I have created this portfolio for my US History class using IPUMS data. IPUMS (Integrated Public Use Microdata Series) converts and publishes records of US census data into a consistent format available online for public use. This resource has been particularly useful for my research because it has pre-selected and randomized 1% samples of US census data. Using the pre-made 1% samples I was able able to manipulate census data in order to study historical trends in the United States.

I completed four projects on the topics of race, immigration, work and family in the United states. The first project analyzes the manner in which the asian race was enumerated from 1880 to 1990. The second project analyzes patterns of Mexican immigration from 1910 to 1970 and how american immigration policy may have affected the observed patterns. For the third project I focus on how race and immigration status impact national and regional female employment rate from 1910 to 1960. In the fourth project I research how the living arrangement for elderly men and women (aged 65 and older) change from 1920 to 1970.

The living arrangements of the elderly are largely dependent on their help seeking behaviors and the resources available to them (Coward 1989, 814). Factors such as increased life expectancy, higher fertility rates, cultural shifts, economic changes and industrialization have all been argued to have altered the living arrangements of elderly americans. I will argue that the higher life expectancy of women than men has led to a rise in single elderly female households. Furthermore I will argue that the increasing economic resources available to the elderly have made living away from family a more feasible alternative.

Data:
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 1920 to 1970 United States censuses. In 1970 I use the 1% state form 1 sample. PUMS has randomly selected the one percent samples used for my analysis. I use the The IPUMS organized variables FAMSIZE and GQ for my analysis. The FAMSIZE variable counts the number of one’s own family members that an individual is living with. This variable is imputed by IPUMS based on several other variables that classify relationships within a household. Family members are anyone related by blood, marriage or adoption. The GQ variable classifies the type of housing unit that an individual is living in (specifically it classifies units as households or group quarters). For all censuses prior to 1940 a “household” was classified as any unit with nine or fewer persons unrelated to the head. From 1940 to 1970, the definition of a household was restricted to four or fewer persons unrelated to the head.  All analyses are weighted by PERWT, the sample weight of each individual.

Methods:

I begin my analyses by restricting the data set to the elderly (individuals over 65 years and older). Then I classify the living arrangements of elderly individuals as living alone, with family members or in group quarters. Individuals living alone are identified as living in a household (GQ==1) and not living with any other family members (FAMSIZE==1). Individuals living with family are identified as living in a household (GQ==1) and living with one or more family members (FAMSIZE>1). Individuals living in group quarters are identified as anyone living with over 4 unrelated persons (GQ>=2). I use this definition of group quarters so that data from before 1940 are comparable to data after 1940. However due to the census’ change in the definition of a household, this may be too wide of a definition for what qualifies as group quarters. Prior to 1940 large households with many servants or boarders was common. Therefore in my analysis, there may be too many individuals in 1920 and 1930 identified as living in group quarters.

After identifying three types of living arrangements, I analyze the living situation of elderly men compared to elderly women. First I graph the population of elderly men and women over time with regard to where they live. Then I graph the proportion of the total elderly men living in each identified living arrangement. I do the same for elderly women. Code for my analysis and visualizations is available here.

Results:

Figure 1:

Figure 1 graphs the population of elderly men and women from 1920 to 1970 with respect to their living arrangement. The population of both men and women grows steadily over the time period. The population elderly men and women is similar from 1920 to 1940. From 1950 to 1970 there are always more elderly women than elderly men.

Figure 2:

Figure 2 graphs the proportion of the total elderly population for each sex living alone, in group quarters or with family. For all years and both sexes, the majority of elderly individuals live with family. The proportion of men who live with family decreases very slightly and unsteadily over time. The proportion of women who live with family decreases steadily over time. Living alone is the next largest living arrangement for both men and women. For men, the proportion of individuals living alone increases very slightly over time. For women, the proportion of individuals living alone increases steadily over time. The smallest proportion of elderly men and women live in group quarters. For men, the proportion of individuals living in group quarters decreases slightly over time. For women, the proportion of individuals living in group quarters stays fairly constant over time.

In my analysis the living arrangement of men does not change significantly because my method of categorization does not distinguish between coresidence with children (or other extended family) and living alone with a spouse. Therefore because women typically outlive their male spouses, the trends of autonomous living arrangements is more significantly seen in the visualization of female living arrangements. The increasing trend in women living alone is likely a reflection of the empty-nester phenomenon (married couples living alone) argued by Brian Gratton and Myron Gutmann. The scholars explain that prior to 1940, rising life expectancy and decreasing fertility rates contribute to elderly parents living away from their children. However after 1940 these factors no longer sufficiently explain changes in living arrangements. Instead, after 1940, the significant gains in economic status for both generations (the elderly and their children) are a more accurate explanation for autonomous living arrangements. Increasing affluence and the decline of the agricultural economy meant multigenerational living arrangements were no longer necessary. Moreover by 1950 old age assistance benefits had rapidly expanded. The expectation of a steady income from social security benefits is another strong causal argument for the increase in elderly individuals living away from family  (Gratton 2010, 332-343). Steven Ruggles supports this explanation of the decreasing multigenerational family. Ruggles explains that from 1850 to 1920 high economic status was closely associated with multigenerational families. However from 1940 to 1970 this phenomenon diminished and multigenerational households became more common in households with a low economic status (Ruggles 2003, 159-160).

Conclusion:

Over the course of the twentieth century as healthcare improved and life expectancy increased, elderly americans simultaneously attained greater economic autonomy, therefore making living away from other family members possible. This trend in the living arrangement of the elderly is most obviously evidenced in my analysis of elderly women. Because women had a longer life expectancy than men and elderly men typically lived alone with their spouses in old age, the trends of autonomous living arrangements are more obvious in the visualization of elderly women.

Bibliography:

Coward, R. T., S. J. Cutler, and F. E. Schmidt. “Differences in the Household Composition of Elders by Age, Gender, and Area of Residence.” The Gerontologist 29.6 (1989): 814-21. Web.

Gratton, Brian, and Myron P. Gutmann. “Emptying the Nest: Older Men in the United States, 1880–2000.” Population and Development Review 36.2 (2010): 331-56. Web.

Ruggles, Steven. “Multigenerational Families in Nineteenth-Century America.” Continuity and Change 18.1 (2003): 139-65. Web.

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.

Data:
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.

Method:

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.
Results:

Figure 1:

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:

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:

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.

Conclusion:

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.

Bibliography:

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.

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.

Data:

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.

Method:

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.

Results:

Figure 1:

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:

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).

Conclusion:

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.

Bibliography:

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.

Collection of census data is a method of discovering who the country is made of. The data collected from the census shapes government policy. More specifically the race classification of the census provides data that are used for racial projects (either to repress or to benefit certain types of people). In this post I analyze the manner in which the asian population in the United States is classified from 1880 to 1990. In contrast to the simple manner in which white and black people are classified in the census, asian people are classified with increasing precision over time. However the label “asian” is representative of a geographic region. In other words the manner in which the Asian race is defined is directly associated with nationality. This contrasts European identification as white or African identification as black. It is important to study the collection of race data because the census data were used as evidence for race science. Race science claimed the superiority of some race groups over others and, therefore, justified exclusionary policies.

Data:

The census data for this analysis are from the Integrated Public-Use Microdata Series (IPUMS). My data set includes the 1880 to 1990 censuses. The 1890 census results are excluded from this data set because the records burned in a fire. My data set excludes Alaska and Hawaii before they achieved statehood in 1959. They were technically part of the U.S. prior to that, but as territories rather than states. The IPUMS data include other states before they were granted statehood, but the problem with Alaska and Hawaii is that they are not in IPUMS samples for 1940-1950. Therefore I also exclude them from the 1900-1930 census data in order to be consistent. I will be using the 1% sample from each census year. Exceptions include the 1970 census, for which I will use the 1% State Form 1 Sample, and the 1980 census, for which I will use the 1% Metro Sample. IPUMS has randomly selected these 1% samples. The IPUMS variable RACED is used for the analysis. For all censuses prior to 1960, the race variable was recorded by an enumerator. Beginning in 1960 the census changed to a self-report format. All analyses are weighted by PERWT, the individual sample weight provided by IPUMS.

Method:

I begin by focussing the analysis of the RACED variable on all races that refer to countries in Asia and the pacific. I classify these race variables into seven categories and label them A-G in the visualization below. Group A includes everyone classified as a Pacific Islander; group C includes everyone classified as Japanese; group D includes everyone classified as Hawaiian; group E includes everyone classified as Chinese; group F includes everyone classified as one of the thirteen race classifications that were added to the census in 1990 (Taiwanese, Vietnamese, Cambodian, Hmong, Laotian, Thai, Bangladeshi, Burmese, Indonesian, Malaysian, Okinawan, Pakistani, and Sri Lankan); group G includes everyone classified as Filipino, Hindu/Asian indian, and Korean; group B includes all other individuals who did not fit into one of these categories. These groups correspond to both geography and patterns of immigration to the United States.

The census only permitted one race classification, so my analysis does not account for the possibility of multiple identification. Additionally before 1960 the enumerators were responsible for reporting people’s race classification. Self-identification may have differed from the census’ reported classification for all years prior to 1960. Code for analysis and visualization is available here.

Figure 1:

Group A: Pacific Islander
Group B: Other
Group C: Japanese
Group D: Hawaiian
Group E: Chinese
Group F: Added in 1990 (Taiwanese, Vietnamese, Cambodian, Hmong, Laotian, Thai, Bangladeshi, Burmese, Indonesian, Malaysian, Okinawan, Pakistani, and Sri Lankan)
Group G: Filipino, Hindu/Asian Indian, and Korean

Figure 1 graphs the asian population from 1880 to 1990. The total asian population for each year is then subdivided into the population of each race category and indicated by color. The asian population increases over time. There is a dramatic shift in asian population growth between the 1950 and 1960 census. Furthermore as time progresses there are an increasing specification in classification of asian. For example in 1880 the census only classifies asians as Chinese (Group E). Beginning in 1900 the Japanese race category (Group C) appears on the census. Then from 1930 onward there is an evident trend of finer and finer gradations of classification. Group G, which first appears in 1930, includes individuals classified as Filipino, Hindu/Asian Indian and Korean. In 1970 the Hawaiian race category appears (Group D). The 1990 census is the only census year to exhibit population in the Pacific Islander category (Group A).

Figure 2: Figure 2. The Asian population with respect to the total American population.

Group A: Pacific Islander
Group B: Other
Group C: Japanese
Group D: Hawaiian
Group E: Chinese
Group F: Added in 1990 (Taiwanese, Vietnamese, Cambodian, Hmong, Laotian, Thai, Bangladeshi, Burmese, Indonesian, Malaysian, Okinawan, Pakistani, and Sri Lankan)
Group G: Filipino, Hindu/Asian Indian, and Korean

Figure 2 graphs the total population of the United States and the total asian population on the same graph. The asian population has always been minuscule in comparison to the total american population. The asian population grew from 113,161 people in 1880 to 7,166,896 people in 1990. The total american population grew from 50,152,560 people in 1880 to 243,878,788 in 1990.

Figure 3:

Group A: Pacific Islander
Group B: Other
Group C: Japanese
Group D: Hawaiian
Group E: Chinese
Group F: Added in 1990 (Taiwanese, Vietnamese, Cambodian, Hmong, Laotian, Thai, Bangladeshi, Burmese, Indonesian, Malaysian, Okinawan, Pakistani, and Sri Lankan)
Group G: Filipino, Hindu/Asian Indian, and Korean

Figure 3 graphs the asian population as a percentage of the total population of the United States. From 1880 to 1970 the asian population remains under 1% of the total american population. At the end of the period of analysis (1990), the Asian population reaches nearly 3% of the total american population.

Conclusion:

The increasing precision of asian race classification demonstrated in the census from 1880 to 1990 is likely a response to increasing asian immigration. Kenneth Prewitt, in What is Your Race? The Census and Our Flawed Efforts to Classify Americans, concludes chapter 5 with a claim of the nature in which racial data influence policy. Prewitt states that racial statistics collected through the census do not cause repressive policies. He does, however, make the claim that without racial statistics, quota-based immigration restriction would not have been possible (2013, 77). The increasing precision of data collection on the asian race, therefore, is indicative of increasing government interest in who is in the United States.

Precision of asian identification was particularly important for the purposes of race science. Race science was developed by nativists with the goal of proving the scientific superiority of certain races over others. Census data were used as evidence for race science and therefore the racial classifications in the census are indicative of the interests of race scientists. Specifically, the precise identification of asians in the census, is a reflection of nativist effort to exclude asian immigrants. As immigration from Asia and the Pacific increased, the census bureau added finer gradations of asian race classification. Jennifer Hochschild and Brenna Powell would explain this pattern as a governmental effort by the white power holders to exclude the “perennial foreigners” (2008, 71). The pattern of precise collection of asian race data reflects how asian people were defined by the census bureau and viewed by nativists as racially distinct from one another.

Grounds for Chinese exclusion from civic american society were made on the theory that the Chinese were radically different than the Japanese. The Japanese were referred to as the “Frenchmen of the east” because of their “Turkish blood” (Hochschild and Powell 2008, 73). Evidence-less race theory such as this perpetuated the distinction between asian people on the census. The collected race data could be used for racial projects or for the sake of blocking naturalization.

Bibliography:

Prewitt, Kenneth. “How Many White Races Are There?” What Is Your Race?: The Census and Our Flawed Efforts to Classify Americans. Princeton, NJ: Princeton UP, 2013. N. pag. Print.

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.” Studies in American Political Development 22.1 (2008): 59-96. Web.