The nineteen forty Albany census provides us with resident’s basic personal information, but it contains a much deeper story in between the lines. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. The data given by the nineteen forty Albany census has a lot of hidden information that one needs to read between the lines to understand. The data consists of all different types of data such as numerical or geographical, based upon how you go about the visualization process. Longitude, latitude and place of birth can help us depict how many persons emigrated from other countries and what countries those people were immigrating from. It offers many different categories of information such as age, occupation, place of birth and value of home. These pieces of information can be put together to create stories of the time period and it’s people. Certain occupations made a lot of money, while other occupations saw little income. If you incorporate age and income together, it can be seen if younger, more energetic workers made more money or if the older, more experienced workers made a higher salary. Instead of age, one could use race or gender to specify which demographic group saw higher incomes. The geographical data offers a perspective on the immigration rates from specific foreign countries, which speaks a lot to the time period because immigration was a huge issue. There is an endless amount of data combinations that one can make with the nineteen forty Albany census and I feel my visualizations can represent that well.
The data in this visual provides us with how many people who were residents of Albany in nineteen forty were born outside of the United States and what countries they were born in, as well as how many people were born in each country. It also includes how many people were born domestically, or within the United States. During this time period they were several laws in place regarding immigration to the United States. Asians had it the worst because of the Chinese Exclusion Act, which prohibited any Chinese laborers from entering the United States. They could barely make their way into the United States legally, which is why the number of people born in Asian countries are very low in Albany, some having no residents and some with one or two, and these residents probably had to go through a long, grueling process just to make it to the United States. Once they got here, it was almost impossible for them to find a job because of the law in place. It was meant to completely stop the flow of workers from china due to the overwhelming amount that were coming to the United States seeking work so that natives could find a job before those foreigners do. European countries also were limited in the amount of people that could migrate to the United States, as they were only allowed to send two percent of the nations’ population each year. All of these laws were passed to ensure that American-born citizens are able to find a job before any foreigners do. Although most these laws were passed prior to the Great Depression, the limited amount of immigrants allowed did play a significant role in aiding the economy after the depression. The anti-immigration laws weren’t the only things that stopped immigrants from moving to the United States. The economic conditions of the nineteen thirties were not a secret to foreigners. Many of the immigrants who immigrated there do so in order to find a better job. Due to the fact that there weren’t enough jobs for native-born citizens, the potential immigrants made the decision to stay where they were, rather than risk having no job in a foreign country.
Foreign countries that consisted of the most persons born there that were living in Albany in nineteen forty were Germany, United Kingdom, Soviet Union/Russia, Greece, Turkey and Italy. All of these nations were heavily populated during the time. This led to the most persons immigrating to Albany because the Immigration Act of nineteen twenty-four, which allowed European countries to limit the amount of citizens it allowed to immigrate to the United States to two percent of the given population of that specific country. So, the countries with the highest populations were allowed to send the most people over to the United States because two percent of their population was larger than those countries with smaller populations.
The nineteen forty Albany census provides us predecessors to get a more statistical point of view of what society was like back then. Although it does not give us individual accounts of those who lived during this time period, it does tell us what age they were in nineteen forty, what state or foreign country they lived in five years prior to nineteen forty and even what level of education they completed. These numbers and data allows us to understand a lot of the city’s trends based off of what jobs people had, how much money they made at these jobs and what level of education was required to acquire those jobs and make a desirable salary within those jobs. It is tough to read all of these random words and letters, therefore we visual the data through program like Tableau to make the data easier to read and also not as boring or unappealing to read.
One of the data visualizations I have put together involve the resident’s home state or country that they were born in. This visualization is a map of every foreign country that an Albany resident in nineteen forty was born in, and how many fellow Albany denizens were also born there. These countries vary in color, but the color scheme represents nothing, meaning that each color has no significance in my visual other than to look nice. Foreign countries had a very small amount of people migrating to the United States. I feel that this is a very likely trend due to the time period and the conditions of the United States during this time period. The United States had just been recovering from the worst economic situation this country has ever seen; The Great Depression. Foreigners obviously heard about the news of the poor economic conditions of the United States and most of them decided to stay where they were. Immigration to the United States was wildly popular prior to the Great Depression because they wanted to be free and believe in their own religions without getting punished by the governing system. Also, immigrants were attracted to moving to the United States because they felt it was the best economic decision they could make. It is still a cliché phrase; “The American Dream”. Immigrants wanted to get a well-paying job, buy a house with a white fence all while raising a family. Once the idea of a well-paying job feel through the cracks, the large sum of potential immigrants did not see the appeal that was once there. With that being said, Albany might not be the most attractive spot for immigrants to relocate to in the first place, but the capital city of the state that most immigrants were arriving in might attract more of said immigrants once they arrived at Ellis Island. If such a small amount of immigrants were immigrating to the capital of the empire state, then it leads me to believe that the national trend stays consistent with the trend of Albany.
The census is a good source to investigate into what type of social and economic trends occurred during a specific time period. Based upon the information given within the nineteen forty census I was able to make the information given a little easier to read and understand through using a visualization and through this visualization it is much easier to make an assumption about the conditions of Albany during the year nineteen forty and the surrounding years as well.
The nineteen forty Albany census provides us with resident’s basic personal information. When simplifying the information and the numbers, many judgments can be made about the circumstances of this time period. Society is ever-changing, socially and politically, and with the aid of the data from the nineteen forty Albany census I was able to create a visualization that represented the views of society in regards to gender and sexism.
My data visualization displays the average age and average income of employed for pay or not employed for pay persons based on gender. On the female side, the average age of women not employed for pay was over forty years old, just about forty-two, and the average age of employed women was just over thirty-five years old. This tells me that the average women in the nineteen forty’s worked for pay for a shorter span of their lives than males did. With that being said, Men in the nineteen forty’s saw an average age of unemployment of just under forty years old, only a few years younger than their female counterparts. The average age of males who were employed for pay is greater than the average of those who are unemployed, being just over forty. This represents that males may have been struggling to find jobs when they were younger, but as they aged and gained experience then they were able to find a job and work there until they retire. This matches the national trend in the United States at the time. Men were likely faced with working at lower-waged jobs or low killed jobs until they were able to find a better paying job. The experience in the work force led to them eventually getting a job. Due to the economic conditions given to the country by the Great Depression a lot of people found themselves unemployed or working a low-wage job just to support their families or themselves. As the economic conditions were healed overtime, more and more individuals were lucky enough to find jobs, but because of this they were much older and that has an impact on why older men were more successful in their job searches. A person whose gender is nullified on the census demonstrates a higher average age of unemployed persons than employed persons. This relationship is similar to that of females so this leads me to believe that most of the persons whose gender is nullified on the census are women. For those whose employment status was nullified in the census they saw a very low average age for both females and males, about 15 and 18 respectively. These persons were most likely nullified when it comes to employment status because they are so young and may not have been expected to work just yet because of academic reasons
There is a clear discrepancy between the incomes of each gender. Males received a much higher average alary than women, and it is a very significant difference. This speaks volumes to the sexism that was still alive during this time period. Women were still seen as “stay-at-home” parents so even when they did get employed for pay, they did not receive close to the same salary as males. Whether or not women were working in similar positions as men, they did not make as much money as their male counterparts, which stills holds true today but not as extreme.
The Great Depression made such a significant impact on the history of the United States and it is quite evident in the nineteen forty census. Even through such broad information it is easy to tell certain stories through the census. Jobs and occupations were necessary in order to acquire currency legally back in nineteen forty and that still holds true today. Albany residents of nineteen forty who were male faced lower employment rates when they were younger versus when they were older, and therefore more experienced. Employers during the nineteen thirty’s would look for workers with more experience. Since the aggregate demand was so low, economics tells us that unemployment rises. What I mean by this is fewer goods and services were being bought by consumers because of the economic conditions, and therefore employers were looking for the best possible workers, or workers with the most experience, in order to make their products as best as possible so that consumers would buy the product. They felt if they had high quality products or services than those goods and services would be highly sought after by consumers. The experience in the work force led to them eventually getting a job. Most males were faced with a tough challenge supporting their families in such a tough economic situation, so this led to them working lower-wage jobs, which are also lower-skill jobs just to feed their families and themselves. As the conditions began to get better, the job market expanded and more workers were able to get hired, literally meaning that the workers are now older than they were before the Great Depression. So, if the people who were hired after the depression were hired before hand, or if the depression didn’t happen, those people would have been working at a younger age and the average age for persons who are employed for pay the nineteen forty would have been a lower age. But due to the occurrence of The Great Depression, the average age of persons employed for pay was higher and it forced workers to be unemployed, work low wage jobs or no-wage jobs before they were able to get a well-paying job.
Persons employed for pay saw a different relationship between employment and age than males. The data says that the average age of unemployed women was over forty. As for women who were employed for pay, the average age was thirty-five. This number represents that as women aged, they were less likely to be working, which is the opposite of males rates. This can be because they had children to look after while her husband worked, or because employers did not want elder women working. This time period was not a “civilized” as society is today. Political Segregation between blacks and whites was still intact within the United States and so was sexism.
Through the likes of Tableau Public I was able to construct my visualizations efficiently and in a manner that is easy to understand. There was a long period of time where trial-and-error was my strategy. I would mix up categories and try to make a useful graph or map out of it. Some of the combinations I was able to create I was also able to ruin by throwing in too many or too little filters or even too many other dimensions. Sometimes the filters were so specific that it was tough to remember what I was filtering, so if I had to start a fresh visual over again it was difficult to get back to where I was. This led to a lot of decent ideas being thrown away involuntarily. I feel I was able to be most interested in my final two visuals and come up with the best stories and arguments. One has to do with immigration and I have a strong interest here. I always find myself reading deeply into articles or anything about immigration, especially immigration to my homeland, the United States and more specifically New York. I was able to make a good argument as to why the immigration numbers were the way they were in nineteen forty. I had to filter a lot of the data out and filter it together. Spelling errors of countries names and other similar kinds of errors from the census forced me to filter some groups or individuals together. The country England or Britain was not a choice on my map as it was named United Kingdom. Any persons who said their birthplace was England did not originally appear on the data map. I used the filter to conjoin the United Kingdom, England and Britain for the most accurate results.
My other visualization of data offers a more in-depth look into the average age and average income of employment for males and females. The stacked bar graph gives the viewer a visual comparison of what average age each gender and whether they were employed for pay or not employed for pay, or even in an institution which represents a institutional home such as prison, hospitals and orphanages. In some cases these adults were not expected to work due to long stays at these institutions. The stacked bars serve well for my argument because it gives viewers an easy-to-look-at comparison of averages ages or males or females. Males who were employed for pay were on average five years older than females who were also employed for pay, and that number five might not be distinguishable from just looking at the graph, but you are able to understand that females were slightly younger when it comes to those who were employed for pay, on average. Also, males saw a much larger average income than females in all categories; employed for pay, not employed for pay or institution. I grouped together two different categories that are both labeled “institution” but one is capitalized and the other is not, so the program did not automatically group them together. I also filtered out any persons under the age of fifteen because they were probably not expected to work, and if they were to work at a younger age it was most likely not for profit but to simply assist their parents or guardians at their place of work such as helping out on the farm or working within the family’s store. This is important because I do not want to have biased data.
After completing my research and visualizations, I feel that I can further research some of the data and patterns. My geographical visualization offers a lot of information about the immigration rates. Many anti-immigration laws were passed several years prior to this census being distributed and I felt that is why the numbers were the way they were in nineteen forty Albany. As for further researching this topic, I should inquire into the nineteen thirty and nineteen twenty censuses and even the ones before that. I feel this is something I should further research so I can get a better idea of when these laws really started to have an impact on the number of immigrants. I did do some research upon the anti-immigrations laws that were passed and they were meant to increase the employment rate of native-born citizens to the United States. Some laws prohibited Asian laborers, while other laws prohibited too many Europeans from entering the United States. These laws were passed in different decades so I am curious as to how the initial anti-immigration laws influenced the following laws. Maybe too many persons of one demographic had desirable jobs and political leaders felt his was not the best-suited situation for the country. Once they saw the results of the initial laws, did it influence the political leaders of the United States to pass even more laws on even more demographic groups? Did these results from the initial laws influence any type of change in the policies behind the laws? My data cannot give a definite answer to this, but it is somewhat clear that this could have very well likely have been the case that the results from the initial laws had an impact on how the following laws were structured and carried out. Also in regards to the geographic visual, I am curious as to why there are only six native-born Canadians living in Albany. Canada is very close to the capital city and I feel six native-Canadians is too little of a number.
Another topic I want to further research in regards to my data is why there were over twice as many females than males living in some kind of institutional housing. It could be because the average age of a male in this type of situation was over fifty-eight years old. This is an old age for the time period and it could be said that most of these males were retired and living in some type of group home. Females’ average age was just over thirty-seven. It is tough to make an assumption off of this number but it could lead me to believe that most of the females within this category were in mental health institutions or possibly even prison. As I mentioned earlier, this time period saw a lot of sexism compared to today’s society. This could potentially led to women being arrested easier for more petty crimes or being placed into an institution easier. I want research how active sexism was in Albany during this time period. It was only twenty years prior to the census that women earned the political right to vote. Although they had some political equality, society potentially was still very sexist.
There is a lot of educated assumptions that can be made from using the data given by the nineteen forty Albany census when you simplify the data, but these assumptions do remain assumptions. Researching furthering into the ideas I had in regards to this data will definitely give me a better understanding of what the data is saying and how it is saying it.