Where My Girls At? (In The Sciences)

Line Charts, Scatter Plots
Intro

In the current educational landscape, there is a constant stream of calls to improve female representation in the sciences. However, the call to action is often framed within the aforementioned nebulous realm of “the sciences”—an umbrella term that ignores the distinct environments across the scientific disciplines. To better understand the true state of women in “the sciences,” we must investigate representation at the discipline level in the context of both undergraduate and doctoral education. As it turns out, National Science Foundation (NSF) open data provides the ability to do just that!

The NSF’s Report on Women, Minorities, and Persons with Disabilities in Science and Engineering includes raw numbers on both undergraduate and doctoral degrees earned by women and men across all science disciplines. With these figures in hand, it’s simple to generate measures of female representation within each field of study—that is, percentages of female degree earners. This NSF report spans the decade 2002–­2012 and provides an immense amount of raw material to investigate.[1]

The static picture: 2012

First, we will zero in on the most recent year of data, 2012, and explicitly compare female representation within and across disciplines.[2]

fig1

The NSF groups science disciplines with similar focus (for example, atmospheric and ocean sciences both focus on environmental science) into classified parent categories. In order to observe not only the variation within each parent category but also across the more granular disciplines themselves, the above graph plots percentage female representation by discipline, with each discipline colored with respect to its NSF classified parent category.

The variation within each parent category can be quite pronounced. In the earth, atmospheric, and ocean sciences, female undergraduate representation ranges from 36% (atmospheric sciences) to 47% (ocean sciences) of total graduates. Among PhD graduates, female representation ranges from 39% (atmospheric sciences) to 48% (ocean sciences). Meanwhile, female representation in the physical sciences has an undergraduate range from 19% (physics) to 47% (chemistry) and a PhD range from 20% (physics) to 39% (chemistry). However, social sciences has the largest spread of all with undergraduate female representation ranging from 30% (economics) to 71% (anthropology) and PhD representation ranging from 33% (economics) to 64% (anthropology).

In line with conventional wisdom, computer sciences and physics are overwhelmingly male (undergraduate and PhD female representation lingers around 20% for both). Other disciplines in which female representation notably lags include: economics, mathematics and statistics, astronomy, and atmospheric sciences. Possible explanations behind the low representation in such disciplines have been debated at length.

Interactions between “innate abilities,” mathematical content, and female representation

Relatively recently, in January 2015, an article in Science “hypothesize[d] that, across the academic spectrum, women are underrepresented in fields whose practitioners believe that raw, innate talent is the main requirement for success, because women are stereotyped as not possessing such talent.” While this explanation was compelling to many, another group of researchers quickly responded by showing that once measures of mathematical content were added into the proposed models, the measures of innate beliefs (based on surveys of faculty members) shed all their statistical significance. Thus, the latter researchers provided evidence that female representation across disciplines is instead associated with the discipline’s mathematical content “and that faculty beliefs about innate ability were irrelevant.”

However, this conclusion does not imply that stereotypical beliefs are unimportant to female representation in scientific disciplines—in fact, the same researchers argue that beliefs of teachers and parents of younger children can play a large role in silently herding women out of math-heavy fields by “becom[ing] part of the self-fulfilling belief systems of the children themselves from a very early age.” Thus, the conclusion only objects to the alleged discovery of a robust causal relationship between one type of belief, university/college faculty beliefs about innate ability, and female representation.

Despite differences, both assessments demonstrate a correlation between measures of innate capabilities and female representation that is most likely driven by (1) women being less likely than men to study math-intensive disciplines and (2) those in math-intensive fields being more likely to describe their capacities as innate.[3]

The second point should hardly be surprising to anyone who has been exposed to mathematical genius tropes—think of all those handsome janitors who write up proofs on chalkboards whose talents are rarely learned. The second point is also incredibly consistent with the assumptions that underlie “the cult of genius” described by Professor Jordan Ellenberg in How Not to Be Wrong: The Power of Mathematical Thinking (p.412):

The genius cult tells students it’s not worth doing mathematics unless you’re the best at mathematics, because those special few are the only ones whose contributions matter. We don’t treat any other subject that way! I’ve never heard a student say, “I like Hamlet, but I don’t really belong in AP English—that kid who sits in the front row knows all the plays, and he started reading Shakespeare when he was nine!”

In short, subjects that are highly mathematical are seen as more driven by innate abilities than are others. In fact, describing someone as a hard worker in mathematical fields is often seen as an implicit insult—an implication I very much understand as someone who has been regularly (usually affectionately) teased as a “try-hard” by many male peers.

The dynamic picture: 2002–2012

Math-intensive subjects are predominately male in the static picture for the year 2012, but how has the gender balance changed over recent years (in these and all science disciplines)? To answer this question, we turn to a dynamic view of female representation over a recent decade by looking at NSF data for the entirety of 2002–2012.

fig2

The above graph plots the percentages of female degree earners in each science discipline for both the undergraduate and doctoral levels for each year from 2002 to 2012. The trends are remarkably varied with overall changes in undergraduate female representation ranging from a decrease of 33.9% (computer sciences) to an increase of 24.4% (atmospheric sciences). Overall changes in doctoral representation ranged from a decline of 8.8% (linguistics) to a rise of 67.6% (astronomy). The following visual more concisely summarizes the overall percentage changes for the decade.

fig3

As this graph illustrates, there were many gains in female representation at the doctoral level between 2002 and 2012. All but three disciplines experienced increased female representation—seems promising, yes? However, substantial losses at the undergraduate level should yield some concern. Only six of the eighteen science disciplines experienced undergraduate gains in female representation over the decade.

The illustrated increases in representation at the doctoral level are likely extensions of gains at the undergraduate level from the previous years—gains that are now being eroded given the presented undergraduate trends. The depicted losses at the undergraduate level could very well lead to similar losses at the doctoral level in the coming decade, which would hamper the widely shared goal to tenure more female professors.

The change for computer sciences is especially important since it provides a basis for the vast, well-documented media and academic focus on women in the field. (Planet Money brought the decline in percentage of female computer science majors to the attention of many in 2014.) The discipline experienced a loss in female representation at the undergraduate level that was more than twice the size of that in any other subject, including physics (-15.6%), earth sciences (-12.2%), and economics (-11.9%).

While the previous discussion of innate talent and stereotype threat focused on math-intensive fields, a category computer sciences fall into, I would argue that this recent decade has seen the effect of those forces on a growing realm of code-intensive fields. The use of computer programming and statistical software has become a standard qualification for many topics in physics, statistics, economics, biology, astronomy, and other fields. In fact, completing degrees in these disciplines now virtually requires coding in some way, shape, or form.

For instance, in my experience, one nontrivial hurdle that stands between students and more advanced classes in statistics or economics is the time necessary to understand how to use software such as R and Stata. Even seemingly simple tasks in these two programs requires some basic level of comfort with structuring commands—an understanding that is not taught in these classes, but rather mentioned as a quick and seemingly obvious sidebar. Despite my extensive coursework in economics and mathematics, I am quick to admit that I only became comfortable with Stata via independent learning in a summer research context, and R via pursuing projects for this blog many months after college graduation.

The implications of coding’s expanding role in many strains of scientific research should not be underestimated. If women are not coding, they are not just missing from computer science—they will increasingly be missing from other disciplines which coding has seeped into.

The big picture: present–future

In other words, I would argue academia is currently faced with the issue of improving female representation in code-intensive fields.[4] As is true with math-intensive fields, the stereotypical beliefs of teachers and parents of younger children “become part of the self-fulfilling belief systems of the children themselves from a very early age” that discourage women from even attempting to enter code-intensive fields. These beliefs when combined with Ellenberg’s described “cult of genius” (a mechanism that surrounded mathematics and now also applies to the atmosphere in computer science) are especially dangerous.

Given the small percentage of women in these fields at the undergraduate level, there is limited potential growth in female representation along the academic pipeline—that is, at the doctoral and professorial levels. While coding has opened up new, incredible directions for research in many of the sciences, its evolving importance also can yield gender imbalances due to the same dynamics that underlie underrepresentation in math-intensive fields.

Footnotes

[1] Unfortunately, we cannot extend this year range back before 2002 since earlier numbers were solely presented for broader discipline categories, or parent science categories—economics and anthropology would be grouped under the broader term “social sciences,” while astronomy and chemistry would be included under the term “physical sciences.”

[2] The NSF differentiates between science and engineering as the latter is often described as an application of the former in academia. While engineering displays an enormous gender imbalance in favor of men, I limit my discussion here to disciplines that fall under the NSF’s science category.

[3] The latter viewpoint does have some scientific backing. The paper “Nonlinear Psychometric Thresholds for Physics and Mathematics” supports the notion that while greater work ethic can compensate for lesser ability in many subjects, those below some threshold of mathematical capacities are very unlikely to succeed in mathematics and physics coursework.

[4] On a positive note, atmospheric sciences, which often involves complex climate modeling techniques, has experienced large gains in female representation at the undergraduate level.

Speaking of coding…

Check out my relevant Github repository for all data and R scripts necessary for reproducing these visuals.

Thank you to:

Ally Seidel for all the edits over the past few months! & members of NYC squad for listening to my ideas and debating terminology with me.


© Alexandra Albright and The Little Dataset That Could, 2016. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts, accompanying visuals, and links may be used, provided that full and clear credit is given to Alex Albright and The Little Dataset That Could with appropriate and specific direction to the original content.
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This Post is Brought to You by the National Science Foundation

Nightingale Graphs, Stacked Area Charts, Stacked Bar Charts, Treemaps
Intro

I have officially finished applying for my PhD. While the application process included many of the same elements that I had previously encountered as a fresh-faced* 17-year-old (think standardized testing without the #2 pencils and lots more button clicking), I am no longer applying as a (relatively) blank slate–a future liberal arts student who will float and skip between disciplines until being neatly slotted into a major. Instead, we PhD applicants have already zeroed in on a particular area of study–in my case, economics. Consequently, each PhD discipline is unlikely to exhibit the same carefully crafted demographics boasted in the pie charts that plaster undergraduate brochures across the country to provide tangible evidence for optimistic, bolded statements about diversity. In formulating responses to a slew of university-specific prompts about diversity in “the sciences,” I grew curiouser and curiouser about two particular questions: What do demographic compositions look like across various PhD disciplines in the sciences? & Have demographic snapshots changed meaningfully over time?

As I continued working to imbue a sense of [academic] self into pdfs composed of tightly structured Times New Roman 12 point font, I repeatedly found myself at the NSF open data portal, seeking to answer these aforementioned questions. However, I would then remind myself that, despite my organic urge to load rows and columns into R Studio, I should be the responsible adult (who I know I can be) and finish my applications before running out to recess. Now that the last of the fateful buttons have been clicked (and a sizable portion of my disposable income has been devoured by application fees and the testing industrial complex), I’m outside and ready to talk science!**

NSF data and sizes of “the sciences”

In this post, I am focusing on the demographics of science PhD degrees awarded as they pertain to citizenship and race/ethnicity, but not gender. In an ideal world, I would be able to discuss the compositions of PhD fields as broken into race/ethnicity-gender combinations, however, the table that includes these types of combinations for US citizens and permanent residents (Table 7-7) only provides the numbers for the broader categories rather than for the desired discipline-level. For instance, social science numbers are provided for 2002-2012 without specific numbers for economics, anthropology, etc. This approach, therefore, would not allow for an investigation into the main topic of interest, which is the demographic differences between the distinct disciplines–there is too much variety within the larger umbrella categories to discuss the fields’ compositions in this way. Therefore, I limit this discussion to demographics with respect to citizenship and race/ethnicity and, accordingly, use Table 7-4 “Doctoral degrees awarded, by citizenship, field, and race or ethnicity: 2002–12” from the NSF Report on Women, Minorities, and Persons with Disabilities in Science and Engineering*** as my data source.

Before getting into the different PhD science fields and their demographics, it’s worth noting the relative sizes of these disciplines. The following treemap depicts the relative sizes of the sciences as defined by NSF data on doctoral degrees awarded in 2012:

treemap2

The size of each squarified rectangle represents the number of degrees awarded within a given field while the color denotes the field’s parent category, as defined by the NSF. (Note that some studies are, in fact, their own parent categories. This is the case for Biological Sciences, Psychology, Computer Sciences, and Agricultural Sciences.) In the upcoming discussion of demographics, we will first discuss raw numbers of degrees earned and the relevant demographic components but will then pivot towards a discussion of percentages, at which point remembering the differences in size will be particularly helpful in piecing together the information into one cohesive idea of the demographics of “the sciences.”****

A decade of demographic snapshots: PhD’s in the sciences

The NSF data specifies two levels of information about the doctoral degrees awarded. The first level identifies the number of degree recipients who are US citizens or permanent residents as well as the number who are temporary residents. Though “[t]emporary [r]esident includes all ethnic and racial groups,” the former category is further broken down into the following subgroups: American Indian or Alaska Native, Asian or Pacific Islander, Black, Hispanic, Other or unknown, and White. In our first exploration of the data, we specify the raw number of degrees awarded to individuals in the specific ethnic and racial categories for US citizens and permanent residents as well as the number awarded to temporary residents. In particular, we start the investigation with the following series of stacked area charts (using flexible y-axes given the vastly different sizes of the disciplines):

raw_plot

In this context and for all following visualizations, the red denotes temporary residents while all other colors (the shades of blue-green and black) are ethnic and racial subsets of the US citizens and permanent residents. By illustrating the raw numbers, this chart allow us to compare the growth of certain PhD’s as well as seeing the distinct demographic breakdowns. While overall the number of science PhD’s increased by 39% from 2002 to 2012, Astronomy, Computer Science, Atmospheric sciences, and Mathematics and statistics PhD’s clearly outpaced other PhD growth rates with increases of 143%, 125% 84%, and 80%, respectively. Meanwhile, the number of Psychology PhD’s actually decreased from 2002 to 2012  by 8%. While this was the only science PhD to experience a decline over the relevant 10-year period, a number of other disciplines grew at modest rates. For instance, the number of Anthropology, Sociology, and Agricultural Sciences PhD’s experienced increases of 15%, 16%, and 18% between 2002 and 2012, which pale in comparison to the vast increases seen in Astronomy, Computer Science, Atmospheric sciences, and Mathematics and statistics.

While it is tempting to use this chart to delve into the demographics of the different fields of study, the use of raw numbers renders a comprehensive comparison of the relative sizes of groups tricky. For this reason, we shift over to visualizations using percentages to best get into the meat of the discussion–this also eliminates the need for different y-axes. In presenting the percentage demographic breakdowns, I supply three different visualizations: a series of stacked area graphs, a series of nightingale graphs (essentially, polar stacked bar charts), and a series of straightforward line graphs, which despite being the least exciting/novel are unambiguous in their interpretation:

percent_area

perc_nightingale

perc_line

One of my main interests in these graphs is the prominence of temporary residents in various disciplines. In fact, it turns out that Economics is actually quite exceptional in terms of its percentage of temporary residents, which lingers around 60% for the decade at hand and is at 58% for 2012. (In 2012, out of the remaining 42% that are US citizens or permanent residents, 70% are white, 11% are asian or pacific islander, 3% are black, 3% are hispanic, 0% are american indian or alaskan native, and 13% are other or unknown.) Economics stands with Computer science, Mathematics and statistics, and Physics as one of the four subjects in the sciences for which temporary residents made up a higher percentage of the PhD population than white US citizens or permanent residents consistently from 2002 to 2012. Furthermore, Economics is also the science PhD with the lowest percentage of white US citizens and permanent residents–that is, a mere 30%.  In this sense, the field stands out as wildly different in these graphs from its social science friends (or, more accurately, frenemies). On another note, it is also not hard to immediately notice that Psychology, which is not a social science in the NSF’s categorization, is so white that its nightingale graph looks like an eye with an immensely overly dilated pupil (though anthropology is not far behind on the dilated pupil front).

Also readily noticeable is the thickness of the blue hues in the case of Area and ethnic studies–an observation that renders it undeniable that this subject is the science PhD with the highest percentage of non-white US citizens and permanent residents. Following this discipline would be the other social sciences Anthropology, Sociology, and Political science and public administration, as well as the separately categorized Psychology. However, it is worth noting that the ambiguity of the temporary residents’ racial and ethnic attributes leaves much of our understanding of the prominence of various groups unclear.

Another focal point of this investigation pertains to the time dimension of these visuals. When homing in on the temporal aspect of these demographic snapshots, there is a discouraging pattern–a lack of much obvious change. This is especially highlighted by the nightingale graphs since the polar coordinates allow the 2012 percentages to loop back next to the 2002 percentages and, thus, facilitate for a simple start-to-end comparison. In most cases, the two points in time look incredibly similar. Of course, this does not necessarily mean there has been no meaningful change. For instance, there have been declines in the percentage of white US citizens and permanent residents in the subjects Area and ethnic studies, Psychology, Sociology, Anthropology, and Political science and public administration, which have then been offset by increases in other groups of individuals. However, the picture is incredibly stagnant for most of the disciplines, especially the hard sciences and the unusually quantitative social science of economics. In pairing the stagnant nature of these demographic snapshots with consistent calls for greater faculty diversity in the wake of campus protests, it is clear that there is a potential bottleneck since such lagging diversity in PhD disciplines can directly contribute to a lack of diversity at the faculty-level.

Endnote

When the public discusses the demographics and diversity of “the sciences,” 1.5 dozen disciplines are being improperly blended together into generalized statements. To better understand the relevant dynamics, individuals should zero in on the discipline-level rather than refer to larger umbrella categories. As it turns out according to our investigation, the demographic breakdowns of these distinct subjects are as fundamentally different as their academic methodologies–methodologies which can be illustrated by the following joke that I can only assume is based on a true story:

As a psychological experiment, an engineer, a chemist, and a theoretical economist are each locked in separate rooms and told they won’t be released until they paint their entire room. They are each given a can of blue paint which holds about half the paint necessary to paint the room and then left alone. A few hours later the psychologist checks up on the three subjects.

(1) The engineer’s walls are completely bare. The engineer explains that he had worked out that there wasn’t enough paint to cover all the walls so he saw no point in starting.

(2) The chemist’s room is painted in faded, streaky blue. “There wasn’t enough paint, so I diluted it,” she explains.

(3) In the economist’s room, the floor and the ceiling are completely blue, and there’s a full can of paint still sitting on the floor. The experimenter is shocked and asks how the economists managed to paint everything. The economist explains, “Oh, I just painted the rational points.”

And with an unwavering appreciation for that bit, I hope to be one of the ~20-30 (who knows?) % of white US citizens/permanent residents in the economics PhD cohort of 2021.

PS-Happy 2016 everyone!

Footnotes

* I had yet to take a driving test at a DMV. I did this successfully at age 21. But, I will not drive your car.

** The NSF divides subjects up into S&E (science and engineering) and non-S&E categories. In this context, I am only discussing the subjects that fall under the umbrella of science. It would be simple to extend the approach and concept to the provided numbers for engineering.

*** This table explains that the exact source for this information is: National Science Foundation, National Center for Science and Engineering Statistics, special tabulations of U.S. Department of Education, National Center for Education Statistics, Integrated Postsecondary Education Data System, Completions Survey, 2002–12.

**** In particular, the tiny size of the group of History of Science PhD’s allows for much more variability year-to-year in terms of demographics. Only 19-34 degrees were given out on an annual basis from 2002-2012. In this case, size of the program is responsible for the wildly evident changes in demographic composition.

Code

Data and R scripts necessary to replicate visualizations are now up on my github! See the NSF_Demographics repo. Let me know if you have any questions or issues with the R script in particular.

Further directions for work
  • Create gif of treemap using years 2002-2012 to replace the static version for just 2012
    • Or use a slider via some D3 magic
  • Follow-up by comparing the gender compositions
  • Look into the development and change history of the US Office of Management and Budget for racial and ethnic categories
    • Just curious as to the timeline of changes and how categorization changes affect our available data

© Alexandra Albright and The Little Dataset That Could, 2016. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts, accompanying visuals, and links may be used, provided that full and clear credit is given to Alex Albright and The Little Dataset That Could with appropriate and specific direction to the original content.

The Rise of the New Kind of Cabbie: A Comparison of Uber and Taxi Drivers

Bar Charts, Stacked Bar Charts
Intro

One day back in the early 2000’s, I commandeered one of my mom’s many spiral notebooks. I’d carry the notebook all around Manhattan, allowing it to accompany me everywhere from pizza parlors to playgrounds, while the notebook waited eagerly for my parents to hail a taxicab so it could fulfill its eventual purpose. Once in a cab, after clicking my seat belt into place (of course!), I’d pull out the notebook in order to develop one of my very first spreadsheets. Not the electronic kind, the paper kind. I made one column for the date of the cab ride, another for the driver’s medallion number (5J31, 3A37, 7P89, etc.) and one last one for the driver’s full name–both the name and number were always readily visible, pressed between two slabs of Plexiglas that intentionally separate the back from the front seat. Taxi drivers always seemed a little nervous when they noticed I was taking down their information–unsure of whether this 8-year-old was planning on calling in a complaint about them to the Taxi and Limousine Commission. I wasn’t planning on it.

Instead, I collected this information in order to discover if I would ever ride in the same cab twice…which I eventually did! On the day that I collected duplicate entries in the second and third columns, I felt an emotional connection to this notebook as it contained a time series of yellow cab rides that ran in parallel with my own development as a tiny human. (Or maybe I just felt emotional because only children can be desperate for friendship, even when it’s friendship with a notebook.) After pages and pages of observations, collected over the years using writing implements ranging from dull pencils to thick Sharpies, I never would have thought that one day yellow cabs would be eclipsed by something else…

Something else

However, today in 2015, according to Taxi and Limousine Commission data, there are officially more Uber cars in New York City than yellow cabs! This is incredible not just because of the speed of Uber’s growth but also since riding with Uber and other similar car services (Lyft, Sidecar) is a vastly different experience than riding in a yellow cab. Never in my pre-Uber life did I think of sitting shotgun. Nor did I consider starting a conversation with the driver. (I most definitely did not tell anyone my name or where I went to school.) Never did my taxi driver need to use an iPhone to get me to my destination. But, most evident to me is the distinction between the identities of the two sets of drivers. It is undoubtedly obvious that compared to traditional cab service drivers, Uber drivers are younger, whiter, more female, and more part-time. Though I have continuously noted these distinctions since growing accustomed to Uber this past summer, I did not think that there was data for illustrating these distinctions quantitatively. However, I recently came across the paper “An Analysis of the Labor Market for Uber’s Driver-Partners in the United States,” written by (Economists!) Jonathan Hall and Alan Krueger. The paper supplies tables that summarize characteristics of both Uber drivers and their conventional taxi driver/chauffeur counterparts. This allows for an exercise in visually depicting the differences between the two opposing sets of drivers—allowing us to then accurately define the characteristics of a new kind of cabbie.  

The rise of the younger cabbie

age.png

The above figure illustrates that Uber drivers are noticeably younger than their taxi counterparts. (From here on, when I discuss taxis I am also implicitly including chauffeurs. If you’d like to learn more about the source of the data and the collection methodology, refer directly to the paper.) For one, the age range including the highest percentage of Uber drivers is the 30-39 range (with 30.1% of drivers) while the range including the highest percentage of taxi drivers is the 50-64 range (with 36.6% of drivers). While about 19.1% of Uber drivers are under 30, only about 8.5% of taxi drivers are this young. Similarly, while only 24.5% of Uber drivers are over 50, 44.3% of taxi drivers are over this threshold. This difference in age is not very surprising given that Uber is a technological innovation and, therefore, participation is skewed to younger individuals.

The rise of the more highly educated cabbie

educ.png

This figure illustrates that Uber drivers, on the whole, are more highly educated than their taxi counterparts. While only 12.2% of Uber drivers do not possess a level of education beyond high school completion, the majority of taxi drivers (52.5%) fall into this category. The percentage of taxi drivers with at least a college degree is a mere 18.8%, but the percentage of Uber drivers with at least a college degree is 47.7%, which is even higher than that percentage for all workers, 41.1%. Thus, Uber’s rise has created a new class of drivers whose higher education level is superior to that of the overall workforce. (Though it is worth noting that the overall workforce boasts a higher percentage of individuals with postgraduate degrees than does Uber–16% to 10.8%.)

The rise of the whiter cabbie

race.png

On the topic of race, conventional taxis boast higher percentages of all non-white racial groups except for the “Other Non-Hispanic” group, which is 3.9 percentage points higher among the Uber population. The most represented race among taxi drivers is black, while the most represented race among Uber drivers is white. 19.5% of Uber drivers are black while 31.6% of taxi drivers are black, and 40.3% of Uber drivers are white while 26.2% of taxi drivers are white. I would be curious to compare the racial breakdown of Uber’s drivers to that of Lyft and Sidecar’s drivers as I suspect the other two might not have populations that are as white (simply based on my own small and insufficient sample size).

The rise of the female cabbie

gender.png

It has been previously documented how Uber has helped women begin to “break into” the taxi industry. While only 1% of NYC yellow cab drivers are women and 8% of taxis (and chauffeurs) as a whole are women, an impressive 14% of Uber drivers are women–a percentage that is likely only possible in the driving industry due to the safety that Uber provides via the information on its riders.

The rise of the very-part-time cabbie

hours.png

A whopping 51% of Uber drivers drive a mere 1-15 hours per week though only 4% of taxis do so. This distinction in driving times between the two sets of drivers makes it clear that Uber drivers are more likely to be supplementing other sources of income with Uber work, while taxi drivers are more likely to be working as a driver full-time (81% of taxis drive more than 35 hours a week on average, but only 19% of Uber drivers do so). In short, it is very clear that Uber drivers treat driving as more of a part-time commitment than do traditional taxi drivers.

Uber by the cities

As a bonus, beyond profiling the demographic and behavioral differences between the two classes of drivers, I present some information about how Uber drivers differ city by city. While this type of comparison could also be extremely interesting for demographic data (gender, race, etc.), hours worked and earnings are the only available pieces of information profiled by city in Hall and Krueger (2015).

Uber by the cities: hours

city.png

New York is the city that possesses the least part-time uberX drivers. (Note: This data is only looking at hours worked for uberX drivers in October 2014.) Only 42% work 1-15 hours while the percentage for the other cities ranges from 53-59%. Similarly, 23% of NYC Uber drivers work 35+ hours while the percentage for other cities ranges from 12-16%. Though these breakdowns are different for each of the six cities, the figure illustrates that Uber driving is treated pretty uniformly as a part-time gig throughout the country.

Uber by the cities: earnings

Also in the report was a breakdown of median earnings per hour by city. An important caveat here is that these are gross pay numbers and, therefore, they do not take into account the costs of driving a Taxi or an Uber. If you’d like to read a quick critique of the paper’s statement that “the net hourly earnings of Uber’s driver-partners exceed the hourly wage of employed taxi drivers and chauffeurs, on average,” read this. However, I will not join this discussion and instead focus only on gross pay numbers since costs are indeed unknown.

earning_by_city.png

According to the report’s information, NYC Uber drivers take in the highest gross earnings per hour ($30.35), followed by SF drivers ($25.77). These are also the same two cities in which the traditional cabbies make the most, however while NYC taxi counterparts make a few dollars more per hour than those in other cities, the NYC Uber drivers make more than 10 dollars per hour more than Boston, Chicago, DC, and LA Uber drivers.

Endnote

There is no doubt that the modern taxi experience is different from the one that I once cataloged in my stout, spiral notebook. Sure, Uber drivers are younger than their conventional cabbie counterparts. They are more often female and more often white. They are more likely to talk to you and tell you about their other jobs or interests. But, the nature of the taxi industry is changing far beyond the scope of the drivers. In particular, information that was once unknown (who took a cab ride with whom and when?) to those not in possession of a taxi notebook is now readily accessible to companies like Uber. Now, this string of recorded Uber rides is just one element in an all-encompassing set of (technologically recorded) sequential occurrences that can at least partially sketch out a skeleton of our lived experiences…No pen or paper necessary.

Bonus: a cartoon!
uberouterspace

The New Yorker Caption Contest for this week with my added caption. The photo was too oddly relevant to my current Uber v. Taxi project for me to not include it!

 Future work (all of which requires access to more data)
  • Investigate whether certain age groups for Uber are dominated by a specific race, e.g. is the 18-39 group disproportionately white while the 40+ group is disproportionately non-white?
  • Request data on gender/race breakdowns for Uber and Taxis by city
    • Looking at the racial breakdowns for NYC would be particularly interesting since the NYC breakdown is likely very different from that of cabbies throughout the rest of the country (this data is not available in the Taxicab Fact Book)
  • Compare characteristics by ride-sharing service: Uber, Lyft, and Sidecar
  • Investigate distribution of types of cars driven by Uber, Lyft, and Sidecar (Toyota, Honda, etc.)
Code

The R notebook for replicating all visuals is available here. See full github repo for the data as well. (Both updated 7-27-17)


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