ai4data/datause-dataset-rechunked huggingface dataset:
ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum).
8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees.
The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households.
Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees.
The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably.
As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor).
**Box 3: The Role of GIS in Project Site Selection**

During project preparation, GIS was used to overlay three spatial datasets: (i) displacement hotspot maps produced by UNHCR's Field Information and Coordination Support section; (ii) a poverty concentration index derived from the national household budget survey; and (iii) road network accessibility scores from the Ministry of Transport's infrastructure database. The GIS analysis identified 47 communities meeting the dual threshold of high displacement concentration and limited infrastructure access. These communities form the initial targeting list for Component 1 activities.
–– Ensuring that a **situation analysis and mapping of relevant actors**
(Government, UN agencies, NGOs, multilaterals and donors) in the health
sector is done in collaboration with the line ministry to inform the design of a
response in each area of public health and nutrition and for every stage of the
response.

–– **Facilitating data driven responses** Facilitate and support the collection,
compilation, analysis, interpretation and dissemination of health program
data. Support inclusion of refugees in national data systems and tools
including disaggregation of data by nationality to the extent possible.

–– **Refugee participation and consultation** : wherever possible, continue to
develop and support consultative processes that enable refugees and host
community members to assist in designing appropriate, accessible and
inclusive responses.

–– **Providing technical expertise & support** : UNHCR will seek to provide
or facilitate technical and general support to partners on program
implementation and support for inclusion of refugees in national data
systems..
18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12).
4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study.
**7.1 Summary of Key M&E Instruments**

| Instrument | Frequency | Managed By | Output |
|---|---|---|---|
| MIS routine data | Continuous | PIU M&E | Monthly dashboard |
| Annual surveys | Yearly | External firm | PDO indicator updates |
| Protection monitoring | Quarterly | UNHCR / NRC | Protection situation reports |
| GRM reports | Monthly | PIU coordinator | Grievance resolution log |
| ESMF field audits | Semi-annual | PIU E&S officer | Compliance report |

All monitoring instruments feed into the annual progress report submitted to the Association by April 30 of each year.
The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy.
7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed.
- **Physical Constraints:** Poor infrastructure, damaged roads, and impassable routes during the rainy season make it difficult
for humanitarian vehicles to access remote areas. Shortages of clean water, fuel, and electricity also affect operations.

- **Lack of Awareness on Inclusion:** Specific needs of diverse at-risk groups, such as people with disabilities and older
individuals, are often considered under one group of ‘the vulnerable’ without an analysis of underlying causal factors.
The limited awareness on inclusion and the specific risks is likely to translate to insufficient support for disability, gender
and age inclusion in humanitarian settings and requires enhanced efforts to include disability inclusion as part of the
existing coordination structures.

**CRITICAL GAPS IN FUNDING AND POPULATION REACHED**

- Limited resources due to conflicts and instability.

- Coordination structures are still in need of awareness in relation to the needs and barriers of people with disabilities and
older persons.

- Accessibility and inclusion are seen as an add on or specific requirements which are not included in budgeting from the
on-set due to perceived high costs of technical support for disability and age inclusion.

- Data gaps that hinder advocacy with donors and humanitarian actors.
Figure 2. Sample and Distribution of Venezuelans in Colombia Notes: The left-hand panel of the ﬁgure illustrates in shades the number of Venezuelans registered in the RAMV census; the red circles depict the surveys carried out per municipality. The right-hand panel illustrates the number of Venezuelans per municipality reported in the 2018 Colombian census, a proxy of the overall distribution of migrants in the country. The correlation between the sample and the 2018 Colombian census registry is 0. 93. 39
E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor.
8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return.
The SEIS sampling frame was validated against UNHCR's registration database prior to fieldwork. SEIS clusters that could not be matched to valid registration records — typically due to secondary displacement between registration and survey fieldwork — were replaced using a random draw from the list of eligible replacement clusters. The SEIS documentation records 31 cluster replacements, representing 5.3 percent of the original SEIS sample, and replacement clusters were confirmed to be statistically comparable to the dropped clusters on observed baseline characteristics.
Income and consumption index: includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007). Labor market index: includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked. Descriptive statistics for each of the outcomes and their components variables are de- scribed in Table 1. III. B Data for the Taliban ’ s Geographical Presence To obtain information on the Taliban ’ s geographical presence, we scraped online data from the Foundation for Defense of Democracies ’ Long War Journal on the districts con- trolled by Taliban, by U. S. Allies, and contested territories between the two groups. 9 The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO ’ s data in Afghanistan, press reports, in- formation provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, ad- ministers the district openly, and oversees local courts (see Appendix B.
To estimate the fraction of income of the poor and non-poor that comes from social protection and transfers, we calibrate �� using the "average per capita transfer from all social protection and labor (for beneficiaries)" and the coverage in each quintile from the ASPIRE database [10] :

<!-- PAGE 18 --> that quintile. Poor and non-poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU- SILC, 2015).