rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6: Participation in local budget events**

|**Indicator**<br>_(in ratio, if not indicated otherwise)_|Someone f<br>househol<br>to influen<br>budgetpro|rom my<br>d tried<br>ce local<br>cess in|Knows t<br>the las<br>hearing i<br>gover|he date of<br>t budget<br>n the local<br>nment|Partic<br>budget<br>Aiyl Ai<br>the last|ipated in<br>hearing of<br>mak during<br>12 months|
|---|---|---|---|---|---|---|
||No|Yes|No|Yes|No|Yes|
|Personal characteristics|||||||
|Age, years|40.2|43.3 **|40.1|44.8 ***|40.1|47.0 ***|
|Female|0.52|0.39 ***|0.53|0.34 ***|0.53|0.30 ***|
|Kyrgyz|0.72|0.87 ***|0.71|0.91 ***|0.71|0.89 ***|
|Years of schooling|10.3|11.2 ***|10.2|11.7 ***|10.3|11.7 ***|
|Risk attitude, 0->10 scale|4.6|6.6 ***|4.6|5.9 ***|4.6|6.2 ***|
|Labor market status|||||||
|Employed|0.60|0.70 ***|0.60|0.80 ***|0.60|0.80 ***|
|Inactive|0.37|0.28 **|0.38|0.19 ***|0.37|0.19 ***|
|Unemployed|0.03|0.02|0.03|0.01 **|0.03|0.01|
|Social capital|||||||
|Lived in the community, years|38|41 **|38|44 ***|38|45 ***|
|Member of a social group|0.11|0.27 ***|0.10|0.39 ***|0.11|0.35 ***|
|Can get financial help from others|0.81|0.88 **|0.81|0.93 ***|0.81|0.91 ***|
|Trust, in a scale from 1->4|||||||
|Trust in people in community|3.2|3.0 ***|3.1|3.3 ***|3.1|3.2|
|Trust in local government|2.7|2.8 *|2.7|2.9 ***|2.7|3.0 ***|
|<br>Trust in central government|2.6|2.7|2.6|2.8 ***|2.6|2.9 ***|
|Trust in police|2.3|2.3|2.3|2.4|2.3|2.4|
|Political attitudes|||||||
|Supports current political system|0.15|0.14|0.14|0.21 ***|0.15|0.18|
|Supports Russian political system|0.20|0.29 ***|0.20|0.24 *|0.20|0.25 *|
|Has no political system preference|0.34|0.27 **|0.24|0.11 ***|0.34|0.32|
|Voted in last Presidential election|0.86|0.94 ***|0.86|0.95 ***|0.86|0.96 ***|
|Is going to vote in local elections|0.77|0.93 ***|0.77|0.88 ***|0.77|0.91 ***|
|Household (HH) characteristics|||||||
|Female head|0.19|0.12 **|0.19|0.17|0.19|0.19|
|HH size, people|5.9|5.6 *|5.9|5.8|6.0|5.4 ***|
|Income per person, '000 Soms/month|3.5|3.6|3.4|4.6 ***|2.0|3.8|
|Land size, ha|1.22|1.68 ***|1.19|1.97 ***|1.21|1.87 ***|
|Livestock units, sheep equivalent|22.0|40.9 ***|21.8|37.3 ***|22.1|37.7 ***|
|Number of cars|0.45|0.49|0.44|0.53 ***|0.45|0.49|
|Asset index, HH level mean=100|107.0|138.6 ***|105.4|152.2 ***|106.6|142.7 ***|
|Housing value, mln.Soms|0.90|0.76 ***|0.90|0.81 **|0.90|0.80 **|
|Community characteristics<br>1|||||||
|Population, '000 people|6.5|5.4 **|6.5|6.6|6.5|5.7 *|
|Kindergarden is available|0.48|0.52|0.47|0.59 ***|0.47|0.59 ***|
|Medical point is available|0.51|0.51|0.51|0.52|0.51|0.49|
|Frequent energy interruptions|0.12|0.08|0.12|0.05 ***|0.12|0.05 ***|
|<br>Clean water availability, %|80|75 **|80|86 ***|80|80|
|Irrigation water available, %|76|85 ***|76|79 *|76|84 ***|
|Number of attending people|153|162|154|151|155|126 *|

Sources: Life in Kyrgyzstan (LiK) Survey, 2011 and 2012 Note: 1/ Community data are from 2011 wave. Significant differences are indicated by * p<0.1, ** p<0.05, *** p<0.01.

17
of formal child care. However, the successfulness of such a policy depends crucially on the extent to which increased public supply crowds out existing private supply, which in turn depends on how private preschool providers respond strategically to increased competition from public providers. Do private providers respond by lowering their prices and competing more aggressively in all market segments? Or do they react by increasingly targeting higherincome households that do not find public providers attractive? If an expansion in free-ofcharge public supply simply induces households to switch from high-quality private suppliers to lower-quality public centers, negative impacts on child development cannot be excluded.

In the present paper we analyze these questions empirically by examining the effect of changes in the supply of public child care services on private child care provision in Brazil. We use rich municipal-level panel data covering the period 2000-2006 to analyze the effect of increased availability of public child care centers on the quantity of private supply, as measured by private enrollment rates and number of private centers. We also check whether increased public supply has any impact on the quality of private child care, as measured by group size, teacher qualification and quality of infrastructure. To plausibly identify exogenous variation in public supply, we exploit unique features of the allocation mechanism of federal transfers to municipalities in Brazil, where the transfers received by local governments exhibit a non-linear and non-monotonic relationship with given population estimates. Results from a regression-discontinuity design reveal that larger federal transfers to a given municipality lead to a significant expansion of public preschool services (as measured by the number of municipal centers and enrollment), but show no evidence that such an expansion crowds-out private enrollment. If anything, the results suggest that private enrollment also increases. We also find no evidence of adverse effects on the quality of private provision.

To guide the interpretation of our empirical results, we develop a simple theoretical model of vertical differentiation, analyzing the optimal pricing response of a private child care provider to entry of a public competitor. In the model, public preschool education is free of charge (zero price), whereas private providers optimally set prices in a profit-maximizing way and supply higher-quality services. Demand for preschool services comes from two 3
# **II  Data and methodology**

The five data sets used are very similar since four of them are standard Demographic and Health Surveys which in addition include HIV testing for a sub-sample of the sample. The Tanzanian Survey is an HIV/AIDS Indicator Survey (AIS) which also includes HIV testing as well as socio-demographic variables (but those are more limited than in the standard DHS)<sup>2</sup> . However, the HIV prevalence among all adults (not only the cohabiting adults included in the sample in Table 1) differs substantially across the five African countries. It is substantially higher in Cameroon (3.9 percent for males, 6.6 percent of females), Kenya (4.6 percent and 8.6 percent), and Tanzania (6.2 percent and 7.6 percent) than in Burkina Faso (1.9 percent and 1.8 percent) and Ghana (1.6 and 2.7 percent). For each of the five countries, I use the couple recode in the survey data. These data sets have been reorganized so that all variables pertaining to a woman and to her male cohabiting partner are assigned to one observation, the couple<sup>3</sup> . Couples include married people as well as people cohabiting but not formally married. The couple recode is then merged with the data set containing the HIV status of the males and females.

It is important to stress that the Demographic and Health Surveys are nationally representative surveys. Previous studies of discordant couples in the medical literature have used non representative samples either because they follow a specific cohort in a particular location (Carpenter and others, 1999; Quinn and others, 2000) or because their sample is a group of pregnant women (Siriwasin and others, 1998) or a group of patients who already know they are HIV positive and are seeking treatment (Carael and others, 1988; N’Gbichi and others, 1995). In the case of a sample where one of the partners at least is a patient, the proportion of concordant positive couples is usually higher because being a patient implies that the individual is seeking some treatment, therefore that he or

> 2 The Demographic and Health Surveys can be downloaded from the website: <u>http://www.measuredhs.com</u> 3 The data from the Tanzania HIV/AIDS Indicator Survey (2003-2004) is not immediately available in couple recode on the web, but I constructed a couple recode based on the instructions of the data provider. The Demographic and Health Survey (DHS) for Lesotho in 2004 will also allow an analysis at the level of the couple, but the data is not yet publicly available. For Lesotho (2004), the final report of the DHS (Lesotho Government and ORC Macro. 2005) includes the statistics about HIV infection in couples, which I discuss briefly in the next sections. The Demographic and Health Surveys for Mali (2001), Zambia (20012002) and the Dominican Republic (2002) included HIV testing results, but the HIV data cannot be merged with the socio-economic variables.

6
46

|||||Legal<br>|Framewo<br>|rk<sup>4 2</sup><br>||fl|Ci|Sifi Idit|
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Restrictions for<br>goinginto<br>reorganizations|Noautomatic<br>stayonassets|Secured<br>creditorpaid<br>first|Managerdoes<br>notstayin<br>reorganizations|Sumof<br>creditor<br>rights|Efficiencyof<br>judicialsystem<br>index|Enforcement<br>ofcreditor<br>rightsindex<sup>43</sup>|Ruleoaw<br>index|oupton<br>index|pecc mpemens|
|Argentina<br>(198042)|0|0|1|0|1<br>|6.0<br>|0.6<br>|5.35<br>|6.02<br>||
|Chile<br>(1981-86)|1|0|1|0|2<br>|7.2<br>|1.4<br>|7.02|5.30||
|Hungary"<br>(1991-95)||1|0|0|2|3.0|0.6|n.a.|n.a.|Thelackofrmodemand<br>efficient institutionsand<br>judicialcapacityisevident<br>inthehugecourtbacklog<br>forliquidationprocedures<br>during1992-96.|
|Japan<br>(1991-ongoing)|0|0|1|1|2|8.0|1.6|8.98|8.52||
|Norway<br>(1987- 93)|1|0|1|0|2|10.0|2.0|10.0|10.0|None|
|Poland<br>(1992- 95)|1|0|1|0|2|3.0|0.6|n.a.|n.a.|Shortageoftrainedjudges;<br>weakenforcementofcourt<br>judgments;highcourt<br>costsforcasefiling;weak<br>administrationofcourts;<br>outdated/inefficient<br>registries forproperty<br>rights,entryandexit.|
|Thailand<br>(1997-ongoing)|I|t|0|1|3|3.2|0.9|6.25|5.18||

12 Sources for indices of creditor rights, efficiency of judicial<sup>system, ruleof law and corruption:La PortaLopez-de-Silanes, and Shleifer1998,Claessens,Djankov andNenova 2000;WorldBank1985;</sup> Barandiaran and Luders 2000, Sanhueza 2000b; Kopanyi<sup>2000b; IntemationalMonetary Fund1999b;Karlsen2000and Norway'sMinistry of Finance;authors'esdmates.</sup> '3 Computed as the product of the creditor rights and efficiency of<sup>judicialsystemindices divided by 10.</sup> " 1993/94 bank and debtor conciliation program was unsuccessful.<sup>Onlyt4% of the eligible enterprisesapplied to the program.Banks wereless willingto write offdebtsand preferredto sellassetspledgedas</sup> collateral. The negotiations were frequently interrupted by<sup>interventions fromthe StateProperty Agency;the selectionprocessof enterpriseswas based on non-financialconsiderations;theagreementsfailedto</sup> specify quantitative performance criteria and targets and to<sup>impose penaltiesfor non-compliance(Kopanyi 2000a).</sup>
# REGULATION, INSTITUTIONS AND COMMITMENT: THE JAMAICAN TELECOMMUNICATIONS SECTOR

Pablo T. Spiller and Cezley 1. Sampson WAfiam B. McKinley Professor of Economics and Public Wities. University of Illinois. and Professor of Govemment and Public Affaims. University of Illinois; and Director. Insfitute of Business. University of West Indims. Jamaica Spiller would like to thank the University of Califomia Universitywide Energy Research Group for providing the faciities and the intelledual environment to conduct the research on which this paper is based. We would like to thank the participants at the World Bank Project on 'Regultion. Instiutions and Economic Effiiency." and in paticular. 3rian Levy. wifh whom much of the onginal ideas for this projeK were developed. tor very useful comments. We are also indebted to Professor Edwin Jones. Dr. Damien King and Mr. Audley Haffis, of the Faculty of Social Sciences. Uniwrsity of West Indies. Mona. for their assance in the preparation ot badcground notes for this study. Finally, we would lke to express our appreciation to senior officrs of TOJ. senior staffers at the Jamaican Ministries ot Finance and Public Utilties and Transporm and to previous commissionems of the Jamiacan Public Utilities Commission who provided much of their scwrce time to infmi us and who provided us with access to company's informtion. The resarch for this paper was panilly funded Sy the World Bank Research Committee.
**TABLE 4. Estimation results for pooled mean group estimation**

||**Aluminum**|**Zinc**|**Copper**|**Oil**|**Coal**|**Gas 1/**|**Gas**|**Rice**|**Wheat**|**Maize 1/**|**Maize**|**Soybeans**<br>**1/**|<br>**Soybeans**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Long run**||||||||||||||
|Log per capita<br>|3.50***|2.60***|2.95***|2.31***|6.04***|0.3|0.38***|1.39***|1.05***|0.28|0.85***|- 0.65|0.84***|
|income|(0.40)|(0.23)|(0.71)|(0.46)|(1.28)|(1.04)|(0.57)|(0.12)|(0.20)|(0.24)|(0.02)|(0.50)|(0.04)|
|Squared log per|- 0.15***|- 0.12***|- 0.12***|- 0.10***|- 0.31***|0.01||- 0.09***|- 0.04***|0.05***||0.10***||
|capita income|(0.02)|(0.01)|(0.04)|(0.02)|(0.06)|(0.05)||(0.01)|(0.01)|(0.02)||(0.03)||
|Lo real rice|- 0.31***|- 0.17***|- 0.36***|- 0.47***|0.15**|- 0.27***|- 0.29***|0.03|0.01|- 0.22***|- 0.19***|- 0.48***|- 0.68***|
|g p|(0.04)|(0.03)|(0.06)|(0.05)|(0.07)|(0.03)|(0.03)|(0.02)|(0.02)|(0.03)|(0.03)|(0.11)|(0.09)|
|**Short run**||||||||||||||
|Adjustment|- 0.26***|- 0.28***|- 0.14***|- 0.07***|- 0.10***|- 0.17***|- 0.17***|- 0.22***|- 0.33***|- 0.19***|- 0.15***|- 0.14***|- 0.13***|
|coefficient|(0.03)|(0.03)|(0.03)|(0.01)|(0.01)|(0.03)|(0.03)|(0.03)|(0.04)|(0.03)|(0.03)|(0.02)|(0.02)|
|Log change in<br>it|- 19.06**|2.9|1.04|4.28*|- 13.41***|31.6|0.63***|- 2.28|- 2.44|- 1.61|0.49***|- 13.54|0.89**|
|per capa<br>income|(9.43)|(13.55)|(7.20)|(2.34)|(3.78)|(21.43)|(0.20)|(6.58)|(6.88)|(4.95)|(0.14)|(21.28)|(0.42)|
|Squared log<br>change in per|1.07**|- 0.01|0.07|- 0.17|0.70***|- 1.51||0.08|0.07|0.15||1.33||
|capita income|(0.47)|(0.67)|(0.36)|(0.11)|(0.18)|(1.06)||(0.46)|(0.38)|(0.32)||(1.33)||
|Log change in|0.09**|0.05|- 0.03|- 0.01*|- 0.01|0.03*|0.03*|- 0.02**|- 0.01|0.02|0.01|- 0.03|- 0.02|
|real price|(0.04)|(0.03)|(0.03)|(0.01)|(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|(0.15)|(0.02)|(0.10)|(0.10)|
|Cntnt|- 4.56***|- 3.50***|- 2.10***|- 0.90***|- 2.85***|- 0.86***|- 0.78***|- 0.40***|- 0.53***|0.61***|0.29***|0.93***|0.36***|
|osa|(0.54)|(0.42)|(0.36)|(0.08)|(0.44)|(0.17)|(0.17)|(0.07)|(0.08)|(0.15)|(0.11)|(0.18)|(0.09)|
|Joint Hausman<br>test- statistic|5.25|7.72|3.26|3.66|4.53|3.02|5.8|2.52|1.45|1.62|5.43|5.86|2.31|
|_p_ - value|0.15|0.05|0.35|0.3|0.21|0.39|0.06|0.47|0.69|0.66|0.07|0.12|0.32|
|log likelihood|886.27|711.2|743.02|3065.46|1557.88|1134.57|1141.82|1647.65|1141.82|1534.65|1462.82|85.7|47.73|
|Observations|1,668|1,658|1,275|1,683|1,366|1,366|1,443|2,692|1,781|2,372|2,372|1,500|1,500|
|Number of<br>countries|33|33|25|33|28|30|30|55|35|47|47|32|32|
|_Memorandum_<br>_item:_||||||||||||||
|Income elasticity<br>at 2017 median<br>income|0.8|0.3|0.7|0.5|0.6|...|0.4|- 0.3|0.3|...|0.8|...|0.8|

Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors in parentheses.

1/ Indicates robustness check but not baseline regression. All other regressions are baseline regressions.

20
mobility. In the case of formal sector loans, a small percentage list paper work as a reason for not seeking loans, in particular among the banked. Interestingly, the percentage of persons who give the high cost of credit as reason for not taking loans is higher in the formal sector than in the informal sector – even though interest rates tend to be higher for informal credit.

28. **In contrast, it is worth noting that the majority of banked and unbanked consider savings and deposits as highly desirable financial services,** as evidenced by other studies such as surveys in Mexico and Brazil. Despite these findings, policy makers tend to focus on credit for the unbanked as a primary financial service, and tend to discount the importance of savings and payment services in this context. As the DANE questionnaire did not include questions about savings habits and costs, a second, more detailed survey could yield important information on these points.

**Figure 3. 14  Reasons for not requesting a loan from the formal sector**

<!-- Start of picture text --> Other 1.7%1.8%<br>Lack of collateral/guarantees 2.6%2.7%<br>High cost of credit 3.3% 7.2%<br>Too much paper w ork 5.9% 11.2%<br>No w ork/job 4.8% 11.9%<br>Prefers ow n resources 10.6% 15.1%<br>Don't like to get into debt 17.6%19.1%<br>Not enough income 8.2% 19.0%<br>Didn't need 22.9% 34.4%<br>0% 5% 10% 15% 20% 25% 30% 35% 40%<br>Banked Unbanked<br><!-- End of picture text --> _Source: DANE household study of Bogotá, 2003_ 58
self-help groups to exchange ideas for improving their business, organize savings and credit, and cooperate on other aspects of their economic activity.

The program was implemented in two phases. In Phase 1, the researchers randomized 60 of the 120 to receive the intervention. The 60 treated were villages villages further randomized into two sub-groups of 30 villages, with one of them additionally receiving the group dynamics training. The remaining 60 villages were assigned to a waitlist group to be treated in Phase 2, 18 months later.

The baseline survey on the 1,800 program participants was administered between April and June 2009. The endline survey was administered between June and August 2012, 16 months after the first grant.

## <u>Variable details:</u>

- to one if the answers

- Self-employment: equal respondent positively question Q110 of Section 4: Economic Activities (”Are you currently doing any business?”).

- Any employment: equal to one if the respondent reports non-zero employment hours in the past month. The activities considered are listed in Section 4 of the survey, before Q99.

- Migration: based on question Q15 of Section 1: Household Characteristics (”Are still in the same location as were when we last interviewed

- you living you you?”).

- Regression controls: the regressions reported in panel B of tables 3 and 4 control for all baseline covariates reported in online Appendix B of Blattman et al. (2016).

# **C.3 Nairobi microfranchising**

The Nairobi microfranchising experiment involved two labor market interventions targeting women aged 18 to 19. The first intervention (”franchise treatment”) combined several elements: business skills training, franchise-specific vocational training, start-up capital (in form of physical capital specific to each franchise), and ongoing business mentoring. The second intervention (”grant treatment”) offered unconditional cash grants of $239. Outcomes were measured at midline, 7 to 10 months after the end of the intervention, and at endline, 14 to 22 months after the intervention.

We rely on the authors own construction of the variables detailing whether each respondent engages in self-employment activities, any income generating activities, and lives in Nairobi. We define migration as an indicator equal to one if the respondent does not live in Nairobi at endline.

60
familiar with such new risk and clients, and as risk mitigation efforts in Malawi improve, it is expected the average cost of risk transfer will also come down.

# **References**

Agroasemex (2006). The Mexican Experience in the Development and Operation of Parametric Insurances Applied to Agriculture, Working Paper, August 2006. Available online at: http://www.agroasemex.gob.mx/media/publicaciones/agricola_in.pdf Collier and Gunning, (1999). Explaining African Economic Performance, Journal of Economic Literature, Vol. XXXVII, March 1999.

Denning, G., Kabambe, P. Harawa, R., Nkhoma, P., Zamba, C., Banda, C., Magombo, C., Keating, M., Malik, A., Flor, R., Wangila, J., Sanchez, P, and Sachs, J. (2007). Smallholder Maize Production in Malawi: Building the Foundation of an African Green Revolution, Science Report, in review, October 2007.

Dorward, A., Chirwa, E., Kelly, V., Jayne, T., Slater, R., Boughton, D. (2008). Evaluation of the 2006/7 Agricultural Input Supply Programme, Malawi. Final Report.

FAO (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements, Irrigation and Drainage Paper No. 56, FAO, Rome.

Frere, M. & Popov, G. (1986). Early Agrometeorological Crop Yield Assessment, Production and Protection Paper No. 73, FAO, Rome.

Gelb, A., Eifert, B. (2006). Improving the Dynamics of Aid: Towards More Predictable Budget Support, Policy Research Working Paper No. WPS 3732, The World Bank, Washington DC.

Hess, U. and Syroka, J. (2005). Weather-based Insurance in Southern Africa: The Case of Malawi, Agriculture and Rural Development Discussion Paper 13, The World Bank, Washington DC. Paper available on the internet at: http://wwwwds.worldbank.org/external/default/WDSContentServer/WDSP/IB/2006/08/23/000310607_2006 0823171231/Rendered/PDF/370510MW0Weath1d0insurance01PUBLIC1.pdf Hess, U. and Im, S. Y. (2007). Saving Livelihoods Through Weather Risk Management: The Role Of Insurance And Financial Markets – A Cast Study of Ethiopia, Afro-Asian Journal of Rural Development, 40 (1), 21-30, 2007.

Hess, U., Wiseman, W., and Robertson, T. (2006). Ethiopia: Integrated Risk Financing to Protect Livelihoods and Foster Development”, Discussion Paper, November 2006.

Ibarra, H., Hess, U., Syroka, J., and Nucifora, A. (2005). Use of Weather Insurance Markets for Managing Food Supply Risk: Malawi Case Study. Available online at: http://www.passlivelihoods.org.uk/site_files%5Cfiles%5Creports%5Cproject_id_240%5CWeath 18
# 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