metadata: authors: 0: author: Noumedem Temgoua,Claudia
1: author: Sharma,Dhiraj
2: author: Wai-Poi,Matthew Grant
count: Iraq
docna: 0: docna: Multidimensional Poverty Assessment of
            Internally Displaced Persons in Iraq
docty: Policy Research Working Paper
subtopic: Inequality,Poverty Assessment,Poverty Lines,Small Area Estimation Poverty Mapping,Poverty Impact Evaluation,Poverty Monitoring & Analysis,Poverty Diagnostics,Hydrology,Water and Human Health,Health and Sanitation,Environmental Engineering,Sanitary Environmental Engineering,Water Supply and Sanitation Economics,Town Water Supply and Sanitation,Sanitation and Sewerage,Small Private Water Supply Providers,Engineering
teratopic: Water Supply and Sanitation,Environment,Science and Technology Development,Health, Nutrition and Population,Poverty Reduction,Water Resources
abstracts: cdata!: Decades of conflict have contributed to
            high flows of internal displacement in Iraq. The incidence
            of these flows on the welfare of internally displaced
            persons is not well understood. This paper attempts to fill
            this gap in the literature by investigating the link between
            internal displacement and multidimensional poverty, using
            one of the most comprehensive household surveys for poverty
            analysis in Iraq. The results show crucial differences
            between internally displaced and non-displaced households
            with respect to multidimensional poverty. Furthermore,
            instrumental variable regression analysis suggests that the
            relationship is causal, that is, the probabilities of
            multidimensional and monetary poverty are higher because of
            internal displacement.
geo_reg_key: 517202,517191
repnme: repnme: Multidimensional Poverty Assessment of
            Internally Displaced Persons in Iraq
display_title: Multidimensional Poverty Assessment
            of Internally Displaced Persons in Iraq
listing_relative_url: /research/2020/04/31933136/multidimensional-poverty-assessment-internally-displaced-persons-iraq
topicv3: Sanitation m1326453 2000,Multi-dimensional Poverty m1326485 1151,Water m1326522 137,Poverty m1326687 695,Poverty Measurement and Analysis m1326941 707
publishtoextweb_dt: 2020-04-06T00:00:00Z
historic_topic: Water Supply and Sanitation,Environment,Science and Technology Development,Health, Nutrition and Population,Poverty Reduction,Water Resources
origu: EFI-MNA-POV-Poverty and Equity (EMNPV)
no_of_pages: 51
author_keys: 0: author_key: 1662675
1: author_key: 1206675
2: author_key: 905143
model_extractions: Policy Research Working Paper 9203 Multidimensional Poverty Assessment of Internally Displaced Persons in Iraq _Claudia Noumedem Temgoua Dhiraj Sharma Matthew Wai-Poi_ Poverty and Equity Global Practice April 2020
Policy Research Working Paper 9203

# **Abstract**

Decades of conflict have contributed to high flows of internal displacement in Iraq. The incidence of these flows on the welfare of internally displaced persons is not well understood. This paper attempts to fill this gap in the literature by investigating the link between internal displacement and multidimensional poverty, using one of the most comprehensive household surveys for poverty analysis in Iraq. The results show crucial differences between internally displaced and non-displaced households with respect to multidimensional poverty. Furthermore, instrumental variable regression analysis suggests that the relationship is causal, that is, the probabilities of multidimensional and monetary poverty are higher because of internal displacement.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cnoumedemtemgoua@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Multidimensional Poverty Assessment of Internally Displaced Persons in Iraq

Claudia NOUMEDEM TEMGOUA<sup>1</sup> , Dhiraj SHARMA<sup>1</sup> , Matthew WAI-POI<sup>1</sup> Contact author:cnoumedemtemgoua@worldbank.org Keywords: Internally displaced people, Forced displacement, Violence and conflict, Multidimensional poverty JEL classification: O15, I32, D6 1World Bank. This work is part of the program ``Building the Evidence on Protracted Forced Displacement: A MultiStakeholder Partnership''. The program is funded by UK aid from the United Kingdom's Department for International Development (DFID), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policymakers. This work does not necessarily reflect the views of DFID, the WBG or UNHCR. We also thank Maria Ana Lugo and Nandini Krishnan at the World Bank for peer-reviewing the paper. All remaining errors and omissions are our own.
# 1. Introduction

Iraq has a long-standing history of internal displacement. Before the Syrian refugee crisis beginning in 2010, Iraqi internally displaced populations (IDPs) and refugees were the world’s second-largest forcibly displaced population, after Afghanistan (Doocy et al., 2009). As of 2008, the number of IDPs was estimated at 2.8 million (IOM, 2008). However, the number of displaced persons increased to over 3.3 million by 2016, following the onset of the 2014 Iraqi Civil War caused by the Islamic State of Iraq and the Levant (ISIL).

Several events in Iraq’s history have triggered massive flows of internal displacement. The Iraqi-Kurdish conflicts caused major flows of displacement from the Kurdistan region in the 1970s. The Iran-Iraq war led to the displacement of populations from the south, mainly the Shi’ite group in the 1980s; the Marsh Arabs were forced to flee following Saddam Hussein’s draining of the marshes in south-eastern Iraq as a repressive response to the Shia insurgents in early 1990s. It is estimated that around one million individuals were forcibly displaced prior to the 2003 US-led invasion. The pre-2003 period can be described as the first of four major forced displacement phases in Iraq.

The 2003 US-led invasion of Iraq marks the beginning of the second phase of displacement; hundreds of thousands of Iraqis were forcibly displaced due to the war (Cockburn, 2007; Stewart, 2007; Steele, 2008). The bombing of the Samarra Al-Askari mosque in February 2006 set off a new surge of violence and insecurity, causing a massive flow of people – third phase. Indeed, the peak of pre-ISIS displacement did not occur right after the US-led invasion but between 2006 and 2008, after the bombing of the Al-Askari mosque (Refugee Studies Center, 2012). The final phase of displacement marks the Daesh insurgency period spanning from 2014 to 2018. The post-2014 period represents one of the most important episodes of internal displacement in Iraq (IOM, 2016). However, the current paper focuses on internal displacement before 2012 due to the use of household data that were collected between 2012 and 2013.

Unlike refugees, IDPs are not covered by any protection within an internationally recognized legal framework. The IDPs in Iraq benefit from little support from the Iraqi government through targeted policies unless they volunteer to return to their original location. The Ministry of Displacement and Migration (MoDM), formed in 2003, has implemented new policies towards displacement in July 2008, mostly aiming at promoting the return of IDPs. However, most IDPs 2
wished to settle in and integrate with their host community, mainly because of security concerns in their place of origin and better opportunities at their destination (IOM, 2013).

Evidence shows IDPs live in precarious conditions. The majority of settlements are self-built overcrowded houses or illegally occupied houses, with limited access to basic services and poor infrastructure, which may lead to poor health outcomes (IOM, 2013). Besides, IDPs might have difficulties finding a formal job due to lack of an identity document, language or cultural barriers. Additionally, forced displacement may have damaging consequences on IDPs’ family structure. For instance, family members may be separated, with working-age adults, and especially males, unable to return to their place of origin. Displacement may also disrupt children’s schooling. This suggests IDPs may be more likely to be deprived in multiple dimensions of well-being; which calls for an in-depth analysis of the deprivation of IDPs to identify the dimensions that demand urgent intervention and to help design policies that are best-suited to the needs of IDPs.

The variation of exposure to violence and insecurity between IDPs and non-IDPs provides a unique quasi-experimental setting, with the war representing an exogenous shock to multiple components of household well-being. The dimensions may be analyzed separately, or combined into a single index of multidimensional poverty, enabling a consolidated analysis of multiple dimensions of deprivation. In the following analysis, we apply the Multidimensional Poverty Index (MPI) methodology developed by Alkire and Foster (2011)<sup>1</sup> to the 2012-2013 Iraqi Household Socio-Economic Survey (IHSES-II)<sup>2</sup> to investigate the effect of forced displacement on multidimensional poverty.

Although there have been numerous works on the macro-level implications of wars and conflicts (e.g. Collier, 1999; Barron et al., 2004; Collier and Hoeffler, 2004; Miguel et al., 2004), 1 See also Alkire and Santos (2010).

2This survey is an outcome from the cooperation between the Central Organization for Statistics of Iraq and the Kurdistan Region Statistics Organization supported by the World Bank, which initiated in 2007 with the first IHSES. The IHSES-II is an update of the first round with additional data and information such as individuals’ past migration history.

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micro-level empirical research on the impact of conflict, and forced displacement in particular, is scant. However, in recent years there has been a growing interest in the literature on how displaced households or individuals are affected (Loaiza et al., 2018), with much of the empirical evidence from African countries (Kondylis, 2008; Ssewanyana et al., 2007). Conflictrelated household displacement has been negatively associated with consumption (Ibáñez and Vélez, 2008; Fiala, 2015), wages and employment (Morales, 2018; Calderón and Ibáñez, 2009), asset ownership (Fransen et al., 2017) and food security (Verwimp and Muñoz-Mora, 2017). This paper intends to contribute to this growing field of literature by exploring the case of a Middle Eastern country, one of the most understudied regions in this empirical literature.

The rest of the paper is organized as follows. In the next section, we discuss various methodologies of poverty measurement. In the third section of the paper, we present our empirical strategy and the results. We conclude the paper in section four.

# 2. Poverty measurement

## 2.1 Monetary vs. Multidimensional Poverty measurements

Sen (1981) distinguishes between two main methods for measuring poverty: the direct and indirect methods. The direct method captures how deprived individuals are with respect to a set of specific basic needs, rights or functioning. The indirect method shows whether individuals’ wealth falls below a certain level that can enable them to meet their basic needs, also known as the poverty line. The indirect method essentially uses monetary poverty measurements such as income, consumption or expenditure; which has the advantage of providing a more straightforward identification of poor individuals.

However, there are some basic limitations to this method, which can be encapsulated in the inability of monetary measurements to fully convey the extent of deprivation, or at least some of the essential needs.<sup>3</sup> Moreover, income can be thought of as means to valuable ends and not

> 3Those basic limitations are mainly connected to comparability issues. For example, the accuracy of the indirect method might be compromised by the fact that people may be exposed to different prices; attaining the poverty

4
an end in itself. The ability to convert income into “functionings” also varies from one individual to another, i.e., even when faced with the same price, people’s needs and their ability to convert resources into “functionings” might differ, which is why it is preferable to measure the achievements directly. Furthermore, the monetary approach is not suited to capture the value of basic services not obtained through the market such as health, education, and water. Nonmonetary indicators may also better identify those who are unable to fully participate in their society due to a lack of resources (Alkire and Santos, 2014). Hence, the use of the direct method has spread in response to these limitations. Multidimensional poverty measurements, for instance, represent variants of the direct method that have increasingly gained the attention of analysts and policy makers despite some criticisms on the way they are built.

Once it is acknowledged that poverty is multidimensional, it raises several practical questions like which poverty dimensions to consider and how to weigh them. There is no consensus on this question in the poverty literature. The construction of multidimensional poverty measures is also complicated by the fact that numerical indicators may not capture the subjective experience of deprivation and that the dimensions of deprivation themselves may be changing across space and over time. This limits the ability of an analyst to construct a universal measure for all social groups for all time periods. But a measure that fulfills all the desirable theoretical properties would come at the cost of simplicity; it would be difficult to convey the relevant information to policy makers. Therefore, scalar indices that aggregate the information from multiple dimensions of deprivation into one quantitative indicator have been widely used (Alkire and Foster, 2011; Maasoumi and Lugo, 2008). Scalar indices are particularly useful for ranking various population groups such as countries or regions (Ferreira and Lugo, 2013).

The Multidimensional Poverty Index (MPI) developed by the Oxford Poverty and Human Development Initiative (OPHI), in collaboration with the United Nations Development Program’s Human Development Report Office between 2009 and 2010, is one of the widelyused multidimensional poverty measures. The MPI was developed as a successor to the Human Poverty Index (HPI). While the HPI is built using aggregated information, the MPI uses line does not seem like the best signal that an individual meets his essential needs to the extent that people may have different patterns of consumption. See Sen (1981) for a detailed description of these limitations.

5
individual-level data to identify individuals who are deprived in multiple dimensions (Alkire and Santos, 2014). Recently, the World Bank (WB) also proposed a global multidimensional poverty measure, which differs from the MPI in crucial ways. Most importantly, the World Bank’s approach to multidimensional poverty measurement is informed by the living standard perspective (The World Bank, 2018). Insomuch as standard monetary measures exclude critical components of welfare, supplementing them with non-monetary components gives a fuller picture of well-being. Our analysis also includes the World Bank multidimensional poverty measure for comparison. We use the MPI methodology proposed by OPHI and the World Bank as they are widely used and they incorporate dimensions and indicators that are globally comparable.

# 2.2 MPI definition

The initial step to estimating multidimensional poverty involves defining a set of indicators that belong to specific poverty dimensions, to which deprivation cutoffs are assigned in the second step. There are three equally weighted poverty dimensions considered in the global MPI analysis: education, health, and standard of living. Ten indicators are defined across these three dimensions, in line with the Millennium Development Goals (MDGs). These indicators are also equally weighted within each dimension as shown in Figure 1.<sup>4</sup>

> 4 The definitions of the indicators are provided in Table B1 in Appendix B. Appendix C explains how the weights are shifted across indicators when the necessary information is missing.

6
<!-- Start of picture text --> 10 Indicators<br>Years of School Child a £ a<br>Schooling | Attendance Mortality » Neem = F - + a: Fa<br>oo= £ = = 8<br>= § © 5 ¢s &<br>(1/6) (1/6) (1/6) (1/6) (1/18 Each)<br>Education (1/3 Health (1/3) Standard of Living (1/3)<br>3 Dimensions<br><!-- End of picture text -->
The next step is to determine the weight of each indicator. The three dimensions of the MPI are equally weighted so that each dimension weight is assigned a value of 1/3. Within each dimension, the indicators _p_ are assigned equal weights _wp_ . The health and education dimensions have two indicators each, therefore each indicator’s weight within these dimensions is 1/6. As for the living standard dimension, each of its six indicators receives a weight of 1/18.

The weights are used to calculate each individual’s deprivation score _ci,_ , a measure of total weighted deprivation which is obtained by a weighted sum of deprivations as follows: Where _0 ≤ ci ≤ 1,_ with _ci_ = 1 if individual _i_ is deprived in all component indicators and _ci_ = 0 otherwise; _Iip_ = 1 if individual _i_ is deprived in indicator _p_ and _Iip_ = 0 otherwise; _wp_ is the weight assigned to the component indicator _p_ so that: In the Alkire-Foster methodology, a poverty cutoff ( _k_ ) is also defined. It is the threshold of total weighted deprivations above which a household is considered to be poor so that _ci ≥ k_ . The value of _k_ is set to 1/3.

Following the identification of deprived individuals, it is possible to compute the headcount ratio ( _H)_ , which is the share of individuals experiencing multidimensional deprivation over the total population. The second measure we consider is the Multidimensional Poverty Index (MPI) or the M0 measure, also known as the adjusted headcount ratio. It is the product of the headcount ratio _H_ and the intensity of multidimensional poverty _A_ : 8
# 3. Methodology

## 3.1 Determinants of household deprivation

### 3.1.1 The binomial logistic model

To assess the relationship between multidimensional poverty and forced displacement, we formulate the following logit model: Consider that a household _j_ is poor ( _Y=1_ ) if its deprivation score is 1/3 or more, or non-poor ( _Y=0_ ) if its deprivation score is below 1/3. The state of deprivation is determined by factors depicting household forced displacement status along with a set of household socio-economic and demographic indicators. These factors are grouped into a vector _X_ so that: Where Xj = Fj + Hj (5) with _Yj_<sup>_*_</sup> being the underlying latent variable capturing the poverty index of household _j_ ; Fj represents the displacement dummy, and Hj other socio-economic and demographic indicators; _uj_ is the stochastic error term; _β_ is a column vector of parameters.

We assume the cumulative distribution of _uj_ is logistic (Greene, 1993) and therefore follows a where _β_ is the raw vector of parameters and ⍺ is a scalar. The logit function to be estimated by the maximum likelihood method is expressed as follows: 9
The natural log of the odds of the household to be MPI poor is the logit variable _Dj_ . The maximum likelihood method used for the estimation of equation (7) does not impose any assumption on the normality or homoskedasticity of errors in the predictors.

where βk is the _kth_ element of β _._

# 3.1.2 The general instrumental variable (IV) strategy

From the relationship in equation (6) it is difficult to draw inference regarding the causal effect of forced displacement on the probability of a household being in multidimensional poverty. Indeed, one could argue that the causality runs in the other directions, i.e., poor households are more likely to be displaced. Also, there may be unobservable factors that affect household deprivation while at the same time serving as the push factors for the household’s movement. For instance, restrictive policies by the state towards certain regions could create uprisings and sectarian violence in these regions and simultaneously contribute to spreading vulnerability and deprivation. These policies might then be indirectly correlated with forced displacement through their strong correlation with conflict and violence. Hence, these issues must be accounted for to avoid attributing any effect of these unobservable factors on household poverty to their forced displacement status. To account for potential endogeneity, we add dummy variables for the interaction between origin and destination governorate fixed effect.<sup>5</sup> Besides, we would need an instrument that explains household forced displacement without being correlated with household deprivation status.

Given that Iraq has a long history of conflict, it is a challenging exercise to find a credible instrument. Nevertheless, we use the Iraq Body Count (IBC), a unique publicly available fatality

> 5 The results are robust to including only destination FE – see Appendix D.

10
database compiling detailed civilian casualties from violence and conflict since 2003, to instrument for the probability of forced displacement. These data mainly come from media sources. The total fatality per governorate determines the intensity of exposure to conflict and as such represents a potentially good instrument to the probability of being forcibly displaced at the time of, or right after the violent outbreak. We assume there is no correlation between household current multidimensional poverty status and the intensity of violence at their governorate of residence at the time they migrated for two reasons: first, the dimensions and indicators of multidimensional poverty are less sensitive to any change caused by violence intensity, which may not be the case for monetary poverty; and second, the time lag between violence and the survey period would significantly mitigate any potential direct effect of violence intensity on multidimensional poverty.

For our IV strategy, we follow the two-stage residual inclusion (2SRI) method (Terza et al., 2008) which has been shown to produce unbiased estimates in a non-linear regression specification. The outcome and auxiliary regressions are formulated as in the following models respectively: where _Y_ denotes our dummy for the household deprivation status; _Xe_ is the dummy for the household displacement status which is our endogenous regressor; _Xo_ is the vector of observable exogenous regressors; _Xu_ is the unobservable variable which is correlated with _Xe_ but not correlated with _Xo_ ; _W_ is our instrumental variable (total fatalities in the household’s governorate of origin two months<sup>6</sup> prior to the migration date); β and α are the vectors of parameters to be estimated; _e_ is the random error term.

> 6 It is important to define a good time window between violence and displacement times, to account for the level of aggregation of the IBC and IDP’s origin places, which are recorded at the governorate level. In general, the closer the violence, the quicker people will be displaced. Since we do not know exactly how close people are to

11
a —r(W@) (10) Y= w(x ;B)t+e
correlated with household poverty status. Furthermore, we control for several household and household-head characteristics in the regression.<sup>9</sup>

**Table 1** Variables’ definition and basic statistics (Total observations = 25,145)

|**Variable**|**Definition**|**Mean**|**Std. Dev. **|**Min **|**Max**|
|---|---|---|---|---|---|
|**Dependent variable**||||||
|MP dummy<br>**Alternative dependent**<br>**variables**|Multidimensional<br>poverty<br>dummy<br>taking<br>value<br>1<br>if<br>household deprivation score is<br>1/3 or more and 0 otherwise|0.059|0.235|0|1|
|Consumption per capita<br>dummy|A dummy taking value 1 if<br>household’spatially deflated<br>monthly consumption per capita<br>is lower than the poverty line<br>which is 105,500.4 dinars<sup>10</sup>and 0<br>otherwise.|0.678|0.467|0|1|
|World Bank MP dummy|Multidimensional<br>poverty<br>dummy<br>taking<br>value<br>1<br>if<br>household deprivation score is<br>1/3 or more and 0 otherwise|0.104|0.305|0|1|
|**Explanatory variables**||||||
|IDH|Internally displaced household; a<br>dummy taking value 1 if at least<br>one household member is an IDP<br>and 0 otherwise.|0.081|0.272|0|1|
|Pre03IDH|Internally<br>displaced<br>household pre-US-led invasion:<br>taking value 1 if at least one<br>household member is an IDP who<br>migrated before 2003 and 0<br>otherwise.|0.046|0.210|0|1|

9 The control variables include age, gender, education, and marital status of the household head, household dependency ratio, shares of working-age males and females in employment, location (rural vs. urban), and access to the public distribution system. We also include household’s spatially deflated monthly consumption per capita to control for whether internal displacement drives multidimensional poverty independently of monetary poverty.

> 10 See Vishwanath et al. (2015) for a detailed description on the poverty line calculation.

13
|O3_05IDH|Internally<br>displaced<br>household from<br>the<br>US-led<br>invasion to the pre-Al-Askari<br>mosque bombing: taking value 1<br>if at least one household member<br>is an IDP who migrated between<br>2003-2005 and 0 otherwise.|0.012|0.111|0|1|
|---|---|---|---|---|---|
|Post05IDH|Internally<br>displaced<br>households post the Al-Askari<br>mosque bombing: taking value 1<br>if at least one household member<br>is an IDP who migrated after<br>2006 and 0 otherwise.|0.022|0.147|0|1|
|Head of household age<br>in years|Head of household age|45.853|13.870|11|104|
|Head of household<br>education<sup>11</sup>|Head of the household total<br>number of years of schooling|2.604|2.235|1|11|
|Dependency ratio|It is the percentage of household<br>members aged under 15 or over<br>64, over the total household<br>population.|43.876|22.528|0|100|
|Head of household married|Dummy taking value 1 if the<br>household head is married and 0<br>otherwise.|0.893|0.310|0|1|
|Head of the household male|Dummy<br>taking<br>value<br>1<br>if<br>household head is male and 0 for<br>female.|0.900|0.301|0|1|
|Household in the rural area|Dummy taking value 1 if the<br>household located in the rural<br>area and 0 for the urban area|0.408|0.491|0|1|
|Public distribution system|Dummy taking value 1 if the<br>household receives ration under|0.986|0.119|0|1|

11 The control variable is household head’s total years of schooling, while the dependent variable is constructed using individual’s educational status. Moreover, the dependent variable is not the MPI but a dummy variable constructed using a threshold of deprivation score. Omitting household head’s total years of schooling from the regression does not affect the results.

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||the public distribution system<br>and 0 otherwise.|||||
|---|---|---|---|---|---|
|Household males share|Share of working-age males<br>who are working(aged 15 and<br>over) in the household.|<br>0.177|0.113|0|1|
|Household females share|Share of working-age females<br>who are working(aged 15 and<br>over) in the household.|<br>0.029|0.078|0|1|
|Consumption per capita|Household’spatially deflated<br>monthly consumption per capita<br>indinars|<br>176,981|112,253|0|3,237,427|

# 3.2 Descriptive statistics

In the present analysis, we use the data gathered in the second round of the IHSES-II which is one of the most comprehensive surveys that provides information on household characteristics and living conditions in Iraq. The fieldwork was conducted between January 2012 and February 2013. The final data cover 25,146 households across 18 governorates of Iraq organized into 2,832 clusters for a total of 176,042 individuals. The respondent in each household is usually the head of the household or an authorized family member, although there are also questions on household members’ characteristics. One important feature of these data is the availability of information on household members’ migration history. It is this information that enables us to identify IDPs. These are people who have lived in another governorate – or their current governorate of residence but a different environment (rural or urban) – for an uninterrupted period of six months, and who have moved from that previous place for one of the following reasons:

- Forced displacement: Individuals who have been deported or forced out of their usual place of residence.

- Conventional armed conflict: People who lived close to battlefronts.

- Civil conflict: People who lived in areas where there is internal insecurity.

15
According to these criteria, 5,939 IDPs are identified in 2,027 IDHs,<sup>12</sup> for a total of 14,402 people living in these households, including IDPs. It is necessary to account for the latter group in our analysis to get an insight into the impact of forced displacement on the whole IDH unit.

Table 2 shows some basic descriptive characteristics of the IDPs in our sample. We also add statistics on all IDH members, since they are the ones on which our analysis will be based.<sup>13</sup> Besides, we include figures on non-IDPs to understand how their characteristics may differ from IDPs rather than for comparison. From the first row, we can see the average age of IDPs is 35 while that of all IDH members is considerably lower (23) and comparable to the one of the non-IDPs population which is 22. This suggests on average IDPs are adults from the working-age population group. Most of the IPDs are male – unlike IDH members and non-IDPs which have a slightly higher proportion of females – and most have not completed the primary level of education.<sup>14</sup> A large majority of IDPs, IDHs members, and non-IDPs get food ration distributed under the public distribution system. In general, the main reason for leaving their place of origin is a general state of insecurity or targeting for persecution.

12 We adopt a broad definition of an IDH, intended as a household unit hosting at least one IDP; since we assume vulnerability embedded in the forced displacement of one member to be a shock with potential externalities to all other members. However, one could think of different approaches to this definition. For instance, a household unit where the head of household is an IDP or more simply, a household unit where all members are IDPs. Following these definitions would reduce our IDH sample, to 1,863 according to the first definition and to 289 as per the second definition. Furthermore, we run the risk of omitting the potential externalities to the entire household and therefore underestimating the real impact of forced displacement on household deprivation status.

> 13 The IDH may include children born after their parents’ migration.

> 14 However, IDPs have more education than non-IDPs.

16
**Table 2** . IDPs and IDHs members’ characteristics

||**IDPs (%)**|**IDH members (%) **|<sup>**Non-IDPs (%)**</sup>|
|---|---|---|---|
||N = 5,939|N = 14,402|N = 161,640|
|Age (mean)|35|23|22|
|Age range||||
|0 - 5|1.75|16.62|18.91|
|6 - 15|16.27|26.34|27.14|
|16 - 25|17.78|20.35|19.28|
|26 - 45|35.49|22.62|22.81|
|45 +|28.71|14.07|11.85|
|Gender||||
|Female|46.54|50.31|50.57|
|Male|53.46|49.69|49.43|
|Highest certificate||||
|Postgraduate level|0.24|0.13|0.13|
|Undergraduate level|6.43|4.21|4.61|
|Intermediate|8.07|5.17|5.57|
|Elementary|18.59|12.64|13.52|
|No certificate|66.67|77.84|71.17|
|Public distribution system|97.76|98.35|99.02|
|Displacement motive||||
|Forced displacement|71.09|||
|Conventional armed conflict|21.74|||
|Civil conflict|7.17|||

Table 3 shows the distribution of IDPs and IDHs by the governorate of origin and residence. Baghdad is the governorate of origin for most of the IDPs and IDHs.<sup>15</sup> Roughly 17 percent of

> 15 As share of total population, Baghdad is the fourth and third largest governorate for origin for IDPs and IDHs respectively.

17
IDPs come from that governorate and another 12 percent are originally from Nainawa. Muthanna and Qadisiya are the governorates that have experienced the lowest outflow of IDPs, altogether accounting for only 2 percent of IDPs. Both governorates are located in the south of Iraq with the least incidence of conflict in the previous decade. As for the governorate of residence, a good portion of IDPs and IDHs has settled in Sulaimaniya (approximately 13 and 17 percent respectively). Sulaimaniya is one of the three Kurdish governorates<sup>16</sup> where there has been little violence after the 2003 US-led invasion. Nainawa and Basrah governorates host around 10 and 9 percent of IDPs respectively. It is interesting to note that Nainawa is an important source and destination governorate for IDPs, suggesting that a large number of individuals are displaced within the governorate. Overall, Table 3 figures show forced displacement has been a common phenomenon to all regions of Iraq since they have all been affected in some way, as both host and origin regions.

**Table 3** . IDPs’ origin and destination locations

|**Governorate**<br>**of residence**|**Total**<br>**IDPs**|**%tage**<br>**IDPs**|**%tage**<br>**IDHs**|**Governorate**<br>**of origin**|**Total**<br>**IDPs**|**%tage**<br>**IDPs**|**%tage**<br>**IDHs**|
|---|---|---|---|---|---|---|---|
|SULAIMANIYA|780|13.13|17.18|BAGHDAD|1,025|17.26|14.81|
|NAINAWA|576|9.7|8.86|NAINAWA|741|12.48|10.09|
|BASRAH|553|9.31|12.51|SULAIMANIYA|634|10.68|14.39|
|DUHOK|533|8.97|8.59|DIYALA|569|9.58|7.89|
|ERBIL|516|8.69|12.83|ERBIL|468|7.88|10.95|
|BAGHDAD|479|8.07|6.55|ANBAR|400|6.74|0.05|
|MAYSAN|412|6.94|5.64|MAYSAN|301|5.07|5.15|
|DIYALA|354|5.96|5.42|DUHOK|274|4.61|5.53|
|ANBAR|338|5.69|4.67|KIRKUK|242|4.07|4.46|
|WASIT|281|4.73|3.92|BASRAH|232|3.91|4.29|
|SALAH AL-DEEN|275|4.63|3.7|BABYLON|202|3.4|3.7|
|THI-QAR|229|3.86|2.58|SALAH AL-DEEN|192|3.23|1.93|
|KIRKUK|179|3.01|1.77|NAJAF|189|3.18|3.92|
|BABYLON|107|1.8|1.29|THI-QAR|123|2.07|2.68|
|KERBELA|105|1.77|1.5|KERBELA|114|1.92|2.15|
|MUTHANNA|94|1.58|0.75|WASIT|106|1.78|1.66|
|QADISIYA|79|1.33|1.13|QADISIYA|63|1.06|0.91|
|NAJAF|49|0.83|1.13|MUTHANNA|63|1.06|0.86|
|Total|5,939|100|100||5,939|100|100|

> 16The two other ones are Duhok and Erbil.

18
Next, we compare the characteristics of IDHs and non-IDHs. Table 4 shows that while both groups are similar in several indicators, they are statistically different on other dimensions as indicated by the results from the t-test<sup>17</sup> in the last column. Displaced households, on average, are slightly older, have an older household head who is also less educated, have lower dependency ratio, and are more likely to be in urban areas. One observation we can draw from these statistics is that internal displacement may not be exogenous to some of the household innate characteristics. These characteristics may drive any empirical relationship we may find between our main variables of interest. Hence, it would be necessary to account for this potential endogeneity with an IV analysis.

**Table 4** . IDHs and non-IDHs general statistics

||**IDHs**|**Non-IDHs**|**P-value**|
|---|---|---|---|
||N=2,027|N=23,119||
|Average household size|7.07|6.99|0.1741<br>|
|Average age|23|22|0.0000|
|Head of household||||
|age|48|46|0.0000|
|Married (%)|88.41|89.35|0.1899|
|Male (%)|89.59|89.96|0.5911|
|Highest certificate (%)||||
|Postgraduate level|0.54|0.66|0.5229|
|Undergraduate level|10.36|12.47|0.0055|
|Intermediate|14.01|15.07|0.2021|
|Elementary|26.05|28.08|0.0505|
|No certificate|49.04|43.72|0.0000|
|Household dependency ratio|41.67|44.07|0.0000|
|Household environment (%)||||
|Rural (area of residence)|26.84|42.03|0.0000|
|Urban (area of residence)|73.16|57.97|0.0000|
|Household’spatially deflatedmonthly<br>consumptionpercapita (Dinars)|176,122|186,625|0.0000|

> 17 A p-value higher than 0.05 means the difference of the mean value of the characteristics between the two cohorts is not different from zero.

19
To get some more insight into the difference in deprivation patterns between IDHs and nonIDHs, we conduct a comparative analysis across dimensions, indicators, and regions. This is discussed in detail in Appendix A.

# 3.3. Results and discussion

# 3.3.1 Determinants of multidimensional poverty

In this section, we present the results of our empirical model on the relationship between a household’s displacement status and multidimensional poverty. The estimates from the baseline logit regression for the multidimensional poverty dummy (MP dummy) are reported in column (1) of table 5. These are variations in the log-odds of the MP dummy from a one-unit change of the explanatory variable obtained through a maximum likelihood procedure. The results show that in general, the logit model fits the data quite well. This is supported by the chi-square test which strongly rejects the hypothesis that the model has no explanatory power and the percentage of correctly predicted observations by the model which is 92.46 percent. Our explanatory variable of interest, the IDH dummy, shows a positive and statistically significant coefficient.

To get further insight into the latter result, we compute the average marginal effect as the partial derivative of the non-linear probability function. We obtain a marginal effect of 0.017, meaning that on average the probability of falling below the poverty line is 1.7 percent higher for IDHs. This supports our initial argument that IDHs are more vulnerable. Households with highly educated head of household tend to be less deprived. Unsurprisingly, the correlation between multidimensional poverty and per capita consumption is negative. The association between share of working-age men and women is positive and negative respectively. In a context where women’s labor force participation is very low and there are low social expectations of women working, a larger share of women in the household working may indicate working by necessity rather than choice.

Column (2) displays results from the linear probability model, which is an OLS regression run on a binary dependent variable. These regression results are reported purely for comparison with the marginal effect from the logit regression, as we cannot fully trust its standard errors since the homoskedasticity and normality of error assumptions of OLS regression are 20
violated.<sup>18</sup> Yet we can comment on the result of the IDH dummy, whose coefficient shows the probability of being deprived is 1.6 percent higher for IDHs. This result is not far from the logit marginal effect of 1.7 percent.

When disentangling the effect of the IDH dummy with respect to the three main historical phases of forced displacement, we find that households with members displaced prior to 2003 do not seem to be affected by deprivation. In contrast, those who were displaced between 2003 and 2005 and after 2005 are significantly more likely to be deprived, 3.2 percent and 3.3 percent respectively. The recency of displacement might explain why those who migrated since 2003 tend to be poorer insofar as they still need some time to be integrated in the host communities.<sup>19</sup> Next, we run a logit regression on another indicator of poverty/welfare, a poverty dummy based on the household monthly consumption per capita – see column (4). The positive sign and significance of the coefficient for the IDH dummy stand as additional evidence that substantiates our a priori argument. We indeed find that IDHs are 1.6 percent more likely to fall below the monetary poverty line as shown by the marginal effect coefficient.

Finally, the results from the logit regression on our alternative multidimensional poverty dummy developed by the WB are displayed in column (5). We get a marginal effect of the IDH dummy of 0.013, which is not too different from the marginal effect we get from the baseline regression with the Alkire and Foster multidimensional poverty dummy.

> 18 In an attempt to correct for this limitation, we run a linear probability model with robust standard errors.

> 19 To test if different types of households were affected by different waves of displacement, we look at the education level of the father of the household head. Most forcibly displaced households have household heads whose fathers have no education, so we cannot test if the education level of household head’s father is significantly different across phases of displacement.

21
**Table 5** . Determinants of poverty

|**Column**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|||||**monetar**|**WB MP**|
|**VARIABLES**|**MP dummy**|**MP dummy**<br>|**MP dummy**|**y**<br>**dummy**|<br>**dummy**|
||logit|Linear<br>probability|logit|logit|logit|
|IDH|0.336***<br>(0.112)|0.016***<br>(0.005)||0.142*<br>(0.076)|0.223**<br>(0.103)|
|Pre03IDH|||0.005<br>(0.168)|||
|O3_05IDH|||0.657***<br>(0.216)|||
|Post05IDH|||0.675***<br>|||
||||(0.178)|||
|Head of household age|0.005**<br>(0.002)|0.001***<br>(0.001)|<br>0.005**<br>(0.002)|0.001<br>(0.001)|-0.010***<br>(0.002)|
|Dependency ratio|-0.001<br>(0.002)|0.001<br>(0.002)|-0.001<br>(0.002)|0.015***<br>(0.001)|0.023***<br>(0.002)|
|Head of household<br>education|-0.391***<br>(0.029)|-0.010***<br>(0.001)|-0.392***<br>(0.029)|-0.246***<br>(0.012)|-1.473***<br>(0.057)|
||-0.234*|-0.009|-0.231|0.362***|0.036|
|Head of household married|(0.142)|(0.009)|(0.142)|(0.107)|(0.134)<br>|
|Head of household male|0.124<br>(0.145)|0.004<br>(0.009)|0.131<br>(0.145)|0.388***<br>(0.106)|0.418***<br>(0.132)<br>|
|Household in rural area|1.414***<br>(0.068)|0.076***<br>(0.003)|1.409***<br>(0.068)|0.692***<br>(0.039)|1.037***<br>(0.057)<br>|
||-0.386|-0.023**|-0.365|-0.013|-0.594**<br>|
|Public distribution system|(0.281)|(0.011)|(0.281)|(0.199)|(0.254)|
|Per capita consumption|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|||
|Household share males|-0.756***<br>(0.275)|-0.041***<br>(0.012)|-0.755***<br>(0.275)|-2.607***<br>(0.198)|-1.660***<br>(0.275)|
|Household share females|1.011***<br>(0.304)|0.043***<br>(0.015)|1.023***<br>(0.304)|-1.642***<br>(0.290)|-0.628*<br>(0.356)|
|Constant|-1.512**<br>(0.616)|0.085***<br>(0.023)|-1.724***<br>(0.596)|-1.978***<br>(0.420)|-0.782<br>(0.579)|
|Marginal effect of IDH|0.017***<br>(0.006)|||0.016*<br>(0.010)|0.013**<br>(0.006)|
|Marginal effect of<br>preUS_IDH|||0.002|||

22
|Marginal effect of<br>withinUShh1<br>Marginal effect of<br>postAskari_IDH|||(0.008)<br>0.032***<br>(0.011)<br>0.033***<br>(0.009)|||
|---|---|---|---|---|---|
|Observations|25,145|25,145|25,145|25,145|25,145|
|LR chi2|2079.87***||2091.45***|4877.09***|3823.37***|
|Degrees of freedom|29||31|28|28|
|Log likelihood|-5613.97||-5613.99|-11481.24|-6991.08|
|McFadden R2|0.185||0.186|0.212|0.273|
|R-squared||0.074||||
|% Predicted right|92.46%||92.43%|88.16%|91.8%|
|Governorate of<br>origin*governorate||||||
|of residence FE|Yes|Yes|Yes|Yes|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

# 3.3.2 IV strategy to identify the impact of forced displacement

In this section, we present the findings from the models formulated to correct for potential endogeneity. The results are presented in Table 6. Since our instrumental variable is only available from 2003 onwards, our sample size is reduced to 23,983.<sup>20</sup> Column (1) displays outcomes from the auxiliary regression of the two-stage residual inclusion (2SRI) model where we instrument for the IDH dummy with the IBC variable – two months fatalities count<sup>21</sup> – and run a generalized linear model (GLM) regression. Results from the first regression show a positive correlation between the total fatality at IDPs’ governorates of origin – two months prior 20 We are left with 865 IDHs of the 2,027 IDHs in our full sample.

21 We did some robustness checks using total fatalities 1 month and 3 months prior to migration and the results did not change much – see Tables D3 and D4 in Appendix D.

23
to migration – and household displacement status. The marginal effect of this variable shows the probability of being displaced increases by 0.1 percentage point per additional person killed. In the second stage, we introduce the residual _Xu(hat)_ in the MP dummy model and run a GLM regression. Results from the second-stage regression are reported in column (2); the point estimate on the IDH dummy is positive and significant. On average, the probability of being deprived is 8.2 percent higher for IDHs, which is much higher than the 1.7 percent in the logit model in Table 5.

Proceeding further, we implement an IV analysis using a Generalized Method of Moments (GMM) model regression, whose results are displayed in column (3). Again, the estimate for the IDH dummy is positive and significant, indicating IDHs are 3 percent more likely to be poor.

The coefficients in columns 2 and 3 in Table 6 are not strictly comparable to those in Table 5 because the IV regression uses only part of the sample. The higher and positive coefficients of the IDH dummy from the GLM and GMM regressions might be attributed to the difference in the sample. Indeed, Appendix A shows that households displaced after 2003 are poorer than the pre-2003 IDHs. For comparison, column 4 reproduces the logit coefficients using the same subset of observations used in the IV regression. The marginal effect of being an IDH for those displaced after 2003 is 1.5 percent, slightly lower than the 1.7 percent obtained from the baseline regression with the full sample in Table 5. This negates the argument that the difference in the sample could be responsible for the difference in the relationship between household displacement status and multidimensional poverty between logit and IV regressions. However, it is still possible that this result does not generalize to those displaced before 2003, so we cannot draw a similar conclusion on pre-2003 IDHs as our data do not allow for a wider time coverage.

Columns (5) and (6) show the regression results for monetary poverty from the second stage of the 2SRI and GMM regressions respectively. In column (5), the marginal effect of the IDH dummy on the monetary poverty dummy returns a value of 0.068, meaning on average IDHs have a 6.8 percent higher probability of falling below the poverty line. From the GMM model in column (6) we get a lower marginal effect of 3.9 percent. Both coefficients from columns (5) and (6) are much higher than the 1.4 percent marginal effect in Table 5.

24
In the last two columns, we perform similar IV analysis with the WB MP dummy. Results from the 2SRI and GMM regressions point to a positive effect of internal forced displacement on that alternative indicator of multidimensional poverty. The second stage of the 2SRI regression returns a marginal effect of 0.071 as displayed in column (7), while the GMM model gives a marginal effect of 0.029 as we can see from column (8).<sup>22</sup>

**Table 6** . IV regression

|**Column**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|---|---|---|---|---|---|---|---|---|
|**VARIABLES**|**IDH**|**MP**<br>**dummy**|**MP**<br>**dummy**|**MP**<br>**dummy**|**monetary**<br>**dummy**|<br>**monetary**<br>**dummy**|**WB MP**<br>**dummy**|<br>**WB MP**<br>**dummy**|
||GLM|GLM|GMM|Logit|GLM|GMM|GLM|GMM|
|IBC|0.001***<br>(0.001)||||||||
|IDH||1.429***<br>(0.246)|0.030***<br>(0.014)|0.630***<br>(0.145)|0.385***<br>(0.112)|0.039**<br>(0.020)|0.854***<br>(0.205)|0.029**<br>(0.014)|
|Household in rural area<br>(origin)|0.383***<br>(0.118)||||||||
|Head of household age|0.001<br>(0.003)|0.003<br>(0.002)|0.001***<br>(0.001)|0.005**<br>(0.002)|-0.001<br>(0.002)|0.001<br>(0.001)|-0.006***<br>(0.001)|-0.001*<br>(0.001)|
|Dependency ratio|-0.003*<br>(0.002)|-0.001<br>(0.002)|-0.001<br>(0.001)|-0.001<br>(0.002)|0.007***<br>(0.002)|0.002***<br>(0.001)|0.015***<br>(0.001)|0.001***<br>(0.001)|
|Head of household<br>education|0.004<br>(0.015)|-0.655***<br>(0.081)|-0.009***<br>(0.001)|-0.387***<br>(0.029)|-0.153***<br>(0.011)|-0.022***<br>(0.001)|-1.691***<br>(0.088)|-0.019***<br>(0.001)|
|Head of household<br>married|-0.033<br>(0.386)|0.011<br>(0.158)|-0.003<br>(0.010)|-0.220<br>(0.144)|0.222***<br>(0.071)|0.036***<br>(0.012)|0.001<br>(0.089)|0.006<br>(0.010)|
|Head of household male|-0.316<br>(0.381)|0.131<br>(0.144)|-0.001<br>(0.010)|0.104<br>(0.146)|0.355***<br>(0.068)|0.034**<br>(0.013)|0.373***<br>(0.086)|0.004<br>(0.011)|
|Household in rural area|-0.086<br>(0.100)|1.215***<br>(0.086)|0.066***<br>(0.003)|1.429***<br>(0.070)|0.393***<br>(0.029)|0.088***<br>(0.005)|0.667***<br>(0.052)|0.070***<br>(0.004)|
|Public distribution|-0.493***|-0.103|-0.033***|-0.422|0.191|-0.014|-0.198|-0.046***|
|system|(0.125)|(0.297)|(0.011)|(0.281)|||(0.188)|(0.012)|

22 Again, for the monetary and WB MP dummies, we cannot attribute our positive and significant results in this section to our sample reduction. We ran logit regressions on the reduced sample for the two dummies separately and obtained significant marginal effect values of 0.019 and 0.006 respectively.

25
|Per capita consumption|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|(0.201)|(0.016)|||
|---|---|---|---|---|---|---|---|---|
|Household share males|0.240<br>(0.281)|-0.862***<br>(0.334)|-0.040***<br>(0.012)|-0.727***<br>(0.280)|-1.575***<br>(0.126)|-0.221***<br>(0.016)|-1.381***<br>(0.235)|<br>-0.080***<br>(0.012)|
|Household share|-0.982**|0.975***|0.029*|0.941***|-1.130***|-0.108***|-0.126|-0.020|
|females|(0.473)|(0.214)|(0.015)|(0.313)|(0.208)|(0.017)|(0.290)|(0.014)|
|Xuhat||-1.136***<br>(0.316)|||-0.363***<br>(0.123)||-0.564**<br>(0.221)||
|Constant|-1.074**|-2.148**|0.110***|-1.501**|-1.566***|0.104***|-0.496|0.101***|
||(0.423)|(0.844)|(0.030)|(0.692)|(0.341)|(0.041)|(0.508)|(0.031)|
|Marginal effect of IBC|0.001***<br>(0.001)||||||||
|Marginal effect of IDH||0.082***<br>(0.014)||0.015***<br>(0.004)|0.068***<br>(0.020)||0.071***<br>(0.017)||
|Observations|23,983|23,983|23,983|23,983|23,983|23,983|23,983|23,983|
|R-squared|0.171|0.126|0.072||0.202|0.181|0.221|0.116|
|McFadden R2||||0.186|||||
|LR chi2||||2015***|||||
|Degrees of freedom||||29|||||
|Log-likelihood||||-4416.60|||||
|Governorate of<br>origin*governorate<br>of residence FE||Yes|Yes|Yes|Yes|Yes|Yes|Yes|

26
# 4. Concluding remarks

This paper presents an analysis of the impact on multidimensional poverty for different waves of internally displaced persons (IDPs) in Iraq. More precisely, we estimate the long-term effects of displacement on multidimensional poverty – and alternatively other monetary measures of poverty – while accounting for potential endogeneity.

Our empirical analysis on the determinants of household multidimensional poverty confirms our initial hypothesis on the poverty vulnerability of IDHs. Findings from the logit model show that the probability of being deprived is 1.7 percent higher for IDPs. To account for potential endogeneity of deprivation, we use the total fatalities in a household’s governorate of origin two months prior to the migration – only available from 2003 onwards – as an instrument to the probability of being displaced. Our IV analysis returns higher marginal coefficients than the simple linear and logit regressions, strengthening the relationship between displacement and multidimensional deprivation for households displaced after 2003. The analysis of alternative measurements of poverty based on consumption per capita and the World Bank’s multidimensional poverty indicator respectively yields similar results. These results too are substantiated by the IV approach.

However, further analysis is needed to strengthen our knowledge of IDPs’ poverty. More robustness checks are necessary, mainly in relation to the construction of our IDH dummy. Indeed, we can exploit the heterogeneity within the concept of IDHs and experiment with different approaches to its definition; such as a household unit where the head of household is an IDP or a household unit where all members are IDPs. This will help us in understanding if the IDH structure matters. Also, the current work can be extended with more up-to-date information, after the new IHSES update scheduled for 2020. This would enable an insight into recent trends of internal displacement and compare them with the ones prior to 2014. And finally, questions might be raised on the validity of the total fatalities count as an appropriate instrument. One might argue that conflicts are more likely to occur in poorer areas, hence it is unlikely that conflict affects poverty only through displacement. We acknowledge this possibility, which calls for exploring other instruments.

27