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 inter- results show crucial differences between internally displaced nal displacement in Iraq. The incidence of these flows on and non-displaced households with respect to multidi- the welfare of internally displaced persons is not well under- mensional poverty. Furthermore, instrumental variable stood. This paper attempts to fill this gap in the literature regression analysis suggests that the relationship is causal, by investigating the link between internal displacement and that is, the probabilities of multidimensional and monetary multidimensional poverty, using one of the most compre- poverty are higher because of internal displacement. hensive household surveys for poverty analysis in Iraq. The 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 TEMGOUA1, Dhiraj SHARMA1, Matthew WAI-POI1 Contact author:cnoumedemtemgoua@worldbank.org Keywords: Internally displaced people, Forced displacement, Violence and conflict, Multidimensional poverty JEL classification: O15, I32, D6 1 World 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) 1 to the 2012-2013 Iraqi Household Socio-Economic Survey (IHSES-II) 2 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). 2 This 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. 3 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). Conflict- related 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. 3 Moreover, income can be thought of as means to valuable ends and not 3 Those 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. Non- monetary 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 widely- used 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. 4 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 Figure 1 MPI dimensions and indicators assigned weights Source: OHPI, 2015 The MPI methodology follows the mathematical structure of one measure of acute multidimensional poverty as proposed by Alkire and Foster (2007, 2011): the adjusted headcount ratio or the Mo measure. To build the Mo measure, it is necessary to go through a process that involves multiple steps. First, there is an evaluation of the household’s standing relative to each indicator deprivation cutoff. Then the household’s deprivations are weighted by the indicator weights. A household is considered MPI poor if the total of its weighted deprivations is 33 percent or more of all possible deprivations. See Santos and Alkire (2011) for a detailed technical description of the MPI, its dimensions, and the indicators. 2.3 MPI methodology The MPI methodology also follows the two main steps for measuring poverty as conceptualized by Sen (1976): identification of poor individuals and the aggregation of the poverty information. One of the basic requirements of the MPI is to set a cutoff zi for each indicator. A cutoff is intended as the minimum level of attainment associated with each indicator. Assuming xi is the achievement of individual i in an indicator, that person is deprived in that indicator if xi falls below the cutoff (xi = 50% (105,500.4 ID) IDHs 7.62 38.7 0.0295 19.31 1. 21.30 Pre03IDH 2.65 38.5 0.0102 20.28 0.12 17.70 03_05IDH 1.63 40.3 0.0065 18.43 0.59 25.33 Post05IDH 3.34 38.1 0.0128 17.62 0.59 26.07 non-IDHs 7.53 39.4 0.0297 19.87 1 24.04 Total population 7.55 39.3 0.0297 19.83 1 23.81 Hence, we proceed to examine the incidence of deprivation in each of the MPI indicators; which is intended as the proportion of the population that is poor and deprived in each indicator, also known as censored headcount ratios. The bar chart in Figure A1 shows these ratios for IDHs’ and non-IDHs’ members. 23 The top two indicators in which more MPI poor people are deprived are school attendance and sanitation. The proportions of poor people with respect to each of these two indicators are around 6 to 7 percent. School attendance appears on the top of the chart for IDHs’ members as the indicator in which more MPI poor people are deprived. For non- IDHs’ members, it is sanitation that drives the most deprivation. The dirt floor is the third indicator in which poor IDHs are concentrated; while for non-IDHs it is not having access to clean water. In general, there is no big difference in the proportion of MPI poor people across the two groups – the only notable differences are with the water, floor, and cooking fuel indicators. What differs is the ranking of each of these indicators, which is also a signal of their importance in the deprivation of poor people. Therefore, doing such analysis would help set 23 Figures for the total population are depicted in Figure B3 in Appendix B. We will not comment on those figures here as they are pretty similar to the ones shown in Figure A1 for non-IDHs’ members. 32 forth priorities for operations and policy intervention towards specific groups, IDPs in particular. Figure A1. Censored headcount ratios Censored Deprivations in each Indicator Percentage of the Population who are MPI poor and deprived in each indicator School attendance 6.78 6.58 Sanitation 6.40 6.83 Water 3.14 5.03 Floor 3.55 3.31 Cooking fuel 2.43 3.55 Nutrition 2.83 2.49 School attainment 1.77 1.72 Child mortality 0.86 0.60 Electricity 0.22 0.06 Assets 0.01 0.00 0 5 10 15 IDH non-IDH Source: IHSES 2012-2013 When accounting for each indicator’s weight, we get a slightly different picture than above as shown in Figure A2. School attendance appears as the indicator with the highest contribution to the total deprivation of poor people in both IDHs and non-IDHs. 24 Its weight is more than as twice the rate of the second indicator in the chart of contributors to deprivation – nutrition – irrespective of an individual’s displacement status. However, school attendance has a higher percentage contribution of 38.3 percent in the deprivation of poor IDHs as compared to only 36.9 percent for poor non-IDHs’ members. This indicates child education as the major driver of the poor’s MPI, is more important for IDHs than for non-IDHs. Similarly, nutrition weight is also higher in the deprivation of poor IDHs – 16.0 percent – than in one of poor non-IDHs – 24 Again here, we do not comment on the indicators’ contribution for the total sample population but report these numbers in Figure B4 in Appendix B as they are very close to what we get for non-IDHs. 33 13.9 percent. One striking observation from all the above is that the two major contributors to poverty account for over half of poor people’s total deprivation. We get a higher percentage of 54.3 percent for IDHs and 50.8 percent for non-IDHs. Moreover, these two major contributors belong to two separate poverty dimensions: education and health. All this points to the necessity of adapting policy interventions to each specific group’s needs while considering the intensity of deprivation they face. It is also critical to avoid focusing on one single sector, but rather favor an inclusive approach that will incorporate key dimensions of poverty into poverty eradication plans. Another notable point we can draw from Figure A2 is the third position of sanitation and its weights in the deprivation of poor IDHs and non-IDHs – 12.0 percent and 12.8 percent, respectively. Although this indicator’s assigned weight is only 1/6 of the total deprivation score, it emerges as one of the major drivers of the intensity of deprivation of the poor. And again here, it belongs to a different poverty dimension than the first two poverty contributors. Figure A2. Contribution of each indicator to overall poverty Indicators' percentage contribution to the deprivation of the multidimensionally poor 38.3 School attendance 36.9 16.0 Nutrition 13.9 12.0 Sanitation 12.8 10.0 School attainment 9.6 5.9 Water 9.4 6.7 Floor 6.2 4.6 Cooking fuel 6.6 6.4 Child mortality 4.1 0.1 Electricity 0.4 0.0 Assets 0.0 0% 10% 20% 30% 40% IDH non_IDH Source: IHSES 2012-2013 In further analysis, we disentangle multidimensional poverty at the governorate level. This is depicted in the maps in Figures A3 and A4 for the incidence of poverty by the governorate. In 34 general, the bulk of the poor is localized in the south and center of Iraq, in governorates such as Muthanna, Wasit and Maysan; although there are some notable differences between the two maps. Some governorates with relatively low poverty headcount ratios for non-IDHs, in fact, host a sizeable share of IDHs’ poor – like for instance Najaf, Diyala and Babylon. Paradoxically, there does not seem to be a correlation between hosting a high share of poor IDPs and hosting a high share of IDPs. Indeed, Sulaimaniya which is the IDPs’ primary governorate of residence, appears as the second area with the lowest poverty headcount ratio for the group of IDHs, a percentage of 0.8. Two other top IDPs hosting governorates, Nainawa in the North and Basrah in the South-East, also host low shares of IDHs – 4.8 and 3.2 percent, respectively. This brings in some new perspective on the key aspects that should be taken into account when designing assistance programs towards IDPs at the geographical level. Areas with the highest shares of IDPs are not necessarily where there is the highest need for assistance, but rather areas where poor IDPs are concentrated should be the primary focus. Figure A3. Multidimensional poverty headcount at the governorate level for IDHs. 35 MP Headcount at the governorate level IDH 3.0 DUHOK 4.8 2.7 NAINAWA ERBIL 0.8 10.8 SULAIMANIYA KIRKUK 11.2 SALAH AL-DEE 7.4 DIYALA 9.8 BAGHDAD 6.5 22.9 4.9 20.9 ANBAR WASIT KERBELA BABYLON 0.0 QADISIYA 19.0 MAYSAN 16.3 24.7 NAJAF THI-QAR 3.2 BASRAH (25.0,31.0] (20.0,25.0] 30.7 (15.0,20.0] MUTHANNA (10.0,15.0] (5.0,10.0] [0.0,5.0] Figure A4. Multidimensional poverty headcount at the governorate level for non-IDHs. 36 MP Headcount at the governorate level non-IDH 3.1 DUHOK 7.1 2.4 NAINAWA ERBIL 2.2 5.8 SULAIMANIYA KIRKUK 10.0 SALAH AL-DEE 2.2 DIYALA 2.7 BAGHDAD 5.5 16.0 5.7 6.9 ANBAR WASIT KERBELA BABYLON 14.0 QADISIYA 17.8 MAYSAN 4.0 12.1 NAJAF THI-QAR 8.0 BASRAH (25.0,31.0] (20.0,25.0] 14.3 (15.0,20.0] MUTHANNA (10.0,15.0] (5.0,10.0] [0.0,5.0] This section has enabled us to put into perspective some key aspects of IDHs’ multidimensional deprivation. We indeed find that deprivation of the poor IDHs is multidimensional. The main indicators that drive up the poverty weight of poor IDHs belong to different dimensions. These indicators are school attendance and nutrition. We also find that the highest headcount ratio of IDHs is found in the school attendance indicator while for non-IDHs it is in the sanitation indicator. Besides, one critical observation resulting from the multidimensional poverty geographical analysis is that areas with the highest shares of IDPs are not necessarily where poor IDPs are concentrated. The latter point highlights the importance of conducting specific poverty analysis for policy design instead of relying on general statistics. 37 Appendix B. Some additional tables and graphs Table B1. MPI dimensions, indicators, cutoffs and weights Dimension Indicator Deprived if... Relative weight Years of No household member has completed five 16.7% years of schooling Schooling/School attainment Education Child Attendance Any school-aged child is not attending 16.7% school in years 1 to 8 To School Mortality Any child has died in the family 16.7% Health Nutrition Any adult to child for whom there is 16.7% nutritional information is malnourished* Electricity The household has no electricity 5.6% Sanitation The household’s sanitation facility is not 5.6% improved (according to MDG guidelines), or it is improved but shared with other households** Water The household does not have access to safe 5.6% Living drinking water (according to MDG guidelines) or safe drinking water is more Standard than 30 minutes walking from home, roundtrip.*** Floor The household has dirt, sand or dung floor. 5.6% 38 Cooking Fuel The household cooks with dung, wood o 5.6% carbon. Assets The household does not own more than one 5.6% of the following assets: radio, television, telephone, bicycle, scooter or refrigerator, and does not own a car or a truck. *Adults are considered malnourished if their BMI is below 18.5. Children are considered malnourished if their z- score of height-for-age is below minus two standard deviations from the median of the reference population. **A household is considered to have access to improved sanitation if it has some type of flush toilet or latrine, or ventilated improved pit or composting toilet, provided that they are not shared. *** A household has access to safe drinking water if the water source is any of the following types: piped water, public tap, borehole or pump, protected well, protected spring or rainwater, and it is within a distance of 30 minutes’ walk (roundtrip). Source: Alkire and Santos, 2014 39 FigureB1. IDPs’ distribution within IDHs Distribution of IDPs within IDHs 10% 8% IDHs share 6% 4% 2% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage of IDPs out of the total IDH members Figure B2. Deprivation cutoffs and deprived population rates Deprivation cutoffs and deprived population rates Standard MPI 100% 90% 80% 70% 60% 50% 40% 30% 20% 7.55% 0% 0% 10% 20% 33% 40% 50% 60% 70% 80% K-Value 40 Figure B3. Censored headcount ratios (total population) Censored Deprivations in each Indicator Percentage of the Population who are MPI poor and deprived in each indicator Sanitation 6.80 School attendance 6.59 Water 4.88 Cooking fuel 3.46 Floor 3.33 Nutrition 2.52 School attainment 1.72 Child mortality 0.63 Electricity 0.20 Assets 0.01 0% 8% Source: IHSES 2012-2013 Figure B4. Contribution of each indicator to overall poverty for the whole population Indicators' percentage contribution to the deprivation of the multidimensionally poor Standard MPI indicators School attendance 36.98 Nutrition 14.11 Sanitation 12.71 School attainment 9.67 Water 9.12 Cooking fuel 6.47 Floor 6.23 Child mortality 4.31 Electricity 0.38 Assets 0.02 0% 10% 20% 30% 40% Source: IHSES 2012-2013 41 Figure B5. Multidimensional poverty headcount at the governorate level (entire population) MP Headcount at the governorate level 3.1 DUHOK 6.9 2.5 NAINAWA ERBIL 2.0 6.0 SULAIMANIYA KIRKUK 10.0 SALAH AL-DEE 2.6 DIYALA 3.2 BAGHDAD 5.6 16.5 5.6 7.4 ANBAR WASIT KERBELA BABYLON 13.7 QADISIYA 17.9 MAYSAN 4.4 12.8 NAJAF THI-QAR 7.1 BASRAH (25.0,31.0] (20.0,25.0] 14.7 (15.0,20.0] MUTHANNA (10.0,15.0] (5.0,10.0] [0.0,5.0] 42 Appendix C: Data limitations The IHSES data were not specifically designed for an Alkire-Foster type of multidimensional analysis; hence the data questions and output might not perfectly fit into the scope of each indicator as defined by the MPI methodology. In the latter, the specification of each indicator’s deprivation cutoffs is very precise. Although some of the questions as formulated in the IHSES survey might help to capture an MPI indicator, the answering options may not incorporate the level of precision needed for the cutoffs. For the sanitation indicator, for instance, options provided to the respondent are not disaggregated to a level that would enable detailed choices such as latrine or ventilated improved pit or composting toilet. Instead, it is limited to the following possible options: siphon/without siphon, shared/not shared. Similar issues are encountered in the water dimension. In general, for such issues, the only available choice would be to accommodate the level of disaggregation offered by our survey. Yet, there is one important issue left to be sorted out with both indicators of the health dimension which is one of the dimensions that poses the main bottleneck. The child mortality indicator of the standard MPI captures whether a child has died in the household within the past 5 years prior to the survey. Our data only provide this information for the past 12 months, causing some potential underestimation of deprivation with respect to the child mortality indicator. Besides, anthropometrics information has only been recorded for a randomly selected subsample – three households per cluster – of the original sample. This means that information is only available for about 34% of our sample. Therefore, in line with Santos and Alkire (2011) recommendations we apply the following strategy as an attempt to account for this limitation: - When we cannot observe the deprivation status about one of the health dimension indicators – nutrition or child mortality – for certain household types – for instance due to the absence of the applicable member in the household – the remaining indicator within the same dimension receives the entire dimension weight of 1/3. - When there are no children under 5 in the household and no other household member’s anthropometric was recorded, we cannot observe the nutritional status of any member. Therefore, child mortality gets the full dimension weight of 1/3. 43 Appendix D: Some robustness checks Table D1. Determinants of poverty (with destination country FE instead of origin-destination FE) Column (1) (2) (3) (4) (5) monetary WB MP MP dummy MP dummy MP dummy VARIABLES dummy dummy Linear logit probability logit logit logit IDH 0.313*** 0.0127** 0.124* 0.189* (0.110) (0.005) (0.074) (0.103) preUS_IDH -0.003 (0.169) withinUShh1 0.565*** (0.215) postAskari_IDH 0.639*** (0.178) Head of household age 0.001 0.001 0.002 -0.001 -0.0131*** (0.002) (0.001) (0.002) (0.001) (0.002) Dependency ratio -0.001 0.001 -0.001 0.015*** 0.024*** (0.002) (0.001) (0.002) (0.001) (0.002) Head of household education -1.265*** -0.0671*** -1.266*** -0.701*** -2.709*** (0.067) (0.00340) (0.0667) (0.0402) (0.075) Head of household married -0.208 -0.005 -0.206 0.359*** 0.0398 (0.142) (0.009) (0.142) (0.107) (0.135) Head of household gender 0.208 0.0130 0.214 0.391*** 0.494*** (0.145) (0.009) (0.145) (0.107) (0.134) Household in rural area 1.415*** 0.073*** 1.411*** 0.735*** 1.069*** (0.068) (0.00317) (0.0683) (0.039) (0.058) Public distribution system -0.141 -0.0129 -0.121 0.286 -0.360 (0.278) (0.0106) (0.278) (0.197) (0.251) Household total males -0.765*** -0.0410*** -0.767*** -2.598*** -1.710*** (0.277) (0.0118) (0.277) (0.198) (0.279) Household total females 0.941*** 0.0358** 0.952*** -1.917*** -0.733** (0.307) (0.0148) (0.308) (0.290) (0.363) Per capita consumption -0.001*** -0.001*** -0.001*** (0.001) (0.001) (0.001) Constant -1.919*** 0.080*** -1.910*** -2.952*** -2.573*** (0.351) (0.017) (0.351) (0.244) (0.312) Marginal effect of IDH 0.015*** (0.004) Marginal effect of preUS_IDH -0.001 (0.008) 44 Marginal effect of withinUShh1 0.028*** (0.010) Marginal effect of postAskari_IDH 0.031*** (0.008) Observations 25,145 25,145 25,145 25,145 25,145 LR chi2 2183.618*** 2193.559*** 4658.19*** 4150.926*** Degrees of freedom 28.000 30.000 27.000 27.000 Log likelihood -5613.97 -5613.99 -11481.24 -6991.08 McFadden R2 0.189 0.190 0.200 0.293 R-squared 0.084 % Predicted right 0.924 0.924 0.883 0.917 Governorate of residence FE Yes Yes Yes Yes Yes Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table D2. IV regressions (with destination country FE instead of origin-destination FE) Column1 (1) (2) (5) (8) (3) (6) (4) (7) MP MP MP monetary monetary WB MP WB MP VARIABLES IDH dummy dummy dummy dummy dummy dummy dummy GLM GLM GMM Logit GLM GMM GLM GMM IBC 0.001*** (0.001) IDH 1.392*** 0.023* 0.584*** 0.391*** 0.0359* 0.871*** 0.027** (0.156) (0.013) (0.140) (0.109) (0.021) (0.182) (0.013) Household in rural area (origin) 0.414*** (0.120) Head of household age -0.001 0.002 0.001 0.001 -0.001 -0.001 -0.007*** -0.001*** (0.003) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Dependency ratio -0.003* -0.001 -0.001 -0.001 0.008*** 0.002*** 0.015*** 0.001*** (0.002) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Head of household education -0.073 -0.919*** -0.064*** -1.272*** -0.353*** -0.087*** -1.997*** -0.156*** (0.076) (0.079) (0.004) (0.068) (0.028) (0.005) (0.082) (0.004) Head of household married -0.072 -0.016 0.001 -0.195 0.200*** 0.039*** -0.002 0.015 (0.332) (0.158) (0.001) (0.144) (0.071) (0.012) (0.089) (0.010) Head of household male -0.243 0.148 0.008 0.192 0.362*** 0.034** 0.372*** 0.027** (0.330) (0.144) (0.010 (0.146) (0.068) (0.013) (0.086) (0.011) Household in rural area -0.121 1.229*** 0.064*** 1.430*** 0.432*** 0.094*** 0.673*** 0.063*** (0.101) (0.082) (0.003) (0.070) (0.030) (0.005) (0.052) (0.004) 45 Public distribution system -0.464*** -0.045 -0.023** -0.178 0.462** 0.016 -0.171 -0.026** (0.117) (0.287) (0.011) (0.278) (0.202) (0.016) (0.187) (0.012) Household share males 0.197 -0.804** -0.041*** -0.746*** -1.585*** -0.222*** -1.391*** -0.082*** (0.293) (0.336) (0.012) (0.282) (0.128) (0.016) (0.235) (0.012) Household share females -1.378*** 0.994*** 0.0223 0.870*** -1.362*** -0.140*** -0.155 -0.0263** (0.320) (0.213) (0.015) (0.316) (0.216) (0.017) (0.293) (0.013) Per capita consumption -0.001*** -0.001*** -0.001*** -0.001*** (0.001) (0.001) (0.001) (0.001) Xuhat -1.231*** -0.359*** -0.592*** (0.223) (0.123) (0.205) Constant -2.886*** -2.043*** 0.113*** -1.785*** -2.891*** 0.0556** -2.378*** 0.145*** (0.372) (0.414) (0.017) (0.353) (0.234) (0.023) (0.243) (0.018) Marginal effect of IBC 0.001*** (0.001) 0.082*** 0.028*** 0.070*** 0.075*** Marginal effect of IDH (0.009) (0.007) (0.019) (0.016) Observations 23,983 23,983 23,983 23,983 23,983 23,983 23,983 23,983 R-squared 0.171 0.126 0.137 0.193 0.317 0.221 0.2288 McFadden R2 0.19 LR chi2 2121*** Degrees of freedom 28 Log likelihood -4363.63 Governorate of origin FE Yes Governorate of residence FE Yes Yes Yes Yes Yes Yes Yes Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table D3. IV regressions with total body count 1-month prior migration as an instrument Column1 (1) (2) (3) (4) (5) (6) (7) (8) MP MP MP Monetary Monetary WB MP WB MP VARIABLES IDH dummy dummy dummy dummy dummy dummy dummy GLM GLM GMM Logit GLM GMM GLM GMM IBC 0.002*** (0.001) IDH 0.810*** 0.034** 0.584*** 0.357*** 0.035 0.699*** 0.0315* (0.283) (0.015) (0.140) (0.118) (0.023) (0.184) (0.016) Household in rural area (origin) 0.371*** (0.132) 46 Head of household age -0.000320 0.002 0.001 0.001 -0.001 -0.001 -0.006*** -0.001*** (0.00283) (0.001) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Dependency ratio -0.002 -0.001 -0.001 -0.001 0.008*** 0.002*** 0.015*** 0.001*** (0.002) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Head of household education -0.0265 -0.914*** -0.069*** -1.272*** -0.354*** -0.087*** -1.995*** -0.158*** (0.078) (0.076) (0.003) (0.068) (0.028) (0.005) (0.082) (0.004) Head of household married 0.0115 -0.0221 -0.005 -0.195 0.199*** 0.039*** -0.004 0.015* (0.361) (0.158) (0.008) (0.144) (0.071) (0.012) (0.0888) (0.009) Head of household male -0.229 0.139 0.0121 0.192 0.361*** 0.033*** 0.370*** 0.027*** (0.359) (0.144) (0.008) (0.146) (0.068) (0.013) (0.086) (0.009) Household in rural area -0.0587 1.189*** 0.073*** 1.430*** 0.431*** 0.094*** 0.668*** 0.064*** (0.102) (0.079) (0.003) (0.070) (0.030) (0.005) (0.052) (0.003) Public distribution system -0.594*** -0.067 -0.014 -0.178 0.462** 0.017 -0.171 -0.025* (0.107) (0.286) (0.012) (0.278) (0.202) (0.019) (0.188) (0.014) Household share male 0.500* -0.759** -0.041*** -0.746*** -1.587*** -0.221*** -1.386*** -0.080*** (0.296) (0.336) (0.013) (0.282) (0.128) (0.019) (0.235) (0.014) Household share female -1.481*** 0.983*** 0.033** 0.870*** -1.361*** -0.133*** -0.158 -0.019 (0.396) (0.214) (0.016) (0.316) (0.216) (0.024) (0.293) (0.017) Per capita consumption -0.001*** -0.001*** -0.001*** -0.001*** (0.001) (0.001) (0.001) (0.001) Xuhat -0.555* -0.318** -0.399* (0.318) (0.135) (0.203) Constant -3.102*** -1.958*** 0.110*** -1.785*** -2.887*** 0.057** -2.366*** 0.147*** (0.448) (0.416) (0.017) (0.353) (0.234) (0.024) (0.244) (0.018) Marginal effect of IBC 0.001*** (0.001) Marginal effect of IDH 0.049*** 0.064*** 0.060*** (0.017) (0.021) (0.016) Observations 23,983 23,983 23,983 23,983 23,983 23,983 23,983 23,983 R-squared 0.162 0.125 0.084 0.193 0.173 0.221 0.161 McFadden R2 0.196 LR chi2 2121*** Degrees of freedom 28 Log likelihood -4363.63 Governorate of origin*governorate of residence FE Yes Yes Yes Yes Yes Yes Yes Yes Robust standard errors in parentheses 47 *** p<0.01, ** p<0.05, * p<0.1 Table D4. IV regressions with total body count 3 months prior migration as an instrument Column1 (1) (2) (3) (4) (5) (6) (7) (8) MP MP MP Monetary Monetary WB MP WB MP VARIABLES IDH dummy dummy dummy dummy dummy dummy dummy GLM GLM GMM Logit GLM GMM GLM GMM IBC 0.001*** (0.001) IDH 1.423*** 0.025* 0.584*** 0.392*** 0.039* 0.911*** 0.027** (0.151) (0.014) (0.140) (0.107) (0.021) (0.168) (0.013) Household in rural area (origin) 0.494*** (0.123) Head of household age -0.001 0.002 0..01 0.001 -0.001 -0.001 -0.006*** -0.001*** (0.003) (0.002) (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Dependency ratio -0.002 -0.001 -0.001 -0.002 0.008*** 0.002*** 0.015*** 0.001*** (0.002) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Head of household education 0.005 -0.922*** -0.064*** -1.272*** -0.354*** -0.087*** -1.999*** -0.156*** (0.088) (0.075) (0.004) (0.068) (0.028) (0.005) (0.081) (0.004) Head of household married -0.094 -0.018 0.001 -0.195 0.200*** 0.039*** -0.002 0.015 (0.339) (0.158) (0.009) (0.144) (0.071) (0.012) (0.089) (0.009) Head of household male -0.265 0.147 0.008 0.192 0.362*** 0.034** 0.372*** 0.027** (0.338) (0.143) (0.009) (0.146) (0.068) (0.013) (0.0857) (0.011) Household in rural area -0.183* 1.238*** 0.064*** 1.430*** 0.432*** 0.094*** 0.675*** 0.0626*** (0.105) (0.082) (0.003) (0.070) (0.030) (0.005) (0.0518) (0.003) Public distribution system -0.308* -0.050 -0.023** -0.178 0.460** 0.017 -0.175 -0.026** (0.158) (0.289) (0.010) (0.278) (0.202) (0.016) (0.187) (0.012) Household share male 0.121 -0.804** -0.040*** -0.746*** -1.584*** -0.222*** -1.389*** -0.082*** (0.293) (0.337) (0.012) (0.282) (0.128) (0.016) (0.235) (0.012) Household share female -1.136*** 0.983*** 0.022 0.870*** -1.367*** -0.140*** -0.165 -0.026** (0.283) (0.213) (0.015) (0.316) (0.216) (0.017) (0.294) (0.013) Per capita consumption -0.001** -0.001*** -0.001*** -0.001*** (0.001) (0.001) (0.001) (0.001) Xuhat -1.272*** -0.363*** -0.636*** (0.217) (0.119) (0.189) Constant -3.167*** -2.040*** 0.113*** -1.785*** -2.887*** 0.055** -2.373*** 0.145*** (0.396) (0.416) (0.017) (0.353) (0.234) (0.023) (0.243) (0.017) Marginal effect of IBC 0.001*** (0.001) Marginal effect of IDH 0.085*** 0.029*** 0.070*** 0.078*** (0.009) (0.007) (0.019) (0.014) Observations 23,983 23,983 23,983 23,983 23,983 23,983 23,983 23,983 48 R-squared 0.171 0.127 0.083 0.193 0.173 0.222 0.161 McFadden R2 0.196 LR chi2 2121 Degrees of freedom 28 Log-likelihood -4363.63 Governorate of origin*governorate of residence FE Yes Yes Yes Yes Yes Yes Yes Yes Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 49