United States Immigrants Admitted: All Countries data was reported at 1,127,167.000 Person in 2017. This records a decrease from the previous number of 1,183,505.000 Person for 2016. United States Immigrants Admitted: All Countries data is updated yearly, averaging 451,510.000 Person from Sep 1900 (Median) to 2017, with 118 observations. The data reached an all-time high of 1,827,167.000 Person in 1991 and a record low of 23,068.000 Person in 1933. United States Immigrants Admitted: All Countries data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s United States – Table US.G087: Immigration.

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards: Immigrants, Refugees, Muslims, Hispanics, Venezuelans News Media Consumption Trust in News Media and Societal Institutions Frequency and Valence of Intergroup Contact Realistic and Symbolic Intergroup Threat Right-wing Authoritarianism Social Dominance Orientation Political Efficacy Personality Characteristics Perceived COVID-threat, and Socio-demographic Characteristics For the adult population aged 25 to 65 in seven European countries: Austria Belgium Germany Hungary Italy Spain Sweden And for ages ranged from 18 to 65 for: United States of America Colombia The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

Estimates for UK immigration, emigration and net migration, year ending June 2012 to year ending December 2024. These are official statistics in development. To access the most up-to-date data for each time period, please use the most recently published dataset.

Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds.For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world™s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.

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

Immigrants Admitted: Philippines data was reported at 53,287.000 Person in 2016. This records a decrease from the previous number of 56,478.000 Person for 2015. Immigrants Admitted: Philippines data is updated yearly, averaging 54,446.000 Person from Sep 1986 (Median) to 2016, with 31 observations. The data reached an all-time high of 74,606.000 Person in 2006 and a record low of 30,943.000 Person in 1999. Immigrants Admitted: Philippines data remains active status in CEIC and is reported by US Department of Homeland Security. The data is categorized under Global Database’s USA – Table US.G086: Immigration.

This table contains 25 series, with data for years 1955 - 2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Last permanent residence (25 items: Total immigrants; France; Great Britain; Total Europe ...).

This dataset is the result of an experiment conducted in nine countries in Latin America and the Caribbean and published as the book "A Better World for Migrants in Latin America and the Caribbean". This project is joint work between the IDB and UNDP. The databases contain data collected for the impact evaluation of an intervention designed to explore which mechanisms are more effective in changing people's beliefs and attitudes toward migrants. The experiment was conducted in nine countries in Latin America and the Caribbean and consisted of two video interventions. The first video, the informative video, aimed to correct misinformation about the impact of migration by providing accurate information about the size of the migrant population and its characteristics. The second video, an emotive video, intended to appeal to the emotions and empathy of the local population.

This paper demonstrates the effect of country level corruption on illicit behavior of individuals in a foreign country. The empirical research investigates the probability of individuals being apprehended overseas due to the influence of corrupt environment in their home countries. Using cross-sectional data for empirical analysis from 104 different countries over the period of 2009– 2011, the authors focused on finding how people from various countries act and behave differently while stationing outside of their home countries. Their findings reveal some evidences that individuals coming to the United States from corruption-ridden countries are more likely to be apprehended than individuals from less corrupt countries are.

The main dataset ("ESB Mobility Database") contains occupational data on 1,200 Irish immigrants who arrived in the U.S. in the Famine years and could be tracked for at least a decade. We also present the most up-to-date version of our Emigrant Savings Bank Depositor Database, which contains data on all 15,000 people who opened accounts at the bank from 1850 to 1858. Also provided are data from the 1855 New York State census documenting the occupations of New York's entire Irish-born population as well as datasets documenting the occupations held by New York's Irish immigrants one year and ten years after their arrival in America,

The Multi-aspect Integrated Migration Indicators (MIMI) dataset is the result of the process of gathering, embedding and combining traditional migration datasets, mostly from sources like Eurostat and UNSD Demographic Statistics Database, and alternative types of data, which consists in multidisciplinary features and measures not typically employed in migration studies, such as the Facebook Social Connectedness Index (SCI). Its purpose is to exploit these novel types of data for: nowcasting migration flows and stocks, studying integration of multiple sources and knowledge, and investigating migration drivers. The MIMI dataset is designed to have a unique pair of countries for each row. Each record contains country-to-country information about: migrations flows and stock their share, their strength of Facebook connectedness and other features, such as corresponding populations, GDP, coordinates, NET migration, and many others. Methodology. After having collected bilateral flows records about international human mobility by citizenship, residence and country of birth (available for both sexes and, in some cases, for different age groups), they have been merged together in order to obtain a unique dataset in which each ordered couple (country-of-origin, country-of-destination) appears once. To avoid duplicate couples, flow records have been selected by following this priority: first migration by citizenship, then migration by residence and lastly by country of birth. The integration process started by choosing, collecting and meaningfully including many other indicators that could be helpful for the dataset final purpose mentioned above. International migration stocks (having a five-year range of measurement) for each couple of countries. Geographical features for each country: ISO3166 name and official name, ISO3166-1 alpha-2 and alpha-3 codes, continent code and name of belonging, latitude and longitude of the centroid, list of bordering countries, country area in square kilometres. Also, the following features have been included for each pair of countries: geodesic distance (in kilometres) computed between their respective centroids. Non-bidirectional migration measures for each country: total number of immigrants and emigrants for each year, NET migration and NET migration rate in a five-year range. Other multidisciplinary indicators (cultural, social, anthropological, demographical, historical features) related to each country: religion (single one or list), yearly GDP at PPP, spoken language (or list of languages), yearly population stocks (and population densities if available), number of Facebook users, percentage of Facebook users, cultural indicators (PDI, IDV, MAS, UAI, LTO). Also the following feature have been included for each pair of countries: Facebook Social Connectedness Index. Once traditional and non-traditional knowledge is gathered and integrated, we move to the pre-processing phase where we manage the data cleaning, preparation and transformation. Here our dataset was subjected to various computational standard processes and additionally reshaped in the final structure established by our design choices. The data quality assessment phase was one of the longest and most delicate, since many values were missing and this could have had a negative impact on the quality of the desired resulting knowledge. They have been integrated from additional sources such as The World Bank, World Population Review, Statista, DataHub, Wikipedia and in some cases extracted from Python libraries such as PyPopulation, CountryInfo and PyCountry. The final dataset has the structure of a huge matrix having countries couples as index (uniquely identified by coupling their ISO 3166-1 alpha-2 codes): it comprises 28725 entries and 485 columns.

RESPOND project produced a high level of empirical material in 11 countries (Sweden, the UK, Germany, Italy, Poland, Austria, Greece, Bulgaria, Turkey, Iraq, and Lebanon) where the research is conducted between the period 2017-2020. The country teams gathered macro (policies), meso (implementation/stakeholders) and micro (individuals/asylum seekers and refuges) level data related to the thematic fields formulated in four work packages: borders, protection regimes, reception, and integration. An important contribution of this research has been its micro/individual focus which enabled the research teams to capture and understand the migration experiences of asylum seekers and refugees and their responses to the policies and obstacles that they have encountered.

Country teams conducted in total 539 interviews with refugees and asylum seekers, and more than 210 interviews with stakeholders (state and non-state actors) working in the field of migration. Additionally, the project has conducted a survey study in Sweden and Turkey (n=700 in each country), covering similar topics.

This dataset is only about the micro part of the Respond research, and reflects data derived out of 539 interviews conducted with asylum seekers and refugees in 11 countries and here presented in a quantitative form. The whole dataset is structured along the work package topics: Border, Protection, Reception and Integration.

This dataset is prepared as part of Work Package D4.4 (Dataset on Reception) the Horizon 2020 RESPOND project as a joint effort of the below listed project partners.

• • Uppsala University (dataset entries from Sweden)
• • Göttingen University (dataset entries from Germany)
• • Glasgow Caledonian University (dataset entries from the UK and Hungary)
• • Istanbul Bilgi University (dataset entries from Turkey)
• • University of Cambridge (dataset entries from the UK, Sweden and Germany)
• • Swedish Research Institute Istanbul (dataset entries from Turkey)
• • University of Florence (dataset entries from Italy)
• • Özyegin University (dataset entries from Turkey)
• • University of Aegean (dataset entries from Greece)
• • University of Warsaw (dataset entries from Poland)
• • Hammurabi Human Rights Organization (dataset entries from Iraq)
• • Lebanon Support (dataset entries from Lebanon)
• • Austrian Academy of Sciences (dataset entries from Austria)

The refugee location data (Geo-Refugee) provides information on the geographical locations, population sizes and accommodation types of refugees and people in refugee-like situations throughout Africa. Based on the United Nations High Commissioner for Refugees' Location and Demographic Composition data as well as information contained in supplemental UNHCR resources, Geo-Refugee assigns administrative unit names and geographic coordinates to refugee camps/ centers, and locations hosting dispersed (self-settled) refugees. Geo-Refugee was collected for the purpose of investigating the relationship between refugees and armed conflict, but can be used for a number of refugee-related studies. The original data for the category refugees and people in a refugee-like situation by accommodation type and location name comes directly from the UNHCR. The category refugees includes: "individuals recognized under the 1951 Convention relating to the Status of Refugees and its 1967 Protocol; the 1969 OAU Convention Governing the Specific Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR statute; individuals granted complementary forms of protection and those enjoying temporary protection.The category people in a refugee-like situation "is descriptive in nature and includes groups of people who are outside their country of origin and who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained" (UNHCR http://www.unhcr.org/45c06c662.html). The unit of the data is the first-level administrative unit (province, region or state). A refugee location is defined as a unit with a known refugee population, as established by UNHCR country offices. The locations data was compiled using statistics provided by the UNHCR Division of Programme Support and Management. Several of the refugee sites in the original UNHCR data are camp names or other lo cations which are not immediately traceable to a particular location using even the most established geographical databases like that of the National Geospatial Intelligence Agency (NGA). Thus, unit-level location of refugees was established and confirmed using supplementary resources including reports, maps, and policy documents compiled by the UNHCR and contained in the Refworld database (see http://www.unhcr.org/cgi-bin/texis/vtx/refworld/rwmain). Refworld was the primary database used for this project. Geographic coordinates were assigned using the database of the National Geospatial-Intelligence Agency. See https://www1.nga.mil/Pages/default.aspx for more information. All attempts were made to find precise coordinates, including cross-referencing with Google Maps. The current version of the data covers 43 African countries and encompasses the period 2000 to 2010. The UNHCR began systematically collecting information on the locations and demographic compositions of refugee populations in 2000.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

This is the World Freedom index, By the State of World Liberty Project. It ranks various countries by various forms of freedom and created an index to see which countries had the most freedom. USA finished 8th, with Estonia in 1st place and North Korea having the least freedom. Source URL: http://www.stateofworldliberty.org/report/rankings.html This Dataset has a ranking for the countries, just to be clear, when you map out the rankings of countries, the highest ranked countries will not be the brightest on the map. Estonia is Ranked #1, but the value of 1 is lower than the value assigned to North Korea (158). so just be aware of that. In short, for mapping, use the Scores not the Ranks.

This data shows where there are interconnections between public transportation modes at aiports, ferry, and intercity rail and bus stations in the United States. More specifically, according to the Bureau of Transportation Statistics: "The Intermodal Passenger Connectivity Database is a nationwide data table of passenger transportation terminals, with data on the availability of connections among the various scheduled public transportation modes at each facility. In addition to geographic data for each terminal, the data elements describe the availability of rail, air, bus, transit, and ferry services. This data has been collected from various public sources to provide the only nationwide measurement of the degree of connectivity available in the national passenger transportation system. At this point, data has been collected for intercity rail stations and airline airports only. Data on terminals of other modes is being collected and will be released when it is available. It is anticipated that the entire database will be complete by December 31, 2008."

RESPOND project produced a high level of empirical material in 11 countries (Sweden, the UK, Germany, Italy, Poland, Austria, Greece, Bulgaria, Turkey, Iraq, and Lebanon) where the research is conducted between the period 2017-2020. The country teams gathered macro (policies), meso (implementation/stakeholders) and micro (individuals/asylum seekers and refuges) level data related to the thematic fields formulated in four work packages: borders, protection regimes, reception, and integration. An important contribution of this research has been its micro/individual focus which enabled the research teams to capture and understand the migration experiences of asylum seekers and refugees and their responses to the policies and obstacles that they have encountered. Country teams conducted in total 539 interviews with refugees and asylum seekers, and more than 210 interviews with stakeholders (state and non-state actors) working in the field of migration. Additionally, the project has conducted a survey study in Sweden and Turkey (n=700 in each country), covering similar topics. This dataset is only about the micro part of the Respond research, and reflects data derived out of 539 interviews conducted with asylum seekers and refugees in 11 countries and here presented in a quantitative form. The whole dataset is structured along the work package topics: Borders, Protection, Reception and Integration. This dataset is prepared as part of Work Package D3.5 (Dataset on refugee protection) the Horizon 2020 RESPOND project as a joint effort of the below listed project partners. • Uppsala University (dataset entries from Sweden) • Göttingen University (dataset entries from Germany) • Glasgow Caledonian University (dataset entries from the UK and Hungary) • Istanbul Bilgi University (dataset entries from Turkey) • University of Cambridge (dataset entries from the UK, Sweden and Germany) • Swedish Research Institute Istanbul (dataset entries from Turkey) • University of Florence (dataset entries from Italy) • Özyegin University (dataset entries from Turkey) • University of Aegean (dataset entries from Greece) • University of Warsaw (dataset entries from Poland) • Hammurabi Human Rights Organization (dataset entries from Iraq) • Lebanon Support (dataset entries from Lebanon) • Austrian Academy of Sciences (dataset entries from Austria)

People who have been granted permanent resident status in Canada. Please note that in these datasets, the figures have been suppressed or rounded to prevent the identification of individuals when the datasets are compiled and compared with other publicly available statistics. Values between 0 and 5 are shown as “--“ and all other values are rounded to the nearest multiple of 5. This may result to the sum of the figures not equating to the totals indicated.