Immigration levels after the 1924 Immigration Act

Immigration has been the focus of many political debates and governmental laws.  Around the middle of the 19th century, “Every branch of the federal government and many state and local governments became embroiled in ideological debates around immigration-as did the Census Office” (Hochschild and Powell, 2008, 75).  Americans felt threatened by the rising number of immigrants for many reasons and engaged in prejudice behavior against immigrants.  Some believed that the native born white population, the dominant race/societal class, was decreasing due do the incoming foreigners taking up space (Hochschild and Powell, 2008, 75).  There were concerns about wether or not these new immigrants had the virtues necessary to participate in the American democratic society (Jacobson, 2000, 69).  Even more, American workers were outraged that immigrant workers were taking their jobs (Jacobson, 2008, 69).  All of these factors and more resulted in extreme tension regarding immigration in the early 20th century.  This tension led to action from the government in the form of the Immigration Act of 1924.  The Immigration Act of 1924 “restricted immigration into the United States to 150,000 a year based on quotas, which were to be allotted to countries in the same proportion that the American people traced their origins to those countries, through immigration or the immigration of their forebears (Ngai, 1999, 67).  This post focuses on levels on immigration from certain countries in the 20th century and how the Immigration Act of 1924 affected those levels.


I gathered data from the Integrated Public-Use Microdata Series (IPUMS) for this project.  I used 1% data samples from the 1900-1960 censuses and took the BPL (Birthplace), SEX, and PERWT variables from these samples.  The PERWT variable represents the sample weight of each individual.  The BPL variable indicates the birthplace of the individual.  I grouped the BPL variable into these categories: U.S. Born, Other North American, Central and South America (Includes Mexico and the Caribbean), Northern Europe, United Kingdom & Ireland, Western EU, Central/Eastern EU, East Asia (Includes China, Japan and Korea), Other Asia, and Other.


I graph the population of the United States by gender and birthplace from 1900-1960 in order to see how immigration levels change over this period.  I graphed immigration levels before and after the Immigration Act of 1924 in so that I could see how much of an impact it had on immigration.  I chose to include the U.S. Born category for two of my graphs in order to view the overall influence of immigrants on the U.S. population.  I graphed the total population to see which birthplace groups most increased population over this time and the population as percentages to better see which birthplace groups gained or lost a presence in the U.S. population over time.  I then created two more graphs without the U.S. Born variable in order to isolate the immigrant population in America.  One graph illustrates the total numerical immigrant population over this period to show how overall immigrant levels changed over time.  I graphed each birthplace category as a percent of the total immigrant population in order to demonstrate which categories were most affected by the Immigration Act of 1924.  The code for this project can be found here.


Figure 1:

Rplot01, non-percent U.S. born

Figure 1 represents the total population of the U.S. by birthplace and gender from 1900-1960.  The increase in population is practically linear and can largely be attributed to the rise in the U.S. Born category between each census.  There are slight changes in the immigrant population that can be seen on this graph, but they are significantly overshadowed by the U.S. Born population.  The immigrant population, according to this figure, was a small fraction of the total U.S. population during this time.  There is no noticeable difference between the male and female population in this figure as well.

Figure 2:


Figure 2, like figure 1, represents the total population of the U.S. by gender and birthplace but as percentages of the total rather than numerical values.  This graph does not show how the total population increases, but it reveals which birthplace categories dominate the population.  The U.S. Born birthplace category makes up about 80% of the total population in this graph.  Like figure 1, it is difficult to see how specific immigrant groups changed in population over this time.  What this graph does reveal is that the immigrant population, as a proportion of the total U.S. population, declines after the Immigration Act of 1924.  The largest percentage of the population that immigrants hold is in the 1910 census.  Immigration from all birthplace categories decreases during this part of the 20th century.  Like figure 1, there is no significant variance between the male and female population here.

Figure 3:


Figure 3 illustrates the numerical value of the total immigrant population in the U.S. from 1900-1960 by gender and birthplace.  This graph shows a much more detailed portrait of how different immigrant groups changed in size during this period.  Male immigrants outnumber female immigrants at the turn of the century and continue to until 1960.  Both genders have a large jump in population from the 1900 to 1910 census, but afterwards the male population plateaus and the female population slightly rises over the next two decades.  The 1930 census is the peak for each gender, followed by a sharp decrease over the following decades.  The male population drops to a level that is lower than its initial 1900 number whereas the female population remains above its 1900 number.  This indicates that restrictions placed on female immigrants could have been lesser than the restrictions placed on male immigrants.  With regards to the separate birthplace categories, this graph shows a steadily decreasing number of immigrants from all parts of Europe and the UK & Ireland and an increase of persons from Central and South America.  There is a drop in immigrants from Central and South America from 1930 to 1940, but the 1960 census indicates that this immigrant group re-populate to its former number.

Figure 4:


Figure 4 illustrates the percentage of the total immigrant population that each birthplace category had from 1900-1960 by gender.  This graph indicates that there is a drop of representation in the immigrant population from all areas of Europe except for Western Europe.  Western Europe ends up with a larger percentage of the total population in 1960 than it had in 1900.  Categories of the immigrant population that increased as a percentage of the total population were Central and South America and Other NA.


These results show the effects that the industrial revolution, American prejudice against immigrants, and the Immigration Act of 1924 had on immigration levels.  Figure 3 shows a rise in the immigrant population until 1930 when it rapidly decreases.  I believe that this highlights the desire for immigrant labor held by factory heads and other employers of the industrial revolution.  Along with this increase in immigrant population came strong prejudice against immigrants for reasons discussed above.  Most of the prejudice came with a fear of Americans losing jobs and having the U.S. population diluted with foreigners.  While it is true that immigrants were becoming a larger percentage of the total population, that percentage was still the significant minority.  I think this indicates that some of the fears brought to the American government about immigrants were born mostly out of prejudice rather than legitimate concerns.  As a direct result of these concerns, the Immigration Act of 1924 clearly did a great job of decreasing immigration levels to the U.S.  But while it very clearly targeted specific immigrant groups (mostly in Europe), other foreign populations began to take their place like Mexican or Canadian immigrants.  Lastly, it is striking how different the overall population of male and female immigrants varies over the course of this period.  By 1960, the female immigrant population is greater than the male immigrant population, revealing how much opportunity has been made for women in this country.  I also believe that this is an indicator of the growth of the role of women, specifically immigrant women, in the labor market.  As certain industries developed, the need for both male and female immigrants became equal as displayed by figure 3.  Immigration was clearly a highly controversial issue during the 20th century and continues to be one in modern times.


Works Cited

Hochschild, Jennifer L., and Brenna Marea Powell. “Racial Reorganization and the United States Census 1850–1930: Mulattoes, Half-Breeds, Mixed Parentage, Hindoos, and the Mexican Race.” Stud. in Am. Pol. Dev. Studies in American Political Development 22.01 (2008): 59-96. Print.

Jacobson, Matthew Frye. Barbarian Virtues: The United States Encounters Foreign Peoples at Home and Abroad, 1876-1917. New York: Hill and Wang, 2000. Print.


Vietnam Veterans Wages and Education

The Vietnam War was one of the United State’s most costly wars. The US formally entered the Vietnam “Conflict” in 1964 after the Gulf of Tonkin Resolution was passed and ended in 1975 with the fall of Saigon and South Vietnam to National Liberation Front (Viet Cong). The war had cost the US $738 billion (2014 USD) (Daggett 2010, 5).

The Vietnam War was also an incredibly divisive war in American history. Many men chose to avoid the draft and protest the war. After hostilities ended in 1975, most Americans simply ignored that there had ever been a war. Ken Meyercord, a draft evader remembers, “There were plenty of people who condemned my actions (to evade the draft), but I was never comfortable with people labelling me as a hero. Mostly though it never came up, people didn’t want to talk about [the Vietnam War]” (Pearl 2016). In this blog post, I will analyze Vietnam veterans’ education and income levels using IPUMS census data compared to non-Veterans by birth year cohorts. I will explore whether or not there are any income and education discrepancies between Vietnam veterans and non-veterans. 


I am using census data from the University of Minnesota’s Integrated Public-Use Microdata Series (IPUMS) database. My samples range from 1970 to 2010. I am working with 1% samples for 1980 to 2000 but I am also using the 1% Form 2 State sample for 1970 and the 1% American Community Study (ACS) sample for 2010. The ACS is also administered by the U.S. Census Bureau. All analyses are weighted with the individual sample weight PERWT variable. 

I will be using the IPUMS’s definition of who is a Vietnam Veteran. The IPUMS Vietnam Veteran definition is broad. It includes any veteran who served between August 1964 and April 1975, irrespective on whether or not they served in the Vietnam War. It does not include Vietnam veterans who served in the U.S. military in Vietnam before 1964 and only includes National Guard and military reserves members if they were called to active duty.

I will be using INCWAGE to calculate the incomes of veterans and non-veterans. INCWAGE does not record net losses for individuals–zero is the lowest possible income. It only records an income paid by employers and includes commission, tips, and cash bonuses. My understanding is that social security, veterans payments and pension payments will be counted in INCWAGE as long as they are paid in cash to recipients. Non-cash benefits will not appear in INCWAGE.


I divide my analyses of Vietnam veterans into Veterans and non-Veterans and only analyze men. I am only analyzing veterans and non-veterans in the labor force (EMPSTAT!=3), including unemployed men. This eliminated veterans too disabled to work and non-veterans who were students in university and not yet searching for work. To remove extreme age outliers, I am analyzing veteran men between the 10th and 90th age percentiles. I am basing the percentiles on who is and is not a veteran in 1980 once the war is over and no one else can become a Vietnam veteran. Somehow IPUMS has men as young as 14 and as old as 73 listed as Vietnam veterans in 1970. By restricting my analysis, I eliminate these impossible outliers. I then separate the remaining men into cohorts by birth years. Each cohort is composed of 2 to 3 birth years.

For each income analysis, I am using 1999 USD. I multiplied the INCWAGE variables by the CPI99 variable to create a common value to compare cohorts, veterans and non-veterans. Without CPI99, it would be meaningless to compare 1980 income to 2010 income. I then created median incomes for each cohort and education level using the PERWT variable.

For the education analysis, I use EDUC. Although EDUCD is more comprehensive, it does not cover 1970 and 1980. I divided education levels based on years of schooling provided in EDUC. For those that attended Grade 12, I assigned “Graduated High school.” Men who attended less than Grade 12 were assigned “did not Graduate High school.” For those that attended college for 4 years, I assigned them “Bachelor’s Degree” and men with 5+ years of college were assigned “Master’s, Doctoral or Professional Degree.” Students who attended college for 1 to 3 years were assigned “Some College.” I recognize that these divisions are rough as years attended does not imply completion and that many students may have graduated or completed degrees faster. It also does not account for trade schools, Associate’s Degrees or other education programs. Finally, the education level is calculated for that census year. Many men may complete a higher level of education later or currently be in the process of completing a degree during enumeration.

Here is my code


Figure 1: Veteran and non-veteran income by birth year cohorts. 1970 - 2010.

Figure 1: Veteran and non-veteran income by birth year cohorts. 1970 – 2010.

Figure 1 shows us that Vietnam veterans who were born before 1952 have nearly equal median incomes to their non-veteran counterparts. Veterans born between 1949 and 1951 however have lower incomes from 1970 to 1990 but by 2000, they are almost identical to the non-veteran cohort. Additionally, Vietnam veterans born between 1937 and 1945 have higher median incomes in 2000 and 2010. This is most likely because the older veterans served in the military as career officers and were able to accrue stable pensions and benefits upon retirement.

Figure 1 also shows us that veterans born between 1946 and 1954 have higher incomes than their non-veteran counterparts in 1970. This may be because veterans were paid more in the military than their non-veteran counterparts in entry level positions. However, by 1980 and 1990, non-veterans born between 1946 and 1954 are earning more than veterans. Non-veterans born between 1952 and 1954 earn substantially more than veterans. This may be because the 1952 to 1954 veterans entered the military directly upon turning 18 and then exited service with less work experience and education than non-veterans.

Veterans born between 1943 and 1945 consistently earn more than non-veterans, however I can not explain why. It may be because of a higher proportion of higher education completed. I will explain this with Figure 2.

Figure 2: Education by Birth year cohort for men above age 39.

Figure 2: Education by Birth year cohort for men above age 39.

Figure 2 shows the education attainment breakdown for each cohort. The education data is taken across all census years for men above age 39. I arbitrarily chose age 39 as I believe that a majority of men would have ended their education by 40 years old. I also did not want to raise the age above 39 as higher ages were more affected by mortality, skewing the results. Figure 2 also shows us which cohort had the most Vietnam veterans. Most veterans were born between 1946 and 1948.

We also see in Figure 2 that for all cohorts, more non-veterans completed Bachelor’s or Master’s, Doctoral, or Professional Degrees than their veteran counterparts did. However, overall there are more non-veterans than veterans, making this an unfair comparison. Looking closer, more non-veterans born after 1945, proportionally, completed a college (attended college 4+ years). Conversely, proportionally more veterans born before 1946 completed a college degree by age 40 compared to non-veterans.

Figure 3: Income by educational attainment. 1970 - 2010.

Figure 3: Veteran and non-veteran income by educational attainment. 1970 – 2010.

Figure 3 show us that both veterans and non-veterans with the same education level earn almost identical median incomes. In fact, veterans with degrees earn slightly more than non-veterans over their lifetime. Non-veterans who completed high school or less earn slightly less than veterans with the same qualifications. This may be due to military pensions and benefits.

Figure 3 helps explain why veterans overall earn less than non-veterans. While veterans earn the same amount when they have the same education, more non-veterans have higher levels of education, pulling up the overall non-veteran population.


Vietnam veteran earnings are primarily determined by how old they were when they entered the military. Younger veterans earn less than their non-veteran counterparts due to entering the military instead of gaining civilian work experience or continuing their education. Veterans and non-veterans earn similar amounts when they both have the same educational qualifications. The 1946 – 1948 cohort, the largest veteran cohort, earns almost identical income to the non-veteran 1946 -1948 cohort. My study would be much more effective if I could analyze the data to see if it is statistically significant and be able to run regressions.

Other studies of Vietnam Veterans’ long term incomes have also concluded that by 2000, veterans were earning as much as non-veterans and that the effect was now negligible (Angrist 2011, 1). Finally, there may be many other variables that cause the discrepancies. For example, race and socioeconomic status may play a factor as many minorities and men from lower/working class backgrounds were over represented in the Vietnam draft (National Vietnam Veterans Foundation).


Angrist, Joshua, Stacey Chen, Jae Song. Long Term Consequences of Vietnam Era Conscription: New Estimates Using Social Security Data. MIT. May 2011.

Daggett, Stephen. Cost of Major U.S. Wars. Congressional Research Service. June 29, 2010.

Pearl, Joshua. Ken Meyercord Interview. oral history conducted 2/26/2016 by Joshua Pearl. Dartmouth Vietnam Project. Rauner Special Collections Library. Dartmouth College. 2016. [Note: This interview is not yet on the Dartmouth Vietnam Project’s website and is not yet transcribed. Available at Rauner by request.]

Sobering Statistics for the Vietnam War. National Vietnam Veterans Foundation.

African American population distribution post-emancipation

Race has always been a highly controversial topic throughout U.S. history.  Race can be defined as a simple characteristic of a person, but it has far greater implications and labels in reality.  Race has become a process of classification that holds significance on social, cultural, and authoritative levels (Omi and Winant, 2014, 105). Starting in 1850, the U.S. Census included the “mulatto” race category as an attempt to study how the mixing of two races affected a person (Hochschild and Powell, 2008, 68).  This was the beginning of an obsession with racial mixtures from racial scientists that had ended by 1930 (Hochschild and Powell, 2008, 71).  This obsession ended and what followed was a specific racial hierarchy (Hochschild and Powell, 2008, 71).  This post focuses on the white and black race categories in this post with regards to population distribution over this time.  The black population in America was heavily restrained up until the 1870 census due to slavery, but after the emancipation proclamation they had the freedom to move.  After the civil war, the U.S. population now had set social definitions of what it meant to be black or white (Hochshild and Powell, 2008, 71).  I look at how these definitions affected the change in black and white population levels in four different regions of the country in this post.



I gathered data from the Integrated Public-Use Microdata Series (IPUMS) for this project.  I included 1% samples from the years 1850-1960.  I used the SEX, RACE, REGION and PERWT variables from these samples for my research.  The PERWT variable represents the sample weight of each individual.  The SEX and RACE variables denote the gender and racial profile of that person.  I separate the RACE variable into white, black, and other.  The white category includes the white population of the United States which incorporates most European immigrants as well as Mexican immigrants until the early 20th century.  The black category encompasses all persons that reports themselves as black as well as those who were classified as mulatto (In the census from 1850-1930), quadroon  or octaroon (In the census from 1890-1930).  The other category of race represents people who do not fall into the white or black race groups.   The REGION variable indicates the region where each individual is currently living during the year of that census.  I separate the REGION variable into these four categories: Northeast, South, Midwest, and West.



I graphed the free population of America by race, sex and region in order to see how the distribution of the black population changed by region following the emancipation of slavery.  I also graphed the different race populations as percents of the total population in each region to see how the percents of the population changed over time.  I created two graphs that give each gender and region its own image.  I used the race categories I created to fill in the bars on each figure.  The blue portion of each bar represents the white population, the red represents the black population, and the green illustrates the other race category.  The Code for my project can be found here.



Figure 1:


Figure 1 illustrates the total population of each region by sex and race from the 1850-1960 census.  There is a steep jump in the black population in the south between the 1860 and 1870 census following the civil war due to the census counting the newly freed black population.  There is also steady rise in the population of each race category in each region and by both genders over this time period.  In the West we can see an exponential rise in the total population over this period.  This rise in population in the west is due to the increase in the ‘white’ population in the region.  After the emancipation of slavery, both male and female black persons became a significant portion of the Southern population.  While the total population of the Southern region increases greatly over this time period, the black population barely increases with it. The white population increase in the South overshadows the black and other population increases.  The Northeast does not start off with a significant increase in its black population after the civil war like the South but steadily over this time period.  This difference between the Northeast and South is due to the significantly lower number of slaves in the Northeast that would have augmented the population upon being freed.  The overall rise in Northeast population is similar to the rise in the Northeast black population.  The same story plays out in the Midwest.  There is a steeper population increase in the Midwest than in the Northeast, but there is also a steady increase in the black population like in the Northeast.  There is no difference between population patterns of male and females.

Figure 2:


Figure 2 illustrates the same data as figure 1, but it displays each race category as a percent of the total population of each region.  This graph reveals more about how the black population migrates from region to region.  In the West, like in figure 1, the black population does not constitute a significant portion of the population during the this time period.  The population of the South after the civil war changes from almost completely white to about 60% white and 40% black in the 1870 census.  As time goes on, the percentage of the black population in the South decreases until it is cut in half to around 20% of the total Southern population by 1960.  There is not much change in the Northeast and Midwest immediately after the civil war, only a few percent increase of the black population as a part of the whole.  The black percentage of the total population increases steadily up to about 5% in 1960.


These results illustrate the inability for the black population to migrate from the South to other regions of the country.  I assumed that the black population would want to leave the South after the civil war due to the extreme racism practiced in the South.  The black population became a smaller percent of the total population in the South over this period not because they left in large numbers, but because the white population grew far more quickly than the black population in that region.  In fact, figure 1 shows that the black population grew each year in the South.  There is evidence for some migration to other regions, but the change in percent of the black population as a portion of the total population in the other regions is very small.  This illustrates that either the black population did not want to move out of the South following the emancipation of slavery, that the South successfully kept the black population in the same region as an act of dominance, or a combination of both.

Works Cited

Tera W. Hunter (1993) Domination and resistance: The politics of wage household labor in New South Atlanta, Labor History, 34:2-3, 205-220.

Hochschild, Jennifer L., and Brenna Marea Powell. “Racial Reorganization and the United States Census 1850–1930: Mulattoes, Half-Breeds, Mixed Parentage, Hindoos, and the Mexican Race.” Stud. in Am. Pol. Dev. Studies in American Political Development 22.01 (2008): 59-96. Print.

Omi, Michael, and Howard Winant. Racial Formation in the United States. Hoboken: Taylor and Franklin, 2014. Print.


Women in the Workforce (1910-1960)

American women are largely considered underprivileged in terms of both the occupations available to them and the salary they make. However this discussion excludes the effects that race and immigrant status may have on women receiving employment. The movement toward intersectionality, according to Mignon Duffy, is imperative to understanding inequality (2007, 314). I am interested in the effects of race and nativity on employment for women in the labor force. In order to understand women’s subordination in the workplace I will analyze the female employment rate for various immigrant and race subgroups on the national and regional level.

The data used for this analysis are from the census data available on the Integrated Public-Use Microdata Series (IPUMS). My data set is composed of one percent samples from the 1910 to 1960 United States censuses. The IPUMS organized variables SEX, AGE, RACE, BPL, STATEFIP, REGION and EMPSTAT are used for my analysis. I exclude the 1920 census because the EMPSTAT variable is not available for that census year. The EMPSTAT variable measures if a respondent was part of the labor force and whether or not they were employed or unemployed. For years prior to 1940 the EMPSTAT variable measures employment status on a specific day. Starting in 1940 the EMPSTAT variable measures employment status in a specific week. For all census years prior to 1960 the census data were collected by an enumerator.

For my analysis I exclude data collected in Hawaii and Alaska for all years prior to 1960. I will be using the one percent sample from each of the census years.  IPUMS has randomly selected the one percent samples used for my analysis. All analyses are weighted by PERWT, the sample weight of each individual.


I begin my analysis with the assumption that all women’s employment status were enumerated correctly. This may not have been true however due to biases of the enumerators. In the process of coding the census data, census officials were instructed to discard any defective cards that may report contradictory information. More specifically, if a woman was reported to have a “male” occupation the card would be discarded or recoded into a more feminine occupation. Therefore some women may not have been accurately reported and others not included in the final census report at all (Conk 2001, 67-69). Folbre confirms this phenomenon and extends it by blaming patriarchal norms for significantly undercounting female labor participation (Folbre 1989, 546-547). However, I proceed by narrowing the data to women over the age of 18, who are in the labor force (either unemployed and looking for work or employed). Therefore my analysis only focuses on women in the workforce and how female employment changes over time. I then categorize each woman based on whether she is native-born or foreign-born (BPL<=99 is native born and BPL>99 is foreign born) and if she is white or nonwhite (RACE==1 is white and RACE!=1 is nonwhite). This method of categorization results in four groups.

By categorizing this way I will analyze how race and immigration status influence female employment rates both nationally and regionally. In order to calculate the national employment rate, for each year I graph the proportion of the work force in each race-immigrant category that was employed. In order to calculate the regional employment rate for each year, I graph the proportion of the work force in each race-immigrant category that was employed for each region of the US. The northeast, midwest, south and west regions are defined by IPUMS as follows: The northeast – New England division, middle Atlantic division, mixed Northeast divisions; the midwest – east north central division, west north central division, mixed midwestern divisions; the south – south atlantic division, east south central division, west south central division, mixed southern divisions; the west – mountain division, pacific division and mixed western divisions. I graph the regions’ employment rates over time using a line graph. Code for analysis and visualization is available here.

Figure 1:

large nat EMP

Figure one graphs the national employment rate for each race-immigrant category from 1910 to 1960. Disparity between all race-immigrant categories is relatively small across all years. Employment rate for women is always above 85%.

Figure 2:

Magnified Nat Emp

Figure two shows a magnified version of the national employment rate for each race-immigrant category from 1910 to 1960. The employment rate is lowest in 1940 for not white foreign-born women. The employment rate is highest in 1950 for white native born women. All race-immigrant groups, except for white foreign-born women, follow the same temporal trends. White foreign-born women unlike all other race-immigrant categories have a higher employment rate in 1940 than in 1930.

Figure 3:

Regional Emp

Figure 3 graphs the regional employment rate for each race-immigrant group from 1910 to 1960. The northeast demonstrates the same trends that were visible with the national employment rates. In the south, employment rates remain fairly consistent over time. However in the south, not white foreign-born women demonstrate the most dramatic changes in employment rate. In the midwest, female employment rate appears to be divided by race. The employment rate for white foreign-born women and white native-born women in the midwest are similar to each other; similarly not white foreign-born women and not white native born women demonstrate similar trends and rates of employment over time. In the west, the employment rate changes very little for all race-immigrant groups. Unlike national or any other region’s trends, not white foreign born women have the highest employment rate for all years except 1960.

Female employment rates over the twentieth century have remained fairly static for all race immigrant categories because the social conditions that influence labor participation decision also influence the demand for labor. For women, the decision for labor force participation is generally influenced by the opportunity cost that women face to perform unpaid household labor. Over the twentieth century as more women obtained higher education, the opportunity cost to being a housewife also rose. Therefore as opportunities for higher education increased an increasing amount of women chose to join the labor force. On the other hand, the increasing opportunities for female higher education is a reflection on changing social conditions that influenced the demand for female labor participation. We see both of these changes in culture conflated in the female employment rates from 1910 to 1960; employment rate conflates labor supply and labor demand together (Cotter 2001, 430-432).

As demand for female labor grew in the twentieth century, the market for female labor was generally limited to reproductive labor. Reproductive labor consisted of work that was considered “necessary to maintain existing life and to reproduce the next generation.” Reproductive labor was not only limited to being a housewive, but could also be paid labor such as domestic service, cleaning, cooking, and child care. This form of labor could be divided into nurturant and non-nurturant occupations. Nurturant work is labor that involves interacting with another person; these occupations were mainly performed by white women and were considered spiritual work. Non-nurturant work on the other hand is labor that maintains daily life; these occupations were mainly performed by racially-ethnic women and were considered dirty or menial work (Duffy 2007, 316-317). Duffy explicates how a woman’s identity impacts the location for the demand of her labor.

The regional trends observed in my analysis are likely a reflection of racial biases in each region. In decades such as 1930 and 1940, following the great depression, nonwhite foreign born women in the midwest and the north suffered sharper drops in employment rate than both the south and the west. Furthermore the racial biases of the north and the south (specifically for african american women) may have an impact on the observed employment rates. The availability of jobs in the developing markets of each region certainly influences employment rates for all groups of women.


Although the results of my analysis do not demonstrate dramatic disparities in employment rate between race-immigrant groups, it is true that the occupations that were held by women were largely dependent on their identity. Therefore a woman’s race and nativity had more influence on what occupation she had and less impact on her likelihood of receiving employment.


Conk, Margo A. “Accuracy, Efficiency, and Bias.” Historical Methods 14.2 (1981): 65-72. ProQuest. Web. 28 Feb. 2016.

Cotter, David A., Joan M. Hermsen, and Reeve Vanneman. “Women’s Work and Working Women: The Demand for Female Labor.” Gender and Society 15.3 (2001): 429-52. Web.

Duffy, Mignon. “Doing the Dirty Work: Gender, Race, and Reproductive Labor in Historical Perspective.” Gender and Society 21.3 (2007): 313-36. Web.

Folbre, Nancy, and Marjorie, Abel. “Women’s Work and Women’s Households: Gender Bias in the U.S. Census.” Social Research 56.3 (1989): 545-69. Web.



Females in the Labor Force 1880-2000


Female labor force participation has increased dramatically over the past century across the board, but the stories vary significantly based on race and marital status. I will be looking at females because there is intersectionality between gender and race when looking at income and participation differences, and I hope isolate one gender to see how race and marital status effect females only. Labor outcomes vary for white and non-white women, just as they are different for males and females. Mignon Duffy points out one of these differences in stories when she argues in “Doing the Dirty Work: Gender, Race, and Reproductive Labor in Historical Perspective” that “treatments that focused… on the entrance of women into the labor force in the 1970s told important stories- but also obscured the empirical reality that Black women, immigrant women, and poor women had been engaged in paid market work in large numbers for many decades” (Duffy 2007, 314). It is therefore important to look at female labor force participation and income by race, rather than aggregately. There are also significant differences between married and unmarried women, so marital status must also be taken into account. In this post, I am interested in isolating the female story by looking at labor force participation and median income by race and marital status.


In order to analyze the changes in female labor force participation and female income in the U.S. census, I used Integrated Public-Use Microdata Series (IPUMS) 1% samples from 1880-2000. For the 1970 data, I used the 1% State Form 1 sample, and for the 1980 data, I use the 1% metro sample.

I use the IPUMS variable RACE to classify individuals in my sample as either white (RACE equal to 1), or non-white (RACE not equal to 1). These are the two race categories I use in my analysis. For all years besides 1950, I use the IPUMS variable PERWT to calculate the number of women in the labor force and not in the labor force for each race in each year. In 1950, I use the IPUMS variable SLWT, which reports the number of persons in the general population represented by each sample-line person in the data. According to the IPUMS website, “each household in the 1950 sample includes one individual who was asked supplemental questions… when analyzing the entire population of the persons in units with more than one individual, cases must be weighted in inverse proportion to household size.” The appropriate weight in this case is SLWT for the income variable, because that was a sample-line question on the 1950 census.

For the income analysis, I use years 1940-2000 due to the fact that the census did not ask for income until the 1940 census, so the data is not available before that year. In my analyses, I only look at females between the ages of 18 and 64, which I am defining to be “working aged.”


I use the IPUMS data to explore changes in female labor force participation, as well as median income, over time for by race and marital status.

I use the IPUMS variable LABFORCE to calculate female labor force participation over time between 1880 and 2000. LABFORCE is a dummy variable representing whether each woman was in the labor force or not. A woman must be either employed or looking for work to be considered “in the labor force.” This is the main variable I use in my labor force participation analysis. I also use the IPUMS variable MARST to categorize women as either “married” or “not married,” including single, divorced, and widowed. In order to calculate the participation rate, I divide the number of women in the labor force in each year by the sum of the number in the labor force and the number not in the labor force. I do so for both races and both marital statuses, so I have labor force participation for each category in each year 1880-2000. The results are shown in Figure 1.

I also observe the median income for both races and marital statuses of women to see how the gap changes between 1940 and 2000. I look at median income in two ways. First, I look at median income for all women. Then, I look at the median income for all women in the labor force, so I can determine whether the key driver of the median income for all women is female labor force participation. I use the IPUMS variable INCWAGE to calculate wages over time. INCWAGE represents the female income for the previous year before taxes, in dollars. This is the main variable I use in my income analysis. I use the variable MARST again in this analysis to categorize women by marital status. To show the changes in income over time for women in these race and marital categories, I take the median income for each race and marriage status in each year, shown in Figure 2. I chose median instead of mean because INCWAGE is top coded, so taking the mean would create bias, likely underestimating the true mean wage. Taking the median does not create the same bias. The median is also a useful indicator of labor force participation. For example, if the median wage is $0, I can conclude that over half of the women were not participating in the labor force that year. To check that the trends I see in income are not just reflecting inflation, I also adjust the median income with price indexes for each year, shown in Figure 3. Because the median wage of all women reflects labor force participation in addition to income, I also calculate the price-adjusted median income for all women in the labor force, shown in Figure 4.

The R code I used to generate the results below can be found here.


Figure 1 shows the female labor force participation rate between 1880 and 2000. For both white and non-white married women, participation in the labor force increased over time. This same increase was seen for white unmarried women, but not non-white unmarried women, who had a stronger presence in the labor force before 1940 than their white counterparts. Although many historians have focused on the sharp increase in women in the work force after 1960, particularly for married women (Duffy 2007), the data show that non-white women were actually in the work force long before this period. Tera Hunter attributed this higher participation rate among non-whites to the high ratio of black women to black men, as well as higher wages for white men then black men. She argues that “the disproportionate sex ratio among blacks made wage work all the more imperative for women, especially for single, divorced, or widowed mothers saddled with the sole responsibility for taking care of their families.” It was not only single women who had to work, because “the low wages paid to black men meant that even married women could rarely escape outside employment and worked in far greater numbers than their white counterparts” (Hunter 1993, 208).

During World War I, the War Department established the “work or fight” rule which entered all unemployed males into the draft. The slogan “labor will win the war” was used to encourage more people to enter the workforce (Departmental Reorganization). Black women were encouraged to work as hired labor in homes during this time, though this was not the beginning of this kind of domestic work for black women. Hunter argues, “black women’s domestic work was essential to the war effort… because it exempted white women from the routine of housework in order that they may do the work which negro women cannot do…domestic work was synonymous with black women in freedom as it was in slavery, and the active efforts by whites to exploit labor clearly circumscribed black lives.” (Hunter 1993).

Over time, the gap between married non-white and white female participation decreased, especially after 1950 when white participation took off due to increasing demand for women in the labor force and the Women’s Rights Movement (Cotter 2001). In 1990, married white women saw higher participation rates than their non-white counterparts for the first time. For unmarried women, whites saw slightly higher participation rates since starting in 1940.

Figure 1:


Figures 2 and 3 show the median income for women by race and marital status between 1940 and 2000. Figure 2 shows median income before adjusting for inflation, while Figure 3 shows median income after the adjustment. Figure 3 is a better graph to show how income was changing relative to prices in each year, which is a more useful metric. The median income for both married race categories is zero in 1940, 1950, and 1960, which means that less than half of the women in each category was participating in the labor force. This finding can be confirmed by observing the participation rate values for those years in Figure 1. Both race and marriage groups experience an increase in wages, both adjusted and not adjusted for inflation. This reflects both a shift in the jobs women performed, as well as an increase in female participation due to the fact that I am observing median income rather than mean income. For most of this time period, unmarried women received higher median wages due to the fact that they participated in wage labor more than married women did in order to support themselves.

Before 1990, the median income for non-white married women was higher than white married women because the non-white participation rate was significantly higher. After 1990, white female participation as well as median white female income were higher for married white women. For unmarried women, the median income was higher across this entire time period.

Figure 2:


Figure 3:


Figure 4 confirms my suspicion that the higher non-white incomes in Figures 2 and 3 are mostly driven by higher labor force participation rates. When only looking at the labor force, white women earn higher median wages for almost all years in both marriage categories, which indicates that white women who chose to participate got higher-paying jobs, or were paid more for the same jobs. Duffy points out that nonwhite women were more overrepresented in low-paying “dirty work” occupations than their white counterparts (Duffy 2007, 329).

Figure 4:



Race and marital status were significant drivers in a woman’s decision to work from 1880 to 2000. Those factors also influenced the median female income. Here, I looked specifically at women, both white and non-white and married and single, to see how their labor outcomes changed between 1880 and 2000. In the first half of this time period, non-whites saw higher labor force participation, especially for married women. They also saw higher median income among married women, but lower median income among unmarried women . The higher income is likely a reflection of the greater participation rates for women without spouses. I confirmed this by looking at income for women in the labor force, and found that white women were paid more almost across the board. Over time, white women saw more participation, especially after the Women’s Rights Movement. They also saw faster income growth than their non-white counterparts. This pattern is consisted for both absolute income, and income adjusted for inflation. These results reflect the racial aspect of the labor systems in the United States today, affording more privileges to white participants.

Works Cited

  • Cotter, David A., Joan M. Hermsen, and Reeve Vanneman. “Women’s Work and Women Working: The Demand for Female Labor.” Gender and Society 15.3 (2001): 429-52. JSTOR. Sage Publications, Inc. Web. 13 Dec. 2014.
  • “Departmental Reorganization Act.” Wikipedia. Wikimedia Foundation, n.d. Web. 13 Feb. 2016.
  • Duffy, M. “Doing the Dirty Work: Gender, Race, and Reproductive Labor in Historical Perspective.” Gender & Society 21.3 (2007): 313-36. Print.
  • Hunter, Tera W. “Domination and Resistance: The Politics of Wage Household Labor in New South Atlanta.” Labor History 34.2-3 (1993): 205-20. Print.

Japanese American Eastward Migration (1900 – 1970)

Japanese immigration to Pacific states began in the 1880s after anti-Chinese legislation led to a shortage of cheap labor. Labor recruiters for factories, rail roads, mining camps, fisheries and plantations looked to Japan to recruit cheap labor from abroad (Takaki 180). The rising Japanese population led to the Census Bureau creating a “Japanese” category for race, distinct from the “Chinese” race and subsequent anti-Japanese legislation was passed in the United States.

Anti-Japanese sentiments culminated with Executive Order 9066 in 1942–3 months after the Japanese attack on Pearl Harbor. The order interned Japanese Americans, including those who were citizens, in camps primarily located in Mountain and MidWest states. Executive Order 9066 did not intern Japanese Americans in Hawaii.

I will analyze how the internment of Japanese Americans affected their migration patterns across the United States. I will also analyze how the citizenship status (the Nisei) of Japanese Americans affected their migration.


I am using census data from the Integrated Public-Use Microdata Series (IPUMS). I am working with 1% samples from 1900 to 1960 and 1970 (Form 1% state sample).

The 1960 and 1970 census are self-reported, mail back forms. Unlike censuses from 1870 to 1950, the 1960 and 1970 censuses are not administered by census enumerators.

I am excluding Hawaii and Alaska from my analyses because there is no Hawaii or Alaska census data for 1940 and 1950. However, I recognize that Hawaii and Alaska were important immigration destinations for Japanese immigrants. Hawaii had a very high Japanese American immigrant and citizen population and did not intern Japanese Americans during WWII.


I divide my analyses of Japanese American migration into 2 segments, Issei and Nisei migration.

Issei are 1st generation Japanese American immigrants born in Japan who were incapable of becoming American due to the 1790 Naturalization Act. The act only permitted “free white persons” to become naturalized citizens. (The Naturalization Act was amended in 1870 to naturalize Blacks born in the US.)  Issei were distinctly classified as “Japanese,” not “White” in the census, denying them naturalization.  The denial of naturalization for Issei was formally upheld in Ozawa v. United States (1920). The court ruled that Issei could not be white, because the Japanese were “clearly of a race which is not Caucasian,” a position upheld by the census for 30 years prior.

However, Nisei, 2nd generation immigrants born in the United States, did have American citizenship. Nisei were able to better integrate into American society because it was harder to legally discriminate against Nisei, as they were American citizens. Nisei were also better culturally integrated and had more education.

I used the IPUMS RACE variable (RACE=5) to identify Japanese Americans and then used IPUMS BPL to separate Issei (BPL=501) and Nisei (BPL<100). All analyses are weighted with the IPUMS individual sample weight PERWT variable.

I mapped the Issei and Nisei populations by STATEFIP and by YEAR. Additionally, I graphed the growth of the Japanese American population by Issei and Nisei by YEAR.

My code can be found here.


Figure 1: Map of Issei im/migration in the US by state. 1910 - 1960.

Figure 1: Map of Issei im/migration in the US by state. 1910 – 1960.

Issei immigration to the U.S. ended in 1924 after the 1924 Immigration Act banned all Asian immigration to the U.S. However, the 1924 Immigration Act was replaced by the 1952 McCarren-Walter Act, which permitted Asian immigration (with a quota based on 1920 census information). (The Mccarren-Walter Act was subsequently replaced by the 1965 Hart-Celler Act, which replaced quota based immigration with skill, refugee and family member based immigration.)

We can see Issei migration from the west to east. Issei originally immigrated to western states, then to the Middle States and then finally branched out to the Midwest in the 1940s and 1950s. The migration starts to dwindle in 1940 and 1950 most likely due to death and no new immigration. In 1960 we start to see fresh migration under the Mccaren-Walter Act. New Japanese immigrants filled in the Southern States and remaining MidWest States.

Figure 2: Map of Nisei migration in the US by state. 1920 - 1970.

Figure 2: Map of Nisei migration in the US by state. 1920 – 1970.

Figure 2 displays the Nisei migration from west to east. Between 1920 and 1930, Nisei begin to migrate from the Western states to the Rocky states, leaving California. This may be due to the 1920 and 1923 amendments to the California Alien Land Law which banned Nisei from owning land on behalf of their non-citizen Issei parents (Lyon).

It is important to remember that until internment, most Nisei were still dependent on their Issei parents. Many Nisei migrated with the Issei or were born in the state. Two Issei parents giving birth to five Nisei children would raise Nisei migration on the maps more than Issei migration would be raised.

The maps also show Nisei migration patterns after the internment. In 1950 we see that Nisei had moved to most of the MidWest and had already started moving to Southern states. Interestingly, Nisei also moved back to California in 1950.

Figure 3: Issei and Nisei population graph by Year. 1900 - 1970.

Figure 3: Issei and Nisei population graph by Year. 1900 – 1970.

Figure 3 supports my claim that the Mccaren-Walter Act and Hart-Celler Act increased Japanese immigration. It explains the increased Issei population growth across the entire US. However, the graph (and maps) does not account for Sansei, 3rd generation Japanese Americans born to Nisei parent(s). I could not find a way to separate Sansei from Nisei without using the IPUMS AGE variable. I found it to be risky to divide AGE based on my assumptions on when the generational Nisei and Sansei cutoff would be. The dramatic increase in the 1960 Nisei population is due to Sansei.


Through the use of IPUMS census data we can visualize and track Japanese American migration from west to east. Anti-Japanese laws and labor discrimination forced Japanese Americans to remain concentrated in the West until the 1942 internment. After WWII and the internment, Japanese Americans, especially the well educated Nisei, were able to travel and settle in new locations. Many Nisei were offered the opportunity to leave internment camps early if they could find work or attend university in the MidWest or east coast (Digest of Points). We see this shift in the Nisei data. By 1970, Nisei had nearly been evenly spread across the US (California remained the outlier).

The graphs also display Issei migration patterns and mortality rates. Ronald Takaki notes that unlike the Nisei, the Issei, “developed a separate Japanese economy and community” in the West and most survivors returned to their Californian roots by 1950 (Takaki 180). We consistently see declining Issei population numbers until 1960, when Japanese immigration is reopened.

The data and graphs overall support my theory that the internment of Japanese-Americans in 1942 eventually led to Nisei migrating elsewhere across the U.S.–primarily MidWest and Eastern states. Many Nisei chose the MidWest because they were barred from returning to the West, usually because of racist policies of resettlement. The Alien Land Laws were not repealed until 1952 (Lyon).

Works Cited: 

“Digest of Points.” Conference of Consideration of the Problems Connected with Relocation of the American-Born Japanese Students Who Have Been Evacuated From Pacific Coast Colleges and Universities. Stevens Hotel, Chicago: Occidental College Library, 29 May 1942.

Immigration and Nationality Act of 1952 (McCarran-Walter Act). Pub. L. 82-414. June 27, 1952.

Immigration and Nationality Act of 1965 (Hart-Celler Act). Pub. L. 89-263. October 3, 1965.

Lyon, Cherstin. “Alien land laws.” Densho Encyclopedia. 23 May 2014, 22:40 PDT. 9 Feb 2016, <>.

Takaki, Ronald. Strangers from a Different Shore: A History of Asian Americans. New York: Little, Brown and Company, 1998.

Mexican Immigration (1910 to 1970)

Although the United States was founded as a country of immigrants, government instated immigration policies have been aimed towards the restriction of immigration. In addition to limiting immigration, restrictive legislation also gave rise to illegal immigration (Gabbaccia 2012, 200). The immigration act of 1924 set immigration quotas that limited the number of immigrants to 150,000 people a year (Ngai 1999, 67). However throughout the 1920s male Mexican immigration was unprecedented. The greater majority of Mexican men that immigrated, came alone to work for a period of time with the intention of returning to Mexico after making some money. Then in the 1930s as job prospects fell, Mexican migrant workers followed through with the circulatory migration strategy and repatriated (willingly returned) to Mexico. In 1942 the Mexican and US governments agreed to the “Bracero Program”; This government sanctioned migration policy allowed Mexican migrants to work in the US for a short period of time, after which they were expected to return to Mexico. However, Braceros overstayed their contracts or even crossed the border illegally throughout the 1940s and into the 1950s (Gratton 2013, 946-949). In 1965 when the Amendments to the Immigration and Nationality Act were passed, the Bracero program was terminated and border protection was increased. However instead of limiting the number of illegal immigrants in the United States, the increase in border control drastically curtailed the emigration of illegal Mexican immigrants (Massey 2012, 9). I will analyze the demographic and quantity of Mexican immigration in relation to immigration legislation from 1910 to 1970.


The data used for this analysis are from the census data available on Integrated Public-Use Microdata Series (IPUMS). My data set is composed of one percent samples from the 1910 to 1970 United States censuses. IPUMS has randomly selected these one percent samples. The IPUMS organized variables BPL, AGE, SEX, PERWT, STATEFIP and YEAR are used for my analysis.  Beginning in 1960 the census form is mailed to US residents and as a result all information resulting from the 1960 census onward is self-reported. Before 1960 census data were collected by an enumerator.

For my analysis I exclude data collected in Hawaii and Alaska for all years prior to 1960 because both states were not granted statehood until 1959. I will be using the one percent sample from each of the census years (1% State Form 1 Sample for 1970). Census data from each of my years of interest are collected for individuals not households. All analyses are weighted by PERWT, the sample weight of each individual.


I begin my analysis with the assumption that the country that a US resident emigrates from is also the country that he or she was born in. Therefore I am identifying immigrant groups by the birthplace (BPL) variable. This is a useful assumption in so far as anyone born in the United States cannot be an immigrant. However when the United States is not the place of birth, birthplace does not necessarily reflect the nationality of the immigrant. Furthermore, birthplace may not necessarily reflect the nation a US resident emigrated from. With that said, I categorized all people who reported the United States as their birthplace together. Then I grouped all people born in Mexico into another category. I also created another category that represents all people reporting birth in a Latin American country. Because Mexico is part of Latin America, I include other Latin American immigration in order to contextualize Mexican immigration. The Latin American category is defined by an IPUMS generated list of countries. Any other reported birthplace is categorized as other.

After graphing the immigrant population over time, I then focussed on the demographic of the Mexican born population. I categorized the AGE variable into ten year age spans (0-9 years old, 10-19 years old, etc.). Then using the SEX variable I divided the data into male or female categories. I summed the population of each age category of both males and females and made a bar graph for each census year. Code for analysis and visualization is available here.


Figure 1:

Percentage Immigrant Population

Figure 1 graphs the immigrant population as a percentage of the total american population for each census year from 1910 to 1970. The percent of american immigrants decreases steadily from 1910 to 1960. The percentage of Mexican immigration, however, differs from the larger patterns of american immigration. From 1910 to 1920 of the percent of the american population born in Mexico increases from 0.3% to 0.5%; this remains true into 1930 (percent of the total population that is born in Mexico is still 0.5%). Then from 1940 to 1960 the percent of the population that is Mexican born falls back to 0.3%. In 1970 the percent of Mexican born immigrants rises back to 0.5%. These changes are fairly insignificant in comparison to the larger shift observed among other immigrant groups. Latin American immigration remains fairly consistent at 0.01% from 1910 to 1960; in 1970 this figure increases to 0.6%.

Figure 2:
Mexican Immigrant Population Pyramid

Figure 2 graphs the Mexican born population from 1930 to 1960 according to gender and age. The 20-29 year old male population is the largest male age category in 1930. Starting in 1940 the male population aged 30 to 39 is the largest. Then in 1950 the male population aged 40 to 49 is the largest. This pattern continues into 1960; In 1960 the male population aged 50 to 59 is the largest. The same pattern of population within the male age categories is seen in the female age categories. From 1910 to 1930 women aged 20 to 29 compose the majority of the female population. Then in 1940 women aged 30 to 39 compose the largest female population. In 1950 women aged 40 to 49 are the largest female population. Finally in 1960 the female population aged 50 to 59 is the largest population. For both genders there are relatively fewer people in the extreme high and low age categories; in other words the population in concentrated in the middle-aged categories.


Figure 1 illustrates the percentage of the american population that were Mexican immigrants, and Figure 2 illustrates the changes in Mexican population according to age and gender. As can be seen in figure 2 the majority of Mexican immigrants were male because the majority of them were emigrating to work. The male Mexican immigrants aged 20-29 were the largest age category because they were incentivized to immigrate for work in the US. Men of working age migrated North with the ultimate intention of returning to Mexico. However, starting in the late 1920s the US policy makers “hardened the difference between legal and illegal immigration” and enforced a stricter deportation policy (Ngai 1999, 90). Although the 1930 census report of the Mexican born population does not reflect these historical events, the results of the stricter deportation policy are visible in the 1940 census data. In 1940 men now aged 30 to 39 represent the largest Mexican born generation in the US. All the way through 1960 we can observe the aging of this generation. The immense increase in population in 1970 correlates with the increased border protection starting in 1965. Following the Immigration Act of 1965 Mexican immigrants could no longer cross boarders with ease. We observe a dramatic increase in population among all age and sex categories because people could not return to Mexico (Massey 2012, 9).


The patterns of population change among those born in Mexico and living in the United States can be explained by changes in immigration legislation. As Brian Gratton and Emily Merchant point out, starting in the 1900s Mexican immigration to the United States grew steadily and ultimately peaked in the 1920s (2013, 947). The duo claims that Mexican migration was a circulatory one consisting mainly of young men looking for temporary work in the United States (2013, 946). However during the great depression employment rates fell and Mexican repatriation prevailed in tandem with stricter deportation policy. Immigration that “averaged 58,747 a year during the late 1920s, dropped to 12,703 in 1930 and 3,333 in 1931” demonstrates a decreasing influx of people (1999, 90).

Following the Immigration Act of 1965 and the termination of the Bracero Program, the new stricter immigration policy resulted in a major increase in Mexican immigration across all Mexican demographics. This significant change in the population is a result of stricter american border patrol. Although estimates of a million illegal immigrants in 1927 still about equaled the estimates of 1971, the illegal immigrants could no longer return to Mexico in 1971 (unlike their counterparts in 1927) (Gabaccia 2012, 200).

Unfortunately the census is only a single snapshot of the population in time. The available census data give us no way of knowing who is a permanent resident and who will eventually emigrate back to Mexico. Furthermore the census gives us know way of knowing which residents are in the US legally and which are not.


Gabaccia, Donna R. Foreign Relations: American Immigration in Global Perspective / Donna R. Gabaccia. Princeton: Princeton University Press, 2012. Web.

Gratton, Brian, and Emily Merchant. “Immigration, Repatriation, and Deportation: The Mexican‐Origin Population in the United States, 1920–1950.” International Migration Review 47.4 (2013): 944-75. Web.

Massey, Douglas S., and Karen A. Pren. “Unintended Consequences of US Immigration Policy: Explaining the Post-1965 Surge from Latin America.” Population and Development Review 38.1 (2012): 1-29. Web.

Ngai, Mae M. “The Architecture of Race in American Immigration Law: A Reexamination of the Immigration Act of 1924.” The Journal of American History 86.1 (1999): 67-92. Web.

Mexican Immigration in the United States 1950-2000

The 1924 Immigration Act restricted immigration into the United States to 150,000 a year based on quotas (Ngai 1999, 67). Mae Ngai argues that “while not subject to numerical quotas or restrictions on naturalization, Mexicans were profoundly affected by restrictive measures enacted in the 1920s, amend them deportation policy, the creation of the Border Patrol, and the criminalization of unlawful entry” (Ngai 1999, 71). The repeal of these acts in 1965 allowed more immigrants from many countries, including those in Africa and Asia. This act did not help immigrants from Mexico legally enter the U.S., however. Douglas Massey and Karen Pren argue that “the surge in immigration from Latin America occurred in spite of rather than because of the new system” (Massey 2012, 1) I will be examining how first generation Mexican immigration changed in the U.S. between 1950 and 2000 to see how their numbers in the U.S. change relative to immigrants from other countries, as well as where they settle.

Data and Methods

In order to analyze the immigration patterns of people born in Mexico, I used Integrated Public-Use Microdata Series (IPUMS) 1% samples from 1950-2000. For the 1970 data, I used the 1% metro form 1 sample, and for 1980, I used the 1% metro sample.

I use PERWT to calculate the number of people born in Mexico in each state in each year. The IPUMS variable BPL represents the birthplace of each observation. The values of BPL include all 50 U.S. states, as well as U.S. territories, and many countries around the world. For my analysis, I will only be looking at the BPL code corresponding to “Mexico.” This is the main variables I use in my analysis, along with the STATEFIP code of the current location of the individual.

I use the IPUMS data to explore the estimated number of first-generation Mexican immigrants in the United States from 1950 to 2000, the percent of the U.S. foreign-born population born in Mexico over the same time period, as well as the states in which these immigrants settle.

First, in order to get a general idea of how Mexican immigration has changed over time, I calculate both the total number of Mexican immigrants, as well as the percent of foreign-born people who were born in Mexico. Figures 1 and 2 show these results.

After gaining a general understanding of the time trends of Mexican immigration, I break down the data by state to see where these first-generation immigrants settled once they arrived. I calculate the percent of foreign-born people born in Mexico in each state to see where they tended to settle. I do this in for two years, 1950 and 2000 (see Figures 3 and 4). I choose these years because they give two snapshots of Mexican immigration- one before the 1965 Immigration Act and end of the Bracero Program, and another that gives a more modern look at where Mexican immigrants have settled over time. I choose to use the percent of the foreign-born population that is Mexican, rather than the number of Mexican-born U.S. citizens, because I think that is a better metric for showing how the number of immigrants is changing relative to other immigrant populations. Using just the number of Mexican immigrants could be misleading if immigration from other countries is growing as well. Therefore, I look at how the Mexican immigrant population is changing relative to immigration from all other countries. The purpose of the maps is to show regions where immigrants from Mexico were settling in the United States.

In this analysis, I am defining a Mexican immigrant as someone who is born in Mexico who currently lives in a U.S. state. It is important to note that being born in Mexico and moving to the U.S. does not mean they are a Mexican race, or identify as “Mexican.” My results are therefore estimations, and could be biased by those born in Mexico who are not Mexican, and then moved to the United States. It is also impossible to tell from my analysis whether a person was born in Mexico, moved to another country, and then moved to the United States.

The R code I used to generate the results below can be found here.


The number of people who were born in Mexico and lived in the United States at the time of the census, which I am roughly defining as a “Mexican immigrant,” increased exponentially from 1950 to 2000, which is shown in Figure 1. The increase in Mexican immigrant population is steady and gradual until 1980, when the immigrant population more than doubles from the previous census. This doubling pattern continues in 1990 and 2000.

Figure 1:


As I mentioned in the “Methods” section, looking only at the number of Mexican-born people can be misleading because their numbers could grow, while their share of the foreign-born population decreases. To see if the increase in immigration from all countries biases my results from Figure 1, I also observe the percent of foreign-born people born in Mexico by year, shown in Figure 2. Almost the exact same results are shown in Figure 2 as in Figure 1, which means that the Mexican population was increasing relative to the rest of the U.S. foreign-born population. Massey and Pren attribute this increase to illegal immigration and to new family reunification policies, which exempted families of citizens and legal permanent residents from quotas (Massey 2012, 3).

Figure 2:


Figure 3 shows the percent of foreign-born people who were born in Mexico in each state in 1950. In the figure, the states with no Mexican immigrants are shown as white, and the rest of the states’ colors are indicated by the legend. In most states on this map, Mexican immigrants represent less than 10% of the immigrant population of that state. The exceptions are New Mexico, Arizona, California, and Texas.

There are a few explanations for why Mexican immigrants would settle in the Southwest, the most important being proximity to Mexico compounded with costs associated with further travel. Another explanation is the tendency for racial groups to work in the same industries, especially leading up to 1950. Matthew Jacobsen argues that “labor contractors and agents… steered their compatriots toward specific work in specific regions of the United States- … Mexicans [were steered] toward the Texas smelter industry…” (Jacobsen 2000, 67). One mechanism for drawing Mexican immigrants to this area was the Bracero Program, which began in 1942, to help bring in temporary labor to the United States (Massey 2012, 2). Once Mexican immigrants move to a particular area, more immigrants are likely to follow to be reunited with their friends and families.

Figure 3:


Figure 4 shows a more modern snapshot of first generation Mexican immigration to the United States. It is important to remember that I am only counting individuals who were born in Mexico, so the data that are shown in the map do not represent the children of Mexican immigrants. These children might also consider themselves to be Mexican, but they would not be counted. Therefore, Figures 3 and 4 are underestimating the total Mexican share of the U.S. foreign-born population.

Figure 4 shows similar trends to Figure 3, but on a much larger scale. That is to say, the region of the U.S. settled by first generation Mexican immigrants remained primarily the Southwest, but this area did expand to include more of the Pacific Northwest and a few states the center of the country. In addition, the map scales show that the share of foreign-born people born in Mexico greatly increased in almost all states between 1950 and 2000. California, for example, went from having 10%-20% of the population represent first generation Mexican immigrants in 1950 to over 40% in 2000. In 1950, only four states had over more than 10% of their foreign-born population born in Mexico. In 2000, only a handful of states outside of New England had under 10%.

Figure 4:



The United States saw a large increase in first-generation Mexican immigrants from 1950 to 2000. Both the absolute number and percent of foreign-born immigrants from Mexico  increased over these 50 years, which implies that the relative number of Mexican immigrants increased relative to the rest of the foreign-born American population. Most of these immigrants settled in the American Southwest due to its proximity to Mexico, preexisting personal ties to this area, as well as job opportunities presented by the Bracero Program and general demand for inexpensive labor in the area. Although I chose to look at one country in particular, the relative increase in the number of Mexican immigrants reflects the general trend of increasing immigrant populations in the U.S. in the second half of the 20th century.

Works Cited

  • Jacobson, Matthew Frye. Barbarian Virtues: The United States Encounters Foreign Peoples at Home and Abroad, 1876-1917. New York: Hill and Wang, 2000. Print.
  • Massey, Douglas S., and Karen A. Pren. “Unintended Consequences of US Immigration Policy: Explaining the Post-1965 Surge from Latin America.” Population and Development Review 38.1 (2012): 1-29. Print.
  • Ngai, Mae M. “The Architecture of Race in American Immigration Law: A Reexamination of the Immigration Act of 1924.” The Journal of American History 86.1 (1999): 67. Organization of American Historians. Web. 29 Nov. 2013.