The impact of foreign born workers on the native born workers’ wages in the United States.
The impact of foreign born workers on the native born workers’ wages in the United States. Christopher Saca Data101 Spring Semester 2021 May 14, 2021
Abstract:
This report will analyze immigrants’ impact on the U.S. labor market, specifically focusing on how wages differ between the two groups. The data analyzed is from the annual labor reports created by the U.S. Bureau of Labor Statistics and covers a span of 10 years, from 2009-2019. The data set includes differences between median weekly wages for native born and foreign born workers on a total scale, and across three categories: education, race, and gender. In the labor market, total median weekly wages for foreign and native born workers increased from 2009-2019, demonstrating how immigration does not have a negative impact on native wages.
Moving on, the notable findings for each category will be disclosed. In terms of education, native born workers had higher wages than foreign born workers in all categories, expect having less than a high school degree. In terms of gender, native-born men are earning more than foreign-born men, and native-born women are earning more than foreign-born women. In terms of race, the only native-born racial group that earned more than their foreign-born counterpart was Hispanic or Latino White. All the wage gaps mentioned above are minor as the highest ones amounted to $100, and demonstrate foreign-born workers are disproportionaly impacted with lower wages.
These trends can help policymakers expand pro-immigration policy as it is evident that foreign-born workers do not negatively affect native-born wages, but rather suffer from comparatively lower wages. The U.S. government should acknowledge this wage gap and support the foreign born population, by creating training programs that provide them with in demand skills, expanding visa programs, and legalizing undocumented immigration.
Topic Sentence:
This report will provide support for President Biden’s Immigration bill that aims to provide undocumented immigrants with a path to citizenship, expand and modernize the visa process especially H1-B visas, and stimulate the economy through a selective green card program (The White House). Furthermore, the analysis will provide Americans with a better understanding of how immigrants impact the labor market, and the differences that occur in wages for foreign and native born individuals. With a more educated population on the issue, both congress and ordinary citizens will be able to recognize the value of immigrants, in this instance, to our economy.
Background:
Foreign-born individuals play an active role in the labor force; in 2018 28.4 million of them were employed and they accounted for 17% of the workforce. The foreign-born population’s contributions to the economy are significant, but economists have differed in their impact to the native workforce, specifically native wages. A group of economist concluded that increasing foreign-born labor participation leads to decreased native wages (De Brauw). They concluded these findings based on the elasticity of immigration, which measures the percentage change in wages given a percentage change in quantity of immigrants (De Brauw). The economists calculated that the elasticity of immigration is -0.2, meaning that a 10% increase in foreign-born labor force participation would cause wages to fall on average by 2% (De Brauw). This relationship does hold true for low-wage native-born workers, as they will experience competition from an increased labor supply of low-skilled immigrants, and their wages will decrease by up to 1% (Nunn, O’Donnell, and Shambaugh).
However, economist David Card and Giovanni Peri conducted further labor analyses and found that wages do not change with an increased supply of immigrants (De Brauw). This theory holds true due to two factors: immigrants and natives are not perfect substitutes in the labor market, and immigrants suffer from a slow economic acclimation process that further decreases their likelihood of competing with native workers (Borjas). Foreign-born individuals have a slow acclimation process to the economy as they must acquire skills demanded by American industries, improve their English-language proficiency, and understanding the industry/labor market, this leads to a large wage gap between foreign-born and native-born citizens (Borjas).
Data Source:
Due to the lack of accurate and available data, the sole dataset for this report is from the Bureau of Labor Statistics, U.S. Department of Labor. The only minor issue with the dataset is that it was originally formatted as a PDF, therefore, it had to be converted into an Excel format. Other than that issue, the dataset provided ample employment and wages information for foreign-born and native- born workers The dataset has four different variables for foreign-born and native-born population, which are gender, education, age, and ethnicity.
Techniques:
The data used for this report all came from the annual Foreign-Born Workers: Labor Force Characteristics report published yearly by the U.S. Department of Labor. The data used in this report comes from the annual foreign-born labor reports from 2009 to 2019. These were compiled into a single excel spreadsheet and manually adjusted into an acceptable R format, that followed the rows and columns data frame model. The technique used to arrange the dataset was purely manually done on Excel, as each data frame was adjust according to the variables to be studied.
The statistical modeling used for this project consist of bar-graphs, scatter plots, and linear and quadratic regressions.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
## ✓ tibble 3.1.1 ✓ dplyr 1.0.5
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(dplyr)library(readxl)
Employment_information <- read_excel("Employment information.xlsx")
Employment_information<-mutate(Employment_information,Year_as_character=as.character(Year))
This bar graph provides an overview of how the labor market participation trends changed over this 10-year period. To create the graph, years was plotted on the x axis against labor participation rate, and year was changed into a character variable in order to clearly represent the yearly breakdown. Overall, the native-born participation rate is slowly decreasing throughout the 10-year track, it starts at roughly 64% (2009) and decreased to approximately 62% in 2019. The foreign born participation rate also decreased from about 63% (2009) to 62% (2019).
ggplot(data=Employment_information, aes(Year_as_character, Employed_total_in_thousands,fill=Nationality))+
geom_col(position="dodge")+
labs(title="Native born and Foreign born employment in thousands from 2009-2019", x="Year",y="Employment in thousands")
This bar graph provides an overview of how labor market employment trends changed over this 10-year period. To create the graph, years was plotted on the x axis against employment in thousands, and year was changed into a character variable in order to clearly represent the yearly breakdown. As can be seen, the total number of employed native-born constantly increased throughout the 10-year period, it started at about 117,000,000 (2009) and ended at 130,000,000 in 2019. The foreign born employment increased from 23,000,000 to 28,000,000.
library(readxl)
Native_vs_foreign_born_wages <- read_excel("Native vs foreign born wages.xlsx")
ggplot(data=Native_vs_foreign_born_wages, aes(x=Year, y=Median_weekly_wages,colour=Nationality))+
geom_point()+
labs(title="Native born and foreign born median weekly wages from 2009-2019", y="Median Weekly Wages")+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
foreign_born_fit<-lm(Median_weekly_wages~Year, data=filter(Native_vs_foreign_born_wages, Nationality=="Foreign born"))
plot(foreign_born_fit)
Native_born_fit<-lm(Median_weekly_wages~Year, data=filter(Native_vs_foreign_born_wages, Nationality=="Native born"))
plot(Native_born_fit)
Foreign_born_wages<-filter(Native_vs_foreign_born_wages, Nationality=="Foreign born")
print(lm(Foreign_born_wages$Median_weekly_wages~Foreign_born_wages$Year))##
## Call:
## lm(formula = Foreign_born_wages$Median_weekly_wages ~ Foreign_born_wages$Year)
##
## Coefficients:
## (Intercept) Foreign_born_wages$Year
## -39806.4 20.1Native_born_wages<-filter(Native_vs_foreign_born_wages, Nationality=="Native born")
print(lm(Native_born_wages$Median_weekly_wages~Native_born_wages$Year))##
## Call:
## lm(formula = Native_born_wages$Median_weekly_wages ~ Native_born_wages$Year)
##
## Coefficients:
## (Intercept) Native_born_wages$Year
## -34484.87 17.54The data frame titled “Native_vs_foreign_born_wages” used for this scatter plot was manually formed on Excel and includes four variables nationality, median weekly wages, total employed, and year.
In 2009, the foreign-born and native-born workers had wages above the linear regression One possible explanation behind this is that on July 24, 2009, the federal minimum wage increased from $5.15 to $7.25 per hour (Filion). This increase in minimum wages benefited about 4.5 million workers and provided an additional $1.6 billion annually in higher wages (Filion).
Moving on, the foreign-born and native-born median wage line’s have negative points that follow a seasonal pattern from 2011 to 2015, and positive points from 2018-2019. In 2018 and 2019, the economy was doing strong as GDP growth was hovering around 3% and unemployment rate averaging 4% (Parkinson). Economic trends will be analyzed to address possible factors behind the 2011-2015 pattern.
In 2011, the U.S. had slow economic growth of 1.5% compared to 2010 economic growth of 3.1%, meaning that businesses and consumer spending were still recovering, thus wages did not grow as expected (Reuters Staff). In 2012, the nation’s economy was suffering from skyrocket levels of debt that totaled $16.05 trillion, to pay for this debt the government increased taxes, which led to businesses reducing wages or workers having less after-tax income (Amadeo). In 2013, there was a 16-day government shutdown that damaged the economy by approximately $24 billion, reduced investor and consumer confidence, and led to thousands of federal employees furloughed (Hicken). In 2014, the economy had a quarter one bleak economy outlook as GDP contracted by 2.1% (Sharf). This led to a disruption across the economy, but mainly to the main sectors impacted were manufacturing and construction (Sharf). Lastly, in 2015 the economy started to grow, evident by the 5.2% increase in household incomes that showed signs of a true recovery (Appelbaum). All in all, these economic events provide some support for the negative residuals found from 2011-2015.
ggplot(data=Native_vs_foreign_born_wages, aes(x=Year, y=Median_weekly_wages,colour=Nationality))+
geom_point()+
labs(title="Native born and foreign born median weekly wages from 2009-2019", y="Median Weekly Wages")+
geom_smooth(method='lm',se = FALSE, formula = y~x+I(x^2))+
scale_x_continuous(name="Year",breaks=(2009:2019))
The residuals vs fitted graph, helped note that a quadratic regression was more suitable for this relationship. This minimized the residuals throughout the 10-year period, especially the ones from 2011-2015, and 2009 and 2019. The quadratic model demonstrates the median weekly wage gap more clearly as well as the narrowing of it, that is seen from 2017-2019. Lastly, by minimizing the residuals, this model aligns more closely with the economic events of that factored into the wage median wage increases.
library(readxl)
Wage_difference <- read_excel("Wage difference.xlsx")
ggplot(data=Wage_difference,aes(x=Year,y=Difference_in_median_weekly_wages_between_native_and_foreign_born,colour=Education))+
geom_point()+
labs(title="Difference in Native-born and Foreign-born median weekly wages from 2009-2019", y="Median Weekly Wages")+
theme(plot.title = element_text(size=12))+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
This scatter plot with linear regressions shows the wage gap between native born and foreign born median weekly wages (calculated by subtracting foreign-born wages from native-born wages) for each year according to four education levels. To build this dataset the following variables were incorporated year, education, and difference in median weekly wages information from the main employment report (2009-2019) was manually organized on Excel.
As can be seen, the median weekly wage difference for workers with a high school degree and some college or an associate degree stayed stable throughout the 10 years. On average, native born workers with a high school degree had $120 higher median weekly wages than foreign-born workers. The linear regression had small points that deviated from the line showing the robustness of the relationship. This wage gap, could be due to the economic acclimation process that leads to foreign-born workers receiving lower wages due to their need to learn U.S. industry demanded skills, acquire English-language proficiency, and understanding the labor market (Borjas). Moreover, the next strong stable wage gap is found in native and foreign workers with some college or an associate degree. Native workers on average earned $80 more than foreign-born workers throughout the 10 years. A possible cause of this gap is that foreign born individuals were taught technological and industry skills that do not directly transfer to U.S. industries, therefore they need to take further training to diminish the wage gap. Lastly, the wage gap between native and foreign born workers with less than a high school degree and bachelor’s degree and higher, is unclear therefore this relationship necessitates a scatterplot of its own.
library(dplyr)
Wage_difference_subgroup<-subset(Wage_difference,Education=='Less_than_high_school'|Education=='Bachelor\'s_degree_and_higher')ggplot(data=Wage_difference_subgroup,aes(x=Year,y=Difference_in_median_weekly_wages_between_native_and_foreign_born,colour=Education))+
geom_point()+
labs(title="Difference in Native born and foreign born median weekly wages from 2009-2019", y="Median Weekly Wages")+
theme(plot.title = element_text(size=12))+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
This scatter plot represents the wage gap between native-born and foreign-born workers median weekly wages from 2009-2019, for having less than high school education level or a bachelor’s degree and higher.
The less than high school linear regression is a downward trend meaning that foreign-born workers will earn more than native-born ones, but it is important to note that the 2011 and 2015 negative wages are outliers that eschew the results. Since, for the majority of the linear regression there is a median wage gap of about $100 favoring native born workers. These regression models have an obscure relationship, as there are more positive points above the linear regression than negative ones. In a future report, the outliers should by analyzed to provide an explanation for their causation and create a more robust model.
The linear regression model for workers with a bachelor’s degree and higher, shows an upward trend that results in native born workers earning higher wages than foreign born workers. This trend should be analyzed with caution as the possible culprit behind it is the 2019 anomalous wage gap (native workers earn $550 more than foreign born). This point has eschewed the linear regression model towards native born workers earning more than foreign workers. Without this outlier, the trend would be negative as there are six significant negative residuals. Overall, in the following years 2011, 2012, 2014-2016 and 2018, foreign born workers earned on average $50 higher wages than native born workers. A possible reason behind this is that foreign born workers are highly demanded in the STEM fields due to short native worker supply, this leads to them earning higher wages (Murray).
library(readxl)
Wage_gender_differences <- read_excel("Wage_gender_differences.xlsx")
ggplot(data=Wage_gender_differences, aes(x=Year, y=Difference_in_median_weekly_wages_between_native_born_and_foreign_born,colour=Gender))+
geom_point()+
labs(title="Difference in Native born and foreign born median weekly wages by gender from 2009-2019", y="Median Weekly Wages")+
theme(plot.title = element_text(size=11))+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
Men_wage_difference_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_gender_differences, Gender=="Men"))
plot(Men_wage_difference_fit)
Women_wage_difference_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_gender_differences, Gender=="Women"))
plot(Women_wage_difference_fit)
Men_wage_difference<-filter(Wage_gender_differences, Gender=="Men")
print(lm(Men_wage_difference$Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Men_wage_difference$Year))##
## Call:
## lm(formula = Men_wage_difference$Difference_in_median_weekly_wages_between_native_born_and_foreign_born ~
## Men_wage_difference$Year)
##
## Coefficients:
## (Intercept) Men_wage_difference$Year
## 10927.545 -5.318Women_wage_difference<-filter(Wage_gender_differences, Gender=="Women")
print(lm(Women_wage_difference$Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Women_wage_difference$Year))##
## Call:
## lm(formula = Women_wage_difference$Difference_in_median_weekly_wages_between_native_born_and_foreign_born ~
## Women_wage_difference$Year)
##
## Coefficients:
## (Intercept) Women_wage_difference$Year
## -1162.7273 0.6364This scatter plot with linear regressions shows the wage gap between native born and foreign born median weekly wages (calculated by subtracting foreign-born wages from native-born wages) for each year, based on gender. To build this dataset the following variables were incorporated: year, education, and difference in median weekly wages information, from the main employment report (2009-2019) was manually organized on Excel.
On average, the men median weekly wage gap (native born workers earn more than foreign born) has been decreasing rapidly over the 10 year period. Moreover, the linear regression presents strong positive points that deviate from the line. In 2009, the native born men earned $180 higher wages compared to foreign born workers. This increased to $280 median weekly wages in 2010, then they decreased approximately by $25 every year until 2019. The residuals vs leverage graph show the significant outliers found during 2010-2012, that may cause the trend to be steeper in the beginning. The line’s equation is y= -5.318x+10927.545. Using this equation, the wage gap will be zero in 2055 based on this 10-year trend. This is a promising trend, that is possibly explained by foreign-born men becoming acclimated to the economy and gaining higher paying jobs.
On average, the women median weekly wage gap (native born workers earn more than foreign born) has been slowly increasing over the 10 year period. The linear regression presents an overall negative trend, with strong negative points and minor positive points that deviate from the line. In 2009, the native born women earned $140 median weekly wages more than foreign born women. This gap decreased to $130 median weekly wages in 2010 then the gap fluctuated periodically every 4 years, by increasing from 2009-2012 then decreasing in 2013, and repeating this pattern throughout.
library(readxl)
Wage_difference_by_race <- read_excel("Wage_difference_by_race.xlsx")
ggplot(data=Wage_difference_by_race, aes(x=Year, y=Difference_in_median_weekly_wages_between_native_born_and_foreign_born, colour=Race))+
geom_point()+
labs(title="Difference in Native born and foreign born median weekly wages by race from 2009-2019", y="Median Weekly Wages")+
theme(plot.title = element_text(size=11))+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
White_non_Hispanic_or_Latino_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_difference_by_race, Race=="White non-Hispanic or Latino"))
plot(White_non_Hispanic_or_Latino_fit)
White_non_Hispanic_or_Latino_lm<-filter(Wage_difference_by_race, Race=="White non-Hispanic or Latino")
print(lm(White_non_Hispanic_or_Latino_lm$Difference_in_median_weekly_wages_between_native_born_and_foreign_born~White_non_Hispanic_or_Latino_lm$Year))##
## Call:
## lm(formula = White_non_Hispanic_or_Latino_lm$Difference_in_median_weekly_wages_between_native_born_and_foreign_born ~
## White_non_Hispanic_or_Latino_lm$Year)
##
## Coefficients:
## (Intercept) White_non_Hispanic_or_Latino_lm$Year
## 18158.491 -9.055Hispanic_or_Latino_lm<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_difference_by_race, Race=="Hispanic or Latino"))
plot(Hispanic_or_Latino_lm)
Hispanic_or_Latino_lm<-filter(Wage_difference_by_race, Race=="Hispanic or Latino")
print(lm(Hispanic_or_Latino_lm$Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Hispanic_or_Latino_lm$Year))##
## Call:
## lm(formula = Hispanic_or_Latino_lm$Difference_in_median_weekly_wages_between_native_born_and_foreign_born ~
## Hispanic_or_Latino_lm$Year)
##
## Coefficients:
## (Intercept) Hispanic_or_Latino_lm$Year
## 9177.873 -4.491This scatter plot with linear regressions shows the wage gap between native born and foreign born median weekly wages for each year, based on different races. To build this dataset the following variables that were incorporated are year, race, and difference in median weekly wages information, from the main employment report (2009-2019) was manually organized on Excel.
On average, the Black non-Hispanic or Latino median weekly wage gap (native born workers earn more than foreign born) has maintained stable over the 10 year period, with foreign born workers earning more than native born workers by roughly $10. Moreover, the linear regression presented a seasonal pattern of strong positive and negative points that have a deviation from the line pattern. These minor wage gaps could be due to foreign-born Black non-Hispanic workers having comparative advantages that led them having higher wages periodically.
On average, Hispanic or Latino native born workers throughout the years earned about $75 more than foreign born Hispanic or Latino. The Hispanic or Latino median weekly wage gap (native born workers earn more than foreign born) has been slowly decreasing over the 10 year period. The linear regression presents an overall negative trend, with strong points that deviate from the line. This line’s equation is y= -4.491x+9177.873, meaning that the wage gap is rapidly decreasing each year. Using the linear regression equation based on the 10 year track, the wage gap will be 0 in 2044. A possible reason behind this decrease is that foreign-born Hispanic workers are acclimating to the economy and are earning higher wages, thus closing the gap.
On average, foreign born White non-Hispanic or Latino workers throughout the 10 year period earned increasingly more than native born workers, presenting an overall downward trend. In 2009, the White non-Hispanic or Latino workers foreign born workers earned about $35 in median weekly wages more than naitve born White non-Hispanic or Latino workers. This gap increased to $100 in median weekly wages in 2015, then increased more to $135 in 2019. This line’s equation is y= -9.055x+18158.491, meaning that the wage gap is rapidly increasing each year. Using this equation the gap will be -200 ( foreign born earning more than native born) in 2027. A possible reason behind this wage gap, is that an increasing of foreign-born White-non Hispanic or Latino workers have brought skills that the native group does not manage, thus creating a comparative advantage and leading to higher wages.
On average, foreign born Asian non-Hispanic or Latino workers throughout the 10 year period earned increasingly more than native born workers. The linear regression presents an overall downward trend, with small points that deviate from the line. In 2009, the Asian non-Hispanic or Latino workers native born workers earned about $35 in median weekly wages more than foreign born Asian non-Hispanic or Latino workers. In 2014, this gap decreased to $-49 meaning that foreign born workers had higher median weekly wages than native born workers. Moreover the gap increased to -75 in 2017, then decreased to -65 in 2018, and decreased to -35. This line’s equation is y= -8.2x+16485.7, according to the slope the wage gap is rapidly increasing each year. Using this equation, the wage gap will be -100 in 2023. A possible reason behind this wage gap is that an increasing of foreign-born Asian non-Hispanic or Latino workers have brought skills that the native group does not manage, thus creating a comparative advantage and leading to higher wages.
Conclusion:
In summation, this investigation provided a clear relationship that between the differences between foreign and native born wages in education, race, and age. For the most part the relationships shown demonstrate that foreign-born workers for the most part are complements to native born workers, rather than direct substitutes. Moreover, foreign-born workers suffer from severe economic acclimation that restricts their income, and leads to them earning proportionally less than native workers.
These finding correspond with economist studies that immigration positively affects the labor market, as they led to economic growth and increased wages for all parties. In terms of education, foreign born workers with less than high school education earn more than native born workers. Following, native born workers with a high school degree native born earn more than foreign born workers. Moreover, native born workers with some college or an associate’s degree earn more than foreign born workers. Lastly, foreign born workers with a bachelor’s degree had higher wages than native born workers, when not taking into account the outlier.
In terms of gender, native-born men workers are earning more than foreign-born men workers, but the wage gap is significantly decreasing. On other other hand, women native-born workers are earning more than foreign-born women, and this gap is rapidly increasing. The last factor is race, native born Hispanic or Latino workers earn higher wages than foreign born workers, but this gap is decreasing at a rapid rate. Moreover, foreign born black non-Hispanic or Latino workers earn higher wage than native born workers and this trend is staying stable. In addition, White non-Hispanic or Latino foreign-born workers are have higher wages than native-born workers. Lastly, foreign-born Asian non-Hispanic or Latino workers have higher wages compared to native-born Asian non-Hispanic or Latino.
With additional funding and time, it is necessary to further research how the factors analyzed above intersect and clearly identify the economic factors affecting the wage gaps between foreign and native born workers.
Works Cited:
Appelbaum, Binyamin. “U.S. Household Income Grew 5.2 Percent in 2015, Breaking Pattern of Stagnation,” The New York Times, Sept. 13, 2016,https://www.nytimes.com/2016/09/14/business/economy/us-census-household-income-poverty-wealth-2015.html.
Amadeo, Kimberly. “US Economy 2012: Summary and Critical Events,” The Balance, February 23, 2021,https://www.thebalance.com/u-s-economy-2012-3305742https://www.thebalance.com/u-s-economy-2012-3305742.
Borjas, George. “Immigration,” National Bureau of Economic Research, December 1999, https://www.nber.org/reporter/winter99/immigration.
De Brauw, Alan. “Does Immigration Reduce Wages?,” Cato Jounral, Fall 2017, https://www.cato.org/cato-journal/fall-2017/does-immigration-reduce-wages.
Filion, Kai. “Fact sheet for 2009 minimum wage increase—Minimum Wage Issue Guide,” Economic Policy Institute, July 20, 2009, https://www.epi.org/publication/mwig_fact_sheet/.
Hicken, Melanie. “Shutdown took $24 billion bite out of economy,” CNN, October 17, 2013, https://money.cnn.com/2013/10/16/news/economy/shutdown-economic-impact/.
Murray, Michael. “To Address STEM Shortage, U.S. Employers Need Talented Immigrants,” Forbes, Feb 9, 2017, https://www.forbes.com/sites/realspin/2017/02/09/to-address-stem-shortage-u-s-employers-need-talented-immigrants/?sh=7cb363145c34.
Nunn, Ryan, O’Donnell, Jimmy, and Shambaugh, Jay. “A Dozen Facts About immigration,” October 9, 2018, https://www.brookings.edu/research/a-dozen-facts-about-immigration/.
Parkinson, Cody. “Labor force participation and employment rates declining for prime-age men and women,” U.S. Bureau of Labor Statistics, July 2018, https://www.bls.gov/opub/mlr/2018/beyond-bls/labor-force-participation-and-employment-rates-declining-for-prime-age-men-and-women.htm.
Reuters Staff. “Economic growth in U.S. states slowed in 2011,” Reuters, June 5, 2012, https://www.reuters.com/article/us-usa-states-growth/economic-growth-in-u-s-states-slowed-in-2011-idUSBRE85414520120605.
The White House. “Fact Sheet: President Biden Sends Immigration Bill to Congress as Part of His Commitment to Modernize our Immigration System,” JANUARY 20, 2021, https://www.whitehouse.gov/briefing-room/statements-releases/2021/01/20/fact-sheet-president-biden-sends-immigration-bill-to-congress-as-part-of-his-commitment-to-modernize-our-immigration-system/.
Data source:
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2019,” U.S. Department of Labor, May 15, 2020, https://www.bls.gov/news.release/pdf/forbrn.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2017,” U.S. Department of Labor, May 17, 2018, https://www.bls.gov/news.release/archives/forbrn_05172018.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2016,” U.S. Department of Labor, May 18, 2017, https://www.bls.gov/news.release/archives/forbrn_05182017.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2015,” U.S. Department of Labor, May 19, 2016, https://www.bls.gov/news.release/archives/forbrn_05192016.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2014,” U.S. Department of Labor, May 21, 2015, https://www.bls.gov/news.release/archives/forbrn_05212015.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2013,” U.S. Department of Labor, May 22, 2014, https://www.bls.gov/news.release/archives/forbrn_05222014.pdf.
U.S. Bureau of Labor Statistics. “FOREIGN-BORN WORKERS: LABOR FORCE CHARACTERISTICS — 2012,” U.S. Department of Labor, May 22, 2013, https://www.bls.gov/news.release/archives/forbrn_05222013.pdf.
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