I chose to analyze the 1940 census data set, which categorizes data for over 10,000 people. The demographics include numerical, text, and geographic data. The categories for the information include: addresses, value of purchased homes, head of households, gender, race, age, marital status, birth year, highest education level, birthplace, residence city, employment, income, parental birth place, and native language. If we were to look at the range of data from the first hundred people, we would see that there are large ranges of numerical and descriptive data. For numerical data, the value of homes ranged from 55 to 10,000 in New York. The age range varied from 1 to 81 years old. Income of people in the 1940s ranged from 0 to 5,000. For geographic and descriptive data, the household ownership had two categories (rented or owned). Marital status ranged from widowed, single and the most common status, married. The relation to the head of household varied from wife and husband to sister-in-law and grandson. Occupations ranged from lawyers, librarians, electricians, maids, in paid family workers, and salary workers in government or private work. School attendance ranged from no educational background to attending college for over 5 years. Over 90% of the dataset consisted of white men and women, with very few black people living in these areas. There was a limited range of street names, for example the first 200 people lived on three blocks: Fleetwood Avenue, Vanschoik Avenue, and Cardinal Avenue. Majority of the residence were born in New York and continued in the same residence.
If we were to compare categories within the 1940 data set, analyzing two columns can raise questions and conclusion about the type of living situations. The first comparison is between the highest level of education and occupation. Those that have only completed some elementary or high school education up until the second year are unemployed. Several people from Vanschoik Avenue that completed the 8th grade were not employed for pay or salary workers in private or government work. Those that have a college education show occupations of Feler Booker and Dental Doctors. Those with no education showed the clear distinction of no occupation. A second comparison is between income and house ownership. It is clear to see those that rented their homes are forced to work harder since they’re making payments more frequently. The income of renters was nearly twice as the income of a home owner ($4,800 vs. $2,200). The census is skewed in showing more people owning homes in the 40s than renting. Another comparison in the data set is between gender and attendance in school or college. The census is unclear in confirming the difference between “attended school or college” and “highest graded completed”. Most of the responses in “attended school or college” said no, but had some sort of educational level in “highest grade completed”. Surprisingly, there was an even amount of males and females that have yes in the category for completing school. Majority of the ones that say “yes” only have elementary or high school education, while very few have attended college. Overall, the census does uncover some patterns in the demographics within certain areas in New York. The downfall to gathering information is the areas that are left black in the data set. It makes it harder to have a detailed account of what happened in the 1940s.
The 1940s was a time where the U.S slowly recovered from the Great Depression. New York specifically since then has maintained the number one spot for highest population since the early 1900s. Looking at demographic information of residents in Albany, New York in 1940 can tell various stories. The addresses, marital status, race, and education are the few parts of the census that raise questions regarding the lifestyles of people in this particular year. Was the residential area rural or urban? Were people financially stable? How did levels of education effect future endeavors? This visualization focuses on how education levels effect occupation decisions.
Initially looking at the data, it is unclear in figuring out who was enrolled in school for that year, and the kinds of occupations at the time. You must take into consideration age, and filter which jobs have different spelling, but are the same title. After making those changes, the visualization shows the variations in occupations and the education background one possessed in that field. The information is represented through a bar graph with a colored key to indicate whether the person attended college, high school, or elementary school. For each occupation there are numerical distinctions for how many people in 1940 worked in the same occupation.
Before looking at the occupations, the census does provide addresses of people in Albany. With some research, I found that Fleetwood Avenue and Cardinal Avenue were in the Whitehall area of Albany. This shows that these residents lived in close proximity of each other, yet obtained various jobs. For example, two people that live on Fleetwood Avenue both in their late 30s/ early 40s white, male, and highest education level is high school. One has a career in sales, while the other is an electrician. You can then compare those two people to a woman in her early 40s, married, with the highest education received in elementary school. Her occupation is not listed in the census.
The comparisons stated show us that creating one story can then lead to others. Were women still suggested to stay in the home in 1940? If she obtained higher education, would she be working?
Looking back at the visualization, something interesting within the story is the placement for those with no educational background. Most work in the same field as those that have went to high school and/or college (housekeeping, inspector, etc…). The highest number of jobs with varying educations obtained were wage/salary workers in government and private businesses, proprietors, owners, laborers, and inspectors. These occupations are closely related to either working for the government or working for themselves. We can build the assumption that this area of Albany is more suburban with many small businesses. Albany today is assumed to be very government orientated because it is the capital, yet many parts in the downtown region do support this assumption created from the data. A final observation following the census is the wide range of jobs that were surprisingly held at the time, especially following the economic downtown a few years before.
Drawing upon the trends shown between race, gender, and income, the census uncovers distinct disparities. There are 128 residents listed in the census that are non-white citizens in Albany, New York. There were Chinese, Filipino, one Japanese, and Negro population. The reference to “negro” alone, and the year 1940 can suggest that racial tensions still existed in the Albany region.
The context of the second visualization connects with the first based on the education levels of races and genders. The group of Chinese residents have a range of education from none to one that attended high school. Filipino residents all attended college except one, whose highest level of education was high school. Only one Japanese resident was listed who attended high school. Majority of the non-white population consisted of Negro residents, with majority of their education levels stopped at elementary school. When comparing these findings to income, the highest income listed between all non-white races was $2,250. The white residents’ education levels varied, however the incomes were at substantially higher levels. If we look at the correlation between gender and education the differences between men and women are surprisingly different than the differences in income. For Chinese men and women, more women were in school or completed higher education levels than men. All Filipino residents listed in the census went to college, with the exception of one woman. However, there were only two males listed in the census, whereas there were eight females. For the Negro population, there was an approximately even distribution of educational levels and listings of men and women. When examining the relationship between gender and income, there are two immediate trends found. Within the Negro population, you see that males makes more money than females.
Other factors such as head of household contribute to this finding. Providing for a family will have affects on income levels for anyone despite gender. In regards to race, the Negro population were systematically disadvantaged. Being a woman and Negro creates a larger disadvantage to make money, especially in the 1940s. The only women that were head of the household in Albany were widowed. If they were married, the chances of receiving any form on income were smaller than single Negro women. The occupations held by women also play into the stereotypes of how society depicts women. They held positions of housework or waitresses, despite their education levels. When looking at white men and women, the incomes of women rarely exceeded $1000. The occupations listed for women who didn’t obtain income were either unpaid family worker or housework. For women that did work, their occupations varied as opposed to non-white women in 1940s Albany. They held positions in medical fields, clerical work, sales, and restaurants. The trend found not only in the census, but in our lives today is the fact that white men hold the highest income. The census itself has less gaps in demographic information than any other gender and race. The variety of incomes, occupations, and education levels can conclude the fact that white men are more privileged that anyone else. It is clear that the stories made from each visualization intersect to show trends that are similar to ones seen today.
The demographic information displayed in the census, can provide a clearer demonstration of trends if visualized. Before creating these visualizations, it is important to know what information you plan on comparing in order to show significant changes in the data. Tableau was recommended as the software to help us further understand our census.
For my first story, I needed to asses whether education levels or occupations would be in rows or columns. There about three graphs that could be used to visualize the data: circle, line, or scatter plot. A scatter plot shows the clearest distinction in the number of people that completed a particular education level and its relation to their occupation. When finalizing that step, I realized there were an exceeding amount of items in each section. Without any categorization, you cannot see any trends or patterns. The range of data for educational levels were specific starting from no educational background to college 5th year or higher. I knew I didn’t want each grade in its own category so I grouped education by none, elementary school, middle school, high school, and college. I decided to filter out null information because it would then complicate the filtering process for occupations. The range of occupations were extremely long, therefore required multiple groupings. I initially based the groupings by related job descriptions, but realized I had to continue to condense and place similar occupations together. For example, I had restaurant related occupations as on group. Bartending was placed in its own group, but can be categorized with restaurant as well. I finalized the visualization with 32 categories of occupations that ranged from accounting to yard work. Factoring age into the visualization helped understand who held these positions at what point in time. There were people that were not of working age, therefore their education levels would not have relevance to the correlation between education and occupations. Once adding this category, my graph changed into a combination of scatter and stacked graph.
My second visualization showing the correlation between race, gender, and income was a simpler process. I made gender and race the columns and income placed in the row section. There were complications in representing the five race categories mentioned earlier. When creating the visualization, the Filipino average income would not show on the graph. The Chinese and Japanese population did not have any numerical income displayed in the census, therefore was removed from the visualization. I chose to use a bar graph so that you can see differences in income based on race and gender. Income was the only dimension that needed to be filtered to show a range in numbers, versus counting each persons’ income.
For both visualizations, it was best to choose colors that were appealing to the eye and made it easy to see the trends that were described in my argument.
When looking at any historical information, you may see a pattern regarding disparities based on societal factors. The 1940s census provides information on a select number of people living on neighboring streets, their marital status, income, gender, education level, occupation, age, and more. Demographic data is a good way to track populations, but requires good observation to find correlations within the data. The patterns we see today are presented in the census once we create visualizations. There is a distinct trend in the societal differences based on class, gender, and race. Three main points will be made within this argument:
1. The higher education obtained by people in society, the better their occupation is.
2. Men make more than women in nearly every profession.
3. There is a presence of white privilege and lack of representation for people of color in any given data set.
The first trend within the argument is education levels influence occupations. The census data lists the number of people who have either attended elementary school to college. Although several names have no information regarding the highest level of education received, a pattern is still present. There is a subset of data that shows people who have only completed elementary school with an occupation as an unpaid family worker, wage or salary worker, janitor, or laborer. Those with only high school education had similar jobs, or some more advanced such as beautician, attendant, and typist. College education residents in Albany were lawyers, civil engineers, stenographers, and accountants. Those job titles/careers advance as the education level increases, showing the value society has always placed on receiving a higher education. The 1940s census does however, lack a large amount of occupations for residents. People in Albany overall did not attend college. You also see that many people that have an elementary school education hold the same positions of those who attended college. After categorizing jobs into the category “Business/City/Police”, there are more people that have an elementary school education than college. I noticed a potential reason for this is my placement of business or private owners into the category. Entrepreneurs don’t necessarily need higher education to be successful. There were also information in the data that assumes college attendees do not always immediately find work in their desired career. There were twelve residents listed in the “housekeeping/cleaning” field who more than likely did not intent to work there. Also ten years prior to 1940, the United States went through economic turmoil. This can have an effect on people who worked in state, local, and financial offices that may have lost their jobs during that time.
The second correlation found within the census is the difference in income based on gender. It is already a known fact that men make more than women today. History has shown us that society has trained women to be comfortable in the household. They are created to be wives, mothers, and attend to duties in the home. All of this work fulfilled and even when doing so, that work is still not measurable to work for pay. When visualized in a bar chart, you can see men made more than women. The census does lack information on occupations for women. The marital status also plays a role, as you can see who is married versus who is the head of the household. Typically, the relation to head of household for women is wife, mother, or daughter. The men would be the head of the household or the son. Both the head and the son still would have occupations listed in the census more frequently than women during this time.
The last pattern within the argument is racial disparities that continue to exist. With a simple glance at the census, it is clear that over ninety percent of the population in Albany was white. It is uncertain if people of color were undocumented purposely, or they simply did not reside in Albany. Four other races were present in the census: Chinese, Filipino, Japanese, and Black. Based on their demographic information, they all lived on Fleetwood Avenue or Vanschoick Avenue. Their education levels, occupations, and incomes varied. The data alone does not show if one minority was more established financially than others. In comparison to white residents, the financial difference is drastically skewed. The sample of information on the minority population is somewhat too small to make any major conclusions. By assessing the information, you can question whether the area in which people of color lived was worse financially, safe or unsafe, encouraged or lacked opportunity for growth in comparison to the neighborhoods where white people resided. Those factors are a part of the present struggle of equal opportunities for all races today.
There are other correlations that can be made with the census data. You can compare martial status to head of household, immigrant status and occupation, home ownership and income, gender and employed for pay/non-pay. The census is simply surface material, yet can unveil many trends about populations in specific areas. These patterns are not new findings to how society is run today. They give additional information for how these patterns came to life.
The following questions presented for research were considered before and during the visualization of the census data.
1. What was the population like 10-20 years prior to 1940?
• There were nearly 15,000 people listed in the census. It would be good to learn how many people lives in Albany in the years prior to compare changes in residency. Was there anything happening in the city that increased or decreased population rates? Albany is the capital of New York, was there something economically attracting people to the city.
2. What brought Chinese, Filipino, and Japanese people to Albany?
• Between the late 1800s and 1920s, Chinese people migrated to Albany, NY and started business. The Chinese Exclusion Act, however, kept their population to a small amount. As late as 2010, the Chinese only makes up nearly 2% of Albany’s population. The percentage of Filipinos and Japanese in Albany has been less than 1% of the population.
3. Why are more women listed in the census than men?
• Initial thoughts behind this question were based on whether men were active in World War II at the time. What did these women do collectively in the city? Where there any places or programs where they came together?
4. Where areas in Albany segregated by race?
• The census provides streets of residence, but further research or visualization of where the streets were in relation to who lived there could show how Albany was form a social standpoint. What jobs were present on the listed streets? How was transportation? Were the areas safe or unsafe?
Overall, the questions and visualizations presented show the need for further research to fully understand life in Albany, NY. Some trends were shown immediately, while others were found through creation of comparisons with the data presented.