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What is the value of home?


Academic year: 2021

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What is the value of home? 

A quantitative study on the effects of natural resource extraction on  conflict-induced displacement 

Theodor Stensö   


Uppsala University  

Department of Peace and Conflict Research   Bachelor's thesis  

Supervisor: Jonathan Hall  10th January 2021   Word count: 10,222 



1. Introduction

2. Theoretical Framework

2.1 Literature Review 6 

2.2 Theory 10 

3. Research Design 14 

3.1 Data 14 

3.2 Dependent variable 15 

3.3 Independent Variable 17 

3.4 Control Variables 18 

4. Results and Analysis 20 

4.1 Descriptive statistics 20 

4.2 Bivariate regression 23 

4.3 Multiple Regression 24 

4.4 Discussion 26 

5. Conclusion 30 

6. Bibliography 33 
















Conflict-induced displacement, a relatively novel term, is well researched but not well understood. 

There is a significant amount of findings on this subject, but many have been disproven at a later  stage, leaving behind a research field largely lacking in substantial findings. As conflict-induced  displacement has steadily increased over the previous years, this is a significant problem. However,  recent findings, hinting at a relationship between natural resource prevalence in armed conflicts  and displacement, could help provide an explanation as for what causes these differences. A large-n  study looking at 207 cases of armed conflict, varying over relative value of natural resource 

extraction, is here conducted. The results find that while there is not a statistically significant  relationship between the two variables, and this relationship varies depending on whether cross- or  within border displacement is the focus, there is some level of covarying relationship. 




1. Introduction 

What causes conflict-induced displacement, and when does it occur? It is per definition a 

consequence of armed conflict, but not all conflicts generate the same quantities of displacement. 

Research within the field of conflict-induced displacement has unfortunately not reached a  consensus on exactly what causes this variation. While the field blossomed in the 90s and 00s,  interest waned over the following decade. During this time however, new findings were made  which promise to provide concrete answers where previously there were few. By embracing these  ideas, the author aims to help reawaken interest within the field, in a time where forced 

displacement is growing (IDMC, 2020a). This paper will attempt to utilize a new classification of  forced displacement to analyze a promising new find, the causal relationship between natural  resources in armed conflict and levels of conflict-induced displacement.  


The theory that armed conflicts over natural resources may see increased displacement was first  presented in Niazi and Heins (2017) paper. Conducting a qualitative study, it was found that the  suggested relationship was not only present, but had a significant impact on all three examined  cases. However, no causal mechanism was convincingly presented, and the results remained  ungeneralizable. If the results produced by Niazi and Hein (2017) were found to be true in a  qualitative study, and if they are statistically significant enough to be deemed generalizable, it could  have significant real world implications. In a field where few causes are consistently found to be  relevant, this could help guide policy makers and spark new questions for the field, and may help  reignite interest in forced displacement as a research topic. The findings presented above are not  the only new additions to this field, and the new definition of conflict-induced displacement, as  presented by IDMC, Internal Displacement Monitoring Centre, may be even more important  (2020b). Dividing forced displacement into disaster- and conflict-induced displacement, a much  greater precision and accuracy can be used in research. Much of the previous findings were based  on forced displacement at large, i.e. all types of involuntary displacements as a direct result of the  actions of others, and may therefore not necessarily satisfy the need for validity. To show how  significant this difference may be, approximately two-thirds of IDPs (Internally Displaced Person)  in 2019 were not displaced by conflict, but would be classified as disaster-induced displacement 


(IDMC, 2020a). Utilizing the new data collected by IDMC may thus finally solve some of the  previous discrepancies, and could help highlight the complex dynamics of displacements.  


This paper will therefore conduct a quantitative study, with the aim of applying the new  available data to accurately find the causes of conflict-induced displacement, and more specifically  find the causal relationship between natural resources in armed conflicts and said displacement. 

The study will be looking at a large array of armed conflicts in which there are various levels of  natural resource extraction, while controlling for variables which previous studies suggest affect  conflict-induced displacement. 


This paper will be divided into five sections. The first, being the introduction, can be found above. 

The second part, theoretical framework, is itself divided into two subsections.  

The first subsection, literature review, gives a summary of the state of the field of conflict-induced  displacement research, explaining the key findings and trends.  

The second subsection is the theory, where this paper's hypothesis and causal mechanism will be  explained and motivated.  

The third section, research design, presents the selected method, as well as data sources and  operationalizations of the variables.  

The fourth section, results and analysis, presentes the results of the paper and will show these in a  concise manner. This will be followed by a discussion and analysis of the results, to conclude if the  hypothesis is correct and discuss other potential findings of note. 

The final section, Conclusion, will summarize the paper and present several avenues for future  research. 



2. Theoretical Framework 

2.1 Literature Review


Conflict-induced displacement research is exhaustive, yet a few paradigms have come to dominate  the field. While collecting them into easily identifiable groups may be simplistic and there is other  important work outside these boundaries, this summary will focus on the most prominent, or to  this case relevant, strands. Two particularly prominent strands are; the economic theory and the  choice-based approach, both of which are necessary to understand the current state of the field.  


First is economic theory. The literature within this branch has found that while low levels of  income causes armed conflict (Chassang & Miguel, 2009:212), and while this conflict in turn may  cause conflict-induced displacement, the economic factors were not a direct cause in- and of itself  and did not directly affect levels of conflict-induced displacement (Mohammed, 2017:26). Prakash  meanwhile found that higher levels of local economic opportunities reduced the likelihood of  displacement due to conflict at communal and individual levels (Prakash, 2013:142). 

Contradictions in the economic field can be found not only by looking at income and 

opportunity, but also at macro-levels. For example, Schmeidl (1997:830) found that economic  development did not have a mediating or causal effect, and variations in economic development  and macro economic trends could not explain the differences in levels of displacement. This was  not the findings of other researchers, however, who found direct links between economic  development in terms of GNP and a reduction in forced displacement (Moore & Shellman,  2004:732). These contradictory findings permeate the field, and muddles the water regarding  economic effects on conflict-induced displacement. The contradictions may be caused by some  studies looking at non-typical cases (Prakash, 2013:142), or variations in premises and definitions  (Moore & Shellman, 2004:725). Other findings within this framework fail to find substantial  results due to low levels of validity in their chosen variables. An example of this last problem can be  found in an article claiming a positive relationship between conflict-induced displacement and  value of land, however it uses population density as a proxy of land value (Raleigh & Hegre, 


2009:32-33). Ultimately, while the economic theory is useful and delivers several key insights, it  does not produce a simple and satisfactory model. 


The second framework is the choice-based approach. It argues that each displacement can be seen  as a choice, and points at each displacement event showing groups of both displaced and 

non-displaced, hinting that each individual here makes a choice between fleeing and remaining. 

Most causal factors can thus, according to the choice-based approach, be viewed as factors 

affecting the choice to leave one's home, and therefore tends to focus on the psychology, safety and  opportunity of those who may be displaced (Moore & Shellman, 2004:725). This theory finds that  state-based discrimination of human rights and states in the process of democratization both  increase the risks of conflict-induced displacements (Davenport, Moore & Poe, 2003:42). 

Melander and Öberg (2007:167) find the reversed relationship with democracies being far less  likely to generate forced displacement along a linear trend however. As for human rights violations,  there are conflicting findings. Schemidl finds no significant effect between human rights violations  and conflict-induced displacement (2007:302), while Moore and Shellman find a substantive  support for a positive causal relationship between the two (2004:739). As the choice-based  approach attempts to find why the decision to flee is made, much of the research is based on  individual and interpersonal factors. The micro-economic theories of income and opportunity can  be fitted into the framework, but as stated above does so with mixed results (Mohammed, 2017:26; 

Prakash, 2013:142). Highlighting interpersonal variables as one of the key factors however is done  by Edwards, who finds that information networks passing person-to-person are the most 

important factor, and that these are drivers of conflict-induced displacement rather than the  violence itself (Edwards, 2009:40). These interpersonal relations are themselves subject to change  during times of armed conflict, and the networks can function as both push and pull factors  (Wood, 2008). Further, the threshold for being subjected to displacement varies from person to  person based on other factors, such as level of education, age, land ownership or national  commitment. Furthermore the idea of all potential victims of forced displacement as unitary is a  false assumption (Melander & Öberg, 2006:146-147). The choice.based approach presents  strongly supported theories, yet while some are broadly applicable, most are case-specific and  highly contextual. This follows naturally from a framework that rejects structural explanations. It 


is therefore, while interesting and useful in certain cases, lacking in generalizability and it’s lack of  simple models makes it less useful for predicting and preventing displacement events. 


Both frameworks produce useful insights, but are by their nature not sufficient to fully explain the  macro-level factors of conflict-induced displacement. Thus, most useful and substantive findings  in the field can be found outside of these, in individual relationships between causal factors and  conflict-induced displacement. The first set of theories are focused on who the perpetrator is and  why. One hypothesis that has been found to be false is the causal link between ethnic conflicts and  increased quantities of displacements (Melander & Öberg, 2006:144). Schmeidl (1997:301) argues  that this may be due to victims alternatively joining the fighting rather than leaving in ethnic  conflicts, yet no explanation is offered as for why this is an alternative here but not in conflicts  without an ethnic dimension. As for the nature of the perpetrator, there does not seem to be a  significant difference between government or rebel forces in relation to displacements (Schmeidl,  1997:302). This leaves a few factors that do seem to affect conflict-induced displacement which  needs to be discussed. First, one paper finds that conflict intensity does have a causal effect on the  quantity of conflict-induced displacements (Melander & Öberg, 2006:144). However, the same  authors later publish results to the contrary, finding no statistical significance in intensity of  fighting with levels of conflict-induced displacement, yet they do not provide any explanation as  for why these results differ (Magnus & Öberg, 2009:166). The same paper does find that 

geographic scope and physical spread of the conflict has a large impact however, following similar  logic as to the information theory presented by Edwards, yet utilizing push rather than pull factors  (Melander & Öberg, 2007:168). The authors argue that a larger geographical spread of conflict will  involve and affect more people, increasing the likelihood of feeling threatened as opposed to  localised conflicts which only impact a smaller quantity of people. Another key factor is the  presence of foreign military personnel in the conflict. Armed interventions result in significantly  larger quantities of forcibly displaced (Schmeidl, 1997:301). Unfortunately, the author cannot  confirm the findings as the data may be skewed as the results are somewhat built on outliers  represented by actions during the cold war, and that the results may not therefore be generalizable  to a post-cold war setting (Schmeidl, 1997:303). This reservation is countered by another study  which finds that conflicts during and after the cold war show no significant difference in terms of 


displacements generated (Melander, Öberg & Hall, 2009:24). Next, there is evidence that past  events of forced displacement increases the risk of new displacement events (Davenport, Moore & 

Poe, 2003:41). Finally, one last important factor is population size, which also correlates positively  with displacement quantity (Magnus & Öberg, 2006). 


Finally, a significant finding that has not gained traction in the field of conflict-induced 

displacement, and which holds importance for this paper, can be found in the research of Niazi  and Hein (2017). In order to put their work into context, it is worth taking a look at another  important paper in the field. Azam and Hoeffler look at why the perpetrator uses forced  displacement as a tactic. They find evidence that displacement is used to lessen insurgents  effectiveness in key strategic areas, to great effect (Azam & Hoeffler, 2002:482). The idea follows  that reducing population levels makes it harder for insurgents to blend in and hide, and therefore  lessens their effectiveness. The paper which provides new key insights finds that conflicts over  natural resources have a significant positive causal effect on forced displacement (Niazi & Hein,  2017:8). This is important for two reasons. First, it has not been previously researched, and is thus  a new finding in the field. Second, if verified, will significantly help predict levels of 

conflict-induced displacement events in the future, as natural resources are largely static and  well-researched (Collier, 2009:127). This may also be beneficial in further research in the field of  displacement as it ties in with another well-researched field, the connection between natural  resources and armed conflict. There are a few reasons that this paper on its own is not satisfactory,  however. First, as a small-n study it is useful for theory building, yet the results are not 

generalizable. Second, it does not effectively argue from a theoretical standpoint for its results. 

Both of these problems must be remedied for the results to be useful. 


This field's frontier, as argued more important than large frameworks above, is building on  individual and separate factors. As such, this paper will attempt to remedy the problems of Niazi  and Hein’s (2017) article. Holding great promise, the aim is to make their findings useful and  generalizable, and building a strong theoretical causal mechanism behind these findings. The first  step will be to ensure generalizability, by conducting a large-n study. Second, building on the  theory will be done relying on the findings of Azam and Hoeffler’s (2002) paper.  



2.2 Theory 

The dependent variable is conflict-induced displacement. This term will here be used as a person  being forced to leave their homes due to the threat of or use of violence. This accounts both for  refugees and IDPs. What constitutes a refugee was established by the 1951 refugee convention, and  refers to a person crossing national borders as a result of fleeing from well-grounded fears of 

violence or persecution (UNHCR, 1951). In order to look at both cross- and within border  displacement, IDPs will also be measured. An IDP is a person who, like a refugee, has to flee their  home but does not cross an international border, and is unlike refugee not a legal term (IOM,  2019:109). By using the term conflict-induced displacement rather than the often used alternatives  of refugees or IDPs, two problems are solved. The first is by including IDPs, which are often  overlooked in favour of refugees, as they are in comparison less visible and more difficult to 

measure. By looking at both categories, we avoid overlooking differences which may affect the two  categories differently (Lischer, 2007:150). The second is that we do not wish to research 

disaster-induced displacement. While this is undoubtedly also important, this paper will focus on  those displaced by armed conflict rather than all human activity (IDMC, 2020a).  


The independent variable will be natural resources extraction. Definitions of these concepts are  rare, and are often simply defined by a predetermined set of resources, such as specifically oil or  minerals, (Ross, 2004:46; Niazi & Hein, 2017:5-7), which the author intends to research. This  paper will attempt to capture a broader scope of resources, and will therefore offer up its own  definition as; a resource which is geographically bound, not produced through artificial means, and  with a substantial monetary value. This should encompass all relevant resources, without including  non-monetary values, such as water or wetlands, or non-geographical resources, such as industries  or taxable incomes. Research on natural resources are plenty, and these are very well mapped and  understood. As such, natural resources are easy to apply to developing theories.  




2.2.1 Causal Mechanism 

This paper is based on the findings of Niazi and Hein (2017). They found evidence that conflicts  over natural resources leads to higher rates of conflict-induced displacement. They find, by doing a  qualitative analysis of three armed conflicts, connections between the natural resources and the  levels of displaced. However, the causal mechanism is never developed in great depth, and the  theory is never tested at a larger scale. My aim is to test this phenomenon on a larger set of data  points, thus thus testing the generalizability, and to test the causal mechanism.  


The causal mechanism proposed by this paper goes as follows; to extract high value resources,  control over the surrounding land is required. This in turn will result in a purposeful displacement  of local populations residing in the area, as a means of ensuring control over the resources revenue. 

The process will be explained below. 


2.2.2 Regional control 

The need to control the land surrounding natural resources to guarantee revenue is 

well-documented. The economic structure of land ownership has been shown to affect how the  conflict develops, however, even more important was the economic value of land (Niazi & Hein,  2017:9). As natural resources require control of land to extract their value, the value of controlling  said land can be considered equal to the resources it inhabits, and is directly correlated to strategic  value (Collier & Hoeffler, 2004:567; Barnet, 2016:240). It is also noted that control of transit  routes to and from the natural resource regions are important, as this is necessary for exports, and  by extension revenue (Niazi & Hein, 2017:9). While the idea of transit routes is likely to be true to  a certain extent, it will not be given attention in this paper, largely due to limitations in data. A  further sign that control of natural resource-rich regions becomes an objective in and of itself, is  that armed conflicts in these areas tend to last longer (Lujala, 2010:20). Further, the same study  finds that active production and potential production, such as large reserves, have similarly large  effects (Lujala, 2010:23). This necessity for control of territory is not just supported by empirics,  but also follows previous theories (Azam & Hoeffler, 2002:482). A further cause of the strategic  importance of natural resources, is that these have been theorized to function as guarantors in 


terms of international support. Collier and Hoeffler (2004:565) proposes a theory that natural  resource access can be promised to external actors, in return for startup resources for rebels, and  support in terms of arms and/or training, and international recognition of post-conflict regimes. 

This is however not found to be the case in another major study (Ross, 2004:51). Instead, no such  relationship was found. 


It should be mentioned here, that the value of exportable resources functions as a self-feeding cycle. 

While the absolute value may not change, the relative value increases with conflict duration, as  armed conflicts have a negative impact on economic development and general exports and thus  weakens other sectors of the economy (Barnett, 2016:239). As the value of the natural resources  thus become a larger section of the economy, and the relative economic gains compared to other  sources increases, the value of the land itself will only grow. To make this effect even stronger,  resources that could be aimed at economic and social development are instead pumped towards the  military, furthering economic deprivation (Barnett, 2016:240). This results in higher relative  demand for the natural resource revenue, exacerbating the situation. 


2.2.3 Displacement 

The means by which natural resources alters armed conflicts and changes the importance of  regional control is established above. Next, this paper will theorize as to why this affects levels of  conflict-induced displacement. The second part of this reasoning is not as steadily researched in the  past, and must follow a rational pathway of its own. The first, most concrete reason for forceful  displacement is to avoid leakage. While leakage of revenue often occurs in favor of foreign actors  (Gunton, 2003:69), this may be intended and is a method of ensuring international legitimacy and  guaranteed support for the occupying force (Le Billion, 2007:95). A greater problem for the  leadership can be internal, and low-tiered leakage of resources (Caselli & Coleman, 2013:162). This  may only be relevant in cases where resources are easily accessible however, resources which in  research are the same as lootables or contraband (Fearon, 2004:284). This mechanism will only be  relevant in a limited amount of cases, as the majority of natural resources do not fit this specific  criteria (Collier & Hoeffler, 2004:40). Unfortunately, the research on the topic is sparse as is the 


available data on the matter, and the second pathway, which will now be presented, is thus more  likely to be observed and remain relevant. 


The second possible pathway begins with the risk of attacks on structures or workers extracting the  natural resources, as a means of denying the occupying actor income. Displacement can, and has  been, used to negate this risk of such attacks. One example is the large-scale displacement campaign  in Darfur, South Sudan in 2002 (HRW, 2003). The theory behind one-sided violence fits well into  settings of valuable land as found in the statement above. The use of violent targeting of civilians is  theorized to be an effective way of maintaining loyalty among said population. One pathway in  which this works, is that the use of selective violence promotes compliance, by showing loyalty or  the desired behaviour the non-combatants are less likely to be targeted, and thus the violence  reduces acts of resistance (Hultman, 2014:291). The use of violence against civilians has been  shown to reduce insurgent attacks, and to be a successful preventative measure of violence from  insurgents among the local population (Lyall, 2009:349). More importantly, the use of 

indiscriminate violence has been a way of clearing out populations, with the express aim of 

reducing insurgents' ability to hide and blend in (Lyall, 2009:336). This tactic has also been shown  to be directly tied to forced displacement as a means of achieving the same goal. Further, displacing  a certain fraction of the population directly lessens the effectiveness of insurgents, for the reasons  stated above (Azam & Hoeffler, 2002:482). The use of strategic displacement has previously been  used to ensure safety in tactically important or valuable areas (Lyall, 2009:336), a theory which is  easily applied to cases of valuable natural resources which require certain zones of control. 


The hypothesis is therefore that ​natural resource extraction in armed conflicts has a positive causal  relationship with conflict-induced displacement​. This stems from the need to control and ensure the  value extracted from the land, which in turn results in displacement campaigns from the 

occupying force. 



3. Research Design 

This paper attempts to find trends in terms of conflict-induced displacement, and as such, will  conduct a large-n study. This will be in the form of a quantitative, comparative analysis. As most  previous research on this issue is qualitative, a quantitative study will be more likely to contribute  to the state of the academic field. This paper will first discuss the available data which will be used. 

After which, the dependent and independent variables will be examined and operationalised,  followed by a discussion on their validities and reliabilities. Finally, control variables will be  discussed.  


3.1 Data 

This paper will rely on four data sources, which have been combined here into one. The data has  been collected from the Internal Displacement Monitoring Centre, IDMC, the World Bank and  UN High Commissioner for Refugees, UNHCR as well as Uppsala Conflict Data Programme,  UCDP (IDMC, 2020a; World Bank, 2020; UNHCR, 2020; UCDP, 2020). By combining these  into one dataset, each individual set of data has to be slightly altered, however as no values are  removed this should not affect the results if done correctly. The reason that a new dataset was  created, instead of relying on older sets of data is twofold. First, several of the older quantitative  studies data are inaccessible at the time of this being written. Secondly, previous 

operationalizations of the dependent variable have been lacking in validity. The new dataset should  solve this issue by more efficiently capturing definitions necessary to understand the phenomenon  in question. This comes with a drawback however, as each added variable is a significant added  workload. Therefore the new dataset has accurate operationalizations, but fewer variables to  examine and to be used as controls. This should not be a problem however, as there are few  potential control variables where data is accessible within the time span and at the correct level of  analysis. As explained in the literature review, there are few concrete and undisputed findings in  the field, and adding variables may only confound the results. As such, this study aims at 

simplicity, and should a causal pathway be found, urges future studies to further test this by adding  more control variables.  



This paper will use the four causal criteria presented by Kellstedt and Whitten as determinants for  causal relationship (2018:85-95). The authors present four criteria which must be fulfilled in order  to claim that there is a causal link from the independent to the dependent variable. These are  covariation, causal mechanism, time order and isolation. Covariation asks whether the two  variables covary, if X changes, it requires to see a change in Y. This will be tested using bivariate  regression. Causal mechanisms require a reasonable mechanism by which X causes change in Y. 

The causal mechanism was explained and motivated in the theory section. Time order requires the  change in X to occur before the change in Y, as to ensure there is no reversed causality. Time order  will not be tested here, simply due to a lack of realistic mechanism for the dependent variable,  conflict-induced displacement, to affect the independent variable, natural resource extraction. 

Isolation requires that no other factor is the cause of change in both X and Y. This will be tested  using a multivariate regression. 


The dataset includes 207 observations across the timespan 2009-2018. The timespan was chosen  because 2009 was the first complete year where the new operationalization of IDPs was in use, and  the data was last updated in 2018. The 207 observations are taken from all countries during this  time-span which experienced armed conflict during that year. The dataset was merged using R  Studio, and is easily replicable. The method used for the regressions was OLS (Ordinary Least  Squares). Regression will be used to satisfy isolation and covariation, as explained above. A  simplified explanation of OLS is: a method which fits a straight line along the observed values of  the dependent variable as such that the total sum of the squared differences will be as small as  possible. Applying this in a bivariate regression shows the extent of covariation, while doing so in a  multivariate regression using control variables will help ascertain isolation (Kellstedt & Whitten,  2018:198). 


3.2 Dependent variable 

This paper attempts to capture conflict-induced displacement as fully as possible. However, most  data collections divide this concept into three subcategories: IDPs, refugees and asylum seekers. 

While these concepts can be somewhat blunt and, occasionally, difficult to interpret (Hruschka & 


Leboeuf, 2019), they are generally standardised over both time and space, and are thus easily  replicable. While some sources do list these as one variable, they tend to use all forms of forced  displacement, including non-violent persecution and disaster-induced displacement, rather than  specifically conflict-induced. For this reason this paper will divide these categories upp individually  and then add them together for a joint conflict-induced displacement value.  


The first of these subconcepts is conflict-induced IDPs. This measure will use a rather thin  operationalization, as used by IDMC. The reason that data is taken from IDMC, is because they  have reliably measured IDPs based on conflict- and disaster-induced displacement separately, and  thus have a wide array of comparable data points. By allowing for the disregard of disaster-induced  displacement, it can be guaranteed that validity is high, and that any results are the effect of the  conflict rather than other factors such as natural hazards. Further, IDMC collects data from a vast  array of primary and secondary sources, reducing the risk of source bias (IDMC, 2020a). However,  there is still bias in which conflicts are more heavily researched, and what groups remain invisible  (Hruschka & Leboeuf, 2019;IDMC, 2020a). A final strength of the IDMC is that other similar  studies use their data as well, and therefore any results will be more comparable. 


Data on refugees will be gathered from UNHCR, and their operationalization will therefore be  used. Refugees, as people who flee from violence or oppression, are functionally IDPs who travel  across borders. While this difference may be interesting for certain research, and is vital for  policymakers, it is outside the scope of this paper. There is one issue with using refugees as an  indicator however. Unlike the data for IDPs, data on refugees separated into conflict-induced and  non-conflict-induced has not been collected. As such, a certain section of refugees will be 

generated by disasters or oppression. As UNHCR uses the legal definition as their 

operationalization, conflict-induction cannot be isolated (UNHCR, 2018:28). However, since  such isolated data is not available, this will be the closest non-primary research can get to a 

functionally valid category of conflict-induced displacement. Similar to IDMC, this data source is  often used by other researchers and will therefore be easily comparable, and as the data is compiled  directly from primary sources it can be expected that the data is reliable (UNHCR, 2020). 



Data on asylum seekers is also taken from primary sources within UNHCR. While asylum seekers  are fewer than refugees and IDPs (UNHCR), they are nonetheless included in the category of  conflict-induced displacements. The difference between refugees and asylum seekers is at which  stage in the process of obtaining legal status (UNHCR, 2015:27). As such, asylum seekers as an  indicator have the same issue as refugees, in that it does not isolate conflict as a cause. Despite this,  ignoring them may have a greater negative impact than including them, and they will thus 

represent the final part of conflict-induced displacements.  


The three categories will be summarised to create a category called total displaced. However, as  there may be other causes of the forced displacements, any cases where there are refugees or asylum  seekers but no IDPs will be excluded from this list. The reasoning follows that the likelihood that  people are forced to flee but that no one is unable to leave the country is highly unlikely, and thus  the cause of the displacement is likely oppression or disaster rather than conflict. While this may  omit certain data points from the analysis, it will increase the validity of results. In the end, both  the total and IDP datasets will be used for the regressions. While the total forced displacements  captures a greater set of cases, and may thus be more accurate in scope, the analysis of IDPs alone  may help be valid as this more accurately captures the definitions proposed in the theory section.  


3.3 Independent Variable 

The independent variable, natural resource extraction, will be using a thin operationalization. In  order to measure the value in a quantitative study, it is made quantifiable. While there are several  ways to do this, each of which may yield interesting results, this paper will use a portion of GDP  based on natural resources. This provides us with several advantages and disadvantages. First, it  provides us with clear quantifiable, continuous values. Further, it directly measures the economic  value of the resource. Since it is the value of natural resources that is hypothesised to result in the  proposed effect, this is the most important aspect. This further shows the value of the resource in  relation to alternate revenue. The function of economic gains in armed conflict is directly 

dependent on alternate sources of income in a manner that people are more likely to fight over  resources which are relatively valuable compared to alternate sources of income (Collier & 


Hoeffler, 2004:569). One problem could be that it does not say that the displacement is occurring  near the extraction, as both are measured at a country level. However, if a strong, statistically  significant link is found this is unlikely to be a significant problem. There may however be a  somewhat different causal mechanism, for example through the mismanagement theory proposed  by Sachs and Warner (2001:836). These causal mechanisms tend to explain why armed conflicts  start however, rather than its specific dynamics and how this affects forced displacement. A final  problem would be what the data taken from the Worldbank considers natural resources; gas, oil,  coal, minerals and forests (World Bank, 2020a). While the first four fit the causal mechanism  proposed well in that they are densely populated and often very valuable, this may not be the case  of forests. However, two results from this may arise. Either, forests are valuable, in which case they  may fit the proposed causal mechanisms, or they are not valuable, in which case they are unlikely to  significantly affect the data. In either case the results should remain relatively valid. Finally, this  data and operationalization are more valid than the majority of previous research in the field. 

Others are either qualitative or leave their exact data unspecified (Ross, 2004; Collier & Hoeffler,  2004:595).  


3.4 Control Variables 

This paper will use three control variables: population size, economic development and conflict  intensity. The first, population size, is taken from the World Bank, and functions as a continuous  variable (World Bank, 2020c). This follows on the findings of Melander and Öberg (2006), which  claim that increased population sizes tend to generate larger quantities of displacements. While this  may at first glance also seem to follow the argument that population density may function as a  proxy for land value (Raleigh & Hegre, 2009:32-33), population is not measured in density  (Worldbank). As previously mentioned, the validity of said claim is dubious, and the population  here measured a total population in said country rather than density, again due to limitations in  available data.  


The GNI per capita values are also taken from the World Bank, and in a similar fashion functions  as a continuous variable. It is operationalized along the world bank's definition as the gross value of 


all citizens' production measured using standard price indexes (World Bank, 2020b). This will give  a feel of the economic development of the country. While there are several criticisms of this as a  measure of economic development (Prakash, 2013:10), it is functional in this case for two reasons. 

First, situations of natural resource extraction are closely linked to inequalities in economic gain  (Sachs and Warner, 2001:837). Thus, it can be expected that inequality will be present in the  majority of cases here examined, and as such economic development may be more relevant than  measures of equality. Second, there is little available data on household income or wages within the  selected time span for relevant countries. As such this is the closest approximation of economic  development to be found. 


The third control variable, intensity levels, is taken from Uppsala Conflict Data Programme, or  UCDP. The dataset called ​Armed Conflict Data​ will be used. This is an ordinal variable, with three  separate values. If there are less than 25 battle-related deaths in the calendar year, it will be 

registered as NA, as any intensity registered is below the armed conflict threshold. The numerical  value one will be given to minor armed conflicts, where battle-related deaths fall between 25 and  999, and the numerical value two given to any major armed conflicts, which will see more than 999  battle-related deaths (UCDP, 2020). This choice was made as continuous versions of these 

variables were given for dyads rather than at a country-level, and would therefore not be applicable  to the rest of the collected data. Further, it is the same data and method used by previous studies,  ensuring the results are comparable (Melander & Öberg, 2006:139). Intensity levels have, as  previously been stated, found mixed results as to their relevance to forced-displacement. Thus,  replicating data following the same methods as previous papers can be useful to understand causes  behind conflict-induced displacement. 





4. Results and Analysis 

While causal mechanism and time order have been discussed, covariation and isolation remains  untested. First, the descriptive statistics of the variables will be discussed and dissected. Second, a  look at the bivariate regressions using both IDPs and total forced displacement will be reviewed  and the correlation investigated. Third, the results will be analyzed including the control variables,  and the causal mechanism will be discussed. Finally, the results will be analysed and examined to  conclude how well the hypothesis fared as compared to the data found.  


4.1 Descriptive statistics 

Below, the descriptive statistics of all the relevant variables can be found summarized, with their  207 observations. As the table was taken directly from R Studio, some of the names may need  clarifying. conflict_new_displacement represents IDPs, sum_displaced is the total displaced value,  i.e. the sum of IDPs, refugees and asylum seekers. gdp_portion is the independent value, 

GNI_value represents the GNI level, and intensity_level is the level of intensity. 




Table 1 Descriptive statistics 

Statistic  Mean  St. Dev.  Min  Max 


conflict_new_displacement  207  195,663.200  387,238.700  3,000,000 

refugees  207  249,708.700  562,455.000  48  3,054,699 

asylum seekers  207  35,536.070  58,656.490  110  369,061 

sum_displaced  207  480,908.000  747,259.300  495  3,523,508 

gdp_portion  207  9.040  10.137  0.001  51.152 

GNI_value  206  6,386.019  5,206.067  770.000  30,080.000 

population  207  127,809,601.000  270,497,960.000  4,248,334  1,352,617,328 

intensity_level  130  1.262  0.441  1.000  2.000 



The first value which needs to be examined is the independent variable, natural resources. By  looking at the max and min values, and comparing these to the mean, it can easily be seen that the  distribution is skewed heavily towards lower values of natural resource extraction, with a few large  outliers with heavy dependence on natural resources. This can be seen in figure 1, showing a  density plot of natural resource value. This can be compared to our measures of the dependent  variable. Total displaced shows a similar distribution, with a heavy quantity of observations of  lower levels of displacements, but a few major outliers, as can be seen in figure 2. IDPs too show a  similar relationship as well, as seen in figure 3. This may hint at some level of covariation, as is  expected at this stage.  



Figure 1 

Figure 2 

Figure 3 


4.2 Bivariate regression 

Across 207 observations, no statistical significance could be tied to the impact of natural resource  extraction to conflict-induced displacement when using the total displaced measure even at the  90% confidence level. This can be seen in table 2, showing the statistical significance of the  covariation. As the p-value is greater than 0.1, it cannot be considered generalizable (Kellstedt & 

Whitten, 2018:179). Further, while the correlation coefficient is positive, it is also small, and there  is little correlation between the two variables. Whether this shows that the null hypothesis is  correct, or if there is a problem with validity, can be tested using our second dependent variable,  IDPs. 


Table 2: Bivariate regression 


Dependent variable:   

---  Total displacements    ---  gdp_portion 3,097.010    (5,143.839)   


Constant 452,910.500***   



---  Observations 207    R2 0.002    Adjusted R2 -0.003   

Residual Std. Error 748,418.300 (df = 205)    F Statistic 0.363 (df = 1; 205)   


Note: *p<0.1; **p<0.05; ***p<0.01 


The same amount of observations for IDPs provides a different view of the correlation. At the 95% 

confidence level, thus having a p-value less than 0.05, the results are statistically significant and can  be generalised to the larger population (Kellstedt & Whitten, 2018:179). Further the correlation  coefficient is higher, showing a stronger correlation between the two variables.  




Table 3: Bivariate regression 


Dependent variable:   

---  IDPs    ---  gdp_portion 5,631.967**   



Constant 144,749.400***   



---  Observations 207    R2 0.022    Adjusted R2 0.017   

Residual Std. Error 383,939.800 (df = 205)    F Statistic 4.555** (df = 1; 205)   


Note: *p<0.1; **p<0.05; ***p<0.01   

4.3 Multiple Regression 

The results of the bivariate regressions were detailed above. Next, the multiple regression using  total displacements should be examined. First, looking at the independent value, it remains  ungeneralizable, as expected. However, the coefficient has switched signs and now has a negative  relationship with the dependent variable. The same negative relationship can be seen with all the  control variables as well, aside from intensity level. As for statistical significance, the only variables  that are generalizable are intensity level and GNI. These results alone do not support this paper's  hypothesis that armed conflict over natural resources results in larger quantities of 

conflict-induced displacements. 




 Table 4: Bivariate regression 


Dependent variable:   

---  Total displacements    ---  gdp_portion -2,599.392    (5,278.480)   


population -0.0002    (0.0002)   


GNI_value -22.783**   



intensity_level 1,138,709.000***   



Constant -593,178.800***   



---  Observations 129    R2 0.398    Adjusted R2 0.379   

Residual Std. Error 669,538.100 (df = 124)    F Statistic 20.527*** (df = 4; 124)   


Note: *p<0.1; **p<0.05; ***p<0.01 


Using IDPs as the measure of the dependent variable provides a different picture. While the  independent variable is no longer statistically significant at the 95% level, it remains significant at  90%. Despite being less generalizable, the coefficient shows a larger correlation between the  independent and dependent variable for the cases shown. All the control variables had a positive  coefficient, as opposed to the previous regression. However, none of the controls was statistically  significant, except for intensity level, which was significant at the 99% level. Based on this 

observation alone, it can therefore be said that while controlling for other causes, natural resource  extraction is likely to have an impact on the level of conflict-induced displacements, but does not  reach the 95% standard for generalizability. 




 Table5: multivariate regression 


Dependent variable:   

---  IDPs    ---  gdp_portion 5,253.133*   



population 0.0001    (0.0001)   


GNI_value 0.203    (6.349)   


intensity_level 340,360.000***   



Constant -234,396.700**   



---  Observations 129    R2 0.164    Adjusted R2 0.138   

Residual Std. Error 372,044.900 (df = 124)    F Statistic 6.103*** (df = 4; 124)   


Note: *p<0.1; **p<0.05; ***p<0.01   

4.4 Discussion 

There are several phenomena of interest in the results of the regressions. First is the variation  between the two selected measurements of the dependent variable. As previously discussed, this  may be caused either by inherent differences between within- and cross border displacements, or  the portion of the total forced displacement measure which was not caused by armed conflict is  larger than anticipated. Both these options should be discussed, to determine how likely they are to  cause this difference of results.  


There are several factors determining whether a person becomes an IDP or refugee in the event of  armed conflict. Two prevailing theories are the expected wages found in neighbouring states  compared to local economic opportunity, and level of violence used against the non-combatants 


(Moore & Shellman, 2006:619; Prakash, 2013:7). Neither of these theories would support lower  levels of displacements across borders in armed conflicts with natural resource extraction. For one,  as previously mentioned, income tends to be lower in states which rely on natural resources (Sachs  and Warner, 2001:836), Therefore likelihood of relative economic opportunity in neighbouring  states is higher. Further, as previously established, geographic areas associated with high monetary  value are correlated with greater levels of targeting of civilians (Lyall, 2009:336). Following this, an  increase in cross-border displacement should thus be observed, as opposed to the results of the  regressions displayed in tables 2 and 4. This leaves a final theory suggesting that a base level of  resources are required for cross border relocation, due to higher social and economic costs, leaving  those without said economic capabilities behind as IDPs (Davenport, Moore & Poe, 2003:41; 

Moore & Shellman, 2006:605). This would support an inherent difference between the 

within-border and cross border displacements that correlate with the observed results, and could  affect the hypothesis. Alternatively, the differences in results could be caused by the portion of  total displaced which are not conflict-induced displacements. However, how large a portion of the  results are represented by this number is difficult to know, as there is no data on levels of 

conflict-induced displacement for cross border displacements. 


Notably, adding armed conflict as a control flipped all other variables to negative in table 4. This  would entail that the independent variable would have a negative impact on displacement. The  theory mentioned above would allow armed conflict intensity to cause a negative relationship with  natural resource extraction. This follows as the theory claims that cross border displacement only  occurs at a base level of wealth (Davenport, Moore & Poe, 2003:41; Moore & Shellman, 

2006:605). As income is often worse in regions dependent on natural resources (Sachs & Warner,  2001:836), this could explain the difference between the measurements. Following this logic, the  same mechanisms as in table 5 are observed, with the exception that lower levels of income  associated with natural resource extraction has a greater negative effect on cross border 

displacement due to high costs than the positive effect on internal displacement. Thus, the sum  total of conflict-induced displacements could be negative.  



As has previously been motivated, intensity levels have had mixed results on displacement levels in  previous studies (Melander & Öberg, 2006:144; Magnus & Öberg, 2009:166). Finding such strong  correlation here is therefore somewhat surprising. However, it is not guaranteed that the causal  mechanism is necessarily as strong as suggested. It is possible that the covariation between the  dependent variable and conflict intensity levels themselves stem from the independent variable. 

Natural resource extraction itself could cause the rising intensity in armed conflict. As explained in  the theory section, the act of extracting natural resources results in grievances both in terms of land  ownership, but also in terms of poor economic development and high levels of inequality, thus  fueling armed conflicts (Collier & Hoeffler, 2004; Sachs and Warner, 2001:837). Further, fighting  is not unlikely to be fiercer surrounding valuable resources, and the fighting over the revenue they  provide could play a part in this data (Ross, 2004:64). If this is true, the independent variable could  cause the change in displacement observed partly through increased conflict intensity, allowing  intensity levels to function as a mediating variable. Since the data collected is at a national level, it  cannot be determined whether fighting was fiercer surrounding the natural resources, and the  concrete effects of natural resource extraction on armed conflict intensity is beyond the scope of  this paper. 


As for the other control variables, these relationships are less surprising, at least concerning table 5. 

Population size was expected to see a positive correlation with conflict-induced displacement  (Magnus & Öberg, 2006), although the results found are not statistically significant in the least. As  for GNI, its effects were both small and statistically insignificant. This too was expected based on  previous research (Prakash, 2013:129). It is however odd that the coefficient was positive. The  results of GNI per capita in table 4 are also expected. An increase in economic activity should  decrease potential economic gain from the crossing of borders, which some have found to be an  contributing factor to cross border displacement (Moore & Shellman, 2004:619). While it is  surprising that contrary to previous large-n studies (Magnus & Öberg, 2009:158), these results are  somewhat generalizable, at the 90% level, they thus follow the results of qualitative research, who  find correlation yet not to a statistically significant level (Prakash, 2013:129-130). The same effect  as was observed with GNI per capita in table 5 can be observed regarding population in table 4. 


The coefficient has the opposite from expected results, but they are neither large nor statistically  significant. 


With these factors considered, it can be determined whether the hypothesis was correct. There was  a somewhat generalizable correlation between conflict-induced IDPs and natural resource 

extraction in armed conflict, and if conflict intensity does have a mediating role, then this is a  statistically significant relationship. This may be caused by both actions to reduce leakage, as well  as means to protect natural resource revenue from sabotage. The same relationship was not found  in regards to total displacement. Whether this difference was caused by inherent differences  between within- and cross border displacement such as difference in costs and opportunities, or a  lack of validity for the broader measurement, is as of now undetermined. However, it is possible  that both these had an effect on the results. 


Finally, the issues with this paper should be discussed. This paper has attempted to capture the  relevant concepts as well as possible. However, questions regarding validity may be raised. The first  is the independent variable. While it does capture both value and quantity of natural resources, it  does include forestry. While this should not be a major problem, it may somewhat affect the data. 

Second, are the measurements of the dependent variable, and has similarly to the independent  variable been discussed previously. Third are the controls. Both conflict intensity and population  are straightforward, yet GNI per capita stands out somewhat. This attempts to capture economic  opportunities and macro-economic trends simultaneously. However, to more accurately capture  economic opportunity, income would fit better. The reason this was not chosen is since accurate  data is lacking within the relevant timeframe. These issues with validity are further discussed in the  research design section. A general lack of appropriate data is a significant issue for this study. Data  at local levels, for all measurements, would have been beneficial and may have provided more  precise results. More importantly, the lack of data on conflict-induced cross border displacement is  particularly problematic. Adding a variable measuring geographic spread would also have been  useful, as it is one a strong predictor of conflict-induced displacement, yet again the data is  nonexistent within the relevant timeframe. Further, there is the possibility of other potential  control variables that could describe the relationship even better, and thus could affect the results. 


While this would be useful to include, not all variables can be included, and the ones presented  above were selected based on relevance and available data. 


5. Conclusion 

This paper attempted to find the effect that natural resource extraction during armed conflict has  on conflict induced displacement. Following the previous literature within the field, the hypothesis  natural resource extraction in armed conflicts has a positive causal relationship with conflict-induced  displacement​ was formed. This follows the logic that in order to reduce leakage and sabotage, the  occupying force will use displacement as a strategy. This theory was tested using a quantitative  study, building a new dataset from IDMC, UNHCR, the World Bank and UCDP. This new  dataset has a key advantage in terms of the research field, as it includes a measure specifically  measuring conflict-induced displacement, although this is only available for IDPs, as opposed to  the broader term forced displacement. Using conflict-induced IDPs, as well as total levels of forced  displacement, two bivariate regressions were done using the portion of GDP represented by  natural resources as the independent variable. After this, a multivariate regression was done for  both measures of conflict induced displacement, controlling for population size, conflict intensity  and economic development. 


This paper found a series of unexpected results. First and foremost, only conflict-induced  displacements of IDPs followed the expected results in a generalizable manner, even if it did not  reach the 95%-threshold when controlling for armed conflict intensity. Whether the broader  operationalization did not have the expected findings due to a difference between within- or  cross-border displacement, or if the new use of specific conflict-induced data was the differing  factor is undetermined. While the results are thus not certain enough to prove the hypothesis  correct, it has shown that there is a likely causal pathway from natural resources to 

conflict-induced displacement regarding at least within-border displacement. 



Other interesting findings were that conflict intensity did have a generalizable and strong positive  causal relationship with both measures of displacement. While this is not a new finding within the  field (Melander & Öberg, 2006:144), it does contradict previous papers (Magnus & Öberg, 

2009:166). Whether intensity levels function as a mediating variable between natural resources and  displacement is as of now unclear, yet would fit the model well. The multivariate regressions  further found support for the theory that there is a higher cost for relocating across borders rather  than within (Davenport, Moore & Poe, 2003:41; Moore & Shellman, 2006:605). Thus, in contexts  with heavy reliance on natural resources, and thus lower average income and wealth (Sachs & 

Warner, 2001:836), fewer can afford the costs associated with crossing borders. As such, these cases  should see a higher proportion of IDPs as compared to cases with lower reliance on natural 

resources, where average income and wealth should be higher. 


Finally, this paper has identified several areas for future research. Quantitative studies using data on  conflict-induced displacement at sub-national level could closer examine the causal pathways, and  look at intervening variables with greater accuracy. This would help establish the exact effects that  armed conflicts over natural resources have on displacement patterns. Possibly the most interesting  puzzle found in this study, is that of effects on within- and cross border displacement. Finding how  the two differ in relation to the independent variable would shed light on many of the questions  raised in this paper. This would be greatly helped by building datasets on cross border 

conflict-induced displacement specifically. Further, while there are plenty of studies examining the  effects of natural resources on intensity levels of armed conflict, looking at how this violence is  directed could be useful. The implications and effects of conflict between two or more armed  groups differ from those where the violence is one-sided. Finally, an interesting future puzzle is  running this data with a reliable dataset using income levels and economic opportunity, to find  differences between the effects of economic development at large and household income in  particular.  


There are several key implications of this study. The results might be useful in predicting 

displacement events. Natural resources tend to be well-mapped (Collier, 2009:127), and as such it  will be easier to predict the movement of displaced people within these regions. Further, greater 


resilience can be built in these communities, and in the management of natural resources in regions  where there is risk of armed conflict. This study has also contributed to the discussion on armed  conflict intensity and its relationship with conflict-induced displacement. Finally, a new dataset  has been built that, while there is room for improvement, allows for testing new more precise  measures of conflict-induced displacement.  





6. Bibliography


Azam, Jean-Paul., Hoeffler, Anke. 2002. “Violence against civilians in civil war: Looting or terror?” 

Journal of Peace Research​. 39 (4): 461-85. 


Barnett, Jon. 2016. “Environmental Security”. In Collins, Alan. (ed). “Contemporary Security  Studies”. 229-147. ​Oxford University Press​. Oxford, England. 


Billion, Philippe Le. 2007. “Securing Transparency: Armed conflicts and the management of  natural resource revenues: Armed Conflicts and the Management of Natural Resource Revenues”. 

March. ​International Journal​. 61(1):93-107   

Caselli, Francesco., Coleman II, Wilbur John. 2013. “On the theory of ethnic conflict”. January. 

Journal of the European Economic Association​. 11(1):160-192. 


Chassang, Sylvain., Miquel, Gerard. 2009. “Economic Shocks and Civil War”. 20 october.

Quarterly journal of Political Science​. 4(3):211-228

Collier, Paul., Hoeffler, Anke. 2004. “Greed and Grievance in Civil War”. 4 October. ​Oxford  Economic Papers​. 56(4):563-595 


Collier, Paul. 2009. “Wars, guns & votes: Democracy in dangerous places”. ​Vintage Books​,  London. 


Davenport, Christian,. Moore, Will,. Poe, Steven. 2003. “SOMETIMES YOU JUST HAVE TO  LEAVE: DOMESTIC THREATS AND FORCED MIGRATION, 1964–1989”. ​International  Interactions​. 29(1):27-55 


Eurostat. 2018. “Expert Group on Refugee and Internally Displaced Persons Statistics”. March. 

[Online]. ​European Union and United Nations​. [Accessed on 2nd January 2021]. Available at: 




Fearon, James. 2004. “Why Do Some Civil Wars Last so Much Longer than Others?”. May. 

Journal of Peace Research​. 41(3):275-301 


Gunton, Thomas. 2003. “Natural resources and regional development: An assessment of 

dependency and comparative advantage paradigms”. January. ​Economic Geography​. 79(1):67-94.  


Hruschka ,C., Leboeuf. 2019. “Vulnerability: A Buzzword or a Standard for Migration”20  January. [Online]. ​Population Europe​. 20. [Accessed on 9th January 2021]. Available at: 




[Online]. ​Human Rights Watch​. [Accessed on 2nd January 2021]. Available at: 



Hultman, Lisa (2014) Violence Against Civilians. In: Edward Newman & Karl Derouen Jr. (eds)  Routledge Handbook of Civil Wars. New York: Routledge, 289–299. 


IDMC. 2020b. “GLOBAL INTERNAL DISPLACEMENT DATABASE”. [Online]. ​Internal  Displacement Monitoring Centre​. [Accessed on 2nd January 2021]. Available at: 



IDMC. 2020a. “Global Report on Internal Displacement 2020”. [Online]. ​Internal Displacement  Monitoring Centre​. [Accessed on 2 January 2021]. Available at: 



IOM. 2019. “Glossary on Migration”. [Online]. ​International Organization for Migration​. 

[Accessed on 10th January 2021]. Available at: 



Kellstedt, Paul., Whitten, Guy. 2018. “The fundamentals of political science research”. Third  edition. ​Cambridge University Press​, USA. 


Lischer, Sarah Kenyon. 2009. “Causes and Consequences of Conflict-Induced Displacement”. 15  August. ​Civil Wars​. 9(2):142-155 


Lyall, Jason (2009) “Does Indiscriminate Violence Incite Insurgent Attacks? Evidence from  Chechnya,” ​Journal of Conflict Resolution​. 53(3): 331–362 


Melander, Erik., Öberg, Magnus. 2006. “Time to Go? Duration Dependence in Forced  Migration”. January 26. ​International interaction​. 32(2):129-152 


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