The impact of Official Development Assistance on the GDP of the donor country

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Bsc Thesis Economics & Business

The impact of Official Development Assistance on

the GDP of the donor country

Author:

Reinier H. de Groot

reinier.degroot@student.uva.nl

Specialization Economics & Finance

Supervisor:

Ieva Rozentale

PhD Candidate

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Abstract

This empirical paper describes the effect of the amount of development aid on a donor country’s GDP. I make use of data on ODA, GDP, Income from tourism, Exports and FDI of the 28 DAC member countries over a period of 30 years. With the use of lagged variables and Granger-causality tests I provide evidence that there is a significant causal effect of ODA on GDP of the donor country through exports and FDI. This effect is best expressed as a positive elasticity of 0.366. A one percent increase in ODA yields a 0.366 percent increase in GDP.

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However, the last couple of years, disputes about the amounts of development aid donated to the developing countries in the world have intensified. Especially in times of crisis, several political parties argue that the money is spent better on for example tax lowering, investments in health care or investments in education. Furthermore, because they believe that a portion of foreign aid is being wasted due to corruption, poor institutional development and inefficiencies, politicians want to cut back on development aid. Although the main goal of development aid is to help countries develop, and the aid indeed proves effective, this still is not enough to convince people to support it.

The Development Assistance Committee (DAC) of the Organization of Economic Cooperation and Development (OECD) has set a target to give a level of 0.7 percent of Official Development Aid as a percentage of the donor’s Gross National Income every year. Only five DAC countries1_{reached this}

target last year: The United Kingdom (0.72%), Denmark (0.85%), Luxembourg (1.00%), Sweden (1.02%) and Norway (1.07%). The Netherlands (0.67%) fell below the target for the first time since 1974.

Countries have different incentives to give foreign aid, such as to maintain trade relations, to help developing countries avoid building up debt and to support humanitarian needs. In this paper I examine an aspect that can be an extra incentive to support development aid, next to the main goal. The research question is as follows:

What is the relationship between the amount of development aid given and the economic performance of the donor country?

In order to answer this question, I perform a regression on data on development aid, foreign direct investment, exports and tourism. Then the separate effects are brought together in one model. This model expresses the effect of foreign aid on the national income in the form of an elasticity.

The proven positive relation is an argument in favor of supporters of development aid. Essentially one “kills two birds with one stone”, as the saying is. Developing countries get support and the economy of the donor country grows at the same time. One can see it as a sustainable investment in both the developing countries and the own country. I, however, do not suggest that the change in national income offsets the costs of foreign aid. It merely is an extra benefit of foreign aid.

The result of this paper is valuable to donor governments or policymakers. As it becomes clear that a higher amount of development aid causes a higher GDP, it encourages a slight increase of the ODA to reach the millennium goals without it costing the economy. A collective raise of ODA worldwide, brings the world closer to reaching the millennium goals.

The next chapter covers part of the literature on the subject, providing a theoretical background for the research. It first examines the behavior of donor countries and later sheds light on several national income determinants. Chapter three explains the methodological aspect of the paper. In the subsequent chapter, several statistical outcomes are shown. The final chapter consists of the conclusion and a recommendation for further research.

1_{The member countries of the DAC are: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland}

France, Germany, Greece, Iceland, Ireland, Italy, Japan, Republic of Korea, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, United Kingdom and the United States.

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2. Literature review

In this chapter I review literature on several aspects of the development aid and its relation to national income in order to point out relevant variables for my analysis. Not much research is done on the effect of the development aid on the economic growth or national income of the donor country yet.The reason probably is that there may be an ethical issue. After all, the purpose of foreign aid is not to benefit from it. Most of the research is done on the recipient countries. Still, effects on the donor other than growth were indeed investigated.

In section 2.1 I review some of the literature on the behavior of donor countries. Section 2.2 consists of studies on different economic indicators and relations with the amount of development aid.

2.1 Behavior of donor countries

The literature on development aid, or Official Development Assistance (ODA) hereafter, can roughly be split up in two parts. One part focuses on the effects on the recipient country. The other focuses on determinants of aid; about the donor countries and their motives. In view of my research question, I focus the most on the latter.

Alesina & Dollar (2000) looked into who gives aid to whom and why. They use data on bilateral aid flows –from one government to another, opposed to multilateral aid, which is aid from multiple countries through institutions such as the Worldbank or UNICEF– and various indicators of strategic interest. Among these indicators are: trade-openness, democracy, colonial past, foreign direct investments, income per capita and UN voting patterns. It appears that good policy in the recipient country is not always the main determinant of the allocation of development aid. Donor countries are inclined to support their former colonies more. A non-colonized democratic country receives less aid than a nondemocratic former colony with similar poverty level and policy stance. Besides, there exist differences in behavior among donors.

Countries such as Norway and Sweden tend to be incentivized by correct measures such as economic openness and democracy, but the “big three” donors, US, France and Japan, all suffer from a distinct distortion. The US gives one third of its foreign aid to Egypt and Israel, France supports its foreign colonies overwhelmingly and Japan’s aid is heavily influenced by the UN voting patterns. In other words, Japan allocates aid especially to “friends” in politics. Even after controlling for strategic interests in former colonies and “friends” in politics, there still is a weak relationship between bilateral aid and poverty or democracy in the recipient country. Overall, however, countries that are democratized and economically open receive more aid, ceteris paribus.

Now one might question if donors do in fact give foreign aid to benefit from it themselves? (Schraeder, Hook, & Taylor, 1998) conducted research specifically on the aid policies of four industrialized democracies: The United States, France, Japan and Sweden. They questioned the determinants of foreign aid of the countries, without generalizing all donors. This way, the outcomes should be more precise. Based on literature, they have performed regressions with the dependent variable Official Development Assistance. The independent variables can be allocated to six different indicators for foreign aid: humanitarian need, strategic importance, economic potential, cultural similarity, ideological stance and region. For each of the indicators they use two or more variables to perform regression on, for each of the four countries. This cross-country analysis yields some conclusions about the nature of foreign aid. Part of these conclusions are merely valid for the four examined countries, but most of them are legitimate for most donor countries. The most meaningful conclusion is that, despite the fact that governmental policymakers try to show otherwise, foreign aid does not merely stem from altruism. Also ideology of the recipient country affects the flows of foreign aid significantly. It depends on the donor country which ideology receives more ODA.

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For the US, aid depends mostly on strategic and ideological interests, associated with the cold war. Japan gives development aid based on economic self-interest. Sweden’s major determinant for aid, contrary to official government statements, is not humanitarian needs, but ideological stance. Socialist regimes receive Sweden’s support mostly. France, as expected, gives aid mostly to former colonies. In fact, the top ten recipient countries of France all are former colonies of France. Likewise, (Burnside & Dollar, 2000) conclude that the allocation of bilateral aid is not always based on the policy in the recipient country. However, the allocation of multilateral aid is indeed impacted slightly by good policy.

(Berthélemy, 2006) carried out research on the altruism or egoism from different countries. The author concludes that most donors behave in a somewhat egoistic way. With the exception of Switzerland, all the countries in the research give some aid to their most important trade partners. Yet, on average, donors do target aid to recipient countries with better governance indicators such as democracy or lack of violent conflicts. Moreover, Berthélemy encounters differences among

countries. Switzerland, Austria, Ireland and the Nordic donors are inclined to be more altruistic, while on the other hand, Australia, Italy, France and, to some extent, Japan and the US are far more

egoistic than other donors.

Besides the reasons mentioned above for allocating foreign aid, there is also the debt burden of a recipient country that influences decision making of both bilateral and multilateral donors. (Birdsall, Claessens, & Diwan, 2003) examined the donor behavior based on the indebtedness of recipient countries. Bilateral donors support poor countries with great debt extensively, in spite of possible bad policies in the recipient countries. This is probably caused by the policy of multilateral donors of giving loans and debt service relief, instead of grants. The debt relief, however, didn’t work out and only increased the debt burden. At the same time, multilateral donors could not accept debt arrears, as it would prevent them from issuing new loans and providing aid. Bilateral donors

therefore had to support the poorest countries by giving aid to prevent recipients from getting into arrears with multilateral donors. Recipient countries benefitted from this by keeping or letting increase their debt stock so they could bargain for more transfers of development aid. This caused a loss of selectivity on good policy, i.e. low and decreasing debt.

These studies point out that there are more incentives to give foreign aid that have little to do with the main goal of development aid. Besides, to which country a donor gives aid and how much differ as well. The latter can be estimated with a quantitative model. Unfortunately, there aren’t many models that explain aspects of foreign aid. One model I found, tries to explain the supply of bilateral foreign aid by (Dudley & Montmarquette, 1976). Their model shows that the probability that a donor gives aid to a country is a decreasing function of the recipient’s income per capita and a function of economic, political and bandwagon aspects. This paper shows that helping a developing country is indeed a reason for giving aid. The negative relation between foreign aid and income per capita supports this.

Apparently, the main reason for giving aid is not always to fight poverty and stimulate the recipients’ economy. Several studies support the vision that there always seems to be a personal interest in terms of ideological interests, trade interests or strategic/military interest. These interests may have an effect on national income. However, it is still not clear through which indicators or determinants one observes such possible economic effects. The next subsection clarifies these indicators.

2.2 Economic determinants and their relations with ODA and GDP

Since there is no previous research on the exact relation I research, I examine it by discussing previous papers on more general variables that influence national income. In the empirical part of this paper, I estimate the effect of development aid on the national income of the donor country by

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means of economic determinants such as foreign direct investments and import or export. In this subsection I clarify in what way these determinants affect national income and in what way the determinants may be affected by Official Development Aid. There are probably several determinants of national income that are being affected by ODA, but for the sake of the length of this paper, I only review FDI, Income from tourism and Exports.

A first economic determinant is Foreign Direct Investments (FDI). The paper of (Borensztein, De Gregorio, & Lee, 1998) is one of many that looked into FDI. They examined how FDI affects economic growth. Their results suggest that FDI is an important vehicle for transferring technology. Through this channel it contributes more to growth than domestic investment, if there is a

complementary minimum stock of human capital. (Alfaro, Chanda, Kalemli-Ozcan, & Sayek, 2004) support the fact that FDI has a beneficial effect on economic growth. However, they do mention that the level of development of local financial markets is very important for the effects to be realized.

Also (Li & Liu, 2005) researched the impact of Foreign Direct Investments on economic growth using paneldata and they found that there exists a significant endogenous relation from the mid-1980s. Not only does FDI directly affect economic growth, but it also does so through interaction terms. There is a strong positive effect with the interaction term human capital and a strong negative effect with the technology gap.

I suspect that a high amount development aid gives a signal to foreign investors of a healthy economy and therefore attracts FDI. Moreover, a high amount of development aid could also create some sort of goodwill of for the donor country that may slightly increase FDI inflows.

Now on to income from tourism as determinant. There are several studies on the relation between tourism development and economic development. Using different estimation techniques and periods, these studies found differing causal relationships. Four out of nine of these studies prior 2007 determined that tourism development leads to economic growth and thus is a determinant of national income: (Balaguer & Cantavella-Jorda, 2002), (Ghali, 1976), (Lanza, Templec, & Urgad, 2003) and (Eugenio-Martin & Morales, 2004). The latter found this relation only valid for low- and medium income countries. Two out of nine studies concluded that economic growth causes tourism

development: (Narayan, 2004) and (Oh, 2005). Lastly, three out of nine studies determined that causality runs both ways: (Dritsakis, 2004), (Durbarry, 2004) and (Kim, Chen, & Jang, 2006). One can easily assume that there indeed is a causal relationship, but which way this causality runs is being doubted. As the majority states that tourism development causes economic growth, I assume the same causality in this paper. I also believe that the possibly created goodwill mentioned above can have a positive effect on the income from tourism.

Further effects on national income arise from trade of goods and services. (Frankel & Romer, 1999) researched if trade increases the country’s income at all. They used several geographical factors of a cross-country dataset as instruments to regress on national income, as there would be no way that national income is affected by geographic factors other than through trade. It turns out that trade indeed raises a country’s income.

But how does aid influence trade? According to (Suwa-Eisenmann & Verdier, 2007) it is possible, through a macroeconomic view, that foreign aid increases domestic saving in the recipient country, which leads to increased investment. This contributes to higher economic growth rates than without foreign aid (White, 1992). Due to the higher growth, the country is able to increase imports, including imports from its foreign aid donor country. This directly points out a possible increase in exports of the donor country. However, as most authors agree on, the relation between aid and trade suffers from reverse causality. Trade can lead to aid if the donor country has commercial ties with the recipient country. The donor maybe wants to reward the recipient for purchasing its goods and services instead of another country’s. I again assume that the aforementioned goodwill, created by ODA, increases exports.

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The relation between aid and exports of the donor country was further investigated by Arvin and Choudhry (1997). They examined whether untied foreign aid disbursements cause exports of the donor country to increase, reflecting the creation of goodwill for the donor country. For this case study on Canada, there appears to be evidence that aid disbursements promote exports. However, it is not sure that this conclusion holds for all countries, given the relatively small sample. Arvin, Cater & Choudhry (2000) later performed the same research for the case of Germany and again found a positive causal impact of untied aid disbursements on its exports to least developed and recipient countries.

The papers of Arvin and Choudhry (1997) and Arvin, Cater and Choudhry (2000) both show some evidence that giving foreign aid promotes exports of the donor country and in this manner creates goodwill for a country.

The most important knowledge gathered from this literature review is about the motivations for giving aid and relations of economic determinants with ODA and GDP. It is unusual for countries to give aid for altruistic reasons. Often they have some self-interest in giving aid. Furthermore, literature shows that ODA has a positive effect on country goodwill, which affects income from tourism, exports and FDI. These economic determinants subsequently all have a positive effect on GDP.

3. Methodology

This chapter presents a description of the data and an explanation of the steps I take to carry out my research.

3.1 Data and variables

In order to research this subject I make use of paneldata on primarily development aid, or Official Development Assistance. ODA comprises all public money that is donated or loaned on non-commercial terms and is used to support welfare or development of developing countries. The iLibrary of the OECD gives access to data on Official Development Assistance: net disbursements. I selected the current 28 member countries of the Development Assistance Committee, with the exception of the European Union as a whole, over the period of 1983 until 2012 to use for the analysis. The countries are:

• Australia • Austria • Belgium • Canada • Czech Republic • Denmark • Finland • France • Germany • Greece • Iceland • Ireland • Italy • Japan

• Korea, Republic of – better known as South Korea • Luxembourg

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• Netherlands, the • New Zealand • Norway • Poland • Portugal • Slovak Republic • Slovenia • Spain • Sweden • Switzerland • United Kingdom • United States

The reason to use these countries is that they are quite comparable in terms of policies, strategies and institutional framework for development cooperation. As a member, a DAC country has met several criteria such as an ODA/GNI ratio over 0.20% or an amount of ODA over USD 100 million that bring the country up to a certain level of development cooperation (OECD, 2014). Moreover, for this specific set of countries, data on several subjects is mostly complete and widely available, which is especially important.

As for the time period, trying to have as many time periods as possible, the fact that data is mostly complete and available is again an important reason. There is sufficient data on ODA in this period of 30 years. 2012 is for most datasets the most recent available year. From that, I took the data for 30 years back, until 1983.

Next to ODA, I collected data on the Gross Domestic Product and the amount of Export of goods and services as a percentage of the GDP of the same group of countries for the same period. I also wanted to use data on international tourism receipts for this period, but unfortunately data on this subject is only available from 1995. Therefore I have to compromise and use data on tourism receipts for the period of 1995 until 2012. The tourism receipts are expenditures by international inbound visitors, including payments to international carriers for international transport (The Worldbank, 2014).

Finally, I use data on net inflows of Foreign Direct Investments. This is the total of equity capital, reinvestment in earnings, other long-term capital and short-term capital as shown in the balance of payments. One speaks of net inflows because divestments are subtracted from the amount of new investments (The Worldbank, 2014). I have chosen for net inflows instead of solely inflows as data on solely inflows isn’t available. This should not be an issue in this research though. Data on GDP, Exports, FDI and tourism receipts, which are the economic determinants to be researched, are available at the online database of the Worldbank, all in current US dollars.

Inconveniently though, the datasets of the Worldbank and the OECD were in different formats than Stata uses. Therefore the datasets first had to be organized in such a way to use it as paneldata in Stata.

Instrumental variables

It is possible that some of the economic determinants suffer from endogeneity. A solution to this issue could be the application of Two Stage Least Squares (2SLS). However, for the regressions of ODA on the different economic determinants, I need strong and relevant instruments. For many researches, it is a challenge to find these and this one is no exception.

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3.2 Methods of analysis

I start with a simple regression of GDP on ODA to see if there is any effect at all in the form of: 𝐺𝐺𝐺𝐺𝐺𝐺 = 𝛼𝛼 + 𝛽𝛽1𝑂𝑂𝐺𝐺𝑂𝑂 + 𝜀𝜀

However, there are some missing values of the GDPmillions variable for the Czech Republic – officially still part of Czechoslovakia until 1993 - before 1990. This is probably due to the occupation by the Warsaw pact countries until 1989. Also, there are missing values for Poland in 1983 and 1984 and for Slovenia for the years until 1989. 1990 coincidentally was the first year with democratic elections in Slovenia and probably the first year for Slovenia to keep track of statistics on GDP, ODA, FDI, exports and tourism. Therefore this results in dropping 16 observations based on missing values of GDP.

As this does not prove a causal relation, I continue by constructing a model with use of the different economic determinants discussed in the literature review. I avoid the use of Two Stage Least Squares for the relation between GDP and ODA because I cannot find a relevant and strong instrument for this relation.

The separate regressions on the determinants of national income have the following form: ∆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽1∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝜀𝜀

∆𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽2∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝜀𝜀

∆𝐹𝐹𝐺𝐺𝐹𝐹 = 𝛼𝛼 + 𝛽𝛽3∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝜀𝜀

The total of the relative separate effects is the relative effect of ODA on GDP. This concise model has the following form:

∆𝐺𝐺𝐺𝐺𝐺𝐺 = 𝑤𝑤1∆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝑤𝑤2∆𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 + 𝑤𝑤3∆𝐹𝐹𝐺𝐺𝐹𝐹

 _{∆𝐺𝐺𝐺𝐺𝐺𝐺 = 𝑤𝑤}₁_𝛽𝛽₁_{𝑂𝑂𝐺𝐺𝑂𝑂 + 𝑤𝑤}₂_𝛽𝛽₂_{𝑂𝑂𝐺𝐺𝑂𝑂 + 𝑤𝑤}₃_𝛽𝛽₃_{∆𝑂𝑂𝐺𝐺𝑂𝑂}  _{∆𝐺𝐺𝐺𝐺𝐺𝐺 = (𝑤𝑤}₁_𝛽𝛽₁_{+ 𝑤𝑤}₂_𝛽𝛽₂_{+ 𝑤𝑤}₃_𝛽𝛽₃_{) ∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂}

Where α is a constant and β1, β2 and β3 are respectively the coefficients of ODA on tourism, exports

and FDI. W1, w2 and w3 are the weights of respectively income from tourism, exports and FDI inflows

as percentage of GDP. ε is the mean zero error term. This model reflects the effect that a change in Official Development Assistance has on the GDP of a country.

To begin with, there is again a problem with missing values. Therefore, for each regression on a separate indicator, I use a slightly different dataset. For the first regression concerning tourism receipts I exclude missing values for both tourism and ODA. Likewise, for the second regression concerning exports, I exclude missing values of exports and ODA. The third regression also excludes missing values for FDI and ODA, but includes negative values. As the FDI variable is expressed as net inflows of Foreign Direct Investment, divestments are also accounted for. Thus, if divestments are larger than investments, the net inflow of FDI is negative.

Instrumental Variable regression

An initial idea for a variable to use as an instrument is some political measure such as the amount of political debates in parliament per year. Development aid is a subject widely discussed in politics. This suggests that there would be more debates per year when a country gives more aid. In other words, a positive correlation between ODA and for example the number of debates per year or the time spent on debating. Unfortunately, this data isn’t widely and completely available.

Finding a strong and relevant instrument for Official Development Aid is an issue for all 10

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researches in this field of economics. Therefore, for the further analysis of this subject I look for different techniques.

Lagged variables

An alternative way to avoid the reverse causality problem is to use lagged variables. For example, the fact that the amount of development aid in the year 2000 is a determinant for the amount of FDI in 2010 is plausible. In fact, this paper’s purpose is to see if previous ODA influences GDP through FDI, among others. It is, however, less plausible that the amount of Foreign Direct Investments in 2010 influences the amount of development aid of ten years back. I therefore use the ODA with a lag of ten years in the next regressions with the determinants of national income. This results in the following models:

∆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽1∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

∆𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽2∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

∆𝐹𝐹𝐺𝐺𝐹𝐹 = 𝛼𝛼 + 𝛽𝛽3∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

Furthermore, I estimate the models with both the ordinary and the lagged ODA variable to see if past development aid assistance has any influence.

∆𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽1∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽2∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

∆𝐸𝐸𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 = 𝛼𝛼 + 𝛽𝛽3∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽4∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

∆𝐹𝐹𝐺𝐺𝐹𝐹 = 𝛼𝛼 + 𝛽𝛽5∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽6∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂(𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙) + 𝜀𝜀

With this regression it still is not sure if ODA genuinely causes an increase in tourism, exports or FDI. To test this, I perform a Granger causality test using lagged (logarithmic) variables of the previous three years. The first step is to run a regression with the following independent lagged variables:

𝑙𝑙𝑙𝑙𝐸𝐸𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 = 𝛽𝛽1𝐿𝐿. 𝑙𝑙𝑙𝑙𝐸𝐸𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝛽𝛽2𝐿𝐿2. 𝑙𝑙𝑙𝑙𝐸𝐸𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝛽𝛽3𝐿𝐿3. 𝑙𝑙𝑙𝑙𝐸𝐸𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 + 𝛽𝛽4𝐿𝐿. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽5𝐿𝐿2. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂

+ 𝛽𝛽6𝐿𝐿3. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂

𝑙𝑙𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 = 𝛽𝛽1𝐿𝐿. 𝑙𝑙𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 + 𝛽𝛽2𝐿𝐿2. 𝑙𝑙𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 + 𝛽𝛽3𝐿𝐿3. 𝑙𝑙𝑙𝑙𝑙𝑙𝐸𝐸𝐸𝐸𝑇𝑇𝑇𝑇𝐸𝐸𝑇𝑇 + 𝛽𝛽4𝐿𝐿. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽5𝐿𝐿2. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂

+ 𝛽𝛽6𝐿𝐿3. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂

𝑙𝑙𝑙𝑙𝐹𝐹𝐺𝐺𝐹𝐹 = 𝛽𝛽1𝐿𝐿. 𝑙𝑙𝑙𝑙𝐹𝐹𝐺𝐺𝐹𝐹 + 𝛽𝛽2𝐿𝐿2. 𝑙𝑙𝑙𝑙𝐹𝐹𝐺𝐺𝐹𝐹 + 𝛽𝛽3𝐿𝐿3. 𝑙𝑙𝑙𝑙𝐹𝐹𝐺𝐺𝐹𝐹 + 𝛽𝛽4𝐿𝐿. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽5𝐿𝐿2. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂 + 𝛽𝛽6𝐿𝐿3. 𝑙𝑙𝑙𝑙𝑂𝑂𝐺𝐺𝑂𝑂

The lagged variables each present a lag of a year. For example, L.lnODA presents a lag of one year, L2.lnODA presents a lag of two years and L3.lnODA presents a lag of three years.

The next step is to jointly test the null hypothesis that the estimated coefficients of the lagged lnODA variables are jointly equal to zero by means of a χ2_{- test with the following null}

hypothesis:

𝐻𝐻0: 𝛽𝛽4= 𝛽𝛽5= 𝛽𝛽6= 0

If I can reject the null hypothesis, then I can say that ODA Granger-causes a change in the dependent variable. Afterwards, I compute the separate means of the income from tourism, exports and FDI inflows as part of GDP to use as weights in the main model.

In the end, I fill in the main model of the effect of ODA on GDP:

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∆𝐺𝐺𝐺𝐺𝐺𝐺 = (𝑤𝑤1𝛽𝛽1+ 𝑤𝑤2𝛽𝛽2+ 𝑤𝑤3𝛽𝛽3) ∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂

This model shows the effect of a one percentage change in foreign aid given on the GDP of the donor country, commonly known as an elasticity.

4. Results 4.1 GDP on ODA

The regression of GDPmillions on ODAmillions yields a significant coefficient on ODAmillions of 328.7925 (Z = 42.04) over the entire dataset. When regressing the logarithm of GDPmillions (lnGDP) on the logarithm of ODAmillions (lnODA), it results in a coefficient of 0.6226965, which can be interpreted as an elasticity because of the logarithms.

This outcome suggests that there is indeed a relation, as can be seen in the graph. However, one cannot immediately assume a causal relation. The coefficient of 1.272503 of the regression of lnODA on lnGDP shows that there is reverse causality or simultaneity, resulting in endogeneity of the regressor. It is not clear if higher ODA causes higher GDP or vice versa.

4.2 Economic indicators on ODA

The three regressions for tourism on ODA, exports on ODA and FDI on ODA yield the following coefficients and standard errors:

(1) (2) (3)

VARIABLES Tourismmillions Exportsmillions FDImillions

ODAmillions 3.017*** 66.45*** 6.585***

(0.102) (1.709) (0.418)

Constant 9,175*** 42,636** 1,264

(2,773) (16,857) (3,703)

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Regressions on logarithms yield a clearer insight as they show relative effects:

(1) (2) (3)

VARIABLES lntourism lnexports lnFDI

lnODA 0.492*** 0.739*** 1.006***

(0.0153) (0.0152) (0.0504)

Constant 5.778*** 6.700*** 1.871***

(0.172) (0.153) (0.378)

The regressions show that income from tourism, exports and FDI all are related to ODA, judging from the significance of the coefficients. Again however, it’s not certain which direction the causality goes. In the case of exports, literature has already provided evidence that ODA causes an increase in exports, but for income from tourism and FDI it is still not clear.

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Lagged variables

The regressions of ten year-lagged logarithms of ODA on Tourism, Exports and FDI yield the following results:

(1) (2) (3)

VARIABLES lnTourism lnExports lnFDI

Lagged lnODA 0.377*** 0.544*** 0.599***

(0.0295) (0.0297) (0.0665)

Constant 6.927*** 8.619*** 5.452***

(0.241) (0.232) (0.451)

When including both the ordinary and the lagged (logarithmic) variables, it shows that the lagged variable still has a significant effect, but it is much smaller.

(1) (2) (3)

lnODA 0.468*** 0.625*** 0.415*** (0.0209) (0.0205) (0.0997) Lagged lnODA 0.0820*** 0.156*** 0.275*** (0.0242) (0.0213) (0.0909) Constant 5.456*** 6.616*** 4.516*** (0.194) (0.149) (0.491)

It seems that past foreign aid assistance does have an influence on all three economic determinants. Although the coefficients all are significant at the one percent level, it is not sure if ODA genuinely causes an increase in tourism, exports or FDI. To test this, I perform a Granger causality test using lagged (logarithmic) variables of the previous three years.

The regression results of ODA and three lagged variables on the three economic determinants are as follows:

(1) (2) (3)

L.lnODA 0.0355 0.0455** 0.0275 (0.0278) (0.0204) (0.169) L2.lnODA 0.0118 0.000761 0.182 (0.0362) (0.0255) (0.233) L3.lnODA -0.0440* -0.0531*** -0.122 (0.0266) (0.0189) (0.161) L.lntourism 1.129*** (0.0509) 13

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L2.lntourism -0.343*** (0.0765) L3.lntourism 0.196*** (0.0513) L.lnexports 1.020*** (0.0408) L2.lnexports -0.315*** (0.0581) L3.lnexports 0.291*** (0.0414) L.lnFDI 0.518*** (0.0446) L2.lnFDI 0.243*** (0.0501) L3.lnFDI 0.0276 (0.0445) Constant 0.185*** 0.182*** 1.378*** (0.0495) (0.0499) (0.201)

The next step is to jointly test the null hypothesis that the estimated coefficients of the lagged lnODA variables are jointly equal to zero by means of a χ2_{- test. The results are shown below.}

lntourism lnexports lnFDI

χ2_statistic _4.02 _16.03 _9.43

Prob > χ2 _0.2592 _0.0011 _0.0240

At a significance level of 5 percent, I can reject the null hypothesis that lnODA does not Granger-cause lnexports and lnFDI. This result suggests that ODA does not have a causal effect on tourism. The weights of the economic determinants in the GDP are as follows:

Tourism Exports FDI

Weight in GDP 0.0311 0.437 0.0432

Using only the significant coefficients from the first simple regression using lnODA and the computed weights, the main model is as follows:

∆𝐺𝐺𝐺𝐺𝐺𝐺 = (0 + 0.437 ∗ 0.739 + 0.0432 ∗ 1.006) ∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂 ∆𝐺𝐺𝐺𝐺𝐺𝐺 = (0.366) ∗ ∆𝑂𝑂𝐺𝐺𝑂𝑂

With ΔODA as the change of the amount of development aid in percent and ΔGDP as the change in GDP in percent. In fact, the coefficient of 0.366 is effectively an elasticity.

5. Conclusion

The aim of this paper is to show what the causal influence is of the amount of development aid a country gives on the GDP of the donor country. The findings of this paper provide evidence that ODA indeed causes GDP. Through the regression of three economic determinants, tourism, exports and FDI, on ODA I test the effect with use of a Granger-causality test.

The individual effects in terms of elasticities of ODA on exports and FDI inflows are respectively 0.739 and 1.006. I found that ODA does not have a significant effect on tourism. By

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giving weights to the elasticities, I can express the effect of ODA on GDP through these economic determinants in a single elasticity: 0.366. Thus, a one percent increase of Official Development Assistance yields a 0.366 percent increase in GDP of the donor country. This confirms my hypothesis that there is indeed a positive effect, but it does not offset the costs of development aid.

This result is valuable to countries as they now have an incentive to increase development aid to a point where the increase in GDP offsets the costs of extra foreign aid. This way the world is a small step closer to reaching the millennium goals. Apart from that, the statement that development aid “just costs us money” can now be refuted.

I must stress, though, that this research may not reveal the complete effect of development aid on the donor country. I only make use of three economic determinants through which ODA affects GDP. A research carried out on multiple determinants probably shows a greater effect on GDP. The length and timespan of this paper, however, make it impossible to examine a much larger number of determinants.

Furthermore, the methods I implement are quite simple and therefore the results may not reflect the total effect on GDP. A more extensive econometrical analysis using Two Stage Least squares may yield more insight in the effect or completely reject the existence of a causal effect. I look forward to any addition or improvement of my research.

Finally, I hope to see an increase in the amount of development aid as a result of my conclusion on the effect on GDP

References

Alesina, A., & Dollar, D. (2000). Who gives foreign aid to whom and why? Journal of Economic

growth, 5(1), 33-63.

Alfaro, L., Chanda, A., Kalemli-Ozcan, S., & Sayek, S. (2004). FDI and economic growth: the role of local financial markets. Journal of International Economics, 64(1), 89-112.

Arvin, M., Cater, B., & Choudhry, S. (2000). A causality analysis of untied foreign assistance and export performance: the case of Germany. Applied Economics Letters, 7(5), 315-319.

Balaguer, J., & Cantavella-Jorda, M. (2002). Tourism as a long-run economic growth factor: The Spanish case. Applied Economics, 34, 877-884.

Berthélemy, J.-C. (2006). Bilateral Donors' Interest vs. Recipients' Development Motives in Aid Allocation: Do All Donors Behave the Same? Review of Development Economics, 10(2), 179-194. Birdall, N., Claessens, S., & Diwan, I. (2003). Policy Selectivity Forgone: Debt and Donor Behavior in Africa. The World Bank Economic Review, 17(3), 409-435.

Borensztein, E., De Gregorio, J., & Lee, J.-W. (1998). How does foreign direct investment affect economic growth? Journal of International Economics, 45(1), 115-135.

Burnside, C., & Dollar, D. (2000). Aid, Policies, and Growth. The American Economic Review, 90(4), 847-868.

Dritsakis, N. (2004). Tourism as a long-run economic growth factor: An empirical investigation for Greece using a causality analysis. Tourism Economics, 10, 305-316.

Dudley, L., & Montmarquette, C. (1976). A Model of the Supply of Bilateral Foreign Aid. The American

Economic Review, 66(1), 132-142.

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Durbarry, R. (2004). Tourism and economic growth: The case of Mauritius. Tourism Economics, 10, 389-401.

Eugenio-Martin, J. L., & Morales, N. M. (2004). Tourism and economic growth in Latin American countries: A panel data approach. Social Science Research Network Electronic Paper.

Frankel, J. A., & Romer, D. (1999). Does trade cause growth? The American Economic Review, 89(3), 370-399.

Ghali, A. (1976). Tourism and economic growth: an empirical study. Economic Development and

Cultural Change, 24, 527-538.

Kim, H. J., Chen, M. H., & Jang, S. C. (2006). Tourism expansion and economic development: The case of Taiwan. Tourism Management, 27(5), 925-933.

Lanza, A., Templec, P., & Urgad, G. (2003). The implications of tourism specialisation in the long-run: An econometric analysis for 13 OECD economies. Tourism Management, 24, 315-321.

Li, X., & Liu, X. (2005). Foreign Direct Investment and Economic Growth: An Increasingly Endogenous Relationship. World Development, 33(3), 393-407.

Mak Arvin, B., & Choudhry, S. A. (1997). Untied Aid and Exports: Do Untied Aid Disbursements Create Goodwill for Donor Exports? Canadian Journal of Development Studies / Revue canadienne d'études

du développement, 18(1), 9-22.

Narayan, P. K. (2004). Economic impact of tourism on Fiji's economy: Empirical evidence from the computable general equilibrium model. Tourism Economics, 10, 419-433.

OECD. (2014, June 22). Joining the Development Assistance Committee. Retrieved from OECD website:

http://www.oecd.org/dac/dac-global-relations/joiningthedevelopmentassistancecommitteedac.htm#Criteria

Oh, C. O. (2005). The contribution of tourism development to economic growth in the Korean economy. Tourism Management, 26, 39-44.

Schraeder, P. J., Hook, S. W., & Taylor, B. (1998). Clarifying the Foreign Aid Puzzle: A Comparison of American, Japanese, French, and Swedish Aid Flows. World Politics, 50(2), 294-323.

Suwa-Eisenmann, A., & Verdier, T. (2007). Aid and Trade. Oxford Review of Economic Policy, 23(3), 481-507.

The Worldbank. (2014, June 22). Foreign direct investment, net inflows (BoP, current US$). Retrieved from Worldbank Database: http://data.worldbank.org/indicator/BX.KLT.DINV.CD.WD

The Worldbank. (2014, June 22). International tourism, receipts (Current US$). Retrieved from Worldbank Database: http://data.worldbank.org/indicator/ST.INT.RCPT.CD

White, H. (1992). The Macroeconomic impact of Development Aid: A Critical Survey. The Journal of

Development Studies, 28(2), 163-240.

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Figure 1: Scatterplot with lnGDP at y-axis and lnODA at x-axis

Appendix

Regression results GDPmillions on ODAmillions

VARIABLES GDPmillions

ODAmillions 328.8***

(7.821)

Constant 97,766

(142,952) Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 Regression results lnGDP on lnODA

VARIABLES lnGDP

lnODA 0.623***

(0.0117)

Constant 8.479***

(0.159) Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1 17 8 10 12 14 16 lnG DP 0 2 4 6 8 10 lnODA

The impact of Official Development Assistance on the GDP of the donor country

Bsc Thesis Economics & Business