Introduction
On Feb 18, 1997, I was born in a small town in China. It was a joyful event where few of my families were cheerful, because I was a girl. My mom was blamed for my birth as a girl and my grandma rarely hugged me before she was gone. When I was 12, my dad tried to hire a surrogate to have a son. When I was 16, I came to study abroad in United States. I lost direct contact but I never felt disconnected from this issue. Former Indian Primer Minister Jawwaharlal Nehru said, “You can tell the condition of a nation by looking at the status of its women”(“STATUS OF WOMEN IN INDIA“)It has been 22 years and I wanted to explore more to see if the situation of women has become better. I choose India as a comparison because of its similar demographics and patriarchal culture remained from history.
Data Source
UNData, GapMinder, Stratista, World Bank Open Data
Reference
“The Legacy of Colonialism: Law and Women ‘ s Rights in India”https://scholarship.law.ufl.edu/cgi/viewcontent.cgi?article=1148&context=facultypub
Wikipedia Feminism in India https://en.wikipedia.org/wiki/Feminism_in_India#Birth_ratio
Women’s Education in India
Women’s Education In India: Facts And Statistics On Importance Of Female Literacy
https://women-s.net/womens-education-in-india/
The History Behind ‘Sati’, A Banned Funeral Custom in Indiahttps://theculturetrip.com/asia/india/articles/the-dark-history-behind-sati-a-banned-funeral-custom-in-india/
Women’s education in India: A situational analysis file:///C:/Users/xwux16/Downloads/Womens-Education-in-India-A-Situational-Analysis%20(1).pdf
Women’s Movement and Change of Women’s Status in China
https://vc.bridgew.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1626&context=jiws
Materials
-OpenRefine
-Tableau
-Photoshop
Visualization Process
For me the most difficult part was finding the data. It was very hard to obtain raw data or consistent data that were available for both China and India. Thus, the datasets that I ended up using are mostly from United Nations and World Data Bank. However, it was still a regret that a lot of the significant data fields such as sexual assaultment and domestic violence were left blank for both countries in the UN consensus. I reviewed and chose most related data per the results of my UX interviews.
I had to process the majority of my datasets by limiting the timeline and filtering the country list. I used OpenRefine to convert the years from column header to a variables so that it is readable in Tableau. On deciding what type of graphs to use, I wanted to use both line graph and bar graph to mix it up. However, after considering my focus of letting audience be able to tell the progress and trend throughout the years, I chose to use line graph for most of the datasets. I also used heat map as an addition of worldwide data to help viewer gain more perspective on how China and India were doing on a large scale.
I used consistent and limit color in all the graphs to prevent viewers from losing track. Men and women were represented by the color blue and pink. India was represented in orange because of its flag color. While red is the color most associated with China, I decided not to use it because red is too close to orange in the color spectrum. In order to make viewers differentiate these two countries with one glance, I chose green. Being aware of the color-blind audience, I used Photoshop to add icon/text to each line for clarification.
After my first draft and user testing, I received the feedback of annotating years with significant events to provide more context to the graph, especially for viewers who are not familiar with the history and culture. The first problem that I had was selecting the events. The events were discussed in the timeline by quoting or paraphrasing relatively objective resources online, however I couldn’t add every event to every graph. Therefore, it was up to me to associate their influence with the data. Despite of my effort in research and trying to fully understand each event, due to time constraint and my background, there was some subjectivity or guessing involved.
After I decided on the events to annotate, in the beginning, I tried to add annotation on Tableau by marking an area. I chose area because I believe the influence of an event rarely shows on the data within one year of the years. It usually takes 5 to 10 years for them to take effect. I didn’t like the visuals of annotation on Tableau. It gave me very few options in stylizing the annotation. Therefore, I exported the graphs as images and annotated the events in Photoshop.
Results and Observation
embed link
Tableau Link:
https://public.tableau.com/profile/xueping.wu#!/vizhome/femalestatus3/Chinaliteracy?publish=yes
Female population % of the General population
In 2017, the male to female ratio of China was 104.81. There were about 42 million more men than women. Although it was still considered as unbalanced, it was making progress to a more balanced sex ratio.
In 2017, the male to female ratio of Inida was 107.55. There were 48.7 million more men than women.
Literacy Rate
The adult literacy was increasing for both China and India.
1990-2000 was a significant period for Chinese women. It was the time that China opened its gate to the world and become part of the international economics. The women adult literacy increased almost 20% in 10 years. As of 2017, the gap between male and female adult literacy was decreased form 28.05% to 3.69%.
For India, it was after 2010 that women started to catch up on literacy rate. In four years, the women adult literacy increased 12.16%. It was more than the improvement of ten years (1981-1991) and almost equal to the improvement made over the course of 1991 – 2001 (14.11%). However, it was worth noticing that in 2017, there was still a gap of 17.96% between men and women.
Female Average Age for 1st Marriage.
Overall, the female average age for 1st marriage in both China and India were slowly increasing. In 2005, the Chinese female average age for 1st marriage was 23.3 and the Indian female average age for 1st marriage was 19.9. Looking wide-widely, at the same time, a lot of countries in Europe, such as France, German, Sweden shared an average age around 30. The age for Canada and United States was also close to each other (25~26 years old). It indicates a pattern where the economics is more developed, the latter women are more likely to get married.
Fertility Rate:
The fertility rate of India women kept decreasing while the fertility rate of Chinese women experienced an roller coaster. It was closely associated with Chinese government policy. In 1950s, not soon after the found of Republic of China, in order to maintain the stability of Chinese society and promote production rate (mainly in farming at the time), Chinese government advertised women domestic duties and rewarded women with more children. This explained why there was a huge increase in fertility rate in early 1960s. Later in 1966-1967, the drastic decrease in fertility rate showed the influence of the Great Leap Forward Movement and the Cultural Revolution both led by Mao Zedong. In 1969, two-child only policy was announced by Chinese government. The fertility rate decreased from 5.75 to 2.3 in ten years. Then after its one-child policy in 1979, the fertility rate experienced a stable decrease until 2000s where it maintained around 1.5. However, after its official release of 2nd child policy in 2011, we can see a slow grow on the fertility rate.
% of Women holding seats in National Parliament
In 20 years (1998-2018), there’s an 3.1% increase in Seats held by Women in National Parliaments. China has the largest parliament of 3000 people, which means in 2017, there were 93 more women in China Parliament compare to 20 years ago.
In 21 years (1997-2018), there’s an 4.6% increase in Seats held by Women in Parliaments
India Parliament has 545 seats, which means in 2017, there were about 25 more women than 21 years ago.
Reflection:
Looking back, I wish I had used sex ratio instead of % of women population. Although viewers could calculate and get a sense of sex ratio from the % of women population change, I think it would have a more direct impact on the viewers. For fertility rate, I think more data on birth control rather than historical event would illustrate the situation better. In the end, I wanted to say it was also difficult for me to select so few movements to annotate because it was truly result of complex history and cultural belief. Poverty was a significant factor. If you want to learn more about the education of Indian women, please read this article.
Upon the completion of this project, my heart couldn’t stop trembling. While the graphs suggest women rights are heading to a better place in China and India, it is heart-breaking to see how slow the development of women rights laws are in both countries. Furthermore, it is frustrating for some women to be unaware of the situation. It was not until 2015 that India defined domestic violence in its laws. It was not until 2019 that Chinese government banned companies posting sexist requirements. I truly hope people will take some time to read the history and understand the graphs and I hope my work will help more men and women from China and India to realize the issue and fight for women rights.