Abstract

Since 1992, Kenya has been a generous host of refugees and asylum seekers, a population which today exceeds 500,000 people. The Kakuma Refugee Camps have long been among the largest hosting sites (about 40% of the total refugees in Kenya), and have become even larger in recent years, with an estimated 67 percent of the current refugee population arriving in the past five years. In 2015, UNHCR, the Government of Kenya, and partners established Kalobeyei Settlement, located 40 kilometers north of Kakuma, to reduce the population burden on the other camps and facilitate a shift towards an area-based development model that addresses the longer term prospects of both refugees and the host community. The refugee population makes up a significant share of the local population (an estimated 40 percent at the district level) and economy, engendering both positive and negative impacts on local Kenyans. While Kenya has emerged as a leader in measuring the impacts of forced displacement, refugees are not systematically included in the national household surveys that serve as the primary tools for measuring and monitoring poverty, labor markets and other welfare indicators at a country-wide level. As a result, comparison of poverty and vulnerability between refugees, host communities and nationals remains difficult. Initiated jointly by UNHCR and the World Bank, this survey replicates the preceding Kalobeyei SES (2018), designed to address these shortcomings and support the wider global vision laid out by the Global Refugee Compact and the Sustainable Development Goals. Data was collected in October 2019 to December 2019, covering about 2,122 households.

Geographic coverage

Kakuma Refugee Camp, Kenya

Analysis unit

Household and individual

Universe

Sampled household survey, representative of all refugees living in Kakuma refugee camp.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kakuma SES utilized a two-stage sampling process where the first stage samples dwellings, stratified by subcamp, followed by second-stage households. Dwellings were drawn as the primary sampling unit (PSU) from an up-to-date list of all dwellings in the camp provided by UNHCR shelter unit, which serves as the sampling frame. The sample was drawn with explicit stratification for the four Kakuma subcamps, with uniform probability for Kakuma 1-3. For Kakuma 4, the selection probability was slightly increased because of higher expected nonresponse

The survey was designed to accurately estimate socioeconomic indicators such as the poverty rate for group sof the population that have at least a 50 percent representation in the population. A 3 percent margin of error at a confidence level of 95 percent is considered accurate, resulting in a sample size of 2,122. Considering a 10 percent nonresponse rate, the target sample size was 2,347.

Sampling deviation

None

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The following sections are included: household roster, education, employment, household characteristics, assets, access, vulnerabilities, social cohesion, coping mechanism, displacement and cunsumption and expenditure.

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, recoding and local suppression).

Response rate

The SES has a non-response rate of about 5%, mainly due to absence of respondent and refusal to participate in the survey

Abstract

Due to persistent instability in the region, Cameroon hosts refugees and asylum seekers from neighboring countries, mainly from the Central African Republic and Nigeria. In 2015, nearly 259,000 Central African refugees arrived in Cameroon, of whom the vast majority settled in the Northern, Eastern and Adamaoua regions. Within these regions, the study identified 11 subsistence zones, of which the 5 zones with the highest refugee concentration were surveyed, in order to inform UNHCR's Livelihoods Strategy 2017-2020 targeting these refugees and to provide a baseline against which to measure the success of its implementation. The survey was conducted among 2,206 refugee households in November 2016. The household data is supplemented with UNHCRs progress data for the purpose of refining the targeting approach of both WFP and UNHCR.

Geographic coverage

Areas hosting Central African refugees in 5 subsistence zones under study within Cameroon's Adamanou, Eastern and Northern regions. This included 7 refugee camps (Gado-Badzere, Lolo, Bile, Timangolo, Borgop, Ngam and Ngarisingo) as well as various non-camp sites.

Analysis unit

Household and individual

Universe

All Central African refugee households residing in the 5 subsistence zones under study within Cameroon's Adamanou, Eastern and Northern regions.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey's objective was to deliver representative data on all refugees living in 5 subsistence zones under study within the Adamanou, Eastern and Northern regions of Cameroon. These subsistence zones were defined along characteristics of geography, production patterns, market access, etc. - a total of 11 such zones were identified within the three regions, and the 5 zones with the highest refugee concentration were retained for the survey. The total Central African refugee population in Cameroon at the time of the survey was estimated at around 52,000 households; the total in the three regions at around 46,800 households; the total in the five selected subsistence zones within the three regions at around 26,000 households.The survey was designed to be representative of the latter 26,000. The refugees within the five zones were located in 7 refugee camps and 11 non-camp sites.

For this survey a stratified, single-stage (i.e. non-clustered) sample design was applied. The camps and non-camp sites were considered sampling strata. Within each of these 18 strata, a systematic sample of households was drawn from UNHCR's registration list, this enabled the later addition of data from UNHCER registration database for the households included in the survey.

The total sample size was 2,206 refugee households.

NB: The original data collection also included a small number of households from the neighbouring host community; however, these observations were dropped from the public-release version of the dataset.

Sampling deviation

None.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SEA questionaires are provided in section "external ressources".

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables).

Response rate

Overall response rate was 92.3%

Abstract

Due to persistent instability in the region, Cameroon hosts refugees and asylum seekers from neighboring countries, mainly from the Central African Republic and Nigeria. In 2015, nearly 259,000 Central African refugees arrived in Cameroon, of whom the vast majority settled in the Northern, Eastern and Adamaoua regions. Within these regions, the study identified 11 subsistence zones, of which the 5 zones with the highest refugee concentration were surveyed, in order to inform UNHCR's Livelihoods Strategy 2017-2020 targeting these refugees and to provide a baseline against which to measure the success of its implementation. The survey was conducted among 2,206 refugee households in November 2016.

Geographic coverage

Areas hosting Central African refugees in 5 subsistence zones under study within Cameroon's Adamanou, Eastern and Northern regions. This included 7 refugee camps (Gado-Badzere, Lolo, Bile, Timangolo, Borgop, Ngam and Ngarisingo) as well as various non-camp sites.

Analysis unit

Household and individual

Universe

All Central African refugee households residing in the 5 subsistence zones under study within Cameroon's Adamanou, Eastern and Northern regions.

UNHCR PPG: 1CMRB

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey's objective was to deliver representative data on all refugees living in 5 subsistence zones under study within the Adamanou, Eastern and Northern regions of Cameroon. These subsistence zones were defined along characteristics of geography, production patterns, market access, etc. - a total of 11 such zones were identified within the three regions, and the 5 zones with the highest refugee concentration were retained for the survey. The total Central African refugee population in Cameroon at the time of the survey was estimated at around 52,000 households; the total in the three regions at around 46,800 households; the total in the five selected subsistence zones within the three regions at around 26,000 households.The survey was designed to be representative of the latter 26,000. The refugees within the five zones were located in 7 refugee camps and 11 non-camp sites.

For this survey a stratified, single-stage (i.e. non-clustered) sample design was applied. The camps and non-camp sites were considered sampling strata. Within each of these 18 strata, a systematic sample of households was drawn from UNHCR's registration list.

The total sample size was 2,206 refugee households.

NB: The original data collection also included a small number of households from the neighbouring host community; however, these observations were dropped from the public-release version of the dataset.

Sampling deviation

None.

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionaires are provided in section "external ressources".

Cleaning operations

The dataset presented here has undergone light checking, cleaning and restructuring (data may still contain errors) as well as anonymization (includes removal of direct identifiers and sensitive variables, and grouping values of select variables). Moreover, households interviewed from host communities were removed.

Response rate

Overall response rate was 92.3%

Abstract

Nine years into the Syria conflict, Lebanon remains at the forefront of one of the worst humanitarian crises. The economic downturn, steep inflation, COVID-19 and finally the Beirut blast have pushed vulnerable communities in Lebanon - including Syrian refugees - to the brink, with thousands of families sinking further into poverty.

The Government of Lebanon (GoL) estimates that the country hosts 1.5 million1 of the 6.6 million Syrians who have fled the conflict since 2011 (including 879,529 registered with UNHCR as of end of September 2020 ). The Syrian refugee population in Lebanon remains one of the largest concentration of refugees per capita in the world.

The 2020 Vulnerability Assessment of Syrian Refugees in Lebanon (VASyR) was the eighth annual survey assessing the situation of Syrian refugees in Lebanon to identify changes and trends in their vulnerabilities. Given the COVID-19 pandemic in Lebanon, most assessments and other activities requiring in person visits were either cancelled or postponed. Considering the prolonged socio-economic status in Lebanon and COVID-19, it was crucial to provide needs-based estimates on Syrian refugees in the country. Thus, the VASyR 2020 was one of the few assessments that were conducted face-to-face; the implementation was accompanied by a comprehensive protocol to ensure the safety of families and field workers. The criticality of conducting the VASyR 2020 was to provide insights about Syrian refugees impacted by the political and economic crisis that hit Lebanon in late 2019 and by the COVID-19 outbreak.

Geographic coverage

National coverage

Analysis unit

Household and individual

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling for the VASyR follows a two-stage cluster approach, keeping with the methodology of previous years. UNHCR database of known Syrian refugees as of June 2020 served as the sample frame. Cases with missing addresses were excluded. Sampling was based on a "30 x 7" two-stage cluster scheme initially developed by the World Health Organization. This method outlines a sample size of 30 clusters per geographical area and seven households per cluster which provides a precision of +/- 10 percentage points. Districts were considered as the geographical level within which 30 clusters were selected. There are 26 districts in Lebanon, where Beirut and Akkar each represent a district and a governorate. As such, to ensure similar representativeness with other governorates, an additional two cluster samples were considered for each, yielding 90 cluster selections for each. The governorate of Baalbek Hermel is made up of only two districts, as such, and to ensure an adequate sample in that governorate, one additional cluster sample was considered.

The primary sampling unit was defined as the village level (i.e. cluster) and UNHCR cases served as the secondary sampling unit. A case was defined as a group of people who are identified together as one unit (usually immediate family/household) under UNHCR databases. Using Emergency Nutrition Assessment (ENA) software, villages were selected using probability proportionate to size where villages with a larger concentration of refugees was more likely to be selected and 30 clusters/villages were selected with four replacement clusters, per district.

In order to estimate the sample size needed to generate results that are representative on a district, governorate and national level, the following assumptions were used: - 50% estimated prevalence - 10% precision - 1.5 design effect - 5% margin of error

Using the above parameters, 165 cases per district/cluster selection was required, leading to a target of 5,115 cases nationally. Due to the known high level of mobility of the Syrian refugee population and based on experience in previous rounds of VASyR and other household level surveys, a 40% non-response rate was considered.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2020 VASyR questionnaire consisted of around 580 questions that collected data at the household level and individual level including demographics, legal documentation, safety and security, shelter, WASH, health, food security, livelihoods, expenditures, food consumption, debt, coping strategies and assistance, as well as questions specifically relating to women, children and people with disabilities.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead