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 6, 2021
Abstract: This report will analyze immigrants impact on the U.S. labor market, specifically focusing on how immigration affects the wages of individuals born in the United States and how wages differ between the two groups. This will be analyzed by investigating the differences between median weekly wages for native born and foreign born workers across three factors: education, race, and gender. In terms of education, 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. In terms of race, White non-Hispanic or Latino foreign-born workers are have higher wages than native-born workers. Furthermore, foreign-born Asian non-Hispanic or Latino workers have higher wages compared to native-born Asian non-Hispanic or Latino. This analysis can help shape immigration policy as we can see that overall native and foreign born wages increase together. Furthermore, the government should work to better support the foreign born population and help minimize the gaps between wages. With additional funding and time, there needs to be more research in the wage discrepancies between foreign and native born wages.
Topic Sentence:
This report will shed light on the positive contributions that immigrants make to our nation’s economy and how for the most part they improve native born wages, rather than restrict them. 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 (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: The nation’s foreign-born population has exponentially increased from 19.8 million in 1990 to 44.8 million in 2018 (Pew research). In 2018, foreign-born individuals made up 13.7% of the nation’s population (Pew Research). 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 academics namely 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 (Cato 2017). 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 (Cato 2017). 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% (Cato 2017). 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 (Bookings). Most of the economic literature estimated that low-wage native-born workers will have a wage reduction of up to 1% (Brookings)
However, economist David Card and Giovanni Peri conducted further labor analyses and found that wages do not change with an increased supply of immigrants (cato jounral). 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 (NBER and cato journal). 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 understading the industry/labor market, this leads to a large wage gap between foreign-born and native-born citizens (NBER). Foreign-born indviiduals are able to reduce the gap by about 10% after their first two decades of immigrating (NBER).
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. 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. These tools helped demonstrate the wage gaps in foreign-born and native-born median weekly wages.
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. As can be seen, the Native-born participation rate is slowly decreasing throughout the 10-year track, it starts at roughly 64% (2009) and decreased to approximately 62%. The U.S. Bureau of Labor Statistics, identified factors that contribute to this decrease mainly limited child-care opporutnties, higher incarceration rates, limited health care accesses, and limited fiscal spending on job-search assistance programs (Labor of Statistics). Moving on, the foreign born participation rate throughout the 10-year track is higher than the native born one, by approximately 2%. A possible explanation for this is that foreign-born individuals have limited access to government welfare therefore depend more on their income to sustain themselves. Furthermore, the native born participation rate decreased from 2009-2016 by about 2%, then increased from 2017-2019 by about 0.5%. These fluctuations could be due to an aging foreign-born population and difficulty finding a job, especially for new immigrants.
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. This is mainly due to an increase in the age eligible work force population. Moving on, foreign born employment has increased throughout the 10 year period, this is can be attributed to a higher age eligible work force population. Lastly, the bars present a minor difference in the proportion of the increases in employment, as native-born employment increases by a larger proportion compared to foreign-born employment.
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 compiled from the U.S. Bureau of Labor Statistics annual foreign-labor reports, that detailed median weekly wages for foreign-born and native-born workers. The U.S. Bureau of Labor Statistics defines the foreign-born population as “persons residing in the U.S. who were not U.S. citizens at birth, including legal immigrants, refugees, temporary residents, and undocumented immigrants” (Cite). The native-born population is defined as persons born in the U.S. or territories and individuals born abroad of at least one parent who was a U.S. citizens (cite).
In 2009, the foreign-born and native-born workers had positive residuals especially for foreign-born workers, meaning that they performed better than expected. One possible rational behind this increase can be that on July 24, 2009 the federal minimum wage increased from $5.15 to $7.25 per hour (Kai Economic Insitute). This increase in minimum wages benefited about 4.5 million workers and provided an additional $1.6 billion annually in higher wages (Kai Economic insititue). Moreover, in 2009 the Obama administration enacted the American Recovery and Reinvestment Act that invigorated the economy by creating jobs and revitlizing industries (Chart book).
Moving on, the foreign-born and native-born median wage line’s have negative residuals that follow a seasonal pattern from 2011 to 2015, and positive residuals for both groups from 2018-2019. In 2018 and 2019, the economy was doing strong as GDP growth was hovering around 3% and unemployment rate averaging 4% (BLS and Bea) During 2011 to 2015, the nation was still recovering from the Great Recession of 2008, leading to lower than expected increase in median weekly wages. To identify the possible rational behind this pattern, the notable economic events of each year will be analyzed.
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). Furthermore, in 2011 the U.S. government’s credit rating downgraded from AAA to AA+ this further reduced business and investor confidence in the nation (CNN). In 2012, the nation’s economy was suffering from skyrocket levels of debt that totaled $16.05 trillion and caused our debt-to-GDP ratio to be 100%, to pay for this debt the government increased taxes which led to businesses reducing wages or workers having less after-tax income (the balance). 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 (CNN). 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 mianly 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 period (NYTimes). This increase was still unable to meet the 10 year median wage linear regression model, meaning that other exogenous factors led to the negative residual. All in all, these economic events provide some support for the negative residuals found from 2011-2015, but the their true nature encompasses a broader set of exogenous factors than the ones studied.
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))
Due to the noted, negative residuals in foreign-born and native-born median wages from 2011-2015 and positive residuals in 2009 and 2019; an improved modeling had to be devised. Therefore, a quadratic regression was plotted, and this significantly minimized the residuals throughout the 10-year period, especially the ones from 2011-2015, and 2009 and 2019. This quadratic model improves the overall analysis of the relationship between native-born and foreign-born median weekly wages, due to the small residuals. 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.
To further investigate the impact foreign-born workers have on the native-born wages there will be an analysis of the differences in median weekly wages between the groups. These median wage differences will further be investigated by incorporating education level, race, and gender observations.
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")+
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 different 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 wage weekly 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 compared to foreign-born workers, and the linear regression has small residuals 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 (NBER). 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. Another possible reason is that native-born workers have the advantage of growing up in the U.S. and are able to hone the nation’s demanded skills for a longer period of time, thus receive higher wages based on years of experience especially U.S. based. Moving on, 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 on 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")+
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, based on having less than high school education level or a bachelor’s degree and higher. The less than high school linear regression presents a downward trend meaning a decreasing median weekly wage gap. For the majority of the linear regression there is a median wage gap of about $100 favoring native born workers. This trend is not robust as there is a significantly positive residual in 2009, and there are two significant negative residuals in 2011 and 2015, that outweigh the 2009 residual and are eschewing the line downwards. With this in mind, the downward regression line is not as promising but it is supported by an economic study which concluded that low-wage native-born workers face a 1% wage decrease when foreign-born workers enter the market, mainly due to labor competition (Bookings). In a future research paper, the outliers can be controlled for and a more robust linear regression model can be built that shows the true wage gap in less than high school workers relationship. Since, the current model has an obscure relationship as there are positive residuals than negative ones, even though the overall trend is going down. This reasoning could be explained by native workers composing the majority of jobs that require less than a high school degree, thus an increase in the supply of foreign-workers has a minimal affect on wages.
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 extremely positive residual 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 $50 higher wages than native born workers. The reasoning behind foreign born workers having higher median weekly wages is that they are highly demanded in the STEM fields, due to companies needing their exceptional skills and training that is in short supply in the United States (Forbes). Moreover, foreign-born workers with a bachelor’s degree and higher have a limited economic acclimation process, as their skills directly transfer to the labor market and are highly sought out for. Lastly, the highly demanded technological and innovative skills of foreign workers lead to them earning higher wages.
library(readxl)
Wage_gender_differences <- read_excel("Wage_gender_differences.xlsx")
ggplot(data=Wage_gender_differences, aes(x=Year, y=Difference_in_median_weekly_wages_between_native_born_and_foreign_born,colour=Gender))+
geom_point()+
labs(title="Difference in Native born and foreign born median weekly wages by gender from 2009-2019", y="Median Weekly Wages")+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
Men_wage_difference_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_gender_differences, Gender=="Men"))
plot(Men_wage_difference_fit)
Women_wage_difference_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_gender_differences, Gender=="Women"))
plot(Women_wage_difference_fit)
Men_wage_difference<-filter(Wage_gender_differences, Gender=="Men")
print(lm(Men_wage_difference$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.
This scatter plot represents the wage gap between native-born and foreign-born workers median weekly wages from 2009-2019, based on gender. On average, the men median weekly wage gap (native born workers earn more than foreign born) has been rapidly decreasing over the 10 year period. Moreover, the linear regression had presented strong positive residuals 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. This is represented in the linear regression line y= -5.318x+10927.545, meaning that the wage gap is decreasing at a considerably rapid rate. Using this linear regression formula based on the 10 year period and algebra, the wage gap will be zero in 2055. This is a promising trend, that can be explained by foreign-born men becoming acclimated to the economy and gaining higher paying jobs. Also, foreign born men are becoming labor complements of native born men, meaning that their wages will start to increase in union.
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 residuals and minor positive residuals 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 $130 median weekly wages in 2010, then the gap fluctuated periodically every 4 years, by increasing from 2009-2012 then decreasing in 2013, and repeating this pattern throughout. This linear linear regression line is y= 0.6364x-1162.7273, meaning that the wage gap is slowly increasing each year. The government must act now to provide higher paying job opportunities for foreign-born workers that decrease the gap, to bridge this slowly widening wage gap.
library(readxl)
Wage_difference_by_race <- read_excel("Wage_difference_by_race.xlsx")
ggplot(data=Wage_difference_by_race, aes(x=Year, y=Difference_in_median_weekly_wages_between_native_born_and_foreign_born, colour=Race))+
geom_point()+
labs(title="Difference in Native born and foreign born median weekly wages by race from 2009-2019", y="Median Weekly Wages")+
geom_smooth(method='lm',se = FALSE)+
scale_x_continuous(name="Year",breaks=(2009:2019))## `geom_smooth()` using formula 'y ~ x'
White_non_Hispanic_or_Latino_fit<-lm(Difference_in_median_weekly_wages_between_native_born_and_foreign_born~Year, data=filter(Wage_difference_by_race, Race=="White non-Hispanic or Latino"))
plot(White_non_Hispanic_or_Latino_fit)
White_non_Hispanic_or_Latino_lm<-filter(Wage_difference_by_race, Race=="White non-Hispanic or Latino")
print(lm(White_non_Hispanic_or_Latino_lm$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 (calculated by subtracting foreign-born wages from native-born 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.
This scatter plot represents the wage gap between native-born and foreign-born workers median weekly wages from 2009-2019, based on race.
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 residuals that deviate from the line. In 2009, the foreign born Black non-Hispanic of Latino earned $10 more than native born Black non-Hispanic or Latino workers. This difference decreased to $0 in 2010, then it increased approximately by $2 (native born workers earning more), then decreased by $15 in 2013, the subsequent years up until 2019 follow the same pattern. These minor 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 residuals that deviate from the line. In 2009, the Hispanic or Latino native born workers earned $155 in median weekly wages more than foreign born Hispanic or Latino. This gap decreased to $145 in median weekly wages in 2014, then steeply decreased to $100 in 2019. This linear linear regression line is y= -4.491x+9177.873, meaning that the wage gap is rapidly decreasing each year. Using the linear regression model and algebra, the 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. Meaning, that the White non-Hispanic or Latino workers median weekly wage gap (foreign born workers earn more than native born) has been increasing throughout the 10 year period. The linear regression presents an overall downward trend, with strong residuals that deviate from the line. 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 linear linear regression line is y= -9.055x+18158.491, meaning that the wage gap is rapidly increasing each year. Using the linear regression model and algebra, 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. Meaning, that the Asian non-Hispanic or Latino workers median weekly wage gap (foreign born workers earn more than native born) has been increasing throughout the 10 year period. The linear regression presents an overall downward trend, with weak residuals 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. The linear linear regression line for this model is y= -8.2x+16485.7, meaning that the wage gap is rapidly increasing each year. Using the linear regression model and algebra, the 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 without taking into account education, race, and age median weekly wages are increasing on a yearly for foreign born and native born workers. This is due to foreign-born workers for the most part being complements to the labor force, rather than direct substitutes of native born workers. Furthermore, foreign-born workers suffer from severe economic assimilation which limits their income, and overall leads to them earning proportionally less than native workers. These finding correspond with economist studies that immigration positively affects the labor market, as it leads to economic growth and increased wages for all parties. After completing the initial analysis, a more granular approach was taken by analyzing the wage difference for foreign and native born workers based on education, gender, and race. 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 additonal funding and time, there needs to be more research in the wage discrepancies between foreign and native born wages.
References:
https://cis.org/Historical-Overview-Immigration-Policy
https://www.cato.org/cato-journal/fall-2017/does-immigration-reduce-wages#references
https://www.nber.org/reporter/winter99/immigration
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