Final Portfolio Overview

I utilize the University of Minnesota’s Integrated Public Microdata Series (IPUMS) census data for all my projects in History 90.01: U.S. History through Census Data (16W). IPUMS census data provides me, and researchers, both snapshot data and longitudinal data on American individuals (since 1850) and households. All my projects use IPUMS data to analyze important trends in American history. I also take advantage of other primary and secondary sources to explain my results. All my projects use R to visualize IPUMS data in the form of charts and graphs. IPUMS data allows researchers to analyze history at a more top-down, macro level.

My projects explore American history through census data. Native Americans and the Census studies how and why Native Americans were enumerated in the census from 1850 to 1950. It also looks at census enumerator instructions and the concepts of race in America. Japanese American Eastward Migration maps Japanese American immigration patterns from the West coast to East coast, Midwest and South. It also pays special attention to the internment and its effects on migration. Vietnam Veterans Wages and Education focuses on comparing income over time and education attainment of veterans and non-veterans by birth year cohorts. Finally, Divorce Rates among Women by Race and Income analyzes divorce rate trends from 1940 to 2010 by income and race and offers explanations on why divorce has rapidly risen.

Divorce Rates among Women by Race and Income

The divorce rate among American women has been rising but by how much and when did the divorce rate begin to climb? And which women are more likely to be divorced?  Is divorce more common among certain races? What are the socioeconomic differences between divorced and married women? For my analyses I will looking at the divorce rate by race and income among American born women to help answer these questions.


I am using census data from the Integrated Public Use Microdata Series (IPUMS) for my analyses. My samples range from 1940 to 2010. I am using 1% samples for 1940 – 1960 and 1980 – 2000. I am using the 1% State Form 1 sample for 1970 and the 1% American Community Study (ACS) for 2010. The ACS is also administered by the U.S. Census Bureau. All analyses are individually weighted with the PERWT variable. I am not weighting samples by household weight (HHWT) because divorce and marriage are individual variables. My analyses do not include women living in Alaska or Hawaii because the census does not include Alaska or Hawaii in the 1940 and 1950 censuses. Adding them would skew my data in 1960. Starting in 1960 the census was collected by mail back form. The 1940 and 1950 censuses were conducted by door to door enumerators. The 1960 census and all censuses after allow self-racial identification.

I define divorced as women listed as divorced, separated or has an absent spouse. I do not use widowed or never married (single) women in my analyses.

I will be using INCWAGE to calculate the incomes of women. 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. It does not include non-cash assets or incomes. Non-cash benefits will not appear in INCWAGE or in my analyses.


My data to only includes women (SEX=2) born in the United States (BPL<= 56). I assign racial categories to each race but combine Chinese, Japanese and Other Asian to one racial group, “All Asian” because of their small population size. I use the HISPAN and RACE variables to create Hispanic as a racial category. Any race that identifies as Hispanic is assigned exclusively to the Hispanic group. This is to prevent double counting when I calculate rates and numbers for race groups. I chose this Hispanic definition of race because according to Clara Rodriguez, “For many Latinos, race is primarily cultural; multiple identities are a normal state of affairs; and “racial mixture” is subject to many different, sometimes fluctuating, definitions (Rodriguez 2000, 5).” Due to Latinos’ different understanding of race, the HISPAN variable is the most accurate way to locate Latinos in the census. Lastly, I exclude “2 Races” and “3+ Races” from my analyses because there is not enough data to make a meaningful analysis with them.

I then use CPI99 and INCWAGE to calculate a comparable income for all decades in 1999 USD. I multiple the INCWAGE variable by the CPI99 variable. Without CPI99 it would be meaningless to compare wages across decades due to inflation. I then created income categories. The categories range from “No Income” to “$100,000+” by $20,000 increments.

I remove never married (single) and widowed women from my analyses (MARST<=4). For my graphs, I graph by raw numbers and by percent. For percent I divide the number of divorced women by the total number of women.

Here is my code. 


Figure 1: Income for divorced women by percent divorced. 1940 - 2010.

Figure 1: Income for divorced women. 1940 – 2010.

Figure 2: Income for married women. 1940 - 2010.

Figure 2: Income for married women. 1940 – 2010.

Figure 1 and Figure 2 show us the percent of divorced and married women earning income and how much they earn. Both Figures 1 and 2 include all races. Between 1940 and 2010, more divorced women are working for an income. Additionally, more divorced women earn higher incomes than married women. Both married and divorced women start earning higher incomes ($20,000+) in 1960. Overall, we observe more women earning an income and we see more women earning higher incomes between 1940 and 2010. Emily Merchant’s analysis of white women living alone may shed some light on why women’s, divorced and married, incomes rose. From 1940 to 1960, more white women entered the labor market and more white women were completing high school (E. R. Merchant et all 2012, 10). With better education and a more accommodating workplace, women were able to enter the labor market and earn higher incomes.

Figure 3: Incomes by race for white, black and Hispanic women. 1940 - 2010.

Figure 3: Incomes by race for divorced white, Hispanic and black women. 1940 – 2010.

Figure 3 is the breakdown of divorced white, Hispanic and black women by income. I chose to analyze White, Hispanic and Black women because they are the three largest racial groups in the U.S. In 1940, more black and Hispanic women are earning an income compared to white divorced women. In 2010, nearly all three groups have equal representation in the labor market, approximately 75% are earning an income. However, more white women earn higher incomes than black and Hispanic women. There are many variables that can cause this preference, such as racial hiring preferences for better jobs or better education for white women.

The 3 graphs match the patterns in Figures 1 and 2: the “No Income” category is declining over time and women are earning more, even if white women are earning more than black and Hispanic women. All women experience decline in wages and rise in “No Income” in 2010, most likely due to the 2008 recession–a hiccup in the data.

Figure 3: Divorce rates by race. 1940 - 2010.

Figure 3: Divorce rates by race. 1940 – 2010.

Figure 4 shows that divorce rates for all women have risen by a large amount since 1940. All Asian women had the smallest change in divorce–rising 6% between 1940 and 2010. Meanwhile, Black women had the largest increase in divorce. Divorce rose by about 30% between 1940 and 2010. Steve Ruggles’ The Origin of African American Family Structure may help explain the high divorce rates among Black women. It “could have been a response to the socioeconomic conditions faced by newly-freed blacks after the Civil War and by free blacks in 1850. Second, the pattern could simply reflect a difference in social norms between blacks and whites, which could have developed either through the experience of slavery or could have its roots in differences between European and African culture. (Ruggles 1994, 147)”

Hispanic, White and All Asian women divorce rates steadily rose after 1960 at similar intervals. Figures 1 to 3 may help explain the patterns in Figure 4. As women entered the labor market and earned more money, they were able to afford divorces and support themselves (and their dependents). From 1960 onwards, more and more women are earning $20,000+, enough to support themselves.


A study of divorce rates among men and their races and incomes will tell us more about why divorce rates began to rise in 1960. However, given the evidence I presented, I am inclined to believe it is because more women were earning an income, giving them the opportunity to get a divorce. 1960 was also the first census where a noticeable amount of women were earning ($20,000+).  But there can be other lurking variables such as education and external support networks among specific races. Steven Mintz and Susan Kellog claim that black women are more likely to live without a spouse because they created “their own distinctive Afro-American kinship network, largely free from interference. (Mintz & Kellog, 68)” Unfortunately I was unable to find Hispanic specific explanations to explain the Hispanic divorce rate and income trends. Finally, my data does not account for women who got divorced and then remarried in between censuses.

Works Cited: 

Merchant, Emily R.; Brian Gratton; Myron P. Gutmann. A Sudden Transition: Household Changes for Middle Aged U.S. Women in the Twentieth Century. Popul Res Policy Rev: Springer Science+Business Media. 27 June 2012.

Mintz, Steven; Susan Kellog. Domestic Revolutions: A Social History of American Family Life. (New York: The Free Press).

Rodriguez, Clara E. Changing Race: Latinos, the Census, and the History of Ethnicity in the United States. (New York: NYU Press, 2000). pp. 3-27.

Ruggles, Steve. The Origins of African American Family Structure.  American Sociological Review, Vol. 59, No. 1 (Feb., 1994), pp. 136-151

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.

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.

Native Americans and the Census

Native Americans were originally only included in the census if they lived under US jurisdiction. Because Native Americans were largely considered independent, they were excluded from the census and were not apportioned Congressional representatives. “Representatives and direct Taxes shall be apportioned among the several States which may be included within this Union, according to their respective Numbers, which shall be determined by adding to the whole Number of free Persons, including those bound to Service for a Term of Years, and excluding Indians not taxed” (Article 1, Section 2 of the U.S. Constitution).

The 1850 census is the first census that allows us to look at individual Native Americans and how they were counted in the census. With Native American census data and enumerator instructions, we can better understand how the federal government understood Native Americans as a race and the relationship between the federal government and Native Americans.

Data & Method:

I collected census data from the Integrated Public Use Microdata Series- United States of American (IPUMS-USA). My census data includes 1% samples from 1850 to 1950.

I exclude Hawaii and Alaska from my analyses, as they were not US territories until 1898 and 1912. Additionally, the 1940 and 1950 censuses do not include Alaska or Hawaii. I also exclude any other US territories and overseas military bases. Hawaii, Alaska and other territories would skew my data and offer little value to my analyses.

Unfortunately, the destruction of the 1890 census prevents us from fully analyzing the affect government policies had on the enumeration of Native Americans between 1881 and 1900. The 1900 census was the first census all Native Americans were enumerated, regardless of tribal affiliations or where they lived.

I first assigned each RACED category a label for the correct race. I weighted each individual’s weight with IPUMS PERWT variable and then summed each racial category by year with the PERWT variable. Next I split the racial categories into “Native American” and “Rest of Population (Not Native).” I then plotted the Native American population. Finally, I used Microsoft Excel to tidy up and display my table.

Click HERE for my R code


Figure 1: Native American population graph. 1850 - 1950.

Figure 1: Native American population graph. 1850 – 1950.

Figure 1 depicts the population of Native Americans from 1850 to 1950. During this time, Native American counted in the census increased from 907 to 329,441, an amazing 36,222% increase. Meanwhile, the overall American population only increased 660%. Figure 2 displays the exact population numbers for each year and compares Native Americans to non-Native Americans. Figure 2 proves that the Native American population boom can only be explained by changes in how Native Americans were enumerated in the census.

Figure 2: Population of Native Americans and Non-Native Americans. 1850 - 1950.  Table.

Figure 2: Population of Native Americans and Non-Native Americans. 1850 – 1950. Table.

Figure 2 displays the exact population numbers for each year and compares Native Americans to non-Native Americans. Figure 2 proves that the Native American population boom can only be explained by changes in how Native Americans were enumerated in the census.

The 1850 Census Enumerator Instructions maintain the same instructions used since the 1790 Census, “Indians not taxed are not to be enumerated in this or any other schedule,” however, in 1860, the instructions for Native Americans start to become more complex. Now, “Indians who have renounced tribal rule, and who under State or Territorial laws exercise the rights of citizens, are to be enumerated,” and were assigned a distinct racial category of “Ind.” Native Americans’ race was determined by their lack of tribal affiliation and U.S. citizenship. Now that Native Americans had been assigned a distinct racial category, their census numbers increase by 3,817%.

In 1870 enumeration instructions again changed for Native Americans. The census found it “highly desirable, for statistical purposes” to count Native Americans, not taxed, living on reservations. However, the 1880 census gave less instructions on how to count Native Americans. Native Americans not taxed were now defined to be those “living on reservations under the care of Government agents” ( Enumerators were also instructed to count Native Americans as “ordinary” (or white) if they lived in society. This new definition and categorization may be because of the Indian Appropriation Act of 1871 which declared, “No Indian nation or tribe within the territory of the United States shall be acknowledged or recognized as an independent nation” and created reservations (25 USC 71, 1871).

By 1890 and 1900, enumerator instructions no longer included any mention of “Indians not-taxed” or instructions on how to classify Native Americans. Enumerators were instructed to write “Ind.” for Native Americans. As Prewitt noted, the Indian Wars ended by 1886 and “the red race [was] assimilated” (Prewitt 2013, 36). By 1900, all Native Americans were counted in the census. The Indian question was solved by 1900 (Prewitt 2013, 36). However, if the Indian question was truly solved then there would be no reason to categorize and assimilate Native Americans as white after 1900.

The loss of the 1890 census data makes it difficult to analyze the affect the Dawes Act had on the counting of Native Americans in the census. The Dawes Act of 1887 allowed the president to forcibly assimilate Native Americans by terminating their reservation, granting those Native Americans citizenship and individual land parcels to live and farm on.

In 1930, Native Americans were classified as Indian according to blood quanta. However, a Native American was capable of being classified as white if “he is regarded as a white person by those in the community where he lives.” The census enumerator instructions supports Prewitt’s claim that Native Americans are able to assimilate and become white. Native Americans classified as white are difficult to find in the census as they are no longer classified as “Ind.” in the census. Other variables such as MBPL and FBPL may help us identify Native Americans reclassified as white. The 1940 and 1950 census enumerator instructions continue to use blood quanta and acceptance in the community to determine if someone is racially Native American.


Native Americans were considered to be an assimilable race by the U.S. government in order to force Native Americans onto reservations and strip them of their sovereign nation rights and treaties. Census enumerator instructions show how over the decades, Native Americans were counted for non-taxable purposes, and then increasingly counted and counted as whites. Native Americans’ racial status changed as the federal government’s desire for Native American land changed. Once the Native Americans had been stripped of their land, they were classified according to conventional blood quanta measurements and community acceptance criteria which allowed them to be categorized as white.

Works Cited

Article 1, Section 2 of the U.S. Constitution.

“Dawes Act.” Dawes Severalty Act Of 1887 (2009): 1. Our Documents. Web. 25 Jan. 2016.

“Indian Apportionment Act of 1871,” 25 US Code 71, 1871.

IPUMS and IPUMS Census Enumerator Instructions (

Prewitt, Kenneth. “The Compromise that Made the Republic and the Nation’s First Statistical Race.” What Is Your Race?: The Census and Our Flawed Efforts to Classify Americans. Princeton, NJ: Princeton UP, 2013. N. pag. Print.