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.

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. ...

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12064410%2F468b9ab69fbaa3eea94ab7c13537052f%2Fimmigration%20flag.png?generation=1673145948097950&alt=media" alt="">

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 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.

At a time when many states are increasing restrictions on immigration, others are using formal agreements on international economic migration to open their borders. The use of international agreements on migration presents a puzzle, as most states can open their borders to migrants unilaterally. I argue that, when states cannot generate large enough flows of migrants or the right type of migrants to fill open positions in the labor market, they turn to the sending state to help them. States that need migrants can negotiate a bilateral labor agreement with a sending state, which then acts as a recruiter, helping to channel labor to the receiving state. This article details the conditions under which immigrant-receiving countries use these treaties and tests the implications of the argument on a new dataset on migration treaties.

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

United States Immigrants Admitted: Malaysia data was reported at 4,109.000 Person in 2017. This records an increase from the previous number of 3,382.000 Person for 2016. United States Immigrants Admitted: Malaysia data is updated yearly, averaging 1,966.000 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 4,109.000 Person in 2017 and a record low of 886.000 Person in 1986. United States Immigrants Admitted: Malaysia 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.G087: 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 ...).

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

Name searchable index to Series S5631 Card Register of Immigrants and Missing Immigrants. This series is a card register of immigrants and missing immigrants dealt with in various correspondence although most cards contain very little information. The cards mainly register the administrative file movement/s of the Immigration Department's correspondence regarding a particular immigrant, but may include the name of the immigrant/missing immigrant, a file number, i.e. 269/24, name of ship, date of arrival, movements of File (dates and file numbers), the name of (correspondence) writer, subject and action. Some cards contain annotations such as "file returned to (other state)" or details concerning an immigrant, for example, age at date of departure etc. There are stamp marks on many of the cards, for example, "Domestic", Decontrolled", "Final", "Decentralised", "Salvation Army", etc

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.

Census: Number of Migrants: Punjab data was reported at 13,735,616.000 Person in 03-01-2011. This records an increase from the previous number of 9,189,438.000 Person for 03-01-2001. Census: Number of Migrants: Punjab data is updated decadal, averaging 9,189,438.000 Person from Mar 1991 (Median) to 03-01-2011, with 3 observations. The data reached an all-time high of 13,735,616.000 Person in 03-01-2011 and a record low of 6,960,431.000 Person in 03-01-1991. Census: Number of Migrants: Punjab data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAG001: Census of India: Migration: Number of Migrants: by States.

Debates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.

Informed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?

In this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.

Migrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.

Who is considered an international migrant? In the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.1

For example, an Argentinian person who spends nine months studying in the United States wouldn’t count as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years would. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.

The same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.

What about illegal migration? “Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.

There are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.

Tracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.2 The rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.

Projected Deaths by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: 2016-2060 // Source: U.S. Census Bureau, Population Division // There are four projection scenarios: 1. Main series, 2. High Immigration series, 3. Low Immigration series, and 4. Zero Immigration series. // Note: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. // For detailed information about the methods used to create the population projections, see https://www2.census.gov/programs-surveys/popproj/technical-documentation/methodology/methodstatement17.pdf. // Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. The Population Estimates and Projections Program provides additional information on its website: https://www.census.gov/programs-surveys/popproj.html.

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.

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.

ABSTRACT

Statements about building walls, deportation and denying services to undocumented immigrants made by the US president may induce fear in Latino populations and create barriers to their health care access. To assess the impact of these statements on undocumented Latino immigrants' (UDLI) and Latino legal residents/citizens' (LLRC) perceptions of safety and their presentations for emergency care, we conducted surveys of adult patients at three county emergency departments (EDs) in California from June 2017 to December 2018. Of 1,684 patients approached, 1,337 (79.4%) agreed to participate: 34.3% UDLI, 36.9% LLRC, and 29.8% non-Latino legal residents/citizens (NLRC). The vast majority of UDLI (95%), LLRC (94%), and NLRC (85%) had heard statements about immigrants by President Trump. Most UDLI (89%), LLRC (88%), and NLRC (87%) either thought that these measures were being enacted now or will be enacted in the future. Most UDLI and LLRC reported that these statements made them feel unsafe living in the US, 75% (95% CI 70 to 80%) and 51% (95% CI 47 to 56%), respectively. More UDLI reported that these statements made them afraid to come to the ED (24% 95% CI 20 to 28%) vs LLRC (4.4% [95% CI 3 to 7%]) and NLRC (3.5% [95% CI 2 to 6%]); 55% of UDLI with this fear stated it caused them to delay coming to the ED (median delay 2-3 days). The vast majority of patients in our California EDs have heard statements about immigrants by the US president, which have induced worry and safety concerns in both UDLI and LLRC patients. These statements may also act as a barrier to some UDLI's access of emergency care. Given California's sanctuary state status, these safety concerns and ED access fears may be greater in a nationwide population of Latinos.

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,

Under the ExpoCast program, United States Environmental Protection Agency (EPA) researchers have developed a high-throughput (HT) framework for estimating aggregate exposures to chemicals from multiple pathways to support rapid prioritization of chemicals. Here, we present methods to estimate HT exposures to chemicals migrating into food from food contact substances (FCS). These methods consisted of combining an empirical model of chemical migration with estimates of daily population food intakes derived from food diaries from the National Health and Nutrition Examination Survey (NHANES). A linear regression model for migration at equilibrium was developed by fitting available migration measurements as a function of temperature, food type (i.e., fatty, aqueous, acidic, alcoholic), initial chemical concentration in the FCS (C0) and chemical properties. The most predictive variables in the resulting model were C0, molecular weight, log Kow, and food type (R2=0.71, p<0.0001). Migration-based concentrations for 1009 chemicals identified via publicly-available data sources as being present in polymer FCSs were predicted for 12 food groups (combinations of 3 storage temperatures and food type). The model was parameterized with screening-level estimates of C0 based on the functional role of chemical in FCS. By combining these concentrations with daily intakes for food groups derived from NHANES, population ingestion exposures of chemical in mg/kg-bodyweight/day (mg/kg-BW/day) were estimated. Calibrated aggregate exposures were estimated for 1931 chemicals by fitting HT FCS and consumer product exposures to exposures inferred from NHANES biomonitoring (R2=0.61, p<0.001); both FCS and consumer product pathway exposures were significantly predictive of inferred exposures. Including the FCS pathway significantly impacted the ratio of predicted exposures to those estimated to produce steady-state blood concentrations equal to in-vitro bioactive concentrations. While these HT methods have large uncertainties (and thus may not be appropriate for assessments of single chemicals), they can provide critical refinement to aggregate exposure predictions used in risk-based chemical priority–setting. This dataset is associated with the following publication: Biryol, D., C. Nicolas, J. Wambaugh, K. Phillips, and K. Isaacs. High-throughput dietary exposure predictions for chemical migrants from food contact substances for use in chemical prioritization. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 108: 185-194, (2017).