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. ...

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.

This annual study provides migration pattern data for the United States by State or by county and are available for inflows (the number of new residents who moved to a State or county and where they migrated from) and outflows (the number of residents who left a State or county and where they moved to). The data include the number of returns filed, number of personal exemptions claimed, total adjusted gross income, and aggregate migration flows at the State level, by the size of adjusted gross income (AGI) and by age of the primary taxpayer. Data are collected and based on year-to-year address changes reported on U.S. Individual Income Tax Returns (Form 1040) filed with the IRS. SOI collects these data as part of its Individual Income Tax Return (Form 1040) Statistics program, Data by Geographic Areas, U.S. Population Migration Data.

The IRS publishes migration data for the US population based upon the individual tax returns filed with the IRS, where they track on a year-by-year basis

• where people were coming from - the prior state of residency
• where people moving to - the new state of residency
• number of returns filed - approximate number of households that migrated
• number of exemptions - approximate number of individuals
• the adjusted gross income (AGI) - recorded in thousands of dollars

The raw data published on the IRS website clearly shows patterns of evolution - changing patterns of what is recorded, how it is record, and naming conventions used - making it a challenge to track changes in the underlying data over time. The current dataset attempts to address these shortcomings by normalizing the record layout, standardizing the conventions, and collecting the annual into a single, coherent dataset.

An individual record is laid out with 9 fields

Y1 Y1_STATE_FIPS Y1_STATE_ABBR Y1_STATE_NAME Y2 Y2_STATE_FIPS Y2_STATE_ABBR Y2_STATE_NAME NUM_RETURNS NUM_EXEMPTIONS AGI Here, Y1 refers to the first year (from where the people are migrating) while Y2 refers to the second year (to where the people are migrating). As this is annual data, Y2 should always be the next year after Y1. Associated with each year are three different ways of identifying a state - the name of the state, it's two-letter abbreviaion, and it's FIPS code. Granted, carrying around three IDs per state is redundant; however, the various IDs are useful in different contexts. One thing to note - the IRS data represents migration into and out of the country via the introduction of a fake state, identified by STATE_NAME=FOREIGN, STATE_ABBR=FR, and STATE_FIPS=57.

From any given state, the dataset records migration to 52 destinations

• either not moving, or staying in the same state
• migrating to one of the other 49 states
• migrating to Washington DC
• migrating overseas (i.e., to the FOREIGN state)

Similarly, the dataset represents the migation into any given state as being from one of 52 destinations. Typically, the numbers associated with "staying put" constitute, by far, the largest contingent of tax payers for the given state. The one exception to this description is the FOREIGN state. The dataset does not record "staying put" outside of the country; there is no record for FOREIGN-to-FOREIGN migration. As such, there are 51, not 52, destinations paired with migration to-and-from the FOREIGN state.

United States US: Net Migration data was reported at 4,500,000.000 Person in 2017. This stayed constant from the previous number of 4,500,000.000 Person for 2012. United States US: Net Migration data is updated yearly, averaging 4,213,405.500 Person from Dec 1962 (Median) to 2017, with 12 observations. The data reached an all-time high of 8,612,074.000 Person in 1997 and a record low of 1,549,465.000 Person in 1967. United States US: Net Migration data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Sum;

This data has been collected by the Government of the United States of America to track the number of people who change their legal status (become permanent resident) through various means. The data represents the number of people who had changed their status in the fiscal year of 2021. It also represents both males and females from a very young age such as 5 years old all the way to 75 years and even older. Furthermore, This data provides an overview of immigration to the United States, with a breakdown by region and country of birth, as well as the type of immigration (adjustments of status and new arrivals) and the various categories of immigrants.

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.

United States Immigrants Admitted: Taiwan data was reported at 4,858.000 Person in 2017. This records a decrease from the previous number of 5,120.000 Person for 2016. United States Immigrants Admitted: Taiwan data is updated yearly, averaging 9,012.000 Person from Sep 1986 (Median) to 2017, with 32 observations. The data reached an all-time high of 16,344.000 Person in 1992 and a record low of 4,697.000 Person in 2014. United States Immigrants Admitted: Taiwan 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.

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

The US Migration dataset contains information about the migration patterns of people in the United States between 1995 and 2000.

United States Naturalization Trends

Naturalization Trends in the US 1999-2017

By Throwback Thursday [source]

About this dataset

The United States Naturalizations 1999-2017 dataset provides comprehensive information on the naturalization trends in the United States over a period of 19 years. It includes data on the year and type of naturalization, as well as the country or region of origin for individuals who were naturalized during this time frame. The dataset offers valuable insights into the overall patterns and shifts in naturalization rates, enabling researchers to analyze and understand the demographic dynamics within the United States. With this dataset, users can explore how factors such as political events, policy changes, and global migration patterns have influenced naturalization trends over time. By examining both new and derivative naturalizations from various countries or regions, researchers can gain a deeper understanding of immigration patterns within specific communities and identify potential factors that contribute to higher rates of citizenship acquisition. Ultimately, this dataset serves as a valuable resource for policymakers, analysts, academics, and anyone interested in studying immigration trends or assessing their impact on American society

How to use the dataset

Understanding the Columns

The dataset consists of several columns that provide valuable information about naturalization trends in the United States from 1999 to 2017. Here's a brief description of each column:

• Year: The year in which the naturalizations took place (numeric).

• Type: The type of naturalization, categorized as either New Naturalizations or Derivative Naturalizations (text).

• Country or Region: The country or region of origin for individuals who were naturalized (text).

Analyzing Yearly Trends

One way you can use this dataset is by analyzing yearly trends in naturalizations. You can group the data by year and explore how many people from different countries or regions became US citizens each year.

For example, you might want to investigate if there are any significant changes in the number of new naturalizations over time or if certain countries show higher rates of derivative naturalizations compared to others.

Comparing Types of Naturalizations

Another interesting analysis could be comparing different types of naturalizations – new and derivative – and examining their patterns over time.

By grouping the data by type and year, you can generate insights into how these categories vary annually and if there are any notable trends between them.

Exploring Country/Region-specific Data

If you're interested in studying specific countries' contribution towards US naturalizations, it's worth exploring data based on country or region.

By filtering the dataset by a particular country or region name, you can gain insight into its citizens' tendencies for migration and becoming US citizens over time.

Visualizing Data for Better Understanding

To visualize this data effectively, consider using charts such as line plots, bar graphs, heatmaps, or even maps (for country/region-specific analysis). Visual representations can help you grasp trends, make comparisons, and communicate your findings more easily.

Drawing Conclusions

By examining this dataset, you can draw conclusions about naturalization trends in the United States from 1999 to 2017 without focusing on specific dates. You may identify patterns that highlight changes in the number of naturalizations by year or uncover interesting insights about countries and their contributions to US naturalizations.

Remember that this dataset provides an overview of naturalization trends; however, it does not include additional factors such as socio-economic conditions or policy changes that may impact these trends. Therefore,

Research Ideas

• Analyzing naturalization trends: This dataset can be used to analyze and understand the trends and patterns of naturalizations in the United States from 1999 to 2017. It can provide insights into how the number of naturalizations has changed over time and identify any significant increases or decreases.
• Identifying countries or regions with high naturalization rates: By analyzing the data, it is possible to identify which countries or regions have higher rates of naturalization in the United States. This information can be useful for studying migration patterns and understanding factors that contribute to higher levels of immigration from certain places.
• Comparing different types of naturalizations: The dataset pro...

CBO Projection: Immigration Rate per 1000 People data was reported at 3.200 Person in 2048. This stayed constant from the previous number of 3.200 Person for 2047. CBO Projection: Immigration Rate per 1000 People data is updated yearly, averaging 3.200 Person from Dec 1987 (Median) to 2048, with 62 observations. The data reached an all-time high of 6.700 Person in 2005 and a record low of 0.300 Person in 2008. CBO Projection: Immigration Rate per 1000 People data remains active status in CEIC and is reported by Congressional Budget Office. The data is categorized under Global Database’s USA – Table US.G087: Immigration: Projection: Congressional Budget Office.

This dataset provides a global gridded (5 arc-min resolution) detailed annual net-migration dataset for 2000-2019. We also provide global annual birth and death rate datasets – that were used to estimate the net-migration – for same years. The dataset is presented in details, with some further analyses, in the following publication. Please cite this paper when using data.

Niva et al. 2023. World's human migration patterns in 2000-2019 unveiled by high-resolution data. Nature Human Behaviour 7: 2023–2037. Doi: https://doi.org/10.1038/s41562-023-01689-4

You can explore the data in our online net-migration explorer: https://wdrg.aalto.fi/global-net-migration-explorer/

Short introduction to the data

For the dataset, we collected, gap-filled, and harmonised:

a comprehensive national level birth and death rate datasets for altogether 216 countries or sovereign states; and

sub-national data for births (data covering 163 countries, divided altogether into 2555 admin units) and deaths (123 countries, 2067 admin units).

These birth and death rates were downscaled with selected socio-economic indicators to 5 arc-min grid for each year 2000-2019. These allowed us to calculate the 'natural' population change and when this was compared with the reported changes in population, we were able to estimate the annual net-migration. See more about the methods and calculations at Niva et al (2023).

We recommend using the data either over multiple years (we provide 3, 5 and 20 year net-migration sums at gridded level) or then aggregated over larger area (we provide adm0, adm1 and adm2 level geospatial polygon files). This is due to some noise in the gridded annual data.

Due to copy-right issues we are not able to release all the original data collected, but those can be requested from the authors.

List of datasets

Birth and death rates:

raster_birth_rate_2000_2019.tif: Gridded birth rate for 2000-2019 (5 arc-min; multiband tif)

raster_death_rate_2000_2019.tif: Gridded death rate for 2000-2019 (5 arc-min; multiband tif)

tabulated_adm1adm0_birth_rate.csv: Tabulated sub-national birth rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

tabulated_ adm1adm0_death_rate.csv: Tabulated sub-national death rate for 2000-2019 at the division to which data was collected (subnational data when available, otherwise national)

Net-migration:

raster_netMgr_2000_2019_annual.tif: Gridded annual net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_3yrSum.tif: Gridded 3-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_5yrSum.tif: Gridded 5-yr sum net-migration 2000-2019 (5 arc-min; multiband tif)

raster_netMgr_2000_2019_20yrSum.tif: Gridded 20-yr sum net-migration 2000-2019 (5 arc-min)

polyg_adm0_dataNetMgr.gpkg: National (adm 0 level) net-migration geospatial file (gpkg)

polyg_adm1_dataNetMgr.gpkg: Provincial (adm 1 level) net-migration geospatial file (gpkg) (if not adm 1 level division, adm 0 used)

polyg_adm2_dataNetMgr.gpkg: Communal (adm 2 level) net-migration geospatial file (gpkg) (if not adm 2 level division, adm 1 used; and if not adm 1 level division either, adm 0 used)

Files to run online net migration explorer

masterData.rds and admGeoms.rds are related to our online ‘Net-migration explorer’ tool (https://wdrg.aalto.fi/global-net-migration-explorer/). The source code of this application is available in https://github.com/vvirkki/net-migration-explorer. Running the application locally requires these two .rds files from this repository.

Metadata

Grids:

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; each band for each year over 2000-2019

Units:

Birth and death rates: births/deaths per 1000 people per year

Net-migration: persons per 1000 people per time period (year, 3yr, 5yr, 20yr, depending on the dataset)

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 2000-2019

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Units:

Net-migration: persons per 1000 people per year

Context

This dataset shows the state-to-state migration in the United States from 2010 to 2019.

Content

The columns in this dataset are: - current state: Current state that people reside in the year of the measurement (include District of Columbia and Puerto Rico) - year: Year of the measurement - population: Population of the current state in the year of the measurement - same house: Number of people reside in the same house as 1 year ago - same state: Number of people reside in the same state as 1 year ago - from different state Total: Total number of people from different states migrate to the current state - abroad Total: Total number of people from abroad migrate to the current state - from: Place from where people migrate to the current state. This includes 50 states, District of Columbia, Puerto Rico, US Island Area, and Foreign Country - number of people: number of people from a different place (from column) migrate to the current state

Acknowledgements

Data source: US Census

Inspiration

Where do people go from/to each year? What are the factors that correlate with the migration into that state (combine with other datasets)?

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 ...).

Note: This COVID-19 data set is no longer being updated as of December 1, 2023. Access current COVID-19 data on the CDPH respiratory virus dashboard (https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/Respiratory-Viruses/RespiratoryDashboard.aspx) or in open data format (https://data.chhs.ca.gov/dataset/respiratory-virus-dashboard-metrics).

As of August 17, 2023, data is being updated each Friday.

For death data after December 31, 2022, California uses Provisional Deaths from the Center for Disease Control and Prevention’s National Center for Health Statistics (NCHS) National Vital Statistics System (NVSS). Prior to January 1, 2023, death data was sourced from the COVID-19 registry. The change in data source occurred in July 2023 and was applied retroactively to all 2023 data to provide a consistent source of death data for the year of 2023.

As of May 11, 2023, data on cases, deaths, and testing is being updated each Thursday. Metrics by report date have been removed, but previous versions of files with report date metrics are archived below.

All metrics include people in state and federal prisons, US Immigration and Customs Enforcement facilities, US Marshal detention facilities, and Department of State Hospitals facilities. Members of California's tribal communities are also included.

The "Total Tests" and "Positive Tests" columns show totals based on the collection date. There is a lag between when a specimen is collected and when it is reported in this dataset. As a result, the most recent dates on the table will temporarily show NONE in the "Total Tests" and "Positive Tests" columns. This should not be interpreted as no tests being conducted on these dates. Instead, these values will be updated with the number of tests conducted as data is received.

These summary metadata refer to the first results on the main demographic developments in the year of reference.

Member States send to Eurostat the first results on the main demographic developments in the year of reference (T), containing the total population figure on 31 December of year T (further published by Eurostat as Population on 1 January of year T+1), total births and total deaths during year T. This data collection is defined under http://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32013R1260&from=EN" target="_blank">Regulation 1260/2013 on European demographic statistics. Countries may also transmit to Eurostat, on voluntary basis, provisional data on total immigration, emigration and net migration during the year (T).

Eurostat's data collection on the above figures is called DEMOBAL and it is carried out in June of each year. Eurostat publishes these first demographic estimates in July of each year in the online database, in the table Population change - Demographic balance and crude rates (demo_gind).

These first demographic estimates may either be confirmed or updated by Eurostat's demographic data collection taking place in December each year (called Unidemo), whereby countries submit detailed breakdowns (e.g. by age and sex) of their yearly population data, including data on migration, both at national and at regional level. The online table Population change - Demographic balance and crude rates (demo-gind) will be accordingly updated. This table includes the latest updates on total population, births and deaths reported by the countries, while the detailed breakdowns by various characteristics included in the rest of the tables of the Eurostat database (Demography domain and Migration, for example the Population by citizenship and by country of birth table) may be transmitted to Eurostat at a subsequent date.

The online table Population change - Demographic balance and crude rates (demo-gind) contains time series going back to 1960; data before 2013 were collected by Eurostat from the national statistical offices on voluntary basis.

The individual metadata files reported by the countries are attached to this metadata file.

Immigration is a highly polarized issue in the United States, and negative attitudes toward immigrants are common. Yet, almost all Americans are descended from people who originated outside the country, a narrative often evoked by the media and taught in school curricula. Can this narrative increase inclusionary attitudes toward migrants? We draw from scholarship showing that perspective-taking decreases prejudice toward outgroups to investigate whether reminding Americans about their own immigration history increases support for immigrants and immigration. We propose that priming family experiences can indirectly stimulate perspective-taking and induce empathy toward the outgroup, which we test with three separate survey experiments conducted over two years. Our findings show that priming family history generates small but consistent inclusionary effects. These effects occur even among partisan subgroups and Americans who approve of President Trump. We provide evidence that increased empathy for immigrants constitutes one mechanism driving these effects.

SSP2 is a “middle-of-the-road” projection, where social, economic and technological trends do not shift markedly from historical patterns, resulting in a U.S. population of 455 million people by 2100. Domestic migration trends remain consistent with the recent past. This version of the ICLUS model does not include climate change projections to dynamically update location-specific amenities when calculating migration. These projections will include the “nocc” label in the file name to indicate this difference.