Bilateral trade ties, elections and foreign aid allocation: A comparison across...

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Bilateral trade ties, elections and foreign aid allocation:

A comparison across donors

Nabeela Alam!

Brandeis University International Business School

Job Market Paper

August 2012

Abstract: This paper informs the aid effectiveness debate by examining non-development motives of bilateral foreign aid flows. Using a panel of five bilateral donors and a hundred recipient countries from

1975-2008, I find that trade ties, recipients' access to donor markets, elections and political

competitiveness in the recipient country are associated with changes in foreign aid commitments. I show

that the US gives more aid to its non-competitive, larger trade partners, but cuts their aid ahead of elections. It substitutes aid with market access for non-competitive countries for which it is an important

export market, but not during election years. Germany, Japan and UK give more aid to countries with

competitive electoral systems, but for these countries Japan and UK substitute aid with trade. The substitution disappears for UK during election years. Japan and UK also reward countries for which they

are important export markets with more aid, but only during non-election years for Japan. During

election years, Germany cuts aid to non-competitive countries, but gives more aid to non-competitive countries for which it is an export destination. There is some evidence that France substitutes aid with

market access for politically competitive countries.

! Email: [email protected]

I would like to thank my advisors, Cathy Mann and Can Erbil, for their support and guidance, and Kathryn Graddy, Nidhya Menon and other seminar participants at Brandeis University and the for their insightful comments and

suggestions. I am also grateful to seminar participants at Babson College and Whitman College for their comments

and suggestions on an earlier version of the paper.

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1 Introduction

Despite the large amounts of aid spent on developing countries, studies give conflicting

results for the impact of aid on growth (Boone 1994; Burnside and Dollar 2000, 2004; Easterly

2003, Easterly, Levine, and Roodman 2003; Rajan and Subramanian 2008). Even when aid has

a positive impact on growth, the effects are modest (Clemens, Radelet, and Bhavnani 2004;

Clemens et al. 2011). However, this is not surprising given that donor interests can drive aid

allocation (Alesina and Dollar 2000; Boone 1996), and that actual patterns of aid allocation

differ from poverty-efficient aid allocation patterns (Collier and Dollar 2002).

While aid based on donor interests can still be consistent with developmental objectives

when there are mutual benefits, this need not be always true. In this light it is important to

identify all the determinants of aid, both developmental and non-developmental. Studies should

take into account extra-development motives when evaluating the impact of aid. Indeed they

should also evaluate whether observed allocation strategies are consistent with the

developmental objectives of foreign assistance. Thus identifying the determinants of foreign aid

will help make the foreign aid allocation process more transparent and accountable.

In this paper, I contribute to an understanding of foreign aid determinants by examining

the relationship between bilateral trade and foreign aid flows for five major donors, namely

France, Germany, Japan, the United Kingdom and the United States. I further test whether

donors differentially adjust foreign aid flows ahead of elections in recipient countries on the

basis of their bilateral trade relationships, and whether donors distinguish between countries with

politically competitive and non-competitive electoral systems.

I add to the literature on aid allocation in three novel ways: First, I ask whether it is the

donor’s trade interest or the recipient’s trade interests that influences foreign aid flows. I define

donor trade interest as the share of donor's total trade with a recipient. Recipient trade interest is

the share of a recipient's total exports flowing to a donor, and therefore measures the recipient's

access to a donor market. Previous literature comparing aid allocation across donors has focused

on donor trade interests (Berthélemy 2006; Berthélemy and Tichit 2004).

Next, I test the aid-trade relationships using bilateral trade ties over different time

horizons to reflect potential short-term and long-term aid allocation strategies. To this end I look

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at a regime-specific measure of trade ties as well as the more conventional year-to-year measure.

The former is the year-to-date cumulative trade ties calculated from the start of a recipient

government’s tenure. The two measures help distinguish whether donor governments are

sensitive to year-to-year differences in trade relationships, or whether it is the cumulative trade

relationship over a recipient regime’s tenure that matters.

Finally, I test for election year effects on foreign aid flows, and if there are differential

effects during elections for recipients with closer trade ties. I also examine if aid allocation

behaviour depends on whether the electoral process in the recipient country is politically

competitive or not.

I find that both donor trade interests and recipient trade interests matter for donors' aid

allocation decisions, but in different ways for each donor. The United States gives more aid to

non-competitive countries with which it has larger trade ties, but cuts aid ahead of elections for

them. It also treats aid and market access as substitutes for non-competitive countries during

non-election years. Germany cuts aid ahead of elections for non-competitive countries with

which it trades more. Germany, Japan and the UK all pledge more aid to countries if they are

politically competitive, but Japan and the UK treat trade and aid as substitutes for these

politically competitive countries. However, the UK mutes this substitution behaviour during

election years so that politically competitive countries that trade more with the UK don't face a

decrease in aid during election years. Both Japan and the UK also give more aid to politically

competitive countries that export more to them. During election years however, Japan decreases

its aid commitments to these countries. France is the only country for which there are no

election year effects and donor trade interests. The latter result is in contrast to previous findings

that France is less altruistic in giving aid (Berthélemy 2006; Rajan and Subramanian 2008).

There is some evidence however that for politically competitive countries, France treats market

access and aid as substitutes.

The objective of this paper is to establish some empirical facts about aid allocation

strategies by different donors. Assessing how foreign aid commitments change with trade

relationships, election events and political competitiveness in recipient countries helps us better

understand the foreign aid process. The overarching goal is to contribute to the aid effectiveness

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literature, which can use these empirical facts to further investigate the motivations behind

foreign aid flows and evaluate the effectiveness of aid based on these objectives.

This paper also contributes to the literature on aid allocation behaviour motivated by

donor interests. Past studies find that donors give aid based on their own geopolitical and

strategic interests (Alesina and Dollar 2000; Boone 1996; Dreher, Nunnenkamp, and Thiele

2008; Faye and Niehaus 2010; Harrigan and Wang 2011; Kuziemko and Werker 2006), or their

economic and commercial interests (Berthélemy 2006; Berthélemy and Tichit 2004; Osei,

Morrissey, and Lloyd 2004). To the best of my knowledge only one other study by Faye and

Niehaus (2010) looks at election year effects of foreign aid flows,1 but they look at differential

effect of elections by political alignment measured by voting coincidence in the United Nations

General Assembly. Moreover, they look at the pooled behaviour of bilateral aid donors and so

constrain all donors in their sample to have on average the same aid allocation strategy. I test the

aid allocation strategies of each donor separately to be able to capture differences in donor

behaviour.

The remainder of the paper is structured as follows. Section 2 discusses the data, and

Section 3 presents the empirical strategy. Section 4 presents and analyzes the results, with

robustness checks discussed in Section 5. Finally, Section 6 concludes. All tables and figures

are in Appendices A and B at the end.

2 Data

I study aid allocation using a dataset composed of five bilateral aid donors and 100

recipient countries from 1975 to 2008, with a total of 12,335 observations. Table 1 lists the

recipient countries. Each observation is a donor-recipient-year triple with information on

bilateral trade and aid, political variables and demographic controls as described below.

The source for aid data is the International Development Statistics (2009) database

compiled by the Development Cooperation Directorate (DCD) and Development Assistance

Committee (DAC) of the Organisation for Economic Cooperation and Development (OECD). I

use annual Official Development Assistance (ODA) figures disaggregated at the donor-recipient

1 A related study by Dreher and Jensen (2007) finds that closer allies (measured by UNGA voting alignment) of the

Group of 7 (G7) countries were given fewer IMF conditions ahead of elections in the recipient countries.

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level, and converted to constant 2000 US dollars. Three conditions must be met in order for

foreign aid to qualify as ODA by the OECD's DAC: (i) the aid transfer must originate from the

official (government) sector;2 (ii) the primary goal of the aid should be to promote economic

development and welfare of the recipient country; and (iii) any loans must be made on

concessional terms and carry a grant element of at least 25 per cent.3 ODA includes flows to

recipient country governments and its institutions, non-governmental organisations, civil society

organisations and multilateral development organisations recognized by the DAC. It does not

constitute military aid, or any flows to or from private individuals.

Figure 1 shows the breakdown between bilateral and multilateral aid commitments.

Overall ODA commitments doubled from 60 billion dollars in 1975 to about 123 billion dollars

in 2007.4 Bilateral aid accounts for roughly 75 per cent of total aid commitments during this

period. I use data for total bilateral commitments, consisting of loans and grants, rather than

bilateral disbursements. Commitments are the more appropriate measure to address the

questions in this paper for several reasons. First, aid disbursements are more likely to reflect

both a donor’s willingness to make a foreign aid transfer, and the recipient government’s

decision to implement or ignore selected donor-approved projects. The response of aid

disbursements to changes in bilateral trade ties would capture both donor response and recipient

response, and makes inference on donor aid allocation behaviour less convincing. Other studies

(Berthélemy 2006; Berthélemy and Tichit 2004; Osei, Morrissey, and Lloyd 2004) also use aid

commitments as it more accurately reflects the donor’s aid allocation decision than do aid

disbursements. The second reason for using commitments has to do with the timing of elections

in recipient countries. To test whether donors adjust aid flows ahead of elections, the variable

for the aid allocation decision must be determined before the elections. While both the lag of aid

disbursements and contemporaneous aid commitments satisfy this property, the problem of

disbursements reflecting both donor and recipient decisions is arguably more acute ahead of

elections.5 In addition, commitments for a given year are announced at the beginning of that

year, with the budget process already underway the previous year. Thus commitments for a

2 Official aid organisations include, for instance, the USAID for the United States, the DFID for the United

Kingdom, and SIDA for Sweden. 3 See OECD (2010) for detailed definitions. 4 ODA amounts in Figures 1 and 2 are in constant 2007 US dollars. 5 See Alam (2012) for a model of how donor-recipient government interactions determine aid disbursements ahead

of elections.

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given year will more accurately reflect election year concerns of the donor country, even for

elections held in the first quarter of the year.6

Figure 2 shows the composition of the OECD DAC’s bilateral aid commitments by a

selected group of donor countries from 1975 to 2008. Ten out of twenty-three7 members account

for approximately 80 per cent of total bilateral commitments each year. The United States is the

largest contributor by aid volume, and accounts for about 25 per cent of total DAC

commitments. The other leading donors are France and Germany (together contributing about

25 per cent), Japan (10 to 20 percent) and the United Kingdom (5 per cent). The Netherlands and

four Scandinavian countries – Denmark, Finland, Norway and Sweden – together contribute

another 10 to 15 percent of total DAC ODA commitments. Based on these numbers I focus on

the aid allocation behaviour of the five largest donors that are most likely to have political and

strategic considerations: France, Germany, Japan, the United Kingdom, and the United States.8

The primary source for bilateral trade data is the International Monetary Fund’s Direction

of Trade Statistics (DOTS) (2010) for the period 1975 to 2008. The database separately reports

both exports and imports between any two countries. I construct two different measures of

bilateral trade ties. The first is the trade intensity of a donor-recipient pair relative to the donor’s

total trade volume, and reflects the importance of the recipient as a trading partner for the donor.

This measures the importance of trade ties from the donor’s perspective, and I use it to test

whether trade links with the recipient drives donor aid allocation. The second measure gauges

the importance of trade ties from the recipient perspective, and is given by the recipient’s exports

to the donor as a share of the recipient country’s total exports. This is in effect an indicator of

the importance of the donor as an export destination for the recipient. I use this measure to test

whether donors view foreign aid as a substitute for giving market access to recipient countries

exports. Table 2 summarizes the variables used in the study and their sources, and Table 3

reports the summary statistics for ODA and trade and export shares by donor.

6 Between 13 and 15 per cent of the election events in the sample occurs in January and February for each donor,

and only about 4.5 per cent occur in January. 7 Number of DAC members increased to twenty-four in January 2010 with the inclusion of South Korea. 8 China has also become a major aid donor, as estimated by several studies (McCormick 2008; Walz and

Ramachandran 2010; Woods 2008), and higher estimates put Chinese bilateral aid amounts close to US bilateral aid

(Lum 2009). However, China and some other new emerging market aid donors such as India do not report their aid

figures to the OECD's DAC.

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Election and political competitiveness variables for the recipient countries are from the

World Bank Database of Political Institutions (DPI) by Beck et al. (2001) and updated by Keefer

(2009). The DPI 2009 version has data available from 1975 to 2008, and this limits the time

period of my analysis. The database reports figures for both executive and legislative elections,

but I use only executive elections as donor-recipient negotiations on aid and other strategic

considerations are more likely to depend on the identity of the head of state rather than the

recipient’s constituent domestic legislature. The measure of political competitiveness, on the

other hand, encompasses competition in both the executive and legislative branches. I expect

that the donors’ perception of the degree of democracy in a recipient country will be determined

by the overall political process, and not only the part of the process that elects the chief

executive.

Other demographic controls such as GDP, population, and infant mortality are from the

World Bank’s World Development Indicators (2009) and are listed in Table 2.

3 Empirical Strategy

This paper uses a simple regression framework to study the effect of donor-recipient

trade ties on ODA flows. The regression specification includes both measures of trade ties: the

donor’s trade interests and the recipient’s trade interests. Equation (1) outlines the baseline

model for estimating the effect of the donor-recipient bilateral trade relationship on ODA flows:

!

ODAdrt = "1Tradedrt#1 + "

2Exportdrt#1 + $ X drt#1b + µrt + %drt . (1)

The variables of interest Tradedrt-1 and Exportdrt-1 are one year lagged time varying measures

that capture donor trade interests and recipient trade interests respectively for each donor (d) and

recipient (r) pair. The coefficient

!

"1 gives the effect of the donor trade interests on foreign aid

flows and

!

"2 gives the effect of the recipient trade interests.

The first measure of bilateral trade ties denoting donor trade interests, Trade, is the

volume of bilateral trade between a donor-recipient pair as a share of the donor’s total trade

volume in a given year. It captures the importance of the recipient as a trading partner to the

donor, and is calculated as:

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!

Tradedrt =Exportdrt + Importdrt

Total Exportdt + Total Importdt,

where Exportdrt is the volume of recipient exports to the donor and Importdrt is the volume of

recipient imports flowing from the donor in year t . Total Exportdt is the donor's total export

volume to all countries and Total Importdt is its total imports during the same time period. Thus

using the above measure, equation (1) tests whether the donor’s share of trade with a recipient in

year t–1 affects ODA flowing from the donor to the recipient in year t. This may have a positive

or negative effect on aid flows as explained below and the final sign on the coefficient

!

"1 will be

an empirical balance.

Donor trade interests can influence foreign aid flows in a number of ways. The donor

may wish to financially support a country that is economically important because it is an

important export market. Tied aid falls into this category, but the causality would go from aid to

trade in this case, with the donor requiring a specified portion of the aid be used in purchases

from the donor itself.9 Alternately, the donor may want to provide financial assistance to a

country from which it imports heavily, especially if the imports are strategically important. The

donor may, on the other hand, simply wish to reward aid recipient countries with which it has

better relations through larger trade ties. Each scenario above is framed in terms of the donor’s

trade interests, and stronger trade ties with a recipient should elicit larger aid flows to the

recipient with

!

"1 being positive.

On the other hand, larger volumes of exports to a recipient may signal an increasing

ability of the recipient country to purchase goods and services, and the donor may decide to

reduce aid. This case is the antithesis of tied aid, and the coefficient on Trade ties will be

negative. We should also expect to see a negative relationship between trade and aid if the donor

sees the two as substitutes, giving less aid to countries with which it trades more. While this

measure captures the donor’s trade interests, it does not distinguish between whether a recipient

is important to the donor via its exports or its imports.

9 Previous studies debating whether aid leads to trade (the basis for tying aid) or whether causality runs in the other

direction find more evidence for the latter case (Osei, Morrissey, and Lloyd 2004; Lloyd et al. 2000).

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The second measure, Export, captures the importance of the donor as an important

export destination for the recipient, and thus reflects the recipient’s trade interests. It is the share

of a recipient’s total exports that flows to the donor:

!

Exportdrt =Exportdrt

Total Exportrt,

where Exportdrt is the recipient’s exports to the donor in year t and Total Exportrt is the

recipient’s total exports for that year. If market access and aid are substitutes, then this measure

of bilateral trade ties will have a negative impact on aid flows, and

!

"2 will be negative. Donors

that have small foreign aid budgets, such as the United Kingdom, are more likely to adopt this

aid allocation strategy. Note that in considering market access and aid as substitutes, the donor is

taking into account the recipient’s perspective by considering whether the donor is an income

source for the recipient country.

The specification in equation (1) assumes that donors base their aid allocation decisions

for year t on trade data for only the previous year. A priori, we can’t rule out the possibility that

historical trade relationships spanning a longer time horizon also matter: trade relationships may

differ from one political regime to another if one government regime is more open to trade than

another. Indeed in their study of politically motivated foreign aid Faye and Niehaus (2010) find

that regimes that are more politically aligned with the five largest bilateral donors receive more

aid from these donors.

To account for the possibility that regimes are important, for each of the two trade

measures discussed I calculate the bilateral trade ties over two time horizons: (i) a year-to-year

measure already described; and (ii) a regime-specific measure where a regime indicates the

period over which a single head of state is in power. For each year, I calculate the cumulative

bilateral trade ties starting from a recipient country regime’s first year in power.10 I use data

from the World Bank’s DPI (2009) to identify the length of time a single head of state (regime)

is in power in the recipient country. I test the baseline model using both the year-to-year and

regime-specific measures of donor’s bilateral trade ties listed above. The regime-specific

bilateral trade measure captures the idea that the head of state matters (Fisman 2001), with some

10 All trade figures are deflated to constant 2000 US dollars using the donor’s GDP deflator for the Trade share

variable, and using the recipient’s GDP deflator for the Export share variable.

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regimes being more open to trade than others. We can reinterpret the discussion above in terms

of whether donors with larger trade ties to regimes see bigger or smaller foreign aid flows, and

whether regimes that export more to a donor country receive more or less aid.

If regimes are important, then donors will also care about elections. There are several

reasons to expect aid commitments during election years to be different than those observed in

non-election years. Faye and Niehaus (2010) show that donors give more aid to closely aligned

regimes facing competitive elections, and argue that the donor may try to influence the election

probabilities. On the flip side, if the government in power in the aid recipient country is not

aligned with the donor in terms of political or economic interests, the donor may withhold aid or

impose increased conditionality on aid to hamper that regime’s chances of winning the elections.

But donors could also scale back foreign aid commitments ahead of elections if they fear the

recipient government will divert funds from donor-prioritized investment projects to more

conspicuous consumption spending such as roads or food subsidies. Regimes that enjoy close

economic and political ties with donor governments may see a smaller or no decrease in aid. Of

course there are other reasons for expecting election year changes in foreign aid flows. Donors

claim that they reward countries with more democratic institutions. To the extent that having

elections signals a more democratic regime, the effect of an election event on foreign aid should

be positive.

In the next set of regressions, I look at differences in election year effects between donor

and recipient pairs on the basis of their trade ties. I extend the baseline model to include political

variables, and test for the effects of elections and political competitiveness by using an empirical

strategy similar to a difference-in-difference estimate:

!

ODAdrt = "1Tradedrt#1

+ "2Exportdrt#1

+ "3EXECrt + "

4EXECrt $Tradedrt#1

+ "5EXECrt $ Exportdrt#1

+ "6PCOMP + "

7PCOMP $ EXECrt

+ "8PCOMP $Tradedrt#1

+ "9EXECrt $PCOMP $Tradedrt#1

+ "10PCOMP $ Exportdrt#1

+ "11EXECrt $PCOMP $ Exportdrt#1

!

+ " X drt#1

b + µrt

+ $drt

(2)

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where EXECrt is an indicator variable that takes a value of 1 during an election year in the

recipient country and is 0 otherwise,11 and PCOMPrt-1 is an indicator variable for political

competition in the recipient country. I follow Block (2002) and construct the political

competitiveness variable from two electoral competition variables in the DPI 2009 dataset, the

Legislative Indicator of Electoral Competition (LIEC) and the Electoral Indicator of Electoral

Competition (EIEC). The DPI (2009) ranks competitiveness on a scale of 1 to 7 as described in

Table 4, with 1 being not competitive. PCOMPrt-1 is equal to 1 if the score on either the EIEC or

the LIEC measure is 6 or higher, and it is 0 otherwise. Table 5 presents the number of elections

year events faced by each donor for the in-sample dataset, and the numbers are further broken

down by whether the regimes are politically competitive or not. Election year events represent

roughly 10 per cent of the observations for each donor.

The signs of the coefficients in equation (2) are difficult to predict. Aid ahead of

elections may be increased or decreased depending on whether a donor favours the incumbent

party, or whether the donor wants to support the electoral process in general. If donors indeed

reward democracy, then the coefficient on political competition, and possibly its interactions,

will be positive.

One concern with the specifications above is the presence of some time invariant donor

specific, recipient specific, or a donor-recipient pair specific bilateral relationship that determines

the level of aid flows. Previous studies have shown that bilateral relationships such as a common

language and former colonial ties (Rajan and Subramanian 2008), or strategic alliances as in the

case of the United States with Israel and Egypt (Kuziemko and Werker 2006), are good

predictors of aid flows. Separating the donor-recipient fixed effects (!dr) from the time varying

component of the error term (!drt) alleviates this concern, and I can use the time varying trade,

election and other control variables to study the time varying component of foreign aid flows.

A second concern is the pooling of all the donors into one fixed effects estimation, as this

measures the average of different aid-giving strategies by different donors. The size of the

dataset allows me to run the estimation described above for each donor separately with recipient

fixed effects, time fixed effects and time trends. I can therefore compare the United States’ aid-

giving behaviour to that of the United Kingdom or Japan. In these estimations, the unit of

11 These are executive elections only, and do not include legislative elections.

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observation is a recipient-year pair and I use recipient fixed effects to capture the time invariant

determinant of foreign aid flows.

Finally, Xdrt-1 is a vector of time varying donor- or recipient-specific demographic control

variables consisting of both donor and recipient GDP and population and recipient country infant

mortality.

4 Results

4.1 Are donor and recipient trade interests associated with ODA

commitments?

In the baseline model given by Equation (1), I test the effect of the donor’s bilateral trade

ties and recipient's export share on foreign aid flows using a pooled panel dataset with the five

largest donors: France, Germany, Japan, the United Kingdom and the United States. Recall that

Tradedrt measures the share of the donor’s total trade with the recipient. If donors tie aid to trade,

then we should expect to see aid positively related to donor-recipient trade ties. If trade and aid

are substitutes, then we should see a negative relationship. Moreover, if donors substitute market

access with aid, then the coefficient on recipients' Export share to the donor will be negative.

However, each donor may have a different aid giving strategy and it is difficult to predict the

signs of the coefficients on donor and recipient trade interests,

!

"1 and

!

"2 respectively.

Table 6 presents the results for the baseline estimation with both the year-to-year and the

regime-specific measures. All columns have donor-recipient pair fixed effects and accordingly

clustered robust standard errors. They also contain the full set of demographic controls for both

donors and recipients.12 The coefficient on the Trade variable is positive, but just significant at

the 10 per cent level and only for the Year-to-Year measure in the regression with time fixed

effects in Column (2). The coefficient on the Export variable is not significant at all. In a panel

of five donors, increased Trade ties with a recipient are weakly associated with higher ODA

flows, but recipient Export shares are not important.

Turning towards the demographic characteristics, recipients with higher real GDP receive

less aid. This is consistent with at least one of the elements of poverty targeting strategies and

12 In the interest of clarity I do not report the fixed effects estimation without controls.

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what we would expect of aid recipient characteristics. Also in keeping with intuition, donors

with higher incomes give more aid. However, the coefficient on infant morality is not significant

or positive as expected, though it may be negatively associated with recipient GDP. Finally,

neither donor nor recipient population size are systematically associated with foreign aid

commitments.

4.2 Do donors differ in ODA allocation strategies?

I now turn to assessing the aid allocation strategies of individual donors. The results

above imply that all five donors in the pooled estimation behave in the same way on average. In

particular, the pooled regression results in Table 6 suggest that Trade is only weakly associated

with ODA commitments from France, Germany, Japan, United Kingdom and the United States.

The results also suggest that there is no relationship between Export shares and ODA

commitments from these donors. But as explained before, pooling the donors together can mask

donor-specific effects.13 If one donor exhibits a positive relationship between aid and Export

while another donor has a negative relationship, these two effects may cancel each other out in

the pooled estimation. It may also be the case that the results observed are driven by one or two

donors in the pool. In the first instance we would incorrectly infer that there is no relationship

between ODA commitments and bilateral trade ties for any of the donors, and in the second

instance we would incorrectly infer that all five donors exhibit the observed relationship. The

purpose of the following set of regressions is to disentangle any such opposing effects.

Table 7 presents the estimation results for equation (1) for each separate donor. I focus

on the regime-specific measures here to keep the discussion tractable. All columns contain

recipient fixed effects, and the even numbered columns also contain year fixed effects. While all

columns contain donor and recipient demographic controls, smoothed real GDP and populations

using logs for France, Germany, UK and USA cause collinearities with year dummies and the

regression drops the Donor Real GDP and Donor Population variables.

The results indicate that the positive coefficient on the Trade variable earlier was being

driven by the positive relationship for the US. United Kingdom exhibits a negative relationship

13 See Osei, Morrissey, and Lloyd (2004) for a discussion on problems with pooled data. While they also stress that

pooling recipients is a problem, I try to identify differences in recipient characteristic that relate to systematic

differences in ODA commitments.

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between Trade and ODA. However, this effect is lost in the pooled estimation as the volume of

ODA flows from the US is typically about fives times that of the UK. Thus the US gives higher

ODA commitments to countries with which it trades more, whereas the UK pledges decreased

aid commitments to countries with which it trades more. The relationship between Export share

and ODA commitments is positive and large relative to the standard errors for Japan and UK,

although it is only significant at the 10 per cent level for Japan in the specification without year

fixed effects in Column (5). Therefore countries that send a higher share of their exports to

Japan receive higher ODA commitments.

ODA commitments are not related to bilateral trade ties for either France or Germany.

Past studies have found that donors generally give more aid to former colonies, and this is

especially true for France (Rajan and Subramanian 2008). If France trades more with former

colonies, then we would expect to see a positive relationship between aid and bilateral trade ties.

In that case the reason we don’t see this effect in the results may be owing to the recipient fixed

effects picking up any effect of colonial ties.

There are also variations from the pooled estimate for the demographic controls. The

negative relationship between ODA commitments and recipient real GDP no longer hold for

Germany and Japan. Increases in recipient population size are associated with lower ODA

commitments from France but larger commitments from the US. Surprisingly, France's ODA

commitments decrease as its real GDP increases. Increases in donor population are related to

higher ODA commitments from France and Japan, and lower commitments from Germany and

the US.

Thus donors differ not only in how their aid allocation relates to trade interests, but also

in the association of aid with donor and recipient income and populations. Given the differences

in donor aid allocation, I focus on a disaggregated analysis of donors in the next two sections.

4.3 Do ODA commitments differ during election years?

So far the analysis has focused on the relationship between aid allocation and trade

interests, and the importance of distinguishing between aid allocation strategies of different

donors. From this point onwards, I look at whether political factors, in addition to trade interests,

are important in shaping foreign aid flows. Elections can matter for several reasons as discussed

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before. With a reduced tolerance towards aid fungibility and the emphasis on conditionality,

donors may fear that recipient countries will misspend aid money or ignore donor-priorities

ahead of elections, and instead focus on visible fiscal spending to boost their chances of winning

the election14. This is of particular concern in countries with weak institutional constraints (Shi

and Svensson 2002). Consequently donors may cut aid commitments ahead of elections. On

the other hand, if donors care about the government regimes holding power in recipient

countries, they may have an interest in election outcomes. To the extent that donors believe

ODA flows can help or hinder election results, and if they further want to affect election

outcomes, donors may withhold or augment foreign aid flows ahead of elections.

In order to test whether election events are important for donors I test whether aid

allocation is different in election years compared to non-election years in recipient countries. I

further ask whether donors distinguish between recipients in whom they have greater trade

interests and recipients for whom they form a larger export market. To this end I estimate a

reduced form of equation (2) given by

!

ODAdrt = "1Tradedrt#1

+ "2Exportdrt#1

+ "3EXECrt + "

4EXECrt $Tradedrt#1

+ "5EXECrt $ Exportdrt#1

+ % X drt#1b + µrt + &drt

, (3)

where

!

"4 and

!

"5, the coefficients on the interaction terms, capture the differential effect of

elections on ODA commitments for the closer trade partners. Table 8 presents the results for the

five donors using the regime-specific measures of Trade and Export.

While the coefficient on the EXEC elections dummy,

!

"3, is not significant, some of the

interaction terms are significant. There are no election year effects for France and Japan, and the

results do not change from the estimation without elections. For Germany,

!

"4

< 0 and

significant at the 1 per cent level, indicating that during election years countries with which

Germany trades more receive lower ODA commitments. The Trade variable in both Columns

(7) and (8) for the UK is now negative and significant, compared to just Column (8) of Table 7 in

the previous section. In addition, the interaction of elections with Export share is negative

!

"5

< 0

and significant at the 10 per cent level, indicating that countries that export more to the UK can

14 Schuknecht (2000) finds that expansionary fiscal policies ahead of elections are mainly due to increased spending

rather than lower revenues.

16

expect to see a fall in ODA commitments during election years. The Trade, Export and election

variables however fail the test of joint significance, but just barely in the estimation with year

fixed effects (p-value = 0.105). Finally, the Trade variable is still positive and significant for

the US, but in addition during election years countries with which the US trades more see a

decrease in foreign aid commitments

!

("4

< 0) . This last result is significant only for the

estimation without year fixed effects in Column (9) and then only at the 10 per cent level.

Moreover, the Trade, Export and election variables fail the test of joint significance (p-value =

0.376). Adding year fixed effects in Column (10) however makes these variables jointly

significant (p-value = 0.053).

The results in this section thus indicate the presence of election year cycles in ODA

commitments for some of the donors, namely Germany, the UK and the US. It is difficult

however to be able to say anything about the motivations behind these cycles. In other words, if

a donor cuts aid allocation during election years, is it because the donor anticipates misuse of

funds or because it does not want to enhance the incumbent government's probability of winning

the election, or is such behaviour perhaps motivated by some other reason? Moreover, are

elections in countries with a de facto democratic system viewed in the same light as elections in

countries that are merely democracies de jure where elections don't matter? If not, then donors

may have different aid allocation strategies for countries with politically competitive and those

with non-competitive electoral processes, and I investigate this angle in the next section.

4.4 Do ODA commitments differ for politically competitive regimes?

In this section I look at whether political competitiveness of recipient regimes matters for

aid allocation, and whether donors treat politically competitive election years differently than

non-competitive ones. If donors fear misuse of funds, then it is worthwhile to note that political

budget cycles marked by increases in fiscal spending are exacerbated ahead of closely contested

elections (Block 2002). On the other hand, Faye and Niehaus (2010) suggest that the US plays a

more active role in politically competitive elections by increasing aid flows to regimes with

which they are more politically aligned to influence election outcomes.

The results in the previous sections suggest that the aid allocation strategies of some

donors are sensitive to regime-specific trade relationships and to election years. If a donor with a

17

positive aid-trade relationship fears misallocation of aid money and cuts aid ahead of elections, it

will likely cut less for the regimes with close trade ties. If it is trying to influence elections, it

should increase aid to closer trading partners facing politically competitive elections. Donors

claim that they reward more democratic countries with more aid, thus more politically

competitive regimes should also see increased foreign aid commitments.

I test the effect of election and political competitiveness in the framework of donor and

recipient trade interests by estimating equation (2). While the purpose of this exercise is to

identify differences in aid allocation strategies when present across donors, I first report the

results of the pooled panel of donors to serve as a benchmark.

Table 9 presents the results of the pooled estimate for both the year-to-year and regime-

specific measures. As before (Table 6), Trade is positively associated with ODA commitments

but is significant only for the year-to-year measure run with year fixed effects in Column (2); and

the coefficient on Export is not significant. However this is the case for the non-competitive

regimes in the estimation of equation (2). We further see that for non-competitive regimes,

donors decrease aid flows during election years for close Trade partners

!

"4

< 0( ) , but regimes

that export more to the donor countries can expect to see an increase in ODA commitments

!

"5

> 0( ) . The last result is significant only with the year-to-year measure.

Politically competitive regimes receive higher ODA commitments than non-competitive

regimes

!

"6

> 0( ) , and experience no election year effects

!

"7

= 0( ) unless mediated by donor or

recipient trade interests. There is no additional effect of political competitiveness on ODA

commitments for countries with which the donors trade more

!

"8

= 0( ) , but there is a differential

positive effect on ODA commitments for these countries during election years

!

"9

> 0( ) . For

countries that trade more, the total effect of elections on politically competitive countries is given

by

!

"4

+ "9. While this may be positive, zero or even negative, any negative effect of elections on

ODA commitments will still be necessarily less for politically competitive countries than the

negative effect faced by non-competitive countries during election years15. Finally politically

competitive countries with larger Export shares receive higher aid commitments

!

"10

> 0( ) but

there is an additional negative effect during election years

!

"11

< 0( ) . Relative to their non-

15 Since

!

"9

> 0 ,

!

"4

+ "9

> "4

.

18

competitive counterparts who receive higher commitments during election year, all else equal

politically competitive regimes see no such change

!

"5

+ "11( ) .

Next, I run identical estimations testing differential effects of elections and trade by

political competitiveness for each of the five donors: France, Germany, Japan, UK and USA.

To keep the discussion tractable I summarize the results for these estimations using the regime-

specific measures of Trade and Export in Table 10, presenting only the signs of the coefficients

under investigation and their significance16. To allow for easier comparison, I also include the

summarized results from Columns (3) and (4) of Table 9 for the pooled panel discussed above.

As expected, individual donor behaviours are either muted or driven by only a subset of

the donors in the pooled estimation. Mirroring the pooled regression result however, neither the

elections dummy by itself nor its interaction with the political competitiveness indicator is

related to foreign aid flows for any of the donors. The positive coefficient for political

competitiveness seen in the pooled regression reflects the behaviour of only some of the donors:

Germany, Japan and the UK favour political competitiveness

!

"6

> 0( ) . The donors otherwise

exhibit very different aid allocation behaviours, and for the remainder of the section I will

document these behaviours.

France

None of the economic or political variables or their interactions is significant for France.

As was mentioned before, France's aid allocation strategies may be driven for the most part by

links to its former colonies and the recipient country fixed effects used in the empirical model

should capture this effect.

Germany

ODA commitments from Germany are positively associated with political

competitiveness. Thus a country that has a competitive electoral system will receive larger aid

commitments on average than one with a non-competitive regime. Germany also exhibits a

weak election year effect, with ODA commitments to non-competitive regimes with which it

16 See Tables B1 through B5 in Appendix B for the regression results for France, Germany, Japan, UK and USA.

19

trades more falling during election years

!

"4

< 0( ) . There are no differential effects for politically

competitive regimes beyond that given by

!

"6.

Japan

Japan too favours political competitiveness

!

"6

> 0( ) . However, if Japan trades more with

these politically competitive countries, the latter can expect to see a decrease in ODA

commitments

!

"8

< 0( ) . Together this suggests that for politically competitive countries, Japan

treats trade and aid as substitutes. While the remainder of the interactions don't have significant

coefficients, tests of linear combinations of the trade and political variables and their interactions

reveal other nuances in Japan's aid allocation behaviour.

Table 11 reports the signs and levels of significance for the tests of linear combinations

of the relevant interactions and correspond to the regressions summarized in Table 10. Even

though the coefficient on Exports,

!

"2, and none of its interaction terms are significant, linear

combination of coefficients given by Export + PCOMP*Export

!

"2

+ "10( ) is positive and

significant for the estimation without year fixed effects. Thus politically competitive countries

that export more to Japan receive higher aid commitments. Moreover, the additional effect of

having elections, given by the linear combination (10)

!

"5

+ "11

< 0( ), is negative and indicates

that these countries see a cut in foreign aid during election years. Thus Japan exhibits election

year cycles in foreign aid commitments.

Note that the effects of Trade and Export interacted with political competitiveness go in

opposite directions, with politically competitive countries that Trade more receiving lower aid

commitments. This suggests that Japan differentiates between its exports and imports when

considering foreign aid allocation decisions.

United Kingdom

Table 10 shows that the UK gives more aid to politically competitive countries than non-

competitive countries

!

"6

> 0( ) . Similar to the case of Japan, politically competitive regimes with

which the UK trades more see lower aid commitments

!

"8

< 0( ) . Thus the UK too treats trade and

aid as substitutes for politically competitive countries. The additional effect of having elections

for politically competitive countries is positive

!

"9

> 0( ) . Table 11 indicates that despite total

20

positive effect of elections on ODA commitments

!

"4

+ "9

> 0( ) , the negative interaction of

political competitiveness with Trade

!

"8

< 0( ) implies that ODA commitments for politically

competitive countries that trade more with the UK still fall during election years

!

"1

+ "4

+ "8

+ "9

< 0( ) . This decrease however is less than that during non-election years.

Finally, politically competitive countries that export more to the UK see increases in

ODA commitments

!

"10

> 0( ) . Thus UK too appears to distinguish between its imports and

exports with relation to ODA allocation decisions.

Trade share and Export share don't affect ODA allocation to non-competitive countries

for the UK. Nor do non-competitive countries experience election year changes in ODA

commitments.

United States of America

In contrast to Germany, Japan and the UK, the US does not give more aid to countries

with competitive electoral systems. Instead it increases ODA commitments to non-competitive

regimes with which it trades more

!

"1

> 0( ) . While the additional impact of political

competitiveness is negative

!

"8

< 0( ) , the test of linear combination in Column (5) of Table 11

reveals that there is no overall association between trade and ODA commitments for politically

competitive countries

!

"1

+ "8

= 0( ) . The US also exhibits additional election year effects. ODA

commitments to non-competitive regimes with which it trades more fall during election years

!

"4

< 0( ) , though politically competitive regimes see no such decrease

!

"4

+ "9

= 0( ) .

Next, the non-competitive regimes that export more to the US receive less foreign aid

!

"2

< 0( ) , suggesting that the US uses market access and aid as substitutes. This effect disappears

during election years

!

"2

+ "5

= 0( ). Although the additional effect of political competitiveness on

countries exporting more to the US is positive

!

"10

> 0( ) , the ODA commitments to politically

competitive regimes US are unaffected by exports

!

"2

+ "10

= 0( ) .

Thus the US shows election year cycles in ODA allocation. In general it allocates more

aid to non-competitive regimes with which it trades more, but cuts their aid during election

years. The US also treats market access and aid as substitutes for non-competitive countries, but

this substitution effect disappears during election years.

21

5 Robustness

The bulk of the analysis thus far has focused on the relationship between ODA

commitments and donor and recipient trade interests using the regime-specific measures for the

Trade and Export variables. However, I also run the same estimations using the more

conventional year-to-year measures described before. While the differences in trade and export

shares calculated using the year-to-year measure is typically small, especially for regimes with

shorter time spans, the difference can increase for longer lasting regimes. We would expect

ODA allocation behaviour to be different for the year-to-year measure if a donor responds more

to recent (previous year) changes than changes in the general orientation of bilateral trade ties of

the regime.

Table 9 already reported the results for the estimation of equation (2) for both the year-to-

year and regime-specific measures for the pooled panel of donors. Tables B1 through B5 in

Appendix B report the corresponding results for the five bilateral donors: France, Germany,

Japan, the UK and the US. The central results for the year-to-year estimation remain unaltered

from those of the regime-specific estimation for the most part. The two notable differences are

in the case of France and Germany.

Recall that France showed no relationship between ODA commitments and any of the

Trade, Export, election or political competitiveness variables and their interactions. With the

year-to-year measure however, the coefficient on the interaction term PCOMP*Export becomes

more negative and acquires significance. Thus for politically competitive countries, France now

treats market access and ODA as substitutes. The trade and political variables however fail the

test of joint significance in both cases (p-values are 0.618 and 0.125).

In Germany's case, using the year-to-year measures of trade ties causes the coefficients

on the interaction terms for EXEC*Trade to become significant for the specification with fixed

effects as well17. Another change is that the coefficient on the interaction term EXEC*Export

increases in magnitude and, for the specification with year fixed effects, becomes significant at

the ten per cent level. Thus during election years Germany lowers ODA commitments to

17 Using the regime-specific measure the coefficient was significant only for the specification without year fixed

effects. The sign is consistently negative throughout.

22

countries with which it trades more, but increases its aid commitments to countries which send a

greater share of their exports to Germany.

6 Summary and Discussion

Using a panel dataset of five aid donors and over a hundred recipients, I document

several empirical relationships between donors' ODA commitments and their regime-based

bilateral trade ties with aid recipient countries. I demonstrate that the nature of these

relationships varies from donor to donor, and that donors exhibit election year cycles in ODA

commitments. I further demonstrate that donors distinguish between countries that have

politically competitive electoral systems and those that do not.

Germany showed a positive association between political competitiveness by itself and

ODA commitments. Germany cuts aid to non-competitive regimes during election years, and

this result is stronger for the year-to-year measure. Using this measure I also find that countries

that export more to Germany experience an increase in ODA commitments during election years.

Japan and the UK also favour politically competitive regimes, but give less aid to these countries

if they account for a larger share of total trade with the respective donor. Thus trade and aid

appear to be substitutes in the case of politically competitive recipients. However, this

substitution of trade with aid is mitigated during election years for UK aid recipients. Both

countries also reward countries that export more to them with higher ODA commitments, but

Japan cuts aid to these countries during election years.

Finally the US pledges more aid to non-competitive regimes with which it trades more

but cuts their aid during election years. It also treats market access and aid as substitutes for

non-competitive regimes. During election years however, countries that export more to the US

do not see this substitution effect and so don't experience the decrease in aid as in non-election

years. ODA flows to politically competitive regimes are not associated with trade relationships

or election year events for the United States.

France is the only country in the study for which there was no relationship between ODA

commitments and regime-specific trade or political variables. With the year-to-year measure of

trade ties however, France treats aid and the market access as substitutes, but only for politically

competitive countries.

23

The goal of this study was to establish some empirical links between aid flows and

interests other than poverty reduction in order to inform the aid effectiveness debate. To this end

I demonstrated how donor trade interests, election events and political competitiveness of

recipient countries are associated with foreign aid commitments from the five largest bilateral aid

donors in the OECD's DAC. Some aid allocation strategies such as substituting aid for market

access and rewarding political competitiveness appears reasonable in supporting development

goals. The implications for development of other strategies are not so clear, for instance the US'

giving more aid to countries with non-competitive regimes if they are larger trading partners.

Along with Germany, Japan and the UK, it also exhibits political aid cycles around election

years. It is difficult to ascertain whether such responses in foreign aid flows help or hinder

economic development. Donor governments may after all be trying to either influence election

outcomes so that a better leader comes to power, or trying to ensure that aid money is not mis-

spent by cutting aid during election years. On the other hand, donors may be strategic about

their own interests.

Further work needs to be done to identify the political and non-political motivations that

drive foreign aid flows. For instance, using closeness of elections as a measure for political

competitiveness will better identify instances of when the donor tries to influence the election

outcome. A measure of the electoral process better proxies a country's degree of democracy and

can be used to gauge whether donors reward democracy. Another venue of research is to explore

the motivations of multilateral aid flows. While multilateral aid is considered to be more in line

with poverty-targeting, the biggest financiers of multilateral aid organisations are the bilateral

donors, and it will be informative to test whether multilateral aid is also influenced, at least in

part, by donors' political motivations and if they exhibit election year cycles.

It is important that any study on aid effectiveness should account for the different

purposes of foreign aid. Assuming all foreign aid flows are for development purposes when it is

not will no doubt show that aid is ineffective. Understanding the donor driven political and

economic motivations behind foreign aid allocation will prepare policymakers in reforming the

foreign aid process, with the hope that it will eventually help identify effective and non-effective

channels of aid.

24

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26

Appendix A Data Summary and Results

Table 1 List of Recipient Countries

Albania Guinea-Bissau Saudi Arabia

Algeria Guyana Senegal Armenia Haiti Sierra Leone

Azerbaijan Honduras Slovenia

Bahrain India South Africa Bangladesh Indonesia Sri Lanka

Barbados Iran St. Lucia

Belize Israel Sudan Benin Jamaica Syria

Bolivia Jordan Tajikistan

Bosnia-Herzegovina Kazakhstan Tanzania

Brunei Kenya Thailand Burkina Faso Korea Togo

Burundi Kuwait Trinidad and Tobago

Cambodia Kyrgyz Republic Tunisia Cameroon Laos Turkey

Cape Verde Lebanon Turkmenistan

Central African Republic Macedonia, FYR Uganda Chad Malawi United Arab Emirates

Chile Malaysia Uruguay

China Maldives Uzbekistan

Colombia Mali Venezuela Comoros Malta Yemen

Congo, Republic Mauritania Zambia

Costa Rica Mauritius Zimbabwe Cote d'Ivoire Mexico

Croatia Moldova

Cyprus Mongolia

Djibouti Morocco Dominican Republic Mozambique

Ecuador Nepal

Egypt Niger El Salvador Nigeria

Ethiopia Oman

Gabon Pakistan Gambia Panama

Georgia Papua New Guinea

Grenada Paraguay

Guatemala Philippines Guinea Rwanda

27

Figure 1 ODA Commitments by Donor Type (1975 – 2007)

Source: OECD (2009)

Figure 2 Share of Total DAC ODA Commitments (1975 – 2007)

Source: OECD (2009)

28

Table 2 Variable Description and Sources

Variable Description Sources

ODA Log of annual ODA total commitments - grants and

loans (constant 2000 US$ millions) OECD (2009)

Trade Lagged volume of donor-recipient trade as share of

total donor trade (%) IMF DOTS (2010)

Export Lagged volume of recipient exports to donor as share

of total recipient exports (%) IMF DOTS (2010)

EXEC Dummy variable for executive elections in recipient

country (= 1 for election year and 0 otherwise) World Bank DPI (2009)

PCOMP

Dummy variable for political competitiveness of the electoral process (= 1 for competitive process and 0

otherwise)

World Bank DPI (2009)

Real GDP Lagged log of Gross Domestic Product of recipient

(constant 2000 $US millions) World Bank WDI (2010)

Pop Lagged recipient population (millions) World Bank WDI (2010)

Infant Mortality Lagged infant mortality (deaths per 1000 births) World Bank WDI (2010)

Donor Real GDP Lagged donor Gross Domestic Product of trading

partner (constant 2000 $US millions) World Bank WDI (2010)

Donor Pop Donor population (millions) World Bank WDI (2010)

29

Table 3 Summary Statistics by Donor

Variable N Mean St. Dev Min Max

Pooled

ODA commitments 12335 65.29 251.53 0.01 15161.08

Donor Trade Share, Year-to-Year 12335 0.16 0.62 0 14.39

Donor Trade share, Regime-specific 12335 0.16 0.57 0 13.62

Recipient Export share, Year-to-Year 12335 5.92 9.80 0 115.16

Recipient Export share, Regime-specific 12335 6.24 9.61 0 83.09

USA

ODA commitments 2320 108.35 429.28 0.01 15161.08



Recipient Export share, Year-to-Year 2320 12.45 15.55 0 115.16 Recipient Export share, Regime-specific 2320 12.45 14.66 0 83.09

France

ODA commitments

2516 35.58 74.59 0.01 1328.69





Germany ODA commitments 2718 38.39 77.42 0.01 1290.04





Japan

ODA commitments 2663 116.13 329.21 0.01 4881.22





UK

ODA commitments 2118 24.01 84.80 0.01 2250.65





NOTE. ODA commitments are in millions of constant 2007 US dollars. Trade and Export shares are in

percentages.

30

Table 4 Legislative and Executive Indices of Electoral Competition

Criteria Score

No legislature 1

Unelected legislature 2

Elected legislature, 1 candidate 3

Elected legislature, 1 party, multiple candidates 4

Multiple parties are legal, but only one party won seats 5

Multiple parties did win seats but the largest party received more than 75% of the seats 6 Largest party got less than 75% of the seats. 7

Source: Reproduced from Keefer (2009)

Table 5 Incidence of Elections by Donor and Political Competitiveness

Political Election Year

Donor Competitiveness No Yes N

Non-Competitive 720 76

Competitive 1506 214 France

All 2226 290 2516

Non-Competitive 809 83 Competitive 1603 223 Germany

All 2412 306 2718


Competitive 1573 221 Japan

All 2360 303 2663


Competitive 1280 183 UK

All 1863 255 2118


Competitive 1410 206 USA

All 2041 279 2320

31

Table 6 Pooled ODA Commitments, Donor Bilateral Trade Ties and Recipient Export Shares

1975-2008

Dependent variable is log ODA commitments

Year-to-Year Regime-Specific

(1) (2) (3) (4)

Tradet-1 0.059 0.113* 0.060 0.095

(0.061) (0.068) (0.071) (0.074)

Exportt-1 0.004 0.002 0.008 0.005

(0.004) (0.004) (0.006) (0.006)

Real GDPt-1 -0.029 -0.053*** -0.030 -0.053***

(0.019) (0.020) (0.019) (0.020)

Popt-1 -0.327 0.500 -0.298 0.485 (0.242) (0.352) (0.244) (0.352)

Infant Mortt-1 -0.001 -0.001 -0.001 -0.001

(0.001) (0.002) (0.001) (0.002)

Donor Real GDPt-1 0.429*** 0.804*** 0.425*** 0.794***

(0.096) (0.135) (0.097) (0.136)

Donor Pop -1.066 0.315 -1.072 0.285

(1.046) (1.120) (1.050) (1.123)

Year -0.030*** -0.028**

(0.011) (0.011)

Year FE's N Y N Y

Observations 12370 12370 12335 12335

R-squared 0.012 0.037 0.012 0.037

Number of donor-recipient pairs 501 501 501 501

Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0.247 0.180 0.162 0.228

NOTE. FE = fixed effects. All regressions are FE estimations. All columns include country fixed effects. Year

dummies are not shown. P-values are reported for the test of joint significance of bilateral Trade Share and Export

Share variables. Robust standard errors are in parentheses and clustered at donor-recipient pairs, *** p<0.01, **

p<0.05, * p<0.10.

32

Table 7

ODA Commitments, Donor Bilateral Trade Ties and Recipient Export Shares by Donor

1975-2008

Regime-Specific


FRA DEU Japan UK USA

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Tradet-1 0.066 0.066 0.098 0.194 -0.019 0.007 -0.760 -0.889* 0.293* 0.300**

(0.176) (0.186) (0.376) (0.397) (0.100) (0.099) (0.478) (0.490) (0.156) (0.117)

Exportt-1 0.007 0.007 -0.006 -0.009 0.027** 0.020 0.027 0.025 -0.001 -0.005

(0.009) (0.008) (0.010) (0.010) (0.012) (0.013) (0.018) (0.018) (0.008) (0.007)

Real GDPt-1 -0.062* -0.072** -0.036 -0.040 0.066 0.045 -0.084* -0.081* -0.090** -0.134***

(0.032) (0.035) (0.031) (0.034) (0.051) (0.051) (0.048) (0.046) (0.037) (0.040)

Popt-1 -1.026** -0.797 0.239 0.528 -0.679 -0.050 0.138 0.394 1.551 2.332**

(0.505) (0.569) (0.462) (0.612) (0.576) (0.679) (0.641) (0.795) (1.003) (1.121)

Infant Mortt-1 -0.002 -0.002 -0.002 -0.003 -0.003 -0.003 0.001 -0.001 0.001 0.002

(0.003) (0.003) (0.003) (0.003) (0.004) (0.004) (0.003) (0.004) (0.004) (0.004)

Donor Real GDPt-1 -0.367*** 0.000 0.574*** 0.000 0.268 0.840*** 0.599*** 0.000 4.378*** 0.000

(0.137) (0.000) (0.113) (0.000) (0.189) (0.298) (0.223) (0.000) (1.052) (0.000)

Donor Pop 5.932** 0.000 -21.294*** 0.000 9.723** 0.000 -7.027 0.000 -16.356*** 0.000

(2.969) (0.000) (2.804) (0.000) (4.325) (0.000) (7.296) (0.000) (3.221) (0.000)

Year 0.013 -0.017 0.009 -0.027 -0.033

(0.015) (0.015) (0.023) (0.025) (0.030)

Year FE's N Y N Y N Y N Y N Y

Observations 2516 2516 2718 2718 2663 2663 2118 2118 2320 2320

R-squared 0.022 0.043 0.077 0.092 0.084 0.102 0.023 0.051 0.034 0.146

Number of recipients 103 103 105 105 104 104 92 92 97 97

Model F (p-value) 0.020 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000

Joint F (p-value) 0.673 0.654 0.813 0.626 0.0972 0.328 0.132 0.103 0.147 0.0390

NOTE. FE = fixed effects. All regressions are FE estimations. Donor and recipient Real GDP and Population variables are in logs. All columns include country

fixed effects. Year dummies are not shown. P-values are reported for the test of joint significance of bilateral Trade Share and Export Share variables. Robust

standard errors are in parentheses and clustered at recipients, *** p<0.01, ** p<0.05, * p<0.10.

33

Table 8

Election Year Differences in ODA Commitments by Donor

1975-2008

Regime-Specific


France Germany Japan UK USA

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Tradet-1 0.072 0.074 0.117 0.211 -0.020 0.007 -0.870* -0.995* 0.306* 0.307**

(0.178) (0.188) (0.369) (0.391) (0.100) (0.099) (0.523) (0.530) (0.157) (0.118)

Exportt-1 0.007 0.007 -0.007 -0.009 0.027** 0.020 0.028 0.027 -0.002 -0.006

(0.009) (0.009) (0.010) (0.010) (0.012) (0.013) (0.018) (0.018) (0.008) (0.007)

EXEC 0.014 0.021 -0.013 -0.024 0.049 0.040 -0.033 -0.025 0.030 0.071

(0.060) (0.060) (0.064) (0.066) (0.077) (0.080) (0.076) (0.080) (0.078) (0.078)

EXEC*Tradet-1 -0.088 -0.122 -0.658*** -0.620*** -0.003 -0.002 0.608 0.593 -0.103* -0.067

(0.097) (0.093) (0.226) (0.235) (0.180) (0.177) (0.444) (0.443) (0.053) (0.042)

EXEC*Exportt-1 -0.000 -0.002 0.014 0.015 -0.009 -0.007 -0.013 -0.015* 0.003 0.003

(0.004) (0.003) (0.010) (0.010) (0.011) (0.010) (0.009) (0.009) (0.004) (0.004)

Year FE's N Y N Y N Y N Y N Y

Observations 2516 2516 2718 2718 2663 2663 2118 2118 2320 2320

R-squared 0.022 0.043 0.078 0.093 0.084 0.103 0.025 0.052 0.034 0.147

Number of recipients 103 103 105 105 104 104 92 92 97 97

Model F (p-value) 0.021 0.000 0.000 0.000 0.000 0.000 0.003 0.000 0.000 0.000

Joint F (p-value) 0.839 0.491 0.0145 0.0642 0.298 0.712 0.197 0.105 0.376 0.0531

NOTE. FE = fixed effects. All regressions are FE estimations. All columns include country fixed effects and donor and recipient demographic controls. Year

dummies are not shown. P-values are reported for the test of joint significance of bilateral Trade Share and Export Share variables, Elections dummy and their

interactions. Robust standard errors are in parentheses and clustered at recipients, *** p<0.01, ** p<0.05, * p<0.10.

34

Table 9

Pooled Election Year Differences in ODA Commitments by Political Competitiveness

1975-2008



(1) (2) (3) (4)

Politically Non-Competitive

Tradet-1 (

!

"1) 0.113 0.170* 0.127 0.165

(0.090) (0.100) (0.109) (0.113)

Exportt-1 (

!

" 2 ) -0.002 -0.003 0.002 -0.000

(0.004) (0.004) (0.006) (0.005)

EXECt (

!

" 3 ) 0.000 0.022 0.014 0.034

(0.062) (0.061) (0.066) (0.065)

EXECt*Tradet-1 (

!

" 4 ) -0.208 -0.268* -0.224* -0.328**

(0.127) (0.138) (0.126) (0.132)

EXECt*Exportt-1 (

!

" 5 ) 0.012** 0.010* 0.009 0.008

(0.006) (0.006) (0.006) (0.005)

Politically Competitive

PCOMPt-1 (

!

" 6 ) 0.106 0.227*** 0.094 0.214***

(0.073) (0.073) (0.077) (0.078)

EXECt*PCOMPt-1 (

!

" 7 ) -0.012 -0.025 -0.015 -0.026

(0.071) (0.070) (0.076) (0.074)

PCOMPt-1*Tradet-1 (

!

" 8 ) -0.223 -0.229 -0.245 -0.253

(0.135) (0.140) (0.162) (0.171)

EXECt*PCOMPt-1*Tradet-1 (

!

" 9 ) 0.180 0.254* 0.226* 0.347**

(0.130) (0.142) (0.130) (0.137)

PCOMPt-1*Exportt-1 (

!

"10) 0.012** 0.009* 0.014** 0.012*

(0.005) (0.005) (0.006) (0.006)

EXECt*PCOMPt-1*Exportt-1 (

!

"11) -0.011* -0.009 -0.010* -0.010

(0.006) (0.006) (0.006) (0.006)

Year FE's N Y N Y

Observations 12370 12370 12335 12335

R-squared 0.016 0.043 0.016 0.043

Number of donor-recipient pairs 501 501 501 501

Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0.0140 0.00497 0.0647 0.00383

NOTE. FE = fixed effects. All regressions are FE estimations. All columns include country fixed effects

and donor and recipient demographic controls. Year dummies are not shown. P-values are reported for the

test of joint significance of bilateral Trade Share and Export Share variables, Elections and Political

Competitiveness dummies and their interactions. Robust standard errors are in parentheses and clustered at

donor-recipient pairs, *** p<0.01, ** p<0.05, * p<0.10.

35

Table 10

Summary of Election Year Differences in ODA Commitments

by Political Competitiveness Disaggregated by Donor

Regime Specific


Pooled France Germany Japan UK USA

(1) (2) (3) (4) (5) (6)

EXECt (

!

" 3 ) (+) (-) (+/-) (+) (+) (-/+)

PCOMPt-1 (

!

" 6 ) (+)/*** (+) (+)* (+)*

/** (+)**

/* (-)

EXECt*PCOMPt-1 (

!

" 7 ) (-) (+) (-/+) (-) (-) (-)

Tradet-1 (

!

"1) (+) (+) (+) (+) (-) (+)***

EXECt*Tradet-1 (

!

" 4 ) (-)*/** (-) (-)*

/ (-) (+/-) (-)

/*

PCOMPt-1*Tradet-1 (

!

" 8 ) (-) (+) (+/-) (-)*** (-)**/* (-)***

EXECt*PCOMPt-1*Tradet-1 (

!

" 9 ) (+)*/** (-) (+) (+) (+)

/* (+)

/*

Exportt-1 (

!

" 2 ) (+/-) (+) (-) (+) (-) (-)*/**

EXECt*Exportt-1 (

!

" 5 ) (+) (+) (+) (+) (+/-) (+)**

PCOMPt-1*Exportt-1 (

!

"10) (+)**/* (-) (-) (+) (+)*** (+)**

EXECt*PCOMPt-1*Exportt-1 (

!

"11) (-)*/ (-) (+) (-) (-) (-)**

NOTE. The results in Column (1) above are a summary of the signs and significance of the Regime-

Specific pooled fixed effects regression in Columns (3) and (4) of Table 9, with the latter specification

including year fixed effects. The remaining columns present analogous results for the five bilateral donors

separately. The full set of regression results for these remaining columns can be found in Tables B1, B2,

B3, B4 and B5 in Appendix B. Asterisks (*) denote the significance of the coefficient signs at the standard

levels. Where signs on the coefficients or the level of significance vary in the two specifications, the

results are separated by a slash (/), where the element before the slash represents the result in the

specification without year fixed effects and the element after the slash denotes the result with fixed effects.

Robust standard errors in the original regressions are clustered at donor-recipient pairs for column (1) and

at recipients for columns (2) - (6), *** p<0.01, ** p<0.05, * p<0.10.

36

Table 11

Tests of Linear Combinations of Interactions by Donor

Regime Specific

France Germany Japan UK USA

Linear Combinations/ Regression (1) (2) (3) (4) (5)

EXEC/PCOMP

(1) EXEC + EXEC*PCOMP

!

" 3 + " 7( ) (+) (-) (+) (-) (+)

(2) PCOMP + EXEC*PCOMP

!

" 6 + " 7( ) (+) (+) (+) (+) (-)

Trade

(3) Trade + EXEC*Trade

!

"1+ " 4( ) (-) (-) (-) (-) (-)

(4) Trade + PCOMP*Trade

!

"1+ " 8( ) (+) (+) (-)*** (-)***/** (+)

(5) Trade + EXEC*Trade + PCOMP*Trade + EXEC*PCOMP*Trade

!

"1+ " 4 + " 8 + " 9( ) (+/-) (-) (-)* (-)**/* (+)

(6) EXEC*Trade + EXEC*PCOMP*Trade

!

" 4 + " 9( ) (-) (-) (+) (+)/* (-)

Export

(7) Export + EXEC*Export

!

" 2 + " 5( ) (+) (+) (+) (-) (+)

(8) Export + PCOMP*Export

!

" 2 + "10( ) (+) (-) (+)**/ (+)*** (+)

(9) Export + EXEC*Export + PCOMP*Export + EXEC*PCOMP*Export

!

" 2 + " 5 + "10 + "11( ) (-) (+) (+) (+)/** (+)

/*

(10) EXEC*Export + EXEC*PCOMP*Export

!

" 5 + "11( ) (-) (+) (-)*/ (-) (-)

NOTE. The tests of linear combinations correspond to Columns (3) and (4) of the regressions presented in Tables B1 through B5 in Appendix B and

summarized above in Table 10. The relevant regressions use Regime-Specific measures of Trade and Export for the five bilateral donors and the specification in

the two columns differ in that Column (4) uses year fixed effects. Asterisks (*) denote the significance of the signs on the coefficient of the linear combinations

at the standard levels. Where signs on the coefficients or the level of significance vary in the two specifications, the results are separated by a slash (/), where the

element before the slash represents the result in the specification without year fixed effects and the element after the slash denotes the result with fixed effects.

*** p<0.01, ** p<0.05, * p<0.10.

37

Appendix B Regressions Summarized in Tables 10 and 11

Table B1

Election Year Differences in ODA Commitments by Political Competitiveness

1975-2008

France



(1) (2) (3) (4)


Tradet-1 -0.014 0.015 0.042 0.047

(0.142) (0.155) (0.195) (0.206)

Exportt-1 0.002 0.003 0.009 0.009

(0.006) (0.006) (0.009) (0.008)

EXECt -0.065 -0.040 -0.089 -0.058

(0.112) (0.112) (0.117) (0.116)

EXECt*Tradet-1 0.004 -0.052 -0.068 -0.109

(0.094) (0.097) (0.089) (0.103)

EXECt*Exportt-1 0.001 -0.001 0.005 0.002

(0.006) (0.007) (0.005) (0.006)


PCOMPt-1 0.174 0.199 0.147 0.173

(0.126) (0.123) (0.129) (0.127)

EXECt*PCOMPt-1 0.081 0.058 0.144 0.113

(0.141) (0.139) (0.148) (0.146)

PCOMPt-1*Tradet-1 0.152 0.125 0.215 0.179

(0.279) (0.279) (0.309) (0.309)

EXECt*PCOMPt-1*Tradet-1 -0.067 0.014 -0.187 -0.126

(0.334) (0.360) (0.358) (0.374)

PCOMPt-1*Exportt-1 -0.012** -0.012* -0.006 -0.006

(0.006) (0.006) (0.006) (0.007)

EXECt*PCOMPt-1*Exportt-1 0.003 0.004 -0.006 -0.004

(0.008) (0.008) (0.008) (0.008)

Year FE's N Y N Y

Observations 2523 2523 2516 2516

R-squared 0.024 0.045 0.025 0.047

Number of recipients 103 103 103 103

Model F (p-value) 0.033 0.000 0.001 0.000

Joint F (p-value) 0.618 0.125 0.400 0.0959

NOTE. FE = fixed effects. All regressions are FE estimations. All columns include country fixed effects and donor

and recipient demographic controls. Year dummies are not shown. P-values are reported for the test of joint

significance of bilateral Trade Share and Export Share variables, Elections and Political Competitiveness dummies

and their interactions. Robust standard errors are in parentheses and clustered at recipients, *** p<0.01, ** p<0.05, *

p<0.10.

38

Table B2


1975-2008

Germany



(1) (2) (3) (4)


Tradet-1 0.138 0.239 0.119 0.222

(0.381) (0.396) (0.395) (0.412)

Exportt-1 -0.003 -0.005 -0.005 -0.006

(0.009) (0.009) (0.013) (0.013)

EXECt -0.066 -0.109 0.014 -0.036

(0.121) (0.120) (0.148) (0.149)

EXECt*Tradet-1 -0.930** -0.857* -0.796* -0.745

(0.432) (0.453) (0.447) (0.458)

EXECt*Exportt-1 0.025 0.026* 0.009 0.011

(0.016) (0.016) (0.023) (0.022)


PCOMPt-1 0.209* 0.220* 0.201* 0.221*

(0.113) (0.117) (0.116) (0.120)

EXECt*PCOMPt-1 0.070 0.110 -0.031 0.016

(0.137) (0.136) (0.164) (0.164)

PCOMPt-1*Tradet-1 0.019 -0.043 0.027 -0.048

(0.480) (0.495) (0.560) (0.565)

EXECt*PCOMPt-1*Tradet-1 0.432 0.421 0.231 0.220

(0.650) (0.685) (0.706) (0.722)

PCOMPt-1*Exportt-1 -0.005 -0.007 -0.003 -0.006

(0.012) (0.013) (0.016) (0.017)

EXECt*PCOMPt-1*Exportt-1 -0.017 -0.015 0.007 0.007

(0.024) (0.023) (0.032) (0.031)

Year FE's N Y N Y

Observations 2725 2725 2718 2718

R-squared 0.082 0.098 0.082 0.097


Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0.124 0.164 0.0251 0.0708





p<0.10.

39

Table B3


1975-2008

Japan



VARIABLES (1) (2) (3) (4)


Tradet-1 0.034 0.082 0.021 0.052

(0.092) (0.093) (0.102) (0.101)

Exportt-1 0.006 0.004 0.019 0.013

(0.007) (0.007) (0.012) (0.012)

EXECt 0.092 0.116 0.149 0.179

(0.164) (0.163) (0.173) (0.174)

EXECt*Tradet-1 0.777 0.798 -0.101 -0.232

(1.134) (1.259) (0.510) (0.545)

EXECt*Exportt-1 0.012 0.010 0.007 0.007

(0.020) (0.020) (0.017) (0.016)


PCOMPt-1 0.329* 0.433** 0.350* 0.452**

(0.167) (0.168) (0.177) (0.177)

EXECt*PCOMPt-1 -0.093 -0.138 -0.125 -0.174

(0.189) (0.187) (0.205) (0.204)

PCOMPt-1*Tradet-1 -0.534*** -0.537*** -0.698*** -0.700***

(0.194) (0.183) (0.175) (0.175)

EXECt*PCOMPt-1*Tradet-1 -0.730 -0.701 0.245 0.386

(1.148) (1.257) (0.569) (0.586)

PCOMPt-1*Exportt-1 0.019 0.012 0.018 0.012

(0.012) (0.012) (0.015) (0.016)

EXECt*PCOMPt-1*Exportt-1 -0.020 -0.016 -0.032 -0.028

(0.026) (0.026) (0.021) (0.020)

Year FE's N Y N Y

Observations 2670 2670 2663 2663

R-squared 0.095 0.116 0.097 0.118


Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0.00590 0.0338 0.000557 0.00457





p<0.10.

40

Table B4


1975-2008

UK



VARIABLES (1) (2) (3) (4)


Tradet-1 0.095 0.031 -0.140 -0.256

(0.527) (0.584) (0.502) (0.527)

Exportt-1 -0.017 -0.020* -0.003 -0.004

(0.011) (0.012) (0.009) (0.009)

EXECt 0.041 0.040 0.045 0.043

(0.128) (0.129) (0.131) (0.133)

EXECt*Tradet-1 -0.046 -0.203 0.095 -0.003

(0.217) (0.240) (0.187) (0.216)

EXECt*Exportt-1 0.002 -0.004 0.001 -0.002

(0.013) (0.012) (0.006) (0.006)


PCOMPt-1 0.387*** 0.372** 0.311** 0.296*

(0.147) (0.158) (0.150) (0.159)

EXECt*PCOMPt-1 -0.117 -0.116 -0.124 -0.122

(0.157) (0.161) (0.160) (0.163)

PCOMPt-1*Tradet-1 -2.029** -1.858* -2.228** -2.053*

(0.936) (1.057) (1.069) (1.165)

EXECt*PCOMPt-1*Tradet-1 1.094 1.458* 0.940 1.203*

(0.846) (0.872) (0.608) (0.609)

PCOMPt-1*Exportt-1 0.045*** 0.046*** 0.053*** 0.053***

(0.016) (0.017) (0.014) (0.014)

EXECt*PCOMPt-1*Exportt-1 -0.023 -0.016 -0.016 -0.011

(0.023) (0.022) (0.021) (0.022)

Year FE's N Y N Y

Observations 2125 2125 2118 2118

R-squared 0.041 0.067 0.058 0.083


Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0.000381 0.00104 8.17e-06 3.23e-05





p<0.10.

41

Table B5


1975-2008

USA



VARIABLES (1) (2) (3) (4)


Tradet-1 1.041*** 0.888*** 0.959*** 0.817***

(0.088) (0.091) (0.090) (0.091)

Exportt-1 -0.016** -0.018*** -0.017* -0.020**

(0.007) (0.006) (0.010) (0.009)

EXECt 0.031 0.120 -0.001 0.088

(0.127) (0.114) (0.140) (0.128)

EXECt*Tradet-1 -3.924 -3.795 -3.326 -3.368*

(2.704) (2.375) (2.014) (1.844)

EXECt*Exportt-1 0.028** 0.022* 0.029** 0.024**

(0.012) (0.013) (0.011) (0.011)


PCOMPt-1 -0.187 -0.020 -0.219 -0.039

(0.230) (0.233) (0.250) (0.254)

EXECt*PCOMPt-1 -0.011 -0.063 0.036 -0.018

(0.155) (0.145) (0.167) (0.157)

PCOMPt-1*Tradet-1 -1.001*** -0.789*** -0.894*** -0.693***

(0.115) (0.118) (0.129) (0.129)

EXECt*PCOMPt-1*Tradet-1 3.872 3.762 3.298 3.362*

(2.706) (2.377) (2.019) (1.847)

PCOMPt-1*Exportt-1 0.025*** 0.024*** 0.027** 0.024**

(0.008) (0.008) (0.010) (0.010)

EXECt*PCOMPt-1*Exportt-1 -0.028** -0.021* -0.030** -0.024**

(0.012) (0.013) (0.012) (0.011)

Year FE's N Y N Y

Observations 2327 2327 2320 2320

R-squared 0.058 0.164 0.054 0.163


Model F (p-value) 0.000 0.000 0.000 0.000

Joint F (p-value) 0 0 0 5.85e-11





p<0.10.

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