6.1Model I – Donors’ Self Interests
The first model is the donors’ self interests’ that signifies what donors’ expect to receive in return for providing development aid. Todaro and Smith(2009) in their study indicated that donors’ primary motivation to provide aid is either a political strategy or economic self-interest. They also stressed on the fact that donor government’s diplomatic strategy influences the aid allocation to the recipient country. This motivation seems inevitable because the policy decisions of donor governments are influenced by many factors that form the domestic conditions, such as value, political party affiliation, constituency interest, public opinion, the country’s deference and decision rules and global competition or alliances (Anderson, 1997). Therefore, a donor country’s aid allocation decisions must become self strategic sometimes to satisfy the best conditions for the donor.
Several studies have shown a strong association between bilateral aid and a donor’s political and economic interests, while the indicators used in the analysis varies as the context changes. An early study by Wittkopf (1972) examines the aid motivation for major donors and finds that the variable of Cold War considerations was the most important factor in the 1960s for aid allocation from the United States. In a series of other case studies of major donors, namely, United Kingdom, Germany, France and the United States – gross term of international liquidity holding, military expenditures, association with communist parties, gross expenditure on the armed forces, GDP and Population were identified as important political indicators. The authors of these series conclude that political interests provide a decent explanation for the aid allocation by these 4 countries in 1960’s. After the end of the cold war in 1989, few of the scholars constructed a new variable for political interests of the donors’ like Voting pattern in the UN by calculating the correlation of the donor and recipient country’s voting record in the UN General Assembly and find that the UN voting pattern is a highly important driver of aid allocation, particularly for Japan’s development aid between 1970 and 1994 (Alesina and Dollar, 2000). Kuziemko and Weker (2006), have revealed that the volume of the development aid from the UN and the United States increases if they notice that the developing country serves a seat on the UN security council. A more recent study by Fuchs and Vadlamannati (2013) on the aid from India from 2008 to 2010 also confirms that the voting pattern in the UN is a significantly important factor that relates to aid allocation.
In various other studies, several economic interests variables have been found out to have strong associations with regard to the overall aid allocation. Davenport(1970) in his study finds out that among several other variables foreign reserve position has been consistently significant. Variable of Trade balance used by Wittkopf (1972) in his study was also a strong explanatory factor for British, German and French aid. McKinlay and Little (1977, 1978 a,b, 1979) and McKinlay (1978) have confirmed the importance of gross size of exports and imports, net balance of private investment as important indicators of donors’ aid allocation. Younas (2008) also confirms the strong association between aid and trade-related indicators but more specifically shows that donors favour countries that import the goods in which the donor country has a comparative advantage in production.
Thus, after a careful analysis of previous literature on aid allocation, the following variables have been used for the present study.
6.2 Variables used in the Regression Analysis
Following the previous literature, the variables described below have been used for the current study.
Dependent variable
Net ODA Given (log)
Since this model is the donor’s self interests’ model, Official Development Assistance given is considered as Y variable. The rationale for using this variable is to understand how the donor country’s aid given is being affected by their own vested self interests.
Independent variables
UN Security Council seat, Gross size of exports, Government expenditure, Total reserves and Net International Investment Position
In this study, we consider Coal production, Coal consumption and Co2 emissions variables as our indicators of sustainable production and consumption. We all know that CO2 emissions are the major pollutants of the environment and 87% percent of all human-produced carbon di-oxide emissions come from the burning of fossil fuels like coal, natural gas and oil. The primary reason behind selecting coal is because it is solely responsible for 43% of the CO2 emissions coming from the energy sector (Le Quere et al, 2013).
Thus, in this model, we take into account coal production, coal consumption and Co2 emissions of 12 donor nations to understand whether the donor country’s own sustainable indicators influence their decisions of providing aid to the developing world. Table 6.2.1 lists the variables used in the donors self interests models’ regression along with their definition and sources.
Table 6.2.1
Donors’ Self – Interests model variables and sources
VARIABLES | DEFINITION | SOURCES |
Aid Given i)Net ODA given | Government aid given/received for economic development & welfare (MN_USD$) | World bank & OECD |
Donors Self- Interests i) UN security council seat ii) Exports iii) Government expenditure iv) Total Reserves v) Net international investment position | Dummy variable(1 = seat,0 = no seat) Gross exports (% of gdp) Govt. Exp( as a share of NI) Total reserves(includes gold, mn_usd) Investment Position(Creditor/Debtor) | United Nation World Bank Our World in Data(IMF) World Bank IMF |
Sustainable indicators i) Coal Production ii) Coal Consumption iii) CO2 Emissions | Coal Production by country(mn_ton) Coal Consumption by country(mn_ton) Annual CO2 emissions per country | International Energy Statistics B.P Statistical Review of Global Energy Global Carbon Project,2017 |
Control Variables i)GDP Per Capita ii) Population | GDP Per Capita, current US$ Population, total( in thousands) | World Bank World Bank |
The Net ODA given data are drawn from the Organization of Economic Co-operation and Development (OECD) Development Assistance Committee (DAC)’s Creditor Reporting System (CRS) data base, which is self-reported information by the DAC countries. Official development assistance (ODA) is defined as government aid designed to promote economic development and welfare of developing countries. Loans and credits for military purposes are excluded. Aid may be provided bilaterally, from donor to recipient, or channelled through a multilateral development agency such as the United Nations or the World bank. The OECD maintains a list of developing countries and territories; only aid to these countries counts as ODA. A long-standing United Nations target is that developed countries should devote 0.7% of their gross national income to ODA. This indicator is measured as a percentage of gross national income and million USD constant prices, using 2015 as the base year.
The data period spans 10 years beginning in 2006 until the most recent collection of data in 2016. Over this period, the data set covers 11 aid-donor countries in total, excluding a few due to data constraints.
The information regarding donors’ political interests is measured by a dummy variable for having a seat on the UN Security Council. The primary aim of the UN Security Council is to maintain global peace and thus it is given the power to authorize multilateral sanctions and military action when an unsecured event occurs, such as during a war or invasion by countries. Holding a seat on the UN Security council means that the country has a representative role in making decisions on behalf of all UN member states for significant/major world events. Therefore, the study hypothesized that some donor countries, might want to increase the development aid if they possess a UN Security council seat to reflect their political will on the Council. Earlier studies by Kuziemko and Werker (2006) reveal that in case of aid from the US and the UN, political interests were significantly related to the volume of development aid and they increased US aid by 59% and UN aid by 8% respectively. Donors’ economic interests have been captured by two variables specifically exports & total reserves. These variables are indicators of the donors’ trading capacity. Net international investment position has also been considered as an independent variable to observe whether how a country’s position of being a debtor or a creditor is affecting the aid given by them.
Following the previous literature, the study also accounts for the effect of potential confounding variables. This process is necessary to observe the effect of the included variables by ensuring that their remaining effect is not due to omitted variable bias. As informed by the previous literature, natural logarithm of GDP per capita and total population were included. Both data are drawn from the World Development Indicators.
6.3 Model Specification
Since the data used in this study is panel data that has two dimensions, namely donor country
and time, firstly, the benchmark fixed effects model includes country-fixed effects to control for the unobserved country- specific and time invariant factor determinants of aid. Thus the specification shown in Eq. 1 includes two groups of explanatory variables and control variables.
Donors Self Interests Fixed effects Model
ln (Net ODA given) it = α + β1(Coal Prod)it+β2(Coal Consumption) it +β3(Co2 emissions) it +β4(UN Seat) it + β5(Exports) it + β6(Total Reserves) it + β7(Government Expenditure) it + β8(Net Investment position) it + δXit + ui + εit -(1)
where the dependent variable Aidit is the amount of ODA provided to the recipient country i in period t. The three variables coal production, coal consumption and CO2 emissions represent our sustainable variables of donor country i in period t. These three variables exhibited strong multi-collinearity and were thus included one by one in the estimation. Therefore these three variables were estimated separately in three different models. For the other variables, variance inflation factors (VIF’s) were calculated to check whether multi-collinearity caused a problem in the estimation, but could not find any evidence that suggests that it seriously affected the estimation results. Variables like UN Seat, Exports, Total reserves, Government expenditure and Net international Investment position capture the donor’s political & commercial interests in country i for period t. Xit is the control variables of GDP Per capita and total population of country i in period t. The term ui is the country fixed effects and denotes the time-invariant differences in providing aid across the donor countries and εit is the error term in the model.
After performing the Hausman test for fixed effects and random effects, the null hypothesis of random effects model is appropriate was rejected and hence fixed effects estimation has been used in this model and its results are as given below in Table 6.3.1
Table 6.3.1
Relation between Net ODA given & donors’ self interests: FE regression
| Fixed effects Estimation |
Coal Production Coal Consumption CO2 emissions (1) (2) (3) |
Donors’ Self Interests UN Security Council seat Exports Government expenditure Total Reserves Net International Investment Position Sustainable variables Coal Production Coal Consumption CO2 Emissions Control Variables GDP Per Capita (log) Population (log) Constant Observations R-squared | -0.064 (0.062) -0.013 (0.008) -0.014** (0.006) -2.63e-07 (3.10e-07) 0.085 (0.093) -0.0002 (0.0004) 0.07 (0.17) 2.19*** (0.77) -29.57** (13.07) 132 0.70 | -0.066 (0.061) -0.013 (0.008) -0.014** (0.006) -2.89e-07 (3.12e-07) 0.081 (0.093) -0.0007 (0.0009) 0.061 (0.17) 2.02** (0.80) -26.57* (13.6) 132 0.69 | -0.07 (0.061) -0.015 (0.008) -0.014** (0.006) -4.03e-07 (3.26e-07) 0.071 (0.093) -0.0003 (0.0002) 0.089 (0.17) 1.846** (0.81) -23.36* (13.7) 132 0.65 |
Note
Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1
Second, the study also included lagged dependent variable in the analysis performed because donors’ tend to allocate aid to a country that has already received a certain amount of aid, rather to a country they do not know about; thus it is hypothesized that the aid flows in the current period tend to be related to the previous periods’ aid allocation. This new specification including lagged version of the dependent variable as one of the independent variable is given in Eq. 2.
Donors Self Interests Lagged Dependent variable Model
ln(Net ODA given) it = α + ρAidt−1 + β1(Coal Production)it+β2(Coal Consumption)it+β3(CO2 emissions)it+β4(UN Seat)it+β5(Exports)it+β6(Total Reserves)it+β7(Govt Expenditure)it+β8(Net Investment position)it+ δXit+ ui + εit -(2)
The results of the model are presented in Table 6.3.2
Table 6.3.2
Relation between Net ODA given & donors’ self interests: Lag dependent variable model, FE regression
| Lagged Dependent Variable model : Fixed effects Estimation |
Coal Production Coal Consumption CO2 emissions (1) (2) (3) |
Donors’ Self Interests UN Security Council seat Exports Government expenditure Total Reserves Net International Investment Position Sustainable variables Coal Production Coal Consumption CO2 Emissions Control Variables GDP Per Capita (log) Population (log) Lagged dependent variable (t-1) Constant Observations R-squared | -0.036 (0.062) -0.008 (0.008) -0.011* (0.006) -2.83e-07 (3.06e-07) 0.043 (0.093) -0.0002 (0.0004) 0.08 (0.18) 1.83** (0.78) 0.107** (0.78) -24.6* (13.34) 131 0.72 | -0.038 (0.061) -0.008 (0.008) -0.011* (0.006) -3.01e-07 (3.08e-07) 0.040 (0.093) -0.0006 (0.0009) 0.077 (0.181) 1.71** (0.81) 0.106** (0.043) -22.3 (13.9) 131 0.72 | -0.043 (0.061) -0.01 (0.009) -0.012* (0.006) -4.03e-07 (3.21e-07) 0.032 (0.093) -0.0003 (0.0002) 0.097 (0.179) 1.53* (0.82) 0.103** (0.043) -19.15 (14.05) 131 0.68 |
Note
Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1
However, in this kind of model specification, Aidit−1 is correlated with the fixed effects in the error term εit which gives rise to a ‘dynamic panel bias’ (Nickell, 1981).This phenomenon could inflate the co-efficient estimate for the lagged aid variable by attributing predictive power to it that actually belongs to the country’s fixed effects. Thus, it could reduce the impact of one-year’s shock on a country’s apparent fixed effects and thereby diminishing endogeneity problem too. To solve these problems, the system generalized method of moments (GMM) estimation developed by Arellano and Bond (1991) has been used. GMM solves the endogenous bias problem that relates to the inclusion of the lagged dependent variables and instrumental variables for other possible endogenous variables. It also addresses the independent variables that are not exogenous, as well as the fixed effects, heteroskedasticity and autocorrelation within countries. The estimation has been run by building a set of two equations, including the original equation in Eq. 2 and the transformed equation or the differenced equation, as specified in Eq. 3.
Donors Self Interests GMM Model
ln(∆Net ODA given) it = ρ(∆ Aidit−1) +β1(∆ Coal Prod)it+β2(∆Coal Consumption)it+β3(∆ CO2 emissions)it+β4(∆UN Seat)it+β5(∆Exports)it+β6(∆Total Reserves)it+β7(∆Government Expenditure)it+β8(∆ Net Investment position)it+ δ(∆Xit)+ ∆ εit -(3)
The lagged difference in the endogenous variables is used as an instrument for the original equation, and the lagged levels of the endogenous variables are used as an instrument for the transformed equation. The results of the system GMM specification are presented below in Table 6.3.3.
Table 6.3.3
Relation between Net ODA given & donors’ self interests: GMM model
| GMM Estimation |
Coal Production Coal Consumption CO2 emissions (1) (2) (3) |
Donors’ Self Interests UN Security Council seat Exports Government expenditure Total Reserves Net International Investment Position Sustainable variables Coal Production Coal Consumption CO2 Emissions Control Variables GDP Per Capita (log) Population (log) Constant Observations R-squared | 0.020 (0.053) -0.004 (0.008) -0.014** (0.005) 6.29e-08 (2.91e-07) 0.095 (0.131) -0.0003 (0.0006) -0.264 (0.17) 2.41*** (0.81) -30.02** (13.96) 108 0.75 | 0.013 (0.053) -0.004 (0.008) -0.014*** (0.005) 2.21e-08 (2.98e-07) 0.094 (0.13) -0.0011 (0.0014) -0.26 (0.17) 2.19** (0.91) -26.2 (15.6) 108 0.72 | 0.009 (0.054) -0.004 (0.008) -0.015*** (0.005) -1.84e-08 (3.07e-07) 0.092 (0.131) -0.0002 (0.0002) -0.25 (0.172) 2.22** (0.86) -26.5 (14.79) 108 0.70 |
Note
Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1
Tables 6.3.1, 6.3.2 & 6.3.3 show the results of association of donors self interests variables with log Net ODA given. In the FE regression, all of the specifications show a moderate fitness to the regression lines, with R-squared values between 0.6 and 0.7. Similarly, the lagged dependent variable model also has a moderate fit, with R-squared values from 0.68 to 0.72. In all the three estimations above (FE, lagged dependent variable model & GMM), government expenditure and population have been significant throughout across models (1), (2) & (3), i.e, coal production, coal consumption & co2 emissions. Government expenditure is a donors’ self interest variable whereas population is a control variable. This evidence reveals that the donor country’s increase aid allocation for sustainable initiatives to a receptor country as their own government expenditure is lowered. Results also state that as a donor country’s population is increasing, their aid allocation to receptor countries is growing. This can be seen from the positive co-efficient of the population (log) variable. The variable of a seat on the UN Security Council showed no significant relation in all specifications. Variables like total reserves & net investment position also did not reveal significant results. From these results, it is clear that donors’ do not provide more aid to countries based on their political interests but they do provide more aid to countries based on their economic interests as revealed by the government expenditure variable.
The sustainability indicators considered in the model also did not reveal significance across the models and thus makes the point clear that donors are not concerned much about their own production, consumption & emissions while giving aid to recipient countries for sustainable initiatives. Overall, donor country’s increase their sustainable aid allocation as their own government expenditure falls and their population is rising.