is legitimate to question the effectiveness of aid in SSA. Almost all of SSA African countries are receiving huge amount foreign aid and the puzzle, therefore, is they are still the poorest in the world.

There is plethora of researches on the relationship between aid and economic growth relationship with a mixed and inconclusive result. Several findings are showing the effectiveness of aid on boosting the economic growth (see Durbary, Gemmell, and Greenaway, 1998; Bhattarai, 2005; Hatemi and Irandoust, 2005; Das and Choudhary, 2011; and Juselius, Moller, and Tarp, 2014). These authors argue that aid accelerates investment and, the growth of investment, in turn, improves the level of economic growth. Their argument stands from the famous neoclassical theory which underlines on the importance of investment to accelerate economic growth (see Burnside and Dollar, 2000; Chenery and Strout, 1966; Levy, 1988; Islam, 1992).

In contrary, several studies are giving alert that the effectiveness of aid in boosting the economic growth is not as expected (for example see Moyo, 2009; Griffin and Enos, 1970; and Easterly, 1999). According to these researchers, aid has a negative impact on economic growth because economic and political instability, corruption, and weak institutional quality lead to inappropriate usage of foreign aid in a host country. Furthermore, Boone (1996) finding reveals a partial crowding out effect of foreign aid over domestic investments; hence, the crowding out effect of foreign aid hurts the domestic economy and develops foreign dependency over time. Adding to this, Hatemi and Irandoust (2005) argued that a negative aid-growth relationship might be caused by unstable business cycles and excessive state intervention.

Burnside and Dollar (2000) and the resent work by Tang and Bundhoo (2017) articulate that the effectiveness of foreign aid in improving the economic growth is conditional to the soundness of policies and prevailing institutions. On the other hand, Khan and Rahim (1993) found that foreign aid has had neither a positive nor negative impact on growth. Thus, the literatures have produced inconsistent and inconclusive e results on the aid-growth linkage.

Therefore, the primary objective of this paper is to analyze the aid-growth relationship in sub-Saharan Africa (SSA) using a panel data which ranges over the period of 1999-2017. The paper uses twelve selected countries as a sample of the study. In addition to its main objective, this paper also examines the role of macroeconomic policies, political and institutional factors in understanding the relationship between aid-economic growths.

Countries considered in this study are the following: Djibouti, Eritrea, Ethiopia, Kenya, Madagascar, Mauritania, Mozambique, Niger, Sierra Leone, Sudan, Tanzania, and Uganda. And, the study is restricted to the period of 1999-2017. The time span is chosen based on the accessibility of data. The researcher hypothesize that foreign aid has a significant positive effect on economic development in sub-Saharan Africa. However, the author suspects that poor policy environment and low institutional quality might cause foreign aid to be ineffective. The paper uses both descriptive and empirical analysis to examine the role of foreign aid on economic growth. The descriptive analysis will be presented with tables and trends. While the empirical analysis discusses using econometrical techniques presentation to identify whether the aid effectiveness is statistically/empirically significant or not using “pooled OLS, fixed-effects and random-effects, first-difference, and two-stage least squares estimation techniques. Having in mind all this, the study will contribute to aid-effectiveness literature, particularly with respect to SSA.

The remaining part of the study are organized as follows: chapter two presents a review of existing relevant literature; chapter three provides a theoretical summary of aid-growth relationship; chapter four devotes to the methodology of the study which briefly discussed model specification and estimation methods; whereas chapter five and six will be devoted to analyze the issue in descriptive and econometric methodology. Finally, chapter seven will conclude the main findings of the paper.

2. Literature Review

2.1 Definition and Concept of Foreign Aid

The term foreign aid is also known as official development assistance (ODA) and it refers to the provision of resources from one country or government to another country (OECD, 2010). In this view, the term “resources” perceptibly include financial aid, human capital or technical assistance, grants, and loans. It mainly intends to promote economic development in host countries.

ODA is defined by OECD Development Assistance Committee as “government aid designed to promote the economic development and welfare of developing countries. Loans and credits for military purposes are excluded. Aid includes grants, "soft" loans (where the grant element is at least 25% of the total) and the provision of technical assistance. The OECD maintains a list of developing countries1 and territories; only aid to these countries counts as ODA” (OECD’s website2 ).

ODA can flow through bilateral or multilateral channels (OECD, 2012). Bilateral aid reflects that financial flow from one country directly to another. While multilateral aid refers to aid flows from multiple countries and other entities. For instance, EU, International Development Association, and Global Fund are the biggest providers of multilateral aid. They provide billions of USD in the form of loans and grants to developing countries aimed at reducing poverty and achieving economic development of those countries.

2.2 Empirical Literature Review

The relationship between aid and growth has been heavily discussed for the last three decades in the academic literature; and shown mixed and inconclusive results. Hence, given those vast findings with heated debates on the effectiveness of aid, it is worth to go through the literature to understand what has been known and identify the gap so that the researcher has a reason to devote his energy.

The existing empirical literature on the aid-growth relationship can be categorized into three phases. The first phase covers the period form 1960-1970, and it has highly been stressed on the relationship between aid and growth of saving. Chenery and Strout (1966) advocated "Two-Gap growth model." They found that aid promotes growth by enhancing domestic saving. Afterward, the second phase, the focus of the researchers was the link between the aid and growth was between the years 1970 to 1990 and emphasized on the link between aid and investment. The underlying assumption is investment in return promotes economic growth. The Harrod-Domar growth model was the dominant model in this time where saving is considered as a basic constraint to investment and growth. Papanek (1972) and Levey (1988) found that foreign aid leads to growth as long as it increases saving and investment. Likewise, Papanek (1973) carried out an empirical analysis which shows that aid has a positive indirect impact on growth. Their assumption was aid promotes consumption of imported commodities by alleviating foreign exchange constraints which impede consumers from importing goods and services. On the other hand, the result shows crowding out of foreign aid on domestic saving. Inline, with the view of Griffin and Enos (1970) which previously suggested that aid does not support domestic resources instead it distorts domestic saving. Papanek (1972), in his conclusion, states that aid should be specific and well designed to be more effective.

The third phase covers the period of 1990s–present, analysis of this period were overflowing by econometrical analysis with wide-range data and variables. One of the first examples of this segment is presented by Boone (1996), in which he looks at the impact of political regimes on the aid-growth relationship; and finds that aid does not affect growth rather it increases the power of government body. Burnside and Dollar (2000) were motivated by Boone’s (1996) findings and replicate the model. And they found that aid is more effective in countries where there are good macroeconomic policies. Later, Easterly, Levine, and Roodman (2003) using a similar approach as of Burnside and Dollar (2000) with large sample size and they found that there is no empirical evidence that supports the conditionality of aid effectiveness on policy’s environment. Similarly, a study by Burke and Estafahami (2006) confirmed to the early work by Burnside and Dollar in 2000, aid is more effective under a good governmental system and good policy. Moyo (2009) in her prominent book "Dead Aid: Why aid is not working and how there is a better way for Africa, " analyses the trend of economic development of Africa for the last three decades. Moyo states that aid has not only refrained economic growth of the region but also crowded out financial and human capital. She also argues that aid causes the region to be more dependent on foreign financial resources.

In contrast, Hatemi and Irandoust (2005) conducted panel co-integration analysis for the period 1974-1996 to examine the effect of aid on economic growth of selected developing countries includes Botswana, Ethiopia, India, Kenya, Sri-Lanka, and Tanzania. Their result shows that international aid has a significant positive effect on growth in developing countries. In the same vein, Asteriou (2009) attempted to examine the relationship between aid and growth for five selected South-Asian countries by using panel unit root approach and found that a positive impact of aid on growth. Furthermore, a similar study by Das and Choudhary (2011) which has used both time-series and panel co-integration approach for 1976-2008 in South-Asian countries; and they found that aid has a strong positive effect on growth in the long-run. Studies by Sakyi (2011) and Arndt, et al. (2015) also confirmed that foreign aid and growth have a positive correlation both in short-run and long-run.

Bhattarai (2005) tried to investigate the impact of aid on growth in Nepal and its relationship with saving, investment and income per capita, using time-series analysis co-integration and ECM for 1970-2002, and found that aid is positively correlated with growth, saving, and investment. This result supports the Harrod-Domar theoretical view which suggests that foreign aid accelerates domestic saving thereafter growth. Moreover, he also found that aid can be more effective in promoting growth in recipient countries with good macroeconomic policies which is similar to the conclusion by Burnside and Dollar (2000) - aid effectiveness is conditional to good policies. A few years earlier, in 1998, Durbary, Gemmell, and Greenaway carried out a study on aid effectiveness using Augmented Fischer-Easterly model for cross-sectional and panel data. Their result also reveals that foreign aid has a positive impact on growth but conditional to environmental circumstance, vary according to geographical location and level of aid allocation.

In contrast, several scholars have argued that aid affect growth negatively. For example, Griffin and Enos (1970), and Weiskopff (1972) examined the aid-growth linkage using empirical analysis, and they concluded that a significant negative relationship between aid and growth. Mosley (1980) stressed that in most developing countries foreign aid increase household and government consumption but not investment. As a result, the author adds, aid has a negative impact on growth. Mosley (1980) argues that increment in aid will reduce tax revenue and increase dependency on foreign aid which afterward reduces investment and growth. However, these studies do not investigate whether aid is conditional on environmental or policies factors.

Nyoni (1998) conducted research on the aid-growth relationship in Tanzania, and the result shows that an increase in aid flows raises the value of the domestic currency (appreciates exchange rate) and increases the price of export goods. Consequently, the author emphasizes, it reduces domestic investment. He concludes that foreign aid has a negative effect on growth through deteriorating investment in the country. Moreover, he stated that government intervention is important to reform and implement convenient policies which enable aid to be more effective and offset the exchange rate problem and playing a positive role in promoting growth.

The existing empirical literature shows not only a positive and/or a negative relationship between aid and growth but also a causal relationship between aid and growth. For example, Bowles (1987) has conducted a study using time series data for the period 1960-1981 for twenty developing countries to examine a causal relationship between foreign and domestic saving. The study found a causal relation between aid and saving, but the direction of the causality between aid and economic growth determine by the donor's national interest. Nonetheless, this causality result does not support to the view of "Two-Gap theory," which was developed by Chenery and Stout in 1966, stressed that foreign aid promote domestic saving and investment and hence raised growth.

2.3 Empirical Literature Review in Sub-Sahara Arica

A growing body of literature has evaluated the effectiveness of foreign aid in the context of Africa in general and SSA countries. Similarly to the world-wide existing literature in aid-growth links, studies also show a mixed and inconclusive result in Africa context.

Gomannee et al. (2005) examined aid effectiveness in twenty-five selected SSA countries using pooled data for the period 1970-1997. They found a positive relationship between aid and growth. The result shows that other things remain constant; a one percent change in AID-GDP ratio contributes 25% point in economic growth rate. In their conclusion, they clearly stated that weak economic performance of the continent does not affect aid ineffectiveness which means that aid plays an important role in promoting the economic growth of the region irrespective of the level of previous economic performance.

Alemayehu (2011) carried out a study “aid and the African dilemma” which evaluated both direct and indirect effect of aid on human development using time series analysis. His study covers 34 African countries over the year 1960-2005. In this study, infant mortality considered as a measurement of human development so as to evaluate the direct effects of aid, and he used the major determinants of infant mortality to assess the indirect effect of aid. The result shows that aid does not have a direct effect on human development (infant mortality) as aid has an insignificant relationship with the educational sector. Alemayehu (2011) concluded that income per capita is the only significant factor in human development or infant mortality.

A study conducted by Rwabutomize (2008) examines the relationship between aid and economic growth in SSA region from 1990-2004 by applying the Arellano-Bond dynamic panel data estimation technique. Rwabutomize (2008) explained that increases in foreign aid inflows will not have a positive effect on promoting economic growth in the region. He suggests that African countries should rely on domestic developmental sources particularly on domestic saving and investment rather on foreign assistance inflows to the region.

Robert Gillanders (2011) analyses the effectiveness of aid on growth use panel data of SSA countries. The study applied a Vector Auto-Regression (VAR) model to evaluate the impact of aid on human development and economic growth simultaneously. The result reveals that a large foreign aid inflow brings a small economic growth in SSA countries. This finding seems to address the aid effectiveness debate as it appears somewhere between the views of aid optimists, and those of aid pessimists. In addition, aid has a small but positive effect on human development, relatively economic growth is found to respond faster to foreign aid inflows. However, the result shows that human development responds more to foreign aid inflows in countries with good institutional and high democracy level.

Nilsson (2013) examines the effect of different sectorial aids on growth. The study is carried out on a panel-data selected sample of SSA countries for the period 1995-2011. The study also analyzed both short-run and long-run effect of sectorial aid on growth. The result reveals that aid has a direct and positive effect on growth when it allocates into a social-infrastructure sector which is included health, education, provision of clean water, and sanitation. While, aid allocated into economic-infrastructure, which includes trade and financial sector, might not generate economic growth in the short-run, but it does in long-run. A similar study conducted by Juselius, Moller, and Tarp (2014) addresses the long-run impact of aid on major macroeconomic variables over 36 SSA countries for the period 1960-2007 using co-integration VAR model. Their estimated model shows that aid has a positive and statistically significant impact on growth in the long run through stimulates investment and national consumption.

Tait, Siddique, and Chatterjee (2016) investigated the aid-growth linkage in SSA for the year 1970-2012, applied fixed effect estimation method. The result reveals that aid has a significant positive effect on growth in the long run. It also indicates that this positive long-run effect of aid is not responsible for diminishing marginal returns and also unconditional to the level of institutional quality. Furthermore, they examined the effectiveness of aid in different sectors over the sub-period 1995-2012, to address the short-run effectiveness of aid. This further investigation reveals that aid allocated into social infrastructure sectors, especially health and education, has a positive significant impact on economic growth. This result is corresponding with findings of Nilsson (2013).

Recently Tang and Bundhoo (2017) conducted research on the effectiveness of aid on growth of SSA countries for the time 1990-2012. They found that aid does not have a direct effect on growth. However, the interaction terms of aid with policy index and institutional quality were found positive and statistically significant. Also, they argue that aid is a key factor in promoting investment in SSA countries and concluded that aid effectiveness is conditional to the political, economic, and institutional level of the host countries. Over all, the literature regarding the nexus between aid and economic growth is seems inconclusive. Therefore, the researcher would like to contribute to this gap by employing both descriptive and empirical analysis with appropriate methodology and data.

3. Theoretical Summary of Aid-Growth Relationship

3.1 The Solow-Swan Model

The Solow-Swan Model is developed by Solow and Swan in 1956 and settled in the neo-classical economic framework. The model is an extension of the Harrod–Domar (HD) model. This section first gives an overview of the HD model, and then it discusses the neoclassical model as well as the Gap theory. Finally, tries to see how foreign aid is described in the models.

The Harrod-Domar model is one of the earliest explanations of economic growth which was developed by Harrod in 1939 and Domar in 1946 independently. However, their similar conclusions imply a joint title, Harrod-Domar model. In line with the Keynesian short-run macroeconomics, the model concentrates on the importance conditions of equilibrium between national saving and investment based on forecasts rather than actual results. The model explains a national economic growth in terms of propensity to save and capital-output ratio by assuming that capita-output ratio is fixed. Accordingly, investment is the only constraint to growth and capital has been a bottleneck to growth. A high propensity to saving brings high economic growth, means that a nation which is able to save a high proportion of national income could grow at higher growth rate than a nation that saved less (Todaro, 1994). Therefore, the implication of the Harrod-Domar model is straight forward. It shows that foreign capital flows promote economic growth if flowing and filling to saving gaps in recipient countries. It is worth pointing out that growth will instantly decrease, even back to previous level, when foreign aid flows decreases or stopped to fill saving gap, unless aid recipients increase their saving propensity which will enable to sustain the required investment level.

The Solow-Swan Model was developed because HD assumes that constant marginal return to capital, which reflects that propensity to saving, capita-output ratio, and population growth are independently constants. In this regard, a steady state of growth could not be occurring, except in some exceptions of factor of production capital and labor growing at the same rate (Solow, 1970). Unlike the HD model, Solow assumes decreasing marginal returns to capital enables the country to achieve steady state growth. This means that to remove the assumption of fixed-proportion function3 in production and to deal with labor-output ration and capital-output ratio in adjusting steady state growth (ibid.).

Under certain assumptions of the model, with constant technical progress, the production function given by:

Abbildung in dieser Leseprobe nicht enthalten

Where: Y is output (income), “K” is capital and “L” is labour. To see production per worker we divided the production function by “L” labour, then expresses as:

Where y = Y/L is output or income per worker, k = K/L is the capital-labour ratio, and the function f(k) = f(k, 1). Thus, the production function can be expressed as:

Abbildung in dieser Leseprobe nicht enthalten

In the model propensity to save is a constant fraction “s” of income. So saving per worker is given by “sy”. This implies:

Abbildung in dieser Leseprobe nicht enthalten

Now, based on the basic model’s assumption that is population is growing at a constant rate, it is essential to articulate the level of investment required to keep the capital-lebor ratio. In this sense, population growth rate and depreciation rate determine the level investment to maintain capital per worker. The amount capital stock growth rate (nxk) plus the investment level needed to replace depreciated capital (dxk) gives the level of investment per labor to maintain capital per worker for the growing population. (See equation 4)

Abbildung in dieser Leseprobe nicht enthalten

Where: ‘n’ is population growth rate ‘d’ is depreciation rate, and ‘k’ is capital per worker

Equation (4) shows the level of investment that needed to maintain capital per worker. Therefore, the fundamental equation of Solow-Swan model essentially tells us that the rate of change on capital per worker (capita-labor ratio) “k” determine by the difference between the amounts saving (investment) per worker and the amount of required investment to maintain capital per worker. This implies that the economy settles in the long run to steady state4. The fundamental equation for the Solow-Swan model is given by the following equation:

Abbildung in dieser Leseprobe nicht enthalten

The steady state is occur where ǩ = 0, or s.f(k) = (n+d)k (see figure 3.1)

Figure 3.1 Steady-state in the neo-classical growth model

Abbildung in dieser Leseprobe nicht enthalten

Source: Adapted from Smriti Chand5

As illustrated in figure 3.1, the steady state is at point E where saving per worker sf(k) curve intersects with the investment requirement line (n+d)k. Then y⃰ is the steady-state income or output per worker, as measured by point “P” on the production function y = f (k) and k⃰ is steady state capital per worker.

The model tells us when saving per worker greater than the investment level then the economy which is measured through output per worker and capital per worker rise till it reaches to its steady-state level (point-E). Then again, when the saving per worker below the level of investment needed to keep capital-labor ratio constant, output per worker or income per worker will decline and capital per worker also falling till it reaches steady-state ‘E’. Thus, the main implication of the model regardless of where the economy starts the economy will be at its steady-state in long-run.

As we have seen above that in steady state, both capita-labor ration and output-labor ration remain unchanged when the economy is growing at the rate of population “n”. Hence, the steady state growth rate or long-run growth does not depend on the saving rate. Changes in the saving rate affect only the short-run growth rate of the economy. This is an important inference of the model, later we will see how foreign aid implies in this model and its implication on saving and growth.

3.1.1 The Effect of Saving on investment and Economic Growth in the Neoclassical Growth Model Analysis

Now to see the impact of saving, we assume that saving rate increases, as illustrated in figure 3.2, an increase in saving rate from “s” to “s1” which saving curve sf(k) shifts upward to s1.f(k) then a new steady state occurs at point E1.

Figure 3.2 Impact of Increase in the Saving Rate in neo-classical model

Abbildung in dieser Leseprobe nicht enthalten

Source: Adapted from Smriti Chand 6

Given that population growth rate is constant, as saving rate rises to s1 the capital-labor ratio will continue to increase till it reaches at k⃰1, and also output-labor ration rise to y⃰1. However, the level output is increasing at the diminishing growth rate in a short-run or transition period that means a long-run growth rate of the economy remains unaffected because the initial growth rate of output is restored over the long-run at the new steady state equilibrium point E1. Therefore, unlike the Harrod-Domar model which argues that saving rate may permanently cause economic growth, the neo-classical model shows that a higher saving rate does not permanently affect the growth rate rather temporally.

3.1.2 The Role of Foreign Aid on Economic Growth in the Neoclassical Growth Model Analysis

This section discusses how foreign aid affects once the economy using the Solow-Swan Model. In fact, foreign aid can be in different forms such as grants, technical assistance, and capital stock. Now, we assume that foreign aid as a financial flow which associates directly with saving. In developing countries, where most of the societies live under the poverty line, the saving rate is near zero. Subsequently, due to this inadequate domestic saving, an active investment might not be undertaken. Hence, economic growth effort must depend on other external capital flows in fact that developmental assistance and foreign aid is considered as one of the major carouses of capital accumulations.

It is important to incorporate foreign aid into the neoclassical model in order to analyse how aid affects growth. Many scholars like Papanek (1972) and Rodan (1961) verify that foreign aid accelerates growth through promoting investment as a result of increment in saving. Hence, similarly, in the model international aid stimulate economic growth through investment as long as saving per worker exceeds the amount required to keep capital per worker constant.

An increment of foreign aid inflow implies higher capital-labour ratio and output per worker (aid-supported per capita income). Figure 3.2 illustrates how higher savings caused by an inflow of aid affects the long-run level of income. The increase in aid flows causes an increment in saving and sf(k) curve shifts upwards to s1f(k), which continues until the new steady state occurs at s1f(k)=(n+d)k. This rise in aid inflow induces high capita per worker k⃰ which is associated with high income per worker (output per worker) y⃰ in a host country. However, as soon as aid is withdrawal an economy back to its previous level as saving per head below the level of investment that requires keeping the capital-labor ratio constant. It means only a domestic saving impotent to maintain the investment required to keep the capital-labor ratio constant. As a result, when aid unable to increase the capital per worker above k⃰ (see figure-3.2), then the role of foreign aid will only be a transistor. On the other hand, if enough foreign capital is driven into the economy, higher income per head levels can be achieved permanently.

3.2 The Two-Gap Model

Two-gap model is introduced by Chenery and Strout (1966). It is considered an extension of HD model. This two-gap model has been used by many researchers in justifying the effectiveness of aid. As it is named “two-gap”, this model suggests that developing countries’ economies face two-gaps, which they must fill otherwise economic growth cannot be achieved.

In this model, the first gap is between investment and domestic saving, which is known as “saving gap”. This saving gap is a main feature of many developing countries economy. There has heated debate among economists on the role of foreign aid in filling this saving gap. Some economists argue that aid is playing a significant role in filling saving gap in host countries. Others argue that developing countries should involve in international trade to overcome their financial needs. This argument is related with the second gap: between exports and imports (trade gap or foreign exchange gap). The economy of most developing countries, especially Sub-Saharan Africa, is fully depends on primary production. Their export dominate by agricultural goods whereas import more industrial goods which are much more expensive than their exportable goods in price term. Consequently, their economy faces current-account deficits, according the model this is called trade/exchange gap. It is important to raise a question how can a country overcome these gaps?

Although there no saving gap or it is very small, a large current-account deficit would constraint productivity because of it limited imports of capital goods that required for investment. It is obviously expected that at least one gap is exist in a foreign aid recipient countries hence aid aims to fill that gap. However, the assumption that foreign financial flows overcome these gaps will hold true only if the host countries committed to use the aid inflow for investment purpose instead of for current consumption aim. In a nation with lack of commitment and poor intensive to invest, aid will not be wasted. Therefore, the effectiveness of foreign aid in filling or solving these gaps depends on productivity of the investment made (Cherkos and Abis, 2011).

4. Methodology and Data

4.1. Data source and Data Measurement

The study mainly depends on secondary data for its analysis. It uses data obtained from international institutions such as: The Organization for Economic Co-operation and Development (OECD), United Nations Conference on Trade and Development (UNCTAD), World Development Indicators, World Governance Indicators (WGI), World Bank (WB), and Freedom House. The main variables are include GGDP, Foreign Aid, macroeconomic indicators (inflation, governmental consumption, and trade openness), Institutional Quality 7 indicator which is constructed from major components of institutional features which includes corruption, government effectiveness, political stability, regulatory quality, voice and accountability, and rule of law which are obtained from the Freedom House database.

Table 4.1 Descriptions of the Variables

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* Represent variables included in instrumental variables in 2SLS model

4.2. Specification of Model

The model used in this study is similar to the one that was used by Burnside and Dollar (2000) and Tang and Bundhoo (2017). To develop my model, I start from the basic neoclassical growth model and adding foreign aid into the model in addition to capital and labor. And, I extend the growth model by adding important factors that significantly affect growth include macroeconomic factors and institution quality index.

Thus, the production function will be:

Abbildung in dieser Leseprobe nicht enthalten

Where:

Y= is gross domestic product (GDP) in real terms,

L = is labor input,

K = is domestic capital stock, and

A= is stock of foreign aid

Similarly to Burnside and Dollar (2000), other researchers like, Karras (2006) and Tang and Bundhoo (2017) included other important variables as a control variables into the model. In addition to the main objective of the study, the paper also examine a range of factors mainly macroeconomic and institutional factors that can help explain the growth performance of the selected sub Saharan African countries. Including employment that is measured as labor, inflation, trade openness, governmental consumption, foreign aid, a dummy variable for British colony and institutional quality index yield the following basic growth models:

Abbildung in dieser Leseprobe nicht enthalten

Where: the letter ‘ i ’ refers countries and ‘ t ’ refers time. The dependent variable (GDP) is the real gross domestic product per capita growth rate which measures the economic growth in the selected sample countries. As Collier Dollar (2002) and Tang and Bundhoo (2017) applied, to capture the ‘catch up effect’ or a convergence effect, I include initial GDP into the model. Moreover, labor and investment present to GDP have been included as explanatory variables to consider their effect on economic growth. Foreign aid as a percentage to GDP is a variable interest, in the model and its square (AID^2) measures diminishing returns to aid has been taken into consideration. The AIDi(t-1) is a one-year lagged value of AID which captures the impact of last year aid on current economic growth.

Furthermore, some interaction terms of aid with policy and governmental indicators has been included in the model to assess conditionality effect of aid on macroeconomic environment of the recipient country. These interaction term represent by aid x inflation, aid x governmental consumption, and aid x trade openness. The Moneyt−1 variable is a one year lagged value of broad money8 as a percentage of GDP and measures the money supply or financial depth of the economy. And the variable IQ strands for Institutional Quality. Moreover, to take into consideration the impact of colonization background on aid effectiveness, I include a Dummy variable of colonization which it takes one if a country was under the British colony otherwise zero. Besides, the interaction, between foreign aid and institutional quality (AIDxIQ), variable reflect the role of institutional feature on aid effectiveness, similarly as in Tang and Bundhoo (2017).

Constructing Institutional Quality index

Using a similar approach to Tang and Bundhoo (2017), I develop an “institutional quality index” using main six determinants of governmental quality. These determinants are consisted “Control of Corruption, Government Effectiveness, Political Stability and Absence of Violence/Terrorism, Regulatory Quality, Voice and Accountability, and Rule of Law”. Now, to construct an institutional quality index, first, I estimate the following equation where gross domestic product is as a dependent variable and these six institutional quality indicators as explanatory variables. And their coefficients (θi) will be obtained as shown in equation (iii).

Abbildung in dieser Leseprobe nicht enthalten

Now, after the coefficients θt are found from the above equation (iii), I replaced them into the “institutional-quality” equation (iv).

Abbildung in dieser Leseprobe nicht enthalten

Where:

Stability is reflecting a “political stability and absence of violence/terrorism”

Effectiveness is “government effectiveness”

Corruption measures the “control of corruption”

Law states the “rule of law”

Accountability captures the “voice and accountability” and Regulatory quality is the “regulatory quality”

4.3. Estimation Methods

The paper uses balanced panel data for a “cross-section” of twelve sub-Saharan Africa countries for the period 1999-2017. The study period has been chosen based on data availability. To analyze the main objective of examining the linkage between aid and growth, the study employs both descriptive and empirical analysis which will be presented in the next chapters. First, the descriptive analysis presents with tables and graphs and gives a description of trends of aid inflows and economic growth. And the empirical analysis discusses using econometrical techniques presentation to identify whether the aid effectiveness is statistically/empirically significant or not. In this empirical analysis different models have employed which includes “Pooled OLS”, “Fixed Effects-FE”, and “Random Effects-RE”, “First Difference-FD”, and “Two-Stage Least Squares-2SLS” methods so as to deal with different effects, such as group/individual-specific effects, time effects, or both.

First, I have estimated the model with Pooled OLS estimation method by assuming that OLS produces efficient and consistent regression result. But, if the individual effect differs from zero; means that unobserved heterogeneity term correlated at least with one of the explanatory variables hence heterogeneity may violate OLS assumption which causes endogeneity, autocorrelation, heteroskedasticity problems. As a result, OLS estimator is no longer the best unbiased linear estimator, consequently pooled OLS is not an appropriate model. For that reason, the paper also uses Fixed-Effect (FE) and First Difference (FD) methods which allow error term to be correlated with explanatory variables and also allow to deal with unobserved heterogeneity term, means that irrespective heteroskedasticity problems these methods may produce consistent coefficients. Additionally, as regards to serial correlation and time effect Random Effect (RE) estimation method has been used.

As we have seen the OLS method consistent under certain assumption which includes that the value of the error term is independent of explanatory variables (zero correlation). When this assumption is violated means that endogeneity problem will occur in the model, the 2SLS technique allows us to deal with the endogenous variable. The estimation technique assumes that some other predictors in the model may correlate to that of an endogenous variable (problematic predictor) but not with the error term. By introducing instrumental variables, I perform the first stage and then the second stage (2SLS model).

In this basic model, I assumed that an AID (ODA percentage to GDP) is an endogenous variable that means there a correlation between aid and error term in the model. Since my variable of interest is AID I suspect that aid may correlate with the error term as Burnside and Dollar (2000) assumed. In order to deal with this assumption that aid may be endogenous, I carry out 2SLS method, where population growth rate, cumulative of human right treats (HRT) participation in the United Nations, five-year lag of aid inflows, life expectancy, and annual military expenditure have been assumed as IVs for the endogenous variable AID. This is clearly show that these IVs do not affect economic growth directly, but indirectly essentially through aid. These instrumental variables are designated based on previous literature, for example, as Arndt, Jones and Tarp (2015) also used “life expectancy” and “population growth rate”, and Burnside and Dollar (2000) and Hansen and Tarp (2002) also used “log of initial GDP” and “lag of foreign aid” and “policy index”, likewise, Magesan (2015) also used “Cumulative participation in human rights treaties at the UN” as instruments in the evaluation of aid effectiveness on economic growth. Thus, once I outlined consistent instruments, I perform 2SLS.

The paper presents the estimates obtained from these five different models to examine the relationship between aid and growth in the selected twelve aid recipient countries. In a nation as well as a continent, like Africa, where endless violation of human right, high corruption, and weak governmental system are impudently present, it is worthy to take political and economic situations into consideration in the aid-growth linkage. Hence, this study tends to observe the role of environmental factors such as social-economic and political stability in an aid-growth relationship.

To see the conditionality of aid on an economic and political environment, I perform two regressions under each model. First, the models will be estimated by excluded a one year lagged values of aid and interaction terms of aid and macroeconomic factors (inflation, governmental consumption, and trade openness) and but later, the same models are regressed which include a lagged values of aid and interaction terms. These distinct regressions clearly show the contribution of these interaction terms that measure economic and institutional factors on aid effectiveness. Likewise, in a separate table, using the same models and estimation methods, I carry out regressions which contain “institutional index” as an explanatory variable. In this paper all models are estimated through “robust standard error” to adjusting heteroscedasticity problem. In addition, I also use the “Hausman-test” and the “Breusch-Pagan-test” to choose the most appropriate model out of Pooled-OLS, FE, RE, and 2SLS models.

The Brush Pagan test is conducted to check heterogeneity in the modes. The “null hypothesis” of the test assumes there is no correlation between the variance of the errors and explanatory variables (homogeneity assumption) means that the variance of the unobserved fixed effects is zero in this case the pooled-OLS model might be the appropriate model. While the alternative hypothesis of the test assumes the presence of correlation between error terms and explanatory variables, in other words, there is a “constant effect” in the error that could occur heterogeneity problem in the model. In this case, the Pooled-OLS will be inappropriate. If there is a violation of the assumption of homoscedasticity then the Housman test will be carried out to choose between RE or FE. The null hypothesis of Housman test assumes no evidence of endogeneity (proceed with random effects) while the alternative hypothesis assumes evidence of endogeneity (fixed effects might be appropriate). The compares tests result reveals that pooled OLS is more convenient and reliable out of these four models in explaining the relationship between aid and growth. But the FD model is not subject to comparison test because it has different coefficients with the other models. Using these given comparison tests the evaluation of aid effectiveness will be based on Pooled OLS model which are more consistent and reliable than others.

5. Foreign aid and Economic Growth in sub-Sahara Africa

5.1 Trends in Foreign Aid Inflows into sub-Sahara Africa

This section presents the distributive analysis of foreign aid in SSA, using tables and graphs. Figure 3.3 illustrates the trend of foreign aid (net official development assistance ODA) inflows into the sub-Saharan African region.

The trend shows that aid allocated to SSA was slightly declined from 1990 till 2000, as shrinking in total world aid and some DAC countries shift their aid allocation from Africa to Asia and Europe. Following the adaption of the United Nations “Millennium Development Goals (MDGs)” in 2000, which foreign aid providers (donors) set 0.7% of their GDP to ODA, aid inflows to Africa has raised by 37% in 2003. Furthermore, aid inflow is increased by 12% in 2006 as the G8 summit in 2005, debt cancellation to support African countries in achieving MDGs. Even with some fluctuations that have been shown in the year 2004 and 2007 because the global financial crisis and euro-zone economy turmoil, the aid inflow to the region continue to increase until the year 2017. Despite the inflow of billions of dollars into the region, weak economic development persists in the region.

Figure 5. 1 ODA Inflows into SSA in Millions of USD, (1990-2017)

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Source: Author’s computation based on OECD data (in 2015 base year)

5.2 Trends in Regional shares of total Foreign Aid (1990-2017)

The figure 4.2 illustrates regional share of foreign aid in billions of USD. For the last ten years, Africa received a large proportion of total foreign aid. For example, in 2009, Africa received $48 billion in which sub-Sahara Africa received $42.2 billion that accounts 25.5% out of total world aid $1,965.4 billon. And in the past ten years the sum of ODA in volume to Africa was $537 billion and sub Saharan Africa received $491 billion ODA, following by Asia received 31%. While the average proportion received by Europe, America, and Oceania were 6%, 9%, 1.2% respectively. These figures clearly illustrate that the poorest region in the world receives the highest volume of aid relative to other regions which in line with the idealist theory9.

Figure 5.2 Regional shares of total ODA (1990-2017)

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Source: Author’s computation based on OECD data

5.3 Trend in Net ODA growth rate and GDP growth rate of SSA

The figure 4.3 shows a comparison of the annual percentage change in foreign aid inflows and GDP growth rate of SSA over period 1990-2017. Compared to GDP growth rate, the trend of foreign aid was very volatile and instable. But it became relatively stable and grew with an average rate of 2.92 % between the 2008 and 2017. Even though in the last seven years foreign aid inflows to the region had almost similar trend with regional GDP growth rate, it is quite difficult to pinpoint whether they are positively or negatively correlate sign. Besides, it is questionable to predict to what extent aid contributed to economic growth in region by only looking over these trends. Thus, it needs further examination with econometric estimation which is presented in the next section of the paper.

Figure 5.3 Annual Growth Trends for Net ODA Inflows and GDP growth rate in SSA (1990-2017)

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Source: Author’s computation based on WB and OECD data

The graph 5.4 presents a scatterplot which shows the relationship between economic growth and foreign aid in SSA countries. The graph shows a positive but very weak relationship between these two variables which are measured as GDP per capita growth rate and aid as a percentage to GDP respectively. This weak effect of aid on growth might be a result of the absence of several important factors which can significantly affect the relationship between aid and growth. Later on, we will see whether the empirical analysis, which includes important factors which influence the aid-growth linkage, confirm this positive but weak effect of aid on growth.

Figure 5.4 Scatterplot of economic growth (GDP) and foreign aid (ODA)

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Source: calculated by author based on WB and OECD data

Table 3.1 presents bilateral aid flows from DAC countries to Africa. In aid provision history the United States, United Kingdom, Germany, and France are the largest ODA providers to SSA, on average (2014-2016) they donated 34%, 15%, 11%, and 8% of total bilateral aid from DAC respectively. Combined these four countries provided more than two-thirds of total bilateral aid to Africa in 2016. Other potential donors are Japan, Canada, and Sweden which provide substantial aid to the continent.

Table 5.1 Top Ten Bilateral Donor Countries to Africa in Million USD (2014-2016)

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Source: Calculated by author from OECD data

Aside from bilateral aid, DAC member countries also supply aid to SSA countries through the multilateral system. For example, they provide large portion of their donations to EU institutions and other regional development banks in the form of multilateral aid.

The following table below presents the major multilateral donors to Africa. International Development Association and EU Institutions are main contributors of multilateral aid provision. These two institutions have provided over 60% of the total multilateral aid allocated to Africa (see table 3.2). “Global Fund”, “African Development Fund”, “Global Alliance for Vaccines and Immunization”, and UNICEF are other potential multilateral aid providers to SSA countries.

Table 5.2 Top Ten Multilateral Donors to Africa in Million USD (2014-2016)

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Source: Calculated by author from OECD data

Table 4.3 shows top ten aid recipients of African countries from both channels bilateral aid and multilateral aid. Ethiopia, Egypt, Tanzania, Nigeria, and Kenya are the main aid beneficiaries which received over 27% of all aid inflows to Africa. Furthermore, D.R. Congo, Mozambique, South Sudan, and Uganda are other aid receiver countries in the SSA region.

Abbildung in dieser Leseprobe nicht enthalten

Table 5.3 Top Ten ODA Recipients in Africa from all Donors in Millions of USD (2014-2016)

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Source: Calculated by Author from OECD data

6. Econometrics Analysis

6.1 Foreign Aid and Economic Growth

As it has been stated in the previous section, I have conducted the Hausman test and the Breusch-Pagan comparison test to choose an appropriate and reliable model between these five models Pooled, FE, RE, and 2SLS. The compares tests result reveals that pooled OLS is more convenient and reliable over these models in explaining the relationship between aid and growth. But there are some exceptions because the FD model is not subject to comparison test because it has different coefficients than the other models.

Using these given comparison tests the evaluation of aid effectiveness will be based on Pooled OLS model which is more consistent and reliable than others. Similar to the work by Tang and Bundhoo (2017), to take into account the role of macroeconomic conditions in aid and growth relationship, the model is regressed with and without the interaction term of aid with some important macroeconomic variables that are inflation, governmental consumption, and trade (see table 6.1). Besides, to examine the role of an institutional factor in the aid-growth relationship, I carry out the same regressions which contain institutional index as an explanatory variable which is illustrated in table 6.2, separately.

By combining all results gained from all estimation models into one table, table 6.1 present all regressed result of all models. As we could see from the table the regression output of these models is mixed. For example, in the model (I), (III), (V), (VII), and (IX), where both interaction terms and lagged value of aid are not included, the coefficient of foreign aid is not statistically significantly different from zero. This seems to support the descriptive analysis which has been present in the previous section and reveals that aid has a positive but weak effect on economic growth. This empirical result tells us foreign assistance alone might not be enough to bring economic change in a host country. High corruption and a weak financial system in the SSA can render aid effectiveness on the economy. For example, when we give a random observation at some SSA countries’ leaders or ruling parts, we surely find that above average African leaders or ruling party’s stay on power irrespective their constitutional term of power. This clearly indicates that leaders might not use foreign financial aid properly rather use it to staying on power. As a result, aid may not be paly its significant role in SSA countries. Burnside and Dollar (2000) and Hansen and Tarp (2002) found that aid has an insignificant effect on the economic growth of a host country if policy and institutional factors are not conducive.

The domestic investment has a positive impact on growth and statistically significant at one percent level of significance and shows that a one percent increase in investment would raise the GDP growth per capita by approximately 0.9 percent other things remain constant. The direct effect of domestic investment seems to confirm and support the Harrod-Domor model which states that increment in investment implies a permanent growth. While labor has a negative significant effect on growth meanly due to the low level of human capital and a high unemployment rate in the region. In the other hand, when aid inflows into the region, the direct effect of the lag of money and inflation will be statistically significant in determining economic growth. Also, the governmental consumption has a positive significant effect on growth. While trade openness and dummy of the British colony on economic growth are found to be statistically insignificant.

The regression result, that includes the interaction terms and lagged variable, shows foreign aid has a positive significant effect on growth, but the lagged value of aid has been found a statistically insignificant effect on growth in models (II), (IV), (VI), (VIII) and (X). This result implies that foreign aid inflow from the previous year would have an insignificant impact on current year economic growth. In other words, foreign aid may not have immediate causes of growth, but it may cause growth in the long run. In fact, a delay may occur between aid financing and its impact on growth, because foreign aid for development activities purposes may not show an immediate effect on growth Moreira (2005). Lack of job opportunities that cause massive unemployment and overpopulation in the region may delay the effectiveness of aid on growth Rabin (2011).

Furthermore, to capture whether the effectiveness of aid varies respect to colonization background, I take into consideration a dummy variable of the British colony, which takes 1 if a country was under British colony otherwise 0. The result shows that the coefficient of interaction term of aid and dummy of British colony is insignificant almost in all models. It implies that foreign aid impact on growth may not be necessary differ between countries due to their historical backgrounds.

Table 6.1 Model Estimation using Macroeconomic Indicators and AID

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Note: In 2SLS model (III) and (IV) AID is assumed as instrumented variable and the instrument variables (IVs) are: log of initial GDP, log of population growth rate, life expectancy, military expenditure, and commutative human right treats in UN. Robust standard errors are presented in parentheses. *** , **, and * represent significance level at the 1%, 5%, and 10% respectively

6.2 The Impact of Economic Policies on the Aid-Growth Relationship

As it has been stated in the introduction section of the paper, one important feature of this paper is the investigation of the role of macroeconomic indicators in the aid and growth relationship. Consequently, this section presents an empirical assessment of the effect of the economic environment on aid-growth linkages. First, I take some important macroeconomic factors and also their interaction term with aid into consideration, in the model, to examine the role of economic policies in determining the effect of foreign aid on growth. In the model (I), (III), (V), (VII), and (IX), where variables such us interaction terms and lagged value of aid are not included, the result shows including foreign aid all economy policy variables are found to have a statistically insignificant effect on growth, except inflation has negative significant impact on growth under FD estimation model.

The estimation result from (II), (IV), (VI), (VIII), and (X) models, which included a “lagged value of aid” and interaction terms and the result shows a significant change in some coefficients. For example, now, the result reveals that a foreign aid inflow to the region has a positive and statistically significant effect on economic growth in the region using model (II), (IV), and (X) models. Since the pooled-OLS model is found more consistent, this empirical analysis also stresses on the pooled OLS regression result which shows aid is statistically significant at 5% level, and it shows that one present increase in foreign aid implies 44 present increments in economic growth in a host county. The effects of some explanatory variables are still unchanged, for instance, the domestic investment’s coefficient found to be positive statistically significant at 1% level. On the other hand, the direct effect of labor on growth still negative but it is found to be insignificant. While now, the macroeconomic indicators that are inflation and governmental consumption have a positive statistically significant effect on growth at 5% level, but the trade openness remains insignificant. The result also shows that interaction terms of aid with inflation and government consumption have a negative and statistically significant at 1% significant level. This implies that the lower the inflation rate level, the greater effect of foreign aid on economic growth and this indicates that aid is more effective in an economy with a strong monetary policy.

Moreover, the result shows that interaction term of aid with governmental consumption, that represent a fiscal policy of an economy, in the model (II), (IV), and (VII) is negative and significant at 5%, 1%, and 10% level respectively. This implies that the lower governmental expenditure on consumption, the great effect of aid on growth. In other words, foreign aid is more significant in promoting economic growth when there are a robust fiscal policy and monetary policy in an economy. Tang and Bundhoo (2017) and Burnside and Dollar (2000) also found similar results which reveal that the higher policy level, the greater effect of aid on growth. The interaction term of aid-trade is not statistically significant in all models mainly as the trade deficit exist in the regional economy. In fact that the SSA’s export highly depends on agricultural productions and its import on capital goods this cause trade deficit in its economy. But inflation and government consumption are still important to explain policy environment.

6.3 The Role of Institutional Factors on the Aid-Growth Relationship

Like macroeconomics factors social-political conditions are important factor in understanding the linkage between aid and growth, especially in a region like SSA where extensive political instability and human right violation perpetual occur. Among other authors, Griffin and Enos (1970) and Easterly (1999) argue that aid inflow into a country with socio-political instability may only be insignificant but also it could be harmful as it makes the country’s economy more dependent on foreign financial sources. Similarly, Moyo (2009) underlined that large share of foreign aid inflows to SSA has been used improperly and mislead its main object as a result of a high rate of corruption and weak institutional quality. She concludes that foreign aid hinders economic growth in the region.

As it has been applied by Tang and Bundhoo (2017), I develop an “institutional quality index” which incorporates six governance indicators which are obtained from WGI data base. These indicators are “corruption”, “government-effectiveness”, “political-stability”, regulatory-quality”, “accountability”, and “rule of law”. Now, in order to develop an “institutional quality index” that measures governance system as well as political condition of a country, first GDP growth rate has been estimated on these six governmental quality indicators and I replace these indicators’ coefficients into the “institutional quality equation” (see question below). Thus, the following institutional quality index has been obtained through.

Institutional-quality = 3.389-2.633*crorruption+4334*Governmental-effectiveness +0.813* political-stability-1.803*regulatory-quality+0.0271*rul-of-law+0.285*accountability (v)

Table 6.2 Institutional Quality Index

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Note: Robust standard errors are presented in parentheses. ***, **, and * represent significance level at the 1%, 5%, and 10% respectively.

The growth model is reliable and statistically significant at the 5% level. The result shows that governmental effectiveness has a positive statistically significant effect on growth, implies good governmental system has an important role in economic development. While, corruption has a negative impact on the economic growth of SSA, mainly due to weak or even no functional anti-corruption regulation in the continent. Both governmental effect and corruption are found to be significant at 1% level. Whereas, the other factors are statistically insignificant, however, as long as, these two important factors are significant, it is readable to think that “institutional quality index” is much better than other measurements of good governance especially in the context of sub-Saharan Africa. Therefore, I include this institutional index into the model to examine its role on aid effectiveness, using the same estimation methods as in table 6.2. The results from all estimators are presented in the following table 6.3.

Table 6.3 Model Estimation Using Institutional Quality Index and AID

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Note: In 2SLS model (III) and (IV) AID is assumed as instrumented variable and the instrument variables (IVs) are: log of initial GDP, log of population growth rate, life expectancy, military expenditure, and commutative human right treats in UN. Robust standard errors are presented in parentheses. ***, **, and * represent significance level at the 1%, 5%, and 10% respectively.

Surprisingly, the inclusion of institutional quality does not alter the estimation output, signs and significance of coefficients present in table 6.3 is exactly same as present in table 6.2, but only it shows an inconsiderable change in their magnitude. In the models without interaction terms and lagged value of aid, I found that a coefficient of institutional quality is statistically insignificant differ from zero which implies that institutional quality does not affect to the aid-growth relationship. Moreover, the interaction term of aid and instructional quality is statistically insignificant in all models. This regression result confirmed the idea that the aid-growth relationship is not conditional to an institutional quality level of a nation. Thus, effectiveness aid does not depend on institutional quality in sub-Saharan Africa, rather depends on policy factors.

Some empirical studies also come to a similar conclusion, for example, Easterly (2003) found the same result that effectiveness of aid is unconditional to institutional quality, but he found that aid is conditional to policy factors. Irrespective this empirical result, there are many witnesses that show massive corruption and political instability in the region impeding the effectiveness of aid in sub-Saharan African countries. For instance, according to the Corruption Perceptions Index, 2017 report by Transparency International shows that sub-Saharan Africa is the worst performing region in the world in preventing and ending corruption. Likewise, Africa Union reported that Africa mislaid over $150 billion a year due to corruption and governmental ineffective. As Easterly (2007) in his respective book “The White Man’s Burden: Why the West’s Efforts to Aid the Rest Have Done So Much Ill and So Little Good“ clearly explained that aid is fruitless unless accountability and independent evaluation on aid implementation permanently predominate. Similarly, Moya (2009) stated that aid cannot be effective in a country with a high level of corruption and low institutional quality rather refraining economic development in the region.

6.4 The Role of Foreign aid in the Two-Gap Model

As we have seen in chapter three of the paper, the two-gap model states that foreign aid inflows have an important role in accelerating growth through relieving saving and foreign exchanges restraints in a county. Using a similar approach to Tang and Bundhoo (2017), the paper examines the effect of aid in the two-gap model. First, I regress import on aid to examine the impact of foreign aid on trade. Then again, to evaluate the role of aid on investment, I regress foreign aid on investment. The result shows that ODA has a positive impact on both investment level and import and it is statistically significant at 1% level. Other things remain constant, one present increment in ODA would raise investment by 40 % and import by 32 %. The overall result of estimating of the two-gap model, reveals that aid have a significant role in promoting economic growth by through stimulate domestic investment and imports in the SSA. Foreign aid is found to be more effective on investment than import in the region (see Appendix iv).

7. Conclusion

The underlining objective of this paper was to determine the impact of foreign aid on economic growth of sub-Sahara Africa during the period of 1999-2017. And the study also examined evaluate whether the effectiveness of foreign aid is conditional on policy and governmental issues. The study has tried to demonstrate the linkage between aid and growth using both descriptive and econometrical analysis.

Following the success story of “Marshal-Plan” in late 1940s, aid inflow to the SSA grew dramatically in the past decades. But the effectiveness of aid still becomes a subject of heated debate among scholars and policy makers. There is a growing literature in the contest of the aid-growth relationship with shown mixed and inconclusive results. However, there are only inadequate numbers of research on the sub-Saharan Africa context. Thus, this paper also adds to a pool of research on the aid-growth relationship in the region.

The descriptive analysis shows that foreign aid has a weak positive relationship with economic growth. However, trend of foreign aid inflows into the region shows that aid does not significantly affect GDP growth rate of the region. For instance, aid to SSA has been continuously declined between the years 1990 to 2000, as shirring in total world aid, but the GDP growth rate was almost stagnant. Again, following, the adaption of the MDGs in 2000, which donors set 0.7% of their GDP to ODA, aid inflows is sharply increasing but there is no significant reflection on GDP growth. Even if it is questionable to predict to what extent aid contributed to economic growth in region by only looking over these trends, this descriptive analysis gives some hint that there is no direct relationship between aid and growth in the sub-Sahara Africa region. Nonetheless, this may differ from country to country, for example in countries such as Ethiopia, Madagascar, Niger, Tanzania, and Uganda the aid-growth link shows a positive correlation, in the other hand, in Mauritania, Djibouti, Niger, and Serra Leone the trend of foreign aid seems negatively correlates with growth, but the trends show no relationship between aid and growth in Kenya and Sudan.

In the econometric analysis, I found that aid by itself has not direct or immediate effect on growth of a country. In fact, aid for infrastructure purpose may not show its effect on growth in short term rather it does in long term. The result shows that the interaction terms of aid with inflation and government consumption are statistically significant while the interaction term of aid and trade openness is found to be insignificant. This implies that both monetary policy and fiscal policy are playing an important role in the effectiveness of aid in a recipient country. Based on the respective work of Tang and Bundhoo (2017), I take into consideration an institutional quality index, which measures socio- political environment of a country, into model. The result shows that institutional quality is found statically insignificant in determining the aid-growth relationship. In conclusion, I tried to see the impact of institutional factors on the nexus between aid and economic growth and found no evidence on it. On the other hand, economic environment of a country has important implication to determine the relationship between aid and growth. In contrast, foreign aid inflow is found to be more effective in a country with sound economic environment irrespective of institutional quality and colonization history. The dummy variable for British colony is also found to be statistically insignificant which implies that aid effectiveness is not depending on historical background of a county.

In summary, this research finds that aid has a positive impact on economic growth in the SSA countries only under sound economic policies. The result also confirms that the aid promotes economic growth hypothesis but only if there is a good economic environment in a host country. Therefore, governments, policy makers, and other responsible bodies should create better economic policies/environment in order to use foreign aid efficiently and effectively.

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Appendixes

Appendix I: List of sub-Saharan Africa countries and countries included in the sample

Abbildung in dieser Leseprobe nicht enthalten

Countries marked with an asterisk (*) countries included in the sample

Appendix II: Summary statistics

Abbildung in dieser Leseprobe nicht enthalten

Source: Author’s computation based on OECD and WB data

Appendix III: Correlation matrixes

Abbildung in dieser Leseprobe nicht enthalten

Source: Author’s computation based on OECD and WB data

Appendix IV: The Role of Foreign aid in the Two-Gap Model

Abbildung in dieser Leseprobe nicht enthalten

Note: Model (I) is a regression of investment-savings gap, on the level of investment. Model (II) a regression of import-export gap on the level of imports. Some important explanatory variables are included as control variable in both modes. Robust standard errors are presented in parentheses. ***, **, and * represent significance level at the 1%, 5%, and 10% respectively.

Appendix V: Trends of AID inflows and Economic growth of selected SSA countries

Abbildung in dieser Leseprobe nicht enthalten

Source: Author’s computation based on OECD data and WB

[...]


1 Based on GNI per capita, with some exception of EU members and G8 members, the list of countries and territories eligible to receive ODA consist all the LDCs as defined by the UN.

2 See https://data.oecd.org/oda/net-oda.htm

3 The fixed proportion production or technology function implies that using fixed factors in production process yield fixed output. It is also well known as a Leontief Production Function.

4 A steady-state growth occurs when factors of production (labor and capital) and output are all growing at the same rate, so output per worker and capital per worker will be constant.

5 Source: http://www.yourarticlelibrary.com

6 Source: http://www.yourarticlelibrary.com

7 Growth rate any variable is computed as

8 Broad money some time refers as ‘M2’ is a money supply in the one’s economy which measures the total money or asset that households and business entities hold or use for payment. It includes currency, saving in bank accounts.

9 The Idealists theory postulates that donors use aid to promote humanitarian concerns such as democracy, human right, and welfare.

66 of 66 pages

Details

Title
The Effect of Foreign Aid on Economic Growth in Developing Countries
Subtitle
Evidence Sub Saharan Africa, Panel Data Analysis 1999-2017
College
University of Trier  (University)
Course
MS.c. in Economics
Grade
2
Author
Year
2019
Pages
66
Catalog Number
V537309
ISBN (Book)
9783346337535
Language
English
Tags
Foreign Aid, Economic Growth, Developing Country, Sub Saharan Africa - SSA, Macroeconomic factor, Institutional factors, Two Gap Model
Quote paper
Semere Tesfamariam Kahsay (Author), 2019, The Effect of Foreign Aid on Economic Growth in Developing Countries, Munich, GRIN Verlag, https://www.grin.com/document/537309

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