INTERNALLY DISPLACED PERSONS CAMP POPULATION DENSITY and MULTIDIMENSIONAL POVERTY in NORTHERN SYRIA a Thesi

FORGOTTEN CITIZENS: INTERNALLY DISPLACED PERSONS CAMP POPULATION DENSITY AND MULTIDIMENSIONAL POVERTY IN NORTHERN SYRIA

A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Arts in Public Policy

Mahir Ali Sheikh B.A.

Washington D.C. April 13, 2021

FORGOTTEN CITIZENS: INTERNALLY DISPLACED PERSONS CAMP POPULATION DENSITY AND MULTIDIMENSIONAL POVERTY IN NORTHERN SYRIA Mahir Ali Sheikh, B.A.

Advisor: Stipica Mudrazijia Ph.D.

Abstract The Syrian humanitarian emergency is one of the largest in recent memory and has deteriorated beyond expectations leaving millions displaced. According to UNHCR, there are 6.6 million

Syrian Internally Displaced Persons (IDPs), many of whom do not have access to basic needs and services to escape protracted displacement. Those who cannot find shelter in urban environments create informal campsites or migrate towards camps set up by humanitarian organizations throughout the country. Campsites increase access to resources and create a stable community that in turn increases economic and social opportunities. Yet, Syria's volatile situation has led camps to be overcrowded, in turn, limiting resources and having downward pressure on livelihoods. The higher population density in these camps increases poverty levels and limits this population's upward mobility opportunities that have already been devastated by war and violent conflict.

Therefore, this thesis aims to test the hypothesis that increased population density within IDP camps is associated with poverty. Data from the Assistance Coordination Unit (a non-profit based in Syria) provides insight into IDP camps' access to basic needs and essential services and population dynamics, giving clarity on the current situation of Syrian IDPs located in the Northern governates of Idlib and Aleppo. This thesis will utilize the Multidimensional Poverty Index (MPI) to identify which camps are multidimensionally poor given access to health, shelter, water, sanitation and hygiene, assets, and education. Once poverty is determined on a camp-to-camp basis, the relationship between population density and poverty will be analyzed to test this thesis's hypothesis and identify the magnitude of the association between the two variables if one exists.

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Acknowledgments Thank you to my professors at Georgetown (Elizabeth Ferris, Jeffrey Glick, Jennifer Wistrand, Franck Weibe) who have provided me with the knowledge to explore humanitarian emergencies and the policies that impact the lives of millions. A special thanks to my advisor Stipica Mudrazijia, Eric Gardner and Pooya Almasi who have supported me and guided me throughout this process. Thank you to my family and friends who have supported me and encouraged me throughout graduate school helping me find the motivation to complete my education at Georgetown University.

Table of Contents Introduction ...... 1

Background ...... 3

Defining an Internally Displaced Persons...... 3

The Case of Syria ...... 3

Internal Displacement ...... 4

Literature Review 5

Factors Causing Displacement ...... 5

Limitations to Humanitarian Assistance ...... 6

Internally Displaced Persons Camps, Populations Density, and Poverty ...... 7

Conceptual Framework ...... 11

Data and Methodology ...... 13

Assistance Coordination Unit Data and Scope ...... 13

Using Multidimensional Poverty Index ...... 14

Variables of Interest ...... 16

Results ...... 18

Descriptive Results ...... 18

Inferential Results: Multidimensional Poverty Index and Population Density ...... 19

Inferential Results: Multidimensional Poverty Index and Camp Area ...... 20

Discussion ...... 23

Discussion of Findings ...... 23

Limitations ...... 24

Policy Recommendations ...... 26

Conclusion ...... 29

Appendix ...... 31

References ...... 38

List of Figures

Figure 1: Implications of Violent Conflict on Displacement and Poverty ...... 12

Figure 2: Visual Representation of Challenges Associated with IDP Classification ...... 25

Figure 3: Structure of UNDP Multidimensional Poverty Index ...... 32

Figure 4: Camp Population Descriptive Statistics ...... 34

Figure 5: WASH Descriptive Statistics ...... 34

Figure 6: Education Descriptive Statistics ...... 35

Figure 7: Shelter Descriptive Statistics ...... 35

Figure 8: Assets Descriptive Statistics ...... 36

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List of Tables

Table 1: Outcomes of Camp-Based, Non-Camp Based IDPs and Host Communities ...... 10

Table 2: Indicators and Variables Used to Derive Multidimensional Poverty Index ...... 17

Table 3: Impact of Population Density on MPI using an OLS Regression Analysis ...... 21

Table 4: Impact of Camp Area on MPI using an OLS Regression Analysis ...... 22

Table 5: Classification for Camp Size ...... 31

Table 6: Summary Statistics of All Observed Variables ...... 33

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Introduction

At the end of 2019, there were 79.5 million individuals who were forcibly displaced, of which 26 million are refugees, while another 45.7 million are internally displaced persons (IDPs)

(UNHCR(b) 2020). In Syria's case, a vast number of the population cannot cross international borders and obtain refugee status leaving no choice but to stay within their home country where violent conflict exacerbates the humanitarian crisis. According to the United Nations High

Commissioner for Refugees (UNHCR), 6.6 million Syrian IDPs are outnumbering the total

Syrian refugee population (UNHCR(b) 2020). These IDPs are highly vulnerable due to instability and limited to no access to public services. For this reason, humanitarian organizations play a crucial role in mitigating some of the challenges that displaced populations face.

Specifically, IDP camps provide housing and resources to those who have lost close to everything and do not have any alternatives. The magnitude of the war and the damage done to public infrastructure has limited Syrian nationals' access to shelter, the documents needed to cross international borders or to identify themselves, subsequently increasing reliance on organizations like UNHCR to provide essential services.

Yet, these camps (whether informal or set up by humanitarian organizations) are meant to support a limited number of households and individuals. High population densities within these camps attribute to over-crowded shelters, limited resources, and deteriorating facilities that fail to meet basic needs and leave displaced populations in a state of acute poverty. With limited opportunities for upward mobility, IDP populations are stuck in situations of protracted displacement. The definition of protracted displacement is as follows: IDPs who are prevented from taking up or unable to take the necessary steps to reduce their vulnerability, impoverishment, and marginalization and find a durable solution to their state of displacement

(Kalin et al. 2017). Syrian IDPs have been in a state of protracted displacement as they cannot make strides to reintegrate into society or make a living due to economic and social constraints. As long as camps are overflowing with IDPs who are constrained by limited access to services and goods, displacement will continue to be a prolonged issue throughout Syria.

This thesis will analyze the impact of population density within IDP camps in Northern

Syria and its effect on poverty levels within those camps. These camps within Northern Syria are a part of the Idlib and Aleppo governates, two regions that the civil war has immensely impacted.

By using data provided by the Assistance Coordination Unit (ACU) 1 the impact of population density on poverty levels will be assessed on a camp-to-camp basis through the use of the

Multidimensional Poverty Index (MPI). MPI utilizes a range of indicators across sectors to determine acute poverty levels within households. This method will be scaled up to determine

MPI in camps, which will provide a clearer understanding of IDP camps' access to basic needs and how that translates to acute poverty and creates protracted displacement situations. This thesis hypothesizes that increased population density within IDP camps has downward pressure on access to services and resources, contributing to acute poverty.

1 ACU is a non-profit, non-governmental, non-political organization that focuses on maximizing the impact of assistance delivered to the Syrian people by coordinating the efforts of donors, implementing agencies, and community representatives (ACU 2020).

Background

Defining an Internally Displaced Persons

According to the Guiding Principles of Internal Displacement, “Internally displaced persons are recognized as persons or groups of persons who been forced or obliged to flee or to leave their homes or places of habitual residence, in particular, to avoid the effects of armed conflict, situations of generalized violence, violations of human rights or natural or man-made disasters, and who have not crossed an internationally recognized State border” (UNOCHA

2004). Those who are unable to cross international borders to obtain refugee status in countries that signed the 1951 Refugee Convention are forced to remain in their home country where violent conflict and poor economic conditions create limited opportunities for displaced populations.

The Case of Syria

The Syrian emergency is one of the most significant humanitarian crises and has left over

6 million displaced since the start of the civil war. The arrest and torture of 15 children that questioned the government and the President, Bashar al-Assad, instigated initial protests in 2011

(IDMC 2020). Following the arrest of those children large protests ensued, with the government responding with repressive tactics giving way to a civilian uprising resulting in a full-scale civil war. Since then, terrorist, and jihadi groups such as ISIS have risen, resulting in widespread violence and international law violations. In recent years, the war in Northern Syria has become even more concerning. The Syrian government planned to retake governorates like Idlib, yet international concern of a large-scale humanitarian crisis (specifically countries involved like

Iran, Russia, and Turkey) prompted an agreement to a ceasefire in 2018 (IDMC 2020). The agreement did not last as conflict resumed and created mass displacement (284,000 new displacements in December) (IDMC 2020). Since then, the situation has progressively worsened, creating more displacements once the U.S. withdrew troops from the country with an end to the war not in sight.

Internal Displacement

As displacement has risen throughout the country, patterns by which Syrian nationals move are influenced by numerous factors, including family ties, religious and ethnic affiliations, access to assistance, and territorial control of armed groups (IDMC 2020). With the possibility of violent conflict always looming, Syrian nationals must be able to move at a moment’s notice and are consistently leaving and reentering specific areas. Border closures throughout the country restrict international movement and force nationals to seek refuge in regions that are not impacted by violence. Lack of civil documentation, increased poverty, and family separation contribute to the increased vulnerability of IDPs (IDMC 2020). By 2018, there were 1.1 million

IDPs located in hard-to-reach locations, increasing the challenges humanitarian organizations face (IDMC 2020). Those IDPs living in camps or informal settlements are faced with overcrowding and deteriorating conditions. Increased population movements have led some camps to be four times above capacity, and with limited space and resources, several households live without roofs and other basic needs (IDMC 2020). As camp population densities rise, protracted displacement and high poverty levels will continue to be the trend in Syria.

Literature Review

Factors Causing Displacement

The displacement situation created by the Syrian civil war is one of the most significant humanitarian emergencies this century. Syrian nationals flee to neighboring countries in the

Middle East and Europe, while those who cannot leave the country relocate within worn-torn districts and governates. For those displaced internally, access to resources and emergency services is severely limited. One of the more significant issues contributing to the internal displacement dilemma is the global governance system used to identify and protect IDPs. The lack of a legal binding framework under international law (the guiding principles on internal displacement) has afforded IDPs less recognition and limited access to humanitarian services that would otherwise prove to be the difference in IDPs' deteriorating livelihoods in Syria (Akbarzada et al. 2018). Along with these legal gaps, the deterioration of public infrastructure (specifically the healthcare system) has exacerbated the problem. From 2011 to 2016, attacks on medical facilities throughout the country totaled 382, killing 757 medical personnel (Akbarzada et al.,

2018). Attacks and violent conflict have decreased the healthcare system's capacity and have contributed to increases in infectious diseases subsequently, making the IDP population more vulnerable. The Syrian constitution specifically identifies the provision and protection of health as a legal right, yet the civil war has violated this right and has left doubts as to whether the country's constitutional framework is enough to protect IDPs (Akbarzada et al. 2018). Lack of government support and public services deterioration contribute to acute poverty and has left

IDPs to rely on humanitarian assistance to meet basic needs.

Limitations to Humanitarian Assistance

When it comes to the IDP communities, populations live in urban areas rather than camps as most rely on public infrastructure for shelter. These individuals are under the jurisdiction of their home country, where the government is still held accountable to provide essential services.

Yet, in many cases, the countries with the highest IDP populations have unstable governments, high rates of corruption, and do not have the economic capacity to address displacement when issues like conflict and food insecurity must be addressed with the resources available.

Therefore, humanitarian assistance can only target a portion of those IDP populations in areas where conflict is minimal, and conditions are safe for humanitarian actors. In Syria, access to humanitarian aid becomes increasingly difficult due to closed borders, hostilities, explosive remnants, and restrictions imposed by the Syrian government (EASO 2020). Syria is also considered as one of the deadliest places to be an aid worker. In 2020, of the 74 fatalities of humanitarian aid workers recorded since the beginning of the year, Syria accounted for over 25% of those deaths (Hodal 2020). This increased violence against aid workers is due to terrorist groups ensuring that local populations must rely on them to obtain basic needs instead of international organizations or NGOs. At the same time, the Syrian government punishes those affiliated with terrorist organizations even though many individuals and households have no other choice. With aid workers more fearful for their lives, access to humanitarian assistance becomes a logistical nightmare and is especially detrimental to internally displaced populations.

Therefore, while humanitarian aid is available to IDPs in Syria, it is not as readily available compared to other countries due to widespread violent conflict.

Internally Displaced Persons Camps, Populations Density, and Poverty

With large numbers of IDPs scattered throughout Syria, this thesis will focus on those residing in camps in Northern Syria. Population density and camp size will be analyzed to understand how these factors can contribute to acute poverty. Without access to economic resources there is downward pressure on IDPs' health, education, security, housing, and social outcomes (Huang et al. 2019). Many IDPs displaced by conflict lack access to resources as the majority of the population (99% of all IDPs) come from low-and-middle-income countries

(LMICs) (Huang et al. 2019). With unstable conditions in their home countries, displacement marginalizes these populations further.

Throughout Northern Syria, camps are set up to address internal displacement and provide access to essential services and resources. The informal nature of many camps makes it difficult to create accurate depictions as all camps range in size, access, and layout. Table 5 provided by Cosgrave 19962 shows the daily requirements for food, water, area, and diameter needed to support those populations. The IDP camps in this study range from small to medium, with the largest camp population falling below 10,000 and the smallest being under 200. Table 5 shows that areas between 0 to 90 hectares are the ideal size needed to support populations ranging from 0 to 15,000 individuals. Generally, displaced populations must also rely on the local environment for resources. While humanitarian agencies can provide resources and services to the population, aid is often limited, so populations must fend for themselves more often than not. This includes upgrading shelters with wood, plastic tarps, and other resources found around campsites.

2 Cosgrave 1996 discusses refugee camp sizes in Africa as its baseline. In this case, Africa may have more or less resources depending on the humanitarian situation and funding, but the same logic for the needs (water, food, area, diameter) of specific population sizes can be used hand in hand for IDP populations in other countries, specifically Northern Syria.

A common misconception around IDPs is that those based in camps are better off than those who are not, given the assumption that those in camps have more access to services (Utz et al. 2019). Yet this is often not true as those who are in camps are the individuals that have the least resources and limited opportunities to improve their livelihood. Utz et al. 2019 clear up this misconception using IDP camp data in five African countries (Nigeria, Somalia, South Sudan,

Sudan, and Ethiopia). Utz et al. 2019 found that in four of the five countries analyzed, IDPs situated in camps were worse off than those in non-camp settings or the surrounding host communities. Table 1 provided by Utz et al. 2019 shows that camp populations are more impoverished and have less access to resources than non-camp populations and host communities. The first row in Table 1 shows poverty levels in each setting for IDPs across the five African countries, of which four country’s IDPs living in camps are poorer than their counterparts. 3 This emphasizes that while IDP populations have access to humanitarian agencies' resources, this does not immediately translate to improved livelihoods. Table 1 shows that overcrowded housing is also an issue in IDP camps than in host communities and non-camp settings. 4 In this study, three out of five countries have IDP camps with higher population densities than their non-camp counterparts and the host community. This fact gives more insight into the relationship this thesis will seek to explore between IDP camp population density and poverty.

In terms of poverty, population density along with the household size can be an efficient predictor. In a World Bank publication analyzing the welfare of Syrian refugees in Lebanon and

Jordan, the authors identify family size and housing as the best predictors of poverty (Paolo et al.

2016). In their findings, family size in Jordan doubled the poverty rate if the family size goes up

3 These values provided in Table 1 are statistically significant at the 10 percent, 5 percent, and 1 percent level. 4 The values provided in Table 1 for overcrowded house in this study were significant at the 10 percent, 5 percent, and 1 percent level.

by one to two members and will increase by 17% if the household increases by one or two children (Paolo et al. 2016). While this finding is in refugees' context, it highlights how household size and population density are associated with poverty for displaced populations, which can be used for IDPs. Increased densities in camps are also associated with increased dependency upon resources. High population densities will create more competition for resources and make displaced populations more passive recipients of services and resources

(Cosgrove 1996). 5 In addition to increased competition over resources, increased densities also raise health concerns. Higher densities in turn increase exposure to infectious diseases due to individuals being in close proximity with those who are more vulnerable to these diseases

(Cosgrave 1996). Therefore, controlling outbreaks and limiting interactions become increasingly difficult, adding stress on already limited health services and resources. Cosgrave et al. 1996 emphasize this by providing studies exploring the association between larger camp sizes and mortality rates. Mercer et al. 1992 was able to find statistically significant evidence that larger refugee camp sizes were associated with high mortality rates in children ages 0 to 4 in Sudanese settlements in 1989. 6 This supports the underlying assumption that limited health services and resources are likely to contribute to increased poverty levels. Overall, the literature provided gives substantive insight into the possible association between high population density in IDP camps and multidimensional poverty that this thesis will explore.

5 This is with the assumption that local populations who are not displaced in these camps also do not have a need for resources (unlikely the case as those who may not be displaced are still living below the poverty line). 6 While this association is statistically significant, it must be noted that children throughout Africa already suffer from higher mortality rates due to limited access to health services and resources. This is especially the case in 1989 where Sudan still had large instances of violent conflict throughout the country due to the coup that occurred at the time.

Table 1: Outcomes of Camp-Based, Non-Camp Based IDPs and Host Communities

(Utz et al. 2019)

Conceptual Framework

IDPs are created by a crisis event whether it is a sudden onset climate disaster or through violent conflict. This study will aim to look at IDPs that are displaced due to violent conflict, as the civil war in Syria has been the main driver of displacement. The civil war has been ongoing for the past decade and while there have been instances of cease fires, until the conflict is resolved there will continue to be high patterns of displacement. While conflict can be tracked throughout the country, it is difficult to pinpoint every instance of conflict that causes displacement since conflict can have economic and social implications that also influence population movements. While many studies have directly connected poverty to conflict, the correlation between the two is far more complex. It should also be noted that displacement is not a singular event. Individuals who are displaced can be displaced on multiple occasions as populations will leave areas effected by conflict and return in hopes of restarting their lives or with hopes that their shelters and belongings are still intact. Yet when those individuals are unable to return to their home or town due to conflict, they are forced out to urban areas where the infrastructure allows them to settle down and addresses immediate safety concerns.

Otherwise displace populations are forced to create informal settlements or must rely on areas where humanitarian organization have set up camps to provide shelter and access to basic needs.

Figure 1 identifies the progressive nature of violent conflict, specifically the Syrian civil war, and how it can create displacement which leads to increased population densities and eventually leads to increases in poverty levels.

In this study the independent variable will be camp population density within camps and the dependent variable will be poverty measured by the MPI. Camp population density influences a number of indicators that play a significant role to poverty levels. Camps that are

over capacity and crowded will have downward pressure on resources and access to facilities that would otherwise provide stability for households. Therefore, the hypothesis this study will undertake is that increased or higher population densities are associated with acute poverty. As more individuals are enclosed in a camp, the access to resources and services becomes limited, which leaves camps in a state of poverty from which they are unable to improve upon. The following section will highlight the process to identify poverty within these IDP camps and how that process proves to be the best strategy needed to understand access to basic needs and resources.

Figure 1: Implications of Violent Conflict on Displacement and Poverty

Data and Methodology

Assistance Coordination Unit Data and Scope

Data sets that include basic needs indicators on IDP populations provide the most efficient way to identify an association between camp population density and poverty. While data representative of the entire IDP population is limited, needs and vulnerability assessments conducted on IDP campsites are available. This thesis will utilize IDP data collected by the

ACU, a national Syrian non-profit institution (with no political affiliations), which focuses on maximizing the impact of humanitarian assistance delivered to Syrians in need. The organization has conducted an IDP camp monitoring study on a month-to-month basis since November 2018.

Utilizing data collected on a month-to-month basis gives the most up-to-date understanding of needs and services rendered for IDPs. The study was conducted for camps in northern Syria in the Aleppo and Idlib governorates. According to UNHCR's CCCM cluster overview taken in

2017, there were 376,571 IDPs in camps or settlements across Syria (UNHCR(a) 2020). This study accounts for 340,557 IDPs across 268 camps and will use data collected from October

2019 to January 2020. The data within these months is consistent as all 268 camps are accounted for, creating an ideal situation to analyze the impact of camp population density on MPI. While data was available and consistent across four months, the variance of the data from October to

January was low. Therefore, the data were collapsed and averaged for each camp to get the best results and reduce excess observations that provided the same information across those four months.

Using Multidimensional Poverty Index

Evaluating each camp's poverty levels will be done using the Multidimensional Poverty

Index (MPI). MPI is an international standard designed to measure acute poverty and has a flexible structure capable of fitting other specifications and models (Santos et al. 2011). Santos et al. 2011 provide a detailed step-by-step process that shows the adjustments that can be made to

MPI to fit models of different specifications. The steps are as follows (Santos et al. 2011) :

1. Defining the data source

2. Choosing the unit of analysis

3. Choosing the dimensions and indicators

4. Choosing the indicators' deprivation cut-offs

5. Choosing the indicators' weights

6. Choosing the poverty cut-off (to identify poor camps)

With the data source identified and the unit of observations set as households, the process of tailoring MPI to this study becomes much more straightforward. MPI consists of 10 indicators across the health sector, education sector, and indicators relevant to living standards. Figure 2 shows this breakdown and the fundamental indicators utilized to measure MPI. Given the structure of the data set used in this study, alternative indicators were selected to calculate MPI for each camp. Table 2 provides the variables and indicators utilized that give the best representation of the quality of life, access to resources and services within these camps. A deprivation cut-off is utilized to determine which households do or do not have access to specific

resources or services using the variables provided in the data set. 7 Then each camp is given a binary score (0 or 1) for that respective indicator depending on whether they are deprived of those resources or services. Each of these indicators is then weighted8 and multiplied by the deprivation score assigned to that camp. The final number derived from these calculations is c(k) which can be identified in Equation 2. Once this number is calculated, each camp can be identified as poor or not poor. The universal threshold for MPI is a c(k) greater than or equal to

.333. Any camp with a calculated c(k) greater than or equal to .333 identifies as poor within this study.

Population density within each camp derives from dividing the total number of individuals by the total camp area, as seen in equation 1. ACU measures camp area by meters squared, so population density is reflected as individuals per meter squared. This number gives insight into population dynamics within the camp and how population density can have downward pressure on resources and services, resulting in increased poverty levels.

Once each camp identifies as poor or not poor, it sets up a scenario to determine the impact of population density on poverty and how strong the relationship is between those two variables. An OLS regression will be the main method of analysis for this study. This model's variables and straightforward data allow for an OLS regression to identify if there is indeed an association between higher population densities and acute poverty.

7 In this study the deprivation threshold is 50% for continuous variables. For binary variables that were used to determine indicators, if the variable is set at 1 then that camp would be coded as 1 or deprived. Example for a binary variable: if a camp is flood prone (coded as 1 for yes and 0 for no) then the assigned indicator would be coded as 1. For a continuous variable: In the case of remittances, if the number of households relying on remittances accounts for less than 50% of total households then the indicator for remittances would be coded as 1 to indicate that that camp is deprived of access to remittances. 8 Each indicator is weighted, and the total adds up to 1. In Figure 2, this is visualized as health, education, and standard of living all combine to equal 1. In this study, the indicators that contribute the most to poverty or can impact poverty the most are given weights of 1/6 (shelter, assistance, Health Services, and Education). Every other indicator is given a weight of 1/18 as they contribute to poverty but not as much as the previously mentioned indicators.

Variables of Interest

There are numerous variables of interest that will be utilized throughout this study. These variables fall in the following groups as they contribute directly to acute poverty: health, water sanitation and hygiene (WASH), access to assets, education, and shelter. The variables were chosen for this model due to the fact that they are the best representation of IDPs access to basic needs and services that reside in camp settlements. These variables provide insight on the day-to- day life for IDPs and some of the shortcomings that are present throughout IDP camps. The control variables used in this study are dummy variables that identify preexisting gaps in healthcare, the sector that needs to be prioritized the most within of each camp, an evaluation of health services, and the priorities for the education sector. Santos et al. 2011 highlight that within the standard structure of MPI, the health and educations indicators are given the highest weights.

While the indicators for MPI were adjusted, the controls used focus on the sectors that have the most influence on poverty levels.

Equation 1: Calculating Population Density

퐼푛푑𝑖푣𝑖푑푢푎푙푠 푃푒푟 퐶푎푚푝 푃표푝푢푙푎푡𝑖표푛 퐷푒푛푠𝑖푡푦 = 퐶푎푚푝 퐴푟푒푎

Equation 2: Calculating Multidimensional Poverty (MPI)

1 1 1 1 푐(푘) = (푓푙표표푑푝푟표푛푒 ∗ ) + (푟푒푚푚𝑖푡푎푛푐푒푠 ∗ ) + (𝑖푛푐표푚푒 ∗ ) + (푤푎푡푒푟푑푟𝑖푛푘푎푏푙푒 ∗ ) 18 18 18 18

1 1 1 1 + (푤푎푡푒푟푎푐푐푒푠푠 ∗ ) + (푠푒푤푎푔푒 ∗ ) + (푒푑푢푐푎푡𝑖표푛 ∗ ) + (ℎ푒푎푙푡ℎ푠푒푟푣𝑖푐푒푠 ∗ ) 18 18 6 6

1 1 + (푠ℎ푒푙푡ℎ푒푟 ∗ ) + (푎푠푠𝑖푠푡푎푛푐푒 ∗ ) 6 6

Table 2: Indicators and Variables Used to Derive Multidimensional Poverty Index

Indicators for Description & Variable Names Type Weight MPI Flood Prone Is camp flood prone? Binary 1/18 (campfloodprone) Remittances If 50% of camp had access to remittances Continuous 1/18 (Percentage_Household_Income) Income If 50% if camp had access to income Continuous 1/18 (Percentage_Houeshold_Income) Water If 50% of households had water that was Drinkable Continuous 1/18 Drinkable (Water_Drinkable) Water Access Number of Households with Access to Water through Continuous 1/18 Network (Percent_Access_water) Sewage If 50% of Households had Functional Sewage System Continuous 1/18 (Sewage_Funtionality) Education Whether Households have Access to Schools Binary 1/6 (Functional_Schools) Health Services Access to Medical Point within Point Binary 1/6 (Medical_Point_Camp) Shelter Tents with More than 6 people/total tents Continuous 1/6 (Shelters_Unstable_Percentage) Need If 50% of Households Rely on Assistance Continuous 1/6 Assistance (Percentage_Assistance)

Notes: Equation 2 provides insight into how those indicators are utilized to determine MPI. The variables highlighted in this table are the ones that are used to derive the depravation score (0 or 1) for each indicator which in turn is weighted and used to determine c(k).

Results

Descriptive Results

When looking at this data set for IDPs in Northern Syrian Camps, there are not many surprises at first glance. In terms of total individuals within these camps, the highest number of individuals within a camp stands at 8822, with the lowest at 212. In terms of households, the smallest number of households within a camp is 39, while the largest in one camp is 1715. These camp population minimums and maximums provide insight into our independent variable of interest of population density.

In terms of our dependent variable of interest, poverty, it was not surprising to see that, on average many of the camps did not have access to essential resources or services. In terms of water, sanitation, and hygiene (WASH), about 40% of camps had access to water through the camp's water system, and only 55% of all households could say that their water was drinkable.

Sewage functionality throughout camps was also mediocre as functionality in camps averaged below 50%, indicating that less than 50% of total camps can effectively dispose of waste (which would negatively affect health conditions). About 25% of households within camps have over six people living in tents, indicating that one-fourth of households are overcrowded yet, many households have access to shelter that is not over-crowded. While this is the case, shelters are unlikely satisfactory to house that many individuals in the first place. In terms of health within camps, few camps have access to medical points. Of the 268 camps within this study, only 19 camps had access to a medical point, indicating limited access to immediate medical services.

Camp area (influencing population density) stood out as the largest camp was 100,750 meters squared, and the smallest was 600 meters squared. Many of these camps are scattered throughout Northern Syria, each ranging in accessibility. More accessible camps are likely to be

larger and house more IDPs. In terms of population density, the largest population density recorded among IDP camps was 15.5 persons per square meter, with the lowest being .016 persons per meter squared. When looking at the dependent variable, MPI, over 90% of camps suffered from acute poverty as 260 of 268 camps were identified as multidimensionally poor.

These values for population density and area will be used in conjunction with MPI to determine the strength of the association between the two.

Inferential Results: Multidimensional Poverty Index and Population Density

Initial results from the OLS regression indicate that there is not a statistically significant relationship between IDP camp population density and multidimensional poverty at the 5 percent level. Once the control variables are added to the regression analysis, the association between population density and poverty does become statistically significant at the 10 percent level. The p-value attached to population density decreases below the .05 threshold by adding these controls, indicating a statistically significant relationship between camp population density and multidimensional poverty. The regression analysis suggests that a one-unit increase in population density (persons per meter squared) is associated with a .132 decrease in multidimensional poverty. In other words, as population density increases by one unit (persons per one meter squared), multidimensional poverty becomes less prevalent within camps. While this relationship may be statistically significant, this finding shows that higher population densities are associated with lower poverty levels, which deviates from our original hypothesis. Also, the p-values attached to many of these control variables fall above the significance threshold of .05. These values can be seen in Table 3. This finding shows that while these control variables add significance to population density, their lack of significance identifies an even weaker

association between our variables of interest. In addition, the value of the R-squared for this specific analysis stands at .1392. This indicates that the model accounts for 14% of the variation, which is low by many standards and could contribute to the weak relationship between the two variables of interest.

Inferential Results: Multidimensional Poverty Index and Camp Area

While there was no substantive association between camp population density and multidimensional poverty, it was worth exploring the relationship between average camp area and poverty. After running the analysis there was no statistically significant relationship between the two at the .05 level, as seen in Table 4. Given that our primary independent variable

(population density) was not statistically significant, this finding was not surprising. Even when adding in the control variables utilized in the previous analysis, the finding was that the association between camp area and multidimensional poverty was still not statistically significant at the .05 level. The issue presented in the previous analysis still stands as most of the control variables have p-values that are above the .05 threshold, indicating that they are not statistically significant. The R-squared in this analysis also stands around 14%, as it did in the previous analysis.

Table 3: Impact of Population Density on MPI using an OLS Regression Analysis

Table 4: Impact of Camp Area on MPI using an OLS Regression Analysis

Discussion

Discussion of Findings

Given the findings, it is clear that population density within IDP camps is not directly associated with poverty levels proving that the initial hypothesis was incorrect and that high population levels do not directly contribute to multidimensional poverty. The lack of statistical significance is attributable to numerous factors. The substantially low variance was one factor.

The data set (while covering four months) did not vary much from month to month, leading to collapsing the data and taking the average values for each variable and observation. This lack of variation contributed to a low R-squared and limited our analysis. In addition, limited observations available within the data set likely contributed to the lack of statistical significance.

Only 268 IDP camps are available for analysis, making it much more challenging to create a model that would provide substantive findings for other IDP populations. Limited observations along with little variance create a difficult situation to analyze any variables with the data available.

Given the circumstances that IDPs face, especially in Syria, there are apparent externalities that directly impact multidimensional poverty. Overarching factors that contribute to long-term, protracted displacement, and poverty, such as conflict and minimal economic prosperity, are the main contributors to multidimensional poverty. In situations of humanitarian crises, access to accurate data is limited and political circumstances exacerbate these challenges, making it hard to quantify factors like conflict. These limitations, along with others, make it challenging to analyze data on displaced populations and define associations between variables of interest.

Limitations

This study looks to shed more light on IDPs' situation throughout Northern Syria, yet some limitations prevent a substantial analysis from being done. One limitation is data availability. The challenges associated with data availability stem from resource capacity limitations making it harder to map IDP populations and efficiently identify the situation's severity (Baal et al. 2017). Another challenge to data availability is the legal framework associated with defining IDPs. The definition provided earlier in this thesis draws a fine line between who should be considered an IDP. Still, the usage of that definition resides with humanitarian actors collecting data in the field, who are the ones drawing the lines on who can or should identify as an IDP (Baal et al. 2017). Due to this, individuals who may not be displaced due to armed conflict or human rights violations and are just poor and have limited access to resources can group themselves with this population creating a situation where humanitarian actors must differentiate between the two. Figure 2 provided by Baal et al. 2017 visualizes this challenge as multiple people ultimately can be included in IDP data, yet this leads data to be inaccurate and unrepresentative of populations in need. A final and substantial limitation, especially in the Syrian case, is the safety concerns associated with humanitarian assistance. As previously mentioned, violent conflict limits the number of humanitarian workers who can have access to populations and increases population movements, limiting accurate data collection.

Lack of accurate data inhibits accurate and efficient research, in turn reducing the literature available on IDPs. Research on refugees is expansive, yet the data challenges have limited the number of empirical papers and studies that accurately connect variables of interest. The recent emergence of IDPs as a vulnerable population contributes to this as the Guiding Principles on

Internal Displacement were only established in 2004. Unlike refugees, there is not a legal

obligation by the international community to provide aid or assistance to this population. As

IDPs continue to emerge as a population of international interest, new and substantive research will provide more insight into the challenges these populations face.

Other limitations include the fact that while identifying poverty in IDP camps can help understand the humanitarian situation, displaced populations are already in distressed situations with limited access to the resources needed to support themselves. These individuals are likely poor or on the cusp of poverty due to their displaced status. Especially in Syria, where the economic situation has deteriorated within the last decade, most IDP populations already suffer from poverty. Thus, the association between specific independent variables and poverty is unlikely to be as strong as first assumed.

Figure 2: Visual Representation of Challenges Associated with IDP Classification

(Baal et al. 2017)

Policy Recommendations

While this thesis does not find a statistically significant relationship between the two variables of interest, there are recommendations on supporting IDPs. When it comes to addressing the IDP situation in Syria, recommendations should ensure that this population is protected to end protracted displacement and reintegrate these individuals back into society. The first recommendation is to ensure that IDPs are garnering increased international attention.

Numerous humanitarian emergencies exist throughout the world, and refugees continue to be the displaced population that receives the bulk of mainstream media attention, yet IDPs are not as publicized. Displaced within their home countries, they are grouped with other vulnerable populations or other conflict-affected populations, decreasing their visibility within the international community (Ferris et al., 2014). 9 Increased attention to IDPs (specifically in Syria), must come from leaders at international organizations (such as the UNOCHA) a must be more outspoken on issues of internal displacement (Ferris et al., 2014). Global leadership can increase

IDP exposure and can highlight how durable solutions allow societies like Syria to reestablish normative economic and social systems that have deteriorated in the wake of violent conflict.

The second recommendation is to ensure national governments are held responsible for resolving internal displacement situations and building a sustainable relationship with humanitarian organizations. The conflict in Syria escalated to a civil war, and the government has done little to address its people's needs instead of focusing on establishing a centralized government. While this may be a long-term solution that would benefit the population, IDPs' immediate needs are not addressed, leaving humanitarian organizations to step in, assuming the

9 Ferris et al. 2014 emphasizes that while those other groups also have urgent needs, lumping IDPs with those groups reduces the exposure to some of the vulnerabilities that IDPs faced that are directly associated with displacement (shelter, documentation, durable solutions etc.)

government's social responsibilities. Given the variety of humanitarian emergencies throughout the world, there is no set standard to approach international governmental collaboration to address internal displacement (Ferris et al., 2014). In the case of Syria, providing a recommendation to increase government involvement becomes difficult due to the conflict and emergency's politicized nature. Without open communication and collaboration with the Syrian government, providing services and resources to IDP remains a challenge. Yet, small compromises the government could make to improve the IDP situation include border openings in the North, allowing organizations easy entry and access to the IDP camps addressed in this study. Positive results and improvements to the humanitarian situation could lead to increased access to other displaced populations promoting a slow but steady economic and social rebuild10.

However, when these organizations are unable to provide satisfactory services or resolve immediate issues, speculation arises as to whether international actors can resolve large-scale crises. The situation in Somalia is an example as the UN has become disadvantaged because the

Humanitarian Country Team was unable to prevent the 2011 famine creating distrust within the country, leading to a more volatile situation (Ferris et al. 2014). Lack of trust between these government and humanitarian actors can create an inefficient distribution of services, which only impacts the populations that need it most (IDPs). In Syria's case, there must first be an acknowledgment of government support for humanitarian agencies to protect its citizens and address deteriorating conditions. Then comes agreements of transparency between transnational organizations and NGOs to create a sustainable communication line to impact high-need areas throughout the country.

10 While increased border access would be a step in the right direction for the Syrian government to improve humanitarian conditions in the country, the threat of extremism and violent conflict from terrorist groups still exists creating difficult circumstances to make amendments to existing policies in place.

A final recommendation is to look forward and create durable solutions that address protracted displacement. Current processes of assistance address short-term needs, yet upward mobility for IDPs continue to be limited. Displacement has implications on several factors that impact livelihood, such as poverty, yet to address displacement, there must be solutions that ease the transition back into society. In protracted displacement cases, additional funding to enable interventions within countries suffering from displacement are not enough, and increased funds are unlikely to have substantial long-term implications (Mundt et al. 2008). Instead, comprehensive political agreements must be used to create circumstances where interventions and durable solutions can have substantive impacts that allow IDPs to regain their livelihoods and provide for themselves (Ferris et al., 2014). There have been agreements to ceasefires in

Syria, yet the emergence of numerous extremist groups creates situations that negate long-term peace agreements. Until a long-term agreement comes to fruition between all acting parties involved in the Syrian conflict, it is unlikely that protracted displacement is resolved, leading to prolonged levels of poverty and instability throughout the country. A possibility to resolve this conflict could involve a third party. Yet, this situation's politicized nature makes that unlikely as countries, and foreign leaders are unlikely and unwilling to facilitate an agreement between numerous actors. The United States' removal of its troops is a prime example. The Syrian situation presents innumerable challenges to address acute poverty and displacement, yet there is hope that the civil war and violent conflict may subside in the near future.

Conclusion

Increased population densities within IDP camps, specifically those in Northern Syria, are overlooked due to limited attention given to IDPs. This thesis's findings suggest that overcrowding and higher population densities within those Syrian IDP camps are not directly associated with increased levels of poverty. Along with this, additional analysis revealed that there was also not a statistically significant relationship between camp size (meters squared) and poverty. While these findings do not directly support the initial hypothesis that this thesis makes, it provides insight into the dynamics of IDP camps, the constraints that come with conducting an in-depth analysis on this population and contributes to a subject that has been overlooked.

Working with data limited in scope and availability highlights the importance of improving data collection and consolidating IDP populations' recognition protocol by humanitarian actors in the field. To address the needs of populations that already lack international attention, improved data sources are required to enhance research methods and processes which will increase the literature on this population. While this may be true, improved data collection is directly associated with humanitarian actors' safety throughout Syria and the world. As long as humanitarian agent's safety is at risk (especially given the high rate of violence against aid workers in Syria), data collection and the distribution of resources and services will remain limited. To work around these limitations, prominent international leaders and organizations must continue to advocate for IDP populations to shed light on the challenges for developing countries with high levels of internal displacement. Along with this, increased government oversight and involvement is needed to directly address internal displacement and mitigate displacement factors. The challenges associated with internal displacement stem from

Syria's political circumstances and must be recognized to have substantive long-term effects on

internal displacement levels. A more compliant government willing to address the issue of displacement opens the door for comprehensive long-term political agreements that can limit or subside violent conflict and facilitate development.

IDP populations less stressed by external factors that impact their livelihoods lead to a higher standard of living conducive to long-term stability. IDPs with more upward mobility opportunities are likely to be less reliant on assistance and reintegrate back into society, diminishing the number of IDPs in camps. This reintegration process and the transition to a normative economic and social structure in Syria and many other countries is the best alternative to limit the vast number of IDPs. Reintegration of displaced populations into society by utilizing the recommendations highlighted provide a foundation for decreasing reliance on humanitarian assistance (specifically camp settlements) and reducing multidimensional poverty.

Appendix

Table 5: Classification For Camp Size (Cosgrave 1996)

Figure 3: Structure of UNDP Multidimensional Poverty Index (Alkire et al. 2020)

Table 6: Summary Statistics of All Observed Variables

Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Camp Area 268 14605.183 13996.516 600 100750 Total Households 268 244.775 261.276 39 1715 Total Individuals 268 1307.151 1323.791 212 8822 Shelters Access Water 268 100.376 187.798 0 1715 Water Functionality 268 .388 .395 0 1 Percent Access Water 268 .412 .472 0 1.387 Sewage System Functionality 268 .491 .341 0 1 Functional Schools 268 .284 .452 0 1 Total Teachers 268 5.276 10.93 0 62 Students Enrolled Inside Camp 268 96.109 261.784 0 2011.5 Tents Camp 268 154.332 161.538 3 1316 Tents 6orMore 268 35.904 63.583 0 450 Tents Stable Living Arrangement 268 118.428 123.018 0 974 Shelters Unstable 268 .226 .205 0 1 Households Assistance 268 139.377 225.231 6 1595 Percentage Household Assistance 268 .491 .247 .057 1 Households with Income 268 74.088 60.694 0 353.75 Households Remittances 268 13.63 16.558 0 134 C_k 268 .586 .152 0 .944 males 268 621.464 628.272 88 4120 females 268 685.687 698.536 111 4702 Population Density Camp 268 .2 .99 .016 15.505 Water Drinkable 268 55.424 31.949 0 100 Percentage Households Income 268 .374 .241 0 .934 Percent Households Remittances 268 .074 .083 0 .75 Min Individuals 268 1288.608 1318.208 212 8822 Max Individuals 268 1325.407 1329.352 212 8822 Medical Point Camp 268 .071 .257 0 1 Water Deprived 268 .582 .494 0 1 Sewage deprived 268 .422 .495 0 1 Edu Deprived 268 .724 .448 0 1 Shelter Deprived 268 .119 .325 0 1 Health Services Deprived 268 .925 .263 0 1 Need Assistance 268 .519 .501 0 1 Drinkable Water Deprived 268 .653 .477 0 1 Income Deprived 268 .668 .472 0 1 Remittance Deprived 268 .993 .086 0 1 Flood Prone 268 .362 .481 0 1 Multidimensionally Poor 268 .963 .177 0 1 Health Services Average 268 .739 .44 0 1 Health Services Good 268 .041 .199 0 1 Health Services Weak 268 .22 .415 0 1 Priority Sector Education 268 .015 .121 0 1 Priority Sector Food Security 268 .201 .402 0 1 Priority Sector Health 268 .037 .19 0 1 Priority Sector Shelter & Non-Food 268 .586 .494 0 1 Priority Sector WASH 268 .201 .402 0 1 Health Condition 25% - 50% Gap in Healthcare 268 .239 .427 0 1 Health Condition 50% - 75% Gap in Healthcare 268 .071 .257 0 1 Health Condition Less than 25% Gap in Healthcare 268 .433 .496 0 1 Health Condition More than 75% Gap in Healthcare 268 .075 .263 0 1 Health Condition No Gap in Healthcare 268 .179 .384 0 1 Priority Edu Establish School 268 .455 .499 0 1 Priority Edu Other 268 .097 .297 0 1 Priority Edu Teaching Materials 268 .507 .501 0 1 MPI Adjusted 268 .97 .17 0 1

Figure 4: Camp Population Descriptive Statistics

Figure 5: WASH Descriptive Statistics

Figure 6: Education Descriptive Statistics

Figure 7: Shelter Descriptive Statistics

Figure 8: Assets Descriptive Statistics

Equation 3: Assessing the Impact of Population Density on MPI

푦 (푀푃퐼퐴푑푗푢푠푡푒푑) = 휷0 + β1 (Population_Density_Camp) + e

Equation 4: Assessing the Impact of Population Density on MPI (with Controls)

MPI_Adjusted = 휷0 + β1 (Population_Density_Camp) + β2 (Health_Condition_No_Gap_HC)+ β3

(Health_Condition_More_75_Gap_HC) + β4 (Health_Condition_Less_25_Gap_HC) + β5

(Health_Condition_50_75_Gap_HC) + β6 (Health_Condition_25_50_Gap_HC) + β7

(Priority_Sector_WASH) + β8 (Priority_Sector_Shelter_NonFood) + β9 (Priority_Sector_Health) + β10

(Priority_Sector_Food_Security) + β11 (Priority_Sector_Education) + β12 (Health_Services_Weak) + β13

(Health_Services_Good) + β14 (Health_Services_Average) + β15 (Priority_Edu_Establish_School) + β16

(Priority_Edu_Teaching_Materials) + β17 (Priority_Edu_Other) + e

Equation 5: Assessing the Impact of Camp Area on MPI

푦 (푀푃퐼퐴푑푗푢푠푡푒푑) = 휷0 + β1 (Camp_Area) + e

Equation 6: Assessing the Impact of Camp Area on MPI (with Controlls)

MPI_Adjusted = 휷0 + β1 (Camp_Area) + β2 (Health_Condition_No_Gap_HC)+ β3

(Health_Condition_More_75_Gap_HC) + β4 (Health_Condition_Less_25_Gap_HC) + β5

(Health_Condition_50_75_Gap_HC) + β6 (Health_Condition_25_50_Gap_HC) + β7

(Priority_Sector_WASH) + β8 (Priority_Sector_Shelter_NonFood) + β9 (Priority_Sector_Health) + β10

(Priority_Sector_Food_Security) + β11 (Priority_Sector_Education) + β12 (Health_Services_Weak) + β13

(Health_Services_Good) + β14 (Health_Services_Average) + β15 (Priority_Edu_Establish_School) + β16

(Priority_Edu_Teaching_Materials) + β17 (Priority_Edu_Other) + e

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