East Asia-related Job Market Data Visualizations (2019-2020), Focused on Time Period
This page provides a deeper dive into the 2019-2020 academic season’s job market data, which is compiled on the previous page.
Here I present additional visualizations based on my own interests in premodern East Asian Studies, highlighting characteristics of job advertisements with a focus on the desired time period of specialization. To ensure clarity for those visiting only this page, I have reproduced some of the content linked above from the main data page (particularly information on the job advertisement compilation process and data categorization). If you have already read the explanations of how this data was collected (and caveats about its content), then you may wish to skip those sections by clicking the label to the right, which will take you straight to the The Data.
About the Data: Sources
The data gathered for this exploration originated from several sites:
This data should NOT be considered 100% comprehensive (see caveats below), merely reflective of information provided on some of the most commonly-used job search portals for academic postings related to East Asia.1
About the Data: Caveats
First, let me say that data is messy. This is a reflection on the job ads themselves, which are even messier. A job ad might ask for something fairly specific, like a historian of contemporary China focusing on the environmental humanities, or simply list “East Asian Studies” with no additional information on a desired time period, specialization, or other qualifications. Because job ads are inconsistent in the terminology used to describe some academic positions, I have chosen not to do a vocabulary-based comparison on desired disciplinary expertise. For the purposes of producing this dataset, job ad language was taken at face value, and cannot account for any internal criteria that informed the scope of searches. How data is represented is also interpreted through the compiler. Others might have organized or categorized this data differently than I have.
The data presented here is also reflective of both an English-language and North American bias, as many of the sites examined tend to focus on those positions, and others presented in other languages are only infrequently submitted to these venues. Some postings also may pass through informal channels only or through alternative lists to which I do not have access. Furthermore, some schools either do not or cannot afford to post their job openings through more widely-known channels.
This list also may not be exhaustive with regards to jobs that one might consider “alt-ac” or “academic adjacent,” as I focused to the best of my ability on those ads that readily circulated among Asian Studies circles. In some cases, job postings may have been removed after their deadline passed or when the position was filled, leading to the disappearance of more specific information on their content. I incorporated advertisements posted between June 1, 2019 and April 29, 2020. Data was collected throughout spring 2020.2
Please note that because of the COVID-19 pandemic, some institutions cut their hiring seasons short in March 2020.
About the Data: Cleaning and Organization
In an effort to produce meaningful visualizations comparing different job ads, I created seven different data categories. To review what each category and its accompanying labels mean (and how they were determined), click each of the tabs below.
The geographic location of the hiring institution.
The geographic regional coverage for which the job ad identified a preference. East Asian Studies and many humanities fields cite a primary region or nation of interest in their advertisements, whereas many social science fields have begun to replace regional or national focus with methodological preferences. This category was narrowed down from the ad content when possible. For example, if an ad listed “East Asian Studies/Asian Studies” for the title or topic of the position, but specified “preference for a candidate who can teach modern China,” then this posting’s “desired region” was labeled as “China.” Ads that included a specific combination of locations, such as “Asia/Africa” are identified separately. Job ads that were vague, such as positions in “non-Western” or “global” studies were not included. Northeast Asia was the central focus of job data collected. - East Asia/Asia
- China/South Asia
Data on the desired discipline identified in job ads was divided into labels that best distinguish the responsibilities of the position, based on the department hiring and/or description of the qualifications. Disciplines for which few postings were area-specific are combined in the interest of visual legibility. The social sciences, in particular, pose challenges to data collection, as they do not often advertise region- or nation-specific positions. Two labels are divided in some visualizations: “Literature & Culture” and “East Asian/Asian Studies.” Often generalist positions in East Asian Studies are filled by specialists in literature and culture, complicating this division. Here, I have chosen to separate general positions that do not list a specific interest in literature/culture from job ads that specifically ask for a literature/culture specialist. Similarly, although there is often overlap between generalist instructors and language instructors at small institutions, the “Language” category applies to jobs exclusively looking for language instructors. - East Asian/Asian Studies
- Literature & Culture
- Film/Media Studies
- Art History/Architecture/Urban Studies
- Religious Studies
- Admin/Program Director
- Social Studies/Education
The time period of the specialist desired in the job advertisement. - modern
- premodern (early modern [c. 18th/19th cen] and before)
- any (not specified or no preference)
- N/A (e.g.: language instructors)
The track/security of the position. Contingent positions have been divided into jobs that are non-tenure track positions and postdocs, as the former typically applies to visiting or adjunct positions and the latter often applies to research positions, sometimes with minimal teaching responsibilities. - TT (tenure track)
- non-TT (non-tenure track, including jobs to which this term does not apply)
- postdoc (non-tenure track, contingent position)
The labeling of institutions is based on its research output and degree-granting programs. This is perhaps the most problematic of the data organized, as, for the sake of comparison, each institution has been forced to fit into the rough equivalent of the US categories based on The Carnegie Classification of Institutions of Higher Education. The Carnegie classifications do NOT consistently correlate with institutional rankings (also problematic). For UK-based institutions, I established R1 equivalents based on the Russell Group categories, and for Australia, the Group of Eight. For universities in Asia and Europe, I consulted with colleagues based in those areas and provided a best guess based on anecdotal advice. I fully acknowledge that these distinctions are problematic and are often considered subjective. The most meaningful observations may be derived from considering the extremes-- whether jobs appear more often at very large doctoral degree-granting institutions or small liberal arts institutions. - R1 - very high research, doctoral program
- R1 regional branch - branch campuses of R1 institutions located in the US or abroad
- R2 - high research, doctoral program
- D/PU - Doctoral/Professional Universities
- M1 - high research, masters program
- M2 - medium research, masters program
- SLAC - small liberal arts college
The type of institution. N/A indicates it is something different from a typical academic degree-granting institution, such as a language training school, etc. - public
The DataPlease review the caveats and labeling system provided above before viewing the data in order to best understand how and why it has been cleaned and organized as presented below. Due to the temporary nature of job ads circulated online, particularly on platforms that delete completed postings or do not provide access to their posts after a certain number of months, I have chosen not to attempt a comparison with last year’s job season, as it may be highly inaccurate. I have created numerous (though not necessarily exhaustive) visualizations based on my data using Tableau Public. Please note that postdocs were counted by the number of positions offered at a single university. For example, Yale University’s Council on East Asian Studies hires four postdocs per year, so this is counted as four positions.
The following visualization is an interactive map of all East Asia-related job postings by time period, track, and location. Clicking on information in the any subsection of the visualization or on the map below will generate its linked data across other fields. Hover over data points on the map to reveal information on the data point provided. Zooming in or out is also possible. Points on the map are sized to frequency, so a place with more jobs postings will appear larger than other points.
Click the title of the subcategory you’re filtering or white space on within the area of the visualization you originally selected to void your selection and start over. Tableau Public can be finicky, so it may take a little experimentation. If interactive visualizations lag or generate an error, please refresh the page.3
Filtering the above visualization for “premodern,” for example, reveals that during the 2019-2020 season there were 15 tenure-track jobs, 4 non-tenure-track jobs, and 9 postdocs specifically seeking a premodernist. This accounts for all disciplines. Further filtering for “TT,” among those premodern job ads, 8 were for institutions in the United States, 2 in Japan and Hong Kong respectively, and 1 each in China, the Netherlands, and the UK.
For a more detailed examination of job distribution by time period, the following visualization adds another dimension, discipline, to the track and geographic location data. The same filter rules as above apply.
In the interest of visual clarity/space, in the visualization above I excluded the “N/A” category and focused on academic positions. Areas such as “Business Investment” or “Admin/Program Director,” where time period was less relevant to the position, were removed.
This chart provides an overview of only premodern jobs, divided by the desired region specified in the job advertisement. Track and discipline are not taken into account here.
Worldwide, twice the number of premodern China jobs were advertised as premodern Japan, with general East Asia or Asia jobs roughly the same in number as Japan. One job expressed an interest in premodern Japan or Korea.
The following visualization is perhaps the most comprehensive. You can filter all East Asia-related job ads by track (tenure-track, non-tenure-track, or postdoc), time period, region of specialization, discipline, and location. This will enable you to target very specific subgroups of job ads.
Click on any subdivision of the visualization to filter results. For example, selecting the “TT” label shows information for only TT jobs across all categories. Clicking on a colored bar (indicating a specific time period within the track labels), will show only that specific subcategory. You may start with any area of the visualization.
For example, clicking the “premodern” bar under the “TT” track, then “Japan,” then “Literature & Culture” reveals that premodern TT jobs for which a Japan specialist on Literature & Culture were sought were advertised in Japan (1) and the US (3) this year. Switching to “History,” only 1 job was advertised anywhere for a premodern Japan historian (in the Netherlands). You may wish to return to the maps above to see information on which specific institutions released these job ads.
Below, all disciplines and time periods are provided with filters for school type and whether or not the institution is public or private. All job locations are included here, but please see the abovementioned caveats regarding the problematic nature of comparing school type labels across region, as not every institution adheres to the same categorization of R1, R2, SLAC, etc.
As explained in the previous visualization, any area of the visualization can be used, and multiple filters can be applied. You might, for example, select “modern” under the “East Asian/Asian Studies” subsection, then “R1,” revealing that there were 12 job advertisements for modern East Asian/Asian Studies at public R1 institutions, with 11 job ads for private R1 institutions.
As stated above, the ephemeral nature of job ads makes it difficult to create any meaningful comparison across job seasons, as there is no guarantee that reliable data survives for the 2018-2019 season or earlier. However, as these visualizations have shown, it is possible to compare various parameters of the 2019-2020 data with the abovementioned caveats in mind.
The outbreak of COVID-19 has had a significant impact on hiring at educational institutions, the impact of which is already being felt as job searches have been postponed or cancelled and hiring freezes implemented for the next 1-2 years (if not more). Where this will leave prospective candidates in the coming years is uncertain, and comparisons of the trends above with future data will need to take these dramatic changes into account. Nevertheless, I hope that the above information will be instructive of the particular moment in time it represents and may become a resource to show both vibrant and underrepresented areas of study within the East Asia field. Click here to return to the previous data page.
A special thank you to Jenna Yoshikawa, Victoria Montrose, Tristan Grunow, and others who generously helped round-out my data with their own collections. ↩
In particular, it was possible to locate data from the June 2019 to January 2020 period using the Association for Asian Studies job board and H-Net/H-Asia forums. A special thanks to AAS for making their older data available to me. ↩
For an unknown reason, Hampden-Sydney College shows up as a data point in the US. Please be advised this is actually in Australia! 🙂 ↩