Foreign Aid and Conflict in Africa

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Foreign Aid and Conflict in Africa

Malin Renate Hatling

Master of Philosophy in Economics Number of credits: 30

Department of Economics Faculty of Social Science

June 2020

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Foreign Aid and Conflict in Africa

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© Malin Renate Hatling

2020

Foreign Aid and Conflict in Africa

http://www.duo.uio.no/

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Preface

Writing this thesis has been a challenging but rewarding experience. It has been a steep learning curve, filled with interesting discussions and analyses, a lot of iced coffee, some late nights and an occasional swear word muttered in frustration. I am pleased to complete my master’s degree with this thesis.

I would first like to thank my supervisor, Andreas Kotsadam, for all his helpful insights and encouraging words throughout this process. He gave me the idea for this thesis and showed great interest in my work. Thank you to my family for all their support and for proofreading the thesis, especially Camilla and Thomas. Thank you to my fiancé, Andreas, for all his helpful tips on writing, proofreading, and support. I appreciate all the dinners you have cooked and dishes you have done while I have been glued to the keyboard.

Malin Renate Hatling Oslo, June 2020

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Abstract

This thesis examines the effect of foreign aid on conflict at a sub-national level in Africa.

Researchers have long relied on cross-country analyses for examining the effectiveness of foreign aid. However, the effects of foreign aid may be too small and localized to produce robust results using this level of aggregation. The literature has therefore increasingly turned to sub-national analyses. This thesis adds to the understanding of how foreign aid affects local outcomes in this growing part of the literature.

The analysis has been done using the UCDP GED version 19.1 (Sundberg & Melander, 2013) and the World Bank Geocoded Research Release version 1.4.1. datasets (AidData, 2016).

Additionally, the thesis uses grid cells as the unit of analysis from the PRIO-GRID dataset (Tollefsen, Strand, & Buhaug, 2012). The datasets are geocoded and used in a fixed effects model with grid cell and year fixed effects, as well as clustered standard errors. The same grid cells are observed before and after having received foreign aid. Furthermore, only observations at the second administrative division, ADM2, or a more precise location are used in the thesis. This controls for differences across countries and regions, so that the results come down to the differences between the affected grid cells in a country.

The results show that foreign aid is not effective in reducing the number of conflicts that occur on the continent, but that it is effective in reducing the number of battle deaths. This suggests that foreign aid reduces the intensity of the ongoing conflicts. Furthermore, foreign aid is more effective in reducing battle deaths in grid cells that are close to the capital and grid cells that experiences exclusion of minorities. Finally, increased economic activity is effective in reducing both the number of conflicts and battle deaths. However, this effect does not run via foreign aid.

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List of figures

Figure 1: Grid cells in Africa………..10

Figure 2: Conflict events in Africa………..………..12

Figure 3: World Bank aid projects in Africa……….………..15

List of tables

Table 1 Descriptive statistics……….………13

Table 2 Distance from capital………17

Table 3 Regression on battle deaths………22

Table 4 Regression on number of conflicts……….23

Table 5 Regression on any conflict………..………….24

Table 6 Forward active regression.………..25

Table 7 Nightlight regression on various conflict measures………..……….27

Table 8 Regression on nightlight………28

Table 9 Far away regression……….…29

Table 10 Not far away regression……….30

Table 11 Far away interaction regression………31

Table 12 No exclusion regression……….32

Table 13 Exclusion regression……….33

Table 14 No exclusion interaction regression………..…35

Table A.1 Far away regression……….41

Table A.2 Not far away regression………41

Table A.3 Far away interaction regression……….42

Table A.4 No exclusion regression………42

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Table A.5 Exclusion regression……….43 Table A.6 No exclusion interaction regression………..43

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Chapter 1 Introduction

The debate of whether, and to what extent, foreign aid is effective is one of the most controversial issues of development economics. It remains to be answered conclusively despite the extensive literature that exists within the field. Despite the controversy, total global foreign aid has continued to increase from 4.65 billion USD in 1960, to 180 billion USD in 2013 (Qian, 2015). In Norway, the amount of foreign aid has likewise grown from 0.5 billion NOK in 1973 (Ministry of Foreign Affairs, 2002) to 37.8 billion NOK in 2019 (Norad, 2020). The fact that foreign aid has increased drastically despite inconclusive research, makes further research on the effect of foreign aid important.

This, of course, also begs the question of what the aid is ‘effective’ at achieving? In this thesis, the focus will be on one specific aspect of this debate; namely, the question about the relationship between foreign aid and conflict levels in recipient countries. More

specifically, this thesis will examine how foreign aid from the World Bank affects conflict at a local level in Africa. In that context, it is important to keep in mind that about 40 % of Sub- Saharan countries have experienced a period of civil war in the period between 1960 and 2000 (Elbadawi & Sambanis, 2000). This is approximately the same time period in which the amount of foreign aid has increased drastically.

Previous literature has largely relied on national-level studies to examine the effects of foreign aid. However, this has been identified as a reason for the lack of robust results in previous research because the effects of foreign aid might be too small to show a significant effect when using this level of aggregation (Dreher & Lohmann, 2015). While the literature increasingly uses sub-national studies to account for local effects, there are seemingly few previous studies examining the relationship between aid and conflict at a local level.

Considering conflict is often contained to one part of a country at a time, while other regions can be relatively unaffected, it is an advantage to examine this at a local level.

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In order to do this I will use the UCDP GED version 19.1 dataset (Sundberg & Melander, 2013) and the World Bank Geocoded Research Release, version 1.4.1. datasets (AidData, 2016). Both datasets are geocoded, enabling an analysis on the local level. Furthermore, grid cells from the PRIO-GRID dataset will be used as the unit of analysis. I will use a fixed effects model with grid cell and year fixed effects. This allows me to compare the same grid cells before and after having received foreign aid. The analysis will be conducted using STATA SE 16.1. The hypothesis is that foreign aid will have a significant effect on conflict, and it may be positive or negative.

My results show that foreign aid is not effective in reducing the number of conflicts occurring on the continent. The results do, however, show that foreign aid is effective in reducing the number of battle deaths. This suggests that foreign aid may be effective in reducing the intensity of the conflicts. Furthermore, I have found that foreign aid is more effective in reducing battle deaths when a project is located close to the capital and in areas that experience the exclusion of minorities. Finally, I have found that while economic growth reduces both the number of conflicts and battle deaths, the effect of foreign aid does not seem to run via economic growth.

The rest of the thesis will be structured as follows. Chapter 2 will provide an overview of the previous research on foreign aid and conflict. This chapter is divided into cross-country and sub-national analyses, as well as theories derived from the previous research. Chapter 3 describes the datasets and Chapter 4 will describe the empirical method. Here I will describe the baseline fixed effects model that will be used to examine how foreign aid affects conflict levels on a local level in recipient countries in Africa. I will also explain why I have chosen to use clustered standard errors, and how I will check the robustness of my results. Chapter 5 provides the results and discussion. Here, I will present my main results, before discussing the forward active regression that was done to check the robustness of these. I also look closer at some mechanisms that can impact the effect of foreign aid and conflict. Namely, the role of economic activity, distance from capital and exclusion. Chapter 6 will conclude the thesis.

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Chapter 2 Previous research

In this chapter, I will outline the relevant previous research. First, I will present some

influential research done using cross-country analyses on foreign aid and conflict. Then I will showcase relevant sub-national studies. Since this thesis also uses a sub-national analysis, I will focus on these. Several of the papers presented in this section use a similar method as this thesis. Finally, I will combine the previous research to present different theories to how foreign aid can impact conflict.

2.1 Cross-country analyses

Cross-country analyses have long been the standard in the field. However, the

inconclusiveness of previous studies on the effect of aid may be because the literature has largely relied on national level studies. A suggested reason for this is that the effects of foreign aid on development outcomes may be too small and localized to affect aggregate outcomes, causing a lack of robust results (Briggs, 2017; Dreher & Lohmann, 2015).

Furthermore, the analyses may fail to control for other differences across countries, leading to spurious relationships between aid and development outcomes (Odokonyero, Ijjo, Marty, Muhumuza, & Moses, 2015). I find it nonetheless relevant to include the following papers as it provides background for this thesis and suggests some theories on how foreign aid may affect conflict.

Alesina and Dollar (2000) argue that political and strategic considerations trump economic need and policy performance when giving foreign aid. They find that colonial past and voting patterns in the UN explain more of the distribution of foreign aid than the political and economic institutions of recipients do. The result of this is that a non-democratic former colony tends to get about twice as much foreign aid as a democratic non-colony does.

Furthermore, countries going through democratization and trade liberalization processes tend to get increases in foreign aid, while countries becoming more authoritarian and closing the economy tend to experience very little decrease in such aid.

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Savun and Tirone (2012) suggest that foreign aid can help reduce the chance of civil war onset during negative economic shocks. Countries with low income per capita are more prone to civil wars. A suggested reason for this is the opportunity cost argument. Poorer people have lower opportunity costs to join a rebellion as they have less to give up or lose.

During an economic shock, the opportunity cost of fighting is temporarily decreased. While high-income countries will increase government expenditures during economic shocks to lessen the impact, low-income countries may have to cut government spending. Foreign aid can then cushion government spending from cuts due to an economic shock, providing the government with resources to make rebellion a less attractive option during the shock.

Similarly, Nielsen, Findley, Davis, Candland, and Nielson (2011) examines the effect of positive and negative foreign aid shocks on the likelihood of conflict. They find that negative aid shocks increase the probability of conflict onset, while positive aid shocks have no significant effect. They suggest that governments may use foreign aid resources on the military, deterring rebels from going to war, or by making side-payments to rebels, effectively buying them out from seeking more through war. Their results suggest that a sudden drop in foreign aid can limit the government’s ability to maintain peace through these methods.

However, Collier and Hoeffler (2007) find that increased military spending is at best ineffective in reducing the risk of civil conflict. Furthermore, they find that African military spending is twice what it would have been in the absence of foreign aid, suggesting that such aid leaks into military budgets. This seems to promote a “neighborhood” arms race, even though the main security threat is likely to be internal for low-income countries.

de Ree and Nillesen (2009) argue that the process of foreign aid allocations is endogenous when it comes to conflict. Donors may reallocate foreign aid when they predict changes in the likelihood of conflict outbreak or ending. Controlling for this endogeneity of foreign aid allocation, they find that such aid significantly reduces the probability of civil war

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continuation. They also do not find a significant effect of foreign aid on the probability of civil conflict onset.

2.2 Sub-national analyses

Given the likely small and local effects foreign aid has, narrowing down both the definitions of aid and the set of outcomes studied can improve the identification of causal channels, as well as account for the heterogenous effect in differences across recipient and donor countries (Qian, 2015). An increasing part of the literature on foreign aid accounts for this using sub-national rather than cross-country analysis. My thesis will use a similar method as several of the papers presented here, and will build on the understanding of how foreign aid affects conflict at a local level.

Dreher and Lohmann (2015) examine the effect of foreign aid on growth at the regional level. They estimate the effect foreign aid from the World Bank has on the growth of nighttime lights in a fixed effects model and average their data to smooth changes over the business cycle. The amount of nighttime lights is often used as a proxy for economic activity and development. They find that growth in nighttime light and foreign aid are significantly correlated in second level administrative regions ADM2, but not in the larger first level administrative regions ADM1.

Briggs (2017) examines how foreign aid is distributed to people at different levels of wealth in African countries. He uses sub-national measures of economic wealth calculated from household surveys against geotagged foreign aid projects in a fixed effects model. The model also controls for area, violence and capital city bias. He finds that foreign aid

disproportionately flows to regions with a higher concentration of the richest people rather than the regions with a higher concentration of the poorest people. The results suggest that donors do not have strong control over how foreign aid is distributed on a sub-national level, and that such aid is not able to reduce extreme poverty since it does not reach the poorest.

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van Weezel (2015) examines the effect of foreign aid in conflict areas on a regional level. He conducts a spatial analysis using a first difference model. The analysis is limited to the Democratic Republic of the Congo, Sudan and Ethiopia. The data show that foreign aid is concentrated around the capitals, while conflict is concentrated in peripheral regions.

Furthermore, the results show that ethnic polarization increases the probability of conflict onset. However, he finds no significant effects of foreign aid on several conflict outcomes.

Similarly, Strandow, Findley, and Young (2016) study the impact of foreign aid on the intensity of ongoing conflicts at a micro level. They find that areas that have a high concentration of foreign aid, experiences more military fatalities but no effect on civilian deaths. They argue that the concentrated foreign aid incentivizes rebels to loot the funding.

If foreign aid is more diffused, there is no increase in military fatalities.

Isaksson and Kotsadam (2018b) examine the effect Chinese development projects have on trade union involvement in Africa. They use geocoded data on sub-national allocation of Chinese aid and Afrobarometer surveys in a fixed effects model. They compare the trade union involvement of people living near an active Chinese development project to people living near an area where a Chinese development project is planned, but not yet initiated.

The results show that Chinese development projects tend to decrease the rate of union involvement in the surrounding areas.

In a similar paper, Isaksson and Kotsadam (2018a) study Chinese aid and local corruption.

Using the same datasets and a similar estimation strategy, they find that corruption becomes more widespread around active Chinese development sites compared to sites where a project has not yet been initiated.

Kotsadam and Østby (2019) investigate how local armed conflict affects maternal mortality in Sub-Saharan Africa. They use data from DHS and UCDP GED in a fixed effects model. While men typically die on the battlefield, more women die due to indirect causes such as

deterioration of the health care system and malnutrition. They find that local exposure to

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armed conflict increases the risk of maternal deaths, and that this risk is stronger in richer, more educated areas.

Kotsadam, Østby, Rustad, Tollefsen, and Urdal (2018) examines how foreign aid affect infant mortality at a sub-national level. They use AidData and DHS data sets in a difference-in- difference method using a fixed effect model. They find that being close to active foreign aid projects reduces infant mortality. Furthermore, foreign aid is more effective in less

privileged groups, but that aid projects are established in areas that on average have lower infant mortality than non-aid locations.

2.3 Theories

There are numerous theories in the literature about how foreign aid affects conflict. In this section I present some relevant theories on the mechanisms of this relationship that will be used to discuss my results.

The opportunity cost argument is often used in the literature, see for example (Collier &

Hoeffler, 2004). Low economic growth and high unemployment provides few opportunities for earning a stable income. Potential rebels will then have a low opportunity cost for joining a rebel group. Foreign aid may help increase the economic growth in a region, thereby raising the opportunity costs of joining a rebel group. If this is the case, foreign aid will help reduce conflict.

Foreign aid given as budget support to the government may affect conflicts levels in several ways. As previously mentioned, governments may use foreign aid to increase their military capacity. This may deter rebels from inciting conflict (de Ree & Nillesen, 2009).

The increased state capacity facilitated through foreign aid can increase the government’s control in central areas of the country. It is therefore more likely that conflict will happen in the periphery, while foreign aid is distributed closer to the center (van Weezel, 2015).

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Foreign aid also increases the financial resources of the government which can invoke rent- seeking behavior in rebel groups. The larger resources of the government increase rebels’

incentives to capture the state. However, it is likely that donors will reduce foreign aid if a conflict breaks out, thereby diminishing the resources to be won. There is also only a 20 per cent chance of rebel victory, suggesting rebels should be more interested in more easily available rents such as natural resources instead. Furthermore, prospective gains of

capturing the state must be heavily discounted by both risk and time, while natural resource revenues do not need discounting as it can be captured during conflict (Collier & Hoeffler, 2002). It therefore seems unlikely that a rebel group will try to capture the state unless it is politically motivated to do so.

Food aid might be the only type of aid that can realistically be captured by rebels during conflict as it acts as a natural resource (Collier & Hoeffler, 2002). In a study on the effect of U.S. food aid on civil conflict, Nunn and Qian (2014) found that such aid increases the incidence and duration of such conflicts. This may be because it prolongs the life of rebellions or because it can reduce the prices faced by local farmers, which can lower the opportunity cost of fighting, thus increasing the risk of conflict.

Conflicts may be more related to economic similarity than economic inequality. Economically similar groups are competing for the same limited resources. If the economy grows

unevenly, one group may pull ahead of others. In this situation, tensions may arise, and a potential conflict may manifest through ethnic lines, creating an “us versus them” situation.

Dividing the competing groups in by ethnicity makes them easier to recognize and exclude by the winning group after a conflict. This situation can be argued to have been a

contributing factor to the genocide in Rwanda, where Hutu people feared Tutsis would seize their land (Ray & Esteban, 2017).

Foreign aid can make communities less reliant on the government. Regions and

communities, perhaps especially peripheral ones, may be reliant on government transfers to sustain the communities. If such transfers are reduced it can incentivize the community to

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take up arms against the government. The decrease in transfers could also increase the risk of communities attacking neighboring communities for resources or transfers. Foreign aid may help make communities more self-sustaining, thus reducing the need for government transfers and the risk of conflict.

In general, then, foreign aid has the potential to both increase and decrease the risk and level of conflict. The risk of increasing conflict comes from unintended consequences such as larger incentives to capture the state or food aid. Foreign aid can also be very volatile and is typically procyclical rather than countercyclical (Bulir & Hamann, 2008). This volatility can create a general uncertainty and instability in the government that may be taken advantage of by insurgents.

However, if foreign aid is effective, its potential to reduce conflict seems larger than its potential to increase it, according to the academic literature. Increased economic activity decreases the opportunity cost of fighting; better infrastructure gives communities access to larger markets and increases the mobility of the work force; and better and more

widespread education provides more opportunities and may help reduce gender-based violence.

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Chapter 3 Data

This chapter will describe the data used in the thesis and how the different datasets are merged. Both datasets are geocoded. When using such datasets, it is important to decide the unit of analysis. For this thesis I will use grid cells as the unit of analysis from the PRIO- GRID dataset (Tollefsen, Strand, & Buhaug, 2012). Using grid cells has two advantages. First, it allows me to have similar units across all of Africa. Secondly, the PRIO-GRID data also contains some important variables that will be used in the analysis to test for mechanisms.

This will be described in more detail later. Figure 1 shows the grid cells across Africa. In total there are 10,678 PRIO-GRID cells over the continent.

Figure 1: Grid cells in Africa

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3.1 Conflict data

For the conflict data I will use the Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset version 19.1 (Sundberg & Melander, 2013). This is a disaggregated dataset covering individual events of organized violence from 1989-2018. It includes events from the whole world, except Syria. The dataset includes 152,616 observations. Each event in the dataset is connected to a specific location defined by a pair of latitude and longitude coordinates (Högbladh, 2019).

The UCDP defines a violent event as: “An incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date” (Högbladh, 2019, p. 4). An organized actor can be the government, or formally or informally organized groups. Furthermore, the UCDP divides conflict into three types. The first is state-based armed conflict, defined as: “A contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in a calendar year.” The second is non-state conflict, defined as: “The use of armed force between two organized armed groups, neither of which is the government of a state which results in at least 25 battle-related deaths is a year.” The last type of conflict is one-sided conflict, which is defined as: “The use of armed force by the government of a state or by a formally organized group against civilians which results in at least 25 deaths” (Högbladh, 2019, pp. 28-30).

The dataset is based on global newswire reporting, local news, NGO reports, and so on.

About 60 per cent of the dataset is based on global newswire reporting¹. In the cases where this have been insufficient to specify and identify a conflict, secondary sources such as local media reporting, NGO reports and government publications are used to further clarify the event.

Since the dataset is primarily based on international media reports, it can be subject to reporting bias. This bias can be divided into two categories, selection and description bias.

1 This reporting comes from Reuters News, Agence France Presse, Associated Press, Xinhua, and BBC Monitoring.

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Selection bias concerns which events are reported. Description bias concerns how the event is reported and relates to the Rashomon effect. This describes how a conflict can be

reported very differently based on political stances, particularly when one side of the conflict involves an authority (Davenport, 2010, p. 3). Furthermore, media reporting is not consistent in quality and quantity over time. In the worst-case scenario, reporting on a conflict is

changed due to aid. International presence often makes conflicts more likely to be reported on. This can lead to an underestimation of the results.

26 per cent of the events are located in Africa, totalling 40,404 events. Since the thesis will focus on local effects, only precision codes 1 and 2 will be kept. This corresponds to the exact location and up to 25 km away from the exact location, respectively. After adjusting the dataset, 28,687 events are left. The conflict points are spatially merged with the grid cells, and the conflicts are shown on the map in Figure 2.

Figure 2: Conflict events in Africa

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Table 1 shows the descriptive statistics. There are 279,277 observations of the different conflict variables. The number of observations comes from the number of grid cells

multiplied by the number of years. The variable battle deaths show that 2.34 people die in battle on average. 2.44 % of the grid cell years have experienced at least one death in combat. Table 1 also shows that every grid cell year experience 0.05 conflicts. 2.78 % of the grid cell years experience at least one conflict over the course of the dataset as seen in the mean for the binary variable any conflict. This amounts to 7,763 grid cell years having at least one conflict.

7.61 % of the grid cell years have had an active foreign aid project over the course of the dataset, which amounts to 21,114 observations.

Table 1 Descriptive statistics

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VARIABLES Obs. Mean Std. Dev.

Main dependent variables

Battle deaths 279,277 2.343 199.2

Number of conflicts 279,277 0.0516 0.503

Any conflict 279,277 0.0278 0.164

Main independent variable

Active 277,628 0.0761 0.265

Other variables

Nightlights mean 234,916 0.348 1.869

Distance from capital 277,628 650.6 416.5

Excluded 208,993 0.463 0.648

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3.2 Foreign aid data

For the data on foreign aid, I will use the World Bank Geocoded Research Release, version 1.4.1. (AidData, 2016). This is also a disaggregated dataset, covering all World Bank aid projects in the International Bank for Reconstruction and Development and the International Development Association lending lines approved from 1995-2014. The dataset covers 5,881 projects in 61,243 locations. The total commitments in these projects exceed 644 billion dollars. The commitments from projects before 2013 are measured in constant 2011 USD.

The commitments from all projects after 2013 are reported in the current USD. 30 % of the projects are located in Africa, totalling 1,764 projects.

Each project is connected to a specific location by a pair of latitude and longitude coordinates. The projects are given a precision coded ranging from 1-8 based on how accurately the project can be assigned to an exact location. Precision code 1-4 contains projects from the first administrative level (ADM1) or a more precise location. Since the thesis will focus on local effects, only precision code 1, which corresponds to the exact location of the project, and 2, which corresponds to a location within 25 km of the

coordinates or a division smaller than the second administrative level (ADM2), will be kept.

Since the dataset is geocoded, there may be some bias in the types of projects that are included. Foreign aid projects such as for example general budget support and infrastructure can be difficult to assign to exact locations and can thus be difficult to include. This means that the grid cells investigated may get this type of aid in addition to the aid projects examined, which may influence the results.

After keeping only African aid projects and narrowing down the precision codes, 688

projects spanning across 6,663 locations in 43 African countries remain. These locations are also merged with the PRIO-GRID data, and the distribution of aid across the continent is shown in Figure 3.

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Figure 3: World Bank aid projects in Africa

3.3 Other variables

In addition to the main dependent and independent variables, I will use a few more variables to test different mechanisms. I will examine how economic activity affects the conflict level, and whether this mechanism seems to run via foreign aid. I will also test how the distance from the capital and ethnic polarization will affect foreign aid and conflict.

In an attempt to see how economic activity can affect conflict and foreign aid I will use change in nighttime lights as an indicator for economic growth. The variable nightlights mean is acquired from the PRIO-GRID dataset and shows the mean nighttime lights within the grid cell (Tollefsen et al., 2012). We see that the mean is close to zero, implying that

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many areas have no nighttime light. A positive change in this variable can indicate an

increase in economic growth and economic activity. This is often associated with a decrease in conflict levels as it raises the opportunity costs for potential rebels.

Henderson, Storeygard, and Weil (2012) was among the first to show that growth in

nightlights as a measurement of economic growth could be a good substitute for GDP. It has since become a common proxy for economic development over the past decade. Growth in nighttime lights has provided a solution to the problem of measuring GDP in low-income countries where GDP is often poorly measured, and a large share of the economic activity runs through the informal sector. It has also provided an opportunity to better measure the difference in growth and inequality within countries.

The variable distance from capital is also sourced from the PRIO-GRID dataset. I will test how foreign aid affects conflict in the center versus the periphery. It is often pointed out that foreign aid is given close to the capital, while conflict often happens in the periphery

(Buhaug, Gates, & Lujala, 2009; van Weezel, 2015). The variable gives the spherical distance in kilometers from the capital to the center of the grid cell (Weidmann, Kuse, & Gleditsch, 2010). In the analysis, the variable will be a binary variable equal to 1 if it is further away from the capital than the median, which is 553 km, and equal to 0 if it is closer to the capital than the median. Table 2 displays the average distance from the capital for active foreign aid projects and conflicts. The average distance from the capital for active aid projects are 378 km and for conflicts it is 560 km. The average for grid cells that do not have an active project is 673 km. This shows that both active projects and conflicts happen closer to the capital in general, even when including country fixed effects so that the regression only looks within the countries.

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Table 2 Distance from capital

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VARIABLES Distance from capital

Distance from capital

Active -294.91*** -134.79***

(2.93) (2.51)

Any conflict -112.98*** -75.85***

(5.38) (4.26)

Observations 277,628 277,628 277,628 277,628

R-squared 0.04 0.00 0.40 0.39

Country FE NO NO YES YES

Mean for non active

673.02 673.02 673.02 673.02

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The final variable that will be used to test mechanisms is the variable excluded. This is also acquired from the PRIO-GRID dataset. The variable counts the number of excluded ethnic groups, defined as discriminated or powerless groups, within the grid cell (Vogt et al., 2015).

Studies show that ethnic polarization increases the risk of conflict (Asal, Findley, & Piazza, 2016; Wimmer, Cederman, & Min, 2009). Excluded will also be a binary variable in the analysis equal to 0 if there is some degree of exclusion and 1 if there is no exclusion. 38.75 per cent of the grid cells experiences some degree of exclusion. This amounts to 80,997 observations.

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Chapter 4 Method

In the following chapter I will describe the method I have chosen to use in my analysis, as well as explain why I have chosen the method. This thesis will use a fixed effects model in all regressions to examine how foreign aid affects conflict levels on a local level in recipient countries in Africa. I will also explain why I have chosen to use clustered standard errors, and how I will check the robustness of my results.

4.1 Fixed effects model

In this thesis I use panel data where every grid cell is observed over several years. I have therefore chosen to work with a fixed effects model as it can exploit the panel data to control for unobserved omitted variables. I add grid cell and time fixed effects to the regressions. The grid cell fixed effects control for omitted variables that vary across the grid cells, but not across time. These are typically for example geographic factors such as

distance to the capital, ruggedness, distance to the sea and so on. Likewise, time fixed effects control for omitted variables that vary across time but are constant across the grid cells, such as continent-wide economic conditions. If the omitted variables does not change over time or across grid cells, any change to the independent variable must be due to other factors than the fixed effects (Stock & Watson, 2015, p. 418).

The dataset contains observations of areas that have received foreign aid and areas that have not received such aid. However, just comparing these two types of observations cross- sectionally may lead to bias. This is partly because foreign aid is not distributed randomly.

Donors choose project locations based on several factors. For example, donors might choose conflict zones if a project is directed at conflict resolution, and more peaceful areas for other projects. Furthermore, the areas receiving foreign aid are likely to differ on many aspects as well, such as the level of poverty, education, or religion. Panel data is well suited to account for these differences. It makes it possible to observe the same grid cells over time, before and after having received foreign aid, thereby minimizing the differences.

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All regressions will be based on the following baseline fixed effects regression model 𝑌_𝑖𝑡 = 𝛽₀+ 𝛽₁𝑎𝑐𝑡𝑖𝑣𝑒_𝑖𝑡 + 𝛼_𝑖+ 𝛾_𝑡+ 𝑢_𝑖𝑡

where 𝑌_𝑖𝑡 is one of the three main conflict variables. The variable 𝑎𝑐𝑡𝑖𝑣𝑒_𝑖𝑡 is a dummy variable equal to 1 if there is an active aid project in grid cell 𝑖 in year 𝑡 and 0 otherwise. 𝛼_𝑖 are the grid cell fixed effects and 𝛾_𝑡 are the year fixed effects. 𝑢_𝑖𝑡 is the error term which will be clustered on the grid cell level.

In the regressions where the variable any conflict is used, the model becomes a linear probability model. 𝑌_𝑖𝑡 is then a dummy variable equal to 1 if there is conflict in grid cell 𝑖 in year 𝑡 and 0 otherwise. The regression coefficient 𝛽₁ is the change in the probability that 𝑌_𝑖𝑡 = 1 associated with a unit change in active, holding the other regressors constant (Stock

& Watson, 2015, p. 435).

4.2 Clustered standard errors

The conflict level in a grid cell is likely to be correlated over time. An area is likely to

experience a similar level of conflict from one year to the next. This means that the conflict level is autocorrelated. Autocorrelation can cause a problem as it violates the OLS

assumption of independence between observations. Since the error term contains omitted time-varying variables that are determinants of the dependent variable, it is likely that some of these omitted variables will be autocorrelated, making the error term autocorrelated as well. To account for this possible issue, the standard errors will be clustered on the grid cell level. Clustering the error terms allow them to have an arbitrary correlation within a cluster, without assuming any correlation across the clusters. In the case that there is no

autocorrelation, the clustered standard errors will still be valid (Stock & Watson, 2015, pp.

412-413).

4.3 Robustness check

It is difficult to know ex-ante when foreign aid has an effect. Does it have a direct effect the year it is initiated, or does it take some time? To investigate this, foreign aid will be lagged in some of the specifications. This allows me to check the timing of the results. Furthermore, I

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will run regressions with the variable forward active. This is a lead variable that will work as a placebo test. Future foreign aid should not have any effect on conflict today. If there is a correlation in these regressions, there might be some other underlying causes not included in the regressions that drive the results. It is also possible a potential correlation could due to post-conflict aid. This type of aid may be planned during conflict and initiated once the conflict is over. This may lead to an apparent effect of future aid on conflict today.

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Chapter 5 Results and discussion

In this chapter I will present my results and discuss them in light of the previous literature.

All results are based on OLS regression with grid cell and year fixed effects. Section 5.1 presents the main results, which are foreign aid regressed on the three different main conflict variables. Section 5.2 is the forward active regression used as a robustness check for the main results. Sections 5.3-5.5 look closer at different mechanisms that can impact the effect of foreign aid on conflict.

5.1 Main results

The main results, displayed in Tables 3-5, use the three different conflict variables as the dependent variables. All the regressions use grid cell and year fixed effects. The only independent variable is the foreign aid variable active. Active will also be lagged in the following regressions.

Table 3 shows the results of active regressed on battle deaths. The variable active has a significant effect on battle deaths in both the present and the lagged versions of the variable. The coefficient of active in column (1) shows that a foreign aid project on average can expect to lower battle deaths by 21.62. Column (1) also shows that the average number of battle deaths in grid cells that do not have an active aid project is 2.14. Foreign aid thus have a large impact on battle death reductions.

The lagged versions of the variable show coefficients in a similar magnitude. These coefficients are all significant at the 5 % level. Since both the lagged and present active variables have a significant impact, it suggests that foreign aid has an immediate, as well as a lasting effect in reducing battle deaths. Foreign aid is thus effective in lowering deaths on the battlefield the same year as the project is initiated.

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Table 3 Regression on battle deaths

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

VARIABLES Battle deaths Battle deaths Battle deaths Battle deaths

Active -21.62** -12.41**

(8.44) (5.22)

Active, lagged -21.83** -1.17

(9.40) (3.57)

Active, lagged 2 -19.62** -8.59**

(9.77) (4.18)

Observations 277,628 267,319 256,994 256,272

R-squared 0.04 0.04 0.05 0.05

Grid cell FE YES YES YES YES

Year FE YES YES YES YES

Mean for non active 2.14 2.13 1.97 1.97

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

In Table 4 the results of active regressed on number of conflicts are shown. There are no significant results except for the coefficient on active lagged twice in column (4), which is significant at the 10 % level. This coefficient may indicate that foreign aid can have a small and late effect in lowering conflicts in Africa. However, the coefficient is close to zero, and the remainder of the results in the table may suggest otherwise. Most of the coefficients are negative, indicating that foreign aid at least does not contribute to more conflicts. However, without any statistical significance it is difficult to determine any positive or negative effect.

Table 4 also shows that the grid cell years that do not have an active project experience on average 0.03 conflicts.

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Table 4 Regression on number of conflicts

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

VARIABLES Number of

conflicts

Number of conflicts

Active -0.00 0.01

(0.01) (0.01)

Active, lagged -0.01 0.02

(0.01) (0.03)

Active, lagged 2 -0.02 -0.05*

(0.01) (0.03)

Observations 277,628 267,319 256,994 256,272

R-squared 0.31 0.33 0.34 0.33

Mean for non active

0.03 0.03 0.03 0.03

*** p<0.01, ** p<0.05, * p<0.1

Table 5 displays the results of active regressed on any conflict. This is the binary variable equal to 1 if there has been a conflict in a grid cell over the course of the data set and 0 otherwise. It can be interpreted as the percentual risk of conflict. The results show that active does not have a significant effect on any conflict. The coefficients are also very close to zero, but negative. Column (1) shows that an active foreign aid project reduces the risk of conflict by 0.2 %.This indicates that foreign aid does not increase the risk of experiencing a conflict, but without statistical significance it is difficult to say anything certain about the

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effect. Table 5 also shows that 2 % of the grid cell years that do not have an active aid project experience conflict.

Table 5 Regression on any conflict

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

VARIABLES Any conflict Any conflict Any conflict Any conflict

Active -0.002 0.001

(0.003) (0.005)

Active, lagged -0.002 -0.006

(0.003) (0.005)

Active, lagged 2 -0.002 0.003

(0.003) (0.005)

Observations 277,628 267,319 256,994 256,272

R-squared 0.221 0.272 0.320 0.240

Mean for non active 0.02 0.02 0.02 0.02

*** p<0.01, ** p<0.05, * p<0.1

The results show that foreign aid does not have a significant effect in lowering the number of conflicts in Africa. It does however suggest that since the effect on battle deaths is

significant, that foreign aid may help reduce the intensity of the conflicts. The same number of conflicts are fought on the continent with foreign aid, but fewer people die in these conflicts.

A possible explanation for this is health aid. Increased capacity and better health care can lead to better treatment of the wounded. This would explain why there is not a reduction in the number of conflicts occurring, but nevertheless a reduction in battle deaths. However, there may be other reasons for this effect, such as increased economic activity. This will be examined in section 5.3.

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5.2 Forward active regression

To check the robustness of the results, the same regressions were run with the variable forward active. A foreign aid project should not have any effect on conflict prior to being implemented. If there is a correlation in these regressions, there might be some other underlying causes not included in the regressions that drive the results in the previous tables.

As can be seen in Table 6, the forward active variable is significant at the 10% level in the battle deaths regression. It is insignificant for number of conflicts and any conflict. The results mirror the previous regressions where the active variable only had a significant effect on battle deaths. This seems to confirm that there are no underlying causes that drive the results for number of conflict and any conflict.

A possible explanation for the significant coefficient in the battle deaths regression might be post-aid conflict. This is foreign aid given to help rebuilding a society after a conflict. It may be that post-conflict aid is not initiated before there has already been a decrease in the intensity of the conflict, thereby resulting in a correlation on battle deaths.

Table 6 forward active regression

(1) (2) (3)

VARIABLES Battle deaths Number of conflicts Any conflict

Forward active -10.03* 0.00 -0.00

(5.32) (0.01) (0.00)

Observations 266,950 266,950 266,950

R-squared 0.05 0.33 0.23

Grid cell FE YES YES YES

Year FE YES YES YES

Mean for non active 2.15 0.03 0.02

*** p<0.01, ** p<0.05, * p<0.1

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5.3 The role of economic activity

In this section I will look at the theory of opportunity cost. First, I will discuss whether increased economic activity reduces conflict and then whether the effect of economic activity on conflict runs via foreign aid.

Table 7 displays the results of nightlights regressed on the conflict variables. Since the regressions include fixed effects, they capture changes in light on changes in conflict.

Nightlights have a clear impact on all the measures of conflict. While the impact is fairly small, it is significant at the 5 % level for battle deaths and number of conflicts, and at the 1

% level for any conflict. A unit change in nighttime lights is associated with a 1 % decrease in the probability of having experienced a conflict over the course of the data set. Furthermore, change in nighttime lights will also lead to one less death on the battlefield. The nightlights variable has also been lagged to see if it takes time before increased economic activity will affect conflict. While the lagged variable is significant at the 1 % level for any conflict, it seems that economic activity has a more immediate effect on the various conflict variables.

These results are well in line with the opportunity cost argument. There is a strong correlation between increased economic activity and lower levels of conflict. Increased economic activity provide more opportunities and increases the opportunity costs for joining a rebellion. This reduces the conflict levels.

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Table 7 Nightlight regression on various conflict measures

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

VARIABLES Battle deaths

Battle deaths

Number of conflicts

Any conflict

Any conflict Nightlights

mean

-1.01** -0.02** -0.01***

(0.43) (0.01) (0.00)

Nightlights mean, lagged

0.17 -0.02* -0.01***

(0.42) (0.01) (0.00)

Observations 234,916 234,916 234,916 234,916 234,916 234,916

R-squared 0.05 0.05 0.37 0.36 0.26 0.25

Grid cell FE YES YES YES YES YES YES

Year FE YES YES YES YES YES YES

Mean for non active

1.95 1.99 0.03 0.03 0.02 0.02

*** p<0.01, ** p<0.05, * p<0.1

These results raise the question of whether the effect of foreign aid and fewer battle deaths run via economic activity. A common goal of foreign aid is to raise economic development, and the correlation between aid and conflict might be connected to economic activity, proxied by nightlights. However, regressions of active on nightlights in Table 8 show that there is not a significant correlation between foreign aid and economic activity. Accordingly, this possible link does not seem to be the case.

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Table 8 Regression on nightlights

(1) (2) (3)

VARIABLES Nightlights Nightlights Nightlights

Active 0.01

(0.02)

Active, lagged 0.01

(0.02)

Active, lagged 2 0.02

(0.02)

Observations 234,916 234,916 234,916

R-squared 0.93 0.93 0.93

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

5.4 The role of distance from capital

Previous literature (Findley, Powell, Strandow, & Tanner, 2011; van Weezel, 2015) have found that foreign aid is typically given close to the capital, while conflict happens in the periphery. I will therefore examine if foreign aid has different effects on conflict depending on whether a project is localized in the center or the periphery. Distance from the capital has been divided into two categories, one being closer to the capital than the median, which is 553 kilometers, and the other being further away. With this distinction, it is possible to see how effective foreign aid is when an active project is placed close to the capital compared to the periphery. Furthermore, an interaction variable, active*far away, has been created to see if the differences in the regressions are statistically significant. In this section I focus solely on the variable battle deaths. Additional results regarding the variables number of conflicts and any conflict are presented in Tables A.1-A.3 in the Appendix.

The regressions in Table 9 are run conditional on being further away from the capital than the median and the regressions in Table 10 are conditional on being closer to the capital than the median. An active foreign aid project seems to be more effective closer to the

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capital than it is in the periphery. Closer to the capital, battle deaths are reduced by almost 22, while the number is almost 7 in the periphery. However, the variable is only significant on the 10% level in the far away regression. Combining these results with the results presented in the Appendix, they show the same tendencies as the main results. Foreign aid does not reduce the number of conflicts, just the intensity of the conflicts, regardless of its placement. Furthermore, grid cells close to the capital that does not have an active foreign aid project experience an average of 3.04 deaths, while areas in the periphery without a project experience an average of 1.33 battle deaths.

Table 9 Far away regression

(1) (2) (3)

VARIABLES Battle deaths Battle deaths Battle deaths

Active -6.94*

(3.86)

Active, lagged -3.41**

(1.52)

Active, lagged 2 -3.00**

(1.48)

Observations 138,797 133,077 127,605

R-squared 0.20 0.28 0.11

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

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Table 10 Not far away regression

(1) (2) (3)

Active -21.97**

(10.46)

(11.65)

Active, lagged 2 -23.25*

(12.28)

Observations 138,831 133,873 128,667

R-squared 0.04 0.05 0.05

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

The differences between the effects of foreign aid in the center and periphery are large. To examine whether the differences are statistically significant, regressions with the interaction term active*far away are run. Note that the regression also includes the variable active. Far away itself is subsumed by the fixed effects as it does not vary over time. The results are shown in Table 11. They are significant at the 10 % level in column (1) and (3), and at the 5 % level in column (2). This implies that foreign aid is more effective in reducing conflict in the center compared to the periphery. A possible explanation may be that the government has more control in the center than in the periphery, making it easier to implement foreign aid projects there.

However, in Table 2, I found that both foreign aid and conflict in general occur closer to the capital than average. This seems to weaken the argument of van Weezel (2015) that the government has more control in the center, and thus conflict should occur in the periphery.

It is still possible that government control makes it easier to implement projects in the center, but nevertheless it does not seem to curb insurgencies.

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Another possible explanation for the large differences between the center and the periphery may then be that foreign aid is more effective in reducing conflict in the center simply

because this is where conflicts happen. Foreign aid cannot reduce conflict if there is no conflict. As both foreign aid and conflict is more present in the center than in the periphery, it is only logical that aid projects prevent more battle deaths there as well.

Table 11 Far away interaction regression

(1) (2) (3)

Active -25.51**

(10.59)

Active*Far_away 18.52*

(11.14)

(11.75)

Active*Far away, lagged 22.99**

(11.67)

(12.20)

Active*Far away 2 20.89*

(12.15)

Observations 277,628 266,950 256,272

R-squared 0.04 0.05 0.05

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

5.5 The role of exclusion

Previous literature (Asal et al., 2016; Wimmer et al., 2009) argue that countries with a high degree of ethnic polarization are more prone to civil conflict. I will therefore examine how foreign aid affects conflict outcomes in areas that exclude minorities, compared to areas that does not exclude. In order to do this, the variable exclusion was turned into a binary variable equal to 1 if there is no exclusion and 0 if there is exclusion in the grid cell. In addition, an interaction variable, active*not excluded, has been created to see if the

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differences in the regressions are statistically significant. This section will again only look at the variable battle deaths. Further results regarding the variables number of conflicts and any conflict can be found in Tables A.4-A.6 in the Appendix.

Table 12 displays the results of the regressions conditional on no exclusion. An active foreign aid project is expected to reduce battle deaths by 4. When the variable is lagged, foreign aid has a somewhat smaller effect, but it is still significant. Foreign aid, then, seems to have a lasting effect in reducing battle deaths. All the results are significant at the 5% level. Table 12 also shows that grid cells that do not have an active project experiences on average one battle death. Table 13 shows the results of the regressions when there is exclusion present.

An active foreign aid project is here expected to reduce battle deaths by 78. The lagged variables show a similar magnitude. The results are all significant at the 10% level. It also shows that grid cells without an active project experiences on average 4.76 battle deaths.

Including the results in the Appendix, these results once again follow the same trends as the ones presented in the main results.

Table 12 No exclusion regression

(1) (2) (3)

Active -4.08**

(1.75)

(1.37)

Active, lagged 2 -1.66***

(0.60)

Observations 127,996 124,136 120,114

R-squared 0.12 0.12 0.14

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

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Table 13 Exclusion regression

(1) (2) (3)

Active -78.55*

(46.22)

Active, lagged -80.38*

(45.90)

(41.98)

Observations 80,997 76,440 72,053

R-squared 0.13 0.13 0.12

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

There are large differences in the effects of foreign aid on expected battle death reductions in grid cells with and without exclusion. To examine if these differences are statistically significant, the regressions are run again with the interaction term, active*not excluded. The variable active is also included, but the exclusion variable is subsumed from the regression as it does not change over time. The results are presented in Table 14 and show that active*not excluded is significant at the 5% level in all the regressions. This suggests that foreign aid has a larger impact on conflict reduction in areas that experience some ethnic polarization. A possible explanation for this may be that different ethnic groups with the same economic possibilities may compete for the same limited resources. If this leads to conflict, foreign aid may reduce battle deaths by increasing the resources available and reducing the competition between the groups.

However, as I have previously found in section 5.3, foreign aid does not seem to increase economic activity. This theory would then be dependent on there being another way for foreign aid to increase resources than through economic growth. It could of course also be that nighttime light is not a good enough measure of economic activity and that there would have been an effect if there was a better measure available.

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It is also possible that foreign aid projects can be a natural place for socializing in a village, particularly if it is a school, hospital, or a marketplace. This could lead to people meeting across ethnic divisions, potentially leading to less hostility between groups.

The results showed that the average number of battle deaths in grid cells that do not have an active aid project are higher in the ones that experiences exclusion than the ones that do not experience exclusion. This leads to another simple possible explanation. Foreign aid may be more effective in reducing battle deaths in areas with the exclusion of minorities than in areas without exclusion, simply because this is where conflicts happen. Foreign aid cannot reduce conflict if there is no conflict.

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Table 14 No exclusion interaction regression

(1) (2) (3)

Active -44.63**

(17.91)

Active*Not excluded 26.03**

(12.93)

(19.71)

Active*Not excluded, lagged 30.02**

(13.86)

Active, lagged 2 -47.68**

(19.81)

Active*Not excluded, lagged 2 33.55**

(13.93)

Observations 208,993 200,576 192,167

R-squared 0.05 0.05 0.05

Year FE YES YES YES

*** p<0.01, ** p<0.05, * p<0.1

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Chapter 6 Conclusion

In this thesis I have examined how foreign aid affects conflict at a sub-national level in Africa.

In order to do this I used the UCDP GED version 19.1 dataset (Sundberg & Melander, 2013) and the World Bank Geocoded Research Release, version 1.4.1. dataset (AidData, 2016) in a fixed effects model. The results show that foreign aid does not reduce the number of conflicts on the continent, but it reduces the intensity of the conflicts. In the following I will summarize my findings, discuss the limitations, and possible ways forward for future research.

Researchers have not reached a consensus regarding the empirical effect of foreign aid. This is partly because the literature has largely relied on cross-country analyses. Foreign aid may have too small an effect to produce robust results in national level studies. While the

literature increasingly uses sub-national studies to account for the local effects, there are, to my knowledge, few studies examining aid and conflict at a local level previously. Included in this thesis are van Weezel (2015), who finds that aid has no significant effect on a number of different conflict outcomes, and Strandow et al. (2016) who find that areas that have a high concentration of aid experiences more military fatalities. This thesis has expanded upon this previous research, in an attempt to understand how foreign aid affects conflict outcomes in Africa at a sub-national level.

In this thesis I have used a fixed effects model with grid cell and year fixed effects, as well as clustered standard errors in all regressions. The main results show that foreign aid is not effective in reducing the number of conflicts that occur on the continent, but it is effective in reducing the number of battle deaths. Accordingly, an active aid project leads, on average, to 21.62 fewer deaths on the battlefield. This suggests that foreign aid reduces the intensity of the ongoing conflicts. Furthermore, I have found that foreign aid is more effective in reducing the intensity when a project is located close to the capital or in an area that experiences exclusion of minorities. Finally, I found that increased economic activity, as

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proxied by nighttime lights, is effective in reducing both the number of conflicts and battle deaths. However, this effect did not run via foreign aid.

Both datasets used in this thesis were geocoded, which allowed me to examine the effects of aid at a local level. This can control for differences across countries that could otherwise have led to spurious relationships, such as the levels of poverty or the quality of institutions.

Furthermore, civil conflict is often contained to one part of a country at a time, while other parts of a country can be relatively unaffected. It is then advantageous to be able to examine areas separately and not the whole country as one. In this thesis only observations at the ADM2 level or a more precise location have been used. This further reduces the differences that can affect the outcomes, and the results then come down to the differences between the affected grid cells in the country.

There were also some limitations inherent in these datasets. The UCDP GED dataset is based on news reports, which can make it subject to reporting bias. This can stem from which conflict events are reported or how an event is reported on. There may also be

inconsistencies in the quality and quantity of the reporting over time, further increasing the risk of bias occurring. The worst-case scenario of this is if the reporting on a conflict changes due to aid. International presence makes a conflict more likely to be reported on, potentially leading to an underestimation of the results.

This thesis has only covered foreign aid given from the World Bank. It is not unlikely that the grid cells examined before and after having received aid may have received aid from other donors than the World Bank at some point as well. In such cases, this may have influenced the results. The geocoding may have produced some bias in this dataset as well. Some types of projects can be difficult to include if they lack an exact location. As an example, this could be projects relating to infrastructure or general budget support aid. The areas examined may have received this type of aid, in addition to the aid projects examined, and may then have influenced the results as well.

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Finally, aid fungibility is always a potential problem when examining aid. There is a possibility that foreign aid may substitute government expenditures. This becomes a problem when evaluating foreign aid as it difficult to know how much a government would have spent without the aid.

More research is needed to understand how foreign aid affects conflict at a local level. The results in this thesis are not necessarily transferable to other donors on the continent or to other regions. For example, Alesina and Dollar (2000) have previously argued that political and strategic considerations trump economic need and policy performance when giving foreign aid. Subsequently, different donors are likely to focus on differing factors when giving aid. Future studies should therefore look at the mechanisms examined in this thesis from other donors or other regions.

Foreign Aid and Conflict in Africa

Foreign Aid and Conflict in Africa

Malin Renate Hatling

Master of Philosophy in Economics Number of credits: 30

Department of Economics Faculty of Social Science

Foreign Aid and Conflict in Africa

© Malin Renate Hatling

2020

Foreign Aid and Conflict in Africa

http://www.duo.uio.no/

Preface

Abstract

Contents

List of figures

List of tables

Chapter 1 Introduction

Chapter 2 Previous research

2.1 Cross-country analyses

2.2 Sub-national analyses

2.3 Theories

Chapter 3 Data

3.1 Conflict data

3.2 Foreign aid data

3.3 Other variables

Chapter 4 Method

4.1 Fixed effects model

4.2 Clustered standard errors

4.3 Robustness check

Chapter 5 Results and discussion

5.1 Main results

5.2 Forward active regression

5.3 The role of economic activity

5.4 The role of distance from capital

5.5 The role of exclusion

Chapter 6 Conclusion