This folder consists of files for a case study of the methods used by Pew Research Center to make direct and indirect estimates for our report on The Religious Composition of the World's Migrants. Two subfolders demonstrate the procedures of the algorithm using two statistical programs, which mirror one another.

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.

Data on religion by immigrant status and period of immigration, age and gender for the population in private households in Canada, provinces and territories, census metropolitan areas, census agglomerations and parts.

The study of the patterns and evolution of international migration often requires high-frequency data on migration flows on a global scale. However, the presently existing databases force a researcher to choose between the frequency of the data and its geographical scale. Yearly data exist but only for a small subset of countries, while most others are only covered every 5 to 10 years. To fill in the gaps in the coverage, the vast majority of databases use some imputation method. Gaps in the stock of migrants are often filled by combining information on migrants based on their country of birth with data based on nationality or using ‘model’ countries and propensity methods. Gaps in the data on the flow of migrants, on the other hand, are often filled by taking the difference in the stock, which the ’demographic accounting’ methods then adjust for demographic evolutions.

This database aims to fill this gap by providing a global, yearly, bilateral database on the stock of migrants according to their country of birth. This database contains close to 2.9 million observations on over 56,000 country pairs from 1960 to 2020, a tenfold increase relative to the second-largest database. In addition, it also produces an estimate of the net flow of migrants. For a subset of countries –over 8,000 country pairs and half a million observations– we also have lower-bound estimates of the gross in- and outflow.

This database was constructed using a novel approach to estimating the most likely values of missing migration stocks and flows. Specifically, we use a Bayesian state-space model to combine the information from multiple datasets on both stocks and flows into a single estimate. Like the demographic accounting technique, the state-space model is built on the demographic relationship between migrant stocks, flows, births and deaths. The most crucial difference is that the state-space model combines the information from multiple databases, including those covering migrant stocks, net flows, and gross flows.

More details on the construction can currently be found in the UNU-CRIS working paper: Standaert, Samuel and Rayp, Glenn (2022) "Where Did They Come From, Where Did They Go? Bridging the Gaps in Migration Data" UNU-CRIS working paper 22.04. Bruges.

https://cris.unu.edu/where-did-they-come-where-did-they-go-bridging-gaps-migration-data

US Naturalizations Statistics

Trends and statistics on US naturalizations from 1999 to 2017

By Throwback Thursday [source]

About this dataset

The dataset US Naturalizations 1999-2017 provides information on the naturalization process of immigrants in the United States during the period from 1999 to 2017. The dataset includes various features or columns, capturing valuable insights into trends and statistics related to immigrants becoming US citizens.

Firstly, there is a column that specifies the year in which each naturalization case occurred, allowing for analysis and comparison over time. Additionally, there is a column indicating the country of birth of each individual who went through the naturalization process. This information allows for an exploration of patterns and trends based on country of origin.

The dataset also includes columns providing details about gender and age groups. By examining the distribution of naturalized individuals across different genders and age ranges, one can gain insights into demographic patterns and changes in immigration over time.

Furthermore, this dataset features columns related to occupation and educational attainment. These variables contribute to understanding the socio-economic characteristics of immigrants who became US citizens. By analyzing occupational trends or educational levels among naturalized individuals, researchers can gain valuable knowledge regarding immigrant integration within various industries or sectors.

Moreover, this dataset contains data on whether an applicant had previous experience as a lawful permanent resident (LPR) before being granted US citizenship. This variable sheds light on pathways to citizenship among those who have already obtained legal status in the United States.

Finally, there are columns providing information about processing times for naturalized cases as well as any special exemptions granted under certain circumstances. These details offer insights into administrative aspects related to applicants' journeys towards acquiring US citizenship.

In summary, this comprehensive dataset offers a wide range of variables that capture important characteristics related to immigrants becoming US citizens between 1999 and 2017. Researchers can use this data to analyze trends based on year, country of origin, gender/age groups, occupation/education levels,and pathways to citizenship such as previous LPR status or special circumstances exemptions

How to use the dataset

• Understand the columns: Familiarize yourself with the different columns available in this dataset to comprehend the information it offers. The columns included are:

  • Year: The year of naturalization.
  • United States: The number of individuals naturalized within the United States.
  • Continents:
    • Africa: Number of individuals born in African countries who were naturalized.
    • Asia: Number of individuals born in Asian countries who were naturalized.
    • Europe: Number of individuals born in European countries who were naturalized.
    • North America (excluding Caribbean): Number of individuals born in North American countries (excluding Caribbean nations) who were naturalized.
    • Oceania: Number of individuals born in Oceanian countries who were naturalized, including Australia and New Zealand.
    • South America: Number of individuals born in South American countries who were naturalized.

• Overview by year: Analyze the total number of people being granted US citizenship over time by examining the United States column. Use statistical methods like mean, median, or mode to understand trends or identify any outliers or significant changes across specific years.

• Continent-specific analysis:

  a) Identify patterns among continents over time by examining each continent's respective column (Africa, Asia, Europe, etc.). Compare growth rates and determine any regions experiencing higher or lower rates compared to others.

  b) Determine which continent contributes most significantly to overall US immigration by calculating continent-wise percentages based on total immigrants for each year.

• Identify region-specific trends:

  a) Analyze immigration patterns within individual continents by dividing them further into specific regions or countries. For example, within Asia, you can examine trends for East Asia (China, Japan, South Korea), Southeast Asia (Vietnam, Philippines), or South Asia (India, Bangladesh).

  b) Perform comparative analysis between regions/countries to identify variations in immigration rates or any interesting factors influencing these variances. ...

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.

The Trout Creek mule deer herd is composed of residents and migrants that make short-range elevational migrations. Mule deer mainly winter at lower elevations surrounding Blue Mountain and the slopes of the Oregon Canyon Mountains. In spring, some of these mule deer migrate to higher elevations in the Oregon Canyon Mountains. Other members of the herd winter in the southwestern portion of the herd’s range, inhabiting areas near Hawks Mountain, the Pueblo Mountains, and the foothills of the Trout Creek Mountains. These mule deer migrate to summer ranges on the crests of Holloway Mountain and the Trout Creek Mountains. Notably, one mule deer formerly wintering on the Trout Creek Mountains migrated south from a summer range on the Nevada border to the Montana Mountains during the second documented winter before returning to Oregon in spring. Habitat on winter ranges consists of A. t. wyomingensis (Wyoming big sagebrush) plant communities and non-native annual grasslands. Summer ranges consist mainly of native grasslands, mountain big sagebrush plant communities, and mountain shrub communities. The Trout Creek mule deer herd faces several threats, including summer wildfires, highway barriers, and competition for resources. In 2012, the Holloway fire burned 462,017 acres (186,972 ha) including most of the Trout Creek and Oregon Canyon Mountains, resulting in the temporary loss of shrub cover at higher elevations and conversion of native forbs and shrubland to invasive annual grasses at lower elevations. Although no migratory mule deer attempt to cross U.S. Highway 95, some resident mule deer have ranges spanning the busy highway, which had an AADT value of 2,095 vehicles in 2018. The Trout Creek mule deer herd also borders the Barren Creek Complex HMA to the north and the Beaty Butte HMA to the east (DOI and BLM, 2020; BLM 2022). The Barren Creek Complex HMA contains approximately 2,500 feral horses while the Beaty Butte HMA contains 463 horses. Both feral horse populations surpass the respective maximum appropriate management levels of 892 and 250 horses, respectively, suggesting that mule deer and horses compete for resources in the few areas where ranges overlap. These mapping layers show the location of the migration corridors for mule deer (Odocoileus hemionus) in the Trout Creek population in Oregon. They were developed from 40 migration sequences collected from a sample size of 10 animals comprising GPS locations collected every 5 hours.

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.

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15,341 DAYS (October 1st, 1979 - September 30th, 2021)

This is a dataset that describes annual statistics regarding US immigration between the 1980-2021 fiscal years.

All data are official figures from the Department of Homeland Security's government website that have been compiled and structured by myself. There are several reasons for the decision to only examine immigration data from 1980 to 2021. Since 1976, a fiscal year for the US government has always started on October 1st and ended the following year on September 30th. If the years prior to 1976 were included, the data may be incorrectly represented and cause further confusion for viewers. Additionally, the United States only tracked refugee arrivals after the Refugee Act of 1980, a statistic that is prominently featured in the dataset. As a result, the start date of 1980 was chosen instead of 1976.

Data Sources

The primary data sources used were the "Yearbook of Immigration Statistics" webpages from the Department of Homeland Security. As a whole, the website not only provided figures about US immigration that were perfect for making time series analyses, but also explored the logistics behind the annual trends found.

1. The Department of Homeland Security's 2021 Yearbook of Immigration Statistics - The Office of Immigration Statistics' 2021 Flow Reports and Population Estimates provide text, tables, and charts on lawful permanent residents, refugees and asylees, nonimmigrant admissions, naturalizations, enforcement actions, and the unauthorized population. Being the latest version released to date, the 2021 yearbook is the most comprehensive report publicly available and tends to feature data of past years for reference.
2. The Department of Homeland Security's Directory of Past Immigration Yearbooks - Past yearbooks were referenced in order to find the missing data from the fiscal years during 2000-2021. There is a single yearbook covering the fiscal years during 1996-1999, but that was the oldest publications featured in the directory.
3. The Center for Immigration Studies's File Library - In order to procure immigration data during the fiscal years of 1980-1999, I found free versions of the Immigration and Naturalization Service's paywalled yearbooks from the Center for Immigration Studies. By doing so, I was able fill in the missing values and finish the dataset.

Statistics Being Tracked

• Immigrants Obtaining Lawful Permanent Resident Status - Number of immigrants who obtained lawful permanent resident status in the United States, otherwise known as green card holders.
• Refugee Arrivals - Number of refugees who arrived in the United States. Excludes Amerasian immigrants except for the fiscal years of 1989 and 1991. Figures are based on refugee's arrival date.
• Noncitizen Apprehensions - Number of noncitizens apprehended in the United States. Data from 2020 to 2021 includes U.S. Customs and Border Protection (CBP) encounters that resulted in expulsion on public health grounds (due to the pandemic).
• Noncitizen Removals - Number of noncitizens removed from the United States. Removals are the compulsory and confirmed movement of an inadmissible or deportable noncitizen out of the United States based on an order of removal.
• Noncitizen Returns - Number of noncitizen returns from the United States. Returns are the confirmed movement of an inadmissible or deportable noncitizen out of the United States not based on an order of removal.

Dataset History

2023-01-07 - Dataset is created (465 days after the end of the 2021 fiscal year).

GitHub Repository - The same data but on GitHub.

Code Starter

Link to Notebook

this graph was created in R:

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Hundreds of millions of people live in a country that is different from the one in which they were born. In some countries, the majority of the population are immigrants.

Migration has played a crucial role in economic development, education and mobility. The transfer of money from migrants working overseas to family or friends in their home country – remittances – can be an important source of income in many countries.

On this page you can find all our data and visualizations relating to migration.

The estimates of the number (or “stock”) of international migrants disaggregated by age, sex and country or area of origin are based on national statistics, in most cases obtained from population censuses. Additionally, population registers and nationally representative surveys provided information on the number and composition of international migrants.

The dataset presents estimates of international migrant by age, sex and origin. Estimates are presented for 1990, 1995, 2000, 2005, 2010, 2015 and 2020 and are available for 232 countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.

The dissertation consists of three chapters relating to the measurement of immigration policies, which developed out of my work as an initial co-author of the International Migration Policy and Law Analysis (IMPALA) Database Project. The first chapter entitled, “Brain Gain? Measuring skill bias in U.S. migrant admissions policy,” develops a conceptual and operational definition of skill bias. I apply the measure to new data revealing the level of skill bias in U.S. migrant admissions policy between 1965 and 2008. Skill bias in U.S. migrant admissions policy is both a critical determinant of the skill composition of the migrant population and a response to economic and public demand for highly skilled migrants. However, despite its central role, this is the first direct, comprehensive, annual measure of skill bias in U.S. migrant admissions policy. The second chapter entitled, “Stalled in the Senate: Explaining change in US migrant admissions policy since 1965,” presents new data characterizing change in U.S. migrant admissions policy as both expansive and infrequent over recent decades. I present a new theory of policy change in U.S. migrant admissions policy that incorporates the role of supermajoritarian decision making procedures and organized anti-immigration groups to better account for both the expansive nature and t he infrequency of policy change. The theory highlights the importance of a coalition of immigrant advocacy groups, employers and unions in achieving policy change and identifies the conditions under which this coalition is most likely to form and least likely to be blocked by an anti-immigration group opposition. The third chapter entitled, “Post-coding aggregation: A methodological principle for independent data collection,” presents a new technique developed to enable independent collection of flexible, high quality data: post-coding aggregation. Post-coding aggregation is a methodological principle that minimizes data loss, increases transparency, and grants data analysts the ability to decide how best to aggregate information to produce measures. I demonstrate how it increases the fl exibility of data use by expanding the utility of data collections for a wider range of research objectives and improves the reliability and the content validity of measures in data analysis.

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.

Elk within the Clarks Fork herd migrate though some of the most rugged and remote terrain in the lower 48 states. The herd, which numbers around 3,000, winters in the Sunlight Basin and the Absaroka foothills just west of Cody, WY. Winter ranges are a mix of sagebrush hills and lodgepole pine forests, within expansive private ranchlands. During migration, animals travel an average one-way distance of 33 miles, with some animals migrating as far as 67 miles. Spring migrations off of winter range head west towards Yellowstone National Park, up several drainages that flow out of the Absaroka Mountains, including the Clarks Fork of the Yellowstone, Crandall Creek, and smaller creeks to the south. Summer ranges consist of alpine and subalpine meadows embedded within spruce-fir and lodgepole pine forest that are predominately within the Park. The Clarks Fork herd is partially migratory, with migrants and resident animals mixing on winter range (residents tend to winter along the foothills further east). Over the last decade, the migratory segment has seen poor recruitment due to drought and increased rates of predation by grizzly bears and wolves, while resident animals have been more productive and continue to expand to the east. Aside from the poor recruitment, the migrations are relatively safe because most of the routes traverse lands within the National Forest or National Park system. These data provide the location of winter ranges for elk (Bison bison) in Yellowstone National Park. They were developed from Brownian bridge movement models using 107 winter sequences collected from a sample size of 46 animals comprising GPS locations collected every 2-8 hours.

Annual number of international migrants by 5-year age groups and gender for Canada, provinces and territories.

This Zenodo repository contains all migration flow estimates associated with the paper "Deep learning four decades of human migration." Evaluation code, training data, trained neural networks, and smaller flow datasets are available in the main GitHub repository, which also provides detailed instructions on data sourcing. Due to file size limits, the larger datasets are archived here.

Data is available in both NetCDF (.nc) and CSV (.csv) formats. The NetCDF format is more compact and pre-indexed, making it suitable for large files. In Python, datasets can be opened as xarray.Dataset objects, enabling coordinate-based data selection.

Each dataset uses the following coordinate conventions:

• Year: 1990–2023
• Birth ISO: Country of birth (UN ISO3)
• Origin ISO: Country of origin (UN ISO3)
• Destination ISO: Destination country (UN ISO3)
• Country ISO: Used for net migration data (UN ISO3)

The following data files are provided:

• T.nc: Full table of flows disaggregated by country of birth. Dimensions: Year, Birth ISO, Origin ISO, Destination ISO
• flows.nc: Total origin-destination flows (equivalent to T summed over Birth ISO). Dimensions: Year, Origin ISO, Destination ISO
• net_migration.nc: Net migration data by country. Dimensions: Year, Country ISO
• stocks.nc: Stock estimates for each country pair. Dimensions: Year, Origin ISO (corresponding to Birth ISO), Destination ISO
• test_flows.nc: Flow estimates on a randomly selected set of test edges, used for model validation

Additionally, two CSV files are provided for convenience:

• mig_unilateral.csv: Unilateral migration estimates per country, comprising:
  • imm: Total immigration flows
  • emi: Total emigration flows
  • net: Net migration
  • imm_pop: Total immigrant population (non-native-born)
  • emi_pop: Total emigrant population (living abroad)
• mig_bilateral.csv: Bilateral flow data, comprising:
  • mig_prev: Total origin-destination flows
  • mig_brth: Total birth-destination flows, where Origin ISO reflects place of birth

Each dataset includes a mean variable (mean estimate) and a std variable (standard deviation of the estimate).

An ISO3 conversion table is also provided.

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