Answering the question of what I want to know took me longer than I expected. Although it is clear that gender inequality is present in most of the aspects of society, motivation to start an investigation of a specific domain came when I could link it to my past and present experiences.

As a teenager, a mentor dissuaded me from studying architecture and to choose a career more appropriate for a woman. This semester I am a TA for the course of Intro to Western Architecture, where the majority of the architectural students are male, and where all historical references are of male architects. Without conducting any research, my initial impression was that the architecture profession is highly male-dominated, especially in decision-making positions. This means that the buildings and spaces inhabited by the whole population are designed mostly by men, even those spaces that have been traditionally linked to women’s activities, such as the house. So, my starting question was how is gender inequality measured in the architecture profession?

The indicators of women’s education in architecture and their participation in the architectural profession at several stages of their careers were the most revealing source of information. But there were many limitations in finding cross-national indicators, as I will discuss later. In the case of the United States, the National Center for Education Statistics (NCED) showed that in 2013, 43% of total enrolled students in architecture programs were females, and according to the National Council of Architectural Registration Boards (NCARB) in 2016, 46% of newly licensed architects were women. This almost gender equality situation drastically changes when considering the percentages of women in top positions of architectural firms or architecture schools. Data from the Association of Collegiate Schools of Architecture (ACSA) was the most cited resource. This 2014 research looked at women architects in different positions, starting from students and interns and reaching leadership positions, such as chair or director in architecture schools, distinguished professors, and winners of renowned International awards. According to the American Institute of Architects, 15% of licensed architects were women, and 19% of 86 deans at US architecture schools were women.

Indicators that considered the architectural practice on a transnational lever were limited. Even surveys that supposedly presented a worldwide view, their data collection came from the United States, Canada, the United Kingdom, Europe, New Zealand, and Australia. Other research used the data available on the websites of the world’s 100 biggest architecture firms (in terms of total workers or projects built), of which only three are headed by women, while only two have management teams with a majority of women. A ranking of countries according to the ratio of male to female architects in the architectural field, presented among the top countries Vietnam (0.7 male architects per female architect) Turkey (0.8 male architects per female architect), Sweden and Norway (1 male architect per female architect), Germany and France ((1.3 male architects per female architect) and Spain (1.4 male architects per female architect), while the countries with a more male-dominated architectural field were Japan, the United States, and the United Kingdom. More details about these statistics required to be purchased. It is important to notice that they did not take into account hierarchy, decision-making or leadership positions.

 

My research was conducted in English, which affected the outcome: most of the data I found was centered in English-speaking countries. It was mainly through articles from online architecture magazines that I found the reference to specific sources of data. One question that I keep having is if research that only looks at the major international firms, instead of looking at a country case-by-case could be relevant. The main reason why I will think it would be valuable is because these companies construct worldwide and often set an example for local architects.

 

Sources:

https://www.acsa-arch.org/resources/data-resources/where-are-the-women-measuring-progress-on-gender-in-architecture/

https://www.arch2o.com/survey-shows-best-and-worst-countries-for-female-architects/

https://www.archdaily.com/200761/reframing-the-stats-about-architecture

https://www.architectmagazine.com/practice/where-gender-inequity-persists-in-architecture-the-technology-sector_o

https://www.architectural-review.com/10017497.article

https://www.architectural-review.com/essays/results-of-the-2016-women-in-architecture-survey-revealed/10003314.article

https://www.bdonline.co.uk/the-top-five-countries-in-the-world-to-be-a-female-architect/5064651.article

https://www.dezeen.com/2017/11/16/survey-leading-architecture-firms-reveals-shocking-lack-gender-diversity-senior-levels/

https://www.ncarb.org/nbtn2017/demographics

https://www.statista.com/statistics/587324/number-of-architects-in-major-architecture-companies-worldwide/

https://www.world-architects.com/en/architecture-news/insight/a-short-survey-of-women-in-architecture

When I initially began this research, I wanted to look at post-graduation placement of women who acquired a doctorate degree in a science, technology, engineering, and mathematics (collectively known as STEM). Unfortunately, the data contained within the Woodrow Wilson Center’s portal was insufficient to answer this question. As a result, I broadened my research to see the rates unemployment (reported as a % of the total labor force) between women in general and women who achieved advanced degrees. Given that education is often touted as ‘the great equalizer,” it would follow that women who attained advanced degrees would have lower rates of unemployment than the women in the general population. In order to test this assumption, I pulled two reports covering women’s unemployment; the first was a 2014 report from the International Labor Organization (ILO) which contains unemployment data on women aged 15-64 from 88 countries, the second was a 2015 report from the World Bank which contains unemployment data for women with advanced degrees from 65 countries. Fortunately, both the ILO and World Bank use the same definition of unemployment which is as follows; “Individuals without work, seeking work in a recent past period, and currently available for work, including people who have lost their jobs or who have voluntarily left work. Persons who did not look for work but have an arrangement for a future job are also counted as unemployed.”  Advanced education was defined as, “…short-cycle tertiary education, a bachelor’s degree or equivalent education level, a master’s degree or equivalent education level, or doctoral degree or equivalent education level” according to the World Bank report. In order to clean up the data, countries that were not on both lists were removed leaving 56 countries for cross comparison. Of those 56 countries, 13 reported a higher unemployment rate for women with advanced degrees than would be expected given the employment rate of women in the general public.

Unfortunately, the reports and data provided by these large organizations tend to conglomerate data in a way that can mask confounding variables. In this case, when looking at general unemployment rates for women compared to those with advanced degrees I had no way of comparing the data by age group, this lead to the inclusion of young women (ages 15-22) who likely could not have finished an advanced degree in the comparison. Aside from differing sociocultural norms, it could be that these 13 countries with higher unemployment for women with advanced degrees merely had a large population of employed young women skewing the data. These unemployment estimates also often do not include forms of informal labor which include seasonal labor (like agricultural/pastoral labor) and household labor. Additionally, while unemployment rates are often used as indicators of economic stability, they can mask other economic issues like chronically low wages, wage inequality, and quality of life. Social scientists can use these reports to guide their inquiry to explore why women with advanced degrees may face higher rates of unemployment in different sociocultural contexts. However, considering this endeavor began as a way to look at post-graduation placement of women with STEM degrees, the limitations and biases baked into the reports makes any critical and contextualized analysis using these reports frustrating, if not impossible.

I decided to do a bit a research to see what data I could easily access regarding women as heads of households across the globe. I was interested to see what countries I could find data for and in particular if the development status of a country had an effect on whether or not women were heads of household. I was also interested to see what other factors I could find data for connected with this information, such as whether I could find data on race, marriage status, parental status, employment, household income, education, or age. Lastly, I was curious if I would be able to find enough data to examine change over time.

I came across three sources for data, in addition to several scholarly articles synthesizing data on US female headed households (FHHs) and race which I didn’t examine beyond titles. The three main sources that I found were “Women’s Health USA 2012” (https://mchb.hrsa.gov/whusa12/pc/pages/hc.html), The World Bank’s “DataBank” (https://data.worldbank.org/indicator/SP.HOU.FEMA.ZS), and a data booklet published by the UN Department of Economic and Social Affairs (DESA), entitled “Household Size and Composition Around the World 2017” (https://www.un.org/en/development/desa/population/publications/pdf/ageing/household_size_and_composition_around_the_world_2017_data_booklet.pdf). Each of these presented the average percentage of FHHs by country. The data they provided was too limited to indicate change over time. In many cases this was because they only had data for one year. I was at times able to see statistics from two or three years for one country, but it was still insufficient for making larger claims about change over time.

One of these three main sources dealt only with data for the United States and so was unhelpful for examining gender inequality across the world. Nevertheless, “Women’s Health USA 2012,” which uses US Census data from 2011 and included correlating statistics for race and age, did include a definition of FHHs that helped to point out to me ways in which such definitions could vary drastically from country to country. For this source, women as heads of household are defined as having children or other family members living with them and no spouse living with them. This includes single moms, single women with a parent or other relative living in their home, and “women with other household compositions.” But, it does not include single women who live alone, women who are the primary source of income for their family, nor women who self-identify as head of household even for census or tax purposes. I was somewhat surprised by this definition because I wasn’t expecting it to be tied to single status and yet not include single women living alone, nor was I expecting that women who identified in the census as head of household didn’t count in these statistics if they had a male spouse living in their house.

The second main source of data regarding FHHs, The World Bank, provided very limited data but for a greater number of (largely, if not entirely, developing) countries. Although data from 77 countries from between 1990 and 2016 was included, there was only data for one year for many of the countries and there was not data for all of these countries for the same year. In addition, the only data I could find simply indicated percentage of FHHs by country. The bits of narrative detail the website provided indicated that there is also correlated data regarding societal pressures or economic changes that seemed to produce an increase in FHHs in developing countries more than cultural patterns, but it doesn’t provide further details. Similarly, the narrative details also indicate differences in marital statuses of FHHs between developing and developed countries without providing the data. Lastly, it indicates that this data is of particular interest for The World Bank because of the information it can provide related to “feminization of poverty – the process whereby poverty becomes more concentrated among Individuals living in female-headed households” and yet there is no mention of data related to poverty supplied here.

The last of these sources, DESA, only tangentially provides data on FHHs. The main concern of this data booklet is household size. However, it does provide two graphs. One contains information about FHHs by continent. The other adds to that information by also providing data about the parental status of FHHs.

Overall, my sense is that the data for examining gender inequality in relation to the role of women as heads of household is somewhat limited. My sense is that this is largely the result of vastly different definitions of FHHs which makes gathering the data not particularly useful or easy. I sensed that this might be a problem I would run up against when I looked at the definition provided by the first source I mentioned. The narrative details provided by The World Bank’s site also indicates that these varying definitions were highly problematic for their analysis of the data.

The dimension of gender inequality I was most interested in was the representation of women in the field of software engineering. This is a particularly important area for me, as I studied computer science in college and worked as a software engineer at an education software company during my third year. When I was hired, I was told “congratulations, you’re number four!”, alluding to the fact that I was the fourth woman to be hired as a software engineer in the over ten years of the company’s existence. The company was a local company, servicing most school districts in Florida (and Texas, for some reason), so the scale at which they hire is much smaller than a traditional tech company, like Google; however, the trend of a lack of representation of women in software continues at the largest scales, as well. 

For example, when you navigate to Google’s “Diversity” site, found through the following link: https://diversity.google/annual-report/, you can see that they have congratulated themselves on diversifying their teams, but there is still a disparity between the representation of women in tech and non-tech positions (22.9% tech, 47.9% non-tech, globally in 2018); this can probably be linked to the tendency of companies to tell women applying for tech positions that they can “work their way up” from a non-tech to a tech position, eventually being able to be hired into the original tech position for which they applied.  The disparity looks even worse when you look into the representation of other groups, particularly non-white women. Of all tech employees hired at Google (in the United States) in 2018, only 0.8% are Black+ women, 1.4% are Latinx+ women, and 0.3% are Native American+ women; the women with the most representation are Asian+ and White+ women at 15.9% and 10.3%, respectively. 

However, this is just one company, and the diversity statistics they release are quite opaque: it would be highly valuable to access information about how long women stay in these tech positions once they are hired, as well as which positions they are actually filling; thus, in keeping their data broad, Google is able to report a greater diversity percentage than might actually exist. I tried finding data specifically related to software engineering globally, but it seems that this data is hard to find, and this makes sense, as I’m sure most tech companies do not want the lack of representation on their software engineering teams to be made public in an easily accessible way. I did find some global data breaking down the representation of women with software engineering skills in different sectors of tech, found here. The data is taken from LinkedIn, which does limit the scope of the data, as it assumes that the representation of employment on LinkedIn parallels that which is found in reality, which it likely does not; however, I still found it interesting, as it was one of the only resources I could find that attempts to show the amount of women in different employment sectors related to software engineering. Moreover, the data is from 2013, so it is a little bit dated, especially considering the growth of tech jobs as a whole in the years since. 

I have chosen to focus on software engineering in particular, as it is a job role that is found at most tech companies and interestingly enough was pioneered by women, when the task of punching code cards was looked at as lesser than the work that men were doing in hardware. I was not surprised to find a general lack of information, especially as it is something that is rarely spoken about in the field, and when it is spoken about, companies and management tend to get defensive when faced with the task of explaining why there are more men named Matt than women who are software engineers in their company. Many will argue that this is due to a lack of women in computer science departments, or women who are even interested in software engineering (you do not need to have a computer science degree to be a software engineer), but this is related to further problems with the rhetoric of tech as a whole. So, it might be interesting to also look at the representation of women in computer science departments, as well as whether these women, once they have graduated, stay or leave the field. The data I could find relating to computer science was limited to the United States, where women make up 18% of computer science bachelor’s degrees. It is often the case that computer science/software engineering/tech can be hostile environments to women due to the existing composition of the fields, so even if a woman works in one of these spaces, how long they stay can be highly dependent upon the workplace experience (though, this can be said for many fields). Overall, it seems that it is hard to find gender disparity data related to specific careers, though it would be highly useful to see the actual composition of tech jobs within companies, not just a broad view that allows an aggregate of positions to represent a higher, though still low, amount of gender diversity.

 

The dimension I chose for gender inequality was advanced degrees awarded at the PhD level. I wanted to learn what the disparity between males and females were for awarded advanced degrees, how this compared to undergraduate degrees, and which countries had the most equal rates of degrees awarded by gender. The most important indicator for this domain was undergraduate degrees awarded and PhD degrees awarded. These two indicators are both incredibly important; the United States awarded forty six percent of their PhD degrees to females. This number appears innocuous and largely equal. However, by examining the indicator of bachelor’s degrees awarded, sixty percent of bachelor’s degrees went to females from 2012 to 2016 in the United States. Why does sixty percent of bachelor’s degrees go to females compared to only forty six percent of doctoral degrees? This number/percentage drop-off certainly complexifies the United State’s level of equality for female doctoral degrees. As such, to obtain a more complete analysis of doctoral degrees awarded by gender, it becomes imperative to also examine undergraduate degrees awarded.

The National Center for Education Statistics and the US Department of Education compile many statistics and data to examine and confront gender inequality within the academy. Not all nations, however, have such thorough data. Although discovering PhD’s awarded by nation was a quick find, undergraduate degrees by gender for countries outside of North America became much more difficult. Many nations do not track or poorly track the gender divide within academia at the undergraduate level. Adding even more complexity to the data, when observing international rates of doctoral degrees awarded by gender, the area/department of study becomes even more difficult to navigate. Tracking degrees-awarded does not offer a nuanced synthesis of gender inequality within the academy; are departments/areas of study at the doctoral level contributing to a more equal number by gender? In other words, are degrees largely segregated by gender? I found trouble navigating in-depth data indicators for doctoral degrees awarded by gender for countries outside of the United States.

The National Science Foundation conducted a powerful study breaking down fifty-six nations by doctoral degrees and gender. Unfortunately, with only fifty-six nations included, a vast amount of data and indicators need to be collected and analyzed.  As a result, I have access to overall doctoral degrees awarded by gender for a wide variety of countries, but many of these nations do not have in-depth (from what I could find) statistics regarding a breakdown of doctoral degrees by gender. In order to write a synthesis and contextualization pertaining to the results, more in-depth and nuanced data is needed.

With the information provided, the countries with the smallest gender gap in PhD’s awarded were Australia, Israel, Macedonia, Croatia, Italy, Estonia, New Zealand, Finland, Ukraine, Kyrgyzstan, Argentina and Mongolia. Each of these nations ranged from fifty to fifty five percent of PhD’s going to women. The countries with the highest number of gender inequality for PhD’s were Taiwan, Georgia, South Korea, Iran, Jordan, Uganda, Malaysia, Saudi Arabia, Armenia, and Colombia. Taiwan had a stunning twenty-six percent of doctoral degrees awarded to females while Colombia reached thirty nine percent. The data is only from 2010, however. As a result, more years need to be included. In doing so, outliers will have a lesser influence on the results.

Above all, it is imperative that more countries participate in the surveys. Fifty-six nations does not offer an adequate analysis. Moreover, countries need to supply both undergraduate and graduate statistics to verify that both tiers are in proportion with one-another. If they are not, then social scientists will have to ask what factors are causing men or women to enter the workforce after obtaining a bachelor’s degree? Is it a correlation with particular departments that are more segregated by gender? Why are certain departments lacking gender diversity? Social scientists are well aware of these questions and will continue to seek more data and indicators in the future.

While exploring the dataset this week I couldn’t (as someone trained in rhetoric and composition) get past the fact that countries with unequal access to power were termed “pursuing parity.” To me that was a way of packaging the blow that these countries were at the bottom of the spectrum in a “works-in-progress” sort of way. This is where the politics of the situation gets involved. I imagine since these countries opted into the study and made their data available then they are considered works in progress? I wonder what is gained by terming it this way as opposed to “unequal” or “no parity” or something like that.

With that in mind I decided to compare a few of the countries at the bottom of the list: China, Saudi Arabia, Russia and Brazil.

 

When I search by education rates there is obviously some data missing. Brazil reports no data, and Saudi Arabia’s data is partially there. Russia and China have datasets. So how do you compile a list with incomplete datasets I wonder?

Likewise, the gender wage gap does not include data on any of the poorer performing countries I listed above. With this in mind I went back to read more about the methodology to see what I was missing. How did they account for working with incomplete data and then drawing up a list based on those data?

Behold! An answer to my question! It looks like there are answers for when data is missing and they are discussed in the limitations part of the methodology. While this information is under “limitations” it also serves as a justification for how data was dealt with then it could not be gathered. I note here they especially talk about the missing educational data. I am not knowledgeable enough in statistical methods to know what this means for their findings, but given the status of the project, and the partners working on it, I do find it comforting that the limitations are acknowledged. Maybe this goes back to why these underperforming countries are termed “pursuing parity?” How can you say something dire about them when you don’t have all the data, after all? (Though educated guesses might help us fill in the blanks here. or is that just my Western point of view talking? It may very well be).

I compare the two examples above to the indicator “# of female heads of state.” What this teaches me about data gathering and data availability is that some indicators researchers choose to use are those that they can get answers for with or without a country’s help. It’s easy enough for a researcher to compile a list of female heads of state and compare across nations that way. The countries themselves don’t need to provide the data. This shows that these kinds of data might be privileged over, say, educational data that may or may not be collected by countries. Furthermore, who is to say if the data collection methods in certain countries would be reliable or not? There are social and political factors involved here too.

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I’m going to add onto this blog post a bit to report on the data availability project that we were to execute.

I was interesting in learning more about women in tech and STEM. I looked at the Statista database first (which, sadly, Pitt doesn’t subscribe to, but I have super shady ways of getting into the database. Librarian skills and all…). Statista is an interesting database because it’s marketed as a “Global business database” so the information therein is ostensibly to help businesses make choices about products, hiring. Or, for individuals who are interested in global trading markets and companies. 

One of the reasons I do like Statista, though, is that oftentimes you can go back to the original dataset, look at methodologies, reports, and decide if you want to use the data, or what it’s “good” for.

I also found datasets through the National Science foundation (nice, because you can download the set) and PEW research (but that is US-based so not cross-cultural).

In terms of my findings, I tend to be more interested in how people access data (as a librarian) and less what they do with it. I understand that this project had to do with both of those things, though. Yet most of the time spent on this project was me imagining how a student or a lay-member of the public might actually get at this information. Mostly, when people Google questions like this they’ll read a report that breaks down the data for them. Fewer people will be able to actually go look at the data itself. Luckily for this issue the datasets are publicly available (like through the NSF). However, some of the more nuanced data I found through Statista: a database you have to subscribe to. So even where there is a lot of data (women and STEM is one of those areas), who has access to it?

Furthermore, how are people actually searching for these things? What keywords are they using? How does that allow them to find information? Or, does it hide information? Statista’s keyword search works differently than a traditional database. The Wikipedia article on Women in STEM is well-researched but are folks going to the works cited list and clicking on those links? Those are the kinds of information behaviors I’m wondering about with the issue of data-access. 

 

For this exercise, I was interested in data regarding gender and museum professions, and more specifically data that provide information on the percentage and professional distribution of women employed by art museums. I was hoping to find cross-national sources of data on the gender composition of art museum employees, but much of what I came across were academic sources in the form of articles and essays and data on gender inequality in cultural institutions considered more broadly. Though I did not come across cross-national information on women employed by art museums, I found two reports on gender in American art museums: one by the Andrew W. Mellon Foundation, Ithaka S+R, the Association of Art Museum Directors (AAMD), and the American Alliance of Museums (AAM), published January 28, 2019, and the other by AAMD and the National Center for Arts Research (NCAR), published in 2017. I thought it might also be useful to consider the United Nations Sustainable Development Goals (SDGs), the Women in Public Service Project and its Global Women’s Leadership Initiative Index (GWLII), and their indicators in thinking about the social, economic, and political factors that may affect the gender composition of art museum employees and the professional roles likely to be held by women in art museums.

The first report, “Art Museum Staff Demographic Survey 2018,” details the results of surveys that were sent to directors of AAMD and AAM member art museums in 2014 and 2018. The report is most concerned with what its authors refer to as “intellectual leadership positions,” which they identify with positions in museum leadership, education, curatorial, and conservation, as potential career pathways to directorship positions. (The involvement of the AAMD (Association of Art Museum Directors) in developing these surveys might be a possible reason for the report’s focus on “intellectual leadership positions,” considered to be pathways to directorships.) Women in roles or departments such as visitor services and front of house staff, membership, development, marketing, administration, registrars, and preparators were not reflected in the report in a disaggregated way, and it is not clear whether women in these positions were included in the report’s percentage of women employed by art museums. The results of the 2014 and 2018 surveys as presented in the 2019 report indicate that American art museums are and have continued to be staffed primarily by women, though men are still more likely to hold senior positions in museum leadership.

The second report, “The Ongoing Gender Gap in Art Museum Directorships,” presents various factors that affect gender representation in American art museum directorships that were identified in the results of surveys conducted in 2013 and 2016. (As noted in my introduction, the AAMD was involved in developing these surveys.) The authors of this report have identified varying relationships among the gender gap in museum directorships, salary disparities, operating budget size, and the type of art museum, providing information on the factors affecting gender disparity in art museums at the most senior position in museum leadership.

These two reports are specific to gender representation in American art museums, but the limited scope of each report does not provide a comprehensive understanding of the gender composition of art museum employees beyond those in “intellectual leadership positions,” including directorships. For gathering data relating to art museums in America and in other nations, I thought it might be useful to consider the SDGs, the GWLII, and their indicators in thinking about what structural factors and socioeconomic situations may contribute to gender representation in art museums.

The United Nations Sustainable Development Goal 5 is to achieve gender equality and empower all women and girls. An indicator related to the social norms and attitudes that may prevent women and girls from seeking paid work and education is the proportion of their time spent on unpaid care and domestic work (SDG indicator 5.4.1). Related SDGs here include SDG 8, which focuses on decent work and economic growth, and SDG 4, meant to ensure inclusive and equitable quality education. SDG 8 indicators that seek in part to measure the average hourly earnings of employed women and men (8.5.1) as well as their respective unemployment rates (8.5.2) may provide socioeconomic context for the number of women employed by art museums and for salary disparities among men and women in comparable professional positions. SDG 4 indicators meant to reflect gender disparities in education and access to education, such as 4.5.1, may be considered similarly.

The Global Women’s Leadership Initiative Index of the Women in Public Service Project is concerned with women’s leadership in public service and has identified three “pillars” of parity: pathways, positions, and power. Though the various professional positions that may be held by women in art museums may not necessarily include a formal leadership component, one could draw from the GWLII, along with the SDG indicators mentioned previously, in considering the availability of access women have to education and museum professions and the representation of women employed by art museums.

Sources:

“Global Women’s Leadership Initiative Index Methodology.” The Women in Public Service Project. 2018. http://data.50x50movement.org/index/methodology.

“Roadmap to 50×50: Power and Parity in Women’s Leadership.” Wilson Center. May 2018. https://www.wilsoncenter.org/sites/default/files/roadmap_to_50x50-_power_and_parity_in_womens_leadership.pdf.

“Sustainable Development Goal 4: Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning.” United Nations. Accessed February 1, 2020. https://sustainabledevelopment.un.org/sdg4.

“Sustainable Development Goal 5: Achieve Gender Equality and Empower All Women and Girls.” United Nations. Accessed February 1, 2020. https://sustainabledevelopment.un.org/sdg5.

“Sustainable Development Goal 8: Promote Sustained, Inclusive and Sustainable Economic Growth, Full and Productive Employment and Decent Work for All.” United Nations. Accessed February 1, 2020. https://sustainabledevelopment.un.org/sdg8.

Treviño, Veronica, Zannie Giraud Voss, Christine Anagnos, and Alison D. Wade. “The Ongoing Gender Gap in Art Museum Directorships.” Association of Art Museum Directors. 2017. https://aamd.org/sites/default/files/document/AAMD%20NCAR%20Gender%20Gap%202017.pdf.

Westermann, Mariët, Roger Schonfeld, and Liam Sweeney. “Art Museum Staff Demographic Survey 2018.” The Andrew W. Mellon Foundation. January 28, 2019. https://mellon.org/media/filer_public/e5/a3/e5a373f3-697e-41e3-8f17-051587468755/sr-mellon-report-art-museum-staff-demographic-survey-01282019.pdf.

In the 1960s and 1970s, on the back of Cold War global politics, several South American countries experienced right-wing, military coups d’état in response to perceived internal and external threats from communism (and other “subversions”). During this tumultuous time, the state violated many individuals’ human rights because of their association with specific social groups – homosexuals, Blacks, Indians, and women, to name a few. While returning to civilian rule during the 1980s and the 1990s, ten of the twelve countries south of Panama rewrote their constitutions (the outliers being Uruguay’s 1967 and Bolivia’s 2008 constitutions). The authors of these new constitutions wrote into them protections based on social groups such as the ones above – including explicit equality between men and women. For the sake of this assignment, I asked: Since the constitutions affected women’s legal status, to what extent did women affect the creation of the constitutions?

To answer this question, I looked at how many women signed the constitutions of the twelve South American nations. The assumption there being that if an individual signed a document, they would have had a hand in its creation. Finding the various countries’ constitutions was an exercise in researching documents and archives. Navigating each country’s government website afforded insight into its priorities and organization. How deeply must one go into the site to access the constitution (often in the form of a downloadable pdf)? Some had a link directly on the homepage, while for others I received a crash course in government structure. Additionally, I learned that Brazil offers its constitution in audio form, and Argentina provides translated videos in sign language.

Immediately, I encountered inconsistencies both between different countries’ documents, and within individual countries. Most frustrating when comparing different countries’ documents were their inconsistencies in listing people involved. Some did not provide a list of signatories and instead opted to sign collectively as “the Assembly” in the case of Colombia, or “the Congress” for Perú, while others used only their titles, “President, Secretaries, and Conventionals” (what is a Conventional?) like Paraguay’s constitution states. Other constitutions simply stopped after their amendments and did not provide any signatories. To know the persons behind the writing and legalization of these countries’ constitutions would require further in-depth archival research. I would need to learn who was part of the Assemblies or Congresses, or held public offices (legislative, executive, and/or judicial) during the years of ratification.

There were also discrepancies within individual countries’ documents. While signatures accompany most of the typed names listed as part of the National Assembly of Venezuela, there are a handful of missing signatures. How do I interpret this? Could I assume they took part in the debates and discussions leading up to the constitution’s creation and they simply chose not to ratify it in the end? Or were they absent the entire time and thus have no input in the document? To what degree would we consider them “decision-makers?” Another interesting digitized document is Brazil’s 1988 Constitution. Click on the “Updated Text” button, and you can read the entirety of the constitution on the site. The site lists thirteen individuals (along with their titles) as signatories at the bottom, one of whom is assumingly female. However, on the pdf version these same thirteen are joined by more than 542 other names. Below these additions, there are 29 more listed as “participants,” followed by 5 people grouped under “in memory.” It would take substantial time to research everyone’s contribution to creating the text.

Even for those countries that publish the signatories’ names, distinguishing between male and female is problematic. My tallies are assumptions of each person’s sex based solely on their name. Additionally, there are many names with Indigenous or African ancestry that are impossible for me to interpret. This problem steams from that fact that the information of the signatories’ sex was not ascribed on the text. To know the sex of everyone who signed the twelve constitutions in South America, and thus speculate the power women had in their creation, would require considerable archival research. Furthermore, to gain a sense of change over time and consequently the impact these constitutions had on women, researchers would need to examine the role women played in the various military dictatorships and compare that to after ratifying the new constitutions. Until I complete such detailed research, the data for the names and sex of those signatories will remain unknown.

Country	Year	Total Signatories	Women Signatories	Mentions of “Women”	Mentions of “Men”
Argentina	1994	4	1	3	3
Bolivia	2008	unknown	unknown	18	10
Brazil	1988	556	unknown	12	10
Chile	1980	17	2	1	2
Colombia	1991	unknown	unknown	7	2
Ecuador	1998	71	6	14	6
Guyana	1980	unknown	unknown	6	3
Paraguay	1992	unknown	unknown	16	8
Peru	1993	unknown	unknown	1	1
Suriname	1987	unknown	unknown	2	1
Uruguay	1967	unknown	unknown	8	5
Venezuela	1999	165	unknown	4	5