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.

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

As of May 15, 2001, 5.4 million people, or 18.4% of the total population, were born outside the country. This was the highest proportion since 1931, when foreign-born people made up 22.2% of the population. In 1996, the proportion was 17.4%. The map shows the percentage of the total population that was foreign-born by census subdivision.

This table provides the number of temporary foreign workers in Canada and in provinces by their country of citizenship.

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.

UK residents by individual countries of birth and citizenship, broken down by UK country, local authority, unitary authority, metropolitan and London boroughs, and counties. Estimates from the Annual Population Survey.

The twenty most common countries of origin for immigrants and mother’s origin for native descendants, with proportional share in the population from the country, and share of students on the specialisation tracks natural science (N); language, social science and economy (S); and other (O), respectively. The total is the summed share from the twenty most common countries, and shares in the whole set of immigrants and descendants, respectively, on the respective specialisation tracks. Note that school classes with too few immigrants to perform our analyses have been excluded. Including the whole population, not only those in our analysis, would increase the figures for “other” specialisations. All figures are in percent.

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 ...).

It is often believed that the Olympic Games have become more migratory. The number of Olympic athletes representing countries in which they weren’t born, is thought to be on the rise. It should, however, be noted that migration in the context of sports is hardly a new phenomenon. To study the question of whether the Olympic Games have become more migratory, we constructed a dataset consisting of approximately 40,000 participants from eleven countries that participated in the Summer Olympics between 1948 and 2012.Based on the data, J. Jansen and G. Engbersen have written an academic paper. The paper is published in the international, peer-reviewed open access journal Comparative Migration Studies. In the paper, the authors show that, as a reflection of global migration patterns and trends, the number of foreign-born Olympians hasn’t necessarily increased in all countries. Rather, the direction of Olympic migration has changed and most teams have become more diverse. Olympic migration is thus primarily a reflection of global migration patterns instead of a discontinuity with the past. The data file has been updated on 11-07-2017. The outdated data file is no longer available. If an athlete’s country of birth is unknown, a question mark is used. If an athlete’s country of birth is the same as the country represented, the cell is left empty. Athletes born in overseas territories of the United Kingdom, France, or the Netherlands (which do not have their own recognised National Olympic Committees) were marked native. Following Czaika and De Haas (2014), athletes are (retroactively) marked foreign-born or native based on today’s territorial borders. The depositor provided the data file in XLSX format. DANS added the CSV format of this file to ensure preservation.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

The research project Political Resocialization of Immigrants (PRI) examines political interest and participation among immigrants. The aim of the project was to study immigrant´s relations to community and politics; their living conditions; experiences of immigration to Sweden; factors possible to stimulate increasing political commitment; political attitudes and political behaviour; representatives and demands; information about the Swedish administrative and political system. More than 2 500 interviews were conducted in 1975-1976 with random samples of immigrants born in Finland, Yugoslavia, Poland and Turkey, and a comparison group of Swedish citizens in the 18-67 age group and domiciled in Stockholm municipality. Stratified samples drawn from among the respondents from the first-wave survey were reinterviewed in an election survey during the weeks following the municipal elections of September 1976, in which immigrants participated for the first time. The first-wave interview included questions on: time of moving to Sweden and Stockholm respectively; places of living; language spoken by the respondent, and language spoken by partner and children; newspaper read (Swedish and from native country) and news listened to (Swedish and from other countries); how the respondent would act in a situation when there is a risk of unemployment; circle of friends; organizational membership and activities; knowledge of who to address in Stockholm in different situations; contacts with authorities; important problems in society; interest in Swedish politics; participation in elections in native country and in Sweden; comparison of the personal situation in a number of areas at present and when living in native country; own situation compared with other immigrants and with Swedes respectively; present and earlier occupation; placement in a ´pyramid of society´ in native country and in Sweden; organizational activities of parents; religiosity, own and parents´. In connection with this interview the respondent had to fill in a questionnaire, in which she/he had to state if she/he agreed or not with a number of general statements and a number of statements concerning her/his own nationality. The election survey included questions about election programs in radio and television, study circles discussing the election, information pamphlets, political meetings, knowledge of candidates, voting, important issues in the election campaign, political parties with special interest in issues concerning immigrants, attempts to influence other people how to vote, comparison between Swedish political parties and parties in the native country, interest in election turn-out, and when the respondent decided to vote/not to vote.

The project aims at providing the data required to study the descriptive representation of citizens of immigrant origin (CIOs). The main aim is to provide an overview of the social and political profile of Member of Parliament (MPs), with a particular focus on identifying MPs of immigrant origin. In addition to the national level dataset described below, a corresponding regional level dataset is available.

Identification variables: Political level (regional, national); country-ID (NUTS); name of region; region-id (NUTS); date of relevant election; full name of district in which elected; level of electoral tier (first / Lower (or single tier); identifier for tier 1 to 3 districts at national level; number of legislatures in the country, as recorded by the parliament itself; date in which the legislature begins and ends; first name, first (second) surname of MP; MP-ID; national MP is also simultaneously a regional MP; which regional MP.

Demography: sex of MP; year of birth of MP; highest level of education (ISCED 1997); last occupation /profession of the MP before first ever becoming an MP (ISCO 2008); occupation sector when first elected; current occupation/ profession of the MP (ISCO 2008); current occupation sector.

Electoral and parliamentary tenure variables: number of times the MP has been previously elected to parliament in this district; type of electoral district; number of times the MP has been previously elected to parliament in this tier; Rookie: MP elected for the first time in this term; number of times the MP has been elected to parliament; number of times the MP has taken up the seat in parliament once elected; year when the MP was first elected to national/regional parliament; total number of years spent in national/regional parliament as MP, prior to this legislature (seniority); when was the MP elected for the last time prior to this legislature (continuity); MP was elected to chamber from inauguration; MP stayed continuously with no interruptions from the moment of taking up the seat until the end of the legislative term; number of months the MP did serve (if he did not serve a full legislative term); MP came back to reclaim the seat if MP left seat at some point; position in party list; rank position in which the MP was elected in district; double candidacy in another tier; MP won seat as incumbent, or as contender; parliamentary group the MP joined at the beginning and at the end of his/her term; full name and acronym of party or list in which elected; party code according to the CMP (Comparative Manifesto Project) dataset; party-ID.

Immigrant origin variables (corresponding coding for MPs mother and father): MP was born in the country of parliament; country (ISO 3166-1), world region (UN Classification for ‘Composition of macro geographical regions’), and country region (NUTS) in which the MP was born; data sources for country of birth (e.g. official parliamentary source, personal blogs, etc.); specific sources for country of birth; reliability of the data regarding the country of birth of the MP (as judged by the coder); year of immigration; born as a national citizen of the country of parliament; country of nationality at birth; data sources country of nationality at birth; specific sources for country of citizenship at birth; reliability of the data regarding citizenship at birth; year in which naturalized as a citizen; data sources year of naturalization; specific sources for date of naturalization; reliability of the data regarding naturalization.

Variables relating to aspects potentially related to discrimination: the MP is a native speaker of an official country language and data sources; specific sources for native language of MP; MP can be perceived by voters as a member of an ‘identifiable’ minority; source where picture found; specific sources for picture of MP; does the MP self-identify as a member of an ethnic minority; ethnicity; sources and specific sources for information on ethnic self-identification of MP; self-identification as a member of a certain religion; religion the MP identifies with.

Party career and committee membership variables: year in which the MP joined the party for which she/he was elected in this legislative term; highest position within the party; MP changed party affiliation during the legislative term; date of change; full name and party acronym of the new party joined, CMP code of the new party and Pathways identifier for party; (corresponding co...

Sweden Number of Immigrants: Female: Ghana data was reported at 66.000 Person in 2017. This records a decrease from the previous number of 72.000 Person for 2016. Sweden Number of Immigrants: Female: Ghana data is updated yearly, averaging 61.500 Person from Dec 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 82.000 Person in 2011 and a record low of 27.000 Person in 2001. Sweden Number of Immigrants: Female: Ghana data remains active status in CEIC and is reported by Statistics Sweden. The data is categorized under Global Database’s Sweden – Table SE.G009: Number of Immigrants: by Sex and Country.

The research project Political Resocialization of Immigrants (PRI) examines political interest and participation among immigrants. The aim of the project was to study immigrant´s relations to community and politics; their living conditions; experiences of immigration to Sweden; factors possible to stimulate increasing political commitment; political attitudes and political behaviour; representatives and demands; information about the Swedish administrative and political system. More than 2 500 interviews were conducted in 1975-1976 with random samples of immigrants born in Finland, Yugoslavia, Poland and Turkey, and a comparison group of Swedish citizens in the 18-67 age group and domiciled in Stockholm municipality. Stratified samples drawn from among the respondents from the first-wave survey were reinterviewed in an election survey during the weeks following the municipal elections of September 1976, in which immigrants participated for the first time. The first-wave interview included questions on: time of moving to Sweden and Stockholm respectively; places of living; language spoken by the respondent, and language spoken by partner and children; newspaper read (Swedish and from native country) and news listened to (Swedish and from other countries); how the respondent would act in a situation when there is a risk of unemployment; circle of friends; organizational membership and activities; knowledge of who to address in Stockholm in different situations; contacts with authorities; important problems in society; interest in Swedish politics; participation in elections in native country and in Sweden; comparison of the personal situation in a number of areas at present and when living in native country; own situation compared with other immigrants and with Swedes respectively; present and earlier occupation; placement in a ´pyramid of society´ in native country and in Sweden; organizational activities of parents; religiosity, own and parents´. In connection with this interview the respondent had to fill in a questionnaire, in which she/he had to state if she/he agreed or not with a number of general statements and a number of statements concerning her/his own nationality. The election survey included questions about election programs in radio and television, study circles discussing the election, information pamphlets, political meetings, knowledge of candidates, voting, important issues in the election campaign, political parties with special interest in issues concerning immigrants, attempts to influence other people how to vote, comparison between Swedish political parties and parties in the native country, interest in election turn-out, and when the respondent decided to vote/not to vote.

Census: Number of Migrants: All India data was reported at 314,541,350.000 Person in 2001. This records an increase from the previous number of 232,112,973.000 Person for 1991. Census: Number of Migrants: All India data is updated yearly, averaging 273,327,161.500 Person from Mar 1991 (Median) to 2001, with 2 observations. The data reached an all-time high of 314,541,350.000 Person in 2001 and a record low of 232,112,973.000 Person in 1991. Census: Number of Migrants: All India data remains active status in CEIC and is reported by Census of India. The data is categorized under Global Database’s India – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

Netherlands Number of Immigrants: Kenya data was reported at 308.000 Person in 2017. This records a decrease from the previous number of 312.000 Person for 2016. Netherlands Number of Immigrants: Kenya data is updated yearly, averaging 206.000 Person from Dec 1995 (Median) to 2017, with 23 observations. The data reached an all-time high of 312.000 Person in 2016 and a record low of 146.000 Person in 1995. Netherlands Number of Immigrants: Kenya data remains active status in CEIC and is reported by Statistics Netherlands. The data is categorized under Global Database’s Netherlands – Table NL.G005: Number of Immigrants: by Country.

This dataset provides values for LABOR FORCE PARTICIPATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.