FOREIGN AID ALLOCATION AND

CONFLICTS WITHIN LEAST DEVELOPED

COUNTRIES

Independent Study

Madiha Samadi

Table of Contents

INTRODUCTION ............................................................................................................................3 FOREIGN AID AND CONFLICTS IN LDCS: AN OVERVIEW ......................................................................... 3

Official Development Assistance (ODA) .............................................................................................. 4 Conflicts in LDCs ................................................................................................................................. 5

LITERATURE REVIEW ................................................................................................................................ 6

RESEARCH QUESTION .................................................................................................................7 VARIABLES AND DATA ............................................................................................................................. 7

CASE STUDY: AFGHANISTAN ................................................................................................... 10

EMPIRICAL MODEL ................................................................................................................... 14 AFGHANISTAN RESULTS ......................................................................................................................... 14 CONFLICT-LDC RESULTS ....................................................................................................................... 17

DISCUSSION ................................................................................................................................. 19

CONCLUSIONS ............................................................................................................................. 20

Tables Table 1: UN Definition of an LDC ................................................................................................ 3

Table 2: Non-conflict and Conflict LDCs ...................................................................................... 8

Table 3: Summary Statistics of Independent and Dependent Variables (Afghanistan) ............... 14

Table 4: Negative Binomial Regression of Sectoral Foreign Aid and Conflict Count ................ 15

Table 5: Summary Statistics of Independent and Dependent Variables (Conflict LDCs-X) ...... 17

Table 6: Negative Binomial Regression of Sectoral Foreign Aid and Conflict Count (LDCs-X)

....................................................................................................................................................... 17

Figures Figure 1: Net ODA Trend 2000-2016 ............................................................................................ 5

Figure 2: Net ODA Distribution to LDCs from DAC Countries 2000-2016 ................................ 5

Figure 3: Afghanistan GNI per capital compared to LDCs ......................................................... 10

Figure 4: Afghanistan Human Assets Index compared to LDCs................................................. 11

Figure 5: Afghanistan Economic Vulnerability Index compared to LDCs ................................. 11

Figure 6: Percentage of US Military Aid to Afghanistan from 2001-2010 ................................. 12

Figure 7: Percentage of Sectoral Foreign Aid by all donors from 2000-2016 ............................ 12

Figure 8: Sectoral Foreign Aid and Conflict Count 2000-2016 .................................................. 13

Figure 9: Government and Civil Service ODA (millions) and Conflict Instances Plot ............... 14

Per capita Income < $1,035

Human Asset Index (HAI) < 60

Economic Vulnerability Index (EVI) < 36

Introduction In Least Developed Countries (LDCs), how should foreign aid as Official Development

Assistance (ODA) be directed in order to decrease the instances of conflict within on-going

conflict countries? Overseas foreign aid remains the largest source of financing for LDCs.

However, in economic literature, little conclusive empirical evidence exists to support the pro-

growth effects from development assistance. Moreover, little economic literature exists on

studying the LDCs as a category in an attempt to identify commonalities between them which

may explain why many of them continue to remain in this category despite receiving millions of

dollars in aid.

The goal of this paper is to assess the impact of sectoral foreign aid on least developed countries

with on-going conflicts, using Afghanistan as a case study. Foreign aid data is collected from the

OECD1 and the conflict data is from the Uppsala Conflict Data Program

2. The regression results

indicate a general relationship between foreign aid and instances of conflict. Moreover, ODA has

a unique impact on instances of conflict particularly when targeted towards specific sectors such

as education, health, energy, and business and financial services. However, a two-way causality

between conflict and foreign aid may exist and the impact of sectoral aid on each other remain

unclear from this study.

The paper is organized in the following manner: the next section provides an overview of LDCs,

foreign aid to LDCs, and on-going conflicts in LDCs, the second section provides a review of

literature, the third section outlines the research question and explains the data and variables, the

fourth section is a case analysis of Afghanistan, the fifth section introduces the empirical model

and the results, while the sixth section presents a discussion of the results.

Foreign Aid and Conflicts in LDCs: An Overview In 1964, the UN Conference on Trade and Development (UNCTAD) created the category of

LDCs during their first session.3 According to the UN, LDCs exhibit structural obstacles to

development and are characterized by the lowest indicators of socioeconomic development and

the lowest human development index ratings in the world. About half of the population in LDCs

live in extreme poverty.

Table 1: UN Definition of an LDC

The UN utilizes three criteria to classify a country as an

LDC: poverty, as measured by Gross National Income

(GNI) per capita and the overall level of resources

available in the country, human resource weakness

(based on nutrition, health, education, and adult literacy), and economic vulnerability.4

The LCDs consist of 32 countries in the African continent, 14 in Asia, and one in Latin America

and the Caribbean region. These countries include small oceanic states as well as conflict prone

1 http://www.oecd.org/about/

2 http://ucdp.uu.se/

3 https://www.un.org/development/desa/dpad/least-developed-country-category/creation-of-the-ldc-category-and-

timeline-of-changes-to-ldc-membership-and-criteria.html 4 https://www.un.org/development/desa/dpad/least-developed-country-category/ldc-criteria.html

“Flows to countries and territories which are provided by official agencies and each transaction

of which is administered with the promotion of the economic development and welfare of

developing countries as its main objective; and is concessional in character and conveys a

grant element of at least 25%.”

and fragile countries like Afghanistan and Yemen. Once a country becomes a member of this

category, it is likely to continue being a member for many years. Since the creation of the LDC

category, only five countries have progressed out: Cape Verde, Botswana, Maldives, Samoa, and

Equatorial Guinea. Two countries, Angola and Vanuatu, are scheduled to “graduate” in the near

future.5

Official Development Assistance (ODA)

While the types of structural impediments differ across the LDCs, collectively, LDCs rely

heavily upon ODA.

ODA is a statistic identified by the Development Assistance Committee (DAC) of the

Organization for Economic Co-operation and Development (OECD) to measure aid. The OCED

definition of ODA is:6

LDCs have little to no access to the international capital and financial markets. The severe

structural impediments within LDCs limit their capacity to attract financial flows from avenues

beyond foreign aid. Between 2000 and 2010, ODA to LDCs increased from $100 billion to $214

billion (Figure 1). 7

In 2014 alone, aid to LDCs was $177 billion. DAC countries alone

distributed $24 billion to LDCs in 2014 (Figure 2). 8

In an attempt to resolve this declining

trend, the donor community during the 2015 financing conference reasserted the UN target of

providing between 0.15% and 0.2% of their GNI to LDCs.

5 https://www.un.org/development/desa/dpad/least-developed-country-category/creation-of-the-ldc-category-and-timeline-of-

changes-to-ldc-membership-and-criteria.html 6http://www.oecd.org/dac/stats/officialdevelopmentassistancedefinitionandcoverage.htm#Definition 7 OECD (2017), Distribution of net ODA (indicator). doi: 10.1787/2334182b-en (Accessed on 21 October 2017) 8 OECD (2017), Distribution of net ODA (indicator). doi: 10.1787/2334182b-en (Accessed on 21 October 2017)

Figure 1: Net ODA Trend 2000-2016

Source: OECD, ODA Statistics

Figure 2: Net ODA Distribution to LDCs from DAC Countries 2000-2016

Source: OECD, ODA Statistics

Conflicts in LDCs

Regardless of the negative trend in ODA, what is challenging for the donor community is

transforming these funds into actual national development. Foreign aid is less likely to be

implemented into successful programs when security concerns, corruption leading to inequality,

weak local systems or a combination of the three are present. While poverty and intra-country

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1998 2000 2002 2004 2006 2008 2010 2012 2014 2016

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violence are victims of each other and together stunt the economic and well-being of LDCs,

simply providing billions in aid clearly has not freed LDCs from poverty or violence. To disrupt

the cycle of poverty and violence, conflicts need to be prevented before they begin and

developed countries need to identify sectors that would benefit the most from the ODA and

avoid the ones that only worsen the security situation.

Literature Review In economic literature, the role of foreign aid in economic development and growth is studied

extensively, yet continues to remain a debated topic. Some have highlighted its positive effects

on developing countries such as Chenery and Strout (1966)9. They studied empirical evidence

from LDCs and concluded that foreign capital and economic growth are positively related.

Others have found neutral effects (Easterly 200510

; Boone, 1996)11

while some researchers have

concluded that aid is negatively related to economic growth (Bobba and Powell, 200712

; Griffin

197013

; Leff 196914

). With multi-year data from 56 countries, Burnside and Dollar (2000)15

argued that foreign aid has a significantly positive relationship with economic growth, only if

these countries have good economic policies. Like Burnside and Dollar (2000), Dalgaard et al.

(2004)16

found that aid has a positive impact on growth in developing countries with favorable

structural characteristics. In contrast, Easterly (2004)17

used the same dataset to show that even

in good policy environments, foreign aid and growth have no positive relationship.

Overall, conclusions on the effects of foreign aid and economic growth vary depending on the

dataset and models, countries being studied and often focus on economic growth and reducing

poverty, two very large and broad variables to be studied from one dataset. It is a lot more

effective to study exactly where and how foreign aid brings about an impact to countries. Findley

et al (2010)18

therefore discussed the importance of sector-by- sector effectiveness of foreign aid.

They argued that since foreign aid plays a vast variety of roles, from improving infrastructure to

healthcare services, to building schools, studies should not be entirely focused on aid’s only role

as promoting economic growth.

Very little conclusive empirical evidence exists to support foreign aid’s growth enhancing

capabilities, especially to LDCs. Wamboye et al. (2014) studied the relationship between foreign

aid, legal origin and economic growth in Africa’s least developed countries. They offered three

conclusions: a) doubling the quantity of bilateral aid hurts Africa’s LDCs, regardless of their

9 Chenery, Hollis B., and Alan M. Strout.(1966) “Foreign Assistance and Economic Development.” The American Economic

Review, vol. 56, pp. 679–733. JSTOR, JSTOR, www.jstor.org/stable/1813524. 10 Easterly, W. (2005), "Can foreign aid buy growth?," Journal of Economic Perspectives. 11 Boone, Peter. (1995) “Politics and the Effectiveness of Foreign Aid.”doi:10.3386/w5308. 12 Bobba, Matteo and Powell, Andrew, Aid and Growth: Politics Matters (2007) 13 Griffin, K., & Enos, J. (1970). Foreign Assistance: Objectives and Consequences. Economic Development and Cultural

Change, 18(3), 313-327. 14 N. H. Leflf, (1969) "Dependency Rates and Savings Rates," Amer. Econ. Rev. 15 Burnside, C., & Dollar, D. (2000). Aid, Policies, and Growth. The American Economic Review, 90(4), 847-868. Retrieved from

http://www.jstor.org/stable/117311 16 Dalgaard, C.-J., Hansen, H. and Tarp, F. (2004), On The Empirics of Foreign Aid and Growth”. The Economic Journal, 114:

F191–F216. doi:10.1111/j.1468-0297.2004.00219.x 17 Easterly, W., Levine, R., & Rodman, D. (2004). Aid, Policies, and Growth: Comment. The American Economic Review, 94(3),

774-780. 18 Findley, Mike et. Al (2010) “To Empower of Impoverish? The Sector by Sector Effectiveness of Foreign Aid,” AidData

Oxford Conference.

legal origin; 2) quantity of multilateral aid is beneficial only in countries with British legal

origin; and 3) growth-enhancing effects of foreign aid are more likely to be present in former

British relative to French colonies. 19

From literature, the importance of sector-by-sector foreign aid and the lack of studies on LDCs

are identified. But how does sectoral foreign aid impact intra-country conflicts within LDCs?

Research shows that foreign aid has had destabilizing effects in Rwanda (Ezemenari 2008)20

and

the Democratic Republic of Congo (Matti 2010)21

.

A study by Collier and Hoeffler (2002)22

found that there is an indirect connection between

levels of foreign aid and armed conflict. Findley and Young (2011)23

, on the other hand, found a

direct connection between changes in aid and conflict. They researched how foreign aid affects

armed conflict. Their results show that sharp increases or decreases in aid potentially induce

violence. They conclude that negative aid shocks significantly increase the probability of armed

conflict onset in that it shifts the domestic balance of power between rebels and the government.

These decreases in aid make governments less able to commit to side payments to appease rebels

or invest in military expenditures to preserve the peace in the future.

Research Question

This paper attempts to combine sectoral foreign aid and intra-country conflict variables in the

context of on-going conflict countries within the LDC category, using Afghanistan as a case

study. Does providing ODA to the government sector increase the number of conflicts in a given

year? If a country benefits from the mining sector, would an increase in ODA for mining lead to

a decrease in the number of conflicts?

Variables and Data The main variables of interest are number of conflicts and sector-by-sector foreign aid.

This paper separates LDCs into conflict and non-conflict LDCs to truly observe the relationship

between the number of conflicts within a year and foreign aid allocation. A conflict country is

described as one that is currently or has recently experienced intra-country violence. Table 2

identifies conflict and non-conflict LDCs and is adapted from a United Nations paper.24

19 Wamboye, Evelyn. (2014), “Foreign aid, legal origin, economic growth and Africa’s least developed countries,” Progress in

Development Studies 14,4 pp. 335-357. 20 Ezemenari, K. (2008), “The Fiscal Impact of Foreign Aid In Rwanda: A Theoretical And Empirical Analysis,” Washington,

D.C: The World Bank 21 Mattie, Stephanie (2010), “Resources and Rent Seeking in the Democratic Republic of the Congo,” Third World Quarterly

Vol. 31 , Iss. 3. 22 Collier, P., & Hoeffler, A. (2002). Aid, policy, and growth in post-conflict societies. Washington, D.C.: World Bank,

Development Research Group. 23 Findley, Young. (2011), “Foreign Aid Shocks as a Cause of Violent Armed Conflict,” American Journal of Political Science,

Vol. 55 pp. 219-232. 24 http://www.un.org/en/development/desa/policy/cdp/cdp_background_papers/bp2012_13.pdf

Table 2: Non-conflict and Conflict LDCs

Data on conflicts within LDCs from 2002 – 2015 is extracted from the UCDP.25

Conflict is

measured as the total number of incidents per year and include armed and unarmed assaults,

assassinations, bombings/explosions, hijacking, hostage taking and facility/infrastructure attack.

Weapons used during these acts of violence include chemical, firearms, explosives/bombs and

vehicles. Targets of the conflicts include businesses, government, police, military, journalists,

private citizens, religious figures, airports and aircrafts, educational institutions, infrastructure

and NGOs.

`

25 http://ucdp.uu.se/

Sector-by-sector ODA is collected from the OECD database.26

Foreign aid data used is in USD

millions and is disbursed from the “All donors” category in the database. This study uses a total

of nine sectors: Education, Healthcare, Government & Civil Society, Economic Infrastructure &

Services, Energy, Banking & Financial Services, Agriculture, Mining, and Humanitarian Aid.

The following paragraphs will describe what these sectors consist of.

Education

The OECD breaks down aid to education into sub-sectors which include education facilities,

teacher training, policy and administrative management and research. The sub-sectors are further

broken down by basic, secondary, and post-secondary levels.

Health

Health sector is defined as aid to health policy and administrative management, medical

education/training, research and basic health level which includes infrastructure, nutrition,

infectious disease control and health personnel development.

Government & Civil Society

The government and civil society sector includes public sector policy and administrative

management, public finance management, anti-corruption organizations and institutions, legal

and judicial development, democratic participation and civil society, elections, legislatures and

political parties, media and free flow of information, human rights, women’s equality

organizations, as well as a conflict, peace and security level.

Economic Infrastructure & Services

The OECD divides this sector into transport and storage grouping, which includes road, rail,

water, air and education and training in transport and storage levels, and communications

grouping. The communications grouping includes communications policy and administrative

management, telecommunications, radio/television/print media, and information and

communication technology.

Energy

The energy grouping includes ODA towards policy and administrative management,

education/training, research, conservation, generation, hydro-electric power plants, solar energy,

wind energy, marine energy, geothermal energy, coal-fired electric power plants, oil-fired

electric power plants and nuclear energy electric power plants.

Business and Financial Services

The business and financial services sector includes business support services and institutions,

privatization, financial policy and administrative management, monetary institutions, formal

sector financial intermediaries and education/training in banking and financial services.

Agriculture

The OECD describes this sector by combining agriculture, forestry, and fishing. This study,

however, only used the agriculture grouping. This grouping includes agricultural policy and

26 http://stats.oecd.org/qwids/

administrative management, development, land resources, inputs, food crop production,

industrial/export crops, livestock, research, alternative development, and reform efforts.

Mineral Resources & Mining Grouping

This sector includes mineral/mining policy and administrative management, mining prospection

and exploration, coal, oil and gas, ferrous metals, industrial minerals, fertilizer minerals, precious

metals/materials, as well as off-shore minerals.

Humanitarian Aid

The OECD includes the following in this grouping: emergency response, reconstruction relief

and rehabilitation and disaster prevention and preparedness subsectors. The emergency response

group includes material relief assistance and services, emergency food aid, and protection and

support services.

Case Study: Afghanistan Afghanistan remains one of the major recipients of foreign aid donated by a variety of countries

and organizations. The war in Afghanistan is the longest war in the United States history,

spanning seventeen years from 2001 to the present. Following the September 11 attacks in 2001,

the US along with a coalition of over 40 countries have been involved in this war and have

appropriated more than $100 billion in aid to reconstruct Afghanistan and rebuild its economy.

In fiscal year 2012 alone US contributed $9.95 billion in non-military aid to Afghanistan.27

However, high levels of political and systemic instability as well as extremist violence remain

pervasive across Afghanistan. Despite the world’s military and financial investment, Afghanistan

remains a country that ranks the lowest in the LDC category with GNI per capita of $672, HAI

of 43.1, and EVI of 35.1 (Figures 3-5).

Figure 3: Afghanistan GNI per capital compared to LDCs

27 http://time.com/43836/afghanistan-is-the-big-winner-in-u-s-foreign-aid/

$- $500.00 $1,000.00 $1,500.00 $2,000.00 $2,500.00 $3,000.00

Afghanistan

LDCs

Graduation Threshold

GNI per capita

Source: UN Afghanistan profile - LDC Status28

Figure 4: Afghanistan Human Assets Index compared to LDCs

Source: UN Afghanistan profile - LDC Status

Figure 5: Afghanistan Economic Vulnerability Index compared to LDCs

Source: UN Afghanistan profile - LDC Status

Afghanistan suffers from multiple poverty traps due to conflict, poor governance and its isolated

geography. As the outlier in the LDC category in receiving the most Aid yet ranking the least,

Afghanistan is used as a case study in this paper. Unlike other LDCs, Afghanistan’s aid is

predominately in the form of military aid. Figure 6 shows that US aid for military expenditure

makes up 95.87% of total US aid to Afghanistan.

28 https://www.un.org/development/desa/dpad/category/ldcs/

0 10 20 30 40 50 60 70

Afghanistan

LDCs

Graduation Threshold

HAI

0 5 10 15 20 25 30 35 40 45

Afghanistan

LDCs

Graduation Threshold

EVI

Figure 6: Percentage of US Military Aid to Afghanistan from 2001-2010

Source: OECD Statistics

Figure 7 demonstrates the percentage of foreign aid by all donors to the following sectors:

Mineral Resources and Mining, Education, Health, Government & Civil Society, Energy,

Agriculture, and Humanitarian Aid. As an agrarian economy in which the majority of the

population rely on agriculture to earn a livelihood,29

it is surprising to witness foreign aid

towards Afghanistan’s primary driver of economic growth to only amount to 4.93%. Similarly,

Afghanistan holds over $1 trillion in proven untapped mineral deposits yet foreign aid from

2000-2016 is only 0.33%. The government & civil society sector has received 33.05% of all

foreign aid despite Afghanistan holding the 7th

most corrupt country ranking in the world.

Figure 7: Percentage of Sectoral Foreign Aid by all donors from 2000-2016

Source: OECD Statistics

29 https://www.usaid.gov/afghanistan/agriculture

4.13%

95.87%

US Military Aid to Afghanistan

Other sectors Military

0.33%

5.11%

4.13%

33.05%

3.19%

4.93%

13.29%

35.98%

Percentage of Foreign Aid by Sector, 2000-2016

MinRes&Mining Education Health Govt Energy Agriculture Humanitarian Aid Other

0

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

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Conflict and Sector-by-Sector Foreign Aid

Counts Govt Agriculture Energy

Humanitarian Aid Health Education MinRes&Mining

Business Financial Services Econ Infrastructure

Figure 8 depicts the sector-by-sector foreign aid and conflict count trend in Afghanistan from

2000-2016. Conflict count refers to the number of instances of conflict that occurred in a given

year.30

The relationship between conflict count and aid to the government and civil society seem

to be related to each other. As aid for government and civil society administration increases, the

number of conflicts in a given year does so as well. A simple plot of government and civil

service ODA in millions and conflict count is shown in Figure 9.

Figure 8: Sectoral Foreign Aid and Conflict Count 2000-2016

Source: OECD Statistics and UCDP Database

30 Having been raised in Afghanistan, I know that these conflict counts are incorrect. There have been many

instances where the actual number of fatalities have not been reported to the media or instances of conflict have not

been reported at all.

Figure 9: Government and Civil Service ODA (millions) and Conflict Instances Plot

Empirical Model The following multiple regression model was applied to the sectoral foreign aid and conflict

count data of Afghanistan and the rest of the conflict LDCs (22 countries, excluding

Afghanistan) from 2000-2016.

Conflict Countit =β1Edui + β

2Miningit + β

3Health it + β

4Govt it + β

5Agri it + β

6Human it + β

7Energy it

+ β8Infra it + β

9BusFin it

The independent variables are non-military foreign aid disaggregated into nine sectors and

expressed in USD millions. The dependent variable is conflict count which refers to the number

of instances of conflict that occur within the countries in a given year. The regression results of

Afghanistan are discussed first and then compared to the regressions of the remaining conflict

LDCs.

Afghanistan Results Table 1 reports the cumulative min, max, average and standard deviation of the independent and

dependent variables for Afghanistan. The sample size consists of 17 observations for each

variable. The year 2016 was Afghanistan’s most violent year with 2,423 recorded instances of

conflict. Within that year, the government and civil society, along with the humanitarian aid

sector, received the most aid whilst the mineral resources and mining and the energy sectors

received the least. In general, the mineral resources and mining sector receives the least amount

of aid and the government and civil society obtains most of the money.

Table 3: Summary Statistics of Independent and Dependent Variables (Afghanistan)

Count BusFin Infra Mining Edu Health Govt Energy Agri Human

Min 207 2.185 13.74 0.8 6.2 13.4 113.1 1.23 1.43 233.3

Median 1114 32.122 671.13 5.32 215.34 201.43 1363.4 101.34 189.3 502.8

Mean 1273 33.3 625.66 12.99 202.93 163.97 1313.6 126.84 196.05 528.2

Max 2423 129.84 1164.52 52.53 435.37 327.71 2749.2 337.13 571.68 841.5

Std. Dev. 851.7627 29.91003 407.3962 15.32633 148.005 102.2526 913.6589 112.4884 177.5566 197.5451

# 17 17 17 17 17 17 17 17 17 17

As an over dispersed count variable with variance greater than its mean, the dependent variable

(conflict count) will be appropriate in a negative binomial regression. The instances of conflicts

is distributed in a way that an ordinary least squares estimation would not make sense. Table 4

displays the effect of sectoral aid on conflict count in a negative binomial regression. In model 2,

the variables are lagged to account for time effects since sectors take a year to bring about an

effect in subsequent years. The regression is performed for a total of 170 observations (10

categories, 17 years).

Table 4: Negative Binomial Regression of Sectoral Foreign Aid and Conflict Count

Note: *, ** and *** indicate significance at 10% level, 5% level, and 1% level, respectively

Variable Model 1

Model 2

(Lagged)

Intercept 5.596 *** 5.894 ***

Edu -0.00277 ** -0.00264 ***

Mining -0.0175 ** -0.0231 ***

Health 0.00334 *** 0.00369 ***

Govt 0.00122 *** 0.00102 ***

Agri -0.000944 -0.000349 **

Human -0.000726 *** -0.000945 ***

Energy 0.00140 * 0.00146 **

Infra 0.000634 *** 0.000869 ***

BusFin -0.00377 ** -0.00549 ***

Table 4 shows that even if foreign aid is not provided to any of the nine sectors, the intercept

remains positive and statistically significant for both models. It can be understood that instances

of conflict will occur regardless of foreign aid to any of the nine sectors. With that in mind, the

next section discusses the multiple regression and estimated relationship between the

independent and dependent variables, assuming that foreign aid may increase or decrease the

number of conflicts in a given year in Afghanistan.

Education

The estimated coefficient for the education sector is negative and significant at the 1% level

under the one-year lagged model. Keeping foreign aid to all other sectors constant, for every

one-percent increase in education aid, the expected log count of the number of conflict instances

decreases by 0.00277. It is implied that providing foreign aid to the education sector can

significantly reduce conflict counts. This needs to be compared to the coefficients of other

conflict LDCs in the event that Afghanistan may be an anomaly.

Mineral Resources and Mining

Like education, the relationship between aid for the mineral resources and mining sector and

conflict is negative. The one year lagged model shows that aid for mining significantly decreases

conflict at the 1% level in the lagged model. This shows that foreign aid takes time to bring

about an effect in the mining sector to truly decrease conflict instances.

Health

The estimated coefficient of health is positive and statistically significant in both models with the

lagged model showing a slightly larger value. For every one-percent increase in aid for the health

sector, the expected log count of the number of conflict instances increases by 0.00369. It is

therefore implied that the donor community’s investment in Afghanistan’s health sector will

increase the number of conflicts. However, it is difficult to ascertain whether investing in this

sector improves the well-being of the country which would then make more conflicts possible.

That relationship is difficult to gauge from this regression and should be studied further.

Government & Civil Society

Similar to the health sector, aid for the government and civil society sector significantly

increases conflict count at the 1% level in both models. For every one percent increase in aid for

government, the expected log count of conflicts increases by 0.00102. The donor community

needs to especially monitor how aid is being utilized by Afghanistan’s government sector given

its history of corruption and tragic inefficiency.

Agriculture

The negative binomial regression shows that the estimated coefficient of the agriculture variable

is negative. The coefficient is insignificant under model 1 but its negative relationship becomes

statistically significant at the 5% level. Investing in agriculture could potentially bring about less

conflicts and violence in the future.

Humanitarian Aid

The coefficient for the humanitarian aid variable is negative for both models, with the lagged

model depicting a larger decrease in the number of conflicts. Both coefficients are statistically

significant at the 1% level.

Energy

Aid for the energy sector is positively related to the number of conflicts. A one-unit increase in

foreign aid results in an increase of 0.00146 in expected log count of conflicts. The lagged model

depicts a higher statistically significant positive relationship at the 1% level.

Economic Infrastructure & Services

Like energy, the estimated coefficients of the economic infrastructure and services sector are

significant at the 1% level for both models. The table suggests that increasing aid to this sector

by one unity would result in an increase in conflict count by 0.000869.

Business and Financial Services

Aid for business and financial services sector is negatively related to conflict count. The

coefficients under both models for this sector are negative. The coefficients are marginally

significant at the 5% level for model but statistically significant at the 1% level for the lagged

model.

Conflict-LDC Results Table 5 reports the cumulative min, max, average and standard deviation of the independent and

dependent variables of the conflict LDCs excluding Afghanistan, hereon afterwards referred to

as LDCs-X. The sample size consists of the following 15 countries: Burundi, Central African

Republic, Chad, Democratic Republic of Congo, Eritrea, Ethiopia, Guinea, Guinea-Bissau,

Liberia, Mozambique, Sierra Leone, Somalia, Sudan, Rwanda and Uganda. The following seven

countries were excluded from the analysis due to lack of data on their counts of conflict from the

UCDP database: Angola, Cambodia, Comoros, Haiti, Nepal, Solomon Islands and Timor-Leste.

Each country has data on counts of conflict and the nine sectors from 2002-2015. The table

shows that collectively, the Humanitarian Aid sector received the most aid. In general, the

Business and Financial Services along with the Mineral Resources and Mining sectors received

the least amount in ODA. The most violent country, based on the count variable, is the

Democratic Republic of Congo with a total of 1,875 recorded instances of conflict.

Table 5: Summary Statistics of Independent and Dependent Variables (Conflict LDCs-X)

Count BusFin Infra Mining Edu Health Govt Energy Agri Human

Min 10 0.001 0.036 0.004 1.383 3.193 1.30 0.0251 0.007 0.040

Median 33 2.026 49.759 0.751 13.591 36.946 66.79 8.373 16.237 59.207

Mean 72.2 8.615 104.786 6.529 61.266 83.978 83.24 31.069 38.878 141.753

Max 547 173.304 592.912 4.866 678.695 648.992 783.87 202.118 386.221 1558.080

Std. Dev. 87.824 19.368 127.303 16.394 84.913 112.768 82.612 46.161 56.026 201.111

# 17 17 17 17 17 17 17 17 17 17

Similar to the Afghanistan case analysis, the conflict count variable is over dispersed and needs

to be assessed in a negative binomial regression. Table 6 displays the relationship between

sectoral aid and conflict count in a negative binomial regression. In model 2, the variables are

lagged to account for time effects since sectors take a year to bring about an effect in subsequent

years. The regression is performed for a total of 2,100 observations (10 categories, 15 countries,

14 years).

Table 6: Negative Binomial Regression of Sectoral Foreign Aid and Conflict Count (LDCs-X)

Note: *, ** and *** indicate significance at 10% level, 5% level, and 1% level, respectively

Variable Model 1 Model 2 (Lagged)

Intercept 4.118 *** 4.082 ***

Edu -0.00482 ** -0.00490 **

Mining 0.00124 0.00365

Health 0.00205 0.00111

Govt -0.00094 -0.00141

Agri 0.00602 * 0.00542 **

Human 0.00112 ** 0.00136 ***

Energy 0.00443 0.00422

Infra -0.00312 -0.00197

BusFin -0.00951 -0.00549 *

Like the Afghanistan case analysis, Table 6 shows that the intercept for both models remain

positive and statistically significant. Regardless of aid to the nine sectors, conflicts exist within

the countries. The following section discusses the multiple regression and estimated relationship

between the independent and dependent variables, assuming that foreign aid may increase or

decrease the number of conflicts in a given year.

Education

The relationship between instances of conflict and aid to the education sector is negative and

significant at the 5% level under both models. Holding foreign aid to the remaining sectors

constant, for every one percent increase in aid for education, the expected log count of the

number of conflict instances decreases by 0.00482. The data suggest that aid to the education

sector has the potential to decrease instances of conflict in conflict LDCs-X. The donor

community should direct their attention towards this sector in order to ensure LDCs-X graduate

from their category in the future.

Mineral Resources and Mining

The estimated coefficient for the mineral resources and mining sector is positive but

insignificantly related to the dependent variable. Keeping foreign aid to all other sectors

constant, for every one percent increase in mineral resources and mining sector aid, the expected

log count of the number of conflict instances increases by 0.00365 under the lagged model.

Health

Like the mining variable, ODA to the health sector is positively related to instances of conflict

but is insignificant.

Government & Civil Society

The estimated coefficient for the government and civil society sector is negative and

insignificant for both models. The regression implies that providing aid to governance leads to a

decrease in the number of conflicts.

Agriculture

Aid for the agriculture sector is positively related to the number of conflicts. A one unit increase

in foreign aid results in an increase of 0.00542 expected log count of conflicts. The lagged model

shows a higher statistically significant relationship at the 5% level.

Humanitarian Aid

Like agriculture, the humanitarian aid variable shows a positive relationship between aid and the

number of conflicts for both models. The coefficients are marginally significant at the 5% level

for model 1, but statistically significant at the 1% level for the lagged model.

Energy

The estimated coefficient for the energy sector is positively related to the dependent variable, but

is insignificant.

Economic Infrastructure & Services

Aid towards economic infrastructure and services is depicted to have a negative relationship with

conflict count, but is insignificant.

Business and Financial Services

Aid for business and financial services sector is negatively related to conflict count. The

coefficient is insignificant under model 1 but statistically significant at the 10% level for the

lagged model.

Discussion Based on the regressions, aid for Afghanistan and the LDCs-X share the following similarities:

(i) Aid to the education sector leads to a statistically decrease in instances of conflict

(ii) Aid to the health sector increases numbers of conflicts and is statistically significant

for Afghanistan but insignificant for the LDCs-X.

(iii) Aid to the energy sector increases instances of conflict and is insignificant for LDCs-

X.

(iv) Aid to the business and financial services sector decreases numbers of conflicts for

both Afghanistan and the LDCs-X.

Apart from the four sectors outlined above, the coefficients of every other sector are the

opposites of each other. For Afghanistan, providing aid towards the mining sector leads to a

decrease in the numbers of conflicts whereas the data for LDCs-X shows a positive relationship

between conflict count and aid to the health sector.

The differences show that it is difficult to assess the effects of aid to countries that differ in

geography, size and reasoning behind the conflict. For example, as a country with a proposed

trillion dollar mineral reserves, aid to the mining sector of Afghanistan should theoretically

decrease its number of conflicts. This relationship, however, may not be observed in conflict

nation such as Guinea, whose economy is not dependent upon mining. It is important for the

donor community to identify sectors which are unique to every country’s geography and

standing that would lead to economic development and a decrease in violence.

Another difficulty to address is the question of the relevance of the conflict. Over the past two

decades, Afghanistan has been a point of interest by the international community. In this case,

donors understandably are to give more aid to countries with higher instances of violence and

terrorism. The data for foreign aid to Afghanistan and the instances of conflict are well-

documented.

In addition, it is important to know that different sectoral aid may have an impact on one another

and error terms to be correlated within the explanatory variables. The issue of causality also

comes into play in that while foreign aid may decrease the number of conflicts in a given year by

promoting economic empowerment, it could also alter power relationships within fragile

countries and thereby increasing conflicts. Moreover, in a given year, conflicts could have

occurred due to an external shock which had nothing to do with foreign aid but did lead to an

increase in conflict. To disentangle the effects of external country-related shocks and movements

in foreign aid, a different model is proposed.

In a revised model, dummy variables could be included to account for both country and time

effects.

Conflict Countit =β1Edui + β

2Miningit + β

3Health it + β

4Govt it + β

5Agri it + β

6Human it + β

7Energy it

+ β8Infra it + β

9BusFin it + factor (Country) + factor (Year)

This revised model could account for the endogeneity.

Conclusions In order for foreign aid to be most beneficial to conflict LDCs, including Afghanistan, foreign

aid allocation needs to target sectors that are most beneficial to an LDCs geography, history,

size, and circumstance. This paper’s result indicate a relationship between conflict and foreign

aid and attempts to show that by disaggregating foreign aid via sectors, the donor community can

study further the effects of their ODA to the level of violence within each country.