Aaron Leichty
Goshen College, Goshen, Indiana
Biology Senior Seminar, Professor Stan Grove
11/25/07
Outline
Thesis Statement: The Pipeline and Life Course models are both helpful for examining the dynamics of gender disparity within the sciences.
For centuries science has been the realm of men. When considering the earliest years in the development of modern science, 17th to 19th centuries, no female scientists come to mind. Male scientists on the other hand, are most often the figures given credit for the development and directions that modern science has taken in its short history. The list of influential names is long: Isaac Newton, Carl Linnaeous, Charles Lyell, Louis Pasteur, Gregor Mendel, and Charles Darwin. Each of these men contributed greatly to science and are deserving of their current glorified statuses.
It is not until the 20th century that science begins to be shaped by the work of women. The history and social dynamics responsible for the lack of female influence in the sciences are inarguable. Sexism, social conditioning, and cultural practices made it next to impossible for the possibility of females to practice science, let alone produce indelible theories. The recognition and influence of females that has arisen in the 20th century was a multifaceted result of numerous social movements throughout industrialized societies. Despite the influx of females participating in the sciences, their recognition is still very low. Marie Curie, Barbara McClintock, and Rosalind Franklin are three of the more well known. Each of these women was awarded the Nobel Prize for their work in science, and this fact is to a great degree the reason they are names familiar to the public (See McGrayne, 1993, for a more complete list of female Nobel winners and the stories of their work).
In recent years, a push to understand the gender disparities in the sciences has resulted in the gathering of huge amounts of statistical data. This data has promoted the development of numerous models and explanations for disparity. Two of the most prominent models that have arisen are the Pipeline and Life Course models. Each of these models has been important to the understanding of gender involvement within the sciences and promise to further understanding into the future. This paper will highlight the findings, advantages, and disadvantages of each model in an attempt to more fully understand the dynamics concerning gender disparities within the sciences.
As mentioned above, there has been an increasing trend to document and study the differences in gender representation through the gathering of statistical data. Both the United States and the European Union have led the way in this documentation. One consensus from these statistics reveals that the number of women receiving higher education is and has been on the rise for decades. According to the National Science Foundation, in the United States, females have consistently outnumbered males in receiving bachelor’s degrees from 1966 to 2004. Although within the sciences, numbers have been reversed. In 1966, the number of women receiving a bachelor’s degree in a science related field was below 50,000. For men this number was approximately 140,000. By 2004, the two gender groups were receiving bachelor’s degrees at almost identical rates, 230,000 each year. Compare this to the 580,000 women and 370,000 men receiving a bachelor’s degree in a non-science field for the same year (NSF, 2006 a).
In 1966 the total number of women in the United States who received a master’s degree outside the sciences was approximately 42,000, for men the number was 58,000. By 1974 the number of men and women receiving this degree was equal, approximately 110,000. From 1975 to 2004, women surpassed men in number of degrees received each year. By 2004, the number of women to men was 278,000 to 170,000. In the sciences, from 1966 to 2004 the number of women receiving a master’s degree has steadily climbed from less than 5,000 to 55,000 each year. For men, the numbers have risen slowly, from 38,000 in 1966 to 65,000 in 2004 (NSF, 2006 b).
The trends for doctoral degrees in the United States have been similar. From 1966 to 2004 the number of women receiving doctorates each year in non-science fields rose from approximately 1,000 to 7,500. For the same time period, the number of females receiving doctorates in the sciences increased from 700 to 7,000. For men, the number of doctorates awarded in non-science and science fields has fluctuated dramatically from 1966 to 2004. A steep rise was seen for both fields from 1966 to 1972, and a gradual decline to original levels was seen thereafter. For non-science fields the number of doctorates received by men was 5,000 in 2004. In the sciences, the number was approximate 8,000 (NSF, 2006 c).
In Europe, the numbers of women receiving higher education has grown similar to the United States. In 2003, doctoral degrees in science and engineering were awarded to approximately 13,000 women in the European Union (EU). For the same year, 24,000 degrees were awarded to men. A closer look at the specific disparities within the sciences reveals that women outnumber men 4,765 to 3,990 in doctorates received in the Life sciences in 2003. In the other fields, men outnumber women 7,416 to 3,656 in doctorates awarded in the Physical sciences and 3,005 to 1,033 in Mathematics and Computing (She Figures, 2006).
In general, there has been a leveling or reversal in gender disparities in higher education. As can be seen by the above statistics, the number of females receiving higher education has grown dramatically and often exceeds the numbers for males. The glaring exception to this prominent leveling trend is in the sciences. Women continue to be the minority gender in degrees received within science. There are two small exceptions to this overall trend. The first occurring in the United States where there was an almost equal rate of bachelor’s degrees given to men and women in 2004. The second is for the European Union, were women outnumbered men in doctorates received in the Life sciences. Both of these exceptions are hopeful, but are not completely indicative of the situation. To be clear, the further along the academic ladder the statistics go, the more disparate the numbers become.
The greatest gender disparity, and yet unmentioned within this paper, is the number of men versus women employed in the field. Intuitively, if the number of women receiving bachelor’s, master’s, and doctorate’s in science has been rising for at least the past 30 years, then the number of women employed as scientists should also be rising at an equal rate. From the available statistics this does not appear to be the case.
In 2003, within the United States, all research institutions employed 72,000 male doctoral science and engineering faculty. These same research institutions employed 29,000 female doctoral science and engineering faculty (NSF, 2006 d). This is a ratio of 2.5 men for every women employed at these institutions. As mentioned previously the ratio of men to women whom obtain their doctorate in the sciences is approximately 1.3.
Again, the trends are similar in Europe. Female researchers comprised 29% of the science work force in the European Union, for 2003, a value being almost identical to the percentage seen in the United States. Interestingly, for the years from 1999 to 2003, a growth rate of 4% was reported for female researchers. A 2% growth rate was reported from males during the same time frame (She Figures, 2006).
These figures raise interesting questions about the education and employment process for the sciences. Are the gender discrepancies seen at the level of profession a result of the lag between attainment of degree and employment? Could it be that the male majority of years past has saturated the employment market and it simply takes time for the employment levels to reach that of education? Or is it a result of various cultural practices and conditionings that dictate at the level of individual? The next section outlines two basic models that have been developed to explain the statistical data using multivariable approaches.
The Pipeline model was introduced in 1983 by Sue Berryman, in her book Who Will Do Science? Minority and Female Attainment of Science and Mathematics Degrees: Trends and Causes. The proposed model used an increasingly narrow pipe as a metaphor for the career path taken in science (Moore, 2006). This model implies that as scientists progress down the pipe (i.e. advancement in science) it narrows and begins to restrict the flow of individuals. Different variations on the pipe theme were soon created. One of the most popular was the idea of the leaky pipe. This modified model accounted for the original model’s increasingly restricted flow and added the additional factor of leaks within the narrowing pipe. These leaks often corresponded to factors causing increased selection in the scientific career path. For example a hole was added to the region of the pipe corresponding to the transitional stage from graduate school to post-doctoral employment.
The entrance to the pipeline is envisioned to be anywhere between middle school and high school. At some point during these early years of education, science begins to be taught to children and young adults. Theoretically the pipes opening should be all inclusive. Science should be a viable career path for any student interested in it. The implication is that all individuals have an equal ability to initially enter the pipeline. This of course is not true, and could be a major flaw in the model. The pipeline is not often interpreted in such a strict manner and it is commonly recognized that the start of a pipe implies initial selection.
The successive stages of schooling that follow high school correspond to the narrowing of the pipe. An obviously problematic implication of this is that an end must be reached and that this end is the highest achievement. A value is placed upon various levels of scientific attainment and the degrading of other levels subsequently results. To become an autonomous researcher is to overcome the competition and exit the pipe victoriously. The model does a poor job of viewing science beyond the limits already placed upon it by a male majority. If an individual drops from the pipeline the model has only one explanation.
What the Pipeline model does do well is give a frame work for statistical investigation. If there are these successive steps in science, and the numbers are reduced at the entrance to the next step, then it should be possible to quantify losses at each step. Not only this, but if a step is found to contain a major leak, then it is much easier to pin-point the source and qualify it. The model also recognized the possibility for multiple factors being responsible for the female minority. By extending the range of years under statistical analysis, the pipeline model has made it possible for new and more thorough models to emerge.
As mentioned previously, the Pipeline model has been the dominate frame work for gender discrepancies in science. To a great degree, many of the current available statistics were collected operating under the models assumptions. Much of the data found in the introduction of this paper is an example of this. Each data set is chosen and collected in an effort to understand the barriers within science that are creating a female minority.
The Life Course model is an answer to the historically narrow focus utilized by those investigating females in science. Surprisingly, this model was not proposed until recent years. Yu Xie and Kimberlee Shauman’s book, Women in Science: Career Processes and Outcomes, was the first to do so. Their self described approach is as follows, “the life course perspective posits that the significant events and transitions in an individual’s life are age-dependent, interrelated, and contingent on (but not determined by) earlier experiences and societal forces” (Xie & Shauman, 2003). The authors recognize a career in science is not rigidly defined and reasons for female exit from the pipeline are numerous. Despite the “multidimensionality” of the science career path, three types of causes are proposed: individual influences, familial influences, and broader social influences.
Individual influence addresses the many person-specific factors contributing to an individual’s career choices. One of the most obvious and controversial of these is intelligence or cognitive ability. An individual is more likely to pursue or achieve a career path complementary to their abilities. Other individual aspects may be the willingness to participate in science and/or the attitude of the individual towards science.
Familial influences are those factors arising outside academics and the career path. The separation from the family and the resulting reduced influence directly coincides with the onset of important career choices. The perceptions of one’s gender role and place in society can influence the choices that the individual makes concerning a career. Also often occurring at this time is the establishment of a new family with new responsibilities. It is undeniable that responsibilities arising from marriage, childbirth, and childbearing are influential for both male and female.
Societal factors entail those influences outside of the family and can often be causative or linked to the individual factors mentioned above. The most influential institutions that individuals participate in outside of the home are educational in nature. Religious involvement is most often associated with familial influences. These large and yet subtle influences often set the limits for what an individual believes to be obtainable.
The most insightful work from this study combined statistical analysis of the previous three influences into single data sets. This allowed for the first time, the quantification of factors occurring outside of the science path. One interesting data set studied post baccalaureates at the career and family level. 5,959 women and 19,211 men were sampled. It was found that of the men married with children, 22.15% were in graduate school, 64.58% were working in science, 11.21% were working in non-science, and 2.06% were not working or in graduate school. Of the women married with children, 13.34% were in graduate school, 34.33% working in science, 20.61% working in non-science, and 31.03% were not working or in graduate school. There are obvious discrepancies. Men married with children are consistently more likely to be in graduate school or working in science, and less likely to be working outside their undergraduate training, or not working at all. Women on the other hand are the complete opposite, being more likely to work outside their undergraduate training or not at all, and less likely to be in graduate school or working in science. What makes this data even more interesting and informative is that for married men and women without children and single men and women, the likelihood of being in graduate school, working in the sciences, working in the non-sciences, and not working at all, are almost identical between genders. The obvious implication of this is that children seem to be the most influential factor determining if women continue in their career path, while this factor is non-existent for men. Similar findings were obtained for men and women following their master’s degree (Xie & Shauman, 2003).
For a number of years statistical data has shown that female scientists tend to receive less funding and have lower salaries than their male counter parts (She Figures, 2003; She Figures, 2006). Xie and Shauman (2003)approach this question similar to that of above. Again, men and women whom were either unmarried, married without children, or married with children were compared with a focus upon earnings and promotional rates. Unmarried females had an average earning of $38,765, while their male counterparts earned $45,000 a year. Women whom were married without children, earned on average $39,198 and males were found to earn $54,769 a year. For women and men married with children, the disparity was almost identical to the previous two categories. Interestingly, unmarried females were found to have the highest promotional rates of all the categories, 0.113. Predictably, females who were married with children had the lowest rate of promotion, 0.031.
In another interesting data set, Xie and Shauman (2003) analyzed the marital statuses of male and female scientists. They found that 37.47% of female scientists studied were unmarried, compared to 19.41% of men. 14.69% of female scientists reported to be married to another scientist, while 2.74% of men reported the same. For male scientists with a spouse holding a non-science doctorate, the percentage was 4.72%, female scientists had a higher percentage of 16.85%. Males with a spouse of another profession were 25.62% of the sample and females with this status comprised 17.79% of their sample. The final category, those scientists married to someone other than those already mentioned, had the greatest discrepancy. For men, 47.51% was reported in contrast with 13.21% reported for females. On the whole, female scientists tend to either be single and those who are not, seem to not discriminate between a potential spouse’s status within another career. Male scientists are least likely to be married to another scientist or doctorate holder in another profession, and are most likely to select a spouse holding a career path different from their own.
Twenty years ago there was no question about females being the minority gender in science. At that time the explanations for the gender discrepancies were poorly supported, and often a result of speculation. The statistics presented throughout this paper demonstrate the dedication and seriousness with which scientists, the scientific community, and society have faced the situation. It is undeniable that sexist and discriminatory views and practices have been present throughout this process. But as the problem continues to be studied, better models and explanations result.
The Pipeline and Life Course models are both frameworks for the study of gender discrepancies within the sciences. The Pipeline model has begun to show its age, but its usefulness and responsibility in the acquisition of much of the current data on the subject are obvious. The Life Course model has brought a fresh new framework to the issue and many of its ideas and data have already been revolutionary.
The number of female scientists is on the rise and numbers obtaining bachelor’s degrees are even with their male counterparts. This fact is hopeful, but does not answer why females are underrepresented in graduate school, employment, and in salaries earned within the sciences. The most recent work points to children as being the most obvious answer as to why the gender differences remain, but does not answer why they are under paid and underfunded. The recent increase of females within the sciences is a promising start and with new and better models being utilized and created, lasting solutions are sure to follow.
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